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+5166.741850 9.637000 0.061000 diff --git a/mltsp/TCP/Algorithms/Debil/README b/mltsp/TCP/Algorithms/Debil/README new file mode 100755 index 00000000..7b33219c --- /dev/null +++ b/mltsp/TCP/Algorithms/Debil/README @@ -0,0 +1,68 @@ +## compile debil +gcc debil.c -o debil -Wall -lm -O4 + +# on a mac, you might need to comment out the line: #include // For MAXFLOAT + + +## preparing the photometry file for Debil + +import fiteb +e = fiteb.EB() +e._make_intable(int(dotastroid)) + +## this will make a file called Phot/dotastro.dat +## see Phot/dotastro220107.dat +## the format is time (UJD) mag mag_err + + +## then prepare a script with: +## filename period +## e.g. cat test.in: +## Phot/dotastro220107.dat 2.43648439 + + +## then run debil +./debil test.in test.out test.err 8000 + + +bloom@betsy:~/TCP/Algorithms/Debil$ more test.out +Phot/dotastro220107.dat 2.436484390000 0.544444 0.530424 0.219584 0.024751 0.113447 0.053615 9.677479 0.460442 12.926126 1.845579 0.997124 0.007358 0.757538 1.419207 263.872047 510.914472 433 8 3.095258 9.934767 2.702335 7.100380 0.963495 -0.149294 0.5126 +8 0.301985 0.123077 0.264675 2.183931 + + +Here's the explanation of the output in test.out: +cols = [("fname", "File name (with full path if not in local directory)"), +("p","Period (in days)"), +("e", "Orbital eccentricity"), +("e_err", "Absolute uncertainty in (3)"), +("r1", "Radius of large star (in units of semimajor axis)"), +("r1_err","Absolute uncertainty in (5)"), +("r2","Radius of small star (in units of semimajor axis)"), +("r2_err","Absolute uncertainty in (7)"), +("b1", "Brightness of large star (magnitudes)"), +("b1_err","Absolute uncertainty in (9)"), +("b2", "Brightness of small star (magnitudes)"), +("br_err","Absolute uncertainty in (11)"), +("sini", "Sine of inclination, sin(i)"), +("sini_err","Absolute uncertainty in (13)"), +("periep", "Phased epoch of periastron"), +("periep_err","Absolute uncertainty in (15)"), +("perarg","Argument of periastron (in degrees)"), +("perarg_err","Absolute uncertainty in (17)"), +("Npoints", "Number of used data points (not including outliers)"), +("Nbad", "Number of outliers"), +("chibest", "Reduced chi squared of the best-fit model"), +("chiavg", "Reduced chi squared of the average value"), +("chispline", "Reduced chi squared of a second order spline (parabolic fit within a sliding window)"), +("chisine", "Reduced chi squared of the sinusoidal best-fit"), +("sig_2d", "Significance of the secondary dip depth (in sigma)"), +("sig_h", "Significance of the hump height (at midpoint between dips, in sigma)"), +("sig_hdiff", "Significance of the hump difference between the two humps (in sigma)"), +("wave", "Waviness (see paper appendix for definition)"), +("score", "Scatter score (see paper appendix for definition)"), +("rho_avg","Mean density (in grams per cm^3 ; see paper appendix for definition)"), +("rho_max", "Max density (in grams per cm^3 ; see paper appendix for definition)")] + + +There's a .data and .fit file written to the top directory with the phased data and the resultant fit: +d = csv2rec("dotastro220107.dat.data",names=("ph","mag","merr","tmp"),delimiter=" ") \ No newline at end of file diff --git a/mltsp/TCP/Algorithms/Debil/debil.c b/mltsp/TCP/Algorithms/Debil/debil.c new file mode 100755 index 00000000..528a4efd --- /dev/null +++ b/mltsp/TCP/Algorithms/Debil/debil.c @@ -0,0 +1,2691 @@ +/****************************************************************************************** + * + * Welcome to the DEBiL fitter (Detached Eclipsing Binary Light-curve fitter) + * Written by Jonathan Devor (CfA), 7 Oct. 2004 + * + * This program fits a given light curve to a model of a binary star system + * It uses the downhill simplex method (Nelder and Mead) with simulated annealing + * for multidimensional continuous optimization (minimization) + * + * The fitter makes the following assumptions- + * + * 1. Both stars are spherical: + * - Detached binary system --> negligible tidal bulge (in Roche lobe) + * - Slow rotation --> negligible centripetal deformation + * + * 2. Both stars have a Sun-like photosphere: + * - Solar limb darkening --> proportional scaling in size and flux per area + * (from Astrophysical Quantities by C.W. Allen) + * - Detached binary system --> negligible gravity darkening + * - Homogeneous --> small star spots and flares + * + * Compile: gcc debil.c -o debil -Wall -lm -O4 + * Run: debil p.txt p.out p.err + * + *******************************************************************************************/ + +#define VERSION "1.1" + +// #define PLOT + +#define GRAPHIC // compile in graphic debugging mode (DOS) + +#define WRITE_CURVE_FIT 500 // The number of fitted points to write + +// Allow the period to be doubled (may cause erroneous fits with equal surface brightness) +#define DO_DOUBLE_PERIOD + +// If the period was doubled, assume that the dips are very similar and only fit r1r2) +//#define DOUBLE_PERIOD_SHORTCUT + +// Allocation of computational resources: +// The default number of iterations (used in amoeba(), greedyFit() and estimateError()) +#define DEFAULT_NUM_ITERATIONS 10000 +// In amoeba(), the last part of the convergence should have the best integration +#define MIN_STEP_ITERATION_FRACTION 0.2 // Fraction of simplex iterations +#define MIN_INTEGRATION_STEP 0.01 // This should be about 0.1 * sqrt(amp error) +// Should be much smaller than (KERNEL_HALF_WIDTH / size), especially when the fit is crude +#define SEARCH_STEP_SIZE 0.0001 + +// Smoothing kernel parameter: +#define NUM_POINTS_HALF_KERNEL 8 // Must be smaller than a half a dip width, but at least 2 + +// Outlier identification parameter: +#define OUTLIER_VARIANCE_LIMIT 7.0 // "sigma clipping" + +//Eccentric anomaly calculation parameter: +#define ECC_ANOMALY_MAX_ERROR 1.0e-4 // Convergence threshold (average error is less than a third of this) + +// Eccentricity calculation parameter: +#define ECCENTRICITY_MAX_ERROR 1.0e-6 + +// General bisection implementation parameter: +// only needed for pathological cases (e.g. overflows or huge roundoff errors) +#define BISECTION_SAFETY_MAX_ITERATIONS 1000 + +// Quadratic limb darkening parameters: +#define DEFAULT_LIMB_QUAD_A 0.3542 // 0.282 [Claret 2003 // Claret 1998] +#define DEFAULT_LIMB_QUAD_B 0.1939 // 0.311 + +// Thermal fluctuation parameters: +#define INIT_TEMPERATURE 0.25 // Temperature (times Boltzmann's constant) at time=0 +#define NUM_E_FOLD_COOL 15.0 // Final_temperature = INIT_TEMPERATURE * exp(-NUM_E_FOLD_COOL) + +// "Greedy" fit parameters: +#define MAX_GREEDY_ITERATION_FRACTION 1.0 // Fraction of simplex iterations +#define GREEDY_CONVERGE_EPSILON 0.001 +#define HALF_MAX_GREEDY_EPSILON 0.1 + +// Error estimation parameters: +#define MAX_ERROR_ESTIMATE_ITERATION_FRACTION 0.1 // Fraction of simplex iterations +#define ERROR_ESTIMATE_EPSILON 0.005 // Should be larger than GREEDY_CONVERGE_EPSILON + +// Error thresholds: +#define MIN_STDDIV_BELOW_MEDIAN 0.0 // threshold for doubling the period (CAUTION) +#define MAX_STDDIV_ABOVE_MEDIAN 1.5 // warning only +// Minimum number of data points in the dips and in the plateau +#define MIN_NUM_DATA 5 // Must be at least a third of DIM + +#define RANDOM_SEED 37435209 + +#define EPSILON 1.0e-9 // To avoid division by zero + +// A useful constant for magnitude error conversion (2.5 / ln(10)) +#define MAGNITUDE_ERROR_FACTOR 1.085736205 + +#ifdef GRAPHIC +#define PLOT_REFRESH_ITERATIONS 10 +#endif + +// ======================= Downhill simplex ================================ + +#define DIM 8 // The number of dimensions (don't change!) + +// list of dimensions: +// physical- +#define D_ECC 0 // Eccentricity of orbit (e) +#define D_R1 1 // Star's radius (in units of the distance between them) +#define D_R2 2 +#define D_B1 3 // Central surface brightness (flux per unit disk area) +#define D_B2 4 +// orientation- +#define D_SIN_I 5 // sin(inclination) +#define D_TMO 6 // (time / 2) - (omega / 4pi) +#define D_TPO 7 // (time / 2) + (omega / 4pi) + +// Time is the time (scan parameter) at perihelion and omega is the argument of perihelion +// (i.e. azimuth of eccentricity alignment +// Note that time and omega become degenerate for small eccentricities. For this +// reason I combined them into orthogonal parameters ,D_TMO and D_TPO, where at +// least the former can be accurately determined and therefor will probably converge +// much faster in the fitting algorithm. + +// NOTE: [valid range of physical parameters] +// 0 <= time0 < 1 (not vital) +// 0 <= omega < 2*pi (not vital) +// 0 < sin_i <= 1 +// 0 <= e < 1 (closed orbit) +// 0 < r1 < 1-e (prevent collisions) +// 0 < r2 < 1-e-r1 (prevent collisions) +// 0 < B1 +// 0 < B2 + +// ==================== Includes =================================== + +#include +#include // For getc() +#include // For malloc() + qsort() +#include // For MAXFLOAT +#include +#include // For memmove() + strlen() + strcmp() + +#ifdef GRAPHIC +#ifdef PLOT +#include +#include // For gotoxy() +#endif +#endif + +// ================ General utility functions ====================== + + +// Returns the square of a given x +double sqr (double x) +{ + return (x * x) ; +} + +// Returns the cube of a given x +double cube (double x) +{ + return (x * x * x) ; +} + + +// Returns the modulo 1 value of x +double mod1 (double x) +{ + return (x - floor(x)) ; +} + + +// Swaps two given values +void swap (double *x1, double *x2) +{ + double tmp = *x1 ; + *x1 = *x2 ; + *x2 = tmp ; +} + +// ----------------------- + +// Prints a given error message (errStr) reported by a given function (functionName) about a +// given light curve file (dataFilename). It is added to a given error repository file (errFilename) +// isFatal: 0 (a warning - try anyways) 1 (an error - go to next) +void printError (char *errFilename, int isFatal, char *dataFilename, char *functionName, char *errStr) +{ + FILE *ferr = fopen (errFilename, "at") ; + + if (!ferr) + { + printf ("%s: [ERROR] Couldn't open the output error file ('%s')\n", dataFilename, errFilename) ; + + if (isFatal) + printf ("%s: [ERROR] %s - %s\n", dataFilename, functionName, errStr) ; + else + printf ("%s: [Warning] %s - %s\n", dataFilename, functionName, errStr) ; + + return ; + } + + if (isFatal) + fprintf (ferr, "%s: [ERROR] %s - %s\n", dataFilename, functionName, errStr) ; + else + fprintf (ferr, "%s: [Warning] %s - %s\n", dataFilename, functionName, errStr) ; + + fclose (ferr) ; +} + + +// =================== Removing outliers ========================== + +// Performs one iteration of statistical summations +void updateVars (float x, float y, + double *A1, double *A2, double *A3, double *A4, + double *B0, double *B1, double *B2, double *C0) +{ + const double x2 = x * x ; + + (*A1) += x ; + (*A2) += x2 ; + (*A3) += x * x2 ; + (*A4) += x2 * x2 ; + (*B0) += y ; + (*B1) += y * x ; + (*B2) += y * x2 ; + (*C0) += y * y ; +} + + +// Calculates a second order regression (parabola: a*x^2 + b*x + c) fit to given data (time, amp) +// around a given instant (sampTime) going up and down by a certain number of data points +// to minimize round off errors, time[] is centered around sampTime and the results are corrected +// for the shift at the end. +// Assumes that the data are sorted in time +// Output: pointers to: a b c and the variance around the fit (pVariance, if not null) +void regressionOrder2 (float sampTime, float *time, float *amp, int size, double *pa, double *pb, double *pc, double *pVariance) +{ + int i, indexDown = 0, indexUp = size - 1 ; + int A0 = NUM_POINTS_HALF_KERNEL + NUM_POINTS_HALF_KERNEL ; + double A1 = 0.0, A2 = 0.0, A3 = 0.0, A4 = 0.0 ; + double B0 = 0.0, B1 = 0.0, B2 = 0.0, C0 = 0.0 ; + double denom, a, b, c ; + + // Step 1: find the closes index above it + // Step 1.1: run the bisection algorithm + while ((indexDown + 1) < indexUp) + { + i = (indexDown + indexUp) / 2 ; + + if (time[i] > sampTime) + indexUp = i ; + else + indexDown = i ; + } + + // Step 1.2: take care of the ends + if (time[indexDown] > sampTime) + indexUp = 0 ; + else + if (time[indexUp] <= sampTime) + indexDown = size - 1 ; + + + // Step 2: do statistics + if (indexUp < NUM_POINTS_HALF_KERNEL) + { + for (i = indexUp + size - NUM_POINTS_HALF_KERNEL ; i < size ; i++) + updateVars (time[i] - 1.0 - sampTime, amp[i], &A1, &A2, &A3, &A4, &B0, &B1, &B2, &C0) ; + + for (i = 0 ; i < indexUp + NUM_POINTS_HALF_KERNEL ; i++) + updateVars (time[i] - sampTime, amp[i], &A1, &A2, &A3, &A4, &B0, &B1, &B2, &C0) ; + } + else if (indexDown >= (size - NUM_POINTS_HALF_KERNEL)) + { + for (i = indexDown + 1 - NUM_POINTS_HALF_KERNEL ; i < size ; i++) + updateVars (time[i] - sampTime, amp[i], &A1, &A2, &A3, &A4, &B0, &B1, &B2, &C0) ; + + for (i = 0 ; i <= (indexDown + NUM_POINTS_HALF_KERNEL - size) ; i++) + updateVars (time[i] + 1.0 - sampTime, amp[i], &A1, &A2, &A3, &A4, &B0, &B1, &B2, &C0) ; + } + else // Normal case + { + for (i = indexUp - NUM_POINTS_HALF_KERNEL ; i < (indexUp + NUM_POINTS_HALF_KERNEL) ; i++) + updateVars (time[i] - sampTime, amp[i], &A1, &A2, &A3, &A4, &B0, &B1, &B2, &C0) ; + } + + denom = (A2 * ((A0*A4) + (2.0*A1*A3) - (A2*A2))) - (A0*A3*A3) - (A1*A1*A4) ; // Denominator + + if (denom == 0.0) + denom = EPSILON ; // Prevents division by zero + + *pa = ((A0 * ((A2*B2) - (A3*B1))) + (A1 * ((A3*B0) - (A1*B2))) + (A2 * ((A1*B1) - (A2*B0)))) / denom ; + *pb = ((A0 * ((A4*B1) - (A3*B2))) + (A1 * ((A2*B2) - (A4*B0))) + (A2 * ((A3*B0) - (A2*B1)))) / denom ; + *pc = ((A1 * ((A3*B2) - (A4*B1))) + (A2 * ((A4*B0) - (A2*B2))) + (A3 * ((A2*B1) - (A3*B0)))) / denom ; + + if (pVariance) + { + a = *pa ; + b = *pb ; + c = *pc ; + *pVariance = ((A4*a*a) + (2.0*a*b*A3) + (A2 * ((b*b) + (2.0*a*c))) + (2.0*b*c*A1) + (A0*c*c) - (2.0 * ((B2*a) + (B1*b) + (B0*c))) + C0) / (A0-3) ; + } + + // Moves back the "zero point": a(x-x0)^2 + b(x-x0) + c --> ax^2 + bx + c + *pc += (((*pa) * sampTime * sampTime) - ((*pb) * sampTime)) ; + *pb -= (2.0 * (*pa) * sampTime) ; +} + + +// Interpolates a 2nd order regression spline (parabola) and returns the amplitude at +// an arbitrary given value (sampTime) +// sampTime - moment to interpolate around (+/- kernelHalfWidth) +// time - input: array of time stamps (may be rearranged) +// amp - input: matching array of amplitudes (may be rearranged) +// size - size of input arrays +// Returns the 2nd order regression at the given point (sampTime) +double interpolateSmooth (double sampTime, float *time, float *amp, int size) +{ + double a, b, c ; + + regressionOrder2 (sampTime, time, amp, size, &a, &b, &c, 0) ; + + return ((a * sampTime * sampTime) + (b * sampTime) + c) ; +} + + + +// Interpolates a 2nd order regression spline (fit to a parabola) and returns the +// time of a given amplitude (goal). It will return the closer (to sampTime) of the +// two solutions, as long as it's within SEARCH_STEP_SIZE +// sampTime - time to fit around +// time - input: array of time stamps (may be rearranged) +// amp - input: matching array of amplitudes (may be rearranged) +// size - size of input arrays +// Returns the nearest x-axis value of the fitted parabola for a given y-axis value +// if it's out of range (sampTime +/- SEARCH_STEP_SIZE), returns sampTime +double findGoalSmooth (double sampTime, double goal, float *time, float *amp, int size) +{ + double a, b, c, D, x1, x2, x ; + + regressionOrder2 (sampTime, time, amp, size, &a, &b, &c, 0) ; + + D = (b*b) - (4.0 * a * (c - goal)) ; // The determinant + + if ((D < 0.0) || (a == 0.0)) + return (sampTime) ; + + x1 = (-b + sqrt(D)) / (2.0 * a) ; + x2 = (-b - sqrt(D)) / (2.0 * a) ; + + if (fabs(x1 - sampTime) < fabs(x2 - sampTime)) + x = x1 ; + else + x = x2 ; + + if (fabs(sampTime - x) > SEARCH_STEP_SIZE) // Should be less than half SEARCH_STEP_SIZE + return (sampTime) ; + + return (mod1(x)) ; +} + + +// Removes the outliers from the input data array +// by comparing the data against a 2nd order regression spline. +// the data is assumed to be sorted by time (modulo period = phase) +// even when points are removes, the sorted order is maintained +// note that it will keep "lonely points" with no other data points near it +// time - input: array of time stamps +// amp - input: matching array of amplitudes +// size - size of input arrays +// *psize = outputs the new size of the array (will be reduced if outliers are found) +// Returns the chi2 around the spline (MAXFLOAT in case of error) +double ridOutliers (float *time, float *amp, float *err, int *psize) +{ + int index, doRid, numVariance ; + double x, sumVariance, variance, varianceFit, sumChi2 ; + double a, b, c ; + + do + { + doRid = 0 ; + index = 0 ; + numVariance = 0 ; + sumVariance = 0.0 ; + sumChi2 = 0.0 ; + + while (index < (*psize)) + { + x = time[index] ; + + regressionOrder2 (x, time, amp, *psize, &a, &b, &c, &varianceFit) ; + + variance = sqr((a * x * x) + (b * x) + c - amp[index]) ; + + if (variance > (varianceFit * OUTLIER_VARIANCE_LIMIT)) + { + doRid = 1 ; + (*psize)-- ; + + if (index < (*psize)) + { + memmove (&time[index], &time[index+1], ((*psize) - index) * sizeof(float)) ; + memmove (&[index], &[index+1], ((*psize) - index) * sizeof(float)) ; + memmove (&err[index], &err[index+1], ((*psize) - index) * sizeof(float)) ; + } + } + else + { + sumVariance += variance ; + sumChi2 += variance / sqr(err[index]) ; + numVariance++ ; + index++ ; + } + } + } + while (doRid) ; + + if (numVariance == 0) + return (MAXFLOAT) ; + + return (sumChi2 / numVariance) ; +} + + +// ============================ Chi2 of alternative models ============================== + +// Returns the reduced chi2 of a flat amplitude model (at the average) +// Assumes: size > 1 +double getAvrChi2 (float *amp, float *err, int size) +{ + double avrAmp = 0.0, sumChi2 = 0.0 ; + int i ; + + for (i = 0 ; i < size ; i++) + avrAmp += amp[i] ; + + avrAmp /= size ; + + for (i = 0 ; i < size ; i++) + sumChi2 += sqr((amp[i] - avrAmp) / err[i]) ; + + return (sumChi2 / (size - 1)) ; +} + + +// Fits the given data to: y = a * sin(2pi * m * x) + b * cos(2pi * m * x) + c +// In other words a sinusoidal perturbation with given amplitude, phase, +// mode (m) and DC component (c) +// The fit weights all the given data points equally. +// Assumes: size > 3 +// Returns the reduced chi2 of the best fit (incorporating error information) +double getSinModeChi2 (float *time, float *amp, float *err, int size, double mode) +{ + const double w = 2.0 * M_PI * mode ; + double A1=0.0, A2=0.0, A3=0.0, B1=0.0, B2=0.0, B3=0.0, C=0.0, D=0.0 ; + double sinx, cosx, denom, a, b, c, chi2 = 0.0; + int i ; + + for (i = 0 ; i < size ; i++) + { + sinx = sin (w * time[i]) ; + cosx = cos (w * time[i]) ; + + A1 += sinx ; + A2 += sinx * sinx ; + A3 += amp[i] * sinx ; + B1 += cosx ; + B2 += cosx * cosx ; + B3 += amp[i] * cosx ; + C += amp[i] ; + D += sinx * cosx ; + } + + denom = (A1*((A1*B2) - (2.0*B1*D))) + (A2*((B1*B1) - (B2*size))) - (D*D*size) ; // Denominator + + if (denom == 0.0) + denom = EPSILON ; // Prevents division by zero + + a = ((A1*((B2*C) - (B1*B3))) + (A3*((B1*B1) - (B2*size))) + (D*((B3*size) - (B1*C)))) / denom ; + b = ((A1*((A1*B3) - (C*D))) + (A2*((B1*C) - (B3*size))) + (A3*((D*size) - (A1*B1)))) / denom ; + c = ((A1*((A3*B2) - (B3*D))) + (A2*((B1*B3) - (B2*C))) + (D*((C*D) - (A3*B1)))) / denom ; + + for (i = 0 ; i < size ; i++) + { + sinx = sin (w * time[i]) ; + cosx = cos (w * time[i]) ; + chi2 += sqr(((a * sinx) + (b * cosx) + c - amp[i]) / err[i]) ; + } + + return (chi2 / (size - 3)) ; +} + + +// fits the given light curve to a sinusoidal with mode 1 and 2 +// returns the best chi square result of the two +double getSinChi2 (float *time, float *amp, float *err, int size) +{ + double m1, m2 ; + + m1 = getSinModeChi2 (time, amp, err, size, 1.0) ; + m2 = getSinModeChi2 (time, amp, err, size, 2.0) ; + + if (m1 <= m2) + return (m1) ; + + return (m2) ; +} + + +//========================= Eccentricity ====================================== + +// Uses a combined Newton-Raphson + bisection method for finding the root (E) of: +// M = E - (e * sin(E)) [Kepler's equation] +// Given: M = mean anomaly ; e = eccentricity of orbit (0 <= e < 1) +// Returns: E = Eccentric Anomaly +double eccentricAnomaly (double M, double e) +{ + int counter = BISECTION_SAFETY_MAX_ITERATIONS ; + double Emin = M - 1.0, Emax = M + 1.0, E = M ; + double f, dfm, dE ; + + do + { + f = M + (e * sin(E)) - E ; // May be optimized by setting cos=sqrt(1-sin^2) + dfm = 1.0 - (e * cos(E)) ; // Minus differential of f (always non-negative) + dE = dfm * ECC_ANOMALY_MAX_ERROR ; + + if (f > dE) + { + Emin = E ; + dE = Emax - Emin ; + + if ((dfm * dE) > f) + E += (f / dfm) ; + else + E = 0.5 * (Emax + Emin) ; + } + else if (f < (-dE)) + { + Emax = E ; + dE = Emax - Emin ; + + if ((dfm * dE) > -f) + E += (f / dfm) ; + else + E = 0.5 * (Emax + Emin) ; + } + else return (E) ; + } + while ((dE > ECC_ANOMALY_MAX_ERROR) && ((--counter) > 0)) ; + + return (E) ; // should almost never exits here +} + + + +// Finds the eccentricity using simple bisection. +// Uses the fact that that tDiff is a monotonically decreasing function of e +// Y = e * sin(omega) ; from the dip widths +// tdiff = the difference between the dip times +double getEccentricity (double tDiff, double Y) +{ + const double Y2 = Y * Y ; + int counter = BISECTION_SAFETY_MAX_ITERATIONS ; + double e2, res, eMid, eMax = 1.0, eMin = fabs(Y) ; + + tDiff *= M_PI ; + + while (((eMax - eMin) > ECCENTRICITY_MAX_ERROR) && ((counter--) > 0)) + { + eMid = 0.5 * (eMax + eMin) ; + e2 = eMid * eMid ; + + res = acos(sqrt((e2 - Y2) / (1.0 - Y2))) - (sqrt((1.0 - e2) * (e2 - Y2)) / (1.0 - Y2)) ; + + if (res > tDiff) + eMin = eMid ; + else + eMax = eMid ; + } + + return (0.5 * (eMax + eMin)) ; +} + + +//========================== Limb Darkening ==================================== + +// Quadratic limb darkening coefficients for a solar-like star in I filter (Claret 1998) +// Note that a "square root" law give a better fit (especially for I filter), +// but the error in assuming a solar star, is much larger and the total CPU requirements +// would almost double (doing a linear fit, on the other hand, won't save much CPU). + +static double limbConstA = 0.0 ; +static double limbConstB = 0.0 ; +static double limbConstC = 0.0 ; +static double limbConstD = 0.0 ; +static double limbConstE = 0.0 ; +static double limbConstF = 0.0 ; +static double limbConstG = 0.0 ; +static double limbConstH = 0.0 ; + + +// Sets the quadratic limb darkening parameters to arbitrary given values. +void setLimbDarkening (double quadA, double quadB) +{ + limbConstA = 1.0 - quadA - quadB ; + limbConstB = quadA + quadB + quadB ; + limbConstC = -quadB ; + limbConstD = M_PI * (1.0 - quadA - quadB - quadB) ; + limbConstE = 0.5 * M_PI * quadB ; + limbConstF = 2.0 * M_PI * (quadA + quadB + quadB) / 3.0 ; + limbConstG = M_PI * (1.0 - quadA - quadB) ; + limbConstH = M_PI * (6.0 - quadA - quadA - quadB) / 6.0 ; +} + +// cosAngle2 = the square of the cosine of the angle between the zenith of +// the star to the given point on the surface (relative to the star's center) +// Returns the solar limb darkening coefficient +// Note that it is normalized so that at the star's zenith (cosAngle2 = 1), it returns 1 +double limbDarkening (double cosAngle2) +{ + return (limbConstA + (limbConstB * sqrt(cosAngle2)) + (limbConstC * cosAngle2)) ; +} + + +// Analytically integrates the limb darkened disk, from r=0 to r=rBack +// rBack = the full radius of the star's disk +double integrateWholeDisk (double rBack) +{ + return (limbConstH * rBack * rBack) ; +} + + +// Analytically integrates the limb darkened disk, from 0 to the given finish +// finish = where to end the integration +// rBack = the radius of the back star's disk (the one seen as a crescent) +double integrateDiskFromStart (double finish, double rBack) +{ + const double x = sqr(finish / rBack) ; + + return (rBack * rBack * (((limbConstD + (limbConstE * x)) * x) + (limbConstF * (1.0 - cube(sqrt(1.0 - x)))))) ; +} + + +// Analytically integrates the limb darkened disk, from the given start to rBack +// start = from where to begin the integration +// rBack = the radius of the back star's disk (the one seen as a crescent) +double integrateDiskToFinish (double start, double rBack) +{ + const double x = 1.0 - sqr(start / rBack) ; + + return (rBack * rBack * (((limbConstG - (limbConstE * x)) * x) + (limbConstF * cube(sqrt(x))))) ; +} + + +// Uses the Secant method (not the most efficient method, but is simple and robust) +// Uses the fact that the function grows monotonically +// Returns finish or -1.0 if error +double invIntegrateDiskFromStart (double S, double rBack) +{ + double r1 = 0.0, S1 = 0.0 ; + double r2 = rBack, S2 = integrateWholeDisk(rBack) ; + double rTry, STry ; + + if ((S < S1) || (S > S2) || ((S2 - S1) < EPSILON)) + return (-1.0) ; // Error (out of bound) + + do + { + rTry = r1 + ((r2 - r1) * (S - S1) / (S2 - S1)) ; + STry = integrateDiskFromStart (rTry, rBack) ; + + if (STry < S) + { + r1 = rTry ; + S1 = STry ; + } + else + { + r2 = rTry ; + S2 = STry ; + } + } + while (fabs(S - STry) > EPSILON) ; + + return (rTry) ; +} + + +// Makes a simple trapezoid numerical integration with varying step size +// (smaller, the closer an asymptote) for finding the brightness of +// a partially hidden star. This method seems to works better than Simpson's. +// start = where to begin the integration +// finish = where to end the integration +// rFront = the radius of the front star's disk (the one seen as a disk) +// rBack = the radius of the back star's disk (the one seen as a crescent) +// D = the distance between the centers of the two stars +// dr0 = the infinitesimal integration step scale (unitless) +// Assumes: D + rFront >= finish >= start >= |D - rFront| >= 0 +// [num iterations] ~= 4 / dr0 ; [abs. error] ~= 0.1 * rFront^2 * dr0^2 +double integrateCrescent (double start, double finish, double rFront, double rBack, + double D, double dr0) +{ + const double rBack_m2 = 1.0 / (rBack * rBack) ; + const double rFront2mD2 = (rFront * rFront) - (D * D) ; + const double middle = 0.5 * (start + finish) ; + const double D2 = 2.0 * D ; + const double limitUp = (((D + rFront) < rBack) ? D + rFront : rBack) ; + const double limitDown = fabs(D - rFront) ; + double dr, r, r2, rStep, sum = 0.0 ; + + if (D < EPSILON) // To avoid division by zero + return (0.0) ; + + // Since dr0 is unitless and we want dr to have units of "distance" (1 = sum semi-major axes) + dr0 *= sqrt(rFront) ; + + // Step 1: integrate from middle up + dr = dr0 * sqrt(limitUp - middle) ; + for (rStep = middle + dr ; rStep < finish ; rStep += dr) + { + r = rStep - (0.5 * dr) ; + r2 = r * r ; + sum += (r * acos((rFront2mD2 - r2) / (D2 * r)) * limbDarkening(1.0 - (rBack_m2 * r2)) * dr) ; + + dr = dr0 * sqrt(limitUp - r) ; + } + + // Step 2: add sliver at upper edge + r = 0.5 * (finish + rStep - dr) ; + r2 = r * r ; + dr += (finish - rStep) ; + sum += (r * acos((rFront2mD2 - r2) / (D2 * r)) * limbDarkening(1.0 - (rBack_m2 * r2)) * dr) ; + + // Step 3: integrate from middle down + dr = dr0 * sqrt(middle - limitDown) ; + for (rStep = middle - dr ; rStep > start ; rStep -= dr) + { + r = rStep + (0.5 * dr) ; + r2 = r * r ; + sum += (r * acos((rFront2mD2 - r2) / (D2 * r)) * limbDarkening(1.0 - (rBack_m2 * r2)) * dr) ; + + dr = dr0 * sqrt(r - limitDown) ; + } + + // Step 4: add sliver at bottom edge + r = 0.5 * (start + rStep + dr) ; + r2 = r * r ; + dr += (rStep - start) ; + sum += (r * acos((rFront2mD2 - r2) / (D2 * r)) * limbDarkening(1.0 - (rBack_m2 * r2)) * dr) ; + + return (2.0 * sum) ; +} + +// =============== Calculate the observed radiation flux from the binary system =============== + +// Calculates the crescent of the back star, when it is partially eclipsed by the front star +// rFront = the radius of the star in front +// rBack = the radius of the star in back +// D = the distance between the centers of the two stars +// dr = the infinitesimal integration step +// Returns the relative brightness of the crescent of the back star +double integrateBackOverlap (double rFront, double rBack, double D, double dr) +{ + if (rFront < D) + { + if (rBack > (D + rFront)) + return (integrateDiskFromStart (D - rFront, rBack) + + integrateCrescent (D - rFront, D + rFront, rFront, rBack, D, dr) + + integrateDiskToFinish (D + rFront, rBack)) ; // E + + return (integrateDiskFromStart (D - rFront, rBack) + + integrateCrescent (D - rFront, rBack, rFront, rBack, D, dr)) ; // B + } + + if (rFront > (D + rBack)) + return (0.0) ; // D + + if (rBack > (D + rFront)) + return (integrateCrescent (rFront - D, rFront + D, rFront, rBack, D, dr) + + integrateDiskToFinish (rFront + D, rBack)) ; // F + + return (integrateCrescent (rFront - D, rBack, rFront, rBack, D, dr)) ; // C +} + + +/****************************************************************** + * Returns the total flux from both stars of the binary. + * Ignores limb darkening, gravity darkening, etc. + * time = scan parameter 0 <= time < 1 + * p[DIM] = parameter vector + * dr = the infinitesimal integration step + * doB12max = 1:recalculate the light curve "plateau" brightness ; 0:don't + * + * NOTE: [definition of 1 unit distance] + * The physical distance between the stars at perihelion (minimal) is (1-e) units + * and the physical distance between the stars at aphelion (maximal) is (1+e) units + * so, 1 unit is the sum of the distances of the two semi-major axes + * + ******************************************************************/ +double flux (double time, double p[DIM], double dr, int doB12max) +{ + static double B12max ; + const double omega = 2.0 * M_PI * (p[D_TPO] - p[D_TMO]) ; + const double meanAnomaly = 2.0 * M_PI * (time - p[D_TPO] - p[D_TMO]) ; // 2pi * (t - t0) + double D2, D, E ; + + if (doB12max) + B12max = (p[D_B1] * integrateWholeDisk(p[D_R1])) + (p[D_B2] * integrateWholeDisk(p[D_R2])) ; + + E = eccentricAnomaly(meanAnomaly, p[D_ECC]) ; + + D2 = sqr(1.0 - (p[D_ECC] * cos(E))) - sqr((((cos(E) - p[D_ECC]) * sin(omega)) + (sqrt(1.0 - sqr(p[D_ECC])) * sin(E) * cos(omega))) * p[D_SIN_I]) ; + + if (D2 > (EPSILON * EPSILON)) // Prevents sqrt of negative or division by zero, later on + D = sqrt (D2) ; + else + D = EPSILON ; + + if (D >= (p[D_R1] + p[D_R2])) // Side by side (A) + return (B12max) ; + + if (sin(E + omega) > 0.0) // Is star 2 in front ? + return ((p[D_B2] * integrateWholeDisk(p[D_R2])) + (p[D_B1] * integrateBackOverlap(p[D_R2], p[D_R1], D, dr))) ; + + // star 1 is in front + return ((p[D_B1] * integrateWholeDisk(p[D_R1])) + (p[D_B2] * integrateBackOverlap(p[D_R1], p[D_R2], D, dr))) ; +} + + + +// Returns the reduced chi squared of the model (a score for how good the fit is) +// If the parameter vector is out of range, returns MAXFLOAT +double scoreFit (double p[DIM], float *time, float *amp, float *err, int size, double dr) +{ + int i ; + double score ; + + // Note that I use MAXFLOAT in a double since is may need to be increased by annealingFluctuation() + if ((p[D_SIN_I] <= 0.0) || (p[D_SIN_I] > 1.0)) return (MAXFLOAT) ; // sin_i + if ((p[D_ECC] < 0.0) || (p[D_ECC] >= 1.0)) return (MAXFLOAT) ; // e + if ((p[D_R1] <= 0.0) || (p[D_R1] >= (1.0 - p[D_ECC]))) return (MAXFLOAT) ; // r1 + if ((p[D_R2] <= 0.0) || (p[D_R2] >= (1.0 - p[D_ECC] - p[D_R1]))) return (MAXFLOAT) ; // r2 + if (p[D_B1] <= 0.0) return (MAXFLOAT) ; // b1 + if (p[D_B2] <= 0.0) return (MAXFLOAT) ; // b2 + + score = sqr((amp[0] - flux(time[0], p, dr, 1)) / err[0]) ; + + for (i = 1 ; i < size ; i++) + score += sqr((amp[i] - flux(time[i], p, dr, 0)) / err[i]) ; + + return (score / (size - DIM)) ; +} + + +// A statistical test +// Returns the correlation of a given residual with the previous residual +// this is to be used to distinguish between models that the mediocre throughout +// and models that are good in some places but very bad in others. My assumption is +// that the latter is far better situation than the former. In the extreme case of +// the latter, chi^2 should be 1, unless there is a problem with the error bars. +// Returns score in the range [-1, 1] (Cauchy-Schwartz inequality) +// A score of 1 means that the model is entirely above or entirely below the data +// A score of 0 is ideal (should have chi^2 = 1) +// A score below 0 is very suspect and probably unphysical +double midScatterScore (double p[DIM], float *time, float *amp, int size) +{ + int i ; + double prevDelta, delta, sumWithPrev = 0.0, sumSqr = 0.0 ; + + // Note that I use MAXFLOAT in a double since is may need to be increased by annealingFluctuation() + if ((p[D_SIN_I] <= 0.0) || (p[D_SIN_I] > 1.0)) return (MAXFLOAT) ; // sin_i + if ((p[D_ECC] < 0.0) || (p[D_ECC] >= 1.0)) return (MAXFLOAT) ; // e + if ((p[D_R1] <= 0.0) || (p[D_R1] >= (1.0 - p[D_ECC]))) return (MAXFLOAT) ; // r1 + if ((p[D_R2] <= 0.0) || (p[D_R2] >= (1.0 - p[D_ECC] - p[D_R1]))) return (MAXFLOAT) ; // r2 + if (p[D_B1] <= 0.0) return (MAXFLOAT) ; // b1 + if (p[D_B2] <= 0.0) return (MAXFLOAT) ; // b2 + + prevDelta = flux(time[size-1], p, MIN_INTEGRATION_STEP, 1) - amp[size-1] ; + + for (i = 0 ; i < size ; i++) + { + delta = flux(time[i], p, MIN_INTEGRATION_STEP, 0) - amp[i] ; + + sumWithPrev += (prevDelta * delta) ; + sumSqr += (delta * delta) ; + + prevDelta = delta ; + } + + if (sumSqr == 0.0) + return (0.0) ; + + return (sumWithPrev / sumSqr) ; +} + + + +#ifdef GRAPHIC +#ifdef PLOT +// Plot the light curve and the fit of the given parameters +// if (p == 0) then ignores the fit +// if (time == 0) then ignores the light curve data +// color = color of the model light curve plot +// if (doInterpol == 1) then plots the spline interpolation of the data +// checkVal = an arbitrary value the user wished to scale like the plot +// Returns the scales Y-axis pixel value which corresponds to (checkVal) +int plotFit (double p[DIM], float *time, float *amp, int size, int color, int doInterpol, double checkVal) +{ + double x, F, Fmin, Fmax ; + int i, y ; + + if (p) + { + if ((p[D_SIN_I] <= 0.0) || (p[D_SIN_I] > 1.0)) return(-1) ; // sin_i + if ((p[D_ECC] < 0.0) || (p[D_ECC] >= 1.0)) return(-2) ; // e + if ((p[D_R1] <= 0.0) || (p[D_R1] >= (1.0 - p[D_ECC]))) return(-3) ; // r1 + if ((p[D_R2] <= 0.0) || (p[D_R2] >= (1.0 - p[D_ECC] - p[D_R1]))) return(-4) ; // r2 + if (p[D_B1] <= 0.0) return(-5) ; // b1 + if (p[D_B2] <= 0.0) return(-6) ; // b2 + } + + Fmin = Fmax = amp[0] ; + + for (i = 1 ; i < size ; i++) + { + if (amp[i] < Fmin) + Fmin = amp[i] ; + else + if (amp[i] > Fmax) + Fmax = amp[i] ; + } + + if (p) + for (x = 0.0 ; x < 1.0 ; x += 0.001) + { + F = flux (x, p, MIN_INTEGRATION_STEP, 1) ; + + y = 400 - (int)(250.0 * (F - Fmin) / (Fmax - Fmin)) ; + if (y > 479) y = 479 ; + if (y < 0) y = 0 ; + + putpixel ((int)(640 * x), y, color) ; + } + + + if (time && doInterpol) + for (x = 0.0 ; x < 1.0 ; x += 0.001) + { + F = interpolateSmooth(x, time, amp, size) ; + + y = 400 - (int)(250.0 * (F - Fmin) / (Fmax - Fmin)) ; + + if ((y <= 479) && (y >= 0)) + putpixel ((int)(640 * x), y, 5) ; + } + + + if (time) + for (i = 0 ; i < size ; i++) + { + y = 400 - (int)(250.0 * (amp[i] - Fmin) / (Fmax - Fmin)) ; + + if ((y <= 479) && (y >= 0)) + putpixel ((int)(640 * time[i]), y, 15) ; + } + + return (400 - (int)(250.0 * (checkVal - Fmin) / (Fmax - Fmin))) ; +} +#endif +#endif + + +#ifdef WRITE_CURVE_FIT +// Writes to files - the raw data and the fitted curve +// Returns: 0 = success otherwise failed +int writeCurveFit (double p[DIM], float *time, float *amp, float *err, int size, char *filename) +{ + FILE *fout ; + char *localFilename, foutName[256] ; + int i ; + + i = strlen(filename) ; + for (i-- ; (i >= 0) && (filename[i] != '/') ; i--) ; // goes to slash or -1 + localFilename = &filename[i+1] ; + + //--------------------- + + sprintf (foutName, "%s.data", localFilename) ; + fout = fopen (foutName, "wt") ; + if (!fout) + return (1) ; + + for (i = 0 ; i < size ; i++) + fprintf (fout, "%f %f %f %f\n", time[i], -2.5 * log10(amp[i]), MAGNITUDE_ERROR_FACTOR * err[i] / amp[i], + 2.5 * log10(flux(time[i], p, MIN_INTEGRATION_STEP, 1) / amp[i])) ; + fclose (fout) ; + + //--------------------- + + sprintf (foutName, "%s.fit", localFilename) ; + fout = fopen (foutName, "wt") ; + if (!fout) + return (2) ; + + for (i = 0 ; i < WRITE_CURVE_FIT ; i++) + fprintf (fout, "%f %f\n", (float)i / WRITE_CURVE_FIT, + -2.5 * log10(flux ((float)i / WRITE_CURVE_FIT, p, MIN_INTEGRATION_STEP, 1))) ; + + fclose (fout) ; + return (0) ; +} +#endif + +//======================= Make a "first guess" for the initial parameters =============================== + +// Data structure of a single light curve observation +struct LCdata +{ + float primary ; + float secondary ; + float tertiary ; +}; + + +// A comparator for a pair-o-float +// Returns: -1 if x.primary < y.primary +// 0 if x.primary = y.primary +// 1 if x.primary > y.primary +int comparLCdata (const void *x, const void *y) +{ + if (((struct LCdata*)x)->primary < ((struct LCdata*)y)->primary) + return (-1) ; + else + return (((struct LCdata*)x)->primary > ((struct LCdata*)y)->primary) ; +} + + +// Returns 0 = success ; 1 = failure (malloc) +int sortSamples (float *time, float *amp, float *err, int size) +{ + int i ; + struct LCdata *arr = (struct LCdata*)malloc (size * sizeof(struct LCdata)) ; + + if (!arr) + return (1) ; + + for (i = 0 ; i < size ; i++) + { + arr[i].primary = time[i] ; + arr[i].secondary = amp[i] ; + arr[i].tertiary = err[i] ; + } + + qsort (arr, size, sizeof(struct LCdata), comparLCdata) ; + + for (i = 0 ; i < size ; i++) + { + time[i] = arr[i].primary ; + amp[i] = arr[i].secondary ; + err[i] = arr[i].tertiary ; + } + + free (arr) ; + return (0) ; +} + + +// A comparator function for sorting an array of floating point values +int comparFloat (const void *x, const void *y) +{ + if ((*((float*)x)) < (*((float*)y))) + return (-1) ; + else + return ((*((float*)x)) > (*((float*)y))) ; +} + + + +// Given a floating point array +// Returns the median amplitude (or MAXFLOAT in case of error - malloc failure) +double getMedian (float *data, int size) +{ + double median ; + float *arr = (float*)malloc (size * sizeof(float)) ; + + if (!arr) + return (MAXFLOAT) ; + + memcpy (arr, data, size * sizeof(float)) ; + + qsort (arr, size, sizeof(float), comparFloat) ; + + if (size & 1) // is odd + median = arr[(size-1)/2] ; + else // is even + median = 0.5 * (arr[size/2] + arr[(size/2)-1]) ; + + free (arr) ; + return (median) ; +} + + + +//----------------------- Find dips ------------------------------------ + +// Finds the two deepest minima of the light curve +// The primary minimum is simply at the smallest value +// The secondary minimum is the place that has the largest difference +// between the value itself and the maxima between it and the primary minimum +// Note: if a secondary minimum can't be found (very unusual), will set *pmin2Amp = MAXFLOAT +// May return an "edge minimum"- near (*pmin1Time + 0.5) +// *pmax1Amp >= *pmax2Amp >= *pmin1Amp ; *pmax1Amp >= *pmin2Amp >= *pmin1Amp +// ideally: *pmax2Amp > *pmin2Amp but there is no guarantee (can be used as a test) +void findDips (float *time, float *amp, int size, + double *pmin1Time, double *pmin2Time, + double *pmin1Amp, double *pmin2Amp) +{ + double maxAmp, min1TimePlusHalf ; + double diff, sampTime, sampAmp ; + + // Step 1: search for the primary dip + *pmin1Time = 0.0 ; + *pmin1Amp = interpolateSmooth (0.0, time, amp, size) ; + + for (sampTime = SEARCH_STEP_SIZE ; sampTime < 1.0 ; sampTime += SEARCH_STEP_SIZE) + { + sampAmp = interpolateSmooth (sampTime, time, amp, size) ; + + if (*pmin1Amp > sampAmp) + { + *pmin1Amp = sampAmp ; + *pmin1Time = sampTime ; + } + } + + // Step 2: search for the secondary dip min1TimePlusHalf = min1Time + 0.5 ; + *pmin2Amp = MAXFLOAT ; + min1TimePlusHalf = *pmin1Time + 0.5 ; + maxAmp = *pmin1Amp ; + diff = 0.0 ; + + if (min1TimePlusHalf < 1.0) + { + // 2.1: look up + for (sampTime = *pmin1Time + SEARCH_STEP_SIZE ; sampTime <= min1TimePlusHalf ; sampTime += SEARCH_STEP_SIZE) + { + sampAmp = interpolateSmooth(sampTime, time, amp, size) ; + + if (maxAmp < sampAmp) + maxAmp = sampAmp ; + + if (diff < maxAmp - sampAmp) + { + diff = maxAmp - sampAmp ; + *pmin2Time = sampTime ; + *pmin2Amp = sampAmp ; + } + } + + // 2.2: look down + maxAmp = *pmin1Amp ; + + for (sampTime = *pmin1Time - SEARCH_STEP_SIZE ; sampTime >= 0.0 ; sampTime -= SEARCH_STEP_SIZE) + { + sampAmp = interpolateSmooth(sampTime, time, amp, size) ; + + if (maxAmp < sampAmp) + maxAmp = sampAmp ; + + if (diff < maxAmp - sampAmp) + { + diff = maxAmp - sampAmp ; + *pmin2Time = sampTime ; + *pmin2Amp = sampAmp ; + } + } + + for (sampTime = 1.0 - SEARCH_STEP_SIZE ; sampTime >= min1TimePlusHalf ; sampTime -= SEARCH_STEP_SIZE) + { + sampAmp = interpolateSmooth(sampTime, time, amp, size) ; + + if (maxAmp < sampAmp) + maxAmp = sampAmp ; + + if (diff < maxAmp - sampAmp) + { + diff = maxAmp - sampAmp ; + *pmin2Time = sampTime ; + *pmin2Amp = sampAmp ; + } + } + } + else // min1TimePlusHalf >= 1.0 + { + min1TimePlusHalf -= 1.0 ; // modulo 1.0 + + // 2.1: look up + for (sampTime = *pmin1Time + SEARCH_STEP_SIZE ; sampTime < 1.0 ; sampTime += SEARCH_STEP_SIZE) + { + sampAmp = interpolateSmooth(sampTime, time, amp, size) ; + + if (maxAmp < sampAmp) + maxAmp = sampAmp ; + + if (diff < (maxAmp - sampAmp)) + { + diff = maxAmp - sampAmp ; + *pmin2Time = sampTime ; + *pmin2Amp = sampAmp ; + } + } + + for (sampTime = 0.0 ; sampTime <= min1TimePlusHalf ; sampTime += SEARCH_STEP_SIZE) + { + sampAmp = interpolateSmooth(sampTime, time, amp, size) ; + + if (maxAmp < sampAmp) + maxAmp = sampAmp ; + + if (diff < maxAmp - sampAmp) + { + diff = maxAmp - sampAmp ; + *pmin2Time = sampTime ; + *pmin2Amp = sampAmp ; + } + } + + // 2.2: look down + maxAmp = *pmin1Amp ; + + for (sampTime = *pmin1Time - SEARCH_STEP_SIZE ; sampTime >= min1TimePlusHalf ; sampTime -= SEARCH_STEP_SIZE) + { + sampAmp = interpolateSmooth(sampTime, time, amp, size) ; + + if (maxAmp < sampAmp) + maxAmp = sampAmp ; + + if (diff < (maxAmp - sampAmp)) + { + diff = maxAmp - sampAmp ; + *pmin2Time = sampTime ; + *pmin2Amp = sampAmp ; + } + } + } +} + + +// Finds the values (should be about max) between the dips (pmax1Amp, pmax2Amp) +// given the average of the two dip times (min1Time, min2Time) +// Note that we can't use a simple max, since it may pick the interpolation +// overshoot at the edges of the dips. This approach is far more robust. +// Sets: *pmax1Amp >= *pmax2Amp +void findMidMaxs (float *time, float *amp, int size, double avrTime, double *pmax1Amp, double *pmax2Amp) +{ + *pmax1Amp = interpolateSmooth(avrTime, time, amp, size) ; + *pmax2Amp = interpolateSmooth(mod1(avrTime + 0.5), time, amp, size) ; + + if (*pmax1Amp < *pmax2Amp) + swap(pmax1Amp, pmax2Amp) ; +} + + + +// Assumed that goalAmp is larger than the amplitude at *pminTime +// Returns the half-width of the given dip, to the point it crosses a given goal amplitude (goalAmp) +// Returns 0.0 upon fatal error (couldn't reach goalAmp) +double findHalfWidth (float *time, float *amp, int size, double goalAmp, double *pminTime) +{ + double minTimePlusHalf = *pminTime + 0.5 ; + double sampAmp, sampTime ; + double limitUp = MAXFLOAT, limitDown = MAXFLOAT ; + double limitUpFineTune, limitDownFineTune ; + + // Step 1: find dip edges + if (minTimePlusHalf < 1.0) + { + // 1.1: look up + for (sampTime = *pminTime + SEARCH_STEP_SIZE ; (limitUp == MAXFLOAT) && (sampTime <= minTimePlusHalf) ; sampTime += SEARCH_STEP_SIZE) + { + sampAmp = interpolateSmooth(sampTime, time, amp, size) ; + + if (sampAmp > goalAmp) + limitUp = sampTime ; + } + + // 1.2: look down + for (sampTime = *pminTime - SEARCH_STEP_SIZE ; (limitDown == MAXFLOAT) && (sampTime >= 0.0) ; sampTime -= SEARCH_STEP_SIZE) + { + sampAmp = interpolateSmooth(sampTime, time, amp, size) ; + + if (sampAmp > goalAmp) + limitDown = sampTime ; + } + + for (sampTime = 1.0 - SEARCH_STEP_SIZE ; (limitDown == MAXFLOAT) && (sampTime >= minTimePlusHalf) ; sampTime -= SEARCH_STEP_SIZE) + { + sampAmp = interpolateSmooth(sampTime, time, amp, size) ; + + if (sampAmp > goalAmp) + limitDown = sampTime ; + } + } + else // minTimePlusHalf >= 1.0 + { + minTimePlusHalf -= 1.0 ; // modulo 1.0 + + // 1.1: look up + for (sampTime = *pminTime + SEARCH_STEP_SIZE ; (limitUp == MAXFLOAT) && (sampTime < 1.0) ; sampTime += SEARCH_STEP_SIZE) + { + sampAmp = interpolateSmooth(sampTime, time, amp, size) ; + + if (sampAmp > goalAmp) + limitUp = sampTime ; + } + + for (sampTime = 0.0 ; (limitUp == MAXFLOAT) && (sampTime <= minTimePlusHalf) ; sampTime += SEARCH_STEP_SIZE) + { + sampAmp = interpolateSmooth(sampTime, time, amp, size) ; + + if (sampAmp > goalAmp) + limitUp = sampTime ; + } + + // 1.2: look down + for (sampTime = *pminTime - SEARCH_STEP_SIZE ; (limitDown == MAXFLOAT) && (sampTime >= minTimePlusHalf) ; sampTime -= SEARCH_STEP_SIZE) + { + sampAmp = interpolateSmooth(sampTime, time, amp, size) ; + + if (sampAmp > goalAmp) + limitDown = sampTime ; + } + } + + if ((limitUp == MAXFLOAT) || (limitDown == MAXFLOAT)) + return (0.0) ; // Fatal error - couldn't reach goalAmp + + + // Step 2: correct for the search overshoot: (error <= 0.5) + limitUp -= (0.5 * SEARCH_STEP_SIZE) ; + if (limitUp < 0.0) limitUp += 1.0 ; + + limitDown += (0.5 * SEARCH_STEP_SIZE) ; + if (limitDown >= 1.0) limitDown -= 1.0 ; + + // Step 3: fine tune output results: + limitUpFineTune = findGoalSmooth (limitUp, goalAmp, time, amp, size) ; + limitDownFineTune = findGoalSmooth (limitDown, goalAmp, time, amp, size) ; + + // Step 4: update minimum: (especially important for square dips) + if (limitDownFineTune < limitUpFineTune) + { + *pminTime = 0.5 * (limitUpFineTune + limitDownFineTune) ; + + return (0.5 * (limitUpFineTune - limitDownFineTune)) ; + } + + *pminTime = 0.5 * (1.0 + limitUpFineTune + limitDownFineTune) ; + + if (*pminTime > 1.0) + *pminTime -= 1.0 ; + + return (0.5 * (1.0 + limitUpFineTune - limitDownFineTune)) ; +} + + + +// Outputs *pvarySin_i, a midway allowable variance for sin(i) +// when the returned value is close to the minimum *pvarySin_i become negative +// this is needed since if it is varied too much, the result will be only MAXFLOATs +// Given: Y = -e * sin(omega), for dip #1 and e * sin(omega), for dip #2 +// doShapeWarnings is for avoiding giving two warnings for the same problem (r1r2) +// Returns 1.0 or best guess upon error +double getSin_i (float *time, float *amp, int size, double e, + double Y, double Rmin, double Rmax, double *pvarySin_i, + double minTime, double minAmp, double medianAmp, + double halfWidth, char *filename, char *errFilename, int doShapeWarnings) +{ + double tmpMinTime, goalAmp, wideHalfWidth, narrowHalfWidth ; + double q, p, H, widthMultiple, cosi2, res ; + const double minSin_i = sqrt(1.0 - sqr(Rmin+Rmax)) ; // the valid range is between this and 1.0 + + *pvarySin_i = 0.5 * (1.0 - minSin_i) ; // default value + + tmpMinTime = minTime ; + goalAmp = (0.75 * medianAmp) + (0.25 * minAmp) ; + wideHalfWidth = findHalfWidth(time, amp, size, goalAmp, &tmpMinTime) ; + + tmpMinTime = minTime ; + goalAmp = (0.25 * medianAmp) + (0.75 * minAmp) ; + narrowHalfWidth = findHalfWidth(time, amp, size, goalAmp, &tmpMinTime) ; + + + if ((narrowHalfWidth >= halfWidth) || (narrowHalfWidth == 0.0) || (wideHalfWidth <= halfWidth)) + { + if (doShapeWarnings) + printError (errFilename, 0, filename, "getSin_i()", "Invalid slope") ; + + return (1.0) ; + } + + if (wideHalfWidth >= 0.25) + { + if (doShapeWarnings) + printError (errFilename, 0, filename, "getSin_i()", "Dip is too wide") ; + + return (1.0) ; + } + + + // Compensate for the widening of the ingress/egress by the smoothing kernel + H = (double)NUM_POINTS_HALF_KERNEL / size ; + q = 0.5 * (wideHalfWidth - narrowHalfWidth) / H ; + + if (q >= 1.0) p = 2.0 * q ; + else if (q > 0.3232309) p = q + sqrt(1.8 - sqrt(3.84 - (3.2 * q))) ; + else if (q > 0.2288838) p = sqrt(1.8 - (0.4 / q) - (q * q)) ; // Rounded up to protect against sqrt of negative + else return (1.0) ; + + widthMultiple = halfWidth / (p * H) ; + + if (widthMultiple > (Rmax / Rmin)) + { + // This error is commented out because it is too common, especially when the (r1r2) guess is wrong + // it could be, in principal, used to cancel the wrong guess, but it is simply too uncertain + + // printError (errFilename, 0, filename, "getSin_i()", "Invalid width multiple") ; + return (1.0) ; + } + + cosi2 = sqr(Rmax * (1.0 + Y) / (1.0 - (e * e))) * (1.0 - (widthMultiple * Rmin / Rmax)) ; + + if (cosi2 < 0.0) + { + printError (errFilename, 0, filename, "getSin_i()", "Negative cos(i)^2") ; + return (1.0) ; + } + + if ((cosi2 >= sqr(Rmin + Rmax)) || (cosi2 >= 1.0)) // The latter should never happen + { + printError (errFilename, 0, filename, "getSin_i()", "Sin(i) is too small") ; + cosi2 = Rmax * Rmax ; + } + + res = sqrt(1.0 - cosi2) ; + + if ((1.0 - res) <= (res - minSin_i)) + *pvarySin_i = 0.5 * (res - minSin_i) ; // Closer to max + else + *pvarySin_i = 0.5 * (res - 1.0) ; // Closer to min - set a negative variance + + return (res) ; +} + + +// =============== Take dips into account ========================= + + +// Returns 1 if t is in the given dip, otherwise returns 0 +int isInDip (double t, double minTime, double halfWidth) +{ + double diff = fabs(t - minTime) ; + + return ((diff < halfWidth) || ((1.0 - diff) < halfWidth)) ; +} + + +// Makes sure that there is a sufficient amount of data to contain a given dip +// minTime = time of dip's minimum +// halfWidth = the dip's half width +// returns: 1 = OK, plenty of data for dip ; 0 = bad, not enough data +int isEnoughDipData (float *time, int size, double minTime, double halfWidth) +{ + int i, dipDataNum = 0 ; + + for (i = 0 ; (dipDataNum < MIN_NUM_DATA) && (i < size) ; i++) + if (isInDip (time[i], minTime, halfWidth)) + dipDataNum++ ; + + return (dipDataNum == MIN_NUM_DATA) ; +} + + +// This may only correct by a small amount. But it's very important to get the +// depth of the small dip as accurately as possible +// pPlateauStdDiv = (output pointer) the standard deviation of the plateau amplitudes +// pPlateauWaviness = (output pointer) a statistical measure for how wavy the plateau is. +// The range is [-1,1], where 1 is very wavy and 0 is not wavy at all (negative values are unphysical). +// note that this is better than stddiv around a spline because a spline, +// has the nasty effect overshooting around the dips even worse, the spline stddiv is strongly +// determined by NUM_POINTS_HALF_KERNEL and might trace the deviations too closely (or not close enough) +// Returns: 0 = success ; 1 = malloc failure ; 2 = goal couldn't be reached ; 3 = not enough data in plateau ; 4 = zero variance +int findPlateauMedian (float *time, float *amp, int size, double min1Time, double min2Time, + double *pMedianAmp, double *pPlateauStdDiv, double *pPlateauWaviness) +{ + int i, reducedSize = 0 ; + double min1HalfWidth, min2HalfWidth ; + double sumVarianceAmp, sumNeighborVariance ; + float *reducedAmp = (float*)malloc (size * sizeof(float)) ; + + if (!reducedAmp) + return (1) ; + + min1HalfWidth = findHalfWidth(time, amp, size, *pMedianAmp, &min1Time) ; + min2HalfWidth = findHalfWidth(time, amp, size, *pMedianAmp, &min2Time) ; + + if ((min1HalfWidth == 0.0) || (min2HalfWidth == 0.0)) + { + free (reducedAmp) ; + return (2) ; + } + + for (i = 0 ; i < size ; i++) + if (!isInDip (time[i], min1Time, min1HalfWidth) && !isInDip(time[i], min2Time, min2HalfWidth)) + { + reducedAmp[reducedSize] = amp[i] ; + reducedSize++ ; + } + + // Check the number of data points in the plateau + if (reducedSize < MIN_NUM_DATA) + { + free (reducedAmp) ; + return (3) ; + } + + *pMedianAmp = getMedian(reducedAmp, size) ; + + // Note that I'm calculating around the median, instead of the average, so to make it more robust + // doing this will make the variance (or sumVarianceAmp) larger than it would have been otherwise. + // but usually the average will be very close to the median, so the difference will be small. + // Also note that I'm calculating the variance in this manner to reduce numerical errors, which + // could become a serious problem. + // Waviness measure - from the ratio of the mean square difference between neighbor to the variance + sumVarianceAmp = sqr(reducedAmp[0] - (*pMedianAmp)) ; + sumNeighborVariance = sqr(reducedAmp[size-1] - reducedAmp[0]) ; + for (i = 1 ; i < reducedSize ; i++) + { + sumVarianceAmp += sqr(reducedAmp[i] - (*pMedianAmp)) ; + sumNeighborVariance += sqr(reducedAmp[i-1] - reducedAmp[i]) ; + } + + if (sumVarianceAmp <= 0.0) + return (4) ; // This should never happen + + *pPlateauStdDiv = sqrt(sumVarianceAmp / reducedSize) ; + *pPlateauWaviness = 1.0 - (0.5 * sumNeighborVariance / sumVarianceAmp) ; + + free (reducedAmp) ; + return (0) ; +} + + +//----------------------- Simplex + simulated annealing ------------------------------------------ + +// Calculates the annealing fluctuation. In converts a flatly distributed random +// variable rand() to a logarithmicly distributed one. +// The temperature (ignoring the Boltzmann's constant) multiplies this +// distribution to create a perturbation above 1.0 + +// Returns a random, logarithmicly distributed perturbation above 1.0 +// probability: p(x) = (1/t) * exp(-(x-1)/t) ; where t = temperature +double annealingFluctuation (double temperature) +{ // May be optimize by subtracting a precalculated: log(1.0 + RAND_MAX) + + return (1.0 - (temperature * log((1.0 + rand()) / (1.0 + RAND_MAX)))) ; +} + +// Part of the downhill simplex method with simulated annealing [press 2002] +// Checks out a given extrapolation factor for the highest point. If it is an improvement +// then it replaces the highest point (makes all the necessary updates). +// p[DIM+1][DIM] - a matrix of the (DIM+1) simplex points in the DIM-dimensional space +// y[DIM+1] - the score (function value) of the (DIM+1) simplex points +// psum[DIM] - the sum of the points' values in each of the DIM coordinates +// ihi - the index for the highest point +// yhi - the value the highest point +// fac - the factor for the highest point, where: +// fac= 1 --> highest point +// fac= 0 --> c.m. of the remaining points +// fac= 0.5 --> mid point between the highest point and the c.m. of the remaining points +// fac= -1 --> reflection of the highest point about the c.m. of the remaining points +// dr - the infinitesimal integration step +// Returns the score (function value) for the extrapolated point +// Note that the arrays: p, y, psum - may get updated +double amotry (double p[DIM+1][DIM], double y[DIM+1], double psum[DIM], int ihi, double *pyhi, + double fac, float *time, float *amp, float *err, int size, double dr, double temperature, + double pElite[DIM], double *pyElite, int *piElite) +{ + int j ; + double ytry, yflu ; + double ptry[DIM] ; + double fac1 = (1.0 - fac) / DIM ; + double fac2 = fac1 - fac ; + + for (j = 0 ; j < DIM ; j++) + ptry[j] = (psum[j] * fac1) - (p[ihi][j] * fac2) ; + + ytry = scoreFit (ptry, time, amp, err, size, dr) ; + + yflu = ytry / annealingFluctuation(temperature) ; // with a random thermal fluctuation subtracted + + // Replaces the highest (worst) value with the new one. The new one will always + // Get copied if it's better than the worst and sometimes even if it's not + if (yflu < (*pyhi)) + { + if (ytry <= (*pyElite)) + { + *pyElite = ytry ; + *piElite = ihi ; + } + else if (ihi == (*piElite)) // About to overwrite the elite + { + memcpy (pElite, p[ihi], DIM * sizeof(double)) ; + *piElite = -1 ; + } + + y[ihi] = ytry ; + *pyhi = yflu ; + + for (j = 0 ; j < DIM ; j++) + psum[j] += (ptry[j] - p[ihi][j]) ; + + memcpy (p[ihi], ptry, DIM * sizeof(double)) ; + } + + return (yflu) ; +} + + +// Based on the downhill simplex method with simulated annealing [press 2002] +// includes Elite refresh [Aldous 1994], where you revert to the +// best fit found so far. This can be done every given number of iterations, +// or as I chose, a certain number of refreshes throughout the convergence +// p[DIM+1][DIM] - a matrix of the (DIM+1) simplex points in the DIM-dimensional space +// y[DIM+1] - the score (function value) of the (DIM+1) simplex points +// numIterations = number of iteration to run the convergence algorithm +// Returns the index for the lowest (best) point in the simplex +int amoeba (double p[DIM+1][DIM], double y[DIM+1], float *time, float *amp, float *err, + int size, long numIterations) +{ + int i, j ; // Temporary + int ilo, ihi ; // Fluctuated + int iElite ; // Exact value + double ytry, yhi, ylo, ynhi, ysave ; // Fluctuated + double sum, psum[DIM], pElite[DIM], yElite ; // Exact values ; pElite is only used when the elite is overwritten (iElite == -1) + double dr, temperature = INIT_TEMPERATURE ; // Utilities + const double coolingFactor = 1.0 - ((double)NUM_E_FOLD_COOL / numIterations) ; // A good approximation, if near 1.0 + const long minStepIteration = (long)(numIterations * MIN_STEP_ITERATION_FRACTION) ; + + // Step 1: [elitism] backup the best candidate + yElite = y[0] ; + iElite = 0 ; + for (i = 1 ; i <= DIM ; i++) + if (y[i] < yElite) + { + yElite = y[i] ; + iElite = i ; + } + + + // Step 2: initializes the sum of values in each coordinate + // (to be used later for calculating the average, or center of mass) + for (j = 0 ; j < DIM ; j++) + { + sum = p[0][j] ; + for (i = 1 ; i <= DIM ; i++) + sum += p[i][j] ; + + psum[j] = sum ; + } + + + // The main loop + while (numIterations > 0) + { + // Step 3: find- + // ihi = index of highest (worst) value + // ilo = index of lowest (best) value + // yhi = highest (worst) value + // ynhi = second highest (worst) value + // ylo = lowest (best) value + + // 3.1: sort the first two + ynhi = ylo = y[0] * annealingFluctuation(temperature) ; + yhi = y[1] * annealingFluctuation(temperature) ; + + if (ylo <= yhi) + { + ihi = 1 ; + ilo = 0 ; + } + else + { + ihi = 0 ; + ilo = 1 ; + ynhi = yhi ; // Swap: ynhi=ylo <--> yhi + yhi = ylo ; + ylo = ynhi ; + } + + // 3.2: sort the rest + for (i = 2 ; i <= DIM ; i++) + { + ytry = y[i] * annealingFluctuation(temperature) ; + + if (ytry <= ylo) + { + ilo = i ; + ylo = ytry ; + } + + if (ytry > yhi) + { + ihi = i ; + ynhi = yhi ; + yhi = ytry ; + } + else if (ytry > ynhi) + { + ynhi = ytry ; + } + } + + + // Step 4: integration step size + if ((numIterations > minStepIteration) && (yElite > 1.0)) + dr = MIN_INTEGRATION_STEP * sqrt(sqrt(yElite)) ; + else + dr = MIN_INTEGRATION_STEP ; + + + // Step 5: try various extrapolations (reflection, expansion and contraction) + + // 5.1: reflect the high point to the other side of the c.m. of the rest + ytry = amotry(p, y, psum, ihi, &yhi, -1.0, time, amp, err, size, dr, temperature, pElite, &yElite, &iElite) ; + numIterations-- ; + + if (ytry <= ylo) + { + // 5.1.1: if the result was the lowest so far, expand it more + ytry = amotry(p, y, psum, ihi, &yhi, 2.0, time, amp, err, size, dr, temperature, pElite, &yElite, &iElite) ; + numIterations-- ; + } + else if (ytry >= ynhi) + { + // 5.1.2: if the result is still the highest, try interpolating + ysave = yhi ; + ytry = amotry(p, y, psum, ihi, &yhi, 0.5, time, amp, err, size, dr, temperature, pElite, &yElite, &iElite) ; + numIterations-- ; + + // 5.1.2.1: if the result is even worse, contract around the lowest (best) point + if (ytry >= ysave) + { + if ((iElite != -1) && (iElite != ilo)) // about to overwrite the elite + { + memcpy (pElite, p[iElite], DIM * sizeof(double)) ; + iElite = -1 ; + } + + for (i = 0 ; i < (DIM+1) ; i++) + if (i != ilo) + { + for (j = 0 ; j < DIM ; j++) + p[i][j] = 0.5 * (p[i][j] + p[ilo][j]) ; + + y[i] = scoreFit(p[i], time, amp, err, size, dr) ; + + if (y[i] < yElite) + { + yElite = y[i] ; + iElite = i ; + } + } + numIterations -= DIM ; + + // 5.1.2.2: reinitialize the sum of values in each coordinate + for (j = 0 ; j < DIM ; j++) + { + sum = 0.0 ; + + for (i = 0 ; i <= DIM ; i++) + sum += p[i][j] ; + + psum[j] = sum ; + } + } + } + + temperature *= coolingFactor ; + +#ifdef GRAPHIC +#ifdef PLOT + if ((numIterations % PLOT_REFRESH_ITERATIONS) == 0) + { + cleardevice() ; + (void)plotFit (p[ihi], time, amp, size, 3, 0, 0.0) ; // Worst - cyan + + if (iElite == -1) + (void)plotFit (pElite, time, amp, size, 15, 0, 0.0) ; // Elite (not in simplex) - white + else + (void)plotFit (p[iElite], time, amp, size, 14, 0, 0.0) ; // Elite (in simplex) - yellow + } +#endif +#endif + } + + // Step 6: [elitism] return the best result + if (iElite == -1) + { + memcpy (p[0], pElite, DIM * sizeof(double)) ; + iElite = 0 ; + } + + return (iElite) ; +} + + + +// This procedure attempts to fine tune the resulting model parameters. +// It tunes the parameters one at a time, shifting each one, in turn, +// a step up and a step down. This is repeated until the chi2 is no longer decreased. +// For error estimation purposes, one parameter p[paramConstNum] is not +// fit. By setting this parameters at a small offset from the optimal +// value, one can measure the chi2 sensitivity to it. +// Set paramConstNum to (-1) for a full greedy fit. +// Returns: 0 = converged ; 1 = didn't converge +int greedyFit(double p[DIM], double *py, float *time, float *amp, float *err, + int size, long numIterations, int paramConstNum) +{ + int i, doCopy, foundImprovement ; + double yTmp, bestVal = 0.0 ; + double paramBack, varyEpsilon = GREEDY_CONVERGE_EPSILON ; + + numIterations = (long)(numIterations * MAX_GREEDY_ITERATION_FRACTION) ; + numIterations /= (2 * DIM) ; + + do + { + numIterations-- ; + foundImprovement = 0 ; + + for (i = 0 ; i < DIM ; i++) + if (i != paramConstNum) + { + doCopy = 0 ; + paramBack = p[i] ; + + p[i] = paramBack * (1.0 - varyEpsilon) ; + yTmp = scoreFit(p, time, amp, err, size, MIN_INTEGRATION_STEP) ; + if (yTmp < (*py)) + { + *py = yTmp ; + bestVal = p[i] ; + doCopy = 1 ; + foundImprovement = 1 ; + } + + p[i] = paramBack * (1.0 + varyEpsilon) ; + yTmp = scoreFit(p, time, amp, err, size, MIN_INTEGRATION_STEP) ; + if (yTmp < (*py)) + { + *py = yTmp ; + bestVal = p[i] ; + doCopy = 1 ; + foundImprovement = 1 ; + } + + if (doCopy) + p[i] = bestVal ; + else + p[i] = paramBack ; + } + + if (foundImprovement) + { + if (varyEpsilon <= HALF_MAX_GREEDY_EPSILON) + varyEpsilon *= 2.0 ; + } + else + varyEpsilon *= 0.5 ; + } + while ((foundImprovement || (varyEpsilon > GREEDY_CONVERGE_EPSILON)) && (numIterations > 0)) ; + + return (foundImprovement || (varyEpsilon > GREEDY_CONVERGE_EPSILON)) ; +} + + + +// Find the best fit for the given data, given initial values of the model parameters +// Returns the node index of the best fit +int runFit(double p[DIM+1][DIM], double y[DIM+1], float *time, float *amp, float *err, + int size, long numIterations, char *filename, char *errFilename) +{ + int best, isUnconverged ; + + best = amoeba(p, y, time, amp, err, size, numIterations) ; + + isUnconverged = greedyFit(p[best], &y[best], time, amp, err, size, numIterations, -1) ; + + if (isUnconverged) + printError (errFilename, 0, filename, "runFit()", "greedyFit() didn't converge") ; + + + // Takes care of the 180 degree turn degeneracy (makes sure that the R1 >= R2) + if (p[best][D_R1] < p[best][D_R2]) + { + swap (&p[best][D_R1], &p[best][D_R2]) ; + swap (&p[best][D_B1], &p[best][D_B2]) ; + p[best][D_TMO] -= 0.25 ; + p[best][D_TPO] += 0.25 ; + } + + return (best) ; +} + +//---------------------- Post-analysis ------------------------------------- + +// Returns the estimated error of a given parameter p[paramNum] +// this is done by offsetting this parameter by a small amount (ERROR_ESTIMATE_EPSILON) +// and seeing how much the chi2 raised, after re-fitting the remaining parameters +// the estimated error is the extrapolated offset needed to double the chi2 +// (compared to the minimum). We assume that the chi2 function is approximately a +// paraboloid around neat the minimum. +// p[DIM] = the given local best fit +// y = the chi2 of the given best fit +// isPhase = 0 for parameters whose error is proportional to their size +// 1 for phase-type parameters (modulo 1) +double estimateError (int paramNum, double p[DIM], double y, int isPhase, + float *time, float *amp, float *err, int size, long numIterations) +{ + double pOffset[DIM], yOffset1, yOffset2 ; + + numIterations = (long)(numIterations * MAX_ERROR_ESTIMATE_ITERATION_FRACTION) ; + numIterations /= 2 ; + + memcpy (pOffset, p, DIM * sizeof(double)) ; + pOffset[paramNum] *= (1.0 + ERROR_ESTIMATE_EPSILON) ; + yOffset1 = scoreFit(pOffset, time, amp, err, size, MIN_INTEGRATION_STEP) ; + greedyFit(pOffset, &yOffset1, time, amp, err, size, numIterations, paramNum) ; + + memcpy (pOffset, p, DIM * sizeof(double)) ; + pOffset[paramNum] *= (1.0 - ERROR_ESTIMATE_EPSILON) ; + yOffset2 = scoreFit(pOffset, time, amp, err, size, MIN_INTEGRATION_STEP) ; + greedyFit(pOffset, &yOffset2, time, amp, err, size, numIterations, paramNum) ; + + if (yOffset1 > yOffset2) + yOffset1 = yOffset2 ; // Set yOffset1 as the minimum + + if (yOffset1 == y) // This should almost never happen + return (-1.0) ; + + if (yOffset1 < y) // This shouldn't happen if greedyFit() converged + swap (&yOffset1, &y) ; + + if (isPhase) + return (ERROR_ESTIMATE_EPSILON * sqrt(y / (yOffset1 - y))) ; + + return (ERROR_ESTIMATE_EPSILON * fabs(p[paramNum]) * sqrt(y / (yOffset1 - y))) ; +} + + + +// Given the period and the radii (in units of a) +// Returns the mean density of the binary system (or maximum if r1=0) +double meanDensity (double period, double r1, double r2) +{ + // 0.01893 = 3 * pi / (G * (seconds per day)^2) [g/cm^3] + return (0.01893 / ((cube(r1) + cube(r2)) * sqr(period))) ; +} + + +#ifdef GRAPHIC +#ifdef PLOT +// Plot the first guess with reference lines +void drawInitLines(double p[DIM+1][DIM], float *time, float *amp, int size, + double min1Time, double min2Time, double min1HalfWidth, double min2HalfWidth, + double min1Amp, double min2Amp, double medianAmp) +{ + int min1AmpPlot, min2AmpPlot, medianAmpPlot, goalAmpPlot ; + + min1AmpPlot = plotFit (0, time, amp, size, 14, 0, min1Amp) ; + min2AmpPlot = plotFit (0, time, amp, size, 14, 0, min2Amp) ; + medianAmpPlot = plotFit (p[0], time, amp, size, 14, 1, medianAmp) ; + + setcolor(7) ; + line (0, medianAmpPlot, 639, medianAmpPlot) ; + + setcolor(5) ; + line ((int)(640 * min1Time), min1AmpPlot-10, (int)(640 * min1Time), 450) ; + line ((int)(640 * min1Time)-5, min1AmpPlot, (int)(640 * min1Time)+5, min1AmpPlot) ; + + setcolor(3) ; + line ((int)(640 * min2Time), min2AmpPlot-10, (int)(640 * min2Time), 450) ; + line ((int)(640 * min2Time)-5, min2AmpPlot, (int)(640 * min2Time)+5, min2AmpPlot) ; + + setcolor(4) ; + goalAmpPlot = (medianAmpPlot + min1AmpPlot) / 2 ; + line ((int)(640 * mod1(min1Time + min1HalfWidth)), goalAmpPlot, (int)(640 * mod1(min1Time + min1HalfWidth)), 450) ; + line ((int)(640 * mod1(min1Time - min1HalfWidth)), goalAmpPlot, (int)(640 * mod1(min1Time - min1HalfWidth)), 450) ; + + goalAmpPlot = (medianAmpPlot + min2AmpPlot) / 2 ; + line ((int)(640 * mod1(min2Time + min2HalfWidth)), goalAmpPlot, (int)(640 * mod1(min2Time + min2HalfWidth)), 450) ; + line ((int)(640 * mod1(min2Time - min2HalfWidth)), goalAmpPlot, (int)(640 * mod1(min2Time - min2HalfWidth)), 450) ; + + //(void)getc (stdin) ; +} +#endif + +// Prints the results of the fit onto the screen +// Note that I assume that p[D_R1] > p[D_R2] for calculating rhoMax +void printResultValue (double p[DIM], double y, float *time, float *amp, int size, int numRid, + double period, double avrChi2, double splineChi2, double sinChi2, + double sigmaMin2Amp, double sigmaMax1Amp, double sigmaMaxDiff, double plateauWaviness) +{ + #ifdef PLOT + + cleardevice() ; + (void)plotFit (p, time, amp, size, 14, 1, 0.0) ; + + gotoxy (1,1) ; + #endif + + + printf ("period=%f e=%f\n", period, p[D_ECC]) ; + printf ("r1/a=%f r2/a=%f B1=%f B2=%f\n", p[D_R1], p[D_R2], + -2.5 * log10(p[D_B1] * integrateWholeDisk(p[D_R1])), + -2.5 * log10(p[D_B2] * integrateWholeDisk(p[D_R2]))) ; + printf ("sin(i)=%f time0=%f omega=%f size=%d numRid=%d\n", p[D_SIN_I], mod1(p[D_TPO] + p[D_TMO]), + 360.0 * mod1(p[D_TPO] - p[D_TMO]), size, numRid) ; + printf ("chi2=%f avrChi2=%f splineChi2=%f sinChi2=%f\n", y, avrChi2, splineChi2, sinChi2) ; + printf ("sig.min2=%.2f sig.max1=%.2f sig.maxDiff=%.2f waviness=%.2f\n", sigmaMin2Amp, sigmaMax1Amp, sigmaMaxDiff, plateauWaviness) ; + printf ("midScatter=%f ", midScatterScore (p, time, amp, size)) ; + printf ("rhoMean=%f rhoMax=%f [g/cm^3]\n", meanDensity(period, p[D_R1], p[D_R2]), meanDensity(period, 0.0, p[D_R2])) ; +} +#endif + + + +// Writes the final (best fit) results to a given file (fout) +void printFinalResult (double p[DIM], double y, float *time, float *amp, float *err, int size, int numRid, double period, + double avrChi2, double splineChi2, double sinChi2, double sigmaMin2Amp, double sigmaMax1Amp, + double sigmaMaxDiff, double plateauWaviness, long numIterations, FILE *fout, char *filename, char *errFilename) +{ + int i ; + double errR1, errR2, errTMPO, errB1, errB2 ; + + // Checks that all the values are valid (not Inf or NaN) - may cause problems in older compilers + for (i = 0 ; (i < DIM) && (p[i] == p[i]) ; i++) ; + + if (i == DIM) + { + numIterations /= DIM ; + + errR1 = estimateError(D_R1, p, y, 0, time, amp, err, size, numIterations) ; + errR2 = estimateError(D_R2, p, y, 0, time, amp, err, size, numIterations) ; + errTMPO = sqrt(sqr(estimateError(D_TMO, p, y, 1, time, amp, err, size, numIterations)) + + sqr(estimateError(D_TPO, p, y, 1, time, amp, err, size, numIterations))) ; + errB1 = sqrt(sqr(estimateError(D_B1, p, y, 0, time, amp, err, size, numIterations) / p[D_B1]) + + sqr(2.0 * errR1 / p[D_R1])) * MAGNITUDE_ERROR_FACTOR ; + errB2 = sqrt(sqr(estimateError(D_B2, p, y, 0, time, amp, err, size, numIterations) / p[D_B2]) + + sqr(2.0 * errR2 / p[D_R2])) * MAGNITUDE_ERROR_FACTOR ; + + fprintf (fout, "%s %.12f ", filename, period) ; + fprintf (fout, "%f %f ", p[D_ECC], + estimateError(D_ECC, p, y, 0, time, amp, err, size, numIterations)) ; + fprintf (fout, "%f %f ", p[D_R1], errR1) ; + fprintf (fout, "%f %f ", p[D_R2], errR2) ; + fprintf (fout, "%f %f ", -2.5 * log10(p[D_B1] * integrateWholeDisk(p[D_R1])), errB1) ; + fprintf (fout, "%f %f ", -2.5 * log10(p[D_B2] * integrateWholeDisk(p[D_R2])), errB2) ; + fprintf (fout, "%f %f ", p[D_SIN_I], + estimateError(D_SIN_I, p, y, 0, time, amp, err, size, numIterations)) ; + fprintf (fout, "%f %f ", mod1(p[D_TPO] + p[D_TMO]), errTMPO) ; + fprintf (fout, "%f %f ", 360.0 * mod1(p[D_TPO] - p[D_TMO]), 360.0 * errTMPO) ; + + fprintf (fout, "%d %d %f %f %f %f ", size, numRid, y, avrChi2, splineChi2, sinChi2) ; + fprintf (fout, "%f %f %f %f ", sigmaMin2Amp, sigmaMax1Amp, sigmaMaxDiff, plateauWaviness) ; + fprintf (fout, "%f %f %f\n", midScatterScore (p, time, amp, size), + meanDensity(period, p[D_R1], p[D_R2]), meanDensity(period, 0.0, p[D_R2])) ; + } + else + printError (errFilename, 0, filename, "printFinalResult()", "One of the fitted parameters is Inf or NaN") ; + +#ifdef WRITE_CURVE_FIT + i = writeCurveFit (p, time, amp, err, size, filename) ; + + if (i) + printError (errFilename, 0, filename, "printFinalResult()", "Couldn't write a data or fit file") ; +#endif + + fflush(fout) ; +} + + +//------------------------------------------------------- + +int main (int argc, char **argv) +{ + int i, isDoublePeriod, best, numRid, size, sizeOrig ; + float *time, *amp, *err ; + double tmpTime, tmpMag, tmpErr ; + double min1Time, min2Time, tDiff, min1Amp, min2Amp, max1Amp, max2Amp ; + double sin_i, r1, r2, B1, B2, e, period, omega, time0 ; + double medianAmp, min1HalfWidth, min2HalfWidth, tmp, Y, varySin_i ; + double goalAmp, plateauStdDiv, plateauWaviness, avrChi2, splineChi2, sinChi2 ; + double sigmaMin1Amp, sigmaMin2Amp, sigmaMax1Amp, sigmaMaxDiff ; + double p[DIM+1][DIM], pPrevBest[DIM], y[DIM+1], yPrevBest ; + double quadA = DEFAULT_LIMB_QUAD_A, quadB = DEFAULT_LIMB_QUAD_B ; + long numIterations = DEFAULT_NUM_ITERATIONS ; + char filename[256], str[64] ; + FILE *finLC, *fout, *finList ; + +#ifdef GRAPHIC +#ifdef PLOT + int ga = 0 , gb = 0 ; + detectgraph (&ga, &gb) ; + initgraph (&ga, &gb, "") ; +#endif + + //argc = 4 ; + //argv[1] = "p.txt" ; + //argv[2] = "pout.txt" ; + //argv[3] = "perr.txt" ; + +#endif + + + if ((argc < 4) || (argc > 7)) + { + printf ("\n Welcome to the DEBiL fitter ver %s (DEBiL=Detached Eclipsing Binary Light-curve fitter)\n\n", VERSION) ; + printf ("Usage: %s [num iteration] [ ]\n", argv[0]) ; + printf (" Defaults: number of iterations = %d\n", DEFAULT_NUM_ITERATIONS) ; + printf (" quadratic limb darkening: a=%f b=%f\n", DEFAULT_LIMB_QUAD_A, DEFAULT_LIMB_QUAD_B) ; + printf ("\n") ; + return (1) ; + } + + if (!strcmp(argv[1], argv[2]) || + !strcmp(argv[1], argv[3]) || + !strcmp(argv[2], argv[3])) + { + printf ("ERROR: all the input/output filenames must be different\n") ; + return (2) ; + } + + + if (!(finList = fopen (argv[1], "rt"))) + { + printf ("ERROR: couldn't open the input LC list file ('%s')\n", argv[1]) ; + return (3) ; + } + + if (!(fout = fopen (argv[2], "at"))) + { + printf ("ERROR: couldn't open the output data file ('%s')\n", argv[2]) ; + fclose (finList) ; + return (4) ; + } + + if ((argc == 5) || (argc == 7)) + numIterations = atol(argv[4]) ; + + if (numIterations < 0) + { + printf ("ERROR: invalid number of iteration (%ld)\n", numIterations) ; + fclose (finList) ; + fclose (fout) ; + return (5) ; + } + + if (argc == 6) + { + quadA = atof(argv[4]) ; + quadB = atof(argv[5]) ; + } + else if (argc == 7) + { + quadA = atof(argv[5]) ; + quadB = atof(argv[6]) ; + } + + setLimbDarkening (quadA, quadB) ; + + printf (" Using settings: \n") ; + printf (" iterations = %ld\n", numIterations) ; + printf (" quadratic limb darkening params: a=%f b=%f\n", quadA, quadB) ; + + srand (RANDOM_SEED) ; + + //----------------------------------------------------------------------- + + while (2 == fscanf(finList, "%s %lf\n", filename, &period)) + { + printf ("%s P=%f\n", filename, period) ; + isDoublePeriod = 0 ; + + if (period <= 0.0) + { + printError (argv[3], 1, filename, "main()", "Invalid period") ; + continue ; + } + + if (!(finLC = fopen (filename, "rt"))) + { + printError (argv[3], 1, filename, "main()", "Couldn't open the input LC file") ; + continue ; + } + + //---------------------------------------------------------------- + + sizeOrig = 0 ; + while (1 == fscanf(finLC, "%*f %*f %lf\n", &tmpErr)) + if (tmpErr > 0.0) // Sign of an invalid magnitude + sizeOrig++ ; + + if (sizeOrig < (3 * MIN_NUM_DATA)) // Need MIN_NUM_DATA for both the dips and the plateau + { + printError (argv[3], 1, filename, "main()", "Not enough data points") ; + fclose (finLC) ; + continue ; + } + + time = (float*)malloc (sizeOrig * sizeof(float)) ; + amp = (float*)malloc (sizeOrig * sizeof(float)) ; + err = (float*)malloc (sizeOrig * sizeof(float)) ; + + if ((!time) || (!amp) || (!err)) + { + if (time) free(time) ; + if (amp) free(amp) ; + if (err) free(err) ; + + printError (argv[3], 1, filename, "main()", "Not enough memory") ; + fclose(finLC) ; + continue ; + } + + //------------------------- + +#ifdef DO_DOUBLE_PERIOD + MAKE_DOUBLE_PERIOD: +#endif + rewind(finLC) ; + size = sizeOrig ; // size is reduced in ridOutliers() when doing a double-period loopback + i = 0 ; + + while ((i < size) && (3 == fscanf(finLC, "%lf %lf %lf\n", &tmpTime, &tmpMag, &tmpErr))) + if (tmpErr > 0.0) // Sign of an invalid magnitude + { + time[i] = (float)mod1(tmpTime / period) ; + amp[i] = (float)pow(10.0, -0.4 * tmpMag) ; // Convert magnitude (logarithmic) to amplitude (linear) + err[i] = (float)(amp[i] * tmpErr / MAGNITUDE_ERROR_FACTOR) ; // Convert to absolute error + i++ ; + } + + if (i != size) + { + printError (argv[3], 1, filename, "main()", "Number mismatch") ; + goto ABORT_LIGHT_CURVE_FITTING ; + } + + i = sortSamples(time, amp, err, size) ; + if (i) + { + printError (argv[3], 1, filename, "main()", "sortSamples() malloc failure") ; + goto ABORT_LIGHT_CURVE_FITTING ; + } + +#ifdef GRAPHIC + #ifdef PLOT + + (void)plotFit (0, time, amp, size, 1, 1, 0.0) ; + //(void)getc(stdin) ; + cleardevice() ; +#endif +#endif + splineChi2 = ridOutliers(time, amp, err, &size) ; + numRid = sizeOrig - size ; + + if (size < (3 * MIN_NUM_DATA)) // Need MIN_NUM_DATA for both the dips and the plateau + { + printError (argv[3], 1, filename, "main()", "Not enough non-outlier data points") ; + goto ABORT_LIGHT_CURVE_FITTING ; + } + + //------------------------------------------------- + + // Find median: + medianAmp = getMedian(amp, size) ; + if (medianAmp == MAXFLOAT) + { + printError (argv[3], 1, filename, "main()", "getMedian() malloc failure") ; + goto ABORT_LIGHT_CURVE_FITTING ; + } + + //-------------------------------------------------- + // Get the location of the dips: + + // Coarse search (secondary may be an "edge minimum") + findDips(time, amp, size, &min1Time, &min2Time, &min1Amp, &min2Amp) ; + + if (medianAmp < min2Amp) + { + if (isDoublePeriod) + { // This should never happen + printError (argv[3], 1, filename, "main()", "No secondary dip- double period was tried and failed (1)") ; + goto ABORT_LIGHT_CURVE_FITTING ; + } + else if ((2.0 * min1Amp) < medianAmp) + { + printError (argv[3], 1, filename, "main()", "No secondary dip- primary dip is too deep for doubling (1)") ; + goto ABORT_LIGHT_CURVE_FITTING ; + } + else // isDoublePeriod = 0 + { +#ifdef DO_DOUBLE_PERIOD + printError (argv[3], 0, filename, "main()", "No secondary dip- try double period (1)") ; + period *= 2.0 ; + isDoublePeriod = 1 ; + goto MAKE_DOUBLE_PERIOD ; +#else + printError (argv[3], 1, filename, "main()", "No secondary dip (1)") ; + goto ABORT_LIGHT_CURVE_FITTING ; +#endif + } + } + + + // Fine tune dips locations and finds their widths + goalAmp = 0.5 * (medianAmp + min1Amp) ; // Half width half min (coarse) + (void)findHalfWidth(time, amp, size, goalAmp, &min1Time) ; + min1Amp = interpolateSmooth (min1Time, time, amp, size) ; + goalAmp = 0.5 * (medianAmp + min2Amp) ; // Half width half min (coarse) + (void)findHalfWidth(time, amp, size, goalAmp, &min2Time) ; + min2Amp = interpolateSmooth (min2Time, time, amp, size) ; + + // find "maxs" midway between the dips + findMidMaxs(time, amp, size, 0.5 * (min1Time + min2Time), &max1Amp, &max2Amp) ; + + // fine-tune median: + i = findPlateauMedian(time, amp, size, min1Time, min2Time, + &medianAmp, &plateauStdDiv, &plateauWaviness) ; + if (i) + { + sprintf (str, "Error #%d in findPlateauMedian()", i) ; + printError (argv[3], 1, filename, "main()", str) ; + goto ABORT_LIGHT_CURVE_FITTING ; + } + + goalAmp = 0.5 * (medianAmp + min1Amp) ; // half width half min (fine tune) + min1HalfWidth = findHalfWidth(time, amp, size, goalAmp, &min1Time) ; + min1Amp = interpolateSmooth (min1Time, time, amp, size) ; + goalAmp = 0.5 * (medianAmp + min2Amp) ; // half width half min (fine tune) + min2HalfWidth = findHalfWidth(time, amp, size, goalAmp, &min2Time) ; + min2Amp = interpolateSmooth (min2Time, time, amp, size) ; + + sigmaMin1Amp = (medianAmp - min1Amp) / plateauStdDiv ; + sigmaMin2Amp = (medianAmp - min2Amp) / plateauStdDiv ; + sigmaMax1Amp = (max1Amp - medianAmp) / plateauStdDiv ; + sigmaMaxDiff = (max1Amp - max2Amp) / plateauStdDiv ; + + //------------- Tests: --------------------- + // Make sure that the dips are okay + // I don't use err[] here, in case the data is inherently volatile + + if (sigmaMin1Amp < MIN_STDDIV_BELOW_MEDIAN) + { + printError (argv[3], 1, filename, "main()", "Primary dip is too small") ; + goto ABORT_LIGHT_CURVE_FITTING ; + } + + if (sigmaMax1Amp > MAX_STDDIV_ABOVE_MEDIAN) + printError (argv[3], 0, filename, "main()", "Large hump at mid-plateau") ; + + if (sigmaMin2Amp < MIN_STDDIV_BELOW_MEDIAN) // consider doubling the period + { + if (isDoublePeriod) + { + printError (argv[3], 1, filename, "main()", "Secondary dip is too small- double period was tried and failed (2)") ; + goto ABORT_LIGHT_CURVE_FITTING ; + } + else if ((2.0 * min1Amp) < medianAmp) + { + printError (argv[3], 1, filename, "main()", "Secondary dip is too small- primary dip is too deep for doubling (2)") ; + goto ABORT_LIGHT_CURVE_FITTING ; + } + else // isDoublePeriod = 0 + { +#ifdef DO_DOUBLE_PERIOD + printError (argv[3], 0, filename, "main()", "Secondary dip is too small- try double period (2)") ; + period *= 2.0 ; + isDoublePeriod = 1 ; + goto MAKE_DOUBLE_PERIOD ; +#else + printError (argv[3], 1, filename, "main()", "No secondary dip (2)") ; + goto ABORT_LIGHT_CURVE_FITTING ; +#endif + } + } + + if ((fabs(min1Time - min2Time) < (min1HalfWidth + min2HalfWidth)) || + (fabs(min1Time - min2Time) > (1.0 - min1HalfWidth - min2HalfWidth))) + { + if (isDoublePeriod) + { + printError (argv[3], 1, filename, "main()", "Dips are overlapping- double period was tried and failed (3)") ; + goto ABORT_LIGHT_CURVE_FITTING ; + } + else if ((2.0 * min1Amp) < medianAmp) + { + printError (argv[3], 1, filename, "main()", "Dips are overlapping- primary dip is too deep for doubling (3)") ; + goto ABORT_LIGHT_CURVE_FITTING ; + } + else // isDoublePeriod = 0 + { +#ifdef DO_DOUBLE_PERIOD + printError (argv[3], 0, filename, "main()", "Dips are overlapping- try double period (3)") ; + period *= 2.0 ; + isDoublePeriod = 1 ; + goto MAKE_DOUBLE_PERIOD ; +#else + printError (argv[3], 1, filename, "main()", "Dips are overlapping (3)") ; + goto ABORT_LIGHT_CURVE_FITTING ; +#endif + } + } + + if ((min1HalfWidth == 0.0) || (min2HalfWidth == 0.0)) + { + printError (argv[3], 1, filename, "main()", "Couldn't reach goal amplitude") ; + goto ABORT_LIGHT_CURVE_FITTING ; + } + + if ((min1HalfWidth >= 0.25) || (min2HalfWidth >= 0.25)) + { + printError (argv[3], 1, filename, "main()", "Out of range half width") ; + goto ABORT_LIGHT_CURVE_FITTING ; + } + + if (!isEnoughDipData(time, size, min1Time, min1HalfWidth) || + !isEnoughDipData(time, size, min2Time, min2HalfWidth)) + { + printError (argv[3], 1, filename, "main()", "Not enough data to constrain one of the dips") ; + goto ABORT_LIGHT_CURVE_FITTING ; + } + + if ((min1Amp + min2Amp) < medianAmp) + { + printError (argv[3], 1, filename, "main()", "The dips are too deep") ; + goto ABORT_LIGHT_CURVE_FITTING ; + } + + // ----------------------------------------------------- + + // Chi2 tests: + + avrChi2 = getAvrChi2 (amp, err, size) ; + sinChi2 = getSinChi2 (time, amp, err, size) ; + + // Calculate the fitting starting point: + + tDiff = fabs(min2Time - min1Time) ; + time0 = 0.5 * (min1Time + min2Time) ; // part 1/3 + + // Convert big-dip/small-dip into first-dip/second-dip (i.e. before/after perihelion) + if ((tDiff > 0.5) ^ (min1Time > min2Time)) + { + swap (&min1Time, &min2Time) ; + swap (&min1Amp, &min2Amp) ; + swap (&min1HalfWidth, &min2HalfWidth) ; + } + + if (tDiff > 0.5) + { + tDiff = 1.0 - tDiff ; + time0 += 0.5 ; // part 2/3 + } + + //------------------ + + // Y = e * sin(omega) + Y = (min1HalfWidth - min2HalfWidth) / (min1HalfWidth + min2HalfWidth) ; + e = getEccentricity(tDiff, Y) ; + omega = asin(Y / e) ; + + tmp = acos(cos(omega) / sqrt(1.0 - (Y*Y))) ; + if (omega < 0.0) + tmp = -tmp ; + + tmp -= (e * sqrt(1.0 - (e*e)) * Y * cos(omega)) / (1.0 - (Y*Y)) ; + + time0 += (tmp / (2.0 * M_PI)) ; + time0 = mod1(time0) ; + + //---------------------------------------- + + if ((e < 0.0) || (e >= 1.0)) + { + printError (argv[3], 1, filename, "main()", "Out of range eccentricity") ; + goto ABORT_LIGHT_CURVE_FITTING ; + } + + //------- [r1 < r2] ------------ + + if (min1Amp < min2Amp) // I chose robustness over accuracy + r2 = sin(2.0 * M_PI * min1HalfWidth) ; + else + r2 = sin(2.0 * M_PI * min2HalfWidth) ; + + if (r2 > ((1.0 - e) / 2.1)) // In case of a large r (must hold: r1+r2 < 1-e) + r2 = (1.0 - e) / 2.1 ; // Theoretically this should be 2.0, but I'm allowing some extra play + + B2 = min2Amp / integrateWholeDisk(r2) ; + + r1 = invIntegrateDiskFromStart((medianAmp - min1Amp) / B2, r2) ; + if (r1 <= 0.0) + { + printError (argv[3], 1, filename, "main()", "Out of bound r1") ; + yPrevBest = MAXFLOAT ; + goto SECOND_ORIENTATION_FITTING ; + } + + B1 = (medianAmp - min2Amp) / integrateWholeDisk(r1) ; + + //------------------------------------------------- + + if (min1Amp < min2Amp) // I chose robustness over accuracy + sin_i = getSin_i (time, amp, size, e, -Y, r1, r2, &varySin_i, + min1Time, min1Amp, medianAmp, min1HalfWidth, filename, argv[3], 1) ; + else + sin_i = getSin_i (time, amp, size, e, Y, r1, r2, &varySin_i, + min2Time, min2Amp, medianAmp, min2HalfWidth, filename, argv[3], 1) ; + + //------------------------------------------------- + // Fit I: + for (i = 0 ; i < DIM+1 ; i++) + { + p[i][D_ECC] = e - ((i == 1) * 0.01 * e) ; + p[i][D_R1] = r1 - ((i == 2) * 0.1 * r1) ; + p[i][D_R2] = r2 - ((i == 3) * 0.1 * r2) ; + p[i][D_B1] = B1 + ((i == 4) * 0.1 * B1) ; + p[i][D_B2] = B2 + ((i == 5) * 0.1 * B2) ; + p[i][D_SIN_I] = sin_i - ((i == 6) * varySin_i) ; + p[i][D_TMO] = (0.5 * time0) - (omega / (4.0 * M_PI)) + ((i == 7) * 0.01) ; + p[i][D_TPO] = (0.5 * time0) + (omega / (4.0 * M_PI)) + ((i == 8) * 0.1) ; + + y[i] = scoreFit (p[i], time, amp, err, size, MIN_INTEGRATION_STEP) ; + } + +#ifdef GRAPHIC + #ifdef PLOT + + drawInitLines(p, time, amp, size, min1Time, min2Time, min1HalfWidth, min2HalfWidth, + min1Amp, min2Amp, medianAmp) ; +#endif +#endif + best = runFit(p, y, time, amp, err, size, numIterations, filename, argv[3]) ; + +#ifdef GRAPHIC + printResultValue (p[best], y[best], time, amp, size, numRid, period, avrChi2, splineChi2, + sinChi2, sigmaMin2Amp, sigmaMax1Amp, sigmaMaxDiff, plateauWaviness) ; +#endif + +#ifdef DOUBLE_PERIOD_SHORTCUT + if (isDoublePeriod) // If B1,B2 and r1,r2 are so close that they make only one minimum + { + printFinalResult (p[best], y[best], time, amp, err, size, numRid, period, avrChi2, splineChi2, sinChi2, + sigmaMin2Amp, sigmaMax1Amp, sigmaMaxDiff, plateauWaviness, numIterations, fout, filename, argv[3]) ; + goto ABORT_LIGHT_CURVE_FITTING ; + } +#endif + + for (i = 0 ; i < DIM ; i++) + pPrevBest[i] = p[best][i] ; + + yPrevBest = y[best] ; + +#ifdef GRAPHIC + //(void)getc(stdin) ; +#endif + + //------------ [r1 > r2] ------------------------------------- + + SECOND_ORIENTATION_FITTING: + + if (min1Amp < min2Amp) // I chose robustness over accuracy + r1 = sin(2.0 * M_PI * min1HalfWidth) ; + else + r1 = sin(2.0 * M_PI * min2HalfWidth) ; + + if (r1 > ((1.0 - e) / 2.1)) // In case of a large r (must hold: r1+r2 < 1-e) + r1 = (1.0 - e) / 2.1 ; // Theoretically this should be 2.0, but I'm allowing some extra play + + B1 = min1Amp / integrateWholeDisk(r1) ; + + r2 = invIntegrateDiskFromStart((medianAmp - min2Amp) / B1, r1) ; + if (r2 <= 0.0) + { + printError (argv[3], 1, filename, "main()", "Out of bound r2") ; + goto ABORT_LIGHT_CURVE_FITTING ; + } + + B2 = (medianAmp - min1Amp) / integrateWholeDisk(r2) ; + + //------------------------------------------------- + + if (min1Amp < min2Amp) // I chose robustness over accuracy + sin_i = getSin_i (time, amp, size, e, -Y, r2, r1, &varySin_i, + min1Time, min1Amp, medianAmp, min1HalfWidth, filename, argv[3], 0) ; + else + sin_i = getSin_i (time, amp, size, e, Y, r2, r1, &varySin_i, + min2Time, min2Amp, medianAmp, min2HalfWidth, filename, argv[3], 0) ; + //------------------------------------------------- + // Fit II: + for (i = 0 ; i < (DIM+1) ; i++) + { + p[i][D_ECC] = e - ((i == 1) * 0.01 * e) ; + p[i][D_R1] = r1 - ((i == 2) * 0.1 * r1) ; + p[i][D_R2] = r2 - ((i == 3) * 0.1 * r2) ; + p[i][D_B1] = B1 + ((i == 4) * 0.1 * B1) ; + p[i][D_B2] = B2 + ((i == 5) * 0.1 * B2) ; + p[i][D_SIN_I] = sin_i - ((i == 6) * varySin_i) ; + p[i][D_TMO] = (0.5 * time0) - (omega / (4.0 * M_PI)) + ((i == 7) * 0.01) ; + p[i][D_TPO] = (0.5 * time0) + (omega / (4.0 * M_PI)) + ((i == 8) * 0.1) ; + + y[i] = scoreFit (p[i], time, amp, err, size, MIN_INTEGRATION_STEP) ; + } + + +#ifdef GRAPHIC + #ifdef PLOT + + drawInitLines(p, time, amp, size, min1Time, min2Time, min1HalfWidth, min2HalfWidth, + min1Amp, min2Amp, medianAmp) ; +#endif +#endif + best = runFit(p, y, time, amp, err, size, numIterations, filename, argv[3]) ; + +#ifdef GRAPHIC + printResultValue (p[best], y[best], time, amp, size, numRid, period, avrChi2, splineChi2, + sinChi2, sigmaMin2Amp, sigmaMax1Amp, sigmaMaxDiff, plateauWaviness) ; +#endif + + //------------------------------------------------ + + if (y[best] < yPrevBest) + printFinalResult (p[best], y[best], time, amp, err, size, numRid, period, avrChi2, splineChi2, sinChi2, + sigmaMin2Amp, sigmaMax1Amp, sigmaMaxDiff, plateauWaviness, numIterations, fout, filename, argv[3]) ; + else + printFinalResult (pPrevBest, yPrevBest, time, amp, err, size, numRid, period, avrChi2, splineChi2, sinChi2, + sigmaMin2Amp, sigmaMax1Amp, sigmaMaxDiff, plateauWaviness, numIterations, fout, filename, argv[3]) ; + + ABORT_LIGHT_CURVE_FITTING: + fclose (finLC) ; + free (time) ; + free (amp) ; + free (err) ; + +#ifdef GRAPHIC + // (void)getc(stdin) ; +#endif + } + + fclose (finList) ; + fclose (fout) ; + + return (0) ; +} + + +/* + To-do: + 1. find best cooling rate (dynamic mutation rate: as the solutions converges, raise the temperature ?) + 2. loop-around convergence for sin_i + + + Note 1. + Calculating the error of the parameters is problematic for two reasons. The first is that we + must determine the size of perturbation that would make a "significant" change in the + optimization score. It is not clear what this "significant" value is. The second, and + probably more sever problem is that while changing each parameter in isolation will + make a relatively large change in the score, there are pairs of parameters (e.g. for e=0, there + is a degeneracy for 2*pi*time0 - omega = const). So lowering one while raising the other will + have a very small change in the score. + + Note 2. + Why does the dip separation vary even when the eccentricity is fixed? + Because when omega = pi/2 (i.e. eclipses occur at perihelion and aphelion), the distance between eclipses + will always be exactly half the period (perfectly symmetric). We assume here that omega = 0 (lower bound?). + That means that from the time difference between dips we can only compute a minimum eccentricity. +*/ diff --git a/mltsp/TCP/Algorithms/Debil/dotastro220107.dat.data b/mltsp/TCP/Algorithms/Debil/dotastro220107.dat.data new file mode 100755 index 00000000..c9c15ecd --- /dev/null +++ b/mltsp/TCP/Algorithms/Debil/dotastro220107.dat.data @@ -0,0 +1,433 @@ +0.002276 9.577000 0.032000 -0.047318 +0.003697 9.589000 0.031000 -0.035318 +0.003948 9.597000 0.039000 -0.027318 +0.006794 9.576000 0.043000 -0.048318 +0.007192 9.589000 0.036000 -0.035318 +0.007453 9.597000 0.036000 -0.027318 +0.015267 9.843000 0.036000 0.218682 +0.015530 9.618000 0.049000 -0.006318 +0.016955 9.667000 0.037000 0.042681 +0.018122 9.573000 0.047000 -0.051318 +0.021023 9.713000 0.031000 0.088682 +0.027230 9.577000 0.027000 -0.047318 +0.028270 9.594000 0.036000 -0.030318 +0.033506 9.570000 0.053000 -0.054318 +0.034735 9.667000 0.057000 0.042681 +0.035575 9.592000 0.025000 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Compile polyfit, a stand-along command line code that fits periodic polynomials piecewise. + + # You'll need GSL! +gcc -L/usr/local/lib polyfit.c -lgsl -lgslcblas -lm -o polyfit + +# you also need Phoebe: http://phoebe.fiz.uni-lj.si (tried: phoebe lib 0.31a +) + + + # optional: make the shared library +gcc -L/usr/local/lib polyfit.c -lgsl -lgslcblas -lm -c polyfit.c -fPIC +gcc -shared -Wl -o polyfit.so polyfit.o -lc -lgsl -lgslcblas -lm + + +On citris33: + gcc -L /global/home/users/dstarr/local/lib/ -I /global/home/users/dstarr/local/include polyfit.c -lgsl -lgslcblas -lm -o polyfit + - where /global/home/users/dstarr/local/lib/ contains libgsl.so + gcc -L /global/home/users/dstarr/local/lib/ -I /global/home/users/dstarr/local/include polyfit.c -lgsl -lgslcblas -lm -c polyfit.c -fPIC + gcc -L /global/home/users/dstarr/local/lib/ -I /global/home/users/dstarr/local/include -shared -Wl -o polyfit.so polyfit.o -lc -lgsl -lgslcblas -lm + + + +2. test out using ipython: + ipython --pylab + >> run eclipse_features.py + >> test2() + >> test() + +This will show you how to get "new" features back. + +One trick is that if you already know the period of the source (like an RRL) you can avoid having to call LS again: + + …fix_initial_period=True,initial_period=0.4422664540092584 + diff --git a/mltsp/TCP/Algorithms/EclFeatures/eclipse_features.py b/mltsp/TCP/Algorithms/EclFeatures/eclipse_features.py new file mode 100644 index 00000000..95c717f1 --- /dev/null +++ b/mltsp/TCP/Algorithms/EclFeatures/eclipse_features.py @@ -0,0 +1,423 @@ +#!/usr/bin/env python +""" +eclipse_features -- generate a dict of features related to + classification of eclipsing systems + in pulsational variables + +is_suspect Is there a reason not to trust the orbital period measurement? +p_pulse Pulsational period (dominant period found by LS) +feature-X-ratio-diff percent of sources more than X sigma fainter than model + relative to X sigma brighter (neg has more faint values) + x = [5,8,15,20,30] +best_orb_period best period found after removing the pulsational period +suspect_reason semicolon separated list why orb_period is suspect +best_orb_chi2 best chi2 fitting orb_period from polyfit +orb_signif LS significance +""" +from __future__ import print_function + +__author__ = "J. S. Bloom, D. Starr" +__version__ = "0.32" + +import os, sys +import numpy as np +from scipy.optimize import fmin + +sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + \ + 'Algorithms/fitcurve')) + +from lomb_scargle_refine import lomb as lombr +import copy +import selectp +from matplotlib import pylab as plt + +def _load_ben_data(fname="LC_246.dat"): + + """loader for Ben's input files""" + + from matplotlib.mlab import csv2rec + ## Get the photometry + name = str(int(fname[fname.find("_")+1:fname.find(".dat")])) + c = csv2rec(fname,delimiter=" ",names=["t","m","merr","rrl"]) + x0 = c['t'] + y = c['m'] + dy = c['merr'] + return x0,y,dy, name + +def _load_dotastro_data(fname="013113-7829.1.xml"): + + """loader for dotastro xml files""" + sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + \ + 'Software/feature_extract/Code')) + import db_importer + + b = db_importer.Source(xml_handle=fname) + kk = b.ts.keys() + ind = 0 + photkey = kk[ind] + ts = b.ts + x0 = np.array(ts[photkey]['t']) + y = np.array(ts[photkey]['m']) + dy = np.array(ts[photkey]['m_err']) + name = fname.split(".xml")[0] + return x0,y,dy, name + +class ebfeature: + + def __init__(self,t=None, m=None, merr=None, name="", allow_plotting=True, sys_err=0.03, \ + verbose=False, fix_initial_period=False, initial_period=1.0, srcid=0): + + self.allow_plotting = allow_plotting + self.name = name + self.t = t ; self.m = m ; self.merr = merr ; self.sys_err = sys_err + self.verbose = verbose + self.fix_initial_period=fix_initial_period ; self.initial_period = initial_period + self.features = {"run": False} + self.srcid = srcid + + def _get_pulsational_period(self,min_freq=10.0,doplot=False,max_pulse_period=400.0): + self.x0 = self.t + self.y = self.m + self.dy = self.merr + self.dy0 = np.sqrt(self.dy**2+self.sys_err**2) + self.x0 -= self.x0.min() + self.nepochs = len(self.x0) + + # define the frequency grid + Xmax = self.x0.max() + if not self.fix_initial_period: + f0 = 1.0/max_pulse_period; df = 0.1/Xmax; fe = min_freq + numf = int((fe-f0)/df) + else: + f0 = 1./self.initial_period + df = 1e-7 + numf = 1 + + psdr,res2 = lombr(self.x0,self.y,self.dy0,f0,df,numf,detrend_order=1) + period=1./res2['freq'] + self.rrlp = period + if self.verbose: + print("Initial pulstional Period is %.8f day" % self.rrlp) + + self.features.update({"p_pulse_initial": self.rrlp}) + + if self.allow_plotting and doplot: + try: + plt.figure(3) + plt.cla() + tt=(self.x0/period) % 1.; s=tt.argsort() + plt.errorbar (tt,self.y,self.dy,fmt='o'); plt.plot(tt[s],res2['model'][s]) + plt.ylim(self.y.max()+0.05,self.y.min()-0.05) + plt.title("P=%f" % (self.rrlp)) + plt.draw() + except: + pass + return res2 + + def gen_outlier_stat_features(self,doplot=False,sig_features=[30,20,15,8,5],\ + min_freq=10.0,dosave=True,max_pulse_period=400.0): + """here we generate outlier features and refine the initial pulsational period + by downweighting those outliers. + """ + + res2 = self._get_pulsational_period(doplot=doplot,min_freq=min_freq) + + ## now sigclip + offs = (self.y - res2['model'])/self.dy0 + moffs = np.median(offs) + offs -= moffs + + ## do some feature creation ... find the statistics of major outliers + for i,s in enumerate(sig_features): + rr = (np.inf,s) if i == 0 else (sig_features[i-1],s) + tmp = (offs < rr[0]) & (offs > rr[1]) + nlow = float(tmp.sum())/self.nepochs + tmp = (offs > -1*rr[0]) & (offs < -1*rr[1]) + nhigh = float(tmp.sum())/self.nepochs + if self.verbose: + print("%i: low = %f high = %f feature-%i-ratio-diff = %f" % (s,nlow,nhigh,s,nhigh - nlow)) + + self.features.update({"feature-%i-ratio-diff" % s: (nhigh - nlow)*100.0}) + + tmp = np.where(abs(offs) > 4) + self.dy_orig = copy.copy(self.merr) + dy = copy.copy(self.merr) + dy[tmp] = np.sqrt(dy[tmp]**2 + res2['model_error'][tmp]**2 + (8.0*(1 - np.exp(-1.0*abs(offs[tmp])/4)))**2) + dy0 = np.sqrt(dy**2+self.sys_err**2) + + #Xmax = self.x0.max() + #f0 = 1.0/max_pulse_period; df = 0.1/Xmax; fe = min_freq + #numf = int((fe-f0)/df) + + #refine around original period + ## Josh's original calcs, which fail for sources like: 221205 + ##df = 0.1/self.x0.max() + ##f0 = res2['freq']*0.95 + ##fe = res2['freq']*1.05 + ##numf = int((fe-f0)/df) + + df = 0.1/self.x0.max() + f0 = res2['freq']*0.95 + fe = res2['freq']*1.05 + numf = int((fe-f0)/df) + if numf == 0: + ## Josh's original calcs, which fail for sources like: 221205 + numf = 100 # kludge / fudge / magic number + df = (fe-f0) / float(numf) + + psdr,res = lombr(self.x0,self.y,dy0,f0,df,numf,detrend_order=1) + period=1./res['freq'] + + self.features.update({"p_pulse": period}) + + if self.allow_plotting and doplot: + try: + tt=(self.x0*res2['freq']) % 1.; s=tt.argsort() + plt.errorbar (tt[tmp],self.y[tmp],self.dy_orig[tmp],fmt='o',c="r") + tt=(self.x0*res['freq']) % 1.; s=tt.argsort() + plt.plot(tt[s],res['model'][s],c="r") + if dosave: + plt.savefig("pulse-%s-p=%f.png" % (os.path.basename(self.name),period)) + if self.verbose: + print("saved...", "pulse-%s-p=%f.png" % (os.path.basename(self.name),period)) + plt.draw() + except: + pass + return offs, res2 + + def gen_orbital_period(self, doplot=False, sig_features=[30,20,15,8,5], min_eclipses=4, + eclipse_shorter=False, dynamic=True, choose_largest_numf=False): + """ + """ + try: + offs,res2 = self.gen_outlier_stat_features(doplot=doplot,sig_features=sig_features) + + ## subtract the model + new_y = self.y - res2['model'] + + # make new weights that penalize sources _near_ the model + dy0 = np.sqrt(self.dy_orig**2+ res2['model_error']**2 + (3*self.sys_err*np.exp(-1.0*abs(offs)/3))**2) ## this downweights data near the model + Xmax = self.x0.max() + #import pdb; pdb.set_trace() + #print + + if choose_largest_numf: + f0 = min_eclipses/Xmax + df = 0.1/Xmax + fe = res2['freq']*0.98 ## dont go near fundamental freq least we find it again + numf = int((fe-f0)/df) + + f0_b = res2['freq']*0.98 + fe_b = 10.0 + df_b = 0.1/Xmax + numf_b = int((fe_b-f0_b)/df_b) + + if numf < numf_b: + f0 = f0_b + fe = fe_b + df = df_b + numf = numf_b + else: + if not eclipse_shorter: + f0 = min_eclipses/Xmax + df = 0.1/Xmax + fe = res2['freq']*0.98 ## dont go near fundamental freq least we find it again + numf = int((fe-f0)/df) + else: + f0 = res2['freq']*0.98 + fe = 10.0 + df = 0.1/Xmax + numf = int((fe-f0)/df) + + freqin = f0 + df*np.arange(numf,dtype='float64') + periodin = 1/freqin + + if self.verbose: + print("P min, max", min(periodin),max(periodin)) + + psdr,res2 = lombr(self.x0,new_y,self.dy0,f0,df,numf) + period=1./res2['freq'] + if self.verbose: + print("orb period = %f sigf = %f" % (period,res2['signif'])) + self.last_res = res2 + s = selectp.selectp(self.x0, new_y, self.dy_orig, period, mults=[1.0,2.0], dynamic=dynamic, verbose=self.verbose, srcid=self.srcid) + s.select() + + self.features.update({"best_orb_period": s.rez['best_period'], "best_orb_chi2": \ + s.rez['best_chi2'], 'orb_signif': res2['signif']}) + + is_suspect = False + reason = [] + if abs(1.0 - self.features['best_orb_period']) < 0.01 or abs(2.0 - self.features['best_orb_period']) < 0.01 or \ + abs(0.5 - self.features['best_orb_period']) < 0.01: + ## likely an alias + is_suspect=True + reason.append("alias") + if self.features['best_orb_chi2'] > 10.0 or self.features['orb_signif'] < 4: + is_suspect=True + reason.append("low significance") + if self.features['best_orb_period'] > Xmax/(2*min_eclipses): + ## probably too long + is_suspect=True + reason.append("too long") + if (0.5 - abs( (self.features['best_orb_period'] / self.features['p_pulse']) % 1.0 - 0.5)) < 0.01: + ## probably an alias of the pulse period + is_suspect=True + reason.append("pulse alias") + + self.features.update({'is_suspect': is_suspect, 'suspect_reason': None if not is_suspect else \ + "; ".join(reason)}) + + + if doplot: + try: + plt.figure(2) + plt.cla() + s.plot_best(extra="suspect=%s %s" % (is_suspect,"" if not is_suspect else "(" + ",".join(reason) + ")")) + plt.savefig("orb-%s-p=%f-sig=%f.png" % (os.path.basename(self.name),period,res2['signif'])) + if self.verbose: + print("saved...", "org-%s-p=%f.png" % (os.path.basename(self.name),period)) + except: + pass + except: + return + + + def old_stuff(self): + print(res2['chi2'], res2['chi0']) + if self.verbose: + print("New Period is %.8f day" % period) + + plt.figure(2) + plt.cla() + tt=(self.x0/period) % 1.; s=tt.argsort() + plt.errorbar (tt[s],new_y[s],self.dy_orig[s],fmt='o',c="b") + plt.plot(tt[s],res2['model'][s],c="r") + + f = open("lc.dat","w") + z = zip(tt[s] - 0.5,new_y[s],self.dy_orig[s]) + for l in z: + f.write("%f %f %f\n" % l) + f.close() + + f = open("lc0.dat","w") + z = zip(self.x0,new_y,self.dy_orig) + for l in z: + f.write("%f %f %f\n" % l) + f.close() + + + psdr,res2 = lombr(self.x0,new_y,self.dy0,f0/2.,df,numf) + period1=1./res2['freq'] + + if self.verbose: + print("New Period is %.8f day" % period1) + + plt.figure(4) + plt.cla() + tt=(self.x0/period1) % 1.; s=tt.argsort() + plt.errorbar (tt[s],new_y[s],self.dy_orig[s],fmt='o',c="b") + plt.plot(tt[s],res2['model'][s],c="r") + print(res2['chi2'], res2['chi0']) + f = open("lc2.dat","w") + z = zip(tt[s] - 0.5,new_y[s],self.dy_orig[s]) + for l in z: + f.write("%f %f %f\n" % l) + f.close() + +def runben(doplot=False): + + import glob + from matplotlib.mlab import csv2rec + import numpy as np + l = glob.glob("/Users/jbloom/Dropbox/LCS/LCnew_??.dat") + if os.path.exists("benfeatures.csv"): + ttt = csv2rec("benfeatures.csv") + header=False + else: + header=True + ttt = np.rec.fromarrays([-1],names='name',formats='i4') + + m = open("benfeatures.csv","a") + has_run = False + for f in l: + if f.find(".dat") != -1: + fname = f + print("working on", f) + x0,y,dy, name =_load_ben_data(fname) + if len(np.where(ttt['name'] == int(os.path.basename(name)))[0]) != 0: + print("... already in list, skipping") + continue + a = ebfeature(t=x0,m=y,merr=dy,name=name) + a.gen_orbital_period(doplot=doplot) + if doplot: + plt.draw() + if not has_run: + ff = a.features.keys() + ff.remove("run") + ff.remove("p_pulse_initial") + if header: + m.write("name," + ",".join(ff) + "\n") + has_run = True + + m.write(os.path.basename(name) + "," + ",".join([str(a.features.get(s)) for s in ff]) + "\n") + m.close() + +def runcand(doplot=False): + + import time + l = os.listdir("BenLike/") + m = open("features.csv","w") + has_run = False + for f in l: + if f.find(".xml") != -1: + fname = "BenLike/" + f + print("working on", f) + x0,y,dy, name = _load_dotastro_data(fname) + a = ebfeature(t=x0,m=y,merr=dy,name=name) + a.gen_orbital_period(doplot=doplot) + if doplot: + plt.draw() + if not has_run: + ff = a.features.keys() + ff.remove("run") + ff.remove("p_pulse_initial") + m.write("name," + ",".join(ff) + "\n") + has_run = True + + m.write(os.path.basename(name) + "," + ",".join([str(a.features.get(s)) for s in ff]) + "\n") + time.sleep(1) + m.close() + + +def test(): + + """This is a test to show how to Ben's input files (t, m, merr)""" + x0,y,dy, name = _load_ben_data() + import pdb; pdb.set_trace() + print() + a = ebfeature(t=x0,m=y,merr=dy,fix_initial_period=True,initial_period=0.4422664540092584,name=name) + a.gen_orbital_period(doplot=True) + print(a.features) + +def test2(): + + """This is a test to show how to use doastro xml files""" + x0,y,dy, name = _load_dotastro_data() + + # note: if you already know the pulsational period, see test() above for + # ebfeature instantiation + a = ebfeature(t=x0,m=y,merr=dy,name=name) + a.gen_orbital_period(doplot=True) + print(a.features) + +if __name__ == '__main__': + ### this section is just for testing + # using t, m, merr: + test() + import pdb; pdb.set_trace() + print() + + ### using xml file: + test2() + diff --git a/mltsp/TCP/Algorithms/EclFeatures/lc0.dat b/mltsp/TCP/Algorithms/EclFeatures/lc0.dat new file mode 100755 index 00000000..b6a40d64 --- /dev/null +++ b/mltsp/TCP/Algorithms/EclFeatures/lc0.dat @@ -0,0 +1,468 @@ +0.000000 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a*a); +} + +inline double SIGN(double a,double b) { + return ((b) >= 0.0 ? fabs(a) : -fabs(a)); +} + +double pythag(double a, double b) { + double absa,absb; + absa=fabs(a); + absb=fabs(b); + if (absa > absb) return absa*sqrt(1.0+SQR(absb/absa)); + else return (absb == 0.0 ? 0.0 : absb*sqrt(1.0+SQR(absa/absb))); +} +""" +tred2_code = """ +inline void tred2(double a[], int n, double d[], double e[]) { + int l,k,j,i; + double scale,hh,h,g,f; + + for (i=n-1;i>=1;i--) { + l=i-1; + h=scale=0.0; + if (l > 0) { + for (k=0;k<=l;k++) + scale += fabs(a[k+i*n]); + if (scale == 0.0) + e[i]=a[l+i*n]; + else { + for (k=0;k<=l;k++) { + a[k+i*n] /= scale; + h += a[k+i*n]*a[k+i*n]; + } + f=a[l+i*n]; + g=(f >= 0.0 ? -sqrt(h) : sqrt(h)); + e[i]=scale*g; + h -= f*g; + a[l+i*n]=f-g; + f=0.0; + for (j=0;j<=l;j++) { + a[i+j*n]=a[j+i*n]/h; + g=0.0; + for (k=0;k<=j;k++) + g += a[k+j*n]*a[k+i*n]; + for (k=j+1;k<=l;k++) + g += a[j+k*n]*a[k+i*n]; + e[j]=g/h; + f += e[j]*a[j+i*n]; + } + hh=f/(h+h); + for (j=0;j<=l;j++) { + f=a[j+i*n]; + e[j]=g=e[j]-hh*f; + for (k=0;k<=j;k++) + a[k+j*n] -= (f*e[k]+g*a[k+i*n]); + } + } + } else + e[i]=a[l+i*n]; + d[i]=h; + } + d[0]=0.0; + e[0]=0.0; + for (i=0;i=l;i--) { + f=s*e[i]; + b=c*e[i]; + e[i+1]=(r=pythag(f,g)); + if (r == 0.0) { + d[i+1] -= p; + e[m]=0.0; + break; + } + s=f/r; + c=g/r; + g=d[i+1]-p; + r=(d[i]-g)*s+2.0*c*b; + d[i+1]=g+(p=s*r); + g=c*r-b; + for (k=0;k= l) continue; + d[l] -= p; + e[l]=g; + e[m]=0.0; + } + } while (m != l); + } +} +""" + +scode = pythag_code + tred2_code + tqli_code +scode += """ +inline void get_eigs(int np,double x[],double d[]) { + double e[np]; + tred2(x,np,d,e); + tqli(d,e,np,x); +} +""" diff --git a/mltsp/TCP/Algorithms/EclFeatures/polyfit.c b/mltsp/TCP/Algorithms/EclFeatures/polyfit.c new file mode 100755 index 00000000..c1567b53 --- /dev/null +++ b/mltsp/TCP/Algorithms/EclFeatures/polyfit.c @@ -0,0 +1,963 @@ +#include +#include +#include +#include + +#include +#include +#include +#include + +void *phoebe_malloc (size_t size); +/* #include */ +#include "polyfit.h" + +#define DEBUG 0 +#define LCPOINTS 400 + +void *phoebe_malloc (size_t size) +{ + /** + * phoebe_malloc: + * + * Allocates the space with the check whether the memory was exhausted. + */ + register void *value = malloc (size); + if (value == 0) + { + printf ("Virtual memory exhauseted.\n"); + exit (-1); + } + return value; +} + + +polyfit_options *polyfit_options_default () +{ + polyfit_options *options = phoebe_malloc (sizeof (*options)); + + options->polyorder = 2; + options->iters = 10000; + options->step_size = 0.001; + options->knots = 4; + options->find_knots = FALSE; + options->find_step = TRUE; + options->chain_length = 10; + options->ann_compat = FALSE; + + return options; +} + +int polyfit_options_free (polyfit_options *options) +{ + free (options); + + return SUCCESS; +} + +polyfit_solution *polyfit_solution_init (polyfit_options *options) +{ + polyfit_solution *solution = phoebe_malloc (sizeof (*solution)); + int k; + + solution->knots = phoebe_malloc (options->knots * sizeof (*(solution->knots))); + + solution->ck = phoebe_malloc (options->knots * sizeof (*(solution->ck))); + for (k = 0; k < options->knots; k++) + solution->ck[k] = phoebe_malloc ((options->polyorder+1) * sizeof (**(solution->ck))); + + solution->npts = phoebe_malloc (options->knots * sizeof (*(solution->npts))); + + return solution; +} + +int polyfit_solution_free (polyfit_solution *solution, polyfit_options *options) +{ + int k; + + free (solution->knots); + for (k = 0; k < options->knots; k++) + free (solution->ck[k]); + free (solution->ck); + free (solution); + + return SUCCESS; +} + +/* Maximum number of observed data points: */ +#define NMAX 10000 + +int polyfit_sort_by_phase (const void *a, const void *b) +{ + const polyfit_triplet *da = (const polyfit_triplet *) a; + const polyfit_triplet *db = (const polyfit_triplet *) b; + + return (da->x > db->x) - (da->x < db->x); +} + +int polyfit_sort_by_value (const void *a, const void *b) +{ + const double *da = (const double *) a; + const double *db = (const double *) b; + + return (*da > *db) - (*da < *db); +} + +int polyfit_find_knots (polyfit_triplet *data, int nobs, double **knots, polyfit_options *options) +{ + int i, j; + double average = 0.0; + int chain_too_short; + int chain_wrapped = 0; + + int chains = 0; + struct { + int len; + int start; + int end; + } chain[2]; + + for (i = 0; i < nobs; i++) + average += data[i].y; + average /= (double) nobs; + + if (!options->ann_compat) { + printf ("# searching for knots automatically:\n"); + printf ("# * average value of the flux: %lf\n", average); + } + + /* To allow wrapping of the phase interval, skip the first chain: */ + i = 0; + while (data[i].y < average) { + if (DEBUG) + printf ("# delaying point %8.4lf (%3d): %lf < %lf\n", data[i].x, i, data[i].y, average); + i++; + } + + for ( ; i < nobs; i++) { + if (data[i].y > average) { + if (DEBUG) + printf ("# skipping point %8.4lf (%3d): %lf > %lf\n", data[i].x, i, data[i].y, average); + continue; + } + + /* Check if the chain is at least CHAIN_LENGTH long: */ + chain_too_short = 0; + if (DEBUG) + printf ("# chain starts at %8.4lf (%3d): %lf < %lf\n", data[i].x, i, data[i].y, average); + for (j = 1; j < options->chain_length; j++) { + if (i+j == nobs) { + i = -j; + chain_wrapped = 1; + } + if (data[i+j].y > average) { + if (DEBUG) + printf ("# chain broken at %8.4lf (%3d): %lf > %lf\n", data[i+j].x, i+j, data[i+j].y, average); + i += j; + chain_too_short = 1; + break; + } + else + if (DEBUG) + printf ("# chain cont'd at %8.4lf (%3d): %lf < %lf\n", data[i+j].x, i+j, data[i+j].y, average); + } + + if (chain_wrapped && chain_too_short) break; + if (chain_too_short) continue; + + while (data[i+j].y < average) { + if (i+j == nobs) { + i = -j; + chain_wrapped = 1; + } + if (DEBUG) + printf ("# chain cont'd at %8.4lf (%3d): %lf < %lf\n", data[i+j].x, i+j, data[i+j].y, average); + j++; + } + + if (chains < 2) { + chains++; + chain[chains-1].len = j-1; + chain[chains-1].start = chain_wrapped*nobs + i; + chain[chains-1].end = i+j-1; + } + else { + chains++; /* Just to count all chains */ + if (j-1 > chain[0].len) { + if (chain[0].len > chain[1].len) { + chain[1].len = chain[0].len; + chain[1].start = chain[0].start; + chain[1].end = chain[0].end; + } + chain[0].len = j-1; + chain[0].start = chain_wrapped*nobs + i; + chain[0].end = i+j-1; + } + else if (j-1 > chain[1].len) { + chain[1].len = j-1; + chain[1].start = chain_wrapped*nobs + i; + chain[1].end = i+j-1; + } + /* else drop through without recording it because it is shorter. */ + } + + if (!options->ann_compat) + printf ("# * found a chain from %lf (index %d/%d) to %lf (index %d/%d)\n", data[chain_wrapped*nobs+i].x, chain_wrapped*nobs+i, nobs-1, data[i+j-1].x, i+j-1, nobs-1); + i += j-1; + if (chain_wrapped) break; + } + + if (!options->ann_compat) + printf ("# * total number of chains found: %d\n", chains); + + /* If the number of chains is less than 2, the search for knots failed. */ + if (chains < 2) + return -1; + + options->knots = 4; + *knots = malloc (options->knots * sizeof (**knots)); + (*knots)[0] = data[chain[0].start].x; (*knots)[1] = data[chain[0].end].x; + (*knots)[2] = data[chain[1].start].x; (*knots)[3] = data[chain[1].end].x; + + return 0; +} + +int polyfit (polyfit_solution *result, polyfit_triplet *data, int nobs, double *knots, int PRINT, polyfit_options *options) +{ + int i, j, k, intervals, kfinal, cum, int1index; + double chisq, chi2tot, knot, dknot; + + gsl_vector **x, **y, **w, **c; + gsl_matrix **A, **cov; + gsl_multifit_linear_workspace **mw; + + for (k = 0; k < options->knots; k++) { + result->knots[k] = knots[k]; + result->npts[k] = 0; + } + + /* Fast-forward to the first knot: */ + i = 0; cum = 0; + while (data[i].x < knots[0]) { i++; cum++; } + result->npts[options->knots-1] = i; + int1index = cum; + + for (j = 0; j < options->knots-1; j++) { + while (data[i].x < knots[j+1] && i < nobs) i++; + result->npts[j] += i-cum; /* we need += because of the wrapped interval */ + if (result->npts[j] <= options->polyorder) { + /* The intervals between knots don't have enough points; bail out. */ + result->chi2 = 1e10; + return -1; + } + cum += result->npts[j]; + } + + /* Add post-last knot data to the last interval: */ + result->npts[j] += nobs-cum; + if (result->npts[j] <= options->polyorder) { + /* The intervals between knots don't have enough points; bail out. */ + result->chi2 = 1e10; + return -1; + } + + /* Allocate arrays of vectors of interval data: */ + x = malloc (options->knots * sizeof (*x)); + y = malloc (options->knots * sizeof (*y)); + w = malloc (options->knots * sizeof (*w)); + + for (j = 0; j < options->knots; j++) { + x[j] = gsl_vector_alloc (result->npts[j]); + y[j] = gsl_vector_alloc (result->npts[j]); + w[j] = gsl_vector_alloc (result->npts[j]); + } + + /* Copy the (phase-shifted) data to these vectors: */ + cum = 0; + for (j = 0; j < options->knots-1; j++) { + for (i = 0; i < result->npts[j]; i++) { + gsl_vector_set (x[j], i, data[int1index+cum+i].x - knots[j]); + gsl_vector_set (y[j], i, data[int1index+cum+i].y); + gsl_vector_set (w[j], i, data[int1index+cum+i].z); + if (DEBUG) + printf ("%d: %lf\t%lf\t%lf\n", j, data[int1index+cum+i].x - knots[j], data[int1index+cum+i].y, data[int1index+cum+i].z); + } + cum += result->npts[j]; + } + + /* Copy the wrapped interval data to these vectors: */ + knot = knots[j]; + for (i = 0; i < result->npts[j]; i++) { + if (int1index+cum+i == nobs) { + cum = -int1index-i; + knot -= 1.0; + } + gsl_vector_set (x[j], i, data[int1index+cum+i].x - knot); + gsl_vector_set (y[j], i, data[int1index+cum+i].y); + gsl_vector_set (w[j], i, data[int1index+cum+i].z); + if (DEBUG) + printf ("%d: %lf\t%lf\t%lf\n", j, data[int1index+cum+i].x - knot, data[int1index+cum+i].y, data[int1index+cum+i].z); + } + + /* If polynomial order is 1, the last interval is determined by constraints: */ + if (options->polyorder == 1) intervals = options->knots-1; else intervals = options->knots; + + /* Allocate all vectors and matrices: */ + A = malloc (intervals * sizeof (*A)); + c = malloc (intervals * sizeof (*c)); + cov = malloc (intervals * sizeof (*cov)); + mw = malloc (intervals * sizeof (*mw)); + + /* The first interval has all polynomial coefficients free. */ + A[0] = gsl_matrix_alloc (result->npts[0], options->polyorder+1); + c[0] = gsl_vector_alloc (options->polyorder+1); + cov[0] = gsl_matrix_alloc (options->polyorder+1, options->polyorder+1); + mw[0] = gsl_multifit_linear_alloc (result->npts[0], options->polyorder+1); + + /* Intervals 1...(N-1) are constrained by the connectivity constraint. */ + for (j = 1; j < options->knots-1; j++) { + A[j] = gsl_matrix_alloc (result->npts[j], options->polyorder); + c[j] = gsl_vector_alloc (options->polyorder); + cov[j] = gsl_matrix_alloc (options->polyorder, options->polyorder); + mw[j] = gsl_multifit_linear_alloc (result->npts[j], options->polyorder); + } + + /* The last interval has two constraints (connectivity and periodicity). */ + if (j < intervals) { + A[j] = gsl_matrix_alloc (result->npts[j], options->polyorder-1); + c[j] = gsl_vector_alloc (options->polyorder-1); + cov[j] = gsl_matrix_alloc (options->polyorder-1, options->polyorder-1); + mw[j] = gsl_multifit_linear_alloc (result->npts[j], options->polyorder-1); + } + + /* Set all elements to all matrices: */ + for (j = 0; j < intervals; j++) { + if (j == 0) kfinal = options->polyorder+1; + else if (j == options->knots-1) kfinal = options->polyorder-1; + else kfinal = options->polyorder; + for (i = 0; i < result->npts[j]; i++) + for (k = 0; k < kfinal; k++) + gsl_matrix_set (A[j], i, k, pow (gsl_vector_get (x[j], i), options->polyorder-k)); + } + + /********************* FITTING THE 1ST INTERVAL: **********************/ + + gsl_multifit_wlinear (A[0], w[0], y[0], c[0], cov[0], &chisq, mw[0]); + + for (k = 0; k < options->polyorder+1; k++) + result->ck[0][k] = gsl_vector_get (c[0], k); + chi2tot = chisq; + + /******************** FITTING INTERVALS 2-(N-1): **********************/ + + for (j = 1; j < options->knots-1; j++) { + /* Satisfy the connectivity constraint: */ + result->ck[j][options->polyorder] = result->ck[j-1][options->polyorder]; + for (k = 0; k < options->polyorder; k++) + result->ck[j][options->polyorder] += result->ck[j-1][k] * pow (knots[j]-knots[j-1], options->polyorder-k); + + /* Apply the connectivity constraint: */ + for (i = 0; i < result->npts[j]; i++) + gsl_vector_set (y[j], i, gsl_vector_get (y[j], i) - result->ck[j][options->polyorder]); + + gsl_multifit_wlinear (A[j], w[j], y[j], c[j], cov[j], &chisq, mw[j]); + chi2tot += chisq; + + for (k = 0; k < options->polyorder; k++) + result->ck[j][k] = gsl_vector_get (c[j], k); + } + + /******************** FITTING THE LAST INTERVAL: **********************/ + + /* Satisfy the connectivity constraint: */ + result->ck[j][options->polyorder] = result->ck[j-1][options->polyorder]; + for (k = 0; k < options->polyorder; k++) + result->ck[j][options->polyorder] += result->ck[j-1][k] * pow (knots[j]-knots[j-1], options->polyorder-k); + + /* Satisfy the periodicity constraint: */ + result->ck[j][options->polyorder-1] = (result->ck[0][options->polyorder]-result->ck[j][options->polyorder])/(knots[0]-knots[j]+1.0); + + /* Apply both constraints: */ + for (i = 0; i < result->npts[j]; i++) + gsl_vector_set (y[j], i, gsl_vector_get (y[j], i) - result->ck[j][options->polyorder] - result->ck[j][options->polyorder-1] * gsl_vector_get (x[j], i)); + + if (options->polyorder > 1) { + for (k = 0; k < options->polyorder-1; k++) + for (i = 0; i < result->npts[j]; i++) + gsl_matrix_set (A[j], i, k, gsl_matrix_get (A[j], i, k) - gsl_vector_get (x[j], i) * pow(knots[0]-knots[j]+1, options->polyorder-k-1)); + + gsl_multifit_wlinear (A[j], w[j], y[j], c[j], cov[j], &chisq, mw[j]); + + for (k = 0; k < options->polyorder-1; k++) + result->ck[j][k] = gsl_vector_get (c[j], k); + chi2tot += chisq; + } + else { + /* + * If we are fitting linear polynomials, there's nothing to fit here + * because both knots are constrained (connectivity and periodicity). + * However, we still need to traverse data points to get chi2. + */ + + chisq = 0.0; + for (i = 0; i < result->npts[j]; i++) + chisq += gsl_vector_get (w[j], i) * gsl_vector_get (y[j], i) * gsl_vector_get (y[j], i); + chi2tot += chisq; + } + + result->chi2 = chi2tot; + + /* Done! Wrap it up: */ + for (i = 0; i < options->knots-1; i++) { + gsl_vector_free (x[i]); + gsl_vector_free (y[i]); + gsl_vector_free (w[i]); + } + + free (x); + free (y); + free (w); + + for (i = 0; i < intervals; i++) { + gsl_vector_free (c[i]); + gsl_matrix_free (A[i]); + gsl_matrix_free (cov[i]); + gsl_multifit_linear_free (mw[i]); + } + + free (c); + free (A); + free (cov); + free (mw); + + return SUCCESS; +} + +int polyfit_print (polyfit_solution *result, polyfit_options *options, int VERBOSE, int PRINT_SOLUTION) +{ + int i, j, k; + double knot, dknot; + + if (VERBOSE && !options->ann_compat) { + printf ("# Phase space partitioning:\n# \n"); + for (k = 0; k < options->knots-1; k++) + printf ("# interval %2d: [% 3.3lf, % 3.3lf), %3d data points\n", k, result->knots[k], result->knots[k+1], result->npts[k]); + printf ("# interval %2d: [% 3.3lf, % 3.3lf), %3d data points\n", k, result->knots[k], result->knots[0], result->npts[k]); + } + + /* Write out a header: */ + if (VERBOSE && !options->ann_compat) { + printf ("# \n# Weighted least-squares solution of the polyfit:\n# \n"); + printf ("# knot\t"); + for (k = 0; k < options->polyorder+1; k++) + printf (" c[%d]\t\t", k); + printf ("\n"); + } + + if (VERBOSE) { + if (options->ann_compat) + printf ("%lf\n", result->knots[0]); + else + printf ("# % lf\t", result->knots[0]); + for (k = 0; k < options->polyorder+1; k++) { + if (options->ann_compat) + printf ("%lf\n", result->ck[0][options->polyorder-k]); + else + printf ("% lf\t", result->ck[0][options->polyorder-k]); + } + if (!options->ann_compat) + printf ("\n"); + } + + for (j = 1; j < options->knots-1; j++) { + if (VERBOSE) { + if (options->ann_compat) + printf ("%lf\n", result->knots[j]); + else + printf ("# % lf\t", result->knots[j]); + for (k = 0; k < options->polyorder+1; k++) { + if (options->ann_compat) + printf ("%lf\n", result->ck[j][options->polyorder-k]); + else + printf ("% lf\t", result->ck[j][options->polyorder-k]); + } + if (!options->ann_compat) + printf ("\n"); + } + } + + if (VERBOSE) { + if (options->ann_compat) + printf ("%lf\n", result->knots[j]); + else + printf ("# % lf\t", result->knots[j]); + for (k = 0; k < options->polyorder+1; k++) { + if (options->ann_compat) + printf ("%lf\n", result->ck[j][options->polyorder-k]); + else + printf ("% lf\t", result->ck[j][options->polyorder-k]); + } + if (!options->ann_compat) + printf ("\n"); + } + + if (!options->ann_compat && PRINT_SOLUTION) { + double phase, flux; + printf ("# \n# Theoretical light curve:\n# \n Phase\t Flux\n"); + for (i = 0; i <= LCPOINTS; i++) { + phase = -0.5 + (double) i / (double) LCPOINTS; + flux = polyfit_compute (result, options, phase); + printf (" % lf\t% lf\n", phase, flux); + } + } +} + +double polyfit_compute (polyfit_solution *result, polyfit_options *options, double phase) +{ + int j, k; + double knot, dknot, flux; + + if (phase < result->knots[0]) { + j = options->knots-1; + knot = result->knots[j]-1.0; + dknot = result->knots[0]-result->knots[options->knots-1]+1.0; + } + else if (phase > result->knots[options->knots-1]) { + j = options->knots-1; + knot = result->knots[j]; + dknot = result->knots[0]-result->knots[options->knots-1]+1.0; + } + else { + j = 0; + while (result->knots[j+1] < phase && j < options->knots-1) j++; + knot = result->knots[j]; + dknot = result->knots[j+1]-result->knots[j]; + } + + flux = result->ck[j][options->polyorder]; + for (k = 0; k < options->polyorder; k++) { + flux += result->ck[j][k] * pow (phase-knot, options->polyorder-k); + if (j == options->knots-1 && k < options->polyorder-1) + flux -= result->ck[j][k] * (phase-knot) * pow (dknot, options->polyorder-k-1); + } + + return flux; +} + + + + +int main (int argc, char **argv) +{ + int i, nobs, iter, status; + polyfit_triplet *data; + FILE *in; + double col1, col2, col3, chi2, chi2test, u; + char line[255]; + + double *knots = NULL; + double *test; + + polyfit_options *options; + polyfit_solution *result; + + + gsl_rng *r; + + if (argc < 2) { + printf ("Usage: ./polyfit [options] lc.dat\n\n"); + printf ("File lc.dat can have 1 column (equidistant fluxes), 2 columns (phase and flux),\n"); + printf ("or 3 columns (phase, flux, and standard deviation)\n\n"); + printf ("Options:\n\n"); + printf (" -o order .. fitting polynomial order (default: 2)\n"); + printf (" -i iters .. number of iterations (default: 10000)\n"); + printf (" -s step .. step for random knot displacement (default: 0.01)\n"); + printf (" -k k1 k2 ... kN .. explicit list of knots\n"); + printf (" --find-knots .. attempt to find knots automatically\n"); + printf (" --find-step .. attempt to find step automatically\n"); + printf (" --chain-length .. minimum chain length for automatic knot search\n"); + printf (" --ann-compatible .. make output ANN-compatible\n\n"); + exit (0); + } + + options = polyfit_options_default (); + options->find_knots = FALSE; + + for (i = 1; i < argc; i++) { + if (strcmp (argv[i], "-o") == 0) + options->polyorder = atoi (argv[++i]); + if (strcmp (argv[i], "-i") == 0) + options->iters = atoi (argv[++i]); + if (strcmp (argv[i], "-s") == 0) + options->step_size = atof (argv[++i]); + if (strcmp (argv[i], "-k") == 0) { + double knot; + i++; + options->knots = 0; + while (sscanf (argv[i], "%lf", &knot) == 1) { + options->knots++; + knots = realloc (knots, options->knots * sizeof (*knots)); + knots[options->knots-1] = knot; + i++; + } + i--; + } + if (strcmp (argv[i], "--find-knots") == 0) + options->find_knots = TRUE; + if (strcmp (argv[i], "--find-step") == 0) + options->find_step = 1; + if (strcmp (argv[i], "--chain-length") == 0) + options->chain_length = atoi (argv[++i]); + if (strcmp (argv[i], "--ann-compatible") == 0) + options->ann_compat = 1; + } + + in = fopen (argv[argc-1], "r"); + if (!in) { + printf ("file %s not found, aborting.\n", argv[argc-1]); + exit (0); + } + + /* Read the input data: */ + + data = malloc (NMAX * sizeof (*data)); + i = 0; + while (!feof (in)) { + if (!fgets (line, 255, in)) break; + if (feof (in)) break; + if (line[0] == '\n') break; + if (1 <= (status = sscanf (line, "%lf\t%lf\t%lf\n", &col1, &col2, &col3))) { + switch (status) { + case 1: + data[i].x = -0.5 + (double) i / (double) LCPOINTS; + data[i].y = col1; + data[i].z = 1.0; + break; + case 2: + data[i].x = col1; + data[i].y = col2; + data[i].z = 1.0; + break; + case 3: + data[i].x = col1; + data[i].y = col2; + data[i].z = 1e-3/col3/col3; + break; + default: + + /* can't ever get here */ + + break; + } + i++; + } + } + fclose (in); + nobs = i; + + if (!options->ann_compat) + printf ("# %d data points read in from %s.\n# \n", nobs, argv[argc-1]); + + /* Sort the data by phase: */ + + qsort (data, nobs, sizeof (*data), polyfit_sort_by_phase); + + if (options->find_knots) { + status = polyfit_find_knots (data, nobs, &knots, options); + if (!options->ann_compat) { + if (status != 0) + printf ("# * automatic search for knots failed, reverting to defaults.\n"); + else + printf ("# * automatic search for knots successful.\n"); + printf ("# \n"); + } + } + + /* The initial phase intervals for knots: */ + + if (!knots) { + knots = malloc (options->knots * sizeof (*knots)); + knots[0] = -0.4; knots[1] = -0.1; knots[2] = 0.2; knots[3] = 0.4; + /* knots[4] = 0.45; + knots[5] = 0.48; */ + } + + /* Sort the knots in ascending order: */ + qsort (knots, options->knots, sizeof (*knots), polyfit_sort_by_value); + + if (options->find_step) { + double diff; + /* Step size would be the minimum width between two knots / 5: */ + diff = fabs (knots[1]-knots[0]); + for (i = 1; i < options->knots-1; i++) + if (fabs (knots[i+1]-knots[i]) < diff) + diff = fabs (knots[i+1]-knots[i]); + if (fabs (knots[i+1]-knots[0]) < diff) + diff = fabs (knots[i+1]-knots[0]); + + options->step_size = diff / 5.0; + } + + if (!options->ann_compat) { + printf ("# Fitting polynomial order: %d\n", options->polyorder); + printf ("# Initial set of knots: {"); + for (i = 0; i < options->knots-1; i++) + printf ("%lf, ", knots[i]); + printf ("%lf}\n", knots[i]); + printf ("# Number of iterations for knot search: %d\n", options->iters); + printf ("# Step size for knot search: %lf\n# \n", options->step_size); + } + + r = gsl_rng_alloc (gsl_rng_mt19937); + gsl_rng_set (r, 1); + + result = polyfit_solution_init (options); + polyfit (result, data, nobs, knots, 0, options); + chi2 = result->chi2; + + if (!options->ann_compat) + printf ("# Original chi2: %lf\n", chi2); + + test = malloc (options->knots * sizeof (*test)); + + for (iter = 0; iter < options->iters; iter++) { + for (i = 0; i < options->knots; i++) { + u = gsl_rng_uniform (r); + test[i] = knots[i] + options->step_size * 2 * u - options->step_size; + if (test[i] < -0.5) test[i] += 1.0; + if (test[i] > 0.5) test[i] -= 1.0; + } + + polyfit (result, data, nobs, test, 0, options); + + if (result->chi2 < chi2) { + chi2 = result->chi2; + for (i = 0; i < options->knots; i++) + knots[i] = test[i]; + } + } + + if (!options->ann_compat) + printf ("# Final chi2: %lf\n# \n", chi2); + + polyfit (result, data, nobs, knots, 0, options); + + polyfit_print (result, options, 1, 1); + + gsl_rng_free (r); + free (data); + free (test); + free (knots); + + polyfit_options_free (options); + + return 0; +} + +float altmain (int argc, char **argv) +{ + int i, nobs, iter, status; + polyfit_triplet *data; + FILE *in; + double col1, col2, col3, chi2, chi2test, u; + char line[255]; + + double *knots = NULL; + double *test; + + polyfit_options *options; + polyfit_solution *result; + + + gsl_rng *r; + + if (argc < 2) { + printf ("Usage: ./polyfit [options] lc.dat\n\n"); + printf ("File lc.dat can have 1 column (equidistant fluxes), 2 columns (phase and flux),\n"); + printf ("or 3 columns (phase, flux, and standard deviation)\n\n"); + printf ("Options:\n\n"); + printf (" -o order .. fitting polynomial order (default: 2)\n"); + printf (" -i iters .. number of iterations (default: 10000)\n"); + printf (" -s step .. step for random knot displacement (default: 0.01)\n"); + printf (" -k k1 k2 ... kN .. explicit list of knots\n"); + printf (" --find-knots .. attempt to find knots automatically\n"); + printf (" --find-step .. attempt to find step automatically\n"); + printf (" --chain-length .. minimum chain length for automatic knot search\n"); + printf (" --ann-compatible .. make output ANN-compatible\n\n"); + exit (0); + } + + options = polyfit_options_default (); + options->find_knots = FALSE; + + for (i = 1; i < argc; i++) { + if (strcmp (argv[i], "-o") == 0) + options->polyorder = atoi (argv[++i]); + if (strcmp (argv[i], "-i") == 0) + options->iters = atoi (argv[++i]); + if (strcmp (argv[i], "-s") == 0) + options->step_size = atof (argv[++i]); + if (strcmp (argv[i], "-k") == 0) { + double knot; + i++; + options->knots = 0; + while (sscanf (argv[i], "%lf", &knot) == 1) { + options->knots++; + knots = realloc (knots, options->knots * sizeof (*knots)); + knots[options->knots-1] = knot; + i++; + } + i--; + } + if (strcmp (argv[i], "--find-knots") == 0) + options->find_knots = TRUE; + if (strcmp (argv[i], "--find-step") == 0) + options->find_step = 1; + if (strcmp (argv[i], "--chain-length") == 0) + options->chain_length = atoi (argv[++i]); + if (strcmp (argv[i], "--ann-compatible") == 0) + options->ann_compat = 1; + } + + in = fopen (argv[argc-1], "r"); + if (!in) { + printf ("file %s not found, aborting.\n", argv[argc-1]); + exit (0); + } + + /* Read the input data: */ + + data = malloc (NMAX * sizeof (*data)); + i = 0; + while (!feof (in)) { + if (!fgets (line, 255, in)) break; + if (feof (in)) break; + if (line[0] == '\n') break; + if (1 <= (status = sscanf (line, "%lf\t%lf\t%lf\n", &col1, &col2, &col3))) { + switch (status) { + case 1: + data[i].x = -0.5 + (double) i / (double) LCPOINTS; + data[i].y = col1; + data[i].z = 1.0; + break; + case 2: + data[i].x = col1; + data[i].y = col2; + data[i].z = 1.0; + break; + case 3: + data[i].x = col1; + data[i].y = col2; + data[i].z = 1e-3/col3/col3; + break; + default: + + /* can't ever get here */ + + break; + } + i++; + } + } + fclose (in); + nobs = i; + + if (!options->ann_compat) + printf ("# %d data points read in from %s.\n# \n", nobs, argv[argc-1]); + + /* Sort the data by phase: */ + + qsort (data, nobs, sizeof (*data), polyfit_sort_by_phase); + + if (options->find_knots) { + status = polyfit_find_knots (data, nobs, &knots, options); + if (!options->ann_compat) { + if (status != 0) + printf ("# * automatic search for knots failed, reverting to defaults.\n"); + else + printf ("# * automatic search for knots successful.\n"); + printf ("# \n"); + } + } + + /* The initial phase intervals for knots: */ + + if (!knots) { + knots = malloc (options->knots * sizeof (*knots)); + knots[0] = -0.4; knots[1] = -0.1; knots[2] = 0.2; knots[3] = 0.4; + /* knots[4] = 0.45; + knots[5] = 0.48; */ + } + + /* Sort the knots in ascending order: */ + qsort (knots, options->knots, sizeof (*knots), polyfit_sort_by_value); + + if (options->find_step) { + double diff; + /* Step size would be the minimum width between two knots / 5: */ + diff = fabs (knots[1]-knots[0]); + for (i = 1; i < options->knots-1; i++) + if (fabs (knots[i+1]-knots[i]) < diff) + diff = fabs (knots[i+1]-knots[i]); + if (fabs (knots[i+1]-knots[0]) < diff) + diff = fabs (knots[i+1]-knots[0]); + + options->step_size = diff / 5.0; + } + + if (!options->ann_compat) { + printf ("# Fitting polynomial order: %d\n", options->polyorder); + printf ("# Initial set of knots: {"); + for (i = 0; i < options->knots-1; i++) + printf ("%lf, ", knots[i]); + printf ("%lf}\n", knots[i]); + printf ("# Number of iterations for knot search: %d\n", options->iters); + printf ("# Step size for knot search: %lf\n# \n", options->step_size); + } + + r = gsl_rng_alloc (gsl_rng_mt19937); + gsl_rng_set (r, 1); + + result = polyfit_solution_init (options); + polyfit (result, data, nobs, knots, 0, options); + chi2 = result->chi2; + + if (!options->ann_compat) + printf ("# Original chi2: %lf\n", chi2); + + test = malloc (options->knots * sizeof (*test)); + + for (iter = 0; iter < options->iters; iter++) { + for (i = 0; i < options->knots; i++) { + u = gsl_rng_uniform (r); + test[i] = knots[i] + options->step_size * 2 * u - options->step_size; + if (test[i] < -0.5) test[i] += 1.0; + if (test[i] > 0.5) test[i] -= 1.0; + } + + polyfit (result, data, nobs, test, 0, options); + + if (result->chi2 < chi2) { + chi2 = result->chi2; + for (i = 0; i < options->knots; i++) + knots[i] = test[i]; + } + } + + if (!options->ann_compat) + printf ("# Final chi2: %lf\n# \n", chi2); + + polyfit (result, data, nobs, knots, 0, options); + + polyfit_print (result, options, 1, 1); + + gsl_rng_free (r); + free (data); + free (test); + free (knots); + + polyfit_options_free (options); + + return (float) chi2; +} + diff --git a/mltsp/TCP/Algorithms/EclFeatures/polyfit.h b/mltsp/TCP/Algorithms/EclFeatures/polyfit.h new file mode 100755 index 00000000..44ed97d9 --- /dev/null +++ b/mltsp/TCP/Algorithms/EclFeatures/polyfit.h @@ -0,0 +1,36 @@ +#include + +typedef struct polyfit_options { + int polyorder; + int iters; + double step_size; + int knots; + bool find_knots; + bool find_step; + int chain_length; + bool ann_compat; +} polyfit_options; + +typedef struct polyfit_triplet { + double x; + double y; + double z; +} polyfit_triplet; + +typedef struct polyfit_solution { + double chi2; + int *npts; /* Number of data points on each interval */ + double *knots; /* Array of knots */ + double **ck; /* Polynomial coefficients */ +} polyfit_solution; + +polyfit_options *polyfit_options_default (); +int polyfit_options_free (polyfit_options *options); +polyfit_solution *polyfit_solution_init (polyfit_options *options); +int polyfit_solution_free (polyfit_solution *solution, polyfit_options *options); +int polyfit_sort_by_phase (const void *a, const void *b); +int polyfit_sort_by_value (const void *a, const void *b); +int polyfit_find_knots (polyfit_triplet *data, int nobs, double **knots, polyfit_options *options); +double polyfit_compute (polyfit_solution *result, polyfit_options *options, double phase); +int polyfit_print (polyfit_solution *result, polyfit_options *options, int VERBOSE, int PRINT_SOLUTION); +int polyfit (polyfit_solution *result, polyfit_triplet *data, int nobs, double *knots, int PRINT, polyfit_options *options); diff --git a/mltsp/TCP/Algorithms/EclFeatures/selectp.py b/mltsp/TCP/Algorithms/EclFeatures/selectp.py new file mode 100644 index 00000000..d36e6fcd --- /dev/null +++ b/mltsp/TCP/Algorithms/EclFeatures/selectp.py @@ -0,0 +1,207 @@ +""" +Select a period. + +Runs polyfit (adapted from PHOEBE) and looks for a period +with the lowest chi-sq. you input the trial period and it will look +at aliases of that period. Assumed to be an eclipsing system. + +See test() for usage + +J.S.Bloom, Aug 2011 + +""" +from __future__ import print_function +import ctypes, sys, tempfile, os +from ctypes import c_char_p +from matplotlib.mlab import csv2rec +from matplotlib import pylab as plt +from numpy import where +import StringIO +import traceback +import time +# path to polyfit executable if dynamic = False + +# gcc -m32 -L/usr/local/lib polyfit.c -lgsl -lgslcblas -lm -o polyfit +# gcc -m32 -shared -Wl -o polyfit.so polyfit.o -lc -lgsl -lgslcblas -lm +exec_path = "./polyfit" + +class selectp: + + + def __init__(self,t, y, dy, period, mults=[0.5,1,2],\ + order=2, iknots=[-0.4,-0.2,0.2,0.4], exec_fpath='', + dynamic=True, mag=True, verbose=False, srcid=0): + + self.verbose = verbose + self.rez = {'models': []} + self.order = order + self.iknots = iknots + self.t = t + self.y = y + self.dy = dy + self.period = period + self.mults = mults + self.mag = mag # y values in mag not flux + self.srcid = srcid + self.dynamic = dynamic + #if len(exec_fpath) > 0: + # self.poly = exec_fpath + #else: + # self.poly = exec_path + self.poly = os.path.abspath(os.environ.get("TCP_DIR") + 'Algorithms/EclFeatures/polyfit') + if dynamic: + ## load the dynamic library + try: + self.polyfit = ctypes.cdll.LoadLibrary(os.path.abspath(os.environ.get("TCP_DIR") + 'Algorithms/EclFeatures/polyfit.so')) + except: + print("cannot load polyfit.so") + + self.ok = True + + def _runp(self,p): + if not self.ok: + return -1 + + ## try running with that period + if self.verbose: + print(" --> running with period ... %f" % p) + # write out phased photometry to the file -- center the most faint point at 0 + tt=(self.t/p) % 1.; s=tt.argsort() + x = tt[s]; y = self.y[s] ; z = self.dy[s] + if self.mag: + mm = where(y == max(y)) + else: + mm = where(y == min(y)) + pmm = x[mm] + tt = (((self.t/p) % 1.0) - pmm + 0.5) % 1.0; s=tt.argsort() + x = tt[s]; y = self.y[s] ; z = self.dy[s] + z = zip(x - 0.5,y,z) + f = tempfile.NamedTemporaryFile(dir="/tmp",suffix=".dat",delete=False) + name = f.name + #(f,name) = tempfile.mkstemp(dir="/tmp",suffix=".dat") + #f = open(name,"w") + lines = [] + for l in z: + lines.append("%f %f %f\n" % l) + write_str = ''.join(lines) + f.write(write_str) + f.flush() # dstarr adds to maybe reduce segfaults + f.close() + + try: + loop_max = 300 + i_loop = 0 + while ((os.stat(name).st_size < len(write_str)) and (i_loop < loop_max)): + time.sleep(1) + i_loop += 1 + if i_loop >= loop_max: + self.rez["models"].append({"period": p, "phase": None, 'f': None, 'chi2': 100000}) + os.remove(name) + return # maybe this is a good way to catch the segfault issue sources prior to segfaulting? + except: + self.rez["models"].append({"period": p, "phase": None, 'f': None, 'chi2': 100000}) + os.remove(name) + return # maybe this is a good way to catch the segfault issue sources prior to segfaulting? + + + tmp = "%s -o %i -k %s --find-step --chain-length 30 %s" % \ + (self.poly,self.order," ".join(["%.2f" % k for k in self.iknots]),name) + alttmp = "%s -o %i -k %s --find-knots --find-step --chain-length 30 %s" % \ + (self.poly,self.order," ".join(["%.2f" % k for k in [-0.45,-0.35,0.40,0.45]]),name) + + #import pdb; pdb.set_trace() + #print + + if self.dynamic: + # make the argv vector + argv = tmp.split() + argc = len(argv) + argv_type = ctypes.c_char_p * len(argv) + argv = argv_type(*argv) + argc = ctypes.c_int(argc) + self.polyfit.altmain.restype = ctypes.c_float + f1 = tempfile.TemporaryFile() + oldstdout = os.dup(sys.stdout.fileno()) + os.dup2(f1.fileno(), 1) + res = self.polyfit.altmain(argc,argv) + os.dup2(oldstdout, 1) + f1.seek(0) + s = csv2rec(f1,delimiter="\t") + self.rez["models"].append({"period": p, "phase": s['phase'], 'f': s['flux'], 'chi2': res}) + else: + from subprocess import PIPE,Popen + + #os.system("touch /global/home/users/dstarr/500GB/debug2/%d" % (self.srcid + 100000000)) + + pp = Popen(tmp, shell=True,stdin=PIPE, stdout=PIPE, \ + stderr=PIPE, close_fds=True) + #sts = os.waitpid(pp.pid, 0)[1] + pp.wait() + + + (child_stdin,child_stdout,child_stderr) = (pp.stdin, pp.stdout, pp.stderr) + ttt = child_stdout.readlines() ; ttt1 = child_stderr.readlines() + child_stdin.close() ; child_stdout.close() ; child_stderr.close() + if len(ttt) == 0 and len(ttt1) == 0: + # probably a seg fault + if self.verbose: + print(" --> trying",alttmp) + pp = Popen(alttmp, shell=True,stdin=PIPE, stdout=PIPE, \ + stderr=PIPE, close_fds=True) + pp.wait() + (child_stdin,child_stdout,child_stderr) = (pp.stdin, pp.stdout, pp.stderr) + ttt = child_stdout.readlines() ; ttt1 = child_stderr.readlines() + child_stdin.close() ; child_stdout.close() ; child_stderr.close() + if len(ttt) == 0 and len(ttt1) == 0: + self.rez["models"].append({"period": p, "phase": None, 'f': None, 'chi2': 100000}) + os.remove(name) + return + ttt1 = StringIO.StringIO("".join(ttt)) # make a new file + s = csv2rec(ttt1,delimiter="\t") + chi2 = 100000 + for l in ttt: + if l.find("Final chi2:") == -1: + continue + chi2 = float(l.split("Final chi2:")[-1]) + self.rez["models"].append({"period": p, "phase": s['phase'], 'f': s['flux'], 'chi2': chi2}) + os.remove(name) + + def select(self): + for m in self.mults: + self._runp(m*self.period) + r = [(x['chi2'],x['period']) for x in self.rez['models']] + r.sort() + if self.verbose: + print(" .... best p = ", r[0][1], "best chi2 = ", r[0][0]) + self.rez.update({"best_period": r[0][1], "best_chi2": r[0][0]}) + + def plot_best(self,extra=""): + b = [x for x in self.rez['models'] if x['period'] == self.rez['best_period']][0] + from matplotlib import pylab as plt + + tt=(self.t/self.rez['best_period']) % 1; s=tt.argsort() + x = tt[s]; y = self.y[s] ; z = self.dy[s] + if self.mag: + mm = where(y== max(y)) + else: + mm = where(y== min(y)) + pmm = x[mm] + tt = (((self.t/self.rez['best_period']) % 1.0) - pmm + 0.5) % 1.0; s=tt.argsort() + x = tt[s] - 0.5; y = self.y[s] ; z = self.dy[s] + + plt.errorbar (x,y,z,fmt='o',c="r") + plt.plot(b['phase'],b['f'],c="b") + plt.ylim(self.y.max()+0.05,self.y.min()-0.05) + plt.xlabel("phase") + plt.ylabel("flux/mag") + plt.title("Best p = %.6f (chi2 = %.3f)" % (self.rez['best_period'],self.rez['best_chi2'])) + plt.text(-0.2,self.y.max() - 0.05, "%s" % extra, ha='center',alpha=0.5) + +def test(): + x = csv2rec("lc.dat",delimiter=" ",names=["t","y","dy"]) + s = selectp(x['t'],x['y'],x['dy'],21.93784630,dynamic=False,verbose=True) + s.select() + print(s.rez) + s.plot_best() + + diff --git a/mltsp/TCP/Algorithms/PTF_SN_classifier.py b/mltsp/TCP/Algorithms/PTF_SN_classifier.py new file mode 100644 index 00000000..eb1b31be --- /dev/null +++ b/mltsp/TCP/Algorithms/PTF_SN_classifier.py @@ -0,0 +1,349 @@ +from scipy.special import erf +from numpy import array,matrix,arange,sqrt,exp,mean,sum,zeros,clip,fix,\ + cumsum,hstack,floor,ceil + +### ### NEED A FEATURE-CHECKING FUNCTION ### ### + +# (1) parse the XML + +# sys.path.append(os.path.expandvars("$TCP_DIR/Software/feature_extract/Code") +# import vosource_parse, pprint +# fname = "test_feature_algorithms.VOSource.xml" +# v = vosource_parse.vosource_parser(fname) +# pprint(v.d) +# note that v is of type xmldict.XmlDictObject +# v.d['ts'] is the parsed timeseries as a list, usually with 4 entries (time, val, valerr, limit) +# it's up to the user to decide how to use those columns...there's almost no reformating +# + + +# from v I need to extract: +# closest_in_light +# closest_in_light_ttype +# closest_in_light_dm +# sdss_best_offset_in_petro_g +# sdss_best_z + sdss_best_dz +# sdss_colors: +# sdss_photo_rest_ug +# sdss_photo_rest_gr +# sdss_photo_rest_ri +# sdss_photo_rest_iz + + +#(2) find host +# +#defaults: +near_z=None +gal_type=0 +sloan_colors=[] +near_z=0 +near_dz=0.1 +nearby=False +used_sdss_colors=False +used_z=False + + +light_threshold=1.5 +if (closest_in_light is not None) and (closest_in_light=conf*mcprob and delta0, fix that gal_type, otherwise use Sloan colors + (either input or defined via sloan type) and their associated + uncertainty when mapping to types. Fractional gal or sloan types + are permitted. + + Sloan colors are [u-g , g-r , r-i , i-z]. + + Galaxy Hubble types indexed according to: + 1=E,2=S0,3=Sa,4=Sb,5=Sc,6=Sd,7=Im . + + Setting sloan_type instead of gal_type is useful in that it provides + a crude propagation of typing uncertainty.""" + + + # + # observed rates + # + obs_rate_Ia = array([4.0018, 5.0504, 5.3397, 5.5446, 5.6459, 5.6683, 5.6113, 5.4954, 5.3387, 5.1593, 4.9450, 4.5844, 4.1014, 3.5384, 2.9377, 2.3418, 1.7929, 1.3334, 1.0057, 0.8486, 0.7604, 0.6771, 0.5985, 0.5249, 0.4561, 0.3922, 0.3331, 0.2790, 0.2297, 0.1854, 0.1459, 0.1113, 0.0816, 0.0569, 0.0370]) + + obs_rate_cc = array([0.8428, 2.3028, 2.3755, 2.2226, 1.9121, 1.6182, 1.3255, 1.0246, 0.7579, 0.5679, 0.4743, 0.3968, 0.3245, 0.2583, 0.1993, 0.1483, 0.1065, 0.0746, 0.0538, 0.0448, 0.0402, 0.0358, 0.0316, 0.0277, 0.0241, 0.0207, 0.0176, 0.0147, 0.0121, 0.0098, 0.0077, 0.0058, 0.0043, 0.0029, 0.0019]) + + obs_rate_IIp =array([0.3377, 0.7043, 0.7691, 0.7210, 0.6151, 0.5115, 0.4047, 0.2936, 0.1958, 0.1289, 0.1013, 0.0808, 0.0626, 0.0467, 0.0332, 0.0221, 0.0135, 0.0073, 0.0037, 0.0025, 0.0023, 0.0020, 0.0018, 0.0016, 0.0014, 0.0012, 0.0011, 0.0009, 0.0008, 0.0007, 0.0006, 0.0005, 0.0004, 0.0004, 0.0003]) + + obs_rate_Ibc =array([0.4616, 1.4410, 1.3800, 1.2553, 1.0378, 0.8448, 0.6681, 0.4958, 0.3484, 0.2459, 0.1970, 0.1582, 0.1234, 0.0929, 0.0666, 0.0449, 0.0277, 0.0154, 0.0080, 0.0056, 0.0049, 0.0043, 0.0038, 0.0033, 0.0028, 0.0024, 0.0020, 0.0016, 0.0013, 0.0011, 0.0008, 0.0006, 0.0004, 0.0003, 0.0002]) + + obs_rate_IIn =array([0.0435, 0.1575, 0.2265, 0.2463, 0.2592, 0.2620, 0.2527, 0.2351, 0.2137, 0.1930, 0.1760, 0.1578, 0.1384, 0.1187, 0.0994, 0.0813, 0.0652, 0.0519, 0.0422, 0.0367, 0.0329, 0.0294, 0.0260, 0.0228, 0.0199, 0.0171, 0.0145, 0.0121, 0.0100, 0.0080, 0.0063, 0.0047, 0.0034, 0.0023, 0.0014]) + + # rates are defined on the following redshift grid + nzbins = len(obs_rate_Ia) + zbin=0.01 + z_grid = arange(nzbins)*zbin + z_grid1 = z_grid - zbin/2. + z_grid1[0] = z_grid[0] + z_grid2 = z_grid + zbin/2. + + + # only do the work if the input-z is in range of interest, otherwise + # we throw up a flag high_z=True + nsig=2. + if (z-nsig*dz0): + zdn = fix( round((z-dz)/dz) )*dz + zup = fix( round((z+dz)/dz) )*dz + + z1sigma = (zdn,zup) + out_dict1 = {'high_z':True,\ + 'Prob_Ia':fix(0.99*100)/100,\ + 'Prob_CC':fix(0.01*100)/100,\ + 'Prob_IIp|CC':fix(0.34*100)/100,\ + 'Prob_Ibc|CC':fix(0.33*100)/100,\ + 'Prob_IIn|CC':fix(0.33*100)/100,\ + 'z_1sigma':z1sigma,'z_1sigma|Ia':z1sigma,'z_1sigma|CC':z1sigma,\ + 'z_1sigma|IIp':z1sigma,'z_1sigma|Ibc':z1sigma,'z_1sigma|IIp':z1sigma} + + + out_dict={ '': { \ + 'class_results':{ \ + 'SN Ia':{'prob': out_dict1['prob_Ia'],\ + 'weight':1.0,\ + 'TUTOR_name': "tia",\ + 'comments': "No Ia subtypes",\ + 'class_value_added_statements': {'name': "z_1sigma",'value' : out_dict1['Ia_z_1sigma'], 'comments': "This is the 1sigma redshift interval if it is a Ia"},\ + 'subclass': { \ + '1991bg-like' : {'prob': None, 'weight': 0.0, 'TUTOR-name': None},\ + 'super-Chandra': {'prob': None, 'weight': 0.0, 'TUTOR-name': "tiasc"},\ + 'Branch-Normal': {'prob': None, 'weight': 0.0, 'TUTOR-name': None},\ + 'peculiar': {'prob': None, 'weight': 0.0, 'TUTOR-name': None}}}, + 'SN CC':{'prob': out_dict1['prob_CC'],\ + 'weight':1.0,\ + 'TUTOR_name': "cc",\ + 'comments': None,\ + 'class_value_added_statements': {'name': "z_1sigma",'value' : out_dict1['cc_z_1sigma'], 'comments': "This is the 1sigma redshift interval if it is a CC-SN"}}, + 'SN Ibc':{'prob': out_dict1['prob_Ibc'],\ + 'weight':1.0,\ + 'TUTOR_name': None,\ + 'comments': "no Ib Ic differenciation",\ + 'class_value_added_statements': {'name': "z_1sigma",'value' : out_dict1['Ibc_z_1sigma'], 'comments': "This is the 1sigma redshift interval if it is a Ibc"},\ + 'subclass': { \ + 'Ib' : {'prob': None, 'weight': 0.0, 'TUTOR-name': "tib"},\ + 'Ic': {'prob': None, 'weight': 0.0, 'TUTOR-name': "tic"},\ + 'peculiar': {'prob': None, 'weight': 0.0, 'TUTOR-name': None}}}, + 'SN IIP':{'prob': out_dict1['prob_IIp'],\ + 'weight':1.0,\ + 'TUTOR_name': "iip",\ + 'comments': "Type II SNe are broken to IIP and IIn",\ + 'class_value_added_statements': {'name': "z_1sigma",'value' : out_dict1['IIp_z_1sigma'], 'comments': "This is the 1sigma redshift interval if it is a IIp"},\ + 'subclass': { \ + 'IIP' : {'prob': None, 'weight': 0.0, 'TUTOR-name': "iip"},\ + 'IIL': {'prob': None, 'weight': 0.0, 'TUTOR-name': "iil"},\ + 'IIb': {'prob': None, 'weight': 0.0, 'TUTOR-name': "iib"},\ + 'peculiar': {'prob': None, 'weight': 0.0, 'TUTOR-name': None}}}, + 'SN IIn':{'prob': out_dict1['prob_IIn'],\ + 'weight':1.0,\ + 'TUTOR_name': "iin",\ + 'comments': "Type II SNe are broken to IIP and IIn",\ + 'class_value_added_statements': {'name': "z_1sigma",'value' : out_dict1['IIn_z_1sigma'], 'comments': "This is the 1sigma redshift interval if it is a IIn"}}}, + 'global_statements_and_flags': [\ + {'name': "interesting_object", 'type': "bool", 'val': None, "comment": None},\ + {'name': "high-z", 'type': "bool", 'val': out_dict1['high_z'], "comment": None}],\ + 'comments' : "in this version weights are 0 or 1, the first for unconstrained questions, the latter for any derived value.",\ + 'version': "v0.1"}} + + return out_dict diff --git a/mltsp/TCP/Algorithms/SpatialClustering/cluster.py b/mltsp/TCP/Algorithms/SpatialClustering/cluster.py new file mode 100644 index 00000000..95e06808 --- /dev/null +++ b/mltsp/TCP/Algorithms/SpatialClustering/cluster.py @@ -0,0 +1,529 @@ +#!/usr/bin/env python + +""" +cluster: Monte Carlo simulation of clustering +""" +from __future__ import print_function + +import os,sys +import datetime +try: + from matplotlib import pylab +except: + pass +try: + from pylab import * +except: + pass +from numarray import random_array +import time +import copy +import math +import random +import numarray + +start_time = time.time() +global blah + + +def is_object_associated_with_source_algorithm_jbloom(n_sources, \ + obj_ra, obj_dec, obj_ra_rms, obj_dec_rms, \ + src_ra, src_dec, src_ra_rms, src_dec_rms, sigma_0): + """ Source matching algorithm + Input: obj & source ra,dec,errors + Return: True/False conditional result + """ + # log (Po(center)*Ps(center)/Po(midpoint)Ps(midpoint)) + #Poc = -1.0* math.log(self.assumed_err[0]*self.assumed_err[1]*2.0*math.pi) + #Psc = -1.0* math.log(s.current_err[0]*s.current_err[1]*2.0*math.pi) + + # This try/except is kudgy, since RMS for both sources or objects should not be 0: + # I think this only occurs for errant cases, while debugging, or integrating a new survey. + try: + midpt = [(obj_ra/obj_ra_rms**2 + src_ra/src_ra_rms**2)/ \ + (1/obj_ra_rms**2 + 1/src_ra_rms**2), \ + (obj_dec/obj_dec_rms**2 + src_dec/src_dec_rms**2)/ \ + (1/obj_dec_rms**2 + 1/src_dec_rms**2)] + except: + if src_ra_rms == 0: + src_ra_rms = 0.2 # default arcsecs + if src_dec_rms == 0: + src_dec_rms = 0.2 # default arcsecs + if obj_ra_rms == 0: + obj_ra_rms = 0.2 # default arcsecs + if obj_dec_rms == 0: + obj_dec_rms = 0.2 # default arcsecs + midpt = [(obj_ra/obj_ra_rms**2 + src_ra/src_ra_rms**2)/ \ + (1/obj_ra_rms**2 + 1/src_ra_rms**2), \ + (obj_dec/obj_dec_rms**2 + src_dec/src_dec_rms**2)/ \ + (1/obj_dec_rms**2 + 1/src_dec_rms**2)] + + + #miderr = [math.sqrt(1.0/(self.assumed_err[0]**(-2) + s.current_err[0]**(-2))),\ + # math.sqrt(1.0/(self.assumed_err[0]**(-2) + s.current_err[0]**(-2)))] + #print (midpt,self.pos,self.assumed_err,s.current_pos,s.current_err) + #Pom = -1.0* math.log(self.assumed_err[0]*self.assumed_err[1]*2.0*math.pi) - 0.5*( ((self.pos[0] - midpt[0])/self.assumed_err[0])**2 + \ + # ((self.pos[1] - midpt[1])/self.assumed_err[1])**2) + #Psm = -1.0* math.log(s.current_err[0]*s.current_err[1]*2.0*math.pi) - 0.5*( ((s.current_pos[0] - midpt[0])/s.current_err[0])**2 + \ + # ((s.current_pos[1] - midpt[1])/s.current_err[1])**2) + #odds = -1.0 * (Poc + Psc - Pom - Psm) + cos_dec_term = math.cos(midpt[1]*math.pi/(180.0*3600.0)) # obj/src positions are in arcsec & need to be converted to radians in cos() + simple_odds = - 0.5*( (cos_dec_term*(src_ra - midpt[0])/src_ra_rms)**2 + \ + ((src_dec - midpt[1])/src_dec_rms)**2) \ + - 0.5*( (cos_dec_term*(obj_ra - midpt[0])/obj_ra_rms)**2 + \ + ((obj_dec - midpt[1])/obj_dec_rms)**2) + #-2*logpop = chi^2 = sigma^2 --> sqrt(10) + num_obs_associated = n_sources + sigma_n = sqrt(2.0*log(num_obs_associated)) + + return ((-2.828*simple_odds < sigma_n**2 + sigma_0**2), simple_odds, sigma_n, midpt) + + +# 20071008 works, original algorihtms, un-normalized +def is_object_associated_with_source_algorithm_jbloom_orig(n_sources, \ + obj_ra, obj_dec, obj_ra_rms, obj_dec_rms, \ + src_ra, src_dec, src_ra_rms, src_dec_rms, sigma_0): + """ Source matching algorithm + Input: obj & source ra,dec,errors + Return: True/False conditional result + """ + # log (Po(center)*Ps(center)/Po(midpoint)Ps(midpoint)) + #Poc = -1.0* math.log(self.assumed_err[0]*self.assumed_err[1]*2.0*math.pi) + #Psc = -1.0* math.log(s.current_err[0]*s.current_err[1]*2.0*math.pi) + midpt = [(obj_ra/obj_ra_rms**2 + src_ra/src_ra_rms**2)/ \ + (1/obj_ra_rms**2 + 1/src_ra_rms**2), \ + (obj_dec/obj_dec_rms**2 + src_dec/src_dec_rms**2)/ \ + (1/obj_dec_rms**2 + 1/src_dec_rms**2)] + #miderr = [math.sqrt(1.0/(self.assumed_err[0]**(-2) + s.current_err[0]**(-2))),\ + # math.sqrt(1.0/(self.assumed_err[0]**(-2) + s.current_err[0]**(-2)))] + #print (midpt,self.pos,self.assumed_err,s.current_pos,s.current_err) + #Pom = -1.0* math.log(self.assumed_err[0]*self.assumed_err[1]*2.0*math.pi) - 0.5*( ((self.pos[0] - midpt[0])/self.assumed_err[0])**2 + \ + # ((self.pos[1] - midpt[1])/self.assumed_err[1])**2) + #Psm = -1.0* math.log(s.current_err[0]*s.current_err[1]*2.0*math.pi) - 0.5*( ((s.current_pos[0] - midpt[0])/s.current_err[0])**2 + \ + # ((s.current_pos[1] - midpt[1])/s.current_err[1])**2) + #odds = -1.0 * (Poc + Psc - Pom - Psm) + simple_odds = - 0.5*( ((src_ra - midpt[0])/src_ra_rms)**2 + \ + ((src_dec - midpt[1])/src_dec_rms)**2) \ + - 0.5*( ((obj_ra - midpt[0])/obj_ra_rms)**2 + \ + ((obj_dec - midpt[1])/obj_dec_rms)**2) + #-2*logpop = chi^2 = sigma^2 --> sqrt(10) + num_obs_associated = n_sources + sigma_n = sqrt(2.0*log(num_obs_associated)) + + return ((-2.0*simple_odds < sigma_n**2 + sigma_0**2), simple_odds, sigma_n) + + + + +class obs: + + def __init__(self,initial_pos,true_err=[[0.09,0.0],[0.0,0.09]],assumed_err=[0.3,0.3],t=None): + self.initial_pos = initial_pos # (true ra and dec) + self.true_err = true_err + self.assumed_err = assumed_err + self.t = t + self.associated_sources = [] + if self.t is None: + ## assign a time + self.t = time.time() - start_time ## this is in seconds + self.pos = [] + + def observe_pos(self): + """ + """ + self.pos = random_array.multivariate_normal(self.initial_pos,self.true_err) + + def plot(self): + scatter([self.pos[0]],[self.pos[1]],s=20) + return + + def is_associated_with_source(self,slist,sigma_0=3.0): + if type(slist) != type([]) or len(slist) == 0: + return {'answer': False, 'sources': []} + + ## todo: put the logic here + yes_source = [] + print(len(slist)) + source_odds = [] + for s in slist: + (match_bool, simple_odds, sigma_n, midpt) = \ + is_object_associated_with_source_algorithm_jbloom(\ + len(s.associated_obs), \ + self.pos[0], self.pos[1], self.assumed_err[0], self.assumed_err[1], \ + s.current_pos[0], s.current_pos[1], s.current_err[0], \ + s.current_err[1], sigma_0) + + if match_bool: + print(("associated",sqrt(-2.0*simple_odds),sqrt(sigma_n**2 + sigma_0**2))) + yes_source.append(s) + source_odds.append(simple_odds) + + + #print (simple_odds) + + if len(yes_source) == 0: + print(("no association",self.pos)) + return {'answer': False, 'sources': []} + + else: + mm= max(source_odds) + ind = source_odds.index(mm) + return {'answer': True, 'best_source': [yes_source[ind]], 'best_odds': mm, 'sources': yes_source, 'odds': source_odds} + + + def is_associated_with_source_orig(self,slist,sigma_0=3.0): + if type(slist) != type([]) or len(slist) == 0: + return {'answer': False, 'sources': []} + + ## todo: put the logic here + yes_source = [] + print(len(slist)) + source_odds = [] + for s in slist: + # log (Po(center)*Ps(center)/Po(midpoint)Ps(midpoint)) + #Poc = -1.0* math.log(self.assumed_err[0]*self.assumed_err[1]*2.0*math.pi) + #Psc = -1.0* math.log(s.current_err[0]*s.current_err[1]*2.0*math.pi) + midpt = [(self.pos[0]/self.assumed_err[0]**2 + s.current_pos[0]/s.current_err[0]**2)/(1/self.assumed_err[0]**2 + 1/s.current_err[0]**2),\ + (self.pos[1]/self.assumed_err[1]**2 + s.current_pos[1]/s.current_err[1]**2)/(1/self.assumed_err[1]**2 + 1/s.current_err[1]**2)] + #miderr = [math.sqrt(1.0/(self.assumed_err[0]**(-2) + s.current_err[0]**(-2))),\ + # math.sqrt(1.0/(self.assumed_err[0]**(-2) + s.current_err[0]**(-2)))] + #print (midpt,self.pos,self.assumed_err,s.current_pos,s.current_err) + #Pom = -1.0* math.log(self.assumed_err[0]*self.assumed_err[1]*2.0*math.pi) - 0.5*( ((self.pos[0] - midpt[0])/self.assumed_err[0])**2 + \ + # ((self.pos[1] - midpt[1])/self.assumed_err[1])**2) + #Psm = -1.0* math.log(s.current_err[0]*s.current_err[1]*2.0*math.pi) - 0.5*( ((s.current_pos[0] - midpt[0])/s.current_err[0])**2 + \ + # ((s.current_pos[1] - midpt[1])/s.current_err[1])**2) + #odds = -1.0 * (Poc + Psc - Pom - Psm) + simple_odds = - 0.5*( ((s.current_pos[0] - midpt[0])/s.current_err[0])**2 + \ + ((s.current_pos[1] - midpt[1])/s.current_err[1])**2) - 0.5*( ((self.pos[0] - midpt[0])/self.assumed_err[0])**2 + \ + ((self.pos[1] - midpt[1])/self.assumed_err[1])**2) + #-2*logpop = chi^2 = sigma^2 --> sqrt(10) + num_obs_associated = len(s.associated_obs) + sigma_n = sqrt(2.0*log(num_obs_associated)) + + if -2.0*simple_odds < sigma_n**2 + sigma_0**2: + print(("associated",sqrt(-2.0*simple_odds),sqrt(sigma_n**2 + sigma_0**2))) + yes_source.append(s) + source_odds.append(simple_odds) + + + #print (simple_odds) + + if len(yes_source) == 0: + print(("no association",self.pos)) + return {'answer': False, 'sources': []} + + else: + mm= max(source_odds) + ind = source_odds.index(mm) + return {'answer': True, 'best_source': [yes_source[ind]], 'best_odds': mm, 'sources': yes_source, 'odds': source_odds} + + + def associate_with_source(self,slist): + if type(slist) != type([]) or len(slist) == 0: + return + # todo: watch out for multiplicity + self.associated_sources.extend(slist) + + def __str__(self): + a = " t=%f" % self.t + a += "\tinitial pos = %s\n" % self.initial_pos + a += "\tobserved pos = %s\n" % self.pos + a += "\ttrue_obsevational_err = %s\n" % self.true_err + return a + +class source: + + def __init__(self,start_pos=[None,None],start_err=[None,None],current_pos=[None,None],current_err=[None,None],\ + associated_obs=[],stype="real"): + """ + possible types: real and constructed + """ + self.start_pos = start_pos + self.start_err = start_err + self.current_pos = current_pos + self.current_err = current_err + self.associated_obs = associated_obs + self.stype = stype + + def add_associated_obs(self,o): + # might want to deepcopy here + self.associated_obs.append(copy.deepcopy(o)) + + def plot(self,code='ys'): + try: + if self.stype=="real": + scatter([self.start_pos[0]],[self.start_pos[1]],c=code[0],marker=code[1],s=60) + else: + scatter([self.current_pos[0]],[self.current_pos[1]],c=code[0],marker=code[1],s=60) + except: + pass + + def recalculate_position(self): + """ + takes all the positions of the associated observation list and recalculated a position + """ + global blah + if self.stype=="real": + print("! not supposed to do this with a real source..") + return + if len(self.associated_obs) == 0: + return + + pos_array = numarray.fromlist([[x.pos[0],x.pos[1],x.assumed_err[0],x.assumed_err[1]] for x in self.associated_obs]) + raa = numarray.fromlist([x[0] for x in pos_array]) + raerra = numarray.fromlist([x[2] for x in pos_array]) + deca = numarray.fromlist([x[1] for x in pos_array]) + decerra = numarray.fromlist([x[3] for x in pos_array]) + + ra = numarray.sum(raa/raerra**2)/numarray.sum(1.0/raerra**2) + dec = numarray.sum(deca/decerra**2)/numarray.sum(1.0/decerra**2) + raerr = math.sqrt(1.0/numarray.sum(1.0/raerra**2)) + decerr = math.sqrt(1.0/numarray.sum(1.0/decerra**2)) + self.current_pos = [ra,dec] + self.current_err = [raerr,decerr] + + def __str__(self): + a = "===== source =====" + a = "type = %s\n" % self.stype + a += "start_pos = %s\n" % self.start_pos + a += "current_pos = %s\n" % self.current_pos + a += "associated sources (total number = %i):\n" % len(self.associated_obs) + a += "-------------------\n" + for o in self.associated_obs: + v = o.__str__() + a += v + + return a + + + +class testreal: + + def __init__(self,fname="./obj_dict_309.pickle"): + self.obj_dict = None + self.load_data(fname=fname) + self.constructed_source_list = [] + self.run() + self.reg_plot_functions() + + def load_data(self,fname="./obj_dict_309.pickle"): + import cPickle + f = open(fname,"r") + obj_dict = cPickle.load(f) + f.close() + print("there are %i observations loaded from the pickle file" % len(obj_dict)) + # dstarr adds this to convert the objects into the format expected by: is_object_associated_with_source_algorithm_jbloom() + # # # # # # # # + self.obj_dict = {} + for obj_key,obj_elem in obj_dict.items(): + obj_ra = obj_dict[obj_key]['ra'] * 3600.0 + obj_dec = obj_dict[obj_key]['decl'] * 3600.0 + new_key = (obj_ra, obj_dec, obj_key[2], obj_key[3]) + self.obj_dict[new_key] = copy.deepcopy(obj_elem) + self.obj_dict[new_key]['ra'] = obj_ra + self.obj_dict[new_key]['decl'] = obj_dec + + + def reg_plot_functions(self): + self.cid1 = connect("key_press_event",self.plot_source_info) + + def plot_source_info(self,event): + + ra = event.xdata + dec = event.ydata + #print (event.key,ra,dec) + if event.key == 's': + #print self.constructed_pos[:,0] + #print self.constructed_pos[:,1] + dist = numarray.sqrt( (self.constructed_pos[:,0] - ra)**2 + (self.constructed_pos[:,1] - dec)**2) + + #print dist + #print "min distance = %f " % min(dist) + the_source_ind = numarray.compress(dist == min(dist), numarray.fromlist(range(len(self.constructed_source_list)))) + #print the_source_ind + #the_source_ind = numarray.compress(dist == min(dist),numarray.arange(len(self.constructed_source_list))) + the_source = self.constructed_source_list[the_source_ind[0]] + print(the_source) + #dist = numarray.sqrt( (the_source.current_pos[0] - self.real_pos[:,0])**2 + (the_source.current_pos[1] - self.real_pos[:,1])**2) + #print "min distances to nearest real source = %f arcsec" % min(dist) + #the_source_ind = numarray.compress(dist == min(dist), numarray.fromlist(range(len(self.real_list)))) + #the_source = self.real_list[the_source_ind[0]] + #print "That real source is at ra=%f dec=%f" % (the_source.start_pos[0],the_source.start_pos[1]) + if event.key == 'r': + dist = numarray.sqrt( (self.real_pos[:,0] - ra)**2 + (self.real_pos[:,1] - dec)**2) + + #print dist + #print "min distance = %f " % min(dist) + the_source_ind = numarray.compress(dist == min(dist), numarray.fromlist(range(len(self.real_list)))) + #print the_source_ind + #the_source_ind = numarray.compress(dist == min(dist),numarray.arange(len(self.constructed_source_list))) + the_source = self.real_list[the_source_ind[0]] + print(the_source) + + + def run(self,shuffle=True): + constructed_source_list = [] + observation_list = [] + obslist = self.obj_dict.keys() + if shuffle: + random.seed() + random.shuffle(obslist) + random.shuffle(obslist) + random.shuffle(obslist) + print("shuffled") + for theo in obslist: + # choose a real source to draw an observation from + o = obs(initial_pos=[theo[0],theo[1]],assumed_err=[theo[2],theo[3]]) + o.pos = [theo[0],theo[1]] + o.plot() + tmp = o.is_associated_with_source(constructed_source_list) + # print tmp + if tmp['answer'] == True: + # o.associate_with_source(tmp['sources']) + for s in tmp['best_source']: + # print (len(tmp['best_source']),o.pos,s.current_pos) + s.add_associated_obs(copy.deepcopy(o)) + s.recalculate_position() + else: + ## make a new source + s = source(start_pos=copy.copy(o.pos),stype='constructed',start_err=copy.copy(o.assumed_err),current_pos=copy.copy(o.pos),\ + current_err=copy.copy(o.assumed_err),associated_obs=[copy.deepcopy(o)]) + print("1 new source") + #print s + #print s.associated_obs + constructed_source_list.append(copy.deepcopy(s)) + + observation_list.append(copy.deepcopy(o)) + + for s in constructed_source_list: + # print s + s.plot('g^') + + self.constructed_source_list = constructed_source_list + self.constructed_pos = (numarray.fromlist([x.current_pos for x in self.constructed_source_list])) + + +class simulate: + + def __init__(self): + + self.constructed_source_list = [] + self.real_list = [] + self.run() + self.reg_plot_functions() + + def reg_plot_functions(self): + + self.cid1 = connect("key_press_event",self.plot_source_info) + + def plot_source_info(self,event): + + ra = event.xdata + dec = event.ydata + #print (event.key,ra,dec) + if event.key == 's': + #print self.constructed_pos[:,0] + #print self.constructed_pos[:,1] + dist = numarray.sqrt( (self.constructed_pos[:,0] - ra)**2 + (self.constructed_pos[:,1] - dec)**2) + + #print dist + #print "min distance = %f " % min(dist) + the_source_ind = numarray.compress(dist == min(dist), numarray.fromlist(range(len(self.constructed_source_list)))) + #print the_source_ind + #the_source_ind = numarray.compress(dist == min(dist),numarray.arange(len(self.constructed_source_list))) + the_source = self.constructed_source_list[the_source_ind[0]] + print(the_source) + dist = numarray.sqrt( (the_source.current_pos[0] - self.real_pos[:,0])**2 + (the_source.current_pos[1] - self.real_pos[:,1])**2) + print("min distances to nearest real source = %f arcsec" % min(dist)) + the_source_ind = numarray.compress(dist == min(dist), numarray.fromlist(range(len(self.real_list)))) + the_source = self.real_list[the_source_ind[0]] + print("That real source is at ra=%f dec=%f" % (the_source.start_pos[0],the_source.start_pos[1])) + if event.key == 'r': + dist = numarray.sqrt( (self.real_pos[:,0] - ra)**2 + (self.real_pos[:,1] - dec)**2) + + #print dist + #print "min distance = %f " % min(dist) + the_source_ind = numarray.compress(dist == min(dist), numarray.fromlist(range(len(self.real_list)))) + #print the_source_ind + #the_source_ind = numarray.compress(dist == min(dist),numarray.arange(len(self.constructed_source_list))) + the_source = self.real_list[the_source_ind[0]] + print(the_source) + + + def run(self,n_sources = 3, n_observations = 21, ra_range = [-20.0,20.0],dec_range=[-20.0,20.0],typical_err=0.3,reuse=True): + + global real_list + + clf() + # make the real sources + if reuse: + try: + type(real_list) == type([]) + except NameError: + reuse = False + real_list = [] + + for ns in range(n_sources): + if not reuse: + real_list.append(source(start_pos=[random_array.uniform(ra_range[0],ra_range[1]),random_array.uniform(dec_range[0],dec_range[1])], + stype='real',start_err=[0.0,0.0],associated_obs=[])) + # print real_list[-1] + real_list[ns].plot() + + + constructed_source_list = [] + observation_list = [] + + ## pick a vector of len n_obsevations sources to choose from from 0 --> n_source - 1 + s_start_ind = random_array.randint(0,n_sources,shape=[n_observations]) + for i in range(n_observations): + # choose a real source to draw an observation from + o = obs(initial_pos=real_list[s_start_ind[i]].start_pos,true_err=[[typical_err**2,0.0],[0.0,typical_err**2]],assumed_err=[1.1*typical_err,1.1*typical_err]) + o.observe_pos() + o.plot() + tmp = o.is_associated_with_source(constructed_source_list) + # print tmp + if tmp['answer'] == True: + # o.associate_with_source(tmp['sources']) + for s in tmp['best_source']: + # print (len(tmp['best_source']),o.pos,s.current_pos) + s.add_associated_obs(copy.deepcopy(o)) + s.recalculate_position() + else: + ## make a new source + s = source(start_pos=copy.copy(o.pos),stype='constructed',start_err=copy.copy(o.assumed_err),current_pos=copy.copy(o.pos),\ + current_err=copy.copy(o.assumed_err),associated_obs=[copy.deepcopy(o)]) + print("new source") + #print s + #print s.associated_obs + constructed_source_list.append(copy.deepcopy(s)) + + observation_list.append(copy.deepcopy(o)) + + for s in constructed_source_list: + # print s + s.plot('g^') + + ## do the comparisons between real and constructed sources + for ns in range(n_sources): + #real_list[ns].plot('ys') + pass + + self.real_list = real_list + self.constructed_source_list = constructed_source_list + self.real_pos = (numarray.fromlist([x.start_pos for x in self.real_list])) + self.constructed_pos = (numarray.fromlist([x.current_pos for x in self.constructed_source_list])) + + + #del observation_list + +if __name__ == '__main__': + #s = simulate() + tr = testreal(fname="/tmp/obj_dict.pickle_309.37471543_0.33168565") +# ipython --pylab cluster.py + +# +# ipython --pylab +#import cluster +#a = cluster.testreal(fname="/tmp/obj_dict.pickle_309.37471543_0.33168565") +#s = cluster.simulate(n_sources = 4, n_observations = 27, ra_range = [180000.0,180040],dec_range=[162000,162040],typical_err=0.3) diff --git a/mltsp/TCP/Algorithms/SpatialClustering/obj_dict.pickle b/mltsp/TCP/Algorithms/SpatialClustering/obj_dict.pickle new file mode 100755 index 00000000..2da4e85d --- /dev/null +++ b/mltsp/TCP/Algorithms/SpatialClustering/obj_dict.pickle @@ -0,0 +1,3507 @@ +(dp1 +(F49.599477999999998 +F-1.0051140000000001 +F0.55002499999999999 +F0.62002800000000002 +I0 +tp2 +(dp3 +S'decl' +p4 +F-1.0051140000000001 +sS'survey_id' +p5 +I0 +sS'src_id' +p6 +I0 +sS'm_err' +p7 +F1.15402 +sS'm' +F22.910900000000002 +sS'dec_rms' +p8 +F0.62002800000000002 +sS'ra' +p9 +F49.599477999999998 +sS'obj_ids' +p10 +(lp11 +L58930212L +aL58930213L +aL58930214L +aL58930215L 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+1 @@ + diff --git a/mltsp/TCP/Algorithms/asas_catalog.R b/mltsp/TCP/Algorithms/asas_catalog.R new file mode 100755 index 00000000..38891271 --- /dev/null +++ b/mltsp/TCP/Algorithms/asas_catalog.R @@ -0,0 +1,543 @@ +##################################################################### +##### run this script to create MACC classification catalog for ASAS +## and save all relevant output, classifications + performance metrics +###################################################################### +n = dim(feat.debos)[1] + +############################ +# load ASAS data +asasdat=read.arff(file=asas_test_arff_fpath) +feat.tmp = data.frame(asasdat) +feat.asas = matrix(0,dim(feat.tmp)[1],dim(feat.debos)[2]) +for(ii in 1:dim(feat.debos)[2]){ + feat.asas[,ii]= feat.tmp[,names(feat.tmp)==names(feat.debos)[ii]] +} +colnames(feat.asas) = names(feat.debos) +feat.asas = data.frame(feat.asas) +class.asas = paste(asasdat$class) # this throws double & low-confidence labels into MISC +class.asas[class.asas=="UNKNOWN"] = "MISC" +class.asas = class.debos(class.asas) +ID.asas = asasdat$source_id +n.epochs.asas = read.table(paste(path,"data/n_epochs_asas.dat",sep=""))[,1] +N = dim(feat.asas)[1] + +### 20120810 dstarr commends out: +## add RRLd feature (0.0035 chosen by considering f2/f1 of known RRd) +#feat.debos$freq_rrd = ifelse(abs(feat.debos$freq_frequency_ratio_21 - 0.746) < 0.0035 | abs(feat.debos$freq_frequency_ratio_31 - 0.746) < 0.0035, 1,0) +#feat.asas$freq_rrd = ifelse(abs(feat.asas$freq_frequency_ratio_21 - 0.746) < 0.0035 | abs(feat.asas$freq_frequency_ratio_31 - 0.746) < 0.0035, 1,0) + + + +## ASAS ACVS classes (as of ACTA ASTRONOMICA Vol. 55 (2005) pp. 275–301): +# to obtain all ACVS classifications (not throwing double labels into MISC) +acvs = read.table(paste(path,"data/ACVS.1.1",sep="")) +# positional information for ASAS and debosscher +ra.dec.asas = read.table(paste(path,"data/ra_dec.csv",sep=""),sep=";") +ra.dec.deb = read.table(paste(path,"data/ra_dec_deb.csv",sep=""),sep=";") + +# fix order of acvs table to be same as our ARFF file (ordered by dotAstro ID) +fix.class = NULL +for(ii in 1:N){ # fix order of class.asas vector + fix.class = c(fix.class,which(ra.dec.asas[,1]==ID.asas[ii])) +} +acvs = acvs[fix.class,] +ra.dec.asas = ra.dec.asas[fix.class,] + + +## sum(acvs[,1]==ra.dec.asas[,9]) # 50124 + + +# # # # # # # # # +# Joey's original asas_catalog.R: +# # # # # # # # # + +feat.train = feat.debos +class.train = class.deb +ID.train = ID + +# keep track of which ASAS data are in training set +intrain = rep("",length(ID.asas)) + +#################################################### +## REMOVE THE FOLLOWING LOW-AMPLITUDE CLASSES: +## k. Lambda Bootis, m. Slowly Puls. B, n. Gamma Doradus, r. Wolf-Rayet +cat("Removing low-amplitude classes\n") +lowamp.rem = c('k. Lambda Bootis', 'm. Slowly Puls. B', 'n. Gamma Doradus', 'r. Wolf-Rayet') +remove = which(class.train %in% lowamp.rem) +feat.train = feat.train[-remove,] +class.train = factor(class.train[-remove]) +ID.train = ID.train[-remove] + + +#################################################### +## add ASAS - debosscher overlap data to training set +# for each match, replace Hipparcos/OGLE features with ASAS features +cat("Adding ASAS training objects that overlap with Debosscher\n") +counter = 0 +for(ii in 1:dim(ra.dec.deb)[1]){ + # kludgey match criterion: rounded versions of RA & Dec both match + ind = which(round(ra.dec.asas[,10],2)==round(ra.dec.deb[ii,10],2) & + round(ra.dec.asas[,12],2)==round(ra.dec.deb[ii,12],2)) + if(length(ind)>0){ + ind1 = which(ID.asas == ra.dec.asas[ind,1]) + ind2 = which(ID == ra.dec.deb[ii,1]) + ind3 = which(ID.train == ra.dec.deb[ii,1]) + if(length(ind2)==1){ + counter = counter+1 + ## remove old Hipparcos data + feat.train = feat.train[-ind3,] + class.train = class.train[-ind3] + ID.train = ID.train[-ind3] + ## add new ASAS data + feat.train = rbind(feat.train,feat.asas[ind1,]) + class.train = c(paste(class.train),paste(class.deb[ind2])) + ID.train = c(ID.train,ID.asas[ind1]) + intrain[ind1] = paste(class.deb[ind2]) + } + } +} +class.train = factor(class.train) +nn = length(class.train) +# total size: +print(table(class.train[(nn-counter+1):nn])[table(class.train[(nn-counter+1):nn])>0]) + ## a. Mira b1. Semireg PV c. RV Tauri + ## 71 9 5 + ## d. Classical Cepheid e. Pop. II Cepheid g. RR Lyrae, FM + ## 76 9 62 + ## h. RR Lyrae, FO j. Delta Scuti j1. SX Phe + ## 15 9 1 + ## l. Beta Cephei o. Pulsating Be p. Per. Var. SG + ## 2 1 2 + ## s1. Class. T Tauri s2. Weak-line T Tauri t. Herbig AE/BE + ## 1 2 1 + ## u. S Doradus v. Ellipsoidal + ## 1 1 +cat("number added:",counter,"\n") +# 268 + + +## #################################################### +## #################################################### +## # RF classifier fit on Debosscher data (OGLE, Hip, ASAS): +## cat("Fitting Original RF on Deb data\n") +## rf.deb = randomForest(x=as.matrix(feat.train),y=class.train,mtry=15,ntree=1000,nodesize=1) +## pred.asas.0 = predict(rf.deb,newdata = feat.asas) +## pred.asas.prob.0 = predict(rf.deb,newdata = feat.asas,type='prob') +## # agreement w/ acvs labels +## par(mar=c(1.5,9,9,1.95)) +## asas.tab = table(pred.asas.0,class.asas) +## asas.tab = asas.tab[,c(1:8,10:13,9)] +## plot.table(asas.tab,title="",cexprob=1,cexaxis=1.22) +## abline(h=1/24,lwd=4,col=4) +## mtext("ACVS Class",2,padj=-6.05,cex=2) +## mtext("RF Predicted Class",3,padj=-5.8,cex=2) + +## ###### performance metrics ########## +## agree.acvs = sum(paste(pred.asas.0)==paste(class.asas))/sum(class.asas!="MISC") +## p.hat.0 = apply(pred.asas.prob.0,1,max) +## mean.p.hat = mean(p.hat.0) +## perc.conf = sum(p.hat.0>0.5)/N + + +#################################################### +#################################################### +# add AL sources to training set +cat("Adding AL data to training set\n") +m=9 # number of AL steps +#### loop through the steps +for(step in 1:m){ + cat("Step",step,"of",m,"\n") + # these files are also in: trunk/Data/allstars/ + instep = read.table(paste(path,"data/AL_addToTrain_",step,".dat",sep="")) + + for(kk in 1:length(instep[,1])){ + if(length(intersect(ID.asas[which(ID.asas==instep[kk,1])],ID.train))==0){ # only add items that are not already included + feat.train = rbind(feat.train,feat.asas[which(ID.asas==instep[kk,1]),]) + class.train = c(paste(class.train),paste(class.debos(instep[kk,2]))) + ID.train = c(ID.train,ID.asas[which(ID.asas==instep[kk,1])]) + intrain[which(ID.asas==instep[kk,1])] = paste(class.debos(instep[kk,2])) + } + else{ cat(ID.asas[which(ID.asas==instep[kk,1])]," already added\n")} + } + class.train = factor(class.train) +} +cat("New training set size:",dim(feat.train)[1],"\n") +cat("Number of unique sources:",length(ID.train),"\n") + + +#################################################### +#################################################### +## add selected SIMBAD confirmed training sources +cat("Adding SIMBAD confirmed sources\n") +# this file is also in: trunk/Data/allstars/ +instep = read.table(paste(path,"data/AL_SIMBAD_confirmed.dat",sep=""))# 94 sources +instep = instep[instep[,1] %in% setdiff(instep[,1],ID.train),] # 80 sources + +### removed symbiotics on 20120310; they are only identifiable spectroscopically (AAM) +#symbiotics = read.table("/Users/jwrichar/Documents/CDI/TCP/trunk/Data/allstars/symbiotics_munari_2002_catalog.dat") +#instep = rbind(instep,symbiotics) + +# loop through SIMBAD-confirmed sources & add to training set +for(kk in 1:length(instep[,1])){ + if(length(intersect(ID.asas[which(ID.asas==instep[kk,1])],ID.train))==0){ # only add items that are not already included + feat.train = rbind(feat.train,feat.asas[which(ID.asas==instep[kk,1]),]) + class.train = c(paste(class.train),paste(class.debos(instep[kk,2]))) + ID.train = c(ID.train,ID.asas[which(ID.asas==instep[kk,1])]) + intrain[which(ID.asas==instep[kk,1])] = paste(class.debos(instep[kk,2])) + } + else{cat("already in training set\n")} +} +class.train = factor(class.train) + +### add more known RCBs: +new.RCB = c(250762, 257713, 251489, 256221, 247066) +for(kk in 1:length(new.RCB)){ + feat.train = rbind(feat.train,feat.asas[which(ID.asas==new.RCB[kk]),]) + class.train = factor(c(paste(class.train),"r1. RCB")) + ID.train = c(ID.train,new.RCB[kk]) + intrain[which(ID.asas==new.RCB[kk])] = c("r1. RCB") +} + + + + + +################################################################################ +## ADD RS CVn and BY Dra training objects from Strassmeier 1988 +################ +## RS CVn +rscvn = read.table(paste(path,"data/RS_CVn_ra_dec_training.dat",sep=""),sep=" ") +rscvn.result = NULL +for(ii in 1:length(rscvn[,1])){ + ind = which(substr(ra.dec.asas[,9],1,4)==paste(substr(rscvn[ii,1],1,2),substr(rscvn[ii,1],4,5),sep="") & substr(ra.dec.asas[,9],7,11)==paste(substr(rscvn[ii,2],1,3),substr(rscvn[ii,2],5,6),sep="")) + if(length(ind)==1){ + if(length(intersect(ID.asas[which(ID.asas==ra.dec.asas[ind,1])],ID.train))==0){ # only add items that are not already included + # add to training set + feat.train = rbind(feat.train,feat.asas[which(ID.asas==ra.dec.asas[ind,1]),]) + class.train = c(paste(class.train),"s3. RS CVn") + ID.train = c(ID.train,ID.asas[which(ID.asas==ra.dec.asas[ind,1])]) + intrain[which(ID.asas==ra.dec.asas[ind,1])] = "s3. RS CVn" + } + } +} +class.train = factor(class.train) + +################ +## BY Dra (added as RS CVn; no point in making an extra class for 1 object) +bydra = read.table(paste(path,"data/BY_Dra_ra_dec_training.dat",sep=""),sep=" ") +bydra.result = NULL +for(ii in 1:length(bydra[,1])){ + ind = which(substr(ra.dec.asas[,9],1,4)==paste(substr(bydra[ii,1],1,2),substr(bydra[ii,1],4,5),sep="") & substr(ra.dec.asas[,9],7,11)==paste(substr(bydra[ii,2],1,3),substr(bydra[ii,2],5,6),sep="")) + if(length(ind)==1){ + if(length(intersect(ID.asas[which(ID.asas==ra.dec.asas[ind,1])],ID.train))==0){ # only add items that are not already included + # add to training set + feat.train = rbind(feat.train,feat.asas[which(ID.asas==ra.dec.asas[ind,1]),]) + class.train = c(paste(class.train),"s3. RS CVn") + ID.train = c(ID.train,ID.asas[which(ID.asas==ra.dec.asas[ind,1])]) + intrain[which(ID.asas==ra.dec.asas[ind,1])] = "s3. RS CVn" + } + } +} +class.train = factor(class.train) + + +N.train = length(ID.train) + + +################################################ +################################################ +# now fit classifier on combined training set +################################################ + +# output ASAS training set IDs and classes +write( rbind(ID.train, paste(class.train)),paste(path,"tables/training_set_id_class.dat",sep=""),ncolumns=2,sep=",\t") + +cat("Final training set size:",dim(feat.train)[1],"\n") +cat("Number of unique sources:",length(unique(ID.train)),"\n") + +################################################ +# tune RF parameters (uncomment below to run, is expensive to run grid search) +################################################ +#source(paste(path,"R/tune_asas_RF.R",sep="")) +#tuneparam = tuneRF(feat.train, class.train, cv = 5, asas = which(ID.train > 200000),ntree=c(5000),mtry=seq(15,21,2),nodesize=2) +## ntree = 5000 mtry = 17 nodesize = 1 : +## CV Error = 0.1915 +################################################ + +# fit the RF with optimal tuning parameters on entire training set +cat("Fitting Final Random Forest\n") +rf.tr = randomForest(x=as.matrix(feat.train),y=class.train,mtry=17,ntree=5000,nodesize=1) +pred.asas.AL = predict(rf.tr,newdata = feat.asas) +pred.asas.prob.AL = predict(rf.tr,newdata = feat.asas,type='prob') + +# save RF object +#save(rf.tr,file=paste(path,"data/asas_randomForest.Rdat",sep="")) +save(rf.tr,file=rf_clfr_fpath) + +agree.acvs = sum(paste(pred.asas.AL)==paste(class.asas))/sum(class.asas!="MISC") +p.hat.AL = apply(pred.asas.prob.AL,1,max) +mean.p.hat = mean(p.hat.AL) +perc.conf = sum(p.hat.AL>0.5)/N + +# performance metrics +cat("Final performance metrics:\n") +cat("Agreement w/ ACVS:",agree.acvs,"\n") +cat("Mean max p-hat:",mean.p.hat,"\n") +cat("Percent w/ prob > 0.5:",perc.conf,"\n") +# Agreement w/ ACVS: 0.8080446 + +# plot confusion matrix w/ ACVS +par(mar=c(1.5,9,9,1.95)) +asas.tab = table(pred.asas.AL,class.asas) +asas.tab = asas.tab[,c(1:8,10:13,9)] +plot.table(asas.tab,title="",cexprob=1,cexaxis=1.06) +abline(h=1/24,lwd=4,col=4) +mtext("ACVS Class",2,padj=-6.05,cex=2) +mtext("RF Predicted Class",3,padj=-5.8,cex=2) + + +######## +## plot feature importance (average over 5 repetitions) +plotfi = FALSE +if(plotfi){ + featimp = matrix(0,length(rf.tr$importance),5) + rownames(featimp) = rownames(rf.tr$importance) + featimp[,1] = rf.tr$importance + for(ii in 2:5){ + rf.tmp = randomForest(x=as.matrix(feat.train),y=class.train,mtry=17,ntree=5000,nodesize=1) + featimp[,ii] = rf.tmp$importance + } + featimp.mean = apply(featimp,1,mean) + featimp.sd = apply(featimp,1,sd) + + featimp.sort = featimp.mean[sort(featimp.mean,index.return=TRUE,decreasing=T)$ix] + featimp.sdsort = featimp.sd[sort(featimp.mean,index.return=TRUE,decreasing=T)$ix] + + pdf(paste(path,"plots/asas_rf_imp.pdf",sep=""),height=6,width=9) + par(mar=c(5,14,1,1)) + barplot(featimp.sort[20:1],horiz=TRUE,xlab="Mean Gini Decrease",names.arg=rep("",20),space=0.2,lwd=2,col=2,xlim=c(0,150)) + axis(2,labels=names(featimp.sort)[20:1],at=0.7+(0:19)*1.2,tick=FALSE,las=2) + arrows(featimp.sort[20:1] - featimp.sdsort[20:1], 0.7+(0:19)*1.2, featimp.sort[20:1] + featimp.sdsort[20:1], 0.7+(0:19)*1.2, code=3, angle=90, length=0.05,lwd=2) + legend('bottomright',"Feature Importance",cex=2,bty="n",text.col=1) + dev.off() +} + + +###################################################### +# compute outlier measure +###################################################### + +## DO DISTANCE-BASED OUTLIER MEASURE +# set to TRUE if you want to compute it, else read it in from file +compoutlier = FALSE +source(paste(path,"R/compute_outlier.R",sep="")) +if(compoutlier){ + cat("Computing classifier confidence values\n") + outlier.score = anomScore(feat.train,class.train, feat.asas, ntree=500, knn=2, metric="rf") + write(outlier.score,paste(path,"data/outScore_asas_class.dat",sep=""),ncolumns=1) + + # find optimal threshold using cross-validation on training set + source(paste(path,"R/confidence_findThresh.R",sep="")) +} else { + outlier.score = read.table(paste(path,"data/outScore_asas_class.dat",sep=""))[,1] + out.thresh = 10.0 +} + + # plot outliers on P-A plot +## pdf(paste(path,"plots/outlier_per_amp.pdf",sep=""),height=8,width=8) +## par(mar=c(5,5,2,1)) +## plot(1/feat.train$freq1_harmonics_freq_0, feat.train$freq1_harmonics_amplitude_0[],pch=19,col="#00000070",log='xy',ylim=c(.003,4),xlab="Period (days)", ylab="Amplitude (V mag)",cex.lab=1.5) +## out.size = (outlier.score[outlier.score >= out.thresh])/5 - 1.5 +## # col.size = paste("#FF0000",ifelse(out.size >= 3, 99, round(out.size/2 * 100)),sep="") +## points(1/feat.asas$freq1_harmonics_freq_0[outlier.score >= out.thresh], feat.asas$freq1_harmonics_amplitude_0[outlier.score >= out.thresh],pch=17,col="#FF000070",cex=out.size) +## legend('topleft',c('Training Set','ASAS Outliers'),col=1:2,pch=c(19,17),cex=1.25) +## dev.off() + + ## plot(feat.train$color_diff_bj, feat.train$skew,pch=19,col="#00000030",xlab="Skew", ylab="Signif. of Period (# of sigma)") + ## points(feat.asas$color_diff_bj[catalog.table$Anomaly >= out.thresh], feat.asas$skew[catalog.table$Anomaly >= out.thresh],pch=17,col="#FF000050") + ## legend('topleft',c('Training Set','ASAS Outliers'),col=1:2,pch=c(19,17)) + + + + + +###################################################### +###################################################### + + +########################### +########################## +# Check probability calibration on AL training data +########################### +########################### +# set this to TRUE if you want to fit calibration parameters +# and produce reliability plots (else uses parameter ab.opt below) +checkcalib = FALSE +if(checkcalib){ + cat("Checking Classifier Calibration\n") + source(paste(path,"R/check_calibration.R",sep="")) # run calibration routine + pred.asas.prob.final = calibrate.sigmoid(pred.asas.prob.AL,ab.opt[1],ab.opt[2]) +} else { + ab.opt = c(-5.270766, 1.754329) # use hard-coded values found from calib. routine + pred.asas.prob.final = calibrate.sigmoid(pred.asas.prob.AL,ab.opt[1],ab.opt[2]) +} +pred.asas.final = pred.asas.AL + + + +##################################################### +# CORRECT THE PERIODS OF THE ECLIPSING SOURCES +########################### +Periods = 1/feat.asas$freq1_harmonics_freq_0 + +# fit RF to set of confirmed eclipsing sources where we are correct and off by factor of 2 +periodic = which(class.asas != "MISC") +half.ID = read.table(paste(path,"data/P_half_ACVS_followup.txt",sep=""),sep=",",header=TRUE)[,1] +same = which( (abs(acvs[periodic,2] - (Periods[periodic])) / acvs[periodic,2]) < .1) +ecl = which(substr(pred.asas.final,1,1) %in% c('w','x','y')) +same.ID = c(ID.asas[intersect(ecl,periodic[same])[1:150]],216273,216323) +feat.half = feat.asas[ID.asas %in% half.ID,] +feat.same = feat.asas[ID.asas %in% same.ID,] +correctP = factor(c(rep("n",length(half.ID)),rep("y",length(same.ID)))) +rf.period = randomForest(rbind(feat.half,feat.same),correctP,ntree=500) # RF + +ecl1 = which(substr(class.asas,1,1) %in% c('w','x','y')) +ecl2 = union(ecl,ecl1) +pred.corP = predict(rf.period,feat.asas[ecl2,]) +Periods[ecl2] =((pred.corP=='n')+1)/feat.asas$freq1_harmonics_freq_0[ecl2] # correct periods + +sum( (abs(acvs[ecl1,2] - (1/feat.asas$freq1_harmonics_freq_0[ecl1])) / acvs[ecl1,2]) < .1) +sum( (abs(acvs[ecl1,2] - Periods[ecl1]) / acvs[ecl1,2]) < .1) + +same = sum(abs(( acvs[periodic,2] - (Periods[periodic])) / acvs[periodic,2]) < .1) / length(periodic) +# 0.7598, up from 0.5386 +half = sum(abs(( acvs[periodic,2] - (2*Periods[periodic])) / acvs[periodic,2]) < .1) / length(periodic) +# 0.152, down from 0.380996 +any = ( sum(abs(( acvs[periodic,2] - (Periods[periodic])) / acvs[periodic,2]) < .1) + + sum(abs(( acvs[periodic,2] - (2*Periods[periodic])) / acvs[periodic,2]) < .1) + + sum(abs(( acvs[periodic,2] - (.5*Periods[periodic])) / acvs[periodic,2]) < .1) )/ length(periodic) +# 0.9287142 +cat("Period Agreement Rate: ",same,", ",half,", ",any,"\n") + + + +###################################################### +##################################################### +# WRITE OUT FINAL CATALOG +########################### +########################### + +# get class names into easily readable format +class.names = unlist(lapply(lapply(levels(pred.asas.final),strsplit,split=". ",fixed=TRUE),function(x) paste(x[[1]][-1],collapse=""))) +class.names = unlist(lapply(lapply(class.names,strsplit,split=",",fixed=TRUE),function(x) paste(x[[1]],collapse=""))) +class.names = unlist(lapply(lapply(class.names,strsplit,split="/",fixed=TRUE),function(x) paste(x[[1]],collapse=""))) +class.names = unlist(lapply(lapply(class.names,strsplit,split="-",fixed=TRUE),function(x) paste(x[[1]],collapse=""))) +class.names = unlist(lapply(lapply(class.names,strsplit,split=".",fixed=TRUE),function(x) paste(x[[1]],collapse=""))) +class.names = unlist(lapply(lapply(class.names,strsplit,split=" ",fixed=TRUE),function(x) paste(x[[1]],collapse="_"))) + +intrain.readable = unlist(lapply(lapply(paste(intrain),strsplit,split=". ",fixed=TRUE),function(x) paste(x[[1]][-1],collapse=""))) +intrain.readable = unlist(lapply(lapply(intrain.readable,strsplit,split=",",fixed=TRUE),function(x) paste(x[[1]],collapse=""))) +intrain.readable = unlist(lapply(lapply(intrain.readable,strsplit,split="/",fixed=TRUE),function(x) paste(x[[1]],collapse=""))) +intrain.readable = unlist(lapply(lapply(intrain.readable,strsplit,split="-",fixed=TRUE),function(x) paste(x[[1]],collapse=""))) +intrain.readable = unlist(lapply(lapply(intrain.readable,strsplit,split=".",fixed=TRUE),function(x) paste(x[[1]],collapse=""))) +intrain.readable = unlist(lapply(lapply(intrain.readable,strsplit,split=" ",fixed=TRUE),function(x) paste(x[[1]],collapse="_"))) + + +# save training set table +class.names.space = unlist(lapply(lapply(class.names,strsplit,split="_",fixed=TRUE),function(x) paste(x[[1]],collapse=" "))) +write.table(cbind(class.names.space,table(class.train),round(table(class.train)/length(class.train),4)),file=paste(path,"tables/training_set.dat",sep=""),quote=FALSE,row.names=FALSE,sep=" & ",eol="\\\\\n",col.names=FALSE) + + +# store RF output with readable class names +pred.asas.final1 = factor(class.names[apply(pred.asas.prob.final,1,which.max)]) +pred.asas.prob.final1 = pred.asas.prob.final +colnames(pred.asas.prob.final1) = class.names + +# add some other important features to the catalog: +catalog.table = cbind(acvs[,1],ID.asas,ra.dec.asas[,c(10,12)],paste(pred.asas.final1),apply(pred.asas.prob.final,1,max),outlier.score,acvs[,6],intrain.readable,pred.asas.prob.final,Periods,feat.asas$freq_signif,n.epochs.asas,acvs[,4],feat.asas$amplitude) +colnames(catalog.table) = c("ASAS_ID","dotAstro_ID","RA","DEC","Class","P_Class","Anomaly","ACVS_Class","Train_Class",class.names,"P","P_signif","N_epochs","V","deltaV") + +#### set this to TRUE if you want to write catalog +write.catalog = TRUE + +if(write.catalog){ + cat("Writing out Catalog\n") + save(pred.asas.final,pred.asas.prob.final,file=paste(path,"data/asas_pred_final_v2_3.Rdat",sep="")) + + write.table(catalog.table,file=paste(path,"catalog/asas_class_catalog_v2_3.dat",sep=""),sep=',',quote=FALSE,row.names=FALSE) + + ### write out latex table to be included in paper + N.table = 40 + catalog.paper = cbind(catalog.table[1:N.table,1:5],round(catalog.table[1:N.table,6:7],3),catalog.table[1:N.table,8:9],round(catalog.table[1:N.table,10],3),rep(" ... ",N.table),round(catalog.table[1:N.table,37:42],3)) + write.table(catalog.paper,file=paste(path,"tables/asas_catalog.dat",sep=""),quote=FALSE,row.names=FALSE,sep=" & ",eol="\\\\\n") + + +#### plot correspondence between us & ACVS + pdf(paste(path,"plots/catalog_rf_acvs.pdf",sep=""),height=8,width=12) + par(mar=c(3,9,9.5,2.75)) + asas.tab = table(pred.asas.final,class.asas) + asas.tab = asas.tab[,c(1:8,10:13,9)] + plot.table(asas.tab,title="",cexprob=0.95,cexaxis=1.35) + abline(h=1/24,lwd=4,col=4) + mtext("ACVS Class",2,padj=-6.05,cex=2) + mtext("MACC Class",3,padj=-6.3,cex=2) + dev.off() + + ### correspondence for confident sources + pdf(paste(path,"plots/catalog_rf_acvs_conf.pdf",sep=""),height=8,width=12) + par(mar=c(3,9,9.5,2.75)) + conf = which(outlier.score < 3) + asas.tab = table(pred.asas.final[conf],class.asas[conf]) + asas.tab = asas.tab[,c(1:8,10:13,9)] + plot.table(asas.tab,title="",cexprob=0.95,cexaxis=1.35) + abline(h=1/24,lwd=4,col=4) + mtext("ACVS Class",2,padj=-6.05,cex=2) + dev.off() + +cat("ACVS agreement rate:",sum(paste(pred.asas.final)==paste(class.asas))/sum(class.asas!="MISC"),"\n") +#ACVS agreement rate: 0.8052965 + +cat("ACVS agreement rate for confident sources:",sum(paste(pred.asas.final[conf])==paste(class.asas[conf]))/sum(class.asas[conf]!="MISC"),"\n") +# ACVS agreement rate for confident sources: 0.9162604 +## length(conf) +## [1] 25158 +} + +########################################################################### +########################### +########################### +# Compare with other papers +########################### +# set this to TRUE if you want to generate comparison tables +docompare = FALSE +if(docompare){ + cat("Comparing classifications to other ASAS papers\n") + source(paste(path,"R/compare_asas_papers.R",sep="")) +} + + + +#################################################### +# plot period-period relationship ACVS vs this work +#################################################### +plotper = FALSE # set to TRUE if you want to generate period plot +if(plotper){ + pdf(paste(path,"plots/period_acvs_us.pdf",sep=""),height=7,width=7) + par(mar=c(5,5,2,1),mfrow=c(1,1)) + # plot(acvs[,2],Periods,log='xy',col="#00000007",pch=20,xlim=c(.03,3000),ylim=c(.03,3000),xlab="Period (ACVS)",ylab="Period (this work)",cex.lab=1.5,cex.axis=1) + # abline(0,1,col=2,lty=3,lwd=2) + # legend('bottomright',"All Sources",cex=1.5,bty="n",text.col=4) + periodic = which(class.asas != "MISC") +#periodic = which(substr(acvs[,6],1,4)!="MISC") + plot(acvs[periodic,2],Periods[periodic],log='xy',col="#00000010",pch=20,xlim=c(.03,1000),ylim=c(.03,1000),xlab="Period (ACVS)",ylab="Period (this work)",cex.lab=1.5,cex.axis=1,cex=1.25) + abline(0,1,col=2,lty=1,lwd=1) + legend('bottomright',"ACVS Periodic Sources",cex=2,bty="n",text.col=4) + legend('topleft',paste("Agreement Rate: ",round(same,3)*100,"%",sep=""),cex=2,bty="n",text.col=2) + dev.off() + + ## half = which( (abs(acvs[periodic,2] - (2/feat.asas$freq1_harmonics_freq_0[periodic])) / acvs[periodic,2]) < .1) + ## half.tab = cbind(catalog.table[periodic[half[1:150]],c(2,5:6,42)], acvs[periodic[half[1:150]],2]) + ## colnames(half.tab)[5] = "P_ACVS" + ## write.table(half.tab, "tables/P_half_ACVS_followup.dat", row.names=FALSE,sep=",",quote=FALSE) + +} diff --git a/mltsp/TCP/Algorithms/class_cv.R b/mltsp/TCP/Algorithms/class_cv.R new file mode 100755 index 00000000..fc06dac4 --- /dev/null +++ b/mltsp/TCP/Algorithms/class_cv.R @@ -0,0 +1,704 @@ + +# CART +rpart.cv = function(x,y,nfolds=5,method="gini",loss=NULL,prior=NULL,seed=sample(1:10^5,1)){ + require(rpart) + set.seed(seed) + + n = length(y) + p = length(table(y)) + folds = sample(1:nfolds,n,replace=TRUE) + predictions = matrix(0,nrow=n,ncol=p) + + # default loss function + if(is.null(loss)){ + loss = matrix(1,p,p) + diag(loss) = rep(0,p)} + + # default prior: prop. to observed class rates + if(is.null(prior)){ + prior = table(y)/n + } + + prior = prior / sum(prior) + + for(ii in 1:nfolds){ + print(paste("fold",ii,"of",nfolds)) + leaveout = which(folds==ii) + # fit tree + y.in = y[-leaveout] + tree.fit = rpart(y.in~.,data=data.frame(x[-leaveout,]),parms=list(split=method,loss=loss,prior=prior),control=rpart.control(minsplit=2,minbucket=1,cp=.001,xval=2)) + if(dim(tree.fit$cptable)[2] < 4) { + print("tree is a stump, skip pruning") + tree.prune = tree.fit } + else { + # print(tree.fit$cptable[which.min(tree.fit$cptable[,"xerror"]),"CP"]) + tree.prune = try(prune(tree.fit,cp=tree.fit$cptable[which.min(tree.fit$cptable[,"xerror"]),"CP"])) + if(length(tree.prune)==1) {tree.prune = prune(tree.fit,cp=2*tree.fit$cptable[which.min(tree.fit$cptable[,"xerror"]),"CP"]) } + } + predictions[leaveout,] = predict(tree.prune,newdata=data.frame(x[leaveout,]),type="prob") + } + pred = levels(y)[apply(predictions,1,which.max)] + pred = factor(pred,levels=levels(y)) + confmat = fixconfmat(table(pred,y),levels(pred),levels(y)) + err.rate = 1-sum(diag(confmat))/n + return(list(predclass=pred,predprob=predictions,confmat=confmat,err.rate=err.rate)) +} + + +### Random Forest +rf.cv = function(x,y,nfolds=5,testset=NULL,prior=NULL,mtry=NULL,n.trees=500,seed=sample(1:10^5,1)){ + # don't train on any of the data in testset + # this is to use in the hierarchical classifier + require(randomForest) + set.seed(seed) + + n = length(y) + p = length(table(y)) + folds = sample(1:nfolds,n,replace=TRUE) + predictions = matrix(0,nrow=n,ncol=p) + + if(is.null(mtry)){ + mtry = ceiling(sqrt(dim(x)[2])) + } + # default prior: prop. to observed class rates + if(is.null(prior)){ + prior = table(y)/n + } + + for(ii in 1:nfolds){ + print(paste("fold",ii,"of",nfolds)) + leaveout = which(folds==ii) + rf.tmp = randomForest(x=as.matrix(x[-union(leaveout,testset),]),y=y[-union(leaveout,testset)],classwt=prior,mtry=mtry,ntree=n.trees,nodesize=5) + predictions[leaveout,] = predict(rf.tmp,newdata=x[leaveout,],type='prob') + } + pred = levels(y)[apply(predictions,1,which.max)] + pred = factor(pred,levels=levels(y)) + confmat = fixconfmat(table(pred,y),levels(pred),names(table(y))) + err.rate = 1-sum(diag(confmat))/n + + return(list(predclass=pred,predprob=predictions,confmat=confmat,err.rate=err.rate)) +} + + + + +### Random Forest (party) +rfc.cv = function(x,y,nfolds=5,testset=NULL,mtry=NULL,weights=NULL,n.trees=500,seed=sample(1:10^5,1)){ + # don't train on any of the data in testset + # this is to use in the hierarchical classifier + require(party) + set.seed(seed) + + n = length(y) + p = length(table(y)) + folds = sample(1:nfolds,n,replace=TRUE) + predictions = matrix(0,nrow=n,ncol=p) + + if(is.null(mtry)){ + mtry = ceiling(sqrt(dim(x)[2])) + } + + for(ii in 1:nfolds){ + print(paste("fold",ii,"of",nfolds)) + leaveout = which(folds==ii) + train = cbind(y[-union(leaveout,testset)],x[-union(leaveout,testset),]) + test = cbind(y[leaveout],x[leaveout,]) + names(train)[1] = names(test)[1] = "y" + rf.tmp = cforest(y~.,data=train,weights=weights[-leaveout],controls=cforest_classical(mtry=mtry,ntree=n.trees)) + predictions[leaveout,] = matrix(unlist(treeresponse(rf.tmp,newdata=test)),length(leaveout),p,byrow=T) + } + pred = levels(y)[apply(predictions,1,which.max)] + pred = factor(pred,levels=levels(y)) + confmat = fixconfmat(table(pred,y),levels(pred),names(table(y))) + err.rate = 1-sum(diag(confmat))/n + + return(list(predclass=pred,predprob=predictions,confmat=confmat,err.rate=err.rate)) +} + + +### Random Forest with MetaCost wrapper +rfMetaCost.cv = function(x,y,nfolds=5,cost=costMat(2),prior=NULL,mtry=NULL,n.trees=500,seed=sample(1:10^5,1)){ + require(randomForest) + set.seed(seed) + + n = length(y) + p = length(table(y)) + folds = sample(1:nfolds,n,replace=TRUE) + predictions = matrix(0,nrow=n,ncol=p) + + if(is.null(mtry)){ + mtry = ceiling(sqrt(dim(x)[2])) + } + # default prior: prop. to observed class rates + if(is.null(prior)){ + prior = table(y)/n + } + + for(ii in 1:nfolds){ + print(paste("fold",ii,"of",nfolds)) + leaveout = which(folds==ii) + rf.init = randomForest(x=as.matrix(x[-leaveout,]),y=y[-leaveout],classwt=prior,mtry=mtry,ntree=n.trees,nodesize=5) + y.adj = factor(apply(rf.init$votes%*%cost,1,which.min)) + rf.adj = randomForest(x=as.matrix(x[-leaveout,]),y=y.adj,mtry=mtry,ntree=n.trees,nodesize=5) + predictions[leaveout] = as.numeric(paste(predict(rf.adj,newdata=x[leaveout,],type='class'))) + } + pred = factor(levels(y)[predictions],levels=levels(y)) + confmat = fixconfmat(table(pred,y),levels(pred),names(table(y))) + err.rate = 1-sum(diag(confmat))/n + + return(list(predclass=pred,predprob=predictions,confmat=confmat,err.rate=err.rate)) +} + +# ############## HSC +# Random Forest hierarchical single class. +rf.hsc = function(x,ymat,y,nfolds=5,mtry=NULL,weights=NULL,n.trees=100,seed=sample(1:10^5,1)){ + require(randomForest) + depth = ymat$depth + ymat = ymat$class + + n = length(ymat[,1]) + p = length(colnames(ymat)[colnames(ymat)==substr(colnames(ymat),1,1)]) + y.hat = matrix(0,nrow=n,ncol=length(ymat[1,])) + predictions = matrix(0,nrow=n,ncol=p) + if(is.null(mtry)){ + mtry = ceiling(sqrt(dim(x)[2])) + } + + print("Classifying at top level") + class.top = factor(colnames(ymat)[depth==1][apply(ymat[,depth==1]==1,1,which)]) + # random forest on top level + rftop = rf.cv(x,class.top,n.trees=n.trees,nfolds=nfolds,mtry=mtry,seed=seed) + y.hat[,depth==1] = rftop$predprob + + print("Classifying within subtrees:") + # go down in the class hierarchy + # do classification at each level within each subtree + for(jj in 2:max(depth)){ + up = which(depth==jj-1) + for(kk in 1:length(up)){ + subtree = up[kk]:min(up[kk+1],length(depth),na.rm=T) + if(sum(depth[subtree]==jj)>0){ # check if the subtree goes deeper + elem.sub = which(ymat[,up[kk]]==1) + out = (1:n)[-elem.sub] + class.sub = rep(NA,n) + class.sub[elem.sub] = colnames(ymat)[depth==jj][apply(ymat[elem.sub,depth==jj]==1,1,which)] + class.sub = factor(class.sub) + rftmp = rf.cv(x,class.sub,testset=out,n.trees=n.trees,mtry=mtry,nfolds=nfolds,seed=seed) + y.hat[,subtree[depth[subtree]==jj]] = rftmp$predprob + } + } + } + + # turn y.hat tree probability estimates into class prob. estimates + kk=0 + for(ii in 1:length(depth)){ + if(ii == 1){ isdeep = (depth[ii] == depth[ii+1]) + } else if(ii == length(depth)){ isdeep = (depth[ii] == depth[ii-1]) + } else { isdeep = depth[ii] == depth[ii-1] | depth[ii] == depth[ii+1]} + if(isdeep){ + kk = kk+1 + tmp = y.hat[,ii] + for(jj in (depth[ii]-1):1){ + ind = which(depth==jj) + tmp = tmp * y.hat[,max(ind[ind0){ # check if the subtree goes deeper + elem.sub = which(ymat[,up[kk]]==1) + out = (1:n)[-elem.sub] + class.sub = rep(NA,n) + class.sub[elem.sub] = colnames(ymat)[depth==jj][apply(ymat[elem.sub,depth==jj]==1,1,which)] + class.sub = factor(class.sub) + rftmp = rfc.cv(x,class.sub,testset=out,n.trees=n.trees,mtry=mtry,nfolds=nfolds,seed=seed) + y.hat[,subtree[depth[subtree]==jj]] = rftmp$predprob + } + } + } + + # turn y.hat tree probability estimates into class prob. estimates + kk=0 + for(ii in 1:length(depth)){ + if(ii == 1){ isdeep = (depth[ii] == depth[ii+1]) + } else if(ii == length(depth)){ isdeep = (depth[ii] == depth[ii-1]) + } else { isdeep = depth[ii] == depth[ii-1] | depth[ii] == depth[ii+1]} + if(isdeep){ + kk = kk+1 + tmp = y.hat[,ii] + for(jj in (depth[ii]-1):1){ + ind = which(depth==jj) + tmp = tmp * y.hat[,max(ind[ind 1L) + sample(y, 1L) + else y +} + +which.is.min.big = function (x,y.in) # break ties to bigger class +{ + y <- seq_along(x)[x == min(x)] + if (length(y) > 1L) + y[which.max(tabulate(y.in)[y.in[y]])] + else y +} + +# pair-wise probability-calculating routines + + +minpair1 <- function(probin) + { ## Count number of classes and construct prob. matrix + nclass <- (1+sqrt(1 + 8*length(probin)))/2 + if(nclass%%1 != 0) stop("Vector has wrong length only one against one problems supported") + probim <- matrix(0, nclass, nclass) + probim[lower.tri(probim)] <- probin + probim = t(probim) + probim[lower.tri(probim)] <- 1 - probin + + sum <- colSums(probim^2) + Q <- diag(sum) + Q[upper.tri(Q)] <- - probin*(1 - probin) + Q[lower.tri(Q)] <- - probin*(1 - probin) + SQ <- matrix(0,nclass +1, nclass +1) + SQ[1:(nclass+1) <= nclass, 1:(nclass+1) <= nclass] <- Q + SQ[1:(nclass+1) > nclass, 1:(nclass+1) <= nclass] <- rep(1,nclass) + SQ[1:(nclass+1) <= nclass, 1:(nclass+1) > nclass] <- rep(1,nclass) + + rhs <- rep(0,nclass+1) + rhs[nclass + 1] <- 1 + + p <- solve(SQ,rhs) + + p <- p[-(nclass+1)]/sum(p[-(nclass+1)]) + return(p) + } + + +couple1 <- function(probin, coupler = "minpair1") +{ + if(is.vector(probin)) + probin <- matrix(probin,1) + m <- dim(probin)[1] + + coupler <- match.arg(coupler, c("minpair1", "pkpd", "vote", "ht")) + +# if(coupler == "ht") +# multiprob <- sapply(1:m, function(x) do.call(coupler, list(probin[x ,], clscnt))) +# else + multiprob <- sapply(1:m, function(x) do.call(coupler, list(probin[x ,]))) + + return(t(multiprob)) +} + + diff --git a/mltsp/TCP/Algorithms/classify_Deb.R b/mltsp/TCP/Algorithms/classify_Deb.R new file mode 100755 index 00000000..74a90965 --- /dev/null +++ b/mltsp/TCP/Algorithms/classify_Deb.R @@ -0,0 +1,365 @@ + +# load data +path = "/Users/jwrichar/Documents/CDI/TCP/debosscher/" +#path = "/accounts/gen/vis/jwrichar/debosscher/" +#.libPaths("/accounts/gen/vis/jwrichar/Rlib") + +source(paste(path,"R/utils_classify.R",sep="")) +source(paste(path,"R/class_cv.R",sep="")) + # load in features & classes +data = load.data(path) +features = data$features +classes = data$classes +ID = data$ID +features = featureConvert(features) # convert freq / amp to ratios to first freq / amp + +features = features[,-which(names(features)=='freq_nharm')] # remove freq_nharm +features = features[,-which(names(features)=='stetson_mean')] # remove stet mean +features = features[,-which(names(features)=='kurtosis')] # remove kurtosis +features = features[,-which(names(features)=='freq_harmonics_offset')] # remove time offset +features = features[,-which(names(features)=='freq_amplitude_ratio_21')] # remove time offset +features = features[,-which(names(features)=='freq_amplitude_ratio_31')] # remove time offset +features = features[,-which(names(features)=='freq_frequency_ratio_21')] # remove time offset +features = features[,-which(names(features)=='freq_frequency_ratio_31')] # remove time offset +classes.mat = class.hier(classes) +n = length(classes) +print(paste("Number of sources analyzed:",n)) + +# feature importance (to screen features for SVM) +library(randomForest) +rf.outinit = randomForest(features,classes,nfolds=10) +featimp = rf.outinit$importance +featimp.q = quantile(featimp,0.7) + + +#################### +# initialize error-rate + computation time matrix +err.rates = matrix(0,10,10) +bad = (costMat(10)==10) # define bad misclassifications +err.rates.bad = matrix(0,10,10) +row.names(err.rates) = c("CART","C4.5","RF","Boost","CART.pw","RF.pw","Boost.pw","SVM.pw","HSC-RF","HMC-RF") +row.names(err.rates.bad) = c("CART","C4.5","RF","Boost","CART.pw","RF.pw","Boost.pw","SVM.pw","HSC-RF","HMC-RF") +comp.times = matrix(0,10,10) +row.names(comp.times) = c("CART","C4.5","RF","Boost","CART.pw","RF.pw","Boost.pw","SVM.pw","HSC-RF","HMC-RF") + +for(ii in 1:10){ +#for(ii in 1:1){ + # keep track of all classifications (to see which sources are hard) + predictions = matrix(0,10,length(classes)) + + seed = sample(1:10^6,1) + + print(err.rates) + print(err.rates.bad) + print(comp.times) + +# CART + t.st = proc.time()[3] + tree.out = rpart.cv(features,classes,nfolds=10,method="gini",seed=seed) + comp.times[1,ii] = proc.time()[3]-t.st + err.rates[1,ii] = tree.out$err.rate + err.rates.bad[1,ii] = sum(tree.out$confmat*bad)/n + predictions[1,] = tree.out$predclass + +# C4.5 + t.st = proc.time()[3] + tree.out = rpart.cv(features,classes,nfolds=10,method="information",seed=seed) + comp.times[2,ii] = proc.time()[3]-t.st + err.rates[2,ii] = tree.out$err.rate + err.rates.bad[2,ii] = sum(tree.out$confmat*bad)/n + predictions[2,] = tree.out$predclass + +# RF + t.st = proc.time()[3] + rf.out = rf.cv(features,classes,n.trees=1000,mtry=15,nfolds=10,seed=seed) + comp.times[3,ii] = proc.time()[3]-t.st + err.rates[3,ii] = rf.out$err.rate + err.rates.bad[3,ii] = sum(rf.out$confmat*bad)/n + predictions[3,] = rf.out$predclass + +# Boosting + t.st = proc.time()[3] + boost.out = boostTree.cv(features,classes,n.trees=50,depth=15,rate="Breiman",nfolds=5,seed=seed) + comp.times[4,ii] = proc.time()[3]-t.st + err.rates[4,ii] = boost.out$err.rate + err.rates.bad[4,ii] = sum(boost.out$confmat*bad)/n + predictions[4,] = boost.out$predclass + +# CART pw vote +# CART pw meta-vote + t.st = proc.time()[3] + cart.pw = vote.pairwise(features,classes,nfolds=10,method='cart',k.meta=10,seed=seed) + comp.times[5,ii] = proc.time()[3]-t.st + err.rates[5,ii] = cart.pw$err.rate.prob + err.rates.bad[5,ii] = sum(cart.pw$confmat.prob*bad)/n + predictions[5,] = cart.pw$predclass.prob + if(ii==1){ + postscript(paste(path,"plots/featImportance_pwCart.ps",sep=""),horizontal=TRUE) + plot.featImp(cart.pw$FeatImp) + dev.off() + } + +# RF pw vote +# RF pw meta-vote + t.st = proc.time()[3] + rf.pw = vote.pairwise(features,classes,nfolds=10,method='rf',n.tree=100,mtry=10,k.meta=10,seed=seed) + comp.times[6,ii] = proc.time()[3]-t.st + err.rates[6,ii] = rf.pw$err.rate.prob + err.rates.bad[6,ii] = sum(rf.pw$confmat.prob*bad)/n + predictions[6,] = rf.pw$predclass.prob + if(ii==1){ + pdf(paste(path,"plots/featImportance_pwRF.pdf",sep=""),height=8,width=10) +# plot.featImp(t(t(rf.pw$FeatImp)/colSums(rf.pw$FeatImp))) # regular + plot.featImp(t(t(sqrt(rf.pw$FeatImp))/colSums(sqrt(rf.pw$FeatImp)))) # square-root (better) + dev.off() + } + + # Boosting pairwise voting + t.st = proc.time()[3] + boost.pw = vote.pairwise(features,classes,nfolds=5,n.tree=50,shrinkage=.05,method='boost',k.meta=10,seed=seed) + comp.times[7,ii] = proc.time()[3]-t.st + err.rates[7,ii] = boost.pw$err.rate.prob + err.rates.bad[7,ii] = sum(boost.pw$confmat.prob*bad)/n + predictions[7,] = boost.pw$predclass.prob + +# SVM pw vote +# SVM pw meta-vote + t.st = proc.time()[3] + svm.pw = vote.pairwise(features[,which(featimp>20)],classes,nfolds=10,method='svm',svmsig=0.05,k.meta=10,seed=seed) + comp.times[8,ii] = proc.time()[3]-t.st + err.rates[8,ii] = svm.pw$err.rate + err.rates.bad[8,ii] = sum(svm.pw$confmat*bad)/n + predictions[8,] = svm.pw$predclass + + ## HSC + t.st = proc.time()[3] + hsc.out = rf.hsc(features,classes.mat,classes,nfolds=10,seed=seed,n.trees=1000,mtry=25) + comp.times[9,ii] = proc.time()[3]-t.st + err.rates[9,ii] = hsc.out$err.rate + err.rates.bad[9,ii] = sum(hsc.out$confmat*bad)/n + predictions[9,] = hsc.out$predclass + + # HMC +# t.st = proc.time()[3] +# hmc.run(out = "hmc1.s",n.trees=500,mtry=25,w0=0.5,seed=seed,path=paste(path,"clus/",sep="")) +# hmc.out = hmc.read(classes,classes.mat$class,file=paste(path,"clus/hmc1.test.pred.arff",sep="")) +# comp.times[10,ii] = proc.time()[3]-t.st +# err.rates[10,ii] = hmc.out$err.rate +# err.rates.bad[10,ii] = sum(hmc.out$confmat*bad)/n +# predictions[10,] = hmc.out$predclass + + + + superClass = factor(levels(classes)[apply(apply(predictions,2,tabulate,nbins=length(table(classes))),2,which.max)]) + superconfmat = fixconfmat(table(superClass,classes),levels(superClass),levels(classes)) + print(paste("Super-classifier mis-class rate:",1-sum(diag(superconfmat))/length(classes))) + # 23.0% + + + if(ii==1){ + periods = 1/features[,"freq1_harmonics_freq_0"] + wrong.all = which(apply(as.numeric(classes)==t(predictions),1,sum)==0) # which sources always misclassified? + # plot folded LCs w/ spline fits for those that are always misclassified + postscript(paste(path,"plots/LCfold_misclass.ps",sep=""),horizontal=F) + par(mfrow=c(4,1),mar=c(0,4,0,.3)) + for(indbad in 1:length(wrong.all)){ + # read in folded LC plus spline fit + perLC = read.table(paste(path,"LC_timefold/deboss_pf",ID[wrong.all[indbad]],".dat",sep="")) + perFit = read.table(paste(path,"LC_timefold/deboss_spline",ID[wrong.all[indbad]],".dat",sep="")) + + plot(perLC[,1],perLC[,2],pch=20,cex=.5,xlim=c(0,1),ylim=range(perLC[,2])[2:1],ylab="mag",xaxt='n') + arrows(perLC[,1],perLC[,2]+perLC[,3],perLC[,1],perLC[,2]-perLC[,3],length=0) + lines(perFit[,1],perFit[,2],col=2,lwd=2) + lines(perFit[,1],perFit[,2]-perFit[,3],col=4,lty=3,lwd=1.5) + lines(perFit[,1],perFit[,2]+perFit[,3],col=4,lty=3,lwd=1.5) + text(0.5,min(perLC[,2]),paste("ID",ID[wrong.all[indbad]],":",classes[wrong.all[indbad]]),pos=1,col="darkred",cex=1.5) + text(1,max(perLC[,2]),paste("Classify as:",superClass[wrong.all[indbad]]),cex=1.5,col="darkblue",pos=2) + text(0,max(perLC[,2]),paste("Period =",signif(periods[wrong.all[indbad]],2),"days"),cex=1.5,col="darkblue",pos=4) + axis(1,labels=FALSE) + if(indbad %% 4 == 0){ + axis(1,padj=-3.5,cex.axis=1) + } + } + dev.off() + } + + print(paste(ii,"of 10 done!")) +} +#boxplot(err.rates~row.names(err.rates)) + +write(t(err.rates),paste(path,"out/errorRates.dat",sep=""),ncolumns=10) +write(t(err.rates.bad),paste(path,"out/errorRates_bad.dat",sep=""),ncolumns=10) +write(t(comp.times),paste(path,"out/compTimes.dat",sep=""),ncolumns=10) + +round(cbind(apply(err.rates,1,mean)*100, +apply(err.rates.bad,1,mean)*100,apply(comp.times,1,mean)),1) + +## CART 32.2 13.7 10.6 +## C4.5 29.8 12.7 14.4 +## RF 22.8 8.0 117.6 +## Boost 25.0 9.9 466.5 +## CART.pw 25.8 8.7 323.2 +## RF.pw 23.4 8.0 290.3 +## Boost.pw 24.1 8.2 301.5 +## SVM.pw 25.3 8.4 273.0 +## HSC-RF 23.5 7.8 230.9 +## HMC-RF 23.4 8.2 946.0 + + +# hmc :0.23365759 0.08171206 945.96260000 + +## [,1] [,2] [,3] [,4] [,5] +## [1,] 0.23151751 0.2341115 0.2392996 0.23281453 2.360571e-01 +## [2,] 0.08106355 0.0843061 0.0843061 0.08236057 8.171206e-02 +## [3,] 940.91800000 901.4580000 888.7930000 958.16100000 1.028320e+03 +## [,6] [,7] [,8] [,9] [,10] +## [1,] 0.23281453 2.347601e-01 0.23994812 0.22827497 0.22697795 +## [2,] 0.08236057 8.106355e-02 0.08041505 0.07782101 0.08171206 +## [3,] 918.74900000 1.001902e+03 898.06400000 964.22500000 959.03600000 + + +# SECOND PART: +# take a couple classifiers and apply to debosscher's features +feat.deb = read.table(paste(path,"debos_features.dat",sep="")) +# hipparcos +ind.hip = read.table("/Users/jwrichar/Documents/CDI/TCP/debosscher/hipparcos_hipind.dat")[,1] +names.deb = paste(feat.deb[,1]) +feat.deb=feat.deb[-which(duplicated(names.deb)),] +names.deb = names.deb[-which(duplicated(names.deb,fromLast=TRUE))] +names.hip = strsplit(paste(read.table("/Users/jwrichar/Documents/CDI/TCP/debosscher/J_A+A_475_1159_list.dat.txt",skip=4,sep="|",strip.white=TRUE)[ind.hip,5]),split=" ") +subset = NULL +for(ii in 1:length(names.hip)){ + subset = c(subset,which(names.deb==names.hip[[ii]][1])) +} +feat.hip = feat.deb[subset,] + duplic = c("c-25473.hip","c-26304.hip","c-33165.hip","c-34042.hip","c-36750.hip","c-39009.hip","c-53461.hip","c-57812.hip","c-58907.hip","c-75377.hip","c-104029.hip") + hip.out = which(feat.hip[,31]=="ROAP" | feat.hip[,31]=="XB" | feat.hip[,31]=="SXPHE" | feat.hip[,1] %in% duplic) +feat.hip = feat.hip[-hip.out,] +class.hip = class.debos(feat.hip[,31]) +feat.hip = feat.hip[,-c(1,31)] +# ogle +feat.ogle = feat.deb[c(which(substr(names.deb,1,4)=="OGLE"),1251:1345),-c(1,31)] +class.ogle = class.debos(feat.deb[c(which(substr(names.deb,1,4)=="OGLE"),1251:1345),31]) + +features.deb = rbind(feat.hip,feat.ogle) +class.deb = factor(c(paste(class.hip),paste(class.ogle))) +class.deb.mat = class.hier(class.deb) + +err.deb = matrix(0,3,10) +for(ii in 1:10){ + seed = sample(1:10^6,1) +# RF + rf.deb = rf.cv(features.deb,class.deb,nfolds=10,n.trees=1000,mtry=10,seed=seed) + err.deb[1,ii] = rf.deb$err.rate #26.4% + +# RF-pw + rfpw.deb = vote.pairwise(features.deb,class.deb,nfolds=10,method='rf',n.tree=100,k.meta=10,seed=seed) + err.deb[2,ii] = rfpw.deb$err.rate.prob #29.0% + + # HSC-RF + hsc.deb = rf.hsc(features.deb,class.deb.mat,class.deb,nfolds=10,seed=seed,n.trees=1000,mtry=10) + err.deb[3,ii] = hsc.deb$err.rate # 28.3% + + print(err.deb) +} +row.names(err.deb)=c("RF","RF.pw","HSC-RF") +write(t(err.deb),paste(path,"out/errorRates_debFeatures.dat",sep=""),ncolumns=10) + +## > apply(err.deb,1,mean) +## RF RF.pw HSC-RF +## 0.2669464 0.2891543 0.2812782 + +# THIRD PART: +# +# run our algorithms on only our LS features +features.ls = features[which(substr(names(features),1,4)=="freq")] + +err.ls = matrix(0,3,10) + +for(ii in 1:10){ + seed = sample(1:10^6,1) +# RF + rf.ls = rf.cv(features.ls,classes,nfolds=10,n.trees=1000,mtry=10,seed=seed) + err.ls[1,ii] = rf.ls$err.rate #26.4% + +# RF-pw + rfpw.ls = vote.pairwise(features.ls,classes,nfolds=10,method='rf',n.tree=100,k.meta=10,seed=seed) + err.ls[2,ii] = rfpw.ls$err.rate.prob #29.0% + + # HSC-RF + hsc.ls = rf.hsc(features.ls,classes.mat,classes,nfolds=10,seed=seed,n.trees=1000,mtry=10) + err.ls[3,ii] = hsc.ls$err.rate # 28.3% + + print(err.ls) +} +row.names(err.ls)=c("RF","RF.pw","HSC-RF") + +write(t(err.ls),paste(path,"out/errorRates_LSFeatures.dat",sep=""),ncolumns=10) + +## > apply(err.ls,1,mean) +## RF RF.pw HSC-RF +## 0.2378080 0.2634890 0.2459144 + +# FOURTH PART: +# +# run our algorithms on only our non-LS features +features.nls = features[which(substr(names(features),1,4)!="freq")] + +err.nls = matrix(0,3,10) + +for(ii in 1:10){ + seed = sample(1:10^6,1) +# RF + rf.nls = rf.cv(features.nls,classes,nfolds=10,n.trees=1000,mtry=7,seed=seed) + err.nls[1,ii] = rf.nls$err.rate #26.4% + +# RF-pw + rfpw.nls = vote.pairwise(features.nls,classes,nfolds=10,method='rf',n.tree=100,k.meta=10,seed=seed) + err.nls[2,ii] = rfpw.nls$err.rate.prob #29.0% + + # HSC-RF + hsc.nls = rf.hsc(features.nls,classes.mat,classes,nfolds=10,seed=seed,n.trees=1000,mtry=7) + err.nls[3,ii] = hsc.nls$err.rate # 28.3% + + print(err.nls) +} +row.names(err.nls)=c("RF","RF.pw","HSC-RF") + +write(t(err.nls),paste(path,"out/errorRates_NLSFeatures.dat",sep=""),ncolumns=10) + +## > apply(err.nls,1,mean) +## RF RF.pw HSC-RF +## 0.2763294 0.2855383 0.2783398 + + +# Debosscher results: +# Gaussian Mixture Model: 31% +# Bayes avg. of ANN: 30% +# 3-dependent Bayesian Network: 34% +# SVM: 50% + +err.rates = read.table(paste(path,"out/errorRates.dat",sep="")) +row.names(err.rates) = c("CART","C4.5","RF","Boost","CART.pw","RF.pw","Boost.pw","SVM.pw","HSC-RF","HMC-RF") +err.deb = read.table(paste(path,"out/errorRates_debFeatures.dat",sep="")) +row.names(err.deb)=c("RF","RF.pw","HSC-RF") +err.ls = read.table(paste(path,"out/errorRates_LSFeatures.dat",sep="")) +row.names(err.ls)=c("RF","RF.pw","HSC-RF") +err.nls = read.table(paste(path,"out/errorRates_NLSFeatures.dat",sep="")) +row.names(err.nls)=c("RF","RF.pw","HSC-RF") + + +# plot boxplot +Errors = rbind(err.rates,err.deb,err.ls,err.nls) + +pdf(paste(path,"plots/misclassRates.pdf",sep=""),width=12,height=8) +par(mar=c(5,4.5,.5,.3)) +boxplot(t(Errors),range=0,names=c(row.names(err.rates),row.names(err.deb),row.names(err.ls),row.names(err.nls)),ylab="Mis-classification Rate",cex.lab=1.5,xaxt="n",boxwex=.5,ylim=c(.21,.3375)) +axis(1,labels=c(row.names(err.rates),row.names(err.deb),row.names(err.ls),row.names(err.nls)),at=1:dim(Errors)[1],las=2,cex.axis= 1,padj=0.5,tick=TRUE) +abline(.3,0,col=2,lty=2,lwd=2.5) +abline(v=c(10.5,13.5,16.5),lwd=2) +legend("topleft","Debosscher et al. (2007)",lwd=2.5,lty=2,col=2,cex=1.45,bty='n') +text(5.5,.215,"LS + non-LS Features\n(this work)",cex=1.25,col='darkblue') +text(12,.215,"LS Features\n(Debosscher)",cex=1.25,col='darkblue') +text(15,.215,"LS Features\n(this work)",cex=1.25,col='darkblue') +text(18.5,.215,"non-LS Feat.\n(this work)",cex=1.25,col='darkblue') +dev.off() diff --git a/mltsp/TCP/Algorithms/compare_randforest_classifiers.py b/mltsp/TCP/Algorithms/compare_randforest_classifiers.py new file mode 100644 index 00000000..9b07ff60 --- /dev/null +++ b/mltsp/TCP/Algorithms/compare_randforest_classifiers.py @@ -0,0 +1,331 @@ +#!/usr/bin/env python +""" +* Get fortran code running for comparisons with R's party:cforest +** install cforest +** have script which runs forest and cforest on some dataset +** also run parf rf classifier on dataset +*** betsy: ~/scratch/rf_parf/parf +** compare results (will need to do crossvalidation) +** First try out on non-missing-value dataset +** Need some missing-feature datasets to try out + +""" +from __future__ import print_function +from __future__ import absolute_import +import os, sys +import numpy + +def example_initial_r_randomforest(): + """ Initial example which trains and classifies a R randomForest classifier + Using 1 40/60 fold of debosscher data. + """ + + algorithms_dirpath = os.path.abspath(os.environ.get("TCP_DIR") + 'Algorithms/') + sys.path.append(algorithms_dirpath) + from . import rpy2_classifiers + rc = rpy2_classifiers.Rpy2Classifier(algorithms_dirpath=algorithms_dirpath) + + train_arff_str = open(os.path.expandvars("$HOME/scratch/full_deboss_1542srcs_20110106.arff")).read() + traindata_dict = rc.parse_full_arff(arff_str=train_arff_str) + + Gen_Fold_Classif = rpy2_classifiers.GenerateFoldedClassifiers() + all_fold_data = Gen_Fold_Classif.generate_fold_subset_data(full_data_dict=traindata_dict, + n_folds=10, + do_stratified=False, + classify_percent=40.) + i_fold = 0 # of 10 folds + fold_data = all_fold_data[i_fold] + do_ignore_NA_features = False + classifier_fpath = os.path.expandvars("$HOME/scratch/classifier_RF_0.rdata")# % (i_fold)) + Gen_Fold_Classif.generate_R_randomforest_classifier_rdata(train_data=fold_data['train_data'], + classifier_fpath=classifier_fpath, + do_ignore_NA_features=do_ignore_NA_features, + algorithms_dirpath=algorithms_dirpath) + + r_name='rf_clfr' + classifier_dict = {'class_name':r_name} + rc.load_classifier(r_name=r_name, + fpath=classifier_fpath) + classif_results = rc.apply_randomforest(classifier_dict=classifier_dict, + data_dict=fold_data['classif_data'], + do_ignore_NA_features=do_ignore_NA_features) + + print("classif_results['error_rate']=", classif_results['error_rate']) + import pdb; pdb.set_trace() + print() + + + +def count_classes(class_list=[]): + """ + """ + count_dict = {} + for class_name in class_list: + if class_name not in count_dict: + count_dict[class_name] = 0 + count_dict[class_name] += 1 + return count_dict + + +def generate_parf_header_with_weighted_classes(count_dict={}, + n_sources=0, arff_header=[]): + """ Given some class information and an existing arff header list, + generate a parf style @attribute class entry which has inverse proportional class weights. + """ + new_arff_header = [] + for line in arff_header: + #if '@attribute source_id' in line.lower(): + # new_arff_header.append('@ignored source_id NUMERIC') + # continue + if not "@attribute class" in line.lower(): + new_arff_header.append(line) + continue + sub_line = line[line.find('{')+1:line.rfind('}')] + class_list = sub_line.split("','") + new_line = line[:line.find('{')+1] + total_weight = 0 # for sanity check only + for quoted_class_name in class_list: + class_name = quoted_class_name.strip("'") + class_weight = count_dict[class_name] / float(n_sources) + + ###Using NO WEIGHT:# + new_line += "'%s', " % (class_name) + ### USING WEIGHTS (which, as calculated seem to worsen the final classification error) + ### - it seems internal weights are used by PARF since nowt error rate agrees with R:randomForest + #new_line += "'%s' (%f), " % (class_name, class_weight) + + total_weight += class_weight + new_line = new_line[:-2] + line[line.rfind('}'):] + print('total_weight=', total_weight) + #print 'line: ', line + #print 'new_line:', new_line + new_arff_header.append(new_line) + + return new_arff_header + + +if __name__ == '__main__': + + #example_initial_r_randomforest() + + # I want to do the following over the same folded datasets + # I also want to use the same parms + #####parf --verbose -t ~/scratch/full_deboss_1542srcs_20110106.arff -a ~/scratch/full_deboss_1542srcs_20110106.arff -n 1000 -m 25 + + # NOTE: this is compiled differently (with different FFLAGS, CFLAGS) than for library/python wrapping version: + parf_exec_fpath = '/home/pteluser/scratch/rf_parf__back_copy2/parf/parf' + + noisify_attribs = [ \ + 'freq1_harmonics_amplitude_0', + 'freq1_harmonics_amplitude_1', + 'freq1_harmonics_amplitude_2', + 'freq1_harmonics_amplitude_3', + 'freq1_harmonics_rel_phase_0', + 'freq1_harmonics_rel_phase_1', + 'freq1_harmonics_rel_phase_2', + 'freq1_harmonics_rel_phase_3', + 'freq2_harmonics_amplitude_0', + 'freq2_harmonics_amplitude_1', + 'freq2_harmonics_amplitude_2', + 'freq2_harmonics_amplitude_3', + 'freq2_harmonics_rel_phase_0', + 'freq2_harmonics_rel_phase_1', + 'freq2_harmonics_rel_phase_2', + 'freq2_harmonics_rel_phase_3', + 'freq3_harmonics_amplitude_0', + 'freq3_harmonics_amplitude_1', + 'freq3_harmonics_amplitude_2', + 'freq3_harmonics_amplitude_3', + 'freq3_harmonics_freq_0', + 'freq3_harmonics_rel_phase_0', + 'freq3_harmonics_rel_phase_1', + 'freq3_harmonics_rel_phase_2', + 'freq3_harmonics_rel_phase_3', + 'freq_amplitude_ratio_31', + 'freq_frequency_ratio_31', + 'freq_signif_ratio_31', + 'skew', + 'qso_log_chi2_qsonu', + 'qso_log_chi2nuNULL_chi2nu', + 'median_absolute_deviation', + 'std', + 'stetson_j', + 'percent_difference_flux_percentile'] + + + ntrees = 100 + mtry=25 + nodesize=5 + use_missing_values = True + prob_source_has_missing=0.3 + prob_misattrib_is_missing=0.5 + + + algorithms_dirpath = os.path.abspath(os.environ.get("TCP_DIR") + 'Algorithms/') + sys.path.append(algorithms_dirpath) + from . import rpy2_classifiers + rc = rpy2_classifiers.Rpy2Classifier(algorithms_dirpath=algorithms_dirpath) + + train_arff_str = open(os.path.expandvars("$HOME/scratch/full_deboss_1542srcs_20110106.arff")).read() + + + if use_missing_values: + train_arff_str = rc.insert_missing_value_features(arff_str=train_arff_str, + noisify_attribs=noisify_attribs, + prob_source_has_missing=prob_source_has_missing, + prob_misattrib_is_missing=prob_misattrib_is_missing) + + traindata_dict = rc.parse_full_arff(arff_str=train_arff_str, fill_arff_rows=True) + arff_header = rc.parse_arff_header(arff_str=train_arff_str)#, ignore_attribs=['source_id']) + + Gen_Fold_Classif = rpy2_classifiers.GenerateFoldedClassifiers() + + all_fold_data = Gen_Fold_Classif.generate_fold_subset_data(full_data_dict=traindata_dict, + n_folds=10, + do_stratified=False, + classify_percent=40.) + + temp_arff_fpath_root = os.path.expandvars("/tmp/parf") + + meta_parf_avgs = [] + meta_R_randomForest_avgs = [] + meta_R_cforest_avgs = [] + for k in range(50): + parf_fpath_dict = {} + results_dict = {} + for i_fold, fold_dict in all_fold_data.items(): + parf_fpath_dict[i_fold] = {} + results_dict[i_fold] = {} + for data_case in fold_dict.keys(): + if data_case == 'train_data': + count_dict = count_classes(class_list=fold_dict['train_data']['class_list']) + n_sources = len(fold_dict['train_data']['class_list']) + new_arff_header = generate_parf_header_with_weighted_classes(count_dict=count_dict, n_sources=n_sources, arff_header=arff_header) + else: + new_arff_header = arff_header + fold_arff_lines = [] + fold_arff_lines.extend(new_arff_header) + fold_arff_lines.extend(fold_dict[data_case]['arff_rows']) + fold_arff_fpath = "%s_%s_%d" % (temp_arff_fpath_root, data_case, i_fold) + if os.path.exists(fold_arff_fpath): + os.system('rm ' + fold_arff_fpath) + fp = open(fold_arff_fpath, 'w') + for line in fold_arff_lines: + fp.write(line + '\n') + fp.close() + parf_fpath_dict[i_fold][data_case] = fold_arff_fpath + + ### Do parf classification + exec_parf_str = '%s -t %s -a %s -n %d -m %d -xs %d -ri source_id -uu source_id' % ( \ + parf_exec_fpath, + parf_fpath_dict[i_fold]['train_data'], + parf_fpath_dict[i_fold]['classif_data'], + ntrees, mtry, nodesize) + print(exec_parf_str) + (a,b,c) = os.popen3(exec_parf_str) + a.close() + c.close() + lines_str = b.read() + b.close() + lines = lines_str.split('\n') + for line in lines: + if not 'Testset classification error' in line: + continue + vals = line.split() + + class_error = float(vals[4].strip('%')) + kappa = float(vals[8]) + results_dict[i_fold]['parf'] = {'class_error':class_error, + 'kappa':kappa} + + if not use_missing_values: + ### Do the R randomForest here: + do_ignore_NA_features = False + for i_fold, fold_data in all_fold_data.items(): + classifier_fpath = os.path.expandvars("$HOME/scratch/classifier_RF_%d.rdata" % (i_fold)) + Gen_Fold_Classif.generate_R_randomforest_classifier_rdata(train_data=fold_data['train_data'], + classifier_fpath=classifier_fpath, + do_ignore_NA_features=do_ignore_NA_features, + algorithms_dirpath=algorithms_dirpath, + ntrees=ntrees, mtry=mtry, + nfolds=10, nodesize=nodesize) + + r_name='rf_clfr' + classifier_dict = {'class_name':r_name} + rc.load_classifier(r_name=r_name, + fpath=classifier_fpath) + classif_results = rc.apply_randomforest(classifier_dict=classifier_dict, + data_dict=fold_data['classif_data'], + do_ignore_NA_features=do_ignore_NA_features) + + print("classif_results['error_rate']=", classif_results['error_rate']) + + results_dict[i_fold]['randomForest'] = {'class_error':classif_results['error_rate']} + + + # # # # # # + # # # # # # + # # # # # # + ### Do the R cforest here: + do_ignore_NA_features = False + for i_fold, fold_data in all_fold_data.items(): + classifier_fpath = os.path.expandvars("$HOME/scratch/classifier_RF_%d.rdata" % (i_fold)) + print('generating cforest...') + Gen_Fold_Classif.generate_R_randomforest_classifier_rdata(train_data=fold_data['train_data'], + classifier_fpath=classifier_fpath, + do_ignore_NA_features=do_ignore_NA_features, + algorithms_dirpath=algorithms_dirpath, + ntrees=ntrees, mtry=mtry, + nfolds=10, nodesize=nodesize, + classifier_type='cforest') + + r_name='rf_clfr' + classifier_dict = {'class_name':r_name} + rc.load_classifier(r_name=r_name, + fpath=classifier_fpath) + + print('applying cforest...') + classif_results_cforest = rc.apply_cforest(classifier_dict=classifier_dict, + data_dict=fold_data['classif_data'], + do_ignore_NA_features=do_ignore_NA_features) + + print("classif_results['error_rate']=", classif_results_cforest['error_rate']) + + results_dict[i_fold]['cforest'] = {'class_error':classif_results_cforest['error_rate']} + + + + + ##### Analyze the results (compare the classifiers): + parf_errors = [] + randomForest_errors = [] + cforest_errors = [] + for i_fold in all_fold_data.keys(): + parf_errors.append(results_dict[i_fold]['parf']['class_error'] / 100.) + randomForest_errors.append(results_dict[i_fold].get('randomForest',{}).get('class_error',-1)) + cforest_errors.append(results_dict[i_fold]['cforest']['class_error']) + + #meta_parf_avgs.append(numpy.mean(parf_errors)) + #meta_R_randomForest_avgs.append(numpy.mean(randomForest_errors)) + #meta_R_cforest_avgs.append(numpy.mean(cforest_errors)) + meta_parf_avgs.extend(parf_errors) + meta_R_randomForest_avgs.extend(randomForest_errors) + meta_R_cforest_avgs.extend(cforest_errors) + + print("PARF mean=%lf, std=%lf" % (numpy.mean(parf_errors), numpy.std(parf_errors))) + print("randomForest mean=%lf, std=%lf" % (numpy.mean(randomForest_errors), numpy.std(randomForest_errors))) + print("cforest mean=%lf, std=%lf" % (numpy.mean(cforest_errors), numpy.std(cforest_errors))) + + + #### Put this within the inner loop so can see how this is improving: + print('META PARF :', numpy.mean(meta_parf_avgs), numpy.std(meta_parf_avgs), k*10 + i_fold) + print('META randomForest:', numpy.mean(meta_R_randomForest_avgs), numpy.std(meta_R_randomForest_avgs), k*10 + i_fold) + print('META cforest :', numpy.mean(meta_R_cforest_avgs), numpy.std(meta_R_cforest_avgs), k*10 + i_fold) + + print('Final META PARF :', numpy.mean(meta_parf_avgs), numpy.std(meta_parf_avgs)) + print('Final META randomForest:', numpy.mean(meta_R_randomForest_avgs), numpy.std(meta_R_randomForest_avgs)) + print('Final META cforest :', numpy.mean(meta_R_cforest_avgs), numpy.std(meta_R_cforest_avgs)) + + import pdb; pdb.set_trace() + print() + diff --git a/mltsp/TCP/Algorithms/count_class_names.py b/mltsp/TCP/Algorithms/count_class_names.py new file mode 100644 index 00000000..9fcb70ce --- /dev/null +++ b/mltsp/TCP/Algorithms/count_class_names.py @@ -0,0 +1,114 @@ +#!/usr/bin/env python +""" Tally the occurances of various science classes found in class_names datafile +""" +from __future__ import print_function +import os, sys +import pprint +import MySQLdb + +class tutor_db: + """ + """ + def __init__(self): + self.pars ={'tcptutor_hostname':'192.168.1.103', + 'tcptutor_username':'tutor', # guest + 'tcptutor_password':'ilove2mass', #'iamaguest', + 'tcptutor_database':'tutor', + 'tcptutor_port':3306} + + + self.tutor_db = MySQLdb.connect(host=self.pars['tcptutor_hostname'], \ + user=self.pars['tcptutor_username'], \ + passwd=self.pars['tcptutor_password'],\ + db=self.pars['tcptutor_database'],\ + port=self.pars['tcptutor_port']) + self.tutor_cursor = self.tutor_db.cursor() + +db = tutor_db() + + +lines = open('class_names').readlines() + +tally_dict = {} +for line in lines: + class_name = line.strip() + if class_name not in tally_dict: + tally_dict[class_name] = [1, class_name] + else: + tally_dict[class_name][0] += 1 + +sorted_elems = tally_dict.values() +sorted_elems.sort(reverse=True) + +for a in sorted_elems: + class_name = a[1].replace(' - ','%') + select_str = 'SELECT * FROM classes WHERE class_name like "' + class_name + '" AND class_is_active="yes" AND class_is_public="yes"' + db.tutor_cursor.execute(select_str) + results = db.tutor_cursor.fetchall() + if len(results) < 1: + select_str = 'SELECT * FROM classes WHERE class_name like "%' + class_name + '%" AND class_is_active="yes" AND class_is_public="yes"' + db.tutor_cursor.execute(select_str) + results = db.tutor_cursor.fetchall() + if len(results) < 1: + select_str = 'SELECT * FROM classes WHERE class_name like "' + class_name + '" AND class_is_active="yes" AND class_is_public="no"' + db.tutor_cursor.execute(select_str) + results = db.tutor_cursor.fetchall() + try: + print("%4d %35s http://dotastro.org/lightcurves/class.php?Class_ID=%d " % (a[0], a[1], int(results[0][0]))) + except: + print("!!!", len(results), class_name) + + +""" +Num sources science class URL to description, class heirarchy +----------- ------------- ----------------------------------- +1048 Classical Cepheid http://dotastro.org/lightcurves/class.php?Class_ID=238 + 889 W Ursae Majoris - W UMa http://dotastro.org/lightcurves/class.php?Class_ID=85 + 515 Beta Persei http://dotastro.org/lightcurves/class.php?Class_ID=253 + 240 Beta Lyrae http://dotastro.org/lightcurves/class.php?Class_ID=251 + 223 Type Ia Supernovae http://dotastro.org/lightcurves/class.php?Class_ID=182 + 189 RR Lyrae, Fundamental Mode http://dotastro.org/lightcurves/class.php?Class_ID=218 + 150 Delta Scuti http://dotastro.org/lightcurves/class.php?Class_ID=211 + 121 W Ursae Majoris http://dotastro.org/lightcurves/class.php?Class_ID=252 + 113 Mira http://dotastro.org/lightcurves/class.php?Class_ID=208 + 110 RR Lyrae, Double Mode http://dotastro.org/lightcurves/class.php?Class_ID=220 + 110 Pulsating Variable http://dotastro.org/lightcurves/class.php?Class_ID=203 + 107 Multiple Mode Cepheid http://dotastro.org/lightcurves/class.php?Class_ID=237 + 63 Microlensing Event http://dotastro.org/lightcurves/class.php?Class_ID=145 + 50 RR Lyrae, First Overtone http://dotastro.org/lightcurves/class.php?Class_ID=219 + 48 Binary http://dotastro.org/lightcurves/class.php?Class_ID=248 + 34 Semiregular Pulsating Variable http://dotastro.org/lightcurves/class.php?Class_ID=214 + 32 Wolf-Rayet http://dotastro.org/lightcurves/class.php?Class_ID=192 + 32 Long Period (W Virginis) http://dotastro.org/lightcurves/class.php?Class_ID=235 + 31 RR Lyrae - Asymmetric http://dotastro.org/lightcurves/class.php?Class_ID=43 + 31 Beta Cephei http://dotastro.org/lightcurves/class.php?Class_ID=213 + 27 Cataclysmic Variable http://dotastro.org/lightcurves/class.php?Class_ID=157 + 26 Gamma Doradus http://dotastro.org/lightcurves/class.php?Class_ID=204 + 17 Population II Cepheid http://dotastro.org/lightcurves/class.php?Class_ID=216 + 16 RR Lyrae http://dotastro.org/lightcurves/class.php?Class_ID=206 + 16 BL Lac http://dotastro.org/lightcurves/class.php?Class_ID=139 + 15 T Tauri http://dotastro.org/lightcurves/class.php?Class_ID=200 + 15 RR Lyrae - First Overtone http://dotastro.org/lightcurves/class.php?Class_ID=219 + 14 Short period (BL Herculis) http://dotastro.org/lightcurves/class.php?Class_ID=234 + 14 Semiregular Pulsating Red Giants http://dotastro.org/lightcurves/class.php?Class_ID=134 + 14 SX Phoenicis http://dotastro.org/lightcurves/class.php?Class_ID=205 + 13 RR Lyrae, Closely Spaced Modes http://dotastro.org/lightcurves/class.php?Class_ID=222 + 13 Lambda Bootis Variable http://dotastro.org/lightcurves/class.php?Class_ID=261 + 13 Ellipsoidal http://dotastro.org/lightcurves/class.php?Class_ID=246 + 10 Blazar http://dotastro.org/lightcurves/class.php?Class_ID=256 + 9 Herbig AE/BE Star http://dotastro.org/lightcurves/class.php?Class_ID=197 + 7 X Ray Binary http://dotastro.org/lightcurves/class.php?Class_ID=260 + 6 RV Tauri http://dotastro.org/lightcurves/class.php?Class_ID=215 + 6 Anomolous Cepheid http://dotastro.org/lightcurves/class.php?Class_ID=236 + 5 S Doradus http://dotastro.org/lightcurves/class.php?Class_ID=191 + 3 Variable Stars [Alt] http://dotastro.org/lightcurves/class.php?Class_ID=154 + 3 Flat Spectrum Radio Quasar http://dotastro.org/lightcurves/class.php?Class_ID=265 + 2 Type II Supernovae http://dotastro.org/lightcurves/class.php?Class_ID=185 + 2 Systems with Planets http://dotastro.org/lightcurves/class.php?Class_ID=254 + 2 Supernovae http://dotastro.org/lightcurves/class.php?Class_ID=180 + 1 SX Phoenicis - Pulsating Subdwarfs http://dotastro.org/lightcurves/class.php?Class_ID=53 + 1 SU Ursae Majoris http://dotastro.org/lightcurves/class.php?Class_ID=169 + 1 SRd http://dotastro.org/lightcurves/class.php?Class_ID=231 + 1 Rotating Variable http://dotastro.org/lightcurves/class.php?Class_ID=240 + 1 AM Herculis (True Polar) http://dotastro.org/lightcurves/class.php?Class_ID=166 +""" diff --git a/mltsp/TCP/Algorithms/cp_noise_files.py b/mltsp/TCP/Algorithms/cp_noise_files.py new file mode 100644 index 00000000..26c6a132 --- /dev/null +++ b/mltsp/TCP/Algorithms/cp_noise_files.py @@ -0,0 +1,142 @@ +#!/usr/bin/env python +""" scps noise trained weka .model files neede for classification +""" +from __future__ import print_function + +import sys, os +import glob + +client_defs = [ \ + {'name':'__local__', + 'hostname':'127.0.0.1', + 'furl_dirpath':'/home/pteluser/.ipython/security', + 'username':'pteluser', + 'ssh_port':22, + 'n_engines':10}, + {'name':'__worms2__', + 'hostname':'localhost', + 'furl_dirpath':'/home/starr/.ipython/security', + 'username':'starr', + 'ssh_port':32151, + 'n_engines':0}, + {'name':'__cch1__', + 'hostname':'localhost', + 'furl_dirpath':'/home/dstarr/.ipython/security', + 'username':'dstarr', + 'nice':19, + 'ssh_port':32161, + 'n_engines':1}, + ] + +""" + {'name':'__trans1__', + 'hostname':'192.168.1.45', + 'furl_dirpath':'/home/pteluser/.ipython/security', + 'username':'pteluser', + 'ssh_port':22, + 'n_engines':0}, + {'name':'__trans2__', + 'hostname':'192.168.1.55', + 'furl_dirpath':'/home/pteluser/.ipython/security', + 'username':'pteluser', + 'ssh_port':22, + 'n_engines':0}, + {'name':'__trans3__', + 'hostname':'192.168.1.65', + 'furl_dirpath':'/home/pteluser/.ipython/security', + 'username':'pteluser', + 'ssh_port':22, + 'n_engines':0}, + {'name':'__sgn02__', + 'hostname':'sgn02.nersc.gov', + 'furl_dirpath':'/global/homes/d/dstarr/datatran/.ipython/security', + 'username':'dstarr', + 'ssh_port':22, + 'n_engines':0}, +""" + +def send_to_other_nodes(glob_mask, dirnames, client_defs): + """ send files on this computer to other node computers. + """ + + for client_def in client_defs: + if client_def['name'] == '__local__': + continue + for dirname in dirnames: + exec_str = "ssh -tp %d %s@%s mkdir scratch/Noisification/%s" % ( \ + client_def['ssh_port'], + client_def['username'], + client_def['hostname'], + dirname) + os.system(exec_str) + + exec_str = "scp -CP %d ~/scratch/Noisification/%s/*arff %s@%s:scratch/Noisification/%s/" % ( \ + client_def['ssh_port'], + dirname, + client_def['username'], + client_def['hostname'], + dirname) + os.system(exec_str) + + exec_str = "scp -CP %d ~/scratch/Noisification/%s/*model %s@%s:scratch/Noisification/%s/" % ( \ + client_def['ssh_port'], + dirname, + client_def['username'], + client_def['hostname'], + dirname) + os.system(exec_str) + + +def retrieve_from_other_node(glob_mask, dirnames, retrieve_host_dict): + """ copy files to this node from other nodes. + """ + # will do an scp to ~/scratch/Noisification/ files: scratch/Noisification/*glob_mask*/*arff *model + + for dirname in dirnames: + exec_str = "mkdir -p ~/scratch/Noisification/%s" % (dirname) + os.system(exec_str) + + exec_str = "scp -CP %d %s@%s:scratch/Noisification/%s/{*arff,*model} ~/scratch/Noisification/%s/" % ( \ + retrieve_host_dict['ssh_port'], + retrieve_host_dict['username'], + retrieve_host_dict['hostname'], + dirname, + dirname) + print(exec_str) + os.system(exec_str) + + + +if __name__ == '__main__': + + #glob_mask = sys.argv[1] # eg: 50nois_*short1 + glob_mask = "50nois_*qk17.9" + dirnames = glob.glob(glob_mask) + + #send_to_other_nodes(glob_mask, dirnames, client_defs) + + retrieve_host_dict = \ + {'name':'__cch1__', + 'hostname':'localhost', + 'furl_dirpath':'/home/dstarr/.ipython/security', + 'username':'dstarr', + 'nice':19, + 'ssh_port':32161, + 'n_engines':1} + + dirnames = ['20nois_19epch_040need_0.050mtrc_j48_17.9', + '20nois_15epch_040need_0.050mtrc_j48_17.9', + '20nois_11epch_040need_0.050mtrc_j48_17.9', + '20nois_21epch_040need_0.050mtrc_j48_17.9', + '20nois_25epch_040need_0.050mtrc_j48_17.9', + '20nois_29epch_040need_0.050mtrc_j48_17.9', + '20nois_17epch_040need_0.050mtrc_j48_17.9', + '20nois_13epch_040need_0.050mtrc_j48_17.9', + '20nois_20epch_040need_0.050mtrc_j48_17.9', + '20nois_27epch_040need_0.050mtrc_j48_17.9', + '20nois_23epch_040need_0.050mtrc_j48_17.9', + '20nois_09epch_040need_0.050mtrc_j48_17.9', + '20nois_10epch_040need_0.050mtrc_j48_17.9', + '20nois_33epch_040need_0.050mtrc_j48_17.9'] + + retrieve_from_other_node(glob_mask, dirnames, retrieve_host_dict) diff --git a/mltsp/TCP/Algorithms/debosscher_vosourcexml_copy.sh b/mltsp/TCP/Algorithms/debosscher_vosourcexml_copy.sh new file mode 100755 index 00000000..53eaeb77 --- /dev/null +++ b/mltsp/TCP/Algorithms/debosscher_vosourcexml_copy.sh @@ -0,0 +1,1393 @@ +#!/bin/sh +cp /home/pteluser/scratch/vosource_xml_writedir/100148010.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148011.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148012.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148013.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148014.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148015.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148016.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148017.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148018.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148019.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148020.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148021.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148022.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148023.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148024.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148025.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148026.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148027.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148028.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148029.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148030.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148031.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148032.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148033.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148034.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148035.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148036.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148037.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148038.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148039.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148040.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148041.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148042.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148043.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148044.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148045.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148046.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148047.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148048.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148049.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148050.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148051.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148052.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148053.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148054.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148055.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148056.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148057.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148058.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148059.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148060.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148061.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148062.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148063.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148064.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148065.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148066.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148067.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148068.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148069.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148070.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148071.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148072.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148073.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148074.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148075.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148076.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148077.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148078.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148079.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148080.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148081.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148082.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148083.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148084.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148085.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148086.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148087.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148088.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148089.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148090.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148091.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148092.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148093.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148094.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148095.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148096.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148097.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148098.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148099.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148100.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148101.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148102.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148103.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148104.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148105.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148106.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148107.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148108.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148109.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148110.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148111.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148112.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148113.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148114.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148115.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148116.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148117.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148118.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148119.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148120.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148121.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148122.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148123.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148124.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148125.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148126.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148127.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148128.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148129.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148130.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148131.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148132.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148133.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148134.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148135.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148136.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148137.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148138.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148139.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148140.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148141.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148142.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148143.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148144.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148145.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148146.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148147.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148148.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148149.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148150.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148151.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148152.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148153.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148154.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148155.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148156.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148157.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148158.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148159.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148160.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148161.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148162.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148163.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148164.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148165.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148166.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148167.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148168.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148169.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148170.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148171.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148172.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148173.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148174.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148175.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148176.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148177.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148178.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148179.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148180.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148181.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148182.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148183.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148184.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148185.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148186.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148187.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148188.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148189.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148190.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148191.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148192.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148193.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148194.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148195.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148196.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148197.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148198.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148199.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148200.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148201.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148202.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148203.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148204.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148205.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148206.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148207.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148208.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148209.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148210.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148211.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148212.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148213.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148214.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148215.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148216.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148217.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148218.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148219.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148220.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148221.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148222.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148223.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148224.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148225.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148226.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148227.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148228.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148229.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148230.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148231.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148232.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148233.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148234.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148235.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148236.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148237.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148238.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148239.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148240.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148241.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148242.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148243.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148244.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148245.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148246.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148247.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148248.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148249.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148250.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148251.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148252.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148253.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148254.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148255.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148256.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148257.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148258.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148259.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148260.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148261.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148262.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148263.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148264.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148265.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148266.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148267.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148268.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148269.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148270.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148271.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148272.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148273.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148274.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148275.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148276.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148277.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148278.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148279.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148280.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148281.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148282.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148283.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148284.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148285.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148286.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148287.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148288.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148289.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148290.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148291.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148292.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148293.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148294.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148295.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148296.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148297.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148298.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148299.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148300.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148301.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148302.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148303.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148304.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148305.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148306.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148307.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148308.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148309.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148310.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148311.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148312.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148313.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148314.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148315.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148316.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148317.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148318.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148319.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148320.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148321.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148322.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148323.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148324.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148325.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148326.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148327.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148328.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148329.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148330.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148331.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148332.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148333.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148334.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148335.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148336.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148337.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148338.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148339.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148340.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148341.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148342.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148343.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148344.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148345.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148346.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148347.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148348.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148349.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148350.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148351.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148352.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148353.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148354.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148355.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148356.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148357.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148358.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148359.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148360.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148361.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148362.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148363.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148364.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148365.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148366.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148367.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148368.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148369.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148370.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148371.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148372.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148373.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148374.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148375.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148376.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148377.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148378.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148379.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148380.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148381.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148382.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148383.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148384.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148385.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148386.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148387.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148388.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148389.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148390.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148391.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148392.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148393.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148394.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148395.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148396.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148397.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148398.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148399.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148400.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148401.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148402.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148403.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148404.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148405.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148406.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148407.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148408.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148409.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148410.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148411.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148412.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148413.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148414.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148415.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148416.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148417.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148418.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148419.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148420.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148421.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148422.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148423.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148424.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148425.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148426.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148427.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148428.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148429.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148430.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148431.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148432.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148433.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148434.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148435.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148436.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148437.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148438.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148439.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148440.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148441.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148442.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148443.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148444.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148445.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148446.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148447.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148448.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148449.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148450.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148451.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148452.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148453.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148454.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148455.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148456.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148457.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148458.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148459.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148460.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148461.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148462.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148463.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148464.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148465.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148466.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148467.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148468.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148469.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148470.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148471.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148472.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148473.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148474.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148475.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148476.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148477.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148478.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148479.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148480.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148481.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148482.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148483.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148484.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148485.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148486.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148487.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148488.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148489.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148490.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148491.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148492.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148493.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148494.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148495.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148496.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148497.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148498.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148499.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148500.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148501.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148502.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148503.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148504.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148505.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148506.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148507.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148508.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148509.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148510.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148511.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148512.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148513.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148514.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148515.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148516.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148517.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148518.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148519.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148520.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148521.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148522.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148523.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148524.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148525.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148526.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148527.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148528.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148529.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148530.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148531.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148532.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148533.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148534.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148535.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148536.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148537.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148538.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148539.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148540.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148541.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148542.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148543.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148544.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148545.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148546.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148547.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148548.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148549.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148550.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148551.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148552.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148553.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148554.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148555.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148556.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148557.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148558.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148559.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148560.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148561.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148562.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148563.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148564.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148565.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148566.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148567.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148568.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148569.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148570.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148571.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148572.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148573.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148574.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148575.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148576.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148577.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148578.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148579.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148580.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148581.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148582.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148583.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148584.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148585.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148586.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148587.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148588.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148589.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148590.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148591.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148592.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148593.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148594.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148595.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148596.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148597.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148598.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148599.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148600.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148601.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148602.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148603.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148604.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148605.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148606.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148607.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148608.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148609.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148610.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148611.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148612.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148613.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148614.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148615.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148616.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148617.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148618.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148619.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148620.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148621.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148622.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148623.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148624.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148625.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148626.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148627.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148628.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148629.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148630.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148631.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148632.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148633.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148634.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148635.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148636.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148637.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148638.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148639.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148640.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148641.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148642.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148643.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148644.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148645.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148646.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148647.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148648.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148649.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148650.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148651.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148652.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148653.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148654.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148655.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148656.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148657.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148658.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148659.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148660.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148661.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148662.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148663.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148664.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148665.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148666.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148667.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148668.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148669.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148670.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148671.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148672.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148673.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148674.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148675.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148676.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148677.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148678.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148679.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148680.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148681.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148682.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148683.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148684.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148685.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148686.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148687.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148688.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148689.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148690.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148691.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148692.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148693.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148694.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148695.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148696.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148697.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148698.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148699.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148700.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148701.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148702.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148703.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148704.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148705.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148706.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148707.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148708.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148709.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148710.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148711.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148712.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148713.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148714.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148715.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148716.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148717.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148718.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148719.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148720.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148721.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148722.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148723.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148724.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148725.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148726.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148727.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148728.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148729.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148730.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148731.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148732.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148733.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148734.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148735.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148736.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148737.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148738.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148739.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148740.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148741.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148742.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148743.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148744.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148745.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148746.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148747.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148748.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148749.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148750.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148751.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148752.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148753.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148754.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148755.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148756.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148757.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148758.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148759.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148760.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148761.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148762.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148763.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148764.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148765.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148766.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148767.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148768.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148769.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148770.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148771.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148772.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148773.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148774.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148775.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148776.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148777.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148778.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148779.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148780.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148781.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148782.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148783.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148784.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148785.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148786.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148787.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148788.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148789.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148790.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148791.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148792.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148793.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148794.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148795.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148796.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148797.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148798.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148799.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148800.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148801.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148802.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148803.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148804.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148805.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148806.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148807.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148808.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148809.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148810.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148811.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148812.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148813.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148814.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148815.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148816.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148817.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148818.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148819.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148820.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148821.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148822.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148823.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148824.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148825.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148826.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148827.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148828.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148829.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148830.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148831.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148832.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148833.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148834.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148835.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148836.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148837.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148838.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148839.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148840.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148841.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148842.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148843.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148844.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148845.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148846.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148847.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148848.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148849.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148850.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148851.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148852.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148853.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148854.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148855.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148856.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148857.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148858.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148859.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148860.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148861.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148862.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148863.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148864.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148865.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148866.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148867.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148868.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148869.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148870.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148871.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148872.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148873.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148874.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148875.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148876.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148877.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148878.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148879.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148880.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148881.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148882.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148883.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148884.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148885.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148886.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148887.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148888.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148889.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148890.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148891.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148892.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148893.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148894.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148895.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148896.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148897.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148898.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148899.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148900.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148901.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148902.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148903.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148904.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148905.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148906.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148907.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148908.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148909.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148910.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148911.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148912.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148913.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148914.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148915.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148916.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148917.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148918.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148919.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148920.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148921.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148922.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148923.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148924.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148925.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148926.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148927.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148928.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148929.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148930.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148931.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148932.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148933.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148934.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148935.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148936.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148937.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148938.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148939.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148940.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148941.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148942.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148943.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148944.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148945.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148946.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148947.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148948.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148949.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148950.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148951.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148952.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148953.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148954.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148955.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148956.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148957.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148958.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148959.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148960.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148961.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148962.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148963.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148964.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148965.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148966.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148967.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148968.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148969.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148970.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148971.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148972.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148973.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148974.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148975.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148976.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148977.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148978.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148979.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148980.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148981.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148982.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148983.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148984.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148985.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148986.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148987.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148988.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148989.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148990.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148991.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148992.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148993.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148994.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148995.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148996.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148997.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148998.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100148999.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149000.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149001.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149002.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149003.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149004.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149005.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149006.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149007.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149008.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149009.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149010.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149011.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149012.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149013.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149014.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149015.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149016.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149017.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149018.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149019.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149020.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149021.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149022.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149023.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149024.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149025.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149026.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149027.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149028.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149029.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149030.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149031.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149032.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149033.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149034.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149035.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149036.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149037.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149038.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149039.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149040.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149041.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149042.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149043.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149044.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149045.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149046.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149047.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149048.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149049.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149050.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149051.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149052.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149053.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149054.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149055.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149056.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149057.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149058.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149059.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149060.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149061.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149062.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149063.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149064.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149065.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149066.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149067.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149068.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149069.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149070.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149071.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149072.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149073.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149074.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149075.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149076.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149077.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149078.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149079.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149080.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149081.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149082.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149083.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149084.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149085.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149086.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149087.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149088.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149089.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149090.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149091.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149092.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149093.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149094.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149095.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149096.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149097.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149098.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149099.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149100.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149101.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149102.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149103.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149104.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149105.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149106.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149107.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149108.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149109.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149110.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149111.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149112.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149113.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149114.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149115.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149116.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149117.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149118.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149119.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149120.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149121.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149122.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149123.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149124.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149125.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149126.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149127.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149128.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149129.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149130.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149131.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149132.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149133.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149134.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149135.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149136.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149137.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149138.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149139.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149140.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149141.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149142.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149143.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149144.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149145.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149146.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149147.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149148.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149149.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149150.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149151.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149152.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149153.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149154.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149155.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149156.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149157.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149158.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149159.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149160.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149161.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149162.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149163.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149164.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149165.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149166.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149167.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149168.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149169.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149170.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149171.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149172.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149173.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149174.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149175.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149176.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149177.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149178.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149179.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149180.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149181.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149182.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149183.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149184.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149185.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149186.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149187.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149188.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149189.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149190.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149191.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149192.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149193.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149194.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149195.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149196.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149197.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149198.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149199.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149200.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149201.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149202.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149203.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149204.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149205.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149206.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149207.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149208.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149209.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149210.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149211.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149212.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149213.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149214.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149215.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149216.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149217.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149218.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149219.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149220.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149221.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149222.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149223.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149224.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149225.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149226.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149227.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149228.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149229.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149230.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149231.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149232.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149233.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149234.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149235.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149236.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149237.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149238.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149239.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149240.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149241.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149242.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149243.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149244.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149245.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149246.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149247.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149248.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149249.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149250.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149251.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149252.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149253.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149254.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149255.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149256.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149257.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149258.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149259.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149260.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149261.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149262.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149263.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149264.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149265.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149266.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149267.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149268.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149269.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149270.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149271.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149272.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149273.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149274.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149275.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149276.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149277.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149278.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149279.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149280.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149281.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149282.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149283.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149284.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149285.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149286.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149287.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149288.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149289.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149290.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149291.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149292.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149293.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149294.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149295.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149296.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149297.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149298.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149299.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149300.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149301.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149302.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149303.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149304.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149305.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149306.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149307.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149308.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149309.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149310.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149311.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149312.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149313.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149314.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149315.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149316.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149317.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149318.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149319.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149320.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149321.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149322.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149323.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149324.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149325.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149326.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149327.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149328.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149329.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149330.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149331.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149332.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149333.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149334.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149335.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149336.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149337.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149338.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149339.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149340.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149341.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149342.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149343.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149344.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149345.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149346.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149347.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149348.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149349.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149350.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149351.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149352.xml ./ +cp 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/home/pteluser/scratch/vosource_xml_writedir/100149383.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149384.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149385.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149386.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149387.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100149388.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100161326.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100161327.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100161328.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100161329.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100161330.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100161331.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100161332.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100161333.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100161334.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100161335.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100161336.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100161337.xml ./ +cp /home/pteluser/scratch/vosource_xml_writedir/100161338.xml ./ diff --git a/mltsp/TCP/Algorithms/do_qsoy.py b/mltsp/TCP/Algorithms/do_qsoy.py new file mode 100644 index 00000000..9cac7256 --- /dev/null +++ b/mltsp/TCP/Algorithms/do_qsoy.py @@ -0,0 +1,28 @@ +#!/usr/bin/env python +""" Nat wrote 20100930, dstarr to adapt as a TCP feature. +""" +from __future__ import print_function +from __future__ import absolute_import +from numpy import median,loadtxt +from .qso_fit import qso_fit +import glob + +if __name__ == '__main__': + + #files=glob.glob('*.dat') + files=['/home/pteluser/scratch/100149386.dat'] + for file in files: + id = file[file.rfind('/'):] + (x,y,dy) = loadtxt(file,unpack=True) + + y0 = 19. + y -= median(y) - y0 + od = qso_fit(x,y,dy,filter='g') + res = od['chi2_qso/nu'],od['chi2_qso/nu_NULL'] + + # QSO-like: res[0]<~2 + # non-QSO: res[1]/res[0]<~2 + + print(("%s %f %f") % (id,res[0],res[1]/res[0])) + import pprint + pprint.pprint(od) diff --git a/mltsp/TCP/Algorithms/evaluate_eclipsing_classifs_using_fiteb.py b/mltsp/TCP/Algorithms/evaluate_eclipsing_classifs_using_fiteb.py new file mode 100644 index 00000000..f607e33b --- /dev/null +++ b/mltsp/TCP/Algorithms/evaluate_eclipsing_classifs_using_fiteb.py @@ -0,0 +1,1480 @@ +#!/usr/bin/env python +""" +Using JSB's fiteb.py, found at: + wget http://commondatastorage.googleapis.com/bloom_code/jsb_eb_fit_v12may2011.tgz + +This code fits eclipsing models in order to determine which type of eclipsing class a TUTOR source is. + +""" +from __future__ import print_function +from __future__ import absolute_import + +import sys, os +import MySQLdb +import glob +from numpy import loadtxt +import numpy +import matplotlib.pyplot as pyplot + +sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + 'Software/ingest_tools')) +from activelearn_utils import Database_Utils + +sys.path.append(os.path.abspath(os.environ.get("HOME") + '/src/install/jsb_eb_fit')) +from . import fiteb + +import pprint +import copy +import cPickle +import datetime +import time + +class IPython_Task_Administrator: + """ Send of Imputation tasks + + Adapted from activelearn_utils.py which was + adapted from generate_weka_classifiers.py:Parallel_Arff_Maker() + + """ + def __init__(self, pars={}): + try: + from IPython.kernel import client + except: + pass + + self.kernel_client = client + + self.pars = pars + # TODO: - initialize ipython modules + self.mec = client.MultiEngineClient() + #self.mec.reset(targets=self.mec.get_ids()) # Reset the namespaces of all engines + self.tc = client.TaskClient() + self.task_id_list = [] + + #### 2011-01-21 added: + self.mec.reset(targets=self.mec.get_ids()) + self.mec.clear_queue() + self.mec.clear_pending_results() + self.tc.task_controller.clear() + + + def initialize_clients(self, mec_str): + """ Instantiate ipython1 clients, import all module dependencies. + """ + #task_str = """cat = os.getpid()""" + #taskid = self.tc.run(client.StringTask(task_str, pull="cat")) + #time.sleep(2) + #print self.tc.get_task_result(taskid, block=False).results + + # 20090815(before): a = arffify.Maker(search=[], skip_class=False, local_xmls=True, convert_class_abrvs_to_names=False, flag_retrieve_class_abrvs_from_TUTOR=True, dorun=False) + import time + + self.mec.execute(mec_str) + time.sleep(2) # This may be needed. + + ### testing: + #task_str = """cat = os.getpid()""" + #taskid = self.tc.run(client.StringTask(task_str, pull="cat")) + #time.sleep(1) + #print self.tc.get_task_result(taskid, block=False).results + + + +class Run_FitEB_Parallel: + """ Do the equivalent of run_fiteb_generate_html_pkl() in parallel. + """ + + def __init__(self): + self.pars = {'tutor_hostname':'192.168.1.103', + 'tutor_username':'dstarr', #'tutor', # guest + 'tutor_password':'ilove2mass', #'iamaguest', + 'tutor_database':'tutor', + 'tutor_port':3306, #33306, + 'tcp_hostname':'192.168.1.25', + 'tcp_username':'pteluser', + 'tcp_port': 3306, #23306, + 'tcp_database':'source_test_db', + 'pkl_fpath':os.path.abspath(os.environ.get("HOME") + '/scratch/evaluate_eclipsing_classifs_using_fiteb__dict.pkl'), + 'fiteb_class_lookup':{'contact':'y. W Ursae Maj.', + 'detached':'w. Beta Persei', + 'semi-detached':'x. Beta Lyrae'}, + 'analysis_params':['chisq', 'i_err', 'q_err', 'l1/l2_err', 'l1/l2', 'q', 'r1', 'r2', 'reflect1'], + 'al_dirpath':os.path.abspath(os.environ.get("TCP_DIR") + 'Data/allstars'), + 'al_glob_str':'AL_*_*.dat', + 'classids_of_interest':[251,#'x. Beta Lyrae', + 253,#'w. Beta Persei', + 252,#'y. W Ursae Maj.', + ], + 'fittype':4, + 'skip_list':[234996, 164830, 164872, 239309, 216110, 225745], + } + + + def client_setup(self, class_id_name={}): + """ Routines for setting up params, database connections for ipython client + """ + + #orig_cwd = os.getcwd() + os.chdir(os.path.abspath(os.environ.get("HOME") + '/src/install/jsb_eb_fit')) + + if len(class_id_name) == 0: + self.DatabaseUtils = Database_Utils(pars=self.pars) + rclass_tutorid_lookup = self.DatabaseUtils.retrieve_tutor_class_ids() + self.class_id_name = dict([[v,k] for k,v in rclass_tutorid_lookup.items()]) + else: + self.class_id_name = class_id_name + + + def client_task(self): + """ Computation task which is to be run on Ipython engines. + """ + pass + + + def add_task(self, srcid=None, period=None, fittype=None, filename=None, classid=None): + """ + """ + tc_exec_str = """ +try: + (alt_rez, rez) = fiteb.period_select(idd=int(srcid), + per=period, + plot=False, + use_xml=True, + try_alt=True, + dosave=False, + show=False, + fittype=fittype) + out_dict = {'alt_rez':alt_rez, + 'rez':rez, + 'srcid':srcid, + 'traceback':"", + 'filename':filename, + 'classid':classid} +except: + out_dict = {'traceback':traceback.format_exc(), + 'srcid':srcid} + """ + tc_exec_str__test = """ +out_dict = {'alt_rez':fiteb.__author__} + """ + task_id = self.ipy_tasks.tc.run(self.ipy_tasks.kernel_client.StringTask(tc_exec_str, + push={'srcid':srcid, + 'period':period, + 'fittype':fittype, + 'filename':filename, + 'classid':classid, + }, + pull='out_dict', + retries=3)) + #print self.ipy_tasks.tc.get_task_result(task_id, block=False) + #import pdb; pdb.set_trace() + #print + self.ipy_tasks.task_id_list.append(task_id) + + + + def ipython_master(self): + """ + """ + self.ipy_tasks = IPython_Task_Administrator() + self.client_setup() # Needed here to make database connections + + mec_str = """ +import sys, os +import MySQLdb +import glob +from numpy import loadtxt +import numpy +import matplotlib.pyplot as pyplot + +sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + 'Software/ingest_tools')) +from activelearn_utils import Database_Utils + +sys.path.append(os.path.abspath(os.environ.get("HOME") + '/src/install/jsb_eb_fit')) +import fiteb + +import pprint +import copy +import cPickle +import traceback + +sys.path.append(os.environ.get('TCP_DIR') + '/Algorithms') +import evaluate_eclipsing_classifs_using_fiteb +RunFitEBParallel = evaluate_eclipsing_classifs_using_fiteb.Run_FitEB_Parallel() +RunFitEBParallel.client_setup(class_id_name=%s) +""" % (str(self.class_id_name)) + self.ipy_tasks.initialize_clients(mec_str=mec_str) + + fpaths = glob.glob("%s/%s" % (self.pars['al_dirpath'], self.pars['al_glob_str'])) + + pickle_dict = {} + + fp = open(os.path.abspath(os.environ.get("HOME") + '/scratch/evaluate_eclipsing_classifs.html'),'w') + fp.write(""" + + """) + + ########### AL*dat sources: + if 1: + for fpath in fpaths: + tup_list = loadtxt(fpath, + dtype={'names': ('src_id', 'class_id'), + 'formats': ('i4', 'i4')}, + usecols=(0,1), + unpack=False) + srcid_list = tup_list['src_id'] + classid_list = tup_list['class_id'] + for i, classid in enumerate(classid_list): + ### NOTE: i corresponds to classid and srcid_list[i] + if classid not in self.pars['classids_of_interest']: + continue + srcid = srcid_list[i] + if int(srcid) in self.pars['skip_list']: + continue + print("srcid:", srcid) + select_str = "SELECT feat_val FROM source_test_db.feat_values JOIN feat_lookup USING (feat_id) WHERE filter_id=8 AND feat_name='freq1_harmonics_freq_0' AND src_id=%d" % (srcid + 100000000) + self.DatabaseUtils.tcp_cursor.execute(select_str) + results = self.DatabaseUtils.tcp_cursor.fetchall() + if len(results) == 0: + raise "Error" + period = 1. / results[0][0] + + if 0: + ### For single-thread testing only: + RunFitEBParallel = Run_FitEB_Parallel() + RunFitEBParallel.client_setup() + (alt_rez, rez) = fiteb.period_select(idd=int(srcid), + per=period, + plot=True, + use_xml=True, + try_alt=True, + dosave=True, + show=False, + fittype=self.pars['fittype']) #) #True,#verbose=False) + import pdb; pdb.set_trace() + print() + + self.add_task(srcid=srcid, + period=period, + fittype=self.pars['fittype'], + filename=fpath[fpath.rfind('/')+1:], + classid=classid) + + ######### Debosscher sources using TUTOR database query: + select_str = "select source_id, class_id from sources where project_id = 123" + self.DatabaseUtils.tutor_cursor.execute(select_str) + results = self.DatabaseUtils.tutor_cursor.fetchall() + if len(results) == 0: + raise "Error" + for row in results: + (srcid, classid) = row + if classid in self.pars['classids_of_interest']: + if int(srcid) in self.pars['skip_list']: + continue + print("22222222", srcid) + + select_str = "SELECT feat_val FROM source_test_db.feat_values JOIN feat_lookup USING (feat_id) WHERE filter_id=8 AND feat_name='freq1_harmonics_freq_0' AND src_id=%d" % (srcid + 100000000) + self.DatabaseUtils.tcp_cursor.execute(select_str) + results = self.DatabaseUtils.tcp_cursor.fetchall() + if len(results) == 0: + raise "Error" + period = 1. / results[0][0] + + self.add_task(srcid=srcid, + period=period, + fittype=self.pars['fittype'], + filename="Debosscher", + classid=classid) + + ######### + #import pdb; pdb.set_trace() + #print + + task_id_list = self.ipy_tasks.task_id_list + tc = self.ipy_tasks.tc + + dtime_pending_1 = None + while ((tc.queue_status()['scheduled'] > 0) or + (tc.queue_status()['pending'] > 0)): + tasks_to_pop = [] + for task_id in self.ipy_tasks.task_id_list: + temp = self.ipy_tasks.tc.get_task_result(task_id, block=False) + if temp is None: + continue + temp2 = temp.results + if temp2 is None: + continue + results = temp['out_dict'] + if len(results.get('traceback',"")) > 0: + print("Task Traceback:", results.get('srcid',""), results.get('traceback',"")) + continue + tasks_to_pop.append(task_id) + srcid = results['srcid'] + rez = results['rez'] + alt_rez = results['alt_rez'] + filename = results['filename'] + classid = results['classid'] + fp.write('\n' % \ + (srcid, srcid, srcid, filename, self.class_id_name[classid], pars['fiteb_class_lookup'].get(rez.get('class',None),'unknown'), str(rez.get('okfit',False)), + rez.get('chisq',False) if rez.get('chisq',99999) is not None else 99999, + rez.get('e',False), + rez.get('i',99999), + rez.get('i_err',99999) if rez.get('i_err',99999) is not None else 99999, + rez.get('q',99999), + rez.get('q_err',99999) if rez.get('q_err',99999) is not None else 99999, + rez.get('l1/l2',99999), + rez.get('l1/l2_err',99999) if rez.get('l1/l2_err',99999) is not None else 99999, + str(rez.get('reflect1',99999) if rez.get('reflect1',99999) is not None else 99999), + str(rez.get('reflect2',99999) if rez.get('reflect2',99999) is not None else 99999), + )) + fp.flush() + pickle_dict[srcid] = rez + pickle_dict[srcid].update({'file':filename, + 'tutor_class':self.class_id_name[classid], + 'fiteb_class':pars['fiteb_class_lookup'].get(rez.get('class',None),'unknown'), + 'okfit':rez.get('okfit',False), + 'chisq':rez.get('chisq',False), + 'chisq_percentiles':alt_rez.get('chisq_percentiles',{}), + 'e_percentiles':alt_rez.get('e_percentiles',{}), + 'i_err_percentiles':alt_rez.get('i_err_percentiles',{}), + 'l1/l2_err_percentiles':alt_rez.get('l1/l2_err_percentiles',{}), + 'l1/l2_percentiles':alt_rez.get('l1/l2_percentiles',{}), + 'l1_percentiles':alt_rez.get('l1_percentiles',{}), + 'l2_percentiles':alt_rez.get('l2_percentiles',{}), + 'omega_deg_percentiles':alt_rez.get('omega_deg_percentiles',{}), + 'primary_eclipse_phase_percentiles':alt_rez.get('primary_eclipse_phase_percentiles',{}), + 'period_percentiles':alt_rez.get('period_percentiles',{}), + 'ratiopass_for_percentile':alt_rez.get('ratiopass_for_percentile',{}), + 'vals_for_percentile':alt_rez.get('vals_for_percentile',{}), + 'nmodels':alt_rez.get('nmodels',{}), + 'q_err_percentiles':alt_rez.get('q_err_percentiles',{}), + 'q_percentiles':alt_rez.get('q_percentiles',{}), + 'r1_percentiles':alt_rez.get('r1_percentiles',{}), + 'r2_percentiles':alt_rez.get('r2_percentiles',{}), + 'reflect1_percentiles':alt_rez.get('reflect1_percentiles',{}), + 'reflect2_percentiles':alt_rez.get('reflect2_percentiles',{}), + 'e':rez.get('e',False), + 'i':rez.get('i',99999), + 'i_err':rez.get('i_err',99999), + 'q':rez.get('q',99999), + 'q_err':rez.get('q_err',99999) if rez.get('q_err',99999) is not None else 99999, + 'l1/l2':rez.get('l1/l2',99999), + 'l1/l2_err':rez.get('l1/l2_err',99999) if rez.get('l1/l2_err',99999) is not None else 99999, + 'reflect1':rez.get('reflect1',99999) if rez.get('reflect1',99999) is not None else 99999, + 'reflect2':rez.get('reflect2',99999) if rez.get('reflect2',99999) is not None else 99999, + }) + #import pdb; pdb.set_trace() + #print + for task_id in tasks_to_pop: + task_id_list.remove(task_id) + # # # + if ((tc.queue_status()['scheduled'] == 0) and + (tc.queue_status()['pending'] <= 2)): + if dtime_pending_1 is None: + dtime_pending_1 = datetime.datetime.now() + else: + now = datetime.datetime.now() + if ((now - dtime_pending_1) >= datetime.timedelta(seconds=300)): + print("dtime_pending=1 timeout break!") + break + print(tc.queue_status()) + print('Sleep... 20', datetime.datetime.utcnow()) + time.sleep(20) + print('> > > > tc.queue_status():', tc.queue_status()) + + ### IN CASE THERE are still tasks which have not been pulled/retrieved: + for task_id in self.ipy_tasks.task_id_list: + temp = self.ipy_tasks.tc.get_task_result(task_id, block=False) + if temp is None: + continue + temp2 = temp.results + if temp2 is None: + continue + results = temp['out_dict'] + if len(results.get('traceback',"")) > 0: + print("Task Traceback:", results.get('srcid',""), results.get('traceback',"")) + continue + tasks_to_pop.append(task_id) + srcid = results['srcid'] + rez = results['rez'] + alt_rez = results['alt_rez'] + filename = results['filename'] + classid = results['classid'] + fp.write('\n' % \ + (srcid, srcid, srcid, filename, self.class_id_name[classid], pars['fiteb_class_lookup'].get(rez.get('class',None),'unknown'), str(rez.get('okfit',False)), + rez.get('chisq',False) if rez.get('chisq',99999) is not None else 99999, + rez.get('e',False), + rez.get('i',99999), + rez.get('i_err',99999) if rez.get('i_err',99999) is not None else 99999, + rez.get('q',99999), + rez.get('q_err',99999) if rez.get('q_err',99999) is not None else 99999, + rez.get('l1/l2',99999), + rez.get('l1/l2_err',99999) if rez.get('l1/l2_err',99999) is not None else 99999, + str(rez.get('reflect1',99999) if rez.get('reflect1',99999) is not None else 99999), + str(rez.get('reflect2',99999) if rez.get('reflect2',99999) is not None else 99999), + )) + fp.flush() + pickle_dict[srcid] = rez + pickle_dict[srcid].update({'file':filename, + 'tutor_class':self.class_id_name[classid], + 'fiteb_class':pars['fiteb_class_lookup'].get(rez.get('class',None),'unknown'), + 'okfit':rez.get('okfit',False), + 'chisq':rez.get('chisq',False), + 'chisq_percentiles':alt_rez.get('chisq_percentiles',{}), + 'e_percentiles':alt_rez.get('e_percentiles',{}), + 'i_err_percentiles':alt_rez.get('i_err_percentiles',{}), + 'l1/l2_err_percentiles':alt_rez.get('l1/l2_err_percentiles',{}), + 'l1/l2_percentiles':alt_rez.get('l1/l2_percentiles',{}), + 'l1_percentiles':alt_rez.get('l1_percentiles',{}), + 'l2_percentiles':alt_rez.get('l2_percentiles',{}), + 'omega_deg_percentiles':alt_rez.get('omega_deg_percentiles',{}), + 'primary_eclipse_phase_percentiles':alt_rez.get('primary_eclipse_phase_percentiles',{}), + 'period_percentiles':alt_rez.get('period_percentiles',{}), + 'ratiopass_for_percentile':alt_rez.get('ratiopass_for_percentile',{}), + 'vals_for_percentile':alt_rez.get('vals_for_percentile',{}), + 'nmodels':alt_rez.get('nmodels',{}), + 'q_err_percentiles':alt_rez.get('q_err_percentiles',{}), + 'q_percentiles':alt_rez.get('q_percentiles',{}), + 'r1_percentiles':alt_rez.get('r1_percentiles',{}), + 'r2_percentiles':alt_rez.get('r2_percentiles',{}), + 'reflect1_percentiles':alt_rez.get('reflect1_percentiles',{}), + 'reflect2_percentiles':alt_rez.get('reflect2_percentiles',{}), + 'e':rez.get('e',False), + 'i':rez.get('i',99999), + 'i_err':rez.get('i_err',99999), + 'q':rez.get('q',99999), + 'q_err':rez.get('q_err',99999) if rez.get('q_err',99999) is not None else 99999, + 'l1/l2':rez.get('l1/l2',99999), + 'l1/l2_err':rez.get('l1/l2_err',99999) if rez.get('l1/l2_err',99999) is not None else 99999, + 'reflect1':rez.get('reflect1',99999) if rez.get('reflect1',99999) is not None else 99999, + 'reflect2':rez.get('reflect2',99999) if rez.get('reflect2',99999) is not None else 99999, + }) + + + + fp.write("""
source_idplotfileTUTOR classfitEB classokfitchi2eccentricityinclination (deg)mass ratiolum ratioreflect 1reflect 2
%dplot%s%s%s%s%4.2f%4.4f%4.2f +- %4.2f%4.2f +- %4.2f%4.2f +- %4.2f%s%s
%dplot%s%s%s%s%4.2f%4.4f%4.2f +- %4.2f%4.2f +- %4.2f%4.2f +- %4.2f%s%s
+ """) + fp.close() + + fp_pickle = open(pars['pkl_fpath'], 'wb') + cPickle.dump(pickle_dict, fp_pickle, 1) + fp_pickle.close() + + import pdb; pdb.set_trace() + print() + + + + + + +def run_fiteb_generate_html_pkl(pars={}): + """ Run fiteb.py on all ASAS AL / Debosscher sources. + Generate .png plots, .html summary, and dictionary .pkl + """ + pars = copy.copy(pars) + pars.update({ \ + 'al_dirpath':os.path.abspath(os.environ.get("TCP_DIR") + 'Data/allstars'), + 'al_glob_str':'AL_*_*.dat', + 'classids_of_interest':[251,#'x. Beta Lyrae', + 253,#'w. Beta Persei', + 252,#'y. W Ursae Maj.', + ], + 'fittype':3, + 'skip_list':[234996, 164830, 164872, 239309, 216110, 225745], + }) + + #period_select(args.did[0],args.per[0],plot=args.plot,use_xml=True,try_alt=args.alt,dosave=args.savefig,show=args.showfig) + orig_cwd = os.getcwd() + os.chdir(os.path.abspath(os.environ.get("HOME") + '/src/install/jsb_eb_fit')) + + DatabaseUtils = Database_Utils(pars=pars) + rclass_tutorid_lookup = DatabaseUtils.retrieve_tutor_class_ids() + class_id_name = dict([[v,k] for k,v in rclass_tutorid_lookup.items()]) + + fpaths = glob.glob("%s/%s" % (pars['al_dirpath'], pars['al_glob_str'])) + + pickle_dict = {} + + fp = open(os.path.abspath(os.environ.get("HOME") + '/scratch/evaluate_eclipsing_classifs.html'),'w') + fp.write(""" + + """) + if 1: + for fpath in fpaths: + tup_list = loadtxt(fpath, + dtype={'names': ('src_id', 'class_id'), + 'formats': ('i4', 'i4')}, + usecols=(0,1), + unpack=False) + srcid_list = tup_list['src_id'] + classid_list = tup_list['class_id'] + for i, classid in enumerate(classid_list): + if int(srcid_list[i]) != 164594: + continue + if classid in pars['classids_of_interest']: + srcid = srcid_list[i] + if int(srcid) in pars['skip_list']: + continue + print("11111111", srcid) + select_str = "SELECT feat_val FROM source_test_db.feat_values JOIN feat_lookup USING (feat_id) WHERE filter_id=8 AND feat_name='freq1_harmonics_freq_0' AND src_id=%d" % (srcid + 100000000) + DatabaseUtils.tcp_cursor.execute(select_str) + results = DatabaseUtils.tcp_cursor.fetchall() + if len(results) == 0: + raise "Error" + period = 1. / results[0][0] + + #try: + if 1: + (alt_rez, rez) = fiteb.period_select(idd=int(srcid), + per=period, + plot=True, + use_xml=True, + try_alt=True, + dosave=True, + show=False, + fittype=pars['fittype']) #True,#verbose=False) + #except: + # print srcid, period + # import pdb; pdb.set_trace() + # print + # continue + + #pprint.pprint(rez) + fp.write('\n' % \ + (srcid, srcid, srcid, fpath[fpath.rfind('/')+1:], class_id_name[classid], pars['fiteb_class_lookup'].get(rez.get('class',None),'unknown'), str(rez.get('okfit',False)), + rez.get('chisq',False), + rez.get('e',False), + rez.get('i',99999), + rez.get('i_err',99999) if rez.get('i_err',99999) is not None else 99999, + rez.get('q',99999), + rez.get('q_err',99999) if rez.get('q_err',99999) is not None else 99999, + rez.get('l1/l2',99999), + rez.get('l1/l2_err',99999) if rez.get('l1/l2_err',99999) is not None else 99999, + str(rez.get('reflect1',99999) if rez.get('reflect1',99999) is not None else 99999), + str(rez.get('reflect2',99999) if rez.get('reflect2',99999) is not None else 99999), + )) + fp.flush() + pickle_dict[srcid] = rez + pickle_dict[srcid].update({'file':fpath[fpath.rfind('/')+1:], + 'tutor_class':class_id_name[classid], + 'fiteb_class':pars['fiteb_class_lookup'].get(rez.get('class',None),'unknown'), + 'okfit':rez.get('okfit',False), + 'chisq':rez.get('chisq',False), + 'e':rez.get('e',False), + 'i':rez.get('i',99999), + 'i_err':rez.get('i_err',99999), + 'q':rez.get('q',99999), + 'q_err':rez.get('q_err',99999) if rez.get('q_err',99999) is not None else 99999, + 'l1/l2':rez.get('l1/l2',99999), + 'l1/l2_err':rez.get('l1/l2_err',99999) if rez.get('l1/l2_err',99999) is not None else 99999, + 'reflect1':rez.get('reflect1',99999) if rez.get('reflect1',99999) is not None else 99999, + 'reflect2':rez.get('reflect2',99999) if rez.get('reflect2',99999) is not None else 99999, + }) + #import pdb; pdb.set_trace() + #print + + select_str = "select source_id, class_id from sources where project_id = 123" + DatabaseUtils.tutor_cursor.execute(select_str) + results = DatabaseUtils.tutor_cursor.fetchall() + if len(results) == 0: + raise "Error" + for row in results: + (srcid, classid) = row + if classid in pars['classids_of_interest']: + if int(srcid) in pars['skip_list']: + continue + if int(srcid) != 164594: + continue + print("22222222", srcid) + + select_str = "SELECT feat_val FROM source_test_db.feat_values JOIN feat_lookup USING (feat_id) WHERE filter_id=8 AND feat_name='freq1_harmonics_freq_0' AND src_id=%d" % (srcid + 100000000) + DatabaseUtils.tcp_cursor.execute(select_str) + results = DatabaseUtils.tcp_cursor.fetchall() + if len(results) == 0: + raise "Error" + period = 1. / results[0][0] + + (alt_rez, rez) = fiteb.period_select(idd=int(srcid), + per=period, + plot=True, + use_xml=True, + try_alt=True, + dosave=True, + show=False, + fittype=pars['fittype'])#, #True,verbose=False) + + #pprint.pprint(rez) + fp.write('\n' % \ + (srcid, srcid, srcid, "Debosscher", class_id_name[classid], pars['fiteb_class_lookup'].get(rez.get('class',None),'unknown'), str(rez.get('okfit',False)), + rez.get('chisq',False), + rez.get('e',False), + rez.get('i',0), + rez.get('i_err',99999) if rez.get('i_err',99999) is not None else 99999, + rez.get('q',0), + rez.get('q_err',0) if rez.get('q_err',0) is not None else 99999, + rez.get('l1/l2',0), + rez.get('l1/l2_err',0) if rez.get('l1/l2_err',0) is not None else 99999, + str(rez.get('reflect1',99999) if rez.get('reflect1',99999) is not None else 99999), + str(rez.get('reflect2',99999) if rez.get('reflect2',99999) is not None else 99999), + )) + + fp.flush() + pickle_dict[srcid] = rez + pickle_dict[srcid].update({'file':"Debosscher", + 'tutor_class':class_id_name[classid], + 'fiteb_class':pars['fiteb_class_lookup'].get(rez.get('class',None),'unknown'), + 'okfit':rez.get('okfit',False), + 'chisq':rez.get('chisq',False), + 'e':rez.get('e',False), + 'i':rez.get('i',99999), + 'i_err':rez.get('i_err',99999), + 'q':rez.get('q',99999), + 'q_err':rez.get('q_err',99999) if rez.get('q_err',99999) is not None else 99999, + 'l1/l2':rez.get('l1/l2',99999), + 'l1/l2_err':rez.get('l1/l2_err',99999) if rez.get('l1/l2_err',99999) is not None else 99999, + 'reflect1':rez.get('reflect1',99999) if rez.get('reflect1',99999) is not None else 99999, + 'reflect2':rez.get('reflect2',99999) if rez.get('reflect2',99999) is not None else 99999, + }) + #break + + fp.write("""
source_idplotfileTUTOR classfitEB classokfitchi2eccentricityinclination (deg)mass ratiolum ratioreflect 1reflect 2
%dplot%s%s%s%s%4.2f%4.4f%4.2f +- %4.2f%4.2f +- %4.2f%4.2f +- %4.2f%s%s
%dplot%s%s%s%s%4.2f%4.4f%4.2f +- %4.2f%4.2f +- %4.2f%4.2f +- %4.2f%s%s
+ """) + fp.close() + + fp_pickle = open(pars['pkl_fpath'], 'wb') + cPickle.dump(pickle_dict, fp_pickle, 1) + fp_pickle.close() + + + +class FitEB_Param_Analysis(Database_Utils): + """ + """ + def __init__(self, pars={}): + self.pars = pars + self.pars['fiteb_class_lookup_rev'] = {} + for k, v in self.pars['fiteb_class_lookup'].items(): + self.pars['fiteb_class_lookup_rev'][v] = k + self.connect_to_db() + + + def reduce_arff_attrib_list_using_SVMranker(self, arff_attrib_list_orig): + """ Parse the WEKA results file returned from the attribute-select SVM-ranker. + Return; a reduced arff_attrib_list + +* want to parse SVM_ranker file and construct ordered lists for each attribute + - storing merit, rank : +** This code runs at: +** then store attribs in final allowed list which: + - have a merit above a certain value + - OR, that attribute has not been stored yet. + - initially allowing 4/7 percentiles for each attribute to pass + - then iterateing and weeding down again and maybe allowing + 2/7 or 1/7 attribs + +average merit average rank attribute +672 +- 0 1 +- 0 658 detached_e_npass_50 +669.3 +- 1.418 3.7 +- 1.42 616 detached_l2_npass_50 + + """ + attrib_added_dict = {} + arff_attrib_list_out = [] + in_header = True + for line in open(self.pars['SVMrank_results_input_fpath']).readlines(): + if len(line) <= 1: + continue + if "average merit" in line: + in_header = False + continue # skip this line + if in_header: + continue + line_rep = line.replace("+-", "") + elems = line_rep.split() + merit = float(elems[0]) + rank = float(elems[2]) + attrib = elems[5] + i_split = attrib.rfind("_") + attrib_root = attrib[:i_split] + if merit < self.pars['SVMrank_allow_cut']: + #import pdb; pdb.set_trace() + #print + pass # we do not use this percentile attribute + elif attrib_root not in attrib_added_dict: + attrib_added_dict[attrib_root] = [attrib] + arff_attrib_list_out.append(attrib) + elif len(attrib_added_dict[attrib_root]) < self.pars['n_attrib_percentiles_cut']: + attrib_added_dict[attrib_root].append(attrib) + arff_attrib_list_out.append(attrib) + else: + pass + arff_attrib_list_out.sort() + return arff_attrib_list_out + + + def parse_trainset_srcid_list(self): + """ Parse a list file of srcids, classified as one of 3 eclipsing classes. + These are to be used as the training set for the WEKA classifier, and are + added to the .arff file in main(). + """ + srcid_class_dict = {} + lines = open(self.pars['trainset_srcid_list_fpath']).readlines() + for line in lines: + if len(line) <= 1: + continue + elems = line.split() + class_name = self.pars['class_short_lookup'][elems[0]] + srcid = int(elems[1]) + srcid_class_dict[srcid] = class_name + + return srcid_class_dict + + + + def main(self, use_trainset_srcid_list=True): + """ + ** Need to generate these plots for: + - different matched classes: EA, EB, EW + - different surveys: Debosscher, ASAS + - different parameters : chi2, incl_ + ** so we need to plot a historgram for: + - sources we agree on (with some tightness cuts) + - all other sources + + Reference existing liklihood code: + - tutor_database_project_insert.py:plot_aperture_mag_relation() + - get_colors_for_tutor_sources.py:determine_color_param_likelyhoods() + + """ + if use_trainset_srcid_list: + trainset_srcid_class_dict = self.parse_trainset_srcid_list() + + fp_pickle = open(self.pars['pkl_fpath']) + data_dict = cPickle.load(fp_pickle) + fp_pickle.close() + + fp_html = open(os.path.abspath(os.environ.get("HOME") + '/scratch/evaluate_eclipsing_classifs.html'),'w') + fp_html.write(""" + + """) + + attribs_for_percentiles = ['chisq', 'i_err', 'q_err', 'l1/l2_err', 'l1/l2', 'q', 'r1', 'r2', 'reflect1', 'reflect2', 'primary_eclipse_phase', 'period', 'omega_deg', 'l1', 'l2', 'e'] # 'primary_eclipse_phase', 'period', 'omega_deg', 'l1', 'l2', 'e'] + arff_attrib_list_orig = copy.copy(self.pars['analysis_params']) + for a_name in attribs_for_percentiles: + for c_name in self.pars['fiteb_class_lookup'].keys(): + for temp_type in ['perc', 'npass']: + for perc in [5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90]: + temp_param_name = "%s_%s_%s_%d" % (c_name, a_name, temp_type, perc) + if temp_param_name in self.pars['percentile_attrib_skip_list']: + continue + arff_attrib_list_orig.append(temp_param_name) + for a_name in attribs_for_percentiles: + for perc in [5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90]: + temp_param_name = "perc_val_%s_%d" % (a_name, perc) + arff_attrib_list_orig.append(temp_param_name) + for a_name in attribs_for_percentiles: + for perc in [5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90]: + temp_param_name = "perc_pass_%s_%d" % (a_name, perc) + arff_attrib_list_orig.append(temp_param_name) + + + + arff_attrib_list = self.reduce_arff_attrib_list_using_SVMranker(arff_attrib_list_orig) + ###DO THIS ONLY ON FIRST ITERATION, prior to running SVM Ranker: + #arff_attrib_list = arff_attrib_list_orig + + fp_arff = open(os.path.abspath(os.environ.get("HOME") + '/scratch/evaluate_eclipsing_classifs.arff'),'w') + fp_arff.write("@RELATION ts\n") + fp_arff.write("@ATTRIBUTE srcid NUMERIC\n") + for param_name in arff_attrib_list: + fp_arff.write("@ATTRIBUTE %s NUMERIC\n" % (param_name)) + fp_arff.write("@ATTRIBUTE class {'contact','detached','semi-detached'}\n@DATA\n") + + + xlim_dict = {'q_err': [0.0, 50], + 'q': [-5.0, 45], + 'l1/l2_err':[0.0, 10], + 'l1/l2': [0.0, 10], + 'chisq': [0.0, 5], + 'reflect1': [0.0, 0.1], + 'r1': [0.0, 2], + 'r2': [0.0, 2], + 'i_err': [0.0, 100]} + + for class_name in self.pars['fiteb_class_lookup'].values(): + for i_param, param_name in enumerate(self.pars['analysis_params']): + match_vals = [] + mismatch_vals = [] + for srcid, src_dict in data_dict.items(): + ### NOTE: cannot apply these constraints: + ## i : even for detached, i spread for matches is >= mismatch i value spread + ## e : doesnt seem to be related to match / mismatch + ## l1 : doesnt seem to be related to match / mismatch + ## dof : probably related to N-epochs + #if ((class_name == src_dict['tutor_class'] == src_dict['fiteb_class']) and + # (src_dict['chisq'] <= 2.4) and + #if ((class_name == src_dict['fiteb_class']) and + # (src_dict['chisq'] <= 0.91) and + + # # # Below list are ambigious sources which may be classified wrong by Debosscher or Allstars users + """ + if ((class_name == src_dict['tutor_class']) and + (src_dict['tutor_class'] != src_dict['fiteb_class']) + ): + """ + + ## ## ## Prior to reclassification of non tutor_class == fiteb_class sources: + # # # Below list are ambigious sources which may be classified wrong by Debosscher or Allstars users + """ + if ((class_name == src_dict['tutor_class'] == src_dict['fiteb_class']) and + ((srcid not in [ \ + +164798, +164636, +164633, +164612, +164869, +221996, +228218, +164768, +164772, +244713, +164850, +164880, +261921, +237428, +249855, +164624, +164649, +164736, +164775, +164790, +164794, +164814, +164822, +234812, +227324, +219432, +252570, +164828, +164839, +164535, +164581, +164598, +164606, +246724, +249004, +226992, +231228]) or (srcid in [ \ +164843, +164847, +164851, +164854, +217524, +232806, +237022, +222870, +164537, +164512, +164518, +164543, +164776, +164786, +])) + ): + """ + + if use_trainset_srcid_list: + if not srcid in trainset_srcid_class_dict.keys(): + continue # skip this source + source_class = trainset_srcid_class_dict[srcid] + #import pdb; pdb.set_trace() + #print + else: + source_class = src_dict['tutor_class'] + + # # # Below list are ambigious sources which may be classified wrong by Debosscher or Allstars users + if ((class_name == source_class) + ): + if i_param == 0: + fp_html.write('\n' % \ + (srcid, srcid, srcid, src_dict['file'], + source_class, + src_dict['fiteb_class'], + str(src_dict['okfit']), + src_dict['chisq'], + src_dict['e'], + src_dict['i'], + src_dict.get('i_err',99999) if src_dict.get('i_err',99999) is not None else 99999, + src_dict['q'], + src_dict['q_err'], + src_dict['l1/l2'], + src_dict['l1/l2_err'], + str(src_dict['reflect1']), + str(src_dict['reflect2']), + )) + ### KLUDGEY: + val_strs = [] + for param_name2 in arff_attrib_list: + #if ((srcid == 264245) and (param_name2 == 'semi-detached_chisq_perc_5')): + #import pdb; pdb.set_trace() + #print + if '_perc_' in param_name2: + perc_num = int(param_name2[param_name2.rfind('_')+1:]) + cls_name = param_name2[:param_name2.find('_')] + attrib_root = param_name2[param_name2.find('_')+1:param_name2.rfind('_perc_')] + attrib_percentiles_name = attrib_root + "_percentiles" + v = src_dict.get(attrib_percentiles_name,{}).get(cls_name,{}).get(perc_num,{}).get('val_at_perc',"?") + elif '_npass_' in param_name2: + perc_num = int(param_name2[param_name2.rfind('_')+1:]) + cls_name = param_name2[:param_name2.find('_')] + attrib_root = param_name2[param_name2.find('_')+1:param_name2.rfind('_npass_')] + attrib_percentiles_name = attrib_root + "_percentiles" + v = src_dict.get(attrib_percentiles_name,{}).get(cls_name,{}).get(perc_num,{}).get('n_pass',"?") + elif 'perc_val_' in param_name2: + perc_num = int(param_name2[param_name2.rfind('_')+1:]) + attrib_root = param_name2[param_name2.find('perc_val_')+9:param_name2.rfind('_')] + v = src_dict.get('vals_for_percentile',{}).get(attrib_root,{}).get(perc_num,"?") + elif 'perc_pass_' in param_name2: + perc_num = int(param_name2[param_name2.rfind('_')+1:]) + attrib_root = param_name2[param_name2.find('perc_pass_')+10:param_name2.rfind('_')] + v = src_dict.get('ratiopass_for_percentile',{}).get(attrib_root,{}).get(perc_num,"?") + else: + v = src_dict.get(param_name2, "?") if src_dict.get(param_name2, "?") is not None else "?" + if numpy.isnan(v): + v = "?" # KLUDGE + if v == 999999: + print('INT 999999') + v = "?" # KLUDGE: catches this N/A value which is generated in fiteb.py::period_select() when EB.run().outrez{} is missing some physical parameters + elif v == 99999: + print('INT 99999') + v = "?" # KLUDGE: catches this N/A value which is generated in fiteb.py::period_select() when EB.run().outrez{} is missing some physical parameters + val_strs.append(str(v)) + a_str = "%d,%s,'%s'\n" % (srcid, ",".join(val_strs), self.pars['fiteb_class_lookup_rev'][class_name]) + fp_arff.write(a_str) + + if 'chi2_perc' in param_name: + perc_num = int(param_name[param_name.rfind('_')+1:]) + cls_name = self.pars['fiteb_class_lookup_rev'][class_name] + if cls_name in src_dict['chisq_percentiles']: + v = src_dict['chisq_percentiles'][cls_name][perc_num]['val_at_perc'] + else: + v = None + elif 'chi2_npass' in param_name: + perc_num = int(param_name[param_name.rfind('_')+1:]) + cls_name = self.pars['fiteb_class_lookup_rev'][class_name] + if cls_name in src_dict['chisq_percentiles']: + v = src_dict['chisq_percentiles'][cls_name][perc_num]['n_pass'] + else: + v = None + else: + v = src_dict[param_name] + if v != None: + match_vals.append(v) + print(" MATCH plot:%s TUTOR:%s fiteb:%s chi2:%f" % (class_name, source_class, src_dict['fiteb_class'], src_dict['chisq'])) + elif ((class_name == source_class) and + (source_class != src_dict['fiteb_class']) and + (srcid in [260456, 220490, 217279, 222948, 219432, 216954, 251878, 226123, 247767, 237081, 235730, 250580, 231228, 222870, 246079, 164512, 164525, 164531, 164533, 164541, 164542, 164543, 164544, 164558, 164556, 164562, 164577, 164579, 164583, 164588, 164596, 164592, 164603, 164611, 164613, 164625, 164628, 164643, 164651, 164667, 164669, 164674, 164683, 164685, 164684, 164690, 164697, 164704, 164703, 164701, 164712, 164707, 164710, 164725, 164744, 164766, 164771, 164778, 164797, 164833, 164867, 164876, 164881]) + ): + if 'chi2_perc' in param_name: + perc_num = int(param_name[param_name.rfind('_')+1:]) + cls_name = self.pars['fiteb_class_lookup_rev'][class_name] + if cls_name in src_dict['chisq_percentiles']: + v = src_dict['chisq_percentiles'][cls_name][perc_num]['val_at_perc'] + else: + v = None + elif 'chi2_npass' in param_name: + perc_num = int(param_name[param_name.rfind('_')+1:]) + cls_name = self.pars['fiteb_class_lookup_rev'][class_name] + if cls_name in src_dict['chisq_percentiles']: + v = src_dict['chisq_percentiles'][cls_name][perc_num]['n_pass'] + else: + v = None + else: + v = src_dict.get(param_name,88888) + if v != None: + mismatch_vals.append(v) + print("MISMATCH plot:%s TUTOR:%s fiteb:%s chi2:%f" % (class_name, source_class, src_dict['fiteb_class'], src_dict['chisq'] if src_dict.get('chisq',99999) is not None else 99999)) + + #mags = vals + #fits = norm.fit(mags) + #dist = norm(fits) + #for m in mags: + # probs.append(dist.pdf(m)[0]) # * len(param_list)/float(len(mag))) + + if (len(xlim_dict.get(param_name,[])) == 0): + minmax_list = [] + for a_list in [match_vals, mismatch_vals]: + if len(a_list) > 0: + minmax_list.extend([min(a_list), max(a_list)]) + x_range = (min(minmax_list), max(minmax_list)) + #if (len(match_vals) > 0): + # x_range = (min(match_vals), max(match_vals)) + #else: + # x_range = (min(mismatch_vals), max(mismatch_vals)) + else: + x_range = (xlim_dict[param_name][0], xlim_dict[param_name][1]) + + pyplot.hist(mismatch_vals, bins=50, normed=False, facecolor='b', alpha=0.3, range=x_range) + pyplot.hist(match_vals, bins=50, normed=False, facecolor='r', alpha=0.3, range=x_range) + #pyplot.plot(mags, probs, 'ro', ms=3) + + title_str = '%s %s' % (class_name, param_name) + pyplot.title(title_str) + fpath = "/tmp/evaluate_eclipsing_classifs__%s.png" % (title_str.replace(' ','_').replace('/','_')) + pyplot.savefig(fpath) + #os.system('eog %s ' % (fpath)) + #pyplot.show() + + print(param_name) + pyplot.clf() + + + fp_html.write("""
source_idplotfileTUTOR classfitEB classokfitchi2eccentricityinclination (deg)mass ratiolum ratioreflect 1reflect 2
%dplot%s%s%s%s%4.2f%4.4f%4.2f +- %4.2f%4.2f +- %4.2f%4.2f +- %4.2f%s%s
+ """) + fp_html.close() + fp_arff.close() + os.system('eog /tmp/evaluate_eclipsing_classifs*png &') + #import pdb; pdb.set_trace() + #print + +class Classify_And_Summarize: + """ Classify and generate summary html, as well as classification distribution summary file. + """ + def __init__(self, pars={}): + self.DatabaseUtils = Database_Utils(pars=pars) + + + def classify_and_summarize(self): + """ Classify and generate summary html, as well as classification distribution summary file. + + ### ### ### + # 1st iteration, producing initial classification distribution with .model generated using + # dstarr's hand selected training-set. + p = {'test_arff_withsrcid':'/home/pteluser/scratch/evaluate_eclipsing_classifs__2of11perc__tutor_fiteb_mismatch_testset2__noclassattribs.arff', + 'test_arff_nosrcid':"/home/pteluser/scratch/evaluate_eclipsing_classifs__2of11perc__tutor_fiteb_mismatch_testset2__noclassattribs__nosrcid.arff", + 'train_arff_withsrcid':'/home/pteluser/scratch/evaluate_eclipsing_classifs__2of11perc__trainset2__noclassattribs.arff', + 'train_model':"/home/pteluser/scratch/evaluate_eclipsing_classifs__RandForest_2of11__nonclassattribs__trainset2_crossvalid.model", + 'comment_file':"/home/pteluser/scratch/evaluate_eclipsing_classifs.comments", + 'classif_distrib_fpath':'/home/pteluser/scratch/evaluate_eclipsing_classifs.class_distrib', + 'classif_distrib_html_fpath':'/home/pteluser/scratch/evaluate_eclipsing_classifs_distribution_summary.html', + 'classes':['contact','detached','semi-detached'], # order used in the .arff / classifier distrib output + 'distribclass_to_classes_lookup':{ + 'detached':'detached', + 'semi-det':'semi-detached', + 'contact':'contact', + }, + } + ### ### ### + # 2nd iteration, using 2 corrberating classifications in papers for a source to be used in the training set. + # Maybe 5-10 sources which were not in trainingset or in the wrong trainingset classes. + ### ### ### + p = {'test_arff_withsrcid':'/home/pteluser/scratch/evaluate_eclipsing_classifs__2of11perc__tutor_fiteb_mismatch_testset2__noclassattribs.arff', + 'test_arff_nosrcid':"/home/pteluser/scratch/evaluate_eclipsing_classifs__2of11perc__tutor_fiteb_mismatch_testset2__noclassattribs__nosrcid.arff", + 'train_arff_withsrcid':'/home/pteluser/scratch/evaluate_eclipsing_classifs__2of11perc__trainset3.arff', + 'train_model':"/home/pteluser/scratch/evaluate_eclipsing_classifs__RandForest_2of11__trainset3_crossvalid.model", + 'comment_file':"/home/pteluser/scratch/evaluate_eclipsing_classifs.comments", + 'classif_distrib_fpath':'/home/pteluser/scratch/evaluate_eclipsing_classifs.class_distrib', + 'classif_distrib_html_fpath':'/home/pteluser/scratch/evaluate_eclipsing_classifs_distribution_summary.html', + 'classes':['contact','detached','semi-detached'], # order used in the .arff / classifier distrib output + 'distribclass_to_classes_lookup':{ + 'detached':'detached', + 'semi-det':'semi-detached', + 'contact':'contact', + }, + } + + ### ### ### + Fourth iteration (there was no third): + p = {'test_arff_withsrcid':'/home/pteluser/scratch/evaluate_eclipsing_classifs__2of11perc__tutor_fiteb_mismatch_testset2__noclassattribs.arff', + 'test_arff_nosrcid':"/home/pteluser/scratch/evaluate_eclipsing_classifs__2of11perc__tutor_fiteb_mismatch_testset2__noclassattribs__nosrcid.arff", + 'train_arff_withsrcid':'/home/pteluser/scratch/evaluate_eclipsing_classifs__2of11perc__trainset4.arff', + 'train_model':"/home/pteluser/scratch/evaluate_eclipsing_classifs__RandForest_2of11__trainset4_crossvalid.model", + 'comment_file':"/home/pteluser/scratch/evaluate_eclipsing_classifs.comments", + 'classif_distrib_fpath':'/home/pteluser/scratch/evaluate_eclipsing_classifs.class_distrib', + 'classif_distrib_html_fpath':'/home/pteluser/scratch/evaluate_eclipsing_classifs_distribution_summary.html', + 'classes':['contact','detached','semi-detached'], # order used in the .arff / classifier distrib output + 'distribclass_to_classes_lookup':{ + 'detached':'detached', + 'semi-det':'semi-detached', + 'contact':'contact', + }, + } + ### ### ### + 5th iteration, re-analysis of attributes using SVM Ranker + p = {'test_arff_withsrcid':'/home/pteluser/scratch/evaluate_eclipsing_classifs__allsrcs__attribsfrom_testset5redone.arff', + 'test_arff_nosrcid':"/home/pteluser/scratch/evaluate_eclipsing_classifs__allsrcs__attribsfrom_testset5redone__nosrcid.arff", + 'train_arff_withsrcid':'/home/pteluser/scratch/evaluate_eclipsing_classifs__2of11perc__trainset5__redoneattribs.arff', + 'train_model':"/home/pteluser/scratch/evaluate_eclipsing_classifs__RandForest_2of11__trainset5__redoneattribs__crossvalid.model", + 'comment_file':"/home/pteluser/scratch/evaluate_eclipsing_classifs.comments", + 'classif_distrib_fpath':'/home/pteluser/scratch/evaluate_eclipsing_classifs.class_distrib', # for output + 'classif_distrib_html_fpath':'/home/pteluser/scratch/evaluate_eclipsing_classifs_distribution_summary.html', # for output + 'classes':['contact','detached','semi-detached'], # order used in the .arff / classifier distrib output + 'distribclass_to_classes_lookup':{ + 'detached':'detached', + 'semi-det':'semi-detached', + 'contact':'contact', + }, + } + + ### ### ### + + """ + p = {'test_arff_withsrcid':'/home/pteluser/scratch/evaluate_eclipsing_classifs__allsrcs__attribsfrom_testset6.arff', + 'test_arff_nosrcid':"/home/pteluser/scratch/evaluate_eclipsing_classifs__allsrcs__attribsfrom_testset6__nosrcid.arff", + 'train_arff_withsrcid':'/home/pteluser/scratch/evaluate_eclipsing_classifs__2of11perc__trainset6.arff', + 'train_model':"/home/pteluser/scratch/evaluate_eclipsing_classifs__RandForest_2of11__trainset6_crossvalid.model", + 'comment_file':"/home/pteluser/scratch/evaluate_eclipsing_classifs.comments", + 'classif_distrib_fpath':'/home/pteluser/scratch/evaluate_eclipsing_classifs.class_distrib', # for output + 'classif_distrib_html_fpath':'/home/pteluser/scratch/evaluate_eclipsing_classifs_distribution_summary.html', # for output + 'classes':['contact','detached','semi-detached'], # order used in the .arff / classifier distrib output + 'distribclass_to_classes_lookup':{ + 'detached':'detached', + 'semi-det':'semi-detached', + 'contact':'contact', + }, + } + + srcid_comment = {} + for line in open(p['comment_file']).readlines(): + if len(line) <= 1: + continue + srcid = int(line[:line.find(' ')]) + comment = line[line.find(' '):] + srcid_comment[srcid] = comment.strip() + + + testing_srcid_list = [] + found_data = False + for line in open(p['test_arff_withsrcid']).readlines(): + if found_data: + srcid = int(line[:line.find(',')]) + testing_srcid_list.append(srcid) + # NOTE: I dont think we need to parse the class, snce this is contained in the class distrib output + elif "@DATA" in line: + found_data = True + + training_srcid_list = [] + found_data = False + for line in open(p['train_arff_withsrcid']).readlines(): + if found_data: + srcid = int(line[:line.find(',')]) + training_srcid_list.append(srcid) + # NOTE: I dont think we need to parse the class, snce this is contained in the class distrib output + elif "@DATA" in line: + found_data = True + + ### Apply classifier to test-set + sys_str = "java weka.classifiers.trees.RandomForest -T %s -l %s -p 1 -distribution > %s" % (p['test_arff_nosrcid'], p['train_model'], p['classif_distrib_fpath']) + os.system(sys_str) + time.sleep(2) + + #training_srcid_list = [] + found_header = False + test_srcid_classifs = {} + test_class_prob_srcid_tups = [] + i_src = 0 + for line in open(p['classif_distrib_fpath']).readlines(): + if found_header and (len(line) > 1): + elems = line[:i_error].split() + tutor_class = p['distribclass_to_classes_lookup'][elems[1][elems[1].find(':') +1:]] + pred_class = elems[2][elems[2].find(':') +1:] + + srcid = testing_srcid_list[i_src] + elems = line[i_error + 5:].split() + prob_elems = elems[0].split(',') + class_probs = [] + for i_class, prob_str in enumerate(prob_elems): + if '*' in prob_str: + i_chosen_class = i_class + prob_num = float(prob_str.replace("*",'')) + class_probs.append(prob_num) + test_srcid_classifs[srcid] = {'primary_class_i':i_chosen_class, + 'class_probs':class_probs, + 'tutor_class':tutor_class, + } + ### Here we append (class_name, primary_classprob, srcid) to a list for sorting: + test_class_prob_srcid_tups.append((p['classes'][i_chosen_class], class_probs[i_chosen_class], srcid)) + i_src += 1 + + elif "inst#" in line: + found_header = True + i_error = line.index('error') + + fp = open(p['classif_distrib_html_fpath'], 'w') + fp.write(""" + + + + + + + + + + + + + + """) + test_class_prob_srcid_tups.sort(reverse=True) + #import pdb; pdb.set_trace() + #print + for (pred_class, pred_prob, srcid) in test_class_prob_srcid_tups: + tutor_matches_pred_str = "" + if pred_class == test_srcid_classifs[srcid]['tutor_class']: + tutor_matches_pred_str = "match" + + in_trainingset_str = "" + if srcid in training_srcid_list: + in_trainingset_str = "train" + + comment_str = "" + if srcid in srcid_comment.keys(): + comment_str = srcid_comment[srcid] + + a_str = """ + + + + + + + + + + + \n""" % ( \ + srcid, + srcid, + srcid, + tutor_matches_pred_str, + in_trainingset_str, + test_srcid_classifs[srcid]['tutor_class'], + p['classes'][test_srcid_classifs[srcid]['primary_class_i']], + test_srcid_classifs[srcid]['class_probs'][test_srcid_classifs[srcid]['primary_class_i']], + test_srcid_classifs[srcid]['class_probs'][0], + test_srcid_classifs[srcid]['class_probs'][1], + test_srcid_classifs[srcid]['class_probs'][2], + comment_str, + ) + #import pdb; pdb.set_trace() + #print + fp.write(a_str) + + + select_str = "select source_ra, source_dec, source_name from sources where source_id = %d" % (srcid) + self.DatabaseUtils.tutor_cursor.execute(select_str) + results = self.DatabaseUtils.tutor_cursor.fetchall() + if len(results) == 0: + raise "Error" + ra = results[0][0] + decl = results[0][1] + source_name = results[0][2] + # ASAS only: if srcid >= 215153: + if ((test_srcid_classifs[srcid]['tutor_class'] != p['classes'][test_srcid_classifs[srcid]['primary_class_i']]) and + ((test_srcid_classifs[srcid]['class_probs'][0] >= 0.8) or + (test_srcid_classifs[srcid]['class_probs'][1] >= 0.8) or + (test_srcid_classifs[srcid]['class_probs'][2] >= 0.8))): + print("%d %s %12.6lf %12.6lf %s %0.7s %0.7s %0.2f %0.2f %0.2f %s" % ( \ + srcid, + source_name, + ra, + decl, + in_trainingset_str if in_trainingset_str == "train" else " ", + test_srcid_classifs[srcid]['tutor_class'], + p['classes'][test_srcid_classifs[srcid]['primary_class_i']], + test_srcid_classifs[srcid]['class_probs'][0], #test_srcid_classifs[srcid]['class_probs'][test_srcid_classifs[srcid]['primary_class_i']], + test_srcid_classifs[srcid]['class_probs'][1], + test_srcid_classifs[srcid]['class_probs'][2], + comment_str, + )) + #import pdb; pdb.set_trace() + #print + + fp.write("""
source_idplotMatchesTrainSetTUTOR/ALLStarsPredictedPred. Prob       contactdetachedsemi-detCOMMENT
%dplot%s%s%s%s%0.2f%0.2f%0.2f%0.2f%s
+ """) + fp.close() + + + +def generate_jktebop_templates(pars={}): + """ + ### generate additional JKTEBOP template files, to better explore param space + # when trying to fit an eclipsing model to a lightcurve. + """ + if len(pars) == 0: + pars = {'template_dirpath':os.path.abspath(os.environ.get("HOME") + '/src/install/jsb_eb_fit/Templates'), + 'reference_template_fpath':os.path.abspath(os.environ.get("HOME") + '/src/install/jsb_eb_fit/Templates/eb.4.template'),#'/home/pteluser/src/install/jsb_eb_fit/Templates/eb.3.template', + 'fname_i_start':2, + 'tweak_params':{'Orbital inclination':{'i_low':0, + 'i_high':4, + 'min':65., + 'max':90., + 'n_samples':5}, + 'Mass ratio of system':{'i_low':5, + 'i_high':9, + 'min':-0.5, + 'max':2.0, + 'n_samples':5}, + 'Ratio of the radii':{'i_low':5, + 'i_high':9, + 'min':0.1, + 'max':3.0, + 'n_samples':5}, + }, + } + lines = open(pars['reference_template_fpath']).readlines() + + i_template = pars['fname_i_start'] + + pname_1 = 'Orbital inclination' + pdict_1 = pars['tweak_params'][pname_1] + val_arr_1 = numpy.linspace(pdict_1['min'], pdict_1['max'], pdict_1['n_samples']) + for val_1 in list(val_arr_1): + new_lines = [] + for i_line, line in enumerate(lines): + if pname_1 in line: + val_1_str = "%0.1f" % (val_1) + new_line = "%s%s%s" % (line[:pdict_1["i_low"]], val_1_str, line[pdict_1["i_low"] + len(val_1_str):]) + print(new_line) + new_lines.append(new_line) + else: + new_lines.append(line) + + lines = new_lines + + pname_2 = 'Mass ratio of system' + pdict_2 = pars['tweak_params'][pname_2] + val_arr_2 = numpy.linspace(pdict_2['min'], pdict_2['max'], pdict_2['n_samples']) + for val_2 in list(val_arr_2): + new_lines = [] + for i_line, line in enumerate(lines): + if pname_2 in line: + val_2_str = "%0.1f" % (val_2) + new_line = "%s%s%s" % (line[:pdict_2["i_low"]], val_2_str, line[pdict_2["i_low"] + len(val_2_str):]) + print(new_line) + new_lines.append(new_line) + else: + new_lines.append(line) + + lines = new_lines + + pname_3 = 'Ratio of the radii' + pdict_3 = pars['tweak_params'][pname_3] + val_arr_3 = numpy.linspace(pdict_3['min'], pdict_3['max'], pdict_3['n_samples']) + for val_3 in list(val_arr_3): + new_lines = [] + for i_line, line in enumerate(lines): + if pname_3 in line: + val_3_str = "%0.1f" % (val_3) + new_line = "%s%s%s" % (line[:pdict_3["i_low"]], val_3_str, line[pdict_3["i_low"] + len(val_3_str):]) + print(new_line) + new_lines.append(new_line) + else: + new_lines.append(line) + + + fpath = "%s/eb.4.alt%d.template" % (pars['template_dirpath'], i_template) + #fpath = "%s/eb.3.alt%d.template" % (pars['template_dirpath'], i_template) + if os.path.exists(fpath): + os.system("rm " + fpath) + fp = open(fpath, 'w') + fp.writelines(new_lines) + fp.close() + i_template += 1 + #import pdb; pdb.set_trace() + #print + + """ + # KLUDGEY: doesnt iterate perfectly over MxN combinations + i_template = pars['fname_i_start'] + for param_str, p_dict in pars['tweak_params'].items(): + val_arr = numpy.linspace(p_dict['min'], p_dict['max'], p_dict['n_samples']) + for val in list(val_arr): + new_lines = [] + for i_line, line in enumerate(lines): + if param_str in line: + new_line = "%s%s%s" % (line[:p_dict["i_low"]], str(val), line[p_dict["i_high"]:]) + #import pdb; pdb.set_trace() + #print + new_lines.append(new_line) + else: + new_lines.append(line) + fpath = "%s/eb.3.alt%d.template" % (pars['template_dirpath'], i_template) + if os.path.exists(fpath): + os.system("rm " + fpath) + fp = open(fpath, 'w') + fp.writelines(new_lines) + fp.close() + i_template += 1 + #import pdb; pdb.set_trace() + #print + lines = new_lines + + """ + + + +if __name__ == '__main__': + pars = {'tutor_hostname':'192.168.1.103', + 'tutor_username':'dstarr', #'tutor', # guest + 'tutor_password':'ilove2mass', #'iamaguest', + 'tutor_database':'tutor', + 'tutor_port':3306, #33306, + 'tcp_hostname':'192.168.1.25', + 'tcp_username':'pteluser', + 'tcp_port': 3306, #23306, + 'tcp_database':'source_test_db', + 'pkl_fpath':os.path.abspath(os.environ.get("HOME") + '/scratch/evaluate_eclipsing_classifs_using_fiteb__dict.pkl'), + 'class_short_lookup':{'EW':'y. W Ursae Maj.', + 'EA':'w. Beta Persei', + 'EB':'x. Beta Lyrae'}, + 'fiteb_class_lookup':{'contact':'y. W Ursae Maj.', + 'detached':'w. Beta Persei', + 'semi-detached':'x. Beta Lyrae'}, + 'analysis_params':['chisq', 'i_err', 'q_err', 'l1/l2_err', 'l1/l2', 'q', 'r1', 'r2', 'reflect1', + 'reflect2', 'primary_eclipse_phase', 'period', 'omega_deg', 'l1', 'e', + ], + 'al_dirpath':os.path.abspath(os.environ.get("TCP_DIR") + 'Data/allstars'), + 'al_glob_str':'AL_*_*.dat', + 'classids_of_interest':[251,#'x. Beta Lyrae', + 253,#'w. Beta Persei', + 252,#'y. W Ursae Maj.', + ], + 'fittype':3, + 'skip_list':[234996, 164830, 164872, 239309, 216110, 225745], + 'percentile_attrib_skip_list':[], + 'SVMrank_results_input_fpath':"/home/pteluser/scratch/evaluate_eclipsing_classifs__SVMranker_3of11perc__trainset6.results", + 'SVMrank_allow_cut':0.1, #30.2,# SKIP attrib when: merit < self.pars['SVMrank_allow_cut'] + 'n_attrib_percentiles_cut':2, #N of percentiles to allow, of 7 percentiles: [5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90] + 'trainset_srcid_list_fpath':'/home/pteluser/scratch/evaluate_eclipsing_classifs__trainset_srcid.list__subset6', + } + + if 0: + ### For generating pickle & html files: + ### IPython-Parallel mode: + RunFitEBParallel = Run_FitEB_Parallel() + RunFitEBParallel.ipython_master() + + ### Single mode (for only for debugging a srcid now): + #run_fiteb_generate_html_pkl(pars=pars) + import pdb; pdb.set_trace() + print() + + if 0: + ### For generating parameter liklihood plots in order to choose param cuts. + new_pars = {} + new_pars.update(pars) + fpa = FitEB_Param_Analysis(pars=new_pars) + fpa.main(use_trainset_srcid_list=False) # use_trainset_srcid_list=True # generate small train-set arff; =False: generate arff with all eclip sources + + if 1: + ### Generate classification and a summary HTML + ### - using an existing WEKA .model, test/train .arff + cas = Classify_And_Summarize(pars=pars) + cas.classify_and_summarize() + + if 0: + ### generate additional JKTEBOP template files, to better explore param space + # when trying to fit an eclipsing model to a lightcurve. + generate_jktebop_templates(pars=pars) diff --git a/mltsp/TCP/Algorithms/example_1_ajax_retrieves_json_from_phpmysql.html b/mltsp/TCP/Algorithms/example_1_ajax_retrieves_json_from_phpmysql.html new file mode 100755 index 00000000..b93a7a34 --- /dev/null +++ b/mltsp/TCP/Algorithms/example_1_ajax_retrieves_json_from_phpmysql.html @@ -0,0 +1,30 @@ + + + + + + + +
+ + + + diff --git a/mltsp/TCP/Algorithms/example_1_givenpost_mysqlqueries_returnsjson.php b/mltsp/TCP/Algorithms/example_1_givenpost_mysqlqueries_returnsjson.php new file mode 100755 index 00000000..932defb9 --- /dev/null +++ b/mltsp/TCP/Algorithms/example_1_givenpost_mysqlqueries_returnsjson.php @@ -0,0 +1,55 @@ +getMessage () . "\n"); + + +## Get the column names from table: +$select_str = "DESCRIBE source_test_db.caltech_classif_summary"; +$result =& $mdb2->query($select_str); +if (PEAR::isError($result)) { + $result =& $mdb2->query($select_str); + die($result->getMessage() . '
' . $result->getDebugInfo()); +} + +$col_names = array(); +$table_data = array(); +while ($row_array = $result->fetchRow()) { + $col_names[] = $row_array[0]; + $table_data[] = array(); +} +$result->free(); + +### Get the table values: +$select_str = "SELECT * FROM source_test_db.caltech_classif_summary"; +$result =& $mdb2->query($select_str); +if (PEAR::isError($result)) { + $result =& $mdb2->query($select_str); + die($result->getMessage() . '
' . $result->getDebugInfo()); +} + +while ($row_array = $result->fetchRow()) { + $i_col = 0; + for ($i=0; $ifree(); + +$output_array = array("table_data" => $table_data, "col_names" => $col_names); +echo json_encode($output_array); + +?> diff --git a/mltsp/TCP/Algorithms/example_parse_vosourcexml.py b/mltsp/TCP/Algorithms/example_parse_vosourcexml.py new file mode 100644 index 00000000..e1756d2b --- /dev/null +++ b/mltsp/TCP/Algorithms/example_parse_vosourcexml.py @@ -0,0 +1,165 @@ +#!/usr/bin/env python +""" This example script parses VOSource style XML. + These XML may be generated TUTOR/DotAstro.org, + or XML generated / used by TCP related programs. + +NOTE: This requires a couple Python packages: + + python-xml # Allows: import xml.etree.cElementTree + +NOTE: This also requires reference to a PATH which contains required Python modules. + - If one svn checks-out TCP, then just add a .bashrc environment variable pointing to the TCP directory: + + export TCP_DIR=/home/pteluser/src/TCP/ + + + +""" +from __future__ import print_function +import os, sys +import pprint + +sys.path.append(os.environ.get('TCP_DIR') + '/Software/feature_extract/Code/extractors') +import mlens3 + +sys.path.append(os.environ.get('TCP_DIR') + '/Software/feature_extract/MLData') +import arffify + +sys.path.append(os.environ.get('TCP_DIR') + '/Software/feature_extract/Code/extractors') +import db_importer + +if __name__ == '__main__': + + + #xml_fpath = "/home/pteluser/scratch/vosource_xml_writedir/100015915.xml" + xml_fpath = "/home/pteluser/Dropbox/work/403.47491.770.xml" + dbi_src = db_importer.Source(xml_handle=xml_fpath)#make_dict_if_given_xml=False + import pdb; pdb.set_trace() + + ##### NOTE: + #print dbi_src.x_sdict.keys() + #['src_id', 'ra', 'dec', 'dec_rms', 'class', 'ra_rms', 'ts'] + #print dbi_src.x_sdict['ts'].keys() + #['B:table13168'] + #print dbi_src.x_sdict['ts']['B:table13168'].keys() + #['ucds', 'm_err', 'ordered_column_names', 'm', 'IDs', 'units', 't', 'limitmags'] + #print dbi_src.x_sdict['ts']['B:table13168']['t'] + #[1165.7560000000001, 1166.7670000000001, .... ] + + ############ + # OBSOLETE: + ############ + + a = arffify.Maker(search=[], skip_class=False, local_xmls=True, convert_class_abrvs_to_names=False, flag_retrieve_class_abrvs_from_TUTOR=False, dorun=False) + a.pars['skip_sci_class_list'] = [] + class_features_dict = a.generate_arff_line_for_vosourcexml(xml_fpath=xml_fpath) + + + pprint.pprint(class_features_dict) + """ +{'class': 'Variable Stars', + 'features': {('amplitude', 'float'): 0.025850000000000001, + ('beyond1std', 'float'): 0.0, + ('flux_percentile_ratio_mid20', 'float'): 0.23181637707700001, + ('flux_percentile_ratio_mid35', 'float'): 0.43537035954600001, + ('flux_percentile_ratio_mid50', 'float'): 0.60144542283299995, + ('flux_percentile_ratio_mid65', 'float'): 0.72181559826399999, + ('flux_percentile_ratio_mid80', 'float'): 0.88061936278599995, + ('freq1_harmonics_amplitude_0', 'string'): None, + ('freq1_harmonics_freq_0', 'string'): None, + ('freq1_harmonics_moments_0', 'string'): None, + ('freq1_harmonics_peak2peak_flux', 'string'): None, + ('freq1_harmonics_rel_phase_0', 'string'): None, + ('freq2_harmonics_amplitude_0', 'string'): None, + ('freq2_harmonics_freq_0', 'string'): None, + ('freq2_harmonics_moments_0', 'string'): None, + ('freq2_harmonics_rel_phase_0', 'string'): None, + ('freq3_harmonics_amplitude_0', 'string'): None, + ('freq3_harmonics_freq_0', 'string'): None, + ('freq3_harmonics_moments_0', 'string'): None, + ('freq3_harmonics_rel_phase_0', 'string'): None, + ('freq_harmonics_offset', 'string'): None, + ('freq_nharm', 'string'): None, + ('freq_signif', 'string'): None, + ('freq_y_offset', 'string'): None, + ('max_slope', 'float'): 4.0, + ('median_buffer_range_percentage', 'float'): 0.25, + ('pair_slope_trend', 'float'): 0.0666666666667, + ('percent_amplitude', 'float'): 34.639465635699999, + ('percent_difference_flux_percentile', 'float'): 0.0384360559729, + ('sdss_petro_radius_g_err', 'string'): None, + ('sdss_photo_z_pztype', 'string'): None, + ('sdss_rosat_flux_in_mJy', 'string'): None, + ('sdss_rosat_log_xray_luminosity', 'string'): None, + ('skew', 'float'): -0.21906825572300001, + ('std', 'float'): 0.0138923614089, + ('ws_variability_bv', 'string'): None, + ('ws_variability_gr', 'string'): None, + ('ws_variability_iz', 'string'): None, + ('ws_variability_ri', 'string'): None, + ('ws_variability_ru', 'string'): None, + ('ws_variability_self', 'float'): 994999.20798299997, + ('ws_variability_ug', 'string'): None}, + 'file': '/home/pteluser/scratch/vosource_xml_writedir/100015915.xml', + 'num': ''} +""" + + d = mlens3.EventData(xml_fpath) + ### NOTE: "d" is an object which contains xmldict.XmlDictObject components among other things. + + + ts_dict = {} + for filter_name, elem_list in d.data['ts'].items(): + ts_dict[filter_name] = {} + for xml_elem in elem_list: + ts_dict[filter_name][xml_elem['name']] = xml_elem['val'] + + pprint.pprint(ts_dict) + """ +{'I:table6235': {'limit': array(['false', 'false', ...], dtype='|S5'), + 'm': array([ 16.2127, 16.2109, ...]), + 'm_err': array([ 0.0061, 0.0081, ...]), + 't': array([ 49086.86378, 49087.78441, ...])}, + 'V:table7886': {'limit': ....}} + """ + + + + + if False: + + ##### The following just kicks us into a PDB prompt rather than exiting, which allows interactive work with variables. + ##### Type "?" for a list of available commands, although print and most Python syntax works + import pdb; pdb.set_trace() + + + + ##### This gives some examples of access to "d"'s XmlDictObject components: + print(d.data['ts'].keys()) + #['I:table6235', 'V:table7886'] + print(d.data['ts']['I:table6235'][2]['name']) + #m_err + + print(d.feat_dict.keys()) + #['I:table6235', 'multiband', 'V:table7886'] + + print(d.feat_dict['I:table6235'].keys()) + #['ratio32', 'ratio31', 'freq3_harmonics_amplitude_error_0', 'freq1_harmonics_peak2peak_flux', 'beyond1std', 'freq1_harmonics_rel_phase_0', 'max_slope', .... ] + + import pprint + pprint.pprint(d.feat_dict['I:table6235']['amplitude']) + #{'description': 'amplitude', + # 'err': {'_text': 'unknown', 'datatype': 'string'}, + # 'filter': {'_text': 'I:table6235', 'datatype': 'string'}, + # 'name': {'_text': 'amplitude', 'class': 'timeseries'}, + # 'origin': {'code_output': {'_text': '"0.02585"', 'datatype': 'string'}, + # 'code_ver': 'db_importer.py 1572 2010-07-06 11:40:46Z pteluser', + # 'description': ' Returns the half the difference between the maximum magnitude and the minimum magnitude. Note this will also work for data that is given in terms of flux. So in a sense, it__SINGLEQUOTE__s a volitile feature across different datasets. Suggestion: use the new percent_amplitude below instead. Turn this one off__qmark__ ', + # 't_gen': {'_text': '2010-07-09T23:54:51.292070', + # 'ucd': 'time.epoch'}}, + # 'val': {'_text': '0.02585', 'datatype': 'float', 'is_reliable': 'True'}} + + + + + diff --git a/mltsp/TCP/Algorithms/fitcurve/av_lombtest_tcp_source(32data).png b/mltsp/TCP/Algorithms/fitcurve/av_lombtest_tcp_source(32data).png new file mode 100755 index 00000000..7cf80140 Binary files /dev/null and b/mltsp/TCP/Algorithms/fitcurve/av_lombtest_tcp_source(32data).png differ diff --git a/mltsp/TCP/Algorithms/fitcurve/bad_fit.png b/mltsp/TCP/Algorithms/fitcurve/bad_fit.png new file mode 100755 index 00000000..c4fb1dbf Binary files /dev/null and b/mltsp/TCP/Algorithms/fitcurve/bad_fit.png differ diff --git a/mltsp/TCP/Algorithms/fitcurve/compare_fit.png b/mltsp/TCP/Algorithms/fitcurve/compare_fit.png new file mode 100755 index 00000000..09bf99d5 Binary files /dev/null and b/mltsp/TCP/Algorithms/fitcurve/compare_fit.png differ diff --git a/mltsp/TCP/Algorithms/fitcurve/cv_dotastro.txt b/mltsp/TCP/Algorithms/fitcurve/cv_dotastro.txt new file mode 100755 index 00000000..a55abe2c --- /dev/null +++ b/mltsp/TCP/Algorithms/fitcurve/cv_dotastro.txt @@ -0,0 +1,62 @@ + m_err_list = [.01,.01,.01,.01,.01,.01,.01,.01,.01,.01,.01,.01,.01,.01,.01,.01,.01,.01,.01,.01,.01,.01,.01,.01,.01,.01,.01,.01,.01,.01] + avail_list2 = [] + avail_list = [2453072.13, + 2453073.1769, + 2453080.196, + 2453081.0925, + 2453081.1887, + 2453082.051, + 2453082.1399, + 2453084.0474, + 2453084.237, + 2453085.0327, + 2453085.1154, + 2453085.220, + 2453089.0485, + 2453104.0592, + 2453104.1665, + 2453108.0916, + 2453109.0451, + 2453109.1489, + 2453110.0888, + 2453111.0473, + 2453111.1455, + 2453112.0303, + 2453112.1564, + 2453114.0266, + 2453114.1342, + 2453116.0093, + 2453116.1329, + 2453117.0429, + 2453118.0103, + 2453118.1034] + m_list = [12.923, + 12.94, + 13.161, + 13.199, + 13.399, + 13.309, + 13.52, + 13.6, + 13.533, + 13.86, + 13.76, + 13.91, + 14.294, + 13.315, + 13.248, + 13.366, + 13.623, + 13.568, + 13.61, + 13.721, + 13.898, + 14.015, + 14.047, + 14.494, + 14.286, + 14.435, + 14.461, + 14.311, + 14.352, + 14.005] diff --git a/mltsp/TCP/Algorithms/fitcurve/fitted_cv.png b/mltsp/TCP/Algorithms/fitcurve/fitted_cv.png new file mode 100755 index 00000000..09bf99d5 Binary files /dev/null and b/mltsp/TCP/Algorithms/fitcurve/fitted_cv.png differ diff --git a/mltsp/TCP/Algorithms/fitcurve/harmonics_phase_(189.312|n_0p05820).png b/mltsp/TCP/Algorithms/fitcurve/harmonics_phase_(189.312|n_0p05820).png new file mode 100755 index 00000000..624cb8e7 Binary files /dev/null and b/mltsp/TCP/Algorithms/fitcurve/harmonics_phase_(189.312|n_0p05820).png differ diff --git a/mltsp/TCP/Algorithms/fitcurve/harmonics_phase_plot.png b/mltsp/TCP/Algorithms/fitcurve/harmonics_phase_plot.png new file mode 100755 index 00000000..a9f95ac6 Binary files /dev/null and b/mltsp/TCP/Algorithms/fitcurve/harmonics_phase_plot.png differ diff --git a/mltsp/TCP/Algorithms/fitcurve/lightcurve.py b/mltsp/TCP/Algorithms/fitcurve/lightcurve.py new file mode 100644 index 00000000..8ef7354e --- /dev/null +++ b/mltsp/TCP/Algorithms/fitcurve/lightcurve.py @@ -0,0 +1,1071 @@ +# TODO remove? +#!/usr/bin/env python + +## Filename: lightcurve.py +## Version: 0.1 (realistically this is nearly the hundredth version) +## Notes: +## -This code uses the pre_whiten from the NOISIFICATION directory. (the dictionary this prewhiten outputs has a 'y_offset' key and uses a shorter nharm_min-max range). +## -Main change for v0.1: lomb_scargle and pre_whiten take input signal errors. Before they were assigning uniform errors to each datapoint. +## pre_whiten has an lower and upper limit in scanning harmonics (4-20). This is significant for phase offset issues +## -TO STORE IN DATABASE: +## In GetPeriodFoldForWeb.generate_lomb_period_fold there is a dictionary called db_dictionary. The values stored in here will be what you want to put online. Need +## to have a method that returns this dictionary though. Also, it may be best to store more harmonics, if we find that the cut I am using is too strict. Currently it +## is that the amplitude/amp_error >=1 (reasonable, although probably too liberal) +## -FOR TCP_EXPLORER PLOT: +## The values stored in db_dictionary are essentially used to create the plot on tcp_explorer. db_dictionary does have extra values not needed in the plot, but useful +## perhaps in feature generation. + +###### +from __future__ import print_function +import sys +import os + +try: + import MySQLdb +except: + pass +import pprint +try: + import matplotlib + import matplotlib.pyplot as pyplot + import matplotlib.mlab as mlab +except: + pass # dstarr doesn't want any extra printed output + #print "matplotlib dependencies could not load" +import scipy +import numpy +try: + import pylab + from pylab import * +except: + pass +from numpy import * # from numpy import loadtxt,long,linspace,pi,arctan2,sin,cos,hstack,array,log10,abs,logical_or,logical_and,var +from scipy.optimize import fmin, brute +from scipy import random +from numpy.random import rand +import lomb_scargle # obsolete? +from lomb_scargle import peak2sigma,lprob2sigma, lomb, get_peak_width # obsolete? +import pre_whiten # obsolete? +from lomb_scargle_refine import lomb as lombr + +import pickle +import copy +from multi_harmonic_fit import multi_harmonic_fit as mh +from scipy.special import gammaincc,gammaln +from scipy.stats import scoreatpercentile + + +class lomb_model(object): + def create_model(self, available, m, m_err, out_dict): + def model(times): + data = zeros(times.size) + for freq in out_dict: + freq_dict = out_dict[freq] + # the if/else is a bit of a hack, + # but I don't want to catch "freq_searched_min" or "freq_searched_max" + if len(freq) > 8: + continue + else: + time_offset = freq_dict["harmonics_time_offset"] + for harmonic in range(freq_dict["harmonics_freq"].size): + f = freq_dict["harmonics_freq"][harmonic] + omega = f * 2 * pi + amp = freq_dict["harmonics_amplitude"][harmonic] + phase = freq_dict["harmonics_rel_phase"][harmonic] + new = amp * sin(omega * (times - time_offset) + phase) + data += new + print("length of data is:", len(data)) + print("data:", data) + return data + return model + +class period_folding_model(lomb_model): + """ contains methods that use period-folding to model data """ + def period_folding(self, needed, available, m , m_err, out_dict, doplot=True): + """ period folds both the needed and available times. Times are not ordered anymore! """ + + # find the first frequency in the lomb scargle dictionary: + f = (out_dict["freq1"]["harmonics_freq"][0]) + if out_dict["freq2"]["signif"] > out_dict["freq1"]["signif"]: + f = out_dict["freq2"]["frequency"] + + # find the phase: + p = out_dict["freq1"]["harmonics_rel_phase"][0] + + #period-fold the available times + t_fold = mod( available + p/(2*pi*f) , (1./f) ) + + #period-fold the needed times + t_fold_model = mod( needed + p/(2*pi*f) , (1./f) ) + ###### DEBUG ###### + early_bool = available < (2.4526e6 + 40) + ###### DEBUG ##### + + period_folded_progenitor_file = file("period_folded_progenitor.txt", "w") + progenitor_file = file("progenitor.txt", "w") + + for n in range(len(t_fold)): + period_folded_progenitor_file.write("%f\t%f\t%f\n" % (t_fold[n], m[n], m_err[n])) + progenitor_file.write("%f\t%f\t%f\n" % (available[n], m[n], m_err[n])) + progenitor_file.close() + + print("RETURNING t_fold:") + print(t_fold) + print("RETURNING t_fold_model:") + print(t_fold_model) + return t_fold, t_fold_model + + + def create_model(self,available, m , m_err, out_dict): + f = out_dict["freq1"]["frequency"] + def model(times): + t_fold, t_fold_model = self.period_folding(times, available, m, m_err, out_dict) + data = empty(0) + rms = empty(0) + for time in t_fold_model: + # we're going to create a window around the desired time and sample a gaussian distribution around that time + period = 1./f + assert period < available.ptp()*1.5, "period is greater than ####SEE VARIABLE CONTSTRAINT#### of the duration of available data" #alterring this. originally 1/3 + # window is 2% of the period + passed = False + for x in arange(0.01, 0.1, 0.01): + t_min = time - x * period + t_max = time + x * period + window = logical_and((t_fold < t_max), (t_fold > t_min)) # picks the available times that are within that window + try: + # there must be more than # points in the window for this to work: + assert (window.sum() >= 2), str(time) # jhiggins changed sum from 5 to 2 + except AssertionError: + continue + else: + passed = True + break + assert passed, "No adequate window found" + m_window = m[window] + mean_window = mean(m_window) + std_window = std(m_window) + + # now we're ready to sample that distribution and create our point + new = (random.normal(loc=mean_window, scale = std_window, size = 1))[0] + data = append(data,new) + rms = append(rms, std_window) + period_folded_model_file = file("period_folded_model.txt", "w") +# model_file = file("model.txt", "w") + for n in range(len(t_fold_model)): + period_folded_model_file.write("%f\t%f\t%f\n" % (t_fold_model[n], data[n], rms[n])) +# model_file.write("%f\t%f\t%f\n" % (available[n], data[n], rms[n])) +# model_file.close() + period_folded_model_file.close() + return {'flux':data, 'rms': rms} + return model + +############################# +# Use this code to generate out_dict as referenced above +############################# + +class observatory_source_interface(object): + def __init__(self): + pass + + + def get_peak_width(self, psd,imax): + pmax = psd[imax] + i = 0 + while ( (psd[imax-i:imax+1+i]>(pmax/2.)).sum()/(1.+2*i)==1 ): + w = 1.+2*i + i+=1 + return w + + + def make_psd_plot(self, psd=None, srcid=None, freqin=None): + """ Make PSD .png plots used in ALLStars webpages. + """ + import time + trys_left = 120 + while trys_left > 0: + try: + from matplotlib import pyplot as plt + ### Plot the PSD(freq) + psd_array = numpy.array(psd) + #import matplotlib + #matplotlib.use('PNG') + #import matplotlib.pyplot as plt + #from matplotlib import pyplot as plt + #from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas + + fig = plt.figure(figsize=(5,3), dpi=100) + ax2 = fig.add_subplot(211) + ax2.plot(freqin, numpy.log10(psd_array)) + ax2.set_ylim(0,3) + ax2.set_yticks([3,2,1,0]) + ax2.set_ylabel("Log10(psd)") + ax2.annotate("freq", xy=(.5, -0.05), xycoords='axes fraction', + horizontalalignment='center', + verticalalignment='top', + fontsize=10) + ax2.set_title(str(srcid) + " Non-prewhitened, Power Spectral Density", fontsize=9) + ax3 = fig.add_subplot(212) + ax3.plot(numpy.log10(freqin), numpy.log10(psd_array)) + ax3.set_ylim(-3,3) + ax3.set_xlim(-3,1) + ax3.set_yticks([3,2,1,0,-1,-2,-3]) + ax3.set_yticklabels(['3','','','0','','','-3']) + ax3.set_xticks([1,0,-1,-2,-3]) + ax3.set_ylabel("Log10(psd)") + ax3.annotate("Log10(freq)", xy=(.5, -0.15), xycoords='axes fraction', + horizontalalignment='center', + verticalalignment='top', + fontsize=10) + #plt.show() + fpath = "/home/pteluser/scratch/tutor_psd_png/psd_%d.png" % (srcid) + plt.savefig(fpath) + fig.clf() + trys_left = 0 + break + #os.system('eog ' + fpath) + #import pdb; pdb.set_trace() + #print + except: + trys_left -= 1 + time.sleep(1) + + + def get_2P_modeled_features(self, x=None, y=None, freq1_freq=None, srcid=None, ls_dict={}): + """ + """ + out_dict = {} + out_dict['model_phi1_phi2'] = 0.0 # NaN / divide by zero default value + out_dict['model_min_delta_mags'] = 0.0 + out_dict['model_max_delta_mags'] = 0.0 + + from scipy.optimize import fmin#, fmin_powell + t_folded_phase = (x / (1./freq1_freq)) % 1. + + A = ls_dict['freq1']['harmonics_amplitude'] + y0 = ls_dict['freq1']['harmonics_y_offset'] + ph = ls_dict['freq1']['harmonics_rel_phase'] + + def model_f(t): + return A[0]*sin(2*pi *t+ph[0]) + \ + A[1]*sin(2*pi*2.*t+ph[1]) + \ + A[2]*sin(2*pi*3.*t+ph[2]) + \ + A[3]*sin(2*pi*4.*t+ph[3]) + \ + A[4]*sin(2*pi*5.*t+ph[4]) + \ + A[5]*sin(2*pi*6.*t+ph[5]) + \ + A[6]*sin(2*pi*7.*t+ph[6]) + \ + A[7]*sin(2*pi*8.*t+ph[7]) + + def model_neg(t): + return -1. * model_f(t) + + + min_1_a = fmin(model_neg, 0.05)[0] # start finding 1st minima, at 5% of phase (fudge/magic number) > 0.018 + max_2_a = fmin(model_f, min_1_a + 0.01)[0] + min_3_a = fmin(model_neg, max_2_a + 0.01)[0] + max_4_a = fmin(model_f, min_3_a + 0.01)[0] + + try: + out_dict['model_phi1_phi2'] = (min_3_a - max_2_a) / (max_4_a / min_3_a) + out_dict['model_min_delta_mags'] = abs(model_f(min_1_a) - model_f(min_3_a)) + out_dict['model_max_delta_mags'] = abs(model_f(max_2_a) - model_f(max_4_a)) + except: + pass # out_dict will just contains defauld 0.0 values + + if 0: + from matplotlib import pyplot as plt + fig = plt.figure() + ax2 = fig.add_subplot(111) + ax2.plot(t_folded_phase, y, 'bo') + + y_median = (max(y) - min(y))/2. + min(y) + t_plot = arange(0.01, 1.0, 0.01) + modl = model_f(t_plot) + y_median + + ax2.plot(t_plot, modl, 'go') + + ax2.plot([max_2_a, max_4_a], numpy.array([model_f(max_2_a), model_f(max_4_a)]) + y_median + 0.5, 'yo') + ax2.plot([min_1_a, min_3_a], numpy.array([model_f(min_1_a), model_f(min_3_a)]) + y_median + 0.5, 'ko') + + ax2.set_title("srcid=%d P=%f min_1=%f max_2=%f min_3=%f max_4=%f" % (srcid, 1. / freq1_freq, min_1_a, max_2_a, min_3_a, max_4_a), fontsize=9) + plt.savefig("/tmp/ggg.png") + #os.system("eog /tmp/ggg.png &") + plt.show() + print() + import pdb; pdb.set_trace() + print() + return out_dict + + + def lomb_code(self, y, dy, x, sys_err=0.05, srcid=0): + """ This function is used for final psd and final L-S freqs which are used as features. + NOTE: lomb_extractor.py..lomb_extractor..extract() also generates psd, but its psd and objects not used for the final L.S. freqs. + + NOTE: currently (20101120) This is adapted from Nat's run_lomb14.py + + """ + ### These are defaults found in run_lomb14.py::run_lomb() definition: + nharm = 8 # nharm = 4 + num_freq_comps = 3 + do_models = True # 20120720: dstarr changes from False -> True + tone_control = 5.0 #1. + ############## + + dy0 = sqrt(dy**2 + sys_err**2) + + wt = 1./dy0**2 + x-=x.min()#needed for lomb() code to run in a fast amount of time + + chi0 = dot(y**2,wt) + + #alias_std = std( x-x.round() ) + + Xmax = x.max() + f0 = 1./Xmax + df = 0.8/Xmax # 20120202 : 0.1/Xmax + fe = 33. #pre 20120126: 10. # 25 + numf = int((fe-f0)/df) + freqin = f0 + df*arange(numf,dtype='float64') # OK + + ytest=1.*y # makes a copy of the array + dof = n0 = len(x) + hh = 1.+arange(nharm) + + out_dict = {} + #prob = gammaincc(0.5*(n0-1.),0.5*chi0) + #if (prob>0): + # lprob=log(prob) + #else: + # lprob= -gammaln(0.5*(n0-1)) - 0.5*chi0 + 0.5*(n0-3)*log(0.5*chi0) + #out_dict['sigma_vary'] = lprob2sigma(lprob) + + lambda0_range=[-log10(n0),8] # these numbers "fix" the strange-amplitude effect + + for i in range(num_freq_comps): + if (i==0): + psd,res = lombr(x,ytest,dy0,f0,df,numf, tone_control=tone_control, + lambda0_range=lambda0_range, nharm=nharm, detrend_order=1) + ### I think it still makes sense to set these here, even though freq1 may be replaced by another non-alias freq. This is because these are parameters that are derived from the first prewhitening application: + out_dict['lambda'] = res['lambda0'] # 20120206 added + out_dict['chi0'] = res['chi0'] + out_dict['time0'] = res['time0'] + out_dict['trend'] = res['trend_coef'][1] #temp_b + out_dict['trend_error'] = res['trend_coef_error'][1] # temp_covar[1][1] # this is the stdev(b)**2 + else: + psd,res = lombr(x,ytest,dy0,f0,df,numf, tone_control=tone_control, + lambda0_range=lambda0_range, nharm=nharm, detrend_order=0) + ytest -= res['model'] + if (i==0): + out_dict['varrat'] = dot(ytest**2,wt) / chi0 + #pre20110426: out_dict['cn0'] -= res['trend']*res['time0'] + dof -= n0 - res['nu'] + dstr = "freq%i" % (i + 1) + + if (do_models==True): + #20120720Commentout#raise # this needs to be moved below after alias stuff + out_dict[dstr+'_model'] = res['model'] + out_dict[dstr] = {} + freq_dict = out_dict[dstr] + freq_dict["frequency"] = res['freq'] + freq_dict["signif"] = res['signif'] + freq_dict["psd"] = psd # 20110804 added just for self.make_psd_plot() use. + freq_dict["f0"] = f0 + freq_dict["df"] = df + freq_dict["numf"] = numf + + freq_dict['harmonics_amplitude'] = res['amplitude'] + freq_dict['harmonics_amplitude_error'] = res['amplitude_error'] + freq_dict['harmonics_rel_phase'] = res['rel_phase'] + freq_dict['harmonics_rel_phase_error'] = res['rel_phase_error'] + freq_dict['harmonics_nharm'] = nharm + freq_dict['harmonics_time_offset'] = res['time0'] + freq_dict['harmonics_y_offset'] = res['cn0'] # 20110429: disable since it was previously mean subtracted and not useful, and not mean subtracted is avg-mag and essentially survey biased # out_dict['cn0'] + + ### Here we check for "1-day" aliases in ASAS / Deboss sources + dstr_alias = [] + dstr_all = ["freq%i" % (i + 1) for i in range(num_freq_comps)] + ### 20120223 co: + #for dstr in dstr_all: + # period = 1./out_dict[dstr]['frequency'] + # if (((period >= 0.93) and (period <= 1.07) and + # (out_dict[dstr]['signif'] < (3.771221/numpy.power(numpy.abs(period - 1.), 0.25) + 3.293027))) or + # ((period >= 0.485) and (period <= 0.515) and (out_dict[dstr]['signif'] < 10.0)) or + # ((period >= 0.325833333) and (period <= 0.340833333) and (out_dict[dstr]['signif'] < 8.0))): + # dstr_alias.append(dstr) # this frequency has a "1 day" alias (or 0.5 or 0.33 + # + ### 20120212 Joey alias re-analysis: + alias = [{'per':1., + 'p_low':0.92, + 'p_high':1.08, + 'alpha_1':8.191855, + 'alpha_2':-7.976243}, + {'per':0.5, + 'p_low':0.48, + 'p_high':0.52, + 'alpha_1':2.438913, + 'alpha_2':0.9837243}, + {'per':0.3333333333, + 'p_low':0.325, + 'p_high':0.342, + 'alpha_1':2.95749, + 'alpha_2':-4.285432}, + {'per':0.25, + 'p_low':0.245, + 'p_high':0.255, + 'alpha_1':1.347657, + 'alpha_2':2.326338}] + + for dstr in dstr_all: + period = 1./out_dict[dstr]['frequency'] + for a in alias: + if ((period >= a['p_low']) and + (period <= a['p_high']) and + (out_dict[dstr]['signif'] < (a['alpha_1']/numpy.power(numpy.abs(period - a['per']), 0.25) + a['alpha_2']))): + dstr_alias.append(dstr) # this frequency has a "1 day" alias (or 0.5 or 0.33 + break # only need to do this once per period, if an alias is found. + + out_dict['n_alias'] = len(dstr_alias) + if 0: + # 20120624 comment out the code which replaces the aliased freq1 with the next non-aliased one: + if len(dstr_alias) > 0: + ### Here we set the next non-alias frequency to freq1, etc: + dstr_diff = list(set(dstr_all) - set(dstr_alias)) + dstr_diff.sort() # want to ensure that the lowest freq is first + reorder = [] + for dstr in dstr_all: + if len(dstr_diff) > 0: + reorder.append(out_dict[dstr_diff.pop(0)]) + else: + reorder.append(out_dict[dstr_alias.pop(0)]) + + for i, dstr in enumerate(dstr_all): + out_dict[dstr] = reorder[i] + + if 0: + ### Write PSD vs freq .png plots for AllStars web visualization: + self.make_psd_plot(psd=out_dict['freq1']['psd'], srcid=srcid, freqin=freqin) + + var0 = var(ytest) - median(dy0)**2 + out_dict['sigma0'] = 0. + if (var0 > 0.): + out_dict['sigma0'] = sqrt(var0) + out_dict['nu'] = dof + out_dict['chi2'] = res['chi2'] #dot(ytest**2,wt) # 20110512: res['chi2'] is the last freq (freq3)'s chi2, which is pretty similar to the old dot(ytest**2,wt) calculation which uses the signal removed ytest + #out_dict['alias_std'] = alias_std + out_dict['freq_binwidth'] = df + out_dict['freq_searched_min']=min(freqin) + out_dict['freq_searched_max']=max(freqin) + out_dict['mad_of_model_residuals'] = median(abs(ytest - median(ytest))) + + ##### This is used for p2p_scatter_2praw feature: + t_2per_fold = x % (2/out_dict['freq1']['frequency']) + tups = zip(t_2per_fold, y)#, range(len(t_2per_fold))) + tups.sort() + t_2fold, m_2fold = zip(*tups) #So: m_2fold[30] == y[i_fold[30]] + m_2fold_array = numpy.array(m_2fold) + sumsqr_diff_folded = numpy.sum((m_2fold_array[1:] - m_2fold_array[:-1])**2) + sumsqr_diff_unfold = numpy.sum((y[1:] - y[:-1])**2) + p2p_scatter_2praw = sumsqr_diff_folded / sumsqr_diff_unfold + out_dict['p2p_scatter_2praw'] = p2p_scatter_2praw + + mad = numpy.median(numpy.abs(y - median(y))) + out_dict['p2p_scatter_over_mad'] = numpy.median(numpy.abs(y[1:] - y[:-1])) / mad + + ### eta feature from arXiv 1101.3316 Kim QSO paper: + out_dict['p2p_ssqr_diff_over_var'] = sumsqr_diff_unfold / ((len(y) - 1) * numpy.var(y)) + + t_1per_fold = x % (1./out_dict['freq1']['frequency']) + tups = zip(t_1per_fold, y)#, range(len(t_2per_fold))) + tups.sort() + t_1fold, m_1fold = zip(*tups) #So: m_1fold[30] == y[i_fold[30]] + m_1fold_array = numpy.array(m_1fold) + out_dict['p2p_scatter_pfold_over_mad'] = \ + numpy.median(numpy.abs(m_1fold_array[1:] - m_1fold_array[:-1])) / mad + + ######################## # # # + ### This section is used to calculate Dubath (10. Percentile90:2P/P) + ### Which requires regenerating a model using 2P where P is the original found period + ### NOTE: this essentially runs everything a second time, so makes feature + ### generation take roughly twice as long. + + model_vals = numpy.zeros(len(y)) + #all_model_vals = numpy.zeros(len(y)) + freq_2p = out_dict['freq1']['frequency'] * 0.5 + ytest_2p=1.*y # makes a copy of the array + + ### So here we force the freq to just 2*freq1_Period + # - we also do not use linear detrending since we are not searching for freqs, and + # we want the resulting model to be smooth when in phase-space. Detrending would result + # in non-smooth model when period folded + psd,res = lombr(x,ytest_2p,dy0,freq_2p,df,1, tone_control=tone_control, + lambda0_range=lambda0_range, nharm=nharm, detrend_order=0)#1) + model_vals += res['model'] + #all_model_vals += res['model'] + + ytest_2p -= res['model'] + for i in range(1,num_freq_comps): + psd,res = lombr(x,ytest_2p,dy0,f0,df,numf, tone_control=tone_control, + lambda0_range=lambda0_range, nharm=nharm, detrend_order=0) + + #all_model_vals += res['model'] + ytest_2p -= res['model'] + + out_dict['medperc90_2p_p'] = scoreatpercentile(numpy.abs(ytest_2p), 90) / \ + scoreatpercentile(numpy.abs(ytest), 90) + + some_feats = self.get_2P_modeled_features(x=x, y=y, freq1_freq=out_dict['freq1']['frequency'], srcid=srcid, ls_dict=out_dict) + out_dict.update(some_feats) + + ### So the following uses the 2*Period model, and gets a time-sorted, folded t and m: + ### - NOTE: if this is succesful, I think a lot of other features could characterize the + ### shapes of the 2P folded data (not P or 2P dependent). + ### - the reason we choose 2P is that occasionally for eclipsing + ### sources the LS code chooses 0.5 of true period (but never 2x + ### the true period). slopes are not dependent upon the actual + ### period so 2P is fine if it gives a high chance of correct fitting. + ### - NOTE: we only use the model from freq1 because this with its harmonics seems to + ### adequately model shapes such as RRLyr skewed sawtooth, multi minima of rvtau + ### without getting the scatter from using additional LS found frequencies. + + + t_2per_fold = x % (1/freq_2p) + tups = zip(t_2per_fold, model_vals) + tups.sort() + t_2fold, m_2fold = zip(*tups) + t_2fold_array = numpy.array(t_2fold) + m_2fold_array = numpy.array(m_2fold) + slopes = (m_2fold_array[1:] - m_2fold_array[:-1]) / (t_2fold_array[1:] - t_2fold_array[:-1]) + out_dict['fold2P_slope_10percentile'] = scoreatpercentile(slopes,10) # this gets the steepest negative slope? + out_dict['fold2P_slope_90percentile'] = scoreatpercentile(slopes,90) # this gets the steepest positive slope? + + return out_dict, ytest + + +class GetPeriodFoldForWeb: + """ + To be called by tcp_html_show_recent_ptf_sources.py, + which is called by a PHP script on lyra. + + Eventually this only prints a JSON javascript structure which + contains period folded data for plottingL like: (mag vs time.) + """ + def __init__(self): + self.pars = { \ + 'mysql_user':"pteluser", + 'mysql_hostname':"192.168.1.25", + 'mysql_database':'source_test_db', + 'mysql_port':3306, + 'featid_lookup_pkl_fpath':os.path.expandvars("$TCP_DATA_DIR/featname_featid_lookup.pkl"), + 'color_chris_folded':"#cc0033", + 'color_chris_model':"#ff3399", + 'color_feature_resampled':"#3399cc", + 'color_folded_data':"#000066", + 'tcptutor_hostname':'lyra.berkeley.edu', + 'tcptutor_username':'pteluser', + 'tcptutor_password':'Edwin_Hubble71', + 'tcptutor_port': 3306, + 'tcptutor_database':'tutor', + } + + + def make_db_connection(self): + """ + """ + self.db = MySQLdb.connect(host=self.pars['mysql_hostname'], + user=self.pars['mysql_user'], + db=self.pars['mysql_database'], + port=self.pars['mysql_port']) + self.cursor = self.db.cursor() + + self.tutor_db = MySQLdb.connect(host=self.pars['tcptutor_hostname'], \ + user=self.pars['tcptutor_username'], \ + passwd=self.pars['tcptutor_password'],\ + db=self.pars['tcptutor_database'],\ + port=self.pars['tcptutor_port']) + self.tutor_cursor = self.tutor_db.cursor() + + + + def generate_featname_featid_lookup(self, filter_id=8): + """ Generate a RDB feature table lookup dictionary for: + feat_id : feat_value + """ + import cPickle + # TODO: if not .pkl file exists... + if os.path.exists(self.pars['featid_lookup_pkl_fpath']): + fp = open(self.pars['featid_lookup_pkl_fpath']) + self.featname_lookup = cPickle.load(fp) + fp.close() + return + else: + select_str = "SELECT feat_name, feat_id FROM source_test_db.feat_lookup WHERE filter_id = %d" % (filter_id) + self.cursor.execute(select_str) + + results = self.cursor.fetchall() + self.cursor.close() + self.featname_lookup = {} + for (feat_name, feat_id) in results: + self.featname_lookup[feat_name] = feat_id + + fp = open(self.pars['featid_lookup_pkl_fpath'], 'w') + cPickle.dump(self.featname_lookup, fp) + fp.close() + return + + + def get_source_arrays(self, source_id): + """ Retrieve source m, t from rdb. + """ + + #select_str = """SELECT object_test_db.obj_srcid_lookup.src_id, + # object_test_db.ptf_events.ujd, + # object_test_db.ptf_events.mag, + # object_test_db.ptf_events.mag_err + # FROM object_test_db.obj_srcid_lookup + # JOIN object_test_db.ptf_events ON (object_test_db.ptf_events.id = object_test_db.obj_srcid_lookup.obj_id) + # WHERE survey_id = 3 AND src_id = %d """ % (source_id) + + # 20091030: dstarr comments out: + #select_str = """SELECT object_test_db.obj_srcid_lookup.src_id, + # object_test_db.ptf_events.ujd, + # (-2.5 * LOG10(object_test_db.ptf_events.flux_aper + object_test_db.ptf_events.f_aper) + object_test_db.ptf_events.ub1_zp_ref) AS m_total, + # object_test_db.ptf_events.mag_err + # FROM object_test_db.obj_srcid_lookup + # JOIN object_test_db.ptf_events ON (object_test_db.ptf_events.id = object_test_db.obj_srcid_lookup.obj_id) + # WHERE survey_id = 3 AND src_id = %d + # ORDER BY object_test_db.ptf_events.ujd""" % (source_id) + + # 20091030: dstarr instead uses: + select_str = """SELECT object_test_db.obj_srcid_lookup.src_id, + object_test_db.ptf_events.ujd, + object_test_db.ptf_events.mag AS m_total, + object_test_db.ptf_events.mag_err + FROM object_test_db.obj_srcid_lookup + JOIN object_test_db.ptf_events ON (object_test_db.ptf_events.id = object_test_db.obj_srcid_lookup.obj_id) + WHERE survey_id = 3 AND src_id = %d + ORDER BY object_test_db.ptf_events.ujd""" % (source_id) + + + #Testing fluxes +## select_str = """SELECT object_test_db.obj_srcid_lookup.src_id, +## object_test_db.ptf_events.ujd, +## object_test_db.ptf_events.flux, +## object_test_db.ptf_events.flux_err +## FROM object_test_db.obj_srcid_lookup +## JOIN object_test_db.ptf_events ON (object_test_db.ptf_events.id = object_test_db.obj_srcid_lookup.obj_id) +## WHERE survey_id = 3 AND src_id = %d +## ORDER BY object_test_db.ptf_events.ujd""" % (source_id) + + self.cursor.execute(select_str) + + t_list = [] + m_list = [] + merr_list = [] + + results = self.cursor.fetchall() + + # TODO: maybe close DB connection too. + for row in results: + t_list.append(row[1]) + m_list.append(row[2]) + merr_list.append(row[3]) + + src_dict = {} + src_dict['src_id'] = results[0][0] + src_dict['t'] = numpy.array(t_list) + src_dict['m'] = numpy.array(m_list) + src_dict['m_err'] = numpy.array(merr_list) + #src_dict['m_err'] = [] #/@/ Justin changed 20090804 + return src_dict + + + def get_source_arrays__dotastro(self, source_id): + """ Retrieve source m, t from rdb. + + This version of the method queries the DotAstro.org / TUTOR database on lyra + """ + + # TODO: need to determine shich filter has the most number of epochs + # TODO: need to retrieve the magnitudes(time) from dotastro database for this fitler + # m_err(time) + srcid_dotastro = source_id - 100000000 + + # first need to use observations.source_id == to get the + # observations.observation_id + # then select count(*) from obs_data.observation_id== and see which observation_id (eg filter) has the most epochs. + # then retrieve all mag, m_err, time for this observation_id and return it. + + ##### First Retrieve the filter for the srcid from the tranx RDB + + select_str = "SELECT feats_used_filt FROM source_test_db.srcid_lookup WHERE src_id = %d" % (source_id) + self.cursor.execute(select_str) + results = self.cursor.fetchall() + if len(results) == 0: + return {} + feat_filter = results[0][0] + + ##### Determine the filter / observation_id which has the most number of epochs, + # since this currently corresponds to the timeseries which the feature algorithms + # were generated using (eg not using the combo_band). + #select_str = """SELECT filters.*, count(obs_data.obsdata_val) AS epoch_count, observations.observation_id + # FROM observations + # JOIN obs_data USING (observation_id) + # JOIN filters USING (filter_id) + # WHERE observations.source_id=%d AND filters.filter_name like "%s%" + # GROUP BY observations.observation_id + # ORDER BY epoch_count DESC""" % (srcid_dotastro, feat_filter[0]) + + select_str = """SELECT obs_data.obsdata_time, obs_data.obsdata_val, obs_data.obsdata_err + FROM observations + JOIN obs_data USING (observation_id) + JOIN filters USING (filter_id) + WHERE observations.source_id=%d AND filters.filter_name like "%s""" % (srcid_dotastro, feat_filter[0]) + '%"' + #'" """ + self.tutor_cursor.execute(select_str) + + t_list = [] + m_list = [] + merr_list = [] + + results = self.tutor_cursor.fetchall() + # TODO: maybe close DB connection too. + for row in results: + t_list.append(row[0]) + m_list.append(row[1]) + merr_list.append(row[2]) + + src_dict = {} + src_dict['src_id'] = source_id + src_dict['t'] = numpy.array(t_list) + src_dict['m'] = numpy.array(m_list) + src_dict['m_err'] = numpy.array(merr_list) + #src_dict['m_err'] = [] #/@/ Justin changed 20090804 + return src_dict + + + ### This function is no longer used by lomb_scargle_extractor. ONly Noisification/Chris related lightcurve.py functions use it. + def generate_lomb_period_fold(self, src_dict, return_option='top4lombfreqs_withharmonics'): + """ Re-generate lomb scargle using Chris code. + + Return period folded m(t) and evenly resampled m(t) in a dictionary. + """ + + obs = observatory_source_interface() + out_dict, cn_output = obs.lomb_code(src_dict['m'], + src_dict['m_err'], + src_dict['t']) + + + return out_dict + + + + def using_features_generate_resampled(self, src_dict): + """ Using features retrieved from RDB and stored in src_dict, + form a re-sampled m(t) and return in a dictionary. + """ + ##### TODO: Get the frequency components from feature tables if + # available. + # - Construct y_axis for some generated time-axis. + + # TODO: using: + # self.featname_lookup + # form a SELECT string which retrieves all features of interest + # then gemerate an out_dict{} so that this works: + + select_str = """SELECT + (SELECT feat_val FROM source_test_db.feat_values WHERE src_id=%d AND feat_id=%d) AS %s, + (SELECT feat_val FROM source_test_db.feat_values WHERE src_id=%d AND feat_id=%d) AS %s, + (SELECT feat_val FROM source_test_db.feat_values WHERE src_id=%d AND feat_id=%d) AS %s, + (SELECT feat_val FROM source_test_db.feat_values WHERE src_id=%d AND feat_id=%d) AS %s, + (SELECT feat_val FROM source_test_db.feat_values WHERE src_id=%d AND feat_id=%d) AS %s, + (SELECT feat_val FROM source_test_db.feat_values WHERE src_id=%d AND feat_id=%d) AS %s, + (SELECT feat_val FROM source_test_db.feat_values WHERE src_id=%d AND feat_id=%d) AS %s, + (SELECT feat_val FROM source_test_db.feat_values WHERE src_id=%d AND feat_id=%d) AS %s, + (SELECT feat_val FROM source_test_db.feat_values WHERE src_id=%d AND feat_id=%d) AS %s, + (SELECT feat_val FROM source_test_db.feat_values WHERE src_id=%d AND feat_id=%d) AS %s, + (SELECT feat_val FROM source_test_db.feat_values WHERE src_id=%d AND feat_id=%d) AS %s + """%(src_dict['src_id'], self.featname_lookup['freq1_harmonics_freq_0'], 'freq1_harmonics_freq_0', + src_dict['src_id'], self.featname_lookup['freq2_harmonics_freq_0'], 'freq2_harmonics_freq_0', + src_dict['src_id'], self.featname_lookup['freq3_harmonics_freq_0'], 'freq3_harmonics_freq_0', + src_dict['src_id'], self.featname_lookup['freq1_harmonics_amplitude_0'], 'freq1_harmonics_amplitude_0', + src_dict['src_id'], self.featname_lookup['freq2_harmonics_amplitude_0'], 'freq2_harmonics_amplitude_0', + src_dict['src_id'], self.featname_lookup['freq3_harmonics_amplitude_0'], 'freq3_harmonics_amplitude_0', + src_dict['src_id'], self.featname_lookup['freq1_harmonics_rel_phase_0'], 'freq1_harmonics_rel_phase_0', + src_dict['src_id'], self.featname_lookup['freq2_harmonics_rel_phase_0'], 'freq2_harmonics_rel_phase_0', + src_dict['src_id'], self.featname_lookup['freq3_harmonics_rel_phase_0'], 'freq3_harmonics_rel_phase_0', + src_dict['src_id'], self.featname_lookup['freq1_signif'], 'freq1_signif', + src_dict['src_id'], self.featname_lookup['freq2_signif'], 'freq2_signif', + ) + + self.cursor.execute(select_str) + + result = self.cursor.fetchall() + + ############# + + freq_list = [] + amp_list = [] + rel_phase = [] + + freq_list.append(result[0][0]) + freq_list.append(result[0][1]) + freq_list.append(result[0][2]) + amp_list.append(result[0][3]) + amp_list.append(result[0][4]) + amp_list.append(result[0][5]) + rel_phase.append(result[0][6]) + rel_phase.append(result[0][7]) + rel_phase.append(result[0][8]) + + freq1_harmonics_freq_0 = result[0][0] + freq2_harmonics_freq_0 = result[0][1] + freq1_signif = result[0][9] + freq2_signif = result[0][10] + freq1_harmonics_rel_phase_0 = result[0][6] + + #try: + # plot_period = 1.0 / freq_list[0] + # x_axis = arange(0,plot_period, .01) + # + # y_axis= amp_list[0]*sin(2*numpy.pi*freq_list[0]*(x_axis-rel_phase[0]))+\ + # amp_list[1]*sin(2*numpy.pi*freq_list[1]*(x_axis-rel_phase[1]))+\ + # amp_list[2]*sin(2*numpy.pi*freq_list[2]*(x_axis-rel_phase[2])) + # + # feature_resampled_dict = {'feature_resampled':{'t':x_axis, 'm':y_axis, + # 'color':self.pars['color_feature_resampled']}} + #except: + # feature_resampled_dict = {'feature_resampled':{'t':[], 'm':[], + # 'color':self.pars['color_feature_resampled']}} + + + ##### Here we period fold the existing data. + f = (freq1_harmonics_freq_0) + if freq2_signif > freq1_signif: + f = freq2_harmonics_freq_0 + + # find the phase: + p = freq1_harmonics_rel_phase_0 + + #period-fold the available times + t_fold = mod( src_dict['t'] + p/(2*pi*f) , (1./f) ) + ##### This is the earlier data: + try: + plot_period = 1.0 / freq_list[0] + x_axis = arange(0,plot_period, .001) + + #KLUDGE: I fake the generated LC mag amplitude & offset using real data: + amp_kludge = max(src_dict['m']) - min(src_dict['m']) + m_offset_kludge = amp_kludge/2. + min(src_dict['m']) + + # 20090720: dstarr replaces the following with something 4-6 lines below + #y_axis= (amp_list[0]*sin(2*numpy.pi*freq_list[0]*(x_axis-rel_phase[0]))+\ + # amp_list[1]*sin(2*numpy.pi*freq_list[1]*(x_axis-rel_phase[1]))+\ + # amp_list[2]*sin(2*numpy.pi*freq_list[2]*(x_axis-rel_phase[2]))) + y_axis= (amp_list[0]*sin(2*numpy.pi*freq_list[0]*(x_axis)))+\ + amp_list[1]*sin(2*numpy.pi*freq_list[1]*(x_axis) + rel_phase[1])+\ + amp_list[2]*sin(2*numpy.pi*freq_list[2]*(x_axis) + rel_phase[2]) + + y_axis= (amp_list[0]*sin(2*numpy.pi*freq_list[0]*(x_axis) + rel_phase[0]))+\ + amp_list[1]*sin(2*numpy.pi*freq_list[1]*(x_axis) + rel_phase[1])+\ + amp_list[2]*sin(2*numpy.pi*freq_list[2]*(x_axis) + rel_phase[2]) + + amp_sampled_m = max(y_axis) - min(y_axis) + amp_sampled_offset = amp_sampled_m / 2. + min(y_axis) + + y_axis = ((y_axis -amp_sampled_offset) / amp_sampled_m) * amp_kludge + m_offset_kludge + + feature_resampled_dict = {'DB features Generated':{'t':x_axis, 'm':y_axis, + 'color':self.pars['color_feature_resampled']}} + except: + feature_resampled_dict = {'DB features Generated':{'t':[], 'm':[], 'points': {'radius': 0.1}, + 'color':self.pars['color_feature_resampled']}} + ##### + + feature_resampled_dict.update({"DB features Period Folded":{'t':t_fold, 'm':src_dict['m'], + 'color':self.pars['color_folded_data']}}) + + html_str = "" + for i in range(len(amp_list)): + html_str += ""+str(i)+""+str(amp_list[i])+""+str(freq_list[i])+""+str(rel_phase[i])+""+str(1/freq_list[i])+" \n" + + if len(sys.argv) >= 2: + if sys.argv[1] == 'get_table_data': + return html_str + + return feature_resampled_dict + + def form_json(self, combo_dict): + """ Given period folded structures, form a JSON-like string, return. + Justin changing order in list since dictionary model blocks folded data + """ + json_list = [] + data_list1 = [] + for i in range(len(combo_dict['Actual Mags folded']['t'])): + data_list1.append([combo_dict['Actual Mags folded']['t'][i],combo_dict['Actual Mags folded']['m'][i]]) + data_list2 = [] + for i in range(len(combo_dict['Folded Model']['t'])): + data_list2.append([combo_dict['Folded Model']['t'][i],combo_dict['Folded Model']['m'][i]]) + data_list3 = [] + for i in range(len(combo_dict['Model with dictionary values']['t'])): + data_list3.append([combo_dict['Model with dictionary values']['t'][i],combo_dict['Model with dictionary values']['m'][i]]) + json_list.append({'label':'Model with dictionary values', + 'color':'#F2BABB', + 'data':data_list3}) + json_list.append({'label':'Folded Model', + 'color':'#BB8800', + 'data':data_list2}) + json_list.append({'label':'Actual Mags folded', + 'color':'#194E84', + 'data':data_list1}) + + json_string_single_quotes = pprint.pformat(json_list) + json_string = json_string_single_quotes.replace("'",'"') + return json_string + + + + def main(self, source_id): + """ + Eventually this function will calculate the period fold + plotting x,y array and return a string with this JSON-like + output, such as: + + [{"label":"Period Fold", "color":#36477b, + "data":[[1,1],[2,4],[3,9],[4,16]]}] + """ + print("make db connect") + self.make_db_connection() + print("before mysql query") + self.generate_featname_featid_lookup() + print("after mysql query") + if source_id >= 100000000: + src_dict = self.get_source_arrays__dotastro(source_id) + else: + src_dict = self.get_source_arrays(source_id) + print("finished generating src_dict") + lc_dict = {} + lomb_folded_dict = self.generate_lomb_period_fold(src_dict) + lc_dict.update(lomb_folded_dict) + + json_string = self.form_json(lc_dict) + return json_string + ####### Justin adding to view fluxes folded ####### + def online_dictionary(self, source_id, return_option="database"): + self.make_db_connection() + self.generate_featname_featid_lookup() + src_dict = self.get_source_arrays(source_id) + + db_dict = self.generate_lomb_period_fold(src_dict,return_option="db_dictionary") + return db_dict + + + + def html_table(self, source_id, return_option="html"): + self.make_db_connection() + self.generate_featname_featid_lookup() + + src_dict = self.get_source_arrays(source_id) + lomb_str = self.generate_lomb_period_fold(src_dict) + db_str = self.using_features_generate_resampled(src_dict) + final_str = " \n" + final_str += " \n" + final_str += "
Chris ValuesDB values
\n" + final_str += " \n" + final_str += lomb_str+"
Harmonic NumAmplitudeFreqOffset 1 / freq
\n" + final_str += " \n" + final_str += " \n" + final_str += db_str+"
Harmonic NumAmplitudeFreqOffset 1 / freq
" + + return final_str + + def for_testing(self, source_id): + pass + + +# 20090806: dstarr does this since sys.argv doesnt work for module imports: +sys_argv_1 = None +sys_argv_2 = None +if len(sys.argv) >= 3: + sys_argv_1 = sys.argv[1] + sys_argv_2 = sys.argv[2] + +if sys_argv_1 == 'get_period_fold2': + source_id = int(sys_argv_2) + GetPeriodFoldForWeb = GetPeriodFoldForWeb() + json_out_string = GetPeriodFoldForWeb.main(source_id) + print(json_out_string) + +if sys_argv_1 == 'get_period_fold4': + from lomb_scargle import * + from pre_whiten import * + from numpy import random + time = arange(0,7.5,0.3) + mags = sin(time) + mags += 15. + 0.1*random.normal(size=len(time)) + psd,freq,signi,simsigni,psdpeak = lomb(time,mags) + i0=psd.argmax(); freq0=freq[i0] + cn, out_dict = pre_whiten(time,mags, freq0) + plot (time,mags,'o') + plot (time,mags-cn) + A = out_dict['amplitude'] + dA = out_dict['amplitude_error'] + ph = out_dict['rel_phase'] + t0 = out_dict['time_offset'] + y0 = out_dict['y_offset'] + f = out_dict['freq'] + tt = min(time) + (max(time)-min(time))*arange(1000)/999. + modl = y0 + A[0]*sin(2*pi*f[0]*(tt-t0)+ph[0]) + for i in range(len(f)-1): + j=i+1 + modl += A[j]*sin(2*pi*f[j]*(tt-t0)+ph[j]) + fig = pyplot.figure() + ax = fig.add_subplot(111) + ax.plot(tt,modl, 'ro') + ax.plot(time, mags, 'bo') + pyplot.show() + +if sys_argv_1 == 'get_period_fold3': + x = arange(0,7.5, 0.3) + y = numpy.sin(x) + y_err = [] + y += 15. + 0.1*random.normal(size=len(x)) + #now add magnitude offest + src_dict = {'t': x, + 'm': y, + 'm_err':y_err} + get = GetPeriodFoldForWeb() + lomb_folded_dict = get.generate_lomb_period_fold(src_dict) + fig = pyplot.figure() + ax1 = fig.add_subplot(111) + ax1.plot(lomb_folded_dict['Actual Mags folded']['t'],lomb_folded_dict['Actual Mags folded']['m'], 'bo') + ax1.plot(lomb_folded_dict['Chris Period Folded: JUSTIN MODIFIED']['t'],lomb_folded_dict['Chris Period Folded: JUSTIN MODIFIED']['m'], 'ro') + ax1.plot(lomb_folded_dict['Chris model generated (w/ new_offset)']['t'],lomb_folded_dict['Chris model generated (w/ new_offset)']['m'], 'yo') + ax1.invert_yaxis() + pyplot.show() + +if sys_argv_1 == 'get_period_fold5': + source_id = int(sys_argv_2) + GetPeriodFoldForWeb = GetPeriodFoldForWeb() + db_dict = GetPeriodFoldForWeb.online_dictionary(source_id) + print(db_dict) + +if __name__ == '__main__': + ### 20101012: Added __main__ for testing use only, to see tracebacks from LombScargle code. + + + source_id = 100149386 + gpffw =GetPeriodFoldForWeb() + #db_dict = gpffw.main(source_id=source_id) + ### The following is done in gpffw.main(), but is hardcoded + # here to keep from doing DB connections + src_dict = {'m': array([ \ + 18.172, 18.556]), + 'm_err': array([ 0.045, 0.046]), + 'src_id': 100149386, + 't': array([ 2451214.70375, 2451215.60842])} + + + lomb_folded_dict = gpffw.generate_lomb_period_fold(src_dict, return_option='top4lombfreqs_withharmonics') + import pprint + pprint.pprint(lomb_folded_dict) + diff --git a/mltsp/TCP/Algorithms/fitcurve/linfit.py b/mltsp/TCP/Algorithms/fitcurve/linfit.py new file mode 100644 index 00000000..15cb9ce7 --- /dev/null +++ b/mltsp/TCP/Algorithms/fitcurve/linfit.py @@ -0,0 +1,31 @@ +from numpy import ones,sqrt + +def linfit(x,y,dy=[]): + """ + m = a+b*x + minimize chi^2 = Sum (y-m)^2/dy^2 + """ + lx=len(x) + if (dy==[]): + dy = ones(lx,dtype='float32') + + wt = 1./dy**2 + ss = wt.sum() + sx = (wt * x).sum() + sy = (wt * y).sum() + t = (x - sx/ss) / dy + b = (t * y / dy).sum() + + st2 = (t*t).sum() + + # parameter estimates + b = b / st2 + a = (sy - sx * b) / ss + + # error estimates + sdeva = sqrt((1. + sx * sx / (ss * st2)) / ss) + sdevb = sqrt(1. / st2) + covar = -sx/(ss*st2) + covar = [[sdeva**2, covar], [covar, sdevb**2]] + + return (a,b,covar) diff --git a/mltsp/TCP/Algorithms/fitcurve/lomb_period_fit(srcid_2570332).png b/mltsp/TCP/Algorithms/fitcurve/lomb_period_fit(srcid_2570332).png new file mode 100755 index 00000000..dcbbd776 Binary files /dev/null and b/mltsp/TCP/Algorithms/fitcurve/lomb_period_fit(srcid_2570332).png differ diff --git a/mltsp/TCP/Algorithms/fitcurve/lomb_period_fit(srcid_2758348).png b/mltsp/TCP/Algorithms/fitcurve/lomb_period_fit(srcid_2758348).png new file mode 100755 index 00000000..f8ebc3d0 Binary files /dev/null and b/mltsp/TCP/Algorithms/fitcurve/lomb_period_fit(srcid_2758348).png differ diff --git a/mltsp/TCP/Algorithms/fitcurve/lomb_scargle.py b/mltsp/TCP/Algorithms/fitcurve/lomb_scargle.py new file mode 100644 index 00000000..8d99503e --- /dev/null +++ b/mltsp/TCP/Algorithms/fitcurve/lomb_scargle.py @@ -0,0 +1,888 @@ +#! /usr/bin/env python + +from __future__ import division +from __future__ import print_function +from numpy import * +from numpy.random import normal +from scipy.stats import norm,betai +from scipy.special import betaln +from pylab import where +from scipy import weave + +def lprob2sigma(lprob): + """ translates a log_e(probability) to units of Gaussian sigmas + """ + + if (lprob>-36.): + sigma = norm.ppf(1.-0.5*exp(1.*lprob)) + else: + # this is good to 5.e-2; just to be crazy, get to 5.e-5 + sigma = sqrt( log(2./pi) - 2.*log(8.2) - 2.*lprob ) + f = 0.5*log(2./pi) - 0.5*sigma**2 - log(sigma) - lprob + df = - sigma - 1./sigma + sigma = sigma - f/df + + return float(sigma) + + +def peak2sigma(psdpeak,n0): + """ translates a psd peak height into a multi-trial NULL-hypothesis probability + NOTE: dstarr replaces '0' with 0.000001 to catch float-point accuracy bugs + Which I otherwise stumble into. + """ + + # Student's-T + prob0 = betai( 0.5*n0-2.,0.5,(n0-1.)/(n0-1.+2.*psdpeak) ) + if (0.5*n0-2.<=0.000001): + lprob0=0. + elif ( (n0-1.)/(n0-1.+2.*psdpeak) <=0.000001 ): + lprob0=-999. + elif (prob0==0): + lprob0=(0.5*n0-2.)*log( (n0-1.)/(n0-1.+2.*psdpeak) +) - log(0.5*n0-2.) - betaln(0.5*n0-2.,0.5) + else: lprob0=log(prob0) + + # ballpark number of independent frequencies + # (Horne and Baliunas, eq. 13) + horne = long(-6.362+1.193*n0+0.00098*n0**2.) + if (horne <= 0): horne=5 + + if (lprob0>log(1.e-4) and prob0>0): + # trials correction, monitoring numerical precision + lprob = log( 1. - exp( horne*log(1-prob0) ) ) + elif (lprob0+log(horne)>log(1.e-4) and prob0>0): + lprob = log( 1. - exp( -horne*prob0 ) ) + else: + lprob = log(horne) + lprob0 + + sigma = lprob2sigma(lprob) + + return sigma + + +def get_peak_width(psd,imax): + pmax = psd[imax] + i = 0 + while ( (psd[imax-i:imax+1+i]>(pmax/2.)).sum()/(1.+2*i)==1 ): + w = 1.+2*i + i+=1 + return w + + +# New as of 20101120: (with run_lomb14.py changes): +def lomb(time, signal, error, f1, df, numf, fit_mean=True, fit_slope=False, subtract_mean=True): + """ + C version of lomb_scargle + + Inputs: + time: time vector + signal: data vector + error: uncertainty on signal + df: frequency step + numf: number of frequencies to consider + + Output: + psd: power spectrum on frequency grid: f1,f1+df,...,f1+numf*df + """ + numt = len(time) + + wth = (1./error).astype('float64') + s0 = dot(wth,wth) + wth /= sqrt(s0) + + if (fit_mean==True): + subtract_mean=True + + if (fit_slope==True): + fit_mean=True + subtract_mean=True + + cn = (signal*wth).astype('float64') + if (subtract_mean==True): + cn -= dot(cn,wth)*wth + + tt = 2*pi*time.astype('float64') + sinx0, cosx0 = sin(df*tt), cos(df*tt) + sinx, cosx = sin(f1*tt)*wth, cos(f1*tt)*wth + + if (fit_slope==True): + tt *= wth + tt -= dot(tt,wth)*wth + tt /= tt.max() + s1 = dot(tt,tt) + cn -= dot(tt,cn)*tt/s1 + + numf = int(numf) + psd = empty(numf,dtype='float64') + + if (subtract_mean==False): + vcn = 1./s0 + else: + vcn = var(cn) + + fit_mean = int(fit_mean) + lomb_scargle_support = """ + inline double SQR(double a) { + return (a == 0.0 ? 0.0 : a*a); + } + + inline void update_sincos (long int numt, double *sinx0_ptr, double *cosx0_ptr, double *sinx_ptr, double *cosx_ptr) { + double tmp,*sinx0 = sinx0_ptr, *cosx0 = cosx0_ptr, *sinx = sinx_ptr, *cosx = cosx_ptr; + for (unsigned long i=0;i1: print('Starting Lomb (standard)...') + + signal = atleast_1d(signal).astype(double) + time = atleast_1d(time).astype(double) + + n0 = len(time) + + # if data error not given, assume all are unity + if (signal_err==[]): + wt = ones(n0,dtype=float) + else: + wt = 1./atleast_1d(signal_err).astype(double)**2; + wt[signal_err<=0] = 1. + + # if delta_time not given, assume 0 + do_sync=True + if (delta_time==[]): + do_sync=False + delta_time = zeros(n0, dtype=float) + else: + delta_time = atleast_1d(delta_time).astype(double) + + # make times manageable (Scargle periodogram is time-shift invariant) + tt = time-min(time) + ii = tt.argsort() + tt = tt[ii]; cn = signal[ii]; wt=wt[ii]; + + s0 = sum(wt) + msignal = sum( cn*wt ) / s0 + cn -= msignal + + # defaults + renorm=1 + if noise == 0: + renorm=0 + noise = sqrt( sum( cn**2*wt )/(n0-1) ) + + # make times manageable (Scargle periodogram is time-shift invariant) + tt = time-min(time) + tt.sort() + max_tt = tt[-1] + + # min.freq is 1/T, go a bit past that + # initial max. freq guess: approx. to Nyquist frequency + df = 0.1/max_tt + fmin = 0.5/max_tt + fmax = n0*fmin + # refine the maximum frequency to be a bit higher + dt = tt[1:] - tt[:-1] + g=where(dt>0) + if (len(g[0])>0): + dt_min = dt[g].min() + fmax = 0.5/dt_min + + # if omega is not given, compute it + if (freqin==[]): + numf = long( ceil( (fmax-fmin)/df ) ) + if (numf>num_freq_max): + if (verbosity>1): print(("Warning: shrinking num_freq %d -> %d (num_freq_max)") % (numf,num_freq_max)) + numf = long(num_freq_max) + #fmax = fmin + numf*df + df = (fmax - fmin)/numf + freqin = fmax - df*arange(numf,dtype=float) + om = 2.*pi*freqin + else: + om = freqin*2*pi + numf = len(om) + + # Bayes term in periodogram gets messy at frequencies lower than this + om0 = pi/max_tt + + if (numf==0): multiple = 0 + + if verbosity>1: print('Setting up periodogram...') + + # Periodogram + # Ref.: W.H. Press and G.B. Rybicki, 1989, ApJ 338, 277 + + # finite bins leads to sinc function; sinc factors drop out if delta_time = const. + # sinc(x) = sin(x*pi)/(x*pi) + + if (multiple > 0): + if verbosity>1: print('Looping...') + sisi=zeros([n0,numf], dtype=float) + coco=zeros([n0,numf], dtype=float) + + # Eq. (6); s2, c2 + ts1 = zeros(numf, dtype=float) + tc1 = zeros(numf, dtype=float) + s1 = zeros(numf, dtype=float) + s2 = zeros(numf, dtype=float) + c2 = zeros(numf, dtype=float) + # Eq. (5); sh and ch + sh = zeros(numf, dtype=float) + ch = zeros(numf, dtype=float) + bayes_term = zeros(numf, dtype=float) + + sync_func = lambda x: 1. + if (do_sync): + sync_func = lambda x: (1.e-99 + sin(pi*x))/(1.e-99 + pi*x) + + + for i in range(numf): + + x = ( om[i]*tt ) % (2*pi) + synct = sync_func(freqin[i]*delta_time) + sinom = sin(x)*synct + cosom = cos(x)*synct + + ts1[i] = sum( sinom*wt ) + tc1[i] = sum (cosom*wt ) + s1[i] = sum( synct**2*wt ) + s2[i] = 2.*sum( sinom*cosom*wt ) + c2[i] = sum( (cosom**2-sinom**2)*wt ) + sh[i] = sum( cn*sinom*wt ) + ch[i] = sum( cn*cosom*wt ) + + if (multiple > 0): + sisi[:,i]=sinom*wt + coco[:,i]=cosom*wt + + # cleanup + sinom = 0. + cosom = 0. + synct = 0. + + # Eq. (2): Definition -> tan(2omtau) + # --- tan(2omtau) = s2 / c2 + omtau = arctan2(s2,c2)/2 + + # cos(tau), sin(tau) + cosomtau = cos(omtau) + sinomtau = sin(omtau) + + tmp = 1.*ts1; + ts1 = cosomtau*tmp - sinomtau*tc1; + tc1 = sinomtau*tmp + cosomtau*tc1; + + tmp = 1.*sh; + sh = cosomtau*tmp - sinomtau*ch; + ch = sinomtau*tmp + cosomtau*ch; + + # Eq. (7); sum(cos(t-tau)**2) and sum(sin(t-tau)**2) + tmp = c2*cos(2.*omtau) + s2*sin(2.*omtau) + tc2 = 0.5*(s1+tmp) # sum(cos(t-tau)**2) + ts2 = 0.5*(s1-tmp) # sum(sin(t-tau)**2) + + norm_sin = sh/ts2; + norm_cos = ch/tc2; + cn0 = ( norm_sin*ts1 + norm_cos*tc1 ) / ( ts1**2/ts2 + tc1**2/tc2 - s0 ); + norm_sin -= cn0*ts1/ts2; + norm_cos -= cn0*tc1/tc2; + + #amplitude = sqrt(norm_sin**2+norm_cos**2) + #damplitude = sqrt(norm_sin**2/ts2+norm_cos**2/tc2)/amplitude*noise + + bayes_term = -0.5*log( s0*ts2*tc2 - tc1**2*ts2 - ts1**2*tc2 ) + 1.5*log(s0) - 0.5*log(4.) - log(freqin) + (log(freqin)).mean() + + # Eq. (3), modified + px = norm_sin**2*ts2 + norm_cos**2*tc2 - cn0**2*s0 + + # be careful here + wh = (tc2<=0) | (ts2<=0) + px[wh] = 0. + + # clean up + tmp = 0. + omtau = 0. + s2 = 0. + c2 = 0. + if multiple <=0 : + ts1 = 0. + tc1 = 0. + tc2 = 0. + ts2 = 0. + + # correct normalization + psd = atleast_1d( 0.5*px/(noise**2) ) + + if (use_bayes): + g=where(om0): + if (use_bayes): + j0 = psd.argmax() + signi = peak2sigma( (psd-bayes_term)[j0],n0) + else: + signi = peak2sigma(psd.max(),n0) + + # --- RUN SIMULATIONS for multiple > 0 + simsigni=[] + psdpeak=[] + if multiple > 0: + if verbosity>1: print('Running Simulations...') + if (multiple*fap < 10): + print('WARNING: Number of iterations (multiple keyword) not large enough for false alarm probability requested (need multiple*FAP > 10 )') + + psdpeak = zeros(multiple, dtype=float) + for m in range(multiple): + if ((m+1)%100 == 0) and (verbosity>0): + print("...working on %ith simulation. (%.2f Done)" % (m,m/multiple)) + + # Gaussian noise simulation + cn = normal(loc=0.0,scale=1.,size=n0)/sqrt(wt) + msignal = sum( cn*wt ) / s0 + cn = cn-msignal # force OBSERVED count rate to zero + if (renorm==0): noise = sqrt( sum( cn**2*wt )/(n0-1) ) + + # Eq. (5); sh and ch + for i in range(numf): + sh[i]=sum(cn*sisi[:,i]) + ch[i]=sum(cn*coco[:,i]) + + # Eq. (3) ; computing the periodogram for each simulation + tmp = sh; + sh = cosomtau*tmp - sinomtau*ch; + ch = sinomtau*tmp + cosomtau*ch; + + norm_sin = sh/ts2; + norm_cos = ch/tc2; + cn0 = ( norm_sin*ts1 + norm_cos*tc1 ) / ( ts1*ts1/ts2 + tc1*tc1/tc2 - s0 ); + norm_sin -= cn0*ts1/ts2; + norm_cos -= cn0*tc1/tc2; + + # Eq. (3), modified + px = norm_sin**2*ts2 + norm_cos**2*tc2 - s0*cn0**2 + + # be careful here + px[wh] = 0. + + psdpeak[m] = 0.5*px.max()/(noise**2) + + # False Alarm Probability according to simulations + if len(psdpeak) != 0: + psdpeak.sort() + psd0 = psdpeak[ long((1-fap)*(multiple-1)) ] + simsigni = peak2sigma(psd0,n0) + + + freq = om/(2.*pi) + + if verbosity>1: print('Done...') + + return (psd,freq,signi,simsigni,psdpeak) + + +if __name__ == '__main__': + from numpy.random import normal + from scipy.stats import betai + + #print('Testing Lomb-Scargle Periodogram with Gaussian noise...') + #freq = 10. # Hz - Sample frequency + #time = 10. #seconds + #noisetime = arange(0,time,1./freq, dtype=float) + #N = len(noisetime) + #dnoisetime=0*noisetime + 1./freq + #noisedata = sin(noisetime*2*pi)*1. + normal(loc=0,scale=1,size=N) + #dnoisedata = noisedata*0.+1. + + file='vosource_9026.dat' + #file='00331_3.dat' + #file='07914_9.dat' + mfile=open(file,'r') + fileList = mfile.readlines() + N = fileList.__len__() + noisedata = zeros(N,dtype=float) + dnoisedata = zeros(N,dtype=float) + noisetime = zeros(N,dtype=float) + dnoisetime = zeros(N,dtype=float) + + i=0 + for line in fileList: + (a,b,c) = line.split() + noisetime[i]=float(a) + noisedata[i]=float(b) + dnoisedata[i]=float(c) + i = i+1 + + noisetime = noisetime - noisetime[0] + + mfile.close() + + # get a careful estimate of the typical time between observations + time = noisetime + time.sort + dt = median( time[1:]-time[:-1] ) + + maxlogx = log(0.5/dt) # max frequency is ~ the sampling rate + minlogx = log(0.5/(time[-1]-time[0])) #min frequency is 0.5/T + + # sample the PSD with 1% fractional precision + M=long(ceil( (maxlogx-minlogx)*100. )) + frequencies = exp(maxlogx-arange(M, dtype=float) / (M-1.) * (maxlogx-minlogx)) + fap = 0.01 # we want to see what psd peak this false alarm probability would correspond to + + # set multiple >0 to get Monte Carlo significane estimate for peak (warning: this is slow) + multiple = 0 # should be >~10/fap + psd, freqs, signi, sim_signi, peak_sort = lomb(noisetime,noisedata,delta_time=dnoisedata, +signal_err=dnoisedata,freqin=frequencies,fap=fap,multiple=multiple) + + #peak location + imax = psd.argmax() + freq_max = freqs[imax] + + mpsd=max(psd) + print(("Peak=%.2f @ %.2f Hz, significance estimate: %.1f-sigma (T-test)") % (mpsd,freq_max,signi)) + + if (len(peak_sort)>0): + + psd0 = peak_sort[ long((1-fap)*(multiple-1)) ] + print(("Expected peak %.2f for False Alarm of %.2e") % (psd0,fap)) + + Prob0 = betai( 0.5*N-2.,0.5,(N-1.)/(N-1.+2.*psd0) ) + Nindep = log(1-fap)/log(1-Prob0) + horne = long(-6.362+1.193*N+0.00098*N**2.) + if (horne <= 0): horne=5 + print(("Estimated number of independent trials: %.2f (horne=%d)") % (Nindep,horne)) + + nover = sum( peak_sort>=mpsd ) + print(("Fraction of simulations with peak greater than observed value: %d/%d") % (nover,multiple)) + +""" +import Gnuplot +import time +plotobj = Gnuplot.Gnuplot() +plotobj.xlabel('Period (s)') +plotobj.ylabel('LS Periodogram') +plotobj('set logscale x') +plotobj('set logscale y') +plotobj.plot(Gnuplot.Data(1./freqs,psd, with = 'l 4 0')) +time.sleep(30) +""" diff --git a/mltsp/TCP/Algorithms/fitcurve/lomb_scargle_refine.py b/mltsp/TCP/Algorithms/fitcurve/lomb_scargle_refine.py new file mode 100644 index 00000000..a6597f54 --- /dev/null +++ b/mltsp/TCP/Algorithms/fitcurve/lomb_scargle_refine.py @@ -0,0 +1,183 @@ +# TODO duplicate? +from numpy import empty,pi,sqrt,sin,cos,dot,where,arange,arctan2,array,diag,ix_,log10,outer,hstack,log,round,zeros +from scipy import weave +from lomb_scargle import lprob2sigma +from scipy.stats import f as fdist +from ls_support import lomb_code,lomb_scargle_support +from numc_eigs import scode as eigs_code + +def lomb(time, signal, error, f1, df, numf, nharm=8, psdmin=6., detrend_order=0,freq_zoom=10.,tone_control=1.,return_model=True,lambda0=1.,lambda0_range=[-8,6]): + """ + C version of lomb_scargle: + Simultaneous fit of a sum of sinusoids by weighted, linear least squares. + model(t) = Sum_k Ck*t^k + Sum_i Sum_j Aij sin(2*pi*j*fi*(t-t0)+phij), i=[1,nfreq], j=[1,nharm] + [t0 defined such that ph11=0] + + Inputs: + time: time vector + signal: data vector + error: data uncertainty vector + df: frequency step + numf: number of frequencies to consider + + detrend_order: order of polynomial detrending (Ck orthogonol polynomial terms above; + 0 floating mean; <0 no detrending) + + psdmin: refine periodogram values with larger psd using multi-harmonic fit + nharm: number of harmonics to use in refinement + lambda0: typical value for regularization parameter (expert parameter) + lambda0_range: allowable range for log10 of regularization parameter + + Output: + psd: power spectrum on frequency grid: f1,f1+df,...,f1+numf*df + out_dict: dictionary describing various parameters of the multiharmonic fit at + the best-fit frequency + """ + numt = len(time) + + freq_zoom = round(freq_zoom/2.)*2. + + dord = detrend_order + if (detrend_order<0): + dord=0 + + if (tone_control<0): + tone_control=0. + + # polynomial terms + coef = empty(dord+1,dtype='float64') + norm = empty(dord+1,dtype='float64') + + wth0 = (1./error).astype('float64') + s0 = dot(wth0,wth0) + wth0 /= sqrt(s0) + + cn = (signal*wth0).astype('float64') + coef[0] = dot(cn,wth0); cn0 = coef[0]; norm[0] = 1. + cn -= coef[0]*wth0 + vcn = 1. + + # sin's and cosin's for later + tt = 2*pi*time.astype('float64') + sinx,cosx = sin(tt*f1)*wth0,cos(tt*f1)*wth0 + sinx_step,cosx_step = sin(tt*df),cos(tt*df) + sinx_back,cosx_back = -sin(tt*df/2.),cos(tt*df/2) + sinx_smallstep,cosx_smallstep = sin(tt*df/freq_zoom),cos(tt*df/freq_zoom) + + npar=2*nharm + hat_matr = empty((npar,numt),dtype='float64') + hat0 = empty((npar,dord+1),dtype='float64') + hat_hat = empty((npar,npar),dtype='float64') + soln = empty(npar,dtype='float64') + psd = zeros(numf,dtype='float64') + + # detrend the data and create the orthogonal detrending basis + if (dord>0): + wth = empty((dord+1,numt),dtype='float64') + wth[0,:] = wth0 + else: + wth = wth0 + + for i in range(detrend_order): + f = wth[i,:]*tt/(2*pi) + for j in range(i+1): + f -= dot(f,wth[j,:])*wth[j,:] + norm[i+1] = sqrt(dot(f,f)); f /= norm[i+1] + coef[i+1] = dot(cn,f) + cn -= coef[i+1]*f + wth[i+1,:] = f + vcn += (f/wth0)**2 + + + chi0 = dot(cn,cn) + varcn = chi0/(numt-1-dord) + psdmin *= 2*varcn + + Tr = array(0.,dtype='float64') + ifreq = array(0,dtype='int32') + lambda0 = array(lambda0/s0,dtype='float64') + lambda0_range = 10**array(lambda0_range,dtype='float64')/s0 + + vars=['numt','numf','nharm','detrend_order','psd','cn','wth','sinx','cosx','sinx_step','cosx_step','sinx_back','cosx_back','sinx_smallstep','cosx_smallstep','hat_matr','hat_hat','hat0','soln','chi0','freq_zoom','psdmin','tone_control','lambda0','lambda0_range','Tr','ifreq'] + weave.inline(lomb_code, vars, support_code = eigs_code + lomb_scargle_support,force=0) + + hat_hat /= s0 + ii = arange(nharm,dtype='int32') + soln[0:nharm] /= (1.+ii)**2; soln[nharm:] /= (1.+ii)**2 + if (detrend_order>=0): + hat_matr0 = outer(hat0[:,0],wth0) + for i in range(detrend_order): + hat_matr0 += outer(hat0[:,i+1],wth[i+1,:]) + + + modl = dot(hat_matr.T,soln); modl0 = dot(hat_matr0.T,soln) + coef0 = dot(soln,hat0) + coef -= coef0 + if (detrend_order>=0): + hat_matr -= hat_matr0 + + out_dict={} + out_dict['chi0'] = chi0*s0 + if (return_model): + if (dord>0): + out_dict['trend'] = dot(coef,wth)/wth0 + else: + out_dict['trend'] = coef[0] + 0*wth0 + out_dict['model'] = modl/wth0 + out_dict['trend'] + + j = psd.argmax() + freq = f1+df*j + (ifreq/freq_zoom - 1/2.)*df + tt = (time*freq) % 1. ; s =tt.argsort() + out_dict['freq'] = freq + out_dict['s0'] = s0 + out_dict['chi2'] = (chi0 - psd[j])*s0 + out_dict['psd'] = psd[j]*0.5/varcn + out_dict['lambda0'] = lambda0*s0 + out_dict['gcv_weight'] = (1-3./numt)/Tr + out_dict['trace'] = Tr + out_dict['nu0'] = numt - npar + npars = (1-Tr)*numt/2 + out_dict['nu'] = numt-npars + out_dict['npars'] = npars + + A0, B0 = soln[0:nharm],soln[nharm:] + hat_hat /= outer( hstack(((1.+ii)**2,(1.+ii)**2)),hstack(((1.+ii)**2,(1.+ii)**2)) ) + err2 = diag(hat_hat) + vA0, vB0 = err2[0:nharm], err2[nharm:] + covA0B0 = hat_hat[(ii,nharm+ii)] + + if (return_model): + vmodl = vcn/s0 + dot( (hat_matr/wth0).T, dot(hat_hat, hat_matr/wth0) ) + vmodl0 = vcn/s0 + dot( (hat_matr0/wth0).T, dot(hat_hat, hat_matr0/wth0) ) + out_dict['model_error'] = sqrt(diag(vmodl)) + out_dict['trend_error'] = sqrt(diag(vmodl0)) + + amp = sqrt(A0**2+B0**2) + damp = sqrt( A0**2*vA0 + B0**2*vB0 + 2.*A0*B0*covA0B0 )/amp + phase = arctan2( B0,A0 ) + rel_phase = phase - phase[0]*(1.+ii) + rel_phase = arctan2( sin(rel_phase),cos(rel_phase) ) + dphase = 0.*rel_phase + for i in range(nharm-1): + j=i+1 + v = array([-A0[0]*(1.+j)/amp[0]**2,B0[0]*(1.+j)/amp[0]**2,A0[j]/amp[j]**2,-B0[j]/amp[j]**2]) + jj=array([0,nharm,j,j+nharm]) + m = hat_hat[ix_(jj,jj)] + dphase[j] = sqrt( dot(dot(v,m),v) ) + + out_dict['amplitude'] = amp + out_dict['amplitude_error'] = damp + out_dict['rel_phase'] = rel_phase + out_dict['rel_phase_error'] = dphase + out_dict['time0'] = -phase[0]/(2*pi*freq) + + ncp = norm.cumprod() + out_dict['trend_coef'] = coef/ncp + out_dict['cn0'] = out_dict['trend_coef'][0] - cn0 + out_dict['trend_coef_error'] = sqrt( ( 1./s0 + diag(dot(hat0.T,dot(hat_hat,hat0))) )/ncp**2 ) + out_dict['cn0_error'] = out_dict['trend_coef_error'][0] + + prob = fdist.sf( 0.5*(numt-1.-dord)*(1.-out_dict['chi2']/out_dict['chi0']), 2,numt-1-dord ) + out_dict['signif'] = lprob2sigma(log(prob)) + + return 0.5*psd/varcn,out_dict diff --git a/mltsp/TCP/Algorithms/fitcurve/lombcheck_1.png b/mltsp/TCP/Algorithms/fitcurve/lombcheck_1.png new file mode 100755 index 00000000..17f9d42f Binary files /dev/null and b/mltsp/TCP/Algorithms/fitcurve/lombcheck_1.png differ diff --git a/mltsp/TCP/Algorithms/fitcurve/lombcheck_2.png b/mltsp/TCP/Algorithms/fitcurve/lombcheck_2.png new file mode 100755 index 00000000..19bf7c54 Binary files /dev/null and b/mltsp/TCP/Algorithms/fitcurve/lombcheck_2.png differ diff --git a/mltsp/TCP/Algorithms/fitcurve/lombcheck_3.png b/mltsp/TCP/Algorithms/fitcurve/lombcheck_3.png new file mode 100755 index 00000000..b019f7c7 Binary files /dev/null and b/mltsp/TCP/Algorithms/fitcurve/lombcheck_3.png differ diff --git a/mltsp/TCP/Algorithms/fitcurve/lombcheck_4.png b/mltsp/TCP/Algorithms/fitcurve/lombcheck_4.png new file mode 100755 index 00000000..7e59dd4a Binary files /dev/null and b/mltsp/TCP/Algorithms/fitcurve/lombcheck_4.png differ diff --git a/mltsp/TCP/Algorithms/fitcurve/lombtest_(45data)_realbogus_p018.png b/mltsp/TCP/Algorithms/fitcurve/lombtest_(45data)_realbogus_p018.png new file mode 100755 index 00000000..2d3d2df2 Binary files /dev/null and b/mltsp/TCP/Algorithms/fitcurve/lombtest_(45data)_realbogus_p018.png differ diff --git a/mltsp/TCP/Algorithms/fitcurve/lombtest_less_samp.png b/mltsp/TCP/Algorithms/fitcurve/lombtest_less_samp.png new file mode 100755 index 00000000..7d94a807 Binary files /dev/null and b/mltsp/TCP/Algorithms/fitcurve/lombtest_less_samp.png differ diff --git a/mltsp/TCP/Algorithms/fitcurve/lombtest_tcp_source(32data).png b/mltsp/TCP/Algorithms/fitcurve/lombtest_tcp_source(32data).png new file mode 100755 index 00000000..1b692dfb Binary files /dev/null and b/mltsp/TCP/Algorithms/fitcurve/lombtest_tcp_source(32data).png differ diff --git a/mltsp/TCP/Algorithms/fitcurve/lombtest_tcp_source(45data).png b/mltsp/TCP/Algorithms/fitcurve/lombtest_tcp_source(45data).png new file mode 100755 index 00000000..81576f06 Binary files /dev/null and b/mltsp/TCP/Algorithms/fitcurve/lombtest_tcp_source(45data).png differ diff --git a/mltsp/TCP/Algorithms/fitcurve/lombtest_well_samp.png b/mltsp/TCP/Algorithms/fitcurve/lombtest_well_samp.png new file mode 100755 index 00000000..e3ec7b69 Binary files /dev/null and b/mltsp/TCP/Algorithms/fitcurve/lombtest_well_samp.png differ diff --git a/mltsp/TCP/Algorithms/fitcurve/ls_support.py b/mltsp/TCP/Algorithms/fitcurve/ls_support.py new file mode 100644 index 00000000..29a227ed --- /dev/null +++ b/mltsp/TCP/Algorithms/fitcurve/ls_support.py @@ -0,0 +1,202 @@ +lomb_scargle_support = """ + inline void copy_sincos (int numt, double sinx0[], double cosx0[], double sinx[], double cosx[]) { + for (int i=0;i0) px = ( c2*sh*sh - 2.*cs*ch*sh + s2*ch*ch ) / detm; + return px; + } + inline void calc_dotprod(int numt, double sinx[], double cosx[], double wt[], int dord, double *st, double *ct) { + int i; + unsigned long n2=numt*dord; + for (*st=0,*ct=0,i=0;ipxmax) { + pxmax=px; + *ifreq = i; + } + update_sincos(numt, sinx_smallstep, cosx_smallstep, sinx1, cosx1, 0); + } + copy_sincos(numt,sinx,cosx,sinx1,cosx1); + if (*ifreqpx_max && Tr>0) { + px_max = px; + lambda_best = lambda; + *Trace = Tr; + } + lambda *= dlambda; + } + if (lambda_best-1.e-5>start) start=lambda_best/dlambda; + else break; + if(lambda_best+1.e-5psd0max && psdmax==0) { + psd0max = psd[j]; + copy_sincos(numt,sinx,cosx,sinx2,cosx2); + jmax = j; + } + // refine the fit around significant sin+cos fits + if (psd[j]>(double)psdmin) { + // first let the frequency vary slightly + px = do_lomb_zoom(numt,detrend_order, cn, sinx, cosx, sinx1, cosx1, sinx_back, cosx_back, sinx_smallstep, cosx_smallstep, wth, freq_zoom, &ifr); + lambda = *lambda0; + // now fit a multi-harmonic model with generalized cross-validation to avoid over-fitting + psd[j] = refine_psd(numt,nharm,detrend_order,hat_matr,hat0,hat_hat,sinx1,cosx1,wth,cn,soln,&lambda,lambda0_range,chi0,tone_control,&Trace,0); + if (psd[j]>psdmax) { + copy_sincos(numt,sinx1,cosx1,sinx2,cosx2); + psdmax=psd[j]; + *ifreq = ifr; + jmax = j; + } + } + update_sincos(numt, sinx_step, cosx_step, sinx, cosx, 0); + } + // finally, rerun at the best-fit period so we get some statistics + psd[jmax] = refine_psd(numt,nharm,detrend_order,hat_matr,hat0,hat_hat,sinx2,cosx2,wth,cn,soln,lambda0,lambda0_range,chi0,tone_control,Tr,1); +""" diff --git a/mltsp/TCP/Algorithms/fitcurve/multi_harmonic_fit.py b/mltsp/TCP/Algorithms/fitcurve/multi_harmonic_fit.py new file mode 100644 index 00000000..d1f3efee --- /dev/null +++ b/mltsp/TCP/Algorithms/fitcurve/multi_harmonic_fit.py @@ -0,0 +1,224 @@ +from __future__ import print_function +from numpy import sin,cos,sqrt,empty,pi,dot,arctan2,atleast_1d,diag,arange,abs,ones,array,zeros,log,trace +from scipy.linalg import cho_solve,cho_factor + +from pre_whiten import chi2sigma + +def CholeskyInverse(t,B): + """ + Computes inverse of matrix given its Cholesky upper Triangular decomposition t. + """ + nrows = len(t) + # Backward step for inverse. + for j in reversed(range(nrows)): + tjj = t[j,j] + S = sum([t[j,k]*B[j,k] for k in range(j+1, nrows)]) + B[j,j] = 1.0/ tjj**2 - S/ tjj + for i in reversed(range(j)): + B[j,i] = B[i,j] = -sum([t[i,k]*B[k,j] for k in range(i+1,nrows)])/t[i,i] + +def multi_harmonic_fit(time,data,error,freq,nharm=4,return_model=False,freq_sep=0.01,fit_mean=True,fit_slope=False): + """ + Simultaneous fit of a sum of sinusoids by weighted, linear least squares. + model(t) = C0 + C1*(t-t0) + Sum_i Sum_j Aij sin(2*pi*j*fi*(t-t0)+phij), i=[1,nfreq], j=[1,nharm] + [t0 defined such that ph11=0] + + Input: + time: x vector + data: y vector + error: uncertainty on data + freq: one or more frequencies freq_i to fit + nharm: number of harmonics of each frequency to fit (nharm=1 is just fundamental) + fij = fi, 2*fi, ... nharm*fi + freq_sep: freq_ij seperated by less than this are ignored (should be the search grid spacing) + fit_slope=False, then C1=0 + fit_mean=False, then C0=0 + + Output: + A dictionary containing the model evaluated on the time grid (if return_model==True) and + the model amplitudes Aij, phases phij, and their uncertainties. + """ + t = time.astype('float64') + r = data.astype('float64') + dr = error.astype('float64') + + numt = len(t) + + wt = 1./dr**2 + s0 = wt.sum() + t0 = (t*wt).sum()/s0 + t -= t0 + + dr *= sqrt(s0) + r0 = (r*wt).sum()/s0 + r -= r0 + + nfit=0 + if (fit_mean==True): + nfit=1 + + if (fit_slope==True): + fit_mean=True + nfit=2 + tm = t.max() + s1 = ((t/tm)**2*wt).sum() + sb = ((t/tm)*r*wt).sum() + slope = sb/s1; s1 /= s0 + r -= slope*t/tm + tt = t/tm/dr + + rr = r/dr + chi0 = dot(rr,rr)*s0 + + matr = empty((nfit+2*nharm,nfit+2*nharm),dtype='float64') + vec = empty(nfit+2*nharm,dtype='float64') + + sx = empty((nharm,numt),dtype='float64') + cx = empty((nharm,numt),dtype='float64') + + # + # We will solve matr*res = vec, for res. Define matr and vec. + # + sx0,cx0 = sin(2*pi*t*freq), cos(2*pi*t*freq) + sx[0,:] = sx0/dr; cx[0,:] = cx0/dr + for i in range(nharm-1): + sx[i+1,:] = cx0*sx[i,:] + sx0*cx[i,:] + cx[i+1,:] = -sx0*sx[i,:] + cx0*cx[i,:] + + if (nfit>0): + vec[0] = 0.; matr[0,0] = 1.; + if (nfit>1): + vec[1] = matr[0,1] = matr[1,0] = 0.; matr[1,1] = s1 + + for i in range(nharm): + vec[i+nfit] = dot(sx[i,:],rr) + vec[nharm+i+nfit] = dot(cx[i,:],rr) + if (nfit>0): + matr[0,i+nfit] = matr[i+nfit,0] = dot(sx[i,:],1./dr) + matr[0,nharm+i+nfit] = matr[nharm+i+nfit,0] = dot(cx[i,:],1./dr) + if (nfit>1): + matr[1,i+nfit] = matr[i+nfit,1] = dot(sx[i,:],tt) + matr[1,nharm+i+nfit] = matr[nharm+i+nfit,1] = dot(cx[i,:],tt) + for j in range(i+1): + matr[j+nfit,i+nfit] = matr[i+nfit,j+nfit] = dot(sx[i,:],sx[j,:]) + matr[j+nfit,nharm+i+nfit] = matr[nharm+i+nfit,j+nfit] = dot(cx[i,:],sx[j,:]) + matr[nharm+j+nfit,i+nfit] = matr[i+nfit,nharm+j+nfit] = dot(sx[i,:],cx[j,:]) + matr[nharm+j+nfit,nharm+i+nfit] = matr[nharm+i+nfit,nharm+j+nfit] = dot(cx[i,:],cx[j,:]) + + + out_dict={} + + # + # Convert to amplitudes and phases and propagate errors + # + out_dict['cn0'] = r0 + out_dict['cn0_error'] = 1./sqrt(s0) + out_dict['trend'] = 0. + out_dict['trend_error']=0. + + A0,B0,vA0,vB0,covA0B0 = zeros((5,nharm),dtype='float64') + amp,phase,rel_phase = zeros((3,nharm),dtype='float64') + damp,dphase = zeros((2,nharm),dtype='float64') + covA0B0 = zeros(nharm,dtype='float64') + res = zeros(nfit+2*nharm,dtype='float64') + err2 = zeros(nfit+2*nharm,dtype='float64') + + out_dict['bayes_factor'] = 0. + + try: + # + # solve the equation and replace matr with its inverse + # + m0 = cho_factor(matr,lower=False) + out_dict['bayes_factor'] = -log(trace(m0[0])) + res = cho_solve(m0,vec) + CholeskyInverse(m0[0],matr) + + A0, B0 = res[nfit:nharm+nfit],res[nharm+nfit:] + amp = sqrt(A0**2+B0**2) + phase = arctan2( B0,A0 ) + + err2 = diag(matr)/s0 + vA0, vB0 = err2[nfit:nharm+nfit], err2[nharm+nfit:] + for i in range(nharm): + covA0B0[i] = matr[nfit+i,nharm+nfit+i]/s0 + + damp = sqrt( A0**2*vA0 + B0**2*vB0 + 2.*A0*B0*covA0B0 )/amp + dphase = sqrt( A0**2*vB0 + B0**2*vA0 - 2.*A0*B0*covA0B0 )/amp**2 + rel_phase = phase - phase[0]*(1.+arange(nharm)) + rel_phase = arctan2( sin(rel_phase),cos(rel_phase) ) + + except: + print ("Failed: singular matrix! (Are your frequencies unique/non-harmonic?)") + + out_dict['time0'] = t0-phase[0]/(2*pi*freq) + out_dict["amplitude"] = amp + out_dict["amplitude_error"] = damp + out_dict["rel_phase"] = rel_phase + out_dict["rel_phase_error"] = dphase + + modl = r0 + dot(A0,sx*dr) + dot(B0,cx*dr) + if (nfit>0): + out_dict['cn0'] += res[0] + out_dict['cn0_error'] = sqrt(err2[0]) + modl += res[0] + if (nfit>1): + out_dict['trend'] = (res[1]+slope)/tm + out_dict['trend_error'] = sqrt(err2[1])/tm + modl += out_dict['trend']*t + ### + #import os + #import matplotlib.pyplot as pyplot + #t_folded = t % (1./freq) + #pyplot.title("nfit=%d After modl += res[0] and modl += out_dict['trend']*t" % (nfit)) + + #pyplot.plot(t_folded, data, 'bo', ms=3) + #pyplot.plot(t_folded, modl, 'ro', ms=3) + #pyplot.plot(t_folded, modl - out_dict['trend']*t, 'mo', ms=3) + #pyplot.plot(t_folded, out_dict['trend']*t, 'go', ms=3) + ##pyplot.plot(t, data, 'bo', ms=3) + ##pyplot.plot(t, modl, 'ro', ms=3) + ##pyplot.plot(t, modl - out_dict['trend']*t, 'mo', ms=3) + ##pyplot.plot(t, out_dict['trend']*t, 'go', ms=3) + ###pyplot.show() + ##fpath = '/tmp/multiharmonic.ps' + ##pyplot.savefig(fpath) + ##os.system('gv %s &' % (fpath)) + #import pdb; pdb.set_trace() + ### + resid = (modl-r-r0-slope*tt*dr)/dr + out_dict['chi2'] = dot(resid,resid)*s0 + out_dict['cn0'] += out_dict['trend']*(out_dict['time0']-t0) + else: + resid = (modl-r-r0)/dr + out_dict['chi2'] = dot(resid,resid)*s0 + + ### + #import os + #import matplotlib.pyplot as pyplot + #t_folded = t % (1./freq) + #pyplot.title("nfit=%d freq=%f End" % (nfit, freq)) + + #pyplot.plot(t_folded, data, 'bo', ms=3) + #pyplot.plot(t_folded, modl, 'ro', ms=3) + #pyplot.plot(t_folded, modl - out_dict['trend']*t, 'mo', ms=3) + #pyplot.plot(t_folded, out_dict['trend']*t, 'go', ms=3) + ##pyplot.plot(t, data, 'bo', ms=3) + ##pyplot.plot(t, modl, 'ro', ms=3) + ##pyplot.plot(t, modl - out_dict['trend']*t, 'mo', ms=3) + ##pyplot.plot(t, out_dict['trend']*t, 'go', ms=3) + ###pyplot.show() + #fpath = '/tmp/multiharmonic.ps' + #pyplot.savefig(fpath) + #os.system('gv %s &' % (fpath)) + #import pdb; pdb.set_trace() + #pyplot.clf() + ### + + + out_dict['nu'] = numt - 2*nharm - nfit + out_dict['signif'] = chi2sigma(chi0,out_dict['chi2'],numt-nfit,nharm) + if (return_model): + out_dict['model'] = modl + + return out_dict diff --git a/mltsp/TCP/Algorithms/fitcurve/numc_eigs.py b/mltsp/TCP/Algorithms/fitcurve/numc_eigs.py new file mode 100644 index 00000000..a1d0078e --- /dev/null +++ b/mltsp/TCP/Algorithms/fitcurve/numc_eigs.py @@ -0,0 +1,143 @@ +pythag_code = """ +inline double SQR(double a) { + return (a == 0.0 ? 0.0 : a*a); +} + +inline double SIGN(double a,double b) { + return ((b) >= 0.0 ? fabs(a) : -fabs(a)); +} + +double pythag(double a, double b) { + double absa,absb; + absa=fabs(a); + absb=fabs(b); + if (absa > absb) return absa*sqrt(1.0+SQR(absb/absa)); + else return (absb == 0.0 ? 0.0 : absb*sqrt(1.0+SQR(absa/absb))); +} +""" +tred2_code = """ +inline void tred2(double a[], int n, double d[], double e[]) { + int l,k,j,i; + double scale,hh,h,g,f; + + for (i=n-1;i>=1;i--) { + l=i-1; + h=scale=0.0; + if (l > 0) { + for (k=0;k<=l;k++) + scale += fabs(a[k+i*n]); + if (scale == 0.0) + e[i]=a[l+i*n]; + else { + for (k=0;k<=l;k++) { + a[k+i*n] /= scale; + h += a[k+i*n]*a[k+i*n]; + } + f=a[l+i*n]; + g=(f >= 0.0 ? -sqrt(h) : sqrt(h)); + e[i]=scale*g; + h -= f*g; + a[l+i*n]=f-g; + f=0.0; + for (j=0;j<=l;j++) { + a[i+j*n]=a[j+i*n]/h; + g=0.0; + for (k=0;k<=j;k++) + g += a[k+j*n]*a[k+i*n]; + for (k=j+1;k<=l;k++) + g += a[j+k*n]*a[k+i*n]; + e[j]=g/h; + f += e[j]*a[j+i*n]; + } + hh=f/(h+h); + for (j=0;j<=l;j++) { + f=a[j+i*n]; + e[j]=g=e[j]-hh*f; + for (k=0;k<=j;k++) + a[k+j*n] -= (f*e[k]+g*a[k+i*n]); + } + } + } else + e[i]=a[l+i*n]; + d[i]=h; + } + d[0]=0.0; + e[0]=0.0; + for (i=0;i=l;i--) { + f=s*e[i]; + b=c*e[i]; + e[i+1]=(r=pythag(f,g)); + if (r == 0.0) { + d[i+1] -= p; + e[m]=0.0; + break; + } + s=f/r; + c=g/r; + g=d[i+1]-p; + r=(d[i]-g)*s+2.0*c*b; + d[i+1]=g+(p=s*r); + g=c*r-b; + for (k=0;k= l) continue; + d[l] -= p; + e[l]=g; + e[m]=0.0; + } + } while (m != l); + } +} +""" + +scode = pythag_code + tred2_code + tqli_code +scode += """ +inline void get_eigs(int np,double x[],double d[]) { + double e[np]; + tred2(x,np,d,e); + tqli(d,e,np,x); +} +""" diff --git a/mltsp/TCP/Algorithms/fitcurve/observatory_source_interface_LGC.py b/mltsp/TCP/Algorithms/fitcurve/observatory_source_interface_LGC.py new file mode 100644 index 00000000..40d95a16 --- /dev/null +++ b/mltsp/TCP/Algorithms/fitcurve/observatory_source_interface_LGC.py @@ -0,0 +1,444 @@ +#!/usr/bin/env python +# encoding: utf-8 +""" +the observatory emits certain demands, fulfilled by this interface +""" +from __future__ import print_function +import sys +import os + +from pylab import * + +from numpy import * +from scipy.optimize import fmin, brute +from scipy import random +from numpy.random import rand + +from xml_reading import xml_reading +from janskies_converter import janskies_converter + +import lomb_scargle +from gcd_utilities import gcd_array +from scipy.fftpack import fftfreq, ifft +import pre_whiten + +import pickle + +import copy + +import ephem + +from spline_fit import spline_fitted_magnitudes, window_creator , window_tttimes, spline_fitted_magnitudes_brute + +from numpy.random import permutation + + +class lomb_model(object): + def create_model(self, available, m, m_err, out_dict): + def model(times): + data = zeros(times.size) + for freq in out_dict: + freq_dict = out_dict[freq] + # the if/else is a bit of a hack, + # but I don't want to catch "freq_searched_min" or "freq_searched_max" + if len(freq) > 8: + continue + else: + time_offset = freq_dict["harmonics_time_offset"] + for harmonic in range(freq_dict["harmonics_freq"].size): + f = freq_dict["harmonics_freq"][harmonic] + omega = f * 2 * pi + amp = freq_dict["harmonics_amplitude"][harmonic] + phase = freq_dict["harmonics_rel_phase"][harmonic] + new = amp * sin(omega * (times - time_offset) + phase) + data += new + return data + return model + +class period_folding_model(lomb_model): + """ contains methods that use period-folding to model data """ + def period_folding(self, needed, available, m , m_err, out_dict, doplot=True): + """ period folds both the needed and available times. Times are not ordered anymore! """ + + # find the first frequency in the lomb scargle dictionary: + f = (out_dict["freq1"]["harmonics_freq"][0]) + if out_dict["freq2"]["signif"] > out_dict["freq1"]["signif"]: + f = out_dict["freq2"]["frequency"] + + # find the phase: + p = out_dict["freq1"]["harmonics_rel_phase"][0] + + #period-fold the available times + t_fold = mod( available + p/(2*pi*f) , (1./f) ) + + #period-fold the needed times + t_fold_model = mod( needed + p/(2*pi*f) , (1./f) ) + ###### DEBUG ###### + early_bool = available < (2.4526e6 + 40) + ###### DEBUG ##### + + period_folded_progenitor_file = file("period_folded_progenitor.txt", "w") + progenitor_file = file("progenitor.txt", "w") + for n in range(len(t_fold)): + period_folded_progenitor_file.write("%f\t%f\t%f\n" % (t_fold[n], m[n], m_err[n])) + progenitor_file.write("%f\t%f\t%f\n" % (available[n], m[n], m_err[n])) + progenitor_file.close() + return t_fold, t_fold_model + + def create_model(self,available, m , m_err, out_dict): + f = out_dict["freq1"]["frequency"] + def model(times): + t_fold, t_fold_model = self.period_folding(times, available, m, m_err, out_dict) + data = empty(0) + rms = empty(0) + for time in t_fold_model: + # we're going to create a window around the desired time and sample a gaussian distribution around that time + period = 1./f + assert period < available.ptp()/3, "period is greater than one third of the duration of available data" + # window is 2% of the period + passed = False + for x in arange(0.01, 0.1, 0.01): + t_min = time - x * period + t_max = time + x * period + window = logical_and((t_fold < t_max), (t_fold > t_min)) # picks the available times that are within that window + try: + # there must be more than 3 points in the window for this to work: + assert (window.sum() > 5), str(time) + except AssertionError: + continue + else: + passed = True + break + assert passed, "No adequate window found" + m_window = m[window] + mean_window = mean(m_window) + std_window = std(m_window) + + # now we're ready to sample that distribution and create our point + new = (random.normal(loc=mean_window, scale = std_window, size = 1))[0] + data = append(data,new) + rms = append(rms, std_window) + period_folded_model_file = file("period_folded_model.txt", "w") +# model_file = file("model.txt", "w") + for n in range(len(t_fold_model)): + period_folded_model_file.write("%f\t%f\t%f\n" % (t_fold_model[n], data[n], rms[n])) +# model_file.write("%f\t%f\t%f\n" % (available[n], data[n], rms[n])) +# model_file.close() + period_folded_model_file.close() + return {'flux':data, 'rms': rms} + return model + +class spline_model(lomb_model): + """ Uses a spline fit as model """ + def create_model(self,available, m , m_err, out_dict): + def model(times): + assert available.ptp() > times.ptp(), "not enough range in available time data to perform spline fit" + delta = available[0] - times[0] + shifted_times = times + delta + mindiff = min(abs(diff(shifted_times))) + possible_range = max(available) - max(shifted_times) + dt = arange(0,possible_range, mindiff/5.*sign(possible_range)) + rand_dt = dt[permutation(dt.size)] + ispassed = False + for t in rand_dt: + try: + trial_times = shifted_times+t # try to advance shifted_times by amount t and see if that works + for i in arange(trial_times.size): + difftimes = available - trial_times[i] + absdiff = abs(difftimes) + nearest = absdiff.argmin() # the distance to the two nearest points + other_side = nearest - 1 * sign(difftimes[nearest]) + distance_to_nearest = absdiff[nearest] + distance_to_other_side = absdiff[other_side] + + # check that the distance to the nearest points is smaller than the minimum separation between desired times + assert distance_to_nearest < mindiff, "distance to the nearest points must be smaller than the minimum separation between desired times" + assert distance_to_other_side < mindiff, "distance to the other side must also be smaller than the minimum separation between desired times" + shift_worked = t + desired_times = shifted_times + t + window = logical_and(available > (desired_times.min()-10.), available < (desired_times.max()+10)) + + data, rms = spline_fitted_magnitudes(available, m, m_err, desired_times, mindiff = mindiff) + except AssertionError as description: + latest_description = description + continue + else: + print("passed") + ispassed = True + break + assert ispassed, "Didn't find a time shift that works, latest reason:" + str(latest_description) + time_fold, time_model_fold = period_folding_model().period_folding(desired_times, available, m , m_err, out_dict, doplot = False) + model_file = file("model.txt", "w") + for n in range(len(desired_times)): + model_file.write("%f\t%f\t%f\n" % (desired_times[n], data[n], rms[n])) + model_file.close() + return {'flux':data, 'rms':rms, 'new_times':desired_times} + return model + +class brutespline(lomb_model): + def create_model(self,available, m , m_err, out_dict): + def model(times): + assert available.ptp() > times.ptp(), "not enough range in available time data to perform spline fit" + delta = available[0] - times[0] + shifted_times = times + delta + possible_range = available[-2] - max(shifted_times) + mindiff = min(abs(diff(shifted_times))) + dt = arange(available[1],possible_range, mindiff/5.*sign(possible_range)) + rand_dt = dt[permutation(dt.size)] + t= rand_dt[0] + ispassed = False + desired_times = shifted_times+t # try to advance shifted_times by amount t and see if that works + data, rms = spline_fitted_magnitudes_brute(available, m, m_err, desired_times) + time_fold, time_model_fold = period_folding_model().period_folding(desired_times, available, m , m_err, out_dict, doplot = False) + model_file = file("model.txt", "w") + for n in range(len(desired_times)): + model_file.write("%f\t%f\t%f\n" % (desired_times[n], data[n], rms[n])) + model_file.close() + return {'flux':data, 'rms':rms, 'new_times':desired_times} + return model + + + +class observatory_source_interface(object): + # # # # # #: dstarr changes this to initially exclude spline models. Only want period_folded. + if len(sys.argv) > 4: + # xxx + if sys.argv[4] == 'tutor': + list_of_models = [period_folding_model()] + else: + list_of_models = [period_folding_model(), spline_model()] + else: + list_of_models = [period_folding_model(), spline_model()] + + def __init__(self): + pass + def get_out_dict(self, available, m, m_err, xml_file): + # time = x + # time.sort() + # 20080520: dstarr finds that SDSS-II data can have "duplicate" data points, which a noisy result of the photometric pipeline(s?). The median() can often be 0.0, which fouls things. So I use a mean here: + #dt = median( time[1:]-time[:-1] ) + dt = median( available[1:]-available[:-1] ) + maxlogx = log(0.5/dt) # max frequency is ~ the sampling rate + minlogx = log(0.5/(available[-1]-available[0])) #min frequency is 0.5/T + # sample the PSD with 1% fractional precision + M=long(ceil( (maxlogx-minlogx)*1000. )) # could change 100 to 1000 for higher resolution + frequencies = exp(maxlogx-arange(M, dtype=float) / (M-1.) * (maxlogx-minlogx)) + out_dict = self.lomb_code(frequencies, m, m_err, available) + f = (out_dict["freq1"]["harmonics_freq"][0]) + if out_dict["freq2"]["signif"] > out_dict["freq1"]["signif"]: + f = out_dict["freq2"]["frequency"] + narrow_frequencies = arange(0.99*f,1.01*f, 0.00001) + out_dict = self.lomb_code(narrow_frequencies, m, m_err, available) + return out_dict + def lomb_code(self,frequencies, m, m_err, available): + len_av = len(available) + dx = zeros(len_av,dtype=float) + num_freq_comps = 4 + out_dict={} + ytest=m + dof = len_av # don't know why we need to have two separate variables for this + if (dof>=5): + out_dict['frequencies'] = frequencies + out_dict['freq_searched_min']=min(frequencies) + out_dict['freq_searched_max']=max(frequencies) + for i in range(num_freq_comps): + psd, freqs, signi, sim_signi, peak_sort = lomb_scargle.lomb(available,ytest,delta_time=dx, signal_err=m_err,freqin=frequencies,verbosity=2) + imax = psd.argmax() + freq_max = freqs[imax] + void_ytest, harm_dict = pre_whiten.pre_whiten(available, ytest, freq_max, delta_time=dx, signal_err=m_err, dof=dof, nharm_min=1, nharm_max=99) + dstr = "freq%i" % (i+1) + # check for nharm and rerun + nharm = harm_dict["nharm"] + if nharm == 0: + break + print("frequency", i+1, "nharm", nharm) + ytest, harm_dict = pre_whiten.pre_whiten(available, ytest, freq_max, delta_time=dx, signal_err=m_err, dof=dof, nharm_min=nharm, nharm_max=nharm) + out_dict[dstr] = {} + freq_dict = out_dict[dstr] + freq_dict["signif"] = signi + freq_dict["frequency"] = freq_max + for elem_k, elem_v in harm_dict.items(): + freq_dict["harmonics_" + elem_k] = elem_v + dof = dof - harm_dict['nharm']*2. + return out_dict + + def form_vsrc_xml_ts(self, old_ts_dict, times, mags, merrs): + """ form a s_dict['ts'] style dict and return it. + """ + new_ts = copy.deepcopy(old_ts_dict) + assert len(new_ts.keys()) == 1 # DEBUG KLUDGE TEST + band_dict = new_ts.values()[0] + band_dict['m'] = mags + band_dict['m_err'] = merrs + band_dict['t'] = times + return new_ts + + + def obs_request(self, target, needed, band="u"): + # first, have xml_reading parse the xml: + xml_file, picked_band = self.pick_object(target,band) + self.xml_file = xml_file + s_dict, source = xml_reading().read_xml(xml_file = xml_file) + # we're going to dig through the dictionary and define a few useful variables + # this is the sub-dictionary that contains the actually time series data, it is defined by db_importer: + ts = s_dict["ts"] + # ts's sub-entries are the different bands available for this source + # we choose the band we want: + picked_band_key = picked_band + for item in ts.keys(): + if picked_band == item.split(":")[0]: + try: + picked_band_key = picked_band + ":" + item.split(":")[1] + except IndexError: + break + print(picked_band_key) + try: + if len(sys.argv) > 4: + if sys.argv[4] != 'tutor': + band_dic = ts[picked_band] + else: + if picked_band == 'any': + band_dic = ts.values()[0] + else: + bands = ts.keys() + band_dic = {} + for vsrc_band in bands: + if picked_band in vsrc_band: + band_dic = ts[vsrc_band] + break + if len(band_dic) == 0: + raise KeyError + else: + band_dic = ts[picked_band] + except KeyError: + print("print ts.keys()", ts.keys()) + raise KeyError + # we then make a copy of this dictionary for us to work with: + my_dic = band_dic.copy() + # we now prepare for the lomb scargle periodogram + available = array(my_dic["t"]) # available times + available = available - min(available) + m = array(my_dic["m"]) + m_err = array(my_dic["m_err"]) + self.out_dict = self.get_out_dict(available, m, m_err, xml_file) + passed = False + for model in self.list_of_models: + print("trying", model) + try: + model_function = model.create_model(available,m,m_err, self.out_dict) + model_output = model_function(needed) + except AssertionError as description: + print("we caught an assertion error %s" % description) + continue + else: + passed = True + break + print(passed, "passed?") + assert passed, "None of the models supplied worked :-/" + # reduce my_dic to these picked times + model_flux = model_output["flux"] + model_rms = model_output["rms"] + m_new = model_flux + model_lightcurve = [] + avg_model_mag = model_flux.mean() + vosource_fpath = sys.argv[1] + + # TODO: replace modeled_lightcurves/ with some explicit, command-line stated dirpath + if vosource_fpath.count('/') == 0: + sourceid = vosource_fpath[:-4] + else: + sourceid = vosource_fpath[vosource_fpath.rfind('/')+1:\ + vosource_fpath.rfind('.')] + ##### The following will automatically contain the TUTOR Classification info: + temp_ts = {} + temp_ts['default_band'] = my_dic + ### OLD (gets full, original t,m,merr): + #s_dict['ts'] = temp_ts + s_dict['ts'] = self.form_vsrc_xml_ts(temp_ts, needed, model_output["flux"], model_output["rms"]) + source.source_dict_to_xml(s_dict) + out_xml_fpath = "OutputVOSources/%s_%s.xml" % (sourceid, str(avg_model_mag)) + # DEBUG/KLUDGE: (the first generated model seems to have an average mag of 0, while subsequent are >>0) I skip the ~0 source: + if avg_model_mag > 3: + source.write_xml(out_xml_fpath=out_xml_fpath) + + first_time = needed[0] + model_lightcurve_file2 = file("model.txt", "w") + model_lightcurve_file = file("modeled_lightcurves/" + sourceid + "_" + str(avg_model_mag), "w") + for n in range(len(needed)): + model_lightcurve_file.write(str(needed[n]) + "\t" + str(model_flux[n]) + "\t" + str(model_rms[n]) + "\n") + model_lightcurve_file2.write(str(needed[n]-first_time) + "\t" + str(model_flux[n]) + "\t" + str(model_rms[n]) + "\n") + model_lightcurve.append([needed[n], model_flux[n], model_rms[n]]) + model_lightcurve_file.close() + model_lightcurve_file2.close() + my_dic["t"] = needed + my_dic["m"] = m_new + m.mean() + my_dic["m_err"] = model_rms + return {"old data": m, "new data": my_dic["m"], "difference": None, "needed": needed, "available":available, "m_err": m_err, "out_dict": self.out_dict, "my_dic": my_dic, "s_dict":s_dict, "source": source, "old_xmlfile":xml_file} + def pick_object(self,target, band = "u"): + if len(sys.argv) > 1: + xml_filename = sys.argv[1] + band = sys.argv[2] + assert xml_filename.split('.')[1] == 'xml' + if xml_filename.count('/') == 0: + xml_fpath = "VOsources/" + xml_filename + else: + xml_fpath = xml_filename # I expect this to be a full, expanded filepath to .xml + return xml_fpath, band + def read_mags_and_convert(self,my_dic,band): + """ reads the magnitudes from the source dictionary and converts them to janskies + this function assumes the vo_source structure + """ + # each band has an entry for the actual data, the magnitudes, which we convert to a numpy array: + magnitudes = array(my_dic['m']) + # and the uncertainties: + errors = array(my_dic["m_err"]) + # send this off to a separate function in janskies_converter for conversion + janskies_dic = janskies_converter().m_to_janskies(magnitudes,errors,band) + my_dic["janskies"] = janskies_dic["janskies"] + my_dic["j_err"] = janskies_dic["errors"] + return my_dic + + +class use_pickle(observatory_source_interface): + """ This class stores the lomb scargle model in a pickle file to speed up simulation of the same source multiple times """ + def get_out_dict(self, available, m, m_err, xml_file): + if '/' in xml_file: + sourcename = xml_file[xml_file.rfind('/')+1:xml_file.rfind('.')] + else: + sourcename = xml_file.split('.')[0] + sourcename = sourcename.split('/')[1] + band = sys.argv[2] + pklfile = 'pickled_models/' + sourcename + "_" + band + '.pkl' + try: + outdict_file = open(pklfile, 'r') + out_dict = pickle.load(outdict_file) + return out_dict + except IOError: + out_dict = super(use_pickle, self).get_out_dict(available, m, m_err, xml_file) + outdict_file = open(pklfile, 'w') + pickle.dump(out_dict, outdict_file) + return out_dict + +def main(): + + def request_noisified(): + my_obs = observatory_PTF.PTF + # make up an object: + vega = my_obs.create_target(ephem.hours('18:36:56.20'), ephem.degrees('38:46:59.0'), "cepheid") # coordinates of vega + for i in range(10): + mindiff_multiplier = i - 5 + if mindiff_multiplier < 1: + mindiff_multiplier = 1 + t = generic_observatory.time_series_generator() + time_series = t.generate_time_series(vega, my_obs) + print("mindiff_multiplier should be: ", mindiff_multiplier) + try: + output = my_obs.observe(target=vega, times = time_series, band = "V") + except AssertionError as description: + print("Failed %s times so far, because of %s" % ((i+1), description)) + else: + return output + return request_noisified() + +if __name__ == '__main__': + output = main() diff --git a/mltsp/TCP/Algorithms/fitcurve/period_folded_model.txt b/mltsp/TCP/Algorithms/fitcurve/period_folded_model.txt new file mode 100755 index 00000000..4a809f1e --- /dev/null +++ b/mltsp/TCP/Algorithms/fitcurve/period_folded_model.txt @@ -0,0 +1,13 @@ +0.083135 0.119154 0.014450 +0.095055 -0.213515 0.052800 +0.106125 -0.200382 0.034450 +0.117155 -0.271233 0.114150 +0.128545 0.353082 0.101350 +0.003258 1.130176 0.017750 +0.069918 0.461138 0.443700 +0.081048 0.083851 0.014450 +0.091998 -0.276903 0.052800 +0.103158 -0.230852 0.034450 +0.114268 -0.197139 0.114150 +0.126648 0.342404 0.101350 +0.002312 1.155161 0.017750 diff --git a/mltsp/TCP/Algorithms/fitcurve/period_folded_progenitor.txt b/mltsp/TCP/Algorithms/fitcurve/period_folded_progenitor.txt new file mode 100755 index 00000000..94c99a26 --- /dev/null +++ b/mltsp/TCP/Algorithms/fitcurve/period_folded_progenitor.txt @@ -0,0 +1,32 @@ +0.440755 1.751870 0.167700 +0.452115 0.764970 0.074400 +0.463535 0.539770 0.061700 +0.474805 0.376470 0.045600 +0.502385 0.013970 0.030800 +0.513525 -0.062230 0.031100 +0.524665 -0.130630 0.035800 +0.536785 -0.155830 0.039500 +0.547545 -0.032630 0.041500 +0.558245 0.126870 0.050200 +0.569275 0.112870 0.051700 +0.580205 0.005170 0.075400 +0.591245 0.245770 0.072100 +0.602555 0.276770 0.071800 +0.613595 0.282770 0.081400 +0.432221 2.355470 0.178200 +0.442461 1.266470 0.107300 +0.452901 0.750970 0.076700 +0.463601 0.551570 0.054300 +0.473911 0.240270 0.053000 +0.484221 0.056470 0.046000 +0.494661 -0.006230 0.039500 +0.505141 -0.170430 0.036400 +0.515561 -0.248130 0.034200 +0.525941 -0.277530 0.038900 +0.536371 -0.246630 0.044500 +0.546891 -0.243230 0.040800 +0.557481 -0.201030 0.044600 +0.567981 -0.059130 0.049600 +0.578451 -0.092630 0.063500 +0.588961 -0.029130 0.070300 +0.541140 -1.172930 0.088900 diff --git a/mltsp/TCP/Algorithms/fitcurve/phase_data.xml b/mltsp/TCP/Algorithms/fitcurve/phase_data.xml new file mode 100755 index 00000000..b7610514 --- /dev/null +++ b/mltsp/TCP/Algorithms/fitcurve/phase_data.xml @@ -0,0 +1,256 @@ + +3356609 +6.95778972201 +1.45039799048 +-0.0682688064713 +7.02937972201 +0.0419597574403 +-0 +6.95938972201 +0.0150078940119 +-0.00359226900613 +6.89018972202 +0.00936571842057 +-0.0471685124956 + + + +2570332 +2.90423691236 +1.14107802901 +-0.159250093555 +2.91059691236 +0.135928910531 +-0.277634049788 +2.92487691236 +0.0299363944642 +-0.143492181815 +2.90767691236 +0.0207236829602 +-0.157715548325 + + + +2570183 +3.65998218581 +0.960095303557 +-0.0614757090546 +3.69630218581 +0.139756744402 +-0.114979776716 +3.62311218581 +0.152238763046 +-0.110402322502 +3.63298218581 +0.133917215004 +-0.0144578888194 + + + +2813201 +3.39987029668 +0.69394819367 +-0.051472551812 +3.43239029668 +0.0115434895399 +-0.182088849454 +3.36443029668 +0.0162810583622 +-0.0445840712313 +3.43239029668 +0.00926812944243 +-0.0582684318253 + + + +3404778 +8.24044481299 +0.608763695285 +-0.0364058017265 +8.15661481299 +0.00168015446065 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+1.04571668807 +-0.0451905358751 +3.35407907238 +0.00172217232879 +-0.0670824972055 +3.28766907238 +0.00315500018751 +-0.030416686655 +3.35407907238 +0.00263756726241 +-0.0894433296073 + + + +3376953 +7.67669446707 +1.2269336628 +-0.00651322000823 +7.75595446707 +0.00802657933796 +-0.0128933196326 +7.60237446707 +0.00572900315897 +-0.0953649419849 +7.75595446707 +0.00523539194396 +-0.0322332990816 + + + +3426086 +7.98069892496 +1.46022524679 +-0.0939767314934 +7.95550892496 +0.519117580264 +-0.0628495303967 +8.04822892495 +0.0557364491031 +-0.106680099012 +8.01666892496 +0.0128354937821 +-0.0719460516789 + + + +405248 +5.93696047921 +1.19271498768 +-0.0168436357881 +5.9977104792 +0.00205243692488 +-0.0666921154976 +5.87895047921 +0.00380451548614 +-0.0807967343287 +5.9977104792 +0.0038088682357 +-0.062523858279 + + + +3393548 +0.197534194176 +70.7453143109 +-0.253120732886 +0.199454194176 +1.03833753812 +-0.250684124275 +0.195514194176 +1.13108473875 +-0.51147181626 +0.199454194176 +1.55511134166 +-0.250684124275 + + + +448268 +6.71066711032 +1.66961233905 +-0.0819590647186 +6.77914711032 +0.0167478731315 +-0.051628913535 +6.64490711032 +0.0309472297837 +-0 +6.77914711032 +0.0320001022031 +-0.051628913535 + + + +2660820 +1.39365768695 +3.79593177713 +-0.287014525694 +1.40045768695 +0.462906728823 +-0.339174831504 +1.37992768695 +0.174817013604 +-0.271753370518 +1.40779768695 +0.232362430081 +-0.248615268547 + + diff --git a/mltsp/TCP/Algorithms/fitcurve/phase_fold_(50 datapoints).png b/mltsp/TCP/Algorithms/fitcurve/phase_fold_(50 datapoints).png new file mode 100755 index 00000000..bba41d40 Binary files /dev/null and b/mltsp/TCP/Algorithms/fitcurve/phase_fold_(50 datapoints).png differ diff --git a/mltsp/TCP/Algorithms/fitcurve/pre_whiten.py b/mltsp/TCP/Algorithms/fitcurve/pre_whiten.py new file mode 100644 index 00000000..5c962739 --- /dev/null +++ b/mltsp/TCP/Algorithms/fitcurve/pre_whiten.py @@ -0,0 +1,289 @@ +#! /usr/bin/env python +# this is Nat's code copied over from the feature_extract project on August 3rd 2008, but I copied over nat's original svn upload, not Dan's modification (the modifications did not apply to this project) + + +from numpy import * + +from lomb_scargle import lprob2sigma + +def chi2sigma(chi0,chi1,nu0,nharm): + from scipy.stats import betai + from scipy.special import betaln + + nu1 = nu0 - 2.*nharm + dfn = nu0-nu1 + dfd = nu1 + sigma = 0. + if (dfn>0 and dfd>0 and chi0>chi1): + fstat = (chi0/chi1-1.)*dfd/dfn + prob = betai( dfd/2., dfn/2., dfd/(dfd+dfn*fstat) ) + if (dfd<=0 or dfn<=0): lprob=0. + elif (chi1==0): lprob=-999. + elif (prob==0): lprob = 0.5*dfd*log( dfd/(dfd+dfn*fstat) )-log(dfd/2.)-betaln(dfd/2.,dfn/2.) + else: lprob = log(prob) + sigma = lprob2sigma(lprob) + + return sigma + + +def pre_whiten(time, signal, freq, delta_time=[], signal_err=[], dof=-999, +nharm_min=4, nharm_max=20): + """Generates a harmonic fit to data (time,signal) with at most + nharm_max harmonics relative to the fundamental frequency freq. + Report statistics for nharm_min of them. + """ + n0 = len(time) + if (dof==-999 or dof>n0): dof=n0 + + A0 = zeros(nharm_max,dtype=float) + dA0 = zeros(nharm_max,dtype=float) + B0 = zeros(nharm_max,dtype=float) + dB0 = zeros(nharm_max,dtype=float) + pha = zeros(nharm_max,dtype=float) + + # if data error not given, assume all are unity + if (signal_err==[]): + wt = ones(n0,dtype=float) + else: + wt = 1./signal_err**2; + wt[signal_err<=0]=1. + + # if delta_time not given, assume 0 + do_sync=True + if (delta_time==[]): + do_sync=False + delta_time = zeros(n0, dtype=float) + + sync_func = lambda x: 1. + if (do_sync): + sync_func = lambda x: (1.e-99 + sin(pi*x))/(1.e-99 + pi*x) + + x = 2*pi*freq*time + cn = 1.*signal + + # just in case it wasn't already subtracted away + s0 = sum(wt) + mn = sum(cn*wt) / s0 + cn_offset = mn + cn -= cn_offset + dof = dof - 1. + + # initial chi^2 value for constant fit + nu0 = dof + chi0 = sum( cn**2*wt ) + + # + n_grid = (1+nharm_max)*20 + x_grid = 2*pi*arange(n_grid,dtype=float)/(1.*n_grid) + modls = zeros([nharm_max,n_grid],dtype=float) + modlc = zeros([nharm_max,n_grid],dtype=float) + modl0 = zeros(n_grid,dtype=float) + vmodl0 = zeros(n_grid,dtype=float) + + # + # do the dirty work, fit for the sinusoid amplitudes and phases + # modulus cn = A*sin(x+pha) , of A0*sin(x)+B0*cos(x) + # + chi1_last = chi0 + stop = 0 + for i in range(nharm_max): + + j = i+1 + + synct = sync_func(freq*j*delta_time) + sinx = sin(j*x)*synct + cosx = cos(j*x)*synct + + ts2 = 2.*sum( sinx*cosx*wt ) + tc2 = sum( (cosx**2-sinx**2)*wt ) + + x0 = 0.5*arctan2(ts2, tc2)/j + sinomtau = sin(j*x0) + cosomtau = cos(j*x0) + sin2omtau = 2.*sinomtau*cosomtau; + cos2omtau = cosomtau**2 - sinomtau**2 + + tmp = tc2*cos2omtau + ts2*sin2omtau; + tc2 = 0.5*(s0+tmp); + ts2 = 0.5*(s0-tmp); + + tmp = sinx + sinx = tmp*cosomtau-cosx*sinomtau + cosx = cosx*cosomtau + tmp*sinomtau + + sh = sum( cn*sinx*wt ) + ts1 = sum( sinx*wt ) + + ch = sum( cn*cosx*wt ) + tc1 = sum( cosx*wt ) + + A0[i] = sh / ts2; dA0[i] = 1./sqrt(ts2); + B0[i] = ch / tc2; dB0[i] = 1./sqrt(tc2); + cn0 = ( A0[i]*ts1 + B0[i]*tc1 ) / ( ts1**2/ts2 + tc1**2/tc2 - s0 ); + A0[i] -= cn0*ts1/ts2; + B0[i] -= cn0*tc1/tc2; + + pha[i] = arctan2(B0[i],A0[i]) - j*x0 + + cn_test = cn - cn0 - ( A0[i]*sinx + B0[i]*cosx ) * synct + + chi1 = sum( cn_test**2*wt ) # chi^2 for harmonic component removed + if (chi1 > chi1_last*(nu0-2*j)/(nu0-2*(j-1)) or j==nharm_max): + stop=1 + nharm = i + sigma = chi2sigma(chi0,chi1,nu0,nharm) + + if (stop==1 and j>nharm_min): # calculate >= nharm_min harmonics + + A0 = A0[:i] + dA0 = dA0[:i] + B0 = B0[:i] + dB0 = dB0[:i] + pha = pha[:i] + modls = modls[:i,:] + modlc = modlc[:i,:] + break + + chi1_last = chi1 + cn = cn_test + cn_offset += cn0 + + modls[i,:] = sin(j*(x_grid-x0)) + modlc[i,:] = cos(j*(x_grid-x0)) + modl0 += A0[i]*modls[i,:] + B0[i]*modlc[i,:] + vmodl0 += (dA0[i]*modls[i,:])**2 + (dB0[i]*modlc[i,:])**2 + + + # find light curve extremum for bookkeeping + pk = argmax(modl0*modl0) + # report phases relative to extremum, time' = time - time_offset + time_offset = - x_grid[pk] / (2*pi*freq) + for i in range(nharm): + pha[i] = pha[i] - (1+i)*x_grid[pk] + pha[i] = arctan2( sin(pha[i]),cos(pha[i]) ) + + x_grid = x_grid-x_grid[pk] + x_grid = arctan2(sin(x_grid),cos(x_grid)) + i0 = argmin(x_grid) + i1 = argmax(x_grid) + + # + # error propagation + # + A = sqrt( A0**2 + B0**2 ) + dA = sqrt( (A0*dA0)**2 + (B0*dB0)**2 ) / A + dpha = 1./(1+(A0/B0)**2) * sqrt( (dA0/B0)**2+(A0*dB0/B0**2)**2 ) + + # use the chi^2 values to get a better (more conservative) error estimate + fac=1. + sigma0 = A[0]/dA[0] + if (sigma0>sigma and sigma>0): fac = sigma0/sigma + # now apply it + dA = fac*dA + vmodl0 = fac**2*vmodl0 + dpha = fac*dpha + + # + # get flux extrema + # + mn = argmin(modl0) + fmin = modl0[mn] + vfmin = vmodl0[mn] + mx = argmax(modl0) + fmax = modl0[mx] + vfmax = vmodl0[mx] + peak2peak_flux = fmax - fmin + peak2peak_flux_error = sqrt( vfmin+vfmax ) + + # + # set flux offset and sign for moment calculation below + # + s0 = 0.5*(modl0[i0]+modl0[i1]) + vs0 = 0.25*(vmodl0[i0]+vmodl0[i1]) + + #import Gnuplot + #import time + #plotobj = Gnuplot.Gnuplot() + #plotobj.xlabel('Time (s)') + #plotobj.ylabel('Folded Light Curve') + #plotobj('unset logscale x') + #plotobj.plot(Gnuplot.Data(x_grid/(2*pi*freq),modl0-s0)) + #time.sleep(3) + + niter=10 + for k in range(niter): + if (k==0): window = 1. + + nmom = 4 # number of moments requested (don't change) + mu = zeros(1+nmom,dtype=float) + vmu = zeros(1+nmom,dtype=float) + x0=0. + norm=1. + for j in range(1+nmom): + xfac = pow(x_grid-x0,j)*window + mu[j] = -s0*mean(xfac)/norm + vmu[j] = vs0*(mean(xfac)/norm)**2 + for k in range(nharm): + mas = mean( xfac*modls[k,:] ) + mac = mean( xfac*modlc[k,:] ) + mu[j] = mu[j] + (A0[k]*mas + B0[k]*mac)/norm + vmu[j] = vmu[j] + (dA0[k]*mas/norm)**2 + (dB0[k]*mac/norm)**2 + if (j==0 and abs(mu[0])>0): norm = mu[0] + if (j==1): x0 = mu[1] + if (j==2): window = 1*( abs(x_grid-x0) < 3.*sqrt(mu[2]) ) + if (sum(window)==n_grid): break + + # defaults + moments = array([0.,0.5/freq,0.,0.]) + dmoments = array([1.,0.5/freq,1.,1.]) + if (abs(mu[0])>0 and mu[2]>0): + av = x0 + var = mu[2] + stdev = sqrt(var) + skewness = mu[3] / mu[2]**1.5 + kurtosis = mu[4] / mu[2]**2 - 3. + # error propagation is approximate + dav = sqrt(vmu[1]) + dstdev = 0.5*sqrt(vmu[2]/mu[2]) + dskewness = sqrt( vmu[3] ) / var**1.5 + dkurtosis = sqrt( vmu[4] ) / var**2. + + moments = array([av,stdev/(2*pi*freq),skewness,kurtosis]) + dmoments = array([dav,dstdev/(2*pi*freq),dskewness,dkurtosis]) + + # + # put it all in a dictionary + # + freqs = freq*(1+arange(nharm_min)) + #out_dict = { 'signif': sigma, 'peak2peak_flux': peak2peak_flux, + # 'peak2peak_flux_error': peak2peak_flux_error, 'amplitude': A[:nharm_min], + # 'freq': freqs, 'amplitude_error': dA[:nharm_min], 'rel_phase': pha[:nharm_min], + # 'rel_phase_error': dpha[:nharm_min], 'moments': moments, + # 'moments_err': dmoments, 'nharm': nharm} + + # 20080508: dstarr wants verbosely labeled dict: + out_dict = { 'signif': sigma, 'peak2peak_flux': peak2peak_flux, + 'peak2peak_flux_error': peak2peak_flux_error, 'nharm': nharm} + + for i in range(len(moments)): + out_dict['moments_' + str(i)] = moments[i] + for i in range(len(dA[:nharm_min])): + out_dict['amplitude_error_' + str(i)] = dA[:nharm_min][i] + for i in range(len(dmoments)): + out_dict['moments_err_' + str(i)] = dmoments[i] + for i in range(len(dpha[:nharm_min])): + out_dict['rel_phase_error_' + str(i)] = dpha[:nharm_min][i] + for i in range(len(A[:nharm_min])): + out_dict['amplitude_' + str(i)] = A[:nharm_min][i] + for i in range(len(pha[:nharm_min])): + out_dict['rel_phase_' + str(i)] = pha[:nharm_min][i] + for i in range(len(freqs)): + out_dict['freq_' + str(i)] = freqs[i] + + out_dict = { 'signif': sigma, 'peak2peak_flux': peak2peak_flux, +'peak2peak_flux_error': peak2peak_flux_error, 'amplitude': A[:nharm_min], +'freq': freqs, 'amplitude_error': dA[:nharm_min], 'rel_phase': pha[:nharm_min], +'rel_phase_error': dpha[:nharm_min], 'moments': moments, +'moments_err': dmoments, 'nharm': nharm, 'time_offset': time_offset,'y_offset':cn_offset} + + return cn, out_dict diff --git a/mltsp/TCP/Algorithms/fitcurve/progenitor.txt b/mltsp/TCP/Algorithms/fitcurve/progenitor.txt new file mode 100755 index 00000000..66231c8b --- /dev/null +++ b/mltsp/TCP/Algorithms/fitcurve/progenitor.txt @@ -0,0 +1,32 @@ +2454939.824650 1.751870 0.167700 +2454939.836010 0.764970 0.074400 +2454939.847430 0.539770 0.061700 +2454939.858700 0.376470 0.045600 +2454939.886280 0.013970 0.030800 +2454939.897420 -0.062230 0.031100 +2454939.908560 -0.130630 0.035800 +2454939.920680 -0.155830 0.039500 +2454939.931440 -0.032630 0.041500 +2454939.942140 0.126870 0.050200 +2454939.953170 0.112870 0.051700 +2454939.964100 0.005170 0.075400 +2454939.975140 0.245770 0.072100 +2454939.986450 0.276770 0.071800 +2454939.997490 0.282770 0.081400 +2454944.838870 2.355470 0.178200 +2454944.849110 1.266470 0.107300 +2454944.859550 0.750970 0.076700 +2454944.870250 0.551570 0.054300 +2454944.880560 0.240270 0.053000 +2454944.890870 0.056470 0.046000 +2454944.901310 -0.006230 0.039500 +2454944.911790 -0.170430 0.036400 +2454944.922210 -0.248130 0.034200 +2454944.932590 -0.277530 0.038900 +2454944.943020 -0.246630 0.044500 +2454944.953540 -0.243230 0.040800 +2454944.964130 -0.201030 0.044600 +2454944.974630 -0.059130 0.049600 +2454944.985100 -0.092630 0.063500 +2454944.995610 -0.029130 0.070300 +2455016.701420 -1.172930 0.088900 diff --git a/mltsp/TCP/Algorithms/fitcurve/scipy_interpolate.png b/mltsp/TCP/Algorithms/fitcurve/scipy_interpolate.png new file mode 100755 index 00000000..003a017e Binary files /dev/null and b/mltsp/TCP/Algorithms/fitcurve/scipy_interpolate.png differ diff --git a/mltsp/TCP/Algorithms/fitcurve/scipy_interpolate_splrep.png b/mltsp/TCP/Algorithms/fitcurve/scipy_interpolate_splrep.png new file mode 100755 index 00000000..7d94a807 Binary files /dev/null and b/mltsp/TCP/Algorithms/fitcurve/scipy_interpolate_splrep.png differ diff --git a/mltsp/TCP/Algorithms/fitcurve/spline_fit.py b/mltsp/TCP/Algorithms/fitcurve/spline_fit.py new file mode 100644 index 00000000..f5ff18c3 --- /dev/null +++ b/mltsp/TCP/Algorithms/fitcurve/spline_fit.py @@ -0,0 +1,119 @@ +from numpy import * +from scipy import array +from scipy.interpolate import splprep, splev, interp1d + + +def append(arr1, arr2): + arr1_list = [] + arr2_list = [] + gen_arr = array([4,5]) + if not(type(arr1) == type(gen_arr)): + arr1_list.append(arr1) + for value in arr2: + arr2_list.append(value) + if not(type(arr2) == type(gen_arr)): + arr2_list.append(arr2) + for value in arr1: + arr1_list.append(value) + if (type(arr1) == type(gen_arr) and (type(arr2) == type(gen_arr))): + for value in arr1: + arr1_list.append(value) + for value in arr2: + arr2_list.append(value) + for value in arr2_list: + arr1_list.append(value) + combined_arr = array(arr1_list) + return combined_arr + + +# Given input arrays of times, magntidues, and requested_times, this function +# returns the spline_fitted_magnitudes. +def spline_fitted_magnitudes(times, magnitudes, errors, requested_times, mindiff = None): + # Spline parameters: + k=5 # spline order + nest=-1 # estimate of number of knots needed (-1 = maximal) + + fitted_errors = empty(0) + fitted_magnitudes = empty(0) + for rtime, window in window_creator(times, requested_times, mindiff = mindiff): + wtimes = times[window] + wmagnitudes = magnitudes[window] + werrors = errors[window] + + # spline parameter: + s=len(wtimes)/100. # smoothness parameter + + # Find the knot points. + tckp, u = splprep([wtimes, wmagnitudes],s=s,k=k,nest=-1) + + # Evaluate spline, including interpolated points. + new_times, new_magnitudes = splev(linspace(0,1,len(wtimes)),tckp) + + # Define an interpolating function along the spline fit. + interpolating_function = interp1d(new_times, new_magnitudes, kind = "linear") + + # Interpolate linerarly along the spline at the requested times. + fitted_magnitude = interpolating_function(rtime) + fitted_magnitudes = append(fitted_magnitudes, fitted_magnitude) + + for m in range(len(wtimes)-1): + if (rtime > wtimes[m]) and (rtime < wtimes[m+1]): + error = (werrors[m] + werrors[m+1])*0.5 + fitted_errors = append(fitted_errors, error) + return fitted_magnitudes, fitted_errors + +def spline_fitted_magnitudes_brute(times, magnitudes, errors, requested_times): + # Spline parameters: + s=len(times)/100. # smoothness parameter + k=5 # spline order + nest=-1 # estimate of number of knots needed (-1 = maximal) + + # Find the knot points. + tckp, u = splprep([times, magnitudes],s=s,k=k,nest=-1) + + # Evaluate spline, including interpolated points. + new_times, new_magnitudes = splev(linspace(0,1,len(times)),tckp) + + # Define an interpolating function along the spline fit. + interpolating_function = interp1d(new_times, new_magnitudes, kind = "linear") + + # Interpolate linerarly along the spline at the requested times. + fitted_magnitudes = interpolating_function(requested_times) + + fitted_errors = array([]) + for n in range(len(requested_times)): + for m in range(len(times)-1): + if (requested_times[n] > times[m]) and (requested_times[n] < times[m+1]): + error = (errors[m] + errors[m+1])*0.5 + fitted_errors = append(fitted_errors, error) + return fitted_magnitudes, fitted_errors + +def window_creator(times, requested_times, mindiff = None): + if not mindiff: + mindiff = min(abs(diff(requested_times))) + for rtime in requested_times: + itime = argmin(abs(times - rtime)) # index of the time that best matches the requested times + diffs = abs(diff(times)) # spacings between times + ibad = where(diffs > mindiff)[0] + 1 # indices where two points are too far apart for a good spline fit + ibad = append(0, ibad) # add the edges + ibad = append(ibad, times.size - 1) + ibadl = ibad[ibad < itime] # indices where two points are too far apart and that are *before* the requested time (l="lower") + ibadu = ibad[ibad > itime] # indices where two points are too far apart and that are *after* the requested time (u="upper") + assert ibadl.size > 0, "the requested time is right at the edge of available times" + assert ibadu.size > 0, "the requested time is right at the edge of available times" + nearestbadl = ibadl[-1] # closest bad time before the requested time + nearestbadu = ibadu[0] # closest bad time after the requested time + assert itime - nearestbadl > 1, "can't be right next to the edge" + assert nearestbadu - itime > 1, "can't be right next to the edge" + window = arange(nearestbadl,nearestbadu) + assert window.size > 5, "window size is too small" + yield rtime, window + +def window_tttimes(times, requested_times, ttimes): + poverlap = zeros(ttimes.size).astype(bool) + for rtime, window in window_creator(times,requested_times): + boundaries = (times[window[0]], times[window[-1]]) + overlap = logical_and( ttimes < boundaries[1] , ttimes > boundaries[0]) + poverlap = logical_or(overlap, poverlap) + wttimes = ttimes[poverlap] + return wttimes \ No newline at end of file diff --git a/mltsp/TCP/Algorithms/fitcurve/test.txt b/mltsp/TCP/Algorithms/fitcurve/test.txt new file mode 100755 index 00000000..1cac6bfc --- /dev/null +++ b/mltsp/TCP/Algorithms/fitcurve/test.txt @@ -0,0 +1,231 @@ +Check that you can query the mysql db to get the harmonic's data rather than running this code: + + + +Source RA, DEC = (188.70795310,8.83488902) + +src_id = 456785, may + +primary: +f = 2.8966 +amp = 0.906 +offset = -0.163985 + +second: +f = 2.95511 +amp = .0010337 +offset = -0.0084599 + +third = +f= 2.8966 +amp = 0.00105 +offset = -0.0431 + +fourth: +f = 2.95511 +amp = .001337 +offset = -0.0084599 + +---------------- +potential first frequency: 386, 692 + +Corresponding mysql query: +feat_id, feat_name in feat_lookup where filter_id=8 ++---------+-------------------------------------------+ +| feat_id | feat_name | ++---------+-------------------------------------------+ +| 1056 | amplitude | +| 282 | beyond1std | +| 435 | chi2 | +| 1218 | chi2_per_deg | +| 1074 | closest_in_light | +| 588 | closest_in_light_absolute_bmag | +| 1425 | closest_in_light_angle_from_major_axis | +| 1740 | closest_in_light_angular_offset_in_arcmin | +| 120 | closest_in_light_dm | +| 57 | closest_in_light_physical_offset_in_kpc | +| 363 | closest_in_light_ttype | +| 183 | dc | +| 777 | distance_in_arcmin_to_nearest_galaxy | +| 1578 | distance_in_kpc_to_nearest_galaxy | +| 714 | dist_from_u | +| 372 | ecpb | +| 1506 | ecpl | +| 1641 | first_lomb | +| 147 | fourier | +| 606 | freq1 | +| 1020 | freq1_harmonics_amplitude_0 | +| 273 | freq1_harmonics_amplitude_1 | +| 480 | freq1_harmonics_amplitude_2 | +| 651 | freq1_harmonics_amplitude_3 | +| 426 | freq1_harmonics_amplitude_error_0 | +| 210 | freq1_harmonics_amplitude_error_1 | +| 1083 | freq1_harmonics_amplitude_error_2 | +| 1200 | freq1_harmonics_amplitude_error_3 | +| 1713 | freq1_harmonics_freq_0 | +| 417 | freq1_harmonics_freq_1 | +| 1533 | freq1_harmonics_freq_2 | +| 1434 | freq1_harmonics_freq_3 | +| 660 | freq1_harmonics_moments_0 | +| 1182 | freq1_harmonics_moments_1 | +| 903 | freq1_harmonics_moments_2 | +| 1335 | freq1_harmonics_moments_3 | +| 219 | freq1_harmonics_moments_err_0 | +| 1092 | freq1_harmonics_moments_err_1 | +| 1362 | freq1_harmonics_moments_err_2 | +| 1551 | freq1_harmonics_moments_err_3 | +| 525 | freq1_harmonics_nharm | +| 246 | freq1_harmonics_peak2peak_flux | +| 1272 | freq1_harmonics_peak2peak_flux_error | +| 1254 | freq1_harmonics_rel_phase_0 | +| 804 | freq1_harmonics_rel_phase_1 | +| 552 | freq1_harmonics_rel_phase_2 | +| 615 | freq1_harmonics_rel_phase_3 | +| 381 | freq1_harmonics_rel_phase_error_0 | +| 696 | freq1_harmonics_rel_phase_error_1 | +| 732 | freq1_harmonics_rel_phase_error_2 | +| 1623 | freq1_harmonics_rel_phase_error_3 | +| 327 | freq1_harmonics_signif | +| 1686 | freq1_signif | +| 894 | freq2 | +| 633 | freq2_harmonics_amplitude_0 | +| 1524 | freq2_harmonics_amplitude_1 | +| 912 | freq2_harmonics_amplitude_2 | +| 237 | freq2_harmonics_amplitude_3 | +| 1704 | freq2_harmonics_amplitude_error_0 | +| 174 | freq2_harmonics_amplitude_error_1 | +| 723 | freq2_harmonics_amplitude_error_2 | +| 1299 | freq2_harmonics_amplitude_error_3 | +| 1515 | freq2_harmonics_freq_0 | +| 1614 | freq2_harmonics_freq_1 | +| 795 | freq2_harmonics_freq_2 | +| 48 | freq2_harmonics_freq_3 | +| 1038 | freq2_harmonics_moments_0 | +| 1461 | freq2_harmonics_moments_1 | +| 12 | freq2_harmonics_moments_2 | +| 786 | freq2_harmonics_moments_3 | +| 165 | freq2_harmonics_moments_err_0 | +| 858 | freq2_harmonics_moments_err_1 | +| 1119 | freq2_harmonics_moments_err_2 | +| 21 | freq2_harmonics_moments_err_3 | +| 1659 | freq2_harmonics_nharm | +| 759 | freq2_harmonics_peak2peak_flux | +| 1245 | freq2_harmonics_peak2peak_flux_error | +| 1146 | freq2_harmonics_rel_phase_0 | +| 1290 | freq2_harmonics_rel_phase_1 | +| 93 | freq2_harmonics_rel_phase_2 | +| 111 | freq2_harmonics_rel_phase_3 | +| 1668 | freq2_harmonics_rel_phase_error_0 | +| 1317 | freq2_harmonics_rel_phase_error_1 | +| 1650 | freq2_harmonics_rel_phase_error_2 | +| 1029 | freq2_harmonics_rel_phase_error_3 | +| 822 | freq2_harmonics_signif | +| 39 | freq2_signif | +| 1416 | freq3 | +| 939 | freq3_harmonics_amplitude_0 | +| 390 | freq3_harmonics_amplitude_1 | +| 336 | freq3_harmonics_amplitude_2 | +| 1695 | freq3_harmonics_amplitude_3 | +| 444 | freq3_harmonics_amplitude_error_0 | +| 399 | freq3_harmonics_amplitude_error_1 | +| 102 | freq3_harmonics_amplitude_error_2 | +| 813 | freq3_harmonics_amplitude_error_3 | +| 84 | freq3_harmonics_freq_0 | +| 1587 | freq3_harmonics_freq_1 | +| 1209 | freq3_harmonics_freq_2 | +| 1002 | freq3_harmonics_freq_3 | +| 318 | freq3_harmonics_moments_0 | +| 687 | freq3_harmonics_moments_1 | +| 291 | freq3_harmonics_moments_2 | +| 975 | freq3_harmonics_moments_3 | +| 75 | freq3_harmonics_moments_err_0 | +| 561 | freq3_harmonics_moments_err_1 | +| 1155 | freq3_harmonics_moments_err_2 | +| 750 | freq3_harmonics_moments_err_3 | +| 1371 | freq3_harmonics_nharm | +| 264 | freq3_harmonics_peak2peak_flux | +| 1497 | freq3_harmonics_peak2peak_flux_error | +| 1128 | freq3_harmonics_rel_phase_0 | +| 768 | freq3_harmonics_rel_phase_1 | +| 570 | freq3_harmonics_rel_phase_2 | +| 1227 | freq3_harmonics_rel_phase_3 | +| 1389 | freq3_harmonics_rel_phase_error_0 | +| 948 | freq3_harmonics_rel_phase_error_1 | +| 849 | freq3_harmonics_rel_phase_error_2 | +| 624 | freq3_harmonics_rel_phase_error_3 | +| 597 | freq3_harmonics_signif | +| 1452 | freq3_signif | +| 1677 | freq_searched_max | +| 1101 | freq_searched_min | +| 1560 | galb | +| 1137 | gall | +| 1470 | interng | +| 885 | intersdss | +| 876 | linear | +| 1326 | lomb | +| 300 | lomb_scargle | +| 471 | max | +| 516 | max_slope | +| 579 | median | +| 993 | median_buffer_range_percentage | +| 1344 | min | +| 642 | n_points | +| 543 | pair_slope_trend | +| 3 | percent_amplitude | +| 498 | position_intermediate | +| 930 | power | +| 1479 | power_spectrum | +| 1542 | ratio21 | +| 129 | ratio31 | +| 138 | ratio32 | +| 1731 | sdss_best_dm | +| 669 | sdss_best_offset_in_kpc | +| 309 | sdss_best_offset_in_petro_g | +| 354 | sdss_best_z | +| 489 | sdss_best_zerr | +| 1353 | sdss_chicago_class | +| 1596 | sdss_dered_g | +| 741 | sdss_dered_i | +| 984 | sdss_dered_r | +| 1632 | sdss_dered_u | +| 1605 | sdss_dered_z | +| 1488 | sdss_dist_arcmin | +| 1164 | sdss_first_flux_in_mjy | +| 534 | sdss_first_offset_in_arcsec | +| 1236 | sdss_in_footprint | +| 507 | sdss_nearest_obj_type | +| 867 | sdss_petro_radius_g | +| 831 | sdss_petro_radius_g_err | +| 1173 | sdss_photo_rest_abs_g | +| 1722 | sdss_photo_rest_abs_i | +| 966 | sdss_photo_rest_abs_r | +| 1398 | sdss_photo_rest_abs_u | +| 1065 | sdss_photo_rest_abs_z | +| 957 | sdss_photo_rest_gr | +| 1443 | sdss_photo_rest_iz | +| 66 | sdss_photo_rest_ri | +| 255 | sdss_photo_rest_ug | +| 1380 | sdss_photo_z_pztype | +| 921 | sdss_rosat_flux_in_mJy | +| 201 | sdss_rosat_log_xray_luminosity | +| 705 | sdss_rosat_offset_in_arcsec | +| 1407 | sdss_rosat_offset_in_sigma | +| 345 | sdss_spec_confidence | +| 1110 | second_lomb | +| 462 | sine_fit | +| 678 | sine_leastsq | +| 1191 | sine_lomb | +| 192 | skew | +| 1308 | std | +| 1569 | stdvs_from_u | +| 408 | tmpned | +| 1011 | weighted_average | +| 453 | wei_av_uncertainty | +| 156 | ws_variability_bv | +| 1047 | ws_variability_gr | +| 1281 | ws_variability_iz | +| 1263 | ws_variability_ri | +| 30 | ws_variability_ru | +| 228 | ws_variability_self | +| 840 | ws_variability_ug | ++---------+-------------------------------------------+ diff --git a/mltsp/TCP/Algorithms/fitcurve/test_lomb_scargle_refine.py b/mltsp/TCP/Algorithms/fitcurve/test_lomb_scargle_refine.py new file mode 100644 index 00000000..603e978b --- /dev/null +++ b/mltsp/TCP/Algorithms/fitcurve/test_lomb_scargle_refine.py @@ -0,0 +1,201 @@ +#!/usr/bin/env python +""" +v 0.1 2011-04-21 Code from Nat + +######### +Heres an example invocation to test: + +from lomb_scargle_refine import lomb as lombr + +sys_err=0.05 +(x,y,dy)=load_some_data +dy0 = sqrt(dy**2+sys_err**2) + +Xmax = x.max() +f0 = 1./Xmax; df = 0.1/Xmax; fe = 10. +numf = int((fe-f0)/df) +freqin = f0 + df*arange(numf,dtype='float64') + +psd,res = lombr(x,y,dy0,f0,df,numf) +plot (freqin,psd) + +### +The default is to fit 8 harmonics to every initial lomb-scargle peak +above 6, with 0th order detrending (fitting mean only). Dan, if you +think I should, I can put the logic to define the frequency grid in +the main code and not in a wrapper like this. + +res is a dictionary containing the stuff previously reported by +pre_whiten: amplitudes, phases, the folded model, etc. + +To iterate 3 times (in not the most elegant fashion): +psd,res = lombr(x,y,dy0,f0,df,numf) +psd1,res1 = lombr(x,y-res['model'],dy0,f0,df,numf) +psd2,res2 = lombr(x,y-res['model']-res1['model'],dy0,f0,df,numf) + +In the way we did this for the Deboscher paper, the first invocation +would have detrend_order=1 set, and we would use nharm=4 throughout. +I suspect we might get more mileage out of leaving nharm=8, but only +using stats on the first 4 as features. + +""" +from __future__ import print_function + + +import sys, os +import random +from numpy import * + + +class Database_Utils: + """ Establish database connections, contains methods related to database tables. + """ + def __init__(self, pars={}): + self.pars = pars + #self.connect_to_db() + + + def connect_to_db(self): + import MySQLdb + self.tcp_db = MySQLdb.connect(host=pars['tcp_hostname'], \ + user=pars['tcp_username'], \ + db=pars['tcp_database'],\ + port=pars['tcp_port']) + self.tcp_cursor = self.tcp_db.cursor() + + self.tutor_db = MySQLdb.connect(host=pars['tutor_hostname'], \ + user=pars['tutor_username'], \ + db=pars['tutor_database'], \ + passwd=pars['tutor_password'], \ + port=pars['tutor_port']) + self.tutor_cursor = self.tutor_db.cursor() + + + def get_timeseries_for_source(self, source_id=None): + """ Get timeseries data from tutor when given a source_id + + """ + select_str = """SELECT sources.source_id, observation_id, obsdata_time, obsdata_val, obsdata_err + FROM sources + JOIN observations USING (source_id) + JOIN obs_data USING (observation_id) + WHERE source_id=%d + """ % (source_id) + self.tutor_cursor.execute(select_str) + results = self.tutor_cursor.fetchall() + if len(results) == 0: + raise "Error" + + t_list = [] + m_list = [] + m_err_list = [] + used_src_id = results[0][0] + used_obs_id = results[0][1] + for (src_id, obs_id, t, m, err) in results: + if ((src_id == used_src_id) and + (obs_id == used_obs_id)): + t_list.append(t) + m_list.append(m) + m_err_list.append(err) + + return {'t':array(t_list), + 'm':array(m_list), + 'm_err':array(m_err_list)} + + + +if __name__ == '__main__': + + ### NOTE: most of the RDB parameters were dupliclated from ingest_toolspy::pars{} + pars = { \ + 'tutor_hostname':'192.168.1.103', #'lyra.berkeley.edu', + 'tutor_username':'dstarr', #'tutor', # guest + 'tutor_password':'ilove2mass', #'iamaguest', + 'tutor_database':'tutor', + 'tutor_port':3306, + 'tcp_hostname':'192.168.1.25', + 'tcp_username':'pteluser', + 'tcp_port': 3306, + 'tcp_database':'source_test_db', + 'high_conf_srcids':range(22), #[241682, 238040, 221547, 225633, 227203, 250761, 219325, 252782, 245584, 236706, 216173, 225396, 233750, 232693, 263653, 216768, 225919, 264626, 230520, 229680, 231266, 221448, 226872, 261712], # Used for session=0, iter=1 (1st) + } + + + dbutil = Database_Utils(pars=pars) + + #import pdb; pdb.set_trace() + #print + + + ######### + #Heres an example invocation to test: + + from lomb_scargle_refine import lomb as lombr + + sys_err=0.05 + #(x,y,dy)=load_some_data + + fpath = '/home/training/scratch/ls_src.dat' + if os.path.exists(fpath): + x = [] + y = [] + dy = [] + fp = open(fpath) + lines = fp.readlines() + for line in lines: + e = line.split() + x.append(float(e[0])) + y.append(float(e[1])) + dy.append(float(e[2])) + fp.close() + x = array(x) + y = array(y) + dy = array(dy) + + + else: + data = dbutil.get_timeseries_for_source(source_id=241682) + x = data['t'] + y = data['m'] + dy = data['m_err'] + + fp = open(fpath, 'w') + for i in range(len(x)): + fp.write("%lf %lf %lf\n" % (x[i], y[i], dy[i])) + fp.close() + + + dy0 = sqrt(dy**2+sys_err**2) + + Xmax = x.max() + f0 = 1./Xmax; df = 0.1/Xmax; fe = 10. + numf = int((fe-f0)/df) + freqin = f0 + df*arange(numf,dtype='float64') + + #psd,res = lombr(x,y,dy0,f0,df,numf) + psd,res = lombr(x,y,dy0,f0,df,numf, detrend_order=1) + import pdb; pdb.set_trace() + print() + psd1,res1 = lombr(x,y-res['model'],dy0,f0,df,numf, detrend_order=0) + plot (freqin,psd) + + ### + """ + The default is to fit 8 harmonics to every initial lomb-scargle peak + above 6, with 0th order detrending (fitting mean only). Dan, if you + think I should, I can put the logic to define the frequency grid in + the main code and not in a wrapper like this. + + res is a dictionary containing the stuff previously reported by + pre_whiten: amplitudes, phases, the folded model, etc. + + To iterate 3 times (in not the most elegant fashion): + psd,res = lombr(x,y,dy0,f0,df,numf) + psd1,res1 = lombr(x,y-res['model'],dy0,f0,df,numf) + psd2,res2 = lombr(x,y-res['model']-res1['model'],dy0,f0,df,numf) + + In the way we did this for the Deboscher paper, the first invocation + would have detrend_order=1 set, and we would use nharm=4 throughout. + I suspect we might get more mileage out of leaving nharm=8, but only + using stats on the first 4 as features. + """ diff --git a/mltsp/TCP/Algorithms/fitcurve/weird_first_generation_phase.png b/mltsp/TCP/Algorithms/fitcurve/weird_first_generation_phase.png new file mode 100755 index 00000000..691394ef Binary files /dev/null and b/mltsp/TCP/Algorithms/fitcurve/weird_first_generation_phase.png differ diff --git a/mltsp/TCP/Algorithms/fiteb.py b/mltsp/TCP/Algorithms/fiteb.py new file mode 100644 index 00000000..85eaa499 --- /dev/null +++ b/mltsp/TCP/Algorithms/fiteb.py @@ -0,0 +1,734 @@ +#!/usr/bin/env python + +""" +EB fitting code wrapping around JKTEBOP (http://www.astro.keele.ac.uk/~jkt/codes/jktebop.html) + +v1.0 written by J. S. Bloom +v1.1 updated by dstarr + +From the command line: + + a) this will fit dotastro source 237224 on a period of 2.55535306 and plot the results + ./fiteb.py 237224 2.55535306 -p + + b) this will fit dotastro source 237224 on a period of 2.55535306 and plot the results using + alternative starting point 0 + ./fiteb.py 237224 2.55535306 -p -a --altnum=0 + + c) this will fit dotastro source 237224 on a period of 2.55535306 and plot the results using + alternative starting point 1 + ./fiteb.py 237224 2.55535306 -p -a --altnum=1 + + d) will fit dotastro source 237224 on a period of 2.55535306 and plot the results using + all three starting points. Finding the best fit result and the best harmonic of the period + ./fiteb.py 237224 2.55535306 -p -s -a + *** this is the preferred usage to make sure you have a wide sweep over parameter space + +From the command line, the meat of the code can be run in three different ways: + + a) Dotastro source, where the XML is grabbed off of the web + e = fiteb.EB(dotastro_id=217688,period=2.76901598*2,use_xml=True) + e.run() + e.plot() + + # you can grab the time-series recarray + newrecarray = numpy.rec.fromarrays([e.ts['V:table542421']['t'],e.ts['V:table542421']['m'],\ + e.ts['V:table542421']['m_err']],names='t,m,m_err',formats='f8,f8,f8') + + b) From a timeseries numpy recarray + b = fiteb.EB(dotastro_id=217688,period=2.76901598*2,use_xml=False,rec_array=newrecarray) + # here the dotastro id isn't really used other than as a placeholder for the bookkeeping + b.run() + b.plot() + + options to run(): + fittype=3,sigma=3.0 + + fittype = same types as on the JKTEBOP website. 3 is the quick and dirty one. 4 does sigma clipping + sigma = 3.0 (used for fittype 4) + + c) You can also try to do model selection when you're not confident which period is correct: + + p = fiteb.period_select(225431,11.9680949,try_alt=True) + # this will search over period 1/2, 1, and 2x the input period, returning the period with the best chisq + # this should also work with a recarray as in case b) above. try_alt will use three different starting points + + + +""" +from __future__ import print_function + +import urllib2, urllib +import os, sys +sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + \ + 'Software/feature_extract/Code')) +import db_importer +import matplotlib + + +from matplotlib import pylab as plt +from matplotlib import mlab +import numpy +import argparse + +__author__ = "JS Bloom (UC Berkeley)" +__version__ = "12 May 2011" + +rez_dict = {"Reduced chi-squared from errorbars:": "chisq", "Number of degrees of freedom:": "dof",\ + "Oblateness of the secondary star:": "ob2", "Oblateness of the primary star:": "ob1", \ + "Geometric reflection coeff (star B):": "reflect2", "Geometric reflection coeff (star A):": "reflect1",\ + "Omega (degrees):": "omega_deg", "Eccentricity:": "e", "Secondary contrib'n to system light:": "l2", \ + "Primary contribut'n to system light:": "l1", "Fractional secondary radius:": "r2", "Fractional primary radius:": "r1", + "Phase of primary eclipse:": "primary_eclipse_phase", "Orbit inclination": "i", "Mass ratio (q)": "q", + "Ephemeris timebase": "t0", "Orbital period (P)": "period", "Surf. bright. ratio": "l1/l2"} + + +class EB: + + dotastro_url = "http://dotastro.org/lightcurves/vosource.php?Source_ID=" + xml_dir = "XML/" + phot_dir = "Phot/" + rez_dir = "Results/" + + # newrecarray = numpy.rec.fromarrays([e.ts['V:table542421']['t'],e.ts['V:table542421']['m'],e.ts['V:table542421']['m_err']],names='t,m,m_err',formats='f8,f8,f8') + + #def __init__(self,dotastro_id=216313,period=0.97154408): + + def __init__(self,dotastro_id=217688,period=2.76901598*2,use_xml=True,rec_array=None,verbose=True): + """ + rec_array should have t, m, m_err at the column names + """ + self.dotastro_id = dotastro_id + self.gotxml = False + self.outname = None + self.photkey = None + self.period = period + self.use_xml = use_xml + self.rec_array = rec_array + self.outrez = {'valid': False, 'chisq':None} + self.verbose= verbose + + def run(self,fittype=3,sigma=3.0,try_alt=False,altnum=1): + + inf, outf, outpar, outfit = self._prep_infiles(fittype=fittype,sigma=sigma,try_alt=try_alt,altnum=altnum) + if inf is None or outf is None: + print("Sorry. Refusing to go further here.") + return + for x in [outf, outpar, outfit]: + if os.path.exists(x): + os.remove(x) + + os.system("./timeout 240 ./jktebop " + inf) + + # replace the output + f = open(outf,"r") + ff = f.readlines() + f.close() + if len(ff) == 0: + print("!!! len(ff) == 0", end=' ') + self.outrez = {'valid': False, 'okfit': False, 'fittype':fittype,'chisq':None, "parameter_file": "", "outfile": ""} + return + ff[0] = ff[0].replace("#"," ") + f = open(outf,"w") + f.writelines(ff) + f.close() + self.outf = outf + + # gobble up the output + self.outrez = self._gobbleout(outpar,fittype=fittype) + if self.verbose: + print("Summary of Results (see %s for more info):" % self.outrez.get('parameter_file')) + print("*"*40) + print("Chisq/dof:", self.outrez.get('chisq')) + print("Is this an Ok fit (chisq aside)?", self.outrez.get("okfit")) + print("period:", self.outrez.get('period'), "day") + print('eccentricity', self.outrez.get('e')) + try: + print("(r1 + r2)/a:", self.outrez.get('r1') + self.outrez.get('r2')) + print("r1/r2:", self.outrez.get('r1')/self.outrez.get('r2')) + except: + pass + + print('r1 overflow:', self.outrez.get('r1overflow')) + print('r2 overflow:', self.outrez.get('r2overflow')) + print('classification:', self.outrez.get('class')) + print("inclination:", self.outrez.get('i'), "+-", self.outrez.get('i_err'), " deg") + print("mass ratio (q):", self.outrez.get('q'), "+-", self.outrez.get('q_err')) + print("lum ratio (l1/l2):", self.outrez.get('l1/l2'), "+-", self.outrez.get('l1/l2_err')) + print("*"*40) + print("") + + def _gobbleout(self,outfile,fittype=None): + rez = {'valid': False, 'okfit': False, 'fittype':fittype,'chisq':None, "parameter_file": outfile, "outfile": self.outf} + if not os.path.exists(outfile): + return rez + + f = open(outfile,"r") + ff = f.readlines() + f.close() + ff.reverse() # look backwards + failed = False + kys = rez_dict.keys() + for l in ff: + for k in kys: + if l.find(k) != -1: + tmp = l.split(k)[-1] + if tmp.find("+/-") != -1: + ## this has an error bar + tmp1 = tmp.split("+/-") + try: + rez.update({rez_dict[k]: float(tmp1[0])}) + except: + failed = True + try: + rez.update({rez_dict[k]+"_err": float(tmp1[1])}) + except: + rez.update({rez_dict[k]+"_err": None}) + + else: + tmp = tmp.strip() + tmp1 = tmp.split(" ") + try: + rez.update({rez_dict[k]: float(tmp1[0])}) + except: + failed = True + kys.remove(k) + break + rez.update({"valid": True}) + try: + if rez['l1/l2'] > -0.05 and rez['reflect1'] > 0.0: + if not failed: + rez['okfit'] = True + except: + rez['okfit'] = False + + if (('r2' not in rez) or ('r1' not in rez)) and (rez['okfit'] == False): + return rez + if rez['r2'] > 0 and rez['r1'] > 0: + + q = (rez['r2']/rez['r1'])**1.534 + vv1 = (0.49*q**(-2./3))/(0.6*q**(-2./3) + numpy.log(1 + q**(-1./3))) + vv2 = (0.49*q**(2./3))/(0.6*q**(2./3) + numpy.log(1 + q**(1./3))) + print(q, rez['r1'], vv1, rez['r2'], vv2) + if q <= 1: + r1over = rez['r1'] > vv1*0.95 + r2over = rez['r2'] > vv2*0.95 + else: + ## fit gave the wrong r1 and r2 (r2 should be smaller) + r2over = rez['r2'] > vv1*0.95 + r1over = rez['r1'] > vv2*0.95 + + rez.update({'r1overflow': r1over, 'r2overflow': r2over}) + if not r1over and not r2over: + cl = 'detached' + elif r1over and r2over: + cl = 'contact' + else: + cl = 'semi-detached' + rez.update({'class': cl}) + else: + rez.update({'class': 'unknown'}) + + return rez + + def plot(self,outf = None,dosave=True,savedir="Plot/",show=True): + if outf is None: + outf = self.outf + #print outf + oo = mlab.csv2rec(outf,delimiter=" ") + #print oo + plt.errorbar(oo['time'] % self.period, oo['magnitude'], oo['error'], fmt="b.") + plt.plot(oo['time'] % self.period, oo['model'],"ro") + plt.title("#%i P=%f d (chisq/dof = %f) r1+r2=%f" % (self.dotastro_id,self.period,self.outrez['chisq'],\ + self.outrez.get('r1') + self.outrez.get('r2'))) + ylim = plt.ylim() + #print ylim + if ylim[0] < ylim[1]: + plt.ylim(ylim[1],ylim[0]) + plt.draw() + if show: + plt.show() + if dosave: + if not os.path.isdir(savedir): + os.mkdir(savedir) + plt.savefig("%splot%i.png" % (savedir,self.dotastro_id))#,self.period)) + print("Saved", "%splot%i.png" % (savedir,self.dotastro_id))#,self.period) + plt.clf() + + def _prep_infiles(self,dotastro_id=None,fittype=3,sigma=3.0,ntries=20,try_alt=False,altnum=1): + if dotastro_id is None: + dotastro_id = self.dotastro_id + + if self.use_xml: + if dotastro_id is None or not isinstance(dotastro_id,int): + return (None,None,None,None) + + print("preppin the infile") + photname, hjd = self._make_intable(dotastro_id=dotastro_id) + if photname is None: + return (None,None,None,None) + + alt = "" if not try_alt else ".alt" + str(altnum) + template_file = "Templates/" + "eb." + str(fittype) + alt + ".template" + print("using template", template_file) + if not os.path.exists(template_file): + print("no template file for that type") + return (None,None,None,None) + + # #import pdb; pdb.set_trace() + # #print + a = open(template_file,"r").readlines() + tmp = self.rez_dir + "dotastro" + str(dotastro_id) + if fittype == 4: + b = "".join(a) % (self.period,hjd,sigma,'"' + photname + '"','"' + tmp + '.par"',\ + '"' + tmp + '.out"', '"' + tmp + '.fit"') + elif fittype == 3: + b = "".join(a) % (self.period,hjd,'"' + photname + '"','"' + tmp + '.par"',\ + '"' + tmp + '.out"', '"' + tmp + '.fit"') + elif fittype == 5: + b = "".join(a) % (self.period,hjd,ntries,'"' + photname + '"','"' + tmp + '.par"',\ + '"' + tmp + '.out"', '"' + tmp + '.fit"') + elif fittype == 6: + b = "".join(a) % (self.period,hjd,'"' + photname + '"','"' + tmp + '.par"',\ + '"' + tmp + '.out"', '"' + tmp + '.fit"') + else: + return (None,None,None,None) + + inname = "dotastro" + str(dotastro_id) + ".in" + f = open(inname,"w") + f.writelines(b) + f.close() + print("wrote %s" % (inname)) + return (inname, tmp + ".out", tmp + ".par", tmp + ".fit") + + + def _make_intable(self,dotastro_id=None,verbose=True,percentile=0.95): + if dotastro_id is None: + dotastro_id = self.dotastro_id + if self.use_xml: + if dotastro_id is None or not isinstance(dotastro_id,int): + return None, None + self._get_xml(dotastro_id,verbose=verbose) + + self._get_timeseries() + + if not os.path.isdir(self.phot_dir): + os.mkdir(self.phot_dir) + + self.photname = self.phot_dir + "dotastro" + str(dotastro_id) + ".dat" + f=open(self.photname,"w") + merr = numpy.array( self.ts[self.photkey]['m_err']) + + merr[numpy.where(merr == 0.0)] += 0.005 + + self.ts[self.photkey]['m_err'] = list(merr) + + try: + rez = zip(self.ts[self.photkey]['t'],self.ts[self.photkey]['m'],self.ts[self.photkey]['m_err']) + except: + print("problem with the timeseries of that source. Bailing") + return (None, None) + + tmp = [f.write("%f %f %f\n" % (x[0], x[1], x[2])) for x in rez] + f.close() + + rez = sorted(rez,key=lambda x: x[1]) ## sort by magnitude + + # return the time near the minimum flux + return (self.photname, rez[int(percentile*len(rez))][0]) + + + def _get_timeseries(self): + if self.use_xml: + if not self.gotxml or self.outname is None: + print("dont have the xml to build the timeseries") + return + try: + self.b = db_importer.Source(xml_handle=self.outname) + except: + print("timeseries import failed. Check your XML file. Maybe rm %s" % self.outname) + return + + kk = self.b.ts.keys() + ind = 0 + if len(kk) > 1: + print("note: lots of phototometry keys to choose from...using the first FIXME") + ind = -1 + for i,k in enumerate(kk): + if k[0].lower() == 'r': + ind = i + break + if ind == -1: + ind = 0 + + self.photkey = kk[ind] + print("phot key = ", kk[ind]) ## FIXME...maybe want to choose V band first + self.ts = self.b.ts + else: + if self.rec_array is None: + print("must give me a recarray!") + return + self.photkey = "V" + self.ts = {self.photkey: self.rec_array} + + def _get_xml(self,dotastro_id=None,verbose=True): + if dotastro_id is None: + dotastro_id = self.dotastro_id + if dotastro_id is None or not isinstance(dotastro_id,int): + self.gotxml = False + print("no dotastro") + return + + if not os.path.isdir(self.xml_dir): + os.mkdir(self.xml_dir) + self.outname = self.xml_dir + "dotastro" + str(dotastro_id) + ".xml" + if not os.path.exists(self.outname): + print(self.dotastro_url + str(dotastro_id)) + urllib.urlretrieve(self.dotastro_url + str(dotastro_id), self.outname) + + #f = open(self.outname,"w") + #r = urllib2.urlopen(self.dotastro_url + str(dotastro_id)) + #f.writelines(r.readlines()) + #f.close() + else: + if verbose: + print("already have file %s locally." % (self.outname)) + self.gotxml = True + + +def period_select(idd=243641,per=2.20770441,use_xml=True,rec_array=None,plot=True,trials=[0.5,1,2],\ + try_alt=False,all_models=True,dosave=False,show=True, fittype=3): + + if try_alt and all_models: + #alts = [-1,0,1] # OLD + #alts = range(-1, 1 + 126) + alts = range(-1, 1 + 5) + else: + alts = [-1] + tt = [] + altuse = [] + result_keys = [] + results = {'chisq':[], + 'class':[], + 'dof':[], + 'e':[], + 'i':[], + 'i_err':[], + 'l1':[], + 'l1/l2':[], + 'l1/l2_err':[], + 'l2':[], + 'ob1':[], + 'ob2':[], + 'okfit':[], + 'omega_deg':[], + 'period':[], + 'primary_eclipse_phase':[], + 'q':[], + 'q_err':[], + 'r1':[], + 'r2':[], + 'r1overflow':[], + 'r2overflow':[], + 'reflect1':[], + 'reflect2':[], + 't0':[], + 't0_err':[], + 'valid':[], + } + + + for i,t in enumerate(trials): + for alt in alts: + a = EB(idd,per*t,use_xml=use_xml,rec_array=rec_array) + print("trying period = %f" % (per*t)) + #if alt < 0: + # a.run(fittype=3,try_alt=False,altnum=alt) + #else: + # a.run(fittype=3,try_alt=True,altnum=alt) + + ###164593: using fittype=4 returns the same detached class and only a slightly tighter chi2: + if alt < 0: + a.run(fittype=fittype,try_alt=False,altnum=alt) + else: + a.run(fittype=fittype,try_alt=True,altnum=alt) + for result_k, result_list in results.items(): + #result_list.append(a.outrez.get(result_k, 999999)) # 999999 is a KLUDGE + result_list.append(a.outrez.get(result_k, numpy.nan)) # 999999 is a KLUDGE + ### doesnt work: + #if a.outrez.has_key(result_k): + # result_list.append(a.outrez[result_k]) + tt.append(t) + altuse.append(alt) + #print '>>>', i, t, alt + + for k in results.keys(): + results[k] = numpy.array(results[k]) + + trials = numpy.array(tt) + altuse = numpy.array(altuse) + + #print ok, trials, chisq, altuse + + oks = numpy.where(results['okfit'] == True)[0] + + #print ok, trials, chisq, altuse + + ok_results = {} + for k in results.keys(): + ok_results[k] = results[k][oks] + + + if len(oks) == 0: + print("warning...no EB models seem like ok fits") + bestt = trials[numpy.where(chisq == min(chisq))] + bestalt = altuse[numpy.where(chisq == min(chisq))] + + rez = {'okfit': False, 'best_period': bestt[0]*per, "best_chisq": min(chisq), "best_alt": bestalt[0]} + else: + trials = trials[oks] + chisq = ok_results['chisq'] + altuse = altuse[oks] + bestt = trials[numpy.where(chisq == min(chisq))] + bestalt = altuse[numpy.where(chisq == min(chisq))] + rez = {'okfit': True, 'best_period': bestt[0]*per, "best_chisq": min(chisq), "best_alt": bestalt[0]} + print(rez) + + from scipy.stats import scoreatpercentile + + results_for_class = {} + for class_name in ['detached', 'semi-detached', 'contact']: + results_for_class[class_name] = {} + for k in ok_results.keys(): + results_for_class[class_name][k] = ok_results[k][numpy.where(ok_results['class'] == class_name)] + results_class_perc = {} + chosen_chisq_class_tups = [] + + attribs_for_percentiles = ['chisq', 'i_err', 'q_err', 'l1/l2_err', 'l1/l2', 'q', 'r1', 'r2', 'reflect1', 'reflect2', 'primary_eclipse_phase', 'period', 'omega_deg', 'l1', 'l2', 'e'] + rez['nmodels'] = {} + #npass_vs_attrib_percentile = {} + vals_for_attribute = {} + for cur_attrib in attribs_for_percentiles: + rez['nmodels'][cur_attrib] = {} + chisq_percentiles = {} + #npass_vs_attrib_percentile[cur_attrib] = {} + vals_for_attribute[cur_attrib] = [] + #for perc in [5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90]: + # npass_vs_attrib_percentile[cur_attrib][perc] = 0 + for class_name in results_for_class.keys(): + subarr = results_for_class[class_name][cur_attrib][numpy.where(results_for_class[class_name][cur_attrib] > -numpy.inf)] # excludes: None, Nan + rez['nmodels'][cur_attrib][class_name] = len(subarr) + vals_for_attribute[cur_attrib].extend(list(subarr)) + if len(results_for_class[class_name][cur_attrib]) <= 1: + continue + chisq_percentiles[class_name] = {} + #subarr = results_for_class[class_name][cur_attrib][numpy.where(results_for_class[class_name][cur_attrib] > -numpy.inf)] # excludes: None, Nan + if len(subarr) > 1: + for perc in [5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90]: + #subarr = results_for_class[class_name][cur_attrib][numpy.where(results_for_class[class_name][cur_attrib] > -numpy.inf)] # excludes: None, Nan + #if len(subarr) <= 1: + # continue + n_at_percentile = len(subarr[numpy.where(subarr <= scoreatpercentile(subarr, perc))]) + chisq_percentiles[class_name][perc] = {'val_at_perc':scoreatpercentile(subarr, perc), + 'n_pass':n_at_percentile/float(len(subarr))} + #npass_vs_attrib_percentile[cur_attrib][perc] += n_at_percentile + if cur_attrib == 'chisq': + ### KLUGEY + results_class_perc[class_name] = {} + chisq_percentile_cut = scoreatpercentile(results_for_class[class_name]['chisq'], 10) + for k in results_for_class[class_name].keys(): + results_class_perc[class_name][k] = results_for_class[class_name][k][numpy.where(results_for_class[class_name]['chisq'] <= chisq_percentile_cut)] + #import pdb; pdb.set_trace() + #print + + source_chisq_at_cut = max(results_for_class[class_name]['chisq'][numpy.where(results_for_class[class_name]['chisq'] <= chisq_percentile_cut)]) + chosen_chisq_class_tups.append((chisq_percentile_cut, + class_name, + trials[numpy.where(chisq == source_chisq_at_cut)], + altuse[numpy.where(chisq == source_chisq_at_cut)], + len(results_class_perc[class_name]['chisq']))) + + #import copy + percentiles_name = "%s_percentiles" % (cur_attrib) + rez[percentiles_name] = chisq_percentiles# copy.deepcopy(chisq_percentiles) # does this help??? (no: the bug was elsewhere) + #import pdb; pdb.set_trace() + #print + rez['ratiopass_for_percentile'] = {} + rez['vals_for_percentile'] = {} + for cur_attrib in attribs_for_percentiles: + total_models_for_attrib = sum(rez['nmodels'][cur_attrib].values()) + rez['ratiopass_for_percentile'][cur_attrib] = {} + rez['vals_for_percentile'][cur_attrib] = {} + for perc in [5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90]: + #rez['ratiopass_for_percentile'][cur_attrib][perc] = npass_vs_attrib_percentile[cur_attrib][perc] / float(total_models_for_attrib) + val_array = numpy.array(vals_for_attribute[cur_attrib]) + rez['vals_for_percentile'][cur_attrib][perc] = scoreatpercentile(val_array, perc) + n_at_percentile = len(val_array[numpy.where(val_array <= rez['vals_for_percentile'][cur_attrib][perc])]) + rez['ratiopass_for_percentile'][cur_attrib][perc] = n_at_percentile / float(total_models_for_attrib) + #import pprint + #import pdb; pdb.set_trace() + #print + + ### NOTE: could probably stick this above so not all attributes are iterated over. + if len(chosen_chisq_class_tups) == 0: + return {}, {} #{}, {'valid': False, 'okfit': False, 'fittype':fittype,'chisq':None, "parameter_file": "", "outfile": ""} + + chosen_chisq_class_tups.sort() + #print "### Sorted chi2 5% percentiles (chisq, class, bestt, bestalt, N_cut): " + #import pprint + #pprint.pprint(chosen_chisq_class_tups) + #for tup in chosen_chisq_class_tups: + # print tup[0], tup[1], results_class_perc[tup[1]]['chisq'] + #import pdb; pdb.set_trace() + #print + + (best_chisq, best_class, bestt, bestalt, n_cut) = chosen_chisq_class_tups[0] + + # results_detach_perc[k] = ok_results[k][numpy.where(numpy.logical_and( \ + # (ok_results['class'] == 'semi-detached'), + # (ok_results['chisq'] < 1.0)))] + + + a = EB(idd,per*bestt[0],use_xml=use_xml,rec_array=rec_array) + a.run(fittype=fittype, try_alt=(bestalt[0] >= 0), altnum=bestalt[0]) + #import pdb; pdb.set_trace() + #print + if plot: + print(dosave,show) + a.plot(dosave=dosave,show=show) + + return rez, a.outrez + + +# OBSOLETE: +def period_select__old(idd=243641,per=2.20770441,use_xml=True,rec_array=None,plot=True,trials=[0.5,1,2],\ + try_alt=False,all_models=True,dosave=False,show=True, fittype=3): + + chisq = [] + ok = [] + if try_alt and all_models: + #alts = [-1,0,1] # OLD + alts = range(-1, 1 + 126) + else: + alts = [-1] + tt = [] + altuse = [] + classif = [] + for i,t in enumerate(trials): + for alt in alts: + a = EB(idd,per*t,use_xml=use_xml,rec_array=rec_array) + print("trying period = %f" % (per*t)) + #if alt < 0: + # a.run(fittype=3,try_alt=False,altnum=alt) + #else: + # a.run(fittype=3,try_alt=True,altnum=alt) + + ###164593: using fittype=4 returns the same detached class and only a slightly tighter chi2: + if alt < 0: + a.run(fittype=fittype,try_alt=False,altnum=alt) + else: + a.run(fittype=fittype,try_alt=True,altnum=alt) + chisq.append(a.outrez['chisq']) + ok.append(a.outrez['okfit']) + tt.append(t) + altuse.append(alt) + classif.append(a.outrez['class']) + print('>>>', i, t, alt) + #import pdb; pdb.set_trace() + #print + + ok = numpy.array(ok) + trials = numpy.array(tt) + chisq = numpy.array(chisq) + altuse = numpy.array(altuse) + classif = numpy.array(classif) + + #import pdb; pdb.set_trace() + #print + #print ok, trials, chisq, altuse + + oks = numpy.where(ok == True)[0] + + #print ok, trials, chisq, altuse + + if len(oks) == 0: + print("warning...no EB models seem like ok fits") + bestt = trials[numpy.where(chisq == min(chisq))] + bestalt = altuse[numpy.where(chisq == min(chisq))] + + rez = {'okfit': False, 'best_period': bestt[0]*per, "best_chisq": min(chisq), "best_alt": bestalt[0]} + else: + trials = trials[oks] + chisq = chisq[oks] + altuse = altuse[oks] + classif = classif[oks] + bestt = trials[numpy.where(chisq == min(chisq))] + bestalt = altuse[numpy.where(chisq == min(chisq))] + rez = {'okfit': True, 'best_period': bestt[0]*per, "best_chisq": min(chisq), "best_alt": bestalt[0]} + + print(rez) + a = EB(idd,per*bestt[0],use_xml=use_xml,rec_array=rec_array) + a.run(fittype=fittype, try_alt=(bestalt[0] >= 0), altnum=bestalt[0]) + #import pdb; pdb.set_trace() + #print + if plot: + print(dosave,show) + a.plot(dosave=dosave,show=show) + + return rez, a.outrez + + +def test(idd=243641,per=2.20770441,plot=True,try_alt=False,altnum=1,dosave=False,show=True): + #e = EB(dotastro_id=217688,period=2.76901598) + #e = EB(216887,4.39149364) ## interesting asmmyetry + #e = EB(216470,2.97443659) ## cannot be fit easily + e = EB(idd,per) + e.run(try_alt=try_alt,altnum=altnum) + if plot: + e.plot(dosave=dosave,show=show) + +if __name__ == "__main__": + + parser = argparse.ArgumentParser(description='Fit eclipsing binary model to dotastro sources') + parser.add_argument('did', metavar='dotastro_id', type=int, nargs=1, + help='dotastro id (e.g. 233474)') + parser.add_argument('per', metavar='period', type=float, nargs=1, + help='period of that source in days (e.g. 1.51608476)') + parser.add_argument('-p', dest='plot', action='store_true', + default=False, + help='plot') + parser.add_argument('-a', dest='alt', action='store_true', + default=False, + help='Try alternative template (closer starting point)') + parser.add_argument('-s', dest='select', action='store_true', + default=False, + help='Do period selection') + + parser.add_argument('--altnum',dest='altnum',type=int,default=1, + help='altnative starting point number 0 or 1') + + parser.add_argument("-f",dest='savefig',action='store_true',default=False,help="save the figure") + parser.add_argument("-x",dest='showfig',action='store_false',default=True,help="dont show the figure. No X11 screen.") + + args = parser.parse_args() + if not args.showfig: + matplotlib.use('Agg') + + if args.select: + out = period_select(args.did[0],args.per[0],plot=args.plot,use_xml=True,try_alt=args.alt,dosave=args.savefig,show=args.showfig, fittype=4) + import pprint + pprint.pprint(out) + else: + test(args.did[0],args.per[0],args.plot,try_alt=args.alt,altnum=args.altnum,dosave=args.savefig,show=args.showfig) + + + # test(230476,2.34910859) + # test(243473,2.21889119) + # test(225492,1.18067021) + # test(255056,1.16160775) + # test(233474,1.51608476) + # test(261052,1.11247858) + # test(216493,1.1935771) + + #test(225332,8.99898396) + #test(216887,8.78298728) diff --git a/mltsp/TCP/Algorithms/generate_arff_using_xml.py b/mltsp/TCP/Algorithms/generate_arff_using_xml.py new file mode 100644 index 00000000..f2b4bbe0 --- /dev/null +++ b/mltsp/TCP/Algorithms/generate_arff_using_xml.py @@ -0,0 +1,49 @@ +#!/usr/bin/env python +""" This is intended to be an example which can be emulated / copied in other code. + +Given a directory with some vosource xmls (which have features generated), +generate a WEKA style .arff file. + +You should be able to run this using: + + python generate_arff_using_xml.py + +NOTE: ASSUMES that the correct directory path pointing to the xmls is defined in this file. + +NOTE: ASSUMES that environment variable TCP_DIR has been defined and works (can be printed/found in os.environ). +""" +import os, sys +import glob + +sys.path.append(os.environ.get("TCP_DIR") + '/Software/feature_extract/MLData') +import arffify + + + + +if __name__ == '__main__': + ### NOTE: this __main__ section will only be executed when the python script is called like: + ### python generate_arff_using_xml.py + ### or + ### ./generate_arff_using_xml.py + + + xml_dirpath = "/home/dstarr/scratch/xml_list" + out_arff_filepath = "/tmp/gen.arff" # to be written + + filepaths = glob.glob("%s/*xml" % (xml_dirpath)) + + vosource_list = [] + for num, fpath in enumerate(filepaths): + ### NOTE: I'm using 'num' as a psedudo source-id for identification in the .arff file. + vosource_list.append((str(num), fpath)) # NOTE: a tuple of this form is needed. + + + a = arffify.Maker(search=[], skip_class=False, local_xmls=True, + convert_class_abrvs_to_names=False, + flag_retrieve_class_abrvs_from_TUTOR=False, + dorun=False, add_srcid_to_arff=True) + a.pars['skip_sci_class_list'] = [] # NOTE: this means that all sources will be added to the .arff, regardless of how ambigous the classification is. + a.populate_features_and_classes_using_local_xmls(srcid_xml_tuple_list=vosource_list) + a.write_arff(outfile=out_arff_filepath, \ + remove_sparse_classes=False)#, remove_sparse_classes=True, n_sources_needed_for_class_inclusion=10) # this parameter allows you to exclude science-classes from the .arff by requiring a certain number of examples to exist. diff --git a/mltsp/TCP/Algorithms/generate_summary.py b/mltsp/TCP/Algorithms/generate_summary.py new file mode 100644 index 00000000..1bf81411 --- /dev/null +++ b/mltsp/TCP/Algorithms/generate_summary.py @@ -0,0 +1,30 @@ +#!/usr/bin/env python +""" Generate a summary of metacost.results Weka output in Noisification/* dirs +""" +from __future__ import print_function + +import sys, os +import glob + + +if __name__ == '__main__': + + + os.chdir('/home/pteluser/scratch/Noisification') + + glob_paths = glob.glob("/home/pteluser/scratch/Noisification/*/metacost.results") + for fpath in glob_paths: + lines = open(fpath).readlines() + i_1st_slash = fpath.rfind('/') + i_2nd_slash = fpath.rfind('/',0,i_1st_slash) + case_name = fpath[i_2nd_slash+1:i_1st_slash] + passed_stratified = False + percent = 0. + for line in lines: + if "=== Stratified cross-validation ===" in line: + passed_stratified = True + elif (passed_stratified and \ + "Correctly Classified Instances" in line): + percent = float(line[52:line.rfind('%')-1]) + break + print("%0.2f %s" % (percent, case_name)) diff --git a/mltsp/TCP/Algorithms/ipengine_kill.py b/mltsp/TCP/Algorithms/ipengine_kill.py new file mode 100644 index 00000000..3101d8e7 --- /dev/null +++ b/mltsp/TCP/Algorithms/ipengine_kill.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python +""" This kills ipython-parallel's ipengine which I cannot +seem to directly ssh-exeucte a pkill on. +""" +import sys, os + +if __name__ == '__main__': + + command_str = "ps awwux | grep bin.ipengine" + + (a,b,c) = os.popen3(command_str) + a.close() + c.close() + lines = b.readlines() + b.close() + + for line in lines: + if len(line) >10: + line_list = line.split() + try: + pid = int(line_list[1]) + exec_str = "kill -9 %d" % (pid) + os.system(exec_str) + except: + pass diff --git a/mltsp/TCP/Algorithms/macc_wrapper.py b/mltsp/TCP/Algorithms/macc_wrapper.py new file mode 100644 index 00000000..57278277 --- /dev/null +++ b/mltsp/TCP/Algorithms/macc_wrapper.py @@ -0,0 +1,114 @@ +#!/usr/bin/env python +""" +* asas_catalog.R in Python +** input parameters: + - deboss arff fpath + - asas arff fpath + - features to exclude +** output: + - asas_randomForest.Rdat fpath + - classifier effeciency metrics +** I want to call the full AL R script, but be able to + modify some bits. +*** wrapping the R code in a python string is less ideal + - but it could just be for a specific version of the AL/MACC code +""" +import os, sys +from rpy2.robjects.packages import importr +from rpy2 import robjects + + + +if __name__ == '__main__': + # TODO: do some popen of the asas_catalog.R script + # source a file which initializes variables + # source asas_catalog.R + + pars = {'root_dirpath':"/Data/dstarr/src/ASASCatalog/", + 'deboss_srcids_arff_fpath':"/Data/dstarr/src/ASASCatalog/data/debosscher_feats_20120305.arff", + 'deboss_train_arff_fpath':"/Data/dstarr/src/ASASCatalog/data/train_20120327_10ntree_5mtry.arff", + 'asas_test_arff_fpath':"/Data/dstarr/src/ASASCatalog/data/test_20120327_10ntree_5mtry.arff", + 'rf_clfr_fpath':"/home/dstarr/scratch/macc_wrapper_rfclfr.rdat", + } + + ### Initialize: + r_str = ''' +set.seed(1) +source("{root_dirpath}R/utils_classify.R") +source("{root_dirpath}R/class_cv.R") +source("{root_dirpath}R/missForest.R") +source("{root_dirpath}R/utils_PTF.R") +source("{root_dirpath}R/runJunkClass.R") +library(randomForest) +library(nnet) +library(foreign) + '''.format(root_dirpath=pars['root_dirpath']) + robjects.r(r_str) + + r_str = ''' +path = "{root_dirpath}" +asas_test_arff_fpath = "{asas_test_arff_fpath}" +rf_clfr_fpath="{rf_clfr_fpath}" + +# Load Debosscher data +debdat=read.arff(file="{deboss_srcids_arff_fpath}") +ID.use = debdat$source_id +debdat=read.arff(file="{deboss_train_arff_fpath}") +use = which(debdat$source_id {isin} ID.use) +debdat = debdat[use,] + +ID = debdat$source_id +debdat$class = paste(debdat$class) +deb.reclassify = read.table(paste(path,"data/reclassified_debosscher_eclipsing.dat",sep="")) +debdat$class[which(ID {isin} deb.reclassify[,1])] = deb.reclassify[,2] + +# straighten out T Tauri subclasses (AAM visual reclassifications) +ttau.cl = c(163375,163434,163445,163480,163585,163762,163907,164145,164355) +debdat$class[debdat$source_id {isin} ttau.cl] = 201 +ttau.wl = c(163981,164277) +debdat$class[debdat$source_id {isin} ttau.wl] = 202 + +class.deb = class.debos(debdat$class) + +# re-label the source that Nat found to be wrong +class.deb[ID==164154] = "y. W Ursae Maj." + +p = dim(debdat)[2] +feat.debos = data.frame(debdat)[,-c(1,p)] # Deb features + '''.format(isin="%in%", + root_dirpath=pars['root_dirpath'], + rf_clfr_fpath=pars['rf_clfr_fpath'], + deboss_srcids_arff_fpath=pars['deboss_srcids_arff_fpath'], + deboss_train_arff_fpath=pars['deboss_train_arff_fpath'], + asas_test_arff_fpath=pars['asas_test_arff_fpath']) + robjects.r(r_str) + + ### Remove useless features from the training data: + r_str = ''' +rem = c(which(substr(names(feat.debos),1,7) == "eclpoly")) +rem = c(rem,which(names(feat.debos)=="color_bv_extinction")) +rem = c(rem,which(names(feat.debos)=="color_diff_bj")) +rem = c(rem,which(names(feat.debos)=="color_diff_hk")) +rem = c(rem,which(names(feat.debos)=="color_diff_jh")) +rem = c(rem,which(names(feat.debos)=="color_diff_rj")) +rem = c(rem,which(names(feat.debos)=="color_diff_vj")) +rem = c(rem,which(names(feat.debos)=="n_points")) +rem = c(rem,which(names(feat.debos)=="freq_rrd")) +rem = c(rem,which(substr(names(feat.debos),17,27)=="rel_phase_0")) +feat.debos = feat.debos[,-rem] + '''.format() + robjects.r(r_str) + #import pdb; pdb.set_trace() + #print + + ### NOTE: must use the local version of asas_catalog.R: + r_str = 'source("asas_catalog.R")' + #r_str = 'source("{root_dirpath}R/asas_catalog.R")'.format(root_dirpath=pars['root_dirpath']) + robjects.r(r_str) + #import pdb; pdb.set_trace() + #print + + + ### TODO: ensure .R files are coming from current path + + ### TODO: retrieve resulting metrics diff --git a/mltsp/TCP/Algorithms/migrate_local_test_mysql_to_raid0.sh b/mltsp/TCP/Algorithms/migrate_local_test_mysql_to_raid0.sh new file mode 100755 index 00000000..e21e86fb --- /dev/null +++ b/mltsp/TCP/Algorithms/migrate_local_test_mysql_to_raid0.sh @@ -0,0 +1,23 @@ +#!/bin/sh + +#This migrates the test-version setup'd mysql table files from local disk to +#transx RAID0 disk. + +# NOTE: the ned_feat_cache tables MUST BE NEWLY created if we wish to +# uncomment the ned_feat_cache tables bit & move it to raid0 +# - otherwise a sym-link to nothing will be move to raid0 & tables lost! + +sudo mysqladmin shutdown +sudo rm -Rf /media/raid_0/object_test_db +sudo rm -Rf /media/raid_0/source_test_db +#sudo rm -Rf /media/raid_0/ned_feat_cache +sudo mv /var/lib/mysql/object_test_db /media/raid_0/ +sudo mv /var/lib/mysql/source_test_db /media/raid_0/ +#sudo mv /var/lib/mysql/ned_feat_cache /media/raid_0/ +sudo ln -s /media/raid_0/object_test_db /var/lib/mysql/object_test_db +sudo ln -s /media/raid_0/source_test_db /var/lib/mysql/source_test_db +#sudo ln -s /media/raid_0/ned_feat_cache /var/lib/mysql/ned_feat_cache +sudo chown -R mysql /media/raid_0/object_test_db +sudo chown -R mysql /media/raid_0/source_test_db +#sudo chown -R mysql /media/raid_0/ned_feat_cache +sudo /usr/local/bin/mysqld_safe --binlog-ignore-db=minor_planet --binlog-ignore-db=source_test_db --binlog-ignore-db=object_test_db --binlog-ignore-db=source_db --binlog-ignore-db=object_db & diff --git a/mltsp/TCP/Algorithms/missForest.R b/mltsp/TCP/Algorithms/missForest.R new file mode 100755 index 00000000..1dc03015 --- /dev/null +++ b/mltsp/TCP/Algorithms/missForest.R @@ -0,0 +1,284 @@ +## +## MissForest - nonparametric missing value imputation for mixed-type data +## +## This R script contains the necessary functions for performing imputation +## using the missForest algorithm in the statistical software R. +## +## An R package 'missForest' will be made available as soon as possible. +## +## Author: D.Stekhoven, stekhoven@stat.math.ethz.ch +############################################################################## + + +missForest <- function(xmis, maxiter = 10, + decreasing = FALSE, + verbose = FALSE, + mtry = floor(sqrt(ncol(xmis))), + ntree = 100, xtrue = NA) +{ ## ---------------------------------------------------------------------- + ## Arguments: + ## xmis = data matrix with missing values + ## maxiter = stop after how many iterations (default = 10) + ## decreasing = (boolean) if TRUE the columns are sorted with decreasing + ## amount of missing values + ## verbose = (boolean) if TRUE then missForest returns error estimates, + ## runtime and if available true error during iterations + ## mtry = how many variables should be tried randomly at each node + ## ntree = how many trees are grown in the forest + ## xtrue = complete data matrix + ## + ## ---------------------------------------------------------------------- + ## Author: Daniel Stekhoven, stekhoven@stat.math.ethz.ch + + n <- nrow(xmis) + p <- ncol(xmis) + + ## perform initial guess on xmis + ximp <- xmis + xAttrib <- lapply(xmis, attributes) + varType <- character(p) + for (t.co in 1:p){ + if (is.null(xAttrib[[t.co]])){ + varType[t.co] <- 'numeric' + ximp[is.na(xmis[,t.co]),t.co] <- mean(xmis[,t.co], na.rm = TRUE) + } else { + varType[t.co] <- xAttrib[[t.co]]$class + ## take the level which is more 'likely' (majority vote) + max.level <- max(table(ximp[,t.co])) + ## if there are several classes which are major, sample one at random + class.assign <- sample(names(which(max.level == summary(ximp[,t.co]))), 1) + ## it shouldn't be the NA class + if (class.assign != "NA's"){ + ximp[is.na(xmis[,t.co]),t.co] <- class.assign + } else { + while (class.assign == "NA's"){ + class.assign <- sample(names(which(max.level == + summary(ximp[,t.co]))), 1) + } + ximp[is.na(xmis[,t.co]),t.co] <- class.assign + } + } + } + + ## extract missingness pattern + NAloc <- is.na(xmis) # where are missings + noNAvar <- apply(NAloc, 2, sum) # how many are missing in the vars + sort.j <- order(noNAvar) # indices of increasing amount of NA in vars + if (decreasing) + sort.j <- rev(sort.j) + sort.noNAvar <- noNAvar[sort.j] + + ## output + Ximp <- vector('list', maxiter) + + ## initialize parameters of interest + iter <- 0 + k <- length(unique(varType)) + convNew <- rep(0, k) + convOld <- rep(Inf, k) + OOBerror <- numeric(p) + names(OOBerror) <- varType + + ## setup convergence variables w.r.t. variable types + if (k == 1){ + if (unique(varType) == 'numeric'){ + names(convNew) <- c('numeric') + } else { + names(convNew) <- c('factor') + } + convergence <- c() + OOBerr <- numeric(1) + } else { + names(convNew) <- c('numeric', 'factor') + convergence <- matrix(NA, ncol = 2) + OOBerr <- numeric(2) + } + + ## function to yield the stopping criterion in the following 'while' loop + stopCriterion <- function(varType, convNew, convOld, iter, maxiter){ + k <- length(unique(varType)) + if (k == 1){ + (convNew < convOld) & (iter < maxiter) + } else { + ((convNew[1] < convOld[1]) | (convNew[2] < convOld[2])) & (iter < maxiter) + } + } + + ## iterate missForest + while (stopCriterion(varType, convNew, convOld, iter, maxiter)){ + if (iter != 0){ + convOld <- convNew + OOBerrOld <- OOBerr + } + cat(" missForest iteration", iter+1, "in progress...") + t.start <- proc.time() + ximp.old <- ximp + for (s in 1:p){ + varInd <- sort.j[s] + if (noNAvar[[varInd]] != 0){ + obsi <- !NAloc[,varInd] # which i's are observed + misi <- NAloc[,varInd] # which i's are missing + obsY <- ximp[obsi, varInd] # training response + obsX <- ximp[obsi, seq(1, p)[-varInd]] # training variables + misX <- ximp[misi, seq(1, p)[-varInd]] # prediction variables + typeY <- varType[varInd] + if (typeY == 'numeric'){ + ## train random forest on observed data + RF <- randomForest(x = obsX, y = obsY, ntree = ntree) + ## record out-of-bag error + OOBerror[varInd] <- RF$mse[ntree] + ## predict missing values in column varInd + misY <- predict(RF, misX) + } else { # if Y is categorical + obsY <- factor(obsY) ## remove empty classes + summarY <- summary(obsY) + if (length(summarY) == 1){ ## if there is only one level left + misY <- factor(rep(names(summarY), sum(misi))) + } else { + ## train random forest on observed data + RF <- randomForest(x = obsX, y = obsY, ntree = ntree) + ## record out-of-bag error + OOBerror[varInd] <- RF$err.rate[[ntree,1]] + ## predict missing values in column varInd + misY <- predict(RF, misX) + } + } + + ## replace old imputed value with prediction + ximp[misi, varInd] <- misY + } + } + cat('done!\n') + + iter <- iter+1 + Ximp[[iter]] <- ximp + + t.co2 <- 1 + ## check the difference between iteration steps + for (t.type in names(convNew)){ + t.ind <- which(varType == t.type) + if (t.type == "numeric"){ + convNew[t.co2] <- sum((ximp[,t.ind]-ximp.old[,t.ind])^2)/sum(ximp[,t.ind]^2) + } else { + dist <- sum(as.character(as.matrix(ximp[,t.ind])) != as.character(as.matrix(ximp.old[,t.ind]))) + convNew[t.co2] <- dist / (n * sum(varType == 'factor')) + } + t.co2 <- t.co2 + 1 + } + + ## compute estimated imputation error + NRMSE <- sqrt(mean(OOBerror[varType=='numeric'])/var(as.vector(as.matrix(xmis[,varType=='numeric'])), na.rm = TRUE)) + PFC <- mean(OOBerror[varType=='factor']) + if (k==1){ + if (unique(varType)=='numeric'){ + OOBerr <- NRMSE + names(OOBerr) <- 'NRMSE' + } else { + OOBerr <- PFC + names(OOBerr) <- 'PFC' + } + } else { + OOBerr <- c(NRMSE, PFC) + names(OOBerr) <- c('NRMSE', 'PFC') + } + + ## return status output, if desired + if (verbose){ + delta.start <- proc.time() - t.start + if (any(!is.na(xtrue))){ + err <- mixError(ximp, xmis, xtrue) + cat(" error(s):", err, "\n") + } + cat(" estimated error(s):", OOBerr, "\n") + cat(" convergence:", convNew, "\n") + cat(" time:", delta.start[3], "seconds\n\n") + } + }#end while((convNew + + + +""" + for row in rows: + out_str += "\n" + for col in row: + out_str += "" % (str(col)) + out_str += "\n" + out_str += """ + +
%s
+ +""" + print(out_str) + + # Then, out_str could be file_pointer.write(out_str) to some .html file. + + # You could also figure out the URL for more information about a PTF source (Caltech or a nersc.gov page, I think) and then print a link column into the above html string: + # something like: + # Click on this link + + + + def some_main_function(self): + """ This is the main'ish function. + + This module should work on the tranx computer (psycopg2 is installed). + + """ + + import psycopg2 + conn = psycopg2.connect("dbname='ptfcands' user='tcp' host='navtara.caltech.edu' password='classify'"); + pg_cursor = conn.cursor() + + column_list = ['shortname', 'ra', 'dec', 'mag', 'type2', 'class', 'isspectra', 'rundate'] + column_str = ', '.join(column_list) + select_str = "SELECT %s FROM saved_cands WHERE shortname > '' AND rundate > '20090101' ORDER BY rundate DESC LIMIT 20" % (column_str) + # The above is the same as: + # SELECT shortname, ra, dec, mag, type2, class, isspectra, rundate FROM saved_cands WHERE shortname > '' AND rundate > '20090101' ORDER BY rundate DESC LIMIT 20 + + pg_cursor.execute(select_str) + + rows = pg_cursor.fetchall() + for row in rows: + print(row) + + # Can also display the results in an html string (which could be written to file): + self.print_rows_in_html_table_form(rows) + + + + def get_ptf_data_epoch_at_classification_time(self): + """ This function gets the datapoint / epoch for a source, which + is closest to the time that Mansi/Robert/Brad identified the source + as interesting / a transient. + + """ + import psycopg2 + conn = psycopg2.connect("dbname='ptfcands' user='tcp' host='navtara.caltech.edu' password='classify'"); + pg_cursor = conn.cursor() + + ### NOTE: The following has some duplicate rows due to there + # occasionally being more than one datesave time for a + # particular source shortname. Thus this isn't useful: + #select_str = "SELECT distinct shortname, datesaved, (cast(to_char(datesaved, 'J')as real) + cast(to_char(datesaved, 'SSSS') as real)/3600./24. - 0.5) as jd FROM annotate order by datesaved ASC" + + ### NOTE: The following only selects the row with the earliest + # datesaved time, thus the first classification of a source. + ### NOTE: This also calculates a Julian-date from the + # string-like datesaved column. + #select_str = "SELECT DISTINCT ON (shortname) shortname, datesaved, (cast(to_char(datesaved, 'J')as real) + cast(to_char(datesaved, 'SSSS') as real)/3600./24. - 0.5) as jd FROM annotate ORDER BY shortname, datesaved" + ### This version JOINs with sources table to also get the + # ra, dec, and source-id: + # # # # # #select_str = "SELECT DISTINCT ON (annotate.shortname) annotate.shortname, sources.id, sources.ra, sources.dec, (cast(to_char(annotate.datesaved, 'J')as real) + cast(to_char(annotate.datesaved, 'SSSS') as real)/3600./24. - 0.5) as jd FROM sources JOIN annotate ON (annotate.shortname=sources.name) ORDER BY annotate.shortname, annotate.datesaved" + #select_str = "SELECT DISTINCT ON (annotate.shortname) annotate.shortname, sources.id, sources.ra, sources.dec, saved_cands.isspectra, (cast(to_char(annotate.datesaved, 'J')as real) + cast(to_char(annotate.datesaved, 'SSSS') as real)/3600./24. - 0.5) as jd FROM sources JOIN annotate ON (annotate.shortname=sources.name) JOIN saved_cands ON (annotate.shortname=saved_cands.shortname) WHERE saved_cands.isspectra = 't' ORDER BY annotate.shortname, annotate.datesaved" + select_str = "SELECT DISTINCT ON (annotate.shortname) annotate.shortname, sources.id, sources.ra, sources.dec, saved_cands.isspectra, (cast(to_char(annotate.datesaved, 'J')as real) + cast(to_char(annotate.datesaved, 'SSSS') as real)/3600./24. - 0.5) as jd FROM sources JOIN annotate ON (annotate.shortname=sources.name) JOIN saved_cands ON (annotate.shortname=saved_cands.shortname) ORDER BY annotate.shortname, annotate.datesaved" + pg_cursor.execute(select_str) + rows = pg_cursor.fetchall() + caltech_sources = {} + for row in rows: + row + caltech_sources[row[0]] = {'shortname':row[0], + 'caltech_srcid':row[1], + 'ra':row[2], + 'dec':row[3], + 'isspectra':row[4], + 'jd_time':row[5]} + print() + + # NOTE: Return a single ptf-events row/epoch/observation which + # most closely correlates to the Caltech classification time. + # - Thus, we believe that the observation at this time + # is what the Caltech person saw when they made the initial + # identification and classification. + db = MySQLdb.connect(host=pars['mysql_hostname'], + user=pars['mysql_user'], + db=pars['mysql_database'], + port=pars['mysql_port']) + cursor = db.cursor() + + for ct_src_dict in caltech_sources.values(): + #select_str = "SELECT T1.*, (T1.ujd - 2454972.8723) AS t_aftr_classif FROM (SELECT id, ra, decl, ujd, mag, mag_err, realbogus, obj_srcid_lookup.src_id FROM ptf_events_htm JOIN obj_srcid_lookup ON (id=obj_id) WHERE obj_srcid_lookup.survey_id=3 AND DIF_HTMCircle(258.96142767, 64.23848418, 0.05)) AS T1 WHERE (T1.ujd - 2454972.8723) > -0.1 ORDER BY src_id, ujd DESC" + select_str = """ +SELECT object_test_db.obj_srcid_lookup.src_id, T1.id, T1.ujd, T1.mag, T1.realbogus, (T1.ujd - %lf) AS t_aftr_classif +FROM + (SELECT id, ra, decl, ujd, mag, mag_err, realbogus + FROM object_test_db.ptf_events_htm + WHERE DIF_HTMCircle(%lf, %lf, 0.05)) AS T1 +JOIN object_test_db.obj_srcid_lookup ON (T1.id=object_test_db.obj_srcid_lookup.obj_id) +WHERE object_test_db.obj_srcid_lookup.survey_id=3 +ORDER BY src_id, ujd DESC + """ % (ct_src_dict['jd_time'], ct_src_dict['ra'], ct_src_dict['dec']) + cursor.execute(select_str) + results = cursor.fetchall() + + ct_src_dict['data'] = [] + for row in results: + ct_src_dict['data'].append({'mysql_srcid':row[0], + 'mysql_objid':row[1], + 'ujd':row[2], + 'mag':row[3], + 'realbogus':row[4], + 't_after_classif':row[5]}) + pprint.pprint(ct_src_dict) + + + def LBL_pgsql__query_realbogus_using_ptf_id(self): + """ + Using a (hardcoded as an example) PTF object/event/epoch id, retrieve + RealBogus values from LBL's PostgreSQL database. + """ + import psycopg2 + conn = psycopg2.connect("dbname='subptf' user='dstarr' host='sgn02.nersc.gov' password='*2ta77' port=6540"); + pg_cursor = conn.cursor() + + select_str = "SELECT candidate_id, bogus, suspect, unclear, maybe, realish, realbogus FROM rb_classifier WHERE candidate_id = 3000001" + pg_cursor.execute(select_str) + rows = pg_cursor.fetchall() + for row in rows: + (candidate_id, bogus, suspect, unclear, maybe, realish, realbogus) = row + print(candidate_id, realbogus) + + +if __name__ == '__main__': + + pars = { \ + 'mysql_user':"pteluser", + 'mysql_hostname':"192.168.1.25", + 'mysql_database':'source_test_db', + 'mysql_port':3306, + } + + + Pg_Db_Class_Example = Postgre_Database_Class_Example(pars) + + Pg_Db_Class_Example.LBL_pgsql__query_realbogus_using_ptf_id() + sys.exit() + Pg_Db_Class_Example.get_ptf_data_epoch_at_classification_time() + sys.exit() + Pg_Db_Class_Example.some_main_function() + + + db = MySQLdb.connect(host=pars['mysql_hostname'], + user=pars['mysql_user'], + db=pars['mysql_database'], + port=pars['mysql_port']) + cursor = db.cursor() + + select_str = "SELECT src_id, ra, decl FROM srcid_lookup LIMIT 3" % () + cursor.execute(select_str) + results = cursor.fetchall() + for row in results: + print(row) + + + # This example queries the TCP Mysql database (on tranx), for a position, + # and returns all the sources, and the magnitude(time) datapoints. + # NOTE: DIF_HTMCircle(258.96142767, 64.23848418, 0.05) means: + # - a 0.05 arc minute (0.05 * 1/60. degrees) radius circle is queried. + # which is around 3 arcseconds, and probably a good standard query for sources. + # - if you query less than 1 arcsecond, you may begin missing a some data for a source. + # NOTE: "ujd" is essentially time, with 1.0 representing 1 day. Aka Julian Date. + # + # NOTE: besides the "realbogus" value, there are also 5 characterstics which GroupThink uses + # in calculating realbogus, and may be pertinant to the real/bogus classification. + # Ideally, these 5 params can be hidden because the realbogus value correctly + # represents whether the subtracted object is real or bogus. + # So, for now you can just concern yourself with the "realbogus" characteristic if you want. + # The other 5 characteristics are: + # bogus | suspect | unclear | maybe | realish + + ### This selects just information from the ptf_events table: + # SELECT id, ra, decl, ujd, mag, mag_err, realbogus, bogus, suspect, unclear, maybe, realish FROM ptf_events_htm WHERE DIF_HTMCircle(258.96142767, 64.23848418, 0.05); + + ### This does the above select (without the 5 extra characteristics), + # but also including the associated TCP source-id. + """ +SELECT id, ra, decl, ujd, mag, mag_err, realbogus, obj_srcid_lookup.src_id FROM ptf_events_htm JOIN obj_srcid_lookup ON (id=obj_id) WHERE obj_srcid_lookup.survey_id=3 AND DIF_HTMCircle(258.96142767, 64.23848418, 0.05); ++---------+--------------+--------------+---------------+---------+---------+-----------+--------+ +| id | ra | decl | ujd | mag | mag_err | realbogus | src_id | ++---------+--------------+--------------+---------------+---------+---------+-----------+--------+ +| 4731119 | 258.96127891 | 64.238416648 | 2454972.8723 | 18.784 | 0.0226 | 0.002 | 50343 | +| 4731815 | 258.96142767 | 64.23848418 | 2454972.91348 | 19.8342 | 0.0788 | 0.019 | 50343 | ++---------+--------------+--------------+---------------+---------+---------+-----------+--------+ + +select sources.name, sources.ra, sources.dec, phot.mag, phot.emag, phot.filter, phot.obsdate from sources JOIN phot ON (phot.sourceid=sources.id) where sources.name='09aa' ORDER BY obsdate; + + name | ra | dec | mag | emag | filter | obsdate +------+------------+----------+---------+----------+--------+------------------------- + 09aa | 173.336234 | -9.41118 | 999 | 999 | R | 2009-02-20 06:46:01.983 + 09aa | 173.336234 | -9.41118 | 999 | 999 | R | 2009-02-20 10:14:15.683 + 09aa | 173.336234 | -9.41118 | 999 | 999 | R | 2009-02-24 10:11:24.683 + 09aa | 173.336234 | -9.41118 | 999 | 999 | g | 2009-02-28 06:05:30.783 + 09aa | 173.336234 | -9.41118 | 999 | 999 | g | 2009-02-28 08:00:10.433 + + +select sources.name, sources.ra, sources.dec, annotations.username, annotations.type, annotations.comment from sources JOIN annotations ON (annotations.sourceid=sources.id) where sources.name='09aa' limit 10; + +name | ra | dec | username | type | comment +------+------------+----------+----------+----------------+----------- + 09aa | 173.336234 | -9.41118 | robert | classification | SN Ia + 09aa | 173.336234 | -9.41118 | robert | redshift | 0.12 + 09aa | 173.336234 | -9.41118 | robert | type | Transient + +###### + +select src_id, ptf_events.id, ptf_events.ujd, ptf_events.mag, ptf_events.realbogus + from obj_srcid_lookup + JOIN ptf_events ON (ptf_events.id=obj_srcid_lookup.obj_id) + WHERE src_id=(SELECT src_id FROM obj_srcid_lookup WHERE obj_id = 4227695 AND survey_id = 3); + + +mysql> select src_id, ptf_events.id, ptf_events.ujd, ptf_events.mag, ptf_events.realbogus from obj_srcid_lookup JOIN ptf_events on (ptf_events.id=obj_srcid_lookup.obj_id) WHERE src_id=(select src_id from obj_srcid_lookup where obj_id = 4227695); ++--------+---------+---------------+---------+------------+ +| src_id | id | ujd | mag | realbogus | ++--------+---------+---------------+---------+------------+ +| 466081 | 4227695 | 2454972.93413 | 19.3241 | 0.0248442 | +| 466081 | 4226964 | 2454972.90348 | 19.8569 | 0.00284416 | ++--------+---------+---------------+---------+------------+ +2 rows in set (0.05 sec) + + + +ptfcands=> select * from sources order by creationdate DESC limit 50; + id | sub_id | cand_id | name | iauname | status | programid | ra | dec | era | edec | classification | redshift | creationdate | lastmodified | priority | scheduling +------+--------+---------+------+---------+--------+-----------+------------+-----------+-----+------+----------------+----------+----------------------------+----------------------------+----------+------------ + 1266 | 12985 | 4447384 | 09ke | | active | -1 | 215.047294 | 51.740923 | | | | | 2009-05-21 20:12:06.438379 | 2009-05-21 20:12:06.438379 | 5 | auto + 1265 | 12433 | 4256479 | 09kd | | active | -1 | 255.859193 | 43.766691 | | | | | 2009-05-21 19:47:39.956362 | 2009-05-21 19:47:39.956362 | 5 | auto + 1264 | 13213 | 4543054 | 09kc | | active | -1 | 186.805834 | 61.72272 | | | | | 2009-05-21 18:16:40.154638 | 2009-05-21 18:16:40.154638 | 5 | auto + 1263 | 11686 | 3985221 | 09kb | | active | -1 | 189.694058 | 79.645662 | | | | | 2009-05-20 23:34:38.119103 | 2009-05-20 23:34:38.119103 | 5 | auto + 1262 | 11444 | 3843122 | 09ka | | active | -1 | 257.394746 | 72.060907 | | | | | 2009-05-20 23:28:03.399104 | 2009-05-20 23:28:03.399104 | 5 | auto + 1261 | 10949 | 3605721 | 09jz | | active | -1 | 195.106203 | 67.460265 | | | | | 2009-05-20 23:07:32.438336 | 2009-05-20 23:07:32.438336 | 5 | auto + 1260 | 11139 | 3692473 | 09jy | | active | -1 | 263.846781 | 68.123137 | | | | | 2009-05-20 22:31:23.594331 | 2009-05-20 22:31:23.594331 | 5 | auto + 1259 | 10799 | 3556880 | 09jx | | active | -1 | 203.348915 | 59.348204 | | | | | 2009-05-20 22:25:08.14293 | 2009-05-20 22:25:08.14293 | 5 | auto + 1258 | 10581 | 3482376 | 09jw | | active | -1 | 193.598873 | 56.732591 | | | | | 2009-05-20 22:23:32.550871 | 2009-05-20 22:23:32.550871 | 5 | auto + + + + + + """ + + + + + + + + + """ +on tranx/192.168.1.25: + +mysql -u pteluser + + +show databases; + +use source_test_db; + +show tables; + +select * from srcid_lookup limit 3; + + +### This gets all features available: + +SELECT feat_name FROM feat_lookup WHERE filter_id=8 AND is_internal=0 ORDER BY feat_name + + +### This gets all features for an (assumed PTF) source & prints them out with feature-names: + +select src_id, feat_id, feat_name, feat_val from feat_values JOIN feat_lookup USING (feat_id) WHERE filter_id=8 AND src_id = 118 ORDER BY feat_name; ++--------+---------+--------------------------------------+------------------+ +| src_id | feat_id | feat_name | feat_val | ++--------+---------+--------------------------------------+------------------+ +| 118 | 1061 | amplitude | 0.5036405 | +| 118 | 287 | beyond1std | 0 | +| 118 | 440 | chi2 | 57122.0446203 | + + +### This mimics the query on the tcp_ptf_summary.php webpage: + +SELECT x.feat_val, y.feat_val FROM feat_values AS x + JOIN srcid_lookup USING (src_id) + INNER JOIN feat_values AS y + ON y.src_id = x.src_id + AND y.feat_id=(SELECT feat_id FROM feat_lookup WHERE ( + feat_lookup.filter_id = 8 AND + feat_lookup.feat_name = 'std')) + WHERE x.feat_id=(SELECT feat_id FROM feat_lookup WHERE ( + feat_lookup.filter_id = 8 AND + feat_lookup.feat_name = 'median')) + AND srcid_lookup.nobjs >= 15; + + +####################### + +# How to connect to Caltech PostgreSQL server: + +# - use tranx +# - password : classify + +psql -d ptfcands -U tcp -h navtara.caltech.edu --password + +### Commands: +# (NOTE: I'm not sure why, by /d and /dt don't work, which would normally allow us to see other tables & databases. Maybe this has been disabled for the tcp user). + +\h # help, lists SQL commands +\h SELECT # gives help on a particular command + +###### +# Here is what columns are in the ptfcands.saved_cands table: +ptfcands=> select * from saved_cands limit 1; + canname | ra | dec | x | y | expname | scanner | ip | class | comments | datesaved | a | b | mag | fwhm | sigma | max2sig | max3sig | flag | type | rundate | visit | field | chip | shortname | isspectra | z | cannum | phase | specdate | date | id | sub_id | obsjd | type2 +---------+------------+-----------+--------+--------+-------------------------------------------------+---------+-----------------+-------+----------+-----------+------+------+---------+------+-------+---------+---------+------+-----------+----------+-------+--------+------+-----------+-----------+-------+--------+-------+---------------+------+----+--------+-------+----------------- + | 120.196928 | 46.948476 | 1786.7 | 3495.8 | PTF200903171418_2_o_14865_00.w_cd.ptf_100224_00 | mansi | 198.202.125.194 | Ia | | | 0.85 | 0.79 | 20.1955 | 2.4 | 7.73 | 5 | 1 | 0 | Transient | 20090317 | 1 | 100224 | 0 | 09h | t | 0.121 | 12 | -2d | Mar 20.360082 | | | | | SurelyTransient + + | 171.386532 | 13.636271 | 1386 | 218.6 | PTF200903023554_1_o_12989_09.w_cd.ptf_002_09 | robert | 75.27.243.136 | | | | 1.31 | 1.27 | 19.0126 | 2.91 | 16.3 | 1 | 0 | 0 | Rock | | | | | None | | | | | | | | | | + + | 171.167212 | 13.376099 | 628.4 | 1144.2 | PTF200903023554_1_o_12989_09.w_cd.ptf_002_09 | robert | 75.27.243.136 | | | | 1.38 | 1.27 | 18.6404 | 3.24 | 21.42 | 0 | 0 | 0 | Rock | | | | | None | | | | | | | | | | + + + + + + +""" diff --git a/mltsp/TCP/Algorithms/qso_fit.py b/mltsp/TCP/Algorithms/qso_fit.py new file mode 100644 index 00000000..94764a6b --- /dev/null +++ b/mltsp/TCP/Algorithms/qso_fit.py @@ -0,0 +1,298 @@ +""" Nat wrote 20100930, dstarr to adapt as a TCP feature. +""" + +from numpy import sqrt,abs,zeros,log,exp,dot,log10,median,atleast_1d,var,shape,pi,where +from scipy.stats import norm +from scipy.linalg import solveh_banded,cholesky_banded +from scipy.special import gammaln,betainc,gammaincc +from scipy import transpose +from scipy import __version__ as scipy_version + +def lprob2sigma(lprob): + """ translates a log_e(probability) to units of Gaussian sigmas """ + if (lprob>-36.): + sigma = norm.ppf(1.-0.5*exp(1.*lprob)) + else: + sigma = sqrt( log(2./pi) - 2.*log(8.2) - 2.*lprob ) + return float(sigma) + + +def chol_inverse_diag(t): + """ Computes inverse of matrix given its Cholesky upper Triangular decomposition t. + matrix form: ab[u + i - j, j] == a[i,j] (here u=1) + (quick version: only calculates diagonal and neighboring elements) """ + (uu,nrows) = shape(t) + B = zeros((uu,nrows),dtype='float64') + B[1,nrows-1] = 1.0/t[1,nrows-1]**2 + B[0,nrows-1] = -t[0,nrows-1]*B[1,nrows-1]/t[1,nrows-2] + for j in reversed(range(nrows-1)): + tjj = t[1,j] + B[1,j] = (1.0/tjj-t[0,j+1]*B[0,j+1])/tjj + B[0,j] = -t[0,j]*B[1,j]/t[1,j-1] + return B + + + +def qso_engine(time,data,error,ltau=3.,lvar=-1.7,sys_err=0.,return_model=False): + """Calculates the fit quality of a damped random walk to a qso lightcurve. + The formalism is from Rybicki & Press (1994; arXiv:comp-gas/9405004) + + Data are modelled with a covariance function + Lij = 0.5*var*tau*exp(-|time_i-time_j|/tau) . + + Input: + time - measurement times, typically days + data - measured magnitudes + error - uncertainty in measured magnitudes + + Output (dictionary): + + chi2/nu - classical variability measure + chi2_qso/nu - for goodness of fit given fixed parameters + chi2_qso/nu_extra - for parameter fitting, add to chi2/nu + chi^2/nu_NULL - expected chi2/nu for non-qso variable + + signif_qso - significance chi^2/nu1 (rule out qso) + signif_vary - significance that source is variable + class - resulting source type (ambiguous, not_qso, qso) + + model - time series prediction for each datum given all others (iff return_model==True) + dmodel - model uncertainty, including uncertainty in data + + Notes: + T = L^(-1) + Data variance is D + Full covariance C^(-1) = (L+D)^(-1) = T [T+D^(-1)]^(-1) D^(-1) + Code takes advantage of the tridiagonality of T and T+D^(-1).""" + + + out_dict={} + out_dict['chi2_qso/nu']=999; out_dict['chi2_qso/nu_extra']=0.; + out_dict['signif_qso']=0.; out_dict['signif_not_qso']=0.; out_dict['signif_vary']=0. + out_dict['chi2_qso/nu_NULL']=0.; out_dict['chi2/nu']=0.; out_dict['nu']=0 + out_dict['model']=[]; out_dict['dmodel']=[]; + out_dict['class']='ambiguous' + + lvar0 = log10(0.5)+lvar+ltau + + ln = len(data) + dt = abs(time[1:]-time[:-1]) + + # first make sure all dt>0 + g=where(dt>0.)[0]; lg = len(g) + # must have at least 2 data points + if (lg<=0): + return out_dict + + if (return_model): + model = 1.*data; dmodel = -1.*error + + if (lg3): + if (out_dict['signif_qso']>3): + out_dict['class']='qso' + elif (out_dict['signif_not_qso']>3): + out_dict['class']='not_qso' + + # best-fit model for the lightcurve + if (return_model): + model[gg] = dat - (u-u0*x0)/diagC + dmodel[gg] = 1./sqrt(diagC) + out_dict['model'] = model + out_dict['dmodel'] = dmodel + + return out_dict + + +def qso_fit(time,data,error,filter='r',sys_err=0.0,return_model=False): + """ Best-fit qso model determined for Sesar Strip82, ugriz-bands (default r). + See additional notes for underlying code qso_engine. + + Input: + time - measurement times [days] + data - measured magnitudes in single filter (also specified) + error - uncertainty in measured magnitudes + + Output: + chi^2/nu - classical variability measure + chi^2_qso/nu - fit statistic + chi^2_qso/nu_NULL - expected fit statistic for non-qso variable + + signif_qso - significance chi^2/nu1 (rule out qso) + signif_vary - significance that source is variable at all + class - source type (ambiguous, not_qso, qso) + + model - time series prediction for each datum given all others (iff return_model==True) + dmodel - model uncertainty, including uncertainty in data + + Note on use (i.e., how class is defined): + + (0) signif_vary < 3: ambiguous, else + (1) signif_qso > 3: qso, else + (2) signif_not_qso > 3: not_qso""" + + pars={} + pars['u'] = [-3.90, 0.12, 2.73, -0.02] + pars['g'] = [-4.10, 0.14, 2.92, -0.07] + pars['r'] = [-4.34, 0.20, 3.12, -0.15] + pars['i'] = [-4.23, 0.05, 2.83, 0.07] + pars['z'] = [-4.44, 0.13, 3.06, -0.07] + if filter.lower() not in pars: + filter='r' + + par = pars[filter.lower()] + mag0 = median(data) + lvar = par[0]+par[1]*(mag0-19.) + ltau = par[2]+par[3]*(mag0-19.) + + time = atleast_1d(time).astype('float64') + data = atleast_1d(data).astype('float64') + error = atleast_1d(error).astype('float64') + + adict = qso_engine(time,data,error,ltau=ltau,lvar=lvar,return_model=return_model,sys_err=sys_err) + + out_dict={} + out_dict['lvar']=lvar + out_dict['ltau']=ltau + out_dict['chi2/nu']=adict['chi2/nu'] + out_dict['nu'] = adict['nu'] + out_dict['chi2_qso/nu']=adict['chi2_qso/nu'] + out_dict['chi2_qso/nu_NULL']=adict['chi2_qso/nu_NULL'] + out_dict['signif_qso']=adict['signif_qso'] + out_dict['signif_not_qso']=adict['signif_not_qso'] + out_dict['signif_vary']=adict['signif_vary'] + out_dict['class']=adict['class']; + out_dict['chi2qso_nu_nuNULL_ratio'] = out_dict['chi2_qso/nu'] / out_dict['chi2_qso/nu_NULL'] + + ### Nat has converged upon the following being the most significant featues, + # Joey believes it is best to jut use these features only (so now the others are disabled in + # __init__.py and qso_extractor.py + out_dict['log_chi2_qsonu'] = log(out_dict['chi2_qso/nu']) + out_dict['log_chi2nuNULL_chi2nu'] = log(out_dict['chi2_qso/nu_NULL'] / out_dict['chi2_qso/nu']) + ### + + if (return_model): + out_dict['model']=adict['model']; out_dict['dmodel']=adict['dmodel'] + + return out_dict diff --git a/mltsp/TCP/Algorithms/randomforest_breiman_v51.f b/mltsp/TCP/Algorithms/randomforest_breiman_v51.f new file mode 100755 index 00000000..b8866187 --- /dev/null +++ b/mltsp/TCP/Algorithms/randomforest_breiman_v51.f @@ -0,0 +1,3434 @@ + program rf5new +c +c Copyright 2002-2003 Leo Breiman and Adele Cutler +c +c This is free open source software but its use,in part or +c in whole,in any commercial product that is sold for profit +c is prohibited without the written consent of Leo Breiman +c and Adele Cutler. +c +c We very much appreciate bug notices and suggested improvements. +c +c leo@stat.berkeley.edu adele@math.usu.edu +c +c SET ALL PARAMETERS FIRST GROUP BELOW. GENERALLY, +c SETTING PARAMETERS TO ZERO TURNS THE CORRESPONDING +c OPTION OFF. +c +c ALL RELEVANT OUTPUT FILES MUST BE GIVEN NAMES--SEE BELOW. +c + integer mdim,ntrain,nclass,maxcat,ntest, + & labelts,labeltr,mtry0,ndsize,jbt,look,lookcls, + & jclasswt,mdim2nd,mselect,imp,interact,impn, + & nprox,nrnn,noutlier,nscale,nprot,missfill,iviz, + & isaverf,isavepar,isavefill,isaveprox, + & irunrf,ireadpar,ireadfill,ireadprox + real code +c +c ------------------------------------------------------- +c CONTROL PARAMETERS +c + parameter( +c DESCRIBE DATA + 1 mdim=9,ntrain=214,nclass=6,maxcat=1, + 1 ntest=0,labelts=0,labeltr=1, +c +c SET RUN PARAMETERS + 2 mtry0=2,ndsize=1,jbt=500,look=100,lookcls=1, + 2 jclasswt=0,mdim2nd=0,mselect=0, +c +c SET IMPORTANCE OPTIONS + 3 imp=1,interact=0,impn=1, +c +c SET PROXIMITY COMPUTATIONS + 4 nprox=1,nrnn=ntrain, +c +c SET OPTIONS BASED ON PROXIMITIES + 5 noutlier=0,nscale=0,nprot=0, +c +c REPLACE MISSING VALUES + 6 code=-999,missfill=0, +c +c GRAPHICS + 7 iviz=0, +c +c SAVING A FOREST + 8 isaverf=0,isavepar=0,isavefill=0,isaveprox=0, +c +c RUNNING A SAVED FOREST + 9 irunrf=0,ireadpar=0,ireadfill=0,ireadprox=0) +c +c ------------------------------------------------------- +c OUTPUT CONTROLS +c + integer isumout,idataout,impfastout,impout,impnout,interout, + & iprotout,iproxout,iscaleout,ioutlierout + parameter( + & isumout = 1,!0/1 1=summary to screen + & idataout= 0,!0/1/2 1=train,2=adds test(7) + & impfastout= 0,!0/1 1=gini fastimp (8) + & impout= 0,!0/1/2 1=imp,2=to screen(9) + & impnout= 0,!0/1 1=impn (10) + & interout= 0,!0/1/2 1=interaction,2=screen(11) + & iprotout= 1,!0/1/2 1=prototypes,2=screen(12) + & iproxout= 1,!0/1/2 1=prox,2=adds test(13) + & iscaleout= 0,!0/1 1=scaling coors (14) + & ioutlierout= 1) !0/1/2 1=train,2=adds test (15) +c +c ------------------------------------------------------- +c DERIVED PARAMETERS (DO NOT CHANGE) +c + integer nsample,nrnodes,mimp,near, + & ifprot,ifscale,iftest,mdim0,ntest0,nprot0,nscale0 + parameter( + & nsample=(2-labeltr)*ntrain, + & nrnodes=2*nsample+1, + & mimp=imp*(mdim-1)+1, + & ifprot=nprot/(nprot-.1), + & ifscale=nscale/(nscale-.1), + & iftest=ntest/(ntest-.1), + & nprot0=(1-ifprot)+nprot, + & nscale0=(1-ifscale)+nscale, + & ntest0=(1-iftest)+ntest, + & mdim0=interact*(mdim-1)+1, + & near=nprox*(nsample-1)+1) +C +c ------------------------------------------------------- +c DIMENSIONING OF ARRAYS +c + real x(mdim,nsample),xts(mdim,ntest0),v5(mdim),v95(mdim), + & tgini(mdim),zt(mdim),avgini(mdim), + & votes(mdim0,jbt),effect(mdim0,mdim0),teffect(mdim0,mdim0), + & hist(0:mdim0,mdim0),g(mdim0),fill(mdim),rinpop(near,jbt), + & dgini(nrnodes),xbestsplit(nrnodes),tnodewt(nrnodes), + & tw(nrnodes),tn(nrnodes),v(nsample),win(nsample),temp(nrnn), + 7 q(nclass,nsample),devout(nclass),classwt(nclass),wr(nclass), + & tmissts(nclass),tmiss(nclass),tclasspop(nclass),wl(nclass), + & rmedout(nclass),tclasscat(nclass,maxcat),qts(nclass,ntest0), + & classpop(nclass,nrnodes),signif(mimp),zscore(mimp),sqsd(mimp), + & avimp(mimp),qimp(nsample),qimpm(nsample,mimp),tout(near), + & outtr(near),xc(maxcat),dn(maxcat),cp(maxcat),cm(maxcat), + & votecat(maxcat),freq(maxcat),wc(nsample),outts(ntest0), + & popclass(nprot0,nclass),protlow(mdim,nprot0,nclass), + & prot(mdim,nprot0,nclass),prothigh(mdim,nprot0,nclass), + & protfreq(mdim,nprot0,nclass,maxcat),rpop(nrnodes), + & protv(mdim,nprot0,nclass),wtx(nsample), + & protvlow(mdim,nprot0,nclass),protvhigh(mdim,nprot0,nclass) + + integer cat(mdim),iv(mdim),msm(mdim), + & muse(mdim),irnk(mdim,jbt),missing(mdim,near),a(mdim,nsample), + & asave(mdim,nsample),b(mdim,nsample), + & cl(nsample),out(nsample),nodextr(nsample),nodexvr(nsample), + & jin(nsample),joob(nsample),pjoob(nsample),ndbegin(near,jbt), + & jvr(nsample),jtr(nsample),jest(nsample),ibest(nrnn), + & isort(nsample),loz(near,nrnn), + & ta(nsample),ncase(nsample),idmove(nsample),kpop(nrnodes), + & jests(ntest0),jts(ntest0),iwork(near), + & nodexts(ntest0),clts(ntest0),imax(ntest0),jinb(near,jbt), + & bestsplitnext(nrnodes),bestvar(nrnodes),bestsplit(nrnodes), + & nodestatus(nrnodes),nodepop(nrnodes),nodestart(nrnodes), + & nodeclass(nrnodes),parent(nrnodes),treemap(2,nrnodes), + & ncts(nclass),nc(nclass),mtab(nclass,nclass),ncn(near), + & its(nsample),jpur(nrnn),npend(nclass),inear(nrnn), + & nrcat(maxcat),kcat(maxcat),ncatsplit(maxcat), + & nbestcat(maxcat,nrnodes),ncp(near),nodexb(near,jbt), + & npcase(near,jbt),ncount(near,jbt),nod(nrnodes),nmfmax, + & ncsplit,ncmax,nmissfill,ndimreps,nmf,nmd,iseed +c +c ------------------------------------------------------- +c USED IN PROXIMITY AND SCALING +c + double precision prox(near,nrnn),y(near),u(near), + & dl(nscale0),xsc(near,nscale0),red(near),ee(near), + & ev(near,nscale0),ppr(near) +c + character*500 text +c +c ------------------------------------------------------- +c SCALAR DECLARATIONS +c + real errtr,errts,tavg,er,randomu +c + integer mtry,n,m,mdimt,k,j,i,m1,jb,nuse,ndbigtree,jj,mr, + & n0,n1,n2,n3,n4,n5,n6,n7 +c +c ------------------------------------------------------- +c READ OLD TREE STRUCTURE AND/OR PARAMETERS +c + if (irunrf.eq.1) + & open(1,file='savedforest',status='old') + if (ireadpar.eq.1) + & open(2,file='savedparams',status='old') + if (ireadfill.eq.1) + & open(3,file='savedmissfill',status='old') + if (ireadprox.eq.1) + & open(4,file='savedprox',status='old') +c +c ------------------------------------------------------- +c NAME OUTPUT FILES FOR SAVING THE FOREST STRUCTURE +c + if (isaverf.eq.1) + & open(1,file='savedforest',status='new') + if (isavepar.eq.1) + & open(2,file='savedparams',status='new') + if (isavefill.eq.1) + & open(3,file='savedmissfill',status='new') + if (isaveprox.eq.1) + & open(4,file='savedprox',status='new') +c +c ------------------------------------------------------- +c NAME OUTPUT FILES TO SAVE DATA FROM CURRENT RUN +c + if (idataout.ge.1) + & open(7,file='save-data-from-run',status='new') + if (impfastout.eq.1) + & open(8,file='save-impfast',status='new') + if (impout.eq.1) + & open(9,file='save-importance-data',status='new') + if (impnout.eq.1) + & open(10,file='save-caseimp-data',status='new') + if (interout.eq.1) + & open(11,file='save-pairwise-effects',status='new') + if (iprotout.eq.1) + & open(12,file='save-protos',status='new') + if (iproxout.ge.1) + & open(13,file='save-run-proximities',status='new') + if (iscaleout.eq.1) + & open(14,file='save-scale',status='new') + if (ioutlierout.ge.1) + & open(15,file='save-outliers',status='new') + if(iviz.eq.1) then +c the graphics program expects files to be named in the +c following way: + open(21,file='data.txt',status='new') + open(22,file='imp.txt',status='new') + open(23,file='info.txt',status='new') + open(24,file='par.txt',status='new') + open(25,file='scale.txt',status='new') + if (iprotout.eq.1) then + open(26,file='proto.txt',status='new') + open(27,file='protinf.txt',status='new') + endif + if(interact.eq.1) open(28,file='inter.txt',status='new') + if(iproxout.ge.1) open(29,file='prox.txt',status='new') + endif +c +c ------------------------------------------------------- +c READ IN DATA--SEE MANUAL FOR FORMAT +c + open(16, file='data.train', status='old') + do n=1,ntrain + read(16,*) (x(m,n),m=1,mdim),cl(n) + enddo + close(16) + if(ntest.gt.0) then + open(17, file='data.test', status='old') + if(labelts.ne.0) then + do n=1,ntest0 + read(17,*) (xts(m,n),m=1,mdim),clts(n) + enddo + else + do n=1,ntest0 + read(17,*) (xts(m,n),m=1,mdim) + enddo + endif + close(17) + endif +c +c ------------------------------------------------------- +c SELECT SUBSET OF VARIABLES TO USE +c + if(mselect.eq.0) then + mdimt=mdim + do k=1,mdim + msm(k)=k + enddo + endif + if (mselect.eq.1) then +c fill in which variables +c mdimt= +c msm(1)= +c --- +c msm(mdimt)= + endif +c +c ------------------------------------------------------- +c SET CATEGORICAL VALUES +c + do m=1,mdim + cat(m)=1 + enddo +c fill in cat(m) for all variables m for which cat(m)>1 +c do m=1,mdim +c cat(m)=4 +c enddo +c +c ------------------------------------------------------- +c SET CLASS WEIGHTS + + if(jclasswt.eq.0) then + do j=1,nclass + classwt(j)=1 + enddo + endif + if(jclasswt.eq.1) then +c fill in classwt(j) for each j: +c classwt(1)=1. +c classwt(2)=10. + endif +c +c ======================================================= +c ************** END OF USER INPUT ******************** +c ======================================================= +c +c ------------------------------------------------------- +c MISC PARAMETERS (SHOULD NOT USUALLY NEED ADJUSTMENT) + iseed=4351 + call sgrnd(iseed) + nmfmax = 5 !number of iterations (iterative fillin) + ncsplit = 25 !number of random splits (big categoricals) + ncmax = 25 !big categorical has more than ncmax levels +c +c ------------------------------------------------------- +c ERROR CHECKING +c + call checkin(labelts,labeltr,nclass,lookcls,jclasswt, + & mselect,mdim2nd,mdim,imp,impn,interact,nprox,nrnn, + & nsample,noutlier,nscale,nprot,missfill,iviz,isaverf, + & irunrf,isavepar,ireadpar,isavefill,ireadfill,isaveprox, + & ireadprox,isumout,idataout,impfastout,impout,interout, + & iprotout,iproxout,iscaleout,ioutlierout,cat,maxcat,cl) +c +c ------------------------------------------------------- +c SPECIAL CASES - QUERY PARAMETERS OR USE SAVED TREE +c +c query parameters + if(ireadpar.eq.1) goto 888 +c +c use saved tree + if(irunrf.eq.1) goto 999 +c +c ------------------------------------------------------- +c INITIALIZATION +c +c mtry will change if mdim2nd>0, so make it a variable + mtry=mtry0 +c +c nmissfill is the number of missing-value-fillin loops + nmissfill=1 + if(missfill.eq.2) nmissfill=nmfmax +c +c ndimreps is 2 if we want to do variable selection, +c otherwise it's 1 + ndimreps=1 + if(mdim2nd.gt.0) ndimreps=2 +c +c assign weights for equal weighting case +c (use getweights for unequal weighting case) + if(jclasswt.eq.0) then + do k=1,nrnodes + tnodewt(k)=1 + enddo + endif +c + if(labeltr.eq.1) then + do n=1,nsample + wtx(n)=classwt(cl(n)) + enddo + else + do n=1,nsample + wtx(n)=1.0 + enddo + endif +c +c ------------------------------------------------------- +c IF DATA ARE UNLABELED, ADD A SYNTHETIC CLASS +c + if(labeltr.eq.0) then + call createclass(x,cl,ntrain,nsample,mdim) + endif +c +c ------------------------------------------------------- +c COUNT CLASS POPULATIONS +c + call zerv(nc,nclass) + do n=1,nsample + nc(cl(n))=nc(cl(n))+1 + enddo + if(ntest.gt.0.and.labelts.eq.1) then + call zerv(ncts,nclass) + do n=1,ntest0 + ncts(clts(n))=ncts(clts(n))+1 + enddo + endif +c +c ------------------------------------------------------- +c DO PRELIMINARY MISSING DATA +c + if(missfill.eq.1) then + call roughfix(x,v,ncase,mdim,nsample, + & cat,code,nrcat,maxcat,fill) + if(ntest.gt.0) then + call xfill(xts,ntest,mdim,fill,code) + endif + endif + if(missfill.eq.2) then + do n=1,near + do m=1,mdim + if(abs(code-x(m,n)).lt.8.232D-11) then + missing(m,n)=1 + else + missing(m,n)=0 + endif + enddo + enddo + call roughfix(x,v,ncase,mdim,nsample, + & cat,code,nrcat,maxcat,fill) + endif +c +c ------------------------------------------------------- +c TOP OF LOOP FOR ITERATIVE MISSING VALUE FILL OR SUBSET SELECTION +c + do nmd=1,ndimreps + + if(imp.gt.0) then + call zervr(sqsd,mimp) + call zervr(avimp,mimp) + endif + call zervr(avgini,mdim) + if(impn.eq.1) then + call zervr(qimp,nsample) + call zermr(qimpm,nsample,mdim) + endif + if(interact.eq.1) call zermr(votes,mdim,jbt) + + do nmf=1,nmissfill +c +c ------------------------------------------------------- +c INITIALIZE FOR RUN +c + call makea(x,mdim,nsample,cat,isort,v,asave,b,mdimt, + & msm,v5,v95,maxcat) +c + call zermr(q,nclass,nsample) + call zerv(out,nsample) + if(nprox.gt.0) call zermd(prox,near,nrnn) + if(ntest.gt.0) then + call zermr(qts,nclass,ntest) + endif +c +c ======================================================= +c ************** BEGIN MAIN LOOP ********************** +c ======================================================= +c + do jb=1,jbt +c +c ------------------------------------------------------- +c INITIALIZE +c + call zervr(win,nsample) + call zerv(jin,nsample) + call zervr(tclasspop,nclass) +c +c ------------------------------------------------------- +c TAKE A BOOTSTRAP SAMPLE +c + do n=1,nsample + k=int(randomu()*nsample) + if(k.lt.nsample) k=k+1 + win(k)=win(k)+classwt(cl(k)) + jin(k)=jin(k)+1 + tclasspop(cl(k))=tclasspop(cl(k))+classwt(cl(k)) + enddo + do n=1,nsample + if(jin(n).eq.0) out(n)=out(n)+1 +c out(n)=number of trees for which the nth +c obs has been out-of-bag + enddo +c +c ------------------------------------------------------- +c PREPARE TO BUILD TREE +c + call moda(asave,a,nuse,nsample,mdim,cat,maxcat,ncase, + & jin,mdimt,msm) +c +c ------------------------------------------------------- +c MAIN TREE-BUILDING +c + call buildtree(a,b,cl,cat,mdim,nsample,nclass,treemap, + & bestvar,bestsplit,bestsplitnext,dgini,nodestatus,nodepop, + & nodestart,classpop,tclasspop,tclasscat,ta,nrnodes, + & idmove,ndsize,ncase,parent,mtry,nodeclass,ndbigtree, + & win,wr,wl,nuse,kcat,ncatsplit,xc,dn,cp,cm,maxcat, + & nbestcat,msm,mdimt,iseed,ncsplit,ncmax) +c +c ------------------------------------------------------- +c SPLIT X +c + call xtranslate(x,mdim,nrnodes,nsample,bestvar,bestsplit, + & bestsplitnext,xbestsplit,nodestatus,cat,ndbigtree) +c +c ------------------------------------------------------- +c ASSIGN CLASSWEIGHTS TO NODES +c + if(jclasswt.eq.1) then + call getweights(x,nsample,mdim,treemap,nodestatus, + & xbestsplit,bestvar,nrnodes,ndbigtree, + & cat,maxcat,nbestcat,jin,win,tw,tn,tnodewt) + endif +c +c ------------------------------------------------------- +c GET OUT-OF-BAG ESTIMATES +c + call testreebag(x,nsample,mdim,treemap,nodestatus, + & xbestsplit,bestvar,nodeclass,nrnodes,ndbigtree,kpop, + & cat,jtr,nodextr,maxcat,nbestcat,rpop,dgini,tgini,jin, + & wtx) + do n=1,nsample + if(jin(n).eq.0) then +c this case is out-of-bag + q(jtr(n),n)=q(jtr(n),n)+tnodewt(nodextr(n)) + endif + enddo + do k=1,mdimt + m=msm(k) + avgini(m)=avgini(m)+tgini(m) + if(interact.eq.1) votes(m,jb)=tgini(m) + enddo +c +c ------------------------------------------------------- +c DO PRE-PROX COMPUTATION +c + if(nprox.eq.1) then + call preprox(near,nrnodes,jbt,nodestatus,ncount,jb, + & nod,nodextr,nodexb,jin,jinb,ncn,ndbegin,kpop,rinpop, + & npcase,rpop,nsample) + endif +c +c ------------------------------------------------------- +c GET TEST SET ERROR ESTIMATES +c + if(ntest.gt.0) then + call testreelite(xts,ntest,mdim,treemap,nodestatus, + & xbestsplit,bestvar,nodeclass,nrnodes,ndbigtree, + & cat,jts,maxcat,nbestcat,nodexts) + do n=1,ntest0 + qts(jts(n),n)=qts(jts(n),n)+tnodewt(nodexts(n)) + enddo + endif +c +c ------------------------------------------------------- +c GIVE RUNNING OUTPUT +c + if(lookcls.eq.1.and.jb.eq.1) then + write(*,*) 'class counts-training data' + write(*,*) (nc(j),j=1,nclass) + if(ntest.gt.0.and.labelts.eq.1) then + print* + write(*,*) 'class counts-test data' + write(*,*) (ncts(j),j=1,nclass) + endif + endif + if(mod(jb,look).eq.0.or.jb.eq.jbt) then + call comperrtr(q,cl,nsample,nclass,errtr, + & tmiss,nc,jest,out) + if(look.gt.0) then + if(lookcls.eq.1) then + write(*,'(i8,100f10.2)') + & jb,100*errtr,(100*tmiss(j),j=1,nclass) + else + write(*,'(i8,2f10.2)') jb,100*errtr + endif + endif + if(ntest.gt.0) then + call comperrts(qts,clts,ntest,nclass,errts, + & tmissts,ncts,jests,labelts) + if(look.gt.0) then + if(labelts.eq.1) then + if(lookcls.eq.1) then + write(*,'(i8,20f10.2)') jb,100*errts, + & (100*tmissts(j),j=1,nclass) + else + write(*,'(i8,2f10.2)') jb,100*errts + endif + print * + endif + endif + endif + endif +c +c ------------------------------------------------------- +c VARIABLE IMPORTANCE +c + if(imp.eq.1.and.nmf.eq.nmissfill) then + call varimp(x,nsample,mdim,cl,nclass,jin,jtr,impn, + & interact,msm,mdimt,qimp,qimpm,avimp,sqsd, + & treemap,nodestatus,xbestsplit,bestvar,nodeclass,nrnodes, + & ndbigtree,cat,jvr,nodexvr,maxcat,nbestcat,tnodewt, + & nodextr,joob,pjoob,iv) + endif +c +c ------------------------------------------------------- +c SEND SAVETREE DATA TO FILE (IF THIS IS THE FINAL FOREST) +c + if(isaverf.eq.1.and.nmd.eq.ndimreps.and.nmf.eq.nmissfill) then + if(jb.eq.1) then + write(1,*) (cat(m),m=1,mdim) + if(missfill.ge.1) write(1,*) (fill(m),m=1,mdim) + endif + write(1,*) ndbigtree + do n=1,ndbigtree + write(1,*) n,nodestatus(n),bestvar(n),treemap(1,n), + & treemap(2,n),nodeclass(n),xbestsplit(n),tnodewt(n), + & (nbestcat(k,n),k=1,maxcat) + enddo + endif + +c ======================================================= +c ************** END MAIN LOOP ************************ +c ======================================================= + enddo !jb +c +c ------------------------------------------------------- +c FIND PROXIMITIES, FILL IN MISSING VALUES AND ITERATE +c + if(nmf.lt.nmissfill) then + write(*,*) 'nrep',nmf + +c compute proximities between each obs in the training +c set and its nrnn closest neighbors + + call comprox(prox,nodexb,jinb,ndbegin, + & npcase,ppr,rinpop,near,jbt,noutlier,outtr,cl, + & loz,nrnn,wtx,nsample,iwork,ibest) + +c use proximities to impute missing values + + call impute(x,prox,near,mdim, + & maxcat,votecat,cat,nrnn,loz,missing) + endif + enddo !nmf +c +c ------------------------------------------------------- +c COMPUTE IMPORTANCES, SELECT SUBSET AND LOOP BACK +c + if(imp.eq.1) then + call finishimp(mdim,sqsd,avimp,signif, + & zscore,jbt,mdimt,msm) + do m=1,mdimt +c zt(m) is the negative z-score for variable msm(m) + zt(m)=-zscore(msm(m)) + muse(m)=msm(m) + enddo +c sort zt from smallest to largest,and sort muse accordingly + call quicksort(zt,muse,1,mdimt,mdim) +c muse(m) refers to the variable that has the mth-smallest zt +c select the mdim2nd most important variables and iterate + if(mdim2nd.gt.0) then + mdimt=mdim2nd + do m=1,mdimt + msm(m)=muse(m) + enddo + mtry=nint(sqrt(real(mdimt))) + endif + endif + enddo !nmd +c +c ------------------------------------------------------- +c END OF ITERATIONS - NOW ENDGAME +c ------------------------------------------------------- +c +c ------------------------------------------------------- +c NORMALIZE VOTES +c + do j=1,nclass + do n=1,nsample + if(q(j,n).gt.0.0.and.out(n).gt.0) q(j,n)=q(j,n)/real(out(n)) + enddo + if(ntest.gt.0) then + do n=1,ntest0 + qts(j,n)=qts(j,n)/jbt + enddo + endif + enddo +c +c ------------------------------------------------------- +c COMPUTE PROXIMITIES AND SEND TO FILE +c + if(nprox.ge.1) then +c compute proximities between each obs in the training +c set and its nrnn closest neighbors + + call comprox(prox,nodexb,jinb,ndbegin, + & npcase,ppr,rinpop,near,jbt,noutlier,outtr,cl, + & loz,nrnn,wtx,nsample,iwork,ibest) + + if(iproxout.ge.1) then + do n=1,near + write(13,'(i5,500(i5,f10.3))') n,(loz(n,k), + & prox(n,k),k=1,nrnn) + enddo + endif + endif + close(13) +c +c ------------------------------------------------------- +c COMPUTE SCALING COORDINATES AND SEND TO FILE +c + if(nprox.eq.1.and.nscale.gt.0) then + call myscale(loz,prox,xsc,y,u,near,nscale,red,nrnn, + & ee,ev,dl) + if(iscaleout.eq.1)then + do n=1,nscale0 + write(14,'(i5,f10.3)') n,dl(n) + enddo + do n=1,near + write(14,'(3i5,15f10.3)') n,cl(n),jest(n), + & (xsc(n,k),k=1,nscale0) + enddo + close(14) + endif + endif +c +c ------------------------------------------------------- +c COMPUTE CASEWISE VARIABLE IMPORTANCE (FOR GRAPHICS) +c AND SEND TO FILE +c + if(impn.eq.1) then + do n=1,nsample + do m1=1,mdimt + mr=msm(m1) + qimpm(n,mr)=100*(qimp(n)-qimpm(n,mr))/jbt + enddo + if(impnout.eq.1) write(10,'(100f10.3)') + & (qimpm(n,msm(m1)),m1=1,mdimt) + enddo + endif +c +c ------------------------------------------------------- +c SEND IMPORTANCES TO FILE OR SCREEN +c + if(imp.eq.1) then + do k=1,min0(mdimt,25) + m=muse(k) + if(impout.eq.1) write(9,'(i5,10f10.3)')m,100*avimp(m), + & zscore(m),signif(m) + if(impout.eq.2) write(*,'(i5,10f10.3)')m,100*avimp(m), + & zscore(m),signif(m) + enddo + close(9) + endif +c +c ------------------------------------------------------- +c COMPUTE INTERACTIONS AND SEND TO FILE OR SCREEN +c + if(interact.eq.1) then + call compinteract(votes,effect,msm,mdim,mdimt, + & jbt,g,iv,irnk,hist,teffect) + if(interout.eq.1) then + write(11,*)'CODE' + do i=1,mdimt + write(11,*) i,msm(i) + enddo + print* + do i=1,mdimt + m=msm(i) + effect(m,m)=0 + write(11,'(40i5)') i, + & (nint(effect(m,msm(j))),j=1,mdimt) + enddo + close(11) + endif + if(interout.eq.2) then + write(*,*)'CODE' + do i=1,mdimt + write(*,*) i,msm(i) + enddo + print* + do i=1,mdimt + m=msm(i) + effect(m,m)=0 + write(*,'(20i5)') i, + & (nint(effect(m,msm(j))),j=1,mdimt) + enddo + endif + endif +c +c ------------------------------------------------------- +c COMPUTE FASTIMP AND SEND TO FILE +c + if(impfastout.eq.1) then + tavg=0 + do k=1,mdimt + m=msm(k) + tavg=tavg+avgini(m) + enddo + tavg=tavg/mdimt + do k=1,mdimt + m=msm(k) + write(8,*) m,avgini(m)/tavg + enddo + close(8) + endif + +c ------------------------------------------------------- +c COMPUTE PROTOTYPES AND SEND TO FILE +c + if(nprox.ge.1.and.nprot.gt.0) then + call compprot(loz,nrnn,nsample,mdim,its, + & cl,wc,nclass,x,mdimt,msm,temp,cat,maxcat, + & jpur,inear,nprot,protlow,prothigh,prot,protfreq, + & protvlow,protvhigh,protv, + & popclass,npend,freq,v5,v95) +c + if (iprotout.eq.1) then + write(12,'(a5,50i10)') ' ',( + & (nint(popclass(i,j)),i=1,npend(j)),j=1,nclass) + write(12,'(a5,50i10)') ' ',( + & (i,i=1,npend(j)),j=1,nclass) + write(12,'(a5,50i10)') ' ',( + & (j,i=1,npend(j)),j=1,nclass) + do k=1,mdimt + m=msm(k) + if(cat(m).eq.1) then + write(12,'(i5,50f10.3)') k, + & ((prot(m,i,j),protlow(m,i,j), + & prothigh(m,i,j),i=1,npend(j)),j=1,nclass) + else + write(12,'(i5,50f10.3)') k, + & (((protfreq(m,i,j,jj),jj=1,cat(m)), + & i=1,npend(j)),j=1,nclass) + endif + enddo + endif + close (12) +c + if(iprotout.eq.2) then + write(*,'(a5,50i10)') ' ',((nint(popclass(i,j)), + & i=1,npend(j)),j=1,nclass) + do k=1,mdimt + m=msm(k) + if(cat(m).eq.1) then + write(*,'(i5,50f10.3)') k, + & ((prot(m,i,j),protlow(m,i,j), + & prothigh(m,i,j),i=1,npend(j)),j=1,nclass) + else + write(*,'(i5,50f10.3)') k, + & (((protfreq(m,i,j,jj),jj=1,cat(m)), + & i=1,npend(j)),j=1,nclass) + endif + enddo + endif + endif +c +c ------------------------------------------------------- +c COMPUTE OUTLIER MEASURE AND SEND TO FILE +c + if(noutlier.ge.1) then + call locateout(cl,tout,outtr,ncp,isort,devout, + & near,nsample,nclass,rmedout) + if(ioutlierout.ge.1) then + do n=1,near + write(15,'(2i5,f10.3)') n,cl(n), + & amax1(outtr(n),0.0) + enddo + endif + endif + +c ------------------------------------------------------- +c SUMMARY OUTPUT +c + if (isumout.eq.1) then + write(*,*) 'final error rate % ',100*errtr + if(ntest.gt.0.and.labelts.ne.0)then + write(*,*) 'final error test % ',100*errts + endif + print * + write(*,*) 'Training set confusion matrix (OOB):' + call zerm(mtab,nclass,nclass) + do n=1,nsample + if(jest(n).gt.0) mtab(cl(n),jest(n))=mtab(cl(n),jest(n))+1 + enddo + write(*,*) ' true class ' + print * + write(*,'(20i6)') (i,i=1,nclass) + print * + do j=1,nclass + write(*,'(20i6)') j,(mtab(i,j),i=1,nclass) + enddo + print * + if(ntest.gt.0.and.labelts.ne.0) then + call zerm(mtab,nclass,nclass) + do n=1,ntest0 + mtab(clts(n),jests(n))=mtab(clts(n),jests(n))+1 + enddo + write(*,*) 'Test set confusion matrix:' + write(*,*) ' true class ' + print * + write(*,'(20i6)') (i,i=1,nclass) + print * + do j=1,nclass + write(*,'(20i6)') j,(mtab(i,j),i=1,nclass) + enddo + print * + endif + endif +c +c ------------------------------------------------------- +c SEND INFO ON TRAINING AND/OR TEST SET DATA TO FILE +c + if(idataout.ge.1) then + do n=1,nsample + write(7,'(3i5,5000f10.3)') n,cl(n),jest(n), + & (q(j,n),j=1,nclass),(x(m,n),m=1,mdim) + enddo + endif + if(idataout.eq.2.and.ntest.gt.0) then + if(labelts.eq.1) then + do n=1,ntest0 + write(7,'(3i5,1000f10.3)') n,clts(n),jests(n), + & (qts(j,n),j=1,nclass),(xts(m,n),m=1,mdim) + enddo + else + do n=1,ntest0 + write(7,'(2i5,1000f10.3)') n,jests(n), + & (qts(j,n),j=1,nclass),(xts(m,n),m=1,mdim) + enddo + endif + endif + close(7) +c +c ------------------------------------------------------- +c SEND GRAPHICS INFO TO FILES +c + if(iviz.eq.1) then + do n=1,nsample + write(21,'(1000f10.3)') (x(msm(m),n),m=1,mdimt) + enddo + do n=1,nsample + write(22,'(100f10.3)')(qimpm(n,msm(m1)),m1=1,mdimt) + enddo + do n=1,nsample + write(23,'(3i5,5000f10.3)') n,cl(n),jest(n), + & (q(j,n),j=1,nclass) + enddo + write(24,*) nsample,mdimt,nscale,nclass,mdimt,nprot,nrnn + do n=1,near + write(25,'(50f10.3)') (xsc(n,k),k=1,nscale0) + enddo + if (iprotout.eq.1) then + do j=1,nclass + do i=1,npend(j) + write(26,'(3i5,5000f10.3)') j,i,1,(protv(msm(m),i,j),m=1,mdimt) + write(26,'(3i5,5000f10.3)') j,i,2,(protvlow(msm(m),i,j),m=1,mdimt) + write(26,'(3i5,5000f10.3)') j,i,3,(protvhigh(msm(m),i,j),m=1,mdimt) + write(27,'(i5)') nint(popclass(i,j)) + enddo + enddo + endif + if(interact.eq.1) then + do i=1,mdimt + m=msm(i) + effect(m,m)=0 + write(28,'(40i5)') + & (nint(effect(m,msm(j))),j=1,mdimt) + enddo + endif + if(iproxout.ge.1) then + do n=1,nsample +c write(29,'(5000(i5,f10.3))') (loz(n,k),prox(n,k),k=1,nrnn) +c NEW???: + write(29,'(5000(i5,f10.3))') (k,prox(n,k),k=1,nrnn) + enddo + endif + endif +c +c ------------------------------------------------------- +c SEND FILL TO FILE (ROUGH FILL ONLY) +c + if(isavefill.eq.1.and.missfill.eq.1) then + write(3,*) (fill(m),m=1,mdim) + endif +c +c ------------------------------------------------------- +c SEND RUN PARAMETERS TO FILE +c + if(isavepar.eq.1) then + write(2,*) ntrain,mdim,maxcat,nclass,jbt, + & jclasswt,missfill,code,nrnodes, + & 100*errtr +c +c type in comments up to 500 characters long +c between the ' ' in the line below. +c + write(2,*) 'this is a test run to verify that my + & descriptive output works.' + close (2) + endif +c +c ------------------------------------------------------- +c END OF USUAL RUN +c +c +c ------------------------------------------------------- +c READ RUN PARAMETERS AND PRINT TO SCREEN +c +888 if(ireadpar.eq.1) then + read(2,*) n0,n1,n2,n3,n4,n5,n6,n7,er + write(*,*) 'parameters' + write(*,*) 'nsample=' ,n0 + write(*,*) 'mdim= ' ,n1 + write(*,*) 'maxcat=',n2 + write(*,*) 'nclass=',n3 + write(*,*) 'jbt= ' ,n4 + write(*,*) 'jclasswt=',n5 + write(*,*) 'code=' ,n6 + write(*,*) 'nrnodes=' ,n7 + print * + write(*,*) 'out-of-bag error=',er,'%' + print * + read(2,'(500a)') text + write(*,*) text +c + stop +c + endif +c +c ------------------------------------------------------- +c RERUN OLD RANDOM FOREST +c +999 if(irunrf.eq.1.and.ntest.gt.0) then + call runforest(mdim,ntest,nclass,maxcat,nrnodes, + & labelts,jbt,clts,xts,xbestsplit,qts,treemap,nbestcat, + & nodestatus,cat,nodeclass,jts,jests,bestvar,tmissts,ncts, + & fill,missfill,code,errts,tnodewt,outts,idataout,imax, + & look,lookcls,nodexts,isumout,mtab) + endif +c +c ======================================================= +c ************** END MAIN ****************************** +c ======================================================= +c + end +c +c ======================================================= +c ************** SUBROUTINES AND FUNCTIONS ************ +c ======================================================= +c +c ------------------------------------------------------- + subroutine runforest(mdim,ntest,nclass,maxcat,nrnodes, + & labelts,jbt,clts,xts,xbestsplit,qts,treemap,nbestcat, + & nodestatus,cat,nodeclass,jts,jests,bestvar,tmissts,ncts, + & fill,missfill,code,errts,tnodewt,outts,idataout,imax, + & look,lookcls,nodexts,isumout,mtab) +c +c reads a forest file and runs new data through it +c + real xts(mdim,ntest),xbestsplit(nrnodes),tmissts(nclass), + & qts(nclass,ntest),fill(mdim),tnodewt(nrnodes),outts(ntest) +c + integer treemap(2,nrnodes),nodestatus(nrnodes), + & cat(mdim),nodeclass(nrnodes),jests(ntest), + & bestvar(nrnodes),jts(ntest),clts(ntest),nodexts(nrnodes), + & ncts(nclass),imax(ntest),nbestcat(maxcat,nrnodes), + & isumout,mtab(nclass,nclass) + +c + integer mdim,ntest,nclass,maxcat,nrnodes,labelts, + & jbt,missfill,look,lookcls,idataout + real errts,code + integer m,j,n,jb,ndbigtree,idummy + call zermr(qts,nclass,ntest) +c + read(1,*) (cat(m),m=1,mdim) +c + if(missfill.eq.1) then + read(1,*) (fill(m),m=1,mdim) +c fast fix on the test data - + call xfill(xts,ntest,mdim,fill,code) + endif +c + if(labelts.eq.1) then + do n=1,ntest + ncts(clts(n))=ncts(clts(n))+1 + enddo + endif +c +c START DOWN FOREST +c + do jb=1,jbt + read(1,*) ndbigtree + do n=1,ndbigtree + read(1,*) idummy,nodestatus(n),bestvar(n), + & treemap(1,n),treemap(2,n),nodeclass(n), + & xbestsplit(n),tnodewt(n),(nbestcat(j,n),j=1,maxcat) + enddo + + call testreelite(xts,ntest,mdim,treemap,nodestatus, + & xbestsplit,bestvar,nodeclass,nrnodes,ndbigtree, + & cat,jts,maxcat,nbestcat,nodexts) + do n=1,ntest + qts(jts(n),n)=qts(jts(n),n)+tnodewt(nodexts(n)) + enddo + + if(labelts.eq.1) then + if(mod(jb,look).eq.0.and.jb.le.jbt) then + call comperrts(qts,clts,ntest,nclass,errts, + & tmissts,ncts,jests,labelts) + if(lookcls.eq.1) then + write(*,'(i8,100f10.2)') + & jb,100*errts,(100*tmissts(j),j=1,nclass) + else + write(*,'(i8,2f10.2)') jb,100*errts + endif + endif + endif +c + enddo !jb +c + if(idataout.eq.2) then + if(labelts.eq.1) then + do n=1,ntest + write(7,'(3i5,1000f10.3)') n,clts(n),jests(n), + & (qts(j,n),j=1,nclass),(xts(m,n),m=1,mdim) + enddo + else + do n=1,ntest + write(7,'(3i5,1000f10.3)') n,jests(n), + & (qts(j,n),j=1,nclass),(xts(m,n),m=1,mdim) + enddo + endif + endif + close(7) + if (isumout.eq.1) then + if(labelts.eq.1)then + write(*,*) 'final error test % ',100*errts + call zerm(mtab,nclass,nclass) + do n=1,ntest + mtab(clts(n),jests(n))=mtab(clts(n),jests(n))+1 + enddo + write(*,*) 'Test set confusion matrix:' + write(*,*) ' true class ' + print * + write(*,'(20i6)') (j,j=1,nclass) + print * + do n=1,nclass + write(*,'(20i6)') n,(mtab(j,n),j=1,nclass) + enddo + print * + endif + endif + end +c +c ------------------------------------------------------- + subroutine makea(x,mdim,nsample,cat,isort,v,asave,b,mdimt, + & msm,v5,v95,maxcat) +c + real x(mdim,nsample),v(nsample),v5(mdim),v95(mdim) + integer cat(mdim),isort(nsample),asave(mdim,nsample), + & b(mdim,nsample),msm(mdim) + integer mdim,nsample,mdimt,maxcat + integer k,mvar,n,n1,n2,ncat,jj +c +c submakea constructs the mdim x nsample integer array a. +c If there are less than 32,000 cases, this can be declared +c integer*2,otherwise integer*4. For each numerical variable +c with values x(m,n),n=1,...,nsample, the x-values are sorted +c from lowest to highest. Denote these by xs(m,n). +c Then asave(m,n) is the case number in which +c xs(m,n) occurs. The b matrix is also constructed here. If the mth +c variable is categorical, then asave(m,n) is the category of the nth +c case number. +c +c input: x,cat +c output: a,b +c work: v,isort +c + do k=1,mdimt + mvar=msm(k) + if (cat(mvar).eq.1) then + do n=1,nsample + v(n)=x(mvar,n) + isort(n)=n + enddo + call quicksort(v,isort,1,nsample,nsample) +c this sorts the v(n) in ascending order. isort(n) is the +c case number of that v(n) nth from the lowest (assume +c the original case numbers are 1,2,...). + n1=nint(.05*nsample) + if(n1.lt.1) n1=1 + v5(mvar)=v(n1) + n2=nint(.95*nsample) + if(n2.gt.nsample) n2=nsample + v95(mvar)=v(n2) + do n=1,nsample-1 + n1=isort(n) + n2=isort(n+1) + asave(mvar,n)=n1 + if(n.eq.1) b(mvar,n1)=1 + if (v(n).lt.v(n+1)) then + b(mvar,n2)=b(mvar,n1)+1 + else + b(mvar,n2)=b(mvar,n1) + endif + enddo + asave(mvar,nsample)=isort(nsample) + else + do ncat=1,nsample + jj=nint(x(mvar,ncat)) + asave(mvar,ncat)=jj + enddo + endif + enddo +c + end +c +c ------------------------------------------------------- + subroutine moda(asave,a,nuse,nsample,mdim,cat,maxcat, + & ncase,jin,mdimt,msm) +c + integer asave(mdim,nsample),a(mdim,nsample),cat(mdim), + & jin(nsample),ncase(nsample),msm(mdim) + integer nuse,nsample,mdim,mdimt,maxcat + integer n,jj,m,k,nt,j +c +c copy rows msm(1),...,msm(mdimt) of asave into the same +c rows of a +c + nuse=0 + do n=1,nsample + do k=1,mdimt + m=msm(k) + a(m,n)=asave(m,n) + enddo + if(jin(n).ge.1) nuse=nuse+1 + enddo + do jj=1,mdimt + m=msm(jj) + k=1 + nt=1 + if(cat(m).eq.1) then + do n=1,nsample + if(k.gt.nsample) goto 37 + if(jin(a(m,k)).ge.1) then + a(m,nt)=a(m,k) + k=k+1 + else + do j=1,nsample-k + if(jin(a(m,k+j)).ge.1) then + a(m,nt)=a(m,k+j) + k=k+j+1 + goto 28 + endif + enddo + endif +28 continue + nt=nt+1 + if(nt.gt.nuse) goto 37 + enddo +37 continue + endif + enddo + if(maxcat.gt.1) then + k=1 + nt=1 + do n=1,nsample + if(jin(k).ge.1) then + ncase(nt)=k + k=k+1 + else + do jj=1,nsample-k + if(jin(k+jj).ge.1) then + ncase(nt)=k+jj + k=k+jj+1 + goto 58 + endif + enddo + endif +58 continue + nt=nt+1 + if(nt.gt.nuse) goto 85 + enddo +85 continue + endif + end +c +c ------------------------------------------------------- + subroutine buildtree(a,b,cl,cat,mdim,nsample,nclass,treemap, + & bestvar,bestsplit,bestsplitnext,dgini,nodestatus,nodepop, + & nodestart,classpop,tclasspop,tclasscat,ta,nrnodes, + & idmove,ndsize,ncase,parent,mtry,nodeclass,ndbigtree, + & win,wr,wl,nuse,kcat,ncatsplit,xc,dn,cp,cm,maxcat, + & nbestcat,msm,mdimt,iseed,ncsplit,ncmax) +c +c Buildtree consists of repeated calls to findbestsplit and movedata. +c Findbestsplit does just that--it finds the best split of the current +c node. Movedata moves the data in the split node right and left so +c that the data corresponding to each child node is contiguous. +c +c The buildtree bookkeeping is different from that in Friedman's +c original CART program: +c ncur is the total number of nodes to date +c nodestatus(k)=1 if the kth node has been split. +c nodestatus(k)=2 if the node exists but has not yet been split +c and=-1 of the node is terminal. +c A node is terminal if its size is below a threshold value, or if it +c is all one class,or if all the x-values are equal. If the current +c node k is split,then its children are numbered ncur+1 (left), and +c ncur+2(right),ncur increases to ncur+2 and the next node to be split +c is numbered k+1. When no more nodes can be split,buildtree +c returns to the main program. +c + integer cl(nsample),cat(mdim),ncatsplit(maxcat), + & treemap(2,nrnodes),bestvar(nrnodes),nodeclass(nrnodes), + & bestsplit(nrnodes),nodestatus(nrnodes),ta(nsample), + & nodepop(nrnodes),nodestart(nrnodes),idmove(nsample), + & bestsplitnext(nrnodes),ncase(nsample),parent(nrnodes), + & kcat(maxcat),msm(mdim),iseed,ncsplit,ncmax + + integer a(mdim,nsample),b(mdim,nsample) + real tclasspop(nclass),classpop(nclass,nrnodes), + & tclasscat(nclass,maxcat),win(nsample),wr(nclass), + & wl(nclass),dgini(nrnodes),xc(maxcat),dn(maxcat), + & cp(maxcat),cm(maxcat) + integer mdim,nsample,nclass,nrnodes,ndsize, + & mtry,ndbigtree,nuse,maxcat,mdimt,j,ncur, + & kbuild,ndstart,ndend,jstat + integer msplit,nbest + integer lcat,i,kn,k,n,ndendl,nc + real decsplit,popt1,popt2,pp + integer nbestcat(maxcat,nrnodes) +c + call zerv(nodestatus,nrnodes) + call zerv(nodestart,nrnodes) + call zerv(nodepop,nrnodes) + call zermr(classpop,nclass,nrnodes) + call zerm(treemap,2,nrnodes) + call zerm(nbestcat,maxcat,nrnodes) + do j=1,nclass + classpop(j,1)=tclasspop(j) + enddo +c + ncur=1 + nodestart(1)=1 + nodepop(1)=nuse + nodestatus(1)=2 +c start main loop +c + do 30 kbuild=1,nrnodes + if (kbuild.gt.ncur) goto 50 + if (nodestatus(kbuild).ne.2) goto 30 +c initialize for next call to findbestsplit +c + ndstart=nodestart(kbuild) + ndend=ndstart+nodepop(kbuild)-1 + do j=1,nclass + tclasspop(j)=classpop(j,kbuild) + enddo + jstat=0 +c + call findbestsplit(a,b,cl,mdim,nsample,nclass,cat, + & ndstart,ndend,tclasspop,tclasscat,msplit,decsplit,nbest, + & ncase,jstat,mtry,win,wr,wl,kcat,ncatsplit,xc,dn, + & cp,cm,maxcat,msm,mdimt,iseed,ncsplit,ncmax) +c + if(jstat.eq.1) then + nodestatus(kbuild)=-1 + goto 30 + else + bestvar(kbuild)=msplit + dgini(kbuild)=decsplit +c + if (cat(msplit).eq.1) then +c continuous + bestsplit(kbuild)=a(msplit,nbest) + bestsplitnext(kbuild)=a(msplit,nbest+1) + else +c categorical + lcat=cat(msplit) + do i=1,lcat + nbestcat(i,kbuild)=ncatsplit(i) + enddo + endif + endif + call movedata(a,ta,mdim,nsample,ndstart,ndend,idmove,ncase, + & msplit,cat,nbest,ndendl,ncatsplit,maxcat,mdimt,msm) +c +c leftnode no.=ncur+1,rightnode no.=ncur+2. +c + nodepop(ncur+1)=ndendl-ndstart+1 + nodepop(ncur+2)=ndend-ndendl + nodestart(ncur+1)=ndstart + nodestart(ncur+2)=ndendl+1 +c +c +c find class populations in both nodes + do n=ndstart,ndendl + nc=ncase(n) + j=cl(nc) + classpop(j,ncur+1)=classpop(j,ncur+1)+win(nc) + enddo + do n=ndendl+1,ndend + nc=ncase(n) + j=cl(nc) + classpop(j,ncur+2)=classpop(j,ncur+2)+win(nc) + enddo +c +c check on nodestatus +c + nodestatus(ncur+1)=2 + nodestatus(ncur+2)=2 + if (nodepop(ncur+1).le.ndsize) nodestatus(ncur+1)=-1 + if (nodepop(ncur+2).le.ndsize) nodestatus(ncur+2)=-1 + popt1=0 + popt2=0 + do j=1,nclass + popt1=popt1+classpop(j,ncur+1) + popt2=popt2+classpop(j,ncur+2) + enddo + do j=1,nclass + if (abs(classpop(j,ncur+1)-popt1) + & .lt.8.232D-11) nodestatus(ncur+1)=-1 + if (abs(classpop(j,ncur+2)-popt2) + & .lt.8.232D-11) nodestatus(ncur+2)=-1 + enddo + treemap(1,kbuild)=ncur+1 + treemap(2,kbuild)=ncur+2 + parent(ncur+1)=kbuild + parent(ncur+2)=kbuild + nodestatus(kbuild)=1 + ncur=ncur+2 + if (ncur.ge.nrnodes) goto 50 +30 continue +50 continue + ndbigtree=nrnodes + do k=nrnodes,1,-1 + if (nodestatus(k).eq.0) ndbigtree=ndbigtree-1 + if (nodestatus(k).eq.2) nodestatus(k)=-1 + enddo + do kn=1,ndbigtree + if(nodestatus(kn).eq.-1) then + pp=0 + do j=1,nclass + if(classpop(j,kn).gt.pp) then + nodeclass(kn)=j + pp=classpop(j,kn) + endif + enddo + endif + enddo + end +c +c ------------------------------------------------------- + subroutine findbestsplit(a,b,cl,mdim,nsample,nclass,cat, + & ndstart,ndend,tclasspop,tclasscat,msplit,decsplit,nbest, + & ncase,jstat,mtry,win,wr,wl,kcat,ncatsplit,xc,dn, + & cp,cm,maxcat,msm,mdimt,iseed,ncsplit,ncmax) +c +c For the best split,msplit is the variable split on. decsplit is the dec. in impurity. +c If msplit is numerical,nsplit is the case number of value of msplit split on, +c and nsplitnext is the case number of the next larger value of msplit. If msplit is +c categorical,then nsplit is the coding into an integer of the categories going left. +c + integer a(mdim,nsample),b(mdim,nsample),iseed,ncsplit,ncmax +c + integer cl(nsample),cat(mdim),ncase(nsample),msm(mdim), + & kcat(maxcat),ncatsplit(maxcat),icat(32) +c + integer mdim,nsample,nclass,ndstart,ndend,mdimt,mtry, + & msplit,nbest,jstat,maxcat,i,j,k,mv,mvar,nc, + & lcat,nnz,nhit,ncatsp,nsp +c + real tclasspop(nclass),tclasscat(nclass,maxcat), + & win(nsample),wr(nclass),wl(nclass),xc(maxcat), + & dn(maxcat),cp(maxcat),cm(maxcat) +c + real decsplit,pno,pdo,rrn,rrd,rln,rld,u, + & crit0,critmax,crit,su +c + real randomu +c + external unpack +c +c compute initial values of numerator and denominator of Gini + pno=0 + pdo=0 + do j=1,nclass + pno=pno+tclasspop(j)*tclasspop(j) + pdo=pdo+tclasspop(j) + enddo + crit0=pno/pdo + jstat=0 +c start main loop through variables to find best split + critmax=-1.0e20 +c + do 20 mv=1,mtry + k=int(mdimt*randomu())+1 + mvar=msm(k) + if(cat(mvar).eq.1) then +c it's not a categorical variable: + rrn=pno + rrd=pdo + rln=0 + rld=0 + call zervr(wl,nclass) + do j=1,nclass + wr(j)=tclasspop(j) + enddo + do nsp=ndstart,ndend-1 + nc=a(mvar,nsp) + u=win(nc) + k=cl(nc) + rln=rln+u*(2*wl(k)+u) + rrn=rrn+u*(-2*wr(k)+u) + rld=rld+u + rrd=rrd-u + wl(k)=wl(k)+u + wr(k)=wr(k)-u + if (b(mvar,nc).lt.b(mvar,a(mvar,nsp+1))) then + if(amin1(rrd,rld).gt.1.0e-5) then + crit=(rln/rld)+(rrn/rrd) + if (crit.gt.critmax) then + nbest=nsp + critmax=crit + msplit=mvar + + endif + endif + endif + enddo + + else +c it's a categorical variable: +c compute the decrease in impurity given by categorical splits + lcat=cat(mvar) + call zermr(tclasscat,nclass,maxcat) + do nsp=ndstart,ndend + nc=ncase(nsp) + k=a(mvar,ncase(nsp)) + tclasscat(cl(nc),k)=tclasscat(cl(nc),k)+win(nc) + enddo + nnz=0 + do i=1,lcat + su=0 + do j=1,nclass + su=su+tclasscat(j,i) + enddo + dn(i)=su + nnz=nnz+1 + enddo + if (nnz.eq.1) then + critmax=-1.0e25 + goto 20 + endif + if(lcat.lt.ncmax) then + call catmax(pdo,tclasscat,tclasspop,nclass,lcat, + & ncatsp,critmax,nhit,maxcat) + if(nhit.eq.1) then + msplit=mvar + call unpack(lcat,ncatsp,icat) + call zerv(ncatsplit,maxcat) + do k=1,lcat + ncatsplit(k)=icat(k) + enddo + endif + else + call catmaxr(ncsplit,tclasscat,tclasspop,icat, + & nclass,lcat,maxcat,ncatsplit,critmax,pdo,nhit, + & iseed) + if(nhit.eq.1)then + msplit=mvar + endif + endif + endif !cat +20 continue +25 continue + decsplit=critmax-crit0 + if (critmax.lt.-1.0e10) jstat=1 + end +c +c ------------------------------------------------------- + subroutine catmaxr(ncsplit,tclasscat,tclasspop,icat, + & nclass,lcat,maxcat,ncatsplit,critmax,pdo,nhit,iseed) +c +c this routine takes the best of ncsplit random splits +c + real tclasscat(nclass,maxcat),tclasspop(nclass),tmpclass(100) + real critmax,pdo + integer icat(32),iseed + integer ncsplit,nclass,lcat,maxcat,ncatsplit(maxcat),nhit +c + integer irbit + real pln,pld,prn,tdec + integer j,n,i,k +c + nhit=0 + do n=1,ncsplit +c generate random split + do k=1,lcat + icat(k)=irbit(iseed) !icat(k) is bernouilli + enddo + do j=1,nclass + tmpclass(j)=0 + do k=1,lcat + if(icat(k).eq.1) then + tmpclass(j)=tmpclass(j)+tclasscat(j,k) + endif + enddo + enddo + pln=0 + pld=0 + do j=1,nclass + pln=pln+tmpclass(j)*tmpclass(j) + pld=pld+tmpclass(j) + enddo + prn=0 + do j=1,nclass + tmpclass(j)=tclasspop(j)-tmpclass(j) + prn=prn+tmpclass(j)*tmpclass(j) + enddo + tdec=(pln/pld)+(prn/(pdo-pld)) + if (tdec.gt.critmax) then + critmax=tdec + nhit=1 + do k=1,lcat + ncatsplit(k)=icat(k) + enddo + endif + enddo + end +c +c ------------------------------------------------------- + subroutine catmax(pdo,tclasscat,tclasspop,nclass,lcat, + & ncatsp,critmax,nhit,maxcat) +c +c this finds the best split of a categorical variable +c with lcat categories and nclass classes, where +c tclasscat(j,k) is the number of cases in +c class j with category value k. The method uses an +c exhaustive search over all partitions of the category +c values. For the two class problem,there is a faster +c exact algorithm. If lcat.ge.10,the exhaustive search +c gets slow and there is a faster iterative algorithm. + real tclasscat(nclass,maxcat),tclasspop(nclass),tmpclass(100) + integer icat(32),n,lcat,nhit,l,j,ncatsp,nclass,maxcat + real critmax,pdo,pln,pld,prn,tdec +c + external unpack + nhit=0 + do n=1,(2**(lcat-1))-1 + call unpack(lcat,n,icat) + do j=1,nclass + tmpclass(j)=0 + do l=1,lcat + if(icat(l).eq.1) then + tmpclass(j)=tmpclass(j)+tclasscat(j,l) + endif + enddo + enddo + pln=0 + pld=0 + do j=1,nclass + pln=pln+tmpclass(j)*tmpclass(j) + pld=pld+tmpclass(j) + enddo + prn=0 + do j=1,nclass + tmpclass(j)=tclasspop(j)-tmpclass(j) + prn=prn+tmpclass(j)*tmpclass(j) + enddo + tdec=(pln/pld)+(prn/(pdo-pld)) + if (tdec.gt.critmax) then + critmax=tdec + ncatsp=n + nhit=1 + endif + enddo + end +c +c ------------------------------------------------------- + subroutine movedata(a,ta,mdim,nsample,ndstart,ndend,idmove, + & ncase,msplit,cat,nbest,ndendl,ncatsplit,maxcat,mdimt,msm) +c +c movedata is the heart of the buildtree construction. +c Based on the best split the data corresponding to the +c current node is moved to the left if it belongs to the +c left child and right if it belongs to the right child. + integer a(mdim,nsample),ta(nsample),idmove(nsample), + & ncase(ndend),cat(mdim),ncatsplit(maxcat),msm(mdimt) + integer mdim,ndstart,ndend,nsample,msplit,nbest,ndendl + integer maxcat,mdimt,nsp,nc,ms,msh,k,n,ih +c compute idmove=indicator of case nos. going left +c + if (cat(msplit).eq.1) then + do nsp=ndstart,nbest + nc=a(msplit,nsp) + idmove(nc)=1 + enddo + do nsp=nbest+1,ndend + nc=a(msplit,nsp) + idmove(nc)=0 + enddo + ndendl=nbest + else + ndendl=ndstart-1 + do nsp=ndstart,ndend + nc=ncase(nsp) + if (ncatsplit(a(msplit,nc)).eq.1) then + idmove(nc)=1 + ndendl=ndendl+1 + else + idmove(nc)=0 + endif + enddo + endif + +c shift case. nos. right and left for numerical variables. + + do ms=1,mdimt + msh=msm(ms) + if (cat(msh).eq.1) then + k=ndstart-1 + do n=ndstart,ndend + ih=a(msh,n) + if (idmove(ih).eq.1) then + k=k+1 + ta(k)=a(msh,n) + endif + enddo + do n=ndstart,ndend + ih=a(msh,n) + if (idmove(ih).eq.0) then + k=k+1 + ta(k)=a(msh,n) + endif + enddo + do k=ndstart,ndend + a(msh,k)=ta(k) + enddo + endif + enddo + +c compute case nos. for right and left nodes. + + if (cat(msplit).eq.1) then + do n=ndstart,ndend + ncase(n)=a(msplit,n) + enddo + else + k=ndstart-1 + do n=ndstart,ndend + if (idmove(ncase(n)).eq.1) then + k=k+1 + ta(k)=ncase(n) + endif + enddo + do n=ndstart,ndend + if (idmove(ncase(n)).eq.0) then + k=k+1 + ta(k)=ncase(n) + endif + enddo + do k=ndstart,ndend + ncase(k)=ta(k) + enddo + endif + end + +c +c ------------------------------------------------------- + subroutine xtranslate(x,mdim,nrnodes,nsample,bestvar, + & bestsplit,bestsplitnext,xbestsplit,nodestatus,cat, + & ndbigtree) +c +c xtranslate takes the splits on numerical variables and translates them +c back into x-values. It also unpacks each categorical split into a 32- +c dimensional vector with components of zero or one--a one indicates +c that the corresponding category goes left in the split. +c + integer cat(mdim),bestvar(nrnodes),bestsplitnext(nrnodes), + & nodestatus(nrnodes),bestsplit(nrnodes) + real x(mdim,nsample),xbestsplit(nrnodes) + integer mdim,nrnodes,nsample,ndbigtree,k,m +c + do k=1,ndbigtree + if (nodestatus(k).eq.1) then + m=bestvar(k) + if (cat(m).eq.1) then + xbestsplit(k)=(x(m,bestsplit(k))+ + & x(m,bestsplitnext(k)))/2 + else + xbestsplit(k)=real(bestsplit(k)) + endif + endif + enddo + end +c +c ------------------------------------------------------- + subroutine getweights(x,nsample,mdim,treemap,nodestatus, + & xbestsplit,bestvar,nrnodes,ndbigtree, + & cat,maxcat,nbestcat,jin,win,tw,tn,tnodewt) +c + real x(mdim,nsample),xbestsplit(nrnodes), + & win(nsample),tw(ndbigtree),tn(ndbigtree),tnodewt(ndbigtree) + integer nsample,mdim,nrnodes,ndbigtree,maxcat + integer treemap(2,nrnodes),bestvar(nrnodes), + & cat(mdim),nodestatus(nrnodes),jin(nsample) +c + integer nbestcat(maxcat,nrnodes) + integer jcat,n,kt,k,m + call zervr(tw,ndbigtree) + call zervr(tn,ndbigtree) + do n=1,nsample + if(jin(n).ge.1) then + kt=1 + do k=1,ndbigtree + if (nodestatus(kt).eq.-1) then + tw(kt)=tw(kt)+win(n) + tn(kt)=tn(kt)+jin(n) + goto 100 + endif + m=bestvar(kt) + if (cat(m).eq.1) then + if (x(m,n).le.xbestsplit(kt)) then + kt=treemap(1,kt) + else + kt=treemap(2,kt) + endif + else + jcat=nint(x(m,n)) + if (nbestcat(jcat,kt).eq.1) then + kt=treemap(1,kt) + else + kt=treemap(2,kt) + endif + endif + enddo +100 continue + endif + enddo + do n=1,ndbigtree + if(nodestatus(n).eq.-1) tnodewt(n)=tw(n)/tn(n) + enddo + end +c +c ------------------------------------------------------- + subroutine testreebag(x,nsample,mdim,treemap,nodestatus, + & xbestsplit,bestvar,nodeclass,nrnodes,ndbigtree,kpop, + & cat,jtr,nodextr,maxcat,nbestcat,rpop,dgini,tgini,jin, + & wtx) +c +c predicts the class of all objects in x +c +c input: + real x(mdim,nsample),xbestsplit(nrnodes),dgini(nrnodes), + & rpop(nrnodes),wtx(nsample) + integer treemap(2,nrnodes),bestvar(nrnodes), + & nodeclass(nrnodes),cat(mdim),nodestatus(nrnodes), + & nbestcat(maxcat,nrnodes),jin(nsample),kpop(nrnodes) +c + integer nsample,mdim,nrnodes,ndbigtree,maxcat +c +c output: + real tgini(mdim) + integer jtr(nsample),nodextr(nsample) +c +c local + integer n,kt,k,m,jcat +c + call zerv(jtr,nsample) + call zerv(nodextr,nsample) + call zervr(tgini,mdim) + call zervr(rpop,nrnodes) + call zerv(kpop,nrnodes) +c + n=1 +903 kt=1 + do k=1,ndbigtree + if (nodestatus(kt).eq.-1) then + jtr(n)=nodeclass(kt) + nodextr(n)=kt + if(jin(n).gt.0)then + rpop(kt)=rpop(kt)+jin(n) + kpop(kt)=kpop(kt)+1 + endif + goto 100 + endif + m=bestvar(kt) + tgini(m)=tgini(m)+dgini(kt) + if (cat(m).eq.1) then + if (x(m,n).le.xbestsplit(kt)) then + kt=treemap(1,kt) + else + kt=treemap(2,kt) + endif + endif + if(cat(m).gt.1) then + jcat=nint(x(m,n)) + if (nbestcat(jcat,kt).eq.1) then + kt=treemap(1,kt) + else + kt=treemap(2,kt) + endif + endif + enddo !k +100 n=n+1 + if(n.le.nsample) goto 903 + + do m=1,mdim + tgini(m)=tgini(m)/nsample + enddo + + end +c +c ------------------------------------------------------- + subroutine testreelite(xts,ntest,mdim,treemap,nodestatus, + & xbestsplit,bestvar,nodeclass,nrnodes,ndbigtree, + & cat,jts,maxcat,nbestcat,nodexts) +c +c predicts the class of all objects in xts +c +c input: + real xts(mdim,ntest),xbestsplit(nrnodes) + integer treemap(2,nrnodes),bestvar(nrnodes),nodeclass(nrnodes), + & cat(mdim),nodestatus(nrnodes),nbestcat(maxcat,nrnodes) + integer ntest,mdim,nrnodes,ndbigtree,maxcat +c +c output: + integer jts(ntest),nodexts(ntest) +c + integer n,kt,k,m,jcat +c + n=1 +903 kt=1 + do k=1,ndbigtree + if (nodestatus(kt).eq.-1) then + jts(n)=nodeclass(kt) + nodexts(n)=kt + goto 100 + endif + m=bestvar(kt) + if (cat(m).eq.1) then + if (xts(m,n).le.xbestsplit(kt)) then + kt=treemap(1,kt) + else + kt=treemap(2,kt) + endif + endif + if(cat(m).gt.1) then + jcat=nint(xts(m,n)) + if (nbestcat(jcat,kt).eq.1) then + kt=treemap(1,kt) + else + kt=treemap(2,kt) + endif + endif + enddo !k +100 n=n+1 + if(n.le.ntest) goto 903 + end +c +c ------------------------------------------------------- + subroutine testreeimp(x,nsample,mdim,joob,pjoob,nout,mr, + & treemap,nodestatus,xbestsplit,bestvar,nodeclass,nrnodes, + & ndbigtree,cat,jvr,nodexvr,maxcat,nbestcat) +c +c predicts the class of out-of-bag-cases for variable importance +c also computes nodexvr +c +c input: + real x(mdim,nsample),xbestsplit(nrnodes) + integer treemap(2,nrnodes),bestvar(nrnodes),nodeclass(nrnodes), + & cat(mdim),nodestatus(nrnodes),nbestcat(maxcat,nrnodes), + & joob(nout),pjoob(nout) + integer nsample,mdim,nout,mr,nrnodes,ndbigtree,maxcat +c +c output: + integer jvr(nout),nodexvr(nout) +c + integer n,kt,k,m,jcat + real xmn +c + call permobmr(joob,pjoob,nout) + n=1 +904 kt=1 + do k=1,ndbigtree + if (nodestatus(kt).eq.-1) then + jvr(n)=nodeclass(kt) + nodexvr(n)=kt + goto 100 + endif + m=bestvar(kt) + if(m.eq.mr) then + xmn=x(m,pjoob(n)) ! permuted value + else + xmn=x(m,joob(n)) + endif + if (cat(m).eq.1) then + if (xmn.le.xbestsplit(kt)) then + kt=treemap(1,kt) + else + kt=treemap(2,kt) + endif + endif + if(cat(m).gt.1) then + jcat=nint(xmn) + if (nbestcat(jcat,kt).eq.1) then + kt=treemap(1,kt) + else + kt=treemap(2,kt) + endif + endif + enddo !k +100 n=n+1 + if(n.le.nout) goto 904 + end +c +c ------------------------------------------------------- + subroutine permobmr(joob,pjoob,nout) +c +c randomly permute the elements of joob and put them in pjoob +c +c input: + integer joob(nout),nout +c output: + integer pjoob(nout) +c local: + integer j,k,jt + real rnd,randomu +c + do j=1,nout + pjoob(j)=joob(j) + enddo + j=nout +11 rnd=randomu() + k=int(j*rnd) + if(k.lt.j) k=k+1 +c switch j and k + jt=pjoob(j) + pjoob(j)=pjoob(k) + pjoob(k)=jt + j=j-1 + if(j.gt.1) go to 11 + end +c +c ------------------------------------------------------- + subroutine comperrtr(q,cl,nsample,nclass,errtr, + & tmiss,nc,jest,out) +c + integer cl(nsample),nc(nclass),jest(nsample),out(nsample) + real q(nclass,nsample),tmiss(nclass),errtr + integer nsample,nclass + real cmax,ctemp + integer n,j,jmax + call zervr(tmiss,nclass) + errtr=0 + do n=1,nsample + cmax=0 + if(out(n).gt.0) then + do j=1,nclass + ctemp=q(j,n)/out(n) + if(ctemp.gt.cmax) then + jmax=j + cmax=ctemp + endif + enddo + else + jmax=1 + endif + jest(n)=jmax + if (jmax.ne.cl(n)) then + tmiss(cl(n))=tmiss(cl(n))+1 + errtr=errtr+1 + endif + enddo + errtr=errtr/nsample + do j=1,nclass + tmiss(j)=tmiss(j)/nc(j) + enddo + end +c +c ------------------------------------------------------- + subroutine comperrts(qts,clts,ntest,nclass,errts, + & tmissts,ncts,jests,labelts) +c +c + integer clts(ntest),ncts(nclass),jests(ntest) + real qts(nclass,ntest),tmissts(nclass) + integer ntest,nclass,labelts + real errts,cmax + integer n,j,jmax + call zervr(tmissts,nclass) + errts=0 + do n=1,ntest + cmax=0 + do j=1,nclass + if (qts(j,n).gt.cmax) then + jmax=j + cmax=qts(j,n) + endif + enddo + jests(n)=jmax + if(labelts.eq.1) then + if (jmax.ne.clts(n)) then + tmissts(clts(n))=tmissts(clts(n))+1 + errts=errts+1 + endif + endif + enddo + if(labelts.eq.1) then + errts=errts/ntest + do j=1,nclass + tmissts(j)=tmissts(j)/ncts(j) + enddo + endif + end +c +c ------------------------------------------------------- + subroutine createclass(x,cl,ns,nsample,mdim) +c + real x(mdim,nsample) + integer cl(nsample) + integer ns,nsample,mdim + real randomu + integer n,m,k +c + do n=1,ns + cl(n)=1 + enddo + do n=ns+1,nsample + cl(n)=2 + enddo + do n=ns+1,nsample + do m=1,mdim + k=int(randomu()*ns) + if(k.lt.ns) k=k+1 + x(m,n)=x(m,k) + enddo + enddo + end +c +c ------------------------------------------------------- + subroutine varimp(x,nsample,mdim,cl,nclass,jin,jtr,impn, + & interact,msm,mdimt,qimp,qimpm,avimp,sqsd, + & treemap,nodestatus,xbestsplit,bestvar,nodeclass,nrnodes, + & ndbigtree,cat,jvr,nodexvr,maxcat,nbestcat,tnodewt, + & nodextr,joob,pjoob,iv) +c + real x(mdim,nsample),xbestsplit(nrnodes), + & avimp(mdim),sqsd(mdim), + & qimp(nsample),qimpm(nsample,mdim),tnodewt(nrnodes) + integer cl(nsample),jin(nsample),jtr(nsample), + & msm(mdimt),treemap(2,nrnodes),nodestatus(nrnodes), + & bestvar(nrnodes),nodeclass(nrnodes),cat(mdim),jvr(nsample), + & nodexvr(nsample),nbestcat(maxcat,nrnodes), + & nodextr(nsample),joob(nsample),pjoob(nsample),iv(mdim) + integer nsample,mdim,nrnodes,ndbigtree,maxcat, + & nclass,impn,interact,mdimt + integer nout,mr,nn,n,jj,k + real right,rightimp +c + nout=0 + right=0 + do n=1,nsample + if(jin(n).eq.0) then +c this case is out-of-bag +c update count of correct oob classifications +c (jtr(n)=cl(n) if case n is correctly classified) + if(jtr(n).eq.cl(n)) right=right+tnodewt(nodextr(n)) +c nout=number of obs out-of-bag for THIS tree + nout=nout+1 + joob(nout)=n + endif + enddo + if(impn.eq.1) then + do n=1,nout + nn=joob(n) + if(jtr(nn).eq.cl(nn)) then + qimp(nn)=qimp(nn)+tnodewt(nodextr(nn))/nout + endif + enddo + endif + call zerv(iv,mdim) + do jj=1,ndbigtree +c iv(j)=1 if variable j was used to split on + if(nodestatus(jj).ne.-1) iv(bestvar(jj))=1 + enddo + do k=1,mdimt + mr=msm(k) +c choose only those that used a split on variable mr + if(iv(mr).eq.1) then + call testreeimp(x,nsample,mdim,joob,pjoob,nout,mr, + & treemap,nodestatus,xbestsplit,bestvar,nodeclass,nrnodes, + & ndbigtree,cat,jvr,nodexvr,maxcat,nbestcat) + rightimp=0 + do n=1,nout +c the nth out-of-bag case is the nnth original case + nn=joob(n) + if(impn.eq.1) then + if(jvr(n).eq.cl(nn)) then + qimpm(nn,mr)=qimpm(nn,mr)+ + & tnodewt(nodexvr(n))/nout + endif + endif + if(jvr(n).eq.cl(nn)) rightimp=rightimp+ + & tnodewt(nodexvr(n)) + enddo + avimp(mr)=avimp(mr)+(right-rightimp)/nout + sqsd(mr)=sqsd(mr)+((right-rightimp)**2)/(nout**2) + else + do n=1,nout +c the nth out-of-bag case is +c the nnth original case + nn=joob(n) + if(impn.eq.1) then + if(jtr(nn).eq.cl(nn)) then + qimpm(nn,mr)=qimpm(nn,mr)+ + & tnodewt(nodextr(nn))/nout + endif + endif + enddo + endif + enddo !k + end +c +c ------------------------------------------------------- + subroutine finishimp(mdim,sqsd,avimp,signif,zscore, + & jbt,mdimt,msm) +c + real sqsd(mdim),avimp(mdim),zscore(mdim),signif(mdim) + integer msm(mdim) + integer mdim,mdimt,k,m1,jbt + real v,av,se + real erfcc + do k=1,mdimt + m1=msm(k) + avimp(m1)=avimp(m1)/jbt + av=avimp(m1) + se=(sqsd(m1)/jbt)-av*av + se=sqrt(se/jbt) + if(se.gt.0.0) then + zscore(m1)=avimp(m1)/se + v=zscore(m1) + signif(m1)=erfcc(v) + else + zscore(m1)=-5 + signif(m1)=1 + endif + enddo + end +c +c ------------------------------------------------------- + subroutine compinteract(votes,effect,msm,mdim,mdimt, + & jbt,g,iv,irnk,hist,teffect) +c + real votes(mdim,jbt),effect(mdim,mdim),g(mdim), + & hist(0:mdim,mdim),teffect(mdim,mdim) + integer msm(mdimt),iv(mdim),irnk(mdim,jbt) + integer mdim,mdimt,jbt,jb,i,nt,irk,j,ii, + & jj,ij,mmin,m,k + real gmin,rcor + do jb=1,jbt + nt=0 + call zerv(iv,mdim) + do i=1,mdimt + m=msm(i) + g(m)=votes(m,jb) + if(abs(g(m)).lt.8.232D-11) then + irnk(m,jb)=0 + iv(m)=1 + nt=nt+1 + endif + enddo + irk=0 + do j=1,8000 + gmin=10000 + do i=1,mdimt + m=msm(i) + if(iv(m).eq.0.and.g(m).lt.gmin) then + gmin=g(m) + mmin=m + endif + enddo + iv(mmin)=1 + irk=irk+1 + irnk(mmin,jb)=irk + nt=nt+1 + if(nt.ge.mdimt) goto 79 + enddo !j +79 continue + enddo !jb + do j=0,mdimt + do i=1,mdimt + m=msm(i) + hist(j,m)=0 + enddo + enddo + do i=1,mdimt + m=msm(i) + do jb=1,jbt + hist(irnk(m,jb),m)=hist(irnk(m,jb),m)+1 + enddo + do j=0,mdimt + hist(j,m)=hist(j,m)/jbt + enddo + enddo !m + + call zermr(effect,mdim,mdim) + do i=1,mdimt + do j=1,mdimt + m=msm(i) + k=msm(j) + do jb=1,jbt + effect(m,k)=effect(m,k)+iabs(irnk(m,jb)-irnk(k,jb)) + enddo + effect(m,k)=effect(m,k)/jbt + enddo + enddo + call zermr(teffect,mdim,mdim) + do i=1,mdimt + do j=1,mdimt + m=msm(i) + k=msm(j) + do ii=0,mdimt + do jj=0,mdimt + teffect(m,k)=teffect(m,k)+abs(ii-jj)*hist(jj,m)*hist(ii,k) + enddo + enddo + rcor=0 + do ij=1,mdimt + rcor=rcor+hist(ij,m)*hist(ij,k) + enddo + teffect(m,k)=teffect(m,k)/(1-rcor) + enddo + enddo + + do i=1,mdimt + do j=1,mdimt + m=msm(i) + k=msm(j) + effect(m,k)=100*(effect(m,k)-teffect(m,k)) + enddo + enddo + end +c +c ------------------------------------------------------- + subroutine compprot(loz,nrnn,ns,mdim,its, + & jest,wc,nclass,x,mdimt,msm,temp,cat,maxcat, + & jpur,inear,nprot,protlow,prothigh,prot,protfreq, + & protvlow,protvhigh,protv,popclass,npend,freq,v5,v95) +c + integer nrnn,ns,mdim,nclass,mdimt,nprot,maxcat + integer loz(ns,nrnn),jest(ns),msm(mdim),its(ns), + & jpur(nrnn),inear(nrnn),npend(nclass),cat(mdim) + real wc(ns),prot(mdim,nprot,nclass), + & protlow(mdim,nprot,nclass),prothigh(mdim,nprot,nclass), + & protfreq(mdim,nprot,nclass,maxcat), + & protvlow(mdim,nprot,nclass),protvhigh(mdim,nprot,nclass), + & x(mdim,ns),temp(nrnn),protv(mdim,nprot,nclass), + & popclass(nprot,nclass),freq(maxcat),v5(mdim),v95(mdim) + integer ii,i,k,n,jp,npu,mm,m,nclose,jj,ll,jmax,nn + real fmax,dt + + + do jp=1,nclass + call zerv(its,ns) +c we try to find nprot prototypes for this class: + npend(jp)=nprot + do i=1,nprot + call zervr(wc,ns) + do n=1,ns + if(its(n).eq.0) then +c wc(n) is the number of unseen neighbors +c of case n that are predicted to be +c in class jp +c loz(n,1),...,loz(n,nrnn) point to +c the nrnn nearest neighbors of +c case n + do k=1,nrnn + nn=loz(n,k) + if(its(nn).eq.0) then + ii=jest(nn) + if(ii.eq.jp) wc(n)=wc(n)+1 + endif + enddo + endif + enddo +c find the unseen case with the largest number +c of unseen predicted-class-jp neighbors + nclose=0 + npu=0 + do n=1,ns + if(wc(n).ge.nclose.and.its(n).eq.0) then + npu=n + nclose=wc(n) + endif + enddo +c if nclose=0,no case has any unseen predicted-class-jp neighbors +c can't find another prototype for this class - reduce npend by 1 and +c start finding prototypes for the next class + if(nclose.eq.0) then + npend(jp)=i-1 + goto 93 + endif +c case npu has the largest number +c of unseen predicted-class-jp neighbors +c put these neighbors in a list of length nclose + ii=0 + do k=1,nrnn + nn=loz(npu,k) + if(its(nn).eq.0.and.jest(nn).eq.jp) then + ii=ii+1 + inear(ii)=nn + endif + enddo +c popclass is a measure of the size of the cluster around +c this prototype + popclass(i,jp)=nclose + do mm=1,mdimt +c m is the index of the mmth variable + m=msm(mm) + if(cat(m).eq.1) then + dt=v95(m)-v5(m) + do ii=1,nclose +c put the value of the mmth variable into the list + temp(ii)=x(m,inear(ii)) + enddo !ii +c sort the list + call quicksort(temp,jpur,1,nclose,nclose) + ii=nclose/4 + if(ii.eq.0) ii=1 +c find the 25th percentile + protvlow(m,i,jp)=temp(ii) + protlow(m,i,jp)=(temp(ii)-v5(m))/dt + ii=nclose/4 + ii=(3*nclose)/4 + if(ii.eq.0) ii=1 +c find the 75th percentile + protvhigh(m,i,jp)=temp(ii) + prothigh(m,i,jp)=(temp(ii)-v5(m))/dt + ii=nclose/2 + if(ii.eq.0) ii=1 +c find the median + protv(m,i,jp)=temp(ii) + prot(m,i,jp)=(temp(ii)-v5(m))/dt + endif + if(cat(m).ge.2) then +c for categorical variables,choose the most frequent class + call zervr(freq,maxcat) + do k=1,nclose + jj=nint(x(m,loz(npu,k))) + freq(jj)=freq(jj)+1 + enddo + jmax=1 + fmax=freq(1) + do ll=2,cat(m) + if(freq(ll).gt.fmax) then + jmax=ll + fmax=freq(ll) + endif + enddo + protv(m,i,jp)=jmax + protvlow(m,i,jp)=jmax + protvhigh(m,i,jp)=jmax + do ll=1,cat(m) + protfreq(m,i,jp,ll)=freq(ll) + enddo + endif + enddo !m +c record that npu and it's neighbors have been 'seen' + its(npu)=1 + do k=1,nclose + nn=loz(npu,k) + its(nn)=1 + enddo + enddo !nprot +93 continue + enddo !jp + + end +c +c ------------------------------------------------------- + subroutine preprox(near,nrnodes,jbt,nodestatus,ncount,jb, + & nod,nodextr,nodexb,jin,jinb,ncn,ndbegin,kpop,rinpop,npcase, + & rpop,nsample) +c + integer near,nrnodes,jbt,jb,nsample, + & nodestatus(nrnodes),ncount(near,jbt),nod(nrnodes), + & nodextr(near),nodexb(near,jbt),jin(nsample),jinb(near,jbt), + & ncn(near),kpop(near),npcase(near,jbt),ndbegin(near,jbt) + real rpop(near),rinpop(near,jbt) + integer n, ntt, k, nterm, kn + + do n=1,near + ncount(n,jb)=0 + ndbegin(n,jb)=0 + enddo + ntt=0 + do k=1,nrnodes + if(nodestatus(k).eq.-1) then + ntt=ntt+1 + nod(k)=ntt + endif + enddo + nterm=ntt + do n=1,near + rinpop(n,jb)=rpop(nodextr(n)) + nodexb(n,jb)=nod(nodextr(n)) + jinb(n,jb)=jin(n) + k=nodexb(n,jb) + ncount(k,jb)=ncount(k,jb)+1 + ncn(n)=ncount(k,jb) + enddo + ndbegin(1,jb)=1 + do k=2,nterm+1 + ndbegin(k,jb)=ndbegin(k-1,jb)+ncount(k-1,jb) + enddo + do n=1,near + kn=ndbegin(nodexb(n,jb),jb)+ncn(n)-1 + npcase(kn,jb)=n + enddo + end +c +c ------------------------------------------------------- + subroutine comprox(prox,nodexb,jinb,ndbegin, + & npcase,ppr,rinpop,near,jbt,noutlier,outtr,cl, + & loz,nrnn,wtx,nsample,iwork,ibest) +c + double precision prox(near,nrnn),ppr(near) + real outtr(near),rinpop(near,jbt),wtx(nsample) + integer nodexb(near,jbt),jinb(near,jbt), + & ndbegin(near,jbt),npcase(near,jbt),cl(nsample), + & loz(near,nrnn),iwork(near),ibest(nrnn), + & near,jbt,noutlier,nrnn,nsample + integer n,jb,k,j,kk + real rsq +c + do n=1,near + call zervd(ppr,near) + do jb=1,jbt + k=nodexb(n,jb) + if(jinb(n,jb).gt.0) then + do j=ndbegin(k,jb),ndbegin(k+1,jb)-1 + kk=npcase(j,jb) + if(jinb(kk,jb).eq.0) then + ppr(kk)=ppr(kk)+(wtx(n)/rinpop(n,jb)) + endif + enddo + endif + if(jinb(n,jb).eq.0) then + do j=ndbegin(k,jb),ndbegin(k+1,jb)-1 + kk=npcase(j,jb) + if(jinb(kk,jb).gt.0) then + ppr(kk)=ppr(kk)+(wtx(kk)/rinpop(kk,jb)) + endif + enddo + endif + enddo !jbt + if(noutlier.eq.1) then + rsq=0 + do k=1,near + if(ppr(k).gt.0.and.cl(k).eq.cl(n)) rsq=rsq+ppr(k)*ppr(k) + enddo + if(rsq.eq.0) rsq=1 + outtr(n)=near/rsq + endif + + if(nrnn.eq.near) then + do k=1,near + prox(n,k)=ppr(k) + loz(n,k)=k + enddo + else + call biggest(ppr,near,nrnn,ibest,iwork) + do k=1,nrnn + prox(n,k)=ppr(ibest(k)) + loz(n,k)=ibest(k) + enddo + endif + enddo !n + end +c +c ------------------------------------------------------ + subroutine biggest(x,n,nrnn,ibest,iwork) +c + double precision x(n) + integer n,nrnn,ibest(nrnn),iwork(n) + integer i,j,ihalfn,jsave +c +c finds the nrnn largest values in the vector x and +c returns their positions in the vector ibest(1),...,ibest(nrnn): +c x(ibest(1)) is the largest +c ... +c x(ibest(nrnn)) is the nrnn-th-largest +c the vector x is not disturbed +c the vector iwork is used as workspace +c + ihalfn=int(n/2) + do i=1,n + iwork(i)=i + enddo + do j=1,ihalfn + i=ihalfn + 1 - j + call sift(x,iwork,n,n,i) + enddo + do j=1,nrnn-1 + i=n-j+1 + ibest(j)=iwork(1) + jsave=iwork(i) + iwork(i)=iwork(1) + iwork(1)=jsave + call sift(x,iwork,n,n-j,1) + enddo + ibest(nrnn)=iwork(1) + end +c +c ------------------------------------------------------ + subroutine sift(x,iwork,n,m,i) +c + double precision x(n) + integer iwork(m),n,m,i + real xsave + integer j,k,jsave,ksave +c +c used by subroutine biggest,to bring the largest element to the +c top of the heap +c + xsave=x(iwork(i)) + ksave=iwork(i) + jsave=i + j=i + i + do k=1,m + if( j.gt.m ) goto 10 + if( j.lt.m ) then + if( x(iwork(j)).lt.x(iwork(j+1)) ) j=j + 1 + endif + if( xsave.ge.x(iwork(j)) ) goto 10 + iwork(jsave)=iwork(j) + jsave=j + j=j + j + enddo +10 continue + iwork(jsave)=ksave + return + end +c +c ------------------------------------------------------ + subroutine locateout(cl,tout,outtr,ncp,isort,devout, + & near,nsample,nclass,rmedout) +c + real outtr(near),tout(near),devout(nclass),rmedout(nclass) + integer cl(nsample),isort(nsample),ncp(near),near,nsample, + & nclass + real rmed, dev + integer jp,nt,n,i + + do jp=1,nclass + nt=0 + do n=1,near + if(cl(n).eq.jp) then + nt=nt+1 + tout(nt)=outtr(n) + ncp(nt)=n + endif + enddo + call quicksort(tout,isort,1,nt,nsample) + rmed=tout((1+nt)/2) + dev=0 + do i=1,nt + dev=dev+amin1(abs(tout(i)-rmed),5*rmed) + enddo + dev=dev/nt + devout(jp)=dev + rmedout(jp)=rmed + do i=1,nt + outtr(ncp(i))=amin1((outtr(ncp(i))-rmed)/dev,20.0) + enddo + enddo !jp + end + +c ------------------------------------------------------- + subroutine myscale(loz,prox,xsc,y,u,near,nscale,red,nrnn, + & ee,ev,dl) +c + double precision prox(near,nrnn),y(near),u(near),dl(nscale), + & xsc(near,nscale),red(near),ee(near),ev(near,nscale),bl(10) + integer loz(near,nrnn) +c + integer near,nscale,nrnn,j,i,it,n,jit,k + double precision dotd,y2,sred,eu,ru,ra,ynorm,sa + do j=1,near + ee(j)=dble(1.) + enddo +c + do j=1,near + red(j)=0 + do i=1,nrnn + red(j)=red(j)+prox(j,i) + enddo + red(j)=red(j)/near + enddo + sred=dotd(ee,red,near) + sred=sred/near + do it=1,nscale + do n=1,near + if(mod(n,2).eq.0) then + y(n)=1 + else + y(n)=-1 + endif + enddo + do jit=1,1000 + y2=dotd(y,y,near) + y2=dsqrt(y2) + do n=1,near + u(n)=y(n)/y2 + enddo + do n=1,near + y(n)=0 + do k=1,nrnn + y(n)=y(n)+prox(n,k)*u(loz(n,k)) + enddo + enddo + eu=dotd(ee,u,near) + ru=dotd(red,u,near) + do n=1,near + y(n)=y(n)-(red(n)-sred)*eu-ru + y(n)=.5*y(n) + enddo + if(it.gt.1) then + do j=1,it-1 + bl(j)=0 + do n=1,near + bl(j)=bl(j)+ev(n,j)*u(n) + enddo + do n=1,near + y(n)=y(n)-bl(j)*dl(j)*ev(n,j) + enddo + enddo + endif + ra=dotd(y,u,near) + ynorm=0 + do n=1,near + ynorm=ynorm+(y(n)-ra*u(n))**2 + enddo + sa=dabs(ra) + if(ynorm.lt.sa*1.0e-7)then + do n=1,near + xsc(n,it)=(dsqrt(sa))*u(n) + ev(n,it)=u(n) + enddo + dl(it)=ra + goto 101 + endif + enddo +101 continue + enddo !nn + end +c +c ------------------------------------------------------- + + subroutine xfill(x,nsample,mdim,fill,code) +c +c input: + real code,fill(mdim) + integer mdim,nsample +c output: + real x(mdim,nsample) +c local: + integer n,m + do n=1,nsample + do m=1,mdim + if(abs(x(m,n)-code).lt.8.232D-11) + & x(m,n)=fill(m) + enddo !m + enddo + end +c +c ------------------------------------------------------- + subroutine roughfix(x,v,ncase,mdim,nsample,cat,code, + & nrcat,maxcat,fill) +c + real x(mdim,nsample),v(nsample),fill(mdim),code + integer ncase(nsample),cat(mdim),nrcat(maxcat) + integer mdim,nsample,maxcat + integer m,n,nt,j,jmax,lcat,nmax + real rmed +c + do m=1,mdim + if(cat(m).eq.1) then +c continuous variable + nt=0 + do n=1,nsample + if(abs(x(m,n)-code).ge.8.232D-11) then + nt=nt+1 + v(nt)=x(m,n) + endif + enddo + call quicksort (v,ncase,1,nt,nsample) + if(nt.gt.0) then + rmed=v((nt+1)/2) + else + rmed=0 + endif + fill(m)=rmed + else +c categorical variable + lcat=cat(m) + call zerv(nrcat,maxcat) + do n=1,nsample + if(abs(x(m,n)-code).ge.8.232D-11) then + j=nint(x(m,n)) + nrcat(j)=nrcat(j)+1 + endif + enddo + nmax=0 + jmax=1 + do j=1,lcat + if(nrcat(j).gt.nmax) then + nmax=nrcat(j) + jmax=j + endif + enddo + fill(m)=real(jmax) + endif + enddo !m + do n=1,nsample + do m=1,mdim + if(abs(x(m,n)-code).lt.8.232D-11) x(m,n)=fill(m) + enddo + enddo + end + +c ------------------------------------------------------- + subroutine impute(x,prox,near,mdim, + & maxcat,votecat,cat,nrnn,loz,missing) +c + real x(mdim,near),votecat(maxcat) + double precision prox(near,nrnn) + integer near,mdim,maxcat,nrnn + integer cat(mdim),loz(near,nrnn), + & missing(mdim,near) + integer i,j,jmax,m,n,k + real sx,dt,rmax +c + do m=1,mdim + if(cat(m).eq.1) then + do n=1,near + if(missing(m,n).eq.1) then + sx=0 + dt=0 + do k=1,nrnn + if(missing(m,loz(n,k)).ne.1) then + sx=sx+real(prox(n,k))*x(m,loz(n,k)) + dt=dt+real(prox(n,k)) + endif + enddo + if(dt.gt.0) x(m,n)=sx/dt + endif + enddo !n + endif + enddo !m + do m=1,mdim + if(cat(m).gt.1) then + do n=1,near + if(missing(m,n).eq.1) then + call zervr(votecat,maxcat) + do k=1,nrnn + if (missing(m,loz(n,k)).ne.1) then + j=nint(x(m,loz(n,k))) + votecat(j)=votecat(j)+real(prox(n,k)) + endif + enddo !k + rmax=-1 + do i=1,cat(m) + if(votecat(i).gt.rmax) then + rmax=votecat(i) + jmax=i + endif + enddo + x(m,n)=real(jmax) + endif + enddo !n + endif + enddo !m + end +c +c ------------------------------------------------------- + subroutine checkin(labelts,labeltr,nclass,lookcls,jclasswt, + & mselect,mdim2nd,mdim,imp,impn,interact,nprox,nrnn, + & nsample,noutlier,nscale,nprot,missfill,iviz,isaverf, + & irunrf,isavepar,ireadpar,isavefill,ireadfill,isaveprox, + & ireadprox,isumout,idataout,impfastout,impout,interout, + & iprotout,iproxout,iscaleout,ioutlierout,cat,maxcat,cl) +c + integer labelts,labeltr,nclass,lookcls,jclasswt,mselect, + & mdim2nd,mdim,imp,impn,interact,nprox,nrnn,nsample,noutlier, + & nscale,nprot,missfill,iviz,isaverf,irunrf,isavepar,ireadpar, + & isavefill,ireadfill,isaveprox,ireadprox,isumout,idataout, + & impfastout,impout,interout,iprotout,iproxout,iscaleout, + & ioutlierout,cat(mdim),maxcat,cl(nsample) +c + integer n,m +c + if(labelts.ne.0.and.labelts.ne.1) then + write(*,*) 'error labelts',labelts + stop + endif + if(labeltr.ne.0.and.labeltr.ne.1) then + write(*,*) 'error labeltr',labeltr + stop + endif + if(labeltr.eq.0.and.nclass.ne.2) then + write(*,*) 'error,nclass should be 2 if labeltr=0' + stop + endif + if(lookcls.ne.0.and.lookcls.ne.1) then + write(*,*) 'error lookcls',lookcls + stop + endif + if(jclasswt.ne.0.and.jclasswt.ne.1) then + write(*,*) 'error jclasswt',jclasswt + stop + endif + if(mselect.ne.0.and.mselect.ne.1) then + write(*,*) 'error mselect',mselect + stop + endif + if(mdim2nd.lt.0.or.mdim2nd.gt.mdim) then + write(*,*) 'error mdim2nd',mdim2nd + stop + endif + if(imp.ne.0.and.imp.ne.1) then + write(*,*) 'error imp',imp + stop + endif + if(impn.ne.0.and.impn.ne.1) then + write(*,*) 'error impn',impn + stop + endif + if(interact.ne.0.and.interact.ne.1) then + write(*,*) 'error interact',interact + stop + endif + if(nprox.lt.0.or.nprox.gt.1) then + write(*,*) 'error nprox',nprox + stop + endif + if(nrnn.lt.0.or.nrnn.gt.nsample) then + write(*,*) 'error - nrnn',nrnn + stop + endif + if(noutlier.lt.0.or.noutlier.gt.2) then + write(*,*) 'error - noutlier',noutlier + stop + endif + if(nscale.lt.0.or.nscale.gt.mdim) then + write(*,*) 'error - nscale',nscale + stop + endif + if(nprot.lt.0.or.nprot.gt.nsample) then + write(*,*) 'error - nprot',nprot + stop + endif + if(missfill.lt.0.or.missfill.gt.2) then + write(*,*) 'error missfill',missfill + stop + endif + if(iviz.ne.0.and.iviz.ne.1) then + write(*,*) 'error iviz',iviz + stop + endif + if(iviz.eq.1.and.impn.ne.1) then + write(*,*) 'error iviz=1 and impn=',impn + stop + endif + if(iviz.eq.1.and.imp.ne.1) then + write(*,*) 'error iviz=1 and imp=',imp + stop + endif + if(iviz.eq.1.and.nprox.ne.1) then + write(*,*) 'error iviz=1 and nprox=',nprox + stop + endif + if(iviz.eq.1.and.nscale.ne.3) then + write(*,*) 'error iviz=1 and nscale=',nscale + stop + endif + if(isaverf.ne.0.and.isaverf.ne.1) then + write(*,*) 'error isaverf',isaverf + stop + endif + if(isaverf.eq.1.and.missfill.eq.2) then + write(*,*) 'error - only rough fix can be saved' + stop + endif + if(irunrf.ne.0.and.irunrf.ne.1) then + write(*,*) 'error irunrf',irunrf + stop + endif + if(isavepar.ne.0.and.isavepar.ne.1) then + write(*,*) 'error isavepar',isavepar + stop + endif + if(ireadpar.ne.0.and.ireadpar.ne.1) then + write(*,*) 'error ireadpar',ireadpar + stop + endif + if(isavefill.ne.0.and.isavefill.ne.1) then + write(*,*) 'error isavefill',isavefill + stop + endif + if(ireadfill.ne.0.and.ireadfill.ne.1) then + write(*,*) 'error ireadfill',ireadfill + stop + endif + if(isaveprox.ne.0.and.isaveprox.ne.1) then + write(*,*) 'error isaveprox',isaveprox + stop + endif + if(ireadprox.ne.0.and.ireadprox.ne.1) then + write(*,*) 'error ireadprox',ireadprox + stop + endif + if(isumout.lt.0.or.isumout.gt.1) then + write(*,*) 'error isumout',isumout + stop + endif + if(idataout.lt.0.or.idataout.gt.2) then + write(*,*) 'error idataout',idataout + stop + endif + if(impfastout.lt.0.or.impfastout.gt.1) then + write(*,*) 'error impfastout',impfastout + stop + endif + if(impout.lt.0.or.impout.gt.2) then + write(*,*) 'error impout',impout + stop + endif + if(interout.lt.0.or.interout.gt.2) then + write(*,*) 'error interout',interout + stop + endif + if(iprotout.lt.0.or.iprotout.gt.2) then + write(*,*) 'error iprotout',iprotout + stop + endif + if(iproxout.lt.0.or.iproxout.gt.2) then + write(*,*) 'error iproxout',iproxout + stop + endif + if(iscaleout.lt.0.or.iscaleout.gt.1) then + write(*,*) 'error iscaleout',iscaleout + stop + endif + if(ioutlierout.lt.0.or.ioutlierout.gt.2) then + write(*,*) 'error ioutlierout',ioutlierout + stop + endif + if(noutlier.gt.0.and.nprox.eq.0) then + write(*,*) 'error - noutlier>0 and nprox=0' + stop + endif + if(nscale.gt.0.and.nprox.eq.0) then + write(*,*) 'error - nscale>0 and nprox=0' + stop + endif + if(nprot.gt.0.and.nprox.eq.0) then + write(*,*) 'error - nprot>0 and nprox=0' + stop + endif + if(mdim2nd.gt.0.and.imp.eq.0) then + write(*,*) 'error - mdim2nd>0 and imp=0' + stop + endif + if(impn.gt.0.and.imp.eq.0) then + write(*,*) 'error - impn>0 and imp=0' + stop + endif + do m=1,mdim + if(cat(m).gt.maxcat) then + write(*,*) 'error in cat',m,cat(m) + stop + endif + enddo + if(labeltr.eq.1) then + do n=1,nsample + if(cl(n).lt.1.or.cl(n).gt.nclass) then + write(*,*) 'error in class label',n,cl(n) + stop + endif + enddo + endif + return + end +c +c ------------------------------------------------------- + subroutine quicksort(v,iperm,ii,jj,kk) +c +c puts into iperm the permutation vector which sorts v into +c increasing order. only elementest from ii to jj are considered. +c array iu(k) and array il(k) permit sorting up to 2**(k+1)-1 elements +c +c this is a modification of acm algorithm #347 by r. c. singleton, +c which is a modified hoare quicksort. +c + real v(kk),vt,vtt + integer t,tt,iperm(kk),iu(32),il(32) + integer ii,jj,kk,m,i,j,k,ij,l +c + m=1 + i=ii + j=jj + 10 if (i.ge.j) go to 80 + 20 k=i + ij=(j+i)/2 + t=iperm(ij) + vt=v(ij) + if (v(i).le.vt) go to 30 + iperm(ij)=iperm(i) + iperm(i)=t + t=iperm(ij) + v(ij)=v(i) + v(i)=vt + vt=v(ij) + 30 l=j + if (v(j).ge.vt) go to 50 + iperm(ij)=iperm(j) + iperm(j)=t + t=iperm(ij) + v(ij)=v(j) + v(j)=vt + vt=v(ij) + if (v(i).le.vt) go to 50 + iperm(ij)=iperm(i) + iperm(i)=t + t=iperm(ij) + v(ij)=v(i) + v(i)=vt + vt=v(ij) + go to 50 + 40 iperm(l)=iperm(k) + iperm(k)=tt + v(l)=v(k) + v(k)=vtt + 50 l=l-1 + if (v(l).gt.vt) go to 50 + tt=iperm(l) + vtt=v(l) + 60 k=k+1 + if (v(k).lt.vt) go to 60 + if (k.le.l) go to 40 + if (l-i.le.j-k) go to 70 + il(m)=i + iu(m)=l + i=k + m=m+1 + go to 90 + 70 il(m)=k + iu(m)=j + j=l + m=m+1 + go to 90 + 80 m=m-1 + if (m.eq.0) return + i=il(m) + j=iu(m) + 90 if (j-i.gt.10) go to 20 + if (i.eq.ii) go to 10 + i=i-1 + 100 i=i+1 + if (i.eq.j) go to 80 + t=iperm(i+1) + vt=v(i+1) + if (v(i).le.vt) go to 100 + k=i + 110 iperm(k+1)=iperm(k) + v(k+1)=v(k) + k=k-1 + if (vt.lt.v(k)) go to 110 + iperm(k+1)=t + v(k+1)=vt + go to 100 + end +c +c ------------------------------------------------------- + subroutine unpack(l,npack,icat) +c + integer icat(32),npack,l,j,n,k + if(l.gt.32) then + write(*,*) 'error in unpack,l=',l + stop + endif + do j=1,32 + icat(j)=0 + enddo + n=npack + icat(1)=mod(n,2) + do k=2,l + n=(n-icat(k-1))/2 + icat(k)=mod(n,2) + enddo + end +c +c ------------------------------------------------------- + subroutine zerv(ix,m1) +c + integer ix(m1),m1,n + do n=1,m1 + ix(n)=0 + enddo + end +c +c ------------------------------------------------------- + subroutine zervr(rx,m1) +c + real rx(m1) + integer m1,n + do n=1,m1 + rx(n)=0 + enddo + end +c +c ------------------------------------------------------- + + subroutine zervd(rx,m1) +c + double precision rx(m1) + integer m1,n + do n=1,m1 + rx(n)=0 + enddo + end +c +c ________________________________________________________ + + subroutine zerm(mx,m1,m2) +c + integer mx(m1,m2),m1,m2,i,j + do j=1,m2 + do i=1,m1 + mx(i,j)=0 + enddo + enddo + end +c +c ------------------------------------------------------- + subroutine zermr(rx,m1,m2) +c + real rx(m1,m2) + integer m1,m2,i,j + do j=1,m2 + do i=1,m1 + rx(i,j)=0 + enddo + enddo + end +c +c ------------------------------------------------------- + subroutine zermd(rx,m1,m2) +c + double precision rx(m1,m2) + integer m1,m2,i,j + do j=1,m2 + do i=1,m1 + rx(i,j)=0 + enddo + enddo + end +c +c ------------------------------------------------------- + real function erfcc(x) +c + real x,t,z + z=abs(x)/1.41421356 + t=1./(1.+0.5*z) + erfcc=t*exp(-z*z-1.26551223+t*(1.00002368+t*(.37409196+t* + * (.09678418+t*(-.18628806+t*(.27886807+t*(-1.13520398+t* + * (1.48851587+t*(-.82215223+t*.17087277))))))))) + erfcc=erfcc/2 + if (x.lt.0.) erfcc=2.-erfcc + return + end +c +c ------------------------------------------------------- + integer function irbit(iseed) +c + integer iseed, ib1, ib2, ib5, ib18, mask + parameter(ib1=1,ib2=2,ib5=16,ib18=131072,mask=ib1+ib2+ib5) + if(iand(iseed,ib18).ne.0) then + iseed=ior(ishft(ieor(iseed,mask),1),ib1) + irbit=1 + else + iseed=iand(ishft(iseed,1),not(ib1)) + irbit=0 + endif + return + end +c +c ------------------------------------------------------- + real function randomu() +c + double precision grnd,u + u=grnd() + randomu=real(u) + end +c +c ------------------------------------------------------- + real function rnorm(i) +c + integer i + real randomu + rnorm=sqrt(-2*log(randomu()))*cos(6.283185*randomu()) + end +c +c ------------------------------------------------------- + double precision function dotd(u,v,ns) +c +c computes the inner product +c input: + double precision u(ns),v(ns) + integer ns +c local: + integer n + dotd=dble(0.0) + do n=1,ns + dotd=dotd+u(n)*v(n) + enddo + end +C************************************************************************** +* A C-program for MT19937: Real number version +* genrand() generates one pseudorandom real number (double) +* which is uniformly distributed on [0,1]-interval,for each +* call. sgenrand(seed) set initial values to the working area +* of 624 words. Before genrand(),sgenrand(seed) must be +* called once. (seed is any 32-bit integer except for 0). +* Integer generator is obtained by modifying two lines. +* Coded by Takuji Nishimura,considering the suggestions by +* Topher Cooper and Marc Rieffel in July-Aug. 1997. +* +* This library is free software; you can redistribute it and/or +* modify it under the terms of the GNU Library General Public +* License as published by the Free Software Foundation; either +* version 2 of the License,or (at your option) any later +* version. +* This library is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. +* See the GNU Library General Public License for more details. +* You should have received a copy of the GNU Library General +* Public License along with this library; if not,write to the +* Free Foundation,Inc.,59 Temple Place,Suite 330,Boston,MA +* 02111-1307 USA +* +* Copyright (C) 1997 Makoto Matsumoto and Takuji Nishimura. +* When you use this,send an email to: matumoto@math.keio.ac.jp +* with an appropriate reference to your work. +* +* Fortran translation by Hiroshi Takano. Jan. 13,1999. +************************************************************************ +* This program uses the following non-standard intrinsics. +* ishft(i,n): If n>0,shifts bits in i by n positions to left. +* If n<0,shifts bits in i by n positions to right. +* iand (i,j): Performs logical AND on corresponding bits of i and j. +* ior (i,j): Performs inclusive OR on corresponding bits of i and j. +* ieor (i,j): Performs exclusive OR on corresponding bits of i and j. +* +************************************************************************ + subroutine sgrnd(seed) +* + implicit integer(a-z) +* +* Period parameters + parameter(n = 624) +* + dimension mt(0:n-1) +* the array for the state vector + common /block/mti,mt + save /block/ +* +* setting initial seeds to mt[n] using +* the generator Line 25 of Table 1 in +* [KNUTH 1981,The Art of Computer Programming +* Vol. 2 (2nd Ed.),pp102] +* + mt(0)=iand(seed,-1) + do 1000 mti=1,n-1 + mt(mti)=iand(69069 * mt(mti-1),-1) +1000 continue +* + return + end +************************************************************************ + double precision function grnd() +* + implicit integer(a-z) +* +* Period parameters + parameter(n = 624) + parameter(n1 = n+1) + parameter(m = 397) + parameter(mata =-1727483681) +* constant vector a + parameter(umask=-2147483648) +* most significant w-r bits + parameter(lmask= 2147483647) +* least significant r bits +* Tempering parameters + parameter(tmaskb=-1658038656) + parameter(tmaskc=-272236544) +* + dimension mt(0:n-1) +* the array for the state vector + common /block/mti,mt + save /block/ + data mti/n1/ +* mti==n+1 means mt[n] is not initialized +* + dimension mag01(0:1) + data mag01/0,mata/ + save mag01 +* mag01(x)=x * mata for x=0,1 +* + TSHFTU(y)=ishft(y,-11) + TSHFTS(y)=ishft(y,7) + TSHFTT(y)=ishft(y,15) + TSHFTL(y)=ishft(y,-18) +* + if(mti.ge.n) then +* generate n words at one time + if(mti.eq.n+1) then +* if sgrnd() has not been called, + call sgrnd(4357) +* a default initial seed is used + endif +* + do 1000 kk=0,n-m-1 + y=ior(iand(mt(kk),umask),iand(mt(kk+1),lmask)) + mt(kk)=ieor(ieor(mt(kk+m),ishft(y,-1)),mag01(iand(y,1))) + 1000 continue + do 1100 kk=n-m,n-2 + y=ior(iand(mt(kk),umask),iand(mt(kk+1),lmask)) + mt(kk)=ieor(ieor(mt(kk+(m-n)),ishft(y,-1)),mag01(iand(y,1))) + 1100 continue + y=ior(iand(mt(n-1),umask),iand(mt(0),lmask)) + mt(n-1)=ieor(ieor(mt(m-1),ishft(y,-1)),mag01(iand(y,1))) + mti=0 + endif +* + y=mt(mti) + mti=mti+1 + y=ieor(y,TSHFTU(y)) + y=ieor(y,iand(TSHFTS(y),tmaskb)) + y=ieor(y,iand(TSHFTT(y),tmaskc)) + y=ieor(y,TSHFTL(y)) +* + if(y.lt.0) then + grnd=(dble(y)+2.0d0**32)/(2.0d0**32-1.0d0) + else + grnd=dble(y)/(2.0d0**32-1.0d0) + endif +* + return + end diff --git a/mltsp/TCP/Algorithms/randomforest_parf_fortran.py b/mltsp/TCP/Algorithms/randomforest_parf_fortran.py new file mode 100644 index 00000000..72cb3071 --- /dev/null +++ b/mltsp/TCP/Algorithms/randomforest_parf_fortran.py @@ -0,0 +1,65 @@ +#!/usr/bin/env python +""" +Compile the python module using fortran code using: + +f2py -c flaplace.f -m flaplace + +""" +from __future__ import print_function +import os, sys +import numpy + + +class Simple_Fortran_Test: + """ + Compile the python module using fortran code using: + + f2py -c flaplace.f -m flaplace + + """ + + def fortranTimeStep(self, u, dx, dy): + """Takes a time step using a simple fortran module that + implements the loop in Fortran. """ + u_new, err = flaplace.timestep(u, dx, dy) + return u_new + + def func3(self, x,y): + return (1- x/2 + x**5 + y**3)*numpy.exp(-x**2-y**2) + + def main(self): + x, y = numpy.meshgrid(numpy.arange(-2,2,1), numpy.arange(-2,2,1)) + u = self.func3(x,y) + dx = 1 + dy = 1 + for i in range(10): + u = self.fortranTimeStep(u, dx, dy) + print(u) + +class RF_Fortran_Test: + """ Wrapping PARF missing-value Fortran re-implementation of RandomForest. + + """ + + def main(self): + """ + """ + pass + + + + +if __name__ == '__main__': + + + RFFortranTest = RF_Fortran_Test() + RFFortranTest.main() + + + ### For just testing that fortran module compiling works: + if 1: + import flaplace + SimpleFortranTest = Simple_Fortran_Test() + SimpleFortranTest.main() + + diff --git a/mltsp/TCP/Algorithms/retrieve_xmls_from_tutor.py b/mltsp/TCP/Algorithms/retrieve_xmls_from_tutor.py new file mode 100644 index 00000000..1772a0ff --- /dev/null +++ b/mltsp/TCP/Algorithms/retrieve_xmls_from_tutor.py @@ -0,0 +1,98 @@ +#!/usr/bin/env python +""" retrieve xmls for a particular tutor/dotastro class_id +""" +from __future__ import print_function +import os, sys +import pprint +import MySQLdb +import datetime + +class tutor_db: + """ + """ + def __init__(self): + self.pars ={'tcptutor_hostname':'192.168.1.103', + 'tcptutor_username':'tutor', # guest + 'tcptutor_password':'ilove2mass', #'iamaguest', + 'tcptutor_database':'tutor', + 'tcptutor_port':3306} + + + self.tutor_db = MySQLdb.connect(host=self.pars['tcptutor_hostname'], \ + user=self.pars['tcptutor_username'], \ + passwd=self.pars['tcptutor_password'],\ + db=self.pars['tcptutor_database'],\ + port=self.pars['tcptutor_port']) + self.tutor_cursor = self.tutor_db.cursor() + + + +class Retrieve_XMLs: + """ + """ + def __init__(self, pars={}): + self.pars = pars + self.db = tutor_db() + + def retrieve_xmls_for_proj_list(self, base_dirpath="", proj_id_list=[]): + """ Given a list of proj_id, retrieve xmls from TUTOR. + """ + + for proj_id in proj_id_list: + + select_str = "SELECT source_id FROM sources WHERE project_id=%d" % (proj_id) + self.db.tutor_cursor.execute(select_str) + results = self.db.tutor_cursor.fetchall() + + if len(results)== 0: + print(datetime.datetime.now(), "No sources for project_id=%d" % (proj_id)) + else: + print(datetime.datetime.now(), "Project_id=", proj_id, "N sources=", len(results)) + + srcid_list = [] + for row in results: + srcid_list.append(row[0]) + + retrieve_dirpath = "%s/tutor_%d" % (base_dirpath, proj_id) + if not os.path.exists(retrieve_dirpath): + os.system("mkdir -p %s" % (retrieve_dirpath)) + + for src_id in srcid_list: + #get_str = "wget -O %s/%d.xml http://dotastro.org/lightcurves/vosource.php?Source_ID=%d" % (pars['retrieve_dirpath'], src_id, src_id) + get_str = "curl --compressed -o %s/%d.xml http://dotastro.org/lightcurves/vosource.php?Source_ID=%d" % (retrieve_dirpath, src_id, src_id) + #os.system(get_str) + #print get_str + (a,b,c) = os.popen3(get_str) + a.close() + c.close() + lines = b.read() + b.close() + + #print " Retrieved: %d" % (src_id) + + + +if __name__ == '__main__': + + pars = { \ + } + # 'project_id':121, # 120 : ASAS (122 : debosscher) + # 'retrieve_dirpath':'/media/raid_0/tutor_121_xmls', #'/tmp/tutor_120_xmls', + #RetrieveXMLs.retrieve_xmls_for_proj_list(base_dirpath="/media/raid_0/all_tutor_xmls", + # proj_id_list=[16, 55]) + + RetrieveXMLs = Retrieve_XMLs(pars=pars) + + proj_id_list = range(1,126 + 1) # 126 is currently the largest tutor project_id + + skip_projids = [121, 123, 126, + 120, 122] # already done, obsolete/old + + for projid in skip_projids: + proj_id_list.remove(projid) + + print() + RetrieveXMLs.retrieve_xmls_for_proj_list(base_dirpath="/media/raid_0/all_tutor_xmls", + proj_id_list=proj_id_list) + + diff --git a/mltsp/TCP/Algorithms/rpy2_classifiers.py b/mltsp/TCP/Algorithms/rpy2_classifiers.py new file mode 100644 index 00000000..fd8b533d --- /dev/null +++ b/mltsp/TCP/Algorithms/rpy2_classifiers.py @@ -0,0 +1,1543 @@ +#!/usr/bin/env python +""" + +Contains Classes which wrap R classifiers using rpy2. + +Tested using: + R 2.11.1 + rpy2 2.1.9 + +""" +from __future__ import print_function +import os, sys +from rpy2.robjects.packages import importr +from rpy2 import robjects +#from numpy import array # only tried out in missforest_parallel_*() +#import rpy2.robjects.numpy2ri # only tried out in missforest_parallel_*() + + +def missforest_parallel_task(varInd, ximp, obsi, misi, varType, ntree, p): + """ Task which is to be spawned off in Parallel onto IPython cluster + and invoked from the activelearn_utils.py:parallelized_imputation() + or activelearn_utils.py::IPython_Task_Administrator() + + This will do a task, atomized by (ntree, impute_feature_i, cross_valid_fold, cross_valid_case) + """ + #import rpy2.robjects.numpy2ri # DEBUG ONLY! + #import numpy # DEBUG ONLY! + robjects.globalenv['varInd'] = robjects.IntVector(varInd) + robjects.globalenv['ximp'] = ximp + robjects.globalenv['obsi'] = robjects.BoolVector(obsi) + robjects.globalenv['misi'] = robjects.BoolVector(misi) + robjects.globalenv['varType'] = varType + robjects.globalenv['ntree'] = ntree + robjects.globalenv['p'] = p + robjects.r(""" +obsY <- ximp[obsi, varInd] # training response +obsX <- ximp[obsi, seq(1, p)[-varInd]] # training variables +misX <- ximp[misi, seq(1, p)[-varInd]] # prediction variables +typeY <- varType[varInd] +blah <- 3 +#ximp[misi, varInd] <- misY""") + #import pdb; pdb.set_trace() + #print + #misY = array(robjects.r("misY")) + #OOBerror_val = list(robjects.r("OOBerror_val"))[0] + #return (misY, OOBerror_val) + #return (0,0) + #return {'obsY':array(robjects.r("obsY")), + # 'obsY_20':robjects.r("obsY[20]"), + # 'obsX_23':robjects.r("obsX[2,3]"), + # 'obsX_1065':robjects.r("obsX[10,65]"), + # 'ntree':robjects.r("ntree"), + # 'typeY':robjects.r("typeY")} + return {'ntree':robjects.r("blah")} + + +class Rpy2Classifier: + """ + """ + def __init__(self, pars={}, + algorithms_dirpath=''): + # # # on citris33: algorithms_dirpath='/global/home/users/dstarr/src/TCP/Algorithms' + self.pars = pars + + ### NOTE: really only need to require() classifiers if we use them + + r_str = ''' + require(randomForest) + require(party) + set.seed(sample(1:10^5,1)) + source("%s/utils_classify.R") + source("%s/missForest.R") + ''' % (algorithms_dirpath, algorithms_dirpath)#, algorithms_dirpath) + #source("%s/class_cv.R") + + #source("/home/pteluser/src/TCP/Algorithms/utils_classify.R") + #NOTNEEDED#source("/home/pteluser/src/TCP/Algorithms/class_cv.R") + robjects.r(r_str) + + + def read_class_dat(self, fpath="/home/pteluser/scratch/features.dat"): + """ Read Joey class.dat + + Taken from tutorial_rpy.py + + """ + lines = open(fpath).readlines() + out_list = [] + for i, line in enumerate(lines): + the_str = line.strip() + if len(the_str) == 0: + continue + out_list.append(the_str) + return out_list + + + def read_features_dat(self, fpath="/home/pteluser/scratch/features.dat"): + """ Read Joey features.dat + + Taken from tutorial_rpy.py + + """ + from rpy2 import robjects + + lines = open(fpath).readlines() + out_list = [] + feat_val_dict = {} + for i, line in enumerate(lines): + if i == 0: + feat_names_with_quotes = line.split() + feat_names = [] + for feat_name in feat_names_with_quotes: + feat_names.append(feat_name.strip('"')) + n_cols = len(feat_names) + for fname in feat_names: + feat_val_dict[fname] = [] + continue # skip the header + line_split = line.split() + for i_f, feat_val in enumerate(line_split): + if feat_val == 'NA': + out_list.append(None) + feat_val_dict[feat_names[i_f]].append(None) + else: + out_list.append(float(feat_val)) + feat_val_dict[feat_names[i_f]].append(float(feat_val)) + + for feat_name, feat_list in feat_val_dict.items(): + feat_val_dict[feat_name] = robjects.FloatVector(feat_list) + + return {'feat_list':out_list, + 'n_cols':n_cols, + 'feat_names':feat_names, + 'feat_val_dict':feat_val_dict} + + + def parse_joey_feature_class_datfile(self, feature_fpath='', class_fpath=''): + """ Parse Joey's features.dat classes.dat. + + """ + f_dict = self.read_features_dat(fpath=feature_fpath) + features = robjects.r['data.frame'](**f_dict['feat_val_dict']) + + class_list = self.read_class_dat(fpath=class_fpath) + classes = robjects.StrVector(class_list) + + ### ??? Do this? : + #robjects.globalenv['features'] = features + #robjects.globalenv['classes'] = classes + + + return {'features':features, + 'classes':classes} + + + def parse_arff_header(self, arff_str='', ignore_attribs=[]): + """ Parse a given ARFF string, replace @attribute with @ignored for attributes in + ignore_attribs list (ala PARF data specification). + + Return arff header string. + """ + lines = arff_str.split('\n') + out_lines = [] + for line in lines: + out_lines.append(line) + if '@data' in line.lower(): + return out_lines + return None # shouldn't get here. + + + def parse_full_arff(self, arff_str='', parse_srcid=True, parse_class=True, + skip_missingval_lines=False, fill_arff_rows=False): + """ Parse class & features from a full arff file. + """ + percent_list = [] + iters_list = [] + featname_list = [] + srcid_list = [] + class_list = [] + arff_rows = [] + #featval_long_list = [] + featname_longfeatval_dict = {} + lines = arff_str.split('\n') + for line in lines: + if len(line) == 0: + continue + elif line[0] == '%': + continue + elif line[:10] == '@ATTRIBUTE': + if (('class' in line) or + ('source_id' in line)): + #if line[11:16] == + continue # I could store the potential classes somewhere + else: + feat_name = line.split()[1] + featname_list.append(feat_name) + featname_longfeatval_dict[feat_name] = [] + elif line[0] == '@': + # -> could add to some feature structure + continue + elif (skip_missingval_lines and ('?' in line)): + continue # we skip souirces/arrf lines which have missing values since R Randomforest (and maybe other classifiers) cannot handle missing values. + else: + ### Then we have a source row with features + if parse_class: + #i_r = line.rfind("'") + #i_l = line.rfind("'", 0, i_r) + if '"' in line: + i_r = line.rfind('"') + i_l = line.rfind('"', 0, i_r) + else: + i_r = line.rfind("'") + i_l = line.rfind("'", 0, i_r) + + a_class = line[i_l+1:i_r] + class_list.append(a_class) #a_class.strip("'")) + shortline = line[:i_l -1] #feat_list[:-1] + elems = shortline.split(',') + if fill_arff_rows: + arff_rows.append(line) + feat_list = elems + if parse_srcid: + if elems[0].count('_') == 0: + src_id = elems[0] + srcid_list.append(src_id) + else: + tups = elems[0].split('_') + srcid = int(tups[0]) + perc = float(tups[1]) + niter = int(tups[2]) + srcid_list.append(srcid) + percent_list.append(perc) + iters_list.append(niter) + feat_list = feat_list[1:] + + for i, elem in enumerate(feat_list): + feat_name = featname_list[i] + if elem == '?': + val = None + else: + try: + val = float(elem) + except: + val = elem + featname_longfeatval_dict[feat_name].append(val) + + #for feat_name, feat_longlist in featname_longfeatval_dict.items(): + # featname_longfeatval_dict[feat_name] = robjects.FloatVector(feat_longlist) + #features = robjects.r['data.frame'](**featname_longfeatval_dict) + #classes = robjects.StrVector(class_list) + #return {'features':features, + # 'classes':classes, + # 'class_list':class_list, + # 'srcid_list':srcid_list, + # 'percent_list':percent_list, + # 'iters_list':iters_list} + + + ### NOTE: We dont do this here anymore. We do it closer to the R classifier building code: + #for feat_name, feat_longlist in featname_longfeatval_dict.items(): + # featname_longfeatval_dict[feat_name] = robjects.FloatVector(feat_longlist) + #features = robjects.r['data.frame'](**featname_longfeatval_dict) + #classes = robjects.StrVector(class_list) + + return {'featname_longfeatval_dict':featname_longfeatval_dict, + 'class_list':class_list, + 'srcid_list':srcid_list, + 'percent_list':percent_list, + 'iters_list':iters_list, + 'arff_rows':arff_rows} + + + + def insert_missing_value_features(self, arff_str='', noisify_attribs=[], + prob_source_has_missing=0.2, + prob_misattrib_is_missing=0.2): + """ Insert some missing-value features to arff rows. + Exepect the input to be a single string representation of arff with \n's. + Returning a similar single string. + """ + import random + out_lines = [] + misattrib_name_to_id = {} + i_attrib = 0 + lines = arff_str.split('\n') + do_attrib_parse = True + for line in lines: + if do_attrib_parse: + if line[:10] == '@ATTRIBUTE': + feat_name = line.split()[1] + if feat_name in noisify_attribs: + misattrib_name_to_id[feat_name] = i_attrib + i_attrib += 1 + elif '@data' in line.lower(): + do_attrib_parse = False + out_lines.append(line) + continue + ### Should only get here after hitting @data line, which means just feature lines + if random.random() > prob_source_has_missing: + out_lines.append(line) + continue # don't set any attributes as missing for this source + attribs = line.split(',') + new_attribs = [] + for i, attrib_val in enumerate(attribs): + if i in misattrib_name_to_id.values(): + if random.random() <= prob_misattrib_is_missing: + new_attribs.append('?') + continue + new_attribs.append(attrib_val) + new_line = ','.join(new_attribs) + out_lines.append(new_line) + new_train_arff_str = '\n'.join(out_lines) + return new_train_arff_str + + + def train_randomforest(self, data_dict, do_ignore_NA_features=False, + ntrees=1000, mtry=25, nfolds=10, nodesize=5): + """ Train a randomForest() R classifier + + Taken from class_cv.R : rf.cv (L40) + """ + featname_longfeatval_dict = data_dict['featname_longfeatval_dict'] + for feat_name, feat_longlist in featname_longfeatval_dict.items(): + featname_longfeatval_dict[feat_name] = robjects.FloatVector(feat_longlist) + data_dict['features'] = robjects.r['data.frame'](**featname_longfeatval_dict) + data_dict['classes'] = robjects.StrVector(data_dict['class_list']) + + robjects.globalenv['x'] = data_dict['features'] + robjects.globalenv['y'] = data_dict['classes'] + + if do_ignore_NA_features: + feat_trim_str = 'x = as.data.frame(x[,-which(substr(names(x),1,4)=="sdss" | substr(names(x),1,3)=="ws_")])' + else: + feat_trim_str = '' + + r_str = ''' + %s + y = class.debos(y) + + ntrees=%d + mtry=%d + nfolds=%d + rf_clfr = randomForest(x=x,y=y,mtry=mtry,ntree=ntrees,nodesize=%d) + ''' % (feat_trim_str, ntrees, mtry, nfolds, nodesize) + ### NOTE: when no classwt is given, this same prior is calculated, so no need to do it again: + #n = length(y) + #prior = table(y)/n + #rf_clfr = randomForest(x=x,y=y,classwt=prior,mtry=mtry,ntree=ntrees,nodesize=%d) + # + # rf_clfr = randomForest(x=x,y=y) + + classifier_out = robjects.r(r_str) + #import pdb; pdb.set_trace() + #print classifier_out + return {'py_obj':classifier_out, + 'r_name':'rf_clfr'} + + + # obsolete / backup: + def actlearn_randomforest__nocost(self, traindata_dict={}, + testdata_dict={}, + do_ignore_NA_features=False, + ntrees=1000, mtry=25, + nfolds=10, nodesize=5, + num_srcs_for_users=100, + random_seed=0, + final_user_classifs={}): + """ Train a randomForest() R classifier + + Taken from class_cv.R : rf.cv (L40) + """ + if do_ignore_NA_features: + print("actlearn_randomforest():: do_ignore_NA_features==True not implemented because obsolete") + raise + + train_featname_longfeatval_dict = traindata_dict['featname_longfeatval_dict'] + for feat_name, feat_longlist in train_featname_longfeatval_dict.items(): + train_featname_longfeatval_dict[feat_name] = robjects.FloatVector(feat_longlist) + traindata_dict['features'] = robjects.r['data.frame'](**train_featname_longfeatval_dict) + traindata_dict['classes'] = robjects.StrVector(traindata_dict['class_list']) + + robjects.globalenv['xtr'] = traindata_dict['features'] + robjects.globalenv['ytr'] = traindata_dict['classes'] + + test_featname_longfeatval_dict = testdata_dict['featname_longfeatval_dict'] + for feat_name, feat_longlist in test_featname_longfeatval_dict.items(): + test_featname_longfeatval_dict[feat_name] = robjects.FloatVector(feat_longlist) + testdata_dict['features'] = robjects.r['data.frame'](**test_featname_longfeatval_dict) + testdata_dict['classes'] = robjects.StrVector(testdata_dict['class_list']) + + robjects.globalenv['xte'] = testdata_dict['features'] + robjects.globalenv['yte'] = testdata_dict['classes'] + + import pdb; pdb.set_trace() + print() + + r_str = ''' + + m=%d + + ntrees=%d + mtry=%d + nfolds=%d + + ytr = class.debos(ytr) + + n.tr = length(ytr) # number of training data + n.te = dim(xte)[1] # number of test data + + if(is.null(mtry)){ mtry = ceiling(sqrt(dim(xtr)[2]))} # set mtry + rf_clfr = randomForest(x=xtr,y=ytr,xtest=xte,ntrees=ntrees,mtry=mtry,proximity=T,nodesize=%d) + rho = rf_clfr$test$proximity # RF proximity matrix, n.tr by n.te matrix + ''' % (num_srcs_for_users, ntrees, mtry, nfolds, nodesize) + + + r_str += ''' + + n.bar = apply(rho[1:n.te,(n.te+1):(n.te+n.tr)],1,sum) # avg. # training data in same terminal node + p.hat = apply(rf_clfr$test$votes,1,max) + err.decr = ((1-p.hat)/(n.bar+1)) %*%rho[1:n.te,1:n.te] # this is Delta V + + # choose probabalistically? or take top choices? + if(FALSE){ # take top m choices + select = which(err.decr >= sort(err.decr,decreasing=TRUE)[m]) + }else{ # sample from distribution defined by err.decr + select = sample(1:n.te,m,prob=err.decr/sum(err.decr),replace=FALSE)} + ''' + + + classifier_out = robjects.r(r_str) + + + #robjects.globalenv['pred_forconfmat'] + #robjects.r("rf_clfr$classes") + + possible_classes = robjects.r("rf_clfr$classes") + + ''' + actlearn_tups = [] + # Nice and kludgey. Could do this in R if I knew it a bit better + #for i, srcid in enumerate(data_dict['srcid_list']): + for i in robjects.globalenv['select']: + # I think the robjects.globalenv['select'] R array has an index starting at i=1 + # so this means if R array gives i=999, then this translates srcid_list[i=998] + # so this means if R array gives i=1, then this translates srcid_list[i=0] + srcid = testdata_dict['srcid_list'][i-1] + tups_list = zip(list(robjects.r("rf_clfr$test$votes[%d,]" % (i))), possible_classes) + tups_list.sort(reverse=True) + for j in range(3): + actlearn_tups.append((int(srcid), j, tups_list[j][0], tups_list[j][1])) + ''' + #import pdb; pdb.set_trace() + #print + + actlearn_tups = [] + # Nice and kludgey. Could do this in R if I knew it a bit better + #for i, srcid in enumerate(data_dict['srcid_list']): + + for i in robjects.globalenv['select']: + # I think the robjects.globalenv['select'] R array has an index starting at i=1 + # so this means if R array gives i=999, then this translates srcid_list[i=998] + # so this means if R array gives i=1, then this translates srcid_list[i=0] + srcid = testdata_dict['srcid_list'][i-1]# index is python so starts at 0 + actlearn_tups.append((int(srcid), robjects.globalenv['err.decr'][i-1]))# I tested this, i starts at 0 + + + allsrc_tups = [] + # Nice and kludgey. Could do this in R if I knew it a bit better + for i, srcid in enumerate(testdata_dict['srcid_list']): + tups_list = zip(list(robjects.r("rf_clfr$test$votes[%d,]" % (i+1))), possible_classes) + tups_list.sort(reverse=True) + for j in range(3): + allsrc_tups.append((int(srcid), j, tups_list[j][0], tups_list[j][1])) + + + return {'actlearn_tups':actlearn_tups, + 'allsrc_tups':allsrc_tups, + 'py_obj':classifier_out, + 'r_name':'rf_clfr', + 'select':robjects.globalenv['select'], + 'select.pred':robjects.r("rf_clfr$test$predicted[select]"), + 'select.predprob':robjects.r("rf_clfr$test$votes[select,]"), + 'err.decr':robjects.globalenv['err.decr'], + 'all.pred':robjects.r("rf_clfr$test$predicted"), + 'all.predprob':robjects.r("rf_clfr$test$votes"), + 'possible_classes':possible_classes, + } + + + def test_missForest_impuation_error(self, feature_data_dict): + """ Do imputation of missing-value feature values in dataset + + - See arxiv 1105.0828v1 for more information on + the R:MissForest imputation code for R:randomForest() + + This function is used to explore the errors which arrise due to imputation of ASAS data + for various "ntree" parameter values used in randomForest() + + 1 - (40588 / 46057.) = 0.11874416483922101 + -> where 46057 is the number of sources with both NA and non-NA attribs + -> where 40588 is the number of sources with non-NA attribs + So we will simulate this NA-source ratio un the 40588 source dataset + by adding + + """ + import datetime + r_data_dict = {} + for feat_name, feat_longlist in feature_data_dict.items(): + r_data_dict[feat_name] = robjects.FloatVector(feat_longlist) + features_r_data = robjects.r['data.frame'](**r_data_dict) + + robjects.globalenv['miss_data'] = features_r_data + robjects.r(""" + miss_data_no_NA = na.omit(miss_data) + """) + ntree_list = [300] + for ntree in ntree_list: + now = datetime.datetime.now() + + robjects.r(""" + miss_data_generated_NA <- miss_data_no_NA + + ### Here we take the non-NA dataset and force NA in a percentage of sources for all of the color features + noNA = 0.118744 + n <- nrow(miss_data_generated_NA) + NAloc <- rep(FALSE, n) + NAloc[sample(n, floor(n*noNA))] <- TRUE + miss_data_generated_NA$color_diff_hk[array(NAloc, dim=n)] <- NA + miss_data_generated_NA$color_diff_jh[array(NAloc, dim=n)] <- NA + miss_data_generated_NA$color_bv_extinction[array(NAloc, dim=n)] <- NA + + miss_data_generated_NA.imp = missForest(miss_data_generated_NA, mtry=5, ntree=%d) + err = mixError(miss_data_generated_NA.imp$Ximp, miss_data_generated_NA, miss_data_no_NA) + """ % (ntree)) + err = float(list(robjects.r('err'))[0]) + fp = open("/home/pteluser/scratch/active_learn/asas_ntree_imputation_tests.dat", "a+") + fp.write("%d %lf %s\n" % (ntree, err, str(datetime.datetime.now() - now))) + fp.close() + import pdb; pdb.set_trace() + print() + + + def generate_imputed_arff_for_ntree(self, feature_data_dict, mtry=None, ntree=None, header_str=None, feature_list=[], srcid_list=[], class_list=[], train_srcids=[]): + """ given a feature_data_dict and mtrey, ntree params, impute the data set + and write arff file. + + - To be run on Ipython task node. + """ + + new_feat_dict = self.imputation_using_missForest(feature_data_dict, mtry=mtry, ntree=ntree) + + n_srcs = len(srcid_list)#new_feat_dict[new_feat_dict.keys()[0]]) + + train_row_lines = [header_str, "@DATA"] + test_row_lines = [header_str, "@DATA"] + for i in range(n_srcs): + srcid = srcid_list[i] + if srcid in train_srcids: + train_row_list = [srcid_list[i]] + ### We use the ordered feature_list, so that attribs in a row match the order of the header + for feat_name in feature_list: + train_row_list.append(str(new_feat_dict[feat_name][i])) + train_row_list.append("'%s'" % (class_list[i])) + new_line = ','.join(train_row_list) + train_row_lines.append(new_line) + else: + test_row_list = [srcid_list[i]] + ### We use the ordered feature_list, so that attribs in a row match the order of the header + for feat_name in feature_list: + test_row_list.append(str(new_feat_dict[feat_name][i])) + test_row_list.append("'%s'" % (class_list[i])) + new_line = ','.join(test_row_list) + test_row_lines.append(new_line) + + train_arff_str = '\n'.join(train_row_lines) + train_arff_fpath = "/home/pteluser/scratch/active_learn/imputed_arffs/train_full_%dntree_%dmtry.arff" % (ntree, mtry) + fp = open(train_arff_fpath, "w") + fp.write(train_arff_str) + fp.close() + + test_arff_str = '\n'.join(test_row_lines) + test_arff_fpath = "/home/pteluser/scratch/active_learn/imputed_arffs/test_full_%dntree_%dmtry.arff" % (ntree, mtry) + fp = open(test_arff_fpath, "w") + fp.write(test_arff_str) + fp.close() + + return {'test_arff_fpath':test_arff_fpath, + 'train_arff_fpath':train_arff_fpath} + + + def imputation_using_missForest(self, feature_data_dict, mtry=None, ntree=None): + """ Do imputation of missing-value feature values in dataset + + - See arxiv 1105.0828v1 for more information on + the R:MissForest imputation code for R:randomForest() + """ + #import numpy + r_data_dict = {} + for feat_name, feat_longlist in feature_data_dict.items(): + try: + r_data_dict[feat_name] = robjects.FloatVector(feat_longlist) + except: + print('feat_longlist.count(None)=', feat_longlist.count(None), '\t', feat_name) + raise # apparently None values are not automatically converted to numpy.nan. Must do earlier. + #print feat_longlist.count(numpy.nan), '\t', feat_name + #import pdb; pdb.set_trace() + #print + features_r_data = robjects.r['data.frame'](**r_data_dict) + + robjects.globalenv['miss_data'] = features_r_data + + r_str = "miss_data.imp = missForest(miss_data, verbose=TRUE, mtry=%d, ntree=%d)$Ximp" % ( \ + mtry, ntree) + blah = robjects.r(r_str) + + out_feat_dict = {} + feature_names = list(robjects.r("names(miss_data.imp)")) + for i, feat_name in enumerate(feature_names): + out_feat_dict[feat_name] = list(robjects.r("miss_data.imp$%s" % (feat_name))) + + return out_feat_dict + + + def actlearn_randomforest(self, traindata_dict={}, + testdata_dict={}, + do_ignore_NA_features=False, + ntrees=1000, mtry=25, + nfolds=10, nodesize=5, + num_srcs_for_users=100, + random_seed=0, + both_user_match_srcid_bool=[], + actlearn_sources_freqsignifs=[]): + """ Train a randomForest() R classifier + + Taken from class_cv.R : rf.cv (L40) + """ + if do_ignore_NA_features: + print("actlearn_randomforest():: do_ignore_NA_features==True not implemented because obsolete") + raise + + train_featname_longfeatval_dict = traindata_dict['featname_longfeatval_dict'] + for feat_name, feat_longlist in train_featname_longfeatval_dict.items(): + train_featname_longfeatval_dict[feat_name] = robjects.FloatVector(feat_longlist) + traindata_dict['features'] = robjects.r['data.frame'](**train_featname_longfeatval_dict) + traindata_dict['classes'] = robjects.StrVector(traindata_dict['class_list']) + + robjects.globalenv['xtr'] = traindata_dict['features'] + robjects.globalenv['ytr'] = traindata_dict['classes'] + + test_featname_longfeatval_dict = testdata_dict['featname_longfeatval_dict'] + for feat_name, feat_longlist in test_featname_longfeatval_dict.items(): + test_featname_longfeatval_dict[feat_name] = robjects.FloatVector(feat_longlist) + testdata_dict['features'] = robjects.r['data.frame'](**test_featname_longfeatval_dict) + testdata_dict['classes'] = robjects.StrVector(testdata_dict['class_list']) + + robjects.globalenv['xte'] = testdata_dict['features'] + robjects.globalenv['yte'] = testdata_dict['classes'] + + #import pdb; pdb.set_trace() + #print + + #robjects.globalenv['instep'] = robjects.IntVector(actlearn_used_srcids_indicies) + #robjects.globalenv['incl_tr'] = robjects.BoolVector(both_user_match_srcid_bool) + robjects.globalenv['actlearn_sources_freqsignifs'] = robjects.FloatVector(actlearn_sources_freqsignifs) + robjects.globalenv['both_user_match_srcid_bool'] = robjects.BoolVector(both_user_match_srcid_bool) + + #for class_name in testdata_dict['class_list']: + # if (('algol' in class_name.lower()) or ('persei' in class_name.lower())): + # print '!', class_name + + r_str = ''' + + m=%d + + ntrees=%d + mtry=%d + nfolds=%d + + ##### ESTIMATE COST FUNCTION FROM AL SAMPLE + ## function taking AL sample (indicator that label found & freq_signif) to fit model for + ## cost (prob. manually label) as fxn. of freq_signif (glm) + getCost = function(sig, labeled){ + cost.fit = glm(labeled~sig,family="binomial") # logistic regression + #sig.vec = round(min(freqSigAll)):round(max(freqSigAll)) # vector to predict + sig.vec = 0:ceiling(max(xte[,'freq_signif'])) # 0:38 # vector to predict (range of freq_signif values in the 50k asas dataset) + cost.pred = predict(cost.fit,newdata = data.frame(sig=sig.vec),se.fit=TRUE) # prediction + + cost = exp(cost.pred$fit) / (1+exp(cost.pred$fit)) # cost function + cost.up = exp(cost.pred$fit+cost.pred$se.fit) / (1+exp(cost.pred$fit+cost.pred$se.fit)) + cost.dn = exp(cost.pred$fit-cost.pred$se.fit) / (1+exp(cost.pred$fit-cost.pred$se.fit)) + return(list(cost=cost,costUp=cost.up,costDn=cost.dn)) + } + + #al.sel.cost = alSelect(feat.debos,class.deb,feat.asas[1:7000,],m=50,ntrees=500,cost=cost.fxn) + + + #cost = getCost(instep,incl_tr,xtr[,'freq_signif']) + cost = getCost(actlearn_sources_freqsignifs, both_user_match_srcid_bool) + cost.fxn = cost$cost + + ytr = class.debos(ytr) + + n.tr = length(ytr) # number of training data + n.te = dim(xte)[1] # number of test data + + if(is.null(mtry)){ mtry = ceiling(sqrt(dim(xtr)[2]))} # set mtry + #xte.imp = missForest(xte, verbose=TRUE)$Ximp + #xtr.imp = missForest(xtr, verbose=TRUE)$Ximp # dstarr hack to get rid of training missingvals + rf_clfr = randomForest(x=xtr,y=ytr,xtest=xte,ntrees=ntrees,mtry=mtry,proximity=TRUE,nodesize=%d) + rho = rf_clfr$test$proximity # RF proximity matrix, n.tr by n.te matrix + + cat("Selecting best objects\n") + n.bar = apply(rho[1:n.te,(n.te+1):(n.te+n.tr)],1,sum) # avg. # training data in same terminal node + p.hat = apply(rf_clfr$test$votes,1,max) + err.decr = ((1-p.hat)/(n.bar+1)) %srho[1:n.te,1:n.te] # this is Delta V + + # if there is a cost vector, use it to alter err.decr + if(length(cost.fxn)>0){ + min.fs = round(min(xte[,'freq_signif'])) + freqsig = round(xte[,'freq_signif']) + te.cost = cost.fxn[freqsig - min.fs + 1] + err.decr = err.decr*te.cost + } + + # choose probabalistically: + select = sample(1:n.te,m,prob=err.decr/sum(err.decr),replace=FALSE) + #return(list(select=select,select.pred=rf_clfr$test$predicted[select],select.predprob=rf_clfr$test$votes[select,],err.decr=err.decr,all.pred=rf_clfr$test$predicted,all.predprob=rf_clfr$test$votes)) + + # # # This is just needed for filling the ASAS catalog tables: + rf_applied_to_train = randomForest(x=xtr,y=ytr,xtest=xtr,ntrees=ntrees,mtry=mtry,proximity=TRUE,nodesize=%d) + # # # + + ''' % (num_srcs_for_users, ntrees, mtry, nfolds, nodesize, "%*%", nodesize) + + classifier_out = robjects.r(r_str) + + #robjects.globalenv['pred_forconfmat'] + #robjects.r("rf_clfr$classes") + + possible_classes = robjects.r("rf_clfr$classes") + + ''' + actlearn_tups = [] + # Nice and kludgey. Could do this in R if I knew it a bit better + #for i, srcid in enumerate(data_dict['srcid_list']): + for i in robjects.globalenv['select']: + # I think the robjects.globalenv['select'] R array has an index starting at i=1 + # so this means if R array gives i=999, then this translates srcid_list[i=998] + # so this means if R array gives i=1, then this translates srcid_list[i=0] + srcid = testdata_dict['srcid_list'][i-1] + tups_list = zip(list(robjects.r("rf_clfr$test$votes[%d,]" % (i))), possible_classes) + tups_list.sort(reverse=True) + for j in range(3): + actlearn_tups.append((int(srcid), j, tups_list[j][0], tups_list[j][1])) + ''' + + actlearn_tups = [] + # Nice and kludgey. Could do this in R if I knew it a bit better + #for i, srcid in enumerate(data_dict['srcid_list']): + + for i in robjects.globalenv['select']: + # I think the robjects.globalenv['select'] R array has an index starting at i=1 + # so this means if R array gives i=999, then this translates srcid_list[i=998] + # so this means if R array gives i=1, then this translates srcid_list[i=0] + srcid = testdata_dict['srcid_list'][i-1]# index is python so starts at 0 + actlearn_tups.append((int(srcid), robjects.globalenv['err.decr'][i-1]))# I tested this, i starts at 0 + + + #import pdb; pdb.set_trace() + #print + allsrc_tups = [] + everyclass_tups = [] + trainset_everyclass_tups = [] + # Nice and kludgey. Could do this in R if I knew it a bit better + for i, srcid in enumerate(testdata_dict['srcid_list']): + tups_list = zip(list(robjects.r("rf_clfr$test$votes[%d,]" % (i+1))), possible_classes) + tups_list.sort(reverse=True) + for j in range(len(tups_list)): + if j < 3: + allsrc_tups.append((int(srcid), j, tups_list[j][0], tups_list[j][1])) + everyclass_tups.append((int(srcid), j, tups_list[j][0], tups_list[j][1])) + + # # # This is just needed for filling the ASAS catalog tables: + for i, srcid in enumerate(traindata_dict['srcid_list']): + tups_list = zip(list(robjects.r("rf_applied_to_train$test$votes[%d,]" % (i+1))), possible_classes) + tups_list.sort(reverse=True) + for j in range(len(tups_list)): + trainset_everyclass_tups.append((int(srcid), j, tups_list[j][0], tups_list[j][1])) + # # # + + #import pdb; pdb.set_trace() + #print + return {'actlearn_tups':actlearn_tups, + 'allsrc_tups':allsrc_tups, + 'everyclass_tups':everyclass_tups, + 'trainset_everyclass_tups':trainset_everyclass_tups, + 'py_obj':classifier_out, + 'r_name':'rf_clfr', + 'select':robjects.globalenv['select'], + 'select.pred':robjects.r("rf_clfr$test$predicted[select]"), + 'select.predprob':robjects.r("rf_clfr$test$votes[select,]"), + 'err.decr':robjects.globalenv['err.decr'], + 'all.pred':robjects.r("rf_clfr$test$predicted"), + 'all.predprob':robjects.r("rf_clfr$test$votes"), + 'possible_classes':possible_classes, + 'all_top_prob':robjects.r("apply(rf_clfr$test$votes,1,max)"), + } + + + def get_confident_sources(self, combo_result_dict={}, n_sources_per_class=10, prob_thresh=0.5): + """ Generate a N-list of confident sources which should be a good representations + of each science class. + """ + robjects.globalenv['pred'] = robjects.IntVector(combo_result_dict['all.pred']) + robjects.globalenv['maxprob'] = robjects.FloatVector(combo_result_dict['all_top_prob']) + + # KLUDGEY + srcid_list = [] + for str_srcid in combo_result_dict['srcid_list']: + srcid_list.append(int(str_srcid)) + robjects.globalenv['ID'] = robjects.IntVector(srcid_list) + + r_str = ''' + m = %d + probThresh= %f + whichConf = which(maxprob>probThresh) # only look at sources with maxProb>probThresh + tabConf = table(pred[whichConf]) # class distribution of confident sources + confAdd = NULL # sources to add + for(ii in 1:length(tabConf)){ + if(tabConf[ii]>0){ # cycle thru confident classes + if(tabConf[ii] 10: + n_folds = 10 + else: + n_folds = min_n_srcs + + + class_indlist = {} + for i, class_name in enumerate(full_data_dict['class_list']): + if class_name not in class_indlist: + class_indlist[class_name] = [] + class_indlist[class_name].append(i) + + + + all_fold_dict = {} + for i in range(n_folds): + all_fold_dict[i] = {'train_data':{'srcid_list':[], + 'featname_longfeatval_dict':{}, + 'iters_list':[], + 'percent_list':[], + 'class_list':[], + 'arff_rows':[]}, + 'classif_data':{'srcid_list':[], + 'featname_longfeatval_dict':{}, + 'iters_list':[], + 'percent_list':[], + 'class_list':[], + 'arff_rows':[]}} + for feat_name in full_data_dict['featname_longfeatval_dict'].keys(): + all_fold_dict[i]['train_data']['featname_longfeatval_dict'][feat_name] = [] + all_fold_dict[i]['classif_data']['featname_longfeatval_dict'][feat_name] = [] + + if do_stratified: + ### Stratified case will have to keep track of which srcids were used in each train/classif fold. + print('do_stratified!!! Case not coded yet!') + + raise + else: + for class_name, ind_list in class_indlist.items(): + src_count = len(ind_list) + if classify_percent is None: + n_to_classify = src_count / n_folds # we exclude only 1 point if n_srcs < (n_folds * 2) + else: + n_to_classify = int(src_count * (classify_percent / 100.)) + + for i_fold, fold_dict in all_fold_dict.items(): + random.shuffle(ind_list) + sub_range = ind_list[:n_to_classify] + for i in sub_range: + fold_dict['classif_data']['srcid_list'].append(full_data_dict['srcid_list'][i]) + for feat_name in full_data_dict['featname_longfeatval_dict'].keys(): + fold_dict['classif_data']['featname_longfeatval_dict'][feat_name].append( \ + full_data_dict['featname_longfeatval_dict'][feat_name][i]) + if len(full_data_dict['iters_list']) > 0: + fold_dict['classif_data']['iters_list'].append(full_data_dict['iters_list'][i]) + if len(full_data_dict['percent_list']) > 0: + fold_dict['classif_data']['percent_list'].append(full_data_dict['percent_list'][i]) + fold_dict['classif_data']['class_list'].append(full_data_dict['class_list'][i]) + if len(full_data_dict['arff_rows']) > 0: + fold_dict['classif_data']['arff_rows'].append(full_data_dict['arff_rows'][i]) + + train_inds = filter(lambda x: x not in sub_range, ind_list) + for i in train_inds: + fold_dict['train_data']['srcid_list'].append(full_data_dict['srcid_list'][i]) + for feat_name in full_data_dict['featname_longfeatval_dict'].keys(): + fold_dict['train_data']['featname_longfeatval_dict'][feat_name].append( \ + full_data_dict['featname_longfeatval_dict'][feat_name][i]) + if len(full_data_dict['iters_list']) > 0: + fold_dict['train_data']['iters_list'].append(full_data_dict['iters_list'][i]) + if len(full_data_dict['percent_list']) > 0: + fold_dict['train_data']['percent_list'].append(full_data_dict['percent_list'][i]) + fold_dict['train_data']['class_list'].append(full_data_dict['class_list'][i]) + if len(full_data_dict['arff_rows']) > 0: + fold_dict['train_data']['arff_rows'].append(full_data_dict['arff_rows'][i]) + + return all_fold_dict + + + def generate_R_randomforest_classifier_rdata(self, train_data={}, + classifier_fpath='', + do_ignore_NA_features=True, + algorithms_dirpath='', + ntrees=1000, mtry=25, nfolds=10, nodesize=5, + classifier_type='randomForest'): + """ Given fpath for an rdata file, file will be generated and (re)written. + """ + rc = Rpy2Classifier(algorithms_dirpath=algorithms_dirpath) + + if os.path.exists(classifier_fpath): + os.system("rm %s" % (classifier_fpath)) + + + if classifier_type == 'randomForest': + classifier_dict = rc.train_randomforest(train_data, + do_ignore_NA_features=do_ignore_NA_features, + ntrees=ntrees, mtry=mtry, nfolds=nfolds, nodesize=nodesize) + elif classifier_type == 'cforest': + classifier_dict = rc.train_cforest(train_data, + do_ignore_NA_features=do_ignore_NA_features, + ntrees=ntrees, mtry=mtry, nfolds=nfolds, nodesize=nodesize) + else: + print('incorrect classifier_type!') + raise + + rc.save_classifier(classifier_dict=classifier_dict, + fpath=classifier_fpath) + print('WROTE:', classifier_fpath) + + + +if __name__ == '__main__': + + + ##### Usage example: + rc = Rpy2Classifier() + + #arff_str = open(os.path.expandvars("$HOME/scratch/full_deboss_20101220.arff")).read() + arff_str = open(os.path.expandvars("$HOME/scratch/full_deboss_1542srcs_20110106.arff")).read() + traindata_dict = rc.parse_full_arff(arff_str=arff_str) + do_ignore_NA_features = False + + #traindata_dict = rc.parse_joey_feature_class_datfile( \ + # feature_fpath=os.path.expandvars("$HOME/scratch/features.dat"), + # class_fpath=os.path.expandvars("$HOME/scratch/class.dat")) + #do_ignore_NA_features = True + + + if 1: + # generate multiple (folded) classifiers: + import cPickle + + Gen_Fold_Classif = GenerateFoldedClassifiers() + all_fold_data = Gen_Fold_Classif.generate_fold_subset_data(full_data_dict=traindata_dict, + n_folds=10, + do_stratified=False, + classify_percent=40.) + + for i_fold, fold_data in all_fold_data.items(): + classifier_fpath = os.path.expandvars("$HOME/scratch/classifier_deboss_RF_%d.rdata" % ( \ + i_fold)) + src_data_pkl_fpath = os.path.expandvars("$HOME/scratch/classifier_deboss_RF_%d.srcs.pkl" % ( \ + i_fold)) + if os.path.exists(src_data_pkl_fpath): + os.system('rm ' + src_data_pkl_fpath) + fp = open(src_data_pkl_fpath, 'wb') + cPickle.dump({'srcid_list':all_fold_data[i_fold]['classif_data']['srcid_list']},fp,1) + fp.close() + + Gen_Fold_Classif.generate_R_randomforest_classifier_rdata(train_data=fold_data['train_data'], + classifier_fpath=classifier_fpath, + do_ignore_NA_features=do_ignore_NA_features) + sys.exit() + + else: + ##### Single classifier case: + classifier_fpath = os.path.expandvars("$HOME/scratch/test_RF_classifier__crap.rdata") + if not os.path.exists(classifier_fpath): + classifier_dict = rc.train_randomforest(traindata_dict, + do_ignore_NA_features=do_ignore_NA_features) + rc.save_classifier(classifier_dict=classifier_dict, + fpath=classifier_fpath) + print('WROTE:', classifier_fpath) + sys.exit() + else: + r_name='rf_clfr' + classifier_dict = {'r_name':r_name} + rc.load_classifier(r_name=r_name, + fpath=classifier_fpath) + + + + classif_results = rc.apply_randomforest(classifier_dict=classifier_dict, + data_dict=traindata_dict, + do_ignore_NA_features=do_ignore_NA_features) + import pdb; pdb.set_trace() + + #TODO: crossvalid_results = rc.get_crossvalid_errors() + diff --git a/mltsp/TCP/Algorithms/simbad_id_lookup.py b/mltsp/TCP/Algorithms/simbad_id_lookup.py new file mode 100644 index 00000000..77a83a00 --- /dev/null +++ b/mltsp/TCP/Algorithms/simbad_id_lookup.py @@ -0,0 +1,97 @@ +#!/usr/bin/env python +""" Adapted from josh's simbad.py +""" +from __future__ import print_function + +import os, sys + +import urllib + + +def query_votable(src_name="HD 27290"): + + #alt_html_pre = "http://simbad.u-strasbg.fr/simbad/sim-coo?" + #html_pre = "http://simbad.harvard.edu/simbad/sim-coo?" + html = "http://simbad.harvard.edu/simbad/sim-id?" + + + #params = urllib.urlencode({'output.format': "VOTABLE", "Coord": "%fd%f" % (ra, dec),\ + # 'Radius': rad, 'Radius.unit': "arcsec"}) + + params = urllib.urlencode({'output.format': "VOTABLE", "Ident":src_name, "NbIdent":1, \ + 'Radius': 2, 'Radius.unit': "arcsec", 'submit':'submit id'}) + f = urllib.urlopen("%s%s" % (html,params)) + s = f.read() + f.close() + return s + + +def parse_class(votable_str): + import amara + + a = amara.parse(votable_str) + #b = a.xml_select("/VOTABLE/RESOURCE/TABLE/FIELD") + #b = a.xml_xpath("/VOTABLE/RESOURCE/TABLE/FIELD[@name='OTYPE']") + b = a.xml_xpath("/VOTABLE/RESOURCE/TABLE/FIELD") + i_col_otype = -1 + for i, elem in enumerate(b): + if elem['name'] == 'OTYPE': + i_col_otype = i + break + b = a.xml_xpath("/VOTABLE/RESOURCE/TABLE/DATA/TABLEDATA/TR/TD") + #print len(b[i_col_otype]) + return str(b[i_col_otype]) + + + + +def query_html(src_name = "HD 27290"): + """ This returns the various associated ids that a source has, unlike in the votable case + """ + #alt_html_pre = "http://simbad.u-strasbg.fr/simbad/sim-coo?" + #html_pre = "http://simbad.harvard.edu/simbad/sim-coo?" + html = "http://simbad.harvard.edu/simbad/sim-id?" + + + #params = urllib.urlencode({'output.format': "VOTABLE", "Coord": "%fd%f" % (ra, dec),\ + # 'Radius': rad, 'Radius.unit': "arcsec"}) + + params = urllib.urlencode({'output.format': "html", "Ident":src_name, "NbIdent":1, \ + 'Radius': 2, 'Radius.unit': "arcsec", 'submit':'submit id'}) + f = urllib.urlopen("%s%s" % (html,params)) + s = f.read() + f.close() + #print s + #a = amara.parse(s) + #b = a.xml_select("/VOTABLE/RESOURCE/TABLE/FIELD") + return s + + +def parse_html_for_ids(html_str, instr_identifier='HIP'): + """ + """ + lines = html_str.split('\n') + out_id_str_list = [] + for line in lines: + if ((' %d AND id <= %d" + # NOTE: this table was filled using the TCP/Software/ingest_tools/sync_with_lbl_footprint_table.py script. + + #(ra, dec) = (336.00127488, 36.490527063) + (ra, dec) = (320.87275, -0.84803) + ##### This gets the PTF limiting magnitude for an (ra,dec): + #select_str = "SELECT filter, ujd, lmt_mg from object_test_db.ptf_candidate_footprint WHERE (MBRContains(radec_region, GeomFromText('POINT(%lf %lf)'))) ORDER BY filter, ujd" % (ra, dec) + select_str = """ +SELECT source_test_db.srcid_lookup_htm.src_id, + object_test_db.sdss_events_a.t, + object_test_db.sdss_events_a.jsb_mag, + object_test_db.sdss_events_a.jsb_mag_err, + object_test_db.sdss_events_a.filt, + object_test_db.sdss_events_a.ra, + object_test_db.sdss_events_a.decl +FROM source_test_db.srcid_lookup_htm +JOIN object_test_db.obj_srcid_lookup USING (src_id) +JOIN object_test_db.sdss_events_a USING (obj_id) +WHERE (DIF_HTMCircle(%lf, %lf, 0.01)) +ORDER BY src_id, t + """ % (ra, dec) + print("SDSS filter numbers translate using {0:'u',1:'g',2:'r',3:'i',4:'z'}") + cursor.execute(select_str) + results = cursor.fetchall() + for row in results: + print(row) diff --git a/mltsp/TCP/Algorithms/stetson_stats.py b/mltsp/TCP/Algorithms/stetson_stats.py new file mode 100644 index 00000000..d3fa832c --- /dev/null +++ b/mltsp/TCP/Algorithms/stetson_stats.py @@ -0,0 +1,51 @@ +from numpy import ones,empty,median,sqrt,mean,abs,sign + + +def stetson_mean( x, weight=100.,alpha=2.,beta=2.,tol=1.e-6,nmax=20): + """An iteratively weighted mean""" + + x0 = median( x ) + + for i in range(nmax): + resid = x - x0 + resid_err = abs(resid)*sqrt(weight) + weight1 = weight/(1. + (resid_err/alpha)**beta) + weight1 /= weight1.mean() + diff = mean( x*weight1 ) - x0 + x0 += diff + if (abs(diff) < tol*abs(x0) or abs(diff) < tol): break + + + return x0 + + +def stetson_j(x,y=[],dx=0.1,dy=0.1): + """Robust covariance statistic between pairs of observations x,y + whose uncertainties are dx,dy. if y is not given, calculates + a robust variance for x.""" + + nels = len(x) + + x0 = stetson_mean(x, 1./dx**2) + delta_x = sqrt(nels / (nels - 1.)) * (x - x0) / dx + + if (y!=[]): + y0 = stetson_mean(y, 1./dy**2) + delta_y = sqrt(nels / (nels - 1.)) * (y - y0) / dy + p_k = delta_x*delta_y + else: + p_k = delta_x**2-1. + + return mean( sign(p_k) * sqrt(abs(p_k)) ) + + +def stetson_k(x,dx=0.1): + """A kurtosis statistic.""" + + nels = len(x) + + x0 = stetson_mean(x, 1./dx**2) + + delta_x = sqrt(nels / (nels - 1.)) * (x - x0) / dx + + return 1./0.798 * mean( abs(delta_x) ) / sqrt( mean(delta_x**2) ) diff --git a/mltsp/TCP/Algorithms/summarize_AL_sources.py b/mltsp/TCP/Algorithms/summarize_AL_sources.py new file mode 100644 index 00000000..74cd2554 --- /dev/null +++ b/mltsp/TCP/Algorithms/summarize_AL_sources.py @@ -0,0 +1,70 @@ +#!/usr/bin/env python +""" Summarizes certain science class sources found in AL*.dat files + +""" +from __future__ import print_function + +import sys, os +import MySQLdb +import glob +from numpy import loadtxt + +sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + 'Software/ingest_tools')) +import activelearn_utils + +if __name__ == '__main__': + + pars = {'tutor_hostname':'192.168.1.103', + 'tutor_username':'dstarr', #'tutor', # guest + 'tutor_password':'ilove2mass', #'iamaguest', + 'tutor_database':'tutor', + 'tutor_port':3306, #33306, + 'tcp_hostname':'192.168.1.25', + 'tcp_username':'pteluser', + 'tcp_port': 3306, #23306, + 'tcp_database':'source_test_db', + 'al_dirpath':'/home/pteluser/src/TCP/Data/allstars', + 'al_glob_str':'AL_*_*.dat', + 'classids_of_interest':[202, # wtts + 201, # ctts + 200, # tt (obsolete) + 267, # Herbig AE + 197], # Herbig AE/BE], + } + + + DatabaseUtils = activelearn_utils.Database_Utils(pars=pars) + rclass_tutorid_lookup = DatabaseUtils.retrieve_tutor_class_ids() + class_id_name = dict([[v,k] for k,v in rclass_tutorid_lookup.items()]) + + fpaths = glob.glob("%s/%s" % (pars['al_dirpath'], pars['al_glob_str'])) + + print(""" + """) + for fpath in fpaths: + tup_list = loadtxt(fpath, + dtype={'names': ('src_id', 'class_id'), + 'formats': ('i4', 'i4')}, + usecols=(0,1), + unpack=False) + srcid_list = tup_list['src_id'] + classid_list = tup_list['class_id'] + for i, classid in enumerate(classid_list): + if classid in pars['classids_of_interest']: + print('' % (srcid_list[i], fpath[fpath.rfind('/')+1:], class_id_name[classid].replace(' ','_'), srcid_list[i], srcid_list[i])) + + select_str = "select source_id, class_id from sources where project_id = 123" + DatabaseUtils.tutor_cursor.execute(select_str) + results = DatabaseUtils.tutor_cursor.fetchall() + if len(results) == 0: + raise "Error" + for row in results: + (source_id, classid) = row + if classid in pars['classids_of_interest']: + print('' % (source_id, "Debosscher", class_id_name[classid].replace(' ','_'), source_id, source_id)) + + print("""
%d (%s) %s %d
%d (%s) %s %d
+ """) + import pdb; pdb.set_trace() + print() + diff --git a/mltsp/TCP/Algorithms/timeout b/mltsp/TCP/Algorithms/timeout new file mode 100755 index 00000000..6a542e9e --- /dev/null +++ b/mltsp/TCP/Algorithms/timeout @@ -0,0 +1,56 @@ +#!/bin/sh + +# Execute a command with a timeout + +# Author: +# http://www.pixelbeat.org/ +# Notes: +# Note there is a timeout command packaged with coreutils since v7.0 +# If the timeout occurs the exit status is 124. +# There is an asynchronous (and buggy) equivalent of this +# script packaged with bash (under /usr/share/doc/ in my distro), +# which I only noticed after writing this. +# I noticed later again that there is a C equivalent of this packaged +# with satan by Wietse Venema, and copied to forensics by Dan Farmer. +# Changes: +# V1.0, Nov 3 2006, Initial release +# V1.1, Nov 20 2007, Brad Greenlee +# Make more portable by using the 'CHLD' +# signal spec rather than 17. +# V1.3, Oct 29 2009, Ján Sáreník +# Even though this runs under dash,ksh etc. +# it doesn't actually timeout. So enforce bash for now. +# Also change exit on timeout from 128 to 124 +# to match coreutils. +# V2.0, Oct 30 2009, Ján Sáreník +# Rewritten to cover compatibility with other +# Bourne shell implementations (pdksh, dash) + +if [ "$#" -lt "2" ]; then + echo "Usage: `basename $0` timeout_in_seconds command" >&2 + echo "Example: `basename $0` 2 sleep 3 || echo timeout" >&2 + exit 1 +fi + +cleanup() +{ + trap - ALRM #reset handler to default + kill -ALRM $a 2>/dev/null #stop timer subshell if running + kill $! 2>/dev/null && #kill last job + exit 124 #exit with 124 if it was running +} + +watchit() +{ + trap "cleanup" ALRM + sleep $1& wait + kill -ALRM $$ +} + +watchit $1& a=$! #start the timeout +shift #first param was timeout for sleep +trap "cleanup" ALRM INT #cleanup after timeout +"$@"& wait $!; RET=$? #start the job wait for it and save its return value +kill -ALRM $a #send ALRM signal to watchit +wait $a #wait for watchit to finish cleanup +exit $RET #return the value diff --git a/mltsp/TCP/Algorithms/tutor_vizier_utils.py b/mltsp/TCP/Algorithms/tutor_vizier_utils.py new file mode 100644 index 00000000..e4725dba --- /dev/null +++ b/mltsp/TCP/Algorithms/tutor_vizier_utils.py @@ -0,0 +1,91 @@ +#!/usr/bin/env python +""" Scripts for retrieving vizier.cfa.harvard.edu data and then +importing into TUTOR database via web-interface. + +Vizier page: + + http://vizier.cfa.harvard.edu/viz-bin/VizieR?-source=J/A+A/461/183 + +Select: + "Max. Entries: 9999" + "Output layout": "tab -Seperated-Values" + *** also "show" the "recno" column, which gives a source-id + + - This downloads a .tsv file of just source info, filenames, ... + + +NOTE: will need to place timeseries data files in lyr3 directory using: + + + +""" +from __future__ import print_function +import sys, os + +def parse_tsv(tsv_fpath): + """ Parse a tsv ';' seperated file which was downloaded from vizier + """ + out_dict = {'data_lists':{}, + 'data_units_dict':{}} + icol_to_colname = {} + lines = open(tsv_fpath) + i_signif_line = 0 + for line_raw in lines: + line = line_raw.strip() + if len(line) == 0: + continue + elif line[0] == '#': + continue + elif i_signif_line == 0: + # _RAJ2000;_DEJ2000;Name;RAJ2000;DEJ2000;Bmag;Vmag;SpType;FileName + col_names = line.split(';') + for i, col_name in enumerate(col_names): + out_dict['data_lists'][col_name] = [] + icol_to_colname[i] = col_name + elif i_signif_line == 1: + unit_str_list = line.split(';') + for i, unit_str in enumerate(unit_str_list): + out_dict['data_units_dict'][icol_to_colname[i]] = unit_str + elif i_signif_line == 2: + pass # skip this but also increment i_signif_line below + else: + elems = line.split(';') + for i, elem in enumerate(elems): + out_dict['data_lists'][icol_to_colname[i]].append(elem) + i_signif_line += 1 + return out_dict + + +def get_timeseries_files_using_tsv(tsv_fpath='', + data_url_prefix='', + data_url_suffix='', + data_download_dirpath=''): + """ +http://vizier.cfa.harvard.edu/viz-bin/nph-Plot/Vgraph/txt?J/A%2bA/461/183/./phot/aaori.dat&P=0&-y&-&-&- + """ + if not os.path.exists(data_download_dirpath): + os.system('mkdir -p %s' % (data_download_dirpath)) + + data_dict = parse_tsv(tsv_fpath) + print(data_dict['data_lists'].keys()) + print('FileName', data_dict['data_lists']['FileName'][10]) + print('RAJ2000', data_dict['data_lists']['RAJ2000'][10]) + + for fname in data_dict['data_lists']['FileName']: + #get_str = "curl -O %s%s%s" % (data_url_prefix, fname, data_url_suffix) + get_str = 'curl "%s%s%s" > "%s/%s"' % (data_url_prefix, fname, data_url_suffix, + data_download_dirpath, fname) + os.system(get_str) + + print("TSV file:\n\t", tsv_fpath) + print("Timeseries dat downloaded to:\n\t", data_download_dirpath) + + + + + +if __name__ == '__main__': + get_timeseries_files_using_tsv(tsv_fpath='/Users/dstarr/Downloads/asu (2).tsv', + data_url_prefix='http://vizier.cfa.harvard.edu/viz-bin/nph-Plot/Vgraph/txt?J/A%2bA/461/183/./phot/', + data_url_suffix='&P=0&-y&-&-&-', + data_download_dirpath='/Users/dstarr/analysis/tutor124ttauri') diff --git a/mltsp/TCP/Algorithms/utils_classify.R b/mltsp/TCP/Algorithms/utils_classify.R new file mode 100755 index 00000000..7678ab86 --- /dev/null +++ b/mltsp/TCP/Algorithms/utils_classify.R @@ -0,0 +1,325 @@ + +# plots confusion matrix as a heat map +plot.confusion = function(confmat,title="Confusion Matrix",cexprob=0.5,cexaxis=1){ + n = dim(confmat)[1] + err.rate = round(1-sum(diag(confmat))/sum(confmat),4) + # flip confusion matrix for plotting + confmat = confmat[,n:1] + confmat.prob = confmat/rowSums(confmat) + + par(mar=c(5,7,5,.5)) + image(matrix(sqrt(confmat.prob),nrow=n,ncol=n),xlab=paste("True Class, Error Rate:",err.rate),xaxt="n",yaxt="n",main=title,cex.lab= 1.5,col=heat.colors(128)) + axis(2,labels=row.names(confmat),at=seq(1,0,length.out=n),las=2,cex.axis= 0.7*cexaxis) + axis(3,labels=substr(colnames(confmat),1,1),at=seq(1,0,length.out=n),las=1,cex.axis= 0.7*cexaxis,padj=2/cexaxis,tick=FALSE) + axis(1,labels=paste(rowSums(confmat)),at=seq(0,1,length.out=n),las=1,cex.axis=.5*cexaxis,tick=F,padj=-4/cexaxis) + abline(v=seq(-1./((n-1)*2),1-1./((n-1)*2),length.out= n)) + abline(h=seq(-1./((n-1)*2),1-1./((n-1)*2),length.out= n)) + for(ii in 1:n){ + for(jj in 1:n){ + if(confmat.prob[ii,jj]>0.005){ + if(confmat.prob[ii,jj]>=0.1){ + text((ii-1)*(1/(n-1)),(jj-1)*(1/(n-1)),round(confmat.prob[ii,jj],2),cex=cexprob,col='blue') + } + else { + text((ii-1)*(1/(n-1)),(jj-1)*(1/(n-1)),round(confmat.prob[ii,jj],2),cex=cexprob,col='white') + } + } + } + } +} + +# set up Debossher classes as factor vector (to compare w/ their work) +class.debos = function(class){ + class.new = paste(class) + class.new[class=="Mira" | class=="MIRA"] = "a. Mira" + class.new[class=="Semiregular Pulsating Variable" | class=="SR"] = "b. Semireg PV" + class.new[class=="RV Tauri" | class=="RVTAU"] = "c. RV Tauri" + class.new[class=="Classical Cepheid" | class=="CLCEP"] = "d. Classical Cepheid" + class.new[class=="Population II Cepheid" | class=="PTCEP"] = "e. Pop. II Cepheid" + class.new[class=="Multiple Mode Cepheid" | class=="DMCEP"] = "f. Multi. Mode Cepheid" + class.new[class=="RR Lyrae, Fundamental Mode" | class=="RRAB"] = "g. RR Lyrae, FM" + class.new[class=="RR Lyrae, First Overtone" | class=="RRC"] = "h. RR Lyrae, FO" + class.new[class=="RR Lyrae, Double Mode" | class=="RRD"] = "i. RR Lyrae, DM" + class.new[class=="Delta Scuti" | class=="DSCUT"] = "j. Delta Scuti" + class.new[class=="Lambda Bootis Variable" | class=="LBOO"] = "k. Lambda Bootis" + class.new[class=="Beta Cephei" | class=="BCEP"] = "l. Beta Cephei" + class.new[class=="Slowly Pulsating B-stars" | class=="SPB"] = "m. Slowly Puls. B" + class.new[class=="Gamma Doradus" | class=="GDOR"] = "n. Gamma Doradus" + class.new[class=="Be Star"] = "o. Pulsating Be" + class.new[class=="Periodically variable supergiants" | class=="PVSG"] = "p. Per. Var. SG" + class.new[class=="Chemically Peculiar Stars" | class=="CP"] = "q. Chem. Peculiar" + class.new[class=="Wolf-Rayet" | class=="WR"] = "r. Wolf-Rayet" + class.new[class=="T Tauri"| class=="TTAU"] = "s. T Tauri" + class.new[class=="Herbig AE/BE Star" | class=="HAEBE"] = "t. Herbig AE/BE" + class.new[class=="S Doradus" | class=="LBV"] = "u. S Doradus" + class.new[class=="Ellipsoidal" | class=="ELL"] = "v. Ellipsoidal" + class.new[class=="Beta Persei" | class=="EA"] = "w. Beta Persei" + class.new[class=="Beta Lyrae" | class=="EB"] = "x. Beta Lyrae" + class.new[class=="W Ursae Majoris" | class=="EW"] = "y. W Ursae Maj." + class.new[class=="Algol (Beta Persei)" | class=="EA"] = "w. Beta Persei" + + class.new = factor(class.new) +# levels(class.new) = sort(c(levels(class.new),"o. BE Star")) + return(class.new) +} + +# straighten out doubly-labeled data +class.double = function(class,ID){ + class[ID==148137] = "u. S Doradus" + class[ID==148583] = "u. S Doradus" + class[ID==148614] = "u. S Doradus" + class[ID==148615] = "u. S Doradus" + class[ID==148038] = "u. S Doradus" + class[ID==148174] = "t. Herbig AE/BE" + class[ID==148375] = "t. Herbig AE/BE" + return(factor(class)) +} + +# loads in OGLE + HIPPARCOS data +load.data = function(path){ + +### HIPPARCOS + hip.ID = read.table(paste(path,"hipparcos_hipIDs.dat",sep=""))[,1] + hip.classes = read.table(paste(path,"hipparcos_classes.dat",sep=""))[[1]] + throwout = c("25473","26304","33165","34042","36750","39009","53461","57812","58907","75377","104029") +#old: "25473", "34042","36750","39009","57812","58907","104029") + hip.out = which(hip.classes=="ROAP" | hip.classes=="XB" | hip.classes=="SXPHE" | hip.ID %in% throwout) + + hip.classes = hip.classes[-hip.out] + hip.ID = hip.ID[-hip.out] + hip.features = read.table(paste(path,"hipparcos_features.dat",sep=""),header=TRUE)[-hip.out,] +# sortfeat = sort(names(hip.features),index.return=TRUE) +# hip.features = hip.features[,sortfeat$ix] +# fix doubly-labeled sources + hip.classes[hip.ID=="5267"] = "LBV" + hip.classes[hip.ID=="23428"] = "LBV" + hip.classes[hip.ID=="26403"] = "HAEBE" + hip.classes[hip.ID=="54413"] = "HAEBE" + hip.classes[hip.ID=="86624"] = "LBV" + hip.classes[hip.ID=="89956"] = "LBV" + hip.classes[hip.ID=="89963"] = "LBV" + ## new double-labels: + hip.classes[hip.ID=="27400"] = "DSCUT" + hip.classes[hip.ID=="30326"] = "MIRA" + hip.classes[hip.ID=="54283"] = "WR" + hip.classes[hip.ID=="84757"] = "WR" + hip.classes[hip.ID=="88856"] = "WR" + hip.classes[hip.ID=="99252"] = "DSCUT" + hip.classes[hip.ID=="109306"] = "LBOO" + hip.classes = class.debos(hip.classes) + +## OGLE + ID = read.table(paste(path,"R/IDs.dat",sep=""))[,1] + features = read.table(paste(path,"R/features.dat",sep=""),header=T) + features = as.data.frame(features[,-which(substr(names(features),1,4)=="sdss" | substr(names(features),1,3)=="ws_")]) # take out sdss & ws features + classes = (read.table(paste(path,"R/class.dat",sep=""),sep="\n"))[[1]] + ogle = which(classes=="W Ursae Majoris" | classes=="RR Lyrae, Double Mode" | classes == "Multiple Mode Cepheid" | classes == "Beta Lyrae" | classes == "Beta Persei") + ogl.features = features[ogle,] + ogl.classes = class.debos(classes[ogle]) + ogl.ID = ID[ogle] + + hip1.features=NULL + for(ii in 1:length(names(ogl.features))){ + ind = which(names(hip.features)==names(ogl.features)[ii]) + hip1.features = cbind(hip1.features,hip.features[,ind]) + } + colnames(hip1.features) = colnames(ogl.features) + hip.features = as.data.frame(hip1.features) + + # hip.features = hip.features[,which(names(hip.features) %in% names(ogl.features))] + + classes = factor(c(paste(hip.classes),paste(ogl.classes))) + features = rbind(hip.features,ogl.features) + names(features)[54:55]= c("QSO","non_QSO") + ID = c(hip.ID,ogl.ID) + return(list(features=features,classes=classes,ID=ID)) +} + +## fixes a confusion matrix w/ too few classes +fixconfmat = function(confmat,pred.names,y.names){ + p = dim(confmat)[2] + + # find which class names are missing + var.LO = setdiff(y.names,pred.names) + var.LOind = sort(which(y.names%in%var.LO)) + len.LO = length(var.LO) + if(len.LO>0){ + cm.tmp = confmat[1:(var.LOind[1]-1),] + for(ii in 1:len.LO){ + cm.tmp = rbind(cm.tmp,rep(0,p)) + + nadd = ifelse(is.na(var.LOind[ii+1]),p-var.LOind[ii],var.LOind[ii+1]-var.LOind[ii]-1) + if(nadd>0){ + cm.tmp = rbind(cm.tmp,confmat[(var.LOind[ii]-ii+1):(var.LOind[ii]-ii+nadd),]) + } + } + confmat = cm.tmp + row.names(confmat) = y.names + } + confmat = t(confmat) + return(confmat) +} + +# plots feature importance matrix (from pairwise CART) +plot.featImp = function(mat,sortLS = TRUE){ + n = dim(mat) + # put LS features first + featNames = row.names(mat) + if(sortLS){ + feat.LS = which(substr(featNames,1,4)=="freq") + mat = rbind(mat[feat.LS,],mat[-feat.LS,]) + featNames = c(featNames[feat.LS],featNames[-feat.LS]) + } + # plot + par(mar=c(11,7,.3,.5)) + image(mat[,n[2]:1],xlab="",xaxt="n",yaxt="n",cex.lab= 1,col=heat.colors(64)) + axis(2,labels=colnames(mat),at=seq(1,0,length.out=n[2]),las=2,cex.axis= 0.7) + axis(1,labels=row.names(mat),at=seq(0,1,length.out=n[1]),las=2,cex.axis= 0.75,padj=0.5,tick=TRUE) + if(sortLS){ + abline(v=(length(feat.LS)-.5)/(n[1]-1),lwd=3,col='darkblue') } + # abline(v=(length(feat.LS)-.5)/(n[1]-1),lwd=2,col=4) + # axis(1,labels=paste(rowSums(mat)),at=seq(0,1,length.out=n[2]),las=1,cex.axis=.5,tick=F,padj=-4) +# abline(v=seq(-1./((n[1]-1)*2),1-1./((n-1)*2),length.out= n[1])) +# abline(h=seq(-1./((n[2]-1)*2),1-1./((n-1)*2),length.out= n[2])) +} + +######## +### create class hierarchy from varstar classes +class.hier = function(class){ + hier = c('1 pv','.1 giant','a','b','c','.2 cepheid','d','e','f','.3 rrl','g','h','i','.4 other','j','k','l','m','n','o','2 ev','p','q','r','s','t','u','3 msv','v','w','x','y') + depth = c(1,2,rep(3,3),2,rep(3,3),2,rep(3,3),2,rep(3,6),1,rep(2,6),1,rep(2,4)) + node = which(substr(hier,1,1)==hier) + intern = which(substr(hier,1,1)!=hier) + top = which(depth==1) + class = factor(substr(paste(class),1,1)) + class.boo = matrix(0,length(class),length(hier)) + class.boo[cbind(1:length(class),node[as.numeric(class)])] = 1 + for(ii in 1:length(class)){ + class.boo[ii,max(intern[intern < node[as.numeric(class)[ii]]])] = 1 + class.boo[ii,max(top[top < node[as.numeric(class)[ii]]])] = 1 + } + colnames(class.boo) = hier + return(list(class=class.boo,depth=depth)) +} + + + + +# construct cost matrix for classifier +costMat = function(val=2){ + p = 25 + cost = matrix(0,p,p) + tax1 = c(rep("pv",15),rep("ev",6),rep("msv",4)) + tax2 = c(rep("giant",3),rep("ceph",3),rep("rrl",3),"ds","lb","bc","spb","gd","be","pvsg","cp","wr","tt","haebe","sd","ell","bp","bl","wum") + + + for(ii in 1:(p-1)){ + for(jj in (ii+1):p){ + if(tax1[ii]==tax1[jj]){ + if(tax2[ii]==tax2[jj]){ + cost[ii,jj] = cost[jj,ii] = .5 + } else { + cost[ii,jj] = cost[jj,ii] = 1 + } + } + else { + cost[ii,jj] = cost[jj,ii] = val + } + } + } + return(cost) +} + +# compute class weights using rescaling approach to cost matrix +costWeights = function(y,cost=costMat(2)){ + n = table(y) + eps = rowSums(cost) + w = (sum(n)*eps)/sum(n*eps) + return(w) +} + +# take ratios of frequencies & amplitudes +featureConvert = function(features){ + + freq1 = features[,"freq1_harmonics_freq_0"] + features[,"freq2_harmonics_freq_0"] = features[,"freq2_harmonics_freq_0"]/freq1 + features[,"freq3_harmonics_freq_0"] = features[,"freq3_harmonics_freq_0"]/freq1 + + amp1 = features[,"freq1_harmonics_amplitude_0"] + features[,"freq1_harmonics_amplitude_1"] = features[,"freq1_harmonics_amplitude_1"]/amp1 + features[,"freq1_harmonics_amplitude_2"] = features[,"freq1_harmonics_amplitude_2"]/amp1 + features[,"freq1_harmonics_amplitude_3"] = features[,"freq1_harmonics_amplitude_3"]/amp1 + features[,"freq2_harmonics_amplitude_0"] = features[,"freq2_harmonics_amplitude_0"]/amp1 + features[,"freq2_harmonics_amplitude_1"] = features[,"freq2_harmonics_amplitude_1"]/amp1 + features[,"freq2_harmonics_amplitude_2"] = features[,"freq2_harmonics_amplitude_2"]/amp1 + features[,"freq2_harmonics_amplitude_3"] = features[,"freq2_harmonics_amplitude_3"]/amp1 + features[,"freq3_harmonics_amplitude_0"] = features[,"freq3_harmonics_amplitude_0"]/amp1 + features[,"freq3_harmonics_amplitude_1"] = features[,"freq3_harmonics_amplitude_1"]/amp1 + features[,"freq3_harmonics_amplitude_2"] = features[,"freq3_harmonics_amplitude_2"]/amp1 + features[,"freq3_harmonics_amplitude_3"] = features[,"freq3_harmonics_amplitude_3"]/amp1 + + return(features) +} + + +# plot prob(correct) for each source, by class +probPlot = function(probMat,pred,classes,thresh = NULL){ + + n = length(classes) + y.names = levels(classes) + p = length(y.names) + probCor = probMat[cbind(1:n,as.numeric(classes))] + +# if(is.null(thresh)) { thresh = rep(1/p,p)} + + par(mar=c(8.75,4,.5,.5)) + # correct class vs. not + col.cor = ifelse(pred==classes,"#00800070",'red') + pch.cor = ifelse(pred==classes,19,4) + # second place + sortProb = apply(probMat,1,sort,decreasing=TRUE) + first = which(probCor==sortProb[1,]) + second = which(probCor==sortProb[2,]) + col.cor[second] = "#00008B70" + pch.cor[second] = 17 + lwd.cor = rep(1.5,n) + lwd.cor[c(first,second)] = 0 + + plot(as.numeric(classes)+runif(n,-.2,.2),probCor,xaxt='n',xlab="",ylab="Pr(Correct Class)",col=col.cor,pch=pch.cor,xlim = c(-.5,p),ylim=c(-.15,1),lwd=lwd.cor) + abline(v=0:length(y.names)+.5) + abline(h=c(0,1),lty=2) + axis(1,labels=y.names,at=1:length(y.names),las=2,cex.axis=.9,padj=0.5,tick=TRUE) + + first.class = tabulate(classes[first])/tabulate(classes) + if(length(tabulate(classes[first])) < length(tabulate(classes))){ + first.class[(length(tabulate(classes[first]))+1) : p] = 0 } + second.class = tabulate(classes[second])/tabulate(classes) + if(length(tabulate(classes[second])) < length(tabulate(classes))){ + second.class[(length(tabulate(classes[second]))+1) : p] = 0 } + none.class = rep(1,p) - first.class - second.class + for(jj in 1:p){ + text(jj,-.05,round(first.class[jj]*100,1),cex=.8,col='darkgreen') + text(jj,-.1,round(second.class[jj]*100,1),cex=.8,col='darkblue') + text(jj,-.15,round(none.class[jj]*100,1),cex=.8,col='red') + } + text(0.5,-.05,"Top (%)",pos=2) + text(0.5,-.1,"2nd (%)",pos=2) + text(0.5,-.15,"X (%)",pos=2) +} + +# plot prob(class) for each source / class +probPlot1 = function(probMat,pred,classes){ + + n = length(classes) + y.names = levels(classes) + m = length(y.names) + par(mar=c(8.75,4,.5,.5)) + plot(rep(1,n)+runif(n,-.2,.2),probMat[,1],xlim=c(1,m),xaxt='n',xlab="",ylab="Pr(Class)",col=ifelse(classes==y.names[1],'blue','grey'),pch=ifelse(classes==y.names[1],20,3),cex=.7,ylim=c(0,1)) + for(ii in 2:length(y.names)){ + points(rep(ii,n)+runif(n,-.2,.2),probMat[,ii],col=ifelse(classes==y.names[ii],'blue','grey'),pch=ifelse(classes==y.names[ii],20,3),cex=.7) + } + abline(v=0:length(y.names)+.5) + abline(h=c(0,1),lty=2) + axis(1,labels=y.names,at=1:length(y.names),las=2,cex.axis=.9,padj=0.5,tick=TRUE) + +} diff --git a/mltsp/TCP/Algorithms/xmlrpc_example.py b/mltsp/TCP/Algorithms/xmlrpc_example.py new file mode 100644 index 00000000..5b701adc --- /dev/null +++ b/mltsp/TCP/Algorithms/xmlrpc_example.py @@ -0,0 +1,57 @@ +#!/usr/bin/env python +""" +An example client / server of xmlrpc transport with python. + +You could have the server using one version of python and the client using another version of python (within reason : maybe not with Python 1.0 and Python3000...). + +To use in it's current simple form: +1) start server by having "if 1:" under __main__ + +2) start client by having "if 0:" under __main__ + +""" +from __future__ import print_function +import os, sys + +server_hostname = "192.168.1.25" +server_port = 23459 + +class Some_Class_We_Want_Remotely_Accessible: + """ Awesome Class which does awesome stuff. + """ + def __init__(self, important_parameter=123): + self.important_parameter = important_parameter + + def some_method(self, passed_value): + print('important_parameter=', self.important_parameter) + print('passed_value=', passed_value) + + +if __name__ == '__main__': + + if 1: + # server: + import SimpleXMLRPCServer + server = SimpleXMLRPCServer.SimpleXMLRPCServer( \ + (server_hostname, server_port), \ + allow_none=True) + server.register_instance( \ + Some_Class_We_Want_Remotely_Accessible(important_parameter=1)) + server.register_multicall_functions() + server.register_introspection_functions() + print('XMLRPC Server is starting at:', server_hostname, server_port) + server.serve_forever() + + else: + # client: + import xmlrpclib + server = xmlrpclib.ServerProxy("http://%s:%d" % \ + (server_hostname, server_port)) + try: + print(server.system.listMethods()) + except: + print('EXCEPT at server.system.listMethods() : Probably XMLRPC server is down!') + sys.exit() + print(server.system.methodHelp("some_method")) + #src_list = server.get_sources_for_radec(ra, dec, box_range) + src_list = server.some_method('hello') diff --git a/mltsp/TCP/Software/__init__.py b/mltsp/TCP/Software/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/mltsp/TCP/Software/__init__.py @@ -0,0 +1 @@ + diff --git a/mltsp/TCP/Software/citris33/__init__.py b/mltsp/TCP/Software/citris33/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/mltsp/TCP/Software/citris33/__init__.py @@ -0,0 +1 @@ + diff --git a/mltsp/TCP/Software/citris33/arff_generation_master.py b/mltsp/TCP/Software/citris33/arff_generation_master.py new file mode 100644 index 00000000..345dec1b --- /dev/null +++ b/mltsp/TCP/Software/citris33/arff_generation_master.py @@ -0,0 +1,2696 @@ +#!/usr/bin/env python +""" +Adapted from test_pairwise_on_citris33_ipython.py + +./arff_generation_master.py +scp -P 10322 ~/scratch/out.arff pteluser@lyra.berkeley.edu:/tmp/citris33_asas.arff + +""" +from __future__ import print_function +import sys, os +import cPickle +import time +import gzip +import copy +import glob +import matplotlib +matplotlib.use('agg') # just needed for lightcurve.py::lomb_code() PSD.png plotting without X11 + +from optparse import OptionParser + +sys.path.append(os.path.abspath(os.environ.get('TCP_DIR') + 'Software/ingest_tools')) + +sciclass_lookup = {'classid_shortname': {0: '_varstar_', + 1: 'GCVS', + 2: 'Eruptive', + 3: 'FU', + 4: 'GCAS', + 5: 'I', + 6: 'IA', + 7: 'IB', + 8: 'IN', + 9: 'INA', + 10: 'INB', + 11: 'INT', + 12: 'IN(YY)', + 13: 'IS', + 14: 'ISA', + 16: 'ISB', + 17: 'RCB', + 18: 'RS', + 19: 'SDOR', + 20: 'UV', + 21: 'UVN', + 22: 'WR', + 23: 'Pulsating', + 24: 'ACYG', + 25: 'BCEP', + 26: 'BCEPS', + 27: 'CEP', + 28: 'CEP(B)', + 29: 'CW', + 30: 'CWA', + 31: 'CWB', + 32: 'DCEP', + 33: 'DCEPS', + 34: 'DSCT', + 35: 'DSCTC', + 36: 'L', + 37: 'LB', + 38: 'LC', + 39: 'M', + 40: 'PVTEL', + 41: 'RR', + 42: 'RR(B)', + 43: 'RRAB', + 44: 'RRC', + 45: 'RV', + 46: 'RVA', + 47: 'RVB', + 48: 'SR', + 49: 'SRA', + 50: 'SRB', + 51: 'SRC', + 52: 'SRD', + 53: 'SXPHE', + 54: 'ZZ', + 55: 'ZZA', + 56: 'ZZB', + 57: 'Rotating', + 58: 'ACV', + 59: 'ACVO', + 60: 'BY', + 61: 'ELL', + 62: 'FKCOM', + 63: 'PSR', + 64: 'SXARI', + 65: 'Cataclysmic', + 66: 'N', + 67: 'NA', + 68: 'NB', + 69: 'NC', + 70: 'NL', + 71: 'NR', + 72: 'SN', + 73: 'SNI', + 74: 'SNII', + 75: 'UG', + 76: 'UGSS', + 77: 'UGSU', + 78: 'UGZ', + 79: 'ZAND', + 80: 'Eclipsing', + 82: 'E', + 83: 'EA', + 84: 'EB', + 85: 'EW', + 86: 'GS', + 87: 'PN', + 88: 'RS', + 89: 'WD', + 90: 'WR(1)', + 91: 'AR', + 92: 'D', + 93: 'DM', + 94: 'DS', + 95: 'DW', + 96: 'K', + 97: 'KE', + 98: 'KW', + 99: 'SD', + 100: 'SNIa', + 101: 'SNIb', + 102: 'SNIc', + 103: 'SNIIP', + 104: 'SNIIN', + 105: 'SNIIL', + 106: 'SNIa-sc', + 107: 'Nonstellar', + 109: 'GalNuclei', + 110: 'AGN', + 111: 'TDE', + 112: 'DrkMatterA', + 113: 'GRB', + 114: 'SHB', + 115: 'LSB', + 116: 'SGR', + 117: 'X', + 118: 'XB', + 119: 'XF', + 120: 'XI', + 121: 'XJ', + 122: 'XND', + 123: 'XNG', + 124: 'XP', + 125: 'XPR', + 126: 'XPRM', + 127: 'XRM', + 128: 'ZZO', + 129: 'NEW', + 130: 'AM', + 131: 'R', + 132: 'BE', + 133: 'EP', + 134: 'SRS', + 135: 'GDOR', + 136: 'RPHS', + 137: 'LPB', + 138: 'BLBOO', + 139: 'BL-Lac', + 140: 'RRcl', + 141: 'RRe', + 142: 'SNIa-pec', + 143: 'SNIc-pec', + 145: 'ML', + 149: 'UXUma', + 150: 'Polars', + 151: 'DQ', + 152: 'EWa', + 153: 'EWs', + 154: 'vs', + 157: 'cv', + 158: 'nov', + 159: 'cn', + 160: 'n-l', + 161: 'sw', + 162: 'vy', + 163: 'ux', + 164: 'amcvn', + 165: 'p', + 166: 'am', + 167: 'dqh', + 168: 'ug', + 169: 'su', + 170: 'er', + 171: 'wz', + 172: 'zc', + 173: 'ssc', + 174: 'rn', + 175: 'sv', + 176: 'grb', + 177: 'lgrb', + 178: 'sgrb', + 179: 'srgrb', + 180: 'sne', + 181: 'cc', + 182: 'tia', + 183: 'tib', + 184: 'tic', + 185: 'tsnii', + 186: 'pi', + 187: 'tsni', + 188: 'ev', + 189: 'rscvn', + 190: 'uv', + 191: 'sdorad', + 192: 'wr', + 193: 'gc', + 194: 'fuor', + 195: 'ov', + 196: 'rcb', + 197: 'haebe', + 198: 'be', + 199: 'shs', + 200: 'tt', + 201: 'ttc', + 202: 'ttw', + 203: 'puls', + 204: 'gd', + 205: 'sx', + 206: 'rr-lyr', + 207: 'ac', + 208: 'mira', + 209: 'pwd', + 211: 'ds', + 212: 'pvt', + 213: 'bc', + 214: 'sreg', + 215: 'rv', + 216: 'piic', + 217: 'c', + 218: 'rr-ab', + 219: 'rr-c', + 220: 'rr-d', + 221: 'rr-e', + 222: 'rr-cl', + 223: 'zz', + 224: 'zzh', + 225: 'zzhe', + 226: 'zzheii', + 227: 'gw', + 228: 'sr-a', + 229: 'sr-b', + 230: 'sr-c', + 231: 'sr-d', + 232: 'rvc', + 233: 'rvv', + 234: 'bl', + 235: 'wv', + 236: 'ca', + 237: 'cm', + 238: 'dc', + 239: 'sdc', + 240: 'rot', + 241: 'sxari', + 242: 'aii', + 243: 'fk', + 244: 'plsr', + 245: 'by', + 246: 'ell', + 247: 'msv', + 248: 'b', + 249: 'iii', + 250: 'xrb', + 251: 'bly', + 252: 'wu', + 253: 'alg', + 254: 'psys', + 255: 'SSO', + 256: 'BLZ', + 257: 'OVV', + 258: 'dsm', + 259: 'lamb', + 260: 'xrbin', + 261: 'lboo', + 262: 'qso', + 263: 'seyf', + 265: 'fsrq', + 266: 'iin', + 267: 'hae', + 268: 'tiapec', + 269: 'tiasc', + 270: 'iil', + 271: 'iip', + 272: 'iib', + 273: 'ticpec', + 274: 'maser', + 275: 'moving', + 276: 'ast', + 277: 'comet', + 278: 'hpm', + 279: 'eclipsing', + 280: 'k', + 281: 'd', + 282: 'sd', + 283: 'unclass', + 284: 'pvsg', + 285: 'cp', + 286: 'spb', + 287: 'sdbv', + 1000000: 'Chemically Peculiar Stars'}, + 'longname_shortname': {'AM Canum Venaticorum': 'amcvn', + 'AM Her': 'AM', + 'AM Herculis (True Polar)': 'am', + 'Active Galactic Nuclei': 'AGN', + 'Algol (Beta Persei)': 'alg', + 'Alpha Cygni': 'ac', + 'Alpha2 CVn - Rapily Oscillating': 'ACVO', + 'Alpha2 Canum Venaticorum': 'aii', + 'Anomalous Cepheids': 'BLBOO', + 'Anomolous Cepheid': 'ca', + 'Asteroid': 'ast', + 'BL Lac': 'BL-Lac', + 'BY Draconis': 'by', + 'Be Star': 'be', + 'Be star': 'BE', + 'Beta Cephei': 'bc', + 'Beta Cephei - Short Period': 'BCEPS', + 'Beta Lyrae': 'bly', + 'Binary': 'b', + 'Blazar': 'BLZ', + 'Cataclysmic (Explosive and Novalike) Variable Stars': 'Cataclysmic', + 'Cataclysmic Variable': 'cv', + 'Cepheid Variable': 'c', + 'Cepheids': 'CEP', + 'Cepheids - Multiple Modes': 'CEP(B)', + 'Chemically Peculiar Stars': 'CP', + 'Classical Cepheid': 'dc', + 'Classical Novae': 'cn', + 'Classical T Tauri': 'ttc', + 'Close Binary Eclipsing Systems': 'eclipsing', + 'Close Binary with Reflection': 'R', + 'Comet': 'comet', + 'Contact Systems': 'k', + 'Contact Systems - Early (O-A)': 'KE', + 'Contact Systems - W Ursa Majoris': 'KW', + 'Core Collapse Supernovae': 'cc', + 'DQ Herculis (Intermdiate Polars)': 'dqh', + 'DQ Herculis Variable (Intermediate Polars)': 'DQ', + 'Dark Matter Anniliation Event': 'DrkMatterA', + 'Delta Cep': 'DCEP', + 'Delta Cep - Symmetrical': 'DCEPS', + 'Delta Scuti': 'ds', + 'Delta Scuti - Low Amplitude': 'DSCTC', + 'Delta Scuti - Multiple Modes': 'dsm', + 'Detached': 'd', + 'Detached - AR Lacertae': 'AR', + 'Detached - Main Sequence': 'DM', + 'Detached - With Subgiant': 'DS', + 'ER Ursae Majoris': 'er', + 'Eclipsed by Planets': 'EP', + 'Eclipsing Binary Systems': 'E', + 'Ellipsoidal': 'ell', + 'Eruptive Variable': 'ev', + 'Eruptive Variable Stars': 'Eruptive', + 'Eruptive Wolf-Rayet': 'WR', + 'FK Comae Berenices': 'fk', + 'FU Orionis': 'fuor', + 'Fast Novae': 'NA', + 'Flaring Orion Variables': 'UVN', + 'Flat Spectrum Radio Quasar': 'fsrq', + 'Fluctuating X-Ray Systems': 'XF', + 'GW Virginis': 'gw', + 'Galaxy Nuclei ': 'GalNuclei', + 'Gamma Cas': 'GCAS', + 'Gamma Cassiopeiae': 'gc', + 'Gamma Doradus': 'gd', + 'Gamma Ray Burst': 'grb', + 'Gamma-ray Bursts': 'GRB', + 'Herbig AE': 'hae', + 'Herbig AE/BE Star': 'haebe', + 'High Proper Motion Star': 'hpm', + 'Irregular': 'I', + 'Irregular Early O-A': 'IA', + 'Irregular Intermediate F-M': 'IB', + 'Irregular Supergiants': 'LC', + 'Lambda Bootis Variable': 'lboo', + 'Lambda Eridani': 'lamb', + 'Long GRB': 'lgrb', + 'Long Gamma-ray Burst': 'LSB', + 'Long Period (W Virginis)': 'wv', + 'Long Period B': 'LPB', + 'Maser': 'maser', + 'Microlensing Event': 'ML', + 'Mira': 'mira', + 'Moving Source': 'moving', + 'Multiple Mode Cepheid': 'cm', + 'Multiple Star Variables': 'msv', + 'New Variability Types': 'NEW', + 'Novae': 'nov', + 'Novalike': 'n-l', + 'Novalike Variables': 'NL', + 'Optically Variable Pulsars': 'PSR', + 'Optically Violent Variable Quasar (OVV)': 'OVV', + 'Orion': 'IN', + 'Orion Early Types (B-A or Ae)': 'INA', + 'Orion Intermediate Types (F-M or Fe-Me)': 'INB', + 'Orion T Tauri': 'INT', + 'Orion Variable': 'ov', + 'Orion with Absorption': 'IN(YY)', + 'PV Telescopii': 'pvt', + 'Pair Instability Supernovae': 'pi', + 'Peculiar Type Ia SN': 'tiapec', + 'Peculiar Type Ia Supernovae': 'SNIa-pec', + 'Peculiar Type Ic Supernovae': 'SNIc-pec', + 'Periodically variable supergiants': 'pvsg', + 'Polars': 'p', + 'Population II Cepheid': 'piic', + 'Pulsar': 'plsr', + 'Pulsating Variable': 'puls', + 'Pulsating Variable Stars': 'Pulsating', + 'Pulsating White Dwarf': 'pwd', + 'Pulsating subdwarf B-stars': 'sdbv', + 'QSO': 'qso', + 'R Coronae Borealis': 'rcb', + 'RR Lyrae': 'rr-lyr', + 'RR Lyrae - Asymmetric': 'RRAB', + 'RR Lyrae - Dual Mode': 'RR(B)', + 'RR Lyrae - Near Symmetric': 'RRC', + 'RR Lyrae -- Closely Spaced Modes': 'RRcl', + 'RR Lyrae -- Second Overtone Pulsations': 'RRe', + 'RR Lyrae, Closely Spaced Modes': 'rr-cl', + 'RR Lyrae, Double Mode': 'rr-d', + 'RR Lyrae, First Overtone': 'rr-c', + 'RR Lyrae, Fundamental Mode': 'rr-ab', + 'RR Lyrae, Second Overtone': 'rr-e', + 'RS Canum Venaticorum': 'rscvn', + 'RV Tauri': 'rv', + 'RV Tauri - Constant Mean Magnitude': 'RVA', + 'RV Tauri - Variable Mean Magnitude': 'RVB', + 'RV Tauri, Constant Mean Brightness': 'rvc', + 'RV Tauri, Variable Mean Brightness': 'rvv', + 'Rapid Irregular': 'IS', + 'Rapid Irregular Early Types (B-A or Ae)': 'ISA', + 'Rapid Irregular Intermediate to Late (F-M and Fe-Me)': 'ISB', + 'Recurrent Novae': 'rn', + 'Rotating Ellipsoidal': 'ELL', + 'Rotating Variable': 'rot', + 'Rotating Variable Stars': 'Rotating', + 'S Doradus': 'sdorad', + 'SRa (Z Aquarii)': 'sr-a', + 'SRb': 'sr-b', + 'SRc': 'sr-c', + 'SRd': 'sr-d', + 'SS Cygni': 'ssc', + 'SU Ursae Majoris': 'su', + 'SW Sextantis': 'sw', + 'SX Arietis': 'sxari', + 'SX Phoenicis': 'sx', + 'SX Phoenicis - Pulsating Subdwarfs': 'SXPHE', + 'Semidetached': 'sd', + 'Semiregular': 'SR', + 'Semiregular - Persistent Periodicity': 'SRA', + 'Semiregular - Poorly Defined Periodicity': 'SRB', + 'Semiregular F, G, or K': 'SRD', + 'Semiregular Pulsating Red Giants': 'SRS', + 'Semiregular Pulsating Variable': 'sreg', + 'Semiregular Supergiants': 'SRC', + 'Seyfert': 'seyf', + 'Shell Star': 'shs', + 'Short GRB': 'sgrb', + 'Short Gamma-ray Burst': 'SHB', + 'Short period (BL Herculis)': 'bl', + 'Slow Irregular': 'L', + 'Slow Irregular - Late Spectral Type (K, M, C, S)': 'LB', + 'Slow Novae': 'NB', + 'Slowly Pulsating B-stars': 'spb', + 'Soft Gamma Ray Repeater': 'srgrb', + 'Soft Gamma-ray Repeater': 'SGR', + 'Solar System Object': 'SSO', + 'Super-chandra Ia supernova': 'SNIa-sc', + 'Super-chandra Type Ia SN': 'tiasc', + 'Supernovae': 'sne', + 'Symbiotic Variable': 'sv', + 'Symbiotic Variables': 'ZAND', + 'Symmetrical': 'sdc', + 'Systems with Planetary Nebulae': 'PN', + 'Systems with Planets': 'psys', + 'Systems with Supergiant(s)': 'GS', + 'Systems with White Dwarfs': 'WD', + 'Systems with Wolf-Rayet Stars': 'WR(1)', + 'T Tauri': 'tt', + 'Three or More Stars': 'iii', + 'Tidal Disruption Event': 'TDE', + 'Type I Supernovae': 'tsni', + 'Type II L supernova': 'iil', + 'Type II N Supernova': 'iin', + 'Type II P supernova': 'iip', + 'Type II Supernovae': 'tsnii', + 'Type II b Supernova': 'iib', + 'Type II-L': 'SNIIL', + 'Type IIN': 'SNIIN', + 'Type IIP': 'SNIIP', + 'Type Ia': 'SNIa', + 'Type Ia Supernovae': 'tia', + 'Type Ib': 'SNIb', + 'Type Ib Supernovae': 'tib', + 'Type Ic': 'SNIc', + 'Type Ic Supernovae': 'tic', + 'Type Ic peculiar': 'ticpec', + 'U Geminorum': 'ug', + 'UV Ceti': 'UV', + 'UV Ceti Variable': 'uv', + 'UX Uma': 'UXUma', + 'UX Ursae Majoris': 'ux', + 'Unclassified': 'unclass', + 'VY Scl': 'vy', + 'Variable Sources (Non-stellar)': 'Nonstellar', + 'Variable Stars': 'GCVS', + 'Variable Stars [Alt]': 'vs', + 'Very Rapidly Pulsating Hot (subdwarf B)': 'RPHS', + 'Very Slow Novae': 'NC', + 'W Ursa Majoris': 'DW', + 'W Ursae Majoris': 'wu', + 'W Ursae Majoris - W UMa': 'EW', + 'W Ursae Majoris- a': 'EWa', + 'W Ursae Majoris- s': 'EWs', + 'W Virginis': 'CW', + 'W Virginis - Long Period': 'CWA', + 'W Virigins - Short Period': 'CWB', + 'WZ Sagittae': 'wz', + 'Weak-lined T Tauri': 'ttw', + 'Wolf-Rayet': 'wr', + 'X Ray Binary': 'xrbin', + 'X Ray Burster': 'xrb', + 'X-Ray Binaries with Jets': 'XJ', + 'X-Ray Bursters': 'XB', + 'X-Ray Pulsar': 'XP', + 'X-Ray Pulsar with late-type dwarf': 'XPRM', + 'X-Ray Pulsar, with Reflection': 'XPR', + 'X-Ray Sources, Optically Variable': 'X', + 'X-Ray with late-type dwarf, un-observed pulsar': 'XRM', + 'X-Ray, Novalike': 'XND', + 'X-Ray, Novalike with Early Type supergiant or giant': 'XNG', + 'X-ray Irregulars': 'XI', + 'Z Camelopardalis': 'zc', + 'ZZ Ceti': 'zz', + 'ZZ Ceti - Only H Absorption': 'ZZA', + 'ZZ Ceti - Only He Absorption': 'ZZB', + 'ZZ Ceti showing HeII': 'ZZO', + 'ZZ Ceti, H Absorption Only': 'zzh', + 'ZZ Ceti, He Absorption Only': 'zzhe', + 'ZZ Ceti, With He-II': 'zzheii', + '_varstar_': '_varstar_'}, + 'shortname_isactive': {'ACVO': 'Yes', + 'AGN': 'Yes', + 'AR': 'Yes', + 'BCEPS': 'Yes', + 'BL-Lac': 'Yes', + 'BLZ': 'Yes', + 'CP': 'No', + 'CW': 'Yes', + 'CWA': 'Yes', + 'CWB': 'Yes', + 'D': 'Yes', + 'DCEP': 'Yes', + 'DCEPS': 'Yes', + 'DM': 'Yes', + 'DS': 'Yes', + 'DSCTC': 'Yes', + 'DrkMatterA': 'Yes', + 'E': 'Yes', + 'ELL': 'Yes', + 'EP': 'Yes', + 'EWa': 'Yes', + 'EWs': 'Yes', + 'Eclipsing': 'Yes', + 'GS': 'Yes', + 'GalNuclei': 'Yes', + 'I': 'Yes', + 'IA': 'Yes', + 'IB': 'Yes', + 'IN(YY)': 'Yes', + 'INA': 'Yes', + 'INB': 'Yes', + 'IS': 'Yes', + 'ISA': 'Yes', + 'ISB': 'Yes', + 'K': 'Yes', + 'KE': 'Yes', + 'KW': 'Yes', + 'L': 'Yes', + 'LB': 'Yes', + 'LC': 'Yes', + 'LPB': 'Yes', + 'ML': 'Yes', + 'NA': 'Yes', + 'NB': 'Yes', + 'NC': 'Yes', + 'NEW': 'Yes', + 'Nonstellar': 'Yes', + 'OVV': 'Yes', + 'PN': 'Yes', + 'PSR': 'Yes', + 'R': 'Yes', + 'RPHS': 'Yes', + 'RR(B)': 'Yes', + 'RRAB': 'Yes', + 'RRC': 'Yes', + 'SD': 'Yes', + 'SNIc-pec': 'Yes', + 'SRA': 'Yes', + 'SRB': 'Yes', + 'SRC': 'Yes', + 'SRD': 'Yes', + 'SRS': 'Yes', + 'SSO': 'Yes', + 'TDE': 'Yes', + 'UVN': 'Yes', + 'WD': 'Yes', + 'WR(1)': 'Yes', + 'X': 'Yes', + 'XF': 'Yes', + 'XI': 'Yes', + 'XJ': 'Yes', + 'XND': 'Yes', + 'XNG': 'Yes', + 'XP': 'Yes', + 'XPR': 'Yes', + 'XPRM': 'Yes', + 'XRM': 'Yes', + '_varstar_': 'No', + 'ac': 'Yes', + 'aii': 'Yes', + 'alg': 'Yes', + 'am': 'Yes', + 'amcvn': 'Yes', + 'ast': 'Yes', + 'b': 'Yes', + 'bc': 'Yes', + 'be': 'Yes', + 'bl': 'Yes', + 'bly': 'Yes', + 'by': 'Yes', + 'c': 'Yes', + 'ca': 'Yes', + 'cc': 'Yes', + 'cm': 'Yes', + 'cn': 'Yes', + 'comet': 'Yes', + 'cp': 'Yes', + 'cv': 'Yes', + 'd': 'Yes', + 'dc': 'Yes', + 'dqh': 'Yes', + 'ds': 'Yes', + 'dsm': 'Yes', + 'eclipsing': 'Yes', + 'ell': 'Yes', + 'er': 'Yes', + 'ev': 'Yes', + 'fk': 'Yes', + 'fsrq': 'Yes', + 'fuor': 'Yes', + 'gc': 'Yes', + 'gd': 'Yes', + 'grb': 'Yes', + 'gw': 'Yes', + 'hae': 'Yes', + 'haebe': 'Yes', + 'hpm': 'Yes', + 'iib': 'Yes', + 'iii': 'Yes', + 'iil': 'Yes', + 'iin': 'Yes', + 'iip': 'Yes', + 'k': 'Yes', + 'lamb': 'Yes', + 'lboo': 'Yes', + 'lgrb': 'Yes', + 'maser': 'Yes', + 'mira': 'Yes', + 'moving': 'Yes', + 'msv': 'Yes', + 'n-l': 'Yes', + 'nov': 'Yes', + 'ov': 'Yes', + 'p': 'Yes', + 'pi': 'Yes', + 'piic': 'Yes', + 'plsr': 'Yes', + 'psys': 'Yes', + 'puls': 'Yes', + 'pvsg': 'Yes', + 'pvt': 'Yes', + 'pwd': 'Yes', + 'qso': 'Yes', + 'rcb': 'Yes', + 'rn': 'Yes', + 'rot': 'Yes', + 'rr-ab': 'Yes', + 'rr-c': 'Yes', + 'rr-cl': 'Yes', + 'rr-d': 'Yes', + 'rr-e': 'Yes', + 'rr-lyr': 'Yes', + 'rscvn': 'Yes', + 'rv': 'Yes', + 'rvc': 'Yes', + 'rvv': 'Yes', + 'sd': 'Yes', + 'sdbv': 'Yes', + 'sdc': 'Yes', + 'sdorad': 'Yes', + 'seyf': 'Yes', + 'sgrb': 'Yes', + 'shs': 'Yes', + 'sne': 'Yes', + 'spb': 'Yes', + 'sr-a': 'Yes', + 'sr-b': 'Yes', + 'sr-c': 'Yes', + 'sr-d': 'Yes', + 'sreg': 'Yes', + 'srgrb': 'Yes', + 'ssc': 'Yes', + 'su': 'Yes', + 'sv': 'Yes', + 'sw': 'Yes', + 'sx': 'Yes', + 'sxari': 'Yes', + 'tia': 'Yes', + 'tiapec': 'Yes', + 'tiasc': 'Yes', + 'tib': 'Yes', + 'tic': 'Yes', + 'ticpec': 'Yes', + 'tsni': 'Yes', + 'tsnii': 'Yes', + 'tt': 'Yes', + 'ttc': 'Yes', + 'ttw': 'Yes', + 'ug': 'Yes', + 'unclass': 'Yes', + 'uv': 'Yes', + 'ux': 'Yes', + 'vs': 'Yes', + 'vy': 'Yes', + 'wr': 'Yes', + 'wu': 'Yes', + 'wv': 'Yes', + 'wz': 'Yes', + 'xrb': 'Yes', + 'xrbin': 'Yes', + 'zc': 'Yes', + 'zz': 'Yes', + 'zzh': 'Yes', + 'zzhe': 'Yes', + 'zzheii': 'Yes'}, + 'shortname_ispublic': {'ACVO': 'No', + 'AGN': 'Yes', + 'AR': 'No', + 'BCEPS': 'No', + 'BL-Lac': 'Yes', + 'BLZ': 'Yes', + 'CP': 'No', + 'CW': 'No', + 'CWA': 'No', + 'CWB': 'No', + 'D': 'No', + 'DCEP': 'No', + 'DCEPS': 'No', + 'DM': 'No', + 'DS': 'No', + 'DSCTC': 'No', + 'DrkMatterA': 'Yes', + 'E': 'No', + 'ELL': 'No', + 'EP': 'No', + 'EWa': 'No', + 'EWs': 'No', + 'Eclipsing': 'No', + 'GS': 'No', + 'GalNuclei': 'Yes', + 'I': 'No', + 'IA': 'No', + 'IB': 'No', + 'IN(YY)': 'No', + 'INA': 'No', + 'INB': 'No', + 'IS': 'No', + 'ISA': 'No', + 'ISB': 'No', + 'K': 'No', + 'KE': 'No', + 'KW': 'No', + 'L': 'No', + 'LB': 'No', + 'LC': 'No', + 'LPB': 'No', + 'ML': 'Yes', + 'NA': 'No', + 'NB': 'No', + 'NC': 'No', + 'NEW': 'No', + 'Nonstellar': 'Yes', + 'OVV': 'Yes', + 'PN': 'No', + 'PSR': 'No', + 'R': 'No', + 'RPHS': 'No', + 'RR(B)': 'No', + 'RRAB': 'No', + 'RRC': 'No', + 'SD': 'No', + 'SNIc-pec': 'No', + 'SRA': 'No', + 'SRB': 'No', + 'SRC': 'No', + 'SRD': 'No', + 'SRS': 'No', + 'SSO': 'Yes', + 'TDE': 'Yes', + 'UVN': 'No', + 'WD': 'No', + 'WR(1)': 'No', + 'X': 'No', + 'XF': 'No', + 'XI': 'No', + 'XJ': 'No', + 'XND': 'No', + 'XNG': 'No', + 'XP': 'No', + 'XPR': 'No', + 'XPRM': 'No', + 'XRM': 'No', + '_varstar_': 'No', + 'ac': 'Yes', + 'aii': 'Yes', + 'alg': 'Yes', + 'am': 'Yes', + 'amcvn': 'Yes', + 'ast': 'Yes', + 'b': 'Yes', + 'bc': 'Yes', + 'be': 'Yes', + 'bl': 'Yes', + 'bly': 'Yes', + 'by': 'Yes', + 'c': 'Yes', + 'ca': 'Yes', + 'cc': 'Yes', + 'cm': 'Yes', + 'cn': 'Yes', + 'comet': 'Yes', + 'cp': 'Yes', + 'cv': 'Yes', + 'd': 'Yes', + 'dc': 'Yes', + 'dqh': 'Yes', + 'ds': 'Yes', + 'dsm': 'Yes', + 'eclipsing': 'No', + 'ell': 'Yes', + 'er': 'Yes', + 'ev': 'Yes', + 'fk': 'Yes', + 'fsrq': 'Yes', + 'fuor': 'Yes', + 'gc': 'Yes', + 'gd': 'Yes', + 'grb': 'Yes', + 'gw': 'Yes', + 'hae': 'Yes', + 'haebe': 'Yes', + 'hpm': 'Yes', + 'iib': 'Yes', + 'iii': 'Yes', + 'iil': 'Yes', + 'iin': 'Yes', + 'iip': 'Yes', + 'k': 'No', + 'lamb': 'Yes', + 'lboo': 'Yes', + 'lgrb': 'Yes', + 'maser': 'Yes', + 'mira': 'Yes', + 'moving': 'Yes', + 'msv': 'Yes', + 'n-l': 'Yes', + 'nov': 'Yes', + 'ov': 'Yes', + 'p': 'Yes', + 'pi': 'Yes', + 'piic': 'Yes', + 'plsr': 'Yes', + 'psys': 'Yes', + 'puls': 'Yes', + 'pvsg': 'Yes', + 'pvt': 'Yes', + 'pwd': 'Yes', + 'qso': 'Yes', + 'rcb': 'Yes', + 'rn': 'Yes', + 'rot': 'Yes', + 'rr-ab': 'Yes', + 'rr-c': 'Yes', + 'rr-cl': 'Yes', + 'rr-d': 'Yes', + 'rr-e': 'Yes', + 'rr-lyr': 'Yes', + 'rscvn': 'Yes', + 'rv': 'Yes', + 'rvc': 'Yes', + 'rvv': 'Yes', + 'sd': 'No', + 'sdbv': 'Yes', + 'sdc': 'Yes', + 'sdorad': 'Yes', + 'seyf': 'Yes', + 'sgrb': 'Yes', + 'shs': 'Yes', + 'sne': 'Yes', + 'spb': 'Yes', + 'sr-a': 'Yes', + 'sr-b': 'Yes', + 'sr-c': 'Yes', + 'sr-d': 'Yes', + 'sreg': 'Yes', + 'srgrb': 'Yes', + 'ssc': 'Yes', + 'su': 'Yes', + 'sv': 'Yes', + 'sw': 'Yes', + 'sx': 'Yes', + 'sxari': 'Yes', + 'tia': 'Yes', + 'tiapec': 'Yes', + 'tiasc': 'Yes', + 'tib': 'Yes', + 'tic': 'Yes', + 'ticpec': 'Yes', + 'tsni': 'Yes', + 'tsnii': 'Yes', + 'tt': 'Yes', + 'ttc': 'Yes', + 'ttw': 'Yes', + 'ug': 'Yes', + 'unclass': 'Yes', + 'uv': 'Yes', + 'ux': 'Yes', + 'vs': 'Yes', + 'vy': 'Yes', + 'wr': 'Yes', + 'wu': 'Yes', + 'wv': 'Yes', + 'wz': 'Yes', + 'xrb': 'Yes', + 'xrbin': 'Yes', + 'zc': 'Yes', + 'zz': 'Yes', + 'zzh': 'Yes', + 'zzhe': 'Yes', + 'zzheii': 'Yes'}, + 'shortname_longname': {'ACVO': 'Alpha2 CVn - Rapily Oscillating', + 'AGN': 'Active Galactic Nuclei', + 'AR': 'Detached - AR Lacertae', + 'BCEPS': 'Beta Cephei - Short Period', + 'BL-Lac': 'BL Lac', + 'BLZ': 'Blazar', + 'CP': 'Chemically Peculiar Stars', + 'CW': 'W Virginis', + 'CWA': 'W Virginis - Long Period', + 'CWB': 'W Virigins - Short Period', + 'D': 'Detached', + 'DCEP': 'Delta Cep', + 'DCEPS': 'Delta Cep - Symmetrical', + 'DM': 'Detached - Main Sequence', + 'DS': 'Detached - With Subgiant', + 'DSCTC': 'Delta Scuti - Low Amplitude', + 'DrkMatterA': 'Dark Matter Anniliation Event', + 'E': 'Eclipsing Binary Systems', + 'ELL': 'Rotating Ellipsoidal', + 'EP': 'Eclipsed by Planets', + 'EWa': 'W Ursae Majoris- a', + 'EWs': 'W Ursae Majoris- s', + 'Eclipsing': 'Close Binary Eclipsing Systems', + 'GS': 'Systems with Supergiant(s)', + 'GalNuclei': 'Galaxy Nuclei ', + 'I': 'Irregular', + 'IA': 'Irregular Early O-A', + 'IB': 'Irregular Intermediate F-M', + 'IN(YY)': 'Orion with Absorption', + 'INA': 'Orion Early Types (B-A or Ae)', + 'INB': 'Orion Intermediate Types (F-M or Fe-Me)', + 'IS': 'Rapid Irregular', + 'ISA': 'Rapid Irregular Early Types (B-A or Ae)', + 'ISB': 'Rapid Irregular Intermediate to Late (F-M and Fe-Me)', + 'K': 'Contact Systems', + 'KE': 'Contact Systems - Early (O-A)', + 'KW': 'Contact Systems - W Ursa Majoris', + 'L': 'Slow Irregular', + 'LB': 'Slow Irregular - Late Spectral Type (K, M, C, S)', + 'LC': 'Irregular Supergiants', + 'LPB': 'Long Period B', + 'ML': 'Microlensing Event', + 'NA': 'Fast Novae', + 'NB': 'Slow Novae', + 'NC': 'Very Slow Novae', + 'NEW': 'New Variability Types', + 'Nonstellar': 'Variable Sources (Non-stellar)', + 'OVV': 'Optically Violent Variable Quasar (OVV)', + 'PN': 'Systems with Planetary Nebulae', + 'PSR': 'Optically Variable Pulsars', + 'R': 'Close Binary with Reflection', + 'RPHS': 'Very Rapidly Pulsating Hot (subdwarf B)', + 'RR(B)': 'RR Lyrae - Dual Mode', + 'RRAB': 'RR Lyrae - Asymmetric', + 'RRC': 'RR Lyrae - Near Symmetric', + 'SD': 'Semidetached', + 'SNIc-pec': 'Peculiar Type Ic Supernovae', + 'SRA': 'Semiregular - Persistent Periodicity', + 'SRB': 'Semiregular - Poorly Defined Periodicity', + 'SRC': 'Semiregular Supergiants', + 'SRD': 'Semiregular F, G, or K', + 'SRS': 'Semiregular Pulsating Red Giants', + 'SSO': 'Solar System Object', + 'TDE': 'Tidal Disruption Event', + 'UVN': 'Flaring Orion Variables', + 'WD': 'Systems with White Dwarfs', + 'WR(1)': 'Systems with Wolf-Rayet Stars', + 'X': 'X-Ray Sources, Optically Variable', + 'XF': 'Fluctuating X-Ray Systems', + 'XI': 'X-ray Irregulars', + 'XJ': 'X-Ray Binaries with Jets', + 'XND': 'X-Ray, Novalike', + 'XNG': 'X-Ray, Novalike with Early Type supergiant or giant', + 'XP': 'X-Ray Pulsar', + 'XPR': 'X-Ray Pulsar, with Reflection', + 'XPRM': 'X-Ray Pulsar with late-type dwarf', + 'XRM': 'X-Ray with late-type dwarf, un-observed pulsar', + '_varstar_': '_varstar_', + 'ac': 'Alpha Cygni', + 'aii': 'Alpha2 Canum Venaticorum', + 'alg': 'Algol (Beta Persei)', + 'am': 'AM Herculis (True Polar)', + 'amcvn': 'AM Canum Venaticorum', + 'ast': 'Asteroid', + 'b': 'Binary', + 'bc': 'Beta Cephei', + 'be': 'Be Star', + 'bl': 'Short period (BL Herculis)', + 'bly': 'Beta Lyrae', + 'by': 'BY Draconis', + 'c': 'Cepheid Variable', + 'ca': 'Anomolous Cepheid', + 'cc': 'Core Collapse Supernovae', + 'cm': 'Multiple Mode Cepheid', + 'cn': 'Classical Novae', + 'comet': 'Comet', + 'cp': 'Chemically Peculiar Stars', + 'cv': 'Cataclysmic Variable', + 'd': 'Detached', + 'dc': 'Classical Cepheid', + 'dqh': 'DQ Herculis (Intermdiate Polars)', + 'ds': 'Delta Scuti', + 'dsm': 'Delta Scuti - Multiple Modes', + 'eclipsing': 'Close Binary Eclipsing Systems', + 'ell': 'Ellipsoidal', + 'er': 'ER Ursae Majoris', + 'ev': 'Eruptive Variable', + 'fk': 'FK Comae Berenices', + 'fsrq': 'Flat Spectrum Radio Quasar', + 'fuor': 'FU Orionis', + 'gc': 'Gamma Cassiopeiae', + 'gd': 'Gamma Doradus', + 'grb': 'Gamma Ray Burst', + 'gw': 'GW Virginis', + 'hae': 'Herbig AE', + 'haebe': 'Herbig AE/BE Star', + 'hpm': 'High Proper Motion Star', + 'iib': 'Type II b Supernova', + 'iii': 'Three or More Stars', + 'iil': 'Type II L supernova', + 'iin': 'Type II N Supernova', + 'iip': 'Type II P supernova', + 'k': 'Contact Systems', + 'lamb': 'Lambda Eridani', + 'lboo': 'Lambda Bootis Variable', + 'lgrb': 'Long GRB', + 'maser': 'Maser', + 'mira': 'Mira', + 'moving': 'Moving Source', + 'msv': 'Multiple Star Variables', + 'n-l': 'Novalike', + 'nov': 'Novae', + 'ov': 'Orion Variable', + 'p': 'Polars', + 'pi': 'Pair Instability Supernovae', + 'piic': 'Population II Cepheid', + 'plsr': 'Pulsar', + 'psys': 'Systems with Planets', + 'puls': 'Pulsating Variable', + 'pvsg': 'Periodically variable supergiants', + 'pvt': 'PV Telescopii', + 'pwd': 'Pulsating White Dwarf', + 'qso': 'QSO', + 'rcb': 'R Coronae Borealis', + 'rn': 'Recurrent Novae', + 'rot': 'Rotating Variable', + 'rr-ab': 'RR Lyrae, Fundamental Mode', + 'rr-c': 'RR Lyrae, First Overtone', + 'rr-cl': 'RR Lyrae, Closely Spaced Modes', + 'rr-d': 'RR Lyrae, Double Mode', + 'rr-e': 'RR Lyrae, Second Overtone', + 'rr-lyr': 'RR Lyrae', + 'rscvn': 'RS Canum Venaticorum', + 'rv': 'RV Tauri', + 'rvc': 'RV Tauri, Constant Mean Brightness', + 'rvv': 'RV Tauri, Variable Mean Brightness', + 'sd': 'Semidetached', + 'sdbv': 'Pulsating subdwarf B-stars', + 'sdc': 'Symmetrical', + 'sdorad': 'S Doradus', + 'seyf': 'Seyfert', + 'sgrb': 'Short GRB', + 'shs': 'Shell Star', + 'sne': 'Supernovae', + 'spb': 'Slowly Pulsating B-stars', + 'sr-a': 'SRa (Z Aquarii)', + 'sr-b': 'SRb', + 'sr-c': 'SRc', + 'sr-d': 'SRd', + 'sreg': 'Semiregular Pulsating Variable', + 'srgrb': 'Soft Gamma Ray Repeater', + 'ssc': 'SS Cygni', + 'su': 'SU Ursae Majoris', + 'sv': 'Symbiotic Variable', + 'sw': 'SW Sextantis', + 'sx': 'SX Phoenicis', + 'sxari': 'SX Arietis', + 'tia': 'Type Ia Supernovae', + 'tiapec': 'Peculiar Type Ia SN', + 'tiasc': 'Super-chandra Type Ia SN', + 'tib': 'Type Ib Supernovae', + 'tic': 'Type Ic Supernovae', + 'ticpec': 'Type Ic peculiar', + 'tsni': 'Type I Supernovae', + 'tsnii': 'Type II Supernovae', + 'tt': 'T Tauri', + 'ttc': 'Classical T Tauri', + 'ttw': 'Weak-lined T Tauri', + 'ug': 'U Geminorum', + 'unclass': 'Unclassified', + 'uv': 'UV Ceti Variable', + 'ux': 'UX Ursae Majoris', + 'vs': 'Variable Stars [Alt]', + 'vy': 'VY Scl', + 'wr': 'Wolf-Rayet', + 'wu': 'W Ursae Majoris', + 'wv': 'Long Period (W Virginis)', + 'wz': 'WZ Sagittae', + 'xrb': 'X Ray Burster', + 'xrbin': 'X Ray Binary', + 'zc': 'Z Camelopardalis', + 'zz': 'ZZ Ceti', + 'zzh': 'ZZ Ceti, H Absorption Only', + 'zzhe': 'ZZ Ceti, He Absorption Only', + 'zzheii': 'ZZ Ceti, With He-II'}, + 'shortname_nsrcs': {'ACV': 0, + 'ACVO': 0, + 'ACYG': 0, + 'AGN': 57, + 'AM': 0, + 'AR': 0, + 'BCEP': 0, + 'BCEPS': 0, + 'BE': 0, + 'BL-Lac': 46, + 'BLBOO': 0, + 'BLZ': 24, + 'BY': 0, + 'CEP': 5, + 'CEP(B)': 0, + 'CP': 0, + 'CW': 0, + 'CWA': 0, + 'CWB': 0, + 'Cataclysmic': 0, + 'Chemically Peculiar Stars': 0, + 'D': 2, + 'DCEP': 0, + 'DCEPS': 0, + 'DM': 0, + 'DQ': 0, + 'DS': 0, + 'DSCT': 149, + 'DSCTC': 0, + 'DW': 0, + 'DrkMatterA': 0, + 'E': 3, + 'EA': 260, + 'EB': 67, + 'ELL': 0, + 'EP': 0, + 'EW': 891, + 'EWa': 0, + 'EWs': 0, + 'Eclipsing': 0, + 'Eruptive': 0, + 'FKCOM': 0, + 'FU': 0, + 'GCAS': 0, + 'GCVS': 712, + 'GDOR': 15, + 'GRB': 0, + 'GS': 0, + 'GalNuclei': 0, + 'I': 0, + 'IA': 0, + 'IB': 0, + 'IN': 0, + 'IN(YY)': 0, + 'INA': 0, + 'INB': 0, + 'INT': 0, + 'IS': 0, + 'ISA': 0, + 'ISB': 0, + 'K': 0, + 'KE': 0, + 'KW': 0, + 'L': 0, + 'LB': 0, + 'LC': 1, + 'LPB': 1, + 'LSB': 0, + 'M': 11, + 'ML': 658, + 'N': 1, + 'NA': 0, + 'NB': 0, + 'NC': 0, + 'NEW': 0, + 'NL': 3, + 'NR': 0, + 'Nonstellar': 0, + 'OVV': 0, + 'PN': 0, + 'PSR': 0, + 'PVTEL': 0, + 'Polars': 0, + 'Pulsating': 1, + 'R': 0, + 'RCB': 0, + 'RPHS': 0, + 'RR': 9, + 'RR(B)': 0, + 'RRAB': 31, + 'RRC': 15, + 'RRcl': 0, + 'RRe': 0, + 'RS': 0, + 'RV': 0, + 'RVA': 0, + 'RVB': 0, + 'Rotating': 0, + 'SD': 0, + 'SDOR': 0, + 'SGR': 0, + 'SHB': 0, + 'SN': 0, + 'SNI': 0, + 'SNII': 0, + 'SNIIL': 0, + 'SNIIN': 1, + 'SNIIP': 0, + 'SNIa': 0, + 'SNIa-pec': 0, + 'SNIa-sc': 0, + 'SNIb': 0, + 'SNIc': 0, + 'SNIc-pec': 0, + 'SR': 0, + 'SRA': 0, + 'SRB': 0, + 'SRC': 0, + 'SRD': 0, + 'SRS': 14, + 'SSO': 0, + 'SXARI': 0, + 'SXPHE': 7, + 'TDE': 0, + 'UG': 3, + 'UGSS': 0, + 'UGSU': 0, + 'UGZ': 1, + 'UV': 0, + 'UVN': 0, + 'UXUma': 0, + 'WD': 0, + 'WR': 173, + 'WR(1)': 0, + 'X': 0, + 'XB': 0, + 'XF': 0, + 'XI': 0, + 'XJ': 0, + 'XND': 0, + 'XNG': 0, + 'XP': 0, + 'XPR': 0, + 'XPRM': 0, + 'XRM': 0, + 'ZAND': 0, + 'ZZ': 0, + 'ZZA': 0, + 'ZZB': 0, + 'ZZO': 0, + '_varstar_': 0, + 'ac': 0, + 'aii': 81, + 'alg': 732, + 'am': 5, + 'amcvn': 0, + 'ast': 93, + 'b': 53, + 'bc': 84, + 'be': 47, + 'bl': 14, + 'bly': 403, + 'by': 0, + 'c': 329, + 'ca': 7, + 'cc': 58, + 'cm': 202, + 'cn': 1, + 'comet': 3, + 'cp': 49, + 'cv': 193, + 'd': 2270, + 'dc': 865, + 'dqh': 5, + 'ds': 845, + 'dsm': 1, + 'eclipsing': 2934, + 'ell': 17, + 'er': 0, + 'ev': 0, + 'fk': 1, + 'fsrq': 3, + 'fuor': 4, + 'gc': 0, + 'gd': 73, + 'grb': 0, + 'gw': 2, + 'hae': 1, + 'haebe': 28, + 'hpm': 4, + 'iib': 27, + 'iii': 0, + 'iil': 0, + 'iin': 111, + 'iip': 74, + 'k': 2758, + 'lamb': 1, + 'lboo': 26, + 'lgrb': 1, + 'maser': 1, + 'mira': 3048, + 'moving': 0, + 'msv': 0, + 'n-l': 3, + 'nov': 3, + 'ov': 0, + 'p': 0, + 'pi': 0, + 'piic': 41, + 'plsr': 0, + 'psys': 3, + 'puls': 250, + 'pvsg': 0, + 'pvt': 0, + 'pwd': 3, + 'qso': 6307, + 'rcb': 2, + 'rn': 0, + 'rot': 4, + 'rr-ab': 1706, + 'rr-c': 452, + 'rr-cl': 13, + 'rr-d': 168, + 'rr-e': 0, + 'rr-lyr': 16, + 'rscvn': 1, + 'rv': 11, + 'rvc': 1, + 'rvv': 0, + 'sd': 879, + 'sdbv': 0, + 'sdc': 53, + 'sdorad': 21, + 'seyf': 0, + 'sgrb': 0, + 'shs': 0, + 'sne': 619, + 'spb': 0, + 'sr-a': 2, + 'sr-b': 2, + 'sr-c': 2, + 'sr-d': 3, + 'sreg': 76, + 'srgrb': 0, + 'ssc': 3, + 'su': 3, + 'sv': 0, + 'sw': 1, + 'sx': 28, + 'sxari': 0, + 'tia': 2176, + 'tiapec': 35, + 'tiasc': 0, + 'tib': 68, + 'tic': 124, + 'ticpec': 5, + 'tsni': 48, + 'tsnii': 749, + 'tt': 32, + 'ttc': 2, + 'ttw': 0, + 'ug': 3, + 'unclass': 61200, + 'uv': 0, + 'ux': 2, + 'vs': 774, + 'vy': 1, + 'wr': 219, + 'wu': 1075, + 'wv': 35, + 'wz': 0, + 'xrb': 0, + 'xrbin': 14, + 'zc': 3, + 'zz': 0, + 'zzh': 0, + 'zzhe': 0, + 'zzheii': 0}, + 'shortname_parent_id': {'ACVO': 58, + 'AGN': 109, + 'AR': 80, + 'BCEPS': 25, + 'BL-Lac': 256, + 'BLZ': 110, + 'CP': 0, + 'CW': 23, + 'CWA': 29, + 'CWB': 29, + 'D': 80, + 'DCEP': 23, + 'DCEPS': 32, + 'DM': 92, + 'DS': 92, + 'DSCTC': 34, + 'DrkMatterA': 107, + 'E': 80, + 'ELL': 57, + 'EP': 129, + 'EWa': 85, + 'EWs': 85, + 'Eclipsing': 1, + 'GS': 80, + 'GalNuclei': 107, + 'I': 2, + 'IA': 2, + 'IB': 2, + 'IN(YY)': 8, + 'INA': 8, + 'INB': 8, + 'IS': 2, + 'ISA': 2, + 'ISB': 13, + 'K': 80, + 'KE': 96, + 'KW': 96, + 'L': 23, + 'LB': 36, + 'LC': 36, + 'LPB': 129, + 'ML': 107, + 'NA': 66, + 'NB': 66, + 'NC': 66, + 'NEW': 1, + 'Nonstellar': 0, + 'OVV': 256, + 'PN': 80, + 'PSR': 57, + 'R': 129, + 'RPHS': 129, + 'RR(B)': 41, + 'RRAB': 41, + 'RRC': 41, + 'SD': 80, + 'SNIc-pec': 102, + 'SRA': 48, + 'SRB': 48, + 'SRC': 48, + 'SRD': 48, + 'SRS': 129, + 'SSO': 107, + 'TDE': 109, + 'UVN': 2, + 'WD': 80, + 'WR(1)': 80, + 'X': 1, + 'XF': 117, + 'XI': 117, + 'XJ': 117, + 'XND': 117, + 'XNG': 117, + 'XP': 117, + 'XPR': 124, + 'XPRM': 124, + 'XRM': 124, + 'ac': 203, + 'aii': 240, + 'alg': 248, + 'am': 165, + 'amcvn': 160, + 'ast': 275, + 'b': 247, + 'bc': 203, + 'be': 193, + 'bl': 216, + 'bly': 248, + 'by': 240, + 'c': 203, + 'ca': 217, + 'cc': 180, + 'cm': 217, + 'cn': 158, + 'comet': 275, + 'cp': 154, + 'cv': 154, + 'd': 279, + 'dc': 217, + 'dqh': 165, + 'ds': 203, + 'dsm': 211, + 'eclipsing': 154, + 'ell': 240, + 'er': 169, + 'ev': 154, + 'fk': 240, + 'fsrq': 256, + 'fuor': 195, + 'gc': 188, + 'gd': 203, + 'grb': 157, + 'gw': 209, + 'hae': 197, + 'haebe': 188, + 'hpm': 275, + 'iib': 185, + 'iii': 247, + 'iil': 185, + 'iin': 185, + 'iip': 185, + 'k': 279, + 'lamb': 198, + 'lboo': 203, + 'lgrb': 176, + 'maser': 107, + 'mira': 203, + 'moving': 0, + 'msv': 154, + 'n-l': 158, + 'nov': 157, + 'ov': 188, + 'p': 158, + 'pi': 181, + 'piic': 203, + 'plsr': 240, + 'psys': 248, + 'puls': 154, + 'pvsg': 154, + 'pvt': 203, + 'pwd': 203, + 'qso': 110, + 'rcb': 188, + 'rn': 158, + 'rot': 154, + 'rr-ab': 206, + 'rr-c': 206, + 'rr-cl': 206, + 'rr-d': 206, + 'rr-e': 206, + 'rr-lyr': 203, + 'rscvn': 188, + 'rv': 203, + 'rvc': 215, + 'rvv': 215, + 'sd': 279, + 'sdbv': 203, + 'sdc': 238, + 'sdorad': 188, + 'seyf': 110, + 'sgrb': 176, + 'shs': 193, + 'sne': 157, + 'spb': 203, + 'sr-a': 214, + 'sr-b': 214, + 'sr-c': 214, + 'sr-d': 214, + 'sreg': 203, + 'srgrb': 176, + 'ssc': 168, + 'su': 168, + 'sv': 157, + 'sw': 160, + 'sx': 203, + 'sxari': 240, + 'tia': 180, + 'tiapec': 182, + 'tiasc': 182, + 'tib': 181, + 'tic': 181, + 'ticpec': 184, + 'tsni': 180, + 'tsnii': 181, + 'tt': 195, + 'ttc': 200, + 'ttw': 200, + 'ug': 158, + 'unclass': 154, + 'uv': 188, + 'ux': 160, + 'vs': 0, + 'vy': 160, + 'wr': 188, + 'wu': 248, + 'wv': 216, + 'wz': 169, + 'xrb': 260, + 'xrbin': 248, + 'zc': 168, + 'zz': 209, + 'zzh': 223, + 'zzhe': 223, + 'zzheii': 223}, + 'shortname_parentshortname': {'ACVO': 'aii', + 'AGN': 'GalNuclei', + 'AR': 'Eclipsing', + 'BCEPS': 'bc', + 'BL-Lac': 'BLZ', + 'BLZ': 'AGN', + 'CP': '_varstar_', + 'CW': 'puls', + 'CWA': 'CW', + 'CWB': 'CW', + 'D': 'Eclipsing', + 'DCEP': 'puls', + 'DCEPS': 'DCEP', + 'DM': 'D', + 'DS': 'D', + 'DSCTC': 'ds', + 'DrkMatterA': 'Nonstellar', + 'E': 'Eclipsing', + 'ELL': 'rot', + 'EP': 'NEW', + 'EWa': 'wu', + 'EWs': 'wu', + 'Eclipsing': 'vs', + 'GS': 'Eclipsing', + 'GalNuclei': 'Nonstellar', + 'I': 'ev', + 'IA': 'ev', + 'IB': 'ev', + 'IN(YY)': 'ov', + 'INA': 'ov', + 'INB': 'ov', + 'IS': 'ev', + 'ISA': 'ev', + 'ISB': 'IS', + 'K': 'Eclipsing', + 'KE': 'K', + 'KW': 'K', + 'L': 'puls', + 'LB': 'L', + 'LC': 'L', + 'LPB': 'NEW', + 'ML': 'Nonstellar', + 'NA': 'nov', + 'NB': 'nov', + 'NC': 'nov', + 'NEW': 'vs', + 'Nonstellar': '_varstar_', + 'OVV': 'BLZ', + 'PN': 'Eclipsing', + 'PSR': 'rot', + 'R': 'NEW', + 'RPHS': 'NEW', + 'RR(B)': 'rr-lyr', + 'RRAB': 'rr-lyr', + 'RRC': 'rr-lyr', + 'SD': 'Eclipsing', + 'SNIc-pec': 'tic', + 'SRA': 'sreg', + 'SRB': 'sreg', + 'SRC': 'sreg', + 'SRD': 'sreg', + 'SRS': 'NEW', + 'SSO': 'Nonstellar', + 'TDE': 'GalNuclei', + 'UVN': 'ev', + 'WD': 'Eclipsing', + 'WR(1)': 'Eclipsing', + 'X': 'vs', + 'XF': 'X', + 'XI': 'X', + 'XJ': 'X', + 'XND': 'X', + 'XNG': 'X', + 'XP': 'X', + 'XPR': 'XP', + 'XPRM': 'XP', + 'XRM': 'XP', + 'ac': 'puls', + 'aii': 'rot', + 'alg': 'b', + 'am': 'p', + 'amcvn': 'n-l', + 'ast': 'moving', + 'b': 'msv', + 'bc': 'puls', + 'be': 'gc', + 'bl': 'piic', + 'bly': 'b', + 'by': 'rot', + 'c': 'puls', + 'ca': 'c', + 'cc': 'sne', + 'cm': 'c', + 'cn': 'nov', + 'comet': 'moving', + 'cp': 'vs', + 'cv': 'vs', + 'd': 'eclipsing', + 'dc': 'c', + 'dqh': 'p', + 'ds': 'puls', + 'dsm': 'ds', + 'eclipsing': 'vs', + 'ell': 'rot', + 'er': 'su', + 'ev': 'vs', + 'fk': 'rot', + 'fsrq': 'BLZ', + 'fuor': 'ov', + 'gc': 'ev', + 'gd': 'puls', + 'grb': 'cv', + 'gw': 'pwd', + 'hae': 'haebe', + 'haebe': 'ev', + 'hpm': 'moving', + 'iib': 'tsnii', + 'iii': 'msv', + 'iil': 'tsnii', + 'iin': 'tsnii', + 'iip': 'tsnii', + 'k': 'eclipsing', + 'lamb': 'be', + 'lboo': 'puls', + 'lgrb': 'grb', + 'maser': 'Nonstellar', + 'mira': 'puls', + 'moving': '_varstar_', + 'msv': 'vs', + 'n-l': 'nov', + 'nov': 'cv', + 'ov': 'ev', + 'p': 'nov', + 'pi': 'cc', + 'piic': 'puls', + 'plsr': 'rot', + 'psys': 'b', + 'puls': 'vs', + 'pvsg': 'vs', + 'pvt': 'puls', + 'pwd': 'puls', + 'qso': 'AGN', + 'rcb': 'ev', + 'rn': 'nov', + 'rot': 'vs', + 'rr-ab': 'rr-lyr', + 'rr-c': 'rr-lyr', + 'rr-cl': 'rr-lyr', + 'rr-d': 'rr-lyr', + 'rr-e': 'rr-lyr', + 'rr-lyr': 'puls', + 'rscvn': 'ev', + 'rv': 'puls', + 'rvc': 'rv', + 'rvv': 'rv', + 'sd': 'eclipsing', + 'sdbv': 'puls', + 'sdc': 'dc', + 'sdorad': 'ev', + 'seyf': 'AGN', + 'sgrb': 'grb', + 'shs': 'gc', + 'sne': 'cv', + 'spb': 'puls', + 'sr-a': 'sreg', + 'sr-b': 'sreg', + 'sr-c': 'sreg', + 'sr-d': 'sreg', + 'sreg': 'puls', + 'srgrb': 'grb', + 'ssc': 'ug', + 'su': 'ug', + 'sv': 'cv', + 'sw': 'n-l', + 'sx': 'puls', + 'sxari': 'rot', + 'tia': 'sne', + 'tiapec': 'tia', + 'tiasc': 'tia', + 'tib': 'cc', + 'tic': 'cc', + 'ticpec': 'tic', + 'tsni': 'sne', + 'tsnii': 'cc', + 'tt': 'ov', + 'ttc': 'tt', + 'ttw': 'tt', + 'ug': 'nov', + 'unclass': 'vs', + 'uv': 'ev', + 'ux': 'n-l', + 'vs': '_varstar_', + 'vy': 'n-l', + 'wr': 'ev', + 'wu': 'b', + 'wv': 'piic', + 'wz': 'su', + 'xrb': 'xrbin', + 'xrbin': 'b', + 'zc': 'ug', + 'zz': 'pwd', + 'zzh': 'zz', + 'zzhe': 'zz', + 'zzheii': 'zz'}} + +def parse_options(): + """ Deal with parsing command line options & --help. Return options object. + """ + parser = OptionParser(usage="usage: %prog cmd [options]") + + +head_str = """ + + + + + + 6930531 + + Best positional information of the source + + + 323.47114731 + -0.79916734036 + + + 0.000277777777778 + 0.000277777777778 + + + + + + MJD + 0.0 + UTC + TOPOCENTER + + + + + + + + + +""" + +tail_str = """ + +
+ + +""" + + + +def generate_xml_str_using_lsd_ts(dat_fpath): + """ Adapted from format_csv_getfeats.py + For converting Ben's generated RRLyrae/eclips lightcurve .dat files + + """ + import csv + + data_str_list = [] + + rows = csv.reader(open(dat_fpath), delimiter=' ') + + t_list = [] + m_list = [] + merr_list = [] + for i,row in enumerate(rows): + t = float(row[0]) + m = float(row[1]) + m_err = float(row[2]) + data_str = ' %lf%lf%lf' % \ + (i, t, m, m_err) + data_str_list.append(data_str) + t_list.append(t) + m_list.append(m) + merr_list.append(m_err) + + all_data_str = '\n'.join(data_str_list) + + out_xml = head_str + all_data_str + tail_str + + return out_xml + + +def get_perc_subset(srcid_list=[], percent_list=[], niters=1, xml_dirpath='', include_header=True, + write_multiinfo_srcids=True, source_xml_dict={}, ParseNomadColorsList=None, use_mtmerr_ts_files=False, do_sigmaclip=True): + """ Adapted from: + - analysis_deboss_tcp_source_compare.py::perc_subset_worker() + - generate_weka_classifiers.py --train_mode : + spawn_off_arff_line_tasks() + + # TODO: might need to convert ids into 100000000 + ids + + # TODO: currently this generates xml-strings with features, + - we eventually want arff rows which can be classified + (ala generate_weka_classifiers.py --train_mode) + ... condense_task_results_and_form_arff() + + """ + import copy + import random + import io + sys.path.append(os.environ.get('TCP_DIR') + '/Software/feature_extract/MLData') + #sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + '/Software/feature_extract/Code/extractors')) + #print os.environ.get("TCP_DIR") + #import mlens3 + import arffify + + sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + \ + 'Software/feature_extract/Code')) + import db_importer + from data_cleaning import sigmaclip_sdict_ts + sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + \ + 'Software/feature_extract')) + from Code import generators_importers + + #out_arff_row_dict = {} + + master_list = [] + master_features_dict = {} + all_class_list = [] + master_classes_dict = {} + + new_srcid_list = [] + for src_id in srcid_list: + + if use_mtmerr_ts_files: + ### Then we want to generate some pseudo XML using the timeseries column file (with lines like: "55243.43624000 19.36500000 ......") + new_xml_str = generate_xml_str_using_lsd_ts(source_xml_dict[src_id]) + else: + + #20120130#tutor_src_id = src_id - 100000000 + tutor_src_id = src_id + if len(source_xml_dict) > 0: + #20120130#xml_fpath = source_xml_dict[str(tutor_src_id)] + xml_fpath = source_xml_dict[tutor_src_id] + else: + xml_fpath = os.path.expandvars("%s/%d.xml" % (xml_dirpath, src_id)) + + xml_str = open(xml_fpath).read() + new_xml_str = ParseNomadColorsList.get_colors_for_srcid(xml_str=xml_str, srcid=tutor_src_id - 100000000) #, srcid=tutor_src_id) + + signals_list = [] + gen_orig = generators_importers.from_xml(signals_list) + gen_orig.signalgen = {} + gen_orig.sig = db_importer.Source(xml_handle=new_xml_str, doplot=False, make_xml_if_given_dict=True) + gen_orig.sdict = gen_orig.sig.x_sdict + gen_orig.set_outputs() # this adds/fills self.signalgen[,multiband]{'input':{filled},'features':{empty},'inter':{empty}} + + signals_list_temp = [] + #import pdb; pdb.set_trace() + #print + + if do_sigmaclip: + try: + ### Do some sigma clipping (Ex: for ASAS data) + sigmaclip_sdict_ts(gen_orig.sig.x_sdict['ts'], sigma_low=4.0, sigma_high=4.0) + except: + continue # probably doesnt have a gen_orig.sig.x_sdict['ts'][]['m'] + + gen_temp = copy.deepcopy(gen_orig) + for perc in percent_list: + ### We generate several random, percent-subsampled vosource in order to include error info: + #if 1: + # i = niters # this should just be a single (integer) subset number/iteration index + #for i in range(niters): + for i in niters: + if write_multiinfo_srcids: + new_srcid = "%d_%2.2f_%d" % (src_id, perc, i) + else: + if type(src_id) == type(12): + new_srcid = "%d" % (src_id) + else: + new_srcid = src_id + + new_srcid_list.append(new_srcid) + + dbi_src = db_importer.Source(make_dict_if_given_xml=False) + + for band, band_dict in gen_orig.sig.x_sdict['ts'].items(): + if ":NOMAD" in band: + i_start = 0 + i_end = len(band_dict['m']) + else: + i_start = int(((len(band_dict['m'])+1) * (1 - perc)) * random.random()) + i_end = i_start + int(perc * (len(band_dict['m'])+1)) + gen_temp.sig.x_sdict['ts'][band]['m'] = band_dict['m'][i_start:i_end] + gen_temp.sig.x_sdict['ts'][band]['m_err'] = band_dict['m_err'][i_start:i_end] + gen_temp.sig.x_sdict['ts'][band]['t'] = band_dict['t'][i_start:i_end] + dbi_src.source_dict_to_xml(gen_temp.sig.x_sdict) + write_xml_str = dbi_src.xml_string + + signals_list = [] + gen = generators_importers.from_xml(signals_list) + gen.generate(xml_handle=write_xml_str) + gen.sig.add_features_to_xml_string(signals_list) + gen.sig.x_sdict['src_id'] = new_srcid + dbi_src.source_dict_to_xml(gen.sig.x_sdict) + + xml_fpath = dbi_src.xml_string + #import pdb; pdb.set_trace() + #print + + a = arffify.Maker(search=[], skip_class=False, local_xmls=True, convert_class_abrvs_to_names=False, flag_retrieve_class_abrvs_from_TUTOR=False, dorun=False) + out_dict = a.generate_arff_line_for_vosourcexml(num=new_srcid, xml_fpath=xml_fpath) + + #out_arff_row_dict[(src_id, perc, i)] = out_dict # ??? TODO: just arff rows? + # dbi_src.xml_string + master_list.append(out_dict) + all_class_list.append(out_dict['class']) + master_classes_dict[out_dict['class']] = 0 + for feat_tup in out_dict['features']: + master_features_dict[feat_tup] = 0 # just make sure there is this key in the dict. 0 is filler + + master_features = master_features_dict.keys() + master_classes = master_classes_dict.keys() + a = arffify.Maker(search=[], skip_class=False, local_xmls=True, + convert_class_abrvs_to_names=False, + flag_retrieve_class_abrvs_from_TUTOR=False, + dorun=False, add_srcid_to_arff=True) + a.master_features = master_features + a.all_class_list = all_class_list + a.master_classes = master_classes + a.master_list = master_list + # # # TODO: ideally just the arff lines / strings will be used + # - although it might be nice to have a disk copy of the arff rows for record, passing to others. + fp_strio = io.StringIO() + a.write_arff(outfile=fp_strio, \ + remove_sparse_classes=True, \ + n_sources_needed_for_class_inclusion=1, + include_header=include_header)#, classes_arff_str='', remove_sparse_classes=False) + + arff_row_list = [] + out_dict = {} + arff_rows_str = fp_strio.getvalue() + + # See pairwise_classification.py 2550 + #Pairwise_Classification parse_arff(self, arff_has_ids=False, arff_has_classes=True, has_srcid=False, get_features=False): + """ + arff_rows = [] + for a_str in arff_rows_str.split('\n'): + if len(a_str) == 0: + continue + if a_str[0] == '@': + continue + if a_str[0] == '%': + continue + arff_rows.append(a_str) + + assert(len(all_class_list) == len(arff_rows)) + + for i, arff_row in enumerate(arff_rows): + class_name = all_class_list[i] + + if not out_dict.has_key(class_name): + out_dict[class_name] = {'srcid_list':[], + 'count':0, + 'arffrow_wo_classnames':[], + } + out_dict[class_name]['srcid_list'].append(new_srcid_list[i]) + out_dict[class_name]['count'] += 1 + out_dict[class_name]['arffrow_wo_classnames'].append( \ + arff_row[:arff_row.rindex("'", 0,arff_row.rindex("'")) - 1]) + + return out_dict # out_dict[class_name][arffrow_wo_classnames:[], count:1, srcid_list:[] ### exclude:'arffrow_with_classnames:[] + + """ + + return arff_rows_str + + +class Arff_Generation_Engine_Tasks: + """ Class contains methods that will be used for arff lines generation + """ + + def task_generate_feature_arff_lines(self, pars, srcid_list=[], xml_dirpath='', include_header=True, + write_multiinfo_srcids=True, source_xml_dict={}, + ParseNomadColorsList=None, + use_mtmerr_ts_files=False, + do_sigmaclip=True): + """ Given a sourceid list, generate features and the resulting ARFF lines. + + """ + #perc_arr = array(list(arange(0.2, 0.6, 0.01))) + #'percent':[str(elem) for elem in perc_arr] + sub_perc_list = [1.0] + sub_iter_list = [1] + #debug = 'hi3' + #import traceback + #try: + arff_str = get_perc_subset(srcid_list, sub_perc_list, sub_iter_list, xml_dirpath=xml_dirpath, + include_header=include_header, + source_xml_dict=source_xml_dict, + write_multiinfo_srcids=write_multiinfo_srcids, + ParseNomadColorsList=ParseNomadColorsList, + use_mtmerr_ts_files=use_mtmerr_ts_files, + do_sigmaclip=do_sigmaclip) + #except: + # debug = traceback.format_exc() + #return {'test':debug, 'arff_rows':[1,2,3,4,5]} + arff_rows = [] + class_list = [] + for row in arff_str.split('\n'): + if len(row) == 0: + continue + elif row[:5] == '@data': + continue + elif row[:16] == '@ATTRIBUTE class': + class_str = row[row.rfind("{'") + 2: row.rfind("'}")] + class_list = class_str.split("','") + else: + arff_rows.append(row) + + return {'arff_rows':arff_rows, 'class_list':class_list} + + + +def master_ipython_arff_generation(pars={}, write_multiinfo_srcids=True, source_xml_dict={}, use_mtmerr_ts_files=False, do_sigmaclip=True): + """ Main code which controls ipython nodes when generating + +This is the task which will be called on ipengines by this function: + +task_generate_feature_arff_lines(pars, srcid_list=[]) + + """ + + import datetime + import time + import cPickle + try: + from IPython.kernel import client + except: + pass + + mec = client.MultiEngineClient() + mec.reset(targets=mec.get_ids()) # Reset the namespaces of all engines + tc = client.TaskClient() + + mec_exec_str = """ +import sys, os +import copy +import matplotlib +matplotlib.use('agg') +sys.path.append(os.path.abspath('/global/home/users/dstarr/src/TCP/Software/ingest_tools')) +sys.path.append(os.path.abspath('/global/home/users/dstarr/src/TCP/Software/citris33')) +from get_colors_for_tutor_sources import Parse_Nomad_Colors_List +ParseNomadColorsList = Parse_Nomad_Colors_List(fpath='/global/home/users/dstarr/src/TCP/Data/best_nomad_src_list') +import arff_generation_master +ArffEngineTasks = arff_generation_master.Arff_Generation_Engine_Tasks()""" + + print('before mec()') + #print mec_exec_str + #import pdb; pdb.set_trace() + engine_ids = mec.get_ids() + pending_result_dict = {} + for engine_id in engine_ids: + pending_result_dict[engine_id] = mec.execute(mec_exec_str, targets=[engine_id], block=False) + n_pending = len(pending_result_dict) + i_count = 0 + while n_pending > 0: + still_pending_dict = {} + for engine_id, pending_result in pending_result_dict.items(): + try: + result_val = pending_result.get_result(block=False) + except: + print("get_result() Except. Still pending on engine: %d" % (engine_id)) + still_pending_dict[engine_id] = pending_result + result_val = None # 20110105 added + if result_val is None: + print("Still pending on engine: %d" % (engine_id)) + still_pending_dict[engine_id] = pending_result + if i_count > 10: + mec.clear_pending_results() + pending_result_dict = {} + mec.reset(targets=still_pending_dict.keys()) + for engine_id in still_pending_dict.keys(): + pending_result_dict[engine_id] = mec.execute(mec_exec_str, targets=[engine_id], block=False) + ### + time.sleep(20) # hack + pending_result_dict = [] # hack + ### + i_count = 0 + else: + print("sleeping...") + time.sleep(5) + pending_result_dict = still_pending_dict + n_pending = len(pending_result_dict) + i_count += 1 + + print('after mec()') + time.sleep(5) # This may be needed, although mec() seems to wait for all the Ipython clients to finish + print('after sleep()') + #import pdb; pdb.set_trace() + + # todo: fill a dict and write to pickle: of srcid:xml_filepath + srcid_list = pars['src_id'] + + task_id_list = [] + class_list = [] + + #if use_mtmerr_ts_files: + # ### This case is used when source timeseries is not stored in XMLs, but rather files with lines like: "55243.43624000 19.36500000 ......" + # return # TODO want to have an else below... + + #for srcid in srcid_list[:4]: + if use_mtmerr_ts_files: + srcid_list_new = srcid_list + else: + srcid_list_new = [] + for srcid in srcid_list: + ##20120130disable: #srcid_list_new.append(int(srcid) + 100000000) + srcid_list_new.append(srcid) + #junktry#srcid_list_new.append(str(srcid)) # late we expect this to be a string, non +100000000 + + ### 20110622: I believe this is just a quick run-through of code in non-parallel mode, for one source: + result_arff_list = [] + from get_colors_for_tutor_sources import Parse_Nomad_Colors_List + ParseNomadColorsList = Parse_Nomad_Colors_List(fpath='/global/home/users/dstarr/src/TCP/Data/best_nomad_src_list') + ArffEngineTasks = Arff_Generation_Engine_Tasks() + out_dict = ArffEngineTasks.task_generate_feature_arff_lines(pars, srcid_list=srcid_list_new[:1], xml_dirpath=pars['xml_dirpath'], include_header=True, write_multiinfo_srcids=write_multiinfo_srcids, source_xml_dict=source_xml_dict, ParseNomadColorsList=ParseNomadColorsList, use_mtmerr_ts_files=use_mtmerr_ts_files, do_sigmaclip=do_sigmaclip) + #out_dict = ArffEngineTasks.task_generate_feature_arff_lines(pars, srcid_list=srcid_list_new, xml_dirpath=pars['xml_dirpath'], include_header=True, write_multiinfo_srcids=write_multiinfo_srcids, source_xml_dict=source_xml_dict, ParseNomadColorsList=ParseNomadColorsList) + ### KLUDGE leave out the last row since it will be reprocessed below: + result_arff_list.extend(out_dict['arff_rows'][:-1]) + #import pdb; pdb.set_trace() + #print + + + n_src_per_task = 10 # 10 # NOTE: is generating PSD(freq) plots within lightcurve.py, should use n_src_per_task = 1, and all tasks should finish.# for ALL_TUTOR, =1 ipcontroller uses 99% memory, so maybe =3? (NOTE: cant do =10 since some TUTOR sources fail) + + imin_list = range(0, len(srcid_list_new), n_src_per_task) + + for i_min in imin_list: + srcid_sublist = srcid_list_new[i_min: i_min + n_src_per_task] + sub_source_xml_dict = {} + if len(source_xml_dict) > 0: + for sid in srcid_sublist: + sub_source_xml_dict[sid] = source_xml_dict[sid] + + #print srcid_list + #import pdb; pdb.set_trace() + #print + ##### FOR DEBUGGING: + ##### - NOTE: will use up memory if run for ~100 iterations + #ArffEngineTasks = Arff_Generation_Engine_Tasks() + #out_dict = ArffEngineTasks.task_generate_feature_arff_lines(pars, srcid_list=srcid_sublist, xml_dirpath=pars['xml_dirpath'], include_header=False, write_multiinfo_srcids=write_multiinfo_srcids, ParseNomadColorsList=ParseNomadColorsList) + #import pdb; pdb.set_trace() + #print + ##### + ### 20110106: This doesn't seem to solve the ipcontroller memory error, but works: + tc_exec_str = """ +tmp_stdout = sys.stdout +sys.stdout = open(os.devnull, 'w') +#os.system('touch /global/home/groups/dstarr/debug_started/%s' % (str(srcid_list[0]))) +out_dict = ArffEngineTasks.task_generate_feature_arff_lines(pars, srcid_list=srcid_list, xml_dirpath=xml_dirpath, include_header=include_header, write_multiinfo_srcids=write_multiinfo_srcids, source_xml_dict=source_xml_dict, ParseNomadColorsList=ParseNomadColorsList, use_mtmerr_ts_files=use_mtmerr_ts_files, do_sigmaclip=do_sigmaclip) +#os.system('touch /global/home/groups/dstarr/debug/%s' % (str(srcid_list[0]))) +sys.stdout.close() +sys.stdout = tmp_stdout + + """ + + if 1: + taskid = tc.run(client.StringTask(tc_exec_str, + push={'pars':pars, + 'srcid_list':srcid_sublist, + 'xml_dirpath':pars['xml_dirpath'], + 'include_header':False, + 'source_xml_dict':sub_source_xml_dict, + 'write_multiinfo_srcids':write_multiinfo_srcids, + 'use_mtmerr_ts_files':use_mtmerr_ts_files, + 'do_sigmaclip':do_sigmaclip}, + pull='out_dict', + retries=3)) # 3 + task_id_list.append(taskid) + #import pdb; pdb.set_trace() + #print # print tc.get_task_result(0, block=False).results + #import pdb; pdb.set_trace() + #### + #combo_results_dict = {} + dtime_pending_1 = None + while ((tc.queue_status()['scheduled'] > 0) or + (tc.queue_status()['pending'] > 0)): + tasks_to_pop = [] + for task_id in task_id_list: + temp = tc.get_task_result(task_id, block=False) + if temp is None: + continue + temp2 = temp.results + if temp2 is None: + continue + results = temp2.get('out_dict',None) + if results is None: + continue # skip some kind of NULL result + if len(results) > 0: + tasks_to_pop.append(task_id) + result_arff_list.extend(results['arff_rows']) + for a_class in results['class_list']: + if not a_class in class_list: + class_list.append(a_class) + #ipython_return_dict = results + #update_combo_results(combo_results_dict=combo_results_dict, + # ipython_return_dict=copy.deepcopy(ipython_return_dict)) + for task_id in tasks_to_pop: + task_id_list.remove(task_id) + + + # (tc.queue_status()['pending'] <= 64)): + # if ((now - dtime_pending_1) >= datetime.timedelta(seconds=300)): + if ((tc.queue_status()['scheduled'] == 0) and + (tc.queue_status()['pending'] <= 7)): + if dtime_pending_1 is None: + dtime_pending_1 = datetime.datetime.now() + else: + now = datetime.datetime.now() + if ((now - dtime_pending_1) >= datetime.timedelta(seconds=1200)): + print("dtime_pending=1 timeout break!") + break + print(tc.queue_status()) + print('Sleep... 60 in test_pairwise_on_citris33_ipython::master_ipython_R_classifiers()', datetime.datetime.utcnow()) + time.sleep(60) + # IN CASE THERE are still tasks which have not been pulled/retrieved: + for task_id in task_id_list: + temp = tc.get_task_result(task_id, block=False) + if temp is None: + continue + temp2 = temp.results + if temp2 is None: + continue + results = temp2.get('out_dict',None) + if results is None: + continue #skip some kind of NULL result + if len(results) > 0: + tasks_to_pop.append(task_id) + result_arff_list.extend(results['arff_rows']) + for a_class in results['class_list']: + if not a_class in class_list: + class_list.append(a_class) + #ipython_return_dict = results + #update_combo_results(combo_results_dict=combo_results_dict, + # ipython_return_dict=copy.deepcopy(ipython_return_dict)) + #### + print(tc.queue_status()) + return {'result_arff_list':result_arff_list, + 'class_list':class_list} + + + +def do_branimir_ptf_timeseries(pars={}): + """ Use Branimir's RRLyrae PTF timeseries files: + TODO: need to create a dict of {srcid:path} + - store this in a .pkl + """ + # TODO: load filename + #20110125#src_list_fpath = "/global/home/users/dstarr/500GB/branimir/IPAC_lightcurves" + src_list_fpath = "/global/home/users/dstarr/500GB/branimir/linear_rr_in_ptf_lightcurves" + fpaths_unstripped = open(src_list_fpath).readlines() + + source_fpath_dict = {} + for fpath_unstripped in fpaths_unstripped: + fpath = fpath_unstripped.strip().replace('/home/bsesar/projects/rrlyr','/global/home/users/dstarr/500GB/branimir') + #src_name = fpath[fpath.rfind('lightcurves') + 12:].replace('_','/') + src_name = fpath[fpath.rfind('_') + 1:] # dstarr has checked that these src_name are all unique and no source is weing overwritten + #if src_name != '6094056391088054904': + if src_name != '6941401624103488574': + continue + source_fpath_dict[src_name] = fpath + + pars['src_id'] = source_fpath_dict.keys() + pars['xml_dirpath'] = None # not needed in our case + out_dict = master_ipython_arff_generation(pars=pars, + source_xml_dict=source_fpath_dict, + write_multiinfo_srcids=False, + use_mtmerr_ts_files=True, + do_sigmaclip=False, + ) #write_multiinfo_srcids=False:only srcid in output arff; True when doing several percent/subset arff rows + result_arff_list = out_dict['result_arff_list'] + + ### Need to find the last ATTRIBUTE in the header, so the classes can be inserted: + in_attibs = False + for i, elem in enumerate(out_dict['result_arff_list']): + if len(elem) == 0: + continue + elif elem[0] == "%": + continue + elif elem[:10] == "@ATTRIBUTE": + in_attibs = True + elif in_attibs: + ### Now we are done parsing the @ATTRIBUTES + class_str = "@ATTRIBUTE class {'%s'}" % ("','".join(out_dict['class_list'])) + out_dict['result_arff_list'].insert(i, class_str) + ### Also want to insert @DATA before the data starts. + out_dict['result_arff_list'].insert(i + 1, '@DATA') + break + + fp = open(os.path.expandvars("$HOME/scratch/out.arff"), 'w') + fp.write('\n'.join(result_arff_list)) + fp.close() + import datetime + print(datetime.datetime.now()) + import pdb; pdb.set_trace() + print() + + + + + +if __name__ == '__main__': + + pars = {'src_id':[], #deboss_srcid_list, #['148875', '148723', '148420', '149144', '149049'], #deboss_srcid_list,#['148875', '148723', '148420', '149144', '149049'], #deboss_srcid_list, #['149144', '149049', '149338', '149049', '149338','149182','149108'], + 'percent':[], #[str(elem) for elem in perc_arr], #[str(elem) for elem in arange(0.90, 1.0, 0.01)], # [str(elem) for elem in arange(0.58, 1.0, 0.01)]#[str(elem) for elem in arange(0.01, 1.01, 0.01)], #[str(elem) for elem in arange(0.8, 1.0, 0.10)], #['0.8', '0.86', '0.88', '0.9', '0.95', '1.0'], + 'niters':'7', #'5', #'6',#'12', # Not a list, string value, will be used to generate list: range(niters) + 'pairwise_classifier_pkl_fpath':"/home/pteluser/Dropbox/work/WEKAj48_dotastro_ge1srcs_period_nonper__exclude_non_debosscher/pairwise_classifier__debosscher_table3.pkl.gz", # This is just debosscher data + 'crossvalid_pairwise_classif_dirpath':'/global/home/users/dstarr/scratch/crossvalid/pairwise_scratch_20101109_4060nostratif_2qso', # NOTE: set to '' if want to do non-crossvalid-folded classifiers + 'taxonomy_prune_defs':{'terminating_classes':['mira', 'sreg', 'rv', 'dc', 'piic', 'cm', 'rr-ab', 'rr-c', 'rr-d', 'ds', 'lboo', 'bc', 'spb', 'gd', 'be', 'pvsg', 'CP', 'wr', 'tt', 'haebe', 'sdorad', 'ell', 'alg', 'bly', 'wu']}, + 'plot_symb':['o','s','v','d','<'], # ,'+','x','.', ,'>','^' + 'feat_distrib_colors':['#000000', + '#ff3366', + '#660000', + '#aa0000', + '#ff0000', + '#ff6600', + '#996600', + '#cc9900', + '#ffff00', + '#ffcc33', + '#ffff99', + '#99ff99', + '#666600', + '#99cc00', + '#00cc00', + '#006600', + '#339966', + '#33ff99', + '#006666', + '#66ffff', + '#0066ff', + '#0000cc', + '#660099', + '#993366', + '#ff99ff', + '#440044'], + 'R_class_lookup':{ \ + 'X Ray Binary':'xrbin', + 'a. Mira':'mira', + 'b. Semireg PV':'sreg', + 'c. RV Tauri':'rv', + 'd. Classical Cepheid':'dc', + 'e. Pop. II Cepheid':'piic', + 'f. Multi. Mode Cepheid':'cm', + 'g. RR Lyrae, FM':'rr-ab', + 'h. RR Lyrae, FO':'rr-c', + 'i. RR Lyrae, DM':'rr-d', + 'j. Delta Scuti':'ds', + 'k. Lambda Bootis':'lboo', + 'l. Beta Cephei':'bc', + 'm. Slowly Puls. B':'spb', + 'n. Gamma Doradus':'gd', + 'o. Pulsating Be':'be', + 'p. Per. Var. SG':'pvsg', + 'q. Chem. Peculiar':'CP', + 'r. Wolf-Rayet':'wr', + 's. T Tauri':'tt', + 't. Herbig AE/BE':'haebe', + 'u. S Doradus':'sdorad', + 'v. Ellipsoidal':'ell', + 'w. Beta Persei':'alg', + 'x. Beta Lyrae':'bly', + 'y. W Ursae Maj.':'wu', + }, + 'R_class_lookup__old':{ \ + 'X Ray Binary':'xrbin', + 'a. Mira':'mira', + 'b. semireg PV':'sreg', + 'c. RV Tauri':'rv', + 'd. Classical Cepheid':'dc', + 'e. Pop. II Cepheid':'piic', + 'f. Multi. Mode Cepheid':'cm', + 'g. RR Lyrae, FM':'rr-ab', + 'h. RR Lyrae, FO':'rr-c', + 'i. RR Lyrae, DM':'rr-d', + 'j. Delta Scuti':'ds', + 'k. Lambda Bootis':'lboo', + 'l. Beta Cephei':'bc', + 'm. Slowly Puls. B':'spb', + 'n. Gamma Doradus':'gd', + 'o. BE':'be', + 'p. Per. Var. SG':'pvsg', + 'q. Chem. Peculiar':'CP', + 'r. Wolf-Rayet':'wr', + 's. T Tauri':'tt', + 't. Herbig AE/BE':'haebe', + 'u. S Doradus':'sdorad', + 'v. Ellipsoidal':'ell', + 'w. Beta Persei':'alg', + 'x. Beta Lyrae':'bly', + 'y. W Ursae Maj.':'wu', + }, + } + # 'srcid':[], + # 'percent'[] + options = parse_options() + #if options.srcid != '': + # pars['src_id'] = options.srcid + + ### Use Branimir's RRLyrae PTF timeseries files: + # TODO: need to create a dict of {srcid:path} + # - store this in a .pkl + if 0: + ### 20120126 Use Branimir's RRLyrae PTF timeseries files: + do_branimir_ptf_timeseries(pars=pars) + sys.exit() + + + if 0: + ### OBSOLETE + ### all_tutor_xmls: Many tutor project_ids case: + pars['xml_dirpath'] = '/global/home/users/dstarr/500GB/all_tutor_xmls_flat' + xmls_dict_pkl_fpath = '/global/home/users/dstarr/500GB/all_tutor_xmls_dict.pkl' + glob_str = '%s/*/*' % (pars['xml_dirpath']) + + if os.path.exists(xmls_dict_pkl_fpath): + source_xml_dict = cPickle.load(open(xmls_dict_pkl_fpath)) + else: + #import pdb; pdb.set_trace() + #print + + source_xml_dict = {} + dirs = os.listdir(pars['xml_dirpath']) + for dir in dirs: + dirpath = "%s/%s" % (pars['xml_dirpath'], dir) + glob_str = '%s/*' % (dirpath) + + xml_fpaths = glob.glob(glob_str) + + for xml_fpath in xml_fpaths: + print(xml_fpath) + num_str = xml_fpath[xml_fpath.rfind('/') + 1:xml_fpath.rfind('.')] + srcid = int(num_str)# - 100000000 + source_xml_dict[str(srcid)] = xml_fpath + + + fp = open(xmls_dict_pkl_fpath, 'wb') + cPickle.dump(source_xml_dict,fp,1) # ,1) means a binary pkl is used. + fp.close() + + + else: + ### Most other TUTOR projects: + #pars['xml_dirpath'] = '/global/home/users/dstarr/500GB/all_tutor_xmls_flat' + #xmls_dict_pkl_fpath = '/global/home/users/dstarr/500GB/all_tutor_xmls_dict.pkl' + #glob_str = '%s/*' % (pars['xml_dirpath']) + + + ### ASAS configs pre20120221 #(?pre 20110511?): + #pars['xml_dirpath'] = '/global/home/users/dstarr/500GB/xmls/proj_126/xmls' + #xmls_dict_pkl_fpath = '/global/home/users/dstarr/500GB/xmls/proj_126/xmls_dict.pkl' + + ### ASAS configs (pre 20110511): + pars['xml_dirpath'] = '/global/home/users/dstarr/500GB/xmls/proj_126/asas_ACVS_50k_new_aper_20120221' + xmls_dict_pkl_fpath = '/global/home/users/dstarr/500GB/xmls/proj_126/asas_ACVS_50k_new_aper_20120221_xmls_dict.pkl' + + ### stipe82 SDSS: + #pars['xml_dirpath'] = '/global/home/groups/dstarr/tutor_121_xmls' + #xmls_dict_pkl_fpath = '/global/home/groups/dstarr/tutor_121_xmls/xmls_dict.pkl' + + glob_str = '%s/*' % (pars['xml_dirpath']) + + + if os.path.exists(xmls_dict_pkl_fpath): + source_xml_dict = cPickle.load(open(xmls_dict_pkl_fpath)) + else: + xml_fpaths = glob.glob(glob_str) + + source_xml_dict = {} + for xml_fpath in xml_fpaths: + #print xml_fpath + num_str = xml_fpath[xml_fpath.rfind('/') + 1:xml_fpath.rfind('.')] + #20120130#srcid = int(num_str) - 100000000 + #20120130#source_xml_dict[str(srcid)] = xml_fpath + source_xml_dict[int(num_str)] = xml_fpath + fp = open(xmls_dict_pkl_fpath, 'wb') + cPickle.dump(source_xml_dict,fp,1) # ,1) means a binary pkl is used. + fp.close() + + #import pdb; pdb.set_trace() + #print + pars['src_id'] = source_xml_dict.keys() + + #import pdb; pdb.set_trace() + #print + out_dict = master_ipython_arff_generation(pars=pars, + source_xml_dict=source_xml_dict, + write_multiinfo_srcids=False) #False:only srcid in output arff; True when doing several percent/subset arff rows + #print result_arff_list + result_arff_list = out_dict['result_arff_list'] + + ### Need to find the last ATTRIBUTE in the header, so the classes can be inserted: + in_attibs = False + for i, elem in enumerate(out_dict['result_arff_list']): + if len(elem) == 0: + continue + elif elem[0] == "%": + continue + elif elem[:10] == "@ATTRIBUTE": + in_attibs = True + elif in_attibs: + ### Now we are done parsing the @ATTRIBUTES + class_str = "@ATTRIBUTE class {'%s'}" % ("','".join(out_dict['class_list'])) + out_dict['result_arff_list'].insert(i, class_str) + ### Also want to insert @DATA before the data starts. + out_dict['result_arff_list'].insert(i + 1, '@DATA') + break + + if 0: + # pre 20120220 + # This section is for recording which LS params were changed for each arff. + # ('nharm', 8), + # ? want to vary the sigmaclipping? + arff_pars = [ \ + ('f_max', 33.0), + ('df_factor', 0.8), + ('nharm', 8), + ('tone_control',5.0), + ('dtrend_order',1)] + fp_dat = open('/global/home/users/dstarr/500GB/LS_param_explore_arffs/LS_param_explore_arffs.dat','a') + arff_fname = "/global/home/users/dstarr/500GB/LS_param_explore_arffs/%f_%f_%d_%f_%d.arff" % ( \ + arff_pars[0][1], + arff_pars[1][1], + arff_pars[2][1], + arff_pars[3][1], + arff_pars[4][1]) + + fp_dat.write("%s %f %f %d %f %d\n" % (arff_fname, + arff_pars[0][1], + arff_pars[1][1], + arff_pars[2][1], + arff_pars[3][1], + arff_pars[4][1])) + fp_dat.close() + + fp = open(arff_fname, 'w') + fp.write('\n'.join(result_arff_list)) + fp.close() + + else: + fp = open(os.path.expandvars("$HOME/scratch/out.arff"), 'w') + fp.write('\n'.join(result_arff_list)) + fp.close() + import datetime + print(datetime.datetime.now()) + import pdb; pdb.set_trace() + print() diff --git a/mltsp/TCP/Software/citris33/arff_generation_master_using_generic_ts_data.py b/mltsp/TCP/Software/citris33/arff_generation_master_using_generic_ts_data.py new file mode 100644 index 00000000..c0d3636d --- /dev/null +++ b/mltsp/TCP/Software/citris33/arff_generation_master_using_generic_ts_data.py @@ -0,0 +1,2506 @@ +#!/usr/bin/env python +""" + +Adapted from arff_generation_master.py + +""" +from __future__ import print_function +import sys, os +import cPickle +import time +import gzip +import copy +import glob +import matplotlib +matplotlib.use('agg') # just needed for lightcurve.py::lomb_code() PSD.png plotting without X11 + +from optparse import OptionParser + +sys.path.append(os.path.abspath(os.environ.get('TCP_DIR') + 'Software/ingest_tools')) + +sciclass_lookup = {'classid_shortname': {0: '_varstar_', + 1: 'GCVS', + 2: 'Eruptive', + 3: 'FU', + 4: 'GCAS', + 5: 'I', + 6: 'IA', + 7: 'IB', + 8: 'IN', + 9: 'INA', + 10: 'INB', + 11: 'INT', + 12: 'IN(YY)', + 13: 'IS', + 14: 'ISA', + 16: 'ISB', + 17: 'RCB', + 18: 'RS', + 19: 'SDOR', + 20: 'UV', + 21: 'UVN', + 22: 'WR', + 23: 'Pulsating', + 24: 'ACYG', + 25: 'BCEP', + 26: 'BCEPS', + 27: 'CEP', + 28: 'CEP(B)', + 29: 'CW', + 30: 'CWA', + 31: 'CWB', + 32: 'DCEP', + 33: 'DCEPS', + 34: 'DSCT', + 35: 'DSCTC', + 36: 'L', + 37: 'LB', + 38: 'LC', + 39: 'M', + 40: 'PVTEL', + 41: 'RR', + 42: 'RR(B)', + 43: 'RRAB', + 44: 'RRC', + 45: 'RV', + 46: 'RVA', + 47: 'RVB', + 48: 'SR', + 49: 'SRA', + 50: 'SRB', + 51: 'SRC', + 52: 'SRD', + 53: 'SXPHE', + 54: 'ZZ', + 55: 'ZZA', + 56: 'ZZB', + 57: 'Rotating', + 58: 'ACV', + 59: 'ACVO', + 60: 'BY', + 61: 'ELL', + 62: 'FKCOM', + 63: 'PSR', + 64: 'SXARI', + 65: 'Cataclysmic', + 66: 'N', + 67: 'NA', + 68: 'NB', + 69: 'NC', + 70: 'NL', + 71: 'NR', + 72: 'SN', + 73: 'SNI', + 74: 'SNII', + 75: 'UG', + 76: 'UGSS', + 77: 'UGSU', + 78: 'UGZ', + 79: 'ZAND', + 80: 'Eclipsing', + 82: 'E', + 83: 'EA', + 84: 'EB', + 85: 'EW', + 86: 'GS', + 87: 'PN', + 88: 'RS', + 89: 'WD', + 90: 'WR(1)', + 91: 'AR', + 92: 'D', + 93: 'DM', + 94: 'DS', + 95: 'DW', + 96: 'K', + 97: 'KE', + 98: 'KW', + 99: 'SD', + 100: 'SNIa', + 101: 'SNIb', + 102: 'SNIc', + 103: 'SNIIP', + 104: 'SNIIN', + 105: 'SNIIL', + 106: 'SNIa-sc', + 107: 'Nonstellar', + 109: 'GalNuclei', + 110: 'AGN', + 111: 'TDE', + 112: 'DrkMatterA', + 113: 'GRB', + 114: 'SHB', + 115: 'LSB', + 116: 'SGR', + 117: 'X', + 118: 'XB', + 119: 'XF', + 120: 'XI', + 121: 'XJ', + 122: 'XND', + 123: 'XNG', + 124: 'XP', + 125: 'XPR', + 126: 'XPRM', + 127: 'XRM', + 128: 'ZZO', + 129: 'NEW', + 130: 'AM', + 131: 'R', + 132: 'BE', + 133: 'EP', + 134: 'SRS', + 135: 'GDOR', + 136: 'RPHS', + 137: 'LPB', + 138: 'BLBOO', + 139: 'BL-Lac', + 140: 'RRcl', + 141: 'RRe', + 142: 'SNIa-pec', + 143: 'SNIc-pec', + 145: 'ML', + 149: 'UXUma', + 150: 'Polars', + 151: 'DQ', + 152: 'EWa', + 153: 'EWs', + 154: 'vs', + 157: 'cv', + 158: 'nov', + 159: 'cn', + 160: 'n-l', + 161: 'sw', + 162: 'vy', + 163: 'ux', + 164: 'amcvn', + 165: 'p', + 166: 'am', + 167: 'dqh', + 168: 'ug', + 169: 'su', + 170: 'er', + 171: 'wz', + 172: 'zc', + 173: 'ssc', + 174: 'rn', + 175: 'sv', + 176: 'grb', + 177: 'lgrb', + 178: 'sgrb', + 179: 'srgrb', + 180: 'sne', + 181: 'cc', + 182: 'tia', + 183: 'tib', + 184: 'tic', + 185: 'tsnii', + 186: 'pi', + 187: 'tsni', + 188: 'ev', + 189: 'rscvn', + 190: 'uv', + 191: 'sdorad', + 192: 'wr', + 193: 'gc', + 194: 'fuor', + 195: 'ov', + 196: 'rcb', + 197: 'haebe', + 198: 'be', + 199: 'shs', + 200: 'tt', + 201: 'ttc', + 202: 'ttw', + 203: 'puls', + 204: 'gd', + 205: 'sx', + 206: 'rr-lyr', + 207: 'ac', + 208: 'mira', + 209: 'pwd', + 211: 'ds', + 212: 'pvt', + 213: 'bc', + 214: 'sreg', + 215: 'rv', + 216: 'piic', + 217: 'c', + 218: 'rr-ab', + 219: 'rr-c', + 220: 'rr-d', + 221: 'rr-e', + 222: 'rr-cl', + 223: 'zz', + 224: 'zzh', + 225: 'zzhe', + 226: 'zzheii', + 227: 'gw', + 228: 'sr-a', + 229: 'sr-b', + 230: 'sr-c', + 231: 'sr-d', + 232: 'rvc', + 233: 'rvv', + 234: 'bl', + 235: 'wv', + 236: 'ca', + 237: 'cm', + 238: 'dc', + 239: 'sdc', + 240: 'rot', + 241: 'sxari', + 242: 'aii', + 243: 'fk', + 244: 'plsr', + 245: 'by', + 246: 'ell', + 247: 'msv', + 248: 'b', + 249: 'iii', + 250: 'xrb', + 251: 'bly', + 252: 'wu', + 253: 'alg', + 254: 'psys', + 255: 'SSO', + 256: 'BLZ', + 257: 'OVV', + 258: 'dsm', + 259: 'lamb', + 260: 'xrbin', + 261: 'lboo', + 262: 'qso', + 263: 'seyf', + 265: 'fsrq', + 266: 'iin', + 267: 'hae', + 268: 'tiapec', + 269: 'tiasc', + 270: 'iil', + 271: 'iip', + 272: 'iib', + 273: 'ticpec', + 274: 'maser', + 275: 'moving', + 276: 'ast', + 277: 'comet', + 278: 'hpm', + 279: 'eclipsing', + 280: 'k', + 281: 'd', + 282: 'sd', + 283: 'unclass', + 284: 'pvsg', + 285: 'cp', + 286: 'spb', + 287: 'sdbv', + 1000000: 'Chemically Peculiar Stars'}, + 'longname_shortname': {'AM Canum Venaticorum': 'amcvn', + 'AM Her': 'AM', + 'AM Herculis (True Polar)': 'am', + 'Active Galactic Nuclei': 'AGN', + 'Algol (Beta Persei)': 'alg', + 'Alpha Cygni': 'ac', + 'Alpha2 CVn - Rapily Oscillating': 'ACVO', + 'Alpha2 Canum Venaticorum': 'aii', + 'Anomalous Cepheids': 'BLBOO', + 'Anomolous Cepheid': 'ca', + 'Asteroid': 'ast', + 'BL Lac': 'BL-Lac', + 'BY Draconis': 'by', + 'Be Star': 'be', + 'Be star': 'BE', + 'Beta Cephei': 'bc', + 'Beta Cephei - Short Period': 'BCEPS', + 'Beta Lyrae': 'bly', + 'Binary': 'b', + 'Blazar': 'BLZ', + 'Cataclysmic (Explosive and Novalike) Variable Stars': 'Cataclysmic', + 'Cataclysmic Variable': 'cv', + 'Cepheid Variable': 'c', + 'Cepheids': 'CEP', + 'Cepheids - Multiple Modes': 'CEP(B)', + 'Chemically Peculiar Stars': 'CP', + 'Classical Cepheid': 'dc', + 'Classical Novae': 'cn', + 'Classical T Tauri': 'ttc', + 'Close Binary Eclipsing Systems': 'eclipsing', + 'Close Binary with Reflection': 'R', + 'Comet': 'comet', + 'Contact Systems': 'k', + 'Contact Systems - Early (O-A)': 'KE', + 'Contact Systems - W Ursa Majoris': 'KW', + 'Core Collapse Supernovae': 'cc', + 'DQ Herculis (Intermdiate Polars)': 'dqh', + 'DQ Herculis Variable (Intermediate Polars)': 'DQ', + 'Dark Matter Anniliation Event': 'DrkMatterA', + 'Delta Cep': 'DCEP', + 'Delta Cep - Symmetrical': 'DCEPS', + 'Delta Scuti': 'ds', + 'Delta Scuti - Low Amplitude': 'DSCTC', + 'Delta Scuti - Multiple Modes': 'dsm', + 'Detached': 'd', + 'Detached - AR Lacertae': 'AR', + 'Detached - Main Sequence': 'DM', + 'Detached - With Subgiant': 'DS', + 'ER Ursae Majoris': 'er', + 'Eclipsed by Planets': 'EP', + 'Eclipsing Binary Systems': 'E', + 'Ellipsoidal': 'ell', + 'Eruptive Variable': 'ev', + 'Eruptive Variable Stars': 'Eruptive', + 'Eruptive Wolf-Rayet': 'WR', + 'FK Comae Berenices': 'fk', + 'FU Orionis': 'fuor', + 'Fast Novae': 'NA', + 'Flaring Orion Variables': 'UVN', + 'Flat Spectrum Radio Quasar': 'fsrq', + 'Fluctuating X-Ray Systems': 'XF', + 'GW Virginis': 'gw', + 'Galaxy Nuclei ': 'GalNuclei', + 'Gamma Cas': 'GCAS', + 'Gamma Cassiopeiae': 'gc', + 'Gamma Doradus': 'gd', + 'Gamma Ray Burst': 'grb', + 'Gamma-ray Bursts': 'GRB', + 'Herbig AE': 'hae', + 'Herbig AE/BE Star': 'haebe', + 'High Proper Motion Star': 'hpm', + 'Irregular': 'I', + 'Irregular Early O-A': 'IA', + 'Irregular Intermediate F-M': 'IB', + 'Irregular Supergiants': 'LC', + 'Lambda Bootis Variable': 'lboo', + 'Lambda Eridani': 'lamb', + 'Long GRB': 'lgrb', + 'Long Gamma-ray Burst': 'LSB', + 'Long Period (W Virginis)': 'wv', + 'Long Period B': 'LPB', + 'Maser': 'maser', + 'Microlensing Event': 'ML', + 'Mira': 'mira', + 'Moving Source': 'moving', + 'Multiple Mode Cepheid': 'cm', + 'Multiple Star Variables': 'msv', + 'New Variability Types': 'NEW', + 'Novae': 'nov', + 'Novalike': 'n-l', + 'Novalike Variables': 'NL', + 'Optically Variable Pulsars': 'PSR', + 'Optically Violent Variable Quasar (OVV)': 'OVV', + 'Orion': 'IN', + 'Orion Early Types (B-A or Ae)': 'INA', + 'Orion Intermediate Types (F-M or Fe-Me)': 'INB', + 'Orion T Tauri': 'INT', + 'Orion Variable': 'ov', + 'Orion with Absorption': 'IN(YY)', + 'PV Telescopii': 'pvt', + 'Pair Instability Supernovae': 'pi', + 'Peculiar Type Ia SN': 'tiapec', + 'Peculiar Type Ia Supernovae': 'SNIa-pec', + 'Peculiar Type Ic Supernovae': 'SNIc-pec', + 'Periodically variable supergiants': 'pvsg', + 'Polars': 'p', + 'Population II Cepheid': 'piic', + 'Pulsar': 'plsr', + 'Pulsating Variable': 'puls', + 'Pulsating Variable Stars': 'Pulsating', + 'Pulsating White Dwarf': 'pwd', + 'Pulsating subdwarf B-stars': 'sdbv', + 'QSO': 'qso', + 'R Coronae Borealis': 'rcb', + 'RR Lyrae': 'rr-lyr', + 'RR Lyrae - Asymmetric': 'RRAB', + 'RR Lyrae - Dual Mode': 'RR(B)', + 'RR Lyrae - Near Symmetric': 'RRC', + 'RR Lyrae -- Closely Spaced Modes': 'RRcl', + 'RR Lyrae -- Second Overtone Pulsations': 'RRe', + 'RR Lyrae, Closely Spaced Modes': 'rr-cl', + 'RR Lyrae, Double Mode': 'rr-d', + 'RR Lyrae, First Overtone': 'rr-c', + 'RR Lyrae, Fundamental Mode': 'rr-ab', + 'RR Lyrae, Second Overtone': 'rr-e', + 'RS Canum Venaticorum': 'rscvn', + 'RV Tauri': 'rv', + 'RV Tauri - Constant Mean Magnitude': 'RVA', + 'RV Tauri - Variable Mean Magnitude': 'RVB', + 'RV Tauri, Constant Mean Brightness': 'rvc', + 'RV Tauri, Variable Mean Brightness': 'rvv', + 'Rapid Irregular': 'IS', + 'Rapid Irregular Early Types (B-A or Ae)': 'ISA', + 'Rapid Irregular Intermediate to Late (F-M and Fe-Me)': 'ISB', + 'Recurrent Novae': 'rn', + 'Rotating Ellipsoidal': 'ELL', + 'Rotating Variable': 'rot', + 'Rotating Variable Stars': 'Rotating', + 'S Doradus': 'sdorad', + 'SRa (Z Aquarii)': 'sr-a', + 'SRb': 'sr-b', + 'SRc': 'sr-c', + 'SRd': 'sr-d', + 'SS Cygni': 'ssc', + 'SU Ursae Majoris': 'su', + 'SW Sextantis': 'sw', + 'SX Arietis': 'sxari', + 'SX Phoenicis': 'sx', + 'SX Phoenicis - Pulsating Subdwarfs': 'SXPHE', + 'Semidetached': 'sd', + 'Semiregular': 'SR', + 'Semiregular - Persistent Periodicity': 'SRA', + 'Semiregular - Poorly Defined Periodicity': 'SRB', + 'Semiregular F, G, or K': 'SRD', + 'Semiregular Pulsating Red Giants': 'SRS', + 'Semiregular Pulsating Variable': 'sreg', + 'Semiregular Supergiants': 'SRC', + 'Seyfert': 'seyf', + 'Shell Star': 'shs', + 'Short GRB': 'sgrb', + 'Short Gamma-ray Burst': 'SHB', + 'Short period (BL Herculis)': 'bl', + 'Slow Irregular': 'L', + 'Slow Irregular - Late Spectral Type (K, M, C, S)': 'LB', + 'Slow Novae': 'NB', + 'Slowly Pulsating B-stars': 'spb', + 'Soft Gamma Ray Repeater': 'srgrb', + 'Soft Gamma-ray Repeater': 'SGR', + 'Solar System Object': 'SSO', + 'Super-chandra Ia supernova': 'SNIa-sc', + 'Super-chandra Type Ia SN': 'tiasc', + 'Supernovae': 'sne', + 'Symbiotic Variable': 'sv', + 'Symbiotic Variables': 'ZAND', + 'Symmetrical': 'sdc', + 'Systems with Planetary Nebulae': 'PN', + 'Systems with Planets': 'psys', + 'Systems with Supergiant(s)': 'GS', + 'Systems with White Dwarfs': 'WD', + 'Systems with Wolf-Rayet Stars': 'WR(1)', + 'T Tauri': 'tt', + 'Three or More Stars': 'iii', + 'Tidal Disruption Event': 'TDE', + 'Type I Supernovae': 'tsni', + 'Type II L supernova': 'iil', + 'Type II N Supernova': 'iin', + 'Type II P supernova': 'iip', + 'Type II Supernovae': 'tsnii', + 'Type II b Supernova': 'iib', + 'Type II-L': 'SNIIL', + 'Type IIN': 'SNIIN', + 'Type IIP': 'SNIIP', + 'Type Ia': 'SNIa', + 'Type Ia Supernovae': 'tia', + 'Type Ib': 'SNIb', + 'Type Ib Supernovae': 'tib', + 'Type Ic': 'SNIc', + 'Type Ic Supernovae': 'tic', + 'Type Ic peculiar': 'ticpec', + 'U Geminorum': 'ug', + 'UV Ceti': 'UV', + 'UV Ceti Variable': 'uv', + 'UX Uma': 'UXUma', + 'UX Ursae Majoris': 'ux', + 'Unclassified': 'unclass', + 'VY Scl': 'vy', + 'Variable Sources (Non-stellar)': 'Nonstellar', + 'Variable Stars': 'GCVS', + 'Variable Stars [Alt]': 'vs', + 'Very Rapidly Pulsating Hot (subdwarf B)': 'RPHS', + 'Very Slow Novae': 'NC', + 'W Ursa Majoris': 'DW', + 'W Ursae Majoris': 'wu', + 'W Ursae Majoris - W UMa': 'EW', + 'W Ursae Majoris- a': 'EWa', + 'W Ursae Majoris- s': 'EWs', + 'W Virginis': 'CW', + 'W Virginis - Long Period': 'CWA', + 'W Virigins - Short Period': 'CWB', + 'WZ Sagittae': 'wz', + 'Weak-lined T Tauri': 'ttw', + 'Wolf-Rayet': 'wr', + 'X Ray Binary': 'xrbin', + 'X Ray Burster': 'xrb', + 'X-Ray Binaries with Jets': 'XJ', + 'X-Ray Bursters': 'XB', + 'X-Ray Pulsar': 'XP', + 'X-Ray Pulsar with late-type dwarf': 'XPRM', + 'X-Ray Pulsar, with Reflection': 'XPR', + 'X-Ray Sources, Optically Variable': 'X', + 'X-Ray with late-type dwarf, un-observed pulsar': 'XRM', + 'X-Ray, Novalike': 'XND', + 'X-Ray, Novalike with Early Type supergiant or giant': 'XNG', + 'X-ray Irregulars': 'XI', + 'Z Camelopardalis': 'zc', + 'ZZ Ceti': 'zz', + 'ZZ Ceti - Only H Absorption': 'ZZA', + 'ZZ Ceti - Only He Absorption': 'ZZB', + 'ZZ Ceti showing HeII': 'ZZO', + 'ZZ Ceti, H Absorption Only': 'zzh', + 'ZZ Ceti, He Absorption Only': 'zzhe', + 'ZZ Ceti, With He-II': 'zzheii', + '_varstar_': '_varstar_'}, + 'shortname_isactive': {'ACVO': 'Yes', + 'AGN': 'Yes', + 'AR': 'Yes', + 'BCEPS': 'Yes', + 'BL-Lac': 'Yes', + 'BLZ': 'Yes', + 'CP': 'No', + 'CW': 'Yes', + 'CWA': 'Yes', + 'CWB': 'Yes', + 'D': 'Yes', + 'DCEP': 'Yes', + 'DCEPS': 'Yes', + 'DM': 'Yes', + 'DS': 'Yes', + 'DSCTC': 'Yes', + 'DrkMatterA': 'Yes', + 'E': 'Yes', + 'ELL': 'Yes', + 'EP': 'Yes', + 'EWa': 'Yes', + 'EWs': 'Yes', + 'Eclipsing': 'Yes', + 'GS': 'Yes', + 'GalNuclei': 'Yes', + 'I': 'Yes', + 'IA': 'Yes', + 'IB': 'Yes', + 'IN(YY)': 'Yes', + 'INA': 'Yes', + 'INB': 'Yes', + 'IS': 'Yes', + 'ISA': 'Yes', + 'ISB': 'Yes', + 'K': 'Yes', + 'KE': 'Yes', + 'KW': 'Yes', + 'L': 'Yes', + 'LB': 'Yes', + 'LC': 'Yes', + 'LPB': 'Yes', + 'ML': 'Yes', + 'NA': 'Yes', + 'NB': 'Yes', + 'NC': 'Yes', + 'NEW': 'Yes', + 'Nonstellar': 'Yes', + 'OVV': 'Yes', + 'PN': 'Yes', + 'PSR': 'Yes', + 'R': 'Yes', + 'RPHS': 'Yes', + 'RR(B)': 'Yes', + 'RRAB': 'Yes', + 'RRC': 'Yes', + 'SD': 'Yes', + 'SNIc-pec': 'Yes', + 'SRA': 'Yes', + 'SRB': 'Yes', + 'SRC': 'Yes', + 'SRD': 'Yes', + 'SRS': 'Yes', + 'SSO': 'Yes', + 'TDE': 'Yes', + 'UVN': 'Yes', + 'WD': 'Yes', + 'WR(1)': 'Yes', + 'X': 'Yes', + 'XF': 'Yes', + 'XI': 'Yes', + 'XJ': 'Yes', + 'XND': 'Yes', + 'XNG': 'Yes', + 'XP': 'Yes', + 'XPR': 'Yes', + 'XPRM': 'Yes', + 'XRM': 'Yes', + '_varstar_': 'No', + 'ac': 'Yes', + 'aii': 'Yes', + 'alg': 'Yes', + 'am': 'Yes', + 'amcvn': 'Yes', + 'ast': 'Yes', + 'b': 'Yes', + 'bc': 'Yes', + 'be': 'Yes', + 'bl': 'Yes', + 'bly': 'Yes', + 'by': 'Yes', + 'c': 'Yes', + 'ca': 'Yes', + 'cc': 'Yes', + 'cm': 'Yes', + 'cn': 'Yes', + 'comet': 'Yes', + 'cp': 'Yes', + 'cv': 'Yes', + 'd': 'Yes', + 'dc': 'Yes', + 'dqh': 'Yes', + 'ds': 'Yes', + 'dsm': 'Yes', + 'eclipsing': 'Yes', + 'ell': 'Yes', + 'er': 'Yes', + 'ev': 'Yes', + 'fk': 'Yes', + 'fsrq': 'Yes', + 'fuor': 'Yes', + 'gc': 'Yes', + 'gd': 'Yes', + 'grb': 'Yes', + 'gw': 'Yes', + 'hae': 'Yes', + 'haebe': 'Yes', + 'hpm': 'Yes', + 'iib': 'Yes', + 'iii': 'Yes', + 'iil': 'Yes', + 'iin': 'Yes', + 'iip': 'Yes', + 'k': 'Yes', + 'lamb': 'Yes', + 'lboo': 'Yes', + 'lgrb': 'Yes', + 'maser': 'Yes', + 'mira': 'Yes', + 'moving': 'Yes', + 'msv': 'Yes', + 'n-l': 'Yes', + 'nov': 'Yes', + 'ov': 'Yes', + 'p': 'Yes', + 'pi': 'Yes', + 'piic': 'Yes', + 'plsr': 'Yes', + 'psys': 'Yes', + 'puls': 'Yes', + 'pvsg': 'Yes', + 'pvt': 'Yes', + 'pwd': 'Yes', + 'qso': 'Yes', + 'rcb': 'Yes', + 'rn': 'Yes', + 'rot': 'Yes', + 'rr-ab': 'Yes', + 'rr-c': 'Yes', + 'rr-cl': 'Yes', + 'rr-d': 'Yes', + 'rr-e': 'Yes', + 'rr-lyr': 'Yes', + 'rscvn': 'Yes', + 'rv': 'Yes', + 'rvc': 'Yes', + 'rvv': 'Yes', + 'sd': 'Yes', + 'sdbv': 'Yes', + 'sdc': 'Yes', + 'sdorad': 'Yes', + 'seyf': 'Yes', + 'sgrb': 'Yes', + 'shs': 'Yes', + 'sne': 'Yes', + 'spb': 'Yes', + 'sr-a': 'Yes', + 'sr-b': 'Yes', + 'sr-c': 'Yes', + 'sr-d': 'Yes', + 'sreg': 'Yes', + 'srgrb': 'Yes', + 'ssc': 'Yes', + 'su': 'Yes', + 'sv': 'Yes', + 'sw': 'Yes', + 'sx': 'Yes', + 'sxari': 'Yes', + 'tia': 'Yes', + 'tiapec': 'Yes', + 'tiasc': 'Yes', + 'tib': 'Yes', + 'tic': 'Yes', + 'ticpec': 'Yes', + 'tsni': 'Yes', + 'tsnii': 'Yes', + 'tt': 'Yes', + 'ttc': 'Yes', + 'ttw': 'Yes', + 'ug': 'Yes', + 'unclass': 'Yes', + 'uv': 'Yes', + 'ux': 'Yes', + 'vs': 'Yes', + 'vy': 'Yes', + 'wr': 'Yes', + 'wu': 'Yes', + 'wv': 'Yes', + 'wz': 'Yes', + 'xrb': 'Yes', + 'xrbin': 'Yes', + 'zc': 'Yes', + 'zz': 'Yes', + 'zzh': 'Yes', + 'zzhe': 'Yes', + 'zzheii': 'Yes'}, + 'shortname_ispublic': {'ACVO': 'No', + 'AGN': 'Yes', + 'AR': 'No', + 'BCEPS': 'No', + 'BL-Lac': 'Yes', + 'BLZ': 'Yes', + 'CP': 'No', + 'CW': 'No', + 'CWA': 'No', + 'CWB': 'No', + 'D': 'No', + 'DCEP': 'No', + 'DCEPS': 'No', + 'DM': 'No', + 'DS': 'No', + 'DSCTC': 'No', + 'DrkMatterA': 'Yes', + 'E': 'No', + 'ELL': 'No', + 'EP': 'No', + 'EWa': 'No', + 'EWs': 'No', + 'Eclipsing': 'No', + 'GS': 'No', + 'GalNuclei': 'Yes', + 'I': 'No', + 'IA': 'No', + 'IB': 'No', + 'IN(YY)': 'No', + 'INA': 'No', + 'INB': 'No', + 'IS': 'No', + 'ISA': 'No', + 'ISB': 'No', + 'K': 'No', + 'KE': 'No', + 'KW': 'No', + 'L': 'No', + 'LB': 'No', + 'LC': 'No', + 'LPB': 'No', + 'ML': 'Yes', + 'NA': 'No', + 'NB': 'No', + 'NC': 'No', + 'NEW': 'No', + 'Nonstellar': 'Yes', + 'OVV': 'Yes', + 'PN': 'No', + 'PSR': 'No', + 'R': 'No', + 'RPHS': 'No', + 'RR(B)': 'No', + 'RRAB': 'No', + 'RRC': 'No', + 'SD': 'No', + 'SNIc-pec': 'No', + 'SRA': 'No', + 'SRB': 'No', + 'SRC': 'No', + 'SRD': 'No', + 'SRS': 'No', + 'SSO': 'Yes', + 'TDE': 'Yes', + 'UVN': 'No', + 'WD': 'No', + 'WR(1)': 'No', + 'X': 'No', + 'XF': 'No', + 'XI': 'No', + 'XJ': 'No', + 'XND': 'No', + 'XNG': 'No', + 'XP': 'No', + 'XPR': 'No', + 'XPRM': 'No', + 'XRM': 'No', + '_varstar_': 'No', + 'ac': 'Yes', + 'aii': 'Yes', + 'alg': 'Yes', + 'am': 'Yes', + 'amcvn': 'Yes', + 'ast': 'Yes', + 'b': 'Yes', + 'bc': 'Yes', + 'be': 'Yes', + 'bl': 'Yes', + 'bly': 'Yes', + 'by': 'Yes', + 'c': 'Yes', + 'ca': 'Yes', + 'cc': 'Yes', + 'cm': 'Yes', + 'cn': 'Yes', + 'comet': 'Yes', + 'cp': 'Yes', + 'cv': 'Yes', + 'd': 'Yes', + 'dc': 'Yes', + 'dqh': 'Yes', + 'ds': 'Yes', + 'dsm': 'Yes', + 'eclipsing': 'No', + 'ell': 'Yes', + 'er': 'Yes', + 'ev': 'Yes', + 'fk': 'Yes', + 'fsrq': 'Yes', + 'fuor': 'Yes', + 'gc': 'Yes', + 'gd': 'Yes', + 'grb': 'Yes', + 'gw': 'Yes', + 'hae': 'Yes', + 'haebe': 'Yes', + 'hpm': 'Yes', + 'iib': 'Yes', + 'iii': 'Yes', + 'iil': 'Yes', + 'iin': 'Yes', + 'iip': 'Yes', + 'k': 'No', + 'lamb': 'Yes', + 'lboo': 'Yes', + 'lgrb': 'Yes', + 'maser': 'Yes', + 'mira': 'Yes', + 'moving': 'Yes', + 'msv': 'Yes', + 'n-l': 'Yes', + 'nov': 'Yes', + 'ov': 'Yes', + 'p': 'Yes', + 'pi': 'Yes', + 'piic': 'Yes', + 'plsr': 'Yes', + 'psys': 'Yes', + 'puls': 'Yes', + 'pvsg': 'Yes', + 'pvt': 'Yes', + 'pwd': 'Yes', + 'qso': 'Yes', + 'rcb': 'Yes', + 'rn': 'Yes', + 'rot': 'Yes', + 'rr-ab': 'Yes', + 'rr-c': 'Yes', + 'rr-cl': 'Yes', + 'rr-d': 'Yes', + 'rr-e': 'Yes', + 'rr-lyr': 'Yes', + 'rscvn': 'Yes', + 'rv': 'Yes', + 'rvc': 'Yes', + 'rvv': 'Yes', + 'sd': 'No', + 'sdbv': 'Yes', + 'sdc': 'Yes', + 'sdorad': 'Yes', + 'seyf': 'Yes', + 'sgrb': 'Yes', + 'shs': 'Yes', + 'sne': 'Yes', + 'spb': 'Yes', + 'sr-a': 'Yes', + 'sr-b': 'Yes', + 'sr-c': 'Yes', + 'sr-d': 'Yes', + 'sreg': 'Yes', + 'srgrb': 'Yes', + 'ssc': 'Yes', + 'su': 'Yes', + 'sv': 'Yes', + 'sw': 'Yes', + 'sx': 'Yes', + 'sxari': 'Yes', + 'tia': 'Yes', + 'tiapec': 'Yes', + 'tiasc': 'Yes', + 'tib': 'Yes', + 'tic': 'Yes', + 'ticpec': 'Yes', + 'tsni': 'Yes', + 'tsnii': 'Yes', + 'tt': 'Yes', + 'ttc': 'Yes', + 'ttw': 'Yes', + 'ug': 'Yes', + 'unclass': 'Yes', + 'uv': 'Yes', + 'ux': 'Yes', + 'vs': 'Yes', + 'vy': 'Yes', + 'wr': 'Yes', + 'wu': 'Yes', + 'wv': 'Yes', + 'wz': 'Yes', + 'xrb': 'Yes', + 'xrbin': 'Yes', + 'zc': 'Yes', + 'zz': 'Yes', + 'zzh': 'Yes', + 'zzhe': 'Yes', + 'zzheii': 'Yes'}, + 'shortname_longname': {'ACVO': 'Alpha2 CVn - Rapily Oscillating', + 'AGN': 'Active Galactic Nuclei', + 'AR': 'Detached - AR Lacertae', + 'BCEPS': 'Beta Cephei - Short Period', + 'BL-Lac': 'BL Lac', + 'BLZ': 'Blazar', + 'CP': 'Chemically Peculiar Stars', + 'CW': 'W Virginis', + 'CWA': 'W Virginis - Long Period', + 'CWB': 'W Virigins - Short Period', + 'D': 'Detached', + 'DCEP': 'Delta Cep', + 'DCEPS': 'Delta Cep - Symmetrical', + 'DM': 'Detached - Main Sequence', + 'DS': 'Detached - With Subgiant', + 'DSCTC': 'Delta Scuti - Low Amplitude', + 'DrkMatterA': 'Dark Matter Anniliation Event', + 'E': 'Eclipsing Binary Systems', + 'ELL': 'Rotating Ellipsoidal', + 'EP': 'Eclipsed by Planets', + 'EWa': 'W Ursae Majoris- a', + 'EWs': 'W Ursae Majoris- s', + 'Eclipsing': 'Close Binary Eclipsing Systems', + 'GS': 'Systems with Supergiant(s)', + 'GalNuclei': 'Galaxy Nuclei ', + 'I': 'Irregular', + 'IA': 'Irregular Early O-A', + 'IB': 'Irregular Intermediate F-M', + 'IN(YY)': 'Orion with Absorption', + 'INA': 'Orion Early Types (B-A or Ae)', + 'INB': 'Orion Intermediate Types (F-M or Fe-Me)', + 'IS': 'Rapid Irregular', + 'ISA': 'Rapid Irregular Early Types (B-A or Ae)', + 'ISB': 'Rapid Irregular Intermediate to Late (F-M and Fe-Me)', + 'K': 'Contact Systems', + 'KE': 'Contact Systems - Early (O-A)', + 'KW': 'Contact Systems - W Ursa Majoris', + 'L': 'Slow Irregular', + 'LB': 'Slow Irregular - Late Spectral Type (K, M, C, S)', + 'LC': 'Irregular Supergiants', + 'LPB': 'Long Period B', + 'ML': 'Microlensing Event', + 'NA': 'Fast Novae', + 'NB': 'Slow Novae', + 'NC': 'Very Slow Novae', + 'NEW': 'New Variability Types', + 'Nonstellar': 'Variable Sources (Non-stellar)', + 'OVV': 'Optically Violent Variable Quasar (OVV)', + 'PN': 'Systems with Planetary Nebulae', + 'PSR': 'Optically Variable Pulsars', + 'R': 'Close Binary with Reflection', + 'RPHS': 'Very Rapidly Pulsating Hot (subdwarf B)', + 'RR(B)': 'RR Lyrae - Dual Mode', + 'RRAB': 'RR Lyrae - Asymmetric', + 'RRC': 'RR Lyrae - Near Symmetric', + 'SD': 'Semidetached', + 'SNIc-pec': 'Peculiar Type Ic Supernovae', + 'SRA': 'Semiregular - Persistent Periodicity', + 'SRB': 'Semiregular - Poorly Defined Periodicity', + 'SRC': 'Semiregular Supergiants', + 'SRD': 'Semiregular F, G, or K', + 'SRS': 'Semiregular Pulsating Red Giants', + 'SSO': 'Solar System Object', + 'TDE': 'Tidal Disruption Event', + 'UVN': 'Flaring Orion Variables', + 'WD': 'Systems with White Dwarfs', + 'WR(1)': 'Systems with Wolf-Rayet Stars', + 'X': 'X-Ray Sources, Optically Variable', + 'XF': 'Fluctuating X-Ray Systems', + 'XI': 'X-ray Irregulars', + 'XJ': 'X-Ray Binaries with Jets', + 'XND': 'X-Ray, Novalike', + 'XNG': 'X-Ray, Novalike with Early Type supergiant or giant', + 'XP': 'X-Ray Pulsar', + 'XPR': 'X-Ray Pulsar, with Reflection', + 'XPRM': 'X-Ray Pulsar with late-type dwarf', + 'XRM': 'X-Ray with late-type dwarf, un-observed pulsar', + '_varstar_': '_varstar_', + 'ac': 'Alpha Cygni', + 'aii': 'Alpha2 Canum Venaticorum', + 'alg': 'Algol (Beta Persei)', + 'am': 'AM Herculis (True Polar)', + 'amcvn': 'AM Canum Venaticorum', + 'ast': 'Asteroid', + 'b': 'Binary', + 'bc': 'Beta Cephei', + 'be': 'Be Star', + 'bl': 'Short period (BL Herculis)', + 'bly': 'Beta Lyrae', + 'by': 'BY Draconis', + 'c': 'Cepheid Variable', + 'ca': 'Anomolous Cepheid', + 'cc': 'Core Collapse Supernovae', + 'cm': 'Multiple Mode Cepheid', + 'cn': 'Classical Novae', + 'comet': 'Comet', + 'cp': 'Chemically Peculiar Stars', + 'cv': 'Cataclysmic Variable', + 'd': 'Detached', + 'dc': 'Classical Cepheid', + 'dqh': 'DQ Herculis (Intermdiate Polars)', + 'ds': 'Delta Scuti', + 'dsm': 'Delta Scuti - Multiple Modes', + 'eclipsing': 'Close Binary Eclipsing Systems', + 'ell': 'Ellipsoidal', + 'er': 'ER Ursae Majoris', + 'ev': 'Eruptive Variable', + 'fk': 'FK Comae Berenices', + 'fsrq': 'Flat Spectrum Radio Quasar', + 'fuor': 'FU Orionis', + 'gc': 'Gamma Cassiopeiae', + 'gd': 'Gamma Doradus', + 'grb': 'Gamma Ray Burst', + 'gw': 'GW Virginis', + 'hae': 'Herbig AE', + 'haebe': 'Herbig AE/BE Star', + 'hpm': 'High Proper Motion Star', + 'iib': 'Type II b Supernova', + 'iii': 'Three or More Stars', + 'iil': 'Type II L supernova', + 'iin': 'Type II N Supernova', + 'iip': 'Type II P supernova', + 'k': 'Contact Systems', + 'lamb': 'Lambda Eridani', + 'lboo': 'Lambda Bootis Variable', + 'lgrb': 'Long GRB', + 'maser': 'Maser', + 'mira': 'Mira', + 'moving': 'Moving Source', + 'msv': 'Multiple Star Variables', + 'n-l': 'Novalike', + 'nov': 'Novae', + 'ov': 'Orion Variable', + 'p': 'Polars', + 'pi': 'Pair Instability Supernovae', + 'piic': 'Population II Cepheid', + 'plsr': 'Pulsar', + 'psys': 'Systems with Planets', + 'puls': 'Pulsating Variable', + 'pvsg': 'Periodically variable supergiants', + 'pvt': 'PV Telescopii', + 'pwd': 'Pulsating White Dwarf', + 'qso': 'QSO', + 'rcb': 'R Coronae Borealis', + 'rn': 'Recurrent Novae', + 'rot': 'Rotating Variable', + 'rr-ab': 'RR Lyrae, Fundamental Mode', + 'rr-c': 'RR Lyrae, First Overtone', + 'rr-cl': 'RR Lyrae, Closely Spaced Modes', + 'rr-d': 'RR Lyrae, Double Mode', + 'rr-e': 'RR Lyrae, Second Overtone', + 'rr-lyr': 'RR Lyrae', + 'rscvn': 'RS Canum Venaticorum', + 'rv': 'RV Tauri', + 'rvc': 'RV Tauri, Constant Mean Brightness', + 'rvv': 'RV Tauri, Variable Mean Brightness', + 'sd': 'Semidetached', + 'sdbv': 'Pulsating subdwarf B-stars', + 'sdc': 'Symmetrical', + 'sdorad': 'S Doradus', + 'seyf': 'Seyfert', + 'sgrb': 'Short GRB', + 'shs': 'Shell Star', + 'sne': 'Supernovae', + 'spb': 'Slowly Pulsating B-stars', + 'sr-a': 'SRa (Z Aquarii)', + 'sr-b': 'SRb', + 'sr-c': 'SRc', + 'sr-d': 'SRd', + 'sreg': 'Semiregular Pulsating Variable', + 'srgrb': 'Soft Gamma Ray Repeater', + 'ssc': 'SS Cygni', + 'su': 'SU Ursae Majoris', + 'sv': 'Symbiotic Variable', + 'sw': 'SW Sextantis', + 'sx': 'SX Phoenicis', + 'sxari': 'SX Arietis', + 'tia': 'Type Ia Supernovae', + 'tiapec': 'Peculiar Type Ia SN', + 'tiasc': 'Super-chandra Type Ia SN', + 'tib': 'Type Ib Supernovae', + 'tic': 'Type Ic Supernovae', + 'ticpec': 'Type Ic peculiar', + 'tsni': 'Type I Supernovae', + 'tsnii': 'Type II Supernovae', + 'tt': 'T Tauri', + 'ttc': 'Classical T Tauri', + 'ttw': 'Weak-lined T Tauri', + 'ug': 'U Geminorum', + 'unclass': 'Unclassified', + 'uv': 'UV Ceti Variable', + 'ux': 'UX Ursae Majoris', + 'vs': 'Variable Stars [Alt]', + 'vy': 'VY Scl', + 'wr': 'Wolf-Rayet', + 'wu': 'W Ursae Majoris', + 'wv': 'Long Period (W Virginis)', + 'wz': 'WZ Sagittae', + 'xrb': 'X Ray Burster', + 'xrbin': 'X Ray Binary', + 'zc': 'Z Camelopardalis', + 'zz': 'ZZ Ceti', + 'zzh': 'ZZ Ceti, H Absorption Only', + 'zzhe': 'ZZ Ceti, He Absorption Only', + 'zzheii': 'ZZ Ceti, With He-II'}, + 'shortname_nsrcs': {'ACV': 0, + 'ACVO': 0, + 'ACYG': 0, + 'AGN': 57, + 'AM': 0, + 'AR': 0, + 'BCEP': 0, + 'BCEPS': 0, + 'BE': 0, + 'BL-Lac': 46, + 'BLBOO': 0, + 'BLZ': 24, + 'BY': 0, + 'CEP': 5, + 'CEP(B)': 0, + 'CP': 0, + 'CW': 0, + 'CWA': 0, + 'CWB': 0, + 'Cataclysmic': 0, + 'Chemically Peculiar Stars': 0, + 'D': 2, + 'DCEP': 0, + 'DCEPS': 0, + 'DM': 0, + 'DQ': 0, + 'DS': 0, + 'DSCT': 149, + 'DSCTC': 0, + 'DW': 0, + 'DrkMatterA': 0, + 'E': 3, + 'EA': 260, + 'EB': 67, + 'ELL': 0, + 'EP': 0, + 'EW': 891, + 'EWa': 0, + 'EWs': 0, + 'Eclipsing': 0, + 'Eruptive': 0, + 'FKCOM': 0, + 'FU': 0, + 'GCAS': 0, + 'GCVS': 712, + 'GDOR': 15, + 'GRB': 0, + 'GS': 0, + 'GalNuclei': 0, + 'I': 0, + 'IA': 0, + 'IB': 0, + 'IN': 0, + 'IN(YY)': 0, + 'INA': 0, + 'INB': 0, + 'INT': 0, + 'IS': 0, + 'ISA': 0, + 'ISB': 0, + 'K': 0, + 'KE': 0, + 'KW': 0, + 'L': 0, + 'LB': 0, + 'LC': 1, + 'LPB': 1, + 'LSB': 0, + 'M': 11, + 'ML': 658, + 'N': 1, + 'NA': 0, + 'NB': 0, + 'NC': 0, + 'NEW': 0, + 'NL': 3, + 'NR': 0, + 'Nonstellar': 0, + 'OVV': 0, + 'PN': 0, + 'PSR': 0, + 'PVTEL': 0, + 'Polars': 0, + 'Pulsating': 1, + 'R': 0, + 'RCB': 0, + 'RPHS': 0, + 'RR': 9, + 'RR(B)': 0, + 'RRAB': 31, + 'RRC': 15, + 'RRcl': 0, + 'RRe': 0, + 'RS': 0, + 'RV': 0, + 'RVA': 0, + 'RVB': 0, + 'Rotating': 0, + 'SD': 0, + 'SDOR': 0, + 'SGR': 0, + 'SHB': 0, + 'SN': 0, + 'SNI': 0, + 'SNII': 0, + 'SNIIL': 0, + 'SNIIN': 1, + 'SNIIP': 0, + 'SNIa': 0, + 'SNIa-pec': 0, + 'SNIa-sc': 0, + 'SNIb': 0, + 'SNIc': 0, + 'SNIc-pec': 0, + 'SR': 0, + 'SRA': 0, + 'SRB': 0, + 'SRC': 0, + 'SRD': 0, + 'SRS': 14, + 'SSO': 0, + 'SXARI': 0, + 'SXPHE': 7, + 'TDE': 0, + 'UG': 3, + 'UGSS': 0, + 'UGSU': 0, + 'UGZ': 1, + 'UV': 0, + 'UVN': 0, + 'UXUma': 0, + 'WD': 0, + 'WR': 173, + 'WR(1)': 0, + 'X': 0, + 'XB': 0, + 'XF': 0, + 'XI': 0, + 'XJ': 0, + 'XND': 0, + 'XNG': 0, + 'XP': 0, + 'XPR': 0, + 'XPRM': 0, + 'XRM': 0, + 'ZAND': 0, + 'ZZ': 0, + 'ZZA': 0, + 'ZZB': 0, + 'ZZO': 0, + '_varstar_': 0, + 'ac': 0, + 'aii': 81, + 'alg': 732, + 'am': 5, + 'amcvn': 0, + 'ast': 93, + 'b': 53, + 'bc': 84, + 'be': 47, + 'bl': 14, + 'bly': 403, + 'by': 0, + 'c': 329, + 'ca': 7, + 'cc': 58, + 'cm': 202, + 'cn': 1, + 'comet': 3, + 'cp': 49, + 'cv': 193, + 'd': 2270, + 'dc': 865, + 'dqh': 5, + 'ds': 845, + 'dsm': 1, + 'eclipsing': 2934, + 'ell': 17, + 'er': 0, + 'ev': 0, + 'fk': 1, + 'fsrq': 3, + 'fuor': 4, + 'gc': 0, + 'gd': 73, + 'grb': 0, + 'gw': 2, + 'hae': 1, + 'haebe': 28, + 'hpm': 4, + 'iib': 27, + 'iii': 0, + 'iil': 0, + 'iin': 111, + 'iip': 74, + 'k': 2758, + 'lamb': 1, + 'lboo': 26, + 'lgrb': 1, + 'maser': 1, + 'mira': 3048, + 'moving': 0, + 'msv': 0, + 'n-l': 3, + 'nov': 3, + 'ov': 0, + 'p': 0, + 'pi': 0, + 'piic': 41, + 'plsr': 0, + 'psys': 3, + 'puls': 250, + 'pvsg': 0, + 'pvt': 0, + 'pwd': 3, + 'qso': 6307, + 'rcb': 2, + 'rn': 0, + 'rot': 4, + 'rr-ab': 1706, + 'rr-c': 452, + 'rr-cl': 13, + 'rr-d': 168, + 'rr-e': 0, + 'rr-lyr': 16, + 'rscvn': 1, + 'rv': 11, + 'rvc': 1, + 'rvv': 0, + 'sd': 879, + 'sdbv': 0, + 'sdc': 53, + 'sdorad': 21, + 'seyf': 0, + 'sgrb': 0, + 'shs': 0, + 'sne': 619, + 'spb': 0, + 'sr-a': 2, + 'sr-b': 2, + 'sr-c': 2, + 'sr-d': 3, + 'sreg': 76, + 'srgrb': 0, + 'ssc': 3, + 'su': 3, + 'sv': 0, + 'sw': 1, + 'sx': 28, + 'sxari': 0, + 'tia': 2176, + 'tiapec': 35, + 'tiasc': 0, + 'tib': 68, + 'tic': 124, + 'ticpec': 5, + 'tsni': 48, + 'tsnii': 749, + 'tt': 32, + 'ttc': 2, + 'ttw': 0, + 'ug': 3, + 'unclass': 61200, + 'uv': 0, + 'ux': 2, + 'vs': 774, + 'vy': 1, + 'wr': 219, + 'wu': 1075, + 'wv': 35, + 'wz': 0, + 'xrb': 0, + 'xrbin': 14, + 'zc': 3, + 'zz': 0, + 'zzh': 0, + 'zzhe': 0, + 'zzheii': 0}, + 'shortname_parent_id': {'ACVO': 58, + 'AGN': 109, + 'AR': 80, + 'BCEPS': 25, + 'BL-Lac': 256, + 'BLZ': 110, + 'CP': 0, + 'CW': 23, + 'CWA': 29, + 'CWB': 29, + 'D': 80, + 'DCEP': 23, + 'DCEPS': 32, + 'DM': 92, + 'DS': 92, + 'DSCTC': 34, + 'DrkMatterA': 107, + 'E': 80, + 'ELL': 57, + 'EP': 129, + 'EWa': 85, + 'EWs': 85, + 'Eclipsing': 1, + 'GS': 80, + 'GalNuclei': 107, + 'I': 2, + 'IA': 2, + 'IB': 2, + 'IN(YY)': 8, + 'INA': 8, + 'INB': 8, + 'IS': 2, + 'ISA': 2, + 'ISB': 13, + 'K': 80, + 'KE': 96, + 'KW': 96, + 'L': 23, + 'LB': 36, + 'LC': 36, + 'LPB': 129, + 'ML': 107, + 'NA': 66, + 'NB': 66, + 'NC': 66, + 'NEW': 1, + 'Nonstellar': 0, + 'OVV': 256, + 'PN': 80, + 'PSR': 57, + 'R': 129, + 'RPHS': 129, + 'RR(B)': 41, + 'RRAB': 41, + 'RRC': 41, + 'SD': 80, + 'SNIc-pec': 102, + 'SRA': 48, + 'SRB': 48, + 'SRC': 48, + 'SRD': 48, + 'SRS': 129, + 'SSO': 107, + 'TDE': 109, + 'UVN': 2, + 'WD': 80, + 'WR(1)': 80, + 'X': 1, + 'XF': 117, + 'XI': 117, + 'XJ': 117, + 'XND': 117, + 'XNG': 117, + 'XP': 117, + 'XPR': 124, + 'XPRM': 124, + 'XRM': 124, + 'ac': 203, + 'aii': 240, + 'alg': 248, + 'am': 165, + 'amcvn': 160, + 'ast': 275, + 'b': 247, + 'bc': 203, + 'be': 193, + 'bl': 216, + 'bly': 248, + 'by': 240, + 'c': 203, + 'ca': 217, + 'cc': 180, + 'cm': 217, + 'cn': 158, + 'comet': 275, + 'cp': 154, + 'cv': 154, + 'd': 279, + 'dc': 217, + 'dqh': 165, + 'ds': 203, + 'dsm': 211, + 'eclipsing': 154, + 'ell': 240, + 'er': 169, + 'ev': 154, + 'fk': 240, + 'fsrq': 256, + 'fuor': 195, + 'gc': 188, + 'gd': 203, + 'grb': 157, + 'gw': 209, + 'hae': 197, + 'haebe': 188, + 'hpm': 275, + 'iib': 185, + 'iii': 247, + 'iil': 185, + 'iin': 185, + 'iip': 185, + 'k': 279, + 'lamb': 198, + 'lboo': 203, + 'lgrb': 176, + 'maser': 107, + 'mira': 203, + 'moving': 0, + 'msv': 154, + 'n-l': 158, + 'nov': 157, + 'ov': 188, + 'p': 158, + 'pi': 181, + 'piic': 203, + 'plsr': 240, + 'psys': 248, + 'puls': 154, + 'pvsg': 154, + 'pvt': 203, + 'pwd': 203, + 'qso': 110, + 'rcb': 188, + 'rn': 158, + 'rot': 154, + 'rr-ab': 206, + 'rr-c': 206, + 'rr-cl': 206, + 'rr-d': 206, + 'rr-e': 206, + 'rr-lyr': 203, + 'rscvn': 188, + 'rv': 203, + 'rvc': 215, + 'rvv': 215, + 'sd': 279, + 'sdbv': 203, + 'sdc': 238, + 'sdorad': 188, + 'seyf': 110, + 'sgrb': 176, + 'shs': 193, + 'sne': 157, + 'spb': 203, + 'sr-a': 214, + 'sr-b': 214, + 'sr-c': 214, + 'sr-d': 214, + 'sreg': 203, + 'srgrb': 176, + 'ssc': 168, + 'su': 168, + 'sv': 157, + 'sw': 160, + 'sx': 203, + 'sxari': 240, + 'tia': 180, + 'tiapec': 182, + 'tiasc': 182, + 'tib': 181, + 'tic': 181, + 'ticpec': 184, + 'tsni': 180, + 'tsnii': 181, + 'tt': 195, + 'ttc': 200, + 'ttw': 200, + 'ug': 158, + 'unclass': 154, + 'uv': 188, + 'ux': 160, + 'vs': 0, + 'vy': 160, + 'wr': 188, + 'wu': 248, + 'wv': 216, + 'wz': 169, + 'xrb': 260, + 'xrbin': 248, + 'zc': 168, + 'zz': 209, + 'zzh': 223, + 'zzhe': 223, + 'zzheii': 223}, + 'shortname_parentshortname': {'ACVO': 'aii', + 'AGN': 'GalNuclei', + 'AR': 'Eclipsing', + 'BCEPS': 'bc', + 'BL-Lac': 'BLZ', + 'BLZ': 'AGN', + 'CP': '_varstar_', + 'CW': 'puls', + 'CWA': 'CW', + 'CWB': 'CW', + 'D': 'Eclipsing', + 'DCEP': 'puls', + 'DCEPS': 'DCEP', + 'DM': 'D', + 'DS': 'D', + 'DSCTC': 'ds', + 'DrkMatterA': 'Nonstellar', + 'E': 'Eclipsing', + 'ELL': 'rot', + 'EP': 'NEW', + 'EWa': 'wu', + 'EWs': 'wu', + 'Eclipsing': 'vs', + 'GS': 'Eclipsing', + 'GalNuclei': 'Nonstellar', + 'I': 'ev', + 'IA': 'ev', + 'IB': 'ev', + 'IN(YY)': 'ov', + 'INA': 'ov', + 'INB': 'ov', + 'IS': 'ev', + 'ISA': 'ev', + 'ISB': 'IS', + 'K': 'Eclipsing', + 'KE': 'K', + 'KW': 'K', + 'L': 'puls', + 'LB': 'L', + 'LC': 'L', + 'LPB': 'NEW', + 'ML': 'Nonstellar', + 'NA': 'nov', + 'NB': 'nov', + 'NC': 'nov', + 'NEW': 'vs', + 'Nonstellar': '_varstar_', + 'OVV': 'BLZ', + 'PN': 'Eclipsing', + 'PSR': 'rot', + 'R': 'NEW', + 'RPHS': 'NEW', + 'RR(B)': 'rr-lyr', + 'RRAB': 'rr-lyr', + 'RRC': 'rr-lyr', + 'SD': 'Eclipsing', + 'SNIc-pec': 'tic', + 'SRA': 'sreg', + 'SRB': 'sreg', + 'SRC': 'sreg', + 'SRD': 'sreg', + 'SRS': 'NEW', + 'SSO': 'Nonstellar', + 'TDE': 'GalNuclei', + 'UVN': 'ev', + 'WD': 'Eclipsing', + 'WR(1)': 'Eclipsing', + 'X': 'vs', + 'XF': 'X', + 'XI': 'X', + 'XJ': 'X', + 'XND': 'X', + 'XNG': 'X', + 'XP': 'X', + 'XPR': 'XP', + 'XPRM': 'XP', + 'XRM': 'XP', + 'ac': 'puls', + 'aii': 'rot', + 'alg': 'b', + 'am': 'p', + 'amcvn': 'n-l', + 'ast': 'moving', + 'b': 'msv', + 'bc': 'puls', + 'be': 'gc', + 'bl': 'piic', + 'bly': 'b', + 'by': 'rot', + 'c': 'puls', + 'ca': 'c', + 'cc': 'sne', + 'cm': 'c', + 'cn': 'nov', + 'comet': 'moving', + 'cp': 'vs', + 'cv': 'vs', + 'd': 'eclipsing', + 'dc': 'c', + 'dqh': 'p', + 'ds': 'puls', + 'dsm': 'ds', + 'eclipsing': 'vs', + 'ell': 'rot', + 'er': 'su', + 'ev': 'vs', + 'fk': 'rot', + 'fsrq': 'BLZ', + 'fuor': 'ov', + 'gc': 'ev', + 'gd': 'puls', + 'grb': 'cv', + 'gw': 'pwd', + 'hae': 'haebe', + 'haebe': 'ev', + 'hpm': 'moving', + 'iib': 'tsnii', + 'iii': 'msv', + 'iil': 'tsnii', + 'iin': 'tsnii', + 'iip': 'tsnii', + 'k': 'eclipsing', + 'lamb': 'be', + 'lboo': 'puls', + 'lgrb': 'grb', + 'maser': 'Nonstellar', + 'mira': 'puls', + 'moving': '_varstar_', + 'msv': 'vs', + 'n-l': 'nov', + 'nov': 'cv', + 'ov': 'ev', + 'p': 'nov', + 'pi': 'cc', + 'piic': 'puls', + 'plsr': 'rot', + 'psys': 'b', + 'puls': 'vs', + 'pvsg': 'vs', + 'pvt': 'puls', + 'pwd': 'puls', + 'qso': 'AGN', + 'rcb': 'ev', + 'rn': 'nov', + 'rot': 'vs', + 'rr-ab': 'rr-lyr', + 'rr-c': 'rr-lyr', + 'rr-cl': 'rr-lyr', + 'rr-d': 'rr-lyr', + 'rr-e': 'rr-lyr', + 'rr-lyr': 'puls', + 'rscvn': 'ev', + 'rv': 'puls', + 'rvc': 'rv', + 'rvv': 'rv', + 'sd': 'eclipsing', + 'sdbv': 'puls', + 'sdc': 'dc', + 'sdorad': 'ev', + 'seyf': 'AGN', + 'sgrb': 'grb', + 'shs': 'gc', + 'sne': 'cv', + 'spb': 'puls', + 'sr-a': 'sreg', + 'sr-b': 'sreg', + 'sr-c': 'sreg', + 'sr-d': 'sreg', + 'sreg': 'puls', + 'srgrb': 'grb', + 'ssc': 'ug', + 'su': 'ug', + 'sv': 'cv', + 'sw': 'n-l', + 'sx': 'puls', + 'sxari': 'rot', + 'tia': 'sne', + 'tiapec': 'tia', + 'tiasc': 'tia', + 'tib': 'cc', + 'tic': 'cc', + 'ticpec': 'tic', + 'tsni': 'sne', + 'tsnii': 'cc', + 'tt': 'ov', + 'ttc': 'tt', + 'ttw': 'tt', + 'ug': 'nov', + 'unclass': 'vs', + 'uv': 'ev', + 'ux': 'n-l', + 'vs': '_varstar_', + 'vy': 'n-l', + 'wr': 'ev', + 'wu': 'b', + 'wv': 'piic', + 'wz': 'su', + 'xrb': 'xrbin', + 'xrbin': 'b', + 'zc': 'ug', + 'zz': 'pwd', + 'zzh': 'zz', + 'zzhe': 'zz', + 'zzheii': 'zz'}} + +def parse_options(): + """ Deal with parsing command line options & --help. Return options object. + """ + parser = OptionParser(usage="usage: %prog cmd [options]") + + + +head_str = """ + + + + + + 6930531 + + Best positional information of the source + + + 323.47114731 + -0.79916734036 + + + 0.000277777777778 + 0.000277777777778 + + + + + + MJD + 0.0 + UTC + TOPOCENTER + + + + + + + + + +""" + +tail_str = """ + +
+ + +""" + + + +def generate_xml_str_using_ben_dat(dat_fpath): + """ Adapted from format_csv_getfeats.py + For converting Ben's generated RRLyrae/eclips lightcurve .dat files + + """ + import csv + + data_str_list = [] + + rows = csv.reader(open(dat_fpath), delimiter=' ') + + t_list = [] + m_list = [] + merr_list = [] + for i,row in enumerate(rows): + t = float(row[0]) + m = float(row[1]) + m_err = float(row[2]) + data_str = ' %lf%lf%lf' % \ + (i, t, m, m_err) + data_str_list.append(data_str) + t_list.append(t) + m_list.append(m) + merr_list.append(m_err) + + all_data_str = '\n'.join(data_str_list) + + out_xml = head_str + all_data_str + tail_str + + return out_xml + + + +def get_dat_arffstrs(dat_fpaths=[], percent_list=[], niters=1, include_header=True, + write_multiinfo_srcids=True, ParseNomadColorsList=None): + """ Adapted from: + - analysis_deboss_tcp_source_compare.py::perc_subset_worker() + - generate_weka_classifiers.py --train_mode : + spawn_off_arff_line_tasks() + + # TODO: might need to convert ids into 100000000 + ids + + # TODO: currently this generates xml-strings with features, + - we eventually want arff rows which can be classified + (ala generate_weka_classifiers.py --train_mode) + ... condense_task_results_and_form_arff() + + """ + import copy + import random + import io + sys.path.append(os.environ.get('TCP_DIR') + '/Software/feature_extract/MLData') + #sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + '/Software/feature_extract/Code/extractors')) + #print os.environ.get("TCP_DIR") + #import mlens3 + import arffify + + sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + \ + 'Software/feature_extract/Code')) + import db_importer + from data_cleaning import sigmaclip_sdict_ts + sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + \ + 'Software/feature_extract')) + from Code import generators_importers + + #out_arff_row_dict = {} + + master_list = [] + master_features_dict = {} + all_class_list = [] + master_classes_dict = {} + + new_srcid_list = [] + for dat_fpath in dat_fpaths: + + # TODO: here I form the xml string from dat file ala format_csv_getfeats.py + + new_xml_str = generate_xml_str_using_ben_dat(dat_fpath) + + #xml_str = open(xml_fpath).read() + #new_xml_str = ParseNomadColorsList.get_colors_for_srcid(xml_str=xml_str, srcid=tutor_src_id) + + signals_list = [] + gen_orig = generators_importers.from_xml(signals_list) + gen_orig.signalgen = {} + gen_orig.sig = db_importer.Source(xml_handle=new_xml_str, doplot=False, make_xml_if_given_dict=True) + gen_orig.sdict = gen_orig.sig.x_sdict + gen_orig.set_outputs() # this adds/fills self.signalgen[,multiband]{'input':{filled},'features':{empty},'inter':{empty}} + + signals_list_temp = [] + + try: + ### Do some sigma clipping (Ex: for ASAS data) + sigmaclip_sdict_ts(gen_orig.sig.x_sdict['ts'], sigma_low=4.0, sigma_high=4.0) + except: + continue # probably doesnt have a gen_orig.sig.x_sdict['ts'][]['m'] + + gen_temp = copy.deepcopy(gen_orig) + for perc in percent_list: + ### We generate several random, percent-subsampled vosource in order to include error info: + #if 1: + # i = niters # this should just be a single (integer) subset number/iteration index + #for i in range(niters): + for i in niters: + if write_multiinfo_srcids: + new_srcid = "%d_%2.2f_%d" % (src_id, perc, i) + else: + new_srcid = '%s' % (dat_fpath[dat_fpath.rfind("_") + 1:dat_fpath.rfind(".")]) + new_srcid_list.append(new_srcid) + + dbi_src = db_importer.Source(make_dict_if_given_xml=False) + + for band, band_dict in gen_orig.sig.x_sdict['ts'].items(): + if ":NOMAD" in band: + i_start = 0 + i_end = len(band_dict['m']) + else: + i_start = int(((len(band_dict['m'])+1) * (1 - perc)) * random.random()) + i_end = i_start + int(perc * (len(band_dict['m'])+1)) + gen_temp.sig.x_sdict['ts'][band]['m'] = band_dict['m'][i_start:i_end] + gen_temp.sig.x_sdict['ts'][band]['m_err'] = band_dict['m_err'][i_start:i_end] + gen_temp.sig.x_sdict['ts'][band]['t'] = band_dict['t'][i_start:i_end] + dbi_src.source_dict_to_xml(gen_temp.sig.x_sdict) + write_xml_str = dbi_src.xml_string + + signals_list = [] + gen = generators_importers.from_xml(signals_list) + gen.generate(xml_handle=write_xml_str) + gen.sig.add_features_to_xml_string(signals_list) + gen.sig.x_sdict['src_id'] = new_srcid + dbi_src.source_dict_to_xml(gen.sig.x_sdict) + + xml_fpath = dbi_src.xml_string + + a = arffify.Maker(search=[], skip_class=False, local_xmls=True, convert_class_abrvs_to_names=False, flag_retrieve_class_abrvs_from_TUTOR=False, dorun=False) + out_dict = a.generate_arff_line_for_vosourcexml(num=new_srcid, xml_fpath=xml_fpath) + + #out_arff_row_dict[(src_id, perc, i)] = out_dict # ??? TODO: just arff rows? + # dbi_src.xml_string + master_list.append(out_dict) + all_class_list.append(out_dict['class']) + master_classes_dict[out_dict['class']] = 0 + for feat_tup in out_dict['features']: + master_features_dict[feat_tup] = 0 # just make sure there is this key in the dict. 0 is filler + + master_features = master_features_dict.keys() + master_classes = master_classes_dict.keys() + a = arffify.Maker(search=[], skip_class=False, local_xmls=True, + convert_class_abrvs_to_names=False, + flag_retrieve_class_abrvs_from_TUTOR=False, + dorun=False, add_srcid_to_arff=True) + a.master_features = master_features + a.all_class_list = all_class_list + a.master_classes = master_classes + a.master_list = master_list + # # # TODO: ideally just the arff lines / strings will be used + # - although it might be nice to have a disk copy of the arff rows for record, passing to others. + fp_strio = io.StringIO() + a.write_arff(outfile=fp_strio, \ + remove_sparse_classes=True, \ + n_sources_needed_for_class_inclusion=1, + include_header=include_header)#, classes_arff_str='', remove_sparse_classes=False) + + arff_row_list = [] + out_dict = {} + arff_rows_str = fp_strio.getvalue() + + # See pairwise_classification.py 2550 + #Pairwise_Classification parse_arff(self, arff_has_ids=False, arff_has_classes=True, has_srcid=False, get_features=False): + """ + arff_rows = [] + for a_str in arff_rows_str.split('\n'): + if len(a_str) == 0: + continue + if a_str[0] == '@': + continue + if a_str[0] == '%': + continue + arff_rows.append(a_str) + + assert(len(all_class_list) == len(arff_rows)) + + for i, arff_row in enumerate(arff_rows): + class_name = all_class_list[i] + + if not out_dict.has_key(class_name): + out_dict[class_name] = {'srcid_list':[], + 'count':0, + 'arffrow_wo_classnames':[], + } + out_dict[class_name]['srcid_list'].append(new_srcid_list[i]) + out_dict[class_name]['count'] += 1 + out_dict[class_name]['arffrow_wo_classnames'].append( \ + arff_row[:arff_row.rindex("'", 0,arff_row.rindex("'")) - 1]) + + return out_dict # out_dict[class_name][arffrow_wo_classnames:[], count:1, srcid_list:[] ### exclude:'arffrow_with_classnames:[] + + """ + + return arff_rows_str + + +class Arff_Generation_Engine_Tasks: + """ Class contains methods that will be used for arff lines generation + """ + + def task_generate_feature_arff_lines(self, pars, dat_fpaths=[], include_header=True, + write_multiinfo_srcids=True, + ParseNomadColorsList=None): + """ Given a sourceid list, generate features and the resulting ARFF lines. + + """ + #perc_arr = array(list(arange(0.2, 0.6, 0.01))) + #'percent':[str(elem) for elem in perc_arr] + sub_perc_list = [1.0] + sub_iter_list = [1] + + arff_str = get_dat_arffstrs(dat_fpaths, sub_perc_list, sub_iter_list, + include_header=include_header, + write_multiinfo_srcids=write_multiinfo_srcids, + ParseNomadColorsList=ParseNomadColorsList) + arff_rows = [] + class_list = [] + for row in arff_str.split('\n'): + if len(row) == 0: + continue + elif row[:5] == '@data': + continue + elif row[:16] == '@ATTRIBUTE class': + class_str = row[row.rfind("{'") + 2: row.rfind("'}")] + class_list = class_str.split("','") + else: + arff_rows.append(row) + + return {'arff_rows':arff_rows, 'class_list':class_list} + + + +def master_ipython_arff_generation(pars={}, write_multiinfo_srcids=True, dat_fpaths=[]): + """ Main code which controls ipython nodes when generating + +This is the task which will be called on ipengines by this function: + +task_generate_feature_arff_lines(pars, srcid_list=[]) + + """ + + import datetime + import time + import cPickle + try: + from IPython.kernel import client + except: + pass + + mec = client.MultiEngineClient() + mec.reset(targets=mec.get_ids()) # Reset the namespaces of all engines + tc = client.TaskClient() + + mec_exec_str = """ +import sys, os +import copy +import matplotlib +matplotlib.use('agg') +sys.path.append(os.path.abspath('/home/pteluser/src/TCP/Software/ingest_tools')) +sys.path.append(os.path.abspath('/home/pteluser/src/TCP/Software/citris33')) +import arff_generation_master_using_generic_ts_data +ArffEngineTasks = arff_generation_master_using_generic_ts_data.Arff_Generation_Engine_Tasks() +""" + + print('before mec()') + #print mec_exec_str + #import pdb; pdb.set_trace() + engine_ids = mec.get_ids() + pending_result_dict = {} + for engine_id in engine_ids: + pending_result_dict[engine_id] = mec.execute(mec_exec_str, targets=[engine_id], block=False) + n_pending = len(pending_result_dict) + i_count = 0 + while n_pending > 0: + still_pending_dict = {} + for engine_id, pending_result in pending_result_dict.items(): + try: + result_val = pending_result.get_result(block=False) + except: + print("get_result() Except. Still pending on engine: %d" % (engine_id)) + still_pending_dict[engine_id] = pending_result + result_val = None # 20110105 added + if result_val is None: + print("Still pending on engine: %d" % (engine_id)) + still_pending_dict[engine_id] = pending_result + if i_count > 10: + mec.clear_pending_results() + pending_result_dict = {} + mec.reset(targets=still_pending_dict.keys()) + for engine_id in still_pending_dict.keys(): + pending_result_dict[engine_id] = mec.execute(mec_exec_str, targets=[engine_id], block=False) + ### + time.sleep(20) # hack + pending_result_dict = [] # hack + ### + i_count = 0 + else: + print("sleeping...") + time.sleep(5) + pending_result_dict = still_pending_dict + n_pending = len(pending_result_dict) + i_count += 1 + + print('after mec()') + time.sleep(5) # This may be needed, although mec() seems to wait for all the Ipython clients to finish + print('after sleep()') + #import pdb; pdb.set_trace() + + # todo: fill a dict and write to pickle: of srcid:xml_filepath + #srcid_list = pars['src_id'] + + #srcid_list_new = [] + task_id_list = [] + class_list = [] + + #for srcid in srcid_list[:4]: + #for srcid in srcid_list: + # srcid_list_new.append(int(srcid) + 100000000) + + ### 20110622: I believe this is just a quick run-through of code in non-parallel mode, for one source: + result_arff_list = [] + from get_colors_for_tutor_sources import Parse_Nomad_Colors_List + ParseNomadColorsList = Parse_Nomad_Colors_List(fpath='/home/pteluser/src/TCP/Data/best_nomad_src_list') + ArffEngineTasks = Arff_Generation_Engine_Tasks() + out_dict = ArffEngineTasks.task_generate_feature_arff_lines(pars, dat_fpaths=dat_fpaths[:1], include_header=True, write_multiinfo_srcids=write_multiinfo_srcids, ParseNomadColorsList=ParseNomadColorsList) + ### KLUDGE leave out the last row since it will be reprocessed below: + result_arff_list.extend(out_dict['arff_rows'][:-1]) + + + n_src_per_task = 10 # NOTE: is generating PSD(freq) plots within lightcurve.py, should use n_src_per_task = 1, and all tasks should finish.# for ALL_TUTOR, =1 ipcontroller uses 99% memory, so maybe =3? (NOTE: cant do =10 since some TUTOR sources fail) + + imin_list = range(0, len(dat_fpaths), n_src_per_task) + + for i_min in imin_list: + dat_sublist = dat_fpaths[i_min: i_min + n_src_per_task] + #print + ##### FOR DEBUGGING: + ##### - NOTE: will use up memory if run for ~100 iterations + #ArffEngineTasks = Arff_Generation_Engine_Tasks() + #out_dict = ArffEngineTasks.task_generate_feature_arff_lines(pars, dat_fpaths=dat_sublist, include_header=False, write_multiinfo_srcids=write_multiinfo_srcids) + #import pdb; pdb.set_trace() + #print + ##### + ### 20110106: This doesn't seem to solve the ipcontroller memory error, but works: + tc_exec_str = """ +tmp_stdout = sys.stdout +sys.stdout = open(os.devnull, 'w') +out_dict = ArffEngineTasks.task_generate_feature_arff_lines(pars, dat_fpaths=dat_fpaths, include_header=include_header, write_multiinfo_srcids=write_multiinfo_srcids) +sys.stdout.close() +sys.stdout = tmp_stdout + """ + if 1: + taskid = tc.run(client.StringTask(tc_exec_str, + push={'pars':pars, + 'dat_fpaths':dat_sublist, + 'include_header':False, + 'write_multiinfo_srcids':write_multiinfo_srcids}, + pull='out_dict', + retries=3)) + task_id_list.append(taskid) + ###for debugging: + #for a,b in inspect.getmembers(tc.get_task_result(taskid, block=False)): print a,b + #import pdb; pdb.set_trace() + #print + #import pdb; pdb.set_trace() + #### + #combo_results_dict = {} + dtime_pending_1 = None + while ((tc.queue_status()['scheduled'] > 0) or + (tc.queue_status()['pending'] > 0)): + tasks_to_pop = [] + for task_id in task_id_list: + temp = tc.get_task_result(task_id, block=False) + if temp is None: + continue + temp2 = temp.results + if temp2 is None: + continue + results = temp2.get('out_dict',None) + if results is None: + continue # skip some kind of NULL result + if len(results) > 0: + tasks_to_pop.append(task_id) + result_arff_list.extend(results['arff_rows']) + for a_class in results['class_list']: + if not a_class in class_list: + class_list.append(a_class) + #ipython_return_dict = results + #update_combo_results(combo_results_dict=combo_results_dict, + # ipython_return_dict=copy.deepcopy(ipython_return_dict)) + for task_id in tasks_to_pop: + task_id_list.remove(task_id) + + if ((tc.queue_status()['scheduled'] == 0) and + (tc.queue_status()['pending'] <= 64)): + if dtime_pending_1 is None: + dtime_pending_1 = datetime.datetime.now() + else: + now = datetime.datetime.now() + if ((now - dtime_pending_1) >= datetime.timedelta(seconds=300)): + print("dtime_pending=1 timeout break!") + break + print(tc.queue_status()) + print('Sleep... 20 in test_pairwise_on_citris33_ipython::master_ipython_R_classifiers()', datetime.datetime.utcnow()) + time.sleep(20) + # IN CASE THERE are still tasks which have not been pulled/retrieved: + for task_id in task_id_list: + temp = tc.get_task_result(task_id, block=False) + if temp is None: + continue + temp2 = temp.results + if temp2 is None: + continue + results = temp2.get('out_dict',None) + if results is None: + continue #skip some kind of NULL result + if len(results) > 0: + tasks_to_pop.append(task_id) + result_arff_list.extend(results['arff_rows']) + for a_class in results['class_list']: + if not a_class in class_list: + class_list.append(a_class) + #ipython_return_dict = results + #update_combo_results(combo_results_dict=combo_results_dict, + # ipython_return_dict=copy.deepcopy(ipython_return_dict)) + #### + return {'result_arff_list':result_arff_list, + 'class_list':class_list} + + + + +if __name__ == '__main__': + + pars = {'src_id':[], #deboss_srcid_list, #['148875', '148723', '148420', '149144', '149049'], #deboss_srcid_list,#['148875', '148723', '148420', '149144', '149049'], #deboss_srcid_list, #['149144', '149049', '149338', '149049', '149338','149182','149108'], + 'percent':[], #[str(elem) for elem in perc_arr], #[str(elem) for elem in arange(0.90, 1.0, 0.01)], # [str(elem) for elem in arange(0.58, 1.0, 0.01)]#[str(elem) for elem in arange(0.01, 1.01, 0.01)], #[str(elem) for elem in arange(0.8, 1.0, 0.10)], #['0.8', '0.86', '0.88', '0.9', '0.95', '1.0'], + 'niters':'7', #'5', #'6',#'12', # Not a list, string value, will be used to generate list: range(niters) + 'pairwise_classifier_pkl_fpath':"/home/pteluser/Dropbox/work/WEKAj48_dotastro_ge1srcs_period_nonper__exclude_non_debosscher/pairwise_classifier__debosscher_table3.pkl.gz", # This is just debosscher data + 'crossvalid_pairwise_classif_dirpath':'/global/home/users/dstarr/scratch/crossvalid/pairwise_scratch_20101109_4060nostratif_2qso', # NOTE: set to '' if want to do non-crossvalid-folded classifiers + 'taxonomy_prune_defs':{'terminating_classes':['mira', 'sreg', 'rv', 'dc', 'piic', 'cm', 'rr-ab', 'rr-c', 'rr-d', 'ds', 'lboo', 'bc', 'spb', 'gd', 'be', 'pvsg', 'CP', 'wr', 'tt', 'haebe', 'sdorad', 'ell', 'alg', 'bly', 'wu']}, + 'plot_symb':['o','s','v','d','<'], # ,'+','x','.', ,'>','^' + 'feat_distrib_colors':['#000000', + '#ff3366', + '#660000', + '#aa0000', + '#ff0000', + '#ff6600', + '#996600', + '#cc9900', + '#ffff00', + '#ffcc33', + '#ffff99', + '#99ff99', + '#666600', + '#99cc00', + '#00cc00', + '#006600', + '#339966', + '#33ff99', + '#006666', + '#66ffff', + '#0066ff', + '#0000cc', + '#660099', + '#993366', + '#ff99ff', + '#440044'], + 'R_class_lookup':{ \ + 'X Ray Binary':'xrbin', + 'a. Mira':'mira', + 'b. Semireg PV':'sreg', + 'c. RV Tauri':'rv', + 'd. Classical Cepheid':'dc', + 'e. Pop. II Cepheid':'piic', + 'f. Multi. Mode Cepheid':'cm', + 'g. RR Lyrae, FM':'rr-ab', + 'h. RR Lyrae, FO':'rr-c', + 'i. RR Lyrae, DM':'rr-d', + 'j. Delta Scuti':'ds', + 'k. Lambda Bootis':'lboo', + 'l. Beta Cephei':'bc', + 'm. Slowly Puls. B':'spb', + 'n. Gamma Doradus':'gd', + 'o. Pulsating Be':'be', + 'p. Per. Var. SG':'pvsg', + 'q. Chem. Peculiar':'CP', + 'r. Wolf-Rayet':'wr', + 's. T Tauri':'tt', + 't. Herbig AE/BE':'haebe', + 'u. S Doradus':'sdorad', + 'v. Ellipsoidal':'ell', + 'w. Beta Persei':'alg', + 'x. Beta Lyrae':'bly', + 'y. W Ursae Maj.':'wu', + }, + 'R_class_lookup__old':{ \ + 'X Ray Binary':'xrbin', + 'a. Mira':'mira', + 'b. semireg PV':'sreg', + 'c. RV Tauri':'rv', + 'd. Classical Cepheid':'dc', + 'e. Pop. II Cepheid':'piic', + 'f. Multi. Mode Cepheid':'cm', + 'g. RR Lyrae, FM':'rr-ab', + 'h. RR Lyrae, FO':'rr-c', + 'i. RR Lyrae, DM':'rr-d', + 'j. Delta Scuti':'ds', + 'k. Lambda Bootis':'lboo', + 'l. Beta Cephei':'bc', + 'm. Slowly Puls. B':'spb', + 'n. Gamma Doradus':'gd', + 'o. BE':'be', + 'p. Per. Var. SG':'pvsg', + 'q. Chem. Peculiar':'CP', + 'r. Wolf-Rayet':'wr', + 's. T Tauri':'tt', + 't. Herbig AE/BE':'haebe', + 'u. S Doradus':'sdorad', + 'v. Ellipsoidal':'ell', + 'w. Beta Persei':'alg', + 'x. Beta Lyrae':'bly', + 'y. W Ursae Maj.':'wu', + }, + } + # 'srcid':[], + # 'percent'[] + options = parse_options() + #if options.srcid != '': + # pars['src_id'] = options.srcid + + if 0: + ### OBSOLETE + ### all_tutor_xmls: Many tutor project_ids case: + pars['xml_dirpath'] = '/global/home/users/dstarr/500GB/all_tutor_xmls_flat' + xmls_dict_pkl_fpath = '/global/home/users/dstarr/500GB/all_tutor_xmls_dict.pkl' + glob_str = '%s/*/*' % (pars['xml_dirpath']) + + if os.path.exists(xmls_dict_pkl_fpath): + source_xml_dict = cPickle.load(open(xmls_dict_pkl_fpath)) + else: + #import pdb; pdb.set_trace() + #print + + source_xml_dict = {} + dirs = os.listdir(pars['xml_dirpath']) + for dir in dirs: + dirpath = "%s/%s" % (pars['xml_dirpath'], dir) + glob_str = '%s/*' % (dirpath) + + xml_fpaths = glob.glob(glob_str) + + for xml_fpath in xml_fpaths: + print(xml_fpath) + num_str = xml_fpath[xml_fpath.rfind('/') + 1:xml_fpath.rfind('.')] + srcid = int(num_str)# - 100000000 + source_xml_dict[str(srcid)] = xml_fpath + + + fp = open(xmls_dict_pkl_fpath, 'wb') + cPickle.dump(source_xml_dict,fp,1) # ,1) means a binary pkl is used. + fp.close() + + + else: + if 0: + ### For testing: + xml_str = generate_xml_str_using_ben_dat("/home/pteluser/Dropbox/LCS/LCnew_417.dat") + import pdb; pdb.set_trace() + print() + + glob_str = '/home/pteluser/Dropbox/LCS/LCnew_*.dat' + + dat_fpaths = glob.glob(glob_str) + + out_dict = master_ipython_arff_generation(pars=pars, + dat_fpaths=dat_fpaths, + write_multiinfo_srcids=False) #False:only srcid in output arff; True when doing several percent/subset arff rows + #print result_arff_list + result_arff_list = out_dict['result_arff_list'] + + ### Need to find the last ATTRIBUTE in the header, so the classes can be inserted: + in_attibs = False + for i, elem in enumerate(out_dict['result_arff_list']): + if len(elem) == 0: + continue + elif elem[0] == "%": + continue + elif elem[:10] == "@ATTRIBUTE": + in_attibs = True + elif in_attibs: + ### Now we are done parsing the @ATTRIBUTES + class_str = "@ATTRIBUTE class {'%s'}" % ("','".join(out_dict['class_list'])) + out_dict['result_arff_list'].insert(i, class_str) + ### Also want to insert @DATA before the data starts. + out_dict['result_arff_list'].insert(i + 1, '@DATA') + break + + fp = open(os.path.expandvars("$HOME/scratch/out.arff"), 'w') + fp.write('\n'.join(result_arff_list)) + fp.close() + import datetime + print(datetime.datetime.now()) + import pdb; pdb.set_trace() + print() diff --git a/mltsp/TCP/Software/citris33/start_ipengines.qsub b/mltsp/TCP/Software/citris33/start_ipengines.qsub new file mode 100755 index 00000000..8904de43 --- /dev/null +++ b/mltsp/TCP/Software/citris33/start_ipengines.qsub @@ -0,0 +1,34 @@ +#!/bin/sh + +#PBS -N ipengines_pwise +#PBS -l nodes=12:ppn=8 +#PBS -l walltime=48:00:00 +#PBS -o outfile +#PBS -e errfile +#PBS -M dstarr@astro.berkeley.edu +#PBS -m abe +#PBS -q long +# +# Export all my environment variables to the job +#PBS -V +# +# +## Run my parallel job +##cd /global/home/users/kmuriki/sample_executables/mpi/matrixmult +cd /global/home/users/dstarr/src/TCP/Software/citris33 + +cat $PBS_NODEFILE +. /usr/Modules/init/bash + +module load intel/11.1.072 +module load gcc +module list + +### full = 74mins per node. 112 nodes (14*8) :: 12*8 =96 +mpiexec -n 96 ipengine --mpi=mpi4py + +##### /global/home/users/dstarr/src/install/epd-6.2-2-rh5-x86_64/lib/python2.6/pdb.py test_pairwise_on_citris33_ipython.py --pairwise_classifier_pkl_fpath=$HOME/scratch/pairwise_classifier__debosscher_table3.pkl.gz --use_hardcoded_sciclass_lookup +##### qsub -I -V -X -l walltime=24:00:00,nodes=1:ppn=1 -q normal +##### qdel 6366.perceus-citris.banatao.berkeley.edu +##### qstat -a +##### qsub start_ipengines.qsub \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/FeatureExtractor.py b/mltsp/TCP/Software/feature_extract/Code/FeatureExtractor.py new file mode 100644 index 00000000..f0a601cd --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/FeatureExtractor.py @@ -0,0 +1,320 @@ +from __future__ import print_function + +import numpy +from numpy import random +from scipy import fftpack, stats, optimize + +from .extractors import * + +import time + +from . import internal_generated_extractors_holder # 20080508 KLUDGE + +#20090321#import amara + +class ExtractException(Exception): + "extractor refused to extract" + pass +class ResultObject(object): + def __init__(self,result): + self.result = result + def __str__(self): + return str(self.result) +class Intermediary(ResultObject): + pass +class Feature(ResultObject): + pass + +class GeneralExtractor(object): + # 20080508 KLUDGE: + igea = internal_generated_extractors_holder.\ + Internal_Gen_Extractors_Accessor() # 20080508 KLUDGE + glob_internally_generated_extractors = igea.glob_internally_generated_extractors + + why_fail = "This didn't fail as far as I know" # implement in subclasses please + minpoints = 0 + maxpoints = 28200 #20090127: dstarr adds this after seeing a ws_variability_extractor related memory balloon possibly due to a +30k dataset. + def __init__(self): + pass + active = False + extname = 'FeatureExtractorctor' #extractor's name + counter = 0 # number of extractions performed + def extr(self,properties,band=None): #general code run before extraction + tic = time.time() + self.properties = properties + self.finddatadic(properties,band=band) # find the dictionary of the signal properties that contains the actual data + if not self.checkalready(): # if it doesn't exist already + try: + self.set_names(self.dic['input']) + self.longenough() # check that there is enough data to run this + result = self.extract() + self.why_fail = False # I didn't fail so far + except ExtractException as e: + result = False + #self.why_fail = "I don't know why it failed" + self.prepare_obj(result) + ### check that resources are not wasted + self.__class__.counter += 1 # number of times this feature has been run, supposed to be used as a check for redundancies, doesn't do anyting right now + #if self.counter > 1: + #dstarr com out# print "I just did this thing twice, that's ridiculous", self.extname, self.counter, "times" + ### + return self.output + def finddatadic(self,properties,band=None): + """ find the dictionary of the signal properties that contains the actual data """ + if band: # if the signal has bands, look for data in respective subdictionary + self.dic = self.properties['data'][band] + self.band = band + else: # otherwise the data is directly in ['data'] (no bands) + self.dic = self.properties['data'] + self.band = None + return None + def checkalready(self): + """ check if this feature has already been extracted in the past """ + for subdic in ['input','features','inter']: + if self.extname in self.dic[subdic]: # test to see if this feature has already been extracted + output = self.dic[subdic][self.extname] + self.output = output + return True # yes it already exists + else: pass + return False # no it doesn't exist yet + + def prepare_obj(self,result): + self.output = self.out_type(result) + self.output.plots = self.plots # give my plot method + self.output.dic = self.dic # give it the signal/band's dictionary (this is kinda crazy actually) + self.output.__doc__ = self.__doc__ # give my docstring + self.output.extname = self.extname # give my name + try: + self.output.uncertainty = self.uncertainty # if the feature extractor specifies an uncertainty value, export it + except AttributeError: + self.output.uncertainty = 0 # otherwise, the uncertainty is zero + #if self.why_fail: #don't pass anything if not failed + self.output.why = self.why_fail # explanation of why a feature extraction failed (or refused to perform) + self.general_obj(self.output) # implement different behavior if this is a feature or an intermediary result + if self.output.result is not False: # no need to do so if failed + self.specific_obj(self.output) # specific feature extractors may be interested in particular behaviors + def general_obj(self,output): + """implement different behavior if this is a feature or an intermediary result""" + pass + def specific_obj(self,output): + """add more information in the output object on a feature-specific basis (implemented at subclass level)""" # this docstring gets overridden down the line, kinda stupid + pass + def extract(self): # delegates the actual implementation to subclasses + pass +# TODO why try / except KeyError? + def set_names(self,where): + """ prepares the most commonly used inputs for easy access """ + try: + self.time_data = where['time_data'] + self.flux_data = where['flux_data'] + except KeyError: + pass + + ####20110512commentout#'frequencies':self.fgen(input_dic['time_data'])}) # 20110512: NOTE: this and self.frequencies are not used by any current features (used to be related to old lomb implementations). About to add a new self.frequencies overwriting declaration in lomb_scargle_extractor.py:extractor(), which will allow the first freq self.frequencies, self.psd to be accessible to outside code. + #try: + # import pdb; pdb.set_trace() + # self.frequencies = where['frequencies'] + #except KeyError: + # self.frequencies = numpy.array([]) + try: + self.rms_data = where['rms_data'] + except KeyError: + try: # sorry this is getting messy, this is for multiband extractors + self.rms_data = numpy.ones(len(self.time_data), dtype=float) + except AttributeError: + pass + try: + self.ra = where['ra'] + self.dec = where['dec'] + except KeyError: + pass + try: + self.ra_rms = where['ra_rms'] + self.dec_rms = where['dec_rms'] + except KeyError: + pass + + try: + self.time_data_unit = where['time_data_unit'] + self.flux_data_unit = where['flux_data_unit'] + self.rms_data_unit = where['rms_data_unit'] + self.time_data_ucd = where['time_data_ucd'] + self.flux_data_ucd = where['flux_data_ucd'] + self.rms_data_ucd = where['rms_data_ucd'] + except KeyError: + pass + + def register_extractor(self): # broken + """ register this extractor as an active extractor""" + from . import feature_interfaces + feature_interfaces.feature_interface.register_extractor(type(self)) + def remove_extractor(self): # broken + """ inactivate this extractor """ + from . import feature_interfaces + feature_interfaces.feature_interface.remove_extractor(type(self)) + def plots(self,properties=None): + if not properties: properties = self.dic + self.set_names(properties['input']) + merge = dict(properties['input'], **properties['features']) + merge = dict(merge, **properties['inter']) + if self == 'Fail': + print("I can't print myself, I'm a failure", self.extname) + else: + self.plot_feature(merge) # delegates at extractor (subclass) level, each extractor knows how to plot itself + legend() + def plot_feature(self,properties): + print("I don't know how to plot myself", self.extname) # implement in subclasses + + + def fetch_extr(self,extractor_name,properties=None,error=True, band=None, returnall = False, return_object = False): + """ Fetch the result from other extractors + error (boolean): True to proagate the error of fetched extractors """ + if not band: band = self.band + if not properties: properties = self.properties + + if not isinstance(extractor_name, str): + print("Method %s is still using old fetch procedure, calling %s" % (self.extname, extractor_name.extname)) + return self.fetch_extr_old(extractor_name,properties,error, band, returnall, return_object) + #print extractor_name, self.properties, self.band + # # # # # # # # dstarr KLUDGE (next single condition:): + from . import feature_interfaces + fetched_extractor = feature_interfaces.feature_interface.request_extractor(extractor_name) # the feature interface is in charge of storing and finding extractors, receives an extractor or False + if not fetched_extractor: #if the feature_interface was unable to find the extractor + self.ex_error("Extractor %s not able to fetch extractor %s" % (self.extname, extractor_name)) + fetched_instance = fetched_extractor() # instantiate + if return_object: + return fetched_instance + elif returnall: # return the entire object + ret_object = fetched_instance.extr(properties,band=band) + returner = ret_object + else: + ret_object = fetched_instance.extr(properties,band=band) + returner = ret_object.result + if ret_object.result is False and error: # if the result is an error + self.ex_error(ret_object.why) # then propagate the error + return returner + def fetch_extr_old( self,extractor_name,properties=None,error=True, band=None, returnall = False, return_object = False): + """ Fetch the result from other extractors + error (boolean): True to proagate the error of fetched extractors """ + if not band: band = self.band + if not properties: properties = self.properties + #print extractor_name, self.properties, self.band + # # # # # # # # dstarr KLUDGE (next single condition:): + if return_object: + ret_object = extractor_name() + #result = ret_object.result + result = True # KLUDGE + returner = ret_object + elif returnall: # return the entire object + ret_object = extractor_name().extr(properties,band=band) + result = ret_object.result + returner = ret_object + else: + ret_object = extractor_name().extr(properties,band=band) + result = ret_object.result + returner = result + if result is False and error: + self.ex_error(ret_object.why) + return returner + def ex_error(self,text="I don't know why"): + """ a feature extractor's way of raising an error cleanly """ + self.why_fail = text + raise ExtractException(text) + def longenough(self): + """ will not perform extraction if there aren't enough data points, minpoints set at extractor level """ + try: + if (len(self.flux_data) < self.minpoints) or \ + (len(self.flux_data) > self.maxpoints): # if 4 or less points, error + self.ex_error("not enough (or too much) data points: %d" % (len(self.flux_data))) + except: + self.ex_error("not enough (or too much) data points") +class FeatureExtractor(GeneralExtractor): + out_type = Feature + internal_use_only = False # dstarr adds this. But a bit of a KLUDGE + # since non-feature extractors should be InterExtractors and + # not FeatureExtractors. But with users making feature extractors + # this parameter may be needed. + def general_obj(self,output): + pass + +class InterExtractor(GeneralExtractor): + out_type = Intermediary + internal_use_only = True # dstarr adds this. But a bit of a KLUDGE + # since non-feature extractors should be InterExtractors and + # not FeatureExtractors. But with users making feature extractors + # this parameter may be needed. + def general_obj(self,output): + pass + +class ContextExtractor(GeneralExtractor): + """ This is a special extractor class for context features. """ + def longenough(self): # we're getting rid of the longenough method because it does not make sense + pass + +class MultiExtractor(ContextExtractor): + """ for feature extractors that need to *compare* multiple bands """ + band1 = 'v' + band2 = 'u' + compared_extr = None + def finddatadic(self,properties,band=None): + """ this needs to be changed so the extractor has access to data from multiple bands """ + assert (band == 'multiband'), 'band should be multiband' + self.dic = self.properties['data']['multiband'] + self.multidic = self.properties['data'] + self.band = band + return None + def set_names(self,where): + """ prepares the most commonly used inputs for easy access """ + ContextExtractor.set_names(self,where) + if self.band1 not in self.multidic: + self.ex_error("Multiband extractor %s did not find band '%s' in '%s'" % (self.extname, self.band1, self.multidic.keys())) + else: pass + if self.band2 not in self.multidic: + self.ex_error("Multiband extractor %s did not find band '%s' in '%s'" % (self.extname, self.band2, self.multidic.keys())) + else: pass + self.dic1 = self.multidic[self.band1] + self.dic2 = self.multidic[self.band2] + self.extr1 = self.fetch_extr(self.compared_extr, band=self.band1) + self.extr2 = self.fetch_extr(self.compared_extr, band=self.band2) + return None + def general_obj(self,output): + output.band1 = self.band1 + output.band2 = self.band2 + output.compared_extr = self.compared_extr + return None + +class MultiFeatureExtractor(MultiExtractor,FeatureExtractor): + pass +class MultiInterExtractor(MultiExtractor,InterExtractor): + pass + +class ContextFeatureExtractor(ContextExtractor,FeatureExtractor): + pass +class ContextInterExtractor(ContextExtractor,InterExtractor): + pass + +# Extractors +####################****************############ +# Extractor Outputs + +"""class Extracted(object): + def __init__(self,data): + self.data = data + def __repr__(self): + return str(self.data) + def newobj(self,new): + self.data = new + return self + def __add__(self,other): + new = self.data + other + self.newobj(new) + def __abs__(self): + new = abs(self.data) + self.newobj(new) + def __getitem__(self,key): + return self.data[key] + def __getattr__(self,name): + print name +# exec "return self.data.%s" % name + out = eval("self.data.%s" % name) + return out""" diff --git a/mltsp/TCP/Software/feature_extract/Code/__init__.py b/mltsp/TCP/Software/feature_extract/Code/__init__.py new file mode 100644 index 00000000..7d148c58 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/__init__.py @@ -0,0 +1,14 @@ +from . import main +from .main import test, xml_print +from .main import signals_list +from .main import generators_importers +from . import signal_objects +#from . import feature_interfaces +#available = feature_interfaces.feature_interface.available_extractors +from . import extractors +from .extractors import * +#from . import feature_interfaces +from . import FeatureExtractor +from . import generators_importers +from . import plotters +from . import signal_objects diff --git a/mltsp/TCP/Software/feature_extract/Code/data_cleaning.py b/mltsp/TCP/Software/feature_extract/Code/data_cleaning.py new file mode 100644 index 00000000..062fe6a1 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/data_cleaning.py @@ -0,0 +1,82 @@ +#!/usr/bin/env python +""" data_cleaning.py + +Tools which are used to clean timeseris data. + +Initially intended to be applied to gen.generate()'s gen.sig.x_sdict object. + +For Example: + +sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + \ + 'Software/feature_extract')) + +from data_cleaning import sigmaclip_sdict_ts + +gen.generate(xml_handle="../../Data/vosource_9026.xml") +sigmaclip_sdict_ts(gen.sig.x_sdict['ts'], sigma_low=4., sigma_high=4.) + + +This is used in: + arff_generation_master.py + +""" +import os, sys +from numpy import array + +def sigmaclip_sdict_ts(sdict_ts={}, sigma_low=4., sigma_high=4.): + """Iterative sigma-clipping of array elements. + + Parameters: + + sdict_ts # A dictionary such as: + gen.sig.x_sdict['ts'] + When previously called: + gen.generate(xml_handle="../../Data/vosource_9026.xml") + + low : lower bound of sigma clipping + high : upper bound of sigma clipping + + NOTE: sdict_ts is mpodified to use the new sigma-clipped timeseries data + + Example: + + gen.generate(xml_handle="../../Data/vosource_9026.xml") + sigmaclip(gen.sig.x_sdict['ts'], low=4., high=4.) + + + Note: satisfy the conditions: + c > mean(c)-std(c)*low and c < mean(c) + std(c)*high + + """ + for band_name, band_dict in sdict_ts.items(): + + if ":NOMAD" in band_name: + continue # skip from sigmaclipping the 1 pseudo epoch NOMAD epoch + elif "extinct" in band_name: + continue # skip from sigmaclipping the 1 pseudo epoch NOMAD epoch + + m = array(band_dict['m']) + + m_std = m.std() + m_mean = m.mean() + keep_inds = ((m > m_mean - (m_std * sigma_low )) & + (m < m_mean + (m_std * sigma_high))) + + ### This limit-mag section is not applicable since limiting mags can + ### potentially have different time sampling/array: + #if ((len(band_dict.get('limitmags', {}).get('lmt_mg',[])) > 0) and + # (len(band_dict.get('limitmags', {}).get('lmt_mg',[])) == len(m))): + # lmt_mg = array(band_dict['limitmags']['lmt_mg']) + # band_dict['limitmags']['lmt_mg'] = list(lmt_mg[keep_inds]) + # + # lmt_t = array(band_dict['limitmags']['t']) + # band_dict['limitmags']['t'] = list(lmt_t[keep_inds]) + + band_dict['m'] = list(m[keep_inds]) + + t = array(band_dict['t']) + band_dict['t'] = list(t[keep_inds]) + + m_err = array(band_dict['m_err']) + band_dict['m_err'] = list(m_err[keep_inds]) + diff --git a/mltsp/TCP/Software/feature_extract/Code/db_importer.py b/mltsp/TCP/Software/feature_extract/Code/db_importer.py new file mode 100644 index 00000000..d5808bf1 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/db_importer.py @@ -0,0 +1,1216 @@ +#!/usr/bin/env python + +""" +main remote interaction client to the transients source database + +NOTE: I've added the "svn propset" characteristic to this file, for $Id: db_importer.py 1573 2010-07-12 18:14:15Z pteluser $ using: + svn propset svn:keywords Id *.py + +To run this locally, you'll want to set up a tunnel from here->lyra->linux + + ssh -L 8000:192.168.1.45:8000 lyra.berkeley.edu + +Leave that window open. + +you'll want to have Amara (XML toolkit installed) + wget http://peak.telecommunity.com/dist/ez_setup.py + sudo python ez_setup.py + sudo /scisoft/i386/Packages/Python/Python.framework/Versions/Current/bin/easy_install amara + (or /sw/bin/easy_install amara) + +Open up a new UNIX window.Typical usage might be: +bash> ipython -pylab +py> import db_importer +py> pe =db_importer.PositionExtractor(pos=(49.599497, -1.0050998),radius=0.001,prefer_only_source_search=True, host="192.168.1.45") +py> pe.search_pos(out_xml_fpath='') # this populates the pe.sources list +py> # to view the dictionary associated with the first source returned +py> import pprint +py> pprint.pprint(pe.sources[0].d) + +If you have an xml that you want to be put in to an internal representation (py dictionary), then do something like this: +bash> ipython -pylab +py> import db_importer +py> s = db_importer.Source(xml_handle="source_5.xml") + +Now s.x_sdict will have a dictionary representation of the xml. +that dictionary (and the one coerced from Dan's db) will look like: +py> pprint.pprint(s.x_sdict) + +{'dec': -1.0142709999999999, + 'dec_rms': 0.000118, + 'ra': 49.591202000000003, + 'ra_rms': 0.00012300000000000001, + 'src_id': 26, + 'ts': {'g': {'m': [24.215399999999999], + 'm_err': [3.4363600000000001], + 't': [53666.468710000001]}, + 'i': {'m': [23.313300000000002], + 'm_err': [1.5199199999999999], + 't': [53666.466222000003]}, + 'r': {'m': [22.273800000000001], + 'm_err': [0.47815600000000003], + 't': [53666.465392999999]}, + 'z': {'m': [22.619199999999999], + 'm_err': [2.7249099999999999], + 't': [53666.467880999997]}}} + +if the source was made from Dan's codes then use s.d for the dictionary. +""" +from __future__ import print_function +from __future__ import absolute_import + +__author__ = "JSB" +__version__ = "9-Aug-2007" +__svn_id__ = "$Id: db_importer.py 1573 2010-07-12 18:14:15Z pteluser $".replace('$','').replace('Id:','').strip() + +__tab__ = " " + +import time, datetime +import os, sys +from logging import * +import copy + +MAX_SEARCH_RADIUS = 0.5 # degrees +#20080225# MAX_SEARCH_RADIUS = 0.125 # degrees +sys.path.append(os.path.abspath(os.environ.get("TCP_DIR","") + \ + '/Software/feature_extract/Code')) # 20090309 dstarr adds this for nosetests use only +sys.path.append(os.path.abspath(os.environ.get("TCP_DIR","") + \ + '/Software/feature_extract/Code/extractors')) # 20090309 dstarr adds this for xmldict load only +from .extractors import xmldict +from . import vo_timeseries +import numpy +try: + import numarray +except: + import numpy as numarray +#import matplotlib +#try: +# from matplotlib import pylab +#except: +# pass +try: + from xml.etree import cElementTree as ElementTree # this is a nicer implementation +except: + # This is caught in M45's python 2.4.3 distro where the elementtree module was installed instead + from elementtree import ElementTree + + +sdss_filters = {'u': 0, 'g': 1, 'r': 2, 'i': 3, 'z': 4, 'V': 5} +sdss_plotting_codes = {'u': 'kd', 'g': 'bo', 'r': 'gs','i': 'rD', 'z': 'md', 'V': 'bo', + 'ptf_g':'go', 'ptf_r':'ro'} + + +def setup_logging(file_level=DEBUG, screen_level=INFO,fname="./db_importer.log"): + + basicConfig(level=file_level,format='%(asctime)s %(levelname)-8s %(message)s', + datefmt='%a, %d %b %Y %H:%M:%S', + filename=fname,filemode='w') + console = StreamHandler() + console.setLevel(screen_level) + getLogger('').addHandler(console) + return + +#try: +# import amara +# use_amara = True +#except: +# warning("cannot use Amara for xml fun") +# use_amara = False + +def add_pretty_indents_to_elemtree(elem, level=0): + """ in-place prettyprint formatter + """ + i = "\n" + level*" " + if len(elem): + if not elem.text or not elem.text.strip(): + elem.text = i + " " + if not elem.tail or not elem.tail.strip(): + elem.tail = i + for elem in elem: + if (elem.tag == "MAG") or (elem.tag == "TIME"): + elem.tail = "\n" + (level+1)*" " + continue + add_pretty_indents_to_elemtree(elem, level+1) + if not elem.tail or not elem.tail.strip(): + elem.tail = i + else: + if level and (not elem.tail or not elem.tail.strip()): + elem.tail = i + + + +class vosource_classification_obj: + """ This deals with forming the part + of the vosource XML. + """ + def __init__(self): + # Internal representation of class:probabilities is: + #self._classes[source_name][class_schema_name][class_name] = prob + self._classes = {} + + + def add_classif_prob(self, class_name="", prob=-1.0, + src_name='None', class_schema_name='None', + human_algo="algorithm", + catalog_tcp="tcp"): + """ This adds a classification to the internal class structure. + + NOTE: human_algo == ["algorithm" OR "human"] + NOTE: catalog_tcp == ["catalog" OR "tcp"] + """ + if human_algo not in self._classes: + self._classes[human_algo] = {} + if catalog_tcp not in self._classes[human_algo]: + self._classes[human_algo][catalog_tcp] = {} + if src_name not in self._classes[human_algo][catalog_tcp]: + self._classes[human_algo][catalog_tcp][src_name] = {} + if class_schema_name not in self._classes[human_algo][catalog_tcp][src_name]: + self._classes[human_algo][catalog_tcp][src_name][class_schema_name] = {} + if class_name not in self._classes[human_algo][catalog_tcp][src_name][class_schema_name]: + self._classes[human_algo][catalog_tcp][src_name][class_schema_name][class_name] = {} + if class_name not in self._classes[human_algo][catalog_tcp][src_name][class_schema_name]: + self._classes[human_algo][catalog_tcp][src_name][class_schema_name][class_name] = {} + self._classes[human_algo][catalog_tcp][src_name][class_schema_name][class_name]['prob'] = prob + + + def get_class_xml_string(self): + """ This forms the ( ) string + and returns this string. + """ + __tab__ = " " # This is a global variable in db_importer.py + + self.class_xml_string = " \n" + for human_algo in self._classes.keys(): + self.class_xml_string += ' \n' % (human_algo) + for catalog_tcp in self._classes[human_algo].keys(): + for src_name in self._classes[human_algo][catalog_tcp].keys(): + self.class_xml_string += """ + + %s + 1.0 + + \n""" % (catalog_tcp, src_name) + for class_schema_name in self._classes[human_algo][catalog_tcp][src_name].keys(): + #self.class_xml_string += ' \n' % (class_schema_name) + for class_name in self._classes[human_algo][catalog_tcp][src_name][class_schema_name].keys(): + #self.class_xml_string += ' \n' % (class_name, \ + #20090420 comment out# self.class_xml_string += ' \n' % (class_name, class_name, self._classes[human_algo][catalog_tcp][src_name][class_schema_name][class_name]['prob']) + self.class_xml_string += ' \n' % (class_name, class_schema_name, self._classes[human_algo][catalog_tcp][src_name][class_schema_name][class_name]['prob']) + self.class_xml_string += " \n" + #self.class_xml_string += " \n" + self.class_xml_string += " \n" + self.class_xml_string += " \n" + return self.class_xml_string + + + def add_class_xml_to_existing_vosource_xml(self, old_vosource_str): + """ Given a vosource XML string, this forms and inserts the + contents and returns a new vosource string. + + Uses get_class_xml_string() + Returns a new vosource_str. + """ + class_xml_string = self.get_class_xml_string() + + # This is KLUDGEY: I'm not sure the re module would be efficient since + # it might search over the enormous string. So I try 3 cases: + r_buffer_size = 1000 + vosource_end_str = old_vosource_str[-r_buffer_size:] + ind = vosource_end_str.rfind(' at end of string!") + return old_vosource_str + + # get the true index of the beginning of : + i_begin_of_vosource = ind + len(old_vosource_str) - r_buffer_size + new_vosource_str = "%s\n%s\n%s" % (\ + old_vosource_str[:i_begin_of_vosource], \ + class_xml_string, \ + old_vosource_str[i_begin_of_vosource:]) + return new_vosource_str + + +class Source: + + def __init__(self,verbose=True, sdict=None, xml_handle=None,\ + make_xml_if_given_dict=True, interactive=False,\ + make_dict_if_given_xml=True, doplot=False,xml_validate=False,\ + out_xml_fpath='', use_source_id_in_xml_fpath=False, \ + write_xml=True, read_simpletimeseries=False): + """ + sdict - the dictionary with the data of the source + as it comes from the database + xml_handle - the string representation, file name, or file h + """ + self.sdict = sdict + self.verbose = verbose + self.xml_handle = xml_handle + self.marshalled_to_xml = False + self.coerced_to_dict = False + self.xml_validate = xml_validate + self.id = "unk" + self.use_source_id = use_source_id_in_xml_fpath + self._handle_start() + + if read_simpletimeseries and make_dict_if_given_xml: + self.simpletimeseriesxml_to_source_dict(self.xml_handle) + return + + if make_xml_if_given_dict and self.coerced_to_dict: + self.source_dict_to_xml(self.d) + if write_xml == True: + self.write_xml(out_xml_fpath=out_xml_fpath) + if doplot: + self.plot_from_dict(self.d,interactive= \ + interactive) + + elif make_dict_if_given_xml and not self.coerced_to_dict: + self.xml_to_source_dict(self.xml_handle) + if doplot: + self.plot_from_dict(self.x_sdict,interactive= \ + interactive,bands=self.x_sdict['ts'].keys()) + + def write_xml(self, out_xml_fpath=''): + if not self.marshalled_to_xml: + return + if type(out_xml_fpath) != type(""): + # This catches filepointers and StringIO + f = out_xml_fpath + else: + if len(out_xml_fpath) != 0: + if not self.use_source_id: + fname = out_xml_fpath + else: + bb = os.path.dirname(out_xml_fpath) + if bb == "": + bb = "./" + if out_xml_fpath.endswith(".xml"): + ttt = "" + else: + ttt = ".xml" + fname = os.path.normpath(bb + "/source" + \ + str(self.id) + "." + os.path.basename(out_xml_fpath) + ttt) + else: + fname = "source_" + str(self.id) + ".xml" + + f = open(fname,"w") + + f.write(self.xml_string) + + if type(out_xml_fpath) == type(""): + f.close() + print("wrote %s " % fname) + + def _handle_start(self): + """ + decides what do to with the initial instantiation + """ + if self.sdict is None and self.xml_handle is None: + # this is a blank instance + if self.verbose: + info("blank instance of Source created") + if self.sdict is not None: + self.coerce_sdict() + + + def coerce_sdict(self,metakeys=["src_id",'ra_rms',"dec_rms","ra",\ + "dec", 'feat_gen_date']): + """ + this makes a reasonable dictionary out of the primitive currently being returned from Dan's codes + """ + self.d = {} + + all_entries = self.sdict.keys() + # TODO: rather than select the first entry, I want to select the entry which has the mst amount of info. + best_represent_entry = all_entries[0] + # NOTE: I want to choose the self.sdict entry which has all of the source info. + # - this is needed because some filters just contain limiting mags and not much source info. + for entry in all_entries: + if 'm' in self.sdict[entry]: + best_represent_entry = entry + break + for m in metakeys: + if m in self.sdict[best_represent_entry]: + self.d.update({m: self.sdict[best_represent_entry][m]}) + for b in all_entries: + if m in self.sdict[b]: + self.sdict[b].pop(m) + self.d.update({'ts': self.sdict}) + if "src_id" in self.d: + self.id = self.d['src_id'] + self.coerced_to_dict = True + return + + + def plot_from_dict(self,dic,bands=['u','g','r','i','z'],plot_title="",interactive=False,save_plot_name=None): + if not self.coerced_to_dict: + return + + if "ts" not in dic: + warning("no timeseries to plot") + return + + import matplotlib + + matplotlib.pylab.hold(False) + matplotlib.pylab.plot([120,111]) + matplotlib.pylab.hold(True) + xxtrema = [[],[]] + yytrema = [[],[]] + #print bands + for i in range(len(bands)): + filt = bands[i] + if filt not in dic['ts']: + continue + #g = [ numpy.array(dic['ts'][filt]['t']),numpy.array(dic['ts'][filt]['m']),numpy.array(dic['ts'][filt]['m_err']) ] + + g = [ numarray.array(dic['ts'][filt]['t']),numarray.array(dic['ts'][filt]['m']),numarray.array(dic['ts'][filt]['m_err']) ] + #print g + matplotlib.pylab.errorbar(g[0],g[1],g[2],fmt=sdss_plotting_codes[filt]) + xxtrema[0].append(min(g[0])) + xxtrema[1].append(max(g[0])) + yytrema[0].append(max(g[1])) + yytrema[1].append(min(g[1])) + + matplotlib.pylab.ylim( (max(yytrema[0]) + 1,min(yytrema[1]) - 0.3) ) + matplotlib.pylab.xlim( (min(xxtrema[0]) - 10,max(xxtrema[1]) + 10) ) + #print (max(yytrema[1]) + 1,min(yytrema[0])) + #print (min(xxtrema[0]) - 10,max(xxtrema[1]) + 10) + #print yytrema + matplotlib.pylab.xlabel("%s - %5.2f [%s]" % (self.timesys, self.timezero, "day")) + matplotlib.pylab.ylabel('magnitude') + + if plot_title=='': + plot_title = "ID: " + repr(dic['src_id']) + matplotlib.pylab.title(plot_title) + + if interactive: + matplotlib.pylab.show() + else: + if save_plot_name is None: + save_plot_name = "source_plot_" + repr(dic['src_id']) + ".png" + matplotlib.pylab.savefig(save_plot_name) + f = open(save_plot_name,'r') + if f.readline().find("PS-Adobe") != -1: + # this is a postscript file + os.rename(save_plot_name,save_plot_name.replace(".png",".ps")) + save_plot_name = save_plot_name.replace(".png",".ps") + f.close() + if self.verbose: + print("+ save light curve %s" % save_plot_name) + + + def normalize_vosource_tags(self, xml_str): + """ Convert specific VOSource tags to expected case + so that it is easier to xpath query, traverse. + """ + xs = xml_str.replace('CLASSIFICATIONS','Classifications')\ + .replace('CLASSIFICATION','Classification')\ + .replace('classification','Classification')\ + .replace(' seems more descriptive than in some cases (9022) + if class_update_dict["class"] != "UNKNOWN": + break # This means we already found a valid classification from xml parsing + for classfn_branch in classification_branches: + correct_branch = False + try: + for source_branch in classfn_branch.findall('source'): + if (source_branch.get('type') == classification_node_type_name): + correct_branch = True + break + except: + pass + if correct_branch: + # Then we get the innermost, longest class dbname & parse last ':' suffix + classes = classfn_branch.findall('.//class') + tmpclasses = [] + dbname_to_name = {} + for c in classes: + class_name = c.get('dbname','') + if len(class_name) > 0: + tmpclasses.append(class_name) + if c.get('name') != None: + verbose_name = c.get('name') + else: + verbose_name = c.get('dbname','') # I'd perfer not having this case arise. + dbname_to_name[class_name] = verbose_name + tmpclasses.sort(key=len,reverse=True) + grandchild_classname = '' + if len(tmpclasses) > 0: + grandchild_classname = dbname_to_name[tmpclasses[0]] + class_update_dict = {"class":grandchild_classname} + break + #else: + # class_update_dict = {"class":"UNKNOWN"} + self.x_sdict.update(class_update_dict) + ###################### + + # self.elemtree.findall('WhereWhen/Position2D/Value2/c1')[0].text + try: + self.x_sdict.update({"ra": float(self.elemtree.\ + findall('WhereWhen/Position2D/Value2/c1')[0].text)}) + except: + self.x_sdict.update({"ra": ''}) + try: + self.x_sdict.update({"dec": float(self.elemtree.\ + findall('WhereWhen/Position2D/Value2/c2')[0].text)}) + except: + self.x_sdict.update({"dec": ''}) + try: + self.x_sdict.update({"dec_rms": float(self.elemtree.\ + findall('WhereWhen/Position2D/Error2/c2')[0].text)}) + except: + self.x_sdict.update({"dec_rms": ''}) + try: + self.x_sdict.update({"ra_rms": float(self.elemtree.\ + findall('WhereWhen/Position2D/Error2/c1')[0].text)}) + except: + self.x_sdict.update({"ra_rms": ''}) + + self.ts = {} + ## now parse through to get all the timeseries data + + ## figure out what kind of timeseries it is + try: + self.timesys = self.elemtree.findall('VOTimeseries/TIMESYS/TimeType')[0].text.upper() + self.timezero = float(self.elemtree.findall('VOTimeseries/TIMESYS/TimeZero')[0].text) + except: + #print "No TIMESYS ... assuming the defaults" + self.timesys = "MJD" + self.timezero = 0.0 + + try: + # 20090311: dstarr not sure if a-mara string case still applicable for etree. Commenting out: + #filt_nodes = [x for x in self.doc.VOSOURCE.VOTIMESERIES.RESOURCE.xml_children if type(x) != type(u"")] + filt_nodes = self.elemtree.findall('VOTimeseries/Resource/TABLE') + except: + filt_nodes = [] + self.durga = filt_nodes + for fn in filt_nodes: + # Here we are iterating over ... + # get the band name + band_split = fn.get('name').split("-") + if len(band_split) == 2: + band = band_split[1] + else: + band = band_split[0] + + units = [] + array_names = [] + ucds = [] + IDs = [] + data = {} + for f in fn.findall('FIELD'): + ucds.append(f.get('ucd')) # appends None otherwise + units.append(f.get('unit')) # appends None otherwise + if f.get('name') != None: + array_names.append(f.get('name')) + else: + array_names.append("unknown") + data.update({array_names[-1]: []}) + if f.get('ID') != None: + IDs.append(f.get('ID')) + else: + IDs.append("unknown") + + data.update({'units': units, 'ucds': ucds, 'IDs': IDs, 'ordered_column_names': array_names, + 'limitmags':{'t':[], 'lmt_mg':[]}}) + ## now go through the timeseries part of the data line by line + try: + rows = fn.findall('DATA/TABLEDATA/TR') + except: + rows = [] + # needed for limit_mag use: + col_name_dict = {} + for i, col_name in enumerate(array_names): + col_name_dict[col_name] = i + + for tr in rows: + cols = tr.findall('TD') + if 'limit' in tr.keys(): + # KLUDGE: for limiting-magnitude XML TABLE row: + # explicitly assume column names exist: XML.TABLE.FIELD.name = {'t','m'} + data['limitmags']['t'].append(float(cols[col_name_dict['t']].text)) + data['limitmags']['lmt_mg'].append(float(cols[col_name_dict['m']].text)) + else: + for i,td in enumerate(cols): + data[array_names[i]].append(float(td.text)) + self.ts.update({band: copy.copy(data)}) + + if 0: + # # # 20100521: dstarr disables this due to complications with watt_per_m2_flux and just the general sloppiness of combining filters and their magnigueds. Ideally there would be some soft of offset magnitude used for each band (like the watt_per_m2_flux, and specific features would act only on this internal combined flux(t) dataset + ############################### + ### KLUDGE: (20090616) + # I'm generating a combined-band which due to a combined number of data points, should + # be used for band-invariant features. This is useful for PTF since data is sparse and + # spread bi-monthly over 2 bands. It is a kludge because astrophysical objects have + # different brightnesses in different bands. Hopefully features which dominate + # effective classification prefer more samples than magnitude accuracy/offsets. + ts_without_combo = {} + for k,v in self.ts.items(): + if k != 'combo_band': + ts_without_combo[k] = v + + band_name_with_data = ts_without_combo.keys()[0] + for band,data_dict in ts_without_combo.items(): + if len(data_dict.get('m',[])) > 0: + band_name_with_data = band + break + combo_band_dict = copy.deepcopy(ts_without_combo[band_name_with_data]) + if 'limitmags' not in combo_band_dict: + combo_band_dict['limitmags'] = {'lmt_mg':[], 't':[]} + for band,data_dict in ts_without_combo.items(): + if band == band_name_with_data: + continue + combo_band_dict['limitmags']['lmt_mg'].extend(data_dict.get('limitmags',{}).get('lmt_mg',[])) + combo_band_dict['limitmags']['t'].extend(data_dict.get('limitmags',{}).get('t',[])) + combo_band_dict['m'].extend(data_dict.get('m',[])) + combo_band_dict['m_err'].extend(data_dict.get('m_err',[])) + combo_band_dict['t'].extend(data_dict.get('t',[])) + t_tup_list = [] + for i,t_val in enumerate(combo_band_dict.get('t',[])): + t_tup_list.append((t_val,i)) + t_tup_list.sort() + i_sorted_list = map(lambda a_b: a_b[1], t_tup_list) + combo_band_dict['m'] = map(lambda i: combo_band_dict['m'][i], i_sorted_list) + combo_band_dict['m_err'] = map(lambda i: combo_band_dict['m_err'][i], i_sorted_list) + combo_band_dict['t'] = map(lambda i: combo_band_dict['t'][i], i_sorted_list) + + t_tup_list = [] + for i,t_val in enumerate(combo_band_dict['limitmags']['t']): + t_tup_list.append((t_val,i)) + t_tup_list.sort() + i_sorted_list = map(lambda a_b1: a_b1[1], t_tup_list) + combo_band_dict['limitmags']['lmt_mg'] = map(lambda i: combo_band_dict['limitmags']['lmt_mg'][i], i_sorted_list) + combo_band_dict['limitmags']['t'] = map(lambda i: combo_band_dict['limitmags']['t'][i], i_sorted_list) + + self.ts['combo_band'] = combo_band_dict + ############################### + self.x_sdict.update({'ts': self.ts}) + if self.verbose: + info("converted xml to dict") + + self.coerced_to_dict = True + + + + # TODO(20100211): I may have the following function take a keyword which specifies xml version, and at some point allows the default of simpletimeseries xml (once all code can make use of this xml... assuming they all use db_importer and not mlens) + def source_dict_to_xml(self,sdict): + sss = vo_timeseries.vo_source_preamble + xmllevel = 1 + + #print sdict + ## put in some versioning to start + sss += """%s\n%s\n%s\n""" % \ + (__tab__*xmllevel,__tab__*(xmllevel+1),str(datetime.datetime.utcnow()),os.path.basename(__file__),__version__,__tab__*xmllevel) + if "src_id" in sdict: + sss += "%s%s\n" % (__tab__*xmllevel,sdict['src_id']) + + ## get the positional info + if "ra" in sdict and "dec" in sdict: + sss += "%s\n" % (__tab__*xmllevel) + sss += '%sBest positional information of the source\n' % (__tab__*(xmllevel + 1)) + sss += '%s\n%s\n' % (__tab__*(xmllevel + 1),__tab__*(xmllevel + 2)) + sss += "%s%s\n%s%s\n%s\n" % \ + (__tab__*(xmllevel + 3),str(sdict['ra']),__tab__*(xmllevel + 3),str(sdict['dec']),__tab__*(xmllevel + 2)) + if "ra_rms" in sdict and "dec_rms" in sdict: + xmllevel += 1 + sss += "%s\n" % (__tab__*(xmllevel + 1)) + sss += "%s%s\n%s%s\n%s\n" % \ + (__tab__*(xmllevel + 2),str(sdict['ra_rms']),__tab__*(xmllevel + 2),str(sdict['dec_rms']),__tab__*(xmllevel + 1)) + xmllevel -= 1 + sss += "%s\n" % (__tab__*(xmllevel + 1)) + sss += "%s\n" % (__tab__*xmllevel) + + ## now do the timeseries + if "ts" in sdict: + sss += __tab__*xmllevel + vo_timeseries.vo_timeseries_preamble + xmllevel += 1 + sss += __tab__*xmllevel + vo_timeseries.vo_timeseries_mjd + filts = sdict['ts'].keys() + if self.verbose: + info(" there are %i band resources for the timeseries ... ") + sss += '%s\n' % (__tab__*xmllevel) + for b in filts: + bdata = copy.deepcopy(sdict['ts'][b]) #20090617 dstarr adds deepcopy due to sdict['ts'] being extended with limitmags while originally this sdict['ts'] structure is still accessible to later classes (which expect only regular magnitudes in sdict['ts']). + + # NOTE: KLUDGE: for now I clump both filters together for limiting magnitudes, since + # it will take more of a rework to seperate filters in all TCP code. + if 't' in bdata: + # KLUDGE: I'm also assuming m, m_err exist. + bdata['is_limit'] = [False] * len(bdata['t']) + is_limit = bdata['is_limit'] # 20091102 dstarr adds this line + elif 'limitmags' in bdata: + # This filter had no normal (t,m,m_err) data. Just lim-mags + bdata['is_limit'] = [] + bdata['t'] = [] + bdata['m'] = [] + bdata['m_err'] = [] + + if 'limitmags' in bdata: + bdata['is_limit'].extend([True] * len(bdata['limitmags']['t'])) + bdata['t'].extend(bdata['limitmags']['t']) + bdata['m'].extend(bdata['limitmags']['lmt_mg']) + bdata['m_err'].extend([0.0] * len(bdata['limitmags']['t'])) + + xmllevel += 1 + sss += '%s
\n' % (__tab__*xmllevel,b) + xmllevel += 1 + col = 1 + has_m = has_m_err = has_t = False + if 't' in bdata: + sss += '%s\n' \ + % (__tab__*xmllevel,col) + t = numarray.array(bdata['t']) + tsort = t.argsort() + t = t[tsort] + has_t = True + col += 1 + else: + warning("no time array given for filter %s" % b) + + if 'limitmags' in bdata: + try: + is_limit = numarray.array(bdata['is_limit'])[tsort] + except: + is_limit = numarray.array(bdata['is_limit']) + if "m" in bdata: + sss += '%s\n' \ + % (__tab__*xmllevel,col,b) + has_m = True + try: + m = numarray.array(bdata['m'])[tsort] + except: + m = numarray.array(bdata['m']) + col += 1 + if "m_err" in bdata: + sss += '%s\n' \ + % (__tab__*xmllevel,col,b) + has_m_err = True + try: + m_err = numarray.array(bdata['m_err'])[tsort] + except: + m_err = numarray.array(bdata['m_err']) + col += 1 + + if "objid_candid" in bdata: + ptf_charac_val_list = [] + ptf_charac_name_list = ['a_elip_candid', 'b_elip_candid', 'chip_id', 'dec_candidate', 'dec_subtract', 'dtime_observe', 'dtime_reductn', 'field_id', 'filter_id', 'fourier_factor', 'fwhm_obj_candid', 'fwhm_obj_subtr', 'hp_il', 'hp_iu', 'hp_kern_radius', 'hp_newskybkg', 'hp_newskysig', 'hp_nsx', 'hp_nsy', 'hp_refskybkg', 'hp_refskysig', 'hp_rss', 'hp_tl', 'hp_tu', 'img_id_candid', 'img_id_refer', 'mag_refer', 'mag_sig_refer', 'mag_sig_subtr', 'mag_subtr', 'nn_a_elip', 'nn_b_elip', 'nn_dec', 'nn_distance', 'nn_mag', 'nn_mag_sig', 'nn_ra', 'nn_star_galaxy', 'nn_x', 'nn_y', 'objid_candid', 'objid_subtract', 'perc_cand_saved', 'percent_incres', 'positive_pix_ratio', 'quality_factor', 'ra_candidate', 'ra_subtract', 'signoise_subt_big_ap', 'signoise_subt_normap', 'surf_bright', 'x_candidate', 'x_subtref', 'y_candidate', 'y_subtref', 'zp_candidate', 'zp_reference'] + for charac in ptf_charac_name_list: + #try: + # ptf_charac_val_list[j] = bdata[charac][tsort] + #except: + # ptf_charac_val_list[j] = bdata[charac] + + #ptf_charac_val_list.append(bdata[charac]) + ptf_charac_val_list.append(numarray.array(bdata[charac])[tsort]) + + sss += '%s\n' % (__tab__*xmllevel,charac,col,b) + col += 1 + + sss += "%s\n" % (__tab__*xmllevel) + if has_t: + xmllevel += 1 + sss += "%s\n" % (__tab__*xmllevel) + xmllevel += 1 + for i in range(len(t)): + # # # # + if is_limit[i]: + sss += '%s' % (__tab__*xmllevel,i + 1) + else: + sss += '%s' % (__tab__*xmllevel,i + 1) + sss += "" % t[i] + if has_m: + sss += "" % m[i] + if has_m_err: + sss += "" % m_err[i] + if "objid_candid" in bdata: + # TODO: I need to have ptf_charac_name_list[j][i] in array form, in case there are >1 ptf datapoint + for j in range(len(ptf_charac_name_list)): + sss += "" % float(ptf_charac_val_list[j][i]) + sss += "\n" + sss += "%s\n" % (__tab__*xmllevel) + xmllevel -= 1 + sss += "%s\n" % (__tab__*xmllevel) + xmllevel -= 1 + sss += "%s
%f%f%f%lf
\n" % (__tab__*xmllevel) + xmllevel -= 1 + sss += "%s\n" % (__tab__*xmllevel) + xmllevel -= 1 + sss += __tab__*xmllevel + "\n" + if "features" in sdict: + sss += "%s\n" % (__tab__*xmllevel) + xmllevel += 1 + for filt_name,filt_dict in sdict["features"].items(): + for feat_name,feat_str_val in filt_dict.items(): + sss += "%s\n" % (__tab__*xmllevel) + xmllevel += 1 + sss += '%s%s\n' % (__tab__*xmllevel, feat_name) # or "class=context" + xmllevel += 1 + sss += '%s%s\n' % (__tab__*xmllevel, feat_name) + xmllevel += 1 + #TODO: WANT "unit" :: sss += "%s%s\n" % (__tab__*xmllevel, feat_str_val) + #if feat_name == 'flux_percentile_ratio_mid50': + # print 'yo' + if 'fail' in feat_str_val.lower(): + sss += """%s%s\n""" % (__tab__*xmllevel, feat_str_val) + else: + # 20090129 KLUDGE to discern strings vs floats: + try: + test = float(feat_str_val) + sss += """%s%s\n""" % (__tab__*xmllevel, feat_str_val) + except: + sss += """%s%s\n""" % (__tab__*xmllevel, feat_str_val) + # TODO: add errors here, if known: + sss += """%sunknown\n""" % (__tab__*xmllevel) + ##### TODO: add features: + sss += """%s%s\n""" % (__tab__*xmllevel, filt_name) + sss += """%s\n""" % (__tab__*xmllevel, sdict["feature_docs"][filt_name].get(feat_name,"").replace('\n','').replace('?','__qmark__').replace('&','__amper__')) + xmllevel += 1 + sss += """%s%s\n""" % (__tab__*xmllevel, __svn_id__) + # TODO: not sure the best way to extract SVN version... (latest version under /feature_extract/Code/ ???) + sss += """%s%s\n""" % (__tab__*xmllevel, sdict['feat_gen_date'].replace(' ','T')) + # TODO: Insert some feature-code generated comment here: + sss += """%s"%s"\n""" % (__tab__*xmllevel, feat_str_val) + xmllevel -= 1 + sss += """%s\n""" % (__tab__*xmllevel) + xmllevel -= 3 + sss += "%s\n" % (__tab__*xmllevel) + xmllevel -= 1 + sss += "%s\n" % (__tab__*xmllevel) + + #### + if "class" in sdict: + vosource_class_obj = vosource_classification_obj() + # 20081023: dstarr comments out: + #vosource_class_obj.add_classif_prob(class_name="tutor", + vosource_class_obj.add_classif_prob(class_name=sdict['class'], + prob=1.0, + class_schema_name="tutor", + human_algo="human") + sss += vosource_class_obj.get_class_xml_string() + sss += "\n" + + self.xml_string = sss + self.marshalled_to_xml = True + + + def add_features_to_xml_string(self, signals_list): + """ Given a signals_list (aka parent .signals_list), + Add features to self.x_sdict and self.xml_string (for XML file write). + """ + # KLUDGE: Assume 1 source in signals_list + self.x_sdict['features'] = {} + self.x_sdict['feature_docs'] = {} + + #import datetime + self.x_sdict['feat_gen_date'] = str(datetime.datetime.utcnow()) + + for filter_name,filt_dict in signals_list[0].properties['data'].items(): + self.x_sdict['features'][filter_name] = {} + self.x_sdict['feature_docs'][filter_name] = {} + # KLUDGE: Since scalar values in ['features'] dict are actually + # of type: Code.FeatureExtractor.outputclass, I explicitly cast str: + for feat_name,value_object in signals_list[0].properties['data']\ + [filter_name]['features'].items(): + self.x_sdict['features'][filter_name][feat_name] = str(value_object) + self.x_sdict['feature_docs'][filter_name][feat_name] = \ + str(value_object.__doc__).replace('&','__AMPERSAND__').replace("'",'__SINGLEQUOTE__').replace('"','__DOUBLEQUOTE__')[:500] + self.source_dict_to_xml(self.x_sdict) + + +class PositionExtractor: + def __init__(self, pos=(None,None), radius=0.001, verbose=True, \ + prefer_only_source_search=True, host="localhost", \ + port=8000, doplot=True, use_source_id_in_xml_name=True, \ + do_remote_connection=1, write_xml=True): + """ + radius - search distance (box) in degrees + use_source_id_in_xml_name - in the case where you've asked for an xml file name, setting + this to True will mean that you get a unique xml file out. + """ + self.verbose = verbose + self.allow_search = False + self.pos = pos + self.radius = radius + self.prefer_only_source_search = prefer_only_source_search + self.doplot = doplot + self.write_xml = write_xml + self.use_source_id_in_xml_name=use_source_id_in_xml_name + + if self.radius > MAX_SEARCH_RADIUS: + warning("search radius exceeded. Cutting down to %f deg" % MAX_SEARCH_RADIUS) + self.radius = MAX_SEARCH_RADIUS + self.pos_well_formed = False + if type(self.pos) == type(()): + if len(self.pos) == 2: + if type(self.pos[0]) == type(1.0) and type(self.pos[1]) == type(1.0): + self.pos_well_formed = True + + if do_remote_connection == 1: + self.setup_remote_connection(host=host, port=port) + + def setup_remote_connection(self,host="localhost",port=8000): + import xmlrpclib + info("connecting to the server") + self.server = xmlrpclib.ServerProxy("http://%s:%i" % (host, port)) + self.remote_meth = self.server.system.listMethods() + if 'system.multicall' in self.remote_meth: + self.multicall = True + else: + self.multicall = False + + if "get_sources_for_radec" in self.remote_meth: + self.allow_search = True + self.search_type = "full" + + if self.prefer_only_source_search and ("get_sources_for_radec_assume_in_src_db" in self.remote_meth): + self.allow_search = True + self.search_type = "src" + + def search_pos(self, out_xml_fpath='', summary_ps_fpath='', \ + get_sources_for_radec_method=None, \ + skip_construct_sources=False): + if not self.allow_search: + warning("No search allowed because the server does not have the appropriate method") + return + info("making the call to the server for position %s" % repr(self.pos)) + if get_sources_for_radec_method != None: + info("this could take awhile") + self.rez = get_sources_for_radec_method(self.pos[0],self.pos[1],self.radius, summary_ps_fpath) + elif self.search_type == "full": + info("this could take awhile") + self.rez = self.server.get_sources_for_radec(self.pos[0],self.pos[1],self.radius, summary_ps_fpath) + else: + self.rez = self.server.get_sources_for_radec_assume_in_src_db(self.pos[0],self.pos[1],self.radius) + + if self.verbose: + info("Got %i sources." % len(self.rez)) + + #20080313 dstarr KLUDGE: I want to call this outside, but + # I believe others might call search_pos() in their own code + # so I leave this as the default action: + if not skip_construct_sources: + self.construct_sources(self.rez, out_xml_fpath=out_xml_fpath) + + def construct_sources(self,slist, out_xml_fpath=''): + """ slist is a list of source dictionaries + """ + if type(slist) != type([]): + warning("slist is not a list") + self.sources = [] + return + if len(slist) == 0: + warning("slist is empty list") + self.sources = [] + return + if type(slist[0]) != type({}): + warning("first entry in slist is not a dictionary") + self.sources = [] + return + # 20080127: dstarr thinks this deepcopy() is unnecissary: + #self.sources = [copy.deepcopy(Source(sdict=s, \ + # out_xml_fpath=out_xml_fpath,doplot=self.doplot,\ + # use_source_id_in_xml_fpath= \ + # self.use_source_id_in_xml_name, \ + # write_xml=self.write_xml)) for s in slist] + self.sources = [Source(sdict=s, \ + out_xml_fpath=out_xml_fpath,doplot=self.doplot,\ + use_source_id_in_xml_fpath= \ + self.use_source_id_in_xml_name, \ + write_xml=self.write_xml) for s in slist] + + def summary_plot(self): + import matplotlib + + matplotlib.pylab.hold(False) + matplotlib.pylab.plot([120,111]) + matplotlib.pylab.hold(True) + rr = [] + dd = [] + for s in self.sources: + matplotlib.pylab.scatter([s.d['ra']],[s.d['dec']]) + rr.append(s.d['ra']) + dd.append(s.d['dec']) + #matplotlib.pylab.ylim( (max(yytrema[0]) + 1,min(yytrema[1]) - 0.3) ) + #matplotlib.pylab.xlim( (min(xxtrema[0]) - 10,max(xxtrema[1]) + 10) ) + #print (max(yytrema[1]) + 1,min(yytrema[0])) + #print (min(xxtrema[0]) - 10,max(xxtrema[1]) + 10) + matplotlib.pylab.ylim( (min(dd) - 0.01,max(dd) + 0.01) ) + matplotlib.pylab.xlim( (min(rr) - 0.01,max(rr) + 0.01) ) + + matplotlib.pylab.xlabel('RA [deg]') + matplotlib.pylab.ylabel('DEC [dec]') + matplotlib.pylab.show() + + def ds9_summary(self,fname="tmp.reg"): + """ + makes a ds9 region file from all the sources. Source number and the number of obs in each filter is given + + TODO: make error ellipses instead of points + """ + pream = \ +""" +# Region file format: DS9 version 4.0 +global color=green font="helvetica 10 normal" select=1 highlite=1 edit=1 move=1 delete=1 include=1 fixed=0 source +fk5 +""" + f = open(fname,"w") + f.write(pream) + for s in self.sources: + (ra,dec) = (str(s.d['ra']),str(s.d['dec'])) + src_id = s.d['src_id'] + vv = s.d['ts'].values() + ss = [band + "=" + str(len(val['t'])) for band, val in s.d['ts'].items()] + label = "s" + str(src_id) + ":" + ";".join(ss) + f.write("point(%s,%s) # point=circle text={%s}\n" % (ra,dec,label)) + f.close() + print("wrote %s" % fname) + + def get_search_help(self): + if self.allow_search: + if self.search_type == "full": + print(self.server.system.methodHelp("get_sources_for_radec")) + else: + print(self.server.system.methodHelp("get_sources_for_radec_assume_in_src_db")) + return + + def __del__(self): + try: + info("disconnecting from the server") + del self.server + except: + pass + +class TestClass: + """ Testing class for python-nose tests. + Useful for refactoring and adding new XML schema + + NOTE: Make sure that environment variable is set to: + PYTHONOPTIMIZE="" + NOTE: Make sure that no ".pyo" files exist in source directories. + + TO RUN python-nose test in SHELL: + nosetests --tests=db_importer + + OR more verbosely: + nosetests -s -vv --tests=db_importer + + TO RUN python-nose test in PDB: + + BREAK on any ERROR: + nosetests --pdb --tests=db_importer + + BREAK on TEST FAILURE: + nosetests --pdb-failures --tests=db_importer + + + TODO TESTS: + - read various things from an testing XML file. + - write a feature & (brute force?) read from xmlfile? + - write a mag,time epoch & (brute force?) read from xmlfile? + - write a classification & (brute force?) read from xmlfile? + """ + def setUp(self): + """ Performed at the beginning of each test + """ + self.temp_rw_vosource_fpath = '/tmp/db_importer_tests.vosource.xml' + + + def tearDown(self): + """ Performed at the end of each test + """ + pass + + + def make_assertions_for__vosource_1990af(self,dbi_src): + """ For $TCP_DIR/Data/1990af.xml + """ + from nose import tools as nt + + nt.assert_true( dbi_src.x_sdict['src_id'] == 14483) + nt.assert_true( dbi_src.x_sdict['class'] =='Type Ia Supernovae') + nt.assert_true( ((dbi_src.x_sdict['ra'] > 323.74216) and + (dbi_src.x_sdict['ra'] < 323.74217))) + nt.assert_true( dbi_src.x_sdict['ts']['B:table4598']['m'][2] == 17.939) + nt.assert_true( ((dbi_src.x_sdict['ts']['B:table4598']['m_err'][3] > 0.030) and + (dbi_src.x_sdict['ts']['B:table4598']['m_err'][3] < 0.032))) + nt.assert_true( ((dbi_src.x_sdict['ts']['B:table4598']['t'][1] > 2448193.53) and + (dbi_src.x_sdict['ts']['B:table4598']['t'][1] < 2448193.55))) + + + def make_assertions_for__vosource_tutor12881(self,dbi_src): + """ For $TCP_DIR/Data/vosource_tutor12881.xml: + """ + from nose import tools as nt + + nt.assert_true( dbi_src.x_sdict['src_id'] == 12881) + nt.assert_true( dbi_src.x_sdict['class'] =='RR Lyrae, Fundamental Mode') + nt.assert_true( ((dbi_src.x_sdict['ra'] > 30.47567) and + (dbi_src.x_sdict['ra'] < 30.47568))) + nt.assert_true( dbi_src.x_sdict['ts']['H:table1384']['m'][2] == 11.5783) + nt.assert_true( ((dbi_src.x_sdict['ts']['H:table1384']['m_err'][3] > 0.0429) and + (dbi_src.x_sdict['ts']['H:table1384']['m_err'][3] < 0.0431))) + nt.assert_true( ((dbi_src.x_sdict['ts']['H:table1384']['t'][1] > 7904.22188) and + (dbi_src.x_sdict['ts']['H:table1384']['t'][1] < 7904.2219))) + + + def test_case_parse_vosource_file(self): + """ Tests related to the parsed vosource xml + """ + # old TCP vosource: + vosource_fpath = os.path.expandvars("$TCP_DIR/Data/vosource_tutor12881.xml") + dbi_src = Source(make_dict_if_given_xml=True, + make_xml_if_given_dict=False, + doplot=False, + xml_handle=vosource_fpath) + self.make_assertions_for__vosource_tutor12881(dbi_src) + + # TUTOR vosource: + vosource_fpath = os.path.expandvars("$TCP_DIR/Data/1990af.xml") + dbi_src = Source(make_dict_if_given_xml=True, + make_xml_if_given_dict=False, + doplot=False, + xml_handle=vosource_fpath) + self.make_assertions_for__vosource_1990af(dbi_src) + + + + + def test_case_generated_vosource_xml_string(self): + """ Tests related to writing a internal sdict to VOSource XML string. + """ + # TODO: test that I want to pickle the test_feature_algorithms's signals_list[0] and open it here + # ( in the class) and then write out a vosource xml string & then test that all the expected are in place. + vosource_fpath = os.path.expandvars("$TCP_DIR/Data/vosource_tutor12881.xml")#old TCP vosource + + sys.path.append(os.path.abspath(os.environ.get("TCP_DIR","") + \ + 'Software/feature_extract')) + sys.path.append(os.path.abspath(os.environ.get("TCP_DIR","") + \ + 'Software/feature_extract/Code')) + from Code import generators_importers + from . import db_importer + + signals_list = [] + gen = generators_importers.from_xml(signals_list) + + gen.generate(xml_handle=vosource_fpath) + gen.sig.add_features_to_xml_string(gen.signals_list) + + from nose import tools as nt + nt.assert_true( ((float(str(signals_list[0].properties['data']['multiband']['features']['ecpl'])) > 25.2720) and + (float(str(signals_list[0].properties['data']['multiband']['features']['ecpl'])) < 25.2721))) + nt.assert_true( ((float(str(signals_list[0].properties['data']['H:table1384']['features']['median'])) > 11.9414) and + (float(str(signals_list[0].properties['data']['H:table1384']['features']['median'])) < 11.9416))) + + # TODO: do schema / formatting tests on the xml string : + # gen.sig.xml_string + + ### Now write this new VOSource XML to file, read again, and test values/format are correct. + gen.sig.write_xml(out_xml_fpath=self.temp_rw_vosource_fpath) + + dbi_src = Source(make_dict_if_given_xml=True, + make_xml_if_given_dict=False, + doplot=False, + xml_handle=self.temp_rw_vosource_fpath) + + # NOTE: This function uses new temp XML, but with original assertions: + self.make_assertions_for__vosource_tutor12881(dbi_src) + + +if __name__ == "__main__": + + tc = TestClass() + tc.setUp() + #tc.test_case_parse_vosource_file() + tc.test_case_generated_vosource_xml_string() + tc.tearDown() + + sys.exit() + setup_logging() + info("started.") + dotest1() + info("finished.") + diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/.__init__.py.swp b/mltsp/TCP/Software/feature_extract/Code/extractors/.__init__.py.swp new file mode 100644 index 00000000..b2dba1ea Binary files /dev/null and b/mltsp/TCP/Software/feature_extract/Code/extractors/.__init__.py.swp differ diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/__init__.py b/mltsp/TCP/Software/feature_extract/Code/extractors/__init__.py new file mode 100644 index 00000000..2eaeb7e0 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/__init__.py @@ -0,0 +1,169 @@ +import os, glob, sys, types +#ext1 = os.environ.get('TCP_DIR') + 'Software/feature_extract/Code/extractors/' + +#20101207#from chi2_per_deg_extractor import chi2_per_deg_extractor +#20101207#from chi2extractor import chi2extractor +#20101207#from dc_extractor import dc_extractor +from .dist_from_u_extractor import dist_from_u_extractor +from .fourier_extractor import fourier_extractor +from .linear_extractor import linear_extractor +from .max_slope_extractor import max_slope_extractor +from .median_extractor import median_extractor +from .beyond1std_extractor import beyond1std_extractor +from .stdvs_from_u_extractor import stdvs_from_u_extractor +#from old_dcextractor import old_dcextractor +# # # 20100912 disabled due to lomb() call# from power_spectrum_extractor import power_spectrum_extractor +# # # 20100912 disabled due to lomb() call# from power_extractor import power_extractor +#20080614#from montecarlo_extractor import montecarlo_extractor +#20080614#from pct_montecarlo_extractor import pct_80_montecarlo_extractor, pct_90_montecarlo_extractor, pct_95_montecarlo_extractor, pct_99_montecarlo_extractor +#20080614#from significant_power_extractor import significant_80_power_extractor, significant_90_power_extractor, significant_95_power_extractor, significant_99_power_extractor +#20080614#from first_freq_extractor import first_freq_extractor +from .sine_fit_extractor import sine_fit_extractor +from .sine_leastsq_extractor import sine_leastsq_extractor +from .skew_extractor import skew_extractor +#from kurtosis_extractor import kurtosis_extractor #20101121 joey recommends disabling since it is very similar to small_kurtosis_extractor +from .s_extractor import s_extractor +#from small_kurtosis_extractor import small_kurtosis_extractor +from .std_extractor import std_extractor +from .median_absolute_deviation_extractor import median_absolute_deviation_extractor +from .wei_av_uncertainty_extractor import wei_av_uncertainty_extractor +from .weighted_average_extractor import weighted_average_extractor +# # # 20100912 disabled due to lomb() call# from lomb_extractor import lomb_extractor +# # # 20100912 disabled due to lomb() call# from first_lomb_extractor import first_lomb_extractor +# # # 20100912 disabled due to lomb() call# from sine_lomb_extractor import sine_lomb_extractor +#20080614#from second_extractor import second_extractor, third_extractor +# # # 20100912 disabled due to lomb() call# from second_lomb_extractor import second_lomb_extractor +# # # 20100912 disabled due to lomb() call# from frequency_ratio_extractor import ratio21, ratio31, ratio32 +#from example_extractor import example_extractor +from .n_points_extractor import n_points_extractor + +#from position_intermediate_extractor import position_intermediate_extractor +#20101207#from galb_extractor import galb_extractor +#20101207#from ecpb_extractor import ecpb_extractor +#20101207#from ecpl_extractor import ecpl_extractor +#20101207#from gall_extractor import gall_extractor +#from tmpned_extractor import tmpned_extractor # 20100914: dstarr disables this for now since running on non lyra LAN clusters, and we dont care about context features at the moment. +#from ratioRUfirst_extractor import ratioRUfirst_extractor +#20101207#from distance_in_kpc_to_nearest_galaxy import distance_in_kpc_to_nearest_galaxy +#20101207#from distance_in_arcmin_to_nearest_galaxy import distance_in_arcmin_to_nearest_galaxy +#20101121commentedout# from lomb_scargle_extractor import lomb_scargle_extractor, freq1_harmonics_amplitude_0_extractor, freq1_harmonics_amplitude_error_0_extractor, freq1_harmonics_freq_0_extractor, freq1_harmonics_moments_0_extractor, freq1_harmonics_moments_err_0_extractor, freq1_harmonics_peak2peak_flux_extractor, freq1_harmonics_peak2peak_flux_error_extractor, freq1_harmonics_rel_phase_0_extractor, freq1_harmonics_rel_phase_error_0_extractor, freq2_harmonics_amplitude_0_extractor, freq2_harmonics_amplitude_error_0_extractor, freq2_harmonics_freq_0_extractor, freq2_harmonics_moments_0_extractor, freq2_harmonics_moments_err_0_extractor, freq2_harmonics_rel_phase_0_extractor, freq2_harmonics_rel_phase_error_0_extractor, freq3_harmonics_amplitude_0_extractor, freq3_harmonics_amplitude_error_0_extractor, freq3_harmonics_freq_0_extractor, freq3_harmonics_moments_0_extractor, freq3_harmonics_moments_err_0_extractor, freq3_harmonics_rel_phase_0_extractor, freq3_harmonics_rel_phase_error_0_extractor, freq_harmonics_offset_extractor, freq_y_offset_extractor, freq_signif_extractor, freq_nharm_extractor, freq1_harmonics_amplitude_1_extractor, freq1_harmonics_amplitude_2_extractor, freq1_harmonics_amplitude_3_extractor, freq1_harmonics_rel_phase_1_extractor, freq1_harmonics_rel_phase_2_extractor, freq1_harmonics_rel_phase_3_extractor, freq1_harmonics_moments_1_extractor, freq1_harmonics_moments_2_extractor, freq1_harmonics_moments_3_extractor, freq2_harmonics_amplitude_1_extractor, freq2_harmonics_amplitude_2_extractor, freq2_harmonics_amplitude_3_extractor, freq2_harmonics_rel_phase_1_extractor, freq2_harmonics_rel_phase_2_extractor, freq2_harmonics_rel_phase_3_extractor, freq2_harmonics_moments_1_extractor, freq2_harmonics_moments_2_extractor, freq2_harmonics_moments_3_extractor, freq3_harmonics_amplitude_1_extractor, freq3_harmonics_amplitude_2_extractor, freq3_harmonics_amplitude_3_extractor, freq3_harmonics_rel_phase_1_extractor, freq3_harmonics_rel_phase_2_extractor, freq3_harmonics_rel_phase_3_extractor, freq3_harmonics_moments_1_extractor, freq3_harmonics_moments_2_extractor, freq3_harmonics_moments_3_extractor, linear_trend_extractor, freq_varrat_extractor, freq_signif_ratio_21_extractor, freq_signif_ratio_31_extractor, freq_frequency_ratio_21_extractor, freq_frequency_ratio_31_extractor, freq_amplitude_ratio_21_extractor, freq_amplitude_ratio_31_extractor + + +from .lomb_scargle_extractor import lomb_scargle_extractor, freq1_harmonics_amplitude_0_extractor, freq1_harmonics_freq_0_extractor, freq1_harmonics_rel_phase_0_extractor, freq2_harmonics_amplitude_0_extractor, freq2_harmonics_freq_0_extractor, freq2_harmonics_rel_phase_0_extractor, freq3_harmonics_amplitude_0_extractor, freq3_harmonics_freq_0_extractor, freq3_harmonics_rel_phase_0_extractor, freq_y_offset_extractor, freq_signif_extractor, freq1_harmonics_amplitude_1_extractor, freq1_harmonics_amplitude_2_extractor, freq1_harmonics_amplitude_3_extractor, freq1_harmonics_rel_phase_1_extractor, freq1_harmonics_rel_phase_2_extractor, freq1_harmonics_rel_phase_3_extractor, freq2_harmonics_amplitude_1_extractor, freq2_harmonics_amplitude_2_extractor, freq2_harmonics_amplitude_3_extractor, freq2_harmonics_rel_phase_1_extractor, freq2_harmonics_rel_phase_2_extractor, freq2_harmonics_rel_phase_3_extractor, freq3_harmonics_amplitude_1_extractor, freq3_harmonics_amplitude_2_extractor, freq3_harmonics_amplitude_3_extractor, freq3_harmonics_rel_phase_1_extractor, freq3_harmonics_rel_phase_2_extractor, freq3_harmonics_rel_phase_3_extractor, linear_trend_extractor, freq_varrat_extractor, freq_signif_ratio_21_extractor, freq_signif_ratio_31_extractor, freq_frequency_ratio_21_extractor, freq_frequency_ratio_31_extractor, freq_amplitude_ratio_21_extractor, freq_amplitude_ratio_31_extractor, p2p_scatter_2praw_extractor, p2p_scatter_over_mad_extractor, p2p_scatter_pfold_over_mad_extractor, medperc90_2p_p_extractor, p2p_ssqr_diff_over_var_extractor, fold2P_slope_10percentile_extractor, fold2P_slope_90percentile_extractor, freq_n_alias_extractor, freq_model_phi1_phi2_extractor, freq_model_min_delta_mags_extractor, freq_model_max_delta_mags_extractor, freq1_lambda_extractor + +from .scatter_res_raw_extractor import scatter_res_raw_extractor +#from psd_example_extractor import psd_example_extractor +from .gskew_extractor import gskew_extractor +# ishivvers additions: +from .phase_dispersion_extractor import phase_dispersion_freq0_extractor, ratio_PDM_LS_freq0_extractor +from .delta_phase_2minima_extractor import delta_phase_2minima_extractor + +#from eclipse_poly_extractor import eclipse_poly_extractor, eclpoly_best_orb_chi2_extractor, eclpoly_best_orb_period_extractor, eclpoly_15_ratio_diff_extractor, eclpoly_20_ratio_diff_extractor, eclpoly_30_ratio_diff_extractor, eclpoly_5_ratio_diff_extractor, eclpoly_8_ratio_diff_extractor, eclpoly_is_suspect_extractor, eclpoly_orb_signif_extractor, eclpoly_final_period_ratio_extractor +#20120130, eclpoly_p_pulse_extractor, eclpoly_p_pulse_initial_extractor + +#20100518 added this line: +from .watt_per_m2_flux_extractor import watt_per_m2_flux_extractor + +from .min_max_extractor import min_extractor, max_extractor +from .amplitude_extractor import amplitude_extractor, percent_amplitude_extractor, percent_difference_flux_percentile_extractor, flux_percentile_ratio_mid20_extractor, flux_percentile_ratio_mid35_extractor, flux_percentile_ratio_mid50_extractor, flux_percentile_ratio_mid65_extractor, flux_percentile_ratio_mid80_extractor +#20101124 disable for now since single band is similar to other features. revisit this when we have multibands# from ws_variability_extractor import ws_variability_self_extractor,ws_variability_bv_extractor, ws_variability_ru_extractor,ws_variability_ug_extractor,ws_variability_gr_extractor,ws_variability_ri_extractor,ws_variability_iz_extractor +from .median_buffer_range_percentage_extractor import median_buffer_range_percentage_extractor +#from lomb_scargle_extractor import ex2_extractor +###from example_extractor import example_extractor +#20110204 comment out since needs reqork to be generally applicable#from pair_slope_trend_extractor import pair_slope_trend_extractor +#ignores = ["min_max_extractor"] ## broken module names should be put here. +# NOTE: qso_extractor: This is not active, and is only used below (if it was active, it would returna a dictionary) +from .qso_extractor import qso_extractor +#from qso_extractor import qso_lvar_extractor, qso_ltau_extractor, qso_chi2nu_extractor, qso_chi2_qsonu_extractor, qso_chi2_qso_nu_NULL_extractor, qso_signif_qso_extractor, qso_signif_not_qso_extractor, qso_signif_vary_extractor, qso_chi2qso_nu_nuNULL_ratio_extractor +from .qso_extractor import qso_log_chi2_qsonu_extractor, qso_log_chi2nuNULL_chi2nu_extractor +from .stetson_extractor import stetson_j_extractor, stetson_k_extractor +#stetson_mean_extractor, # this is essentially the mean lightcurve magnitude + +#from .color_diff_extractor import static_colors_extractor, color_diff_jh_extractor, color_diff_hk_extractor, color_diff_bj_extractor, color_diff_vj_extractor, color_diff_rj_extractor, color_bv_extinction_extractor + +from .lcmodel_extractor import lcmodel_extractor, lcmodel_pos_mag_ratio_extractor, lcmodel_pos_n_ratio_extractor, lcmodel_median_n_per_day_extractor, lcmodel_pos_n_per_day_extractor, lcmodel_neg_n_per_day_extractor, lcmodel_pos_area_ratio_extractor + +from .ar_is_extractor import ar_is_theta_extractor, ar_is_sigma_extractor + +## JSB additions +from .interng_extractor import interng_extractor + + +from .old_dc_extractor import old_dc_extractor +#20101207#from closest_in_light import closest_in_light +#20101207#from closest_in_light_absolute_bmag import closest_in_light_absolute_bmag +#20101207#from closest_in_light_angle_from_major_axis import closest_in_light_angle_from_major_axis +#20101207#from closest_in_light_angular_offset_in_arcmin import closest_in_light_angular_offset_in_arcmin +#20101207#from closest_in_light_dm import closest_in_light_dm +#20101207#from closest_in_light_physical_offset_in_kpc import closest_in_light_physical_offset_in_kpc +#20101207#from closest_in_light_ttype import closest_in_light_ttype + +#20101207#from intersdss_extractor import intersdss_extractor +#20101207#from sdss_dist_arcmin import sdss_dist_arcmin +#20101207#from sdss_best_dm import sdss_best_dm +#20101207#from sdss_best_z import sdss_best_z +#20101207#from sdss_best_zerr import sdss_best_zerr +#20101207#from sdss_photo_z_pztype import sdss_photo_z_pztype +#20101207#from sdss_chicago_class import sdss_chicago_class +#20101207#from sdss_best_offset_in_kpc import sdss_best_offset_in_kpc +#20101207#from sdss_best_z import sdss_best_z +#20101207#from sdss_best_zerr import sdss_best_zerr +#20101207#from sdss_dered_g import sdss_dered_g +#20101207#from sdss_dered_i import sdss_dered_i +#20101207#from sdss_dered_z import sdss_dered_z +#20101207#from sdss_dered_r import sdss_dered_r +#20101207#from sdss_dered_u import sdss_dered_u +#20101207#from sdss_in_footprint import sdss_in_footprint +#20101207#from sdss_nearest_obj_type import sdss_nearest_obj_type +#20101207#from sdss_spec_confidence import sdss_spec_confidence + +#20101207#from sdss_photo_rest_abs_g import sdss_photo_rest_abs_g +#20101207#from sdss_photo_rest_abs_i import sdss_photo_rest_abs_i +#20101207#from sdss_photo_rest_abs_r import sdss_photo_rest_abs_r +#20101207#from sdss_photo_rest_abs_u import sdss_photo_rest_abs_u +#20101207#from sdss_photo_rest_abs_z import sdss_photo_rest_abs_z +#20101207#from sdss_photo_rest_gr import sdss_photo_rest_gr +#20101207#from sdss_photo_rest_iz import sdss_photo_rest_iz +#20101207#from sdss_photo_rest_ri import sdss_photo_rest_ri +#20101207#from sdss_photo_rest_ug import sdss_photo_rest_ug +#20101207#from sdss_best_offset_in_petro_g import sdss_best_offset_in_petro_g +#20101207#from sdss_petro_radius_g import sdss_petro_radius_g +#20101207#from sdss_petro_radius_g_err import sdss_petro_radius_g_err + +#20101207#from sdss_first_flux_in_mjy import sdss_first_flux_in_mjy +#20101207#from sdss_first_offset_in_arcsec import sdss_first_offset_in_arcsec +#20101207#from sdss_rosat_flux_in_mJy import sdss_rosat_flux_in_mJy +#20101207#from sdss_rosat_log_xray_luminosity import sdss_rosat_log_xray_luminosity +#20101207#from sdss_rosat_offset_in_arcsec import sdss_rosat_offset_in_arcsec +#20101207#from sdss_rosat_offset_in_sigma import sdss_rosat_offset_in_sigma + +#tmp = glob.glob(ext1 + "*extract*.py") +#tmp = [os.path.basename(x).split(".py")[0] for x in tmp] +#for i in ignores: +# if i in tmp: +# tmp.remove(i) + +#for n in tmp: +# try: +# print "import %s" %n +# exec "import %s" % n +# ## get all the classes from this module +# tmp = \ +#"""zclass = [] +#f = %s.__file__.replace(".pyc",".py") +#tmp = open(f,"r") +#ff = tmp.readlines() +#tmp.close() +#for l in ff: +# if l[:15].find("class ") != -1: +# zclass.append(l.split("class ")[1].split("(")[0]) +#if len(zclass) > 0: +# tmp = "from %s import " + ", ".join([x for x in zclass]) +# exec tmp""" % (n,n) +# exec tmp +# except: +# print "could not import %s" % n + + + +#__all__=["chi2extractor","dc_extractor","dist_from_u_extractor","fourierextractor","linear_extractor","max_slope_extractor","medianextractor","beyond1std_extractor","stdvs_from_u_extractor","old_dcextractor","power_spectrum_extractor","power_extractor","pct_80_montecarlo_extractor","pct_90_montecarlo_extractor","pct_95_montecarlo_extractor","pct_99_montecarlo_extractor","significant_80_power_extractor","significant_90_power_extractor","significant_95_power_extractor","significant_99_power_extractor","first_freq_extractor","sine_fit_extractor","sine_leastsq_extractor","skew_extractor","stdextractor","wei_av_uncertainty_extractor","weighted_average_extractor","lomb_extractor","first_lomb_extractor","sine_lomb_extractor","second_extractor","third_extractor","second_lomb_extractor"] diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/amplitude_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/amplitude_extractor.py new file mode 100644 index 00000000..d4369f7b --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/amplitude_extractor.py @@ -0,0 +1,198 @@ +try: + from ..FeatureExtractor import FeatureExtractor +except: + import os + ppath = os.environ.get('PYTHONPATH') + os.environ.update({'PYTHONPATH': ppath + ":" + os.path.realpath("..")}) + #print os.environ.get("PYTHONPATH") + from FeatureExtractor import FeatureExtractor + +import unittest +import sys +from scipy import stats +import numpy + +# TODO scipy.stats.scoreatpercentile is deprecated; should use numpy.percentile +class amplitude_extractor(FeatureExtractor): + """ Returns the half the difference between the maximum magnitude and the minimum magnitude. + Note this will also work for data that is given in terms of flux. So in a sense, it's + a volitile feature across different datasets. + + Suggestion: use the new percent_amplitude below instead. Turn this one off? + + """ + active = True + extname = 'amplitude' #extractor's name + def extract(self): + maxm = self.fetch_extr('max') # maximum magnitude + minm = self.fetch_extr('min') # minimum magnitude + try: + amplitude = maxm - minm + except: + return None + #print ("absolute",amplitude) + + return amplitude/2.0 + +# TODO what is up with this assumed_unit stuff? this seems well-defined for any units +# in general, should we assume things are log-scale? +class percent_amplitude_extractor(FeatureExtractor): + """ Returns the largest percentage difference between the maximum + magnitude and the minimum magnitude relative to the median. + + assumes that the flux data is in units of magnitudes unless flux_data_unit has been set otherwise. + """ + active = True + extname = 'percent_amplitude' #extractor's name + assumed_unit = "mag" # 20100518: actually, I think a unitless percentage is returned. + def extract(self): +# TODO is there really any reason to call these extractors instead of just Python functions... + maxm = self.fetch_extr('max') # maximum magnitude + minm = self.fetch_extr('min') # minimum magnitude + medd = self.fetch_extr("median") + try: + unit = self.flux_data_unit + except NameError: + unit = self.assumed_unit + + try: + if unit in ['mag','mags','magnitude']: + maxm = 10**(-0.4*maxm) + minm = 10**(-0.4*minm) + medd = 10**(-0.4*medd) + + amplitude = maxm - minm + #20100916dstarr comments this out since the different numerators seems not very useful/consistant: + #tmp = [(maxm - medd)/medd, medd/(maxm - medd), (minm - medd)/medd, medd/(minm - medd)] + #20100916dstarr adds this instead: + tmp = [abs((maxm - medd)/medd), abs((minm - medd)/medd)] + #print ("percent",max(tmp)) + return max(tmp) + except: + return None + + +class percent_difference_flux_percentile_extractor(FeatureExtractor): + """ The 2nd & 98th flux percentiles are subtracted and converted into magnitude. + Assumes that the flux data is in units of magnitudes unless flux_data_unit has been set otherwise. + This algorithms is mentioned by Eyer (2005) arXiv:astro-ph/0511458v1 + and he references Evans & Belokurov (2005). + + I actually use 5th and 95th since we could be interested in sources with less than 50-100 epochs. + + """ + active = True + extname = 'percent_difference_flux_percentile' #extractor's name + assumed_unit = "mag" # 20100518: actually, I think a unitless percentage is returned. + def extract(self): + watts_m2 = self.fetch_extr('watt_per_m2_flux') + try: + flux_high = stats.scoreatpercentile(watts_m2, 95) + flux_low = stats.scoreatpercentile(watts_m2, 5) + flux_50 = stats.scoreatpercentile(watts_m2, 50) + return (flux_high - flux_low) / flux_50 + except: + return None + + +class flux_percentile_ratio_mid20_extractor(FeatureExtractor): + """ + mid20: A ratio of (60 flux percentile - 40 flux percentile) / + (95 flux percentile - 5 flux percentile) + """ + active = True + extname = 'flux_percentile_ratio_mid20' #extractor's name + assumed_unit = "mag" # 20100518: actually, I think a unitless percentage is returned. + def extract(self): + watts_m2 = self.fetch_extr('watt_per_m2_flux') + try: + flux_high = stats.scoreatpercentile(watts_m2, 60) + flux_low = stats.scoreatpercentile(watts_m2, 40) + flux_diff_ref = stats.scoreatpercentile(watts_m2, 95) - \ + stats.scoreatpercentile(watts_m2, 5) + return (flux_high - flux_low) / flux_diff_ref + except: + return None + + +class flux_percentile_ratio_mid35_extractor(FeatureExtractor): + """ + mid35: A ratio of (67.5 flux percentile - 32.5 flux percentile) / + (95 flux percentile - 5 flux percentile) + """ + active = True + extname = 'flux_percentile_ratio_mid35' #extractor's name + assumed_unit = "mag" # 20100518: actually, I think a unitless percentage is returned. + def extract(self): + watts_m2 = self.fetch_extr('watt_per_m2_flux') + try: + flux_high = stats.scoreatpercentile(watts_m2, 67.5) + flux_low = stats.scoreatpercentile(watts_m2, 32.5) + flux_diff_ref = stats.scoreatpercentile(watts_m2, 95) - \ + stats.scoreatpercentile(watts_m2, 5) + return (flux_high - flux_low) / flux_diff_ref + except: + return None + + +class flux_percentile_ratio_mid50_extractor(FeatureExtractor): + """ + mid50: A ratio of (75 flux percentile - 25 flux percentile) / + (95 flux percentile - 5 flux percentile) + """ + active = True + extname = 'flux_percentile_ratio_mid50' #extractor's name + assumed_unit = "mag" # 20100518: actually, I think a unitless percentage is returned. + def extract(self): + watts_m2 = self.fetch_extr('watt_per_m2_flux') + try: + flux_high = stats.scoreatpercentile(watts_m2, 75) + flux_low = stats.scoreatpercentile(watts_m2, 25) + flux_diff_ref = stats.scoreatpercentile(watts_m2, 95) - \ + stats.scoreatpercentile(watts_m2, 5) + return (flux_high - flux_low) / flux_diff_ref + except: + return None + + +class flux_percentile_ratio_mid65_extractor(FeatureExtractor): + """ + mid65: A ratio of (82.5 flux percentile - 17.5 flux percentile) / + (95 flux percentile - 5 flux percentile) + """ + active = True + extname = 'flux_percentile_ratio_mid65' #extractor's name + assumed_unit = "mag" # 20100518: actually, I think a unitless percentage is returned. + def extract(self): + watts_m2 = self.fetch_extr('watt_per_m2_flux') + try: + flux_high = stats.scoreatpercentile(watts_m2, 82.5) + flux_low = stats.scoreatpercentile(watts_m2, 17.5) + flux_diff_ref = stats.scoreatpercentile(watts_m2, 95) - \ + stats.scoreatpercentile(watts_m2, 5) + return (flux_high - flux_low) / flux_diff_ref + except: + return None + +class flux_percentile_ratio_mid80_extractor(FeatureExtractor): + """ + mid80: A ratio of (90 flux percentile - 10 flux percentile) / + (95 flux percentile - 5 flux percentile) + """ + active = True + extname = 'flux_percentile_ratio_mid80' #extractor's name + assumed_unit = "mag" # 20100518: actually, I think a unitless percentage is returned. + def extract(self): + watts_m2 = self.fetch_extr('watt_per_m2_flux') + try: + flux_high = stats.scoreatpercentile(watts_m2, 90) + flux_low = stats.scoreatpercentile(watts_m2, 10) + flux_diff_ref = stats.scoreatpercentile(watts_m2, 95) - \ + stats.scoreatpercentile(watts_m2, 5) + return (flux_high - flux_low) / flux_diff_ref + except: + return None + + +if __name__ == '__main__': + unittest.main() diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/ar_is_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/ar_is_extractor.py new file mode 100644 index 00000000..8cecc3c9 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/ar_is_extractor.py @@ -0,0 +1,76 @@ +from ..FeatureExtractor import FeatureExtractor + +import scipy.optimize as optim +import numpy + +class ar_is_theta_extractor(FeatureExtractor): + ''' + AR-IS(1) model of Erdogan et al. (2005) + http://www.siam.org/proceedings/datamining/2005/dm05_74erdogane.pdf + + This model should be fit on the *** Lomb-Scargle model subtracted data *** + + Model: x(t + delta) = theta^delta * x(t) + sigma_delta * eps + eps ~ N(0,1) + ''' + active = True + extname = 'ar_is_theta' + + + def sum_of_sq(self, theta,delta,x): + return numpy.sum((x[1:] - theta**delta * x[:-1])**2) + + def extract(self): + try: + t = self.time_data + m = self.flux_data + n = len(t) + +# TODO could be more flexible here; do we always want to subtract the full LS fit? +# maybe just the polynomial fit? looks like that's what the paper does + # get the LS model residuals (for principal frequency) + lomb_dict = self.fetch_extr('lomb_scargle') + model = lomb_dict.get('freq1_model',[]) + x = m - model + + delta = t[1:] - t[:-1] + + # estimate theta + theta = optim.fminbound(self.sum_of_sq, x1=0., x2=1., args=(delta, x)) + + return theta + except: + return 0.0 + + + + +class ar_is_sigma_extractor(FeatureExtractor): + ''' returns the ratio of phase_dispersion-estimated and lomb_scargle-estimated frequencies ''' + active = True + extname = 'ar_is_sigma' + def extract(self): + try: + t = self.time_data + m = self.flux_data + n = len(t) + + # get the LS model residuals (for principal frequency) + lomb_dict = self.fetch_extr('lomb_scargle') + model = lomb_dict.get('freq1_model',[]) + x = m - model + + delta = t[1:] - t[:-1] + + # get theta + theta = self.fetch_extr('ar_is_theta') + + # estimate sigma + z = x[1:] - theta**delta * x[:-1] + sigma = numpy.sqrt( 1./n * numpy.sum( z**2 / (1-theta**(2*delta))/(1-theta**2))) + return sigma + except: + return 0.0 + + # estimate sigma + diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/beyond1std_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/beyond1std_extractor.py new file mode 100644 index 00000000..b637c556 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/beyond1std_extractor.py @@ -0,0 +1,22 @@ +from ..FeatureExtractor import FeatureExtractor + +# TODO simplify +class beyond1std_extractor(FeatureExtractor): + """ calculates the percentage of points that lie beyond one standard deviation from the weighted mean """ + active = True + extname = 'beyond1std' #extractor's name + def extract(self): + self.x_devs() + stdvs_from_u = self.fetch_extr('stdvs_from_u') + dvs_from_u = 0 + for i in stdvs_from_u: + if i > self.devs: + dvs_from_u += 1 + try: + ret_val = dvs_from_u / float(len(stdvs_from_u)) + except: + self.ex_error(text="beyond1std_extractor") + return ret_val +# TODO why + def x_devs(self): # how many deviations ? + self.devs = 1.0 diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/chi2_data_error_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/chi2_data_error_extractor.py new file mode 100644 index 00000000..1d4e584a --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/chi2_data_error_extractor.py @@ -0,0 +1,9 @@ +from ..FeatureExtractor import FeatureExtractor + +class chi2_data_error_extractor(FeatureExtractor): + active = True + extname = 'chi2_data_error' # identifier used in final extracted value dict. + def extract(self): + mean_val = self.fetch_extr('weighted_average') + chi2 = sum( ((self.flux_data-mean_val)/self.rms_data)**2 ) + return chi2 diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/chi2_per_deg_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/chi2_per_deg_extractor.py new file mode 100644 index 00000000..1b23c36f --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/chi2_per_deg_extractor.py @@ -0,0 +1,11 @@ +from ..FeatureExtractor import FeatureExtractor + +class chi2_per_deg_extractor(FeatureExtractor): #Chi Square per degree of freedom + active = 1 + active = True + extname = 'chi2_per_deg' #extractor's name + def extract(self): + chi2 = self.fetch_extr('chi2') + degrees = len(self.flux_data) - 1 + chi2_per_degrees = chi2 / degrees + return chi2_per_degrees diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/chi2extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/chi2extractor.py new file mode 100644 index 00000000..fff70c0b --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/chi2extractor.py @@ -0,0 +1,11 @@ +from __future__ import absolute_import +from ..FeatureExtractor import FeatureExtractor +from .common_functions import ChiSquare + +class chi2extractor(FeatureExtractor,ChiSquare): + active = True + extname = 'chi2' #extractor's name + def extract(self): + dc = self.fetch_extr('dc') + chisquare = self.chi_square_sum(self.flux_data,lambda x: dc,x=self.time_data,rms=self.rms_data) + return chisquare \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/closest_in_light.py b/mltsp/TCP/Software/feature_extract/Code/extractors/closest_in_light.py new file mode 100644 index 00000000..a4155ec0 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/closest_in_light.py @@ -0,0 +1,25 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class closest_in_light(ContextFeatureExtractor): + """distance_in_arcmin_to_nearest_galaxy""" + active = True + extname = 'closest_in_light' #extractor's name + + light_cutoff = 4.0 ## dont report anything farther away than this + verbose = False + def extract(self): + n = self.fetch_extr('interng') + + try: + tmp = n["closest_in_light"] + except: + return None # 20081010 dstarr adds try/except in case NED mysql cache server is down + + if tmp is None or tmp > self.light_cutoff: + rez = None + else: + rez = tmp + if self.verbose: + print(n) + return rez diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/closest_in_light_absolute_bmag.py b/mltsp/TCP/Software/feature_extract/Code/extractors/closest_in_light_absolute_bmag.py new file mode 100644 index 00000000..7cfbbbd2 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/closest_in_light_absolute_bmag.py @@ -0,0 +1,26 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class closest_in_light_absolute_bmag(ContextFeatureExtractor): + """closest_light_absolute_bmag""" + active = True + extname = 'closest_light_absolute_bmag' #extractor's name + + light_cutoff = 4.0 ## dont report anything farther away than this + verbose = False + def extract(self): + n = self.fetch_extr('interng') + + try: + tmp = n["closest_in_light"] + ret = n["closest_in_light_absolute_bmag"] + except: + return None # 20081010 dstarr adds try/except in case NED mysql cache server is down + + if tmp is None or tmp > self.light_cutoff: + rez = None + else: + rez = ret + if self.verbose: + print(n) + return rez diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/closest_in_light_angle_from_major_axis.py b/mltsp/TCP/Software/feature_extract/Code/extractors/closest_in_light_angle_from_major_axis.py new file mode 100644 index 00000000..1246a10f --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/closest_in_light_angle_from_major_axis.py @@ -0,0 +1,26 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class closest_in_light_angle_from_major_axis(ContextFeatureExtractor): + """closest_light_angle_from_major_axis""" + active = True + extname = 'closest_light_angle_from_major_axis' #extractor's name + + light_cutoff = 4.0 ## dont report anything farther away than this + verbose = False + def extract(self): + n = self.fetch_extr('interng') + + try: + tmp = n["closest_in_light"] + ret = n["closest_in_light_angle_from_major_axis"] + except: + return None # 20081010 dstarr adds try/except in case NED mysql cache server is down + + if tmp is None or tmp > self.light_cutoff: + rez = None + else: + rez = ret + if self.verbose: + print(n) + return rez diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/closest_in_light_angular_offset_in_arcmin.py b/mltsp/TCP/Software/feature_extract/Code/extractors/closest_in_light_angular_offset_in_arcmin.py new file mode 100644 index 00000000..9cb48827 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/closest_in_light_angular_offset_in_arcmin.py @@ -0,0 +1,26 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class closest_in_light_angular_offset_in_arcmin(ContextFeatureExtractor): + """closest_light_angular_offset_in_arcmin""" + active = True + extname = 'closest_light_angular_offset_in_arcmin' #extractor's name + + light_cutoff = 4.0 ## dont report anything farther away than this + verbose = False + def extract(self): + n = self.fetch_extr('interng') + + try: + tmp = n["closest_in_light"] + ret = n["closest_in_light_angular_offset_in_arcmin"] + except: + return None # 20081010 dstarr adds try/except in case NED mysql cache server is down + + if tmp is None or tmp > self.light_cutoff: + rez = None + else: + rez = ret + if self.verbose: + print(n) + return rez diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/closest_in_light_dm.py b/mltsp/TCP/Software/feature_extract/Code/extractors/closest_in_light_dm.py new file mode 100644 index 00000000..a6eebb53 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/closest_in_light_dm.py @@ -0,0 +1,26 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class closest_in_light_dm(ContextFeatureExtractor): + """closest_light_dm""" + active = True + extname = 'closest_light_dm' #extractor's name + + light_cutoff = 4.0 ## dont report anything farther away than this + verbose = False + def extract(self): + n = self.fetch_extr('interng') + + try: + tmp = n["closest_in_light"] + ret = n["closest_in_light_dm"] + except: + return None # 20081010 dstarr adds try/except in case NED mysql cache server is down + + if tmp is None or tmp > self.light_cutoff: + rez = None + else: + rez = ret + if self.verbose: + print(n) + return rez diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/closest_in_light_physical_offset_in_kpc.py b/mltsp/TCP/Software/feature_extract/Code/extractors/closest_in_light_physical_offset_in_kpc.py new file mode 100644 index 00000000..26044d72 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/closest_in_light_physical_offset_in_kpc.py @@ -0,0 +1,26 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class closest_in_light_physical_offset_in_kpc(ContextFeatureExtractor): + """closest_light_physical_offset_in_kpc""" + active = True + extname = 'closest_light_physical_offset_in_kpc' #extractor's name + + light_cutoff = 4.0 ## dont report anything farther away than this + verbose = False + def extract(self): + n = self.fetch_extr('interng') + + try: + tmp = n["closest_in_light"] + ret = n["closest_in_light_physical_offset_in_kpc"] + except: + return None # 20081010 dstarr adds try/except in case NED mysql cache server is down + + if tmp is None or tmp > self.light_cutoff: + rez = None + else: + rez = ret + if self.verbose: + print(n) + return rez diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/closest_in_light_ttype.py b/mltsp/TCP/Software/feature_extract/Code/extractors/closest_in_light_ttype.py new file mode 100644 index 00000000..c92d24ae --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/closest_in_light_ttype.py @@ -0,0 +1,26 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class closest_in_light_ttype(ContextFeatureExtractor): + """closest_light_ttype""" + active = True + extname = 'closest_light_ttype' #extractor's name + + light_cutoff = 4.0 ## dont report anything farther away than this + verbose = False + def extract(self): + n = self.fetch_extr('interng') + + try: + tmp = n["closest_in_light"] + ret = n["closest_in_light_ttype"] + except: + return None # 20081010 dstarr adds try/except in case NED mysql cache server is down + + if tmp is None or tmp > self.light_cutoff: + rez = None + else: + rez = ret + if self.verbose: + print(n) + return rez diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/color_diff_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/color_diff_extractor.py new file mode 100644 index 00000000..6be9e7b4 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/color_diff_extractor.py @@ -0,0 +1,103 @@ +from ..FeatureExtractor import FeatureExtractor, InterExtractor + +#class static_colors_extractor(FeatureExtractor): # Using this will add a 'X' feature in vosource xml whose value is a string representation of the returned od dict. (using internal_use_only=False, active=False) +class static_colors_extractor(InterExtractor): + """ Retrieve non timeseries colors (average, or single measurment) + + These colors are not intended to be used as features, but instead used + in color difference features. + + Initially coded to use cdsclient NOMAD colors. + + """ + #active = False # TODO probably want False + internal_use_only = False # if set True, then seems to run all X code for each sub-feature + active = True # if set False, then seems to run all X code for each sub-feature + extname = 'static_colors' #extractor's name + + def extract(self): + """ TODO: want to check if self.static_colors exists of len() != 0 + - if not, then want to retrieve from NOMAD servers + - Maybe these values will be inserted prior to feature generation by some code which has cached / inmemory access to NOMAD info for all TUTOR sources - in order to remove the need for netowrk or disk access. + """ + #self.static_colors = {'B':15.900, + # 'V':15.600, # None + # 'R':15.370, + # 'J':15.617, + # 'H':15.496, + # 'K':14.641} + + self.static_band_names = ['B:NOMAD', 'V:NOMAD', 'R:NOMAD', 'J:NOMAD', 'H:NOMAD', 'K:NOMAD', 'extinct_bv'] + self.static_colors = {} + for band in self.static_band_names: + #self.static_colors[band] = self.properties['data'][band]['input']['flux_data'][0] + self.static_colors[band] = self.properties['data'].get(band,{}) \ + .get('input',{}) \ + .get('flux_data',[None])[0] + return self.static_colors + + +class color_diff_jh_extractor(FeatureExtractor): + """ Generate color difference features using non timeseries, static or + average color magnitude measurements. Initially coded to use + cdsclient NOMAD colors. + """ + active = True + extname = 'color_diff_jh' #extractor's name + color_name1 = 'J:NOMAD' + color_name2 = 'H:NOMAD' + + def extract(self): + + self.static_colors = self.properties['data'][self.band]['inter']['static_colors'].result + + color_1 = self.static_colors[self.color_name1] + color_2 = self.static_colors[self.color_name2] + + if ((color_1 != -99) and (color_1 != None) and (color_2 != -99) and (color_2 != None)): + return color_1 - color_2 + else: + return None + + +class color_diff_hk_extractor(color_diff_jh_extractor): + extname = 'color_diff_hk' + color_name1 = 'H:NOMAD' + color_name2 = 'K:NOMAD' + +class color_diff_bj_extractor(color_diff_jh_extractor): + """ See color_diff_jh_extractor docstring. + B-J chosen since it combines a survey with 2mass (which we have J-H and H-K features) + """ + extname = 'color_diff_bj' + color_name1 = 'B:NOMAD' + color_name2 = 'J:NOMAD' + +class color_diff_vj_extractor(color_diff_jh_extractor): + """ See color_diff_jh_extractor docstring. + V-J chosen since it combines a survey with 2mass (which we have J-H and H-K features) + """ + extname = 'color_diff_vj' + color_name1 = 'V:NOMAD' + color_name2 = 'J:NOMAD' + +class color_diff_rj_extractor(color_diff_jh_extractor): + """ See color_diff_jh_extractor docstring. + R-J chosen since it combines a survey with 2mass (which we have J-H and H-K features) + """ + extname = 'color_diff_rj' + color_name1 = 'R:NOMAD' + color_name2 = 'J:NOMAD' + +class color_bv_extinction_extractor(FeatureExtractor): + """ NED galactic extinction estimate from ned.ipac.caltech.edu given an ra,dec. + This is the E(B-V) value from G. Schlegel et al. 1998ApJ..500..525S. + This value is stored in TCP/Data/best_nomad_src_list for a srcid, along with NOMAD colors. + """ + active = True + extname = 'color_bv_extinction' #extractor's name + + def extract(self): + self.static_colors = self.properties['data'][self.band]['inter']['static_colors'].result + + return self.static_colors['extinct_bv'] diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/ChiSquare.py b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/ChiSquare.py new file mode 100644 index 00000000..70cb6dc1 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/ChiSquare.py @@ -0,0 +1,26 @@ +import numpy +from numpy import random +from scipy import fftpack, stats, optimize +try: + from pylab import * +except: + pass + +# TODO I think this is all pretty unnecessary, just use weighted least squares +class ChiSquare(object): #gives extractors the ability to calculate chi squares + def chi_square_sum(self,y,f,x=None,rms=None): + """ inputs: [y]-data (array) [f]unction (function), [x]-axis [rms] noise (array)""" + chi2=self.chi_square(y,f,x,rms) + chi2_sum = chi2.sum() + return chi2_sum + def chi_square(self,y,f,x=None,rms=None): + """ inputs: [y]-data (array) [f]unction (function), [x]-axis [rms] noise (array)""" + if rms is None: + rms = ones(len(y)) + if x is None: + x = range(len(y)) + chi2_total = 0.0 + fx = f(x) +# print 'fx',fx + chi2 = numpy.power(y - fx,2)/numpy.power(rms,2) + return chi2 diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/Example_Methods.py b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/Example_Methods.py new file mode 100644 index 00000000..164acf79 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/Example_Methods.py @@ -0,0 +1,28 @@ +import numpy +from numpy import random +from scipy import fftpack, stats, optimize +try: + from pylab import * +except: + pass + +class Example_Methods(object): # Extractors which inherit this object can make use of these methods. + def example_main_method(self,y,f,x=None,rms=None): + """ Inputs: + [y]-data (array) + [f]unction (function) + [x]-axis (array) + [rms] noise (array) + """ + # Always initialize these in-case they aren't defined: + if rms is None: + rms = ones(len(y)) + if x is None: + x = range(len(y)) + + # Do work here. Call inherited methods if needed. + # - NOTE: use numpy module for basic math/array tasks. + + # Return a scalar float: + return sum(y) + diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/__init__.py b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/__init__.py new file mode 100644 index 00000000..86b7fc69 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/__init__.py @@ -0,0 +1,2 @@ +from .ChiSquare import ChiSquare +from .Example_Methods import Example_Methods diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/_eigs.h b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/_eigs.h new file mode 100644 index 00000000..e8aeb446 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/_eigs.h @@ -0,0 +1,138 @@ +#include + +inline double SQR(double a) { + return (a == 0.0 ? 0.0 : a*a); +} + +inline double SIGN(double a,double b) { + return ((b) >= 0.0 ? fabs(a) : -fabs(a)); +} + +double pythag(double a, double b) { + double absa,absb; + absa=fabs(a); + absb=fabs(b); + if (absa > absb) return absa*sqrt(1.0+SQR(absb/absa)); + else return (absb == 0.0 ? 0.0 : absb*sqrt(1.0+SQR(absa/absb))); +} + +static inline void tred2(double a[], int n, double d[], double e[]) { + int l,k,j,i; + double scale,hh,h,g,f; + + for (i=n-1;i>=1;i--) { + l=i-1; + h=scale=0.0; + if (l > 0) { + for (k=0;k<=l;k++) + scale += fabs(a[k+i*n]); + if (scale == 0.0) + e[i]=a[l+i*n]; + else { + for (k=0;k<=l;k++) { + a[k+i*n] /= scale; + h += a[k+i*n]*a[k+i*n]; + } + f=a[l+i*n]; + g=(f >= 0.0 ? -sqrt(h) : sqrt(h)); + e[i]=scale*g; + h -= f*g; + a[l+i*n]=f-g; + f=0.0; + for (j=0;j<=l;j++) { + a[i+j*n]=a[j+i*n]/h; + g=0.0; + for (k=0;k<=j;k++) + g += a[k+j*n]*a[k+i*n]; + for (k=j+1;k<=l;k++) + g += a[j+k*n]*a[k+i*n]; + e[j]=g/h; + f += e[j]*a[j+i*n]; + } + hh=f/(h+h); + for (j=0;j<=l;j++) { + f=a[j+i*n]; + e[j]=g=e[j]-hh*f; + for (k=0;k<=j;k++) + a[k+j*n] -= (f*e[k]+g*a[k+i*n]); + } + } + } else + e[i]=a[l+i*n]; + d[i]=h; + } + d[0]=0.0; + e[0]=0.0; + for (i=0;i=l;i--) { + f=s*e[i]; + b=c*e[i]; + e[i+1]=(r=pythag(f,g)); + if (r == 0.0) { + d[i+1] -= p; + e[m]=0.0; + break; + } + s=f/r; + c=g/r; + g=d[i+1]-p; + r=(d[i]-g)*s+2.0*c*b; + d[i+1]=g+(p=s*r); + g=c*r-b; + for (k=0;k= l) continue; + d[l] -= p; + e[l]=g; + e[m]=0.0; + } + } while (m != l); + } +} + +static inline void get_eigs(int np,double x[],double d[]) { + double e[np]; + tred2(x,np,d,e); + tqli(d,e,np,x); +} diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/_lomb_scargle.h b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/_lomb_scargle.h new file mode 100644 index 00000000..1c399669 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/_lomb_scargle.h @@ -0,0 +1,226 @@ +#include +#include "_eigs.h" + +static inline void copy_sincos (int numt, double sinx0[], double cosx0[], double sinx[], double cosx[]) { + int i; + for (i=0;i0) px = ( c2*sh*sh - 2.*cs*ch*sh + s2*ch*ch ) / detm; + return px; +} + +static inline void calc_dotprod(int numt, double sinx[], double cosx[], double wt[], int dord, double *st, double *ct) { + int i; + unsigned long n2=numt*dord; + for (*st=0,*ct=0,i=0;ipxmax) { + pxmax=px; + *ifreq = i; + } + update_sincos(numt, sinx_smallstep, cosx_smallstep, sinx1, cosx1, 0); + } + copy_sincos(numt,sinx,cosx,sinx1,cosx1); + if (*ifreqpx_max && Tr>0) { + px_max = px; + lambda_best = lambda; + *Trace = Tr; + } + lambda *= dlambda; + } + if (lambda_best-1.e-5>start) start=lambda_best/dlambda; + else break; + if(lambda_best+1.e-5psd0max && psdmax==0) { + psd0max = psd[j]; + copy_sincos(numt,sinx,cosx,sinx2,cosx2); + jmax = j; + } + // refine the fit around significant sin+cos fits + if (psd[j]>(double)psdmin) { + // first let the frequency vary slightly + px = do_lomb_zoom(numt,detrend_order, cn, sinx, cosx, sinx1, cosx1, sinx_back, cosx_back, sinx_smallstep, cosx_smallstep, wth, freq_zoom, &ifr); + lambda = *lambda0; + // now fit a multi-harmonic model with generalized cross-validation to avoid over-fitting + psd[j] = refine_psd(numt,nharm,detrend_order,hat_matr,hat0,hat_hat,sinx1,cosx1,wth,cn,soln,&lambda,lambda0_range,chi0,tone_control,&Trace,0); + if (psd[j]>psdmax) { + copy_sincos(numt,sinx1,cosx1,sinx2,cosx2); + psdmax=psd[j]; + *ifreq = ifr; + jmax = j; + } + } + update_sincos(numt, sinx_step, cosx_step, sinx, cosx, 0); + } + // finally, rerun at the best-fit period so we get some statistics + psd[jmax] = refine_psd(numt,nharm,detrend_order,hat_matr,hat0,hat_hat,sinx2,cosx2,wth,cn,soln,lambda0,lambda0_range,chi0,tone_control,Tr,1); +} diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/_lomb_scargle.pxd b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/_lomb_scargle.pxd new file mode 100644 index 00000000..a18c0518 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/_lomb_scargle.pxd @@ -0,0 +1,12 @@ +cdef extern from "_lomb_scargle.h": + void lomb_scargle(int numt, int numf, int nharm, int detrend_order, + double psd[], double cn[], double wth[], + double sinx[], double cosx[], double sinx_step[], + double cosx_step[], double sinx_back[], + double cosx_back[], double sinx_smallstep[], + double cosx_smallstep[], double hat_matr[], + double hat_hat[], double hat0[], + double soln[], double chi0, double freq_zoom, + double psdmin, double tone_control, + double lambda0[], double lambda0_range[], + double Tr[], int ifreq[]) diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/_lomb_scargle.pyx b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/_lomb_scargle.pyx new file mode 100644 index 00000000..15646621 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/_lomb_scargle.pyx @@ -0,0 +1,29 @@ +from _lomb_scargle cimport lomb_scargle as _lomb_scargle + +cimport numpy as cnp +import numpy as np + +def lomb_scargle(int numt, int numf, int nharm, int detrend_order, + double[:] psd, double[:] cn, cnp.ndarray wth, + double[:] sinx, double[:] cosx, double[:] sinx_step, + double[:] cosx_step, double[:] sinx_back, + double[:] cosx_back, double[:] sinx_smallstep, + double[:] cosx_smallstep, double[:, :] hat_matr, + double[:, :] hat_hat, double[:, :] hat0, + double[:] soln, double chi0, double freq_zoom, + double psdmin, double tone_control, + cnp.ndarray[dtype=double, ndim=0] lambda0, + double[:] lambda0_range, + cnp.ndarray[dtype=double, ndim=0] Tr, + cnp.ndarray[dtype=cnp.int32_t, ndim=0] ifreq): + + assert wth.dtype == np.double + + _lomb_scargle(numt, numf, nharm, detrend_order, &psd[0], &cn[0], + (wth.data), &sinx[0], &cosx[0], &sinx_step[0], + &cosx_step[0], &sinx_back[0], &cosx_back[0], + &sinx_smallstep[0], &cosx_smallstep[0], &hat_matr[0, 0], + &hat_hat[0, 0], &hat0[0, 0], + &soln[0], chi0, freq_zoom, psdmin, tone_control, + (lambda0.data), &lambda0_range[0], + (Tr.data), (ifreq.data)) diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/build/temp.linux-x86_64-3.3/_lomb_scargle.o b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/build/temp.linux-x86_64-3.3/_lomb_scargle.o new file mode 100644 index 00000000..5b16c192 Binary files /dev/null and b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/build/temp.linux-x86_64-3.3/_lomb_scargle.o differ diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/lightcurve.py b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/lightcurve.py new file mode 100644 index 00000000..06cc34cb --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/lightcurve.py @@ -0,0 +1,973 @@ +#!/usr/bin/env python + +""" +- This code uses the pre_whiten from the NOISIFICATION + directory. (the dictionary this prewhiten outputs has a + 'y_offset' key and uses a shorter nharm_min-max range). + +- Main change for v0.1: lomb_scargle and pre_whiten take input + signal errors. Before they were + assigning uniform errors to each + datapoint. pre_whiten has an lower and + upper limit in scanning harmonics + (4-20). This is significant for phase + offset issues + +-TO STORE IN DATABASE: + In GetPeriodFoldForWeb.generate_lomb_period_fold there is a + dictionary called db_dictionary. The values stored in here + will be what you want to put online. Need to have a method + that returns this dictionary though. Also, it may be best + to store more harmonics, if we find that the cut I am using + is too strict. Currently it is that the amplitude/amp_error + >=1 (reasonable, although probably too liberal) + +-FOR TCP_EXPLORER PLOT: + The values stored in db_dictionary are essentially used to + create the plot on tcp_explorer. db_dictionary does have + extra values not needed in the plot, but useful perhaps + feature generation. +""" + +import sys +import os + +try: + import MySQLdb +except: + pass +import pprint + +import numpy as np +# TODO use namespace +from numpy import * + +from scipy import random +from .lomb_scargle_refine import lomb as lombr + +from scipy.stats import scoreatpercentile + + +class lomb_model(object): + def create_model(self, available, m, m_err, out_dict): + def model(times): + data = zeros(times.size) + for freq in out_dict: + freq_dict = out_dict[freq] + # the if/else is a bit of a hack, + # but I don't want to catch "freq_searched_min" or "freq_searched_max" + if len(freq) > 8: + continue + else: + time_offset = freq_dict["harmonics_time_offset"] + for harmonic in range(freq_dict["harmonics_freq"].size): + f = freq_dict["harmonics_freq"][harmonic] + omega = f * 2 * pi + amp = freq_dict["harmonics_amplitude"][harmonic] + phase = freq_dict["harmonics_rel_phase"][harmonic] + new = amp * sin(omega * (times - time_offset) + phase) + data += new + return data + return model + +class period_folding_model(lomb_model): + """ contains methods that use period-folding to model data """ + def period_folding(self, needed, available, m , m_err, out_dict, doplot=True): + """ period folds both the needed and available times. Times are not ordered anymore! """ + + # find the first frequency in the lomb scargle dictionary: + f = (out_dict["freq1"]["harmonics_freq"][0]) + if out_dict["freq2"]["signif"] > out_dict["freq1"]["signif"]: + f = out_dict["freq2"]["frequency"] + + # find the phase: + p = out_dict["freq1"]["harmonics_rel_phase"][0] + + #period-fold the available times + t_fold = mod( available + p/(2*pi*f) , (1./f) ) + + #period-fold the needed times + t_fold_model = mod( needed + p/(2*pi*f) , (1./f) ) + ###### DEBUG ###### + early_bool = available < (2.4526e6 + 40) + ###### DEBUG ##### + + period_folded_progenitor_file = file("period_folded_progenitor.txt", "w") + progenitor_file = file("progenitor.txt", "w") + + for n in range(len(t_fold)): + period_folded_progenitor_file.write("%f\t%f\t%f\n" % (t_fold[n], m[n], m_err[n])) + progenitor_file.write("%f\t%f\t%f\n" % (available[n], m[n], m_err[n])) + progenitor_file.close() + + return t_fold, t_fold_model + + + def create_model(self,available, m , m_err, out_dict): + f = out_dict["freq1"]["frequency"] + def model(times): + t_fold, t_fold_model = self.period_folding(times, available, m, m_err, out_dict) + data = empty(0) + rms = empty(0) + for time in t_fold_model: + # we're going to create a window around the desired time and sample a gaussian distribution around that time + period = 1./f + assert period < available.ptp()*1.5, "period is greater than ####SEE VARIABLE CONTSTRAINT#### of the duration of available data" #alterring this. originally 1/3 + # window is 2% of the period + passed = False + for x in arange(0.01, 0.1, 0.01): + t_min = time - x * period + t_max = time + x * period + window = logical_and((t_fold < t_max), (t_fold > t_min)) # picks the available times that are within that window + try: + # there must be more than # points in the window for this to work: + assert (window.sum() >= 2), str(time) # jhiggins changed sum from 5 to 2 + except AssertionError: + continue + else: + passed = True + break + assert passed, "No adequate window found" + m_window = m[window] + mean_window = mean(m_window) + std_window = std(m_window) + + # now we're ready to sample that distribution and create our point + new = (random.normal(loc=mean_window, scale = std_window, size = 1))[0] + data = append(data,new) + rms = append(rms, std_window) + period_folded_model_file = file("period_folded_model.txt", "w") +# model_file = file("model.txt", "w") + for n in range(len(t_fold_model)): + period_folded_model_file.write("%f\t%f\t%f\n" % (t_fold_model[n], data[n], rms[n])) +# model_file.write("%f\t%f\t%f\n" % (available[n], data[n], rms[n])) +# model_file.close() + period_folded_model_file.close() + return {'flux':data, 'rms': rms} + return model + +############################# +# Use this code to generate out_dict as referenced above +############################# + +class observatory_source_interface(object): + def __init__(self): + pass + + + def get_peak_width(self, psd,imax): + pmax = psd[imax] + i = 0 + while ( (psd[imax-i:imax+1+i]>(pmax/2.)).sum()/(1.+2*i)==1 ): + w = 1.+2*i + i+=1 + return w + + + def make_psd_plot(self, psd=None, srcid=None, freqin=None): + """ Make PSD .png plots used in ALLStars webpages. + """ + import time + trys_left = 120 + while trys_left > 0: + try: + from matplotlib import pyplot as plt + ### Plot the PSD(freq) + psd_array = np.array(psd) + #import matplotlib + #matplotlib.use('PNG') + #import matplotlib.pyplot as plt + #from matplotlib import pyplot as plt + #from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas + + fig = plt.figure(figsize=(5,3), dpi=100) + ax2 = fig.add_subplot(211) + ax2.plot(freqin, np.log10(psd_array)) + ax2.set_ylim(0,3) + ax2.set_yticks([3,2,1,0]) + ax2.set_ylabel("Log10(psd)") + ax2.annotate("freq", xy=(.5, -0.05), xycoords='axes fraction', + horizontalalignment='center', + verticalalignment='top', + fontsize=10) + ax2.set_title(str(srcid) + " Non-prewhitened, Power Spectral Density", fontsize=9) + ax3 = fig.add_subplot(212) + ax3.plot(np.log10(freqin), np.log10(psd_array)) + ax3.set_ylim(-3,3) + ax3.set_xlim(-3,1) + ax3.set_yticks([3,2,1,0,-1,-2,-3]) + ax3.set_yticklabels(['3','','','0','','','-3']) + ax3.set_xticks([1,0,-1,-2,-3]) + ax3.set_ylabel("Log10(psd)") + ax3.annotate("Log10(freq)", xy=(.5, -0.15), xycoords='axes fraction', + horizontalalignment='center', + verticalalignment='top', + fontsize=10) + #plt.show() + fpath = "/home/pteluser/scratch/tutor_psd_png/psd_%d.png" % (srcid) + plt.savefig(fpath) + fig.clf() + trys_left = 0 + break + #os.system('eog ' + fpath) + #import pdb; pdb.set_trace() + #print + except: + trys_left -= 1 + time.sleep(1) + + + def get_2P_modeled_features(self, x=None, y=None, freq1_freq=None, srcid=None, ls_dict={}): + """ + """ + out_dict = {} + out_dict['model_phi1_phi2'] = 0.0 # NaN / divide by zero default value + out_dict['model_min_delta_mags'] = 0.0 + out_dict['model_max_delta_mags'] = 0.0 + + from scipy.optimize import fmin#, fmin_powell + t_folded_phase = (x / (1./freq1_freq)) % 1. + + A = ls_dict['freq1']['harmonics_amplitude'] + y0 = ls_dict['freq1']['harmonics_y_offset'] + ph = ls_dict['freq1']['harmonics_rel_phase'] + + def model_f(t): + return A[0]*sin(2*pi *t+ph[0]) + \ + A[1]*sin(2*pi*2.*t+ph[1]) + \ + A[2]*sin(2*pi*3.*t+ph[2]) + \ + A[3]*sin(2*pi*4.*t+ph[3]) + \ + A[4]*sin(2*pi*5.*t+ph[4]) + \ + A[5]*sin(2*pi*6.*t+ph[5]) + \ + A[6]*sin(2*pi*7.*t+ph[6]) + \ + A[7]*sin(2*pi*8.*t+ph[7]) + + def model_neg(t): + return -1. * model_f(t) + + + min_1_a = fmin(model_neg, 0.05)[0] # start finding 1st minima, at 5% of phase (fudge/magic number) > 0.018 + max_2_a = fmin(model_f, min_1_a + 0.01)[0] + min_3_a = fmin(model_neg, max_2_a + 0.01)[0] + max_4_a = fmin(model_f, min_3_a + 0.01)[0] + + try: +# TODO is this wrong? seems like it should be a minus + out_dict['model_phi1_phi2'] = (min_3_a - max_2_a) / (max_4_a / min_3_a) + out_dict['model_min_delta_mags'] = abs(model_f(min_1_a) - model_f(min_3_a)) + out_dict['model_max_delta_mags'] = abs(model_f(max_2_a) - model_f(max_4_a)) + except: + pass # out_dict will just contains defauld 0.0 values + + if 0: + from matplotlib import pyplot as plt + fig = plt.figure() + ax2 = fig.add_subplot(111) + ax2.plot(t_folded_phase, y, 'bo') + + y_median = (max(y) - min(y))/2. + min(y) + t_plot = arange(0.01, 1.0, 0.01) + modl = model_f(t_plot) + y_median + + ax2.plot(t_plot, modl, 'go') + + ax2.plot([max_2_a, max_4_a], np.array([model_f(max_2_a), model_f(max_4_a)]) + y_median + 0.5, 'yo') + ax2.plot([min_1_a, min_3_a], np.array([model_f(min_1_a), model_f(min_3_a)]) + y_median + 0.5, 'ko') + + ax2.set_title("srcid=%d P=%f min_1=%f max_2=%f min_3=%f max_4=%f" % (srcid, 1. / freq1_freq, min_1_a, max_2_a, min_3_a, max_4_a), fontsize=9) + plt.savefig("/tmp/ggg.png") + #os.system("eog /tmp/ggg.png &") + plt.show() + print + import pdb; pdb.set_trace() + print + return out_dict + + + def lomb_code(self, y, dy, x, sys_err=0.05, srcid=0): + """ This function is used for final psd and final L-S freqs which are used as features. + NOTE: lomb_extractor.py..lomb_extractor..extract() also generates psd, but its psd and objects not used for the final L.S. freqs. + + NOTE: currently (20101120) This is adapted from Nat's run_lomb14.py + + """ + ### These are defaults found in run_lomb14.py::run_lomb() definition: + nharm = 8 # nharm = 4 + num_freq_comps = 3 + do_models = True # 20120720: dstarr changes from False -> True + tone_control = 5.0 #1. + ############## + + dy0 = sqrt(dy**2 + sys_err**2) + + wt = 1./dy0**2 + x-=x.min()#needed for lomb() code to run in a fast amount of time + + chi0 = dot(y**2,wt) + + #alias_std = std( x-x.round() ) + + Xmax = x.max() +# TODO why? seems arbitrary; applies to this whole section... + f0 = 1./Xmax + df = 0.8/Xmax # 20120202 : 0.1/Xmax + fe = 33. #pre 20120126: 10. # 25 + numf = int((fe-f0)/df) + freqin = f0 + df*arange(numf,dtype='float64') # OK + + ytest=1.*y # makes a copy of the array + dof = n0 = len(x) + hh = 1.+arange(nharm) + + out_dict = {} + #prob = gammaincc(0.5*(n0-1.),0.5*chi0) + #if (prob>0): + # lprob=log(prob) + #else: + # lprob= -gammaln(0.5*(n0-1)) - 0.5*chi0 + 0.5*(n0-3)*log(0.5*chi0) + #out_dict['sigma_vary'] = lprob2sigma(lprob) + + lambda0_range=[-log10(n0),8] # these numbers "fix" the strange-amplitude effect + + for i in range(num_freq_comps): + if (i==0): + psd,res = lombr(x,ytest,dy0,f0,df,numf, tone_control=tone_control, + lambda0_range=lambda0_range, nharm=nharm, detrend_order=1) + ### I think it still makes sense to set these here, even though freq1 may be replaced by another non-alias freq. This is because these are parameters that are derived from the first prewhitening application: + out_dict['lambda'] = res['lambda0'] # 20120206 added + out_dict['chi0'] = res['chi0'] + out_dict['time0'] = res['time0'] + out_dict['trend'] = res['trend_coef'][1] #temp_b + out_dict['trend_error'] = res['trend_coef_error'][1] # temp_covar[1][1] # this is the stdev(b)**2 + else: + psd,res = lombr(x,ytest,dy0,f0,df,numf, tone_control=tone_control, + lambda0_range=lambda0_range, nharm=nharm, detrend_order=0) + + ytest -= res['model'] + if (i==0): + out_dict['varrat'] = dot(ytest**2,wt) / chi0 + #pre20110426: out_dict['cn0'] -= res['trend']*res['time0'] + dof -= n0 - res['nu'] + dstr = "freq%i" % (i + 1) + + if (do_models==True): + #20120720Commentout#raise # this needs to be moved below after alias stuff + out_dict[dstr+'_model'] = res['model'] + out_dict[dstr] = {} + freq_dict = out_dict[dstr] + freq_dict["frequency"] = res['freq'] + freq_dict["signif"] = res['signif'] + freq_dict["psd"] = psd # 20110804 added just for self.make_psd_plot() use. + freq_dict["f0"] = f0 + freq_dict["df"] = df + freq_dict["numf"] = numf + + freq_dict['harmonics_amplitude'] = res['amplitude'] + freq_dict['harmonics_amplitude_error'] = res['amplitude_error'] + freq_dict['harmonics_rel_phase'] = res['rel_phase'] + freq_dict['harmonics_rel_phase_error'] = res['rel_phase_error'] + freq_dict['harmonics_nharm'] = nharm + freq_dict['harmonics_time_offset'] = res['time0'] + freq_dict['harmonics_y_offset'] = res['cn0'] # 20110429: disable since it was previously mean subtracted and not useful, and not mean subtracted is avg-mag and essentially survey biased # out_dict['cn0'] + +# TODO what is going on here? also, decouple + ### Here we check for "1-day" aliases in ASAS / Deboss sources + dstr_alias = [] + dstr_all = ["freq%i" % (i + 1) for i in range(num_freq_comps)] + ### 20120223 co: + #for dstr in dstr_all: + # period = 1./out_dict[dstr]['frequency'] + # if (((period >= 0.93) and (period <= 1.07) and + # (out_dict[dstr]['signif'] < (3.771221/np.power(np.abs(period - 1.), 0.25) + 3.293027))) or + # ((period >= 0.485) and (period <= 0.515) and (out_dict[dstr]['signif'] < 10.0)) or + # ((period >= 0.325833333) and (period <= 0.340833333) and (out_dict[dstr]['signif'] < 8.0))): + # dstr_alias.append(dstr) # this frequency has a "1 day" alias (or 0.5 or 0.33 + # + ### 20120212 Joey alias re-analysis: + alias = [{'per':1., + 'p_low':0.92, + 'p_high':1.08, + 'alpha_1':8.191855, + 'alpha_2':-7.976243}, + {'per':0.5, + 'p_low':0.48, + 'p_high':0.52, + 'alpha_1':2.438913, + 'alpha_2':0.9837243}, + {'per':0.3333333333, + 'p_low':0.325, + 'p_high':0.342, + 'alpha_1':2.95749, + 'alpha_2':-4.285432}, + {'per':0.25, + 'p_low':0.245, + 'p_high':0.255, + 'alpha_1':1.347657, + 'alpha_2':2.326338}] + + for dstr in dstr_all: + period = 1./out_dict[dstr]['frequency'] + for a in alias: + if ((period >= a['p_low']) and + (period <= a['p_high']) and + (out_dict[dstr]['signif'] < (a['alpha_1']/np.power(np.abs(period - a['per']), 0.25) + a['alpha_2']))): + dstr_alias.append(dstr) # this frequency has a "1 day" alias (or 0.5 or 0.33 + break # only need to do this once per period, if an alias is found. + + out_dict['n_alias'] = len(dstr_alias) + if 0: + # 20120624 comment out the code which replaces the aliased freq1 with the next non-aliased one: + if len(dstr_alias) > 0: + ### Here we set the next non-alias frequency to freq1, etc: + dstr_diff = list(set(dstr_all) - set(dstr_alias)) + dstr_diff.sort() # want to ensure that the lowest freq is first + reorder = [] + for dstr in dstr_all: + if len(dstr_diff) > 0: + reorder.append(out_dict[dstr_diff.pop(0)]) + else: + reorder.append(out_dict[dstr_alias.pop(0)]) + + for i, dstr in enumerate(dstr_all): + out_dict[dstr] = reorder[i] + + if 0: + ### Write PSD vs freq .png plots for AllStars web visualization: + self.make_psd_plot(psd=out_dict['freq1']['psd'], srcid=srcid, freqin=freqin) + + var0 = var(ytest) - median(dy0)**2 + out_dict['sigma0'] = 0. + if (var0 > 0.): + out_dict['sigma0'] = sqrt(var0) + out_dict['nu'] = dof + out_dict['chi2'] = res['chi2'] #dot(ytest**2,wt) # 20110512: res['chi2'] is the last freq (freq3)'s chi2, which is pretty similar to the old dot(ytest**2,wt) calculation which uses the signal removed ytest + #out_dict['alias_std'] = alias_std + out_dict['freq_binwidth'] = df + out_dict['freq_searched_min']=min(freqin) + out_dict['freq_searched_max']=max(freqin) + out_dict['mad_of_model_residuals'] = median(abs(ytest - median(ytest))) + + ##### This is used for p2p_scatter_2praw feature: + t_2per_fold = x % (2/out_dict['freq1']['frequency']) + tups = list(zip(t_2per_fold, y))#, range(len(t_2per_fold))) + tups.sort() + t_2fold, m_2fold = zip(*tups) #So: m_2fold[30] == y[i_fold[30]] + m_2fold_array = np.array(m_2fold) + sumsqr_diff_folded = np.sum((m_2fold_array[1:] - m_2fold_array[:-1])**2) + sumsqr_diff_unfold = np.sum((y[1:] - y[:-1])**2) + p2p_scatter_2praw = sumsqr_diff_folded / sumsqr_diff_unfold + out_dict['p2p_scatter_2praw'] = p2p_scatter_2praw + + mad = np.median(np.abs(y - median(y))) + out_dict['p2p_scatter_over_mad'] = np.median(np.abs(y[1:] - y[:-1])) / mad + + ### eta feature from arXiv 1101.3316 Kim QSO paper: + out_dict['p2p_ssqr_diff_over_var'] = sumsqr_diff_unfold / ((len(y) - 1) * np.var(y)) + + t_1per_fold = x % (1./out_dict['freq1']['frequency']) + tups = list(zip(t_1per_fold, y))#, range(len(t_2per_fold))) + tups.sort() + t_1fold, m_1fold = zip(*tups) #So: m_1fold[30] == y[i_fold[30]] + m_1fold_array = np.array(m_1fold) + out_dict['p2p_scatter_pfold_over_mad'] = \ + np.median(np.abs(m_1fold_array[1:] - m_1fold_array[:-1])) / mad + + ######################## # # # + ### This section is used to calculate Dubath (10. Percentile90:2P/P) + ### Which requires regenerating a model using 2P where P is the original found period + ### NOTE: this essentially runs everything a second time, so makes feature + ### generation take roughly twice as long. + + model_vals = np.zeros(len(y)) + #all_model_vals = np.zeros(len(y)) + freq_2p = out_dict['freq1']['frequency'] * 0.5 + ytest_2p=1.*y # makes a copy of the array + + ### So here we force the freq to just 2*freq1_Period + # - we also do not use linear detrending since we are not searching for freqs, and + # we want the resulting model to be smooth when in phase-space. Detrending would result + # in non-smooth model when period folded + psd,res = lombr(x,ytest_2p,dy0,freq_2p,df,1, tone_control=tone_control, + lambda0_range=lambda0_range, nharm=nharm, detrend_order=0)#1) + model_vals += res['model'] + #all_model_vals += res['model'] + + ytest_2p -= res['model'] + for i in range(1,num_freq_comps): + psd,res = lombr(x,ytest_2p,dy0,f0,df,numf, tone_control=tone_control, + lambda0_range=lambda0_range, nharm=nharm, detrend_order=0) + + #all_model_vals += res['model'] + ytest_2p -= res['model'] + + out_dict['medperc90_2p_p'] = scoreatpercentile(np.abs(ytest_2p), 90) / \ + scoreatpercentile(np.abs(ytest), 90) + + some_feats = self.get_2P_modeled_features(x=x, y=y, freq1_freq=out_dict['freq1']['frequency'], srcid=srcid, ls_dict=out_dict) + out_dict.update(some_feats) + + ### So the following uses the 2*Period model, and gets a time-sorted, folded t and m: + ### - NOTE: if this is succesful, I think a lot of other features could characterize the + ### shapes of the 2P folded data (not P or 2P dependent). + ### - the reason we choose 2P is that occasionally for eclipsing + ### sources the LS code chooses 0.5 of true period (but never 2x + ### the true period). slopes are not dependent upon the actual + ### period so 2P is fine if it gives a high chance of correct fitting. + ### - NOTE: we only use the model from freq1 because this with its harmonics seems to + ### adequately model shapes such as RRLyr skewed sawtooth, multi minima of rvtau + ### without getting the scatter from using additional LS found frequencies. + + + t_2per_fold = x % (1/freq_2p) + tups = list(zip(t_2per_fold, model_vals)) +# TODO why? + tups.sort() + t_2fold, m_2fold = zip(*tups) + t_2fold_array = np.array(t_2fold) + m_2fold_array = np.array(m_2fold) + slopes = (m_2fold_array[1:] - m_2fold_array[:-1]) / (t_2fold_array[1:] - t_2fold_array[:-1]) +# TODO why not just get the (almost) steepest positive/negative slopes directly? +# wrong for strictly increasing/decreasing +# TODO why is this here instead of another extractor... + out_dict['fold2P_slope_10percentile'] = scoreatpercentile(slopes,10) # this gets the steepest negative slope? + out_dict['fold2P_slope_90percentile'] = scoreatpercentile(slopes,90) # this gets the steepest positive slope? + + return out_dict, ytest + + +class GetPeriodFoldForWeb: + """ + To be called by tcp_html_show_recent_ptf_sources.py, + which is called by a PHP script on lyra. + + Eventually this only prints a JSON javascript structure which + contains period folded data for plottingL like: (mag vs time.) + """ + def __init__(self): + self.pars = { \ + 'mysql_user':"pteluser", + 'mysql_hostname':"192.168.1.25", + 'mysql_database':'source_test_db', + 'mysql_port':3306, + 'featid_lookup_pkl_fpath':os.path.expandvars("$TCP_DATA_DIR/featname_featid_lookup.pkl"), + 'color_chris_folded':"#cc0033", + 'color_chris_model':"#ff3399", + 'color_feature_resampled':"#3399cc", + 'color_folded_data':"#000066", + 'tcptutor_hostname':'lyra.berkeley.edu', + 'tcptutor_username':'pteluser', + 'tcptutor_password':'Edwin_Hubble71', + 'tcptutor_port': 3306, + 'tcptutor_database':'tutor', + } + + + def make_db_connection(self): + """ + """ + self.db = MySQLdb.connect(host=self.pars['mysql_hostname'], + user=self.pars['mysql_user'], + db=self.pars['mysql_database'], + port=self.pars['mysql_port']) + self.cursor = self.db.cursor() + + self.tutor_db = MySQLdb.connect(host=self.pars['tcptutor_hostname'], \ + user=self.pars['tcptutor_username'], \ + passwd=self.pars['tcptutor_password'],\ + db=self.pars['tcptutor_database'],\ + port=self.pars['tcptutor_port']) + self.tutor_cursor = self.tutor_db.cursor() + + + + def generate_featname_featid_lookup(self, filter_id=8): + """ Generate a RDB feature table lookup dictionary for: + feat_id : feat_value + """ + import cPickle + # TODO: if not .pkl file exists... + if os.path.exists(self.pars['featid_lookup_pkl_fpath']): + fp = open(self.pars['featid_lookup_pkl_fpath']) + self.featname_lookup = cPickle.load(fp) + fp.close() + return + else: + select_str = "SELECT feat_name, feat_id FROM source_test_db.feat_lookup WHERE filter_id = %d" % (filter_id) + self.cursor.execute(select_str) + + results = self.cursor.fetchall() + self.cursor.close() + self.featname_lookup = {} + for (feat_name, feat_id) in results: + self.featname_lookup[feat_name] = feat_id + + fp = open(self.pars['featid_lookup_pkl_fpath'], 'w') + cPickle.dump(self.featname_lookup, fp) + fp.close() + return + + + def get_source_arrays(self, source_id): + """ Retrieve source m, t from rdb. + """ + + #select_str = """SELECT object_test_db.obj_srcid_lookup.src_id, + # object_test_db.ptf_events.ujd, + # object_test_db.ptf_events.mag, + # object_test_db.ptf_events.mag_err + # FROM object_test_db.obj_srcid_lookup + # JOIN object_test_db.ptf_events ON (object_test_db.ptf_events.id = object_test_db.obj_srcid_lookup.obj_id) + # WHERE survey_id = 3 AND src_id = %d """ % (source_id) + + # 20091030: dstarr comments out: + #select_str = """SELECT object_test_db.obj_srcid_lookup.src_id, + # object_test_db.ptf_events.ujd, + # (-2.5 * LOG10(object_test_db.ptf_events.flux_aper + object_test_db.ptf_events.f_aper) + object_test_db.ptf_events.ub1_zp_ref) AS m_total, + # object_test_db.ptf_events.mag_err + # FROM object_test_db.obj_srcid_lookup + # JOIN object_test_db.ptf_events ON (object_test_db.ptf_events.id = object_test_db.obj_srcid_lookup.obj_id) + # WHERE survey_id = 3 AND src_id = %d + # ORDER BY object_test_db.ptf_events.ujd""" % (source_id) + + # 20091030: dstarr instead uses: + select_str = """SELECT object_test_db.obj_srcid_lookup.src_id, + object_test_db.ptf_events.ujd, + object_test_db.ptf_events.mag AS m_total, + object_test_db.ptf_events.mag_err + FROM object_test_db.obj_srcid_lookup + JOIN object_test_db.ptf_events ON (object_test_db.ptf_events.id = object_test_db.obj_srcid_lookup.obj_id) + WHERE survey_id = 3 AND src_id = %d + ORDER BY object_test_db.ptf_events.ujd""" % (source_id) + + + #Testing fluxes +## select_str = """SELECT object_test_db.obj_srcid_lookup.src_id, +## object_test_db.ptf_events.ujd, +## object_test_db.ptf_events.flux, +## object_test_db.ptf_events.flux_err +## FROM object_test_db.obj_srcid_lookup +## JOIN object_test_db.ptf_events ON (object_test_db.ptf_events.id = object_test_db.obj_srcid_lookup.obj_id) +## WHERE survey_id = 3 AND src_id = %d +## ORDER BY object_test_db.ptf_events.ujd""" % (source_id) + + self.cursor.execute(select_str) + + t_list = [] + m_list = [] + merr_list = [] + + results = self.cursor.fetchall() + + # TODO: maybe close DB connection too. + for row in results: + t_list.append(row[1]) + m_list.append(row[2]) + merr_list.append(row[3]) + + src_dict = {} + src_dict['src_id'] = results[0][0] + src_dict['t'] = np.array(t_list) + src_dict['m'] = np.array(m_list) + src_dict['m_err'] = np.array(merr_list) + #src_dict['m_err'] = [] #/@/ Justin changed 20090804 + return src_dict + + + def get_source_arrays__dotastro(self, source_id): + """ Retrieve source m, t from rdb. + + This version of the method queries the DotAstro.org / TUTOR database on lyra + """ + + # TODO: need to determine shich filter has the most number of epochs + # TODO: need to retrieve the magnitudes(time) from dotastro database for this fitler + # m_err(time) + srcid_dotastro = source_id - 100000000 + + # first need to use observations.source_id == to get the + # observations.observation_id + # then select count(*) from obs_data.observation_id== and see which observation_id (eg filter) has the most epochs. + # then retrieve all mag, m_err, time for this observation_id and return it. + + ##### First Retrieve the filter for the srcid from the tranx RDB + + select_str = "SELECT feats_used_filt FROM source_test_db.srcid_lookup WHERE src_id = %d" % (source_id) + self.cursor.execute(select_str) + results = self.cursor.fetchall() + if len(results) == 0: + return {} + feat_filter = results[0][0] + + ##### Determine the filter / observation_id which has the most number of epochs, + # since this currently corresponds to the timeseries which the feature algorithms + # were generated using (eg not using the combo_band). + #select_str = """SELECT filters.*, count(obs_data.obsdata_val) AS epoch_count, observations.observation_id + # FROM observations + # JOIN obs_data USING (observation_id) + # JOIN filters USING (filter_id) + # WHERE observations.source_id=%d AND filters.filter_name like "%s%" + # GROUP BY observations.observation_id + # ORDER BY epoch_count DESC""" % (srcid_dotastro, feat_filter[0]) + + select_str = """SELECT obs_data.obsdata_time, obs_data.obsdata_val, obs_data.obsdata_err + FROM observations + JOIN obs_data USING (observation_id) + JOIN filters USING (filter_id) + WHERE observations.source_id=%d AND filters.filter_name like "%s""" % (srcid_dotastro, feat_filter[0]) + '%"' + #'" """ + self.tutor_cursor.execute(select_str) + + t_list = [] + m_list = [] + merr_list = [] + + results = self.tutor_cursor.fetchall() + # TODO: maybe close DB connection too. + for row in results: + t_list.append(row[0]) + m_list.append(row[1]) + merr_list.append(row[2]) + + src_dict = {} + src_dict['src_id'] = source_id + src_dict['t'] = np.array(t_list) + src_dict['m'] = np.array(m_list) + src_dict['m_err'] = np.array(merr_list) + #src_dict['m_err'] = [] #/@/ Justin changed 20090804 + return src_dict + + + ### This function is no longer used by lomb_scargle_extractor. ONly Noisification/Chris related lightcurve.py functions use it. + def generate_lomb_period_fold(self, src_dict, return_option='top4lombfreqs_withharmonics'): + """ Re-generate lomb scargle using Chris code. + + Return period folded m(t) and evenly resampled m(t) in a dictionary. + """ + + obs = observatory_source_interface() + out_dict, cn_output = obs.lomb_code(src_dict['m'], + src_dict['m_err'], + src_dict['t']) + + + return out_dict + + + + def using_features_generate_resampled(self, src_dict): + """ Using features retrieved from RDB and stored in src_dict, + form a re-sampled m(t) and return in a dictionary. + """ + ##### TODO: Get the frequency components from feature tables if + # available. + # - Construct y_axis for some generated time-axis. + + # TODO: using: + # self.featname_lookup + # form a SELECT string which retrieves all features of interest + # then gemerate an out_dict{} so that this works: + + select_str = """SELECT + (SELECT feat_val FROM source_test_db.feat_values WHERE src_id=%d AND feat_id=%d) AS %s, + (SELECT feat_val FROM source_test_db.feat_values WHERE src_id=%d AND feat_id=%d) AS %s, + (SELECT feat_val FROM source_test_db.feat_values WHERE src_id=%d AND feat_id=%d) AS %s, + (SELECT feat_val FROM source_test_db.feat_values WHERE src_id=%d AND feat_id=%d) AS %s, + (SELECT feat_val FROM source_test_db.feat_values WHERE src_id=%d AND feat_id=%d) AS %s, + (SELECT feat_val FROM source_test_db.feat_values WHERE src_id=%d AND feat_id=%d) AS %s, + (SELECT feat_val FROM source_test_db.feat_values WHERE src_id=%d AND feat_id=%d) AS %s, + (SELECT feat_val FROM source_test_db.feat_values WHERE src_id=%d AND feat_id=%d) AS %s, + (SELECT feat_val FROM source_test_db.feat_values WHERE src_id=%d AND feat_id=%d) AS %s, + (SELECT feat_val FROM source_test_db.feat_values WHERE src_id=%d AND feat_id=%d) AS %s, + (SELECT feat_val FROM source_test_db.feat_values WHERE src_id=%d AND feat_id=%d) AS %s + """%(src_dict['src_id'], self.featname_lookup['freq1_harmonics_freq_0'], 'freq1_harmonics_freq_0', + src_dict['src_id'], self.featname_lookup['freq2_harmonics_freq_0'], 'freq2_harmonics_freq_0', + src_dict['src_id'], self.featname_lookup['freq3_harmonics_freq_0'], 'freq3_harmonics_freq_0', + src_dict['src_id'], self.featname_lookup['freq1_harmonics_amplitude_0'], 'freq1_harmonics_amplitude_0', + src_dict['src_id'], self.featname_lookup['freq2_harmonics_amplitude_0'], 'freq2_harmonics_amplitude_0', + src_dict['src_id'], self.featname_lookup['freq3_harmonics_amplitude_0'], 'freq3_harmonics_amplitude_0', + src_dict['src_id'], self.featname_lookup['freq1_harmonics_rel_phase_0'], 'freq1_harmonics_rel_phase_0', + src_dict['src_id'], self.featname_lookup['freq2_harmonics_rel_phase_0'], 'freq2_harmonics_rel_phase_0', + src_dict['src_id'], self.featname_lookup['freq3_harmonics_rel_phase_0'], 'freq3_harmonics_rel_phase_0', + src_dict['src_id'], self.featname_lookup['freq1_signif'], 'freq1_signif', + src_dict['src_id'], self.featname_lookup['freq2_signif'], 'freq2_signif', + ) + + self.cursor.execute(select_str) + + result = self.cursor.fetchall() + + ############# + + freq_list = [] + amp_list = [] + rel_phase = [] + + freq_list.append(result[0][0]) + freq_list.append(result[0][1]) + freq_list.append(result[0][2]) + amp_list.append(result[0][3]) + amp_list.append(result[0][4]) + amp_list.append(result[0][5]) + rel_phase.append(result[0][6]) + rel_phase.append(result[0][7]) + rel_phase.append(result[0][8]) + + freq1_harmonics_freq_0 = result[0][0] + freq2_harmonics_freq_0 = result[0][1] + freq1_signif = result[0][9] + freq2_signif = result[0][10] + freq1_harmonics_rel_phase_0 = result[0][6] + + #try: + # plot_period = 1.0 / freq_list[0] + # x_axis = arange(0,plot_period, .01) + # + # y_axis= amp_list[0]*sin(2*np.pi*freq_list[0]*(x_axis-rel_phase[0]))+\ + # amp_list[1]*sin(2*np.pi*freq_list[1]*(x_axis-rel_phase[1]))+\ + # amp_list[2]*sin(2*np.pi*freq_list[2]*(x_axis-rel_phase[2])) + # + # feature_resampled_dict = {'feature_resampled':{'t':x_axis, 'm':y_axis, + # 'color':self.pars['color_feature_resampled']}} + #except: + # feature_resampled_dict = {'feature_resampled':{'t':[], 'm':[], + # 'color':self.pars['color_feature_resampled']}} + + + ##### Here we period fold the existing data. + f = (freq1_harmonics_freq_0) + if freq2_signif > freq1_signif: + f = freq2_harmonics_freq_0 + + # find the phase: + p = freq1_harmonics_rel_phase_0 + + #period-fold the available times + t_fold = mod( src_dict['t'] + p/(2*pi*f) , (1./f) ) + ##### This is the earlier data: + try: + plot_period = 1.0 / freq_list[0] + x_axis = arange(0,plot_period, .001) + + #KLUDGE: I fake the generated LC mag amplitude & offset using real data: + amp_kludge = max(src_dict['m']) - min(src_dict['m']) + m_offset_kludge = amp_kludge/2. + min(src_dict['m']) + + # 20090720: dstarr replaces the following with something 4-6 lines below + #y_axis= (amp_list[0]*sin(2*np.pi*freq_list[0]*(x_axis-rel_phase[0]))+\ + # amp_list[1]*sin(2*np.pi*freq_list[1]*(x_axis-rel_phase[1]))+\ + # amp_list[2]*sin(2*np.pi*freq_list[2]*(x_axis-rel_phase[2]))) + y_axis= (amp_list[0]*sin(2*np.pi*freq_list[0]*(x_axis)))+\ + amp_list[1]*sin(2*np.pi*freq_list[1]*(x_axis) + rel_phase[1])+\ + amp_list[2]*sin(2*np.pi*freq_list[2]*(x_axis) + rel_phase[2]) + + y_axis= (amp_list[0]*sin(2*np.pi*freq_list[0]*(x_axis) + rel_phase[0]))+\ + amp_list[1]*sin(2*np.pi*freq_list[1]*(x_axis) + rel_phase[1])+\ + amp_list[2]*sin(2*np.pi*freq_list[2]*(x_axis) + rel_phase[2]) + + amp_sampled_m = max(y_axis) - min(y_axis) + amp_sampled_offset = amp_sampled_m / 2. + min(y_axis) + + y_axis = ((y_axis -amp_sampled_offset) / amp_sampled_m) * amp_kludge + m_offset_kludge + + feature_resampled_dict = {'DB features Generated':{'t':x_axis, 'm':y_axis, + 'color':self.pars['color_feature_resampled']}} + except: + feature_resampled_dict = {'DB features Generated':{'t':[], 'm':[], 'points': {'radius': 0.1}, + 'color':self.pars['color_feature_resampled']}} + ##### + + feature_resampled_dict.update({"DB features Period Folded":{'t':t_fold, 'm':src_dict['m'], + 'color':self.pars['color_folded_data']}}) + + html_str = "" + for i in range(len(amp_list)): + html_str += ""+str(i)+""+str(amp_list[i])+""+str(freq_list[i])+""+str(rel_phase[i])+""+str(1/freq_list[i])+" \n" + + if len(sys.argv) >= 2: + if sys.argv[1] == 'get_table_data': + return html_str + + return feature_resampled_dict + + def form_json(self, combo_dict): + """ Given period folded structures, form a JSON-like string, return. + Justin changing order in list since dictionary model blocks folded data + """ + json_list = [] + data_list1 = [] + for i in range(len(combo_dict['Actual Mags folded']['t'])): + data_list1.append([combo_dict['Actual Mags folded']['t'][i],combo_dict['Actual Mags folded']['m'][i]]) + data_list2 = [] + for i in range(len(combo_dict['Folded Model']['t'])): + data_list2.append([combo_dict['Folded Model']['t'][i],combo_dict['Folded Model']['m'][i]]) + data_list3 = [] + for i in range(len(combo_dict['Model with dictionary values']['t'])): + data_list3.append([combo_dict['Model with dictionary values']['t'][i],combo_dict['Model with dictionary values']['m'][i]]) + json_list.append({'label':'Model with dictionary values', + 'color':'#F2BABB', + 'data':data_list3}) + json_list.append({'label':'Folded Model', + 'color':'#BB8800', + 'data':data_list2}) + json_list.append({'label':'Actual Mags folded', + 'color':'#194E84', + 'data':data_list1}) + + json_string_single_quotes = pprint.pformat(json_list) + json_string = json_string_single_quotes.replace("'",'"') + return json_string + + + + def main(self, source_id): + """ + Eventually this function will calculate the period fold + plotting x,y array and return a string with this JSON-like + output, such as: + + [{"label":"Period Fold", "color":#36477b, + "data":[[1,1],[2,4],[3,9],[4,16]]}] + """ + self.make_db_connection() + self.generate_featname_featid_lookup() + if source_id >= 100000000: + src_dict = self.get_source_arrays__dotastro(source_id) + else: + src_dict = self.get_source_arrays(source_id) + lc_dict = {} + lomb_folded_dict = self.generate_lomb_period_fold(src_dict) + lc_dict.update(lomb_folded_dict) + + json_string = self.form_json(lc_dict) + return json_string + ####### Justin adding to view fluxes folded ####### + def online_dictionary(self, source_id, return_option="database"): + self.make_db_connection() + self.generate_featname_featid_lookup() + src_dict = self.get_source_arrays(source_id) + + db_dict = self.generate_lomb_period_fold(src_dict,return_option="db_dictionary") + return db_dict + + + + def html_table(self, source_id, return_option="html"): + self.make_db_connection() + self.generate_featname_featid_lookup() + + src_dict = self.get_source_arrays(source_id) + lomb_str = self.generate_lomb_period_fold(src_dict) + db_str = self.using_features_generate_resampled(src_dict) + final_str = " \n" + final_str += " \n" + final_str += "
Chris ValuesDB values
\n" + final_str += " \n" + final_str += lomb_str+"
Harmonic NumAmplitudeFreqOffset 1 / freq
\n" + final_str += " \n" + final_str += " \n" + final_str += db_str+"
Harmonic NumAmplitudeFreqOffset 1 / freq
" + + return final_str + + def for_testing(self, source_id): + pass diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/linfit.py b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/linfit.py new file mode 100644 index 00000000..d5fdfb2f --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/linfit.py @@ -0,0 +1,32 @@ +from numpy import ones,sqrt + +# TODO don't see any reason to use this instead of polyfit from numpy +def linfit(x,y,dy=[]): + """ + m = a+b*x + minimize chi^2 = Sum (y-m)^2/dy^2 + """ + lx=len(x) + if (dy==[]): + dy = ones(lx,dtype='float32') + + wt = 1./dy**2 + ss = wt.sum() + sx = (wt * x).sum() + sy = (wt * y).sum() + t = (x - sx/ss) / dy + b = (t * y / dy).sum() + + st2 = (t*t).sum() + + # parameter estimates + b = b / st2 + a = (sy - sx * b) / ss + + # error estimates + sdeva = sqrt((1. + sx * sx / (ss * st2)) / ss) + sdevb = sqrt(1. / st2) + covar = -sx/(ss*st2) + covar = [[sdeva**2, covar], [covar, sdevb**2]] + + return (a,b,covar) diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/lomb_scargle_refine.py b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/lomb_scargle_refine.py new file mode 100644 index 00000000..c542f2b9 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/lomb_scargle_refine.py @@ -0,0 +1,211 @@ +from numpy import exp,empty,pi,sqrt,sin,cos,dot,where,arange,arctan2,array,diag,ix_,log10,outer,hstack,log,round,zeros +from scipy.stats import f as fdist, norm +from ._lomb_scargle import lomb_scargle + + +def lprob2sigma(lprob): + """ translates a log_e(probability) to units of Gaussian sigmas + """ + + if (lprob>-36.): + sigma = norm.ppf(1.-0.5*exp(1.*lprob)) + else: + # this is good to 5.e-2; just to be crazy, get to 5.e-5 + sigma = sqrt( log(2./pi) - 2.*log(8.2) - 2.*lprob ) + f = 0.5*log(2./pi) - 0.5*sigma**2 - log(sigma) - lprob + df = - sigma - 1./sigma + sigma = sigma - f/df + + return float(sigma) + + +def lomb(time, signal, error, f1, df, numf, nharm=8, psdmin=6., detrend_order=0, + freq_zoom=10., tone_control=1., return_model=True, + lambda0=1., lambda0_range=[-8,6]): + """ + C version of lomb_scargle: + Simultaneous fit of a sum of sinusoids by weighted, linear least squares. + model(t) = Sum_k Ck*t^k + Sum_i Sum_j Aij sin(2*pi*j*fi*(t-t0)+phij), i=[1,nfreq], j=[1,nharm] + [t0 defined such that ph11=0] + + Inputs: + time: time vector + signal: data vector + error: data uncertainty vector + df: frequency step + numf: number of frequencies to consider + + detrend_order: order of polynomial detrending (Ck orthogonol polynomial terms above; + 0 floating mean; <0 no detrending) + + psdmin: refine periodogram values with larger psd using multi-harmonic fit + nharm: number of harmonics to use in refinement + lambda0: typical value for regularization parameter (expert parameter) + lambda0_range: allowable range for log10 of regularization parameter + + Output: + psd: power spectrum on frequency grid: f1,f1+df,...,f1+numf*df + out_dict: dictionary describing various parameters of the multiharmonic fit at + the best-fit frequency + """ + numt = len(time) + +# TODO what is this? + freq_zoom = round(freq_zoom/2.)*2. + + dord = detrend_order + if (detrend_order<0): + dord=0 + + if (tone_control<0): + tone_control=0. + + # polynomial terms + coef = empty(dord+1,dtype='float64') + norm = empty(dord+1,dtype='float64') + + wth0 = (1./error).astype('float64') + s0 = dot(wth0,wth0) + wth0 /= sqrt(s0) + +# TODO what is this step + cn = (signal*wth0).astype('float64') + coef[0] = dot(cn,wth0); cn0 = coef[0]; norm[0] = 1. + cn -= coef[0]*wth0 + vcn = 1. + + # sin's and cosin's for later + tt = 2*pi*time.astype('float64') + sinx,cosx = sin(tt*f1)*wth0,cos(tt*f1)*wth0 + sinx_step,cosx_step = sin(tt*df),cos(tt*df) + sinx_back,cosx_back = -sin(tt*df/2.),cos(tt*df/2) + sinx_smallstep,cosx_smallstep = sin(tt*df/freq_zoom),cos(tt*df/freq_zoom) + + npar=2*nharm + hat_matr = empty((npar,numt),dtype='float64') + hat0 = empty((npar,dord+1),dtype='float64') + hat_hat = empty((npar,npar),dtype='float64') + soln = empty(npar,dtype='float64') + psd = zeros(numf,dtype='float64') + + # detrend the data and create the orthogonal detrending basis + if (dord>0): + wth = empty((dord+1,numt),dtype='float64') + wth[0,:] = wth0 + else: + wth = wth0 + +# TODO can we just use polyfit from numpy +# TODO not clear why this has to live inside lomb_scargle code, should be decoupled + for i in range(detrend_order): + f = wth[i,:]*tt/(2*pi) + for j in range(i+1): + f -= dot(f,wth[j,:])*wth[j,:] + norm[i+1] = sqrt(dot(f,f)); f /= norm[i+1] + coef[i+1] = dot(cn,f) + cn -= coef[i+1]*f + wth[i+1,:] = f + vcn += (f/wth0)**2 + + + chi0 = dot(cn,cn) + varcn = chi0/(numt-1-dord) + psdmin *= 2*varcn + + Tr = array(0.,dtype='float64') + ifreq = array(0,dtype='int32') + lambda0 = array(lambda0/s0,dtype='float64') + lambda0_range = 10**array(lambda0_range,dtype='float64')/s0 + + +# TODO why? + vars=['numt','numf','nharm','detrend_order','psd','cn','wth','sinx','cosx','sinx_step','cosx_step','sinx_back','cosx_back','sinx_smallstep','cosx_smallstep','hat_matr','hat_hat','hat0','soln','chi0','freq_zoom','psdmin','tone_control','lambda0','lambda0_range','Tr','ifreq'] + + lomb_scargle(numt, numf, nharm, detrend_order, psd, cn, wth, sinx, + cosx, sinx_step, cosx_step, sinx_back, cosx_back, + sinx_smallstep, cosx_smallstep, hat_matr, hat_hat, hat0, + soln, chi0, freq_zoom, psdmin, tone_control, lambda0, + lambda0_range, Tr, ifreq) + + hat_hat /= s0 + ii = arange(nharm,dtype='int32') +# TODO why? + soln[0:nharm] /= (1.+ii)**2; soln[nharm:] /= (1.+ii)**2 + if (detrend_order>=0): + hat_matr0 = outer(hat0[:,0],wth0) + for i in range(detrend_order): + hat_matr0 += outer(hat0[:,i+1],wth[i+1,:]) + + + modl = dot(hat_matr.T,soln); modl0 = dot(hat_matr0.T,soln) + coef0 = dot(soln,hat0) + coef -= coef0 + if (detrend_order>=0): + hat_matr -= hat_matr0 + + out_dict={} + out_dict['chi0'] = chi0*s0 + if (return_model): + if (dord>0): + out_dict['trend'] = dot(coef,wth)/wth0 + else: + out_dict['trend'] = coef[0] + 0*wth0 + out_dict['model'] = modl/wth0 + out_dict['trend'] + + j = psd.argmax() + freq = f1+df*j + (ifreq/freq_zoom - 1/2.)*df + tt = (time*freq) % 1. ; s =tt.argsort() + out_dict['freq'] = freq + out_dict['s0'] = s0 + out_dict['chi2'] = (chi0 - psd[j])*s0 + out_dict['psd'] = psd[j]*0.5/varcn + out_dict['lambda0'] = lambda0*s0 + out_dict['gcv_weight'] = (1-3./numt)/Tr + out_dict['trace'] = Tr + out_dict['nu0'] = numt - npar + npars = (1-Tr)*numt/2 + out_dict['nu'] = numt-npars + out_dict['npars'] = npars + + A0, B0 = soln[0:nharm],soln[nharm:] + hat_hat /= outer( hstack(((1.+ii)**2,(1.+ii)**2)),hstack(((1.+ii)**2,(1.+ii)**2)) ) + err2 = diag(hat_hat) + vA0, vB0 = err2[0:nharm], err2[nharm:] + covA0B0 = hat_hat[(ii,nharm+ii)] + + if (return_model): + vmodl = vcn/s0 + dot( (hat_matr/wth0).T, dot(hat_hat, hat_matr/wth0) ) + vmodl0 = vcn/s0 + dot( (hat_matr0/wth0).T, dot(hat_hat, hat_matr0/wth0) ) + out_dict['model_error'] = sqrt(diag(vmodl)) + out_dict['trend_error'] = sqrt(diag(vmodl0)) + + amp = sqrt(A0**2+B0**2) + damp = sqrt( A0**2*vA0 + B0**2*vB0 + 2.*A0*B0*covA0B0 )/amp + phase = arctan2( B0,A0 ) + rel_phase = phase - phase[0]*(1.+ii) + rel_phase = arctan2( sin(rel_phase),cos(rel_phase) ) + dphase = 0.*rel_phase +# TODO what is this + for i in range(nharm-1): + j=i+1 + v = array([-A0[0]*(1.+j)/amp[0]**2,B0[0]*(1.+j)/amp[0]**2,A0[j]/amp[j]**2,-B0[j]/amp[j]**2]) + jj=array([0,nharm,j,j+nharm]) + m = hat_hat[ix_(jj,jj)] + dphase[j] = sqrt( dot(dot(v,m),v) ) + + out_dict['amplitude'] = amp + out_dict['amplitude_error'] = damp + out_dict['rel_phase'] = rel_phase + out_dict['rel_phase_error'] = dphase + out_dict['time0'] = -phase[0]/(2*pi*freq) + + ncp = norm.cumprod() + out_dict['trend_coef'] = coef/ncp + out_dict['cn0'] = out_dict['trend_coef'][0] - cn0 + out_dict['trend_coef_error'] = sqrt( ( 1./s0 + diag(dot(hat0.T,dot(hat_hat,hat0))) )/ncp**2 ) + out_dict['cn0_error'] = out_dict['trend_coef_error'][0] + + prob = fdist.sf( 0.5*(numt-1.-dord)*(1.-out_dict['chi2']/out_dict['chi0']), 2,numt-1-dord ) + out_dict['signif'] = lprob2sigma(log(prob)) + + return 0.5*psd/varcn,out_dict diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/ls_support.py b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/ls_support.py new file mode 100644 index 00000000..d83a8a78 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/ls_support.py @@ -0,0 +1,203 @@ +# TODO remove? +lomb_scargle_support = """ + inline void copy_sincos (int numt, double sinx0[], double cosx0[], double sinx[], double cosx[]) { + for (int i=0;i0) px = ( c2*sh*sh - 2.*cs*ch*sh + s2*ch*ch ) / detm; + return px; + } + inline void calc_dotprod(int numt, double sinx[], double cosx[], double wt[], int dord, double *st, double *ct) { + int i; + unsigned long n2=numt*dord; + for (*st=0,*ct=0,i=0;ipxmax) { + pxmax=px; + *ifreq = i; + } + update_sincos(numt, sinx_smallstep, cosx_smallstep, sinx1, cosx1, 0); + } + copy_sincos(numt,sinx,cosx,sinx1,cosx1); + if (*ifreqpx_max && Tr>0) { + px_max = px; + lambda_best = lambda; + *Trace = Tr; + } + lambda *= dlambda; + } + if (lambda_best-1.e-5>start) start=lambda_best/dlambda; + else break; + if(lambda_best+1.e-5psd0max && psdmax==0) { + psd0max = psd[j]; + copy_sincos(numt,sinx,cosx,sinx2,cosx2); + jmax = j; + } + // refine the fit around significant sin+cos fits + if (psd[j]>(double)psdmin) { + // first let the frequency vary slightly + px = do_lomb_zoom(numt,detrend_order, cn, sinx, cosx, sinx1, cosx1, sinx_back, cosx_back, sinx_smallstep, cosx_smallstep, wth, freq_zoom, &ifr); + lambda = *lambda0; + // now fit a multi-harmonic model with generalized cross-validation to avoid over-fitting + psd[j] = refine_psd(numt,nharm,detrend_order,hat_matr,hat0,hat_hat,sinx1,cosx1,wth,cn,soln,&lambda,lambda0_range,chi0,tone_control,&Trace,0); + if (psd[j]>psdmax) { + copy_sincos(numt,sinx1,cosx1,sinx2,cosx2); + psdmax=psd[j]; + *ifreq = ifr; + jmax = j; + } + } + update_sincos(numt, sinx_step, cosx_step, sinx, cosx, 0); + } + // finally, rerun at the best-fit period so we get some statistics + psd[jmax] = refine_psd(numt,nharm,detrend_order,hat_matr,hat0,hat_hat,sinx2,cosx2,wth,cn,soln,lambda0,lambda0_range,chi0,tone_control,Tr,1); +""" diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/multi_harmonic_fit.py b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/multi_harmonic_fit.py new file mode 100644 index 00000000..1a226f74 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/multi_harmonic_fit.py @@ -0,0 +1,225 @@ +from __future__ import print_function +from __future__ import absolute_import +from numpy import sin,cos,sqrt,empty,pi,dot,arctan2,atleast_1d,diag,arange,abs,ones,array,zeros,log,trace +from scipy.linalg import cho_solve,cho_factor + +from .pre_whiten import chi2sigma + +def CholeskyInverse(t,B): + """ + Computes inverse of matrix given its Cholesky upper Triangular decomposition t. + """ + nrows = len(t) + # Backward step for inverse. + for j in reversed(range(nrows)): + tjj = t[j,j] + S = sum([t[j,k]*B[j,k] for k in range(j+1, nrows)]) + B[j,j] = 1.0/ tjj**2 - S/ tjj + for i in reversed(range(j)): + B[j,i] = B[i,j] = -sum([t[i,k]*B[k,j] for k in range(i+1,nrows)])/t[i,i] + +def multi_harmonic_fit(time,data,error,freq,nharm=4,return_model=False,freq_sep=0.01,fit_mean=True,fit_slope=False): + """ + Simultaneous fit of a sum of sinusoids by weighted, linear least squares. + model(t) = C0 + C1*(t-t0) + Sum_i Sum_j Aij sin(2*pi*j*fi*(t-t0)+phij), i=[1,nfreq], j=[1,nharm] + [t0 defined such that ph11=0] + + Input: + time: x vector + data: y vector + error: uncertainty on data + freq: one or more frequencies freq_i to fit + nharm: number of harmonics of each frequency to fit (nharm=1 is just fundamental) + fij = fi, 2*fi, ... nharm*fi + freq_sep: freq_ij seperated by less than this are ignored (should be the search grid spacing) + fit_slope=False, then C1=0 + fit_mean=False, then C0=0 + + Output: + A dictionary containing the model evaluated on the time grid (if return_model==True) and + the model amplitudes Aij, phases phij, and their uncertainties. + """ + t = time.astype('float64') + r = data.astype('float64') + dr = error.astype('float64') + + numt = len(t) + + wt = 1./dr**2 + s0 = wt.sum() + t0 = (t*wt).sum()/s0 + t -= t0 + + dr *= sqrt(s0) + r0 = (r*wt).sum()/s0 + r -= r0 + + nfit=0 + if (fit_mean==True): + nfit=1 + + if (fit_slope==True): + fit_mean=True + nfit=2 + tm = t.max() + s1 = ((t/tm)**2*wt).sum() + sb = ((t/tm)*r*wt).sum() + slope = sb/s1; s1 /= s0 + r -= slope*t/tm + tt = t/tm/dr + + rr = r/dr + chi0 = dot(rr,rr)*s0 + + matr = empty((nfit+2*nharm,nfit+2*nharm),dtype='float64') + vec = empty(nfit+2*nharm,dtype='float64') + + sx = empty((nharm,numt),dtype='float64') + cx = empty((nharm,numt),dtype='float64') + + # + # We will solve matr*res = vec, for res. Define matr and vec. + # + sx0,cx0 = sin(2*pi*t*freq), cos(2*pi*t*freq) + sx[0,:] = sx0/dr; cx[0,:] = cx0/dr + for i in range(nharm-1): + sx[i+1,:] = cx0*sx[i,:] + sx0*cx[i,:] + cx[i+1,:] = -sx0*sx[i,:] + cx0*cx[i,:] + + if (nfit>0): + vec[0] = 0.; matr[0,0] = 1.; + if (nfit>1): + vec[1] = matr[0,1] = matr[1,0] = 0.; matr[1,1] = s1 + + for i in range(nharm): + vec[i+nfit] = dot(sx[i,:],rr) + vec[nharm+i+nfit] = dot(cx[i,:],rr) + if (nfit>0): + matr[0,i+nfit] = matr[i+nfit,0] = dot(sx[i,:],1./dr) + matr[0,nharm+i+nfit] = matr[nharm+i+nfit,0] = dot(cx[i,:],1./dr) + if (nfit>1): + matr[1,i+nfit] = matr[i+nfit,1] = dot(sx[i,:],tt) + matr[1,nharm+i+nfit] = matr[nharm+i+nfit,1] = dot(cx[i,:],tt) + for j in range(i+1): + matr[j+nfit,i+nfit] = matr[i+nfit,j+nfit] = dot(sx[i,:],sx[j,:]) + matr[j+nfit,nharm+i+nfit] = matr[nharm+i+nfit,j+nfit] = dot(cx[i,:],sx[j,:]) + matr[nharm+j+nfit,i+nfit] = matr[i+nfit,nharm+j+nfit] = dot(sx[i,:],cx[j,:]) + matr[nharm+j+nfit,nharm+i+nfit] = matr[nharm+i+nfit,nharm+j+nfit] = dot(cx[i,:],cx[j,:]) + + + out_dict={} + + # + # Convert to amplitudes and phases and propagate errors + # + out_dict['cn0'] = r0 + out_dict['cn0_error'] = 1./sqrt(s0) + out_dict['trend'] = 0. + out_dict['trend_error']=0. + + A0,B0,vA0,vB0,covA0B0 = zeros((5,nharm),dtype='float64') + amp,phase,rel_phase = zeros((3,nharm),dtype='float64') + damp,dphase = zeros((2,nharm),dtype='float64') + covA0B0 = zeros(nharm,dtype='float64') + res = zeros(nfit+2*nharm,dtype='float64') + err2 = zeros(nfit+2*nharm,dtype='float64') + + out_dict['bayes_factor'] = 0. + + try: + # + # solve the equation and replace matr with its inverse + # + m0 = cho_factor(matr,lower=False) + out_dict['bayes_factor'] = -log(trace(m0[0])) + res = cho_solve(m0,vec) + CholeskyInverse(m0[0],matr) + + A0, B0 = res[nfit:nharm+nfit],res[nharm+nfit:] + amp = sqrt(A0**2+B0**2) + phase = arctan2( B0,A0 ) + + err2 = diag(matr)/s0 + vA0, vB0 = err2[nfit:nharm+nfit], err2[nharm+nfit:] + for i in range(nharm): + covA0B0[i] = matr[nfit+i,nharm+nfit+i]/s0 + + damp = sqrt( A0**2*vA0 + B0**2*vB0 + 2.*A0*B0*covA0B0 )/amp + dphase = sqrt( A0**2*vB0 + B0**2*vA0 - 2.*A0*B0*covA0B0 )/amp**2 + rel_phase = phase - phase[0]*(1.+arange(nharm)) + rel_phase = arctan2( sin(rel_phase),cos(rel_phase) ) + + except: + print ("Failed: singular matrix! (Are your frequencies unique/non-harmonic?)") + + out_dict['time0'] = t0-phase[0]/(2*pi*freq) + out_dict["amplitude"] = amp + out_dict["amplitude_error"] = damp + out_dict["rel_phase"] = rel_phase + out_dict["rel_phase_error"] = dphase + + modl = r0 + dot(A0,sx*dr) + dot(B0,cx*dr) + if (nfit>0): + out_dict['cn0'] += res[0] + out_dict['cn0_error'] = sqrt(err2[0]) + modl += res[0] + if (nfit>1): + out_dict['trend'] = (res[1]+slope)/tm + out_dict['trend_error'] = sqrt(err2[1])/tm + modl += out_dict['trend']*t + ### + #import os + #import matplotlib.pyplot as pyplot + #t_folded = t % (1./freq) + #pyplot.title("nfit=%d After modl += res[0] and modl += out_dict['trend']*t" % (nfit)) + + #pyplot.plot(t_folded, data, 'bo', ms=3) + #pyplot.plot(t_folded, modl, 'ro', ms=3) + #pyplot.plot(t_folded, modl - out_dict['trend']*t, 'mo', ms=3) + #pyplot.plot(t_folded, out_dict['trend']*t, 'go', ms=3) + ##pyplot.plot(t, data, 'bo', ms=3) + ##pyplot.plot(t, modl, 'ro', ms=3) + ##pyplot.plot(t, modl - out_dict['trend']*t, 'mo', ms=3) + ##pyplot.plot(t, out_dict['trend']*t, 'go', ms=3) + ###pyplot.show() + ##fpath = '/tmp/multiharmonic.ps' + ##pyplot.savefig(fpath) + ##os.system('gv %s &' % (fpath)) + #import pdb; pdb.set_trace() + ### + resid = (modl-r-r0-slope*tt*dr)/dr + out_dict['chi2'] = dot(resid,resid)*s0 + out_dict['cn0'] += out_dict['trend']*(out_dict['time0']-t0) + else: + resid = (modl-r-r0)/dr + out_dict['chi2'] = dot(resid,resid)*s0 + + ### + #import os + #import matplotlib.pyplot as pyplot + #t_folded = t % (1./freq) + #pyplot.title("nfit=%d freq=%f End" % (nfit, freq)) + + #pyplot.plot(t_folded, data, 'bo', ms=3) + #pyplot.plot(t_folded, modl, 'ro', ms=3) + #pyplot.plot(t_folded, modl - out_dict['trend']*t, 'mo', ms=3) + #pyplot.plot(t_folded, out_dict['trend']*t, 'go', ms=3) + ##pyplot.plot(t, data, 'bo', ms=3) + ##pyplot.plot(t, modl, 'ro', ms=3) + ##pyplot.plot(t, modl - out_dict['trend']*t, 'mo', ms=3) + ##pyplot.plot(t, out_dict['trend']*t, 'go', ms=3) + ###pyplot.show() + #fpath = '/tmp/multiharmonic.ps' + #pyplot.savefig(fpath) + #os.system('gv %s &' % (fpath)) + #import pdb; pdb.set_trace() + #pyplot.clf() + ### + + + out_dict['nu'] = numt - 2*nharm - nfit + out_dict['signif'] = chi2sigma(chi0,out_dict['chi2'],numt-nfit,nharm) + if (return_model): + out_dict['model'] = modl + + return out_dict diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/plot_methods.py b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/plot_methods.py new file mode 100644 index 00000000..a26cb69b --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/plot_methods.py @@ -0,0 +1,44 @@ +from __future__ import print_function +try: + import pylab +except: + pass +try: + from pylab import * +except: + pass + +class plot_vs_time(object): + """ inheritable function to plot oneself against the time axis """ + extname = 'plot vs time inheritable method' #extractor's name + def plot_feature(self,properties): + plot(self.time_data, properties[self.extname], label=self.extname) + +class plot_vs_frequencies(object): + """ inheritable function to plot oneself against the frequency axis """ + extname = 'plot vs frequency inheritable method' + def plot_feature(self,properties): + # dstarr hacks this since some 'results' are tuples with frequencies as the [1] element + #plot(self.frequencies, properties[self.extname], label=self.extname) + if len(properties[self.extname]) == 2: + pylab.plot(properties[self.extname][1],properties[self.extname][0], 'bo') + pylab.title(self.extname) + pylab.show() + else: + #plot(self.frequencies, properties[self.extname], label=self.extname) + pylab.plot(properties[self.extname], 'bo') + pylab.title(self.extname) + pylab.show() + +class plot_vertical_line(object): + """ inheritable function to plot oneself against the frequency axis """ + extname = 'plot vertical line inheritable method' #extractor's name + def plot_feature(self,properties): + print(self.extname) + print(properties[self.extname]) + axvline(x=properties[self.extname],label=self.extname) +class plot_horizontal_line(object): + """ inheritable function to plot oneself horizontally """ + extname = 'plot horizontal line inheritable method' #extractor's name + def plot_feature(self,properties): + axhline(x=properties[self.extname],label=self.extname) diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/pre_whiten.py b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/pre_whiten.py new file mode 100644 index 00000000..63bffe40 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/pre_whiten.py @@ -0,0 +1,291 @@ +#! /usr/bin/env python +# this is Nat's code copied over from the feature_extract project on August 3rd 2008, but I copied over nat's original svn upload, not Dan's modification (the modifications did not apply to this project) + +from __future__ import division +from __future__ import absolute_import +from numpy import * + +from .lomb_scargle_refine import lprob2sigma + + +def chi2sigma(chi0,chi1,nu0,nharm): + from scipy.stats import betai + from scipy.special import betaln + + nu1 = nu0 - 2.*nharm + dfn = nu0-nu1 + dfd = nu1 + sigma = 0. + if (dfn>0 and dfd>0 and chi0>chi1): + fstat = (chi0/chi1-1.)*dfd/dfn + prob = betai( dfd/2., dfn/2., dfd/(dfd+dfn*fstat) ) + if (dfd<=0 or dfn<=0): lprob=0. + elif (chi1==0): lprob=-999. + elif (prob==0): lprob = 0.5*dfd*log( dfd/(dfd+dfn*fstat) )-log(dfd/2.)-betaln(dfd/2.,dfn/2.) + else: lprob = log(prob) + sigma = lprob2sigma(lprob) + + return sigma + + +def pre_whiten(time, signal, freq, delta_time=[], signal_err=[], dof=-999, +nharm_min=4, nharm_max=20): + """Generates a harmonic fit to data (time,signal) with at most + nharm_max harmonics relative to the fundamental frequency freq. + Report statistics for nharm_min of them. + """ + n0 = len(time) + if (dof==-999 or dof>n0): dof=n0 + + A0 = zeros(nharm_max,dtype=float) + dA0 = zeros(nharm_max,dtype=float) + B0 = zeros(nharm_max,dtype=float) + dB0 = zeros(nharm_max,dtype=float) + pha = zeros(nharm_max,dtype=float) + + # if data error not given, assume all are unity + if (signal_err==[]): + wt = ones(n0,dtype=float) + else: + wt = 1./signal_err**2; + wt[signal_err<=0]=1. + + # if delta_time not given, assume 0 + do_sync=True + if (delta_time==[]): + do_sync=False + delta_time = zeros(n0, dtype=float) + + sync_func = lambda x: 1. + if (do_sync): + sync_func = lambda x: (1.e-99 + sin(pi*x))/(1.e-99 + pi*x) + + x = 2*pi*freq*time + cn = 1.*signal + + # just in case it wasn't already subtracted away + s0 = sum(wt) + mn = sum(cn*wt) / s0 + cn_offset = mn + cn -= cn_offset + dof = dof - 1. + + # initial chi^2 value for constant fit + nu0 = dof + chi0 = sum( cn**2*wt ) + + # + n_grid = (1+nharm_max)*20 + x_grid = 2*pi*arange(n_grid,dtype=float)/(1.*n_grid) + modls = zeros([nharm_max,n_grid],dtype=float) + modlc = zeros([nharm_max,n_grid],dtype=float) + modl0 = zeros(n_grid,dtype=float) + vmodl0 = zeros(n_grid,dtype=float) + + # + # do the dirty work, fit for the sinusoid amplitudes and phases + # modulus cn = A*sin(x+pha) , of A0*sin(x)+B0*cos(x) + # + chi1_last = chi0 + stop = 0 + for i in range(nharm_max): + + j = i+1 + + synct = sync_func(freq*j*delta_time) + sinx = sin(j*x)*synct + cosx = cos(j*x)*synct + + ts2 = 2.*sum( sinx*cosx*wt ) + tc2 = sum( (cosx**2-sinx**2)*wt ) + + x0 = 0.5*arctan2(ts2, tc2)/j + sinomtau = sin(j*x0) + cosomtau = cos(j*x0) + sin2omtau = 2.*sinomtau*cosomtau; + cos2omtau = cosomtau**2 - sinomtau**2 + + tmp = tc2*cos2omtau + ts2*sin2omtau; + tc2 = 0.5*(s0+tmp); + ts2 = 0.5*(s0-tmp); + + tmp = sinx + sinx = tmp*cosomtau-cosx*sinomtau + cosx = cosx*cosomtau + tmp*sinomtau + + sh = sum( cn*sinx*wt ) + ts1 = sum( sinx*wt ) + + ch = sum( cn*cosx*wt ) + tc1 = sum( cosx*wt ) + + A0[i] = sh / ts2; dA0[i] = 1./sqrt(ts2); + B0[i] = ch / tc2; dB0[i] = 1./sqrt(tc2); + cn0 = ( A0[i]*ts1 + B0[i]*tc1 ) / ( ts1**2/ts2 + tc1**2/tc2 - s0 ); + A0[i] -= cn0*ts1/ts2; + B0[i] -= cn0*tc1/tc2; + + pha[i] = arctan2(B0[i],A0[i]) - j*x0 + + cn_test = cn - cn0 - ( A0[i]*sinx + B0[i]*cosx ) * synct + + chi1 = sum( cn_test**2*wt ) # chi^2 for harmonic component removed + if (chi1 > chi1_last*(nu0-2*j)/(nu0-2*(j-1)) or j==nharm_max): + stop=1 + nharm = i + sigma = chi2sigma(chi0,chi1,nu0,nharm) + + if (stop==1 and j>nharm_min): # calculate >= nharm_min harmonics + + A0 = A0[:i] + dA0 = dA0[:i] + B0 = B0[:i] + dB0 = dB0[:i] + pha = pha[:i] + modls = modls[:i,:] + modlc = modlc[:i,:] + break + + chi1_last = chi1 + cn = cn_test + cn_offset += cn0 + + modls[i,:] = sin(j*(x_grid-x0)) + modlc[i,:] = cos(j*(x_grid-x0)) + modl0 += A0[i]*modls[i,:] + B0[i]*modlc[i,:] + vmodl0 += (dA0[i]*modls[i,:])**2 + (dB0[i]*modlc[i,:])**2 + + + # find light curve extremum for bookkeeping + pk = argmax(modl0*modl0) + # report phases relative to extremum, time' = time - time_offset + time_offset = - x_grid[pk] / (2*pi*freq) + for i in range(nharm): + pha[i] = pha[i] - (1+i)*x_grid[pk] + pha[i] = arctan2( sin(pha[i]),cos(pha[i]) ) + + x_grid = x_grid-x_grid[pk] + x_grid = arctan2(sin(x_grid),cos(x_grid)) + i0 = argmin(x_grid) + i1 = argmax(x_grid) + + # + # error propagation + # + A = sqrt( A0**2 + B0**2 ) + dA = sqrt( (A0*dA0)**2 + (B0*dB0)**2 ) / A + dpha = 1./(1+(A0/B0)**2) * sqrt( (dA0/B0)**2+(A0*dB0/B0**2)**2 ) + + # use the chi^2 values to get a better (more conservative) error estimate + fac=1. + sigma0 = A[0]/dA[0] + if (sigma0>sigma and sigma>0): fac = sigma0/sigma + # now apply it + dA = fac*dA + vmodl0 = fac**2*vmodl0 + dpha = fac*dpha + + # + # get flux extrema + # + mn = argmin(modl0) + fmin = modl0[mn] + vfmin = vmodl0[mn] + mx = argmax(modl0) + fmax = modl0[mx] + vfmax = vmodl0[mx] + peak2peak_flux = fmax - fmin + peak2peak_flux_error = sqrt( vfmin+vfmax ) + + # + # set flux offset and sign for moment calculation below + # + s0 = 0.5*(modl0[i0]+modl0[i1]) + vs0 = 0.25*(vmodl0[i0]+vmodl0[i1]) + + #import Gnuplot + #import time + #plotobj = Gnuplot.Gnuplot() + #plotobj.xlabel('Time (s)') + #plotobj.ylabel('Folded Light Curve') + #plotobj('unset logscale x') + #plotobj.plot(Gnuplot.Data(x_grid/(2*pi*freq),modl0-s0)) + #time.sleep(3) + + niter=10 + for k in range(niter): + if (k==0): window = 1. + + nmom = 4 # number of moments requested (don't change) + mu = zeros(1+nmom,dtype=float) + vmu = zeros(1+nmom,dtype=float) + x0=0. + norm=1. + for j in range(1+nmom): + xfac = pow(x_grid-x0,j)*window + mu[j] = -s0*mean(xfac)/norm + vmu[j] = vs0*(mean(xfac)/norm)**2 + for k in range(nharm): + mas = mean( xfac*modls[k,:] ) + mac = mean( xfac*modlc[k,:] ) + mu[j] = mu[j] + (A0[k]*mas + B0[k]*mac)/norm + vmu[j] = vmu[j] + (dA0[k]*mas/norm)**2 + (dB0[k]*mac/norm)**2 + if (j==0 and abs(mu[0])>0): norm = mu[0] + if (j==1): x0 = mu[1] + if (j==2): window = 1*( abs(x_grid-x0) < 3.*sqrt(mu[2]) ) + if (sum(window)==n_grid): break + + # defaults + moments = array([0.,0.5/freq,0.,0.]) + dmoments = array([1.,0.5/freq,1.,1.]) + if (abs(mu[0])>0 and mu[2]>0): + av = x0 + var = mu[2] + stdev = sqrt(var) + skewness = mu[3] / mu[2]**1.5 + kurtosis = mu[4] / mu[2]**2 - 3. + # error propagation is approximate + dav = sqrt(vmu[1]) + dstdev = 0.5*sqrt(vmu[2]/mu[2]) + dskewness = sqrt( vmu[3] ) / var**1.5 + dkurtosis = sqrt( vmu[4] ) / var**2. + + moments = array([av,stdev/(2*pi*freq),skewness,kurtosis]) + dmoments = array([dav,dstdev/(2*pi*freq),dskewness,dkurtosis]) + + # + # put it all in a dictionary + # + freqs = freq*(1+arange(nharm_min)) + #out_dict = { 'signif': sigma, 'peak2peak_flux': peak2peak_flux, + # 'peak2peak_flux_error': peak2peak_flux_error, 'amplitude': A[:nharm_min], + # 'freq': freqs, 'amplitude_error': dA[:nharm_min], 'rel_phase': pha[:nharm_min], + # 'rel_phase_error': dpha[:nharm_min], 'moments': moments, + # 'moments_err': dmoments, 'nharm': nharm} + + # 20080508: dstarr wants verbosely labeled dict: + out_dict = { 'signif': sigma, 'peak2peak_flux': peak2peak_flux, + 'peak2peak_flux_error': peak2peak_flux_error, 'nharm': nharm} + + for i in range(len(moments)): + out_dict['moments_' + str(i)] = moments[i] + for i in range(len(dA[:nharm_min])): + out_dict['amplitude_error_' + str(i)] = dA[:nharm_min][i] + for i in range(len(dmoments)): + out_dict['moments_err_' + str(i)] = dmoments[i] + for i in range(len(dpha[:nharm_min])): + out_dict['rel_phase_error_' + str(i)] = dpha[:nharm_min][i] + for i in range(len(A[:nharm_min])): + out_dict['amplitude_' + str(i)] = A[:nharm_min][i] + for i in range(len(pha[:nharm_min])): + out_dict['rel_phase_' + str(i)] = pha[:nharm_min][i] + for i in range(len(freqs)): + out_dict['freq_' + str(i)] = freqs[i] + + out_dict = { 'signif': sigma, 'peak2peak_flux': peak2peak_flux, +'peak2peak_flux_error': peak2peak_flux_error, 'amplitude': A[:nharm_min], +'freq': freqs, 'amplitude_error': dA[:nharm_min], 'rel_phase': pha[:nharm_min], +'rel_phase_error': dpha[:nharm_min], 'moments': moments, +'moments_err': dmoments, 'nharm': nharm, 'time_offset': time_offset,'y_offset':cn_offset} + + return cn, out_dict diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/setup.py b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/setup.py new file mode 100644 index 00000000..39e785a3 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/common_functions/setup.py @@ -0,0 +1,21 @@ +import os +import numpy as np +from Cython.Build import cythonize + +base_path = os.path.abspath(os.path.dirname(__file__)) + + +def configuration(parent_package='', top_path=None): + from numpy.distutils.misc_util import Configuration + config = Configuration('common_functions', parent_package, top_path) + + cythonize(os.path.join(base_path, '_lomb_scargle.pyx')) + + config.add_extension('_lomb_scargle', '_lomb_scargle.c', + include_dirs=[np.get_include()]) + + return config + +if __name__ == '__main__': + from numpy.distutils.core import setup + setup(**configuration(top_path='').todict()) diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/dc_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/dc_extractor.py new file mode 100644 index 00000000..96ea5c6c --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/dc_extractor.py @@ -0,0 +1,25 @@ +from __future__ import absolute_import +from ..FeatureExtractor import FeatureExtractor + +from scipy import optimize +from .common_functions import ChiSquare +from .common_functions.plot_methods import plot_horizontal_line + + +class dc_extractor(plot_horizontal_line,FeatureExtractor,ChiSquare): + active = True + extname = 'dc' #extractor's name + def extract(self): +# print 'xdata',self.time_data,'ydata', self.flux_data,'rms_data',self.rms_data + dc = optimize.fminbound(self.weighted_average,-10,10,args=(self.time_data,self.flux_data,self.rms_data)) + # KLUDGY 20090810: (on some systems optimizes.fminbound returns an array, other ones it returns a numpy.float). The least painful fix is this: + try: + return(dc[0]) + except: + return(dc) + def weighted_average(self,u,x,y,rms): + def average(x): + dc = x.copy() + dc[:] = u + return dc + return self.chi_square_sum(y,average,x=x,rms=rms) diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/delta_phase_2minima_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/delta_phase_2minima_extractor.py new file mode 100644 index 00000000..8e185b69 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/delta_phase_2minima_extractor.py @@ -0,0 +1,147 @@ +try: + from ..FeatureExtractor import FeatureExtractor +except: + import os + ppath = os.environ.get('PYTHONPATH') + os.environ.update({'PYTHONPATH': ppath + ":" + os.path.realpath("..")}) + #print os.environ.get("PYTHONPATH") + from FeatureExtractor import FeatureExtractor + +import numpy#, pdb +from scipy.optimize import fmin +try: + import matplotlib.pyplot as plt +except: + pass + +class delta_phase_2minima_extractor(FeatureExtractor): + ''' + returns a best guess of the phase difference between the two + lowest minima of a lightcurve. + + only relevant for eclipsing sources, and best used + to identify orbital eccentricity + + author: I.Shivvers, June 2012 + ''' + active = True + extname = 'delta_phase_2minima' + def extract(self): + try: + # get info + t = self.time_data + m = self.flux_data + pdm_period = 1./self.fetch_extr('phase_dispersion_freq0') + p = self.fold(t, pdm_period) + + # find the proper window for the model + best_GCV, optimal_window = self.minimize_GCV(p, m) + mins = self.findMins(p,m, optimal_window) + val = abs(mins[0]-mins[1]) + if val > .5: val = 1.-val # keep between 0. and .5 + if val == 0.: val = .5 # only found one minimum; probably got the period wrong by factor of 2 + return val + except: + return 0.0 + + def findMins(self, p, m, optimal_window): + """ + Find and return the phase of two lowest minima of unsmoothed data + If only one minima, returns that phase twice. + Couched in error-catching, so that it returns 0.,0. if it can't + make sense of the specific LC """ + try: + # calculate smoothed model + bandwidth = float(optimal_window)/len(p) + model = self.kernelSmooth(p, m, bandwidth) + # resort into proper order + zpm = list(zip(p, model)) + zpm.sort() + zpm_arr = numpy.array(zpm) + phs = zpm_arr[:,0] + model = zpm_arr[:,1] + # identify and rank the peaks + peaks_ind = self.find_peaks(model) + zppi = list(zip(model[peaks_ind], phs[peaks_ind], peaks_ind)) + zppi.sort(reverse=True) + # only report a minima as seperate if the model goes back past the mean and returns in between them + threshold = numpy.mean(model) + min1 = zppi[0] + min2 = zppi[0] + for zed in zppi[1:]: + test_values = model[ min([zed[2], min1[2]]): max([zed[2], min1[2]])] + if min(test_values) < threshold and zed[0] > threshold: + min2 = zed + break + return [min1[1], min2[1]] + except (IndexError, UnboundLocalError): + return [0., 0.] + + def fold(self, times, period): + ''' return phases for folded at ''' + t0 = times[0] + phase = ((times-t0)%period)/period + return phase + + def rolling_window(self, b, window): + """ Call: numpy.mean(rolling_window(observations, n), 1) + """ + # perform smoothing using strides trick + shape = b.shape[:-1] + (b.shape[-1] - window + 1, window) + strides = b.strides + (b.strides[-1],) + return numpy.lib.stride_tricks.as_strided(b, shape=shape, strides=strides) + + def GCV(self, window, X,Y): + # put in proper order + zpm = list(zip(X,Y)) + zpm.sort() + zpm_arr = numpy.array(zpm) + phs = zpm_arr[:,0] + mags = zpm_arr[:,1] + # handle edges properly by extending array in both directions + if window>1: + b = numpy.concatenate((mags[-window/2:], mags, mags[:window/2-1])) + else: + b = mags + # calculate smoothed model and corresponding smoothing matrix diagonal value + model = numpy.mean(self.rolling_window(b, window), 1) + Lii = 1./window + # return the Generalized Cross-Validation criterion + GCV = 1./len(phs) * numpy.sum( ((mags-model)/(1.-Lii))**2 ) + return GCV + + def minimize_GCV(self,X,Y, window_range=(10,50,2)): + ''' quick way to pick best GCV value ''' + windows = numpy.arange(*window_range) + GCVs = numpy.array( [self.GCV(window, X,Y) for window in windows] ) + best_GCV = numpy.min(GCVs) + optimal_window = windows[ numpy.argmin(GCVs) ] + return best_GCV, optimal_window + + def GaussianKernel(self, x): + return (1./numpy.sqrt(2.*numpy.pi)) * numpy.exp(-x**2 / 2.) + + def kernelSmooth(self, X, Y, bandwidth): + ''' slow implementation of gaussian kernel smoothing ''' + L = numpy.zeros([len(Y),len(X)]) + diags = [] + for i in range(len(X)): + diff = abs(X[i] - X) + # wrap around X=1; i.e. diff cannot be more than .5 + diff[diff>.5] = 1. - diff[diff>.5] + # renormalize, and operate on l vector + l = diff/bandwidth + # calculate the Gaussian for the values within 4sigma and plug it in + # anything beyond 4sigma is basically zero + tmp = self.GaussianKernel(l[l<4]) + diags.append(numpy.max(tmp)) + L[i,l<4] = tmp/numpy.sum(tmp) + # model is the smoothing matrix dotted into the data + return numpy.dot(L, Y.T) + + def find_peaks(self, x): + """ find peaks in """ + xmid = x[1:-1] # orig array with ends removed + xm1 = x[2:] # orig array shifted one up + xp1 = x[:-2] # orig array shifted one back + return numpy.where(numpy.logical_and(xmid > xm1, xmid > xp1))[0] + 1 diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/dist_from_u_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/dist_from_u_extractor.py new file mode 100644 index 00000000..4a7cabcf --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/dist_from_u_extractor.py @@ -0,0 +1,12 @@ +from ..FeatureExtractor import InterExtractor + +class dist_from_u_extractor(InterExtractor): + active = True + extname = 'dist_from_u' #extractor's name + def extract(self): + u = self.fetch_extr('weighted_average') + sd = self.fetch_extr('wei_av_uncertainty') + diff = self.flux_data - u + uncer = self.rms_data + sd + self.uncertainty = uncer + return diff diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/distance_in_arcmin_to_nearest_galaxy.py b/mltsp/TCP/Software/feature_extract/Code/extractors/distance_in_arcmin_to_nearest_galaxy.py new file mode 100644 index 00000000..8835e82e --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/distance_in_arcmin_to_nearest_galaxy.py @@ -0,0 +1,30 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class distance_in_arcmin_to_nearest_galaxy(ContextFeatureExtractor): + """distance_in_arcmin_to_nearest_galaxy""" + active = True + extname = 'distance_in_arcmin_to_nearest_galaxy' #extractor's name + + cutoff = 100.0 ## arcmin + verbose = False + def extract(self): + n = self.fetch_extr('tmpned') + + #if not isinstance(n,ned.NED): + # self.ex_error("bad ned instance") + + try: + tmp = n.distance_in_arcmin_to_nearest_galaxy() + except: + return None # 20081010 dstarr adds try/except in case NED mysql cache server is down + + if tmp['distance'] is None or tmp['distance'] > self.cutoff: + ## JSB change to None because we assume we dont have a result here + rez = None + else: + rez = tmp['distance'] + if self.verbose: + print(tmp) + return rez + diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/distance_in_kpc_to_nearest_galaxy.py b/mltsp/TCP/Software/feature_extract/Code/extractors/distance_in_kpc_to_nearest_galaxy.py new file mode 100644 index 00000000..373e3ff3 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/distance_in_kpc_to_nearest_galaxy.py @@ -0,0 +1,32 @@ +from __future__ import print_function +from __future__ import absolute_import +from ..FeatureExtractor import ContextFeatureExtractor + +from . import ned + +class distance_in_kpc_to_nearest_galaxy(ContextFeatureExtractor): + """distance_in_kpc_to_nearest_galaxy""" + active = True + extname = 'distance_in_kpc_to_nearest_galaxy' #extractor's name + + cutoff = 1000.0 ## kpc + verbose = False + def extract(self): + n = self.fetch_extr('tmpned') + + #if not isinstance(n,ned.NED): + # self.ex_error("bad ned instance") + + try: + tmp = n.distance_in_kpc_to_nearest_galaxy() + except: + return None # 20081010 dstarr adds try/except in case NED mysql cache server is down + if tmp['distance'] is None or tmp['distance'] > self.cutoff: + ## JSB change to None because we assume we dont have a result here + rez = None + else: + rez = tmp['distance'] + if self.verbose: + print(tmp) + return rez + diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/eclipse_poly_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/eclipse_poly_extractor.py new file mode 100644 index 00000000..4fb1c1be --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/eclipse_poly_extractor.py @@ -0,0 +1,171 @@ +from ..FeatureExtractor import FeatureExtractor, InterExtractor + +import os, sys + +class eclipse_poly_extractor(InterExtractor): + """ +Josh's 20110809 eclipsing features code, which characterizes various +aspects of eclipsing timeseries. Applies a polyfit algorithm which is +not 2nd deriv continuous constrained as the splinefits in Nat's +lombscargle code. + +Requires GSL and requires C code compilation of polyfit.c into +polyfit.so for Python bindings. + + """ + #active = False # TODO probably want False + internal_use_only = False # if set True, then seems to run all X code for each sub-feature + active = True # if set False, then seems to run all X code for each sub-feature + extname = 'eclipse_poly' #extractor's name + + def extract(self): + """ Note: this is an internal feature which calculates data structures which + are used by other features. + """ + lomb_dict = self.fetch_extr('lomb_scargle') + + sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + \ + 'Algorithms/EclFeatures')) + import eclipse_features + a = eclipse_features.ebfeature(t=self.properties['data'][self.band]['input']['time_data'], + m=self.properties['data'][self.band]['input']['flux_data'], + merr=self.properties['data'][self.band]['input']['rms_data'], + srcid=self.properties['data'][self.band]['input']['srcid'], + allow_plotting=False, + fix_initial_period=True, + initial_period=1./lomb_dict['freq1_harmonics_freq_0']) + a.gen_orbital_period(doplot=False, dynamic=False, eclipse_shorter=True, choose_largest_numf=True)#, dynamic=True) + a.features['final_period_ratio'] = a.features['p_pulse'] * lomb_dict['freq1_harmonics_freq_0'] + #import pprint + #pprint.pprint(a.features) + #import pdb; pdb.set_trace() + #print + return a.features + + + +class eclpoly_best_orb_chi2_extractor(FeatureExtractor): + """ A feature generated by jbloom 20110809 eclipsing polyfit eclipse_features.py code. + """ + active = True + extname = 'eclpoly_best_orb_chi2' #extractor's name + def extract(self): + val = self.properties['data'][self.band]['inter']['eclipse_poly'].result['best_orb_chi2'] + return val + + +class eclpoly_best_orb_period_extractor(FeatureExtractor): + """ A feature generated by jbloom 20110809 eclipsing polyfit eclipse_features.py code. + """ + active = True + extname = 'eclpoly_best_orb_period' #extractor's name + def extract(self): + val = self.properties['data'][self.band]['inter']['eclipse_poly'].result['best_orb_period'] + return val + + +class eclpoly_15_ratio_diff_extractor(FeatureExtractor): + """ A feature generated by jbloom 20110809 eclipsing polyfit eclipse_features.py code. + """ + active = True + extname = 'eclpoly_15_ratio_diff' #extractor's name + def extract(self): + val = self.properties['data'][self.band]['inter']['eclipse_poly'].result['feature-15-ratio-diff'] + return val + + +class eclpoly_20_ratio_diff_extractor(FeatureExtractor): + """ A feature generated by jbloom 20110809 eclipsing polyfit eclipse_features.py code. + """ + active = True + extname = 'eclpoly_20_ratio_diff' #extractor's name + def extract(self): + val = self.properties['data'][self.band]['inter']['eclipse_poly'].result['feature-20-ratio-diff'] + return val + + +class eclpoly_30_ratio_diff_extractor(FeatureExtractor): + """ A feature generated by jbloom 20110809 eclipsing polyfit eclipse_features.py code. + """ + active = True + extname = 'eclpoly_30_ratio_diff' #extractor's name + def extract(self): + val = self.properties['data'][self.band]['inter']['eclipse_poly'].result['feature-30-ratio-diff'] + return val + + +class eclpoly_5_ratio_diff_extractor(FeatureExtractor): + """ A feature generated by jbloom 20110809 eclipsing polyfit eclipse_features.py code. + """ + active = True + extname = 'eclpoly_5_ratio_diff' #extractor's name + def extract(self): + val = self.properties['data'][self.band]['inter']['eclipse_poly'].result['feature-5-ratio-diff'] + return val + + +class eclpoly_8_ratio_diff_extractor(FeatureExtractor): + """ A feature generated by jbloom 20110809 eclipsing polyfit eclipse_features.py code. + """ + active = True + extname = 'eclpoly_8_ratio_diff' #extractor's name + def extract(self): + val = self.properties['data'][self.band]['inter']['eclipse_poly'].result['feature-8-ratio-diff'] + return val + + +class eclpoly_is_suspect_extractor(FeatureExtractor): + """ A feature generated by jbloom 20110809 eclipsing polyfit eclipse_features.py code. + """ + active = True + extname = 'eclpoly_is_suspect' #extractor's name + def extract(self): + val = self.properties['data'][self.band]['inter']['eclipse_poly'].result['is_suspect'] + return int(val) + + +class eclpoly_orb_signif_extractor(FeatureExtractor): + """ A feature generated by jbloom 20110809 eclipsing polyfit eclipse_features.py code. + """ + active = True + extname = 'eclpoly_orb_signif' #extractor's name + def extract(self): + val = self.properties['data'][self.band]['inter']['eclipse_poly'].result['orb_signif'] + return val + + +class eclpoly_p_pulse_extractor(FeatureExtractor): + """ A feature generated by jbloom 20110809 eclipsing polyfit eclipse_features.py code. + + This is essentially freq1, so we disable in __init__.py + """ + active = True + extname = 'eclpoly_p_pulse' #extractor's name + def extract(self): + val = self.properties['data'][self.band]['inter']['eclipse_poly'].result['p_pulse'] + return val + + +class eclpoly_p_pulse_initial_extractor(FeatureExtractor): + """ A feature generated by jbloom 20110809 eclipsing polyfit eclipse_features.py code. + This is essentially freq1, so we disable in __init__.py + """ + active = True + extname = 'eclpoly_p_pulse_initial' #extractor's name + def extract(self): + val = self.properties['data'][self.band]['inter']['eclipse_poly'].result['p_pulse_initial'] + return val + + +class eclpoly_final_period_ratio_extractor(FeatureExtractor): + """ A feature added by dstarr on 20120206: ratio of eclip poly fit final period with LS freq1 period. + This represents how different the eclipsing period is from freq1, without duplicating a freq1 feature for + most sources (which would give more power than intended). + + """ + active = True + extname = 'eclpoly_final_period_ratio' #extractor's name + def extract(self): + val = self.properties['data'][self.band]['inter']['eclipse_poly'].result['final_period_ratio'] + return val + diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/ecpb_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/ecpb_extractor.py new file mode 100644 index 00000000..e9c0778b --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/ecpb_extractor.py @@ -0,0 +1,15 @@ +from ..FeatureExtractor import ContextFeatureExtractor + +class ecpb_extractor(ContextFeatureExtractor): + """the Ecliptic coordinate b (latitude) in degrees""" + active = True + extname = 'ecpb' #extractor's name + + def extract(self): + posdict = self.fetch_extr('position_intermediate') + + if 'ecb' not in posdict or posdict['ecb'] is None: + self.ex_error("bad ecb in the intermediate extractor. check install of pyephem and input coordinate") + + return posdict['ecb'] + diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/ecpl_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/ecpl_extractor.py new file mode 100644 index 00000000..80249692 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/ecpl_extractor.py @@ -0,0 +1,15 @@ +from ..FeatureExtractor import ContextFeatureExtractor + +class ecpl_extractor(ContextFeatureExtractor): + """the Ecliptic coordinate l (longitude) in degrees""" + active = True + extname = 'ecpl' #extractor's name + + def extract(self): + posdict = self.fetch_extr('position_intermediate') + + if 'ecl' not in posdict or posdict['ecl'] is None: + self.ex_error("bad ecl in the intermediate extractor. check install of pyephem and input coordiantes") + + return posdict['ecl'] + diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/example_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/example_extractor.py new file mode 100644 index 00000000..6e24205e --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/example_extractor.py @@ -0,0 +1,17 @@ +from __future__ import absolute_import +from ..FeatureExtractor import FeatureExtractor + +from .common_functions.Example_Methods import Example_Methods + +class example_extractor(FeatureExtractor,Example_Methods): + """ Just an example extractor skeleton. For full example, see: + http://lyra.berkeley.edu/dokuwiki/doku.php?id=tcp:feature_testing + """ + internal_use_only = True + active = True + extname = 'example' # identifier used in final extracted value dict. + def extract(self): + ls_result_dict = self.fetch_extr('lomb_scargle') + median_val = self.fetch_extr('median') # fetches the result from the media extractor (median_val is now the media value of the timecurve) + summed_val = self.example_main_method(self.flux_data,lambda x: median_val,x=self.time_data,rms=self.rms_data) # returns sum(self.flux_data) + return float(summed_val)/median_val diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/first_freq_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/first_freq_extractor.py new file mode 100644 index 00000000..f833f293 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/first_freq_extractor.py @@ -0,0 +1,18 @@ +from __future__ import absolute_import +from ..FeatureExtractor import FeatureExtractor +from .common_functions import plot_methods + +from .common_functions.plot_methods import plot_vertical_line + +class first_freq_extractor(plot_vertical_line,FeatureExtractor): + """grabs the highest frequency from an fft or lomb power spectrum""" + active = True + extname = 'first_freq' #extractor's name + def extract(self): + power = self.fetch_extr('power') + freqs = self.frequencies + max_index = power[1:].argmax() + 1 #power[1:len(power)/2+1].argmax() + 1 + max_freq = freqs[max_index] + #20080123dstarr comments: #assert max_freq < 0.5, "maximum frequency higher than 0.5" +# print "max_freq", max_freq, type(max_freq) + return max_freq diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/first_lomb_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/first_lomb_extractor.py new file mode 100644 index 00000000..81968e30 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/first_lomb_extractor.py @@ -0,0 +1,16 @@ +from __future__ import absolute_import +from ..FeatureExtractor import FeatureExtractor + +from .first_freq_extractor import first_freq_extractor +from .common_functions.plot_methods import plot_vertical_line + +class first_lomb_extractor(first_freq_extractor): + """ extracts the first frequency from the lomb periodogram""" + active = False + extname = 'first_lomb' #extractor's name + def extract(self): + power = self.fetch_extr('lomb') + freqs = self.frequencies + max_index = power[1:].argmax() + 1 + max_freq = freqs[max_index]#freqs[max_index] + return max_freq diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/fourier_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/fourier_extractor.py new file mode 100644 index 00000000..9e0ab7c3 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/fourier_extractor.py @@ -0,0 +1,25 @@ +from ..FeatureExtractor import InterExtractor +import numpy +from numpy import random, round +from scipy import fftpack, stats, optimize +try: + from pylab import * +except: + pass +from .common_functions import * +from .common_functions.plot_methods import plot_vs_frequencies + +class fourier_extractor(plot_vs_frequencies,InterExtractor): + active = 1 + active = False + extname = 'fourier' #extractor's name + def extract(self): + self.test_even() + #frequencies = self.frequencies #self.time_data / len(self.time_data) + fft = fftpack.fft(self.flux_data) + return fft + def test_even(self): + for x in range(len(self.time_data)-3): + slicex = self.time_data[x:x+3] # slice with three elements + if round((slicex[2] - slicex[1]),2) != round((slicex[1] - slicex[0]),2): + self.ex_error("Unevenly Spaced Data") diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/fourierextractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/fourierextractor.py new file mode 100644 index 00000000..7ab74ba5 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/fourierextractor.py @@ -0,0 +1,27 @@ +# TODO remove? +from __future__ import absolute_import +from ..FeatureExtractor import InterExtractor +import numpy +from numpy import random, round +from scipy import fftpack, stats, optimize +try: + from pylab import * +except: + pass +from .common_functions import * +from .common_functions.plot_methods import plot_vs_frequencies + +class fourierextractor(plot_vs_frequencies,InterExtractor): + active = 1 + active = False + extname = 'fourier' #extractor's name + def extract(self): + self.test_even() + #frequencies = self.frequencies #self.time_data / len(self.time_data) + fft = fftpack.fft(self.flux_data) + return fft + def test_even(self): + for x in range(len(self.time_data)-3): + slicex = self.time_data[x:x+3] # slice with three elements + if round((slicex[2] - slicex[1]),2) != round((slicex[1] - slicex[0]),2): + self.ex_error("Unevenly Spaced Data") diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/frequency_ratio_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/frequency_ratio_extractor.py new file mode 100644 index 00000000..0c8f8407 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/frequency_ratio_extractor.py @@ -0,0 +1,25 @@ +from ..FeatureExtractor import FeatureExtractor + +class ratio21(FeatureExtractor): + """ Computes the ratio from the second frequency to the first frequency""" + active = True + extname = 'ratio21' #extractor's name + topex = 'second' + bottomex = 'first_freq' + def extract(self): + top = self.fetch_extr(self.topex) + bottom = self.fetch_extr(self.bottomex) + result = top/bottom + return result +class ratio31(ratio21): + """ Computes the ratio from the third frequency to the first frequency""" + active = True + extname = 'ratio31' #extractor's name + topex = 'third' + bottomex = 'first_freq' +class ratio32(ratio21): + """ Computes the ratio from the third frequency to the second frequency""" + active = True + extname = 'ratio32' #extractor's name + topex = 'third' + bottomex = 'second' diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/galb_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/galb_extractor.py new file mode 100644 index 00000000..eda74a10 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/galb_extractor.py @@ -0,0 +1,15 @@ +from ..FeatureExtractor import ContextFeatureExtractor + +class galb_extractor(ContextFeatureExtractor): + """the Galactic coordinate b (latitude) in degrees""" + active = True + extname = 'galb' #extractor's name + + def extract(self): + posdict = self.fetch_extr('position_intermediate') + + if 'galb' not in posdict or posdict['galb'] is None: + self.ex_error("bad gal-b in the intermediate extractor. check install pyephem and input coordinates") + + return posdict['galb'] + diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/gall_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/gall_extractor.py new file mode 100644 index 00000000..e8df93bb --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/gall_extractor.py @@ -0,0 +1,15 @@ +from ..FeatureExtractor import ContextFeatureExtractor + +class gall_extractor(ContextFeatureExtractor): + """the Galactic coordinate l (longitude) in degrees""" + active = True + extname = 'gall' #extractor's name + + def extract(self): + posdict = self.fetch_extr('position_intermediate') + + if 'gall' not in posdict or posdict['gall'] is None: + self.ex_error("bad gal-l in the intermediate extractor. check install of pyephem and input coordinates") + + return posdict['gall'] + diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/geohash2.py b/mltsp/TCP/Software/feature_extract/Code/extractors/geohash2.py new file mode 100644 index 00000000..29c9efd9 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/geohash2.py @@ -0,0 +1,215 @@ +#!/usr/bin/python +""" + implementation of http://en.wikipedia.org/wiki/Geohash + + This module was written in May 2008 by Schuyler Erle and is made + available in the public domain. Cheers to Christopher Schmidt for + suggesting it. Works in Python 2.3 and up. Run it on the command + line to try the doctests. Please send patches, comments, etc. to + schuyler@geocoder.us. + + You can instantiate a Geohash object with a (lon,lat) tuple, or with + a string. The bbox() and point() methods return the bounding box + and the center point of the area indicated by the geohash. Casting + the Geohash object to a string gives the hash itself. + + Adding two Geohash objects together gives the Geohash of their + minimal bounding box. + + The Geohash class inherits from a Geostring class, which provides an + identical API, but returns strings consisting of interleaved 0 and + 1 bits. Geostring is a less compact representation than the Geohash + class, but *much* more suitable for use when storing and comparing + bounding boxes in a B-Tree or similar index. If you are storing + bounding boxes in a database and space is not a major concern, you + should use the Geostring class instead of the Geohash. + +>>> hash = Geohash((-0.25, 51.5)) +>>> str(hash) +'gcpufr3cnxf6q' +>>> hash.bbox() +(-0.25, 51.5, -0.25, 51.5) +>>> hash.point() +(-0.25, 51.5) +>>> hash.bbox(4) +(-0.35156300000000001, 51.328125, 0.0, 51.503906000000001) + +*** note, floating point representations are weird +>>> -0.35156300000000001 == -0.351563 +True + +>>> hash = Geohash('9q8yykc03ycmh') +>>> hash.bbox() +(-122.41920399999999, 37.775196000000001, -122.41920399999999, 37.775196000000001) +>>> hash.point() +(-122.41920399999999, 37.775196000000001) +>>> hash.bbox(3) +(-123.75, 36.5625, -120.9375, 37.96875) +>>> union = hash + Geohash('9q8yze443z') +>>> str(union) +'9q8y' + +*** see note above +>>> -122.419204 == -122.41920399999999 +True + +A degenerate example, but at least it works lexically: + +>>> hash = Geoindex((-0.25,51.5),depth=8) +>>> str(hash) +'0222202022202022' +>>> hash.bbox() +(-1.40625, 51.328125, 0.0, 52.03125) +>>> hash2 = Geoindex((0.25,52.5),depth=8) +>>> str(hash2) +'2202000002000200' +>>> str(hash+hash2) +'1111111111111111' +>>> str(hash+hash2) > str(hash) +True +>>> str(hash+hash2) < str(hash2) +True + +>>> hash = Geostring((-0.25,51.5),depth=8) +>>> str(hash) +'0111101011101011' +>>> hash.bbox() +(-1.40625, 51.328125, 0.0, 52.03125) + +Some degenerate cases: + +>>> west = Geostring("0") +>>> west.bbox() +(-180.0, -90.0, 0.0, 90.0) +>>> east = Geostring("1") +>>> east.bbox() +(0.0, -90.0, 180.0, 90.0) +>>> str(east+west) +'' +>>> (east+west).bbox() +(-180.0, -90.0, 180.0, 90.0) +""" +from __future__ import print_function +from functools import reduce + +defbound = (0,-90,360,90) # (-180,-90,180,90) ## make this more astronomy friendly Josh Bloom +defdepth = 35 + +class Geostring (object): + def _to_bits (cls,f,depth=defdepth): + f *= (1 << depth) + return [(long(f) >> (depth-i)) & 1 for i in range(1,depth+1)] + _to_bits = classmethod(_to_bits) + + def bitstring (cls, xxx_todo_changeme,bound=defbound,depth=defdepth): + (x,y) = xxx_todo_changeme + x = cls._to_bits((x-bound[0])/float(bound[2]-bound[0]),depth) + y = cls._to_bits((y-bound[1])/float(bound[3]-bound[1]),depth) + bits = reduce(lambda x,y:x+list(y), zip(x,y), []) + return "".join(map(str,bits)) + bitstring = classmethod(bitstring) + + def __init__ (self, data, bound=defbound, depth=defdepth): + self.bound = bound + self.depth = depth + self.origin = bound[0:2] + self.size = (bound[2]-bound[0], bound[3]-bound[1]) + if isinstance(data,tuple) or isinstance(data,list): + self.hash = self.bitstring(data,bound,depth) + else: + self.hash = data + + def __str__ (self): + return self.hash + + def _to_bbox (self, bits): + depth = len(bits)/2 + minx = miny = 0.0 + maxx = maxy = 1.0 + for i in range(depth+1): + try: + minx += float(bits[i*2])/(2<>(4-i)&1 for i in range(5)] + for n in map(self.BASE_32.find, self.hash[:prefix])] + bits = reduce(lambda x,y:x+y, bits, []) + return self._to_bbox(bits) + +if __name__ == "__main__": + import sys + if len(sys.argv) == 1: + import doctest + doctest.testmod(verbose=True) + elif len(sys.argv) == 2: + print(Geohash(sys.argv[1]).bbox()) + else: + print(Geohash(map(float, sys.argv[1:3]))) \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/gskew_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/gskew_extractor.py new file mode 100644 index 00000000..16b8d406 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/gskew_extractor.py @@ -0,0 +1,20 @@ +from ..FeatureExtractor import FeatureExtractor +from numpy import median, sort, round +class gskew_extractor(FeatureExtractor): + """ Doesnt really have anything to do with skew at all. I tested this on the RCBs that we found and its clear that they all have fairly negative ghetto skew. Of course, for RCBs near the detection limit this wont be that helpful, and for highly active RCBs it may not be great, but for your typical RCB is should separate out from other things. Im curious to see what this produces for SRPV and Mira sources. + +Note - Ive arbitrarily selected 3 percent here. It could be 5 percent or 10, we should probably test a few different values to see how much difference it makes. + + """ + active = True + extname = 'gskew' # identifier used in final extracted value dict. + def extract(self): + """2012-06-12 Adam Miller coded + """ + medmag = median(self.flux_data) + sortmag = sort(self.flux_data) + three_per = int(round(0.03*len(self.flux_data))) +# TODO don't love this name... +# TODO this is just 2*np.median(x) - np.percentile(x, 98.5) - np.percentile(x, 1.5) w/ weird rounding of indices + ghetto_skew = (medmag - median(sortmag[-three_per:])) + (medmag-median(sortmag[0:three_per])) + return ghetto_skew diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/interng_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/interng_extractor.py new file mode 100644 index 00000000..64f87744 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/interng_extractor.py @@ -0,0 +1,46 @@ +from __future__ import absolute_import +from ..FeatureExtractor import ContextInterExtractor + +#from power_extractor import power_extractor as power_extractor +from . import ng + +class interng_extractor(ContextInterExtractor): + """intermediate call to the ng 200 Mpc galaxy server + should get something like + {'closest_in_light': 0.17192566500632125, + 'closest_in_light_absolute_bmag': -22.190000000000005, + 'closest_in_light_angle_from_major_axis': -125.26712677240506, + 'closest_in_light_dm': 33.200000000000003, + 'closest_in_light_physical_offset_in_kpc': 7.9431382807271822, + 'closest_in_light_position': (154.2234, 73.400639999999996), + 'closest_in_light_semimajor_r25_arcsec': 244.42816668246769, + 'closest_in_light_semiminor_r25_arcsec': 208.04211027151902, + 'closest_in_light_ttype': 3.8999999999999999, + 'closest_in_lightangular_offset_in_arcmin': 0.62555572920077607, + 'closest_units': 'galaxy_surface_brightness'} + + Explanation: + closest_in_light = "the nearest galaxy, in units of that galaxies r25 surface brightness ellipse". Less than ~1.5 should indicate that the source is reasonably sure of being physically associated with that galaxy + + closest_in_light_absolute_bmag: extinction corrected absolute magnitude of the nearest galaxy + + closest_in_light_angle_from_major_axis: offset angle from the semi-major axis (I'd think that cc would be +-20 deg around 0 deg and 180 deg. + + closest_in_light_dm: best distance modulus known + + closest_in_light_ttype: galaxy t-type if reasonable certain otherwise None + """ + active = True + extname = 'interng' #extractor's name + + n = None + def extract(self): + posdict = self.fetch_extr('position_intermediate') + + if 'ra' not in posdict or posdict['dec'] is None: + self.ex_error("bad RA or DEC in the intermediate ng extractor. check install pyephem and input coordinates") + + if not self.n: + self.n = ng.get_closest_by_light(pos=(posdict['ra'],posdict['dec'])) + + return self.n diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/intersdss_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/intersdss_extractor.py new file mode 100644 index 00000000..11e90de4 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/intersdss_extractor.py @@ -0,0 +1,57 @@ +from __future__ import absolute_import +from ..FeatureExtractor import ContextInterExtractor + +#from power_extractor import power_extractor as power_extractor +from . import sdss + +class intersdss_extractor(ContextInterExtractor): + """intermediate call to the sdss server + {'best_dl': 4455.1248913899999, + 'best_dm': 43.244299416029733, + 'best_offset_in_kpc': 7.0559739096982508, + 'bestz': 0.72108298999999998, + 'dec': 0.79265430000000003, + 'dered_g': 19.441016999999999, + 'dered_i': 19.391967999999999, + 'dered_r': 19.373417, + 'dered_u': 19.644580999999999, + 'dered_z': 19.353552000000001, + 'dist_in_arcmin': 0.016127780000000001, + 'in_footprint': True, + 'objid': 587731513946275936L, + 'photo2_flag': None, + 'photo2_z_cc': None, + 'photo2_z_d1': None, + 'photo2_zerr_cc': None, + 'photo2_zerr_d1': None, + 'photo_z': None, + 'photo_zerr': None, + 'ra': 15.008567559999999, + 'spec_confidence': 0.58286400000000005, + 'spec_veldisp': None, + 'spec_z': 0.72108300000000003, + 'spec_zStatus': 'xcorr_loc', + 'type': 'qso', + 'url': 'http://cas.sdss.org/astrodr7/en/tools/explore/obj.asp?id=587731513946275936', + 'urlalt': 'http://cas.sdss.org/astrodr7/en/tools/chart/navi.asp?ra=15.008568&dec=0.792654'} + + """ + active = True + extname = 'intersdss' #extractor's name + verbose = True + n = None + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + def extract(self): + posdict = self.fetch_extr('position_intermediate') + + if 'ra' not in posdict or posdict['dec'] is None: + self.ex_error("bad RA or DEC in the intermediate ng extractor. check install pyephem and input coordinates") + + #20090126 dstarr comments this conditional out: #if not self.n: + if 'sdss_internal_struct' in self.properties['data']['multiband']['inter']: + self.s = self.properties['data']['multiband']['inter']['sdss_internal_struct'] + else: + self.s = sdss.sdssq(pos=(posdict['ra'],posdict['dec']),verbose=self.verbose,maxd=self.light_cutoff*1.05) + + return self.s.feature diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/jansky_flux_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/jansky_flux_extractor.py new file mode 100644 index 00000000..5bf88606 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/jansky_flux_extractor.py @@ -0,0 +1,84 @@ +from __future__ import print_function +from ..FeatureExtractor import InterExtractor +from numpy import * + +class jansky_flux_extractor(InterExtractor): + """ Convert the flux from magnitudes to janskies """ + active = True + extname = 'jansky_flux' #extractor's name + # def extract(self): + # table1 = { \ + # "u": {"central":3650 , "width":680 , "f_lambda(0)": 4.27e-9 , "f_nu(0)": 1.90e-23, "constant": 13.84}, \ + # "b": {"central":4400 , "width":980 , "f_lambda(0)": 6.61e-9 , "f_nu(0)": 4.27e-23, "constant": 12.97}, \ + # "v": {"central":5500 , "width":890 , "f_lambda(0)": 3.64e-9 , "f_nu(0)": 3.67e-23, "constant": 13.72}, \ + # "r": {"central":7000 , "width":2200 , "f_lambda(0)": 1.74e-9 , "f_nu(0)": 2.84e-23, "constant": 13.54}, \ + # "i": {"central":9000 , "width":2400 , "f_lambda(0)": 8.32e-10, "f_nu(0)": 2.25e-23, "constant": 14.25}, \ + # "j": {"central":12500, "width":3000 , "f_lambda(0)": 3.18e-10, "f_nu(0)": 1.65e-23, "constant": 15.05}, \ + # "h": {"central":16500, "width":4000 , "f_lambda(0)": 1.18e-10, "f_nu(0)": 1.07e-23, "constant": 15.82}, \ + # "k": {"central":22000, "width":6000 , "f_lambda(0)": 4.17e-11, "f_nu(0)": 6.73e-24, "constant": 16.50}, \ + # "l": {"central":36000, "width":12000, "f_lambda(0)": 6.23e-12, "f_nu(0)": 2.69e-24, "constant": 17.82}, \ + # } # table 1 from Misconceptions About Astronomical Magnitudes," E. Schulman and C. V. Cox, American Journal of Physics, Vol. 65, pg. 1003 (1997). + # watts_m2 = self.fetch_extr('watt_per_m2_flux') + # centerA = table1[self.band.lower()]['central'] # Angstrom + # width = table1[self.band.lower()]['width'] # Angstrom + # minA = centerA - width / 2 + # maxA = centerA + width / 2 + # minm = minA * 1e-10 # Angstrom to m + # maxm = maxA * 1e-10 + # c = 299792458 # m / s + # maxHz = c / minm + # minHz = c / maxm + # width_Hz = maxHz - minHz + # jansky = watts_m2 / width_Hz # Jansky = W / m^2 / Hz + # return jansky + def extract(self): + try: + unit = self.flux_data_unit + except NameError: + unit = self.assumed_unit + if unit in ['mag','mags','magnitude']: + """ table from http://ssc.spitzer.caltech.edu/tools/magtojy/ref.html """ + zero_magnitude_fluxes = { \ + "u": 1823, \ + "b": 4130, \ + "v": 3781, \ + "r": 2941, \ + "i": 2635, \ + "j": 1603, \ + "h": 1075, \ + "k": 667 , \ + "l": 288 , \ + "m": 170 , \ + "n": 36 , \ + "o": 9.4 } + try: + """ This is inside a try/except to catch the possibility of the band not being in the zero_magnitude_fluxes dictionary """ + zero_magnitude = zero_magnitude_fluxes[self.band.lower()] + except KeyError: + self.ex_error("Band %s not found" % (self.band)) + """ follow the conversion method from http://ircamera.as.arizona.edu/astr_250/Lectures/Lec13_sml.htm """ + janskies = zero_magnitude * power(10, self.flux_data / (-2.5) ) + self.uncertainty = self.uncer_calc(janskies) + return janskies + else: + print("units not recognized", self.flux_data_unit, self.extname) + self.uncertainty = self.rms_data + return self.flux_data # else assume it's already in those units, no unit conversion implemented for the moment + def uncer_calc(self, flux_wm2): + """ calculate the uncertainty in the SI flux + Latex for the approximation (valid if the flux uncertainty is less than 10%): + + \sigma_m &=& \sqrt{ \sigma_{m,higher} \times \sigma_{m, lower}} \\ + &=& \sqrt{ \left( 2.5 \log_{10}(f - \sigma_f) - 2.5\log_{10}f \right) \times \left( -2.5 \log_{10}(f + \sigma_f) + 2.5\log_{10}f \right) } \\ + &=& \sqrt{ -2.5^2 \log_{10}\left(\frac{f}{f-\sigma_f}\right) \left(\log_{10}\frac{f}{f+\sigma_f}\right)} \\ + &=& \sqrt{ -2.5^2 \log_{10}\left(\frac{f-\sigma_f}{f}\right) \log_{10}\left(\frac{f+\sigma_f}{f}\right)} \\ + &=& 2.5\sqrt{ -\log_{10}\left( 1 - \frac{\sigma_f}{f} \right) \log_{10}\left( 1 + \frac{\sigma_f}{f} \right)} \\ + &=& \frac{2.5 }{\ln 10} \sqrt{ -\ln\left( 1 - \frac{\sigma_f}{f} \right) \ln\left( 1 + \frac{\sigma_f}{f} \right)} \\ + &\approx & \frac{2.5 }{\ln 10} \sqrt{ \left(\frac{\sigma_f}{f} \right) ^2 } \\ + &\approx & \frac{\sigma_f}{f} + + """ + f = flux_wm2 + sigmam = self.rms_data + sigmaf = f * sigmam + return sigmaf \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/kurtosis_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/kurtosis_extractor.py new file mode 100644 index 00000000..3d25ce57 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/kurtosis_extractor.py @@ -0,0 +1,12 @@ +from ..FeatureExtractor import FeatureExtractor + +from scipy import stats + +class kurtosis_extractor(FeatureExtractor): + """ calculates the kurtosis of the signal using scipy.stats.kurtosis + """ + active = True + extname = 'kurtosis' #extractor's name + def extract(self): + kurtosis = stats.kurtosis(self.flux_data) + return kurtosis diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/lcmodel_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/lcmodel_extractor.py new file mode 100644 index 00000000..440c5fb5 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/lcmodel_extractor.py @@ -0,0 +1,372 @@ +from ..FeatureExtractor import FeatureExtractor, InterExtractor + +import numpy as np +import scipy.integrate + +class lcmodel_extractor(InterExtractor): + """ Base class for the unfolded lightcurve model features. + dstarr coded 2012-07-20 + + Gaussian Kernel smoothing adapted from I.Shivvers' delta_phase_2minima_extractor.py + """ + internal_use_only = False # if set True, then seems to run all X code for each sub-feature + active = True # if set False, then seems to run all X code for each sub-feature + extname = 'lcmodel' #extractor's name + + def debug_compare_gp_and_shivv_models(self, X, y, dy, shivv_model, debug_x=[], debug_y=[], pos_delta_mag=None, neg_delta_mag=None): + """ Compare & Plot the fit generated by Shivver's code and the + scikits GaussianProcess function + + For debugging only. + """ + ##### + from sklearn.gaussian_process import GaussianProcess + from matplotlib import pyplot as pl + + t_min = np.min(X) + t_max = np.max(X) + x = np.atleast_2d(np.linspace(t_min, t_max, 10000)).T + + X = np.atleast_2d(X).T + + ### Instanciate a Gaussian Process model + #gp = GaussianProcess(corr='squared_exponential', theta0=1e-3, + # thetaL=1e-3, thetaU=1, + # nugget=(dy / y) ** 2, + # random_start=20) + gp = GaussianProcess(corr='absolute_exponential', theta0=1e-3, + nugget=(dy / y) ** 2, + random_start=10) + # Fit to data using Maximum Likelihood Estimation of the parameters + gp.fit(X, y) + + # Make the prediction on the meshed x-axis (ask for MSE as well) + y_pred, MSE = gp.predict(x, eval_MSE=True) + sigma = np.sqrt(MSE) + # Plot the function, the prediction and the 95% confidence interval based on + # the MSE + from matplotlib import rcParams + rcParams.update({'legend.fontsize':8}) + ms = 4 + fig = pl.figure() + pl.plot(x, [np.median(y)]*len(x), 'c', label='median') + #pl.plot(x, [np.mean(y)]*len(x), 'm', label=u'mean') + #pl.plot(X, y, 'r:', label=u'orig $m(t)$') + pl.errorbar(X.ravel(), y, dy, fmt='ro', ms=5, label='Observations') + pl.plot(X, shivv_model, 'g', ms=ms, label='Shivvers model') + pl.plot(X, shivv_model, 'go', ms=ms) + pl.plot(x, y_pred, 'b-', ms=ms, label='exp corr prediction') + pl.fill(np.concatenate([x, x[::-1]]), \ + np.concatenate([y_pred - 1.9600 * sigma, + (y_pred + 1.9600 * sigma)[::-1]]), \ + alpha=.5, fc='b', ec='None', label='95% confidence interval') + if len(debug_x) > 0: + pl.plot(debug_x, debug_y, 'y*', ms=10, label='cross threshhold') + if pos_delta_mag != None: + pl.plot(x, [pos_delta_mag]*len(x), 'y', label='median delta thresh') + pl.plot(x, [neg_delta_mag]*len(x), 'y', label='median delta thresh') + + + pl.xlabel('$t$') + pl.ylabel('$m(t)$') + pl.legend(loc='upper left') + srcid = 244888 + img_fpath = '/home/dstarr/scratch/lcmodel_feature_asas_examples/%d.png' % (srcid) + pl.title("Source ID=%d" % (srcid)) + pl.savefig(img_fpath) + #import os + #os.system("eog %s" % (img_fpath)) + #pl.show() + #import pdb; pdb.set_trace() + #print + + +# TODO abomination + def get_dmag_at_median_threshold(self, sign, normalized_model_mags): + """ + """ + if sign == 'pos': + thresh_list = np.linspace(0,np.max(normalized_model_mags),20) + elif sign == 'neg': + thresh_list = np.linspace(np.min(normalized_model_mags),0,20) + thresh_passthru = np.zeros(len(thresh_list), dtype=np.int) + for i_th, thresh in enumerate(thresh_list): + if sign == 'pos': + past_thresh = np.where(normalized_model_mags > thresh)[0] + elif sign == 'neg': + past_thresh = np.where(normalized_model_mags < thresh)[0]#[::-1] + j_prev = -10 # some value < -1 + for j in past_thresh: + if j-1 == j_prev: + j_prev = j + continue # we've already caught this threshold passthrough + thresh_passthru[i_th] += 1 + j_prev = j + + sum_n_passthru_2 = np.sum(thresh_passthru)/2. + cumul = 0 + delta_mag_median = 0. # This should always get a found value. + for i, n in enumerate(thresh_passthru): + cumul += n + if cumul >= (sum_n_passthru_2): + delta_mag_median = thresh_list[i] + break + + """ + from matplotlib import pyplot as pl + fig = pl.figure() + pl.plot(thresh_list, thresh_passthru, 'bo') + pl.xlabel('(threshold mag - median mag)') + pl.ylabel('N intersects for single direction') + + srcid = 244888 + pl.title("Source ID=%d" % (srcid)) + img_fpath = '/home/dstarr/scratch/lcmodel_feature_asas_examples/%d_%s_thresh.png' % (srcid, sign) + pl.savefig(img_fpath) + #pl.show() + pl.clf() + """ + + return {'delta_mag_median':delta_mag_median, + 'n_thresh_median':n} + + +# TODO off by 1? + def get_n_passing_median(self, normalized_model_mags): + """ Get number of positive slope intersections of delta_mag=0.0 median + """ + past_thresh = np.where(normalized_model_mags > 0.0)[0] + n_thresh_at_median = 0 + j_prev = -10 # some value < -1 + for j in past_thresh: + if j-1 == j_prev: + j_prev = j + continue # we've already caught this threshold passthrough + n_thresh_at_median += 1 + j_prev = j + return n_thresh_at_median + + + def get_area(self, sign, m, t): + """ Get area under model, for the postive (above median) and negative parts. + + I break the ( > median) points into segments which I integrate + over using trapazoid method. Otherwise, the interpolation + over ( < median) gaps would skew the results. + """ + if sign == 'pos': + past_thresh = np.where(m > 0.0)[0] + elif sign == 'neg': + past_thresh = np.where(m < 0.0)[0] + total_area = 0. + t_segment = [t[past_thresh[0]]] + m_segment = [m[past_thresh[0]]] + j_prev = past_thresh[0] + #import pdb; pdb.set_trace() + #print + for j in past_thresh[1:]: + if j-1 == j_prev: + j_prev = j + t_segment.append(t[j]) + m_segment.append(m[j]) + else: + if len(t_segment) >= 2: + total_area += scipy.integrate.trapz(m_segment, t_segment) + # then we integrate the current segment if long enough + t_segment = [t[j]] + m_segment = [m[j]] + j_prev = j + if len(t_segment) >= 2: + total_area += scipy.integrate.trapz(m_segment, t_segment) + return total_area + + + def extract(self): + """ Base, initial internal extractor for the unfolded lightcurve model features. + """ + try: + # get info + t = self.time_data + m = self.flux_data + + # find the proper window for the model + best_GCV, optimal_window = self.minimize_GCV(t, m) + bandwidth = 500*float(optimal_window)/len(t) # 1000 too smooth, 100 is ok but a little too coupled to orig data. + shivv_model = self.kernelSmooth(t, m, bandwidth) + + m_median = np.median(m) + normalized_model_mags = shivv_model - m_median + + n_thresh_at_median = self.get_n_passing_median(normalized_model_mags) + pos_dict = self.get_dmag_at_median_threshold('pos', normalized_model_mags) + neg_dict = self.get_dmag_at_median_threshold('neg', normalized_model_mags) + + pos_area = self.get_area('pos', normalized_model_mags, t) + neg_area = self.get_area('neg', normalized_model_mags, t) + + #self.debug_compare_gp_and_shivv_models(t, m, self.rms_data, shivv_model, + # pos_delta_mag=pos_dict['delta_mag_median'] + m_median, + # neg_delta_mag=neg_dict['delta_mag_median'] + m_median) + + + delta_t = np.max(t) - np.min(t) + + self.lc_feats = {'pos_mag_ratio': pos_dict['delta_mag_median']/(pos_dict['delta_mag_median'] + abs(neg_dict['delta_mag_median'])), + 'pos_n_ratio': pos_dict['n_thresh_median']/float(pos_dict['n_thresh_median'] + neg_dict['n_thresh_median']), + 'median_n_per_day': n_thresh_at_median / delta_t, + 'pos_n_per_day': pos_dict['n_thresh_median'] / delta_t, + 'neg_n_per_day': neg_dict['n_thresh_median'] / delta_t, + 'pos_area_ratio': pos_area / (pos_area + abs(neg_area)), + } + except: + self.lc_feats = {} + return self.lc_feats + + + def fold(self, times, period): + ''' return phases for folded at ''' + t0 = times[0] + phase = ((times-t0)%period)/period + return phase + + def rolling_window(self, b, window): + """ Call: np.mean(rolling_window(observations, n), 1) + """ + # perform smoothing using strides trick + shape = b.shape[:-1] + (b.shape[-1] - window + 1, window) + strides = b.strides + (b.strides[-1],) + return np.lib.stride_tricks.as_strided(b, shape=shape, strides=strides) + + def GCV(self, window, X,Y): + # put in proper order + zpm = list(zip(X,Y)) + zpm.sort() + zpm_arr = np.array(zpm) + phs = zpm_arr[:,0] + mags = zpm_arr[:,1] + # handle edges properly by extending array in both directions + if window>1: + b = np.concatenate((mags[-window/2:], mags, mags[:window/2-1])) + else: + b = mags + # calculate smoothed model and corresponding smoothing matrix diagonal value + model = np.mean(self.rolling_window(b, window), 1) + Lii = 1./window + # return the Generalized Cross-Validation criterion + GCV = 1./len(phs) * np.sum( ((mags-model)/(1.-Lii))**2 ) + return GCV + + def minimize_GCV(self,X,Y, window_range=(10,50,2)): + ''' quick way to pick best GCV value ''' + windows = np.arange(*window_range) + GCVs = np.array( [self.GCV(window, X,Y) for window in windows] ) + best_GCV = np.min(GCVs) + optimal_window = windows[ np.argmin(GCVs) ] + return best_GCV, optimal_window + + def GaussianKernel(self, x): + return (1./np.sqrt(2.*np.pi)) * np.exp(-x**2 / 2.) + + def kernelSmooth(self, X, Y, bandwidth): + ''' slow implementation of gaussian kernel smoothing ''' + L = np.zeros([len(Y),len(X)]) + diags = [] + for i in range(len(X)): + diff = abs(X[i] - X) + # wrap around X=1; i.e. diff cannot be more than .5 + diff[diff>.5] = 1. - diff[diff>.5] + # renormalize, and operate on l vector + l = diff/bandwidth + # calculate the Gaussian for the values within 4sigma and plug it in + # anything beyond 4sigma is basically zero + tmp = self.GaussianKernel(l[l<4]) + diags.append(np.max(tmp)) + L[i,l<4] = tmp/np.sum(tmp) + # model is the smoothing matrix dotted into the data + return np.dot(L, Y.T) + + def find_peaks(self, x): + """ find peaks in """ + xmid = x[1:-1] # orig array with ends removed + xm1 = x[2:] # orig array shifted one up + xp1 = x[:-2] # orig array shifted one back + return np.where(np.logical_and(xmid > xm1, xmid > xp1))[0] + 1 + + + +class lcmodel_pos_mag_ratio_extractor(FeatureExtractor): + """ this is a ratio of positive threshold's delta magnitude divided +by the sum of both the positive and negative threshold's delta +magnitudes + """ + active = True + extname = 'lcmodel_pos_mag_ratio' #extractor's name + + def extract(self): + lc_feats = self.properties['data'][self.band]['inter']['lcmodel'].result + return lc_feats.get('pos_mag_ratio', 0.0) + + +class lcmodel_pos_n_ratio_extractor(FeatureExtractor): + """ this is the number of intersections through the positive +threshold divided by the sum of both the positive and negative +threshold's number of intersections + """ + active = True + extname = 'lcmodel_pos_n_ratio' #extractor's name + + def extract(self): + lc_feats = self.properties['data'][self.band]['inter']['lcmodel'].result + return lc_feats.get('pos_n_ratio', 0.0) + +class lcmodel_median_n_per_day_extractor(FeatureExtractor): + """ this is the number of intersections through the median, + divided by the total time span of observations, so that +short surveys or observations of a source can be compared with longer +baselined sources. + """ + active = True + extname = 'lcmodel_median_n_per_day' #extractor's name + + def extract(self): + lc_feats = self.properties['data'][self.band]['inter']['lcmodel'].result + return lc_feats.get('median_n_per_day', 0.0) + +class lcmodel_pos_n_per_day_extractor(FeatureExtractor): + """ this is the number of intersections through the positive +threshold, divided by the total time span of observations, so that +short surveys or observations of a source can be compared with longer +baselined sources. + """ + active = True + extname = 'lcmodel_pos_n_per_day' #extractor's name + + def extract(self): + lc_feats = self.properties['data'][self.band]['inter']['lcmodel'].result + return lc_feats.get('pos_n_per_day', 0.0) + +class lcmodel_neg_n_per_day_extractor(FeatureExtractor): + """ this is the number of intersections through the negative +threshold, divided by the total time span of observations, so that +short surveys or observations of a source can be compared with longer +baselined sources. + """ + active = True + extname = 'lcmodel_neg_n_per_day' #extractor's name + + def extract(self): + lc_feats = self.properties['data'][self.band]['inter']['lcmodel'].result + return lc_feats.get('neg_n_per_day', 0.0) + +class lcmodel_pos_area_ratio_extractor(FeatureExtractor): + """describes the area above the median in relation to the abs() combined area of +both above and below the median. In other words, it is +(magnitude-days area above median) / ( (magnitude-days area above +median) + abs(magnitude-days area below median)). + """ + active = True + extname = 'lcmodel_pos_area_ratio' #extractor's name + + def extract(self): + lc_feats = self.properties['data'][self.band]['inter']['lcmodel'].result + return lc_feats.get('pos_area_ratio', 0.0) diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/linear_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/linear_extractor.py new file mode 100644 index 00000000..dfd7180c --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/linear_extractor.py @@ -0,0 +1,23 @@ +from __future__ import absolute_import +from ..FeatureExtractor import InterExtractor +import numpy +from scipy import optimize +from .common_functions import ChiSquare + +# TODO just use weighted least squares, e.g. polyfit +class linear_extractor(InterExtractor,ChiSquare): # fits ax+b + ''' produces a linear fit, returns in the format 'a(slope), b (y-intercept) ''' + active = True + extname = 'linear' #extractor's name + def extract(self): + a = 0 + b = 0 + init = numpy.array([a,b]) + linear = optimize.fmin(self.linear_fit,init,args=(self.time_data,self.flux_data,self.rms_data),disp=0) + return(linear) + def linear_fit(self,ab,x,y,rms): + def linear(x): + return ab[0]*x +ab[1] + return self.chi_square_sum(y,linear,x=x,rms=rms) + def plot_feature(self,properties): + plot( self.time_data,properties[self.extname][1]+ properties[self.extname][0]* self.time_data, label=self.extname) diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/lomb_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/lomb_extractor.py new file mode 100644 index 00000000..4ea3cdd0 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/lomb_extractor.py @@ -0,0 +1,51 @@ +from __future__ import absolute_import +from ..FeatureExtractor import InterExtractor +from .common_functions import lomb_scargle +from .common_functions.plot_methods import plot_vs_frequencies +from numpy import arange + +# TODO what is this +class lomb_extractor(plot_vs_frequencies,InterExtractor): + """extracts a lomb scargle periodogram from the data""" + active = True + extname = 'lomb' #extractor's name + minpoints = 5 # minimum number of points for the extractor to run + def extract(self): + """ NOTE: lightcurve.py:L228 actually calls lomb_scargle.lomb() and generates the psd and final L.S. freqs which are used as features. + """ + self.test_uneven() + #noisedata = normal(loc=0,scale=5,size=int(round(freq*time))) + #noisetime = self.time_data + #noisedata = self.flux_data + var = { 'x': self.time_data, 'y': self.flux_data, 'ylabel': 'Amplitude', + 'xlabel':'Time (s)' } + #N= len(noisetime) + #dt = 1.0 #findAverageSampleTime(var,0) + #maxlogx = log(1/(2*dt)) # max frequency is the sampling rate + #minlogx = log(1/(max(var['x'])-min(var['x']))) #min frequency is 1/T + #frequencies = self.frequencies#exp(arange(N, dtype = float) / (N-1.) * (maxlogx-minlogx)+minlogx) + psd, freqs, signi, simsigni, psdpeaks = lomb_scargle.lomb(var['x'], + var['y'],freqin=self.frequencies,verbosity=0) + #20071206 dstarr comment out: + #result = psd + #import pdb; pdb.set_trace() + if 0: + ### TEST / DEBUGGING only: + from .common_functions import plot_analysis_psd + plot_analysis_psd.do_plot(psd, freqs, signi, simsigni, psdpeaks, x=var['x'], y=var['y']) + + result = psd + return result + def test_uneven(self): + uneven = False + for x in arange(len(self.time_data)-3): + slicex = self.time_data[x:x+3] # slice with three elements + if round((slicex[2] - slicex[1]),2) != round((slicex[1] - slicex[0]),2): + uneven = True + break + if not uneven: + self.ex_error("Evenly Spaced Data (don't waste my time!)") + + + + diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/lomb_scargle_alternative_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/lomb_scargle_alternative_extractor.py new file mode 100644 index 00000000..1ce4926c --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/lomb_scargle_alternative_extractor.py @@ -0,0 +1,103 @@ +# TODO what is this +""" This module is an implementation of lomb_scargle using type( """ +from __future__ import print_function +from __future__ import absolute_import + +from ..FeatureExtractor import FeatureExtractor +from ..FeatureExtractor import InterExtractor +from .common_functions.lomb_scargle import lomb +from .common_functions.pre_whiten import pre_whiten + +from pylab import * + +class lomb_scargle_extractor(InterExtractor): + """ wrapper for common_functions lomb_scargle and pre_whiten + """ + internal_use_only = False + active = False + extname = 'lomb_scargle' + def extract(self): + x = self.time_data + nx = len(x) + dx = zeros(nx,dtype=float) + y = self.flux_data + dy = self.rms_data + + time = x + time.sort + dt = median( time[1:]-time[:-1] ) + maxlogx = log(0.5/dt) # max frequency is ~ the sampling rate + minlogx = log(0.5/(time[-1]-time[0])) #min frequency is 0.5/T + # sample the PSD with 1% fractional precision + M=long(ceil( (maxlogx-minlogx)*100. )) + frequencies = exp(maxlogx-arange(M, dtype=float) / (M-1.) * (maxlogx-minlogx)) + num_freq_comps = 3 + out_dict={} + ytest=y + dof = len(x) + if (dof>=5): + + for i in range(num_freq_comps): + psd, freqs, signi, sim_signi, peak_sort = lomb(x,ytest,delta_time=dx, signal_err=dy,freqin=frequencies,verbosity=0) + imax = psd.argmax() + freq_max = freqs[imax] + ytest, harm_dict = pre_whiten(x, ytest, freq_max, delta_time=dx, signal_err=dy, dof=dof, nharm_min=4, nharm_max=100) + dstr = "freq%i" % (i+1) + out_dict['freq_searched_min']=min(freqs) + out_dict['freq_searched_max']=max(freqs) + out_dict[dstr] = freq_max + out_dict[dstr+"_signif"] = signi + #if (dof>0 and harm_dict['nharm']>0 and harm_dict['signif']>0): + # out_dict[dstr+"_harmonics"] = harm_dict + #else: + # out_dict[dstr+"_harmonics"] = {} + #dof = dof - harm_dict['nharm']*2. + + # 20080508: dstarr modifies harm_dict so it is a shallow dict which we can out_dict.update() + if (dof>0 and harm_dict['nharm']>0 and harm_dict['signif']>0): + for elem_k, elem_v in harm_dict.items(): + out_dict[dstr + "_harmonics_" + elem_k] = elem_v + # Do we even want to include this case as empty dict??? : + #else: + # out_dict[dstr+"_harmonics"] = {} + dof = dof - harm_dict['nharm']*2. + #print out_dict.keys() + return out_dict + +class lomb_generic(FeatureExtractor): + """ Generic lomb extractor grabs value from dictionary """ + internal_use_only = False + active = True + extname = 'to_be_overloaded' # identifier used in final extracted value dict. + lomb_key = 'to_be_overloaded' + def extract(self): + lomb_dict = self.fetch_extr('lomb_scargle') # fetches the dictionary from lomb_scargle_extractor with the useful lomb scargle results in it + # If lomb_dict is partially filled, most likely lomb couldn't compute completely due to FALSE condition: (dof>0 and harm_dict['nharm']>0 and harm_dict['signif']>0) + if self.lomb_key in lomb_dict: + return lomb_dict[self.lomb_key] # finds the correct keyword that this class is assigned to, this could be replaced by self.extname if it wasn't for the _alt + else: + self.exerror('Lomb Scargle Dictionary does not have key %s' % (self.lomb_key)) + +lomb_features = ['freq_searched_min', 'freq1_harmonics_rel_phase_error_1', 'freq1_harmonics_peak2peak_flux', 'freq1_harmonics_rel_phase_error_3', 'freq1_harmonics_rel_phase_error_2', 'freq1_harmonics_rel_phase_0', 'freq1_harmonics_rel_phase_1', 'freq1_harmonics_rel_phase_2', 'freq1_harmonics_rel_phase_3', 'freq1_harmonics_amplitude_2', 'freq1_harmonics_amplitude_3', 'freq1_harmonics_amplitude_0', 'freq1_harmonics_amplitude_1', 'freq2_signif', 'freq1_harmonics_peak2peak_flux_error', 'freq1_harmonics_signif', 'freq3', 'freq2', 'freq1', 'freq1_harmonics_rel_phase_error_0', 'freq1_harmonics_freq_0', 'freq1_harmonics_freq_1', 'freq1_harmonics_freq_2', 'freq1_harmonics_freq_3', 'freq3_signif', 'freq1_harmonics_nharm', 'freq1_harmonics_moments_err_0', 'freq1_harmonics_moments_err_1', 'freq1_harmonics_moments_err_2', 'freq1_harmonics_moments_err_3', 'freq1_signif', 'freq1_harmonics_moments_0', 'freq1_harmonics_moments_1', 'freq1_harmonics_moments_2', 'freq1_harmonics_moments_3', 'freq1_harmonics_amplitude_error_3', 'freq1_harmonics_amplitude_error_2', 'freq1_harmonics_amplitude_error_1', 'freq1_harmonics_amplitude_error_0', 'freq_searched_max'] + +for feature in lomb_features: + print("About to prepare type", feature) + newclass = type(feature, (lomb_generic,), {'__doc__': feature, 'extname':feature, 'lomb_key':feature}) # see http://docs.python.org/lib/built-in-funcs.html for documentation of type() + exec "%s = newclass" % (feature) + print("new extname:") + exec "print %s().extname" % (feature) + + + + + + + + +# regex from !'(\S+)',\n to +# class $1_extractor(lomb_generic): +# """ $1 """ +# extname = "$1" +# lomb_key = "$1" +# +# (need a newline at the end) diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/lomb_scargle_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/lomb_scargle_extractor.py new file mode 100644 index 00000000..1a37f09a --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/lomb_scargle_extractor.py @@ -0,0 +1,903 @@ +"""This module is a low-tech implementation of lomb_scargle_extractor using +regular expressions +""" +from __future__ import print_function +from __future__ import absolute_import + +from ..FeatureExtractor import FeatureExtractor +from ..FeatureExtractor import InterExtractor +from .common_functions.pre_whiten import pre_whiten + +# TODO use namespace +try: + from pylab import * +except: + pass +from numpy import log, exp, arange, median, ceil +from .common_functions import lightcurve +import copy # 20100902 added + +class lomb_scargle_extractor(InterExtractor): + """ wrapper for common_functions lomb_scargle and pre_whiten + """ + internal_use_only = False + active = True + extname = 'lomb_scargle' + def __init__(self): + pass + + def extract(self): + src_dict = {} + src_dict['t'] = copy.copy(self.time_data) # 20100902 added the copy() + src_dict['m'] = copy.copy(self.flux_data) # 20100902 addde the copy() + src_dict['m_err'] = copy.copy(self.rms_data) # 20100902 added the copy() + + if len(self.time_data) == 0: + self.ex_error(text="self.time_data of len()==0") + + obs = lightcurve.observatory_source_interface() + # 20110611 dstarr added just for lightcurve.py:lomb_code(): debug/allstars-plot use. + db_dictionary, cn_output = obs.lomb_code(src_dict['m'], + src_dict['m_err'], + src_dict['t'], + srcid=self.dic['input'].get('srcid',0))# 20110611 dstarr added just for lightcurve.py:lomb_code(): debug/allstars-plot use. + + out_dict = {} + dstr_list = ['freq1','freq2','freq3']#,'freq4'] + for dstr in dstr_list: + lomb_dict = db_dictionary[dstr] + if len(lomb_dict['harmonics_amplitude']) < 4: + n_harm_iters = len(lomb_dict['harmonics_amplitude']) + else: + n_harm_iters = 1 + 3 # includes primary component + +# TODO don't like this but is there a simpler way? where/how else could these be stored in general? + out_dict["%s_harmonics_freq_0" % (dstr)] = lomb_dict['frequency'] + for i in range(n_harm_iters): + out_dict["%s_harmonics_amplitude_%d" % (dstr, i)] = \ + lomb_dict['harmonics_amplitude'][i] + out_dict["%s_harmonics_amplitude_error_%d" % (dstr, i)] = \ + lomb_dict['harmonics_amplitude_error'][i] + out_dict["%s_harmonics_rel_phase_%d" % (dstr, i)] = \ + lomb_dict['harmonics_rel_phase'][i] + out_dict["%s_harmonics_rel_phase_error_%d" % (dstr, i)] = \ + lomb_dict['harmonics_rel_phase_error'][i] + out_dict['freq_harmonics_offset'] = db_dictionary['freq1']['harmonics_time_offset'] + out_dict['freq_nharm'] = db_dictionary['freq1']['harmonics_nharm'] + out_dict['freq_signif'] = db_dictionary['freq1']['signif'] + out_dict['freq_y_offset'] = db_dictionary['freq1']['harmonics_y_offset'] + + out_dict['lambda'] = db_dictionary['lambda'] + out_dict['trend'] = db_dictionary['trend'] + out_dict['varrat'] = db_dictionary['varrat'] + out_dict['n_alias'] = db_dictionary['n_alias'] + out_dict['model_phi1_phi2'] = db_dictionary['model_phi1_phi2'] + out_dict['model_min_delta_mags'] = db_dictionary['model_min_delta_mags'] + out_dict['model_max_delta_mags'] = db_dictionary['model_max_delta_mags'] + + out_dict['freq1_psd'] = db_dictionary['freq1']["psd"] + out_dict['freq1_f0'] = db_dictionary['freq1']["f0"] + out_dict['freq1_df'] = db_dictionary['freq1']["df"] + out_dict['freq1_numf'] = db_dictionary['freq1']["numf"] + out_dict['freq1_model'] = db_dictionary['freq1_model'] + + # 20100916 dstarr adds a couple more features: + try: + out_dict['freq_signif_ratio_21'] = db_dictionary['freq2']['signif'] / db_dictionary['freq1']['signif'] + except: + out_dict['freq_signif_ratio_21'] = 0.0 + + try: + out_dict['freq_signif_ratio_31'] = db_dictionary['freq3']['signif'] / db_dictionary['freq1']['signif'] + except: + out_dict['freq_signif_ratio_31'] = 0.0 + + try: + out_dict['freq_frequency_ratio_21'] = db_dictionary['freq2']['frequency'] / db_dictionary['freq1']['frequency'] + except: + out_dict['freq_frequency_ratio_21'] = 0.0 + + try: + out_dict['freq_frequency_ratio_31'] = db_dictionary['freq3']['frequency'] / db_dictionary['freq1']['frequency'] + except: + out_dict['freq_frequency_ratio_31'] = 0.0 + + + try: + out_dict['freq_amplitude_ratio_21'] = db_dictionary['freq2']['harmonics_amplitude'][0] / db_dictionary['freq1']['harmonics_amplitude'][0] + except: + out_dict['freq_amplitude_ratio_21'] = 0.0 + + try: + out_dict['freq_amplitude_ratio_31'] = db_dictionary['freq3']['harmonics_amplitude'][0] / db_dictionary['freq1']['harmonics_amplitude'][0] + except: + out_dict['freq_amplitude_ratio_31'] = 0.0 + + try: + out_dict['mad_of_model_residuals'] = db_dictionary['mad_of_model_residuals'] + except: + out_dict['mad_of_model_residuals'] = 0.0 + + + try: + out_dict['p2p_scatter_2praw'] = db_dictionary['p2p_scatter_2praw'] + except: + out_dict['p2p_scatter_2praw'] = 0.0 + + try: + out_dict['p2p_scatter_over_mad'] = db_dictionary['p2p_scatter_over_mad'] + except: + out_dict['p2p_scatter_over_mad'] = 0.0 + + try: + out_dict['p2p_scatter_pfold_over_mad'] = db_dictionary['p2p_scatter_pfold_over_mad'] + except: + out_dict['p2p_scatter_pfold_over_mad'] = 0.0 + + try: + out_dict['medperc90_2p_p'] = db_dictionary['medperc90_2p_p'] + except: + out_dict['medperc90_2p_p'] = 0.0 + + + + try: + out_dict['p2p_ssqr_diff_over_var'] = db_dictionary['p2p_ssqr_diff_over_var'] + except: + out_dict['p2p_ssqr_diff_over_var'] = 0.0 + + try: + out_dict['fold2P_slope_10percentile'] = db_dictionary['fold2P_slope_10percentile'] + except: + out_dict['fold2P_slope_10percentile'] = 0.0 + + try: + out_dict['fold2P_slope_90percentile'] = db_dictionary['fold2P_slope_90percentile'] + except: + out_dict['fold2P_slope_90percentile'] = 0.0 + + return out_dict + + + +class lomb_generic(FeatureExtractor): + """ Generic lomb extractor grabs value from dictionary """ + internal_use_only = False + active = True + extname = 'to_be_overloaded' # identifier used in final extracted value dict. + lomb_key = 'to_be_overloaded' + def extract(self): + lomb_dict = self.fetch_extr('lomb_scargle') # fetches the dictionary from lomb_scargle_extractor with the useful lomb scargle results in it + # If lomb_dict is partially filled, most likely lomb couldn't compute completely due to FALSE condition: (dof>0 and harm_dict['nharm']>0 and harm_dict['signif']>0) + if self.lomb_key in lomb_dict: + return lomb_dict[self.lomb_key] # finds the correct keyword that this class is assigned to, this could be replaced by self.extname if it wasn't for the _alt + else: + self.ex_error('Lomb Scargle Dictionary does not have key %s' % (self.lomb_key)) + +#[!'freq_searched_min', +# !'freq1_harmonics_rel_phase_error_1', +# !'freq1_harmonics_peak2peak_flux', +# !'freq1_harmonics_rel_phase_error_3', +# !'freq1_harmonics_rel_phase_error_2', +# !'freq1_harmonics_rel_phase_0', +# !'freq1_harmonics_rel_phase_1', +# !'freq1_harmonics_rel_phase_2', +# !'freq1_harmonics_rel_phase_3', +# !'freq1_harmonics_amplitude_2', +# !'freq1_harmonics_amplitude_3', +# !'freq1_harmonics_amplitude_0', +# !'freq1_harmonics_amplitude_1', +# !'freq2_signif', +# !'freq1_harmonics_peak2peak_flux_error', +# !'freq1_harmonics_signif', +# !'freq3', +# !'freq2', +# !'freq1', +# !'freq1_harmonics_rel_phase_error_0', +# !'freq1_harmonics_freq_0', +# !'freq1_harmonics_freq_1', +# !'freq1_harmonics_freq_2', +# !'freq1_harmonics_freq_3', +# !'freq3_signif', +# !'freq1_harmonics_nharm', +# !'freq1_harmonics_moments_err_0', +# !'freq1_harmonics_moments_err_1', +# !'freq1_harmonics_moments_err_2', +# !'freq1_harmonics_moments_err_3', +# !'freq1_signif', +# !'freq1_harmonics_moments_0', +# !'freq1_harmonics_moments_1', +# !'freq1_harmonics_moments_2', +# !'freq1_harmonics_moments_3', +# !'freq1_harmonics_amplitude_error_3', +# !'freq1_harmonics_amplitude_error_2', +# !'freq1_harmonics_amplitude_error_1', +# !'freq1_harmonics_amplitude_error_0', +# !'freq_searched_max'] + +# regex from !'(\S+)',\n to +# class $1_extractor(lomb_generic): +# """ $1 """ +# extname = "$1" +# lomb_key = "$1" +# +# (need a newline at the end) + + + +#class freq1_extractor(lomb_generic): +# """ freq1 """ +# extname = "freq1" +# lomb_key = "freq1" + +class freq1_harmonics_amplitude_0_extractor(lomb_generic): + """ freq1_harmonics_amplitude_0 """ + extname = "freq1_harmonics_amplitude_0" + lomb_key = "freq1_harmonics_amplitude_0" + +class freq1_harmonics_amplitude_1_extractor(lomb_generic): + """ freq1_harmonics_amplitude_1 """ + extname = "freq1_harmonics_amplitude_1" + lomb_key = "freq1_harmonics_amplitude_1" + +class freq1_harmonics_amplitude_2_extractor(lomb_generic): + """ freq1_harmonics_amplitude_2 """ + extname = "freq1_harmonics_amplitude_2" + lomb_key = "freq1_harmonics_amplitude_2" + +class freq1_harmonics_amplitude_3_extractor(lomb_generic): + """ freq1_harmonics_amplitude_3 """ + extname = "freq1_harmonics_amplitude_3" + lomb_key = "freq1_harmonics_amplitude_3" + +class freq1_harmonics_amplitude_error_0_extractor(lomb_generic): + """ freq1_harmonics_amplitude_error_0 """ + extname = "freq1_harmonics_amplitude_error_0" + lomb_key = "freq1_harmonics_amplitude_error_0" + +#class freq1_harmonics_amplitude_error_1_extractor(lomb_generic): +# """ freq1_harmonics_amplitude_error_1 """ +# extname = "freq1_harmonics_amplitude_error_1" +# lomb_key = "freq1_harmonics_amplitude_error_1" +# +#class freq1_harmonics_amplitude_error_2_extractor(lomb_generic): +# """ freq1_harmonics_amplitude_error_2 """ +# extname = "freq1_harmonics_amplitude_error_2" +# lomb_key = "freq1_harmonics_amplitude_error_2" +# +#class freq1_harmonics_amplitude_error_3_extractor(lomb_generic): +# """ freq1_harmonics_amplitude_error_3 """ +# extname = "freq1_harmonics_amplitude_error_3" +# lomb_key = "freq1_harmonics_amplitude_error_3" + +class freq1_harmonics_freq_0_extractor(lomb_generic): + """ freq1_harmonics_freq_0 """ + extname = "freq1_harmonics_freq_0" + lomb_key = "freq1_harmonics_freq_0" + +#class freq1_harmonics_freq_1_extractor(lomb_generic): +# """ freq1_harmonics_freq_1 """ +# extname = "freq1_harmonics_freq_1" +# lomb_key = "freq1_harmonics_freq_1" +# +#class freq1_harmonics_freq_2_extractor(lomb_generic): +# """ freq1_harmonics_freq_2 """ +# extname = "freq1_harmonics_freq_2" +# lomb_key = "freq1_harmonics_freq_2" +# +#class freq1_harmonics_freq_3_extractor(lomb_generic): +# """ freq1_harmonics_freq_3 """ +# extname = "freq1_harmonics_freq_3" +# lomb_key = "freq1_harmonics_freq_3" + +class freq1_harmonics_moments_0_extractor(lomb_generic): + """ freq1_harmonics_moments_0 """ + extname = "freq1_harmonics_moments_0" + lomb_key = "freq1_harmonics_moments_0" + +class freq1_harmonics_moments_1_extractor(lomb_generic): + """ freq1_harmonics_moments_1 """ + extname = "freq1_harmonics_moments_1" + lomb_key = "freq1_harmonics_moments_1" + +class freq1_harmonics_moments_2_extractor(lomb_generic): + """ freq1_harmonics_moments_2 """ + extname = "freq1_harmonics_moments_2" + lomb_key = "freq1_harmonics_moments_2" + +class freq1_harmonics_moments_3_extractor(lomb_generic): + """ freq1_harmonics_moments_3 """ + extname = "freq1_harmonics_moments_3" + lomb_key = "freq1_harmonics_moments_3" + +class freq1_harmonics_moments_err_0_extractor(lomb_generic): + """ freq1_harmonics_moments_err_0 """ + extname = "freq1_harmonics_moments_err_0" + lomb_key = "freq1_harmonics_moments_err_0" + +#class freq1_harmonics_moments_err_1_extractor(lomb_generic): +# """ freq1_harmonics_moments_err_1 """ +# extname = "freq1_harmonics_moments_err_1" +# lomb_key = "freq1_harmonics_moments_err_1" +# +#class freq1_harmonics_moments_err_2_extractor(lomb_generic): +# """ freq1_harmonics_moments_err_2 """ +# extname = "freq1_harmonics_moments_err_2" +# lomb_key = "freq1_harmonics_moments_err_2" +# +#class freq1_harmonics_moments_err_3_extractor(lomb_generic): +# """ freq1_harmonics_moments_err_3 """ +# extname = "freq1_harmonics_moments_err_3" +# lomb_key = "freq1_harmonics_moments_err_3" + +## class freq1_harmonics_nharm_extractor(lomb_generic): +## """ freq1_harmonics_nharm """ +## extname = "freq1_harmonics_nharm" +## lomb_key = "freq1_harmonics_nharm" + +class freq1_harmonics_peak2peak_flux_extractor(lomb_generic): + """ freq1_harmonics_peak2peak_flux """ + extname = "freq1_harmonics_peak2peak_flux" + lomb_key = "freq1_harmonics_peak2peak_flux" + +class freq1_harmonics_peak2peak_flux_error_extractor(lomb_generic): + """ freq1_harmonics_peak2peak_flux_error """ + extname = "freq1_harmonics_peak2peak_flux_error" + lomb_key = "freq1_harmonics_peak2peak_flux_error" + +class freq1_harmonics_rel_phase_0_extractor(lomb_generic): + """ freq1_harmonics_rel_phase_0 """ + extname = "freq1_harmonics_rel_phase_0" + lomb_key = "freq1_harmonics_rel_phase_0" + +class freq1_harmonics_rel_phase_1_extractor(lomb_generic): + """ freq1_harmonics_rel_phase_1 """ + extname = "freq1_harmonics_rel_phase_1" + lomb_key = "freq1_harmonics_rel_phase_1" + +class freq1_harmonics_rel_phase_2_extractor(lomb_generic): + """ freq1_harmonics_rel_phase_2 """ + extname = "freq1_harmonics_rel_phase_2" + lomb_key = "freq1_harmonics_rel_phase_2" + +class freq1_harmonics_rel_phase_3_extractor(lomb_generic): + """ freq1_harmonics_rel_phase_3 """ + extname = "freq1_harmonics_rel_phase_3" + lomb_key = "freq1_harmonics_rel_phase_3" + +class freq1_harmonics_rel_phase_error_0_extractor(lomb_generic): + """ freq1_harmonics_rel_phase_error_0 """ + extname = "freq1_harmonics_rel_phase_error_0" + lomb_key = "freq1_harmonics_rel_phase_error_0" + +class freq1_harmonics_rel_phase_error_0_extractor(lomb_generic): + """ freq1_harmonics_rel_phase_error_0 """ + extname = "freq1_harmonics_rel_phase_error_0" + lomb_key = "freq1_harmonics_rel_phase_error_0" + +class freq1_lambda_extractor(lomb_generic): + """ freq1_lambda """ + extname = "freq1_lambda" + lomb_key = "lambda" + + + +#class freq1_harmonics_rel_phase_error_1_extractor(lomb_generic): +# """ freq1_harmonics_rel_phase_error_1 """ +# extname = "freq1_harmonics_rel_phase_error_1" +# lomb_key = "freq1_harmonics_rel_phase_error_1" +# +#class freq1_harmonics_rel_phase_error_2_extractor(lomb_generic): +# """ freq1_harmonics_rel_phase_error_2 """ +# extname = "freq1_harmonics_rel_phase_error_2" +# lomb_key = "freq1_harmonics_rel_phase_error_2" +# +#class freq1_harmonics_rel_phase_error_3_extractor(lomb_generic): +# """ freq1_harmonics_rel_phase_error_3 """ +# extname = "freq1_harmonics_rel_phase_error_3" +# lomb_key = "freq1_harmonics_rel_phase_error_3" + +## class freq1_harmonics_signif_extractor(lomb_generic): +## """ freq1_harmonics_signif """ +## extname = "freq1_harmonics_signif" +## lomb_key = "freq1_harmonics_signif" + +## class freq1_signif_extractor(lomb_generic): +## """ freq1_signif """ +## extname = "freq1_signif" +## lomb_key = "freq1_signif" + +## class freq2_extractor(lomb_generic): +## """ freq2 """ +## extname = "freq2" +## lomb_key = "freq2" + +class freq2_harmonics_amplitude_0_extractor(lomb_generic): + """ freq2_harmonics_amplitude_0 """ + extname = "freq2_harmonics_amplitude_0" + lomb_key = "freq2_harmonics_amplitude_0" + +class freq2_harmonics_amplitude_1_extractor(lomb_generic): + """ freq2_harmonics_amplitude_1 """ + extname = "freq2_harmonics_amplitude_1" + lomb_key = "freq2_harmonics_amplitude_1" + +class freq2_harmonics_amplitude_2_extractor(lomb_generic): + """ freq2_harmonics_amplitude_2 """ + extname = "freq2_harmonics_amplitude_2" + lomb_key = "freq2_harmonics_amplitude_2" + +class freq2_harmonics_amplitude_3_extractor(lomb_generic): + """ freq2_harmonics_amplitude_3 """ + extname = "freq2_harmonics_amplitude_3" + lomb_key = "freq2_harmonics_amplitude_3" + +class freq2_harmonics_amplitude_error_0_extractor(lomb_generic): + """ freq2_harmonics_amplitude_error_0 """ + extname = "freq2_harmonics_amplitude_error_0" + lomb_key = "freq2_harmonics_amplitude_error_0" + +#class freq2_harmonics_amplitude_error_1_extractor(lomb_generic): +# """ freq2_harmonics_amplitude_error_1 """ +# extname = "freq2_harmonics_amplitude_error_1" +# lomb_key = "freq2_harmonics_amplitude_error_1" +# +#class freq2_harmonics_amplitude_error_2_extractor(lomb_generic): +# """ freq2_harmonics_amplitude_error_2 """ +# extname = "freq2_harmonics_amplitude_error_2" +# lomb_key = "freq2_harmonics_amplitude_error_2" +# +#class freq2_harmonics_amplitude_error_3_extractor(lomb_generic): +# """ freq2_harmonics_amplitude_error_3 """ +# extname = "freq2_harmonics_amplitude_error_3" +# lomb_key = "freq2_harmonics_amplitude_error_3" + +class freq2_harmonics_freq_0_extractor(lomb_generic): + """ freq2_harmonics_freq_0 """ + extname = "freq2_harmonics_freq_0" + lomb_key = "freq2_harmonics_freq_0" + +#class freq2_harmonics_freq_1_extractor(lomb_generic): +# """ freq2_harmonics_freq_1 """ +# extname = "freq2_harmonics_freq_1" +# lomb_key = "freq2_harmonics_freq_1" +# +#class freq2_harmonics_freq_2_extractor(lomb_generic): +# """ freq2_harmonics_freq_2 """ +# extname = "freq2_harmonics_freq_2" +# lomb_key = "freq2_harmonics_freq_2" +# +#class freq2_harmonics_freq_3_extractor(lomb_generic): +# """ freq2_harmonics_freq_3 """ +# extname = "freq2_harmonics_freq_3" +# lomb_key = "freq2_harmonics_freq_3" + +class freq2_harmonics_moments_0_extractor(lomb_generic): + """ freq2_harmonics_moments_0 """ + extname = "freq2_harmonics_moments_0" + lomb_key = "freq2_harmonics_moments_0" + +class freq2_harmonics_moments_1_extractor(lomb_generic): + """ freq2_harmonics_moments_1 """ + extname = "freq2_harmonics_moments_1" + lomb_key = "freq2_harmonics_moments_1" + +class freq2_harmonics_moments_2_extractor(lomb_generic): + """ freq2_harmonics_moments_2 """ + extname = "freq2_harmonics_moments_2" + lomb_key = "freq2_harmonics_moments_2" + +class freq2_harmonics_moments_3_extractor(lomb_generic): + """ freq2_harmonics_moments_3 """ + extname = "freq2_harmonics_moments_3" + lomb_key = "freq2_harmonics_moments_3" + +class freq2_harmonics_moments_err_0_extractor(lomb_generic): + """ freq2_harmonics_moments_err_0 """ + extname = "freq2_harmonics_moments_err_0" + lomb_key = "freq2_harmonics_moments_err_0" + +#class freq2_harmonics_moments_err_1_extractor(lomb_generic): +# """ freq2_harmonics_moments_err_1 """ +# extname = "freq2_harmonics_moments_err_1" +# lomb_key = "freq2_harmonics_moments_err_1" +# +#class freq2_harmonics_moments_err_2_extractor(lomb_generic): +# """ freq2_harmonics_moments_err_2 """ +# extname = "freq2_harmonics_moments_err_2" +# lomb_key = "freq2_harmonics_moments_err_2" +# +#class freq2_harmonics_moments_err_3_extractor(lomb_generic): +# """ freq2_harmonics_moments_err_3 """ +# extname = "freq2_harmonics_moments_err_3" +# lomb_key = "freq2_harmonics_moments_err_3" + +## class freq2_harmonics_nharm_extractor(lomb_generic): +## """ freq2_harmonics_nharm """ +## extname = "freq2_harmonics_nharm" +## lomb_key = "freq2_harmonics_nharm" + +## class freq2_harmonics_peak2peak_flux_extractor(lomb_generic): +## """ freq2_harmonics_peak2peak_flux """ +## extname = "freq2_harmonics_peak2peak_flux" +## lomb_key = "freq2_harmonics_peak2peak_flux" + +## class freq2_harmonics_peak2peak_flux_error_extractor(lomb_generic): +## """ freq2_harmonics_peak2peak_flux_error """ +## extname = "freq2_harmonics_peak2peak_flux_error" +## lomb_key = "freq2_harmonics_peak2peak_flux_error" + +class freq2_harmonics_rel_phase_0_extractor(lomb_generic): + """ freq2_harmonics_rel_phase_0 """ + extname = "freq2_harmonics_rel_phase_0" + lomb_key = "freq2_harmonics_rel_phase_0" + +class freq2_harmonics_rel_phase_1_extractor(lomb_generic): + """ freq2_harmonics_rel_phase_1 """ + extname = "freq2_harmonics_rel_phase_1" + lomb_key = "freq2_harmonics_rel_phase_1" + +class freq2_harmonics_rel_phase_2_extractor(lomb_generic): + """ freq2_harmonics_rel_phase_2 """ + extname = "freq2_harmonics_rel_phase_2" + lomb_key = "freq2_harmonics_rel_phase_2" + +class freq2_harmonics_rel_phase_3_extractor(lomb_generic): + """ freq2_harmonics_rel_phase_3 """ + extname = "freq2_harmonics_rel_phase_3" + lomb_key = "freq2_harmonics_rel_phase_3" + +class freq2_harmonics_rel_phase_error_0_extractor(lomb_generic): + """ freq2_harmonics_rel_phase_error_0 """ + extname = "freq2_harmonics_rel_phase_error_0" + lomb_key = "freq2_harmonics_rel_phase_error_0" + +#class freq2_harmonics_rel_phase_error_1_extractor(lomb_generic): +# """ freq2_harmonics_rel_phase_error_1 """ +# extname = "freq2_harmonics_rel_phase_error_1" +# lomb_key = "freq2_harmonics_rel_phase_error_1" +# +#class freq2_harmonics_rel_phase_error_2_extractor(lomb_generic): +# """ freq2_harmonics_rel_phase_error_2 """ +# extname = "freq2_harmonics_rel_phase_error_2" +# lomb_key = "freq2_harmonics_rel_phase_error_2" +# +#class freq2_harmonics_rel_phase_error_3_extractor(lomb_generic): +# """ freq2_harmonics_rel_phase_error_3 """ +# extname = "freq2_harmonics_rel_phase_error_3" +# lomb_key = "freq2_harmonics_rel_phase_error_3" + +## class freq2_harmonics_signif_extractor(lomb_generic): +## """ freq2_harmonics_signif """ +## extname = "freq2_harmonics_signif" +## lomb_key = "freq2_harmonics_signif" + +## class freq2_signif_extractor(lomb_generic): +## """ freq2_signif """ +## extname = "freq2_signif" +## lomb_key = "freq2_signif" + +## class freq3_extractor(lomb_generic): +## """ freq3 """ +## extname = "freq3" +## lomb_key = "freq3" + +class freq3_harmonics_amplitude_0_extractor(lomb_generic): + """ freq3_harmonics_amplitude_0 """ + extname = "freq3_harmonics_amplitude_0" + lomb_key = "freq3_harmonics_amplitude_0" + +class freq3_harmonics_amplitude_1_extractor(lomb_generic): + """ freq3_harmonics_amplitude_1 """ + extname = "freq3_harmonics_amplitude_1" + lomb_key = "freq3_harmonics_amplitude_1" + +class freq3_harmonics_amplitude_2_extractor(lomb_generic): + """ freq3_harmonics_amplitude_2 """ + extname = "freq3_harmonics_amplitude_2" + lomb_key = "freq3_harmonics_amplitude_2" + +class freq3_harmonics_amplitude_3_extractor(lomb_generic): + """ freq3_harmonics_amplitude_3 """ + extname = "freq3_harmonics_amplitude_3" + lomb_key = "freq3_harmonics_amplitude_3" + +class freq3_harmonics_amplitude_error_0_extractor(lomb_generic): + """ freq3_harmonics_amplitude_error_0 """ + extname = "freq3_harmonics_amplitude_error_0" + lomb_key = "freq3_harmonics_amplitude_error_0" + +#class freq3_harmonics_amplitude_error_1_extractor(lomb_generic): +# """ freq3_harmonics_amplitude_error_1 """ +# extname = "freq3_harmonics_amplitude_error_1" +# lomb_key = "freq3_harmonics_amplitude_error_1" +# +#class freq3_harmonics_amplitude_error_2_extractor(lomb_generic): +# """ freq3_harmonics_amplitude_error_2 """ +# extname = "freq3_harmonics_amplitude_error_2" +# lomb_key = "freq3_harmonics_amplitude_error_2" +# +#class freq3_harmonics_amplitude_error_3_extractor(lomb_generic): +# """ freq3_harmonics_amplitude_error_3 """ +# extname = "freq3_harmonics_amplitude_error_3" +# lomb_key = "freq3_harmonics_amplitude_error_3" + +class freq3_harmonics_freq_0_extractor(lomb_generic): + """ freq3_harmonics_freq_0 """ + extname = "freq3_harmonics_freq_0" + lomb_key = "freq3_harmonics_freq_0" + +#class freq3_harmonics_freq_1_extractor(lomb_generic): +# """ freq3_harmonics_freq_1 """ +# extname = "freq3_harmonics_freq_1" +# lomb_key = "freq3_harmonics_freq_1" +# +#class freq3_harmonics_freq_2_extractor(lomb_generic): +# """ freq3_harmonics_freq_2 """ +# extname = "freq3_harmonics_freq_2" +# lomb_key = "freq3_harmonics_freq_2" +# +#class freq3_harmonics_freq_3_extractor(lomb_generic): +# """ freq3_harmonics_freq_3 """ +# extname = "freq3_harmonics_freq_3" +# lomb_key = "freq3_harmonics_freq_3" + +class freq3_harmonics_moments_0_extractor(lomb_generic): + """ freq3_harmonics_moments_0 """ + extname = "freq3_harmonics_moments_0" + lomb_key = "freq3_harmonics_moments_0" + +class freq3_harmonics_moments_1_extractor(lomb_generic): + """ freq3_harmonics_moments_1 """ + extname = "freq3_harmonics_moments_1" + lomb_key = "freq3_harmonics_moments_1" + +class freq3_harmonics_moments_2_extractor(lomb_generic): + """ freq3_harmonics_moments_2 """ + extname = "freq3_harmonics_moments_2" + lomb_key = "freq3_harmonics_moments_2" + +class freq3_harmonics_moments_3_extractor(lomb_generic): + """ freq3_harmonics_moments_3 """ + extname = "freq3_harmonics_moments_3" + lomb_key = "freq3_harmonics_moments_3" + +class freq3_harmonics_moments_err_0_extractor(lomb_generic): + """ freq3_harmonics_moments_err_0 """ + extname = "freq3_harmonics_moments_err_0" + lomb_key = "freq3_harmonics_moments_err_0" + +#class freq3_harmonics_moments_err_1_extractor(lomb_generic): +# """ freq3_harmonics_moments_err_1 """ +# extname = "freq3_harmonics_moments_err_1" +# lomb_key = "freq3_harmonics_moments_err_1" +# +#class freq3_harmonics_moments_err_2_extractor(lomb_generic): +# """ freq3_harmonics_moments_err_2 """ +# extname = "freq3_harmonics_moments_err_2" +# lomb_key = "freq3_harmonics_moments_err_2" +# +#class freq3_harmonics_moments_err_3_extractor(lomb_generic): +# """ freq3_harmonics_moments_err_3 """ +# extname = "freq3_harmonics_moments_err_3" +# lomb_key = "freq3_harmonics_moments_err_3" + +## class freq3_harmonics_nharm_extractor(lomb_generic): +## """ freq3_harmonics_nharm """ +## extname = "freq3_harmonics_nharm" +## lomb_key = "freq3_harmonics_nharm" + +## class freq3_harmonics_peak2peak_flux_extractor(lomb_generic): +## """ freq3_harmonics_peak2peak_flux """ +## extname = "freq3_harmonics_peak2peak_flux" +## lomb_key = "freq3_harmonics_peak2peak_flux" + +## class freq3_harmonics_peak2peak_flux_error_extractor(lomb_generic): +## """ freq3_harmonics_peak2peak_flux_error """ +## extname = "freq3_harmonics_peak2peak_flux_error" +## lomb_key = "freq3_harmonics_peak2peak_flux_error" + +class freq3_harmonics_rel_phase_0_extractor(lomb_generic): + """ freq3_harmonics_rel_phase_0 """ + extname = "freq3_harmonics_rel_phase_0" + lomb_key = "freq3_harmonics_rel_phase_0" + +class freq3_harmonics_rel_phase_1_extractor(lomb_generic): + """ freq3_harmonics_rel_phase_1 """ + extname = "freq3_harmonics_rel_phase_1" + lomb_key = "freq3_harmonics_rel_phase_1" + +class freq3_harmonics_rel_phase_2_extractor(lomb_generic): + """ freq3_harmonics_rel_phase_2 """ + extname = "freq3_harmonics_rel_phase_2" + lomb_key = "freq3_harmonics_rel_phase_2" + +class freq3_harmonics_rel_phase_3_extractor(lomb_generic): + """ freq3_harmonics_rel_phase_3 """ + extname = "freq3_harmonics_rel_phase_3" + lomb_key = "freq3_harmonics_rel_phase_3" + +class freq3_harmonics_rel_phase_error_0_extractor(lomb_generic): + """ freq3_harmonics_rel_phase_error_0 """ + extname = "freq3_harmonics_rel_phase_error_0" + lomb_key = "freq3_harmonics_rel_phase_error_0" + +#class freq3_harmonics_rel_phase_error_1_extractor(lomb_generic): +# """ freq3_harmonics_rel_phase_error_1 """ +# extname = "freq3_harmonics_rel_phase_error_1" +# lomb_key = "freq3_harmonics_rel_phase_error_1" +# +#class freq3_harmonics_rel_phase_error_2_extractor(lomb_generic): +# """ freq3_harmonics_rel_phase_error_2 """ +# extname = "freq3_harmonics_rel_phase_error_2" +# lomb_key = "freq3_harmonics_rel_phase_error_2" +# +#class freq3_harmonics_rel_phase_error_3_extractor(lomb_generic): +# """ freq3_harmonics_rel_phase_error_3 """ +# extname = "freq3_harmonics_rel_phase_error_3" +# lomb_key = "freq3_harmonics_rel_phase_error_3" + +## class freq3_harmonics_signif_extractor(lomb_generic): +## """ freq3_harmonics_signif """ +## extname = "freq3_harmonics_signif" +## lomb_key = "freq3_harmonics_signif" + +## class freq3_signif_extractor(lomb_generic): +## """ freq3_signif """ +## extname = "freq3_signif" +## lomb_key = "freq3_signif" + +## class freq_searched_max_extractor(lomb_generic): +## """ freq_searched_max """ +## extname = "freq_searched_max" +## lomb_key = "freq_searched_max" + +## class freq_searched_min_extractor(lomb_generic): +## """ freq_searched_min """ +## extname = "freq_searched_min" +## lomb_key = "freq_searched_min" + +###### + +class freq_harmonics_offset_extractor(lomb_generic): + """ freq_harmonics_offset """ + extname = "freq_harmonics_offset" + lomb_key = "freq_harmonics_offset" + +class freq_y_offset_extractor(lomb_generic): + """ freq_y_offset """ + extname = "freq_y_offset" + lomb_key = "freq_y_offset" + +class freq_signif_extractor(lomb_generic): + """ freq_signif """ + extname = "freq_signif" + lomb_key = "freq_signif" + +class freq_nharm_extractor(lomb_generic): + """ freq_nharm """ + extname = "freq_nharm" + lomb_key = "freq_nharm" + +class linear_trend_extractor(lomb_generic): + """ slope (b) of linear trend fitted to unfolded data using linfit.py: m = a+b*x; minimize chi^2 = Sum (y-m)^2/dy^2""" + extname = "linear_trend" + lomb_key = "trend" + +class freq_varrat_extractor(lomb_generic): + """ Ratio of variances: V_freq1_subtracted / V_linear_trend_subtracted """ + extname = "freq_varrat" + lomb_key = "varrat" + +######## + +class freq_signif_ratio_21_extractor(lomb_generic): + """ freq_signif_ratio_21 """ + extname = "freq_signif_ratio_21" + lomb_key = "freq_signif_ratio_21" + +class freq_signif_ratio_31_extractor(lomb_generic): + """ freq_signif_ratio_31 """ + extname = "freq_signif_ratio_31" + lomb_key = "freq_signif_ratio_31" + +class freq_frequency_ratio_21_extractor(lomb_generic): + """ freq_frequency_ratio_21 """ + extname = "freq_frequency_ratio_21" + lomb_key = "freq_frequency_ratio_21" + +class freq_frequency_ratio_31_extractor(lomb_generic): + """ freq_frequency_ratio_31 """ + extname = "freq_frequency_ratio_31" + lomb_key = "freq_frequency_ratio_31" + +class freq_amplitude_ratio_21_extractor(lomb_generic): + """ freq_amplitude_ratio_21 """ + extname = "freq_amplitude_ratio_21" + lomb_key = "freq_amplitude_ratio_21" + +class freq_amplitude_ratio_31_extractor(lomb_generic): + """ freq_amplitude_ratio_31 """ + extname = "freq_amplitude_ratio_31" + lomb_key = "freq_amplitude_ratio_31" + +class p2p_scatter_2praw_extractor(lomb_generic): + """ From arXiv 1101_2406v1 Dubath 20110112 paper. +sum of the squares of the magnitude +differences between pairs of successive data points in the light +curve folded around twice the period divided by the same quantity +derived from the raw light curve. """ + extname = "p2p_scatter_2praw" + lomb_key = "p2p_scatter_2praw" + +class p2p_scatter_over_mad_extractor(lomb_generic): + """ From arXiv 1101_2406v1 Dubath 20110112 paper. +median of the absolute values of the differences +between successive magnitudes in the raw light curve normalized +by the Median Absolute Deviation (MAD) around the median. +""" + extname = "p2p_scatter_over_mad" + lomb_key = "p2p_scatter_over_mad" + + +class p2p_scatter_pfold_over_mad_extractor(lomb_generic): + """ From arXiv 1101_2406v1 Dubath 20110112 paper. +median of the absolute values of the +differences between successive magnitudes in the folded light +curve normalized by the Median Absolute Deviation (MAD) +around the median of the raw lightcurve. """ + extname = "p2p_scatter_pfold_over_mad" + lomb_key = "p2p_scatter_pfold_over_mad" + + +class medperc90_2p_p_extractor(lomb_generic): + """ From arXiv 1101_2406v1 Dubath 20110112 paper. +Percentile90:2P/P: +the 90-th percentile of the absolute residual values around the 2P model +divided by the same quantity +for the residuals around the P model. The 2P model is a model +recomputed using twice the period value. +""" + extname = "medperc90_2p_p" + lomb_key = "medperc90_2p_p" + + +class p2p_ssqr_diff_over_var_extractor(lomb_generic): + """ eta feature from arXiv 1101.3316 Kim QSO paper. + if there exists positive serial correlation, the eta is small, negative serial correlation then eta is large. Note that the linear trend is not use in the model""" + extname = "p2p_ssqr_diff_over_var" + lomb_key = "p2p_ssqr_diff_over_var" + + +class fold2P_slope_10percentile_extractor(lomb_generic): + """ Using point-to-point slopes calculated from LS freq1 model and 2 Period folded data, this is 10 percentile median of the slopes. Note that the linear trend is not use in the model""" + extname = "fold2P_slope_10percentile" + lomb_key = "fold2P_slope_10percentile" + + +class fold2P_slope_90percentile_extractor(lomb_generic): + """ Using point-to-point slopes calculated from LS freq1 model and 2 Period folded data, this is 90 percentile median of the slopes. Note that the linear trend is not use in the model""" + extname = "fold2P_slope_90percentile" + lomb_key = "fold2P_slope_90percentile" + +class freq_n_alias_extractor(lomb_generic): + """ freq_n_alias """ + extname = "freq_n_alias" + lomb_key = "n_alias" + +class freq_model_phi1_phi2_extractor(lomb_generic): + """ freq_model_phi1_phi2 : ratio of model phase between max1 and min2 over phase between min2 to max3 """ + extname = "freq_model_phi1_phi2" + lomb_key = "model_phi1_phi2" + + +class freq_model_min_delta_mags_extractor(lomb_generic): + """ freq_model_min_delta_mags : ratio of model phase between max1 and min2 over phase between min2 to max3 """ + extname = "freq_model_min_delta_mags" + lomb_key = "model_min_delta_mags" + +class freq_model_max_delta_mags_extractor(lomb_generic): + """ freq_model_max_delta_mags : ratio of model phase between max1 and min2 over phase between min2 to max3 """ + extname = "freq_model_max_delta_mags" + lomb_key = "model_max_delta_mags" + diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/max_slope_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/max_slope_extractor.py new file mode 100644 index 00000000..5d1e5cba --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/max_slope_extractor.py @@ -0,0 +1,18 @@ +from ..FeatureExtractor import FeatureExtractor + +class max_slope_extractor(FeatureExtractor): + active = True + extname = 'max_slope' #extractor's name + def extract(self): + max_slope = 0 + max_slope_i = 0 +# TODO np.diff +# TODO is this really supposed to return max_slope_i instead of max_slope...? + for i in range(len(self.time_data)-1): + ydiff = self.flux_data[i+1]-self.flux_data[i] + xdiff = self.time_data[i+1]-self.time_data[i] + slope = ydiff / xdiff + if abs(slope) > abs(max_slope): + max_slope = slope + max_slope_i = i + return(max_slope_i) diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/median_absolute_deviation_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/median_absolute_deviation_extractor.py new file mode 100644 index 00000000..52ac558f --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/median_absolute_deviation_extractor.py @@ -0,0 +1,13 @@ +from ..FeatureExtractor import FeatureExtractor + +from numpy import median + +class median_absolute_deviation_extractor(FeatureExtractor): + """ MAD: Median Absolute Deviation : median(abs(mag[] - median(mag[]))) + """ + active = True + extname = 'median_absolute_deviation' #extractor's name + def extract(self): + mad = median(abs(self.flux_data - median(self.flux_data))) + return mad + diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/median_buffer_range_percentage_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/median_buffer_range_percentage_extractor.py new file mode 100644 index 00000000..af90eb80 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/median_buffer_range_percentage_extractor.py @@ -0,0 +1,27 @@ +from ..FeatureExtractor import FeatureExtractor + +class median_buffer_range_percentage_extractor(FeatureExtractor): + """ extracts the percentage of points that fall within the buffer reang of + the median """ + active = True + extname = 'median_buffer_range_percentage' #extractor's name +# TODO simplify logic + def extract(self): + magic_number = 1/10.0 #defines size of buffer range with respect to abs(max) - abs(min) + max = self.fetch_extr('max') + min = self.fetch_extr('min') + median = self.fetch_extr('median') + # TODO lots of this try/except -> return None... + try: +# TODO almost certainly wrong for inputs w/ different signs + buffer_range = (abs(max) - abs(min))*(magic_number) + points_within_buffer_range_of_median = 0 + for n in self.flux_data: + if abs(n - median) < buffer_range: + points_within_buffer_range_of_median += 1 + else: + pass + #print '!!!!', points_within_buffer_range_of_median/float(len(self.flux_data)) + return points_within_buffer_range_of_median/float(len(self.flux_data)) + except: + return None diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/median_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/median_extractor.py new file mode 100644 index 00000000..73a4b4be --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/median_extractor.py @@ -0,0 +1,13 @@ +from ..FeatureExtractor import FeatureExtractor +from numpy import median as med +from .common_functions.plot_methods import plot_horizontal_line + +class median_extractor(plot_horizontal_line,FeatureExtractor): + active = True + extname = 'median' #extractor's name + def extract(self): + try: + median = float(med(self.flux_data)) + except: + self.ex_error("EXCEPT in medianextractor() most likely flux_data=[]") + return(median) diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/medianextractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/medianextractor.py new file mode 100644 index 00000000..6608c8a5 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/medianextractor.py @@ -0,0 +1,15 @@ +from __future__ import absolute_import +from ..FeatureExtractor import FeatureExtractor +from numpy import median as med +from .common_functions.plot_methods import plot_horizontal_line + +# TODO duplicate +class medianextractor(plot_horizontal_line,FeatureExtractor): + active = True + extname = 'median' #extractor's name + def extract(self): + try: + median = float(med(self.flux_data)) + except: + self.ex_error("EXCEPT in medianextractor() most likely flux_data=[]") + return(median) diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/min_max_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/min_max_extractor.py new file mode 100644 index 00000000..02ca374e --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/min_max_extractor.py @@ -0,0 +1,27 @@ +from ..FeatureExtractor import FeatureExtractor + +class min_extractor(FeatureExtractor): + """ extracts the minimum magnitude (i.e. brightest points)""" + active = True + extname = 'min' #extractor's name + def extract(self): + try: + min_index = self.flux_data.argmin() + self.vplot = self.time_data[min_index] # tells plotting method where to plot this line + minimum = self.flux_data[min_index] + except: + minimum = None + return minimum + +class max_extractor(FeatureExtractor): + """ extracts the maximum magnitude (i.e. faintest points)""" + active = True + extname = 'max' #extractor's name + def extract(self): + try: + max_index = self.flux_data.argmax() + self.vplot = self.time_data[max_index] # tells plotting method where to plot this line + maximum = self.flux_data[max_index] + except: + maximum = None + return maximum diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/mlens3.py b/mltsp/TCP/Software/feature_extract/Code/extractors/mlens3.py new file mode 100755 index 00000000..ab46ba24 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/mlens3.py @@ -0,0 +1,760 @@ +#!/usr/bin/env python +"""Mlens + a plugin to the classification engine that makes statements about probabilities of a microlens in a light curve + + v1.0 -- JSB + + Usage: + import mlens3 + ## make a data model instance from a VOSource + d = mlens3.EventData("124.22024.4124.xml") + + ## run the fitter (turn off doplot for running without pylab) + m = mlens3.Mlens(datamodel=d,doplot=True) + + ## print the results + print m + + ## grab the results (prob_mlens should be between 0 and 1...anything above 0.8 is pretty sure bet) + prob_mlens = m.final_results["final"]["probabilities"]["single-lens"] + + TODO: + - combine multiple filters to make stronger statements + - search for anamolies in the data + - make statements about multiple lens + +""" +from __future__ import print_function +from __future__ import absolute_import +from numpy import * +from math import degrees, pi +from scipy.optimize import leastsq, fmin, fmin_powell +from scipy import random as r +from scipy.odr import * + +from scipy.stats import trim_mean, normaltest, moment, chisqprob +try: + from scipy.stats import median +except: + from scipy import median +try: + from scipy.stats import samplestd +except: + from scipy import std as samplestd + +from scipy.stats.kde import gaussian_kde as kde + +from scipy.stats import norm as statnorm +c = 2.998e10 +g = 6.674e-8 +import copy, sys, os +try: + from matplotlib.pylab import * +except: + print("unable to import matplotlib") +from . import vosource_parse, xmldict +#from pprint import pprint, pformat# dstarr disables pprint function since I'd rather not have this module constantly in memory if just for debugging use. + +__version__ = "1.0.2" + + +from .. import db_importer + +class Mlens: + + ## basic lens equations + theta_e = lambda s,ml, dl: sqrt(2.0*g*ml*1.99e33/(dl*3.085e18))/c + theta_e_arcsec = lambda s,ml, dl: 3600.0*degrees(theta_e(ml,dl)) + re = lambda s,ml, dl: dl*3.085e18*theta_e(ml,dl) + te = lambda s,ml, dl, vl: re(ml,dl)/(vl*1e5) + te_in_days = lambda s,ml, dl, vl: te(ml, dl, vl)/(24.0*3600.0) + u = lambda s,ml, dl, vl, umin, t0, t: sqrt(umin**2 + ((t - t0)/te_in_days(ml,dl,vl))**2) + u_te = lambda s,the_te, umin, t0, t: sqrt(umin**2 + ((t - t0)/the_te)**2) + + final_results = {} + + def __init__(self,datamodel=None,doplot=True,verbose=False): + self.doplot = doplot + self.verbose = verbose + if self.doplot: + cla() + if datamodel is None: + self.test() + else: + self.data = datamodel + self.run() + + def __str__(self): + a = "Results of microlens fitting\n" + # dstarr disables pprint function since I'd rather not have this module constantly in memory if just for debugging use. + #a += pformat(self.final_results) + a += str(self.final_results) + return a + + def out_print(self,rez): + + if not isinstance(rez,dict): + return "" + + a = "********* Filter = %s ************\n" % rez.keys()[0] + a += "_"*50 + "\n" + tmp = rez.values()[0] + for k,v in rez.values()[0].iteritems(): + a += " --> sub-set name: %s <-- \n" % k + a += " + metadata ---\n" + ndata = -1 if "ndata" not in v else v['ndata'] + normalcy_prob = -1 if "normalcy_prob" not in v else v['normalcy_prob'] + p_chi = -1 if "p_chi" not in v else v['p_chi'] + res_var = -1 if "resid_var" not in v else v['resid_var'] + chisq = -1 if "chisq" not in v else v['chisq'] + dof = -1 if "dof" not in v else v['dof'] + frac_data_remaining = 1 if "frac_data_remaining" not in v else v['frac_data_remaining'] + beta = [-1,-1,-1,-1,-1,-1] if "beta" not in v else v['beta'] + if len(beta) == 6: + eq = "f(t) = %8.3e + (%7.3f + %8.3e x t) * g(u[t])" % (beta[5],beta[3],beta[4]) + elif len(beta) == 5: + eq = "f(t) = (%7.3f + %8.3e x t) * g(u[t])" % (beta[3],beta[4]) + else: + eq = "" + a += "\tndata = %i chisq = %f dof = %f\n" % (ndata,chisq,dof) + a += "\tp_chi = %f p_normalcy= %f resid_variance = %f\n" % (p_chi,normalcy_prob,res_var) + a += "\tfrac data = %7.2f\n" % frac_data_remaining + a += "\tequation: %s\n" % eq + a += " + derived parameters ---\n" + a += "\tt_0 = %6.3f +/- %6.3f d t_e = %6.3f +/- %6.3f d\n" % \ + (v["t0"]["val"],v["t0"]["err"],v["te"]["val"],v["te"]["err"]) + a += "\tAmax = %6.3f +/- %6.3f u_min = %6.3f +/- %6.3f\n" % \ + (v["Amax"]["val"],v["Amax"]["err"],v["umin"]["val"],v["umin"]["err"]) + return a + def renorm_data(self,dat,rez,dset="sig5"): + + if dset not in rez.values()[0]: + dset = "all" + if dset not in rez.values()[0]: + durga + return (dat[0],dat[1],dat[2]) + v=rez.values()[0][dset] + if "beta" not in v: + durga + return (dat[0],dat[1],dat[2]) + + beta = v['beta'] + if len(beta) == 6: + zp = beta[5] + else: + zp = 0.0 + + f = (dat[1] - zp)/(beta[3] + beta[4]*dat[0]/dat[0][0]) + ferr = (f/dat[1])*dat[2] + + return (dat[0],f,ferr) + + + def run(self,multifilt=False): + + if not multifilt: + print("Warning: we're only going to use the filter with the most data for now") + print(" in the future we'll combine the results") + self.all_fits = [] + self.all_out = [] + if self.doplot: + cla() + minx, maxx = 1e9,-99 + miny, maxy = 1e9,-99 + maxdata = 0 + thef = "" + for f in self.data.filts: + if len(self.data.t(f)) > maxdata: + maxdata = len(self.data.t(f)) + thef = f + + f = thef + tt, ff, fferr = [], [], [] + print("using %i epochs from filter %s" % (maxdata,f)) + dat = [self.data.t(f),self.data.flux(f),self.data.flux_err(f)] + minx = min([min(self.data.t(f)),minx]) + miny = min([min(self.data.flux(f)),miny]) + maxx = max([max(self.data.t(f)),maxx]) + maxy = max([max(self.data.flux(f)),maxy]) + # 20090212: dstarr re-enables try/except to see if we handle it fine (when n_epochs==1) + try: + self.all_fits.append({f: self._filt_run(dat,f)}) + self.all_out.append({f: self.inspect_output_by_filter(self.all_fits[-1],dat,doplot=self.doplot)}) + if self.verbose: + print(self.out_print(self.all_out[-1])) + except: + print("EXCEPT: mlens3.py:168 self.inspect_output_by_filter(..). Probably not enough datapoints.") + return + tnorm, fluxnorm, flux_errnorm = self.renorm_data(dat,self.all_out[-1],dset="sig5") + tt.extend(list(tnorm)) + ff.extend(list(fluxnorm)) + fferr.extend(list(flux_errnorm)) + self.single_lens_statements(self.all_out[-1]) + a = self.out_print(self.all_out[-1]) + self.final_results.update({"description": a}) + if self.doplot: + xlim([minx,maxx]) + ylim([0.9*miny,maxy*1.1]) + return + else: + if not isinstance(self.data,EventData): + print("bad data model") + + self.all_fits = [] + self.all_out = [] + if self.doplot: + cla() + ## loop over the filters and collect the results + minx, maxx = 1e9,-99 + miny, maxy = 1e9,-99 + tt, ff, fferr = [], [], [] + for f in self.data.filts: + print("filter %s" % f) + dat = [self.data.t(f),self.data.flux(f),self.data.flux_err(f)] + minx = min([min(self.data.t(f)),minx]) + miny = min([min(self.data.flux(f)),miny]) + maxx = max([max(self.data.t(f)),maxx]) + maxy = max([max(self.data.flux(f)),maxy]) + try: + self.all_fits.append({f: self._filt_run(dat,f)}) + self.all_out.append({f: self.inspect_output_by_filter(self.all_fits[-1],dat,doplot=self.doplot)}) + if self.verbose: + print(self.out_print(self.all_out[-1])) + except: + continue + tnorm, fluxnorm, flux_errnorm = self.renorm_data(dat,self.all_out[-1],dset="sig5") + tt.extend(list(tnorm)) + ff.extend(list(fluxnorm)) + fferr.extend(list(flux_errnorm)) + self.single_lens_statements(self.all_out[-1]) + + if self.doplot: + xlim([minx,maxx]) + ylim([0.9*miny,maxy*1.1]) + + print(self.data.filts) + if len(self.data.filts) > 1: + print("combined fit") + print(dat) + #dat = [array(tt),array(ff),array(fferr)] + #self.all_fits.append({"all": self._filt_run(dat,"all")}) + #self.all_out.append({"all": self.inspect_output_by_filter(self.all_fits[-1],dat)}) + #self.out_print(self.all_out[-1]) + + def single_lens_statements(self,rez): + + f = rez.keys()[0] + rez = rez.values()[0] + rel_import = array([1/1.0,1/0.8,1/2.0]) ## importance of knowing umin, te, t0 + rel_pimport = array([1.0,2.0,3.0]) ## inverse of importance + bestp = besta = 0.0 + ret = [] + for k,v in rez.iteritems(): + effective_prob = sqrt(max([v['p_chi'], exp(-1.0*v['resid_var'])])) + expected_te = 40.0 + best_te_dif = min( [abs(expected_te - v['te']['val'] + v['te']['err']),\ + abs(expected_te - v['te']['val'] - v['te']['err']),\ + abs(expected_te - v['te']['val'])] ) + var = log10(12.0) + te_prob_1 = exp( -( log10(expected_te) - log10(v['te']['val'] + v['te']['err']) )**2/var**2) + te_prob_2 = exp( -(log10(expected_te) - log10(v['te']['val'] - v['te']['err']) )**2/var**2) + te_prob_3 = exp( -(log10(expected_te) - log10(v['te']['val']) )**2/var**2) + + #print (te_prob_1,te_prob_2,te_prob_3) + te_prob = max([te_prob_1,te_prob_2,te_prob_3]) + #effective_prob *= te_prob + + ## how well is umin and te measured? + umin_sig = v['umin']['val']/v['umin']['err'] + te_sig = v['te']['val']/v['te']['err'] + + ## how well is t0 measured (relative to t_e)? + t0_sig = v['te']['val']/v['t0']['err'] + + ## what's the chance that the peak was before the first data? + p_peak_before = statnorm.cdf((self.data.t(f)[0] - v['t0']['val'])/v['t0']['err']) + + ## what's the chance that the peak was the after the last data? + p_peak_after = 1 - statnorm.cdf((self.data.t(f)[-1] - v['t0']['val'])/v['t0']['err']) + + pvals = array([1.0 - math.exp(-1.0*umin_sig/2), 1.0 - math.exp(-1.0*te_sig/2),1.0 - math.exp(-1.0*t0_sig/2)]) + avgpval = sqrt( ( (pvals/rel_import)**2 ).sum() / ( (1.0/rel_import)**2 ).sum() ) + final_p = sqrt( ((avgpval/rel_pimport[0])**2 + (te_prob/rel_pimport[1])**2 + (effective_prob/rel_pimport[2])**2 ) / ((1.0/rel_pimport)**2).sum()) + bestp = bestp if bestp > final_p or isnan(final_p) else final_p + besta = besta if besta > avgpval or isnan(avgpval) else avgpval + + tmp = {k: {"probabilities": \ + {"peak_before_first_obs": p_peak_before, \ + "peak_after_last_obs": p_peak_after, \ + "well measured parameters": avgpval, \ + "combined_is_microlens": final_p, \ + "reasonable effetive time (te)": te_prob,\ + "statistical normalcy of fit": effective_prob}, \ + "parameters": \ + {"te": {"val": v['te']['val'], "err": v['te']['err'], "sig": te_sig},\ + "t0": {"val": v['t0']['val'], "err": v['t0']['err'], "sig": t0_sig},\ + "umin": {"val": v['umin']['val'], "err": v['umin']['err'], "sig": umin_sig}}}} + ret.append(tmp) + if self.verbose: + print((k, final_p, avgpval, te_prob, effective_prob, umin_sig,te_sig,t0_sig,p_peak_before,p_peak_after)) + ## what's the robustness of the result (how much do the parameters change on clipping)? + ## how well observed are all the data (how much do the parameters change on clipping)? + if self.verbose: + print("Best probability of a being a single microlens = %f" % bestp) + print("Robustness of the 3 parameter fit = %f " % besta) + ret = {"value_added_properties": ret} + #tmp = {"probabilities": {"single-lens": bestp, "single-lens-parameter-robustness": besta}} + #dstarr prefers a more generic form: ("weight" is just a 0..1 quantifier) + tmp = {"probabilities": {"single-lens": {'prob':bestp, "prob_weight": besta}}} + ret.update(tmp) + self.final_results = ret + + def _extract_info(self,b,berr,extra): + + umin_found, umin_found_sigma = b[1], berr[1] + dadu = -(umin_found**2 + 2)/(umin_found**2*sqrt(umin_found + 4)) - \ + (umin_found**2 + 2)/(2*umin_found*(umin_found + 4)**(1.5)) + \ + 2/sqrt(umin_found + 4) + + Amax = (umin_found**2 + 2)/(umin_found*sqrt(umin_found**2 + 4)) + Amax_sigma = sqrt(dadu**2)*umin_found_sigma + + ret = {"beta": b, + "t0": {"val": b[2], "err": berr[2]}, + "te": {"val": abs(b[0]), "err": berr[0]}, + "umin": {"val": abs(b[1]), "err": berr[1]}, + "Amax": {"val": abs(Amax), "err": Amax_sigma}, + "resid_var": extra.res_var} + + return ret + + def inspect_output_by_filter(self,rez,dat,doplot=False,test=False, + sig_clips=[5, 3, 2], sig_test=[False,False,True]): + p = rez.values()[0][1] + myoutput = rez.values()[0][0] + new = rez.values()[0][2] + filt = rez.keys()[0] + + ret = {} + ret.update({"all": self._extract_info(p,myoutput.sd_beta,myoutput)}) + err = dat[2] + tmp = (dat[1] - self.modelfunc_small_te(p,dat[0]))/err + dof = tmp.shape[0] - myoutput.beta.shape[0] + chisq = (tmp**2).sum() + ret['all'].update({"ndata": dat[0].shape[0], \ + "chisq": chisq, "dof": dof, "p_chi": chisqprob(chisq,dof), + "normalcy_prob": normaltest(tmp)[1]}) + + for s in enumerate(sig_clips): + if sig_test[s[0]] and not test: + continue + sig = s[1] + # get the indices of those inside and out of the clip area + tmpisig = (abs(tmp) < sig).nonzero()[0] + tmpisige = (abs(tmp) > sig).nonzero()[0] + frac_less_than_sig = float(tmpisig.shape[0])/dat[0].shape[0] + # print frac_less_than_sig + if frac_less_than_sig < 1.0: + out = self._filt_run([dat[0][tmpisig],dat[1][tmpisig],err[tmpisig]],\ + filt,do_sim=False,vplot=False) + p = out[1] + myoutput = out[0] + t = "-test" if sig_test[s[0]] else "" + + ret.update({"sig" + str(sig) + t: self._extract_info(p,myoutput.sd_beta,myoutput)}) + tmp = (dat[1][tmpisig] - self.modelfunc_small_te(p,dat[0][tmpisig]))/err[tmpisig] + dof = tmp.shape[0] - myoutput.beta.shape[0] + chisq = (tmp**2).sum() + try: + ntest = normaltest(tmp)[1] + except: + ntest = 0.0 + ret["sig" + str(sig) + t].update({"ndata": dat[0][tmpisig].shape[0], \ + "chisq": chisq, "dof": dof, "p_chi": chisqprob(chisq,dof), + "normalcy_prob": ntest, "frac_data_remaining": frac_less_than_sig }) + if doplot: + plot(dat[0][tmpisige],dat[1][tmpisige],".") + + return ret + + def _filt_run(self,dat,filt,do_sim=False,vplot=True,nrange=1): + + if self.doplot and vplot: + errorbar(dat[0],dat[1],dat[2],fmt="o") + + new = True + if new: + mymodel = Model(self.fitfunc_small_te,extra_args=[dat[1],dat[2],False]) + else: + mymodel = Model(self.fitfunc_te) #,extra_args=[dat[1],dat[2],False]) + + # get some good guesses + try: + scale = trim_mean(dat[1],0.3) + except: + scale = mean(dat[1]) + offset = 1.0 #trim_mean(dat[1],0.3) + t0 = median(dat[0]) + umin = 1.0 + b = 0.0 ## trending slope + mydata = RealData(dat[0],dat[1],sx=1.0/(60*24),sy=dat[2]) + + trange = list(linspace(min(dat[0]),max(dat[0]),nrange)) + maxi = (dat[1] == max(dat[1])).nonzero()[0] + trange.extend(list(dat[0][maxi])) + trange.extend([t0, max(dat[0]) + 10, max(dat[0]) + 100]) + + final_output = None + for t0i in trange: + for te in 10**linspace(log10(2),log10(200),nrange): + if new: + pinit = [te,umin,t0i] # ,scale,offset,b] + else: + pinit = [te,umin,t0i ,scale,offset,b] + + myodr = ODR(mydata,mymodel,beta0=pinit) + myoutput = myodr.run() + if final_output is None: + final_output = myoutput + old_sd_beta = final_output.sd_beta + continue + + if trim_mean(log10(myoutput.sd_beta / final_output.sd_beta),0.0) < 0.0 and \ + myoutput.res_var <= final_output.res_var and (myoutput.sd_beta == 0.0).sum() <= (final_output.sd_beta == 0.0).sum(): + final_output = myoutput + + if 1: + t = linspace(min(dat[0]),max([max(dat[0]),final_output.beta[2] + 6*final_output.beta[0]]),1500) + if new: + tmp = self.fitfunc_small_te(final_output.beta,dat[0],dat[1],dat[2],True) + #print tmp, "***" + p = list(final_output.beta) + p.extend([tmp[0],tmp[1],tmp[2]]) + y = array(self.modelfunc_small_te(p,t)) + else: + p = final_output.beta + y = self.fitfunc_te(final_output.beta,t) + #print final_output.beta + if self.doplot: + plot(t,y) + xlabel('Time [days]') + ylabel('Relative Flux Density') + + if do_sim: + for i in range(10): + tmp = r.multivariate_normal(myoutput.beta, myoutput.cov_beta) + if self.doplot: + plot(t, self.a_te(tmp[0],tmp[1],tmp[2],tmp[3],tmp[4],tmp[5],t),"-") + + return (final_output, p, new) + + def exp_model(self,scale): + pass + + def a_te(self,the_te, umin, t0, scale, offset, b, t): + uu = self.u_te(the_te,umin,t0,t) + return scale*(offset + b*(t/t[0]))*(uu**2 + 2)/(uu*sqrt(uu**2 + 4.0)) + + #return scale*((uu**2 + 2)/(uu*sqrt(uu**2 + 4.0)) + offset + b*(t/t[0])) + + def a(self,ml, dl, vl, umin, t0, t): + uu = self.u(ml, dl, vl, umin, t0, t) + return (uu**2 + 2)/(uu*sqrt(uu**2 + 4.0)) + + def fitfunc_te(self,p, t): + return self.a_te(p[0],p[1],p[2],p[3],p[4],p[5],t) + + def errfunc_small_te(self,p, t, y, err): + the_te, umin, t0 = p[0],p[1],p[2] + uu = self.u_te(the_te,umin,t0,t) + tp = t/t[0] + c1, c2, c3, c4, c5 = (uu**2/err**2).sum(), (uu*tp/err**2).sum(), \ + (uu*y/err**2).sum(), (tp**2*uu**2/err**2).sum(), (tp*y*uu/err**2).sum() + a = (c5*c2 - c3*c4)/(c4*c1 - c2**2) + b = (-1.0*c3 - a*c1)/c2 + + f = (a + b*tp)*(uu**2 + 2)/(uu*sqrt(uu**2 + 4.0)) + return (y - f) / err + + def modelfunc_small_te(self,p,t): + the_te, umin, t0, a, b = abs(p[0]),p[1],p[2],p[3],p[4] + if len(p) > 5: + c = p[5] + else: + c = 0 + #the_te, umin, t0 = 6.55809283e+01 , 1.01096831e+00 , 1.85432274e+03 + #a = 1.22903731e+00* 1.00337302e+00 + #b = -2.41483071e-04 * 1.22903731e+00 + + uu = self.u_te(the_te,umin,t0,t) + tp = t/t[0] + return c + (a + b*tp)*(uu**2 + 2)/(uu*sqrt(uu**2 + 4.0)) + + def fitfunc_small_te(self,p, t, y,err,retab): + #the_te, umin, t0 = 6.55809283e+01 , 1.01096831e+00 , 1.85432274e+03 + + the_te, umin, t0 = abs(p[0]),p[1],p[2] + uu = self.u_te(the_te,umin,t0,t) + uu = (uu**2 + 2)/(uu*sqrt(uu**2 + 4.0)) + tp = t/t[0] + #err[:] = 1 + old = False + if old: + c1, c2, c3, c4, c5 = (uu**2/err**2).sum(), (tp*uu**2/err**2).sum(), \ + (uu*y/err**2).sum(), (tp**2*uu**2/err**2).sum(), (tp*y*uu/err**2).sum() + # print c1, c2, c3, c4, c5 + a = (-1.0*c5*c2 + c3*c4)/(c4*c1 - c2**2) + b = (c3 - a*c1)/c2 + c = 0 + ## + #a = 1.0*c3/c1 + #b = 0.0 + # (uu**2 + 2)/(uu*sqrt(uu**2 + 4.0)) + else: + c1 = (y/err**2).sum() + c2 = (1/err**2).sum() + c3 = (uu/err**2).sum() + c4 = (tp*uu/err**2).sum() + c5 = (y*uu/err**2).sum() + c6 = c4 + c7 = (uu**2/err**2).sum() + c8 = (tp*uu**2/err**2).sum() + c9 = (tp*y*uu/err**2).sum() + c10 = c4 + c11 = c8 + c12 = (tp**2*uu**2/err**2).sum() + c= (c3*(c12*c5-c8*c9)+c4*(c7*c9-c11*c5)+c1*(c11*c8-c12*c7))/(c2*(c11*c8-c12*c7)+c3*(c12*c6-c10*c8)+c4*(c10*c7-c11*c6)) + a=-(c2*(c12*c5-c8*c9)+c4*(c6*c9-c10*c5)+c1*(c10*c8-c12*c6))/(c2*(c11*c8-c12*c7)+c3*(c12*c6-c10*c8)+c4*(c10*c7-c11*c6)) + b= (c2*(c11*c5-c7*c9)+c3*(c6*c9-c10*c5)+c1*(c10*c7-c11*c6))/(c2*(c11*c8-c12*c7)+c3*(c12*c6-c10*c8)+c4*(c10*c7-c11*c6)) + + f = (a + b*tp)*uu + c + #cc = (((f - y)/err)**2).sum() + #print (a,b,the_te,umin,t0) + #a = 1.22903731e+00* 1.00337302e+00 + #b = -2.41483071e-04 * 1.22903731e+00 + #f = (a + b*tp)*uu # (uu**2 + 2)/(uu*sqrt(uu**2 + 4.0)) + #cc1 = (((f - y)/err)**2).sum() + #print cc, cc1, cc/cc1 + #f = (a + b*tp)*uu # (uu**2 + 2)/(uu*sqrt(uu**2 + 4.0)) + if not retab: + return f # (y - f) / err + else: + return (a,b,c) + def plot_rez(self): + cla() + +class EventData: + + def __init__(self,data): + ## should be + if isinstance(data,xmldict.XmlDictObject) or isinstance(data,dict): + self.data = data + if isinstance(data,str): + ## maybe it's a file? + if data.endswith(".xml"): + v = vosource_parse.vosource_parser(data) + self.fname = data + #self.elemtree = v.elemtree # 20090225 dstarr added + self.data = v.d + else: + # The we assume it is a large string of XML + v = vosource_parse.vosource_parser(data, is_xmlstring=True) + self.fname = data + #self.elemtree = v.elemtree # 20090225 dstarr added + self.data = v.d + # self.fiter_data/dict = .... ?etree? + + self.filts = self.data['ts'].keys() + + # We get feature data (not for mlens use, but for other classifier's use) + self.get_features_from_data() + + + def get_features_from_data(self): + """ Retrieve features from self.data (vosource_parser) + inserts into self.feat_dict{} + WHERE: + self.feat_dict[] = {\ + 'description': 'freq1_harmonics_peak2peak_flux', + 'err': {'_text': 'unknown', 'datatype': 'string'}, + 'filter': {'_text': 'H', 'datatype': 'string'}, + 'name': {'_text': 'freq1_harmonics_peak2peak_flux', 'class': 'timeseries'}, + 'origin': {'code_output': {'_text': '"0.307688969542"', + 'datatype': 'string'}, + 'code_ver': 'db_importer.py 885 2008-10-24 19:47:03Z pteluser', + 'description': '', + 't_gen': {'_text': '2008-10-28T04:50:35.258144', + 'ucd': 'time.epoch'}}, + 'val': {'_text': '0.307688969542', + 'datatype': 'float', + 'is_reliable': 'True'}} + """ + self.feat_dict = {'multiband':{}} + # TODO: there are different filters. Use these. + + for feat_obj in self.data.get('VOSOURCE',{}).get('Features',{}).get('Feature',[]): + a_feat_dict = dict(feat_obj) + feat_name = a_feat_dict.get('name',{}).get('_text','NOT_A_FEATURE') + filt_name = a_feat_dict.get('filter',{}).get('_text','NOT_A_FILTER') + if filt_name not in self.feat_dict: + self.feat_dict[filt_name] = {} + self.feat_dict[filt_name][feat_name] = a_feat_dict # ??? need to copy.deepcopy() this? + + + def t(self,filt): + """assumes unit = day for now""" + + if isinstance(filt,str): + if filt not in self.filts: + return array([]) + else: + ret = array([]) + for c in self.data['ts'][filt]: + if "name" in c: + if c['name'] == "t": + ret = c['val'] + break + if "system" in c: + if c['system'] == "TIMESYS": + ret = c["val"] + break + return ret + return array([]) + + def flux(self,filt,zp=None): + """assumes unit = day for now""" + if not zp: + zp = 3000e3 ## just choose something to get us close to mJy + + if isinstance(filt,str): + if filt not in self.filts: + return array([]) + else: + ret = array([]) + for c in self.data['ts'][filt]: + if "name" in c: + if c['name'].find("err") != -1: + continue + #if c['name'] == "f" or c['name'].find("flux") != -1: + # ret = c['val'] + # break + #if c['name'] == "m" or c['name'].find("mag") != -1: + # ret = zp*10**(-0.4*c["val"]) + # break + if "ucd" in c: + if c['ucd'].find("err") != -1: + continue + if c['ucd'].find("rel flux") != -1: + ret = c["val"] + 1.0 ## for OGLE data + break + if c['ucd'].find("flux") != -1: + ret = c["val"] + break + if c['ucd'].find("phot.mag") != -1: + ret = zp*10**(-0.4*c["val"]) + break + if "unit" in c: + if c['unit'].find("Jy") != -1 or c['unit'].find("erg") != -1: + ret = c["val"] + break + if c["unit"].find("rel flux") != -1: + ret = c["val"] + 1.0 ## for OGLE + break + if c['unit'].find("mag") != -1: + ret = zp*10**(-0.4*c["val"]) + break + + return ret + return array([]) + + def flux_err(self,filt,zp=None): + """assumes unit = day for now + not a correct calculation for large errors + """ + + if not zp: + zp = 3000e3 ## just choose something to get us close to mJy + + if isinstance(filt,str): + if filt not in self.filts: + return array([]) + else: + ret = array([]) + for c in self.data['ts'][filt]: + #if c.has_key("name"): + # if c['name'] == "f_err": + # ret = c['val'] + # break + # if c['name'] == "m_err": + # ret = c["val"]*self.flux(filt,zp) + # break + if "ucd" in c: + if c['ucd'].find("flux") != -1 and c['ucd'].find("err") != -1: + ret = c["val"] + break + if c['ucd'].find("phot.mag") and c['ucd'].find("err") != -1: + ret = c["val"]*self.flux(filt,zp) + break + + if (ret == 0).sum() == ret.shape[0]: + ## all zeros! figure out the scatter + tmp = self.flux(filt,zp) + for i in range(5): + med = median(tmp) + sigma = sqrt(((tmp - med)**2).sum()/ret.shape[0]) + tmpi = (abs(tmp - med) < 2.5*sigma).nonzero()[0] + tmp = tmp[tmpi] + #print med, sigma + ret = sigma*ones(ret.shape[0]) + + return ret + return array([]) + +def test(): + + #d = EventData("test_feature_algorithms.VOSource.xml") + #m = Mlens(datamodel=d) + #d = EventData("MM1-1190.xml") + #d = EventData("sc33-290665.xml") + #d = EventData("124.22024.4124.xml") + #d = EventData("142.27776.3952.xml") + #d = EventData("167.24169.3673.xml") + #d = EventData("168.25079.1100.xml") + #d = EventData("307.36884.258.xml") + #d = EventData("sc39-468687.xml") + d = EventData("sc39-697584.xml") + #d = EventData("sc34-451887.xml") + #d = EventData("MM1-1192.xml") # 2 filters + #d = EventData("BLG157.7-132318.xml") # multi + #d = EventData("MM1-0307.xml") #not a lens + #d = EventData("ON231.xml") #not a lens + #d = EventData("SN1998dk.xml") #not a lens + #pprint(d.data) + #pprint(d.data['ts']) + #pprint(d.t("B")) + #pprint(d.flux("B")) + #pprint(d.flux_err("B")) + #durga + m = Mlens(datamodel=d,doplot=True) + print(m) + #errorbar(d.t("I"),d.flux("I"),d.flux_err("I")) + #print d.t("I").median() + +if __name__ == '__main__': + + ##### dstarr added these testing statements: + + d = EventData(os.path.abspath(os.environ.get("TCP_DIR") + "/Data/124.22024.4124.xml")) + + ## run the fitter (turn off doplot for running without pylab) + m = Mlens(datamodel=d,doplot=True) + + ###m.run() + ## print the results + import pprint + pprint.pprint(m.final_results) + + ## grab the results (prob_mlens should be between 0 and 1...anything above 0.8 is pretty sure bet) + prob_mlens = m.final_results["probabilities"]["single-lens"] + + pass # for pdb breakpoint diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/montecarlo_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/montecarlo_extractor.py new file mode 100644 index 00000000..ff640dc6 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/montecarlo_extractor.py @@ -0,0 +1,29 @@ +from __future__ import absolute_import +from ..FeatureExtractor import InterExtractor +import numpy + +from ..generators_importers.montecarlo_gen import montecarlo_gen +from .power_extractor import power_extractor +class montecarlo_extractor(InterExtractor): + """ Performs a montecarlo bootstrap on the data to determine the significance of a power spectrum """ + active = True + extname = 'montecarlo' #extractor's name + how_many = 50 # TEN IS PROBABLY NOT ENOUGH BUT IT SPEEDS THINGS UP FOR NOW + def extract(self): + list_for_bootstraps = [] + spectra = [] + for i in range(self.how_many): + gen = montecarlo_gen() + try: + gen.generate(self.dic,list_for_bootstraps) + except KeyError: + self.ex_error("(KeyError in montecarlo generator) No rms data available in dictionary:%s" % self.dic['input'].keys().__str__()) + freqs = self.frequencies#self.fetch_extr('power_spectrum')[0] + for sig in list_for_bootstraps: + power = sig.update(power_extractor()).result + if power == "Fail": + self.ex_error(power.why) + spectra.append(power) + spec_stack = numpy.vstack(spectra) + spec_stack.sort(axis=0) + return spec_stack \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/n_points_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/n_points_extractor.py new file mode 100644 index 00000000..7016d411 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/n_points_extractor.py @@ -0,0 +1,8 @@ +from ..FeatureExtractor import FeatureExtractor + +class n_points_extractor(FeatureExtractor): + active = True + extname = 'n_points' # identifier used in final extracted value dict. + def extract(self): + n_val = len(self.flux_data) # number of photometric points in the light curve + return n_val diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/ned.py b/mltsp/TCP/Software/feature_extract/Code/extractors/ned.py new file mode 100644 index 00000000..26553cda --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/ned.py @@ -0,0 +1,460 @@ +from __future__ import print_function +import os,sys +import urllib +#20090321#import amara +import copy +import threading +import warnings + +class NED(object): + + max_dist_search_arcmin = 100.0 ## 5 degree radius at most + max_z_for_local_search = 0.003 ## otherwise it aint local and could hang + max_small_dist_search_arcmin = 1.0 + max_field_dist_search_arcmin = 30.0 + + local_results = None + nearest_results = None + field_results = None + + indict = {"in_csys": "Equatorial", "in_equinox": "J2000.0",\ + "out_csys": "Equatorial", "out_equinox": "J2000.0",\ + "obj_sort": "Distance to search center",\ + "of": "pre_text","zv_breaker": 30000.0, \ + "list_limit": 5, "img_stamp": "NO", "z_constraint": "Available",\ + "z_value1": "", "z_value2": "", "z_unit": "z", "ot_include": "ANY", \ + "in_objtypes1": "Galaxies", "nmp_op": "ANY", "search_type": "Near Position Search"} + + name_search_dict = {"objname": "", "extend": "no","of":"xml_derved","img_stamp":"NO"} + + ned_url = "http://ned.ipac.caltech.edu/cgi-bin/nph-objsearch?" + + ucd_lookup = {'name': "meta.id;meta.main", "ra": "pos.eq.ra;meta.main", + "dec": "pos.eq.dec;meta.main", "z": "src.redshift", "zflag": "meta.code;src.redshift", + "distance_arcmin": "pos.distance"} + + def __init__(self,pos=(None,None),verbose=False,object_types="Galaxies",precompute_on_instantiation=True, + do_threaded=True,do_local=True, do_field = True, do_nearest=True): + self.good_pos = False + self.ra = None + self.dec = None + self.parse_pos(pos) + self.verbose = verbose + self.indict.update({"in_objtypes1": object_types}) + self.objects = [] + self.threads = [] + self.do_threaded = do_threaded + self.do_local = do_local + self.do_nearest = do_nearest + self.do_field = do_field + + if precompute_on_instantiation: + if self.do_threaded: + self._run_threaded() + else: + if self.verbose: + print("precomuting") + if self.do_local: + self._local_gal_search() + if self.do_nearest: + self._nearest_galaxy_search() + if self.do_field: + self._field_galaxy_search() + + def _run_threaded(self): + if self.do_local: + if self.verbose: + print("Starting local search in a thread") + self.threads.append(threading.Thread(target=self._local_gal_search,name="local")) + self.threads[-1].start() + if self.do_nearest: + if self.verbose: + print("Starting nearest search in a thread") + self.threads.append(threading.Thread(target=self._nearest_galaxy_search,name="nearest")) + self.threads[-1].start() + if self.do_field: + if self.verbose: + print("Starting field search in a thread") + + self.threads.append(threading.Thread(target=self._field_galaxy_search,name="field")) + self.threads[-1].start() + ### dstarr adds, to insure all threads join before returning: + #for t in self.threads: + # t.join() + + def _local_gal_search(self,z_max=0.002,rad=100.0): + + local_dict = copy.copy(self.indict) + if rad > self.max_dist_search_arcmin: + rad = self.max_dist_search_arcmin + if self.verbose: + print("!NED: setting search radius to max_dist_search_arcmin (%f)" % self.max_dist_search_arcmin) + + if z_max > self.max_z_for_local_search: + z_max = self.max_z_for_local_search + if self.verbose: + print("!NED: setting max z to max_z_for_local_search (%f)" % self.max_z_for_local_search) + + ## get the xml_main for this, maybe nothing + local_dict.update({"lon": self.ra, "lat": self.dec, "radius": rad, "of": "xml_main",\ + "z_constraint": "Less Than","z_value1": z_max}) + self.local_results = self._do_search(local_dict) + + def print_local_gal_search(self, z_max=0.002,rad=150.0,timeout=60.0): + """ will do very wide search for local galaxies (z < z_max) consistent with a position """ + + if self.do_threaded: + for t in self.threads: + if t.getName() == 'local': + if self.verbose: + print("Joining the local thread and waiting for it to finish") + t.join(timeout) + + if self.local_results is None: + self._local_gal_search(z_max = z_max, rad= rad) + + print("*"*30) + print("Local Galaxy Results") + print("*"*30) + self._rez_print(self.local_results,key='kpc_offset') + + + def _nearest_galaxy_search(self,rad=0.2): + """ searches really nearby for all types of galaxies whether z is known or not ... default is 12 arcsec """ + local_dict = copy.copy(self.indict) + if rad > self.max_field_dist_search_arcmin: + rad = self.max_field_dist_search_arcmin + + ## get the xml_main for this, maybe nothing + local_dict.update({"lon": self.ra, "lat": self.dec, "radius": rad, "of": "xml_main",\ + "z_constraint": "Unconstrained"}) + + # we really only care about the nearest few guys + self.nearest_results = self._do_search(local_dict,max_derived=3) + + def print_nearest_galaxy_search(self,rad=0.2,timeout=60): + if self.do_threaded: + for t in self.threads: + if t.getName() == 'nearest': + if self.verbose: + print("Joining the 'nearest' thread and waiting for it to finish") + t.join(timeout) + + if self.nearest_results is None: + self._nearest_galaxy_search(rad= rad) + + print("*"*30) + print("Nearest Galaxy Results") + print("*"*30) + self._rez_print(self.nearest_results,key='distance_arcmin') + + def _field_galaxy_search(self,rad=10): + """ searches field galaxies """ + + local_dict = copy.copy(self.indict) + if rad > self.max_field_dist_search_arcmin: + rad = self.max_field_dist_search_arcmin + + ## get the xml_main for this, maybe nothing + local_dict.update({"lon": self.ra, "lat": self.dec, "radius": rad, "of": "xml_main",\ + "z_constraint": "Available", "z_value1": "", "z_value2": ""}) + + # we really only care about the nearest few guys + self.field_results = self._do_search(local_dict,max_derived=100) + + def print_field_galaxy_search(self,rad=5,timeout=60): + if self.do_threaded: + for t in self.threads: + if t.getName() == 'field': + if self.verbose: + print("Joining the 'field' thread and waiting for it to finish") + t.join(timeout) + + if self.field_results is None: + self._field_galaxy_search(rad= rad) + + print("*"*30) + print("Field Galaxy Results") + print("*"*30) + self._rez_print(self.field_results,key='kpc_offset') + + def distance_in_kpc_to_nearest_galaxy(self,timeout=60): + """ cutoff in kpc ... dont return anything if more than that """ + ans = {"request": "distance_in_kpc_to_nearest_galaxy","distance": None} + if self.do_threaded: + # we need to join field and local results + for t in self.threads: + if t.getName() in ['field','local']: + t.join(timeout) + else: + if self.field_results is None: + self._field_galaxy_search() + if self.local_results is None: + self._local_galaxy_search() + + if self.field_results is None or self.local_results is None: + warnings.warn("no field or local result return") + ans.update({'feedback': 'no field or local result return'}) + return ans + + tmp = copy.copy(self.field_results) + tmp.extend(copy.copy(self.local_results)) + + ## sort by kpc offset + key = 'kpc_offset' + obj = copy.copy(tmp) + for o in tmp: + if key not in o: + obj.remove(o) + + if len(obj) == 0: + ans.update({'feedback': 'no sources found with spatial position values known'}) + return ans + + obj.sort(key=lambda x: x[key]) + ans.update({'feedback': 'seems good','distance': obj[0][key], 'source_info': copy.copy(obj[0])}) + return ans + + def distance_in_arcmin_to_nearest_galaxy(self,timeout=60): + """ """ + ans = {"request": "distance_in_arcmin_to_nearest_galaxy","distance": None} + if self.do_threaded: + # we need to join field and local results + for t in self.threads: + if t.getName() in ['field','local','nearest']: + t.join(timeout) + else: + if self.field_results is None: + self._field_galaxy_search() + if self.nearest_results is None: + self._nearest_galaxy_search() + if self.local_results is None: + self._local_galaxy_search() + + if self.field_results is None or self.local_results is None or self.nearest_results is None: + warnings.warn("no field or local or nearest result return") + ans.update({'feedback': 'no field or local or nearest result return'}) + return ans + + tmp = copy.copy(self.field_results) + tmp.extend(copy.copy(self.local_results)) + + ## sort by kpc offset + key = 'distance_arcmin' + obj = copy.copy(tmp) + for o in tmp: + if key not in o: + obj.remove(o) + + if len(obj) == 0: + ans.update({'feedback': 'no sources found with spatial position values known'}) + return ans + + obj.sort(key=lambda x: x[key]) + ans.update({'feedback': 'seems good','distance': obj[0][key], \ + 'source_info': copy.copy(obj[0])}) + return ans + def _rez_print(self,objects,key='kpc_offset'): + + ## sort by kpc offset + other = [] + obj = copy.copy(objects) + + for o in objects: + if key not in o: + other.append(o) + obj.remove(o) + + obj.sort(key=lambda x: x[key]) + + print("%-30s\t%9s\t%9s\t%9s\t%9s" % ("Name","z","dm","dist","offset")) + print("%-30s\t%9s\t%9s\t%9s\t%9s" % ("","","mag","arcmin","kpc")) + for o in obj: + if "dm" in o: + dm = o['dm'] + else: + dm = "---" + if 'kpc_offset' in o: + ko = o['kpc_offset'] + else: + ko = "---" + if 'z' in o: + z = o['z'] + else: + z = "---" + + print("%-30s\t%9s\t%9s\t%9.3f\t%9s" % (o['name'],str(z),str(dm),o["distance_arcmin"],str(ko))) + + if len(other) > 0: + print(" *** OTHER (those that cannot be sorted by requested sort key)**** ") + print("%-30s\t%9s\t%9s\t%9s\t%9s" % ("Name","z","dm","dist","offset")) + print("%-30s\t%9s\t%9s\t%9s\t%9s" % ("","","mag","arcmin","kpc")) + for o in other: + if "dm" in o: + dm = o['dm'] + else: + dm = "---" + if 'kpc_offset' in o: + ko = o['kpc_offset'] + else: + ko = "---" + if 'z' in o: + z = o['z'] + else: + z = "---" + + print("%-30s\t%9s\t%9s\t%9.1f\t%9s" % (o['name'],str(z),str(dm),o["distance_arcmin"],str(ko))) + + def _do_search(self,local,get_derived_obj_info=True,max_derived=500): + """ actually performs the search and parses the output """ + + if not self.good_pos: + return [] + + params = urllib.urlencode(local) + if self.verbose: + print(self.ned_url + params) + f = urllib.urlopen(self.ned_url + params) + + tmp = f.read() + try: + doc = amara.parse(tmp) + except: + print("EXCEPT: ned() extractor") + return [] + self.doc = doc + if self.verbose: + print("got the document from NED") + try: + main_table = doc.xml_xpath(u'//TABLE[@ID="NED_MainTable"]')[0] + except: + print("no main table") + return [] + objs = self._get_objects_from_main_table(main_table) + if get_derived_obj_info: + objs = self._get_derived_info(objs,max_derived=max_derived) + + return objs + + def _get_derived_info(self,objs,max_derived=500): + + ret = [] + local = copy.copy(self.name_search_dict) + for o in objs[:max_derived]: + ## do a search on the name + if "name" not in o: + ret.append(o) + else: + tmp = copy.copy(o) + + local.update({"objname": o['name']}) + params = urllib.urlencode(local) + if self.verbose: + print(self.ned_url + params) + f = urllib.urlopen(self.ned_url + params) + tmp1 = f.read() + doc = amara.parse(tmp1) + if self.verbose: + print("got the derived document from NED for source %s" % o['name']) + try: + d= doc.xml_xpath(u'//TABLE[@ID="NED_DerivedValuesTable"]')[0] + except: + print("no dervived table") + continue + + dfields = d[0].xml_xpath(u'FIELD') + dat = d[0].xml_xpath(u'DATA/TABLEDATA/TR/TD') + for i in range(len(dfields)): + if dfields[i].xml_xpath(u'@ucd="pos.distance;scale;hubble.flow.galactocentric" and @unit="kpc/arcmin"'): + tmp1 = unicode(dat[i]) + try: + ug1 = float(tmp1) + except: + ug1 = str(tmp1).strip() + tmp.update({'kpc_arcmin': ug1}) + try: + tmp.update({'kpc_offset': tmp['kpc_arcmin']*tmp['distance_arcmin']}) + except: + pass + if dfields[i].xml_xpath(u'@ucd="pos.distance;luminosity_moduli" and @unit="mag"'): + tmp1 = unicode(dat[i]) + try: + ug1 = float(tmp1) + except: + ug1 = str(tmp1).strip() + tmp.update({'dm': ug1}) + ret.append(tmp) + return ret + + def _get_objects_from_main_table(self,main_table): + vals = self.ucd_lookup.values() + table_lookup = self.ucd_lookup.fromkeys(self.ucd_lookup) + + fields = main_table.xml_xpath(u'FIELD') + for i in range(len(fields)): + f = fields[i] + if f.ucd in vals: + for k,v in self.ucd_lookup.items(): + if v == f.ucd: + table_lookup[k] = i + break + + objs = main_table.xml_xpath(u'DATA/TABLEDATA/TR') + objects = [] + for o in objs: + ug = o.xml_xpath(u'TD') + tmp = {} + for k, i in table_lookup.items(): + tmp1 = unicode(ug[i]) + try: + ug1 = float(tmp1) + except: + ug1 = str(tmp1).strip() + tmp.update({k: ug1}) + objects.append(tmp) + + return objects + + def parse_pos(self,pos): + """ parses the position and set local variables """ + ## TODO: more error checking ... assume degrees now + #print pos + ra = pos[0] + dec = pos[1] + if type(ra) != type(1.2) or type(dec) != type(1.2): + warnings.warn("RA and/or DEC is not a type of float") + self.good_pos = False + return + + if ra < 0.0 or ra >= 360.0 or dec < -90.0 or dec > 90.0: + warnings.warn("RA and/or DEC out of range") + self.good_pos = False + return + + self.good_pos = True + self.ra = "%fd" % ra + self.dec = "%fd" % dec + + return + +def test(): + ra = 199.83412 + dec = 8.92897 + #ra = 286.61986 + #dec = 68.79320 + #ra = 185.1282480 + #dec = 28.346232 + b = NED(pos=(ra,dec),verbose=False) + #b.print_local_gal_search(rad=150) + #b.print_nearest_galaxy_search(rad=0.2) + #b.print_field_galaxy_search(rad=8) + print(b.distance_in_kpc_to_nearest_galaxy()) + print(b.distance_in_arcmin_to_nearest_galaxy()) + b.print_local_gal_search() + b.print_nearest_galaxy_search() + b.print_field_galaxy_search() + +if __name__ == "__main__": + test() + + diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/ned_cache_server.py b/mltsp/TCP/Software/feature_extract/Code/extractors/ned_cache_server.py new file mode 100644 index 00000000..63159749 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/ned_cache_server.py @@ -0,0 +1,1093 @@ +#!/usr/bin/env python +""" + v0.2 ned_cache_server.py : Code is now more generic and allows caching of + SDSS server characteristics as well as NED characs. I'm coding + such that new SDSS extractadd new + v0.1 ned_cache_server.py : Code which caches and delegates NED + 'nearest object feature' information for various (ra,dec). + This information is returned to feature-extractors. + The purpose behind this is that the NED database only allows 1 query + per second, so we need to regulate & cache our queries. + +NOTE: This system works as follows: + - a server continually queries a shared MySQL table for new (retrieved=0) + table rows + - if >= 1 row such as this exists, the 'server' retrieves these items + from SDSS/NED and populates the MySQL tables, setting (retrieved=1) + - a client call, generally from tmpned_extractor.py feature extractor + will, by using a ned_cache_server.py Class for accessing, + will query for an (ra,dec) to see if previously retrieved feature + data exists. + - if no data exists, it places a (retrieved=0) row in MySQL table + - if data exists for (ra,dec), it retrieves and fills sdss and ned + structures which other ned and sdss feature extractors expect. + + +import ned_cache_server +ncc = ned_cache_server.Ned_Cache_Client(ned_cache_server.pars) +ned_dict = ncc.retrieve_queue_ned_dict(49.599486,-1.005111) +print ned_dict + +### Simple DEBUG snippet to submit ra,dec & get ned dict: +import socket +s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) +s.connect(('127.0.0.1', 45623)) +#s.send("%lf@%lf" % (49.599486,-1.005111)) +s.send("%lf@%lf" % (48.0,-3.005111)) +data = s.recv(512) +s.close() +rec_data = repr(data).replace("@"," ") +print rec_data + +""" +from __future__ import print_function +from __future__ import absolute_import +import sys, os +import time +import MySQLdb +import copy + +sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + \ + 'Software/feature_extract/Code/extractors/')) +from . import ned +from . import sdss + +pars = { + 'ned_cache_hostname':'192.168.1.25', + 'ned_cache_username':'pteluser', #'dstarr', + 'ned_cache_database':'ned_feat_cache', + 'ned_cache_db_port':3306, #3306, + 'tablename__ch_id_defs':'ch_id_defs', + 'tablename__ch_spatial':'ch_spatial', + 'tablename__ch_vals':'ch_vals', + 'tablename__ptf_footprint':'ptf_footprint', + 'ptf_postgre_dbname':'subptf', # KLUDGE: these ptf* are taken from ingest_tools.py + 'ptf_postgre_user':'dstarr', + 'ptf_postgre_host':'sgn02.nersc.gov', + 'ptf_postgre_password':'*2ta77', + 'ptf_postgre_port':6540, + 'ptf_postgre_sub_tablename':'subtraction', + 'ptf_postgre_candidate_tablename':'candidate', + 'htm_level':17, #BIGINT, ~0.024-0.053 arcsec resolution + 'htm_query_radius':0.016666 * 5.0, # in arcmins, used in SQL/HTM spatial query + 'while_loop_sleep_time':1, # seconds. Not too short: SQL query done for each + 'socket_loop_sleep_time':0.1, # seconds. May be short. + 'socket_bind_wait_time':3.0, # seconds. + 'socket_server_port':45623, + 'socket_n_backlog_connections':50, + 'do_ned_query':False, + 'do_sdss_query':True, + 'char_replace':{'-':'_', + '+':'_', + '"':'_', + "'":'_', + " ":'_'}, + } +pars['tablename__ch_spatial_htm'] = pars['tablename__ch_spatial'] + '_htm' + + +# For return to Feature extractors: +class Sdss_Obj: + def __init__(self): + self.feature = {} + + + + +# For return to Feature extractors: +class Ned_Dictlike_Obj: + def __init__(self): + self.__dict__ = {} + + + def __getitem__(self, key): + return self.__dict__[key] + + + def __setitem__(self, key, value): + self.__dict__[key] = value + + + def __str__(self): + return str(self.__dict__) + + + def __len__(self): + return len(self.__dict__) + + + def distance_in_arcmin_to_nearest_galaxy(self): + # NOTE: -1 is N/A + ret_dict = {'distance':self.__dict__.get('source_info',{})\ + .get('distance_arcmin', -1)} + return ret_dict + + def distance_in_kpc_to_nearest_galaxy(self): + # NOTE: -1 is N/A + ret_dict = {'distance':self.__dict__.get('source_info',{})\ + .get('kpc_offset', -1)} + return ret_dict + + +def select_ned_data_from_table(ra, dec, cursor): + """ Select NN info for a (ra,dec) and place in ned.py-like dict. + """ + ned_out_dict = Ned_Dictlike_Obj() + sdss_out_dict = Sdss_Obj() + + select_str = "SELECT spatial_id FROM %s WHERE (DIF_HTMCircle(%lf, %lf, %lf)) LIMIT 1" % \ + (pars['tablename__ch_spatial_htm'], \ + ra, dec, pars['htm_query_radius']) + cursor.execute(select_str) + results = cursor.fetchall() + + spatial_id = -1 + try: + if results[0][0] >= 0: + spatial_id = results[0][0] + except: + pass + + if spatial_id == -1: + return (ned_out_dict, sdss_out_dict) # Default "empty" dict-like objects + + select_str = "SELECT y.ch_group, y.ch_name, x.ch_val_dbl, x.ch_val_str, y.ch_type FROM %s AS x JOIN %s AS y ON y.ch_id = x.ch_id WHERE spatial_id=%d" %\ + (pars['tablename__ch_vals'], + pars['tablename__ch_id_defs'], + spatial_id) + cursor.execute(select_str) + results = cursor.fetchall() + + ################ + # Here we form the feature-extractor expected structures from RDB SELECT + + if len(results) > 0: + ned_out_dict['source_info'] = {} + for (ch_group, ch_name, ch_val_dbl, ch_val_str, ch_type) in results: + if ch_group == 0: + # NED + if ch_name == 'distance': + ned_out_dict['distance'] = ch_val_dbl + if ch_type: + ned_out_dict['source_info'][ch_name] = ch_val_str + else: + ned_out_dict['source_info'][ch_name] = ch_val_dbl + else: + # SDSS (ch_group == 1) + if ch_type: + sdss_out_dict.feature[ch_name] = ch_val_str + else: + sdss_out_dict.feature[ch_name] = ch_val_dbl + + return (ned_out_dict, sdss_out_dict) + + +class Ned_Cache_Server: + """ Caches and delegates NED (ra,dec) queries for 'nearest object' features. + """ + #import socket + def __init__(self, pars): + self.pars = pars + self.NED_connection_is_free = True # whether method can query NED server + self.threads = [] + + # Make general connection to MySQL server: + self.db = MySQLdb.connect(host=self.pars['ned_cache_hostname'], + user=self.pars['ned_cache_username'], + db=self.pars['ned_cache_database'], + port=self.pars['ned_cache_db_port']) + self.cursor = self.db.cursor() + + + def sig_handler(self, signum, frame): + """ Catch: 'kill':SIGTERM, ^C: SIGINT + """ + print("Got signal: %i" % (signum)) + self.do_loop = False + + + def drop_tables(self): + """ Drop tables and MySQL stuff. + """ + # Drop main tables: + try: + table_list = [('TABLE', self.pars['tablename__ch_id_defs']), + ('TABLE', self.pars['tablename__ch_spatial']), + ('TABLE', self.pars['tablename__ch_vals'])] + for t_type, table_name in table_list: + drop_table_str = "DROP %s IF EXISTS %s" % (t_type, + table_name) + print(drop_table_str) + self.cursor.execute(drop_table_str) + + # Drop HTM / dif triggers, views: + drop_table_str = """DROP view %s.%s; + """ % ( \ + self.pars['ned_cache_database'], + self.pars['tablename__ch_spatial_htm']) + self.cursor.execute(drop_table_str) + except: + print('!!! unable to drop tables / views') + + + def create_tables(self): + """ Create MySQL Tables. + """ + # NOTE: ch_type SMALLINT, ### 0:double, 1:string + create_str = """CREATE TABLE %s ( + ch_id SMALLINT, + ch_group TINYINT, + ch_type SMALLINT, + ch_name VARCHAR(40), + PRIMARY KEY (ch_id), + INDEX(ch_group, ch_name)) + """ % (self.pars['tablename__ch_id_defs']) + self.cursor.execute(create_str) + + #20090504: ch_val_str VARCHAR(500), # if this is changed again, also edit L357 + create_str = """CREATE TABLE %s ( + ch_id SMALLINT, + ch_val_dbl DOUBLE, + ch_val_str VARCHAR(20), + spatial_id INT UNSIGNED, + PRIMARY KEY (spatial_id, ch_id)) + """ % (self.pars['tablename__ch_vals']) + self.cursor.execute(create_str) + + # 20090724: dstarr removes obsolete index below: + # INDEX(local_retr_dtime) + create_str = """CREATE TABLE %s ( + ra DOUBLE, + decl DOUBLE, + spatial_id INT UNSIGNED NOT NULL AUTO_INCREMENT, + retrieved TINYINT DEFAULT 0, + local_retr_dtime DATETIME DEFAULT NULL, + PRIMARY KEY (spatial_id), + INDEX(retrieved)) + """ % (self.pars['tablename__ch_spatial']) + self.cursor.execute(create_str) + + create_str = """CREATE TABLE %s ( + spatial_id INT UNSIGNED NOT NULL, + ujd DOUBLE, + lmt_mg FLOAT, + PRIMARY KEY (spatial_id, ujd)) + """ % (self.pars['tablename__ptf_footprint']) + self.cursor.execute(create_str) + + make_dif = \ + "$HOME/bin/dif --index-htm %s %s %d ra decl < /dev/null" % \ + (self.pars['ned_cache_database'], \ + self.pars['tablename__ch_spatial'], \ + self.pars['htm_level']) + os.system(make_dif) + + + def retrieve_insert_ned_data_to_table(self, ra, dec, spatial_id, cursor, \ + update_only=False): + """ Insert ned.py extracted NN dict info into RDB table. + """ + insert_list = ["INSERT INTO %s (%s.spatial_id, %s.ch_id, %s.ch_val_dbl, %s.ch_val_str) VALUES " % (\ + self.pars['tablename__ch_vals'], + self.pars['tablename__ch_vals'], + self.pars['tablename__ch_vals'], + self.pars['tablename__ch_vals'], + self.pars['tablename__ch_vals'])] + + if self.pars['do_ned_query']: + ### NED INSERT: + n = ned.NED(pos=(ra,dec),verbose=False, do_threaded=False) + nn_dict = n.distance_in_arcmin_to_nearest_galaxy() + if 'kpc_offset' not in nn_dict.get('source_info',{}): + print('ERROR: odd nn_dict:', nn_dict) + else: + nice_name = nn_dict['source_info']['name'] + for old,new in self.pars['char_replace'].items(): + nice_name = nice_name.replace(old,new) + if nn_dict['source_info']['dm'] == '': + float_dm = -1 + else: + float_dm = float(nn_dict['source_info']['dm']) + + ################ + # Here we parse the feature-extractor structures for INSERT: + nn_dict['source_info']['distance'] = nn_dict['distance'] + for ch_name,ch_val in nn_dict['source_info'].items(): + if (0,ch_name) not in self.chname_chid_lookup: + continue # skip this ch_name since its not a charac. + if self.chname_type_lookup[(0,ch_name)]: + ch_val_dbl = 'NULL' + ch_val_str = "'%s'" % (ch_val) + else: + ch_val_dbl = "%f" % (ch_val) + ch_val_str = 'NULL' + insert_list.append("(%d, %d, %s, %s), " % ( \ + spatial_id, self.chname_chid_lookup[(0,ch_name)], \ + ch_val_dbl, ch_val_str)) + + if self.pars['do_sdss_query']: + ### SDSS INSERT: + s = sdss.sdssq(pos=(ra,dec),verbose=True,maxd=0.2*1.05) # 0.2*1.05 :: dont report anything farther away than this in arcmin + + sdss_dict = s.feature + if 'in_footprint' not in sdss_dict: + print('ERROR: odd sdss_dict:', sdss_dict) + else: + ################ + # Here we parse the feature-extractor structures for INSERT: + for ch_name,ch_val in sdss_dict.items(): + if (1,ch_name) not in self.chname_chid_lookup: + continue # skip this ch_name since its not a charac. + if ch_val is None: + ch_val_dbl = 'NULL' + ch_val_str = 'NULL' + elif self.chname_type_lookup[(1,ch_name)] == 1: + ch_val_dbl = 'NULL' + ch_val_str = "'%s'" % (ch_val[-20:]) + elif self.chname_type_lookup[(1,ch_name)] == 2: + ch_val_dbl = 'NULL' + ch_val_str = 'NULL' + else: + ch_val_dbl = "%f" % (ch_val) + ch_val_str = 'NULL' + insert_list.append("(%d, %d, %s, %s), " % ( \ + spatial_id, self.chname_chid_lookup[(1,ch_name)], \ + ch_val_dbl, ch_val_str)) + + if len(insert_list) > 1: + insert_str = ''.join(insert_list)[:-2] + ' ON DUPLICATE KEY UPDATE spatial_id=VALUES(spatial_id), ch_id=VALUES(ch_id), ch_val_dbl=VALUES(ch_val_dbl), ch_val_str=VALUES(ch_val_str)' + cursor.execute(insert_str) + + update_str = \ + "UPDATE %s SET retrieved=1 WHERE (spatial_id=%d)" % (\ + self.pars['tablename__ch_spatial'], spatial_id) + cursor.execute(update_str) + + # obsolete: + def retrieve_from_pgsql_insert_into_local_table(self, pgsql_cursor=None, + mysql_cursor=None, + rdb_rows=[]): + """ Given a list of soruce-positions (ra,dec,id) which are retrieved from + MysqlDB accounting table, retrieve: + - footprint limiting magnitude data from PGSQL-lbl database + and store locally. + - (? other non-delayed tasks (like SDSS/NED) ?) + """ + # NOTE: the following is not intended to be used as an INSERT, since the row already exists, but I use this to allow UPDATEING of many rows at once (using INSERT ... UPDATE syntax): + #chspatial_update_list = ["INSERT INTO %s (spatial_id) VALUES " % (self.pars['tablename__ch_spatial'])] + chspatial_update_list = ["UPDATE %s SET local_retr_dtime=NOW() WHERE " % (self.pars['tablename__ch_spatial'])] + + footprint_insert_list = ["INSERT INTO %s (spatial_id, ujd, lmt_mg) VALUES " % (self.pars['tablename__ptf_footprint'])] + offset_deg = 0.0005556 + for (ra, dec, spatial_id) in rdb_rows: + chspatial_update_list.append("(spatial_id=%d) OR " % (spatial_id)) + select_str = "select obsjd, lmt_mg from proc_image where box(polygon'((%lf, %lf), (%lf, %lf), (%lf, %lf), (%lf, %lf), (%lf, %lf))') && box(image_footprint)" % ( \ + ra - offset_deg, + dec + offset_deg, + ra + offset_deg, + dec + offset_deg, + ra + offset_deg, + dec - offset_deg, + ra - offset_deg, + dec - offset_deg, + ra - offset_deg, + dec + offset_deg) + pgsql_cursor.execute(select_str) + rdb_rows = pgsql_cursor.fetchall() + + for (ujd, lmt_mg) in rdb_rows: + if ujd is None: + continue # this happens occasionally + footprint_insert_list.append("(%d, %lf, %lf), " % (spatial_id, ujd, lmt_mg)) + + footprint_insert_str = ''.join(footprint_insert_list)[:-2] + ' ON DUPLICATE KEY UPDATE spatial_id=VALUES(spatial_id), ujd=VALUES(ujd), lmt_mg=VALUES(lmt_mg)' + mysql_cursor.execute(footprint_insert_str) + + # NOTE: the following is not intended to be used as an INSERT, since the row already exists, but I use this to allow UPDATEING of many rows at once (using INSERT ... UPDATE syntax): + chspatial_update_str = ''.join(chspatial_update_list)[:-3] + mysql_cursor.execute(chspatial_update_str) + + # TODO: also update the accounting table with a datetimed local_retr_dtime == NOW() + + + def rdb_table_watcher(self): + """ This method continually polls the local NED MySQL table for + new position additions which have not had NED data retrieved. + This then queues a list of positions & retrieves NED data, updating + the RDB table. When in NED-querying mode, it locks/flags other methods + from querying the remote NED service. + """ + import time + + # ??? Why did I choose to have this DB cursor locally instantiated? + + db = MySQLdb.connect(host=self.pars['ned_cache_hostname'], + user=self.pars['ned_cache_username'], + db=self.pars['ned_cache_database']) + cursor = db.cursor() + + while self.do_loop: + try: + select_str = \ + "SELECT ra,decl,spatial_id FROM %s WHERE NOT retrieved LIMIT 50000" % \ + (self.pars['tablename__ch_spatial']) + #print 'select_str=', select_str + cursor.execute(select_str) + results = cursor.fetchall() + if len(results) == 0: + time.sleep(self.pars['while_loop_sleep_time']) + print('.', end=' ') + continue + else: + self.NED_connection_is_free = False + for (ra, dec, spatial_id) in results: + self.retrieve_insert_ned_data_to_table(ra, dec, spatial_id,\ + cursor, update_only=True) + self.NED_connection_is_free = True + except: + print('EXCEPT during cursor.execute(), DB down? Sleeping for a bit...') + print('select_str=', select_str) + time.sleep(self.pars['while_loop_sleep_time']) + print("Out of while loop") + + #### + + # obsolete: + def local_retr_table_watcher(self): + """ This method continually polls the local NED MySQL table for + new position additions which have not had NED data retrieved. + This then queues a list of positions & retrieves NED data, updating + the RDB table. When in NED-querying mode, it locks/flags other methods + from querying the remote NED service. + """ + import time + import psycopg2 + + # I chose to have this DB cursor locally instantiated so that multiple threads can poll DB. + + mysql_db = MySQLdb.connect(host=self.pars['ned_cache_hostname'], + user=self.pars['ned_cache_username'], + db=self.pars['ned_cache_database']) + mysql_cursor = mysql_db.cursor() + + pg_conn = psycopg2.connect(\ + "dbname='%s' user='%s' host='%s' password='%s' port=%d" % \ + (self.pars['ptf_postgre_dbname'],\ + self.pars['ptf_postgre_user'],\ + self.pars['ptf_postgre_host'],\ + self.pars['ptf_postgre_password'],\ + self.pars['ptf_postgre_port'])) + pg_cursor = pg_conn.cursor() + + while self.do_loop: + #try: + if 1: + select_str = \ + "SELECT ra,decl,spatial_id FROM %s ORDER BY local_retr_dtime ASC LIMIT 100" %\ + (self.pars['tablename__ch_spatial']) + mysql_cursor.execute(select_str) + results = mysql_cursor.fetchall() + if len(results) == 0: + time.sleep(self.pars['while_loop_sleep_time']) + print('.', end=' ') + continue + else: + #self.NED_connection_is_free = False + #TODO: the passed in method should take the full list of ra,dec, spatial_id. + # and then fill some other table with values retrieved from PGSQL. + self.retrieve_from_pgsql_insert_into_local_table( \ + pgsql_cursor=pg_cursor, + mysql_cursor=mysql_cursor, + rdb_rows=results) + + #for (ra, dec, spatial_id) in results: + # self.retrieve_insert_ned_data_to_table(ra, dec, spatial_id,\ + # mysql_cursor, update_only=True) + #self.NED_connection_is_free = True + #except: + if 0: + print('EXCEPT during cursor.execute(), DB down? Sleeping for a bit...') + print('select_str=', select_str) + time.sleep(self.pars['while_loop_sleep_time']) + print("Out of while loop") + + + #### + + + def get_chdefs_from_srccode(self): + """ Generate 'ch_id_defs_list' list(dict) from features which exist + in current software extractors (source code). + + KLUDGE: Currently ch_id_defs_list[{}] is explicitly filled below + for testing, development. + + TODO: Eventually parse (sdss,ned) features from + feature_extract/Code/extractor/ directory. + + For now I just hardcode a list{dict} + """ + ch_id_defs_list = [ \ + {'ch_id':0, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'best_dl', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':0, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'best_dm', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':0, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'best_offset_in_kpc', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':1, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'best_offset_in_petro_g', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':2, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'bestz', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':3, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'bestz_err', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':4, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'chicago_class', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':5, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'chicago_status', + 'val_type':0, # 0:double, 1:string ###20090504 1->0 + }, + {'ch_id':6, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'chicago_z', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':7, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'dec', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':8, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'dered_g', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':9, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'dered_i', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':10, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'dered_r', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':11, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'dered_u', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':12, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'dered_z', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':13, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'dist_in_arcmin', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':14, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'first_flux_in_mJy', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':15, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'first_offset_in_arcsec', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':16, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'in_footprint', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':17, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'objid', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':18, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'petroRadErr_g', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':19, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'petroRad_g', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':20, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'photo2_flag', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':21, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'photo2_z_cc', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':22, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'photo2_z_d1', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':23, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'photo2_zerr_cc', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':24, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'photo2_zerr_d1', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':25, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'photo_rest_abs_g', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':26, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'photo_rest_abs_i', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':27, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'photo_rest_abs_r', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':28, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'photo_rest_abs_u', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':29, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'photo_rest_abs_z', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':30, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'photo_rest_gr', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':31, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'photo_rest_iz', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':32, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'photo_rest_ri', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':33, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'photo_rest_ug', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':34, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'photo_z', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':35, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'photo_zerr', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':36, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'ra', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':37, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'rosat_cps', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':38, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'rosat_flux_in_microJy', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':39, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'rosat_hardness_1', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':40, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'rosat_hardness_2', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':41, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'rosat_log_xray_luminosity', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':42, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'rosat_offset_in_arcsec', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':43, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'rosat_offset_in_sigma', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':44, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'rosat_poserr', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':45, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'spec_confidence', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':46, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'spec_veldisp', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':47, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'spec_z', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':48, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'spec_zStatus', + 'val_type':1, # 0:double, 1:string + }, + {'ch_id':49, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'type', + 'val_type':1, # 0:double, 1:string + }, + {'ch_id':50, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'url', + 'val_type':1, # 0:double, 1:string + }, + {'ch_id':51, + 'ch_group':1, #0:NED, 1:SDSS featclass + 'ch_name':'urlalt', + 'val_type':2, # 0:double, 1:string, 2:NULL + }, + {'ch_id':52, + 'ch_group':0, #0:NED, 1:SDSS featclass + 'ch_name':'distance', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':53, + 'ch_group':0, #0:NED, 1:SDSS featclass + 'ch_name':'z', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':54, + 'ch_group':0, #0:NED, 1:SDSS featclass + 'ch_name':'dm', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':55, + 'ch_group':0, #0:NED, 1:SDSS featclass + 'ch_name':'name', + 'val_type':1, # 0:double, 1:string + }, + {'ch_id':56, + 'ch_group':0, #0:NED, 1:SDSS featclass + 'ch_name':'distance_arcmin', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':57, + 'ch_group':0, #0:NED, 1:SDSS featclass + 'ch_name':'kpc_offset', + 'val_type':0, # 0:double, 1:string + }, + {'ch_id':58, + 'ch_group':0, #0:NED, 1:SDSS featclass + 'ch_name':'kpc_arcmin', + 'val_type':0, # 0:double, 1:string + }] + return ch_id_defs_list + + + def get_chdefs_from_table(self): + """ This retrieves existing charac_id entries from 'ch_id_defs' TABLE + and returns results in a ch_defs[{}] + """ + ch_defs = [] + select_str = "SELECT ch_id, ch_group, ch_type, ch_name FROM %s" % ( \ + self.pars['tablename__ch_id_defs']) + self.cursor.execute(select_str) + results = self.cursor.fetchall() + for result in results: + if len(result) != 4: + print('ERROR: %s returns an unexpected result: %s' % ( \ + select_str, str(result))) + result_dict = {'ch_id':result[0], + 'ch_group':result[1], #0:NED, 1:SDSS featclass + 'ch_name':result[3], + 'val_type':result[2], # 0:double, 1:string + } + ch_defs.append(result_dict) + return ch_defs + + + def determine_chdefs_not_in_table(self, existing_table_chdefs, + srccode_chdefs): + """Updates srccode_chdefs[{}] with 'ch_id' numbers from existing + entries in existing_table_chdefs[{}] + Then, for entries not in " " " ", they be given a ch_id = MAX(ch_id)+1 + """ + existing_ch_id_list = [] + for exist_chdef in existing_table_chdefs: + existing_ch_id_list.append(exist_chdef['ch_id']) + if len(existing_ch_id_list) == 0: + max_ch_id = -1 # so first incremented id is 0 + else: + max_ch_id = max(existing_ch_id_list) + + new_chdefs = [] + all_chdefs = copy.deepcopy(existing_table_chdefs) + + for srccode_chdef in srccode_chdefs: + is_in_exist_chdef = False + # This is done brute force & CPU slow: + for exist_chdef in all_chdefs: + if ((srccode_chdef['ch_name'] == exist_chdef['ch_name']) and + (srccode_chdef['ch_group'] == exist_chdef['ch_group'])): + is_in_exist_chdef = True + break + if not is_in_exist_chdef: + updated_chdef = copy.deepcopy(srccode_chdef) + max_ch_id += 1 + updated_chdef['ch_id'] = max_ch_id + all_chdefs.append(updated_chdef) + new_chdefs.append(updated_chdef) + + self.chname_chid_lookup = {} + self.chname_type_lookup = {} + for chdef in all_chdefs: + self.chname_chid_lookup[(chdef['ch_group'], chdef['ch_name'])] = chdef['ch_id'] + self.chname_type_lookup[(chdef['ch_group'], chdef['ch_name'])] = chdef['val_type'] + + # NOTE: now we have syncd 'ch_id_defs' TABLE which matches all_chdefs[] + self.all_chdefs = all_chdefs # This will be referenced in later code. + + return new_chdefs + + + def update_chdefs_table(self, new_ch_defs): + """ using new_chdefs, we add new entries to 'ch_id_defs' table + """ + if len(new_ch_defs) == 0: + return # don't INSERT anything. + insert_list = \ + ["INSERT INTO %s (ch_id, ch_group, ch_type, ch_name) VALUES " % (\ + self.pars['tablename__ch_id_defs'])] + for ch_def in new_ch_defs: + insert_list.append("(%d, %d, %d, '%s'), " % \ + (ch_def['ch_id'], + ch_def['ch_group'], + ch_def['val_type'], + ch_def['ch_name'])) + + self.cursor.execute(''.join(insert_list)[:-2]) + + + def initialize_loop_threads(self): + """ Start table-watching and socket server threads. + Both threads while(self.do_loop), which is coupled to SIGINT event. + """ + import signal + import threading + + self.do_loop = True + signal.signal(signal.SIGINT, self.sig_handler) # Handle ^C + + existing_table_chdefs = self.get_chdefs_from_table() + + srccode_chdefs = self.get_chdefs_from_srccode() + + new_chdefs = self.determine_chdefs_not_in_table(existing_table_chdefs, + srccode_chdefs) + self.update_chdefs_table(new_chdefs) + + # TODO: now we use all_chdefs[] when storing queried SDSS & NED data. + #self.rdb_table_watcher() + + # TODO: eventually thread this off: + # 20090524: I just coded this and then decided not to use it: + #self.local_retr_table_watcher() + + t = threading.Thread(target=self.rdb_table_watcher, args=[]) + self.threads.append(t) + t.start() + + + ### Disable socket server for now: + #self.socket_listening_server() + #t = threading.Thread(target=self.socket_listening_server, args=[]) + #self.threads.append(t) + #t.start() + + + + def wait_for_thread_joins(self): + """ Wait for all loop threads to join. + """ + for t in self.threads: + t.join() + print("All threads have joined") + + + def insert_radec_into_cachetable_for_retrieval(self, ra, dec, cursor): + """ This INSERTs ra,dec only row into local cache table, so + table-watching thread will eventually retrieve from NED and UPDATE + it's entry in the local cache table. + """ + insert_str="INSERT INTO %s (ra, decl, retrieved) VALUES (%lf, %lf, 0)"%\ + (self.pars['ned_cache_tablename_root'], ra, dec) + cursor.execute(insert_str) + + + def get_ned_dict_trying_local_or_remote(self, ra, dec): + """ Attempt to retrieve NED dict from local cache table, or from + the NED service, or lastly just queue in table for eventual NED + retrieval by other thread. + """ + (ned_dict, sdss_dict) = select_ned_data_from_table(ra, dec, self.cursor) + if len(ned_dict) > 0: + if str(ned_dict['distance']) != "NULL": + return ned_dict + update_only=True + else: + update_only=False + if self.NED_connection_is_free: + self.NED_connection_is_free = False + # NOTE: OK to use self.cursor since other thread cursor is independt + self.retrieve_insert_ned_data_to_table(ra, dec, self.cursor, \ + update_only=update_only) + self.NED_connection_is_free = True + (ned_dict, sdss_dict) = select_ned_data_from_table(ra, dec, self.cursor) + return ned_dict + else: + self.insert_radec_into_cachetable_for_retrieval(ra, dec,self.cursor) + return {} + + + def socket_listening_server(self): + """ This loop waits for a socket connection, which passes (ra,dec) + and then tries to retrieve a NED dict, which it returns. + The NED dict retrieval can be done via direct NED server query or + retrieving from local NED cache database, or queue retrieval & return {} + """ + s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + try_to_bind = True + while try_to_bind: + try: + s.bind(('', self.pars['socket_server_port'])) + try_to_bind = False + except: + print("socket %d still in use"%(self.pars['socket_server_port'])) + time.sleep(self.pars['socket_bind_wait_time']) + + print("In socket listening loop...") + s.listen(self.pars['socket_n_backlog_connections']) + while self.do_loop: + time.sleep(self.pars['socket_loop_sleep_time']) + conn, addr = s.accept() + rec_str = conn.recv(64).split("@") + if len(rec_str) != 2: + continue # skip blank lines, which terminate queries + try: + ra = float(rec_str[0]) + dec = float(rec_str[1]) + except: + continue # skip weird values + + ned_dict = self.get_ned_dict_trying_local_or_remote(ra, dec) + + dict_str = str(ned_dict) + #out_str = "Hello@Joe,@%s %s" % (rec_str[0], rec_str[1]) + conn.send(dict_str) + conn.close() + + +class Ned_Cache_Client: + """ Client class called within NED feature extractor, which retrieves + ned_dict{} from cache table, if available; or queues todo-task by + adding a "retrieve==0" row (for ra,dec) to cache table. + """ + def __init__(self, pars): + self.pars = pars + # Make general connection to MySQL server: + self.db = MySQLdb.connect(host=self.pars['ned_cache_hostname'], + user=self.pars['ned_cache_username'], + db=self.pars['ned_cache_database'], + port=self.pars['ned_cache_db_port']) + self.cursor = self.db.cursor() + + + def retrieve_queue_ned_dict(self, ra, dec): + """ Given an (ra,dec), Attempt to retrieve filled ned_dict{} from + ned_cache table; if row is queued/empty, return {}; if (ra,dec) not + in table, add todo (retrieve=0) entry to table (for (ra,dec)). + """ + (ned_obj, sdss_obj) = select_ned_data_from_table(ra, dec, self.cursor) + if len(ned_obj) > 0: + #20090123: dstarr comments out: #if str(ned_obj['distance']) == "NULL": + if len(sdss_obj.feature) <= 0: + return (Ned_Dictlike_Obj(), Sdss_Obj()) + else: + return (ned_obj, sdss_obj) + else: + # This is non-existant row case. Need to add a (retrieved==0) row. + query_str = "INSERT INTO " + self.pars['tablename__ch_spatial'] + " (retrieved, ra, decl) VALUES (0, %lf, %lf)" + insert_str = query_str % (ra, dec) + self.cursor.execute(insert_str) + return (Ned_Dictlike_Obj(), Sdss_Obj()) + + +if __name__ == '__main__': + + # Can populate ned-cache-server using ra,decs from MySQL outfile: + # select ra,decl from sh_spatial into outfile '/tmp/out'; + # The results can be loaded back in once the ned_cach_server.py is running: + # load data infile '/tmp/out' into table ch_spatial (ra,decl); + + ##### Do Index server: + ncs = Ned_Cache_Server(pars) + #20081026: I don't want to drop all this hard work. I comment out: + #ncs.drop_tables() + #ncs.create_tables() + #sys.exit() + + ncs.initialize_loop_threads() + + ncs.wait_for_thread_joins() diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/ng.py b/mltsp/TCP/Software/feature_extract/Code/extractors/ng.py new file mode 100644 index 00000000..a8a4268f --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/ng.py @@ -0,0 +1,461 @@ +#!/bin/env python +""" +ng -- gets the nearest galaxies + +USAGE: + ./ng.py [ra] +""" +from __future__ import print_function +from __future__ import absolute_import +import csv +import os +import sqlite3 +import numpy, math +from . import geohash2 +from math import sin, cos, radians, sqrt, atan2, degrees +import copy + +__author__ = "Josh Bloom" +__version__ = "10 Nov 2008" + +if "TCP_DIR" in os.environ: + DATADIR = os.environ.get("TCP_DIR") + "Data/" +else: + DATADIR = "" + +def_min_depth = 11 + +class GalGetter: + + dbname = "gal.db" + rez = [] + query = "" + index_type="index" + + def __init__(self,inname=DATADIR + "200MpcGalaxyCatalog_v2.dat",dbname=DATADIR + "gal_v2.db",max_rows=-1,make_db_on_instance=True,verbose=False): + self.inname = inname + self.dbname = dbname + self.verbose = verbose + + if make_db_on_instance: + self.make_db(max_rows=max_rows) + + # NOTE: 20090227: dstarr changes some default values so this works with normal feature extractor: + # def make_db(self,in_memory=False,clobber=False,skip_headers=[''],\ + def make_db(self,in_memory=False,clobber=False,skip_headers=[''],\ + intnames=['pgc'],default_hash_depth=18,max_rows=-1): + + if not os.path.exists(self.inname): + if self.verbose: + print("inname = %s does not exist" % self.inname) + return None + + r = csv.reader(open(self.inname), delimiter="|") + + ## figure out the headers + tmp = numpy.array([s.strip() for s in next(r)]) + use_indices = [] + for i in range(len(tmp)): + if not (tmp[i] in skip_headers): + use_indices.append(i) + tmp = list(tmp.take(use_indices)) + + self.headers = copy.copy(tmp) + self.headers.append('ghash') + + ra_ind = tmp.index('al2000') + dec_ind = tmp.index('de2000') + d_ind = tmp.index('logd25') + + if clobber: + if not in_memory: + if os.path.exists(self.dbname): + os.remove(self.dbname) + else: + if os.path.exists(self.dbname): + return self.dbname + + if in_memory: + self.dbname = ":memory:" + + # Make the DB + conn = sqlite3.connect(self.dbname) + c = conn.cursor() + tmp1 = [] + for s in tmp: + if s in intnames: + tmp1.append(s + " int") + else: + tmp1.append(s + " real") + + tmp1.append("ghash text") + tmp1 = ",".join(tmp1) + c.execute('''create table galaxies (%s)''' % tmp1) + #c.execute('select * from galaxies') + #print "*" + #for r in c: + # print r + #print "*" + i=0 + min_depth = 35 + if self.index_type == "hash": + g = geohash2.Geohash + else: + g = geohash2.Geoindex + for l in r: + if i > max_rows and max_rows > 1: + break + try: + tmp = numpy.array([s.strip() for s in l]) + tmp = list(tmp.take(use_indices)) + tmp[ra_ind] = str(float(tmp[ra_ind])*15.0) + hashpos = (float(tmp[ra_ind]),float(tmp[dec_ind])) + if float(tmp[d_ind]) > 0: + hashpos_depth = int(math.floor(18.25707 - \ + 3.333333*math.log10(60.0*10**(float(tmp[d_ind])*0.1)))) + else: + hashpos_depth = default_hash_depth + if hashpos_depth < min_depth: + min_depth = hashpos_depth + #hashpos_depth = 18 + #print hashpos, hashpos_depth + tmp.append("'%s'" % str(g(hashpos,depth=hashpos_depth))) + tmp = ",".join(tmp) + c.execute("""insert into galaxies values (%s)""" % tmp) + i+=1 + except: + if self.verbose: + print("row %i" % i) + i+=1 + continue + conn.commit() + conn.close() + if self.verbose: + print("min_depth ", min_depth) + return self.dbname + + def getgi(self,pos=(None,None),error=1.0,min_depth=def_min_depth): + mult = 3600.0 + depth =int(math.floor(18.25707 - 3.333333*math.log10(mult*error))) + if depth > min_depth: + depth = min_depth + #depth=18 + return geohash2.Geoindex(pos,depth=depth) + + def getgh(self,pos=(None,None),error=1.0,min_depth=def_min_depth): + mult = 3600.0 + depth =int(math.floor(18.25707 - 3.333333*math.log10(mult*error))) + if depth > min_depth: + depth = min_depth + #depth=18 + return geohash2.Geohash(pos,depth=depth) + + def getgals(self,pos=(49.362750 , 41.405417),radius=5,min_depth=def_min_depth,sort_by='dist',max_d=500.0): + if self.index_type == "hash": + g = self.getgh + else: + g = self.getgi + ## radius in degrees + ## max d in Mpc + dm_max = 5.0*math.log10(max_d*1e5) + gh= g(pos=pos,error=radius*3,min_depth=min_depth) + #gh1 = self.getgh(pos=(pos[0],pos[1]+radius),error=radius,min_depth=min_depth) + #gh2 = self.getgh(pos=(pos[0],pos[1]-radius),error=radius,min_depth=min_depth) + #gh3 = self.getgh(pos=(pos[0]-radius*cos(radians(pos[1])),pos[1]),error=radius,min_depth=min_depth) + #gh4 = self.getgh(pos=(pos[0]+radius*cos(radians(pos[1])),pos[1]),error=radius,min_depth=min_depth) + #print gh.bbox() + #print gh.point() + #print gh1.bbox(), (pos[0],pos[1]+radius), str(gh1) + #print gh2.bbox(), (pos[0],pos[1]-radius), str(gh2) + #print gh3.bbox(), (pos[0]-radius*cos(radians(pos[1])),pos[1]), str(gh3) + #print gh4.bbox(), (pos[0]+radius*cos(radians(pos[1])),pos[1]), str(gh4) + conn = sqlite3.connect(self.dbname) + c = conn.cursor() + self.query = 'pos = %s, radius = %f, sort_by=%s max_d=%f \nselect * from galaxies where galaxies.ghash glob %s and galaxies.mucin < %s' % \ + (repr(pos), radius, sort_by, max_d, str(gh)[:-2] + "*",dm_max) + c.execute('select * from galaxies where galaxies.ghash glob ? and galaxies.mucin < ?', (str(gh)[:-2] + "*",dm_max)) + tmp = c.fetchall() + d_ind = self.headers.index('logd25') + r_ind = self.headers.index('logr25') + pa_ind = self.headers.index('pa') + mucin = self.headers.index('mucin') + muc = self.headers.index('mup') + #semim = 60 * 10.**(r1[0][d_ind])*0.1 + #semimin = semim / 10**r1[0][r_ind] + #pa = r1[0][pa_ind] + + tmp1 = [] + for r in tmp: + d = self.distance(pos[0],pos[1],r[1],r[2]) + if d < radius: + dl = self.distlight(pos[0],pos[1],r[1],r[2],60 * 10.**(r[d_ind])*0.1,60 * 10.**(r[d_ind])*0.1/(10**r[r_ind]),r[pa_ind]) + if r[muc] > 0: + off = 1e3*radians(d)*1e-5*10**(r[muc]/5.0) + else: + off = 1e3*radians(d)*1e-5*10**(r[mucin]/5.0) + #print off, d, r[mucin] + tmp1.append( (r,d,dl,off)) + + tmp = tmp1 + #print len(tmp) + if sort_by == 'dist': + tmp.sort(key=lambda x: x[1]) + elif sort_by == 'dm': + tmp.sort(key=lambda x: x[0][self.headers.index('mucin')]) + elif sort_by == 'mag': + ## get the distance modulus and the b-band mag corrected + tmp.sort(key=lambda x: x[0][self.headers.index('btc')] - x[0][self.headers.index('mucin')]) + elif sort_by == 'light': + ## distance in light units + tmp.sort(key=lambda x: x[2][0]) + elif sort_by == 'phys': + tmp.sort(key=lambda x: x[3]) + self.rez = tmp + conn.close() + + def grab_rez(self,retkey="light",prefix="closest_in_"): + + + try: + r1=self.rez[0] + if retkey == 'light': + val= r1[2][0] + sb = "light" + units = "galaxy_surface_brightness" + alt_dict = {prefix + sb + "_physical_offset_in_kpc": r1[3], prefix + sb + "_angular_offset_in_arcmin": r1[1]*60} + elif retkey == 'phys': + val= r1[3] + sb = "physical_offset_in_kpc" + units = "kpc" + alt_dict = {prefix + sb + "_light": r1[2][0], prefix + sb + "_angular_offset_in_arcmin": r1[1]*60} + elif retkey == "dist": + val= r1[1]*60 + sb = "angular_offset_in_arcmin" + units = "arcmin" + alt_dict = {prefix + sb + "_light": r1[2][0], prefix + sb + "_physical_offset_in_kpc": r1[3]} + + else: + return {} + + d_ind = self.headers.index('logd25') + r_ind = self.headers.index('logr25') + pa_ind = self.headers.index('pa') + mucin = self.headers.index('mucin') + muc = self.headers.index('mup') + t = self.headers.index('t') + te = self.headers.index('e_t') + b = self.headers.index('btc') + ra = self.headers.index('al2000') + dec = self.headers.index('de2000') + + smj,sminor,pa = (60 * 10.**(r1[0][d_ind])*0.1, 60 * 10.**(r1[0][d_ind])*0.1/(10**r1[0][r_ind]), r1[0][pa_ind]) + dm = r1[0][muc] if r1[0][muc] > 0 else r1[0][mucin] + + if smj == 6.0e-99: + smj = None + sminor = None + + ## look at the t-type + ttype = r1[0][t] if r1[0][te] < 3 and r1[0][t] != -99.0 else None + + ## look at the absolute mag of the closest galaxy + absb = r1[0][b] - dm if r1[0][b] > 5.0 else None + + ## angle from major + angle_major = r1[2][1] or None + + ## position (for internal purposes if we want it) + ra, dec = r1[0][ra], r1[0][dec] + + except: + return {} + + alt_dict.update({prefix + sb: val, prefix + sb + "_units": units, prefix + sb + "_semimajor_r25_arcsec": smj, \ + prefix + sb + "_semiminor_r25_arcsec": sminor, prefix + sb + "_dm": dm, \ + prefix + sb + "_angle_from_major_axis": angle_major, prefix + sb + "_ttype": ttype, \ + prefix + sb + "_absolute_bmag": absb, prefix + sb + "_galaxy_position": (ra, dec)}) + return copy.copy(alt_dict) + + def __str__(self): + a = "%s\n%s\n%s\n" % ("*"*50, self.query,"*"*50) + a += "dist(') offset(kpc) dist(light) angle_from_major" + for h in self.headers: + a += "%9s" % h + a += "\n" + for r1 in self.rez: + r = r1[0] + #print r1[2] + a += "%7.4f %7.4f %7.4f %7.1f " % (r1[1]*60, r1[3], r1[2][0], r1[2][1] or -999) + a += " ".join([str(x) for x in r]) + " \n" + # a += "%f %f %f %f %s\n" % (r[1], r[2], r[7], r1[1], r[-1]) + return a + + def writeds9(self,fname='ds9.reg'): + ra_ind = self.headers.index('al2000') + dec_ind = self.headers.index('de2000') + d_ind = self.headers.index('logd25') + r_ind = self.headers.index('logr25') + pa_ind = self.headers.index('pa') + pgc_ind = self.headers.index('pgc') + mucin = self.headers.index('mucin') + muc = self.headers.index('mup') + + f = open(fname,'w') + f.write("# Region file\n") + f.write('global color=green font="helvetica 10 normal" select=1 highlite=1 edit=1 move=1 delete=1 include=1 fixed=0 source\nfk5\n') + mind = 100 + maxd = 0 + for r1 in self.rez: + if r1[0][muc] != -99: + m = r1[0][muc] + else: + m = r1[0][mucin] + if m < mind: mind = m + if m > maxd: maxd = m + + # max sure that maxd and mind aren't the same + if mind == maxd: + mind /= 1.02 + for r1 in self.rez: + d = r1[0] + semim = 60 * 10.**(r1[0][d_ind])*0.1 + semimin = semim / 10**r1[0][r_ind] + pa = r1[0][pa_ind] + if r1[0][muc] != -99: + m = r1[0][muc] + else: + m = r1[0][mucin] + #print r1, m, mind, maxd + cc = str(hex(int(255.0 - 255.0*(m - mind)/(maxd - mind)))).split("x")[1] + if len(cc) == 1: cc = "0" + cc + + col = '"#' + cc*2 + '44"' + width = int(5 - 4.0*(m - mind)/(maxd - mind)) + d= 1e-5*10**(m/5.0) + f.write('ellipse(%f,%f,%f",%f",%f) # text={pgc=%i, d=%4.1f Mpc} color=%s width=%i\n' % (r1[0][ra_ind],r1[0][dec_ind],\ + semim,semimin,pa - 90.0,r1[0][pgc_ind],d,col,width)) + f.close() + + def distance(self,lon0, lat0, lon, lat): + """ + Calculates the distance between two points (decimal) + """ + d_lat = radians(lat0 - lat) + d_lon = radians(lon0 - lon) + x = sin(d_lat/2) ** 2 + \ + cos(radians(lat0)) * cos(radians(lat)) *\ + sin(d_lon/2) ** 2 + y = 2 * atan2(sqrt(x), sqrt(1.0 - x)) + distance = y*180.0/math.pi + return distance + + def distlight(self,lon, lat, lon0, lat0,semimajor,semiminor,pa,assumed_size_if_none=15.0): + """assumed size = 15.0 arcsec""" + d = self.distance(lon0,lat0,lon,lat) + if pa == -99.0 and semimajor == 6e-99: + + if self.verbose: + print("bad pa or size: returning %f %f" % (d/(assumed_size_if_none/3600.0),d)) + return (d/(assumed_size_if_none/3600.0), None) + ## get the angle from the center of this galaxy to the source + dra = self.distance(lon0,lat0,lon,lat0) + ddec = self.distance(lon0,lat0,lon0,lat) + + if lat < lat0: + ddec *= -1 + if lon < lon0: + dra *= -1 + #if ((lon - lon0) > -180.0) and ((lon - lon0) < 180.0): + # dra *= -1 + + ## this is the angle between the center of the galaxy and the source (east of North) + a = atan2(dra,ddec) + + ## relative to the semi-major axis the angle is + t = a - radians(pa) + + ## here's the r25 along this direction + r = numpy.sqrt((semimajor**2)*(semiminor**2)/( (semimajor*numpy.sin(t))**2 + (semiminor*numpy.cos(t))**2)) + + #print r, d, d*3600.0/r, lon0, lat0, lon, lat + #print "*"*50 + return (d*3600.0/r, degrees(t)) + +def test(): + global ddd + ddd = GalGetter(max_rows=-1) + ddd.getgals() + print(ddd) + ddd.writeds9() + +def test1(): + conn = sqlite3.connect(ddd.dbname) + c = conn.cursor() + c.execute('select * from galaxies') + for r in c: print(r) + +def get_closest_by_light(pos=(None,None),max_d=300.0,radius=1.0): + """to be called by the extrators""" + ddd = GalGetter(verbose=False) + ddd.getgals(pos=pos,radius=1.0,sort_by="light",max_d=max_d) + return ddd.grab_rez("light") + +def get_closest_by_physical_offset(pos=(None,None),max_d=300.0,radius=1.0): + """to be called by the extrators in kpc""" + ddd = GalGetter(verbose=False) + ddd.getgals(pos=pos,radius=1.0,sort_by="phys",max_d=max_d) + return ddd.grab_rez("phys") + +def get_closest_by_angular_offset(pos=(None,None),max_d=300.0,radius=1.0): + """to be called by the extrators in arcmin""" + ddd = GalGetter(verbose=False) + ddd.getgals(pos=pos,radius=1.0,sort_by="dist",max_d=max_d) + return ddd.grab_rez("dist") + +if __name__ == "__main__": + from optparse import OptionParser + usage = "usage: %prog [options] -p ra dec\n" + parser = OptionParser(usage) + + parser.add_option("--ds9name", dest="ds9name", \ + help="Name of the output ds9 region file",\ + default="ds9.reg") + + parser.add_option("--nds9",dest="no_ds9", action="store_true",\ + help="dont write the ds9 file",default=False) + + parser.add_option("--radius",dest="radius",\ + help="Search radius in arcmin",type="float",default=60.0) + + parser.add_option("--maxd",dest="max_d",\ + help="Maximum distance to search (in Mpc); default = 500 Mpc",type="float",default=500.0) + + parser.add_option("-v","--verbose",dest="verbose",action="store_true",\ + help="Be verbose",default=False) + + parser.add_option("--ntop",dest="ntop",action="store_false",\ + help="Dont print the top result",default=False) + + parser.add_option("--sortkey",dest="sortkey",choices = ['dist', 'dm', 'mag', 'light','phys'], \ + help="Sort key: (dist; default) angular distance from source, " + \ + "(dm) proximity to Earth in Mpc, (mag) galaxy absolute mag, (light) light units, (phys) offset in kpc",default="dist") + + parser.add_option("-p", type="float", nargs=2, dest="pos") + + (options, args) = parser.parse_args() + +# if len(args) != 2: +# print "You must supply RA and DEC in decimal degrees" +# print usage +# parser.parse_args(['-h']) + + if not options.pos: + parser.parse_args(['-h']) + + ddd = GalGetter(verbose=options.verbose) + ddd.getgals(pos=options.pos,radius=float(options.radius)/60.,sort_by=options.sortkey,max_d=options.max_d) + if not options.no_ds9: + ddd.writeds9(fname=options.ds9name) + + if not options.ntop: + print(ddd) diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/noisify.py b/mltsp/TCP/Software/feature_extract/Code/extractors/noisify.py new file mode 100644 index 00000000..c6c97318 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/noisify.py @@ -0,0 +1,87 @@ +from ..FeatureExtractor import InterExtractor +from numpy import inf +import numpy + +from ..generators_importers.copy_gen import noisified_gen # the class that will generate the new (noisified) signal + +class noisify(InterExtractor): + """ Noisifies the data as wished, and re-extracts all the features""" + active = False + extname = 'noisify' #extractor's name + list_of_noisifiers = [badpoints(p=0.001, sigma=10), addgaussian(sigma = 2), cliptop(maxvalue = 20), clipbottom(minvalue = 3)] # list of noisifying functions that will be applied in order to the signal, presumably this list should be defined elsewhere to make it more flexible and modifiable at runtime. Order matters, and entries can of course be repeated. + dic_of_noisifiers = {'badpoints':badpoints, 'addgaussian':addgaussian, 'cliptop':cliptop, 'clipbottom':clipbottom, 'makefainter':makefainter} # this dictionary (dic_of_noisifiers) is simply to be able to export all the noisifying functions outside of this module + def extract(self): + for i in range(len(self.list_of_noisifiers)): + self.list_of_noisifiers[i] = self.list_of_noisifiers[i].noisify # we want the actual function, not the class + generator = noisified_gen(self.list_of_noisifiers) # generate a new signal + signal = generator.generate(self.dic) # send the generator the list of functions to apply to the signal + return signal # the result is the actual signal, so far we haven't applied any feature extractions to it + + +class noisifier(object): + def noisify(self,inputdic): + self.flux_data = inputdic['flux_data'] + self.time_data = inputdic['time_data'] + self.n = len(self.flux_data) # because this is often useful + return self.myaction(inputdic) + def myaction(self,inputdic): + pass + + +class cliptop(noisifier): + """ clips the faintest points """ + def __init__(self,maxvalue=inf): + self.maxvalue = maxvalue + def myaction(self,inputdic): + self.flux_data = self.flux_data.clip(min = -inf, max = self.maxvalue) + inputdic['flux_data'] = self.flux_data + return inputdic + +class clipbottom(noisifier): + """ clips the brightest points """ + def __init__(self,minvalue=-inf): + self.minvalue = minvalue + def myaction(self,inputdic): + self.flux_data = self.flux_data.clip(min = self.minvalue, max = inf) + inputdic['flux_data'] = self.flux_data + return inputdic + +class addgaussian(noisifier): + """ _add_ gaussian noise to the signal + Here we will specify a sigma, and add corresponding noise to the signal. + The array of RMS values will simply be augmented by this sigma + """ + def __init__(self, sigma = 1): + self.sigma = sigma + def myaction(self,inputdic): + gauss = numpy.random.normal(loc=0.0,scale=self.sigma,size=self.n) # create the gaussian noise + self.flux_data = self.flux_data + gauss # add the gaussian noise to the signal + inputdic['flux_data'] = self.flux_data + inputdic['rms_data'] += self.sigma + return inputdic + +class badpoints(noisifier): + """ simulate bad points + input: the probability of a bad point, and the sigma associated with such a bad point + Note: it would make sense to apply the bad points _before_ clipping, to avoid points outside the clipping boundaries + """ + def __init__(self, p=0.001, sigma = 10): + self.p = p + self.sigma = sigma + def myaction(self,inputdic): + isitbad = numpy.random.binomial(1, self.p, size=self.n) # Create a bunch of zeros and ones using a binomial distribution. The ones will become bad points. Each point is just one trial (by definition), hence the first input. + nbadpoints = isitbad.sum() # number of bad points + isitbad = isitbad.astype(bool) # convert to True/False so we can use it to index things + gauss = numpy.random.normal(loc=0.0,scale=self.sigma,size=nbadpoints) # create the bad points + self.flux_data[isitbad] = gauss # the bad points are replaced by the random values we just created + inputdic['flux_data'] = self.flux_data + return inputdic + +class makefainter(noisifier): + """ Make an object fainter by a fixed number of magnitudes """ + def __init__(self, mags = 1): + self.mags = mags + def myaction(self,inputdic): + self.flux_data += self.mags # add that number of magnitudes (-> make fainter) + inputdic['flux_data'] = self.flux_data + return inputdic \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/old_dc_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/old_dc_extractor.py new file mode 100644 index 00000000..5b99971f --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/old_dc_extractor.py @@ -0,0 +1,18 @@ +from ..FeatureExtractor import FeatureExtractor + +from numpy import ones, mean +from scipy import stats +from pylab import * + +# TODO ...also remove? +class old_dc_extractor(FeatureExtractor): + """ Old DC + """ + active = True + extname = 'old_dc' #extractor's name + def extract(self): + return(mean(self.flux_data)) + def plot_feature(self,properties): + dc_line = ones(len(self.time_data),dtype=float) + dc_line[:] = properties['old dc'] + plot(self.time_data,dc_line,label='old dc') diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/old_dcextractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/old_dcextractor.py new file mode 100644 index 00000000..057beafa --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/old_dcextractor.py @@ -0,0 +1,18 @@ +from ..FeatureExtractor import FeatureExtractor + +from numpy import ones +from scipy import stats +from pylab import * + +# TODO remove? +class old_dcextractor(FeatureExtractor): + """ Old DC + """ + active = True + extname = 'old_dc' #extractor's name + def extract(self): + return(stats.mean(self.flux_data)) + def plot_feature(self,properties): + dc_line = ones(len(self.time_data),dtype=float) + dc_line[:] = properties['old dc'] + plot(self.time_data,dc_line,label='old dc') diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/pair_slope_trend_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/pair_slope_trend_extractor.py new file mode 100644 index 00000000..515954a0 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/pair_slope_trend_extractor.py @@ -0,0 +1,35 @@ +from ..FeatureExtractor import FeatureExtractor + +class pair_slope_trend_extractor(FeatureExtractor): + """percentage of pairs of points which continually rise, over total number number of pairs. + + We only want to run this on the last MAX_PAIRS points to see if there is an + overall trend to the rise/fall. + + To account for plateaus, we devide by the total number of pairs examined, and + not the total of rising/falling pairs. + + The slopes are not weighted, but you can change the value of PLATEAU_SLOPE + to included values close to 0 as 0 slope. + + Blame: john m. brewer + """ + active = True + extname = 'pair_slope_trend' # extractor name + MAX_PAIRS = 30 # maximum number of pairs to examine from end of epochs + PLATEAU_SLOPE = 0.0 # slope considered equal to 0 + + def extract(self): + rising = 0; + falling = 0; + lastpoint = len(self.time_data) - 1 + firstpoint = max(0,lastpoint-self.MAX_PAIRS) + + for i in range(firstpoint,lastpoint): + fluxDiff = self.flux_data[i+1] - self.flux_data[i] + timeDiff = self.time_data[i+1] - self.time_data[i] + slope = fluxDiff/timeDiff + rising += (slope > self.PLATEAU_SLOPE) + falling += (slope < (0.0 - self.PLATEAU_SLOPE)) + + return float(rising - falling)/max(1.0,(lastpoint - firstpoint)) diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/pct_montecarlo_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/pct_montecarlo_extractor.py new file mode 100644 index 00000000..1673b4c6 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/pct_montecarlo_extractor.py @@ -0,0 +1,40 @@ +from __future__ import absolute_import +from ..FeatureExtractor import InterExtractor + +from .common_functions.plot_methods import plot_vs_frequencies + + +class pct_80_montecarlo_extractor(plot_vs_frequencies,InterExtractor): + """ picks the right montecarlo cruve according to the desired significance """ + active = True + extname = 'pct_80_montecarlo' #extractor's name + percent_significance = 80 + def extract(self): + self.spectra = self.fetch_extr('montecarlo') + spectr_index = self.calc_index() + if spectr_index == len(self.spectra): + self.ex_error("too high degree of certainty for this number of montecarlo iteration") + nth_spectrum = self.spectra[spectr_index] + return nth_spectrum + def calc_index(self): + " will calculate the correct index for the sorted array of spectra according to the desired percent significance " + how_many = self.spectra.shape[0] + sign = float(self.percent_significance) + ind_rough = sign/100.0 * how_many + ind = int(round(ind_rough)) + return ind +class pct_90_montecarlo_extractor(pct_80_montecarlo_extractor): + """ picks the right montecarlo cruve according to the desired significance """ + active = True + extname = 'pct_90_montecarlo' #extractor's name + percent_significance = 90 +class pct_95_montecarlo_extractor(pct_80_montecarlo_extractor): + """ picks the right montecarlo cruve according to the desired significance """ + active = True + extname = 'pct_95_montecarlo' #extractor's name + percent_significance = 95 +class pct_99_montecarlo_extractor(pct_80_montecarlo_extractor): + """ picks the right montecarlo cruve according to the desired significance """ + active = True + extname = 'pct_99_montecarlo' #extractor's name + percent_significance = 99 diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/phase_dispersion_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/phase_dispersion_extractor.py new file mode 100644 index 00000000..5cdcf815 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/phase_dispersion_extractor.py @@ -0,0 +1,144 @@ +try: + from ..FeatureExtractor import FeatureExtractor +except: + import os + ppath = os.environ.get('PYTHONPATH') + os.environ.update({'PYTHONPATH': ppath + ":" + os.path.realpath("..")}) + #print os.environ.get("PYTHONPATH") + from FeatureExtractor import FeatureExtractor + +import numpy#, pdb +#from scipy.optimize import fmin +#from common_functions.lomb_scargle_refine import lomb + + +class phase_dispersion_freq0_extractor(FeatureExtractor): + ''' + returns the frequency estimated by phase dispersion minimization. + + NOTE: designed to identify correct period of eccentric eclipsing sources. + + by I.Shivvers, June 2012 + ''' + active = True + extname = 'phase_dispersion_freq0' + + def extract(self): + try: + t = self.time_data + m = self.flux_data + e = self.rms_data + + # find set of periods to test + test_periods = self.preSelect_periods() + + # choose best period by minimizing GCV + GCVs = [] + for iii, period in enumerate(test_periods): + #print iii,'of', len(self.test_periods), '...' + p = self.fold(t, period) + best_GCV, optimal_window = self.minimize_GCV(p, m) + GCVs.append(best_GCV) + b_period = test_periods[ numpy.argmin(GCVs) ] + # return the period with the lowest GCV + return 1./b_period + except: + return 0.0 + + # DEFINITIONS + def fold(self, times, period): + ''' return phases for folded at ''' + t0 = times[0] + phase = ((times-t0)%period)/period + return phase + + def rolling_window(self, a, window): + """ Call: numpy.mean(rolling_window(observations, n), 1) + """ + shape = a.shape[:-1] + (a.shape[-1] - window + 1, window) + strides = a.strides + (a.strides[-1],) + return numpy.lib.stride_tricks.as_strided(a, shape=shape, strides=strides) + + def GCV(self, window, X,Y): + # put in proper order + zpm = list(zip(X,Y)) + zpm.sort() + zpm_arr = numpy.array(zpm) + phs = zpm_arr[:,0] + mags = zpm_arr[:,1] + # handle edges properly by extending array in both directions + if window>1: + b = numpy.concatenate((mags[-window/2:], mags, mags[:window/2-1])) + else: + b = mags + # calculate smoothed model and corresponding smoothing matrix diagonal value + model = numpy.mean(self.rolling_window(b, window), 1) + Lii = 1./window + # return the Generalized Cross-Validation criterion + GCV = 1./len(phs) * numpy.sum( ((mags-model)/(1.-Lii))**2 ) + return GCV + + def minimize_GCV(self,X,Y, window_range=(10,50,2)): + ''' quick way to pick best GCV value; GCV is not smooth ''' + windows = numpy.arange(*window_range) + GCVs = numpy.array( [self.GCV(window, X,Y) for window in windows] ) + best_GCV = numpy.min(GCVs) + optimal_window = windows[ numpy.argmin(GCVs) ] + return best_GCV, optimal_window + + def preSelect_periods(self, numpeaks=5): + """ use LS to select trial periods for use in find_period + returns top most-likely periods """ + lomb_dict = self.fetch_extr('lomb_scargle') + psd = lomb_dict.get('freq1_psd',[]) + f0 = lomb_dict.get('freq1_f0',0.) + df = lomb_dict.get('freq1_df',0.) + numf = lomb_dict.get('freq1_numf',0.) + frequencies = numpy.linspace( f0, f0+df*numf, numf ) + periods = 1./frequencies + peaks_ind = self.find_peaks(psd) + zpp = list(zip(psd[peaks_ind], periods[peaks_ind])) + zpp.sort(reverse=True) + test_periods = numpy.array([zed[1] for zed in zpp[:numpeaks]]) + # return periods and a few harmonics + return numpy.concatenate( [test_periods, test_periods*2, test_periods*3, test_periods/2., test_periods/3. ] ) + ''' + def preSelect_periods(self, t,m,e, numpeaks=10): + ### calculate raw psd ### + # longest period (shortest frequency) is .5*(total time span) + f0 = 2./(t[-1]-t[0]) + # shortest period (largest frequency) is physically motivated at period = .1 days + fend = 1./.1 + numf = int(136121) + df = (fend-f0)/numf + periods = 1./numpy.arange(f0, fend, df) + # run the smoothed ls periodogram, and find all of the peaks + psd,lombdict = lomb(t,m,e, f0, df, numf) + peaks_ind = self.find_peaks(psd) + zpp = zip(psd[peaks_ind], periods[peaks_ind]) + zpp.sort(reverse=True) + test_periods = numpy.array([zed[1] for zed in zpp[:numpeaks]]) + # return periods and a few harmonics + return numpy.concatenate( [test_periods, test_periods*2, test_periods*3, test_periods/2., test_periods/3. ] ) + ''' + def find_peaks(self, x): + """ find peaks in x """ + xmid = x[1:-1] # orig array with ends removed + xm1 = x[2:] # orig array shifted one up + xp1 = x[:-2] # orig array shifted one back + return numpy.where(numpy.logical_and(xmid > xm1, xmid > xp1))[0] + 1 + + +class ratio_PDM_LS_freq0_extractor(FeatureExtractor): + ''' returns the ratio of phase_dispersion-estimated and lomb_scargle-estimated frequencies ''' + active = True + extname = 'ratio_PDM_LS_freq0' + def extract(self): + try: + pdm_freq = self.fetch_extr('phase_dispersion_freq0') + lomb_dict = self.fetch_extr('lomb_scargle') + LS_freq = lomb_dict.get('freq1_harmonics_freq_0',0.) + return pdm_freq/LS_freq + except: + return 0.0 + diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/position_intermediate_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/position_intermediate_extractor.py new file mode 100644 index 00000000..c9a003f9 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/position_intermediate_extractor.py @@ -0,0 +1,59 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextInterExtractor +#from ..FeatureExtractor import FeatureExtractor +import math +rad2deg = 180.0/math.pi + +ok_to_use = True +try: + import ephem +except: + print("!position_intermedite_extractor: pyephem not installed. The position features will fail.") + ok_to_use = False + +default = {'galb': None, 'gall': None, 'ecb': None, 'ecl': None, 'ra': None, 'dec': None} + +class position_intermediate_extractor(ContextInterExtractor): +#class position_intermediate_extractor(FeatureExtractor): + """ intermediate position extractor """ + active = True + extname = 'position_intermediate' + exttype = "context" + + if active and not ok_to_use: + active = False + + def extract(self): + if not self.active: + return default + + ret = default + ## do some tests on the data + try: + if self.ra < 0.0 or self.ra >= 360.0: + ret.update({'ra': None}) + else: + ret.update({'ra': ephem.degrees(str(self.ra))}) + except: + ret.update({'ra': None}) + + try: + if self.dec < -90.0 or self.dec > 90.0: + ret.update({'dec': None}) + else: + ret.update({'dec': ephem.degrees(str(self.dec))}) + except: + ret.update({'dec': None}) + + if ret['dec'] is not None and ret['ra'] is not None: + np = ephem.Equatorial(ret['ra'],ret['dec'],epoch='2000') + else: + return ret + g = ephem.Galactic(np) + e = ephem.Ecliptic(np) + + ret = {'galb': rad2deg*float(g.lat), 'gall': rad2deg*float(g.long), \ + 'ecb': rad2deg*float(e.lat), 'ecl': rad2deg*float(e.long), \ + 'ra': rad2deg*float(np.ra), 'dec': rad2deg*float(np.dec)} + + return ret diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/power_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/power_extractor.py new file mode 100644 index 00000000..5eea7d2f --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/power_extractor.py @@ -0,0 +1,34 @@ +from __future__ import absolute_import +from ..FeatureExtractor import InterExtractor +from .common_functions.plot_methods import plot_vs_frequencies + +class power_extractor(plot_vs_frequencies,InterExtractor): + """ extracts a periodogram, chooses either a lomb or fft extraction method """ + active = True + extname = 'power' + def extract(self): + self.longenough() + if 0:#self.iseven(): # determines if the data is evenly sampled / commented out assuming all our data is uneven + #print "Evenly sampled data detected" + result = self.fetch_extr('power_spectrum') + else: # otherwise use lomb-scargle + #print "Unevenly sampled data detected" + power = self.fetch_extr('lomb') + result = power + return result + def longenough(self): + try: + if len(self.flux_data) < 5: # if 4 or less points, error + self.ex_error("not enough data points") + return None + except: + self.ex_error("not enough data points") + + def iseven(self): + iseven = True + for x in range(len(self.time_data)-3): + slicex = self.time_data[x:x+3] # slice with three elements + if round((slicex[2] - slicex[1]),2) != round((slicex[1] - slicex[0]),2): + iseven = False + break + return iseven diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/power_spectrum_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/power_spectrum_extractor.py new file mode 100644 index 00000000..d2536803 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/power_spectrum_extractor.py @@ -0,0 +1,13 @@ +from __future__ import absolute_import +from ..FeatureExtractor import InterExtractor +from .common_functions.plot_methods import plot_vs_frequencies + +class power_spectrum_extractor(plot_vs_frequencies,InterExtractor): + active = False + extname = 'power_spectrum' #extractor's name + def extract(self): + fourier = self.fetch_extr('fourier') + if not isinstance(fourier,numpy.ndarray): + self.ex_error("Did not receive the correct type as input") + power_spec = abs(fourier)**2 + return power_spec diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/psd_example_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/psd_example_extractor.py new file mode 100644 index 00000000..4ff1ce53 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/psd_example_extractor.py @@ -0,0 +1,14 @@ +from ..FeatureExtractor import FeatureExtractor + +class psd_example_extractor(FeatureExtractor): + """ Example use of the first freq LombScargle derived PSD + """ + active = True + extname = "psd_example" + def extract(self): + lomb_dict = self.fetch_extr('lomb_scargle') + psd = lomb_dict.get('freq1_psd',[]) + f0 = lomb_dict.get('freq1_f0',0.) + df = lomb_dict.get('freq1_df',0.) + numf = lomb_dict.get('freq1_numf',0.) + return f0*df*numf + sum(psd) diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/qso_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/qso_extractor.py new file mode 100755 index 00000000..4be6a06b --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/qso_extractor.py @@ -0,0 +1,107 @@ +import os,sys +from numpy import median +from ..FeatureExtractor import FeatureExtractor, InterExtractor + +from .....Algorithms.qso_fit import qso_fit + + +#class qso_extractor(FeatureExtractor): # Using this will add a 'qso' feature in vosource xml whose value is a string representation of the returned od dict. (using internal_use_only=False, active=False) +class qso_extractor(InterExtractor): + """ calculates the skew of the signal using scipy.stats.skew + biased skew?""" + internal_use_only = False # if set True, then seems to run all qso code for each sub-feature + active = True # if set False, then seems to run all qso code for each sub-feature + extname = 'qso' #extractor's name + def extract(self): + y0 = 19. + y = self.flux_data - median(self.flux_data) + y0 + try: + od = qso_fit(self.time_data, + y, + self.rms_data,filter='g') + except: + self.ex_error(text="qso_extractor.qso_extractor()") + + #res = od['chi2_qso/nu'],od['chi2_qso/nu_NULL'] + # QSO-like: res[0]<~2 + # non-QSO: res[1]/res[0]<~2 + return od + + +class qso_generic(FeatureExtractor): + """ Generic qso extractor grabs value from dictionary """ + internal_use_only = False + active = True + extname = 'to_be_overloaded' # identifier used in final extracted value dict. + qso_key = 'to_be_overloaded' + def extract(self): + qso_dict = self.fetch_extr('qso') + if self.qso_key in qso_dict: + return qso_dict[self.qso_key] + else: + self.ex_error('qso_extractor dictionary does not have key %s' % (self.qso_key)) + +##### Nat has converged upon the following being the most significant featues, +# Joey believes it is best to jut use these features only (so now the others are disabled in +# __init__.py and qso_extractor.py + +class qso_log_chi2_qsonu_extractor(qso_generic): + """ qso_log_chi2_qsonu """ + extname = "qso_log_chi2_qsonu" + qso_key = "log_chi2_qsonu" + +class qso_log_chi2nuNULL_chi2nu_extractor(qso_generic): + """ qso_log_chi2nuNULL_chi2nu """ + extname = "qso_log_chi2nuNULL_chi2nu" + qso_key = "log_chi2nuNULL_chi2nu" + +##### + +### eventually get rid of: +#class qso_lvar_extractor(qso_generic): +# """ qso_lvar """ +# extname = "qso_lvar" +# qso_key = "lvar" + +### eventually get rid of: +#class qso_ltau_extractor(qso_generic): +# """ qso_ltau """ +# extname = "qso_ltau" +# qso_key = "ltau" + +### eventually get rid of: (since is not related to QSO classifier) +#class qso_chi2nu_extractor(qso_generic): +# """ qso_chi2nu """ +# extname = "qso_chi2nu" +# qso_key = "chi2/nu" + +#class qso_chi2_qsonu_extractor(qso_generic): +# """ qso_chi2_qsonu """ +# extname = "qso_chi2_qsonu" +# qso_key = "chi2_qso/nu" + +#class qso_chi2_qso_nu_NULL_extractor(qso_generic): +# """ chi2_qso_nu_NULL """ +# extname = "qso_chi2_qso_nu_NULL" +# qso_key = "chi2_qso/nu_NULL" + +#class qso_signif_qso_extractor(qso_generic): +# """ qso_signif_qso """ +# extname = "qso_signif_qso" +# qso_key = "signif_qso" + +#class qso_signif_not_qso_extractor(qso_generic): +# """ qso_signif_not_qso """ +# extname = "qso_signif_not_qso" +# qso_key = "signif_not_qso" + +#class qso_signif_vary_extractor(qso_generic): +# """ qso_signif_vary """ +# extname = "qso_signif_vary" +# qso_key = "signif_vary" + +#class qso_chi2qso_nu_nuNULL_ratio_extractor(qso_generic): +# """ qso_chi2qso_nu_nuNULL_ratio """ +# extname = "qso_chi2qso_nu_nuNULL_ratio" +# qso_key = "chi2qso_nu_nuNULL_ratio" + diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/ratioRUfirst_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/ratioRUfirst_extractor.py new file mode 100644 index 00000000..129245d0 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/ratioRUfirst_extractor.py @@ -0,0 +1,17 @@ +from __future__ import print_function +from ..FeatureExtractor import MultiFeatureExtractor + +class ratioRUfirst_extractor(MultiFeatureExtractor): + """ calculates the ratio of the first frequency in the V band to the U band """ + active = False + extname = 'ratioRUfirst' #extractor's name + band1 = 'r' + band2 = 'u' + compared_extr = 'first_freq' + def extract(self): + self.extr1 = self.extr1 # just a reminder + self.extr2 = self.extr2 # just a reminder + ratioRU = self.extr1 / self.extr2 + print("ratioRU", ratioRU) + print("band",self.band) + return ratioRU \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/s_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/s_extractor.py new file mode 100644 index 00000000..31978279 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/s_extractor.py @@ -0,0 +1,20 @@ +from ..FeatureExtractor import InterExtractor +from numpy import sqrt + +class s_extractor(InterExtractor): + """ extracts the values s as shown in http://www.xycoon.com/peakedness_small_sample_test_1.htm for example + This is used by small_kurtosis_extractor""" + active = True + extname = 's' #extractor's name + minpoints = 2 # minimum number of points for the extractor to run +# TODO the quantity in the link is just std_extractor rescaled by sqrt(n/(n-1)) +# however, this uses the weighted average for x_bar, so maybe we want both versions? +# but this is different from the "weighted standard deviation"... + def extract(self): + n = float(self.fetch_extr('n_points')) + average = self.fetch_extr('weighted_average') + xi_minus_average = self.flux_data - average + squared = xi_minus_average**2 + sum_of_squared = squared.sum() + s = sqrt((1/(n-1)) * sum_of_squared) + return s diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sc.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sc.py new file mode 100644 index 00000000..ff920594 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sc.py @@ -0,0 +1,153 @@ +#!/usr/bin/env python + +""" +dumbass tool to run skycoor on random assortment of coordinate string formats +""" +from __future__ import print_function +import os,string +skycoor = "/scisoft/i386/bin/skycoor" + +def string_metrics(sss): + + if type(sss) != type("sss"): + return {'digits': None, 'separators': None, 'letters': None} + + n_digits = len([x for x in sss if x in string.digits]) + n_letters = len([x for x in sss if x in string.ascii_letters]) + n_digits = len([x for x in sss if x in ",'=&+-: hmsd" or x in '"']) + return {'digits': n_digits, 'separators': n_letters, 'letters': n_digits, 'len': len(sss)} + +def sexify(sss): + + met = string_metrics(sss) + if met['separators'] > 0: + ## figure out what the separator is + nsplit ={} + for s in ":hmsd": + nsplit.update({s: len(sss.split(s)) - 1}) + #print nsplit + + ## remove the letters + sss = "".join([s for s in sss if s in string.digits or s in ["."]]) + + hh = sss[0:2] + mm = sss[2:4] + ss = sss[4:] + val = ":".join([hh,mm,ss]) + if sss.find(".") not in [6,-1]: + val = sss + + return val + +def reckonpos(a): + + ra, dec = None, None + + if len(a) == 1: + ## try removing letters + #print a[0] + seps = "hmso'" + '""' + b = a[0] + print(b) + for s in seps: + ttt = b.index(s) + print(ttt) + if ttt != -1: + if string.digits.find(b[ttt - 1]) != -1 and string.digits.find(b[ttt - 2]) == -1: + b[ttt - 2] = "0" + a[0] = b + print(b + "***") + tmp = [a for a in a[0] if string.ascii_letters.find(a) == -1 and "=".find(a) == -1 and "'".find(a) == -1 \ + and '"'.find(a) == -1] + tmp = "".join(tmp) + sss = "".join(tmp) + sss = sss.strip() + sss.replace("="," ") + sss.replace(" "," ") + sss.replace("\t","") + sss.replace("\n","") + if sss.count(".") > 1: + ## too many periods, remove all those at the beginning and the end + sss = string.strip(sss,".") + + print(sss) + tmp = [x for x in sss.split(" ") if x != ""] + if len(tmp) == 6: + ra = ":".join(tmp[0:3]) + dec = ":".join(tmp[3:6]) + return (ra,dec) + + + + tmp = [x for x in sss.split(":") if x != ""] + if len(tmp) == 6: + ra = ":".join(tmp[0:3]) + dec = ":".join(tmp[3:6]) + return (ra,dec) + + # probably like: J000356.67+010007.3 + met = string_metrics(sss) + + ## try to break on + or - + notgood = False + tmp = sss.split("-") + decsign = "-" + if len(tmp) != 2: + tmp = sss.split("+") + decsign="+" + if len(tmp) != 2: + notgood = True + if not notgood: + ra = sexify(tmp[0]) + dec = decsign + sexify(tmp[1]) + else: + tmp = [x for x in sss.split(" ") if x != ""] + if len(tmp) == 2: + ra, dec = tmp[0], tmp[1] + return (ra,dec) + else: + return (None,None) + if len(a) == 6: + ## this is probably HH MM SS DD MM SS + pass + + return (ra,dec) + #print (ra, dec) + +def clean(sss): + + if sss.count(".") > 1: + ## too many periods, remove all those at the beginning and the end + sss = string.strip(sss,".") + + return sss +if __name__ == "__main__": + from optparse import OptionParser + usage = "usage: %prog [options] 'ra dec'\n" + parser = OptionParser(usage) + + (options, args) = parser.parse_args() + + if len(args) < 1: + parser.parse_args(['-h']) + + #print args + ra,dec = reckonpos(args) + + ra = clean(ra) + dec = clean(dec) + if ra.find(":") != -1: + os.system(skycoor + " -dv %s %s" % (ra,dec)) + else: + os.system(skycoor + " -v %s %s" % (ra,dec)) + + #os.system(skycoor + " -v %s %s" % (ra,dec)) + +## +""" +./sc.py "3 33 48.97 +0 42 33.6" +./sc.py "RA 09:43:26.22 Dec 25:10:21.9 " +./sc.py "ra=55.497287&dec=-0.782905" +/sc.py " ra=55.49728745, dec=-0.78290499" + +""" \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/scatter_res_raw_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/scatter_res_raw_extractor.py new file mode 100644 index 00000000..a66c6d14 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/scatter_res_raw_extractor.py @@ -0,0 +1,18 @@ +from ..FeatureExtractor import FeatureExtractor + +class scatter_res_raw_extractor(FeatureExtractor): + """ From arXiv 1101_2406v1 Dubath 20110112 paper. + + Scatter:res/raw: + Median Absolute Deviation (MAD) of the residuals + (obtained by subtracting model values from the raw light curve) + divided by the MAD of the raw light-curve values + around the median. + """ + active = True + extname = "scatter_res_raw" + def extract(self): + median_absolute_deviation = self.fetch_extr('median_absolute_deviation') + lomb_dict = self.fetch_extr('lomb_scargle') + mad_of_model_residuals = lomb_dict.get('mad_of_model_residuals',0.) + return mad_of_model_residuals / median_absolute_deviation diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss.py new file mode 100644 index 00000000..cf017f3d --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss.py @@ -0,0 +1,609 @@ +""" +sdss -- query tools +182.520649 ++18.534407 +""" +from __future__ import print_function + +import os, sys, string,urllib,copy +from math import log10, radians, pi +import time, traceback, datetime +import threading +import StringIO + +global limit_per_min, recent_query_list, do_limit +limit_per_min = 19.99 +recent_query_list = [] +do_limit = True + +dr7_mirrors = [{'url': "http://cas.sdss.org/astrodr7/en/tools/search/x_sql.asp", "second_per_query": 1.02, + "active": True, "last": None, "running": False}, + {'url': "http://skyserver.sdss.org/astro/en/tools/search/x_sql.asp", "second_per_query": 1.02, "active": True, "last": None, + "running": False}, + {'url': "http://www.sdss.org.uk/dr7/en/tools/search/x_sql.asp", "second_per_query": 1.02, "active": True, "last": None, + "running": False}] + +class sdssq: + #dr_url='http://skyserver.sdss.org/dr6/en/tools/search/x_sql.asp' + dr_url="http://cas.sdss.org/astrodr7/en/tools/search/x_sql.asp" + formats = ['csv','xml','html'] + def_fmt = "csv" + all_completed = False + completed = False + z_completed = False + completed_master = False + threads = [] + z_results = None + results = None + results_master = None + feature = None + in_footprint = False + + def _get_dr_url(self,old=False,max_wait=10.0): + ## get the next available dr7 mirror + if old: + return self.dr_url + + ## loop over the dr7 mirrors + now = time.time() + while time.time() - now < max_wait: + for dr7 in dr7_mirrors: + if not dr7['active'] or dr7['running'] == True: + continue + if dr7['last'] is None: + ## we haven't used this guy yet! + dr7['last'] = time.time() + dr7['running'] =True + return dr7['url'] + if time.time() - dr7['last'] > dr7['second_per_query']: + dr7['last'] = time.time() + dr7['running'] =True + return dr7['url'] + time.sleep(0.01) + + return None + + + def __init__(self,pos=(None,None),threaded=True,verbose=False,block=True,maxd=1,timeout=10.0, \ + run_on_instance=False,run_feature_maker=True,runfoot=False): + + """maxd in arcmin""" + self.runfoot = runfoot + self.threaded = threaded + self.verbose = verbose + self.pos = pos + self.maxd = maxd + self.timeout = timeout + self.block =block + + self.run_on_instance = run_on_instance + self.run_feature_maker = run_feature_maker + self.threaded = threaded + self.do_runs() + return + t = threading.Thread(target=self.do_runs, args=[]) + t.start() + t.join(120.0) # wait 120 seconds before giving up on SDSS queries (I've seen some take ~50 seconds) + if t.isAlive(): + print("! Thread has not returned!") + + def do_runs(self): + """ This is to be threaded so that a timeout in thread .join() can be used. + """ + if self.runfoot: + ss = self.run_footprint_check() + if self.verbose: + print("Position in footprint: %s" % repr(ss)) + + if self.run_on_instance: + if not self.threaded: + self.run_all() + else: + self.run_all_threaded() + if self.verbose: + print(self.__str__()) + + if self.run_feature_maker: + self.feature_maker() + + + def run_all(self): + self._get_z_objs() + self._get_objs(force_galaxy=True,include_photo_z=True) + self._get_objs(force_galaxy=False,include_photo_z=False) + if self.completed and self.z_completed and self.completed_master: + self.all_completed = True + + def run_footprint_check(self,old=False): + sss = """SELECT count(*) as total from dbo.fFootprintEq(%f,%f,%f)""" % (self.pos[0],self.pos[1],0.2) + if old: + fff = self._query(sss) + else: + fff = self._new_query(sss) + + line = fff.readline() + if line.startswith("ERROR") or line.find("HTTP Error 404") != -1: + if self.verbose: + print(line) + print(fff.readlines()) + return False + ## get the keys + #kkk = line.split(",") + ## now go line by line + line = fff.readline() + vvv = line.strip().split(",") + if vvv[0] == "0" or vvv[0].find("The page cannot be found") != -1: + self.in_footprint = False + else: + self.in_footprint = True + return self.in_footprint + + def run_all_threaded(self): + + import threading + self.threads.append(threading.Thread(target=self._get_objs,name="master",kwargs={'force_galaxy': False,'include_photo_z': False})) + self.threads[-1].start() + self.threads.append(threading.Thread(target=self._get_objs,name="near",kwargs={'force_galaxy': True,'include_photo_z': True})) + self.threads[-1].start() + self.threads.append(threading.Thread(target=self._get_z_objs,name="z")) + self.threads[-1].start() + + if self.block: + for t in self.threads: + #print "joining %s" % repr(t) + t.join(self.timeout) + + if self.completed and self.z_completed and self.completed_master: + self.all_completed = True + + def feature_maker(self,force=False,old=False,verbose=False): + """http://www.sdss.org/dr6/algorithms/redshift_type.html#eclass""" + if not self.in_footprint: + self.feature = {"in_footprint": False} + if not force: + return + else: + self.feature = {"in_footprint": True} + + sss = """SELECT TOP 3 p.objid, case + WHEN S.bestObjID is NOT NULL then dbo.fSpecClassN(S.specClass) + WHEN p.probPSF = 0 THEN "galaxy" + WHEN p.probPSF = 1 THEN "star" + ELSE "unknown" + end as type, p.ra, p.dec,p.dered_u, p.dered_g, p.dered_r, p.dered_i, p.dered_z, p.err_u, p.err_g, p.err_r, p.err_i, p.err_z, + n.distance as dist_in_arcmin, R.spz as chicago_z, R.eclass as chicago_class, R.spz_status as chicago_status, + T.z as photo_z, T.zerr as photo_zerr, T.pzType as photo_z_pztype, + T.rest_ug as photo_rest_ug,T.rest_gr as photo_rest_gr,T.rest_ri as photo_rest_ri, + T.rest_iz as photo_rest_iz,T.absMag_g as photo_rest_abs_g,T.absMag_u as photo_rest_abs_u, + T.absMag_r as photo_rest_abs_r,T.absMag_i as photo_rest_abs_i,T.absMag_z as photo_rest_abs_z, + Q.photozcc2 as photo2_z_cc,Q.photozerrcc2 as photo2_zerr_cc,Q.photozd1 as photo2_z_d1, + Q.photozerrd1 as photo2_zerr_d1,Q.flag as photo2_flag,S.z as spec_z, S.velDisp as spec_veldisp, dbo.fSpecZWarningN(S.zWarning) as spec_zWarning, + dbo.fSpecZStatusN(S.zStatus) as spec_zStatus, S.zConf as spec_confidence, S.zErr as spec_zerr, case + WHEN S.bestObjID is NOT NULL AND S.zConf > 0.5 THEN S.z + WHEN Q.objID IS NOT NULL AND p.r > 20 then Q.photozcc2 + WHEN Q.objID IS NOT NULL AND p.r < 20 then Q.photozd1 + WHEN T.objID is NOT NULL AND T.z != -9999 THEN T.z + ELSE NULL + end as bestz, case + WHEN S.bestObjID is NOT NULL AND S.zConf > 0.5 THEN S.zErr + WHEN Q.objID IS NOT NULL AND p.r > 20 then Q.photozerrcc2 + WHEN Q.objID IS NOT NULL AND p.r < 20 then Q.photozerrd1 + WHEN T.objID is NOT NULL AND T.z != -9999 THEN T.zerr + ELSE NULL + end as bestz_err, + dbo.fCosmoDl(case + WHEN S.bestObjID is NOT NULL AND S.zConf > 0.5 AND S.z < 0.0001 THEN 0.00001 + WHEN S.bestObjID is NOT NULL AND S.zConf > 0.5 AND S.z > 0.0001 THEN S.z + WHEN Q.objID IS NOT NULL AND p.r > 20 then Q.photozcc2 + WHEN Q.objID IS NOT NULL AND p.r < 20 then Q.photozd1 + WHEN T.objID is NOT NULL AND T.z != -9999 THEN T.z + ELSE 1500 + end) as best_dl,case + WHEN p.petroRad_g > 0.0 THEN p.petroRad_g + ELSE NULL + end as petroRad_g, case + WHEN p.petroRad_g > 0.0 THEN p.petroRadErr_g + ELSE NULL + end as petroRadErr_g, case + WHEN p.petroRad_g > 0.0 THEN n.distance * 60.0 / p.petroRad_g + ELSE NULL + end as best_offset_in_petro_g, f.delta as first_offset_in_arcsec, f.integr as first_flux_in_mJy, + X.delta as rosat_offset_in_arcsec, X.hard1 as rosat_hardness_1,X.hard2 as rosat_hardness_2, X.cps as rosat_cps, X.posErr as rosat_poserr, case + WHEN X.objID is NOT NULL THEN X.delta / X.posErr + end as rosat_offset_in_sigma, X.cps * 6.9 as rosat_flux_in_microJy, R.seguetargetclass as classtype, + R.sptypea as spectral_stellar_type, R.hammersptype as spectral_hammer_type, R.flag as spectral_flag, R.zbclass as segue_class, R.zbsubclass as segue_star_type + FROM PhotoPrimary p + JOIN dbo.fGetNearbyObjEq(%f,%f,%f) as n ON p.objID = n.objID + LEFT JOIN photoz as T ON p.objID=T.objID + LEFT JOIN photoz2 as Q ON p.objID=Q.objID + LEFT JOIN SpecObjAll as S ON p.objId=S.bestObjId + LEFT JOIN sppParams as R ON S.specObjID=R.specObjID + LEFT JOIN First as f ON p.objID=f.objID + LEFT JOIN Rosat as X on p.objID=X.objID + ORDER by n.distance + """ % (self.pos[0],self.pos[1],self.maxd) + ## as per Dovi's suggestion, get the nearest guy, no questions asked. + #print sss + #"""""" + if old: + fff = self._query(sss) + else: + fff = self._new_query(sss,verbose=verbose) + + line = fff.readline() + if line.startswith("ERROR") or line.startswith("No objects"): + if self.verbose: + print(line) + print(fff.readlines()) + return self.feature + ## get the keys + kkk = line.split(",") + ## now go line by line + line = fff.readline() + rez = [] + try: + if not self.in_footprint: + self.in_footprint = True + self.feature = {"in_footprint": True} + while line: + tmp = {} + vvv = line.strip().split(",") + for k,v in dict(zip(kkk,vvv)).items(): + #print "***" + k + "****" + str(v) + "****" + if v == "0" or v == "null": + v = None + if k.strip() in ['objid',"photo2_flag"]: + if v != None: + v1 = long(v) + else: + v1 = None + elif k.strip() in ['type','spec_zStatus','spec_zWarning',"spectral_flag","spectral_hammer_type",\ + "spectral_stellar_type","spectral_flag","classtype","segue_class","segue_star_type"]: + v1 = v + if v1 != None: + v1 = v1.lower() + if v1 == 'null': + v1 = None + else: + if v != None: + if v != "-9999": + try: + v1 = float(v) + except: + print(traceback.print_exc()) + if v.startswith("ERR"): + time.sleep(60.0) + v1 = None + else: + v1 = None + #v1 = float(v) if v != "-9999" else None + else: + v1 = None + tmp.update({k.strip(): v1}) + tmp.update({"url": "http://cas.sdss.org/astrodr7/en/tools/explore/obj.asp?id=%i" % tmp['objid']}) + tmp.update({"urlalt": "http://cas.sdss.org/astrodr7/en/tools/chart/chart.asp?ra=%f&dec=%f" % (tmp['ra'],tmp['dec'])}) + if 'best_dl' in tmp: + if tmp['best_dl'] > 1e6: + ## kludge here + tmp.update({"best_dl": None}) + if tmp['best_dl'] != None: + tmp.update({"best_dm": 5.0*log10(tmp['best_dl']*1e5)}) + if 'bestz' in tmp and 'dist_in_arcmin' in tmp: + angdist =tmp['best_dl']/(1.0 + tmp['bestz'])**2 + tmp.update({"best_offset_in_kpc": 1e3*angdist*radians(tmp['dist_in_arcmin']/60.0)}) + if "rosat_flux_in_microJy" in tmp and "best_dl" in tmp and "best_z" in tmp: + if tmp["rosat_flux_in_microJy"] > 0.0 and tmp["best_dl"] and tmp["best_z"]: + l = ((3.085e24)**2)*4.0*pi*tmp["best_dl"]*tmp["best_dl"]*(1.0 + tmp["best_z"])*1e-29 + tmp.update({"rosat_log_xray_luminosity": log10(l)}) + else: + tmp.update({"rosat_log_xray_luminosity": None}) + rez.append(copy.copy(tmp)) + line = fff.readline() + except: + print(traceback.format_exc()) + print(line) + # 20090211: dstarr adds try/except: + try: + if len(rez) == 0: + self.feature.update(copy.copy(rez[0])) + elif len(rez) > 1: + ## make sure there isn't a more nearby galaxy + if rez[0]['type'] != 'galaxy' and rez[0]['best_offset_in_petro_g'] > 2.0: + ## we're sort of far from the closest star. Just check to make sure there isn't a galaxy closer in petro + usei = 0 + bestp = 100.0 + for i,r in enumerate(rez[1:]): + if r['type'] == 'galaxy' and r['best_offset_in_petro_g'] < bestp: + bestp = r['best_offset_in_petro_g'] + usei = i+1 + if bestp < 3.0: + self.feature.update(copy.copy(rez[usei])) + self.feature.update({"note": "nearer star not used. Instead nearby galaxy"}) + + else: + self.feature.update(copy.copy(rez[0])) + else: + self.feature.update(copy.copy(rez[0])) + except: + print("EXCEPT: sdss.py...self.feature.update(rez[0])") + return self.feature + return + + def nearest(self): + ret = None + if self.completed and self.results: + if len(self.results) > 0: + ret = copy.copy(self.results[0]) + return ret + + def nearest_all(self): + ret = None + if self.completed_master and self.results_master: + if len(self.results_master) > 0: + ret = copy.copy(self.results_master[0]) + return ret + + def nearest_with_z(self): + ret = None + if self.z_completed and self.z_results: + if len(self.z_results) > 0: + ret = copy.copy(self.z_results[0]) + return ret + + def __str__(self): + printone = False + a = "" + if self.z_completed: + a += "*** redshift search results within %6.1f arcmin of pos = %s *** \n" % (self.maxd, self.pos) + if self.z_results: + kkk = self.z_results[0].keys() + a += " ".join(kkk) + "\n" + if printone: + nn = 1 + else: + nn = len(self.z_results[0]) + for i in range(nn): + a += " ".join([str(x) for x in self.z_results[i].values()]) + "\n" + else: + a += " nothing found \n" + else: + a += "*** [redshift search not completed] \n" + + if self.completed: + a += "*** galaxy search results within %6.1f arcmin of pos = %s *** \n" % (self.maxd, self.pos) + if self.results: + kkk = self.results[0].keys() + a += " ".join(kkk) + "\n" + if printone: + nn = 1 + else: + nn= len(self.results[0]) + for i in range(nn): + a += " ".join([str(x) for x in self.results[i].values()]) + "\n" + else: + a += " nothing found \n" + else: + a += "*** [galaxy search not completed] \n" + + if self.completed_master: + a += "*** galaxy/star search results within %6.1f arcmin of pos = %s *** \n" % (self.maxd, self.pos) + if self.results_master: + kkk = self.results_master[0].keys() + a += " ".join(kkk) + "\n" + if printone: + nn = 1 + else: + nn = len(self.results_master[0]) + for i in range(nn): + a += " ".join([str(x) for x in self.results_master[i].values()]) + "\n" + else: + a += " nothing found \n" + else: + a += "*** [galaxy search not completed] \n" + + return a + + def _get_z_objs(self,force_galaxy=True,old=False): + if force_galaxy: + the_table = "Galaxy" + else: + the_table = "PhotoPrimary" + sss = """SELECT p.objid, p.ra, p.dec,p.dered_u, p.dered_g, p.dered_r, p.dered_i, p.dered_z, n.distance, S.z, S.velDisp, dbo.fSpecZStatusN(S.zStatus) as zStatus, S.zConf + FROM %s p + JOIN SpecObjAll as S ON p.objId=S.bestObjId + JOIN dbo.fGetNearbyObjEq(%f, %f,%f) as n ON p.objID = n.objID + WHERE + S.zStatus != dbo.fSpecZStatus('FAILED') + ORDER BY n.distance""" % (the_table,self.pos[0],self.pos[1],self.maxd) + + if old: + fff = self._query(sss) + else: + fff = self._new_query(sss) + line = fff.readline() + if line.startswith("ERROR") or line.startswith("No objects"): + self.z_completed = True + if self.verbose: + print(line) + print(fff.readlines()) + return + ## get the keys + kkk = line.split(",") + ## now go line by line + line = fff.readline() + rez = [] + while line: + tmp = {} + vvv = line.strip().split(",") + for k,v in dict(zip(kkk,vvv)).items(): + if k == 'objid': + v1 = long(v) + elif k == 'zStatus': + v1 = v + else: + if v != "-9999": + v1 = float(v) + else: + v1 = None + + tmp.update({k.strip(): v1}) + rez.append(copy.copy(tmp)) + line = fff.readline() + self.z_results = copy.copy(rez[0]) + self.z_completed = True + return + + def _get_objs(self,force_galaxy=True,include_photo_z=True,old=False): + if force_galaxy: + the_table = "Galaxy" + else: + the_table = "PhotoPrimary" + + if include_photo_z: + extra_col = ",T.z, T.zerr,T.dmod,T.rest_ug,T.rest_gr,T.rest_ri,T.rest_iz,T.absMag_g" + extra_join = "JOIN photoz as T ON p.objID=T.objID " + else: + extra_col = " " + extra_join = " " + + if include_photo_z or force_galaxy: + complete_flag = "self.completed" + results = "self.results" + else: + complete_flag = "self.completed_master" + results = "self.results_master" + + + sss = """SELECT p.objid, p.ra, p.dec,p.dered_u, p.dered_g, p.dered_r, p.dered_i, p.dered_z, n.distance %s + FROM %s p + JOIN dbo.fGetNearbyObjEq(%f, %f,%f) as n ON p.objID = n.objID + %s + ORDER BY n.distance""" % (extra_col,the_table,self.pos[0],self.pos[1],self.maxd,extra_join) + + #print sss, complete_flag, results + if old: + fff = self._query(sss) + else: + fff = self._new_query(sss) + + #print fff.readlines() + line = fff.readline() + #if line.startswith("ERROR") or line.startswith("No objects"): + # dstarr 20090127 adds len() condition: + if line.startswith("ERROR") or \ + line.startswith("No objects") or \ + (len(line) == 0): + exec(complete_flag + " = True") + if self.verbose: + print(line) + print(fff.readlines()) + return + ## get the keys + kkk = line.split(",") + ## now go line by line + line = fff.readline() + rez = [] + while line: + tmp = {} + vvv = line.strip().split(",") + for k,v in dict(zip(kkk,vvv)).items(): + if k == 'objid': + v1 = long(v) + elif k == 'zStatus': + v1 = v + else: + v1 = float(v) + tmp.update({k.strip(): v1}) + rez.append(copy.copy(tmp)) + line = fff.readline() + exec(results + " = copy.copy(rez)") + exec(complete_flag + " = True") + #print complete_flag, self.completed, self.completed_master + return + + def _filtercomment(self,sql): + "Get rid of comments starting with --" + fsql = '' + for line in sql.split('\n'): + fsql += line.split('--')[0] + ' ' + os.linesep; + return fsql + + def _new_query(self,sql,fmt=def_fmt,wait_period=62,verbose=True): + #print dr7_mirrors + url = self._get_dr_url(old=False) + if verbose: + print(url) + #print dr7_mirrors + if url is None: + return StringIO.StringIO() # This is an empty filehandler + + fsql = self._filtercomment(sql) + params = urllib.urlencode({'cmd': fsql, 'format': fmt}) + try: + ttt = urllib.urlopen(url+'?%s' % params) + for d in dr7_mirrors: + if d['url'] == url: + d['running'] = False + break + #print dr7_mirrors + return ttt + except: + print("TRIED: " + url+'?%s' % params) + print("EXCEPT: sdss.py._query()") + for d in dr7_mirrors: + if d['url'] == url: + d['running'] = False + d['active'] = False + break + return StringIO.StringIO() # This is an empty filehandler + + + def _query(self,sql,url=dr_url,fmt=def_fmt,wait_period=62): + "Run query and return file object" + global limit_per_min, recent_query_list, do_limit + if do_limit: + recent_query_list.append((time.time(),sql)) + ## cull out all the old calls + recent_query_list = [x for x in recent_query_list if time.time() - x[0] < wait_period] + if len(recent_query_list) > limit_per_min: + ## ug, we've got to wait + tmp = [time.time() - x[0] for x in recent_query_list if time.time() - x[0] < wait_period] + wait = wait_period - max(tmp) + 1 + if self.verbose: + print("Date: %s Query length is %i in the last %f sec" % (str(datetime.datetime.now()),len(recent_query_list) - 1 , wait_period)) + print("waiting %f sec, so as not to block the SDSS query %s" % (wait,sql)) + time.sleep(wait) + + fsql = self._filtercomment(sql) + params = urllib.urlencode({'cmd': fsql, 'format': fmt}) + try: + return urllib.urlopen(url+'?%s' % params) + except: + print("TRIED: " + url+'?%s' % params) + print("EXCEPT: sdss.py._query()") + return StringIO.StringIO() # This is an empty filehandler + +def test(verbose=True): + + dr7_mirrors[0]["second_per_query"] = 10 + dr7_mirrors[1]["second_per_query"] = 7 + + s = sdssq(pos=(36.522917 ,-0.540500),verbose=verbose) + #s = sdssq(pos=(38.522917 ,1.540500),verbose=verbose) + #s = sdssq(pos=(40.522917 ,2.540500),verbose=verbose) + #s = sdssq(pos=(42.522917 ,3.540500),verbose=verbose) + #s = sdssq(pos=(170.76476675499998,-8.5050723541500002), verbose=verbose) + + #print s.nearest() + #print s.nearest_with_z() + s.feature_maker(force=True,verbose=True) + import pprint + pprint.pprint(s.feature) + import webbrowser + try: + webbrowser.open_new_tab(s.feature["urlalt"]) + except: + pass + +if __name__ == "__main__": + test(verbose=False) diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_best_dm.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_best_dm.py new file mode 100644 index 00000000..9bdabf66 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_best_dm.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_best_dm(ContextFeatureExtractor): + """If a redshift is known, what is the distance modulus?""" + active = True + extname = 'sdss_best_dm' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "best_dm" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["best_dm"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_best_offset_in_kpc.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_best_offset_in_kpc.py new file mode 100644 index 00000000..28d24069 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_best_offset_in_kpc.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_best_offset_in_kpc(ContextFeatureExtractor): + """If a redshift is known, what is the offset in kpc?""" + active = True + extname = 'sdss_best_offset_in_kpc' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "best_offset_in_kpc" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["best_offset_in_kpc"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_best_offset_in_petro_g.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_best_offset_in_petro_g.py new file mode 100644 index 00000000..b58ab7ed --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_best_offset_in_petro_g.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_best_offset_in_petro_g(ContextFeatureExtractor): + """What is the offset in Petrosian radii (gband)?""" + active = True + extname = 'sdss_best_offset_in_petro_g' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "best_offset_in_petro_g" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["best_offset_in_petro_g"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_best_z.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_best_z.py new file mode 100644 index 00000000..6271c42e --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_best_z.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_best_z(ContextFeatureExtractor): + """If a redshift is known, what is it?""" + active = True + extname = 'sdss_best_z' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "bestz" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["bestz"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_best_zerr.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_best_zerr.py new file mode 100644 index 00000000..4305ce12 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_best_zerr.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_best_zerr(ContextFeatureExtractor): + """If a redshift is known, what is it's error?""" + active = True + extname = 'sdss_best_zerr' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "bestz_err" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["bestz_err"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_chicago_class.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_chicago_class.py new file mode 100644 index 00000000..972e68d8 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_chicago_class.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_chicago_class(ContextFeatureExtractor): + """What is the PCA parameter for the galaxy type? from sppParams""" + active = True + extname = 'sdss_chicago_class' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "chicago_class" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["chicago_class"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_dered_g.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_dered_g.py new file mode 100644 index 00000000..360d7e7e --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_dered_g.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_dered_g(ContextFeatureExtractor): + """What is the dereddened g magnitude?""" + active = True + extname = 'sdss_dered_g' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "dered_g" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["dered_g"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_dered_i.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_dered_i.py new file mode 100644 index 00000000..349e76fb --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_dered_i.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_dered_i(ContextFeatureExtractor): + """What is the dereddened g magnitude?""" + active = True + extname = 'sdss_dered_i' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "dered_i" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["dered_i"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_dered_r.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_dered_r.py new file mode 100644 index 00000000..8d7998e6 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_dered_r.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_dered_r(ContextFeatureExtractor): + """What is the dereddened r magnitude?""" + active = True + extname = 'sdss_dered_r' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "dered_r" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["dered_r"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_dered_u.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_dered_u.py new file mode 100644 index 00000000..70c9869b --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_dered_u.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_dered_u(ContextFeatureExtractor): + """What is the dereddened u magnitude?""" + active = True + extname = 'sdss_dered_u' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "dered_u" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["dered_u"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_dered_z.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_dered_z.py new file mode 100644 index 00000000..0aedf663 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_dered_z.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_dered_z(ContextFeatureExtractor): + """What is the dereddened z magnitude?""" + active = True + extname = 'sdss_dered_z' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "dered_z" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["dered_z"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_dist_arcmin.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_dist_arcmin.py new file mode 100644 index 00000000..7cb96393 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_dist_arcmin.py @@ -0,0 +1,40 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_dist_arcmin(ContextFeatureExtractor): + """distance to sdss source""" + active = True + extname = 'sdss_dist_arcmin' #extractor's name + + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["dist_in_arcmin"] + if self.verbose: + print(n) + return rez diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_first_flux_in_mjy.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_first_flux_in_mjy.py new file mode 100644 index 00000000..4825f978 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_first_flux_in_mjy.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_first_flux_in_mjy(ContextFeatureExtractor): + """What is the flux of the first source?""" + active = True + extname = 'sdss_first_flux_in_mjy' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "first_flux_in_mJy" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["first_flux_in_mJy"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_first_offset_in_arcsec.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_first_offset_in_arcsec.py new file mode 100644 index 00000000..641a6d93 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_first_offset_in_arcsec.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_first_offset_in_arcsec(ContextFeatureExtractor): + """What is the offset of the first source?""" + active = True + extname = 'sdss_first_offset_in_arcsec' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "first_offset_in_arcsec" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["first_offset_in_arcsec"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_in_footprint.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_in_footprint.py new file mode 100644 index 00000000..c82f33a8 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_in_footprint.py @@ -0,0 +1,29 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_in_footprint(ContextFeatureExtractor): + """Is the source in the sdss footprint""" + active = True + extname = 'sdss_in_footprint' #extractor's name + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return 0 + else: + return 1 + diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_nearest_obj_type.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_nearest_obj_type.py new file mode 100644 index 00000000..33cdecd8 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_nearest_obj_type.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_nearest_obj_type(ContextFeatureExtractor): + """What is classification of the nearest object?""" + active = True + extname = 'sdss_nearest_obj_type' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "type" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["type"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_petro_radius_g.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_petro_radius_g.py new file mode 100644 index 00000000..598b96e2 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_petro_radius_g.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_petro_radius_g(ContextFeatureExtractor): + """What is the Petrosian radius in arcsec?""" + active = True + extname = 'sdss_petro_radius_g' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "petroRad_g" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["petroRad_g"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_petro_radius_g_err.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_petro_radius_g_err.py new file mode 100644 index 00000000..9428d531 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_petro_radius_g_err.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_petro_radius_g_err(ContextFeatureExtractor): + """What is the Petrosian radius error in arcsec?""" + active = True + extname = 'sdss_petro_radius_g_err' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "petroRad_g_err" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["petroRad_g_err"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_abs_g.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_abs_g.py new file mode 100644 index 00000000..23e329c7 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_abs_g.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_photo_rest_abs_g(ContextFeatureExtractor): + """What is the sdss_photo_rest_abs_g?""" + active = True + extname = 'sdss_photo_rest_abs_g' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "photo_rest_abs_g" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["photo_rest_abs_g"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_abs_i.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_abs_i.py new file mode 100644 index 00000000..6cb23819 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_abs_i.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_photo_rest_abs_i(ContextFeatureExtractor): + """What is the sdss_photo_rest_abs_i?""" + active = True + extname = 'sdss_photo_rest_abs_i' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "photo_rest_abs_i" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["photo_rest_abs_i"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_abs_r.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_abs_r.py new file mode 100644 index 00000000..ec114333 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_abs_r.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_photo_rest_abs_r(ContextFeatureExtractor): + """What is the sdss_photo_rest_abs_r?""" + active = True + extname = 'sdss_photo_rest_abs_r' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "photo_rest_abs_r" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["photo_rest_abs_r"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_abs_u.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_abs_u.py new file mode 100644 index 00000000..b83ceb32 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_abs_u.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_photo_rest_abs_u(ContextFeatureExtractor): + """What is the sdss_photo_rest_abs_u?""" + active = True + extname = 'sdss_photo_rest_abs_u' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "photo_rest_abs_u" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["photo_rest_abs_u"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_abs_z.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_abs_z.py new file mode 100644 index 00000000..359a0291 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_abs_z.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_photo_rest_abs_z(ContextFeatureExtractor): + """What is the sdss_photo_rest_abs_z?""" + active = True + extname = 'sdss_photo_rest_abs_z' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "photo_rest_abs_z" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["photo_rest_abs_z"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_gr.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_gr.py new file mode 100644 index 00000000..9b397d84 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_gr.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_photo_rest_gr(ContextFeatureExtractor): + """What is the restframe g - r color?""" + active = True + extname = 'sdss_photo_rest_gr' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "photo_rest_gr" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["photo_rest_gr"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_iz.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_iz.py new file mode 100644 index 00000000..e7ccc588 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_iz.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_photo_rest_iz(ContextFeatureExtractor): + """What is the restframe i - z color?""" + active = True + extname = 'sdss_photo_rest_iz' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "photo_rest_iz" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["photo_rest_iz"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_ri.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_ri.py new file mode 100644 index 00000000..740322bb --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_ri.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_photo_rest_ri(ContextFeatureExtractor): + """What is the restframe r - i color?""" + active = True + extname = 'sdss_photo_rest_ri' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "photo_rest_ri" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["photo_rest_ri"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_ug.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_ug.py new file mode 100644 index 00000000..649874eb --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_rest_ug.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_photo_rest_ug(ContextFeatureExtractor): + """What is the restframe u - g color?""" + active = True + extname = 'sdss_photo_rest_ug' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "photo_rest_ug" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["photo_rest_ug"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_z_pztype.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_z_pztype.py new file mode 100644 index 00000000..d4f3bb86 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_photo_z_pztype.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_photo_z_pztype(ContextFeatureExtractor): + """From the photoz pipeline, what is the galaxy type (0 - 1)? See photoz for more details""" + active = True + extname = 'sdss_photo_z_pztype' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "photo_z_pztype" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["photo_z_pztype"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_rosat_flux_in_mJy.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_rosat_flux_in_mJy.py new file mode 100644 index 00000000..95426144 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_rosat_flux_in_mJy.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_rosat_flux_in_mJy(ContextFeatureExtractor): + """What is the flux of the rosat source?""" + active = True + extname = 'sdss_rosat_flux_in_mJy' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "rosat_flux_in_mJy" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["rosat_flux_in_mJy"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_rosat_log_xray_luminosity.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_rosat_log_xray_luminosity.py new file mode 100644 index 00000000..cdda18f2 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_rosat_log_xray_luminosity.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_rosat_log_xray_luminosity(ContextFeatureExtractor): + """What is the luminosity of the rosat source?""" + active = True + extname = 'sdss_rosat_log_xray_luminosity' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "rosat_log_xray_luminosity" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["rosat_log_xray_luminosity"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_rosat_offset_in_arcsec.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_rosat_offset_in_arcsec.py new file mode 100644 index 00000000..0d886283 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_rosat_offset_in_arcsec.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_rosat_offset_in_arcsec(ContextFeatureExtractor): + """What is the offset of the rosat source?""" + active = True + extname = 'sdss_rosat_offset_in_arcsec' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "rosat_offset_in_arcsec" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["rosat_offset_in_arcsec"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_rosat_offset_in_sigma.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_rosat_offset_in_sigma.py new file mode 100644 index 00000000..ca79ac81 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_rosat_offset_in_sigma.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_rosat_offset_in_sigma(ContextFeatureExtractor): + """What is the offset of the rosat source?""" + active = True + extname = 'sdss_rosat_offset_in_sigma' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "rosat_offset_in_sigma" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["rosat_offset_in_sigma"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_spec_confidence.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_spec_confidence.py new file mode 100644 index 00000000..3f5ed05b --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sdss_spec_confidence.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from ..FeatureExtractor import ContextFeatureExtractor + +class sdss_spec_confidence(ContextFeatureExtractor): + """If there is a spectrum, what is the confidence in the redshift derived from it?""" + active = True + extname = 'sdss_spec_confidence' #extractor's name + light_cutoff = 0.2 ## dont report anything farther away than this in arcmin + + verbose = False + def extract(self): + n = self.fetch_extr('intersdss') + + if n is None: + if self.verbose: + print("Nothing in the sdss extractor") + return None + + if "in_footprint" not in n: + if self.verbose: + print("No footprint info in the sdss extractor. Should never happen.") + return None + + if not n['in_footprint']: + if self.verbose: + print("Not in the footprint") + return None + + if "spec_confidence" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if "dist_in_arcmin" not in n: + if self.verbose: + print("Desired parameter was not determined") + return None + + if n["dist_in_arcmin"] > self.light_cutoff: + return None + else: + rez = n["spec_confidence"] + if self.verbose: + print(n) + return rez \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/second_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/second_extractor.py new file mode 100644 index 00000000..05160188 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/second_extractor.py @@ -0,0 +1,74 @@ +from __future__ import absolute_import + +from ..FeatureExtractor import FeatureExtractor +import numpy +from numpy import random +from scipy import fftpack, stats, optimize +try: + from pylab import * +except: + pass +from .common_functions import * + +#from power_extractor import power_extractor as power_extractor # could also use one of the x% significant extractors +from scipy import stats +from .common_functions.plot_methods import plot_vertical_line + + +class second_extractor(plot_vertical_line,FeatureExtractor): + """ extracts the second highest peak from a power spectrum""" + active = True + extname = 'second' #extractor's name + gauss_scale = 0.005 + def set_inputs(self): + #self.frequencies = self.fetch_extr('power_spectrum')[0] + #self.frequencies = self.frequencies[:len(self.frequencies)+1] + result = self.fetch_extr('significant_90_power') + (power, freq) = result + self.power = power + self.frequencies = freq + self.previous = self.fetch_extr('first_freq') + def extract(self): + # 20071215 dstarr adds try/except: + try: + self.set_inputs() + except: + why = "Except: {second,third}_extractor.set_inputs(). Probably Failure of self.fetch_extr(...) for second_extractor,first_freq_extractor, or power_extractor." + self.ex_error(text=why) + + gaussian = self.make_gaussian() + self.subtracted_gauss = self.power - gaussian + max_index = self.subtracted_gauss[1:].argmax() + 1 + max_freq = self.frequencies[max_index] + if self.subtracted_gauss[max_index] < 0.1: + why = "No %s frequency, power spectrum is zero/low at all points" % self.extname + self.ex_error(text=why) + #if self.extname == 'second lomb': + # plot(self.frequencies,subtracted_gauss,label="subtracted gaussian") + # plot(self.frequencies,gaussian,label="gaussian") + return max_freq + def make_gaussian(self): + gaussian = stats.norm.pdf(self.frequencies,loc=self.previous,scale = self.gauss_scale) +# plot(self.frequencies,gaussian,label = "early gaussian") + index = self.frequencies.searchsorted(self.previous) + ratio = self.power[index]/gaussian[index] + gaussian *= ratio + return gaussian + def specific_obj(self,output): + output.subtracted_gauss = self.subtracted_gauss + +class third_extractor(second_extractor): + """ extracts the third highest peak from a power spectrum""" + active = True + extname = 'third' #extractor's name + def set_inputs(self): + #self.power = self.fetch_extr('second').subtracted_gauss # result object has data about the subtracted gaussian + # 20071215 dstarr changes to this: + result = self.fetch_extr('second') + + self.power = result.subtracted_gauss + self.frequencies = result.frequencies + + # 20071215 dstarr notes this can probably be done instead: + ##### self.previous = result + self.previous = self.fetch_extr('second') diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/second_lomb_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/second_lomb_extractor.py new file mode 100644 index 00000000..01aaa326 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/second_lomb_extractor.py @@ -0,0 +1,27 @@ +from __future__ import absolute_import + +from ..FeatureExtractor import FeatureExtractor +import numpy +from numpy import random +from scipy import fftpack, stats, optimize +try: + from pylab import * +except: + pass +from .common_functions import * + +from .second_extractor import second_extractor +from .lomb_extractor import lomb_extractor +from .first_lomb_extractor import first_lomb_extractor +#from common_functions.plot_methods import plot_vertical_line + +class second_lomb_extractor(second_extractor): + """ Extracts the second frequency from a lomb power spectrum""" + active = False + extname = 'second_lomb' #extractor's name + gauss_scale = 0.002 + def set_inputs(self): + #self.frequencies = self.fetch_extr('lomb')[0] + (power,freq) = self.fetch_extr('lomb') + self.power = power + self.first = self.fetch_extr('first_lomb') diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/significant_power_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/significant_power_extractor.py new file mode 100644 index 00000000..66aea42f --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/significant_power_extractor.py @@ -0,0 +1,41 @@ +from __future__ import absolute_import +from ..FeatureExtractor import InterExtractor +from .common_functions.plot_methods import plot_vs_frequencies +from numpy import select + +class significant_80_power_extractor(plot_vs_frequencies,InterExtractor): + """ removes noise power from the periodogram """ + active = True + percent_significance = 80 + extname = 'significant_80_power' + def noise_p(self): #noise power + pct_montecarlo_extractor = 'pct_80_montecarlo' + return pct_montecarlo_extractor + def extract(self): + power = self.fetch_extr('power') + noise = self.fetch_extr(self.noise_p()) + subtract = power - noise + #>>> from numpy import * + #>>> x = array([5., -2., 1., 0., 4., -1., 3., 10.]) + #>>> select([x < 0, x == 0, x <= 5], [x-0.1, 0.0, x+0.2], default = 100.) + #array([ 5.2, -2.1, 1.2, 0. , 4.2, -1.1, 3.2, 100. ]) + result = select([subtract < 0],[0],default = subtract) + return result +class significant_90_power_extractor(significant_80_power_extractor): + percent_significance = 90 + extname = 'significant_90_power' + def noise_p(self): + pct_montecarlo_extractor = 'pct_90_montecarlo' + return pct_montecarlo_extractor +class significant_95_power_extractor(significant_80_power_extractor): + percent_significance = 95 + extname = 'significant_95_power' + def noise_p(self): + pct_montecarlo_extractor = 'pct_95_montecarlo' + return pct_montecarlo_extractor +class significant_99_power_extractor(significant_80_power_extractor): + percent_significance = 99 + extname = 'significant_99_power' + def noise_p(self): + pct_montecarlo_extractor = 'pct_99_montecarlo' + return pct_montecarlo_extractor diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sine_fit_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sine_fit_extractor.py new file mode 100644 index 00000000..f0d5575d --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sine_fit_extractor.py @@ -0,0 +1,43 @@ +from __future__ import absolute_import +from ..FeatureExtractor import InterExtractor +import numpy +from numpy import random +from scipy import fftpack, stats, optimize +try: + from pylab import * +except: + pass +from .common_functions import ChiSquare + +# TODO remove? +class sine_fit_extractor(InterExtractor,ChiSquare): + active = True + extname = 'sine_fit' #extractor's name + def extract(self): + a = self.fetch_extr('old_dc') + try: + b = self.properties['amplitude'] #amplitude + except: + b = 1 + c = 2*numpy.pi * self.fetch_extr('first_freq') # related to amplitude + d = 0 # x shift + init = numpy.array([a,b,c,d]) + sine = optimize.fmin_l_bfgs_b( self.sine_fit,init,args= (self.time_data, self.flux_data, self.rms_data), approx_grad=1, bounds=[(-10,10),(10,20), (c-1/1000,c+1/1000),(0,0)]) + abcd = sine[0] + return(abcd) + def sine_fit(self,abcd,x,y,rms): + fx = self.sine_wave(abcd,x) + chi2 = numpy.power(y - fx,2)/numpy.power(rms,2) + chi2_sum = chi2.sum() + return chi2_sum + def sine_wave(self,abcd,x): + sine = abcd[0] + abcd[1]*numpy.sin(abcd[2]*x-abcd[3]) + return sine + def plot_feature(self,properties): + a = properties['sine_fit'][0] + b = properties['sine_fit'][1] + c = properties['sine_fit'][2] + d = properties['sine_fit'][3] + abcd = array([a,b,c,d]) + # y = a + b * sin(cx - d) + plot(self.time_data,self.sine_wave(abcd,self.time_data),label='sine fit') diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sine_leastsq_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sine_leastsq_extractor.py new file mode 100644 index 00000000..e2ccdf00 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sine_leastsq_extractor.py @@ -0,0 +1,51 @@ +from __future__ import absolute_import +from ..FeatureExtractor import InterExtractor +import numpy +from numpy import random, pi +from scipy import fftpack, stats, optimize +try: + from pylab import * +except: + pass +from .common_functions import * + +# TODO remove? +class sine_leastsq_extractor(InterExtractor,ChiSquare): + active = True + extname = 'sine_leastsq' #extractor's name + def extract(self): + a = self.dc_estimate() + b = 15#properties['amplitude'] #amplitude + c = self.frequency_estimate() * 2 * pi + d = 0 # x shift + init = numpy.array([a,b,c,d]) +# print 'real', 'a:', properties['dc_real'], 'b:', properties['amplitude'], 'c:', 2* numpy.pi / properties['period'] + sine = optimize.leastsq(self.sine_fit,init,args=(self.time_data,self.flux_data,self.rms_data)) + return(sine[0]) +# def sine_fit(self,abcd,x,y,rms): +# def sine(x): +# # y = a + b * sin(cx - d) +# return abcd[0] + abcd[1]*numpy.sin(abcd[2]*x-abcd[3])#ab[0]*x +ab[1] +# print self.chi_square(y,sine,x=x,rms=rms) +# return self.chi_square(y,sine,x=x,rms=rms) + def frequency_estimate(self): + freq = self.fetch_extr('first_freq') #2*numpy.pi/20 + return freq + def dc_estimate(self): + dc = self.fetch_extr('weighted_average') + return dc + def sine_fit(self,abcd,x,y,rms): + fx = self.sine_wave(abcd,x) + chi2 = numpy.power(y - fx,2)/numpy.power(rms,2) + return chi2 + def sine_wave(self,abcd,x): + sine = abcd[0] + abcd[1]*numpy.sin(abcd[2]*x-abcd[3]) + return sine + def plot_feature(self,properties): + a = properties[self.extname][0] + b = properties[self.extname][1] + c = properties[self.extname][2] + d = properties[self.extname][3] + abcd = array([a,b,c,d]) + # y = a + b * sin(cx - d) + plot(self.time_data,self.sine_wave(abcd,self.time_data),label=self.extname) diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/sine_lomb_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/sine_lomb_extractor.py new file mode 100644 index 00000000..2984810e --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/sine_lomb_extractor.py @@ -0,0 +1,12 @@ +from __future__ import absolute_import +from ..FeatureExtractor import InterExtractor + +from .sine_leastsq_extractor import sine_leastsq_extractor + +class sine_lomb_extractor(sine_leastsq_extractor): + """Fits a sine wave using the lomb result as an estimate """ + active = False + extname = 'sine_lomb' #extractor's name + def frequency_estimate(self): + freq = self.fetch_extr('first_lomb') #2*numpy.pi/20 + return freq diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/skew_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/skew_extractor.py new file mode 100644 index 00000000..ae42d6fa --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/skew_extractor.py @@ -0,0 +1,12 @@ +from ..FeatureExtractor import FeatureExtractor + +from scipy import stats + +class skew_extractor(FeatureExtractor): + """ calculates the skew of the signal using scipy.stats.skew + biased skew?""" + active = True + extname = 'skew' #extractor's name + def extract(self): + skew = stats.skew(self.flux_data) + return skew \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/small_kurtosis_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/small_kurtosis_extractor.py new file mode 100644 index 00000000..6ab40e92 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/small_kurtosis_extractor.py @@ -0,0 +1,29 @@ +from ..FeatureExtractor import FeatureExtractor + +from numpy import * + +class small_kurtosis_extractor(FeatureExtractor): + """ calculates the kurtosis of the signal using the kurtosis formula for small samples from http://www.xycoon.com/peakedness_small_sample_test_1.htm""" + active = True + extname = 'small_kurtosis' #extractor's name + minpoints = 4 # minimum number of points for the extractor to run + def extract(self): + kurtosis = self.kurtosis_calc() + self.uncertainty = self.uncertainty_calc() + return kurtosis + def kurtosis_calc(self): + """ simply follows http://www.xycoon.com/skewness_small_sample_test_1.htm """ + n = float(self.fetch_extr('n_points')) + average = self.fetch_extr('weighted_average') + s = self.fetch_extr('s') + xi_minus_average = self.flux_data - average + xi_minus_average_over_s = xi_minus_average / s + to_the_fourth = power(xi_minus_average_over_s,4) + sum_fourth_s = to_the_fourth.sum() + kurtosis = ( n*(n+1)/((n-1)*(n-2)*(n-3)) ) * sum_fourth_s - 3*(n-1)**2 / ((n-2) * (n-3)) + return kurtosis + def uncertainty_calc(self): + n = float(self.fetch_extr('n_points')) + ss = sqrt(6*n*(n-1)/(n-2)/(n+1)/(n+3)) + sk = sqrt(4*(n**2-1)*(ss**2)/(n-3)/(n+5)) + return sk diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/small_skew_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/small_skew_extractor.py new file mode 100644 index 00000000..30bf97fe --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/small_skew_extractor.py @@ -0,0 +1,29 @@ +from ..FeatureExtractor import FeatureExtractor + +from pylab import * + +class small_skew_extractor(FeatureExtractor): + """ calculates the skew of the signal using the skewness formula for small samples from http://www.xycoon.com/skewness_small_sample_test_1.htm""" + active = True + extname = 'small_skew' #extractor's name + minpoints = 3 # minimum number of points for the extractor to run + def extract(self): + skew = self.skew_calc() + self.uncertainty = self.uncertainty_calc() + return skew + def skew_calc(self): + """ simply follows http://www.xycoon.com/skewness_small_sample_test_1.htm """ + n = float(len(self.time_data)) + average = self.fetch_extr('weighted_average') + xi_minus_average = self.flux_data - average + cubed = xi_minus_average**3 + sum_of_cubed = cubed.sum() + m3 = (1/n) * sum_of_cubed + s = self.fetch_extr('s') + skewness = (n**2 * m3) / ((n-1)*(n-2)*(s**3)) + return skewness + def uncertainty_calc(self): + n = float(len(self.time_data)) + ss = sqrt(6*n*(n-1)/(n-2)/(n+1)/(n+3)) + return ss + diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/std_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/std_extractor.py new file mode 100644 index 00000000..2f615eb2 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/std_extractor.py @@ -0,0 +1,8 @@ +from ..FeatureExtractor import FeatureExtractor +from numpy import std + +class std_extractor(FeatureExtractor): + active = True + extname = 'std' #extractor's name + def extract(self): + return(std(self.flux_data)) diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/stdextractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/stdextractor.py new file mode 100644 index 00000000..2039acae --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/stdextractor.py @@ -0,0 +1,8 @@ +from ..FeatureExtractor import FeatureExtractor +from numpy import std + +class stdextractor(FeatureExtractor): + active = True + extname = 'std' #extractor's name + def extract(self): + return(std(self.flux_data)) diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/stdvs_from_u_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/stdvs_from_u_extractor.py new file mode 100644 index 00000000..0f68bf6a --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/stdvs_from_u_extractor.py @@ -0,0 +1,28 @@ +from __future__ import absolute_import + +from ..FeatureExtractor import InterExtractor +import numpy +from numpy import random +from scipy import fftpack, stats, optimize +try: + from pylab import * +except: + pass +from .common_functions import * + +from .dist_from_u_extractor import dist_from_u_extractor +# TODO remove +from .wei_av_uncertainty_extractor import wei_av_uncertainty_extractor + +class stdvs_from_u_extractor(InterExtractor): + active = True + extname = 'stdvs_from_u' #extractor's name + def extract(self): + dist_from_u = self.fetch_extr('dist_from_u', returnall=True) + dist = dist_from_u.result + sd = self.fetch_extr('weighted_average', returnall=True).uncertainty # returns the uncertainty in the weighted average + num = dist/sd # number of standard deviations from the weighted average + uncer = dist_from_u.uncertainty/sd # scales the uncertainty (not sure this is correct) +# TODO uncertainty isn't tested anywhere yet + self.uncertainty = uncer + return num diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/stetson_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/stetson_extractor.py new file mode 100644 index 00000000..18f18445 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/stetson_extractor.py @@ -0,0 +1,35 @@ +import os,sys +from ..FeatureExtractor import FeatureExtractor + +from .....Algorithms.stetson_stats import stetson_mean, stetson_j, stetson_k + + +class stetson_mean_extractor(FeatureExtractor): + """ An iteratively weighted mean""" + active = True + extname = 'stetson_mean' #extractor's name + def extract(self): + value = stetson_mean(self.flux_data) + return value + + +class stetson_j_extractor(FeatureExtractor): + """Robust covariance statistic between pairs of observations x,y + whose uncertainties are dx,dy. if y is not given, calculates + a robust variance for x.""" + active = True + extname = 'stetson_j' #extractor's name + minpoints = 2 # minimum number of points for the extractor to run + def extract(self): + value = stetson_j(self.flux_data) + return value + + +class stetson_k_extractor(FeatureExtractor): + """A kurtosis statistic.""" + active = True + extname = 'stetson_k' #extractor's name + minpoints = 2 # minimum number of points for the extractor to run + def extract(self): + value = stetson_k(self.flux_data) + return value diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/tmpned_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/tmpned_extractor.py new file mode 100644 index 00000000..bf65eec7 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/tmpned_extractor.py @@ -0,0 +1,45 @@ +from __future__ import print_function +from __future__ import absolute_import +#from ..FeatureExtractor import FeatureExtractor +from ..FeatureExtractor import ContextInterExtractor +#from ..FeatureExtractor import MultiExtractor + +#from power_extractor import power_extractor as power_extractor +from . import ned + +class tmpned_extractor(ContextInterExtractor): + """the Galactic coordinate b (latitude) in degrees""" + active = True + extname = 'tmpned' #extractor's name + + n = None + def extract(self): + #20080617_dstarr_comments_out#self.ex_error("let's not do this today") + posdict = self.fetch_extr('position_intermediate') + + if 'ra' not in posdict or posdict['dec'] is None: + self.ex_error("bad RA or DEC in the intermediate extractor. check install pyephem and input coordinates") + + if not self.n: + #sys.path.append(os.path.abspath(os.environ.get("../../")) + # This module should exist in the local path: + #try: ### 20090123 dstarr comments this out. + try: + from . import ned_cache_server + ncc = ned_cache_server.Ned_Cache_Client(ned_cache_server.pars) + #self.n = ncc.retrieve_queue_ned_dict(posdict['ra'], \ + # posdict['dec']) + (ned_obj, sdss_obj) = ncc.retrieve_queue_ned_dict(posdict['ra'], \ + posdict['dec']) + self.n = ned_obj + #self.s = sdss_obj # 20090126: I added this line a couple days ago, but I don't think it is useful/accessed. So, I add the following line instead: + self.properties['data']['multiband']['inter']['sdss_internal_struct'] = sdss_obj + + except: + print("MySQL connection to NED cache server FAILED") + ###self.n = ned.NED(pos=(posdict['ra'],posdict['dec']),verbose=True, do_threaded=True) # dstarr changes flags to be verbose, ... + + + return self.n #{'distance_in_kpc_to_nearest_galaxy': self.n.distance_in_kpc_to_nearest_galaxy(),\ + #'distance_in_arcmin_to_nearest_galaxy': self.n.distance_in_arcmin_to_nearest_galaxy()} + diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/vosource_parse.py b/mltsp/TCP/Software/feature_extract/Code/extractors/vosource_parse.py new file mode 100644 index 00000000..76c5a855 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/vosource_parse.py @@ -0,0 +1,121 @@ +""" +like dbimporter but more general. + +try it like: + +import vosource_parse, pprint + +fname = "test_feature_algorithms.VOSource.xml" +v = vosource_parse.vosource_parser(fname) +pprint(v.d) + +## note that v is of type xmldict.XmlDictObject + +v['ts'] is the parsed timeseries as a list, usually with 4 entries (time, val, valerr, limit) + +it's up to the user to decide how to use those columns...there's almost no reformating + +""" +from scipy import * +from . import xmldict +import os, sys +from xml.etree import cElementTree as ElementTree # this is a nicer implementation +from pprint import pprint + +class vosource_parser: + def __init__(self, fname, is_xmlstring=False): + self.fname = fname + self.d = {} + + if is_xmlstring: + self._parse_xmlstring() + elif os.path.exists(self.fname): + self._parse() + + if self.d: + self._make_timeseries() + + def _parse(self): + try: + # 20090225 dstarr adds: + self.elemtree = ElementTree.parse(self.fname).getroot() + self.d = xmldict.ConvertXmlToDict(self.elemtree) + except Exception as e: + print(e) + self.d = {} + return + + def _parse_xmlstring(self): + try: + self.elemtree = ElementTree.fromstring(self.fname) + self.d = xmldict.ConvertXmlToDict(self.elemtree) + except Exception as e: + print("EXCEPTION:", e) + self.d = {} + return + + def _make_timeseries(self): + self.d.update({"ts": {}}) + try: + if isinstance(self.d['VOSOURCE']['VOTimeseries']['Resource']['TABLE'], xmldict.XmlDictObject): + ts_dict = [self.d['VOSOURCE']['VOTimeseries']['Resource']['TABLE']] + else: + ts_dict = self.d['VOSOURCE']['VOTimeseries']['Resource']['TABLE'] + except: + print("Error") + return + + for filt in ts_dict: + name = filt['name'] + if not isinstance(filt.get('FIELD',''),list): + print("field is not a list") + continue + allcol = [] + ncols = len(filt['FIELD']) + for col in filt['FIELD']: + allcol.append(col) + allcol[-1].update({"val": []}) + try: + xml_obj = filt['DATA']['TABLEDATA']['TR'] + except: + continue # sometimes there is a blank line and no data. Skip this filter. + #20090212: dstarr adds this first condition to catch n_epochs=1, when there was just the 2nd previously: + if isinstance(xml_obj,xmldict.XmlDictObject): + xml_obj = [filt['DATA']['TABLEDATA']['TR']] + elif not isinstance(xml_obj,list): + print("data is not a list") + continue + ndata = len(xml_obj) + limit = empty(ndata,dtype="S5") + limit[:] = "false" + for d in xml_obj: + if len(d['TD']) != ncols: + continue + for c in enumerate(d['TD']): + if isinstance(c[1],str): + allcol[c[0]]['val'].append(float(c[1])) + elif isinstance(c[1],dict): + if "_text" in c[1]: + if isinstance(c[1]['_text'],str): + allcol[c[0]]['val'].append(float(c[1]['_text'])) + if "limit" in c[1]: + limit[c[0]] = c[1]['limit'] + #make the limits + lim = {'ID': 'col%i' % (ncols + 1), 'datatype': 'string', 'name': "limit", \ + "ucd": "stat.max;stat.min", "val": limit} + for col in allcol: + col.update({"val": array(col['val'])}) + + allcol.append(lim) + self.d['ts'].update({name: allcol}) + + #print ts_dict + +def test(): + + fname = "test_feature_algorithms.VOSource.xml" + v = vosource_parser(fname) + pprint(v.d) + +if __name__ == "__main__": + test() diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/watt_per_m2_flux_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/watt_per_m2_flux_extractor.py new file mode 100644 index 00000000..b5ff8be1 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/watt_per_m2_flux_extractor.py @@ -0,0 +1,66 @@ +from __future__ import print_function +from __future__ import absolute_import +from ..FeatureExtractor import InterExtractor +import numpy +from numpy import random +from scipy import fftpack, stats, optimize +from pylab import * +from .common_functions import * + +# TODO this should def. be a function, not an extractor +class watt_per_m2_flux_extractor(InterExtractor): + """ Convert the magnitudes to SI units""" + active = True + extname = 'watt_per_m2_flux' #extractor's name + def extract(self): + #import pdb; pdb.set_trace() + try: + unit = self.flux_data_unit + except NameError: + unit = self.assumed_unit + if unit in ['mag','mags','magnitude']: + constants = \ + {"u": 13.84 \ + ,"b": 12.97 \ + ,"v": 13.72 \ + ,"r": 13.54 \ + ,"i": 14.25 \ + ,"j": 15.05 \ + ,"h": 15.82 \ + ,"k": 16.50 \ + ,"l": 17.82} # from Misconceptions About Astronomical Magnitudes," E. Schulman and C. V. Cox, American Journal of Physics, Vol. 65, pg. 1003 (1997). Table 1 + try: + if 'clear' in self.band.lower(): + constant = constants['v'] # 20100722 kludge to get some debosscher data ingested + else: + # 20100518 dstarr adds a kludge which tries assuming that the first character of band string is a filter. Realistically, we need to have constants{} entries for all 24 bands found in the lyra:tutor:filters (take first elem + constant = constants[self.band[0].lower()] # find the right constant by enforcing lower case + except KeyError: + self.ex_error("Band %s not found" % (self.band)) + f = 10**(-0.4*(self.flux_data + constant)) # ergs per second per cm^2, equation 11 of the above-mentioned paper + fwatt = f * 1e-7 # conversion from erg/s to watts + fm2 = fwatt * 1e4 # 10 000 cm^2 in a m^2 + self.uncertainty = self.uncer_calc(fm2) + return fm2 + else: + print("units not recognized", self.flux_data_unit, self.extname) + self.uncertainty = self.rms_data + return self.flux_data # else assume it's already in those units, no unit conversion implemented for the moment + def uncer_calc(self, flux_wm2): + """ calculate the uncertainty in the SI flux + Latex for the approximation (valid if the flux uncertainty is less than 10%): + + \sigma_m &=& \sqrt{ \sigma_{m,higher} \times \sigma_{m, lower}} \\ + &=& \sqrt{ \left( 2.5 \log_{10}(f - \sigma_f) - 2.5\log_{10}f \right) \times \left( -2.5 \log_{10}(f + \sigma_f) + 2.5\log_{10}f \right) } \\ + &=& \sqrt{ -2.5^2 \log_{10}\left(\frac{f}{f-\sigma_f}\right) \left(\log_{10}\frac{f}{f+\sigma_f}\right)} \\ + &=& \sqrt{ -2.5^2 \log_{10}\left(\frac{f-\sigma_f}{f}\right) \log_{10}\left(\frac{f+\sigma_f}{f}\right)} \\ + &=& 2.5\sqrt{ -\log_{10}\left( 1 - \frac{\sigma_f}{f} \right) \log_{10}\left( 1 + \frac{\sigma_f}{f} \right)} \\ + &=& \frac{2.5 }{\ln 10} \sqrt{ -\ln\left( 1 - \frac{\sigma_f}{f} \right) \ln\left( 1 + \frac{\sigma_f}{f} \right)} \\ + &\approx & \frac{2.5 }{\ln 10} \sqrt{ \left(\frac{\sigma_f}{f} \right) ^2 } \\ + &\approx & \frac{\sigma_f}{f} + + """ + f = flux_wm2 + sigmam = self.rms_data + sigmaf = f * sigmam + return sigmaf diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/wei_av_uncertainty_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/wei_av_uncertainty_extractor.py new file mode 100644 index 00000000..d39c8a48 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/wei_av_uncertainty_extractor.py @@ -0,0 +1,9 @@ +from ..FeatureExtractor import FeatureExtractor + +# TODO should just be a function +class wei_av_uncertainty_extractor(FeatureExtractor): ### REDUNDANT + active = False + extname = 'wei_av_uncertainty' #extractor's name + def extract(self): + uncertainty = 1.0/(self.rms_data**(2)).sum() + return uncertainty diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/weighted_average_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/weighted_average_extractor.py new file mode 100644 index 00000000..c8efc4d8 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/weighted_average_extractor.py @@ -0,0 +1,23 @@ +from __future__ import absolute_import +from ..FeatureExtractor import FeatureExtractor + +try: + from pylab import * +except: + pass +from .common_functions.plot_methods import plot_horizontal_line + +class weighted_average_extractor(plot_horizontal_line,FeatureExtractor): + active = True + extname = 'weighted_average' #extractor's name + def extract(self): +# TODO do we compute these error weights a lot? could be a library function +# cf. ChiSquare module + we_av = (self.flux_data / (self.rms_data)**2).sum()/((1/self.rms_data)**2).sum() + self.uncertainty = sqrt(1.0/(self.rms_data**(2)).sum()) +# print 'weighted',we_av + return we_av + def plot_feature(self,properties): + dc_line = ones(len(self.time_data),dtype=float) + dc_line[:] = properties[extname] + plot(self.time_data,dc_line,label=extname) diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/ws_variability_extractor.py b/mltsp/TCP/Software/feature_extract/Code/extractors/ws_variability_extractor.py new file mode 100644 index 00000000..13af2366 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/ws_variability_extractor.py @@ -0,0 +1,125 @@ +from __future__ import print_function +from ..FeatureExtractor import MultiFeatureExtractor +from ..FeatureExtractor import FeatureExtractor + +from numpy import * + +class ws_variability_self_extractor(FeatureExtractor): + """ JSB adds this...a self variability for a single band + http://articles.adsabs.harvard.edu/cgi-bin/nph-iarticle_query?db_key=AST&bibcode=1996PASP..108..851S&letter=0&classic=YES&defaultprint=YES&whole_paper=YES&page=852&epage=852&send=Send+PDF&filetype=.pdf + page 853, equation in top left of the page + """ + active = True + extname = 'ws_variability_self' #extractor's name + compared_extr = 'n_points' + minpoints = 2 # 20081010: dstarr adds this variable, although it doesn't seem to used anywhere... # 20081108: changed min_points to minpoints, as expected by FeatureExtractor (this was a typo) + maxpoints = 10000 #20090127: dstarr adds this after seeing a ws_variability_extractor related memory balloon possibly due to a +30k dataset. + def extract(self): + band1= self.band + band2= self.band + n1 = self.extr # n_points from band 1 + n2 = self.extr + self.dic1 = self.dic # the dictionary in band 1 + self.dic2 = self.dic + timeindices = indexb, indexv = self.find_close_points(self.dic['input']['time_data'] , self.dic['input']['time_data']) # find the indices of the points + meanb = self.fetch_extr('dc') # b bar JSB changed to dc instead of old_dc + meanv = self.fetch_extr('dc') # v bar + bi = self.dic['input']['flux_data'][indexb] + vi = self.dic['input']['flux_data'][indexv] + sigbi = self.dic['input']['rms_data'][indexb] + sigvi = self.dic['input']['rms_data'][indexv] + insum = ((bi - meanb) / sigbi) * ( (vi - meanv) / sigvi) + summed = insum.sum() + n = float(len(indexb)) + try: + I = sqrt(1 / (n * (n-1) ) ) * summed + print("ws_variability_self for band %s = %f" % (self.band,I)) + except: + # if p'time_data'] just contains multiple same times, then n==1 & Excepts. + I = None + return I + + + def find_close_points(self, times1, times2): + """ find points in the two bands that were sampled close in time to each other """ + times1x, times2x = ix_(times1, times2) # http://www.scipy.org/Tentative_NumPy_Tutorial#head-05b0dc978ba9ce86c363b3fa92a6e4869e6c72a9 + # 20080702: dstarr noticies that we occasionally get a python MemoryError here. Due to large timeseries datasets? Is this an inefficeint algorithm?: + timediff = abs(times1x - times2x) # the difference in time between any two points + zeromatrix = zeros((len(times1),len(times2)) ) # a matrix full of zeros of the same dimensions as timediff + min1 = timediff.argmin(axis=0) # find the index of the minimum down each column + min2 = timediff.argmin(axis=1) # find the index of the minimum across each row + minmatrix1 = zeromatrix + minmatrix2 = zeromatrix.copy() + minmatrix1[min1 , arange(len(times2))] = 1 # mark the column minima with 1 + minmatrix2[arange(len(times1)), min2] = 1 # mark the row minima with 1 + boolmatrix = logical_and(minmatrix1,minmatrix2) # intersection + return where(boolmatrix) # the indices the of intersection points + + +class ws_variability_bv_extractor(MultiFeatureExtractor): + """ http://articles.adsabs.harvard.edu/cgi-bin/nph-iarticle_query?db_key=AST&bibcode=1996PASP..108..851S&letter=0&classic=YES&defaultprint=YES&whole_paper=YES&page=852&epage=852&send=Send+PDF&filetype=.pdf + page 853, equation in top left of the page + """ + active = True + extname = 'ws_variability_bv' #extractor's name + band1 = 'b' + band2 = 'v' + compared_extr = 'n_points' + def extract(self): + n1 = self.extr1 # n_points from band 1 + n2 = self.extr2 + self.dic1 = self.dic1 # the dictionary in band 1 + self.dic2 = self.dic2 + timeindices = indexb, indexv = self.find_close_points(self.dic1['input']['time_data'] , self.dic2['input']['time_data']) # find the indices of the points + meanb = self.fetch_extr('dc', band=self.band1) # b bar JSB changed to dc instead of old_dc + meanv = self.fetch_extr('dc', band=self.band2) # v bar + bi = self.dic1['input']['flux_data'][indexb] + vi = self.dic2['input']['flux_data'][indexv] + sigbi = self.dic1['input']['rms_data'][indexb] + sigvi = self.dic2['input']['rms_data'][indexv] + insum = ((bi - meanb) / sigbi) * ( (vi - meanv) / sigvi) + summed = insum.sum() + n = float(len(indexb)) + I = sqrt(1 / (n * (n-1) ) ) * summed + return I + + + def find_close_points(self, times1, times2): + """ find points in the two bands that were sampled close in time to each other """ + times1x, times2x = ix_(times1, times2) # http://www.scipy.org/Tentative_NumPy_Tutorial#head-05b0dc978ba9ce86c363b3fa92a6e4869e6c72a9 + timediff = abs(times1x - times2x) # the difference in time between any two points + zeromatrix = zeros((len(times1),len(times2)) ) # a matrix full of zeros of the same dimensions as timediff + min1 = timediff.argmin(axis=0) # find the index of the minimum down each column + min2 = timediff.argmin(axis=1) # find the index of the minimum across each row + minmatrix1 = zeromatrix + minmatrix2 = zeromatrix.copy() + minmatrix1[min1 , arange(len(times2))] = 1 # mark the column minima with 1 + minmatrix2[arange(len(times1)), min2] = 1 # mark the row minima with 1 + boolmatrix = logical_and(minmatrix1,minmatrix2) # intersection + return where(boolmatrix) # the indices the of intersection points + +class ws_variability_ru_extractor(ws_variability_bv_extractor): + extname = 'ws_variability_ru' + band1 = 'r' + band2 = 'u' + +class ws_variability_ug_extractor(ws_variability_bv_extractor): + extname = 'ws_variability_ug' + band1 = 'u' + band2 = 'g' + +class ws_variability_gr_extractor(ws_variability_bv_extractor): + extname = 'ws_variability_gr' + band1 = 'g' + band2 = 'r' + +class ws_variability_ri_extractor(ws_variability_bv_extractor): + extname = 'ws_variability_ri' + band1 = 'r' + band2 = 'i' + +class ws_variability_iz_extractor(ws_variability_bv_extractor): + extname = 'ws_variability_iz' + band1 = 'i' + band2 = 'z' + diff --git a/mltsp/TCP/Software/feature_extract/Code/extractors/xmldict.py b/mltsp/TCP/Software/feature_extract/Code/extractors/xmldict.py new file mode 100644 index 00000000..1c9e7de1 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/extractors/xmldict.py @@ -0,0 +1,137 @@ +from __future__ import print_function +from xml.etree import ElementTree +from pprint import pprint + +# calling example +def main(): + configdict = ConvertXmlToDict(ElementTree.parse('test_tdf.xml').getroot()) + return configdict + pprint(configdict) + + # you can access the data as a dictionary + #print configdict['settings']['color'] + #configdict['settings']['color'] = 'red' + + # or you can access it like object attributes + print(configdict.tdf.created_by) + configdict.tdf.created_by = 'red' + + root = ConvertDictToXml(configdict) + + tree = ElementTree.ElementTree(root) + tree.write('config.new.xml') + + +# Module Code: + +class XmlDictObject(dict): + def __init__(self, initdict=None): + if initdict is None: + initdict = {} + dict.__init__(self, initdict) + + def __getattr__(self, item): + return self.__getitem__(item) + + def __setattr__(self, item, value): + self.__setitem__(item, value) + + def __str__(self): + if '_text' in self: + return self.__getitem__('_text') + else: + return '' + + @staticmethod + def Wrap(x): + if isinstance(x, dict): + return XmlDictObject((k, XmlDictObject.Wrap(v)) for (k, v) in x.items()) + elif isinstance(x, list): + return [XmlDictObject.Wrap(v) for v in x] + else: + return x + + @staticmethod + def _UnWrap(x): + if isinstance(x, dict): + return dict((k, XmlDictObject._UnWrap(v)) for (k, v) in x.items()) + elif isinstance(x, list): + return [XmlDictObject._UnWrap(v) for v in x] + else: + return x + + def UnWrap(self): + return XmlDictObject._UnWrap(self) + +def _ConvertDictToXmlRecurse(parent, dictitem): + assert type(dictitem) is not type([]) + + if isinstance(dictitem, dict): + for (tag, child) in dictitem.items(): + if str(tag) == '_text': + parent.text = str(child) + elif type(child) is type([]): + for listchild in child: + elem = ElementTree.Element(tag) + parent.append(elem) + _ConvertDictToXmlRecurse(elem, listchild) + else: + elem = ElementTree.Element(tag) + parent.append(elem) + _ConvertDictToXmlRecurse(elem, child) + else: + parent.text = str(dictitem) + +def ConvertDictToXml(xmldict): + roottag = xmldict.keys()[0] + root = ElementTree.Element(roottag) + _ConvertDictToXmlRecurse(root, xmldict[roottag]) + return root + +def _ConvertXmlToDictRecurse(node, dictclass): + nodedict = dictclass() + + if len(node.items()) > 0: + # if we have attributes, set them + nodedict.update(dict(node.items())) + + for child in node: + # recursively add the element's children + newitem = _ConvertXmlToDictRecurse(child, dictclass) + if child.tag in nodedict: + # found duplicate tag, force a list + if type(nodedict[child.tag]) is type([]): + # append to existing list + nodedict[child.tag].append(newitem) + else: + # convert to list + nodedict[child.tag] = [nodedict[child.tag], newitem] + else: + # only one, directly set the dictionary + nodedict[child.tag] = newitem + + if node.text is None: + text = '' + else: + text = node.text.strip() + + if len(nodedict) > 0: + # if we have a dictionary add the text as a dictionary value (if there is any) + if len(text) > 0: + nodedict['_text'] = text + else: + # if we don't have child nodes or attributes, just set the text + try: + nodedict = node.text.strip() + except: + #print node.text + nodedict = None + #print nodedict + return nodedict + +def ConvertXmlToDict(root, dictclass=XmlDictObject): + return dictclass({root.tag: _ConvertXmlToDictRecurse(root, dictclass)}) + + +if __name__ == '__main__': + main() diff --git a/mltsp/TCP/Software/feature_extract/Code/feature_interfaces.py b/mltsp/TCP/Software/feature_extract/Code/feature_interfaces.py new file mode 100644 index 00000000..410b0039 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/feature_interfaces.py @@ -0,0 +1,168 @@ +from __future__ import print_function + +import numpy as np +from . import extractors +from . import FeatureExtractor +from . import internal_generated_extractors_holder + + +avtype = [('extname', 'S100'), + ('extractor', np.object_), + ('active', np.bool_)] # format of available_extractors + + +class FeatureInterface(object): + """This serves as an interface between signals and extractors. + An instance of this object is generated when the module is imported + """ + def __init__(self): + igea = internal_generated_extractors_holder.Internal_Gen_Extractors_Accessor() + # obsolete: + self.glob_internally_generated_extractors = \ + igea.glob_internally_generated_extractors + + # 20081216: dstarr sees that we keep on appending signals here (and growing memory) when he thinks only one signal is needed in self.subscribing_signals[] to do the feature extractions for a source. : (original function == True): + self.debug__original_do_append_signals_to_subscribing_signals = False + self.subscribing_signals = [] + self.available_extractors = np.empty(0, avtype) # declare the recipient numpy array for extractors + + #def register_signal(self,signal): + # self.subscribing_signals.append(signal) + # for ext_name,extractor in self.available_extractors.\ + # items(): + # signal.update(extractor()) + # 20071215: dstarr modifies this since he thinks order of load matters + # and a dictionary .items() or .values() is not applicable: + + def register_signal(self, signal, list_of_extractors, initialize=True): + """ initialize determines whether all the active extractors are immediately applied to the signal """ + if self.debug__original_do_append_signals_to_subscribing_signals: + self.subscribing_signals.append(signal) + else: + # 20081216: dstarr sees that we keep on appending signals here (and growing memory) when he thinks only one signal is needed in self.subscribing_signals[] to do the feature extractions for a source. + self.subscribing_signals = [signal] + if initialize: # check that we want to initialize the signal + for an_extractor in self.available_extractors[self.available_extractors['active']]: # loop through all active extractors + extractor_obj = an_extractor['extractor']() # instantiate + signal.update(extractor_obj) + + def register_extractor(self, extractor): + self.available_extractors = np.append(self.available_extractors, + np.array((extractor.extname, + extractor, + extractor.active), avtype)) # append a tuple of format avtype containing (extname, extractor object, active) + if extractor.active: self.notify(extractor) + + def notify(self, extractor): + #print "New active extractor available!" + for signal in self.subscribing_signals: + signal.update(extractor()) + + def remove_signal(self,signal): + self.subscribing_signals.remove(signal) + + + def remove_extractor(self,extractor): + """ Remove an extractor from the available extractor list. + Input is a type. To remove by name, using remove_extname """ + sizebeforeremoving = self.available_extractors.size + self.available_extractors = self.available_extractors[ where( \ + self.available_extractors['extractor'] != extractor)] # slice off the corresponding extractor + sizeafterremoving = self.available_extractors.size + if sizebeforeremoving == sizeafterremoving: + print("Key does not exist, can't be removed from active list", extractor.extname) + + + def remove_extname(self,extname): + """ Remove an extractor from the available extractor list by + its name. Input is a string. + To remove by type, using remove_extractor """ + sizebeforeremoving = self.available_extractors.size + self.available_extractors = self.available_extractors[ where( \ + self.available_extractors['extname'] != extname)] # slice off the corresponding extractor + sizeafterremoving = self.available_extractors.size + if sizebeforeremoving == sizeafterremoving: + print("Key does not exist, can't be removed from active list", extractor.extname) + + + def switch_extname(self,extractor_name,activate=False,deactivate=False): + extractor_index = self.find_extname(extractor_name, index=True) + if extractor_index: # check that find_extname worked + extractor_row = self.available_extractors[\ + extractor_index] + active = extractor_row['active'] + print("This extractor %s was in state %s" % (\ + extractor_name, active)) + if activate: + active = True + elif deactivate: + active = False + else: + active = not active + print("This extractor %s is now in state %s" % (\ + extractor_name,active)) + self.available_extractors[extractor_index] = array(\ + (extractor_row['extname'][0], \ + extractor_row['extractor'][0], active),avtype) + return "done" + else: + return False + + + def find_extname(self,extractor_name, index = False): # linked if want to modify the array directly + extractor_index = np.where(self.available_extractors['extname'] ==\ + np.compat.asbytes(extractor_name))[0] + + if np.size(extractor_index) is 0: + print("find_extname couldn't find extractor %s" % \ + (extractor_name)) + return False # if we didn't find the object + if index: + return extractor_index[0] + extractor_row = self.available_extractors[extractor_index][0] # return the corresponding extractor row (extname, extractor and active) + return extractor_row + + + def request_extractor(self,extractor_name): + extractor_row = self.find_extname(extractor_name) + if extractor_row: # check that find_extname worked + return extractor_row['extractor'] + else: + return False + + +feature_interface = FeatureInterface() + + +def initialize(list_of_extractors): + from ...ingest_tools import feature_extraction_interface + fs = feature_extraction_interface.Internal_Feature_Extractors() + for key_name in fs.feature_ordered_keys: + list_of_extractors.append(key_name) + # The following list is no-longer explicitly defined here. + # Rather, I build the list in feature_extraction_interface.py + #list_of_extractors.extend([ weighted_average_extractor , chi2extractor , dc_extractor , dist_from_u_extractor , fourierextractor , linear_extractor , max_slope_extractor , medianextractor , beyond1std_extractor , stdvs_from_u_extractor , old_dcextractor , power_spectrum_extractor , power_extractor , montecarlo_extractor , pct_80_montecarlo_extractor , pct_90_montecarlo_extractor , pct_95_montecarlo_extractor , pct_99_montecarlo_extractor , significant_80_power_extractor , significant_90_power_extractor , significant_95_power_extractor , significant_99_power_extractor , first_freq_extractor , sine_fit_extractor , sine_leastsq_extractor , skew_extractor , stdextractor , wei_av_uncertainty_extractor , lomb_extractor , first_lomb_extractor , sine_lomb_extractor , second_extractor , third_extractor , second_lomb_extractor , ratio21, ratio31, ratio32]) # order matters! + #for extractor in extractors.__dict__.values(): + #for extractor in list_of_extractors: + list_of_extractor_objects = [] + for extractor_name in list_of_extractors: + extractor = getattr(extractors, extractor_name) + list_of_extractor_objects.append(extractor) + + if isinstance(extractor,type): + instance = extractor() + if isinstance(instance,FeatureExtractor.GeneralExtractor): + instance.register_extractor() + else: + pass + else: + pass + + return list_of_extractor_objects + + +def fetch_extract(extractor_name,properties,band=None): + """ we want the result of this extractor """ + extractor = feature_interface.request_extractor(extractor_name) + result = extractor.extr(properties,band=band) + return result diff --git a/mltsp/TCP/Software/feature_extract/Code/generators_importers/__init__.py b/mltsp/TCP/Software/feature_extract/Code/generators_importers/__init__.py new file mode 100644 index 00000000..351d9b81 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/generators_importers/__init__.py @@ -0,0 +1,11 @@ +from .double_ind_gen import double_ind_gen +from .double_sig_gen import double_sig_gen +from .double_uneven_gen import double_uneven_gen +from .from_xml import from_xml +from .generator import generator +from .lin_inc_gen import lin_inc_gen +from .sgwindn_gen import sgwindn_gen +from .uneven_sine_gen import uneven_sine_gen +from .vizier_importer import vizier_importer +#20080614#from montecarlo_gen import montecarlo_gen +from .copy_gen import copy_gen, noisified_gen diff --git a/mltsp/TCP/Software/feature_extract/Code/generators_importers/copy_gen.py b/mltsp/TCP/Software/feature_extract/Code/generators_importers/copy_gen.py new file mode 100644 index 00000000..c1905d8f --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/generators_importers/copy_gen.py @@ -0,0 +1,50 @@ +""" +This generates creates a simple copy of a signal. It was written for the noisification process. + +The noisifier generator first noisifies the data. + +Created by Maxime Rischard on 2008-05-26. + +""" +from __future__ import absolute_import + +import numpy + +from .gen_or_imp import gen_or_imp + +class copy_gen(gen_or_imp): + name = "copy generator" + def generate(self,dic): + inputdic = dic['input'] + rms = inputdic['rms_data'] + self.time_data = inputdic['time_data'] + # 200712076 dstarr try/excepts this: + try: + self.frequencies = inputdic['frequencies'] + except: + self.frequencies = numpy.array([]) + self.s = inputdic['flux_data'] + self.set_outputs(inputdic) + return self.store(self.signalgen) + def set_outputs(self, inputdic): + self.make_dics() + self.for_input = self.sub_dics(self.signaldata) + self.for_input = inputdic + def store(self,data): + from ..signal_objects import signal + return signal(data,register=False) + +class noisified_gen(copy_gen): + name = "noisifier" + def __init__(self, list_of_noisifiers): + """ we'll be using __init__ to initialize the string of actions to apply to the signal """ + self.list_of_noisifiers = list_of_noisifiers + def set_outputs(self, inputdic): + self.make_dics() + self.for_input = self.sub_dics(self.signaldata) + self.for_input['old inputs'] = inputdic + for noisifier in self.list_of_noisifiers: + inputdic = noisifier(inputdic) + self.for_input.update(inputdic) + + \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/generators_importers/double_ind_gen.py b/mltsp/TCP/Software/feature_extract/Code/generators_importers/double_ind_gen.py new file mode 100644 index 00000000..67b800f8 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/generators_importers/double_ind_gen.py @@ -0,0 +1,24 @@ +from __future__ import absolute_import +import numpy +from .storage import storage + +from .sgwindn_gen import sgwindn_gen +from .double_sig_gen import double_sig_gen + +class double_ind_gen(sgwindn_gen,double_sig_gen): + name = 'double sine wave with individual noise, evenly sampled' + def generate(self,stdev = 1.0): + self.stdev = stdev + self.s = self.randomsine() + # self.s = 5 * numpy.ones(len(self.t)) + self.noise_tuple = self.noise(stdev_in=self.stdev,size_in=len(self.s)) + self.n = self.noise_tuple[1] + self.set_outputs() + self.signaldata = {'period':(self.p/(self.r+1)) ,'period2':(self.p2/(self.r2+1)), 'amplitude':self.amp,'amplitude2':self.amp2 , 'time_data':self.t , 'flux_data':(self.s+self.n) , 'clean_flux_data':self.s ,'dc_real':self.dc} + self.store(self.signalgen) + def set_outputs(self): +# generator.set_outputs(self) +# self.for_input['rms_data'] = self.noise_tuple[0] + sgwindn_gen.set_outputs(self) + self.for_input['period2']=(self.p2/(self.r2+1)) + self.for_input['amplitude2']=self.amp2 \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/generators_importers/double_sig_gen.py b/mltsp/TCP/Software/feature_extract/Code/generators_importers/double_sig_gen.py new file mode 100644 index 00000000..d6b58a58 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/generators_importers/double_sig_gen.py @@ -0,0 +1,27 @@ +from __future__ import absolute_import +import numpy +from .storage import storage + +from .generator import generator + +class double_sig_gen(generator): + source = 'double_sig_gen' + def randomsine(self): #generates a random sine wave + self.set_vars() + self.t2 = self.tgen() + self.r2 = self.rgen() + self.amp2 = self.ampgen() + self.p2 = self.pgen() + s = self.dc + (self.amp * numpy.sin(2*numpy.pi*self.t*(self.r+1)/self.p)) + (self.amp2 * numpy.sin(2*numpy.pi*self.t2*(self.r2+1)/self.p2)) # if r=0, period=p, if r=1, period = p/2 + return(s) + def set_outputs(self): + super(double_sig_gen,self).set_outputs() + self.for_input['period2']=(self.p2/(self.r2+1)) + self.for_input['amplitude2']=self.amp2 + def generate(self,stdev = 1.0): + self.stdev = stdev + s = self.randomsine() + n = self.noise(stdev_in=self.stdev,size_in=len(self.s)) # generate noise with correct length + self.make_dics() + self.signaldata = {'period':(self.p/(self.r+1)) ,'period2':(self.p2/(self.r2+1)), 'amplitude':self.amp,'amplitude2':self.amp2 , 'time_data':self.t , 'flux_data':(s+n) , 'clean_flux_data':s ,'dc_real':self.dc} + self.store(self.signalgen) \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/generators_importers/double_uneven_gen.py b/mltsp/TCP/Software/feature_extract/Code/generators_importers/double_uneven_gen.py new file mode 100644 index 00000000..d4b6372c --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/generators_importers/double_uneven_gen.py @@ -0,0 +1,9 @@ +from __future__ import absolute_import +import numpy +from .storage import storage + +from .uneven_sine_gen import uneven_sine_gen +from .double_ind_gen import double_ind_gen + +class double_uneven_gen(uneven_sine_gen,double_ind_gen): + name = 'unevenly sampled, double signal (two sine waves), uneven noise' \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/generators_importers/from_xml.py b/mltsp/TCP/Software/feature_extract/Code/generators_importers/from_xml.py new file mode 100644 index 00000000..5b3df22a --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/generators_importers/from_xml.py @@ -0,0 +1,171 @@ +from __future__ import print_function +from __future__ import absolute_import +import numpy, sys +from .. import db_importer +from .storage import storage +from numpy import log, exp, arange, median, ceil, pi + +from .gen_or_imp import gen_or_imp +import types + +class from_xml(gen_or_imp): + name = 'xml' + def __init__(self,signals_list=[]): + self.storer = storage() + self.signals_list = signals_list + def generate(self, xml_handle="/home/maxime/feature_extract/Code/source_5.xml", make_xml_if_given_dict=True, register = True): + self.signalgen = {} + self.sig = db_importer.Source(xml_handle=xml_handle,doplot=False, make_xml_if_given_dict=make_xml_if_given_dict) + self.sdict = self.sig.x_sdict + self.set_outputs() # this adds/fills self.signalgen[,multiband]{'input':{filled},'features':{empty},'inter':{empty}} + # see (1) at EOF for output from above function + self.storer.store(self.signalgen,self.signals_list, register=register) + + + # see (2) at EOF for output from above function + + def reckon_err_axis(self,d,obs_ind,ucd_error_values=['stat.error','error']): + """ figure out the column number for the error associated with index obs_ind """ + if 'units' in d and 'ordered_column_names' in d and 'ucds' in d: + ucd_obs = d['ucds'][obs_ind] + for i in range(len(d['ucds'])): + if i == obs_ind: + continue + if type(d['ucds'][i]) == str and d['ucds'][i].find(ucd_obs) != -1: + for e in ucd_error_values: + if d['ucds'][i].find(e) != -1: + return i + return None + + def set_outputs(self): + self.make_dics() + nominal_flux_or_mag_err = 0.10 + nominal_time_dependent_positional_err = 0.001 # degrees + + for band in self.sig.ts: + dic = self.sig.ts[band] + self.signaldata[band] = {} #20071206 dstarr adds this + input_dic = self.sub_dics(self.signaldata[band]) # creates sub-dictionaries in this particular band, chooses the right one to receive input data + #input_dic = dict(time_data=numpy.array(dic['t']), flux_data=numpy.array(dic['m']), rms_data=numpy.array(dic['m_err'])) + if "t" in dic: + time = numpy.array(dic['t']) + else: + time = numpy.array([]) + + ## JSB...adding units to the dictionary. Note that there is also UCD availability + ## TODO....allow for multiple flux measurements in the same instance + if 'units' in dic and 'ordered_column_names' in dic and 'ucds' in dic: + # PTF characteristics: + if 'mag_subtr' in dic['ordered_column_names']: + # NOTE: for PTF VOSource, which db_importer generated, the first 3 items (t,m,merr) are handled below. + for i_colname in range(3,len(dic['ordered_column_names'])): + colname = dic['ordered_column_names'][i_colname] + input_dic.update({(colname + '_unit'): dic['units'][i_colname], + (colname + '_ucd'): dic['ucds'][i_colname], + colname: numpy.array(dic[dic['ordered_column_names'][i_colname]])}) + ## TIME AXIS: REQUIRED + time_axis = 0 + try: + input_dic.update({'time_data_unit': dic['units'][time_axis], + 'time_data_ucd': dic['ucds'][time_axis], + 'time_data': numpy.array(dic[dic['ordered_column_names'][time_axis]])}) + input_dic['srcid'] = self.sdict.get("src_id",0) # 20110611 dstarr added just for lightcurve.py:lomb_code(): debug/allstars-plot use. + #input_dic.update({})#20110512commentout#'frequencies':self.fgen(input_dic['time_data'])}) # 20110512: NOTE: this and self.frequencies are not used by any current features (used to be related to old lomb implementations). About to add a new self.frequencies overwriting declaration in lomb_scargle_extractor.py:extractor(), which will allow the first freq self.frequencies, self.psd to be accessible to outside code. + except: + pass + ## FLUX/MAG AXIS: REQUIRED (or does it? Maybe we just want position versus time. Oh well....) + flux_or_mag_axis = 1 + try: + input_dic.update({'flux_data_unit': dic['units'][flux_or_mag_axis], + 'flux_data_ucd': dic['ucds'][flux_or_mag_axis],\ + 'flux_data': numpy.array(dic[dic['ordered_column_names'][flux_or_mag_axis]])}) + except: + input_dic.update({'flux_data': numpy.array([])}) + + ## UNCERTAINTY IN FLUX/MAX: OPTIONAL + try: + flux_or_mag_err_axis = self.reckon_err_axis(dic,flux_or_mag_axis) + if flux_or_mag_err_axis: + input_dic.update({'rms_data_unit': dic['units'][flux_or_mag_err_axis], + 'rms_data_ucd': dic['ucds'][flux_or_mag_err_axis], + 'rms_data': numpy.array(dic[dic['ordered_column_names'][flux_or_mag_err_axis]])}) + else: + ## assume that the UCD and units are the same as the flux axis + input_dic.update({'rms_data_unit': dic['units'][flux_or_mag_axis], + 'rms_data_ucd': dic['ucds'][flux_or_mag_axis], \ + 'rms_data': nominal_flux_or_mag_err*numpy.ones((len(input_dic['time_data'])))}) + except: + pass + ## POSITIONAL INFORMATION: OPTIONAL + ## positional info will have a UCD like pos.something.something + pos_header_names = [x for x in dic['ucds'][1:] if x.find("pos.") != -1] + if len(pos_header_names) in [2,4]: + print("FUTURE: There appears to be time_dependent positional information passed. You'll want to load input_dict with this.") + else: + print(" NOTE: No apparent time depdendent position information passes to from_xml().") + + else: + ## warning! + ## TODO: this is volitile because 1) we might not require m_err and 2) the name of the flux and time axes could be different + if "t" in dic: + time = numpy.array(dic['t']) + else: + time = numpy.array([]) + + input_dic.update({'time_data': time, 'flux_data':numpy.array(dic['m']), 'rms_data':numpy.array(dic['m_err']), \ + 'frequencies':self.fgen(time)}) + + input_dic.update( {'ra':self.sdict['ra'], 'dec':self.sdict['dec'], 'ra_rms':self.sdict['ra_rms'], 'dec_rms':self.sdict['dec_rms']}) + # 20090616 added: + input_dic['limit_mag_dict'] = dic.get('limitmags',{}) # 20090624 c/o dic['limitmags'] + self.signalgen['source']='xml' + self.signaldata['multiband'] = {} + input_dic = self.sub_dics(self.signaldata['multiband']) + input_dic.update( {'ra':self.sdict['ra'], 'dec':self.sdict['dec'], 'ra_rms':self.sdict['ra_rms'], 'dec_rms':self.sdict['dec_rms']}) # copied from line 28 + + + def fgen(self, time_data): + #var = { 'x': noisetime, 'y': noisedata, 'ylabel': 'Amplitude', 'xlabel':'Time (s)' } + + N= len(time_data) + + if N > 1: + # NOTE: 20090717: dstarr replaces frequency definition with freq defs from lightcurve.py->get_out_dict() + #maxt = max(time_data) + #mint = min(time_data) + #dt = (maxt - mint)/N #findAverageSampleTime(var,0) + #maxlogx = log(1/(2*dt)) # max frequency is the sampling rate + #minlogx = log(1/(maxt-mint)) #min frequency is 1/T + #frequencies = exp(arange(N, dtype = float) / (N-1.) * (maxlogx-minlogx)+minlogx) + + ### 20091122: + #tt = time_data - min(time_data) + #fmin = 0.5/max(tt) + #fmax = 48 # 48cyc/day : this cooresponds to 30 minute period, a minimum period of interest. #n0 / (2.*max(tt)) + #df_over_f=0.001 + #numf = long( ceil( log(fmax/fmin)/df_over_f ) ) + #freqin = exp( log(fmax)-log(fmax/fmin)*arange(numf, dtype=float)/(numf-1.) ) + #om = 2.*pi*freqin + #frequencies = om/(2.*pi) + ### + + tt = time_data - min(time_data) + fmin = 0.5/max(tt) + fmax = 48 # 48cyc/day : this cooresponds to 30 minute period, a minimum period of interest. #n0 / (2.*max(tt)) + + df = 0.1/max(tt) + numf = long( ceil( (fmax-fmin)/df ) ) + num_freq_max=10000 + + if (numf>num_freq_max): + numf = num_freq_max + df = (fmax - fmin)/numf + freqin = fmax - df*arange(numf,dtype=float) + + om = 2.*pi*freqin + frequencies = om/(2.*pi) + else: + frequencies = numpy.array([1]) + + return frequencies + diff --git a/mltsp/TCP/Software/feature_extract/Code/generators_importers/gen_or_imp.py b/mltsp/TCP/Software/feature_extract/Code/generators_importers/gen_or_imp.py new file mode 100644 index 00000000..b1bc31f2 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/generators_importers/gen_or_imp.py @@ -0,0 +1,12 @@ +import numpy + +class gen_or_imp(object): + name = 'gen_or_imp' + def make_dics(self): + self.signalgen = {'source':self.name,'data':{}} + self.signaldata = self.signalgen['data'] + def sub_dics(self,where): + where['input'] = {} # to receive time, flux and rms data from the generators + where['features'] = {} # to receive the extracted features later one + where['inter'] = {} # to receive intermediary features later on (not a single number, but useful for extractors down the line) + return where['input'] \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/generators_importers/generator.py b/mltsp/TCP/Software/feature_extract/Code/generators_importers/generator.py new file mode 100644 index 00000000..66950d89 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/generators_importers/generator.py @@ -0,0 +1,58 @@ +from __future__ import absolute_import +import numpy +from .storage import storage + +from .gen_or_imp import gen_or_imp + +class generator(gen_or_imp): + name = 'generator' + def __init__(self,signals_list=[]): + self.storer = storage() + self.signals_list = signals_list + def ampgen(self): + amp = (10.0+10.0*float(numpy.random.rand(1))) # one random number determines the amplitude between 10 and 20 + return amp + def tgen(self): # x axis (time) + return numpy.arange(500,dtype=float) #number of data points + def fgen(self): + frequencies = self.t / len(self.t) #evenly spaced data + def dcgen(self): + return(-10 + 20*float(numpy.random.rand(1))) # sets dc level randomly between -10 and +10 + def rgen(self): + return(float(numpy.random.rand(1))) # one random number determines the period + def pgen(self): + return(30) #period + def randomsine(self): #generates a random sine wave + self.set_vars() + s = self.dc + self.amp * numpy.sin(2*numpy.pi*self.t*(self.r+1)/self.p) # if r=0, period=p, if r=1, period = p/2 + return(s) + def noise(self,stdev_in = 1.0,size_in=1): #gaussian with standard deviation as input + # gauss = numpy.random.normal(loc=0.0,scale=stdev,size=size2) #gaussian noise signal in numpy + gauss = numpy.random.normal(loc=0.0,scale=stdev_in,size=size_in) + # return numarray.array(list(gauss)) #convert to numarray (in a horrible fashion) because couldn't plot with numpy + return gauss + def set_vars(self): # sets all the variables + self.t = self.tgen() + self.f = self.fgen() + self.r = self.rgen() + self.amp = self.ampgen() + self.p = self.pgen() + self.dc = self.dcgen() + def generate(self,stdev = 1.0): + self.stdev = stdev + self.s = self.randomsine() + self.n = self.noise(stdev_in=self.stdev,size_in=len(self.s)) # generate noise with correct length + self.set_outputs() + self.store(self.signalgen) + def set_outputs(self): + self.make_dics() + self.for_input = self.sub_dics(self.signaldata) + self.for_input['period']=(self.p/(self.r+1)) + self.for_input['amplitude']=self.amp + self.for_input['time_data']=self.t + self.for_input['flux_data']=(self.s+self.n) + self.for_input['clean_flux_data']=self.s + self.for_input['dc_real']=self.dc + self.for_input['frequencies'] = self.f + def store(self,data): + self.storer.store(data,self.signals_list) diff --git a/mltsp/TCP/Software/feature_extract/Code/generators_importers/lin_inc_gen.py b/mltsp/TCP/Software/feature_extract/Code/generators_importers/lin_inc_gen.py new file mode 100644 index 00000000..ed61e944 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/generators_importers/lin_inc_gen.py @@ -0,0 +1,18 @@ +from __future__ import absolute_import +import numpy +from .storage import storage + +from .sgwindn_gen import sgwindn_gen + +class lin_inc_gen(sgwindn_gen): #data with a slope + name = 'linearly increasing generator' + def slopegen(self): + a = float(numpy.random.normal(loc=0.1,scale=2,size=1)) + return a + def set_vars(self): + super(lin_inc,self).set_vars() + self.a = self.slopegen() + def set_outputs(self): + super(lin_inc,self).set_outputs() + self.for_input['real_slope'] = self.a + self.for_input['flux_data']=self.signalgen['flux_data'] + self.t * self.a \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/generators_importers/montecarlo_gen.py b/mltsp/TCP/Software/feature_extract/Code/generators_importers/montecarlo_gen.py new file mode 100644 index 00000000..bfdff93b --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/generators_importers/montecarlo_gen.py @@ -0,0 +1,33 @@ +from __future__ import absolute_import +import numpy + +from .gen_or_imp import gen_or_imp + +class montecarlo_gen(gen_or_imp): + name = "montecarlo bootstrap generator" + def generate(self,dic,output): + inputdic = dic['input'] + rms = inputdic['rms_data'] + self.time_data = inputdic['time_data'] + # 200712076 dstarr try/excepts this: + try: + self.frequencies = inputdic['frequencies'] + except: + self.frequencies = numpy.array([]) + noise_signal = [] + for i in range(len(rms)): + noise = float(numpy.random.normal(loc=0.0,scale=rms[i],size=1)) + noise_signal.append(noise) + self.s = numpy.array(noise_signal) + self.set_outputs() + self.store(self.signalgen,output) + def set_outputs(self): + self.make_dics() + self.for_input = self.sub_dics(self.signaldata) + self.for_input['time_data'] = self.time_data + self.for_input['flux_data'] = self.s + self.for_input['frequencies'] = self.frequencies + def store(self,data,output): + from ..signal_objects import signal + signal_obj = signal(data,register=False) + output.append(signal_obj) diff --git a/mltsp/TCP/Software/feature_extract/Code/generators_importers/sgwindn_gen.py b/mltsp/TCP/Software/feature_extract/Code/generators_importers/sgwindn_gen.py new file mode 100644 index 00000000..5ec36bd4 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/generators_importers/sgwindn_gen.py @@ -0,0 +1,33 @@ +from __future__ import absolute_import +import numpy +from .storage import storage + +from .generator import generator + +class sgwindn_gen(generator): #signal with individual noise (each data point has a different standard deviation) + name = "signal with individual noise" + def noise(self,stdev_in = 1.0,size_in=1): #gaussian with standard deviation as input + #indiv_noise_std = abs(numpy.random.normal(loc=stdev_in,scale=1.0,size=size_in)) # each point gets a different noise with a different st. dev., absolute value avoids negative values (not perfect, could be linear instead of gaussian) + indiv_noise_std = numpy.random.rand(size_in)+1 +# indiv_noise_std[:] = 1 +# indiv_noise_std[5] = 20 +# indiv_noise_std[2] = 20 + indiv_noise = [] + for i in range(size_in): + noise = float(numpy.random.normal(loc=0.0,scale=indiv_noise_std[i],size=1)) + indiv_noise.append(noise) +# print 'indiv_noise_std',numpy.array(indiv_noise_std).sum(),'n','indiv_noise',numpy.array(indiv_noise).sum() + return (indiv_noise_std,indiv_noise) + def generate(self,stdev = 1.0): + self.stdev = stdev + self.s = self.randomsine() +# self.s = 5 * numpy.ones(len(self.t)) + self.noise_tuple = self.noise(stdev_in=self.stdev,size_in=len(self.s)) + self.n = self.noise_tuple[1] + self.set_outputs() + self.store(self.signalgen) + def set_vars(self): + super(sgwindn_gen,self).set_vars() + def set_outputs(self): + super(sgwindn_gen,self).set_outputs() + self.for_input['rms_data'] = self.noise_tuple[0] \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/generators_importers/storage.py b/mltsp/TCP/Software/feature_extract/Code/generators_importers/storage.py new file mode 100644 index 00000000..e22a3435 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/generators_importers/storage.py @@ -0,0 +1,14 @@ +import numpy +from ..signal_objects import signal_generator, signal_with_bands, signal_xml, signal + +class storage(object): + def store(self,signalfromgen,signals_list, register=True): + if signalfromgen['source'] =='generator': + signals_list.append(signal_generator(signalfromgen, register=register)) + elif signalfromgen['source'] == 'vizier cepheids': + signals_list.append(signal_with_bands(signalfromgen, register=register)) + elif signalfromgen['source'] == 'xml': + # generally going here... + out = signal_xml(signalfromgen, register=register) + signals_list.append(out) + else: signals_list.append(signal(signalfromgen, register=register)) diff --git a/mltsp/TCP/Software/feature_extract/Code/generators_importers/uneven_sine_gen.py b/mltsp/TCP/Software/feature_extract/Code/generators_importers/uneven_sine_gen.py new file mode 100644 index 00000000..fe847648 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/generators_importers/uneven_sine_gen.py @@ -0,0 +1,25 @@ +from __future__ import absolute_import +import numpy +from numpy import log, exp, random, ones, arange +from .storage import storage + +from .sgwindn_gen import sgwindn_gen + +class uneven_sine_gen(sgwindn_gen): + name = 'uneven sine with individual noise' + def tgen(self): + poiss = random.poisson(lam = 1000, size = 200) # 1000 = 1 day = expected time between measuremens + poiss = poiss / 1000.0 + x_axis = ones(len(poiss),dtype=float) + for i in arange(len(x_axis))[:-1]: + x_axis[i+1] = x_axis[i] + poiss[i] + return x_axis + def fgen(self): + noisetime = self.t + #var = { 'x': noisetime, 'y': noisedata, 'ylabel': 'Amplitude', 'xlabel':'Time (s)' } + N= len(noisetime) + dt = 1.0 #findAverageSampleTime(var,0) + maxlogx = log(1/(2*dt)) # max frequency is the sampling rate + minlogx = log(1/(max(noisetime)-min(noisetime))) #min frequency is 1/T + frequencies = exp(arange(N, dtype = float) / (N-1.) * (maxlogx-minlogx)+minlogx) + return frequencies \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/generators_importers/vizier_importer.py b/mltsp/TCP/Software/feature_extract/Code/generators_importers/vizier_importer.py new file mode 100644 index 00000000..154d5f16 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/generators_importers/vizier_importer.py @@ -0,0 +1,43 @@ +from __future__ import absolute_import +import numpy +from .storage import storage + +from .gen_or_imp import gen_or_imp + +class vizier_importer(gen_or_imp): + name = 'vizier cepheids' + def __init__(self,signals_list=[]): + self.storer = storage() + self.signals_list = signals_list + def generate(self): +# filepath = 'table.dat' + filepath = 'table2.dat' #smaller version for testing purposes + data = open(filepath, mode='r') + self.signalgen={} + first_line = data.readline() + self.name = first_line[0:9] + self.dates = [float(first_line[10:22])] + self.mags = [float(first_line[24:29])] + for line in data: + if line[0:9] != self.name: + self.set_outputs() + self.store(self.signalgen) + self.name = line[0:9] + self.dates=[] + self.mags=[] + jd = line[10:22] #julian date, specified from http://vizier.u-strasbg.fr/viz-bin/Cat?II/217 + mag = line[24:29] #[-0.93/16.0]? V (Johnson) magnitude + if mag != ' ': + self.dates.append(float(jd)) + self.mags.append(float(mag)) + self.set_outputs() + self.store(self.signalgen) + def set_outputs(self): + self.make_dics() + self.for_input = self.sub_dics(self.signaldata['Vmag']) + self.for_input = dict(time_data=numpy.array(self.dates,dtype=float), flux_data=numpy.array(self.mags,dtype=float), rms_data=numpy.ones(len(self.dates),dtype=float)/10.0) +# self.signalgen['time_data']= numpy.array(self.dates,dtype=float) +# self.signalgen['flux_data']= numpy.array(self.mags,dtype=float) + self.signalgen['object_name'] = self.name + def store(self,data): + self.storer.store(data,self.signals_list) \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/generators_importers/vo_timeseries.py b/mltsp/TCP/Software/feature_extract/Code/generators_importers/vo_timeseries.py new file mode 100644 index 00000000..6a441246 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/generators_importers/vo_timeseries.py @@ -0,0 +1,20 @@ + +vo_table_preamble = """ + + + +""" + +vo_timeseries_preamble = """\n""" +vo_timeseries_mjd = """ +\t\tMJD +\t\t0.0 +\t\tUTC +\t\tTOPOCENTER + + +""" + +vo_source_preamble = """\n\n\t\n""" + diff --git a/mltsp/TCP/Software/feature_extract/Code/import_vizier.py b/mltsp/TCP/Software/feature_extract/Code/import_vizier.py new file mode 100644 index 00000000..1c4ce08a --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/import_vizier.py @@ -0,0 +1,17 @@ +""" read files into numpy arrays """ +from __future__ import print_function + +import numpy +class vizier_importer(object): + def open_file(self): + filepath = 'table.dat' + data = open(filepath, mode='r') + for line in data: + jd = line[10:21] #julian date, specified from http://vizier.u-strasbg.fr/viz-bin/Cat?II/217 + mag = line[23:28] #[-0.93/16.0]? V (Johnson) magnitude + if mag != ' ': + print(jd, mag) + return None +if __name__ == '__main__': + importer = vizier_importer() + importer.open_file() \ No newline at end of file diff --git a/mltsp/TCP/Software/feature_extract/Code/internal_generated_extractors_holder.py b/mltsp/TCP/Software/feature_extract/Code/internal_generated_extractors_holder.py new file mode 100644 index 00000000..e1eafe5e --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/internal_generated_extractors_holder.py @@ -0,0 +1,12 @@ +glob_internally_generated_extractors = {} # 20080508: dstarr KLUDGE. Nat's Lomb Scargle algorithms generate a bunch of features, which due to the current architecture, must be represented in seperate extractor objects that are available many stacks higher (to be added to a "signal_list"). Existing architecture assumed all extractors are explicitly defined (listed as ordered __init__.py imports), but this dynamic feature-extractor generation breaks this assumption. So, I must use a global list which is accessible by signals_list related methods, and still updateable by the Nat-Lomb_scargle feature extractor. +class Internal_Gen_Extractors_Accessor: + """ Used only to allow access to a global list, by a + class which does: import feature_interfaces + + KLUDGE. + """ + def __init__(self): + global glob_internally_generated_extractors + self.glob_internally_generated_extractors = glob_internally_generated_extractors + def main(self): + pass diff --git a/mltsp/TCP/Software/feature_extract/Code/jsb_fake_data.py b/mltsp/TCP/Software/feature_extract/Code/jsb_fake_data.py new file mode 100644 index 00000000..c2b1c189 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/jsb_fake_data.py @@ -0,0 +1,62 @@ +#/bin/env python + +""" +makes some fake data for Maxime's codes +""" + +import datetime +import os, sys +import numpy +import cPickle +__version__ = "v0.01" + +class DataSet: + + def __init__(self,outname="tmp",clobber=True,odir="./"): + self.dict = None # the dictionary representation + self.xml = None # the XML representation of this data + self.outname = outname + self.odir = odir + + if outname == "time": + ## generate the outname from datetime + outname = "data" + str(datetime.datetime.now()).replace(" ","") + + if clobber: + if os.path.exists(odir + outname + ".pkl"): + os.remove(odir + outname + ".pkl") + if os.path.exists(odir + outname + ".xml"): + os.remove(odir + outname + ".xml") + + def make_dict(self): + self.data = {"B": {"t": numpy.array([23455.12,23455.23,23455.56,23455.901,23456.102,23456.9,123457.8]),\ + "f": numpy.array([12.02, 13.4, 12.00, 17.00, 12.01, 13.4, 13.56]), \ + "rms": numpy.array([0.02, 0.78, 0.05, 0.10, 0.03, 0.89, 0.45]), \ + "flux_units": "mag"},\ + "V": {"t": numpy.array([23455.12,23455.23,23455.56,23455.901,23456.102,23456.9,123457.8]),\ + "f": numpy.array([15.02, 16.5, 16.00, 19.00, 14.2, 17.6, 19.56]), \ + "rms": numpy.array([0.02, 0.78, 0.05, 0.10, -999, 0.89, -99]), \ + "flux_units": "mag"},\ + "I": {"t": numpy.array([23455.12,23455.23,23455.56,23455.901,23456.102,23456.9,123457.8]),\ + "f": numpy.array([15.02, 16.5, 17.00, 19.00, 19.2, 17.6, 11.56]), \ + "rms": numpy.array([0.2, 3.4, 5.0 , -999, 1.0, 3.0, -99.0]), \ + "flux_units": "uJy"}} + + self.dict = {"id": self.outname, "version": __version__, "lower_limit_code": -99, "upper_limit_code": -999, 'data': self.data} + + def get_dict(self): + if self.dict is None: + self.make_dict() + return self.dict + + def make_xml(self): + ## probably want to make the XML first then create the dictionary parse of it + pass + + def pickle(self): + output = open(self.odir + self.outname + ".pkl", 'wb') + cPickle.dump(self.dict, output,-1) + output.close() + return self.odir + self.outname + ".pkl", 'wb' + + diff --git a/mltsp/TCP/Software/feature_extract/Code/main.py b/mltsp/TCP/Software/feature_extract/Code/main.py new file mode 100644 index 00000000..a5ed98ab --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/main.py @@ -0,0 +1,42 @@ +""" +First, import this module +> import random_number +then run its test, assigning it a name +> sig = random_number.test() +then you can plot more things if you want, e.g.: +> sig.plots('dc') +""" +from __future__ import print_function +from __future__ import absolute_import + +from . import generators_importers + +signals_list = [] + + +def test(stdev = 1.0, clear=True, signals_list=signals_list): + """ performs a basic test and plots stuff + change the generator to sgwindn (line 13) to get an evenly sampled signal + """ +# gen = generators_importers.vizier_importer(signals_list) +# gen = generators_importers.sgwindn_gen(signals_list) + gen = generators_importers.from_xml(signals_list) +# gen = generators_importers.uneven_sine_gen(signals_list) +# gen = generators_importers.double_uneven_gen(signals_list) +# gen = generators_importers.double_sig_gen(signals_list) +# gen = generators_importers.test_lomb(signals_list) + i=0 + while i < 1: + gen.generate() + i += 1 + sig = signals_list[-1] +# sig.properties['data']['inter']['99pct significant power'].plots() + #sig.properties['data']['inter']['power'].plots() + #sig.properties['data']['inter']['pct 80 montecarlo'].plots() + #sig.properties['data']['inter']['pct 90 montecarlo'].plots() + #sig.properties['data']['inter']['pct 95 montecarlo'].plots() + #sig.properties['data']['inter']['pct 99 montecarlo'].plots() + return sig +def xml_print(): + for signal in signals_list: + print(signal.xml_print.xml()) diff --git a/mltsp/TCP/Software/feature_extract/Code/plotters.py b/mltsp/TCP/Software/feature_extract/Code/plotters.py new file mode 100644 index 00000000..34605d3a --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/plotters.py @@ -0,0 +1,32 @@ +from __future__ import print_function +from __future__ import absolute_import +try: + from pylab import * +except: + pass +from numpy import * +import numpy +from . import feature_interfaces + + +class plotter(object): + def plots(self,properties,what = 'data'): + if what =='data': + plot(properties['time_data'],properties['flux_data'],marker='d',label='signal data') + elif what =='real': + plot(properties['time_data'],properties['clean_flux_data'],label='clean data from generator',marker='o') + elif what =='dc_real': + dc_line = ones(len(properties['time_data']),dtype=float) + dc_line[:] = properties['dc_real'] + plot(properties['time_data'],dc_line,label='real dc from generator') + else: +# try: + extractor = feature_interfaces.extractor_fetch(what) + if what in properties: + pass + else: + print(what,'not available') + return None + extractor.plots(properties) +# except KeyError: +# print "can't plot", what diff --git a/mltsp/TCP/Software/feature_extract/Code/signal_objects.py b/mltsp/TCP/Software/feature_extract/Code/signal_objects.py new file mode 100644 index 00000000..8bfdd182 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/signal_objects.py @@ -0,0 +1,131 @@ +from __future__ import print_function +from __future__ import absolute_import +from . import feature_interfaces +from . import plotters +from . import FeatureExtractor + +#global_list_of_extractors = [] # Global KLUDGE: # 20071215 dstarr. Apparently this is done since feature_interfaces.initialize() is called outside of everything +global_list_of_extractors = feature_interfaces.initialize([]) +#20090321#import amara + +class signal(object): + def __init__(self,data,register=True): + self.properties = data + self.choose_plotter() + if register: self.register_signal() + def choose_plotter(self): + self.plotter = plotters.plotter() + def update(self,extract_method): + control = self.properties.copy() + result = extract_method.extr(self.properties) + assert (control == self.properties), "this bastard changed my dictionary" # this is just a test, can be removed if we trust our code + write_where = self.find_where() + self.write_result(write_where,result) + return result + def write_result(self,write_where,result): + """ the type (class) of result I get informs me of where to put it """ + if isinstance(result,FeatureExtractor.Feature): + write_where['features'][result.extname] = result + elif isinstance(result,FeatureExtractor.Intermediary): + write_where['inter'][result.extname] = result + else: raise "weird kinda of result, don't know what to do with it" + def register_signal(self, initialize = True): + """ says to the interface: i'm interested in hearing about new features + initialize determines whether active features should immediately be applied + """ + feature_interfaces.feature_interface.register_signal(self, \ + global_list_of_extractors, initialize=initialize) + def remove_signal(self): + """ says to the interface: I don't care about new features anymore """ + feature_interfaces.feature_interface.remove_signal(self) + #def plots(self,what = 'data'): + # self.plotter.plots(what) + def find_where(self,band=None): + return self.properties['data'] + def plots(self,list_what,band=None): + self.iplot(list_what,band) + def iplot(self,list_what,band=None): + for what in list_what: + self.plotter.plots(self.find_where(band)['input'],what) + plotters.grid(True) + plotters.legend() + def xml_print(self): + xml = amara.create_document(u"root") + xml_where = xml + waiting_dics = [] + dic = self.properties + while 1: + for key in dic: + value = dic[key] + xml_where[unicode(key)] = None + if isinstance(value,dict): + waiting_dics.append((value,xml_where[unicode(key)])) + if isinstance(value,ResultObject): + xml_where[unicode(key)] = value.xml_print() + else: + xml_where[unicode(key)] = value + try: + next_dic = waiting_dics.pop() + except IndexError: + break + dic = next_dic[0] + xml_where[1] + return xml + + + + + +class signal_generator(signal): #signal from random sine wave generator + pass +class signal_with_bands(signal): + def update(self,extract_method): + for key in self.properties['data'].keys(): + if key == 'multiband': + continue + elif 'NOMAD' in key: + # 20110517: dstarr adds this case since I want NOMAD color info available to color_diff_extractor.py, but I do not want to extract features for this "*:NOMAD" band. + continue + elif 'extinct_' in key: + # 20110517: dstarr adds this case since I want (NED extinction) color info available to color_diff_extractor.py, but I do not want to extract features for this "extinct_*" band. + continue + if isinstance(extract_method,FeatureExtractor.ContextExtractor): + write_where = self.find_where(band='multiband') + myband = 'multiband' + else: + write_where = self.find_where(band=key) + myband = key + result = extract_method.extr(self.properties.copy(),band=myband) # we could decide not to send a copy if we trust our code + if isinstance(extract_method,FeatureExtractor.FeatureExtractor): + # 20080508: dstarr adds condition: + if not extract_method.internal_use_only: + write_where['features'][result.extname] = result + elif isinstance(extract_method,FeatureExtractor.InterExtractor): + write_where['inter'][result.extname] = result + else: raise "weird kinda of result, don't know what to do with it" + #self.write_result(write_where,result) + def default_band(self): + print("Warning: using default band") + return 'Vmag' + def choose_plotter(self): + self.plotter = plotters.plotter() + def prinprop(self,list_what,band=None): + if band == 'all': + for aband in self.properties['data']: + print(aband, end=' ') + self.iprint(list_what,aband) + else: self.iprint(list_what,band) + def iprint(self,list_what,band=None): + for what in list_what: + print(self.find_where(band)[what]) + def plots(self,list_what,band=None): + if band == 'all': + for aband in self.properties['data']: + self.iplot(list_what,aband) + else: self.iplot(list_what,band) + def find_where(self,band=None): + return self.properties['data'][band] +class signal_xml(signal_with_bands): + def default_band(self): + return 'z' + diff --git a/mltsp/TCP/Software/feature_extract/Code/source_5.xml b/mltsp/TCP/Software/feature_extract/Code/source_5.xml new file mode 100755 index 00000000..700b52a5 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/source_5.xml @@ -0,0 +1,238 @@ + + + + + + + 5 + + Best positional information of the source + + + 49.599486 + -1.005111 + + + 0.000125 + 0.000066 + + + + + + MJD + 0.0 + UTC + TOPOCENTER + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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995 +TT AQL 2449623.1883 7.531 1.504 1.586 .827 995 +TT AQL 2449624.1980 7.659 1.601 1.599 .838 995 +TT AQL 2449625.2011 7.650 1.549 1.561 .833 995 +TT AQL 2449626.2214 7.510 1.500 .795 995 +TT AQL 2449631.1875 6.792 1.191 .662 995 +TT AQL 2449632.2074 6.906 1.011 1.286 .708 995 +TT AQL 2449633.1903 7.031 1.370 .735 995 +TT AQL 2449634.1885 7.181 1.423 .800 995 +TT AQL 2449934.3211 6.869 1.255 .723 1.343 998 +TT AQL 2449935.3299 6.966 1.353 .737 1.414 998 +TT AQL 2449936.3026 7.107 1.433 .781 1.493 998 +TT AQL 2449937.2714 7.223 1.489 .802 998 +TT AQL 2449938.2933 7.387 .814 1.578 998 +TT AQL 2449939.2920 7.531 .846 1.609 998 +TT AQL 2449941.2881 7.612 1.619 998 +TT AQL 2449942.2624 7.600 1.571 998 +TT AQL 2449943.2524 7.385 .788 1.506 998 +TT AQL 2449944.2632 7.372 1.445 998 +TT AQL 2449945.2598 6.811 1.251 998 +TT AQL 2449946.2387 6.678 1.230 998 +TT AQL 2449947.2343 6.780 1.312 998 +TT AQL 2449948.2309 6.912 1.384 998 +TT AQL 2449949.2333 7.061 1.457 998 +TT AQL 2449950.2304 7.118 1.487 998 +TT AQL 2449952.2358 7.399 1.572 998 +TT AQL 2449953.2586 7.563 1.621 998 +TT AQL 2449954.2338 7.647 1.609 998 +TT AQL 2449955.2309 7.658 1.605 998 +TT AQL 2450305.2751 6.816 1.180 .691 971 +TT AQL 2450306.2974 6.931 1.261 .741 971 +TT AQL 2450307.2507 7.088 1.297 .773 971 +TT AQL 2450310.2568 7.447 1.529 .829 971 +TT AQL 2450311.1840 7.562 1.578 .836 971 +TT AQL 2450312.1846 7.631 1.568 .846 971 +TT AQL 2450313.2072 7.614 1.495 .823 971 +TT AQL 2450314.1810 7.439 1.414 .795 971 +TT AQL 2450315.1879 7.330 1.308 .756 971 +TT AQL 2450316.1985 7.174 1.198 .682 971 +TT AQL 2450317.2056 6.537 .952 .586 971 +TT AQL 2450318.1922 6.722 1.086 .655 971 +TT AQL 2450319.1862 6.832 1.175 .684 971 +TT AQL 2450320.1989 6.951 1.268 .733 971 +TT AQL 2450321.1797 7.059 1.346 .760 971 +TT AQL 2450322.1786 7.176 1.408 .791 971 +TT AQL 2450323.1761 7.316 1.474 .810 971 +TT AQL 2450324.1955 7.459 1.524 .840 971 +TT AQL 2450325.1687 7.580 1.545 .839 971 +TT AQL 2450326.1563 7.663 1.533 .854 971 +BC AQL 2446284.2064 13.030 .867 .493 987 +BC AQL 2446285.2122 13.623 .984 .576 987 +BC AQL 2446286.2062 12.734 .598 .386 987 +BC AQL 2446287.1982 13.119 .883 .516 987 +BC AQL 2446288.2109 13.639 1.001 .586 987 +BC AQL 2446289.2254 12.752 .610 .383 987 +BC AQL 2446290.2069 13.150 .956 .512 987 +BC AQL 2446291.1968 13.651 1.031 .590 987 +BC AQL 2446292.1982 12.791 .643 .396 987 +BC AQL 2446293.2292 13.216 .930 .544 987 +BC AQL 2446294.1947 13.734 1.020 .600 987 +BC AQL 2446295.1745 12.824 .623 .407 987 +BC AQL 2446296.1827 13.256 .961 .529 987 +BC AQL 2446297.1905 13.676 1.034 .582 987 +BC AQL 2446298.2110 12.863 .684 .447 987 +BC AQL 2446299.1785 13.314 .905 .572 987 +BC AQL 2446300.1774 13.689 .937 .566 987 +BC AQL 2446301.1931 12.861 .725 .428 987 +BC AQL 2446302.1908 13.331 1.064 .564 987 +BC AQL 2447737.3534 13.328 .975 .580 991 +BC AQL 2447738.3175 13.701 .948 .609 991 +BC AQL 2447740.3148 13.419 .954 .582 991 +BC AQL 2447741.2892 13.589 .889 .575 991 +BC AQL 2447742.2995 12.917 .725 .470 991 +BC AQL 2447743.2872 13.378 .995 .556 991 +BC AQL 2447744.2652 13.459 .847 .535 991 +BC AQL 2447745.2709 12.947 .741 .470 991 +BC AQL 2447746.2762 13.441 .986 .576 991 +BC AQL 2447747.2706 13.027 .721 .442 991 +BC AQL 2447748.2705 12.976 .784 .505 991 +BC AQL 2447749.2650 13.483 .979 .597 991 +BC AQL 2447759.2275 12.740 .586 .380 991 +BC AQL 2447760.2499 13.164 .883 .495 991 +BC AQL 2447761.2330 13.661 1.025 .570 991 +BC AQL 2447762.2183 12.774 .579 991 +BC AQL 2447767.2353 13.642 1.011 .533 991 +BC AQL 2447768.2380 .694 991 +BC AQL 2447770.2221 13.647 .997 .566 991 +BC AQL 2447771.2172 12.836 .727 .438 991 +BC AQL 2447772.2152 13.339 .957 .582 991 +BC AQL 2447773.2412 13.592 .919 .571 991 +BC AQL 2447774.2451 12.877 .764 .450 991 +BC AQL 2447775.2103 13.394 .988 .543 991 +BC AQL 2447776.2106 13.469 .884 .513 991 +EV AQL 2445489.3514 12.347 1.555 1.011 982 +EV AQL 2445490.2968 12.314 1.633 1.782 .948 982 +EV AQL 2445493.3397 12.167 1.185 1.659 .958 982 +EV AQL 2445497.2655 11.713 .989 1.410 .830 982 +EV AQL 2445498.2772 .929 1.411 .840 982 +EV AQL 2445501.3242 11.566 .921 1.396 .846 982 +EV AQL 2445502.3006 11.590 1.048 1.374 .861 982 +EV AQL 2445503.3203 11.622 1.030 1.384 .862 982 +EV AQL 2445505.3046 11.644 1.126 1.495 .853 982 +EV AQL 2445508.2968 11.728 1.597 .887 982 +EV AQL 2445509.3242 11.805 1.590 982 +EV AQL 2445512.3280 11.858 1.247 1.669 .925 982 +EV AQL 2445513.3476 11.889 .941 982 +EV AQL 2445514.3203 11.966 1.726 982 +EV AQL 2445646.1328 12.357 1.803 .984 982 +EV AQL 2445648.1171 12.147 1.701 .897 982 +EV AQL 2445649.1405 12.070 1.547 .923 982 +EV AQL 2445650.1250 11.994 1.413 .921 982 +EV AQL 2445864.3788 11.974 1.297 1.722 .962 982 +EV AQL 2445866.3984 12.025 1.711 .967 982 +EV AQL 2445867.3945 12.062 1.709 .971 982 +EV AQL 2445868.3476 12.068 1.749 .972 982 +EV AQL 2445869.3671 12.111 1.747 .980 982 +EV AQL 2445870.3554 12.137 1.781 .982 982 +EV AQL 2445871.3593 12.160 1.786 .977 982 +EV AQL 2445872.3631 12.187 1.790 .986 982 +EV AQL 2445873.3631 12.222 1.747 .993 982 +EV AQL 2445874.3514 12.242 1.802 .980 982 +EV AQL 2445875.3437 12.290 1.759 .985 982 +EV AQL 2445876.3631 12.271 1.770 .980 982 +EV AQL 2445877.3437 12.274 1.750 .990 982 +EV AQL 2445878.3476 12.246 1.713 .957 982 +EV AQL 2445879.3359 12.219 1.675 .968 982 +EV AQL 2445880.3476 12.123 1.660 .933 982 +EV AQL 2445881.3397 12.044 1.585 .907 982 +EV AQL 2445882.3320 11.923 1.524 .889 982 +EV AQL 2445883.3476 11.793 1.446 .865 982 +EV AQL 2445886.3514 11.592 1.391 .825 982 +EV AQL 2445887.3593 11.585 1.378 .811 982 +EV AQL 2446252.3335 12.026 1.726 .976 987 +EV AQL 2446253.2953 12.030 1.761 .979 987 +EV AQL 2446255.3232 12.102 1.754 .983 987 +EV AQL 2446256.3444 12.129 1.773 .996 987 +EV AQL 2446257.3528 12.168 1.786 .985 987 +EV AQL 2446258.3004 12.209 1.780 1.003 987 +EV AQL 2446259.3115 12.225 1.793 .995 987 +EV AQL 2446260.2905 12.277 1.784 1.007 987 +EV AQL 2446261.2963 12.285 1.777 1.002 987 +EV AQL 2446262.3183 12.290 1.773 .986 987 +EV AQL 2446263.3149 12.307 1.794 .984 987 +EV AQL 2446265.3037 12.301 1.730 .983 987 +EV AQL 2446266.3013 12.214 1.702 .962 987 +EV AQL 2446267.2795 12.141 1.645 .954 987 +EV AQL 2446268.2721 11.985 1.581 .909 987 +EV AQL 2446269.2648 11.862 1.508 .877 987 +EV AQL 2446270.2775 11.705 1.084 1.422 .857 987 +EV AQL 2446271.2596 11.628 1.058 1.410 .833 987 +EV AQL 2446272.2330 11.564 1.025 1.347 .818 987 +EV AQL 2446273.3033 11.585 1.070 1.374 .816 987 +EV AQL 2446274.2700 11.557 .991 1.351 .831 987 +EV AQL 2446275.2888 11.566 1.034 1.408 .823 987 +EV AQL 2446278.3088 11.598 1.547 .837 987 +EV AQL 2446279.2614 11.651 1.521 .858 987 +EV AQL 2446280.2606 11.709 1.510 .889 987 +EV AQL 2446283.2112 11.816 1.263 1.625 .916 987 +EV AQL 2446284.1869 11.823 1.648 .920 987 +EV AQL 2446285.2043 11.877 1.638 .943 987 +EV AQL 2446286.2002 11.904 1.689 .943 987 +EV AQL 2446287.1929 11.906 1.715 .932 987 +EV AQL 2446288.2048 11.958 1.710 .950 987 +EV AQL 2446289.2184 11.958 1.729 .947 987 +EV AQL 2446290.2006 11.998 1.748 .953 987 +EV AQL 2446291.1885 12.027 1.760 .982 987 +EV AQL 2446292.1909 12.059 1.754 .970 987 +EV AQL 2446293.2241 12.087 1.761 .990 987 +EV AQL 2446294.1883 12.113 1.748 .985 987 +EV AQL 2446295.1670 12.126 1.797 .976 987 +EV AQL 2446296.1749 12.186 1.795 .984 987 +EV AQL 2446297.1807 12.189 1.782 .992 987 +EV AQL 2446298.2019 12.268 1.775 1.012 987 +EV AQL 2446299.1681 12.245 1.782 .992 987 +EV AQL 2446300.1673 12.300 1.769 .987 987 +EV AQL 2446301.1789 12.304 1.735 .996 987 +EV AQL 2446302.1763 12.305 1.778 .975 987 +EV AQL 2446303.1568 12.277 1.745 .976 987 +EV AQL 2446304.1456 12.262 1.714 .956 987 +EV AQL 2446606.2889 12.208 1.799 1.000 988 +EV AQL 2446607.3589 12.258 1.765 .991 988 +EV AQL 2446608.2992 12.279 1.771 .998 988 +EV AQL 2446609.3461 12.295 1.765 1.001 988 +EV AQL 2446610.3316 12.292 1.745 .991 988 +EV AQL 2446611.3203 12.294 1.761 .979 988 +EV AQL 2446612.3196 12.267 1.704 .982 988 +EV AQL 2446613.3093 12.196 1.438 1.671 .955 988 +EV AQL 2446614.3236 12.123 1.608 .947 988 +EV AQL 2446615.2943 11.997 1.575 .902 988 +EV AQL 2446616.3050 11.883 1.500 .893 988 +EV AQL 2446617.3021 11.747 1.451 .842 988 +EV AQL 2446618.3022 11.660 1.388 .836 988 +EV AQL 2446619.3031 11.585 1.039 1.341 .825 988 +EV AQL 2446620.2802 11.565 1.061 1.386 .805 988 +EV AQL 2446621.2744 11.574 1.032 1.406 .824 988 +EV AQL 2446622.2809 11.577 1.055 1.421 .813 988 +EV AQL 2446623.3197 11.616 1.444 .842 988 +EV AQL 2446624.3216 11.621 1.458 .840 988 +EV AQL 2446625.2605 11.675 1.448 .880 988 +EV AQL 2446626.2729 11.679 1.197 1.484 .876 988 +EV AQL 2446627.2563 11.701 1.199 1.545 .883 988 +EV AQL 2446628.2614 11.744 1.532 .901 988 +EV AQL 2446629.2642 11.790 1.545 .927 988 +EV AQL 2446631.2363 11.836 1.611 .926 988 +EV AQL 2446632.2655 11.874 1.668 .941 988 +EV AQL 2446635.2425 11.936 1.713 .937 988 +EV AQL 2446636.2708 11.997 1.710 .959 988 +EV AQL 2446638.2811 12.015 1.768 .956 988 +EV AQL 2446992.3374 12.252 1.801 1.005 989 +EV AQL 2446994.3973 12.295 1.784 1.006 989 +EV AQL 2446995.3461 12.291 1.803 .994 989 +EV AQL 2446996.2801 12.292 1.802 .996 989 +EV AQL 2446997.2806 12.264 1.815 .967 989 +EV AQL 2446998.3026 12.248 1.752 .989 989 +EV AQL 2446999.2699 12.210 1.705 .951 989 +EV AQL 2447000.3153 12.083 1.637 .924 989 +EV AQL 2447001.2829 12.000 1.546 .915 989 +EV AQL 2447002.2831 11.848 1.494 .865 989 +EV AQL 2447003.2658 11.684 .835 989 +EV AQL 2447082.1601 11.556 1.348 .811 989 +EV AQL 2447083.1307 11.567 1.326 .802 989 +EV AQL 2447084.1198 11.553 1.366 .809 989 +EV AQL 2447087.1700 1.415 .840 989 +EV AQL 2447088.0908 11.634 1.452 .851 989 +EV AQL 2447091.0874 11.729 1.535 .901 989 +EV AQL 2447098.0702 11.964 1.713 .962 989 +EV AQL 2447399.2510 11.747 1.509 .901 990 +EV AQL 2447400.2348 11.769 1.561 .889 990 +EV AQL 2447401.2224 11.770 1.583 .894 990 +EV AQL 2447402.2066 11.806 1.631 .891 990 +EV AQL 2447403.1960 11.892 1.580 .918 990 +EV AQL 2447404.1932 11.892 1.610 .919 990 +EV AQL 2447407.2081 11.989 1.701 .965 990 +EV AQL 2447408.1770 11.988 1.706 .950 990 +EV AQL 2447409.1937 12.022 1.704 .975 990 +EV AQL 2447410.1987 12.058 1.771 .961 990 +EV AQL 2447411.2196 12.099 1.745 .972 990 +EV AQL 2447412.2652 12.165 1.771 .983 990 +EV AQL 2447413.1843 12.126 1.766 .970 990 +EV AQL 2447414.1692 12.176 1.775 .975 990 +EV AQL 2447415.1728 12.208 1.791 .987 990 +EV AQL 2447416.1780 12.255 1.788 .988 990 +EV AQL 2447417.1621 12.237 1.818 .969 990 +EV AQL 2447418.1637 12.280 1.783 .978 990 +EV AQL 2447419.1531 12.248 1.784 .958 990 +EV AQL 2447420.1581 12.308 1.787 .960 990 +EV AQL 2447421.1507 12.299 1.746 .973 990 +EV AQL 2447422.1543 12.312 1.765 .959 990 +EV AQL 2447423.1436 12.239 1.705 .953 990 +EV AQL 2447424.1464 12.179 1.648 .942 990 +EV AQL 2447425.1520 12.023 .905 990 +EV AQL 2447427.1585 11.802 1.448 .839 990 +EV AQL 2447428.1432 11.662 1.399 .811 990 +EV AQL 2447429.1408 11.612 1.399 .817 990 +EV AQL 2447430.1282 11.560 1.364 .807 990 +EV AQL 2447431.1291 11.567 1.381 .794 990 +EV AQL 2447432.1239 11.561 1.399 .795 990 +EV AQL 2447433.1296 11.606 1.425 .838 990 +EV AQL 2447434.1347 11.611 1.433 .815 990 +EV AQL 2447734.2811 12.067 1.641 .907 991 +EV AQL 2447735.3387 11.933 1.542 .880 991 +EV AQL 2447736.3132 11.792 1.449 .855 991 +EV AQL 2447737.3051 11.654 1.441 .833 991 +EV AQL 2447738.3079 11.614 1.352 .815 991 +EV AQL 2447739.2803 11.559 1.371 .815 991 +EV AQL 2447740.3036 1.333 .803 991 +EV AQL 2447741.2824 11.544 1.355 .820 991 +EV AQL 2447742.2936 11.547 1.394 .831 991 +EV AQL 2447743.2804 11.566 1.407 .833 991 +EV AQL 2447744.2583 11.625 1.457 .849 991 +EV AQL 2447745.2648 11.639 1.481 .853 991 +EV AQL 2447746.2693 11.681 1.520 .868 991 +EV AQL 2447748.2647 11.712 1.570 .892 991 +EV AQL 2447749.2551 11.757 1.579 .896 991 +EV AQL 2447750.2464 11.783 1.610 .910 991 +EV AQL 2447751.2408 11.814 1.648 .926 991 +EV AQL 2447752.2200 11.839 1.650 .914 991 +EV AQL 2447753.2069 11.901 1.670 .956 991 +EV AQL 2447754.2479 11.925 1.719 .927 991 +EV AQL 2447755.2436 11.946 1.693 .966 991 +EV AQL 2447756.2650 11.974 1.691 .963 991 +EV AQL 2447757.2341 12.026 1.724 .995 991 +EV AQL 2447758.2430 12.043 1.722 .980 991 +EV AQL 2447759.2184 12.068 1.751 .969 991 +EV AQL 2447760.2376 12.103 1.767 .978 991 +EV AQL 2447761.2208 12.135 1.814 .947 991 +EV AQL 2447762.2005 12.171 1.766 .962 991 +EV AQL 2447763.1846 12.171 1.757 .994 991 +EV AQL 2447764.1886 12.216 1.774 .980 991 +EV AQL 2447766.1893 12.281 1.788 .997 991 +EV AQL 2447767.2301 12.268 1.753 .978 991 +EV AQL 2447768.2283 1.787 .993 991 +EV AQL 2447770.2147 12.273 1.750 .975 991 +EV AQL 2447771.2116 12.239 1.683 .975 991 +EV AQL 2447772.2077 12.180 1.640 .960 991 +EV AQL 2447773.2336 12.050 1.589 .937 991 +EV AQL 2447774.2388 11.918 1.516 .897 991 +EV AQL 2447775.2033 11.769 1.489 .835 991 +EV AQL 2447776.2033 11.635 1.388 .839 991 +EV AQL 2448503.2240 12.298 1.771 .982 993 +EV AQL 2448504.1902 12.309 1.772 .985 993 +EV AQL 2448505.1812 12.300 1.773 .983 993 +EV AQL 2448506.2337 12.254 1.740 .960 993 +EV AQL 2448507.2111 12.218 1.697 .950 993 +EV AQL 2448508.1894 12.145 1.657 .922 993 +EV AQL 2448509.1971 12.030 1.593 .894 993 +EV AQL 2448510.1911 11.871 1.533 .882 993 +EV AQL 2448511.1970 11.763 1.479 .851 993 +EV AQL 2448512.2032 11.613 1.409 .793 993 +EV AQL 2448513.2099 11.633 1.305 .852 993 +EV AQL 2448514.2114 11.557 1.387 .791 993 +EV AQL 2448515.2083 11.603 1.322 .823 993 +EV AQL 2448516.2021 11.594 1.403 .842 993 +EV AQL 2448517.1899 11.612 1.429 .817 993 +EV AQL 2448518.2016 11.633 1.439 .828 993 +EV AQL 2448519.2235 11.628 1.477 .836 993 +EV AQL 2448520.1900 11.672 1.493 .841 993 +EV AQL 2448521.2149 11.685 1.560 .858 993 +EV AQL 2448522.1991 11.731 1.551 .864 993 +EV AQL 2448523.1936 11.781 1.564 .893 993 +EV AQL 2448854.2683 12.253 1.751 .997 994 +EV AQL 2448856.2509 12.116 1.623 .942 994 +EV AQL 2448858.2630 11.822 1.520 .865 994 +EV AQL 2448860.2486 11.596 1.429 .822 994 +EV AQL 2448862.2784 11.547 1.382 .793 994 +EV AQL 2448870.2326 11.732 1.572 .884 994 +EV AQL 2448872.2362 11.769 1.657 .908 994 +EV AQL 2448874.2299 11.855 1.703 .927 994 +EV AQL 2448876.1860 11.928 1.742 .935 994 +EV AQL 2448877.2038 11.935 1.774 .942 994 +EV AQL 2448878.2169 11.993 1.750 .970 994 +EV AQL 2448880.1591 12.050 1.757 .971 994 +EV AQL 2448881.1524 12.059 1.775 .963 994 +EV AQL 2448882.1638 12.100 1.781 .970 994 +EV AQL 2448883.1689 12.113 1.808 .978 994 +EV AQL 2448884.1750 12.165 1.787 .987 994 +EV AQL 2448885.1633 12.197 1.783 1.003 994 +EV AQL 2448886.1639 12.236 1.786 .997 994 +EV AQL 2448888.1677 12.289 1.792 1.013 994 +EV AQL 2448889.1790 12.300 1.840 .992 994 +EV AQL 2448890.1565 12.302 1.810 .991 994 +EV AQL 2448891.1554 12.305 1.784 .985 994 +EV AQL 2448892.1704 12.276 1.744 .969 994 +EV AQL 2448893.1583 12.253 1.747 .964 994 +EV AQL 2448894.1882 12.201 1.668 .937 994 +EV AQL 2449521.8569 11.612 996 +EV AQL 2449522.7479 11.569 1.398 996 +EV AQL 2449528.8070 11.749 1.611 .875 .839 996 +EV AQL 2449529.7341 11.810 1.588 .906 .849 996 +EV AQL 2449530.7346 11.779 1.632 .847 .868 996 +EV AQL 2449530.7385 11.852 1.673 .886 .852 996 +EV AQL 2449543.6977 12.185 1.785 .938 .888 996 +EV AQL 2449545.6766 12.342 1.392 1.814 .986 .892 996 +EV AQL 2449561.8111 11.610 1.406 .766 996 +EV AQL 2449563.7360 11.695 1.465 .871 .823 996 +EV AQL 2449564.7181 11.725 1.469 .871 .840 996 +EV AQL 2449617.1234 12.774 1.734 .946 995 +EV AQL 2449620.2216 12.283 1.835 .997 995 +EV AQL 2449621.2232 12.346 1.827 .991 995 +EV AQL 2449623.1899 12.316 1.805 .980 995 +EV AQL 2449624.2049 12.323 1.864 .982 995 +EV AQL 2449625.2027 12.354 1.846 .983 995 +EV AQL 2449626.2226 12.313 1.776 .967 995 +EV AQL 2449631.1894 11.939 1.540 .905 995 +EV AQL 2449632.2095 11.725 1.443 .803 995 +EV AQL 2449633.1872 11.670 1.370 .832 995 +EV AQL 2449634.1899 11.560 1.407 .789 995 +EV AQL 2449933.3509 12.294 1.813 1.082 1.906 998 +EV AQL 2449934.3228 12.295 1.815 .968 1.896 998 +EV AQL 2449935.3310 12.281 1.003 1.893 998 +EV AQL 2449936.2957 12.349 1.034 1.938 998 +EV AQL 2449937.2788 12.298 1.747 .973 998 +EV AQL 2449938.3052 12.277 .981 1.904 998 +EV AQL 2449939.2951 12.212 1.882 998 +EV AQL 2449941.2936 1.797 998 +EV AQL 2449942.2679 11.871 1.688 998 +EV AQL 2449943.2567 11.734 1.667 998 +EV AQL 2449944.2674 11.648 1.614 998 +EV AQL 2449945.2624 11.608 1.606 998 +EV AQL 2449946.2557 11.578 1.601 998 +EV AQL 2449947.2375 11.575 1.584 998 +EV AQL 2449948.2339 11.619 1.627 998 +EV AQL 2449949.2368 11.673 1.661 998 +EV AQL 2449950.2348 11.634 1.641 998 +EV AQL 2449952.2387 11.695 1.675 998 +EV AQL 2449953.2962 11.758 1.717 998 +EV AQL 2449954.2371 11.742 1.731 998 +EV AQL 2449955.2338 11.795 1.739 998 +EV AQL 2449956.2858 11.808 1.783 998 +EV AQL 2449957.2180 11.894 1.842 998 +EV AQL 2449958.1935 11.899 1.818 998 +EV AQL 2449959.2266 11.935 1.872 998 +EV AQL 2449960.2411 11.952 1.727 .972 1.882 998 +EV AQL 2449962.2034 12.061 1.824 998 +EV AQL 2449963.3635 12.044 1.836 998 +EV AQL 2449985.2359 11.513 1.368 .839 1.592 998 +EV AQL 2449986.1460 11.546 1.577 998 +EV AQL 2449987.1811 11.598 1.595 998 +EV AQL 2449992.1289 11.679 1.718 998 +EV AQL 2449993.1578 11.847 1.719 998 +EV AQL 2450009.1529 12.232 1.930 998 +EV AQL 2450011.1336 12.273 1.903 998 +EV AQL 2450017.0991 12.177 1.822 998 +EV AQL 2450018.1560 12.029 1.740 998 +EV AQL 2450020.1162 11.758 1.643 998 +EV AQL 2450305.2149 11.818 1.608 .908 971 +EV AQL 2450306.2683 11.914 1.657 .958 971 +EV AQL 2450306.2692 11.859 1.638 .951 971 +EV AQL 2450307.2489 11.894 1.668 .933 971 +EV AQL 2450310.2551 11.971 1.723 .951 971 +EV AQL 2450311.1818 12.012 1.726 .960 971 +EV AQL 2450312.1816 12.028 1.723 .961 971 +EV AQL 2450313.1969 12.067 1.690 .979 971 +EV AQL 2450314.1780 12.095 1.725 .984 971 +EV AQL 2450315.1862 12.102 1.754 .966 971 +EV AQL 2450316.1952 12.149 1.740 .947 971 +EV AQL 2450317.1951 12.187 1.795 .963 971 +EV AQL 2450318.1846 12.232 1.748 .993 971 +EV AQL 2450319.1801 12.239 1.728 .982 971 +EV AQL 2450320.1963 12.266 1.766 .972 971 +EV AQL 2450321.1773 12.281 1.795 .969 971 +EV AQL 2450322.1755 12.290 1.783 .975 971 +EV AQL 2450323.1731 12.315 1.728 .982 971 +EV AQL 2450324.1926 12.287 1.715 .977 971 +EV AQL 2450325.1651 12.260 1.768 .963 971 +EV AQL 2450326.1502 12.231 1.605 .955 971 +EV AQL 2450327.2123 12.099 1.687 971 +EV AQL 2450328.3479 12.003 1.514 .891 971 +EV AQL 2450329.1756 12.086 1.492 .877 971 +EV AQL 2450330.1688 11.780 1.425 .857 971 +EV AQL 2450332.1649 11.551 1.369 .763 971 +EV AQL 2450333.1648 11.539 1.385 .799 971 +EV AQL 2450334.1793 11.570 1.385 .808 971 +EV AQL 2450335.1726 11.601 1.415 .824 971 +EV AQL 2450336.1790 11.594 1.426 .845 971 +EV AQL 2450337.1648 11.633 1.458 .857 971 +EV AQL 2450338.2227 11.655 1.474 .857 971 +EV AQL 2450340.1526 11.706 1.555 .887 971 +EV AQL 2450341.1605 11.738 1.564 .891 971 +EV AQL 2450342.1655 11.747 1.582 .907 971 +EV AQL 2450344.1769 11.808 1.643 .870 971 +EV AQL 2450347.1696 11.935 1.665 .970 971 +EV AQL 2450349.1559 11.977 1.682 .954 971 +EV AQL 2450357.1409 12.165 1.805 .952 971 +FF AQL 2448854.1922 5.347 .372 .795 .466 994 +FF AQL 2448858.1667 5.459 .413 .835 .479 994 +FF AQL 2448860.1567 5.304 .313 .828 .459 994 +FF AQL 2448870.1401 5.489 .416 .869 .490 994 +FF AQL 2448872.1416 5.386 .327 .817 .472 994 +FF AQL 2448874.1386 5.426 .343 .848 .476 994 +FF AQL 2448876.1331 5.486 .374 .842 .487 994 +FF AQL 2448877.1282 5.294 .256 .778 .449 994 +FF AQL 2448878.2094 5.357 .335 .822 .456 994 +FF AQL 2448880.1261 5.529 .410 .889 .491 994 +FF AQL 2448881.1161 5.362 .331 .794 .462 994 +FF AQL 2448882.1186 5.282 .262 .801 .447 994 +FF AQL 2448883.1165 5.436 .360 .852 .490 994 +FF AQL 2448884.1144 5.560 .392 .855 .487 994 +FF AQL 2448885.1111 5.469 .382 .842 .481 994 +FF AQL 2448886.1121 5.268 .248 .784 .445 994 +FF AQL 2448888.1096 5.508 .392 .883 .500 994 +FF AQL 2448889.1104 5.520 .399 .901 .483 994 +FF AQL 2448890.1067 5.376 .311 .799 .478 994 +FF AQL 2448891.1042 5.289 .254 .792 .463 994 +FF AQL 2448892.1030 5.439 .348 .843 .477 994 +FF AQL 2448893.1029 5.546 .418 .893 .499 994 +FF AQL 2448894.1019 5.437 .332 .834 .486 994 +FF AQL 2449521.8543 5.331 .691 996 +FF AQL 2449522.7458 5.459 .785 996 +FF AQL 2449529.6937 5.191 .675 .429 .388 996 +FF AQL 2449530.7308 5.245 .731 .466 .402 996 +FF AQL 2449534.7487 5.133 .690 .453 .398 996 +FF AQL 2449543.6902 5.106 .658 .444 .374 996 +FF AQL 2449545.6720 5.521 .791 .498 .441 996 +FF AQL 2449617.1111 5.435 .586 .854 .492 995 +FF AQL 2449619.2067 5.168 .529 .709 .405 995 +FF AQL 2449621.0996 5.403 .557 .810 .469 995 +FF AQL 2449621.1914 5.393 .523 .861 .464 995 +FF AQL 2449622.0980 .854 .476 995 +FF AQL 2449623.0926 5.326 .529 .758 .440 995 +FF AQL 2449624.0954 5.106 .494 .696 .396 995 +FF AQL 2449625.0956 5.322 .519 .796 .441 995 +FF AQL 2449631.0943 5.479 .537 .850 .480 995 +FF AQL 2449632.1031 5.276 .512 .738 .434 995 +FF AQL 2449634.0989 5.360 .539 .811 .456 995 +FF AQL 2449809.9069 5.525 .842 .464 .464 997 +FF AQL 2449810.9064 5.358 .757 .424 .306 997 +FF AQL 2449811.8686 5.184 .677 .403 .392 997 +FF AQL 2449813.9032 5.466 .846 .438 .478 997 +FF AQL 2449814.8870 5.478 .778 .465 .436 997 +FF AQL 2449817.9059 5.456 .820 .474 .445 997 +FF AQL 2449818.8972 5.500 .823 .469 997 +FF AQL 2449821.9010 5.361 .782 .439 .437 997 +FF AQL 2449822.9093 5.483 .834 .469 .481 997 +FF AQL 2449823.8851 5.460 .797 .470 .436 997 +FF AQL 2449825.8769 5.290 .731 .420 .432 997 +FF AQL 2449934.1685 5.502 .850 .491 .925 998 +FF AQL 2449935.1647 5.555 .826 .486 .916 998 +FF AQL 2449936.1586 5.352 .773 .435 .839 998 +FF AQL 2449937.2231 5.203 .729 .414 998 +FF AQL 2449938.1600 5.396 .816 .457 .863 998 +FF AQL 2449939.1641 5.494 .864 .478 .920 998 +FF AQL 2449942.1587 5.269 .760 .422 .812 998 +FF AQL 2449943.1522 .849 .466 .882 998 +FF AQL 2449944.1523 5.514 .852 .462 .900 998 +FF AQL 2449945.1533 5.315 .765 .428 .825 998 +FF AQL 2449946.1533 5.177 .715 .397 .771 998 +FF AQL 2449947.1520 5.404 .821 .461 .869 998 +FF AQL 2449948.1481 5.511 .869 .477 .910 998 +FF AQL 2449949.1545 5.409 .803 .448 .867 998 +FF AQL 2449950.1504 5.204 .713 .406 .783 998 +FF AQL 2449952.1504 5.479 .837 .472 .918 998 +FF AQL 2449953.1748 5.506 .831 .466 .898 998 +FF AQL 2449954.1623 5.335 .757 .451 .823 998 +FF AQL 2449955.1550 5.239 .725 .413 .798 998 +FF AQL 2449958.1411 5.453 .801 .456 .884 998 +FF AQL 2449959.1364 5.228 .693 .415 .829 998 +FF AQL 2449962.1494 5.495 .843 .480 .922 998 +FF AQL 2450009.0926 5.236 .747 .415 .795 998 +FF AQL 2450011.0895 5.481 .847 .472 .887 998 +FF AQL 2450012.1299 5.322 .830 998 +FF AQL 2450017.0768 5.194 .708 .405 .786 998 +FF AQL 2450018.1220 5.287 .761 .439 .850 998 +FF AQL 2450020.0698 5.471 .849 .471 .910 998 +FF AQL 2450305.1736 5.429 .805 .487 971 +FF AQL 2450306.1648 5.507 .837 .502 971 +FF AQL 2450307.1842 5.357 .776 .442 971 +FF AQL 2450310.2270 5.478 .850 .480 971 +FF AQL 2450311.1578 5.429 .824 .449 971 +FF AQL 2450312.1524 5.199 .731 .406 971 +FF AQL 2450313.1585 5.265 .752 .434 971 +FF AQL 2450314.1432 5.439 .831 .467 971 +FF AQL 2450315.1445 5.497 .848 .484 971 +FF AQL 2450316.1424 5.349 .784 .430 971 +FF AQL 2450317.1709 5.187 .685 971 +FF AQL 2450318.1421 5.359 .770 .455 971 +FF AQL 2450319.1403 5.488 .816 .485 971 +FF AQL 2450320.1414 5.434 .793 .476 971 +FF AQL 2450321.1364 5.208 .700 .422 971 +FF AQL 2450322.1312 5.285 .733 .459 971 +FF AQL 2450323.1315 5.433 .799 .499 971 +FF AQL 2450324.1367 5.498 .823 .484 971 +FF AQL 2450325.1278 5.366 .750 .444 971 +FF AQL 2450326.1242 5.187 .704 .400 971 +FF AQL 2450327.1844 5.350 .816 971 +FF AQL 2450328.3433 5.499 .819 .491 971 +FF AQL 2450329.2084 .766 .456 971 +FF AQL 2450330.2291 5.225 .711 .416 971 +FF AQL 2450332.2212 5.447 .815 971 +FF AQL 2450333.2023 5.479 .832 .462 971 +FF AQL 2450334.2200 5.295 .735 .421 971 +FF AQL 2450335.2278 5.213 .714 .422 971 +FF AQL 2450336.2270 5.381 .815 .481 971 +FF AQL 2450337.1903 5.489 .857 .488 971 +FF AQL 2450340.1783 5.288 .782 .442 971 +FF AQL 2450341.2007 5.454 .830 .450 971 +FF AQL 2450342.2149 5.462 .837 .475 971 +FF AQL 2450344.2473 5.226 .716 971 +FF AQL 2450347.2581 5.402 .791 .471 971 +FF AQL 2450349.2025 5.315 .787 .445 971 +FM AQL 2446606.2956 8.337 1.028 1.411 .797 988 +FM AQL 2446607.3625 8.565 1.135 1.473 .835 988 +FM AQL 2446608.3025 8.608 1.093 1.461 .830 988 +FM AQL 2446609.3491 8.098 .804 1.199 .716 988 +FM AQL 2446610.3358 7.978 .827 1.185 .702 988 +FM AQL 2446611.3238 8.196 .939 1.324 .765 988 +FM AQL 2446612.3231 8.354 1.018 1.400 .804 988 +FM AQL 2446613.3169 8.542 1.096 1.458 .828 988 +FM AQL 2446614.3265 8.630 1.109 1.467 .837 988 +FM AQL 2446615.2967 8.205 .829 1.234 .751 988 +FM AQL 2446616.3076 7.960 .804 1.179 .699 988 +FM AQL 2446617.3043 8.165 .927 1.313 .757 988 +FM AQL 2446618.3039 8.349 1.017 1.382 .813 988 +FM AQL 2446619.3086 8.528 1.088 1.451 .829 988 +FM AQL 2446620.2847 8.639 1.138 1.477 .834 988 +FM AQL 2446621.2800 8.268 .872 1.279 .754 988 +FM AQL 2446622.2868 7.924 .799 1.153 .688 988 +FM AQL 2446623.3212 8.133 .915 1.289 .758 988 +FM AQL 2446624.3228 8.321 1.380 .793 988 +FM AQL 2446625.2629 8.508 1.097 1.436 .836 988 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2448517.1923 7.957 1.151 .669 993 +FM AQL 2448518.2059 8.035 1.230 .718 993 +FM AQL 2448519.2243 8.229 1.351 .775 993 +FM AQL 2448520.1943 8.398 1.413 .795 993 +FM AQL 2448521.2197 8.548 1.486 .806 993 +FM AQL 2448522.2034 8.587 1.419 .813 993 +FM AQL 2448523.1982 8.041 1.178 .697 993 +FM AQL 2448854.2718 7.971 1.204 .709 994 +FM AQL 2448856.2533 8.356 1.412 .796 994 +FM AQL 2448858.2657 8.623 1.481 .837 994 +FM AQL 2448860.2510 7.946 1.197 .694 994 +FM AQL 2448862.2801 8.314 1.426 .783 994 +FM AQL 2448870.2369 8.635 1.524 .836 994 +FM AQL 2448872.2392 7.896 1.162 .678 994 +FM AQL 2448874.2328 8.297 1.396 .788 994 +FM AQL 2448876.2240 8.643 1.517 .830 994 +FM AQL 2448877.2072 8.412 1.372 .775 994 +FM AQL 2448878.2220 7.903 1.156 .678 994 +FM AQL 2448880.1609 8.274 1.395 .781 994 +FM AQL 2448881.1569 8.432 1.444 .811 994 +FM AQL 2448882.1651 8.610 1.524 .836 994 +FM AQL 2448883.1733 8.488 1.408 .783 994 +FM AQL 2448884.1788 7.909 1.155 .682 994 +FM AQL 2448885.1669 8.058 1.246 .725 994 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2449626.2276 8.292 1.358 .767 995 +FM AQL 2449631.1938 8.065 1.255 .726 995 +FM AQL 2449632.2118 8.279 1.337 .778 995 +FM AQL 2449633.1914 8.441 1.433 .809 995 +FM AQL 2449634.1929 8.606 1.502 .821 995 +FM AQL 2449934.3295 8.691 1.476 .887 1.647 998 +FM AQL 2449935.3364 8.197 .733 1.439 998 +FM AQL 2449936.3157 7.981 .712 1.376 998 +FM AQL 2449937.2878 8.157 1.321 .768 998 +FM AQL 2449938.3140 8.369 .821 1.580 998 +FM AQL 2449939.3046 8.563 1.645 998 +FM AQL 2449941.2990 8.296 1.474 998 +FM AQL 2449942.2729 7.986 1.371 998 +FM AQL 2449943.2602 8.152 1.488 998 +FM AQL 2449944.2715 8.340 1.546 998 +FM AQL 2449945.2658 8.518 1.617 998 +FM AQL 2449946.2403 8.654 1.659 998 +FM AQL 2449947.2353 8.421 1.534 998 +FM AQL 2449948.2314 7.935 1.344 998 +FM AQL 2449949.2339 8.148 1.461 998 +FM AQL 2449950.2321 8.293 1.555 998 +FM AQL 2449952.2361 8.631 1.636 998 +FM AQL 2449953.2616 8.476 1.555 998 +FM AQL 2449954.2348 7.929 1.338 998 +FM AQL 2449955.2318 8.091 1.443 998 +FM AQL 2450305.2704 8.339 1.387 .798 971 +FM AQL 2450306.3004 8.539 1.432 .847 971 +FM AQL 2450307.2523 8.658 1.414 .850 971 +FM AQL 2450310.2595 8.152 1.273 .759 971 +FM AQL 2450311.1864 8.320 1.364 .795 971 +FM AQL 2450312.1869 8.497 1.416 .835 971 +FM AQL 2450313.2093 8.637 1.439 .836 971 +FM AQL 2450314.1832 8.340 1.294 .769 971 +FM AQL 2450315.1900 7.915 1.129 .680 971 +FM AQL 2450316.2017 8.128 1.246 .725 971 +FM AQL 2450317.2086 8.303 1.339 .798 971 +FM AQL 2450318.1945 8.503 1.411 .824 971 +FM AQL 2450319.1886 8.628 1.460 .828 971 +FM AQL 2450320.2010 8.400 1.312 .779 971 +FM AQL 2450321.1824 7.908 1.119 .680 971 +FM AQL 2450322.1819 8.102 1.222 .743 971 +FM AQL 2450323.1802 8.282 1.339 .793 971 +FM AQL 2450324.2020 8.469 1.405 .822 971 +FM AQL 2450325.1716 8.614 1.445 .835 971 +FM AQL 2450326.1577 8.508 1.345 .805 971 +FN AQL 2446606.2997 8.416 .836 1.227 .714 988 +FN AQL 2446607.3668 8.296 .815 1.173 .685 988 +FN AQL 2446608.3058 8.113 .783 1.099 .660 988 +FN AQL 2446609.3516 8.150 .807 1.171 .688 988 +FN AQL 2446610.3379 8.262 .909 1.241 .718 988 +FN AQL 2446611.3265 8.414 1.013 1.328 .753 988 +FN AQL 2446612.3248 8.581 1.066 1.386 .786 988 +FN AQL 2446613.3197 8.682 1.097 1.389 .790 988 +FN AQL 2446614.3281 8.636 1.016 1.344 .775 988 +FN AQL 2446615.2985 8.469 .860 1.260 .730 988 +FN AQL 2446616.3096 8.353 .813 1.199 .695 988 +FN AQL 2446617.3062 8.199 .788 1.135 .668 988 +FN AQL 2446618.3057 8.150 .783 1.131 .679 988 +FN AQL 2446619.3105 8.210 .834 1.199 .707 988 +FN AQL 2446620.2863 8.340 .960 1.275 .739 988 +FN AQL 2446621.2819 8.499 1.054 1.338 .775 988 +FN AQL 2446622.2886 8.619 1.088 1.401 .787 988 +FN AQL 2446623.3234 8.677 1.080 1.382 .783 988 +FN AQL 2446624.3243 8.541 1.294 .750 988 +FN AQL 2446625.2650 8.436 .859 1.220 .720 988 +FN AQL 2446626.2838 8.280 .810 1.171 .683 988 +FN AQL 2446627.2661 8.120 .755 1.110 .664 988 +FN AQL 2446628.2671 8.142 .817 1.161 .678 988 +FN AQL 2446629.2698 8.288 .880 1.238 .727 988 +FN AQL 2446631.2424 8.573 1.072 1.372 .785 988 +FN AQL 2446632.2734 8.676 1.097 1.409 .794 988 +FN AQL 2446635.2492 8.346 .839 1.187 .697 988 +FN AQL 2446636.2820 8.211 .795 1.125 .671 988 +FN AQL 2447409.2006 8.610 1.396 .774 990 +FN AQL 2447410.2187 8.690 1.045 1.378 .776 990 +FN AQL 2447411.2292 8.553 .854 1.300 .744 990 +FN AQL 2447413.1899 8.292 .726 1.190 .679 990 +FN AQL 2447414.1780 8.105 .657 1.107 .647 990 +FN AQL 2447415.1792 8.124 .698 1.184 .663 990 +FN AQL 2447416.1848 8.225 .798 1.251 .699 990 +FN AQL 2447417.1782 8.362 .906 1.330 .730 990 +FN AQL 2447418.1812 8.543 1.037 1.384 .760 990 +FN AQL 2447419.1587 8.638 1.051 1.409 .767 990 +FN AQL 2447420.1637 8.642 1.367 .755 990 +FN AQL 2447421.1560 8.453 .874 1.267 .709 990 +FN AQL 2447422.1609 8.343 1.199 .669 990 +FN AQL 2447423.1502 8.205 .725 1.157 .657 990 +FN AQL 2447424.1566 8.145 .715 1.157 .673 990 +FN AQL 2447425.1620 8.172 .767 1.197 .677 990 +FN AQL 2447427.1700 8.480 .984 1.358 .743 990 +FN AQL 2447428.1532 8.604 1.046 1.417 .761 990 +FN AQL 2447429.1497 8.683 1.015 1.422 .773 990 +FN AQL 2447430.1342 8.551 .818 1.317 .732 990 +FN AQL 2447431.1376 8.428 .777 1.235 .703 990 +FN AQL 2447432.1306 8.296 .741 1.187 .674 990 +FN AQL 2447433.1363 8.122 .671 1.125 .642 990 +FN AQL 2447434.1400 8.126 .702 1.165 .660 990 +FN AQL 2447734.2929 8.450 .777 1.251 .720 991 +FN AQL 2447735.3445 8.293 1.179 .676 991 +FN AQL 2447736.3174 8.148 1.092 .669 991 +FN AQL 2447737.3215 8.100 .734 1.152 .661 991 +FN AQL 2447738.3118 8.221 .790 1.205 .700 991 +FN AQL 2447739.2827 8.355 .866 1.287 .735 991 +FN AQL 2447740.3065 8.432 1.010 1.350 .768 991 +FN AQL 2447741.2851 8.615 1.058 1.400 .779 991 +FN AQL 2447742.2964 8.651 1.018 1.351 .781 991 +FN AQL 2447743.2830 8.465 .836 1.292 .726 991 +FN AQL 2447744.2616 8.405 .748 1.212 .702 991 +FN AQL 2447745.2684 8.286 .728 1.151 .677 991 +FN AQL 2447746.2723 8.101 .677 1.115 .652 991 +FN AQL 2447747.2667 8.114 .720 1.179 .679 991 +FN AQL 2447748.2677 8.273 .794 1.271 .712 991 +FN AQL 2447749.2583 8.413 .966 1.312 .747 991 +FN AQL 2447751.2446 8.668 1.054 1.385 .791 991 +FN AQL 2447752.2309 8.619 .948 1.338 .757 991 +FN AQL 2447753.2259 8.444 .778 1.270 .712 991 +FN AQL 2447754.2532 8.327 .736 1.185 .683 991 +FN AQL 2447755.2477 8.148 .665 1.115 .660 991 +FN AQL 2447756.2857 8.076 .675 1.150 .667 991 +FN AQL 2447757.2520 8.188 .774 1.211 .687 991 +FN AQL 2447758.2483 8.342 .858 1.282 .742 991 +FN AQL 2447759.2232 8.518 .990 1.336 .770 991 +FN AQL 2447760.2418 8.627 1.056 1.420 .775 991 +FN AQL 2447761.2248 8.678 .969 1.366 .778 991 +FN AQL 2447762.2173 8.517 1.264 .735 991 +FN AQL 2447763.1873 8.389 1.199 .712 991 +FN AQL 2447764.1920 8.244 1.150 .675 991 +FN AQL 2447766.1937 8.156 1.152 .704 991 +FN AQL 2447767.2332 8.251 1.246 .709 991 +FN AQL 2447768.2341 8.359 .913 1.330 .740 991 +FN AQL 2447770.2187 8.677 1.046 1.410 .789 991 +FN AQL 2447771.2140 8.593 .915 1.340 .773 991 +FN AQL 2447772.2115 8.441 .773 1.246 .721 991 +FN AQL 2447773.2367 8.322 .732 1.165 .702 991 +FN AQL 2447774.2420 8.140 .675 1.114 .652 991 +FN AQL 2447775.2062 8.129 .666 1.155 .674 991 +FN AQL 2447776.2081 8.199 .769 1.219 .719 991 +FN AQL 2448503.2274 8.327 1.213 .671 993 +FN AQL 2448504.1840 8.191 .693 1.119 .667 993 +FN AQL 2448505.1746 8.143 .692 1.135 .667 993 +FN AQL 2448506.2304 8.201 1.214 .693 993 +FN AQL 2448507.2040 8.337 .888 1.300 .733 993 +FN AQL 2448508.1847 8.485 1.361 .760 993 +FN AQL 2448509.1922 8.637 1.050 1.414 .778 993 +FN AQL 2448510.1860 8.660 .989 1.383 .782 993 +FN AQL 2448511.1930 8.519 .829 1.302 .728 993 +FN AQL 2448512.1971 8.421 .750 1.218 .712 993 +FN AQL 2448513.2070 8.297 .695 1.161 .675 993 +FN AQL 2448514.2086 8.140 .644 1.129 .667 993 +FN AQL 2448515.2028 8.170 1.148 .688 993 +FN AQL 2448516.1978 8.259 1.244 .718 993 +FN AQL 2448517.1849 8.405 .951 1.330 .737 993 +FN AQL 2448518.1973 8.580 1.369 .768 993 +FN AQL 2448519.2158 8.645 1.412 .776 993 +FN AQL 2448520.1866 8.607 1.358 .748 993 +FN AQL 2448521.2089 8.426 1.272 .712 993 +FN AQL 2448522.1949 8.374 1.157 .664 993 +FN AQL 2448523.1892 8.196 1.122 .668 993 +FN AQL 2448856.2479 8.144 1.155 .693 994 +FN AQL 2448858.2623 8.372 1.331 .738 994 +FN AQL 2448860.2461 8.658 1.444 .783 994 +FN AQL 2448862.2746 8.469 1.300 .706 994 +FN AQL 2448870.2303 8.690 1.423 .785 994 +FN AQL 2448872.2314 8.415 1.251 .712 994 +FN AQL 2448874.2259 8.120 1.136 .649 994 +FN AQL 2448876.2221 8.249 .602 1.243 .696 994 +FN AQL 2448877.2000 8.370 1.310 .735 994 +FN AQL 2448878.2144 8.536 1.407 .777 994 +FN AQL 2448880.1530 8.673 1.399 .772 994 +FN AQL 2448881.1494 8.486 1.290 .731 994 +FN AQL 2448882.1595 8.401 1.227 .704 994 +FN AQL 2448883.1676 8.248 1.178 .677 994 +FN AQL 2448884.1735 8.105 1.138 .658 994 +FN AQL 2448885.1617 8.151 1.200 .686 994 +FN AQL 2448886.1616 8.301 1.277 .720 994 +FN AQL 2448888.1650 8.603 1.417 .781 994 +FN AQL 2448889.1759 8.713 1.422 .791 994 +FN AQL 2448890.1537 8.594 1.341 .766 994 +FN AQL 2448891.1525 8.448 1.255 .713 994 +FN AQL 2448892.1637 8.333 1.190 .688 994 +FN AQL 2448893.1549 8.156 1.136 .654 994 +FN AQL 2448894.1852 8.175 1.169 .678 994 +FN AQL 2449521.8824 8.352 1.320 996 +FN AQL 2449522.8209 8.485 1.354 996 +FN AQL 2449528.8623 8.101 1.112 .660 .620 996 +FN AQL 2449529.7591 8.204 1.166 .674 .649 996 +FN AQL 2449530.8082 8.216 .915 1.293 .707 .685 996 +FN AQL 2449534.7952 8.626 1.331 .751 .711 996 +FN AQL 2449543.7979 8.611 1.109 1.379 .772 .733 996 +FN AQL 2449545.7667 8.396 .984 1.193 .699 .672 996 +FN AQL 2449561.8261 8.622 1.384 .767 .735 996 +FN AQL 2449617.1640 8.551 1.213 1.368 .771 995 +FN AQL 2449619.2467 8.703 1.363 .802 995 +FN AQL 2449620.2290 8.592 1.297 .750 995 +FN AQL 2449621.2118 8.462 1.229 .726 995 +FN AQL 2449623.1963 8.160 .762 1.112 .659 995 +FN AQL 2449624.2118 8.140 .870 1.166 .664 995 +FN AQL 2449625.2105 8.227 .885 1.209 .699 995 +FN AQL 2449626.2295 8.376 1.303 .726 995 +FN AQL 2449631.1961 8.394 1.190 .702 995 +FN AQL 2449632.2136 8.273 1.159 .669 995 +FN AQL 2449633.1933 8.127 1.136 .655 995 +FN AQL 2449634.1950 8.167 1.196 .682 995 +FN AQL 2449934.3384 8.485 1.243 .759 1.406 998 +FN AQL 2449935.3423 8.325 .699 1.333 998 +FN AQL 2449936.3195 8.146 .655 1.279 998 +FN AQL 2449937.3004 8.173 1.167 .696 998 +FN AQL 2449938.3256 8.318 .735 1.413 998 +FN AQL 2449939.3273 8.426 .757 1.463 998 +FN AQL 2449941.3070 8.704 1.567 998 +FN AQL 2449942.2789 8.723 1.510 998 +FN AQL 2449943.2646 8.482 1.278 .728 1.409 998 +FN AQL 2449944.2758 8.398 1.353 998 +FN AQL 2449945.2692 8.264 1.329 998 +FN AQL 2449946.2424 8.156 1.323 998 +FN AQL 2449947.2366 8.214 1.380 998 +FN AQL 2449948.2333 8.347 1.431 998 +FN AQL 2449949.2361 8.542 1.506 998 +FN AQL 2449950.2342 8.630 1.529 998 +FN AQL 2449952.2380 8.575 1.460 998 +FN AQL 2449953.2711 8.469 1.408 998 +FN AQL 2449954.2360 8.321 1.325 998 +FN AQL 2449955.2331 8.150 1.286 998 +FN AQL 2450305.2724 8.296 1.186 .689 971 +FN AQL 2450306.3032 8.126 1.108 .678 971 +FN AQL 2450307.2547 8.184 1.141 .691 971 +FN AQL 2450310.2618 8.590 1.357 .789 971 +FN AQL 2450311.1891 8.696 1.381 .791 971 +FN AQL 2450312.1891 8.634 1.317 .778 971 +FN AQL 2450313.2118 8.476 1.219 .724 971 +FN AQL 2450314.1857 8.358 1.175 .703 971 +FN AQL 2450315.1930 8.208 1.109 .674 971 +FN AQL 2450316.2041 8.164 1.119 .651 971 +FN AQL 2450317.2114 8.215 1.182 .706 971 +FN AQL 2450318.1977 8.371 1.263 .738 971 +FN AQL 2450319.1939 8.501 1.329 .765 971 +FN AQL 2450320.2056 8.648 1.363 .783 971 +FN AQL 2450321.2006 8.672 1.346 .791 971 +FN AQL 2450322.1845 8.542 1.277 .745 971 +FN AQL 2450323.1828 8.427 1.211 .709 971 +FN AQL 2450324.2041 8.309 1.147 .691 971 +FN AQL 2450325.1733 8.127 1.099 .650 971 +FN AQL 2450326.1599 8.167 1.144 .688 971 +KL AQL 2447734.3611 10.519 1.140 .626 991 +KL AQL 2447735.3841 991 +KL AQL 2447736.3876 9.801 .770 .438 991 +KL AQL 2447737.3807 9.961 .929 .502 991 +KL AQL 2447738.3618 10.214 1.011 .560 991 +KL AQL 2447739.3366 10.323 1.092 .582 991 +KL AQL 2447740.3688 10.527 1.155 .591 991 +KL AQL 2447741.2615 10.432 1.055 .576 991 +KL AQL 2447742.2489 9.863 .770 .462 991 +KL AQL 2447743.2404 9.917 .535 .881 .461 991 +KL AQL 2447744.2301 10.132 .670 1.022 .552 991 +KL AQL 2447745.2254 10.277 .754 1.089 .581 991 +KL AQL 2447746.2352 10.465 .835 1.161 .606 991 +KL AQL 2447747.2224 10.477 1.102 .594 991 +KL AQL 2447749.2084 9.920 .521 .823 .512 991 +KL AQL 2447750.2122 10.126 .691 .982 .545 991 +KL AQL 2447751.2172 10.262 1.072 .576 991 +KL AQL 2447752.2101 10.429 .856 1.139 .587 991 +KL AQL 2447753.2134 10.525 1.134 .593 991 +KL AQL 2447754.2385 9.981 .487 .856 .485 991 +KL AQL 2447755.2091 9.883 .837 .482 991 +KL AQL 2447756.2313 10.096 .984 .527 991 +KL AQL 2447757.2066 10.227 .759 1.062 .576 991 +KL AQL 2447758.1997 10.405 .811 1.151 .590 991 +KL AQL 2447759.2101 10.519 1.138 .596 991 +KL AQL 2447760.2200 10.075 .889 .498 991 +KL AQL 2447761.2052 9.857 .457 .832 .455 991 +KL AQL 2447762.2048 10.084 .970 .513 991 +KL AQL 2447763.2027 10.228 1.038 .580 991 +KL AQL 2447764.2058 10.417 1.126 .601 991 +KL AQL 2447766.2085 10.148 .912 .531 991 +KL AQL 2447767.2138 9.844 .804 .459 991 +KL AQL 2447768.2119 10.022 .957 .506 991 +KL AQL 2447770.1979 10.395 1.135 .599 991 +KL AQL 2447771.1958 10.538 1.148 .606 991 +KL AQL 2447772.1915 10.186 .957 .519 991 +KL AQL 2447773.1940 9.835 .774 .447 991 +KL AQL 2447774.2245 10.024 .940 .519 991 +KL AQL 2447775.1886 10.212 1.026 .571 991 +KL AQL 2447776.1889 10.360 1.153 .586 991 +KL AQL 2449522.7695 10.344 1.100 996 +KL AQL 2449529.7493 10.515 1.163 .551 .527 996 +KL AQL 2449530.7535 10.509 1.066 .567 .522 996 +KL AQL 2449534.7746 10.325 1.044 .521 .503 996 +KL AQL 2449545.7566 10.105 .946 .509 .458 996 +KL AQL 2449564.7308 10.198 1.014 .544 .494 996 +KL AQL 2449621.2822 10.526 1.146 .590 995 +KL AQL 2449623.2682 9.986 .857 .465 995 +KL AQL 2449624.2438 9.869 .837 .469 995 +KL AQL 2449625.2603 10.106 .952 .529 995 +KL AQL 2449626.2750 10.221 1.050 .553 995 +KL AQL 2449631.2352 10.066 .933 .521 995 +KL AQL 2449632.2577 1.050 .562 995 +KL AQL 2449633.2436 10.388 1.164 .583 995 +KL AQL 2449634.2394 10.558 1.152 .587 995 +KL AQL 2449635.2755 10.107 .890 .493 995 +KL AQL 2450570.6299 9.830 .861 972 +KL AQL 2450573.6094 10.376 1.109 972 +KL AQL 2450575.5927 10.323 1.030 972 +KL AQL 2450575.6437 10.266 1.008 972 +KL AQL 2450576.6291 9.825 .865 972 +KL AQL 2450576.6590 9.827 .875 972 +KL AQL 2450577.6089 10.009 .964 972 +KL AQL 2450577.6534 10.012 .969 972 +KL AQL 2450578.5986 10.200 1.052 972 +KL AQL 2450578.6539 10.204 1.064 972 +KL AQL 2450580.5892 10.514 1.147 972 +KL AQL 2450580.6381 10.547 1.132 972 +KL AQL 2450582.5694 9.837 .847 972 +KL AQL 2450582.6168 9.839 .854 972 +KL AQL 2450582.6632 9.813 .839 972 +KL AQL 2450583.5613 9.983 .940 972 +KL AQL 2450584.5505 10.175 1.052 972 +KL AQL 2450584.6087 10.182 1.048 972 +KL AQL 2450584.6539 10.193 1.057 972 +PP AQL 2445644.2070 .739 950 +PP AQL 2445646.1484 12.807 1.628 .884 950 +PP AQL 2445648.1210 12.924 1.497 .827 950 +PP AQL 2445649.1484 12.879 1.385 .855 950 +PP AQL 2445650.1406 12.528 1.421 950 +PP AQL 2445659.1054 11.441 1.230 .630 950 +PP AQL 2445665.1015 11.840 1.500 .736 950 +PP AQL 2445666.0859 12.015 1.543 .767 950 +PP AQL 2445668.0898 12.357 1.596 .823 950 +PP AQL 2445674.1054 12.483 1.343 .771 950 +PP AQL 2445675.0820 12.300 1.311 .734 950 +PP AQL 2445676.0898 12.169 1.217 .707 950 +PP AQL 2445877.3632 11.555 1.329 .679 950 +PP AQL 2445878.3554 11.607 1.381 .686 950 +PP AQL 2445879.3437 11.719 1.395 .714 950 +PP AQL 2445880.3515 11.773 1.463 .704 950 +PP AQL 2445881.3437 11.934 1.442 .765 950 +PP AQL 2445882.3359 12.052 1.488 .768 950 +PP AQL 2445883.3515 12.198 1.481 .760 950 +PP AQL 2445886.3593 12.572 1.486 .805 950 +PP AQL 2445887.3632 12.656 1.462 .795 950 +V336 AQL 2450305.2835 10.091 1.473 .862 971 +V336 AQL 2450306.2956 10.215 1.516 .855 971 +V336 AQL 2450307.2481 10.054 1.340 .817 971 +V336 AQL 2450310.2546 9.749 1.311 .784 971 +V336 AQL 2450311.1813 9.811 1.386 .793 971 +V336 AQL 2450312.1807 10.009 1.451 .836 971 +V336 AQL 2450313.1961 10.185 1.502 .863 971 +V336 AQL 2450314.1772 10.148 1.454 .830 971 +V336 AQL 2450315.1852 9.711 1.222 .745 971 +V336 AQL 2450316.1943 9.523 1.174 .673 971 +V336 AQL 2450317.1939 9.727 1.291 .756 971 +V336 AQL 2450318.1832 9.764 1.360 .786 971 +V336 AQL 2450319.1786 9.978 1.464 .816 971 +V336 AQL 2450320.1923 10.129 1.529 .836 971 +V336 AQL 2450321.1760 10.205 1.484 .842 971 +V336 AQL 2450322.1735 9.862 1.304 .769 971 +V336 AQL 2450323.1719 9.502 1.159 .693 971 +V336 AQL 2450324.1905 9.670 1.265 .764 971 +V336 AQL 2450325.1631 9.726 1.326 .775 971 +V336 AQL 2450326.1489 9.943 1.399 .830 971 +V493 AQL 2448101.3080 11.211 1.365 .803 992 +V493 AQL 2448102.2531 11.279 1.328 .808 992 +V493 AQL 2448103.2237 10.793 1.170 .712 992 +V493 AQL 2448104.2509 11.179 1.376 .788 992 +V493 AQL 2448108.2345 11.260 1.361 .782 992 +V493 AQL 2448109.2243 10.817 1.179 .707 992 +V493 AQL 2448110.2185 11.173 1.388 .794 992 +V493 AQL 2448111.2334 11.278 1.327 .808 992 +V493 AQL 2448112.2271 10.810 1.184 .699 992 +V493 AQL 2448113.2205 11.183 1.363 .793 992 +V493 AQL 2448114.2494 11.296 1.331 .808 992 +V493 AQL 2448115.2019 10.847 1.160 .719 992 +V493 AQL 2448116.2129 11.227 1.329 .828 992 +V493 AQL 2448117.2471 11.303 1.341 .790 992 +V493 AQL 2448118.2433 10.840 .747 1.212 .721 992 +V493 AQL 2448119.2212 11.260 1.373 .816 992 +V493 AQL 2448123.2082 11.275 1.334 .796 992 +V493 AQL 2448126.2285 11.268 1.319 .796 992 +V493 AQL 2448127.2085 10.814 1.203 .717 992 +V493 AQL 2450570.6134 10.756 1.349 972 +V493 AQL 2450572.5716 11.306 1.589 972 +V493 AQL 2450572.6237 11.301 1.600 972 +V493 AQL 2450573.5140 10.842 1.386 972 +V493 AQL 2450573.6017 10.746 1.355 972 +V493 AQL 2450574.5374 11.006 1.504 972 +V493 AQL 2450574.5962 11.019 1.504 972 +V493 AQL 2450575.5813 11.305 1.593 972 +V493 AQL 2450575.6443 11.295 1.572 972 +V493 AQL 2450576.5447 10.813 1.384 972 +V493 AQL 2450576.6239 10.724 1.354 972 +V493 AQL 2450576.6590 10.699 1.350 972 +V493 AQL 2450577.5516 11.020 1.508 972 +V493 AQL 2450577.6086 11.042 1.520 972 +V493 AQL 2450577.6502 11.051 1.529 972 +V493 AQL 2450578.5297 11.296 1.583 972 +V493 AQL 2450578.5953 11.293 1.593 972 +V493 AQL 2450578.6515 11.294 1.594 972 +V493 AQL 2450580.4743 11.004 1.501 972 +V493 AQL 2450580.5837 11.030 1.531 972 +V493 AQL 2450580.6350 11.074 1.526 972 +V493 AQL 2450582.5656 10.751 1.352 972 +V493 AQL 2450582.6131 10.734 1.344 972 +V493 AQL 2450582.6605 10.694 1.332 972 +V493 AQL 2450583.5567 11.051 1.521 972 +V493 AQL 2450583.6143 11.080 1.535 972 +V493 AQL 2450584.5477 11.298 1.588 972 +V493 AQL 2450584.6038 11.311 1.585 972 +V493 AQL 2450584.6517 11.319 1.598 972 +V496 AQL 2446995.2968 7.853 .842 1.207 .674 989 +V496 AQL 2446996.2509 7.641 .694 1.098 .628 989 +V496 AQL 2446997.2738 7.561 .781 1.101 .627 989 +V496 AQL 2446998.2781 7.669 .802 1.144 .657 989 +V496 AQL 2446999.2646 7.784 .876 1.215 .673 989 +V496 AQL 2447000.2687 7.872 .879 1.245 .693 989 +V496 AQL 2447001.2776 7.972 .910 1.266 .702 989 +V496 AQL 2447002.2686 7.808 .783 1.180 .665 989 +V496 AQL 2447003.2589 7.632 .713 1.081 .627 989 +V496 AQL 2449520.9184 1.163 996 +V496 AQL 2449521.8759 7.628 1.094 996 +V496 AQL 2449528.8530 7.585 1.098 .620 .602 996 +V496 AQL 2449529.8032 7.635 1.122 .635 .614 996 +V496 AQL 2449530.8034 7.694 1.192 .682 .655 996 +V496 AQL 2449534.7921 7.690 1.147 .679 .635 996 +V496 AQL 2449543.7943 7.636 1.149 .669 .633 996 +V496 AQL 2449545.7635 7.887 1.236 .698 .656 996 +V496 AQL 2449558.8515 1.260 .632 .685 996 +V496 AQL 2449559.8477 7.932 1.284 996 +V496 AQL 2449561.8244 7.782 1.145 .652 .619 996 +V496 AQL 2449564.8332 7.698 1.162 996 +V496 AQL 2449617.1275 7.697 1.082 .680 995 +V496 AQL 2449620.2059 7.866 .859 1.197 .685 995 +V496 AQL 2449621.1767 7.928 1.242 .674 995 +V496 AQL 2449623.1657 7.832 .807 1.153 .673 995 +V496 AQL 2449624.1767 7.668 .811 1.061 .643 995 +V496 AQL 2449625.1805 7.613 .802 1.099 .631 995 +V496 AQL 2449626.2092 7.736 1.152 .662 995 +V496 AQL 2449631.1490 7.603 1.071 .612 995 +V496 AQL 2449632.1612 1.118 .665 995 +V496 AQL 2449633.1638 7.783 1.172 .693 995 +V496 AQL 2449634.1562 7.860 1.241 .673 995 +V496 AQL 2449934.3287 7.976 1.259 .741 1.390 998 +V496 AQL 2449935.3360 8.001 .709 1.359 998 +V496 AQL 2449936.3142 7.807 .660 1.265 998 +V496 AQL 2449937.2868 7.711 .644 998 +V496 AQL 2449938.3132 7.728 .656 1.282 998 +V496 AQL 2449939.3030 7.753 1.294 998 +V496 AQL 2449941.2985 7.912 1.402 998 +V496 AQL 2449942.2722 8.022 1.360 998 +V496 AQL 2449943.2597 7.781 1.269 998 +V496 AQL 2449944.2710 7.653 1.197 998 +V496 AQL 2449945.2653 7.714 1.247 998 +V496 AQL 2449946.2397 7.744 1.289 998 +V496 AQL 2449947.2350 7.858 1.326 998 +V496 AQL 2449948.2324 7.982 1.360 998 +V496 AQL 2449949.2349 7.997 1.362 998 +V496 AQL 2449950.2332 7.757 1.249 998 +V496 AQL 2449952.2372 7.715 1.267 998 +V496 AQL 2449953.2662 7.805 1.304 998 +V496 AQL 2449954.2346 7.896 1.347 998 +V496 AQL 2449955.2316 7.991 1.368 998 +V496 AQL 2447400.2367 7.738 1.184 .674 990 +V496 AQL 2447401.2246 7.799 .849 1.229 .678 990 +V496 AQL 2450305.2856 7.596 1.084 .632 971 +V496 AQL 2450306.3002 7.671 1.121 .671 971 +V496 AQL 2450307.2603 7.828 1.127 .689 971 +V496 AQL 2450310.2592 7.897 1.169 .690 971 +V496 AQL 2450311.1861 7.693 1.097 .651 971 +V496 AQL 2450312.1867 7.589 1.063 .650 971 +V496 AQL 2450313.2091 7.693 1.101 .660 971 +V496 AQL 2450314.1830 7.770 1.173 .693 971 +V496 AQL 2450315.1899 7.881 1.194 .710 971 +V496 AQL 2450316.2008 7.978 1.188 .688 971 +V496 AQL 2450317.2079 7.845 1.145 .687 971 +V496 AQL 2450318.1941 7.668 1.072 .651 971 +V496 AQL 2450319.1880 7.623 1.061 .639 971 +V496 AQL 2450320.2004 7.710 1.128 .666 971 +V496 AQL 2450321.1818 7.794 1.164 .694 971 +V496 AQL 2450322.1811 7.905 1.201 .705 971 +V496 AQL 2450323.1794 7.967 1.208 .709 971 +V496 AQL 2450324.2013 7.825 1.127 .678 971 +V496 AQL 2450325.1708 7.621 1.052 .626 971 +V496 AQL 2447734.2864 7.738 .822 1.225 .675 991 +V496 AQL 2447736.3152 7.949 1.253 .704 991 +V496 AQL 2447737.3063 7.801 .805 1.193 .654 991 +V496 AQL 2447738.3092 7.657 .660 1.091 .634 991 +V496 AQL 2447739.2818 7.622 .692 1.091 .626 991 +V496 AQL 2447740.3041 7.720 1.161 .652 991 +V496 AQL 2447741.2826 7.758 .838 1.199 .683 991 +V496 AQL 2447742.2937 7.857 .893 1.232 .684 991 +V496 AQL 2447743.2806 7.890 1.265 .682 991 +V496 AQL 2447744.2593 7.784 .736 1.168 .661 991 +V496 AQL 2447745.2676 7.598 .659 1.065 .619 991 +V496 AQL 2447746.2704 7.596 .682 1.097 .619 991 +V496 AQL 2447747.2647 7.672 .793 1.148 .668 991 +V496 AQL 2447748.2655 7.778 .821 1.225 .677 991 +V496 AQL 2447750.2486 7.919 1.248 .679 991 +V496 AQL 2447751.2427 7.760 .730 1.133 .668 991 +V496 AQL 2447752.2219 7.570 .645 1.074 .616 991 +V496 AQL 2447754.2515 7.710 .797 1.176 .658 991 +V496 AQL 2447755.2455 7.800 .856 1.239 .681 991 +V496 AQL 2447756.2678 7.903 .892 1.269 .696 991 +V496 AQL 2447757.2492 7.906 .870 1.222 .690 991 +V496 AQL 2447758.2457 7.722 .685 1.131 .652 991 +V496 AQL 2447759.2216 7.593 .629 1.052 .633 991 +V496 AQL 2447760.2397 7.621 .724 1.127 .625 991 +V496 AQL 2447761.2230 7.734 .788 1.172 .666 991 +V496 AQL 2447762.2162 7.836 1.227 .689 991 +V496 AQL 2447763.1865 7.905 1.250 .693 991 +V496 AQL 2447764.1912 7.892 1.225 .690 991 +V496 AQL 2447766.1926 7.562 1.082 .619 991 +V496 AQL 2447767.2321 7.639 .752 1.132 .646 991 +V496 AQL 2447768.2321 7.689 .833 1.194 .671 991 +V496 AQL 2447770.2177 7.942 .977 1.259 .701 991 +V496 AQL 2447771.2123 7.835 .809 1.213 .685 991 +V496 AQL 2447772.2083 7.645 .689 1.081 .639 991 +V496 AQL 2447773.2345 7.589 .677 1.087 .639 991 +V496 AQL 2447774.2406 7.660 .751 1.175 .637 991 +V496 AQL 2447775.2044 7.763 .808 1.195 .677 991 +V496 AQL 2447776.2060 7.851 .875 1.254 .697 991 +V526 AQL 2446262.3227 12.800 1.668 .909 987 +V526 AQL 2446263.3193 13.237 1.771 .946 987 +V526 AQL 2446265.3070 12.422 1.388 .815 987 +V526 AQL 2446266.3041 12.735 1.628 .902 987 +V526 AQL 2446267.2831 13.071 1.749 .933 987 +V526 AQL 2446268.2750 13.221 1.579 .909 987 +V526 AQL 2446269.2684 12.297 1.347 .772 987 +V526 AQL 2446270.2842 12.586 1.611 .877 987 +V526 AQL 2446272.2422 13.417 1.715 .941 987 +V526 AQL 2446273.3085 12.285 1.295 .758 987 +V526 AQL 2446274.2749 12.491 1.533 .842 987 +V526 AQL 2446275.2989 12.908 1.772 .916 987 +V526 AQL 2446278.3166 12.456 .843 987 +V526 AQL 2446279.2669 12.812 1.689 .911 987 +V526 AQL 2446280.2660 13.309 1.759 .950 987 +V526 AQL 2446283.2235 12.755 1.633 .896 987 +V526 AQL 2446284.1896 13.116 1.757 .935 987 +V526 AQL 2446285.2067 13.052 1.527 .852 987 +V526 AQL 2446286.2025 12.317 1.352 .772 987 +V526 AQL 2446287.1947 12.631 1.607 .858 987 +V526 AQL 2446288.2067 13.025 1.759 .901 987 +V526 AQL 2446289.2213 13.336 .929 987 +V526 AQL 2446290.2027 12.264 1.295 .740 987 +V526 AQL 2446291.1913 12.547 1.538 .862 987 +V526 AQL 2446292.1937 12.959 1.749 .909 987 +V526 AQL 2446294.1918 12.229 1.224 .718 987 +V526 AQL 2446295.1703 12.484 1.507 .828 987 +V526 AQL 2446296.1780 12.859 1.716 .898 987 +V526 AQL 2446297.1845 13.374 1.797 .949 987 +V526 AQL 2446298.2077 12.323 1.200 .730 987 +V526 AQL 2446299.1732 12.423 1.414 .821 987 +V526 AQL 2446300.1719 12.782 1.670 .890 987 +V526 AQL 2446301.1847 13.204 .931 987 +V526 AQL 2446302.1820 12.697 1.362 .771 987 +V526 AQL 2446303.1643 12.364 1.392 .807 987 +V526 AQL 2449625.2215 12.523 1.549 .848 995 +V526 AQL 2449626.2335 12.909 1.773 .847 995 +V526 AQL 2449631.2000 13.196 1.913 .878 995 +V526 AQL 2449632.2186 12.745 1.474 .762 995 +V526 AQL 2449633.1962 12.319 1.446 .737 995 +V526 AQL 2449634.1980 12.691 1.726 .870 995 +V526 AQL 2449521.8948 13.270 1.758 996 +V526 AQL 2449522.7609 12.807 1.335 996 +V526 AQL 2449534.8051 13.596 1.823 .927 .870 996 +V526 AQL 2449543.8015 12.889 1.284 .892 .805 996 +V526 AQL 2449561.7887 12.419 1.440 .810 .741 996 +V526 AQL 2449563.7657 13.149 1.784 .931 .825 996 +V526 AQL 2449564.7122 13.155 1.105 1.521 .877 .815 996 +V600 AQL 2449934.3409 9.995 1.504 .954 1.765 998 +V600 AQL 2449935.3510 9.939 .908 1.770 998 +V600 AQL 2449936.3212 10.164 .945 1.844 998 +V600 AQL 2449937.3034 10.317 1.688 .981 998 +V600 AQL 2449938.3287 10.446 .989 1.918 998 +V600 AQL 2449939.3288 10.053 .899 1.761 998 +V600 AQL 2449941.3126 9.908 1.736 998 +V600 AQL 2449942.2881 9.960 1.786 998 +V600 AQL 2449943.2662 10.125 1.840 998 +V600 AQL 2449944.2770 10.279 1.883 998 +V600 AQL 2449945.2708 10.407 1.910 998 +V600 AQL 2449946.2627 10.162 1.797 998 +V600 AQL 2449947.2504 9.782 1.621 998 +V600 AQL 2449948.2398 9.838 1.690 998 +V600 AQL 2449949.2469 9.971 1.798 998 +V600 AQL 2449950.2380 10.057 1.821 998 +V600 AQL 2449952.2434 10.387 1.898 998 +V600 AQL 2449953.2894 10.289 1.845 998 +V600 AQL 2449954.2424 9.894 1.707 998 +V600 AQL 2449955.2379 9.798 1.674 998 +V600 AQL 2448854.2745 9.713 1.394 .832 994 +V600 AQL 2448856.2559 9.916 1.555 .893 994 +V600 AQL 2448858.2706 10.199 1.668 .949 994 +V600 AQL 2448860.2546 10.225 1.605 .926 994 +V600 AQL 2448862.2817 9.774 1.419 .835 994 +V600 AQL 2448870.2389 9.898 1.527 .869 994 +V600 AQL 2448872.2417 10.099 1.644 .922 994 +V600 AQL 2448874.2357 10.356 1.688 .949 994 +V600 AQL 2448876.2260 9.731 1.388 .828 994 +V600 AQL 2448877.2092 9.835 1.504 .847 994 +V600 AQL 2448878.2238 9.916 1.548 .887 994 +V600 AQL 2448880.1625 10.243 1.674 .960 994 +V600 AQL 2448881.1580 10.372 1.695 .959 994 +V600 AQL 2448882.1674 10.141 1.565 .907 994 +V600 AQL 2448883.1746 9.714 1.388 .805 994 +V600 AQL 2448884.1801 9.775 1.467 .849 994 +V600 AQL 2448885.1685 9.946 1.518 .911 994 +V600 AQL 2448886.1671 10.062 1.572 .923 994 +V600 AQL 2448888.1711 10.365 1.697 .968 994 +V600 AQL 2448889.1822 10.258 1.606 .932 994 +V600 AQL 2448890.1595 9.840 1.387 .880 994 +V600 AQL 2448891.1587 9.774 1.411 .854 994 +V600 AQL 2448892.1737 9.923 1.508 .901 994 +V600 AQL 2448893.1617 10.014 1.567 .928 994 +V600 AQL 2448894.1914 10.191 1.623 .952 994 +V600 AQL 2446606.3052 10.100 1.237 1.611 .943 988 +V600 AQL 2446607.3713 10.275 1.358 1.673 .973 988 +V600 AQL 2446608.3090 10.366 1.382 1.652 .981 988 +V600 AQL 2446609.3570 10.057 1.059 1.480 .908 988 +V600 AQL 2446610.3413 9.716 .957 1.347 .835 988 +V600 AQL 2446611.3291 9.864 1.074 1.465 .890 988 +V600 AQL 2446612.3300 9.945 1.148 1.502 .917 988 +V600 AQL 2446613.3220 10.092 1.192 1.602 .942 988 +V600 AQL 2446614.3318 10.231 1.342 1.649 .969 988 +V600 AQL 2446615.3012 10.363 1.384 1.687 .969 988 +V600 AQL 2446616.3133 10.184 1.098 1.557 .931 988 +V600 AQL 2446617.3086 9.766 .994 1.383 .835 988 +V600 AQL 2446618.3085 9.817 1.038 1.419 .878 988 +V600 AQL 2446619.3136 9.937 1.100 1.512 .902 988 +V600 AQL 2446620.2893 10.033 1.206 1.564 .934 988 +V600 AQL 2446621.2867 10.178 1.266 1.641 .953 988 +V600 AQL 2446622.2914 10.337 1.394 1.697 .974 988 +V600 AQL 2446623.3252 10.275 1.593 .955 988 +V600 AQL 2446624.3258 9.869 1.414 .858 988 +V600 AQL 2446625.2672 9.756 1.376 .852 988 +V600 AQL 2446626.2866 9.902 1.498 .901 988 +V600 AQL 2446627.2690 9.955 1.158 1.531 .912 988 +V600 AQL 2446628.2700 10.147 1.593 .953 988 +V600 AQL 2446629.2729 10.328 1.669 .976 988 +V600 AQL 2446631.2453 10.011 1.458 .888 988 +V600 AQL 2446632.2778 9.733 1.369 .839 988 +V600 AQL 2446635.2520 10.106 1.589 .942 988 +V600 AQL 2446636.2851 10.286 1.652 .979 988 +V600 AQL 2448504.1891 10.317 1.659 .964 993 +V600 AQL 2448505.1807 10.353 1.635 .960 993 +V600 AQL 2448506.2329 9.952 1.457 .859 993 +V600 AQL 2448507.2087 9.720 1.356 .830 993 +V600 AQL 2448508.1876 9.895 1.466 .878 993 +V600 AQL 2448509.1956 9.923 1.530 .895 993 +V600 AQL 2448510.1892 10.105 1.633 .932 993 +V600 AQL 2448511.1966 10.273 1.678 .947 993 +V600 AQL 2448512.1976 10.370 1.671 .960 993 +V600 AQL 2448513.2094 10.054 1.486 .887 993 +V600 AQL 2448514.2110 9.733 1.363 .831 993 +V600 AQL 2448515.2056 9.880 1.416 .872 993 +V600 AQL 2448516.1995 9.912 1.520 .890 993 +V600 AQL 2448517.1879 10.103 1.580 .935 993 +V600 AQL 2448518.1999 10.240 1.648 .952 993 +V600 AQL 2448519.2183 10.356 1.686 .961 993 +V600 AQL 2448520.1886 10.138 1.549 .886 993 +V600 AQL 2448521.2126 9.714 1.364 .811 993 +V600 AQL 2448522.1980 9.782 1.423 .852 993 +V600 AQL 2448523.1918 9.934 1.514 .900 993 +V733 AQL 2448856.2760 9.763 .775 .459 994 +V733 AQL 2448858.2810 9.972 .959 .513 994 +V733 AQL 2448860.2671 10.199 1.033 .546 994 +V733 AQL 2448862.2916 9.758 .797 .434 994 +V733 AQL 2448870.2522 9.920 .956 .490 994 +V733 AQL 2448872.2577 10.138 1.043 .535 994 +V733 AQL 2448874.2751 9.817 .863 .442 994 +V733 AQL 2448876.2334 9.942 .927 .520 994 +V733 AQL 2448877.2252 10.003 1.019 .501 994 +V733 AQL 2448878.2393 10.139 1.043 .542 994 +V733 AQL 2448880.1726 9.925 .857 .487 994 +V733 AQL 2448881.1989 9.745 .784 .462 994 +V733 AQL 2448882.1754 9.874 .930 .482 994 +V733 AQL 2448883.1810 9.991 .984 .502 994 +V733 AQL 2448884.1876 10.109 1.030 .543 994 +V733 AQL 2448885.1760 10.186 1.051 .533 994 +V733 AQL 2448886.1744 9.954 .912 .469 994 +V733 AQL 2448887.2550 9.742 .802 .436 994 +V733 AQL 2448888.1779 9.860 .880 .477 994 +V733 AQL 2448889.1890 9.997 .968 .518 994 +V733 AQL 2448890.1668 10.102 1.010 .535 994 +V733 AQL 2448891.1656 10.207 1.033 .561 994 +V733 AQL 2448892.1797 9.999 .914 .480 994 +V733 AQL 2448893.1692 9.769 .805 .457 994 +V733 AQL 2448894.1978 9.863 .855 .486 994 +V733 AQL 2449935.3744 10.201 .572 1.054 998 +V733 AQL 2449936.3699 10.070 .517 .963 998 +V733 AQL 2449937.3398 9.825 .796 .462 998 +V733 AQL 2449938.3663 9.868 .468 .937 998 +V733 AQL 2449939.3523 9.969 .978 998 +V733 AQL 2449941.3416 10.231 998 +V733 AQL 2449942.3113 10.143 1.013 998 +V733 AQL 2449943.3051 9.835 .889 998 +V733 AQL 2449944.3399 9.828 .903 998 +V733 AQL 2449945.3508 9.996 .965 998 +V733 AQL 2449946.3225 10.101 1.018 998 +V733 AQL 2449947.2894 10.193 1.030 998 +V733 AQL 2449948.2692 10.174 1.014 998 +V733 AQL 2449949.2715 9.911 .897 998 +V733 AQL 2449950.2638 9.767 .884 998 +V733 AQL 2449952.2695 10.028 .984 998 +V733 AQL 2449953.3264 10.194 1.060 998 +V733 AQL 2449954.2633 10.229 1.024 998 +V733 AQL 2449955.2561 9.969 .930 998 +V733 AQL 2448503.2544 9.916 .842 .464 993 +V733 AQL 2448504.2097 9.744 .778 .445 993 +V733 AQL 2448505.2324 9.900 .856 .496 993 +V733 AQL 2448506.2507 9.991 .951 .525 993 +V733 AQL 2448507.2242 10.107 1.019 .526 993 +V733 AQL 2448508.2008 10.175 1.029 .520 993 +V733 AQL 2448509.1853 10.007 .903 .500 993 +V733 AQL 2448510.1775 9.752 .778 .442 993 +V733 AQL 2448511.1814 9.836 .901 .468 993 +V733 AQL 2448512.1884 9.977 .938 .505 993 +V733 AQL 2448513.1973 10.104 .995 .540 993 +V733 AQL 2448514.1962 10.197 1.035 .540 993 +V733 AQL 2448515.1918 10.050 .898 .505 993 +V733 AQL 2448516.1923 9.767 .782 .441 993 +V733 AQL 2448517.1804 9.830 .848 .454 993 +V733 AQL 2448518.1931 9.944 .957 .482 993 +V733 AQL 2448519.2130 10.050 1.003 .512 993 +V733 AQL 2448520.1826 10.178 1.028 .534 993 +V733 AQL 2448521.2052 10.064 .968 .490 993 +V733 AQL 2448522.1915 9.792 .810 .443 993 +V733 AQL 2448523.1862 9.791 .840 .467 993 +V733 AQL 2449620.2763 10.243 .970 .564 995 +V733 AQL 2449621.2756 10.078 .880 .512 995 +V733 AQL 2449623.2611 9.811 .843 .464 995 +V733 AQL 2449624.2385 9.912 .850 .929 .472 995 +V733 AQL 2449625.2573 10.071 .980 .524 995 +V733 AQL 2449626.2707 10.158 .968 .547 995 +V733 AQL 2449631.2323 10.037 .969 .507 995 +V733 AQL 2449632.2552 10.172 1.027 .526 995 +V733 AQL 2449633.2409 10.117 .963 .536 995 +V733 AQL 2449634.2342 9.836 .808 .481 995 +V733 AQL 2449635.2721 9.747 .819 .464 995 +V733 AQL 2446617.3160 10.201 .746 1.013 .538 988 +V733 AQL 2446618.3122 10.078 .589 .919 .517 988 +V733 AQL 2446619.3177 9.783 .493 .786 .449 988 +V733 AQL 2446620.2923 9.820 .563 .833 .467 988 +V733 AQL 2446621.2892 9.951 .652 .923 .503 988 +V733 AQL 2446622.2953 10.051 .695 .990 .523 988 +V733 AQL 2446623.3275 10.175 1.026 .531 988 +V733 AQL 2446624.3279 10.114 .942 .518 988 +V733 AQL 2446625.2688 9.829 .807 .465 988 +V733 AQL 2446626.2939 9.772 .821 .451 988 +V733 AQL 2446627.2772 9.930 .607 .911 .494 988 +V733 AQL 2446628.2786 10.047 .969 .515 988 +V733 AQL 2446629.2819 10.164 .712 1.029 .551 988 +V733 AQL 2446631.2510 9.883 .825 .466 988 +V733 AQL 2446632.2837 9.768 .825 .448 988 +V733 AQL 2446635.2576 10.114 1.010 .533 988 +V733 AQL 2446636.2932 10.193 .720 1.005 .538 988 +V765 AQL 2449521.9278 12.334 .706 996 +V765 AQL 2449522.8676 12.321 .763 996 +V765 AQL 2449558.8581 12.330 .375 .531 996 +V765 AQL 2449559.8560 12.257 996 +V765 AQL 2449561.8347 12.326 .741 .403 .492 996 +V765 AQL 2449564.8402 12.340 .702 996 +V801 AQL 2444825.2734 13.492 1.151 950 +V801 AQL 2444827.1875 13.194 1.101 950 +V801 AQL 2444836.2617 13.613 1.390 950 +V801 AQL 2444840.2226 13.437 1.247 950 +V801 AQL 2444841.2031 13.260 1.182 950 +V801 AQL 2444844.1796 13.243 1.314 950 +V801 AQL 2444845.1718 13.299 1.371 950 +V801 AQL 2444846.1992 13.460 1.408 950 +V801 AQL 2444847.1757 13.654 1.460 950 +V801 AQL 2444848.1718 13.788 1.439 950 +V801 AQL 2444849.1757 13.747 1.403 950 +V801 AQL 2444850.1796 13.721 1.239 950 +V801 AQL 2444851.1757 13.706 1.277 950 +V801 AQL 2444852.1757 13.667 1.219 950 +V801 AQL 2444853.1757 13.516 1.203 950 +V801 AQL 2444854.1875 13.384 1.157 950 +V801 AQL 2444859.1406 13.417 950 +V801 AQL 2444880.1679 13.678 1.242 950 +V801 AQL 2444881.1523 13.591 1.143 950 +V801 AQL 2445173.3085 13.667 1.500 950 +V801 AQL 2445174.3125 13.898 1.601 950 +V801 AQL 2445175.3359 13.957 1.486 950 +V801 AQL 2445176.3242 13.817 1.304 950 +V801 AQL 2445178.3125 13.629 1.205 950 +V801 AQL 2445191.2421 13.716 1.308 950 +V801 AQL 2445192.2460 13.656 1.230 950 +V801 AQL 2445193.2265 13.545 1.195 950 +V801 AQL 2445194.2265 13.426 1.144 950 +V801 AQL 2445195.2421 13.280 1.159 950 +V801 AQL 2445196.2812 13.270 1.187 950 +V801 AQL 2445198.2460 13.336 1.328 950 +V801 AQL 2445199.2460 13.431 1.390 950 +V801 AQL 2445201.2460 13.745 1.559 950 +V801 AQL 2447735.3202 13.440 1.549 .728 950 +V801 AQL 2447736.3070 13.716 1.560 .788 950 +V801 AQL 2447737.2966 13.950 1.548 .793 950 +V801 AQL 2447738.2997 14.124 1.482 .867 950 +V801 AQL 2447739.2740 13.938 1.406 .763 950 +V801 AQL 2447740.2995 13.746 1.308 .750 950 +V801 AQL 2447741.2770 13.630 1.278 .703 950 +V801 AQL 2447742.2885 13.540 1.186 .687 950 +V801 AQL 2447743.2749 13.409 1.216 .671 950 +V801 AQL 2447744.2542 13.319 1.131 .692 950 +V801 AQL 2447745.2603 13.214 1.163 .681 950 +V801 AQL 2447746.2628 13.200 1.243 .727 950 +V801 AQL 2447747.2568 13.229 1.314 .742 950 +V801 AQL 2447748.2580 13.281 1.414 .725 950 +V801 AQL 2447749.2453 13.424 1.400 .763 950 +V916 AQL 2449934.3322 10.610 1.751 1.028 1.912 998 +V916 AQL 2449935.3381 10.673 1.043 1.925 998 +V916 AQL 2449936.3170 10.802 1.048 1.982 998 +V916 AQL 2449937.2941 10.932 2.008 1.050 998 +V916 AQL 2449938.3171 11.077 1.072 2.053 998 +V916 AQL 2449939.3067 11.182 2.067 998 +V916 AQL 2449941.3018 11.175 2.052 998 +V916 AQL 2449942.2748 11.103 1.997 998 +V916 AQL 2449943.2614 10.967 1.952 998 +V916 AQL 2449944.2728 10.843 1.877 998 +V916 AQL 2449945.2667 10.372 1.744 998 +V916 AQL 2449946.2572 10.368 1.755 998 +V916 AQL 2449947.2400 10.511 1.830 998 +V916 AQL 2449948.2359 10.642 1.906 998 +V916 AQL 2449949.2391 10.784 1.951 998 +V916 AQL 2449950.2366 10.869 2.008 998 +V916 AQL 2449952.2410 11.138 2.073 998 +V916 AQL 2449953.2815 11.243 2.073 998 +V916 AQL 2449955.2360 11.169 2.059 998 +V916 AQL 2444825.3397 10.433 1.641 982 +V916 AQL 2444827.2343 10.688 1.836 982 +V916 AQL 2444829.2500 10.915 1.953 982 +V916 AQL 2444830.2264 11.054 1.982 982 +V916 AQL 2444831.2343 11.160 2.037 982 +V916 AQL 2444832.2343 11.200 2.020 982 +V916 AQL 2444833.2264 11.174 1.975 982 +V916 AQL 2444844.2109 11.111 2.026 982 +V916 AQL 2444845.1992 11.203 2.000 982 +V916 AQL 2444846.2304 11.233 2.013 982 +V916 AQL 2444847.2070 11.102 1.928 982 +V916 AQL 2444848.1992 10.944 1.815 982 +V916 AQL 2444849.2304 10.881 1.764 982 +V916 AQL 2444850.2264 10.397 1.537 982 +V916 AQL 2444851.2226 10.318 1.567 982 +V916 AQL 2444852.2147 10.433 1.646 982 +V916 AQL 2444853.2147 10.563 1.755 982 +V916 AQL 2444854.2147 10.690 1.839 982 +V916 AQL 2444855.1913 10.804 1.893 982 +V916 AQL 2444856.1953 10.930 1.964 982 +V916 AQL 2444857.1992 11.072 1.975 982 +V916 AQL 2444858.2617 11.189 2.020 982 +V916 AQL 2444880.1953 10.575 1.781 982 +V916 AQL 2444881.1835 10.700 1.855 982 +V916 AQL 2444882.2226 10.836 1.883 982 +V916 AQL 2444883.1405 10.925 1.967 982 +V916 AQL 2444884.1445 11.074 1.995 982 +V916 AQL 2444885.1679 11.189 2.048 982 +V916 AQL 2445173.3593 10.335 1.558 982 +V916 AQL 2445175.3710 10.512 1.708 982 +V916 AQL 2445178.3476 10.859 1.939 982 +V916 AQL 2445181.2343 11.194 2.020 982 +V916 AQL 2445182.2381 11.199 1.990 982 +V916 AQL 2445183.2421 11.091 1.923 982 +V916 AQL 2445184.2109 10.955 1.805 982 +V916 AQL 2445186.2538 10.394 1.550 982 +V916 AQL 2445187.2343 10.310 1.560 982 +V916 AQL 2445188.2304 10.427 1.652 982 +V916 AQL 2445189.2812 10.566 1.766 982 +V916 AQL 2445190.2851 10.690 1.843 982 +V916 AQL 2445191.2929 10.816 1.890 982 +V916 AQL 2445192.2929 10.923 1.958 982 +V916 AQL 2445193.2695 11.055 1.983 982 +V916 AQL 2445194.2695 11.147 2.007 982 +V916 AQL 2445198.2889 10.916 1.788 982 +V916 AQL 2445199.2617 10.663 1.673 982 +V916 AQL 2445200.2772 10.332 1.552 982 +V916 AQL 2445201.2695 10.388 1.616 982 +V916 AQL 2445203.3203 10.631 1.803 982 +V916 AQL 2445211.1796 10.932 1.816 982 +V916 AQL 2445212.1992 10.864 1.747 982 +V916 AQL 2445213.1913 10.375 1.540 982 +V916 AQL 2445214.1913 10.319 1.588 982 +V916 AQL 2445648.1210 10.778 1.913 1.020 982 +V916 AQL 2445648.1210 10.793 1.900 1.021 982 +V916 AQL 2445649.1484 10.913 1.976 1.050 982 +V916 AQL 2445650.1367 11.021 2.043 1.057 982 +V916 AQL 2445665.0976 11.210 2.054 1.081 982 +V916 AQL 2445666.0780 11.252 2.043 1.091 982 +V916 AQL 2445668.0820 10.938 1.885 1.003 982 +V916 AQL 2445864.3006 10.910 1.672 1.961 1.044 982 +V916 AQL 2445867.3085 11.227 1.891 2.014 1.086 982 +V916 AQL 2445868.3514 11.179 1.731 1.954 1.061 982 +V916 AQL 2445869.3710 11.010 1.546 1.860 1.030 982 +V916 AQL 2445870.3593 10.911 1.432 1.791 1.002 982 +V916 AQL 2445871.3631 10.669 1.276 1.665 .930 982 +V916 AQL 2445872.3671 10.328 1.166 1.546 .889 982 +V916 AQL 2445873.3671 10.375 1.224 1.616 .917 982 +V916 AQL 2445874.3554 10.507 1.346 1.728 .945 982 +V916 AQL 2445875.3514 10.639 1.565 1.809 .970 982 +V916 AQL 2445876.3631 10.756 1.714 1.870 1.011 982 +V916 AQL 2445877.3476 10.872 1.917 1.047 982 +V916 AQL 2445878.3514 11.001 1.979 1.060 982 +V916 AQL 2445879.3397 11.139 2.009 1.070 982 +V916 AQL 2445880.3476 11.218 2.017 1.089 982 +V916 AQL 2445881.3397 11.241 1.961 1.085 982 +V916 AQL 2445882.3359 11.098 1.895 1.045 982 +V916 AQL 2445883.3514 10.946 1.832 1.003 982 +V916 AQL 2445886.3514 10.346 1.194 1.581 .898 982 +V916 AQL 2445887.3631 10.481 1.673 .941 982 +V916 AQL 2447409.1986 10.799 1.879 1.016 990 +V916 AQL 2447410.2009 10.929 1.959 1.028 990 +V916 AQL 2447411.2226 11.070 1.990 1.060 990 +V916 AQL 2447413.1863 11.212 2.017 1.070 990 +V916 AQL 2447414.1708 11.183 1.971 1.039 990 +V916 AQL 2447415.1741 11.019 1.883 1.015 990 +V916 AQL 2447416.1803 10.925 1.803 .983 990 +V916 AQL 2447417.1642 10.718 1.691 .940 990 +V916 AQL 2447418.1657 10.340 1.567 .863 990 +V916 AQL 2447419.1538 10.329 1.578 990 +V916 AQL 2447420.1606 10.504 1.712 .924 990 +V916 AQL 2447421.1528 10.615 1.778 .969 990 +V916 AQL 2447422.1566 10.695 1.856 .986 990 +V916 AQL 2447423.1452 10.876 1.923 1.028 990 +V916 AQL 2447424.1493 11.000 1.986 1.045 990 +V916 AQL 2447425.1572 11.118 1.990 1.051 990 +V916 AQL 2447427.1622 11.236 1.985 1.053 990 +V916 AQL 2447428.1459 11.097 1.934 1.019 990 +V916 AQL 2447429.1430 10.953 1.834 .992 990 +V916 AQL 2447430.1301 10.875 1.734 .962 990 +V916 AQL 2447431.1315 10.420 1.528 .874 990 +V916 AQL 2447432.1265 10.317 1.571 .881 990 +V916 AQL 2447433.1309 10.457 1.658 .919 990 +V916 AQL 2447434.1361 10.578 1.729 .952 990 +V916 AQL 2450305.2862 11.039 1.864 1.025 971 +V916 AQL 2450306.3011 10.924 1.791 1.004 971 +V916 AQL 2450307.2531 10.783 1.666 .954 971 +V916 AQL 2450310.2601 10.501 1.625 .947 971 +V916 AQL 2450311.1872 10.622 1.750 .978 971 +V916 AQL 2450312.1875 10.723 1.832 1.015 971 +V916 AQL 2450313.2101 10.846 1.889 1.025 971 +V916 AQL 2450314.1841 10.970 1.963 1.047 971 +V916 AQL 2450315.1914 11.072 2.005 1.047 971 +V916 AQL 2450316.2027 11.199 1.997 1.037 971 +V916 AQL 2450317.2098 11.221 1.973 1.062 971 +V916 AQL 2450318.1958 11.151 1.923 1.042 971 +V916 AQL 2450319.1909 10.961 1.828 .990 971 +V916 AQL 2450320.2022 10.895 1.768 .975 971 +V916 AQL 2450321.1832 10.504 1.576 .896 971 +V916 AQL 2450322.1829 10.332 1.522 .881 971 +V916 AQL 2450323.1809 10.427 1.615 .925 971 +V916 AQL 2450324.2027 10.578 1.698 .972 971 +V916 AQL 2450325.1721 10.680 1.813 .990 971 +V916 AQL 2450326.1588 10.793 1.873 1.015 971 +V916 AQL 2448503.2364 11.160 1.950 1.046 993 +V916 AQL 2448504.1974 11.005 1.834 1.025 993 +V916 AQL 2448505.1876 10.902 1.820 .969 993 +V916 AQL 2448506.2390 10.615 1.634 .916 993 +V916 AQL 2448507.2150 10.336 1.548 .884 993 +V916 AQL 2448508.1918 10.391 1.606 .900 993 +V916 AQL 2448509.1998 10.545 1.723 .943 993 +V916 AQL 2448510.1938 10.639 1.810 .985 993 +V916 AQL 2448511.2016 10.773 1.897 .990 993 +V916 AQL 2448512.2059 10.896 1.964 1.023 993 +V916 AQL 2448514.2139 11.160 2.038 1.062 993 +V916 AQL 2448515.2121 11.248 1.991 1.081 993 +V916 AQL 2448516.2060 11.203 1.960 1.067 993 +V916 AQL 2448517.1992 11.072 1.897 1.011 993 +V916 AQL 2448518.2082 10.948 1.793 1.013 993 +V916 AQL 2448519.2256 10.848 1.716 .967 993 +V916 AQL 2448520.1954 10.390 1.491 .874 993 +V916 AQL 2448521.2210 10.327 1.610 .891 993 +V916 AQL 2448522.2055 10.482 1.659 .929 993 +V916 AQL 2448523.1995 10.619 1.758 .976 993 +V979 AQL 2449522.8334 14.129 .683 996 +V979 AQL 2449534.8244 14.030 .671 .304 .414 996 +V979 AQL 2449543.8203 14.071 996 +V979 AQL 2449561.8171 13.882 .670 .426 .462 996 +V979 AQL 2449563.7712 13.716 .488 .353 .377 996 +V979 AQL 2449564.7215 14.023 .670 .424 .440 996 +V1162 AQL 2446608.3248 7.621 .486 .760 .448 988 +V1162 AQL 2446610.3492 7.815 .610 .928 .518 988 +V1162 AQL 2446611.3316 7.941 .697 .999 .537 988 +V1162 AQL 2446613.3240 7.787 .474 .828 .478 988 +V1162 AQL 2446614.3351 7.567 .499 .767 .443 988 +V1162 AQL 2446616.3154 7.910 .637 .989 .529 988 +V1162 AQL 2446617.3173 8.012 .697 1.023 .548 988 +V1162 AQL 2446618.3135 8.006 .610 .929 .542 988 +V1162 AQL 2446619.3190 7.583 .435 .749 .436 988 +V1162 AQL 2446620.2937 7.673 .527 .839 .476 988 +V1162 AQL 2446621.2908 7.859 .937 .520 988 +V1162 AQL 2446622.2967 7.956 .685 1.005 .538 988 +V1162 AQL 2446623.3286 8.060 .660 .997 .548 988 +V1162 AQL 2446624.3285 7.703 .797 .467 988 +V1162 AQL 2446625.2700 7.617 .493 .797 .459 988 +V1162 AQL 2446626.2906 7.763 .588 .928 .495 988 +V1162 AQL 2446627.2740 7.939 .657 .980 .534 988 +V1162 AQL 2446628.2748 8.074 .716 1.014 .537 988 +V1162 AQL 2446629.2774 7.900 .514 .894 .500 988 +V1162 AQL 2446631.2535 7.726 .550 .856 .500 988 +V1162 AQL 2446632.2853 7.877 .628 .979 .526 988 +V1162 AQL 2446635.2598 7.565 .468 .775 .429 988 +V1162 AQL 2446636.2896 7.684 .525 .834 .470 988 +V1162 AQL 2449520.9255 8.107 .994 996 +V1162 AQL 2449521.9127 7.735 .837 996 +V1162 AQL 2449522.8437 7.484 .802 996 +V1162 AQL 2449528.8658 7.662 .838 .494 996 +V1162 AQL 2449529.8070 7.851 .947 .523 .480 996 +V1162 AQL 2449530.8120 7.886 .892 1.011 .523 .499 996 +V1162 AQL 2449534.8318 7.747 .920 .520 .483 996 +V1162 AQL 2449543.8466 7.545 .468 .784 .431 .447 996 +V1162 AQL 2449545.7968 7.775 .596 .935 .513 .496 996 +V1162 AQL 2449558.8558 8.025 .696 .979 .531 .490 996 +V1162 AQL 2449559.8542 7.664 .532 .754 .443 .438 996 +V1162 AQL 2449561.8325 7.791 .712 .922 .499 .476 996 +V1162 AQL 2449564.8388 7.794 .827 996 +V1162 AQL 2448854.1733 8.020 1.052 .548 994 +V1162 AQL 2448856.1597 7.522 .382 .803 .437 994 +V1162 AQL 2448858.1588 7.865 .592 .993 994 +V1162 AQL 2448860.1493 7.963 .773 .969 .519 994 +V1162 AQL 2448870.1334 8.026 .679 1.030 .550 994 +V1162 AQL 2448872.1355 7.619 .743 .472 994 +V1162 AQL 2448874.1330 7.863 .977 .525 994 +V1162 AQL 2448876.1292 8.059 .598 .974 .553 994 +V1162 AQL 2448877.1224 7.680 .773 .446 994 +V1162 AQL 2448880.1215 7.935 .657 1.037 .535 994 +V1162 AQL 2448881.1142 8.045 1.034 .549 994 +V1162 AQL 2448882.1173 7.780 .872 .471 994 +V1162 AQL 2448883.1159 7.562 .779 .445 994 +V1162 AQL 2448884.1126 7.760 .918 .500 994 +V1162 AQL 2448885.1098 7.893 .984 .545 994 +V1162 AQL 2448886.1104 8.041 1.015 .530 994 +V1162 AQL 2448888.1083 7.545 .778 .426 994 +V1162 AQL 2448889.1085 7.682 .848 .470 994 +V1162 AQL 2448890.1059 7.883 .611 .944 .555 994 +V1162 AQL 2448891.1031 7.988 1.004 .553 994 +V1162 AQL 2448892.1018 8.068 1.007 .542 994 +V1162 AQL 2448893.1016 7.675 .803 .455 994 +V1162 AQL 2448894.0995 7.586 .424 .803 .454 994 +V1162 AQL 2448102.1951 7.915 .507 .956 .516 992 +V1162 AQL 2448103.1707 .408 992 +V1162 AQL 2448104.1748 7.608 .497 .840 .499 992 +V1162 AQL 2448108.1739 7.653 .455 .810 .442 992 +V1162 AQL 2448109.1609 7.570 .490 .802 .464 992 +V1162 AQL 2448110.1689 7.788 .534 .938 .525 992 +V1162 AQL 2448111.1731 7.874 .643 .998 .528 992 +V1162 AQL 2448112.1680 7.990 .676 1.008 .550 992 +V1162 AQL 2448113.1635 7.788 .473 .876 .497 992 +V1162 AQL 2448114.1722 7.504 .418 .741 .417 992 +V1162 AQL 2448115.2269 7.714 .545 .864 .495 992 +V1162 AQL 2448116.1724 7.829 .613 .973 .486 992 +V1162 AQL 2448117.1756 7.930 .546 992 +V1162 AQL 2448119.1649 7.628 .368 .771 .444 992 +V1162 AQL 2448123.1463 8.054 .658 .999 .561 992 +V1162 AQL 2448127.1528 7.865 .673 .987 .543 992 +V1162 AQL 2448503.2032 7.875 .607 .962 993 +V1162 AQL 2448505.1377 8.038 .650 .960 .543 993 +V1162 AQL 2448506.1406 7.617 .440 .770 .464 993 +V1162 AQL 2448507.1260 7.610 993 +V1162 AQL 2448508.1258 7.814 .572 .952 .520 993 +V1162 AQL 2448509.1261 7.948 1.000 .541 993 +V1162 AQL 2448510.1266 8.050 1.042 .525 993 +V1162 AQL 2448511.1249 7.791 .876 .476 993 +V1162 AQL 2448512.1250 7.568 .419 .774 .436 993 +V1162 AQL 2448513.1222 7.747 .541 .888 .491 993 +V1162 AQL 2448514.1227 7.919 .645 .972 .535 993 +V1162 AQL 2448515.1169 7.962 .698 1.011 .529 993 +V1162 AQL 2448516.1198 7.927 .574 .939 .520 993 +V1162 AQL 2448517.1187 7.551 .390 .728 .445 993 +V1162 AQL 2448518.1253 7.694 .486 .851 .493 993 +V1162 AQL 2448519.1357 7.830 .603 .969 .512 993 +V1162 AQL 2448520.1133 7.971 .667 1.008 .535 993 +V1162 AQL 2448521.1203 8.052 .662 1.013 .549 993 +V1162 AQL 2448522.1181 7.682 .469 .774 .470 993 +V1162 AQL 2448523.1095 7.611 .449 .813 .444 993 +V1162 AQL 2449619.2236 7.573 .560 .766 .440 995 +V1162 AQL 2449620.2088 7.702 .553 .802 .454 995 +V1162 AQL 2449621.1793 7.853 .669 .932 .506 995 +V1162 AQL 2449623.1678 8.119 .724 1.011 .559 995 +V1162 AQL 2449624.1792 7.746 .556 .802 .455 995 +V1162 AQL 2449625.1828 7.583 .562 .749 .442 995 +V1162 AQL 2449626.2137 7.787 .882 .524 995 +V1162 AQL 2449631.1508 7.720 .835 .486 995 +V1162 AQL 2449632.1636 7.935 .941 .550 995 +V1162 AQL 2449633.1658 8.042 .997 .562 995 +V1162 AQL 2449634.1583 8.068 .979 .529 995 +V1162 AQL 2447734.3540 7.813 .648 .992 .548 991 +V1162 AQL 2447735.3777 7.983 .659 1.018 .539 991 +V1162 AQL 2447736.3827 8.065 .639 .932 .541 991 +V1162 AQL 2447738.3583 7.647 .412 .846 .461 991 +V1162 AQL 2447739.3332 7.771 .622 .932 .499 991 +V1162 AQL 2447740.3335 7.914 .664 1.006 .561 991 +V1162 AQL 2447741.2570 8.051 .686 .999 .561 991 +V1162 AQL 2447742.2462 7.815 .405 .867 .476 991 +V1162 AQL 2447743.2361 7.569 .391 .796 .442 991 +V1162 AQL 2447744.2266 7.702 .529 .865 .489 991 +V1162 AQL 2447745.2216 7.852 .603 .969 .527 991 +V1162 AQL 2447746.2303 7.981 .631 .992 .550 991 +V1162 AQL 2447747.2189 7.996 .597 .950 .534 991 +V1162 AQL 2447748.2252 7.554 .364 .764 .433 991 +V1162 AQL 2447749.2038 7.594 .483 .821 .484 991 +V1162 AQL 2447750.2084 7.765 .601 .948 .508 991 +V1162 AQL 2447751.2140 7.935 .663 1.012 .542 991 +V1162 AQL 2447752.2071 8.033 .664 1.023 .538 991 +V1162 AQL 2447753.2101 7.701 .424 .848 .462 991 +V1162 AQL 2447754.2352 7.550 .419 .799 .443 991 +V1162 AQL 2447755.2054 7.740 .536 .913 .502 991 +V1162 AQL 2447756.2273 7.906 .620 .971 .537 991 +V1162 AQL 2447757.1989 8.019 .700 .985 .571 991 +V1162 AQL 2447758.1937 7.880 .492 .920 .498 991 +V1162 AQL 2447759.2064 7.535 .362 .714 .449 991 +V1162 AQL 2447760.2166 7.674 .504 .857 .468 991 +V1162 AQL 2447761.2003 7.864 .589 .950 .534 991 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2450315.1986 7.909 .944 .546 971 +V1162 AQL 2450316.2108 8.047 .986 971 +V1162 AQL 2450317.2197 7.974 .908 .527 971 +V1162 AQL 2450318.2293 7.576 .773 .446 971 +V1162 AQL 2450319.2213 7.697 .843 .495 971 +V1162 AQL 2450320.2222 7.851 .943 .525 971 +V1162 AQL 2450321.2109 7.988 .958 .570 971 +V1162 AQL 2450322.2177 8.050 .974 .568 971 +V1162 AQL 2450323.2441 7.661 .794 .457 971 +V1162 AQL 2450324.2146 7.640 .798 .475 971 +V1162 AQL 2450325.2117 7.808 .918 .515 971 +V1162 AQL 2449808.9177 7.797 .897 .495 .476 997 +V1162 AQL 2449809.9022 7.907 .992 .513 .503 997 +V1162 AQL 2449810.8966 8.022 1.014 .535 997 +V1162 AQL 2449811.8973 7.925 .898 .496 .480 997 +V1162 AQL 2449813.8875 7.687 .843 .471 .457 997 +V1162 AQL 2449814.8893 7.859 .935 .523 .533 997 +V1162 AQL 2449817.8919 7.650 .769 .454 .426 997 +V1162 AQL 2449818.8907 7.629 .801 .447 .420 997 +V1162 AQL 2449821.9033 8.035 1.004 .538 .499 997 +V1162 AQL 2449822.9034 7.776 .832 .471 .469 997 +V1162 AQL 2449823.8929 7.572 .750 .443 .434 997 +V1162 AQL 2449825.8933 7.891 .964 .517 .509 997 +V1344 AQL 2448860.2467 7.897 1.474 .778 994 +V1344 AQL 2448862.2754 7.759 1.346 .752 994 +V1344 AQL 2448870.2317 7.680 1.345 .727 994 +V1344 AQL 2448872.2334 7.654 1.340 .729 994 +V1344 AQL 2448874.2278 7.836 1.439 .793 994 +V1344 AQL 2448876.2231 7.926 1.411 .795 994 +V1344 AQL 2448877.2013 7.754 1.357 .750 994 +V1344 AQL 2448878.2157 7.639 1.309 .726 994 +V1344 AQL 2448880.1540 7.715 1.363 .760 994 +V1344 AQL 2448881.1502 7.766 1.425 .764 994 +V1344 AQL 2448882.1606 7.880 1.462 .790 994 +V1344 AQL 2448883.1683 7.908 1.444 .785 994 +V1344 AQL 2448884.1743 7.817 1.403 .771 994 +V1344 AQL 2448885.1626 7.700 1.319 .744 994 +V1344 AQL 2448886.1620 7.645 1.322 .732 994 +V1344 AQL 2448888.1655 7.739 1.398 .761 994 +V1344 AQL 2448889.1763 7.849 1.437 .778 994 +V1344 AQL 2448890.1540 7.914 1.448 .803 994 +V1344 AQL 2448891.1529 7.886 1.407 .780 994 +V1344 AQL 2448892.1646 7.755 1.348 .745 994 +V1344 AQL 2448893.1555 7.657 1.333 .714 994 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2448512.2016 7.636 .857 1.299 .722 993 +V1344 AQL 2448513.2074 7.683 1.319 .735 993 +V1344 AQL 2448514.2090 7.751 .941 1.376 .763 993 +V1344 AQL 2448515.2040 7.843 1.374 .777 993 +V1344 AQL 2448516.1985 7.896 1.420 .790 993 +V1344 AQL 2448517.1869 7.900 1.037 1.402 .767 993 +V1344 AQL 2448518.1979 7.786 1.330 .748 993 +V1344 AQL 2448519.2167 7.635 1.303 .720 993 +V1344 AQL 2448520.1870 7.657 1.312 .714 993 +V1344 AQL 2448521.2095 7.678 1.343 .742 993 +V1344 AQL 2448522.1959 7.756 1.385 .755 993 +V1344 AQL 2448523.1898 7.877 1.422 .791 993 +V1344 AQL 2449521.8807 7.636 1.329 996 +V1344 AQL 2449522.8174 7.635 1.330 996 +V1344 AQL 2449528.8596 7.621 1.297 .744 .686 996 +V1344 AQL 2449529.7576 1.320 .740 .713 996 +V1344 AQL 2449530.8058 7.655 1.362 .761 .729 996 +V1344 AQL 2449534.7935 7.809 1.367 .765 .733 996 +V1344 AQL 2449543.7961 7.593 1.318 .757 .709 996 +V1344 AQL 2449545.7652 7.679 1.364 .751 .718 996 +V1344 AQL 2449561.8279 7.776 1.236 1.372 .777 .716 996 +V1344 AQL 2450305.2736 7.766 1.330 .757 971 +V1344 AQL 2450306.3039 7.648 1.271 .750 971 +V1344 AQL 2450307.2557 7.699 1.259 .754 971 +V1344 AQL 2450310.2624 7.879 1.388 .789 971 +V1344 AQL 2450311.1897 7.925 1.400 .790 971 +V1344 AQL 2450312.1897 7.829 1.341 .774 971 +V1344 AQL 2450313.2125 7.694 1.267 .737 971 +V1344 AQL 2450314.1862 7.649 1.272 .739 971 +V1344 AQL 2450315.1936 7.678 1.303 .744 971 +V1344 AQL 2450316.2048 7.765 1.323 .746 971 +V1344 AQL 2450317.2123 7.842 1.370 .786 971 +V1344 AQL 2450318.1985 7.934 1.394 .793 971 +V1344 AQL 2450319.1951 7.892 1.361 .784 971 +V1344 AQL 2450320.2067 7.768 1.302 .755 971 +V1344 AQL 2450321.2014 7.644 1.245 .747 971 +V1344 AQL 2450322.1854 7.672 1.268 .744 971 +V1344 AQL 2450323.1835 7.715 1.324 .759 971 +V1344 AQL 2450324.2049 7.800 1.361 .776 971 +V1344 AQL 2450325.1742 7.875 1.389 .787 971 +V1344 AQL 2450326.1607 7.927 1.391 .799 971 +V1344 AQL 2449934.3357 7.771 1.379 .765 1.475 998 +V1344 AQL 2449935.3412 7.802 .801 1.498 998 +V1344 AQL 2449936.3184 7.897 .805 1.525 998 +V1344 AQL 2449937.2993 7.938 1.444 .804 998 +V1344 AQL 2449938.3238 7.908 .787 1.523 998 +V1344 AQL 2449939.3260 7.710 .737 1.437 998 +V1344 AQL 2449941.3060 7.718 1.489 998 +V1344 AQL 2449942.2777 7.812 1.496 998 +V1344 AQL 2449943.2635 7.820 1.429 .776 1.499 998 +V1344 AQL 2449944.2749 7.949 1.545 998 +V1344 AQL 2449945.2687 7.934 1.529 998 +V1344 AQL 2449946.2417 7.805 1.487 998 +V1344 AQL 2449947.2361 7.675 1.433 998 +V1344 AQL 2449948.2328 7.678 1.429 998 +V1344 AQL 2449949.2356 7.769 1.473 998 +V1344 AQL 2449950.2338 7.775 1.492 998 +V1344 AQL 2449952.2376 7.948 1.550 998 +V1344 AQL 2449953.2683 7.878 1.525 998 +V1344 AQL 2449954.2356 7.731 1.426 998 +V1344 AQL 2449955.2326 7.671 1.442 998 +V1359 AQL 2449809.9089 9.019 1.355 .752 .736 916 +V1359 AQL 2449810.9020 8.990 1.360 .742 .633 916 +V1359 AQL 2449811.9049 9.012 1.340 .747 .741 916 +V1359 AQL 2449813.8923 8.994 1.343 .751 .706 916 +V1359 AQL 2449814.8917 9.003 1.317 .759 .724 916 +V1359 AQL 2449817.9017 8.998 1.336 .766 .721 916 +V1359 AQL 2449818.8921 9.002 1.335 .754 .707 916 +V1359 AQL 2449621.2870 9.076 1.396 .734 995 +V1359 AQL 2449623.2733 9.081 1.345 .773 995 +V1359 AQL 2449624.2453 9.063 1.353 .758 995 +V1359 AQL 2449625.2678 9.052 1.272 1.344 .760 995 +V1359 AQL 2449626.2765 8.991 1.337 .777 995 +V1359 AQL 2449631.2361 9.046 1.340 .770 995 +V1359 AQL 2449632.2592 9.051 1.343 .758 995 +V1359 AQL 2449633.2448 9.032 1.387 .754 995 +V1359 AQL 2449634.2404 9.044 1.347 .763 995 +V1359 AQL 2449522.8713 9.034 1.365 996 +V1359 AQL 2449528.8698 9.115 1.359 .779 .710 996 +V1359 AQL 2449529.7628 9.099 1.335 .768 .721 996 +V1359 AQL 2449530.8144 1.358 .756 .706 996 +V1359 AQL 2449534.8338 9.077 1.331 .747 .719 996 +V1359 AQL 2449543.8288 9.084 1.359 .736 .721 996 +V1359 AQL 2449545.7722 9.072 1.324 .771 .711 996 +V1359 AQL 2449558.8616 9.067 1.330 .790 .718 996 +V1359 AQL 2449559.8583 9.067 1.363 996 +V1359 AQL 2449561.8396 9.075 1.336 .775 .718 996 +V1359 AQL 2449564.8428 9.062 1.343 996 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10.651 .954 1.773 972 +V340 ARA 2450577.5935 10.655 .949 1.771 972 +V340 ARA 2450577.6359 10.657 .949 1.787 972 +V340 ARA 2450578.4842 10.560 .936 1.748 972 +V340 ARA 2450578.5765 10.565 .935 1.758 972 +V340 ARA 2450578.6313 10.555 .939 1.747 972 +V340 ARA 2450579.5359 10.478 .907 1.707 972 +V340 ARA 2450580.4573 10.446 .881 1.677 972 +V340 ARA 2450580.5699 10.442 .894 1.679 972 +V340 ARA 2450580.6193 10.426 .879 1.674 972 +V340 ARA 2450582.4725 9.643 .727 1.417 972 +V340 ARA 2450582.5950 9.675 .727 1.418 972 +V340 ARA 2450582.6427 9.632 .726 1.401 972 +V340 ARA 2450583.4776 9.663 .734 1.433 972 +V340 ARA 2450583.5954 9.679 .743 1.437 972 +V340 ARA 2450584.4349 9.740 .760 1.488 972 +V340 ARA 2450584.5870 9.763 .779 1.499 972 +Y AUR 2450007.5172 9.824 1.226 998 +Y AUR 2450008.4950 10.019 1.213 998 +Y AUR 2450009.4267 9.241 .885 998 +Y AUR 2450011.4149 9.887 1.208 998 +Y AUR 2450017.4156 9.311 .932 998 +Y AUR 2450018.4208 9.631 1.109 998 +Y AUR 2450019.3958 9.960 1.214 998 +Y AUR 2450020.3259 9.912 1.181 998 +Y AUR 2450332.4446 9.955 1.082 .719 971 +Y AUR 2450333.4572 9.388 .792 .617 971 +Y AUR 2450337.4204 9.267 .793 .493 971 +Y AUR 2450341.4261 9.175 .745 .466 971 +Y AUR 2450344.4314 9.956 1.080 .656 971 +Y AUR 2450347.4208 9.854 1.088 .623 971 +RT AUR 2450007.5199 5.418 .640 998 +RT AUR 2450008.4954 5.230 .565 998 +RT AUR 2450009.4533 5.599 .719 .392 .736 998 +RT AUR 2450010.4481 5.792 .808 .410 .800 998 +RT AUR 2450011.4157 5.200 .516 998 +RT AUR 2450017.4173 5.813 .795 998 +RT AUR 2450018.4210 5.736 .738 998 +RT AUR 2450019.3963 5.109 .501 998 +RT AUR 2450020.3265 5.431 .703 998 +RX AUR 2445666.3828 7.309 .509 .779 .474 982 +RX AUR 2445676.3828 7.673 .597 .938 .548 982 +RX AUR 2445679.3671 7.404 .548 .892 .510 982 +RX AUR 2445687.2851 7.763 .611 .970 .562 982 +RX AUR 2445689.4022 7.370 .533 .800 .484 982 +RX AUR 2445690.3242 7.331 .537 .816 .490 982 +RX AUR 2445691.3203 7.475 .594 .904 .536 982 +RX AUR 2445692.3163 7.561 .655 .980 .561 982 +RX AUR 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990 +GV AUR 2447408.4812 12.252 1.373 .808 990 +GV AUR 2447409.4783 12.453 1.428 .806 990 +GV AUR 2447410.4775 11.916 1.066 .710 990 +GV AUR 2447411.4864 11.745 1.055 .692 990 +GV AUR 2447413.4783 12.247 1.299 .816 990 +GV AUR 2447414.4864 12.431 1.387 .819 990 +GV AUR 2447415.4867 12.233 1.236 .756 990 +GV AUR 2447416.4807 11.637 1.027 .649 990 +GV AUR 2447417.4776 11.965 1.199 .737 990 +GV AUR 2447418.4765 12.173 1.287 .801 990 +GV AUR 2447419.4782 12.281 1.393 .793 990 +GV AUR 2447420.4777 12.418 1.357 .791 990 +GV AUR 2447421.4813 11.574 .967 .632 990 +GV AUR 2447422.4785 11.836 1.099 .722 990 +GV AUR 2447423.4991 12.117 1.276 .780 990 +GV AUR 2447424.4168 12.321 1.311 .820 990 +GV AUR 2447425.4631 12.506 1.358 .815 990 +GV AUR 2447427.4718 11.827 1.150 .710 990 +GV AUR 2447428.3920 12.064 1.237 .774 990 +GV AUR 2447430.4196 12.472 1.396 .854 990 +GV AUR 2447431.4788 11.952 1.100 .700 990 +GV AUR 2447432.4685 11.726 1.032 .707 990 +GV AUR 2447433.4463 12.013 1.252 .753 990 +GV AUR 2447434.4602 12.206 1.296 .787 990 +GV AUR 2448504.3937 12.073 1.181 .768 993 +GV AUR 2448505.4129 11.715 1.089 .689 993 +GV AUR 2448506.4079 12.031 1.204 .780 993 +GV AUR 2448507.4135 12.187 1.324 .798 993 +GV AUR 2448508.4138 12.420 1.367 .833 993 +GV AUR 2448509.4086 12.353 1.267 .810 993 +GV AUR 2448510.4056 11.631 1.030 .649 993 +GV AUR 2448511.3954 11.921 1.195 .743 993 +GV AUR 2448512.3867 12.185 1.293 .800 993 +GV AUR 2448513.3963 12.351 1.387 .827 993 +GV AUR 2448514.4060 12.487 1.360 .838 993 +GV AUR 2448515.3911 11.579 .959 .652 993 +GV AUR 2448516.4022 11.836 1.174 .722 993 +GV AUR 2448517.4012 12.095 1.269 .795 993 +GV AUR 2448518.4007 12.280 1.364 .807 993 +GV AUR 2448519.3557 12.465 1.393 .820 993 +GV AUR 2448520.3598 11.814 1.066 .699 993 +GV AUR 2448521.4109 11.765 1.120 .685 993 +GV AUR 2448522.3991 12.030 1.241 .767 993 +GV AUR 2448523.3806 12.232 .795 993 +GV AUR 2450332.4533 12.149 1.315 971 +GV AUR 2450333.4649 12.365 1.367 971 +GV AUR 2450337.4472 12.157 1.310 .827 971 +GV AUR 2450341.4346 11.773 1.122 .706 971 +GV AUR 2450344.4405 12.450 1.416 .882 971 +GV AUR 2450347.4302 11.958 1.262 .773 971 +HR AUR 2448102.4736 11.293 1.079 .629 992 +HR AUR 2448103.4536 11.328 1.173 992 +HR AUR 2448109.4661 11.959 1.073 992 +HR AUR 2448111.4698 11.259 1.106 .646 992 +HR AUR 2448112.4504 12.093 1.139 .756 992 +HR AUR 2448113.4653 11.033 1.078 .625 992 +HR AUR 2448114.4793 11.659 1.115 .707 992 +HR AUR 2448118.4857 11.084 1.065 .629 992 +HR AUR 2448119.4843 11.456 1.052 .703 992 +HR AUR 2448122.4848 11.915 1.108 .749 992 +HR AUR 2448123.4834 11.106 .998 .619 992 +HR AUR 2448126.4834 11.126 1.032 .616 992 +IN AUR 2446296.4496 13.599 1.355 .877 987 +IN AUR 2446297.4550 13.774 1.552 .902 987 +IN AUR 2446298.4605 13.906 1.508 .924 987 +IN AUR 2446299.4333 14.084 1.512 .981 987 +IN AUR 2446300.4342 13.712 1.387 .866 987 +IN AUR 2446301.4600 13.653 1.381 .896 987 +IN AUR 2446302.4582 13.795 1.528 .916 987 +IN AUR 2446303.4721 13.953 1.526 .959 987 +IN AUR 2446304.4499 14.096 1.574 .946 987 +IN AUR 2447087.4136 13.616 1.372 .895 989 +IN AUR 2447088.4025 13.843 1.456 .959 989 +IN AUR 2448103.4346 13.572 1.404 .869 992 +IN AUR 2448111.4544 14.006 1.670 992 +IN AUR 2448112.4332 13.729 1.396 .890 992 +IN AUR 2448113.4407 13.570 1.336 .920 992 +IN AUR 2448114.4602 13.829 1.464 992 +IN AUR 2448117.4836 13.820 .929 992 +IN AUR 2448118.4745 13.660 1.372 .878 992 +IN AUR 2448119.4700 13.818 1.522 .911 992 +IN AUR 2448122.4744 13.624 1.482 .840 992 +IN AUR 2448123.4704 13.618 1.417 .881 992 +IN AUR 2448126.4702 14.137 1.554 .970 992 +IN AUR 2448503.4649 13.972 1.472 .976 993 +IN AUR 2448504.3774 14.101 1.498 .983 993 +IN AUR 2448505.3941 13.744 1.396 .899 993 +IN AUR 2448506.3974 993 +IN AUR 2448507.4009 13.805 1.509 .941 993 +IN AUR 2448508.3994 13.985 1.544 .986 993 +IN AUR 2448509.3988 14.114 1.541 .962 993 +IN AUR 2448510.3988 13.660 1.444 .839 993 +IN AUR 2448511.3888 13.633 1.393 .891 993 +IN AUR 2448512.3807 13.840 1.486 .935 993 +IN AUR 2448513.3902 13.958 1.525 .945 993 +IN AUR 2448514.3995 14.097 1.571 .976 993 +IN AUR 2448515.3856 13.626 1.374 .872 993 +IN AUR 2448516.3940 13.643 1.407 .865 993 +IN AUR 2448517.3952 13.818 1.494 .953 993 +IN AUR 2448518.3944 13.957 1.546 .923 993 +IN AUR 2448519.3465 14.106 1.424 .946 993 +IN AUR 2448520.3536 13.550 1.338 .854 993 +IN AUR 2448521.3987 13.629 1.403 .891 993 +IN AUR 2448522.3757 1.506 993 +IN AUR 2448523.3642 13.978 1.606 993 +V335 AUR 2446287.4816 11.941 .943 .623 987 +V335 AUR 2446291.4741 12.322 1.140 .712 987 +V335 AUR 2446293.4635 12.862 .820 987 +V335 AUR 2446294.4575 12.041 1.016 .636 987 +V335 AUR 2446295.4398 12.554 1.270 .762 987 +V335 AUR 2446296.4536 12.852 1.347 .787 987 +V335 AUR 2446297.4582 11.990 .957 .630 987 +V335 AUR 2446298.4635 12.392 1.181 .728 987 +V335 AUR 2446299.4369 12.719 1.299 .789 987 +V335 AUR 2446300.4387 12.831 1.311 .800 987 +V335 AUR 2446301.4635 12.210 1.049 .691 987 +V335 AUR 2446302.4633 12.614 1.286 .777 987 +V335 AUR 2446303.4761 12.836 1.340 .805 987 +V335 AUR 2446304.4547 11.909 .964 .602 987 +V335 AUR 2447082.4852 11.919 .878 .600 989 +V335 AUR 2447083.4483 12.446 1.157 .747 989 +V335 AUR 2447084.3953 12.700 1.307 .784 989 +V335 AUR 2447085.4363 12.779 1.186 .798 989 +V335 AUR 2447087.4364 12.587 .847 1.230 .782 989 +V335 AUR 2447088.4306 12.842 1.285 .814 989 +V335 AUR 2447402.4932 12.871 1.325 .804 990 +V335 AUR 2447404.5027 12.527 1.224 .763 990 +V335 AUR 2447408.4777 12.660 1.314 .764 990 +V335 AUR 2447409.4757 12.880 1.286 .807 990 +V335 AUR 2447410.4749 12.123 .971 .658 990 +V335 AUR 2447411.4840 12.596 1.198 .785 990 +V335 AUR 2447413.4759 11.946 .914 .609 990 +V335 AUR 2447414.4831 12.440 1.153 .735 990 +V335 AUR 2447415.4853 12.757 1.287 .787 990 +V335 AUR 2447416.4780 12.797 1.207 .785 990 +V335 AUR 2447417.4759 12.228 1.051 .678 990 +V335 AUR 2447418.4736 12.635 1.269 .783 990 +V335 AUR 2447419.4753 12.756 1.328 .782 990 +V335 AUR 2447420.4769 11.938 .956 .600 990 +V335 AUR 2447421.4791 12.474 1.237 .740 990 +V335 AUR 2447422.4775 12.728 1.261 .786 990 +V335 AUR 2447423.4950 12.416 1.082 .700 990 +V335 AUR 2447424.4153 12.282 1.067 .706 990 +V335 AUR 2447425.4601 12.695 1.250 .777 990 +V335 AUR 2447427.4709 12.060 1.028 .629 990 +V335 AUR 2447428.3891 12.528 1.194 .760 990 +V335 AUR 2447430.4154 12.122 1.018 .628 990 +V335 AUR 2447431.4760 12.388 1.179 .703 990 +V335 AUR 2447432.4651 12.706 1.263 .816 990 +V335 AUR 2447433.4455 12.822 1.309 .778 990 +V335 AUR 2447434.4549 12.162 1.037 .658 990 +V335 AUR 2448503.4808 12.537 1.263 .755 993 +V335 AUR 2448504.3860 12.813 1.317 .836 993 +V335 AUR 2448505.4107 12.194 1.026 .677 993 +V335 AUR 2448506.4067 12.392 1.145 .737 993 +V335 AUR 2448507.4105 12.720 1.290 .815 993 +V335 AUR 2448508.4124 12.872 1.276 .805 993 +V335 AUR 2448509.4076 12.126 1.028 .662 993 +V335 AUR 2448510.4042 12.615 1.250 .773 993 +V335 AUR 2448511.3942 12.846 1.298 .802 993 +V335 AUR 2448512.3858 11.931 .953 .601 993 +V335 AUR 2448513.3953 12.432 1.208 .732 993 +V335 AUR 2448514.4052 12.755 1.318 .797 993 +V335 AUR 2448515.3900 12.771 1.229 .790 993 +V335 AUR 2448516.4009 12.233 1.103 .687 993 +V335 AUR 2448517.3971 12.641 1.260 .798 993 +V335 AUR 2448518.3998 12.846 1.337 .801 993 +V335 AUR 2448519.3550 11.902 .970 .589 993 +V335 AUR 2448520.3565 12.453 1.187 .744 993 +V335 AUR 2448521.4073 12.764 1.278 .793 993 +V335 AUR 2448522.3942 12.392 1.085 .699 993 +V335 AUR 2448523.3763 12.310 1.133 .706 993 +V335 AUR 2450332.4503 12.449 1.210 .809 971 +V335 AUR 2450333.4621 12.774 1.262 .911 971 +V335 AUR 2450337.4427 12.891 1.351 .827 971 +V335 AUR 2450341.4313 12.319 1.097 .693 971 +V335 AUR 2450344.4366 12.838 1.315 .799 971 +RU CAM 2449617.3410 8.288 1.099 .301 915 +RU CAM 2449620.3961 8.434 1.059 1.145 .308 915 +RU CAM 2449621.3942 8.505 .992 1.138 .325 915 +RU CAM 2449622.4469 8.537 1.046 1.139 .337 915 +RU CAM 2449623.4003 8.519 1.006 1.158 .308 915 +RU CAM 2449624.3486 8.565 1.151 1.147 .323 915 +RU CAM 2449624.4256 8.578 1.039 1.121 .330 915 +RU CAM 2449625.3688 8.581 1.170 1.144 .324 915 +RU CAM 2449631.3586 8.553 1.135 .318 915 +RU CAM 2449632.3634 8.525 .993 1.118 .328 915 +RU CAM 2449632.4487 8.542 .969 1.115 .323 915 +RU CAM 2449633.3309 8.477 1.011 1.122 .330 915 +RU CAM 2449633.4283 8.521 1.009 1.103 .311 915 +RU CAM 2449634.3447 8.483 1.087 1.127 .326 915 +RU CAM 2449635.3970 8.480 1.101 .324 915 +RU CAM 2449635.4695 8.484 1.090 .320 915 +RW CAM 2445660.3671 8.828 1.412 .840 982 +RW CAM 2445665.4101 8.423 1.409 .802 982 +RW CAM 2445666.3163 8.503 1.449 .821 982 +RW CAM 2445676.3125 8.797 1.421 .830 982 +RW CAM 2445679.3085 8.207 1.244 .730 982 +RW CAM 2445686.2421 8.813 1.542 .883 982 +RW CAM 2445687.2187 8.920 1.520 .888 982 +RW CAM 2445688.2500 9.005 1.534 .896 982 +RW CAM 2445689.3437 9.057 .832 1.497 .894 982 +RW CAM 2445690.2147 9.053 .807 1.480 .889 982 +RW CAM 2445691.2304 8.954 .798 1.447 .867 982 +RW CAM 2445692.2187 8.829 .776 1.407 .842 982 +RW CAM 2445693.2030 8.839 .785 1.376 .830 982 +RW CAM 2445694.2109 8.598 .774 1.313 .779 982 +RW CAM 2445695.2304 8.261 .779 1.215 .728 982 +RW CAM 2445705.1367 9.017 .808 1.512 .890 982 +RW CAM 2445706.1562 9.046 .797 1.491 .886 982 +RW CAM 2445707.1601 9.012 .806 1.471 .876 982 +RW CAM 2447401.4698 8.590 1.294 .755 990 +RW CAM 2447402.4411 8.250 1.210 .716 990 +RW CAM 2447403.4651 8.251 1.241 .737 990 +RW CAM 2447404.4634 8.344 1.305 .759 990 +RW CAM 2447407.4465 8.585 1.452 .831 990 +RW CAM 2447408.4432 8.657 1.514 .844 990 +RW CAM 2447409.4200 8.780 1.494 .870 990 +RW CAM 2447410.4376 8.881 1.512 .894 990 +RW CAM 2447411.4490 8.967 1.530 .893 990 +RW CAM 2447413.3841 9.068 1.462 .889 990 +RW CAM 2447413.4575 9.089 1.456 .899 990 +RW CAM 2447414.4520 9.008 1.451 .863 990 +RW CAM 2447415.4508 8.880 .757 1.425 .839 990 +RW CAM 2447416.3913 8.801 1.399 .817 990 +RW CAM 2447417.3933 8.779 1.359 .805 990 +RW CAM 2447418.3944 8.360 1.212 .753 990 +RW CAM 2447419.3797 8.224 1.217 .730 990 +RW CAM 2447420.3800 8.284 1.282 .741 990 +RW CAM 2447421.3748 8.379 1.361 .773 990 +RW CAM 2447422.3593 1.412 .781 990 +RW CAM 2447423.4488 8.541 .919 1.430 .830 990 +RW CAM 2447424.3849 8.650 1.439 .854 990 +RW CAM 2447425.4370 8.737 .884 1.485 .851 990 +RW CAM 2447427.4338 8.913 .849 1.568 .866 990 +RW CAM 2447428.3513 8.991 1.520 .881 990 +RW CAM 2447428.4513 9.009 1.519 .875 990 +RW CAM 2447430.3636 9.047 1.483 .876 990 +RW CAM 2447430.4734 9.046 1.489 .874 990 +RW CAM 2447431.4136 8.938 1.450 .844 990 +RW CAM 2447432.4137 8.781 1.402 .841 990 +RW CAM 2447433.3795 8.859 1.388 .832 990 +RW CAM 2447434.3857 8.510 .734 1.256 .758 990 +RW CAM 2447434.5149 8.486 1.231 .757 990 +RW CAM 2448858.4063 9.038 1.517 .882 994 +RW CAM 2448860.4082 8.838 1.440 .846 994 +RW CAM 2448862.4621 8.574 1.326 .768 994 +RW CAM 2448870.3799 8.749 1.535 .868 994 +RW CAM 2448872.3530 8.953 1.517 .904 994 +RW CAM 2448874.3907 9.055 1.533 .886 994 +RW CAM 2448875.4457 9.016 1.499 .865 994 +RW CAM 2448876.4357 8.892 1.455 .859 994 +RW CAM 2448877.3391 8.830 1.412 .826 994 +RW CAM 2448879.4121 8.355 1.259 .737 994 +RW CAM 2448880.3560 8.215 1.231 .730 994 +RW CAM 2448881.3641 8.287 1.305 .748 994 +RW CAM 2448882.3669 8.372 1.362 .780 994 +RW CAM 2448883.3728 8.479 1.430 .821 994 +RW CAM 2448885.3694 8.649 1.506 .859 994 +RW CAM 2448886.3549 8.733 1.518 .884 994 +RW CAM 2448887.3649 8.829 1.528 .889 994 +RW CAM 2448888.3347 8.918 1.527 .900 994 +RW CAM 2448889.3331 8.992 1.547 .890 994 +RW CAM 2448890.3110 9.036 1.530 .888 994 +RW CAM 2448891.3764 9.045 1.509 .876 994 +RW CAM 2448893.3858 8.808 1.433 .832 994 +RX CAM 2449948.4574 8.080 1.559 998 +RX CAM 2449986.4283 7.832 1.498 998 +RX CAM 2449987.4634 8.048 1.535 998 +RX CAM 2449992.4827 7.533 1.367 998 +RX CAM 2450007.5009 7.498 1.379 998 +RX CAM 2450008.4861 7.514 1.374 998 +RX CAM 2450009.4197 7.751 1.319 .778 1.478 998 +RX CAM 2450010.4095 7.896 1.526 998 +RX CAM 2450011.3890 8.064 1.556 998 +RX CAM 2450017.4070 7.785 1.340 .773 1.463 998 +RX CAM 2450018.4149 7.879 1.504 998 +RX CAM 2450019.3527 8.053 1.543 998 +RX CAM 2450020.3203 7.944 1.497 998 +RX CAM 2450323.4680 7.473 1.143 .697 971 +RX CAM 2450326.3957 7.799 1.316 .763 971 +RX CAM 2450327.4471 7.930 1.367 .783 971 +RX CAM 2450328.4555 8.026 1.385 .805 971 +RX CAM 2450332.3704 7.461 1.163 .726 971 +RX CAM 2450333.3735 7.640 1.240 .790 971 +RX CAM 2450334.3904 7.780 1.325 .775 971 +RX CAM 2450335.3964 7.978 1.394 .835 971 +RX CAM 2450337.3578 7.799 1.263 .759 971 +RX CAM 2450338.4416 7.330 1.049 .669 971 +RX CAM 2450340.3594 7.455 1.160 .723 971 +RX CAM 2450341.3667 7.673 1.270 .773 971 +RX CAM 2450342.3828 7.780 1.320 .792 971 +RX CAM 2450344.3646 8.042 1.394 .817 971 +RX CAM 2450347.3760 7.453 1.139 .685 971 +TV CAM 2448103.4097 11.251 .918 .581 992 +TV CAM 2448104.4499 11.575 1.117 .679 992 +TV CAM 2448109.4030 11.468 1.060 .657 992 +TV CAM 2448111.4175 11.978 1.314 .773 992 +TV CAM 2448112.3782 12.150 1.390 .797 992 +TV CAM 2448113.3646 11.600 1.086 .657 992 +TV CAM 2448114.4285 11.422 1.032 .654 992 +TV CAM 2448116.4391 11.909 1.292 .776 992 +TV CAM 2448118.4230 11.940 1.213 .720 992 +TV CAM 2448119.4238 11.298 .968 .616 992 +TV CAM 2448122.4297 12.069 1.354 .788 992 +TV CAM 2448123.3989 12.153 1.310 .769 992 +TV CAM 2448126.3721 11.783 1.288 .742 992 +TV CAM 2448127.3366 11.948 1.322 .783 992 +TV CAM 2450323.4783 11.774 1.185 .743 971 +TV CAM 2450326.3987 11.972 1.206 .722 971 +TV CAM 2450327.4495 11.278 .964 .596 971 +TV CAM 2450328.4575 11.605 1.173 .711 971 +TV CAM 2450332.3733 11.219 .941 .608 971 +TV CAM 2450333.3767 11.525 1.089 .715 971 +TV CAM 2450334.3925 11.777 1.252 .732 971 +TV CAM 2450335.3982 12.016 1.332 .815 971 +TV CAM 2450337.3599 11.546 1.037 .663 971 +TV CAM 2450338.4430 11.445 1.063 .667 971 +TV CAM 2450340.3609 11.955 1.273 .771 971 +TV CAM 2450341.3687 12.144 1.351 .818 971 +TV CAM 2450342.3843 11.822 1.153 .715 971 +TV CAM 2450344.3707 11.624 1.183 .727 971 +TV CAM 2450347.3773 12.081 1.282 .756 971 +AB CAM 2448102.4367 11.466 1.078 .643 992 +AB CAM 2448103.3958 11.808 1.218 .743 992 +AB CAM 2448104.4460 11.975 1.313 .772 992 +AB CAM 2448109.3974 11.846 1.245 .748 992 +AB CAM 2448111.4120 12.267 1.394 .820 992 +AB CAM 2448112.3727 12.253 1.364 .791 992 +AB CAM 2448113.3617 11.362 .983 .620 992 +AB CAM 2448114.4250 11.681 1.144 .712 992 +AB CAM 2448116.4326 12.120 1.376 .808 992 +AB CAM 2448118.4194 12.128 1.268 .798 992 +AB CAM 2448119.4205 11.440 1.015 .641 992 +AB CAM 2448122.4267 12.182 1.407 .814 992 +AB CAM 2448123.3947 12.355 1.423 .821 992 +AB CAM 2448126.3679 11.758 1.205 .730 992 +AB CAM 2448127.3321 11.894 1.340 .753 992 +AB CAM 2450323.4660 12.084 1.308 .745 971 +AB CAM 2450326.3883 11.946 1.278 .747 971 +AB CAM 2450327.4207 12.141 1.391 .797 971 +AB CAM 2450328.4506 12.297 1.419 .811 971 +AB CAM 2450332.3677 11.928 1.315 .797 971 +AB CAM 2450333.3701 12.197 1.381 .860 971 +AB CAM 2450334.3870 12.338 1.417 .801 971 +AB CAM 2450335.3937 11.687 1.122 .695 971 +AB CAM 2450337.3266 11.799 1.275 .741 971 +AB CAM 2450338.4395 11.988 1.320 .785 971 +AB CAM 2450340.3573 12.324 1.460 .823 971 +AB CAM 2450341.3645 11.455 1.029 .634 971 +AB CAM 2450342.3796 11.568 1.136 .659 971 +AB CAM 2450344.3622 12.026 1.333 .807 971 +AB CAM 2450347.3731 11.375 .998 .584 971 +AC CAM 2446260.4535 12.775 1.617 1.023 987 +AC CAM 2446269.4520 12.880 1.646 1.023 987 +AC CAM 2446270.4620 12.514 1.496 .935 987 +AC CAM 2446274.4618 12.670 1.542 .945 987 +AC CAM 2446283.4497 12.294 1.370 .893 987 +AC CAM 2446284.4621 12.587 1.570 .964 987 +AC CAM 2446285.4370 12.730 1.601 1.011 987 +AC CAM 2446287.3991 12.258 1.398 .881 987 +AC CAM 2446288.4361 12.496 1.549 .949 987 +AC CAM 2446289.4386 12.733 1.610 .997 987 +AC CAM 2446290.4532 12.907 1.673 .997 987 +AC CAM 2446291.4312 12.326 1.399 .884 987 +AC CAM 2446293.4052 12.699 1.665 .985 987 +AC CAM 2446294.4062 12.866 1.679 1.008 987 +AC CAM 2446295.3909 12.527 1.476 .930 987 +AC CAM 2446296.4032 12.418 1.459 .934 987 +AC CAM 2446297.4141 12.672 1.598 .986 987 +AC CAM 2446298.4282 12.854 1.643 1.023 987 +AC CAM 2446299.3949 12.683 1.541 .953 987 +AC CAM 2446300.3742 12.361 1.429 .897 987 +AC CAM 2446301.4318 12.680 1.605 .999 987 +AC CAM 2446302.4146 12.809 1.697 987 +AC CAM 2446303.4098 12.797 1.559 .978 987 +AC CAM 2446304.3553 12.295 1.350 .886 987 +AC CAM 2450323.4634 12.426 1.429 .897 971 +AC CAM 2450326.3832 12.854 1.657 .984 971 +AC CAM 2450327.4178 12.601 1.515 .951 971 +AC CAM 2450328.4450 12.360 1.476 .895 971 +AC CAM 2450332.3643 12.316 1.402 .924 971 +AC CAM 2450333.3653 12.599 1.598 1.004 971 +AC CAM 2450334.3786 12.790 1.675 .990 971 +AC CAM 2450335.3908 12.867 1.643 1.017 971 +AC CAM 2450337.3233 12.549 1.612 .959 971 +AC CAM 2450338.4356 12.795 1.630 1.016 971 +AC CAM 2450340.3548 12.260 1.400 .883 971 +AC CAM 2450341.3617 12.536 1.550 .973 971 +AC CAM 2450342.3742 12.715 1.589 .976 971 +AC CAM 2450344.3596 12.298 1.424 .877 971 +AC CAM 2450347.3685 12.863 1.669 .982 971 +AD CAM 2444829.3984 12.875 1.748 982 +AD CAM 2444830.4256 13.009 1.791 982 +AD CAM 2444831.3593 13.034 1.761 982 +AD CAM 2444832.4218 12.682 1.639 982 +AD CAM 2444833.4101 12.708 1.576 982 +AD CAM 2444834.4375 11.962 1.300 982 +AD CAM 2444835.4375 12.187 1.473 982 +AD CAM 2444880.3867 12.167 1.484 982 +AD CAM 2444881.3593 12.293 1.525 982 +AD CAM 2444883.3163 12.526 1.649 982 +AD CAM 2444884.3593 12.670 1.731 982 +AD CAM 2445179.4256 13.014 1.846 982 +AD CAM 2445180.3905 13.011 1.791 982 +AD CAM 2445181.4022 12.762 1.656 982 +AD CAM 2445182.3984 12.702 1.614 982 +AD CAM 2445183.4062 12.059 1.395 982 +AD CAM 2445184.3828 12.146 1.418 982 +AD CAM 2445186.4256 12.387 1.581 982 +AD CAM 2445187.3828 12.514 1.673 982 +AD CAM 2445188.4022 12.635 1.729 982 +AD CAM 2445189.4296 12.818 1.770 982 +AD CAM 2445190.4101 12.968 1.800 982 +AD CAM 2445191.4022 13.029 1.788 982 +AD CAM 2445192.4101 12.844 1.704 982 +AD CAM 2445193.4218 12.734 1.587 982 +AD CAM 2445198.4296 12.486 1.632 982 +AD CAM 2445199.4139 12.612 1.716 982 +AD CAM 2445200.4375 12.778 1.763 982 +AD CAM 2445665.4022 12.824 982 +AD CAM 2445666.3085 12.678 1.016 982 +AD CAM 2445676.3046 12.887 1.047 982 +AD CAM 2445686.2304 13.021 1.076 982 +AD CAM 2445687.2109 12.941 1.043 982 +AD CAM 2445688.2421 12.726 982 +AD CAM 2445689.3320 12.713 1.609 1.000 982 +AD CAM 2445690.2070 12.046 1.365 .852 982 +AD CAM 2445691.2187 12.179 1.437 .921 982 +AD CAM 2445692.2109 12.291 1.529 .947 982 +AD CAM 2445693.1913 12.421 1.549 1.008 982 +AD CAM 2445694.2030 12.511 1.690 1.021 982 +AD CAM 2445695.2226 12.669 1.745 1.061 982 +AD CAM 2445701.2070 12.542 1.524 .945 982 +AD CAM 2445705.1328 12.502 1.608 1.027 982 +AD CAM 2445706.1562 12.613 1.697 1.055 982 +AD CAM 2445707.1562 12.774 1.763 1.071 982 +AD CAM 2447401.4658 12.614 1.528 .957 990 +AD CAM 2447402.4291 11.946 1.287 .836 990 +AD CAM 2447403.4582 12.265 1.494 .912 990 +AD CAM 2447404.4558 12.345 1.549 .961 990 +AD CAM 2447407.4355 12.738 1.773 1.060 990 +AD CAM 2447408.4355 12.893 1.826 1.067 990 +AD CAM 2447409.4102 13.051 1.834 1.075 990 +AD CAM 2447410.4294 13.003 1.771 1.043 990 +AD CAM 2447411.4393 12.733 1.647 1.039 990 +AD CAM 2447413.3777 11.981 1.285 .850 990 +AD CAM 2447414.4443 12.202 1.462 .899 990 +AD CAM 2447415.4423 12.329 1.515 .957 990 +AD CAM 2447416.3836 12.431 1.604 .983 990 +AD CAM 2447417.3864 12.542 1.678 1.011 990 +AD CAM 2447418.3864 12.700 1.717 1.051 990 +AD CAM 2447419.3688 12.830 1.782 1.073 990 +AD CAM 2447420.3607 13.018 1.818 1.086 990 +AD CAM 2447421.3665 13.036 1.778 1.059 990 +AD CAM 2447422.3503 12.845 1.631 1.027 990 +AD CAM 2447423.4416 12.690 1.574 .965 990 +AD CAM 2447424.3774 12.214 1.379 .864 990 +AD CAM 2447425.3545 12.136 1.348 .888 990 +AD CAM 2447427.4250 12.416 1.622 .980 990 +AD CAM 2447428.3393 12.504 1.642 1.013 990 +AD CAM 2447430.3492 12.811 1.764 1.082 990 +AD CAM 2447431.3943 12.981 1.768 1.075 990 +AD CAM 2447432.3975 13.018 1.772 1.091 990 +AD CAM 2447433.3704 12.923 1.709 1.046 990 +AD CAM 2447434.3734 12.741 1.603 .993 990 +AD CAM 2448852.4800 12.913 1.803 1.034 994 +AD CAM 2448854.4818 12.415 1.486 .952 994 +AD CAM 2448858.4014 12.493 1.665 1.030 994 +AD CAM 2448860.4035 12.803 1.768 1.097 994 +AD CAM 2448862.4556 13.047 1.828 1.085 994 +AD CAM 2448870.3766 12.575 1.740 1.043 994 +AD CAM 2448872.3516 12.912 1.785 1.094 994 +AD CAM 2448874.3855 13.043 1.734 1.098 994 +AD CAM 2448875.4324 12.770 1.638 1.018 994 +AD CAM 2448876.4271 12.722 1.607 .985 994 +AD CAM 2448877.3350 12.023 1.340 .850 994 +AD CAM 2448879.4090 12.353 1.547 .964 994 +AD CAM 2448880.3399 12.440 1.582 .995 994 +AD CAM 2448881.3606 12.527 1.721 1.007 994 +AD CAM 2448882.3622 12.704 1.710 1.075 994 +AD CAM 2448883.3674 12.860 1.798 1.084 994 +AD CAM 2448885.3627 13.059 1.810 1.101 994 +AD CAM 2448886.3535 12.837 1.693 1.034 994 +AD CAM 2448887.3610 12.752 1.622 1.003 994 +AD CAM 2448888.3251 12.352 1.457 .940 994 +AD CAM 2448889.3301 12.088 1.377 .899 994 +AD CAM 2448890.2966 12.295 1.537 .954 994 +AD CAM 2448891.3153 12.387 1.594 .978 994 +AD CAM 2448892.3168 1.684 1.035 994 +AD CAM 2448893.3835 12.672 1.741 1.076 994 +AM CAM 2446269.4595 13.263 1.487 .918 987 +AM CAM 2446283.4570 13.640 1.672 1.022 987 +AM CAM 2446284.4709 13.786 1.702 1.033 987 +AM CAM 2446285.4416 13.256 1.423 .926 987 +AM CAM 2446287.4047 13.607 1.638 1.019 987 +AM CAM 2446288.4401 13.776 1.672 1.040 987 +AM CAM 2446289.4420 13.274 1.431 .928 987 +AM CAM 2446290.4574 13.414 1.616 .958 987 +AM CAM 2446291.4353 13.595 1.709 .994 987 +AM CAM 2446293.4108 13.305 1.497 .937 987 +AM CAM 2446294.4118 13.399 1.582 .975 987 +AM CAM 2446295.3990 13.623 1.664 1.024 987 +AM CAM 2446296.4080 13.777 1.658 1.043 987 +AM CAM 2446297.4243 13.277 1.439 .911 987 +AM CAM 2446298.4314 13.400 1.575 .965 987 +AM CAM 2446299.3993 13.650 1.678 1.023 987 +AM CAM 2446300.3778 13.775 1.693 1.017 987 +AM CAM 2446301.4349 13.320 1.450 .919 987 +AM CAM 2446302.4190 13.388 1.594 .954 987 +AM CAM 2446303.4140 13.641 1.641 1.045 987 +AM CAM 2446304.3615 13.750 1.726 1.031 987 +AM CAM 2447402.4330 13.580 1.690 .986 990 +AM CAM 2447404.4590 13.424 1.476 .943 990 +AM CAM 2447407.4438 13.750 1.669 1.028 990 +AM CAM 2447408.4404 13.410 1.554 .947 990 +AM CAM 2447409.4147 13.356 1.557 .940 990 +AM CAM 2447410.4354 13.607 1.614 1.011 990 +AM CAM 2447411.4466 13.747 1.727 990 +AM CAM 2447413.3815 13.351 1.505 .951 990 +AM CAM 2447414.4467 13.574 1.672 .984 990 +AM CAM 2447415.4468 13.751 1.670 1.013 990 +AM CAM 2447416.3882 13.475 1.538 .948 990 +AM CAM 2447417.3903 13.343 1.484 .937 990 +AM CAM 2447418.3910 13.605 1.650 1.023 990 +AM CAM 2447419.3731 13.745 1.659 1.031 990 +AM CAM 2447420.3771 13.445 1.565 .929 990 +AM CAM 2447421.3717 13.321 1.523 .940 990 +AM CAM 2447422.3528 13.480 1.617 .978 990 +AM CAM 2447423.4438 13.693 1.674 .985 990 +AM CAM 2447424.3819 13.498 1.486 .953 990 +AM CAM 2447425.3560 13.380 1.473 .946 990 +AM CAM 2447427.4272 13.734 1.715 1.027 990 +AM CAM 2447428.3456 13.490 1.488 .926 990 +AM CAM 2447430.3528 13.582 1.667 .976 990 +AM CAM 2447431.4093 13.748 1.633 1.018 990 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2450312.3420 7.304 .890 .572 923 +CK CAM 2450314.3428 7.814 1.102 .664 923 +CK CAM 2450314.4746 7.795 1.081 .653 923 +CK CAM 2450315.3352 7.227 .833 .537 923 +CK CAM 2450315.4506 7.241 .852 .549 923 +CK CAM 2450316.4355 7.593 1.041 .643 923 +CK CAM 2450317.4789 7.801 1.102 .670 923 +CK CAM 2450318.3112 7.372 .888 .557 923 +CK CAM 2450318.4680 7.245 .833 .543 923 +CK CAM 2450319.3408 7.458 .974 .612 923 +CK CAM 2450320.3198 7.738 1.087 .665 923 +CK CAM 2450321.3042 7.699 1.011 .628 923 +CK CAM 2450322.3015 7.328 .909 .573 923 +CK CAM 2450322.4339 7.390 .935 .595 923 +CK CAM 2450323.3132 7.688 1.037 .677 923 +CK CAM 2450324.3567 7.791 1.072 .652 923 +TW CAP 2450350.6862 10.717 .760 .841 999 +TW CAP 2450351.6393 10.645 .464 .706 999 +TW CAP 2450352.7095 10.214 .371 .581 999 +TW CAP 2450353.6027 9.990 .344 .534 999 +TW CAP 2450354.6263 9.981 .367 .562 999 +TW CAP 2450354.6968 9.981 .377 .567 999 +TW CAP 2450355.5913 10.104 .417 .627 999 +TW CAP 2450355.6481 10.133 .441 .635 999 +TW CAP 2450357.5965 10.257 .499 .712 999 +TW CAP 2450357.6958 10.236 .497 .721 999 +TW CAP 2450358.6319 10.304 .748 999 +TW CAP 2450359.6223 10.325 .793 999 +TW CAP 2450359.7014 10.345 .820 999 +TW CAP 2450361.6362 10.383 .840 999 +TW CAP 2450362.6450 10.398 .854 999 +TW CAP 2450363.6225 10.402 .859 999 +TW CAP 2450379.6243 10.886 .799 999 +TW CAP 2450380.6236 10.594 .463 .704 999 +TW CAP 2450381.6267 10.244 .598 999 +TW CAP 2450382.6164 10.089 .586 999 +TW CAP 2450383.6240 10.065 .602 999 +TW CAP 2450384.6198 10.157 .673 999 +TW CAP 2450386.6084 10.275 .763 999 +TW CAP 2450387.5889 10.310 .767 999 +TW CAP 2450388.5881 10.337 .814 999 +TW CAP 2450389.5816 10.360 .820 999 +TW CAP 2450390.5741 10.382 .841 999 +TW CAP 2450391.5712 10.372 .653 .849 999 +TW CAP 2450392.5781 10.396 .878 999 +TW CAP 2450393.5713 10.450 .852 999 +VX CAP 2450351.6448 14.841 .180 .435 999 +VX CAP 2450352.7140 14.328 .179 999 +VX CAP 2450353.6122 15.401 .424 .655 999 +VX CAP 2450355.5945 14.667 .248 .354 999 +VX CAP 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CAR 2449825.7408 8.370 .799 .443 .415 997 +V397 CAR 2449826.6702 8.300 .728 .423 .399 997 +V397 CAR 2449827.6423 8.327 .786 .443 .396 997 +V397 CAR 2450352.8785 8.270 .821 999 +V397 CAR 2450354.8711 8.302 .826 999 +V397 CAR 2450355.8776 8.321 .829 999 +V397 CAR 2450357.8857 8.296 .856 999 +V397 CAR 2450358.8678 8.353 .868 999 +V397 CAR 2450359.8596 8.277 .831 999 +V397 CAR 2450361.8507 8.263 .808 999 +V397 CAR 2450362.8563 8.396 .803 .864 999 +V397 CAR 2450363.8533 8.233 .811 999 +V397 CAR 2450379.8068 8.243 .794 999 +V397 CAR 2450380.8023 8.355 .886 999 +V397 CAR 2450381.8024 8.260 .821 999 +V397 CAR 2450383.8009 8.262 .818 999 +V397 CAR 2450384.7822 8.304 .850 999 +V397 CAR 2450386.7835 8.292 .828 999 +V397 CAR 2450387.7842 8.335 .864 999 +V397 CAR 2450388.7776 8.268 .844 999 +V397 CAR 2450390.7800 8.250 .835 999 +V397 CAR 2450391.7595 8.393 .875 999 +V397 CAR 2450392.7605 8.223 .823 999 +V397 CAR 2450393.7670 8.386 .882 999 +V397 CAR 2450394.7623 8.201 .804 999 +V397 CAR 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1.370 972 +L CAR 2450571.1998 4.061 1.297 972 +L CAR 2450572.1933 4.077 1.303 972 +L CAR 2450573.1912 4.064 1.285 972 +L CAR 2450573.1934 4.054 1.282 972 +L CAR 2450575.1907 4.042 1.259 972 +L CAR 2450576.1916 4.023 1.234 972 +L CAR 2450578.1872 1.176 972 +L CAR 2450579.1861 3.721 1.137 972 +L CAR 2450579.2737 1.152 972 +L CAR 2450580.1892 3.597 1.083 972 +L CAR 2450581.1929 3.484 1.044 972 +L CAR 2450581.2066 3.486 1.058 972 +L CAR 2450582.1818 3.415 1.031 972 +L CAR 2450582.2555 3.393 1.022 972 +L CAR 2450583.2139 3.354 1.025 972 +L CAR 2450583.2509 3.355 1.029 972 +RS CAS 2446608.4305 9.680 1.033 1.383 .865 988 +RS CAS 2446611.4337 10.239 1.648 .980 988 +RS CAS 2446613.4201 9.787 1.023 1.375 .852 988 +RS CAS 2446614.4322 9.623 1.047 1.366 .844 988 +RS CAS 2446615.3965 9.829 1.111 1.470 .904 988 +RS CAS 2446616.4291 10.001 1.162 1.544 .945 988 +RS CAS 2446617.3892 10.182 1.258 1.632 988 +RS CAS 2446618.4387 10.296 1.654 .994 988 +RS CAS 2446620.3945 9.556 .991 1.304 .809 988 +RS CAS 2446621.4189 9.761 1.055 1.435 .880 988 +RS CAS 2446622.4079 9.959 1.151 1.546 .930 988 +RS CAS 2446624.4088 10.320 1.643 .999 988 +RS CAS 2446625.3744 10.231 1.547 .958 988 +RS CAS 2446626.4010 9.546 1.280 .810 988 +RS CAS 2446627.3958 9.708 1.393 .872 988 +RS CAS 2446628.4076 9.922 1.160 1.514 .932 988 +RS CAS 2446629.3549 10.025 1.558 .956 988 +RS CAS 2446631.3789 10.326 1.618 .979 988 +RS CAS 2446632.4127 9.694 1.354 .828 988 +RS CAS 2446636.3917 10.210 1.632 .987 988 +RS CAS 2448854.3722 10.049 1.556 .907 994 +RS CAS 2448856.3599 9.739 1.437 .851 994 +RS CAS 2448860.3328 10.274 1.603 .960 994 +RS CAS 2448862.3384 9.685 1.399 .843 994 +RS CAS 2448870.3138 9.953 1.573 .909 994 +RS CAS 2448872.2905 10.319 1.676 .971 994 +RS CAS 2448874.3175 9.557 1.298 .814 994 +RS CAS 2448875.3330 9.764 1.446 .877 994 +RS CAS 2448876.3205 9.964 1.551 .930 994 +RS CAS 2448877.2725 10.113 1.647 .940 994 +RS CAS 2448878.3415 10.288 1.686 .987 994 +RS CAS 2448880.2573 9.577 1.315 .823 994 +RS CAS 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8.080 .377 .764 .431 992 +TU CAS 2448126.4199 8.075 .385 .763 .437 992 +TU CAS 2448126.4901 8.102 .375 .732 .443 992 +TU CAS 2448127.2978 7.541 .283 .537 .317 992 +TU CAS 2448127.3972 7.647 .293 .587 .346 992 +TU CAS 2448852.4168 7.222 .380 .228 905 +TU CAS 2448854.3529 7.251 .206 .385 .227 905 +TU CAS 2448854.4117 7.324 .233 .428 .245 905 +TU CAS 2448854.4546 7.396 .239 .455 .270 905 +TU CAS 2448856.3973 7.709 .245 .562 .338 905 +TU CAS 2448856.4647 7.652 .240 .566 .328 905 +TU CAS 2448856.4955 7.643 .242 .541 .325 905 +TU CAS 2448857.3295 7.736 .273 .628 .368 905 +TU CAS 2448857.5016 8.112 .428 .726 .429 905 +TU CAS 2448858.2250 8.064 .357 .747 .408 905 +TU CAS 2448858.3009 8.049 .352 .724 .405 905 +TU CAS 2448858.3521 8.029 .322 .693 .403 905 +TU CAS 2448858.3740 7.997 .306 .671 .398 905 +TU CAS 2448858.4002 7.953 .274 .661 .389 905 +TU CAS 2448858.4522 7.805 .246 .598 .362 905 +TU CAS 2448858.5024 7.573 .218 .516 .318 905 +TU CAS 2448859.2848 7.790 .308 .676 .380 905 +TU CAS 2448859.4178 7.886 .328 .705 .401 905 +TU CAS 2448859.5043 7.916 .317 .731 .409 905 +TU CAS 2448860.2257 8.039 .326 .737 .409 905 +TU CAS 2448860.2853 8.004 .313 .716 .406 905 +TU CAS 2448860.3572 7.897 .288 .654 .366 905 +TU CAS 2448860.3586 7.887 .248 .658 .371 905 +TU CAS 2448860.4656 7.717 .237 .632 .335 905 +TU CAS 2448860.4765 7.725 .243 .578 .342 905 +TU CAS 2448860.4991 7.683 .251 .556 .329 905 +TU CAS 2448861.3602 7.734 .624 .363 905 +TU CAS 2448862.3289 8.012 .372 .708 .420 905 +TU CAS 2448862.3452 7.981 .307 .728 .403 905 +TU CAS 2448862.3710 8.003 .349 .713 .414 905 +TU CAS 2448862.4381 8.002 .333 .725 .381 905 +TU CAS 2448862.4964 8.004 .354 .724 .388 905 +TU CAS 2448863.3303 7.481 .258 .518 .317 905 +TU CAS 2448863.5035 7.641 .597 .353 905 +TU CAS 2448870.2672 7.950 .369 .735 .407 905 +TU CAS 2448870.3136 7.964 .378 .726 .406 905 +TU CAS 2448870.3371 7.970 .385 .748 .408 905 +TU CAS 2448870.3883 8.007 .351 .747 .432 905 +TU CAS 2448870.4015 7.982 .370 .736 .411 905 +TU CAS 2448870.4049 7.988 .379 .755 .419 905 +TU CAS 2448870.4654 8.012 .377 .745 .414 905 +TU CAS 2448870.4808 7.993 .340 .743 .406 905 +TU CAS 2448870.5108 8.014 .382 .742 .429 905 +TU CAS 2448871.3151 7.608 .246 .540 .326 905 +TU CAS 2448871.4313 7.579 .247 .530 .321 905 +TU CAS 2448872.1772 7.709 .259 .644 .343 905 +TU CAS 2448872.2232 7.734 .245 .633 .364 905 +TU CAS 2448872.3038 7.761 .297 .621 .360 905 +TU CAS 2448872.3276 7.768 .286 .632 .361 905 +TU CAS 2448872.3598 7.775 .296 .639 .375 905 +TU CAS 2448872.3865 7.792 .290 .644 .379 905 +TU CAS 2448872.4212 7.802 .300 .649 .381 905 +TU CAS 2448872.4303 7.793 .303 .663 .364 905 +TU CAS 2448872.5103 7.843 .287 .670 .399 905 +TU CAS 2448873.2476 8.004 .317 .722 .396 905 +TU CAS 2448873.4224 7.908 .253 .665 .373 905 +TU CAS 2448874.1777 7.651 .248 .612 .345 905 +TU CAS 2448874.2436 7.695 .301 .644 .356 905 +TU CAS 2448874.2623 7.718 .263 .651 .364 905 +TU CAS 2448874.3052 7.750 .658 .373 905 +TU CAS 2448874.3401 7.787 .291 .680 .384 905 +TU CAS 2448874.4083 7.835 .311 .706 .389 905 +TU CAS 2448874.4205 7.843 .309 .700 .390 905 +TU CAS 2448874.5156 7.893 .329 .745 .397 905 +TU CAS 2448875.2144 8.107 .366 .751 .416 905 +TU CAS 2448875.3122 8.053 .308 .731 .411 905 +TU CAS 2448875.3547 7.981 .263 .731 .384 905 +TU CAS 2448875.3951 7.898 .252 .654 .376 905 +TU CAS 2448875.4326 7.808 .248 .612 .353 905 +TU CAS 2448875.4506 7.773 .234 .595 .351 905 +TU CAS 2448875.5095 7.565 .224 .525 .300 905 +TU CAS 2448876.2130 7.728 .300 .634 .261 905 +TU CAS 2448876.2160 7.761 .307 .636 .384 905 +TU CAS 2448876.2813 7.794 .376 .652 .378 905 +TU CAS 2448876.3184 7.799 .303 .649 .382 905 +TU CAS 2448876.3190 7.790 .298 .662 .377 905 +TU CAS 2448876.3476 7.803 .315 .652 .382 905 +TU CAS 2448876.4127 7.825 .321 .696 .378 905 +TU CAS 2448876.4285 7.826 .314 .679 .380 905 +TU CAS 2448876.5207 7.859 .313 .686 .386 905 +TU CAS 2448877.1882 7.967 .331 .718 .389 905 +TU CAS 2448877.2269 7.952 .313 .705 .393 905 +TU CAS 2448877.2595 7.970 .340 .703 .402 905 +TU CAS 2448877.2965 7.975 .339 .698 .395 905 +TU CAS 2448877.3513 7.965 .315 .709 .393 905 +TU CAS 2448877.4147 7.943 .316 .714 .365 905 +TU CAS 2448877.4536 7.931 .283 .692 .384 905 +TU CAS 2448877.5163 7.930 .271 .680 .388 905 +TU CAS 2448878.1814 7.384 .242 .462 .289 905 +TU CAS 2448878.2010 7.377 .228 .448 .268 905 +TU CAS 2448878.2952 7.418 .243 .489 .276 905 +TU CAS 2448878.3329 7.449 .234 .498 .292 905 +TU CAS 2448878.3670 7.490 .222 .506 .310 905 +TU CAS 2448878.4161 7.528 .246 .537 .323 905 +TU CAS 2448879.1803 7.999 .347 .745 .406 905 +TU CAS 2448879.2964 8.036 .360 .747 .430 905 +TU CAS 2448879.3470 8.053 .359 .771 .420 905 +TU CAS 2448879.3594 8.051 .357 .754 .419 905 +TU CAS 2448879.3673 8.054 .370 .763 .414 905 +TU CAS 2448879.4015 8.076 .390 .755 .421 905 +TU CAS 2448879.4397 8.075 .773 .421 905 +TU CAS 2448879.5183 8.092 .754 .423 905 +TU CAS 2448880.1787 7.383 .265 .452 .297 905 +TU CAS 2448880.1897 7.378 .228 .447 .285 905 +TU CAS 2448880.2260 7.439 .272 .482 .308 905 +TU CAS 2448880.2460 7.442 .248 .490 .290 905 +TU CAS 2448880.2754 7.484 .260 .506 .312 905 +TU CAS 2448880.2845 7.485 .236 .497 .318 905 +TU CAS 2448880.3134 7.519 .246 .543 .304 905 +TU CAS 2448880.3330 7.549 .218 .560 .331 905 +TU CAS 2448880.3571 7.557 .259 .567 .320 905 +TU CAS 2448880.3875 7.605 .259 .574 .336 905 +TU CAS 2448880.4200 7.631 .299 .592 .348 905 +TU CAS 2448880.4214 7.623 .264 .607 .334 905 +TU CAS 2448880.5171 7.706 .297 .641 .358 905 +TU CAS 2448881.1671 7.981 .341 .738 .407 905 +TU CAS 2448881.1846 8.000 .380 .718 .427 905 +TU CAS 2448881.2376 7.988 .331 .748 .420 905 +TU CAS 2448881.2653 8.004 .354 .736 .426 905 +TU CAS 2448881.3403 8.059 .338 .724 .429 905 +TU CAS 2448881.3974 8.030 .362 .745 .414 905 +TU CAS 2448881.4112 8.033 .350 .744 .411 905 +TU CAS 2448881.4624 8.029 .332 .743 .408 905 +TU CAS 2448881.5108 8.047 .344 .733 .417 905 +TU CAS 2448882.1574 7.604 .264 .550 .326 905 +TU CAS 2448882.1911 7.598 .247 .554 .313 905 +TU CAS 2448882.2161 7.609 .238 .543 .330 905 +TU CAS 2448882.2414 7.579 .231 .556 .311 905 +TU CAS 2448882.2762 7.585 .254 .546 .310 905 +TU CAS 2448882.3108 7.583 .249 .550 .311 905 +TU CAS 2448882.3174 7.599 .287 .556 .330 905 +TU CAS 2448882.3626 7.587 .248 .553 .312 905 +TU CAS 2448882.4071 7.601 .253 .552 .322 905 +TU CAS 2448882.4191 7.609 .253 .549 .334 905 +TU CAS 2448882.4359 7.606 .259 .550 .325 905 +TU CAS 2448883.1490 7.809 .278 .665 .384 905 +TU CAS 2448883.2274 7.848 .299 .679 .392 905 +TU CAS 2448883.2673 7.863 .308 .687 .395 905 +TU CAS 2448883.3045 7.875 .315 .696 .386 905 +TU CAS 2448883.3588 7.897 .325 .707 .390 905 +TU CAS 2448883.3921 7.916 .338 .718 .402 905 +TU CAS 2448883.4009 7.898 .336 .709 .386 905 +TU CAS 2448883.4374 7.949 .320 .712 .417 905 +TU CAS 2448883.5162 7.949 .358 .735 .399 905 +TU CAS 2448884.1569 7.816 .240 .615 .355 905 +TU CAS 2448884.2041 7.658 .197 .543 .324 905 +TU CAS 2448884.2425 7.648 .167 .445 .286 905 +TU CAS 2448885.1972 7.909 .364 .733 .409 905 +TU CAS 2448885.2027 7.903 .312 .737 .390 905 +TU CAS 2448885.2397 7.966 .354 .746 .417 905 +TU CAS 2448885.2693 7.982 .364 .764 .399 905 +TU CAS 2448885.3399 7.980 .371 .752 .406 905 +TU CAS 2448885.3996 8.004 .372 .763 .424 905 +TU CAS 2448885.4328 8.015 .348 .757 .424 905 +TU CAS 2448885.5232 8.050 .372 .751 .427 905 +TU CAS 2448886.1578 7.709 .218 .565 .343 905 +TU CAS 2448886.1993 7.581 .218 .522 .309 905 +TU CAS 2448886.2497 7.461 .217 .470 .277 905 +TU CAS 2448886.2587 7.453 .209 .456 .274 905 +TU CAS 2448886.2665 7.434 .231 .446 .274 905 +TU CAS 2448886.2843 7.407 .229 .445 .267 905 +TU CAS 2448886.2940 7.419 .264 .458 .285 905 +TU CAS 2448886.2974 7.403 .225 .438 .273 905 +TU CAS 2448886.3103 7.403 .231 .429 .276 905 +TU CAS 2448886.3245 7.396 .222 .447 .277 905 +TU CAS 2448886.3462 7.382 .219 .447 .265 905 +TU CAS 2448886.3716 7.400 .231 .444 .267 905 +TU CAS 2448886.3914 7.409 .243 .456 .272 905 +TU CAS 2448886.4139 7.437 .288 .474 .295 905 +TU CAS 2448886.4293 7.424 .227 .470 .277 905 +TU CAS 2448886.5186 7.493 .241 .502 .303 905 +TU CAS 2448887.2333 7.843 .304 .670 .391 905 +TU CAS 2448887.2818 7.838 .328 .681 .391 905 +TU CAS 2448887.2859 7.835 .290 .686 .379 905 +TU CAS 2448887.3164 7.851 .310 .682 .395 905 +TU CAS 2448888.1451 7.959 .301 .691 .390 905 +TU CAS 2448888.1967 7.940 .319 .711 .400 905 +TU CAS 2448888.2352 7.945 .307 .689 .387 905 +TU CAS 2448888.2658 7.932 .293 .684 .391 905 +TU CAS 2448888.2892 7.933 .295 .670 .394 905 +TU CAS 2448888.3089 7.915 .295 .677 .394 905 +TU CAS 2448888.3355 7.904 .298 .650 .382 905 +TU CAS 2448888.3675 7.884 .265 .646 .376 905 +TU CAS 2448888.3721 7.882 .275 .651 .374 905 +TU CAS 2448888.3854 7.866 .278 .654 .371 905 +TU CAS 2448888.4189 7.851 .264 .636 .372 905 +TU CAS 2448888.5246 7.728 .260 .590 .349 905 +TU CAS 2448889.1435 7.539 .266 .556 .327 905 +TU CAS 2448889.2055 7.612 .275 .586 .330 905 +TU CAS 2448889.2417 7.641 .275 .598 .351 905 +TU CAS 2448889.2606 7.666 .266 .604 .350 905 +TU CAS 2448889.2695 7.665 .272 .610 .363 905 +TU CAS 2448889.3035 7.675 .276 .637 .353 905 +TU CAS 2448889.3422 7.711 .287 .657 .356 905 +TU CAS 2448890.1481 8.086 .333 .758 .419 905 +TU CAS 2448890.1851 8.098 .373 .769 .420 905 +TU CAS 2448890.2201 8.109 .375 .766 .426 905 +TU CAS 2448890.2485 8.099 .361 .778 .418 905 +TU CAS 2448890.2707 8.101 .355 .769 .419 905 +TU CAS 2448890.3084 8.094 .356 .761 .410 905 +TU CAS 2448890.3484 8.074 .333 .742 .411 905 +TU CAS 2448890.3632 8.065 .305 .724 .401 905 +TU CAS 2448890.3739 8.051 .325 .722 .400 905 +TU CAS 2448890.3795 8.035 .306 .712 .398 905 +TU CAS 2448890.3932 8.022 .291 .709 .395 905 +TU CAS 2448890.4035 8.012 .300 .687 .399 905 +TU CAS 2448890.4133 7.985 .304 .680 .386 905 +TU CAS 2448890.5237 7.573 .237 .504 .292 905 +TU CAS 2448891.1413 7.684 .268 .609 .363 905 +TU CAS 2448891.1818 7.700 .262 .632 .356 905 +TU CAS 2448891.2115 7.730 .304 .635 .369 905 +TU CAS 2448891.2189 7.730 .278 .647 .377 905 +TU CAS 2448891.2440 7.729 .281 .679 .354 905 +TU CAS 2448891.2816 7.776 .303 .649 .374 905 +TU CAS 2448891.3129 7.779 .317 .680 .366 905 +TU CAS 2448891.3247 7.806 .309 .680 .377 905 +TU CAS 2448891.3616 7.835 .311 .691 .387 905 +TU CAS 2448891.3772 7.834 .321 .691 .379 905 +TU CAS 2448891.4177 7.855 .324 .709 .391 905 +TU CAS 2448891.4470 7.863 .315 .702 .393 905 +TU CAS 2448891.5267 7.918 .314 .708 .416 905 +TU CAS 2448892.1398 8.009 .330 .737 .403 905 +TU CAS 2448892.1970 8.012 .321 .720 .414 905 +TU CAS 2448892.2365 7.999 .305 .719 .412 905 +TU CAS 2448892.2616 7.999 .309 .729 .401 905 +TU CAS 2448892.2997 8.023 .295 .724 .410 905 +TU CAS 2448892.3314 8.016 .318 .712 .401 905 +TU CAS 2448893.1473 7.566 .274 .543 .325 905 +TU CAS 2448893.1865 7.574 .245 .515 .336 905 +TU CAS 2448893.2270 7.564 .223 .532 .322 905 +TU CAS 2448893.2409 7.546 .245 .542 .310 905 +TU CAS 2448893.2463 7.545 .236 .537 .311 905 +TU CAS 2448893.2542 7.545 .234 .543 .318 905 +TU CAS 2448893.2588 7.550 .231 .549 .318 905 +TU CAS 2448893.2685 7.561 .240 .529 .323 905 +TU CAS 2448893.2752 7.556 .249 .539 .324 905 +TU CAS 2448893.2823 7.554 .244 .544 .320 905 +TU CAS 2448893.2868 7.561 .244 .534 .323 905 +TU CAS 2448893.3080 7.562 .236 .542 .325 905 +TU CAS 2448893.3150 7.559 .235 .545 .319 905 +TU CAS 2448893.3322 7.589 .253 .555 .334 905 +TU CAS 2448893.3335 7.560 .230 .557 .324 905 +TU CAS 2448893.3393 7.561 .236 .550 .315 905 +TU CAS 2448893.3540 7.572 .243 .549 .326 905 +TU CAS 2448893.3603 7.567 .240 .544 .323 905 +TU CAS 2448893.3725 7.577 .255 .540 .321 905 +TU CAS 2448893.3786 7.578 .252 .549 .323 905 +TU CAS 2448893.4155 7.586 .243 .556 .332 905 +TU CAS 2448893.4247 7.605 .254 .562 .330 905 +TU CAS 2448894.1372 7.938 .342 .729 .403 905 +TU CAS 2448894.1790 7.974 .325 .733 .414 905 +TU CAS 2448894.2066 7.966 .323 .733 .414 905 +TU CAS 2448894.2209 7.966 .324 .737 .411 905 +TU CAS 2448894.2459 7.979 .339 .744 .417 905 +TU CAS 2448894.2621 7.974 .329 .736 .411 905 +TU CAS 2448894.2732 7.977 .324 .752 .406 905 +TU CAS 2448894.2850 7.994 .329 .732 .419 905 +TU CAS 2448894.2940 7.989 .357 .735 .408 905 +TU CAS 2448894.2945 7.995 .329 .736 .417 905 +TU CAS 2448894.3135 8.014 .339 .733 .421 905 +TU CAS 2448894.3217 8.034 .324 .745 .413 905 +TU CAS 2448894.3352 8.028 .342 .760 .407 905 +TU CAS 2448894.3460 8.052 .335 .752 .417 905 +TU CAS 2448503.3881 7.251 .377 .230 993 +TU CAS 2448504.3130 7.888 .709 .387 993 +TU CAS 2448504.4223 7.931 .364 .735 .409 993 +TU CAS 2448505.2037 7.920 .638 .391 993 +TU CAS 2448505.3307 7.786 .252 .622 .348 993 +TU CAS 2448505.4430 7.691 .257 .574 .333 993 +TU CAS 2448506.2106 7.620 .577 .325 993 +TU CAS 2448506.3389 7.665 .251 .600 .351 993 +TU CAS 2448506.3789 7.684 .261 .603 .352 993 +TU CAS 2448506.4397 7.714 .261 .620 .366 993 +TU CAS 2448506.5063 7.721 .651 .343 993 +TU CAS 2448507.1722 8.020 .378 .727 .432 993 +TU CAS 2448507.3142 8.049 .372 .740 .400 993 +TU CAS 2448507.3465 8.044 .355 .741 .409 993 +TU CAS 2448507.3752 8.046 .335 .732 .411 993 +TU CAS 2448507.4281 8.039 .724 .406 993 +TU CAS 2448507.5115 8.015 .708 .410 993 +TU CAS 2448508.1661 7.487 .253 .505 .300 993 +TU CAS 2448508.2689 7.593 .571 .336 993 +TU CAS 2448508.2994 7.622 .270 .582 .341 993 +TU CAS 2448508.3436 7.652 .286 .618 .345 993 +TU CAS 2448508.3877 7.711 .278 .626 .360 993 +TU CAS 2448508.4310 7.747 .303 .642 .374 993 +TU CAS 2448508.5142 7.807 .664 .395 993 +TU CAS 2448509.1745 8.066 .364 .755 .427 993 +TU CAS 2448509.2161 8.071 .366 .767 .428 993 +TU CAS 2448509.2779 8.082 .354 .771 .419 993 +TU CAS 2448509.2946 8.115 .371 .736 .439 993 +TU CAS 2448509.3371 8.108 .358 .762 .419 993 +TU CAS 2448509.3726 8.088 .362 .742 .420 993 +TU CAS 2448509.4234 8.084 .323 .727 .421 993 +TU CAS 2448509.5102 7.977 .666 .402 993 +TU CAS 2448510.1685 7.612 .258 .563 .331 993 +TU CAS 2448510.2585 7.654 .273 .584 .345 993 +TU CAS 2448510.3207 7.691 .294 .598 .355 993 +TU CAS 2448510.3845 7.722 .271 .616 .358 993 +TU CAS 2448510.4188 7.744 .278 .626 .354 993 +TU CAS 2448510.5136 7.787 .293 .638 .384 993 +TU CAS 2448511.1701 7.908 .320 .717 .396 993 +TU CAS 2448511.2729 7.933 .336 .709 .394 993 +TU CAS 2448511.3314 7.943 .346 .710 .397 993 +TU CAS 2448511.3775 7.952 .330 .721 .386 993 +TU CAS 2448511.4245 7.970 .356 .701 .394 993 +TU CAS 2448511.5113 7.967 .699 .393 993 +TU CAS 2448512.1753 7.349 .230 .418 .265 993 +TU CAS 2448512.2621 7.277 .216 .406 .236 993 +TU CAS 2448512.3173 7.284 .206 .415 .255 993 +TU CAS 2448512.3675 7.321 .209 .423 .263 993 +TU CAS 2448512.4231 7.378 .213 .442 .285 993 +TU CAS 2448512.5164 7.463 .480 .305 993 +TU CAS 2448513.1840 7.980 .377 .719 .421 993 +TU CAS 2448513.2719 7.992 .357 .750 .404 993 +TU CAS 2448513.3288 8.008 .365 .754 .423 993 +TU CAS 2448513.3773 8.031 .345 .760 .417 993 +TU CAS 2448513.4266 8.048 .361 .752 .424 993 +TU CAS 2448514.1817 7.182 .199 .352 .229 993 +TU CAS 2448514.2775 7.306 .310 .410 .251 993 +TU CAS 2448514.3337 7.367 .238 .449 .265 993 +TU CAS 2448514.3858 7.434 .255 .490 .282 993 +TU CAS 2448514.4303 7.499 .282 .487 .308 993 +TU CAS 2448515.1729 7.954 .392 .705 .410 993 +TU CAS 2448515.3125 7.962 .346 .720 .409 993 +TU CAS 2448515.3748 7.981 .347 .718 .424 993 +TU CAS 2448515.4170 7.983 .338 .743 .413 993 +TU CAS 2448515.5214 8.002 .708 .414 993 +TU CAS 2448516.1641 7.742 .279 .572 .348 993 +TU CAS 2448516.2493 7.680 .259 .576 .317 993 +TU CAS 2448516.3425 7.642 .244 .553 .312 993 +TU CAS 2448516.3797 7.622 .256 .549 .312 993 +TU CAS 2448516.4303 7.601 .253 .535 .320 993 +TU CAS 2448517.1623 7.727 .307 .586 .366 993 +TU CAS 2448517.2405 7.765 .306 .626 .371 993 +TU CAS 2448517.3353 7.818 .310 .655 .384 993 +TU CAS 2448517.3805 7.827 .317 .684 .374 993 +TU CAS 2448517.4236 7.862 .303 .689 .393 993 +TU CAS 2448517.5037 7.898 .324 .708 .391 993 +TU CAS 2448518.1771 8.045 .346 .704 .398 993 +TU CAS 2448518.2552 7.973 .300 .665 .391 993 +TU CAS 2448518.3385 7.739 .244 .575 .339 993 +TU CAS 2448518.3873 7.486 .242 .476 .277 993 +TU CAS 2448518.5186 7.075 .315 .213 993 +TU CAS 2448519.1908 7.824 .321 .672 .392 993 +TU CAS 2448519.3092 7.888 .346 .710 .395 993 +TU CAS 2448519.3779 7.931 .351 .725 .411 993 +TU CAS 2448519.4163 7.944 .366 .733 .411 993 +TU CAS 2448519.5237 7.976 .742 .414 993 +TU CAS 2448520.1611 8.005 .326 .695 .387 993 +TU CAS 2448520.2968 7.783 .270 .578 .358 993 +TU CAS 2448520.3841 7.596 .242 .529 .298 993 +TU CAS 2448520.5195 7.473 .475 .284 993 +TU CAS 2448521.1811 7.732 .307 .628 .358 993 +TU CAS 2448521.3460 7.780 .642 .377 993 +TU CAS 2448522.1725 7.962 .352 .710 .397 993 +TU CAS 2448522.3015 7.999 .331 .725 .414 993 +TU CAS 2448523.1637 7.410 .238 .488 .286 993 +TU CAS 2448523.2975 7.547 .552 .315 993 +TU CAS 2448523.5154 7.756 .639 .385 993 +TU CAS 2449199.4587 7.824 .354 .668 .384 911 +TU CAS 2449201.4275 7.681 .326 .559 .346 911 +TU CAS 2449202.3738 7.970 .692 .405 911 +TU CAS 2449202.4582 7.982 .391 .695 .399 911 +TU CAS 2449203.3170 7.294 .297 .396 .252 911 +TU CAS 2449203.4053 7.407 .329 .460 .285 911 +TU CAS 2449203.4635 7.470 .327 .479 .300 911 +TU CAS 2449204.3068 8.029 .430 .731 .416 911 +TU CAS 2449204.3917 8.050 .418 .734 .423 911 +TU CAS 2449204.4688 8.059 .739 .417 911 +TU CAS 2449205.3160 7.492 .305 .473 .298 911 +TU CAS 2449205.3994 7.560 .330 .506 .319 911 +TU CAS 2449205.4651 7.610 .313 .542 .329 911 +TU CAS 2449206.3392 7.915 .676 .394 911 +TU CAS 2449208.4036 7.934 .673 .401 911 +TU CAS 2449208.4685 7.941 .690 .398 911 +TU CAS 2449207.3247 7.659 .531 .322 911 +TU CAS 2449207.4061 7.540 .303 .479 .308 911 +TU CAS 2449207.4692 7.459 .450 .287 911 +TU CAS 2449209.3728 7.195 .361 .220 911 +TU CAS 2449210.3410 7.933 .396 .698 .404 911 +TU CAS 2449210.4273 7.955 .710 .405 911 +TU CAS 2449211.3143 7.756 .574 .343 911 +TU CAS 2449211.4567 7.641 .517 .320 911 +TU CAS 2449212.3348 7.698 .585 .350 911 +TU CAS 2449212.4531 7.742 .601 .358 911 +TU CAS 2449218.3364 7.326 .431 .260 911 +TU CAS 2449219.2904 8.003 .722 .418 911 +TU CAS 2449219.3935 8.032 .737 .414 911 +TU CAS 2449219.4960 8.069 .743 .423 911 +TU CAS 2449220.2852 7.353 .410 .262 911 +TU CAS 2449220.4262 7.521 .501 .309 911 +TU CAS 2449221.3362 7.982 .707 .409 911 +TU CAS 2449221.4357 7.999 .707 .411 911 +TU CAS 2449221.4958 8.009 .704 .412 911 +TU CAS 2449222.2874 7.627 .531 .313 911 +TU CAS 2449222.4131 7.608 .508 .322 911 +TU CAS 2449222.4993 7.587 .501 .322 911 +TU CAS 2449224.2996 7.789 .581 .347 911 +TU CAS 2449224.4228 7.178 .353 .227 911 +TU CAS 2449225.2659 7.876 .383 .687 .391 911 +TU CAS 2449225.4146 7.948 .719 .405 911 +TU CAS 2449225.4976 7.984 .719 .412 911 +TU CAS 2449226.2677 7.767 .558 .349 911 +TU CAS 2449226.4096 7.492 .444 .283 911 +TU CAS 2449226.4975 7.450 .446 .281 911 +TU CAS 2449227.2613 7.785 .626 .372 911 +TU CAS 2449227.4113 7.819 .635 .375 911 +TU CAS 2449227.5031 7.831 .644 .388 911 +TU CAS 2449228.2699 7.975 .400 .674 .403 911 +TU CAS 2449228.4252 7.951 .655 .388 911 +TU CAS 2449228.5009 7.918 .633 .381 911 +TU CAS 2449229.2607 7.556 .318 .512 .325 911 +TU CAS 2449229.4226 7.697 .583 .360 911 +TU CAS 2449229.5009 7.758 .625 .370 911 +TU CAS 2449230.2657 8.091 .730 .419 911 +TU CAS 2449230.4017 8.112 .729 .419 911 +TU CAS 2449230.4995 8.056 .692 .407 911 +TU CAS 2449231.2656 7.651 .589 .338 911 +TU CAS 2449231.4090 7.768 .632 .370 911 +TU CAS 2449231.4989 7.821 .653 .377 911 +TU CAS 2449232.2514 7.996 .696 .404 911 +TU CAS 2449232.4005 7.979 .681 .398 911 +TU CAS 2449232.4991 7.940 .661 .385 911 +TU CAS 2449233.2564 7.526 .484 .308 911 +TU CAS 2449233.4038 7.519 .488 .307 911 +TU CAS 2449234.2528 7.942 .692 .401 911 +TU CAS 2449234.4191 8.009 .710 .411 911 +TU CAS 2449234.4986 8.043 .702 .417 911 +TU CAS 2449235.2473 7.165 .352 .211 911 +TU CAS 2449235.4187 7.423 .466 .285 911 +TU CAS 2449235.5030 7.518 .508 .314 911 +TU CAS 2449236.2306 7.996 .726 .416 911 +TU CAS 2449236.4180 8.025 .727 .416 911 +TU CAS 2449236.5038 8.044 .737 .419 911 +TU CAS 2449237.2246 7.515 .469 .292 911 +TU CAS 2449237.4086 7.574 .510 .313 911 +TU CAS 2449238.2822 7.823 .640 .374 911 +TU CAS 2449238.4102 7.869 .646 .389 911 +TU CAS 2449239.2181 7.912 .645 .376 911 +TU CAS 2449239.4039 7.554 .483 .303 911 +TU CAS 2449239.5115 7.232 .374 .230 911 +TU CAS 2449240.2295 7.806 .643 .384 911 +TU CAS 2449240.3416 7.881 .689 .396 911 +TU CAS 2449240.5125 7.967 .721 .413 911 +TU CAS 2449241.2317 7.933 .647 .380 911 +TU CAS 2449241.3623 7.478 .450 .277 911 +TU CAS 2449241.5133 7.278 .379 .240 911 +TU CAS 2449242.3301 7.877 .668 .391 911 +TU CAS 2449242.4162 7.892 .687 .389 911 +TU CAS 2449243.2117 7.937 .656 .384 911 +TU CAS 2449243.3614 7.880 .631 .371 911 +TU CAS 2449244.2354 7.525 .516 .305 911 +TU CAS 2449244.3904 7.632 .559 .340 911 +TU CAS 2449244.5094 7.703 .606 .348 911 +TU CAS 2449245.2323 8.052 .730 .413 911 +TU CAS 2449245.3672 8.072 .729 .411 911 +TU CAS 2449298.1402 7.924 .695 .398 911 +TU CAS 2449298.2965 8.095 .626 .477 911 +TU CAS 2449617.3238 8.082 .737 .265 914 +TU CAS 2449617.3623 8.041 .467 .762 .268 914 +TU CAS 2449618.3919 7.610 .576 .212 914 +TU CAS 2449618.4572 7.659 .385 .580 .217 914 +TU CAS 2449619.1962 7.941 .365 .680 .246 914 +TU CAS 2449620.3307 7.557 .331 .487 .179 914 +TU CAS 2449620.3514 7.513 .329 .488 .176 914 +TU CAS 2449620.3595 7.505 .326 .499 .170 914 +TU CAS 2449620.3831 7.499 .341 .488 .169 914 +TU CAS 2449620.4213 7.438 .321 .493 .178 914 +TU CAS 2449620.4496 7.460 .337 .465 .180 914 +TU CAS 2449621.1741 7.811 .647 .232 914 +TU CAS 2449621.3012 7.893 .405 .647 .252 914 +TU CAS 2449621.3585 7.923 .401 .692 .255 914 +TU CAS 2449621.3815 7.997 .419 .716 .262 914 +TU CAS 2449621.4154 7.946 .425 .700 .245 914 +TU CAS 2449621.4408 7.959 .459 .691 .250 914 +TU CAS 2449621.5106 7.990 .702 .256 914 +TU CAS 2449622.3899 7.114 .334 .301 .115 914 +TU CAS 2449622.4310 7.127 .414 .345 .108 914 +TU CAS 2449622.4536 7.171 .317 .365 .135 914 +TU CAS 2449622.4738 7.216 .293 .365 .134 914 +TU CAS 2449622.5094 7.287 .340 .415 .130 914 +TU CAS 2449623.1427 7.910 .411 .687 .266 914 +TU CAS 2449623.2952 7.886 .400 .710 .259 914 +TU CAS 2449623.3190 7.934 .413 .734 .259 914 +TU CAS 2449623.3308 8.048 .409 .703 .263 914 +TU CAS 2449623.3556 7.996 .407 .731 .263 914 +TU CAS 2449623.3837 8.001 .724 .270 914 +TU CAS 2449623.4076 7.981 .440 .734 .261 914 +TU CAS 2449623.4297 7.985 .400 .726 .261 914 +TU CAS 2449623.4516 8.017 .433 .734 .262 914 +TU CAS 2449623.4701 8.009 .425 .729 .275 914 +TU CAS 2449623.4819 8.026 .462 .705 .274 914 +TU CAS 2449623.5078 8.030 .387 .724 .267 914 +TU CAS 2449624.1577 7.780 .596 .209 914 +TU CAS 2449624.2802 7.647 .343 .513 .189 914 +TU CAS 2449624.3203 7.618 .326 .509 .247 914 +TU CAS 2449624.3607 7.589 .341 .501 .187 914 +TU CAS 2449624.3819 7.550 .324 .496 .181 914 +TU CAS 2449624.3987 7.546 .322 .483 .183 914 +TU CAS 2449624.4252 7.529 .337 .495 .174 914 +TU CAS 2449624.4439 7.540 .294 .513 .178 914 +TU CAS 2449624.4667 7.547 .328 .488 .188 914 +TU CAS 2449624.4780 7.552 .496 .187 914 +TU CAS 2449624.4870 7.572 .335 .493 .190 914 +TU CAS 2449624.5064 7.557 .293 .518 .176 914 +TU CAS 2449624.5150 7.574 .307 .530 .181 914 +TU CAS 2449625.1716 7.736 .332 .593 .227 914 +TU CAS 2449625.2397 7.773 .327 .589 .229 914 +TU CAS 2449625.3156 7.780 .322 .608 .229 914 +TU CAS 2449625.3405 7.766 .353 .630 .227 914 +TU CAS 2449625.3588 7.768 .399 .631 .231 914 +TU CAS 2449625.3781 7.779 .371 .627 .224 914 +TU CAS 2449625.3973 7.805 .334 .621 .237 914 +TU CAS 2449625.4312 7.799 .356 .628 .235 914 +TU CAS 2449625.4508 7.803 .397 .612 .245 914 +TU CAS 2449625.4824 7.798 .639 .227 914 +TU CAS 2449625.5013 7.833 .377 .638 .249 914 +TU CAS 2449625.5140 7.825 .642 .238 914 +TU CAS 2449626.2044 8.046 .412 .686 .260 914 +TU CAS 2449631.1773 7.387 .316 .448 .161 914 +TU CAS 2449631.2207 7.398 .275 .455 .159 914 +TU CAS 2449631.2999 7.412 .317 .458 .196 914 +TU CAS 2449631.3264 7.508 .286 .448 .172 914 +TU CAS 2449631.3461 7.451 .475 .176 914 +TU CAS 2449631.3562 7.431 .476 .180 914 +TU CAS 2449632.1928 8.005 .397 .719 .260 914 +TU CAS 2449632.2352 8.011 .356 .713 .252 914 +TU CAS 2449632.2980 8.086 .412 .697 .259 914 +TU CAS 2449632.3252 8.042 .393 .716 .266 914 +TU CAS 2449632.3506 7.986 .462 .741 .263 914 +TU CAS 2449632.3514 8.046 .730 .268 914 +TU CAS 2449632.3741 8.060 .419 .725 .262 914 +TU CAS 2449632.3911 8.074 .431 .712 .267 914 +TU CAS 2449632.3979 8.056 .730 .262 914 +TU CAS 2449632.4113 8.082 .436 .731 .267 914 +TU CAS 2449632.4365 8.083 .420 .736 .270 914 +TU CAS 2449632.4542 8.071 .423 .738 .267 914 +TU CAS 2449632.4787 8.101 .751 .269 914 +TU CAS 2449632.5077 8.100 .460 .730 .269 914 +TU CAS 2449633.1570 7.263 .302 .374 .139 914 +TU CAS 2449633.2036 7.314 .357 .411 .154 914 +TU CAS 2449633.2306 7.370 .276 .442 .158 914 +TU CAS 2449633.2860 7.474 .339 .469 .164 914 +TU CAS 2449633.3029 7.462 .328 .472 .179 914 +TU CAS 2449633.3163 7.462 .337 .489 .179 914 +TU CAS 2449633.3362 7.483 .361 .519 .191 914 +TU CAS 2449633.3574 7.532 .315 .511 .195 914 +TU CAS 2449633.3723 7.553 .404 .509 .191 914 +TU CAS 2449633.3935 7.562 .364 .545 .209 914 +TU CAS 2449633.4102 7.564 .385 .555 .204 914 +TU CAS 2449633.4334 7.598 .388 .568 .195 914 +TU CAS 2449633.4526 7.624 .412 .549 .209 914 +TU CAS 2449633.4646 7.631 .355 .586 .209 914 +TU CAS 2449633.4953 7.657 .378 .596 .219 914 +TU CAS 2449634.1781 7.987 .427 .709 .257 914 +TU CAS 2449634.2131 7.997 .392 .707 .242 914 +TU CAS 2449634.2543 7.997 .343 .716 .248 914 +TU CAS 2449634.2798 8.049 .375 .706 .250 914 +TU CAS 2449634.2948 8.056 .720 .262 914 +TU CAS 2449634.3313 8.016 .485 .707 .257 914 +TU CAS 2449634.3532 8.029 .415 .712 .262 914 +TU CAS 2449635.3431 7.596 .502 .201 914 +TU CAS 2449635.3620 7.590 .499 .200 914 +TU CAS 2449635.3743 7.603 .525 .192 914 +TU CAS 2449635.3894 7.603 .551 .201 914 +TU CAS 2449635.4025 7.604 .522 .192 914 +TU CAS 2449635.4124 7.584 .526 .186 914 +TU CAS 2449635.4374 7.604 .541 .198 914 +TU CAS 2449635.4584 7.607 .552 .191 914 +TU CAS 2449635.4695 7.627 .550 .212 914 +TU CAS 2449635.4789 7.623 .550 .212 914 +TU CAS 2449635.4872 7.617 .547 .208 914 +TU CAS 2449635.5042 7.609 .583 .190 914 +TU CAS 2449640.2521 7.851 .649 .241 914 +TU CAS 2449640.3318 7.859 .663 .240 914 +TU CAS 2449640.3995 7.872 .657 .243 914 +TU CAS 2449641.3461 7.896 .644 .235 914 +TU CAS 2449647.3231 7.987 .701 .257 914 +TU CAS 2449647.4045 8.014 .719 .261 914 +TU CAS 2449933.4007 7.884 .668 .401 .749 998 +TU CAS 2449933.4404 7.846 .695 .386 .760 998 +TU CAS 2449934.4498 7.965 .642 .412 .731 998 +TU CAS 2449935.3180 7.628 .594 .353 .655 998 +TU CAS 2449935.4397 7.766 .383 .715 998 +TU CAS 2449936.3589 8.141 .753 .431 .800 998 +TU CAS 2449936.4661 8.159 .694 .427 .797 998 +TU CAS 2449937.3947 7.802 .681 .385 998 +TU CAS 2449937.4423 7.825 .685 .378 998 +TU CAS 2449937.4608 7.847 .688 .395 998 +TU CAS 2449938.4753 7.967 .678 .401 .770 998 +TU CAS 2449939.4695 7.628 .613 998 +TU CAS 2449941.4738 7.613 .582 998 +TU CAS 2449942.4678 8.084 .742 .420 .817 998 +TU CAS 2449943.4048 7.646 .552 .322 .653 998 +TU CAS 2449943.4490 7.706 .555 .336 .678 998 +TU CAS 2449944.4757 7.957 .699 .422 .795 998 +TU CAS 2449946.4703 8.046 .725 .430 .820 998 +TU CAS 2449947.4421 7.228 .377 .225 .448 998 +TU CAS 2449948.4012 7.963 .695 .406 .788 998 +TU CAS 2449948.4791 7.973 .722 .401 .777 998 +TU CAS 2449949.3657 7.846 .645 .361 .694 998 +TU CAS 2449949.4027 7.841 .640 .361 .683 998 +TU CAS 2449952.3535 7.733 .643 .361 .718 998 +TU CAS 2449953.4126 8.071 .712 .414 .784 998 +TU CAS 2449954.2920 7.628 .609 .321 .648 998 +TU CAS 2449954.3672 7.701 .612 .352 .677 998 +TU CAS 2449955.3272 7.951 .718 .399 .787 998 +TU CAS 2449956.4807 7.442 .506 .300 .582 998 +TU CAS 2449957.4078 8.060 .770 .423 .809 998 +TU CAS 2449959.3794 7.959 .729 .395 .766 998 +TU CAS 2449960.4175 7.647 .523 .311 .627 998 +TU CAS 2449962.3907 7.296 .403 .244 .503 998 +TU CAS 2449963.4179 7.928 .725 .410 .783 998 +TU CAS 2449985.3759 8.004 .724 .405 .779 998 +TU CAS 2449986.3625 7.556 .510 .308 .591 998 +TU CAS 2449987.3934 7.996 .742 .402 .778 998 +TU CAS 2449992.4172 7.668 .546 .316 .625 998 +TU CAS 2450007.3730 7.696 .572 .341 .659 998 +TU CAS 2450008.4151 7.854 .742 998 +TU CAS 2450009.3609 7.805 .587 .349 .671 998 +TU CAS 2450010.3078 7.884 .702 .391 .756 998 +TU CAS 2450010.3225 7.889 .707 .392 .753 998 +TU CAS 2450010.3401 7.894 .718 .391 .765 998 +TU CAS 2450010.3691 7.893 .723 .398 .756 998 +TU CAS 2450010.3856 7.892 .718 .400 .772 998 +TU CAS 2450010.4040 7.906 .735 .402 .770 998 +TU CAS 2450010.4187 7.925 .730 .408 .799 998 +TU CAS 2450010.4444 7.930 .731 .413 .799 998 +TU CAS 2450010.4647 7.943 .739 .403 .781 998 +TU CAS 2450010.4760 7.938 .740 .402 .783 998 +TU CAS 2450011.3039 7.859 .638 .367 .715 998 +TU CAS 2450011.3221 7.828 .635 .369 .708 998 +TU CAS 2450011.3712 7.774 .597 .355 .684 998 +TU CAS 2450011.3882 7.765 .588 .351 .672 998 +TU CAS 2450011.4198 7.729 .582 .339 .650 998 +TU CAS 2450011.4324 7.725 .565 .341 .647 998 +TU CAS 2450017.2950 7.975 .712 .400 .759 998 +TU CAS 2450017.3064 7.973 .710 .391 .752 998 +TU CAS 2450017.3372 7.973 .710 .400 .767 998 +TU CAS 2450017.3634 8.002 .709 .400 .760 998 +TU CAS 2450017.3839 8.032 .719 .403 .763 998 +TU CAS 2450017.4061 8.036 .723 .398 .770 998 +TU CAS 2450017.4409 8.053 .721 .395 .744 998 +TU CAS 2450018.3213 7.343 .427 .261 .515 998 +TU CAS 2450018.3457 7.317 .437 .257 .497 998 +TU CAS 2450018.3926 7.349 .415 .268 .533 998 +TU CAS 2450018.4378 7.334 .438 .251 .503 998 +TU CAS 2450019.2919 7.931 .730 .411 .787 998 +TU CAS 2450019.3066 7.950 .722 .407 .802 998 +TU CAS 2450019.3227 7.953 .731 .409 .797 998 +TU CAS 2450019.3413 7.951 .735 .405 .785 998 +TU CAS 2450019.3569 7.962 .734 .412 .797 998 +TU CAS 2450019.3779 7.972 .736 .415 .813 998 +TU CAS 2450019.4033 7.990 .728 .415 .822 998 +TU CAS 2450019.4183 7.989 .734 .423 .796 998 +TU CAS 2450020.2702 7.160 .361 .206 .419 998 +TU CAS 2450020.2914 7.193 .364 .222 .428 998 +TU CAS 2450020.3080 7.203 .380 .222 .435 998 +TU CAS 2450020.3349 7.248 .397 .241 .455 998 +TU CAS 2450020.3531 7.260 .411 .239 .476 998 +TU CAS 2450020.3749 7.310 .409 .260 .486 998 +TU CAS 2450305.3270 7.651 .647 .352 971 +TU CAS 2450305.3970 7.667 .655 .368 971 +TU CAS 2450306.1973 7.922 .690 .352 971 +TU CAS 2450306.4616 8.053 .796 .347 971 +TU CAS 2450307.3398 7.532 .621 .329 971 +TU CAS 2450307.3964 7.575 .654 .356 971 +TU CAS 2450307.4420 7.630 .681 .370 971 +TU CAS 2450310.4468 7.949 .761 .417 971 +TU CAS 2450311.3058 7.379 .506 .273 971 +TU CAS 2450311.3314 7.334 .511 .279 971 +TU CAS 2450311.3474 7.315 .495 .278 971 +TU CAS 2450311.4561 7.337 .479 .299 971 +TU CAS 2450312.2083 7.870 .709 .495 971 +TU CAS 2450312.4341 7.997 .819 .430 971 +TU CAS 2450313.2655 7.160 .423 .201 971 +TU CAS 2450313.3102 7.170 .447 .215 971 +TU CAS 2450313.3298 7.189 .454 .218 971 +TU CAS 2450314.2065 7.922 .740 .399 971 +TU CAS 2450314.2520 7.942 .746 .401 971 +TU CAS 2450314.4183 7.976 .772 .422 971 +TU CAS 2450315.2547 7.771 .622 .458 971 +TU CAS 2450315.3566 7.679 .645 .372 971 +TU CAS 2450315.4469 7.602 .653 .317 971 +TU CAS 2450316.1710 7.625 .582 .332 971 +TU CAS 2450316.3150 7.737 .685 .357 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2448117.4699 10.131 1.203 .703 992 +XY CAS 2448118.4008 10.211 1.275 .700 992 +XY CAS 2448119.4008 10.079 1.166 .677 992 +XY CAS 2448122.3979 10.131 1.274 .703 992 +XY CAS 2448123.3607 10.263 1.262 .700 992 +XY CAS 2448126.3495 10.044 1.232 .681 992 +XY CAS 2448127.3117 10.204 1.260 .709 992 +XY CAS 2448127.4076 10.220 1.276 .707 992 +XY CAS 2449936.4550 10.198 .687 1.331 998 +XY CAS 2449943.4644 9.790 1.177 998 +XY CAS 2449947.4491 9.818 1.187 998 +XY CAS 2449948.4161 9.842 1.228 998 +XY CAS 2449950.4671 10.222 1.365 998 +XY CAS 2449952.4610 9.747 1.127 998 +XY CAS 2449953.4656 9.973 1.307 998 +XY CAS 2449955.3911 10.255 1.357 998 +XY CAS 2449957.4554 9.812 1.221 998 +XY CAS 2449960.4196 10.231 1.324 998 +XY CAS 2449962.4286 9.942 1.306 998 +XY CAS 2449985.3786 10.017 1.315 998 +XY CAS 2449986.3684 10.197 1.367 998 +XY CAS 2449987.3953 10.215 1.318 998 +XY CAS 2449992.4191 9.872 1.181 998 +XY CAS 2450310.4645 10.146 1.276 .725 971 +XY CAS 2450311.4691 10.202 1.197 .732 971 +XY CAS 2450312.4585 9.700 .997 .593 971 +XY CAS 2450314.4307 10.091 1.227 .704 971 +XY CAS 2450315.3862 10.206 1.292 .727 971 +XY CAS 2450316.3817 9.953 1.123 .679 971 +XY CAS 2450317.4016 9.733 1.078 .619 971 +XY CAS 2450318.3978 9.977 1.186 .686 971 +XY CAS 2450319.4057 10.131 1.273 .706 971 +XY CAS 2450320.3885 10.215 1.249 .700 971 +XY CAS 2450321.3906 9.651 1.007 .592 971 +XY CAS 2450322.3726 9.873 1.146 .659 971 +XY CAS 2450323.3752 10.099 1.263 .667 971 +XY CAS 2450324.3483 10.203 1.289 .706 971 +XY CAS 2450325.3513 9.981 1.128 .650 971 +XY CAS 2450326.2861 9.729 1.030 .610 971 +AP CAS 2448101.4418 11.786 1.493 .866 992 +AP CAS 2448102.3859 11.405 1.315 .785 992 +AP CAS 2448103.3568 11.290 1.277 .773 992 +AP CAS 2448104.3842 11.441 1.381 .802 992 +AP CAS 2448104.4176 11.439 1.389 .811 992 +AP CAS 2448108.3975 11.728 1.477 .861 992 +AP CAS 2448109.3631 11.388 1.265 .786 992 +AP CAS 2448110.3615 11.324 1.305 .782 992 +AP CAS 2448111.3781 11.486 1.398 .831 992 +AP CAS 2448112.3390 11.560 1.451 .836 992 +AP CAS 2448113.3291 11.697 1.525 .874 992 +AP CAS 2448114.4003 11.905 1.573 .924 992 +AP CAS 2448116.3937 11.372 1.277 .801 992 +AP CAS 2448117.4588 1.307 .803 992 +AP CAS 2448118.3936 11.502 1.417 .844 992 +AP CAS 2448119.3869 11.626 1.514 .869 992 +AP CAS 2448122.3634 11.651 1.418 .855 992 +AP CAS 2448123.3535 11.299 1.260 .770 992 +AP CAS 2448126.3397 11.629 1.496 .851 992 +AP CAS 2448127.3000 11.749 992 +AP CAS 2448127.4000 11.778 1.526 .895 992 +AP CAS 2449943.4626 11.837 1.683 998 +AP CAS 2449947.4459 11.612 1.520 1.637 998 +AP CAS 2449948.4034 11.790 1.720 998 +AP CAS 2449950.4570 11.678 1.642 998 +AP CAS 2449952.4515 11.478 1.590 998 +AP CAS 2449953.4577 11.559 1.665 998 +AP CAS 2449954.4725 1.798 998 +AP CAS 2449955.3835 11.849 1.786 998 +AP CAS 2450310.4515 11.631 1.497 .886 971 +AP CAS 2450311.4618 11.787 1.511 .914 971 +AP CAS 2450312.4491 11.969 1.611 .912 971 +AP CAS 2450314.4205 11.308 1.270 .783 971 +AP CAS 2450315.3794 11.391 1.380 .814 971 +AP CAS 2450316.3733 11.497 1.436 .892 971 +AP CAS 2450317.3948 11.643 1.494 .874 971 +AP CAS 2450318.3823 11.793 1.569 .892 971 +AP CAS 2450319.3983 11.857 1.549 .893 971 +AP CAS 2450320.3819 11.576 1.417 .816 971 +AP CAS 2450321.3823 11.292 1.318 .777 971 +AP CAS 2450322.3697 11.456 1.358 .844 971 +AP CAS 2450323.3629 11.556 1.499 .808 971 +AP CAS 2450324.3362 11.659 1.477 .874 971 +AP CAS 2450325.3438 11.798 1.575 .884 971 +AP CAS 2450326.2682 11.882 1.527 .880 971 +AS CAS 2447401.3881 12.382 1.451 .888 990 +AS CAS 2447402.3789 12.559 1.456 .879 990 +AS CAS 2447403.4207 12.173 1.320 .821 990 +AS CAS 2447404.4128 12.402 1.443 .874 990 +AS CAS 2447409.3494 11.866 1.179 .766 990 +AS CAS 2447410.3753 12.451 1.486 .921 990 +AS CAS 2447411.3727 12.429 1.394 .886 990 +AS CAS 2447413.3322 12.227 1.326 .842 990 +AS CAS 2447414.3316 12.423 1.423 .871 990 +AS CAS 2447415.3097 11.830 1.181 .789 990 +AS CAS 2447416.3038 12.283 1.422 .869 990 +AS CAS 2447417.2987 12.594 1.484 .927 990 +AS CAS 2447418.2950 11.980 1.282 .800 990 +AS CAS 2447419.2717 12.303 1.389 .856 990 +AS CAS 2447420.2678 12.363 1.411 .855 990 +AS CAS 2447421.2584 12.278 1.380 .832 990 +AS CAS 2447422.2726 12.163 1.369 .821 990 +AS CAS 2447423.3992 12.555 1.480 .899 990 +AS CAS 2447424.2900 11.774 1.118 .732 990 +AS CAS 2447425.3106 12.380 1.456 .866 990 +AS CAS 2447427.3692 12.170 1.342 .810 990 +AS CAS 2447428.4316 12.224 1.329 .841 990 +AS CAS 2447429.3221 12.440 1.419 .862 990 +AS CAS 2447430.2819 12.016 1.293 .777 990 +AS CAS 2447430.4622 11.734 1.172 .724 990 +AS CAS 2447431.3412 12.268 1.408 .854 990 +AS CAS 2447432.3198 12.578 1.513 .897 990 +AS CAS 2447434.3068 12.344 1.441 .866 990 +AS CAS 2447741.4684 12.101 1.316 .821 991 +AS CAS 2447742.4390 12.263 1.399 .841 991 +AS CAS 2447743.4215 12.348 1.334 .884 991 +AS CAS 2447744.4708 12.468 1.447 991 +AS CAS 2447745.4661 11.962 1.200 991 +AS CAS 2447746.4694 12.489 1.475 .876 991 +AS CAS 2447747.3995 12.357 1.356 .860 991 +AS CAS 2447748.4676 12.227 1.348 .841 991 +AS CAS 2447749.4697 12.440 1.400 991 +AS CAS 2447750.4677 12.363 1.346 991 +AS CAS 2447751.4691 12.004 1.229 991 +AS CAS 2447752.4654 12.319 1.392 .862 991 +AS CAS 2447753.4656 12.562 1.344 .914 991 +AS CAS 2447754.4342 12.043 1.208 .815 991 +AS CAS 2447755.4694 12.497 1.460 .913 991 +AS CAS 2447756.4623 12.174 1.304 .818 991 +AS CAS 2447757.4524 12.208 1.329 .877 991 +AS CAS 2447758.4037 12.263 1.335 .854 991 +AS CAS 2447759.3867 12.456 1.399 .882 991 +AS CAS 2447760.4389 11.848 1.168 .778 991 +AS CAS 2447761.4628 12.423 1.469 .887 991 +AS CAS 2447762.4403 12.433 1.389 .884 991 +AS CAS 2447763.4574 12.173 1.342 .841 991 +AS CAS 2447765.4395 12.320 1.290 .853 991 +AS CAS 2447766.4343 11.975 1.297 .779 991 +AS CAS 2447767.4420 12.276 1.370 .869 991 +AS CAS 2447768.4018 12.498 1.496 .910 991 +AS CAS 2447770.3491 12.456 1.440 .915 991 +AS CAS 2447771.3385 12.393 1.395 .881 991 +AS CAS 2447771.4003 12.387 1.368 .883 991 +AS CAS 2447772.3342 12.170 1.302 .844 991 +AS CAS 2447772.4423 12.194 1.349 991 +AS CAS 2447773.3605 12.238 1.348 .846 991 +AS CAS 2447773.4116 12.274 1.344 .861 991 +AS CAS 2447774.3655 12.446 1.412 .890 991 +AS CAS 2447774.4231 12.468 1.417 .909 991 +AS CAS 2447775.3359 11.774 1.141 .755 991 +AS CAS 2447775.3865 11.752 1.168 .735 991 +AS CAS 2447776.3469 12.329 1.436 .884 991 +AS CAS 2447776.4086 12.355 1.433 .882 991 +AS CAS 2448101.4357 12.076 1.301 .809 992 +AS CAS 2448102.3799 12.222 1.345 .844 992 +AS CAS 2448103.3527 12.319 1.387 .887 992 +AS CAS 2448104.3782 12.431 1.411 .888 992 +AS CAS 2448104.4141 12.425 1.419 .889 992 +AS CAS 2448108.3894 12.184 1.348 .868 992 +AS CAS 2448109.3563 12.453 1.425 .888 992 +AS CAS 2448110.3554 12.299 1.354 .852 992 +AS CAS 2448111.3697 12.054 1.277 .823 992 +AS CAS 2448112.3343 12.302 1.392 .883 992 +AS CAS 2448113.3237 12.541 1.462 .899 992 +AS CAS 2448113.3939 12.530 1.427 992 +AS CAS 2448114.3964 12.048 1.374 .808 992 +AS CAS 2448116.3895 12.262 1.328 .854 992 +AS CAS 2448117.4552 12.302 1.326 .864 992 +AS CAS 2448118.3839 12.328 1.371 .876 992 +AS CAS 2448119.3825 12.503 1.414 .901 992 +AS CAS 2448122.3601 12.482 1.452 .894 992 +AS CAS 2448123.3493 12.149 1.332 .848 992 +AS CAS 2448123.4117 12.180 1.353 .852 992 +AS CAS 2448126.3345 12.078 1.294 .813 992 +AS CAS 2448126.4190 12.050 1.277 .816 992 +AS CAS 2448127.2963 12.275 1.352 .872 992 +AS CAS 2448127.3953 12.291 1.413 .873 992 +AS CAS 2448443.4605 12.377 1.055 902 +AS CAS 2448444.4515 12.292 1.390 .851 902 +AS CAS 2448445.4516 12.653 1.507 902 +AS CAS 2448448.4434 12.324 1.415 902 +AS CAS 2448449.4408 12.473 1.303 .858 902 +AS CAS 2448453.4506 12.428 1.350 902 +AS CAS 2448455.4485 12.303 1.315 902 +AS CAS 2448456.4464 12.160 1.291 .798 902 +AS CAS 2448457.4439 12.616 1.433 .863 902 +AS CAS 2448458.4528 12.349 1.344 .829 902 +AS CAS 2448460.4473 12.700 1.468 .904 902 +AS CAS 2448461.4445 12.006 1.236 902 +AS CAS 2448462.4542 12.290 1.344 902 +AS CAS 2448463.4464 12.312 1.393 .861 902 +AS CAS 2448464.4536 12.339 1.367 .857 902 +AS CAS 2448465.4575 11.973 1.283 902 +AS CAS 2448466.4647 12.492 1.486 .921 902 +AS CAS 2448467.4457 12.792 .569 902 +AS CAS 2448477.4503 12.222 1.340 .834 902 +AS CAS 2448479.4672 12.297 1.442 .858 902 +AS CAS 2448482.4514 12.147 1.295 .816 902 +AS CAS 2448485.4161 12.186 1.312 .829 902 +AS CAS 2448486.4160 12.021 .455 .691 .401 902 +AS CAS 2448487.4259 12.309 .800 1.401 .864 902 +AS CAS 2448489.3972 12.024 .393 .675 .384 902 +AS CAS 2448490.4165 12.471 .966 1.448 .907 902 +AS CAS 2448491.3749 12.190 .923 1.303 .843 902 +AS CAS 2448493.3325 12.004 .694 .392 902 +AS CAS 2448494.3835 12.392 1.422 .866 902 +AS CAS 2448498.3969 12.111 1.287 .829 902 +AS CAS 2448499.3389 12.349 .935 1.416 .878 902 +AS CAS 2448503.3517 12.551 1.454 .897 902 +AS CAS 2448504.3758 11.867 1.217 .776 902 +AS CAS 2448505.4105 12.469 1.443 .897 902 +AS CAS 2448506.4245 12.267 1.324 902 +AS CAS 2448507.4144 12.189 1.327 902 +AS CAS 2448508.3909 12.254 1.288 .854 902 +AS CAS 2448509.4098 12.460 1.021 1.387 .904 902 +AS CAS 2448510.3942 11.722 1.165 .720 902 +AS CAS 2448511.4153 12.291 1.408 .866 902 +AS CAS 2448512.4166 12.473 1.424 902 +AS CAS 2448513.4157 12.040 1.276 902 +AS CAS 2448514.4138 12.358 1.417 902 +AS CAS 2448515.4136 12.342 1.343 .859 902 +AS CAS 2448516.3918 12.072 1.278 .775 902 +AS CAS 2448517.3929 12.177 1.347 .818 902 +AS CAS 2448520.3843 12.371 1.451 .851 902 +AS CAS 2448521.3643 12.414 1.374 .864 902 +AS CAS 2448522.2997 12.146 1.279 .824 902 +AS CAS 2448523.3380 12.228 1.337 .846 902 +AS CAS 2448533.3595 12.491 1.483 .902 902 +AS CAS 2448537.3231 12.112 .887 1.279 .803 902 +AS CAS 2448541.3980 12.266 1.376 .858 902 +AS CAS 2448542.2482 12.467 1.133 1.464 .871 902 +AS CAS 2448543.2920 11.864 .977 1.173 .752 902 +AS CAS 2448549.3561 11.673 1.129 .691 902 +AS CAS 2448551.2763 12.454 1.478 .876 902 +AS CAS 2448552.2750 12.139 1.265 .825 902 +AS CAS 2448553.2429 12.200 1.300 .828 902 +AS CAS 2448556.2820 12.090 1.325 .801 902 +AS CAS 2448557.2307 12.573 1.443 .930 902 +AS CAS 2448558.2320 11.883 1.176 .761 902 +AS CAS 2448559.2501 12.256 1.383 .836 902 +AS CAS 2448560.2376 12.324 1.387 .829 902 +AS CAS 2448561.2585 12.327 .940 1.346 .853 902 +AS CAS 2448562.2889 12.029 .897 1.256 .809 902 +AS CAS 2449198.4712 12.362 1.447 .892 909 +AS CAS 2449199.4535 12.389 1.273 .866 909 +AS CAS 2449201.4332 12.386 1.454 .897 909 +AS CAS 2449202.3661 12.114 1.251 .838 909 +AS CAS 2449202.4537 11.984 1.219 .769 909 +AS CAS 2449203.3105 12.132 1.323 .841 909 +AS CAS 2449203.3992 12.162 1.339 .877 909 +AS CAS 2449203.4606 12.269 1.375 .869 909 +AS CAS 2449204.3027 12.479 1.472 .884 909 +AS CAS 2449204.3872 12.502 1.406 .907 909 +AS CAS 2449204.4662 12.472 1.499 .887 909 +AS CAS 2449205.3122 12.230 1.340 .817 909 +AS CAS 2449205.3956 12.175 1.339 .796 909 +AS CAS 2449205.4625 12.231 1.343 .841 909 +AS CAS 2449206.3354 12.072 1.258 .790 909 +AS CAS 2449206.4005 12.109 1.305 .815 909 +AS CAS 2449206.4636 12.117 1.290 .821 909 +AS CAS 2449207.3223 12.310 1.367 .866 909 +AS CAS 2449207.4015 12.369 1.393 .871 909 +AS CAS 2449207.4657 12.384 1.441 .894 909 +AS CAS 2449209.3689 12.058 1.305 .808 909 +AS CAS 2449210.3373 12.530 1.487 .927 909 +AS CAS 2449210.4233 12.502 1.492 .891 909 +AS CAS 2449211.3106 12.204 1.329 .826 909 +AS CAS 2449211.4542 12.049 1.266 .789 909 +AS CAS 2449212.3315 12.262 1.336 .859 909 +AS CAS 2449212.4507 12.275 1.357 .871 909 +AS CAS 2449218.3324 12.151 1.304 .825 909 +AS CAS 2449219.2867 12.486 1.414 .927 909 +AS CAS 2449219.3909 12.461 1.454 .894 909 +AS CAS 2449219.4887 12.448 1.485 .879 909 +AS CAS 2449220.2814 12.306 1.362 .847 909 +AS CAS 2449220.4235 12.229 1.370 .823 909 +AS CAS 2449221.3329 12.106 1.274 .828 909 +AS CAS 2449221.4335 12.065 1.291 .807 909 +AS CAS 2449222.2835 12.272 1.369 .851 909 +AS CAS 2449222.4105 12.326 1.406 .871 909 +AS CAS 2449224.2962 11.940 1.217 .775 909 +AS CAS 2449224.4204 12.049 1.271 .833 909 +AS CAS 2449225.2618 12.486 1.474 .915 909 +AS CAS 2449225.4116 12.527 1.482 .918 909 +AS CAS 2449225.4933 12.545 1.450 .908 909 +AS CAS 2449226.2641 12.360 1.360 .866 909 +AS CAS 2449226.4068 12.238 1.278 .828 909 +AS CAS 2449226.4928 12.118 1.290 .801 909 +AS CAS 2449227.2572 12.159 1.337 .812 909 +AS CAS 2449227.4087 12.252 1.345 .862 909 +AS CAS 2449227.4982 12.278 1.388 .836 909 +AS CAS 2449228.2669 12.328 1.406 .887 909 +AS CAS 2449228.4228 12.325 1.375 .879 909 +AS CAS 2449228.4967 12.310 1.405 .874 909 +AS CAS 2449229.2579 12.416 1.416 .887 909 +AS CAS 2449229.4200 12.432 1.428 .919 909 +AS CAS 2449229.4966 12.441 1.424 .894 909 +AS CAS 2449230.2620 11.803 1.173 .748 909 +AS CAS 2449230.3991 11.862 1.190 .762 909 +AS CAS 2449230.4947 11.903 1.221 .770 909 +AS CAS 2449231.2631 12.367 1.409 .912 909 +AS CAS 2449231.4051 12.357 1.512 .889 909 +AS CAS 2449231.4941 12.465 1.499 .919 909 +AS CAS 2449232.2475 12.554 1.502 .875 909 +AS CAS 2449232.4033 12.489 1.405 .887 909 +AS CAS 2449232.4948 12.338 1.392 .847 909 +AS CAS 2449233.2538 12.069 1.261 .816 909 +AS CAS 2449233.4012 12.065 1.338 .821 909 +AS CAS 2449234.2489 12.445 1.493 .878 909 +AS CAS 2449234.4167 12.453 1.404 .903 909 +AS CAS 2449234.4941 12.441 1.463 .864 909 +AS CAS 2449235.2435 12.334 1.370 .850 909 +AS CAS 2449235.4161 12.323 1.313 .887 909 +AS CAS 2449235.4982 12.299 1.361 .849 909 +AS CAS 2449236.2265 12.197 1.283 .839 909 +AS CAS 2449236.4155 12.103 1.273 .816 909 +AS CAS 2449236.4993 12.077 1.261 .802 909 +AS CAS 2449237.2221 12.238 1.305 .859 909 +AS CAS 2449237.4061 12.262 1.405 .843 909 +AS CAS 2449237.5045 12.371 1.401 .897 909 +AS CAS 2449238.2782 12.536 1.478 .897 909 +AS CAS 2449238.4077 12.570 1.475 .909 909 +AS CAS 2449239.2142 11.773 1.172 .723 909 +AS CAS 2449239.4014 11.954 1.212 .784 909 +AS CAS 2449239.5074 12.049 1.266 .806 909 +AS CAS 2449240.2257 12.428 1.457 .907 909 +AS CAS 2449240.3369 12.457 1.475 .888 909 +AS CAS 2449240.5081 12.540 1.550 .883 909 +AS CAS 2449241.2275 12.461 1.387 .873 909 +AS CAS 2449241.3597 12.383 1.354 .867 909 +AS CAS 2449241.5091 12.274 1.383 .840 909 +AS CAS 2449242.3273 12.172 1.313 .836 909 +AS CAS 2449242.4123 12.209 1.354 .858 909 +AS CAS 2449242.5087 12.213 1.363 .818 909 +AS CAS 2449243.2081 12.287 1.369 .863 909 +AS CAS 2449243.3588 12.310 1.361 .884 909 +AS CAS 2449243.5112 12.339 1.423 .859 909 +AS CAS 2449244.2311 12.410 1.415 .883 909 +AS CAS 2449244.3877 12.438 1.424 .880 909 +AS CAS 2449244.5049 12.460 1.440 .887 909 +AS CAS 2449245.2288 11.874 1.183 .765 909 +AS CAS 2449245.3646 11.838 1.146 .768 909 +AS CAS 2449298.1243 12.394 1.408 .893 909 +AS CAS 2449298.2933 12.452 1.410 .935 909 +AS CAS 2448503.3830 12.572 1.456 .894 993 +AS CAS 2448504.3102 11.847 1.173 .760 993 +AS CAS 2448504.4200 11.935 1.266 .793 993 +AS CAS 2448505.1989 12.403 1.425 .889 993 +AS CAS 2448505.3263 12.465 1.459 .899 993 +AS CAS 2448505.4387 12.477 1.471 .890 993 +AS CAS 2448506.2070 12.469 1.456 .862 993 +AS CAS 2448506.3369 12.323 1.386 .886 993 +AS CAS 2448506.3755 12.380 1.360 .881 993 +AS CAS 2448506.4361 12.305 1.411 .838 993 +AS CAS 2448507.1686 12.164 1.292 .849 993 +AS CAS 2448507.3123 12.192 1.324 .830 993 +AS CAS 2448507.3431 12.185 1.343 .830 993 +AS CAS 2448507.3719 12.193 1.333 .828 993 +AS CAS 2448507.4261 12.200 1.349 .839 993 +AS CAS 2448508.1623 12.266 1.373 .854 993 +AS CAS 2448508.2668 12.265 1.379 .855 993 +AS CAS 2448508.2974 12.257 1.382 .844 993 +AS CAS 2448508.3415 12.246 1.405 .846 993 +AS CAS 2448508.3851 12.281 1.394 .862 993 +AS CAS 2448508.4288 12.280 1.356 .869 993 +AS CAS 2448509.1704 12.427 1.489 .882 993 +AS CAS 2448509.2122 12.408 1.442 .871 993 +AS CAS 2448509.2732 12.449 1.415 .898 993 +AS CAS 2448509.3350 12.446 1.428 .895 993 +AS CAS 2448509.3706 12.445 1.424 .900 993 +AS CAS 2448509.4201 12.444 1.411 .891 993 +AS CAS 2448510.1650 11.941 1.217 .786 993 +AS CAS 2448510.2565 11.834 1.173 .756 993 +AS CAS 2448510.3188 11.786 1.173 .741 993 +AS CAS 2448510.3826 11.769 1.155 .740 993 +AS CAS 2448510.4166 11.805 1.153 .741 993 +AS CAS 2448511.1665 12.239 1.400 .841 993 +AS CAS 2448511.2709 12.297 1.422 .876 993 +AS CAS 2448511.3294 12.329 1.430 .864 993 +AS CAS 2448511.3740 12.363 1.440 .875 993 +AS CAS 2448511.4221 12.358 1.465 .904 993 +AS CAS 2448512.1732 12.596 1.502 .923 993 +AS CAS 2448512.2600 12.611 1.489 .908 993 +AS CAS 2448512.3153 12.583 1.500 .904 993 +AS CAS 2448512.3653 12.591 1.506 .901 993 +AS CAS 2448512.4191 12.543 1.494 .894 993 +AS CAS 2448513.1817 11.967 1.237 .785 993 +AS CAS 2448513.2698 12.010 1.278 .792 993 +AS CAS 2448513.3258 12.036 1.268 .797 993 +AS CAS 2448513.3742 12.069 1.288 .811 993 +AS CAS 2448513.4233 12.097 1.295 .826 993 +AS CAS 2448514.1784 12.412 1.424 .912 993 +AS CAS 2448514.2742 12.419 1.447 .895 993 +AS CAS 2448514.3303 12.399 1.462 .876 993 +AS CAS 2448514.3838 12.422 1.413 .876 993 +AS CAS 2448514.4268 12.434 1.411 .889 993 +AS CAS 2448515.1682 12.391 1.399 .864 993 +AS CAS 2448515.3092 12.378 1.404 .869 993 +AS CAS 2448515.3728 12.383 1.377 .881 993 +AS CAS 2448515.4149 12.366 1.395 .887 993 +AS CAS 2448516.1608 12.249 1.323 .850 993 +AS CAS 2448516.2468 12.211 1.326 .816 993 +AS CAS 2448516.3393 12.157 1.314 .813 993 +AS CAS 2448516.3800 12.089 1.354 .771 993 +AS CAS 2448516.4264 12.099 1.288 .817 993 +AS CAS 2448517.1587 12.145 1.306 .824 993 +AS CAS 2448517.2369 12.167 1.331 .839 993 +AS CAS 2448517.3327 12.206 1.350 .863 993 +AS CAS 2448517.3783 12.207 1.357 .827 993 +AS CAS 2448517.4239 12.239 1.368 993 +AS CAS 2448518.1720 12.523 1.433 .910 993 +AS CAS 2448518.2500 12.551 1.452 .913 993 +AS CAS 2448518.3336 12.551 1.445 .897 993 +AS CAS 2448518.3782 12.567 1.452 .884 993 +AS CAS 2448519.1868 11.719 1.118 .719 993 +AS CAS 2448519.3074 11.787 1.170 .727 993 +AS CAS 2448519.3740 11.839 1.192 .760 993 +AS CAS 2448519.4133 11.880 1.223 .775 993 +AS CAS 2448520.1563 12.353 1.415 .879 993 +AS CAS 2448520.2929 12.408 1.406 .895 993 +AS CAS 2448520.3790 12.439 1.457 .890 993 +AS CAS 2448521.1748 12.520 1.452 .892 993 +AS CAS 2448521.3412 12.448 1.389 .880 993 +AS CAS 2448522.1664 12.128 1.265 .817 993 +AS CAS 2448522.2960 12.165 1.324 .830 993 +AS CAS 2448523.1592 12.261 1.358 .873 993 +AS CAS 2448523.2929 12.202 1.321 .816 993 +AS CAS 2449617.3164 12.322 1.255 .586 913 +AS CAS 2449617.3504 12.259 1.386 .578 913 +AS CAS 2449618.3861 12.354 1.298 .601 913 +AS CAS 2449620.3324 11.918 1.205 .496 913 +AS CAS 2449620.3539 11.908 1.132 .495 913 +AS CAS 2449620.3766 11.887 1.165 .508 913 +AS CAS 2449620.4128 11.819 1.211 .485 913 +AS CAS 2449620.4432 11.895 1.152 .515 913 +AS CAS 2449621.1692 12.280 1.343 .582 913 +AS CAS 2449621.3032 12.390 1.383 .594 913 +AS CAS 2449621.3502 12.435 1.434 .586 913 +AS CAS 2449621.3744 12.441 1.420 .594 913 +AS CAS 2449621.4077 12.454 1.472 .596 913 +AS CAS 2449621.4436 12.455 1.417 .586 913 +AS CAS 2449622.3680 12.693 1.498 .617 913 +AS CAS 2449622.4203 12.637 1.444 .614 913 +AS CAS 2449623.3008 12.048 1.256 .540 913 +AS CAS 2449623.3332 12.169 1.289 .534 913 +AS CAS 2449623.3576 12.137 1.325 .542 913 +AS CAS 2449623.3780 12.132 1.310 .553 913 +AS CAS 2449623.4090 12.112 1.314 .552 913 +AS CAS 2449623.4310 12.123 1.298 .551 913 +AS CAS 2449623.4534 12.151 1.329 .566 913 +AS CAS 2449623.4716 12.191 1.324 .577 913 +AS CAS 2449623.4832 12.170 1.336 .563 913 +AS CAS 2449624.1588 12.503 1.538 .596 913 +AS CAS 2449624.2814 12.569 1.490 .619 913 +AS CAS 2449624.3650 12.584 1.506 .628 913 +AS CAS 2449624.3841 12.565 1.518 .619 913 +AS CAS 2449624.4003 12.515 1.523 .608 913 +AS CAS 2449624.4195 12.542 1.530 .604 913 +AS CAS 2449624.4383 12.528 1.505 .622 913 +AS CAS 2449624.4607 12.505 1.484 .605 913 +AS CAS 2449625.1723 12.529 1.504 .595 913 +AS CAS 2449625.2322 12.521 1.468 .602 913 +AS CAS 2449625.3175 12.533 1.463 .592 913 +AS CAS 2449625.3804 12.498 1.406 .603 913 +AS CAS 2449625.3995 12.492 1.451 .604 913 +AS CAS 2449625.4333 12.460 1.453 .600 913 +AS CAS 2449625.4523 12.441 1.461 .607 913 +AS CAS 2449626.2049 12.403 1.348 .596 913 +AS CAS 2449631.1784 12.654 1.550 .601 913 +AS CAS 2449631.3015 12.686 1.508 .635 913 +AS CAS 2449632.1944 12.210 1.336 .563 913 +AS CAS 2449632.2367 12.218 1.327 .559 913 +AS CAS 2449632.2991 12.293 1.321 .559 913 +AS CAS 2449632.3259 12.208 1.379 .558 913 +AS CAS 2449632.3449 12.159 1.286 .558 913 +AS CAS 2449632.3457 12.182 1.395 .546 913 +AS CAS 2449632.3758 12.250 1.407 .560 913 +AS CAS 2449632.3922 12.285 1.345 .588 913 +AS CAS 2449632.3940 12.116 1.293 .550 913 +AS CAS 2449632.4123 12.244 1.406 .563 913 +AS CAS 2449632.4317 12.288 1.374 .591 913 +AS CAS 2449632.4552 12.280 1.399 .576 913 +AS CAS 2449632.4740 12.238 1.334 .572 913 +AS CAS 2449633.2048 12.309 1.395 .570 913 +AS CAS 2449633.2313 12.401 1.346 .561 913 +AS CAS 2449633.3173 12.427 1.383 .589 913 +AS CAS 2449633.3380 12.391 1.356 .583 913 +AS CAS 2449633.3590 12.339 1.345 .593 913 +AS CAS 2449633.3734 12.311 1.400 .552 913 +AS CAS 2449633.3949 12.342 1.369 .592 913 +AS CAS 2449633.4108 12.312 1.400 .592 913 +AS CAS 2449633.4340 12.333 1.337 .586 913 +AS CAS 2449633.4456 12.324 1.362 .590 913 +AS CAS 2449633.4655 12.308 1.419 .579 913 +AS CAS 2449634.2147 12.439 1.474 .577 913 +AS CAS 2449634.3259 12.408 1.483 .596 913 +AS CAS 2449635.3464 11.917 1.198 .515 913 +AS CAS 2449635.3636 11.916 1.183 .524 913 +AS CAS 2449635.3751 11.891 1.211 .510 913 +AS CAS 2449635.3899 11.911 1.182 .538 913 +AS CAS 2449635.4033 11.870 1.193 .482 913 +AS CAS 2449635.4134 11.818 1.198 .485 913 +AS CAS 2449635.4379 11.851 1.199 .512 913 +AS CAS 2449635.4521 11.837 1.236 .503 913 +AS CAS 2449635.4655 11.833 1.241 .500 913 +AS CAS 2449635.4751 11.847 1.203 .524 913 +AS CAS 2449635.4833 11.830 1.191 .507 913 +AS CAS 2449635.4998 11.824 1.193 .501 913 +AS CAS 2449640.2452 12.335 1.401 .596 913 +AS CAS 2449640.3355 12.357 1.387 .588 913 +AS CAS 2449640.4015 12.321 1.378 .582 913 +AS CAS 2449641.3499 12.202 1.274 .556 913 +AS CAS 2449647.3170 12.096 1.285 .545 913 +AS CAS 2449647.4003 12.080 1.284 .531 913 +AS CAS 2448852.4116 12.255 1.355 .859 952 +AS CAS 2448854.3561 12.430 1.359 .892 952 +AS CAS 2448854.4076 12.426 1.446 .884 952 +AS CAS 2448854.4504 12.463 1.276 .891 952 +AS CAS 2448856.3932 12.466 1.497 .915 952 +AS CAS 2448856.4611 12.440 1.531 .889 952 +AS CAS 2448858.3016 12.057 1.349 .800 952 +AS CAS 2448858.3517 12.132 1.336 .842 952 +AS CAS 2448858.3768 12.126 1.347 .843 952 +AS CAS 2448858.4564 12.158 1.413 .829 952 +AS CAS 2448860.2206 12.349 1.413 .880 952 +AS CAS 2448860.2853 12.328 1.386 .854 952 +AS CAS 2448860.3529 12.329 1.370 .869 952 +AS CAS 2448860.4672 12.279 1.416 .831 952 +AS CAS 2448862.3297 12.267 1.406 .899 952 +AS CAS 2448862.3668 12.267 1.396 .854 952 +AS CAS 2448862.4340 12.312 1.418 .861 952 +AS CAS 2448870.2625 11.779 1.203 .709 952 +AS CAS 2448870.3093 11.766 1.217 .720 952 +AS CAS 2448870.3319 11.802 1.178 .750 952 +AS CAS 2448870.3842 11.832 1.208 .759 952 +AS CAS 2448870.4045 11.846 1.226 .763 952 +AS CAS 2448870.4135 11.858 1.208 .753 952 +AS CAS 2448870.4628 11.880 1.225 .750 952 +AS CAS 2448870.5070 11.886 1.209 .763 952 +AS CAS 2448872.1721 12.528 1.580 .871 952 +AS CAS 2448872.3000 12.587 1.485 .917 952 +AS CAS 2448872.3218 12.596 1.477 .912 952 +AS CAS 2448872.3602 12.568 1.470 .910 952 +AS CAS 2448872.3865 12.564 1.446 .911 952 +AS CAS 2448872.4301 12.506 1.448 .866 952 +AS CAS 2448874.1783 12.402 1.454 .894 952 +AS CAS 2448874.2557 12.396 1.494 .871 952 +AS CAS 2448874.3052 12.420 1.463 .882 952 +AS CAS 2448874.3349 12.354 1.558 .826 952 +AS CAS 2448874.4113 12.442 1.451 .884 952 +AS CAS 2448874.4638 12.432 1.472 .885 952 +AS CAS 2448875.3076 12.377 1.495 .894 952 +AS CAS 2448875.3482 12.343 1.405 .856 952 +AS CAS 2448875.3950 12.346 1.417 .857 952 +AS CAS 2448876.2090 12.194 1.344 .843 952 +AS CAS 2448876.2741 12.119 1.383 .800 952 +AS CAS 2448876.3135 12.139 1.301 .820 952 +AS CAS 2448876.3426 12.115 1.304 .807 952 +AS CAS 2448876.4080 12.101 1.332 .841 952 +AS CAS 2448876.5103 12.047 1.269 .809 952 +AS CAS 2448877.1839 12.175 1.328 .820 952 +AS CAS 2448877.2550 12.188 1.350 .836 952 +AS CAS 2448877.2919 12.219 1.331 .855 952 +AS CAS 2448877.3455 12.217 1.399 .846 952 +AS CAS 2448877.4109 12.242 1.412 .828 952 +AS CAS 2448877.5104 12.285 1.424 952 +AS CAS 2448878.1958 12.484 1.504 .855 952 +AS CAS 2448878.2894 12.531 1.498 .895 952 +AS CAS 2448878.3283 12.550 1.564 .917 952 +AS CAS 2448878.3634 12.577 1.400 .924 952 +AS CAS 2448879.2920 11.807 1.184 .747 952 +AS CAS 2448879.3421 11.855 1.238 .768 952 +AS CAS 2448879.3628 11.862 1.237 .757 952 +AS CAS 2448879.3967 11.906 1.233 .787 952 +AS CAS 2448879.4409 11.926 1.252 .774 952 +AS CAS 2448879.5115 11.988 1.258 .806 952 +AS CAS 2448880.1848 12.371 1.476 .890 952 +AS CAS 2448880.2404 12.402 1.396 .901 952 +AS CAS 2448880.2712 12.400 1.352 .879 952 +AS CAS 2448880.3080 12.434 1.516 .883 952 +AS CAS 2448880.3522 12.433 1.497 .894 952 +AS CAS 2448880.3872 12.369 1.595 .822 952 +AS CAS 2448880.4232 12.441 1.495 .874 952 +AS CAS 2448880.4535 12.495 1.442 952 +AS CAS 2448880.5061 12.507 1.491 .927 952 +AS CAS 2448881.1794 12.507 1.481 .889 952 +AS CAS 2448881.2330 12.559 1.541 .975 952 +AS CAS 2448881.2611 12.399 1.538 .902 952 +AS CAS 2448881.3360 12.451 1.438 .885 952 +AS CAS 2448881.3893 12.382 1.470 .871 952 +AS CAS 2448881.4110 12.398 1.385 .871 952 +AS CAS 2448881.5140 12.310 1.392 .828 952 +AS CAS 2448882.1873 12.143 1.310 .841 952 +AS CAS 2448882.2355 12.128 1.346 .830 952 +AS CAS 2448882.2716 12.153 1.314 .839 952 +AS CAS 2448882.3060 12.148 1.356 .818 952 +AS CAS 2448882.3585 12.164 1.339 .819 952 +AS CAS 2448882.4022 12.209 1.300 .865 952 +AS CAS 2448882.4309 12.207 1.325 .840 952 +AS CAS 2448883.2227 12.270 1.384 .840 952 +AS CAS 2448883.2650 12.295 1.383 .869 952 +AS CAS 2448883.3000 12.288 1.391 .863 952 +AS CAS 2448883.3543 12.286 1.355 .861 952 +AS CAS 2448883.3883 12.296 1.364 .865 952 +AS CAS 2448883.4336 12.307 1.412 .874 952 +AS CAS 2448883.5091 12.291 1.413 .854 952 +AS CAS 2448884.1990 12.402 1.426 .889 952 +AS CAS 2448884.2402 12.437 1.426 .844 952 +AS CAS 2448885.1933 11.997 1.222 .780 952 +AS CAS 2448885.2342 11.923 1.233 .761 952 +AS CAS 2448885.2650 11.895 1.222 .760 952 +AS CAS 2448885.3365 11.818 1.184 .755 952 +AS CAS 2448885.3960 11.826 1.153 .780 952 +AS CAS 2448885.4280 11.805 1.162 .759 952 +AS CAS 2448885.5147 11.837 1.149 952 +AS CAS 2448886.1957 12.240 1.332 .874 952 +AS CAS 2448886.2460 12.260 1.394 .876 952 +AS CAS 2448886.2984 12.281 1.417 .860 952 +AS CAS 2448886.3220 12.286 1.456 .874 952 +AS CAS 2448886.3424 12.307 1.460 .873 952 +AS CAS 2448886.3850 12.346 1.412 952 +AS CAS 2448886.4252 12.361 1.422 .882 952 +AS CAS 2448886.5107 12.407 1.464 952 +AS CAS 2448887.2297 12.580 1.567 .904 952 +AS CAS 2448887.2747 12.609 1.477 .937 952 +AS CAS 2448887.3129 12.593 1.494 .910 952 +AS CAS 2448888.1923 11.931 1.214 952 +AS CAS 2448888.2318 11.951 1.234 .779 952 +AS CAS 2448888.2495 11.951 1.237 .770 952 +AS CAS 2448888.2623 11.952 1.275 .766 952 +AS CAS 2448888.2874 11.997 1.252 .800 952 +AS CAS 2448888.3055 11.988 1.284 .810 952 +AS CAS 2448888.3321 12.020 1.246 .807 952 +AS CAS 2448888.3633 12.002 1.299 .786 952 +AS CAS 2448888.3815 12.034 1.281 .802 952 +AS CAS 2448888.4144 12.044 1.296 .818 952 +AS CAS 2448888.5163 12.097 1.347 952 +AS CAS 2448889.2020 12.389 1.437 .877 952 +AS CAS 2448889.2381 12.413 1.450 .901 952 +AS CAS 2448889.2647 12.426 1.466 .906 952 +AS CAS 2448889.2996 12.382 1.468 .892 952 +AS CAS 2448889.3388 12.406 1.467 .892 952 +AS CAS 2448890.1817 12.409 1.423 .891 952 +AS CAS 2448890.2164 12.395 1.413 .886 952 +AS CAS 2448890.2449 12.373 1.424 .862 952 +AS CAS 2448890.2673 12.364 1.452 .865 952 +AS CAS 2448890.3049 12.376 1.425 .854 952 +AS CAS 2448890.3081 12.374 1.411 .872 952 +AS CAS 2448890.3443 12.363 1.434 .867 952 +AS CAS 2448890.3611 12.385 1.395 .888 952 +AS CAS 2448890.3907 12.357 1.422 .865 952 +AS CAS 2448890.4061 12.372 1.393 .870 952 +AS CAS 2448890.5153 12.343 1.415 952 +AS CAS 2448891.1776 12.258 1.383 .855 952 +AS CAS 2448891.2157 12.252 1.377 .849 952 +AS CAS 2448891.2411 12.233 1.360 .833 952 +AS CAS 2448891.2779 12.232 1.324 .835 952 +AS CAS 2448891.3089 12.217 1.331 .840 952 +AS CAS 2448891.3201 12.205 1.319 .839 952 +AS CAS 2448891.3576 12.182 1.339 .809 952 +AS CAS 2448891.3735 12.166 1.341 .828 952 +AS CAS 2448891.4132 12.136 1.330 .821 952 +AS CAS 2448891.5177 12.074 1.310 952 +AS CAS 2448892.1930 12.130 1.347 .822 952 +AS CAS 2448892.2327 12.151 1.337 .832 952 +AS CAS 2448892.2579 12.179 1.354 .851 952 +AS CAS 2448892.2942 12.217 1.391 .855 952 +AS CAS 2448892.3274 12.261 1.410 .861 952 +AS CAS 2448893.1830 12.487 1.498 .900 952 +AS CAS 2448893.2233 12.507 1.480 .916 952 +AS CAS 2448893.2408 12.505 1.463 .902 952 +AS CAS 2448893.2540 12.508 1.497 .925 952 +AS CAS 2448893.2707 12.507 1.462 .908 952 +AS CAS 2448893.2823 12.517 1.488 .902 952 +AS CAS 2448893.3104 12.513 1.485 .897 952 +AS CAS 2448893.3356 12.538 1.457 .913 952 +AS CAS 2448893.3558 12.530 1.494 .918 952 +AS CAS 2448893.3748 12.548 1.500 .914 952 +AS CAS 2448893.4181 12.521 1.509 .892 952 +AS CAS 2448894.1754 11.730 1.175 .733 952 +AS CAS 2448894.1789 11.729 1.143 .720 952 +AS CAS 2448894.2021 11.710 1.138 .734 952 +AS CAS 2448894.2090 11.712 1.162 .737 952 +AS CAS 2448894.2189 11.700 1.141 .716 952 +AS CAS 2448894.2228 11.690 1.152 .714 952 +AS CAS 2448894.2424 11.703 1.149 .724 952 +AS CAS 2448894.2472 11.704 1.141 .725 952 +AS CAS 2448894.2576 11.705 1.154 .729 952 +AS CAS 2448894.2643 11.692 1.160 .722 952 +AS CAS 2448894.2698 11.720 1.153 .729 952 +AS CAS 2448894.2729 11.719 1.157 .735 952 +AS CAS 2448894.2808 11.711 1.132 .717 952 +AS CAS 2448894.2849 11.718 1.160 .715 952 +AS CAS 2448894.2926 11.723 1.160 .732 952 +AS CAS 2448894.3023 11.735 .637 1.170 .727 952 +AS CAS 2448894.3183 11.779 1.167 .732 952 +AS CAS 2448894.3333 11.803 1.155 .747 952 +AS CAS 2448894.3441 11.807 1.152 .739 952 +AS CAS 2449933.3939 12.345 1.381 .864 1.692 998 +AS CAS 2449933.4433 12.317 1.386 .875 1.701 998 +AS CAS 2449934.4434 12.343 1.383 .922 1.713 998 +AS CAS 2449935.4404 11.994 .793 1.596 998 +AS CAS 2449936.4414 12.471 .875 1.745 998 +AS CAS 2449937.4374 11.902 1.212 .778 998 +AS CAS 2449937.4575 11.794 .782 998 +AS CAS 2449941.4496 12.135 1.599 998 +AS CAS 2449942.4235 12.299 1.379 .892 1.730 998 +AS CAS 2449943.3982 12.418 1.396 .839 1.665 998 +AS CAS 2449944.4423 12.091 1.354 .877 1.646 998 +AS CAS 2449946.4631 12.031 1.264 .766 1.601 998 +AS CAS 2449947.4357 12.212 1.357 .885 1.715 998 +AS CAS 2449948.3954 12.292 1.409 .878 1.680 998 +AS CAS 2449949.3465 12.321 1.464 .874 1.638 998 +AS CAS 2449952.3468 12.350 1.394 .884 1.674 998 +AS CAS 2449953.4071 12.194 1.347 .839 1.635 998 +AS CAS 2449954.2853 12.480 1.451 .974 1.773 998 +AS CAS 2449954.3618 12.397 1.485 .895 1.715 998 +AS CAS 2449955.3216 12.259 1.405 .845 1.650 998 +AS CAS 2449956.4708 12.071 1.282 .870 1.688 998 +AS CAS 2449957.3962 12.378 998 +AS CAS 2449962.3834 12.224 1.300 .842 1.658 998 +AS CAS 2449963.3953 12.316 1.412 .875 1.714 998 +AS CAS 2449985.3680 12.232 1.387 .848 1.623 998 +AS CAS 2449986.3568 12.079 1.281 .841 1.598 998 +AS CAS 2449987.3862 12.198 1.363 .867 1.661 998 +AS CAS 2449992.4112 12.120 1.339 .843 1.645 998 +AS CAS 2450007.3679 12.120 1.323 .836 1.668 998 +AS CAS 2450008.4116 12.277 1.692 998 +AS CAS 2450009.3568 12.415 1.440 .926 1.778 998 +AS CAS 2450010.3044 12.084 1.256 .810 1.557 998 +AS CAS 2450010.3192 11.950 1.269 .800 1.492 998 +AS CAS 2450010.3361 12.005 1.270 .797 1.542 998 +AS CAS 2450010.3654 11.940 1.240 .791 1.514 998 +AS CAS 2450010.3823 11.901 1.229 .770 1.482 998 +AS CAS 2450010.4007 11.896 1.229 .787 1.525 998 +AS CAS 2450010.4155 11.875 1.237 .772 1.514 998 +AS CAS 2450010.4407 11.829 1.242 .768 1.514 998 +AS CAS 2450010.4609 11.846 1.219 .765 1.504 998 +AS CAS 2450010.4729 11.827 1.185 .785 1.529 998 +AS CAS 2450011.3001 12.190 1.359 .872 1.666 998 +AS CAS 2450011.3186 12.207 1.421 .877 1.672 998 +AS CAS 2450011.3674 12.228 1.417 .881 1.708 998 +AS CAS 2450017.2918 12.176 1.297 .855 1.612 998 +AS CAS 2450017.3025 12.157 1.333 .827 1.624 998 +AS CAS 2450017.3343 12.163 1.379 .836 1.650 998 +AS CAS 2450017.3593 12.190 1.359 .857 1.646 998 +AS CAS 2450017.3804 12.218 1.361 .856 1.620 998 +AS CAS 2450017.4014 12.221 1.381 .845 1.650 998 +AS CAS 2450017.4372 12.261 1.372 .843 1.659 998 +AS CAS 2450018.3171 12.499 1.484 .905 1.774 998 +AS CAS 2450018.3414 12.494 1.488 .907 1.773 998 +AS CAS 2450018.3880 12.506 1.485 .920 1.755 998 +AS CAS 2450018.4322 12.518 1.506 .933 1.781 998 +AS CAS 2450019.2882 11.725 1.146 .751 1.468 998 +AS CAS 2450019.3036 11.728 1.143 .748 1.450 998 +AS CAS 2450019.3195 11.719 1.133 .745 1.435 998 +AS CAS 2450019.3376 11.690 1.124 .729 1.457 998 +AS CAS 2450019.3539 11.689 1.129 .748 1.455 998 +AS CAS 2450019.3744 11.674 1.146 .726 1.432 998 +AS CAS 2450019.4001 11.693 1.127 .736 1.444 998 +AS CAS 2450019.4149 11.699 1.142 .731 1.441 998 +AS CAS 2450020.2656 12.309 1.407 .895 1.714 998 +AS CAS 2450020.2876 12.316 1.418 .893 1.719 998 +AS CAS 2450020.3310 12.306 1.455 .879 1.678 998 +AS CAS 2450305.2482 12.519 1.508 .897 971 +AS CAS 2450305.3239 12.576 1.499 .927 971 +AS CAS 2450306.1942 12.176 1.209 .786 971 +AS CAS 2450306.4559 12.004 1.283 .717 971 +AS CAS 2450307.3372 12.273 1.434 .881 971 +AS CAS 2450307.3907 12.243 1.462 .883 971 +AS CAS 2450307.4381 12.290 1.442 .890 971 +AS CAS 2450310.4423 12.012 1.319 .829 971 +AS CAS 2450311.3011 12.433 1.420 .906 971 +AS CAS 2450311.3271 12.471 1.454 .933 971 +AS CAS 2450311.3438 12.472 1.436 .954 971 +AS CAS 2450311.4524 12.484 1.439 .954 971 +AS CAS 2450312.2043 12.495 1.390 .979 971 +AS CAS 2450312.4309 12.098 1.331 .838 971 +AS CAS 2450313.2616 12.147 1.320 .825 971 +AS CAS 2450313.3068 12.175 1.376 .854 971 +AS CAS 2450313.3262 12.201 1.335 .865 971 +AS CAS 2450314.2028 12.507 1.438 .910 971 +AS CAS 2450314.2483 12.516 1.462 .902 971 +AS CAS 2450314.4147 12.501 1.454 .915 971 +AS CAS 2450315.2507 12.304 1.321 .931 971 +AS CAS 2450315.3537 12.238 1.355 .884 971 +AS CAS 2450315.4415 12.168 1.407 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+CF CAS 2447409.3338 11.370 1.323 .772 990 +CF CAS 2447410.3591 11.113 1.211 .708 990 +CF CAS 2447411.3561 10.896 1.076 .688 990 +CF CAS 2447413.3183 11.275 1.282 .767 990 +CF CAS 2447413.4341 11.306 1.308 .771 990 +CF CAS 2447414.3173 11.402 1.309 .783 990 +CF CAS 2447415.2974 11.071 1.187 .700 990 +CF CAS 2447416.2924 10.895 1.131 .665 990 +CF CAS 2447417.2840 11.109 1.245 .716 990 +CF CAS 2447418.2826 11.271 1.345 .771 990 +CF CAS 2447419.2588 11.375 1.315 .753 990 +CF CAS 2447420.2538 11.029 1.137 .688 990 +CF CAS 2447420.5108 10.885 1.056 990 +CF CAS 2447421.2467 10.948 1.159 .677 990 +CF CAS 2447421.5083 10.994 1.136 990 +CF CAS 2447422.2611 11.174 1.300 .742 990 +CF CAS 2447422.5078 11.225 1.290 990 +CF CAS 2447423.3844 11.306 1.315 .755 990 +CF CAS 2447423.5074 11.328 1.320 990 +CF CAS 2447424.2766 11.451 1.311 .798 990 +CF CAS 2447424.5049 11.381 1.281 .770 990 +CF CAS 2447425.2961 10.935 1.133 .670 990 +CF CAS 2447425.5057 10.931 1.041 990 +CF CAS 2447427.3389 11.204 1.311 .720 990 +CF CAS 2447427.5034 11.178 1.317 .747 990 +CF CAS 2447428.2715 11.275 1.346 .751 990 +CF CAS 2447428.4202 11.328 1.344 .758 990 +CF CAS 2447428.4957 11.384 1.354 .754 990 +CF CAS 2447429.3070 11.392 1.339 .754 990 +CF CAS 2447430.2564 10.879 1.080 .653 990 +CF CAS 2447430.4516 10.871 1.078 .660 990 +CF CAS 2447430.5110 10.848 1.091 .655 990 +CF CAS 2447431.3047 10.985 1.202 .692 990 +CF CAS 2447431.5102 11.045 1.186 .722 990 +CF CAS 2447432.2934 11.160 1.299 .748 990 +CF CAS 2447432.5095 11.283 1.277 .779 990 +CF CAS 2447433.2840 11.332 1.322 .765 990 +CF CAS 2447434.2768 11.377 1.324 .763 990 +CF CAS 2447434.4991 11.281 1.189 .756 990 +CF CAS 2447734.4776 11.131 1.271 .739 991 +CF CAS 2447736.4717 11.373 1.298 .752 991 +CF CAS 2447738.4673 11.019 1.168 .693 991 +CF CAS 2447739.4621 11.156 1.280 .758 991 +CF CAS 2447741.4013 11.336 1.309 .736 991 +CF CAS 2447742.4289 10.836 1.072 .640 991 +CF CAS 2447743.4140 11.020 1.180 .712 991 +CF CAS 2447744.4434 11.217 1.283 .750 991 +CF CAS 2447745.4399 11.372 1.314 .802 991 +CF CAS 2447746.4428 11.301 1.317 .727 991 +CF CAS 2447747.3933 10.825 1.063 .659 991 +CF CAS 2447748.4469 11.080 1.186 .716 991 +CF CAS 2447749.4469 11.247 1.238 .765 991 +CF CAS 2447750.4431 11.414 1.300 .785 991 +CF CAS 2447751.4501 11.217 1.216 .720 991 +CF CAS 2447752.4410 10.837 1.061 .667 991 +CF CAS 2447753.4318 11.061 1.196 .726 991 +CF CAS 2447754.4173 1.306 .754 991 +CF CAS 2447755.4537 11.370 1.321 .787 991 +CF CAS 2447756.4514 11.098 1.172 .714 991 +CF CAS 2447757.4239 10.860 .653 1.066 .689 991 +CF CAS 2447758.3846 11.064 1.201 .737 991 +CF CAS 2447759.3727 11.231 1.337 .745 991 +CF CAS 2447759.4730 11.236 1.353 .755 991 +CF CAS 2447760.4041 11.385 1.323 .804 991 +CF CAS 2447761.4553 11.041 1.174 .700 991 +CF CAS 2447762.4325 10.879 1.120 .694 991 +CF CAS 2447763.2780 11.072 1.250 .721 991 +CF CAS 2447764.2835 11.246 1.311 .745 991 +CF CAS 2447765.2867 11.361 1.348 .769 991 +CF CAS 2447766.2966 11.038 1.185 .689 991 +CF CAS 2447767.4333 10.933 1.119 .693 991 +CF CAS 2447768.3951 11.109 1.275 .727 991 +CF CAS 2447770.3433 11.383 1.338 .764 991 +CF CAS 2447771.3326 10.980 1.115 .700 991 +CF CAS 2447771.3947 10.946 1.083 .684 991 +CF CAS 2447772.3287 10.943 1.153 .694 991 +CF CAS 2447772.4345 10.960 1.158 .716 991 +CF CAS 2447773.3548 11.149 1.271 .748 991 +CF CAS 2447773.4060 11.166 1.248 .760 991 +CF CAS 2447774.3597 11.292 1.328 .761 991 +CF CAS 2447774.4163 11.317 1.332 .771 991 +CF CAS 2447775.3258 11.390 1.323 .769 991 +CF CAS 2447775.3800 11.389 1.317 .774 991 +CF CAS 2447776.3410 10.904 1.087 .678 991 +CF CAS 2447776.4030 10.893 1.057 .675 991 +CF CAS 2448101.4318 11.347 1.350 .793 992 +CF CAS 2448102.3766 11.239 1.242 .727 992 +CF CAS 2448103.3495 10.841 1.052 .652 992 +CF CAS 2448104.3693 11.030 1.205 .716 992 +CF CAS 2448104.4101 11.034 1.234 .718 992 +CF CAS 2448108.3838 10.829 1.100 .664 992 +CF CAS 2448109.3507 11.077 1.219 .733 992 +CF CAS 2448110.3495 11.254 1.306 .768 992 +CF CAS 2448111.3662 11.372 1.388 .770 992 +CF CAS 2448112.3295 11.138 1.188 .705 992 +CF CAS 2448112.4564 11.065 1.169 .709 992 +CF CAS 2448113.3197 10.866 1.064 .681 992 +CF CAS 2448114.3936 11.132 1.258 .772 992 +CF CAS 2448116.3851 11.396 1.378 .794 992 +CF CAS 2448117.4496 .691 992 +CF CAS 2448118.3801 10.940 1.126 .700 992 +CF CAS 2448119.3788 11.144 1.307 .752 992 +CF CAS 2448122.3557 10.976 1.126 .704 992 +CF CAS 2448123.3452 10.926 1.167 .692 992 +CF CAS 2448123.4072 10.952 1.155 .700 992 +CF CAS 2448126.3298 11.403 1.358 .770 992 +CF CAS 2448127.2912 10.949 1.113 .683 992 +CF CAS 2448127.3918 10.899 1.082 .667 992 +CF CAS 2448503.3752 10.893 1.132 .657 993 +CF CAS 2448504.3061 11.078 1.230 .734 993 +CF CAS 2448505.2125 11.261 1.295 .770 993 +CF CAS 2448505.3232 11.274 1.320 .775 993 +CF CAS 2448506.2059 11.368 1.345 .771 993 +CF CAS 2448506.3340 11.400 1.342 .791 993 +CF CAS 2448506.4352 11.409 1.363 .780 993 +CF CAS 2448507.1679 11.179 1.203 .737 993 +CF CAS 2448507.3099 11.068 1.184 .692 993 +CF CAS 2448507.3409 11.059 1.168 .685 993 +CF CAS 2448507.4241 11.028 1.122 .700 993 +CF CAS 2448508.1602 10.876 1.095 .664 993 +CF CAS 2448508.2642 10.874 1.143 .649 993 +CF CAS 2448508.2912 10.891 1.138 .669 993 +CF CAS 2448508.3392 10.896 1.152 .674 993 +CF CAS 2448508.3835 10.940 1.120 993 +CF CAS 2448508.4266 10.941 1.122 .694 993 +CF CAS 2448509.1684 11.105 1.267 .737 993 +CF CAS 2448509.2100 11.089 1.279 .725 993 +CF CAS 2448509.2713 11.126 1.277 .749 993 +CF CAS 2448509.3327 11.132 1.265 .738 993 +CF CAS 2448509.3683 11.125 1.251 .734 993 +CF CAS 2448510.3157 11.272 1.324 .758 993 +CF CAS 2448510.3806 11.287 1.331 .774 993 +CF CAS 2448511.2684 11.384 1.382 .767 993 +CF CAS 2448511.3267 11.387 1.360 .763 993 +CF CAS 2448512.3119 11.020 1.124 .700 993 +CF CAS 2448512.3632 10.997 1.120 .689 993 +CF CAS 2448512.4156 10.970 1.109 .687 993 +CF CAS 2448513.2677 10.921 1.137 .667 993 +CF CAS 2448513.3229 10.923 1.146 .692 993 +CF CAS 2448513.3734 10.955 1.150 .695 993 +CF CAS 2448514.3265 11.137 1.282 .733 993 +CF CAS 2448514.3814 11.173 1.267 .763 993 +CF CAS 2448515.3366 11.311 1.330 .780 993 +CF CAS 2448516.1582 11.403 1.326 .771 993 +CF CAS 2448517.3294 10.952 1.096 .682 993 +CF CAS 2448518.2459 10.949 1.142 .692 993 +CF CAS 2448519.3036 11.163 1.280 .742 993 +CF CAS 2448520.2892 11.329 1.330 .785 993 +CF CAS 2448521.3367 11.388 1.334 .742 993 +CF CAS 2448522.2930 10.885 1.091 .652 993 +CF CAS 2448523.2882 10.975 1.167 .695 993 +CF CAS 2448852.3820 1.362 .790 994 +CF CAS 2448854.3827 10.889 1.117 .680 994 +CF CAS 2448856.3696 11.263 1.315 .760 994 +CF CAS 2448858.3356 11.062 1.173 .697 994 +CF CAS 2448860.3428 11.132 1.259 .747 994 +CF CAS 2448862.3452 11.398 1.351 .781 994 +CF CAS 2448870.3196 11.162 1.300 .734 994 +CF CAS 2448872.3083 11.402 1.331 .770 994 +CF CAS 2448874.3250 10.990 1.196 .703 994 +CF CAS 2448875.3304 11.194 1.316 .755 994 +CF CAS 2448876.3285 11.342 1.345 .778 994 +CF CAS 2448877.2788 11.373 1.342 .753 994 +CF CAS 2448878.3484 10.835 1.079 .646 994 +CF CAS 2448879.3514 11.033 1.212 .715 994 +CF CAS 2448880.2631 11.204 1.335 .761 994 +CF CAS 2448881.3954 11.369 1.338 .792 994 +CF CAS 2448882.2617 11.324 1.322 .753 994 +CF CAS 2448883.2894 10.852 1.078 .664 994 +CF CAS 2448885.2570 11.236 1.300 .766 994 +CF CAS 2448886.3039 11.353 1.356 .767 994 +CF CAS 2448887.3011 11.264 1.249 .730 994 +CF CAS 2448888.2526 10.839 1.103 .653 994 +CF CAS 2448888.3982 10.855 1.118 .660 994 +CF CAS 2448889.2567 11.041 1.248 .710 994 +CF CAS 2448890.2346 11.214 1.358 .744 994 +CF CAS 2448890.3529 11.254 1.321 .763 994 +CF CAS 2448890.4098 11.261 1.325 .758 994 +CF CAS 2448891.2316 11.366 1.345 .776 994 +CF CAS 2448891.3491 11.384 1.355 .760 994 +CF CAS 2448892.2490 11.226 1.249 .749 994 +CF CAS 2448893.2513 10.863 1.088 .677 994 +CF CAS 2448894.2907 11.081 1.260 .721 994 +CF CAS 2449617.3071 11.381 .774 995 +CF CAS 2449620.3714 11.014 1.151 .723 995 +CF CAS 2449620.4403 11.011 1.180 .723 995 +CF CAS 2449621.3707 11.226 1.306 .778 995 +CF CAS 2449621.4353 11.184 1.278 .744 995 +CF CAS 2449622.4175 11.391 1.297 .785 995 +CF CAS 2449622.4708 11.379 1.277 .783 995 +CF CAS 2449623.3745 11.327 1.297 .743 995 +CF CAS 2449623.4441 11.285 1.212 .726 995 +CF CAS 2449624.4363 10.823 1.091 .625 995 +CF CAS 2449624.4763 10.878 1.081 .682 995 +CF CAS 2449625.3565 11.049 1.190 .714 995 +CF CAS 2449625.4082 11.088 1.221 .716 995 +CF CAS 2449625.4463 11.086 1.204 .741 995 +CF CAS 2449625.4806 11.063 1.248 .702 995 +CF CAS 2449625.5114 11.113 1.248 .736 995 +CF CAS 2449631.3437 11.289 1.345 .767 995 +CF CAS 2449632.3425 1.353 .771 995 +CF CAS 2449632.4297 11.418 1.355 .762 995 +CF CAS 2449632.4715 11.424 1.357 .778 995 +CF CAS 2449633.3239 11.242 1.143 .710 995 +CF CAS 2449633.3863 11.147 1.159 .723 995 +CF CAS 2449633.4436 11.082 1.153 .684 995 +CF CAS 2449634.3230 1.092 .646 995 +CF CAS 2449635.3505 11.097 1.201 .728 995 +CF CAS 2449635.4512 11.118 1.309 .717 995 +CF CAS 2449933.4559 11.287 1.306 .789 1.470 998 +CF CAS 2449934.4778 11.398 .839 1.532 998 +CF CAS 2449935.4524 11.315 .734 1.418 998 +CF CAS 2449936.4481 .658 1.301 998 +CF CAS 2449937.4551 11.040 .740 998 +CF CAS 2449938.4697 11.289 .779 1.488 998 +CF CAS 2449939.4635 11.441 1.501 998 +CF CAS 2449941.4667 10.883 1.306 998 +CF CAS 2449942.4606 11.131 1.424 998 +CF CAS 2449943.4448 11.337 1.516 998 +CF CAS 2449944.4633 11.458 1.529 998 +CF CAS 2449946.4569 10.923 1.326 998 +CF CAS 2449947.4243 11.134 1.415 998 +CF CAS 2449948.3847 11.322 1.488 998 +CF CAS 2449949.3916 11.458 1.525 998 +CF CAS 2449955.4207 11.027 1.334 998 +CF CAS 2449956.4300 10.965 1.370 998 +CF CAS 2449957.4506 11.201 1.466 998 +CF CAS 2449960.3976 11.006 1.300 998 +CF CAS 2449962.4260 11.205 1.478 998 +CF CAS 2449985.3351 10.877 1.272 998 +CF CAS 2449986.3528 11.104 1.423 998 +CF CAS 2449992.4060 11.324 1.481 998 +CF CAS 2450007.3587 11.349 1.533 998 +CF CAS 2450008.4035 11.350 1.465 998 +CF CAS 2450009.3527 10.857 1.263 998 +CF CAS 2450010.3011 11.017 1.368 998 +CF CAS 2450011.3136 11.250 1.488 998 +CF CAS 2450017.2978 11.384 1.487 998 +CF CAS 2450018.3115 11.264 1.424 998 +CF CAS 2450019.2994 10.817 1.278 998 +CF CAS 2450020.2486 11.053 1.392 998 +CF CAS 2450305.3302 11.402 1.404 .787 971 +CF CAS 2450307.3725 10.896 1.220 .704 971 +CF CAS 2450310.4339 11.366 1.380 .769 971 +CF CAS 2450311.3234 10.942 1.122 .682 971 +CF CAS 2450311.3590 10.941 1.109 .657 971 +CF CAS 2450312.4238 10.975 1.184 .738 971 +CF CAS 2450313.3233 11.159 1.267 .751 971 +CF CAS 2450314.3974 11.337 1.326 .791 971 +CF CAS 2450315.3530 11.365 1.360 .799 971 +CF CAS 2450315.4356 11.339 1.382 .757 971 +CF CAS 2450316.3652 10.865 1.123 .712 971 +CF CAS 2450317.3783 10.998 1.213 .714 971 +CF CAS 2450318.3647 11.199 1.294 .764 971 +CF CAS 2450319.3723 11.309 1.357 .833 971 +CF CAS 2450320.3742 11.357 1.354 .757 971 +CF CAS 2450321.3379 10.841 1.127 .751 971 +CF CAS 2450321.3624 10.838 1.111 .698 971 +CF CAS 2450322.3606 11.029 1.242 .710 971 +CF CAS 2450323.3422 11.236 1.345 .777 971 +CF CAS 2450324.3239 11.340 1.361 .779 971 +CF CAS 2450325.3136 11.322 1.320 .756 971 +CF CAS 2450326.2569 10.870 1.096 .652 971 +CF CAS 2450327.3148 11.016 1.206 .734 971 +CF CAS 2450328.3958 11.205 1.355 .725 971 +CF CAS 2450330.2662 11.340 1.263 .732 971 +CF CAS 2450332.2432 11.041 1.196 .637 971 +CF CAS 2450333.2510 11.210 1.308 .716 971 +CF CAS 2450334.2609 11.362 1.347 .751 971 +CF CAS 2450335.2952 11.208 1.227 .729 971 +CF CAS 2450336.3231 10.853 1.126 .640 971 +CF CAS 2450337.2292 11.076 1.246 .710 971 +CF CAS 2450338.3582 11.247 1.366 .764 971 +CF CAS 2450340.2385 11.150 1.233 .702 971 +CF CAS 2450341.2360 10.894 1.108 .664 971 +CF CAS 2450342.2541 11.081 1.286 .743 971 +CF CAS 2450344.2704 11.398 1.360 .677 971 +CF CAS 2450347.2473 11.127 1.269 .744 971 +CF CAS 2450349.2211 11.397 1.392 .776 971 +CF CAS 2450357.2057 11.186 1.270 .731 971 +CG CAS 2444827.3671 11.136 1.183 982 +CG CAS 2444829.3788 11.734 1.406 982 +CG CAS 2444830.3984 11.574 1.327 982 +CG CAS 2444831.3359 10.990 1.080 982 +CG CAS 2444832.3631 11.404 1.288 982 +CG CAS 2444833.3359 11.625 1.390 982 +CG CAS 2444848.4785 1.047 982 +CG CAS 2444851.4804 11.723 1.415 982 +CG CAS 2444880.3593 11.368 982 +CG CAS 2444880.4843 11.366 982 +CG CAS 2444881.3320 1.423 982 +CG CAS 2444881.4062 1.421 982 +CG CAS 2444883.2889 10.953 1.048 982 +CG CAS 2444883.4647 10.889 1.042 982 +CG CAS 2444884.2851 1.213 982 +CG CAS 2445182.4647 11.576 1.376 982 +CG CAS 2445183.4647 11.726 1.420 982 +CG CAS 2445184.4530 11.007 1.056 982 +CG CAS 2445187.4256 11.682 1.436 982 +CG CAS 2445188.3828 11.572 1.309 982 +CG CAS 2445189.4101 11.021 1.102 982 +CG CAS 2445199.4804 11.468 1.337 982 +CG CAS 2445205.4921 11.750 1.436 982 +CG CAS 2445877.4296 11.740 1.444 .845 982 +CG CAS 2445878.4375 11.231 1.166 .726 982 +CG CAS 2445880.4218 11.457 1.327 .805 982 +CG CAS 2445881.4296 11.711 1.390 .829 982 +CG CAS 2445882.4335 11.673 1.356 .806 982 +CG CAS 2445883.4335 11.000 1.072 .670 982 +CG CAS 2445886.4296 11.783 1.401 .847 982 +CG CAS 2445887.4256 10.930 1.057 .648 982 +CG CAS 2447741.4615 11.689 1.396 991 +CG CAS 2447742.4326 11.517 1.257 .771 991 +CG CAS 2447743.4170 11.048 1.081 .716 991 +CG CAS 2447744.4452 11.435 1.302 .793 991 +CG CAS 2447745.4444 11.670 1.373 .829 991 +CG CAS 2447746.4446 11.763 1.420 .803 991 +CG CAS 2447747.3952 10.912 1.033 .650 991 +CG CAS 2447748.4488 11.326 1.221 .758 991 +CG CAS 2447749.4488 11.581 1.343 .824 991 +CG CAS 2447750.4475 1.398 991 +CG CAS 2447751.4518 11.078 1.090 991 +CG CAS 2447752.4435 11.150 1.145 .731 991 +CG CAS 2447753.4355 11.509 1.294 .818 991 +CG CAS 2447754.4241 11.692 1.388 .829 991 +CG CAS 2447755.4585 11.568 1.297 .794 991 +CG CAS 2447756.4562 10.983 1.075 .677 991 +CG CAS 2447757.4327 11.357 1.266 .795 991 +CG CAS 2447758.3913 11.579 1.378 .802 991 +CG CAS 2447759.3787 11.751 1.389 991 +CG CAS 2447760.4355 10.924 1.023 .664 991 +CG CAS 2447761.4591 11.238 1.264 .742 991 +CG CAS 2447762.4373 11.535 1.357 .826 991 +CG CAS 2447763.2804 11.724 1.386 .848 991 +CG CAS 2447764.2858 11.468 1.236 .758 991 +CG CAS 2447765.4366 11.094 1.154 .702 991 +CG CAS 2447766.2993 11.405 1.307 .793 991 +CG CAS 2447767.4355 11.664 1.396 .826 991 +CG CAS 2447768.3980 11.609 1.386 .794 991 +CG CAS 2447770.3454 11.312 1.254 .768 991 +CG CAS 2447771.3343 11.594 1.357 .836 991 +CG CAS 2447771.3958 11.614 1.359 .821 991 +CG CAS 2447772.3303 11.763 1.407 .843 991 +CG CAS 2447772.4373 11.754 1.381 .844 991 +CG CAS 2447773.3572 10.997 1.037 .661 991 +CG CAS 2447773.4080 10.970 1.045 .676 991 +CG CAS 2447774.3620 11.196 1.195 .735 991 +CG CAS 2447774.4188 11.233 1.191 .750 991 +CG CAS 2447775.3312 11.513 1.325 .808 991 +CG CAS 2447775.3819 11.506 1.344 .812 991 +CG CAS 2447776.3427 11.715 1.401 .851 991 +CG CAS 2447776.4048 11.746 1.387 .861 991 +CG CAS 2448104.3739 11.730 1.386 .824 992 +CG CAS 2448104.4110 11.718 1.382 .842 992 +CG CAS 2448108.3856 11.724 1.418 .844 992 +CG CAS 2448109.3529 11.211 1.123 .720 992 +CG CAS 2448110.3517 11.128 1.189 .718 992 +CG CAS 2448111.3676 11.483 1.347 .806 992 +CG CAS 2448112.3310 11.687 1.407 .829 992 +CG CAS 2448113.3212 11.673 1.329 .808 992 +CG CAS 2448114.3946 11.021 1.117 .707 992 +CG CAS 2448116.3866 11.675 1.400 .861 992 +CG CAS 2448117.4516 1.382 .839 992 +CG CAS 2448118.3813 10.946 1.018 .670 992 +CG CAS 2448119.3803 11.274 1.251 .767 992 +CG CAS 2448122.3566 11.335 1.196 .753 992 +CG CAS 2448123.3474 11.126 1.118 .720 992 +CG CAS 2448123.4079 11.105 1.171 .718 992 +CG CAS 2448126.3319 11.739 1.369 .821 992 +CG CAS 2448127.2926 10.967 1.036 .666 992 +CG CAS 2448127.3926 10.964 1.094 .657 992 +CG CAS 2448503.3821 11.204 1.162 .728 993 +CG CAS 2448504.3091 11.477 1.324 .796 993 +CG CAS 2448505.3252 11.705 1.401 .840 993 +CG CAS 2448506.3346 11.595 1.319 .793 993 +CG CAS 2448507.3422 11.020 1.101 .684 993 +CG CAS 2448508.2956 11.381 1.293 .783 993 +CG CAS 2448510.3165 11.766 1.416 .841 993 +CG CAS 2448511.3274 10.906 1.056 .658 993 +CG CAS 2448512.3127 11.266 1.219 .763 993 +CG CAS 2448513.3254 11.556 1.373 .820 993 +CG CAS 2448514.3297 11.746 1.397 .851 993 +CG CAS 2448515.3082 11.287 1.172 .720 993 +CG CAS 2448516.1598 11.080 1.100 .698 993 +CG CAS 2448517.3303 11.473 1.319 .801 993 +CG CAS 2448518.2469 11.684 1.404 .837 993 +CG CAS 2448519.3043 11.699 1.363 .804 993 +CG CAS 2448520.2905 10.970 1.051 .670 993 +CG CAS 2448521.3382 11.362 1.272 .780 993 +CG CAS 2448522.2951 11.602 1.411 .811 993 +CG CAS 2448523.2904 11.780 1.364 .851 993 +CG CAS 2449625.4164 11.220 1.133 1.188 .719 995 +CH CAS 2445648.3203 10.405 1.375 .894 982 +CH CAS 2445649.3280 10.671 1.492 .990 982 +CH CAS 2445660.2734 11.213 1.705 1.074 982 +CH CAS 2445665.2929 10.766 1.591 1.003 982 +CH CAS 2445666.2147 10.852 1.673 1.040 982 +CH CAS 2445668.2304 11.031 1.801 1.073 982 +CH CAS 2445674.2381 11.377 1.830 1.081 982 +CH CAS 2445676.2343 11.122 1.693 1.041 982 +CH CAS 2445677.1913 10.790 1.539 .980 982 +CH CAS 2445679.1875 10.568 1.491 .948 982 +CH CAS 2445683.2070 11.049 1.839 1.103 982 +CH CAS 2445688.1562 11.470 1.099 982 +CH CAS 2445689.2030 11.375 1.778 1.118 982 +CH CAS 2445691.1639 11.141 1.227 1.683 1.036 982 +CH CAS 2445694.1445 10.546 1.108 1.447 .945 982 +CH CAS 2445695.1484 10.722 1.198 1.560 .991 982 +CH CAS 2445701.1522 11.371 1.577 1.882 1.128 982 +CH CAS 2445706.1093 11.145 1.253 1.682 1.037 982 +CH CAS 2445707.1093 10.935 1.164 1.574 .994 982 +CH CAS 2447399.4565 10.630 1.476 .949 990 +CH CAS 2447400.3531 10.710 1.574 .955 990 +CH CAS 2447401.3378 10.810 1.625 .996 990 +CH CAS 2447402.3338 10.933 1.698 1.038 990 +CH CAS 2447403.3627 11.021 1.791 1.043 990 +CH CAS 2447404.3656 11.157 1.809 1.078 990 +CH CAS 2447408.3273 11.411 1.859 1.076 990 +CH CAS 2447409.3217 11.350 1.772 1.081 990 +CH CAS 2447410.3456 11.252 1.742 1.068 990 +CH CAS 2447411.3443 11.137 1.652 1.031 990 +CH CAS 2447413.3073 10.405 1.371 .887 990 +CH CAS 2447413.4230 10.427 1.323 .898 990 +CH CAS 2447414.3073 10.517 1.477 .909 990 +CH CAS 2447415.2861 10.726 1.579 .970 990 +CH CAS 2447416.2819 10.819 1.611 1.004 990 +CH CAS 2447417.2725 10.902 1.705 1.021 990 +CH CAS 2447418.2696 11.001 1.759 1.052 990 +CH CAS 2447419.2491 11.140 1.798 1.084 990 +CH CAS 2447420.2441 11.228 1.864 1.105 990 +CH CAS 2447421.2366 11.367 1.884 1.108 990 +CH CAS 2447422.2508 11.524 1.890 1.130 990 +CH CAS 2447423.3745 11.432 1.860 1.097 990 +CH CAS 2447424.2647 11.427 1.771 1.090 990 +CH CAS 2447425.2842 11.248 1.747 1.051 990 +CH CAS 2447427.3254 10.975 1.600 .965 990 +CH CAS 2447428.2529 10.407 1.375 .869 990 +CH CAS 2447428.4099 10.397 1.381 .873 990 +CH CAS 2447429.2542 10.502 1.454 .902 990 +CH CAS 2447430.2397 10.691 1.569 .963 990 +CH CAS 2447430.4411 10.760 1.610 .982 990 +CH CAS 2447431.2892 10.820 1.645 .997 990 +CH CAS 2447432.2776 10.901 1.662 1.036 990 +CH CAS 2447433.2693 11.009 1.737 1.072 990 +CH CAS 2447434.2641 11.104 1.833 1.067 990 +CH CAS 2449617.2985 10.540 1.400 .928 995 +CH CAS 2449620.3651 1.710 1.057 995 +CH CAS 2449620.4352 10.913 1.669 1.031 995 +CH CAS 2449621.3639 1.765 1.089 995 +CH CAS 2449621.4301 1.783 1.070 995 +CH CAS 2449622.4099 11.149 1.797 1.081 995 +CH CAS 2449622.4666 11.155 1.815 1.092 995 +CH CAS 2449623.3674 11.288 1.868 1.117 995 +CH CAS 2449623.4392 11.273 1.928 1.091 995 +CH CAS 2449624.3139 1.936 995 +CH CAS 2449624.4166 11.393 1.127 995 +CH CAS 2449624.4741 11.394 1.995 1.087 995 +CH CAS 2449625.3532 11.457 1.945 1.110 995 +CH CAS 2449625.4409 11.482 1.949 1.141 995 +CH CAS 2449625.4776 11.454 1.902 1.102 995 +CH CAS 2449631.3383 10.484 1.400 .897 995 +CH CAS 2449632.3389 10.485 1.416 .895 995 +CH CAS 2449632.4208 10.524 1.422 .918 995 +CH CAS 2449632.4691 10.481 1.446 .900 995 +CH CAS 2449633.2997 .994 995 +CH CAS 2449633.3807 10.738 1.534 .991 995 +CH CAS 2449633.4400 10.718 1.529 .978 995 +CH CAS 2449634.3187 10.780 1.588 .992 995 +CH CAS 2449635.3391 10.907 1.716 1.027 995 +CH CAS 2449635.4486 10.910 1.696 1.017 995 +CH CAS 2449935.4374 10.738 1.020 1.945 998 +CH CAS 2449936.4310 10.960 1.065 2.019 998 +CH CAS 2449942.4374 11.492 2.203 998 +CH CAS 2449943.4130 11.531 2.215 998 +CH CAS 2449944.4301 11.403 2.160 998 +CH CAS 2449946.4339 11.209 2.048 998 +CH CAS 2449947.4008 10.999 1.949 998 +CH CAS 2449948.3703 10.473 1.762 998 +CH CAS 2449949.3740 10.573 1.822 998 +CH CAS 2449950.4151 10.763 1.534 1.019 1.941 998 +CH CAS 2449952.4113 11.062 2.024 998 +CH CAS 2449953.4281 2.120 998 +CH CAS 2449954.3930 11.186 2.150 998 +CH CAS 2449955.3607 11.264 2.160 998 +CH CAS 2450310.3999 10.452 1.450 .915 971 +CH CAS 2450311.4185 10.504 1.392 .924 971 +CH CAS 2450312.3969 10.706 1.543 .981 971 +CH CAS 2450314.3751 10.897 1.661 1.065 971 +CH CAS 2450315.3250 10.989 1.761 1.062 971 +CH CAS 2450316.3244 11.101 1.814 1.095 971 +CH CAS 2450317.3340 11.201 1.899 1.098 971 +CH CAS 2450318.3416 11.321 1.887 1.102 971 +CH CAS 2450319.3135 11.420 1.919 1.129 971 +CH CAS 2450320.3378 11.461 1.919 1.170 971 +CH CAS 2450321.3267 11.407 1.838 1.195 971 +CH CAS 2450322.3308 11.323 1.765 1.109 971 +CH CAS 2450323.3217 11.216 1.748 1.079 971 +CH CAS 2450324.3133 11.054 1.640 1.016 971 +CH CAS 2450325.2988 10.563 1.473 .920 971 +CH CAS 2450326.2389 10.475 1.403 .905 971 +CS CAS 2449943.4345 11.582 .783 998 +CS CAS 2449946.4458 11.916 1.088 998 +CS CAS 2449947.4127 11.909 1.066 998 +CS CAS 2449948.3783 11.949 1.069 998 +CS CAS 2449949.3848 12.123 1.148 998 +CS CAS 2449953.4491 12.894 1.188 998 +CS CAS 2449954.4128 12.679 1.033 998 +CS CAS 2449955.3778 12.389 .949 998 +CT CAS 2447000.4614 12.428 1.520 .869 989 +CT CAS 2447003.4598 12.264 1.374 .842 989 +CT CAS 2447085.3438 12.453 1.333 .838 989 +CT CAS 2447086.2592 11.997 1.199 .777 989 +CT CAS 2447087.3101 12.285 1.331 .865 989 +CT CAS 2447088.1750 12.468 1.386 .881 989 +CT CAS 2447098.2279 12.153 1.255 .822 989 +CT CAS 2447401.3785 12.532 1.424 .868 990 +CT CAS 2447402.3739 11.903 1.180 .716 990 +CT CAS 2447403.4035 12.234 1.332 .829 990 +CT CAS 2447404.4048 12.418 1.446 .841 990 +CT CAS 2447407.3533 12.307 1.404 .838 990 +CT CAS 2447408.3548 12.469 1.405 .855 990 +CT CAS 2447409.3421 12.393 1.324 .839 990 +CT CAS 2447410.3667 12.036 1.242 .781 990 +CT CAS 2447411.3621 12.308 1.395 .858 990 +CT CAS 2447413.3247 12.227 1.254 .813 990 +CT CAS 2447414.3222 12.050 1.285 .782 990 +CT CAS 2447415.3020 12.345 1.423 .850 990 +CT CAS 2447416.2969 12.497 1.454 .866 990 +CT CAS 2447417.2916 12.052 1.196 .775 990 +CT CAS 2447418.2865 12.119 1.340 .814 990 +CT CAS 2447419.2636 12.345 1.381 .859 990 +CT CAS 2447420.2605 12.509 1.442 .873 990 +CT CAS 2447421.2518 11.958 1.170 .732 990 +CT CAS 2447422.2649 12.231 1.323 .835 990 +CT CAS 2447423.3918 12.423 1.425 .853 990 +CT CAS 2447424.2814 12.575 1.395 .884 990 +CT CAS 2447425.3020 11.941 1.190 .750 990 +CT CAS 2447427.3531 12.469 1.506 .847 990 +CT CAS 2447428.4287 12.386 1.346 .833 990 +CT CAS 2447429.3130 12.015 1.242 .767 990 +CT CAS 2447430.2635 12.292 1.396 .853 990 +CT CAS 2447431.3147 12.461 1.459 .858 990 +CT CAS 2447432.3018 12.298 1.328 .831 990 +CT CAS 2447433.2893 12.063 1.250 .781 990 +CT CAS 2447434.2837 12.345 1.439 .864 990 +CY CAS 2445649.3476 12.154 2.059 1.167 982 +CY CAS 2445665.3320 12.149 1.925 1.128 982 +CY CAS 2445666.2500 12.004 1.813 1.128 982 +CY CAS 2445668.2578 11.508 1.556 .977 982 +CY CAS 2445674.2500 11.657 1.880 1.072 982 +CY CAS 2445676.2460 11.906 1.969 1.141 982 +CY CAS 2445677.2030 12.029 2.030 1.133 982 +CY CAS 2445679.2109 12.189 1.962 1.134 982 +CY CAS 2445683.1835 11.039 1.371 .816 982 +CY CAS 2445688.1796 11.590 1.825 1.065 982 +CY CAS 2445689.2226 11.691 1.837 1.100 982 +CY CAS 2445690.1522 11.822 1.861 1.126 982 +CY CAS 2445691.1756 11.982 1.961 1.146 982 +CY CAS 2445694.1562 12.121 1.858 1.134 982 +CY CAS 2445695.1601 11.951 1.380 1.802 1.092 982 +CY CAS 2445701.1601 11.406 1.278 1.699 1.011 982 +CY CAS 2445706.1131 12.050 1.962 1.136 982 +CY CAS 2445707.1171 12.171 1.773 1.936 1.164 982 +CY CAS 2447400.3723 11.732 1.860 1.088 990 +CY CAS 2447401.3491 11.846 1.903 1.091 990 +CY CAS 2447402.3503 12.013 1.930 1.127 990 +CY CAS 2447403.3744 12.140 1.968 1.136 990 +CY CAS 2447404.3768 12.188 1.921 1.137 990 +CY CAS 2447408.3336 11.036 1.377 .859 990 +CY CAS 2447409.3278 11.123 1.434 .918 990 +CY CAS 2447410.3520 11.247 1.541 .957 990 +CY CAS 2447411.3499 11.338 1.594 1.003 990 +CY CAS 2447413.3129 11.536 1.779 1.056 990 +CY CAS 2447413.4250 11.609 1.728 1.084 990 +CY CAS 2447414.3123 11.662 1.828 1.085 990 +CY CAS 2447415.2907 11.767 1.106 990 +CY CAS 2447416.2883 11.956 1.900 1.127 990 +CY CAS 2447417.2788 12.061 1.982 1.119 990 +CY CAS 2447418.2760 12.144 1.956 1.140 990 +CY CAS 2447419.2547 12.137 1.877 1.112 990 +CY CAS 2447420.2501 11.984 1.821 1.066 990 +CY CAS 2447421.2418 11.965 1.729 1.063 990 +CY CAS 2447422.2572 11.617 1.566 .978 990 +CY CAS 2447423.3793 11.038 1.396 .859 990 +CY CAS 2447424.2713 11.136 1.478 .932 990 +CY CAS 2447425.2903 11.294 1.607 .965 990 +CY CAS 2447427.3333 11.538 1.763 1.031 990 +CY CAS 2447428.2634 11.598 1.787 1.059 990 +CY CAS 2447428.4127 11.653 1.796 1.081 990 +CY CAS 2447429.2987 11.796 1.835 1.105 990 +CY CAS 2447430.2500 11.862 1.931 1.094 990 +CY CAS 2447430.4435 11.957 1.902 1.131 990 +CY CAS 2447431.2990 12.052 2.018 1.127 990 +CY CAS 2447432.2871 12.136 1.902 1.141 990 +CY CAS 2447433.2768 12.178 1.900 1.139 990 +CY CAS 2447434.2708 12.074 1.864 1.094 990 +CY CAS 2449936.4341 12.115 1.139 2.131 998 +CY CAS 2449942.4427 11.467 2.010 998 +CY CAS 2449943.4233 11.582 2.075 998 +CY CAS 2449944.4358 11.640 2.124 998 +CY CAS 2449946.4381 11.971 2.238 998 +CY CAS 2449947.4051 12.074 2.227 998 +CY CAS 2449948.3721 12.175 2.244 998 +CY CAS 2449949.3761 12.203 2.246 998 +CY CAS 2449952.4161 2.049 998 +CY CAS 2449953.4350 11.025 1.747 998 +CY CAS 2449954.3978 11.223 1.832 998 +CY CAS 2449955.3654 11.289 1.907 998 +CY CAS 2450310.4083 11.972 1.858 1.111 971 +CY CAS 2450311.4253 11.972 1.741 1.075 971 +CY CAS 2450312.4087 11.359 1.480 .972 971 +CY CAS 2450314.3842 11.224 1.462 .977 971 +CY CAS 2450315.3291 11.313 1.605 .996 971 +CY CAS 2450316.3416 11.412 1.695 1.037 971 +CY CAS 2450317.3415 11.501 1.734 1.057 971 +CY CAS 2450318.3491 11.611 1.807 1.065 971 +CY CAS 2450319.3196 11.749 1.915 1.113 971 +CY CAS 2450320.3395 11.897 1.990 1.166 971 +CY CAS 2450321.3318 12.034 1.965 971 +CY CAS 2450322.3354 12.159 1.980 1.167 971 +CY CAS 2450323.3333 12.207 1.976 1.166 971 +CY CAS 2450324.3173 12.034 1.909 1.099 971 +CY CAS 2450325.3031 11.899 1.801 1.075 971 +CY CAS 2450326.2421 11.907 1.714 1.059 971 +CZ CAS 2446615.3984 11.547 1.361 .869 988 +CZ CAS 2446616.4312 11.800 1.503 .918 988 +CZ CAS 2446617.3929 11.973 1.569 .943 988 +CZ CAS 2446620.3963 11.394 1.260 .805 988 +CZ CAS 2446621.4211 11.640 1.414 .885 988 +CZ CAS 2446622.4098 11.819 1.528 988 +CZ CAS 2446624.4112 12.110 1.567 .958 988 +CZ CAS 2446625.3758 11.446 1.240 .808 988 +CZ CAS 2446626.4026 11.469 1.292 .829 988 +CZ CAS 2446627.3976 11.716 1.462 .905 988 +CZ CAS 2446628.4102 11.910 1.527 .946 988 +CZ CAS 2446629.3571 12.061 1.575 .958 988 +CZ CAS 2446631.3811 11.333 1.227 .782 988 +CZ CAS 2446632.4151 11.572 1.374 .858 988 +CZ CAS 2446636.3930 11.711 1.407 .852 988 +CZ CAS 2448854.3768 11.852 1.591 .904 994 +CZ CAS 2448856.3612 12.084 1.560 .920 994 +CZ CAS 2448858.3330 11.504 1.325 .837 994 +CZ CAS 2448860.3342 11.925 1.535 .946 994 +CZ CAS 2448862.3415 11.940 1.488 .899 994 +CZ CAS 2448870.3161 11.638 1.455 .855 994 +CZ CAS 2448872.2917 12.041 1.595 .937 994 +CZ CAS 2448874.3180 11.399 1.254 .787 994 +CZ CAS 2448875.3336 11.497 1.354 .830 994 +CZ CAS 2448876.3231 11.749 1.485 .902 994 +CZ CAS 2448877.2756 11.921 1.561 .912 994 +CZ CAS 2448878.3422 12.089 1.623 .945 994 +CZ CAS 2448880.2580 11.329 1.275 .787 994 +CZ CAS 2448881.2481 11.551 1.385 .851 994 +CZ CAS 2448882.2566 11.785 1.512 .910 994 +CZ CAS 2448883.2812 11.990 1.589 .943 994 +CZ CAS 2448885.2522 11.741 1.411 .861 994 +CZ CAS 2448886.3068 11.398 1.300 .811 994 +CZ CAS 2448887.2954 11.669 1.418 .899 994 +CZ CAS 2448888.2465 11.833 1.549 .899 994 +CZ CAS 2448889.2539 12.044 1.622 .962 994 +CZ CAS 2448890.2303 12.096 1.609 .932 994 +CZ CAS 2448890.3504 12.087 1.558 .926 994 +CZ CAS 2448891.2289 11.437 1.299 .793 994 +CZ CAS 2448891.3442 11.387 1.244 .783 994 +CZ CAS 2448892.2469 11.478 1.343 .836 994 +CZ CAS 2448892.3422 1.386 .839 994 +CZ CAS 2448893.2474 11.694 1.499 .876 994 +CZ CAS 2448894.2879 11.889 1.570 .922 994 +CZ CAS 2449937.4485 12.132 .976 998 +CZ CAS 2449939.4504 11.372 1.515 998 +CZ CAS 2449941.4444 11.831 1.760 998 +CZ CAS 2449942.4484 12.041 1.865 998 +CZ CAS 2449943.4281 12.221 1.889 998 +CZ CAS 2449944.4486 11.863 1.747 998 +CZ CAS 2449946.4417 11.695 1.721 998 +CZ CAS 2449947.4089 11.862 1.788 998 +CZ CAS 2449948.3749 12.055 1.860 998 +CZ CAS 2449949.3795 12.201 1.866 998 +CZ CAS 2449950.4428 11.527 1.592 998 +CZ CAS 2449952.4358 11.780 1.746 998 +CZ CAS 2449953.4412 11.931 1.846 998 +CZ CAS 2449954.4048 12.113 1.862 998 +CZ CAS 2449955.3721 12.105 1.844 998 +CZ CAS 2450310.4144 11.885 1.551 .948 971 +CZ CAS 2450311.4348 12.158 1.506 .976 971 +CZ CAS 2450312.4131 11.946 1.533 .924 971 +CZ CAS 2450314.3887 11.570 1.381 .876 971 +CZ CAS 2450315.3320 11.779 1.523 .917 971 +CZ CAS 2450316.3602 11.963 1.592 .989 971 +CZ CAS 2450317.3722 12.116 1.662 .950 971 +CZ CAS 2450318.3593 11.731 1.420 .857 971 +CZ CAS 2450319.3653 11.373 1.320 971 +CZ CAS 2450320.3629 11.634 1.458 .865 971 +CZ CAS 2450321.3512 11.807 1.549 .955 971 +CZ CAS 2450322.3380 12.054 1.624 .971 971 +CZ CAS 2450323.3357 12.157 1.648 .961 971 +CZ CAS 2450324.3191 11.477 1.337 .808 971 +CZ CAS 2450325.3089 11.417 1.380 .804 971 +CZ CAS 2450326.2447 11.716 1.482 .864 971 +DD CAS 2446608.4360 9.688 .837 1.158 .683 988 +DD CAS 2446613.4252 10.075 .983 1.282 .733 988 +DD CAS 2446614.4353 9.921 .891 1.221 .693 988 +DD CAS 2446615.4182 9.803 .810 1.141 .664 988 +DD CAS 2446616.4341 9.676 .773 1.096 .640 988 +DD CAS 2446617.3959 9.640 .753 1.123 .655 988 +DD CAS 2446618.4372 1.187 .676 988 +DD CAS 2446619.4420 9.870 1.289 .715 988 +DD CAS 2446620.3985 10.030 1.103 1.366 .745 988 +DD CAS 2446621.4227 10.141 1.404 .758 988 +DD CAS 2446622.4121 10.180 1.116 1.374 .746 988 +DD CAS 2446624.4137 9.903 1.188 .683 988 +DD CAS 2446625.3774 9.790 1.121 .656 988 +DD CAS 2446626.4045 9.614 1.079 .619 988 +DD CAS 2446627.3994 9.648 1.124 .646 988 +DD CAS 2446628.4125 9.747 .859 1.208 .684 988 +DD CAS 2446629.3604 9.876 1.021 1.277 .729 988 +DD CAS 2446631.3832 10.166 1.395 .762 988 +DD CAS 2446632.4168 10.191 1.359 .746 988 +DD CAS 2446636.3950 9.601 1.068 .632 988 +DD CAS 2448503.3723 9.874 1.270 .718 993 +DD CAS 2448504.3036 10.007 1.349 .741 993 +DD CAS 2448505.3197 10.160 1.388 .759 993 +DD CAS 2448506.3315 10.189 1.365 .755 993 +DD CAS 2448507.3385 9.999 1.284 .697 993 +DD CAS 2448508.2892 9.878 1.207 .671 993 +DD CAS 2448509.3299 9.780 1.117 .641 993 +DD CAS 2448510.3128 9.597 1.083 .611 993 +DD CAS 2448511.3181 9.627 1.124 .641 993 +DD CAS 2448512.3032 9.739 1.212 .702 993 +DD CAS 2448513.3160 9.875 1.309 .717 993 +DD CAS 2448514.3190 10.049 1.377 .748 993 +DD CAS 2448515.3054 10.188 1.389 .762 993 +DD CAS 2448516.2426 10.192 1.373 .748 993 +DD CAS 2448517.3271 9.974 1.237 .703 993 +DD CAS 2448518.2435 9.879 1.185 .678 993 +DD CAS 2448519.2955 9.768 1.141 .651 993 +DD CAS 2448520.2844 9.601 1.042 .634 993 +DD CAS 2448521.3319 9.635 1.161 .652 993 +DD CAS 2448522.2900 9.753 1.233 .684 993 +DD CAS 2448523.2846 9.914 1.310 .721 993 +DD CAS 2449936.4397 .749 1.406 998 +DD CAS 2449937.4533 10.080 .771 998 +DD CAS 2449943.4394 9.719 1.262 998 +DD CAS 2449944.4614 9.769 1.322 998 +DD CAS 2449946.4482 10.061 1.450 998 +DD CAS 2449947.4150 10.189 1.452 998 +DD CAS 2449948.3799 10.238 1.467 998 +DD CAS 2449949.3870 10.140 1.405 998 +DD CAS 2449955.4160 9.925 1.392 998 +DD CAS 2449956.4247 10.042 1.426 998 +DD CAS 2449957.4397 10.183 1.469 998 +DD CAS 2449959.4050 1.409 998 +DD CAS 2449960.3771 9.984 1.343 998 +DD CAS 2449962.4149 9.669 1.272 998 +DD CAS 2449963.4357 9.706 1.316 998 +DD CAS 2449985.3232 9.942 1.383 998 +DD CAS 2449986.3318 10.096 1.447 998 +DD CAS 2449987.3711 10.195 1.457 998 +DD CAS 2449992.3813 9.686 1.226 998 +DD CAS 2450310.4303 10.108 1.409 .752 971 +DD CAS 2450311.4465 10.229 1.366 .812 971 +DD CAS 2450312.4206 10.068 1.338 .757 971 +DD CAS 2450314.3949 9.816 1.148 .679 971 +DD CAS 2450315.3614 9.684 1.123 .688 971 +DD CAS 2450316.3635 9.616 1.119 .706 971 +DD CAS 2450317.3761 9.698 1.205 .681 971 +DD CAS 2450318.3630 9.845 1.263 .720 971 +DD CAS 2450319.3702 9.990 1.355 .820 971 +DD CAS 2450320.3701 10.158 1.394 .774 971 +DD CAS 2450321.3545 10.182 1.408 .800 971 +DD CAS 2450322.3583 10.068 1.315 .742 971 +DD CAS 2450323.3402 9.953 1.249 .714 971 +DD CAS 2450324.3221 9.792 1.164 .665 971 +DD CAS 2450325.3116 9.655 1.129 .640 971 +DD CAS 2450326.2545 9.667 1.128 .643 971 +DD CAS 2450327.3120 9.684 1.203 .687 971 +DD CAS 2450330.2640 10.181 1.392 .760 971 +DD CAS 2450332.2405 10.044 1.251 .651 971 +DD CAS 2450333.2482 9.895 1.174 .647 971 +DD CAS 2450334.2565 9.788 1.152 .645 971 +DD CAS 2450335.2928 9.622 1.081 .642 971 +DD CAS 2450336.3206 9.612 1.147 .640 971 +DD CAS 2450337.2269 9.734 1.203 .668 971 +DD CAS 2450338.3564 9.892 1.332 .725 971 +DD CAS 2450340.2357 10.175 1.366 .752 971 +DD CAS 2450341.2329 10.193 1.356 .757 971 +DD CAS 2450342.2519 9.958 1.293 .706 971 +DD CAS 2450344.2686 9.771 1.148 .556 971 +DD CAS 2450347.2449 9.755 1.229 .691 971 +DD CAS 2450349.2178 10.065 1.376 .766 971 +DF CAS 2448858.3996 11.144 1.300 .765 994 +DF CAS 2448860.3970 10.760 1.175 .689 994 +DF CAS 2448862.4511 11.138 1.293 .724 994 +DF CAS 2448870.3698 11.069 1.266 .717 994 +DF CAS 2448872.3457 10.902 1.227 .708 994 +DF CAS 2448874.3828 10.951 1.185 .693 994 +DF CAS 2448875.4122 10.659 1.128 .646 994 +DF CAS 2448876.4233 10.960 1.306 .727 994 +DF CAS 2448877.3314 11.119 1.330 .730 994 +DF CAS 2448879.4046 10.741 1.123 .654 994 +DF CAS 2448880.3371 10.967 1.281 .712 994 +DF CAS 2448881.3564 11.125 1.330 .727 994 +DF CAS 2448882.3511 10.673 1.034 .635 994 +DF CAS 2448883.3641 10.778 1.148 .682 994 +DF CAS 2448885.3602 11.153 1.294 .735 994 +DF CAS 2448886.3483 10.567 .992 .615 994 +DF CAS 2448887.3536 10.830 1.141 .701 994 +DF CAS 2448888.3211 11.047 1.283 .749 994 +DF CAS 2448889.3291 11.126 1.305 .719 994 +DF CAS 2448890.2953 10.549 1.009 .601 994 +DF CAS 2448891.3143 10.833 1.205 .687 994 +DF CAS 2448892.3155 11.130 1.309 .746 994 +DF CAS 2448893.3796 11.053 1.234 .697 994 +DF CAS 2450314.4844 11.120 1.293 .762 971 +DF CAS 2450315.4285 10.896 1.193 .669 971 +DF CAS 2450316.4258 10.674 1.117 .656 971 +DF CAS 2450317.4554 10.946 1.262 .716 971 +DF CAS 2450318.4512 11.120 1.289 .762 971 +DF CAS 2450319.4547 10.727 1.092 .649 971 +DF CAS 2450320.4517 10.717 1.127 .668 971 +DF CAS 2450321.4573 10.978 1.273 .711 971 +DF CAS 2450322.4151 11.147 1.297 .747 971 +DF CAS 2450323.4207 10.627 1.079 .630 971 +DF CAS 2450325.3735 11.003 1.280 .720 971 +DF CAS 2450326.3225 11.156 1.286 .721 971 +DL CAS 2445490.4179 9.173 1.245 .713 982 +DL CAS 2445502.4335 8.866 1.173 .701 982 +DL CAS 2445503.4335 8.988 1.226 .720 982 +DL CAS 2445505.4296 9.222 1.331 .753 982 +DL CAS 2445508.4296 8.667 1.066 .624 982 +DL CAS 2445509.4218 8.797 1.118 .672 982 +DL CAS 2445512.4453 9.110 1.289 .740 982 +DL CAS 2445513.4375 9.217 1.312 .741 982 +DL CAS 2445514.4296 9.201 1.258 .732 982 +DL CAS 2445649.3671 9.234 1.335 .756 982 +DL CAS 2445656.4101 9.142 .733 982 +DL CAS 2445665.3514 9.248 1.339 .759 982 +DL CAS 2445666.2655 9.232 1.310 .748 982 +DL CAS 2445668.2812 8.689 1.044 .640 982 +DL CAS 2445674.2695 9.284 1.322 .742 982 +DL CAS 2445676.2617 8.689 1.064 .637 982 +DL CAS 2445677.2147 8.776 1.096 .666 982 +DL CAS 2445679.2226 8.959 1.238 .717 982 +DL CAS 2445683.2187 8.988 1.175 .675 982 +DL CAS 2445686.1679 8.840 1.167 .692 982 +DL CAS 2445687.1484 8.949 1.236 .712 982 +DL CAS 2445688.1953 9.092 1.302 .742 982 +DL CAS 2445689.2460 9.240 1.357 .755 982 +DL CAS 2445690.1601 9.258 1.328 .753 982 +DL CAS 2445691.1913 8.992 1.195 .691 982 +DL CAS 2445692.1445 8.711 1.071 .637 982 +DL CAS 2445693.1484 8.772 1.111 .663 982 +DL CAS 2445694.1718 8.838 1.176 .687 982 +DL CAS 2445695.1796 8.950 1.236 .715 982 +DL CAS 2445701.1756 8.769 1.128 .658 982 +DL CAS 2445705.1171 9.219 1.346 .753 982 +DL CAS 2445706.1250 9.251 1.321 .744 982 +DL CAS 2445707.1288 9.006 1.191 .686 982 +DL CAS 2445878.4375 8.838 .826 1.183 .699 982 +DL CAS 2445879.4179 9.015 .903 1.252 .727 982 +DL CAS 2445880.4179 9.115 .939 1.316 .749 982 +DL CAS 2445881.4218 9.257 .974 1.352 .747 982 +DL CAS 2445882.4218 9.217 .911 1.299 .738 982 +DL CAS 2445883.4256 8.942 .750 1.161 .678 982 +DL CAS 2445886.4218 8.860 .804 1.194 .695 982 +DL CAS 2445887.4179 8.990 .882 1.267 .717 982 +DL CAS 2446259.4373 8.936 .784 1.155 .687 987 +DL CAS 2446260.4104 8.704 .736 1.070 .632 987 +DL CAS 2446263.4504 8.995 .909 1.251 .721 987 +DL CAS 2446265.4320 9.284 1.361 .766 987 +DL CAS 2446266.4361 9.216 .917 1.310 .738 987 +DL CAS 2446267.4371 8.966 .765 1.158 .686 987 +DL CAS 2446268.4488 8.683 .757 1.062 987 +DL CAS 2446269.4113 8.807 .796 1.135 .676 987 +DL CAS 2446270.4233 8.842 .812 1.190 .693 987 +DL CAS 2446272.4297 9.114 .940 1.334 .747 987 +DL CAS 2446273.4438 9.258 1.002 1.346 .757 987 +DL CAS 2446274.4272 9.225 1.291 .744 987 +DL CAS 2446275.4374 8.934 .757 1.168 .684 987 +DL CAS 2446279.4365 8.992 1.272 .729 987 +DL CAS 2446280.4199 9.109 .976 1.327 .740 987 +DL CAS 2446283.4076 8.949 .740 1.154 .679 987 +DL CAS 2446284.4192 8.705 1.062 .636 987 +DL CAS 2446287.3493 8.958 .888 1.271 .714 987 +DL CAS 2446288.3893 9.132 1.331 .747 987 +DL CAS 2446289.3937 9.244 1.360 .754 987 +DL CAS 2446290.4097 9.248 1.306 .737 987 +DL CAS 2446291.3811 8.945 .815 1.173 .684 987 +DL CAS 2446293.3763 8.776 .758 1.149 .675 987 +DL CAS 2446294.3671 8.836 .829 1.193 .688 987 +DL CAS 2446295.4819 8.981 .883 1.277 .716 987 +DL CAS 2446295.3549 8.971 .866 1.265 .718 987 +DL CAS 2446296.3455 9.112 .953 1.322 .747 987 +DL CAS 2446297.3435 9.250 1.005 1.366 .755 987 +DL CAS 2446298.3482 9.218 .909 1.317 .742 987 +DL CAS 2446299.3500 8.970 .766 1.185 .686 987 +DL CAS 2446300.3370 8.693 .711 1.070 .636 987 +DL CAS 2446301.3831 8.816 .797 1.125 .681 987 +DL CAS 2446302.3778 8.845 .794 1.207 .680 987 +DL CAS 2446302.4938 8.847 1.211 .681 987 +DL CAS 2446303.3253 8.976 1.255 .714 987 +DL CAS 2446304.2909 9.101 .919 1.337 .730 987 +DL CAS 2446304.4846 9.121 .954 1.352 .736 987 +DL CAS 2446608.4489 9.118 1.307 .757 988 +DL CAS 2446613.4449 8.805 .755 1.151 .666 988 +DL CAS 2446614.4461 8.855 1.190 .701 988 +DL CAS 2446615.4392 8.987 1.260 .723 988 +DL CAS 2446616.4401 9.130 .953 1.318 .746 988 +DL CAS 2446617.4391 9.264 1.021 1.345 .755 988 +DL CAS 2446620.4275 8.708 .719 1.060 .636 988 +DL CAS 2446621.4549 8.793 1.128 .673 988 +DL CAS 2446622.4504 8.852 1.195 .684 988 +DL CAS 2446623.4577 8.974 1.264 .720 988 +DL CAS 2446624.4497 9.125 1.304 .757 988 +DL CAS 2446625.4534 9.243 1.343 .756 988 +DL CAS 2446626.4500 9.216 1.296 .747 988 +DL CAS 2446627.4537 8.943 .771 1.161 .683 988 +DL CAS 2446628.4518 8.694 .734 1.061 .642 988 +DL CAS 2446629.4569 8.784 .787 1.141 .672 988 +DL CAS 2446631.4407 8.992 .869 1.248 .723 988 +DL CAS 2446632.4537 9.116 1.329 .745 988 +DL CAS 2446636.4508 8.692 .707 1.082 .632 988 +DL CAS 2447401.4052 9.238 .953 1.356 .748 990 +DL CAS 2447402.3819 9.233 1.342 .725 990 +DL CAS 2447403.4234 8.983 1.175 .686 990 +DL CAS 2447404.4143 8.689 1.081 .616 990 +DL CAS 2447407.3677 8.988 .764 1.268 .715 990 +DL CAS 2447408.3687 9.121 .925 1.321 .737 990 +DL CAS 2447409.3519 9.226 .960 1.343 .757 990 +DL CAS 2447410.3764 9.242 1.333 .741 990 +DL CAS 2447411.3767 8.988 .699 1.183 .692 990 +DL CAS 2447413.3345 8.788 .679 1.112 .673 990 +DL CAS 2447413.4452 8.832 1.105 .683 990 +DL CAS 2447414.3319 8.823 .730 1.187 .674 990 +DL CAS 2447415.3108 8.961 .780 1.248 .707 990 +DL CAS 2447416.3040 9.098 1.308 .733 990 +DL CAS 2447417.3003 9.206 1.343 .743 990 +DL CAS 2447417.5101 9.278 1.330 .763 990 +DL CAS 2447418.2955 9.249 1.322 .756 990 +DL CAS 2447418.5109 9.211 1.282 .747 990 +DL CAS 2447419.2732 .678 990 +DL CAS 2447419.5121 8.947 1.182 .667 990 +DL CAS 2447420.2695 8.706 .625 1.074 .635 990 +DL CAS 2447420.5133 8.703 1.072 .631 990 +DL CAS 2447421.2601 8.788 1.142 .653 990 +DL CAS 2447421.5126 8.801 1.117 .664 990 +DL CAS 2447422.2729 8.872 .705 1.203 .681 990 +DL CAS 2447422.5120 8.854 1.193 .727 990 +DL CAS 2447423.4028 8.968 .854 1.242 .712 990 +DL CAS 2447423.5112 9.006 1.243 .716 990 +DL CAS 2447424.2921 9.107 .923 1.300 .737 990 +DL CAS 2447424.5082 9.116 1.275 .745 990 +DL CAS 2447425.3109 9.234 .919 1.375 .753 990 +DL CAS 2447425.5137 9.314 1.324 .755 990 +DL CAS 2447427.3728 8.992 .694 1.203 .672 990 +DL CAS 2447427.5129 8.924 1.192 .677 990 +DL CAS 2447428.2789 8.691 .631 1.075 .619 990 +DL CAS 2447428.4339 8.713 1.061 .629 990 +DL CAS 2447428.4996 8.740 1.073 .622 990 +DL CAS 2447429.3243 8.786 .640 1.135 .664 990 +DL CAS 2447430.2824 8.833 .724 1.199 .677 990 +DL CAS 2447430.4639 8.862 1.199 .693 990 +DL CAS 2447430.5168 8.855 1.209 .694 990 +DL CAS 2447431.3442 8.952 .822 1.276 .691 990 +DL CAS 2447431.5183 9.014 1.223 .723 990 +DL CAS 2447432.3203 9.057 .942 1.307 .736 990 +DL CAS 2447432.5181 9.161 1.308 .753 990 +DL CAS 2447433.3049 9.236 .917 1.352 .756 990 +DL CAS 2447434.3083 9.242 .865 1.344 .737 990 +DL CAS 2447434.5080 9.228 1.264 .747 990 +DL CAS 2447741.4691 8.736 1.140 .639 991 +DL CAS 2447742.4625 8.815 .765 1.189 991 +DL CAS 2447743.4248 8.965 .845 1.254 .735 991 +DL CAS 2447744.4735 9.124 1.310 .742 991 +DL CAS 2447745.4675 9.247 1.348 .778 991 +DL CAS 2447746.4709 9.229 1.341 .717 991 +DL CAS 2447747.4003 8.941 1.168 .689 991 +DL CAS 2447748.4704 8.721 1.033 .629 991 +DL CAS 2447749.4720 8.811 .673 991 +DL CAS 2447750.4696 8.877 1.179 .694 991 +DL CAS 2447751.4706 8.971 1.235 .720 991 +DL CAS 2447752.4665 9.084 1.303 .742 991 +DL CAS 2447753.4684 9.242 1.327 .757 991 +DL CAS 2447754.4385 9.234 .872 1.281 .745 991 +DL CAS 2447755.4738 8.938 1.164 .688 991 +DL CAS 2447756.4715 1.058 .619 991 +DL CAS 2447757.4555 8.760 1.118 .677 991 +DL CAS 2447758.4065 8.839 .721 1.140 .695 991 +DL CAS 2447759.3901 8.941 .792 1.285 .690 991 +DL CAS 2447759.4822 8.947 1.275 .700 991 +DL CAS 2447760.4412 9.099 1.301 .755 991 +DL CAS 2447761.4656 9.227 1.367 .760 991 +DL CAS 2447762.4431 9.208 1.322 .752 991 +DL CAS 2447763.4590 8.958 1.176 .693 991 +DL CAS 2447765.4411 8.788 1.126 .671 991 +DL CAS 2447766.4352 8.849 1.176 .692 991 +DL CAS 2447767.4426 8.970 .808 1.249 .717 991 +DL CAS 2447768.4051 .877 1.315 .740 991 +DL CAS 2447770.3959 9.243 .847 1.308 .763 991 +DL CAS 2447771.3773 8.970 .712 1.156 .701 991 +DL CAS 2447772.3364 8.704 .602 1.058 .641 991 +DL CAS 2447772.4445 8.699 .602 1.061 .644 991 +DL CAS 2447773.3628 8.768 .670 1.113 .657 991 +DL CAS 2447773.4139 8.788 .667 1.116 .676 991 +DL CAS 2447773.4864 8.789 1.129 .675 991 +DL CAS 2447774.3676 8.840 .727 1.186 .689 991 +DL CAS 2447774.4247 8.851 .718 1.168 .697 991 +DL CAS 2447775.3376 8.951 .772 1.242 .724 991 +DL CAS 2447775.3883 8.957 .786 1.242 .720 991 +DL CAS 2447776.3493 9.095 .869 1.316 .747 991 +DL CAS 2447776.4100 9.111 .893 1.282 .765 991 +DL CAS 2447776.4870 9.107 1.286 .760 991 +DL CAS 2448503.3893 8.962 1.252 .702 993 +DL CAS 2448504.3130 9.064 1.279 .741 993 +DL CAS 2448505.3315 9.219 1.349 .754 993 +DL CAS 2448506.3395 9.253 1.335 .756 993 +DL CAS 2448507.3466 9.005 1.199 .684 993 +DL CAS 2448508.2998 8.718 1.080 .639 993 +DL CAS 2448509.3379 8.774 1.138 .661 993 +DL CAS 2448510.3253 8.835 1.200 .684 993 +DL CAS 2448511.3321 8.939 1.261 .712 993 +DL CAS 2448512.3177 9.084 1.313 .748 993 +DL CAS 2448513.3285 9.223 1.346 .767 993 +DL CAS 2448514.3336 9.253 1.328 .748 993 +DL CAS 2448515.3130 9.029 1.197 .698 993 +DL CAS 2448516.1706 8.770 1.082 .646 993 +DL CAS 2448517.3362 8.771 1.106 .676 993 +DL CAS 2448518.2558 8.841 1.174 .694 993 +DL CAS 2448518.3393 8.839 1.195 .685 993 +DL CAS 2448519.3097 8.942 1.234 .713 993 +DL CAS 2448520.2969 9.102 1.276 .761 993 +DL CAS 2448521.3460 9.216 1.365 .757 993 +DL CAS 2448522.3023 9.262 1.347 .753 993 +DL CAS 2448523.2978 9.018 1.218 .694 993 +DL CAS 2448852.4197 8.707 1.065 .631 994 +DL CAS 2448854.4122 8.843 1.204 .682 994 +DL CAS 2448856.4021 9.091 1.327 .742 994 +DL CAS 2448858.3544 9.257 1.328 .753 994 +DL CAS 2448860.3577 8.725 1.067 .642 994 +DL CAS 2448862.3731 8.862 1.174 .689 994 +DL CAS 2448870.3379 8.821 1.217 .678 994 +DL CAS 2448872.3273 9.075 1.337 .726 994 +DL CAS 2448874.3418 9.258 1.332 .754 994 +DL CAS 2448875.3562 9.011 1.213 .694 994 +DL CAS 2448876.3486 8.722 1.081 .641 994 +DL CAS 2448877.2977 8.762 1.140 .639 994 +DL CAS 2448879.3683 8.960 1.260 .721 994 +DL CAS 2448880.2755 9.081 1.310 .759 994 +DL CAS 2448881.2654 9.209 1.368 .767 994 +DL CAS 2448881.4166 9.231 1.365 .764 994 +DL CAS 2448882.3122 9.255 .896 1.351 .750 994 +DL CAS 2448883.3055 9.028 1.216 .702 994 +DL CAS 2448883.5172 8.962 1.176 .684 994 +DL CAS 2448885.2706 8.778 1.127 .659 994 +DL CAS 2448886.3256 8.846 1.194 .696 994 +DL CAS 2448886.5200 8.841 1.211 .689 994 +DL CAS 2448887.3163 8.937 1.250 .710 994 +DL CAS 2448888.2654 9.081 1.314 .745 994 +DL CAS 2448889.2701 9.211 1.373 .769 994 +DL CAS 2448890.2484 9.262 1.359 .751 994 +DL CAS 2448890.3638 9.249 1.358 .748 994 +DL CAS 2448890.4115 9.249 1.328 .742 994 +DL CAS 2448892.2614 8.763 1.087 .649 994 +DL CAS 2448893.2751 8.740 1.135 .656 994 +DL CAS 2448894.3139 8.858 1.207 .682 994 +DL CAS 2449617.3264 9.260 1.315 .758 995 +DL CAS 2449620.3852 8.734 1.042 .615 995 +DL CAS 2449620.4509 8.732 1.041 .661 995 +DL CAS 2449621.3829 8.859 1.095 .683 995 +DL CAS 2449621.4516 8.847 1.118 .654 995 +DL CAS 2449622.4319 8.848 1.147 .669 995 +DL CAS 2449622.4745 8.852 1.156 .691 995 +DL CAS 2449623.3900 8.942 1.231 .706 995 +DL CAS 2449623.4575 8.965 1.214 .728 995 +DL CAS 2449624.3214 9.145 1.284 995 +DL CAS 2449624.4472 9.101 1.264 .735 995 +DL CAS 2449624.4782 9.102 1.272 .722 995 +DL CAS 2449624.5071 9.123 1.319 .734 995 +DL CAS 2449625.3592 9.226 1.346 .741 995 +DL CAS 2449625.4576 9.253 1.324 .769 995 +DL CAS 2449625.4824 9.244 1.323 .742 995 +DL CAS 2449625.5020 9.264 1.350 .764 995 +DL CAS 2449631.3468 8.945 1.210 .706 995 +DL CAS 2449632.3535 9.104 1.274 .736 995 +DL CAS 2449632.4417 9.110 1.259 .746 995 +DL CAS 2449632.4791 9.124 1.298 .729 995 +DL CAS 2449633.3259 9.294 1.333 .759 995 +DL CAS 2449633.3994 9.250 1.315 .751 995 +DL CAS 2449633.4532 9.263 1.301 .754 995 +DL CAS 2449634.3329 9.282 1.314 .725 995 +DL CAS 2449635.3690 9.031 1.160 .703 995 +DL CAS 2449635.4589 9.016 1.166 .694 995 +DL CAS 2449935.4581 9.007 .738 1.383 998 +DL CAS 2449936.4523 9.184 .756 1.447 998 +DL CAS 2449937.4610 9.229 1.335 .768 998 +DL CAS 2449938.4732 9.300 1.319 .777 1.484 998 +DL CAS 2449939.4674 9.063 1.329 998 +DL CAS 2449941.4712 8.873 1.259 998 +DL CAS 2449942.4653 8.898 1.348 998 +DL CAS 2449943.4497 9.057 1.421 998 +DL CAS 2449944.4696 9.161 1.476 998 +DL CAS 2449946.4676 9.301 1.493 998 +DL CAS 2449947.4429 9.032 1.208 .710 1.349 998 +DL CAS 2449948.4016 8.781 1.275 998 +DL CAS 2449950.4469 8.876 1.336 998 +DL CAS 2449952.4437 9.144 1.434 998 +DL CAS 2449953.4544 9.254 1.492 998 +DL CAS 2449954.4180 9.310 1.464 998 +DL CAS 2449955.3809 9.086 1.402 998 +DL CAS 2450305.4051 9.207 1.376 .784 971 +DL CAS 2450306.4653 9.287 1.350 .682 971 +DL CAS 2450310.4493 8.836 1.203 .712 971 +DL CAS 2450311.4595 8.970 1.219 .762 971 +DL CAS 2450312.4373 9.045 1.347 .761 971 +DL CAS 2450314.4190 9.269 1.330 .766 971 +DL CAS 2450315.3712 9.022 1.227 .732 971 +DL CAS 2450316.3720 8.729 1.106 .692 971 +DL CAS 2450317.3870 8.753 1.147 .665 971 +DL CAS 2450318.3811 8.848 1.195 .699 971 +DL CAS 2450319.3898 8.917 1.262 .784 971 +DL CAS 2450320.3806 9.079 1.326 .743 971 +DL CAS 2450321.3727 9.197 1.376 .790 971 +DL CAS 2450322.3683 9.273 1.374 .761 971 +DL CAS 2450323.3609 9.079 1.245 .666 971 +DL CAS 2450324.3344 8.735 1.116 .646 971 +DL CAS 2450325.3432 8.745 1.145 .660 971 +DL CAS 2450326.2662 8.863 1.168 .693 971 +DL CAS 2450327.3571 8.904 1.280 .694 971 +DL CAS 2450329.1812 1.348 .758 971 +DL CAS 2450330.2005 9.249 1.349 .759 971 +DL CAS 2450332.1954 8.777 1.108 .760 971 +DL CAS 2450333.2192 8.724 1.081 .699 971 +DL CAS 2450333.4794 8.766 1.093 .758 971 +DL CAS 2450334.2134 8.822 1.190 .669 971 +DL CAS 2450334.4322 8.824 1.203 .667 971 +DL CAS 2450335.2093 8.880 1.253 .705 971 +DL CAS 2450335.4421 8.935 1.250 .719 971 +DL CAS 2450336.2052 9.040 1.311 .732 971 +DL CAS 2450337.2028 9.175 1.344 .746 971 +DL CAS 2450337.4535 9.287 1.352 .743 971 +DL CAS 2450338.4805 9.241 1.304 .720 971 +DL CAS 2450340.2000 8.769 1.111 .651 971 +DL CAS 2450341.1905 8.726 1.117 .647 971 +DL CAS 2450341.4470 8.750 1.117 .625 971 +DL CAS 2450342.1919 8.819 1.187 .678 971 +DL CAS 2450344.1903 9.043 1.323 .758 971 +DL CAS 2450347.1804 9.104 1.287 .726 971 +DL CAS 2450347.4360 9.001 1.207 .692 971 +DL CAS 2450349.1872 8.688 1.138 .628 971 +EX CAS 2446253.4488 13.018 1.623 .945 987 +EX CAS 2446255.4435 12.551 1.370 .860 987 +EX CAS 2446256.4187 12.784 1.510 .925 987 +EX CAS 2446257.4301 12.962 1.626 .933 987 +EX CAS 2446258.4152 13.047 1.592 .951 987 +EX CAS 2446259.4114 12.472 1.329 .842 987 +EX CAS 2446260.3597 12.735 1.475 987 +EX CAS 2446261.3611 12.949 1.576 .960 987 +EX CAS 2446263.4331 12.573 1.346 .849 987 +EX CAS 2446265.4071 12.907 1.557 .959 987 +EX CAS 2446266.3856 13.002 1.642 .966 987 +EX CAS 2446267.4161 12.857 1.473 .914 987 +EX CAS 2446268.4011 12.550 1.393 987 +EX CAS 2446269.3705 12.813 1.548 .942 987 +EX CAS 2446270.3967 13.000 1.582 .962 987 +EX CAS 2446272.3914 12.492 1.360 .838 987 +EX CAS 2446273.4214 12.763 1.483 .930 987 +EX CAS 2446274.4021 12.950 1.603 .948 987 +EX CAS 2446275.3950 13.106 1.569 .999 987 +EX CAS 2446279.4267 13.080 .971 987 +EX CAS 2446280.3847 12.786 1.459 .900 987 +EX CAS 2446283.3782 13.017 1.598 .982 987 +EX CAS 2448852.3993 12.773 1.526 .914 994 +EX CAS 2448854.4018 13.076 1.610 .942 994 +EX CAS 2448856.3886 12.698 1.452 .887 994 +EX CAS 2448858.3472 13.076 1.622 .975 994 +EX CAS 2448860.3491 12.583 1.422 .856 994 +EX CAS 2448862.3612 13.020 1.616 .962 994 +EX CAS 2448870.3268 12.907 1.591 .940 994 +EX CAS 2448872.3201 12.737 1.434 .866 994 +EX CAS 2448874.3312 12.849 1.555 .935 994 +EX CAS 2448875.3421 13.030 1.611 .972 994 +EX CAS 2448876.3376 12.981 1.547 .927 994 +EX CAS 2448877.2873 12.481 1.396 .799 994 +EX CAS 2448878.3557 12.821 1.538 .950 994 +EX CAS 2448879.3564 13.026 1.637 .979 994 +EX CAS 2448881.2574 12.522 1.366 .839 994 +EX CAS 2448882.2670 12.699 1.445 .907 994 +EX CAS 2448883.2951 12.938 1.532 .945 994 +EX CAS 2448885.2612 12.740 1.436 .869 994 +EX CAS 2448886.3137 12.624 1.421 .891 994 +EX CAS 2448887.3090 12.867 1.555 .942 994 +EX CAS 2448888.2592 13.049 1.610 .985 994 +EX CAS 2448888.4086 13.054 1.600 .961 994 +EX CAS 2448889.2602 12.971 1.549 .918 994 +EX CAS 2448890.2416 12.537 1.343 .869 994 +EX CAS 2448890.3579 12.561 1.377 .857 994 +EX CAS 2448891.2399 12.766 1.558 .897 994 +EX CAS 2448891.3562 12.819 1.532 .915 994 +EX CAS 2448892.2529 13.001 1.626 .970 994 +EX CAS 2448892.3509 1.621 .980 994 +EX CAS 2448893.2625 13.090 1.579 .968 994 +EX CAS 2448894.3014 12.492 1.365 .831 994 +FM CAS 2450330.2693 9.065 1.002 .571 971 +FM CAS 2450332.2514 9.317 1.126 971 +FM CAS 2450333.2574 9.415 1.143 .609 971 +FM CAS 2450334.2730 9.036 .952 .533 971 +FM CAS 2450335.2976 8.894 .920 .541 971 +FM CAS 2450336.3261 9.048 1.065 .574 971 +FM CAS 2450337.2326 9.188 1.093 .604 971 +FM CAS 2450338.3899 9.363 1.185 .639 971 +FM CAS 2450340.2431 8.961 .917 .539 971 +FM CAS 2450341.2472 8.930 .934 .548 971 +FM CAS 2450342.2591 9.065 1.092 .591 971 +FM CAS 2450344.2838 9.377 1.165 .652 971 +FM CAS 2450347.2496 8.944 .974 .553 971 +FM CAS 2450357.2133 9.179 1.004 .572 971 +FO CAS 2446255.4493 14.067 .905 987 +FO CAS 2446256.4270 14.282 1.431 .914 987 +FO CAS 2446257.4220 14.512 1.439 .961 987 +FO CAS 2446258.4070 14.693 1.503 .995 987 +FO CAS 2446259.4032 14.664 1.420 .937 987 +FO CAS 2446260.3635 14.001 1.219 .812 987 +FO CAS 2446261.3669 14.070 1.263 .829 987 +FO CAS 2446263.4386 14.384 1.451 .912 987 +FO CAS 2446265.4111 14.711 1.529 .965 987 +FO CAS 2446266.3896 14.609 1.442 .926 987 +FO CAS 2446267.4198 13.870 1.214 .785 987 +FO CAS 2446268.4047 14.074 1.300 .844 987 +FO CAS 2446269.3759 14.059 1.378 .864 987 +FO CAS 2446270.4007 14.409 1.479 .939 987 +FO CAS 2446272.4033 14.693 1.591 .932 987 +FO CAS 2446273.4254 14.501 1.374 .929 987 +FO CAS 2446274.4063 13.873 1.203 .814 987 +FO CAS 2446275.4191 14.072 1.316 .895 987 +FO CAS 2446284.3801 14.527 1.454 .944 987 +FO CAS 2446285.3547 14.657 1.586 .950 987 +FO CAS 2446287.3036 14.249 1.336 .862 987 +FO CAS 2446288.3677 13.992 1.249 .816 987 +FO CAS 2446289.3630 14.045 1.322 .853 987 +FO CAS 2446290.3719 14.330 1.363 .891 987 +FO CAS 2446291.3444 14.493 1.483 .930 987 +FO CAS 2446292.3399 14.760 1.558 1.006 987 +FO CAS 2446294.3264 14.053 1.232 .799 987 +FO CAS 2446295.3225 14.025 1.277 .847 987 +FO CAS 2446296.3123 14.088 1.352 .895 987 +FO CAS 2446297.2811 14.402 1.407 .974 987 +FO CAS 2446298.3182 14.550 1.485 .993 987 +FO CAS 2446299.3121 14.764 1.641 1.020 987 +FO CAS 2446300.3036 14.584 1.477 .973 987 +FO CAS 2446301.3378 13.894 1.194 .789 987 +FO CAS 2446302.3239 14.102 1.310 .775 987 +FO CAS 2446303.3066 14.060 .881 987 +FO CAS 2446304.3819 14.416 1.468 .916 987 +FO CAS 2450333.2683 13.889 1.177 .847 971 +FO CAS 2450334.2886 14.148 1.232 .862 971 +FO CAS 2450335.3115 14.127 1.308 .904 971 +FO CAS 2450336.3389 14.448 1.476 .940 971 +FO CAS 2450337.2502 14.560 971 +FO CAS 2450338.3701 14.745 1.589 .952 971 +FO CAS 2450340.2454 13.905 1.218 .818 971 +FO CAS 2450341.2508 14.113 1.294 .872 971 +FO CAS 2450342.2638 14.139 1.410 .896 971 +FO CAS 2450344.2870 14.644 1.436 1.005 971 +FO CAS 2450344.4666 14.611 1.450 .991 971 +FO CAS 2450347.2877 13.988 1.262 .839 971 +FO CAS 2450347.4489 14.007 1.284 .824 971 +FW CAS 2446253.4384 12.356 1.530 .917 987 +FW CAS 2446256.4443 12.689 1.637 .976 987 +FW CAS 2446257.4162 12.384 1.482 .898 987 +FW CAS 2446258.3994 12.188 1.396 .865 987 +FW CAS 2446259.3993 12.316 1.509 .910 987 +FW CAS 2446260.3683 12.441 1.585 .939 987 +FW CAS 2446263.4444 12.473 1.494 .916 987 +FW CAS 2446265.4191 12.326 1.501 .912 987 +FW CAS 2446266.3962 12.390 1.564 .941 987 +FW CAS 2446267.3887 12.589 1.615 .971 987 +FW CAS 2446268.4111 12.684 1.664 .979 987 +FW CAS 2446269.3835 12.608 1.525 .975 987 +FW CAS 2446270.4073 12.201 1.401 .859 987 +FW CAS 2446272.4114 12.377 1.535 .934 987 +FW CAS 2446273.4338 12.531 1.577 .975 987 +FW CAS 2446274.4140 12.661 1.644 .982 987 +FW CAS 2446275.4271 12.647 1.614 .961 987 +FW CAS 2446279.3656 12.495 1.535 .959 987 +FW CAS 2446280.3964 12.606 1.687 .956 987 +FW CAS 2446283.3844 12.186 1.407 .860 987 +FW CAS 2447401.4130 12.366 1.529 .897 990 +FW CAS 2447402.3839 12.469 1.599 .937 990 +FW CAS 2447403.4270 12.588 1.661 .932 990 +FW CAS 2447404.4165 12.732 1.615 .952 990 +FW CAS 2447407.3737 12.350 1.501 .894 990 +FW CAS 2447408.3727 12.439 1.512 .920 990 +FW CAS 2447409.3536 12.556 1.601 .956 990 +FW CAS 2447410.3800 12.721 1.658 .970 990 +FW CAS 2447411.3782 12.533 1.507 .924 990 +FW CAS 2447413.3360 12.288 1.461 .894 990 +FW CAS 2447414.3352 12.381 1.536 .907 990 +FW CAS 2447415.3119 12.536 1.620 .935 990 +FW CAS 2447416.3067 12.699 1.635 .975 990 +FW CAS 2447417.3028 12.659 1.570 .942 990 +FW CAS 2447418.2975 12.296 1.410 .874 990 +FW CAS 2447419.2743 12.171 1.382 .859 990 +FW CAS 2447420.2708 12.372 1.527 .925 990 +FW CAS 2447421.2613 12.503 1.600 .938 990 +FW CAS 2447422.2766 12.653 1.635 .939 990 +FW CAS 2447423.4039 12.712 1.579 .965 990 +FW CAS 2447424.2933 12.416 1.449 .892 990 +FW CAS 2447425.3143 12.206 1.412 .856 990 +FW CAS 2447427.3761 12.479 1.586 .933 990 +FW CAS 2447428.2803 12.598 1.638 .972 990 +FW CAS 2447428.4371 12.640 1.606 .947 990 +FW CAS 2447429.3261 12.719 1.618 .942 990 +FW CAS 2447430.2863 12.453 1.487 .903 990 +FW CAS 2447431.3492 12.174 1.416 .841 990 +FW CAS 2447432.3248 12.271 1.515 .870 990 +FW CAS 2447433.3080 12.435 1.540 .945 990 +FW CAS 2447434.3115 12.554 1.663 .940 990 +FW CAS 2448852.4266 12.670 1.601 .951 994 +FW CAS 2448854.4173 12.232 1.452 .859 994 +FW CAS 2448856.4035 12.504 1.556 .954 994 +FW CAS 2448858.3558 12.725 1.673 .979 994 +FW CAS 2448860.3587 12.197 1.406 .861 994 +FW CAS 2448862.3580 12.461 1.568 .955 994 +FW CAS 2448870.3388 12.696 1.670 .973 994 +FW CAS 2448872.3302 12.233 1.409 .861 994 +FW CAS 2448874.3428 12.379 1.570 .928 994 +FW CAS 2448875.3573 12.518 1.653 .952 994 +FW CAS 2448876.3494 12.658 1.642 .970 994 +FW CAS 2448877.2986 12.687 1.644 .951 994 +FW CAS 2448879.3708 12.211 1.446 .874 994 +FW CAS 2448880.2774 12.361 1.546 .919 994 +FW CAS 2448881.2675 12.489 1.588 .952 994 +FW CAS 2448881.4194 12.518 1.644 .967 994 +FW CAS 2448882.3148 12.625 1.670 .965 994 +FW CAS 2448883.3083 12.724 1.649 .965 994 +FW CAS 2448885.2715 12.228 1.427 .866 994 +FW CAS 2448886.3290 12.348 1.506 .903 994 +FW CAS 2448887.3184 12.448 1.592 .960 994 +FW CAS 2448888.2677 12.606 1.607 .980 994 +FW CAS 2448889.2709 12.730 1.672 .988 994 +FW CAS 2448890.2506 12.514 1.533 .929 994 +FW CAS 2448890.3669 12.467 1.517 .914 994 +FW CAS 2448891.2468 12.169 1.415 .854 994 +FW CAS 2448892.2638 12.292 1.492 .897 994 +FW CAS 2448893.2778 12.413 1.535 .943 994 +FW CAS 2448894.3146 12.589 1.626 .970 994 +GL CAS 2446260.4322 12.554 1.198 .749 987 +GL CAS 2446265.4563 12.902 1.364 .857 987 +GL CAS 2446267.4583 12.659 1.268 .744 987 +GL CAS 2446269.4332 12.859 1.380 .840 987 +GL CAS 2446270.4460 13.055 1.423 .868 987 +GL CAS 2446272.4441 12.544 1.211 .753 987 +GL CAS 2446274.4459 13.050 1.412 .867 987 +GL CAS 2446275.4575 12.678 1.216 .755 987 +GL CAS 2446279.4493 12.724 1.261 .799 987 +GL CAS 2446280.4431 12.522 1.206 .749 987 +GL CAS 2446283.4320 12.752 1.246 .776 987 +GL CAS 2446284.4450 12.546 1.182 .764 987 +GL CAS 2446285.4210 12.859 1.371 .861 987 +GL CAS 2446287.3803 12.817 1.308 .774 987 +GL CAS 2446288.4211 12.526 1.205 .741 987 +GL CAS 2446289.4223 12.858 1.369 .857 987 +GL CAS 2446290.4380 13.072 1.427 .837 987 +GL CAS 2446291.4174 12.771 1.262 .782 987 +GL CAS 2446293.3910 12.833 1.399 .804 987 +GL CAS 2446294.3883 13.050 1.439 .863 987 +GL CAS 2446295.3783 12.870 1.257 .801 987 +GL CAS 2446296.3831 12.505 1.198 .758 987 +GL CAS 2446297.3988 12.822 1.372 .814 987 +GL CAS 2446298.4120 13.037 1.441 .851 987 +GL CAS 2446299.3782 12.876 1.293 .795 987 +GL CAS 2446300.3589 12.492 1.188 .742 987 +GL CAS 2446301.4181 12.866 1.371 .825 987 +GL CAS 2446302.4018 13.025 1.459 .839 987 +GL CAS 2446303.3899 12.874 1.293 .807 987 +GL CAS 2446304.3314 12.461 1.158 .722 987 +GL CAS 2446615.4496 13.108 1.432 .838 988 +GL CAS 2446620.4485 12.347 1.109 .685 988 +GL CAS 2446621.4489 12.656 1.281 .779 988 +GL CAS 2446622.4372 12.931 1.428 .834 988 +GL CAS 2446623.4465 13.069 1.463 .847 988 +GL CAS 2446624.4407 12.338 1.100 .691 988 +GL CAS 2446625.4338 12.645 1.262 .786 988 +GL CAS 2446626.4345 12.905 1.413 .844 988 +GL CAS 2446627.4388 13.104 1.403 .869 988 +GL CAS 2446628.4381 12.330 1.115 .673 988 +GL CAS 2446629.4415 12.658 1.249 .796 988 +GL CAS 2446631.4227 13.085 1.389 .852 988 +GL CAS 2446632.4348 12.336 1.109 .680 988 +GL CAS 2446636.4281 12.346 1.098 .688 988 +GL CAS 2447401.4374 12.827 1.291 .768 990 +GL CAS 2447402.4162 12.493 1.221 .729 990 +GL CAS 2447403.4422 12.844 1.364 .819 990 +GL CAS 2447404.4463 13.009 1.436 .823 990 +GL CAS 2447407.4223 12.820 1.376 .818 990 +GL CAS 2447408.4200 13.032 1.467 .858 990 +GL CAS 2447409.4019 12.924 1.297 .788 990 +GL CAS 2447410.4145 12.494 1.205 .741 990 +GL CAS 2447411.4262 12.820 1.369 .826 990 +GL CAS 2447413.3601 12.964 1.319 .822 990 +GL CAS 2447414.3747 12.446 1.195 .707 990 +GL CAS 2447415.3382 12.815 1.253 .816 990 +GL CAS 2447416.3329 13.019 1.418 .825 990 +GL CAS 2447417.3262 12.985 1.338 .814 990 +GL CAS 2447418.3237 12.460 1.155 .724 990 +GL CAS 2447419.3054 12.706 1.315 .776 990 +GL CAS 2447420.2931 12.981 1.424 .819 990 +GL CAS 2447421.2895 13.020 1.360 .818 990 +GL CAS 2447422.3006 12.465 1.137 .725 990 +GL CAS 2447424.3691 13.019 1.390 .847 990 +GL CAS 2447425.3470 13.015 1.339 .832 990 +GL CAS 2447427.4133 12.835 1.381 .804 990 +GL CAS 2447428.3125 12.991 1.397 .846 990 +GL CAS 2447430.3278 12.468 1.178 .730 990 +GL CAS 2447431.3799 12.797 1.339 .806 990 +GL CAS 2447432.3762 13.004 1.409 .859 990 +GL CAS 2447433.3520 13.022 1.393 .860 990 +GL CAS 2447434.3601 12.442 1.139 .722 990 +GL CAS 2447758.4546 12.351 1.059 .698 991 +GL CAS 2447759.4354 12.608 1.293 .753 991 +GL CAS 2447760.4637 12.930 1.381 991 +GL CAS 2447762.4586 12.360 1.063 .702 991 +GL CAS 2447763.4739 12.636 1.270 991 +GL CAS 2447765.4614 13.102 1.405 .842 991 +GL CAS 2447766.4585 12.364 1.104 .694 991 +GL CAS 2447767.4599 12.624 1.264 .773 991 +GL CAS 2447768.4409 1.403 .845 991 +GL CAS 2447770.4250 12.391 1.087 .717 991 +GL CAS 2447771.4286 12.623 1.248 .783 991 +GL CAS 2447772.3580 12.891 1.384 .833 991 +GL CAS 2447773.3826 13.097 1.379 .859 991 +GL CAS 2447773.4316 13.076 1.414 .844 991 +GL CAS 2447774.3895 12.437 1.110 .703 991 +GL CAS 2447774.4478 12.392 1.098 .698 991 +GL CAS 2447775.3592 12.613 1.221 .791 991 +GL CAS 2447775.4194 12.597 1.264 .751 991 +GL CAS 2447776.3747 12.902 1.387 .849 991 +GL CAS 2447776.4329 12.922 1.371 .842 991 +GM CAS 2446284.4492 11.659 1.731 1.103 987 +GM CAS 2446285.4246 11.729 1.816 1.127 987 +GM CAS 2446287.3848 11.916 1.938 1.179 987 +GM CAS 2446288.4250 12.067 1.923 1.209 987 +GM CAS 2446289.4271 12.153 1.931 1.211 987 +GM CAS 2446290.4419 11.976 1.839 1.143 987 +GM CAS 2446291.4201 11.673 1.734 1.101 987 +GM CAS 2446293.3935 11.788 1.843 1.142 987 +GM CAS 2446294.3931 11.854 1.917 1.154 987 +GM CAS 2446295.3810 11.985 1.923 1.181 987 +GM CAS 2446296.3866 12.119 1.968 1.205 987 +GM CAS 2446297.4018 12.113 1.915 1.191 987 +GM CAS 2446298.4150 11.825 1.805 1.132 987 +GM CAS 2446299.3809 11.629 1.742 1.082 987 +GM CAS 2446300.3621 11.750 1.790 1.136 987 +GM CAS 2446301.4207 11.863 1.824 1.171 987 +GM CAS 2446302.4046 11.931 1.933 1.170 987 +GM CAS 2446303.3929 12.067 1.959 1.191 987 +GM CAS 2446304.3390 12.140 1.964 1.195 987 +GO CAS 2446260.4365 13.190 1.628 1.000 987 +GO CAS 2446269.4388 12.943 1.494 .966 987 +GO CAS 2446270.4514 13.265 1.700 1.032 987 +GO CAS 2446272.4467 12.942 1.512 .935 987 +GO CAS 2446274.4510 13.403 1.729 1.037 987 +GO CAS 2446275.4616 13.120 1.568 .980 987 +GO CAS 2446280.4492 13.335 1.706 1.045 987 +GO CAS 2446283.4378 13.274 1.703 1.042 987 +GO CAS 2446284.4530 13.475 1.752 1.061 987 +GO CAS 2446285.4271 12.916 1.481 .933 987 +GO CAS 2446287.3878 13.410 1.736 1.054 987 +GO CAS 2446288.4283 13.101 1.587 .955 987 +GO CAS 2446289.4300 13.118 1.618 .988 987 +GO CAS 2446290.4444 13.414 1.748 1.033 987 +GO CAS 2446291.4231 13.347 1.650 1.022 987 +GO CAS 2446293.3964 13.350 1.711 1.057 987 +GO CAS 2446294.3957 13.423 1.736 1.026 987 +GO CAS 2446295.3838 12.929 1.504 .936 987 +GO CAS 2446296.3894 13.280 1.726 1.045 987 +GO CAS 2446297.4050 13.455 1.702 1.062 987 +GO CAS 2446298.4173 12.932 1.500 .943 987 +GO CAS 2446299.3841 13.212 1.694 1.022 987 +GO CAS 2446300.3646 13.408 1.749 1.033 987 +GO CAS 2446301.4233 13.125 1.532 .997 987 +GO CAS 2446302.4075 13.121 1.652 .996 987 +GO CAS 2446303.3953 13.411 1.733 1.065 987 +GO CAS 2446304.3427 13.344 1.663 .999 987 +HK CAS 2446265.4271 13.934 .870 .500 987 +HK CAS 2446266.4305 14.441 1.278 .643 987 +HK CAS 2446267.4318 14.683 1.076 .563 987 +HK CAS 2446268.4453 14.110 1.080 .560 987 +HK CAS 2446269.3944 14.821 1.351 .673 987 +HK CAS 2446270.4180 13.890 .857 .503 987 +HK CAS 2446272.4231 14.686 1.126 .599 987 +HK CAS 2446273.4400 14.127 1.076 .579 987 +HK CAS 2446274.4205 14.862 1.295 .705 987 +HK CAS 2446275.4324 13.901 .843 .491 987 +HK CAS 2446280.4142 13.907 .796 .506 987 +HK CAS 2446284.4027 14.863 1.341 .672 987 +HK CAS 2446285.3770 13.879 .795 .499 987 +HK CAS 2446287.3437 14.723 1.094 .639 987 +HK CAS 2446288.3836 14.132 1.052 .577 987 +HK CAS 2446289.3878 14.803 1.370 .611 987 +HK CAS 2446290.3986 13.901 .816 .466 987 +HK CAS 2446291.3771 14.428 1.263 .621 987 +HK CAS 2446293.3696 14.108 1.076 .620 987 +HK CAS 2446294.3550 14.787 1.367 .652 987 +HK CAS 2446295.3483 13.872 .784 .485 987 +HK CAS 2446296.3402 14.469 1.363 .657 987 +HK CAS 2446297.3356 14.663 1.194 .611 987 +HK CAS 2446298.3445 14.138 1.035 .583 987 +HK CAS 2446299.3410 14.777 1.331 .625 987 +HK CAS 2446300.3304 13.859 .820 .464 987 +HK CAS 2446301.3693 14.458 1.174 .674 987 +HK CAS 2446302.3532 14.731 1.119 .589 987 +HK CAS 2446303.3185 14.047 .557 987 +HK CAS 2446615.4430 13.931 .985 .519 988 +HK CAS 2446616.4444 14.598 1.345 .653 988 +HK CAS 2446617.4484 14.271 1.018 .515 988 +HK CAS 2446620.4319 13.990 .875 .571 988 +HK CAS 2446625.4436 13.929 .918 .503 988 +HK CAS 2446626.4451 14.521 1.298 .663 988 +HK CAS 2446627.4478 14.191 .957 .540 988 +HK CAS 2446628.4478 14.155 1.191 .557 988 +HK CAS 2446629.4508 14.886 1.317 .696 988 +HK CAS 2446631.4342 14.545 1.195 .689 988 +HK CAS 2446632.4464 14.185 .855 988 +HK CAS 2446636.4427 14.551 1.236 .667 988 +HK CAS 2447407.3788 13.864 .773 .462 990 +HK CAS 2447408.3808 14.276 1.196 990 +HK CAS 2447409.3600 14.759 1.130 .643 990 +HK CAS 2447410.3857 14.045 .961 .552 990 +HK CAS 2447411.4026 14.697 1.330 .654 990 +HK CAS 2447413.3408 14.271 1.166 .610 990 +HK CAS 2447414.3375 14.838 1.274 .630 990 +HK CAS 2447415.3156 13.990 .981 .512 990 +HK CAS 2447416.3141 14.633 1.291 .670 990 +HK CAS 2447417.3082 13.878 .805 .439 990 +HK CAS 2447418.3036 14.254 1.140 .601 990 +HK CAS 2447420.2767 13.991 .892 .544 990 +HK CAS 2447421.2655 14.634 1.288 .683 990 +HK CAS 2447422.2822 13.969 .838 .432 990 +HK CAS 2447423.4113 14.302 1.180 .606 990 +HK CAS 2447424.2979 14.762 1.269 .663 990 +HK CAS 2447425.3194 14.020 .973 .536 990 +HK CAS 2447427.3821 13.851 .723 990 +HK CAS 2447430.3020 13.986 .970 .499 990 +HK CAS 2447432.3387 13.880 .762 .466 990 +HK CAS 2447433.3279 14.270 1.182 990 +HK CAS 2447434.3377 14.746 1.298 .650 990 +HK CAS 2447743.4271 14.380 1.221 .626 991 +HK CAS 2447757.4585 13.902 .815 .503 991 +HK CAS 2447758.4100 14.391 1.207 .633 991 +HK CAS 2447759.4020 14.674 1.112 .572 991 +HK CAS 2447760.4447 14.085 1.029 991 +HK CAS 2447762.4455 13.870 .776 991 +HK CAS 2447763.4603 14.422 1.208 991 +HK CAS 2447765.4423 14.075 1.070 .535 991 +HK CAS 2447766.4430 14.800 1.260 .641 991 +HK CAS 2447767.4492 13.865 .804 .480 991 +HK CAS 2447768.4066 14.310 1.244 .655 991 +HK CAS 2447770.3976 14.067 1.020 .571 991 +HK CAS 2447771.3821 14.768 1.251 .710 991 +HK CAS 2447772.3427 13.827 .761 .454 991 +HK CAS 2447773.3716 14.360 1.186 .648 991 +HK CAS 2447774.3772 14.673 1.166 .621 991 +HK CAS 2447774.4350 14.707 1.080 .601 991 +HK CAS 2447775.3435 14.068 .980 .581 991 +HK CAS 2447775.3947 14.084 .995 .575 991 +HK CAS 2447776.3618 14.766 1.275 .700 991 +HK CAS 2447776.4192 14.764 991 +HK CAS 2448852.4394 14.004 .895 .515 994 +HK CAS 2448854.4289 14.139 .906 .489 994 +HK CAS 2448856.4118 14.834 1.267 .607 994 +HK CAS 2448858.3614 14.563 1.233 .683 994 +HK CAS 2448860.3643 14.154 1.149 .599 994 +HK CAS 2448862.3801 13.987 .877 994 +HK CAS 2448862.4408 13.987 .930 .521 994 +HK CAS 2448870.3462 14.170 1.090 .618 994 +HK CAS 2448872.3686 13.918 .933 .513 994 +HK CAS 2448874.3477 14.434 .980 .573 994 +HK CAS 2448875.3682 14.179 1.130 .658 994 +HK CAS 2448876.3615 14.906 1.228 .761 994 +HK CAS 2448880.3018 14.188 .804 1.132 .628 994 +HK CAS 2448881.2814 14.837 .690 994 +HK CAS 2448882.3030 13.896 .865 .500 994 +HK CAS 2448883.3360 14.522 1.295 .626 994 +HK CAS 2448885.2848 14.183 1.100 .620 994 +HK CAS 2448886.2686 14.846 1.342 .695 994 +HK CAS 2448887.3238 13.942 .908 .495 994 +HK CAS 2448888.2787 14.440 1.259 .663 994 +HK CAS 2448889.2875 14.665 1.089 .626 994 +HK CAS 2448890.2606 14.099 1.102 .550 994 +HK CAS 2448891.2610 14.815 1.372 .691 994 +HK CAS 2448892.2720 13.903 .824 .536 994 +HK CAS 2448893.2883 14.495 1.243 .684 994 +HK CAS 2448894.3239 14.549 1.010 .596 994 +IO CAS 2446259.4534 14.078 1.250 .812 987 +IO CAS 2446260.4233 13.502 1.034 .661 987 +IO CAS 2446269.4249 13.873 1.327 .775 987 +IO CAS 2446270.4369 14.072 1.325 .798 987 +IO CAS 2446272.4381 13.351 1.037 .647 987 +IO CAS 2446273.4551 13.572 1.182 .730 987 +IO CAS 2446274.4381 13.735 1.252 .762 987 +IO CAS 2446275.4507 13.959 1.335 .789 987 +IO CAS 2446280.4344 13.842 1.208 .807 987 +IO CAS 2446283.4237 13.298 1.011 .622 987 +IO CAS 2446284.4324 13.568 1.153 .730 987 +IO CAS 2446285.4024 13.603 1.229 .739 987 +IO CAS 2446287.3666 14.083 1.371 .794 987 +IO CAS 2446288.4005 13.523 .675 987 +IO CAS 2446289.4023 13.372 1.078 .671 987 +IO CAS 2446290.4265 13.593 1.172 .724 987 +IO CAS 2446291.3956 13.790 1.238 .779 987 +IO CAS 2446294.3798 13.275 .979 .628 987 +IO CAS 2446295.3665 13.467 1.114 .690 987 +IO CAS 2446296.3710 13.605 1.186 .755 987 +IO CAS 2446297.3782 13.877 1.265 .793 987 +IO CAS 2446298.3925 14.098 1.304 .815 987 +IO CAS 2446299.3658 13.699 1.141 .693 987 +IO CAS 2446300.3498 13.315 .997 .642 987 +IO CAS 2446301.3978 13.623 1.153 .770 987 +IO CAS 2446302.3903 13.716 1.232 .762 987 +IO CAS 2446303.3704 13.938 1.344 .804 987 +IO CAS 2446304.3151 14.056 .795 987 +IO CAS 2448858.3849 13.848 1.243 .801 994 +IO CAS 2448860.3814 13.824 1.222 .759 994 +IO CAS 2448862.4119 13.589 1.141 .748 994 +IO CAS 2448870.3588 13.985 1.286 .798 994 +IO CAS 2448872.3901 13.310 .970 .646 994 +IO CAS 2448874.3748 13.603 1.204 .727 994 +IO CAS 2448875.3810 13.858 1.288 .788 994 +IO CAS 2448877.3229 13.708 1.169 994 +IO CAS 2448880.3278 13.685 1.246 .769 994 +IO CAS 2448882.3412 14.084 1.317 .793 994 +IO CAS 2448883.3432 13.386 1.004 .672 994 +IO CAS 2448885.3519 13.596 1.215 .724 994 +IO CAS 2448886.2931 13.825 1.285 .805 994 +IO CAS 2448887.3351 14.029 1.344 .808 994 +IO CAS 2448888.2988 13.945 1.243 .801 994 +IO CAS 2448889.3070 13.292 1.010 .655 994 +IO CAS 2448890.2789 13.513 1.174 .705 994 +IO CAS 2448891.2850 13.621 1.207 .731 994 +IO CAS 2448892.2851 13.930 1.336 .808 994 +IO CAS 2448893.3203 14.104 1.313 .823 994 +KK CAS 2447000.4501 12.161 1.792 .998 989 +KK CAS 2447002.4518 11.658 1.466 .901 989 +KK CAS 2447003.4334 11.774 1.565 .942 989 +KK CAS 2447082.2939 12.228 1.423 1.683 1.034 989 +KK CAS 2447083.3554 11.932 1.605 .943 989 +KK CAS 2447084.2583 11.654 1.162 1.454 .891 989 +KK CAS 2447085.3339 11.734 1.538 .917 989 +KK CAS 2447086.2526 11.739 1.535 .942 989 +KK CAS 2447087.3027 11.877 1.645 .980 989 +KK CAS 2447088.1673 12.020 1.693 1.003 989 +KK CAS 2447098.2196 12.256 1.743 1.035 989 +KK CAS 2447400.3760 12.219 1.805 990 +KK CAS 2447401.3528 12.255 1.760 1.028 990 +KK CAS 2447402.3546 12.068 1.690 .970 990 +KK CAS 2447403.3770 11.773 1.514 .921 990 +KK CAS 2447404.3886 11.688 1.553 .881 990 +KK CAS 2447407.3387 11.977 1.708 .991 990 +KK CAS 2447408.3368 12.123 1.775 1.003 990 +KK CAS 2447409.3281 12.225 1.764 1.031 990 +KK CAS 2447410.3539 12.145 1.688 .993 990 +KK CAS 2447411.3514 11.828 1.532 .942 990 +KK CAS 2447413.3143 11.758 1.576 .930 990 +KK CAS 2447413.4287 11.744 1.589 .907 990 +KK CAS 2447414.3127 11.748 1.602 .941 990 +KK CAS 2447415.2923 11.923 1.691 .982 990 +KK CAS 2447416.2890 12.077 1.752 1.005 990 +KK CAS 2447417.2796 12.215 1.795 1.023 990 +KK CAS 2447418.2788 12.180 1.722 1.014 990 +KK CAS 2447419.2563 11.908 1.579 .946 990 +KK CAS 2447420.2506 11.661 1.493 .904 990 +KK CAS 2447421.2436 11.796 1.569 .937 990 +KK CAS 2447422.2576 11.773 1.565 .943 990 +KK CAS 2447423.3812 11.915 1.676 .978 990 +KK CAS 2447424.2719 12.090 1.709 1.012 990 +KK CAS 2447425.2908 12.194 1.797 1.026 990 +KK CAS 2447427.3341 12.002 1.625 .945 990 +KK CAS 2447428.2654 11.647 1.496 .877 990 +KK CAS 2447428.4151 11.671 1.481 .911 990 +KK CAS 2447429.3009 11.764 1.587 .920 990 +KK CAS 2447430.2508 11.716 1.577 .939 990 +KK CAS 2447430.4463 11.764 1.589 .947 990 +KK CAS 2447431.3005 11.906 1.672 .974 990 +KK CAS 2447432.2888 11.990 1.694 1.007 990 +KK CAS 2447433.2791 12.227 990 +KK CAS 2447434.2714 12.257 1.764 1.025 990 +KK CAS 2449957.4326 12.300 1.992 998 +KK CAS 2449960.3674 11.794 1.825 998 +KK CAS 2449962.4186 11.912 1.937 998 +KK CAS 2449963.4402 12.182 2.062 998 +KK CAS 2449963.4471 12.193 2.009 998 +KK CAS 2449985.3266 11.768 1.819 998 +KK CAS 2449986.3470 11.724 1.816 998 +KK CAS 2449987.3740 11.912 1.912 998 +LT CAS 2446260.4454 12.541 1.292 .806 987 +LT CAS 2446269.4459 12.918 1.391 .863 987 +LT CAS 2446270.4564 12.013 1.031 .679 987 +LT CAS 2446272.4510 12.559 1.336 .833 987 +LT CAS 2446274.4562 12.951 1.389 .884 987 +LT CAS 2446275.4669 12.824 1.351 .850 987 +LT CAS 2446280.4554 12.965 1.451 .892 987 +LT CAS 2446283.4440 12.348 1.222 .750 987 +LT CAS 2446284.4575 12.645 1.302 .860 987 +LT CAS 2446285.4328 12.799 1.401 .886 987 +LT CAS 2446287.3932 12.695 1.318 .811 987 +LT CAS 2446288.4317 12.060 1.094 .675 987 +LT CAS 2446289.4353 12.388 1.223 .787 987 +LT CAS 2446290.4499 12.645 1.355 .825 987 +LT CAS 2446291.4284 12.793 1.368 .860 987 +LT CAS 2446293.4018 12.580 1.264 .793 987 +LT CAS 2446294.4024 12.105 1.099 .687 987 +LT CAS 2446295.3877 12.405 1.237 .783 987 +LT CAS 2446296.4007 12.615 1.359 .835 987 +LT CAS 2446297.4104 12.813 1.418 .875 987 +LT CAS 2446298.4225 12.981 1.473 .882 987 +LT CAS 2446299.3916 12.480 1.177 .767 987 +LT CAS 2446300.3710 12.133 1.083 .699 987 +LT CAS 2446301.4284 12.475 1.227 .809 987 +LT CAS 2446302.4111 12.632 1.383 .838 987 +LT CAS 2446303.4012 12.842 1.442 .868 987 +LT CAS 2446304.3509 12.976 1.461 .862 987 +LT CAS 2447401.4547 12.811 1.428 .843 990 +LT CAS 2447402.4237 12.974 1.456 .884 990 +LT CAS 2447403.4500 12.544 1.214 .763 990 +LT CAS 2447404.4527 12.096 1.082 .683 990 +LT CAS 2447407.4275 12.815 1.407 .868 990 +LT CAS 2447408.4254 12.990 1.436 .889 990 +LT CAS 2447409.4068 12.501 1.176 .785 990 +LT CAS 2447410.4211 12.138 1.098 .709 990 +LT CAS 2447411.4334 12.441 1.241 .809 990 +LT CAS 2447413.3642 12.830 1.394 .876 990 +LT CAS 2447414.3801 12.974 1.449 .854 990 +LT CAS 2447415.3457 12.436 1.163 .748 990 +LT CAS 2447416.3386 12.135 1.080 .698 990 +LT CAS 2447417.3310 12.427 1.233 .788 990 +LT CAS 2447418.3273 12.645 1.346 .836 990 +LT CAS 2447419.3102 12.775 1.404 .845 990 +LT CAS 2447420.3013 12.957 1.453 .858 990 +LT CAS 2447421.2937 12.379 1.171 .734 990 +LT CAS 2447422.3073 12.174 1.081 .718 990 +LT CAS 2447423.4379 12.449 1.258 .798 990 +LT CAS 2447424.3757 12.655 1.329 .834 990 +LT CAS 2447425.3511 12.897 1.402 .878 990 +LT CAS 2447427.4192 12.078 1.073 .654 990 +LT CAS 2447428.3228 12.160 1.161 .698 990 +LT CAS 2447430.3322 12.666 1.392 .828 990 +LT CAS 2447431.3878 12.862 1.456 .841 990 +LT CAS 2447432.3857 12.950 1.438 .875 990 +LT CAS 2447433.3604 12.061 1.041 .673 990 +LT CAS 2447434.3662 12.215 1.139 .726 990 +NP CAS 2446284.3956 13.850 1.202 987 +NP CAS 2446285.3657 13.926 1.891 1.180 987 +NP CAS 2446287.3108 13.230 1.612 1.039 987 +NP CAS 2446288.3747 13.470 1.729 1.104 987 +NP CAS 2446289.3780 13.609 1.886 1.126 987 +NP CAS 2446290.3867 13.795 1.837 1.164 987 +NP CAS 2446291.3647 13.894 1.869 1.189 987 +NP CAS 2446294.3468 13.406 1.719 1.093 987 +NP CAS 2446295.3434 13.556 1.811 1.130 987 +NP CAS 2446296.3334 13.718 1.826 1.176 987 +NP CAS 2446297.3246 13.873 1.876 1.178 987 +NP CAS 2446298.3393 13.852 1.837 1.189 987 +NP CAS 2446299.3344 13.282 1.614 1.051 987 +NP CAS 2446300.3254 13.355 1.683 1.098 987 +NP CAS 2446301.3579 13.564 1.796 1.161 987 +NP CAS 2446302.3456 13.719 1.872 1.150 987 +NP CAS 2446303.3628 13.854 1.878 1.190 987 +NP CAS 2446304.4113 13.842 1.855 1.163 987 +NP CAS 2447404.4064 13.383 1.652 1.066 990 +NP CAS 2447407.3555 13.864 1.900 1.155 990 +NP CAS 2447408.3571 13.872 1.137 990 +NP CAS 2447409.3429 13.315 1.603 1.055 990 +NP CAS 2447410.3706 13.370 1.682 1.077 990 +NP CAS 2447411.3675 13.519 1.777 1.105 990 +NP CAS 2447413.3269 13.846 1.904 1.156 990 +NP CAS 2447414.3271 13.899 1.821 1.172 990 +NP CAS 2447415.3061 13.450 1.691 1.071 990 +NP CAS 2447416.3007 13.313 1.679 1.056 990 +NP CAS 2447417.2928 13.456 1.771 1.070 990 +NP CAS 2447418.2911 13.688 1.841 1.162 990 +NP CAS 2447419.2649 13.777 1.824 1.147 990 +NP CAS 2447420.2617 13.888 1.857 1.171 990 +NP CAS 2447421.2532 13.601 1.740 1.083 990 +NP CAS 2447422.2689 13.287 1.640 1.017 990 +NP CAS 2447423.3932 13.492 1.725 1.079 990 +NP CAS 2447424.2881 13.674 1.786 1.149 990 +NP CAS 2447425.3034 13.781 1.878 1.146 990 +NP CAS 2447427.3640 13.631 1.797 1.093 990 +NP CAS 2447429.3190 13.462 1.741 1.067 990 +NP CAS 2447430.2661 13.609 1.812 1.118 990 +NP CAS 2447431.3163 13.770 1.867 1.150 990 +NP CAS 2447432.3034 13.892 1.851 1.185 990 +NP CAS 2447433.2991 13.792 1.788 1.151 990 +NP CAS 2447434.2850 13.257 1.614 1.030 990 +NP CAS 2450330.2751 13.832 1.952 1.150 971 +NP CAS 2450332.2559 13.539 1.716 1.021 971 +NP CAS 2450333.2606 13.252 1.617 1.005 971 +NP CAS 2450334.2777 13.458 1.773 1.078 971 +NP CAS 2450335.3196 13.632 1.887 1.164 971 +NP CAS 2450336.3295 13.747 1.875 1.137 971 +NP CAS 2450337.2410 13.952 1.924 1.155 971 +NP CAS 2450338.3766 13.593 1.797 1.116 971 +NP CAS 2450340.2531 13.436 1.722 1.107 971 +NP CAS 2450341.2577 13.622 1.848 1.127 971 +NP CAS 2450342.2740 13.724 1.919 1.147 971 +NP CAS 2450344.2943 13.713 1.807 1.146 971 +NP CAS 2450344.4552 13.602 1.766 1.101 971 +NP CAS 2450347.2945 13.572 1.881 1.137 971 +NP CAS 2450347.4570 13.592 1.869 1.127 971 +NY CAS 2448101.4514 13.287 .827 .559 992 +NY CAS 2448103.3618 13.204 .776 .515 992 +NY CAS 2448104.3876 13.296 .860 .525 992 +NY CAS 2448104.4191 13.323 .858 .562 992 +NY CAS 2448108.3997 13.446 .876 .573 992 +NY CAS 2448109.3688 13.053 .718 .475 992 +NY CAS 2448110.3683 13.430 .828 .581 992 +NY CAS 2448111.3818 13.395 .857 .579 992 +NY CAS 2448112.3433 13.075 .793 .497 992 +NY CAS 2448113.3334 13.473 .906 .558 992 +NY CAS 2448114.4037 13.354 .824 .581 992 +NY CAS 2448116.3996 13.537 .986 .591 992 +NY CAS 2448117.4651 .799 .486 992 +NY CAS 2448118.3966 13.283 .806 .537 992 +NY CAS 2448119.3908 13.589 .906 .592 992 +NY CAS 2448122.3663 13.517 .916 .598 992 +NY CAS 2448123.3570 13.127 .749 .519 992 +NY CAS 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2448505.3140 13.039 1.233 .806 993 +PW CAS 2448506.3267 12.886 1.233 .793 993 +PW CAS 2448507.3324 13.148 1.392 .848 993 +PW CAS 2448508.2830 13.331 1.423 .881 993 +PW CAS 2448509.3235 13.035 1.238 .793 993 +PW CAS 2448510.3061 12.871 1.245 .779 993 +PW CAS 2448511.3112 13.126 1.386 .841 993 +PW CAS 2448512.2994 13.337 1.422 .877 993 +PW CAS 2448513.3104 13.022 1.279 .795 993 +PW CAS 2448514.3153 12.877 1.203 .790 993 +PW CAS 2448515.3005 13.148 1.348 .854 993 +PW CAS 2448516.2400 13.337 1.415 .885 993 +PW CAS 2448517.3245 13.020 1.245 .786 993 +PW CAS 2448518.2400 12.851 1.244 .768 993 +PW CAS 2448519.2914 13.130 1.368 .839 993 +PW CAS 2448520.2761 13.312 1.456 .861 993 +PW CAS 2448521.3245 13.009 1.313 .774 993 +PW CAS 2448522.2813 12.864 1.171 .788 993 +PW CAS 2448523.2806 13.133 1.364 .836 993 +V342 CAS 2446253.4008 11.908 1.258 .760 987 +V342 CAS 2446255.4239 12.254 1.381 .825 987 +V342 CAS 2446256.3974 11.836 1.153 .737 987 +V342 CAS 2446257.4474 11.934 1.289 .782 987 +V342 CAS 2446258.4375 12.124 1.389 .803 987 +V342 CAS 2446259.4292 12.247 1.390 .832 987 +V342 CAS 2446260.3414 11.843 1.168 .715 987 +V342 CAS 2446261.3447 11.931 .906 1.260 .769 987 +V342 CAS 2446263.4175 12.263 1.368 .819 987 +V342 CAS 2446265.3795 11.970 .859 1.280 .793 987 +V342 CAS 2446266.3724 12.144 1.372 .824 987 +V342 CAS 2446267.4005 12.266 1.396 .821 987 +V342 CAS 2446268.3842 11.749 .791 1.146 .718 987 +V342 CAS 2446269.3559 11.981 .879 1.272 .803 987 +V342 CAS 2446270.3800 12.176 1.041 1.370 .817 987 +V342 CAS 2446272.3759 11.749 .772 1.162 .711 987 +V342 CAS 2446273.3819 11.997 1.302 .789 987 +V342 CAS 2446274.3671 12.193 1.398 .827 987 +V342 CAS 2446275.3789 12.228 1.348 .816 987 +V342 CAS 2446279.3910 12.215 1.361 .784 987 +V342 CAS 2446280.3633 11.761 1.173 .717 987 +V342 CAS 2446283.3555 12.196 1.312 .806 987 +V342 CAS 2446284.3672 11.801 1.189 .730 987 +V342 CAS 2446285.3279 12.051 1.283 .803 987 +V342 CAS 2446286.3014 12.231 1.385 .828 987 +V342 CAS 2446287.2863 12.170 1.299 .803 987 +V342 CAS 2446288.3579 11.824 1.238 .728 987 +V342 CAS 2446289.3538 12.074 1.346 .804 987 +V342 CAS 2446290.3620 12.251 1.379 .818 987 +V342 CAS 2446291.3243 12.109 1.270 .791 987 +V342 CAS 2446292.3268 11.818 1.230 .736 987 +V342 CAS 2446294.2888 12.236 1.398 .808 987 +V342 CAS 2446295.2899 12.066 1.262 .769 987 +V342 CAS 2446296.2816 11.841 1.228 .744 987 +V342 CAS 2446297.2707 12.093 1.363 .811 987 +V342 CAS 2446298.3078 12.242 1.388 .841 987 +V342 CAS 2446299.2756 12.031 1.247 .776 987 +V342 CAS 2446300.2727 11.881 1.219 .749 987 +V342 CAS 2446301.3173 12.116 1.370 .806 987 +V342 CAS 2446302.2922 12.241 1.411 .818 987 +V342 CAS 2446303.2916 11.929 1.228 .747 987 +V342 CAS 2446304.2760 11.886 1.258 .753 987 +V342 CAS 2449956.3923 12.002 1.480 998 +V342 CAS 2449957.4235 11.956 1.488 998 +V342 CAS 2449959.3873 1.574 998 +V342 CAS 2449960.3586 11.997 1.461 998 +V342 CAS 2449962.4047 12.202 1.617 998 +V342 CAS 2449985.3109 12.002 1.525 998 +V342 CAS 2449986.3260 12.195 1.592 998 +V342 CAS 2449987.3652 12.230 1.576 998 +V342 CAS 2449992.3745 11.859 1.469 998 +V342 CAS 2450007.3549 11.949 1.469 998 +V342 CAS 2450009.3495 12.109 1.568 998 +V342 CAS 2450011.3122 11.948 1.479 998 +V342 CAS 2450017.2893 12.147 1.587 998 +V342 CAS 2450018.3089 12.264 1.564 998 +V342 CAS 2450019.2968 11.812 1.447 998 +V342 CAS 2450020.2456 11.922 1.497 998 +V379 CAS 2450306.4345 9.291 1.237 .687 971 +V379 CAS 2450307.3430 9.176 1.186 .700 971 +V379 CAS 2450310.4477 9.233 1.199 .724 971 +V379 CAS 2450311.4579 9.255 1.148 .738 971 +V379 CAS 2450312.4355 8.977 1.119 .663 971 +V379 CAS 2450314.4095 9.201 1.183 .708 971 +V379 CAS 2450315.3695 9.251 1.221 .736 971 +V379 CAS 2450316.3709 9.098 1.124 .713 971 +V379 CAS 2450317.3854 8.947 1.080 .644 971 +V379 CAS 2450318.3799 9.149 1.168 .700 971 +V379 CAS 2450319.3790 9.253 1.216 971 +V379 CAS 2450320.3791 9.178 1.174 .682 971 +V379 CAS 2450321.3713 8.916 1.068 .665 971 +V379 CAS 2450322.3672 9.098 1.147 .684 971 +V379 CAS 2450323.3589 9.277 1.218 .675 971 +V379 CAS 2450325.3425 9.003 1.099 .647 971 +V379 CAS 2450326.2643 9.028 1.087 .648 971 +V379 CAS 2450327.3253 9.164 1.198 .692 971 +V379 CAS 2450328.4033 9.247 1.223 .680 971 +V379 CAS 2450330.3000 8.986 1.067 .631 971 +V379 CAS 2450332.2790 9.259 1.198 .669 971 +V379 CAS 2450333.2817 9.175 1.146 971 +V379 CAS 2450334.3045 8.942 1.054 .669 971 +V379 CAS 2450335.3311 9.094 1.160 .706 971 +V379 CAS 2450336.3556 9.233 1.199 .697 971 +V379 CAS 2450337.2632 9.245 1.201 .695 971 +V379 CAS 2450338.3913 8.968 1.082 .634 971 +V379 CAS 2450340.2692 9.224 1.167 .739 971 +V379 CAS 2450341.2721 9.272 1.191 .701 971 +V379 CAS 2450342.2991 9.046 1.136 .646 971 +V379 CAS 2450344.3037 9.143 1.196 .727 971 +V379 CAS 2450347.2551 8.924 1.072 .628 971 +V379 CAS 2450349.2253 9.240 1.216 .707 971 +V379 CAS 2450357.2234 9.172 1.176 .684 971 +V395 CAS 2449959.4752 11.023 1.394 998 +V395 CAS 2449960.4692 10.493 1.193 998 +V395 CAS 2449962.4805 10.937 1.450 998 +V395 CAS 2449985.4358 10.599 1.277 998 +V395 CAS 2449986.4096 10.891 1.395 998 +V395 CAS 2449987.4378 11.003 1.391 998 +V395 CAS 2449992.4703 10.653 1.244 998 +V395 CAS 2450008.4726 10.675 1.238 998 +V395 CAS 2450009.3923 10.544 1.221 998 +V395 CAS 2450010.3930 10.805 1.414 998 +V395 CAS 2450011.3613 10.959 1.396 998 +V395 CAS 2450017.3525 10.544 1.219 998 +V395 CAS 2450018.3761 10.794 1.316 998 +V395 CAS 2450019.3424 10.931 1.233 .721 1.392 998 +V395 CAS 2450020.3102 10.848 1.315 998 +V395 CAS 2448102.4258 10.749 1.152 .661 992 +V395 CAS 2448103.3834 10.448 1.016 .606 992 +V395 CAS 2448104.4341 10.729 1.174 .692 992 +V395 CAS 2448111.3982 10.433 1.006 .620 992 +V395 CAS 2448112.3617 10.700 1.173 .683 992 +V395 CAS 2448113.3523 10.919 1.247 .721 992 +V395 CAS 2448114.4160 10.924 1.214 .724 992 +V395 CAS 2448116.4206 10.704 1.222 .685 992 +V395 CAS 2448118.4072 10.948 1.209 .706 992 +V395 CAS 2448119.4113 10.460 1.015 .623 992 +V395 CAS 2448122.4185 10.957 1.204 .707 992 +V395 CAS 2448123.3821 10.412 .989 .602 992 +V395 CAS 2448126.3568 10.951 1.246 .703 992 +V395 CAS 2448127.3192 10.409 1.006 .604 992 +V407 CAS 2446253.4536 12.182 1.416 .842 987 +V407 CAS 2446255.4400 11.760 1.211 .747 987 +V407 CAS 2446256.4151 11.942 1.337 .794 987 +V407 CAS 2446257.4365 12.121 1.424 .824 987 +V407 CAS 2446258.4193 12.198 1.449 .841 987 +V407 CAS 2446259.4166 11.875 1.247 .760 987 +V407 CAS 2446260.3580 11.830 1.282 .748 987 +V407 CAS 2446261.3582 12.041 1.374 .813 987 +V407 CAS 2446263.4299 12.151 1.380 .814 987 +V407 CAS 2446265.4045 11.935 1.334 .800 987 +V407 CAS 2446266.3829 12.089 1.419 .829 987 +V407 CAS 2446267.4137 12.218 1.451 .838 987 +V407 CAS 2446268.3984 11.936 1.295 .770 987 +V407 CAS 2446269.3683 11.798 1.275 .748 987 +V407 CAS 2446270.3938 11.993 1.388 .805 987 +V407 CAS 2446272.3884 12.196 1.415 .822 987 +V407 CAS 2446273.4190 11.765 1.225 .738 987 +V407 CAS 2446274.3994 11.898 1.314 .773 987 +V407 CAS 2446275.3912 12.081 1.421 .833 987 +V407 CAS 2446279.4107 11.969 1.399 .784 987 +V407 CAS 2446280.3792 12.122 1.425 .812 987 +V407 CAS 2446283.3724 11.861 1.305 .762 987 +V553 CAS 2450312.2153 13.739 927 +V553 CAS 2450312.4655 1.134 927 +V553 CAS 2450314.2106 13.038 1.592 .949 927 +V553 CAS 2450314.3009 13.126 1.534 1.025 927 +V553 CAS 2450314.4355 13.163 1.597 1.022 927 +V553 CAS 2450315.2670 13.356 1.763 927 +V553 CAS 2450315.3500 13.348 1.802 1.081 927 +V553 CAS 2450315.3930 13.369 1.786 1.073 927 +V553 CAS 2450316.1790 13.498 927 +V553 CAS 2450316.3874 13.566 1.905 1.112 927 +V553 CAS 2450317.2735 13.740 1.908 1.093 927 +V553 CAS 2450317.3553 13.713 1.983 1.076 927 +V553 CAS 2450317.4068 13.737 1.982 1.117 927 +V553 CAS 2450318.2191 13.360 1.649 1.023 927 +V553 CAS 2450318.3108 13.295 1.615 .988 927 +V553 CAS 2450318.4024 13.187 1.620 .978 927 +V553 CAS 2450319.2132 13.090 1.526 .951 927 +V553 CAS 2450319.3402 13.136 1.651 .983 927 +V553 CAS 2450319.4169 13.132 1.670 .990 927 +V553 CAS 2450320.3198 13.405 1.790 1.062 927 +V553 CAS 2450320.4014 13.400 1.804 1.068 927 +V553 CAS 2450321.1947 13.538 1.083 927 +V553 CAS 2450321.4214 13.576 1.886 1.098 927 +V553 CAS 2450322.2075 13.715 1.069 927 +V553 CAS 2450322.4710 13.783 1.840 1.131 927 +V553 CAS 2450323.2221 13.285 1.605 .992 927 +V553 CAS 2450323.4582 13.031 1.593 .933 927 +V553 CAS 2450324.2215 13.148 .952 927 +V553 CAS 2450324.4056 13.178 .999 927 +V553 CAS 2450325.1947 13.363 1.054 927 +V553 CAS 2450325.3389 13.423 1.740 1.082 927 +V636 CAS 2450008.5231 7.184 1.520 998 +V636 CAS 2450009.4743 7.269 1.525 998 +V636 CAS 2450010.4681 7.277 1.561 998 +V636 CAS 2450011.4305 7.252 1.523 998 +V636 CAS 2450017.4357 7.214 1.523 998 +V636 CAS 2450018.4512 7.276 1.497 998 +V636 CAS 2450019.4138 7.246 1.529 998 +V636 CAS 2450020.3735 7.182 1.495 998 +V636 CAS 2449617.3294 7.275 1.398 .782 995 +V636 CAS 2449620.3872 7.104 .984 1.334 .737 995 +V636 CAS 2449620.4528 7.125 1.006 1.312 .783 995 +V636 CAS 2449621.3879 7.126 .961 1.341 .758 995 +V636 CAS 2449622.4388 7.171 1.059 1.360 .775 995 +V636 CAS 2449622.4763 7.166 1.373 .776 995 +V636 CAS 2449622.5105 7.181 1.022 1.371 .762 995 +V636 CAS 2449623.3934 7.221 1.392 .776 995 +V636 CAS 2449623.4602 7.224 1.112 1.396 .780 995 +V636 CAS 2449623.5082 7.228 1.059 1.377 .792 995 +V636 CAS 2449624.3227 1.419 995 +V636 CAS 2449624.4496 7.281 1.107 1.423 .792 995 +V636 CAS 2449624.4791 7.283 1.402 .797 995 +V636 CAS 2449624.5083 1.428 995 +V636 CAS 2449625.3611 7.295 1.075 1.418 .790 995 +V636 CAS 2449625.4602 7.307 1.061 1.417 .791 995 +V636 CAS 2449625.4834 7.284 1.413 .794 995 +V636 CAS 2449625.5031 7.287 1.083 1.416 .795 995 +V636 CAS 2449631.3486 7.207 1.388 .786 995 +V636 CAS 2449632.3557 7.279 1.102 1.399 .794 995 +V636 CAS 2449632.4441 7.277 1.106 1.411 .795 995 +V636 CAS 2449632.4799 7.282 1.406 .791 995 +V636 CAS 2449633.3269 7.289 1.108 1.429 .782 995 +V636 CAS 2449633.4016 7.302 1.119 1.407 .789 995 +V636 CAS 2449633.4551 7.310 1.087 1.400 .800 995 +V636 CAS 2449634.3381 7.261 1.088 1.375 .786 995 +V636 CAS 2449635.3826 7.201 1.347 .770 995 +V636 CAS 2449635.4610 7.197 1.349 .767 995 +V636 CAS 2450305.4022 7.184 1.380 .809 971 +V636 CAS 2450306.4203 7.168 1.371 .763 971 +V636 CAS 2450310.4777 7.196 1.438 .811 971 +V636 CAS 2450312.4807 7.276 1.416 .823 971 +V636 CAS 2450314.4530 7.124 1.361 .776 971 +V636 CAS 2450315.4117 7.065 1.355 .740 971 +V636 CAS 2450316.4080 7.116 1.344 .785 971 +V636 CAS 2450317.4446 7.095 1.381 .779 971 +V636 CAS 2450318.4265 7.177 1.399 .798 971 +V636 CAS 2450319.4409 7.222 1.423 .785 971 +V636 CAS 2450320.4233 7.259 1.415 .808 971 +V636 CAS 2450321.4413 7.206 1.389 .776 971 +V636 CAS 2450322.4010 7.176 1.372 .780 971 +V636 CAS 2450323.3994 7.112 1.361 .781 971 +V636 CAS 2450324.3775 7.084 1.365 .769 971 +V636 CAS 2450325.3618 7.103 1.365 .776 971 +V636 CAS 2450326.3045 7.167 1.383 .787 971 +V636 CAS 2450327.3688 7.221 1.438 .791 971 +V636 CAS 2450328.4046 7.260 1.456 .781 971 +V636 CAS 2450330.3243 7.193 1.370 .785 971 +V636 CAS 2450332.2870 7.101 1.332 .716 971 +V636 CAS 2450333.2891 7.103 1.357 971 +V636 CAS 2450334.3144 7.145 1.363 .823 971 +V636 CAS 2450335.3325 7.205 1.416 .820 971 +V636 CAS 2450336.3611 7.267 1.415 .802 971 +V636 CAS 2450337.2689 7.267 1.428 .793 971 +V636 CAS 2450338.3960 7.218 1.400 .782 971 +V636 CAS 2450340.2759 7.127 1.342 .743 971 +V636 CAS 2450341.2737 7.111 1.330 .774 971 +V636 CAS 2450342.3010 7.082 1.374 .759 971 +V636 CAS 2450344.3050 7.225 1.432 .835 971 +V636 CAS 2450347.2559 7.188 1.376 .772 971 +V636 CAS 2450349.2317 7.084 1.340 .763 971 +V CEN 2449802.8858 6.616 .786 .490 .480 997 +V CEN 2449803.8830 6.883 .938 .571 .527 997 +V CEN 2449804.8600 7.057 1.023 .584 .570 997 +V CEN 2449805.8364 7.209 1.068 .598 .579 997 +V CEN 2449807.8461 6.487 .706 .435 .470 997 +V CEN 2449808.8515 6.746 .862 .522 .509 997 +V CEN 2449809.7699 6.930 .967 .571 .544 997 +V CEN 2449809.8225 6.929 .977 .564 .550 997 +V CEN 2449810.8179 7.126 1.053 .598 .567 997 +V CEN 2449811.7779 7.211 1.039 .595 .568 997 +V CEN 2449811.8106 7.201 1.031 .592 .565 997 +V CEN 2449813.7911 6.591 .783 .484 .482 997 +V CEN 2449813.8379 6.599 .798 .479 .479 997 +V CEN 2449814.8002 6.832 .924 .547 .521 997 +V CEN 2449815.7550 7.006 1.009 .583 .555 997 +V CEN 2449815.8030 7.016 1.006 .585 .562 997 +V CEN 2449817.7351 6.977 .889 .546 .523 997 +V CEN 2449817.8265 6.910 .860 .532 .514 997 +V CEN 2449818.7471 6.470 .695 .446 .436 997 +V CEN 2449818.8117 6.462 .694 .438 .434 997 +V CEN 2449821.7385 7.091 1.044 .595 .560 997 +V CEN 2449821.8099 7.111 1.054 .600 .573 997 +V CEN 2449822.7333 7.203 1.044 .593 .573 997 +V CEN 2449822.8036 7.181 1.034 .589 .571 997 +V CEN 2449823.7239 6.534 .697 .450 .455 997 +V CEN 2449823.7960 6.471 .687 .435 .436 997 +V CEN 2449825.7161 6.823 .920 .546 .524 997 +V CEN 2449825.7809 6.829 .919 .550 .533 997 +V CEN 2449826.7494 6.991 1.011 .575 .546 997 +V CEN 2449827.7033 7.188 1.058 .603 .569 997 +V CEN 2450350.5462 7.151 1.156 999 +V CEN 2450351.4938 6.423 .848 999 +V CEN 2450352.5490 1.024 999 +V CEN 2450353.4810 6.883 1.105 999 +V CEN 2450354.4973 7.082 1.160 999 +V CEN 2450355.4820 7.215 1.176 999 +V CEN 2450357.4813 6.524 .921 999 +V CEN 2450357.5443 6.553 .973 999 +V CEN 2450358.4817 6.785 1.056 999 +V CEN 2450358.5520 6.792 1.060 999 +V CEN 2450359.4828 6.940 1.115 999 +V CEN 2450360.4824 7.156 1.171 999 +V CEN 2450361.4831 7.117 1.160 999 +V CEN 2450362.4809 6.414 .853 999 +V CEN 2450363.4838 6.661 .994 999 +V CEN 2450379.4941 6.519 .968 999 +V CEN 2450542.6471 7.154 1.133 972 +V CEN 2450568.5010 7.017 1.130 972 +V CEN 2450570.4256 6.955 1.061 972 +V CEN 2450572.3863 6.694 1.030 972 +V CEN 2450572.5126 6.720 1.040 972 +V CEN 2450573.3749 6.898 1.112 972 +V CEN 2450573.4622 6.905 1.097 972 +V CEN 2450573.5559 6.913 1.104 972 +V CEN 2450574.4918 7.117 1.170 972 +V CEN 2450575.3585 7.231 1.190 972 +V CEN 2450575.4567 7.200 1.150 972 +V CEN 2450575.5452 7.175 1.136 972 +V CEN 2450576.3624 6.565 .911 972 +V CEN 2450576.5059 6.460 .880 972 +V CEN 2450576.5695 6.437 .874 972 +V CEN 2450577.5139 6.604 .974 972 +V CEN 2450577.5751 6.619 .978 972 +V CEN 2450578.3914 6.813 1.072 972 +V CEN 2450578.4631 6.827 1.082 972 +V CEN 2450578.5584 6.850 1.092 972 +V CEN 2450579.5139 7.013 1.149 972 +V CEN 2450579.5504 7.023 1.154 972 +V CEN 2450580.4296 7.185 1.175 972 +V CEN 2450580.5117 7.192 1.173 972 +V CEN 2450582.4539 6.442 .876 972 +V CEN 2450582.5382 6.462 .885 972 +V CEN 2450583.4548 6.720 1.024 972 +V CEN 2450583.5795 6.725 1.030 972 +V CEN 2450584.4173 6.910 1.107 972 +V CEN 2450584.4872 6.913 1.108 972 +TX CEN 2449520.7166 10.730 1.958 996 +TX CEN 2449521.6939 10.856 1.988 996 +TX CEN 2449522.6258 10.845 1.974 996 +TX CEN 2449526.6640 10.891 1.973 1.026 .920 996 +TX CEN 2449528.6801 10.764 1.850 .985 .916 996 +TX CEN 2449529.6491 10.245 1.522 .892 .839 996 +TX CEN 2449530.6744 9.954 1.461 .844 .792 996 +TX CEN 2449532.6293 10.153 1.621 .875 .889 996 +TX CEN 2449534.6626 10.353 1.807 .954 .900 996 +TX CEN 2449535.6311 10.530 1.799 996 +TX CEN 2449536.6235 10.641 1.847 .922 996 +TX CEN 2449543.6282 10.933 1.945 1.034 .946 996 +TX CEN 2449544.5602 10.808 1.847 1.003 .936 996 +TX CEN 2449545.5718 10.815 1.847 1.020 .927 996 +TX CEN 2449558.6189 11.102 2.058 1.098 .980 996 +TX CEN 2449560.6534 1.064 .964 996 +TX CEN 2449563.5865 10.493 1.607 996 +TX CEN 2449564.5712 9.916 1.397 .818 .831 996 +TX CEN 2450568.5028 11.092 2.081 972 +TX CEN 2450570.4268 10.761 1.980 972 +TX CEN 2450572.3873 10.413 1.852 972 +TX CEN 2450572.5133 10.286 1.789 972 +TX CEN 2450573.3755 9.902 1.681 972 +TX CEN 2450573.4628 9.901 1.675 972 +TX CEN 2450573.5564 9.911 1.669 972 +TX CEN 2450574.4923 10.005 1.737 972 +TX CEN 2450575.3592 10.121 1.819 972 +TX CEN 2450575.4574 10.124 1.796 972 +TX CEN 2450575.5458 10.114 1.796 972 +TX CEN 2450576.4493 10.223 1.868 972 +TX CEN 2450576.5064 10.235 1.875 972 +TX CEN 2450576.5701 10.252 1.891 972 +TX CEN 2450577.5151 10.327 1.925 972 +TX CEN 2450577.5775 10.324 1.916 972 +TX CEN 2450578.3919 10.433 1.961 972 +TX CEN 2450578.4636 10.428 1.963 972 +TX CEN 2450578.5590 10.435 1.963 972 +TX CEN 2450579.5143 10.538 2.010 972 +TX CEN 2450579.5510 10.550 2.016 972 +TX CEN 2450580.4304 10.633 2.028 972 +TX CEN 2450580.5556 10.658 2.051 972 +TX CEN 2450582.4545 10.872 2.087 972 +TX CEN 2450582.5389 10.874 2.074 972 +TX CEN 2450583.4556 10.977 2.100 972 +TX CEN 2450583.5802 10.959 2.089 972 +TX CEN 2450584.4182 11.025 2.095 972 +TX CEN 2450584.4957 11.010 2.085 972 +UZ CEN 2449520.6567 9.064 .919 996 +UZ CEN 2449521.6323 8.530 .648 996 +UZ CEN 2449522.5795 8.634 .698 996 +UZ CEN 2449526.6181 8.762 .495 .810 .456 .475 996 +UZ CEN 2449528.5729 8.222 .344 .510 .338 .355 996 +UZ CEN 2449529.5742 8.763 .785 .465 .478 996 +UZ CEN 2449530.5887 9.076 .577 .893 .531 .500 996 +UZ CEN 2449532.5834 8.639 .731 .429 .442 996 +UZ CEN 2449534.5873 9.047 .529 .864 .506 .496 996 +UZ CEN 2449535.5894 8.310 .380 .575 .293 .312 996 +UZ CEN 2449536.5791 8.862 .435 .808 .425 .453 996 +UZ CEN 2449536.5842 8.865 .428 .804 .417 .447 996 +UZ CEN 2449542.5549 8.487 .405 .648 .405 .456 996 +UZ CEN 2449543.5578 8.911 .521 .882 .472 .506 996 +UZ CEN 2449544.5304 9.129 .585 .919 .492 .520 996 +UZ CEN 2449545.5181 8.401 .412 .586 .390 .388 996 +UZ CEN 2449546.5290 8.869 .860 .462 .492 996 +UZ CEN 2449558.5497 8.574 .391 .647 .395 .424 996 +UZ CEN 2449561.5680 8.712 .373 .695 .433 .427 996 +UZ CEN 2449564.5367 8.963 .841 996 +UZ CEN 2449803.8269 9.069 .883 .529 .505 997 +UZ CEN 2449804.8209 8.878 .773 .455 .458 997 +UZ CEN 2449805.7916 8.559 .660 .398 .406 997 +UZ CEN 2449807.8006 9.054 .879 .498 .501 997 +UZ CEN 2449808.7926 8.235 .498 .328 .347 997 +UZ CEN 2449808.8000 8.222 .510 .317 .336 997 +UZ CEN 2449809.7206 8.775 .777 .466 .462 997 +UZ CEN 2449809.7998 8.799 .808 .475 .470 997 +UZ CEN 2449810.7750 9.064 .895 .512 .519 997 +UZ CEN 2449811.7445 8.559 .630 .390 .405 997 +UZ CEN 2449812.7530 8.722 .729 .455 .455 997 +UZ CEN 2449813.7574 8.906 .833 .491 .478 997 +UZ CEN 2449813.8200 8.921 .836 .490 .476 997 +UZ CEN 2449814.7625 8.989 .833 .482 .478 997 +UZ CEN 2449815.7105 8.357 .551 .363 .347 997 +UZ CEN 2449815.7871 8.380 .587 .364 .365 997 +UZ CEN 2449817.6954 9.125 .885 .521 .506 997 +UZ CEN 2449817.7997 9.131 .918 .520 .508 997 +UZ CEN 2449818.6983 8.325 .540 .353 .370 997 +UZ CEN 2449818.7775 8.384 .574 .362 .362 997 +UZ CEN 2449821.7092 8.791 .715 .443 .447 997 +UZ CEN 2449821.7730 8.752 .721 .434 .437 997 +UZ CEN 2449822.6829 8.542 .668 .403 .411 997 +UZ CEN 2449822.7867 8.567 .683 .415 .440 997 +UZ CEN 2449823.6788 8.903 .830 .491 .486 997 +UZ CEN 2449823.7791 8.921 .868 .486 .487 997 +UZ CEN 2449825.6652 8.384 .578 .365 .379 997 +UZ CEN 2449825.7597 8.448 .617 .389 .394 997 +UZ CEN 2449826.7138 8.898 .853 .486 .490 997 +UZ CEN 2449827.6640 9.057 .879 .504 .494 997 +UZ CEN 2450384.8559 9.011 .849 .995 999 +UZ CEN 2450386.8491 8.836 .844 .965 999 +UZ CEN 2450387.8485 9.083 .912 1.024 999 +UZ CEN 2450388.8463 8.373 .733 999 +UZ CEN 2450390.8388 8.933 .862 .974 999 +UZ CEN 2450391.8333 8.854 .776 .914 999 +UZ CEN 2450392.8365 8.466 .643 .798 999 +UZ CEN 2450393.8443 8.896 .861 .991 999 +UZ CEN 2450394.8305 9.086 .889 1.011 999 +UZ CEN 2450542.5843 8.575 .421 .825 972 +UZ CEN 2450568.4532 8.934 .482 .948 972 +UZ CEN 2450570.3807 8.844 .486 .958 972 +UZ CEN 2450572.3409 8.333 .345 .717 972 +UZ CEN 2450573.3353 8.822 .488 .956 972 +UZ CEN 2450573.4280 8.833 .473 .946 972 +UZ CEN 2450574.4582 9.004 .498 .993 972 +UZ CEN 2450575.3244 8.713 .425 .852 972 +UZ CEN 2450575.4200 8.649 .424 .837 972 +UZ CEN 2450576.3274 8.593 .428 .856 972 +UZ CEN 2450576.4174 8.605 .420 .861 972 +UZ CEN 2450576.4754 8.634 .438 .875 972 +UZ CEN 2450578.3607 9.090 .511 .998 972 +UZ CEN 2450578.4373 9.066 .501 .977 972 +UZ CEN 2450579.4399 8.502 .401 .818 972 +UZ CEN 2450580.3401 8.924 .501 .988 972 +UZ CEN 2450580.4057 8.941 .508 .991 972 +UZ CEN 2450582.3566 8.509 .397 .785 972 +UZ CEN 2450582.4249 8.519 .393 .796 972 +UZ CEN 2450583.3497 8.785 .472 .929 972 +UZ CEN 2450583.4218 8.792 .470 .929 972 +UZ CEN 2450584.3410 8.954 .492 .974 972 +UZ CEN 2450584.4041 8.960 .501 .972 972 +VW CEN 2450542.6368 10.490 1.626 972 +VW CEN 2450568.4910 10.013 1.454 972 +VW CEN 2450570.4141 10.208 1.533 972 +VW CEN 2450572.5035 10.468 1.616 972 +VW CEN 2450573.3683 10.587 1.664 972 +VW CEN 2450573.4485 10.575 1.633 972 +VW CEN 2450573.5513 10.583 1.631 972 +VW CEN 2450574.4844 10.700 1.654 972 +VW CEN 2450575.3431 10.742 1.651 972 +VW CEN 2450575.4438 10.732 1.617 972 +VW CEN 2450575.5382 10.725 1.646 972 +VW CEN 2450576.3555 10.689 1.629 972 +VW CEN 2450576.4381 10.685 1.618 972 +VW CEN 2450576.4982 10.678 1.636 972 +VW CEN 2450577.5075 10.493 1.558 972 +VW CEN 2450578.3827 10.406 1.520 972 +VW CEN 2450578.4571 10.389 1.500 972 +VW CEN 2450578.5518 10.402 1.499 972 +VW CEN 2450580.4245 9.706 1.260 972 +VW CEN 2450580.5061 9.709 1.263 972 +VW CEN 2450582.4487 9.877 1.370 972 +VW CEN 2450582.4843 9.881 1.370 972 +VW CEN 2450583.4501 10.033 1.466 972 +VW CEN 2450584.4121 10.112 1.492 972 +VW CEN 2450584.4831 10.127 1.506 972 +XX CEN 2450351.4960 8.220 1.229 999 +XX CEN 2450353.4835 7.828 1.053 999 +XX CEN 2450354.4982 7.703 1.006 999 +XX CEN 2450355.4837 7.378 .902 999 +XX CEN 2450357.4830 7.645 1.063 999 +XX CEN 2450358.4869 7.770 1.118 999 +XX CEN 2450359.4849 7.913 1.171 999 +XX CEN 2450360.4868 8.094 1.242 999 +XX CEN 2450361.4864 8.263 1.310 999 +XX CEN 2450362.4837 8.203 1.232 999 +XX CEN 2450363.4878 8.069 1.140 999 +XX CEN 2450542.6430 7.443 .955 972 +XX CEN 2450568.4945 8.086 1.226 972 +XX CEN 2450570.4171 8.211 1.215 972 +XX CEN 2450572.3799 7.886 1.078 972 +XX CEN 2450572.5074 7.842 1.059 972 +XX CEN 2450573.3706 7.709 1.013 972 +XX CEN 2450573.4571 7.689 .994 972 +XX CEN 2450573.5537 7.692 1.012 972 +XX CEN 2450574.4877 7.389 .909 972 +XX CEN 2450575.3461 7.400 .948 972 +XX CEN 2450575.4474 7.435 .936 972 +XX CEN 2450575.5409 7.453 .952 972 +XX CEN 2450576.3583 7.645 1.065 972 +XX CEN 2450576.4408 7.655 1.060 972 +XX CEN 2450576.5010 7.649 1.060 972 +XX CEN 2450577.5101 7.792 1.121 972 +XX CEN 2450578.3861 7.915 1.178 972 +XX CEN 2450578.4596 7.925 1.183 972 +XX CEN 2450578.5545 7.948 1.187 972 +XX CEN 2450579.5103 8.088 1.232 972 +XX CEN 2450580.4264 8.203 1.248 972 +XX CEN 2450580.5080 8.219 1.251 972 +XX CEN 2450582.4506 8.067 1.154 972 +XX CEN 2450582.4866 8.056 1.151 972 +XX CEN 2450583.4521 7.857 1.068 972 +XX CEN 2450584.4141 7.711 1.005 972 +XX CEN 2450584.4854 7.709 1.021 972 +AY CEN 2450568.4274 9.045 .627 1.210 972 +AY CEN 2450570.3649 8.525 .506 .987 972 +AY CEN 2450572.3338 8.875 .618 1.190 972 +AY CEN 2450573.3278 8.998 .625 972 +AY CEN 2450573.4227 9.010 .627 1.207 972 +AY CEN 2450574.4515 9.026 .603 1.169 972 +AY CEN 2450575.3197 8.623 .516 1.030 972 +AY CEN 2450575.4167 8.572 .513 1.002 972 +AY CEN 2450576.3223 8.623 .531 1.062 972 +AY CEN 2450576.4125 8.646 .554 1.085 972 +AY CEN 2450578.3548 8.974 .616 1.206 972 +AY CEN 2450578.4321 8.988 .617 1.201 972 +AY CEN 2450579.4342 9.094 .633 1.219 972 +AY CEN 2450580.3325 8.762 .546 1.076 972 +AY CEN 2450580.4012 8.732 .537 1.054 972 +AY CEN 2450582.3517 8.769 .566 1.127 972 +AY CEN 2450582.4190 8.779 .581 1.123 972 +AY CEN 2450583.3449 8.927 .611 1.193 972 +AY CEN 2450583.4140 8.936 .619 1.191 972 +AY CEN 2450584.3365 9.062 .622 1.213 972 +AY CEN 2450584.3988 9.062 .632 1.216 972 +AZ CEN 2449520.6263 8.420 .581 996 +AZ CEN 2449521.6030 8.581 .679 996 +AZ CEN 2449522.5660 8.756 .709 996 +AZ CEN 2449526.5914 8.488 .609 .390 .376 996 +AZ CEN 2449528.5653 8.744 .757 .435 .401 996 +AZ CEN 2449529.5643 8.589 .659 .399 .414 996 +AZ CEN 2449530.5834 8.434 .625 .380 .376 996 +AZ CEN 2449532.5809 8.643 .710 .395 .409 996 +AZ CEN 2449534.5839 8.707 .725 .433 .408 996 +AZ CEN 2449535.5808 8.728 .704 .363 .403 996 +AZ CEN 2449542.5485 8.552 .664 .392 .386 996 +AZ CEN 2449543.5512 8.479 .618 .395 .381 996 +AZ CEN 2449544.5208 8.729 .772 .426 .425 996 +AZ CEN 2449545.5120 8.647 .661 .414 .396 996 +AZ CEN 2449546.5243 8.439 .598 .379 .373 996 +AZ CEN 2449558.5619 8.523 .647 .378 .377 996 +AZ CEN 2449559.4698 8.461 .636 .358 .388 996 +AZ CEN 2449561.5394 8.648 .666 .400 .390 996 +AZ CEN 2449563.4719 8.720 .715 .417 .429 996 +AZ CEN 2449564.5318 8.750 .712 996 +AZ CEN 2449803.8188 8.588 .637 .395 .394 997 +AZ CEN 2449804.8147 8.806 .751 .441 .427 997 +AZ CEN 2449805.7829 8.627 .644 .385 .388 997 +AZ CEN 2449807.7944 8.759 .726 .428 .430 997 +AZ CEN 2449808.7865 8.711 .688 .413 .397 997 +AZ CEN 2449809.7142 .585 .363 .378 997 +AZ CEN 2449809.7946 8.465 .344 .358 997 +AZ CEN 2449810.7634 8.706 .710 .417 .415 997 +AZ CEN 2449811.7388 8.778 .720 .432 .418 997 +AZ CEN 2449812.7235 8.475 .595 .359 .364 997 +AZ CEN 2449813.7520 8.629 .677 .404 .399 997 +AZ CEN 2449814.7591 8.796 .733 .425 .424 997 +AZ CEN 2449815.7075 8.530 .607 .378 .351 997 +AZ CEN 2449815.7842 8.496 .615 .359 .353 997 +AZ CEN 2449817.6896 8.798 .738 .440 .419 997 +AZ CEN 2449817.7962 8.809 .750 .448 .429 997 +AZ CEN 2449818.6944 8.594 .641 .393 .392 997 +AZ CEN 2449818.7754 8.592 .637 .387 .368 997 +AZ CEN 2449821.6881 8.680 .664 .409 .394 997 +AZ CEN 2449821.7690 8.642 .668 .394 .396 997 +AZ CEN 2449822.6796 8.466 .594 .359 .363 997 +AZ CEN 2449822.7827 8.477 .594 .377 .376 997 +AZ CEN 2449823.6757 8.716 .710 .426 .410 997 +AZ CEN 2449823.7755 8.735 .734 .425 .412 997 +AZ CEN 2449824.6660 8.743 .707 .418 .410 997 +AZ CEN 2449825.6617 8.454 .589 .355 .361 997 +AZ CEN 2449825.7560 8.463 .580 .363 .359 997 +AZ CEN 2449826.7119 8.659 .698 .405 .404 997 +AZ CEN 2449827.6626 8.770 .752 .431 .402 997 +AZ CEN 2450379.8634 8.751 .848 999 +AZ CEN 2450380.8802 8.573 .783 999 +AZ CEN 2450381.8609 8.515 .755 999 +AZ CEN 2450384.8495 8.437 .734 999 +AZ CEN 2450386.8416 8.714 .830 999 +AZ CEN 2450387.8501 8.437 .729 999 +AZ CEN 2450388.8485 8.642 .817 999 +AZ CEN 2450390.8410 8.453 .718 999 +AZ CEN 2450391.8341 8.582 .794 999 +AZ CEN 2450392.8410 8.765 .847 999 +AZ CEN 2450393.8452 8.515 .766 999 +AZ CEN 2450394.8313 8.523 .773 999 +AZ CEN 2450541.5694 8.534 .762 972 +AZ CEN 2450542.5714 8.533 .763 972 +AZ CEN 2450568.4251 8.567 .790 972 +AZ CEN 2450570.3638 8.557 .752 972 +AZ CEN 2450572.3330 8.764 .863 972 +AZ CEN 2450573.3272 8.651 .805 972 +AZ CEN 2450573.4219 8.609 .785 972 +AZ CEN 2450574.4507 8.471 .742 972 +AZ CEN 2450575.3190 8.717 .849 972 +AZ CEN 2450575.4160 8.731 .844 972 +AZ CEN 2450576.3216 8.734 .840 972 +AZ CEN 2450576.4119 8.702 .836 972 +AZ CEN 2450578.3536 8.670 .844 972 +AZ CEN 2450578.4313 8.685 .828 972 +AZ CEN 2450579.4336 8.762 .849 972 +AZ CEN 2450580.3319 8.485 .729 972 +AZ CEN 2450580.4005 8.475 .723 972 +AZ CEN 2450582.3511 8.800 .862 972 +AZ CEN 2450582.4184 8.787 .856 972 +AZ CEN 2450583.3443 8.537 .758 972 +AZ CEN 2450583.4133 8.511 .749 972 +AZ CEN 2450584.3358 8.543 .762 972 +AZ CEN 2450584.3983 8.564 .774 972 +BB CEN 2449520.6639 10.043 .972 996 +BB CEN 2449521.6359 9.888 .875 996 +BB CEN 2449522.5826 10.133 .986 996 +BB CEN 2449526.6357 10.166 1.024 .576 996 +BB CEN 2449528.6265 10.041 .948 .570 .556 996 +BB CEN 2449529.5993 9.869 .903 .539 .520 996 +BB CEN 2449530.6060 .726 1.062 .610 996 +BB CEN 2449532.5915 10.066 .940 .574 .550 996 +BB CEN 2449534.6040 10.186 .694 1.053 .613 .569 996 +BB CEN 2449536.5942 10.074 .574 .944 996 +BB CEN 2449542.5693 .702 1.028 .611 996 +BB CEN 2449543.5746 10.262 .707 1.085 .594 .597 996 +BB CEN 2449544.5445 10.050 .597 .964 .592 .527 996 +BB CEN 2449545.5376 9.842 .443 .890 .545 .515 996 +BB CEN 2449546.5397 10.129 1.011 .602 .579 996 +BB CEN 2449558.5826 10.133 1.053 .609 996 +BB CEN 2449564.5440 10.070 996 +BB CEN 2449803.8369 10.256 1.034 .611 .592 997 +BB CEN 2449804.8277 9.953 .868 .549 .551 997 +BB CEN 2449805.8003 9.991 .901 .569 .542 997 +BB CEN 2449807.8120 10.244 1.022 .604 .613 997 +BB CEN 2449808.8053 9.946 .852 .549 .541 997 +BB CEN 2449809.7266 9.990 .914 .565 .554 997 +BB CEN 2449809.8032 9.994 .912 .572 .559 997 +BB CEN 2449810.7870 10.236 1.050 .621 .611 997 +BB CEN 2449811.7539 10.242 1.035 .599 .622 997 +BB CEN 2449812.7569 9.953 .899 .548 .546 997 +BB CEN 2449813.7664 9.979 .899 .552 .575 997 +BB CEN 2449813.8249 9.988 .929 .569 .562 997 +BB CEN 2449814.7657 10.233 1.046 .626 .598 997 +BB CEN 2449815.7291 10.238 1.032 .608 .613 997 +BB CEN 2449815.7947 10.231 1.006 .625 .581 997 +BB CEN 2449817.7058 9.964 .909 .557 .564 997 +BB CEN 2449817.8036 9.999 .929 .586 .556 997 +BB CEN 2449818.7015 10.191 1.045 .612 .592 997 +BB CEN 2449818.7815 10.268 1.028 .638 .614 997 +BB CEN 2449821.7148 9.957 .902 .566 .564 997 +BB CEN 2449821.7862 9.959 .925 .560 .557 997 +BB CEN 2449822.6863 10.216 1.040 .612 .601 997 +BB CEN 2449822.7934 10.227 1.044 .612 .634 997 +BB CEN 2449823.6844 10.259 1.052 .605 .615 997 +BB CEN 2449823.7848 10.245 1.015 .625 .585 997 +BB CEN 2449825.6703 9.954 .897 .555 .559 997 +BB CEN 2449825.7644 9.973 .901 .562 .560 997 +BB CEN 2449826.7211 10.225 1.038 .612 .615 997 +BB CEN 2449827.6699 10.261 1.014 .621 .597 997 +BB CEN 2450542.5906 10.264 1.243 972 +BB CEN 2450568.4575 9.889 1.082 972 +BB CEN 2450570.3853 10.221 1.227 972 +BB CEN 2450572.3444 9.934 1.103 972 +BB CEN 2450573.3390 9.951 1.125 972 +BB CEN 2450573.4311 9.983 1.133 972 +BB CEN 2450574.4611 10.230 1.235 972 +BB CEN 2450575.3262 10.246 1.234 972 +BB CEN 2450575.4231 10.213 1.215 972 +BB CEN 2450576.3300 9.943 1.098 972 +BB CEN 2450576.4201 9.910 1.092 972 +BB CEN 2450576.4779 9.910 1.092 972 +BB CEN 2450578.3653 10.242 1.231 972 +BB CEN 2450578.4399 10.252 1.233 972 +BB CEN 2450579.4426 10.211 1.218 972 +BB CEN 2450580.3426 9.942 1.090 972 +BB CEN 2450580.4076 9.912 1.083 972 +BB CEN 2450582.3593 10.226 1.223 972 +BB CEN 2450582.4273 10.238 1.225 972 +BB CEN 2450583.3523 10.233 1.211 972 +BB CEN 2450583.4244 10.220 1.213 972 +BB CEN 2450584.3428 9.946 1.098 972 +BB CEN 2450584.4058 9.922 1.090 972 +BK CEN 2449520.6599 10.002 .826 996 +BK CEN 2449521.6337 10.222 .911 996 +BK CEN 2449522.5808 10.423 .941 996 +BK CEN 2449526.6233 9.995 .592 .808 .534 .467 996 +BK CEN 2449528.6211 10.336 .741 .982 .535 .538 996 +BK CEN 2449529.5831 9.755 .753 .438 .450 996 +BK CEN 2449530.6010 10.122 .598 .899 .573 .503 996 +BK CEN 2449534.6004 10.330 .649 .971 .611 .527 996 +BK CEN 2449536.5877 9.922 .453 .753 .426 .358 996 +BK CEN 2449542.5632 9.974 .521 .785 .538 .466 996 +BK CEN 2449543.5693 10.142 .606 .863 .562 .493 996 +BK CEN 2449544.5385 10.437 .693 .994 .625 .520 996 +BK CEN 2449545.5321 9.616 .670 .426 .412 996 +BK CEN 2449546.5362 10.226 .976 .601 .512 996 +BK CEN 2449558.5761 9.822 .514 .731 .441 .469 996 +BK CEN 2449561.5762 9.814 .549 .713 .453 .473 996 +BK CEN 2449564.5413 10.008 .774 996 +BK CEN 2449803.8320 10.219 .871 .568 .528 997 +BK CEN 2449804.8244 10.485 .975 .587 .567 997 +BK CEN 2449805.7951 9.693 .634 .407 .425 997 +BK CEN 2449807.8090 10.428 .965 .578 .557 997 +BK CEN 2449808.8020 9.972 .767 .460 .492 997 +BK CEN 2449809.7249 10.102 .832 .514 .515 997 +BK CEN 2449809.8015 10.099 .828 .529 .513 997 +BK CEN 2449810.7822 10.312 .953 .554 .562 997 +BK CEN 2449811.7489 10.206 .854 .517 .521 997 +BK CEN 2449812.7551 9.991 .802 .479 .524 997 +BK CEN 2449813.7616 10.407 .998 .563 .571 997 +BK CEN 2449813.8216 10.426 1.011 .576 .577 997 +BK CEN 2449814.7641 10.026 .755 .477 .483 997 +BK CEN 2449815.7273 10.066 .824 .507 .502 997 +BK CEN 2449815.7924 10.091 .824 .518 .518 997 +BK CEN 2449817.6982 10.335 .939 .563 .546 997 +BK CEN 2449817.8012 10.326 .922 .559 .552 997 +BK CEN 2449818.6998 9.724 .689 .424 .440 997 +BK CEN 2449818.7789 9.796 .653 .452 .440 997 +BK CEN 2449821.7114 9.817 .707 .441 .465 997 +BK CEN 2449821.7757 9.859 .743 .451 .474 997 +BK CEN 2449822.6845 10.274 .939 .552 .550 997 +BK CEN 2449822.7882 10.310 .938 .573 .565 997 +BK CEN 2449823.6807 10.329 .933 .549 .553 997 +BK CEN 2449823.7809 10.310 .942 .543 .543 997 +BK CEN 2449825.6670 9.996 .801 .484 .512 997 +BK CEN 2449825.7613 10.032 .812 .500 .510 997 +BK CEN 2449826.7177 10.377 .966 .564 .556 997 +BK CEN 2449827.6672 9.865 .691 .447 .454 997 +BK CEN 2450384.8629 10.350 .997 1.155 999 +BK CEN 2450387.8471 10.194 .914 1.072 999 +BK CEN 2450388.8447 10.289 .939 1.102 999 +BK CEN 2450390.8372 10.096 .895 1.051 999 +BK CEN 2450391.8318 10.461 1.018 1.162 999 +BK CEN 2450392.8351 9.705 .672 .863 999 +BK CEN 2450393.8430 10.207 .934 1.097 999 +BK CEN 2450394.8292 10.310 .935 1.108 999 +BK CEN 2450542.5859 10.066 .505 1.021 972 +BK CEN 2450568.4550 10.049 .510 1.023 972 +BK CEN 2450570.3822 9.704 .419 .840 972 +BK CEN 2450572.3424 10.362 .577 1.135 972 +BK CEN 2450573.3369 10.146 .530 1.032 972 +BK CEN 2450573.4294 10.096 .502 .999 972 +BK CEN 2450574.4594 9.943 .472 .964 972 +BK CEN 2450575.3253 10.237 .557 1.108 972 +BK CEN 2450575.4211 10.259 .565 1.108 972 +BK CEN 2450576.3284 10.445 .579 1.127 972 +BK CEN 2450576.4184 10.408 .564 1.121 972 +BK CEN 2450576.4763 10.354 .540 1.084 972 +BK CEN 2450578.3617 10.357 .581 1.153 972 +BK CEN 2450578.4383 10.380 .587 1.140 972 +BK CEN 2450579.4410 10.245 .525 1.068 972 +BK CEN 2450580.3409 9.990 .487 .978 972 +BK CEN 2450580.4065 10.003 .488 .993 972 +BK CEN 2450582.3577 10.344 .563 1.106 972 +BK CEN 2450582.4258 10.354 .577 1.116 972 +BK CEN 2450583.3506 9.704 .418 .853 972 +BK CEN 2450583.4228 9.678 .420 .849 972 +BK CEN 2450584.3420 10.137 .529 1.066 972 +BK CEN 2450584.4050 10.168 .545 1.067 972 +IU CEN 2450348.5257 12.542 .665 .914 999 +IU CEN 2450351.5267 13.331 .949 1.175 999 +IU CEN 2450352.5442 12.546 .998 999 +IU CEN 2450353.5019 12.926 .940 1.174 999 +IU CEN 2450354.5155 13.493 1.091 1.344 999 +IU CEN 2450355.5005 12.400 .678 .881 999 +IU CEN 2450357.5023 13.227 1.017 1.268 999 +IU CEN 2450358.5083 12.538 .656 .894 999 +IU CEN 2450359.5030 12.611 .824 1.072 999 +IU CEN 2450360.5048 13.115 1.011 1.253 999 +IU CEN 2450361.5076 13.282 .911 1.221 999 +IU CEN 2450362.5034 12.514 .757 1.002 999 +IU CEN 2450363.5071 12.962 .999 1.208 999 +IZ CEN 2450386.8596 13.106 2.312 999 +IZ CEN 2450387.8408 12.720 1.769 2.155 999 +IZ CEN 2450388.8384 12.615 1.705 2.153 999 +IZ CEN 2450390.8341 12.900 1.903 2.281 999 +IZ CEN 2450391.8282 13.039 1.926 2.307 999 +IZ CEN 2450392.8309 13.084 1.927 2.304 999 +IZ CEN 2450393.8361 12.668 1.704 2.154 999 +IZ CEN 2450394.8247 12.615 1.709 2.150 999 +KK CEN 2449520.6494 10.928 1.083 996 +KK CEN 2449522.5752 11.309 1.277 996 +KK CEN 2449528.5803 11.903 1.459 .843 .791 996 +KK CEN 2449529.5802 11.701 1.406 .815 .752 996 +KK CEN 2449530.5950 11.488 1.290 .747 .698 996 +KK CEN 2449532.5862 10.871 1.085 .620 .634 996 +KK CEN 2449534.5916 11.271 1.289 .728 .731 996 +KK CEN 2449542.5593 11.514 1.318 .783 .707 996 +KK CEN 2449543.5634 11.307 1.216 .720 .698 996 +KK CEN 2449544.5340 10.855 1.073 .615 .647 996 +KK CEN 2449545.5284 11.086 1.187 .663 .708 996 +KK CEN 2449546.5329 11.229 1.293 .745 .715 996 +KK CEN 2449558.5803 11.254 1.221 .756 .744 996 +KK CEN 2449561.5738 11.648 1.462 .857 .816 996 +KK CEN 2449564.5390 11.919 1.504 996 +KN CEN 2449802.8799 10.088 1.807 1.057 .947 997 +KN CEN 2449803.8786 10.163 1.801 1.076 .966 997 +KN CEN 2449804.8554 10.193 1.810 1.044 .971 997 +KN CEN 2449805.8293 10.234 1.778 1.073 .966 997 +KN CEN 2449807.8387 10.285 1.796 1.064 .967 997 +KN CEN 2449808.8487 10.294 1.780 1.050 .945 997 +KN CEN 2449809.7676 10.314 1.759 1.051 .950 997 +KN CEN 2449809.8206 10.311 1.786 1.054 .953 997 +KN CEN 2449810.8158 10.339 1.797 1.050 .956 997 +KN CEN 2449811.7739 10.358 1.774 1.061 .955 997 +KN CEN 2449811.8087 10.352 1.046 .966 997 +KN CEN 2449813.7845 10.353 1.741 1.033 .955 997 +KN CEN 2449814.7919 10.351 1.728 1.043 .921 997 +KN CEN 2449815.7528 10.318 1.708 1.030 .925 997 +KN CEN 2449815.7997 10.294 1.703 1.036 .923 997 +KN CEN 2449817.7717 9.828 1.523 .942 .881 997 +KN CEN 2449817.8227 9.813 1.519 .933 .895 997 +KN CEN 2449818.7431 9.552 1.421 .894 .832 997 +KN CEN 2449818.8082 9.524 1.439 .880 .835 997 +KN CEN 2449821.7322 9.328 1.387 .853 .822 997 +KN CEN 2449821.8041 9.338 1.414 .859 .840 997 +KN CEN 2449822.7277 9.404 1.439 .875 .864 997 +KN CEN 2449822.7995 9.402 1.454 .881 .863 997 +KN CEN 2449823.7210 9.466 1.504 .901 .876 997 +KN CEN 2449823.7924 9.459 1.494 .896 .864 997 +KN CEN 2449825.7129 9.571 1.585 .929 .885 997 +KN CEN 2449825.7773 9.572 1.571 .951 .888 997 +KN CEN 2449826.7459 9.619 1.623 .951 .888 997 +KN CEN 2449827.6994 9.662 1.651 .971 .898 997 +KN CEN 2450542.6398 9.670 1.890 972 +KN CEN 2450568.4924 9.299 1.670 972 +KN CEN 2450570.4157 9.352 1.715 972 +KN CEN 2450572.3781 9.476 1.794 972 +KN CEN 2450572.5047 9.467 1.775 972 +KN CEN 2450573.3692 9.520 1.817 972 +KN CEN 2450573.4495 9.516 1.810 972 +KN CEN 2450573.5524 9.525 1.802 972 +KN CEN 2450574.4864 9.595 1.852 972 +KN CEN 2450575.3448 9.636 1.876 972 +KN CEN 2450575.4455 9.631 1.864 972 +KN CEN 2450575.5397 9.625 1.853 972 +KN CEN 2450576.3571 9.681 1.896 972 +KN CEN 2450576.4395 9.687 1.888 972 +KN CEN 2450576.4997 9.681 1.893 972 +KN CEN 2450577.5090 9.733 1.907 972 +KN CEN 2450578.3872 9.775 1.916 972 +KN CEN 2450578.4585 9.785 1.936 972 +KN CEN 2450578.5533 9.792 1.934 972 +KN CEN 2450580.4252 9.854 1.964 972 +KN CEN 2450580.5068 9.866 1.965 972 +KN CEN 2450582.4494 9.939 1.975 972 +KN CEN 2450582.4852 9.931 1.966 972 +KN CEN 2450583.4507 10.001 1.983 972 +KN CEN 2450584.4129 10.022 1.986 972 +KN CEN 2450584.4842 10.033 1.989 972 +LV CEN 2450384.8530 12.829 1.545 1.991 999 +LV CEN 2450386.8461 12.313 1.414 1.809 999 +LV CEN 2450387.8386 12.528 1.554 1.907 999 +LV CEN 2450388.8357 12.683 1.627 1.950 999 +LV CEN 2450390.8316 12.349 1.383 1.794 999 +LV CEN 2450391.8246 12.311 1.399 1.800 999 +LV CEN 2450392.8285 12.503 1.600 1.877 999 +LV CEN 2450393.8334 12.679 1.623 1.954 999 +LV CEN 2450394.8212 12.790 1.614 1.954 999 +LV CEN 2450568.4454 12.790 1.005 972 +LV CEN 2450568.4489 12.760 .991 972 +LV CEN 2450570.3677 12.215 .870 972 +LV CEN 2450572.3365 12.615 1.000 972 +LV CEN 2450573.3318 12.769 1.009 972 +LV CEN 2450573.4246 .985 972 +LV CEN 2450574.4538 12.652 .928 972 +LV CEN 2450575.3215 12.215 .850 972 +LV CEN 2450576.2680 12.436 .933 972 +LV CEN 2450576.3242 12.447 .944 972 +LV CEN 2450576.4147 12.440 .923 972 +LV CEN 2450578.3577 12.791 .992 972 +LV CEN 2450578.4343 12.773 .997 972 +LV CEN 2450579.4362 12.670 .958 972 +LV CEN 2450580.3360 12.226 .868 972 +LV CEN 2450580.3375 12.211 .852 972 +LV CEN 2450580.4029 12.213 .863 972 +LV CEN 2450582.3538 12.630 .972 972 +LV CEN 2450582.4210 12.635 .984 972 +LV CEN 2450583.3470 12.785 .997 972 +LV CEN 2450583.4190 12.783 1.001 972 +LV CEN 2450584.3384 12.716 .962 972 +LV CEN 2450584.4011 12.690 .967 972 +MZ CEN 2449521.6773 11.712 1.533 996 +MZ CEN 2449522.6047 11.497 1.447 996 +MZ CEN 2449528.6689 11.828 1.731 .954 .881 996 +MZ CEN 2449529.6215 11.912 1.001 .877 996 +MZ CEN 2449530.6535 11.952 1.708 .979 .875 996 +MZ CEN 2449532.6109 11.610 1.538 .859 .840 996 +MZ CEN 2449534.6450 11.268 1.363 .825 .764 996 +MZ CEN 2449535.6111 1.380 .811 996 +MZ CEN 2449542.5879 1.599 .846 996 +MZ CEN 2449543.6099 11.504 1.491 .877 .819 996 +MZ CEN 2449544.5497 11.342 1.410 .836 .796 996 +MZ CEN 2449545.5446 11.215 1.380 .849 .779 996 +MZ CEN 2449546.5591 11.338 1.486 .855 .810 996 +MZ CEN 2449558.6010 11.548 1.694 .949 .893 996 +MZ CEN 2449560.5640 11.904 1.779 1.009 .887 996 +MZ CEN 2449563.5808 11.617 1.544 996 +MZ CEN 2449564.5618 11.435 1.454 996 +QY CEN 2449530.6640 11.294 1.675 1.090 1.004 996 +QY CEN 2449530.6707 11.261 1.628 1.043 1.031 996 +QY CEN 2449532.6205 11.303 1.741 1.119 1.041 996 +QY CEN 2449536.6152 11.796 2.168 1.106 996 +QY CEN 2449544.5560 12.290 2.387 1.337 1.187 996 +QY CEN 2449546.5658 12.052 2.252 1.234 1.150 996 +QY CEN 2449558.6128 12.159 2.477 1.384 1.195 996 +QY CEN 2449560.5892 12.197 2.473 1.412 1.268 996 +QY CEN 2449563.5835 12.080 2.245 996 +QY CEN 2449564.5673 12.054 2.163 996 +QY CEN 2450568.4984 11.885 2.554 972 +QY CEN 2450570.4231 12.088 2.601 972 +QY CEN 2450572.3828 12.245 2.628 972 +QY CEN 2450572.5107 12.243 2.610 972 +QY CEN 2450573.3727 12.259 2.620 972 +QY CEN 2450573.4601 12.248 2.593 972 +QY CEN 2450574.4898 12.175 2.572 972 +QY CEN 2450575.3480 12.046 2.535 972 +QY CEN 2450575.4495 12.036 2.512 972 +QY CEN 2450575.5426 12.020 2.520 972 +QY CEN 2450576.3603 12.023 2.481 972 +QY CEN 2450576.4425 12.047 2.502 972 +QY CEN 2450576.5032 12.061 2.510 972 +QY CEN 2450576.5671 12.054 2.499 972 +QY CEN 2450577.5118 11.692 2.369 972 +QY CEN 2450577.5760 11.641 2.344 972 +QY CEN 2450578.3896 11.299 2.244 972 +QY CEN 2450578.4613 11.289 2.249 972 +QY CEN 2450578.5566 11.291 2.252 972 +QY CEN 2450579.5121 11.289 2.284 972 +QY CEN 2450579.5483 11.292 2.282 972 +QY CEN 2450580.4279 11.376 2.332 972 +QY CEN 2450580.5099 11.385 2.349 972 +QY CEN 2450582.4521 11.550 2.438 972 +QY CEN 2450582.4883 11.538 2.421 972 +QY CEN 2450583.4595 11.663 2.490 972 +QY CEN 2450584.4156 11.716 2.491 972 +QY CEN 2450584.4939 11.725 2.511 972 +V378 CEN 2449521.6847 8.497 1.016 996 +V378 CEN 2449522.6076 .908 996 +V378 CEN 2449526.6444 8.693 .709 1.102 .665 .627 996 +V378 CEN 2449528.6751 8.323 .923 .581 .562 996 +V378 CEN 2449529.6424 8.341 .952 .598 .577 996 +V378 CEN 2449530.6571 8.431 .597 .996 .631 .579 996 +V378 CEN 2449532.6151 8.659 1.112 .665 .621 996 +V378 CEN 2449534.6499 8.462 .590 .990 .604 .581 996 +V378 CEN 2449535.6223 .605 .905 996 +V378 CEN 2449536.6064 8.385 .646 .950 .577 996 +V378 CEN 2449543.6138 8.449 .634 1.011 .634 .584 996 +V378 CEN 2449544.5526 8.544 .739 1.085 .633 .614 996 +V378 CEN 2449545.5502 8.645 .700 1.134 .646 .622 996 +V378 CEN 2449546.5617 8.678 1.106 .648 .623 996 +V378 CEN 2449558.6093 8.635 .821 1.117 .649 .627 996 +V378 CEN 2449560.5273 8.413 .667 .992 .581 .588 996 +V378 CEN 2449561.5299 8.296 .666 .914 .579 .575 996 +V378 CEN 2449563.4666 8.494 .744 1.053 .620 .598 996 +V378 CEN 2449564.4544 8.590 1.079 996 +V378 CEN 2449803.8666 1.077 .634 997 +V378 CEN 2449804.8502 8.688 1.108 .646 .646 997 +V378 CEN 2449805.8243 8.509 .968 .617 .602 997 +V378 CEN 2449807.8341 8.373 .968 .594 .590 997 +V378 CEN 2449808.8434 8.481 1.029 .616 .606 997 +V378 CEN 2449809.7642 8.590 1.084 .650 .625 997 +V378 CEN 2449809.8191 8.585 1.087 .639 .636 997 +V378 CEN 2449810.8082 8.655 1.109 .654 .632 997 +V378 CEN 2449811.7705 8.609 1.063 .629 .626 997 +V378 CEN 2449811.8075 8.611 1.043 .636 .622 997 +V378 CEN 2449813.7811 8.319 .924 .578 .577 997 +V378 CEN 2449813.8360 8.320 .928 .582 .565 997 +V378 CEN 2449814.7900 8.415 .987 .606 .588 997 +V378 CEN 2449815.7513 8.530 1.070 .629 .620 997 +V378 CEN 2449815.7984 8.529 1.063 .634 .620 997 +V378 CEN 2449817.7308 8.672 1.084 .661 .635 997 +V378 CEN 2449817.8244 8.677 1.090 .658 .645 997 +V378 CEN 2449818.7449 8.491 .986 .609 .600 997 +V378 CEN 2449818.8099 8.461 .990 .600 .590 997 +V378 CEN 2449821.7358 8.452 1.029 .618 .599 997 +V378 CEN 2449821.8067 8.466 1.038 .623 .615 997 +V378 CEN 2449822.7309 8.579 1.072 .643 .641 997 +V378 CEN 2449822.8015 8.574 1.080 .638 .645 997 +V378 CEN 2449823.7193 8.664 1.107 .652 .651 997 +V378 CEN 2449823.7906 8.653 1.119 .646 .637 997 +V378 CEN 2449825.7112 8.333 .918 .571 .575 997 +V378 CEN 2449826.7432 8.317 .932 .576 .561 997 +V378 CEN 2449827.6976 8.424 .985 .615 .583 997 +V378 CEN 2450542.6332 8.336 1.164 972 +V378 CEN 2450568.4883 8.344 1.161 972 +V378 CEN 2450570.4115 8.373 1.193 972 +V378 CEN 2450572.4364 8.637 1.291 972 +V378 CEN 2450573.3556 8.660 1.305 972 +V378 CEN 2450573.4466 8.644 1.283 972 +V378 CEN 2450573.5494 8.656 1.294 972 +V378 CEN 2450574.4821 8.492 1.220 972 +V378 CEN 2450575.3414 8.309 1.156 972 +V378 CEN 2450575.4421 8.288 1.135 972 +V378 CEN 2450576.3537 8.330 1.176 972 +V378 CEN 2450576.4362 8.326 1.182 972 +V378 CEN 2450576.4929 8.341 1.197 972 +V378 CEN 2450577.5056 8.463 1.229 972 +V378 CEN 2450578.3805 8.565 1.272 972 +V378 CEN 2450578.4556 8.587 1.275 972 +V378 CEN 2450579.4723 1.304 972 +V378 CEN 2450580.4229 8.618 1.276 972 +V378 CEN 2450580.5045 8.611 1.278 972 +V378 CEN 2450582.4465 8.327 1.162 972 +V378 CEN 2450582.4828 8.293 1.146 972 +V378 CEN 2450583.4482 8.416 1.202 972 +V378 CEN 2450584.4108 8.512 1.235 972 +V378 CEN 2450584.4546 8.526 1.263 972 +V381 CEN 2450348.5234 7.755 .891 .957 999 +V381 CEN 2450351.4944 7.325 .714 999 +V381 CEN 2450353.4821 7.744 .966 999 +V381 CEN 2450355.4827 7.982 .985 999 +V381 CEN 2450357.4821 7.478 .839 999 +V381 CEN 2450358.4843 7.717 .936 999 +V381 CEN 2450359.4837 7.891 .984 999 +V381 CEN 2450360.4840 7.958 .965 999 +V381 CEN 2450361.4844 7.408 .803 999 +V381 CEN 2450362.4824 7.472 .815 999 +V381 CEN 2450363.4851 7.733 .947 999 +V381 CEN 2450542.6448 7.948 .523 1.005 972 +V381 CEN 2450568.4962 7.997 .520 972 +V381 CEN 2450570.4213 7.377 .393 .771 972 +V381 CEN 2450572.3809 7.848 .502 .990 972 +V381 CEN 2450572.5084 7.848 .510 .980 972 +V381 CEN 2450573.3714 7.986 .536 1.013 972 +V381 CEN 2450573.4582 7.992 .517 .996 972 +V381 CEN 2450573.5546 7.988 .507 .994 972 +V381 CEN 2450574.4886 7.602 .431 .836 972 +V381 CEN 2450575.3469 7.355 .383 .781 972 +V381 CEN 2450575.4482 7.393 .402 .774 972 +V381 CEN 2450575.5416 7.378 .388 .767 972 +V381 CEN 2450576.3591 7.602 .459 .891 972 +V381 CEN 2450576.4415 7.621 .457 .905 972 +V381 CEN 2450576.5018 7.639 .474 .921 972 +V381 CEN 2450576.5661 7.657 .456 .904 972 +V381 CEN 2450577.5109 7.845 .505 .971 972 +V381 CEN 2450578.3885 7.973 .520 1.004 972 +V381 CEN 2450578.4603 7.980 .525 .996 972 +V381 CEN 2450578.5553 7.998 .528 1.011 972 +V381 CEN 2450579.5111 7.645 .439 .858 972 +V381 CEN 2450579.5470 7.618 .431 .855 972 +V381 CEN 2450580.4271 7.344 .380 .761 972 +V381 CEN 2450580.5087 7.364 .387 .774 972 +V381 CEN 2450582.4513 7.809 .497 .968 972 +V381 CEN 2450582.4873 7.804 .497 .950 972 +V381 CEN 2450583.4529 7.984 .520 1.003 972 +V381 CEN 2450584.4147 7.796 .460 .895 972 +V381 CEN 2450584.4861 7.736 .465 .885 972 +V419 CEN 2450379.8646 8.253 .895 999 +V419 CEN 2450380.8815 8.052 .811 999 +V419 CEN 2450381.8619 8.065 .807 999 +V419 CEN 2450384.8508 8.294 .913 999 +V419 CEN 2450386.8429 8.011 .791 999 +V419 CEN 2450387.8489 8.091 .834 999 +V419 CEN 2450388.8473 8.216 .877 999 +V419 CEN 2450390.8396 8.268 .884 999 +V419 CEN 2450391.8349 8.037 .789 999 +V419 CEN 2450392.8427 8.029 .783 999 +V419 CEN 2450393.8479 8.157 .865 999 +V419 CEN 2450394.8381 8.260 .896 999 +V419 CEN 2449803.8234 .698 .435 .402 997 +V419 CEN 2449804.8180 8.235 .788 .439 .435 997 +V419 CEN 2449805.7867 8.326 .821 .468 .440 997 +V419 CEN 2449805.7900 8.336 .813 .472 .433 997 +V419 CEN 2449807.7971 8.134 .727 .408 .413 997 +V419 CEN 2449808.7899 8.045 .676 .402 .391 997 +V419 CEN 2449808.7975 8.035 .679 .398 .383 997 +V419 CEN 2449809.7177 8.158 .740 .426 .424 997 +V419 CEN 2449809.7980 8.146 .752 .428 .409 997 +V419 CEN 2449810.7666 8.274 .817 .455 .443 997 +V419 CEN 2449811.7419 8.343 .825 .468 .445 997 +V419 CEN 2449812.7458 8.272 .773 .441 997 +V419 CEN 2449813.7550 8.023 .679 .385 .394 997 +V419 CEN 2449814.7612 8.073 .695 .408 .397 997 +V419 CEN 2449815.7094 8.207 .768 .451 .412 997 +V419 CEN 2449815.7859 8.204 .790 .434 .427 997 +V419 CEN 2449817.6928 8.333 .815 .463 .437 997 +V419 CEN 2449817.7986 8.327 .836 .455 .452 997 +V419 CEN 2449818.6970 8.154 .723 .425 .409 997 +V419 CEN 2449821.6904 8.259 .798 .454 .437 997 +V419 CEN 2449821.7713 8.260 .813 .452 .437 997 +V419 CEN 2449822.6815 8.330 .828 .460 .440 997 +V419 CEN 2449822.7852 8.326 .821 .471 .449 997 +V419 CEN 2449823.6775 8.292 .782 .453 .432 997 +V419 CEN 2449823.7776 8.263 .781 .440 .428 997 +V419 CEN 2449825.6637 8.050 .694 .402 .397 997 +V419 CEN 2449825.7580 8.059 .701 .404 .396 997 +V419 CEN 2449826.7158 8.186 .774 .436 .416 997 +V419 CEN 2449827.6659 8.296 .827 .461 .432 997 +V419 CEN 2449520.6423 8.309 .799 996 +V419 CEN 2449526.5956 8.151 .409 .756 .452 .417 996 +V419 CEN 2449528.5689 8.104 .454 .746 .424 .429 996 +V419 CEN 2449529.5333 8.189 .847 .435 .423 996 +V419 CEN 2449530.5606 8.320 .531 .852 .472 .468 996 +V419 CEN 2449532.5624 8.095 .359 .724 .426 .425 996 +V419 CEN 2449534.5662 8.156 .789 .448 .435 996 +V419 CEN 2449536.5706 8.361 .486 .815 .456 996 +V419 CEN 2449542.5513 8.332 .477 .824 .478 .459 996 +V419 CEN 2449543.5536 8.082 .385 .736 .408 .434 996 +V419 CEN 2449544.5275 8.047 .474 .710 .421 .420 996 +V419 CEN 2449545.5146 8.154 .443 .775 .447 .441 996 +V419 CEN 2449546.5262 8.285 .831 .475 .461 996 +V419 CEN 2449558.5667 8.309 .530 .845 .463 .439 996 +V419 CEN 2449559.4732 8.216 .498 .820 .419 .434 996 +V419 CEN 2449561.5354 8.066 .494 .703 .404 .408 996 +V419 CEN 2449563.4700 8.318 .596 .834 .451 .443 996 +V419 CEN 2449564.4577 8.340 .801 996 +V419 CEN 2450542.5754 8.168 .857 972 +V419 CEN 2450568.4296 8.002 .780 972 +V419 CEN 2450570.3665 8.198 .855 972 +V419 CEN 2450572.3354 8.313 .895 972 +V419 CEN 2450573.4238 8.080 .801 972 +V419 CEN 2450574.4526 8.030 .802 972 +V419 CEN 2450575.3208 8.141 .855 972 +V419 CEN 2450575.4177 8.140 .840 972 +V419 CEN 2450576.3234 8.256 .891 972 +V419 CEN 2450576.4136 8.270 .912 972 +V419 CEN 2450578.3564 8.231 .861 972 +V419 CEN 2450578.4332 8.223 .852 972 +V419 CEN 2450579.4352 8.013 .792 972 +V419 CEN 2450580.3338 8.074 .815 972 +V419 CEN 2450580.4022 8.076 .812 972 +V419 CEN 2450582.3527 8.314 .908 972 +V419 CEN 2450582.4200 8.308 .911 972 +V419 CEN 2450583.3459 8.327 .896 972 +V419 CEN 2450583.4177 8.317 .894 972 +V419 CEN 2450584.3374 8.122 .814 972 +V419 CEN 2450584.3998 8.104 .809 972 +V420 CEN 2450542.5799 10.532 .940 972 +V420 CEN 2450568.4516 10.112 .402 972 +V420 CEN 2450570.3799 9.405 .279 972 +V420 CEN 2450572.3401 9.512 .343 972 +V420 CEN 2450573.3350 9.607 .366 972 +V420 CEN 2450573.4277 9.610 .366 972 +V420 CEN 2450574.4577 9.705 .402 972 +V420 CEN 2450575.3239 9.741 .408 972 +V420 CEN 2450575.4194 .398 972 +V420 CEN 2450576.3267 9.818 .460 972 +V420 CEN 2450576.4171 9.814 .432 972 +V420 CEN 2450578.3602 9.905 .477 972 +V420 CEN 2450578.4369 9.912 .471 972 +V420 CEN 2450579.4387 9.970 .487 972 +V420 CEN 2450580.4025 10.014 .493 972 +V420 CEN 2450582.3559 10.137 .512 972 +V420 CEN 2450582.4239 10.139 .523 972 +V420 CEN 2450583.3492 10.230 .537 972 +V420 CEN 2450583.4214 10.222 .515 972 +V420 CEN 2450584.3407 10.313 .528 972 +V420 CEN 2450584.4036 10.314 .526 972 +V496 CEN 2450542.6305 9.773 .679 1.363 972 +V496 CEN 2450568.4863 9.617 .631 1.274 972 +V496 CEN 2450570.4095 10.059 .743 1.474 972 +V496 CEN 2450572.3712 9.884 .682 1.355 972 +V496 CEN 2450572.4998 9.770 .655 1.308 972 +V496 CEN 2450573.3540 9.722 .669 1.357 972 +V496 CEN 2450573.4457 9.735 .651 1.344 972 +V496 CEN 2450573.5480 9.769 .678 1.368 972 +V496 CEN 2450574.4803 10.006 .739 1.460 972 +V496 CEN 2450575.3398 10.164 .763 1.505 972 +V496 CEN 2450575.4406 10.167 .731 1.486 972 +V496 CEN 2450576.3516 10.164 .744 1.472 972 +V496 CEN 2450576.4345 10.124 .734 1.452 972 +V496 CEN 2450576.4913 10.094 .715 1.442 972 +V496 CEN 2450577.5045 9.662 .643 1.295 972 +V496 CEN 2450578.3787 9.884 .713 1.413 972 +V496 CEN 2450578.4534 9.911 .727 1.433 972 +V496 CEN 2450579.4708 10.117 .756 1.509 972 +V496 CEN 2450580.4211 10.209 .766 1.494 972 +V496 CEN 2450580.5031 10.228 .765 1.501 972 +V496 CEN 2450582.4454 9.783 .671 1.359 972 +V496 CEN 2450582.4814 9.801 .695 1.367 972 +V496 CEN 2450583.4462 10.043 .736 1.465 972 +V496 CEN 2450584.4100 10.180 .766 1.495 972 +V496 CEN 2450584.4532 10.194 .768 1.516 972 +V553 CEN 2450348.5057 8.589 .715 .731 999 +V553 CEN 2450351.5323 8.301 .695 999 +V553 CEN 2450353.5044 8.262 .664 999 +V553 CEN 2450354.5172 8.715 .776 999 +V553 CEN 2450355.5146 8.229 .638 999 +V553 CEN 2450357.4959 8.201 .639 999 +V553 CEN 2450358.5006 8.726 .789 999 +V553 CEN 2450359.4973 8.222 .621 999 +V553 CEN 2450360.4991 8.704 .793 999 +V553 CEN 2450361.5003 8.275 .651 999 +V553 CEN 2450362.4977 8.669 .790 999 +V553 CEN 2450363.5017 8.295 .615 999 +V553 CEN 2449807.8543 8.445 .767 .389 .346 997 +V553 CEN 2449808.8721 8.552 .653 .363 .329 997 +V553 CEN 2449809.7890 8.388 .716 .381 .326 997 +V553 CEN 2449809.8338 8.403 .725 .387 .335 997 +V553 CEN 2449810.8401 8.570 .700 .372 .310 997 +V553 CEN 2449811.7981 8.334 .696 .368 .326 997 +V553 CEN 2449811.8250 8.340 .711 .367 .325 997 +V553 CEN 2449813.8167 8.321 .682 .368 .320 997 +V553 CEN 2449814.8170 8.609 .733 .390 .338 997 +V553 CEN 2449815.7724 8.287 .634 .349 .314 997 +V553 CEN 2449815.8152 8.300 .656 .356 .325 997 +V553 CEN 2449817.7860 8.273 .622 .348 .310 997 +V553 CEN 2449817.8392 8.294 .634 .353 .336 997 +V553 CEN 2449818.7602 8.738 .824 .424 .370 997 +V553 CEN 2449818.8228 8.700 .789 .407 .339 997 +V553 CEN 2449821.7598 8.239 .577 .324 .293 997 +V553 CEN 2449821.8299 8.216 .592 .325 .311 997 +V553 CEN 2449822.7517 8.694 .853 .424 .362 997 +V553 CEN 2449822.8199 8.701 .849 .427 .362 997 +V553 CEN 2449823.7444 8.227 .569 .316 .309 997 +V553 CEN 2449823.8133 8.241 .586 .326 .298 997 +V553 CEN 2449825.7336 8.252 .557 .318 .294 997 +V553 CEN 2449825.7974 8.232 .559 .318 .300 997 +V553 CEN 2449826.7672 8.655 .862 .430 .345 997 +V553 CEN 2449827.7218 8.280 .558 .311 .292 997 +V641 CEN 2449520.7262 10.733 2.031 996 +V641 CEN 2449521.7043 10.841 2.021 996 +V641 CEN 2449522.6303 10.554 1.893 996 +V641 CEN 2449528.6852 10.458 1.846 1.108 .970 996 +V641 CEN 2449529.6538 10.411 1.850 1.055 .975 996 +V641 CEN 2449530.6804 10.310 1.825 1.053 .971 996 +V641 CEN 2449532.6321 10.310 1.820 1.018 .962 996 +V641 CEN 2449534.6655 10.290 1.813 1.049 .958 996 +V641 CEN 2449535.6426 10.301 1.748 .849 .895 996 +V641 CEN 2449536.6465 10.316 1.753 .852 .921 996 +V641 CEN 2449542.6186 10.022 1.737 1.019 1.001 996 +V641 CEN 2449543.6333 10.138 1.819 1.036 .949 996 +V641 CEN 2449545.6043 10.114 1.815 1.024 .946 996 +V641 CEN 2449546.5998 10.123 1.818 1.042 .963 996 +V641 CEN 2449558.6231 10.389 1.950 1.127 1.018 996 +V641 CEN 2449561.6900 10.349 2.030 1.162 1.030 996 +V641 CEN 2449563.5884 10.431 2.030 1.158 1.059 996 +V641 CEN 2449564.5755 10.425 2.046 996 +V641 CEN 2449802.8966 10.031 1.797 1.071 .986 997 +V641 CEN 2449803.8857 10.063 1.073 997 +V641 CEN 2449804.8629 10.037 1.832 1.071 .997 997 +V641 CEN 2449807.8482 10.013 1.885 1.069 1.012 997 +V641 CEN 2449808.8530 10.012 1.079 997 +V641 CEN 2449809.7715 10.023 1.939 1.091 .996 997 +V641 CEN 2449809.8239 10.012 1.908 1.079 .999 997 +V641 CEN 2449810.8242 10.044 1.902 1.096 1.007 997 +V641 CEN 2449811.7793 10.057 1.918 1.107 1.008 997 +V641 CEN 2449811.8118 10.054 1.932 1.105 1.006 997 +V641 CEN 2449813.7927 10.083 1.944 1.116 1.010 997 +V641 CEN 2449814.8021 10.085 1.955 1.105 1.013 997 +V641 CEN 2449815.7574 10.130 2.016 1.128 1.019 997 +V641 CEN 2449815.8043 10.116 1.973 1.123 1.022 997 +V641 CEN 2449817.7758 10.170 2.030 1.139 1.031 997 +V641 CEN 2449817.8278 10.180 2.031 1.139 1.038 997 +V641 CEN 2449818.7484 10.200 2.075 1.151 1.022 997 +V641 CEN 2449818.8130 10.192 2.039 1.129 1.015 997 +V641 CEN 2449821.7408 10.269 2.098 1.170 1.037 997 +V641 CEN 2449821.8122 10.268 2.121 1.163 1.045 997 +V641 CEN 2449822.7358 10.314 2.112 1.176 1.058 997 +V641 CEN 2449822.8055 10.318 2.127 1.185 1.062 997 +V641 CEN 2449823.7257 10.361 2.148 1.188 1.070 997 +V641 CEN 2449823.7979 10.343 2.204 1.182 1.050 997 +V641 CEN 2449825.7176 10.439 2.192 1.205 1.073 997 +V641 CEN 2449825.7823 10.439 2.149 1.218 1.070 997 +V641 CEN 2449826.7513 10.483 2.188 1.220 1.076 997 +V641 CEN 2449827.7048 10.525 2.185 1.219 1.109 997 +V641 CEN 2450568.5073 10.177 1.112 2.153 972 +V641 CEN 2450570.4318 10.136 1.102 2.130 972 +V641 CEN 2450572.3899 10.149 1.116 2.148 972 +V641 CEN 2450572.5150 10.147 1.094 2.143 972 +V641 CEN 2450573.3773 10.145 1.100 2.142 972 +V641 CEN 2450573.4646 10.135 1.096 2.134 972 +V641 CEN 2450573.5585 10.139 1.089 2.127 972 +V641 CEN 2450574.4943 10.166 1.102 2.141 972 +V641 CEN 2450575.3616 10.197 1.117 2.171 972 +V641 CEN 2450575.4598 10.193 1.110 2.159 972 +V641 CEN 2450575.5474 10.180 1.119 2.148 972 +V641 CEN 2450576.4513 10.206 1.130 2.174 972 +V641 CEN 2450576.5087 10.202 1.119 2.172 972 +V641 CEN 2450576.5734 10.228 1.132 2.173 972 +V641 CEN 2450577.5168 10.224 1.132 2.169 972 +V641 CEN 2450577.5793 10.214 1.112 2.161 972 +V641 CEN 2450578.3943 10.251 1.149 2.189 972 +V641 CEN 2450578.4652 10.233 1.131 2.173 972 +V641 CEN 2450578.5607 10.242 1.133 2.178 972 +V641 CEN 2450579.5165 10.257 1.140 2.187 972 +V641 CEN 2450579.5542 10.271 1.122 2.131 972 +V641 CEN 2450580.4320 10.271 1.145 2.190 972 +V641 CEN 2450580.5577 10.274 1.145 2.193 972 +V641 CEN 2450582.4568 10.326 1.159 2.207 972 +V641 CEN 2450582.5413 10.315 1.145 2.196 972 +V641 CEN 2450583.4583 10.363 1.154 2.210 972 +V641 CEN 2450583.5823 10.335 1.152 2.199 972 +V641 CEN 2450584.4201 10.371 1.170 2.227 972 +V641 CEN 2450584.4885 10.370 1.162 2.228 972 +V659 CEN 2450351.4972 6.500 .821 999 +V659 CEN 2450353.4848 6.674 .907 999 +V659 CEN 2450354.4993 6.748 .926 999 +V659 CEN 2450355.4847 6.692 .891 999 +V659 CEN 2450357.4845 6.483 .832 999 +V659 CEN 2450358.4857 6.600 .887 999 +V659 CEN 2450359.4862 6.677 .903 999 +V659 CEN 2450360.4888 6.739 .945 999 +V659 CEN 2450361.4878 6.666 .901 999 +V659 CEN 2450362.4851 6.506 .828 999 +V659 CEN 2450363.4868 6.541 .821 999 +V659 CEN 2450542.6349 6.476 .425 .828 972 +V659 CEN 2450568.4896 6.735 .477 .921 972 +V659 CEN 2450570.4128 6.483 .420 .821 972 +V659 CEN 2450572.3745 .438 .866 972 +V659 CEN 2450572.5024 6.630 .456 .892 972 +V659 CEN 2450573.3674 6.709 .480 .928 972 +V659 CEN 2450573.4476 6.701 .459 .906 972 +V659 CEN 2450573.5505 6.711 .464 .924 972 +V659 CEN 2450574.4833 6.729 .463 .918 972 +V659 CEN 2450575.3422 6.620 .444 .885 972 +V659 CEN 2450575.4430 6.595 .434 .852 972 +V659 CEN 2450575.5372 6.564 .432 .840 972 +V659 CEN 2450576.3545 6.480 .426 .831 972 +V659 CEN 2450576.4371 6.480 .418 .825 972 +V659 CEN 2450576.4945 6.481 .405 .845 972 +V659 CEN 2450577.5065 6.557 .437 .865 972 +V659 CEN 2450578.3814 6.654 .462 .905 972 +V659 CEN 2450578.4564 6.662 .467 .908 972 +V659 CEN 2450578.5510 6.678 .474 .910 972 +V659 CEN 2450579.4733 6.759 .447 .891 972 +V659 CEN 2450580.4237 6.699 .453 .899 972 +V659 CEN 2450580.5054 6.699 .464 .902 972 +V659 CEN 2450582.4476 6.490 .419 .830 972 +V659 CEN 2450582.4836 6.490 .426 .831 972 +V659 CEN 2450583.4490 6.599 .451 .883 972 +V659 CEN 2450584.4115 6.689 .469 .915 972 +V659 CEN 2450584.4553 6.688 .467 .918 972 +V737 CEN 2450351.4929 6.778 1.064 999 +V737 CEN 2450352.5504 6.916 1.106 999 +V737 CEN 2450353.4798 6.909 1.085 999 +V737 CEN 2450354.4960 6.773 1.013 999 +V737 CEN 2450355.4808 6.561 .943 999 +V737 CEN 2450357.4808 6.659 1.017 999 +V737 CEN 2450357.5456 6.642 .995 999 +V737 CEN 2450358.4832 6.757 1.057 999 +V737 CEN 2450358.5533 6.776 1.050 999 +V737 CEN 2450359.4821 6.848 1.080 999 +V737 CEN 2450360.4805 6.915 1.079 999 +V737 CEN 2450361.4828 6.793 1.072 999 +V737 CEN 2450362.4801 6.586 .967 999 +V737 CEN 2450363.4831 6.578 .961 999 +V737 CEN 2450363.5538 6.591 .974 999 +V737 CEN 2450379.4930 6.710 1.073 999 +V737 CEN 2450542.6486 6.789 .559 1.061 972 +V737 CEN 2450570.4294 6.738 .546 1.049 972 +V737 CEN 2450572.3885 6.932 .592 1.111 972 +V737 CEN 2450572.5140 6.915 .573 1.090 972 +V737 CEN 2450573.3763 6.782 .544 1.032 972 +V737 CEN 2450573.4635 6.757 .531 1.015 972 +V737 CEN 2450573.5575 6.720 .511 1.011 972 +V737 CEN 2450574.4932 6.568 .495 .956 972 +V737 CEN 2450575.3605 6.564 .498 .980 972 +V737 CEN 2450575.4586 6.565 .485 .955 972 +V737 CEN 2450575.5464 6.562 .500 .963 972 +V737 CEN 2450576.4502 6.652 .538 1.032 972 +V737 CEN 2450576.5077 6.653 .531 1.032 972 +V737 CEN 2450576.5712 6.658 .521 1.015 972 +V737 CEN 2450577.5158 6.747 .553 1.057 972 +V737 CEN 2450577.5783 6.751 .555 1.068 972 +V737 CEN 2450578.3932 6.846 .574 1.088 972 +V737 CEN 2450578.4642 6.850 .577 1.096 972 +V737 CEN 2450578.5597 6.865 .579 1.094 972 +V737 CEN 2450579.5155 6.922 .580 1.104 972 +V737 CEN 2450579.5530 6.915 .560 1.088 972 +V737 CEN 2450580.4310 6.791 .538 1.044 972 +V737 CEN 2450580.5567 6.767 .537 1.027 972 +V737 CEN 2450582.4557 6.558 .497 .963 972 +V737 CEN 2450582.5403 6.561 .495 .958 972 +V737 CEN 2450583.4570 6.654 .527 1.013 972 +V737 CEN 2450583.5813 6.640 .520 1.010 972 +V737 CEN 2450584.4192 6.727 .549 1.050 972 +AK CEP 2449934.4593 11.118 .825 1.543 998 +AK CEP 2449935.4290 11.163 .809 1.595 998 +AK CEP 2449936.4182 11.370 .864 1.681 998 +AK CEP 2449937.4243 11.467 .884 998 +AK CEP 2449938.4462 11.524 .892 1.700 998 +AK CEP 2449941.4244 1.525 998 +AK CEP 2449942.3981 11.206 1.604 998 +AK CEP 2449943.3890 11.328 1.668 998 +AK CEP 2449944.4205 11.439 1.707 998 +AK CEP 2449946.4182 11.315 1.581 998 +AK CEP 2449947.3758 10.911 1.389 998 +AK CEP 2449948.3429 10.992 1.468 998 +AK CEP 2449949.3382 11.176 1.562 998 +AK CEP 2449952.3221 11.520 1.688 998 +AK CEP 2449953.3988 11.410 1.639 998 +AK CEP 2449954.3494 10.974 1.421 998 +AK CEP 2449955.3111 10.953 1.424 998 +AK CEP 2450311.3990 11.140 1.340 .803 971 +AK CEP 2450312.3889 11.300 1.397 .854 971 +AK CEP 2450314.3603 11.512 1.460 .861 971 +AK CEP 2450315.3198 11.226 1.339 .799 971 +AK CEP 2450316.3184 10.886 1.191 .741 971 +AK CEP 2450317.3282 10.967 1.300 .756 971 +AK CEP 2450318.3371 11.155 1.359 .801 971 +AK CEP 2450319.3081 11.205 1.447 .816 971 +AK CEP 2450320.2922 11.377 1.497 .855 971 +AK CEP 2450321.3224 11.501 1.533 971 +AK CEP 2450322.3206 11.353 1.415 .837 971 +AK CEP 2450323.3149 10.925 1.214 .732 971 +AK CEP 2450324.3094 10.958 1.271 .744 971 +AK CEP 2450325.2960 11.080 1.362 .813 971 +CN CEP 2446284.3738 12.666 1.945 1.188 987 +CN CEP 2446285.3366 12.469 1.865 1.144 987 +CN CEP 2446286.3083 12.178 1.716 1.085 987 +CN CEP 2446287.2931 12.136 1.680 1.095 987 +CN CEP 2446288.3627 12.134 1.756 1.091 987 +CN CEP 2446289.3584 12.216 1.797 1.118 987 +CN CEP 2446290.3681 12.443 1.874 1.165 987 +CN CEP 2446291.3277 12.540 1.932 1.199 987 +CN CEP 2446294.2952 12.609 1.892 1.167 987 +CN CEP 2446295.2954 12.341 1.802 1.121 987 +CN CEP 2446296.2867 12.124 1.698 1.092 987 +CN CEP 2446297.2774 12.153 1.742 1.090 987 +CN CEP 2446298.3144 12.126 1.751 1.108 987 +CN CEP 2446299.2807 12.332 1.835 1.151 987 +CN CEP 2446300.2755 12.484 1.895 1.191 987 +CN CEP 2446301.3203 12.598 1.977 1.207 987 +CN CEP 2446302.2998 12.714 1.973 1.219 987 +CN CEP 2446303.2986 12.667 1.938 1.191 987 +CN CEP 2446304.2825 12.472 1.833 1.136 987 +CN CEP 2447739.4430 12.423 1.763 1.139 991 +CN CEP 2447741.4335 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2447768.3548 13.110 1.771 .998 991 +DR CEP 2447770.3120 13.339 1.767 1.000 991 +DR CEP 2447771.3017 13.369 1.749 .984 991 +DR CEP 2447772.2970 13.322 1.733 .947 991 +DR CEP 2447773.3262 13.195 1.588 .949 991 +DR CEP 2447774.3312 13.196 1.581 .932 991 +DR CEP 2447775.2948 13.113 1.499 .894 991 +DR CEP 2447776.3055 12.345 1.177 .743 991 +DR CEP 2449934.4002 12.466 .790 1.540 998 +DR CEP 2449935.3950 12.503 .821 1.625 998 +DR CEP 2449936.3880 12.624 .856 1.688 998 +DR CEP 2449937.3670 12.695 .912 998 +DR CEP 2449938.4077 12.721 .903 1.743 998 +DR CEP 2449939.4051 12.879 1.809 998 +DR CEP 2449941.3748 13.010 1.825 998 +DR CEP 2449942.3548 13.141 1.900 998 +DR CEP 2449943.3550 13.227 1.923 998 +DR CEP 2449944.3922 13.295 1.928 998 +DR CEP 2449945.3918 13.406 1.912 998 +DR CEP 2449946.3800 13.422 1.947 998 +DR CEP 2449947.3381 13.373 1.910 998 +DR CEP 2449948.3311 13.226 1.854 998 +DR CEP 2449949.3206 13.280 1.796 998 +DR CEP 2449950.3069 13.128 1.715 998 +DR CEP 2449952.3030 12.374 1.509 998 +DR CEP 2449953.3779 12.446 1.552 998 +DR CEP 2449954.3203 12.535 1.589 998 +DR CEP 2449955.2942 12.610 1.675 998 +DR CEP 2450310.3830 1.622 .974 971 +DR CEP 2450311.3678 13.127 1.592 .905 971 +DR CEP 2450312.3659 13.239 1.585 .905 971 +DR CEP 2450313.3433 12.585 1.267 .779 971 +DR CEP 2450314.3156 12.365 1.219 .796 971 +DR CEP 2450315.2963 12.403 1.243 .798 971 +DR CEP 2450316.2892 12.478 1.372 .804 971 +DR CEP 2450317.3092 12.545 1.443 .841 971 +DR CEP 2450318.3177 12.624 1.524 .885 971 +DR CEP 2450319.2991 12.693 1.599 .916 971 +DR CEP 2450320.2732 12.801 1.602 .945 971 +DR CEP 2450321.2802 12.876 1.611 .966 971 +DR CEP 2450322.2710 12.971 .942 971 +DR CEP 2450323.2704 13.069 1.743 1.001 971 +DR CEP 2450324.2792 13.142 1.713 .996 971 +DR CEP 2450325.2573 13.225 1.773 1.006 971 +DR CEP 2450326.2150 13.325 1.864 1.001 971 +DR CEP 2450327.2927 13.378 1.770 .965 971 +DR CEP 2450330.2433 13.193 1.569 .955 971 +DR CEP 2450332.2288 12.776 1.301 .809 971 +DR CEP 2450333.2313 12.354 1.209 .822 971 +DR CEP 2450334.2397 12.376 1.259 .746 971 +DR CEP 2450335.2481 12.463 1.322 .784 971 +DR CEP 2450336.3084 12.489 1.403 .790 971 +DR CEP 2450337.2164 12.611 1.477 .831 971 +DR CEP 2450338.3485 12.680 1.515 .874 971 +DR CEP 2450340.2151 12.859 1.659 .925 971 +DR CEP 2450341.2151 12.968 1.653 .965 971 +DR CEP 2450342.2380 13.006 1.767 .961 971 +DR CEP 2450344.2567 13.207 1.739 .925 971 +DR CEP 2450347.2328 13.370 1.745 .973 971 +DR CEP 2450349.1941 13.133 1.662 .924 971 +DR CEP 2450357.1984 12.704 1.508 .905 971 +IR CEP 2446608.3744 7.868 .612 .940 .555 988 +IR CEP 2446609.4293 7.731 .562 .861 .517 988 +IR CEP 2446611.3863 7.823 .575 .898 .536 988 +IR CEP 2446612.4204 7.804 .597 .914 .550 988 +IR CEP 2446613.3734 7.931 .584 .940 .558 988 +IR CEP 2446614.3844 7.748 .585 .887 .533 988 +IR CEP 2446615.3713 7.956 .615 .962 .559 988 +IR CEP 2446616.3846 7.697 .572 .866 .513 988 +IR CEP 2446617.3578 7.971 .610 .985 .575 988 +IR CEP 2446618.3708 7.651 .560 .841 .505 988 +IR CEP 2446619.4513 7.971 .978 .574 988 +IR CEP 2446620.3694 7.605 .555 .819 .487 988 +IR CEP 2446621.3766 7.962 .621 .978 .567 988 +IR CEP 2446622.3649 7.613 .541 .817 .490 988 +IR CEP 2446623.3923 7.934 .625 .972 .566 988 +IR CEP 2446624.3733 7.640 .539 .818 .494 988 +IR CEP 2446625.3446 7.912 .608 .935 .565 988 +IR CEP 2446626.3565 7.701 .551 .860 .504 988 +IR CEP 2446627.3523 7.863 .606 .928 .553 988 +IR CEP 2446628.3666 7.793 .562 .890 .523 988 +IR CEP 2446629.3277 7.808 .585 .915 .544 988 +IR CEP 2446631.3266 7.757 .588 .887 .527 988 +IR CEP 2446632.3447 7.935 .598 .954 .553 988 +IR CEP 2446636.3586 7.978 .623 .998 .568 988 +IR CEP 2447409.3136 7.739 .515 .879 .533 990 +IR CEP 2447410.3358 7.934 .538 .977 .546 990 +IR CEP 2447411.3323 7.691 .483 .858 .519 990 +IR CEP 2447413.3014 7.646 .483 .825 .500 990 +IR CEP 2447413.4121 7.730 .506 .811 .534 990 +IR CEP 2447414.2994 7.955 .534 1.007 .559 990 +IR CEP 2447415.2761 7.619 .457 .823 .492 990 +IR CEP 2447416.2733 7.948 .556 .984 .561 990 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CEP 2448507.3283 7.983 .559 .982 .579 993 +IR CEP 2448508.2795 7.605 .483 .823 .493 993 +IR CEP 2448509.3192 7.973 .519 .990 .579 993 +IR CEP 2448510.2995 7.596 .468 .817 .492 993 +IR CEP 2448511.3072 7.916 .524 .977 .570 993 +IR CEP 2448512.2918 7.655 .435 .838 .500 993 +IR CEP 2448513.3048 7.896 .518 .947 .571 993 +IR CEP 2448514.3113 7.717 .480 .854 .515 993 +IR CEP 2448515.2960 7.858 .539 .930 .564 993 +IR CEP 2448516.2320 7.845 .920 .539 993 +IR CEP 2448517.3191 7.809 .920 .548 993 +IR CEP 2448518.2342 7.927 .941 .563 993 +IR CEP 2448519.2855 7.741 .896 .527 993 +IR CEP 2448520.2717 7.942 .951 .556 993 +IR CEP 2448521.3166 7.712 .883 .521 993 +IR CEP 2448522.2757 7.978 .977 .580 993 +IR CEP 2448523.2688 7.667 .859 .501 993 +IR CEP 2448854.3619 7.915 .494 .976 .563 994 +IR CEP 2448856.3460 7.970 .993 .581 994 +IR CEP 2448858.3223 7.981 .993 .561 994 +IR CEP 2448860.3214 7.951 .565 .980 .568 994 +IR CEP 2448862.3185 7.929 .984 .571 994 +IR CEP 2448870.3039 7.747 .518 .930 .526 994 +IR CEP 2448872.2843 7.724 .502 .898 .516 994 +IR CEP 2448874.3103 7.674 .445 .871 .502 994 +IR CEP 2448875.3154 7.969 .531 1.003 .575 994 +IR CEP 2448876.3052 7.648 .483 .842 .515 994 +IR CEP 2448877.2662 7.963 .549 1.009 .544 994 +IR CEP 2448878.3017 7.603 .470 .828 .490 994 +IR CEP 2448880.2508 7.626 .464 .844 .495 994 +IR CEP 2448881.2418 7.900 .540 .977 .581 994 +IR CEP 2448882.2446 7.664 .465 .852 .500 994 +IR CEP 2448883.2702 7.879 .541 .952 .561 994 +IR CEP 2448884.2452 7.774 .508 .892 994 +IR CEP 2448885.2415 7.851 .522 .949 .543 994 +IR CEP 2448886.2548 7.820 .466 .920 .540 994 +IR CEP 2448887.2866 7.793 .508 .929 .543 994 +IR CEP 2448888.2378 7.918 .505 .970 .556 994 +IR CEP 2448889.2434 7.734 .480 .911 .537 994 +IR CEP 2448890.2222 7.941 .542 .995 .549 994 +IR CEP 2448890.3959 7.893 .936 .553 994 +IR CEP 2448891.2205 7.666 .461 .866 .513 994 +IR CEP 2448891.3347 7.730 .482 .896 .526 994 +IR CEP 2448892.2388 7.962 .520 .994 .564 994 +IR CEP 2448892.3358 1.013 .585 994 +IR CEP 2448893.2313 7.642 .452 .856 .502 994 +IR CEP 2448894.2671 7.958 .521 .988 .572 994 +IR CEP 2449617.2551 .944 .555 995 +IR CEP 2449618.4031 .595 .830 .512 995 +IR CEP 2449620.3183 7.581 .564 .804 .513 995 +IR CEP 2449620.4281 7.592 .581 .823 .494 995 +IR CEP 2449621.3277 7.983 .621 .946 .587 995 +IR CEP 2449621.4197 8.004 .648 .956 .557 995 +IR CEP 2449622.3915 7.602 .589 .775 .495 995 +IR CEP 2449622.4550 7.595 .821 .503 995 +IR CEP 2449623.3073 7.892 .619 .919 .566 995 +IR CEP 2449623.4168 7.916 .950 .550 995 +IR CEP 2449624.2971 7.711 .562 .857 995 +IR CEP 2449624.3732 7.673 .529 .829 .509 995 +IR CEP 2449624.4672 7.644 .813 .497 995 +IR CEP 2449625.3013 7.850 .636 .913 .549 995 +IR CEP 2449625.3408 7.841 .594 .929 .555 995 +IR CEP 2449625.3830 7.893 .617 .948 .562 995 +IR CEP 2449625.4703 7.937 .968 .549 995 +IR CEP 2449631.3205 7.705 .870 .519 995 +IR CEP 2449632.2932 7.971 .644 .941 .562 995 +IR CEP 2449632.3849 7.941 .589 .958 .556 995 +IR CEP 2449632.3992 7.944 .618 .940 .568 995 +IR CEP 2449633.2757 7.666 .844 .513 995 +IR CEP 2449633.2883 7.653 .544 .854 .494 995 +IR CEP 2449633.3500 7.692 .547 .856 .513 995 +IR CEP 2449633.4175 7.725 .839 .524 995 +IR CEP 2449634.2845 7.967 .968 .560 995 +IR CEP 2449634.3128 7.972 .645 .961 .570 995 +IR CEP 2449635.3244 7.620 .811 .492 995 +IR CEP 2449635.4065 7.655 .827 .509 995 +IR CEP 2449934.4365 8.003 .980 .596 1.119 998 +IR CEP 2449935.4205 7.630 .524 1.000 998 +IR CEP 2449936.4073 7.988 .583 1.130 998 +IR CEP 2449937.4044 7.639 .848 .503 998 +IR CEP 2449938.4217 7.971 .601 1.158 998 +IR CEP 2449939.4137 7.685 .845 .510 .997 998 +IR CEP 2449941.4133 7.775 1.009 998 +IR CEP 2449942.3841 7.879 1.101 998 +IR CEP 2449943.3783 7.889 1.085 998 +IR CEP 2449944.4071 7.824 1.103 998 +IR CEP 2449946.3966 7.807 1.065 998 +IR CEP 2449947.3692 7.995 1.114 998 +IR CEP 2449948.3326 7.685 .871 .509 .991 998 +IR CEP 2449949.3306 8.036 1.138 998 +IR CEP 2449952.3157 7.612 .830 .502 .984 998 +IR CEP 2449953.3932 8.003 1.160 998 +IR CEP 2449954.3440 7.668 1.009 998 +IR CEP 2449955.3032 7.948 .978 .577 1.118 998 +IR CEP 2450307.3514 7.626 .914 .528 971 +IR CEP 2450311.2968 7.822 .911 .560 971 +IR CEP 2450312.3301 7.839 .951 .565 971 +IR CEP 2450313.3184 7.849 .952 .557 971 +IR CEP 2450314.2879 7.759 .926 .583 971 +IR CEP 2450315.2882 7.945 .998 .577 971 +IR CEP 2450316.2806 7.740 .932 .539 971 +IR CEP 2450317.2414 8.001 .993 .582 971 +IR CEP 2450318.2745 7.664 .884 .514 971 +IR CEP 2450319.2851 7.970 1.040 .574 971 +IR CEP 2450320.2769 7.645 .864 .518 971 +IR CEP 2450321.2748 7.976 1.015 .587 971 +IR CEP 2450322.2694 7.596 .858 .520 971 +IR CEP 2450323.2690 7.956 1.035 .589 971 +IR CEP 2450324.2731 7.645 .885 .522 971 +IR CEP 2450325.2523 7.917 .992 .567 971 +IR CEP 2450326.2086 7.758 .883 .536 971 +IR CEP 2450326.4746 7.619 .807 .463 971 +IR CEP 2450332.4696 7.783 .914 .592 971 +IR CEP 2450333.4995 7.840 .944 971 +IR CEP 2450335.4942 7.788 .903 .540 971 +IR CEP 2450337.4983 7.815 .862 971 +IR CEP 2450338.4823 7.937 .965 .555 971 +IR CEP 2450341.4835 7.660 .873 .546 971 +IY CEP 2446252.4489 12.672 1.476 .856 987 +IY CEP 2446253.3702 12.842 1.575 .896 987 +IY CEP 2446255.4112 13.159 1.669 .937 987 +IY CEP 2446256.3861 12.770 1.452 .858 987 +IY CEP 2446257.4552 12.565 1.376 .839 987 +IY CEP 2446258.4464 12.740 1.512 .894 987 +IY CEP 2446259.3379 12.898 1.600 .910 987 +IY CEP 2446260.3202 13.074 1.608 .942 987 +IY CEP 2446261.3286 13.164 1.565 .938 987 +IY CEP 2446263.3831 12.603 1.402 .846 987 +IY CEP 2446265.3483 12.961 1.560 .937 987 +IY CEP 2446266.3536 13.087 1.646 .947 987 +IY CEP 2446267.3545 13.042 1.561 .916 987 +IY CEP 2446268.3546 12.500 1.309 .794 987 +IY CEP 2446269.3225 12.651 1.441 .838 987 +IY CEP 2446270.3166 12.853 1.543 .912 987 +IY CEP 2446272.3565 13.128 1.696 .929 987 +IY CEP 2446273.3548 12.818 1.421 .858 987 +IY CEP 2446274.3298 12.543 1.400 .830 987 +IY CEP 2446275.3526 12.739 1.495 987 +IY CEP 2446279.3708 12.646 1.376 .823 987 +IY CEP 2446280.3329 12.588 1.411 .830 987 +IY CEP 2446283.3347 13.134 1.620 .959 987 +IY CEP 2446284.3298 13.021 1.569 .879 987 +IY CEP 2446286.2770 12.667 1.452 .849 987 +IY CEP 2446288.3370 13.024 1.617 .918 987 +IY CEP 2446289.3290 13.176 1.634 .958 987 +IY CEP 2446290.3362 12.838 1.427 .862 987 +IY CEP 2446291.2772 12.528 1.331 .805 987 +IY CEP 2446292.2876 12.741 1.487 .880 987 +IY CEP 2446294.2541 13.065 1.643 .931 987 +IY CEP 2446295.2641 13.158 1.612 .943 987 +IY CEP 2446296.2515 12.673 1.355 .825 987 +IY CEP 2446297.2503 12.611 1.383 .839 987 +IY CEP 2446298.2620 12.820 1.505 .906 987 +IY CEP 2446299.2423 12.956 1.598 .926 987 +IY CEP 2446300.2454 13.118 1.626 .950 987 +IY CEP 2446301.2909 13.043 1.557 .912 987 +IY CEP 2446302.3040 12.525 1.330 .806 987 +IY CEP 2446303.2593 12.642 1.454 .836 987 +IY CEP 2446304.2433 12.838 1.539 .892 987 +IY CEP 2447738.4436 12.991 1.506 .885 991 +IY CEP 2447739.4202 12.463 1.304 .774 991 +IY CEP 2447740.4366 12.690 1.419 .854 991 +IY CEP 2447741.4103 12.824 1.523 .887 991 +IY CEP 2447742.4057 13.032 1.617 .941 991 +IY CEP 2447743.3881 13.159 1.638 .933 991 +IY CEP 2447744.4210 12.788 1.452 .827 991 +IY CEP 2447745.4149 12.614 1.356 .866 991 +IY CEP 2447746.4251 12.761 1.518 .859 991 +IY CEP 2447747.3734 12.881 1.573 .889 991 +IY CEP 2447748.4206 13.091 1.619 .926 991 +IY CEP 2447749.4237 13.129 1.535 .914 991 +IY CEP 2447750.4200 12.631 1.295 .839 991 +IY CEP 2447751.4205 12.589 1.408 .813 991 +IY CEP 2447753.3961 12.963 .919 991 +IY CEP 2447754.3827 13.113 1.571 .940 991 +IY CEP 2447755.4133 12.949 1.533 .906 991 +IY CEP 2447756.4059 12.446 1.346 .773 991 +IY CEP 2447757.3859 12.673 1.413 .894 991 +IY CEP 2447758.3541 12.837 1.534 .907 991 +IY CEP 2447759.3433 12.992 1.612 .899 991 +IY CEP 2447760.4221 13.152 1.610 .959 991 +IY CEP 2447761.3105 12.785 1.456 .849 991 +IY CEP 2447762.4104 12.532 1.347 .828 991 +IY CEP 2447763.2539 12.726 1.464 .877 991 +IY CEP 2447764.2619 12.872 1.553 .899 991 +IY CEP 2447766.2735 13.141 1.616 .930 991 +IY CEP 2447767.3637 12.564 1.364 .783 991 +IY CEP 2447768.3602 12.549 1.411 .813 991 +IY CEP 2447770.3150 12.959 1.576 .940 991 +IY CEP 2447771.3114 13.105 1.610 .940 991 +IY CEP 2447772.3127 13.030 1.545 .910 991 +IY CEP 2447773.3404 12.484 1.331 .740 991 +IY CEP 2447774.3461 12.679 1.440 .871 991 +IY CEP 2447775.3097 12.868 1.508 .920 991 +IY CEP 2447776.3243 13.026 1.625 .946 991 +IY CEP 2450311.3759 13.053 .919 971 +IY CEP 2450312.3749 13.213 1.639 .955 971 +IY CEP 2450314.3228 12.627 1.384 .888 971 +IY CEP 2450315.3021 12.798 1.561 .893 971 +IY CEP 2450316.2953 12.954 1.669 .921 971 +IY CEP 2450317.3156 13.117 1.717 .945 971 +IY CEP 2450318.3257 13.024 1.551 .906 971 +IY CEP 2450319.3044 12.502 1.349 .795 971 +IY CEP 2450320.2803 12.673 1.438 .858 971 +IY CEP 2450321.2882 12.859 1.617 .922 971 +IY CEP 2450322.2827 13.019 1.650 .956 971 +IY CEP 2450323.2810 13.164 1.714 .950 971 +IY CEP 2450324.2857 12.838 1.471 .880 971 +IY CEP 2450325.2660 12.545 1.305 .830 971 +IY CEP 2450326.2288 12.747 1.498 .858 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7.550 .464 .925 972 +BP CIR 2450579.5172 7.667 .473 .955 972 +BP CIR 2450580.4327 7.388 .422 .859 972 +BP CIR 2450580.5585 7.419 .433 .871 972 +BP CIR 2450582.4592 7.402 .412 .847 972 +BP CIR 2450582.5420 7.384 .413 .844 972 +BP CIR 2450583.4610 7.596 .464 .941 972 +BP CIR 2450583.5833 7.599 .460 .933 972 +BP CIR 2450584.4208 7.643 .468 .936 972 +BP CIR 2450584.4897 7.592 .469 .924 972 +SS CMA 2445666.4921 9.990 .910 1.250 982 +SS CMA 2445676.4843 10.376 1.210 1.478 .818 982 +SS CMA 2445687.4256 10.272 1.524 .899 982 +SS CMA 2445691.4453 9.898 1.218 .720 982 +SS CMA 2445692.3476 9.832 .873 1.151 .709 982 +SS CMA 2445693.3631 9.455 .755 1.061 .632 982 +SS CMA 2445694.3671 9.499 .807 1.070 .657 982 +SS CMA 2445704.3437 9.836 1.205 .708 982 +SS CMA 2445705.3320 9.709 .812 1.117 .681 982 +SS CMA 2445706.3280 9.416 .763 1.028 .650 982 +SS CMA 2445707.3397 9.665 .822 1.175 .704 982 +VZ CMA 2449803.6656 9.183 .856 .556 .593 997 +VZ CMA 2449807.6505 9.494 1.004 .625 .653 997 +VZ CMA 2449808.6709 9.605 1.031 .656 .664 997 +VZ CMA 2449809.6132 9.249 .865 .575 .596 997 +VZ CMA 2449810.6421 9.420 .988 .609 .642 997 +VZ CMA 2449811.6137 9.638 1.048 .657 .664 997 +VZ CMA 2449812.6313 9.277 .885 .568 .614 997 +VZ CMA 2449813.6428 9.395 .942 .627 .616 997 +VZ CMA 2449814.6425 9.618 1.049 .670 .660 997 +VZ CMA 2449815.6122 9.328 .921 .584 .610 997 +VZ CMA 2449816.6007 9.308 .916 .593 .606 997 +VZ CMA 2449822.5766 9.220 .876 .562 .596 997 +VZ CMA 2449823.5662 9.561 1.023 .648 .653 997 +VZ CMA 2449824.5662 9.500 .984 .629 .636 997 +VZ CMA 2449825.5613 9.203 .867 .559 .591 997 +VZ CMA 2449826.5739 9.510 1.015 .636 .662 997 +VZ CMA 2449827.5718 9.563 .988 .644 .653 997 +VZ CMA 2450351.8190 9.509 1.296 999 +VZ CMA 2450352.8020 9.529 1.007 1.291 999 +VZ CMA 2450354.7931 9.479 1.289 999 +VZ CMA 2450355.7967 9.544 1.289 999 +VZ CMA 2450355.9055 9.541 1.300 999 +VZ CMA 2450357.7943 9.407 1.255 999 +VZ CMA 2450358.7971 9.585 1.317 999 +VZ CMA 2450359.8100 9.228 1.167 999 +VZ CMA 2450361.7923 9.608 1.324 999 +VZ CMA 2450362.7942 9.286 1.176 999 +VZ CMA 2450363.7849 9.323 1.221 999 +VZ CMA 2450379.7427 9.445 1.265 999 +VZ CMA 2450380.7306 9.574 1.324 999 +VZ CMA 2450381.7262 9.190 1.117 999 +VZ CMA 2450382.7280 9.403 1.243 999 +VZ CMA 2450383.7042 9.597 1.320 999 +VZ CMA 2450384.6984 9.256 1.187 999 +VZ CMA 2450386.6932 9.555 1.337 999 +VZ CMA 2450386.6945 9.549 1.322 999 +VZ CMA 2450387.6932 9.311 1.204 999 +VZ CMA 2450388.7164 9.279 1.209 999 +VZ CMA 2450389.6856 9.561 1.295 999 +VZ CMA 2450390.7009 9.364 1.217 999 +VZ CMA 2450391.6867 9.222 1.182 999 +VZ CMA 2450392.6934 9.544 1.312 999 +VZ CMA 2450393.7192 9.410 1.241 999 +VZ CMA 2450394.7164 9.196 1.174 999 +VZ CMA 2450543.2573 9.635 1.334 972 +VZ CMA 2450568.2454 9.602 1.326 972 +VZ CMA 2450570.2294 9.359 1.234 972 +VZ CMA 2450571.2157 9.597 1.331 972 +VZ CMA 2450572.2127 9.333 1.215 972 +VZ CMA 2450573.2245 9.308 1.224 972 +VZ CMA 2450575.2141 9.365 1.230 972 +VZ CMA 2450575.2374 9.357 1.218 972 +VZ CMA 2450576.2085 9.265 1.190 972 +VZ CMA 2450576.2434 9.275 1.192 972 +VZ CMA 2450578.2135 9.414 1.235 972 +VZ CMA 2450578.2386 9.421 1.234 972 +VZ CMA 2450579.1967 9.223 1.194 972 +VZ CMA 2450579.2779 9.263 1.198 972 +VZ CMA 2450580.2119 9.561 1.316 972 +VZ CMA 2450580.2470 9.592 1.312 972 +VZ CMA 2450581.2288 9.488 1.262 972 +VZ CMA 2450581.2351 9.490 1.267 972 +VZ CMA 2450581.2393 9.491 1.267 972 +VZ CMA 2450582.1997 9.227 1.176 972 +VZ CMA 2450582.2579 9.229 1.180 972 +VZ CMA 2450583.2220 9.481 1.296 972 +VZ CMA 2450583.2560 9.540 1.304 972 +VZ CMA 2450584.2120 9.524 1.276 972 +VZ CMA 2450584.2430 9.527 1.293 972 +AO CMA 2445694.3788 12.424 1.518 .913 982 +AO CMA 2445704.3397 12.239 1.496 .896 982 +AO CMA 2445705.3280 12.450 1.533 .938 982 +AO CMA 2445706.3242 12.284 1.431 .895 982 +AO CMA 2445707.3320 11.658 .843 1.152 .765 982 +AO CMA 2449803.6557 12.070 1.380 .854 .833 997 +AO CMA 2449804.6700 12.247 1.478 .866 .854 997 +AO CMA 2449807.6567 11.720 1.167 .727 .760 997 +AO CMA 2449808.6729 11.990 1.289 .851 .799 997 +AO CMA 2449809.6157 12.109 1.363 .855 .837 997 +AO CMA 2449810.6448 12.344 1.455 .906 .860 997 +AO CMA 2449811.6160 12.460 1.498 .886 .892 997 +AO CMA 2449812.6339 11.960 1.220 .775 .787 997 +AO CMA 2449813.6449 11.773 1.169 .749 .770 997 +AO CMA 2449814.6481 11.988 1.333 .820 .820 997 +AO CMA 2449815.6166 12.132 1.428 .857 .848 997 +BC CMA 2445706.3203 13.370 1.249 .794 982 +BC CMA 2445707.3280 12.600 .986 .644 982 +BC CMA 2449804.6601 12.950 1.138 .679 .712 997 +BC CMA 2449807.6476 12.595 .901 .576 .654 997 +BC CMA 2449808.6658 12.962 1.167 .699 .722 997 +BC CMA 2449809.6087 13.239 1.189 .775 .763 997 +BC CMA 2449810.6356 13.375 1.252 .755 .758 997 +BC CMA 2449811.6094 12.573 .934 .577 .628 997 +BC CMA 2449812.6282 12.899 1.075 .693 .725 997 +BC CMA 2449813.6386 13.195 1.206 .753 .782 997 +BC CMA 2449814.6361 13.363 1.273 .765 .778 997 +BC CMA 2449815.6048 12.750 .932 .636 .647 997 +CP CMA 2445704.3242 13.272 1.168 .763 982 +CP CMA 2445705.3085 13.429 1.270 .763 982 +CP CMA 2445706.3125 13.585 1.262 .800 982 +CP CMA 2445707.3163 13.203 1.353 .716 982 +CP CMA 2449804.6527 13.391 1.239 .729 .765 997 +CP CMA 2449808.6608 13.288 1.255 .711 .749 997 +CP CMA 2449809.6019 13.470 1.227 .757 .759 997 +CP CMA 2449810.6250 13.619 1.349 .758 .738 997 +CP CMA 2449811.6041 12.948 .987 .593 .648 997 +CP CMA 2449812.6232 13.109 1.097 .671 .693 997 +CP CMA 2449813.6314 13.369 1.188 .752 .777 997 +CP CMA 2449814.6283 13.515 1.241 .782 .760 997 +VY CMI 2449804.6424 14.409 .226 -.117 .103 997 +VY CMI 2449806.6518 15.243 .452 .187 .042 997 +VY CMI 2449808.6435 15.370 .464 .220 -.015 997 +VY CMI 2449809.5787 15.357 .366 .312 .198 997 +VY CMI 2449810.6179 14.244 .024 .017 -.033 997 +VY CMI 2449812.6154 14.988 .290 .189 .189 997 +VY CMI 2449813.5444 15.102 .280 .283 .392 997 +VY CMI 2449825.4891 14.454 .125 .119 .088 997 +VY CMI 2449826.4962 14.925 .282 .258 .157 997 +AL CRA 2450348.6177 12.430 1.243 1.271 999 +AL CRA 2450351.5926 12.291 .902 1.105 999 +AL CRA 2450352.6425 12.191 .792 1.026 999 +AL CRA 2450353.5597 12.118 .800 .985 999 +AL CRA 2450354.5938 11.900 .705 .904 999 +AL CRA 2450355.5703 11.632 .652 .861 999 +AL CRA 2450355.6292 11.638 .713 .840 999 +AL CRA 2450357.5821 11.490 .771 .932 999 +AL CRA 2450358.5835 11.452 .975 999 +AL CRA 2450358.6703 11.471 .973 999 +AL CRA 2450359.5718 11.458 1.004 999 +AL CRA 2450359.6634 11.473 .985 999 +AL CRA 2450361.6059 11.631 1.010 1.066 999 +AL CRA 2450362.5803 11.770 1.126 999 +AL CRA 2450363.5792 11.940 1.184 999 +AL CRA 2450379.5872 11.738 1.130 999 +AL CRA 2450380.6063 12.011 1.186 999 +AL CRA 2450381.5958 12.252 1.242 999 +AL CRA 2450383.5885 12.571 1.256 999 +AL CRA 2450384.5831 12.478 1.206 999 +AL CRA 2450386.5499 12.248 1.034 999 +BE CRA 2450351.6011 13.717 .515 .774 999 +BE CRA 2450352.6692 14.253 .742 .840 999 +BE CRA 2450353.5731 13.583 .350 .499 999 +BE CRA 2450354.6174 13.571 .468 .663 999 +BE CRA 2450355.5826 13.994 .626 .836 999 +BE CRA 2450355.6400 14.048 .646 .832 999 +BE CRA 2450357.5897 13.407 .375 .569 999 +BE CRA 2450358.5963 13.842 .582 .765 999 +BE CRA 2450358.6853 13.900 .597 .783 999 +BE CRA 2450359.5839 14.266 .675 .850 999 +BE CRA 2450359.6758 14.349 .624 .866 999 +BE CRA 2450361.6168 13.704 .526 .700 999 +BE CRA 2450362.6334 14.186 .664 .875 999 +BE CRA 2450363.6133 13.475 .333 .463 999 +BE CRA 2450379.5980 14.264 .733 .915 999 +BE CRA 2450380.6138 13.356 .345 .514 999 +BE CRA 2450381.6035 13.645 .543 .649 999 +BE CRA 2450382.5903 14.092 .709 .840 999 +BE CRA 2450383.5945 13.611 .345 .528 999 +BE CRA 2450384.5925 13.548 .495 .645 999 +BE CRA 2450386.5835 14.166 .790 999 +BE CRA 2450389.5429 14.284 .652 .871 999 +BE CRA 2450392.5333 14.090 .654 .846 999 +BE CRA 2450392.5734 14.123 .658 .840 999 +BE CRA 2450393.5345 13.811 .412 .602 999 +BE CRA 2450393.5660 13.728 .422 .601 999 +BQ CRA 2450348.6373 12.886 .128 .222 999 +BQ CRA 2450351.5954 14.046 .546 .696 999 +BQ CRA 2450352.6460 14.058 .514 .747 999 +BQ CRA 2450353.5668 13.829 .392 .641 999 +BQ CRA 2450354.6019 13.649 .361 .594 999 +BQ CRA 2450355.5778 13.403 .268 .468 999 +BQ CRA 2450355.6364 13.511 .344 .521 999 +BQ CRA 2450357.5865 13.388 .306 .401 999 +BQ CRA 2450358.5915 14.141 .489 .677 999 +BQ CRA 2450358.6802 13.951 .448 .629 999 +BQ CRA 2450359.5783 14.098 .460 .702 999 +BQ CRA 2450359.6691 14.103 .464 .719 999 +BQ CRA 2450361.6118 13.971 .464 .688 999 +BQ CRA 2450362.6275 13.835 .432 .656 999 +BQ CRA 2450363.6093 13.616 .398 .591 999 +BQ CRA 2450379.5918 13.882 999 +BQ CRA 2450380.6107 13.806 .400 .641 999 +BQ CRA 2450381.5987 13.575 .273 .546 999 +BQ CRA 2450383.5917 12.851 .176 .195 999 +BQ CRA 2450384.5867 14.143 .468 .655 999 +BQ CRA 2450390.5377 13.387 .274 .461 999 +BQ CRA 2450391.5372 13.025 .177 .304 999 +BQ CRA 2450392.5361 13.546 .345 .462 999 +HQ CRA 2450348.6090 15.249 .805 .914 999 +HQ CRA 2450351.5844 15.095 .596 .900 999 +HQ CRA 2450353.5545 14.233 .324 .447 999 +HQ CRA 2450354.5794 15.164 .564 .767 999 +HQ CRA 2450355.5595 15.051 .549 .774 999 +HQ CRA 2450357.5775 14.566 .425 .576 999 +HQ CRA 2450358.5771 15.243 .569 .779 999 +HQ CRA 2450359.5413 14.855 .534 .655 999 +HQ CRA 2450361.5786 15.133 .571 .784 999 +HQ CRA 2450362.6178 14.912 .610 .754 999 +HQ CRA 2450363.5931 14.318 .386 .513 999 +HQ CRA 2450379.5631 14.975 .586 .750 999 +HQ CRA 2450380.5989 14.523 .259 .448 999 +HQ CRA 2450381.5906 14.776 .528 .467 999 +HQ CRA 2450382.5632 14.993 .698 .556 999 +HQ CRA 2450383.5838 14.731 .543 .574 999 +HQ CRA 2450384.5755 14.318 .501 .441 999 +HQ CRA 2450386.5792 14.880 .482 .776 999 +HQ CRA 2450387.5717 14.169 .341 .444 999 +HQ CRA 2450388.5660 14.917 .498 .523 999 +HQ CRA 2450389.5625 14.940 .651 .716 999 +HQ CRA 2450390.5551 14.505 .432 .559 999 +HQ CRA 2450391.5544 14.573 .344 .480 999 +HQ CRA 2450392.5570 15.085 .669 .722 999 +HQ CRA 2450393.5528 14.989 .745 999 +KQ CRA 2450348.6022 12.590 .875 1.096 999 +KQ CRA 2450351.5792 12.313 .811 1.043 999 +KQ CRA 2450352.6216 12.242 .793 1.068 999 +KQ CRA 2450353.5484 12.107 .832 1.006 999 +KQ CRA 2450354.5694 12.083 .834 1.065 999 +KQ CRA 2450355.5526 12.046 .838 1.037 999 +KQ CRA 2450355.6221 12.019 .829 1.063 999 +KQ CRA 2450357.5692 11.967 .855 1.085 999 +KQ CRA 2450358.5692 11.984 1.084 999 +KQ CRA 2450359.5346 11.984 1.090 999 +KQ CRA 2450360.5691 12.097 1.107 999 +KQ CRA 2450361.5708 12.052 .889 1.113 999 +KQ CRA 2450362.5787 12.109 1.142 999 +KQ CRA 2450363.5776 12.149 1.150 999 +KQ CRA 2450379.5564 12.452 1.001 999 +KQ CRA 2450380.5893 12.342 .966 999 +KQ CRA 2450381.5845 12.208 .959 999 +KQ CRA 2450382.5546 12.223 .956 999 +KQ CRA 2450383.5778 12.150 .989 999 +KQ CRA 2450384.5559 12.192 1.043 999 +KQ CRA 2450386.5459 12.160 1.092 999 +KQ CRA 2450387.5668 12.081 1.092 999 +KQ CRA 2450388.5443 12.067 1.115 999 +KQ CRA 2450389.5558 12.060 1.122 999 +KQ CRA 2450390.5393 12.042 1.129 999 +KQ CRA 2450391.5385 12.083 1.122 999 +KQ CRA 2450392.5391 12.104 1.153 999 +KQ CRA 2450393.5472 12.151 1.147 999 +V347 CRA 2450348.6152 12.560 .759 .948 999 +V347 CRA 2450351.5892 12.227 .800 .943 999 +V347 CRA 2450352.6397 12.264 .780 1.018 999 +V347 CRA 2450353.5630 12.270 .913 1.021 999 +V347 CRA 2450354.5973 12.349 .955 1.063 999 +V347 CRA 2450355.5734 12.485 .996 1.120 999 +V347 CRA 2450355.6328 12.500 1.022 1.105 999 +V347 CRA 2450357.5837 12.985 1.166 1.221 999 +V347 CRA 2450358.5849 13.161 1.254 999 +V347 CRA 2450359.5736 13.120 1.183 999 +V347 CRA 2450361.6088 12.859 1.063 999 +V347 CRA 2450362.5822 12.878 1.022 999 +V347 CRA 2450363.5804 12.702 .938 999 +V347 CRA 2450379.5707 12.516 .920 999 +V347 CRA 2450380.6042 12.305 .884 999 +V347 CRA 2450381.5941 12.256 .952 999 +V347 CRA 2450382.5796 .987 999 +V347 CRA 2450383.5871 12.185 .995 999 +V347 CRA 2450384.5593 12.319 1.005 999 +V347 CRA 2450386.5479 12.548 1.109 999 +V347 CRA 2450387.5680 12.828 1.222 999 +V347 CRA 2450388.5462 13.014 1.242 999 +V347 CRA 2450389.5574 13.200 1.178 999 +V347 CRA 2450390.5411 13.166 1.192 999 +V347 CRA 2450391.5403 12.976 1.081 999 +V347 CRA 2450392.5519 12.894 1.021 999 +V347 CRA 2450393.5488 12.806 .984 999 +V449 CRA 2450351.6059 14.340 1.063 1.086 999 +V449 CRA 2450352.6839 14.464 .789 1.219 999 +V449 CRA 2450353.5781 13.932 .718 .851 999 +V449 CRA 2450354.6210 13.425 .507 .679 999 +V449 CRA 2450355.5863 13.075 .418 .591 999 +V449 CRA 2450355.6431 13.052 .432 .533 999 +V449 CRA 2450357.5928 13.015 .514 .677 999 +V449 CRA 2450358.6194 13.262 .700 .825 999 +V449 CRA 2450358.6901 13.267 .748 .839 999 +V449 CRA 2450359.5905 13.327 .827 .876 999 +V449 CRA 2450359.6860 13.358 .820 .889 999 +V449 CRA 2450361.6316 13.363 .917 .927 999 +V449 CRA 2450362.6398 13.422 .969 .915 999 +V449 CRA 2450363.6169 13.541 1.010 .967 999 +V449 CRA 2450379.6070 13.462 .929 .976 999 +V449 CRA 2450380.6170 13.714 1.063 1.038 999 +V449 CRA 2450381.6067 13.877 1.077 1.033 999 +V449 CRA 2450382.5981 14.043 1.221 1.004 999 +V449 CRA 2450383.5996 14.183 1.228 .989 999 +V449 CRA 2450384.5994 14.284 .993 1.068 999 +V449 CRA 2450386.5959 13.906 .645 .823 999 +V449 CRA 2450387.5754 13.420 .492 .662 999 +V449 CRA 2450388.5708 13.105 .389 .605 999 +R CRU 2450541.5931 6.462 .388 972 +R CRU 2450542.6142 6.713 .446 972 +R CRU 2450568.4778 7.158 .534 972 +R CRU 2450570.4018 6.410 .365 972 +R CRU 2450572.3557 6.836 .484 972 +R CRU 2450572.4946 6.842 .493 972 +R CRU 2450573.3479 7.027 .524 972 +R CRU 2450573.4399 7.052 .530 972 +R CRU 2450574.4738 7.151 .521 972 +R CRU 2450575.3346 6.694 .416 972 +R CRU 2450575.4340 6.610 .394 972 +R CRU 2450576.3392 6.435 .377 972 +R CRU 2450576.4288 6.458 .381 972 +R CRU 2450576.4862 6.470 .386 972 +R CRU 2450577.4994 6.704 .454 972 +R CRU 2450578.3736 6.860 .498 972 +R CRU 2450578.4480 6.868 .495 972 +R CRU 2450579.4511 7.082 .534 972 +R CRU 2450580.4163 7.140 .527 972 +R CRU 2450580.4978 7.124 .513 972 +R CRU 2450582.3677 6.479 .394 972 +R CRU 2450582.4354 6.496 .398 972 +R CRU 2450583.3605 6.714 .460 972 +R CRU 2450583.4325 6.732 .463 972 +R CRU 2450584.3499 6.873 .493 972 +R CRU 2450584.4482 6.896 .499 972 +S CRU 2450542.6287 6.857 .936 972 +S CRU 2450568.4808 6.601 .913 972 +S CRU 2450570.4046 6.923 .983 972 +S CRU 2450572.3583 6.383 .816 972 +S CRU 2450572.4967 6.410 .820 972 +S CRU 2450573.3505 6.659 .953 972 +S CRU 2450573.4423 6.671 .932 972 +S CRU 2450573.5451 6.670 .926 972 +S CRU 2450574.4764 6.859 .985 972 +S CRU 2450575.3368 6.913 .985 972 +S CRU 2450575.4362 6.891 .959 972 +S CRU 2450576.3412 6.209 .707 972 +S CRU 2450576.4311 6.208 .722 972 +S CRU 2450576.4885 6.211 .718 972 +S CRU 2450577.5015 6.507 .870 972 +S CRU 2450578.3756 6.722 .958 972 +S CRU 2450578.4502 6.742 .964 972 +S CRU 2450579.4533 6.907 1.008 972 +S CRU 2450580.4184 6.686 .886 972 +S CRU 2450580.5000 6.608 .864 972 +S CRU 2450582.3698 6.550 .895 972 +S CRU 2450582.4376 6.556 .882 972 +S CRU 2450583.3626 6.774 .971 972 +S CRU 2450583.4353 6.796 .979 972 +S CRU 2450584.3519 6.917 .987 972 +S CRU 2450584.4503 6.932 .992 972 +T CRU 2450541.5902 6.577 .523 .997 972 +T CRU 2450542.6008 6.702 .532 972 +T CRU 2450568.4764 6.566 .517 972 +T CRU 2450570.4007 6.811 .551 972 +T CRU 2450572.3548 6.422 .470 972 +T CRU 2450572.4936 6.386 .448 972 +T CRU 2450573.3471 6.364 .472 972 +T CRU 2450573.4391 6.362 .448 972 +T CRU 2450574.4727 6.500 .501 972 +T CRU 2450575.3339 6.585 .515 972 +T CRU 2450575.4332 6.608 .530 972 +T CRU 2450576.3385 6.712 .540 972 +T CRU 2450576.4280 6.735 .560 972 +T CRU 2450576.4855 6.745 .549 972 +T CRU 2450577.4982 6.825 .555 972 +T CRU 2450578.3728 6.615 .505 972 +T CRU 2450578.4472 6.603 .508 972 +T CRU 2450579.4503 6.340 .443 972 +T CRU 2450580.4156 6.401 .467 972 +T CRU 2450580.4971 6.430 .484 972 +T CRU 2450582.3669 6.617 .528 972 +T CRU 2450582.4346 6.621 .524 972 +T CRU 2450583.3598 6.753 .547 972 +T CRU 2450583.4317 6.767 .548 972 +T CRU 2450583.4332 6.767 .548 972 +T CRU 2450584.3492 6.807 .542 972 +T CRU 2450584.4473 6.799 .544 972 +X CRU 2450542.6275 8.543 .615 972 +X CRU 2450568.4730 8.670 .620 972 +X CRU 2450570.3950 8.150 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CRU 2450574.4641 11.862 .874 1.743 972 +VV CRU 2450575.3286 12.040 .932 1.847 972 +VV CRU 2450576.3326 12.179 .949 1.862 972 +VV CRU 2450576.4228 12.191 .946 1.869 972 +VV CRU 2450576.4801 12.196 .961 1.884 972 +VV CRU 2450577.4929 12.316 .935 1.868 972 +VV CRU 2450578.3672 12.217 .937 1.838 972 +VV CRU 2450578.4422 12.186 .945 1.822 972 +VV CRU 2450579.4446 11.686 .828 1.658 972 +VV CRU 2450580.4108 11.833 .868 1.731 972 +VV CRU 2450582.3613 12.141 .957 1.862 972 +VV CRU 2450582.4294 12.166 .948 1.870 972 +VV CRU 2450583.3547 12.311 .960 1.885 972 +VV CRU 2450583.4267 12.316 .972 1.885 972 +VV CRU 2450584.3446 12.292 .947 1.854 972 +VV CRU 2450584.4078 12.257 .954 1.845 972 +VV CRU 2450584.4423 12.239 .937 1.845 972 +VW CRU 2450542.6211 9.760 .864 1.701 972 +VW CRU 2450568.4704 9.697 .857 972 +VW CRU 2450570.3927 9.795 .843 1.670 972 +VW CRU 2450572.3506 9.445 .798 1.596 972 +VW CRU 2450573.3434 9.633 .859 1.682 972 +VW CRU 2450573.4352 9.639 .823 1.658 972 +VW CRU 2450574.4659 9.795 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2449543.5983 11.910 1.732 .986 .924 996 +VX CRU 2449546.5447 12.240 1.784 .998 .975 996 +VX CRU 2449558.5885 1.779 1.101 .995 996 +VX CRU 2449560.5443 12.460 1.711 1.078 .979 996 +VX CRU 2449560.5510 12.466 1.776 1.076 .953 996 +VX CRU 2449563.5733 11.968 1.570 996 +VX CRU 2449564.5465 11.565 1.388 996 +VX CRU 2449803.8495 1.876 1.022 997 +VX CRU 2449804.8366 12.412 1.832 1.087 1.018 997 +VX CRU 2449805.8109 12.310 1.720 1.065 .975 997 +VX CRU 2449807.8207 11.954 1.592 .969 .945 997 +VX CRU 2449808.8325 11.479 997 +VX CRU 2449809.7511 11.504 1.441 .888 .882 997 +VX CRU 2449809.8071 11.513 1.451 .897 .897 997 +VX CRU 2449810.7922 11.724 1.512 .977 .927 997 +VX CRU 2449811.7599 11.832 1.621 .992 .951 997 +VX CRU 2449812.7639 12.008 1.697 1.058 .987 997 +VX CRU 2449814.7797 12.278 1.869 1.105 1.000 997 +VX CRU 2449815.7342 12.354 1.843 1.110 1.011 997 +VX CRU 2449817.7581 12.349 1.790 1.083 .993 997 +VX CRU 2449817.8089 12.340 1.750 1.086 .986 997 +VX CRU 2449818.7261 12.190 1.646 1.045 .960 997 +VX CRU 2449818.7958 12.171 1.695 1.020 .961 997 +VX CRU 2449821.7206 11.442 1.385 .891 .857 997 +VX CRU 2449821.7920 11.455 1.404 .884 .871 997 +VX CRU 2449822.6911 11.691 1.530 .947 .912 997 +VX CRU 2449823.7085 11.845 1.662 1.016 .960 997 +VX CRU 2449825.6761 12.067 1.745 1.048 .998 997 +VX CRU 2449826.7269 12.204 1.832 1.072 .992 997 +VX CRU 2449827.6755 12.314 1.819 1.083 1.019 997 +AG CRU 2450542.6239 8.100 .868 972 +AG CRU 2450568.4719 7.796 .696 972 +AG CRU 2450570.3941 8.408 .983 972 +AG CRU 2450572.3520 7.780 .682 972 +AG CRU 2450572.4909 7.749 .674 972 +AG CRU 2450573.3445 8.113 .879 972 +AG CRU 2450573.4365 8.134 .868 972 +AG CRU 2450574.4699 8.457 1.001 972 +AG CRU 2450575.3315 8.606 1.022 972 +AG CRU 2450575.4307 8.587 1.013 972 +AG CRU 2450576.3361 7.757 .692 972 +AG CRU 2450576.4257 7.779 .710 972 +AG CRU 2450576.4828 7.806 .723 972 +AG CRU 2450577.4959 8.249 .928 972 +AG CRU 2450578.3705 8.496 1.013 972 +AG CRU 2450578.4450 8.524 1.014 972 +AG CRU 2450579.4473 8.554 .990 972 +AG CRU 2450580.4135 7.841 .736 972 +AG CRU 2450582.3647 8.522 1.007 972 +AG CRU 2450582.4321 8.529 1.009 972 +AG CRU 2450583.3576 8.501 .967 972 +AG CRU 2450583.4296 8.429 .936 972 +AG CRU 2450584.3473 7.899 .761 972 +AG CRU 2450584.4450 7.953 .792 972 +BG CRU 2449521.6444 5.503 996 +BG CRU 2449526.6401 5.460 .574 .353 .337 996 +BG CRU 2449528.6324 5.362 .579 .361 .341 996 +BG CRU 2449529.6034 5.357 .574 .388 .330 996 +BG CRU 2449530.6113 5.519 .628 .416 .366 996 +BG CRU 2449532.5947 5.347 .561 .367 .328 996 +BG CRU 2449534.6078 5.519 .656 .444 .360 996 +BG CRU 2449535.6042 5.402 .554 .313 996 +BG CRU 2449536.6023 5.452 .616 .325 996 +BG CRU 2449543.5780 5.509 .633 .406 .365 996 +BG CRU 2449544.5471 5.565 .650 .415 .367 996 +BG CRU 2449545.5410 5.373 .577 .373 .348 996 +BG CRU 2449546.5422 5.383 .566 .352 .334 996 +BG CRU 2449558.5738 5.456 .622 .342 .343 996 +BG CRU 2449559.4757 5.404 .569 .379 .373 996 +BG CRU 2449560.5239 5.579 .639 .397 .367 996 +BG CRU 2449561.5339 5.506 .625 996 +BG CRU 2449563.4691 5.474 .625 996 +BG CRU 2449564.4562 5.561 .651 .377 .360 996 +BG CRU 2449803.8434 .600 .346 997 +BG CRU 2449804.8317 5.584 .673 .374 .379 997 +BG CRU 2449805.8029 5.488 .605 .367 .344 997 +BG CRU 2449807.8169 5.538 .656 .368 .365 997 +BG CRU 2449808.8093 5.545 .640 .375 .358 997 +BG CRU 2449809.7492 5.394 .570 .341 .334 997 +BG CRU 2449809.8053 5.390 .572 .340 .334 997 +BG CRU 2449810.7900 5.470 .613 .360 .365 997 +BG CRU 2449811.7570 5.572 .655 .389 .371 997 +BG CRU 2449812.7594 5.445 .588 .353 .350 997 +BG CRU 2449813.7698 5.409 .579 .351 .338 997 +BG CRU 2449813.8267 5.399 .596 .335 .339 997 +BG CRU 2449814.7679 5.562 .686 997 +BG CRU 2449815.7313 5.501 .611 .373 .348 997 +BG CRU 2449815.7967 5.479 .619 .361 .348 997 +BG CRU 2449817.7292 5.515 .633 .381 .366 997 +BG CRU 2449817.8066 5.540 .634 .385 .368 997 +BG CRU 2449818.7230 5.562 .643 .381 .359 997 +BG CRU 2449818.7910 5.566 .625 .384 .355 997 +BG CRU 2449821.7172 5.562 .653 .394 .358 997 +BG CRU 2449821.7891 5.556 .663 .380 .372 997 +BG CRU 2449822.7161 5.438 .589 .356 .343 997 +BG CRU 2449822.7977 5.422 .588 .354 .360 997 +BG CRU 2449823.6869 5.402 .578 .354 .338 997 +BG CRU 2449823.7884 5.391 .605 .344 .335 997 +BG CRU 2449825.6738 5.519 .615 .370 .359 997 +BG CRU 2449825.7734 5.489 .611 .368 .352 997 +BG CRU 2449826.7244 5.366 .562 .345 .316 997 +BG CRU 2449827.6732 5.486 .640 .371 .342 997 +BG CRU 2450542.6179 5.427 .704 972 +BG CRU 2450568.4840 5.378 .674 972 +BG CRU 2450570.4073 5.549 .744 972 +BG CRU 2450572.4988 5.396 .684 972 +BG CRU 2450573.3528 5.535 .766 972 +BG CRU 2450573.4444 5.556 .695 972 +BG CRU 2450574.4790 5.498 .723 972 +BG CRU 2450575.3388 5.377 .687 972 +BG CRU 2450575.4383 5.361 .662 972 +BG CRU 2450576.3433 5.485 .729 972 +BG CRU 2450576.4329 5.492 .741 972 +BG CRU 2450576.4903 5.493 .723 972 +BG CRU 2450577.5033 5.538 .743 972 +BG CRU 2450578.3775 5.399 .686 972 +BG CRU 2450578.4520 5.396 .682 972 +BG CRU 2450579.4552 5.437 .712 972 +BG CRU 2450580.4202 5.564 .758 972 +BG CRU 2450580.5021 5.569 .761 972 +BG CRU 2450582.3716 5.369 .675 972 +BG CRU 2450582.4397 5.383 .675 972 +BG CRU 2450583.3642 5.535 .743 972 +BG CRU 2450583.4369 5.543 .746 972 +BG CRU 2450584.3536 5.519 .725 972 +BG CRU 2450584.4522 5.513 .726 972 +BX CRU 2449803.8596 12.820 1.847 1.855 1.979 918 +BX CRU 2449804.8442 12.819 1.823 1.833 1.998 918 +BX CRU 2449805.8160 12.767 1.858 1.804 1.992 918 +BX CRU 2449807.8286 12.746 1.848 1.800 2.001 918 +BX CRU 2449808.8375 12.720 1.854 1.792 1.979 918 +BX CRU 2449809.7552 12.692 1.881 1.810 1.973 918 +BX CRU 2449809.8141 12.659 1.867 1.784 1.967 918 +BX CRU 2449810.7955 12.632 1.877 1.764 1.984 918 +BX CRU 2449811.7634 12.639 1.829 1.782 1.968 918 +BX CRU 2449813.8321 12.573 1.876 1.757 1.938 918 +BX CRU 2449814.7850 12.564 1.848 1.759 1.941 918 +BX CRU 2449815.7404 12.550 1.872 1.758 1.939 918 +BX CRU 2449817.7642 12.529 1.879 1.765 1.943 918 +BX CRU 2449817.8151 12.518 1.856 1.756 1.940 918 +BX CRU 2449818.7366 12.498 1.867 1.744 1.930 918 +BX CRU 2449818.8039 12.491 1.884 1.742 1.922 918 +BX CRU 2449821.7267 12.431 1.877 1.731 1.914 918 +BX CRU 2449821.7981 12.443 1.962 1.752 1.914 918 +BX CRU 2449822.7196 12.421 1.936 1.736 1.920 918 +BX CRU 2449823.7168 12.414 1.826 1.718 1.926 918 +BX CRU 2449825.7033 12.403 1.892 1.729 1.922 918 +BX CRU 2449826.7363 12.385 1.857 1.720 1.900 918 +BX CRU 2449827.6929 12.386 1.854 1.707 1.901 918 +BX CRU 2449529.5937 12.558 1.909 1.889 1.922 996 +BX CRU 2449532.6035 12.613 1.899 1.870 1.914 996 +BX CRU 2449534.6378 12.674 1.952 1.909 1.891 996 +BX CRU 2449546.5521 12.508 1.871 1.771 1.912 996 +BX CRU 2449558.5947 1.912 996 +BX CRU 2449560.5581 12.496 1.861 1.723 1.879 996 +BX CRU 2449563.5769 12.466 996 +X CYG 2445488.2538 6.140 .844 1.117 .583 982 +X CYG 2445502.3163 5.904 .655 .889 .497 982 +X CYG 2445503.2695 6.001 .683 .967 .527 982 +X CYG 2445505.2617 6.181 .929 1.159 .590 982 +X CYG 2445508.2421 6.518 1.176 1.362 .667 982 +X CYG 2445509.2500 6.650 1.262 1.395 .692 982 +X CYG 2445512.2578 6.892 1.262 1.432 .706 982 +X CYG 2445513.2929 6.859 1.171 .693 982 +X CYG 2445514.2578 6.708 .970 1.285 .652 982 +X CYG 2445515.2655 6.588 .835 1.190 .627 982 +X CYG 2445644.3046 6.896 1.403 .705 982 +X CYG 2445648.2381 6.021 .567 .885 .489 982 +X CYG 2445649.2538 5.838 .590 .856 .474 982 +X CYG 2447432.0893 6.567 .875 1.208 .614 990 +X CYG 2447433.0854 6.632 .852 1.184 .621 990 +X CYG 2447434.0868 6.145 .565 .958 .506 990 +X CYG 2447734.4554 6.233 .937 1.207 .601 991 +X CYG 2447735.4379 6.314 1.059 1.269 .623 991 +X CYG 2447736.4409 6.448 1.170 1.321 .649 991 +X CYG 2447738.4149 6.715 1.357 1.410 .692 991 +X CYG 2447739.3850 6.778 1.396 1.428 .707 991 +X CYG 2447740.4132 6.867 1.346 1.445 .692 991 +X CYG 2447741.3691 6.854 1.338 1.410 .692 991 +X CYG 2447742.3740 6.744 1.115 1.333 .664 991 +X CYG 2447743.3623 6.556 .853 1.216 .639 991 +X CYG 2447744.3403 6.590 .867 1.207 .611 991 +X CYG 2447745.3442 6.115 .584 .946 .509 991 +X CYG 2447746.3489 5.848 .466 .916 .463 991 +X CYG 2447747.3470 5.886 .570 .910 .510 991 +X CYG 2447748.3447 5.998 .655 1.011 .528 991 +X CYG 2447749.3447 6.098 .790 1.088 .586 991 +X CYG 2447750.3433 6.216 .899 1.180 .624 991 +X CYG 2447751.3172 6.272 1.032 1.247 .616 991 +X CYG 2447752.3009 6.373 1.097 1.323 .644 991 +X CYG 2447753.2779 6.493 1.203 1.348 .664 991 +X CYG 2447754.3417 6.620 1.278 1.421 .686 991 +X CYG 2447755.3672 6.742 1.358 1.446 .708 991 +X CYG 2447756.3625 6.803 1.352 1.455 .701 991 +X CYG 2447757.3376 6.840 1.316 1.437 .710 991 +X CYG 2447758.3251 6.780 1.179 1.357 .685 991 +X CYG 2447759.3089 6.636 .953 1.264 .631 991 +X CYG 2447760.3056 6.596 .821 1.215 .616 991 +X CYG 2447761.2792 6.442 .751 1.105 .583 991 +X CYG 2447762.2574 5.919 .523 .844 .475 991 +X CYG 2447763.2271 5.837 .538 .876 .480 991 +X CYG 2447764.2330 5.941 .602 .946 .515 991 +X CYG 2447766.2350 6.158 .837 1.143 .596 991 +X CYG 2447767.2999 6.252 .978 1.222 .607 991 +X CYG 2447768.2819 6.319 1.068 1.292 .641 991 +X 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2450310.2895 10.462 1.693 1.017 971 +BZ CYG 2450311.2661 10.478 1.653 1.013 971 +BZ CYG 2450312.2924 10.308 1.616 .973 971 +BZ CYG 2450313.2845 10.091 1.490 .934 971 +BZ CYG 2450314.2734 10.016 1.474 .922 971 +BZ CYG 2450315.2831 10.003 1.526 .916 971 +BZ CYG 2450316.2156 10.001 1.536 .911 971 +BZ CYG 2450317.2370 10.170 1.613 .956 971 +BZ CYG 2450318.2656 10.234 1.658 .976 971 +BZ CYG 2450319.2448 10.340 1.717 .988 971 +BZ CYG 2450320.2375 10.472 1.746 1.000 971 +BZ CYG 2450321.2283 10.481 1.734 1.003 971 +BZ CYG 2450322.2626 10.332 1.616 .979 971 +BZ CYG 2450323.2617 10.144 1.572 .923 971 +BZ CYG 2450324.2642 10.009 1.512 .911 971 +BZ CYG 2450325.2284 10.022 1.525 .911 971 +CD CYG 2445502.3242 9.205 1.391 .791 982 +CD CYG 2445503.2460 9.240 1.385 .794 982 +CD CYG 2445505.2421 8.386 .754 1.003 .632 982 +CD CYG 2445508.2226 8.689 .955 1.291 .740 982 +CD CYG 2445509.2187 8.814 1.052 1.343 .780 982 +CD CYG 2445512.2264 9.112 1.373 1.529 .843 982 +CD CYG 2445513.2460 9.266 1.602 1.589 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2445880.2500 8.293 .704 .949 .585 982 +CD CYG 2445881.2343 8.452 .794 1.020 .633 982 +CD CYG 2445882.2226 8.539 .843 1.122 .674 982 +CD CYG 2445883.2304 8.634 .932 1.209 .716 982 +CD CYG 2445886.2304 8.917 1.263 1.451 .797 982 +CD CYG 2445887.2264 9.036 1.380 1.490 .815 982 +CD CYG 2448503.3064 9.442 1.650 .869 993 +CD CYG 2448504.2407 9.490 1.606 .875 993 +CD CYG 2448505.2623 9.489 1.603 .854 993 +CD CYG 2448506.2802 9.379 1.507 .847 993 +CD CYG 2448507.2549 9.239 1.415 .798 993 +CD CYG 2448508.2257 9.246 1.412 .777 993 +CD CYG 2448509.2524 8.514 1.015 .634 993 +CD CYG 2448510.2311 8.395 1.015 .641 993 +CD CYG 2448511.2453 8.487 1.101 .660 993 +CD CYG 2448512.2434 8.593 1.194 .697 993 +CD CYG 2448513.2473 8.697 1.269 .744 993 +CD CYG 2448514.2529 8.794 1.376 .773 993 +CD CYG 2448515.2299 8.891 1.415 .792 993 +CD CYG 2448516.2164 8.995 1.489 .817 993 +CD CYG 2448517.2105 9.097 1.541 .826 993 +CD CYG 2448518.2182 9.234 1.574 .862 993 +CD CYG 2448519.2495 9.350 1.621 .878 993 +CD CYG 2448520.2193 9.443 1.644 .877 993 +CD CYG 2448522.2211 9.508 1.599 .878 993 +CD CYG 2448523.2103 9.407 1.520 .847 993 +CD CYG 2448854.2994 8.646 1.238 .729 994 +CD CYG 2448856.2898 8.840 1.415 .785 994 +CD CYG 2448858.2816 9.053 1.538 .827 994 +CD CYG 2448860.2680 9.285 1.647 .861 994 +CD CYG 2448862.2917 9.478 1.637 .887 994 +CD CYG 2448870.2550 8.530 1.168 .670 994 +CD CYG 2448872.2594 8.709 1.324 .752 994 +CD CYG 2448874.2767 8.927 1.489 .802 994 +CD CYG 2448876.2356 9.173 1.560 .857 994 +CD CYG 2448877.2263 9.261 1.637 .851 994 +CD CYG 2448878.2415 9.382 1.659 .871 994 +CD CYG 2448880.2333 9.480 1.657 .856 994 +CD CYG 2448881.2000 9.433 1.600 .851 994 +CD CYG 2448882.2066 9.326 1.529 .818 994 +CD CYG 2448883.2377 9.240 1.427 .798 994 +CD CYG 2448884.2255 9.136 1.337 .766 994 +CD CYG 2448885.2239 8.306 .965 .583 994 +CD CYG 2448886.2356 8.439 1.059 .644 994 +CD CYG 2448887.2565 8.529 1.130 .678 994 +CD CYG 2448888.2173 8.620 1.246 .713 994 +CD CYG 2448889.2238 8.706 1.346 .735 994 +CD CYG 2448890.2058 8.809 1.399 .773 994 +CD CYG 2448891.2039 8.904 1.491 .794 994 +CD CYG 2448892.2232 9.024 1.540 .813 994 +CD CYG 2448893.2107 9.137 1.576 .844 994 +CD CYG 2448894.2149 9.255 1.623 .864 994 +CD CYG 2449934.3840 9.091 1.538 .877 1.596 998 +CD CYG 2449935.3847 9.205 .887 1.616 998 +CD CYG 2449936.3756 9.355 .884 1.664 998 +CD CYG 2449937.3582 9.474 .915 998 +CD CYG 2449938.3898 9.573 998 +CD CYG 2449939.3833 9.512 .872 1.660 998 +CD CYG 2449941.3541 9.283 1.566 998 +CD CYG 2449942.3362 9.280 1.548 998 +CD CYG 2449943.3254 8.700 1.331 998 +CD CYG 2449944.3769 8.404 1.238 998 +CD CYG 2449945.3786 8.521 1.280 998 +CD CYG 2449946.3637 8.638 1.353 998 +CD CYG 2449947.2919 8.719 1.421 998 +CD CYG 2449948.2774 8.795 1.487 998 +CD CYG 2449949.2752 8.914 1.552 998 +CD CYG 2449950.2676 8.988 1.587 998 +CD CYG 2449952.2734 9.216 1.645 998 +CD CYG 2449953.3310 9.347 1.674 998 +CD CYG 2449954.2670 9.469 1.673 998 +CD CYG 2449955.2595 9.538 1.707 998 +CD CYG 2450305.2910 8.642 1.205 .704 971 +CD CYG 2450306.3108 8.725 1.282 .744 971 +CD CYG 2450307.2934 8.855 1.327 .772 971 +CD CYG 2450310.2763 9.120 1.515 .834 971 +CD CYG 2450311.2499 9.248 1.564 .847 971 +CD CYG 2450312.2831 9.369 1.590 .862 971 +CD CYG 2450313.2759 9.450 1.615 .874 971 +CD CYG 2450314.2643 9.515 1.590 .864 971 +CD CYG 2450315.2144 9.471 1.582 .838 971 +CD CYG 2450316.2121 9.371 1.467 .803 971 +CD CYG 2450317.2259 9.232 1.376 .784 971 +CD CYG 2450318.2423 9.218 1.350 .752 971 +CD CYG 2450319.2287 8.364 .979 .586 971 +CD CYG 2450320.2286 8.422 1.035 .604 971 +CD CYG 2450321.2190 8.508 1.089 .651 971 +CD CYG 2450322.2517 8.610 1.197 .695 971 +CD CYG 2450323.2528 8.711 1.284 .724 971 +CD CYG 2450324.2553 8.814 1.362 .760 971 +CD CYG 2450325.2179 8.900 1.421 .781 971 +CD CYG 2450326.1803 9.013 1.468 .806 971 +DT CYG 2446615.1802 5.837 .372 .558 .297 988 +DT CYG 2446616.1784 5.617 .290 .504 .262 988 +DT CYG 2446626.1756 5.622 .327 .502 .275 988 +DT CYG 2446627.1773 .379 .580 .329 988 +DT CYG 2446628.1765 5.652 .288 .533 .263 988 +DT CYG 2446629.1619 5.809 .345 .560 .305 988 +DT CYG 2446630.1894 5.845 .371 .572 .308 988 +DT CYG 2446631.1607 5.604 .302 .492 .273 988 +DT CYG 2446631.4544 5.675 .337 .518 .276 988 +DT CYG 2446632.1643 5.873 .325 .586 .343 988 +DT CYG 2446632.4630 5.870 .348 .321 988 +DT CYG 2446635.1631 5.836 .348 .589 .320 988 +DT CYG 2446636.1618 5.641 .331 .524 .262 988 +DT CYG 2446636.4612 5.672 .325 .541 .281 988 +DT CYG 2446637.1584 5.837 .375 .567 .331 988 +DT CYG 2446996.4512 5.691 .385 .513 .297 989 +DT CYG 2446997.4508 5.865 .416 .575 .331 989 +DT CYG 2446998.4680 .451 .268 989 +DT CYG 2446999.4624 5.844 .398 .584 .321 989 +DT CYG 2447000.4671 5.665 .321 .493 .279 989 +DT CYG 2447002.4724 .399 .560 .310 989 +DT CYG 2447003.4664 5.629 .274 .487 .277 989 +DT CYG 2447004.1600 5.785 .440 .569 .326 989 +DT CYG 2447005.1537 5.841 .444 .599 .324 989 +DT CYG 2447088.0715 5.701 .281 .488 .297 989 +DT CYG 2447091.0691 5.667 .345 .514 .298 989 +DT CYG 2447098.0421 .537 .324 989 +DT CYG 2447399.1306 5.897 .293 .543 .360 990 +DT CYG 2447400.1227 5.843 .242 .502 .329 990 +DT CYG 2447401.1143 5.708 .298 .460 .319 990 +DT CYG 2447402.1122 5.927 .295 .644 .364 990 +DT CYG 2447403.1136 5.686 .235 .400 .329 990 +DT CYG 2447404.1122 5.854 .270 .521 .359 990 +DT CYG 2447407.1096 5.898 .245 .671 .303 990 +DT CYG 2447408.1086 .282 .401 .323 990 +DT CYG 2447409.1090 5.829 .328 .531 .330 990 +DT CYG 2447410.1120 5.836 .207 .333 990 +DT CYG 2447411.1078 5.754 .294 .405 .333 990 +DT CYG 2447412.1109 5.956 .212 .698 .371 990 +DT CYG 2447413.1018 5.665 .264 .392 .303 990 +DT CYG 2447414.1049 5.891 .299 .531 .368 990 +DT CYG 2447415.1037 5.852 .271 .471 .367 990 +DT CYG 2447416.1011 5.729 .283 .444 .332 990 +DT CYG 2447417.1022 5.931 .278 .606 .373 990 +DT CYG 2447418.1007 5.668 .253 .423 .296 990 +DT CYG 2447419.0970 5.802 .286 .542 .328 990 +DT CYG 2447420.0983 5.845 .269 .540 .350 990 +DT CYG 2447421.0973 5.708 .261 .472 .330 990 +DT CYG 2447422.0912 5.898 .303 .611 .334 990 +DT CYG 2447423.0940 5.687 .243 .441 .318 990 +DT CYG 2447424.0922 5.828 .196 .634 .340 990 +DT CYG 2447425.0950 5.818 .234 .548 .325 990 +DT CYG 2447427.0926 5.867 .272 .644 .326 990 +DT CYG 2447428.0878 5.679 .249 .415 .321 990 +DT CYG 2447429.0906 5.851 .270 .544 .356 990 +DT CYG 2447430.0865 5.839 .253 .513 .348 990 +DT CYG 2447431.0862 5.706 .277 .439 .324 990 +DT CYG 2447432.0854 5.908 .321 .569 .365 990 +DT CYG 2447433.0821 5.668 .278 .398 .322 990 +DT CYG 2447434.0832 5.849 .258 .565 .352 990 +DT CYG 2448101.1522 5.802 .551 .307 992 +DT CYG 2448102.1744 5.893 .328 .596 .333 992 +DT CYG 2448103.1497 5.662 .359 .496 .267 992 +DT CYG 2448104.1522 5.911 .339 .609 .346 992 +DT CYG 2448108.1454 5.664 .397 .510 .280 992 +DT CYG 2448109.1418 5.888 .365 .599 .327 992 +DT CYG 2448110.1477 5.736 .369 .528 .288 992 +DT CYG 2448111.1541 5.758 .365 .563 .300 992 +DT CYG 2448112.1476 5.887 .360 .586 .327 992 +DT CYG 2448113.1480 5.641 .387 .501 .282 992 +DT CYG 2448114.1390 5.938 .357 .610 992 +DT CYG 2448115.2188 5.683 .402 .511 .275 992 +DT CYG 2448116.1645 5.780 .374 .562 .297 992 +DT CYG 2448117.1474 5.883 .347 .327 992 +DT CYG 2448118.1431 5.675 .377 .525 .287 992 +DT CYG 2448119.1361 .337 .623 .338 992 +DT CYG 2448123.1321 5.659 .411 .499 .277 992 +DT CYG 2448127.1393 5.888 .582 .333 992 +DT CYG 2448503.1180 .176 .503 .303 993 +DT CYG 2448504.1181 5.910 .246 .612 .344 993 +DT CYG 2448505.1199 5.690 .184 .524 .290 993 +DT CYG 2448506.1186 5.786 .223 .587 .330 993 +DT CYG 2448507.1159 5.880 .241 .584 .339 993 +DT CYG 2448508.1161 5.663 .198 .509 .303 993 +DT CYG 2448509.1155 5.892 .249 .619 .352 993 +DT CYG 2448510.1163 5.679 .226 .506 .294 993 +DT CYG 2448511.1143 5.793 .248 .579 .320 993 +DT CYG 2448512.1151 5.873 .255 .583 .338 993 +DT CYG 2448513.1149 5.664 .202 .495 .300 993 +DT CYG 2448514.1144 5.907 .268 .609 .348 993 +DT CYG 2448515.1079 5.663 .225 .486 .286 993 +DT CYG 2448516.1126 5.760 .242 .566 .315 993 +DT CYG 2448517.1105 5.872 .253 .560 .346 993 +DT CYG 2448518.1167 5.665 .234 .495 .303 993 +DT CYG 2448519.1104 5.894 .275 .606 .345 993 +DT CYG 2448520.1058 5.688 .216 .493 .300 993 +DT CYG 2448521.1118 5.779 .258 .570 .324 993 +DT CYG 2448522.1055 5.869 .273 .591 .341 993 +DT CYG 2448523.1018 5.666 .223 .503 .295 993 +DT CYG 2448856.1394 5.776 .214 .606 .318 994 +DT CYG 2448858.1350 5.663 .192 .544 .300 994 +DT CYG 2448860.1333 5.651 .165 .548 .298 994 +DT CYG 2448870.1200 5.671 .157 .551 .284 994 +DT CYG 2448872.1177 5.877 .237 .584 .367 994 +DT CYG 2448874.1179 5.910 .207 .651 .338 994 +DT CYG 2448876.1143 5.827 .190 .606 .351 994 +DT CYG 2448877.1101 5.906 .201 .592 .346 994 +DT CYG 2448878.2507 5.720 .148 .578 .321 994 +DT CYG 2448880.1106 5.671 .152 .571 .285 994 +DT CYG 2448881.1050 5.796 .202 .604 .333 994 +DT CYG 2448882.1072 5.867 .208 .625 .332 994 +DT CYG 2448883.1075 5.679 .152 .553 .313 994 +DT CYG 2448884.1034 5.900 .222 .629 .358 994 +DT CYG 2448885.1007 5.671 .154 .560 .296 994 +DT CYG 2448886.1006 5.816 .198 .613 .334 994 +DT CYG 2448888.0986 5.692 .140 .548 .313 994 +DT CYG 2448889.0970 5.907 .253 .649 .346 994 +DT CYG 2448890.0967 5.695 .164 .531 .315 994 +DT CYG 2448891.0946 5.813 .202 .609 .341 994 +DT CYG 2448892.0933 5.847 .194 .594 .328 994 +DT CYG 2448893.0933 5.687 .156 .559 .305 994 +DT CYG 2448894.0918 5.876 .213 .629 .356 994 +DT CYG 2449617.2169 5.603 .301 .511 .273 995 +DT CYG 2449620.1878 5.548 .323 .496 .267 995 +DT CYG 2449620.1957 5.537 .347 .483 .266 995 +DT CYG 2449621.0963 5.845 .357 .566 .353 995 +DT CYG 2449621.1884 5.832 .320 .604 .347 995 +DT CYG 2449622.0959 5.624 .233 .539 .306 995 +DT CYG 2449623.0902 5.700 .337 .510 .313 995 +DT CYG 2449624.0940 5.850 .353 .598 .339 995 +DT CYG 2449625.0942 5.609 .294 .486 .282 995 +DT CYG 2449631.0938 .328 .585 .330 995 +DT CYG 2449632.1007 .523 .299 995 +DT CYG 2449632.3091 5.643 .242 .502 .283 995 +DT CYG 2449633.0892 5.691 .318 .526 .307 995 +DT CYG 2449634.0966 5.889 .329 .612 .346 995 +DT CYG 2449934.1571 5.876 .607 .294 .592 998 +DT CYG 2449935.1572 5.704 .506 .332 .553 998 +DT CYG 2449937.2169 5.742 .516 .301 998 +DT CYG 2449938.1527 5.806 .581 .307 .569 998 +DT CYG 2449939.1581 5.865 .605 .329 .646 998 +DT CYG 2449942.1499 5.688 .517 .288 .560 998 +DT CYG 2449943.1443 .562 .307 .545 998 +DT CYG 2449944.1429 5.909 .593 .294 .607 998 +DT CYG 2449945.1431 5.634 .506 .289 .574 998 +DT CYG 2449946.1464 5.841 .622 .335 .634 998 +DT CYG 2449947.1431 5.717 .539 .285 .576 998 +DT CYG 2449947.2756 5.677 .511 .282 .560 998 +DT CYG 2449948.1417 5.765 .557 .320 .611 998 +DT CYG 2449949.1461 5.850 .587 .326 .623 998 +DT CYG 2449950.1436 5.631 .502 .289 .547 998 +DT CYG 2449952.1421 5.695 .507 .288 .575 998 +DT CYG 2449953.1714 .547 .318 .603 998 +DT CYG 2449954.1563 5.840 .582 .324 .605 998 +DT CYG 2449955.1518 5.688 .463 .271 .513 998 +DT CYG 2449958.1367 5.834 .557 .326 .624 998 +DT CYG 2449959.1325 .597 .329 .638 998 +DT CYG 2449962.1457 5.729 .532 .299 .586 998 +DT CYG 2450009.0902 5.909 .592 .331 .635 998 +DT CYG 2450011.0876 5.878 .604 .332 .634 998 +DT CYG 2450012.1363 5.686 .547 998 +DT CYG 2450017.0763 5.741 .526 .295 .577 998 +DT CYG 2450018.1318 5.761 .553 .311 .604 998 +DT CYG 2450020.0685 5.637 .484 .277 .541 998 +DT CYG 2450305.1569 5.677 .505 .301 971 +DT CYG 2450306.1569 5.907 .609 .348 971 +DT CYG 2450307.1800 5.662 .524 .279 971 +DT CYG 2450310.2076 5.658 .530 .297 971 +DT CYG 2450311.1476 5.857 .650 .316 971 +DT CYG 2450312.1444 5.641 .556 .334 971 +DT CYG 2450313.1473 5.768 .623 .323 971 +DT CYG 2450314.1331 5.827 .601 .323 971 +DT CYG 2450315.1355 5.660 .559 .298 971 +DT CYG 2450316.1371 5.885 .689 .342 971 +DT CYG 2450317.1664 5.652 .524 .228 971 +DT CYG 2450318.1370 5.768 .591 .368 971 +DT CYG 2450319.1351 5.829 .584 .391 971 +DT CYG 2450320.1362 5.629 .514 .305 971 +DT CYG 2450321.1319 5.900 .616 .356 971 +DT CYG 2450322.1252 5.668 .509 .318 971 +DT CYG 2450323.1276 5.763 .561 .344 971 +DT CYG 2450324.1329 5.840 .603 .324 971 +DT CYG 2450325.1232 5.645 .526 .297 971 +DT CYG 2450326.1204 5.886 .632 .337 971 +DT CYG 2450327.1837 5.624 .556 971 +DT CYG 2450328.3430 5.783 .667 .390 971 +DT CYG 2450329.2085 .576 .337 971 +DT CYG 2450330.2286 5.738 .565 .288 971 +DT CYG 2450332.2244 5.620 .526 .238 971 +DT CYG 2450333.2053 5.794 .614 .339 971 +DT CYG 2450334.2303 5.794 .583 .300 971 +DT CYG 2450335.2345 5.672 .567 .294 971 +DT CYG 2450336.2310 5.872 .643 .347 971 +DT CYG 2450337.1940 5.610 .558 .270 971 +DT CYG 2450340.2108 5.641 .573 .304 971 +DT CYG 2450341.2097 5.885 .639 .322 971 +DT CYG 2450342.2230 5.615 .540 .273 971 +DT CYG 2450344.2560 5.779 .584 .266 971 +DT CYG 2450347.2646 5.634 .547 .257 971 +DT CYG 2450349.2116 5.818 .600 .319 971 +EP CYG 2448101.3239 12.917 1.361 .794 992 +EP CYG 2448103.2418 12.340 1.043 .661 992 +EP CYG 2448104.2595 12.554 1.179 .724 992 +EP CYG 2448108.2430 12.448 1.116 .703 992 +EP CYG 2448109.2308 12.784 1.258 .796 992 +EP CYG 2448111.2422 13.084 1.366 .814 992 +EP CYG 2448112.2329 12.368 1.024 .672 992 +EP CYG 2448113.2282 12.695 1.278 .770 992 +EP CYG 2448113.3674 12.733 1.281 .796 992 +EP CYG 2448114.2549 12.994 1.378 .845 992 +EP CYG 2448115.2110 13.141 1.385 .846 992 +EP CYG 2448116.2193 12.318 1.045 .684 992 +EP CYG 2448117.2731 1.244 .742 992 +EP CYG 2448118.2568 12.901 1.361 .809 992 +EP CYG 2448119.2257 13.131 1.395 .855 992 +EP CYG 2448122.2486 12.804 1.326 .791 992 +EP CYG 2448123.2247 13.048 1.365 .848 992 +EP CYG 2448126.2550 12.769 1.285 .805 992 +EP CYG 2448126.3770 12.809 1.306 .802 992 +EP CYG 2448127.2192 12.973 1.423 .812 992 +EP CYG 2448127.3403 12.961 1.378 .831 992 +EP CYG 2449934.3523 12.855 .768 1.473 998 +EP CYG 2449935.3590 12.466 .703 1.372 998 +EP CYG 2449936.3341 12.761 .757 1.482 998 +EP CYG 2449937.3179 13.011 .854 998 +EP CYG 2449938.3548 13.147 .868 1.684 998 +EP CYG 2449939.3450 12.381 1.358 998 +EP CYG 2449941.3311 12.930 1.554 998 +EP CYG 2449942.3030 13.163 1.708 998 +EP CYG 2449943.2955 12.359 1.305 998 +EP CYG 2449944.2909 12.603 1.492 998 +EP CYG 2449945.2830 12.908 1.627 998 +EP CYG 2449946.2767 13.099 1.633 998 +EP CYG 2449947.2556 12.819 1.473 998 +EP CYG 2449948.2445 12.542 1.430 998 +EP CYG 2449949.2528 12.822 1.545 998 +EP CYG 2449950.2431 13.014 1.621 998 +EP CYG 2449952.2480 12.409 1.356 998 +EP CYG 2449953.3098 12.731 1.542 998 +EP CYG 2449954.2483 13.000 1.639 998 +EP CYG 2449955.2430 13.152 1.655 998 +EU CYG 2445649.1913 14.468 1.749 982 +EU CYG 2445650.1992 14.332 1.629 1.035 982 +EU CYG 2445665.1562 14.284 982 +EU CYG 2445666.1093 14.006 1.508 1.039 982 +EU CYG 2445667.1367 13.337 1.367 .805 982 +EU CYG 2445668.1328 13.452 1.401 .898 982 +EU CYG 2445674.1367 13.951 1.046 982 +EU CYG 2445675.1367 14.005 1.762 1.039 982 +EU CYG 2445676.1093 14.160 1.797 1.096 982 +EU CYG 2445683.1210 13.528 .938 982 +EU CYG 2445691.0859 14.248 1.765 1.047 982 +EU CYG 2445692.0742 14.331 1.660 1.067 982 +EU CYG 2445693.0780 14.308 1.693 1.041 982 +EU CYG 2445695.0820 14.095 1.619 1.003 982 +EU CYG 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2447421.1752 13.387 1.364 .861 990 +EU CYG 2447422.1874 13.572 1.490 .910 990 +EU CYG 2447424.1847 13.790 1.604 .992 990 +EU CYG 2447425.2068 13.832 1.646 1.011 990 +EU CYG 2447427.2128 14.062 1.703 1.014 990 +EU CYG 2447432.1613 14.217 1.653 1.028 990 +EU CYG 2447433.1779 14.107 1.610 1.008 990 +EU CYG 2447434.1755 14.072 1.517 .983 990 +EU CYG 2449945.3016 14.105 1.989 998 +EU CYG 2449946.2883 14.137 2.117 998 +EU CYG 2449947.2645 14.263 2.055 998 +EU CYG 2449948.2633 14.272 2.021 998 +EU CYG 2449949.2587 14.305 2.144 998 +EU CYG 2449950.2552 14.134 1.991 998 +EU CYG 2449952.2597 14.018 1.964 998 +EU CYG 2449953.3167 13.409 1.657 998 +EU CYG 2449954.2545 13.442 1.682 998 +EU CYG 2449955.2485 13.654 1.928 998 +EU CYG 2449957.2692 13.814 2.056 998 +EU CYG 2449958.2510 13.904 2.013 998 +EU CYG 2449959.2769 14.057 2.154 998 +EU CYG 2449962.3129 14.317 2.089 998 +EU CYG 2449986.2268 13.631 1.868 998 +EU CYG 2449987.2476 13.681 1.921 998 +EU CYG 2449992.1870 14.292 2.079 998 +EU CYG 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2447413.2325 11.415 1.712 .931 990 +EZ CYG 2447414.2341 11.476 1.744 .922 990 +EZ CYG 2447415.2189 11.440 1.664 .907 990 +EZ CYG 2447416.2113 11.203 1.508 .855 990 +EZ CYG 2447417.2098 11.071 1.455 .813 990 +EZ CYG 2447418.2037 10.913 1.339 .773 990 +EZ CYG 2447419.1897 10.557 1.264 .730 990 +EZ CYG 2447420.1907 10.699 1.369 .806 990 +EZ CYG 2447421.1823 10.850 1.457 .811 990 +EZ CYG 2447422.1918 10.975 1.562 .851 990 +EZ CYG 2447423.1879 11.148 1.640 .883 990 +EZ CYG 2447424.1935 11.332 1.705 .928 990 +EZ CYG 2447425.2167 11.438 1.709 .916 990 +EZ CYG 2447427.2246 11.420 1.586 .882 990 +EZ CYG 2447428.1853 11.149 1.494 .826 990 +EZ CYG 2447429.1816 11.072 1.388 .816 990 +EZ CYG 2447430.1561 10.734 1.275 .740 990 +EZ CYG 2447431.2036 10.667 1.305 .760 990 +EZ CYG 2447432.1877 10.767 1.366 .789 990 +EZ CYG 2447433.1899 10.934 1.499 .845 990 +EZ CYG 2447434.1882 11.060 1.578 .874 990 +EZ CYG 2449945.3326 11.486 1.762 998 +EZ CYG 2449946.3116 11.308 1.694 998 +EZ CYG 2449947.3101 11.168 1.619 998 +EZ CYG 2449948.2896 11.016 1.568 998 +EZ CYG 2449949.2923 10.619 1.415 998 +EZ CYG 2449950.2792 10.680 1.493 998 +EZ CYG 2449952.2856 10.977 1.637 998 +EZ CYG 2449953.3534 11.133 1.708 998 +EZ CYG 2449954.2958 11.265 1.752 998 +EZ CYG 2449955.2694 11.415 1.767 998 +GH CYG 2446606.3315 9.577 .784 1.175 .702 988 +GH CYG 2446607.4276 9.740 .893 1.239 .743 988 +GH CYG 2446608.3341 9.770 .907 1.276 .759 988 +GH CYG 2446609.2264 10.020 .978 1.384 .817 988 +GH CYG 2446610.3582 10.184 1.039 1.443 .841 988 +GH CYG 2446611.2313 10.295 1.023 1.450 .845 988 +GH CYG 2446612.2177 10.157 .944 1.336 .818 988 +GH CYG 2446613.2176 9.656 .806 1.153 .709 988 +GH CYG 2446614.2193 9.605 .855 1.160 .708 988 +GH CYG 2446615.2187 9.738 .858 1.235 .740 988 +GH CYG 2446616.2203 9.818 .897 1.300 .770 988 +GH CYG 2446617.2157 10.021 1.011 1.411 .814 988 +GH CYG 2446618.2140 10.196 1.034 1.433 .847 988 +GH CYG 2446619.2129 10.303 1.008 1.430 .846 988 +GH CYG 2446620.2119 10.079 .850 1.348 .799 988 +GH CYG 2446621.3099 9.557 .826 1.117 .678 988 +GH CYG 2446622.2104 9.626 .831 1.188 .715 988 +GH CYG 2446623.2006 9.712 .897 1.227 .742 988 +GH CYG 2446624.1961 9.871 1.316 .781 988 +GH CYG 2446625.1904 10.066 1.412 .810 988 +GH CYG 2446626.1980 10.207 1.439 .843 988 +GH CYG 2446627.1980 10.294 .977 1.437 .839 988 +GH CYG 2446628.2000 10.011 .882 1.297 .774 988 +GH CYG 2446629.2106 9.538 .813 1.116 .684 988 +GH CYG 2446630.2105 9.620 1.174 .711 988 +GH CYG 2446631.1939 9.716 1.227 .740 988 +GH CYG 2446632.2073 9.947 1.355 .795 988 +GH CYG 2446635.2967 10.238 1.410 .813 988 +GH CYG 2446636.2068 9.917 1.267 .756 988 +GH CYG 2446637.2093 9.533 1.123 .690 988 +GH CYG 2449621.2779 10.343 1.470 .833 995 +GH CYG 2449623.2648 9.565 1.127 .677 995 +GH CYG 2449624.2418 9.600 1.156 .710 995 +GH CYG 2449625.2586 9.732 1.265 .739 995 +GH CYG 2449626.2733 9.869 1.320 .784 995 +GH CYG 2449631.2334 9.543 1.115 .684 995 +GH CYG 2449632.2564 9.664 1.204 .701 995 +GH CYG 2449633.2423 9.676 1.242 .742 995 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11.993 1.573 .922 992 +GI CYG 2448112.2688 11.416 1.319 .811 992 +GI CYG 2448113.2648 11.544 1.385 .857 992 +GI CYG 2448113.3821 11.520 1.421 .865 992 +GI CYG 2448114.3097 11.760 1.541 .898 992 +GI CYG 2448116.2883 12.094 1.600 1.005 992 +GI CYG 2448117.3750 11.845 1.457 .882 992 +GI CYG 2448118.3251 11.435 1.305 .833 992 +GI CYG 2448122.3074 12.042 1.643 .942 992 +GI CYG 2448123.2815 11.768 1.421 .876 992 +GI CYG 2448126.2844 11.806 1.571 .916 992 +GI CYG 2448126.3948 11.825 1.549 .941 992 +GI CYG 2448127.2435 11.973 1.589 .957 992 +GI CYG 2448127.3637 12.003 1.608 992 +GI CYG 2449944.3420 11.956 1.812 998 +GI CYG 2449945.3617 11.456 1.610 998 +GI CYG 2449946.3422 11.592 1.697 998 +GI CYG 2449947.3233 11.724 1.731 998 +GI CYG 2449948.2968 11.885 1.842 998 +GI CYG 2449949.3058 12.051 1.869 998 +GI CYG 2449950.2882 11.887 1.765 998 +GI CYG 2449952.2906 11.586 1.685 998 +GI CYG 2449953.3627 11.786 1.821 998 +GI CYG 2449954.3069 11.978 1.810 998 +GI CYG 2449955.2818 12.069 1.872 998 +GL CYG 2448101.3849 13.944 1.284 .819 992 +GL CYG 2448102.3221 14.027 1.328 .807 992 +GL CYG 2448103.2999 13.319 1.008 .664 992 +GL CYG 2448104.3338 13.753 1.259 .765 992 +GL CYG 2448108.3416 1.259 992 +GL CYG 2448109.2979 13.861 1.257 .744 992 +GL CYG 2448110.2874 13.482 1.061 .697 992 +GL CYG 2448111.2990 13.925 .818 992 +GL CYG 2448112.2799 14.077 1.329 .839 992 +GL CYG 2448113.2729 13.280 1.004 .640 992 +GL CYG 2448114.3452 13.818 1.242 .806 992 +GL CYG 2448117.3966 13.674 1.193 .736 992 +GL CYG 2448118.3376 13.968 1.263 .792 992 +GL CYG 2448122.3196 13.994 1.326 .806 992 +GL CYG 2448123.2900 13.294 .993 .632 992 +GL CYG 2448126.2938 13.679 1.157 .715 992 +GL CYG 2448126.4014 13.482 1.038 992 +GL CYG 2448127.2659 13.556 1.138 .712 992 +GL CYG 2448127.3706 13.607 1.107 .727 992 +IU CYG 2448101.3371 12.988 1.064 .703 992 +IU CYG 2448102.2777 12.992 1.144 .700 992 +IU CYG 2448103.2470 13.043 1.137 .734 992 +IU CYG 2448104.2643 13.043 1.181 .707 992 +IU CYG 2448108.2531 13.247 1.254 .790 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11.521 .733 .426 992 +MZ CYG 2448126.4055 11.499 .700 .452 992 +MZ CYG 2448127.2701 11.235 .606 .396 992 +MZ CYG 2448127.3767 11.145 .614 .379 992 +MZ CYG 2448854.3402 11.180 .629 .382 994 +MZ CYG 2448856.3351 11.053 .676 .402 994 +MZ CYG 2448858.3162 11.239 .928 .502 994 +MZ CYG 2448860.3161 11.301 1.053 .537 994 +MZ CYG 2448862.3168 11.421 1.220 .575 994 +MZ CYG 2448870.2175 12.628 1.295 .636 994 +MZ CYG 2448872.1832 12.485 1.120 .662 994 +MZ CYG 2448874.1902 11.794 .960 .513 994 +MZ CYG 2448876.2709 11.116 .665 .404 994 +MZ CYG 2448877.4009 11.068 .683 .399 994 +MZ CYG 2448878.2581 11.141 .427 .774 .450 994 +MZ CYG 2448879.3363 11.221 .872 .469 994 +MZ CYG 2448880.2001 11.247 .934 .507 994 +MZ CYG 2448881.2297 11.283 .978 .532 994 +MZ CYG 2448882.1975 11.327 1.071 .552 994 +MZ CYG 2448883.2108 11.377 1.108 .556 994 +MZ CYG 2448884.2329 11.482 1.154 .606 994 +MZ CYG 2448885.2067 11.583 1.253 .576 994 +MZ CYG 2448886.2159 11.716 1.274 .603 994 +MZ CYG 2448887.2644 11.880 1.269 .631 994 +MZ CYG 2448888.2263 12.081 1.372 .666 994 +MZ CYG 2448889.2325 12.244 1.337 .681 994 +MZ CYG 2448890.1912 12.353 1.393 .642 994 +MZ CYG 2448891.1869 12.466 1.302 .669 994 +MZ CYG 2448892.2042 12.457 1.290 .615 994 +MZ CYG 2448893.1919 12.373 1.191 .628 994 +MZ CYG 2449934.4165 11.601 .626 1.125 998 +MZ CYG 2449935.4145 11.795 998 +MZ CYG 2449936.4043 11.902 .670 1.187 998 +MZ CYG 2449937.4145 12.103 1.636 .670 998 +MZ CYG 2449938.4310 12.221 .671 998 +MZ CYG 2449939.4310 12.398 1.253 998 +MZ CYG 2449941.4025 12.403 1.193 998 +MZ CYG 2449942.3777 12.326 1.201 998 +MZ CYG 2449943.3659 12.186 1.115 998 +MZ CYG 2449944.4005 11.865 .978 998 +MZ CYG 2449945.4042 11.530 .848 998 +MZ CYG 2449946.3912 11.210 .767 998 +MZ CYG 2449947.3503 11.141 .775 998 +MZ CYG 2449948.3156 11.194 .838 998 +MZ CYG 2449949.3147 11.237 .888 998 +MZ CYG 2449950.3015 11.169 .917 998 +MZ CYG 2449952.2979 11.253 1.032 998 +MZ CYG 2449953.3727 11.320 1.057 998 +MZ CYG 2449954.3145 11.430 1.074 998 +MZ CYG 2449955.2904 11.536 1.109 998 +MZ CYG 2449957.3685 1.149 998 +MZ CYG 2449959.3496 1.201 998 +MZ CYG 2449987.3305 11.934 1.004 998 +MZ CYG 2449992.3619 11.139 .897 998 +QY CYG 2448108.2952 14.317 .823 992 +QY CYG 2448112.2621 14.253 .989 .530 992 +QY CYG 2448113.2581 14.690 .984 992 +QY CYG 2448114.3024 14.985 1.023 992 +QY CYG 2448116.2766 14.437 .933 .634 992 +QY CYG 2448117.3561 14.760 1.202 992 +QY CYG 2448118.2983 14.968 1.119 992 +QY CYG 2448119.2689 14.547 .923 .639 992 +QY CYG 2448122.2837 14.977 1.246 992 +QY CYG 2448123.2565 14.333 .867 .540 992 +QY CYG 2448126.2776 15.037 1.145 .760 992 +QY CYG 2448127.2382 14.199 .788 .543 992 +QY CYG 2448127.3589 14.127 .782 992 +V343 CYG 2445665.1835 13.803 1.535 .896 982 +V343 CYG 2445666.1445 13.669 1.411 .822 982 +V343 CYG 2445667.1679 13.679 1.440 .792 982 +V343 CYG 2445668.2030 13.089 1.187 .762 982 +V343 CYG 2445674.1639 13.678 1.555 .911 982 +V343 CYG 2445675.1679 13.772 1.605 .913 982 +V343 CYG 2445676.1405 14.040 1.617 .938 982 +V343 CYG 2445690.1131 13.666 .833 982 +V343 CYG 2445691.1054 13.649 1.422 .830 982 +V343 CYG 2445692.0976 13.133 1.150 .733 982 +V343 CYG 2445693.0976 13.189 1.254 .783 982 +V343 CYG 2445694.1014 13.315 1.355 .813 982 +V343 CYG 2445695.0937 13.452 .845 982 +V343 CYG 2445701.1014 13.933 1.523 .907 982 +V343 CYG 2445877.2617 13.854 1.589 .953 982 +V343 CYG 2445878.2381 13.959 1.633 .949 982 +V343 CYG 2447402.2668 13.648 1.553 990 +V343 CYG 2447403.3109 13.772 1.628 .923 990 +V343 CYG 2447404.2708 13.935 1.624 .945 990 +V343 CYG 2447407.2693 13.714 1.447 .841 990 +V343 CYG 2447408.2572 13.686 1.405 .869 990 +V343 CYG 2447409.2644 13.366 1.216 .770 990 +V343 CYG 2447410.2785 13.181 1.235 .753 990 +V343 CYG 2447411.2844 13.281 1.300 .809 990 +V343 CYG 2447413.2601 13.545 1.476 .891 990 +V343 CYG 2447414.2676 13.669 1.571 .917 990 +V343 CYG 2447415.2468 13.772 1.585 .892 990 +V343 CYG 2447416.2372 13.910 1.638 .927 990 +V343 CYG 2447417.2332 14.015 1.593 .930 990 +V343 CYG 2447418.2328 13.832 1.469 .916 990 +V343 CYG 2447419.2123 13.695 1.446 .864 990 +V343 CYG 2447420.2119 13.671 1.443 .840 990 +V343 CYG 2447421.2003 13.347 1.264 .773 990 +V343 CYG 2447422.2158 13.154 1.252 .760 990 +V343 CYG 2447423.2221 13.276 1.352 .803 990 +V343 CYG 2447424.2247 13.411 1.471 .835 990 +V343 CYG 2447425.2444 13.514 1.536 .866 990 +V343 CYG 2447427.2775 13.859 1.609 .938 990 +V343 CYG 2447431.2477 13.695 1.514 .864 990 +V343 CYG 2447432.2252 13.682 1.409 .833 990 +V343 CYG 2447433.2292 13.272 1.186 .768 990 +V343 CYG 2447434.2339 13.165 1.264 .755 990 +V343 CYG 2447734.4129 13.385 1.402 .887 991 +V343 CYG 2447735.4169 13.504 1.482 .874 991 +V343 CYG 2447736.4259 13.646 1.529 .893 991 +V343 CYG 2447737.4065 13.722 1.606 .911 991 +V343 CYG 2447738.3951 13.868 991 +V343 CYG 2447739.3664 13.997 1.577 .936 991 +V343 CYG 2447740.4073 13.931 1.547 .871 991 +V343 CYG 2447741.3555 13.682 1.429 .853 991 +V343 CYG 2447742.3607 13.649 1.421 .835 991 +V343 CYG 2447743.3501 13.230 1.198 .761 991 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2447767.2954 13.183 1.139 .754 991 +V343 CYG 2447768.2842 13.158 1.250 .783 991 +V343 CYG 2447770.2864 13.379 1.422 .825 991 +V343 CYG 2447771.2766 13.520 1.499 .867 991 +V343 CYG 2447772.2696 13.683 1.586 .914 991 +V343 CYG 2447773.3032 13.816 1.559 .912 991 +V343 CYG 2447774.3060 13.966 1.622 .964 991 +V343 CYG 2447775.2688 14.054 1.571 .975 991 +V343 CYG 2447776.2801 13.908 1.538 .941 991 +V343 CYG 2449957.3317 13.938 1.769 998 +V343 CYG 2449959.3431 13.834 1.797 998 +V343 CYG 2450011.2548 13.248 1.556 998 +V343 CYG 2450017.2116 14.059 1.804 998 +V343 CYG 2450018.2584 14.000 1.783 998 +V343 CYG 2450019.2512 13.711 1.664 998 +V343 CYG 2450020.2037 13.675 1.652 998 +V347 CYG 2446252.4101 12.441 1.664 1.046 987 +V347 CYG 2446253.3205 12.174 1.607 .993 987 +V347 CYG 2446255.3660 12.199 1.652 1.021 987 +V347 CYG 2446256.3112 12.454 1.788 1.073 987 +V347 CYG 2446257.3239 12.594 1.835 1.101 987 +V347 CYG 2446258.2734 12.764 1.861 1.117 987 +V347 CYG 2446259.2740 12.857 1.880 1.149 987 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990 +V347 CYG 2447407.2958 12.300 1.641 1.006 990 +V347 CYG 2447408.3019 12.167 1.650 .970 990 +V347 CYG 2447409.2959 12.431 1.771 990 +V347 CYG 2447410.3101 12.616 1.809 1.094 990 +V347 CYG 2447411.3140 12.767 1.849 1.110 990 +V347 CYG 2447413.2911 12.801 1.810 1.119 990 +V347 CYG 2447414.2878 12.466 1.709 1.039 990 +V347 CYG 2447415.2663 12.212 1.587 .980 990 +V347 CYG 2447416.2637 12.297 1.629 1.004 990 +V347 CYG 2447417.2549 12.190 1.642 .985 990 +V347 CYG 2447418.2535 12.495 1.777 1.067 990 +V347 CYG 2447419.2309 12.634 1.823 1.114 990 +V347 CYG 2447420.2279 12.771 1.906 1.109 990 +V347 CYG 2447421.2187 12.920 1.885 1.125 990 +V347 CYG 2447422.2302 12.713 1.769 1.052 990 +V347 CYG 2447423.2354 12.397 1.648 1.019 990 +V347 CYG 2447424.2456 12.216 1.589 1.011 990 +V347 CYG 2447425.2645 12.280 1.620 1.013 990 +V347 CYG 2447428.2292 12.656 1.840 1.088 990 +V347 CYG 2447429.2356 12.843 1.857 1.121 990 +V347 CYG 2447430.2216 12.878 1.949 1.117 990 +V347 CYG 2447431.2690 12.658 1.767 1.056 990 +V347 CYG 2447432.2501 12.281 1.584 1.000 990 +V347 CYG 2447433.2502 12.274 1.643 .995 990 +V347 CYG 2447434.2474 12.216 1.634 1.003 990 +V356 CYG 2447399.4235 12.081 1.140 .711 990 +V356 CYG 2447401.3141 12.587 1.445 .831 990 +V356 CYG 2447402.3146 12.769 1.477 .866 990 +V356 CYG 2447403.3466 12.707 1.432 .834 990 +V356 CYG 2447404.3223 12.042 1.151 .687 990 +V356 CYG 2447407.3037 12.782 1.477 .876 990 +V356 CYG 2447408.3112 12.727 1.448 .839 990 +V356 CYG 2447409.3040 12.060 1.128 .707 990 +V356 CYG 2447410.3172 12.326 1.310 .776 990 +V356 CYG 2447411.3215 12.580 1.393 .836 990 +V356 CYG 2447413.2943 12.771 1.412 .873 990 +V356 CYG 2447414.2914 12.043 1.173 .692 990 +V356 CYG 2447415.2697 12.288 1.291 .773 990 +V356 CYG 2447416.2663 12.551 1.396 .844 990 +V356 CYG 2447417.2586 12.721 1.462 .854 990 +V356 CYG 2447418.2550 12.792 1.438 .857 990 +V356 CYG 2447419.2340 12.122 1.124 .728 990 +V356 CYG 2447420.2308 12.255 1.280 .765 990 +V356 CYG 2447421.2208 12.529 1.422 .819 990 +V356 CYG 2447422.2346 12.725 1.428 .831 990 +V356 CYG 2447423.2386 12.807 1.467 .879 990 +V356 CYG 2447424.2484 12.072 1.163 .719 990 +V356 CYG 2447425.2674 12.264 1.255 .773 990 +V356 CYG 2447427.3086 12.774 1.459 .861 990 +V356 CYG 2447428.2353 12.782 1.451 .833 990 +V356 CYG 2447429.2383 12.177 1.158 .721 990 +V356 CYG 2447430.2232 12.217 1.255 .758 990 +V356 CYG 2447431.2727 12.514 1.411 .827 990 +V356 CYG 2447432.2532 12.719 1.447 .867 990 +V356 CYG 2447433.2538 12.812 1.486 .866 990 +V356 CYG 2447434.2505 12.162 1.220 .708 990 +V356 CYG 2446252.4286 12.367 1.319 .799 987 +V356 CYG 2446255.4004 12.644 1.406 .837 987 +V356 CYG 2446256.3301 12.025 1.165 .706 987 +V356 CYG 2446257.3340 12.324 1.314 .797 987 +V356 CYG 2446258.2811 12.578 1.412 .828 987 +V356 CYG 2446259.2846 12.732 1.497 .882 987 +V356 CYG 2446260.2521 12.754 1.404 .856 987 +V356 CYG 2446261.2768 12.039 1.150 .704 987 +V356 CYG 2446263.2581 12.568 1.400 .867 987 +V356 CYG 2446265.2386 12.777 1.463 .852 987 +V356 CYG 2446266.2497 12.053 1.167 .696 987 +V356 CYG 2446267.3338 12.286 1.299 .775 987 +V356 CYG 2446268.3364 12.542 1.422 .835 987 +V356 CYG 2446269.3138 12.725 1.462 .875 987 +V356 CYG 2446270.3643 12.731 1.450 .847 987 +V356 CYG 2446272.3453 12.274 1.274 .784 987 +V356 CYG 2446273.2809 12.524 1.410 .816 987 +V356 CYG 2446274.2413 12.691 1.437 .861 987 +V356 CYG 2446275.2538 12.777 1.485 .849 987 +V356 CYG 2446278.2759 12.424 1.462 .805 987 +V356 CYG 2446279.3146 12.724 1.459 .859 987 +V356 CYG 2446280.2998 12.777 1.477 .868 987 +V356 CYG 2447735.4516 12.605 1.439 .832 991 +V356 CYG 2447736.4518 12.781 1.466 .838 991 +V356 CYG 2447738.4201 12.124 1.157 .726 991 +V356 CYG 2447739.3896 12.387 1.308 .821 991 +V356 CYG 2447740.4179 12.641 1.449 .861 991 +V356 CYG 2447741.3720 12.774 1.484 .853 991 +V356 CYG 2447742.3780 12.518 1.337 .817 991 +V356 CYG 2447743.3662 1.202 .706 991 +V356 CYG 2447744.3449 12.347 1.334 .802 991 +V356 CYG 2447745.3485 12.585 1.433 .861 991 +V356 CYG 2447746.3534 12.769 1.516 .849 991 +V356 CYG 2447747.3502 12.595 1.360 .826 991 +V356 CYG 2447748.3485 12.039 1.163 .703 991 +V356 CYG 2447749.3472 12.336 1.282 .799 991 +V356 CYG 2447750.3252 12.597 1.394 .866 991 +V356 CYG 2447751.3232 12.752 1.431 .868 991 +V356 CYG 2447752.3030 12.636 1.374 .811 991 +V356 CYG 2447753.3039 12.034 1.125 .702 991 +V356 CYG 2447754.3460 12.325 1.321 .798 991 +V356 CYG 2447755.3712 12.564 1.433 .841 991 +V356 CYG 2447756.3653 12.765 1.455 .867 991 +V356 CYG 2447757.3430 12.663 1.377 .859 991 +V356 CYG 2447758.3300 12.028 1.129 .715 991 +V356 CYG 2447759.3132 12.285 1.320 .753 991 +V356 CYG 2447760.3108 12.554 1.410 .839 991 +V356 CYG 2447761.2914 12.733 1.482 .861 991 +V356 CYG 2447762.2697 12.737 1.424 .842 991 +V356 CYG 2447763.2384 12.046 1.101 991 +V356 CYG 2447764.2447 12.236 1.267 991 +V356 CYG 2447766.2524 12.731 1.456 .897 991 +V356 CYG 2447767.3199 12.733 1.425 .854 991 +V356 CYG 2447768.3360 1.156 .722 991 +V356 CYG 2447770.2933 12.515 1.409 .819 991 +V356 CYG 2447771.2847 12.712 1.481 .864 991 +V356 CYG 2447772.2799 12.777 1.426 .847 991 +V356 CYG 2447773.3091 12.055 1.128 .715 991 +V356 CYG 2447774.3140 12.232 1.298 .760 991 +V356 CYG 2447775.2744 12.554 1.368 .828 991 +V356 CYG 2447776.2873 12.718 1.471 .858 991 +V356 CYG 2446606.3943 12.354 1.304 .815 988 +V356 CYG 2446611.3614 12.319 1.326 .784 988 +V356 CYG 2446612.3798 12.600 1.472 .857 988 +V356 CYG 2446613.3528 12.772 1.478 .864 988 +V356 CYG 2446614.3544 12.702 1.422 .862 988 +V356 CYG 2446615.3516 12.051 .910 1.129 .716 988 +V356 CYG 2446616.3500 12.323 1.296 .793 988 +V356 CYG 2446618.3361 12.740 1.510 .868 988 +V356 CYG 2446620.3167 12.047 .896 1.136 .712 988 +V356 CYG 2446621.3599 12.292 1.312 .776 988 +V356 CYG 2446622.3339 12.554 1.047 1.408 .846 988 +V356 CYG 2446625.3114 12.074 .890 1.142 .726 988 +V356 CYG 2446629.3085 12.804 1.426 .881 988 +V356 CYG 2446632.3212 12.524 1.397 .833 988 +V383 CYG 2447399.3424 10.643 1.495 .914 990 +V383 CYG 2447400.2811 10.866 1.611 .993 990 +V383 CYG 2447401.2827 11.072 1.710 1.019 990 +V383 CYG 2447402.2832 11.159 1.708 1.018 990 +V383 CYG 2447403.3203 10.657 1.481 .914 990 +V383 CYG 2447404.2833 10.739 1.553 .938 990 +V383 CYG 2447407.2854 11.073 1.660 .987 990 +V383 CYG 2447408.2942 10.588 1.495 .884 990 +V383 CYG 2447409.2879 10.858 1.578 .977 990 +V383 CYG 2447410.3019 11.069 1.699 1.023 990 +V383 CYG 2447411.3007 11.161 1.720 1.028 990 +V383 CYG 2447413.2511 10.695 1.537 .944 990 +V383 CYG 2447414.2593 10.915 1.670 .989 990 +V383 CYG 2447415.2374 11.084 1.034 990 +V383 CYG 2447416.2292 11.171 1.692 1.014 990 +V383 CYG 2447417.2240 10.616 1.466 .904 990 +V383 CYG 2447418.2222 10.779 1.562 .947 990 +V383 CYG 2447419.2053 10.949 1.721 .992 990 +V383 CYG 2447420.2052 11.118 1.746 1.015 990 +V383 CYG 2447421.1938 11.028 1.647 .965 990 +V383 CYG 2447422.2080 10.638 1.464 .926 990 +V383 CYG 2447423.2122 10.867 1.641 .980 990 +V383 CYG 2447424.2165 11.116 1.743 1.036 990 +V383 CYG 2447425.2353 11.184 1.738 1.020 990 +V383 CYG 2447427.2546 10.780 1.553 .927 990 +V383 CYG 2447428.2045 10.929 1.692 .978 990 +V383 CYG 2447429.1997 11.118 1.691 1.011 990 +V383 CYG 2447430.1929 11.126 1.709 1.005 990 +V383 CYG 2447431.2324 10.607 1.475 .899 990 +V383 CYG 2447432.2146 10.840 1.564 .970 990 +V383 CYG 2447433.2210 11.057 1.704 1.019 990 +V383 CYG 2447434.2195 11.165 1.711 1.037 990 +V383 CYG 2446992.3995 10.938 1.599 .975 989 +V383 CYG 2447001.3060 11.112 1.660 1.019 989 +V383 CYG 2447002.3121 10.580 1.459 .906 989 +V383 CYG 2447003.2897 10.785 1.593 .971 989 +V383 CYG 2449986.2868 10.599 1.765 998 +V383 CYG 2449987.2966 10.752 1.870 998 +V383 CYG 2449992.3262 10.881 1.923 998 +V383 CYG 2450007.3086 11.097 1.977 998 +V383 CYG 2450009.3067 10.639 1.777 998 +V383 CYG 2450011.2687 11.002 1.970 998 +V383 CYG 2450017.2618 11.036 2.001 998 +V383 CYG 2450018.2792 10.801 1.824 998 +V383 CYG 2450019.2685 10.544 1.811 998 +V383 CYG 2450020.2322 10.909 1.939 998 +V386 CYG 2446274.2487 9.408 1.190 1.492 .912 987 +V386 CYG 2446275.2582 9.638 1.279 1.647 .967 987 +V386 CYG 2446277.2284 10.010 1.706 1.013 987 +V386 CYG 2446278.2832 9.464 1.103 1.498 .886 987 +V386 CYG 2446279.3194 9.357 1.475 .893 987 +V386 CYG 2446280.3047 9.581 1.291 1.602 .958 987 +V386 CYG 2446283.3020 9.679 1.576 .935 987 +V386 CYG 2446284.2814 9.304 1.109 1.434 .870 987 +V386 CYG 2446285.2537 9.546 1.582 .944 987 +V386 CYG 2446286.2453 9.741 1.291 1.678 .991 987 +V386 CYG 2446287.2391 9.895 1.730 1.002 987 +V386 CYG 2446288.2833 9.854 1.637 .980 987 +V386 CYG 2446289.3031 9.265 1.072 1.411 .855 987 +V386 CYG 2446290.3034 9.495 1.533 .921 987 +V386 CYG 2446291.2566 9.692 1.263 1.648 .976 987 +V386 CYG 2446292.2776 9.877 1.712 .998 987 +V386 CYG 2446294.2435 9.353 1.076 1.421 .870 987 +V386 CYG 2446295.2455 9.396 1.495 .902 987 +V386 CYG 2446296.2384 9.646 1.271 1.626 .961 987 +V386 CYG 2446297.2412 9.815 1.701 .996 987 +V386 CYG 2446298.2500 9.972 1.712 1.018 987 +V386 CYG 2446299.2342 9.556 1.137 1.506 .917 987 +V386 CYG 2446300.2366 9.336 1.127 1.458 .880 987 +V386 CYG 2446300.3925 9.370 1.484 .887 987 +V386 CYG 2446301.2427 9.586 1.241 1.589 .959 987 +V386 CYG 2446302.2575 9.757 1.724 .975 987 +V386 CYG 2446303.2374 9.928 1.432 1.738 1.001 987 +V386 CYG 2446304.2284 9.726 1.183 1.622 .932 987 +V386 CYG 2450305.3066 9.762 1.588 .955 971 +V386 CYG 2450306.3188 9.268 1.417 .853 971 +V386 CYG 2450307.3018 9.526 1.537 .923 971 +V386 CYG 2450310.2916 9.854 1.653 .958 971 +V386 CYG 2450311.2673 9.283 1.372 .850 971 +V386 CYG 2450312.2978 9.446 1.500 .904 971 +V386 CYG 2450313.2894 9.651 1.614 .954 971 +V386 CYG 2450314.2746 9.837 1.680 .982 971 +V386 CYG 2450315.2846 9.920 1.696 .980 971 +V386 CYG 2450316.2180 9.469 1.454 .876 971 +V386 CYG 2450317.2387 9.415 1.447 .873 971 +V386 CYG 2450318.2676 9.612 1.589 .937 971 +V386 CYG 2450319.2562 9.764 1.677 .961 971 +V386 CYG 2450320.2395 9.949 1.717 .983 971 +V386 CYG 2450321.2302 9.642 1.535 .923 971 +V386 CYG 2450322.2644 9.303 1.421 .866 971 +V386 CYG 2450323.2633 9.556 1.597 .920 971 +V386 CYG 2450324.2660 9.738 1.656 .973 971 +V396 CYG 2449617.2058 11.727 2.482 1.552 995 +V396 CYG 2449619.3131 11.761 1.549 995 +V396 CYG 2449620.3022 11.848 2.569 1.568 995 +V396 CYG 2449621.2888 11.889 2.625 1.560 995 +V396 CYG 2449623.2372 11.835 1.553 995 +V396 CYG 2449624.2620 11.816 2.576 1.533 995 +V396 CYG 2449625.2885 11.851 2.586 1.527 995 +V396 CYG 2449626.2880 11.772 2.507 1.526 995 +V396 CYG 2449631.2551 11.260 2.174 1.422 995 +V396 CYG 2449632.2782 11.116 2.100 1.396 995 +V396 CYG 2449633.2606 11.041 2.023 1.367 995 +V396 CYG 2449634.2650 10.998 2.042 1.384 995 +V396 CYG 2446606.3569 11.148 2.072 1.404 988 +V396 CYG 2446607.2666 11.034 1.587 1.964 1.405 988 +V396 CYG 2446608.2134 10.952 2.013 1.371 988 +V396 CYG 2446609.1936 10.945 1.660 2.000 1.385 988 +V396 CYG 2446610.3919 11.002 2.036 1.408 988 +V396 CYG 2446611.2017 11.029 2.057 1.424 988 +V396 CYG 2446612.1879 11.101 1.730 2.099 1.452 988 +V396 CYG 2446613.1869 11.131 1.755 2.146 1.449 988 +V396 CYG 2446614.1870 11.180 1.879 2.179 1.481 988 +V396 CYG 2446615.1921 11.243 2.201 1.495 988 +V396 CYG 2446616.1927 11.268 1.902 2.273 1.495 988 +V396 CYG 2446617.1867 11.261 2.309 1.485 988 +V396 CYG 2446618.1865 11.327 2.262 1.514 988 +V396 CYG 2446619.1853 11.357 2.348 1.513 988 +V396 CYG 2446620.1832 11.419 2.329 1.536 988 +V396 CYG 2446621.3375 11.485 2.386 1.550 988 +V396 CYG 2446622.1836 11.513 2.369 1.548 988 +V396 CYG 2446623.1849 11.565 2.398 1.562 988 +V396 CYG 2446624.1828 11.616 2.390 1.575 988 +V396 CYG 2446625.1856 11.662 2.408 1.559 988 +V396 CYG 2446626.1918 11.668 2.414 1.557 988 +V396 CYG 2446627.1922 11.732 2.466 1.563 988 +V396 CYG 2446628.1917 11.775 2.434 1.589 988 +V396 CYG 2446629.1996 11.770 2.430 1.566 988 +V396 CYG 2446630.2004 11.811 2.397 1.561 988 +V396 CYG 2446631.1876 11.799 2.398 1.576 988 +V396 CYG 2446632.1992 11.805 2.415 1.557 988 +V396 CYG 2446635.2743 11.812 2.336 1.549 988 +V396 CYG 2446636.1792 11.787 2.336 1.533 988 +V396 CYG 2446637.1851 11.663 2.299 1.500 988 +V396 CYG 2444826.3554 11.462 2.397 982 +V396 CYG 2444827.2968 11.517 2.415 982 +V396 CYG 2444829.3163 11.587 2.465 982 +V396 CYG 2444830.2968 11.681 2.458 982 +V396 CYG 2444831.2851 11.691 982 +V396 CYG 2444832.2968 11.755 2.489 982 +V396 CYG 2444833.2812 11.762 2.460 982 +V396 CYG 2444834.3984 11.799 2.478 982 +V396 CYG 2444835.4022 11.797 2.440 982 +V396 CYG 2444836.4375 11.807 2.438 982 +V396 CYG 2444845.2304 11.097 2.040 982 +V396 CYG 2444847.2460 10.946 2.006 982 +V396 CYG 2444848.2381 10.985 2.042 982 +V396 CYG 2444849.2578 11.030 2.091 982 +V396 CYG 2444850.2578 11.062 2.130 982 +V396 CYG 2444851.2538 11.122 2.190 982 +V396 CYG 2444852.2460 11.189 2.195 982 +V396 CYG 2444853.2617 11.222 2.241 982 +V396 CYG 2444854.2538 11.252 2.278 982 +V396 CYG 2444855.2460 11.315 2.285 982 +V396 CYG 2444856.2421 11.350 2.326 982 +V396 CYG 2444857.2421 11.394 2.368 982 +V396 CYG 2444880.2304 10.979 2.057 982 +V396 CYG 2444881.2226 10.982 2.093 982 +V396 CYG 2444882.2734 11.041 2.078 982 +V396 CYG 2444883.2030 11.074 2.140 982 +V396 CYG 2444884.2304 11.133 2.145 982 +V396 CYG 2445174.4375 11.729 2.334 982 +V396 CYG 2445175.4570 11.577 2.240 982 +V396 CYG 2445176.4256 11.374 2.158 982 +V396 CYG 2445178.4139 10.986 2.020 982 +V396 CYG 2445179.3514 10.932 1.998 982 +V396 CYG 2445180.3437 10.962 2.030 982 +V396 CYG 2445181.2772 11.000 2.077 982 +V396 CYG 2445182.2851 11.043 2.111 982 +V396 CYG 2445183.3085 11.097 2.131 982 +V396 CYG 2445184.3320 11.144 2.178 982 +V396 CYG 2445186.3046 11.217 2.252 982 +V396 CYG 2445187.3359 11.270 2.295 982 +V396 CYG 2445188.3242 11.301 2.314 982 +V396 CYG 2445189.3554 11.355 2.351 982 +V396 CYG 2445190.3750 11.392 2.365 982 +V396 CYG 2445191.3710 11.428 2.381 982 +V396 CYG 2445192.3671 11.467 2.385 982 +V396 CYG 2445193.3905 11.538 2.395 982 +V396 CYG 2445194.3828 11.551 2.426 982 +V396 CYG 2445198.3945 11.740 2.452 982 +V396 CYG 2445199.3359 11.755 2.456 982 +V396 CYG 2445201.3671 11.794 2.441 982 +V396 CYG 2445203.2695 11.781 2.430 982 +V396 CYG 2445204.2617 11.790 2.408 982 +V396 CYG 2445205.2851 11.791 2.406 982 +V396 CYG 2445207.2929 11.761 2.347 982 +V396 CYG 2445208.3280 11.661 2.270 982 +V396 CYG 2445209.2734 11.465 2.187 982 +V396 CYG 2445210.3163 11.200 2.077 982 +V396 CYG 2445211.2772 11.044 2.020 982 +V396 CYG 2445212.2812 10.970 1.999 982 +V396 CYG 2445213.2812 10.940 2.020 982 +V396 CYG 2445214.2812 11.004 2.043 982 +V396 CYG 2445488.2421 11.299 1.499 982 +V396 CYG 2445490.2617 11.398 1.515 982 +V396 CYG 2445493.2264 11.498 1.547 982 +V396 CYG 2445496.2655 11.643 2.462 1.559 982 +V396 CYG 2445498.2264 11.699 1.550 982 +V396 CYG 2445501.2772 11.744 2.409 1.540 982 +V396 CYG 2445503.2500 2.408 1.550 982 +V396 CYG 2445505.2421 11.747 1.532 982 +V396 CYG 2445508.2226 11.468 1.440 982 +V396 CYG 2445509.2381 11.253 2.152 1.414 982 +V396 CYG 2445512.2381 10.929 2.040 1.375 982 +V396 CYG 2445513.2578 10.998 2.046 1.418 982 +V396 CYG 2445514.2460 10.995 2.077 1.411 982 +V396 CYG 2445515.2381 11.024 2.151 1.417 982 +V396 CYG 2445648.2187 11.064 2.139 1.414 982 +V396 CYG 2445649.2381 11.082 2.199 1.453 982 +V396 CYG 2445658.1718 11.525 1.535 982 +V396 CYG 2449933.4134 10.981 2.047 1.421 2.688 998 +V396 CYG 2449934.3866 10.977 1.428 2.712 998 +V396 CYG 2449935.3866 10.966 1.455 2.739 998 +V396 CYG 2449936.3794 11.093 1.475 2.790 998 +V396 CYG 2449937.3445 11.120 2.191 1.495 998 +V396 CYG 2449939.3727 11.189 2.856 998 +V396 CYG 2449941.3590 11.301 2.891 998 +V396 CYG 2449942.3403 11.324 2.927 998 +V396 CYG 2449943.3419 11.380 2.965 998 +V396 CYG 2449944.3799 11.384 2.962 998 +V396 CYG 2449945.3808 11.468 2.970 998 +V396 CYG 2449946.3658 11.556 3.030 998 +V396 CYG 2449947.3256 11.536 2.980 998 +V396 CYG 2449948.3216 11.598 3.030 998 +V396 CYG 2449949.3258 11.655 3.013 998 +V396 CYG 2449950.3126 11.657 3.008 998 +V396 CYG 2449952.3074 11.734 3.037 998 +V396 CYG 2449953.3816 11.763 3.031 998 +V396 CYG 2449954.3395 11.766 3.024 998 +V396 CYG 2449955.2985 11.794 3.007 998 +V396 CYG 2449956.3705 11.776 3.023 998 +V396 CYG 2449957.3586 11.770 3.022 998 +V396 CYG 2449958.3220 11.792 3.000 998 +V396 CYG 2449959.3465 11.811 3.003 998 +V396 CYG 2449960.3065 11.810 2.977 998 +V396 CYG 2449962.3691 11.591 2.894 998 +V396 CYG 2449985.2842 11.655 2.992 998 +V396 CYG 2449986.2807 11.695 2.997 998 +V396 CYG 2449987.2838 11.708 2.973 998 +V396 CYG 2449992.2102 11.780 2.966 998 +V396 CYG 2449993.2163 11.769 2.956 998 +V396 CYG 2450007.3050 11.226 2.865 998 +V396 CYG 2450009.3028 11.297 2.880 998 +V396 CYG 2450011.2658 11.397 2.930 998 +V396 CYG 2450017.2573 11.758 2.994 998 +V396 CYG 2450018.2746 11.723 3.031 998 +V396 CYG 2450019.2651 11.756 3.007 998 +V396 CYG 2448503.3275 10.984 2.032 1.364 993 +V396 CYG 2448504.2611 11.003 2.017 1.388 993 +V396 CYG 2448505.2856 11.044 2.057 1.394 993 +V396 CYG 2448506.2982 11.089 2.087 1.412 993 +V396 CYG 2448507.2835 11.118 2.150 1.416 993 +V396 CYG 2448508.2410 11.154 2.185 1.440 993 +V396 CYG 2448509.2659 11.214 2.216 1.474 993 +V396 CYG 2448510.2521 11.259 2.240 1.478 993 +V396 CYG 2448511.2638 11.297 2.281 1.484 993 +V396 CYG 2448512.2554 11.345 2.289 1.512 993 +V396 CYG 2448513.2518 11.395 2.312 1.511 993 +V396 CYG 2448514.2576 11.444 2.347 1.521 993 +V396 CYG 2448515.2330 11.473 2.352 1.518 993 +V396 CYG 2448516.2229 11.516 2.396 1.529 993 +V396 CYG 2448517.2185 11.575 1.549 993 +V396 CYG 2448518.2241 11.605 2.450 1.553 993 +V396 CYG 2448519.2544 11.670 2.444 1.565 993 +V396 CYG 2448520.2256 11.707 2.444 1.569 993 +V396 CYG 2448521.2752 11.723 2.483 1.552 993 +V396 CYG 2448522.2459 11.796 2.480 1.582 993 +V396 CYG 2448523.2376 11.812 2.491 1.545 993 +V396 CYG 2450305.2191 11.227 2.245 1.451 971 +V396 CYG 2450306.2812 11.254 2.274 1.466 971 +V396 CYG 2450307.2950 11.348 2.266 1.481 971 +V396 CYG 2450310.2778 11.427 2.373 1.500 971 +V396 CYG 2450311.2513 11.490 2.418 1.507 971 +V396 CYG 2450312.2847 11.543 2.391 1.521 971 +V396 CYG 2450313.2772 11.566 2.456 1.522 971 +V396 CYG 2450314.2657 11.626 2.458 1.523 971 +V396 CYG 2450315.2166 11.602 2.488 1.479 971 +V396 CYG 2450316.2215 11.652 2.461 1.495 971 +V396 CYG 2450317.2286 11.699 2.434 1.509 971 +V396 CYG 2450318.2436 11.718 2.480 1.479 971 +V396 CYG 2450319.2304 11.737 2.478 1.492 971 +V396 CYG 2450320.2301 11.769 2.457 1.483 971 +V396 CYG 2450321.2204 11.777 2.431 1.497 971 +V396 CYG 2450322.2536 11.790 2.437 1.495 971 +V396 CYG 2450323.2551 11.790 2.441 1.473 971 +V396 CYG 2450324.2574 11.777 2.435 1.467 971 +V396 CYG 2450325.2229 11.786 2.439 1.471 971 +V396 CYG 2450326.1811 11.771 2.417 1.475 971 +V396 CYG 2450327.2652 11.682 2.317 971 +V396 CYG 2450328.3741 11.520 2.234 1.421 971 +V396 CYG 2450330.2204 11.187 2.065 1.348 971 +V396 CYG 2450332.2084 10.948 2.056 1.313 971 +V396 CYG 2450333.1923 10.947 2.095 1.339 971 +V396 CYG 2450334.2024 11.004 2.149 1.343 971 +V396 CYG 2450335.2173 11.027 2.219 1.407 971 +V396 CYG 2450336.2219 11.100 2.191 1.377 971 +V396 CYG 2450337.1859 11.120 2.274 1.386 971 +V396 CYG 2450338.2650 11.175 2.264 1.413 971 +V396 CYG 2450340.1746 11.239 2.353 1.438 971 +V396 CYG 2450341.1965 11.279 2.411 1.477 971 +V396 CYG 2450342.2100 11.305 2.365 1.441 971 +V396 CYG 2450344.2396 11.405 2.405 1.436 971 +V396 CYG 2450347.2247 11.549 2.416 1.483 971 +V396 CYG 2450349.1744 11.638 1.489 971 +V396 CYG 2450357.1875 11.772 2.450 1.477 971 +V402 CYG 2449992.2947 9.610 1.103 998 +V402 CYG 2449993.2281 9.855 1.233 998 +V402 CYG 2450007.2980 10.038 1.321 998 +V402 CYG 2450009.2958 9.575 1.064 998 +V402 CYG 2450011.2609 9.981 1.273 998 +V402 CYG 2450017.2525 10.129 1.340 998 +V402 CYG 2450018.2694 9.567 1.030 998 +V402 CYG 2450019.2600 9.842 1.216 998 +V402 CYG 2450020.2288 10.014 1.336 998 +V402 CYG 2446606.3463 9.912 .762 1.115 .644 988 +V402 CYG 2446607.2805 10.073 .833 1.149 .664 988 +V402 CYG 2446608.2226 10.066 .758 1.099 .645 988 +V402 CYG 2446609.2041 9.557 .600 .878 .543 988 +V402 CYG 2446610.3865 9.850 1.079 .619 988 +V402 CYG 2446611.2110 10.002 .807 1.140 .658 988 +V402 CYG 2446612.1967 10.151 .830 1.147 .681 988 +V402 CYG 2446613.1973 9.658 .613 .921 .555 988 +V402 CYG 2446614.1993 9.703 .668 .992 .595 988 +V402 CYG 2446615.2009 9.960 .741 1.130 .646 988 +V402 CYG 2446616.2011 10.093 .825 1.175 .658 988 +V402 CYG 2446617.1921 9.925 .678 1.030 .618 988 +V402 CYG 2446618.1938 9.597 .634 .912 .567 988 +V402 CYG 2446619.1931 9.871 .732 1.076 .635 988 +V402 CYG 2446620.1894 10.039 .844 1.150 .668 988 +V402 CYG 2446621.3306 10.055 .747 1.103 .639 988 +V402 CYG 2446622.1886 9.549 .597 .885 .534 988 +V402 CYG 2446623.1918 9.774 1.025 .605 988 +V402 CYG 2446624.1870 9.992 1.134 .656 988 +V402 CYG 2446625.1890 10.126 1.147 .669 988 +V402 CYG 2446626.1966 9.703 .935 .577 988 +V402 CYG 2446627.1967 9.694 .964 .581 988 +V402 CYG 2446628.1983 9.932 .798 1.103 .644 988 +V402 CYG 2446629.2091 10.081 .826 1.152 .671 988 +V402 CYG 2446631.1924 9.595 .896 .549 988 +V402 CYG 2446632.2053 9.871 1.066 .626 988 +V402 CYG 2446635.2819 9.546 .610 .890 .531 988 +V402 CYG 2446636.1846 9.750 1.034 .596 988 +V402 CYG 2446637.1892 9.977 1.121 .662 988 +V402 CYG 2448854.3180 9.936 1.145 .641 994 +V402 CYG 2448856.3191 9.956 1.075 .615 994 +V402 CYG 2448858.2947 9.833 1.087 .623 994 +V402 CYG 2448860.2815 10.121 1.163 .664 994 +V402 CYG 2448862.3045 9.759 1.032 .606 994 +V402 CYG 2448870.2569 9.545 .926 .522 994 +V402 CYG 2448872.2757 10.017 1.162 .654 994 +V402 CYG 2448874.2950 9.616 .916 .552 994 +V402 CYG 2448877.2449 10.086 1.211 .649 994 +V402 CYG 2448878.2439 9.910 1.068 .612 994 +V402 CYG 2448880.2346 9.864 1.095 .629 994 +V402 CYG 2448881.2210 10.050 1.178 .658 994 +V402 CYG 2448882.2245 10.103 1.159 .650 994 +V402 CYG 2448883.2551 9.550 .917 .533 994 +V402 CYG 2448884.2262 9.783 1.058 .619 994 +V402 CYG 2448885.2247 9.989 1.182 .636 994 +V402 CYG 2448886.2364 10.126 1.177 .666 994 +V402 CYG 2448887.2571 9.679 .958 .555 994 +V402 CYG 2448888.2184 9.678 .995 .581 994 +V402 CYG 2448889.2246 9.928 1.137 .637 994 +V402 CYG 2448890.2063 10.078 1.183 .674 994 +V402 CYG 2448891.2052 10.024 1.047 .660 994 +V402 CYG 2448892.2245 9.578 .921 .548 994 +V402 CYG 2448893.2121 9.830 1.109 .619 994 +V402 CYG 2448894.2156 10.017 1.195 .662 994 +V438 CYG 2449957.3612 10.884 2.353 998 +V438 CYG 2449962.3744 11.223 2.395 998 +V438 CYG 2449986.2834 10.929 2.248 998 +V438 CYG 2449987.2931 10.718 2.192 998 +V438 CYG 2449992.3083 11.093 2.397 998 +V438 CYG 2450007.3066 11.200 2.354 998 +V438 CYG 2450009.3049 10.872 2.201 998 +V438 CYG 2450011.2672 10.610 2.178 998 +V438 CYG 2450018.2774 11.260 2.421 998 +V438 CYG 2450019.2668 11.058 2.272 998 +V438 CYG 2450020.2308 10.907 2.245 998 +V438 CYG 2447409.2731 10.430 1.684 1.058 990 +V438 CYG 2447410.2888 10.632 1.829 1.123 990 +V438 CYG 2447411.2944 10.751 1.894 1.164 990 +V438 CYG 2447413.2645 11.071 2.057 1.246 990 +V438 CYG 2447414.2734 11.200 2.164 1.244 990 +V438 CYG 2447415.2513 11.373 2.141 1.273 990 +V438 CYG 2447416.2431 11.393 2.094 1.238 990 +V438 CYG 2447417.2404 11.281 2.045 1.235 990 +V438 CYG 2447418.2410 11.085 1.984 1.190 990 +V438 CYG 2447419.2175 10.942 1.915 1.156 990 +V438 CYG 2447420.2174 10.556 1.760 1.081 990 +V438 CYG 2447421.2050 10.529 1.796 1.091 990 +V438 CYG 2447422.2194 10.679 1.892 1.155 990 +V438 CYG 2447423.2240 10.853 1.995 1.189 990 +V438 CYG 2447424.2298 11.021 2.069 1.233 990 +V438 CYG 2447425.2520 11.193 2.114 1.243 990 +V438 CYG 2447427.2827 11.469 2.130 1.266 990 +V438 CYG 2447428.2142 11.330 2.086 1.229 990 +V438 CYG 2447429.2119 11.155 1.976 1.197 990 +V438 CYG 2447430.2024 10.950 1.909 1.149 990 +V438 CYG 2447431.2518 10.664 1.808 1.091 990 +V438 CYG 2447432.2346 10.469 1.722 1.069 990 +V438 CYG 2447433.2356 10.723 1.868 1.153 990 +V438 CYG 2447434.2360 10.801 1.965 1.156 990 +V438 CYG 2444825.4218 11.295 2.150 982 +V438 CYG 2444827.2812 11.304 2.113 982 +V438 CYG 2444829.3006 10.944 1.921 982 +V438 CYG 2444831.2734 10.441 1.770 982 +V438 CYG 2444832.2812 10.697 1.880 982 +V438 CYG 2444833.2655 10.806 1.963 982 +V438 CYG 2444834.3867 10.988 2.043 982 +V438 CYG 2444835.3905 11.139 2.106 982 +V438 CYG 2444836.4256 11.297 2.148 982 +V438 CYG 2444845.2226 10.911 2.037 982 +V438 CYG 2444846.2655 11.090 2.099 982 +V438 CYG 2444847.2381 11.243 2.156 982 +V438 CYG 2444848.2304 11.377 2.159 982 +V438 CYG 2444849.2538 11.374 2.141 982 +V438 CYG 2444850.2500 11.232 2.026 982 +V438 CYG 2444851.2460 11.007 1.942 982 +V438 CYG 2444852.2421 10.863 1.875 982 +V438 CYG 2444853.2500 10.388 1.712 982 +V438 CYG 2444854.2460 10.637 1.853 982 +V438 CYG 2444855.2343 10.761 1.926 982 +V438 CYG 2444856.2343 10.894 2.006 982 +V438 CYG 2444857.2343 11.059 2.063 982 +V438 CYG 2444880.2147 11.129 2.130 982 +V438 CYG 2444881.2070 11.264 2.180 982 +V438 CYG 2444882.2538 11.393 2.128 982 +V438 CYG 2444883.1875 11.351 2.085 982 +V438 CYG 2444884.2187 11.138 1.998 982 +V438 CYG 2445174.4218 11.354 2.124 982 +V438 CYG 2445175.4413 11.157 2.014 982 +V438 CYG 2445178.4101 10.364 1.705 982 +V438 CYG 2445179.3280 10.624 1.852 982 +V438 CYG 2445180.3163 10.741 1.921 982 +V438 CYG 2445181.2734 10.874 2.006 982 +V438 CYG 2445182.2812 11.021 2.078 982 +V438 CYG 2445183.3046 11.182 2.124 982 +V438 CYG 2445184.3085 11.332 2.147 982 +V438 CYG 2445186.3006 11.257 2.063 982 +V438 CYG 2445187.3280 11.069 1.959 982 +V438 CYG 2445188.3006 10.889 1.905 982 +V438 CYG 2445189.3397 10.464 1.713 982 +V438 CYG 2445190.3710 10.584 1.834 982 +V438 CYG 2445191.3671 10.702 1.917 982 +V438 CYG 2445192.3631 10.837 1.980 982 +V438 CYG 2445194.3788 11.155 2.107 982 +V438 CYG 2445198.3905 11.090 1.998 982 +V438 CYG 2445199.3320 10.925 1.910 982 +V438 CYG 2445200.4139 10.540 1.748 982 +V438 CYG 2445201.3631 10.498 1.789 982 +V438 CYG 2445203.3006 10.802 1.980 982 +V438 CYG 2445209.3085 11.118 2.007 982 +V438 CYG 2445210.3125 10.953 1.920 982 +V438 CYG 2445211.2734 10.770 1.846 982 +V438 CYG 2445212.3085 10.405 1.724 982 +V438 CYG 2445213.2772 10.646 1.878 982 +V438 CYG 2445648.2226 10.868 1.898 1.140 982 +V438 CYG 2445649.2421 10.370 1.715 1.069 982 +V438 CYG 2445660.2226 10.478 1.069 982 +V438 CYG 2445665.1992 11.164 1.258 982 +V438 CYG 2445666.1601 11.300 1.270 982 +V438 CYG 2445668.2109 11.269 1.238 982 +V438 CYG 2445674.1718 10.793 1.197 982 +V438 CYG 2445676.1445 11.118 1.252 982 +V438 CYG 2450305.3078 10.986 2.057 1.214 971 +V438 CYG 2450306.3140 11.117 2.102 1.228 971 +V438 CYG 2450307.2968 11.319 2.115 1.264 971 +V438 CYG 2450310.2818 11.117 1.996 1.192 971 +V438 CYG 2450311.2556 10.969 1.880 1.160 971 +V438 CYG 2450312.2872 10.781 1.816 1.121 971 +V438 CYG 2450313.2798 10.379 1.696 1.053 971 +V438 CYG 2450314.2689 10.667 1.861 1.124 971 +V438 CYG 2450315.2595 10.764 1.965 971 +V438 CYG 2450316.2231 10.904 2.043 1.174 971 +V438 CYG 2450317.2308 11.066 2.097 1.202 971 +V438 CYG 2450318.2453 11.242 2.171 1.227 971 +V438 CYG 2450319.2324 11.341 2.190 1.235 971 +V438 CYG 2450320.2329 11.372 2.095 1.220 971 +V438 CYG 2450321.2224 11.183 2.014 1.198 971 +V438 CYG 2450322.2557 10.996 1.921 1.153 971 +V438 CYG 2450323.2569 10.844 1.923 1.092 971 +V438 CYG 2450324.2591 10.360 1.675 1.026 971 +V438 CYG 2450325.2248 10.619 1.866 1.098 971 +V438 CYG 2450326.1824 10.723 1.943 1.135 971 +V459 CYG 2449986.2996 10.453 1.672 998 +V459 CYG 2449987.3133 10.656 1.781 998 +V459 CYG 2449992.3472 10.359 1.578 998 +V459 CYG 2450007.3156 10.456 1.683 998 +V459 CYG 2450009.3169 10.709 1.772 998 +V459 CYG 2450010.2663 10.858 1.820 998 +V459 CYG 2450011.2754 10.959 1.832 998 +V459 CYG 2450017.2690 10.876 1.859 998 +V459 CYG 2450018.2879 10.977 1.869 998 +V459 CYG 2450019.2730 10.659 1.726 998 +V459 CYG 2450020.2367 10.298 1.552 998 +V492 CYG 2446252.2625 12.164 1.571 .955 987 +V492 CYG 2446253.2440 12.235 1.602 .995 987 +V492 CYG 2446255.3457 12.457 1.700 1.040 987 +V492 CYG 2446256.2876 12.569 1.777 1.051 987 +V492 CYG 2446257.3051 12.698 1.812 1.067 987 +V492 CYG 2446258.2533 12.545 1.727 1.024 987 +V492 CYG 2446259.2552 12.277 1.585 .995 987 +V492 CYG 2446260.2258 12.186 1.563 .959 987 +V492 CYG 2446261.2490 12.255 1.638 .991 987 +V492 CYG 2446262.2218 12.336 1.713 1.027 987 +V492 CYG 2446263.2274 12.454 1.712 1.052 987 +V492 CYG 2446265.2075 12.640 1.852 1.062 987 +V492 CYG 2446266.2255 12.397 1.703 1.008 987 +V492 CYG 2446267.2317 12.197 1.573 .970 987 +V492 CYG 2446268.2449 12.222 1.599 .987 987 +V492 CYG 2446269.2255 12.290 1.657 1.021 987 +V492 CYG 2446270.2379 12.368 1.708 1.045 987 +V492 CYG 2446272.3101 12.646 1.775 1.088 987 +V492 CYG 2446273.2473 12.591 1.757 1.039 987 +V492 CYG 2446275.2305 12.186 1.589 .980 987 +V492 CYG 2446277.1854 1.699 1.005 987 +V492 CYG 2446278.1820 12.386 1.837 1.031 987 +V492 CYG 2446279.2278 12.601 1.821 1.066 987 +V492 CYG 2446280.2152 12.661 1.787 1.062 987 +V495 CYG 2449986.2766 10.618 1.956 998 +V495 CYG 2449987.2891 10.743 1.980 998 +V495 CYG 2449992.3053 10.547 1.924 998 +V495 CYG 2450007.3034 10.751 1.977 998 +V495 CYG 2450009.3013 10.772 1.977 998 +V495 CYG 2450011.2646 10.441 1.859 998 +V495 CYG 2450017.2564 10.513 1.892 998 +V495 CYG 2450018.2734 10.472 1.890 998 +V495 CYG 2450020.2277 10.653 1.960 998 +V495 CYG 2447409.2673 10.795 1.647 1.010 990 +V495 CYG 2447410.2833 10.449 1.545 .957 990 +V495 CYG 2447411.2926 10.444 1.543 .965 990 +V495 CYG 2447413.2552 10.677 1.669 1.015 990 +V495 CYG 2447414.2631 10.798 1.742 1.026 990 +V495 CYG 2447415.2402 10.873 1.674 1.048 990 +V495 CYG 2447416.2322 10.678 1.628 .995 990 +V495 CYG 2447417.2267 10.411 1.559 .947 990 +V495 CYG 2447418.2250 10.464 1.573 .947 990 +V495 CYG 2447419.2080 10.543 1.639 .987 990 +V495 CYG 2447420.2077 10.691 1.689 1.012 990 +V495 CYG 2447421.1958 10.807 1.705 1.020 990 +V495 CYG 2447422.2113 10.812 1.674 1.031 990 +V495 CYG 2447423.2137 10.558 1.575 .974 990 +V495 CYG 2447424.2185 10.470 1.575 .981 990 +V495 CYG 2447425.2386 10.496 1.606 .959 990 +V495 CYG 2447427.2579 10.805 1.687 1.018 990 +V495 CYG 2447428.2079 10.839 1.719 1.023 990 +V495 CYG 2447429.2045 10.817 1.677 1.010 990 +V495 CYG 2447430.1962 10.462 1.570 .954 990 +V495 CYG 2447431.2351 10.435 1.573 .950 990 +V495 CYG 2447432.2193 10.527 1.586 .985 990 +V495 CYG 2447433.2237 10.694 1.659 1.033 990 +V495 CYG 2447434.2220 10.780 1.710 1.025 990 +V495 CYG 2446992.4058 10.833 1.677 1.042 989 +V495 CYG 2447001.3086 10.424 1.531 .962 989 +V495 CYG 2447002.3143 10.503 1.618 .982 989 +V495 CYG 2447003.2919 10.635 1.652 1.021 989 +V495 CYG 2447082.2322 10.447 1.031 1.527 .970 989 +V495 CYG 2447083.1769 10.542 1.055 1.592 .985 989 +V495 CYG 2447084.1663 10.679 1.100 1.647 1.013 989 +V495 CYG 2447087.1722 10.627 1.601 .992 989 +V495 CYG 2447088.1129 10.438 1.025 1.515 .968 989 +V495 CYG 2447091.1035 10.709 1.701 1.035 989 +V495 CYG 2447098.0929 10.754 1.677 1.033 989 +V495 CYG 2446615.3118 10.817 1.115 1.704 1.046 988 +V495 CYG 2446616.3256 10.823 1.709 1.045 988 +V495 CYG 2446617.3261 10.472 1.085 1.599 .970 988 +V495 CYG 2446618.3218 10.407 1.138 1.548 .969 988 +V495 CYG 2446619.3274 10.501 1.140 1.580 .985 988 +V495 CYG 2446620.3016 10.615 1.099 1.645 1.019 988 +V495 CYG 2446621.3429 10.744 1.695 1.036 988 +V495 CYG 2446622.3047 10.834 1.076 1.706 1.052 988 +V495 CYG 2446623.3397 10.759 1.653 1.032 988 +V495 CYG 2446624.3344 10.426 1.576 .959 988 +V495 CYG 2446625.2879 10.424 1.548 .978 988 +V495 CYG 2446626.3095 10.514 1.621 .987 988 +V495 CYG 2446627.2942 10.659 1.653 1.028 988 +V495 CYG 2446628.2882 10.776 1.713 1.038 988 +V495 CYG 2446629.2906 10.851 1.698 1.053 988 +V495 CYG 2446631.2696 10.430 1.542 .961 988 +V495 CYG 2446632.2955 10.441 1.568 .970 988 +V495 CYG 2446635.3238 10.799 1.705 1.047 988 +V495 CYG 2446636.3016 10.839 1.684 1.035 988 +V495 CYG 2450305.3103 10.666 1.653 .988 971 +V495 CYG 2450306.3119 10.422 1.538 .951 971 +V495 CYG 2450307.2946 10.470 1.536 .956 971 +V495 CYG 2450310.2773 10.793 1.685 1.034 971 +V495 CYG 2450311.2506 10.851 1.658 1.029 971 +V495 CYG 2450312.2838 10.553 1.549 .969 971 +V495 CYG 2450313.2766 10.397 1.524 .952 971 +V495 CYG 2450314.2651 10.481 1.572 .970 971 +V495 CYG 2450315.2162 10.593 1.650 .985 971 +V495 CYG 2450316.2130 10.721 1.690 .992 971 +V495 CYG 2450317.2270 10.836 1.670 1.035 971 +V495 CYG 2450318.2432 10.822 1.662 1.018 971 +V495 CYG 2450319.2294 10.443 1.552 .942 971 +V495 CYG 2450320.2297 10.418 1.544 .933 971 +V495 CYG 2450321.2197 10.493 1.590 .966 971 +V495 CYG 2450322.2525 10.632 1.663 .999 971 +V495 CYG 2450323.2541 10.761 1.707 1.006 971 +V495 CYG 2450324.2563 10.860 1.656 1.039 971 +V495 CYG 2450325.2187 10.739 1.663 .981 971 +V514 CYG 2446252.2777 11.393 1.392 1.691 1.099 987 +V514 CYG 2446253.2513 11.601 1.794 1.132 987 +V514 CYG 2446255.3527 11.382 1.633 1.044 987 +V514 CYG 2446256.2967 11.070 1.538 1.002 987 +V514 CYG 2446257.3145 11.368 1.706 1.080 987 +V514 CYG 2446258.2620 11.587 1.795 1.130 987 +V514 CYG 2446259.2585 11.758 1.831 1.147 987 +V514 CYG 2446260.2332 11.618 1.509 1.720 1.090 987 +V514 CYG 2446261.2524 11.031 1.505 .997 987 +V514 CYG 2446263.2368 11.566 1.456 1.766 1.126 987 +V514 CYG 2446265.2128 11.715 1.757 1.132 987 +V514 CYG 2446266.2309 10.987 1.509 .982 987 +V514 CYG 2446267.3105 11.324 1.352 1.662 1.076 987 +V514 CYG 2446268.3102 11.557 1.415 1.778 1.116 987 +V514 CYG 2446269.2948 11.718 1.841 1.129 987 +V514 CYG 2446270.3476 11.669 1.741 1.111 987 +V514 CYG 2446271.2337 11.020 1.497 .981 987 +V514 CYG 2446272.3159 11.297 1.666 1.067 987 +V514 CYG 2446273.2561 11.514 1.778 1.109 987 +V514 CYG 2446274.2057 11.697 1.825 1.143 987 +V514 CYG 2446275.1824 11.791 1.796 1.154 987 +V514 CYG 2446277.1978 11.226 1.643 1.053 987 +V514 CYG 2446278.2312 11.481 1.806 1.110 987 +V514 CYG 2446279.2958 11.745 1.831 1.144 987 +V514 CYG 2446280.2293 11.801 1.806 1.143 987 +V514 CYG 2446283.2930 11.536 1.765 1.116 987 +V514 CYG 2446284.2669 11.710 1.827 1.138 987 +V514 CYG 2446286.2416 11.121 1.527 .994 987 +V514 CYG 2446287.2339 11.195 1.621 1.027 987 +V514 CYG 2446288.2773 11.484 1.724 1.112 987 +V514 CYG 2446289.2966 11.684 1.820 1.145 987 +V514 CYG 2446291.2499 11.203 1.543 1.014 987 +V514 CYG 2446292.2724 11.209 1.578 1.034 987 +V514 CYG 2446294.2384 11.664 1.794 1.144 987 +V514 CYG 2446295.2394 11.792 1.852 1.134 987 +V514 CYG 2446296.2337 11.322 1.589 1.027 987 +V514 CYG 2446297.2366 11.140 1.560 1.013 987 +V514 CYG 2446298.2447 11.431 1.708 1.093 987 +V514 CYG 2446299.2286 11.647 1.804 1.148 987 +V514 CYG 2446300.3887 11.811 1.830 1.141 987 +V514 CYG 2446300.2321 11.818 1.844 1.154 987 +V514 CYG 2446301.2371 11.415 1.629 1.064 987 +V514 CYG 2446302.2538 11.124 1.581 1.011 987 +V514 CYG 2446303.2340 11.399 1.731 1.085 987 +V514 CYG 2446304.2247 11.623 1.790 1.122 987 +V514 CYG 2449960.3117 11.654 2.237 998 +V514 CYG 2449986.2909 11.688 2.194 998 +V514 CYG 2449987.2988 11.753 2.209 998 +V514 CYG 2449992.3294 11.759 2.177 998 +V514 CYG 2450007.3111 11.780 2.204 998 +V514 CYG 2450009.3087 11.127 1.972 998 +V514 CYG 2450011.2713 11.607 2.188 998 +V514 CYG 2450017.2636 11.824 2.231 998 +V514 CYG 2450018.2813 11.489 2.079 998 +V514 CYG 2450019.2701 11.005 1.920 998 +V514 CYG 2450020.2340 11.318 2.083 998 +V520 CYG 2446252.4148 11.091 1.474 .842 987 +V520 CYG 2446253.3239 10.976 1.377 .816 987 +V520 CYG 2446255.3706 10.890 1.403 .817 987 +V520 CYG 2446256.3147 11.056 1.476 .835 987 +V520 CYG 2446257.3275 11.010 1.399 .812 987 +V520 CYG 2446258.2768 10.573 1.209 .726 987 +V520 CYG 2446259.2763 10.834 .993 1.385 .800 987 +V520 CYG 2446260.2494 11.073 1.464 .838 987 +V520 CYG 2446261.2673 11.048 .951 1.402 .828 987 +V520 CYG 2446263.2514 10.835 .935 1.374 .798 987 +V520 CYG 2446265.2289 11.116 1.031 1.437 .841 987 +V520 CYG 2446266.2443 10.510 .856 1.215 .724 987 +V520 CYG 2446267.3292 10.848 1.383 .800 987 +V520 CYG 2446268.3320 11.063 1.439 .842 987 +V520 CYG 2446269.3072 11.073 1.026 1.433 .822 987 +V520 CYG 2446270.3593 10.531 .869 1.206 .716 987 +V520 CYG 2446272.3302 11.048 1.453 .840 987 +V520 CYG 2446273.2732 11.108 1.087 1.445 .822 987 +V520 CYG 2446274.2321 10.527 .860 1.190 .710 987 +V520 CYG 2446275.2444 10.795 .979 1.355 .794 987 +V520 CYG 2446277.2194 11.146 1.422 .828 987 +V520 CYG 2446278.2468 10.514 1.203 .699 987 +V520 CYG 2446279.3055 10.796 1.378 .791 987 +V520 CYG 2446280.2375 11.021 1.435 .836 987 +V520 CYG 2446283.2965 10.792 1.346 .793 987 +V520 CYG 2446284.2772 11.029 1.455 .845 987 +V520 CYG 2446286.2362 10.560 .823 1.210 .709 987 +V520 CYG 2446287.2360 10.740 1.332 .777 987 +V520 CYG 2446288.2796 11.038 1.415 .859 987 +V520 CYG 2446289.2994 11.124 1.463 .830 987 +V520 CYG 2446290.2994 10.578 1.193 .719 987 +V520 CYG 2446291.2517 10.738 1.305 .777 987 +V520 CYG 2446292.2738 11.004 1.462 .833 987 +V520 CYG 2446294.2400 10.632 1.219 .739 987 +V520 CYG 2446295.2414 10.701 1.313 .765 987 +V520 CYG 2446296.2354 10.989 1.436 .828 987 +V520 CYG 2446297.2381 11.124 1.454 .845 987 +V520 CYG 2446298.2464 10.674 1.214 .756 987 +V520 CYG 2446299.2307 10.702 1.293 .770 987 +V520 CYG 2446300.2350 10.974 1.445 .824 987 +V520 CYG 2446300.3906 11.017 1.441 .839 987 +V520 CYG 2446301.2389 11.117 1.483 .846 987 +V520 CYG 2446302.2566 10.682 1.286 .729 987 +V520 CYG 2446303.2361 10.674 1.318 .757 987 +V520 CYG 2446304.2268 10.944 1.460 .812 987 +V520 CYG 2449957.3635 11.076 1.639 998 +V520 CYG 2449960.3154 10.920 1.609 998 +V520 CYG 2449986.2954 11.091 1.605 998 +V520 CYG 2449987.3027 10.518 1.396 998 +V520 CYG 2449992.3345 10.805 1.556 998 +V520 CYG 2450007.3133 10.592 1.430 998 +V520 CYG 2450009.3114 10.998 1.612 998 +V520 CYG 2450010.2638 11.114 1.638 .792 1.615 998 +V520 CYG 2450011.2734 10.663 1.471 998 +V520 CYG 2450017.2672 11.022 1.605 998 +V520 CYG 2450018.2850 11.107 1.659 998 +V520 CYG 2450019.2716 10.708 1.464 998 +V520 CYG 2450020.2354 10.652 1.484 998 +V532 CYG 2449957.3665 9.157 1.284 998 +V532 CYG 2449960.3440 9.257 1.314 998 +V532 CYG 2449992.3415 9.171 1.267 998 +V532 CYG 2450007.3179 8.936 1.173 998 +V532 CYG 2450009.3198 9.243 1.298 998 +V532 CYG 2450010.2676 9.061 1.222 998 +V532 CYG 2450010.2891 9.028 1.206 998 +V532 CYG 2450011.2770 8.951 1.212 998 +V532 CYG 2450017.2720 8.997 1.180 998 +V532 CYG 2450018.2899 9.073 1.241 998 +V532 CYG 2450019.2746 9.222 1.321 998 +V532 CYG 2450020.2377 9.000 1.210 998 +V532 CYG 2450311.2680 9.233 1.145 .665 971 +V532 CYG 2450312.2983 9.082 1.077 .625 971 +V532 CYG 2450313.2904 8.918 1.034 .597 971 +V532 CYG 2450314.2755 9.202 1.135 .662 971 +V532 CYG 2450315.2850 9.175 1.113 .650 971 +V532 CYG 2450316.2186 8.936 1.019 .588 971 +V532 CYG 2450317.2397 9.161 1.096 .650 971 +V532 CYG 2450318.2707 9.268 1.139 .662 971 +V532 CYG 2450319.2570 8.938 1.030 .588 971 +V532 CYG 2450320.2404 9.058 1.081 .621 971 +V532 CYG 2450321.2310 9.253 1.159 .666 971 +V532 CYG 2450322.2651 9.052 1.048 .632 971 +V532 CYG 2450323.2644 8.961 1.052 .608 971 +V532 CYG 2450324.2669 9.224 1.164 .669 971 +V532 CYG 2450325.2378 9.154 1.079 .646 971 +V532 CYG 2450326.1860 8.942 1.013 .600 971 +V538 CYG 2449956.3760 10.229 1.397 998 +V538 CYG 2449957.3805 10.314 1.454 998 +V538 CYG 2449992.3657 10.501 1.484 998 +V538 CYG 2450007.3194 10.404 1.506 998 +V538 CYG 2450009.3213 10.692 1.563 998 +V538 CYG 2450010.2907 10.652 1.539 998 +V538 CYG 2450011.2786 10.268 1.397 998 +V538 CYG 2450017.2735 10.383 1.401 998 +V538 CYG 2450018.2915 10.244 1.414 998 +V538 CYG 2450019.2759 10.362 1.477 998 +V538 CYG 2450020.2396 10.479 1.529 998 +V538 CYG 2448101.4086 10.519 1.327 .784 992 +V538 CYG 2448102.3519 10.167 1.187 .697 992 +V538 CYG 2448103.3235 10.234 1.242 .745 992 +V538 CYG 2448104.3471 10.391 1.354 .775 992 +V538 CYG 2448108.3595 10.190 1.199 .705 992 +V538 CYG 2448109.3324 10.259 1.243 .732 992 +V538 CYG 2448110.3172 10.386 1.319 .765 992 +V538 CYG 2448111.3340 10.536 1.426 .798 992 +V538 CYG 2448112.2959 10.671 1.458 .819 992 +V538 CYG 2448113.2884 10.669 1.387 .797 992 +V538 CYG 2448113.3869 10.606 1.381 .796 992 +V538 CYG 2448114.3653 10.262 1.233 .725 992 +V538 CYG 2448116.3593 10.410 1.323 .778 992 +V538 CYG 2448117.4223 10.600 1.388 .801 992 +V538 CYG 2448118.3583 10.675 1.451 .815 992 +V538 CYG 2448119.3476 10.707 1.433 .819 992 +V538 CYG 2448122.3324 10.368 1.340 .775 992 +V538 CYG 2448123.3085 10.498 1.394 .794 992 +V538 CYG 2448126.3067 10.369 1.275 .744 992 +V538 CYG 2448126.4091 10.336 1.245 .749 992 +V538 CYG 2448127.2731 10.214 1.197 .710 992 +V538 CYG 2448127.3796 10.208 1.217 .715 992 +V538 CYG 2450305.3118 10.200 1.186 .698 971 +V538 CYG 2450306.3204 10.284 1.284 .709 971 +V538 CYG 2450307.3046 10.427 1.328 .752 971 +V538 CYG 2450310.2976 10.539 1.338 .744 971 +V538 CYG 2450311.2701 10.213 1.177 .691 971 +V538 CYG 2450312.3012 10.280 1.239 .725 971 +V538 CYG 2450314.2808 10.535 1.389 .784 971 +V538 CYG 2450315.2878 10.664 1.457 .791 971 +V538 CYG 2450316.2660 10.642 1.384 .756 971 +V538 CYG 2450318.2728 10.272 1.224 .710 971 +V538 CYG 2450319.2593 10.394 1.334 .750 971 +V538 CYG 2450320.2434 10.550 1.390 .768 971 +V538 CYG 2450321.2349 10.669 1.420 .802 971 +V538 CYG 2450322.2684 10.663 1.381 .801 971 +V538 CYG 2450323.2682 10.298 1.223 .708 971 +V538 CYG 2450324.2721 10.233 1.244 .706 971 +V538 CYG 2450325.2399 10.399 1.297 .754 971 +V547 CYG 2448101.3533 13.291 1.490 .944 992 +V547 CYG 2448102.2887 13.426 1.632 .974 992 +V547 CYG 2448103.2604 13.691 .995 992 +V547 CYG 2448104.2757 13.846 992 +V547 CYG 2448108.2879 13.436 1.597 .977 992 +V547 CYG 2448109.2833 13.613 1.694 1.009 992 +V547 CYG 2448110.2599 13.727 1.805 992 +V547 CYG 2448111.2649 13.696 1.659 .971 992 +V547 CYG 2448112.2507 12.948 1.328 .838 992 +V547 CYG 2448113.2488 13.107 1.444 .882 992 +V547 CYG 2448114.2896 13.423 1.602 .960 992 +V547 CYG 2448116.2570 13.785 1.778 1.063 992 +V547 CYG 2448117.3132 1.676 1.037 992 +V547 CYG 2448118.2897 13.085 1.327 .873 992 +V547 CYG 2448119.2573 13.091 1.437 .904 992 +V547 CYG 2448122.2747 13.720 1.752 1.019 992 +V547 CYG 2448123.2448 13.841 1.745 1.036 992 +V547 CYG 2448126.2668 13.288 1.518 .968 992 +V547 CYG 2448126.3911 13.316 1.548 .944 992 +V547 CYG 2448127.2303 13.462 1.627 .991 992 +V547 CYG 2448127.3547 13.483 1.604 .984 992 +V547 CYG 2449957.2864 13.475 1.964 998 +V547 CYG 2449958.2615 13.672 1.970 998 +V547 CYG 2449959.3084 13.910 998 +V547 CYG 2450009.2612 13.849 1.987 998 +V547 CYG 2450011.2183 12.998 1.683 998 +V547 CYG 2450017.1802 12.998 1.622 998 +V547 CYG 2450018.2261 13.152 1.752 998 +V554 CYG 2446252.2523 13.773 .996 .639 987 +V554 CYG 2446253.2349 14.121 1.116 .758 987 +V554 CYG 2446256.2797 13.934 .986 .661 987 +V554 CYG 2446257.2982 13.989 1.144 .691 987 +V554 CYG 2446258.2430 14.288 1.216 .764 987 +V554 CYG 2446259.2484 14.446 1.294 .814 987 +V554 CYG 2446260.2207 14.439 1.257 .742 987 +V554 CYG 2446261.2437 13.848 1.045 .658 987 +V554 CYG 2446262.2147 14.221 1.226 .741 987 +V554 CYG 2446263.2215 14.415 1.253 .805 987 +V554 CYG 2446265.2037 13.733 1.024 .615 987 +V554 CYG 2446266.2202 14.041 1.183 .725 987 +V554 CYG 2446267.2270 14.381 1.272 .788 987 +V554 CYG 2446268.2399 14.485 1.258 .795 987 +V554 CYG 2446269.2208 13.970 1.032 .676 987 +V554 CYG 2446270.2197 13.938 1.129 .711 987 +V554 CYG 2446272.3046 14.499 1.261 .848 987 +V554 CYG 2446273.2372 14.354 1.218 .715 987 +V554 CYG 2446275.2243 14.175 1.219 .760 987 +V554 CYG 2446280.2070 14.356 1.189 .797 987 +V609 CYG 2446606.4018 11.601 2.257 1.317 988 +V609 CYG 2446607.4527 11.537 2.236 1.282 988 +V609 CYG 2446608.3575 11.349 1.802 2.107 1.241 988 +V609 CYG 2446609.3907 10.962 1.603 1.909 1.156 988 +V609 CYG 2446610.4220 10.511 1.371 1.733 1.058 988 +V609 CYG 2446611.3663 10.428 1.394 1.711 1.052 988 +V609 CYG 2446612.3839 10.474 1.432 1.731 1.071 988 +V609 CYG 2446613.3583 10.524 1.437 1.769 1.084 988 +V609 CYG 2446614.3596 10.574 1.481 1.810 1.115 988 +V609 CYG 2446615.3595 10.647 1.563 1.859 1.130 988 +V609 CYG 2446616.3549 10.687 1.579 1.937 1.145 988 +V609 CYG 2446617.3411 10.726 1.587 1.973 1.174 988 +V609 CYG 2446618.3412 10.790 1.711 2.002 1.201 988 +V609 CYG 2446619.3642 10.861 2.064 1.221 988 +V609 CYG 2446620.3215 10.893 2.093 1.228 988 +V609 CYG 2446621.3637 10.937 2.131 1.232 988 +V609 CYG 2446622.3426 10.984 2.029 2.147 1.247 988 +V609 CYG 2446623.3780 11.037 2.178 1.271 988 +V609 CYG 2446624.3617 11.109 2.202 1.281 988 +V609 CYG 2446625.3201 11.164 2.191 1.299 988 +V609 CYG 2446626.3388 11.192 2.226 1.297 988 +V609 CYG 2446627.3339 11.256 2.081 2.258 1.302 988 +V609 CYG 2446628.3396 11.325 2.270 1.312 988 +V609 CYG 2446629.3146 11.341 2.301 1.308 988 +V609 CYG 2446631.2877 11.401 2.243 1.307 988 +V609 CYG 2446632.3270 11.419 2.255 1.313 988 +V609 CYG 2446635.3582 11.620 2.252 1.333 988 +V609 CYG 2446636.3254 11.631 2.297 1.320 988 +V609 CYG 2444826.3867 11.271 2.256 982 +V609 CYG 2444829.3242 11.374 2.263 982 +V609 CYG 2444830.3514 11.450 2.250 982 +V609 CYG 2444831.2889 11.414 2.254 982 +V609 CYG 2444832.3046 11.509 2.270 982 +V609 CYG 2444833.2851 11.589 2.273 982 +V609 CYG 2444834.4062 11.617 2.271 982 +V609 CYG 2444835.4101 11.560 2.221 982 +V609 CYG 2444836.4453 11.510 2.189 982 +V609 CYG 2444845.2460 10.686 1.926 982 +V609 CYG 2444847.2538 10.764 1.984 982 +V609 CYG 2444850.2655 10.908 2.116 982 +V609 CYG 2444851.2578 10.962 2.145 982 +V609 CYG 2444852.2500 11.078 2.198 982 +V609 CYG 2444853.2695 11.082 2.196 982 +V609 CYG 2444854.2578 11.136 2.217 982 +V609 CYG 2444856.2500 11.189 2.249 982 +V609 CYG 2444857.2460 11.285 2.258 982 +V609 CYG 2444880.2381 10.938 2.114 982 +V609 CYG 2444881.2304 10.917 2.142 982 +V609 CYG 2444882.2734 10.979 2.152 982 +V609 CYG 2444883.2070 11.032 2.195 982 +V609 CYG 2444884.2343 11.100 2.160 982 +V609 CYG 2445174.4530 11.524 2.268 982 +V609 CYG 2445176.4375 11.598 2.291 982 +V609 CYG 2445178.4256 11.473 2.186 982 +V609 CYG 2445179.3397 11.281 2.055 982 +V609 CYG 2445180.3554 10.869 1.903 982 +V609 CYG 2445181.2968 10.546 1.763 982 +V609 CYG 2445182.2968 10.456 1.727 982 +V609 CYG 2445183.3203 10.489 1.754 982 +V609 CYG 2445184.3397 10.548 1.795 982 +V609 CYG 2445186.3750 10.631 1.880 982 +V609 CYG 2445187.3437 10.691 1.929 982 +V609 CYG 2445188.3359 10.732 1.956 982 +V609 CYG 2445189.3631 10.785 2.025 982 +V609 CYG 2445190.3867 10.833 2.045 982 +V609 CYG 2445191.3788 10.881 2.074 982 +V609 CYG 2445192.3867 10.910 2.107 982 +V609 CYG 2445193.4022 10.991 2.135 982 +V609 CYG 2445194.3867 11.017 2.172 982 +V609 CYG 2445198.3984 11.237 2.266 982 +V609 CYG 2445199.3397 11.268 2.257 982 +V609 CYG 2445201.3710 11.348 2.243 982 +V609 CYG 2445203.2772 11.385 2.254 982 +V609 CYG 2445204.2655 11.433 2.273 982 +V609 CYG 2445205.2889 11.497 2.270 982 +V609 CYG 2445207.2968 11.622 2.263 982 +V609 CYG 2445208.3320 11.604 2.250 982 +V609 CYG 2445209.2812 11.505 2.192 982 +V609 CYG 2445210.3203 11.302 2.098 982 +V609 CYG 2445211.2812 10.926 1.928 982 +V609 CYG 2445212.2889 10.569 1.758 982 +V609 CYG 2445213.2889 10.453 1.737 982 +V609 CYG 2445214.2851 10.515 1.751 982 +V609 CYG 2445489.2772 11.481 2.129 1.262 982 +V609 CYG 2445493.2500 10.421 1.748 1.037 982 +V609 CYG 2445496.2851 10.574 1.839 1.127 982 +V609 CYG 2445498.2421 10.677 1.951 1.150 982 +V609 CYG 2445501.2929 10.812 2.078 1.217 982 +V609 CYG 2445503.2851 10.908 2.143 1.230 982 +V609 CYG 2445505.2734 2.178 1.249 982 +V609 CYG 2445508.2655 11.180 1.275 982 +V609 CYG 2445509.2889 11.239 1.300 982 +V609 CYG 2445512.2968 11.333 1.343 982 +V609 CYG 2445513.3163 11.370 1.299 982 +V609 CYG 2445514.2929 11.398 1.303 982 +V609 CYG 2445515.2929 11.431 1.313 982 +V609 CYG 2445648.2655 10.474 1.717 1.050 982 +V609 CYG 2445649.2929 10.474 1.706 1.055 982 +V609 CYG 2445658.2109 10.965 2.086 1.230 982 +V609 CYG 2445695.1131 11.235 2.257 1.297 982 +V609 CYG 2449617.2462 11.469 2.316 1.282 995 +V609 CYG 2449620.3069 11.731 2.394 1.328 995 +V609 CYG 2449621.3161 11.730 2.218 2.312 1.312 995 +V609 CYG 2449623.2418 11.406 2.100 1.243 995 +V609 CYG 2449624.2723 11.008 1.968 1.148 995 +V609 CYG 2449625.2953 10.582 1.728 1.062 995 +V609 CYG 2449626.2954 10.347 1.697 1.035 995 +V609 CYG 2449631.2620 10.686 1.932 1.140 995 +V609 CYG 2449632.2880 10.797 1.951 1.175 995 +V609 CYG 2449633.2681 10.809 2.012 1.161 995 +V609 CYG 2449635.3177 10.907 2.114 1.212 995 +V609 CYG 2449933.4299 11.492 2.187 1.288 2.421 998 +V609 CYG 2449934.4182 11.339 1.299 2.412 998 +V609 CYG 2449935.4168 10.831 1.146 2.193 998 +V609 CYG 2449936.3923 10.564 1.071 2.094 998 +V609 CYG 2449937.3785 10.476 1.756 1.075 998 +V609 CYG 2449938.4162 10.542 1.099 2.137 998 +V609 CYG 2449939.4071 10.585 2.146 998 +V609 CYG 2449941.4066 10.713 2.200 998 +V609 CYG 2449942.3802 10.746 2.282 998 +V609 CYG 2449943.3681 10.860 2.344 998 +V609 CYG 2449944.4026 10.829 2.365 998 +V609 CYG 2449945.4063 10.905 2.371 998 +V609 CYG 2449946.3926 10.968 2.421 998 +V609 CYG 2449947.3516 10.991 2.417 998 +V609 CYG 2449948.3151 11.029 2.435 998 +V609 CYG 2449949.3156 11.116 2.453 998 +V609 CYG 2449950.3023 11.113 2.527 998 +V609 CYG 2449952.2990 11.220 2.538 998 +V609 CYG 2449953.3733 11.267 2.540 998 +V609 CYG 2449954.3153 11.330 2.548 998 +V609 CYG 2449955.2912 11.368 2.555 998 +V609 CYG 2449956.3632 11.363 2.534 998 +V609 CYG 2449957.3786 11.413 2.564 998 +V609 CYG 2449958.3236 11.419 2.546 998 +V609 CYG 2449959.3594 11.492 2.558 998 +V609 CYG 2449960.3484 11.540 2.563 998 +V609 CYG 2449985.2900 11.275 2.534 998 +V609 CYG 2449986.3074 11.312 2.528 998 +V609 CYG 2449987.3169 11.317 2.508 998 +V609 CYG 2449992.3533 11.580 2.539 998 +V609 CYG 2448503.3480 11.534 2.197 1.264 993 +V609 CYG 2448504.2789 11.373 2.089 1.257 993 +V609 CYG 2448505.3081 10.965 1.900 1.161 993 +V609 CYG 2448506.3150 10.573 1.752 1.086 993 +V609 CYG 2448507.2980 10.483 1.721 1.051 993 +V609 CYG 2448508.2575 10.482 1.758 1.053 993 +V609 CYG 2448509.3132 10.554 1.760 1.097 993 +V609 CYG 2448510.2958 10.596 1.844 1.105 993 +V609 CYG 2448511.3036 10.661 1.880 1.132 993 +V609 CYG 2448512.2887 10.737 1.909 1.186 993 +V609 CYG 2448513.3003 10.768 1.984 1.183 993 +V609 CYG 2448514.3033 10.800 2.007 1.194 993 +V609 CYG 2448515.2382 10.864 2.009 1.210 993 +V609 CYG 2448516.2278 10.890 2.103 1.213 993 +V609 CYG 2448517.3145 10.953 2.092 1.253 993 +V609 CYG 2448518.2305 10.999 2.150 1.255 993 +V609 CYG 2448519.2780 11.053 2.156 1.271 993 +V609 CYG 2448520.2637 11.113 2.172 1.284 993 +V609 CYG 2448521.3066 11.145 2.241 1.263 993 +V609 CYG 2448522.2695 11.210 2.273 1.291 993 +V609 CYG 2448523.2633 11.271 2.272 1.299 993 +V609 CYG 2450306.2773 11.544 2.158 1.272 971 +V609 CYG 2450307.3015 11.353 2.087 1.209 971 +V609 CYG 2450310.2943 10.439 1.699 1.017 971 +V609 CYG 2450311.2679 10.480 1.713 1.047 971 +V609 CYG 2450312.2990 10.532 1.738 1.067 971 +V609 CYG 2450313.2903 10.576 1.824 1.080 971 +V609 CYG 2450314.2757 10.646 1.850 1.111 971 +V609 CYG 2450315.2850 10.668 1.904 1.117 971 +V609 CYG 2450316.2265 10.732 1.944 1.149 971 +V609 CYG 2450317.2786 10.784 2.003 1.166 971 +V609 CYG 2450318.2707 10.841 2.022 1.181 971 +V609 CYG 2450319.2569 10.858 2.087 1.200 971 +V609 CYG 2450320.2405 10.940 2.145 1.199 971 +V609 CYG 2450321.2314 10.975 2.140 1.224 971 +V609 CYG 2450322.2653 11.023 2.161 1.244 971 +V609 CYG 2450323.2653 11.077 2.261 1.237 971 +V609 CYG 2450324.2675 11.135 2.128 1.262 971 +V609 CYG 2450325.2379 11.178 2.207 1.259 971 +V609 CYG 2450326.1863 11.232 2.277 1.268 971 +V609 CYG 2450327.2676 11.285 2.238 971 +V609 CYG 2450328.3836 11.296 2.307 1.290 971 +V609 CYG 2450330.2222 11.422 2.273 1.271 971 +V609 CYG 2450332.2111 11.422 2.253 1.241 971 +V609 CYG 2450333.1945 11.481 2.299 1.277 971 +V609 CYG 2450334.2313 11.564 2.262 1.258 971 +V609 CYG 2450335.2205 11.604 2.333 1.329 971 +V609 CYG 2450336.2243 11.601 2.221 1.277 971 +V609 CYG 2450337.1881 11.545 2.269 1.250 971 +V609 CYG 2450338.2671 11.358 2.130 1.215 971 +V609 CYG 2450340.1767 10.604 1.812 1.069 971 +V609 CYG 2450341.1984 10.461 1.759 1.071 971 +V609 CYG 2450342.2126 10.452 1.753 1.029 971 +V609 CYG 2450344.2416 10.565 1.820 1.061 971 +V609 CYG 2450347.2266 10.726 1.921 1.146 971 +V609 CYG 2450349.1771 10.807 2.061 1.160 971 +V609 CYG 2450357.1910 11.213 2.230 1.261 971 +V621 CYG 2446274.2543 12.157 1.424 .786 987 +V621 CYG 2446275.2618 11.805 1.203 .706 987 +V621 CYG 2446277.2377 11.678 1.146 .703 987 +V621 CYG 2446278.2928 11.790 1.344 .722 987 +V621 CYG 2446279.3224 12.086 1.380 .799 987 +V621 CYG 2446280.3112 12.177 1.352 .799 987 +V621 CYG 2446283.3045 11.677 1.182 .717 987 +V621 CYG 2446284.2849 11.884 1.291 .768 987 +V621 CYG 2446285.2568 12.100 1.385 .785 987 +V621 CYG 2446286.2491 12.177 1.364 .795 987 +V621 CYG 2446287.2414 11.522 1.062 .641 987 +V621 CYG 2446288.2855 11.415 1.053 .643 987 +V621 CYG 2446289.3055 11.700 1.197 .715 987 +V621 CYG 2446290.3062 11.891 1.311 .749 987 +V621 CYG 2446291.2594 12.103 1.375 .782 987 +V621 CYG 2446292.2807 12.145 1.361 .790 987 +V621 CYG 2446294.2470 11.438 1.079 .642 987 +V621 CYG 2446295.2492 11.694 1.249 .710 987 +V621 CYG 2446296.2417 11.905 1.301 .767 987 +V621 CYG 2446297.2441 12.124 1.387 .779 987 +V621 CYG 2446298.2530 12.128 1.315 .774 987 +V621 CYG 2446299.2367 11.318 .956 .609 987 +V621 CYG 2446300.2397 11.494 1.073 .680 987 +V621 CYG 2446300.3954 11.526 1.117 .677 987 +V621 CYG 2446301.2456 11.747 .875 1.245 .735 987 +V621 CYG 2446302.2601 11.924 1.355 .754 987 +V621 CYG 2446303.2419 12.144 1.391 .787 987 +V621 CYG 2446304.2321 12.044 1.311 .740 987 +V924 CYG 2447399.2817 10.770 .871 .505 990 +V924 CYG 2447400.2457 10.597 .802 .490 990 +V924 CYG 2447401.2313 10.619 .821 .487 990 +V924 CYG 2447402.2280 10.736 .926 .497 990 +V924 CYG 2447403.2300 10.839 .930 .532 990 +V924 CYG 2447404.2034 10.838 .914 .518 990 +V924 CYG 2447407.2190 10.685 .868 .497 990 +V924 CYG 2447408.1875 10.774 .904 .519 990 +V924 CYG 2447409.2062 10.853 .909 .540 990 +V924 CYG 2447410.2231 10.806 .881 .520 990 +V924 CYG 2447411.2335 10.596 .798 .470 990 +V924 CYG 2447413.2033 10.716 .882 .523 990 +V924 CYG 2447414.1916 10.803 .917 .529 990 +V924 CYG 2447415.2032 10.855 .918 .524 990 +V924 CYG 2447416.1965 10.693 .861 .491 990 +V924 CYG 2447417.1925 10.560 .795 .480 990 +V924 CYG 2447418.1886 10.683 .831 .497 990 +V924 CYG 2447419.1758 10.722 .900 .510 990 +V924 CYG 2447420.1720 10.822 .943 .519 990 +V924 CYG 2447421.1639 10.768 .919 .505 990 +V924 CYG 2447422.1713 10.595 .799 .480 990 +V924 CYG 2447423.1631 10.568 .815 .475 990 +V924 CYG 2447424.1660 10.710 .888 .517 990 +V924 CYG 2447425.1904 10.830 .936 .534 990 +V924 CYG 2447427.2006 10.799 .867 .502 990 +V924 CYG 2447428.1761 10.551 .824 .455 990 +V924 CYG 2447429.1590 10.644 .829 .488 990 +V924 CYG 2447430.1471 10.734 .911 .507 990 +V924 CYG 2447431.1598 10.834 .941 .526 990 +V924 CYG 2447432.1456 10.821 .938 .531 990 +V924 CYG 2447433.1516 10.665 .839 .495 990 +V924 CYG 2447434.1533 10.556 .817 .472 990 +V924 CYG 2449957.2390 10.598 .949 998 +V924 CYG 2449958.2086 10.666 .960 998 +V924 CYG 2449959.2425 10.739 1.011 998 +V924 CYG 2449960.2624 10.812 1.013 998 +V924 CYG 2449962.2311 10.774 .952 998 +V924 CYG 2449986.1723 10.623 .967 998 +V924 CYG 2449987.2204 10.696 .971 998 +V924 CYG 2449992.1488 10.627 .947 998 +V924 CYG 2449993.1895 10.752 1.016 998 +V924 CYG 2450007.2626 10.631 .918 998 +V924 CYG 2450009.1923 10.703 .901 .463 .978 998 +V924 CYG 2450011.1886 10.829 1.036 998 +V924 CYG 2450017.1108 10.847 1.010 998 +V924 CYG 2450018.1769 10.673 .966 998 +V924 CYG 2450020.1286 10.656 .986 998 +V924 CYG 2449620.2538 10.834 .918 .549 995 +V924 CYG 2449621.2631 10.888 .914 .541 995 +V924 CYG 2449623.2226 10.572 .779 .479 995 +V924 CYG 2449624.2318 10.576 .879 .809 .493 995 +V924 CYG 2449625.2516 10.724 .946 .868 .524 995 +V924 CYG 2449626.2646 10.787 .904 .528 995 +V924 CYG 2449631.2277 10.772 .909 .546 995 +V924 CYG 2449632.2508 10.836 .900 .529 995 +V924 CYG 2449633.2241 10.728 .878 .491 995 +V924 CYG 2449634.2293 10.545 .792 .438 995 +V924 CYG 2450305.3172 10.787 .921 .522 971 +V924 CYG 2450306.3043 10.837 .943 .528 971 +V924 CYG 2450307.2835 10.799 .883 .525 971 +V924 CYG 2450310.2651 10.699 .891 .512 971 +V924 CYG 2450311.1925 10.825 .934 .527 971 +V924 CYG 2450312.2739 10.838 .915 .514 971 +V924 CYG 2450313.2499 10.712 .882 .497 971 +V924 CYG 2450314.1903 10.590 .832 .460 971 +V924 CYG 2450315.1937 10.606 .875 .466 971 +V924 CYG 2450316.2057 10.760 .913 .501 971 +V924 CYG 2450317.2150 10.848 .922 .544 971 +V924 CYG 2450318.2244 10.796 .919 .497 971 +V924 CYG 2450319.2060 10.610 .833 .471 971 +V924 CYG 2450320.2161 10.599 .819 .470 971 +V924 CYG 2450321.2053 10.673 .890 .495 971 +V924 CYG 2450322.2128 10.797 .944 .539 971 +V924 CYG 2450323.2395 10.841 .949 .524 971 +V924 CYG 2450324.2092 10.750 .887 .512 971 +V924 CYG 2450325.2069 10.583 .820 .476 971 +V924 CYG 2450326.1650 10.654 .832 .476 971 +V924 CYG 2450327.2398 10.741 .920 971 +V924 CYG 2450328.3615 10.845 .918 .527 971 +V924 CYG 2450329.2130 .510 971 +V924 CYG 2450330.1853 10.717 .859 .478 971 +V924 CYG 2450332.1774 10.658 .903 .438 971 +V924 CYG 2450333.1762 10.770 .934 .528 971 +V924 CYG 2450334.1879 10.845 .962 .513 971 +V924 CYG 2450335.1907 10.750 .931 .508 971 +V924 CYG 2450336.1928 10.568 .826 .482 971 +V924 CYG 2450337.1742 10.609 .857 .475 971 +V924 CYG 2450338.2344 10.717 .922 .503 971 +V924 CYG 2450340.1607 10.829 .971 .527 971 +V924 CYG 2450341.1689 10.687 .881 .490 971 +V924 CYG 2450342.1781 10.554 .849 .460 971 +V924 CYG 2450344.2018 10.775 .936 .527 971 +V924 CYG 2450347.1902 10.617 .853 .468 971 +V924 CYG 2450349.1652 10.686 .914 .478 971 +V924 CYG 2450357.1590 10.835 .931 .533 971 +V1020 CYG 2447403.2724 13.917 1.982 1.245 990 +V1020 CYG 2447404.2470 14.050 1.978 1.278 990 +V1020 CYG 2447407.2446 13.706 1.896 1.205 990 +V1020 CYG 2447408.2320 13.911 1.926 1.263 990 +V1020 CYG 2447409.2435 14.060 1.944 1.264 990 +V1020 CYG 2447410.2559 13.714 1.826 1.199 990 +V1020 CYG 2447411.2637 13.452 1.791 1.165 990 +V1020 CYG 2447413.2338 13.938 1.961 1.276 990 +V1020 CYG 2447414.2358 14.059 1.949 1.286 990 +V1020 CYG 2447415.2231 13.671 1.766 1.201 990 +V1020 CYG 2447416.2165 13.471 1.792 1.151 990 +V1020 CYG 2447417.2139 13.736 1.853 1.228 990 +V1020 CYG 2447418.2063 13.967 1.951 1.268 990 +V1020 CYG 2447419.1945 14.021 1.889 1.267 990 +V1020 CYG 2447420.1945 13.589 1.790 1.173 990 +V1020 CYG 2447421.1839 13.481 1.785 1.147 990 +V1020 CYG 2447422.1986 13.722 1.874 1.248 990 +V1020 CYG 2447424.2021 14.151 1.920 1.290 990 +V1020 CYG 2447425.2239 13.517 1.757 1.163 990 +V1020 CYG 2447427.2324 13.835 1.905 990 +V1020 CYG 2447431.2058 13.550 1.819 1.178 990 +V1020 CYG 2447432.1905 13.805 1.877 1.266 990 +V1020 CYG 2447433.1916 14.017 1.997 1.299 990 +V1020 CYG 2447434.1899 14.067 1.916 1.264 990 +V1020 CYG 2446607.3048 14.100 1.961 1.324 988 +V1020 CYG 2446608.2381 13.690 1.817 1.181 988 +V1020 CYG 2446609.2220 13.438 1.772 1.167 988 +V1020 CYG 2446610.3642 13.730 1.942 1.249 988 +V1020 CYG 2446611.2259 13.942 1.937 1.271 988 +V1020 CYG 2446612.2136 14.071 1.920 1.274 988 +V1020 CYG 2446613.2131 13.629 1.813 1.193 988 +V1020 CYG 2446614.2148 13.467 1.811 1.187 988 +V1020 CYG 2446615.2146 13.745 1.867 1.267 988 +V1020 CYG 2446616.2155 13.948 1.943 1.293 988 +V1020 CYG 2446617.2104 14.064 1.932 1.282 988 +V1020 CYG 2446618.2089 13.503 1.799 1.163 988 +V1020 CYG 2446619.2093 13.499 1.771 1.174 988 +V1020 CYG 2446620.2088 13.755 1.868 1.258 988 +V1020 CYG 2446621.3138 13.957 1.886 1.292 988 +V1020 CYG 2446622.2054 14.034 1.933 1.279 988 +V1020 CYG 2446629.3994 13.596 1.898 1.221 988 +V1020 CYG 2446636.2008 13.971 1.941 1.287 988 +V1020 CYG 2446637.2043 14.025 1.923 1.253 988 +V1020 CYG 2446252.2119 13.923 2.000 1.264 987 +V1020 CYG 2446253.2128 14.049 1.962 1.293 987 +V1020 CYG 2446255.2716 13.530 1.846 1.188 987 +V1020 CYG 2446256.2584 13.765 1.930 1.242 987 +V1020 CYG 2446257.2708 14.000 1.929 1.298 987 +V1020 CYG 2446258.2004 14.071 1.916 1.267 987 +V1020 CYG 2446259.2292 13.354 1.697 1.153 987 +V1020 CYG 2446260.2036 13.573 1.840 1.192 987 +V1020 CYG 2446261.2313 13.801 1.965 1.286 987 +V1020 CYG 2446262.1958 13.955 1.958 1.294 987 +V1020 CYG 2446263.2020 14.051 1.851 1.295 987 +V1020 CYG 2446264.2020 13.312 1.713 1.127 987 +V1020 CYG 2446265.1893 13.591 1.772 1.228 987 +V1020 CYG 2446266.2067 13.778 1.913 1.262 987 +V1020 CYG 2446267.2129 14.026 1.919 1.313 987 +V1020 CYG 2446268.2201 14.000 1.952 1.256 987 +V1020 CYG 2446269.2032 13.293 1.773 1.134 987 +V1020 CYG 2446270.1999 13.537 1.813 1.210 987 +V1020 CYG 2446271.2183 13.831 1.934 1.275 987 +V1020 CYG 2446272.2764 13.993 1.935 1.283 987 +V1020 CYG 2446273.2134 14.002 1.841 1.260 987 +V1020 CYG 2446275.2001 13.595 1.892 1.204 987 +V1020 CYG 2446279.1752 13.378 1.680 1.141 987 +V1020 CYG 2446280.1800 13.648 1.800 1.230 987 +V1020 CYG 2449957.2965 13.976 2.517 998 +V1020 CYG 2449958.2703 14.108 2.540 998 +V1020 CYG 2449959.3317 13.433 2.325 998 +V1020 CYG 2450009.2682 13.529 2.317 998 +V1020 CYG 2447734.3743 1.266 991 +V1020 CYG 2447735.3945 13.342 1.129 991 +V1020 CYG 2447736.3989 13.567 1.806 1.189 991 +V1020 CYG 2447737.3851 13.754 1.932 1.237 991 +V1020 CYG 2447738.3709 13.981 1.917 1.237 991 +V1020 CYG 2447739.3407 14.010 1.937 1.262 991 +V1020 CYG 2447740.3756 13.342 1.752 1.099 991 +V1020 CYG 2447741.3284 13.553 1.843 1.167 991 +V1020 CYG 2447742.3385 13.803 1.917 1.239 991 +V1020 CYG 2447743.3276 13.961 1.964 1.261 991 +V1020 CYG 2447744.3067 14.061 1.911 1.281 991 +V1020 CYG 2447745.3079 13.316 1.742 1.122 991 +V1020 CYG 2447746.3123 13.588 1.867 1.208 991 +V1020 CYG 2447747.3078 13.784 1.963 1.243 991 +V1020 CYG 2447748.3070 13.973 1.960 1.266 991 +V1020 CYG 2447749.3043 13.991 1.959 1.243 991 +V1020 CYG 2447770.2552 13.412 1.729 1.164 991 +V1020 CYG 2447771.2396 13.678 1.896 1.248 991 +V1020 CYG 2447772.2416 13.889 1.985 1.253 991 +V1020 CYG 2447773.2707 14.072 1.942 1.269 991 +V1020 CYG 2447774.2748 13.768 1.863 1.220 991 +V1020 CYG 2447775.2333 13.423 1.719 1.150 991 +V1020 CYG 2447776.2391 13.690 1.897 1.237 991 +V1020 CYG 2448503.2611 13.415 1.743 1.140 993 +V1020 CYG 2448504.2202 13.680 1.826 1.233 993 +V1020 CYG 2448505.2356 13.916 1.955 1.271 993 +V1020 CYG 2448506.2534 14.051 1.975 1.291 993 +V1020 CYG 2448507.2277 13.923 1.937 1.232 993 +V1020 CYG 2448508.2046 13.402 1.757 1.132 993 +V1020 CYG 2448509.2395 13.706 1.901 1.223 993 +V1020 CYG 2448510.2113 13.880 1.976 1.284 993 +V1020 CYG 2448511.2205 14.063 2.004 1.276 993 +V1020 CYG 2448512.2256 13.861 1.910 1.236 993 +V1020 CYG 2448513.2239 13.450 1.775 1.143 993 +V1020 CYG 2448514.2311 13.737 1.920 1.238 993 +V1020 CYG 2448515.2506 13.927 1.928 1.253 993 +V1020 CYG 2448516.2755 14.094 1.939 1.304 993 +V1020 CYG 2448517.2248 13.805 1.842 1.221 993 +V1020 CYG 2448518.2708 13.507 1.758 1.172 993 +V1025 CYG 2448101.3735 12.929 1.774 1.048 992 +V1025 CYG 2448102.3142 13.114 1.832 1.085 992 +V1025 CYG 2448104.3067 13.264 1.807 992 +V1025 CYG 2448111.2910 13.306 1.874 1.082 992 +V1025 CYG 2448112.2719 12.817 1.561 .966 992 +V1025 CYG 2448113.2684 12.649 1.574 .955 992 +V1025 CYG 2448114.3194 12.874 1.751 992 +V1025 CYG 2448116.3022 13.146 1.842 1.071 992 +V1025 CYG 2448117.3877 13.387 1.912 1.094 992 +V1025 CYG 2448118.3294 13.264 1.796 1.089 992 +V1025 CYG 2448119.2903 12.811 1.625 .980 992 +V1025 CYG 2448122.3109 12.932 1.764 1.031 992 +V1025 CYG 2448123.2834 13.182 1.864 1.080 992 +V1025 CYG 2448126.2870 12.778 1.608 .977 992 +V1025 CYG 2448126.3980 12.720 1.549 .950 992 +V1025 CYG 2448127.2607 12.690 1.589 .983 992 +V1025 CYG 2448127.3658 12.708 1.607 .972 992 +V1025 CYG 2449957.3016 12.761 1.910 998 +V1025 CYG 2449958.2767 12.695 1.874 998 +V1025 CYG 2449959.3374 12.864 1.998 998 +V1025 CYG 2450009.2726 12.922 2.015 998 +V1025 CYG 2450011.2277 13.322 2.138 998 +V1025 CYG 2450017.1951 13.121 2.020 998 +V1025 CYG 2450018.2348 13.355 2.148 998 +V1025 CYG 2450020.1867 12.640 1.840 998 +V1033 CYG 2446252.2266 13.213 1.795 1.054 987 +V1033 CYG 2446253.2210 13.353 1.779 1.092 987 +V1033 CYG 2446255.2820 12.762 1.540 .972 987 +V1033 CYG 2446256.2727 13.050 1.692 1.048 987 +V1033 CYG 2446257.2914 13.266 1.790 1.089 987 +V1033 CYG 2446258.2369 13.369 1.827 1.082 987 +V1033 CYG 2446260.2132 12.783 1.563 .956 987 +V1033 CYG 2446261.2397 13.049 1.708 1.041 987 +V1033 CYG 2446262.2089 13.258 1.810 1.071 987 +V1033 CYG 2446263.2145 13.360 1.786 1.072 987 +V1033 CYG 2446265.1987 12.789 1.543 .993 987 +V1033 CYG 2446266.2162 13.038 1.698 1.053 987 +V1033 CYG 2446267.2225 13.292 1.750 1.099 987 +V1033 CYG 2446268.2317 13.336 1.792 1.083 987 +V1033 CYG 2446269.2143 12.957 1.582 .999 987 +V1033 CYG 2446270.2134 12.758 1.543 .985 987 +V1033 CYG 2446271.2297 13.079 1.723 1.057 987 +V1033 CYG 2446272.2973 13.284 1.771 1.085 987 +V1033 CYG 2446273.2285 13.366 1.827 1.094 987 +V1033 CYG 2446275.2156 12.810 1.599 .984 987 +V1033 CYG 2446279.2206 12.860 1.530 .995 987 +V1033 CYG 2446280.1976 12.832 1.526 .994 987 +V1033 CYG 2446607.2870 13.031 1.672 1.039 988 +V1033 CYG 2446608.2271 13.224 1.759 1.083 988 +V1033 CYG 2446610.3806 13.075 1.634 1.014 988 +V1033 CYG 2446611.2143 12.717 1.494 .963 988 +V1033 CYG 2446612.2011 13.020 1.672 1.054 988 +V1033 CYG 2446613.2008 13.228 1.718 1.077 988 +V1033 CYG 2446614.2028 13.380 1.756 1.093 988 +V1033 CYG 2446615.2037 13.190 1.669 1.043 988 +V1033 CYG 2446616.2043 12.750 1.534 .959 988 +V1033 CYG 2446617.1978 13.005 1.655 1.015 988 +V1033 CYG 2446618.1975 13.231 1.759 1.092 988 +V1033 CYG 2446619.1961 13.396 1.723 1.117 988 +V1033 CYG 2446620.1937 13.153 1.630 1.036 988 +V1033 CYG 2446621.3257 12.781 1.529 .985 988 +V1033 CYG 2446622.1925 13.014 1.658 1.044 988 +V1033 CYG 2446629.3918 13.359 1.796 1.097 988 +V1033 CYG 2446636.1869 12.787 1.582 .968 988 +V1033 CYG 2446637.1918 13.074 1.663 1.063 988 +V1033 CYG 2447403.2821 12.950 1.605 1.023 990 +V1033 CYG 2447404.2516 13.165 1.726 1.053 990 +V1033 CYG 2447407.2481 12.675 1.520 .912 990 +V1033 CYG 2447408.2357 12.935 1.602 .995 990 +V1033 CYG 2447409.2460 13.233 1.703 1.057 990 +V1033 CYG 2447410.2582 13.368 1.704 1.092 990 +V1033 CYG 2447411.2661 13.334 1.746 1.068 990 +V1033 CYG 2447413.2401 12.966 1.618 1.026 990 +V1033 CYG 2447414.2429 13.180 1.768 1.049 990 +V1033 CYG 2447415.2279 13.355 1.775 1.075 990 +V1033 CYG 2447416.2210 13.351 1.755 1.052 990 +V1033 CYG 2447417.2182 12.652 1.460 .917 990 +V1033 CYG 2447418.2137 12.983 1.612 1.030 990 +V1033 CYG 2447419.1986 13.141 1.752 1.048 990 +V1033 CYG 2447420.1994 13.334 1.781 1.062 990 +V1033 CYG 2447421.1886 13.330 1.718 1.037 990 +V1033 CYG 2447422.2005 12.661 1.492 .935 990 +V1033 CYG 2447423.2034 12.963 1.641 1.026 990 +V1033 CYG 2447424.2051 13.288 1.761 1.114 990 +V1033 CYG 2447425.2261 13.355 1.771 1.063 990 +V1033 CYG 2447428.1879 12.986 1.620 1.016 990 +V1033 CYG 2447429.1829 13.213 1.727 1.057 990 +V1033 CYG 2447430.1699 13.330 1.744 1.059 990 +V1033 CYG 2447431.2208 13.268 1.726 1.025 990 +V1033 CYG 2447432.2038 12.708 1.467 .939 990 +V1033 CYG 2447433.2084 13.037 1.638 1.030 990 +V1033 CYG 2447434.2057 13.245 1.738 1.080 990 +V1033 CYG 2448503.2944 13.308 1.805 1.041 993 +V1033 CYG 2448504.2332 13.404 1.113 993 +V1033 CYG 2448505.2574 12.760 1.492 .960 993 +V1033 CYG 2448506.2737 12.873 1.604 .986 993 +V1033 CYG 2448507.2493 13.154 1.736 1.052 993 +V1033 CYG 2448508.2195 13.318 1.791 1.089 993 +V1033 CYG 2448510.2261 12.684 1.497 .959 993 +V1033 CYG 2448511.2409 12.904 1.610 1.005 993 +V1033 CYG 2448512.2390 13.158 1.741 1.063 993 +V1033 CYG 2448513.2419 13.348 1.776 1.094 993 +V1033 CYG 2448514.2464 13.431 1.740 1.094 993 +V1033 CYG 2448515.2634 12.717 1.467 .941 993 +V1033 CYG 2448516.2996 12.968 1.579 1.009 993 +V1033 CYG 2448517.2658 13.204 1.728 1.071 993 +V1033 CYG 2448518.2983 13.354 1.783 1.091 993 +V1046 CYG 2447734.4455 12.138 1.608 .968 991 +V1046 CYG 2447735.4342 12.393 1.739 1.010 991 +V1046 CYG 2447736.4361 12.585 1.760 1.029 991 +V1046 CYG 2447737.4215 12.559 1.755 1.021 991 +V1046 CYG 2447738.4118 12.073 1.520 .921 991 +V1046 CYG 2447739.3801 12.167 1.635 .959 991 +V1046 CYG 2447740.4016 12.466 1.751 1.028 991 +V1046 CYG 2447741.3665 12.583 1.778 1.021 991 +V1046 CYG 2447742.3704 12.551 1.764 1.002 991 +V1046 CYG 2447743.3590 12.040 1.549 .922 991 +V1046 CYG 2447744.3369 12.203 1.626 .965 991 +V1046 CYG 2447745.3391 12.421 1.720 1.004 991 +V1046 CYG 2447746.3386 12.582 1.842 1.021 991 +V1046 CYG 2447747.3357 12.527 1.755 1.010 991 +V1046 CYG 2447748.3344 12.038 1.526 .899 991 +V1046 CYG 2447749.3350 12.192 1.630 .956 991 +V1046 CYG 2447750.3103 12.423 1.734 1.023 991 +V1046 CYG 2447751.3055 12.582 1.815 1.029 991 +V1046 CYG 2447752.2755 12.556 1.727 1.014 991 +V1046 CYG 2447753.2755 12.063 1.527 .926 991 +V1046 CYG 2447754.3259 12.241 1.663 991 +V1046 CYG 2447755.3527 12.437 1.760 1.015 991 +V1046 CYG 2447758.3157 12.020 1.509 .911 991 +V1046 CYG 2447759.2988 12.210 1.662 .946 991 +V1046 CYG 2447760.2933 12.451 1.778 1.014 991 +V1046 CYG 2447761.2768 12.578 1.813 1.012 991 +V1046 CYG 2447762.2566 12.534 1.719 .993 991 +V1046 CYG 2447763.2255 12.019 1.532 .911 991 +V1046 CYG 2447764.2324 12.212 1.643 .974 991 +V1046 CYG 2447766.2340 12.598 1.773 1.052 991 +V1046 CYG 2447767.2937 12.489 1.706 .999 991 +V1046 CYG 2447768.2795 11.981 1.541 .914 991 +V1046 CYG 2447770.2807 12.465 1.737 1.016 991 +V1046 CYG 2447771.2705 12.610 1.814 1.048 991 +V1046 CYG 2447772.2641 12.494 1.697 1.014 991 +V1046 CYG 2447773.2956 12.053 1.562 .917 991 +V1046 CYG 2447774.2992 12.268 1.678 .981 991 +V1046 CYG 2447775.2585 12.477 1.763 1.026 991 +V1046 CYG 2447776.2673 12.617 1.807 1.053 991 +V1046 CYG 2446606.3611 12.055 1.550 .938 988 +V1046 CYG 2446607.2622 12.254 1.683 .996 988 +V1046 CYG 2446608.2099 12.468 1.746 1.033 988 +V1046 CYG 2446609.1885 12.629 1.708 1.063 988 +V1046 CYG 2446610.3944 12.355 1.684 .988 988 +V1046 CYG 2446611.1977 12.024 1.549 .927 988 +V1046 CYG 2446612.1822 12.256 1.658 .977 988 +V1046 CYG 2446613.1810 12.478 1.709 1.051 988 +V1046 CYG 2446614.1816 12.628 1.725 1.082 988 +V1046 CYG 2446615.1871 12.476 1.684 1.005 988 +V1046 CYG 2446616.1863 12.062 1.593 .928 988 +V1046 CYG 2446617.1820 12.255 1.631 1.009 988 +V1046 CYG 2446618.1819 12.479 1.722 1.044 988 +V1046 CYG 2446619.1804 12.603 1.787 1.043 988 +V1046 CYG 2446620.1785 12.461 1.619 1.037 988 +V1046 CYG 2446621.3398 12.112 1.575 .954 988 +V1046 CYG 2446622.1792 12.279 1.654 1.017 988 +V1046 CYG 2446623.1802 12.508 1.716 1.062 988 +V1046 CYG 2446624.1780 12.662 1.659 1.080 988 +V1046 CYG 2446625.1799 12.435 1.638 1.021 988 +V1046 CYG 2446626.1855 12.070 1.544 .941 988 +V1046 CYG 2446627.1876 12.315 1.643 1.008 988 +V1046 CYG 2446628.1866 12.510 1.717 1.059 988 +V1046 CYG 2446629.1919 12.602 1.803 1.048 988 +V1046 CYG 2446630.1952 12.370 1.607 .991 988 +V1046 CYG 2446631.1834 12.112 1.522 .974 988 +V1046 CYG 2446632.1912 12.319 1.686 1.012 988 +V1046 CYG 2446635.2697 12.268 1.627 .985 988 +V1046 CYG 2446636.1744 12.100 1.571 .931 988 +V1046 CYG 2446637.1803 12.307 1.726 1.002 988 +V1046 CYG 2446274.2216 12.308 1.649 .963 987 +V1046 CYG 2446275.1794 12.094 1.608 .947 987 +V1046 CYG 2446277.1921 12.450 1.896 1.035 987 +V1046 CYG 2446278.2050 12.604 1.043 987 +V1046 CYG 2446279.2350 12.290 1.634 .987 987 +V1046 CYG 2446280.2222 12.124 1.574 .950 987 +V1046 CYG 2446283.2871 12.694 1.801 1.057 987 +V1046 CYG 2446284.2634 12.200 1.626 .947 987 +V1046 CYG 2446285.2478 12.143 1.623 .964 987 +V1046 CYG 2446286.2385 12.362 1.731 1.023 987 +V1046 CYG 2446287.2310 12.531 1.816 1.036 987 +V1046 CYG 2446288.2734 12.647 1.814 1.047 987 +V1046 CYG 2446289.2717 12.160 1.570 .939 987 +V1046 CYG 2446290.2598 12.147 1.608 .950 987 +V1046 CYG 2446291.2261 12.338 1.725 .995 987 +V1046 CYG 2446292.2484 12.569 1.787 1.046 987 +V1046 CYG 2446294.2238 12.147 1.586 .942 987 +V1046 CYG 2446295.2031 12.124 1.612 .947 987 +V1046 CYG 2446296.2100 12.403 1.703 1.038 987 +V1046 CYG 2446297.2168 12.555 1.793 1.039 987 +V1046 CYG 2446298.2312 12.637 1.754 1.049 987 +V1046 CYG 2446299.2038 12.140 1.569 .932 987 +V1046 CYG 2446300.2070 12.153 1.628 .961 987 +V1046 CYG 2446300.3849 12.205 1.628 1.074 987 +V1046 CYG 2446301.2247 12.401 1.766 1.013 987 +V1046 CYG 2446302.2287 12.594 1.826 1.052 987 +V1046 CYG 2446303.2012 12.639 1.784 1.035 987 +V1046 CYG 2446304.1928 12.124 1.589 .935 987 +V1046 CYG 2447402.2787 12.061 1.541 .920 990 +V1046 CYG 2447403.3171 12.283 1.639 .988 990 +V1046 CYG 2447404.2806 12.483 1.720 1.031 990 +V1046 CYG 2447407.2771 12.070 1.529 .918 990 +V1046 CYG 2447408.2665 12.276 1.675 .985 990 +V1046 CYG 2447409.2706 12.511 1.760 1.041 990 +V1046 CYG 2447410.2845 12.672 1.794 1.069 990 +V1046 CYG 2447411.2890 12.524 1.695 1.019 990 +V1046 CYG 2447413.2571 12.274 1.667 .996 990 +V1046 CYG 2447414.2657 12.470 1.781 1.013 990 +V1046 CYG 2447415.2416 12.641 1.781 1.057 990 +V1046 CYG 2447416.2343 12.512 1.684 1.008 990 +V1046 CYG 2447417.2288 12.057 1.526 .921 990 +V1046 CYG 2447418.2269 12.283 1.636 .990 990 +V1046 CYG 2447419.2102 12.466 1.743 1.028 990 +V1046 CYG 2447420.2096 12.628 1.795 1.047 990 +V1046 CYG 2447421.1982 12.502 1.680 1.021 990 +V1046 CYG 2447422.2130 12.060 1.513 .927 990 +V1046 CYG 2447423.2162 12.321 1.670 1.011 990 +V1046 CYG 2447424.2211 12.582 1.761 1.074 990 +V1046 CYG 2447425.2415 12.641 1.829 1.036 990 +V1046 CYG 2447427.2615 12.164 1.572 .937 990 +V1046 CYG 2447428.2103 12.315 1.692 .988 990 +V1046 CYG 2447429.2067 12.540 1.762 1.043 990 +V1046 CYG 2447430.1986 12.635 1.039 990 +V1046 CYG 2447431.2370 12.448 1.673 1.000 990 +V1046 CYG 2447432.2208 12.106 1.535 .935 990 +V1046 CYG 2447433.2244 12.352 1.703 1.017 990 +V1046 CYG 2447434.2248 12.556 1.759 1.040 990 +V1154 CYG 2447410.2958 9.045 .459 .840 .502 990 +V1154 CYG 2447411.3078 9.202 .948 .548 990 +V1154 CYG 2447413.2852 9.393 .982 .577 990 +V1154 CYG 2447414.2047 9.115 .857 .492 990 +V1154 CYG 2447415.1922 9.023 .412 .852 .496 990 +V1154 CYG 2447416.1674 9.184 .952 .530 990 +V1154 CYG 2447417.1663 9.282 1.016 .549 990 +V1154 CYG 2447418.1680 9.370 .997 .556 990 +V1154 CYG 2447419.1674 .834 .464 990 +V1154 CYG 2447420.1729 9.006 .849 .472 990 +V1154 CYG 2447421.1649 9.160 .941 .525 990 +V1154 CYG 2447422.1745 9.293 .996 .558 990 +V1154 CYG 2447423.1656 9.350 .991 .548 990 +V1154 CYG 2447424.1682 9.088 .860 .497 990 +V1154 CYG 2447425.1946 9.065 .853 .502 990 +V1154 CYG 2447427.1989 9.385 1.030 .546 990 +V1154 CYG 2447428.1568 9.341 .998 .539 990 +V1154 CYG 2447429.1617 9.102 .816 .485 990 +V1154 CYG 2447430.1580 9.055 .425 .867 .499 990 +V1154 CYG 2447431.1617 9.246 .958 .528 990 +V1154 CYG 2447432.1669 9.327 1.007 .549 990 +V1154 CYG 2447433.1528 9.357 .999 .551 990 +V1154 CYG 2447434.1554 9.024 .831 .480 990 +V1154 CYG 2449957.2516 9.120 1.011 998 +V1154 CYG 2449959.2538 1.074 998 +V1154 CYG 2449960.2760 9.281 1.028 998 +V1154 CYG 2449962.2712 1.005 998 +V1154 CYG 2449986.1785 9.008 .937 998 +V1154 CYG 2449987.2298 9.167 1.028 998 +V1154 CYG 2449992.1597 9.155 1.012 998 +V1154 CYG 2449993.1993 9.283 1.079 998 +V1154 CYG 2450007.2927 9.209 1.041 998 +V1154 CYG 2450009.2126 9.355 1.048 998 +V1154 CYG 2450011.2030 9.050 .956 998 +V1154 CYG 2450017.1271 9.245 1.040 998 +V1154 CYG 2450018.1879 9.341 1.076 998 +V1154 CYG 2450020.1389 9.012 .926 998 +V1154 CYG 2448854.3284 9.164 .959 .529 994 +V1154 CYG 2448858.2359 9.009 .404 .836 .498 994 +V1154 CYG 2448860.2312 9.289 .574 1.033 .572 994 +V1154 CYG 2448862.3095 9.094 .852 .487 994 +V1154 CYG 2448870.2230 9.301 .593 1.023 .544 994 +V1154 CYG 2448872.1880 9.091 .843 .502 994 +V1154 CYG 2448874.1966 9.191 .965 .543 994 +V1154 CYG 2448876.2617 9.356 .976 .557 994 +V1154 CYG 2448877.1709 9.069 .851 .488 994 +V1154 CYG 2448878.2067 9.047 .864 .504 994 +V1154 CYG 2448880.1764 9.318 1.008 .573 994 +V1154 CYG 2448881.1676 9.352 .994 .558 994 +V1154 CYG 2448882.1786 9.040 .862 .481 994 +V1154 CYG 2448883.1846 9.040 .881 .492 994 +V1154 CYG 2448884.1909 9.183 .986 .538 994 +V1154 CYG 2448885.1797 9.319 1.029 .554 994 +V1154 CYG 2448886.1782 9.343 .997 .546 994 +V1154 CYG 2448887.2612 8.993 .826 .474 994 +V1154 CYG 2448888.2021 9.058 .897 .508 994 +V1154 CYG 2448889.2111 9.223 .995 .534 994 +V1154 CYG 2448890.1850 9.322 1.030 .552 994 +V1154 CYG 2448891.1816 9.323 .994 .542 994 +V1154 CYG 2448892.1972 8.987 .810 .481 994 +V1154 CYG 2448893.2158 9.087 .900 .509 994 +V1154 CYG 2448894.2543 9.235 .994 .551 994 +V1154 CYG 2447734.3405 .490 .854 .495 991 +V1154 CYG 2447735.3733 8.980 .831 .476 991 +V1154 CYG 2447736.3785 9.179 .923 .538 991 +V1154 CYG 2447737.3794 9.258 1.012 .546 991 +V1154 CYG 2447738.3582 9.325 991 +V1154 CYG 2447739.3256 9.069 .391 .828 .491 991 +V1154 CYG 2447740.2676 9.003 .439 .836 .485 991 +V1154 CYG 2447741.2655 9.154 .548 .925 .548 991 +V1154 CYG 2447742.2537 9.292 .594 1.003 .581 991 +V1154 CYG 2447743.2438 9.346 .994 .557 991 +V1154 CYG 2447744.2333 9.100 .429 .826 .506 991 +V1154 CYG 2447745.2282 9.021 .451 .822 .508 991 +V1154 CYG 2447746.2387 9.179 .533 .981 .528 991 +V1154 CYG 2447747.2263 9.283 1.006 .561 991 +V1154 CYG 2447748.2317 9.354 .547 1.005 .549 991 +V1154 CYG 2447749.2139 9.080 .439 .816 .480 991 +V1154 CYG 2447750.2202 9.029 .461 .864 .512 991 +V1154 CYG 2447751.2218 9.181 .551 .953 .546 991 +V1154 CYG 2447752.2163 9.304 .625 .990 .554 991 +V1154 CYG 2447753.2173 9.328 .545 .998 .540 991 +V1154 CYG 2447754.2443 9.038 .395 .829 .487 991 +V1154 CYG 2447755.2126 9.021 .452 .861 .501 991 +V1154 CYG 2447756.2358 9.204 .537 .949 .555 991 +V1154 CYG 2447757.2105 9.303 .597 1.001 .565 991 +V1154 CYG 2447758.2059 9.336 .546 .983 .557 991 +V1154 CYG 2447759.2147 9.009 .409 .816 .477 991 +V1154 CYG 2447760.2239 9.052 .457 .876 .499 991 +V1154 CYG 2447761.2084 9.218 .549 .959 .540 991 +V1154 CYG 2447762.2085 9.340 .608 1.002 .556 991 +V1154 CYG 2447763.2060 9.331 .980 .542 991 +V1154 CYG 2447764.2089 8.993 .799 .486 991 +V1154 CYG 2447766.2117 9.227 .983 .555 991 +V1154 CYG 2447767.2174 9.341 1.000 .561 991 +V1154 CYG 2447768.2146 9.278 .506 .981 .516 991 +V1154 CYG 2447769.2142 8.991 .407 .807 .483 991 +V1154 CYG 2447770.2018 9.070 .486 .888 .512 991 +V1154 CYG 2447771.1992 9.219 .556 .983 .540 991 +V1154 CYG 2447772.1946 9.328 .572 1.009 .555 991 +V1154 CYG 2447773.1978 9.338 .517 .927 .561 991 +V1154 CYG 2447774.2281 8.997 .411 .786 .486 991 +V1154 CYG 2447775.1913 9.085 .486 .882 .523 991 +V1154 CYG 2447776.1917 9.224 .578 .973 .545 991 +V1154 CYG 2446274.2164 9.288 .659 1.006 .553 987 +V1154 CYG 2446275.1758 9.384 .631 1.022 .567 987 +V1154 CYG 2446277.1736 8.980 .463 .835 .481 987 +V1154 CYG 2446278.1733 9.141 .580 .929 .534 987 +V1154 CYG 2446279.1707 9.310 .632 .977 .568 987 +V1154 CYG 2446280.1763 9.357 .670 .999 .568 987 +V1154 CYG 2446283.2746 9.182 .580 .951 .535 987 +V1154 CYG 2446284.2540 9.306 1.002 .563 987 +V1154 CYG 2446285.2459 9.379 .998 .570 987 +V1154 CYG 2446286.2335 9.115 .495 .862 .499 987 +V1154 CYG 2446287.2217 9.007 .479 .841 .484 987 +V1154 CYG 2446288.2712 9.190 .571 .961 .536 987 +V1154 CYG 2446289.2704 9.302 .627 1.016 .557 987 +V1154 CYG 2446290.2580 9.370 .624 1.004 .554 987 +V1154 CYG 2446291.2246 9.095 .852 .500 987 +V1154 CYG 2446292.2454 9.036 .512 .854 .491 987 +V1154 CYG 2446294.2207 9.318 .637 1.007 .559 987 +V1154 CYG 2446295.2013 9.365 .613 .999 .552 987 +V1154 CYG 2446296.2087 9.093 .488 .843 .496 987 +V1154 CYG 2446297.2153 9.031 .511 .872 .496 987 +V1154 CYG 2446298.2301 9.208 .955 .551 987 +V1154 CYG 2446299.2022 9.321 .646 1.007 .558 987 +V1154 CYG 2446300.2055 9.353 1.008 .540 987 +V1154 CYG 2446300.3835 9.346 .590 .962 .540 987 +V1154 CYG 2446301.2230 9.055 .474 .835 .488 987 +V1154 CYG 2446302.2234 9.043 .498 .896 .491 987 +V1154 CYG 2446303.1977 9.204 .591 .969 .539 987 +V1154 CYG 2446304.1900 9.329 .632 1.034 .548 987 +V1154 CYG 2450305.3181 9.161 .888 .494 971 +V1154 CYG 2450306.3047 9.004 .845 .469 971 +V1154 CYG 2450307.2840 9.150 .940 .517 971 +V1154 CYG 2450310.2652 9.144 .883 .495 971 +V1154 CYG 2450311.1926 9.007 .847 .470 971 +V1154 CYG 2450312.2739 9.160 .951 .512 971 +V1154 CYG 2450313.2499 9.295 1.000 .543 971 +V1154 CYG 2450314.1908 9.365 1.003 .541 971 +V1154 CYG 2450315.1940 9.142 .898 .480 971 +V1154 CYG 2450316.2060 9.013 .853 971 +V1154 CYG 2450317.2154 9.184 .942 .522 971 +V1154 CYG 2450318.2249 9.304 .997 .551 971 +V1154 CYG 2450319.2066 9.355 1.004 .540 971 +V1154 CYG 2450320.2169 9.117 .871 .480 971 +V1154 CYG 2450321.2057 9.003 .853 .476 971 +V1154 CYG 2450322.2133 9.177 .943 .542 971 +V1154 CYG 2450323.2399 9.307 1.020 .536 971 +V1154 CYG 2450324.2099 9.377 .994 .551 971 +V1154 CYG 2450325.2076 9.098 .852 .481 971 +V1154 CYG 2450326.1654 9.050 .849 .481 971 +V1154 CYG 2450327.2424 9.217 .974 971 +V1154 CYG 2450328.3636 9.336 .984 .547 971 +V1154 CYG 2450330.3181 9.059 .842 .465 971 +V1154 CYG 2450332.2948 9.212 .976 .520 971 +V1154 CYG 2450333.3061 9.316 .999 .519 971 +V1154 CYG 2450334.3372 9.338 .983 .533 971 +V1154 CYG 2450335.3513 9.013 .845 .479 971 +V1154 CYG 2450337.2876 9.250 .989 .537 971 +V1154 CYG 2450340.2976 9.010 .799 .469 971 +V1154 CYG 2450341.3025 9.097 .916 .522 971 +V1154 CYG 2450342.3185 9.207 1.002 .515 971 +V1154 CYG 2450344.3196 9.292 .964 .485 971 +V1154 CYG 2450347.3232 9.261 .980 .562 971 +V1154 CYG 2450349.2776 9.290 .959 .529 971 +V1334 CYG 2448856.1343 5.775 .099 .504 .290 994 +V1334 CYG 2448858.1320 5.940 .138 .516 .331 994 +V1334 CYG 2448860.1309 5.845 .093 .545 .309 994 +V1334 CYG 2448870.1180 5.856 .110 .546 .297 994 +V1334 CYG 2448872.1153 .130 .510 994 +V1334 CYG 2448874.1134 5.932 .077 .574 .308 994 +V1334 CYG 2448876.1129 5.832 .079 .515 .325 994 +V1334 CYG 2448877.1075 5.956 .089 .543 .317 994 +V1334 CYG 2448878.2522 5.941 .083 .556 .327 994 +V1334 CYG 2448880.1088 5.853 .109 .546 .298 994 +V1334 CYG 2448881.1033 5.941 .130 .558 .320 994 +V1334 CYG 2448882.1024 5.873 .546 .308 994 +V1334 CYG 2448883.1049 5.814 .089 .524 .310 994 +V1334 CYG 2448884.1010 5.923 .088 .552 .327 994 +V1334 CYG 2448885.0983 5.914 .096 .535 .318 994 +V1334 CYG 2448886.0984 5.810 .087 .523 .296 994 +V1334 CYG 2448888.0961 5.951 .083 .555 .329 994 +V1334 CYG 2448889.0946 5.817 .102 .530 .292 994 +V1334 CYG 2448890.0944 5.880 .110 .517 .330 994 +V1334 CYG 2448891.0923 5.956 .096 .563 .329 994 +V1334 CYG 2448892.0907 5.872 .060 .517 .312 994 +V1334 CYG 2448893.0908 5.831 .075 .528 .307 994 +V1334 CYG 2448894.0894 5.905 .073 .543 .325 994 +V1334 CYG 2447414.1034 5.938 .168 .508 .311 990 +V1334 CYG 2447415.1019 5.941 .167 .498 .320 990 +V1334 CYG 2447416.0986 5.860 .183 .403 .325 990 +V1334 CYG 2447417.1010 5.884 .159 .466 .341 990 +V1334 CYG 2447418.0985 5.959 .142 .577 .300 990 +V1334 CYG 2447419.0958 5.825 .176 .453 .290 990 +V1334 CYG 2447420.0961 5.838 .182 .470 .308 990 +V1334 CYG 2447421.0950 5.914 .140 .563 .310 990 +V1334 CYG 2447422.0893 5.908 .110 .562 .295 990 +V1334 CYG 2447423.0928 5.824 .160 .504 .298 990 +V1334 CYG 2447424.0909 5.906 .563 .320 990 +V1334 CYG 2447425.0931 5.915 .150 .535 .297 990 +V1334 CYG 2447427.0903 .189 .472 990 +V1334 CYG 2447428.0862 5.951 .145 .549 .322 990 +V1334 CYG 2447429.0886 5.864 .151 .486 .300 990 +V1334 CYG 2447430.0852 5.835 .160 .434 .316 990 +V1334 CYG 2447431.0842 5.928 .160 .526 .322 990 +V1334 CYG 2447432.0844 5.894 .203 .464 .319 990 +V1334 CYG 2447433.0807 5.834 .175 .400 .333 990 +V1334 CYG 2447434.0815 5.887 .141 .548 .306 990 +V1334 CYG 2449933.4387 5.832 .560 .311 .616 998 +V1334 CYG 2449934.1539 5.904 .280 .602 998 +V1334 CYG 2449935.1543 5.925 .520 .582 998 +V1334 CYG 2449936.1480 5.797 .475 .278 .570 998 +V1334 CYG 2449937.2150 5.985 .551 .337 998 +V1334 CYG 2449938.1504 5.974 .551 .320 .603 998 +V1334 CYG 2449942.1474 5.805 .492 .291 .565 998 +V1334 CYG 2449943.1419 .502 .278 .519 998 +V1334 CYG 2449944.1405 5.944 .535 .284 .595 998 +V1334 CYG 2449945.1406 5.863 .495 .311 .585 998 +V1334 CYG 2449946.1429 5.748 .480 .271 .528 998 +V1334 CYG 2449947.1408 5.953 .533 .321 .619 998 +V1334 CYG 2449947.2740 5.969 .557 .324 .632 998 +V1334 CYG 2449948.1397 5.910 .530 .300 .592 998 +V1334 CYG 2449949.1439 5.751 .453 .276 .528 998 +V1334 CYG 2449950.1416 5.889 .540 .309 .595 998 +V1334 CYG 2449952.1401 5.795 .474 .273 .554 998 +V1334 CYG 2449953.1703 .480 .276 .544 998 +V1334 CYG 2449954.1514 .522 .312 .585 998 +V1334 CYG 2449955.1502 5.858 .491 .295 .562 998 +V1334 CYG 2449958.1346 5.972 .493 .314 .584 998 +V1334 CYG 2449959.1307 .465 .298 998 +V1334 CYG 2449962.1440 5.878 .486 .312 .589 998 +V1334 CYG 2450009.0892 5.827 .477 .283 .551 998 +V1334 CYG 2450011.0865 5.952 .531 .309 .603 998 +V1334 CYG 2450012.1370 5.814 .557 998 +V1334 CYG 2450017.0751 5.960 .514 .324 .628 998 +V1334 CYG 2450018.1330 5.906 .518 .316 .613 998 +V1334 CYG 2450020.0678 5.899 .505 .304 .599 998 +V1334 CYG 2446996.4550 5.764 .251 .435 .287 989 +V1334 CYG 2446997.4495 5.860 .287 .486 .302 989 +V1334 CYG 2446998.4627 5.911 .241 .483 .314 989 +V1334 CYG 2446999.4605 5.795 .265 .461 .278 989 +V1334 CYG 2447000.4713 5.807 .220 .467 .288 989 +V1334 CYG 2447002.4709 5.810 .264 .462 .277 989 +V1334 CYG 2447003.4644 5.734 .286 .432 .266 989 +V1334 CYG 2447004.1646 5.868 .304 .500 .315 989 +V1334 CYG 2447005.1573 5.962 .288 .529 .321 989 +V1334 CYG 2447088.0730 5.973 .170 .515 .328 989 +V1334 CYG 2447091.0705 5.932 .228 .529 .324 989 +V1334 CYG 2447098.0431 .546 .345 989 +V1334 CYG 2446615.1842 5.940 .216 .489 .310 988 +V1334 CYG 2446616.1827 5.808 .160 .512 .270 988 +V1334 CYG 2446626.1802 5.803 .230 .453 .280 988 +V1334 CYG 2446627.1821 5.852 .189 .474 .288 988 +V1334 CYG 2446628.1801 5.934 .220 .488 .309 988 +V1334 CYG 2446629.1653 5.914 .226 .485 .295 988 +V1334 CYG 2446630.1921 5.781 .219 .492 .273 988 +V1334 CYG 2446631.1647 5.898 .180 .524 .309 988 +V1334 CYG 2446631.4574 5.924 .179 .541 .305 988 +V1334 CYG 2446632.1677 5.894 .155 .525 .311 988 +V1334 CYG 2446632.4655 5.851 .212 .502 .290 988 +V1334 CYG 2446635.1669 5.910 .202 .509 .310 988 +V1334 CYG 2446636.1646 5.827 .209 .507 .279 988 +V1334 CYG 2446636.4640 5.771 .206 .491 .270 988 +V1334 CYG 2446637.1611 5.774 .207 .502 .281 988 +V1334 CYG 2449617.2185 5.861 .150 .527 .309 995 +V1334 CYG 2449618.2051 .192 .498 .298 995 +V1334 CYG 2449619.2155 5.753 .183 .465 .305 995 +V1334 CYG 2449620.1864 5.816 .217 .514 .302 995 +V1334 CYG 2449621.0940 5.919 .204 .492 .338 995 +V1334 CYG 2449621.1864 5.904 .186 .522 .329 995 +V1334 CYG 2449622.0942 .107 .496 .311 995 +V1334 CYG 2449623.0878 5.731 .213 .466 .279 995 +V1334 CYG 2449624.0927 5.868 .208 .535 .329 995 +V1334 CYG 2449625.0929 5.816 .182 .490 .299 995 +V1334 CYG 2449631.0929 .169 .525 .323 995 +V1334 CYG 2449632.1992 5.747 .121 .472 .285 995 +V1334 CYG 2449633.0872 .192 .492 .309 995 +V1334 CYG 2449634.0943 5.950 .184 .528 .321 995 +V1334 CYG 2450305.1845 5.852 .521 .296 971 +V1334 CYG 2450307.1954 5.955 .565 .318 971 +V1334 CYG 2450311.1455 5.894 .585 .315 971 +V1334 CYG 2450312.1426 5.790 .551 .344 971 +V1334 CYG 2450313.1455 5.823 .578 .295 971 +V1334 CYG 2450314.1312 5.935 .567 .319 971 +V1334 CYG 2450315.1338 5.847 .559 .303 971 +V1334 CYG 2450316.1357 5.803 .603 .292 971 +V1334 CYG 2450317.1655 5.923 .556 .261 971 +V1334 CYG 2450318.1356 5.882 .543 .353 971 +V1334 CYG 2450319.1337 5.786 .498 .349 971 +V1334 CYG 2450320.1395 5.878 .541 .321 971 +V1334 CYG 2450321.1291 5.938 .556 .340 971 +V1334 CYG 2450322.1517 5.800 .520 .286 971 +V1334 CYG 2450323.1428 5.852 .524 .304 971 +V1334 CYG 2450325.1400 5.833 .530 .302 971 +V1334 CYG 2450326.1543 5.816 .534 .296 971 +V1334 CYG 2450327.1710 5.895 .605 971 +V1334 CYG 2450328.3376 5.817 .608 971 +V1334 CYG 2450329.2062 .510 .303 971 +V1334 CYG 2450330.1496 5.945 .573 .303 971 +V1334 CYG 2450332.2074 5.790 .540 .266 971 +V1334 CYG 2450333.2014 5.845 .556 .309 971 +V1334 CYG 2450334.2272 5.939 .563 .289 971 +V1334 CYG 2450335.2329 5.825 .544 .290 971 +V1334 CYG 2450336.2297 5.790 .536 .303 971 +V1334 CYG 2450337.1931 5.919 .595 .310 971 +V1334 CYG 2450340.2093 5.876 .583 .322 971 +V1334 CYG 2450341.2084 5.912 .561 .292 971 +V1334 CYG 2450342.2212 5.779 .534 .274 971 +V1334 CYG 2450344.2522 5.936 .574 .279 971 +V1334 CYG 2450347.2632 5.939 .583 .297 971 +V1334 CYG 2450349.2097 5.817 .534 .291 971 +V1336 CYG 2448503.3399 14.275 1.170 .656 993 +V1336 CYG 2448504.2755 14.275 1.195 .624 993 +V1336 CYG 2448505.2964 14.299 1.256 .586 993 +V1336 CYG 2448506.3116 14.307 1.221 .646 993 +V1336 CYG 2448507.2879 14.358 1.217 .646 993 +V1336 CYG 2448508.2482 14.333 1.222 .622 993 +V1336 CYG 2448509.3006 14.403 1.192 .633 993 +V1336 CYG 2448510.2886 14.464 1.219 .657 993 +V1336 CYG 2448511.2915 14.381 1.320 .629 993 +V1336 CYG 2448512.2771 14.467 1.255 .634 993 +V1336 CYG 2448513.2920 14.501 1.191 .633 993 +V1336 CYG 2448514.2929 14.475 1.208 .611 993 +V1336 CYG 2448515.2881 14.550 1.126 .647 993 +V1336 CYG 2448516.3314 14.491 1.188 .578 993 +V1336 CYG 2448517.2987 14.496 1.130 .661 993 +V1336 CYG 2448518.3205 14.466 1.137 993 +V1336 CYG 2448519.2690 14.455 1.029 993 +V1336 CYG 2448520.2581 14.393 1.128 993 +V1336 CYG 2448521.2978 14.328 1.083 .530 993 +V1336 CYG 2448522.2605 14.343 1.129 993 +V1336 CYG 2448523.2469 14.359 1.135 993 +V1336 CYG 2445663.1914 1.090 950 +V1336 CYG 2445666.1757 14.416 .711 950 +V1336 CYG 2445668.2187 14.479 1.342 .554 950 +V1336 CYG 2445674.1875 14.656 1.268 .790 950 +V1336 CYG 2445675.1914 14.593 1.161 .777 950 +V1336 CYG 2445676.1875 14.628 1.403 .679 950 +V1336 CYG 2445679.1562 14.721 1.126 .772 950 +V1336 CYG 2445683.1445 14.869 1.214 .789 950 +V1336 CYG 2445701.1132 14.529 1.232 .678 950 +V1336 CYG 2445705.0937 14.601 1.277 .691 950 +V1336 CYG 2445706.0820 14.617 1.322 .659 950 +V1336 CYG 2445707.0820 14.656 1.285 .654 950 +V1336 CYG 2445867.4453 14.734 1.143 .720 950 +V1336 CYG 2445869.4062 14.808 1.287 .733 950 +V1336 CYG 2445870.3945 14.841 1.321 .647 950 +V1336 CYG 2445871.4140 14.865 1.344 .631 950 +V1336 CYG 2445872.3945 14.961 1.229 .680 950 +V1336 CYG 2445873.4062 14.957 1.230 .704 950 +V1336 CYG 2445874.4023 14.872 1.335 .596 950 +V1336 CYG 2445875.3984 14.901 1.229 .647 950 +V1336 CYG 2445876.4101 14.851 1.119 .624 950 +V1336 CYG 2445877.3945 14.858 1.178 .654 950 +V1336 CYG 2445878.3906 14.736 1.073 .603 950 +V1336 CYG 2445879.3750 14.709 1.093 .633 950 +V1336 CYG 2445880.3789 14.605 1.087 .548 950 +V1336 CYG 2445881.3710 14.622 1.094 .634 950 +V1336 CYG 2445882.3671 14.642 1.087 .573 950 +V1336 CYG 2445883.3789 14.560 1.130 .576 950 +V1336 CYG 2445886.3828 14.562 1.154 .663 950 +V1336 CYG 2445887.3867 14.532 1.196 .640 950 +V1336 CYG 2447735.4674 14.539 1.119 .626 950 +V1336 CYG 2447736.4633 14.587 1.196 .609 950 +V1336 CYG 2447738.4244 14.630 1.233 .658 950 +V1336 CYG 2447739.4045 14.625 1.228 .674 950 +V1336 CYG 2447740.4198 14.622 1.328 .657 950 +V1336 CYG 2447741.3806 14.654 1.306 .641 950 +V1336 CYG 2447742.3799 14.698 1.325 .662 950 +V1336 CYG 2447743.3684 14.679 1.344 .648 950 +V1336 CYG 2447744.3468 14.661 1.354 .633 950 +V1336 CYG 2447745.3541 14.783 1.362 .648 950 +V1336 CYG 2447746.3560 14.774 1.335 .624 950 +V1336 CYG 2447747.3571 14.839 1.314 .677 950 +V1336 CYG 2447748.3506 14.796 1.366 .649 950 +V1336 CYG 2447749.3552 14.883 1.279 .638 950 +V1336 CYG 2447750.3376 14.893 1.266 .685 950 +V1336 CYG 2447751.3397 14.860 1.319 .609 950 +V1336 CYG 2447752.3198 14.948 1.144 .697 950 +V1336 CYG 2447754.3614 14.754 1.291 .534 950 +V1336 CYG 2447755.3817 14.805 1.209 .617 950 +V1336 CYG 2447756.3858 14.660 1.147 .545 950 +V1336 CYG 2447757.3664 14.595 1.227 .540 950 +V1336 CYG 2447758.3420 14.622 1.090 .631 950 +V1336 CYG 2447759.3175 14.564 1.079 .545 950 +V1336 CYG 2447760.3155 14.534 1.090 .556 950 +V1336 CYG 2447761.2932 14.468 1.081 .578 950 +V1336 CYG 2447762.2718 14.481 1.139 .585 950 +V1336 CYG 2447763.2456 14.483 1.114 950 +V1336 CYG 2447764.2521 14.468 1.095 950 +V1336 CYG 2447766.2550 14.442 .631 950 +V1336 CYG 2447767.3216 14.496 1.186 .623 950 +V1336 CYG 2447768.3377 14.497 1.189 .700 950 +V1336 CYG 2447770.2950 14.574 1.263 .668 950 +V1336 CYG 2447771.2865 14.613 1.218 .632 950 +V1336 CYG 2447772.2816 14.662 1.297 .616 950 +V1336 CYG 2447773.3110 14.654 1.305 .591 950 +V1336 CYG 2447774.3161 14.662 1.354 .644 950 +V1336 CYG 2447775.2782 14.730 1.298 .661 950 +V1336 CYG 2447776.2892 14.765 1.277 .700 950 +V1364 CYG 2445174.4101 12.920 1.788 982 +V1364 CYG 2445175.4296 12.864 1.853 982 +V1364 CYG 2445176.3945 13.003 1.905 982 +V1364 CYG 2445178.3984 13.253 2.061 982 +V1364 CYG 2445179.3046 13.358 2.117 982 +V1364 CYG 2445180.3359 13.475 2.156 982 +V1364 CYG 2445181.3671 13.578 2.178 982 +V1364 CYG 2445182.3593 13.610 2.142 982 +V1364 CYG 2445183.3631 13.529 2.148 982 +V1364 CYG 2445184.2812 13.440 2.039 982 +V1364 CYG 2445186.3554 13.214 1.893 982 +V1364 CYG 2445187.2929 12.929 1.841 982 +V1364 CYG 2445188.2889 12.882 1.851 982 +V1364 CYG 2445189.3203 13.008 1.885 982 +V1364 CYG 2445190.3476 13.120 1.984 982 +V1364 CYG 2445191.3514 13.256 2.033 982 +V1364 CYG 2445192.3514 13.346 2.108 982 +V1364 CYG 2445193.3788 13.466 2.177 982 +V1364 CYG 2445194.3593 13.553 2.192 982 +V1364 CYG 2445195.3437 13.615 2.181 982 +V1364 CYG 2445198.3710 13.340 1.984 982 +V1364 CYG 2445199.3242 13.220 1.914 982 +V1364 CYG 2445200.3320 12.929 1.791 982 +V1364 CYG 2445201.3476 12.900 1.821 982 +V1364 CYG 2445203.2617 13.109 1.999 982 +V1364 CYG 2445204.2538 13.246 2.049 982 +V1364 CYG 2445205.2734 13.368 2.109 982 +V1364 CYG 2445207.2812 13.560 2.189 982 +V1364 CYG 2445208.3085 13.637 2.164 982 +V1364 CYG 2445209.2578 13.561 2.108 982 +V1364 CYG 2445210.2851 13.413 2.050 982 +V1364 CYG 2445211.2578 13.339 2.002 982 +V1364 CYG 2445213.2655 12.911 1.813 982 +V1364 CYG 2445214.2695 12.935 1.811 982 +V1364 CYG 2445665.1953 13.360 1.972 1.209 982 +V1364 CYG 2445866.4139 13.318 2.069 1.240 982 +V1364 CYG 2445868.3788 13.547 2.143 1.286 982 +V1364 CYG 2445869.3945 13.601 2.191 1.280 982 +V1364 CYG 2445870.3867 13.595 2.157 1.266 982 +V1364 CYG 2445871.3984 13.515 2.058 1.254 982 +V1364 CYG 2445872.3828 13.369 1.204 982 +V1364 CYG 2445873.3905 13.365 1.939 1.211 982 +V1364 CYG 2445874.3867 13.037 1.845 1.130 982 +V1364 CYG 2445875.3750 12.885 1.807 1.122 982 +V1364 CYG 2445876.3867 12.942 1.832 1.145 982 +V1364 CYG 2445877.3750 13.045 1.939 1.181 982 +V1364 CYG 2445878.3710 13.165 2.016 1.209 982 +V1364 CYG 2445879.3593 13.299 2.084 1.236 982 +V1364 CYG 2445880.3631 13.408 2.161 1.256 982 +V1364 CYG 2445881.3554 13.549 2.144 1.272 982 +V1364 CYG 2445882.3514 13.601 2.190 1.258 982 +V1364 CYG 2445883.3671 13.608 2.185 1.256 982 +V1364 CYG 2445886.3710 13.333 1.925 1.197 982 +V1364 CYG 2447734.4332 13.235 2.111 1.236 991 +V1364 CYG 2447735.4291 13.370 2.129 1.223 991 +V1364 CYG 2447736.4318 13.519 2.179 1.243 991 +V1364 CYG 2447737.4167 13.573 2.132 1.269 991 +V1364 CYG 2447738.4032 13.632 2.085 1.254 991 +V1364 CYG 2447739.3734 13.471 2.108 1.211 991 +V1364 CYG 2447740.3972 13.404 2.039 1.214 991 +V1364 CYG 2447741.3574 13.319 1.959 1.162 991 +V1364 CYG 2447742.3665 13.054 1.869 1.112 991 +V1364 CYG 2447743.3549 12.896 1.742 1.113 991 +V1364 CYG 2447744.3338 12.923 1.883 1.128 991 +V1364 CYG 2447745.3339 13.043 1.954 1.170 991 +V1364 CYG 2447746.3348 13.184 2.018 1.195 991 +V1364 CYG 2447747.3316 13.260 2.066 1.231 991 +V1364 CYG 2447748.3302 13.409 2.126 1.221 991 +V1364 CYG 2447749.3261 13.532 2.133 1.259 991 +V1364 CYG 2447750.3018 13.601 2.129 1.273 991 +V1364 CYG 2447751.2977 13.586 2.095 1.235 991 +V1364 CYG 2447752.2651 13.530 2.084 1.235 991 +V1364 CYG 2447753.2656 13.414 1.942 1.217 991 +V1364 CYG 2447754.3147 13.338 1.912 991 +V1364 CYG 2447755.3479 13.053 1.919 1.133 991 +V1364 CYG 2447756.3416 12.873 1.851 1.095 991 +V1364 CYG 2447757.3227 12.907 1.867 1.139 991 +V1364 CYG 2447758.3098 13.012 1.907 1.133 991 +V1364 CYG 2447759.2780 13.170 2.025 1.176 991 +V1364 CYG 2447760.2837 13.278 2.129 1.208 991 +V1364 CYG 2447761.2693 13.394 2.124 1.231 991 +V1364 CYG 2447762.2516 13.517 2.196 1.243 991 +V1364 CYG 2447763.2198 13.600 2.155 991 +V1364 CYG 2447764.2249 13.593 2.150 1.237 991 +V1364 CYG 2447766.2299 13.400 1.941 1.207 991 +V1364 CYG 2447767.2843 13.314 1.938 1.189 991 +V1364 CYG 2447768.2750 13.009 1.863 1.137 991 +V1364 CYG 2447770.2739 12.938 1.825 1.129 991 +V1364 CYG 2447771.2621 13.058 1.954 1.182 991 +V1364 CYG 2447772.2571 13.178 2.020 1.195 991 +V1364 CYG 2447773.2883 13.293 2.066 1.219 991 +V1364 CYG 2447774.2910 13.414 2.145 1.233 991 +V1364 CYG 2447775.2508 13.544 2.135 1.260 991 +V1364 CYG 2447776.2564 13.597 2.180 1.268 991 +V1467 CYG 2446606.3368 13.297 2.476 1.545 988 +V1467 CYG 2446607.2935 13.344 1.554 988 +V1467 CYG 2446608.2312 13.387 2.570 1.569 988 +V1467 CYG 2446609.2163 13.376 2.487 1.566 988 +V1467 CYG 2446610.3709 13.423 2.537 1.579 988 +V1467 CYG 2446611.2191 13.451 2.512 1.584 988 +V1467 CYG 2446612.2058 13.445 2.555 1.587 988 +V1467 CYG 2446613.2070 13.483 2.574 1.597 988 +V1467 CYG 2446614.2093 13.515 2.608 1.618 988 +V1467 CYG 2446615.2087 13.536 2.603 1.582 988 +V1467 CYG 2446616.2098 13.586 2.583 1.613 988 +V1467 CYG 2446617.2038 13.627 2.609 1.627 988 +V1467 CYG 2446618.2022 13.583 2.613 1.603 988 +V1467 CYG 2446619.2015 13.650 2.581 1.597 988 +V1467 CYG 2446620.1992 13.663 2.629 1.606 988 +V1467 CYG 2446621.3195 13.771 2.646 1.661 988 +V1467 CYG 2446622.1978 13.721 2.619 1.624 988 +V1467 CYG 2446623.1948 13.779 2.608 1.639 988 +V1467 CYG 2446624.1899 13.796 1.629 988 +V1467 CYG 2446625.2798 13.887 2.746 1.660 988 +V1467 CYG 2446626.3027 13.844 2.714 1.610 988 +V1467 CYG 2446627.2870 13.925 2.661 1.635 988 +V1467 CYG 2446628.2942 13.957 2.627 1.624 988 +V1467 CYG 2446629.3787 13.986 2.604 1.641 988 +V1467 CYG 2446631.2932 14.047 2.573 1.647 988 +V1467 CYG 2446632.3746 14.099 2.469 1.672 988 +V1467 CYG 2446635.2881 13.851 2.448 1.598 988 +V1467 CYG 2446636.1920 13.762 2.598 1.576 988 +V1467 CYG 2446637.1982 13.564 2.500 1.519 988 +V1467 CYG 2446638.2147 13.416 2.345 1.475 988 +V1467 CYG 2448503.2705 13.501 2.593 1.569 993 +V1467 CYG 2448504.2226 13.539 2.509 1.602 993 +V1467 CYG 2448505.2447 13.580 2.652 1.581 993 +V1467 CYG 2448506.2708 13.566 2.682 1.577 993 +V1467 CYG 2448507.2380 13.594 2.653 1.552 993 +V1467 CYG 2448508.2103 13.639 2.706 1.583 993 +V1467 CYG 2448509.2506 13.656 2.723 1.571 993 +V1467 CYG 2448510.2138 13.698 2.587 1.622 993 +V1467 CYG 2448511.2302 13.745 2.695 1.615 993 +V1467 CYG 2448512.2366 13.746 2.624 1.585 993 +V1467 CYG 2448513.2312 13.795 2.692 1.613 993 +V1467 CYG 2448514.2437 13.838 2.760 1.606 993 +V1467 CYG 2448515.2529 13.863 2.672 1.592 993 +V1467 CYG 2448516.2972 13.918 2.779 1.607 993 +V1467 CYG 2448517.2784 13.968 2.651 1.627 993 +V1467 CYG 2448518.2792 13.995 2.677 1.640 993 +V1467 CYG 2448519.2373 14.078 2.650 1.667 993 +V1467 CYG 2448520.2166 14.046 2.732 1.654 993 +V1467 CYG 2448521.2523 13.993 1.591 993 +V1467 CYG 2448522.2285 14.010 1.605 993 +V1467 CYG 2448523.2198 14.030 1.595 993 +V1467 CYG 2445174.3828 13.989 2.713 982 +V1467 CYG 2445175.4218 14.027 2.667 982 +V1467 CYG 2445176.3867 14.055 2.683 982 +V1467 CYG 2445178.3905 13.930 2.623 982 +V1467 CYG 2445179.2929 13.862 2.600 982 +V1467 CYG 2445180.3280 13.688 2.506 982 +V1467 CYG 2445181.3593 13.548 2.432 982 +V1467 CYG 2445182.3476 13.378 2.344 982 +V1467 CYG 2445183.3514 13.206 2.309 982 +V1467 CYG 2445184.2655 13.141 2.193 982 +V1467 CYG 2445186.3437 13.050 2.248 982 +V1467 CYG 2445187.2812 13.069 2.279 982 +V1467 CYG 2445188.2772 13.038 2.252 982 +V1467 CYG 2445189.3046 13.095 2.289 982 +V1467 CYG 2445190.3359 13.110 2.338 982 +V1467 CYG 2445191.3397 13.143 2.369 982 +V1467 CYG 2445192.3397 13.144 2.370 982 +V1467 CYG 2445193.3710 13.208 2.366 982 +V1467 CYG 2445194.3476 13.191 2.417 982 +V1467 CYG 2445195.3280 13.228 2.462 982 +V1467 CYG 2445198.3593 13.317 2.533 982 +V1467 CYG 2445199.3006 13.338 2.526 982 +V1467 CYG 2445200.3203 13.362 2.551 982 +V1467 CYG 2445201.3359 13.395 2.521 982 +V1467 CYG 2445203.2538 13.429 2.594 982 +V1467 CYG 2445204.2460 13.464 2.596 982 +V1467 CYG 2445205.2655 13.525 2.569 982 +V1467 CYG 2445207.2695 13.529 2.632 982 +V1467 CYG 2445208.2929 13.594 2.626 982 +V1467 CYG 2445209.2421 13.599 2.627 982 +V1467 CYG 2445210.2695 13.661 2.692 982 +V1467 CYG 2445211.2343 13.647 2.724 982 +V1467 CYG 2445212.2734 13.737 2.602 982 +V1467 CYG 2445213.2460 13.703 2.760 982 +V1467 CYG 2445214.2500 13.740 2.707 982 +V1467 CYG 2445649.2109 13.635 982 +V1467 CYG 2445665.1756 13.748 1.531 982 +V1467 CYG 2445666.1367 13.540 1.502 982 +V1467 CYG 2445667.1601 13.554 1.475 982 +V1467 CYG 2445674.1522 13.104 1.462 982 +V1467 CYG 2445675.1562 13.159 1.478 982 +V1467 CYG 2445676.1288 13.166 1.450 982 +V1467 CYG 2445864.4022 13.067 2.223 1.426 982 +V1467 CYG 2445866.4022 13.047 2.216 1.420 982 +V1467 CYG 2445868.3710 13.038 2.341 1.423 982 +V1467 CYG 2445869.3867 13.120 2.361 1.457 982 +V1467 CYG 2445870.3788 13.145 2.348 1.492 982 +V1467 CYG 2445871.3945 13.168 2.366 1.477 982 +V1467 CYG 2445872.3788 13.177 2.422 1.489 982 +V1467 CYG 2445873.3828 13.231 2.478 1.513 982 +V1467 CYG 2445874.3828 13.231 2.472 1.516 982 +V1467 CYG 2445875.3671 13.297 2.519 1.543 982 +V1467 CYG 2445876.3828 13.284 2.496 1.528 982 +V1467 CYG 2445877.3671 13.306 2.557 1.548 982 +V1467 CYG 2445878.3631 13.349 2.517 1.552 982 +V1467 CYG 2445879.3514 13.387 2.577 1.556 982 +V1467 CYG 2445880.3593 13.393 2.601 1.574 982 +V1467 CYG 2445881.3514 13.430 2.652 1.572 982 +V1467 CYG 2445882.3437 13.451 2.632 1.573 982 +V1467 CYG 2445883.3593 13.476 2.641 1.578 982 +V1467 CYG 2445886.3671 13.556 2.615 1.594 982 +V1467 CYG 2445887.3710 13.584 2.707 1.603 982 +V1467 CYG 2446252.3995 13.065 2.237 1.407 987 +V1467 CYG 2446253.2265 13.055 2.222 1.414 987 +V1467 CYG 2446255.2763 13.058 2.289 1.442 987 +V1467 CYG 2446256.2626 13.088 2.270 1.433 987 +V1467 CYG 2446257.2836 13.130 2.297 1.466 987 +V1467 CYG 2446258.2293 13.166 2.306 1.483 987 +V1467 CYG 2446259.2339 13.136 2.409 1.469 987 +V1467 CYG 2446260.2080 13.213 2.396 1.487 987 +V1467 CYG 2446261.2340 13.209 2.414 1.496 987 +V1467 CYG 2446262.2061 13.237 2.416 1.513 987 +V1467 CYG 2446263.2068 13.252 2.452 1.514 987 +V1467 CYG 2446265.1949 13.324 2.504 1.557 987 +V1467 CYG 2446266.2117 13.318 2.501 1.559 987 +V1467 CYG 2446267.2180 13.373 2.500 1.563 987 +V1467 CYG 2446268.2254 13.381 2.532 1.566 987 +V1467 CYG 2446269.2091 13.411 2.624 1.575 987 +V1467 CYG 2446270.2072 13.395 2.596 1.583 987 +V1467 CYG 2446271.2235 13.425 2.600 1.566 987 +V1467 CYG 2446272.2828 13.476 2.630 1.591 987 +V1467 CYG 2446273.2218 13.535 1.608 987 +V1467 CYG 2446274.3824 13.539 2.637 1.590 987 +V1467 CYG 2446275.2088 13.575 2.603 1.629 987 +V1467 CYG 2446279.1817 13.686 1.627 987 +V1467 CYG 2446279.2102 13.695 1.625 987 +V1467 CYG 2446280.1900 13.710 2.682 1.624 987 +V1467 CYG 2446283.2806 13.855 2.780 1.618 987 +V1467 CYG 2446284.2577 13.889 2.724 1.627 987 +V1467 CYG 2446285.2396 13.901 2.649 1.629 987 +V1467 CYG 2446286.2230 13.948 2.740 1.629 987 +V1467 CYG 2446287.2245 13.972 2.689 1.631 987 +V1467 CYG 2446288.2581 13.996 2.677 1.635 987 +V1467 CYG 2446289.2597 14.039 2.666 1.644 987 +V1467 CYG 2446290.2289 14.089 2.724 1.661 987 +V1467 CYG 2446291.2150 14.036 2.676 1.621 987 +V1467 CYG 2446292.2357 14.060 2.707 1.645 987 +V1467 CYG 2446294.2130 13.942 2.597 1.613 987 +V1467 CYG 2446295.1854 13.789 2.561 1.558 987 +V1467 CYG 2446296.1975 13.720 2.500 1.552 987 +V1467 CYG 2446297.2050 13.524 2.419 1.506 987 +V1467 CYG 2446298.2241 13.390 2.360 1.481 987 +V1467 CYG 2446299.1933 13.216 2.309 1.437 987 +V1467 CYG 2446300.1943 13.126 2.291 1.405 987 +V1467 CYG 2446301.2150 13.059 2.265 1.405 987 +V1467 CYG 2446302.2057 13.063 2.292 1.411 987 +V1467 CYG 2446303.1893 13.051 2.244 1.408 987 +V1467 CYG 2446304.1701 13.071 2.291 1.416 987 +V1467 CYG 2449620.2966 13.625 1.571 995 +V1467 CYG 2449621.2558 13.677 1.557 995 +V1467 CYG 2449623.2293 1.571 995 +V1467 CYG 2449624.2552 13.767 1.614 995 +V1467 CYG 2449625.2745 13.801 1.601 995 +V1467 CYG 2449626.2807 13.787 1.636 995 +V1467 CYG 2449631.2403 13.831 1.590 995 +V1467 CYG 2449632.2694 13.933 1.615 995 +V1467 CYG 2449633.2494 13.923 1.624 995 +V1467 CYG 2449634.2559 14.006 1.620 995 +V1467 CYG 2449635.2801 14.005 1.598 995 +V1467 CYG 2447734.3973 13.654 2.703 1.607 991 +V1467 CYG 2447735.4094 13.647 2.621 1.590 991 +V1467 CYG 2447736.4170 13.732 1.597 991 +V1467 CYG 2447737.4028 13.693 2.704 1.566 991 +V1467 CYG 2447738.3863 13.865 2.727 1.619 991 +V1467 CYG 2447739.3585 13.761 2.679 1.581 991 +V1467 CYG 2447740.3895 13.888 2.741 1.635 991 +V1467 CYG 2447741.3468 13.877 2.733 1.600 991 +V1467 CYG 2447742.3535 13.912 2.721 1.617 991 +V1467 CYG 2447743.3434 13.960 2.728 1.635 991 +V1467 CYG 2447744.3185 14.015 2.786 1.632 991 +V1467 CYG 2447745.3215 14.029 2.703 1.632 991 +V1467 CYG 2447746.3279 14.042 1.594 991 +V1467 CYG 2447747.3237 14.025 2.714 1.607 991 +V1467 CYG 2447748.3226 14.079 2.702 1.625 991 +V1467 CYG 2447749.3235 13.981 2.628 1.579 991 +V1467 CYG 2447750.2909 13.927 2.659 1.589 991 +V1467 CYG 2447751.2956 13.788 2.531 1.539 991 +V1467 CYG 2447752.2622 13.664 2.472 1.501 991 +V1467 CYG 2447753.2633 13.492 1.473 991 +V1467 CYG 2447754.3003 13.305 2.340 1.411 991 +V1467 CYG 2447755.3260 13.144 1.396 991 +V1467 CYG 2447756.3228 13.054 2.339 1.390 991 +V1467 CYG 2447757.3063 13.050 1.392 991 +V1467 CYG 2447758.2952 13.011 2.238 1.374 991 +V1467 CYG 2447759.2643 13.041 2.250 1.387 991 +V1467 CYG 2447760.2819 13.067 2.269 1.407 991 +V1467 CYG 2447761.2672 13.097 2.270 1.425 991 +V1467 CYG 2447762.2403 13.150 2.302 1.448 991 +V1467 CYG 2447763.2140 13.153 2.340 1.468 991 +V1467 CYG 2447764.2194 13.167 2.342 1.463 991 +V1467 CYG 2447766.2191 13.257 2.373 1.492 991 +V1467 CYG 2447767.2820 13.266 2.419 1.489 991 +V1467 CYG 2447768.2651 13.242 2.465 1.506 991 +V1467 CYG 2447770.2725 13.331 2.493 1.506 991 +V1467 CYG 2447771.2543 13.377 2.525 1.532 991 +V1467 CYG 2447772.2508 13.398 2.515 1.548 991 +V1467 CYG 2447773.2867 13.421 2.554 1.532 991 +V1467 CYG 2447774.2891 13.446 2.599 1.544 991 +V1467 CYG 2447775.2489 13.484 2.597 1.547 991 +V1467 CYG 2447776.2547 13.523 2.590 1.596 991 +V1467 CYG 2446992.3894 13.281 2.487 1.518 989 +V1467 CYG 2446994.4079 13.363 2.546 1.552 989 +V1467 CYG 2446995.3248 13.309 2.633 1.552 989 +V1467 CYG 2446996.2628 13.321 2.640 1.541 989 +V1467 CYG 2446997.2940 13.382 2.686 1.565 989 +V1467 CYG 2446998.2890 13.422 2.586 1.590 989 +V1467 CYG 2446999.2904 13.445 2.637 1.576 989 +V1467 CYG 2447000.3003 13.478 2.653 1.610 989 +V1467 CYG 2447001.2991 13.549 2.751 1.605 989 +V1467 CYG 2447002.2996 13.545 2.740 1.615 989 +V1467 CYG 2447003.2849 13.496 2.807 1.574 989 +V1467 CYG 2447082.2219 13.121 2.253 1.448 989 +V1467 CYG 2447083.1578 13.130 2.252 1.460 989 +V1467 CYG 2447084.1468 13.128 2.292 1.438 989 +V1467 CYG 2447087.1578 13.225 2.483 1.489 989 +V1467 CYG 2447088.1072 13.243 2.414 1.500 989 +V1467 CYG 2447091.0970 13.307 2.537 1.530 989 +V1467 CYG 2447098.0868 13.495 2.606 1.586 989 +V1467 CYG 2448854.3123 13.803 2.820 1.589 994 +V1467 CYG 2448856.2992 13.903 2.794 1.615 994 +V1467 CYG 2448858.2828 14.013 2.687 1.660 994 +V1467 CYG 2448860.2689 14.016 2.705 1.621 994 +V1467 CYG 2448862.3009 14.032 2.763 1.582 994 +V1467 CYG 2448870.2879 13.256 2.336 1.428 994 +V1467 CYG 2448872.2605 13.080 2.318 1.393 994 +V1467 CYG 2448874.2796 13.069 2.335 1.422 994 +V1467 CYG 2448876.2544 13.150 2.453 1.448 994 +V1467 CYG 2448877.2286 13.096 1.418 994 +V1467 CYG 2448878.2827 13.136 2.325 1.435 994 +V1467 CYG 2448879.3228 13.182 2.415 1.473 994 +V1467 CYG 2448880.2230 13.164 2.363 1.457 994 +V1467 CYG 2448881.2024 13.218 2.446 1.491 994 +V1467 CYG 2448882.2081 13.253 2.456 1.495 994 +V1467 CYG 2448883.2390 13.301 2.527 1.540 994 +V1467 CYG 2448884.2155 13.277 2.514 1.529 994 +V1467 CYG 2448885.2125 13.298 2.572 1.509 994 +V1467 CYG 2448886.2303 13.340 2.522 1.524 994 +V1467 CYG 2448887.2372 13.349 2.566 1.530 994 +V1467 CYG 2448888.2045 13.384 2.642 1.529 994 +V1467 CYG 2448889.2133 13.419 2.574 1.534 994 +V1467 CYG 2448890.2018 13.447 2.638 1.564 994 +V1467 CYG 2448891.1904 13.463 2.642 1.552 994 +V1467 CYG 2448892.2162 13.486 2.674 1.572 994 +V1467 CYG 2448893.2034 13.521 2.673 1.572 994 +V1467 CYG 2448894.2282 13.547 2.665 1.583 994 +V1467 CYG 2449933.4211 13.826 1.633 2.968 998 +V1467 CYG 2449934.3709 13.721 2.264 1.640 2.972 998 +V1467 CYG 2449935.3774 13.415 1.533 2.818 998 +V1467 CYG 2449936.3454 13.317 1.435 2.749 998 +V1467 CYG 2449937.3527 13.208 1.439 998 +V1467 CYG 2449938.3756 13.137 1.449 2.783 998 +V1467 CYG 2449939.3588 13.016 2.655 998 +V1467 CYG 2449941.3500 13.084 2.740 998 +V1467 CYG 2449943.3239 13.039 2.740 998 +V1467 CYG 2449944.3756 13.024 2.728 998 +V1467 CYG 2449945.3742 13.135 2.757 998 +V1467 CYG 2449947.2447 13.156 2.798 998 +V1467 CYG 2449948.2700 13.169 2.826 998 +V1467 CYG 2449949.2719 13.212 2.883 998 +V1467 CYG 2449950.2648 13.220 2.882 998 +V1467 CYG 2449952.2708 13.292 2.945 998 +V1467 CYG 2449953.3278 13.300 2.912 998 +V1467 CYG 2449954.2647 13.342 2.941 998 +V1467 CYG 2449955.2573 13.373 2.929 998 +V1467 CYG 2449956.3547 13.425 3.010 998 +V1467 CYG 2449957.2587 13.390 2.944 998 +V1467 CYG 2449958.2406 13.508 3.058 998 +V1467 CYG 2449959.2587 13.549 3.063 998 +V1467 CYG 2449960.2981 13.487 3.009 998 +V1467 CYG 2449962.2853 13.542 2.986 998 +V1467 CYG 2449985.2783 13.266 2.822 998 +V1467 CYG 2449986.1999 13.115 2.771 998 +V1467 CYG 2449987.2408 13.045 2.731 998 +V1467 CYG 2450009.2824 13.465 3.052 998 +V1467 CYG 2450011.2512 13.560 3.002 998 +V1467 CYG 2450017.2068 13.738 3.059 998 +V1467 CYG 2450018.2461 13.750 3.027 998 +V1467 CYG 2450020.1982 13.807 3.054 998 +V1467 CYG 2447399.3310 13.863 1.617 990 +V1467 CYG 2447400.2580 13.841 2.781 1.609 990 +V1467 CYG 2447401.2459 13.862 2.688 990 +V1467 CYG 2447402.2464 13.977 2.719 1.626 990 +V1467 CYG 2447403.3004 13.984 2.790 1.615 990 +V1467 CYG 2447404.2654 13.989 2.719 1.607 990 +V1467 CYG 2447407.2631 14.079 2.735 1.609 990 +V1467 CYG 2447408.2511 14.061 2.675 1.620 990 +V1467 CYG 2447409.2505 14.089 2.669 1.604 990 +V1467 CYG 2447410.2642 14.037 2.582 1.598 990 +V1467 CYG 2447411.2715 13.965 2.624 1.574 990 +V1467 CYG 2447413.2448 13.696 2.457 1.529 990 +V1467 CYG 2447414.2556 13.516 2.445 1.475 990 +V1467 CYG 2447415.2343 13.349 2.336 1.449 990 +V1467 CYG 2447416.2240 13.227 2.252 1.407 990 +V1467 CYG 2447417.2223 13.098 2.267 1.382 990 +V1467 CYG 2447418.2178 13.088 2.198 1.395 990 +V1467 CYG 2447419.2038 2.198 1.375 990 +V1467 CYG 2447420.2031 13.047 2.237 1.385 990 +V1467 CYG 2447422.2043 13.067 2.269 1.421 990 +V1467 CYG 2447423.2102 13.116 2.262 1.430 990 +V1467 CYG 2447424.2099 13.210 2.308 1.475 990 +V1467 CYG 2447425.2339 13.177 2.320 1.455 990 +V1467 CYG 2447427.2441 13.294 2.428 1.478 990 +V1467 CYG 2447428.2018 13.270 2.502 1.495 990 +V1467 CYG 2447429.1936 13.318 1.515 990 +V1467 CYG 2447430.1861 13.293 1.487 990 +V1467 CYG 2447431.2238 13.326 2.543 1.504 990 +V1467 CYG 2447432.2132 13.375 2.529 1.518 990 +V1467 CYG 2447433.2185 13.395 2.587 1.525 990 +V1467 CYG 2447434.2093 13.442 2.520 1.548 990 +V1467 CYG 2450305.2231 13.688 2.570 1.583 971 +V1467 CYG 2450306.2880 13.691 2.622 1.596 971 +V1467 CYG 2450307.2905 13.755 2.729 1.608 971 +V1467 CYG 2450310.2711 13.791 2.696 1.561 971 +V1467 CYG 2450311.2470 13.865 2.646 1.580 971 +V1467 CYG 2450312.2790 13.900 2.672 1.595 971 +V1467 CYG 2450313.2724 13.921 2.585 1.590 971 +V1467 CYG 2450314.1960 13.976 1.608 971 +V1467 CYG 2450315.2114 13.923 2.777 1.551 971 +V1467 CYG 2450316.2596 13.941 2.644 1.532 971 +V1467 CYG 2450317.2233 14.007 2.658 1.561 971 +V1467 CYG 2450318.2390 14.031 2.655 1.560 971 +V1467 CYG 2450319.2239 14.001 2.572 1.563 971 +V1467 CYG 2450320.2251 14.003 2.595 1.536 971 +V1467 CYG 2450321.2155 13.944 2.560 1.526 971 +V1467 CYG 2450322.2487 13.852 2.590 1.523 971 +V1467 CYG 2450323.2495 13.734 2.679 1.468 971 +V1467 CYG 2450324.2514 13.545 2.530 1.433 971 +V1467 CYG 2450325.2155 13.418 2.321 1.445 971 +V1467 CYG 2450326.1785 13.270 2.233 1.420 971 +V1467 CYG 2450327.2558 13.125 2.247 971 +V1467 CYG 2450330.2152 13.103 2.218 1.382 971 +V1467 CYG 2450332.1857 13.020 2.380 1.308 971 +V1467 CYG 2450333.1867 13.057 2.305 1.408 971 +V1467 CYG 2450334.1967 13.098 2.338 1.402 971 +V1467 CYG 2450335.1986 13.136 2.411 1.444 971 +V1467 CYG 2450336.2186 13.162 2.271 1.448 971 +V1467 CYG 2450337.1817 13.185 2.373 1.449 971 +V1467 CYG 2450338.2616 13.201 2.411 1.458 971 +V1467 CYG 2450340.1694 13.250 2.473 1.483 971 +V1467 CYG 2450341.1768 13.290 2.449 1.500 971 +V1467 CYG 2450342.2054 13.292 2.437 1.505 971 +V1467 CYG 2450344.2307 13.325 2.554 1.473 971 +V1467 CYG 2450347.1980 13.424 2.480 1.507 971 +V1467 CYG 2450357.1830 13.733 2.630 1.572 971 +V1726 CYG 2449934.4256 9.050 .556 1.037 998 +V1726 CYG 2449935.4184 8.901 .534 1.010 998 +V1726 CYG 2449936.4059 9.035 .541 1.054 998 +V1726 CYG 2449937.4181 9.115 .555 998 +V1726 CYG 2449938.4368 9.068 .568 1.085 998 +V1726 CYG 2449939.4316 8.942 .995 998 +V1726 CYG 2449941.4103 9.120 1.074 998 +V1726 CYG 2449942.3834 9.098 1.083 998 +V1726 CYG 2449943.3701 9.011 1.041 998 +V1726 CYG 2449944.4056 8.956 1.057 998 +V1726 CYG 2449945.4096 9.097 1.062 998 +V1726 CYG 2449946.3949 9.138 1.090 998 +V1726 CYG 2449947.3539 9.013 1.021 998 +V1726 CYG 2449948.3180 8.937 1.004 998 +V1726 CYG 2449949.3178 9.064 1.046 998 +V1726 CYG 2449950.3042 9.115 1.098 998 +V1726 CYG 2449952.3010 8.925 1.000 998 +V1726 CYG 2449953.3755 9.011 1.049 998 +V1726 CYG 2449954.3172 9.134 1.088 998 +V1726 CYG 2449955.2927 9.099 1.077 998 +V1726 CYG 2449617.2420 .839 .474 995 +V1726 CYG 2449619.3235 9.071 .949 .530 995 +V1726 CYG 2449620.3106 9.040 .658 .913 .551 995 +V1726 CYG 2449621.3215 8.947 .553 .844 .525 995 +V1726 CYG 2449623.2800 9.059 .685 .918 .537 995 +V1726 CYG 2449624.2920 9.067 .682 .916 .626 995 +V1726 CYG 2449625.2980 8.968 .681 .869 .515 995 +V1726 CYG 2449631.2776 8.955 .594 .892 .565 995 +V1726 CYG 2449632.2899 9.088 .700 .915 .547 995 +V1726 CYG 2449632.3808 9.071 .650 .920 .545 995 +V1726 CYG 2449633.2719 .700 .892 .539 995 +V1726 CYG 2449633.3452 9.043 .589 .901 .534 995 +V1726 CYG 2449633.4146 9.025 .879 .548 995 +V1726 CYG 2449634.2809 8.921 .544 .855 .504 995 +V1726 CYG 2449635.3205 8.947 .837 .522 995 +V1726 CYG 2448503.3527 8.905 .863 .510 993 +V1726 CYG 2448504.2814 9.002 .882 .539 993 +V1726 CYG 2448505.3092 9.083 .930 .550 993 +V1726 CYG 2448506.3173 9.038 .915 .548 993 +V1726 CYG 2448507.2995 8.925 .500 .854 .518 993 +V1726 CYG 2448508.2596 8.967 .480 .885 .535 993 +V1726 CYG 2448509.3144 9.086 .517 .928 .566 993 +V1726 CYG 2448510.2979 9.067 .538 .906 .555 993 +V1726 CYG 2448511.3048 8.940 .460 .878 .518 993 +V1726 CYG 2448512.2899 8.958 .465 .862 .535 993 +V1726 CYG 2448513.3020 9.057 .492 .931 .563 993 +V1726 CYG 2448514.3058 9.089 .510 .931 .551 993 +V1726 CYG 2448515.2397 8.991 .859 .527 993 +V1726 CYG 2448516.2308 8.931 .856 .528 993 +V1726 CYG 2448517.3170 9.021 .920 .545 993 +V1726 CYG 2448518.2328 9.109 .906 .569 993 +V1726 CYG 2448519.2807 9.006 .898 .528 993 +V1726 CYG 2448520.2675 8.901 .854 .514 993 +V1726 CYG 2448521.3101 9.001 .919 .535 993 +V1726 CYG 2448522.2714 9.087 .971 .556 993 +V1726 CYG 2448523.2673 9.044 .915 .540 993 +V1726 CYG 2445489.2851 9.063 .904 .536 982 +V1726 CYG 2445490.2734 8.934 .860 .509 982 +V1726 CYG 2445493.2381 9.056 .930 .541 982 +V1726 CYG 2445496.2734 9.011 .897 .549 982 +V1726 CYG 2445497.2264 9.132 .926 .549 982 +V1726 CYG 2445498.2343 9.031 .890 .542 982 +V1726 CYG 2445501.2851 .941 .542 982 +V1726 CYG 2445503.2929 8.930 .826 .526 982 +V1726 CYG 2445505.2812 9.067 .947 .549 982 +V1726 CYG 2445508.2695 8.952 .857 .522 982 +V1726 CYG 2445509.3006 9.095 .900 .554 982 +V1726 CYG 2445512.3085 8.916 .854 .526 982 +V1726 CYG 2445513.3280 9.056 .908 .553 982 +V1726 CYG 2445514.3006 9.097 .927 .545 982 +V1726 CYG 2445515.3046 8.992 .862 .540 982 +V1726 CYG 2445648.2655 8.962 .855 .521 982 +V1726 CYG 2445649.2968 9.083 .882 .552 982 +V1726 CYG 2445658.2147 .935 .531 982 +V1726 CYG 2445658.2421 .921 .556 982 +V1726 CYG 2445695.1171 8.992 .905 .529 982 +V1726 CYG 2445877.4022 9.029 .905 .545 982 +V1726 CYG 2445878.3984 9.082 .614 .946 .558 982 +V1726 CYG 2445881.3788 8.998 .614 .886 .533 982 +V1726 CYG 2445882.3710 9.091 .616 .945 .557 982 +V1726 CYG 2445883.3828 9.091 .595 .927 .549 982 +V1726 CYG 2445886.3867 9.073 .617 .938 .564 982 +V1726 CYG 2450305.3194 9.024 .922 .526 971 +V1726 CYG 2450306.3193 9.089 .967 .535 971 +V1726 CYG 2450307.3035 9.021 .915 .508 971 +V1726 CYG 2450310.2957 9.049 .960 .517 971 +V1726 CYG 2450311.2693 9.044 .917 .524 971 +V1726 CYG 2450312.3003 8.914 .864 .500 971 +V1726 CYG 2450313.2915 8.938 .912 .496 971 +V1726 CYG 2450314.2777 9.065 .959 .527 971 +V1726 CYG 2450315.2866 9.060 .936 .530 971 +V1726 CYG 2450316.2195 8.957 .887 .503 971 +V1726 CYG 2450317.2405 8.968 .874 .516 971 +V1726 CYG 2450318.2723 9.059 .949 .524 971 +V1726 CYG 2450319.2586 9.071 .948 .536 971 +V1726 CYG 2450320.2422 8.994 .901 .505 971 +V1726 CYG 2450321.2334 8.914 .874 .508 971 +V1726 CYG 2450322.2674 9.030 .910 .547 971 +V1726 CYG 2450323.2672 9.087 .969 .535 971 +V1726 CYG 2450324.2697 9.011 .911 .530 971 +V1726 CYG 2450325.2394 8.910 .858 .507 971 +V1726 CYG 2450326.1882 9.013 .914 .526 971 +V1726 CYG 2450327.2715 9.130 971 +V1726 CYG 2450328.3869 9.015 .932 .534 971 +V1726 CYG 2450330.2254 9.021 .913 .516 971 +V1726 CYG 2450332.2150 9.057 .939 .516 971 +V1726 CYG 2450333.2288 8.955 .860 971 +V1726 CYG 2450334.2342 8.930 .870 .482 971 +V1726 CYG 2450335.2377 9.056 .946 .539 971 +V1726 CYG 2450336.2350 9.070 .945 .525 971 +V1726 CYG 2450337.2117 8.978 .905 .502 971 +V1726 CYG 2450338.3458 8.916 .887 .492 971 +V1726 CYG 2450340.2117 9.088 .972 .557 971 +V1726 CYG 2450341.2113 9.006 .899 .510 971 +V1726 CYG 2450342.2247 8.885 .883 .491 971 +V1726 CYG 2450344.2444 9.097 .968 .512 971 +V1726 CYG 2450347.2288 8.971 .898 .514 971 +V1726 CYG 2450349.1809 9.081 .538 971 +TX DEL 2449624.2711 9.125 .666 .393 995 +TX DEL 2449625.2946 8.907 .573 .323 995 +TX DEL 2449632.2274 9.000 .636 .361 995 +TX DEL 2449633.2657 9.090 .766 .381 995 +TX DEL 2449634.2682 9.382 .929 .463 995 +BX DEL 2448102.3401 11.844 .337 .228 992 +BX DEL 2448103.3035 12.246 .539 .300 992 +BX DEL 2448104.3107 12.489 .650 .357 992 +BX DEL 2448108.3469 12.301 .682 992 +BX DEL 2448109.3060 12.144 .563 .333 992 +BX DEL 2448110.2955 12.068 .543 .317 992 +BX DEL 2448111.3065 11.908 .513 .370 992 +BX DEL 2448112.2216 11.811 .375 .214 992 +BX DEL 2448113.2133 11.982 .400 .257 992 +BX DEL 2448114.2327 12.251 .550 .320 992 +BX DEL 2448116.3485 12.499 .612 .384 992 +BX DEL 2448117.2603 12.531 .669 .387 992 +BX DEL 2448118.2515 12.426 .655 .391 992 +BX DEL 2448119.3067 12.399 .684 .373 992 +BX DEL 2448122.3238 12.115 .539 .337 992 +BX DEL 2448123.2190 11.923 .412 992 +W GEM 2450007.5226 7.086 1.134 998 +W GEM 2450008.5098 7.275 1.131 .602 1.162 998 +W GEM 2450009.4593 7.437 1.180 998 +W GEM 2450010.4500 7.229 1.095 998 +W GEM 2450011.4196 6.694 .870 998 +W GEM 2450017.4246 7.433 1.183 998 +W GEM 2450018.4379 7.156 1.041 998 +W GEM 2450019.4035 6.669 .858 998 +W GEM 2450020.3643 6.751 .911 998 +RZ GEM 2450007.5281 9.673 1.179 998 +RZ GEM 2450008.5151 9.730 1.200 998 +RZ GEM 2450009.4629 10.043 1.343 998 +RZ GEM 2450010.4532 10.240 1.440 998 +RZ GEM 2450011.4236 10.392 1.459 998 +RZ GEM 2450017.4300 10.506 1.467 998 +RZ GEM 2450018.4436 9.908 1.201 998 +RZ GEM 2450019.4076 9.687 1.163 998 +RZ GEM 2450020.3677 9.999 1.297 998 +AA GEM 2445666.4179 10.005 .852 1.218 .666 982 +AA GEM 2445676.4062 10.043 1.033 1.300 .692 982 +AA GEM 2445679.3905 9.796 .644 1.061 .616 982 +AA GEM 2445687.3242 9.976 1.306 .691 982 +AA GEM 2445690.3631 9.783 .677 1.082 .611 982 +AA GEM 2445691.3631 9.728 .664 1.035 .594 982 +AA GEM 2445692.4296 9.489 .568 .933 .538 982 +AA GEM 2445693.4335 9.422 .573 .931 .546 982 +AA GEM 2445694.3359 9.487 .662 .973 .586 982 +AA GEM 2445695.3046 9.587 .761 1.079 .610 982 +AA GEM 2445705.2617 9.450 .606 .956 .543 982 +AA GEM 2445706.2734 9.533 .705 1.055 .590 982 +AA GEM 2445707.2772 9.658 .813 1.148 .623 982 +AA GEM 2447408.4991 9.792 1.102 .611 990 +AA GEM 2447409.5031 9.757 .999 .593 990 +AA GEM 2447410.5035 9.527 .900 .568 990 +AA GEM 2447411.5082 .522 .893 .578 990 +AA GEM 2447413.5012 9.638 1.062 990 +AA GEM 2447414.5001 9.750 1.167 .648 990 +AA GEM 2447415.5019 9.879 1.240 .663 990 +AA GEM 2447417.4967 10.084 1.258 .696 990 +AA GEM 2447418.4944 9.953 .670 990 +AA GEM 2447419.4997 9.763 1.075 .609 990 +AA GEM 2447420.4985 9.761 1.070 .594 990 +AA GEM 2447421.4986 9.592 .958 .561 990 +AA GEM 2447422.5014 9.438 .872 .549 990 +AA GEM 2447424.4941 9.554 1.034 .598 990 +AA GEM 2447425.4942 9.727 1.122 .626 990 +AA GEM 2447427.4965 9.972 1.288 .674 990 +AA GEM 2447428.4865 10.094 1.287 .677 990 +AA GEM 2447430.5011 9.839 1.109 .629 990 +AA GEM 2447431.5017 9.767 1.052 .591 990 +AA GEM 2447432.4983 9.644 .989 .580 990 +AA GEM 2447433.4749 9.433 .931 .543 990 +AA GEM 2447434.4903 9.431 .927 .555 990 +AA GEM 2448504.4388 9.816 1.138 .621 993 +AA GEM 2448505.4501 9.756 1.057 .623 993 +AA GEM 2448506.4241 9.666 .970 .577 993 +AA GEM 2448507.4493 9.422 .918 .551 993 +AA GEM 2448508.4585 9.456 .965 .572 993 +AA GEM 2448510.4397 9.711 1.146 .637 993 +AA GEM 2448511.4143 9.816 1.206 .654 993 +AA GEM 2448512.3986 9.981 1.279 .697 993 +AA GEM 2448513.4037 10.051 1.294 .690 993 +AA GEM 2448514.4134 10.044 1.263 .672 993 +AA GEM 2448515.3988 9.900 1.117 .661 993 +AA GEM 2448516.4112 9.768 1.085 .594 993 +AA GEM 2448517.4077 9.702 .984 .590 993 +AA GEM 2448518.4068 9.438 .901 .533 993 +AA GEM 2448519.3978 9.416 .947 .566 993 +AA GEM 2448520.4087 9.508 1.040 .586 993 +AA GEM 2448521.4223 9.635 1.125 .622 993 +AA GEM 2448522.4107 9.748 1.189 .654 993 +AA GEM 2448523.3968 9.905 1.250 .671 993 +AA GEM 2450007.5262 9.851 1.228 998 +AA GEM 2450008.5134 9.812 1.143 998 +AA GEM 2450009.4614 9.763 1.116 998 +AA GEM 2450010.4519 9.472 1.042 998 +AA GEM 2450011.4218 9.450 1.065 998 +AA GEM 2450017.4283 10.116 1.325 998 +AA GEM 2450018.4410 9.916 1.203 998 +AA GEM 2450019.4062 9.846 1.179 998 +AA GEM 2450020.3665 9.799 1.099 998 +AA GEM 2450326.4512 9.574 .970 .572 971 +AA GEM 2450327.4818 9.406 .934 .545 971 +AA GEM 2450332.4566 9.949 1.276 971 +AA GEM 2450333.4852 10.051 1.262 971 +AA GEM 2450335.4609 9.830 1.113 .631 971 +AA GEM 2450337.4699 9.680 1.024 .610 971 +AA GEM 2450341.4639 9.645 1.146 .629 971 +AA GEM 2450344.4773 10.003 1.300 971 +AA GEM 2450347.4656 9.783 1.096 .655 971 +DX GEM 2449803.5241 10.614 .895 .576 .547 997 +DX GEM 2449804.5126 10.898 1.027 .640 .590 997 +DX GEM 2449805.5020 10.726 .927 .563 .575 997 +DX GEM 2449806.4970 10.574 .901 .543 .544 997 +DX GEM 2449807.4943 10.902 1.003 .620 997 +DX GEM 2449808.4960 10.810 .944 .624 .570 997 +DX GEM 2449809.4922 10.541 .878 .545 .536 997 +DX GEM 2449810.4928 10.859 .996 .621 .583 997 +DX GEM 2449811.4938 10.830 .970 .596 .562 997 +DX GEM 2449812.4885 10.551 .883 .531 .544 997 +DX GEM 2449813.4921 10.819 1.005 .611 .584 997 +DX GEM 2449814.4919 10.898 1.005 .598 .597 997 +DX GEM 2449815.4921 10.581 .864 .529 .560 997 +DX GEM 2449817.4856 10.880 1.014 .621 .574 997 +DX GEM 2449818.4956 10.603 .917 .536 .561 997 +DX GEM 2449819.4879 10.736 .951 .597 .577 997 +DX GEM 2449823.4863 10.919 1.021 .609 .584 997 +DX GEM 2449824.4775 10.696 .942 .565 .549 997 +DX GEM 2449825.4753 10.641 .918 .558 .557 997 +DX GEM 2449826.4815 10.924 1.027 .622 997 +DX GEM 2449827.4804 10.721 .937 .558 .568 997 +DX GEM 2450315.4665 10.782 1.091 .574 971 +DX GEM 2450316.4798 10.855 1.086 .619 971 +DX GEM 2450317.4816 10.578 .948 .600 971 +DX GEM 2450318.4776 10.777 1.016 .650 971 +DX GEM 2450326.4899 10.727 .963 .647 971 +DX GEM 2450335.4870 10.827 1.008 .674 971 +DX GEM 2450337.4871 10.849 1.081 .657 971 +DX GEM 2450341.4712 10.903 1.068 .648 971 +DX GEM 2450344.4719 10.900 1.091 .645 971 +BB HER 2446606.2841 9.789 .634 .944 .542 988 +BB HER 2446607.3510 9.942 1.049 .577 988 +BB HER 2446608.2906 9.997 1.084 .606 988 +BB HER 2446609.3305 10.204 .638 988 +BB HER 2446610.3108 10.338 .968 1.255 .670 988 +BB HER 2446611.3075 10.410 .958 1.228 .668 988 +BB HER 2446612.3073 10.146 .692 1.061 .607 988 +BB HER 2446613.2952 9.799 .626 .907 .531 988 +BB HER 2446614.2876 9.884 .720 .994 .571 988 +BB HER 2446615.2918 9.958 .772 1.046 .594 988 +BB HER 2446616.3022 10.125 .826 1.161 .628 988 +BB HER 2446617.2994 10.281 .951 1.223 .663 988 +BB HER 2446618.2988 10.420 1.017 1.254 .683 988 +BB HER 2446619.2982 10.322 .815 1.161 .643 988 +BB HER 2446620.2763 9.963 .670 .986 .559 988 +BB HER 2446621.2703 9.805 .651 .933 .531 988 +BB HER 2446622.2780 9.949 .774 1.045 .588 988 +BB HER 2446623.3179 9.993 1.090 .597 988 +BB HER 2446624.3198 10.204 1.175 .653 988 +BB HER 2446625.2584 10.345 1.232 .670 988 +BB HER 2446627.2532 10.141 1.084 .599 988 +BB HER 2446628.2577 9.792 .628 .908 .527 988 +BB HER 2446629.2608 9.880 1.002 .564 988 +BB HER 2446631.2329 10.112 1.135 .628 988 +BB HER 2446632.2605 10.256 1.204 .663 988 +BB HER 2446635.2389 9.977 .987 .567 988 +BB HER 2446636.2669 9.814 .929 .544 988 +BB HER 2449934.3174 10.036 .608 1.183 998 +BB HER 2449935.3260 10.196 1.193 .642 1.242 998 +BB HER 2449936.2915 10.377 .709 1.291 998 +BB HER 2449937.2744 10.443 1.255 .676 998 +BB HER 2449941.2894 9.941 1.152 998 +BB HER 2449942.2638 10.150 1.216 998 +BB HER 2449943.2535 10.265 1.264 998 +BB HER 2449944.2645 10.418 1.258 998 +BB HER 2449945.2603 10.372 1.244 998 +BB HER 2449946.2597 10.004 1.102 998 +BB HER 2449947.2481 9.827 1.044 998 +BB HER 2449948.2370 9.964 1.131 998 +BB HER 2449949.2397 10.038 1.171 998 +BB HER 2449950.2309 10.186 1.231 998 +BB HER 2449952.2417 10.445 1.287 998 +BB HER 2449953.2855 10.197 1.167 998 +BB HER 2449954.2408 9.823 1.019 998 +V LAC 2446608.3948 8.951 .622 .980 .573 988 +V LAC 2446609.4513 9.200 1.097 .613 988 +V LAC 2446611.4045 8.686 .517 .779 .473 988 +V LAC 2446612.4332 8.645 .543 .812 .490 988 +V LAC 2446613.3887 8.959 .615 .993 .567 988 +V LAC 2446614.3969 9.195 .725 1.088 .623 988 +V LAC 2446615.3845 9.352 .763 1.126 .629 988 +V LAC 2446616.4124 8.650 .520 .780 .460 988 +V LAC 2446617.3742 8.616 .556 .812 .478 988 +V LAC 2446618.4455 8.944 1.005 .570 988 +V LAC 2446619.4463 9.205 1.094 .618 988 +V LAC 2446620.3819 9.354 .748 1.129 .632 988 +V LAC 2446621.3895 8.647 .510 .769 .462 988 +V LAC 2446623.4205 8.955 1.002 .571 988 +V LAC 2446624.3857 9.208 1.092 .615 988 +V LAC 2446625.3584 9.364 1.101 .639 988 +V LAC 2446626.3722 8.641 .513 .785 .462 988 +V LAC 2446627.3694 8.629 .550 .808 .487 988 +V LAC 2446628.4560 8.979 1.009 .578 988 +V LAC 2446629.3408 9.188 .705 1.078 .620 988 +V LAC 2446631.3442 8.665 .513 .778 .467 988 +V LAC 2446632.3927 8.665 .547 .823 .484 988 +V LAC 2446636.3735 8.604 .518 .775 .448 988 +V LAC 2447399.4513 8.550 .426 .740 .461 990 +V LAC 2447400.3481 8.857 .487 .930 .542 990 +V LAC 2447401.3335 9.095 .619 1.059 .580 990 +V LAC 2447402.3277 9.300 .703 1.145 .620 990 +V LAC 2447403.3594 9.162 1.035 .571 990 +V LAC 2447404.3611 8.486 .737 .426 990 +V LAC 2447407.3216 9.326 .691 1.140 .626 990 +V LAC 2447408.3223 9.148 1.021 .561 990 +V LAC 2447409.3186 8.470 .418 .705 .447 990 +V LAC 2447410.3420 8.864 .499 .955 .545 990 +V LAC 2447411.3398 9.127 .629 1.046 .614 990 +V LAC 2447413.3047 9.182 1.002 .589 990 +V LAC 2447413.4199 9.109 .516 .887 .584 990 +V LAC 2447414.3042 8.469 .409 .750 .427 990 +V LAC 2447415.2825 8.844 .513 .936 .539 990 +V LAC 2447416.2784 9.113 .631 1.057 .593 990 +V LAC 2447417.2693 9.289 .689 1.110 .611 990 +V LAC 2447418.2669 9.185 1.026 .581 990 +V LAC 2447419.2462 8.460 .402 .724 .428 990 +V LAC 2447420.2407 8.830 .497 .941 .533 990 +V LAC 2447421.2335 9.118 1.067 .596 990 +V LAC 2447422.2465 9.283 .659 1.120 .631 990 +V LAC 2447423.3670 9.117 .548 .934 .571 990 +V LAC 2447424.2615 8.455 .354 .729 .452 990 +V LAC 2447425.2802 8.847 .502 .958 .540 990 +V LAC 2447427.3216 1.164 .619 990 +V LAC 2447428.2496 9.149 .569 1.015 .562 990 +V LAC 2447428.4058 9.012 .504 .920 .538 990 +V LAC 2447429.2508 8.489 .388 .720 .442 990 +V LAC 2447430.2364 8.829 .503 .951 .532 990 +V LAC 2447430.4366 8.930 .518 .996 .558 990 +V LAC 2447431.2853 9.131 .627 1.089 .601 990 +V LAC 2447432.2709 9.294 1.121 .624 990 +V LAC 2447433.2649 9.170 .538 .969 .581 990 +V LAC 2447434.2601 8.474 .397 .757 .431 990 +V LAC 2447738.4614 8.588 .751 .471 991 +V LAC 2447739.4366 8.882 .573 .928 .563 991 +V LAC 2447740.4511 9.169 1.078 .603 991 +V LAC 2447741.4279 9.302 1.127 .616 991 +V LAC 2447742.4183 8.875 .481 .884 .520 991 +V LAC 2447743.4020 8.566 .466 .750 .482 991 +V LAC 2447744.4347 8.936 .980 .569 991 +V LAC 2447745.4298 9.178 .694 1.070 .624 991 +V LAC 2447746.4339 9.343 1.158 .612 991 +V LAC 2447747.3847 8.886 .877 .520 991 +V LAC 2447748.4356 8.603 .462 .761 .470 991 +V LAC 2447749.4378 8.966 .564 .930 .541 991 +V LAC 2447750.4317 9.220 .642 1.078 .634 991 +V LAC 2447751.4420 9.321 1.107 .619 991 +V LAC 2447752.4313 8.773 .836 .496 991 +V LAC 2447753.4091 8.576 .451 .749 .479 991 +V LAC 2447754.4023 8.937 .538 .943 .563 991 +V LAC 2447755.4313 9.129 .653 1.074 .621 991 +V LAC 2447756.4258 9.291 .678 1.122 .616 991 +V LAC 2447757.3983 8.801 .462 .811 .530 991 +V LAC 2447758.3710 8.547 .412 .747 .466 991 +V LAC 2447758.4794 8.584 .743 .492 991 +V LAC 2447759.3582 8.897 .530 .982 .534 991 +V LAC 2447759.4772 8.909 1.019 .555 991 +V LAC 2447760.4172 9.157 .650 1.082 .629 991 +V LAC 2447761.4481 9.322 1.143 .621 991 +V LAC 2447762.4235 8.732 .812 .497 991 +V LAC 2447762.4920 8.637 .801 .464 991 +V LAC 2447763.2675 8.513 .753 .465 991 +V LAC 2447764.2731 8.877 .939 .547 991 +V LAC 2447765.4843 9.180 1.094 .602 991 +V LAC 2447766.2883 9.314 1.134 .628 991 +V LAC 2447767.3794 8.768 .440 .844 .482 991 +V LAC 2447768.3754 8.529 .435 .780 .494 991 +V LAC 2447770.3337 9.154 .633 1.083 .626 991 +V LAC 2447771.3215 9.322 .708 1.121 .644 991 +V LAC 2447772.3170 8.870 .419 .874 .519 991 +V LAC 2447772.4926 8.596 .737 991 +V LAC 2447773.3449 8.583 .427 .763 .478 991 +V LAC 2447773.4902 8.599 .793 991 +V LAC 2447774.3500 8.919 .558 .972 .563 991 +V LAC 2447775.3141 9.161 .647 1.069 .618 991 +V LAC 2447775.4844 9.179 1.074 .626 991 +V LAC 2447776.3286 9.352 .710 1.118 .662 991 +V LAC 2447776.4936 9.334 1.107 .624 991 +V LAC 2448103.4652 8.982 .987 992 +V LAC 2448109.4707 9.219 1.100 .619 992 +V LAC 2448110.3378 9.335 1.133 .615 992 +V LAC 2448111.3603 8.557 .740 .451 992 +V LAC 2448111.4740 8.447 .709 .423 992 +V LAC 2448112.3251 8.616 .799 .481 992 +V LAC 2448112.4516 8.666 .840 .510 992 +V LAC 2448113.3152 8.940 .987 .562 992 +V LAC 2448113.4691 9.012 1.044 .592 992 +V LAC 2448114.4817 9.246 1.118 .632 992 +V LAC 2448116.3803 8.517 .709 .432 992 +V LAC 2448117.4453 .841 .510 992 +V LAC 2448118.3757 8.960 1.008 .584 992 +V LAC 2448119.3749 9.212 1.122 .634 992 +V LAC 2448119.4911 9.245 1.152 .625 992 +V LAC 2448122.3516 8.647 .832 .504 992 +V LAC 2448123.3406 8.973 1.007 .580 992 +V LAC 2448126.3240 8.540 .740 .447 992 +V LAC 2448126.4856 8.433 .647 .435 992 +V LAC 2448127.2862 8.620 .827 .476 992 +V LAC 2448127.3875 8.661 .851 .490 992 +V LAC 2448503.3611 9.252 1.105 .621 993 +V LAC 2448504.2953 9.331 1.091 .614 993 +V LAC 2448505.3134 8.416 .690 .427 993 +V LAC 2448506.3233 8.746 .900 .520 993 +V LAC 2448507.3315 9.049 1.053 .583 993 +V LAC 2448508.2820 9.255 1.096 .634 993 +V LAC 2448509.3223 9.342 1.092 .623 993 +V LAC 2448510.3039 8.403 .677 .427 993 +V LAC 2448511.3102 8.749 .900 .511 993 +V LAC 2448512.2954 9.057 .583 1.039 .597 993 +V LAC 2448513.3087 9.269 .673 1.099 .648 993 +V LAC 2448514.3148 9.329 1.092 .623 993 +V LAC 2448515.2997 8.413 .692 .419 993 +V LAC 2448516.2354 8.755 .880 .521 993 +V LAC 2448517.3224 9.062 1.035 .598 993 +V LAC 2448518.2378 9.256 1.100 .626 993 +V LAC 2448519.2885 9.323 1.095 .605 993 +V LAC 2448520.2753 8.416 .673 .424 993 +V LAC 2448521.3232 8.781 .900 .534 993 +V LAC 2448522.2791 9.048 1.060 .589 993 +V LAC 2448523.2719 9.262 1.121 .621 993 +V LAC 2448854.3694 8.455 .738 .429 994 +V LAC 2448856.3527 9.104 1.055 .602 994 +V LAC 2448858.3288 9.215 1.032 .584 994 +V LAC 2448860.3285 8.818 .939 .530 994 +V LAC 2448862.3242 9.305 1.144 .631 994 +V LAC 2448870.3073 8.822 .940 .534 994 +V LAC 2448872.2873 9.304 1.135 .614 994 +V LAC 2448874.3143 8.478 .736 .438 994 +V LAC 2448875.3230 8.835 .501 .958 .535 994 +V LAC 2448876.3089 9.112 1.063 .610 994 +V LAC 2448877.2700 9.302 1.132 .623 994 +V LAC 2448878.3391 9.150 1.010 .576 994 +V LAC 2448880.2550 8.822 .944 .537 994 +V LAC 2448881.2451 9.093 1.075 .596 994 +V LAC 2448882.2515 9.293 .674 1.141 .636 994 +V LAC 2448883.2770 9.205 .542 1.036 .583 994 +V LAC 2448884.2508 8.475 .395 .745 .444 994 +V LAC 2448885.2476 8.843 .510 .944 .531 994 +V LAC 2448886.2638 9.103 .608 1.075 .599 994 +V LAC 2448887.2907 9.294 .699 1.143 .630 994 +V LAC 2448888.2436 9.199 .548 1.054 .581 994 +V LAC 2448889.2493 8.462 .419 .741 .431 994 +V LAC 2448890.2273 8.821 .514 .931 .536 994 +V LAC 2448890.3988 8.887 .965 .548 994 +V LAC 2448891.2243 9.098 .604 1.087 .599 994 +V LAC 2448891.3405 9.147 .658 1.065 .605 994 +V LAC 2448892.2422 9.289 .676 1.138 .631 994 +V LAC 2448892.3390 9.355 1.158 .638 994 +V LAC 2448893.2370 9.202 .541 1.037 .580 994 +V LAC 2448894.2786 8.464 .391 .731 .442 994 +V LAC 2449617.2663 8.610 .782 .495 995 +V LAC 2449618.4151 1.006 .585 995 +V LAC 2449620.3258 1.093 .639 995 +V LAC 2449620.4255 9.355 .892 1.099 .629 995 +V LAC 2449621.3350 8.437 .500 .658 .445 995 +V LAC 2449621.4246 8.437 .667 .412 995 +V LAC 2449622.4027 8.703 .859 .505 995 +V LAC 2449622.4583 8.716 .877 .513 995 +V LAC 2449623.3158 9.024 .698 .977 .606 995 +V LAC 2449623.4240 9.018 .997 .586 995 +V LAC 2449624.3079 9.266 1.085 995 +V LAC 2449624.3951 9.248 .847 1.066 .618 995 +V LAC 2449624.4707 9.264 1.113 .596 995 +V LAC 2449625.3476 9.355 1.094 .627 995 +V LAC 2449625.3923 9.356 .885 1.081 .621 995 +V LAC 2449625.4748 9.352 1.090 .596 995 +V LAC 2449631.3328 8.408 .672 .415 995 +V LAC 2449632.3353 8.704 .839 .502 995 +V LAC 2449632.4060 8.722 .648 .868 .514 995 +V LAC 2449632.4662 8.743 .859 .529 995 +V LAC 2449633.2950 9.027 .727 .993 .587 995 +V LAC 2449633.3674 9.032 .684 .981 .580 995 +V LAC 2449633.4244 9.048 1.015 .594 995 +V LAC 2449634.2915 9.248 1.081 .616 995 +V LAC 2449635.3321 9.350 1.092 .613 995 +V LAC 2449635.4332 9.351 1.103 .611 995 +V LAC 2449934.4658 9.373 .653 1.206 998 +V LAC 2449935.4348 8.410 .434 .839 998 +V LAC 2449936.4261 8.791 .553 1.034 998 +V LAC 2449937.4296 9.086 .610 998 +V LAC 2449938.4543 9.295 .649 1.232 998 +V LAC 2449939.4481 9.386 1.216 998 +V LAC 2449941.4339 8.830 1.015 998 +V LAC 2449942.4139 9.072 1.156 998 +V LAC 2449943.4087 9.312 .644 1.258 998 +V LAC 2449944.4261 9.353 1.228 998 +V LAC 2449946.4304 8.819 1.055 998 +V LAC 2449947.3819 9.080 1.161 998 +V LAC 2449948.3474 9.283 1.210 998 +V LAC 2449949.3427 9.381 1.185 998 +V LAC 2449950.3894 8.471 .716 .447 .862 998 +V LAC 2449952.3447 9.082 1.143 998 +V LAC 2449953.4050 9.292 1.228 998 +V LAC 2449954.3582 9.370 1.197 998 +V LAC 2449955.3195 8.442 .842 998 +V LAC 2450305.3507 8.817 .933 .520 971 +V LAC 2450306.3327 9.100 1.099 .614 971 +V LAC 2450307.3304 9.290 1.176 .636 971 +V LAC 2450311.2849 9.072 1.050 .584 971 +V LAC 2450312.3422 9.306 1.120 .662 971 +V LAC 2450313.3211 9.246 1.071 .608 971 +V LAC 2450314.2956 8.455 .771 .470 971 +V LAC 2450315.2930 8.801 .955 .532 971 +V LAC 2450316.2864 9.089 1.086 .600 971 +V LAC 2450317.2453 9.301 1.126 .631 971 +V LAC 2450318.2774 9.273 1.077 .604 971 +V LAC 2450319.2875 8.424 .787 .422 971 +V LAC 2450320.3290 8.837 .965 .602 971 +V LAC 2450321.2778 9.089 1.084 .612 971 +V LAC 2450322.3098 9.304 1.142 .678 971 +V LAC 2450323.3113 9.242 1.088 .608 971 +V LAC 2450324.2763 8.453 .788 .445 971 +V LAC 2450325.2552 8.805 .954 .530 971 +V LAC 2450326.2079 9.102 1.059 .599 971 +V LAC 2450327.2745 9.318 1.117 971 +V LAC 2450330.2404 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2446618.4442 8.552 1.013 .585 988 +X LAC 2446619.4452 8.277 .865 .523 988 +X LAC 2446620.3804 8.220 .565 .858 .509 988 +X LAC 2446621.3881 8.347 .616 .947 .542 988 +X LAC 2446622.3765 8.514 .657 1.019 .577 988 +X LAC 2446623.4190 8.590 .704 1.040 .587 988 +X LAC 2446624.3846 8.513 .958 .565 988 +X LAC 2446625.3571 8.219 .836 .503 988 +X LAC 2446626.3705 8.262 .602 .904 .523 988 +X LAC 2446627.3677 8.433 .639 .987 .571 988 +X LAC 2446628.3938 8.584 .682 1.032 .599 988 +X LAC 2446629.3393 8.585 .658 1.012 .587 988 +X LAC 2446631.3425 8.220 .571 .861 .509 988 +X LAC 2446632.3912 8.378 .650 .956 .551 988 +X LAC 2446636.3717 8.217 .555 .863 .506 988 +X LAC 2447399.4486 8.299 .515 .892 .535 990 +X LAC 2447400.3496 8.417 .547 .979 .564 990 +X LAC 2447401.3341 8.547 .612 1.036 .579 990 +X LAC 2447402.3310 8.593 .616 1.040 .584 990 +X LAC 2447403.3610 8.298 .904 .507 990 +X LAC 2447404.3622 8.206 .848 .495 990 +X LAC 2447407.3220 8.616 .588 1.072 .590 990 +X LAC 2447408.3228 8.498 .562 .978 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2447775.4852 8.290 .900 .533 991 +X LAC 2447776.3309 8.450 .586 1.006 .576 991 +X LAC 2447776.4959 8.474 .992 .571 991 +X LAC 2448103.4664 8.509 1.006 .598 992 +X LAC 2448109.4718 8.585 1.023 .596 992 +X LAC 2448110.3388 8.582 1.011 .579 992 +X LAC 2448111.3611 8.304 .851 .537 992 +X LAC 2448111.4749 8.266 .851 .526 992 +X LAC 2448112.3257 8.178 .844 .502 992 +X LAC 2448112.4524 8.206 .853 .525 992 +X LAC 2448113.3158 8.308 .918 .536 992 +X LAC 2448113.4702 8.353 .972 .538 992 +X LAC 2448114.4826 8.541 1.051 .599 992 +X LAC 2448116.3809 8.489 .947 .564 992 +X LAC 2448117.4458 8.220 .830 .502 992 +X LAC 2448118.3763 8.265 .893 .517 992 +X LAC 2448119.3751 8.442 1.029 .578 992 +X LAC 2448119.4923 8.479 1.040 .595 992 +X LAC 2448122.3519 8.241 .878 .525 992 +X LAC 2448123.3413 8.212 .857 .507 992 +X LAC 2448126.3248 8.587 1.070 .581 992 +X LAC 2448126.4865 8.629 1.006 .612 992 +X LAC 2448127.2867 8.462 .950 .557 992 +X LAC 2448127.3882 8.416 .937 .553 992 +X LAC 2448503.3620 8.304 .892 .516 993 +X LAC 2448504.2956 8.194 .836 .507 993 +X LAC 2448505.3141 8.328 .935 .539 993 +X LAC 2448506.3239 8.492 1.016 .586 993 +X LAC 2448507.3324 8.588 1.044 .583 993 +X LAC 2448508.2828 8.518 .980 .560 993 +X LAC 2448509.3233 8.220 .845 .511 993 +X LAC 2448510.3042 8.259 .884 .536 993 +X LAC 2448511.3112 8.419 .977 .566 993 +X LAC 2448512.2970 8.565 .582 1.040 .598 993 +X LAC 2448513.3102 8.576 .566 1.025 .593 993 +X LAC 2448514.3156 8.311 .865 .523 993 +X LAC 2448515.3005 8.217 .857 .503 993 +X LAC 2448516.2357 8.342 .938 .543 993 +X LAC 2448517.3226 8.508 1.014 .594 993 +X LAC 2448518.2383 8.591 1.036 .594 993 +X LAC 2448519.2891 8.468 .969 .553 993 +X LAC 2448520.2761 8.205 .830 .514 993 +X LAC 2448521.3242 8.284 .907 .520 993 +X LAC 2448522.2793 8.437 .998 .565 993 +X LAC 2448523.2728 8.566 1.028 .584 993 +X LAC 2448854.3699 8.420 1.013 .563 994 +X LAC 2448856.3566 8.571 1.008 .586 994 +X LAC 2448858.3306 8.232 .844 .527 994 +X LAC 2448860.3289 8.515 1.022 .584 994 +X LAC 2448862.3246 8.465 .943 .561 994 +X LAC 2448870.3080 8.348 .976 .540 994 +X LAC 2448872.2885 8.591 1.041 .585 994 +X LAC 2448874.3150 8.203 .855 .502 994 +X LAC 2448875.3234 8.302 .485 .934 .550 994 +X LAC 2448876.3098 8.452 1.021 .575 994 +X LAC 2448877.2708 8.563 1.066 .582 994 +X LAC 2448878.3398 8.528 1.010 .566 994 +X LAC 2448880.2559 8.235 .879 .511 994 +X LAC 2448881.2453 8.371 .958 .563 994 +X LAC 2448882.2518 8.527 .583 1.052 .586 994 +X LAC 2448883.2773 8.584 .599 1.042 .586 994 +X LAC 2448884.2518 8.395 .503 .946 .542 994 +X LAC 2448885.2479 8.211 .458 .880 .496 994 +X LAC 2448886.2641 8.305 .509 .935 .539 994 +X LAC 2448887.2929 8.470 .596 1.002 .572 994 +X LAC 2448888.2439 8.578 .583 1.052 .588 994 +X LAC 2448889.2495 8.545 .578 1.010 .578 994 +X LAC 2448890.2277 8.214 .472 .858 .503 994 +X LAC 2448890.3997 8.210 .860 .515 994 +X LAC 2448891.2265 8.223 .483 .903 .525 994 +X LAC 2448891.3408 8.260 .492 .900 .519 994 +X LAC 2448892.2447 8.379 .512 .975 .555 994 +X LAC 2448892.3397 1.004 .573 994 +X LAC 2448893.2374 8.533 .603 1.040 .586 994 +X LAC 2448894.2788 8.560 .559 1.043 .587 994 +X LAC 2449617.2701 .976 .562 995 +X LAC 2449618.4163 .993 .599 995 +X LAC 2449620.3282 8.221 .557 .832 .506 995 +X LAC 2449620.4264 8.173 .578 .866 .508 995 +X LAC 2449621.3369 8.312 .578 .902 .543 995 +X LAC 2449621.4254 8.362 .919 .531 995 +X LAC 2449622.4035 8.496 1.012 .584 995 +X LAC 2449622.4590 8.496 1.012 .586 995 +X LAC 2449623.3167 8.603 .779 1.004 .607 995 +X LAC 2449623.4247 8.584 1.015 .588 995 +X LAC 2449624.3961 8.497 .663 .931 .566 995 +X LAC 2449624.4714 8.491 .960 .558 995 +X LAC 2449625.3496 8.196 .829 .493 995 +X LAC 2449625.3933 8.197 .577 .807 .507 995 +X LAC 2449625.4755 8.199 .831 .481 995 +X LAC 2449631.3340 8.208 .829 .500 995 +X LAC 2449632.3360 8.348 .918 .545 995 +X LAC 2449632.4071 8.371 .622 .950 .555 995 +X LAC 2449632.4670 8.363 .954 .550 995 +X LAC 2449633.2959 8.525 .732 .978 .592 995 +X LAC 2449633.3684 8.528 .698 .999 .574 995 +X LAC 2449633.4253 8.530 1.003 .579 995 +X LAC 2449634.2923 8.617 1.015 .601 995 +X LAC 2449635.4346 8.460 .933 .561 995 +X LAC 2449934.4664 8.598 .625 1.148 998 +X LAC 2449935.4354 8.222 .533 1.016 998 +X LAC 2449936.4266 8.262 .519 1.009 998 +X LAC 2449937.4303 8.422 .562 998 +X LAC 2449938.4548 8.558 .605 1.149 998 +X LAC 2449939.4485 8.643 1.138 998 +X LAC 2449941.4346 8.244 .981 998 +X LAC 2449942.4145 8.330 1.065 998 +X LAC 2449943.4093 8.537 .596 1.163 998 +X LAC 2449944.4267 8.614 1.188 998 +X LAC 2449946.4298 8.254 1.001 998 +X LAC 2449947.3825 8.274 1.028 998 +X LAC 2449948.3479 8.417 1.094 998 +X LAC 2449949.3433 8.572 1.138 998 +X LAC 2449950.3935 8.637 1.044 .607 1.171 998 +X LAC 2449952.3450 8.233 .978 998 +X LAC 2449953.4054 8.349 1.047 998 +X LAC 2449954.3586 8.518 1.110 998 +X LAC 2449955.3202 8.597 1.142 998 +X LAC 2450305.3513 8.322 .894 .512 971 +X LAC 2450306.3333 8.230 .919 .529 971 +X LAC 2450307.3310 8.337 1.008 .561 971 +X LAC 2450311.2863 8.209 .860 .495 971 +X LAC 2450312.3430 8.300 .918 .567 971 +X LAC 2450313.3216 8.421 1.012 .576 971 +X LAC 2450314.2963 8.564 1.053 .624 971 +X LAC 2450315.2934 8.586 1.054 .580 971 +X LAC 2450316.2873 8.308 .919 .536 971 +X LAC 2450317.2458 8.228 .888 .510 971 +X LAC 2450318.2782 8.352 .971 .545 971 +X LAC 2450319.2880 8.489 1.068 .573 971 +X LAC 2450320.3294 8.615 1.072 .654 971 +X LAC 2450321.2783 8.480 .993 .565 971 +X LAC 2450322.3104 8.217 .892 .560 971 +X LAC 2450323.3119 8.283 .957 .543 971 +X LAC 2450324.2768 8.442 1.042 .584 971 +X LAC 2450325.2557 8.562 1.065 .587 971 +X LAC 2450326.2082 8.610 1.016 .591 971 +Y LAC 2446608.3807 9.259 .489 .839 .480 988 +Y LAC 2446611.3869 8.858 .397 .644 .389 988 +Y LAC 2446612.4222 9.183 .819 .465 988 +Y LAC 2446613.3757 9.401 .527 .871 .501 988 +Y LAC 2446614.3864 9.419 .489 .859 .492 988 +Y LAC 2446615.3724 8.786 .386 .584 .362 988 +Y LAC 2446616.3857 9.084 .445 .779 .442 988 +Y LAC 2446617.3594 9.312 .529 .869 .488 988 +Y LAC 2446618.3723 9.467 .544 .870 .508 988 +Y LAC 2446619.4485 8.808 .584 .367 988 +Y LAC 2446620.3706 8.983 .422 .705 .417 988 +Y LAC 2446621.3776 9.254 .495 .844 .473 988 +Y LAC 2446622.3662 9.428 .526 .882 .499 988 +Y LAC 2446623.4066 9.154 .425 .725 .424 988 +Y LAC 2446624.3740 8.882 .648 .388 988 +Y LAC 2446625.3458 9.189 .477 .784 .469 988 +Y LAC 2446626.3578 9.370 .525 .884 .497 988 +Y LAC 2446627.3538 9.427 .505 .844 .492 988 +Y LAC 2446628.3670 8.783 .400 .593 .354 988 +Y LAC 2446629.3289 9.078 .434 .750 .450 988 +Y LAC 2446631.3280 9.471 .531 .874 .500 988 +Y LAC 2446632.3465 8.868 .379 .607 .364 988 +Y LAC 2446636.3602 9.201 .428 .746 .444 988 +Y LAC 2449617.2591 .450 995 +Y LAC 2449620.3206 .407 .565 .381 995 +Y LAC 2449621.3301 9.108 .734 .469 995 +Y LAC 2449622.3960 9.328 .550 .829 .509 995 +Y LAC 2449623.3109 9.471 .587 .869 .526 995 +Y LAC 2449623.4201 9.470 .857 .504 995 +Y LAC 2449624.3015 8.985 .620 995 +Y LAC 2449624.3764 8.860 .434 .587 .365 995 +Y LAC 2449625.3047 8.928 .450 .656 .413 995 +Y LAC 2449625.3868 8.966 .454 .682 .421 995 +Y LAC 2449625.4719 9.015 .729 .419 995 +Y LAC 2449631.3237 9.372 .844 .493 995 +Y LAC 2449632.3320 9.432 .814 .488 995 +Y LAC 2449632.4012 9.421 .832 .492 995 +Y LAC 2449633.2909 8.767 .390 .566 .360 995 +Y LAC 2449633.3530 8.765 .390 .567 .357 995 +Y LAC 2449633.4202 8.770 .577 .360 995 +Y LAC 2449634.2872 .730 .435 995 +Y LAC 2449635.3275 9.302 .840 .483 995 +Y LAC 2449635.4099 9.323 .837 .485 995 +Y LAC 2449934.4546 9.481 .862 .537 .998 998 +Y LAC 2449935.4268 9.140 .425 .844 998 +Y LAC 2449936.4173 8.883 .376 .765 998 +Y LAC 2449937.4232 9.216 .485 998 +Y LAC 2449938.4440 9.429 .530 1.016 998 +Y LAC 2449939.4378 9.444 .495 .947 998 +Y LAC 2449941.4215 9.158 .834 998 +Y LAC 2449942.3949 9.378 .981 998 +Y LAC 2449943.3866 9.541 .995 998 +Y LAC 2449944.4193 8.826 .746 998 +Y LAC 2449946.4026 9.315 .957 998 +Y LAC 2449947.3746 9.473 .979 998 +Y LAC 2449948.3410 9.247 .879 998 +Y LAC 2449949.3366 8.900 .750 998 +Y LAC 2449950.3606 9.215 .926 998 +Y LAC 2449952.3210 9.438 .948 998 +Y LAC 2449953.3984 8.819 .745 998 +Y LAC 2449954.3488 9.106 .891 998 +Y LAC 2449955.3097 9.361 .971 998 +Z LAC 2445648.3006 8.225 .739 1.086 .623 982 +Z LAC 2445649.3125 8.373 .875 1.165 .657 982 +Z LAC 2445660.2500 8.357 .893 1.210 .657 982 +Z LAC 2445664.2421 8.809 1.351 .715 982 +Z LAC 2445665.2734 8.604 .921 1.184 .669 982 +Z LAC 2445666.1953 8.453 .704 1.107 .635 982 +Z LAC 2445668.2500 7.905 .584 .858 .522 982 +Z LAC 2445674.2109 8.842 1.085 1.400 .727 982 +Z LAC 2445676.2226 8.583 .785 1.196 .675 982 +Z LAC 2445679.1756 7.886 .594 .857 .524 982 +Z LAC 2445683.1913 8.555 .982 1.299 .702 982 +Z LAC 2445688.1484 8.433 .731 1.090 .633 982 +Z LAC 2445689.1756 8.296 .731 1.044 .592 982 +Z LAC 2445691.1445 8.161 .724 1.050 .596 982 +Z LAC 2445695.1367 8.731 1.126 1.368 .728 982 +Z LAC 2445706.1014 8.727 1.141 1.378 .714 982 +Z LAC 2445707.0976 8.847 1.148 1.385 .724 982 +Z LAC 2447399.4412 7.969 .555 .907 .543 990 +Z LAC 2447400.3413 8.186 .634 1.045 .606 990 +Z LAC 2447401.3269 8.269 .729 1.157 .624 990 +Z LAC 2447402.3235 8.415 .899 1.257 .652 990 +Z LAC 2447403.3570 8.608 1.333 .704 990 +Z LAC 2447404.3588 8.731 .713 990 +Z LAC 2447407.3174 8.548 .709 1.192 .646 990 +Z LAC 2447408.3200 8.379 1.092 .603 990 +Z LAC 2447409.3162 8.193 .575 .992 .575 990 +Z LAC 2447410.3396 7.945 .542 .931 .535 990 +Z LAC 2447411.3354 8.174 .689 1.069 .604 990 +Z LAC 2447413.3031 8.438 1.249 .687 990 +Z LAC 2447413.4155 8.500 .918 .699 990 +Z LAC 2447414.3016 8.594 1.007 1.349 .694 990 +Z LAC 2447415.2799 8.771 1.112 1.380 .722 990 +Z LAC 2447416.2761 8.854 1.109 1.369 .724 990 +Z LAC 2447417.2675 8.757 1.277 .687 990 +Z LAC 2447418.2657 8.530 1.158 .649 990 +Z LAC 2447419.2443 8.379 .664 1.078 .603 990 +Z LAC 2447420.2388 8.173 .572 .981 .560 990 +Z LAC 2447421.2319 7.959 .538 .931 .538 990 +Z LAC 2447422.2442 8.161 .603 1.099 .603 990 +Z LAC 2447423.3639 8.348 .814 1.166 .659 990 +Z LAC 2447424.2594 8.419 .837 1.263 .687 990 +Z LAC 2447425.2774 8.613 1.007 1.347 .704 990 +Z LAC 2447427.3188 8.883 1.074 1.404 .709 990 +Z LAC 2447428.2468 8.731 .927 1.269 .676 990 +Z LAC 2447428.4025 8.733 .882 1.254 .685 990 +Z LAC 2447429.2487 8.531 .676 1.155 .639 990 +Z LAC 2447430.2337 8.372 .649 1.082 .606 990 +Z LAC 2447430.4339 8.382 .655 1.086 .608 990 +Z LAC 2447431.2824 8.094 .547 .966 .545 990 +Z LAC 2447432.2681 7.995 .641 .956 .555 990 +Z LAC 2447433.2624 8.217 .679 1.076 .624 990 +Z LAC 2447434.2583 8.300 .768 1.195 .640 990 +Z LAC 2447738.4596 8.252 1.096 .620 991 +Z LAC 2447739.4341 8.331 .863 1.183 .655 991 +Z LAC 2447740.4496 8.512 1.289 .684 991 +Z LAC 2447741.4250 8.658 1.184 1.361 .716 991 +Z LAC 2447742.4156 8.801 1.177 1.398 .723 991 +Z LAC 2447743.3995 8.831 1.045 1.318 .723 991 +Z LAC 2447744.4329 8.646 1.190 .686 991 +Z LAC 2447745.4260 8.452 .720 1.094 .632 991 +Z LAC 2447746.4327 8.323 1.091 .573 991 +Z LAC 2447747.3835 7.877 .860 .519 991 +Z LAC 2447748.4324 8.165 .649 1.020 .602 991 +Z LAC 2447749.4355 8.266 .740 1.076 .623 991 +Z LAC 2447750.4295 8.433 .867 1.208 .680 991 +Z LAC 2447751.4314 8.529 1.001 1.285 .703 991 +Z LAC 2447752.4295 8.694 1.346 .726 991 +Z LAC 2447753.4062 8.816 1.107 1.384 .722 991 +Z LAC 2447754.3981 8.794 1.001 1.321 .712 991 +Z LAC 2447755.4272 8.550 .758 1.184 .684 991 +Z LAC 2447756.4222 8.368 .649 1.113 .604 991 +Z LAC 2447757.3948 8.277 .653 1.014 .621 991 +Z LAC 2447758.3684 7.875 .491 .840 .511 991 +Z LAC 2447758.4819 7.849 .863 .528 991 +Z LAC 2447759.3547 8.125 .623 1.058 .582 991 +Z LAC 2447759.4751 8.134 1.077 .601 991 +Z LAC 2447760.4147 8.240 .740 1.120 .657 991 +Z LAC 2447761.4470 8.375 1.244 .661 991 +Z LAC 2447762.4225 8.526 1.301 .712 991 +Z LAC 2447762.4897 8.558 1.331 .714 991 +Z LAC 2447763.2663 8.681 1.364 .720 991 +Z LAC 2447764.2719 8.803 1.375 .721 991 +Z LAC 2447765.4818 8.768 1.299 .695 991 +Z LAC 2447766.2871 8.578 1.204 .667 991 +Z LAC 2447767.3760 8.386 .696 1.068 .610 991 +Z LAC 2447768.3720 8.238 .607 1.039 .613 991 +Z LAC 2447770.3316 8.142 .633 1.050 .603 991 +Z LAC 2447771.3188 8.247 .732 1.131 .645 991 +Z LAC 2447772.3142 8.406 .841 1.224 .676 991 +Z LAC 2447772.4901 8.435 1.220 991 +Z LAC 2447773.3419 8.554 .965 1.309 .703 991 +Z LAC 2447773.4883 8.551 1.314 991 +Z LAC 2447774.3474 8.728 1.064 1.365 .734 991 +Z LAC 2447775.3111 8.846 1.116 1.355 .736 991 +Z LAC 2447775.4823 8.824 1.347 .742 991 +Z LAC 2447776.3258 8.803 .957 1.305 .735 991 +Z LAC 2447776.4895 8.754 1.274 .707 991 +Z LAC 2448109.4689 8.352 1.197 .662 992 +Z LAC 2448110.3360 8.486 1.279 .690 992 +Z LAC 2448111.3586 8.649 1.364 .718 992 +Z LAC 2448111.4719 8.688 1.388 .720 992 +Z LAC 2448112.3233 8.790 1.387 .733 992 +Z LAC 2448112.4497 8.814 1.390 .748 992 +Z LAC 2448113.3135 8.835 1.338 .731 992 +Z LAC 2448114.4796 8.646 1.245 .681 992 +Z LAC 2448116.3781 8.368 1.059 .608 992 +Z LAC 2448118.3740 8.119 1.016 .598 992 +Z LAC 2448119.3736 8.218 1.138 .631 992 +Z LAC 2448119.4886 8.260 1.137 .651 992 +Z LAC 2448122.3484 8.663 1.367 .728 992 +Z LAC 2448123.3389 8.813 1.401 .741 992 +Z LAC 2448126.3223 8.469 1.148 .631 992 +Z LAC 2448126.4837 8.457 1.078 .636 992 +Z LAC 2448127.2841 8.363 1.048 .613 992 +Z LAC 2448127.3861 8.328 1.063 .604 992 +Z LAC 2448503.3591 8.679 1.351 .721 993 +Z LAC 2448504.2940 8.808 1.361 .731 993 +Z LAC 2448505.3117 8.842 1.342 .729 993 +Z LAC 2448506.3218 8.646 .822 1.215 .681 993 +Z LAC 2448507.3302 8.463 1.132 .631 993 +Z LAC 2448508.2807 8.350 1.069 .601 993 +Z LAC 2448509.3210 7.919 .876 .535 993 +Z LAC 2448510.3017 8.122 .624 1.024 .593 993 +Z LAC 2448511.3084 8.220 1.123 .616 993 +Z LAC 2448512.2930 8.373 .812 1.219 .664 993 +Z LAC 2448513.3065 8.521 .952 1.308 .697 993 +Z LAC 2448514.3124 8.699 1.356 .725 993 +Z LAC 2448515.2983 8.834 1.387 .742 993 +Z LAC 2448516.2340 8.842 1.338 .722 993 +Z LAC 2448517.3210 8.617 1.186 .680 993 +Z LAC 2448518.2360 8.468 1.115 .635 993 +Z LAC 2448519.2872 8.333 1.056 .602 993 +Z LAC 2448520.2739 7.896 .865 .521 993 +Z LAC 2448521.3219 8.149 1.055 .600 993 +Z LAC 2448522.2777 8.247 1.137 .639 993 +Z LAC 2448523.2703 8.379 1.236 .661 993 +Z LAC 2448854.3649 8.723 1.282 .682 994 +Z LAC 2448856.3485 8.360 1.088 .598 994 +Z LAC 2448858.3244 8.002 .993 .558 994 +Z LAC 2448860.3255 8.316 1.192 .656 994 +Z LAC 2448862.3210 8.637 1.365 .713 994 +Z LAC 2448870.3053 8.186 1.128 .601 994 +Z LAC 2448872.2858 8.493 1.287 .687 994 +Z LAC 2448874.3126 8.803 1.402 .737 994 +Z LAC 2448875.3191 8.835 1.022 1.373 .725 994 +Z LAC 2448876.3065 8.697 .831 1.242 .703 994 +Z LAC 2448877.2684 8.491 1.165 .628 994 +Z LAC 2448878.3373 8.353 1.066 .615 994 +Z LAC 2448880.2534 8.081 1.015 .575 994 +Z LAC 2448881.2430 8.200 1.115 .621 994 +Z LAC 2448882.2460 8.331 .790 1.215 .657 994 +Z LAC 2448883.2714 8.505 .940 1.288 .695 994 +Z LAC 2448884.2486 8.669 1.021 1.377 .723 994 +Z LAC 2448885.2428 8.820 1.110 1.401 .726 994 +Z LAC 2448886.2592 8.829 1.005 1.376 .716 994 +Z LAC 2448887.2880 8.656 .827 1.228 .689 994 +Z LAC 2448888.2405 8.481 .679 1.138 .649 994 +Z LAC 2448889.2466 8.344 .662 1.087 .607 994 +Z LAC 2448890.2246 7.937 .522 .903 .521 994 +Z LAC 2448890.3973 7.898 .898 .516 994 +Z LAC 2448891.2218 8.101 .591 1.036 .590 994 +Z LAC 2448891.3376 8.141 .638 1.055 .592 994 +Z LAC 2448892.2400 8.206 .692 1.120 .616 994 +Z LAC 2448892.3379 1.155 .639 994 +Z LAC 2448893.2342 8.348 .789 1.226 .654 994 +Z LAC 2448894.2762 8.495 .905 1.305 .692 994 +Z LAC 2449617.2647 8.509 995 +Z LAC 2449618.4094 8.239 1.052 .616 995 +Z LAC 2449620.3232 8.049 .674 .930 .567 995 +Z LAC 2449620.4230 8.017 .669 .985 .565 995 +Z LAC 2449621.3323 8.191 .703 1.059 .622 995 +Z LAC 2449621.4225 8.232 1.080 .599 995 +Z LAC 2449622.3990 8.322 1.190 .646 995 +Z LAC 2449622.4568 8.336 1.190 .667 995 +Z LAC 2449623.3131 8.485 1.122 1.231 .682 995 +Z LAC 2449623.4215 8.483 1.263 .689 995 +Z LAC 2449624.3047 8.685 1.320 995 +Z LAC 2449624.3911 8.666 1.256 1.301 .706 995 +Z LAC 2449624.4691 8.683 1.343 .696 995 +Z LAC 2449625.3435 8.809 1.365 .724 995 +Z LAC 2449625.3881 8.809 1.275 1.340 .742 995 +Z LAC 2449625.4733 8.844 1.377 .720 995 +Z LAC 2449631.3291 8.057 .962 .567 995 +Z LAC 2449632.3329 8.203 1.063 .600 995 +Z LAC 2449632.4023 8.232 .776 1.084 .628 995 +Z LAC 2449632.4626 8.227 .802 1.103 .621 995 +Z LAC 2449633.2921 8.336 .838 1.168 .651 995 +Z LAC 2449633.3638 8.349 .843 1.164 .655 995 +Z LAC 2449633.4213 8.357 1.164 .663 995 +Z LAC 2449634.2885 8.496 1.249 .692 995 +Z LAC 2449635.3287 8.666 1.323 .710 995 +Z LAC 2449635.4294 8.673 1.337 .691 995 +Z LAC 2449934.4628 8.383 .651 1.191 998 +Z LAC 2449935.4333 7.897 .539 1.036 998 +Z LAC 2449936.4247 8.180 .615 1.167 998 +Z LAC 2449937.4283 8.269 .644 998 +Z LAC 2449938.4524 8.413 .694 1.338 998 +Z LAC 2449939.4462 8.596 1.365 998 +Z LAC 2449941.4312 8.900 1.399 998 +Z LAC 2449942.4115 8.855 1.408 998 +Z LAC 2449943.4068 8.680 .682 1.334 998 +Z LAC 2449944.4247 8.471 1.260 998 +Z LAC 2449946.4277 7.935 1.047 998 +Z LAC 2449947.3803 8.177 1.162 998 +Z LAC 2449948.3461 8.272 1.234 998 +Z LAC 2449949.3415 8.429 1.282 998 +Z LAC 2449950.3775 8.592 1.365 998 +Z LAC 2449952.3428 8.873 1.415 998 +Z LAC 2449953.4039 8.848 1.396 998 +Z LAC 2449954.3569 8.622 1.274 998 +Z LAC 2449955.3183 8.469 1.221 998 +Z LAC 2450305.3494 7.962 .920 .521 971 +Z LAC 2450306.3316 8.096 1.049 .601 971 +Z LAC 2450307.3280 8.245 1.147 .644 971 +Z LAC 2450311.2791 8.802 1.353 .723 971 +Z LAC 2450312.3392 8.855 1.326 .764 971 +Z LAC 2450313.3195 8.664 1.235 .698 971 +Z LAC 2450314.2902 8.494 1.131 .684 971 +Z LAC 2450315.2898 8.371 1.090 .610 971 +Z LAC 2450316.2833 7.950 .927 .539 971 +Z LAC 2450317.2428 8.123 1.026 .585 971 +Z LAC 2450318.2757 8.225 1.129 .620 971 +Z LAC 2450319.2863 8.331 1.241 .651 971 +Z LAC 2450320.3276 8.527 1.312 .756 971 +Z LAC 2450321.2766 8.677 1.370 .732 971 +Z LAC 2450322.3081 8.831 1.397 .784 971 +Z LAC 2450323.3097 8.851 1.382 .731 971 +Z LAC 2450324.2743 8.653 1.254 .690 971 +Z LAC 2450325.2536 8.477 1.139 .634 971 +Z LAC 2450326.2054 8.384 1.074 .608 971 +RR LAC 2446608.3906 8.670 .574 .873 .513 988 +RR LAC 2446609.4468 8.873 .987 .564 988 +RR LAC 2446612.4301 9.110 .647 1.014 .587 988 +RR LAC 2446613.3858 8.509 .499 .736 .455 988 +RR LAC 2446614.3939 8.571 .551 .817 .493 988 +RR LAC 2446615.3813 8.805 .610 .937 .554 988 +RR LAC 2446616.4081 8.969 .684 1.033 .581 988 +RR LAC 2446617.3702 9.150 .766 1.109 .613 988 +RR LAC 2446618.4472 9.177 1.096 .606 988 +RR LAC 2446619.4475 8.736 .846 .506 988 +RR LAC 2446620.3787 8.494 .526 .754 .455 988 +RR LAC 2446621.3865 8.706 .584 .890 .524 988 +RR LAC 2446622.3745 8.891 .617 .995 .565 988 +RR LAC 2446623.4172 9.085 .755 1.088 .605 988 +RR LAC 2446624.3835 9.230 1.114 .623 988 +RR LAC 2446625.3559 9.076 .977 .585 988 +RR LAC 2446626.3687 8.445 .508 .725 .435 988 +RR LAC 2446627.3656 8.602 .545 .836 .495 988 +RR LAC 2446628.3922 8.837 .624 .957 .562 988 +RR LAC 2446629.3365 8.979 .667 1.028 .595 988 +RR LAC 2446631.3396 9.205 .716 1.068 .608 988 +RR LAC 2446632.3882 8.670 .516 .807 .480 988 +RR LAC 2446636.3695 9.135 .756 1.100 .612 988 +RR LAC 2447399.4437 9.037 .664 1.045 .596 990 +RR LAC 2447400.3439 9.211 .667 1.115 .596 990 +RR LAC 2447402.3235 8.659 .441 .819 .477 990 +RR LAC 2447403.3582 8.539 .785 .476 990 +RR LAC 2447404.3600 8.743 .925 .523 990 +RR LAC 2447407.3194 9.231 .695 1.138 .611 990 +RR LAC 2447408.3211 8.953 .950 .535 990 +RR LAC 2447409.3180 8.426 .418 .715 .445 990 +RR LAC 2447410.3401 8.635 .463 .875 .497 990 +RR LAC 2447411.3359 8.842 .575 .960 .560 990 +RR LAC 2447413.3038 9.196 1.107 .625 990 +RR LAC 2447413.4174 9.243 .730 1.073 .641 990 +RR LAC 2447414.3030 9.144 .618 1.068 .583 990 +RR LAC 2447415.2807 8.575 .421 .767 .462 990 +RR LAC 2447416.2780 8.528 .444 .795 .459 990 +RR LAC 2447417.2688 8.739 .480 .937 .523 990 +RR LAC 2447418.2659 8.915 1.011 .583 990 +RR LAC 2447419.2448 9.121 1.107 .601 990 +RR LAC 2447420.2402 9.226 1.123 .611 990 +RR LAC 2447421.2325 8.919 .930 .534 990 +RR LAC 2447422.2461 8.430 .390 .754 .447 990 +RR LAC 2447423.3659 8.726 .514 .866 .535 990 +RR LAC 2447424.2596 8.823 .506 .991 .564 990 +RR LAC 2447425.2776 9.035 .638 1.098 .583 990 +RR LAC 2447427.3192 9.140 .591 1.032 .571 990 +RR LAC 2447428.2473 8.469 .431 .736 .436 990 +RR LAC 2447428.4039 8.446 .391 .736 .433 990 +RR LAC 2447429.2492 8.565 .437 .814 .475 990 +RR LAC 2447430.2344 8.764 .524 .961 .536 990 +RR LAC 2447430.4348 8.852 .528 .980 .559 990 +RR LAC 2447431.2842 8.962 .618 1.041 .584 990 +RR LAC 2447432.2693 9.146 .753 1.103 .618 990 +RR LAC 2447433.2644 9.231 .676 1.082 .621 990 +RR LAC 2447434.2587 8.751 .453 .890 .495 990 +RR LAC 2447738.4599 8.853 .947 .564 991 +RR LAC 2447739.4357 8.949 .675 1.006 .592 991 +RR LAC 2447740.4503 9.189 1.098 .623 991 +RR LAC 2447741.4272 9.153 1.045 .583 991 +RR LAC 2447742.4176 8.643 .457 .803 .465 991 +RR LAC 2447743.4015 8.536 .445 .747 .491 991 +RR LAC 2447744.4343 8.756 .920 .539 991 +RR LAC 2447745.4267 8.906 .593 1.000 .596 991 +RR LAC 2447746.4330 9.141 1.122 .605 991 +RR LAC 2447747.3838 9.199 1.118 .614 991 +RR LAC 2447748.4351 8.946 .563 .907 .543 991 +RR LAC 2447749.4362 8.447 .435 .681 .440 991 +RR LAC 2447750.4312 8.741 .451 .810 .524 991 +RR LAC 2447751.4410 8.824 .966 .552 991 +RR LAC 2447752.4303 9.019 1.035 .614 991 +RR LAC 2447753.4085 9.191 .733 1.098 .613 991 +RR LAC 2447754.4002 9.144 .609 1.029 .596 991 +RR LAC 2447755.4290 8.486 .418 .734 .473 991 +RR LAC 2447756.4236 8.499 .451 .785 .476 991 +RR LAC 2447757.3977 8.728 .528 .922 .555 991 +RR LAC 2447758.3689 8.893 .583 .987 .581 991 +RR LAC 2447758.4775 8.907 .984 .575 991 +RR LAC 2447759.3563 9.115 .682 1.108 .598 991 +RR LAC 2447759.4761 9.119 1.112 .615 991 +RR LAC 2447760.4159 9.211 .665 1.079 .638 991 +RR LAC 2447761.4472 8.776 .878 .507 991 +RR LAC 2447762.4227 8.428 .745 .464 991 +RR LAC 2447762.4908 8.456 .778 .457 991 +RR LAC 2447763.2670 8.632 .855 .514 991 +RR LAC 2447764.2726 8.830 .964 .563 991 +RR LAC 2447765.4829 9.073 1.067 .596 991 +RR LAC 2447766.2873 9.181 1.122 .618 991 +RR LAC 2447767.3787 9.066 .573 .998 .581 991 +RR LAC 2447768.3747 8.427 .396 .709 .457 991 +RR LAC 2447770.3333 8.782 .529 .926 .565 991 +RR LAC 2447771.3203 8.936 .608 1.007 .597 991 +RR LAC 2447772.3164 9.158 .675 1.105 .628 991 +RR LAC 2447772.4916 9.193 1.081 991 +RR LAC 2447773.3442 9.209 .676 1.077 .615 991 +RR LAC 2447773.4894 9.160 1.042 991 +RR LAC 2447774.3492 8.764 .440 .828 .505 991 +RR LAC 2447775.3131 .411 991 +RR LAC 2447775.4834 8.489 .764 .475 991 +RR LAC 2447776.3277 8.705 .482 .893 .544 991 +RR LAC 2447776.4918 8.720 .882 .539 991 +RR LAC 2448103.4637 8.673 .877 992 +RR LAC 2448109.4698 8.584 .817 .496 992 +RR LAC 2448110.3371 8.769 .943 .537 992 +RR LAC 2448111.3596 8.936 1.023 .584 992 +RR LAC 2448111.4729 8.962 1.057 .578 992 +RR LAC 2448112.3244 9.141 1.084 .619 992 +RR LAC 2448112.4508 9.170 1.086 .634 992 +RR LAC 2448113.3145 9.212 1.098 .618 992 +RR LAC 2448114.4806 8.729 .842 .504 992 +RR LAC 2448116.3796 8.704 .880 .536 992 +RR LAC 2448117.4447 8.915 .984 .565 992 +RR LAC 2448118.3750 9.075 1.082 .603 992 +RR LAC 2448119.3740 9.239 1.125 .645 992 +RR LAC 2448119.4898 9.225 1.157 .634 992 +RR LAC 2448122.3497 8.589 .833 .523 992 +RR LAC 2448123.3396 8.833 .947 .564 992 +RR LAC 2448126.3230 9.196 1.112 .612 992 +RR LAC 2448126.4848 9.186 1.053 .589 992 +RR LAC 2448127.2852 8.734 .831 .503 992 +RR LAC 2448127.3869 8.644 .799 .485 992 +RR LAC 2448503.3607 9.080 1.077 .610 993 +RR LAC 2448504.2943 9.214 1.102 .631 993 +RR LAC 2448505.3129 9.080 .983 .578 993 +RR LAC 2448506.3222 8.450 .724 .452 993 +RR LAC 2448507.3311 8.598 .834 .495 993 +RR LAC 2448508.2815 8.802 .950 .553 993 +RR LAC 2448509.3218 8.983 1.049 .589 993 +RR LAC 2448510.3024 9.174 .729 1.101 .633 993 +RR LAC 2448511.3096 9.204 1.071 .610 993 +RR LAC 2448512.2949 8.712 .408 .831 .500 993 +RR LAC 2448513.3081 8.503 .420 .766 .475 993 +RR LAC 2448514.3141 8.734 .906 .531 993 +RR LAC 2448515.2992 8.898 1.000 .575 993 +RR LAC 2448516.2345 9.119 1.075 .619 993 +RR LAC 2448517.3213 9.228 1.127 .629 993 +RR LAC 2448518.2367 9.036 .965 .561 993 +RR LAC 2448519.2875 8.440 .711 .446 993 +RR LAC 2448520.2749 8.617 .847 .492 993 +RR LAC 2448521.3228 8.839 .969 .556 993 +RR LAC 2448522.2780 9.020 1.050 .596 993 +RR LAC 2448523.2713 9.204 1.104 .629 993 +RR LAC 2448854.3685 8.706 .921 .528 994 +RR LAC 2448856.3503 9.103 1.093 .608 994 +RR LAC 2448858.3253 9.020 .952 .566 994 +RR LAC 2448860.3263 8.612 .854 .508 994 +RR LAC 2448862.3232 9.014 1.065 .594 994 +RR LAC 2448870.3065 9.208 1.115 .617 994 +RR LAC 2448872.2868 8.453 .744 .447 994 +RR LAC 2448874.3139 8.857 .994 .564 994 +RR LAC 2448875.3206 9.043 .622 1.080 .602 994 +RR LAC 2448876.3082 9.205 1.125 .625 994 +RR LAC 2448877.2694 9.142 1.063 .581 994 +RR LAC 2448878.3386 8.486 .752 .457 994 +RR LAC 2448880.2547 8.770 .944 .538 994 +RR LAC 2448881.2441 8.908 1.038 .573 994 +RR LAC 2448882.2482 9.140 .692 1.118 .607 994 +RR LAC 2448883.2748 9.233 .694 1.114 .621 994 +RR LAC 2448884.2489 8.856 .452 .922 .525 994 +RR LAC 2448885.2455 8.468 .427 .742 .444 994 +RR LAC 2448886.2617 8.673 .472 .884 .518 994 +RR LAC 2448887.2903 8.870 .553 .991 .577 994 +RR LAC 2448888.2411 9.055 .647 1.095 .599 994 +RR LAC 2448889.2470 9.222 .713 1.134 .630 994 +RR LAC 2448890.2249 9.089 .595 1.032 .586 994 +RR LAC 2448890.3982 9.030 .957 .572 994 +RR LAC 2448891.2235 8.467 .401 .750 .448 994 +RR LAC 2448891.3380 8.457 .409 .728 .435 994 +RR LAC 2448892.2418 8.565 .422 .828 .493 994 +RR LAC 2448892.3386 .869 .505 994 +RR LAC 2448893.2346 8.799 .517 .971 .560 994 +RR LAC 2448894.2766 8.949 .568 1.049 .596 994 +RR LAC 2449618.4129 .929 .564 995 +RR LAC 2449620.3245 .783 1.085 .639 995 +RR LAC 2449620.4242 9.174 1.103 .629 995 +RR LAC 2449621.3337 9.209 .672 1.055 995 +RR LAC 2449621.4237 9.221 1.070 .596 995 +RR LAC 2449622.4001 8.622 .777 .476 995 +RR LAC 2449622.4577 8.598 .768 .486 995 +RR LAC 2449623.3139 8.481 .568 .770 .476 995 +RR LAC 2449623.4223 8.498 .777 .460 995 +RR LAC 2449624.3064 8.753 .878 995 +RR LAC 2449624.3928 8.731 .607 .889 .531 995 +RR LAC 2449624.4699 8.781 .910 .547 995 +RR LAC 2449625.3449 8.902 .982 .558 995 +RR LAC 2449625.3903 8.893 .733 .989 .567 995 +RR LAC 2449625.4740 8.931 1.011 .561 995 +RR LAC 2449631.3302 8.839 .956 .550 995 +RR LAC 2449632.3337 9.008 1.014 .586 995 +RR LAC 2449632.4037 9.041 .800 1.035 .608 995 +RR LAC 2449632.4639 9.049 1.028 .603 995 +RR LAC 2449633.2932 9.195 .743 1.116 .609 995 +RR LAC 2449633.3651 9.197 .743 1.080 .622 995 +RR LAC 2449633.4224 9.206 1.086 .623 995 +RR LAC 2449634.2896 9.193 1.043 .604 995 +RR LAC 2449635.3299 8.565 .756 .466 995 +RR LAC 2449635.4307 8.502 .735 .456 995 +RR LAC 2449934.4649 9.152 .620 1.155 998 +RR LAC 2449935.4341 .658 1.202 998 +RR LAC 2449936.4254 8.951 .557 1.049 998 +RR LAC 2449937.4291 8.490 .484 998 +RR LAC 2449938.4537 8.670 .529 1.032 998 +RR LAC 2449939.4476 8.903 1.075 998 +RR LAC 2449941.4329 9.276 1.187 998 +RR LAC 2449942.4131 9.165 1.170 998 +RR LAC 2449943.4081 8.591 .473 .928 998 +RR LAC 2449944.4255 8.583 .992 998 +RR LAC 2449946.4288 8.990 1.163 998 +RR LAC 2449947.3813 9.174 1.193 998 +RR LAC 2449948.3469 9.277 1.217 998 +RR LAC 2449949.3422 8.926 1.054 998 +RR LAC 2449950.3845 8.502 .907 998 +RR LAC 2449952.3440 8.894 1.099 998 +RR LAC 2449953.4045 9.095 1.181 998 +RR LAC 2449954.3575 9.256 1.203 998 +RR LAC 2449955.3190 9.132 1.150 998 +RR LAC 2450307.3370 9.209 1.159 .649 971 +RR LAC 2450311.2830 8.806 .957 .546 971 +RR LAC 2450312.3406 8.990 1.045 .622 971 +RR LAC 2450313.3205 9.163 1.129 .624 971 +RR LAC 2450314.2938 9.211 1.090 .646 971 +RR LAC 2450315.2922 8.703 .867 .505 971 +RR LAC 2450316.2853 8.501 .833 .469 971 +RR LAC 2450317.2446 8.736 .926 .531 971 +RR LAC 2450318.2769 8.897 1.024 .571 971 +RR LAC 2450319.2869 9.076 1.142 .597 971 +RR LAC 2450320.3282 9.241 1.140 .685 971 +RR LAC 2450321.2772 8.998 1.001 .562 971 +RR LAC 2450322.3092 8.448 .787 .493 971 +RR LAC 2450323.3108 8.633 .921 .513 971 +RR LAC 2450324.2754 8.846 1.019 .562 971 +RR LAC 2450325.2545 8.998 1.066 .589 971 +RR LAC 2450326.2063 9.209 1.119 .619 971 +BG LAC 2446608.3700 8.597 .539 .823 .489 988 +BG LAC 2446609.4256 8.814 .684 .982 .557 988 +BG LAC 2446611.3831 9.106 .770 1.103 .607 988 +BG LAC 2446612.4031 9.119 .700 1.054 .592 988 +BG LAC 2446613.3700 8.610 .522 .811 .487 988 +BG LAC 2446614.3811 8.726 .607 .926 .538 988 +BG LAC 2446615.3681 8.932 .705 1.038 .579 988 +BG LAC 2446616.3812 9.082 .779 1.095 .605 988 +BG LAC 2446617.3543 9.175 .760 1.110 .610 988 +BG LAC 2446618.3678 8.754 .548 .873 .515 988 +BG LAC 2446620.3663 8.868 .663 1.013 .566 988 +BG LAC 2446621.3731 9.018 .752 1.082 .590 988 +BG LAC 2446622.3620 9.162 .754 1.109 .610 988 +BG LAC 2446623.3891 8.930 .599 .959 .554 988 +BG LAC 2446624.3704 8.612 .833 .493 988 +BG LAC 2446625.3414 8.804 .631 .944 .560 988 +BG LAC 2446626.3534 8.957 1.062 .588 988 +BG LAC 2446627.3488 9.119 .776 1.095 .605 988 +BG LAC 2446628.3629 9.135 .712 1.062 .590 988 +BG LAC 2446629.3244 8.618 .530 .821 .491 988 +BG LAC 2446631.3223 8.919 .690 1.030 .576 988 +BG LAC 2446632.3401 9.078 .753 1.095 .603 988 +BG LAC 2446636.3550 8.873 .675 1.016 .567 988 +BG LAC 2449617.2253 8.881 1.030 .557 995 +BG LAC 2449620.3178 8.705 .547 .840 .528 995 +BG LAC 2449621.3270 8.717 .632 .868 .516 995 +BG LAC 2449623.2864 9.050 1.050 .599 995 +BG LAC 2449624.2969 9.161 1.088 .694 995 +BG LAC 2449625.3009 8.922 .922 .551 995 +BG LAC 2449631.3204 8.583 .811 .477 995 +BG LAC 2449632.2930 8.742 .904 .531 995 +BG LAC 2449632.3845 8.741 .929 .524 995 +BG LAC 2449633.2755 8.955 1.037 .570 995 +BG LAC 2449633.3497 8.959 .723 1.004 .584 995 +BG LAC 2449633.4173 8.952 1.038 .586 995 +BG LAC 2449634.2842 9.093 1.064 .596 995 +BG LAC 2449635.3239 9.174 1.055 .594 995 +BG LAC 2449934.4536 9.006 .963 .614 1.093 998 +BG LAC 2449935.4266 8.572 .480 .965 998 +BG LAC 2449936.4172 8.816 .569 1.068 998 +BG LAC 2449938.4432 9.126 .626 1.212 998 +BG LAC 2449939.4366 9.182 .602 1.157 998 +BG LAC 2449941.4200 8.761 1.004 998 +BG LAC 2449942.3937 8.929 1.133 998 +BG LAC 2449943.3861 9.123 1.201 998 +BG LAC 2449944.4191 9.184 1.221 998 +BG LAC 2449946.4015 8.687 1.005 998 +BG LAC 2449948.3403 9.018 1.139 998 +BG LAC 2449950.3536 9.095 1.142 998 +BG LAC 2449952.3205 8.780 .931 .554 1.086 998 +BG LAC 2449953.3970 8.987 1.165 998 +BG LAC 2449955.3077 9.199 1.175 998 +BG LAC 2450007.3375 9.029 1.158 998 +BG LAC 2450009.3234 8.837 1.021 998 +BG LAC 2450010.2929 8.609 .938 998 +BG LAC 2450011.3064 8.824 1.081 998 +BG LAC 2450017.2852 8.898 1.127 998 +BG LAC 2450018.3037 9.084 1.160 998 +BG LAC 2450019.2778 9.137 1.141 998 +BG LAC 2450020.2409 8.679 .981 998 +DF LAC 2448101.4232 1.304 .779 992 +DF LAC 2448102.3705 11.732 1.080 .669 992 +DF LAC 2448103.3426 11.665 1.076 .649 992 +DF LAC 2448104.3625 11.907 1.235 .722 992 +DF LAC 2448108.3751 11.793 1.210 .720 992 +DF LAC 2448109.3445 12.053 1.326 .755 992 +DF LAC 2448110.3415 12.211 1.292 .774 992 +DF LAC 2448111.3552 11.731 1.098 .652 992 +DF LAC 2448112.3204 11.682 1.088 .664 992 +DF LAC 2448113.3099 11.926 1.254 .717 992 +DF LAC 2448114.3883 12.132 1.328 .780 992 +DF LAC 2448116.3747 11.591 1.002 .637 992 +DF LAC 2448117.4365 1.208 .724 992 +DF LAC 2448118.3714 12.075 1.297 .752 992 +DF LAC 2448119.3706 12.213 1.332 .769 992 +DF LAC 2448122.3459 11.955 1.268 .735 992 +DF LAC 2448123.3263 12.146 1.309 .765 992 +DF LAC 2448126.3186 11.883 1.225 .724 992 +DF LAC 2448126.4122 11.867 1.224 .742 992 +DF LAC 2448127.2819 12.054 1.291 .746 992 +DF LAC 2448127.3835 12.059 1.314 .753 992 +FQ LAC 2445666.2109 13.944 1.164 .654 950 +FQ LAC 2445668.2421 13.463 1.053 .695 950 +FQ LAC 2445674.2187 14.212 1.406 .804 950 +FQ LAC 2445676.2304 14.194 1.219 .744 950 +FQ LAC 2445679.1835 13.444 1.030 .646 950 +FQ LAC 2445683.1992 13.691 .779 950 +FQ LAC 2445689.1914 13.658 1.070 .679 950 +FQ LAC 2445690.1367 13.363 1.018 .600 950 +FQ LAC 2445691.1562 13.356 1.101 .658 950 +FQ LAC 2445692.1210 13.462 1.150 .722 950 +FQ LAC 2445693.1210 13.520 1.251 .736 950 +FQ LAC 2445694.1406 13.589 1.310 .753 950 +FQ LAC 2445695.1445 13.731 1.352 .780 950 +FQ LAC 2445701.1445 13.437 .998 .736 950 +FQ LAC 2445705.1015 13.576 1.330 .735 950 +FQ LAC 2445706.1054 13.684 1.383 .770 950 +FQ LAC 2445707.1015 13.948 1.426 .789 950 +IT LAC 2446274.3528 15.156 .895 .627 987 +IT LAC 2446284.3485 14.807 .905 .445 987 +IT LAC 2446285.2949 15.357 .891 .660 987 +IT LAC 2446286.2857 15.425 .910 .620 987 +IT LAC 2446287.2716 15.051 .882 .569 987 +IT LAC 2446288.3437 15.436 .992 .660 987 +IT LAC 2446289.3411 14.735 .768 .481 987 +IT LAC 2446290.3402 15.168 .965 .588 987 +IT LAC 2446291.2915 15.367 1.074 .657 987 +IT LAC 2446292.3105 14.922 .852 .629 987 +IT LAC 2446294.2666 15.240 .973 .541 987 +IT LAC 2446295.2798 15.195 .837 .579 987 +IT LAC 2446296.2658 15.427 .868 .574 987 +IT LAC 2446297.2618 14.793 .684 .499 987 +IT LAC 2446298.2738 15.169 .968 .607 987 +IT LAC 2446299.2603 15.376 1.049 .538 987 +IT LAC 2446300.2614 15.068 .730 .580 987 +IT LAC 2446301.3053 15.329 1.089 .655 987 +IT LAC 2446302.3166 15.050 .773 987 +IT LAC 2446303.2754 15.100 .832 .573 987 +GH LUP 2450541.6556 7.541 1.267 972 +GH LUP 2450542.6610 7.551 1.258 972 +GH LUP 2450568.5225 7.580 1.249 972 +GH LUP 2450570.4372 7.541 1.240 972 +GH LUP 2450572.3946 7.658 1.309 972 +GH LUP 2450572.5188 7.648 1.294 972 +GH LUP 2450573.3808 7.686 1.307 972 +GH LUP 2450573.4683 7.682 1.303 972 +GH LUP 2450573.5621 7.685 1.298 972 +GH LUP 2450574.4985 7.717 1.311 972 +GH LUP 2450575.3649 7.679 1.289 972 +GH LUP 2450575.4629 7.693 1.300 972 +GH LUP 2450575.5505 7.675 1.288 972 +GH LUP 2450575.6144 7.679 1.296 972 +GH LUP 2450576.4554 7.632 1.276 972 +GH LUP 2450576.5118 7.626 1.274 972 +GH LUP 2450576.5772 7.635 1.279 972 +GH LUP 2450577.5207 7.589 1.260 972 +GH LUP 2450577.5828 7.581 1.251 972 +GH LUP 2450578.3974 7.559 1.244 972 +GH LUP 2450578.4682 7.551 1.252 972 +GH LUP 2450578.5651 7.550 1.247 972 +GH LUP 2450578.6173 7.549 1.249 972 +GH LUP 2450579.5196 7.557 1.261 972 +GH LUP 2450580.4353 7.576 1.271 972 +GH LUP 2450580.5608 7.576 1.272 972 +GH LUP 2450580.6099 7.584 1.282 972 +GH LUP 2450582.4616 7.674 1.303 972 +GH LUP 2450582.5444 7.680 1.297 972 +GH LUP 2450583.4634 7.718 1.307 972 +GH LUP 2450583.5858 7.705 1.301 972 +GH LUP 2450584.4232 7.695 1.294 972 +GH LUP 2450584.4920 7.686 1.298 972 +V473 LYR 2449619.2499 -.834 .662 .244 912 +V473 LYR 2449620.2326 -.750 .766 .696 .249 912 +V473 LYR 2449621.1950 -.625 .825 .794 .286 912 +V473 LYR 2449621.2284 -.622 .869 .771 .270 912 +V473 LYR 2449621.2957 -.581 .867 .760 .293 912 +V473 LYR 2449622.1124 -.919 .788 .630 .245 912 +V473 LYR 2449623.0957 -.633 .842 .721 .268 912 +V473 LYR 2449623.1813 -.708 .789 .709 .250 912 +V473 LYR 2449623.2006 -.730 .776 .686 .253 912 +V473 LYR 2449623.2104 -.735 .782 .678 .249 912 +V473 LYR 2449623.2494 -.791 .777 .669 .237 912 +V473 LYR 2449623.2899 -.832 .787 .661 .245 912 +V473 LYR 2449624.0972 -.654 .857 .755 .279 912 +V473 LYR 2449624.1408 -.615 .833 .753 .287 912 +V473 LYR 2449624.1635 -.618 .847 .763 .270 912 +V473 LYR 2449624.1994 -.605 .848 .763 .277 912 +V473 LYR 2449624.2186 -.608 .871 .763 .276 912 +V473 LYR 2449624.2469 -.585 .858 .757 .277 912 +V473 LYR 2449624.2745 -.583 .845 .758 .287 912 +V473 LYR 2449624.2877 -.590 .852 .764 .274 912 +V473 LYR 2449624.3259 -.593 .869 .760 .275 912 +V473 LYR 2449625.0985 -.909 .790 .617 .228 912 +V473 LYR 2449625.1573 -.879 .820 .637 .230 912 +V473 LYR 2449625.1784 -.856 .799 .661 .239 912 +V473 LYR 2449625.1968 -.857 .797 .651 .236 912 +V473 LYR 2449625.2386 -.815 .834 .656 .252 912 +V473 LYR 2449625.2608 -.813 .787 .680 .244 912 +V473 LYR 2449625.2842 -.780 .814 .675 .250 912 +V473 LYR 2449625.3057 -.800 .811 .684 .257 912 +V473 LYR 2449625.3219 -.783 .812 .701 .250 912 +V473 LYR 2449626.2075 -.757 .758 .686 .250 912 +V473 LYR 2449626.2260 -.771 .641 .247 912 +V473 LYR 2449626.2394 -.801 .755 .658 .247 912 +V473 LYR 2449626.2612 -.808 .761 .661 .241 912 +V473 LYR 2449631.0968 -.893 .799 .643 .229 912 +V473 LYR 2449631.1650 -.839 .807 .656 .235 912 +V473 LYR 2449631.1809 -.822 .815 .669 .242 912 +V473 LYR 2449631.2086 -.822 .821 .671 .243 912 +V473 LYR 2449631.2515 -.790 .823 .677 .250 912 +V473 LYR 2449631.2900 -.775 .821 .692 .254 912 +V473 LYR 2449632.1085 -.678 .784 .699 .257 912 +V473 LYR 2449632.1550 -.731 .793 .689 .252 912 +V473 LYR 2449632.1803 -.766 .763 .665 .242 912 +V473 LYR 2449632.1968 -.792 .764 .665 .237 912 +V473 LYR 2449632.2295 -.830 .751 .645 .240 912 +V473 LYR 2449632.2611 -.866 .774 .632 .218 912 +V473 LYR 2449632.3033 -.913 .786 .636 .195 912 +V473 LYR 2449633.0987 -.632 .841 .767 .278 912 +V473 LYR 2449633.1553 -.605 .836 .782 .275 912 +V473 LYR 2449633.1807 -.604 .818 .787 .282 912 +V473 LYR 2449633.2117 -.600 .849 .770 .271 912 +V473 LYR 2449633.2333 -.589 .831 .781 .282 912 +V473 LYR 2449633.2799 -.578 .866 .772 .287 912 +V473 LYR 2449633.3015 -.604 .849 .782 .273 912 +V473 LYR 2449634.1015 -.882 .798 .666 .234 912 +V473 LYR 2449634.1330 -.875 .816 .644 .235 912 +V473 LYR 2449634.1801 -.824 .814 .672 .237 912 +V473 LYR 2449634.2060 -.824 .778 .700 .235 912 +V473 LYR 2449634.2470 -.793 .813 .703 .249 912 +V473 LYR 2449634.2698 -.780 .848 .704 .260 912 +V473 LYR 2449634.2933 -.770 .851 .691 .245 912 +V473 LYR 2449635.3079 -.930 .598 .226 912 +V473 LYR 2449811.9005 6.214 .654 .358 .357 919 +V473 LYR 2449813.8983 6.136 .620 .342 .349 919 +V473 LYR 2449814.8984 6.187 .640 .356 .362 919 +V473 LYR 2449817.8986 6.200 .664 .368 .362 919 +V473 LYR 2449818.8989 6.108 .625 .350 .298 919 +V473 LYR 2449821.8963 6.096 .608 .355 .330 919 +V473 LYR 2449822.9003 6.147 .601 .374 .352 919 +V473 LYR 2449823.9009 6.204 .648 .389 .350 919 +V473 LYR 2449825.9011 6.154 .603 .353 .356 919 +V473 LYR 2449933.3627 6.111 .611 .703 920 +V473 LYR 2449933.4085 6.119 .620 .700 920 +V473 LYR 2449934.1873 6.150 .650 .707 920 +V473 LYR 2449934.2580 6.150 .641 .724 920 +V473 LYR 2449934.3119 6.135 .646 .692 920 +V473 LYR 2449934.3798 6.160 .643 .715 920 +V473 LYR 2449934.4314 6.138 .652 .682 920 +V473 LYR 2449935.2248 6.118 .629 .694 920 +V473 LYR 2449935.2704 6.116 .634 .689 920 +V473 LYR 2449935.3146 6.117 .640 .698 920 +V473 LYR 2449935.3657 6.123 .636 .693 920 +V473 LYR 2449936.1690 6.136 .624 .706 920 +V473 LYR 2449936.2328 6.119 .627 .693 920 +V473 LYR 2449936.3101 6.111 .633 .700 920 +V473 LYR 2449936.3569 6.119 .627 .698 920 +V473 LYR 2449936.4130 6.108 .630 .676 920 +V473 LYR 2449936.4730 6.114 .645 .698 920 +V473 LYR 2449937.2261 6.140 .645 .366 920 +V473 LYR 2449937.2668 6.146 .645 .359 920 +V473 LYR 2449937.3299 6.142 .641 .367 920 +V473 LYR 2449937.3916 6.145 .643 .368 920 +V473 LYR 2449937.4365 6.143 .638 .361 920 +V473 LYR 2449937.4748 6.144 .658 .346 920 +V473 LYR 2449938.1841 6.110 .633 .354 .686 920 +V473 LYR 2449938.2302 6.111 .630 .363 .701 920 +V473 LYR 2449938.2802 6.113 .632 .356 .688 920 +V473 LYR 2449938.3349 6.119 .637 .360 .702 920 +V473 LYR 2449938.3856 6.119 .641 .353 .699 920 +V473 LYR 2449938.4616 6.133 .652 .358 .699 920 +V473 LYR 2449939.1809 6.122 .633 .360 .705 920 +V473 LYR 2449939.2325 6.118 .623 .362 .693 920 +V473 LYR 2449939.3200 6.118 .628 .359 .694 920 +V473 LYR 2449939.3636 6.109 .628 .357 .693 920 +V473 LYR 2449939.4589 6.105 .627 .347 .699 920 +V473 LYR 2449941.2276 6.102 .614 .357 .676 920 +V473 LYR 2449941.2801 6.119 .624 .361 .695 920 +V473 LYR 2449941.3252 6.119 .640 .357 .710 920 +V473 LYR 2449941.3870 6.129 .625 .366 .710 920 +V473 LYR 2449941.4643 6.166 .598 .390 920 +V473 LYR 2449942.1614 6.115 .632 .358 .693 920 +V473 LYR 2449942.2332 6.113 .626 .356 .688 920 +V473 LYR 2449942.2825 6.122 .625 .361 .698 920 +V473 LYR 2449942.3256 6.108 .631 .354 .679 920 +V473 LYR 2449942.3641 6.109 .621 .357 .682 920 +V473 LYR 2449942.4064 6.110 .633 .351 .692 920 +V473 LYR 2449942.4575 6.111 .633 .353 .679 920 +V473 LYR 2449943.1669 6.158 .652 .376 .724 920 +V473 LYR 2449943.2201 6.152 .644 .372 .716 920 +V473 LYR 2449943.2705 6.145 .646 .366 .702 920 +V473 LYR 2449943.3362 6.144 .652 .362 .693 920 +V473 LYR 2449943.3766 6.145 .647 .359 .706 920 +V473 LYR 2449943.4205 6.141 .661 .350 .690 920 +V473 LYR 2449943.4543 6.142 .657 .350 .697 920 +V473 LYR 2449944.1666 6.108 .630 .363 .693 920 +V473 LYR 2449944.2322 6.109 .635 .354 .684 920 +V473 LYR 2449944.2797 6.114 .634 .359 .696 920 +V473 LYR 2449944.3170 6.118 .636 .359 .693 920 +V473 LYR 2449944.3671 6.131 .634 .367 .706 920 +V473 LYR 2449944.4131 6.118 .641 .355 .676 920 +V473 LYR 2449944.4558 6.125 .653 .355 .697 920 +V473 LYR 2449945.1662 6.127 .637 .359 .695 920 +V473 LYR 2449945.2270 6.125 .624 .364 .692 920 +V473 LYR 2449945.2741 6.118 .622 .366 .698 920 +V473 LYR 2449945.3213 6.118 .625 .361 .701 920 +V473 LYR 2449945.3539 6.101 .627 .360 .679 920 +V473 LYR 2449945.4153 6.102 .632 .349 .663 920 +V473 LYR 2449946.2101 6.151 .643 .369 .705 920 +V473 LYR 2449946.2510 6.148 .644 .364 .704 920 +V473 LYR 2449946.2923 6.153 .642 .367 .706 920 +V473 LYR 2449946.3725 6.137 .651 .363 .708 920 +V473 LYR 2449946.4245 6.145 .632 .357 .702 920 +V473 LYR 2449946.4529 6.146 .652 .349 .692 920 +V473 LYR 2449947.1677 6.114 .628 .365 .696 920 +V473 LYR 2449947.2257 6.119 .630 .367 .700 920 +V473 LYR 2449947.2700 6.127 .632 .363 .704 920 +V473 LYR 2449947.2786 6.114 .634 .361 .693 920 +V473 LYR 2449947.3188 6.119 .648 .356 .693 920 +V473 LYR 2449947.3661 6.127 .643 .359 .710 920 +V473 LYR 2449947.4224 6.124 .640 .355 .677 920 +V473 LYR 2449948.1602 6.121 .628 .361 .696 920 +V473 LYR 2449948.2035 6.115 .630 .359 .692 920 +V473 LYR 2449948.2497 6.115 .632 .361 .695 920 +V473 LYR 2449948.3042 6.109 .631 .361 .693 920 +V473 LYR 2449948.3595 6.115 .624 .364 .711 920 +V473 LYR 2449948.4111 6.116 .628 .364 .696 920 +V473 LYR 2449949.1654 6.147 .642 .367 .704 920 +V473 LYR 2449949.2020 6.143 .642 .367 .702 920 +V473 LYR 2449949.2429 6.153 .648 .364 .712 920 +V473 LYR 2449949.2993 6.153 .644 .367 .714 920 +V473 LYR 2449949.3631 6.140 .628 .364 .695 920 +V473 LYR 2449950.1640 6.108 .625 .361 .686 920 +V473 LYR 2449950.1955 6.117 .626 .357 .689 920 +V473 LYR 2449950.2490 6.115 .635 .361 .692 920 +V473 LYR 2449950.2945 6.124 .635 .361 .702 920 +V473 LYR 2449950.3194 6.115 .640 .356 .693 920 +V473 LYR 2449952.1637 6.150 .647 .369 .707 920 +V473 LYR 2449952.2209 6.145 .643 .367 .705 920 +V473 LYR 2449952.2532 6.147 .646 .368 .708 920 +V473 LYR 2449952.3134 6.144 .646 .365 .697 920 +V473 LYR 2449952.3630 6.142 .643 .363 .697 920 +V473 LYR 2449953.1821 6.111 .629 .360 .696 920 +V473 LYR 2449953.2344 6.118 .630 .361 .695 920 +V473 LYR 2449953.3000 6.125 .631 .362 .700 920 +V473 LYR 2449953.3580 6.131 .637 .364 .693 920 +V473 LYR 2449953.3875 6.126 .635 .351 .678 920 +V473 LYR 2449954.1704 6.116 .627 .353 .688 920 +V473 LYR 2449954.2292 6.115 .627 .360 .692 920 +V473 LYR 2449954.2808 6.119 .617 .361 .691 920 +V473 LYR 2449954.3353 6.112 .619 .354 .682 920 +V473 LYR 2449954.3777 6.102 .635 .354 .681 920 +V473 LYR 2449955.1628 6.145 .646 .366 .702 920 +V473 LYR 2449955.1887 6.153 .640 .374 .716 920 +V473 LYR 2449955.2268 6.154 .634 .368 .709 920 +V473 LYR 2449955.2781 6.154 .628 .372 .716 920 +V473 LYR 2449955.3382 6.126 .641 .358 .703 920 +V473 LYR 2449957.1518 6.118 .629 .359 .688 920 +V473 LYR 2449957.1837 6.119 .625 .360 .695 920 +V473 LYR 2449957.2354 6.120 .622 .361 .700 920 +V473 LYR 2449957.2748 6.116 .626 .360 .700 920 +V473 LYR 2449957.3240 6.104 .616 .355 .690 920 +V473 LYR 2449957.3917 6.119 .623 .357 .695 920 +V473 LYR 2449958.1580 6.144 .646 .364 .698 920 +V473 LYR 2449958.2122 6.157 .641 .369 .704 920 +V473 LYR 2449958.2922 6.152 .640 .363 .700 920 +V473 LYR 2449959.1508 6.117 .632 .355 .698 920 +V473 LYR 2449959.2228 6.123 .629 .359 .691 920 +V473 LYR 2449959.2639 6.134 .629 .367 .706 920 +V473 LYR 2449959.3206 6.133 .639 .365 .697 920 +V473 LYR 2449959.3640 6.136 .640 .354 .706 920 +V473 LYR 2449962.1647 6.118 .631 .363 .698 920 +V473 LYR 2449962.3446 6.131 .641 .356 .692 920 +V473 LYR 2449963.3529 6.098 .649 .339 .664 920 +V473 LYR 2449982.2794 6.139 .650 .355 .689 920 +V473 LYR 2449985.2017 6.126 .649 .366 .691 920 +V473 LYR 2449985.2576 6.130 .638 .369 .707 920 +V473 LYR 2449986.1375 6.135 .623 .374 .701 920 +V473 LYR 2449986.1847 6.122 .646 .365 .691 920 +V473 LYR 2449986.2639 6.130 .655 .364 .686 920 +V473 LYR 2449987.1910 6.105 .637 .359 .676 920 +V473 LYR 2449987.2632 6.097 .633 .356 .675 920 +V473 LYR 2449992.1226 6.117 .646 .363 .685 920 +V473 LYR 2449992.2496 6.131 .651 .362 .689 920 +V473 LYR 2449993.1616 6.098 .636 .354 .675 920 +V473 LYR 2449993.2559 6.097 .629 .352 .682 920 +V473 LYR 2450003.0896 6.119 .640 .363 .685 920 +V473 LYR 2450006.1168 6.139 .638 .359 .687 920 +V473 LYR 2450006.1424 6.138 .623 .366 .685 920 +V473 LYR 2450006.1824 6.139 .636 .365 .682 920 +V473 LYR 2450006.1940 6.164 .638 .367 .714 920 +V473 LYR 2450007.1823 6.137 .654 .359 .692 920 +V473 LYR 2450007.2270 6.149 .648 .372 .702 920 +V473 LYR 2450007.2706 6.115 .669 .344 .669 920 +V473 LYR 2450009.1361 6.132 .645 .367 .692 920 +V473 LYR 2450009.1731 6.129 .646 .359 .680 920 +V473 LYR 2450009.2393 6.133 .639 .359 .684 920 +V473 LYR 2450011.1133 6.093 .629 .356 920 +V473 LYR 2450011.1498 6.107 .628 .361 .689 920 +V473 LYR 2450011.2295 6.113 .639 .360 .681 920 +V473 LYR 2450011.2795 6.131 .622 .364 .684 920 +V473 LYR 2450012.1360 6.128 .647 .367 .690 920 +V473 LYR 2450017.1404 6.106 .635 .359 .688 920 +V473 LYR 2450017.2268 6.119 .635 .356 .695 920 +V473 LYR 2450018.1329 6.117 .651 .356 .674 920 +V473 LYR 2450018.2125 6.128 .624 .366 .691 920 +V473 LYR 2450018.2482 6.101 .662 .346 920 +V473 LYR 2450019.2378 6.152 .660 .360 .713 920 +V473 LYR 2450020.0934 6.103 .630 .359 .684 920 +V473 LYR 2450020.1450 6.105 .628 .360 .677 920 +V473 LYR 2450020.2131 6.103 .646 .353 .681 920 +V473 LYR 2450305.1712 6.131 .579 .357 921 +V473 LYR 2450305.2111 6.139 .590 .357 921 +V473 LYR 2450305.2474 6.144 .597 .351 921 +V473 LYR 2450305.2991 6.146 .599 .353 921 +V473 LYR 2450305.3440 6.159 .602 .354 921 +V473 LYR 2450305.3870 6.152 .622 .346 921 +V473 LYR 2450305.4144 6.154 .609 .349 921 +V473 LYR 2450305.4317 6.167 .611 .355 921 +V473 LYR 2450306.1627 6.115 .564 .343 921 +V473 LYR 2450306.1882 6.110 .567 .340 921 +V473 LYR 2450306.2582 6.114 .561 .346 921 +V473 LYR 2450306.2919 6.092 .577 .341 921 +V473 LYR 2450306.3307 6.101 .571 .344 921 +V473 LYR 2450306.3638 6.094 .563 .348 921 +V473 LYR 2450306.3972 6.110 .575 .343 921 +V473 LYR 2450306.4479 6.098 .573 921 +V473 LYR 2450307.1971 6.189 .589 .362 921 +V473 LYR 2450307.2282 6.178 .608 .356 921 +V473 LYR 2450307.3268 6.165 .600 .353 921 +V473 LYR 2450307.3601 6.157 .591 .349 921 +V473 LYR 2450307.4042 6.151 .596 .351 921 +V473 LYR 2450310.2229 6.176 .602 .355 921 +V473 LYR 2450310.2514 6.172 .598 .358 921 +V473 LYR 2450310.2861 6.153 .595 .349 921 +V473 LYR 2450310.3180 6.155 .597 .353 921 +V473 LYR 2450310.4237 6.146 .597 .341 921 +V473 LYR 2450311.1554 6.129 .586 .348 921 +V473 LYR 2450311.1973 6.139 .593 .349 921 +V473 LYR 2450311.2317 6.143 .598 .351 921 +V473 LYR 2450311.2614 6.154 .592 .363 921 +V473 LYR 2450311.2926 6.155 .601 .352 921 +V473 LYR 2450311.3192 6.160 .600 .352 921 +V473 LYR 2450311.3423 6.157 .604 .346 921 +V473 LYR 2450311.3640 6.163 .603 .358 921 +V473 LYR 2450311.4070 6.164 .613 .354 921 +V473 LYR 2450312.1500 6.118 .568 .347 921 +V473 LYR 2450312.1682 6.114 .572 .339 921 +V473 LYR 2450312.2015 6.111 .572 .345 921 +V473 LYR 2450312.2335 6.111 .572 .344 921 +V473 LYR 2450312.2721 6.110 .572 .341 921 +V473 LYR 2450312.3209 6.096 .570 .346 921 +V473 LYR 2450312.3615 6.095 .578 .334 921 +V473 LYR 2450312.4033 6.106 .576 .342 921 +V473 LYR 2450312.4469 6.113 .588 .334 921 +V473 LYR 2450313.1559 6.186 .594 .351 921 +V473 LYR 2450313.1736 6.179 .608 .357 921 +V473 LYR 2450313.2046 6.175 .607 .353 921 +V473 LYR 2450313.2528 6.170 .601 .358 921 +V473 LYR 2450313.2997 6.167 .596 .353 921 +V473 LYR 2450313.3381 6.162 .596 .356 921 +V473 LYR 2450314.1405 6.143 .579 .356 921 +V473 LYR 2450314.1606 6.136 .594 .351 921 +V473 LYR 2450314.1998 6.148 .592 .350 921 +V473 LYR 2450314.2432 6.156 .596 .354 921 +V473 LYR 2450314.2835 6.160 .600 .352 921 +V473 LYR 2450314.3310 6.163 .602 .353 921 +V473 LYR 2450314.3537 6.168 .605 .356 921 +V473 LYR 2450314.4044 6.177 .605 .357 921 +V473 LYR 2450315.1417 6.124 .559 .348 921 +V473 LYR 2450315.2067 6.111 .561 .337 921 +V473 LYR 2450315.2449 6.111 .564 .341 921 +V473 LYR 2450315.3078 6.107 .574 .335 921 +V473 LYR 2450315.3460 6.109 .569 .338 921 +V473 LYR 2450315.4011 6.112 .576 .339 921 +V473 LYR 2450315.4522 6.118 .340 921 +V473 LYR 2450316.1413 6.193 .598 .365 921 +V473 LYR 2450316.1541 6.192 .595 .363 921 +V473 LYR 2450316.2294 6.182 .598 .354 921 +V473 LYR 2450316.2571 6.183 .592 .364 921 +V473 LYR 2450316.3024 6.181 .598 .361 921 +V473 LYR 2450316.3522 6.171 .585 .351 921 +V473 LYR 2450316.4041 6.144 .574 .348 921 +V473 LYR 2450316.4459 6.135 .569 .339 921 +V473 LYR 2450317.1557 6.157 .586 .355 921 +V473 LYR 2450317.2010 6.164 .591 .356 921 +V473 LYR 2450317.2501 6.166 .598 .348 921 +V473 LYR 2450317.2983 6.175 .596 .357 921 +V473 LYR 2450317.3489 6.181 .598 .358 921 +V473 LYR 2450317.4158 6.187 .592 .352 921 +V473 LYR 2449522.7649 6.198 .744 996 +V473 LYR 2449529.7373 5.983 .642 .375 .319 996 +V473 LYR 2449530.7458 6.116 .354 .680 .372 .348 996 +V473 LYR 2449534.7701 6.229 .746 .425 .376 996 +T MON 2445649.5000 5.594 .656 .910 .484 982 +T MON 2445665.4960 6.524 1.264 1.469 .702 982 +T MON 2445687.3671 6.264 1.193 1.398 .686 982 +T MON 2445690.4256 6.492 1.324 1.413 .701 982 +T MON 2445691.4022 6.512 1.305 1.433 .702 982 +T MON 2445692.4764 6.561 1.335 1.434 .699 982 +T MON 2445693.2929 6.601 1.266 1.419 .707 982 +T MON 2445694.2617 6.602 1.236 1.417 .697 982 +T MON 2445695.3242 6.607 1.185 1.374 .697 982 +T MON 2445704.3006 5.715 .651 .937 .533 982 +T MON 2445705.2812 5.787 .705 1.001 .528 982 +T MON 2445706.2889 5.874 .735 1.070 .573 982 +T MON 2445707.2968 5.908 .828 1.117 .587 982 +T MON 2447082.5129 .695 .897 .512 989 +T MON 2447083.4698 5.811 .656 .937 .535 989 +T MON 2447084.4073 5.847 .711 .989 .553 989 +T MON 2447085.4553 5.919 .821 1.047 989 +T MON 2447087.4453 .978 1.186 .614 989 +T MON 2447088.4388 1.052 1.228 .629 989 +T MON 2447407.5063 5.671 .927 .557 990 +T MON 2447408.5042 5.774 1.040 .556 990 +T MON 2447409.5079 5.844 .804 1.064 .566 990 +T MON 2447410.5074 5.864 .944 1.085 .591 990 +T MON 2447411.5125 5.974 .943 1.182 .629 990 +T MON 2447413.5081 6.082 1.107 1.231 .664 990 +T MON 2447414.5043 6.111 1.133 1.292 .669 990 +T MON 2447415.5058 6.178 1.161 1.331 .667 990 +T MON 2447417.5041 6.339 1.233 1.375 .695 990 +T MON 2447418.5031 6.321 1.274 1.396 .689 990 +T MON 2447419.5070 6.409 1.298 1.443 .705 990 +T MON 2447420.5076 6.481 1.307 1.437 .699 990 +T MON 2447421.5055 6.520 1.332 1.422 .697 990 +T MON 2447422.5058 6.523 1.319 1.448 .687 990 +T MON 2447424.4986 6.605 1.312 1.385 .692 990 +T MON 2447425.5024 6.625 1.201 1.347 .689 990 +T MON 2447427.5009 6.589 1.089 1.343 .679 990 +T MON 2447428.4907 6.612 1.028 1.327 .650 990 +T MON 2447430.5068 6.085 .682 1.074 .567 990 +T MON 2447431.5056 5.780 .569 .882 .536 990 +T MON 2447432.5033 5.633 .615 .811 .523 990 +T MON 2447433.4790 5.642 .564 .880 .518 990 +T MON 2447434.4949 5.704 .623 .907 .546 990 +T MON 2448504.4451 6.606 1.405 1.413 .730 993 +T MON 2448505.4555 6.642 1.279 1.384 .737 993 +T MON 2448506.4480 6.630 1.159 1.376 .715 993 +T MON 2448507.4519 6.561 1.182 1.352 .707 993 +T MON 2448508.4605 6.585 1.143 1.313 .683 993 +T MON 2448509.4500 6.581 1.061 1.270 .685 993 +T MON 2448510.4421 6.452 .979 1.211 .629 993 +T MON 2448511.4345 6.144 .714 1.056 .586 993 +T MON 2448512.4318 5.792 .591 .917 .520 993 +T MON 2448513.4366 5.625 .526 .850 .497 993 +T MON 2448514.4383 5.607 .581 .894 .503 993 +T MON 2448515.4270 5.686 .681 .932 .535 993 +T MON 2448516.4377 5.748 .740 .998 .552 993 +T MON 2448517.4327 5.816 .785 1.046 .583 993 +T MON 2448518.4231 5.914 .864 1.135 .589 993 +T MON 2448519.4251 5.904 .954 1.204 .606 993 +T MON 2448520.4276 5.996 1.000 1.272 .614 993 +T MON 2448521.4389 6.036 1.284 .630 993 +T MON 2448522.4271 6.078 1.142 1.293 .650 993 +T MON 2448523.4179 6.164 1.172 1.336 .667 993 +T MON 2448872.4944 6.009 1.275 .631 994 +T MON 2448874.5010 6.098 1.372 .636 994 +T MON 2448875.4950 6.183 1.183 1.355 .668 994 +T MON 2448876.4999 6.229 1.225 1.367 .674 994 +T MON 2448877.4957 6.306 1.226 1.412 .685 994 +T MON 2448879.4986 6.422 1.315 1.455 .688 994 +T MON 2448880.4962 6.492 1.305 1.461 .715 994 +T MON 2448881.4958 6.522 1.327 1.454 .707 994 +T MON 2448883.4972 6.567 1.277 1.435 .705 994 +T MON 2448885.5058 6.588 1.132 1.387 .693 994 +T MON 2448886.5000 6.563 1.082 1.376 .679 994 +T MON 2448888.5073 6.506 .974 1.290 .659 994 +T MON 2448890.5062 5.916 .557 1.010 .530 994 +T MON 2448891.5094 5.667 .486 .913 .502 994 +T MON 2449621.4976 5.715 .675 .867 .473 995 +T MON 2449622.4926 5.670 .620 .814 .472 995 +T MON 2449623.4962 5.678 .622 .894 .496 995 +T MON 2449624.4979 5.698 .758 .978 .543 995 +T MON 2449625.4935 5.820 .781 1.020 .537 995 +T MON 2449632.4957 6.196 1.241 1.331 995 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2448890.4792 11.589 1.038 .631 994 +XX MON 2448891.4776 11.685 1.149 .669 994 +XX MON 2449804.5939 1.293 .735 997 +XX MON 2449805.5823 12.247 1.382 997 +XX MON 2449806.5578 12.069 1.201 .710 .722 997 +XX MON 2449807.5713 11.551 .969 .631 .635 997 +XX MON 2449808.5715 11.768 1.160 .684 .708 997 +XX MON 2449809.5487 11.979 1.257 .744 .748 997 +XX MON 2449810.5543 12.170 1.316 .781 .743 997 +XX MON 2449811.5464 12.259 1.356 .751 .769 997 +XX MON 2449812.5666 11.641 1.006 .618 .651 997 +XX MON 2449813.6072 11.690 1.064 .660 .722 997 +XX MON 2449814.5888 11.926 1.212 .737 .722 997 +XX MON 2449815.5372 12.078 1.284 .757 .753 997 +XX MON 2449816.5677 12.240 .758 .760 997 +XX MON 2449817.5431 12.070 1.185 .739 .724 997 +XX MON 2449825.5396 11.912 1.229 .718 .732 997 +YY MON 2449805.6252 13.212 997 +YY MON 2449806.5907 13.743 1.104 .690 .698 997 +YY MON 2449807.5891 14.177 .771 997 +YY MON 2449808.6110 13.898 1.145 .676 .733 997 +YY MON 2449809.5605 13.562 1.065 .645 .694 997 +YY MON 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10.723 1.156 .717 993 +BE MON 2448517.4506 10.439 1.114 .662 993 +BE MON 2448518.4397 10.816 1.280 .740 993 +BE MON 2448519.4414 10.332 1.071 .616 993 +BE MON 2448520.4480 10.604 1.218 .694 993 +BE MON 2448521.4624 10.820 1.290 .720 993 +BE MON 2448522.4422 10.233 .975 .615 993 +BE MON 2448523.4226 10.673 1.229 .724 993 +BV MON 2448515.5075 11.651 1.156 993 +BV MON 2448516.5106 11.192 1.035 993 +BV MON 2448518.4936 11.703 1.158 .775 993 +BV MON 2448519.4854 11.122 1.054 .644 993 +BV MON 2448520.4949 11.559 1.241 993 +BV MON 2448521.5042 11.675 1.207 993 +BV MON 2448523.4852 11.565 1.191 .749 993 +BV MON 2448881.4704 11.310 1.127 .683 994 +BV MON 2448882.4709 11.707 1.235 .784 994 +BV MON 2448883.4654 11.245 1.000 .649 994 +BV MON 2448885.4701 11.640 1.205 .757 994 +BV MON 2448886.4643 11.254 1.032 .638 994 +BV MON 2448888.4724 11.648 1.235 .769 994 +BV MON 2448890.4704 11.278 1.107 .672 994 +BV MON 2448891.4663 11.625 1.253 .740 994 +BV MON 2449805.6353 11.412 1.113 .643 .697 997 +BV 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2448523.4193 10.769 1.041 .648 993 +CU MON 2447084.4698 13.905 1.530 .921 989 +CU MON 2447085.4910 13.359 1.215 .790 989 +CU MON 2447087.4890 13.607 1.414 .890 989 +CU MON 2448504.4808 13.604 .861 993 +CU MON 2448505.4890 13.855 .956 993 +CU MON 2448506.4810 13.998 1.534 .952 993 +CU MON 2448507.4760 13.234 1.241 .811 993 +CU MON 2448508.4847 13.487 1.349 993 +CU MON 2448509.4729 13.684 1.489 .937 993 +CU MON 2448510.4802 13.918 1.514 .960 993 +CU MON 2448511.4621 13.879 1.434 .912 993 +CU MON 2448512.4552 13.302 1.241 .805 993 +CU MON 2448513.4570 13.541 1.422 .870 993 +CU MON 2448514.4562 13.712 1.515 .935 993 +CU MON 2448515.4501 13.895 1.518 .940 993 +CU MON 2448516.4538 13.523 1.358 .821 993 +CU MON 2448517.4527 13.346 1.306 .837 993 +CU MON 2448518.4450 13.664 1.491 .909 993 +CU MON 2448519.4490 13.753 1.541 .910 993 +CU MON 2448520.4502 13.977 1.568 .934 993 +CV MON 2447084.4882 10.510 1.477 .890 989 +CV MON 2447085.5046 10.634 1.470 .895 989 +CV MON 2447087.5076 10.031 1.243 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10.620 1.527 .889 994 +CV MON 2448877.4549 10.123 1.244 .782 994 +CV MON 2448879.4736 10.267 1.432 .806 994 +CV MON 2448880.4730 10.432 1.491 .872 994 +CV MON 2448881.4560 10.621 1.537 .917 994 +CV MON 2448882.4554 10.462 1.370 .862 994 +CV MON 2448883.4533 9.961 1.185 .768 994 +CV MON 2448885.4478 10.393 1.438 .887 994 +CV MON 2448886.4519 10.513 1.562 .883 994 +CV MON 2448888.4516 9.967 1.214 .743 994 +CV MON 2448890.4509 10.305 1.444 .857 994 +CV MON 2448891.4496 10.481 1.514 .882 994 +CV MON 2449620.4741 9.962 .777 1.152 .774 995 +CV MON 2449803.5064 9.996 1.241 .752 .790 997 +CV MON 2449804.5087 10.239 1.332 .855 .814 997 +CV MON 2449805.4991 10.399 1.447 .839 .869 997 +CV MON 2449806.4925 10.585 1.463 .902 .865 997 +CV MON 2449807.4908 10.503 1.365 .851 .862 997 +CV MON 2449808.4934 9.955 1.145 .744 .755 997 +CV MON 2449809.4896 10.158 1.288 .813 .812 997 +CV MON 2449810.4891 10.342 1.380 .850 .850 997 +CV MON 2449811.4910 10.529 1.493 .881 .872 997 +CV MON 2449812.4854 10.604 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6.370 1.208 1.512 .856 987 +Y OPH 2446288.1605 6.328 1.178 1.483 .859 987 +Y OPH 2446289.1632 6.291 1.132 1.430 .840 987 +Y OPH 2446290.1532 6.164 1.073 1.393 .825 987 +Y OPH 2446291.1660 6.036 1.028 1.308 .810 987 +Y OPH 2446292.1547 5.918 .968 1.294 .771 987 +Y OPH 2446293.2103 5.918 1.013 1.275 .780 987 +Y OPH 2446294.1490 5.949 1.047 1.297 .798 987 +Y OPH 2446295.1470 6.000 1.061 1.340 .807 987 +Y OPH 2446296.1486 6.051 1.104 1.375 .817 987 +Y OPH 2446297.1524 6.110 1.134 1.434 .835 987 +Y OPH 2446298.1457 6.171 1.176 1.469 .854 987 +Y OPH 2446299.1442 6.247 1.219 1.499 .868 987 +Y OPH 2446300.1476 6.295 1.252 1.524 .872 987 +Y OPH 2446301.1467 6.338 1.236 1.536 .893 987 +Y OPH 2446302.1531 6.360 1.258 1.541 .885 987 +Y OPH 2446303.1460 6.370 1.259 1.546 .887 987 +Y OPH 2446304.1378 6.360 1.213 1.524 .865 987 +Y OPH 2446606.2536 6.097 1.206 1.480 .841 988 +Y OPH 2446607.2418 6.221 1.233 1.481 .874 988 +Y OPH 2446608.2651 6.265 1.262 1.520 .879 988 +Y OPH 2446609.2655 6.319 1.522 988 +Y OPH 2446610.2627 6.330 1.277 1.522 .897 988 +Y OPH 2446611.2720 6.352 1.284 1.540 .884 988 +Y OPH 2446612.2611 6.346 1.247 1.497 .889 988 +Y OPH 2446613.2655 6.313 1.203 1.477 .865 988 +Y OPH 2446614.2553 6.244 1.172 1.445 .857 988 +Y OPH 2446615.2543 6.143 1.103 1.421 .830 988 +Y OPH 2446616.2630 6.020 1.044 1.334 .809 988 +Y OPH 2446617.2590 5.880 1.035 1.287 .762 988 +Y OPH 2446618.2574 5.845 1.007 1.270 .783 988 +Y OPH 2446619.2555 5.877 1.019 1.291 .780 988 +Y OPH 2446619.2574 5.878 1.024 1.298 .780 988 +Y OPH 2446620.2450 5.914 1.333 988 +Y OPH 2446621.2304 5.981 1.368 988 +Y OPH 2446622.2477 6.037 1.114 1.418 988 +Y OPH 2446623.2305 6.123 1.170 1.454 .843 988 +Y OPH 2446624.2316 6.171 1.210 1.478 .867 988 +Y OPH 2446625.2297 6.257 1.239 1.504 .882 988 +Y OPH 2446626.2226 6.311 1.269 1.524 .888 988 +Y OPH 2446627.2211 6.357 1.263 1.528 .888 988 +Y OPH 2446628.2214 6.356 1.283 1.534 .884 988 +Y OPH 2446629.2335 6.354 1.243 1.514 .881 988 +Y OPH 2446630.2314 1.227 1.474 .868 988 +Y OPH 2446631.2157 6.292 1.166 1.449 .855 988 +Y OPH 2446632.2271 6.195 1.119 1.409 .839 988 +Y OPH 2446635.2006 5.813 1.010 1.270 .761 988 +Y OPH 2446636.2341 5.866 1.032 1.292 .779 988 +Y OPH 2446994.2654 5.882 .935 1.271 .782 989 +Y OPH 2446995.2282 5.879 1.001 1.279 .787 989 +Y OPH 2446996.1989 5.873 .980 1.308 .794 989 +Y OPH 2446997.2123 5.932 1.049 1.366 .812 989 +Y OPH 2446998.2371 6.005 1.063 1.385 .822 989 +Y OPH 2446999.2235 6.084 1.083 1.432 .843 989 +Y OPH 2447000.2233 6.149 1.082 1.471 .858 989 +Y OPH 2447001.2244 6.219 1.156 1.507 .874 989 +Y OPH 2447002.2265 6.257 1.192 1.519 .898 989 +Y OPH 2447003.2093 6.331 1.208 1.537 .896 989 +Y OPH 2447004.1804 6.320 1.286 1.542 .901 989 +Y OPH 2447005.1786 6.397 1.245 1.553 .891 989 +Y OPH 2447399.1555 6.396 1.225 1.537 .851 990 +Y OPH 2447400.2171 6.378 1.521 .889 990 +Y OPH 2447401.1304 6.302 1.156 1.482 .846 990 +Y OPH 2447402.1324 6.292 1.097 1.430 .848 990 +Y OPH 2447403.1280 6.160 .997 1.385 .806 990 +Y OPH 2447404.1303 6.008 .907 1.322 .769 990 +Y OPH 2447408.1243 1.334 .806 990 +Y OPH 2447409.1256 6.025 .973 1.402 .812 990 +Y OPH 2447410.1388 6.041 1.054 1.398 .831 990 +Y OPH 2447411.1350 6.127 1.105 1.441 .834 990 +Y OPH 2447412.1446 6.198 1.151 1.493 .851 990 +Y OPH 2447413.1276 6.257 1.161 1.534 .856 990 +Y OPH 2447414.1307 6.322 1.200 1.546 .868 990 +Y OPH 2447415.1365 6.322 1.201 1.550 .864 990 +Y OPH 2447416.1271 6.361 1.190 1.525 .874 990 +Y OPH 2447417.1265 6.343 1.189 1.512 .873 990 +Y OPH 2447418.1258 6.320 1.133 1.499 .831 990 +Y OPH 2447420.1241 6.173 1.056 1.406 .818 990 +Y OPH 2447421.1177 6.049 .977 1.361 .795 990 +Y OPH 2447422.1184 5.910 .922 1.295 .767 990 +Y OPH 2447423.1184 5.868 .889 1.276 .755 990 +Y OPH 2447424.1180 5.885 .952 1.305 .778 990 +Y OPH 2447425.1191 5.945 .954 1.341 .786 990 +Y OPH 2447427.1232 6.043 1.042 1.412 .813 990 +Y OPH 2447428.1147 6.134 1.092 1.450 .827 990 +Y OPH 2447429.1217 6.197 1.136 1.505 .849 990 +Y OPH 2447430.1124 6.267 1.202 1.459 .865 990 +Y OPH 2447431.1132 6.320 1.186 1.520 .877 990 +Y OPH 2447432.1106 6.340 1.186 1.547 .864 990 +Y OPH 2447433.1065 6.352 1.189 1.525 .864 990 +Y OPH 2447434.1104 6.370 1.190 1.508 .867 990 +Y OPH 2447735.2280 6.058 1.015 1.430 .830 991 +Y OPH 2447736.2173 6.118 1.067 1.460 .851 991 +Y OPH 2447737.2174 6.196 1.144 1.452 .873 991 +Y OPH 2447738.2349 6.205 1.162 1.506 .868 991 +Y OPH 2447739.2036 6.291 1.205 1.516 .876 991 +Y OPH 2447740.2077 6.325 1.173 1.541 .878 991 +Y OPH 2447741.1997 6.377 1.188 1.512 .893 991 +Y OPH 2447742.2158 6.383 1.128 1.506 .872 991 +Y OPH 2447743.1996 6.328 1.124 1.487 .859 991 +Y OPH 2447744.1912 6.294 1.100 1.462 .839 991 +Y OPH 2447745.1902 6.230 1.060 1.395 .832 991 +Y OPH 2447746.1918 6.055 .975 1.328 .803 991 +Y OPH 2447747.1843 5.936 .890 1.290 .775 991 +Y OPH 2447748.1848 5.877 .895 1.248 .765 991 +Y OPH 2447748.1856 5.868 1.270 .768 991 +Y OPH 2447749.1624 5.851 .900 1.271 .768 991 +Y OPH 2447750.1625 5.905 .940 1.298 .771 991 +Y OPH 2447751.1718 5.959 .963 1.347 .793 991 +Y OPH 2447752.1586 6.042 1.031 1.389 .829 991 +Y OPH 2447753.1554 6.096 1.089 1.425 .838 991 +Y OPH 2447754.1651 6.166 1.115 1.476 .849 991 +Y OPH 2447755.1530 6.222 1.172 1.486 .849 991 +Y OPH 2447756.1830 6.307 1.190 1.510 .879 991 +Y OPH 2447757.1561 6.315 1.205 1.531 .878 991 +Y OPH 2447758.1529 6.350 1.199 1.529 .883 991 +Y OPH 2447759.1478 6.341 1.173 1.499 .869 991 +Y OPH 2447760.1670 6.336 1.121 1.490 .882 991 +Y OPH 2447761.1492 6.310 1.104 1.446 .856 991 +Y OPH 2447762.1515 6.209 1.036 1.419 .824 991 +Y OPH 2447763.1414 6.094 .991 1.358 .804 991 +Y OPH 2447764.1448 5.956 .923 1.286 .791 991 +Y OPH 2447766.1405 5.876 .885 1.276 .763 991 +Y OPH 2447767.1514 5.908 .890 1.318 .791 991 +Y OPH 2447768.1437 6.012 .977 1.316 .811 991 +Y OPH 2447769.1441 6.023 1.388 .813 991 +Y OPH 2447770.1390 6.078 1.021 1.447 .830 991 +Y OPH 2447771.1382 6.160 1.082 1.475 .845 991 +Y OPH 2447772.1332 6.220 1.131 1.466 .864 991 +Y OPH 2447773.1388 6.273 1.178 1.521 .879 991 +Y OPH 2447774.1542 6.303 1.141 1.540 .872 991 +Y OPH 2447775.1318 6.328 1.186 1.546 .879 991 +Y OPH 2447776.1352 6.321 1.165 1.523 .862 991 +Y OPH 2448101.1947 6.364 1.164 1.553 .876 992 +Y OPH 2448102.2092 6.348 1.161 1.532 .864 992 +Y OPH 2448103.1878 6.319 1.128 1.487 .849 992 +Y OPH 2448104.1879 6.270 1.069 1.486 .832 992 +Y OPH 2448108.1877 5.957 .978 1.295 .751 992 +Y OPH 2448109.1740 5.958 .984 1.313 .742 992 +Y OPH 2448110.1815 6.003 .985 1.357 .770 992 +Y OPH 2448111.1847 6.046 1.007 1.427 .776 992 +Y OPH 2448112.1818 6.108 1.053 1.443 .807 992 +Y OPH 2448113.1747 6.169 1.107 1.485 .839 992 +Y OPH 2448114.1819 6.236 1.157 1.530 .839 992 +Y OPH 2448115.1888 6.320 1.515 .867 992 +Y OPH 2448116.1891 6.358 1.179 1.579 .864 992 +Y OPH 2448117.1870 6.349 1.186 1.618 .891 992 +Y OPH 2448118.1869 6.360 1.199 1.561 .859 992 +Y OPH 2448119.1578 6.402 1.153 1.533 .877 992 +Y OPH 2448123.1579 6.131 .989 1.372 .788 992 +Y OPH 2448127.1691 6.006 1.010 1.356 .780 992 +Y OPH 2448503.1253 .892 1.288 .787 993 +Y OPH 2448504.1216 5.963 .913 1.340 .783 993 +Y OPH 2448505.1237 6.026 .958 1.367 .811 993 +Y OPH 2448506.1238 6.115 .989 1.427 .846 993 +Y OPH 2448507.1409 6.162 1.064 1.463 .845 993 +Y OPH 2448508.1348 6.224 1.118 1.488 .864 993 +Y OPH 2448509.1345 6.288 1.170 1.499 .875 993 +Y OPH 2448510.1351 6.332 1.208 1.529 .869 993 +Y OPH 2448511.1327 6.374 1.204 1.531 .887 993 +Y OPH 2448512.1317 6.376 1.196 1.528 .879 993 +Y OPH 2448513.1326 6.372 1.155 1.497 .884 993 +Y OPH 2448514.1315 6.365 1.122 1.474 .860 993 +Y OPH 2448515.1266 6.258 1.083 1.450 .839 993 +Y OPH 2448516.1301 6.172 1.007 1.410 .823 993 +Y OPH 2448517.1311 6.084 .948 1.321 .820 993 +Y OPH 2448518.1333 5.961 .913 1.273 .785 993 +Y OPH 2448519.1530 5.866 .859 1.287 .751 993 +Y OPH 2448520.1220 5.914 .891 1.297 .773 993 +Y OPH 2448521.1343 5.957 .941 1.332 .798 993 +Y OPH 2448522.1276 6.000 .996 1.351 .810 993 +Y OPH 2448523.1175 6.092 1.013 1.441 .821 993 +Y OPH 2448860.1708 .881 1.341 .788 994 +Y OPH 2448870.1576 6.341 1.177 1.525 .871 994 +Y OPH 2448872.2071 6.334 1.172 1.556 .857 994 +Y OPH 2448874.2191 6.309 1.037 1.524 .847 994 +Y OPH 2448876.1429 6.168 .965 1.384 .825 994 +Y OPH 2448877.1384 6.058 .885 1.354 .805 994 +Y OPH 2448880.1335 5.945 .885 1.325 .788 994 +Y OPH 2448881.1222 5.983 .932 1.363 .808 994 +Y OPH 2448882.1234 6.077 .958 1.384 .828 994 +Y OPH 2448883.1229 6.107 1.005 1.430 .843 994 +Y OPH 2448884.1209 6.197 1.055 1.471 .854 994 +Y OPH 2448885.1194 6.238 1.123 1.494 .869 994 +Y OPH 2448886.1195 6.308 1.143 1.524 .864 994 +Y OPH 2448888.1174 6.377 1.159 1.543 .871 994 +Y OPH 2448889.1205 6.383 1.168 1.525 .869 994 +Y OPH 2448890.1121 6.395 1.143 1.503 .898 994 +Y OPH 2448891.1116 6.352 1.090 1.474 .866 994 +Y OPH 2448892.1099 6.296 1.033 1.441 .838 994 +Y OPH 2448893.1100 6.189 .975 1.403 .826 994 +Y OPH 2448894.1111 6.044 .900 1.333 .793 994 +Y OPH 2449620.1308 6.162 1.186 1.441 .852 995 +Y OPH 2449621.1149 6.234 1.307 1.439 .881 995 +Y OPH 2449622.1083 6.303 1.329 1.484 .899 995 +Y OPH 2449623.1041 6.330 1.259 1.526 .884 995 +Y OPH 2449624.1061 6.345 1.198 1.541 .873 995 +Y OPH 2449625.1057 6.389 1.275 1.523 .876 995 +Y OPH 2449631.1045 5.949 .906 1.292 .778 995 +Y OPH 2449632.1166 5.906 .896 1.282 .776 995 +Y OPH 2449633.1114 5.905 .940 1.291 .777 995 +Y OPH 2449634.1116 5.954 .896 1.357 .789 995 +Y OPH 2449803.9210 5.924 1.245 .783 .763 997 +Y OPH 2449804.9182 5.978 1.271 .787 .799 997 +Y OPH 2449805.9153 6.011 1.298 .809 .791 997 +Y OPH 2449808.9247 6.207 1.427 .853 .835 997 +Y OPH 2449809.9166 6.244 1.475 .857 .831 997 +Y OPH 2449810.9170 6.277 1.505 .860 997 +Y OPH 2449811.9213 6.344 1.503 .868 .846 997 +Y OPH 2449813.9220 6.372 1.493 .879 .837 997 +Y OPH 2449814.8820 6.366 1.477 .870 .834 997 +Y OPH 2449817.9209 6.183 1.354 .829 .786 997 +Y OPH 2449818.9230 6.008 1.291 .790 .750 997 +Y OPH 2449821.9138 5.913 1.264 .780 .754 997 +Y OPH 2449822.9171 6.004 1.303 .796 .795 997 +Y OPH 2449823.9109 6.049 1.359 .823 .806 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998 +Y OPH 2449962.1546 6.137 1.449 .857 1.658 998 +Y OPH 2450009.0925 1.306 .787 1.498 998 +Y OPH 2450011.0893 5.977 1.365 .804 1.519 998 +Y OPH 2450012.1265 1.585 998 +Y OPH 2450017.0768 6.342 1.561 .889 1.727 998 +Y OPH 2450018.1170 6.312 1.578 .938 1.734 998 +Y OPH 2450020.0696 6.363 1.523 .867 1.701 998 +Y OPH 2450305.1810 6.145 1.402 .869 971 +Y OPH 2450306.1809 6.196 1.439 .872 971 +Y OPH 2450307.1925 6.286 1.474 .881 971 +Y OPH 2450310.2363 6.390 1.484 .890 971 +Y OPH 2450311.1655 6.340 1.478 .875 971 +Y OPH 2450312.1603 6.322 1.439 .878 971 +Y OPH 2450313.1663 6.297 1.411 .868 971 +Y OPH 2450314.1527 6.209 1.363 .851 971 +Y OPH 2450315.1517 6.080 1.296 .833 971 +Y OPH 2450316.1478 5.947 1.236 .798 971 +Y OPH 2450317.1771 5.894 1.212 .743 971 +Y OPH 2450318.1487 5.902 1.249 .788 971 +Y OPH 2450319.1468 5.959 1.263 .811 971 +Y OPH 2450320.1488 5.989 1.311 .824 971 +Y OPH 2450321.1439 6.074 1.340 .837 971 +Y OPH 2450322.1382 6.137 1.383 .853 971 +Y OPH 2450323.1381 6.202 1.381 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991 +BF OPH 2447754.1631 7.003 .351 .722 .415 991 +BF OPH 2447755.1518 7.212 .506 .821 .473 991 +BF OPH 2447756.1618 7.497 .695 .998 .543 991 +BF OPH 2447757.1525 7.618 .670 1.024 .549 991 +BF OPH 2447758.1505 7.086 .354 .828 .449 991 +BF OPH 2447759.1465 7.222 .436 .798 .531 991 +BF OPH 2447760.1647 7.464 .645 .957 .567 991 +BF OPH 2447761.1527 7.646 .658 1.014 .574 991 +BF OPH 2447762.1540 7.093 .379 .786 .470 991 +BF OPH 2447763.1456 7.152 .890 .447 991 +BF OPH 2447764.1478 7.454 .663 1.024 .533 991 +BF OPH 2447766.1428 7.196 .500 .817 .439 991 +BF OPH 2447767.1548 7.151 .409 .862 .470 991 +BF OPH 2447768.1473 7.485 .913 .544 991 +BF OPH 2447769.1482 7.607 .640 1.063 .569 991 +BF OPH 2447770.1436 7.282 .428 .803 .504 991 +BF OPH 2447771.1418 7.091 .383 .860 .470 991 +BF OPH 2447772.1375 7.404 .628 .867 .548 991 +BF OPH 2447773.1446 7.582 .608 1.135 .608 991 +BF OPH 2447775.1353 7.067 .454 .803 .439 991 +BF OPH 2447776.1395 .578 1.001 991 +BF OPH 2448101.1971 7.271 .499 .842 .535 992 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7.059 .695 .350 971 +BF OPH 2450318.1498 7.305 .927 .539 971 +BF OPH 2450319.1479 7.593 .924 .578 971 +BF OPH 2450320.1506 7.590 .931 .557 971 +BF OPH 2450321.1451 6.997 .658 .427 971 +BF OPH 2450322.1392 7.310 .853 .466 971 +BF OPH 2450323.1394 7.553 .852 .533 971 +BF OPH 2450324.1433 7.608 .850 .558 971 +BF OPH 2450325.1362 6.973 .680 .451 971 +BF OPH 2450326.1326 7.274 .863 .507 971 +BH OPH 2449520.8304 11.677 .478 996 +BH OPH 2449521.7374 11.513 .441 996 +BH OPH 2449522.6834 11.656 .495 996 +BH OPH 2449528.7503 12.510 .729 .464 .490 996 +BH OPH 2449529.7098 12.717 .711 .358 .400 996 +BH OPH 2449530.7192 12.344 .624 .375 .409 996 +BH OPH 2449534.6941 11.855 .372 .412 996 +BH OPH 2449543.6852 11.552 .431 .263 .325 996 +BH OPH 2449545.6590 11.843 .560 .388 .409 996 +BH OPH 2449559.7858 12.269 996 +BH OPH 2449623.1699 11.791 .604 .385 995 +BH OPH 2449624.1888 12.214 .581 .449 995 +BH OPH 2449625.1878 12.182 .811 .475 995 +BH OPH 2449632.1701 11.615 .464 .316 995 +BH OPH 2449633.1714 11.586 .470 .316 995 +BH OPH 2449634.1623 11.764 .611 .389 995 +BH OPH 2449934.2708 12.068 .469 998 +BH OPH 2449935.2805 12.156 .504 .953 998 +BH OPH 2449936.2524 12.264 .488 .923 998 +BH OPH 2449944.2262 11.842 .710 998 +BH OPH 2449945.2380 12.028 .835 998 +BH OPH 2449946.1976 12.190 .931 998 +BH OPH 2449947.2129 12.289 .991 998 +BH OPH 2449948.2117 12.350 .977 998 +BH OPH 2449949.2161 12.449 1.030 998 +BH OPH 2449950.2116 12.445 .924 998 +BH OPH 2449952.2055 11.980 .700 998 +BH OPH 2449953.2289 11.683 .662 998 +BH OPH 2449954.2236 11.633 .664 998 +BH OPH 2449955.2217 .747 998 +V477 OPH 2448102.2197 14.086 .680 .468 992 +V477 OPH 2448103.1948 13.590 .548 .414 992 +V477 OPH 2448104.2022 14.136 .706 992 +V477 OPH 2448109.1801 13.653 .479 992 +V477 OPH 2448111.1941 13.604 .627 992 +V477 OPH 2448112.1944 14.171 .848 .451 992 +V477 OPH 2448113.1804 13.558 .594 .373 992 +V477 OPH 2448114.1882 14.248 .800 .505 992 +V477 OPH 2448115.1747 13.600 .555 992 +V477 OPH 2448117.1969 13.609 .585 .399 992 +V477 OPH 2448118.1984 14.255 .764 .505 992 +V477 OPH 2448119.1810 13.635 .508 .399 992 +V477 OPH 2448123.1663 13.567 .513 .412 992 +V477 OPH 2448127.1790 13.557 .503 .373 992 +V477 OPH 2448858.1833 14.066 .607 .434 994 +V477 OPH 2448883.1267 13.558 .580 .377 994 +V477 OPH 2448884.1299 14.254 .788 .504 994 +V477 OPH 2448885.1216 13.550 .572 .408 994 +V477 OPH 2448886.1217 14.274 .769 .540 994 +V477 OPH 2448887.1366 13.566 .574 .373 994 +V477 OPH 2448888.1198 14.237 .756 .470 994 +V477 OPH 2448889.1266 13.546 .574 .314 994 +V477 OPH 2448890.1202 14.231 .762 .522 994 +V477 OPH 2448891.1139 13.565 .524 .378 994 +V477 OPH 2448892.1130 14.177 .789 .550 994 +V477 OPH 2448893.1135 13.589 .525 .404 994 +V477 OPH 2448894.1142 14.229 .720 .566 994 +V478 OPH 2449521.7330 12.949 .793 996 +V478 OPH 2449522.6770 12.749 .928 996 +V478 OPH 2449534.7292 13.326 1.077 .701 .626 996 +V478 OPH 2449543.6736 12.764 1.060 .625 .624 996 +V478 OPH 2449564.5968 13.496 1.340 996 +V554 OPH 2444841.1757 13.120 1.323 950 +V554 OPH 2444844.1562 13.327 1.423 950 +V554 OPH 2444845.1562 13.543 1.486 950 +V554 OPH 2444846.1796 13.892 1.573 950 +V554 OPH 2444847.1562 13.908 1.546 950 +V554 OPH 2444848.1601 14.001 1.386 950 +V554 OPH 2444849.1562 13.846 1.210 950 +V554 OPH 2444850.1640 13.434 1.212 950 +V554 OPH 2444851.1562 12.954 1.031 950 +V554 OPH 2444852.1562 12.735 .966 950 +V554 OPH 2444853.1523 12.669 1.043 950 +V554 OPH 2444854.1523 12.963 1.013 950 +V554 OPH 2444857.1367 13.057 950 +V554 OPH 2444857.1406 13.040 1.294 950 +V554 OPH 2445174.2695 13.991 950 +V554 OPH 2445175.2148 13.947 1.416 950 +V554 OPH 2445176.2109 13.814 1.181 950 +V554 OPH 2445189.1953 14.173 1.411 950 +V554 OPH 2445190.1796 14.063 1.405 950 +V554 OPH 2445191.1796 13.731 1.361 950 +V554 OPH 2445192.1796 13.359 1.148 950 +V554 OPH 2445193.1718 12.891 1.010 950 +V554 OPH 2445194.1679 12.587 .988 950 +V554 OPH 2445195.1757 12.821 1.101 950 +V554 OPH 2445196.1718 13.112 950 +V554 OPH 2445198.1718 13.093 1.253 950 +V554 OPH 2445199.1757 13.196 1.300 950 +V554 OPH 2445200.1718 13.327 1.344 950 +V554 OPH 2445201.1679 13.573 1.373 950 +V554 OPH 2447735.2071 12.935 1.108 .628 950 +V554 OPH 2447736.2020 13.066 1.209 .811 950 +V554 OPH 2447737.2022 13.124 1.243 .826 950 +V554 OPH 2447738.2230 13.036 1.399 .813 950 +V554 OPH 2447739.1917 13.227 1.472 .752 950 +V554 OPH 2447740.1918 13.227 1.472 .793 950 +V554 OPH 2447742.2044 13.889 1.546 .911 950 +V554 OPH 2447743.1881 14.166 1.484 .947 950 +V554 OPH 2447744.1805 14.041 1.491 .902 950 +V554 OPH 2447745.1783 13.889 1.282 .739 950 +V554 OPH 2447746.1791 13.621 1.047 .793 950 +V554 OPH 2447747.1721 13.094 1.028 .687 950 +V554 OPH 2447748.1706 12.815 .941 .563 950 +V775 OPH 2444832.2148 13.501 .871 950 +V775 OPH 2444833.1992 13.391 .795 950 +V775 OPH 2444836.2812 13.300 .829 950 +V775 OPH 2444840.2500 13.508 1.050 950 +V775 OPH 2444841.2265 13.685 .950 950 +V775 OPH 2444844.1992 13.494 .840 950 +V775 OPH 2444845.1914 13.413 .798 950 +V775 OPH 2444846.2187 13.304 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2445175.3593 14.935 1.214 950 +V833 OPH 2445176.3359 14.869 1.012 950 +V833 OPH 2445178.3320 14.604 .791 950 +V833 OPH 2445191.2734 14.818 .999 950 +V833 OPH 2445192.2773 14.631 .831 950 +V833 OPH 2445193.2539 14.448 .810 950 +V833 OPH 2445194.2539 14.145 .658 950 +V833 OPH 2445198.2734 13.830 .841 950 +V833 OPH 2447735.2251 14.680 .902 .564 950 +V833 OPH 2447736.2098 14.551 .782 .523 950 +V833 OPH 2447737.2100 14.165 .662 .483 950 +V833 OPH 2447738.2318 13.760 .588 .398 950 +V833 OPH 2447739.1998 13.670 .655 .427 950 +V833 OPH 2447740.2012 13.763 .751 .490 950 +V833 OPH 2447741.1920 13.873 .792 .481 950 +V833 OPH 2447742.2122 13.797 .886 .528 950 +V833 OPH 2447743.1962 13.794 .866 .551 950 +V833 OPH 2447744.1880 13.988 .937 .538 950 +V833 OPH 2447745.1870 14.222 1.055 .573 950 +V833 OPH 2447746.1831 14.559 1.111 .674 950 +V833 OPH 2447747.1791 14.801 1.196 950 +V833 OPH 2447748.1782 14.961 .975 950 +V2338 OPH 2450312.1726 12.121 1.130 .658 922 +V2338 OPH 2450312.3365 12.153 1.109 .647 922 +V2338 OPH 2450313.1876 12.161 1.018 .648 922 +V2338 OPH 2450313.3034 12.140 1.043 .636 922 +V2338 OPH 2450314.1637 12.044 1.013 .607 922 +V2338 OPH 2450314.3073 12.045 .983 .640 922 +V2338 OPH 2450315.1685 12.032 .945 .600 922 +V2338 OPH 2450315.2797 12.030 .981 .601 922 +V2338 OPH 2450316.1630 11.979 .919 .573 922 +V2338 OPH 2450316.2712 11.942 .941 .566 922 +V2338 OPH 2450317.1624 11.838 .887 .566 922 +V2338 OPH 2450317.3024 11.822 .906 .533 922 +V2338 OPH 2450318.1684 11.707 .894 .555 922 +V2338 OPH 2450318.2885 11.698 .883 .545 922 +V2338 OPH 2450319.1702 11.638 .887 .558 922 +V2338 OPH 2450319.2905 11.629 .874 .557 922 +V2338 OPH 2450320.1721 11.566 .957 .554 922 +V2338 OPH 2450320.3035 11.575 .964 .560 922 +V2338 OPH 2450321.1587 11.557 .995 .578 922 +V2338 OPH 2450321.2988 11.566 .997 .576 922 +V2338 OPH 2450322.1529 11.619 1.039 .609 922 +V2338 OPH 2450322.3008 11.631 1.020 .593 922 +V2338 OPH 2450323.1558 11.700 1.058 .619 922 +V2338 OPH 2450323.2988 11.738 1.069 .610 922 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13.730 1.187 1.455 999 +V336 ORI 2450357.8633 14.438 1.345 1.849 999 +V336 ORI 2450358.8532 13.624 1.090 1.430 999 +V336 ORI 2450361.8656 13.793 1.150 1.532 999 +V336 ORI 2450362.8649 14.279 1.387 1.784 999 +V336 ORI 2450363.8615 14.435 1.454 1.767 999 +V336 ORI 2450379.7305 14.180 1.374 1.713 999 +V336 ORI 2450380.7364 14.409 1.628 1.745 999 +V336 ORI 2450381.7378 13.729 1.219 1.499 999 +V336 ORI 2450382.7464 14.270 1.362 1.729 999 +V336 ORI 2450383.7416 14.409 1.611 1.711 999 +V336 ORI 2450384.7392 13.863 1.200 1.571 999 +V336 ORI 2450386.7353 14.410 1.726 999 +V336 ORI 2450387.7384 13.922 1.267 1.539 999 +V336 ORI 2450388.7571 14.292 1.457 1.745 999 +V336 ORI 2450389.7548 14.269 1.403 1.678 999 +V336 ORI 2450390.7492 14.022 1.358 1.641 999 +V336 ORI 2450391.7324 14.352 1.432 1.806 999 +V336 ORI 2450392.7184 14.011 1.295 1.557 999 +V336 ORI 2450393.7122 14.093 1.350 1.678 999 +KAPPA PAV 2450348.4949 4.169 .624 .676 999 +KAPPA PAV 2450349.5595 4.430 .813 .781 999 +KAPPA PAV 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8.172 1.765 972 +RY SCO 2450584.5937 7.798 1.637 972 +RY SCO 2450584.6416 7.782 1.634 972 +WY SCO 2444840.1523 13.041 1.201 950 +WY SCO 2444841.1562 13.002 1.056 950 +WY SCO 2444844.1484 12.540 .991 950 +WY SCO 2444845.1484 12.478 1.039 950 +WY SCO 2444846.1757 12.575 1.148 950 +WY SCO 2444847.1445 1.171 950 +WY SCO 2444848.1445 12.658 1.245 950 +WY SCO 2444849.1406 12.804 1.299 950 +WY SCO 2444850.1484 13.122 1.378 950 +WY SCO 2444851.1445 13.414 1.377 950 +WY SCO 2445173.2070 13.315 1.364 950 +WY SCO 2445174.1953 13.438 950 +WY SCO 2445189.1796 13.051 1.308 950 +WY SCO 2445190.1718 12.926 1.067 950 +WY SCO 2445191.1718 12.734 .986 950 +WY SCO 2445192.1718 12.684 1.031 950 +WY SCO 2445193.1679 12.491 .972 950 +WY SCO 2445194.1640 12.537 1.064 950 +WY SCO 2445195.1679 12.576 1.130 950 +WY SCO 2445196.1640 12.713 950 +WY SCO 2445198.1679 12.996 1.305 950 +WY SCO 2445199.1718 13.332 1.364 950 +WY SCO 2445200.1640 13.457 1.397 950 +WY SCO 2445201.1601 13.189 1.207 950 +V470 SCO 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972 +V636 SCO 2450580.5987 6.422 .917 972 +V636 SCO 2450582.5789 6.639 1.032 972 +V636 SCO 2450582.6256 6.657 1.034 972 +V636 SCO 2450582.6731 6.650 1.030 972 +V636 SCO 2450583.5713 6.799 1.082 972 +V636 SCO 2450584.5594 6.916 1.103 972 +V636 SCO 2450584.6183 6.912 1.100 972 +V636 SCO 2450584.6626 6.924 1.107 972 +V950 SCO 2449520.7697 7.461 .876 996 +V950 SCO 2449521.7641 7.247 .755 996 +V950 SCO 2449526.7024 7.378 .840 .498 .470 996 +V950 SCO 2449528.7254 7.136 .605 .751 .445 .426 996 +V950 SCO 2449529.7684 7.274 .804 .477 .461 996 +V950 SCO 2449530.7769 7.456 .604 .887 .503 .483 996 +V950 SCO 2449532.6521 7.128 .758 .438 .434 996 +V950 SCO 2449534.7145 7.380 .841 .484 .467 996 +V950 SCO 2449535.7169 7.124 .604 .699 .453 .421 996 +V950 SCO 2449536.6971 7.304 .639 .798 .490 .463 996 +V950 SCO 2449543.6623 7.338 .841 .490 .464 996 +V950 SCO 2449545.6286 7.135 .735 .438 .424 996 +V950 SCO 2449558.6422 .745 .458 .444 996 +V950 SCO 2449560.5442 7.324 .614 .801 .468 .470 996 +V950 SCO 2449561.5051 7.490 .593 .824 .489 .468 996 +V950 SCO 2449561.7206 7.396 .581 .801 .469 .469 996 +V950 SCO 2449563.4554 7.255 .751 .439 .459 996 +V950 SCO 2449564.4517 7.473 .840 996 +V950 SCO 2449804.8926 7.486 .826 .485 .495 997 +V950 SCO 2449805.8706 7.203 .696 .432 .431 997 +V950 SCO 2449808.8860 7.309 .736 .452 .448 997 +V950 SCO 2449809.8377 7.175 .704 .431 .426 997 +V950 SCO 2449810.8404 7.408 .824 .485 .441 997 +V950 SCO 2449811.7982 7.420 .812 .477 .464 997 +V950 SCO 2449811.8196 7.407 .811 .465 .469 997 +V950 SCO 2449813.8101 7.310 .785 .461 .459 997 +V950 SCO 2449813.8415 7.315 .790 .459 .453 997 +V950 SCO 2449814.8118 7.471 .845 .484 .480 997 +V950 SCO 2449815.7672 7.256 .739 .447 .427 997 +V950 SCO 2449815.8103 7.242 .725 .443 .431 997 +V950 SCO 2449817.7792 7.452 .853 .495 .479 997 +V950 SCO 2449817.8343 7.472 .840 .504 .489 997 +V950 SCO 2449818.7555 7.376 .787 .466 .445 997 +V950 SCO 2449818.8184 7.350 .776 .457 .447 997 +V950 SCO 2449821.7541 7.435 .834 .491 .466 997 +V950 SCO 2449821.8244 7.438 .830 .483 .476 997 +V950 SCO 2449822.7470 7.183 .711 .423 .435 997 +V950 SCO 2449822.8152 7.157 .701 .425 .429 997 +V950 SCO 2449823.7385 7.256 .762 .448 .449 997 +V950 SCO 2449823.8071 7.266 .773 .456 .448 997 +V950 SCO 2449825.7279 7.300 .753 .457 .427 997 +V950 SCO 2449825.7924 7.281 .736 .448 .443 997 +V950 SCO 2449826.7618 7.160 .707 .427 .413 997 +V950 SCO 2449827.7161 7.400 .834 .471 .478 997 +V950 SCO 2450348.5782 7.449 .858 .973 999 +V950 SCO 2450350.6628 7.212 .842 999 +V950 SCO 2450351.5606 7.404 .949 999 +V950 SCO 2450352.5826 7.442 .957 999 +V950 SCO 2450353.5297 7.169 .834 999 +V950 SCO 2450354.5425 7.288 .904 999 +V950 SCO 2450355.5289 7.478 .974 999 +V950 SCO 2450357.5281 7.185 .858 999 +V950 SCO 2450358.5317 7.440 .956 999 +V950 SCO 2450359.5244 7.379 .920 999 +V950 SCO 2450360.5311 7.147 .833 999 +V950 SCO 2450361.5372 7.360 .924 999 +V950 SCO 2450362.5368 7.471 .972 999 +V950 SCO 2450363.5352 7.191 .848 999 +V950 SCO 2450379.5543 7.427 .948 999 +V950 SCO 2450380.5533 7.183 .858 999 +V950 SCO 2450381.5374 7.260 .883 999 +V950 SCO 2450382.5332 .973 999 +V950 SCO 2450383.5330 7.279 .885 999 +V950 SCO 2450384.5389 7.176 .870 999 +V950 SCO 2450385.5192 7.426 .958 999 +V950 SCO 2450386.5287 7.372 .921 999 +V950 SCO 2450387.5240 7.177 .842 999 +V950 SCO 2450388.5279 7.331 .921 999 +V950 SCO 2450389.5274 7.464 .969 999 +V950 SCO 2450390.5231 7.209 .870 999 +V950 SCO 2450391.5224 7.226 .881 999 +V950 SCO 2450392.5162 7.444 .968 999 +V950 SCO 2450393.5192 7.327 .908 999 +V950 SCO 2450570.6432 7.225 .868 972 +V950 SCO 2450572.6435 7.352 .914 972 +V950 SCO 2450573.5228 7.116 .827 972 +V950 SCO 2450573.6200 7.134 .853 972 +V950 SCO 2450573.6569 7.135 .852 972 +V950 SCO 2450574.5506 7.357 .945 972 +V950 SCO 2450574.6089 7.385 .957 972 +V950 SCO 2450575.5086 7.457 .967 972 +V950 SCO 2450575.6017 7.459 .969 972 +V950 SCO 2450575.6516 7.421 .939 972 +V950 SCO 2450576.5545 7.175 .845 972 +V950 SCO 2450576.6373 7.156 .850 972 +V950 SCO 2450577.5628 7.252 .900 972 +V950 SCO 2450577.6196 7.263 .903 972 +V950 SCO 2450577.6639 7.273 .912 972 +V950 SCO 2450578.5404 7.448 .978 972 +V950 SCO 2450578.6072 7.445 .977 972 +V950 SCO 2450578.6621 7.449 .976 972 +V950 SCO 2450580.4849 7.150 .851 972 +V950 SCO 2450580.5980 7.172 .863 972 +V950 SCO 2450582.5781 7.379 .931 972 +V950 SCO 2450582.6248 7.373 .916 972 +V950 SCO 2450582.6721 7.354 .918 972 +V950 SCO 2450583.5705 7.147 .841 972 +V950 SCO 2450584.5587 7.316 .921 972 +V950 SCO 2450584.6176 7.331 .932 972 +X SCT 2449934.2461 10.075 .769 998 +X SCT 2449935.2626 9.802 .680 1.357 998 +X SCT 2449936.2111 10.175 .797 1.523 998 +X SCT 2449939.2486 9.708 1.347 998 +X SCT 2449942.2242 10.413 1.588 998 +X SCT 2449943.2111 9.594 1.276 998 +X SCT 2449944.2066 10.072 1.459 998 +X SCT 2449945.2048 10.376 1.624 998 +X SCT 2449946.1964 10.424 1.601 998 +X SCT 2449947.2116 9.590 1.231 998 +X SCT 2449948.2105 9.948 1.461 998 +X SCT 2449949.2151 10.285 1.586 998 +X SCT 2449950.2106 10.423 1.589 998 +X SCT 2449952.2033 9.877 1.404 998 +X SCT 2449953.2271 10.254 1.557 998 +X SCT 2449954.2130 10.426 1.592 998 +X SCT 2449955.2125 10.124 1.444 998 +X SCT 2450568.5623 9.551 .616 972 +X SCT 2450570.5248 10.226 972 +X SCT 2450572.4623 9.843 .691 972 +X SCT 2450572.5523 9.677 .655 972 +X SCT 2450573.4953 9.762 .679 972 +X SCT 2450573.5905 9.815 .698 972 +X SCT 2450573.6621 9.843 .704 972 +X SCT 2450574.5266 10.143 .778 972 +X SCT 2450574.5882 10.157 .779 972 +X SCT 2450575.4866 10.359 .788 972 +X SCT 2450575.5725 10.375 .806 972 +X SCT 2450575.6364 10.379 .797 972 +X SCT 2450576.5332 10.036 .710 972 +X SCT 2450576.6144 9.914 .693 972 +X SCT 2450576.6519 9.843 .684 972 +X SCT 2450577.5427 9.717 .667 972 +X SCT 2450577.6014 9.747 .677 972 +X SCT 2450577.6418 9.765 .682 972 +X SCT 2450578.5208 10.084 .764 972 +X SCT 2450578.5871 10.099 .777 972 +X SCT 2450578.6442 10.110 .780 972 +X SCT 2450579.5685 10.340 .800 972 +X SCT 2450580.4650 10.307 .787 972 +X SCT 2450580.5765 10.211 .758 972 +X SCT 2450580.6264 10.171 .755 972 +X SCT 2450582.5579 10.027 .746 972 +X SCT 2450582.6054 10.074 .756 972 +X SCT 2450582.6535 10.060 .756 972 +X SCT 2450583.5488 10.310 .782 972 +X SCT 2450583.6053 10.319 .801 972 +X SCT 2450584.5407 10.353 .792 972 +X SCT 2450584.5968 10.337 .781 972 +X SCT 2450584.6446 10.310 .783 972 +Y SCT 2445488.3710 9.697 1.763 .976 982 +Y SCT 2445489.4101 9.944 1.839 1.034 982 +Y SCT 2445493.4062 9.654 1.267 1.584 .928 982 +Y SCT 2445496.3397 9.375 1.090 1.549 .910 982 +Y SCT 2445497.3125 9.513 1.364 1.645 .950 982 +Y SCT 2445498.3554 9.645 1.466 1.690 .984 982 +Y SCT 2445502.3437 9.884 1.391 1.696 .976 982 +Y SCT 2445503.3514 9.758 1.323 1.652 .967 982 +Y SCT 2445505.3671 9.325 1.062 1.437 .870 982 +Y SCT 2445508.3476 9.600 1.737 .978 982 +Y SCT 2445515.3593 9.430 1.500 .891 982 +Y SCT 2445872.3397 9.893 1.582 1.791 1.003 982 +Y SCT 2445873.3397 9.988 1.659 1.791 1.015 982 +Y SCT 2445874.3280 9.942 1.732 .995 982 +Y SCT 2445875.3163 9.724 1.614 .936 982 +Y SCT 2445876.3320 9.580 1.165 1.519 .910 982 +Y SCT 2445877.3242 9.462 1.123 1.470 .897 982 +Y SCT 2445878.3163 9.241 1.084 1.417 .852 982 +Y SCT 2445879.3125 9.386 1.179 1.541 .896 982 +Y SCT 2445880.3203 9.494 1.326 1.625 .937 982 +Y SCT 2445881.3125 9.734 1.519 1.691 1.003 982 +Y SCT 2445882.3046 9.850 1.781 1.000 982 +Y SCT 2445883.3163 10.038 1.789 1.043 982 +Y SCT 2445886.3203 9.643 1.191 1.550 .921 982 +Y SCT 2445887.3006 9.492 1.131 1.503 .882 982 +Y SCT 2447400.2267 9.497 1.597 .937 990 +Y SCT 2447401.1851 9.624 1.685 .935 990 +Y SCT 2447402.1786 9.797 1.741 .972 990 +Y SCT 2447403.1736 9.974 1.767 .993 990 +Y SCT 2447404.1593 10.012 1.763 .972 990 +Y SCT 2447408.1561 9.315 .989 1.417 990 +Y SCT 2447409.1710 9.304 1.488 .861 990 +Y SCT 2447410.1759 9.402 1.551 .897 990 +Y SCT 2447411.1785 9.572 1.646 .934 990 +Y SCT 2447412.1834 9.787 1.763 .999 990 +Y SCT 2447413.1661 9.881 1.782 .993 990 +Y SCT 2447414.1523 10.012 1.827 .995 990 +Y SCT 2447415.1572 9.944 1.765 .983 990 +Y SCT 2447416.1510 9.723 1.609 .929 990 +Y SCT 2447417.1458 9.564 1.553 .886 990 +Y SCT 2447418.1469 9.477 1.502 .854 990 +Y SCT 2447419.1394 9.254 1.437 .839 990 +Y SCT 2447420.1429 9.370 1.532 .881 990 +Y SCT 2447421.1368 9.512 1.610 .915 990 +Y SCT 2447422.1502 9.685 1.717 .964 990 +Y SCT 2447423.1399 9.820 1.801 .965 990 +Y SCT 2447424.1430 9.944 1.815 1.003 990 +Y SCT 2447425.1483 9.983 1.747 .984 990 +Y SCT 2447427.1548 9.682 1.545 .914 990 +Y SCT 2447428.1400 9.528 1.496 .882 990 +Y SCT 2447429.1375 9.225 1.435 .820 990 +Y SCT 2447430.1255 9.353 1.503 .872 990 +Y SCT 2447431.1267 9.457 1.590 .906 990 +Y SCT 2447432.1210 9.614 1.702 .930 990 +Y SCT 2447433.1161 9.782 1.746 .964 990 +Y SCT 2447434.1228 9.946 1.780 .990 990 +Y SCT 2449934.2769 9.597 .974 1.848 998 +Y SCT 2449935.2857 9.758 1.025 1.901 998 +Y SCT 2449936.2609 9.937 1.021 1.932 998 +Y SCT 2449939.2685 9.862 1.887 998 +Y SCT 2449942.2489 9.284 .862 1.659 998 +Y SCT 2449943.2377 9.413 1.754 998 +Y SCT 2449944.2439 9.559 1.781 998 +Y SCT 2449945.2206 9.687 1.891 998 +Y SCT 2449946.2162 9.817 1.920 998 +Y SCT 2449947.2168 10.021 1.971 998 +Y SCT 2449948.2189 10.064 1.966 998 +Y SCT 2449949.2230 9.948 1.905 998 +Y SCT 2449950.2187 9.732 1.603 .960 1.849 998 +Y SCT 2449952.2252 9.355 1.682 998 +Y SCT 2449953.2410 9.418 1.745 998 +Y SCT 2449954.2140 9.481 1.782 998 +Y SCT 2449955.2133 9.656 1.866 998 +Z SCT 2445488.3867 9.601 1.353 1.479 .807 982 +Z SCT 2445489.4179 9.739 1.462 1.547 .865 982 +Z SCT 2445490.3476 9.836 1.418 1.617 .866 982 +Z SCT 2445493.3945 10.024 .864 982 +Z SCT 2445496.3320 9.534 .958 1.290 .765 982 +Z SCT 2445497.3085 9.073 .824 1.085 .673 982 +Z SCT 2445498.3476 9.211 .851 1.193 .730 982 +Z SCT 2445501.3593 9.581 1.462 .815 982 +Z SCT 2445502.3514 9.690 1.393 1.535 .824 982 +Z SCT 2445503.3593 9.864 1.404 1.614 .868 982 +Z SCT 2445505.3710 10.043 1.449 1.605 .860 982 +Z SCT 2445508.3593 9.673 .999 1.359 .775 982 +Z SCT 2445515.3631 9.666 1.534 .825 982 +Z SCT 2445872.3437 9.206 .890 1.200 .701 982 +Z SCT 2445873.3437 9.288 1.002 1.262 .742 982 +Z SCT 2445874.3280 9.424 1.122 1.374 .771 982 +Z SCT 2445875.3203 9.551 1.447 .808 982 +Z SCT 2445876.3359 9.688 1.346 1.522 .828 982 +Z SCT 2445877.3242 9.837 1.453 1.570 .856 982 +Z SCT 2445878.3163 9.962 1.557 1.608 .859 982 +Z SCT 2445879.3125 10.047 1.619 .860 982 +Z SCT 2445880.3203 9.993 1.391 1.551 .863 982 +Z SCT 2445881.3163 9.825 1.225 1.422 .838 982 +Z SCT 2445882.3046 9.685 1.039 1.361 .776 982 +Z SCT 2445883.3163 9.574 1.295 .738 982 +Z SCT 2445886.3242 9.311 .993 1.278 .735 982 +Z SCT 2445887.3046 9.407 1.108 1.374 .767 982 +Z SCT 2447401.1876 10.029 1.643 .847 990 +Z SCT 2447402.1813 10.067 1.610 .850 990 +Z SCT 2447403.1765 9.959 1.503 .814 990 +Z SCT 2447404.1600 9.730 1.381 .755 990 +Z SCT 2447408.1593 9.241 .871 1.230 .704 990 +Z SCT 2447409.1737 9.343 1.329 .746 990 +Z SCT 2447410.1765 9.486 1.398 .789 990 +Z SCT 2447411.1795 9.625 1.505 .812 990 +Z SCT 2447412.1844 9.804 1.609 .862 990 +Z SCT 2447413.1668 9.895 1.599 .853 990 +Z SCT 2447414.1534 10.015 1.645 .847 990 +Z SCT 2447415.1583 10.036 1.628 .854 990 +Z SCT 2447416.1521 9.917 1.511 .815 990 +Z SCT 2447417.1465 9.711 1.394 .776 990 +Z SCT 2447418.1481 9.635 1.343 .727 990 +Z SCT 2447419.1404 9.355 1.218 .690 990 +Z SCT 2447420.1437 9.085 1.133 .646 990 +Z SCT 2447421.1376 9.242 1.257 .700 990 +Z SCT 2447422.1521 9.358 1.362 .748 990 +Z SCT 2447423.1411 9.491 1.431 .769 990 +Z SCT 2447424.1449 9.640 1.500 .808 990 +Z SCT 2447425.1503 9.772 1.548 .822 990 +Z SCT 2447427.1567 10.068 1.588 .844 990 +Z SCT 2447428.1416 10.068 1.574 .854 990 +Z SCT 2447429.1388 9.914 1.531 .801 990 +Z SCT 2447430.1269 9.730 1.355 .785 990 +Z SCT 2447431.1279 9.642 1.314 .756 990 +Z SCT 2447432.1223 9.304 1.187 .679 990 +Z SCT 2447433.1173 9.122 1.147 .657 990 +Z SCT 2447434.1236 9.274 1.228 .708 990 +Z SCT 2449934.2890 9.677 1.327 .792 1.480 998 +Z SCT 2449935.2952 9.326 .696 1.337 998 +Z SCT 2449936.2751 9.180 .683 1.329 998 +Z SCT 2449937.2626 9.314 1.245 .742 998 +Z SCT 2449938.2865 9.458 .778 1.496 998 +Z SCT 2449939.2838 9.563 1.541 998 +Z SCT 2449942.2582 9.976 1.645 998 +Z SCT 2449943.2483 10.057 1.669 998 +Z SCT 2449944.2525 10.107 1.634 998 +Z SCT 2449945.2531 9.967 1.627 998 +Z SCT 2449946.2247 9.726 1.520 998 +Z SCT 2449947.2224 9.685 1.494 998 +Z SCT 2449948.2261 9.337 1.363 998 +Z SCT 2449949.2288 9.228 1.349 998 +Z SCT 2449950.2263 9.276 1.401 998 +Z SCT 2449952.2312 9.554 1.547 998 +Z SCT 2449953.2461 9.712 1.608 998 +Z SCT 2449954.2189 9.837 1.610 998 +Z SCT 2449955.2180 9.979 1.664 998 +RU SCT 2445488.3437 9.724 1.593 1.970 1.148 982 +RU SCT 2445489.4022 9.798 1.981 1.143 982 +RU SCT 2445490.3320 9.875 1.822 2.020 1.143 982 +RU SCT 2445493.3867 9.886 1.571 1.908 1.126 982 +RU SCT 2445496.3163 9.787 1.363 1.807 1.087 982 +RU SCT 2445497.2968 9.388 1.067 1.618 1.000 982 +RU SCT 2445498.3397 8.829 1.021 1.398 .893 982 +RU SCT 2445501.3476 9.162 1.140 1.594 .999 982 +RU SCT 2445502.3554 9.211 1.262 1.682 1.008 982 +RU SCT 2445503.3476 9.296 1.323 1.735 1.045 982 +RU SCT 2445505.3320 9.477 1.476 1.855 1.092 982 +RU SCT 2445508.3359 9.768 1.751 1.982 1.129 982 +RU SCT 2445515.3476 9.798 1.591 1.840 1.102 982 +RU SCT 2445871.3397 9.560 1.367 1.686 1.030 982 +RU SCT 2445872.3514 8.894 1.137 1.409 .887 982 +RU SCT 2445873.3514 8.877 1.164 1.435 .893 982 +RU SCT 2445874.3359 9.022 1.220 1.521 .938 982 +RU SCT 2445875.3242 9.097 1.289 1.566 .969 982 +RU SCT 2445876.3437 9.180 1.300 1.654 1.000 982 +RU SCT 2445877.3359 9.262 1.418 1.703 1.039 982 +RU SCT 2445878.3280 9.347 1.538 1.783 1.052 982 +RU SCT 2445879.3203 9.442 1.653 1.814 1.078 982 +RU SCT 2445880.3280 9.503 1.690 1.876 1.103 982 +RU SCT 2445881.3242 9.638 1.882 1.895 1.139 982 +RU SCT 2445882.3125 9.721 1.791 1.947 1.139 982 +RU SCT 2445883.3242 9.822 1.981 1.985 1.148 982 +RU SCT 2445886.3320 9.934 1.851 1.945 1.147 982 +RU SCT 2445887.3125 9.879 1.708 1.899 1.122 982 +RU SCT 2447735.2873 9.779 1.937 1.139 991 +RU SCT 2447736.2725 9.852 1.959 1.132 991 +RU SCT 2447737.2730 9.892 1.939 1.127 991 +RU SCT 2447738.2671 9.874 1.950 1.110 991 +RU SCT 2447739.2360 9.888 1.893 1.118 991 +RU SCT 2447740.2537 9.832 1.860 1.089 991 +RU SCT 2447741.2411 9.852 1.859 1.097 991 +RU SCT 2447742.2607 9.843 1.784 1.091 991 +RU SCT 2447743.2519 9.564 1.647 1.002 991 +RU SCT 2447744.2257 8.867 1.391 .871 991 +RU SCT 2447745.2208 8.908 1.418 .887 991 +RU SCT 2447746.2258 9.020 1.489 .927 991 +RU SCT 2447747.2188 9.120 1.553 .981 991 +RU SCT 2447748.2252 9.181 1.631 .984 991 +RU SCT 2447749.2035 9.231 1.690 1.029 991 +RU SCT 2447750.2024 9.304 1.778 1.028 991 +RU SCT 2447751.2089 9.445 1.792 1.074 991 +RU SCT 2447752.1861 9.536 1.854 1.085 991 +RU SCT 2447753.1921 9.623 1.902 1.107 991 +RU SCT 2447754.2068 9.708 1.954 1.114 991 +RU SCT 2447755.2055 9.800 1.960 1.134 991 +RU SCT 2447756.2273 9.891 1.958 1.138 991 +RU SCT 2447757.1983 9.908 1.955 1.137 991 +RU SCT 2447758.1937 9.925 1.937 1.137 991 +RU SCT 2447759.1838 9.881 1.884 1.101 991 +RU SCT 2447760.1996 9.861 1.844 1.094 991 +RU SCT 2447761.1753 9.861 1.861 1.076 991 +RU SCT 2447762.1757 9.795 1.791 1.055 991 +RU SCT 2447763.1667 9.362 1.585 .973 991 +RU SCT 2447764.1677 8.814 1.385 .863 991 +RU SCT 2447766.1671 9.056 1.513 .949 991 +RU SCT 2447767.1863 9.133 1.582 .971 991 +RU SCT 2447768.1816 9.202 1.646 .998 991 +RU SCT 2447769.1818 9.272 1.679 1.033 991 +RU SCT 2447770.1704 9.347 1.767 1.048 991 +RU SCT 2447771.1689 9.455 1.823 1.065 991 +RU SCT 2447772.1664 9.547 1.850 1.089 991 +RU SCT 2447773.1687 9.645 1.899 1.109 991 +RU SCT 2447774.1972 9.748 1.921 1.120 991 +RU SCT 2447775.1610 9.829 1.962 1.138 991 +RU SCT 2447776.1646 9.887 1.930 1.136 991 +RU SCT 2449934.2839 9.137 .996 1.915 998 +RU SCT 2449935.2907 9.165 .983 1.903 998 +RU SCT 2449936.2701 9.306 1.031 2.006 998 +RU SCT 2449937.2581 9.440 1.795 1.059 998 +RU SCT 2449939.2751 9.564 2.155 998 +RU SCT 2449942.2528 9.826 2.219 998 +RU SCT 2449943.2430 9.853 2.209 998 +RU SCT 2449944.2493 9.975 2.225 998 +RU SCT 2449945.2501 9.979 2.227 998 +RU SCT 2449946.2219 9.936 2.200 998 +RU SCT 2449947.2195 9.930 2.188 998 +RU SCT 2449948.2237 9.943 2.190 998 +RU SCT 2449949.2259 9.940 2.157 998 +RU SCT 2449950.2237 9.703 2.064 998 +RU SCT 2449952.2287 8.933 1.779 998 +RU SCT 2449953.2435 9.094 1.853 998 +RU SCT 2449954.2164 9.150 1.895 998 +RU SCT 2449955.2157 9.247 1.962 998 +SS SCT 2447735.2899 8.053 .886 .508 991 +SS SCT 2447736.2746 8.085 .943 .547 991 +SS SCT 2447737.2742 8.299 1.054 .582 991 +SS SCT 2447738.2686 8.404 1.068 .604 991 +SS SCT 2447739.2366 7.923 .824 .507 991 +SS SCT 2447740.2700 8.187 .558 .954 991 +SS SCT 2447741.2421 8.371 1.054 .613 991 +SS SCT 2447742.2612 8.363 .547 .969 .591 991 +SS SCT 2447743.2523 7.938 .845 .495 991 +SS SCT 2447744.2344 8.253 .597 1.030 .578 991 +SS SCT 2447745.2396 8.406 1.072 .616 991 +SS SCT 2447746.2419 8.134 .885 .551 991 +SS SCT 2447747.2331 8.048 .913 .546 991 +SS SCT 2447748.2379 8.297 1.077 .598 991 +SS SCT 2447749.2194 8.414 .661 1.079 .588 991 +SS SCT 2447750.2070 7.912 .797 .505 991 +SS SCT 2447751.2130 8.151 .974 .571 991 +SS SCT 2447752.1909 8.388 1.064 .604 991 +SS SCT 2447753.1971 8.378 1.031 .587 991 +SS SCT 2447754.2129 7.967 .856 .502 991 +SS SCT 2447755.2197 8.239 1.028 .580 991 +SS SCT 2447756.2453 8.411 1.063 .622 991 +SS SCT 2447757.2207 8.148 .493 .901 .559 991 +SS SCT 2447758.2220 8.050 .465 .936 .533 991 +SS SCT 2447759.1902 8.321 .624 1.026 .613 991 +SS SCT 2447760.2037 8.434 1.079 .601 991 +SS SCT 2447761.1794 7.964 .436 .829 .507 991 +SS SCT 2447762.1805 8.163 .970 .560 991 +SS SCT 2447763.1702 8.355 1.054 .626 991 +SS SCT 2447764.1719 8.385 1.032 .594 991 +SS SCT 2447766.1722 8.221 .999 .592 991 +SS SCT 2447767.1906 8.440 1.058 .627 991 +SS SCT 2447768.1866 8.212 .535 .912 .555 991 +SS SCT 2447769.1856 8.038 .454 .925 .526 991 +SS SCT 2447770.1754 8.295 .594 1.045 .619 991 +SS SCT 2447771.1734 8.437 .621 1.081 .614 991 +SS SCT 2447772.1711 7.959 .429 .815 .504 991 +SS SCT 2447773.1737 8.143 .964 .572 991 +SS SCT 2447774.2028 8.359 .655 1.062 .614 991 +SS SCT 2447775.1658 8.416 .593 1.037 .603 991 +SS SCT 2447776.1692 7.909 .438 .837 .497 991 +SS SCT 2448503.2122 7.942 .904 .495 993 +SS SCT 2448504.1708 8.239 1.018 .592 993 +SS SCT 2448505.1592 8.434 1.061 .616 993 +SS SCT 2448506.1545 8.182 .936 .550 993 +SS SCT 2448507.1481 8.056 .914 .540 993 +SS SCT 2448508.1444 8.320 1.061 .601 993 +SS SCT 2448509.1488 8.442 1.091 .602 993 +SS SCT 2448510.1499 7.956 .463 .839 .488 993 +SS SCT 2448511.1481 8.151 .554 .992 .563 993 +SS SCT 2448512.1479 8.374 1.094 .609 993 +SS SCT 2448513.1524 8.387 1.049 .598 993 +SS SCT 2448514.1538 7.965 .850 .511 993 +SS SCT 2448515.1464 8.207 1.013 .571 993 +SS SCT 2448517.1414 8.214 .923 .564 993 +SS SCT 2448518.1435 8.074 .912 .568 993 +SS SCT 2448519.1662 8.302 1.076 .587 993 +SS SCT 2448520.1348 8.451 1.077 .611 993 +SS SCT 2448521.1507 7.943 .838 .494 993 +SS SCT 2448522.1413 8.098 .975 .551 993 +SS SCT 2448523.1317 8.381 1.094 .600 993 +SS SCT 2449934.2915 8.249 .950 .578 1.073 998 +SS SCT 2449935.2961 8.111 .556 1.072 998 +SS SCT 2449936.2806 8.362 .598 1.177 998 +SS SCT 2449937.2638 8.507 1.088 .628 998 +SS SCT 2449938.2890 8.062 .532 1.029 998 +SS SCT 2449939.2857 8.199 .570 1.130 998 +SS SCT 2449941.2850 8.453 1.193 998 +SS SCT 2449942.2598 8.014 .996 998 +SS SCT 2449943.2499 8.245 .586 1.130 998 +SS SCT 2449944.2545 8.498 1.183 998 +SS SCT 2449945.2545 8.285 1.106 998 +SS SCT 2449946.2254 8.031 1.022 998 +SS SCT 2449947.2230 8.358 1.182 998 +SS SCT 2449948.2267 8.491 1.198 998 +SS SCT 2449949.2295 8.094 1.042 998 +SS SCT 2449950.2269 8.130 1.084 998 +SS SCT 2449952.2321 8.465 1.186 998 +SS SCT 2449953.2469 7.997 1.012 998 +SS SCT 2449954.2196 8.261 1.120 998 +SS SCT 2449955.2187 8.471 1.201 998 +SS SCT 2450306.1713 8.062 .918 .549 971 +SS SCT 2450307.1882 8.349 1.052 .605 971 +SS SCT 2450310.2319 8.180 .984 .577 971 +SS SCT 2450311.1617 8.340 1.081 .605 971 +SS SCT 2450312.1562 8.346 1.026 .590 971 +SS SCT 2450313.1618 7.953 .867 .513 971 +SS SCT 2450314.1474 8.240 1.021 .593 971 +SS SCT 2450315.1828 8.422 1.038 .621 971 +SS SCT 2450316.1929 8.158 .893 .496 971 +SS SCT 2450317.1927 8.113 .913 .538 971 +SS SCT 2450318.1819 8.365 1.065 .611 971 +SS SCT 2450319.1735 8.452 1.057 .604 971 +SS SCT 2450320.1906 7.939 .842 .495 971 +SS SCT 2450321.1743 8.171 .968 .565 971 +SS SCT 2450322.1724 8.386 1.050 .607 971 +SS SCT 2450323.1704 8.393 .993 .593 971 +SS SCT 2450324.1883 7.987 .855 .531 971 +SS SCT 2450325.1613 8.254 1.003 .593 971 +SU SCT 2446252.3207 13.266 .672 .438 987 +SU SCT 2446253.2896 13.997 .959 .673 987 +SU SCT 2446255.3176 13.356 .627 .491 987 +SU SCT 2446256.3382 14.023 .954 .594 987 +SU SCT 2446257.3464 13.747 1.016 .599 987 +SU SCT 2446258.2961 13.412 .669 .486 987 +SU SCT 2446259.3053 14.051 1.026 .627 987 +SU SCT 2446260.2852 13.789 .982 .576 987 +SU SCT 2446261.2889 13.398 .692 .513 987 +SU SCT 2446262.2846 14.092 1.063 .607 987 +SU SCT 2446263.3078 13.807 1.058 .639 987 +SU SCT 2446265.2967 14.009 .955 .582 987 +SU SCT 2446266.2655 13.893 1.072 .584 987 +SU SCT 2446268.2664 13.785 .870 .525 987 +SU SCT 2446269.2587 13.902 1.026 .615 987 +SU SCT 2446270.2601 13.421 .874 .498 987 +SU SCT 2446271.2535 13.532 .744 .488 987 +SU SCT 2446272.2230 13.939 1.034 .637 987 +SU SCT 2446273.2936 13.617 .838 .643 987 +SU SCT 2446283.1922 13.288 .666 .430 987 +SU SCT 2446609.3402 13.262 .649 .459 988 +SU SCT 2446610.3187 13.931 1.036 .618 988 +SU SCT 2446611.3144 13.613 .955 .575 988 +SU SCT 2446612.3127 13.273 .655 .450 988 +SU SCT 2446613.3037 13.961 .961 .618 988 +SU SCT 2446614.3154 13.684 .936 .584 988 +SU SCT 2446615.2721 13.209 .419 .704 .403 988 +SU SCT 2446616.2838 13.981 .998 .622 988 +SU SCT 2446617.2794 13.624 .997 .600 988 +SU SCT 2446618.2781 13.260 .505 .734 .436 988 +SU SCT 2446619.2781 14.049 .950 .664 988 +SU SCT 2446620.2601 13.717 .997 .589 988 +SU SCT 2446621.2541 13.344 .433 .714 .485 988 +SU SCT 2446622.2609 14.017 1.030 .587 988 +SU SCT 2447401.2061 13.822 1.085 .568 990 +SU SCT 2447402.1972 13.855 1.063 .562 990 +SU SCT 2447403.1895 13.442 .805 .486 990 +SU SCT 2447404.1775 13.703 .828 .539 990 +SU SCT 2447408.1664 13.852 1.080 .603 990 +SU SCT 2447409.1801 13.491 .947 .538 990 +SU SCT 2447410.1796 13.239 .660 .412 990 +SU SCT 2447411.1840 13.877 1.056 .563 990 +SU SCT 2447412.1910 13.632 1.029 .579 990 +SU SCT 2447413.1698 13.216 .705 .408 990 +SU SCT 2447414.1561 13.881 1.042 .599 990 +SU SCT 2447415.1615 13.641 .982 .585 990 +SU SCT 2447416.1539 13.245 .691 .430 990 +SU SCT 2447417.1505 13.878 1.039 .563 990 +SU SCT 2447418.1494 13.713 .971 .552 990 +SU SCT 2447419.1425 13.357 .764 .444 990 +SU SCT 2447420.1450 13.982 1.003 .616 990 +SU SCT 2447421.1385 13.719 1.062 .621 990 +SU SCT 2447433.1197 13.857 1.094 .586 990 +SU SCT 2447434.1246 13.493 .883 .535 990 +SU SCT 2447735.2988 13.415 .821 .494 991 +SU SCT 2447736.2811 13.902 .936 .596 991 +SU SCT 2447737.2790 1.010 991 +SU SCT 2447738.2743 13.409 .899 .524 991 +SU SCT 2447739.2487 13.684 .871 .519 991 +SU SCT 2447740.2797 13.970 1.086 .586 991 +SU SCT 2447741.2490 13.453 .816 .537 991 +SU SCT 2447742.2736 13.239 .652 .420 991 +SU SCT 2447743.2582 13.862 1.049 .624 991 +SU SCT 2447744.2405 13.494 .903 .520 991 +SU SCT 2447745.2434 13.232 .598 .406 991 +SU SCT 2447746.2515 13.909 .976 .662 991 +SU SCT 2447747.2378 13.580 .945 .581 991 +SU SCT 2447748.2436 13.222 .667 .424 991 +SU SCT 2447749.2263 13.912 .924 991 +SU SCT 2447760.2069 13.361 .815 .480 991 +SU SCT 2447761.1814 14.010 .995 .595 991 +SU SCT 2447762.1815 13.812 1.011 991 +SU SCT 2447763.1715 13.332 .799 991 +SU SCT 2447764.1732 13.910 .981 991 +SU SCT 2447766.1736 13.407 .821 991 +SU SCT 2447767.1979 13.630 .752 .571 991 +SU SCT 2447768.1944 13.879 1.035 .586 991 +SU SCT 2447769.1919 13.463 .881 .521 991 +SU SCT 2447770.1799 13.299 .666 .427 991 +SU SCT 2447771.1745 13.883 1.043 .588 991 +SU SCT 2447772.1719 13.542 .861 .560 991 +SU SCT 2447773.1755 13.220 .640 .434 991 +SU SCT 2447774.2046 13.856 1.019 .577 991 +SU SCT 2447775.1720 13.567 .992 .561 991 +SU SCT 2447776.1738 13.219 .637 .434 991 +SU SCT 2448881.1279 13.230 .685 994 +SU SCT 2448882.1440 13.926 1.027 .588 994 +SU SCT 2448883.1395 13.659 .901 .622 994 +SU SCT 2448884.1356 13.309 .689 .443 994 +SU SCT 2448885.1310 13.944 .991 .599 994 +SU SCT 2448886.1390 13.681 1.018 .550 994 +SU SCT 2448887.1501 13.259 .812 .350 994 +SU SCT 2448888.1337 14.040 1.018 .634 994 +SU SCT 2448889.1555 13.804 1.116 994 +SU SCT 2448890.1312 13.389 .748 .509 994 +SU SCT 2448891.1295 13.997 1.096 .577 994 +SU SCT 2448892.1252 13.805 1.012 .581 994 +SU SCT 2448893.1279 13.426 .813 .516 994 +SU SCT 2448894.1294 13.952 .954 .600 994 +SU SCT 2449522.8860 13.457 .796 996 +SU SCT 2449543.7864 13.755 .935 .489 .491 996 +SU SCT 2449559.8330 13.771 .935 996 +SU SCT 2449561.7689 13.900 1.051 .543 .560 996 +SU SCT 2449563.7591 13.324 .670 .435 .431 996 +SU SCT 2449564.6435 13.975 .954 .592 .579 996 +SU SCT 2449945.1819 13.337 .854 998 +SU SCT 2449946.1801 13.940 998 +SU SCT 2449947.1832 13.649 1.153 998 +SU SCT 2449948.1852 13.320 .907 998 +SU SCT 2449949.1869 13.943 1.186 998 +SU SCT 2449952.1944 13.977 1.227 998 +SU SCT 2449954.2044 13.370 .936 998 +SU SCT 2450009.1082 13.789 1.189 998 +SU SCT 2450011.1039 13.803 1.095 998 +SU SCT 2450012.1141 13.811 1.140 998 +SU SCT 2450017.0829 13.362 .797 998 +SU SCT 2450018.1065 13.979 1.119 998 +SU SCT 2450020.0788 13.289 .896 998 +TY SCT 2445489.3945 10.573 1.650 .986 982 +TY SCT 2445490.3203 10.280 1.531 .931 982 +TY SCT 2445493.3828 10.857 1.816 1.093 982 +TY SCT 2445496.3242 11.201 1.948 1.145 982 +TY SCT 2445497.3046 11.186 1.906 1.129 982 +TY SCT 2445498.3437 11.045 1.830 1.104 982 +TY SCT 2445501.3554 10.312 1.505 .946 982 +TY SCT 2445502.3593 10.556 1.671 1.011 982 +TY SCT 2445503.3437 10.673 1.769 1.047 982 +TY SCT 2445505.3359 11.011 1.914 1.120 982 +TY SCT 2445508.3397 11.195 1.905 1.115 982 +TY SCT 2445515.3554 10.790 1.825 1.087 982 +TY SCT 2445871.3359 11.153 1.940 1.136 982 +TY SCT 2445872.3514 11.203 1.959 1.127 982 +TY SCT 2445873.3514 11.140 1.885 1.117 982 +TY SCT 2445874.3359 10.977 1.803 1.061 982 +TY SCT 2445875.3242 10.754 1.667 1.023 982 +TY SCT 2445876.3397 10.575 1.599 .998 982 +TY SCT 2445877.3320 10.305 1.512 .948 982 +TY SCT 2445878.3280 10.570 1.674 1.011 982 +TY SCT 2445879.3203 10.698 1.759 1.044 982 +TY SCT 2445880.3280 10.823 1.844 1.087 982 +TY SCT 2445881.3242 11.032 1.822 1.874 1.127 982 +TY SCT 2445882.3125 11.152 1.963 1.123 982 +TY SCT 2445883.3242 11.243 1.960 1.129 982 +TY SCT 2445886.3280 10.776 1.444 1.683 1.026 982 +TY SCT 2445887.3085 10.565 1.247 1.626 .976 982 +TY SCT 2447735.2850 10.529 1.655 .993 991 +TY SCT 2447736.2716 10.676 1.741 1.034 991 +TY SCT 2447737.2714 10.795 1.825 1.055 991 +TY SCT 2447738.2667 10.957 1.878 1.094 991 +TY SCT 2447739.2345 11.115 1.929 1.113 991 +TY SCT 2447740.2531 11.217 1.928 1.114 991 +TY SCT 2447741.2406 11.152 1.869 1.099 991 +TY SCT 2447742.2595 11.032 1.748 1.084 991 +TY SCT 2447743.2508 10.839 1.674 1.013 991 +TY SCT 2447744.2245 10.600 1.630 .972 991 +TY SCT 2447745.2204 10.287 1.515 .921 991 +TY SCT 2447746.2250 10.511 1.655 .980 991 +TY SCT 2447747.2175 10.693 1.715 1.033 991 +TY SCT 2447748.2238 10.795 1.056 991 +TY SCT 2447749.2023 10.942 1.888 1.090 991 +TY SCT 2447750.2005 11.094 1.936 1.098 991 +TY SCT 2447751.2074 11.236 1.910 1.145 991 +TY SCT 2447752.1847 11.181 1.912 1.084 991 +TY SCT 2447753.1906 11.048 1.812 1.067 991 +TY SCT 2447754.2060 10.812 1.701 1.004 991 +TY SCT 2447755.2038 10.629 1.641 .979 991 +TY SCT 2447756.2260 10.304 1.498 .936 991 +TY SCT 2447757.1968 10.500 1.631 .982 991 +TY SCT 2447758.1924 10.637 1.728 1.028 991 +TY SCT 2447759.1823 10.813 1.775 1.067 991 +TY SCT 2447760.1973 10.957 1.872 1.081 991 +TY SCT 2447761.1740 11.116 1.916 1.103 991 +TY SCT 2447762.1743 11.209 1.932 1.115 991 +TY SCT 2447763.1654 11.192 1.862 1.109 991 +TY SCT 2447764.1672 11.032 1.819 1.062 991 +TY SCT 2447766.1666 10.662 1.645 .976 991 +TY SCT 2447767.1855 10.320 1.493 .935 991 +TY SCT 2447768.1802 10.492 1.647 .957 991 +TY SCT 2447769.1808 10.642 1.678 1.041 991 +TY SCT 2447770.1697 10.772 1.796 1.053 991 +TY SCT 2447771.1678 10.954 1.868 1.095 991 +TY SCT 2447772.1645 11.103 1.913 1.109 991 +TY SCT 2447773.1676 11.216 1.916 1.128 991 +TY SCT 2447774.1956 11.194 1.901 1.107 991 +TY SCT 2447775.1599 11.080 1.803 1.097 991 +TY SCT 2447776.1638 10.836 1.733 1.031 991 +TY SCT 2449934.2862 10.375 .913 1.820 998 +TY SCT 2449935.2929 10.563 1.009 1.930 998 +TY SCT 2449936.2724 10.787 1.089 2.062 998 +TY SCT 2449939.2795 11.242 2.215 998 +TY SCT 2449942.2559 11.042 2.091 998 +TY SCT 2449943.2446 10.776 1.980 998 +TY SCT 2449944.2503 10.630 1.932 998 +TY SCT 2449945.2510 10.371 1.861 998 +TY SCT 2449946.2229 10.581 1.965 998 +TY SCT 2449947.2203 10.765 2.079 998 +TY SCT 2449948.2245 10.911 2.111 998 +TY SCT 2449949.2274 11.113 2.184 998 +TY SCT 2449950.2245 11.200 2.198 998 +TY SCT 2449952.2295 11.212 2.169 998 +TY SCT 2449953.2446 11.057 2.087 998 +TY SCT 2449954.2173 10.829 2.007 998 +TY SCT 2449955.2164 10.650 1.941 998 +UZ SCT 2445493.4256 11.340 1.898 1.116 982 +UZ SCT 2445496.3631 10.764 1.670 1.046 982 +UZ SCT 2445497.3280 10.850 1.769 1.048 982 +UZ SCT 2445498.3710 11.029 1.823 1.099 982 +UZ SCT 2445501.3828 11.288 2.007 1.125 982 +UZ SCT 2445502.3750 11.429 2.078 1.172 982 +UZ SCT 2445503.3710 11.536 2.057 1.166 982 +UZ SCT 2445505.3867 11.641 2.089 1.204 982 +UZ SCT 2445508.3750 11.397 1.167 982 +UZ SCT 2445515.3867 11.193 1.993 1.135 982 +UZ SCT 2447737.2485 10.888 1.033 991 +UZ SCT 2447738.2546 10.764 1.698 1.037 991 +UZ SCT 2447739.2232 10.886 1.769 1.076 991 +UZ SCT 2447740.2402 11.074 1.856 1.090 991 +UZ SCT 2447741.2275 11.123 1.890 1.109 991 +UZ SCT 2447742.2391 11.272 1.966 1.132 991 +UZ SCT 2447743.2224 11.336 1.990 1.172 991 +UZ SCT 2447744.2096 11.451 2.062 1.169 991 +UZ SCT 2447745.2087 11.568 2.058 1.179 991 +UZ SCT 2447746.2125 11.625 2.050 1.187 991 +UZ SCT 2447747.2067 11.649 2.074 1.161 991 +UZ SCT 2447748.2084 11.598 2.020 1.162 991 +UZ SCT 2447749.1880 11.436 1.956 1.167 991 +UZ SCT 2447750.1857 11.341 1.916 1.117 991 +UZ SCT 2447751.1919 11.201 1.865 1.074 991 +UZ SCT 2447752.1695 10.867 1.695 1.036 991 +UZ SCT 2447753.1757 10.799 1.721 1.032 991 +UZ SCT 2447754.1883 10.932 1.798 1.071 991 +UZ SCT 2447755.1822 11.068 1.882 1.079 991 +UZ SCT 2447756.2077 11.174 1.967 1.134 991 +UZ SCT 2447757.1800 11.282 1.987 1.146 991 +UZ SCT 2447758.1732 11.367 2.067 1.147 991 +UZ SCT 2447759.1671 11.498 2.038 1.196 991 +UZ SCT 2447760.1836 11.593 2.095 1.174 991 +UZ SCT 2447761.1628 11.641 2.059 1.190 991 +UZ SCT 2447762.1633 11.633 2.067 1.177 991 +UZ SCT 2447763.1566 11.546 2.010 1.172 991 +UZ SCT 2447764.1575 11.416 1.928 1.148 991 +UZ SCT 2447766.1565 11.130 1.778 1.063 991 +UZ SCT 2447767.1741 10.794 1.719 1.001 991 +UZ SCT 2447768.1668 10.847 1.722 1.061 991 +UZ SCT 2447769.1605 10.937 1.803 1.084 991 +UZ SCT 2447770.1567 11.069 1.856 1.111 991 +UZ SCT 2447771.1545 11.171 1.981 1.123 991 +UZ SCT 2447772.1522 11.279 1.992 1.136 991 +UZ SCT 2447773.1568 11.381 2.059 1.160 991 +UZ SCT 2447774.1854 11.490 2.083 1.165 991 +UZ SCT 2447775.1494 11.597 2.074 1.191 991 +UZ SCT 2447776.1525 11.629 2.074 1.177 991 +UZ SCT 2448101.2268 11.694 2.064 1.225 992 +UZ SCT 2448102.2274 11.574 2.023 1.172 992 +UZ SCT 2448103.2058 11.448 1.931 1.154 992 +UZ SCT 2448104.2178 11.365 1.906 1.144 992 +UZ SCT 2448108.1992 10.945 1.806 1.073 992 +UZ SCT 2448109.1919 11.053 1.908 1.111 992 +UZ SCT 2448110.1938 11.143 1.871 1.110 992 +UZ SCT 2448111.2068 11.261 1.961 1.141 992 +UZ SCT 2448112.2008 11.358 2.051 1.157 992 +UZ SCT 2448113.1908 11.511 2.043 1.227 992 +UZ SCT 2448114.2091 11.577 2.148 1.202 992 +UZ SCT 2448115.1911 11.663 2.084 1.193 992 +UZ SCT 2448117.2154 11.543 2.070 1.189 992 +UZ SCT 2448118.2159 11.417 1.949 1.153 992 +UZ SCT 2448119.1881 11.419 1.894 992 +UZ SCT 2448123.1814 11.004 1.812 1.107 992 +UZ SCT 2448126.2077 11.301 1.990 1.164 992 +UZ SCT 2448127.1863 11.396 2.035 1.194 992 +UZ SCT 2449934.2495 11.201 1.114 2.061 998 +UZ SCT 2449935.2671 10.900 1.063 1.989 998 +UZ SCT 2449936.2171 10.914 1.080 2.033 998 +UZ SCT 2449942.2282 11.593 2.267 998 +UZ SCT 2449943.2151 11.643 2.289 998 +UZ SCT 2449944.1903 11.779 2.275 998 +UZ SCT 2449945.1993 11.722 2.287 998 +UZ SCT 2449946.1923 11.634 2.283 998 +UZ SCT 2449947.2066 11.474 2.225 998 +UZ SCT 2449948.1814 11.433 2.159 998 +UZ SCT 2449949.1829 11.110 2.082 998 +UZ SCT 2449950.1850 10.904 2.029 998 +UZ SCT 2449952.1898 11.104 2.121 998 +UZ SCT 2449953.2229 11.232 2.173 998 +UZ SCT 2449954.2083 11.319 2.230 998 +UZ SCT 2449955.2084 11.408 2.206 998 +BX SCT 2446252.3062 12.141 2.006 1.326 987 +BX SCT 2446253.2774 12.258 2.081 1.366 987 +BX SCT 2446255.3093 12.637 2.138 1.404 987 +BX SCT 2446256.3327 12.237 1.968 1.322 987 +BX SCT 2446257.3361 11.816 1.830 1.232 987 +BX SCT 2446258.2924 12.058 1.938 1.299 987 +BX SCT 2446259.2954 12.224 2.029 1.349 987 +BX SCT 2446260.2798 12.451 2.127 1.372 987 +BX SCT 2446261.2852 12.591 2.141 1.421 987 +BX SCT 2446262.2808 12.520 2.091 1.365 987 +BX SCT 2446263.3039 11.843 1.812 1.226 987 +BX SCT 2446265.2916 12.230 2.024 1.338 987 +BX SCT 2446266.2612 12.376 2.072 1.386 987 +BX SCT 2446267.2507 12.558 2.172 1.424 987 +BX SCT 2446268.2624 12.613 2.125 1.394 987 +BX SCT 2446269.2547 12.191 1.944 1.305 987 +BX SCT 2446270.2561 11.812 1.851 1.244 987 +BX SCT 2446271.2483 12.071 1.952 1.313 987 +BX SCT 2446272.2164 12.240 2.077 1.342 987 +BX SCT 2446273.2879 12.495 1.402 987 +BX SCT 2446275.2810 12.425 2.089 1.362 987 +BX SCT 2446283.1884 11.894 1.918 1.234 987 +BX SCT 2446296.1712 11.901 1.931 1.234 987 +BX SCT 2446297.1764 12.146 2.002 1.327 987 +BX SCT 2449521.8215 12.060 1.963 996 +BX SCT 2449522.7873 12.211 1.905 996 +BX SCT 2449534.7433 12.127 1.935 1.318 1.221 996 +BX SCT 2449543.7730 12.650 2.197 1.380 1.276 996 +BX SCT 2449559.8287 12.086 1.868 996 +BX SCT 2449561.7634 12.345 2.010 1.371 1.250 996 +BX SCT 2449563.7542 12.665 2.139 1.401 1.287 996 +BX SCT 2449564.6370 12.257 1.916 1.298 1.232 996 +BX SCT 2449623.1583 11.879 1.807 1.225 995 +BX SCT 2449624.1732 12.115 1.997 1.305 995 +BX SCT 2449625.1509 12.336 2.258 1.319 995 +BX SCT 2449632.1461 12.371 2.183 1.361 995 +BX SCT 2449633.1404 12.554 2.170 1.359 995 +BX SCT 2449634.1506 12.680 2.325 1.382 995 +BX SCT 2449934.2956 12.533 1.428 2.682 998 +BX SCT 2449935.2989 12.637 1.371 2.653 998 +BX SCT 2449936.2850 12.360 1.326 2.551 998 +BX SCT 2449944.2581 12.009 2.454 998 +BX SCT 2449945.2576 12.179 2.579 998 +BX SCT 2449946.2019 12.339 2.623 998 +BX SCT 2449948.2283 12.672 2.732 998 +BX SCT 2449949.2309 12.358 2.585 998 +BX SCT 2449950.2281 11.845 2.391 998 +BX SCT 2449952.2334 12.277 2.623 998 +BX SCT 2449953.2484 12.480 2.683 998 +BX SCT 2449954.2216 12.637 2.714 998 +BX SCT 2449955.2202 12.561 2.647 998 +CK SCT 2448101.2404 10.382 1.522 .905 992 +CK SCT 2448102.2486 10.465 1.588 .934 992 +CK SCT 2448103.2165 10.582 1.618 .975 992 +CK SCT 2448104.2298 10.699 1.677 .994 992 +CK SCT 2448108.2121 10.357 1.487 .898 992 +CK SCT 2448109.2048 10.455 1.558 .933 992 +CK SCT 2448110.2034 10.501 1.625 .940 992 +CK SCT 2448111.2162 10.652 1.648 .981 992 +CK SCT 2448112.2071 10.772 1.714 .986 992 +CK SCT 2448113.1990 10.825 1.672 1.010 992 +CK SCT 2448114.2178 10.576 1.588 .961 992 +CK SCT 2448115.1987 10.381 1.446 .914 992 +CK SCT 2448116.2059 10.437 1.530 .935 992 +CK SCT 2448117.2229 10.518 1.599 .942 992 +CK SCT 2448118.2205 10.632 1.659 .971 992 +CK SCT 2448119.1953 10.773 1.681 .995 992 +CK SCT 2448123.1945 10.379 1.481 .927 992 +CK SCT 2448126.2166 10.678 1.653 .985 992 +CK SCT 2448127.1925 10.777 1.716 1.003 992 +CK SCT 2449934.2806 10.575 .979 1.868 998 +CK SCT 2449936.2671 10.831 1.011 1.945 998 +CK SCT 2449944.2461 10.886 1.925 998 +CK SCT 2449945.2227 10.843 1.940 998 +CK SCT 2449946.2190 10.572 1.820 998 +CK SCT 2449947.2184 10.409 1.769 998 +CK SCT 2449948.2220 10.511 1.822 998 +CK SCT 2449949.2243 10.587 1.862 998 +CK SCT 2449950.2219 10.677 1.907 998 +CK SCT 2449952.2266 10.931 2.020 998 +CK SCT 2449953.2422 10.727 1.885 998 +CK SCT 2449954.2155 10.450 1.769 998 +CK SCT 2449955.2142 10.471 1.792 998 +CN SCT 2444840.2147 12.530 2.168 982 +CN SCT 2444841.1953 12.648 2.222 982 +CN SCT 2444844.1718 12.733 2.142 982 +CN SCT 2444845.1679 12.477 2.063 982 +CN SCT 2444846.1953 12.286 2.032 982 +CN SCT 2444847.1679 12.195 2.004 982 +CN SCT 2444848.1679 12.162 1.994 982 +CN SCT 2444849.1718 12.376 2.092 982 +CN SCT 2444850.1756 12.494 2.163 982 +CN SCT 2444851.1679 12.640 2.218 982 +CN SCT 2444852.1679 12.760 2.230 982 +CN SCT 2444853.1718 12.816 2.246 982 +CN SCT 2444854.1756 12.711 2.187 982 +CN SCT 2444880.1601 12.489 2.181 982 +CN SCT 2444881.1445 12.635 2.254 982 +CN SCT 2445173.2929 12.798 2.168 982 +CN SCT 2445174.3046 12.629 2.190 982 +CN SCT 2445175.3280 12.417 2.048 982 +CN SCT 2445178.3046 12.271 2.049 982 +CN SCT 2445180.2304 12.542 2.139 982 +CN SCT 2445190.2460 12.530 2.199 982 +CN SCT 2445191.2343 12.699 2.228 982 +CN SCT 2445192.2381 12.797 2.235 982 +CN SCT 2445193.2187 12.780 2.216 982 +CN SCT 2445194.2187 12.674 2.159 982 +CN SCT 2445195.2381 12.404 2.019 982 +CN SCT 2445196.2734 12.301 1.981 982 +CN SCT 2445198.2381 12.236 2.043 982 +CN SCT 2445199.2381 12.422 2.127 982 +CN SCT 2445201.2381 12.691 2.223 982 +CN SCT 2445871.3320 12.753 2.295 1.412 982 +CN SCT 2445872.3476 12.794 2.301 1.417 982 +CN SCT 2445873.3476 12.725 2.181 1.419 982 +CN SCT 2445874.3320 12.541 2.040 1.382 982 +CN SCT 2445875.3203 12.310 2.005 1.312 982 +CN SCT 2445876.3397 12.202 1.998 1.307 982 +CN SCT 2445877.3320 12.186 2.010 1.326 982 +CN SCT 2445878.3242 12.363 2.133 1.349 982 +CN SCT 2445879.3163 12.509 2.160 1.373 982 +CN SCT 2445880.3242 12.606 2.232 1.418 982 +CN SCT 2445881.3203 12.813 2.223 1.466 982 +CN SCT 2445882.3085 12.822 2.217 1.443 982 +CN SCT 2445883.3203 12.761 2.180 1.410 982 +CN SCT 2445886.3242 12.204 1.990 1.302 982 +CN SCT 2445887.3046 12.138 2.005 1.306 982 +CN SCT 2447735.2830 12.149 1.944 1.264 991 +CN SCT 2447736.2695 12.266 2.040 1.304 991 +CN SCT 2447737.2686 12.403 2.089 1.333 991 +CN SCT 2447738.2649 12.533 2.192 1.390 991 +CN SCT 2447739.2305 12.688 2.255 1.399 991 +CN SCT 2447740.2498 12.819 2.265 1.407 991 +CN SCT 2447741.2378 12.794 1.402 991 +CN SCT 2447742.2588 12.677 2.101 1.350 991 +CN SCT 2447743.2474 12.439 2.018 1.314 991 +CN SCT 2447744.2226 12.281 1.989 1.291 991 +CN SCT 2447745.2176 12.145 1.954 1.260 991 +CN SCT 2447746.2226 12.254 2.007 1.327 991 +CN SCT 2447747.2140 12.428 2.107 1.336 991 +CN SCT 2447748.2220 12.554 2.171 1.362 991 +CN SCT 2447749.2011 12.684 1.401 991 +CN SCT 2447750.1950 12.785 2.229 1.407 991 +CN SCT 2447751.2047 12.800 2.231 1.400 991 +CN SCT 2447752.1827 12.641 2.192 1.328 991 +CN SCT 2447753.1861 12.441 2.021 1.329 991 +CN SCT 2447754.2010 12.292 2.012 1.276 991 +CN SCT 2447755.2024 12.145 1.954 1.261 991 +CN SCT 2447756.2246 12.282 2.013 1.310 991 +CN SCT 2447757.1952 12.409 2.160 1.339 991 +CN SCT 2447758.1913 12.542 1.362 991 +CN SCT 2447759.1815 12.709 2.250 1.395 991 +CN SCT 2447760.1964 12.786 1.391 991 +CN SCT 2447761.1702 12.800 2.226 1.384 991 +CN SCT 2447762.1729 12.672 2.163 1.353 991 +CN SCT 2447763.1623 12.429 2.047 1.319 991 +CN SCT 2447764.1653 12.299 1.985 1.293 991 +CN SCT 2447766.1636 12.233 2.038 1.301 991 +CN SCT 2447767.1831 12.419 2.118 1.334 991 +CN SCT 2447768.1759 12.521 2.164 1.355 991 +CN SCT 2447769.1768 12.688 2.248 1.397 991 +CN SCT 2447770.1676 12.791 2.246 1.395 991 +CN SCT 2447771.1631 12.806 2.226 1.405 991 +CN SCT 2447772.1613 12.689 2.084 1.377 991 +CN SCT 2447773.1653 12.404 2.057 1.302 991 +CN SCT 2447774.1928 12.293 2.014 1.281 991 +CN SCT 2447775.1571 12.144 1.956 1.268 991 +CN SCT 2447776.1608 12.223 2.009 1.283 991 +EV SCT 2446606.2689 10.063 1.190 .728 988 +EV SCT 2446607.3153 10.274 .917 1.275 .786 988 +EV SCT 2446608.2717 10.105 .831 1.165 .741 988 +EV SCT 2446609.3177 10.049 .820 1.173 .717 988 +EV SCT 2446610.2997 10.263 1.270 .774 988 +EV SCT 2446611.2930 10.108 .843 1.183 .737 988 +EV SCT 2446612.2968 10.063 1.139 .742 988 +EV SCT 2446613.2837 10.266 .877 1.248 .774 988 +EV SCT 2446614.2762 10.148 .809 1.185 .756 988 +EV SCT 2446615.2812 9.993 .852 1.168 .707 988 +EV SCT 2446616.2913 .852 1.235 .785 988 +EV SCT 2446617.2874 10.163 .814 1.230 .744 988 +EV SCT 2446618.2894 10.018 .787 1.155 .719 988 +EV SCT 2446619.2869 10.231 .871 1.251 .768 988 +EV SCT 2446620.2668 10.201 .841 1.227 .756 988 +EV SCT 2446621.2625 10.004 .804 1.140 .724 988 +EV SCT 2446622.2692 10.203 .824 1.252 .763 988 +EV SCT 2446623.3073 10.232 1.240 .755 988 +EV SCT 2446624.2564 10.005 .799 1.136 .712 988 +EV SCT 2446625.2504 10.185 1.239 .764 988 +EV SCT 2446626.2464 10.244 1.245 .773 988 +EV SCT 2446627.2441 10.006 .827 1.144 .708 988 +EV SCT 2446628.2444 10.142 .831 1.219 .757 988 +EV SCT 2446629.2528 10.285 1.257 .781 988 +EV SCT 2446631.2231 10.120 1.209 .743 988 +EV SCT 2446632.2503 10.266 1.280 .774 988 +EV SCT 2446635.2267 10.256 1.273 .782 988 +EV SCT 2446636.2578 10.055 1.150 .730 988 +EV SCT 2449558.6825 1.191 .736 .749 996 +EV SCT 2449559.7789 10.213 .908 1.196 .745 .740 996 +EV SCT 2449561.6778 10.154 .854 1.185 .734 .741 996 +EV SCT 2449561.7534 10.163 1.204 .739 .742 996 +EV SCT 2449563.7046 10.018 .803 1.117 .680 .710 996 +EV SCT 2449564.6075 10.124 .848 1.149 .737 .732 996 +EV SCT 2449564.6771 10.158 .853 1.158 .733 .748 996 +EV SCT 2449621.1426 10.377 1.291 .763 995 +EV SCT 2449623.1476 10.153 1.191 .726 995 +EV SCT 2449624.1326 10.367 1.297 .787 995 +EV SCT 2449625.1385 10.191 1.195 .724 995 +EV SCT 2449631.1267 10.175 1.260 .736 995 +EV SCT 2449632.1304 10.029 1.152 .723 995 +EV SCT 2449633.1301 10.292 1.246 .770 995 +EV SCT 2449634.1398 10.229 1.181 .755 995 +EV SCT 2449808.9056 10.174 1.168 .724 .764 997 +EV SCT 2449809.8689 10.294 1.253 .750 .781 997 +EV SCT 2449810.8678 10.028 1.133 .685 997 +EV SCT 2449811.8575 10.092 1.166 .701 .745 997 +EV SCT 2449813.8619 10.054 1.139 .687 .731 997 +EV SCT 2449814.8416 10.066 1.151 .707 .730 997 +EV SCT 2449817.8678 10.069 1.129 .717 .729 997 +EV SCT 2449818.8498 10.269 1.247 .742 .773 997 +EV SCT 2449822.8940 10.118 1.154 .716 .750 997 +EV SCT 2449823.8819 10.041 1.137 .706 .744 997 +EV SCT 2449825.8709 10.171 1.178 .715 .757 997 +EV SCT 2450306.3245 10.081 1.205 .726 971 +EV SCT 2450307.2366 10.307 1.241 .792 971 +EV SCT 2450310.2442 10.276 1.261 .776 971 +EV SCT 2450311.1791 10.175 1.208 .754 971 +EV SCT 2450312.1780 9.998 1.146 .730 971 +EV SCT 2450313.1935 10.241 1.232 .780 971 +EV SCT 2450314.1749 10.189 1.212 .773 971 +EV SCT 2450315.1782 10.005 1.101 .725 971 +EV SCT 2450316.1907 10.245 1.196 .717 971 +EV SCT 2450317.1915 10.248 1.202 .750 971 +EV SCT 2450318.1803 10.029 1.145 .727 971 +EV SCT 2450319.1695 10.213 1.206 .768 971 +EV SCT 2450320.1880 10.235 1.230 .761 971 +EV SCT 2450321.1731 10.005 1.108 .717 971 +EV SCT 2450322.1710 10.184 1.211 .753 971 +EV SCT 2450323.1687 10.281 1.205 .765 971 +EV SCT 2450324.1869 10.039 1.112 .737 971 +EV SCT 2450325.1584 10.154 1.185 .756 971 +EV SCT 2450326.1469 10.282 1.216 .783 971 +EV SCT 2450327.2049 10.037 1.152 .800 971 +EV SCT 2450330.1598 10.022 1.183 .754 971 +EV SCT 2450332.1513 10.284 1.248 .755 971 +EV SCT 2450333.1528 10.062 1.157 .700 971 +EV SCT 2450334.1637 10.080 1.200 .777 971 +EV SCT 2450335.1638 10.276 1.309 .768 971 +EV SCT 2450336.1638 10.091 1.207 .769 971 +EV SCT 2450337.1561 10.073 1.171 .744 971 +EV SCT 2450340.1450 10.048 1.171 .754 971 +EV SCT 2450341.1524 10.266 1.256 .776 971 +EV SCT 2450342.1562 10.144 1.200 .777 971 +EV SCT 2450344.1667 10.230 1.270 .698 971 +EV SCT 2450347.1604 10.217 1.264 .789 971 +EV SCT 2450349.1456 9.974 1.192 .730 971 +EV SCT 2450568.5755 10.006 1.413 972 +EV SCT 2450570.6051 10.216 1.482 972 +EV SCT 2450572.4756 10.139 1.477 972 +EV SCT 2450572.5624 10.173 1.497 972 +EV SCT 2450573.5048 10.243 1.491 972 +EV SCT 2450573.5978 10.235 1.502 972 +EV SCT 2450573.6486 10.230 1.501 972 +EV SCT 2450574.5334 10.004 1.417 972 +EV SCT 2450574.5933 9.999 1.417 972 +EV SCT 2450575.4935 10.128 1.463 972 +EV SCT 2450575.5775 10.172 1.495 972 +EV SCT 2450575.6410 10.163 1.474 972 +EV SCT 2450576.5405 10.265 1.513 972 +EV SCT 2450576.6201 10.260 1.520 972 +EV SCT 2450577.5475 10.030 1.430 972 +EV SCT 2450577.6058 10.013 1.415 972 +EV SCT 2450577.6460 10.008 1.418 972 +EV SCT 2450578.5254 10.111 1.480 972 +EV SCT 2450578.5917 10.125 1.493 972 +EV SCT 2450578.6485 10.144 1.492 972 +EV SCT 2450579.5735 10.288 1.524 972 +EV SCT 2450580.4709 10.072 1.447 972 +EV SCT 2450580.5807 10.025 1.424 972 +EV SCT 2450580.6317 10.021 1.429 972 +EV SCT 2450582.5626 10.265 1.511 972 +EV SCT 2450582.6102 10.292 1.516 972 +EV SCT 2450582.6576 10.274 1.521 972 +EV SCT 2450583.5536 10.077 1.428 972 +EV SCT 2450583.6099 10.075 1.437 972 +EV SCT 2450584.5448 10.066 1.447 972 +EV SCT 2450584.6010 10.092 1.461 972 +EV SCT 2450584.6486 10.084 1.433 972 +EW SCT 2446994.3261 8.229 1.466 1.843 1.166 989 +EW SCT 2446995.2780 8.268 1.519 1.862 1.176 989 +EW SCT 2446996.2374 7.960 1.357 1.725 1.107 989 +EW SCT 2446997.2613 7.809 1.386 1.698 1.082 989 +EW SCT 2446998.2665 7.956 1.386 1.744 1.114 989 +EW SCT 2446999.2502 8.033 1.410 1.780 1.129 989 +EW SCT 2447000.2543 8.024 1.380 1.764 1.124 989 +EW SCT 2447001.2617 8.091 1.423 1.792 1.136 989 +EW SCT 2447002.2506 8.133 1.445 1.813 1.145 989 +EW SCT 2447400.2231 7.921 1.288 1.730 1.104 990 +EW SCT 2447401.1813 8.100 1.488 1.800 1.122 990 +EW SCT 2447402.1734 8.172 1.490 1.796 1.128 990 +EW SCT 2447403.1714 8.074 1.722 1.097 990 +EW SCT 2447404.1568 7.983 1.339 1.688 1.076 990 +EW SCT 2447408.1534 8.044 1.458 1.772 1.117 990 +EW SCT 2447409.1665 8.217 1.498 1.838 1.147 990 +EW SCT 2447410.1312 7.958 1.326 1.725 1.080 990 +EW SCT 2447411.1270 7.648 1.236 1.602 1.028 990 +EW SCT 2447412.1332 7.854 1.317 1.730 1.081 990 +EW SCT 2447413.1193 8.098 1.504 1.823 1.110 990 +EW SCT 2447414.1232 8.257 1.820 1.170 990 +EW SCT 2447415.1237 8.024 1.422 1.774 1.088 990 +EW SCT 2447416.1190 7.871 1.282 1.688 1.062 990 +EW SCT 2447417.1164 7.925 1.324 1.756 1.084 990 +EW SCT 2447418.1188 7.932 1.740 1.069 990 +EW SCT 2447419.1142 7.973 1.394 1.739 1.094 990 +EW SCT 2447420.1131 8.039 1.426 1.774 1.109 990 +EW SCT 2447421.1110 8.193 1.495 1.832 1.142 990 +EW SCT 2447422.1114 7.942 1.341 1.666 1.074 990 +EW SCT 2447423.1102 7.664 1.262 1.634 1.010 990 +EW SCT 2447424.1081 7.803 1.339 1.724 1.096 990 +EW SCT 2447425.1109 8.148 1.468 1.809 1.139 990 +EW SCT 2447427.1114 7.997 1.357 1.739 1.088 990 +EW SCT 2447428.1060 7.775 1.204 1.649 1.058 990 +EW SCT 2447429.1143 7.921 1.718 1.092 990 +EW SCT 2447430.1044 8.056 1.442 1.738 1.106 990 +EW SCT 2447431.1031 8.075 1.424 1.788 1.117 990 +EW SCT 2447432.1016 8.019 1.386 1.762 1.092 990 +EW SCT 2447433.0991 8.097 1.753 1.114 990 +EW SCT 2447434.1028 7.927 1.307 1.721 1.069 990 +EW SCT 2447735.2773 8.240 1.833 1.160 991 +EW SCT 2447736.2629 7.855 1.290 1.667 1.071 991 +EW SCT 2447737.2543 7.729 1.267 1.616 1.048 991 +EW SCT 2447738.2594 7.924 1.364 1.753 1.094 991 +EW SCT 2447739.2286 8.094 1.792 1.134 991 +EW SCT 2447740.2454 8.135 1.749 1.129 991 +EW SCT 2447741.2344 7.997 1.715 1.104 991 +EW SCT 2447742.2441 7.999 1.304 1.707 1.106 991 +EW SCT 2447743.2447 7.869 1.279 1.686 1.083 991 +EW SCT 2447744.2195 7.827 1.308 1.673 1.063 991 +EW SCT 2447745.2151 7.922 1.346 1.709 1.103 991 +EW SCT 2447746.2194 8.130 1.470 1.792 1.144 991 +EW SCT 2447747.2123 8.253 1.813 1.154 991 +EW SCT 2447748.2144 7.834 1.232 1.652 1.033 991 +EW SCT 2447749.1942 7.706 1.599 1.054 991 +EW SCT 2447750.1934 7.944 1.727 1.113 991 +EW SCT 2447751.1980 8.163 1.491 1.844 1.148 991 +EW SCT 2447752.1754 8.213 1.456 1.799 1.151 991 +EW SCT 2447753.1825 7.956 1.320 1.707 1.089 991 +EW SCT 2447754.1988 7.891 1.316 1.679 1.078 991 +EW SCT 2447755.1918 7.927 1.325 1.713 1.090 991 +EW SCT 2447756.2167 7.956 1.341 1.709 1.106 991 +EW SCT 2447757.1882 7.953 1.367 1.728 1.106 991 +EW SCT 2447758.1846 8.100 1.429 1.802 1.127 991 +EW SCT 2447759.1734 8.185 1.791 1.138 991 +EW SCT 2447760.1907 7.807 1.629 1.061 991 +EW SCT 2447761.1681 7.712 1.277 1.606 1.050 991 +EW SCT 2447762.1687 7.950 1.735 1.103 991 +EW SCT 2447763.1611 8.193 1.822 1.156 991 +EW SCT 2447764.1627 8.235 1.805 1.146 991 +EW SCT 2447766.1619 7.834 1.647 1.082 991 +EW SCT 2447767.1804 7.992 1.726 1.106 991 +EW SCT 2447768.1729 8.071 1.444 1.743 1.093 991 +EW SCT 2447769.1732 8.028 1.395 1.722 1.120 991 +EW SCT 2447770.1644 8.063 1.380 1.743 1.133 991 +EW SCT 2447771.1604 8.078 1.394 1.770 1.108 991 +EW SCT 2447772.1581 7.803 1.255 1.624 1.062 991 +EW SCT 2447773.1629 7.748 1.660 1.051 991 +EW SCT 2447774.1902 7.957 1.394 1.725 1.114 991 +EW SCT 2447775.1539 8.221 1.501 1.816 1.168 991 +EW SCT 2447776.1587 8.231 1.821 1.140 991 +EW SCT 2448101.2376 8.257 1.518 1.855 1.176 992 +EW SCT 2448102.2355 8.106 1.793 1.126 992 +EW SCT 2448103.2138 7.777 1.646 1.065 992 +EW SCT 2448104.2271 7.868 1.721 1.097 992 +EW SCT 2448108.2083 8.132 1.800 1.135 992 +EW SCT 2448109.2007 8.025 1.739 1.122 992 +EW SCT 2448110.2011 7.730 1.639 1.060 992 +EW SCT 2448111.2132 7.808 1.699 1.072 992 +EW SCT 2448112.2056 8.030 1.796 1.137 992 +EW SCT 2448113.1979 8.249 1.867 1.182 992 +EW SCT 2448114.2164 8.083 1.776 1.129 992 +EW SCT 2448115.1971 7.699 1.599 1.039 992 +EW SCT 2448116.2050 7.911 1.676 1.106 992 +EW SCT 2448117.2215 8.088 1.827 1.135 992 +EW SCT 2448118.2194 8.157 1.788 1.149 992 +EW SCT 2448119.1945 8.097 1.760 1.126 992 +EW SCT 2448123.1870 7.879 1.704 1.105 992 +EW SCT 2448126.2143 8.053 1.727 1.114 992 +EW SCT 2448127.1907 7.629 1.589 1.043 992 +EW SCT 2448418.4566 7.897 1.361 1.691 1.091 903 +EW SCT 2448419.3896 7.854 1.339 1.737 1.073 903 +EW SCT 2448420.3850 7.868 1.395 1.746 1.118 903 +EW SCT 2448425.4002 7.666 1.376 1.601 1.038 903 +EW SCT 2448425.4088 7.921 1.381 1.741 1.113 903 +EW SCT 2448426.3508 8.128 1.472 1.832 1.149 903 +EW SCT 2448427.3879 8.209 1.505 1.826 1.161 903 +EW SCT 2448428.3896 8.039 1.362 1.740 1.069 903 +EW SCT 2448429.4016 7.891 1.708 1.094 903 +EW SCT 2448430.3802 7.929 1.371 1.723 1.095 903 +EW SCT 2448436.3674 7.665 1.591 1.043 903 +EW SCT 2448437.3780 7.913 1.745 1.105 903 +EW SCT 2448438.3748 8.171 1.850 1.201 903 +EW SCT 2448439.3599 8.259 1.849 1.179 903 +EW SCT 2448440.3647 8.001 1.722 1.110 903 +EW SCT 2448443.3258 8.012 1.762 1.122 903 +EW SCT 2448444.3185 8.020 1.781 1.126 903 +EW SCT 2448445.3226 8.039 1.789 1.125 903 +EW SCT 2448448.3133 7.720 1.310 1.629 1.055 903 +EW SCT 2448449.3679 7.898 1.737 903 +EW SCT 2448451.3084 8.284 1.875 1.172 903 +EW SCT 2448453.3259 7.774 1.370 1.640 1.069 903 +EW SCT 2448454.3407 7.982 1.763 1.119 903 +EW SCT 2448455.3470 8.086 1.806 903 +EW SCT 2448456.3415 8.091 1.783 1.135 903 +EW SCT 2448457.3451 8.020 1.771 1.124 903 +EW SCT 2448458.3158 8.052 1.442 1.768 1.127 903 +EW SCT 2448459.2750 7.891 1.349 1.700 1.089 903 +EW SCT 2448460.3452 7.785 1.317 1.676 1.072 903 +EW SCT 2448461.2821 7.903 1.739 1.106 903 +EW SCT 2448462.2919 8.205 1.483 1.840 1.185 903 +EW SCT 2448463.2567 8.249 1.539 1.836 1.143 903 +EW SCT 2448464.2640 7.913 1.332 1.676 1.071 903 +EW SCT 2448465.3253 7.726 1.322 1.618 1.044 903 +EW SCT 2448466.3234 7.966 1.777 1.118 903 +EW SCT 2448467.3220 8.172 1.835 1.160 903 +EW SCT 2448472.3820 7.811 1.508 1.680 1.078 903 +EW SCT 2448473.3265 7.917 1.398 1.716 1.091 903 +EW SCT 2448474.3175 8.127 1.535 1.754 1.137 903 +EW SCT 2448475.3035 8.186 1.570 1.791 1.137 903 +EW SCT 2448477.2465 7.704 1.295 1.594 1.048 903 +EW SCT 2448478.2428 7.984 1.452 1.744 1.085 903 +EW SCT 2448479.3143 8.194 1.896 1.172 903 +EW SCT 2448480.2405 8.235 1.516 1.822 1.170 903 +EW SCT 2448481.2696 7.946 1.342 1.687 1.107 903 +EW SCT 2448482.2729 7.865 1.686 1.097 903 +EW SCT 2448484.2437 8.007 1.409 1.758 1.124 903 +EW SCT 2448485.2175 7.984 1.428 1.756 1.125 903 +EW SCT 2448486.2174 8.079 1.493 1.786 1.149 903 +EW SCT 2448487.2075 8.152 1.487 1.803 1.154 903 +EW SCT 2448488.1834 7.837 1.336 1.648 1.079 903 +EW SCT 2448489.2084 7.735 1.350 1.630 1.069 903 +EW SCT 2448490.2253 7.935 1.436 1.728 1.122 903 +EW SCT 2448491.2338 8.194 1.576 1.884 1.164 903 +EW SCT 2448493.2133 7.851 1.349 1.670 1.081 903 +EW SCT 2448498.2444 8.043 1.451 1.774 1.129 903 +EW SCT 2448499.2183 8.053 1.436 1.766 1.129 903 +EW SCT 2448503.2047 8.191 1.853 1.178 903 +EW SCT 2448504.2192 8.247 1.838 1.164 903 +EW SCT 2448505.1814 7.800 1.642 1.073 903 +EW SCT 2448506.1804 7.752 1.652 903 +EW SCT 2448507.2121 8.023 1.775 1.141 903 +EW SCT 2448508.2044 8.163 1.514 1.843 1.162 903 +EW SCT 2448509.1937 8.124 1.804 903 +EW SCT 2448510.1872 7.967 1.727 1.112 903 +EW SCT 2448511.1925 7.959 1.729 1.114 903 +EW SCT 2448512.1997 7.892 1.379 1.696 1.100 903 +EW SCT 2448513.1875 7.881 1.694 903 +EW SCT 2448514.1820 7.947 1.415 1.750 1.117 903 +EW SCT 2448515.1931 8.162 1.493 1.829 1.170 903 +EW SCT 2448516.1923 8.204 1.829 1.161 903 +EW SCT 2448517.1863 7.746 1.635 1.055 903 +EW SCT 2448518.2007 7.765 1.631 1.079 903 +EW SCT 2448520.1851 8.224 1.850 1.172 903 +EW SCT 2448521.1427 8.179 1.814 1.157 903 +EW SCT 2448522.1434 7.885 1.687 1.094 903 +EW SCT 2448523.1808 7.888 1.695 1.096 903 +EW SCT 2448536.1421 8.037 1.422 1.777 1.144 903 +EW SCT 2448537.1260 8.106 1.438 1.789 1.144 903 +EW SCT 2448541.1507 7.810 1.325 1.648 1.076 903 +EW SCT 2448542.1124 7.811 1.339 1.670 1.086 903 +EW SCT 2448543.1275 8.014 1.446 1.761 1.137 903 +EW SCT 2448854.2185 8.081 1.773 1.145 905 +EW SCT 2448856.1995 7.814 1.708 1.071 905 +EW SCT 2448856.2412 7.837 1.721 1.084 905 +EW SCT 2448857.1542 8.089 1.450 1.824 1.140 905 +EW SCT 2448857.3402 8.137 1.444 1.851 1.147 905 +EW SCT 2448858.1728 8.247 1.518 1.862 1.162 905 +EW SCT 2448858.1976 8.243 1.521 1.857 1.167 905 +EW SCT 2448858.2560 8.233 1.869 1.161 905 +EW SCT 2448858.3319 8.225 1.580 1.889 1.139 905 +EW SCT 2448859.1916 8.078 1.372 1.774 1.122 905 +EW SCT 2448859.3351 8.042 1.767 1.122 905 +EW SCT 2448860.1611 7.857 1.279 1.689 1.077 905 +EW SCT 2448860.2055 7.857 1.718 1.074 905 +EW SCT 2448860.2392 7.836 1.276 1.718 1.063 905 +EW SCT 2448860.2931 7.856 1.270 1.695 1.076 905 +EW SCT 2448861.1508 7.922 1.335 1.726 1.095 905 +EW SCT 2448861.3141 7.917 1.747 1.079 905 +EW SCT 2448863.1971 7.973 1.352 1.750 1.108 905 +EW SCT 2448863.2754 7.986 1.327 1.737 1.120 905 +EW SCT 2448870.1581 8.282 1.554 1.873 1.166 905 +EW SCT 2448870.1801 8.260 1.551 1.880 1.164 905 +EW SCT 2448870.2778 8.274 1.551 1.879 1.171 905 +EW SCT 2448871.2777 8.043 1.336 1.792 1.104 905 +EW SCT 2448872.1529 7.756 1.238 1.628 1.062 905 +EW SCT 2448872.1540 7.743 1.233 1.643 1.046 905 +EW SCT 2448872.2197 7.707 1.632 1.029 905 +EW SCT 2448872.2588 7.758 1.209 1.661 1.057 905 +EW SCT 2448873.1865 7.913 1.336 1.740 1.101 905 +EW SCT 2448873.2633 7.945 1.313 1.734 1.103 905 +EW SCT 2448874.1538 8.027 1.795 1.113 905 +EW SCT 2448874.2022 8.038 1.392 1.789 1.122 905 +EW SCT 2448874.2056 8.034 1.821 1.129 905 +EW SCT 2448874.2558 8.044 1.361 1.802 1.134 905 +EW SCT 2448875.1704 8.063 1.419 1.782 1.129 905 +EW SCT 2448875.2820 8.018 1.352 1.853 1.130 905 +EW SCT 2448876.1664 8.005 1.401 1.770 1.113 905 +EW SCT 2448876.1810 8.012 1.764 1.112 905 +EW SCT 2448876.2602 8.011 1.397 1.789 1.122 905 +EW SCT 2448877.1312 8.097 1.422 1.803 1.120 905 +EW SCT 2448877.1934 8.081 1.818 1.109 905 +EW SCT 2448877.2670 8.100 1.438 1.803 1.133 905 +EW SCT 2448878.1596 8.002 1.319 1.729 1.104 905 +EW SCT 2448878.1790 7.978 1.739 1.096 905 +EW SCT 2448878.2418 7.964 1.346 1.737 1.097 905 +EW SCT 2448879.1635 7.751 1.253 1.639 1.062 905 +EW SCT 2448879.2362 7.744 1.238 1.649 1.066 905 +EW SCT 2448880.1476 7.858 1.708 1.099 905 +EW SCT 2448880.1563 7.849 1.318 1.712 1.091 905 +EW SCT 2448880.2650 7.864 1.309 1.723 1.089 905 +EW SCT 2448881.1429 8.077 1.463 1.822 1.132 905 +EW SCT 2448881.1440 8.075 1.825 1.137 905 +EW SCT 2448881.2541 8.119 1.418 1.843 1.154 905 +EW SCT 2448882.1364 8.293 1.584 1.869 1.170 905 +EW SCT 2448882.1538 8.286 1.892 1.174 905 +EW SCT 2448882.2487 8.295 1.606 1.885 1.166 905 +EW SCT 2448883.1318 8.081 1.352 1.752 1.112 905 +EW SCT 2448883.1520 8.058 1.742 1.114 905 +EW SCT 2448883.2548 8.020 1.331 1.741 1.112 905 +EW SCT 2448884.1411 7.666 1.254 1.595 1.038 905 +EW SCT 2448884.1513 7.671 1.610 1.040 905 +EW SCT 2448884.2375 7.660 1.206 1.646 1.036 905 +EW SCT 2448885.1467 7.891 1.724 1.112 905 +EW SCT 2448885.1536 7.919 1.354 1.722 1.109 905 +EW SCT 2448885.2309 7.913 1.382 1.765 1.094 905 +EW SCT 2448886.1368 8.109 1.474 1.804 1.139 905 +EW SCT 2448886.1506 8.116 1.804 1.139 905 +EW SCT 2448886.2181 8.116 1.425 1.829 1.144 905 +EW SCT 2448888.1267 8.025 1.414 1.766 1.108 905 +EW SCT 2448888.1526 8.036 1.775 1.107 905 +EW SCT 2448888.2326 8.008 1.310 1.767 1.101 905 +EW SCT 2448889.1242 7.995 1.393 1.734 1.105 905 +EW SCT 2448889.1705 8.046 1.752 1.109 905 +EW SCT 2448889.2297 7.996 1.345 1.767 1.099 905 +EW SCT 2448890.1266 7.951 1.327 1.722 1.100 905 +EW SCT 2448890.1417 7.965 1.715 1.121 905 +EW SCT 2448890.2123 7.922 1.301 1.730 1.097 905 +EW SCT 2448891.1214 7.847 1.280 1.697 1.079 905 +EW SCT 2448891.1408 7.840 1.697 1.076 905 +EW SCT 2448891.2221 7.830 1.270 1.693 1.065 905 +EW SCT 2448892.1224 7.887 1.334 1.722 1.094 905 +EW SCT 2448892.1421 7.888 1.718 1.099 905 +EW SCT 2448892.2156 7.896 1.288 1.746 1.100 905 +EW SCT 2448893.1275 8.068 1.428 1.802 1.137 905 +EW SCT 2448893.1389 8.083 1.822 1.125 905 +EW SCT 2448893.2101 8.096 1.403 1.807 1.141 905 +EW SCT 2448894.1194 8.261 1.496 1.864 1.165 905 +EW SCT 2448894.1495 8.271 1.861 1.162 905 +EW SCT 2448894.2142 8.247 1.505 1.879 1.163 905 +EW SCT 2449197.3698 7.948 1.730 1.115 907 +EW SCT 2449198.2252 7.881 1.404 1.697 1.097 907 +EW SCT 2449198.3195 7.892 1.375 1.704 1.099 907 +EW SCT 2449199.3265 7.926 1.368 1.737 1.114 907 +EW SCT 2449200.2336 7.935 1.389 1.722 1.109 907 +EW SCT 2449201.2402 7.956 1.430 1.742 1.123 907 +EW SCT 2449201.3581 7.948 1.360 1.751 1.121 907 +EW SCT 2449202.2454 8.071 1.460 1.795 1.150 907 +EW SCT 2449202.3606 8.098 1.817 1.154 907 +EW SCT 2449203.2287 8.201 1.545 1.824 1.164 907 +EW SCT 2449203.3499 8.199 1.483 1.843 1.167 907 +EW SCT 2449204.2258 7.885 1.359 1.684 1.086 907 +EW SCT 2449204.3484 7.852 1.349 1.662 1.091 907 +EW SCT 2449205.2293 7.672 1.337 1.610 1.058 907 +EW SCT 2449205.3585 7.704 1.276 1.625 1.072 907 +EW SCT 2449206.2559 7.894 1.751 1.112 907 +EW SCT 2449206.3683 7.947 1.755 1.129 907 +EW SCT 2449207.2359 8.194 1.823 1.179 907 +EW SCT 2449207.3527 8.186 1.889 1.173 907 +EW SCT 2449209.2200 7.942 1.391 1.706 1.109 907 +EW SCT 2449209.3607 7.910 1.650 1.090 907 +EW SCT 2449210.2900 7.812 1.355 1.665 1.080 907 +EW SCT 2449211.2675 7.964 1.757 1.123 907 +EW SCT 2449212.2708 8.040 1.777 1.140 907 +EW SCT 2449218.2849 7.923 1.386 1.726 1.119 907 +EW SCT 2449219.2760 8.177 1.858 1.175 907 +EW SCT 2449220.2711 8.266 1.854 1.180 907 +EW SCT 2449221.3253 7.820 1.689 1.074 907 +EW SCT 2449222.2742 7.752 1.647 1.072 907 +EW SCT 2449224.2848 8.126 1.833 1.153 907 +EW SCT 2449225.2462 8.088 1.438 1.792 1.140 907 +EW SCT 2449226.2482 7.991 1.402 1.744 1.119 907 +EW SCT 2449227.2403 8.000 1.400 1.762 1.119 907 +EW SCT 2449228.2499 7.885 1.383 1.684 1.096 907 +EW SCT 2449229.2403 7.808 1.376 1.687 1.087 907 +EW SCT 2449230.2433 7.902 1.748 1.114 907 +EW SCT 2449231.2446 8.149 1.853 1.160 907 +EW SCT 2449232.2318 8.253 1.847 1.175 907 +EW SCT 2449233.2339 7.812 1.653 1.074 907 +EW SCT 2449234.2333 7.725 1.628 1.071 907 +EW SCT 2449235.2271 7.975 1.804 1.124 907 +EW SCT 2449236.2112 8.172 1.873 1.164 907 +EW SCT 2449237.2034 8.152 1.826 1.154 907 +EW SCT 2449238.1957 7.928 1.730 1.113 907 +EW SCT 2449239.1998 7.913 1.707 1.106 907 +EW SCT 2449240.2122 7.925 1.721 1.109 907 +EW SCT 2449241.2145 7.930 1.720 1.116 907 +EW SCT 2449243.1931 8.117 1.825 1.156 907 +EW SCT 2449244.2177 8.188 1.827 1.156 907 +EW SCT 2449245.2152 7.779 1.638 1.075 907 +EW SCT 2449298.1021 7.744 1.631 1.064 907 +EW SCT 2448503.2099 8.156 1.868 1.127 993 +EW SCT 2448504.1671 8.256 1.814 1.155 993 +EW SCT 2448505.1579 7.828 1.638 1.069 993 +EW SCT 2448505.2179 7.828 1.564 1.086 993 +EW SCT 2448506.1529 7.751 1.658 1.062 993 +EW SCT 2448506.2222 7.773 1.650 1.063 993 +EW SCT 2448507.1468 8.012 1.758 1.119 993 +EW SCT 2448508.1428 8.178 1.830 1.164 993 +EW SCT 2448508.1773 8.171 1.812 1.143 993 +EW SCT 2448509.1432 8.168 1.758 1.158 993 +EW SCT 2448509.1870 8.124 1.775 1.124 993 +EW SCT 2448510.1448 7.980 1.725 1.092 993 +EW SCT 2448510.1810 7.979 1.726 1.098 993 +EW SCT 2448511.1411 7.972 1.734 1.101 993 +EW SCT 2448511.1855 7.956 1.724 1.089 993 +EW SCT 2448512.1388 7.907 1.709 1.089 993 +EW SCT 2448512.1918 7.902 1.674 1.076 993 +EW SCT 2448513.1406 7.885 1.717 1.079 993 +EW SCT 2448513.2007 7.900 1.654 1.101 993 +EW SCT 2448514.1409 7.966 1.743 1.112 993 +EW SCT 2448514.2010 7.984 1.740 1.112 993 +EW SCT 2448515.1389 8.142 1.820 1.138 993 +EW SCT 2448515.1960 8.174 1.783 1.153 993 +EW SCT 2448516.1379 8.208 1.820 1.142 993 +EW SCT 2448517.1389 7.792 1.607 1.061 993 +EW SCT 2448518.1414 7.768 1.627 1.066 993 +EW SCT 2448520.1332 8.225 1.828 1.170 993 +EW SCT 2448521.1476 8.172 1.804 1.144 993 +EW SCT 2448522.1396 7.868 1.671 1.084 993 +EW SCT 2448523.1297 7.904 1.713 1.083 993 +EW SCT 2449514.3505 7.752 1.644 .737 914 +EW SCT 2449514.4166 7.758 1.659 .742 914 +EW SCT 2449515.3758 7.933 1.754 .777 914 +EW SCT 2449516.3930 8.220 1.861 .811 914 +EW SCT 2449517.3920 8.259 1.828 .806 914 +EW SCT 2449519.4161 7.792 1.668 .746 914 +EW SCT 2449521.3289 8.145 1.819 .798 914 +EW SCT 2449522.3669 8.082 1.775 .783 914 +EW SCT 2449522.4278 8.077 1.786 .784 914 +EW SCT 2449523.3250 8.004 1.757 .776 914 +EW SCT 2449523.4044 8.009 1.744 .771 914 +EW SCT 2449524.3050 8.013 1.763 .773 914 +EW SCT 2449524.3773 8.014 1.748 .776 914 +EW SCT 2449524.4418 8.007 1.764 .771 914 +EW SCT 2449525.3014 7.889 1.680 .758 914 +EW SCT 2449526.2907 7.820 1.686 .747 914 +EW SCT 2449529.2910 8.245 1.854 .810 914 +EW SCT 2449530.2756 7.786 1.653 .727 914 +EW SCT 2449530.3598 7.747 1.627 .728 914 +EW SCT 2449530.4150 7.719 1.633 .722 914 +EW SCT 2449533.2763 8.196 1.861 .808 914 +EW SCT 2449533.3543 8.199 1.866 .805 914 +EW SCT 2449533.4084 8.204 1.864 .806 914 +EW SCT 2449534.2965 8.140 1.816 .892 914 +EW SCT 2449534.3444 8.148 1.799 .794 914 +EW SCT 2449534.4006 8.118 1.793 .788 914 +EW SCT 2449535.2654 7.920 1.715 .756 914 +EW SCT 2449535.3318 7.919 1.691 .755 914 +EW SCT 2449535.4003 7.915 1.700 .760 914 +EW SCT 2449536.2586 7.929 1.713 .766 914 +EW SCT 2449536.3799 7.933 1.720 .765 914 +EW SCT 2449537.2611 7.929 1.724 .764 914 +EW SCT 2449537.3266 7.930 1.732 .763 914 +EW SCT 2449537.3996 7.931 1.728 .766 914 +EW SCT 2449538.2840 7.930 1.727 .768 914 +EW SCT 2449538.3328 7.928 1.725 .768 914 +EW SCT 2449539.2585 7.949 1.738 .772 914 +EW SCT 2449539.3445 7.949 1.749 .770 914 +EW SCT 2449539.3826 7.960 1.738 .773 914 +EW SCT 2449540.2474 8.143 1.832 .801 914 +EW SCT 2449540.3405 8.152 1.828 .798 914 +EW SCT 2449541.2516 8.183 1.834 .796 914 +EW SCT 2449541.3167 8.172 1.807 .793 914 +EW SCT 2449541.3840 8.160 1.794 .796 914 +EW SCT 2449542.2532 7.778 1.628 .739 914 +EW SCT 2449542.3059 7.743 1.636 .725 914 +EW SCT 2449542.3754 7.747 1.605 .732 914 +EW SCT 2449543.2529 7.734 1.648 .734 914 +EW SCT 2449543.3171 7.754 1.652 .741 914 +EW SCT 2449545.2402 8.229 1.863 .818 914 +EW SCT 2449545.2950 8.233 1.883 .818 914 +EW SCT 2449546.2441 8.192 1.818 .797 914 +EW SCT 2449546.3076 8.164 1.816 .793 914 +EW SCT 2449546.3826 8.154 1.807 .788 914 +EW SCT 2449547.2328 7.838 1.662 .741 914 +EW SCT 2449547.2888 7.821 1.663 .739 914 +EW SCT 2449548.3187 7.877 1.704 .760 914 +EW SCT 2449548.3584 7.884 1.707 .753 914 +EW SCT 2449549.2327 7.988 1.766 .778 914 +EW SCT 2449549.3056 7.994 1.766 .777 914 +EW SCT 2449549.3532 7.998 1.773 .780 914 +EW SCT 2449550.3050 8.010 1.785 .770 914 +EW SCT 2449551.3001 7.997 1.750 .778 914 +EW SCT 2449552.2994 8.076 1.808 .786 914 +EW SCT 2449553.2950 8.077 1.782 .784 914 +EW SCT 2449554.3074 7.775 1.652 .738 914 +EW SCT 2449556.2972 8.014 1.777 .788 914 +EW SCT 2449557.2504 8.230 1.862 .808 914 +EW SCT 2449559.2233 7.739 1.616 .726 914 +EW SCT 2449559.3000 7.727 1.608 .727 914 +EW SCT 2449560.2314 7.824 1.689 .748 914 +EW SCT 2449560.3365 7.853 1.700 .757 914 +EW SCT 2449561.3126 8.061 1.784 .791 914 +EW SCT 2449563.3152 8.036 1.759 .778 914 +EW SCT 2449564.2392 8.013 1.753 .777 914 +EW SCT 2449617.0997 7.938 1.575 1.698 .758 914 +EW SCT 2449620.2012 7.938 1.320 1.714 .768 914 +EW SCT 2449621.1548 8.036 1.497 1.792 .766 914 +EW SCT 2449624.1679 7.680 1.373 1.587 .725 914 +EW SCT 2449625.1553 7.872 1.367 1.683 .750 914 +EW SCT 2449626.2098 8.149 1.889 .795 914 +EW SCT 2449631.1458 8.047 1.786 .770 914 +EW SCT 2449632.1564 7.996 1.600 1.761 .773 914 +EW SCT 2449633.1592 8.030 1.453 1.745 .778 914 +EW SCT 2449634.1468 8.180 1.803 .799 914 +EW SCT 2449520.8780 8.135 1.812 996 +EW SCT 2449521.7443 8.099 1.840 996 +EW SCT 2449522.6910 8.040 1.786 996 +EW SCT 2449528.7600 8.242 1.925 1.199 1.107 996 +EW SCT 2449529.8001 8.023 1.757 1.130 1.093 996 +EW SCT 2449534.8608 7.987 1.772 1.123 1.091 996 +EW SCT 2449535.7591 7.924 1.348 1.654 996 +EW SCT 2449536.7779 7.832 1.340 1.699 996 +EW SCT 2449543.7534 7.821 1.394 1.744 1.111 1.067 996 +EW SCT 2449545.7219 8.220 1.923 1.178 1.107 996 +EW SCT 2449558.8372 7.797 1.733 1.072 1.041 996 +EW SCT 2449559.8249 7.820 1.323 1.697 1.057 1.015 996 +EW SCT 2449561.7572 8.108 1.445 1.787 1.143 1.079 996 +EW SCT 2449804.9143 7.957 1.716 1.092 1.090 997 +EW SCT 2449805.9011 8.045 1.743 1.130 1.097 997 +EW SCT 2449808.9102 8.141 1.776 1.126 1.104 997 +EW SCT 2449809.8879 7.993 1.716 1.093 1.090 997 +EW SCT 2449809.9191 7.995 1.703 1.090 1.076 997 +EW SCT 2449810.8849 7.739 1.612 1.029 .980 997 +EW SCT 2449810.9126 7.720 1.626 1.033 .956 997 +EW SCT 2449811.8389 7.826 1.675 1.070 1.056 997 +EW SCT 2449811.8813 7.828 1.688 1.062 1.060 997 +EW SCT 2449811.9129 7.849 1.671 1.068 1.066 997 +EW SCT 2449813.8677 8.279 1.878 1.167 1.120 997 +EW SCT 2449813.9095 8.278 1.872 1.164 1.124 997 +EW SCT 2449814.8519 8.064 1.734 1.106 1.071 997 +EW SCT 2449815.8242 7.999 1.646 .998 .958 997 +EW SCT 2449817.8505 8.105 1.799 1.138 1.104 997 +EW SCT 2449817.9109 8.095 1.794 1.136 1.095 997 +EW SCT 2449818.8393 8.128 1.806 1.133 1.093 997 +EW SCT 2449818.8706 8.125 1.792 1.131 1.103 997 +EW SCT 2449818.8858 8.099 1.782 1.126 1.082 997 +EW SCT 2449818.9057 8.094 1.785 1.133 1.071 997 +EW SCT 2449818.9167 8.098 1.796 1.121 1.078 997 +EW SCT 2449821.8571 7.959 1.710 1.088 1.083 997 +EW SCT 2449822.8370 7.798 1.659 1.060 1.053 997 +EW SCT 2449823.8244 7.890 1.704 1.089 1.062 997 +EW SCT 2449825.8347 8.270 1.852 1.159 1.126 997 +EW SCT 2449934.2673 7.958 1.772 1.138 2.207 998 +EW SCT 2449935.2789 8.193 1.181 2.242 998 +EW SCT 2449936.2493 8.323 1.167 2.270 998 +EW SCT 2449937.2548 7.957 1.734 1.130 998 +EW SCT 2449938.2487 7.807 1.675 1.093 2.105 998 +EW SCT 2449939.2654 8.005 1.807 1.134 2.199 998 +EW SCT 2449942.2459 8.059 1.146 2.204 998 +EW SCT 2449943.2358 8.080 1.803 1.164 2.232 998 +EW SCT 2449944.2231 7.975 1.726 1.084 2.127 998 +EW SCT 2449945.2167 7.803 1.675 1.095 2.102 998 +EW SCT 2449946.2149 7.868 1.766 1.116 2.154 998 +EW SCT 2449947.2049 8.168 1.869 1.178 2.260 998 +EW SCT 2449948.2178 8.320 1.882 1.206 2.323 998 +EW SCT 2449949.2222 7.980 1.741 1.108 2.143 998 +EW SCT 2449950.2176 7.704 1.650 1.041 2.042 998 +EW SCT 2449952.2115 8.193 1.876 1.184 2.276 998 +EW SCT 2449953.2214 8.205 1.854 1.174 2.262 998 +EW SCT 2449954.2005 8.020 1.762 1.136 2.179 998 +EW SCT 2449955.1972 8.016 1.766 1.137 2.195 998 +EW SCT 2449956.1423 8.019 1.761 1.122 2.157 998 +EW SCT 2449957.1493 7.906 1.737 1.102 2.126 998 +EW SCT 2449958.1890 7.956 1.772 1.128 2.179 998 +EW SCT 2449959.1881 8.196 2.267 998 +EW SCT 2450009.1456 7.942 2.157 998 +EW SCT 2450011.1282 7.965 1.798 1.110 2.134 998 +EW SCT 2450012.1193 8.043 2.195 998 +EW SCT 2450017.0896 8.255 1.919 1.190 2.287 998 +EW SCT 2450018.1144 8.292 2.286 998 +EW SCT 2450020.0866 7.813 1.738 1.079 2.112 998 +EW SCT 2450348.5931 7.908 1.720 2.160 999 +EW SCT 2450351.5704 7.863 1.636 2.103 999 +EW SCT 2450352.5930 7.693 1.596 2.066 999 +EW SCT 2450353.5387 7.909 1.726 2.174 999 +EW SCT 2450354.5520 8.170 1.835 2.260 999 +EW SCT 2450355.5324 8.230 1.817 2.256 999 +EW SCT 2450358.5355 7.961 1.735 2.174 999 +EW SCT 2450359.5277 8.029 1.750 2.194 999 +EW SCT 2450360.5344 7.997 1.742 2.169 999 +EW SCT 2450361.5406 8.030 1.747 2.186 999 +EW SCT 2450362.5400 8.103 1.769 2.220 999 +EW SCT 2450363.5386 7.833 1.670 2.096 999 +EW SCT 2450379.5266 8.229 1.807 2.264 999 +EW SCT 2450380.5279 7.793 1.615 2.086 999 +EW SCT 2450381.5533 7.726 1.631 2.073 999 +EW SCT 2450382.5384 8.003 1.741 2.210 999 +EW SCT 2450383.5468 8.177 1.846 2.251 999 +EW SCT 2450384.5431 8.132 2.221 999 +EW SCT 2450386.5417 7.889 1.723 2.143 999 +EW SCT 2450387.5459 7.935 1.701 2.173 999 +EW SCT 2450388.5386 7.892 1.731 2.149 999 +EW SCT 2450389.5378 7.915 1.724 2.147 999 +EW SCT 2450390.5332 8.102 1.827 2.221 999 +EW SCT 2450391.5324 8.158 1.795 2.228 999 +EW SCT 2450392.5271 7.734 1.617 2.062 999 +EW SCT 2450393.5286 7.713 1.639 2.091 999 +EW SCT 2450305.2326 7.912 1.714 1.104 971 +EW SCT 2450306.1909 7.924 1.675 1.101 971 +EW SCT 2450307.2373 7.992 1.670 1.127 971 +EW SCT 2450310.2454 8.126 1.742 1.142 971 +EW SCT 2450312.1790 7.752 1.617 1.087 971 +EW SCT 2450313.1946 8.030 1.737 1.143 971 +EW SCT 2450314.1757 8.233 1.826 1.187 971 +EW SCT 2450314.2490 8.248 1.832 1.176 971 +EW SCT 2450315.1792 8.136 1.702 1.143 971 +EW SCT 2450316.1917 7.782 1.557 1.015 971 +EW SCT 2450317.1877 7.892 1.636 1.066 971 +EW SCT 2450318.1788 8.021 1.734 1.128 971 +EW SCT 2450319.1657 8.053 1.698 1.133 971 +EW SCT 2450320.1859 7.954 1.698 1.104 971 +EW SCT 2450321.1687 8.045 1.715 1.124 971 +EW SCT 2450322.1672 8.046 1.697 1.111 971 +EW SCT 2450323.1643 7.774 1.577 1.060 971 +EW SCT 2450324.1836 7.808 1.598 1.093 971 +EW SCT 2450325.1570 7.992 1.702 1.129 971 +EW SCT 2450326.1459 8.229 1.788 1.169 971 +EW SCT 2450327.2003 8.140 1.777 1.225 971 +EW SCT 2450329.1647 7.639 1.687 1.089 971 +EW SCT 2450330.1571 8.034 1.770 1.155 971 +EW SCT 2450332.1489 8.021 1.717 1.102 971 +EW SCT 2450333.1541 7.939 1.692 1.072 971 +EW SCT 2450334.1657 7.964 1.726 1.139 971 +EW SCT 2450335.1655 7.846 1.679 1.077 971 +EW SCT 2450336.1654 7.831 1.686 1.121 971 +EW SCT 2450337.1588 8.004 1.721 1.131 971 +EW SCT 2450338.2172 8.214 1.806 1.175 971 +EW SCT 2450340.1471 7.659 1.579 1.059 971 +EW SCT 2450341.1541 7.827 1.657 1.090 971 +EW SCT 2450342.1587 8.066 1.782 1.168 971 +EW SCT 2450344.1696 8.017 1.736 1.039 971 +EW SCT 2450347.1629 7.929 1.714 1.128 971 +EW SCT 2450349.1483 7.929 1.742 1.133 971 +EW SCT 2450357.1344 7.726 1.579 .993 971 +EW SCT 2450568.5776 7.761 1.058 2.106 972 +EW SCT 2450570.5158 8.203 1.148 2.263 972 +EW SCT 2450570.6079 8.221 1.149 2.262 972 +EW SCT 2450572.4776 7.709 1.013 2.056 972 +EW SCT 2450572.5654 7.714 1.033 2.071 972 +EW SCT 2450573.5070 7.840 1.052 2.117 972 +EW SCT 2450573.5987 7.874 1.076 2.152 972 +EW SCT 2450573.6499 7.882 1.083 2.153 972 +EW SCT 2450574.5342 7.987 1.104 2.192 972 +EW SCT 2450574.5939 7.997 1.110 2.200 972 +EW SCT 2450575.4944 8.007 1.084 2.184 972 +EW SCT 2450575.5782 8.023 1.094 2.192 972 +EW SCT 2450575.6417 8.030 1.106 2.197 972 +EW SCT 2450576.5413 7.952 1.085 2.174 972 +EW SCT 2450576.6210 7.948 1.093 2.173 972 +EW SCT 2450576.6569 7.943 1.090 2.171 972 +EW SCT 2450577.5484 8.027 1.106 2.193 972 +EW SCT 2450577.6065 8.029 1.107 2.196 972 +EW SCT 2450577.6466 8.033 1.110 2.200 972 +EW SCT 2450578.5261 7.989 1.097 2.178 972 +EW SCT 2450578.5924 7.972 1.095 2.171 972 +EW SCT 2450578.6418 7.948 1.092 2.166 972 +EW SCT 2450578.6492 7.947 1.095 2.163 972 +EW SCT 2450578.6706 7.950 1.091 2.160 972 +EW SCT 2450579.5742 7.780 1.040 2.079 972 +EW SCT 2450580.4716 7.776 1.050 2.118 972 +EW SCT 2450580.5813 7.784 1.055 2.119 972 +EW SCT 2450580.6323 7.791 1.059 2.127 972 +EW SCT 2450582.5632 8.209 1.142 2.261 972 +EW SCT 2450582.6109 8.235 1.144 2.258 972 +EW SCT 2450582.6582 8.218 1.137 2.252 972 +EW SCT 2450583.5543 8.053 1.099 2.184 972 +EW SCT 2450583.6107 8.029 1.089 2.166 972 +EW SCT 2450584.5453 7.625 1.017 2.036 972 +EW SCT 2450584.6016 7.624 1.014 2.035 972 +EW SCT 2450584.6494 7.619 .999 2.032 972 +V367 SCT 2445489.4335 11.756 1.896 1.232 982 +V367 SCT 2445490.3710 11.603 1.823 1.171 982 +V367 SCT 2445493.4101 11.502 1.844 1.180 982 +V367 SCT 2445496.3514 11.889 2.029 1.261 982 +V367 SCT 2445497.3203 11.604 1.846 1.192 982 +V367 SCT 2445498.3631 11.252 1.721 1.115 982 +V367 SCT 2445501.3750 11.841 1.932 1.237 982 +V367 SCT 2445502.3631 11.721 1.882 1.199 982 +V367 SCT 2445503.3631 11.495 1.749 1.188 982 +V367 SCT 2445505.3788 11.547 1.875 1.171 982 +V367 SCT 2445508.3631 11.738 2.109 1.234 982 +V367 SCT 2445515.3710 11.662 2.046 1.214 982 +V367 SCT 2445872.3359 11.670 1.899 1.219 982 +V367 SCT 2445873.3359 11.822 1.951 1.248 982 +V367 SCT 2445874.3242 11.735 1.855 1.212 982 +V367 SCT 2445875.3163 11.325 1.691 1.128 982 +V367 SCT 2445876.3280 11.394 1.755 1.162 982 +V367 SCT 2445877.3203 11.624 1.862 1.217 982 +V367 SCT 2445878.3125 11.796 1.898 1.242 982 +V367 SCT 2445879.3085 11.738 1.900 1.208 982 +V367 SCT 2445880.3163 11.614 1.789 1.216 982 +V367 SCT 2445881.3085 11.558 1.759 1.199 982 +V367 SCT 2445882.3006 11.521 1.804 1.182 982 +V367 SCT 2445886.3163 11.816 1.949 1.235 982 +V367 SCT 2445887.2968 11.620 1.819 1.184 982 +V367 SCT 2446606.2625 11.512 1.499 1.749 1.179 988 +V367 SCT 2446607.3103 11.461 1.781 1.163 988 +V367 SCT 2446608.2678 11.541 1.811 1.183 988 +V367 SCT 2446609.3144 11.767 1.619 1.886 1.240 988 +V367 SCT 2446610.2963 11.837 1.870 1.246 988 +V367 SCT 2446611.2909 11.552 1.871 1.154 988 +V367 SCT 2446612.2831 11.274 1.445 1.709 1.115 988 +V367 SCT 2446613.2814 11.525 1.879 1.157 988 +V367 SCT 2446614.2715 11.718 1.913 1.249 988 +V367 SCT 2446615.2790 11.810 1.909 1.246 988 +V367 SCT 2446616.2886 11.732 1.847 1.216 988 +V367 SCT 2446617.2841 11.469 1.811 1.156 988 +V367 SCT 2446618.2863 11.519 1.772 1.180 988 +V367 SCT 2446619.2833 11.559 1.806 1.185 988 +V367 SCT 2446620.2646 11.553 1.807 1.200 988 +V367 SCT 2446621.2577 11.590 1.801 1.203 988 +V367 SCT 2446622.2661 11.726 1.921 1.219 988 +V367 SCT 2446623.2453 11.784 1.880 1.232 988 +V367 SCT 2446624.2544 11.520 1.745 1.180 988 +V367 SCT 2446625.2478 11.275 1.698 1.121 988 +V367 SCT 2446626.2432 11.491 1.804 1.177 988 +V367 SCT 2446627.2392 11.744 1.911 1.230 988 +V367 SCT 2446628.2404 11.853 1.921 1.255 988 +V367 SCT 2446629.2501 11.710 1.818 1.214 988 +V367 SCT 2446632.2454 11.626 1.862 1.221 988 +V367 SCT 2446635.2214 11.682 1.872 1.231 988 +V367 SCT 2446636.2541 11.661 1.827 1.214 988 +V367 SCT 2447401.1731 11.555 1.824 1.164 990 +V367 SCT 2447402.1705 11.668 1.854 1.183 990 +V367 SCT 2447403.1673 11.776 1.876 1.196 990 +V367 SCT 2447404.1541 11.657 1.793 1.153 990 +V367 SCT 2447408.1460 11.848 1.921 1.224 990 +V367 SCT 2447409.1610 11.806 1.921 1.208 990 +V367 SCT 2447410.1605 11.477 1.762 1.132 990 +V367 SCT 2447411.1612 11.378 1.743 1.127 990 +V367 SCT 2447412.1663 11.550 1.856 1.172 990 +V367 SCT 2447413.1500 11.635 1.915 1.172 990 +V367 SCT 2447414.1473 11.654 1.879 1.175 990 +V367 SCT 2447415.1527 11.636 1.894 1.182 990 +V367 SCT 2447416.1444 11.668 1.884 1.182 990 +V367 SCT 2447417.1403 11.561 1.790 1.167 990 +V367 SCT 2447418.1418 11.417 1.762 1.109 990 +V367 SCT 2447419.1335 11.417 1.791 1.131 990 +V367 SCT 2447420.1368 11.633 1.882 1.172 990 +V367 SCT 2447421.1300 11.846 1.938 1.233 990 +V367 SCT 2447422.1404 11.665 1.931 1.170 990 +V367 SCT 2447423.1292 11.406 1.757 1.120 990 +V367 SCT 2447424.1304 11.307 1.754 1.120 990 +V367 SCT 2447425.1373 11.575 1.836 1.162 990 +V367 SCT 2447427.1380 11.744 1.872 1.197 990 +V367 SCT 2447428.1286 11.648 1.836 1.162 990 +V367 SCT 2447429.1268 11.587 1.862 1.158 990 +V367 SCT 2447430.1168 11.517 1.824 1.136 990 +V367 SCT 2447431.1185 11.462 1.809 1.137 990 +V367 SCT 2447432.1144 11.462 1.774 1.142 990 +V367 SCT 2447433.1104 11.654 1.880 1.173 990 +V367 SCT 2447434.1166 11.842 1.943 1.210 990 +V367 SCT 2447735.2675 11.600 1.823 1.197 991 +V367 SCT 2447736.2450 11.772 1.924 1.213 991 +V367 SCT 2447737.2502 11.753 1.834 1.206 991 +V367 SCT 2447738.2564 11.407 1.711 1.129 991 +V367 SCT 2447739.2252 11.270 1.716 1.112 991 +V367 SCT 2447740.2408 11.565 1.856 1.190 991 +V367 SCT 2447741.2305 11.774 1.890 1.235 991 +V367 SCT 2447742.2398 11.876 1.879 1.213 991 +V367 SCT 2447743.2249 11.651 1.821 1.182 991 +V367 SCT 2447744.2113 11.398 1.739 1.129 991 +V367 SCT 2447745.2110 11.490 1.788 1.152 991 +V367 SCT 2447746.2147 11.591 1.827 1.205 991 +V367 SCT 2447747.2076 11.616 1.844 1.179 991 +V367 SCT 2447748.2105 11.612 1.885 1.157 991 +V367 SCT 2447749.1903 11.694 1.877 1.200 991 +V367 SCT 2447750.1886 11.649 1.814 1.190 991 +V367 SCT 2447751.1939 11.407 1.762 1.133 991 +V367 SCT 2447752.1706 11.328 1.719 1.114 991 +V367 SCT 2447753.1772 11.542 1.824 1.173 991 +V367 SCT 2447754.1917 11.775 1.939 1.209 991 +V367 SCT 2447755.1856 11.857 1.904 1.214 991 +V367 SCT 2447756.2121 11.566 1.828 1.155 991 +V367 SCT 2447757.1816 11.313 1.699 1.119 991 +V367 SCT 2447758.1782 11.499 1.855 1.157 991 +V367 SCT 2447759.1684 11.671 1.864 1.203 991 +V367 SCT 2447760.1850 11.707 1.882 1.196 991 +V367 SCT 2447761.1635 11.664 1.811 1.185 991 +V367 SCT 2447762.1653 11.648 1.843 1.190 991 +V367 SCT 2447763.1574 11.571 1.780 1.168 991 +V367 SCT 2447764.1593 11.478 1.731 1.160 991 +V367 SCT 2447766.1585 11.597 1.818 1.197 991 +V367 SCT 2447767.1766 11.801 1.935 1.218 991 +V367 SCT 2447768.1683 11.843 1.878 1.216 991 +V367 SCT 2447769.1612 11.535 1.735 1.164 991 +V367 SCT 2447770.1589 11.278 1.661 1.105 991 +V367 SCT 2447771.1562 11.539 1.796 1.170 991 +V367 SCT 2447772.1533 11.721 1.847 1.208 991 +V367 SCT 2447773.1588 11.753 1.909 1.197 991 +V367 SCT 2447774.1860 11.652 1.843 1.185 991 +V367 SCT 2447775.1504 11.562 1.777 1.170 991 +V367 SCT 2447776.1542 11.506 1.771 1.153 991 +V367 SCT 2448101.2107 11.873 1.940 1.248 992 +V367 SCT 2448102.2307 11.653 1.837 1.170 992 +V367 SCT 2448103.2086 11.286 1.673 1.106 992 +V367 SCT 2448104.2207 11.465 1.782 1.177 992 +V367 SCT 2448108.2025 11.631 1.837 1.184 992 +V367 SCT 2448109.1955 11.559 1.817 1.173 992 +V367 SCT 2448110.1961 11.511 1.776 1.172 992 +V367 SCT 2448111.2085 11.445 1.782 1.142 992 +V367 SCT 2448112.2018 11.503 1.808 1.185 992 +V367 SCT 2448113.1937 11.710 1.924 1.215 992 +V367 SCT 2448114.2113 11.823 1.926 1.243 992 +V367 SCT 2448115.1930 11.623 1.810 1.172 992 +V367 SCT 2448116.1999 11.283 1.646 1.108 992 +V367 SCT 2448117.2166 11.467 1.866 1.165 992 +V367 SCT 2448118.2171 11.726 1.887 1.209 992 +V367 SCT 2448119.1893 11.854 1.936 1.205 992 +V367 SCT 2448123.1823 11.566 1.818 1.187 992 +V367 SCT 2448126.2087 11.740 1.846 1.220 992 +V367 SCT 2448127.1881 11.792 1.889 1.238 992 +V367 SCT 2448854.2170 11.592 1.905 1.213 905 +V367 SCT 2448856.1951 11.826 1.940 1.208 905 +V367 SCT 2448856.2380 11.830 1.925 1.225 905 +V367 SCT 2448857.1725 11.623 1.814 1.182 905 +V367 SCT 2448858.1828 11.439 1.779 1.149 905 +V367 SCT 2448858.1949 11.455 1.743 1.154 905 +V367 SCT 2448858.2529 11.422 1.784 1.137 905 +V367 SCT 2448858.3497 11.451 1.780 1.149 905 +V367 SCT 2448859.1989 11.540 1.833 1.170 905 +V367 SCT 2448859.3376 11.519 1.905 1.160 905 +V367 SCT 2448860.1741 11.604 1.851 1.187 905 +V367 SCT 2448860.2014 11.597 1.867 1.177 905 +V367 SCT 2448860.2360 11.582 1.881 1.172 905 +V367 SCT 2448860.3029 11.565 1.872 1.162 905 +V367 SCT 2448861.1614 11.609 1.860 1.175 905 +V367 SCT 2448861.3185 11.560 1.832 1.180 905 +V367 SCT 2448863.2062 11.774 1.882 1.209 905 +V367 SCT 2448863.2865 11.760 1.928 1.198 905 +V367 SCT 2448870.1706 11.563 1.786 1.158 905 +V367 SCT 2448870.1749 11.524 1.816 1.142 905 +V367 SCT 2448870.2691 11.535 1.764 1.174 905 +V367 SCT 2448872.1702 11.564 1.789 1.177 905 +V367 SCT 2448872.2510 11.597 1.775 1.183 905 +V367 SCT 2448873.1791 11.687 1.896 1.206 905 +V367 SCT 2448873.2554 11.693 1.994 1.203 905 +V367 SCT 2448874.1482 11.655 1.916 1.187 905 +V367 SCT 2448874.1883 11.651 1.902 1.172 905 +V367 SCT 2448874.2004 11.678 1.961 1.218 905 +V367 SCT 2448874.2495 11.693 1.885 1.190 905 +V367 SCT 2448875.1644 11.668 1.893 1.188 905 +V367 SCT 2448875.2748 11.625 1.988 1.154 905 +V367 SCT 2448876.1617 11.696 1.914 1.196 905 +V367 SCT 2448876.1731 11.696 1.872 1.172 905 +V367 SCT 2448876.2550 11.714 1.968 1.214 905 +V367 SCT 2448877.1260 11.639 1.887 1.157 905 +V367 SCT 2448877.1478 11.597 1.831 1.167 905 +V367 SCT 2448877.2606 11.689 1.990 1.207 905 +V367 SCT 2448878.1551 11.452 1.806 1.153 905 +V367 SCT 2448878.1736 11.431 1.769 1.152 905 +V367 SCT 2448878.2372 11.444 1.783 1.156 905 +V367 SCT 2448879.1594 11.434 1.770 1.170 905 +V367 SCT 2448879.2316 11.446 1.762 1.163 905 +V367 SCT 2448880.1465 11.638 1.940 1.190 905 +V367 SCT 2448880.1514 11.659 1.878 1.203 905 +V367 SCT 2448880.2586 11.684 1.997 1.198 905 +V367 SCT 2448881.1364 11.845 1.943 1.215 905 +V367 SCT 2448881.1382 11.827 1.968 1.232 905 +V367 SCT 2448881.2472 11.867 1.974 1.235 905 +V367 SCT 2448882.1307 11.852 1.891 1.216 905 +V367 SCT 2448882.1519 11.821 1.932 1.211 905 +V367 SCT 2448882.2435 11.803 1.916 1.193 905 +V367 SCT 2448883.1197 11.515 1.770 1.159 905 +V367 SCT 2448883.1491 11.451 1.755 1.141 905 +V367 SCT 2448883.2489 11.413 1.828 1.142 905 +V367 SCT 2448884.1299 11.392 1.740 1.139 905 +V367 SCT 2448884.1483 11.364 1.728 1.121 905 +V367 SCT 2448885.1360 11.659 1.866 1.197 905 +V367 SCT 2448885.1399 11.570 1.860 1.185 905 +V367 SCT 2448885.2249 11.590 1.891 1.182 905 +V367 SCT 2448886.1260 11.757 1.919 1.220 905 +V367 SCT 2448886.1479 11.766 1.934 1.220 905 +V367 SCT 2448886.2118 11.745 1.939 1.208 905 +V367 SCT 2448888.1172 11.665 1.860 1.183 905 +V367 SCT 2448888.1482 11.679 1.859 1.190 905 +V367 SCT 2448888.2271 11.667 1.841 1.190 905 +V367 SCT 2448889.1150 11.577 1.846 1.167 905 +V367 SCT 2448889.1683 11.626 1.851 1.170 905 +V367 SCT 2448889.2241 11.595 1.806 1.174 905 +V367 SCT 2448890.1153 11.565 1.814 1.167 905 +V367 SCT 2448890.1394 11.558 1.797 1.184 905 +V367 SCT 2448890.2073 11.530 1.825 1.162 905 +V367 SCT 2448891.1118 11.509 1.807 1.155 905 +V367 SCT 2448891.1386 11.493 1.806 1.155 905 +V367 SCT 2448891.2168 11.495 1.823 1.172 905 +V367 SCT 2448892.1142 11.476 1.810 1.159 905 +V367 SCT 2448892.1392 11.451 1.820 1.132 905 +V367 SCT 2448892.2101 11.392 1.858 1.112 905 +V367 SCT 2448893.1188 11.652 1.873 1.188 905 +V367 SCT 2448893.1360 11.672 1.887 1.199 905 +V367 SCT 2448893.2042 11.672 1.878 1.192 905 +V367 SCT 2448894.1094 11.817 1.926 1.210 905 +V367 SCT 2448894.1469 11.840 1.951 1.227 905 +V367 SCT 2448894.2086 11.817 1.946 1.218 905 +V367 SCT 2449197.3653 11.770 1.867 1.225 906 +V367 SCT 2449198.2323 11.525 1.785 1.170 906 +V367 SCT 2449198.3152 11.501 1.761 1.160 906 +V367 SCT 2449199.3218 11.347 1.766 1.126 906 +V367 SCT 2449200.2285 11.597 1.890 1.210 906 +V367 SCT 2449201.3534 11.887 1.275 906 +V367 SCT 2449202.2407 11.932 1.926 1.287 906 +V367 SCT 2449202.3558 11.927 1.994 1.266 906 +V367 SCT 2449203.2251 11.714 1.854 1.205 906 +V367 SCT 2449203.3454 11.677 1.905 1.192 906 +V367 SCT 2449204.2220 11.431 1.765 1.154 906 +V367 SCT 2449204.3444 11.417 1.757 1.126 906 +V367 SCT 2449205.2256 11.522 1.832 1.169 906 +V367 SCT 2449205.3538 11.567 1.811 1.195 906 +V367 SCT 2449206.2525 11.679 1.885 1.222 906 +V367 SCT 2449206.3647 11.645 1.932 1.209 906 +V367 SCT 2449207.2329 11.710 1.859 1.219 906 +V367 SCT 2449207.3490 11.666 1.923 1.196 906 +V367 SCT 2449209.2162 11.759 1.896 1.228 906 +V367 SCT 2449209.3562 11.751 1.965 1.219 906 +V367 SCT 2449210.2858 11.701 1.823 1.209 906 +V367 SCT 2449211.2639 11.501 1.770 1.164 906 +V367 SCT 2449212.2673 11.376 1.761 1.142 906 +V367 SCT 2449218.2807 11.584 1.837 1.201 906 +V367 SCT 2449219.2718 11.737 1.891 1.228 906 +V367 SCT 2449220.2672 11.768 1.917 1.227 906 +V367 SCT 2449221.3206 11.705 1.890 1.194 906 +V367 SCT 2449222.2706 11.685 1.869 1.210 906 +V367 SCT 2449224.2812 11.496 1.810 1.170 906 +V367 SCT 2449225.2417 11.478 1.781 1.170 906 +V367 SCT 2449226.2434 11.625 1.852 1.208 906 +V367 SCT 2449227.2354 11.867 1.918 1.288 906 +V367 SCT 2449228.2448 11.895 1.987 1.260 906 +V367 SCT 2449229.2357 11.559 1.825 1.159 906 +V367 SCT 2449230.2385 11.314 1.707 1.122 906 +V367 SCT 2449231.2409 11.584 1.813 1.192 906 +V367 SCT 2449232.2282 11.807 1.932 1.253 906 +V367 SCT 2449233.2299 11.859 1.927 1.252 906 +V367 SCT 2449234.2295 11.754 1.874 1.240 906 +V367 SCT 2449235.2232 11.564 1.843 1.192 906 +V367 SCT 2449236.2080 11.558 1.795 1.179 906 +V367 SCT 2449237.1994 11.610 1.808 1.216 906 +V367 SCT 2449238.1924 11.585 1.796 1.200 906 +V367 SCT 2449239.1965 11.640 1.852 1.215 906 +V367 SCT 2449240.2080 11.829 1.909 1.260 906 +V367 SCT 2449241.2094 11.833 1.908 1.238 906 +V367 SCT 2449243.1895 11.321 1.734 1.124 906 +V367 SCT 2449244.2140 11.617 1.856 1.214 906 +V367 SCT 2449245.2053 11.811 1.972 1.253 906 +V367 SCT 2449298.0968 11.777 1.947 1.222 906 +V367 SCT 2448503.2079 11.694 1.903 1.205 993 +V367 SCT 2448504.1634 11.845 1.924 1.245 993 +V367 SCT 2448505.1561 11.710 1.830 1.189 993 +V367 SCT 2448505.2223 11.701 1.776 1.199 993 +V367 SCT 2448506.1502 11.284 1.692 1.105 993 +V367 SCT 2448506.2251 11.293 1.628 1.125 993 +V367 SCT 2448507.1453 11.411 1.752 1.144 993 +V367 SCT 2448508.1403 11.656 1.881 1.204 993 +V367 SCT 2448508.1792 11.688 1.848 1.220 993 +V367 SCT 2448509.1403 11.834 1.938 1.234 993 +V367 SCT 2448509.1863 11.804 1.923 1.215 993 +V367 SCT 2448510.1433 11.769 1.914 1.192 993 +V367 SCT 2448510.1791 11.769 1.874 1.213 993 +V367 SCT 2448511.1375 11.585 1.805 1.180 993 +V367 SCT 2448511.1833 11.556 1.811 1.157 993 +V367 SCT 2448512.1367 11.485 1.777 1.157 993 +V367 SCT 2448512.1902 11.510 1.732 1.170 993 +V367 SCT 2448513.1375 11.549 1.805 1.159 993 +V367 SCT 2448513.1991 11.576 1.772 1.190 993 +V367 SCT 2448514.1376 11.593 1.839 1.190 993 +V367 SCT 2448514.1975 11.586 1.828 1.184 993 +V367 SCT 2448515.1360 11.511 1.826 1.166 993 +V367 SCT 2448515.1928 11.541 1.764 1.162 993 +V367 SCT 2448516.1334 11.684 1.832 1.211 993 +V367 SCT 2448517.1343 11.803 1.870 1.235 993 +V367 SCT 2448518.1379 11.632 1.830 1.170 993 +V367 SCT 2448519.1609 11.294 1.674 1.099 993 +V367 SCT 2448520.1291 11.416 1.782 1.153 993 +V367 SCT 2448521.1433 11.659 1.917 1.197 993 +V367 SCT 2448522.1359 11.841 1.914 1.253 993 +V367 SCT 2448523.1258 11.825 1.891 1.220 993 +V367 SCT 2449514.3396 11.440 1.755 .784 913 +V367 SCT 2449514.4089 11.467 1.757 .810 913 +V367 SCT 2449515.3672 11.525 1.774 .820 913 +V367 SCT 2449516.3873 11.786 1.913 .861 913 +V367 SCT 2449517.3863 11.928 1.956 .871 913 +V367 SCT 2449519.4093 11.304 1.674 .779 913 +V367 SCT 2449521.3388 11.697 1.910 .849 913 +V367 SCT 2449522.3603 11.843 1.916 .859 913 +V367 SCT 2449522.4212 11.841 1.938 .859 913 +V367 SCT 2449523.3170 11.812 1.882 .860 913 +V367 SCT 2449523.3971 11.730 1.921 .827 913 +V367 SCT 2449524.2973 11.618 1.821 .818 913 +V367 SCT 2449524.3667 11.632 1.796 .830 913 +V367 SCT 2449524.4299 11.601 1.861 .813 913 +V367 SCT 2449525.2927 11.574 1.778 .835 913 +V367 SCT 2449526.2811 11.519 1.847 .791 913 +V367 SCT 2449529.2835 11.823 1.931 .868 913 +V367 SCT 2449530.2677 11.892 1.946 .884 913 +V367 SCT 2449530.3525 11.890 2.012 .876 913 +V367 SCT 2449530.4074 11.848 1.986 .844 913 +V367 SCT 2449533.2697 11.487 1.792 .805 913 +V367 SCT 2449533.3473 11.510 1.798 .821 913 +V367 SCT 2449533.4014 11.511 1.822 .823 913 +V367 SCT 2449534.2910 11.732 1.917 .855 913 +V367 SCT 2449534.3296 11.743 1.947 .844 913 +V367 SCT 2449534.3365 11.732 1.952 .849 913 +V367 SCT 2449535.2592 11.895 1.961 .868 913 +V367 SCT 2449535.3229 11.913 1.990 .873 913 +V367 SCT 2449535.3923 11.902 1.970 .873 913 +V367 SCT 2449536.2505 11.804 1.878 .859 913 +V367 SCT 2449536.3729 11.792 1.880 .855 913 +V367 SCT 2449537.2542 11.516 1.796 .800 913 +V367 SCT 2449537.3209 11.531 1.765 .817 913 +V367 SCT 2449537.3932 11.520 1.771 .808 913 +V367 SCT 2449538.2774 11.493 1.783 .808 913 +V367 SCT 2449538.3272 11.485 1.824 .798 913 +V367 SCT 2449539.2520 11.611 1.830 .828 913 +V367 SCT 2449539.3396 11.604 1.861 .819 913 +V367 SCT 2449539.3777 11.575 1.882 .809 913 +V367 SCT 2449540.2415 11.640 1.866 .836 913 +V367 SCT 2449540.3342 11.656 1.840 .832 913 +V367 SCT 2449541.2447 11.617 1.871 .823 913 +V367 SCT 2449541.3105 11.611 1.855 .837 913 +V367 SCT 2449541.3780 11.638 1.829 .832 913 +V367 SCT 2449542.2461 11.747 1.883 .841 913 +V367 SCT 2449542.2997 11.744 1.892 .856 913 +V367 SCT 2449542.3691 11.777 1.947 .858 913 +V367 SCT 2449543.2439 11.803 1.918 .851 913 +V367 SCT 2449543.3125 11.789 1.926 .849 913 +V367 SCT 2449545.2329 11.339 1.701 .773 913 +V367 SCT 2449545.2881 11.365 1.685 .788 913 +V367 SCT 2449546.2361 11.505 1.786 .813 913 +V367 SCT 2449546.3026 11.508 1.801 .808 913 +V367 SCT 2449546.3769 11.535 1.811 .819 913 +V367 SCT 2449547.2263 11.752 1.926 .855 913 +V367 SCT 2449547.2830 11.773 1.932 .862 913 +V367 SCT 2449548.3120 11.915 1.969 .870 913 +V367 SCT 2449548.3508 11.934 1.971 .874 913 +V367 SCT 2449549.2258 11.788 1.908 .839 913 +V367 SCT 2449549.2979 11.793 1.868 .854 913 +V367 SCT 2449549.3458 11.756 1.898 .845 913 +V367 SCT 2449550.2969 11.421 1.687 .796 913 +V367 SCT 2449551.2928 11.501 1.761 .813 913 +V367 SCT 2449552.2914 11.678 1.825 .845 913 +V367 SCT 2449553.2830 11.716 1.843 .834 913 +V367 SCT 2449554.2971 11.714 1.815 .844 913 +V367 SCT 2449556.2860 11.736 1.949 .864 913 +V367 SCT 2449557.2403 11.576 1.812 .833 913 +V367 SCT 2449559.2140 11.502 1.825 .821 913 +V367 SCT 2449559.2894 11.512 1.854 .818 913 +V367 SCT 2449560.2235 11.776 1.940 .854 913 +V367 SCT 2449560.3277 11.775 1.977 .859 913 +V367 SCT 2449561.3035 11.924 2.013 .862 913 +V367 SCT 2449563.3070 11.322 1.684 .773 913 +V367 SCT 2449564.2313 11.478 1.814 .808 913 +V367 SCT 2449621.1487 11.572 1.811 .788 913 +V367 SCT 2449623.1519 11.958 1.935 .865 913 +V367 SCT 2449624.1356 11.938 1.978 .866 913 +V367 SCT 2449625.1434 11.685 1.877 .805 913 +V367 SCT 2449631.1407 11.771 2.036 .823 913 +V367 SCT 2449632.1379 11.659 1.859 .828 913 +V367 SCT 2449633.1322 11.348 1.715 .788 913 +V367 SCT 2449634.1418 11.512 1.856 .809 913 +V367 SCT 2449520.8696 11.653 1.894 996 +V367 SCT 2449521.7985 11.750 1.917 996 +V367 SCT 2449522.7288 11.889 1.891 996 +V367 SCT 2449528.8346 11.768 1.912 1.158 1.106 996 +V367 SCT 2449536.7632 11.672 1.827 1.074 1.065 996 +V367 SCT 2449543.7410 11.743 1.866 1.158 1.086 996 +V367 SCT 2449545.7078 11.382 1.774 1.153 1.079 996 +V367 SCT 2449559.8183 11.785 1.948 996 +V367 SCT 2449561.7425 11.844 1.859 1.213 1.159 996 +V367 SCT 2449564.6503 11.583 1.791 1.184 1.132 996 +V367 SCT 2449804.8967 11.538 1.805 1.162 1.162 997 +V367 SCT 2449808.9029 11.328 1.661 1.108 1.094 997 +V367 SCT 2449809.8602 11.498 1.776 1.169 1.140 997 +V367 SCT 2449810.8617 11.670 1.915 1.198 1.103 997 +V367 SCT 2449811.8413 11.777 1.926 1.213 1.163 997 +V367 SCT 2449811.8865 11.805 1.913 1.224 1.175 997 +V367 SCT 2449813.8646 11.608 1.776 1.176 1.127 997 +V367 SCT 2449814.8447 11.550 1.765 1.171 1.125 997 +V367 SCT 2449817.8547 11.555 1.799 1.182 1.138 997 +V367 SCT 2449818.8329 11.758 1.916 1.233 1.151 997 +V367 SCT 2449818.8637 11.761 1.949 1.233 1.166 997 +V367 SCT 2449821.8388 11.240 1.650 1.076 1.101 997 +V367 SCT 2449822.8313 11.472 1.778 1.160 1.128 997 +V367 SCT 2449823.8289 11.739 1.872 1.219 1.166 997 +V367 SCT 2449825.8105 11.752 1.763 1.192 1.160 997 +V367 SCT 2449934.2627 11.688 1.164 2.296 998 +V367 SCT 2449935.2747 11.382 1.116 2.175 998 +V367 SCT 2449936.2411 11.446 1.752 1.174 2.265 998 +V367 SCT 2449942.2393 11.655 1.219 2.325 998 +V367 SCT 2449943.2284 11.708 1.933 1.236 2.380 998 +V367 SCT 2449944.2131 11.760 1.883 1.192 2.335 998 +V367 SCT 2449945.2450 11.707 1.910 1.215 2.344 998 +V367 SCT 2449946.1836 11.672 1.909 1.177 2.314 998 +V367 SCT 2449947.1999 11.593 1.842 1.189 2.274 998 +V367 SCT 2449948.1777 11.500 1.772 1.163 2.284 998 +V367 SCT 2449949.1767 11.465 1.780 1.179 2.279 998 +V367 SCT 2449950.1820 11.746 1.935 1.241 2.361 998 +V367 SCT 2449953.2176 11.430 1.748 1.147 2.217 998 +V367 SCT 2449954.1961 11.424 1.762 1.157 2.230 998 +V367 SCT 2449955.1931 11.644 1.905 1.210 2.350 998 +V367 SCT 2449957.2074 11.770 1.918 1.247 2.376 998 +V367 SCT 2449958.1849 11.693 1.874 1.210 2.322 998 +V367 SCT 2449959.1856 11.652 2.308 998 +V367 SCT 2450011.1253 11.478 2.229 998 +V367 SCT 2450012.1169 11.554 2.325 998 +V367 SCT 2450017.0857 11.604 2.316 998 +V367 SCT 2450018.1113 11.655 2.323 998 +V367 SCT 2450020.0821 11.720 2.343 998 +V367 SCT 2450348.5866 11.599 1.855 2.336 999 +V367 SCT 2450350.6658 11.633 1.835 2.354 999 +V367 SCT 2450351.5636 11.519 1.785 2.305 999 +V367 SCT 2450352.5895 11.509 1.789 2.313 999 +V367 SCT 2450353.5406 11.705 1.880 2.374 999 +V367 SCT 2450354.5535 11.839 1.941 2.420 999 +V367 SCT 2450355.5349 11.674 1.794 2.341 999 +V367 SCT 2450357.5348 11.411 1.752 2.278 999 +V367 SCT 2450358.5383 11.670 1.891 2.376 999 +V367 SCT 2450359.5288 11.832 1.940 2.418 999 +V367 SCT 2450360.5355 11.783 1.877 2.386 999 +V367 SCT 2450361.5416 11.539 1.774 2.289 999 +V367 SCT 2450362.5413 11.472 1.750 2.284 999 +V367 SCT 2450363.5396 11.539 1.823 2.320 999 +V367 SCT 2450379.5288 11.680 1.871 2.362 999 +V367 SCT 2450380.5296 11.690 1.846 2.334 999 +V367 SCT 2450381.5549 11.530 1.788 2.293 999 +V367 SCT 2450382.5402 11.382 1.696 2.245 999 +V367 SCT 2450383.5482 11.450 1.833 2.304 999 +V367 SCT 2450384.5449 11.721 1.868 2.398 999 +V367 SCT 2450386.5431 11.702 1.864 2.334 999 +V367 SCT 2450387.5484 11.285 1.709 2.178 999 +V367 SCT 2450388.5394 11.406 1.782 2.267 999 +V367 SCT 2450389.5395 11.644 1.883 2.362 999 +V367 SCT 2450390.5345 11.759 1.913 2.408 999 +V367 SCT 2450391.5344 11.709 1.881 2.347 999 +V367 SCT 2450392.5280 11.613 1.858 2.347 999 +V367 SCT 2450393.5300 11.548 1.822 2.298 999 +V367 SCT 2450305.2375 11.528 1.778 1.175 971 +V367 SCT 2450306.1932 11.505 1.750 1.162 971 +V367 SCT 2450307.2339 11.595 1.726 1.190 971 +V367 SCT 2450310.2417 11.841 1.873 1.240 971 +V367 SCT 2450311.1756 11.787 1.855 1.230 971 +V367 SCT 2450312.1751 11.452 1.705 1.161 971 +V367 SCT 2450313.1904 11.343 1.679 1.147 971 +V367 SCT 2450314.1714 11.569 1.874 1.213 971 +V367 SCT 2450314.2457 11.598 1.822 1.209 971 +V367 SCT 2450315.1812 11.792 1.816 1.245 971 +V367 SCT 2450316.1965 11.867 1.778 1.210 971 +V367 SCT 2450317.1982 11.649 1.798 1.181 971 +V367 SCT 2450318.1767 11.429 1.725 1.149 971 +V367 SCT 2450319.1847 11.534 1.740 1.179 971 +V367 SCT 2450320.1833 11.599 1.768 1.193 971 +V367 SCT 2450321.1712 11.622 1.820 1.193 971 +V367 SCT 2450322.1695 11.661 1.807 1.194 971 +V367 SCT 2450323.1661 11.752 1.776 1.217 971 +V367 SCT 2450324.1853 11.694 1.765 1.215 971 +V367 SCT 2450325.1672 11.452 1.662 1.147 971 +V367 SCT 2450326.1518 11.356 1.668 1.142 971 +V367 SCT 2450327.2251 11.548 1.821 1.252 971 +V367 SCT 2450329.1697 11.509 1.918 1.230 971 +V367 SCT 2450330.1616 11.540 1.772 1.204 971 +V367 SCT 2450332.1468 11.553 1.763 1.169 971 +V367 SCT 2450333.1504 11.664 1.864 1.165 971 +V367 SCT 2450334.1604 11.713 1.869 1.264 971 +V367 SCT 2450335.1598 11.669 1.850 1.199 971 +V367 SCT 2450336.1617 11.648 1.856 1.235 971 +V367 SCT 2450337.1540 11.614 1.748 1.185 971 +V367 SCT 2450340.1429 11.632 1.826 1.216 971 +V367 SCT 2450341.1489 11.825 1.905 1.230 971 +V367 SCT 2450342.1529 11.840 1.887 1.253 971 +V367 SCT 2450344.1633 11.289 1.702 1.025 971 +V367 SCT 2450568.5645 11.792 1.219 2.368 972 +V367 SCT 2450570.5950 11.434 1.118 2.234 972 +V367 SCT 2450572.4697 11.517 1.160 2.320 972 +V367 SCT 2450572.5560 11.545 1.186 2.338 972 +V367 SCT 2450572.5592 11.543 1.179 2.328 972 +V367 SCT 2450573.5006 11.754 1.218 2.399 972 +V367 SCT 2450573.5947 11.768 1.215 2.402 972 +V367 SCT 2450573.6671 11.796 1.227 2.420 972 +V367 SCT 2450574.5298 11.866 1.233 2.421 972 +V367 SCT 2450574.5910 11.857 1.217 2.417 972 +V367 SCT 2450575.4899 11.632 1.149 2.300 972 +V367 SCT 2450575.5747 11.606 1.164 2.303 972 +V367 SCT 2450575.6383 11.579 1.157 2.295 972 +V367 SCT 2450576.5356 11.296 1.086 2.206 972 +V367 SCT 2450576.6171 11.317 1.124 2.222 972 +V367 SCT 2450576.6536 11.308 1.106 2.210 972 +V367 SCT 2450577.5446 11.493 1.152 2.294 972 +V367 SCT 2450577.6031 11.511 1.156 2.300 972 +V367 SCT 2450577.6434 11.512 1.152 2.306 972 +V367 SCT 2450578.5227 11.662 1.207 2.365 972 +V367 SCT 2450578.5888 11.659 1.200 2.366 972 +V367 SCT 2450578.6395 11.666 1.205 2.375 972 +V367 SCT 2450578.6458 11.663 1.194 2.368 972 +V367 SCT 2450579.5705 11.714 1.190 2.364 972 +V367 SCT 2450580.4672 11.662 1.185 2.354 972 +V367 SCT 2450580.5782 11.641 1.177 2.334 972 +V367 SCT 2450580.6291 11.647 1.182 2.355 972 +V367 SCT 2450582.5599 11.540 1.138 2.281 972 +V367 SCT 2450582.6074 11.570 1.146 2.294 972 +V367 SCT 2450582.6552 11.547 1.148 2.289 972 +V367 SCT 2450583.5508 11.490 1.140 2.273 972 +V367 SCT 2450583.6070 11.478 1.146 2.272 972 +V367 SCT 2450584.5423 11.411 1.135 2.267 972 +V367 SCT 2450584.5985 11.406 1.129 2.255 972 +AA SER 2444825.3085 11.958 2.164 982 +AA SER 2444827.2030 11.791 982 +AA SER 2444829.2070 11.989 2.267 982 +AA SER 2444831.2109 12.236 2.459 982 +AA SER 2444832.2030 12.287 2.440 982 +AA SER 2444833.1875 12.411 2.438 982 +AA SER 2444836.2734 12.673 2.487 982 +AA SER 2444840.2381 12.463 2.401 982 +AA SER 2444841.2147 12.371 2.284 982 +AA SER 2444844.1875 11.787 2.162 982 +AA SER 2444845.1835 11.886 2.169 982 +AA SER 2444846.2109 11.991 2.249 982 +AA SER 2444847.1835 12.085 2.311 982 +AA SER 2444848.1835 12.174 2.388 982 +AA SER 2444849.2070 12.290 2.430 982 +AA SER 2444850.1913 12.359 2.479 982 +AA SER 2444851.1835 12.458 2.469 982 +AA SER 2444852.1875 12.562 2.456 982 +AA SER 2444853.1953 12.622 2.517 982 +AA SER 2444854.2030 12.666 2.431 982 +AA SER 2444855.1484 12.632 2.436 982 +AA SER 2444856.1562 12.518 2.390 982 +AA SER 2444858.2538 12.402 2.300 982 +AA SER 2444880.1718 11.965 2.251 982 +AA SER 2444881.1601 12.107 2.304 982 +AA SER 2445173.3203 12.083 2.306 982 +AA SER 2445175.3437 12.325 2.444 982 +AA SER 2445178.3203 12.568 2.517 982 +AA SER 2445179.2264 12.620 2.509 982 +AA SER 2445180.2421 12.643 2.484 982 +AA SER 2445181.2147 12.593 2.463 982 +AA SER 2445182.2187 12.473 2.416 982 +AA SER 2445186.2460 11.764 2.114 982 +AA SER 2445187.2187 11.813 2.124 982 +AA SER 2445188.2147 11.938 2.192 982 +AA SER 2445189.2734 12.011 2.269 982 +AA SER 2445190.2655 12.092 2.324 982 +AA SER 2445191.2578 12.193 2.337 982 +AA SER 2445192.2617 12.312 2.433 982 +AA SER 2445193.2421 12.378 2.443 982 +AA SER 2445194.2381 12.495 2.436 982 +AA SER 2445195.2578 12.554 2.526 982 +AA SER 2445198.2578 12.620 2.442 982 +AA SER 2445200.2695 12.403 2.331 982 +AA SER 2445201.2617 12.373 2.287 982 +AA SER 2445646.1210 12.374 2.325 1.403 982 +AA SER 2445648.1014 11.880 2.176 1.323 982 +AA SER 2445650.1131 11.829 2.142 1.306 982 +AA SER 2445665.0897 11.950 2.197 1.303 982 +AA SER 2445864.3671 12.601 2.488 1.461 982 +AA SER 2445866.3867 12.614 2.519 1.428 982 +AA SER 2445867.3671 12.560 2.456 1.429 982 +AA SER 2445868.3437 12.436 2.380 1.412 982 +AA SER 2445869.3631 12.378 2.283 1.391 982 +AA SER 2445870.3280 12.199 2.207 1.331 982 +AA SER 2445871.3437 11.815 2.107 1.283 982 +AA SER 2445872.3554 11.758 2.112 1.282 982 +AA SER 2445873.3554 11.859 2.161 1.325 982 +AA SER 2445874.3397 11.981 2.225 1.354 982 +AA SER 2445875.3280 12.046 2.313 1.370 982 +AA SER 2445876.3437 12.145 2.367 1.385 982 +AA SER 2445877.3359 12.270 2.421 1.433 982 +AA SER 2445878.3320 12.332 2.430 1.426 982 +AA SER 2445879.3242 12.440 2.488 1.451 982 +AA SER 2445880.3320 12.481 2.491 1.444 982 +AA SER 2445881.3280 12.575 2.427 1.443 982 +AA SER 2445882.3163 12.653 2.488 1.458 982 +AA SER 2445883.3280 12.653 2.511 1.440 982 +AA SER 2445886.3320 12.417 2.291 1.404 982 +AA SER 2445887.3163 12.214 2.307 1.320 982 +AA SER 2447401.1916 12.136 2.279 1.362 990 +AA SER 2447402.1867 12.179 2.372 1.359 990 +AA SER 2447403.1801 12.301 2.362 1.379 990 +AA SER 2447404.1649 12.360 2.387 1.390 990 +AA SER 2447408.1475 12.687 2.462 1.438 990 +AA SER 2447409.1637 12.627 2.471 1.426 990 +AA SER 2447410.1728 12.567 2.389 1.412 990 +AA SER 2447411.1727 12.443 2.355 1.394 990 +AA SER 2447412.1788 12.418 2.307 1.374 990 +AA SER 2447413.1606 12.175 2.230 1.326 990 +AA SER 2447414.1500 11.840 2.108 1.275 990 +AA SER 2447415.1538 11.756 2.123 1.245 990 +AA SER 2447416.1461 11.868 2.140 1.298 990 +AA SER 2447417.1422 11.965 2.205 1.331 990 +AA SER 2447418.1431 12.076 2.321 1.329 990 +AA SER 2447419.1367 12.180 2.332 1.367 990 +AA SER 2447420.1388 12.252 2.399 1.379 990 +AA SER 2447421.1323 12.354 2.421 1.402 990 +AA SER 2447422.1424 12.425 2.458 1.395 990 +AA SER 2447423.1349 12.517 2.457 1.405 990 +AA SER 2447424.1375 12.588 2.477 1.425 990 +AA SER 2447425.1410 12.648 2.439 1.417 990 +AA SER 2447427.1431 12.618 2.474 1.417 990 +AA SER 2447428.1313 12.483 2.333 1.397 990 +AA SER 2447429.1317 12.431 2.350 1.376 990 +AA SER 2447430.1216 12.256 2.182 1.331 990 +AA SER 2447431.1233 11.873 2.118 1.269 990 +AA SER 2447432.1177 11.778 2.097 1.270 990 +AA SER 2447433.1123 11.877 2.155 1.285 990 +AA SER 2447434.1194 11.985 2.199 1.323 990 +AA SER 2450570.6103 11.889 2.568 972 +AA SER 2450572.4801 12.060 2.646 972 +AA SER 2450572.5674 12.063 2.660 972 +AA SER 2450573.5088 12.135 2.677 972 +AA SER 2450573.5999 12.154 2.687 972 +AA SER 2450574.5354 12.241 2.701 972 +AA SER 2450574.5948 12.255 2.718 972 +AA SER 2450575.4961 12.329 2.728 972 +AA SER 2450575.5795 12.357 2.731 972 +AA SER 2450575.6428 12.362 2.741 972 +AA SER 2450576.5427 12.419 2.745 972 +AA SER 2450576.6222 12.452 2.767 972 +AA SER 2450577.5496 12.518 2.775 972 +AA SER 2450577.6074 12.536 2.785 972 +AA SER 2450577.6476 12.527 2.766 972 +AA SER 2450578.5275 12.591 2.781 972 +AA SER 2450578.5936 12.586 2.778 972 +AA SER 2450578.6502 12.587 2.790 972 +AA SER 2450579.5755 12.657 2.782 972 +AA SER 2450580.4725 12.604 2.758 972 +AA SER 2450580.5825 12.588 2.763 972 +AA SER 2450580.6334 12.616 2.777 972 +AA SER 2450582.5642 12.381 2.667 972 +AA SER 2450582.6117 12.394 2.661 972 +AA SER 2450582.6592 12.387 2.676 972 +AA SER 2450583.5552 12.372 2.657 972 +AA SER 2450583.6125 12.383 2.664 972 +AA SER 2450584.5464 12.065 2.554 972 +AA SER 2450584.6026 12.052 2.559 972 +AA SER 2450584.6504 12.019 2.536 972 +BQ SER 2446606.2730 9.329 .970 1.408 .836 988 +BQ SER 2446607.3203 9.410 1.048 1.466 .877 988 +BQ SER 2446610.3028 9.614 1.112 1.503 .921 988 +BQ SER 2446611.2969 9.290 1.015 1.395 .835 988 +BQ SER 2446612.2993 9.362 1.002 1.429 .868 988 +BQ SER 2446613.2867 9.647 1.140 1.552 .932 988 +BQ SER 2446614.2788 9.729 1.099 1.558 .933 988 +BQ SER 2446615.2875 9.193 1.010 1.327 .817 988 +BQ SER 2446616.2993 9.480 1.076 1.504 .888 988 +BQ SER 2446617.2964 9.674 1.141 1.578 .922 988 +BQ SER 2446618.2956 9.572 1.119 1.510 .906 988 +BQ SER 2446619.2959 9.494 1.064 1.465 .883 988 +BQ SER 2446620.2742 9.358 1.019 1.418 .854 988 +BQ SER 2446621.2675 9.438 1.080 1.458 .886 988 +BQ SER 2446622.2751 9.663 1.130 1.560 .926 988 +BQ SER 2446623.3158 9.564 1.488 .890 988 +BQ SER 2446624.3181 9.223 1.366 .822 988 +BQ SER 2446625.2563 9.529 1.509 .903 988 +BQ SER 2446627.2513 9.516 1.450 .891 988 +BQ SER 2446628.2535 9.390 1.053 1.442 .858 988 +BQ SER 2446629.2588 9.482 1.468 .890 988 +BQ SER 2446631.2302 9.656 1.539 .919 988 +BQ SER 2446632.2580 9.419 1.431 .859 988 +BQ SER 2446635.2341 9.756 1.178 1.596 .943 988 +BQ SER 2446636.2639 9.303 1.376 .839 988 +BQ SER 2447400.2196 9.766 1.577 .930 990 +BQ SER 2447401.1778 9.215 .836 1.374 .797 990 +BQ SER 2447404.1557 9.609 1.510 .871 990 +BQ SER 2447408.1520 9.612 1.516 .891 990 +BQ SER 2447409.1659 9.624 1.535 .894 990 +BQ SER 2447410.1747 9.182 1.342 990 +BQ SER 2447411.1770 9.472 1.524 .875 990 +BQ SER 2447412.1819 9.726 1.591 .940 990 +BQ SER 2447413.1627 9.512 1.499 .868 990 +BQ SER 2447414.1520 9.340 1.441 .829 990 +BQ SER 2447415.1554 9.518 1.534 .877 990 +BQ SER 2447416.1490 9.547 1.512 .884 990 +BQ SER 2447417.1441 9.558 1.513 .887 990 +BQ SER 2447418.1456 9.496 1.467 .850 990 +BQ SER 2447419.1377 9.249 1.388 .823 990 +BQ SER 2447420.1413 9.528 1.518 .889 990 +BQ SER 2447421.1341 9.764 1.588 .921 990 +BQ SER 2447422.1479 9.301 1.402 .806 990 +BQ SER 2447423.1376 9.327 1.472 .831 990 +BQ SER 2447424.1397 9.643 1.564 .914 990 +BQ SER 2447425.1462 9.596 1.499 .871 990 +BQ SER 2447427.1527 9.425 1.419 .833 990 +BQ SER 2447428.1382 9.379 1.421 .841 990 +BQ SER 2447429.1361 9.584 1.537 .901 990 +BQ SER 2447430.1240 9.735 1.556 .907 990 +BQ SER 2447431.1253 9.205 1.346 .795 990 +BQ SER 2447432.1197 9.403 1.476 .859 990 +BQ SER 2447433.1145 9.715 1.589 .928 990 +BQ SER 2447434.1216 9.664 1.525 .889 990 +BQ SER 2447735.2712 9.292 1.369 .837 991 +BQ SER 2447736.2477 9.528 1.494 .887 991 +BQ SER 2447737.2529 9.753 1.568 .923 991 +BQ SER 2447738.2575 9.284 1.387 .828 991 +BQ SER 2447739.2266 9.282 1.409 .847 991 +BQ SER 2447740.2438 9.660 1.522 .927 991 +BQ SER 2447741.2332 9.643 1.540 .902 991 +BQ SER 2447742.2430 9.461 1.419 .884 991 +BQ SER 2447743.2272 9.445 1.443 .856 991 +BQ SER 2447744.2144 9.419 1.437 .857 991 +BQ SER 2447745.2132 9.539 1.493 .900 991 +BQ SER 2447746.2175 9.692 1.561 .896 991 +BQ SER 2447747.2104 9.217 1.337 .819 991 +BQ SER 2447748.2132 9.402 1.452 .855 991 +BQ SER 2447749.1930 9.673 1.568 .922 991 +BQ SER 2447750.1916 9.665 1.542 .897 991 +BQ SER 2447751.1966 9.290 1.387 .832 991 +BQ SER 2447752.1737 9.475 1.484 .871 991 +BQ SER 2447753.1812 9.549 1.490 .882 991 +BQ SER 2447754.1961 9.549 1.533 .878 991 +BQ SER 2447755.1899 9.609 1.506 .901 991 +BQ SER 2447756.2138 9.244 1.360 .828 991 +BQ SER 2447757.1861 9.430 1.473 .882 991 +BQ SER 2447758.1800 9.726 1.593 .930 991 +BQ SER 2447759.1717 9.588 1.484 .881 991 +BQ SER 2447760.1895 9.263 1.371 .824 991 +BQ SER 2447761.1658 9.564 1.514 .905 991 +BQ SER 2447762.1675 9.642 1.548 .891 991 +BQ SER 2447763.1600 9.516 1.460 .892 991 +BQ SER 2447764.1615 9.458 1.458 .859 991 +BQ SER 2447766.1603 9.508 1.455 .897 991 +BQ SER 2447767.1790 9.752 1.576 .923 991 +BQ SER 2447768.1713 9.363 1.412 .816 991 +BQ SER 2447769.1694 9.299 .890 1.387 .847 991 +BQ SER 2447770.1622 9.606 1.033 1.538 .916 991 +BQ SER 2447771.1587 9.708 1.578 .911 991 +BQ SER 2447772.1568 9.392 1.388 .854 991 +BQ SER 2447773.1614 9.437 1.434 .863 991 +BQ SER 2447774.1889 9.473 1.459 .866 991 +BQ SER 2447775.1527 9.545 1.475 .904 991 +BQ SER 2447776.1569 9.672 1.667 .926 991 +BQ SER 2448101.2304 9.502 1.517 .884 992 +BQ SER 2448101.2319 9.514 1.495 .889 992 +BQ SER 2448102.2343 9.318 1.416 .848 992 +BQ SER 2448103.2116 9.411 1.443 .878 992 +BQ SER 2448104.2235 9.659 1.585 .919 992 +BQ SER 2448104.2241 9.641 1.596 .918 992 +BQ SER 2448109.1987 9.513 1.477 .882 992 +BQ SER 2448110.1992 9.394 1.455 .860 992 +BQ SER 2448111.2113 9.454 1.470 .864 992 +BQ SER 2448112.2046 9.487 1.500 .877 992 +BQ SER 2448113.1972 9.640 1.548 .914 992 +BQ SER 2448114.2139 9.425 1.453 .862 992 +BQ SER 2448115.1952 9.279 1.365 .853 992 +BQ SER 2448116.2029 9.579 1.548 .884 992 +BQ SER 2448117.2203 9.803 1.590 .937 992 +BQ SER 2448118.2190 9.353 1.409 .841 992 +BQ SER 2448119.1929 9.438 1.452 .871 992 +BQ SER 2448123.1853 9.389 1.398 .852 992 +BQ SER 2448126.2118 9.743 1.588 .914 992 +BQ SER 2448127.1900 9.163 1.352 .808 992 +BQ SER 2448854.2203 9.446 1.516 .891 905 +BQ SER 2448856.2003 9.583 1.527 .896 905 +BQ SER 2448856.2418 9.576 1.526 .894 905 +BQ SER 2448857.1615 9.512 1.507 .884 905 +BQ SER 2448857.3472 9.513 1.518 .876 905 +BQ SER 2448858.1771 9.386 1.437 .860 905 +BQ SER 2448858.2021 9.377 1.436 .861 905 +BQ SER 2448858.2584 9.366 1.405 .844 905 +BQ SER 2448858.3394 9.353 1.471 .846 905 +BQ SER 2448859.1932 9.429 1.482 .866 905 +BQ SER 2448859.3422 9.479 1.482 .891 905 +BQ SER 2448860.1638 9.654 1.569 .912 905 +BQ SER 2448860.2071 9.656 1.597 .916 905 +BQ SER 2448860.2402 9.674 1.588 .927 905 +BQ SER 2448861.1536 9.690 1.539 .896 905 +BQ SER 2448861.3221 9.578 1.497 .892 905 +BQ SER 2448863.2009 9.532 1.514 .899 905 +BQ SER 2448863.2802 9.548 1.544 .907 905 +BQ SER 2448870.1647 9.482 1.451 .864 905 +BQ SER 2448870.1829 9.462 1.450 .857 905 +BQ SER 2448870.2859 9.398 1.444 .856 905 +BQ SER 2448871.2984 9.298 1.440 .845 905 +BQ SER 2448872.1566 9.560 1.556 .902 905 +BQ SER 2448872.1605 9.565 1.556 .900 905 +BQ SER 2448872.2217 9.547 1.568 .908 905 +BQ SER 2448872.2677 9.594 1.577 .905 905 +BQ SER 2448873.1941 9.787 1.619 .935 905 +BQ SER 2448873.2724 9.787 1.601 .937 905 +BQ SER 2448874.1552 9.366 1.430 .854 905 +BQ SER 2448874.1961 9.364 1.395 .852 905 +BQ SER 2448874.2075 9.333 1.417 .838 905 +BQ SER 2448874.2631 9.325 1.414 .842 905 +BQ SER 2448875.1776 9.418 1.468 .860 905 +BQ SER 2448875.2916 9.457 1.475 .882 905 +BQ SER 2448876.1728 9.598 1.555 .899 905 +BQ SER 2448876.1822 9.601 1.554 .900 905 +BQ SER 2448876.2678 9.628 1.540 .923 905 +BQ SER 2448877.1387 9.602 1.530 .900 905 +BQ SER 2448877.1949 9.588 1.534 .902 905 +BQ SER 2448877.2774 9.594 1.516 .906 905 +BQ SER 2448878.1665 9.597 1.530 .892 905 +BQ SER 2448878.1818 9.596 1.537 .898 905 +BQ SER 2448878.2502 9.592 1.449 .844 905 +BQ SER 2448879.1679 9.398 1.404 .851 905 +BQ SER 2448879.2414 9.363 1.449 .844 905 +BQ SER 2448880.1487 9.352 1.438 .843 905 +BQ SER 2448880.1631 9.362 1.442 .852 905 +BQ SER 2448880.2743 9.390 1.472 .869 905 +BQ SER 2448881.1451 9.585 1.571 .892 905 +BQ SER 2448881.1502 9.613 1.564 .909 905 +BQ SER 2448881.2645 9.631 1.591 .905 905 +BQ SER 2448882.1424 9.776 1.604 .925 905 +BQ SER 2448882.1547 9.779 1.619 .920 905 +BQ SER 2448882.2579 9.757 1.575 .928 905 +BQ SER 2448883.1267 9.239 1.358 .825 905 +BQ SER 2448883.1529 9.196 1.336 .806 905 +BQ SER 2448883.2646 9.210 1.368 .822 905 +BQ SER 2448884.1359 9.440 1.500 .878 905 +BQ SER 2448884.1523 9.434 1.487 .875 905 +BQ SER 2448885.1468 9.687 1.584 .919 905 +BQ SER 2448885.1477 9.684 1.592 .910 905 +BQ SER 2448885.2378 9.708 1.609 .917 905 +BQ SER 2448886.1317 9.616 1.551 .892 905 +BQ SER 2448886.1510 9.617 1.514 .913 905 +BQ SER 2448886.2225 9.597 1.534 .905 905 +BQ SER 2448888.1221 9.438 1.470 .867 905 +BQ SER 2448888.1536 9.441 1.455 .881 905 +BQ SER 2448888.2366 9.414 1.469 .865 905 +BQ SER 2448889.1198 9.458 1.487 .884 905 +BQ SER 2448889.1718 9.484 1.506 .873 905 +BQ SER 2448889.2341 9.449 1.528 .861 905 +BQ SER 2448890.1211 9.629 1.559 .901 905 +BQ SER 2448890.1422 9.644 1.555 .936 905 +BQ SER 2448890.2166 9.648 1.563 .907 905 +BQ SER 2448891.1166 9.680 1.556 .911 905 +BQ SER 2448891.1417 9.655 1.554 .898 905 +BQ SER 2448891.2261 9.623 1.535 .904 905 +BQ SER 2448892.1183 9.206 1.362 .825 905 +BQ SER 2448892.1441 9.153 1.364 .808 905 +BQ SER 2448892.2205 9.215 1.374 .831 905 +BQ SER 2448893.1234 9.504 1.530 .899 905 +BQ SER 2448893.1407 9.505 1.532 .894 905 +BQ SER 2448893.2153 9.538 1.528 .898 905 +BQ SER 2448894.1148 9.745 1.624 .931 905 +BQ SER 2448894.1511 9.765 1.624 .928 905 +BQ SER 2448894.2196 9.761 1.621 .935 905 +BQ SER 2449197.3755 9.632 1.547 .927 908 +BQ SER 2449198.2273 9.778 1.590 .946 908 +BQ SER 2449198.3235 9.773 1.566 .955 908 +BQ SER 2449199.3321 9.207 1.318 .830 908 +BQ SER 2449200.2375 9.429 1.442 .885 908 +BQ SER 2449201.2437 9.726 1.571 .945 908 +BQ SER 2449201.3630 9.735 1.586 .941 908 +BQ SER 2449202.2491 9.633 1.542 .902 908 +BQ SER 2449202.3635 9.616 1.512 .914 908 +BQ SER 2449203.2318 9.414 1.414 .872 908 +BQ SER 2449203.3535 9.424 1.433 .878 908 +BQ SER 2449204.2292 9.477 1.456 .888 908 +BQ SER 2449204.3528 9.475 1.459 .894 908 +BQ SER 2449205.2326 9.502 1.462 .907 908 +BQ SER 2449205.3627 9.516 1.474 .901 908 +BQ SER 2449206.2591 9.615 1.539 .927 908 +BQ SER 2449206.3721 9.633 1.551 .914 908 +BQ SER 2449207.2389 9.681 1.515 .929 908 +BQ SER 2449207.3558 9.624 1.486 .904 908 +BQ SER 2449209.2223 9.484 1.489 .895 908 +BQ SER 2449209.3636 9.521 1.505 .921 908 +BQ SER 2449210.2941 9.772 1.606 .951 908 +BQ SER 2449211.2693 9.559 1.476 .891 908 +BQ SER 2449212.2726 9.348 1.396 .876 908 +BQ SER 2449218.2878 9.558 1.503 .922 908 +BQ SER 2449219.2781 9.805 1.589 .963 908 +BQ SER 2449220.2735 9.341 1.375 .850 908 +BQ SER 2449221.3286 9.378 1.440 .871 908 +BQ SER 2449222.2763 9.648 1.572 .924 908 +BQ SER 2449224.2880 9.479 1.472 .879 908 +BQ SER 2449225.2501 9.455 1.430 .883 908 +BQ SER 2449226.2522 9.420 1.425 .882 908 +BQ SER 2449227.2446 9.573 1.539 .916 908 +BQ SER 2449228.2541 9.755 1.583 .940 908 +BQ SER 2449229.2445 9.221 1.330 .827 908 +BQ SER 2449230.2469 9.421 1.458 .892 908 +BQ SER 2449231.2479 9.726 1.595 .946 908 +BQ SER 2449232.2356 9.673 1.533 .935 908 +BQ SER 2449233.2373 9.359 1.389 .859 908 +BQ SER 2449234.2370 9.499 1.486 .894 908 +BQ SER 2449235.2304 9.536 1.507 .902 908 +BQ SER 2449236.2145 9.601 1.505 .919 908 +BQ SER 2449237.2066 9.640 1.524 .913 908 +BQ SER 2449238.1978 9.242 1.356 .839 908 +BQ SER 2449239.2023 9.474 1.485 .897 908 +BQ SER 2449240.2145 9.772 1.609 .951 908 +BQ SER 2449241.2169 9.578 1.489 .905 908 +BQ SER 2449243.1952 9.577 1.529 .917 908 +BQ SER 2449244.2201 9.640 1.545 .927 908 +BQ SER 2449245.2177 9.559 1.496 .912 908 +BQ SER 2449298.1059 9.353 1.382 .861 908 +BQ SER 2448503.2142 9.218 1.361 .793 993 +BQ SER 2448504.1718 9.364 1.443 .861 993 +BQ SER 2448505.1603 9.686 1.574 .925 993 +BQ SER 2448505.2201 9.714 1.543 .954 993 +BQ SER 2448506.1555 9.690 1.544 .903 993 +BQ SER 2448506.2231 9.666 1.558 .896 993 +BQ SER 2448507.1487 9.300 1.385 .828 993 +BQ SER 2448508.1449 9.476 1.494 .884 993 +BQ SER 2448508.1783 9.487 1.480 .877 993 +BQ SER 2448509.1493 9.575 1.511 1.003 993 +BQ SER 2448509.1879 9.570 1.498 .905 993 +BQ SER 2448510.1514 9.553 1.518 .892 993 +BQ SER 2448510.1816 9.549 1.509 .888 993 +BQ SER 2448511.1485 9.615 1.532 .898 993 +BQ SER 2448511.1867 9.587 1.507 .875 993 +BQ SER 2448512.1495 9.256 1.375 .820 993 +BQ SER 2448512.1928 9.250 1.374 .817 993 +BQ SER 2448513.1537 9.432 1.472 .876 993 +BQ SER 2448513.2017 9.454 1.455 .879 993 +BQ SER 2448514.1547 9.716 1.619 .909 993 +BQ SER 2448514.2025 9.764 1.574 .940 993 +BQ SER 2448515.1481 9.570 1.504 .863 993 +BQ SER 2448515.1975 9.589 1.430 .899 993 +BQ SER 2448516.1392 9.227 1.393 .801 993 +BQ SER 2448517.1435 9.560 1.514 .912 993 +BQ SER 2448518.1494 9.688 1.543 .928 993 +BQ SER 2448519.1686 9.514 1.485 .870 993 +BQ SER 2448520.1363 9.487 1.452 .879 993 +BQ SER 2448521.1525 9.362 1.427 .856 993 +BQ SER 2448522.1426 9.484 1.479 .891 993 +BQ SER 2448523.1332 9.732 1.605 .915 993 +BQ SER 2449514.3464 9.755 1.568 .649 913 +BQ SER 2449514.4133 9.751 1.575 .641 913 +BQ SER 2449515.3724 9.241 1.315 .556 913 +BQ SER 2449516.3902 9.409 1.458 .603 913 +BQ SER 2449517.3892 9.709 1.592 .649 913 +BQ SER 2449519.4131 9.364 1.391 .588 913 +BQ SER 2449521.3329 9.540 1.480 .621 913 +BQ SER 2449522.3627 9.576 1.518 .625 913 +BQ SER 2449522.4247 9.597 1.513 .622 913 +BQ SER 2449523.3214 9.656 1.521 .629 913 +BQ SER 2449523.4009 9.648 1.491 .624 913 +BQ SER 2449524.3012 9.230 1.355 .561 913 +BQ SER 2449524.3704 9.244 1.331 .574 913 +BQ SER 2449524.4329 9.238 1.342 .573 913 +BQ SER 2449525.2976 9.457 1.473 .618 913 +BQ SER 2449526.2867 9.733 1.584 .646 913 +BQ SER 2449529.2874 9.553 1.541 .626 913 +BQ SER 2449530.2713 9.652 1.541 .629 913 +BQ SER 2449530.3559 9.650 1.530 .630 913 +BQ SER 2449530.4108 9.636 1.534 .623 913 +BQ SER 2449533.2732 9.318 1.400 .576 913 +BQ SER 2449533.3506 9.321 1.399 .580 913 +BQ SER 2449533.4046 9.322 1.409 .585 913 +BQ SER 2449534.2937 9.509 1.491 .618 913 +BQ SER 2449534.3406 9.524 1.489 .621 913 +BQ SER 2449534.3965 9.522 1.520 .615 913 +BQ SER 2449535.2625 9.750 1.585 .639 913 +BQ SER 2449535.3275 9.771 1.583 .652 913 +BQ SER 2449535.3956 9.769 1.604 .649 913 +BQ SER 2449536.2553 9.458 1.424 .588 913 +BQ SER 2449536.3761 9.367 1.367 .580 913 +BQ SER 2449537.2575 9.288 1.393 .581 913 +BQ SER 2449537.3236 9.320 1.397 .581 913 +BQ SER 2449537.3965 9.342 1.415 .583 913 +BQ SER 2449538.2810 9.621 1.548 .626 913 +BQ SER 2449538.3301 9.639 1.567 .636 913 +BQ SER 2449539.2555 9.718 1.558 .641 913 +BQ SER 2449539.3417 9.699 1.551 .634 913 +BQ SER 2449539.3794 9.703 1.534 .636 913 +BQ SER 2449540.2445 9.446 1.431 .603 913 +BQ SER 2449540.3371 9.420 1.429 .591 913 +BQ SER 2449541.2485 9.450 1.453 .594 913 +BQ SER 2449541.3138 9.468 1.432 .601 913 +BQ SER 2449541.3805 9.459 1.449 .601 913 +BQ SER 2449542.2496 9.440 1.479 .601 913 +BQ SER 2449542.3022 9.463 1.457 .609 913 +BQ SER 2449542.3715 9.466 1.449 .601 913 +BQ SER 2449543.2500 9.534 1.520 .616 913 +BQ SER 2449543.3144 9.549 1.508 .629 913 +BQ SER 2449545.2367 9.291 1.355 .571 913 +BQ SER 2449545.2912 9.260 1.356 .567 913 +BQ SER 2449546.2404 9.376 1.416 .602 913 +BQ SER 2449546.3047 9.386 1.433 .594 913 +BQ SER 2449546.3794 9.406 1.458 .598 913 +BQ SER 2449547.2296 9.663 1.573 .639 913 +BQ SER 2449547.2857 9.696 1.578 .652 913 +BQ SER 2449548.3153 9.714 1.541 .627 913 +BQ SER 2449548.3546 9.693 1.527 .636 913 +BQ SER 2449549.2296 9.294 1.362 .568 913 +BQ SER 2449549.3012 9.290 1.367 .569 913 +BQ SER 2449549.3496 9.310 1.364 .583 913 +BQ SER 2449550.3007 9.474 1.494 .601 913 +BQ SER 2449551.2960 9.592 1.510 .618 913 +BQ SER 2449552.2953 9.566 1.514 .619 913 +BQ SER 2449553.2908 9.607 1.509 .618 913 +BQ SER 2449554.3017 9.270 1.351 .563 913 +BQ SER 2449556.2931 9.729 1.595 .645 913 +BQ SER 2449557.2447 9.632 1.462 .623 913 +BQ SER 2449559.2195 9.545 1.520 .625 913 +BQ SER 2449559.2952 9.567 1.522 .618 913 +BQ SER 2449560.2280 9.701 1.546 .646 913 +BQ SER 2449560.3322 9.682 1.563 .631 913 +BQ SER 2449561.3083 9.517 1.478 .603 913 +BQ SER 2449563.3111 9.391 1.419 .591 913 +BQ SER 2449564.2363 9.498 1.485 .614 913 +BQ SER 2449617.0962 9.627 1.486 .602 913 +BQ SER 2449620.2112 9.785 1.542 .641 913 +BQ SER 2449621.1813 9.551 1.461 .593 913 +BQ SER 2449623.1762 9.500 1.444 .603 913 +BQ SER 2449625.1939 9.651 1.545 .621 913 +BQ SER 2449626.2175 9.438 1.423 .596 913 +BQ SER 2449631.1572 9.406 1.478 .583 913 +BQ SER 2449632.2027 9.620 1.530 .633 913 +BQ SER 2449633.1754 9.634 1.497 .635 913 +BQ SER 2449634.1830 9.591 1.518 .631 913 +BQ SER 2449521.7417 9.526 1.496 996 +BQ SER 2449522.6890 9.651 1.545 996 +BQ SER 2449528.7559 9.382 1.465 .834 .850 996 +BQ SER 2449529.7280 9.636 1.541 .935 .877 996 +BQ SER 2449530.7828 9.534 1.123 1.510 .891 .884 996 +BQ SER 2449534.7356 9.624 1.548 .916 .898 996 +BQ SER 2449535.7411 9.800 1.057 1.476 .793 .996 996 +BQ SER 2449536.7719 9.206 .817 1.241 .763 .668 996 +BQ SER 2449543.7436 9.631 1.583 .894 .924 996 +BQ SER 2449545.7124 9.161 1.332 .809 .813 996 +BQ SER 2449559.7892 9.692 1.079 1.556 .905 .922 996 +BQ SER 2449561.7491 9.478 .979 1.401 .867 .865 996 +BQ SER 2449563.7498 9.423 1.020 1.388 .857 .863 996 +BQ SER 2449564.6326 9.591 1.109 1.497 .894 .899 996 +BQ SER 2449804.9115 9.535 1.413 .861 .919 997 +BQ SER 2449805.8980 9.288 .884 997 +BQ SER 2449808.9084 9.670 1.401 .920 997 +BQ SER 2449809.8714 9.475 1.402 .857 .878 997 +BQ SER 2449810.8826 9.267 1.351 .817 .773 997 +BQ SER 2449811.8365 9.546 1.514 .881 .901 997 +BQ SER 2449811.8763 9.572 1.497 .896 .887 997 +BQ SER 2449813.8733 9.323 1.329 .825 .842 997 +BQ SER 2449813.9071 9.301 1.335 .821 .835 997 +BQ SER 2449814.8494 9.393 1.399 .856 .859 997 +BQ SER 2449815.8221 9.638 1.497 .904 .889 997 +BQ SER 2449817.8486 9.591 1.428 .904 .890 997 +BQ SER 2449817.9084 9.568 1.433 .893 .883 997 +BQ SER 2449818.8373 9.399 1.377 .830 .873 997 +BQ SER 2449818.8689 9.390 1.380 .848 .858 997 +BQ SER 2449818.8844 9.366 1.395 .840 .851 997 +BQ SER 2449818.9034 9.351 1.362 .843 .824 997 +BQ SER 2449818.9152 9.346 1.385 .832 .819 997 +BQ SER 2449821.8524 9.754 1.537 .921 .929 997 +BQ SER 2449822.8406 9.161 1.288 .800 .825 997 +BQ SER 2449823.8206 9.446 1.435 .871 .875 997 +BQ SER 2449825.8304 9.624 1.470 .884 .904 997 +BQ SER 2449934.2655 9.247 1.393 .832 1.631 998 +BQ SER 2449935.2770 9.494 .908 1.745 998 +BQ SER 2449937.2527 9.553 1.512 .900 998 +BQ SER 2449938.2464 9.403 .883 1.707 998 +BQ SER 2449939.2604 9.579 1.780 998 +BQ SER 2449942.2429 9.559 .896 1.747 998 +BQ SER 2449943.2333 9.257 1.408 .842 1.650 998 +BQ SER 2449944.2204 9.590 1.531 .907 1.767 998 +BQ SER 2449945.2148 9.786 1.613 .950 1.842 998 +BQ SER 2449946.2133 9.394 1.425 .875 1.704 998 +BQ SER 2449947.2032 9.362 1.458 .858 1.666 998 +BQ SER 2449948.2158 9.650 1.566 .927 1.811 998 +BQ SER 2449949.2205 9.687 1.567 .923 1.807 998 +BQ SER 2449950.2159 9.530 1.500 .903 1.746 998 +BQ SER 2449952.2095 9.414 1.450 .880 1.718 998 +BQ SER 2449953.2200 9.639 1.550 .927 1.811 998 +BQ SER 2449954.1994 9.782 1.605 .942 1.836 998 +BQ SER 2449955.1961 9.249 1.368 .822 1.607 998 +BQ SER 2449957.2110 9.738 1.605 .963 1.859 998 +BQ SER 2449958.1879 9.698 1.550 .945 1.816 998 +BQ SER 2449959.1873 9.426 1.681 998 +BQ SER 2450009.1468 9.697 1.773 998 +BQ SER 2450011.1275 9.446 1.475 .881 1.691 998 +BQ SER 2450012.1214 9.435 1.702 998 +BQ SER 2450017.0881 9.704 1.581 .956 1.875 998 +BQ SER 2450018.1133 9.742 1.825 998 +BQ SER 2450020.0848 9.471 1.496 .900 1.745 998 +BQ SER 2450348.5910 9.438 1.444 1.742 999 +BQ SER 2450351.5716 9.716 1.521 1.811 999 +BQ SER 2450352.5964 9.234 1.312 1.642 999 +BQ SER 2450353.5377 9.376 1.394 1.724 999 +BQ SER 2450354.5507 9.678 1.546 1.830 999 +BQ SER 2450355.5312 9.698 1.816 999 +BQ SER 2450357.5307 9.476 1.447 1.761 999 +BQ SER 2450358.5342 9.583 1.483 1.791 999 +BQ SER 2450359.5269 9.555 1.480 1.774 999 +BQ SER 2450360.5336 9.592 1.488 1.787 999 +BQ SER 2450361.5398 9.273 1.342 1.658 999 +BQ SER 2450362.5393 9.448 1.448 1.759 999 +BQ SER 2450363.5377 9.720 1.578 1.857 999 +BQ SER 2450379.5251 9.521 1.464 1.795 999 +BQ SER 2450380.5259 9.563 1.496 1.793 999 +BQ SER 2450381.5516 9.656 1.572 1.795 999 +BQ SER 2450382.5364 9.247 1.335 1.661 999 +BQ SER 2450383.5454 9.378 1.461 1.670 999 +BQ SER 2450384.5418 9.673 1.584 1.842 999 +BQ SER 2450386.5402 9.206 1.351 1.646 999 +BQ SER 2450388.5370 9.613 1.542 1.808 999 +BQ SER 2450389.5363 9.538 1.506 1.751 999 +BQ SER 2450390.5320 9.536 1.470 1.753 999 +BQ SER 2450391.5304 9.329 1.351 1.686 999 +BQ SER 2450392.5198 9.409 1.473 1.750 999 +BQ SER 2450393.5222 9.686 1.586 1.833 999 +BQ SER 2450305.2409 9.266 1.392 .824 971 +BQ SER 2450306.1948 9.437 1.478 .875 971 +BQ SER 2450307.2355 9.598 1.511 .918 971 +BQ SER 2450310.2429 9.276 1.381 .834 971 +BQ SER 2450311.1775 9.408 1.451 .869 971 +BQ SER 2450312.1765 9.679 1.567 .931 971 +BQ SER 2450313.1919 9.649 1.498 .909 971 +BQ SER 2450314.1724 9.187 1.331 .818 971 +BQ SER 2450314.2476 9.221 1.363 .826 971 +BQ SER 2450315.1765 9.505 1.491 .888 971 +BQ SER 2450316.1890 9.701 1.541 .877 971 +BQ SER 2450317.1862 9.547 1.451 .874 971 +BQ SER 2450318.1725 9.477 1.464 .876 971 +BQ SER 2450319.1641 9.388 1.408 .855 971 +BQ SER 2450320.1799 9.469 1.497 .878 971 +BQ SER 2450321.1668 9.684 1.570 .913 971 +BQ SER 2450322.1655 9.469 1.412 .863 971 +BQ SER 2450323.1626 9.256 1.371 .827 971 +BQ SER 2450324.1817 9.596 1.521 .917 971 +BQ SER 2450325.1550 9.767 1.556 .939 971 +BQ SER 2450326.1446 9.427 1.403 .870 971 +BQ SER 2450327.1979 9.434 1.488 1.014 971 +BQ SER 2450329.1620 9.637 1.504 .910 971 +BQ SER 2450330.1637 9.679 1.547 .923 971 +BQ SER 2450332.1529 9.335 1.417 .855 971 +BQ SER 2450333.1565 9.613 1.550 .913 971 +BQ SER 2450334.1748 9.756 1.584 .935 971 +BQ SER 2450335.1681 9.251 1.375 .822 971 +BQ SER 2450336.1676 9.445 1.496 .905 971 +BQ SER 2450337.1617 9.650 1.547 .938 971 +BQ SER 2450338.2195 9.568 1.513 .901 971 +BQ SER 2450340.1491 9.351 1.429 .867 971 +BQ SER 2450341.1568 9.423 1.469 .876 971 +BQ SER 2450342.1620 9.652 1.555 .945 971 +BQ SER 2450344.1720 9.172 1.360 .766 971 +BQ SER 2450347.1662 9.538 1.474 .912 971 +BQ SER 2450349.1517 9.409 1.478 .874 971 +BQ SER 2450357.1373 9.368 1.428 .827 971 +BQ SER 2450570.5192 9.268 .824 1.671 972 +BQ SER 2450570.6025 9.313 .828 1.692 972 +BQ SER 2450572.4735 9.643 .868 1.781 972 +BQ SER 2450572.5606 9.630 .905 1.799 972 +BQ SER 2450573.5031 9.512 .864 1.751 972 +BQ SER 2450573.5962 9.531 .884 1.772 972 +BQ SER 2450573.6461 9.525 .872 1.766 972 +BQ SER 2450574.5316 9.457 .857 1.737 972 +BQ SER 2450574.5919 9.448 .860 1.744 972 +BQ SER 2450575.4920 9.316 .836 1.704 972 +BQ SER 2450575.5760 9.317 .826 1.697 972 +BQ SER 2450575.6394 9.334 .837 1.698 972 +BQ SER 2450576.5392 9.515 .893 1.793 972 +BQ SER 2450576.6185 9.535 .893 1.793 972 +BQ SER 2450576.6547 9.552 .897 1.796 972 +BQ SER 2450577.5459 9.756 .927 1.858 972 +BQ SER 2450577.6042 9.747 .924 1.852 972 +BQ SER 2450577.6445 9.760 .926 1.852 972 +BQ SER 2450578.5240 9.345 .837 1.678 972 +BQ SER 2450578.5902 9.297 .830 1.673 972 +BQ SER 2450578.6407 9.263 .823 1.661 972 +BQ SER 2450578.6471 9.264 .831 1.659 972 +BQ SER 2450578.6721 9.244 .818 1.647 972 +BQ SER 2450579.5721 9.384 .849 1.740 972 +BQ SER 2450580.4692 9.637 .917 1.831 972 +BQ SER 2450580.5793 9.659 .927 1.846 972 +BQ SER 2450580.6304 9.670 .892 1.824 972 +BQ SER 2450582.5612 9.403 .848 1.704 972 +BQ SER 2450582.6088 9.420 .849 1.700 972 +BQ SER 2450582.6563 9.390 .846 1.706 972 +BQ SER 2450583.5520 9.475 .875 1.760 972 +BQ SER 2450583.6086 9.465 .863 1.742 972 +BQ SER 2450584.5435 9.473 .882 1.752 972 +BQ SER 2450584.5997 9.481 .888 1.761 972 +BQ SER 2450584.6473 9.473 .867 1.762 972 +CR SER 2446994.2974 10.923 1.243 1.713 1.041 989 +CR SER 2446995.2562 11.172 1.796 1.087 989 +CR SER 2446996.2208 11.238 1.791 1.081 989 +CR SER 2446998.2504 10.584 1.541 .944 989 +CR SER 2446999.2361 10.843 1.707 1.011 989 +CR SER 2447000.2371 11.022 1.760 1.055 989 +CR SER 2447001.2347 11.211 1.835 1.063 989 +CR SER 2447002.2366 1.622 1.002 989 +CR SER 2447003.2193 10.537 1.480 .928 989 +CR SER 2447402.1415 10.856 1.663 1.008 990 +CR SER 2447403.1368 11.044 1.743 1.028 990 +CR SER 2447404.1392 11.196 1.765 1.023 990 +CR SER 2447408.1308 11.006 1.692 1.031 990 +CR SER 2447409.1325 11.125 1.842 1.035 990 +CR SER 2447410.1507 11.081 1.694 1.016 990 +CR SER 2447411.1507 10.441 1.452 .879 990 +CR SER 2447412.1557 10.713 1.633 .975 990 +CR SER 2447413.1395 10.929 1.755 1.013 990 +CR SER 2447414.1369 11.117 1.816 1.028 990 +CR SER 2447415.1424 11.199 1.823 1.038 990 +CR SER 2447416.1336 10.537 1.506 .895 990 +CR SER 2447417.1310 10.615 1.584 .944 990 +CR SER 2447418.1319 10.856 1.716 .983 990 +CR SER 2447419.1249 11.075 1.786 1.032 990 +CR SER 2447420.1293 11.226 1.799 1.061 990 +CR SER 2447421.1208 10.838 1.606 .965 990 +CR SER 2447422.1235 10.448 1.603 .899 990 +CR SER 2447423.1228 10.774 1.665 .973 990 +CR SER 2447424.1244 10.950 1.766 1.019 990 +CR SER 2447425.1265 11.175 1.818 1.046 990 +CR SER 2447427.1301 10.451 1.450 .887 990 +CR SER 2447428.1219 10.728 1.606 .970 990 +CR SER 2447433.1084 10.617 1.579 .925 990 +CR SER 2447434.1125 10.894 1.716 1.008 990 +CR SER 2449520.8200 10.509 1.479 996 +CR SER 2449521.7834 10.640 1.549 996 +CR SER 2449528.7864 11.008 1.778 1.010 .978 996 +CR SER 2449536.7474 10.520 1.017 1.415 .872 .907 996 +CR SER 2449543.7291 1.641 .953 .948 996 +CR SER 2449559.8013 10.869 1.671 996 +CR SER 2449561.7313 11.204 1.794 1.073 1.013 996 +CR SER 2449564.6564 10.780 1.587 .978 .973 996 +CR SER 2449625.1405 11.206 1.842 1.067 995 +CR SER 2449632.1267 10.591 1.528 .953 995 +CR SER 2449633.1263 10.698 1.564 .979 995 +CR SER 2449805.8837 11.242 1.766 1.068 1.041 997 +CR SER 2449808.8915 10.852 1.645 1.003 .996 997 +CR SER 2449809.8486 11.006 1.721 1.025 1.032 997 +CR SER 2449810.8508 11.178 1.785 1.055 997 +CR SER 2449811.8328 11.020 1.641 1.007 .994 997 +CR SER 2449813.8498 10.735 1.601 .966 .975 997 +CR SER 2449814.8274 10.946 1.722 1.010 1.010 997 +CR SER 2449815.8340 11.157 1.746 1.068 1.040 997 +CR SER 2449817.8469 10.475 1.430 .887 .915 997 +CR SER 2449818.8298 10.690 1.535 .957 .955 997 +CR SER 2449821.8607 11.238 1.757 997 +CR SER 2449822.8467 10.624 1.467 .922 .935 997 +CR SER 2449823.8359 10.609 1.497 .936 .950 997 +CR SER 2449825.8218 11.028 1.714 1.030 1.023 997 +DV SER 2450348.5953 13.365 2.834 999 +DV SER 2450352.6015 13.718 2.684 3.019 999 +DV SER 2450354.5464 13.846 2.694 3.054 999 +DV SER 2450355.5140 13.881 2.690 3.035 999 +DV SER 2450357.5151 13.946 2.752 3.025 999 +DV SER 2450358.5203 13.987 2.690 3.034 999 +DV SER 2450359.5154 14.030 2.743 3.054 999 +DV SER 2450360.5205 14.023 2.633 3.026 999 +DV SER 2450361.5258 14.098 2.619 3.046 999 +DV SER 2450362.5208 14.066 2.658 3.017 999 +DV SER 2450363.5235 14.035 2.618 3.001 999 +DV SER 2450379.5195 13.954 2.624 3.079 999 +DV SER 2450380.5208 13.997 3.079 999 +DV SER 2450381.5436 13.951 2.843 3.006 999 +DV SER 2450383.5385 13.983 2.729 2.973 999 +DV SER 2450386.5339 14.049 2.740 3.020 999 +DV SER 2450387.5425 13.886 2.527 2.919 999 +DV SER 2450388.5325 13.562 2.366 2.836 999 +DV SER 2450389.5321 13.240 2.251 2.701 999 +DV SER 2450390.5275 13.174 2.249 2.697 999 +DV SER 2450391.5266 13.193 2.276 2.723 999 +DV SER 2450392.5236 13.228 2.294 2.746 999 +S SGE 2446992.2991 5.924 .806 1.030 .517 989 +S SGE 2447001.1563 5.977 .791 1.031 .515 989 +S SGE 2447002.1680 5.879 .657 .925 .484 989 +S SGE 2447003.1564 5.487 .718 .398 989 +S SGE 2447004.1490 5.335 .649 .397 989 +S SGE 2447005.1447 5.454 .726 .432 989 +S SGE 2447084.0808 5.970 .956 .512 989 +S SGE 2447087.0665 .436 .742 .415 989 +S SGE 2447088.0645 5.357 .417 .633 .383 989 +S SGE 2447091.0608 5.679 .652 .901 .483 989 +S SGE 2447399.1424 5.453 .507 .681 .409 990 +S SGE 2447400.1313 5.390 .459 .687 .388 990 +S SGE 2447401.1221 5.684 .581 .891 .491 990 +S SGE 2447402.1193 5.855 .653 .975 .552 990 +S SGE 2447403.1189 5.992 .736 1.031 .540 990 +S SGE 2447404.1181 5.979 .650 1.007 .500 990 +S SGE 2447408.1173 .668 .420 990 +S SGE 2447409.1194 5.631 .533 .811 .495 990 +S SGE 2447410.1205 5.811 .556 1.000 .540 990 +S SGE 2447411.1164 5.938 .741 1.018 .538 990 +S SGE 2447412.1182 6.036 .711 1.021 .535 990 +S SGE 2447413.1102 5.879 .518 .854 .517 990 +S SGE 2447414.1132 5.466 .426 .616 .401 990 +S SGE 2447415.1114 5.311 .458 .600 .368 990 +S SGE 2447416.1099 5.416 .459 .700 .411 990 +S SGE 2447417.1093 5.416 .496 .764 .423 990 +S SGE 2447418.1100 5.706 .556 .910 .496 990 +S SGE 2447419.1056 5.816 .685 .971 .515 990 +S SGE 2447420.1051 5.992 .773 1.053 .516 990 +S SGE 2447421.1032 5.924 .612 1.006 .486 990 +S SGE 2447422.1000 5.574 .411 990 +S SGE 2447423.1017 5.298 .415 .575 .381 990 +S SGE 2447424.0992 5.457 .520 .423 990 +S SGE 2447425.1027 5.364 .449 .690 .401 990 +S SGE 2447427.1022 5.780 .623 .974 .515 990 +S SGE 2447428.0965 5.932 .726 1.055 .512 990 +S SGE 2447429.1031 6.001 .719 1.004 .516 990 +S SGE 2447430.0947 5.776 .469 .814 .487 990 +S SGE 2447431.0945 5.308 .414 .597 .349 990 +S SGE 2447432.0930 5.341 .499 .626 .382 990 +S SGE 2447433.0904 5.377 .463 .670 .413 990 +S SGE 2447434.0919 5.526 .482 .831 .446 990 +S SGE 2447734.3584 5.399 .441 .741 .421 991 +S SGE 2447735.3808 5.361 .385 .722 .431 991 +S SGE 2447736.3862 5.656 .544 .897 .498 991 +S SGE 2447737.3798 5.725 .710 .958 .496 991 +S SGE 2447738.3605 5.978 .779 1.005 .538 991 +S SGE 2447739.3360 5.946 .685 .978 .509 991 +S SGE 2447740.3528 5.673 .383 .857 .442 991 +S SGE 2447741.2592 5.319 .293 .632 .405 991 +S SGE 2447742.2485 5.392 .364 .701 .443 991 +S SGE 2447743.2378 5.393 .357 .732 .436 991 +S SGE 2447744.2282 5.541 .458 .803 .484 991 +S SGE 2447745.2233 5.677 .582 .979 .485 991 +S SGE 2447746.2322 5.855 .710 1.012 .507 991 +S SGE 2447747.2217 5.969 .752 1.037 .521 991 +S SGE 2447748.2276 5.854 .545 .934 .470 991 +S SGE 2447749.1396 5.528 .379 .741 .446 991 +S SGE 2447749.2078 5.525 .373 .693 .466 991 +S SGE 2447750.2163 5.311 .291 .673 .416 991 +S SGE 2447751.1447 5.382 .386 .730 .423 991 +S SGE 2447751.2165 5.438 .411 .733 .446 991 +S SGE 2447752.1416 5.384 .408 .707 .440 991 +S SGE 2447752.2097 5.395 .401 .732 .449 991 +S SGE 2447753.1384 5.622 .541 .939 .483 991 +S SGE 2447753.2127 5.650 .538 .950 .480 991 +S SGE 2447754.1462 5.746 .683 .962 .495 991 +S SGE 2447754.2377 5.792 .693 .982 .504 991 +S SGE 2447755.1355 5.914 .766 1.005 .512 991 +S SGE 2447755.2079 5.955 .756 1.040 .525 991 +S SGE 2447756.1377 5.950 .699 .972 .513 991 +S SGE 2447756.2298 5.957 .657 .961 .506 991 +S SGE 2447757.1345 5.638 .421 .869 .449 991 +S SGE 2447757.2013 5.627 .409 .846 .444 991 +S SGE 2447758.1327 5.299 .306 .618 .385 991 +S SGE 2447758.1967 5.278 .263 .654 .381 991 +S SGE 2447759.1307 5.400 .396 .682 .430 991 +S SGE 2447759.2087 5.411 .356 .721 .426 991 +S SGE 2447760.1296 5.385 .342 .725 .450 991 +S SGE 2447760.2191 5.373 .342 .736 .429 991 +S SGE 2447761.1319 5.590 .468 .852 .503 991 +S SGE 2447761.2029 5.605 .458 .880 .490 991 +S SGE 2447762.1346 5.685 .622 .937 .484 991 +S SGE 2447762.2044 5.720 .630 .953 .488 991 +S SGE 2447763.1264 5.870 .751 .997 .528 991 +S SGE 2447763.2024 5.888 1.007 .528 991 +S SGE 2447764.1289 5.976 .713 .995 .517 991 +S SGE 2447764.2053 5.982 .714 1.006 .524 991 +S SGE 2447766.1252 5.472 .338 .661 .428 991 +S SGE 2447766.2079 5.423 .309 .658 .434 991 +S SGE 2447767.1329 5.345 .324 .659 .416 991 +S SGE 2447767.2135 5.373 .349 .662 .423 991 +S SGE 2447768.1242 .359 .775 991 +S SGE 2447768.2108 5.358 .374 .757 .406 991 +S SGE 2447769.1265 5.421 .395 .768 .451 991 +S SGE 2447769.2090 5.462 .408 .800 .461 991 +S SGE 2447770.1194 5.641 .529 .984 .483 991 +S SGE 2447770.1972 5.691 .562 .940 .511 991 +S SGE 2447771.1197 5.800 .652 1.026 .504 991 +S SGE 2447771.1949 5.807 .685 1.014 .502 991 +S SGE 2447772.1171 5.973 .758 1.006 .531 991 +S SGE 2447772.1910 5.970 .750 1.027 .528 991 +S SGE 2447773.1197 5.941 .627 .984 .512 991 +S SGE 2447773.1934 5.927 .623 .944 .500 991 +S SGE 2447774.1348 5.618 .360 .765 .454 991 +S SGE 2447774.2241 5.590 .399 .725 .457 991 +S SGE 2447775.1148 5.274 .298 .641 .393 991 +S SGE 2447775.1876 5.292 .291 .645 .389 991 +S SGE 2447776.1193 5.418 .403 .712 .431 991 +S SGE 2447776.1879 5.423 .380 .730 .437 991 +S SGE 2448101.1633 5.550 .442 .758 .427 992 +S SGE 2448102.1790 5.295 .450 .674 .380 992 +S SGE 2448103.1567 5.393 .538 .750 .400 992 +S SGE 2448104.1586 5.367 .472 .775 .419 992 +S SGE 2448108.1504 5.955 .806 .979 .515 992 +S SGE 2448109.1462 5.668 .514 .836 .453 992 +S SGE 2448110.1543 5.295 .562 .642 .363 992 +S SGE 2448111.1584 5.373 .556 .743 .386 992 +S SGE 2448112.1530 5.362 .538 .748 .402 992 +S SGE 2448113.1525 5.540 .650 .860 .456 992 +S SGE 2448114.1445 5.725 .752 .948 .493 992 +S SGE 2448115.2242 5.870 .916 1.007 .513 992 +S SGE 2448116.1688 5.969 .840 1.019 .522 992 +S SGE 2448117.1511 5.819 .570 .936 .495 992 +S SGE 2448118.1474 5.452 .450 .400 992 +S SGE 2448119.1423 5.355 .530 .704 .394 992 +S SGE 2448123.1367 5.816 .862 .966 .514 992 +S SGE 2448127.1450 5.269 .656 .368 992 +S SGE 2448503.1247 5.772 .413 .822 .457 993 +S SGE 2448504.1218 5.331 .267 .657 .372 993 +S SGE 2448505.1239 5.413 .345 .716 .405 993 +S SGE 2448506.1238 5.389 .342 .766 .414 993 +S SGE 2448507.1203 5.583 .484 .863 .454 993 +S SGE 2448508.1204 5.740 .596 .948 .495 993 +S SGE 2448509.1198 5.912 .718 1.021 .523 993 +S SGE 2448510.1206 6.027 .742 1.028 .525 993 +S SGE 2448511.1200 5.876 .546 .923 .483 993 +S SGE 2448512.1195 5.468 .337 .707 .400 993 +S SGE 2448513.1184 5.348 .332 .686 .382 993 +S SGE 2448514.1182 5.431 .393 .758 .418 993 +S SGE 2448515.1118 5.400 .420 .774 .413 993 +S SGE 2448516.1160 5.659 .586 .919 .474 993 +S SGE 2448517.1142 5.836 .694 .985 .513 993 +S SGE 2448518.1207 6.014 .761 1.037 .532 993 +S SGE 2448519.1196 5.968 .636 .967 .508 993 +S SGE 2448520.1091 5.621 .427 .769 .436 993 +S SGE 2448521.1151 5.297 .335 .647 .363 993 +S SGE 2448522.1096 5.414 .419 .746 .409 993 +S SGE 2448523.1050 5.369 .385 .751 .403 993 +S SGE 2448854.1636 5.954 .689 1.007 994 +S SGE 2448856.1544 5.301 .247 .695 .378 994 +S SGE 2448858.1514 5.389 .346 .759 .434 994 +S SGE 2448860.1438 5.719 .604 .971 .495 994 +S SGE 2448870.1287 5.999 .713 1.053 .521 994 +S SGE 2448872.1301 5.683 .364 .787 .472 994 +S SGE 2448874.1289 5.459 .290 .799 .419 994 +S SGE 2448876.1228 5.665 .468 .897 .499 994 +S SGE 2448877.1189 5.815 .621 .981 .505 994 +S SGE 2448878.2382 5.963 .686 1.054 .530 994 +S SGE 2448880.1187 5.790 .468 .900 .467 994 +S SGE 2448881.1120 5.382 .253 .728 .392 994 +S SGE 2448882.1143 5.421 .269 .767 .417 994 +S SGE 2448883.1129 5.424 .308 .782 .434 994 +S SGE 2448884.1098 5.505 .374 .852 .460 994 +S SGE 2448885.1075 5.717 .558 .943 .497 994 +S SGE 2448886.1080 5.887 .663 1.013 .518 994 +S SGE 2448888.1055 5.930 .525 .946 .505 994 +S SGE 2448889.1043 5.554 .354 .788 .419 994 +S SGE 2448890.1029 5.363 .227 .710 .417 994 +S SGE 2448891.1011 5.473 .317 .797 .435 994 +S SGE 2448892.0992 5.381 .294 .775 .431 994 +S SGE 2448893.0991 5.680 .497 .921 .489 994 +S SGE 2448894.0974 5.774 .590 .990 .512 994 +S SGE 2449810.9071 5.640 .797 .424 .269 997 +S SGE 2449814.8947 5.607 .839 .461 .441 997 +S SGE 2449817.8952 6.029 1.009 .537 .458 997 +S SGE 2449818.8940 5.783 .861 .461 .393 997 +S SGE 2449821.8927 5.371 .727 .400 .376 997 +S SGE 2449822.8961 5.461 .792 .434 .413 997 +S SGE 2449823.8964 5.700 .922 .491 .446 997 +S SGE 2449825.8965 6.010 1.016 .521 .492 997 +S SGE 2449934.1641 5.873 1.037 .533 .987 998 +S SGE 2449935.1606 6.007 1.004 .516 1.005 998 +S SGE 2449936.1547 5.856 .895 .497 .917 998 +S SGE 2449937.2209 5.401 .680 .386 998 +S SGE 2449938.1568 5.374 .687 .378 .710 998 +S SGE 2449939.1610 5.389 .756 .408 .774 998 +S SGE 2449942.1540 5.829 .992 .501 .945 998 +S SGE 2449943.1482 5.918 1.034 .522 .971 998 +S SGE 2449944.1466 5.965 .953 .474 .930 998 +S SGE 2449945.1469 5.582 .781 .429 .802 998 +S SGE 2449946.1497 5.298 .653 .350 .668 998 +S SGE 2449947.1469 5.426 .758 .400 .761 998 +S SGE 2449947.2773 5.437 .758 .407 .781 998 +S SGE 2449948.1449 5.369 .742 .407 .786 998 +S SGE 2449949.1496 5.610 .886 .464 .871 998 +S SGE 2449950.1469 5.787 .980 .500 .948 998 +S SGE 2449952.1454 6.014 1.007 .509 .977 998 +S SGE 2449953.1736 5.750 .824 .446 .846 998 +S SGE 2449954.1599 5.329 .623 .347 .663 998 +S SGE 2449955.1533 5.431 .716 .392 .751 998 +S SGE 2449958.1391 5.765 .925 .495 .933 998 +S SGE 2449959.1350 .996 .528 .975 998 +S SGE 2449962.1479 5.505 .716 .407 .772 998 +S SGE 2450009.0905 5.807 .984 .497 .931 998 +S SGE 2450011.0875 5.943 .962 .502 .925 998 +S SGE 2450012.1325 5.591 .799 998 +S SGE 2450017.0758 5.743 .954 .493 .927 998 +S SGE 2450018.1273 5.942 1.033 .525 .996 998 +S SGE 2450020.0687 5.768 .860 .458 .880 998 +S SGE 2450305.1648 5.747 .821 .467 971 +S SGE 2450306.1606 5.330 .644 .385 971 +S SGE 2450307.1826 5.379 .714 .395 971 +S SGE 2450311.1530 5.829 .996 .492 971 +S SGE 2450312.1479 5.990 1.014 .521 971 +S SGE 2450313.1538 5.857 .932 .474 971 +S SGE 2450314.1377 5.500 .734 .395 971 +S SGE 2450314.1381 5.501 .729 .396 971 +S SGE 2450315.1396 5.300 .671 .380 971 +S SGE 2450316.1397 5.412 .773 .407 971 +S SGE 2450317.1693 5.382 .730 971 +S SGE 2450318.1397 5.646 .862 .479 971 +S SGE 2450319.1380 5.796 .907 .524 971 +S SGE 2450320.1384 5.948 .973 .537 971 +S SGE 2450321.1340 5.989 .937 .530 971 +S SGE 2450322.1275 5.666 .774 .476 971 +S SGE 2450323.1297 5.263 .616 .384 971 +S SGE 2450324.1348 5.403 .707 .410 971 +S SGE 2450325.1255 5.382 .717 .410 971 +S SGE 2450326.1226 5.579 .845 .452 971 +S SGE 2450327.1768 5.726 .953 971 +S SGE 2450329.1594 1.000 .521 971 +S SGE 2450330.2315 5.830 .853 .478 971 +S SGE 2450332.2200 5.348 .692 971 +S SGE 2450333.1979 5.371 .742 .401 971 +S SGE 2450334.2249 5.473 .784 .426 971 +S SGE 2450335.2247 5.686 .913 .506 971 +S SGE 2450336.2321 5.843 .979 .535 971 +S SGE 2450337.2097 5.995 .995 .534 971 +S SGE 2450340.1819 5.269 .668 .378 971 +S SGE 2450342.2188 5.345 .746 .407 971 +S SGE 2450344.2503 5.782 .940 971 +S SGE 2450347.2609 5.672 .812 .461 971 +S SGE 2450349.2070 5.401 .725 .415 971 +DG SGE 2446252.3440 13.254 1.928 1.251 987 +DG SGE 2446253.3028 13.452 2.093 1.273 987 +DG SGE 2446255.3354 12.817 1.781 1.121 987 +DG SGE 2446256.3534 13.103 1.944 1.198 987 +DG SGE 2446257.3607 13.422 1.983 1.285 987 +DG SGE 2446258.3110 13.545 2.003 1.269 987 +DG SGE 2446259.3205 13.125 1.882 1.172 987 +DG SGE 2446260.2992 12.962 1.877 1.138 987 +DG SGE 2446261.3070 13.286 1.965 1.250 987 +DG SGE 2446262.3434 13.502 2.003 1.304 987 +DG SGE 2446263.3439 13.479 2.049 1.225 987 +DG SGE 2446265.3183 13.198 1.903 1.223 987 +DG SGE 2446266.3152 13.437 2.072 1.267 987 +DG SGE 2446267.2942 13.547 2.076 1.271 987 +DG SGE 2446268.2871 13.012 1.854 1.146 987 +DG SGE 2446269.2803 12.991 1.854 1.165 987 +DG SGE 2446270.2943 13.294 1.965 1.236 987 +DG SGE 2446272.2598 13.476 1.952 1.257 987 +DG SGE 2446273.3210 12.851 1.852 1.102 987 +DG SGE 2446274.2921 13.154 1.933 1.208 987 +DG SGE 2446275.3174 13.433 2.119 1.263 987 +DG SGE 2446279.2791 13.310 2.066 1.216 987 +DG SGE 2446280.2839 13.514 2.044 1.262 987 +DG SGE 2446283.2429 13.217 2.047 1.188 987 +DG SGE 2449957.2304 13.254 998 +DG SGE 2449958.2047 13.505 2.457 998 +DG SGE 2449959.2382 13.576 2.455 998 +DG SGE 2449962.2262 13.382 2.358 998 +DG SGE 2449986.1675 13.459 2.415 998 +DG SGE 2449987.2085 12.831 2.169 998 +DG SGE 2449992.1440 12.984 2.247 998 +DG SGE 2450007.2114 13.501 2.418 998 +DG SGE 2450009.1961 12.879 2.176 998 +DG SGE 2450017.1141 13.552 2.431 998 +DG SGE 2450018.1782 12.859 2.201 998 +DG SGE 2450020.1306 13.372 2.376 998 +GX SGE 2444825.3514 12.906 2.269 982 +GX SGE 2444827.2421 12.848 2.209 982 +GX SGE 2444830.2343 12.150 1.784 982 +GX SGE 2444831.2381 12.003 1.812 982 +GX SGE 2444832.2421 12.131 1.850 982 +GX SGE 2444833.2304 12.251 1.984 982 +GX SGE 2444845.2070 12.124 1.904 982 +GX SGE 2444846.2343 12.234 2.007 982 +GX SGE 2444847.2109 12.373 2.082 982 +GX SGE 2444848.2030 12.508 2.151 982 +GX SGE 2444849.2343 12.634 2.254 982 +GX SGE 2444850.2304 12.780 2.264 982 +GX SGE 2444851.2264 12.907 2.300 982 +GX SGE 2444852.2187 12.968 2.258 982 +GX SGE 2444853.2226 12.846 2.168 982 +GX SGE 2444854.2187 12.609 2.058 982 +GX SGE 2444855.2070 12.569 1.996 982 +GX SGE 2444856.1992 11.998 1.726 982 +GX SGE 2444857.2030 12.013 1.820 982 +GX SGE 2444858.2695 12.139 1.939 982 +GX SGE 2444880.1992 12.584 2.054 982 +GX SGE 2444881.1875 12.523 2.013 982 +GX SGE 2444882.2304 11.875 1.708 982 +GX SGE 2444883.1639 12.031 1.833 982 +GX SGE 2444884.1522 12.166 1.911 982 +GX SGE 2444885.1756 12.280 2.055 982 +GX SGE 2445173.3671 12.870 2.246 982 +GX SGE 2445175.3788 12.893 2.215 982 +GX SGE 2445176.3554 12.677 2.091 982 +GX SGE 2445178.3554 12.320 1.877 982 +GX SGE 2445179.2421 11.924 1.732 982 +GX SGE 2445180.2772 12.046 1.880 982 +GX SGE 2445181.2381 12.189 1.929 982 +GX SGE 2445184.2226 12.573 2.213 982 +GX SGE 2445186.2617 12.851 2.275 982 +GX SGE 2445187.2500 12.934 2.277 982 +GX SGE 2445188.2381 12.892 2.233 982 +GX SGE 2445190.2889 12.559 2.075 982 +GX SGE 2445191.2968 12.284 1.891 982 +GX SGE 2445192.3006 11.947 1.799 982 +GX SGE 2445193.2734 12.059 1.877 982 +GX SGE 2445194.2734 12.193 1.948 982 +GX SGE 2445198.2968 12.742 2.249 982 +GX SGE 2445201.2734 12.875 2.206 982 +GX SGE 2445203.3242 12.569 2.027 982 +GX SGE 2445204.2030 12.270 1.870 982 +GX SGE 2445205.2147 11.969 1.804 982 +GX SGE 2445207.2187 12.197 1.986 982 +GX SGE 2445648.1288 12.521 2.188 1.246 982 +GX SGE 2445649.1522 12.635 2.228 1.269 982 +GX SGE 2445650.1484 12.771 1.272 982 +GX SGE 2445665.1093 12.973 1.306 982 +GX SGE 2445668.0937 12.571 2.030 1.223 982 +GX SGE 2445674.1131 12.474 2.173 1.295 982 +GX SGE 2445675.0897 12.670 2.244 1.321 982 +GX SGE 2445864.3359 12.119 1.884 1.168 982 +GX SGE 2445866.3750 12.364 2.133 1.229 982 +GX SGE 2445867.3514 12.490 2.146 1.256 982 +GX SGE 2445868.3046 12.666 2.150 1.293 982 +GX SGE 2445869.3397 12.788 2.240 1.317 982 +GX SGE 2445870.3125 12.889 1.307 982 +GX SGE 2445871.3125 12.942 1.305 982 +GX SGE 2445872.2889 12.828 2.194 1.290 982 +GX SGE 2445873.2851 12.625 2.106 1.235 982 +GX SGE 2445874.2772 12.571 2.078 1.218 982 +GX SGE 2445875.2695 12.018 1.769 1.100 982 +GX SGE 2445876.2734 12.004 1.837 1.131 982 +GX SGE 2445877.2889 12.103 1.929 1.163 982 +GX SGE 2445878.2655 12.231 2.017 1.196 982 +GX SGE 2445879.2734 12.375 2.076 1.248 982 +GX SGE 2445880.2812 12.502 2.158 1.282 982 +GX SGE 2445881.2617 12.657 2.186 1.308 982 +GX SGE 2445882.2655 12.779 2.252 1.302 982 +GX SGE 2445883.2734 12.882 2.281 1.306 982 +GX SGE 2445886.2655 12.616 2.074 1.244 982 +GX SGE 2445887.2617 12.552 1.981 1.198 982 +GX SGE 2447734.3107 12.027 1.852 1.132 991 +GX SGE 2447735.3542 12.169 2.001 1.157 991 +GX SGE 2447736.3563 12.343 2.028 1.215 991 +GX SGE 2447737.3611 12.424 2.117 1.238 991 +GX SGE 2447738.3388 1.296 991 +GX SGE 2447739.3051 12.729 2.208 1.290 991 +GX SGE 2447740.3401 12.857 2.324 1.331 991 +GX SGE 2447741.2952 12.920 2.241 1.292 991 +GX SGE 2447742.3044 12.875 2.220 1.283 991 +GX SGE 2447743.2929 12.666 2.053 1.243 991 +GX SGE 2447744.2706 12.585 2.042 1.215 991 +GX SGE 2447745.2786 12.298 1.880 1.147 991 +GX SGE 2447746.2829 11.957 1.788 1.079 991 +GX SGE 2447747.2762 12.024 1.875 1.114 991 +GX SGE 2447748.2751 12.191 2.001 1.171 991 +GX SGE 2447749.2702 12.330 2.044 1.197 991 +GX SGE 2447750.2555 12.452 2.109 1.245 991 +GX SGE 2447751.2531 12.566 2.138 1.248 991 +GX SGE 2447752.2347 12.730 2.229 1.289 991 +GX SGE 2447753.2328 12.890 1.309 991 +GX SGE 2447754.2646 12.986 2.255 1.339 991 +GX SGE 2447755.2836 12.869 2.252 1.273 991 +GX SGE 2447756.2950 12.617 2.184 1.219 991 +GX SGE 2447757.2626 12.576 2.029 1.198 991 +GX SGE 2447758.2545 12.223 1.926 1.112 991 +GX SGE 2447759.2339 11.975 1.772 1.097 991 +GX SGE 2447760.2557 12.072 1.891 1.112 991 +GX SGE 2447761.2414 12.220 1.975 1.192 991 +GX SGE 2447762.2222 12.335 2.079 1.199 991 +GX SGE 2447763.1895 12.442 2.133 1.224 991 +GX SGE 2447764.1962 12.572 2.187 1.253 991 +GX SGE 2447766.1964 12.907 2.267 1.323 991 +GX SGE 2447767.2459 12.948 2.268 1.288 991 +GX SGE 2447768.2434 12.802 2.209 1.270 991 +GX SGE 2447770.2333 12.554 2.031 1.204 991 +GX SGE 2447771.2218 12.167 1.816 1.121 991 +GX SGE 2447772.2207 11.971 1.830 1.098 991 +GX SGE 2447773.2475 12.084 1.888 1.138 991 +GX SGE 2447774.2525 12.235 1.959 1.192 991 +GX SGE 2447775.2156 12.353 2.047 1.220 991 +GX SGE 2447776.2189 12.483 2.144 1.280 991 +GX SGE 2449956.2941 12.525 2.472 998 +GX SGE 2449957.2219 12.665 2.468 998 +GX SGE 2449958.1968 12.815 2.536 998 +GX SGE 2449959.2303 12.944 2.558 998 +GX SGE 2449960.2505 12.979 2.578 998 +GX SGE 2449962.2094 12.747 2.453 998 +GX SGE 2449986.1517 12.945 2.530 998 +GX SGE 2449987.1876 12.808 2.509 998 +GX SGE 2449992.1338 12.122 2.273 998 +GX SGE 2449993.1701 12.236 2.342 998 +GX SGE 2450009.1557 12.675 2.504 998 +GX SGE 2450011.1375 12.933 2.537 998 +GX SGE 2450017.1038 12.055 2.189 998 +GX SGE 2450018.1694 12.184 2.295 998 +GX SGE 2450020.1192 12.413 2.393 998 +GY SGE 2444825.3631 10.254 2.420 982 +GY SGE 2444827.2460 10.327 2.435 982 +GY SGE 2444829.2617 10.373 2.429 982 +GY SGE 2444830.2381 10.367 2.432 982 +GY SGE 2444831.2460 10.429 2.430 982 +GY SGE 2444832.2460 10.432 2.447 982 +GY SGE 2444833.2381 10.454 2.449 982 +GY SGE 2444835.3593 10.481 2.464 982 +GY SGE 2444841.2421 10.498 2.400 982 +GY SGE 2444844.2226 10.360 2.320 982 +GY SGE 2444846.2381 10.207 2.250 982 +GY SGE 2444847.2147 10.120 2.215 982 +GY SGE 2444848.2070 10.056 2.176 982 +GY SGE 2444849.2381 9.972 2.145 982 +GY SGE 2444850.2343 9.922 2.129 982 +GY SGE 2444851.2304 9.882 2.120 982 +GY SGE 2444852.2226 9.862 2.111 982 +GY SGE 2444853.2264 9.857 2.103 982 +GY SGE 2444854.2226 9.843 2.121 982 +GY SGE 2444855.2109 9.870 2.124 982 +GY SGE 2444856.2070 9.859 2.120 982 +GY SGE 2444857.2109 9.868 2.138 982 +GY SGE 2444858.2734 9.859 982 +GY SGE 2444859.2226 9.911 2.173 982 +GY SGE 2444880.2030 10.318 2.457 982 +GY SGE 2444881.1913 10.341 2.460 982 +GY SGE 2444882.2343 10.388 2.411 982 +GY SGE 2444883.1679 10.422 2.424 982 +GY SGE 2444884.1601 10.461 2.417 982 +GY SGE 2444885.1835 10.450 2.448 982 +GY SGE 2445173.3750 10.051 2.291 982 +GY SGE 2445175.3828 10.094 2.314 982 +GY SGE 2445178.3593 10.148 2.345 982 +GY SGE 2445179.2460 10.173 2.366 982 +GY SGE 2445181.2460 10.208 2.383 982 +GY SGE 2445182.2460 10.227 2.415 982 +GY SGE 2445183.2460 10.241 2.407 982 +GY SGE 2445184.2304 10.266 2.424 982 +GY SGE 2445186.2734 10.301 2.452 982 +GY SGE 2445187.2538 10.332 2.436 982 +GY SGE 2445188.2460 10.350 2.450 982 +GY SGE 2445189.2851 10.371 2.443 982 +GY SGE 2445190.2968 10.384 2.458 982 +GY SGE 2445191.3006 10.421 2.469 982 +GY SGE 2445192.3046 10.415 2.464 982 +GY SGE 2445193.2772 10.459 2.431 982 +GY SGE 2445194.2772 10.459 2.448 982 +GY SGE 2445198.3006 10.477 2.444 982 +GY SGE 2445199.2655 10.469 2.415 982 +GY SGE 2445200.2812 10.431 2.396 982 +GY SGE 2445201.2772 10.406 2.372 982 +GY SGE 2445203.2187 10.312 2.315 982 +GY SGE 2445204.2109 10.245 2.273 982 +GY SGE 2445205.2187 10.164 2.224 982 +GY SGE 2445207.2109 10.016 2.154 982 +GY SGE 2445208.2421 9.959 2.129 982 +GY SGE 2445209.1953 9.911 2.118 982 +GY SGE 2445210.2030 9.889 2.114 982 +GY SGE 2445211.1835 9.868 2.110 982 +GY SGE 2445212.2109 9.882 2.107 982 +GY SGE 2445213.1953 9.868 2.123 982 +GY SGE 2445214.1953 9.889 2.130 982 +GY SGE 2445489.3203 10.199 2.497 2.445 1.402 982 +GY SGE 2445497.2538 10.402 2.525 1.429 982 +GY SGE 2445498.2695 10.407 2.488 1.433 982 +GY SGE 2445501.3163 10.452 2.522 1.436 982 +GY SGE 2445503.3085 10.496 1.429 982 +GY SGE 2445505.2968 10.506 1.415 982 +GY SGE 2445508.2851 10.491 2.465 1.413 982 +GY SGE 2445509.3085 10.452 2.466 1.402 982 +GY SGE 2445512.3125 10.295 2.282 1.374 982 +GY SGE 2445513.3397 10.208 2.296 1.351 982 +GY SGE 2445514.3125 10.129 2.260 1.325 982 +GY SGE 2445515.3046 10.027 2.220 1.316 982 +GY SGE 2445644.2147 10.265 2.506 982 +GY SGE 2445646.1601 10.322 2.511 1.386 982 +GY SGE 2445648.1328 10.331 2.512 1.414 982 +GY SGE 2445649.1562 10.328 1.413 982 +GY SGE 2445659.1093 10.494 1.425 982 +GY SGE 2445665.1131 10.372 2.453 1.377 982 +GY SGE 2445666.0937 10.329 2.409 1.367 982 +GY SGE 2445668.1014 10.152 2.304 1.331 982 +GY SGE 2445674.1171 9.794 2.173 1.276 982 +GY SGE 2445675.0937 9.846 2.188 1.268 982 +GY SGE 2445676.0937 9.873 2.195 1.284 982 +GY SGE 2445679.1171 9.882 2.255 1.284 982 +GY SGE 2445683.1014 9.979 2.330 1.332 982 +GY SGE 2445686.0859 10.054 2.381 1.360 982 +GY SGE 2445690.0820 10.129 2.373 1.378 982 +GY SGE 2445864.3397 10.475 2.465 2.489 1.437 982 +GY SGE 2445866.3788 10.475 2.270 2.430 1.426 982 +GY SGE 2445867.3554 10.475 2.480 2.445 1.426 982 +GY SGE 2445868.3085 10.477 2.345 2.432 1.410 982 +GY SGE 2445869.3437 10.449 2.406 2.380 1.421 982 +GY SGE 2445870.3163 10.408 2.124 2.378 1.408 982 +GY SGE 2445871.3125 10.353 2.352 1.391 982 +GY SGE 2445872.2968 10.302 2.143 2.304 1.373 982 +GY SGE 2445873.2889 10.228 1.961 2.297 1.355 982 +GY SGE 2445874.2812 10.144 1.916 2.258 1.340 982 +GY SGE 2445875.2734 10.071 1.890 2.184 1.323 982 +GY SGE 2445876.2772 9.985 1.837 2.170 1.291 982 +GY SGE 2445877.2929 9.948 1.823 2.131 1.298 982 +GY SGE 2445878.2695 9.897 1.795 2.105 1.289 982 +GY SGE 2445879.2734 9.879 1.763 2.090 1.277 982 +GY SGE 2445880.2851 9.855 2.114 1.285 982 +GY SGE 2445881.2617 9.877 2.084 1.295 982 +GY SGE 2445882.2695 9.845 1.818 2.114 1.280 982 +GY SGE 2445883.2772 9.839 1.847 2.113 1.278 982 +GY SGE 2445886.2695 9.899 1.844 2.156 1.312 982 +GY SGE 2445887.2695 9.884 1.891 2.186 1.297 982 +GY SGE 2446252.3378 9.946 2.226 1.337 987 +GY SGE 2446253.2989 9.952 2.253 1.346 987 +GY SGE 2446255.3308 9.994 2.270 1.353 987 +GY SGE 2446256.3481 10.006 2.283 1.361 987 +GY SGE 2446257.3566 10.042 2.308 1.374 987 +GY SGE 2446258.3047 10.059 2.164 2.338 1.371 987 +GY SGE 2446259.3151 10.073 2.193 2.330 1.382 987 +GY SGE 2446260.2938 10.107 2.337 1.377 987 +GY SGE 2446261.3005 10.126 2.241 2.346 1.391 987 +GY SGE 2446262.3365 10.137 2.212 2.345 1.401 987 +GY SGE 2446263.3377 10.173 2.191 2.378 1.400 987 +GY SGE 2446265.3110 10.236 2.312 2.391 1.415 987 +GY SGE 2446266.3085 10.244 2.323 2.406 1.424 987 +GY SGE 2446267.2876 10.251 2.314 2.409 1.423 987 +GY SGE 2446268.2799 10.259 2.369 2.412 1.422 987 +GY SGE 2446269.2726 10.291 2.355 2.408 1.423 987 +GY SGE 2446270.2884 10.308 2.363 2.414 1.427 987 +GY SGE 2446272.2520 10.353 2.427 1.423 987 +GY SGE 2446273.3149 10.397 2.373 2.424 1.430 987 +GY SGE 2446274.2811 10.378 2.445 1.433 987 +GY SGE 2446275.3057 10.407 2.414 2.460 1.430 987 +GY SGE 2446278.3418 10.497 1.454 987 +GY SGE 2446279.2735 10.486 2.446 1.436 987 +GY SGE 2446280.2726 10.480 2.459 2.444 1.427 987 +GY SGE 2446283.2329 10.519 2.474 2.416 1.426 987 +GY SGE 2446284.2115 10.461 2.408 1.410 987 +GY SGE 2446285.2196 10.436 2.398 1.397 987 +GY SGE 2446286.2097 10.390 2.364 1.389 987 +GY SGE 2446287.2052 10.303 2.109 2.320 1.367 987 +GY SGE 2446288.2196 10.236 2.291 1.347 987 +GY SGE 2446289.2299 10.156 2.242 1.331 987 +GY SGE 2446290.2121 10.090 1.899 2.201 1.312 987 +GY SGE 2446291.2029 10.000 2.178 1.300 987 +GY SGE 2446292.2034 9.937 2.151 1.279 987 +GY SGE 2446293.2347 9.880 2.124 1.283 987 +GY SGE 2446294.2010 9.876 2.118 1.279 987 +GY SGE 2446295.1791 9.858 2.104 1.282 987 +GY SGE 2446296.1874 9.870 2.106 1.280 987 +GY SGE 2446297.1958 9.835 2.123 1.273 987 +GY SGE 2446298.2158 9.873 2.125 1.291 987 +GY SGE 2446299.1835 9.862 2.140 1.286 987 +GY SGE 2446300.1828 9.899 2.173 1.294 987 +GY SGE 2446301.1997 9.895 2.164 1.301 987 +GY SGE 2446302.1973 9.923 2.218 1.309 987 +GY SGE 2446303.1710 9.929 2.212 1.315 987 +GY SGE 2446304.1526 9.965 2.243 1.314 987 +GY SGE 2446606.3101 9.868 2.106 1.285 988 +GY SGE 2446607.3810 9.886 2.098 1.284 988 +GY SGE 2446608.3141 9.867 1.828 2.089 1.293 988 +GY SGE 2446609.2388 9.862 1.775 2.119 1.296 988 +GY SGE 2446610.3438 9.879 2.138 1.297 988 +GY SGE 2446611.2402 9.892 1.913 2.143 1.302 988 +GY SGE 2446612.2264 9.915 1.869 2.152 1.318 988 +GY SGE 2446613.2272 9.925 1.928 2.166 1.323 988 +GY SGE 2446614.2287 9.934 2.183 1.335 988 +GY SGE 2446615.2263 9.954 1.944 2.220 1.332 988 +GY SGE 2446616.2300 9.981 1.983 2.231 1.344 988 +GY SGE 2446617.2254 9.969 2.040 2.234 1.345 988 +GY SGE 2446618.2274 10.013 2.023 2.248 1.366 988 +GY SGE 2446619.2234 10.026 2.081 2.266 1.368 988 +GY SGE 2446620.2208 10.043 2.110 2.275 1.373 988 +GY SGE 2446621.3023 10.062 2.286 1.374 988 +GY SGE 2446622.2196 10.061 2.157 2.323 1.375 988 +GY SGE 2446623.2085 10.099 2.318 1.381 988 +GY SGE 2446624.2041 10.120 2.338 1.390 988 +GY SGE 2446625.2001 10.145 2.347 1.392 988 +GY SGE 2446626.2067 10.134 2.351 1.397 988 +GY SGE 2446627.2083 10.177 2.366 1.404 988 +GY SGE 2446628.2093 10.193 2.366 1.407 988 +GY SGE 2446629.2215 10.204 2.396 1.411 988 +GY SGE 2446630.2206 10.211 2.390 1.411 988 +GY SGE 2446632.2182 10.294 2.401 1.431 988 +GY SGE 2446635.3096 10.344 2.435 1.424 988 +GY SGE 2446636.2186 10.373 2.433 1.436 988 +GY SGE 2446637.2196 10.389 2.420 1.423 988 +GY SGE 2446638.2257 10.404 2.427 1.440 988 +GY SGE 2446992.3195 10.279 2.483 1.434 989 +GY SGE 2446994.3735 10.333 2.492 1.441 989 +GY SGE 2446995.3432 10.338 2.491 1.441 989 +GY SGE 2446996.2769 10.351 2.509 1.436 989 +GY SGE 2446997.3073 10.352 1.434 989 +GY SGE 2446998.2998 10.385 2.518 1.447 989 +GY SGE 2446999.2749 10.427 2.520 1.445 989 +GY SGE 2447000.3113 10.421 2.521 1.439 989 +GY SGE 2447001.2867 10.463 2.517 1.451 989 +GY SGE 2447002.2866 10.491 2.530 1.450 989 +GY SGE 2447003.2702 10.490 2.558 1.448 989 +GY SGE 2447083.1412 10.020 2.254 1.340 989 +GY SGE 2447084.1275 10.031 2.277 1.347 989 +GY SGE 2447087.1672 10.100 2.364 1.369 989 +GY SGE 2447088.0929 10.088 2.351 1.366 989 +GY SGE 2447091.0896 10.205 2.400 1.417 989 +GY SGE 2447098.0742 10.346 1.428 989 +GY SGE 2447399.2658 10.164 2.341 1.378 990 +GY SGE 2447400.2393 10.189 2.345 1.387 990 +GY SGE 2447401.2261 10.212 2.359 1.378 990 +GY SGE 2447402.2239 10.229 2.364 1.379 990 +GY SGE 2447403.2254 10.265 2.376 1.382 990 +GY SGE 2447404.1991 10.258 2.379 1.378 990 +GY SGE 2447407.2154 10.338 2.415 1.407 990 +GY SGE 2447408.1839 10.343 2.414 1.395 990 +GY SGE 2447409.2034 10.360 2.416 1.404 990 +GY SGE 2447410.2065 10.399 2.460 1.402 990 +GY SGE 2447411.2319 10.439 2.412 1.421 990 +GY SGE 2447413.1910 10.464 2.441 1.425 990 +GY SGE 2447414.1791 10.467 2.438 1.414 990 +GY SGE 2447415.1807 10.502 2.456 1.415 990 +GY SGE 2447416.1862 10.503 2.437 1.402 990 +GY SGE 2447417.1808 10.500 2.437 1.406 990 +GY SGE 2447418.1824 10.540 2.424 1.430 990 +GY SGE 2447419.1593 10.455 2.406 1.383 990 +GY SGE 2447420.1648 10.491 2.424 1.385 990 +GY SGE 2447421.1565 10.463 2.411 1.387 990 +GY SGE 2447422.1621 10.465 2.387 1.371 990 +GY SGE 2447423.1525 10.396 2.358 1.356 990 +GY SGE 2447424.1567 10.354 2.327 1.367 990 +GY SGE 2447425.1682 10.283 2.280 1.336 990 +GY SGE 2447427.1826 10.184 2.209 1.305 990 +GY SGE 2447428.1535 10.029 2.191 1.266 990 +GY SGE 2447429.1501 10.006 2.153 1.281 990 +GY SGE 2447430.1350 9.930 2.108 1.256 990 +GY SGE 2447431.1458 9.910 2.091 1.251 990 +GY SGE 2447432.1328 9.866 2.115 1.250 990 +GY SGE 2447433.1373 9.890 2.127 1.263 990 +GY SGE 2447434.1417 9.864 2.119 1.251 990 +GY SGE 2447734.3144 10.250 2.280 1.339 991 +GY SGE 2447735.3562 10.178 2.213 1.330 991 +GY SGE 2447736.3587 10.122 2.142 1.326 991 +GY SGE 2447737.3646 9.987 2.147 1.280 991 +GY SGE 2447738.3427 10.004 2.110 1.291 991 +GY SGE 2447739.3065 9.934 2.083 1.274 991 +GY SGE 2447740.3422 9.879 2.112 1.291 991 +GY SGE 2447741.3016 9.846 2.110 1.263 991 +GY SGE 2447742.3084 9.846 2.091 1.260 991 +GY SGE 2447743.2987 9.854 2.095 1.256 991 +GY SGE 2447744.2748 9.865 2.119 1.263 991 +GY SGE 2447745.2812 9.887 2.109 1.287 991 +GY SGE 2447746.2857 9.895 2.156 1.263 991 +GY SGE 2447747.2794 9.884 2.157 1.263 991 +GY SGE 2447748.2777 9.927 2.174 1.289 991 +GY SGE 2447749.2721 9.964 2.166 1.299 991 +GY SGE 2447750.2601 9.962 2.188 1.320 991 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1.426 991 +GY SGE 2447776.2227 10.528 2.457 1.442 991 +GY SGE 2448503.2405 10.505 2.412 1.397 993 +GY SGE 2448504.2003 10.491 2.382 1.397 993 +GY SGE 2448505.2265 10.461 2.360 1.390 993 +GY SGE 2448506.2400 10.377 2.365 1.365 993 +GY SGE 2448507.2160 10.341 2.319 1.357 993 +GY SGE 2448508.1930 10.252 2.293 1.334 993 +GY SGE 2448509.2017 10.192 2.210 1.336 993 +GY SGE 2448510.1962 10.069 2.183 1.304 993 +GY SGE 2448511.2046 10.012 2.167 1.286 993 +GY SGE 2448512.2137 9.965 2.143 1.277 993 +GY SGE 2448513.2143 9.928 2.116 1.263 993 +GY SGE 2448514.2167 9.909 2.121 1.266 993 +GY SGE 2448515.2146 9.927 2.083 1.283 993 +GY SGE 2448516.2082 9.894 2.086 1.278 993 +GY SGE 2448517.2020 9.893 2.129 1.271 993 +GY SGE 2448518.2103 9.906 2.113 1.274 993 +GY SGE 2448519.2280 9.907 2.123 1.296 993 +GY SGE 2448520.1986 9.920 2.133 1.275 993 +GY SGE 2448521.2242 9.914 2.178 1.290 993 +GY SGE 2448522.2089 9.962 2.174 1.313 993 +GY SGE 2448523.2024 9.991 2.179 1.329 993 +GY SGE 2448856.2571 10.480 2.482 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2449535.7529 7.056 1.033 1.281 .726 .685 996 +U SGR 2449536.7560 6.689 .765 1.058 .664 .625 996 +U SGR 2449543.7357 6.587 .671 1.031 .622 .582 996 +U SGR 2449545.7020 6.530 .798 1.082 .638 .605 996 +U SGR 2449558.8265 6.416 .706 1.008 .615 .613 996 +U SGR 2449558.8281 6.403 .695 1.037 .602 .621 996 +U SGR 2449559.7839 6.597 .807 1.132 .644 .629 996 +U SGR 2449561.7383 6.939 .998 1.267 .698 .673 996 +U SGR 2449564.7347 6.363 .675 .893 .556 .548 996 +U SGR 2449617.0891 7.017 .864 1.179 .687 995 +U SGR 2449620.1292 6.639 .699 1.107 .631 995 +U SGR 2449621.1123 6.724 .997 1.146 .658 995 +U SGR 2449622.1071 6.915 1.003 1.265 .663 995 +U SGR 2449623.1019 7.103 1.022 1.298 .707 995 +U SGR 2449624.1050 6.953 .748 1.163 .697 995 +U SGR 2449625.1047 6.397 .608 .919 .582 995 +U SGR 2449631.1023 6.793 .688 1.073 .647 995 +U SGR 2449632.1145 6.380 .536 .900 .566 995 +U SGR 2449633.1084 6.546 .736 1.060 .616 995 +U SGR 2449634.1096 6.725 .748 1.099 .675 995 +U SGR 2449805.9117 7.047 1.213 .704 .683 997 +U SGR 2449808.8963 6.649 1.084 .617 .640 997 +U SGR 2449809.8556 6.671 1.130 .652 .652 997 +U SGR 2449810.8569 6.875 1.244 .679 997 +U SGR 2449811.8528 7.036 1.290 .705 .682 997 +U SGR 2449813.8586 6.411 .921 .559 .559 997 +U SGR 2449817.8646 6.932 1.255 .707 .673 997 +U SGR 2449818.8471 7.075 1.297 .706 .693 997 +U SGR 2449821.8779 6.489 1.004 .598 .612 997 +U SGR 2449822.8649 6.631 1.115 .644 .641 997 +U SGR 2449823.8581 6.804 1.213 .686 .667 997 +U SGR 2449825.8650 7.070 1.264 .704 .686 997 +U SGR 2449936.2376 6.465 .985 .604 1.159 998 +U SGR 2449937.2514 6.681 1.110 .680 998 +U SGR 2449956.1364 6.494 .947 .585 1.125 998 +U SGR 2449957.1453 6.656 1.085 .646 1.232 998 +U SGR 2449958.1527 6.713 1.155 .663 1.291 998 +U SGR 2449959.1447 6.941 1.246 .718 1.377 998 +U SGR 2449962.1563 6.512 .991 .585 1.138 998 +U SGR 2450009.0952 1.080 .617 1.192 998 +U SGR 2450011.0911 6.630 1.091 .640 1.228 998 +U SGR 2450012.1256 1.252 998 +U SGR 2450017.0787 6.455 1.029 .565 1.142 998 +U SGR 2450020.0717 6.908 1.251 .703 1.374 998 +U SGR 2450354.5909 6.461 1.177 999 +U SGR 2450355.5437 6.639 1.273 999 +U SGR 2450355.6188 6.628 1.266 999 +U SGR 2450357.5436 6.930 1.374 999 +U SGR 2450357.6224 6.947 1.377 999 +U SGR 2450358.5514 7.075 1.388 999 +U SGR 2450358.6155 7.077 1.379 999 +U SGR 2450359.5545 6.769 1.268 999 +U SGR 2450359.6077 6.753 1.260 999 +U SGR 2450360.5486 6.326 1.098 999 +U SGR 2450361.5563 6.504 1.200 999 +U SGR 2450361.6262 6.532 1.208 999 +U SGR 2450362.5607 6.631 1.276 999 +U SGR 2450362.5992 6.630 1.274 999 +U SGR 2450363.5530 6.817 1.344 999 +U SGR 2450363.6040 6.826 1.344 999 +U SGR 2450380.5659 6.374 1.117 999 +U SGR 2450381.5682 6.422 1.161 999 +U SGR 2450382.5531 6.664 1.273 999 +U SGR 2450383.5636 6.732 1.321 999 +U SGR 2450384.5528 6.943 1.388 999 +U SGR 2450386.5717 6.753 1.260 999 +U SGR 2450388.4932 6.491 1.192 999 +U SGR 2450389.4930 6.648 1.278 999 +U SGR 2450390.4945 6.818 1.351 999 +U SGR 2450391.4936 6.976 1.377 999 +U SGR 2450392.4938 7.065 1.386 999 +U SGR 2450393.4953 6.695 1.222 999 +U SGR 2450306.1725 6.435 .920 .574 971 +U SGR 2450307.1898 6.434 .979 .583 971 +U SGR 2450310.2324 6.954 1.215 .716 971 +U SGR 2450311.1624 7.017 1.281 .723 971 +U SGR 2450312.1569 6.848 1.137 .685 971 +U SGR 2450313.1626 6.354 .897 .570 971 +U SGR 2450314.1484 6.452 .991 .619 971 +U SGR 2450315.1485 6.638 1.050 .692 971 +U SGR 2450316.1463 6.753 1.115 .707 971 +U SGR 2450317.1752 6.942 1.203 .647 971 +U SGR 2450318.1463 7.040 1.256 .721 971 +U SGR 2450319.1440 6.757 1.016 .659 971 +U SGR 2450320.1456 6.313 .891 .567 971 +U SGR 2450321.1406 6.511 .983 .632 971 +U SGR 2450322.1352 6.647 1.072 .653 971 +U SGR 2450323.1353 6.825 1.108 .694 971 +U SGR 2450324.1400 6.963 1.164 .712 971 +U SGR 2450325.1315 7.080 1.214 .715 971 +U SGR 2450326.1279 6.620 1.005 .617 971 +U SGR 2450570.6545 6.489 1.200 972 +U SGR 2450573.5308 6.953 1.368 972 +U SGR 2450573.6233 6.953 1.388 972 +U SGR 2450573.6692 6.967 1.392 972 +U SGR 2450574.5580 7.065 1.393 972 +U SGR 2450575.5117 6.709 1.252 972 +U SGR 2450575.6044 6.706 1.249 972 +U SGR 2450575.6541 6.641 1.217 972 +U SGR 2450576.5575 6.346 1.121 972 +U SGR 2450576.6674 6.357 1.135 972 +U SGR 2450577.5652 6.533 1.232 972 +U SGR 2450577.6218 6.540 1.236 972 +U SGR 2450577.6662 6.548 1.241 972 +U SGR 2450578.5427 6.627 1.290 972 +U SGR 2450578.6095 6.625 1.292 972 +U SGR 2450578.6647 6.629 1.289 972 +U SGR 2450580.4879 6.987 1.389 972 +U SGR 2450580.6006 7.000 1.399 972 +U SGR 2450582.5804 6.552 1.182 972 +U SGR 2450582.6274 6.532 1.174 972 +U SGR 2450582.6757 6.490 1.158 972 +U SGR 2450583.5730 6.392 1.150 972 +U SGR 2450584.5608 6.572 1.253 972 +U SGR 2450584.6196 6.588 1.261 972 +U SGR 2450584.6661 6.590 1.257 972 +W SGR 2449956.1379 5.070 .944 .492 .955 998 +W SGR 2449957.1460 5.177 .990 .487 .951 998 +W SGR 2449958.1540 4.932 .844 .438 .852 998 +W SGR 2449959.1462 4.536 .654 .425 .766 998 +W SGR 2449962.1567 4.659 .814 .423 .838 998 +W SGR 2450354.5871 4.300 .652 999 +W SGR 2450355.5385 4.502 .766 999 +W SGR 2450355.6028 4.483 .743 999 +W SGR 2450357.5384 4.765 .896 999 +W SGR 2450357.6170 4.794 .906 999 +W SGR 2450358.5470 4.904 .932 999 +W SGR 2450358.6108 4.916 .935 999 +W SGR 2450359.5507 5.065 .967 999 +W SGR 2450359.6034 5.058 .943 999 +W SGR 2450360.5436 4.937 .893 999 +W SGR 2450361.5521 4.408 .680 999 +W SGR 2450361.6215 4.384 .662 999 +W SGR 2450362.5566 4.349 .689 999 +W SGR 2450362.5950 4.354 .691 999 +W SGR 2450363.5485 4.515 .785 999 +W SGR 2450363.6000 4.512 .779 999 +W SGR 2450379.4826 4.479 .789 999 +W SGR 2450380.4780 4.826 .916 999 +W SGR 2450380.5593 4.811 .911 999 +W SGR 2450381.5095 4.923 .933 999 +W SGR 2450382.5470 .977 999 +W SGR 2450383.4824 4.953 .879 999 +W SGR 2450383.5564 4.836 .854 999 +W SGR 2450384.5468 4.319 999 +W SGR 2450385.4827 4.417 .686 999 +W SGR 2450386.4846 4.508 .781 999 +W SGR 2450386.5659 4.501 .767 999 +W SGR 2450387.5522 4.663 .863 999 +W SGR 2450388.4848 4.838 .916 999 +W SGR 2450389.4836 5.021 .957 999 +W SGR 2450390.4856 5.072 .950 999 +W SGR 2450391.4849 4.689 .794 999 +W SGR 2450392.4852 4.292 .649 999 +W SGR 2450393.4868 4.503 .758 999 +W SGR 2450310.2253 .648 .421 971 +W SGR 2450311.1571 4.426 .719 .425 971 +W SGR 2450312.1516 4.710 .835 .502 971 +W SGR 2450313.1575 4.904 .877 .532 971 +W SGR 2450314.1420 5.035 .915 .532 971 +W SGR 2450315.1436 4.912 .695 .526 971 +W SGR 2450316.1427 4.297 .502 .383 971 +W SGR 2450317.1711 4.392 .572 971 +W SGR 2450318.1484 4.476 .714 .431 971 +W SGR 2450319.1462 4.667 .681 .480 971 +W SGR 2450320.1483 4.793 .823 .500 971 +W SGR 2450321.1430 5.011 .830 .514 971 +W SGR 2450322.1376 5.055 .821 .460 971 +W SGR 2450323.1377 4.685 .568 .403 971 +W SGR 2450324.1419 4.271 .442 .339 971 +W SGR 2450325.1342 4.479 .624 .430 971 +W SGR 2450326.1300 4.458 .664 .425 971 +W SGR 2450570.6586 4.804 .904 972 +W SGR 2450573.5334 4.781 .821 972 +W SGR 2450573.6259 4.731 .824 972 +W SGR 2450573.6724 4.708 .818 972 +W SGR 2450574.5605 4.285 .653 972 +W SGR 2450575.5151 4.420 .735 972 +W SGR 2450575.6068 4.466 .752 972 +W SGR 2450575.6563 4.446 .738 972 +W SGR 2450576.5599 4.491 .796 972 +W SGR 2450576.6698 4.482 .797 972 +W SGR 2450577.5675 4.708 .882 972 +W SGR 2450577.6241 4.728 .890 972 +W SGR 2450577.6684 4.733 .889 972 +W SGR 2450578.5451 4.846 .937 972 +W SGR 2450578.6124 4.848 .935 972 +W SGR 2450578.6674 4.861 .935 972 +W SGR 2450580.4906 5.035 .940 972 +W SGR 2450580.6027 5.010 .933 972 +W SGR 2450582.5827 4.304 .667 972 +W SGR 2450582.6296 4.320 .667 972 +W SGR 2450582.6779 4.315 .671 972 +W SGR 2450583.5754 4.521 .785 972 +W SGR 2450584.5635 4.483 .783 972 +W SGR 2450584.6217 4.503 .801 972 +W SGR 2450584.6680 4.496 .790 972 +X SGR 2449956.1395 4.361 .598 .406 .730 998 +X SGR 2449957.1469 4.356 .688 .418 .761 998 +X SGR 2449958.1546 4.490 .814 .418 .851 998 +X SGR 2449959.1469 4.652 .855 .486 .919 998 +X SGR 2449962.1573 4.725 .812 .477 .920 998 +X SGR 2450354.5857 4.786 .958 999 +X SGR 2450355.5373 4.397 .809 999 +X SGR 2450355.6015 4.357 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2450389.4848 4.841 .978 999 +X SGR 2450390.4871 4.434 .831 999 +X SGR 2450391.4861 4.252 .772 999 +X SGR 2450392.4866 4.403 .860 999 +X SGR 2450393.4881 4.548 .927 999 +X SGR 2450305.1816 4.822 .815 .524 971 +X SGR 2450306.1816 4.450 .614 .416 971 +X SGR 2450307.1936 4.291 .578 .383 971 +X SGR 2450310.2380 4.792 .798 .523 971 +X SGR 2450311.1667 4.772 .890 .547 971 +X SGR 2450312.1617 4.775 .843 .546 971 +X SGR 2450313.1673 4.522 .659 .473 971 +X SGR 2450314.1543 4.206 .596 .428 971 +X SGR 2450315.1565 4.405 .578 .519 971 +X SGR 2450316.1489 4.485 .674 .527 971 +X SGR 2450317.1784 4.700 .758 .437 971 +X SGR 2450318.1498 4.786 .883 .549 971 +X SGR 2450319.1479 4.875 .770 .551 971 +X SGR 2450320.1504 4.506 .669 .465 971 +X SGR 2450321.1449 4.256 .530 .417 971 +X SGR 2450322.1393 4.352 .626 .409 971 +X SGR 2450323.1394 4.516 .628 .474 971 +X SGR 2450324.1433 4.639 .699 .508 971 +X SGR 2450325.1361 4.783 .816 .556 971 +X SGR 2450326.1327 4.856 .815 .533 971 +X SGR 2450330.1367 .504 971 +X SGR 2450332.1364 4.786 .840 .377 971 +X SGR 2450570.6599 4.805 1.002 972 +X SGR 2450573.5350 4.219 .762 972 +X SGR 2450573.6271 4.228 .782 972 +X SGR 2450573.6736 4.225 .784 972 +X SGR 2450574.5618 4.355 .853 972 +X SGR 2450575.5180 4.497 .917 972 +X SGR 2450575.6086 4.520 .920 972 +X SGR 2450575.6575 4.501 .913 972 +X SGR 2450576.5610 4.655 .974 972 +X SGR 2450576.6710 4.670 .977 972 +X SGR 2450577.5689 4.804 1.012 972 +X SGR 2450577.6253 4.804 1.008 972 +X SGR 2450577.6695 4.807 1.013 972 +X SGR 2450578.5462 4.832 1.006 972 +X SGR 2450578.6135 4.824 1.007 972 +X SGR 2450578.6685 4.814 .995 972 +X SGR 2450580.4916 4.239 .776 972 +X SGR 2450580.6038 4.233 .777 972 +X SGR 2450582.5838 4.492 .915 972 +X SGR 2450582.6308 4.501 .912 972 +X SGR 2450582.6790 4.496 .908 972 +X SGR 2450583.5766 4.665 .978 972 +X SGR 2450584.5645 4.800 1.008 972 +X SGR 2450584.6227 4.813 1.011 972 +X SGR 2450584.6690 4.806 1.000 972 +Y SGR 2449934.2320 6.085 1.123 .602 1.086 998 +Y SGR 2449935.2528 5.981 .510 .975 998 +Y SGR 2449936.2008 5.406 .418 .797 998 +Y SGR 2449938.2369 5.827 .958 .524 .985 998 +Y SGR 2449939.2218 6.020 1.013 .579 1.103 998 +Y SGR 2449942.2111 5.415 .778 998 +Y SGR 2449943.2019 5.653 .848 .491 .896 998 +Y SGR 2449944.1981 5.890 .980 998 +Y SGR 2449945.2117 6.028 1.086 998 +Y SGR 2449946.2351 6.161 1.063 .557 1.063 998 +Y SGR 2449947.1868 5.688 .810 .472 .900 998 +Y SGR 2449948.1587 5.454 .729 .426 .831 998 +Y SGR 2449949.1640 5.672 .875 .486 .942 998 +Y SGR 2449950.1627 5.884 .968 .545 1.014 998 +Y SGR 2449952.1595 6.118 1.012 .559 1.068 998 +Y SGR 2449953.1807 5.513 .709 .438 .844 998 +Y SGR 2449954.1694 5.521 .751 .434 .861 998 +Y SGR 2449955.1615 5.733 .892 .514 .974 998 +Y SGR 2450354.5897 5.876 1.030 999 +Y SGR 2450355.5423 6.065 1.080 999 +Y SGR 2450355.6175 6.037 1.075 999 +Y SGR 2450357.5422 5.385 .797 999 +Y SGR 2450357.6209 5.376 .796 999 +Y SGR 2450358.5504 5.522 .885 999 +Y SGR 2450358.6145 5.541 .893 999 +Y SGR 2450359.5534 5.728 .983 999 +Y SGR 2450359.6068 5.733 .979 999 +Y SGR 2450360.5466 5.905 1.056 999 +Y SGR 2450361.5549 6.080 1.078 999 +Y SGR 2450361.6248 6.109 1.087 999 +Y SGR 2450362.5595 5.850 .978 999 +Y SGR 2450362.5979 5.822 .969 999 +Y SGR 2450363.5520 5.367 .801 999 +Y SGR 2450363.6027 5.378 .794 999 +Y SGR 2450379.4815 6.042 1.069 999 +Y SGR 2450380.4770 5.471 .820 999 +Y SGR 2450380.5633 5.406 .818 999 +Y SGR 2450381.4775 5.515 .873 999 +Y SGR 2450381.5662 5.507 .891 999 +Y SGR 2450382.4840 .961 999 +Y SGR 2450382.5510 5.760 .982 999 +Y SGR 2450383.5213 5.903 1.038 999 +Y SGR 2450384.5502 6.067 1.089 999 +Y SGR 2450385.4812 5.971 1.010 999 +Y SGR 2450386.5263 5.360 .777 999 +Y SGR 2450388.4838 5.769 1.000 999 +Y SGR 2450389.4822 5.939 1.056 999 +Y SGR 2450390.4846 6.084 1.077 999 +Y SGR 2450391.4841 5.822 .954 999 +Y SGR 2450392.4840 5.381 .806 999 +Y SGR 2450393.4856 5.609 .917 999 +Y SGR 2450305.1791 5.606 .765 .470 971 +Y SGR 2450306.1792 5.408 .698 .425 971 +Y SGR 2450307.1913 5.696 .863 .488 971 +Y SGR 2450310.2345 6.082 .967 .552 971 +Y SGR 2450311.1644 5.437 .733 .432 971 +Y SGR 2450312.1591 5.441 .764 .456 971 +Y SGR 2450313.1648 5.687 .873 .520 971 +Y SGR 2450314.1507 5.839 .958 .558 971 +Y SGR 2450315.1496 6.044 .971 .605 971 +Y SGR 2450316.1472 5.974 .903 .560 971 +Y SGR 2450317.1762 5.376 .663 971 +Y SGR 2450318.1474 5.506 .795 .469 971 +Y SGR 2450319.1451 5.751 .861 .537 971 +Y SGR 2450320.1469 5.853 .908 .573 971 +Y SGR 2450321.1419 6.071 .976 .584 971 +Y SGR 2450322.1364 5.863 .883 .503 971 +Y SGR 2450323.1363 5.371 .616 .419 971 +Y SGR 2450324.1408 5.561 .758 .483 971 +Y SGR 2450325.1327 5.799 .903 .545 971 +Y SGR 2450326.1289 5.939 .975 .564 971 +Y SGR 2450570.6565 5.672 .903 972 +Y SGR 2450573.5321 5.783 1.003 972 +Y SGR 2450573.6246 5.798 1.019 972 +Y SGR 2450573.6710 5.805 1.028 972 +Y SGR 2450574.5592 5.983 1.072 972 +Y SGR 2450575.5138 6.077 1.069 972 +Y SGR 2450575.6303 6.072 1.052 972 +Y SGR 2450576.5586 5.602 .889 972 +Y SGR 2450576.6687 5.511 .845 972 +Y SGR 2450577.5662 5.437 .842 972 +Y SGR 2450577.6229 5.455 .857 972 +Y SGR 2450577.6672 5.463 .863 972 +Y SGR 2450578.5439 5.648 .968 972 +Y SGR 2450578.6109 5.654 .976 972 +Y SGR 2450578.6662 5.671 .978 972 +Y SGR 2450580.4893 6.018 1.080 972 +Y SGR 2450580.6016 6.021 1.077 972 +Y SGR 2450582.5815 5.423 .811 972 +Y SGR 2450582.6284 5.408 .807 972 +Y SGR 2450582.6767 5.375 .788 972 +Y SGR 2450583.5741 5.499 .885 972 +Y SGR 2450584.5622 5.700 .969 972 +Y SGR 2450584.6206 5.721 .999 972 +Y SGR 2450584.6670 5.718 .977 972 +VY SGR 2446994.3043 11.844 1.733 2.366 1.331 989 +VY SGR 2446995.2627 11.998 2.389 1.341 989 +VY SGR 2446996.2255 11.969 2.325 1.327 989 +VY SGR 2447001.2423 11.027 1.825 1.152 989 +VY SGR 2447002.2391 11.126 1.888 1.202 989 +VY SGR 2447003.2212 11.295 2.011 1.230 989 +VY SGR 2447401.1542 11.839 2.267 1.281 990 +VY SGR 2447402.1476 12.006 2.315 1.324 990 +VY SGR 2447403.1437 11.959 2.213 1.274 990 +VY SGR 2447404.1424 11.763 2.103 1.233 990 +VY SGR 2447408.1353 11.030 1.799 1.127 990 +VY SGR 2447409.1410 11.094 1.987 1.147 990 +VY SGR 2447410.1526 11.214 1.972 1.217 990 +VY SGR 2447411.1534 11.368 2.080 1.236 990 +VY SGR 2447412.1577 11.561 2.173 1.271 990 +VY SGR 2447413.1414 11.632 2.233 1.252 990 +VY SGR 2447414.1393 11.808 2.288 1.288 990 +VY SGR 2447415.1447 11.894 2.330 1.288 990 +VY SGR 2447416.1385 11.959 2.290 1.304 990 +VY SGR 2447417.1334 11.881 2.227 1.283 990 +VY SGR 2447418.1342 11.639 2.124 1.213 990 +VY SGR 2447419.1270 11.697 2.067 1.231 990 +VY SGR 2447420.1314 11.157 1.876 1.125 990 +VY SGR 2447421.1222 10.956 1.813 1.109 990 +VY SGR 2447422.1256 11.108 1.942 1.165 990 +VY SGR 2447423.1255 11.146 1.993 1.154 990 +VY SGR 2447425.1291 2.116 1.271 990 +VY SGR 2447427.1348 11.723 2.233 1.305 990 +VY SGR 2447428.1244 11.884 2.272 1.319 990 +VY SGR 2447735.2359 11.228 2.008 1.209 991 +VY SGR 2447736.2238 11.342 2.121 1.222 991 +VY SGR 2447737.2262 11.480 2.107 1.259 991 +VY SGR 2447738.2402 11.569 2.214 1.285 991 +VY SGR 2447739.2085 11.756 2.233 1.296 991 +VY SGR 2447740.2146 11.864 2.255 1.313 991 +VY SGR 2447741.2067 12.017 2.258 1.337 991 +VY SGR 2447742.2200 12.009 2.180 1.291 991 +VY SGR 2447743.2027 11.703 2.104 1.268 991 +VY SGR 2447744.1947 11.625 2.007 1.216 991 +VY SGR 2447745.1938 11.426 1.901 1.189 991 +VY SGR 2447746.1941 10.835 1.644 1.096 991 +VY SGR 2447747.1890 10.995 1.824 1.125 991 +VY SGR 2447748.1900 11.123 1.958 1.165 991 +VY SGR 2447749.1692 11.191 2.063 1.195 991 +VY SGR 2447750.1688 11.300 2.122 1.194 991 +VY SGR 2447753.1629 11.780 2.288 1.288 991 +VY SGR 2447754.1736 11.921 2.219 1.316 991 +VY SGR 2447756.1932 11.880 2.246 1.279 991 +VY SGR 2447757.1655 11.663 2.142 1.240 991 +VY SGR 2447758.1613 11.570 2.036 1.218 991 +VY SGR 2447759.1538 11.078 1.749 1.130 991 +VY SGR 2447760.1713 10.961 1.791 1.111 991 +VY SGR 2447761.1560 11.069 1.817 1.160 991 +VY SGR 2447762.1572 11.169 1.989 1.182 991 +VY SGR 2447763.1495 11.310 2.040 1.218 991 +VY SGR 2447764.1532 11.443 2.111 1.264 991 +VY SGR 2447766.1474 11.741 2.203 1.300 991 +VY SGR 2447767.1618 11.896 2.271 1.316 991 +VY SGR 2447768.1535 11.976 2.244 1.308 991 +VY SGR 2447769.1539 11.937 2.211 1.316 991 +VY SGR 2447770.1503 11.768 2.118 1.264 991 +VY SGR 2447771.1478 11.620 2.058 1.237 991 +VY SGR 2447772.1431 11.494 1.901 1.200 991 +VY SGR 2447773.1502 10.828 1.676 1.088 991 +VY SGR 2447774.1788 10.947 1.841 1.109 991 +VY SGR 2447775.1414 11.129 1.893 1.183 991 +VY SGR 2447776.1445 11.237 2.000 1.170 991 +VY SGR 2449934.2270 11.591 1.284 2.432 998 +VY SGR 2449935.2499 11.806 1.387 2.501 998 +VY SGR 2449936.1929 11.909 1.353 2.481 998 +VY SGR 2449938.2085 12.050 1.330 2.506 998 +VY SGR 2449939.2162 11.963 998 +VY SGR 2449942.2052 11.046 2.143 998 +VY SGR 2449943.1945 10.959 2.141 998 +VY SGR 2449944.1825 11.185 2.224 998 +VY SGR 2449945.1936 11.321 2.326 998 +VY SGR 2449946.1894 11.450 2.351 998 +VY SGR 2449947.1783 11.578 2.455 998 +VY SGR 2449948.1724 11.716 2.465 998 +VY SGR 2449949.1730 11.787 2.511 998 +VY SGR 2449950.1777 11.976 2.480 998 +VY SGR 2449952.1821 12.006 2.515 998 +VY SGR 2449954.1836 11.697 2.353 998 +VY SGR 2449955.1783 11.554 2.305 998 +WZ SGR 2445488.4453 8.352 1.475 1.531 .839 982 +WZ SGR 2445496.4062 7.801 1.152 1.407 .780 982 +WZ SGR 2445497.3593 7.900 1.212 1.462 .790 982 +WZ SGR 2445498.3984 7.952 1.305 1.475 .817 982 +WZ SGR 2445501.3984 8.270 1.547 1.648 .842 982 +WZ SGR 2445502.4022 8.314 1.623 1.636 .894 982 +WZ SGR 2445503.3905 8.378 1.655 1.717 .872 982 +WZ SGR 2445505.3554 8.529 1.876 1.726 .886 982 +WZ SGR 2445515.3203 7.486 .884 1.160 .679 982 +WZ SGR 2445872.3242 8.154 1.600 1.626 .850 982 +WZ SGR 2445873.3280 8.237 1.710 1.634 .864 982 +WZ SGR 2445874.3163 8.314 1.766 1.709 .865 982 +WZ SGR 2445875.3085 8.410 1.813 1.697 .862 982 +WZ SGR 2445876.3203 8.488 1.844 1.734 .901 982 +WZ SGR 2445877.3163 8.546 1.879 1.704 .903 982 +WZ SGR 2445878.3085 8.542 1.814 1.712 .874 982 +WZ SGR 2445879.3006 8.525 1.687 1.665 .872 982 +WZ SGR 2445880.3085 8.418 1.497 1.603 .867 982 +WZ SGR 2445881.3046 8.425 1.355 1.568 .892 982 +WZ SGR 2445882.2968 8.353 1.318 1.524 .824 982 +WZ SGR 2445883.3085 8.111 1.113 1.378 .763 982 +WZ SGR 2445886.2968 7.526 .911 1.155 .673 982 +WZ SGR 2445887.2929 7.572 .969 1.225 .694 982 +WZ SGR 2447402.1537 8.219 1.603 .868 990 +WZ SGR 2447403.1481 8.269 1.674 .838 990 +WZ SGR 2447404.1457 8.357 1.666 .837 990 +WZ SGR 2447408.1400 8.575 1.673 .874 990 +WZ SGR 2447409.1443 8.461 1.712 .847 990 +WZ SGR 2447410.1542 8.364 1.556 .836 990 +WZ SGR 2447411.1553 8.309 1.520 .805 990 +WZ SGR 2447412.1600 8.354 1.534 .816 990 +WZ SGR 2447413.1440 7.866 .934 1.301 .689 990 +WZ SGR 2447414.1415 7.521 .739 1.133 .654 990 +WZ SGR 2447415.1461 7.420 .751 1.148 .634 990 +WZ SGR 2447416.1405 7.498 1.195 .662 990 +WZ SGR 2447417.1348 7.613 .945 1.290 .698 990 +WZ SGR 2447418.1358 7.710 1.368 .705 990 +WZ SGR 2447419.1289 7.869 1.487 .760 990 +WZ SGR 2447420.1326 7.876 1.509 .783 990 +WZ SGR 2447421.1251 7.957 1.551 .799 990 +WZ SGR 2447422.1282 8.065 1.642 .821 990 +WZ SGR 2447423.1276 8.078 1.636 .800 990 +WZ SGR 2447424.1288 8.186 1.679 .844 990 +WZ SGR 2447425.1329 8.278 1.681 .847 990 +WZ SGR 2447427.1365 8.439 1.700 .883 990 +WZ SGR 2447428.1276 8.508 1.719 .867 990 +WZ SGR 2447429.1249 8.516 1.777 .851 990 +WZ SGR 2447430.1156 8.528 1.676 .845 990 +WZ SGR 2447431.1170 8.485 1.670 .860 990 +WZ SGR 2447432.1119 8.416 1.617 .828 990 +WZ SGR 2447433.1097 8.290 1.524 .777 990 +WZ SGR 2447434.1137 8.308 1.496 .770 990 +WZ SGR 2449934.2297 8.015 .862 1.590 998 +WZ SGR 2449935.2514 8.145 .881 1.552 998 +WZ SGR 2449938.2349 8.390 1.708 .867 1.659 998 +WZ SGR 2449939.2199 8.470 .893 1.689 998 +WZ SGR 2449942.2090 8.579 1.691 998 +WZ SGR 2449943.1995 8.589 .865 1.614 998 +WZ SGR 2449944.1850 8.577 1.631 998 +WZ SGR 2449945.2128 8.415 1.605 998 +WZ SGR 2449946.2364 8.322 1.556 998 +WZ SGR 2449948.2069 7.759 1.317 998 +WZ SGR 2449949.2102 7.551 1.263 998 +WZ SGR 2449950.2056 7.514 1.257 998 +WZ SGR 2449952.1996 7.750 1.392 998 +WZ SGR 2449953.2242 7.853 1.476 998 +WZ SGR 2449954.2101 7.906 1.493 998 +WZ SGR 2449955.2099 7.995 1.558 998 +WZ SGR 2450354.5891 8.400 1.663 999 +WZ SGR 2450355.5404 8.485 1.693 999 +WZ SGR 2450355.6061 8.474 1.693 999 +WZ SGR 2450357.5404 8.528 1.667 999 +WZ SGR 2450357.6186 8.540 1.674 999 +WZ SGR 2450358.5493 8.518 1.654 999 +WZ SGR 2450358.6129 8.518 1.649 999 +WZ SGR 2450359.5523 8.457 1.627 999 +WZ SGR 2450359.6053 8.453 1.617 999 +WZ SGR 2450360.5454 8.353 1.591 999 +WZ SGR 2450361.5541 8.353 1.573 999 +WZ SGR 2450361.6238 8.381 1.564 999 +WZ SGR 2450362.5586 8.193 1.512 999 +WZ SGR 2450362.5971 8.173 1.505 999 +WZ SGR 2450363.5506 7.645 1.311 999 +WZ SGR 2450363.6018 7.633 1.307 999 +WZ SGR 2450380.5617 8.505 1.659 999 +WZ SGR 2450381.5643 8.426 1.619 999 +WZ SGR 2450382.5493 8.399 1.587 999 +WZ SGR 2450383.5602 8.326 1.582 999 +WZ SGR 2450384.5489 8.128 1.489 999 +WZ SGR 2450386.5681 7.436 1.248 999 +WZ SGR 2450388.4953 7.594 1.362 999 +WZ SGR 2450389.4953 7.686 1.408 999 +WZ SGR 2450390.4973 7.761 1.463 999 +WZ SGR 2450391.4965 7.847 1.502 999 +WZ SGR 2450392.4965 7.912 1.548 999 +WZ SGR 2450393.4985 8.005 1.562 999 +WZ SGR 2450305.1807 7.906 1.465 .806 971 +WZ SGR 2450306.1803 7.959 1.483 .806 971 +WZ SGR 2450307.1922 8.109 1.557 .820 971 +WZ SGR 2450310.2351 8.380 1.654 .873 971 +WZ SGR 2450311.1650 8.384 1.716 .882 971 +WZ SGR 2450312.1596 8.451 1.695 .887 971 +WZ SGR 2450313.1653 8.551 1.694 .904 971 +WZ SGR 2450314.1513 8.525 1.662 .895 971 +WZ SGR 2450315.1743 8.519 1.534 .884 971 +WZ SGR 2450316.1871 8.465 1.509 971 +WZ SGR 2450317.1845 8.374 1.450 .796 971 +WZ SGR 2450318.1704 8.357 1.486 .824 971 +WZ SGR 2450319.1605 8.069 1.271 .758 971 +WZ SGR 2450321.1637 7.446 1.037 .655 971 +WZ SGR 2450322.1614 7.507 1.113 .650 971 +WZ SGR 2450323.1601 7.615 1.159 .689 971 +WZ SGR 2450324.1791 7.729 1.215 .755 971 +WZ SGR 2450325.1526 7.782 1.333 .773 971 +WZ SGR 2450326.1436 7.850 1.395 .781 971 +WZ SGR 2450568.5607 8.069 1.581 972 +WZ SGR 2450570.5094 8.165 1.613 972 +WZ SGR 2450572.4599 8.324 1.677 972 +WZ SGR 2450572.5515 8.345 1.658 972 +WZ SGR 2450573.5895 8.415 1.669 972 +WZ SGR 2450573.6455 8.426 1.682 972 +WZ SGR 2450574.5257 8.484 1.690 972 +WZ SGR 2450574.5847 8.469 1.685 972 +WZ SGR 2450575.4855 8.516 1.685 972 +WZ SGR 2450575.5716 8.519 1.691 972 +WZ SGR 2450575.6357 8.518 1.680 972 +WZ SGR 2450576.5322 8.517 1.674 972 +WZ SGR 2450576.6133 8.511 1.670 972 +WZ SGR 2450576.6513 8.506 1.667 972 +WZ SGR 2450577.5420 8.481 1.648 972 +WZ SGR 2450577.6000 8.477 1.642 972 +WZ SGR 2450577.6412 8.468 1.640 972 +WZ SGR 2450578.5202 8.405 1.616 972 +WZ SGR 2450578.5864 8.399 1.616 972 +WZ SGR 2450578.6436 8.390 1.616 972 +WZ SGR 2450579.5676 8.342 1.594 972 +WZ SGR 2450580.4645 8.339 1.567 972 +WZ SGR 2450580.5760 8.330 1.559 972 +WZ SGR 2450580.6258 8.312 1.556 972 +WZ SGR 2450582.5573 7.529 1.275 972 +WZ SGR 2450582.6049 7.539 1.280 972 +WZ SGR 2450582.6529 7.510 1.277 972 +WZ SGR 2450583.4923 7.455 1.277 972 +WZ SGR 2450583.6046 7.465 1.279 972 +WZ SGR 2450584.5402 7.525 1.316 972 +WZ SGR 2450584.5964 7.529 1.330 972 +WZ SGR 2450584.6441 7.531 1.335 972 +XX SGR 2449934.2427 9.360 .819 1.552 998 +XX SGR 2449935.2609 9.218 .741 1.427 998 +XX SGR 2449936.2085 8.532 .617 1.191 998 +XX SGR 2449938.2448 8.903 .710 1.368 998 +XX SGR 2449939.2454 9.060 1.482 998 +XX SGR 2449942.2194 8.852 1.324 998 +XX SGR 2449943.2080 8.489 1.190 998 +XX SGR 2449944.2030 8.852 1.327 998 +XX SGR 2449945.2035 8.960 1.433 998 +XX SGR 2449946.1955 9.158 1.481 998 +XX SGR 2449947.2107 9.345 1.558 998 +XX SGR 2449948.2096 9.153 1.443 998 +XX SGR 2449949.2137 8.487 1.163 998 +XX SGR 2449950.2093 8.673 1.306 998 +XX SGR 2449952.2023 9.118 1.489 998 +XX SGR 2449953.2261 9.340 1.572 998 +XX SGR 2449954.2119 9.322 1.487 998 +XX SGR 2449955.2116 8.707 1.243 998 +XX SGR 2450354.5915 8.546 1.245 999 +XX SGR 2450355.5448 8.773 1.362 999 +XX SGR 2450355.6195 8.751 1.354 999 +XX SGR 2450357.5441 9.155 1.509 999 +XX SGR 2450357.6229 9.159 1.498 999 +XX SGR 2450358.5522 9.280 1.517 999 +XX SGR 2450358.6162 9.280 1.514 999 +XX SGR 2450359.5571 8.941 1.377 999 +XX SGR 2450359.6084 8.912 1.363 999 +XX SGR 2450360.5493 8.417 1.188 999 +XX SGR 2450361.5576 8.667 1.319 999 +XX SGR 2450361.6270 8.697 1.329 999 +XX SGR 2450362.5612 8.834 1.411 999 +XX SGR 2450362.5996 8.835 1.409 999 +XX SGR 2450363.5537 9.079 1.503 999 +XX SGR 2450363.6047 9.092 1.500 999 +XX SGR 2450380.5641 8.584 1.281 999 +XX SGR 2450381.5669 8.805 1.392 999 +XX SGR 2450382.5517 9.040 1.477 999 +XX SGR 2450383.5622 9.182 1.520 999 +XX SGR 2450384.5513 9.262 1.513 999 +XX SGR 2450386.5704 8.514 1.234 999 +XX SGR 2450388.4941 8.870 1.413 999 +XX SGR 2450389.4943 9.139 1.493 999 +XX SGR 2450390.4958 9.272 1.529 999 +XX SGR 2450391.4950 9.080 1.436 999 +XX SGR 2450392.4954 8.407 1.177 999 +XX SGR 2450393.4971 8.628 1.292 999 +XX SGR 2450306.1784 9.140 1.310 .761 971 +XX SGR 2450307.1910 9.304 1.323 .788 971 +XX SGR 2450310.2338 8.703 1.102 .678 971 +XX SGR 2450311.1637 8.802 1.207 .718 971 +XX SGR 2450312.1582 9.039 1.283 .776 971 +XX SGR 2450313.1639 9.255 1.348 .797 971 +XX SGR 2450314.1499 9.182 1.255 .768 971 +XX SGR 2450315.1758 8.486 .914 .625 971 +XX SGR 2450316.1880 8.601 .994 .579 971 +XX SGR 2450317.1857 8.834 1.133 .685 971 +XX SGR 2450319.1624 9.217 1.279 .796 971 +XX SGR 2450321.1654 8.848 1.070 .696 971 +XX SGR 2450322.1642 8.477 .965 .606 971 +XX SGR 2450323.1612 8.728 1.076 .672 971 +XX SGR 2450324.1805 8.895 1.142 .743 971 +XX SGR 2450325.1538 9.128 1.275 .786 971 +YZ SGR 2446607.3535 7.227 .730 1.032 .581 988 +YZ SGR 2446609.3335 7.516 .920 1.171 .633 988 +YZ SGR 2446610.3131 7.661 .970 1.219 .659 988 +YZ SGR 2446611.3095 7.690 .946 1.198 .642 988 +YZ SGR 2446612.3092 7.532 .730 1.069 .620 988 +YZ SGR 2446613.2993 7.282 .624 .963 .543 988 +YZ SGR 2446614.3116 7.183 .654 .926 .541 988 +YZ SGR 2446615.2689 7.070 .600 .902 .524 988 +YZ SGR 2446617.2757 7.246 .766 1.053 .582 988 +YZ SGR 2446618.2735 7.425 .857 1.136 .635 988 +YZ SGR 2446619.2745 7.570 .945 1.202 .650 988 +YZ SGR 2446620.2570 7.701 .984 1.228 .671 988 +YZ SGR 2446621.2464 7.681 .878 1.151 .644 988 +YZ SGR 2446622.2587 7.412 .660 1.041 .588 988 +YZ SGR 2446623.3094 7.194 .590 .937 .531 988 +YZ SGR 2446624.2464 7.201 .641 .931 .543 988 +YZ SGR 2446625.2438 7.016 .580 .888 .515 988 +YZ SGR 2446626.2389 7.203 .690 1.004 .565 988 +YZ SGR 2446627.2366 7.325 .760 1.101 .592 988 +YZ SGR 2446628.2351 7.464 .920 1.167 .612 988 +YZ SGR 2446629.2474 7.666 .987 1.224 .668 988 +YZ SGR 2446631.2195 7.577 .761 1.113 .625 988 +YZ SGR 2446632.2406 7.325 .624 .980 .572 988 +YZ SGR 2446635.2185 7.012 .609 .928 .523 988 +YZ SGR 2446636.2488 7.236 .781 1.041 .585 988 +YZ SGR 2447735.2922 7.284 1.080 .592 991 +YZ SGR 2447736.2760 7.478 1.154 .644 991 +YZ SGR 2447737.2759 7.579 1.181 .640 991 +YZ SGR 2447738.2691 7.656 .905 1.240 .649 991 +YZ SGR 2447739.2426 7.609 .812 1.143 .621 991 +YZ SGR 2447740.2733 7.374 .602 1.003 .577 991 +YZ SGR 2447741.2433 7.170 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.477 .438 996 +AP SGR 2449620.1300 7.276 .952 .503 995 +AP SGR 2449621.1138 6.573 .618 .387 995 +AP SGR 2449622.1076 6.830 .839 .413 995 +AP SGR 2449623.1032 7.122 .949 .517 995 +AP SGR 2449624.1054 7.333 1.028 .550 995 +AP SGR 2449625.1050 7.360 .900 .555 995 +AP SGR 2449631.1036 6.556 .634 .382 995 +AP SGR 2449632.1161 6.909 .805 .498 995 +AP SGR 2449633.1103 7.127 .947 .540 995 +AP SGR 2449634.1108 7.359 1.011 .602 995 +AP SGR 2449805.8769 7.212 .964 .538 .508 997 +AP SGR 2449808.8941 6.762 .730 .425 .422 997 +AP SGR 2449809.8537 6.996 .879 .494 .481 997 +AP SGR 2449810.8550 7.172 .966 .522 997 +AP SGR 2449811.8513 7.346 .998 .538 .511 997 +AP SGR 2449813.8569 6.723 .714 .426 .404 997 +AP SGR 2449814.8323 6.969 .863 .488 .466 997 +AP SGR 2449815.8368 7.178 .960 997 +AP SGR 2449817.8632 6.807 .692 .425 .403 997 +AP SGR 2449818.8421 6.704 .698 .417 .397 997 +AP SGR 2449818.9129 6.690 .710 .420 .390 997 +AP SGR 2449821.9110 7.334 .998 .545 .505 997 +AP SGR 2449822.9134 6.795 .696 .419 .421 997 +AP SGR 2449823.8909 6.699 .702 .423 .420 997 +AP SGR 2449825.8835 7.159 .943 .523 .508 997 +AP SGR 2449934.2057 6.786 .804 .400 .803 998 +AP SGR 2449935.2350 6.810 .421 .808 998 +AP SGR 2449936.1804 7.023 .895 .514 .951 998 +AP SGR 2449937.2330 7.260 .974 .534 998 +AP SGR 2449938.1921 7.425 1.008 .553 1.044 998 +AP SGR 2449939.1898 6.919 .776 .446 .886 998 +AP SGR 2449942.1812 7.233 .982 .569 1.045 998 +AP SGR 2449943.1755 7.402 1.019 .541 1.079 998 +AP SGR 2449944.1958 7.061 .860 998 +AP SGR 2449945.2092 6.732 .820 998 +AP SGR 2449946.2305 7.054 .848 .471 .903 998 +AP SGR 2449947.1612 7.198 .966 .528 1.043 998 +AP SGR 2449948.1560 7.397 1.033 .551 1.090 998 +AP SGR 2449949.1616 7.080 .802 .488 .935 998 +AP SGR 2449950.1601 6.688 .669 .401 .767 998 +AP SGR 2449952.1569 7.225 .943 .558 1.049 998 +AP SGR 2449953.1790 7.342 .992 .559 1.052 998 +AP SGR 2449954.1676 7.142 .842 .481 .910 998 +AP SGR 2449955.1596 6.681 .639 .416 .769 998 +AP SGR 2449958.1504 7.365 1.016 .550 1.061 998 +AP SGR 2449959.1423 7.215 .886 .500 .957 998 +AP SGR 2449962.1540 7.172 .937 .539 1.011 998 +AP SGR 2450354.5878 6.577 .747 999 +AP SGR 2450355.5392 6.854 .907 999 +AP SGR 2450355.6051 6.855 .895 999 +AP SGR 2450357.5395 7.247 1.044 999 +AP SGR 2450357.6179 7.260 1.040 999 +AP SGR 2450358.5480 7.258 1.005 999 +AP SGR 2450358.6116 7.212 .976 999 +AP SGR 2450359.5514 6.553 .732 999 +AP SGR 2450359.6042 6.555 .752 999 +AP SGR 2450360.5445 6.808 .892 999 +AP SGR 2450361.5528 7.063 .986 999 +AP SGR 2450361.6224 7.084 .978 999 +AP SGR 2450362.5574 7.237 1.043 999 +AP SGR 2450362.5958 7.240 1.038 999 +AP SGR 2450363.5493 7.276 1.018 999 +AP SGR 2450363.6008 7.259 1.003 999 +AP SGR 2450380.5604 6.747 .877 999 +AP SGR 2450381.5631 7.011 .967 999 +AP SGR 2450382.5480 7.238 1.022 999 +AP SGR 2450383.5590 7.302 1.042 999 +AP SGR 2450384.5482 6.632 .769 999 +AP SGR 2450386.5668 6.996 .948 999 +AP SGR 2450388.4963 7.334 1.046 999 +AP SGR 2450389.4965 6.747 .796 999 +AP SGR 2450390.4986 6.709 .829 999 +AP SGR 2450391.4977 6.975 .956 999 +AP SGR 2450392.4979 7.162 1.033 999 +AP SGR 2450393.4997 7.339 1.044 999 +AP SGR 2450306.1809 7.067 .903 .492 971 +AP SGR 2450307.1926 7.330 .981 .535 971 +AP SGR 2450310.2362 6.947 .824 .488 971 +AP SGR 2450311.1655 7.070 .942 .536 971 +AP SGR 2450312.1603 7.226 .991 .560 971 +AP SGR 2450313.1662 7.164 .859 .518 971 +AP SGR 2450314.1520 6.552 .650 .402 971 +AP SGR 2450315.1501 6.871 .767 .517 971 +AP SGR 2450316.1477 7.072 .875 .543 971 +AP SGR 2450317.1766 7.276 .952 .477 971 +AP SGR 2450318.1479 7.202 .923 .520 971 +AP SGR 2450319.1456 6.586 .614 .395 971 +AP SGR 2450320.1476 6.809 .794 .470 971 +AP SGR 2450321.1424 7.083 .879 .528 971 +AP SGR 2450322.1371 7.265 .953 .529 971 +AP SGR 2450323.1372 7.265 .835 .527 971 +AP SGR 2450324.1413 6.534 .577 .374 971 +AP SGR 2450325.1338 6.823 .780 .477 971 +AP SGR 2450326.1294 7.044 .907 .524 971 +AP SGR 2450330.1398 .856 .481 971 +AP SGR 2450332.1395 7.219 .953 .447 971 +AP SGR 2450333.1438 7.253 .952 .389 971 +AP SGR 2450334.1531 6.530 .677 .451 971 +AP SGR 2450336.1450 7.063 .949 .575 971 +AP SGR 2450337.1421 7.265 .897 .590 971 +AP SGR 2450570.6408 7.335 1.032 972 +AP SGR 2450572.6418 6.741 .850 972 +AP SGR 2450573.5211 6.933 .943 972 +AP SGR 2450573.6183 6.969 .964 972 +AP SGR 2450573.6553 6.978 .973 972 +AP SGR 2450574.5488 7.158 1.025 972 +AP SGR 2450575.5072 7.321 1.048 972 +AP SGR 2450575.6003 7.344 1.044 972 +AP SGR 2450575.6503 7.319 1.034 972 +AP SGR 2450576.5531 6.816 .843 972 +AP SGR 2450576.6360 6.725 .803 972 +AP SGR 2450576.6662 6.699 .797 972 +AP SGR 2450577.5614 6.689 .821 972 +AP SGR 2450577.6183 6.703 .832 972 +AP SGR 2450577.6627 6.716 .840 972 +AP SGR 2450578.5388 6.937 .956 972 +AP SGR 2450578.6056 6.946 .960 972 +AP SGR 2450578.6609 6.962 .966 972 +AP SGR 2450580.4836 7.313 1.052 972 +AP SGR 2450580.5968 7.317 1.052 972 +AP SGR 2450582.5768 6.669 .814 972 +AP SGR 2450582.6237 6.693 .817 972 +AP SGR 2450582.6709 6.697 .821 972 +AP SGR 2450583.5692 6.950 .952 972 +AP SGR 2450584.5576 7.133 1.014 972 +AP SGR 2450584.6162 7.154 1.025 972 +AP SGR 2450584.6604 7.153 1.020 972 +AV SGR 2446994.2792 11.074 2.107 1.326 989 +AV SGR 2446996.2024 11.327 2.265 1.338 989 +AV SGR 2446997.2161 11.424 1.786 2.393 1.369 989 +AV SGR 2446998.2399 11.593 2.377 1.383 989 +AV SGR 2446999.2271 11.732 2.447 1.386 989 +AV SGR 2447000.2259 11.827 2.470 1.397 989 +AV SGR 2447001.2268 11.877 2.446 1.383 989 +AV SGR 2447002.2286 11.794 2.334 1.399 989 +AV SGR 2447003.2113 11.707 2.240 1.371 989 +AV SGR 2447004.2257 11.617 2.180 1.341 989 +AV SGR 2447401.1456 11.816 2.375 1.352 990 +AV SGR 2447402.1382 11.864 2.241 1.371 990 +AV SGR 2447403.1350 11.720 2.288 1.299 990 +AV SGR 2447404.1332 11.576 2.200 1.252 990 +AV SGR 2447408.1274 10.903 1.895 1.192 990 +AV SGR 2447409.1279 10.954 2.048 1.197 990 +AV SGR 2447410.1469 11.040 2.053 1.245 990 +AV SGR 2447411.1471 11.168 2.160 1.263 990 +AV SGR 2447412.1527 11.398 2.249 1.269 990 +AV SGR 2447413.1361 11.404 2.327 1.291 990 +AV SGR 2447414.1341 11.595 2.312 1.356 990 +AV SGR 2447415.1397 11.663 2.411 1.331 990 +AV SGR 2447416.1315 11.784 2.378 1.364 990 +AV SGR 2447417.1284 11.862 2.349 1.379 990 +AV SGR 2447418.1292 11.762 2.318 1.298 990 +AV SGR 2447419.1226 11.768 2.322 1.334 990 +AV SGR 2447420.1271 11.533 2.204 1.295 990 +AV SGR 2447421.1189 11.411 2.087 1.273 990 +AV SGR 2447422.1215 10.714 1.856 1.112 990 +AV SGR 2447423.1204 10.739 1.944 1.114 990 +AV SGR 2447424.1206 10.958 1.958 1.217 990 +AV SGR 2447425.1225 11.046 2.045 1.222 990 +AV SGR 2447427.1270 11.235 2.237 1.299 990 +AV SGR 2447428.1198 11.418 2.182 1.336 990 +AV SGR 2447735.2311 11.247 2.196 1.301 991 +AV SGR 2447736.2211 11.352 1.288 991 +AV SGR 2447737.2198 11.535 2.275 1.359 991 +AV SGR 2447738.2374 11.576 2.374 1.354 991 +AV SGR 2447739.2059 11.753 2.348 1.366 991 +AV SGR 2447740.2109 11.786 2.391 1.340 991 +AV SGR 2447741.2005 11.845 2.338 1.368 991 +AV SGR 2447742.2168 11.793 2.239 1.317 991 +AV SGR 2447743.2001 11.534 2.158 1.310 991 +AV SGR 2447744.1916 11.511 2.148 1.243 991 +AV SGR 2447745.1915 11.050 1.909 1.163 991 +AV SGR 2447746.1935 10.655 1.750 1.127 991 +AV SGR 2447747.1864 10.857 1.870 991 +AV SGR 2447748.1878 10.943 2.011 1.203 991 +AV SGR 2447749.1661 11.016 2.085 1.268 991 +AV SGR 2447750.1670 11.106 2.111 1.246 991 +AV SGR 2447751.1754 11.300 2.184 1.316 991 +AV SGR 2447752.1599 11.452 2.238 1.326 991 +AV SGR 2447753.1610 11.517 2.352 1.314 991 +AV SGR 2447754.1669 11.675 2.330 1.362 991 +AV SGR 2447755.1540 11.748 2.421 1.337 991 +AV SGR 2447756.1869 11.845 2.442 1.382 991 +AV SGR 2447757.1578 11.790 2.293 1.377 991 +AV SGR 2447758.1547 11.651 2.276 1.328 991 +AV SGR 2447759.1524 11.545 2.139 1.314 991 +AV SGR 2447760.1687 11.372 2.072 1.241 991 +AV SGR 2447761.1547 10.649 1.750 1.119 991 +AV SGR 2447762.1551 10.761 1.878 1.168 991 +AV SGR 2447763.1489 10.902 1.935 1.191 991 +AV SGR 2447764.1494 11.025 2.040 1.245 991 +AV SGR 2447766.1469 11.246 2.195 1.280 991 +AV SGR 2447767.1604 11.400 2.228 1.311 991 +AV SGR 2447768.1519 11.521 2.267 1.302 991 +AV SGR 2447769.1503 11.650 2.364 1.365 991 +AV SGR 2447770.1476 11.715 2.356 1.341 991 +AV SGR 2447771.1467 11.795 2.373 1.355 991 +AV SGR 2447772.1394 11.800 2.243 1.368 991 +AV SGR 2447773.1470 11.696 2.270 1.347 991 +AV SGR 2447774.1783 11.528 2.182 1.285 991 +AV SGR 2447775.1390 11.510 2.124 1.278 991 +AV SGR 2447776.1418 10.892 1.854 1.143 991 +AV SGR 2449934.2221 10.639 1.832 1.140 2.166 998 +AV SGR 2449935.2426 10.914 1.169 2.242 998 +AV SGR 2449936.1887 11.033 1.237 2.342 998 +AV SGR 2449938.1960 11.302 1.286 2.427 998 +AV SGR 2449939.2050 11.351 1.353 2.589 998 +AV SGR 2449942.2014 2.629 998 +AV SGR 2449943.1916 11.671 2.592 998 +AV SGR 2449944.1788 2.632 998 +AV SGR 2449945.1912 11.848 2.658 998 +AV SGR 2449946.1873 11.621 2.551 998 +AV SGR 2449947.1761 11.582 2.546 998 +AV SGR 2449949.1722 10.935 2.248 998 +AV SGR 2449950.1757 10.856 2.252 998 +AV SGR 2449952.1786 11.082 2.070 1.263 2.414 998 +AV SGR 2449953.2066 11.225 2.498 998 +AV SGR 2449954.1816 11.290 2.461 998 +AV SGR 2449955.1761 11.387 2.275 1.333 2.517 998 +AY SGR 2449934.2403 10.412 .877 1.788 998 +AY SGR 2449935.2596 .989 1.828 998 +AY SGR 2449936.2038 10.717 .984 1.881 998 +AY SGR 2449938.2396 11.069 1.046 2.008 998 +AY SGR 2449939.2253 10.546 1.539 .771 1.823 998 +AY SGR 2449943.2052 10.771 1.921 998 +AY SGR 2449944.2009 11.046 1.967 998 +AY SGR 2449945.2015 10.936 1.949 998 +AY SGR 2449946.1943 10.174 1.643 998 +AY SGR 2449947.2098 10.340 1.778 998 +AY SGR 2449948.2082 10.544 1.830 998 +AY SGR 2449949.2123 10.696 1.933 998 +AY SGR 2449950.2067 10.900 1.969 998 +AY SGR 2449952.2009 10.644 1.812 998 +AY SGR 2449953.2253 10.248 1.688 998 +AY SGR 2449954.2112 10.423 1.767 998 +AY SGR 2449955.2108 10.620 1.889 998 +BB SGR 2446994.3299 7.009 .741 1.110 .602 989 +BB SGR 2446995.2806 7.194 .890 1.178 .629 989 +BB SGR 2446996.2402 7.197 .769 1.138 .616 989 +BB SGR 2446997.2629 6.802 .673 .941 .538 989 +BB SGR 2446998.2683 6.673 .586 .860 .511 989 +BB SGR 2446999.2534 6.848 .699 .977 .557 989 +BB SGR 2447000.2565 6.918 .685 1.051 .587 989 +BB SGR 2447001.2645 7.111 .789 1.125 .617 989 +BB SGR 2447002.2529 7.235 .893 1.157 .646 989 +BB SGR 2447003.2412 7.139 1.053 .602 989 +BB SGR 2447401.1970 7.188 .780 1.129 .608 990 +BB SGR 2447402.1881 6.814 .537 .903 .513 990 +BB SGR 2447403.1813 6.647 .490 .856 .460 990 +BB SGR 2447404.1648 6.840 .967 .535 990 +BB SGR 2447408.1604 7.077 .653 1.037 .587 990 +BB SGR 2447409.1742 6.689 .474 .893 .497 990 +BB SGR 2447410.1287 6.655 .574 .941 .502 990 +BB SGR 2447411.1237 6.860 .661 1.022 .563 990 +BB SGR 2447412.1259 7.000 .789 1.091 .589 990 +BB SGR 2447413.1162 7.106 .864 1.169 .573 990 +BB SGR 2447414.1201 7.279 .859 1.159 .625 990 +BB SGR 2447415.1206 6.905 .584 1.011 .532 990 +BB SGR 2447416.1156 6.643 .501 .886 .486 990 +BB SGR 2447417.1143 6.805 .571 1.016 .539 990 +BB SGR 2447418.1160 6.863 .710 1.042 .533 990 +BB SGR 2447419.1107 7.124 .812 1.168 .587 990 +BB SGR 2447420.1110 7.219 .852 1.232 .636 990 +BB SGR 2447421.1087 7.227 .794 1.139 .624 990 +BB SGR 2447422.1059 6.815 .560 .899 .516 990 +BB SGR 2447423.1074 6.659 .515 .894 .494 990 +BB SGR 2447424.1045 6.860 .615 1.024 .529 990 +BB SGR 2447425.1078 6.937 .708 1.114 .576 990 +BB SGR 2447427.1087 7.213 .872 1.193 .625 990 +BB SGR 2447428.1032 7.072 .646 1.048 .601 990 +BB SGR 2447429.1117 6.639 .487 .891 .483 990 +BB SGR 2447430.1011 6.721 .589 .931 .510 990 +BB SGR 2447431.1010 6.880 .658 1.034 .579 990 +BB SGR 2447432.0987 7.009 .727 1.127 .571 990 +BB SGR 2447433.0961 7.125 .849 1.126 .590 990 +BB SGR 2447434.0990 7.259 .806 1.182 .612 990 +BB SGR 2448504.1335 6.763 .512 .905 .514 993 +BB SGR 2448505.1319 6.715 .527 .862 .544 993 +BB SGR 2448507.1292 7.001 1.032 .612 993 +BB SGR 2448508.1263 7.140 .830 1.147 .623 993 +BB SGR 2448509.1266 7.263 .873 1.140 .639 993 +BB SGR 2448510.1273 6.994 .594 1.022 .555 993 +BB SGR 2448511.1252 6.674 .484 .864 .504 993 +BB SGR 2448512.1257 6.787 .596 .938 .539 993 +BB SGR 2448513.1239 6.909 .674 1.012 .589 993 +BB SGR 2448514.1238 7.079 .769 1.093 .619 993 +BB SGR 2448515.1179 7.127 .880 1.158 .606 993 +BB SGR 2448516.1206 7.188 .815 1.109 .629 993 +BB SGR 2448517.1199 6.868 .554 .923 .553 993 +BB SGR 2448518.1260 6.721 .509 .858 .535 993 +BB SGR 2448519.1379 6.822 .606 1.006 .548 993 +BB SGR 2448520.1141 6.926 .697 1.051 .598 993 +BB SGR 2448521.1220 7.063 .832 1.141 .609 993 +BB SGR 2448522.1193 7.211 .911 1.153 .623 993 +BB SGR 2448523.1108 7.125 .706 1.095 .573 993 +BB SGR 2448854.1965 7.249 .886 1.168 .667 994 +BB SGR 2448858.1690 6.858 .677 1.040 .565 994 +BB SGR 2448860.1602 7.153 .816 1.218 .606 994 +BB SGR 2448870.1435 6.679 .510 .906 .491 994 +BB SGR 2448872.1907 6.980 .701 1.130 .579 994 +BB SGR 2448874.2104 7.259 .798 1.188 .662 994 +BB SGR 2448876.1349 6.695 .457 .830 .505 994 +BB SGR 2448877.1298 6.844 .547 .977 .563 994 +BB SGR 2448880.1276 7.181 .847 1.187 .618 994 +BB SGR 2448881.1170 7.222 .821 1.158 .622 994 +BB SGR 2448882.1195 6.925 .544 .948 .561 994 +BB SGR 2448883.1184 6.660 .490 .865 .511 994 +BB SGR 2448884.1153 6.880 .602 1.025 .558 994 +BB SGR 2448885.1126 6.913 .667 1.053 .582 994 +BB SGR 2448886.1142 7.100 .781 1.117 .595 994 +BB SGR 2448888.1116 7.139 .663 1.101 .598 994 +BB SGR 2448889.1126 6.743 .495 .898 .488 994 +BB SGR 2448890.1081 6.758 .545 .891 .551 994 +BB SGR 2448891.1058 6.896 .644 1.009 .581 994 +BB SGR 2448892.1046 6.991 .731 1.066 .601 994 +BB SGR 2448893.1048 7.137 .816 1.149 .609 994 +BB SGR 2448894.1037 7.262 .830 1.161 .629 994 +BB SGR 2449520.9064 6.766 .932 996 +BB SGR 2449521.8090 6.840 1.048 996 +BB SGR 2449521.8527 6.842 1.066 996 +BB SGR 2449521.9094 6.876 1.046 996 +BB SGR 2449522.6961 6.975 1.079 996 +BB SGR 2449522.7205 7.030 1.102 996 +BB SGR 2449522.7381 6.942 1.097 996 +BB SGR 2449522.7807 6.926 1.080 996 +BB SGR 2449522.8638 6.968 1.119 996 +BB SGR 2449522.8914 6.992 1.119 996 +BB SGR 2449522.9158 7.003 1.145 996 +BB SGR 2449522.9236 6.992 1.132 996 +BB SGR 2449526.5619 6.633 .505 .875 .518 .484 996 +BB SGR 2449526.6079 6.656 .556 .874 .542 .485 996 +BB SGR 2449526.6526 6.620 .875 .523 .482 996 +BB SGR 2449526.7074 6.656 .473 .847 .542 .494 996 +BB SGR 2449528.7136 6.878 .666 1.049 .597 .539 996 +BB SGR 2449528.7309 6.889 .706 1.046 .601 .552 996 +BB SGR 2449528.7795 6.899 1.065 .615 .571 996 +BB SGR 2449528.8124 6.950 1.052 .620 .549 996 +BB SGR 2449528.8432 6.903 1.066 .615 .551 996 +BB SGR 2449528.8758 6.915 1.059 .605 .561 996 +BB SGR 2449529.6890 7.054 1.123 .635 .577 996 +BB SGR 2449529.7723 7.046 1.116 .636 .578 996 +BB SGR 2449529.8108 7.047 1.118 .634 .576 996 +BB SGR 2449529.8410 7.074 1.133 .642 .587 996 +BB SGR 2449529.8560 7.081 1.130 .634 .584 996 +BB SGR 2449529.8668 7.073 1.138 .641 .568 996 +BB SGR 2449530.8221 7.278 .813 1.134 .653 996 +BB SGR 2449534.8695 6.885 1.038 .608 .563 996 +BB SGR 2449535.7294 6.970 .728 1.037 996 +BB SGR 2449536.6349 7.123 .802 1.094 996 +BB SGR 2449536.6734 7.124 .811 1.102 996 +BB SGR 2449536.7378 7.141 .810 1.103 996 +BB SGR 2449536.7835 .760 1.161 996 +BB SGR 2449543.8366 7.206 1.186 .641 .588 996 +BB SGR 2449543.8373 .866 996 +BB SGR 2449545.6824 6.967 .591 .998 .582 .547 996 +BB SGR 2449545.7526 6.916 .581 .975 .589 .537 996 +BB SGR 2449545.7868 6.903 .540 .987 .580 .536 996 +BB SGR 2449558.7482 6.975 .674 .991 .550 .551 996 +BB SGR 2449559.7142 6.679 .596 .810 .499 .514 996 +BB SGR 2449559.7748 6.649 .584 .816 .490 .504 996 +BB SGR 2449561.6852 6.886 .730 1.002 .573 .546 996 +BB SGR 2449561.8413 6.889 .763 1.010 .565 .523 996 +BB SGR 2449563.6478 7.159 .866 1.131 .617 .591 996 +BB SGR 2449564.6013 7.258 .853 1.124 .633 .580 996 +BB SGR 2449564.8442 7.224 1.086 996 +BB SGR 2449617.1061 7.319 .994 1.137 .646 995 +BB SGR 2449620.1247 6.744 .641 .850 .540 995 +BB SGR 2449623.0981 7.169 .919 1.123 .615 995 +BB SGR 2449624.1016 7.316 1.011 1.144 .650 995 +BB SGR 2449625.1012 7.060 .646 .976 .594 995 +BB SGR 2449631.0984 7.295 .860 1.122 .624 995 +BB SGR 2449632.1110 6.923 .642 .890 .569 995 +BB SGR 2449633.1032 6.703 .548 .874 .490 995 +BB SGR 2449634.1064 .793 .925 .560 995 +BB SGR 2449805.9087 6.714 .860 .508 .518 997 +BB SGR 2449808.9152 7.146 1.112 .612 .592 997 +BB SGR 2449809.9004 7.257 1.163 .613 .637 997 +BB SGR 2449810.8961 7.033 1.002 .567 997 +BB SGR 2449811.8652 6.674 .842 .485 .492 997 +BB SGR 2449813.8752 6.891 1.003 .564 .546 997 +BB SGR 2449813.9118 6.896 .991 .572 .553 997 +BB SGR 2449814.8543 7.018 1.081 .594 .556 997 +BB SGR 2449817.8762 6.925 .935 .553 .523 997 +BB SGR 2449818.8721 6.688 .841 .492 .499 997 +BB SGR 2449818.9069 6.640 .835 .497 .470 997 +BB SGR 2449821.8647 7.069 1.099 .602 .589 997 +BB SGR 2449822.8533 7.230 1.159 .629 .588 997 +BB SGR 2449823.8403 7.191 1.086 .611 .573 997 +BB SGR 2449825.8543 6.709 .859 .509 .506 997 +BB SGR 2449934.1933 7.037 1.142 .601 1.175 998 +BB SGR 2449935.2298 7.253 .640 1.176 998 +BB SGR 2449936.1743 7.339 1.164 .650 1.208 998 +BB SGR 2449937.2295 7.030 .969 .571 998 +BB SGR 2449938.1891 6.760 .859 .515 .976 998 +BB SGR 2449939.1860 6.862 .976 .557 1.095 998 +BB SGR 2449942.1758 7.318 1.176 .650 1.251 998 +BB SGR 2449943.1716 7.173 1.094 .626 1.185 998 +BB SGR 2449944.1925 6.918 .993 998 +BB SGR 2449945.2061 6.773 1.027 998 +BB SGR 2449946.2338 6.942 1.015 .563 1.091 998 +BB SGR 2449947.1578 7.023 1.052 .592 1.158 998 +BB SGR 2449948.1524 7.190 1.113 .625 1.225 998 +BB SGR 2449949.1587 7.323 1.150 .652 1.241 998 +BB SGR 2449950.1568 7.171 1.061 .600 1.136 998 +BB SGR 2449952.1539 6.845 .906 .552 1.062 998 +BB SGR 2449953.1771 7.003 1.031 .585 1.103 998 +BB SGR 2449954.1647 7.068 1.099 .602 1.143 998 +BB SGR 2449955.1572 7.229 1.145 .633 1.201 998 +BB SGR 2449956.1340 7.392 1.119 .648 1.243 998 +BB SGR 2449957.1439 7.016 .972 .564 1.076 998 +BB SGR 2449958.1438 6.751 .817 .521 .993 998 +BB SGR 2449958.1483 6.743 .826 .519 1.008 998 +BB SGR 2449959.1391 6.901 .947 .564 1.089 998 +BB SGR 2449962.1515 7.256 1.186 .622 1.188 998 +BB SGR 2450305.1755 6.879 .984 .587 971 +BB SGR 2450306.1669 7.004 1.034 .581 971 +BB SGR 2450307.1856 7.223 1.145 .595 971 +BB SGR 2450310.2285 6.721 .853 .521 971 +BB SGR 2450311.1593 6.789 .987 .559 971 +BB SGR 2450312.1537 6.878 1.034 .595 971 +BB SGR 2450313.1597 7.079 1.076 .633 971 +BB SGR 2450314.1445 7.207 1.138 .638 971 +BB SGR 2450315.1459 7.117 .995 .631 971 +BB SGR 2450316.1445 6.705 .838 .528 971 +BB SGR 2450317.1734 6.720 .879 .445 971 +BB SGR 2450318.1438 6.844 1.005 .571 971 +BB SGR 2450319.1416 7.006 .993 .613 971 +BB SGR 2450320.1428 7.094 1.093 .622 971 +BB SGR 2450321.1378 7.261 1.075 .635 971 +BB SGR 2450322.1325 6.964 .938 .551 971 +BB SGR 2450323.1326 6.649 .757 .505 971 +BB SGR 2450324.1380 6.770 .869 .541 971 +BB SGR 2450325.1289 6.932 .985 .583 971 +BB SGR 2450326.1253 7.036 1.062 .600 971 +BB SGR 2450570.6355 6.893 1.104 972 +BB SGR 2450573.5173 7.221 1.177 972 +BB SGR 2450573.6147 7.218 1.197 972 +BB SGR 2450573.6511 7.209 1.202 972 +BB SGR 2450574.5455 6.899 1.066 972 +BB SGR 2450574.6043 6.889 1.065 972 +BB SGR 2450575.5035 6.653 .981 972 +BB SGR 2450575.5970 6.686 .997 972 +BB SGR 2450575.6472 6.683 1.002 972 +BB SGR 2450576.5499 6.822 1.087 972 +BB SGR 2450576.6328 6.828 1.093 972 +BB SGR 2450576.6631 6.832 1.091 972 +BB SGR 2450577.5582 6.921 1.133 972 +BB SGR 2450577.6154 6.924 1.133 972 +BB SGR 2450577.6578 6.931 1.141 972 +BB SGR 2450578.5357 7.065 1.184 972 +BB SGR 2450578.6028 7.066 1.188 972 +BB SGR 2450578.6580 7.071 1.187 972 +BB SGR 2450580.4800 7.168 1.174 972 +BB SGR 2450580.5937 7.118 1.154 972 +BB SGR 2450582.5737 6.710 1.019 972 +BB SGR 2450582.6209 6.735 1.024 972 +BB SGR 2450582.6677 6.729 1.024 972 +BB SGR 2450583.5659 6.884 1.103 972 +BB SGR 2450584.5548 6.974 1.150 972 +BB SGR 2450584.6126 6.983 1.156 972 +BB SGR 2450584.6577 6.989 1.154 972 +V350 SGR 2449620.1511 7.128 .745 .445 995 +V350 SGR 2449621.1245 7.351 .648 .883 .516 995 +V350 SGR 2449622.1201 7.612 .819 1.012 .559 995 +V350 SGR 2449623.1076 7.747 .803 1.072 .596 995 +V350 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7.241 .982 972 +V350 SGR 2450584.6591 7.241 .971 972 +V773 SGR 2449520.7921 12.272 2.241 996 +V773 SGR 2449521.7723 12.478 2.343 996 +V773 SGR 2449522.7048 12.723 2.354 996 +V773 SGR 2449528.7733 12.707 2.406 1.496 1.323 996 +V773 SGR 2449529.7823 12.775 2.452 1.480 1.348 996 +V773 SGR 2449534.8482 12.851 2.333 1.481 1.320 996 +V773 SGR 2449543.7199 12.342 2.191 1.372 1.278 996 +V773 SGR 2449545.6882 12.643 2.495 1.461 1.357 996 +V773 SGR 2449561.7264 12.411 2.275 1.481 1.347 996 +V773 SGR 2449564.6098 12.669 2.249 1.514 1.354 996 +V773 SGR 2446262.2408 12.429 2.247 1.463 987 +V773 SGR 2446263.2700 12.547 2.356 1.496 987 +V773 SGR 2446266.2531 12.113 2.133 1.399 987 +V773 SGR 2446267.2380 12.257 2.189 1.475 987 +V773 SGR 2446268.2506 12.409 2.338 1.503 987 +V773 SGR 2446269.2315 12.634 2.346 1.537 987 +V773 SGR 2446270.2447 12.665 2.424 1.512 987 +V773 SGR 2446271.2373 12.293 2.161 1.400 987 +V773 SGR 2446272.2058 12.039 2.123 1.373 987 +V773 SGR 2446283.1714 12.015 2.136 1.323 987 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2450573.5859 12.375 2.842 972 +V773 SGR 2450573.6652 12.390 2.850 972 +V773 SGR 2450574.5243 12.533 2.883 972 +V773 SGR 2450574.5835 12.546 2.882 972 +V773 SGR 2450575.4839 12.716 2.919 972 +V773 SGR 2450575.5701 12.708 2.904 972 +V773 SGR 2450575.6343 12.718 2.907 972 +V773 SGR 2450576.4737 12.496 2.817 972 +V773 SGR 2450576.5306 12.437 2.800 972 +V773 SGR 2450576.6117 12.389 2.782 972 +V773 SGR 2450576.6502 12.349 2.768 972 +V773 SGR 2450577.5407 11.993 2.653 972 +V773 SGR 2450577.5990 12.002 2.651 972 +V773 SGR 2450577.6401 12.021 2.669 972 +V773 SGR 2450578.5187 12.208 2.778 972 +V773 SGR 2450578.5851 12.201 2.779 972 +V773 SGR 2450578.6372 12.208 2.779 972 +V773 SGR 2450579.5403 12.420 2.863 972 +V773 SGR 2450579.5663 12.417 2.851 972 +V773 SGR 2450580.4630 12.581 2.899 972 +V773 SGR 2450580.5748 12.597 2.905 972 +V773 SGR 2450580.6247 12.606 2.899 972 +V773 SGR 2450582.5561 12.246 2.716 972 +V773 SGR 2450582.6035 12.225 2.700 972 +V773 SGR 2450582.6517 12.160 2.673 972 +V773 SGR 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2446302.4850 12.612 1.531 .935 987 +AV TAU 2446303.4942 12.388 1.358 .877 987 +AV TAU 2446304.4729 12.102 1.353 .826 987 +AV TAU 2447082.4809 12.241 .847 989 +AV TAU 2447083.4423 12.589 1.449 .870 989 +AV TAU 2447084.3881 12.563 1.394 .882 989 +AV TAU 2447085.4314 12.029 1.266 .806 989 +AV TAU 2447088.4227 11.901 1.190 .763 989 +AV TAU 2447408.4906 12.490 1.536 990 +AV TAU 2447409.4927 12.701 1.520 .918 990 +AV TAU 2447410.4879 11.911 1.187 .767 990 +AV TAU 2447411.4961 12.347 1.448 .886 990 +AV TAU 2447413.4904 12.566 1.376 .894 990 +AV TAU 2447414.4949 12.090 1.309 990 +AV TAU 2447415.4962 12.476 1.439 990 +AV TAU 2447416.4951 12.649 1.476 .922 990 +AV TAU 2447417.4903 11.880 1.204 .726 990 +AV TAU 2447418.4879 12.246 1.366 .871 990 +AV TAU 2447419.4935 12.480 1.510 .892 990 +AV TAU 2447420.4929 12.653 1.523 .894 990 +AV TAU 2447421.4923 11.965 1.221 .777 990 +AV TAU 2447422.4926 12.382 1.404 .889 990 +AV TAU 2447424.4883 12.247 1.300 .825 990 +AV TAU 2447425.4874 12.158 1.331 .826 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2448520.4056 11.845 1.219 .744 993 +AV TAU 2448521.4187 12.273 1.392 .869 993 +AV TAU 2448522.4080 12.558 1.454 .919 993 +AV TAU 2448523.3914 12.643 1.480 .898 993 +EF TAU 2446294.4767 13.044 .910 .608 987 +EF TAU 2446295.4595 13.296 1.000 .695 987 +EF TAU 2446297.4749 12.911 .852 .584 987 +EF TAU 2446298.4793 13.171 1.002 .625 987 +EF TAU 2446299.4500 13.389 .974 .639 987 +EF TAU 2446300.4536 12.724 .763 .527 987 +EF TAU 2446301.4770 13.078 .985 .599 987 +EF TAU 2446302.4789 13.265 1.007 .649 987 +EF TAU 2446303.4892 13.290 .990 .621 987 +EF TAU 2446304.4696 12.922 .891 .581 987 +EF TAU 2447083.4341 12.795 .776 .560 989 +EF TAU 2447084.3800 13.107 .918 .600 989 +EF TAU 2447085.4240 13.293 .981 .660 989 +EF TAU 2447087.4226 12.950 .865 .610 989 +EF TAU 2447088.4158 13.207 1.005 .618 989 +EF TAU 2447407.4998 12.698 .809 .497 990 +EF TAU 2447408.4861 13.096 .963 990 +EF TAU 2447409.4858 13.278 1.027 .610 990 +EF TAU 2447410.4807 13.156 .900 .587 990 +EF TAU 2447411.4901 13.010 .867 .616 990 +EF TAU 2447413.4841 13.439 .980 .673 990 +EF TAU 2447414.4915 12.791 .799 990 +EF TAU 2447415.4932 13.161 .962 990 +EF TAU 2447416.4874 13.328 .977 .665 990 +EF TAU 2447417.4836 12.970 .823 .560 990 +EF TAU 2447418.4830 12.985 .924 .606 990 +EF TAU 2447419.4863 13.219 .983 .652 990 +EF TAU 2447420.4852 13.423 .955 .669 990 +EF TAU 2447421.4872 12.791 .880 .533 990 +EF TAU 2447422.4877 13.148 .886 .630 990 +EF TAU 2447424.4817 12.770 .763 .545 990 +EF TAU 2447425.4803 12.969 .941 .683 990 +EF TAU 2447427.4841 13.301 1.036 .617 990 +EF TAU 2447428.4745 12.823 .937 .588 990 +EF TAU 2447430.4866 13.258 .896 .633 990 +EF TAU 2447431.4890 12.754 .773 .425 990 +EF TAU 2447432.4849 13.031 .900 .576 990 +EF TAU 2447760.4830 13.180 .918 991 +EF TAU 2447768.4878 13.303 1.029 991 +EF TAU 2447770.4584 13.058 .953 .631 991 +EF TAU 2447771.4633 13.270 1.001 .675 991 +EF TAU 2447772.4695 13.270 .902 .635 991 +EF TAU 2447773.4644 12.918 .896 .576 991 +EF TAU 2447774.4809 13.218 1.004 991 +EF TAU 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.855 998 +EU TAU 2450008.5082 8.243 .739 .435 .856 998 +EU TAU 2450009.4646 8.004 .718 998 +EU TAU 2450010.4548 8.233 .861 998 +EU TAU 2450011.4250 8.041 .758 998 +EU TAU 2450017.4312 8.277 .811 998 +EU TAU 2450018.4448 8.052 .759 998 +EU TAU 2450019.4091 8.277 .834 998 +EU TAU 2450020.3689 8.032 .738 998 +EU TAU 2450380.7186 8.234 .879 999 +EU TAU 2450381.7190 7.919 .727 999 +EU TAU 2450382.7242 8.211 .865 999 +EU TAU 2450383.7199 7.964 .740 999 +EU TAU 2450384.7171 8.168 .855 999 +EU TAU 2450386.7101 8.103 .846 999 +EU TAU 2450387.7119 8.028 .772 999 +EU TAU 2450387.8781 7.941 .746 999 +EU TAU 2450388.7365 8.121 .835 999 +EU TAU 2450388.8785 8.167 .848 999 +EU TAU 2450389.7023 8.087 .808 999 +EU TAU 2450390.7024 8.078 .811 999 +EU TAU 2450390.8728 8.141 .841 999 +EU TAU 2450391.6973 8.176 .846 999 +EU TAU 2450391.8677 8.066 .795 999 +EU TAU 2450392.7036 8.006 .790 999 +EU TAU 2450392.8710 8.091 .818 999 +EU TAU 2450393.6982 8.208 .856 999 +EU TAU 2450393.8762 8.128 .807 999 +EU TAU 2450394.6959 7.973 .766 999 +EU TAU 2450394.8637 8.056 .812 999 +EU TAU 2450307.4703 8.157 .780 .414 971 +EU TAU 2450315.4631 8.211 .799 .450 971 +EU TAU 2450316.4511 7.906 .714 .375 971 +EU TAU 2450317.4752 8.195 .762 .464 971 +EU TAU 2450318.4644 7.991 .668 .431 971 +EU TAU 2450319.4902 8.178 .754 .464 971 +EU TAU 2450320.4868 8.029 .674 .452 971 +EU TAU 2450322.4396 8.096 .723 .463 971 +EU TAU 2450326.4759 8.220 .735 .474 971 +EU TAU 2450335.4787 7.935 .648 .469 971 +EU TAU 2450337.4830 7.956 .677 .444 971 +EU TAU 2450338.4838 8.192 .767 .451 971 +EU TAU 2450341.4687 8.035 .703 .416 971 +EU TAU 2450344.4851 8.076 .751 .527 971 +R TRA 2450351.4896 6.601 .765 999 +R TRA 2450352.5547 6.577 .788 999 +R TRA 2450353.4776 6.812 .899 999 +R TRA 2450354.5579 6.832 .859 999 +R TRA 2450355.4774 6.383 .712 999 +R TRA 2450357.4770 6.897 .900 999 +R TRA 2450357.5489 6.899 .902 999 +R TRA 2450358.4768 6.402 .690 999 +R TRA 2450358.5577 6.371 .679 999 +R TRA 2450359.4784 6.589 .811 999 +R TRA 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2450574.5035 6.142 .719 972 +S TRA 2450575.3710 6.308 .816 972 +S TRA 2450575.4685 6.323 .807 972 +S TRA 2450575.5551 6.340 .814 972 +S TRA 2450575.6198 6.366 .807 972 +S TRA 2450576.4615 6.458 .876 972 +S TRA 2450576.5187 6.471 .875 972 +S TRA 2450576.5817 6.487 .871 972 +S TRA 2450577.5294 6.669 .925 972 +S TRA 2450577.5884 6.675 .930 972 +S TRA 2450577.6310 6.675 .927 972 +S TRA 2450578.4055 6.766 .927 972 +S TRA 2450578.4789 6.758 .930 972 +S TRA 2450578.5716 6.746 .924 972 +S TRA 2450578.6263 6.733 .915 972 +S TRA 2450579.5307 6.281 .739 972 +S TRA 2450580.4518 6.053 .669 972 +S TRA 2450580.5653 6.075 .687 972 +S TRA 2450580.6144 6.085 .688 972 +S TRA 2450582.4666 6.386 .839 972 +S TRA 2450582.5897 6.408 .848 972 +S TRA 2450582.6364 6.409 .841 972 +S TRA 2450583.4728 6.615 .922 972 +S TRA 2450583.5903 6.622 .912 972 +S TRA 2450584.4301 6.748 .932 972 +S TRA 2450584.5816 6.759 .933 972 +S TRA 2450584.6294 6.759 .934 972 +U TRA 2449520.7522 8.123 .686 996 +U TRA 2449522.6444 8.137 .711 996 +U TRA 2449526.6945 7.692 .352 .499 .296 .244 996 +U TRA 2449528.7020 7.385 .387 .250 .262 996 +U TRA 2449529.6724 7.936 .695 .397 .373 996 +U TRA 2449530.7713 8.167 .700 .395 .349 996 +U TRA 2449532.6469 8.051 .665 .379 .367 996 +U TRA 2449534.7088 7.917 .441 .617 .368 .348 996 +U TRA 2449535.7082 8.276 .418 .775 .362 .362 996 +U TRA 2449536.7138 7.716 .421 .531 .384 .339 996 +U TRA 2449543.6514 8.159 .438 .677 .375 .350 996 +U TRA 2449545.6176 8.145 .473 .698 .378 .373 996 +U TRA 2449558.6355 8.293 .403 .720 .396 .402 996 +U TRA 2449560.5392 7.897 .424 .703 .377 .378 996 +U TRA 2449561.5258 8.242 .415 .744 .430 .402 996 +U TRA 2449561.7035 8.071 .346 .657 .378 .368 996 +U TRA 2449563.4617 8.067 .401 .692 .386 .381 996 +U TRA 2449802.9078 8.131 .699 .404 .385 997 +U TRA 2449803.9049 7.679 .476 .321 .304 997 +U TRA 2449804.8777 8.078 .696 .398 .391 997 +U TRA 2449805.8469 8.201 .679 .401 .390 997 +U TRA 2449807.8777 8.233 .758 .420 .414 997 +U TRA 2449808.8584 7.791 .541 .325 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2450577.3648 8.250 1.552 972 +RY VEL 2450578.2665 8.293 1.581 972 +RY VEL 2450578.3068 8.261 1.576 972 +RY VEL 2450580.2798 8.385 1.630 972 +RY VEL 2450580.3639 8.385 1.632 972 +RY VEL 2450582.3029 8.476 1.669 972 +RY VEL 2450582.3742 8.476 1.669 972 +RY VEL 2450583.2903 8.548 1.685 972 +RY VEL 2450583.3302 8.544 1.683 972 +RY VEL 2450584.2828 8.597 1.697 972 +RY VEL 2450584.3536 8.596 1.696 972 +RZ VEL 2449802.7421 6.901 1.185 .631 .569 997 +RZ VEL 2449803.7203 6.997 1.211 .676 .613 997 +RZ VEL 2449804.7297 7.056 1.310 .682 .611 997 +RZ VEL 2449805.7081 7.148 1.341 .705 .635 997 +RZ VEL 2449806.7262 7.257 1.405 .726 .646 997 +RZ VEL 2449807.7141 7.360 1.451 .740 .663 997 +RZ VEL 2449808.7152 7.464 1.471 .765 .668 997 +RZ VEL 2449809.6569 7.542 1.505 .755 .694 997 +RZ VEL 2449810.6873 7.613 1.506 .772 .674 997 +RZ VEL 2449811.6661 7.635 1.471 .765 .676 997 +RZ VEL 2449812.6807 7.601 1.422 .744 .670 997 +RZ VEL 2449813.6901 7.590 1.348 .740 .654 997 +RZ VEL 2449814.6141 7.611 1.365 .728 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2450379.8784 7.404 1.443 999 +RZ VEL 2450380.7880 7.517 1.437 999 +RZ VEL 2450380.8273 7.522 1.455 999 +RZ VEL 2450381.7952 7.597 1.467 999 +RZ VEL 2450381.8727 7.605 1.467 999 +RZ VEL 2450383.7409 7.611 1.414 999 +RZ VEL 2450383.8293 7.583 1.415 999 +RZ VEL 2450383.8775 7.595 1.418 999 +RZ VEL 2450384.7793 7.569 1.396 999 +RZ VEL 2450384.8743 7.564 1.383 999 +RZ VEL 2450386.7967 7.594 1.353 999 +RZ VEL 2450386.8743 7.558 1.352 999 +RZ VEL 2450387.8632 7.190 1.201 999 +RZ VEL 2450388.8134 6.425 .904 999 +RZ VEL 2450388.8608 6.418 .903 999 +RZ VEL 2450389.7245 6.451 .931 999 +RZ VEL 2450390.7172 6.561 1.008 999 +RZ VEL 2450390.8575 6.563 1.007 999 +RZ VEL 2450391.7487 6.666 1.082 999 +RZ VEL 2450391.8513 6.662 1.081 999 +RZ VEL 2450392.7186 6.769 1.139 999 +RZ VEL 2450392.8556 6.757 1.142 999 +RZ VEL 2450393.7369 6.834 1.201 999 +RZ VEL 2450393.8585 6.847 1.204 999 +RZ VEL 2450394.7426 6.910 1.238 999 +RZ VEL 2450394.8490 6.936 1.259 999 +RZ VEL 2450543.2409 7.475 1.438 972 +RZ VEL 2450568.2194 7.576 1.389 972 +RZ VEL 2450570.2199 7.556 1.344 972 +RZ VEL 2450571.2099 7.366 1.266 972 +RZ VEL 2450572.2066 6.524 .950 972 +RZ VEL 2450573.2168 6.467 .950 972 +RZ VEL 2450573.2176 6.459 .942 972 +RZ VEL 2450575.2039 1.073 972 +RZ VEL 2450576.2015 6.743 1.139 972 +RZ VEL 2450576.2424 6.741 1.137 972 +RZ VEL 2450578.1980 6.886 1.253 972 +RZ VEL 2450580.1983 7.100 1.329 972 +RZ VEL 2450581.2140 7.201 1.363 972 +RZ VEL 2450582.1964 7.295 1.387 972 +RZ VEL 2450582.2847 7.293 1.400 972 +RZ VEL 2450583.2214 7.383 1.418 972 +RZ VEL 2450583.2553 7.387 1.424 972 +RZ VEL 2450584.1966 7.481 1.433 972 +RZ VEL 2450584.2569 7.479 1.432 972 +RZ VEL 2450584.2709 7.483 1.435 972 +ST VEL 2450351.8625 9.357 1.249 999 +ST VEL 2450352.8485 9.507 1.359 999 +ST VEL 2450354.8414 9.848 1.490 999 +ST VEL 2450355.8427 10.022 1.534 999 +ST VEL 2450357.8379 9.343 1.259 999 +ST VEL 2450358.8343 9.521 1.367 999 +ST VEL 2450359.8388 9.714 1.451 999 +ST VEL 2450361.8370 10.051 1.527 999 +ST VEL 2450362.8344 9.744 1.397 999 +ST VEL 2450363.8118 9.378 1.269 999 +ST VEL 2450379.7653 10.048 1.529 999 +ST VEL 2450380.7565 9.567 1.322 999 +ST VEL 2450381.7557 9.425 1.311 999 +ST VEL 2450383.7769 9.753 1.475 999 +ST VEL 2450384.7570 9.952 1.533 999 +ST VEL 2450386.7525 9.473 1.282 999 +ST VEL 2450387.7597 9.463 1.335 999 +ST VEL 2450388.7391 9.649 1.430 999 +ST VEL 2450389.7348 9.785 1.478 999 +ST VEL 2450390.7253 9.965 1.520 999 +ST VEL 2450391.7063 10.006 1.500 999 +ST VEL 2450392.7247 9.425 1.276 999 +ST VEL 2450393.7266 9.470 1.341 999 +ST VEL 2450394.7230 9.665 1.433 999 +ST VEL 2450542.4259 9.856 .771 972 +ST VEL 2450568.2954 9.518 .657 972 +ST VEL 2450570.2707 9.650 .721 972 +ST VEL 2450571.2424 9.784 .756 972 +ST VEL 2450572.2344 9.965 .785 972 +ST VEL 2450573.2569 9.997 .776 972 +ST VEL 2450575.2552 9.482 .669 972 +ST VEL 2450576.2609 9.677 .736 972 +ST VEL 2450578.2377 9.995 .783 972 +ST VEL 2450578.2894 9.983 .776 972 +ST VEL 2450580.2240 9.401 .633 972 +ST VEL 2450580.2658 9.410 .637 972 +ST VEL 2450582.2229 9.700 .729 972 +ST VEL 2450582.2788 9.704 .748 972 +ST VEL 2450583.2409 9.842 .762 972 +ST VEL 2450583.2734 9.848 .764 972 +ST VEL 2450584.2106 10.016 .778 972 +ST VEL 2450584.2643 10.033 .792 972 +SV VEL 2449802.7919 8.520 1.179 .663 .644 997 +SV VEL 2449803.7399 8.643 1.246 .689 .662 997 +SV VEL 2449804.7731 8.777 1.350 .707 .678 997 +SV VEL 2449805.7291 8.912 1.359 .737 .698 997 +SV VEL 2449807.7601 9.089 1.383 .737 .715 997 +SV VEL 2449808.7577 9.026 1.322 .713 .690 997 +SV VEL 2449809.6879 8.936 1.237 .694 .669 997 +SV VEL 2449810.7374 8.836 1.161 .667 .640 997 +SV VEL 2449811.6918 8.573 1.027 .599 .594 997 +SV VEL 2449812.7009 7.930 .753 .470 .495 997 +SV VEL 2449813.7099 8.132 .888 .528 .545 997 +SV VEL 2449814.7327 8.286 .998 .573 .588 997 +SV VEL 2449815.6866 8.390 1.066 .618 .618 997 +SV VEL 2449816.6730 8.501 1.156 .643 .651 997 +SV VEL 2449817.6663 8.602 1.242 .687 .645 997 +SV VEL 2449818.6671 8.740 1.314 .718 .686 997 +SV VEL 2449821.6598 9.081 1.369 .751 .706 997 +SV VEL 2449822.6569 9.046 1.316 .732 .684 997 +SV VEL 2449822.7597 9.033 1.317 .726 .693 997 +SV VEL 2449823.6499 8.943 1.253 .697 .661 997 +SV VEL 2449823.7515 8.911 1.270 .671 .669 997 +SV VEL 2449824.6430 8.829 1.173 .668 .654 997 +SV VEL 2449825.6400 8.657 1.069 .622 .618 997 +SV VEL 2449825.7398 8.623 1.048 .622 .592 997 +SV VEL 2449826.6692 7.961 .764 .474 .503 997 +SV VEL 2449827.6415 8.089 .856 .524 .529 997 +SW VEL 2449802.7480 8.764 1.468 .762 .702 997 +SW VEL 2449803.7312 8.725 1.428 .755 .707 997 +SW VEL 2449804.7344 8.740 1.364 .744 .668 997 +SW VEL 2449805.7126 8.353 1.127 .651 .624 997 +SW VEL 2449806.7308 7.626 .810 .494 .486 997 +SW VEL 2449807.7184 7.510 .783 .471 .491 997 +SW VEL 2449808.7189 7.597 .830 .508 .526 997 +SW VEL 2449809.6641 7.688 .909 .548 .556 997 +SW VEL 2449810.6914 7.755 1.015 .578 .575 997 +SW VEL 2449811.6707 7.823 1.079 .616 .591 997 +SW VEL 2449812.6843 7.885 1.159 .636 .627 997 +SW VEL 2449813.6936 7.958 1.216 .674 .623 997 +SW VEL 2449814.6160 8.028 1.274 .698 .657 997 +SW VEL 2449814.7013 8.017 1.281 .694 .643 997 +SW VEL 2449815.6670 8.088 1.321 .706 .664 997 +SW VEL 2449816.6567 8.156 1.356 .715 .683 997 +SW VEL 2449817.6439 8.215 1.417 .735 .675 997 +SW VEL 2449818.6354 8.308 1.427 .750 .686 997 +SW VEL 2449819.6128 8.387 1.454 .766 .703 997 +SW VEL 2449821.6263 8.528 1.489 .775 .714 997 +SW VEL 2449822.6296 8.567 1.491 .779 .699 997 +SW VEL 2449823.6176 8.594 1.478 .766 .701 997 +SW VEL 2449824.6131 8.618 1.457 .769 .703 997 +SW VEL 2449825.6115 8.679 1.458 .762 .699 997 +SW VEL 2449826.6383 8.732 1.427 .771 .703 997 +SW VEL 2449827.6122 8.750 1.367 .766 .691 997 +SW VEL 2450351.9074 7.875 1.249 999 +SW VEL 2450352.8978 7.962 1.325 999 +SW VEL 2450354.8959 8.112 1.384 999 +SW VEL 2450355.8946 8.194 1.404 999 +SW VEL 2450357.8925 8.327 1.453 999 +SW VEL 2450358.8917 8.399 1.472 999 +SW VEL 2450359.8843 8.484 1.490 999 +SW VEL 2450361.8840 8.580 1.476 999 +SW VEL 2450362.8921 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8.121 1.384 972 +SW VEL 2450568.2802 8.324 1.443 972 +SW VEL 2450570.2650 8.435 1.454 972 +SW VEL 2450571.2403 8.531 1.481 972 +SW VEL 2450572.2361 8.577 1.484 972 +SW VEL 2450573.2548 8.588 1.470 972 +SW VEL 2450575.2534 8.715 1.483 972 +SW VEL 2450576.2594 8.732 1.471 972 +SW VEL 2450578.2141 8.598 1.376 972 +SW VEL 2450578.2878 8.542 1.354 972 +SW VEL 2450580.2113 7.507 .970 972 +SW VEL 2450580.2640 7.527 .965 972 +SW VEL 2450581.2829 7.582 1.021 972 +SW VEL 2450582.2212 7.648 1.085 972 +SW VEL 2450582.2771 7.642 1.091 972 +SW VEL 2450583.2394 7.712 1.142 972 +SW VEL 2450583.2720 7.718 1.150 972 +SW VEL 2450584.2093 7.789 1.181 972 +SW VEL 2450584.2626 7.783 1.193 972 +SX VEL 2450351.8606 8.060 .911 999 +SX VEL 2450352.8473 8.034 .918 999 +SX VEL 2450354.8403 8.082 .974 999 +SX VEL 2450355.8407 8.285 1.036 999 +SX VEL 2450357.8368 8.553 1.157 999 +SX VEL 2450358.8334 8.651 1.173 999 +SX VEL 2450359.8378 8.549 1.094 999 +SX VEL 2450361.8360 8.018 .891 999 +SX VEL 2450362.8269 8.017 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2450582.2220 8.063 .472 972 +SX VEL 2450582.2778 8.050 .475 972 +SX VEL 2450583.2400 7.927 .449 972 +SX VEL 2450583.2726 7.930 .453 972 +SX VEL 2450584.2098 8.145 .496 972 +SX VEL 2450584.2633 8.179 .510 972 +AE VEL 2450351.8773 10.394 1.553 999 +AE VEL 2450352.8647 9.823 1.351 999 +AE VEL 2450354.8563 10.157 1.536 999 +AE VEL 2450355.8583 10.309 1.583 999 +AE VEL 2450357.8517 10.636 1.674 999 +AE VEL 2450358.8442 10.472 1.588 999 +AE VEL 2450359.8467 9.838 1.345 999 +AE VEL 2450361.8451 10.170 1.534 999 +AE VEL 2450362.8449 10.251 1.572 999 +AE VEL 2450363.8410 10.512 1.659 999 +AE VEL 2450379.7876 10.641 1.648 999 +AE VEL 2450380.7955 10.155 1.459 999 +AE VEL 2450381.7832 9.867 1.374 999 +AE VEL 2450383.7962 10.156 1.540 999 +AE VEL 2450384.7720 10.428 1.632 999 +AE VEL 2450386.7776 10.677 1.666 999 +AE VEL 2450387.7795 10.257 1.519 999 +AE VEL 2450388.7722 9.837 1.363 999 +AE VEL 2450390.7750 10.158 1.538 999 +AE VEL 2450391.7559 10.386 1.630 999 +AE VEL 2450392.7572 10.554 1.679 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2450582.1863 5.785 .688 972 +AH VEL 2450582.2554 5.747 .672 972 +AH VEL 2450583.2144 5.512 .590 972 +AH VEL 2450583.2512 5.511 .586 972 +AH VEL 2450584.1867 5.687 .659 972 +AH VEL 2450584.1875 5.686 .655 972 +AH VEL 2450584.2557 5.698 .672 972 +AM VEL 2450351.8549 13.266 1.270 1.501 999 +AM VEL 2450352.8412 13.397 1.337 1.546 999 +AM VEL 2450354.8337 13.401 1.185 1.512 999 +AM VEL 2450355.8327 12.842 .948 1.237 999 +AM VEL 2450357.8312 13.092 1.120 1.430 999 +AM VEL 2450358.8244 13.164 1.151 1.462 999 +AM VEL 2450359.8324 13.367 1.224 1.553 999 +AM VEL 2450361.8244 13.528 1.317 1.566 999 +AM VEL 2450362.8238 13.170 1.095 1.416 999 +AM VEL 2450363.8280 12.868 .941 1.279 999 +AM VEL 2450379.7763 12.989 1.049 1.353 999 +AM VEL 2450380.7668 13.137 1.194 1.439 999 +AM VEL 2450381.7707 13.170 1.225 1.467 999 +AM VEL 2450383.7851 13.471 1.292 1.576 999 +AM VEL 2450384.7645 13.479 1.303 1.548 999 +AM VEL 2450386.7601 12.938 .994 1.295 999 +AM VEL 2450387.7667 13.094 1.111 1.405 999 +AM VEL 2450388.7584 13.164 1.157 1.456 999 +AM VEL 2450389.7646 13.367 1.227 1.545 999 +AM VEL 2450390.7657 13.450 1.294 1.566 999 +AM VEL 2450391.7454 13.527 1.311 1.591 999 +AM VEL 2450392.7424 13.270 1.143 1.452 999 +AM VEL 2450393.7509 12.852 .917 1.268 999 +AM VEL 2450394.7469 12.996 1.033 1.378 999 +AP VEL 2449802.7454 10.212 1.114 .690 .647 997 +AP VEL 2449803.7278 10.405 1.185 .700 .691 997 +AP VEL 2449804.7324 9.756 .916 .566 .557 997 +AP VEL 2449805.7105 10.285 1.145 .698 .689 997 +AP VEL 2449806.7292 10.247 1.058 .653 .641 997 +AP VEL 2449807.7169 9.909 .979 .602 .604 997 +AP VEL 2449808.7174 9.985 1.006 .638 .623 997 +AP VEL 2449809.6607 10.329 1.164 .690 .675 997 +AP VEL 2449810.6897 9.565 .788 .537 .522 997 +AP VEL 2449811.6677 10.101 1.088 .654 .668 997 +AP VEL 2449812.6826 10.399 1.158 .716 .694 997 +AP VEL 2449813.6917 9.787 .898 .575 .577 997 +AP VEL 2449814.6094 10.128 1.059 .664 .664 997 +AP VEL 2449814.6993 10.099 1.066 .660 .644 997 +AP VEL 2449815.6655 10.188 1.085 .676 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10.293 1.164 1.388 999 +AP VEL 2450383.7690 10.012 1.052 1.290 999 +AP VEL 2450384.7546 10.259 1.142 1.367 999 +AP VEL 2450386.7494 9.874 .968 1.195 999 +AP VEL 2450387.7574 10.129 1.106 1.331 999 +AP VEL 2450388.7258 10.339 1.150 1.394 999 +AP VEL 2450389.7295 9.723 .917 1.160 999 +AP VEL 2450390.7476 10.305 1.196 1.391 999 +AP VEL 2450391.7325 10.146 1.037 1.302 999 +AP VEL 2450392.7273 9.929 .994 1.222 999 +AP VEL 2450393.7293 10.029 1.048 1.292 999 +AP VEL 2450394.7265 10.244 1.121 1.362 999 +AP VEL 2450541.4350 10.224 .663 1.330 972 +AP VEL 2450542.4188 9.989 .632 1.243 972 +AP VEL 2450568.2780 9.963 .636 1.267 972 +AP VEL 2450570.2499 9.950 .606 1.213 972 +AP VEL 2450571.2375 9.986 .641 1.266 972 +AP VEL 2450572.2305 10.101 .675 1.330 972 +AP VEL 2450573.2524 10.291 .694 1.355 972 +AP VEL 2450575.2297 10.264 .700 1.375 972 +AP VEL 2450575.2524 10.277 .698 1.391 972 +AP VEL 2450576.2268 10.279 .685 1.338 972 +AP VEL 2450576.2584 10.245 .663 1.323 972 +AP VEL 2450578.2124 10.165 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2449813.6764 8.146 .641 .394 .378 997 +AX VEL 2449814.5989 8.215 .691 .414 .418 997 +AX VEL 2449814.6813 8.237 .679 .432 .414 997 +AX VEL 2449815.6493 8.215 .670 .413 .404 997 +AX VEL 2449816.6396 8.210 .681 .415 .407 997 +AX VEL 2449817.6263 8.393 .761 .448 .448 997 +AX VEL 2449818.6222 7.951 .563 .359 .366 997 +AX VEL 2449819.6032 8.253 .722 .428 .435 997 +AX VEL 2449821.6123 8.025 .581 .369 .372 997 +AX VEL 2449822.6128 8.278 .717 .434 .430 997 +AX VEL 2449823.6036 8.277 .696 .422 .414 997 +AX VEL 2449824.6003 8.210 .686 .414 .414 997 +AX VEL 2449825.5980 8.268 .697 .417 .414 997 +AX VEL 2449826.6220 8.032 .609 .378 .379 997 +AX VEL 2449827.5969 8.352 .756 .451 .438 997 +AX VEL 2450351.8328 8.318 .878 999 +AX VEL 2450352.8098 8.225 .683 .839 999 +AX VEL 2450355.8001 8.071 .763 999 +AX VEL 2450357.8004 8.129 .781 999 +AX VEL 2450358.7978 8.102 .799 999 +AX VEL 2450359.8104 8.395 .906 999 +AX VEL 2450361.7937 8.243 .841 999 +AX VEL 2450362.7885 8.161 .798 999 +AX VEL 2450363.7803 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8.084 .387 .791 972 +AX VEL 2450578.2042 8.092 .380 .783 972 +AX VEL 2450578.2217 8.119 .386 .776 972 +AX VEL 2450578.2477 8.113 .377 .769 972 +AX VEL 2450580.2054 8.424 .460 .912 972 +AX VEL 2450580.2570 8.445 .465 .906 972 +AX VEL 2450582.2134 8.248 .427 .843 972 +AX VEL 2450582.2693 8.253 .425 .849 972 +AX VEL 2450583.2319 8.178 .412 .810 972 +AX VEL 2450583.2652 8.176 .406 .809 972 +AX VEL 2450584.2036 8.197 .411 .827 972 +AX VEL 2450584.2194 8.203 .418 .825 972 +BG VEL 2450351.9051 7.433 1.220 999 +BG VEL 2450352.8990 7.506 1.277 999 +BG VEL 2450354.8949 7.697 1.357 999 +BG VEL 2450355.8929 7.829 1.366 999 +BG VEL 2450357.8909 7.597 1.279 999 +BG VEL 2450358.8905 7.411 1.221 999 +BG VEL 2450359.8827 7.504 1.279 999 +BG VEL 2450361.8817 7.725 1.368 999 +BG VEL 2450363.8885 7.857 1.374 999 +BG VEL 2450379.8030 7.435 1.219 999 +BG VEL 2450379.8759 7.393 1.238 999 +BG VEL 2450380.7900 7.535 1.281 999 +BG VEL 2450380.8293 7.516 1.289 999 +BG VEL 2450381.7968 7.620 1.335 999 +BG VEL 2450381.8701 7.629 1.346 999 +BG VEL 2450383.7424 7.857 1.395 999 +BG VEL 2450383.8310 7.837 1.397 999 +BG VEL 2450383.8745 7.859 1.398 999 +BG VEL 2450384.7807 7.819 1.367 999 +BG VEL 2450384.8716 7.809 1.370 999 +BG VEL 2450386.7987 7.460 1.219 999 +BG VEL 2450386.8711 7.436 1.232 999 +BG VEL 2450387.8603 7.541 1.298 999 +BG VEL 2450388.8150 7.616 1.337 999 +BG VEL 2450388.8580 7.621 1.334 999 +BG VEL 2450389.7590 7.764 1.358 999 +BG VEL 2450390.7844 7.861 1.406 999 +BG VEL 2450390.8542 7.872 1.398 999 +BG VEL 2450391.7506 7.822 1.367 999 +BG VEL 2450391.8490 7.781 1.350 999 +BG VEL 2450392.7759 7.539 1.262 999 +BG VEL 2450392.8526 7.530 1.259 999 +BG VEL 2450393.7379 7.422 1.227 999 +BG VEL 2450393.8562 7.434 1.227 999 +BG VEL 2450394.7434 7.528 1.287 999 +BG VEL 2450394.8467 7.548 1.299 999 +BG VEL 2450542.4307 7.777 .702 972 +BG VEL 2450568.3002 7.597 .663 972 +BG VEL 2450570.2743 7.802 .696 972 +BG VEL 2450571.2580 7.906 .735 972 +BG VEL 2450572.2503 7.684 .668 972 +BG VEL 2450573.2616 7.441 .623 972 +BG VEL 2450575.2593 7.579 .661 972 +BG VEL 2450576.2628 7.680 .677 972 +BG VEL 2450578.2403 7.894 .703 972 +BG VEL 2450578.2920 7.878 .719 972 +BG VEL 2450580.2276 7.453 .606 972 +BG VEL 2450580.2681 7.467 .614 972 +BG VEL 2450580.3511 7.420 .602 972 +BG VEL 2450582.2254 7.589 .662 972 +BG VEL 2450582.2812 7.596 .684 972 +BG VEL 2450583.2433 7.694 .686 972 +BG VEL 2450583.2764 7.701 .688 972 +BG VEL 2450584.2274 7.839 .707 972 +BG VEL 2450584.2670 7.826 .704 972 +BH VEL 2450352.8342 12.712 1.647 1.992 999 +BH VEL 2450354.8269 12.522 1.608 1.951 999 +BH VEL 2450355.8258 1.684 999 +BH VEL 2450358.8166 12.900 1.786 2.082 999 +BH VEL 2450359.8267 12.782 1.716 2.025 999 +BH VEL 2450361.8167 12.530 1.584 1.947 999 +BH VEL 2450362.8195 12.581 1.668 1.984 999 +BH VEL 2450363.8177 12.688 1.705 2.042 999 +BH VEL 2450379.7713 12.869 1.766 2.092 999 +BH VEL 2450380.7600 12.894 1.782 2.052 999 +BH VEL 2450381.7657 12.687 1.667 1.976 999 +BH VEL 2450383.7798 12.530 1.596 1.967 999 +BH VEL 2450384.7595 12.622 1.648 2.016 999 +BH VEL 2450386.7557 12.864 1.788 2.067 999 +BH VEL 2450387.7616 12.909 1.806 2.078 999 +BH VEL 2450388.7415 12.755 2.028 999 +BH VEL 2450389.7375 12.537 1.565 1.941 999 +BH VEL 2450390.7595 12.527 1.613 1.957 999 +BH VEL 2450391.7425 12.603 1.644 2.012 999 +BH VEL 2450392.7496 12.694 1.716 2.029 999 +BH VEL 2450393.7577 12.812 1.759 2.071 999 +BH VEL 2450394.7526 12.910 1.781 2.087 999 +BQ VEL 2450352.8245 14.790 1.265 1.348 999 +BQ VEL 2450354.8225 14.365 .934 1.197 999 +BQ VEL 2450355.8199 14.707 1.136 1.366 999 +BQ VEL 2450357.8136 14.086 .828 1.037 999 +BQ VEL 2450358.8119 14.691 1.062 1.384 999 +BQ VEL 2450359.8216 14.906 1.236 1.399 999 +BQ VEL 2450361.8123 14.520 1.010 1.275 999 +BQ VEL 2450362.8023 14.848 1.163 1.441 999 +BQ VEL 2450363.7922 15.041 1.178 1.475 999 +BQ VEL 2450379.7499 14.864 1.228 1.429 999 +BQ VEL 2450380.7421 15.007 1.257 1.348 999 +BQ VEL 2450381.7428 14.261 .979 1.150 999 +BQ VEL 2450383.7515 14.962 1.425 999 +BQ VEL 2450384.7457 14.090 .747 1.088 999 +BQ VEL 2450386.7389 14.887 1.259 1.425 999 +BQ VEL 2450387.7463 14.861 1.001 1.323 999 +BQ VEL 2450388.7056 14.450 1.028 1.258 999 +BQ VEL 2450389.7160 14.805 1.121 1.389 999 +BQ VEL 2450390.7538 15.026 1.119 1.423 999 +BQ VEL 2450391.7372 14.236 .891 1.128 999 +BQ VEL 2450392.7371 14.724 1.084 1.363 999 +BQ VEL 2450393.7449 14.936 1.219 1.453 999 +BQ VEL 2450394.7318 14.210 .792 1.117 999 +CO VEL 2450352.8309 12.506 2.218 999 +CO VEL 2450354.8297 12.942 2.030 2.354 999 +CO VEL 2450355.8298 12.206 2.025 999 +CO VEL 2450357.8266 12.717 2.311 999 +CO VEL 2450358.8200 12.908 2.350 999 +CO VEL 2450359.8295 12.587 2.180 999 +CO VEL 2450361.8196 12.672 2.282 999 +CO VEL 2450362.8131 12.886 2.343 999 +CO VEL 2450363.8071 12.881 2.298 999 +CO VEL 2450379.7603 12.861 2.346 999 +CO VEL 2450380.7531 12.936 2.315 999 +CO VEL 2450381.7516 12.159 2.018 999 +CO VEL 2450383.7659 12.749 2.302 999 +CO VEL 2450384.7527 12.951 2.360 999 +CO VEL 2450386.7474 12.439 2.178 999 +CO VEL 2450387.7551 12.683 2.277 999 +CO VEL 2450388.7235 12.927 2.377 999 +CO VEL 2450389.7276 12.583 2.171 999 +CO VEL 2450390.7494 12.323 2.111 999 +CO VEL 2450391.7340 12.664 2.300 999 +CO VEL 2450392.7515 12.866 2.333 999 +CO VEL 2450393.7593 12.863 2.301 999 +CO VEL 2450394.7551 12.214 2.062 999 +CP VEL 2450351.8694 12.830 2.001 999 +CP VEL 2450352.8509 12.670 1.523 1.921 999 +CP VEL 2450354.8432 12.403 1.460 1.854 999 +CP VEL 2450355.8444 12.525 1.565 1.906 999 +CP VEL 2450357.8440 12.776 1.715 2.052 999 +CP VEL 2450358.8359 12.943 1.788 2.098 999 +CP VEL 2450359.8401 13.063 1.791 2.115 999 +CP VEL 2450361.8382 12.840 1.606 1.999 999 +CP VEL 2450362.8357 12.667 1.548 1.930 999 +CP VEL 2450363.8306 12.557 1.526 1.888 999 +CP VEL 2450379.7798 13.053 1.798 2.093 999 +CP VEL 2450380.7720 12.945 1.686 2.037 999 +CP VEL 2450381.7750 12.797 1.542 1.995 999 +CP VEL 2450383.7896 12.479 1.455 1.879 999 +CP VEL 2450384.7676 12.492 1.509 1.903 999 +CP VEL 2450386.7631 12.727 1.701 2.020 999 +CP VEL 2450387.7697 12.870 1.729 2.074 999 +CP VEL 2450388.7617 13.006 1.800 2.113 999 +CP VEL 2450390.7690 12.922 1.698 2.042 999 +CP VEL 2450391.7664 12.773 1.595 1.970 999 +CP VEL 2450392.7450 12.662 1.535 1.939 999 +CP VEL 2450393.7533 12.447 1.467 1.873 999 +CP VEL 2450394.7489 12.514 1.523 1.924 999 +CS VEL 2450351.8921 11.459 1.548 999 +CS VEL 2450352.8656 11.682 1.660 999 +CS VEL 2450354.8576 12.013 1.761 999 +CS VEL 2450355.8594 11.940 1.681 999 +CS VEL 2450357.8526 11.439 1.546 999 +CS VEL 2450359.8554 11.875 1.727 999 +CS VEL 2450361.8457 11.943 1.693 999 +CS VEL 2450362.8462 11.319 1.458 999 +CS VEL 2450363.8420 11.501 1.563 999 +CS VEL 2450379.7893 11.762 1.625 999 +CS VEL 2450380.7972 11.319 1.463 999 +CS VEL 2450381.7844 11.570 1.614 999 +CS VEL 2450383.7970 11.921 1.733 999 +CS VEL 2450384.7729 12.082 1.779 999 +CS VEL 2450386.7788 11.352 1.482 999 +CS VEL 2450387.7801 11.576 1.626 999 +CS VEL 2450388.7731 11.762 1.708 999 +CS VEL 2450390.7756 12.066 1.754 999 +CS VEL 2450391.7706 11.654 1.574 999 +CS VEL 2450392.7537 11.348 1.493 999 +CS VEL 2450393.7606 11.577 1.630 999 +CS VEL 2450394.7559 11.744 1.680 999 +CS VEL 2450542.4795 11.795 .892 972 +CS VEL 2450568.3177 12.035 .867 972 +CS VEL 2450570.2876 11.458 .780 972 +CS VEL 2450571.2689 11.685 .846 972 +CS VEL 2450572.2622 11.814 .871 972 +CS VEL 2450573.2719 12.005 .890 972 +CS VEL 2450575.2682 11.363 .734 972 +CS VEL 2450576.2748 11.485 .787 972 +CS VEL 2450576.3631 11.494 .783 972 +CS VEL 2450577.3575 11.736 .839 972 +CS VEL 2450578.3005 11.860 .865 972 +CS VEL 2450580.2467 11.952 .859 972 +CS VEL 2450580.2914 11.952 .860 972 +CS VEL 2450580.3588 11.917 .861 972 +CS VEL 2450582.2365 11.508 .792 972 +CS VEL 2450582.2965 11.516 .810 972 +CS VEL 2450583.2845 11.720 .848 972 +CS VEL 2450583.3241 11.733 .854 972 +CS VEL 2450584.2372 11.882 .879 972 +CS VEL 2450584.3088 11.898 .872 972 +CX VEL 2450351.8840 11.562 1.596 1.845 999 +CX VEL 2450352.8549 11.714 1.638 1.881 999 +CX VEL 2450354.8500 11.066 1.282 1.623 999 +CX VEL 2450355.8479 11.160 1.683 999 +CX VEL 2450357.8478 11.488 1.567 1.830 999 +CX VEL 2450358.8398 11.664 1.630 1.869 999 +CX VEL 2450359.8431 11.684 1.569 1.852 999 +CX VEL 2450361.8411 11.149 1.335 1.675 999 +CX VEL 2450362.8421 11.322 1.491 1.761 999 +CX VEL 2450363.8376 11.462 1.530 1.828 999 +CX VEL 2450379.7827 11.076 1.270 1.625 999 +CX VEL 2450380.7750 11.151 1.368 1.683 999 +CX VEL 2450381.7780 11.346 1.479 1.785 999 +CX VEL 2450383.7923 11.665 1.608 1.880 999 +CX VEL 2450384.7693 11.724 1.869 999 +CX VEL 2450386.7655 11.126 1.306 1.657 999 +CX VEL 2450387.7714 11.327 1.442 1.779 999 +CX VEL 2450388.7649 11.436 1.531 1.824 999 +CX VEL 2450390.7712 11.734 1.640 1.877 999 +CX VEL 2450391.7544 11.339 1.427 1.727 999 +CX VEL 2450392.7559 11.069 1.300 1.638 999 +CX VEL 2450393.7627 11.235 1.437 1.738 999 +CX VEL 2450394.7576 11.382 1.791 999 +CX VEL 2450542.4279 11.066 .789 972 +CX VEL 2450568.2979 11.159 .842 972 +CX VEL 2450570.2725 11.435 .910 972 +CX VEL 2450571.2450 11.637 .941 972 +CX VEL 2450572.2485 11.735 .951 972 +CX VEL 2450573.2591 11.306 .850 972 +CX VEL 2450575.2576 11.290 .876 972 +CX VEL 2450576.2622 11.428 .920 972 +CX VEL 2450578.2389 11.751 .944 972 +CX VEL 2450578.2905 11.736 .935 972 +CX VEL 2450580.2260 11.071 .797 972 +CX VEL 2450580.2669 11.094 .814 972 +CX VEL 2450582.2241 11.405 .912 972 +CX VEL 2450582.2800 11.397 .911 972 +CX VEL 2450583.2422 11.564 .927 972 +CX VEL 2450583.2751 11.582 .941 972 +CX VEL 2450584.2262 11.736 .961 972 +CX VEL 2450584.2656 11.724 .956 972 +DK VEL 2450351.8867 10.688 .843 1.029 999 +DK VEL 2450352.8620 10.830 .888 1.078 999 +DK VEL 2450355.8512 10.572 .822 .940 999 +DK VEL 2450357.8492 10.806 1.076 999 +DK VEL 2450358.8415 10.534 .969 999 +DK VEL 2450359.8454 10.832 1.097 999 +DK VEL 2450361.8426 10.713 1.042 999 +DK VEL 2450362.8435 10.810 1.056 999 +DK VEL 2450363.8396 10.559 .964 999 +DK VEL 2450379.7846 10.856 1.110 999 +DK VEL 2450380.7926 10.544 .966 999 +DK VEL 2450381.7801 10.721 .871 1.040 999 +DK VEL 2450383.7940 10.560 .980 999 +DK VEL 2450384.7705 10.850 1.090 999 +DK VEL 2450386.7676 10.728 1.031 999 +DK VEL 2450387.7735 10.784 1.057 999 +DK VEL 2450388.7662 10.570 .984 999 +DK VEL 2450390.7729 10.546 .959 999 +DK VEL 2450391.7681 10.749 1.058 999 +DK VEL 2450392.7470 10.758 1.040 999 +DK VEL 2450393.7549 10.585 1.000 999 +DK VEL 2450394.7501 10.846 1.103 999 +DK VEL 2450542.4340 10.535 .484 972 +DK VEL 2450568.3020 10.841 .542 972 +DK VEL 2450570.2772 10.688 .505 972 +DK VEL 2450571.2598 10.855 .549 972 +DK VEL 2450572.2515 10.542 .488 972 +DK VEL 2450573.2628 10.814 .541 972 +DK VEL 2450575.2605 10.701 .522 972 +DK VEL 2450578.2511 10.861 .545 972 +DK VEL 2450578.2932 10.862 .559 972 +DK VEL 2450580.2287 10.710 .515 972 +DK VEL 2450580.2692 10.734 .518 972 +DK VEL 2450582.2267 10.567 .482 972 +DK VEL 2450582.2893 10.566 .486 972 +DK VEL 2450583.2445 10.859 .555 972 +DK VEL 2450583.2776 10.854 .543 972 +DK VEL 2450584.2286 10.611 .491 972 +DK VEL 2450584.3006 10.579 .487 972 +DP VEL 2450351.8903 11.909 1.758 999 +DP VEL 2450352.8631 12.088 1.817 999 +DP VEL 2450354.8556 11.586 1.576 999 +DP VEL 2450355.8567 11.583 1.583 999 +DP VEL 2450357.8505 11.964 1.790 999 +DP VEL 2450358.8426 12.136 1.816 999 +DP VEL 2450359.8459 11.928 1.714 999 +DP VEL 2450361.8443 11.705 1.669 999 +DP VEL 2450362.8443 11.902 1.746 999 +DP VEL 2450363.8401 12.069 1.792 999 +DP VEL 2450379.7857 11.989 1.785 999 +DP VEL 2450380.7942 12.163 1.837 999 +DP VEL 2450381.7820 11.934 1.718 999 +DP VEL 2450383.7950 11.698 1.684 999 +DP VEL 2450384.7710 11.924 1.770 999 +DP VEL 2450386.7691 12.162 1.795 999 +DP VEL 2450387.7746 11.557 1.569 999 +DP VEL 2450388.7712 11.571 1.610 999 +DP VEL 2450390.7739 11.998 1.812 999 +DP VEL 2450391.7693 12.169 1.830 999 +DP VEL 2450392.7522 11.934 1.705 999 +DP VEL 2450393.7599 11.466 1.545 999 +DP VEL 2450394.7548 11.692 1.665 999 +DP VEL 2450541.4537 11.544 .809 972 +DP VEL 2450542.4467 11.608 1.630 972 +DP VEL 2450568.3129 11.920 .857 972 +DP VEL 2450570.2841 11.712 .830 972 +DP VEL 2450571.2654 11.934 .909 972 +DP VEL 2450572.2577 12.083 .924 972 +DP VEL 2450573.2681 12.171 .931 972 +DP VEL 2450575.2652 11.617 .816 972 +DP VEL 2450576.2716 11.840 .864 972 +DP VEL 2450577.3170 12.046 .899 972 +DP VEL 2450578.2554 12.187 .922 972 +DP VEL 2450578.2979 12.171 .932 972 +DP VEL 2450580.2894 11.503 .776 972 +DP VEL 2450580.3559 11.513 .787 972 +DP VEL 2450582.2336 11.930 .896 972 +DP VEL 2450582.2941 11.929 .903 972 +DP VEL 2450583.2820 12.108 .913 972 +DP VEL 2450584.2342 12.182 .894 972 +DP VEL 2450584.3057 12.163 .899 972 +EX VEL 2449804.7636 11.839 1.703 .968 .920 997 +EX VEL 2449805.7209 11.668 1.603 .910 .906 997 +EX VEL 2449807.7209 11.546 1.471 .897 .873 997 +EX VEL 2449808.7220 11.251 1.382 .821 .825 997 +EX VEL 2449809.6707 11.247 1.417 .837 .853 997 +EX VEL 2449810.6943 11.319 1.480 .881 .858 997 +EX VEL 2449811.6733 11.416 1.576 .910 .875 997 +EX VEL 2449812.6872 11.536 1.689 .922 .918 997 +EX VEL 2449813.6969 11.677 1.720 .973 .926 997 +EX VEL 2449814.7044 11.767 1.806 .984 .924 997 +EX VEL 2449815.6706 11.878 1.804 .996 .930 997 +EX VEL 2449816.6599 11.955 1.783 1.030 .934 997 +EX VEL 2449817.6475 11.861 1.787 .972 .922 997 +EX VEL 2449818.6390 11.708 1.642 .953 .896 997 +EX VEL 2449819.6154 11.648 1.592 .911 .901 997 +EX VEL 2449821.6367 11.362 1.429 .847 .848 997 +EX VEL 2449822.6375 11.235 1.405 .840 .837 997 +EX VEL 2449823.6210 11.279 1.464 .859 .863 997 +EX VEL 2449824.6167 11.385 1.572 .891 .879 997 +EX VEL 2449825.6150 11.490 1.654 .922 .901 997 +EX VEL 2449826.6446 11.621 1.713 .953 .913 997 +EX VEL 2449827.6182 11.738 1.758 .978 .922 997 +EZ VEL 2449807.7296 12.276 1.776 1.064 .991 997 +EZ VEL 2449808.7298 12.317 1.854 1.076 1.009 997 +EZ VEL 2449809.6753 12.361 1.880 1.082 1.027 997 +EZ VEL 2449810.6993 12.379 1.897 1.069 1.038 997 +EZ VEL 2449811.6778 12.434 1.915 1.109 1.038 997 +EZ VEL 2449812.6907 12.489 1.918 1.086 1.055 997 +EZ VEL 2449813.7012 12.534 1.948 1.122 1.042 997 +EZ VEL 2449814.7073 12.562 1.978 1.131 1.036 997 +EZ VEL 2449815.6740 12.621 1.960 1.126 1.069 997 +EZ VEL 2449816.6646 12.649 1.966 1.142 1.062 997 +EZ VEL 2449817.6569 12.674 2.029 1.139 1.047 997 +EZ VEL 2449818.6508 12.729 2.045 1.159 1.073 997 +EZ VEL 2449821.6460 12.834 1.969 1.148 1.074 997 +EZ VEL 2449822.6459 12.891 1.909 1.165 1.063 997 +EZ VEL 2449823.6376 12.917 1.970 1.151 1.076 997 +EZ VEL 2449824.6235 12.942 1.944 1.156 1.077 997 +EZ VEL 2449825.6293 12.933 1.996 1.130 1.064 997 +EZ VEL 2449826.6573 12.951 2.024 1.126 1.061 997 +EZ VEL 2449827.6245 12.982 2.016 1.134 1.052 997 +EZ VEL 2450542.4391 12.633 2.195 972 +EZ VEL 2450568.3076 12.291 2.072 972 +EZ VEL 2450570.2803 12.334 2.104 972 +EZ VEL 2450571.2634 12.378 2.115 972 +EZ VEL 2450572.2549 12.431 2.162 972 +EZ VEL 2450573.2661 12.455 2.151 972 +EZ VEL 2450575.2633 12.564 2.173 972 +EZ VEL 2450576.2701 12.600 2.187 972 +EZ VEL 2450577.3145 12.671 2.200 972 +EZ VEL 2450578.2536 12.702 2.204 972 +EZ VEL 2450578.2960 12.698 2.228 972 +EZ VEL 2450580.2405 12.764 2.226 972 +EZ VEL 2450580.2866 12.782 2.227 972 +EZ VEL 2450580.2880 12.780 2.220 972 +EZ VEL 2450580.3542 12.776 2.229 972 +EZ VEL 2450582.2296 12.834 2.208 972 +EZ VEL 2450582.2922 12.842 2.230 972 +EZ VEL 2450583.2802 12.874 2.225 972 +EZ VEL 2450584.2321 12.907 2.232 972 +EZ VEL 2450584.3033 12.911 2.230 972 +FN VEL 2450351.8793 10.097 1.361 999 +FN VEL 2450352.8751 10.087 1.389 999 +FN VEL 2450354.8611 10.447 1.535 999 +FN VEL 2450355.8625 10.585 1.546 999 +FN VEL 2450357.8811 10.028 1.100 1.366 999 +FN VEL 2450358.8646 10.222 1.197 1.475 999 +FN VEL 2450359.8568 10.415 1.299 1.519 999 +FN VEL 2450361.8474 10.485 1.499 999 +FN VEL 2450362.8548 9.985 1.058 1.327 999 +FN VEL 2450363.8511 10.162 1.174 1.426 999 +FN VEL 2450379.7914 10.161 1.168 1.423 999 +FN VEL 2450380.7993 10.343 1.276 1.515 999 +FN VEL 2450381.7865 10.477 1.549 999 +FN VEL 2450383.7984 10.099 1.360 999 +FN VEL 2450384.7754 10.072 1.122 1.388 999 +FN VEL 2450386.7808 10.446 1.280 1.529 999 +FN VEL 2450387.7813 10.562 1.550 999 +FN VEL 2450388.7749 10.288 1.163 1.442 999 +FN VEL 2450390.7770 10.208 1.460 999 +FN VEL 2450391.7573 10.397 1.270 1.540 999 +FN VEL 2450392.7583 10.542 1.307 1.570 999 +FN VEL 2450393.7647 10.468 1.254 1.516 999 +FN VEL 2450394.7600 9.977 1.049 1.329 999 +FN VEL 2450542.4851 10.580 .791 972 +FN VEL 2450568.3199 10.539 .797 972 +FN VEL 2450570.2901 10.032 .663 972 +FN VEL 2450571.2710 10.133 .710 972 +FN VEL 2450572.2640 10.320 .764 972 +FN VEL 2450573.2743 10.465 .771 972 +FN VEL 2450575.2699 10.212 .696 972 +FN VEL 2450576.2767 10.069 .681 972 +FN VEL 2450576.3649 10.090 .696 972 +FN VEL 2450577.3600 10.302 .743 972 +FN VEL 2450578.2616 10.468 .780 972 +FN VEL 2450578.3019 10.434 .767 972 +FN VEL 2450580.2487 10.453 .752 972 +FN VEL 2450580.2754 10.425 .743 972 +FN VEL 2450580.3603 10.361 .738 972 +FN VEL 2450582.2382 10.221 .739 972 +FN VEL 2450582.2981 10.206 .729 972 +FN VEL 2450583.2861 10.396 .765 972 +FN VEL 2450583.3256 10.398 .766 972 +FN VEL 2450584.2390 10.552 .794 972 +S VUL 2445489.3006 9.270 1.863 2.099 1.125 982 +S VUL 2445493.2617 9.321 1.857 2.140 1.123 982 +S VUL 2445496.4296 9.298 1.832 2.085 1.117 982 +S VUL 2445498.4530 9.244 1.751 2.020 1.099 982 +S VUL 2445501.4413 9.177 1.584 1.974 1.087 982 +S VUL 2445502.4570 9.109 1.590 1.931 1.071 982 +S VUL 2445503.4570 9.052 1.581 1.894 1.054 982 +S VUL 2445505.4570 8.931 1.524 1.825 1.034 982 +S VUL 2445508.4570 8.817 1.365 1.778 .992 982 +S VUL 2445512.4530 8.741 1.720 .983 982 +S VUL 2445513.4453 8.719 1.388 1.699 .971 982 +S VUL 2445514.4453 8.727 1.301 1.714 .973 982 +S VUL 2445644.2187 8.908 1.825 982 +S VUL 2445646.1835 .980 982 +S VUL 2445648.1484 8.778 1.426 1.733 .988 982 +S VUL 2445649.1756 8.725 1.337 1.731 .990 982 +S VUL 2445658.1131 8.751 1.426 1.774 1.000 982 +S VUL 2445659.1131 8.747 1.444 1.806 1.002 982 +S VUL 2445663.1131 8.781 1.494 1.856 1.043 982 +S VUL 2445665.1288 8.796 1.462 1.885 1.021 982 +S VUL 2445666.1014 8.814 1.525 1.898 1.035 982 +S VUL 2445667.1288 1.568 1.891 1.014 982 +S VUL 2445668.1171 8.828 1.546 1.937 1.036 982 +S VUL 2445674.1250 8.860 1.657 1.990 1.069 982 +S VUL 2445675.1131 8.924 1.640 2.017 1.085 982 +S VUL 2445676.0976 8.949 1.601 2.026 1.077 982 +S VUL 2445679.1250 8.985 1.764 2.063 1.084 982 +S VUL 2445683.1054 9.057 1.740 2.081 1.108 982 +S VUL 2445686.0897 9.110 2.109 1.112 982 +S VUL 2445688.1131 9.156 1.131 982 +S VUL 2445694.0742 9.230 2.071 1.126 982 +S VUL 2445695.0703 9.251 2.078 1.142 982 +S VUL 2445864.3046 8.720 1.455 1.748 .988 982 +S VUL 2445866.3476 8.740 1.490 1.779 .995 982 +S VUL 2445867.3203 8.743 1.510 1.790 1.003 982 +S VUL 2445868.2500 8.770 1.512 1.804 1.013 982 +S VUL 2445869.3046 8.763 1.591 1.797 1.020 982 +S VUL 2445870.2889 8.775 1.565 1.821 1.018 982 +S VUL 2445871.2578 8.785 1.599 1.825 1.021 982 +S VUL 2445872.2772 8.800 1.619 1.841 1.034 982 +S VUL 2445873.2695 8.811 1.624 1.860 1.038 982 +S VUL 2445874.2655 8.818 1.630 1.875 1.038 982 +S VUL 2445875.2578 8.816 1.679 1.880 1.031 982 +S VUL 2445876.2578 8.832 1.678 1.889 1.042 982 +S VUL 2445877.2812 8.877 1.706 1.883 1.070 982 +S VUL 2445878.2538 8.875 1.737 1.903 1.060 982 +S VUL 2445879.2617 8.890 1.742 1.924 1.064 982 +S VUL 2445880.2695 8.890 1.747 1.934 1.071 982 +S VUL 2445881.2500 8.935 1.816 1.921 1.080 982 +S VUL 2445882.2578 8.932 1.839 1.951 1.072 982 +S VUL 2445883.2617 8.921 1.804 1.952 1.075 982 +S VUL 2445886.2578 8.969 1.876 1.981 1.082 982 +S VUL 2445887.2500 8.977 1.984 1.986 1.083 982 +S VUL 2446252.1921 9.292 2.012 2.042 1.115 987 +S VUL 2446253.1942 9.280 2.005 2.030 1.123 987 +S VUL 2446255.2630 9.250 1.905 1.999 1.111 987 +S VUL 2446256.2478 9.226 1.865 1.985 1.091 987 +S VUL 2446257.2606 9.176 1.815 1.967 1.084 987 +S VUL 2446258.1917 9.167 1.795 1.952 1.073 987 +S VUL 2446259.2223 9.098 1.721 1.913 1.070 987 +S VUL 2446260.1967 9.091 1.697 1.901 1.052 987 +S VUL 2446261.2249 9.002 1.630 1.860 1.041 987 +S VUL 2446262.1895 8.962 1.580 1.821 1.037 987 +S VUL 2446263.1959 8.901 1.531 1.803 1.015 987 +S VUL 2446264.1952 8.828 1.492 1.793 1.020 987 +S VUL 2446265.1831 8.824 1.486 1.758 1.005 987 +S VUL 2446266.1991 8.778 1.455 1.746 .997 987 +S VUL 2446267.2068 8.799 1.441 1.740 .992 987 +S VUL 2446268.2126 8.729 1.447 1.730 .975 987 +S VUL 2446269.1971 8.707 1.435 1.718 .982 987 +S VUL 2446270.4634 8.722 1.450 1.711 .985 987 +S VUL 2446272.4655 8.731 1.417 1.708 .988 987 +S VUL 2446273.4609 8.677 1.439 1.716 .966 987 +S VUL 2446274.4641 8.718 1.480 1.703 1.001 987 +S VUL 2446275.4698 8.706 1.489 1.760 .992 987 +S VUL 2446279.4594 8.726 1.769 1.004 987 +S VUL 2446283.2672 8.829 1.583 1.861 1.029 987 +S VUL 2446284.2481 8.832 1.590 1.868 1.028 987 +S VUL 2446285.2327 8.846 1.634 1.874 1.039 987 +S VUL 2446286.2175 8.857 1.635 1.893 987 +S VUL 2446287.2119 8.855 1.669 1.896 1.045 987 +S VUL 2446288.2508 8.875 1.701 1.901 1.055 987 +S VUL 2446289.2501 8.887 1.686 1.915 1.056 987 +S VUL 2446290.2230 8.898 1.715 1.936 1.044 987 +S VUL 2446291.2102 8.894 1.758 1.948 1.059 987 +S VUL 2446292.2298 8.918 1.754 1.957 1.063 987 +S VUL 2446293.3495 8.936 1.787 1.955 1.081 987 +S VUL 2446294.2077 8.951 1.761 1.963 1.078 987 +S VUL 2446295.1911 8.948 1.794 1.986 1.071 987 +S VUL 2446296.1913 8.989 1.841 1.970 1.082 987 +S VUL 2446297.1993 8.979 1.835 1.989 1.079 987 +S VUL 2446298.2195 9.028 1.888 1.985 1.095 987 +S VUL 2446299.1873 9.021 1.869 2.000 1.088 987 +S VUL 2446300.1871 9.050 1.891 2.026 1.088 987 +S VUL 2446301.2086 9.066 1.940 2.015 1.106 987 +S VUL 2446302.2164 9.077 1.865 2.061 1.093 987 +S VUL 2446303.1803 9.088 1.940 2.034 1.104 987 +S VUL 2446304.1595 9.117 1.906 2.051 1.097 987 +S VUL 2446605.4426 8.977 1.850 1.049 988 +S VUL 2446606.3141 8.938 1.557 1.833 1.038 988 +S VUL 2446607.3850 8.916 1.522 1.814 1.016 988 +S VUL 2446608.3209 8.850 1.527 1.766 1.012 988 +S VUL 2446609.2345 8.808 1.478 1.769 1.001 988 +S VUL 2446610.3471 8.783 1.459 1.739 .991 988 +S VUL 2446611.2382 8.770 1.445 1.732 .989 988 +S VUL 2446612.2248 8.758 1.440 1.716 .995 988 +S VUL 2446613.2235 8.734 1.417 1.702 .989 988 +S VUL 2446614.2250 8.721 1.439 1.718 .989 988 +S VUL 2446615.2247 8.721 1.429 1.718 .988 988 +S VUL 2446616.2275 8.727 1.447 1.720 .985 988 +S VUL 2446617.2236 8.688 1.472 1.724 .980 988 +S VUL 2446618.2227 8.717 1.468 1.724 1.003 988 +S VUL 2446619.2216 8.723 1.501 1.735 .998 988 +S VUL 2446620.2191 8.726 1.483 1.751 1.002 988 +S VUL 2446621.3044 8.735 1.756 1.003 988 +S VUL 2446622.2175 8.737 1.507 1.769 1.017 988 +S VUL 2446623.2068 8.750 1.585 1.775 1.011 988 +S VUL 2446624.2012 8.766 1.558 1.781 1.017 988 +S VUL 2446625.1983 8.783 1.780 1.027 988 +S VUL 2446626.2044 8.767 1.687 1.800 1.026 988 +S VUL 2446627.2068 8.794 1.632 1.821 1.032 988 +S VUL 2446628.2076 8.797 1.682 1.843 1.036 988 +S VUL 2446629.2196 8.809 1.644 1.858 1.048 988 +S VUL 2446630.2176 8.804 1.859 1.048 988 +S VUL 2446631.2028 8.854 1.668 1.871 1.052 988 +S VUL 2446632.2145 8.870 1.708 1.869 1.061 988 +S VUL 2446635.3056 8.896 1.766 1.915 1.073 988 +S VUL 2446636.2143 8.903 1.766 1.935 1.078 988 +S VUL 2446637.2162 8.952 1.791 1.921 1.095 988 +S VUL 2446638.2230 8.931 1.810 1.947 1.085 988 +S VUL 2446992.2605 9.090 2.061 1.122 989 +S VUL 2446994.3552 9.141 1.886 2.081 1.132 989 +S VUL 2446995.3347 9.121 1.887 2.078 1.120 989 +S VUL 2446996.2727 9.133 2.006 2.087 1.127 989 +S VUL 2446997.3027 9.140 1.933 2.115 1.122 989 +S VUL 2446998.2982 9.168 1.886 2.095 1.133 989 +S VUL 2446999.2785 9.200 1.958 2.105 1.128 989 +S VUL 2447000.3068 9.192 1.914 2.110 1.125 989 +S VUL 2447001.2903 9.238 1.954 2.081 1.134 989 +S VUL 2447002.2908 9.238 1.956 2.113 1.130 989 +S VUL 2447003.2754 9.242 1.898 2.123 1.128 989 +S VUL 2447082.1702 1.775 1.951 1.075 989 +S VUL 2447082.2156 9.173 1.807 1.955 1.072 989 +S VUL 2447083.1480 9.167 1.689 1.937 1.080 989 +S VUL 2447084.1340 9.131 1.646 1.910 1.065 989 +S VUL 2447087.1650 8.964 1.559 1.834 1.020 989 +S VUL 2447088.0979 8.924 1.818 1.033 989 +S VUL 2447091.0910 8.807 1.468 1.757 .999 989 +S VUL 2447098.0771 8.724 1.404 1.724 .986 989 +S VUL 2447399.2917 9.005 1.966 1.075 990 +S VUL 2447400.2490 9.038 1.990 1.101 990 +S VUL 2447401.2359 9.037 1.833 1.991 1.079 990 +S VUL 2447402.2316 9.063 1.815 2.008 1.084 990 +S VUL 2447403.2343 9.081 1.817 1.998 1.084 990 +S VUL 2447404.2059 9.075 1.909 2.008 1.071 990 +S VUL 2447407.2191 9.135 2.039 1.105 990 +S VUL 2447408.1919 9.119 1.887 2.028 1.090 990 +S VUL 2447409.2211 9.177 2.029 1.117 990 +S VUL 2447410.2274 9.202 1.955 2.040 1.115 990 +S VUL 2447411.2398 9.214 1.932 2.022 1.117 990 +S VUL 2447413.1953 9.225 1.933 2.064 1.124 990 +S VUL 2447414.1819 9.231 1.921 2.062 1.102 990 +S VUL 2447415.1839 9.259 1.935 2.086 1.115 990 +S VUL 2447416.1889 9.270 1.977 2.056 1.110 990 +S VUL 2447417.1852 9.255 1.972 2.085 1.107 990 +S VUL 2447418.1838 9.278 2.081 1.101 990 +S VUL 2447419.1620 9.219 2.046 1.088 990 +S VUL 2447420.1667 9.270 2.044 1.085 990 +S VUL 2447421.1592 9.256 2.042 1.095 990 +S VUL 2447422.1656 9.266 2.026 1.095 990 +S VUL 2447423.1574 9.254 1.826 2.010 1.099 990 +S VUL 2447424.1615 9.245 2.003 1.091 990 +S VUL 2447425.1837 9.236 1.736 1.978 1.087 990 +S VUL 2447427.1867 9.218 1.695 1.945 1.067 990 +S VUL 2447428.1703 9.088 1.916 1.038 990 +S VUL 2447429.1538 9.088 1.897 1.057 990 +S VUL 2447430.1386 9.006 1.522 1.845 1.029 990 +S VUL 2447431.1503 8.966 1.492 1.823 1.015 990 +S VUL 2447432.1361 8.886 1.461 1.809 1.008 990 +S VUL 2447433.1398 8.870 1.351 1.799 1.001 990 +S VUL 2447434.1449 8.804 1.338 1.768 .971 990 +S VUL 2447734.3486 8.800 1.708 1.883 1.038 991 +S VUL 2447735.3750 8.840 1.647 1.906 1.027 991 +S VUL 2447736.3800 8.902 1.893 1.057 991 +S VUL 2447737.3720 8.867 1.743 1.936 1.056 991 +S VUL 2447738.3506 8.947 1.766 1.910 1.072 991 +S VUL 2447739.3145 8.935 1.705 1.950 1.060 991 +S VUL 2447740.3509 8.961 1.777 1.956 1.069 991 +S VUL 2447741.3078 8.940 1.762 1.991 1.062 991 +S VUL 2447742.3174 8.960 1.878 1.976 1.086 991 +S VUL 2447743.3063 8.969 1.846 1.965 1.063 991 +S VUL 2447744.2859 8.994 1.848 1.995 1.091 991 +S VUL 2447745.2910 9.028 1.823 1.995 1.096 991 +S VUL 2447746.2958 9.047 1.826 2.045 1.072 991 +S VUL 2447747.2854 9.030 1.869 2.007 1.079 991 +S VUL 2447748.2837 9.076 1.890 2.016 1.105 991 +S VUL 2447749.2805 9.111 1.888 2.028 1.082 991 +S VUL 2447750.2689 9.098 1.819 2.034 1.114 991 +S VUL 2447751.2683 9.112 1.909 2.035 1.101 991 +S VUL 2447752.2470 9.142 1.931 2.040 1.096 991 +S VUL 2447753.2472 9.170 1.948 2.027 1.105 991 +S VUL 2447754.2858 9.189 2.036 1.114 991 +S VUL 2447755.3147 9.186 1.963 2.052 1.124 991 +S VUL 2447756.3111 9.182 1.970 2.061 1.106 991 +S VUL 2447757.2776 9.233 2.047 1.119 991 +S VUL 2447758.2698 9.236 1.931 2.031 1.102 991 +S VUL 2447759.2491 9.255 2.011 2.066 1.112 991 +S VUL 2447760.2672 9.266 2.080 1.100 991 +S VUL 2447761.2560 9.279 1.924 2.067 1.132 991 +S VUL 2447762.2372 9.304 1.979 2.024 1.114 991 +S VUL 2447763.1994 9.263 2.051 1.104 991 +S VUL 2447764.2173 9.271 2.042 1.118 991 +S VUL 2447766.2165 9.271 2.021 1.105 991 +S VUL 2447767.2554 9.241 1.803 1.988 1.097 991 +S VUL 2447768.2616 9.180 1.774 1.985 1.099 991 +S VUL 2447770.2445 9.160 1.721 1.928 1.089 991 +S VUL 2447771.2353 9.110 1.643 1.925 1.078 991 +S VUL 2447772.2293 9.068 1.593 1.921 1.053 991 +S VUL 2447773.2585 9.041 1.522 1.861 1.052 991 +S VUL 2447774.2629 8.974 1.474 1.844 1.028 991 +S VUL 2447775.2274 8.916 1.423 1.808 1.012 991 +S VUL 2447776.2306 8.880 1.386 1.804 1.015 991 +S VUL 2448503.2427 9.118 2.050 1.099 993 +S VUL 2448504.2013 9.151 2.037 1.109 993 +S VUL 2448505.2278 9.177 2.034 1.125 993 +S VUL 2448506.2413 9.178 2.052 1.117 993 +S VUL 2448507.2175 9.204 2.076 1.108 993 +S VUL 2448508.1947 9.210 2.088 1.107 993 +S VUL 2448509.2043 9.236 2.089 1.119 993 +S VUL 2448510.1983 9.246 2.070 1.137 993 +S VUL 2448511.2057 9.264 2.090 1.117 993 +S VUL 2448512.2150 9.280 2.095 1.120 993 +S VUL 2448513.2157 9.296 2.097 1.122 993 +S VUL 2448514.2176 9.317 2.071 1.132 993 +S VUL 2448515.2166 9.346 2.055 1.129 993 +S VUL 2448516.2092 9.315 2.054 1.119 993 +S VUL 2448517.2031 9.317 2.059 1.127 993 +S VUL 2448518.2123 9.304 2.056 1.120 993 +S VUL 2448519.2301 9.289 2.046 1.113 993 +S VUL 2448520.1996 9.290 2.027 1.117 993 +S VUL 2448521.2265 9.240 2.030 1.098 993 +S VUL 2448522.2110 9.225 1.994 1.091 993 +S VUL 2448523.2043 9.207 1.959 1.097 993 +S VUL 2448854.2943 9.239 2.087 1.119 994 +S VUL 2448856.2666 9.280 2.092 1.124 994 +S VUL 2448858.2770 9.281 2.094 1.122 994 +S VUL 2448860.2618 9.284 2.078 1.115 994 +S VUL 2448862.2878 9.252 2.050 1.092 994 +S VUL 2448870.2478 8.952 1.872 1.010 994 +S VUL 2448872.2534 8.858 1.807 1.004 994 +S VUL 2448874.2688 8.789 1.815 .973 994 +S VUL 2448876.1971 8.747 1.756 .984 994 +S VUL 2448877.2194 8.748 1.740 .986 994 +S VUL 2448878.2352 8.744 1.732 .988 994 +S VUL 2448879.3300 8.735 1.742 .953 994 +S VUL 2448880.2291 8.726 1.732 .980 994 +S VUL 2448881.1956 8.716 1.734 .981 994 +S VUL 2448882.2034 8.722 1.777 .970 994 +S VUL 2448883.2341 8.733 1.759 .980 994 +S VUL 2448884.2223 8.737 1.792 .986 994 +S VUL 2448885.2200 8.749 1.786 .974 994 +S VUL 2448886.2322 8.753 1.784 .996 994 +S VUL 2448887.2519 8.746 1.802 .985 994 +S VUL 2448888.2149 8.753 1.826 .997 994 +S VUL 2448889.2208 8.784 1.816 1.016 994 +S VUL 2448890.2032 8.774 1.839 1.006 994 +S VUL 2448891.2006 8.778 1.865 1.012 994 +S VUL 2448892.2209 8.799 1.873 1.019 994 +S VUL 2448893.2084 8.810 1.882 1.033 994 +S VUL 2448894.2120 8.810 1.895 1.036 994 +S VUL 2449617.1845 9.236 1.974 1.052 995 +S VUL 2449619.2986 9.124 1.907 1.062 995 +S VUL 2449620.2650 9.068 1.930 1.061 995 +S VUL 2449621.2698 9.039 1.840 1.035 995 +S VUL 2449623.2558 8.889 1.795 1.004 995 +S VUL 2449624.2362 8.811 1.806 .999 995 +S VUL 2449625.2547 8.803 1.752 .979 995 +S VUL 2449626.2683 8.768 1.723 .985 995 +S VUL 2449631.2302 8.709 1.711 .969 995 +S VUL 2449632.2531 8.700 1.736 .971 995 +S VUL 2449633.2386 8.694 1.750 .980 995 +S VUL 2449634.2322 8.696 1.714 .968 995 +S VUL 2449635.2681 8.679 1.733 .965 995 +S VUL 2449933.3757 9.055 2.022 1.134 2.067 998 +S VUL 2449934.3588 9.096 2.035 1.174 2.135 998 +S VUL 2449935.3678 9.011 1.148 2.107 998 +S VUL 2449936.3537 9.093 2.038 1.159 2.127 998 +S VUL 2449937.3350 9.100 2.089 1.129 998 +S VUL 2449938.3618 9.160 1.135 2.171 998 +S VUL 2449939.3471 9.138 2.131 998 +S VUL 2449941.3354 9.193 2.140 998 +S VUL 2449942.3050 9.212 2.169 998 +S VUL 2449943.3000 9.226 2.179 998 +S VUL 2449944.2950 9.244 2.168 998 +S VUL 2449945.2867 9.248 2.151 998 +S VUL 2449946.2792 9.294 2.174 998 +S VUL 2449947.2661 9.327 2.168 998 +S VUL 2449948.2656 9.285 2.157 998 +S VUL 2449949.2667 9.339 2.179 998 +S VUL 2449950.2596 9.307 2.186 998 +S VUL 2449952.2658 9.310 2.174 998 +S VUL 2449953.3218 9.342 2.170 998 +S VUL 2449954.2596 9.324 2.132 998 +S VUL 2449955.2523 9.306 2.136 998 +S VUL 2449956.3160 9.248 2.142 998 +S VUL 2449957.2549 9.235 2.124 998 +S VUL 2449958.2373 9.233 2.022 1.098 2.115 998 +S VUL 2449959.2555 9.206 2.096 998 +S VUL 2449960.2829 9.155 2.067 998 +S VUL 2449962.2761 9.070 2.001 998 +S VUL 2449963.3840 9.008 2.012 998 +S VUL 2449985.2661 8.769 1.982 998 +S VUL 2449986.1930 8.763 1.996 998 +S VUL 2449987.2331 8.795 1.997 998 +S VUL 2449992.1628 8.839 2.025 998 +S VUL 2449993.2017 8.851 2.032 998 +S VUL 2450007.2564 9.115 2.095 998 +S VUL 2450009.2138 9.148 2.100 998 +S VUL 2450011.2035 9.186 2.138 998 +S VUL 2450017.1279 9.292 2.160 998 +S VUL 2450018.1883 9.293 2.151 998 +S VUL 2450020.1394 9.287 2.145 998 +S VUL 2450305.2181 8.990 1.831 1.019 971 +S VUL 2450306.2746 8.952 1.766 1.027 971 +S VUL 2450307.2862 8.893 1.777 .998 971 +S VUL 2450310.2672 8.778 1.701 .987 971 +S VUL 2450311.1946 8.763 1.712 .974 971 +S VUL 2450312.2756 8.738 1.691 .975 971 +S VUL 2450313.2687 8.708 1.690 .972 971 +S VUL 2450314.1927 8.715 1.696 .971 971 +S VUL 2450315.1958 8.679 1.713 .948 971 +S VUL 2450316.2079 8.706 1.697 .938 971 +S VUL 2450317.2170 8.707 1.696 .969 971 +S VUL 2450318.2265 8.705 1.704 .977 971 +S VUL 2450319.2186 8.675 1.725 .977 971 +S VUL 2450320.2187 8.699 1.732 .973 971 +S VUL 2450321.2076 8.698 1.722 .993 971 +S VUL 2450322.2151 8.715 1.729 1.011 971 +S VUL 2450323.2417 8.724 1.773 .991 971 +S VUL 2450324.2118 8.744 1.777 .999 971 +S VUL 2450325.2094 8.749 1.783 1.005 971 +S VUL 2450326.1668 8.770 1.795 1.009 971 +S VUL 2450327.2469 8.766 1.830 971 +S VUL 2450328.3680 8.793 1.773 1.025 971 +S VUL 2450329.2801 1.804 1.036 971 +S VUL 2450330.2078 8.864 1.819 1.037 971 +S VUL 2450332.1799 8.809 1.889 .978 971 +S VUL 2450333.1800 8.818 1.895 1.047 971 +S VUL 2450334.1919 8.847 1.900 1.035 971 +S VUL 2450335.1940 8.857 1.924 1.068 971 +S VUL 2450336.1955 8.863 1.905 1.055 971 +S VUL 2450337.1760 8.893 1.935 1.053 971 +S VUL 2450338.2573 8.903 1.930 1.059 971 +S VUL 2450340.1630 8.935 1.977 1.074 971 +S VUL 2450341.1713 8.956 1.965 1.071 971 +S VUL 2450342.1814 8.937 1.972 1.064 971 +S VUL 2450344.2044 8.984 1.997 1.089 971 +S VUL 2450347.1928 9.066 2.020 1.079 971 +S VUL 2450349.1707 9.082 2.033 1.074 971 +S VUL 2450357.1611 9.243 2.033 1.110 971 +T VUL 2446615.4594 5.441 .420 .409 .300 988 +T VUL 2446616.4557 5.700 .425 .696 .352 988 +T VUL 2446617.4591 5.896 .535 .750 .394 988 +T VUL 2446618.4539 6.033 .493 .759 .394 988 +T VUL 2446619.4587 5.401 .409 .366 .283 988 +T VUL 2446620.4615 5.587 .425 .585 .321 988 +T VUL 2446621.4611 5.813 .458 .767 .380 988 +T VUL 2446622.4586 5.998 .506 .783 .398 988 +T VUL 2446623.4612 5.706 .353 .631 .324 988 +T VUL 2446624.4573 5.521 .405 .456 .328 988 +T VUL 2446625.4622 5.729 .393 .734 .357 988 +T VUL 2446626.4595 5.934 .525 .769 .400 988 +T VUL 2446627.4608 6.002 .454 .736 .393 988 +T VUL 2446628.4627 5.406 .403 .350 .291 988 +T VUL 2446629.4658 5.618 .410 .623 .343 988 +T VUL 2446631.4519 6.027 .512 .753 .411 988 +T VUL 2446632.1609 5.872 .385 .647 .376 988 +T VUL 2446632.4610 5.598 .373 .534 .306 988 +T VUL 2446635.1601 5.891 .522 .764 .401 988 +T VUL 2446636.1592 6.073 .518 .776 .406 988 +T VUL 2446636.4589 5.958 .432 .722 .383 988 +T VUL 2446637.1563 5.379 .388 .419 .262 988 +T VUL 2446992.2821 5.363 .463 .267 989 +T VUL 2447000.1537 5.865 .534 .720 .430 989 +T VUL 2447001.1504 5.383 .390 .458 .267 989 +T VUL 2447002.1633 5.671 .430 .638 .370 989 +T VUL 2447003.1535 5.911 .524 .756 .412 989 +T VUL 2447004.1566 6.044 .594 .786 .429 989 +T VUL 2447005.1503 5.517 .466 .523 .292 989 +T VUL 2447088.0694 6.031 .768 .432 989 +T VUL 2447091.0676 5.745 .424 .695 .381 989 +T VUL 2447098.0415 .621 .373 989 +T VUL 2447399.1337 6.081 .423 .823 .419 990 +T VUL 2447400.1256 5.420 .312 .334 .277 990 +T VUL 2447401.1166 5.635 .352 .576 .364 990 +T VUL 2447402.1139 5.900 .398 .759 .437 990 +T VUL 2447404.1149 .261 990 +T VUL 2447407.1116 5.954 .375 .858 .386 990 +T VUL 2447408.1098 6.064 .433 .770 .399 990 +T VUL 2447410.1137 5.732 .279 .632 .417 990 +T VUL 2447411.1096 5.924 .414 .775 .428 990 +T VUL 2447412.1141 6.068 .446 .803 .429 990 +T VUL 2447413.1036 5.656 .282 .467 .349 990 +T VUL 2447414.1066 5.585 .298 .502 .353 990 +T VUL 2447415.1054 5.841 .378 .665 .429 990 +T VUL 2447416.1032 5.999 .395 .851 .425 990 +T VUL 2447417.1037 5.979 .335 .795 .401 990 +T VUL 2447418.1025 5.425 .295 .391 .295 990 +T VUL 2447419.0988 5.676 .344 .597 .377 990 +T VUL 2447420.0991 5.916 .415 .826 .407 990 +T VUL 2447421.0984 6.029 .403 .857 .412 990 +T VUL 2447422.0934 5.526 .230 .470 .277 990 +T VUL 2447423.0955 5.609 .309 .539 .370 990 +T VUL 2447424.0933 5.830 .298 .797 .407 990 +T VUL 2447425.0968 6.003 .382 .858 .414 990 +T VUL 2447427.0952 5.422 .343 .396 .295 990 +T VUL 2447428.0893 5.753 .337 .647 .403 990 +T VUL 2447430.0877 6.037 .373 .853 .411 990 +T VUL 2447431.0874 5.431 .318 .393 .279 990 +T VUL 2447432.0876 5.579 .367 .548 .354 990 +T VUL 2447433.0837 5.895 .381 .718 .444 990 +T VUL 2447434.0851 6.010 .393 .881 .411 990 +T VUL 2447739.3829 5.870 .365 .807 .411 991 +T VUL 2447740.3550 6.018 .425 .772 .424 991 +T VUL 2447741.1483 5.817 .245 .608 .373 991 +T VUL 2447742.1679 5.457 .192 .548 .323 991 +T VUL 2447743.1475 5.717 .345 .772 .384 991 +T VUL 2447744.1436 5.916 .421 .777 .421 991 +T VUL 2447745.1409 5.977 .413 .777 .405 991 +T VUL 2447746.1477 5.494 .210 .376 .358 991 +T VUL 2447746.4836 5.464 .220 .520 .329 991 +T VUL 2447747.1431 5.586 .287 .668 .351 991 +T VUL 2447748.1457 5.854 .418 .754 .424 991 +T VUL 2447748.4783 5.924 .483 .718 .419 991 +T VUL 2447749.1375 6.005 .457 .788 .428 991 +T VUL 2447749.4787 6.011 .455 .742 .411 991 +T VUL 2447750.1408 5.629 .264 .607 .334 991 +T VUL 2447750.4791 5.413 .184 .490 .269 991 +T VUL 2447751.1423 5.497 .245 .524 .335 991 +T VUL 2447751.1433 5.492 .240 .535 .333 991 +T VUL 2447751.4816 5.586 .260 .639 .366 991 +T VUL 2447752.1400 5.766 .369 .741 .399 991 +T VUL 2447752.4757 5.811 .414 .738 .393 991 +T VUL 2447753.1305 5.940 .466 .789 .418 991 +T VUL 2447753.4785 6.009 .468 .747 .453 991 +T VUL 2447754.1445 5.966 .396 .734 .398 991 +T VUL 2447754.4683 5.748 .260 .666 .367 991 +T VUL 2447755.1334 5.431 .230 .407 .321 991 +T VUL 2447755.4872 5.474 .575 .328 991 +T VUL 2447756.1355 5.627 .292 .710 .354 991 +T VUL 2447756.4867 5.705 .364 .660 .378 991 +T VUL 2447757.1330 5.845 .418 .761 .411 991 +T VUL 2447758.1304 6.005 .456 .781 .419 991 +T VUL 2447758.4853 5.952 .393 .750 .400 991 +T VUL 2447759.1290 5.553 .236 .524 .328 991 +T VUL 2447760.1279 5.564 .234 .585 .359 991 +T VUL 2447761.1302 5.780 .338 .765 .402 991 +T VUL 2447762.1331 5.944 .459 .784 .407 991 +T VUL 2447763.1250 5.913 .371 .695 .396 991 +T VUL 2447764.1272 5.418 .197 .465 .319 991 +T VUL 2447766.1235 5.924 .438 .773 .417 991 +T VUL 2447767.1308 6.008 .439 .796 .411 991 +T VUL 2447768.1224 5.571 .179 .483 .288 991 +T VUL 2447769.1247 5.591 .264 .569 .371 991 +T VUL 2447770.1177 5.788 .351 .811 .376 991 +T VUL 2447771.1179 5.981 .445 .799 .408 991 +T VUL 2447772.1155 5.850 .289 .676 .386 991 +T VUL 2447773.1177 5.458 .228 .512 .329 991 +T VUL 2447774.1321 5.726 .302 .698 .385 991 +T VUL 2447774.1338 5.742 .305 .677 .407 991 +T VUL 2447775.1128 5.920 .378 .827 .437 991 +T VUL 2447776.1173 6.000 .423 .775 .419 991 +T VUL 2448101.1566 5.430 .375 .478 .277 992 +T VUL 2448102.1755 5.704 .335 .661 .351 992 +T VUL 2448103.1519 5.947 .402 .735 .379 992 +T VUL 2448104.1540 6.073 .415 .789 .400 992 +T VUL 2448108.1471 6.032 .551 .766 .382 992 +T VUL 2448109.1433 5.938 .303 .684 .359 992 +T VUL 2448110.1494 5.451 .421 .489 .286 992 +T VUL 2448111.1552 5.715 .410 .669 .343 992 +T VUL 2448112.1491 5.967 .484 .746 .388 992 +T VUL 2448113.1496 6.062 .444 .756 .397 992 +T VUL 2448114.1410 5.458 .316 .524 .270 992 +T VUL 2448115.2201 5.616 .478 .594 .325 992 +T VUL 2448116.1656 5.865 .467 .737 .368 992 +T VUL 2448117.1484 6.065 .459 .795 .408 992 +T VUL 2448118.1444 5.859 .261 .655 .354 992 +T VUL 2448119.1376 5.510 .341 .547 .294 992 +T VUL 2448123.1343 5.388 .446 .477 .264 992 +T VUL 2448127.1410 5.681 .415 .598 .319 992 +T VUL 2444468.3085 5.422 .485 .456 900 +T VUL 2444468.3501 5.440 .505 .473 900 +T VUL 2444468.4355 5.427 .501 .466 900 +T VUL 2444469.2043 5.619 .530 .598 900 +T VUL 2444469.2661 5.635 .519 .618 900 +T VUL 2444469.3432 5.651 .515 .626 900 +T VUL 2444469.3897 5.671 .511 .632 900 +T VUL 2444472.2848 5.788 .469 .623 900 +T VUL 2444472.3348 5.745 .482 .590 900 +T VUL 2444472.3793 5.707 .462 .579 900 +T VUL 2444472.4126 5.674 .472 .538 900 +T VUL 2444476.2418 6.060 .571 .752 900 +T VUL 2444476.2911 6.033 .546 .766 900 +T VUL 2444476.3342 6.029 .550 .748 900 +T VUL 2444476.3994 6.021 .545 .721 900 +T VUL 2444476.4460 5.994 .506 .720 900 +T VUL 2444477.1335 5.466 .519 .470 900 +T VUL 2444477.1626 5.472 .514 .467 900 +T VUL 2444477.2057 5.428 .515 .445 900 +T VUL 2444477.2703 5.467 .516 .460 900 +T VUL 2444477.3300 5.465 .512 .463 900 +T VUL 2444477.3737 5.457 .521 .476 900 +T VUL 2444477.4369 5.443 .466 .482 900 +T VUL 2444478.3460 5.683 .511 .657 900 +T VUL 2448503.1225 6.098 .424 .785 .433 993 +T VUL 2448504.1200 5.752 .236 .609 .336 993 +T VUL 2448505.1222 5.515 .217 .553 .321 993 +T VUL 2448506.1224 5.789 .332 .714 .402 993 +T VUL 2448507.1175 5.981 .418 .786 .421 993 +T VUL 2448508.1174 6.032 .380 .757 .418 993 +T VUL 2448509.1188 5.412 .173 .481 .288 993 +T VUL 2448510.1189 5.663 .298 .642 .361 993 +T VUL 2448511.1188 5.925 .415 .766 .424 993 +T VUL 2448512.1182 6.060 .435 .792 .437 993 +T VUL 2448513.1174 5.627 .230 .554 .325 993 +T VUL 2448514.1172 5.548 .246 .585 .331 993 +T VUL 2448515.1109 5.785 .368 .723 .390 993 +T VUL 2448516.1152 5.969 .441 .796 .415 993 +T VUL 2448517.1133 5.986 .357 .709 .415 993 +T VUL 2448518.1199 5.443 .226 .489 .302 993 +T VUL 2448519.1175 5.691 .315 .669 .375 993 +T VUL 2448520.1084 5.923 .422 .758 .422 993 +T VUL 2448521.1142 6.069 .455 .784 .442 993 +T VUL 2448522.1085 5.503 .233 .513 .293 993 +T VUL 2448523.1044 5.584 .263 .595 .330 993 +T VUL 2448856.1416 5.659 .247 .691 .367 994 +T VUL 2448858.1374 6.043 .430 .801 .435 994 +T VUL 2448860.1350 5.549 .195 .640 .346 994 +T VUL 2448870.1218 5.846 .357 .779 .391 994 +T VUL 2448872.1195 5.909 .286 .681 .410 994 +T VUL 2448874.1209 5.750 .261 .738 .380 994 +T VUL 2448876.1158 6.076 .379 .801 .443 994 +T VUL 2448877.1118 5.488 .102 .557 .298 994 +T VUL 2448878.2490 5.674 .226 .687 .373 994 +T VUL 2448880.1122 6.028 .419 .830 .415 994 +T VUL 2448881.1063 5.760 .234 .652 .357 994 +T VUL 2448882.1079 5.532 .156 .628 .333 994 +T VUL 2448883.1078 5.775 .298 .735 .399 994 +T VUL 2448884.1038 5.958 .382 .810 .432 994 +T VUL 2448885.1012 6.034 .376 .771 .409 994 +T VUL 2448886.1027 5.448 .092 .575 .297 994 +T VUL 2448888.0991 5.910 .364 .785 .419 994 +T VUL 2448889.0989 6.049 .434 .820 .427 994 +T VUL 2448890.0971 5.683 .194 .595 .360 994 +T VUL 2448891.0951 5.561 .171 .623 .349 994 +T VUL 2448892.0935 5.767 .290 .733 .391 994 +T VUL 2448893.0937 5.999 .399 .816 .429 994 +T VUL 2448894.0922 5.961 .306 .757 .406 994 +T VUL 2449617.2145 5.781 .346 .666 .343 995 +T VUL 2449619.2126 5.679 .383 .672 .378 995 +T VUL 2449620.1893 5.877 .495 .784 .406 995 +T VUL 2449620.1972 5.869 .504 .770 .400 995 +T VUL 2449621.0975 6.052 .478 .768 .439 995 +T VUL 2449621.1893 6.020 .453 .791 .430 995 +T VUL 2449622.0973 5.378 .258 .511 .300 995 +T VUL 2449623.0918 5.551 .345 .569 .336 995 +T VUL 2449624.0950 5.820 .445 .725 .403 995 +T VUL 2449625.0951 6.006 .462 .776 .424 995 +T VUL 2449631.0947 5.306 .310 .456 .258 995 +T VUL 2449632.1013 5.584 .336 .616 .341 995 +T VUL 2449632.3101 5.647 .325 .641 .381 995 +T VUL 2449633.0910 5.840 .441 .738 .408 995 +T VUL 2449634.0979 6.016 .483 .812 .424 995 +T VUL 2449934.1598 5.765 .701 .717 998 +T VUL 2449935.1586 6.015 .774 .474 .796 998 +T VUL 2449936.1523 6.083 .786 .446 .832 998 +T VUL 2449937.2180 5.429 .489 .275 998 +T VUL 2449938.1546 5.658 .634 .338 .648 998 +T VUL 2449939.1594 5.854 .772 .402 .778 998 +T VUL 2449942.1518 5.468 .544 .303 .570 998 +T VUL 2449943.1464 .709 .380 .708 998 +T VUL 2449944.1446 5.993 .804 .388 .774 998 +T VUL 2449945.1450 6.039 .776 .421 .785 998 +T VUL 2449946.1481 5.412 .486 .266 .508 998 +T VUL 2449947.1449 5.650 .654 .347 .673 998 +T VUL 2449947.2764 5.698 .676 .363 .705 998 +T VUL 2449948.1433 5.899 .770 .410 .785 998 +T VUL 2449949.1476 6.021 .783 .422 .802 998 +T VUL 2449950.1451 5.660 .599 .325 .628 998 +T VUL 2449952.1437 5.794 .727 .387 .747 998 +T VUL 2449953.1728 6.032 .781 .411 .776 998 +T VUL 2449954.1586 5.978 .720 .390 .739 998 +T VUL 2449955.1526 .490 .281 .536 998 +T VUL 2449958.1378 6.112 .795 .423 .821 998 +T VUL 2449959.1339 5.627 .545 .312 .578 998 +T VUL 2449962.1465 6.035 .798 .434 .825 998 +T VUL 2450009.0911 5.614 .611 .332 .636 998 +T VUL 2450011.0880 6.014 .793 .417 .789 998 +T VUL 2450012.1345 5.756 .661 998 +T VUL 2450017.0766 5.403 .467 .266 .519 998 +T VUL 2450018.1305 5.644 .647 .353 .673 998 +T VUL 2450020.0693 6.109 .791 .416 .802 998 +T VUL 2450305.1589 5.496 .503 .306 971 +T VUL 2450306.1584 5.573 .596 .343 971 +T VUL 2450310.2087 5.445 .539 .305 971 +T VUL 2450311.1491 5.677 .709 .346 971 +T VUL 2450312.1459 5.919 .792 .405 971 +T VUL 2450313.1491 6.022 .805 .409 971 +T VUL 2450314.1345 5.421 .508 .273 971 +T VUL 2450315.1371 5.604 .638 .352 971 +T VUL 2450316.1381 5.846 .785 .397 971 +T VUL 2450317.1670 6.018 .776 .371 971 +T VUL 2450318.1382 5.773 .630 .356 971 +T VUL 2450319.1365 5.462 .531 .320 971 +T VUL 2450320.1370 5.729 .678 .397 971 +T VUL 2450321.1327 5.962 .760 .437 971 +T VUL 2450322.1260 6.044 .740 .444 971 +T VUL 2450323.1283 5.376 .476 .301 971 +T VUL 2450324.1337 5.627 .644 .347 971 +T VUL 2450325.1240 5.894 .745 .417 971 +T VUL 2450326.1211 6.015 .787 .414 971 +U VUL 2446615.4561 7.464 1.111 1.454 .846 988 +U VUL 2446616.4514 7.200 .916 1.317 .785 988 +U VUL 2446617.4556 6.768 .874 1.148 .687 988 +U VUL 2446618.4504 6.900 .886 1.223 .724 988 +U VUL 2446619.4552 6.939 .920 1.269 .764 988 +U VUL 2446620.4581 7.171 1.016 1.381 .806 988 +U VUL 2446621.4580 7.318 1.110 1.447 .842 988 +U VUL 2446622.4554 7.462 1.150 1.498 .853 988 +U VUL 2446623.4579 7.454 1.108 1.443 .840 988 +U VUL 2446624.4543 7.197 .935 1.310 .786 988 +U VUL 2446625.4587 6.770 .816 1.155 .685 988 +U VUL 2446626.4554 6.879 .923 1.221 .733 988 +U VUL 2446627.4570 6.955 .902 1.265 .755 988 +U VUL 2446628.4587 7.179 1.013 1.379 .820 988 +U VUL 2446629.4619 7.328 1.138 1.445 .843 988 +U VUL 2446631.4484 7.481 1.095 1.430 .851 988 +U VUL 2446632.1691 7.275 .975 1.349 .808 988 +U VUL 2446632.4577 7.156 .924 1.314 .777 988 +U VUL 2446635.1703 6.913 .919 1.265 .751 988 +U VUL 2446636.1661 7.089 .980 1.341 .796 988 +U VUL 2446636.4560 7.157 1.055 1.394 .809 988 +U VUL 2446637.1626 7.259 1.073 1.425 .840 988 +U VUL 2447084.0882 7.230 1.014 1.358 .801 989 +U VUL 2447091.0669 6.970 .931 1.275 .764 989 +U VUL 2447098.0408 6.965 .879 1.229 .737 989 +U VUL 2447399.1459 7.362 .955 1.386 .794 990 +U VUL 2447400.1338 6.896 .742 1.176 .687 990 +U VUL 2447401.1237 6.814 .767 1.192 .683 990 +U VUL 2447402.1235 6.947 .820 1.282 .755 990 +U VUL 2447404.1214 7.266 1.013 1.415 .818 990 +U VUL 2447408.1196 6.976 .808 1.167 .737 990 +U VUL 2447409.1217 6.832 .752 1.165 .707 990 +U VUL 2447410.1221 6.951 .780 1.293 .743 990 +U VUL 2447411.1177 7.069 .886 1.309 .788 990 +U VUL 2447412.1229 7.278 .957 1.431 .832 990 +U VUL 2447413.1115 7.414 1.089 1.477 .853 990 +U VUL 2447414.1146 7.543 1.101 1.476 .853 990 +U VUL 2447415.1134 7.332 .912 1.389 .803 990 +U VUL 2447416.1118 6.893 .752 1.206 .702 990 +U VUL 2447417.1106 6.804 .730 1.227 .700 990 +U VUL 2447418.1111 6.902 .819 1.264 .717 990 +U VUL 2447419.1072 6.959 .888 1.284 .740 990 +U VUL 2447420.1064 7.268 .999 1.442 .814 990 +U VUL 2447421.1055 7.401 1.084 1.491 .845 990 +U VUL 2447422.1012 7.452 1.101 1.458 .807 990 +U VUL 2447423.1030 7.329 .929 1.395 .807 990 +U VUL 2447424.1004 6.893 1.212 .720 990 +U VUL 2447425.1040 6.828 .731 1.195 .698 990 +U VUL 2447427.1041 7.002 .863 1.342 .748 990 +U VUL 2447428.0979 7.269 .982 1.448 .820 990 +U VUL 2447429.1063 7.418 1.062 1.475 .849 990 +U VUL 2447430.0966 7.492 1.083 1.497 .840 990 +U VUL 2447431.0960 7.336 .932 1.403 .798 990 +U VUL 2447432.0946 6.859 .780 1.181 .701 990 +U VUL 2447433.0913 6.846 .719 990 +U VUL 2447434.0935 6.951 .794 1.289 .742 990 +U VUL 2447734.3188 7.455 1.003 1.415 .827 991 +U VUL 2447735.3590 7.048 .822 1.264 .742 991 +U VUL 2447736.3615 6.767 .745 1.127 .694 991 +U VUL 2447737.3660 6.862 .822 1.256 .726 991 +U VUL 2447738.3440 6.974 .845 1.265 .759 991 +U VUL 2447739.3075 7.173 .952 1.387 .808 991 +U VUL 2447740.3430 7.323 1.044 1.448 .831 991 +U VUL 2447741.3035 7.424 1.121 1.498 .844 991 +U VUL 2447742.3099 7.420 1.040 1.435 .838 991 +U VUL 2447743.2980 7.077 .822 1.279 .754 991 +U VUL 2447744.2759 6.752 .733 1.157 .690 991 +U VUL 2447745.2818 6.890 .815 1.234 .736 991 +U VUL 2447746.2870 6.938 .823 1.303 .744 991 +U VUL 2447747.2807 7.143 .972 1.404 .801 991 +U VUL 2447748.2793 7.331 1.066 1.467 .840 991 +U VUL 2447749.2730 7.469 1.115 1.474 .846 991 +U VUL 2447750.2614 7.436 1.005 1.427 .847 991 +U VUL 2447751.2563 7.091 .838 1.267 .765 991 +U VUL 2447752.2403 6.764 .726 1.157 .686 991 +U VUL 2447753.2378 6.889 .769 1.239 .728 991 +U VUL 2447754.2687 6.939 .822 1.282 .753 991 +U VUL 2447755.2870 7.172 .969 1.390 .810 991 +U VUL 2447756.2999 7.297 1.040 1.468 .827 991 +U VUL 2447757.2659 7.450 1.102 1.498 .851 991 +U VUL 2447758.2591 7.423 1.003 1.431 .831 991 +U VUL 2447759.2371 7.117 .819 1.300 .753 991 +U VUL 2447760.2585 6.766 .715 1.155 .689 991 +U VUL 2447761.1342 6.883 .777 1.214 .746 991 +U VUL 2447761.2441 6.906 .782 1.226 .740 991 +U VUL 2447762.1363 6.916 .816 1.278 .732 991 +U VUL 2447762.2277 6.954 .822 1.263 .753 991 +U VUL 2447763.1281 7.133 .940 1.372 .806 991 +U VUL 2447763.1946 7.155 .923 1.382 .813 991 +U VUL 2447764.1304 7.282 1.028 1.443 .820 991 +U VUL 2447764.1988 7.309 1.047 1.451 .830 991 +U VUL 2447766.1270 7.470 1.046 1.437 .848 991 +U VUL 2447766.2026 7.442 .993 1.456 .837 991 +U VUL 2447767.1342 7.154 .825 1.311 .764 991 +U VUL 2447767.2490 7.093 .815 1.279 .757 991 +U VUL 2447768.1264 .729 1.154 .674 991 +U VUL 2447768.2498 .710 1.164 .691 991 +U VUL 2447769.1279 6.851 .797 1.238 .716 991 +U VUL 2447770.1214 6.915 .800 1.295 .743 991 +U VUL 2447770.2366 6.920 .819 1.285 .744 991 +U VUL 2447771.1227 7.144 .923 1.379 .798 991 +U VUL 2447771.2254 7.159 .929 1.404 .818 991 +U VUL 2447772.1188 7.313 1.024 1.441 .849 991 +U VUL 2447772.2228 7.316 1.042 1.475 .828 991 +U VUL 2447773.1210 7.451 1.107 1.513 .856 991 +U VUL 2447773.2515 7.477 1.088 1.486 .860 991 +U VUL 2447774.1368 7.446 1.021 1.474 .828 991 +U VUL 2447774.2559 7.438 .986 1.438 .837 991 +U VUL 2447775.1163 7.161 .816 1.314 .785 991 +U VUL 2447775.2202 7.115 .809 1.297 .761 991 +U VUL 2447776.1208 6.749 .726 1.155 .676 991 +U VUL 2447776.2243 6.785 .736 1.161 .705 991 +U VUL 2448101.1686 7.472 1.103 1.492 .862 992 +U VUL 2448102.1814 7.360 1.012 1.409 .823 992 +U VUL 2448103.1586 6.993 .846 1.185 .730 992 +U VUL 2448104.1605 6.838 .834 1.174 .697 992 +U VUL 2448108.1526 7.386 1.117 1.480 .844 992 +U VUL 2448109.1483 7.476 1.117 1.493 .854 992 +U VUL 2448110.1564 7.376 .997 1.412 .816 992 +U VUL 2448111.1608 6.979 .857 1.238 .710 992 +U VUL 2448112.1546 6.841 .840 1.157 .695 992 +U VUL 2448114.1468 7.025 .924 1.345 .755 992 +U VUL 2448115.2259 7.267 1.042 1.435 .826 992 +U VUL 2448116.1706 7.355 1.128 1.491 .826 992 +U VUL 2448117.1525 7.500 1.132 1.522 .869 992 +U VUL 2448118.1491 7.390 1.021 1.414 .821 992 +U VUL 2448119.1443 7.023 .847 1.250 .735 992 +U VUL 2448123.1391 7.275 1.040 1.418 .828 992 +U VUL 2448127.1471 6.983 .875 1.235 .717 992 +U VUL 2448504.1427 6.864 .767 1.208 .721 993 +U VUL 2448505.1396 6.944 .816 1.277 .749 993 +U VUL 2448506.1416 7.108 .935 1.368 .791 993 +U VUL 2448507.1212 7.315 1.028 1.448 .826 993 +U VUL 2448508.1212 7.454 1.094 1.514 .861 993 +U VUL 2448509.1202 7.502 1.028 1.486 .849 993 +U VUL 2448510.1217 7.251 .858 1.342 .793 993 +U VUL 2448511.1203 6.822 .731 1.176 .695 993 +U VUL 2448512.1199 6.880 .757 1.229 .727 993 +U VUL 2448513.1187 6.954 .797 1.278 .751 993 +U VUL 2448514.1182 7.123 .905 1.371 .796 993 +U VUL 2448515.1118 7.285 1.019 1.453 .816 993 +U VUL 2448516.1160 7.419 1.108 1.508 .839 993 +U VUL 2448517.1142 7.499 1.067 1.466 .859 993 +U VUL 2448518.1214 7.261 .887 1.336 .796 993 +U VUL 2448519.1296 6.810 .726 1.164 .696 993 +U VUL 2448520.1095 6.867 .776 1.199 .727 993 +U VUL 2448521.1156 6.950 .841 1.270 .748 993 +U VUL 2448522.1096 7.102 .927 1.370 .794 993 +U VUL 2448523.1053 7.318 1.032 1.460 .829 993 +U VUL 2448854.1689 7.012 .819 1.253 .747 994 +U VUL 2448856.1555 6.907 .755 1.280 .740 994 +U VUL 2448858.1525 7.212 .957 1.426 .821 994 +U VUL 2448860.1454 7.473 1.118 1.536 .855 994 +U VUL 2448870.1302 7.044 .778 1.282 .737 994 +U VUL 2448872.1314 6.949 .793 1.252 .770 994 +U VUL 2448874.1300 7.217 .917 1.447 .808 994 +U VUL 2448876.1233 7.515 1.077 1.507 .879 994 +U VUL 2448877.1191 7.467 .966 1.442 .829 994 +U VUL 2448878.2346 6.995 .734 1.244 .742 994 +U VUL 2448880.1190 6.926 .797 1.278 .739 994 +U VUL 2448881.1119 6.959 .810 1.306 .761 994 +U VUL 2448882.1148 7.225 .967 1.446 .805 994 +U VUL 2448883.1130 7.363 1.029 1.507 .851 994 +U VUL 2448884.1100 7.463 1.106 1.522 .855 994 +U VUL 2448885.1074 7.423 .974 1.444 .822 994 +U VUL 2448886.1080 7.054 .780 1.272 .737 994 +U VUL 2448888.1057 6.931 .749 1.272 .745 994 +U VUL 2448889.1062 6.964 .801 1.323 .763 994 +U VUL 2448890.1030 7.232 .951 1.410 .828 994 +U VUL 2448891.1010 7.365 1.049 1.493 .840 994 +U VUL 2448892.0994 7.451 1.073 1.506 .852 994 +U VUL 2448893.0993 7.420 .972 1.443 .823 994 +U VUL 2448894.0975 7.008 .745 1.272 .747 994 +U VUL 2449522.7733 7.404 1.519 996 +U VUL 2449529.7458 7.209 1.436 .843 .808 996 +U VUL 2449545.7328 7.270 1.103 1.449 .842 .794 996 +U VUL 2449563.6151 7.492 1.479 996 +U VUL 2449564.7281 7.315 .974 1.340 .805 .746 996 +U VUL 2449617.1761 1.410 .829 995 +U VUL 2449619.2953 7.524 1.474 .870 995 +U VUL 2449620.2474 7.424 .961 1.420 .837 995 +U VUL 2449621.2614 7.025 .816 1.242 .743 995 +U VUL 2449623.2096 6.926 .834 1.243 .744 995 +U VUL 2449624.2297 6.953 .914 1.269 .759 995 +U VUL 2449625.2500 7.216 1.006 1.410 .814 995 +U VUL 2449626.2637 7.373 1.471 .856 995 +U VUL 2449631.2266 6.917 1.235 .742 995 +U VUL 2449632.2485 6.960 .894 1.310 .755 995 +U VUL 2449633.2225 7.217 .999 1.416 .817 995 +U VUL 2449634.2267 7.348 1.461 .830 995 +U VUL 2449934.1678 6.840 1.233 .717 1.426 998 +U VUL 2449936.1577 7.071 1.332 .792 1.541 998 +U VUL 2449937.2225 7.296 1.454 .856 998 +U VUL 2449938.1591 7.454 1.505 .866 1.673 998 +U VUL 2449939.1632 7.504 1.518 .875 1.687 998 +U VUL 2449942.1574 6.825 1.190 .721 1.397 998 +U VUL 2449943.1512 6.882 1.270 .756 1.441 998 +U VUL 2449944.1511 7.060 1.345 .763 1.507 998 +U VUL 2449945.1509 7.284 1.453 .850 1.634 998 +U VUL 2449946.1523 7.404 1.504 .861 1.653 998 +U VUL 2449947.1508 7.527 1.524 .869 1.667 998 +U VUL 2449948.1475 7.379 1.397 .833 1.605 998 +U VUL 2449949.1522 6.896 1.198 .727 1.405 998 +U VUL 2449950.1493 6.853 1.201 .727 1.424 998 +U VUL 2449952.1491 7.043 1.337 .788 1.521 998 +U VUL 2449953.1746 .838 1.628 998 +U VUL 2449954.1615 7.465 1.498 .869 1.677 998 +U VUL 2449955.1542 7.560 1.503 .874 1.673 998 +U VUL 2449958.1407 1.192 .738 1.433 998 +U VUL 2449959.1363 1.281 .777 998 +U VUL 2449962.1489 7.431 1.508 .868 1.682 998 +U VUL 2450009.0918 7.262 1.442 .833 1.601 998 +U VUL 2450011.0897 7.480 1.509 .864 1.651 998 +U VUL 2450012.1315 7.320 1.575 998 +U VUL 2450017.0770 7.261 1.450 .843 1.614 998 +U VUL 2450018.1238 7.411 1.525 .878 1.653 998 +U VUL 2450020.0697 7.332 1.390 .826 1.588 998 +U VUL 2450305.1690 7.307 1.418 .840 971 +U VUL 2450306.1614 7.455 1.467 .875 971 +U VUL 2450307.1834 7.474 1.427 .847 971 +U VUL 2450310.2147 6.880 1.208 .734 971 +U VUL 2450311.1541 6.908 1.263 .743 971 +U VUL 2450312.1491 7.119 1.365 .795 971 +U VUL 2450313.1549 7.287 1.460 .828 971 +U VUL 2450314.1394 7.451 1.474 .848 971 +U VUL 2450315.1407 7.460 1.445 .847 971 +U VUL 2450316.1406 7.179 1.330 .773 971 +U VUL 2450317.1701 6.786 1.122 .652 971 +U VUL 2450318.1411 6.872 1.176 .735 971 +U VUL 2450319.1392 6.931 1.207 .769 971 +U VUL 2450320.1404 7.122 1.330 .812 971 +U VUL 2450321.1351 7.312 1.411 .847 971 +U VUL 2450322.1300 7.455 1.449 .877 971 +U VUL 2450323.1306 7.437 1.405 .856 971 +U VUL 2450324.1359 7.180 1.283 .781 971 +U VUL 2450325.1268 6.818 1.128 .705 971 +U VUL 2450326.3371 6.913 1.209 .744 971 +U VUL 2450330.3215 7.439 1.476 .847 971 +U VUL 2450332.2932 7.122 1.257 .723 971 +U VUL 2450333.3046 6.777 1.150 .645 971 +U VUL 2450334.3354 6.925 1.241 .748 971 +U VUL 2450335.3500 6.951 1.264 .763 971 +U VUL 2450337.2909 7.360 1.462 .831 971 +U VUL 2450340.3006 7.113 1.223 .776 971 +U VUL 2450341.3056 6.806 1.161 .732 971 +U VUL 2450342.3213 6.876 1.259 .720 971 +U VUL 2450344.3222 7.187 1.369 971 +U VUL 2450349.2809 6.757 1.145 .726 971 +X VUL 2446606.3256 9.062 1.284 1.619 .919 988 +X VUL 2446607.3940 9.222 1.369 1.637 .922 988 +X VUL 2446608.3297 8.967 1.119 1.493 .862 988 +X VUL 2446609.2291 8.439 .960 1.278 .752 988 +X VUL 2446610.3541 8.639 1.037 1.412 .819 988 +X VUL 2446611.2343 8.813 1.156 1.517 .861 988 +X VUL 2446612.2204 8.974 1.239 1.569 .906 988 +X VUL 2446613.2193 9.163 1.358 1.608 .924 988 +X VUL 2446614.2211 9.181 1.303 1.575 .922 988 +X VUL 2446615.2207 8.583 .990 1.319 .773 988 +X VUL 2446616.2230 8.566 1.007 1.354 .784 988 +X VUL 2446617.2185 8.709 1.138 1.481 .833 988 +X VUL 2446618.2175 8.884 1.185 1.527 .893 988 +X VUL 2446619.2166 9.120 1.349 1.610 .925 988 +X VUL 2446620.2148 9.215 1.359 1.627 .931 988 +X VUL 2446621.3078 8.761 1.033 1.387 .811 988 +X VUL 2446622.2132 8.466 .987 1.300 .761 988 +X VUL 2446623.2024 8.684 1.085 1.420 .834 988 +X VUL 2446624.1975 8.861 1.502 .878 988 +X VUL 2446625.1921 9.059 1.589 .915 988 +X VUL 2446626.1992 9.177 1.646 .916 988 +X VUL 2446627.2016 9.025 1.136 1.531 .860 988 +X VUL 2446628.2027 8.438 .998 1.272 .747 988 +X VUL 2446629.2141 8.608 1.061 1.388 .810 988 +X VUL 2446630.2121 8.788 1.172 1.477 .856 988 +X VUL 2446631.1957 8.969 1.237 1.571 .896 988 +X VUL 2446632.2094 9.176 1.638 .927 988 +X VUL 2446635.2981 8.557 1.023 1.362 .789 988 +X VUL 2446636.2084 8.755 1.109 1.491 .852 988 +X VUL 2446637.2110 8.873 1.222 1.544 .883 988 +X VUL 2447734.3659 .875 1.284 .758 991 +X VUL 2447735.3875 8.637 1.416 .798 991 +X VUL 2447736.3912 8.831 1.507 .859 991 +X VUL 2447737.3830 9.004 1.585 .897 991 +X VUL 2447738.3633 9.201 1.634 .918 991 +X VUL 2447739.3387 9.047 1.526 .877 991 +X VUL 2447740.3708 8.454 .820 1.252 .738 991 +X VUL 2447741.3264 8.585 .911 1.389 .786 991 +X VUL 2447742.3315 8.727 1.099 1.524 .853 991 +X VUL 2447743.3211 8.899 1.175 1.564 .885 991 +X VUL 2447744.3019 9.113 1.278 1.647 .917 991 +X VUL 2447745.3041 9.163 1.628 .918 991 +X VUL 2447746.3083 8.640 .891 1.381 .784 991 +X VUL 2447747.3032 .873 1.336 .763 991 +X VUL 2447748.3006 8.718 1.021 1.463 .828 991 +X VUL 2447749.2980 8.834 1.091 1.521 .849 991 +X VUL 2447750.2885 9.081 1.617 .902 991 +X VUL 2447751.2826 9.179 1.636 .914 991 +X VUL 2447752.2521 8.918 1.460 .837 991 +X VUL 2447753.2529 8.452 .846 1.282 .741 991 +X VUL 2447754.2889 8.646 .900 1.427 .811 991 +X VUL 2447755.3218 8.821 1.087 1.512 .876 991 +X VUL 2447756.3182 9.000 1.187 1.606 .889 991 +X VUL 2447757.3008 9.156 1.273 1.648 .911 991 +X VUL 2447758.2915 9.074 1.092 1.504 .876 991 +X VUL 2447759.2615 8.451 .841 1.291 .730 991 +X VUL 2447760.2740 8.577 .895 1.371 .793 991 +X VUL 2447761.2598 8.776 1.051 1.486 .846 991 +X VUL 2447762.2391 8.927 1.537 .880 991 +X VUL 2447763.2007 9.107 1.654 .919 991 +X VUL 2447764.2186 9.178 1.617 .914 991 +X VUL 2447766.2181 8.513 1.322 .765 991 +X VUL 2447767.2751 8.706 1.459 .821 991 +X VUL 2447768.2639 8.815 1.115 1.522 .859 991 +X VUL 2447770.2509 9.203 1.627 .900 991 +X VUL 2447771.2374 8.884 .968 1.478 .844 991 +X VUL 2447772.2335 8.446 .829 1.287 .748 991 +X VUL 2447773.2624 8.660 .943 1.418 .819 991 +X VUL 2447774.2662 8.831 1.075 1.521 .858 991 +X VUL 2447775.2293 9.012 1.603 .889 991 +X VUL 2447776.2338 9.175 1.668 .917 991 +X VUL 2448503.2559 9.212 1.653 .912 993 +X VUL 2448504.2107 8.973 1.478 .849 993 +X VUL 2448505.2329 8.467 1.280 .758 993 +X VUL 2448506.2510 8.626 1.436 .802 993 +X VUL 2448507.2247 8.831 1.545 .850 993 +X VUL 2448508.2020 8.985 1.613 .879 993 +X VUL 2448509.2331 9.180 1.648 .911 993 +X VUL 2448510.2015 9.117 1.586 .881 993 +X VUL 2448511.2114 8.511 .839 1.323 .744 993 +X VUL 2448512.2180 8.567 1.378 .783 993 +X VUL 2448513.2217 8.788 1.513 .842 993 +X VUL 2448514.2249 8.926 1.559 .884 993 +X VUL 2448515.2251 9.150 1.628 .914 993 +X VUL 2448516.2115 9.215 1.629 .918 993 +X VUL 2448517.2058 8.752 1.393 .807 993 +X VUL 2448518.2142 8.521 1.305 .773 993 +X VUL 2448519.2317 8.718 1.468 .835 993 +X VUL 2448520.2014 8.869 1.531 .862 993 +X VUL 2448521.2283 9.084 1.622 .906 993 +X VUL 2448522.2133 9.208 1.673 .914 993 +X VUL 2448523.2061 8.972 1.478 .859 993 +X VUL 2448854.2969 8.739 1.507 .841 994 +X VUL 2448856.2716 9.102 1.656 .906 994 +X VUL 2448858.2791 8.847 1.465 .830 994 +X VUL 2448860.2651 8.672 .936 1.463 .824 994 +X VUL 2448862.2892 9.050 1.633 .889 994 +X VUL 2448870.2497 9.180 1.644 .897 994 +X VUL 2448872.2550 8.518 1.358 .783 994 +X VUL 2448874.2723 8.866 1.584 .860 994 +X VUL 2448876.2300 9.236 1.685 .924 994 +X VUL 2448877.2210 8.858 1.480 .820 994 +X VUL 2448878.2367 8.478 1.312 .759 994 +X VUL 2448880.2318 8.848 1.546 .874 994 +X VUL 2448881.1971 9.027 1.618 .899 994 +X VUL 2448882.2050 9.200 1.685 .922 994 +X VUL 2448883.2361 9.056 1.571 .861 994 +X VUL 2448884.2244 8.478 1.287 .764 994 +X VUL 2448885.2223 8.605 1.407 .791 994 +X VUL 2448886.2345 8.818 1.533 .862 994 +X VUL 2448887.2535 8.948 1.591 .876 994 +X VUL 2448888.2167 9.153 1.672 .926 994 +X VUL 2448889.2227 9.189 1.637 .901 994 +X VUL 2448890.2047 8.625 1.377 .769 994 +X VUL 2448891.2023 8.523 1.378 .765 994 +X VUL 2448892.2225 8.756 1.496 .841 994 +X VUL 2448893.2099 8.876 1.560 .871 994 +X VUL 2448894.2135 9.086 1.648 .909 994 +X VUL 2449934.3630 8.660 1.387 .862 1.597 998 +X VUL 2449935.3713 8.806 .894 1.695 998 +X VUL 2449936.3638 8.999 1.594 .916 1.758 998 +X VUL 2449937.3403 9.177 1.680 .940 998 +X VUL 2449938.3665 9.197 .908 1.784 998 +X VUL 2449939.3676 8.509 1.305 .767 1.487 998 +X VUL 2449941.3380 8.797 1.654 998 +X VUL 2449942.3082 8.930 1.730 998 +X VUL 2449943.3024 9.140 1.803 998 +X VUL 2449944.3378 9.241 1.819 998 +X VUL 2449945.3487 8.847 1.624 998 +X VUL 2449946.3201 8.541 1.520 998 +X VUL 2449947.2691 8.736 1.630 998 +X VUL 2449948.2676 8.877 1.713 998 +X VUL 2449949.2697 9.098 1.759 998 +X VUL 2449950.2623 9.206 1.825 998 +X VUL 2449952.2679 8.470 1.493 998 +X VUL 2449953.3248 8.669 1.622 998 +X VUL 2449954.2618 8.842 1.689 998 +X VUL 2449955.2546 8.987 1.740 998 +X VUL 2450307.1816 8.579 1.395 .793 971 +X VUL 2450310.2109 9.128 1.622 .904 971 +X VUL 2450311.1515 9.135 1.581 .884 971 +X VUL 2450313.1525 8.505 1.368 .761 971 +X VUL 2450314.1366 8.735 1.444 .835 971 +X VUL 2450315.1987 8.880 1.536 .857 971 +X VUL 2450316.2107 9.128 1.608 .892 971 +X VUL 2450317.2195 9.228 1.603 .917 971 +X VUL 2450318.2295 8.806 1.403 .815 971 +X VUL 2450319.2213 8.487 1.292 .770 971 +X VUL 2450320.2221 8.703 1.436 .817 971 +X VUL 2450321.2110 8.855 1.499 .871 971 +X VUL 2450322.2438 9.098 1.584 .912 971 +X VUL 2450323.2440 9.215 1.640 .911 971 +X VUL 2450324.2462 8.997 1.498 .859 971 +X VUL 2450325.2119 8.476 1.248 .752 971 +X VUL 2450326.1682 8.643 1.377 .813 971 +SV VUL 2445489.3085 7.240 1.383 1.635 .850 982 +SV VUL 2445493.2734 7.354 1.698 .859 982 +SV VUL 2445496.4375 7.454 1.555 1.733 .880 982 +SV VUL 2445498.4570 7.494 1.595 1.729 .877 982 +SV VUL 2445501.4453 7.602 1.607 1.759 .892 982 +SV VUL 2445502.4609 7.640 1.623 1.743 .892 982 +SV VUL 2445505.4609 7.705 1.570 1.746 .897 982 +SV VUL 2445508.4609 7.760 1.482 1.746 .881 982 +SV VUL 2445509.4492 1.449 1.685 .877 982 +SV VUL 2445512.4570 7.416 1.120 1.485 .803 982 +SV VUL 2445513.4492 7.183 1.008 1.357 .748 982 +SV VUL 2445514.4492 6.950 .914 1.236 .692 982 +SV VUL 2445644.2226 7.743 1.570 1.694 982 +SV VUL 2445646.1875 7.737 1.391 1.648 .835 982 +SV VUL 2445648.1522 7.450 1.172 1.415 .767 982 +SV VUL 2445649.1796 7.081 .954 1.298 .740 982 +SV VUL 2445650.1875 6.797 .942 1.231 .620 982 +SV VUL 2445658.1171 6.817 1.040 1.302 .719 982 +SV VUL 2445659.1131 6.875 1.066 1.346 .733 982 +SV VUL 2445663.1171 7.017 1.156 1.489 .817 982 +SV VUL 2445665.1367 7.067 1.267 1.550 .799 982 +SV VUL 2445666.1014 7.106 1.288 1.580 .820 982 +SV VUL 2445667.1367 7.164 1.309 1.607 .818 982 +SV VUL 2445668.1250 7.168 1.369 1.619 .827 982 +SV VUL 2445674.1288 7.313 1.520 1.708 .875 982 +SV VUL 2445675.1250 7.405 1.563 .872 982 +SV VUL 2445676.1054 7.426 1.517 1.738 .881 982 +SV VUL 2445679.1288 7.503 1.586 1.766 .882 982 +SV VUL 2445683.1131 7.653 1.586 1.785 .900 982 +SV VUL 2445686.0976 7.742 1.584 1.773 .897 982 +SV VUL 2445688.1171 7.775 1.437 1.786 .901 982 +SV VUL 2445694.0780 7.134 1.047 1.332 .739 982 +SV VUL 2445695.0742 6.918 .938 1.215 .697 982 +SV VUL 2445864.3125 7.700 1.659 1.757 .899 982 +SV VUL 2445866.3514 7.759 1.656 1.752 .893 982 +SV VUL 2445867.3203 7.763 1.633 1.738 .895 982 +SV VUL 2445868.2538 7.775 1.605 1.713 .897 982 +SV VUL 2445869.3125 7.753 1.582 1.700 .891 982 +SV VUL 2445870.2889 7.725 1.517 1.664 .879 982 +SV VUL 2445871.2617 7.645 1.418 1.601 .853 982 +SV VUL 2445872.2812 7.502 1.272 1.532 982 +SV VUL 2445873.2734 7.312 1.124 1.411 .779 982 +SV VUL 2445874.2655 7.054 .962 1.305 .717 982 +SV VUL 2445875.2617 6.859 .893 1.188 .674 982 +SV VUL 2445876.2617 6.761 .868 1.164 .653 982 +SV VUL 2445877.2812 6.754 .886 1.149 .669 982 +SV VUL 2445878.2578 6.739 .875 1.167 .660 982 +SV VUL 2445879.2655 6.758 .910 1.180 .670 982 +SV VUL 2445880.2734 6.780 .918 1.217 .693 982 +SV VUL 2445881.2538 6.827 .980 1.233 .710 982 +SV VUL 2445882.2578 6.851 .995 1.292 .709 982 +SV VUL 2445883.2655 6.852 1.035 1.314 .721 982 +SV VUL 2445886.2578 6.967 1.179 1.422 .766 982 +SV VUL 2445887.2538 6.993 1.194 1.456 .771 982 +SV VUL 2446252.1985 7.160 1.405 1.546 .827 987 +SV VUL 2446253.1997 7.184 1.441 1.563 .844 987 +SV VUL 2446255.2667 7.253 1.481 1.621 .851 987 +SV VUL 2446256.2518 7.278 1.513 1.648 .846 987 +SV VUL 2446257.2630 7.299 1.531 1.655 .856 987 +SV VUL 2446258.1943 7.345 1.558 1.669 .858 987 +SV VUL 2446259.2252 7.350 1.570 1.678 .867 987 +SV VUL 2446260.1996 7.424 1.602 1.692 .867 987 +SV VUL 2446261.2268 7.423 1.619 1.684 .880 987 +SV VUL 2446262.1911 7.467 1.623 1.675 .897 987 +SV VUL 2446263.1975 7.469 1.634 1.704 .881 987 +SV VUL 2446264.1982 7.463 1.622 1.745 .890 987 +SV VUL 2446265.1853 7.532 1.656 1.717 .893 987 +SV VUL 2446266.2032 7.549 1.663 1.718 .893 987 +SV VUL 2446267.2089 7.627 1.670 1.728 .895 987 +SV VUL 2446268.2148 7.624 1.685 1.736 .893 987 +SV VUL 2446269.1999 7.658 1.692 1.735 .903 987 +SV VUL 2446270.4660 7.729 1.681 1.746 .909 987 +SV VUL 2446272.4697 7.772 1.683 1.731 .905 987 +SV VUL 2446273.4635 7.741 1.634 1.704 .885 987 +SV VUL 2446274.4688 7.757 1.659 1.678 .892 987 +SV VUL 2446275.4735 7.742 1.564 1.646 .874 987 +SV VUL 2446279.4639 7.143 1.105 1.370 .755 987 +SV VUL 2446283.2709 6.741 .866 1.159 .657 987 +SV VUL 2446284.2508 6.739 .878 1.171 .661 987 +SV VUL 2446285.2356 6.767 .905 1.193 .676 987 +SV VUL 2446286.2196 6.791 .919 1.230 .682 987 +SV VUL 2446287.2149 6.806 .960 1.271 .699 987 +SV VUL 2446288.2536 6.846 .994 1.299 .720 987 +SV VUL 2446289.2520 6.882 1.040 1.339 .730 987 +SV VUL 2446290.2248 6.912 1.070 1.386 .736 987 +SV VUL 2446291.2115 6.934 1.120 1.406 .761 987 +SV VUL 2446292.2325 6.972 1.161 1.454 .766 987 +SV VUL 2446293.3531 6.999 1.212 1.481 .784 987 +SV VUL 2446294.2095 7.040 1.248 1.512 .796 987 +SV VUL 2446295.1943 7.056 1.302 1.536 .799 987 +SV VUL 2446296.1943 7.105 1.356 1.552 .814 987 +SV VUL 2446297.2021 7.128 1.386 1.586 .821 987 +SV VUL 2446298.2216 7.184 1.403 1.600 .839 987 +SV VUL 2446299.1902 7.202 1.447 1.619 .835 987 +SV VUL 2446300.1901 7.244 1.468 1.647 .844 987 +SV VUL 2446301.2105 7.269 1.513 1.653 .858 987 +SV VUL 2446302.2201 7.296 1.491 1.698 .851 987 +SV VUL 2446303.1843 7.315 1.544 1.692 .855 987 +SV VUL 2446304.1636 7.370 1.558 1.704 .858 987 +SV VUL 2446605.4497 6.852 1.347 .727 988 +SV VUL 2446606.3187 6.885 1.091 1.380 .739 988 +SV VUL 2446607.3897 6.973 1.141 1.396 .759 988 +SV VUL 2446608.3241 6.969 1.184 1.429 .770 988 +SV VUL 2446609.2307 6.988 1.213 1.468 .776 988 +SV VUL 2446610.3491 7.039 1.274 1.494 .796 988 +SV VUL 2446611.2363 7.075 1.330 1.525 .807 988 +SV VUL 2446612.2222 7.124 1.336 1.549 .831 988 +SV VUL 2446613.2215 7.137 1.376 1.555 .825 988 +SV VUL 2446614.2236 7.171 1.421 1.579 .846 988 +SV VUL 2446615.2227 7.206 1.419 1.610 .845 988 +SV VUL 2446616.2257 7.251 1.473 1.621 .855 988 +SV VUL 2446617.2213 7.242 1.518 1.639 .854 988 +SV VUL 2446618.2203 7.295 1.516 1.647 .873 988 +SV VUL 2446619.2191 7.338 1.557 1.650 .877 988 +SV VUL 2446620.2170 7.349 1.569 1.671 .873 988 +SV VUL 2446621.3058 7.391 1.585 1.679 .881 988 +SV VUL 2446622.2155 7.410 1.585 1.688 .889 988 +SV VUL 2446623.2047 7.446 1.611 1.688 .884 988 +SV VUL 2446624.1994 7.479 1.623 1.699 .891 988 +SV VUL 2446625.1950 7.514 1.679 1.694 .903 988 +SV VUL 2446626.2022 7.511 1.679 1.709 .891 988 +SV VUL 2446627.2038 7.574 1.640 1.715 .895 988 +SV VUL 2446628.2049 7.584 1.674 1.718 .901 988 +SV VUL 2446629.2167 7.632 1.675 1.727 .907 988 +SV VUL 2446630.2158 7.634 1.641 1.739 .903 988 +SV VUL 2446631.2000 7.709 1.669 1.734 .904 988 +SV VUL 2446632.2119 7.742 1.647 1.733 .912 988 +SV VUL 2446635.3022 7.746 1.598 1.694 .897 988 +SV VUL 2446636.2117 7.716 1.531 1.675 .889 988 +SV VUL 2446637.2131 7.662 1.483 1.624 .878 988 +SV VUL 2446638.2195 7.557 1.354 1.573 .839 988 +SV VUL 2446992.2683 7.746 1.646 1.762 .911 989 +SV VUL 2446994.3691 7.796 1.597 1.754 .913 989 +SV VUL 2446995.3398 7.769 1.533 1.730 .898 989 +SV VUL 2446996.2690 7.747 1.513 1.704 .894 989 +SV VUL 2446997.3005 7.680 1.452 1.678 .870 989 +SV VUL 2446998.2957 7.587 1.301 1.593 .852 989 +SV VUL 2446999.2824 7.444 1.194 1.503 .807 989 +SV VUL 2447000.3044 7.190 .986 1.373 .752 989 +SV VUL 2447001.2920 6.973 .920 1.229 .707 989 +SV VUL 2447002.2941 6.796 .854 1.168 .663 989 +SV VUL 2447003.2783 6.720 .833 1.143 .652 989 +SV VUL 2447082.2174 7.735 1.653 1.726 .898 989 +SV VUL 2447083.1509 7.773 1.610 1.702 .898 989 +SV VUL 2447084.1364 7.775 1.569 1.712 .886 989 +SV VUL 2447087.1629 7.710 1.456 1.641 .867 989 +SV VUL 2447088.1009 7.617 1.380 1.579 .853 989 +SV VUL 2447091.0928 7.023 .981 1.271 .722 989 +SV VUL 2447098.0787 .973 1.235 .707 989 +SV VUL 2447399.2987 7.790 1.491 1.720 .889 990 +SV VUL 2447400.2498 7.789 1.706 .901 990 +SV VUL 2447401.2377 7.759 1.474 1.687 .868 990 +SV VUL 2447402.2331 7.707 1.401 1.648 .854 990 +SV VUL 2447403.2363 7.608 1.285 1.563 .837 990 +SV VUL 2447404.2096 7.422 1.136 1.469 .783 990 +SV VUL 2447407.2202 6.794 1.174 .655 990 +SV VUL 2447408.1927 6.722 .759 1.148 .633 990 +SV VUL 2447409.2218 6.755 1.135 .652 990 +SV VUL 2447410.2325 6.759 .769 1.159 .659 990 +SV VUL 2447411.2413 6.770 .812 1.167 .669 990 +SV VUL 2447413.1990 6.814 .861 1.263 .703 990 +SV VUL 2447414.1867 6.853 .909 1.298 .705 990 +SV VUL 2447415.1891 6.893 .948 1.343 .729 990 +SV VUL 2447416.1934 6.915 1.005 1.395 .727 990 +SV VUL 2447417.1862 6.931 1.032 1.421 .740 990 +SV VUL 2447418.1847 7.001 1.094 1.440 .770 990 +SV VUL 2447419.1630 6.955 1.161 1.448 .755 990 +SV VUL 2447420.1671 7.044 1.203 1.492 .774 990 +SV VUL 2447421.1601 7.059 1.526 .784 990 +SV VUL 2447422.1656 7.118 1.257 1.556 .809 990 +SV VUL 2447423.1601 7.149 1.345 1.572 .815 990 +SV VUL 2447424.1627 7.206 1.329 1.607 .841 990 +SV VUL 2447425.1866 7.240 1.375 1.623 .841 990 +SV VUL 2447427.1914 7.346 1.460 1.650 .844 990 +SV VUL 2447428.1722 7.311 1.456 1.665 .840 990 +SV VUL 2447429.1554 7.364 1.487 1.684 .866 990 +SV VUL 2447430.1423 7.381 1.494 1.686 .860 990 +SV VUL 2447431.1537 7.400 1.554 1.698 .859 990 +SV VUL 2447432.1416 7.421 1.583 1.700 .872 990 +SV VUL 2447433.1459 7.476 1.553 1.726 .882 990 +SV VUL 2447434.1505 7.490 1.569 1.714 .874 990 +SV VUL 2447734.3530 6.969 1.176 1.479 .798 991 +SV VUL 2447735.3775 7.017 1.204 1.490 .780 991 +SV VUL 2447736.3810 7.078 1.508 .798 991 +SV VUL 2447737.3757 7.090 1.319 1.560 .813 991 +SV VUL 2447738.3542 7.177 1.374 1.560 .833 991 +SV VUL 2447739.3182 7.178 1.350 1.571 .828 991 +SV VUL 2447740.3514 7.204 1.390 1.617 .827 991 +SV VUL 2447741.3130 7.230 1.426 1.641 .838 991 +SV VUL 2447742.3178 7.253 1.482 1.650 .856 991 +SV VUL 2447743.3067 7.288 1.478 1.641 .845 991 +SV VUL 2447744.2889 7.332 1.487 1.670 .870 991 +SV VUL 2447745.2914 7.375 1.553 1.674 .878 991 +SV VUL 2447746.2963 7.406 1.513 1.718 .866 991 +SV VUL 2447747.2907 7.411 1.542 1.703 .873 991 +SV VUL 2447748.2880 7.464 1.566 1.704 .879 991 +SV VUL 2447749.2862 7.499 1.575 1.691 .872 991 +SV VUL 2447750.2738 7.512 1.565 1.701 .887 991 +SV VUL 2447751.2707 7.528 1.594 1.707 .882 991 +SV VUL 2447752.2512 7.593 1.617 1.714 .884 991 +SV VUL 2447753.2522 7.639 1.608 1.717 .892 991 +SV VUL 2447754.2862 7.662 1.585 1.740 .894 991 +SV VUL 2447755.3214 7.687 1.625 1.731 .904 991 +SV VUL 2447756.3178 7.696 1.594 1.737 .887 991 +SV VUL 2447757.2846 7.746 1.597 1.731 .902 991 +SV VUL 2447758.2765 7.751 1.579 1.705 .887 991 +SV VUL 2447759.2611 7.774 1.551 1.723 .887 991 +SV VUL 2447760.2737 7.766 1.482 1.701 .877 991 +SV VUL 2447761.2595 7.738 1.468 1.671 .881 991 +SV VUL 2447762.2376 7.670 1.376 1.621 .849 991 +SV VUL 2447763.1998 7.544 1.538 .833 991 +SV VUL 2447764.2177 7.346 1.437 .788 991 +SV VUL 2447766.2173 6.878 .769 1.199 .687 991 +SV VUL 2447767.2745 6.721 .764 1.137 .638 991 +SV VUL 2447768.2619 6.631 .745 1.138 .649 991 +SV VUL 2447770.2504 6.686 .773 1.170 .652 991 +SV VUL 2447771.2357 6.706 .807 1.198 .671 991 +SV VUL 2447772.2329 6.756 .830 1.239 .683 991 +SV VUL 2447773.2617 6.807 .846 1.271 .698 991 +SV VUL 2447774.2658 6.839 .910 1.303 .714 991 +SV VUL 2447775.2286 6.870 .951 1.345 .728 991 +SV VUL 2447776.2330 6.914 .998 1.394 .760 991 +SV VUL 2448503.2435 7.156 1.328 1.586 .812 993 +SV VUL 2448504.2025 7.201 1.586 .836 993 +SV VUL 2448505.2287 7.248 1.395 1.602 .846 993 +SV VUL 2448506.2418 7.248 1.434 1.646 .841 993 +SV VUL 2448507.2179 7.296 1.465 1.656 .845 993 +SV VUL 2448508.1951 7.317 1.473 1.678 .853 993 +SV VUL 2448509.2051 7.363 1.486 1.681 .865 993 +SV VUL 2448510.1989 7.373 1.516 1.692 .882 993 +SV VUL 2448511.2068 7.412 1.567 1.713 .864 993 +SV VUL 2448512.2158 7.451 1.573 1.728 .877 993 +SV VUL 2448513.2165 7.481 1.584 1.713 .876 993 +SV VUL 2448514.2193 7.505 1.606 1.734 .884 993 +SV VUL 2448515.2174 7.557 1.642 1.697 .899 993 +SV VUL 2448516.2104 7.559 1.721 .897 993 +SV VUL 2448517.2043 7.588 1.636 1.729 .889 993 +SV VUL 2448518.2130 7.637 1.721 .909 993 +SV VUL 2448519.2306 7.663 1.745 .907 993 +SV VUL 2448520.2006 7.702 1.749 .892 993 +SV VUL 2448521.2275 7.716 1.753 .891 993 +SV VUL 2448522.2117 7.790 1.699 .934 993 +SV VUL 2448523.2048 7.786 1.744 .905 993 +SV VUL 2448854.2955 6.892 1.360 .737 994 +SV VUL 2448856.2691 6.966 1.428 .759 994 +SV VUL 2448858.2776 7.031 1.495 .793 994 +SV VUL 2448860.2626 7.098 1.245 1.561 .809 994 +SV VUL 2448862.2886 7.164 1.594 .825 994 +SV VUL 2448870.2489 7.409 1.742 .861 994 +SV VUL 2448872.2543 7.459 1.730 .885 994 +SV VUL 2448874.2706 7.536 1.760 .889 994 +SV VUL 2448876.1982 7.601 1.747 .906 994 +SV VUL 2448877.2203 7.636 1.765 .897 994 +SV VUL 2448878.2360 7.685 1.772 .909 994 +SV VUL 2448879.3316 7.731 1.752 .888 994 +SV VUL 2448880.2302 7.757 1.748 .907 994 +SV VUL 2448881.1961 7.763 1.759 .906 994 +SV VUL 2448882.2043 7.783 1.767 .905 994 +SV VUL 2448883.2345 7.795 1.737 .901 994 +SV VUL 2448884.2234 7.781 1.721 .893 994 +SV VUL 2448885.2215 7.757 1.703 .874 994 +SV VUL 2448886.2336 7.718 1.617 .870 994 +SV VUL 2448887.2529 7.559 1.575 .830 994 +SV VUL 2448888.2155 7.382 1.486 .797 994 +SV VUL 2448889.2217 7.168 1.338 .744 994 +SV VUL 2448890.2041 6.927 1.233 .692 994 +SV VUL 2448891.2016 6.782 1.187 .654 994 +SV VUL 2448892.2214 6.749 1.155 .662 994 +SV VUL 2448893.2089 6.731 1.160 .654 994 +SV VUL 2448894.2128 6.730 1.180 .665 994 +SV VUL 2449617.1875 6.946 1.062 1.357 .721 995 +SV VUL 2449619.2998 6.943 1.401 .747 995 +SV VUL 2449620.2697 7.014 1.438 .781 995 +SV VUL 2449621.2710 1.282 1.493 .794 995 +SV VUL 2449623.2573 7.119 1.355 1.526 .804 995 +SV VUL 2449624.2372 7.122 1.433 1.542 .820 995 +SV VUL 2449625.2558 7.200 1.585 .831 995 +SV VUL 2449626.2693 7.211 1.593 .833 995 +SV VUL 2449631.2312 7.380 1.673 .857 995 +SV VUL 2449632.2540 7.424 1.689 .866 995 +SV VUL 2449633.2395 7.425 1.698 .881 995 +SV VUL 2449634.2331 7.453 1.706 .864 995 +SV VUL 2449635.2702 7.457 1.683 .866 995 +SV VUL 2449933.3783 6.975 1.417 .802 1.455 998 +SV VUL 2449934.3597 7.034 1.441 .846 1.524 998 +SV VUL 2449935.3683 6.972 .811 1.498 998 +SV VUL 2449936.3515 7.065 1.500 .847 1.552 998 +SV VUL 2449937.3362 7.097 1.558 .830 998 +SV VUL 2449938.3637 7.145 .828 1.595 998 +SV VUL 2449939.3483 7.141 1.592 .830 1.588 998 +SV VUL 2449941.3365 7.230 1.610 998 +SV VUL 2449942.3061 7.274 1.647 998 +SV VUL 2449943.3010 7.293 1.663 998 +SV VUL 2449944.2958 7.328 1.670 998 +SV VUL 2449945.2876 7.348 1.671 998 +SV VUL 2449946.2799 7.408 1.671 998 +SV VUL 2449947.2670 7.452 1.669 998 +SV VUL 2449948.2661 7.426 1.677 998 +SV VUL 2449949.2674 7.500 1.703 998 +SV VUL 2449950.2601 7.474 1.697 998 +SV VUL 2449952.2663 7.545 1.724 998 +SV VUL 2449953.3224 7.597 1.720 998 +SV VUL 2449954.2602 7.635 1.712 998 +SV VUL 2449955.2529 7.688 1.750 998 +SV VUL 2449956.3191 7.688 1.734 998 +SV VUL 2449957.2555 7.728 1.739 998 +SV VUL 2449958.2381 7.782 1.741 998 +SV VUL 2449959.2559 7.811 1.749 998 +SV VUL 2449960.2918 7.801 1.745 998 +SV VUL 2449962.2790 7.816 1.688 998 +SV VUL 2449963.3871 7.713 1.647 998 +SV VUL 2449985.2714 7.150 1.609 998 +SV VUL 2449986.1951 7.173 1.611 998 +SV VUL 2449987.2366 7.216 1.622 998 +SV VUL 2449992.1659 7.361 1.660 998 +SV VUL 2449993.2036 7.375 1.651 998 +SV VUL 2450007.2570 7.763 1.662 998 +SV VUL 2450009.2145 7.613 1.606 998 +SV VUL 2450011.2040 7.209 1.430 998 +SV VUL 2450017.1285 6.752 1.300 998 +SV VUL 2450018.1891 6.791 1.329 998 +SV VUL 2450020.1404 6.835 1.382 998 +SV VUL 2450305.2190 7.351 1.657 .862 971 +SV VUL 2450306.2758 7.392 1.642 .881 971 +SV VUL 2450307.2869 7.412 1.679 .876 971 +SV VUL 2450310.2678 7.494 1.689 .887 971 +SV VUL 2450311.1951 7.537 1.703 .885 971 +SV VUL 2450312.2762 7.559 1.689 .890 971 +SV VUL 2450313.2693 7.588 1.702 .890 971 +SV VUL 2450314.1932 7.626 1.727 .883 971 +SV VUL 2450315.1963 7.644 1.737 .869 971 +SV VUL 2450316.2086 7.705 1.719 .865 971 +SV VUL 2450317.2174 7.742 1.707 .894 971 +SV VUL 2450318.2272 7.767 1.704 .901 971 +SV VUL 2450319.2194 7.747 1.704 .894 971 +SV VUL 2450320.2202 7.770 1.696 .879 971 +SV VUL 2450321.2088 7.755 1.666 .888 971 +SV VUL 2450322.2160 7.723 1.632 .893 971 +SV VUL 2450323.2425 7.637 1.600 .846 971 +SV VUL 2450324.2128 7.506 1.519 .817 971 +SV VUL 2450325.2101 7.310 1.395 .773 971 +SV VUL 2450326.1675 7.103 1.273 .725 971 +SV VUL 2450327.2503 6.860 1.171 971 +SV VUL 2450328.3708 6.767 1.081 .663 971 +SV VUL 2450329.2822 1.104 .650 971 +SV VUL 2450330.2096 6.791 1.116 .657 971 +SV VUL 2450332.1824 6.762 1.198 .608 971 +SV VUL 2450333.1829 6.790 1.216 .697 971 +SV VUL 2450334.1942 6.837 1.266 .697 971 +SV VUL 2450335.1952 6.880 1.299 .743 971 +SV VUL 2450336.1963 6.907 1.310 .743 971 +SV VUL 2450337.1782 6.946 1.380 .750 971 +SV VUL 2450338.2555 6.970 1.410 .760 971 +SV VUL 2450340.1642 7.033 1.486 .792 971 +SV VUL 2450341.1731 7.069 1.498 .806 971 +SV VUL 2450342.1822 7.084 1.512 .801 971 +SV VUL 2450344.2056 7.163 1.571 .838 971 +SV VUL 2450347.1940 7.286 1.625 .842 971 +SV VUL 2450349.1728 7.329 1.644 .845 971 +SV VUL 2450357.1636 7.563 1.699 .887 971 +AS VUL 2444825.3867 12.564 2.009 982 +AS VUL 2444827.2578 12.350 982 +AS VUL 2444829.2812 11.833 1.754 982 +AS VUL 2444830.2578 11.939 1.801 982 +AS VUL 2444831.2578 12.085 1.884 982 +AS VUL 2444832.2617 12.210 1.990 982 +AS VUL 2444833.2500 12.323 2.043 982 +AS VUL 2444835.3710 12.657 2.167 982 +AS VUL 2444844.2304 12.173 1.952 982 +AS VUL 2444845.2109 12.297 2.026 982 +AS VUL 2444846.2500 12.434 2.080 982 +AS VUL 2444847.2226 12.547 2.116 982 +AS VUL 2444848.2147 12.707 2.150 982 +AS VUL 2444849.2460 12.680 2.079 982 +AS VUL 2444850.2421 12.451 1.979 982 +AS VUL 2444851.2381 12.347 1.889 982 +AS VUL 2444852.2304 12.227 1.798 982 +AS VUL 2444854.2343 11.878 1.763 982 +AS VUL 2444855.2226 12.032 1.843 982 +AS VUL 2444856.2187 12.180 1.908 982 +AS VUL 2444857.2187 12.312 1.991 982 +AS VUL 2444880.2070 12.078 1.907 982 +AS VUL 2444881.1953 12.206 1.977 982 +AS VUL 2444882.2421 12.351 2.048 982 +AS VUL 2444884.1679 12.651 2.092 982 +AS VUL 2445175.4062 12.308 2.040 982 +AS VUL 2445178.3788 12.679 2.139 982 +AS VUL 2445179.2772 12.667 2.082 982 +AS VUL 2445180.3046 12.442 1.976 982 +AS VUL 2445181.2538 12.349 1.903 982 +AS VUL 2445182.2578 12.224 1.818 982 +AS VUL 2445183.2851 11.788 1.667 982 +AS VUL 2445184.2500 11.852 1.763 982 +AS VUL 2445186.2851 12.123 1.937 982 +AS VUL 2445187.2617 12.263 2.017 982 +AS VUL 2445188.2578 12.380 982 +AS VUL 2445190.3046 12.653 2.130 982 +AS VUL 2445191.3085 12.685 2.080 982 +AS VUL 2445192.3125 12.479 1.966 982 +AS VUL 2445193.3593 12.355 1.883 982 +AS VUL 2445194.3359 12.275 1.815 982 +AS VUL 2445198.3476 12.108 1.943 982 +AS VUL 2445201.3242 12.519 2.091 982 +AS VUL 2445203.3280 12.692 2.096 982 +AS VUL 2445204.2889 12.574 2.006 982 +AS VUL 2445649.1718 11.908 1.753 1.084 982 +AS VUL 2445665.1250 12.377 2.055 1.165 982 +AS VUL 2445668.1171 12.638 2.083 1.188 982 +AS VUL 2445675.1093 12.143 1.978 1.120 982 +AS VUL 2445864.3163 12.572 2.079 1.172 982 +AS VUL 2445866.3554 12.331 1.861 1.125 982 +AS VUL 2445867.3280 11.953 1.386 1.729 1.031 982 +AS VUL 2445868.2578 11.803 1.259 1.739 1.033 982 +AS VUL 2445869.3163 11.905 1.415 1.774 1.078 982 +AS VUL 2445870.2929 12.053 1.529 1.852 1.120 982 +AS VUL 2445871.2889 12.149 1.832 1.969 1.137 982 +AS VUL 2445872.2851 12.307 2.020 1.171 982 +AS VUL 2445873.2772 12.434 2.122 1.181 982 +AS VUL 2445874.2695 12.577 2.150 1.217 982 +AS VUL 2445875.2617 12.677 2.128 1.207 982 +AS VUL 2445876.2617 12.626 2.086 1.188 982 +AS VUL 2445877.2851 12.419 1.939 1.154 982 +AS VUL 2445878.2617 12.328 1.866 1.131 982 +AS VUL 2445879.2655 12.141 1.777 1.085 982 +AS VUL 2445880.2772 11.802 1.694 1.035 982 +AS VUL 2445881.2538 11.879 1.759 1.056 982 +AS VUL 2445882.2617 12.003 1.886 1.090 982 +AS VUL 2445883.2655 12.133 1.935 1.143 982 +AS VUL 2445886.2617 12.533 2.138 1.197 982 +AS VUL 2445887.2538 12.641 2.134 1.202 982 +AS VUL 2447734.3313 12.570 2.060 1.183 991 +AS VUL 2447735.3693 12.457 1.951 1.131 991 +AS VUL 2447736.3739 12.332 1.854 1.108 991 +AS VUL 2447737.3709 12.153 1.811 1.064 991 +AS VUL 2447738.3480 11.782 1.613 1.015 991 +AS VUL 2447739.3119 11.819 1.732 1.035 991 +AS VUL 2447740.3472 11.961 1.819 1.083 991 +AS VUL 2447741.3068 12.066 1.919 1.090 991 +AS VUL 2447742.3134 12.213 1.977 1.152 991 +AS VUL 2447743.3018 12.359 2.016 1.162 991 +AS VUL 2447744.2826 12.496 2.088 1.191 991 +AS VUL 2447745.2847 12.647 2.118 1.208 991 +AS VUL 2447746.2909 12.702 2.087 1.200 991 +AS VUL 2447747.2846 12.476 1.994 1.150 991 +AS VUL 2447748.2829 12.334 1.900 1.115 991 +AS VUL 2447749.2765 12.285 1.844 1.080 991 +AS VUL 2447750.2663 11.777 1.623 1.001 991 +AS VUL 2447751.2619 11.791 1.683 991 +AS VUL 2447752.2442 11.930 1.805 1.052 991 +AS VUL 2447753.2458 12.062 1.898 1.091 991 +AS VUL 2447754.2737 12.208 1.996 1.134 991 +AS VUL 2447755.3083 12.327 2.087 1.167 991 +AS VUL 2447756.3097 12.454 2.137 1.187 991 +AS VUL 2447757.2762 12.586 2.128 1.188 991 +AS VUL 2447758.2683 12.681 2.062 1.192 991 +AS VUL 2447759.2477 12.603 2.021 1.180 991 +AS VUL 2447760.2634 12.359 1.926 1.116 991 +AS VUL 2447761.2511 12.329 1.830 1.126 991 +AS VUL 2447762.2308 11.968 1.676 1.027 991 +AS VUL 2447763.1975 11.806 1.687 1.032 991 +AS VUL 2447764.2015 11.871 1.769 1.041 991 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10.777 1.565 .919 987 +BR VUL 2446256.3576 10.964 1.625 .948 987 +BR VUL 2446257.3651 11.036 1.606 .940 987 +BR VUL 2446258.3192 10.301 .930 1.279 .777 987 +BR VUL 2446259.3246 10.476 .992 1.426 .843 987 +BR VUL 2446260.3029 10.729 1.086 1.549 .888 987 +BR VUL 2446261.3116 10.928 1.249 1.596 .940 987 +BR VUL 2446262.3486 11.054 1.230 1.641 .948 987 +BR VUL 2446263.3490 10.413 .948 1.330 .798 987 +BR VUL 2446265.3230 10.719 1.100 1.527 .899 987 +BR VUL 2446266.3198 10.896 1.202 1.598 .940 987 +BR VUL 2446267.2988 11.047 1.286 1.660 .949 987 +BR VUL 2446268.2925 10.645 1.006 1.429 .849 987 +BR VUL 2446269.2845 10.340 .956 1.332 .795 987 +BR VUL 2446270.2999 10.639 1.082 1.473 .893 987 +BR VUL 2446272.2641 11.019 1.279 1.630 .946 987 +BR VUL 2446273.3248 10.837 1.528 .893 987 +BR VUL 2446274.2977 10.291 .960 1.307 .780 987 +BR VUL 2446275.3241 10.597 1.469 .875 987 +BR VUL 2446279.2856 10.270 1.276 .768 987 +BR VUL 2446280.2891 10.516 1.423 .856 987 +BR VUL 2446283.2489 11.074 1.634 .943 987 +BR VUL 2446284.2135 10.337 1.296 .784 987 +BR VUL 2446286.2113 10.727 1.539 .895 987 +BR VUL 2446287.2096 10.897 1.620 .925 987 +BR VUL 2446288.2212 11.056 1.671 .934 987 +BR VUL 2446289.2316 10.531 1.351 .819 987 +BR VUL 2446290.2188 10.411 1.374 .799 987 +BR VUL 2446291.2040 10.668 1.506 .890 987 +BR VUL 2446292.2054 10.864 1.618 .913 987 +BR VUL 2446293.2379 11.033 1.656 .951 987 +BR VUL 2446294.2030 10.738 1.453 .873 987 +BR VUL 2446295.1809 10.332 1.304 .797 987 +BR VUL 2446296.1889 10.613 1.490 .870 987 +BR VUL 2446297.1974 10.827 1.582 .918 987 +BR VUL 2446298.2176 11.049 1.642 .955 987 +BR VUL 2446299.1853 10.918 1.557 .907 987 +BR VUL 2446300.1842 10.293 1.302 .771 987 +BR VUL 2446301.2019 10.569 1.445 .863 987 +BR VUL 2446302.1998 10.810 1.614 .905 987 +BR VUL 2446303.1740 10.981 1.637 .936 987 +BR VUL 2446304.1558 11.055 1.615 .930 987 +BR VUL 2447409.2175 10.831 1.572 .899 990 +BR VUL 2447410.2256 11.005 1.621 .945 990 +BR VUL 2447411.2363 11.027 1.547 .927 990 +BR VUL 2447413.1939 10.510 1.419 .850 990 +BR VUL 2447414.1806 10.740 1.573 .888 990 +BR VUL 2447415.1827 10.936 1.636 .919 990 +BR VUL 2447416.1877 11.058 1.616 .917 990 +BR VUL 2447417.1822 10.368 1.285 .780 990 +BR VUL 2447418.1830 10.457 1.378 .822 990 +BR VUL 2447419.1607 10.639 1.505 .864 990 +BR VUL 2447420.1653 10.880 1.609 .909 990 +BR VUL 2447421.1580 11.021 1.661 .922 990 +BR VUL 2447422.1634 10.577 1.393 .827 990 +BR VUL 2447423.1541 10.371 1.366 .786 990 +BR VUL 2447424.1597 10.663 1.504 .882 990 +BR VUL 2447425.1702 10.852 1.577 .896 990 +BR VUL 2447427.1849 10.803 1.469 .854 990 +BR VUL 2447428.1683 10.325 1.307 .762 990 +BR VUL 2447429.1517 10.610 1.472 .861 990 +BR VUL 2447430.1368 10.818 1.565 .910 990 +BR VUL 2447431.1476 11.007 1.624 .917 990 +BR VUL 2447432.1344 10.920 1.569 .889 990 +BR VUL 2447433.1384 10.299 1.283 .775 990 +BR VUL 2447434.1433 10.548 1.448 .843 990 +BR VUL 2449621.2671 10.365 1.293 .776 995 +BR VUL 2449623.2534 10.814 1.469 1.554 .913 995 +BR VUL 2449624.2352 11.010 1.578 .957 995 +BR VUL 2449625.2534 11.027 1.593 .920 995 +BR VUL 2449626.2673 10.269 1.242 .808 995 +BR VUL 2449631.2293 10.345 1.298 .785 995 +BR VUL 2449632.2521 10.472 1.419 .817 995 +BR VUL 2449633.2263 10.721 1.524 .894 995 +BR VUL 2449634.2309 10.910 1.618 .905 995 +BR VUL 2449957.2483 11.063 1.859 998 +BR VUL 2449958.2235 10.839 1.741 998 +BR VUL 2449960.2708 10.652 1.725 998 +BR VUL 2449962.2514 11.065 1.821 998 +BR VUL 2449986.1761 10.594 1.693 998 +BR VUL 2449987.2241 10.821 1.797 998 +BR VUL 2449992.1520 10.772 1.757 998 +BR VUL 2449993.1926 10.963 1.818 998 +BR VUL 2450007.2501 10.686 1.693 998 +BR VUL 2450009.2051 11.091 1.831 998 +BR VUL 2450011.1991 10.353 1.555 998 +BR VUL 2450017.1225 10.557 1.652 998 +BR VUL 2450018.1841 10.805 1.763 998 +BR VUL 2450020.1339 11.049 1.810 998 +DG VUL 2444825.3945 10.734 1.710 982 +DG VUL 2444827.2655 11.106 1.907 982 +DG VUL 2444829.2889 11.350 2.075 982 +DG VUL 2444830.2655 11.468 2.108 982 +DG VUL 2444831.2617 11.594 2.197 982 +DG VUL 2444832.2695 11.733 2.219 982 +DG VUL 2444833.2578 11.811 2.232 982 +DG VUL 2444835.3788 11.748 2.114 982 +DG VUL 2444844.2381 11.508 2.131 982 +DG VUL 2444845.2147 11.644 2.185 982 +DG VUL 2444846.2538 11.762 2.245 982 +DG VUL 2444847.2304 11.816 2.201 982 +DG VUL 2444848.2187 11.827 2.162 982 +DG VUL 2444849.2500 11.711 2.098 982 +DG VUL 2444850.2460 11.596 2.057 982 +DG VUL 2444851.2421 11.408 1.954 982 +DG VUL 2444852.2343 10.784 1.719 982 +DG VUL 2444853.2460 10.927 1.814 982 +DG VUL 2444854.2381 11.076 1.901 982 +DG VUL 2444855.2264 11.192 1.975 982 +DG VUL 2444856.2226 11.320 2.017 982 +DG VUL 2444857.2226 11.448 2.101 982 +DG VUL 2444880.2109 10.823 1.767 982 +DG VUL 2444881.1992 11.026 1.898 982 +DG VUL 2444882.2460 11.159 1.934 982 +DG VUL 2444883.1796 11.272 2.017 982 +DG VUL 2444884.1718 11.422 2.062 982 +DG VUL 2445175.4101 11.735 2.116 982 +DG VUL 2445178.3828 11.042 1.803 982 +DG VUL 2445179.2851 10.740 1.717 982 +DG VUL 2445181.2617 11.120 1.919 982 +DG VUL 2445182.2655 11.238 1.972 982 +DG VUL 2445183.2929 11.343 2.079 982 +DG VUL 2445184.2538 11.485 2.106 982 +DG VUL 2445186.2929 11.746 2.215 982 +DG VUL 2445187.2695 11.828 2.215 982 +DG VUL 2445188.2655 11.816 2.173 982 +DG VUL 2445189.2968 11.705 2.119 982 +DG VUL 2445190.3125 11.591 2.035 982 +DG VUL 2445191.3163 11.451 1.973 982 +DG VUL 2445192.3163 10.815 1.715 982 +DG VUL 2445193.3631 10.881 1.770 982 +DG VUL 2445194.3397 11.065 1.879 982 +DG VUL 2445198.3514 11.545 2.166 982 +DG VUL 2445201.3320 11.849 2.194 982 +DG VUL 2445203.3359 11.657 2.082 982 +DG VUL 2445204.2929 11.559 2.020 982 +DG VUL 2445208.2812 11.109 1.895 982 +DG VUL 2445644.2304 11.131 982 +DG VUL 2445646.1875 11.473 982 +DG VUL 2445648.1562 11.692 1.272 982 +DG VUL 2445649.1835 11.755 1.271 982 +DG VUL 2445650.1913 11.873 1.268 982 +DG VUL 2445658.1250 11.191 1.150 982 +DG VUL 2445659.1210 11.329 1.234 982 +DG VUL 2445665.1405 11.772 1.249 982 +DG VUL 2445666.1014 11.652 1.216 982 +DG VUL 2445668.1250 11.165 1.890 1.115 982 +DG VUL 2445675.1250 11.615 1.260 982 +DG VUL 2445864.3242 11.431 2.106 1.234 982 +DG VUL 2445866.3593 11.705 2.211 1.268 982 +DG VUL 2445867.3397 11.807 2.228 1.274 982 +DG VUL 2445868.2734 11.861 2.017 2.186 1.293 982 +DG VUL 2445869.3280 11.778 1.956 2.133 1.264 982 +DG VUL 2445870.3006 11.623 1.747 2.087 1.225 982 +DG VUL 2445871.3046 11.541 1.709 2.010 1.201 982 +DG VUL 2445872.2889 11.127 1.841 1.106 982 +DG VUL 2445873.2812 10.759 1.730 1.047 982 +DG VUL 2445874.2734 10.997 1.509 1.856 1.114 982 +DG VUL 2445875.2655 11.110 1.921 1.139 982 +DG VUL 2445876.2695 11.219 1.705 2.007 1.174 982 +DG VUL 2445877.2889 11.366 2.088 1.222 982 +DG VUL 2445878.2655 11.488 2.134 1.247 982 +DG VUL 2445879.2695 11.632 2.184 1.268 982 +DG VUL 2445880.2812 11.754 2.220 1.293 982 +DG VUL 2445881.2578 11.839 2.220 1.292 982 +DG VUL 2445882.2655 11.827 2.183 1.274 982 +DG VUL 2445883.2695 11.711 2.100 1.244 982 +DG VUL 2445886.2655 10.843 1.745 1.047 982 +DG VUL 2445887.2578 10.821 1.451 1.764 1.047 982 +DG VUL 2447409.2370 11.558 1.964 1.180 990 +DG VUL 2447410.2492 11.042 1.770 1.074 990 +DG VUL 2447411.2579 10.813 1.695 1.045 990 +DG VUL 2447413.2018 11.154 1.923 1.154 990 +DG VUL 2447414.1883 11.259 1.974 1.172 990 +DG VUL 2447415.2007 11.391 2.077 1.212 990 +DG VUL 2447416.1943 11.518 2.131 1.230 990 +DG VUL 2447417.1884 11.614 2.195 1.230 990 +DG VUL 2447418.1856 11.780 2.216 1.253 990 +DG VUL 2447419.1640 11.785 2.171 1.248 990 +DG VUL 2447420.1693 11.826 2.144 1.224 990 +DG VUL 2447421.1615 11.703 2.078 1.209 990 +DG VUL 2447422.1687 11.588 2.019 1.190 990 +DG VUL 2447423.1608 11.442 1.973 1.150 990 +DG VUL 2447424.1634 10.838 1.747 1.036 990 +DG VUL 2447425.1874 10.915 1.792 1.053 990 +DG VUL 2447427.1946 11.242 1.966 1.131 990 +DG VUL 2447428.1739 11.287 2.041 1.173 990 +DG VUL 2447429.1564 11.434 2.111 1.206 990 +DG VUL 2447430.1439 11.567 2.150 1.235 990 +DG VUL 2447431.1555 11.712 2.171 1.265 990 +DG VUL 2447432.1429 11.798 2.224 1.263 990 +DG VUL 2447433.1466 11.872 2.239 1.270 990 +DG VUL 2447434.1510 11.800 2.135 1.235 990 +DG VUL 2448503.2585 11.298 2.029 1.188 993 +DG VUL 2448504.2120 11.424 2.070 1.239 993 +DG VUL 2448505.2347 11.573 2.117 1.255 993 +DG VUL 2448506.2524 11.679 2.224 1.251 993 +DG VUL 2448507.2266 11.796 2.264 1.255 993 +DG VUL 2448508.2038 11.838 2.247 1.250 993 +DG VUL 2448509.2342 11.809 2.148 1.229 993 +DG VUL 2448510.2021 11.676 2.094 1.240 993 +DG VUL 2448511.2132 11.604 2.016 1.193 993 +DG VUL 2448512.2189 11.311 1.903 1.136 993 +DG VUL 2448513.2227 10.769 1.689 1.026 993 +DG VUL 2448514.2257 10.990 1.854 1.094 993 +DG VUL 2448515.2272 11.127 1.868 1.128 993 +DG VUL 2448516.2131 11.241 1.944 1.164 993 +DG VUL 2448517.2073 11.363 2.029 1.167 993 +DG VUL 2448518.2151 11.476 2.100 1.230 993 +DG VUL 2448519.2323 11.626 2.150 1.266 993 +DG VUL 2448520.2031 11.764 2.185 1.278 993 +DG VUL 2448521.2305 11.835 2.214 1.273 993 +DG VUL 2448522.2139 11.868 2.241 1.277 993 +DG VUL 2448523.2067 11.781 2.130 1.250 993 +DG VUL 2449957.2912 11.060 2.217 998 +DG VUL 2449958.2668 11.205 2.287 998 +DG VUL 2449959.3272 11.284 2.316 998 +DG VUL 2449992.2806 11.805 2.432 998 +DG VUL 2449993.2200 11.711 2.385 998 +DG VUL 2450009.2649 11.265 2.200 998 +DG VUL 2450011.2205 10.973 2.121 998 +DG VUL 2450017.1826 11.736 2.444 998 +DG VUL 2450018.2292 11.874 2.486 998 +DG VUL 2450020.1796 11.751 2.400 998 +ET VUL 2448854.2884 12.064 1.564 .923 994 +ET VUL 2448856.2621 12.043 1.608 .929 994 +ET VUL 2448858.2736 12.017 1.552 .922 994 +ET VUL 2448860.2576 12.012 1.559 .934 994 +ET VUL 2448862.2868 11.999 1.588 .930 994 +ET VUL 2448870.2437 12.055 1.727 .947 994 +ET VUL 2448872.2478 12.081 1.718 .980 994 +ET VUL 2448874.2423 12.129 1.721 .998 994 +ET VUL 2448876.1937 12.165 1.755 1.015 994 +ET VUL 2448877.2146 12.170 1.747 .985 994 +ET VUL 2448878.2301 12.182 1.796 1.010 994 +ET VUL 2448879.3258 12.225 1.740 .984 994 +ET VUL 2448880.1702 12.251 1.755 1.029 994 +ET VUL 2448881.1621 12.270 1.784 1.008 994 +ET VUL 2448882.1730 12.272 1.823 1.010 994 +ET VUL 2448883.1776 12.294 1.814 1.026 994 +ET VUL 2448884.1833 12.303 1.774 1.032 994 +ET VUL 2448885.1724 12.353 1.795 1.051 994 +ET VUL 2448886.1694 12.382 1.816 1.041 994 +ET VUL 2448887.2475 12.399 1.802 1.049 994 +ET VUL 2448888.1734 12.398 1.840 1.038 994 +ET VUL 2448889.1844 12.427 1.841 1.058 994 +ET VUL 2448890.1619 12.440 1.814 1.055 994 +ET VUL 2448891.1608 12.445 1.826 1.043 994 +ET VUL 2448892.1764 12.429 1.806 1.041 994 +ET VUL 2448893.1648 12.452 1.809 1.038 994 +ET VUL 2448894.1935 12.438 1.812 1.033 994 +ET VUL 2449620.2429 12.170 1.665 .969 995 +ET VUL 2449621.2427 .971 995 +ET VUL 2449623.2071 12.153 1.707 .992 995 +ET VUL 2449624.2263 12.168 1.733 .999 995 +ET VUL 2449625.2466 12.193 1.780 1.014 995 +ET VUL 2449626.2516 1.712 995 +ET VUL 2449631.2229 12.332 1.822 1.023 995 +ET VUL 2449632.2435 12.344 1.916 1.025 995 +ET VUL 2449633.2183 12.370 1.813 1.023 995 +ET VUL 2449634.2220 12.415 1.848 1.049 995 +ET VUL 2449934.3450 12.068 1.614 .986 1.881 998 +ET VUL 2449935.3536 11.999 .976 1.857 998 +ET VUL 2449936.3256 12.053 .967 1.858 998 +ET VUL 2449937.3128 12.007 1.001 998 +ET VUL 2449938.3457 12.119 1.016 1.963 998 +ET VUL 2449939.3349 12.111 1.913 998 +ET VUL 2449941.3211 12.182 1.960 998 +ET VUL 2449942.2937 12.164 1.913 998 +ET VUL 2449943.2768 12.168 1.954 998 +ET VUL 2449944.2859 12.179 1.921 998 +ET VUL 2449945.2798 12.199 1.955 998 +ET VUL 2449946.2676 12.284 2.034 998 +ET VUL 2449947.2523 12.300 2.003 998 +ET VUL 2449948.2422 12.298 1.984 998 +ET VUL 2449949.2491 12.299 1.999 998 +ET VUL 2449950.2399 12.305 2.023 998 +ET VUL 2449952.2450 12.314 1.993 998 +ET VUL 2449953.3057 12.366 2.035 998 +ET VUL 2449954.2447 12.379 2.012 998 +ET VUL 2449955.2398 12.420 2.029 998 +ET VUL 2449956.3084 12.426 2.069 998 +ET VUL 2449957.2253 12.457 2.038 998 +ET VUL 2449958.1997 12.482 2.048 998 +ET VUL 2449959.2333 12.471 2.015 998 +ET VUL 2449960.2570 12.474 2.053 998 +ET VUL 2449962.2162 12.486 1.956 998 +ET VUL 2449985.2450 11.969 1.836 998 +ET VUL 2449986.1607 12.011 1.839 998 +ET VUL 2449987.2021 12.003 1.837 998 +ET VUL 2449992.1392 12.040 1.860 998 +ET VUL 2449993.1849 12.063 1.873 998 +ET VUL 2450007.2244 12.362 1.962 998 +ET VUL 2450011.1403 12.404 1.984 998 +ET VUL 2450017.1065 12.413 1.997 998 +ET VUL 2450018.1727 12.377 1.943 998 +ET VUL 2450020.1239 12.315 1.945 998 +ET VUL 2450305.4255 12.013 1.546 .917 971 +ET VUL 2450306.2847 12.041 1.550 .971 971 +ET VUL 2450307.2576 12.092 1.570 .958 971 +ET VUL 2450310.2638 12.086 1.566 .982 971 +ET VUL 2450311.1909 12.117 1.643 .986 971 +ET VUL 2450312.1923 12.115 1.644 .987 971 +ET VUL 2450313.2137 12.149 1.674 .995 971 +ET VUL 2450314.1880 12.171 1.685 .998 971 +ET VUL 2450315.2091 12.154 1.710 .986 971 +ET VUL 2450316.2642 12.188 1.713 .984 971 +ET VUL 2450317.2136 12.194 1.723 1.007 971 +ET VUL 2450318.2037 12.267 1.733 .982 971 +ET VUL 2450319.1971 12.217 1.725 .999 971 +ET VUL 2450320.2086 12.244 1.774 .997 971 +ET VUL 2450321.2032 12.265 1.726 1.025 971 +ET VUL 2450322.1890 12.294 1.740 1.015 971 +ET VUL 2450323.1853 12.312 1.782 1.033 971 +ET VUL 2450324.2069 12.326 1.734 1.019 971 +ET VUL 2450325.2047 12.344 1.768 1.028 971 +ET VUL 2450326.1632 12.399 1.773 1.034 971 +ET VUL 2450327.2353 12.380 1.826 971 +ET VUL 2450328.3579 12.416 1.754 1.025 971 +ET VUL 2450329.1948 1.748 1.023 971 +ET VUL 2450330.1803 12.499 1.797 1.040 971 +ET VUL 2450332.1712 12.430 1.809 .996 971 +ET VUL 2450333.1716 12.420 1.792 1.026 971 +ET VUL 2450334.1845 12.427 1.784 1.012 971 +ET VUL 2450335.1817 12.420 1.766 1.023 971 +ET VUL 2450336.1880 12.388 1.728 1.015 971 +ET VUL 2450337.1707 12.369 1.717 .986 971 +ET VUL 2450338.2289 12.314 1.719 .980 971 +ET VUL 2450340.1573 12.262 1.647 .968 971 +ET VUL 2450341.1660 12.201 1.632 .950 971 +ET VUL 2450342.1724 12.152 1.611 .937 971 +ET VUL 2450344.1988 12.106 1.590 .943 971 +ET VUL 2450347.1861 12.044 1.541 .903 971 +ET VUL 2450349.1613 12.021 1.531 .915 971 +ET VUL 2450357.1483 12.001 1.608 .927 971 +GQ VUL 2445194.3125 14.029 2.281 982 +GQ VUL 2445195.3006 13.946 2.242 982 +GQ VUL 2445198.3397 13.072 2.065 982 +GQ VUL 2445199.2889 13.302 2.162 982 +GQ VUL 2445201.3006 13.535 2.249 982 +GQ VUL 2445204.2304 13.884 2.321 982 +GQ VUL 2445205.2421 14.001 2.354 982 +GQ VUL 2445207.2460 14.043 2.252 982 +GQ VUL 2445208.2695 13.903 2.244 982 +GQ VUL 2445209.2226 13.767 2.241 982 +GQ VUL 2445210.2304 13.524 2.150 982 +GQ VUL 2445211.2187 13.082 2.054 982 +GQ VUL 2445212.2460 13.337 2.195 982 +GQ VUL 2445213.2304 13.420 2.216 982 +GQ VUL 2445214.2343 13.582 2.227 982 +GQ VUL 2445646.1679 13.874 1.480 982 +GQ VUL 2445649.1679 14.085 1.505 982 +GQ VUL 2445665.1210 13.622 1.420 982 +GQ VUL 2445668.1054 13.430 1.433 982 +GQ VUL 2445675.1014 14.004 1.473 982 +GQ VUL 2445866.4218 13.793 2.330 1.447 982 +GQ VUL 2445868.3631 13.079 2.140 1.315 982 +GQ VUL 2445869.3788 13.274 2.177 1.371 982 +GQ VUL 2445870.3710 13.389 2.193 1.412 982 +GQ VUL 2445871.3828 13.512 2.237 1.451 982 +GQ VUL 2445872.3750 13.607 2.329 1.458 982 +GQ VUL 2445873.3750 13.748 2.381 1.497 982 +GQ VUL 2445874.3750 13.915 2.308 982 +GQ VUL 2445875.3631 14.007 2.284 1.519 982 +GQ VUL 2445876.3750 14.097 2.296 1.521 982 +GQ VUL 2445877.3554 14.013 2.327 1.480 982 +GQ VUL 2445878.3554 13.875 2.287 1.466 982 +GQ VUL 2445879.3476 13.762 2.241 1.449 982 +GQ VUL 2445880.3514 13.532 2.244 1.400 982 +GQ VUL 2445881.3437 13.083 2.050 1.339 982 +GQ VUL 2445882.3397 13.350 2.166 1.401 982 +GQ VUL 2445883.3554 13.464 2.155 1.445 982 +GQ VUL 2445886.3593 13.789 2.326 1.501 982 +GQ VUL 2445887.3671 13.923 2.360 1.499 982 +NN VUL 2445193.3085 14.878 1.554 950 +NN VUL 2445194.2929 14.778 1.523 950 +NN VUL 2445195.2851 14.635 1.446 950 +NN VUL 2445198.3125 13.999 1.181 950 +NN VUL 2445199.2812 13.875 1.189 950 +NN VUL 2445200.2890 13.853 1.233 950 +NN VUL 2445201.2929 13.964 1.245 950 +NN VUL 2445203.2265 14.059 1.360 950 +NN VUL 2445204.2187 14.102 1.372 950 +NN VUL 2445205.2265 14.075 1.423 950 +NN VUL 2445207.2304 14.110 1.455 950 +NN VUL 2445208.2539 14.090 1.512 950 +NN VUL 2445209.2070 14.109 1.502 950 +NN VUL 2445210.2187 14.141 1.553 950 +NN VUL 2445211.2031 14.186 1.528 950 +NN VUL 2445212.2304 14.227 1.588 950 +NN VUL 2445213.2109 14.293 1.574 950 diff --git a/mltsp/TCP/Software/feature_extract/Code/table2.dat b/mltsp/TCP/Software/feature_extract/Code/table2.dat new file mode 100755 index 00000000..6579279b --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/Code/table2.dat @@ -0,0 +1,549 @@ +U AQL 2446615.4520 6.793 .909 1.264 .645 988 +U AQL 2446626.2882 6.323 .717 1.054 .594 988 +U AQL 2446627.2710 6.513 .797 1.142 .632 988 +U AQL 2446628.2724 6.688 .915 1.206 .652 988 +U AQL 2446629.2748 6.843 .914 1.240 .684 988 +U AQL 2446631.2478 6.078 .605 .849 .506 988 +U AQL 2446632.2798 6.183 .649 .974 .546 988 +U AQL 2446635.2545 6.630 .894 1.210 .658 988 +U AQL 2446636.2871 6.869 .917 1.248 .680 988 +U AQL 2446996.2535 6.150 .591 .882 .528 989 +U AQL 2446997.2764 6.099 .928 .531 989 +U AQL 2446998.2797 6.314 .692 1.048 .595 989 +U AQL 2446999.2665 6.404 .752 1.097 .614 989 +U AQL 2447000.2711 6.622 .818 1.199 .658 989 +U AQL 2447001.2792 6.803 .897 1.246 .683 989 +U AQL 2447002.2707 6.712 .765 1.164 .650 989 +U AQL 2447003.2607 6.174 .608 .907 .530 989 +U AQL 2447084.0855 6.598 .795 1.123 .636 989 +U AQL 2447087.0710 .682 1.064 .609 989 +U AQL 2447088.0685 6.115 .598 .836 .523 989 +U AQL 2447091.0649 6.566 1.165 .659 989 +U AQL 2447098.0397 .818 1.140 .655 989 +U AQL 2447399.1484 6.387 .700 1.076 .589 990 +U AQL 2447401.1255 6.675 .908 1.241 .644 990 +U AQL 2447402.2208 6.817 .853 1.233 .652 990 +U AQL 2447403.2231 6.444 .580 1.014 .570 990 +U AQL 2447404.1975 6.060 .490 .875 .490 990 +U AQL 2447407.2130 6.574 .786 1.204 .631 990 +U AQL 2447408.1824 6.684 .874 1.231 .653 990 +U AQL 2447409.2012 6.800 1.237 .657 990 +U AQL 2447410.1259 6.440 .593 1.063 .584 990 +U AQL 2447411.1197 6.091 .502 .861 .513 990 +U AQL 2447412.1246 .576 1.016 990 +U AQL 2447413.1132 6.332 .674 1.059 .577 990 +U AQL 2447414.1158 6.579 .804 1.167 .630 990 +U AQL 2447415.1187 6.665 .884 1.244 .646 990 +U AQL 2447416.1140 6.830 .863 1.265 .669 990 +U AQL 2447417.1113 6.493 .603 1.108 .589 990 +U AQL 2447418.1134 6.033 .532 .862 .485 990 +U AQL 2447419.1084 6.220 .614 .985 .540 990 +U AQL 2447420.1080 6.341 .654 1.096 .590 990 +U AQL 2447421.1064 6.550 .747 1.209 .646 990 +U AQL 2447422.1035 .883 990 +U AQL 2447423.1047 6.814 .886 1.245 .661 990 +U AQL 2447424.1018 6.555 .567 1.099 .603 990 +U AQL 2447425.1054 6.061 .494 .886 .491 990 +U AQL 2447427.1058 6.303 .674 1.079 .579 990 +U AQL 2447428.0998 6.525 .750 1.170 .634 990 +U AQL 2447429.1086 6.657 .853 1.240 .652 990 +U AQL 2447430.0982 6.806 .893 1.256 .651 990 +U AQL 2447431.0979 6.539 .626 1.097 .592 990 +U AQL 2447432.0964 6.041 .476 .913 .490 990 +U AQL 2447433.0933 6.188 .581 .970 .545 990 +U AQL 2447434.0952 6.344 .650 1.092 .583 990 +U AQL 2447734.3034 6.087 .485 .871 .506 991 +U AQL 2447735.3474 6.249 .595 .984 .576 991 +U AQL 2447736.3518 6.352 .648 1.075 .604 991 +U AQL 2447737.3571 6.523 .847 1.164 .627 991 +U AQL 2447738.3367 .901 1.227 .687 991 +U AQL 2447739.3005 6.816 .885 1.216 .670 991 +U AQL 2447740.3355 6.412 .576 1.068 .608 991 +U AQL 2447741.2932 6.045 .522 .868 .515 991 +U AQL 2447742.3027 6.205 .604 .972 .561 991 +U AQL 2447743.2910 6.272 .666 1.052 .594 991 +U AQL 2447744.2688 6.526 .757 1.161 .634 991 +U AQL 2447745.2755 6.681 .880 1.217 .662 991 +U AQL 2447746.2811 6.819 .859 1.217 .669 991 +U AQL 2447747.2738 6.442 .612 1.043 .591 991 +U AQL 2447748.2735 6.045 .491 .864 .530 991 +U AQL 2447749.2670 6.202 .585 .961 .558 991 +U AQL 2447751.2484 6.514 .780 1.154 .649 991 +U AQL 2447752.2312 6.669 .887 1.217 .646 991 +U AQL 2447753.2296 6.798 .874 1.243 .663 991 +U AQL 2447754.2565 6.484 .612 1.052 .588 991 +U AQL 2447755.2508 6.028 .488 .868 .498 991 +U AQL 2447756.2896 6.147 .579 .991 .557 991 +U AQL 2447757.2554 6.296 .663 1.065 .584 991 +U AQL 2447758.2511 6.500 .732 1.172 .644 991 +U AQL 2447759.2311 6.682 .867 1.198 .664 991 +U AQL 2447760.2524 6.807 .876 1.229 .652 991 +U AQL 2447761.1378 6.587 .662 1.079 .625 991 +U AQL 2447761.2375 6.518 .617 1.058 .608 991 +U AQL 2447762.1396 6.039 .478 .866 .498 991 +U AQL 2447762.2209 6.033 .491 .855 .500 991 +U AQL 2447763.1330 6.162 .593 .950 .536 991 +U AQL 2447764.1928 6.325 .658 1.060 .595 991 +U AQL 2447766.1309 6.654 .857 1.224 .659 991 +U AQL 2447766.1951 6.651 .835 1.203 .675 991 +U AQL 2447767.1390 6.807 .880 1.216 .682 991 +U AQL 2447767.2417 6.782 .865 1.242 .667 991 +U AQL 2447768.1311 6.602 .661 1.079 .590 991 +U AQL 2447768.2419 6.473 .646 1.079 .612 991 +U AQL 2447769.1325 6.069 .507 .841 .553 991 +U AQL 2447770.1264 6.138 .533 .973 .543 991 +U AQL 2447770.2287 6.185 .584 .946 .561 991 +U AQL 2447771.1270 6.312 .658 1.086 .591 991 +U AQL 2447771.2205 6.296 .664 1.066 .606 991 +U AQL 2447772.1228 6.456 1.086 .630 991 +U AQL 2447772.2188 6.464 .764 1.140 .621 991 +U AQL 2447773.1256 6.627 .821 1.252 .652 991 +U AQL 2447773.2448 6.652 .866 1.195 .659 991 +U AQL 2447774.1401 6.763 .845 1.274 .649 991 +U AQL 2447774.2488 6.786 .887 1.249 .662 991 +U AQL 2447775.1209 6.619 .671 1.107 .634 991 +U AQL 2447775.2141 6.541 .659 1.093 .603 991 +U AQL 2447776.1252 6.050 .465 .885 .500 991 +U AQL 2447776.2174 6.036 .475 .870 .520 991 +U AQL 2448101.1701 6.324 .683 .987 .601 992 +U AQL 2448102.1931 6.430 .773 1.076 .621 992 +U AQL 2448103.1727 6.617 .870 1.137 .642 992 +U AQL 2448104.1774 6.768 .920 1.210 .676 992 +U AQL 2448108.1762 6.337 .704 1.039 .581 992 +U AQL 2448109.1633 6.417 .749 1.073 .608 992 +U AQL 2448110.1715 6.651 .841 1.159 .654 992 +U AQL 2448111.1749 6.779 .898 1.216 .657 992 +U AQL 2448112.1723 6.666 .734 1.082 .636 992 +U AQL 2448113.1651 6.105 .582 .844 .516 992 +U AQL 2448114.1738 6.137 .626 .901 .522 992 +U AQL 2448115.2287 6.353 .716 1.001 .594 992 +U AQL 2448116.1745 6.406 .624 992 +U AQL 2448117.1779 6.621 .818 .672 992 +U AQL 2448118.1769 6.804 .903 1.223 .667 992 +U AQL 2448119.1634 6.729 .726 1.110 .640 992 +U AQL 2448123.1484 6.389 .768 1.061 .601 992 +U AQL 2448127.1567 6.138 .631 .858 .524 992 +U AQL 2448503.2050 6.597 .866 1.202 .643 993 +U AQL 2448504.1376 6.751 .881 1.239 .673 993 +U AQL 2448505.1358 6.818 .818 1.176 .674 993 +U AQL 2448506.1394 6.370 .556 1.005 .584 993 +U AQL 2448507.1236 6.103 .506 .912 .519 993 +U AQL 2448508.1236 6.297 .612 1.032 .582 993 +U AQL 2448509.1228 6.358 .648 1.092 .598 993 +U AQL 2448510.1237 6.603 .805 1.181 .646 993 +U AQL 2448511.1225 6.756 .891 1.258 .667 993 +U AQL 2448512.1226 6.825 .837 1.216 .669 993 +U AQL 2448513.1205 6.404 .568 1.008 .578 993 +U AQL 2448514.1206 6.112 .506 .885 .520 993 +U AQL 2448515.1147 6.250 .629 1.010 .568 993 +U AQL 2448516.1179 6.316 .668 1.069 .596 993 +U AQL 2448517.1163 6.600 .810 1.152 .668 993 +U AQL 2448518.1232 6.763 .903 1.239 .677 993 +U AQL 2448519.1318 6.801 .822 1.227 .654 993 +U AQL 2448520.1111 6.411 .577 .997 .584 993 +U AQL 2448521.1174 6.089 .527 .874 .520 993 +U AQL 2448522.1160 6.279 .642 .999 .581 993 +U AQL 2448523.1072 6.357 .652 1.081 .592 993 +U AQL 2448854.1815 6.561 .795 1.189 .667 994 +U AQL 2448856.1656 6.801 .822 1.254 .670 994 +U AQL 2448858.1623 6.061 .499 .878 .523 994 +U AQL 2448860.1542 6.325 .641 1.105 .603 994 +U AQL 2448870.1365 6.812 .865 1.254 .657 994 +U AQL 2448872.1383 6.110 .471 .895 .528 994 +U AQL 2448874.1349 6.337 .598 1.092 .594 994 +U AQL 2448876.1296 6.724 .853 1.235 .677 994 +U AQL 2448877.1234 6.890 .849 1.251 .684 994 +U AQL 2448880.1231 6.230 .563 1.011 .564 994 +U AQL 2448881.1152 6.335 .625 1.113 .604 994 +U AQL 2448882.1177 6.570 .755 1.188 .646 994 +U AQL 2448883.1169 6.706 .872 1.221 .677 994 +U AQL 2448884.1136 6.852 .862 1.256 .682 994 +U AQL 2448885.1106 6.526 .624 1.090 .616 994 +U AQL 2448886.1120 6.097 .468 .881 .505 994 +U AQL 2448888.1096 6.357 .621 1.080 .603 994 +U AQL 2448889.1098 6.538 .760 1.179 .638 994 +U AQL 2448890.1070 6.715 .848 1.228 .681 994 +U AQL 2448891.1043 6.843 .849 1.257 .682 994 +U AQL 2448892.1030 6.542 .605 1.069 .608 994 +U AQL 2448893.1028 6.082 .464 .890 .504 994 +U AQL 2448894.1016 6.199 .512 .976 .566 994 +U AQL 2449521.9006 6.604 1.204 996 +U AQL 2449522.8230 1.252 996 +U AQL 2449528.8639 6.652 1.245 .696 .716 996 +U AQL 2449529.8050 6.747 1.259 .704 .642 996 +U AQL 2449530.8107 6.669 .901 1.213 .685 .637 996 +U AQL 2449534.8110 6.332 1.109 .661 .608 996 +U AQL 2449543.8130 .779 1.289 .748 .655 996 +U AQL 2449545.7690 6.289 .738 .993 .620 .561 996 +U AQL 2449558.8536 6.745 .820 1.197 .649 .632 996 +U AQL 2449559.8467 .956 996 +U AQL 2449561.8304 6.297 .722 1.013 .572 .564 996 +U AQL 2449564.8376 6.749 1.225 996 +U AQL 2449619.2059 6.490 .827 1.098 .629 995 +U AQL 2449620.1988 6.629 .854 1.193 .663 995 +U AQL 2449621.1022 6.802 .967 1.208 .691 995 +U AQL 2449621.1936 6.806 .960 1.230 .665 995 +U AQL 2449622.1007 6.753 .741 1.184 .656 995 +U AQL 2449623.0948 6.334 .611 .957 .560 995 +U AQL 2449624.1006 6.155 .617 .913 .534 995 +U AQL 2449625.0974 6.350 .689 1.042 .604 995 +U AQL 2449631.0958 6.137 .621 .911 .539 995 +U AQL 2449632.1054 6.367 .684 1.041 .610 995 +U AQL 2449633.0974 6.370 .733 1.074 .631 995 +U AQL 2449634.1005 6.644 .940 1.219 .658 995 +U AQL 2449808.9166 6.437 1.072 .611 .608 997 +U AQL 2449809.9044 6.643 1.187 .648 .633 997 +U AQL 2449810.8994 6.780 1.236 .662 997 +U AQL 2449811.9138 6.740 1.129 .635 .630 997 +U AQL 2449813.9168 6.143 .886 .533 .531 997 +U AQL 2449814.8824 6.339 1.023 .589 .578 997 +U AQL 2449817.9124 6.796 1.216 .680 .634 997 +U AQL 2449818.9179 6.738 1.134 .643 .603 997 +U AQL 2449821.9066 6.319 1.024 .590 .570 997 +U AQL 2449822.9064 6.378 1.065 .605 .607 997 +U AQL 2449823.9053 6.637 1.179 .665 .634 997 +U AQL 2449825.9050 6.753 1.144 .641 .636 997 +U AQL 2449934.1714 6.295 1.093 .584 1.138 998 +U AQL 2449935.1671 6.449 1.095 .659 1.165 998 +U AQL 2449936.1614 6.627 1.200 .661 1.244 998 +U AQL 2449937.2264 6.804 1.246 .686 998 +U AQL 2449938.1641 6.876 1.218 .660 1.253 998 +U AQL 2449939.1666 6.321 .990 .561 1.108 998 +U AQL 2449942.1614 6.371 1.084 .624 1.176 998 +U AQL 2449943.1550 6.548 1.203 .649 1.242 998 +U AQL 2449944.1555 6.820 1.254 .636 1.262 998 +U AQL 2449945.1565 6.805 1.216 .677 1.286 998 +U AQL 2449946.1555 6.395 .996 .552 1.084 998 +U AQL 2449947.1550 6.130 .906 .535 1.034 998 +U AQL 2449948.1512 6.332 1.057 .584 1.138 998 +U AQL 2449949.1574 6.330 1.068 .603 1.175 998 +U AQL 2449950.1536 1.212 .685 998 +U AQL 2449952.1527 6.813 1.210 .660 1.276 998 +U AQL 2449953.1769 6.403 .984 .565 1.093 998 +U AQL 2449954.1644 6.143 .906 .519 1.015 998 +U AQL 2449955.1570 .598 1.132 998 +U AQL 2449958.1432 6.813 1.251 .675 1.312 998 +U AQL 2449959.1386 6.874 1.217 .677 1.313 998 +U AQL 2449962.1514 6.330 1.046 .587 1.142 998 +U AQL 2450009.0938 6.426 1.051 .583 1.128 998 +U AQL 2450011.0905 6.250 1.002 .570 1.085 998 +U AQL 2450012.1294 6.308 1.158 998 +U AQL 2450017.0777 6.095 .866 .514 1.028 998 +U AQL 2450018.1206 6.236 .993 .595 1.080 998 +U AQL 2450020.0707 6.533 1.162 .642 1.249 998 +U AQL 2450305.1716 6.090 .851 .532 971 +U AQL 2450306.1633 6.246 .966 .582 971 +U AQL 2450307.1849 6.372 1.045 .599 971 +U AQL 2450310.2163 6.820 1.183 .664 971 +U AQL 2450311.1559 6.406 1.025 .587 971 +U AQL 2450312.1507 6.032 .877 .521 971 +U AQL 2450313.1568 6.239 1.007 .578 971 +U AQL 2450314.1411 6.325 1.039 .603 971 +U AQL 2450315.1424 6.580 1.117 .683 971 +U AQL 2450316.1420 6.699 1.184 .675 971 +U AQL 2450317.1715 6.824 1.155 .621 971 +U AQL 2450318.1426 6.455 .999 .605 971 +U AQL 2450319.1408 6.088 .815 .545 971 +U AQL 2450320.1420 6.229 .951 .588 971 +U AQL 2450321.1367 6.348 1.004 .624 971 +U AQL 2450322.1317 6.568 1.100 .666 971 +U AQL 2450324.1372 6.815 1.148 .682 971 +U AQL 2450325.1281 6.502 1.002 .612 971 +U AQL 2450326.1244 6.061 .854 .513 971 +SZ AQL 2445502.3359 8.927 1.171 1.535 .848 982 +SZ AQL 2445503.3242 8.518 .879 1.298 .762 982 +SZ AQL 2445505.3163 8.126 .847 1.205 .699 982 +SZ AQL 2445508.3046 8.425 1.097 1.474 .807 982 +SZ AQL 2445509.3320 8.523 1.196 1.541 .834 982 +SZ AQL 2445512.3397 8.890 1.731 .898 982 +SZ AQL 2445513.3905 9.012 1.791 .912 982 +SZ AQL 2445514.3359 9.116 1.775 .930 982 +SZ AQL 2445648.1093 8.697 1.418 1.641 .878 982 +SZ AQL 2445650.1171 8.955 1.598 1.773 .921 982 +SZ AQL 2445872.3593 8.872 1.620 1.714 .897 982 +SZ AQL 2445873.3593 8.991 1.687 1.740 .918 982 +SZ AQL 2445874.3476 9.087 1.747 1.767 .920 982 +SZ AQL 2445875.3397 9.171 1.759 .935 982 +SZ AQL 2445876.3554 9.152 1.616 1.726 .916 982 +SZ AQL 2445877.3397 9.059 1.452 1.666 .895 982 +SZ AQL 2445878.3437 8.941 1.238 1.577 .862 982 +SZ AQL 2445879.3320 8.922 1.213 1.518 .838 982 +SZ AQL 2445880.3437 8.544 .969 1.344 .761 982 +SZ AQL 2445881.3359 7.962 .823 1.086 .654 982 +SZ AQL 2445882.3280 8.128 .885 1.220 .693 982 +SZ AQL 2445883.3437 8.234 .979 1.293 .733 982 +SZ AQL 2445886.3476 8.544 1.327 1.540 .837 982 +SZ AQL 2445887.3554 8.647 1.410 1.596 .857 982 +SZ AQL 2447399.2436 9.054 1.716 1.778 .903 990 +SZ AQL 2447400.2285 9.151 1.744 1.782 .924 990 +SZ AQL 2447402.2003 9.150 1.520 1.699 .887 990 +SZ AQL 2447403.1935 9.031 1.588 .861 990 +SZ AQL 2447404.1955 8.896 1.502 .824 990 +SZ AQL 2447407.2101 8.058 .730 1.164 .669 990 +SZ AQL 2447408.1795 8.146 .818 1.249 .688 990 +SZ AQL 2447409.1954 8.253 1.321 .738 990 +SZ AQL 2447410.2033 8.372 1.030 1.428 .770 990 +SZ AQL 2447411.2247 8.479 1.151 1.500 .813 990 +SZ AQL 2447413.1877 8.652 1.625 .850 990 +SZ AQL 2447414.1741 8.781 1.473 1.678 .875 990 +SZ AQL 2447415.1752 8.903 1.573 1.745 .889 990 +SZ AQL 2447416.1826 8.999 1.654 1.799 .884 990 +SZ AQL 2447417.1765 9.090 1.777 .905 990 +SZ AQL 2447418.1795 9.152 1.775 .896 990 +SZ AQL 2447420.1618 9.020 1.642 .858 990 +SZ AQL 2447421.1543 8.884 1.534 .839 990 +SZ AQL 2447422.1583 8.911 1.517 .816 990 +SZ AQL 2447423.1468 8.309 .798 1.234 .694 990 +SZ AQL 2447424.1528 7.973 .725 1.130 .641 990 +SZ AQL 2447425.1593 8.131 .809 1.225 .682 990 +SZ AQL 2447427.1659 8.357 1.028 1.397 .762 990 +SZ AQL 2447428.1486 8.441 1.149 1.481 .793 990 +SZ AQL 2447429.1451 8.546 1.220 1.588 .821 990 +SZ AQL 2447430.1311 8.655 1.368 1.596 .849 990 +SZ AQL 2447431.1347 8.763 1.446 1.664 .866 990 +SZ AQL 2447432.1282 8.875 1.725 .882 990 +SZ AQL 2447433.1331 9.027 1.761 .909 990 +SZ AQL 2447434.1374 9.123 1.766 .913 990 +SZ AQL 2448503.2163 8.027 .676 1.108 .629 993 +SZ AQL 2448504.1802 8.055 1.152 .674 993 +SZ AQL 2448505.1652 8.168 .832 1.243 .703 993 +SZ AQL 2448506.2282 8.273 1.337 .742 993 +SZ AQL 2448507.2013 8.372 1.426 .784 993 +SZ AQL 2448508.1821 8.461 1.508 .806 993 +SZ AQL 2448509.1893 8.580 1.571 .846 993 +SZ AQL 2448510.1838 8.682 1.622 .861 993 +SZ AQL 2448511.1896 8.830 1.676 .866 993 +SZ AQL 2448512.1947 8.932 1.734 .892 993 +SZ AQL 2448513.2040 9.061 1.743 .921 993 +SZ AQL 2448514.2053 9.156 1.776 .904 993 +SZ AQL 2448515.2008 9.197 1.710 .932 993 +SZ AQL 2448516.1954 9.131 1.698 .884 993 +SZ AQL 2448517.1820 9.025 1.589 .862 993 +SZ AQL 2448518.1942 8.902 1.515 .840 993 +SZ AQL 2448519.2140 8.869 1.525 .822 993 +SZ AQL 2448520.1849 8.180 1.173 .650 993 +SZ AQL 2448521.2064 7.996 1.175 .639 993 +SZ AQL 2448522.1924 8.144 1.244 .698 993 +SZ AQL 2448523.1869 8.279 1.315 .732 993 +SZ AQL 2448856.2467 9.092 1.776 .935 994 +SZ AQL 2448858.2606 9.185 1.794 .925 994 +SZ AQL 2448860.2449 8.997 1.638 .866 994 +SZ AQL 2448862.2716 8.807 1.477 .795 994 +SZ AQL 2448870.2285 8.684 1.673 .845 994 +SZ AQL 2448872.2296 8.924 1.735 .902 994 +SZ AQL 2448874.2241 9.143 1.797 .920 994 +SZ AQL 2448876.2202 9.155 1.733 .907 994 +SZ AQL 2448877.1985 9.033 1.641 .878 994 +SZ AQL 2448878.2130 8.885 1.561 .851 994 +SZ AQL 2448880.1516 8.154 1.187 .676 994 +SZ AQL 2448881.1472 8.031 1.177 .657 994 +SZ AQL 2448882.1568 8.175 1.268 .711 994 +SZ AQL 2448883.1659 8.260 1.346 .744 994 +SZ AQL 2448884.1719 8.360 1.430 .779 994 +SZ AQL 2448885.1599 8.447 1.536 .803 994 +SZ AQL 2448886.1599 8.571 1.587 .824 994 +SZ AQL 2448888.1616 8.795 1.696 .886 994 +SZ AQL 2448889.1740 8.950 1.730 .912 994 +SZ AQL 2448890.1522 9.045 1.777 .941 994 +SZ AQL 2448891.1508 9.130 1.787 .920 994 +SZ AQL 2448892.1614 9.205 1.773 .940 994 +SZ AQL 2448893.1514 9.168 1.735 .919 994 +SZ AQL 2448894.1835 9.056 1.663 .880 994 +SZ AQL 2449521.8687 8.748 1.673 996 +SZ AQL 2449522.7907 8.830 1.693 996 +SZ AQL 2449528.8491 9.012 1.627 .849 .793 996 +SZ AQL 2449529.7535 8.984 1.511 .832 .793 996 +SZ AQL 2449530.7899 8.764 1.250 1.463 .804 .775 996 +SZ AQL 2449534.7786 8.333 1.354 .733 .729 996 +SZ AQL 2449543.7907 9.228 1.732 1.766 .927 .839 996 +SZ AQL 2449545.7283 9.005 1.448 1.642 .856 .796 996 +SZ AQL 2449561.7820 9.148 1.993 1.714 .908 .827 996 +SZ AQL 2449617.1148 8.170 1.171 .675 995 +SZ AQL 2449619.2375 8.222 1.239 .721 995 +SZ AQL 2449620.2166 8.266 1.298 .737 995 +SZ AQL 2449621.2076 8.372 1.414 .789 995 +SZ AQL 2449623.1848 8.580 1.354 1.565 .836 995 +SZ AQL 2449624.1957 8.706 1.629 .856 995 +SZ AQL 2449625.1991 8.817 1.678 1.679 .878 995 +SZ AQL 2449626.2198 8.935 1.744 .894 995 +SZ AQL 2449631.1854 9.085 1.672 .858 995 +SZ AQL 2449632.2056 8.934 1.567 .851 995 +SZ AQL 2449633.1847 8.955 1.525 .829 995 +SZ AQL 2449634.1867 8.369 1.234 .712 995 +SZ AQL 2449934.3203 8.905 1.738 .926 1.729 998 +SZ AQL 2449935.3292 9.017 1.736 .964 1.771 998 +SZ AQL 2449936.3037 9.140 1.792 .978 1.801 998 +SZ AQL 2449937.2722 9.188 1.793 .944 998 +SZ AQL 2449938.2911 9.225 .937 1.783 998 +SZ AQL 2449941.2867 8.890 1.651 998 +SZ AQL 2449942.2613 8.802 1.554 998 +SZ AQL 2449943.2514 7.949 .637 1.255 998 +SZ AQL 2449944.2624 8.165 1.364 998 +SZ AQL 2449945.2586 8.245 1.431 998 +SZ AQL 2449946.2380 8.315 1.484 998 +SZ AQL 2449947.2338 8.430 1.552 998 +SZ AQL 2449948.2304 8.544 1.600 998 +SZ AQL 2449949.2326 8.679 1.648 998 +SZ AQL 2449950.2299 8.747 1.695 998 +SZ AQL 2449952.2353 9.000 1.766 998 +SZ AQL 2449953.2564 9.121 1.761 998 +SZ AQL 2449954.2333 9.179 1.780 998 +SZ AQL 2449955.2304 9.191 1.773 998 +SZ AQL 2450305.2764 8.220 1.266 .738 971 +SZ AQL 2450306.2986 8.305 1.337 .778 971 +SZ AQL 2450307.2514 8.448 1.359 .815 971 +SZ AQL 2450310.2574 8.726 1.606 .873 971 +SZ AQL 2450311.1845 8.838 1.666 .891 971 +SZ AQL 2450312.1853 8.940 1.700 .911 971 +SZ AQL 2450313.2076 9.058 1.724 .913 971 +SZ AQL 2450314.1815 9.130 1.751 .928 971 +SZ AQL 2450315.1883 9.166 1.702 .920 971 +SZ AQL 2450316.1989 9.135 1.636 .869 971 +SZ AQL 2450317.2061 9.003 1.565 .869 971 +SZ AQL 2450318.1926 8.921 1.492 .860 971 +SZ AQL 2450319.1865 8.855 1.451 .816 971 +SZ AQL 2450320.1993 7.981 1.070 .642 971 +SZ AQL 2450321.1802 8.078 1.155 .678 971 +SZ AQL 2450322.1792 8.181 1.218 .719 971 +SZ AQL 2450323.1773 8.295 1.322 .755 971 +SZ AQL 2450324.1960 8.396 1.384 .809 971 +SZ AQL 2450325.1692 8.477 1.474 .823 971 +SZ AQL 2450326.1567 8.586 1.535 .847 971 +U TRA 2449520.7522 8.123 .686 996 +U TRA 2449522.6444 8.137 .711 996 +U TRA 2449526.6945 7.692 .352 .499 .296 .244 996 +U TRA 2449528.7020 7.385 .387 .250 .262 996 +U TRA 2449529.6724 7.936 .695 .397 .373 996 +U TRA 2449530.7713 8.167 .700 .395 .349 996 +U TRA 2449532.6469 8.051 .665 .379 .367 996 +U TRA 2449534.7088 7.917 .441 .617 .368 .348 996 +U TRA 2449535.7082 8.276 .418 .775 .362 .362 996 +U TRA 2449536.7138 7.716 .421 .531 .384 .339 996 +U TRA 2449543.6514 8.159 .438 .677 .375 .350 996 +U TRA 2449545.6176 8.145 .473 .698 .378 .373 996 +U TRA 2449558.6355 8.293 .403 .720 .396 .402 996 +U TRA 2449560.5392 7.897 .424 .703 .377 .378 996 +U TRA 2449561.5258 8.242 .415 .744 .430 .402 996 +U TRA 2449561.7035 8.071 .346 .657 .378 .368 996 +U TRA 2449563.4617 8.067 .401 .692 .386 .381 996 +U TRA 2449802.9078 8.131 .699 .404 .385 997 +U TRA 2449803.9049 7.679 .476 .321 .304 997 +U TRA 2449804.8777 8.078 .696 .398 .391 997 +U TRA 2449805.8469 8.201 .679 .401 .390 997 +U TRA 2449807.8777 8.233 .758 .420 .414 997 +U TRA 2449808.8584 7.791 .541 .325 .316 997 +U TRA 2449809.7781 7.956 .660 .364 .373 997 +U TRA 2449809.8267 7.974 .650 .380 .367 997 +U TRA 2449810.8301 8.196 .729 .423 .365 997 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-0,0 +1,20 @@ + +vo_table_preamble = """ + + + +""" + +vo_timeseries_preamble = """\n""" +vo_timeseries_mjd = """ +\t\t\tMJD +\t\t\t0.0 +\t\t\tUTC +\t\t\tTOPOCENTER +\t\t + +""" + +vo_source_preamble = """\n\n\t\n""" + diff --git a/mltsp/TCP/Software/feature_extract/MLData/__init__.py b/mltsp/TCP/Software/feature_extract/MLData/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/MLData/__init__.py @@ -0,0 +1 @@ + diff --git a/mltsp/TCP/Software/feature_extract/MLData/arffify.py b/mltsp/TCP/Software/feature_extract/MLData/arffify.py new file mode 100644 index 00000000..82a52293 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/MLData/arffify.py @@ -0,0 +1,1070 @@ +#!/usr/bin/env python + +""" +arffify + + makes ARFF files out of queries to the TCP/TUTOR DB. + +USAGE: + [0] mkdir XML + [1] In a separate window, make a tunnel to the test XML-RPC server on the linux boxes + print "ssh -L 34583:192.168.1.65:34583 lyra.berkeley.edu" + [2] Start python + +py> import arffify +py> a = arffify.Maker(search=["Cepheids","RR Lyrae - Asymmetric","Mira","W Ursae Majoris",]) ## search is a list of string names to look up + +""" +from __future__ import print_function +from __future__ import unicode_literals +import os,sys +#try: +# import amara +#except: +# pass +try: + import xmlrpclib +except ImportError: + import xmlrpc.client as xmlrpclib +import urllib +import copy +import datetime +try: + import MySQLdb +except: + pass +import time +import glob + +from ...feature_extract.Code.extractors import mlens3 + +# pre 20091117: +#skip_features = ['beyond1std', 'chi2', 'chi2_per_deg', 'dc', 'example', 'first_freq', 'freq1_harmonics_freq_1', 'freq1_harmonics_freq_2', 'freq1_harmonics_freq_3', 'freq2_harmonics_amplitude_error_1', 'freq2_harmonics_amplitude_error_2', 'freq2_harmonics_amplitude_error_3', 'freq2_harmonics_freq_1', 'freq2_harmonics_freq_2', 'freq2_harmonics_freq_3', 'freq3_harmonics_amplitude_error_1', 'freq3_harmonics_amplitude_error_2', 'freq3_harmonics_amplitude_error_3', 'freq3_harmonics_freq_1', 'freq3_harmonics_freq_2', 'freq3_harmonics_freq_3', 'freq_searched_max', 'max', 'median', 'min', 'old_dc', 'ratio21', 'ratio31', 'ratio32', 'ratioRUfirst', 'second', 'std', 'third', 'wei_av_uncertainty', 'weighted_average', 'distance_in_arcmin_to_nearest_galaxy', 'distance_in_kpc_to_nearest_galaxy', 'freq1_harmonics_nharm', 'freq2_harmonics_nharm', 'freq2_harmonics_signif', 'freq3_harmonics_nharm', 'freq3_harmonics_signif', 'percent_amplitude', 'max_slope'] #, 'n_points' + +skip_features =['chi2', + 'chi2_per_deg', + 'dc', + 'distance_in_arcmin_to_nearest_galaxy', + 'distance_in_kpc_to_nearest_galaxy', + 'example', + 'first_freq', + 'freq1_harmonics_freq_1', + 'freq1_harmonics_freq_2', + 'freq1_harmonics_freq_3', + 'freq1_harmonics_nharm', + 'freq2_harmonics_amplitude_error_1', + 'freq2_harmonics_amplitude_error_2', + 'freq2_harmonics_amplitude_error_3', + 'freq2_harmonics_freq_1', + 'freq2_harmonics_freq_2', + 'freq2_harmonics_freq_3', + 'freq2_harmonics_nharm', + 'freq2_harmonics_signif', + 'freq3_harmonics_amplitude_error_1', + 'freq3_harmonics_amplitude_error_2', + 'freq3_harmonics_amplitude_error_3', + 'freq3_harmonics_freq_1', + 'freq3_harmonics_freq_2', + 'freq3_harmonics_freq_3', + 'freq3_harmonics_nharm', + 'freq3_harmonics_signif', + 'freq_searched_max', + 'max', + #'max_slope', # 2010525 dstarr re-enables this since it looks like the algorithm might have worth when looking at old .arffs and also I dont recall why I disabled it + 'median', + 'min', + 'old_dc', + #'percent_amplitude', # 2010517 dstarr re-enables this to see if useful, althoguh return output of extractor looks kludgey and nonlinear. + 'ratio21', + 'ratio31', + 'ratio32', + 'ratioRUfirst', + 'second', + #'std', # 2010525 dstarr re-enables this since skew has been useful (although it may not be useful) + 'third', + 'wei_av_uncertainty', + 'weighted_average', + 'closest_in_light', + 'closest_light_absolute_bmag', + 'closest_light_angle_from_major_axis', + 'closest_light_angular_offset_in_arcmin', + 'closest_light_dm', + 'closest_light_physical_offset_in_kpc', + 'closest_light_ttype', + 'freq1_harmonics_amplitude_error_0', + 'freq1_harmonics_moments_err_0', + 'freq1_harmonics_peak2peak_flux_error', + 'freq1_harmonics_rel_phase_error_0', + 'freq2_harmonics_amplitude_error_0', + 'freq2_harmonics_moments_err_0', + 'freq2_harmonics_rel_phase_error_0', + 'freq3_harmonics_amplitude_error_0', + 'freq3_harmonics_moments_err_0', + 'freq3_harmonics_rel_phase_error_0', + #20120126 commentout'n_points', # This is required for internal use by plugin_classifier.py Thankfully it doesn't seem to be used much in classifier decision trees, though. + 'sdss_best_dm', + 'sdss_best_offset_in_kpc', + 'sdss_best_offset_in_petro_g', + 'sdss_best_z', + 'sdss_best_zerr', + 'sdss_dered_g', + 'sdss_dered_i', + 'sdss_dered_r', + 'sdss_dered_u', + 'sdss_dered_z', + 'sdss_dist_arcmin', + 'sdss_in_footprint', + 'sdss_nearest_obj_type', + 'sdss_petro_radius_g', + 'sdss_photo_rest_abs_g', + 'sdss_photo_rest_abs_i', + 'sdss_photo_rest_abs_r', + 'sdss_photo_rest_abs_u', + 'sdss_photo_rest_abs_z', + 'sdss_photo_rest_gr', + 'sdss_photo_rest_iz', + 'sdss_photo_rest_ri', + 'sdss_photo_rest_ug', + 'sdss_chicago_class', # 2010525 dstarr adds + 'sdss_first_flux_in_mjy', # 2010525 dstarr adds + 'sdss_first_offset_in_arcsec', # 2010525 dstarr adds + 'sdss_spec_confidence', # 2010525 dstarr adds + 'sdss_rosat_offset_in_arcsec', + 'sdss_rosat_offset_in_sigma'] + +positional_features = ["ecpb", "galb", "gall", "ecpl"] #"distance_in_arcmin_to_nearest_galaxy", "distance_in_kpc_to_nearest_galaxy" + + +def get_class_abrv_lookup_from_header(train_arff_fpath): + """ Given a .arff which is to be used for training, return the + {class_abrev:class_fullname} dictionary parsed from the header, if + it exists. + """ + lines = open(train_arff_fpath).readlines() + class_lookup_dict = {} + for line in lines: + if line[:20] == "%% class_lookup_dict": + exec(line[3:]) + break # get out of loop + return class_lookup_dict + +class Maker: + + #print "ssh lyra.berkeley.edu -c blowfish -X -L 13306:127.0.0.1:3306" + # 'skip_sci_class_list':['vs', 'GCVS', 'NEW', ''], # Science classes to skip from adding to .arff + # 'disambiguate_sci_class_dict':{'CEP':'c', # Ambiguous key classes are given value class_name + # 'dc':'c', + # 'RR':'rr-lyr', + # 'Nonstellar':'ML', + # 'Pulsating':'puls', + # }, + pars = {'tcptutor_hostname':'lyra.berkeley.edu', + 'tcptutor_username':'pteluser', + 'tcptutor_password':'Edwin_Hubble71', + 'tcptutor_port': 3306, # 13306, + 'tcptutor_database':'tutor', + 'classdb_hostname':'127.0.0.1', #'192.168.1.25', + 'classdb_username':'dstarr', #'pteluser', + 'classdb_port': 3306, + 'classdb_database':'source_test_db', #'source_db', + 't_sleep':0.2, + 'number_threads':4, + 'tcp_tutor_srcid_offset':100000000, + 'local_xmls_fpath':os.path.expandvars('$HOME/scratch/TUTOR_vosources'), + 'skip_sci_class_list':['','Variable Stars'], #20110202: dstarr excludes 'UNKNOWN', since unclassified ASAS sources need o be included in the arff files. # Science classes to skip from adding to .arff + 'disambiguate_sci_class_dict':{'Pulsating Variable Stars':'Pulsating Variable',#'Pulsating Variable':'Pulsating Variable Stars', #20100702 disable: 'Pulsating Variable Stars':'Pulsating Variable', # Ambiguous key classes are given value class_name + 'Cepheid Variable':'Classical Cepheid', + 'Cepheids':'Classical Cepheid', + 'Classical Cepheids':'Classical Cepheid', #20091115: dstarr adds + 'Classical Cepheids, Symmetrical':'Classical Cepheid', # 20091119: kludgy subclass assumption + 'Symmetrical':'Classical Cepheid', # 20091119: kludgy subclass assumption + 'Classical Cepheid Multiple Modes Symmetrical':'Multiple Mode Cepheid', #20091115: dstarr adds + 'RR-Lyrae stars, subtype ab':'RR Lyrae, Fundamental Mode', #20091117: would like: 'RR-Lyrae stars, subtype ab':'RR Lyrae, Fundamental Mode', + 'RR Lyrae - Near Symmetric':'RR Lyrae - First Overtone', + 'Contact Systems':'Binary', + 'Delta-Scuti stars':'Delta Scuti', + 'Algol (Beta Persei)':'Beta Persei', + 'Algol, with third?':'Beta Persei', + 'Algol, semidetached, pulsating component':'Beta Persei', + 'Beta Cephei, massive, rapidly rotating, multiperiodicity':'Beta-Cephei stars', + }, #20091117: disabling this since seems incorrect since seperate sub-types: 'RR Lyrae, First Overtone':'RR Lyrae, Fundamental Mode', + 'hardcoded_class_abrv_lookup': \ + {'ACV': 'Alpha2 Canum Venaticorum', + 'ACVO': 'Alpha2 CVn - Rapily Oscillating', + 'ACYG': 'Alpha Cygni', + 'AGN': 'Active Galactic Nuclei', + 'AM': 'AM Her', + 'AR': 'Detached - AR Lacertae', + 'BCEP': 'Beta Cephei', + 'BCEPS': 'Beta Cephei - Short Period', + 'BE': 'Be star', + 'BL-Lac': 'BL Lac', + 'BLBOO': 'Anomalous Cepheids', + 'BLZ': 'Blazar', + 'BY': 'BY Draconis', + 'CEP': 'Cepheids', + 'CEP(B)': 'Cepheids - Multiple Modes', + 'CW': 'W Virginis', + 'CWA': 'W Virginis - Long Period', + 'CWB': 'W Virigins - Short Period', + 'Cataclysmic': 'Cataclysmic (Explosive and Novalike) Variable Stars', + 'D': 'Detached', + 'DCEP': 'Delta Cep', + 'DCEPS': 'Delta Cep - Symmetrical', + 'DM': 'Detached - Main Sequence', + 'DQ': 'DQ Herculis Variable (Intermediate Polars)', + 'DS': 'Detached - With Subgiant', + 'DSCT': 'Delta Scuti', + 'DSCTC': 'Delta Scuti - Low Amplitude', + 'DW': 'W Ursa Majoris', + 'DrkMatterA': 'Dark Matter Anniliation Event', + 'E': 'Eclipsing Binary Systems', + 'EA': 'Algol (Beta Persei)', + 'EB': 'Beta Lyrae', + 'ELL': 'Rotating Ellipsoidal', + 'EP': 'Eclipsed by Planets', + 'EW': 'W Ursae Majoris - W UMa', + 'EWa': 'W Ursae Majoris- a', + 'EWs': 'W Ursae Majoris- s', + 'Eclipsing': 'Close Binary Eclipsing Systems', + 'Eruptive': 'Eruptive Variable Stars', + 'FKCOM': 'FK Comae Berenices', + 'FU': 'FU Orionis', + 'GCAS': 'Gamma Cas', + 'GCVS': 'Variable Stars', + 'GDOR': 'Gamma Doradus', + 'GRB': 'Gamma-ray Bursts', + 'GS': 'Systems with Supergiant(s)', + 'GalNuclei': 'Galaxy Nuclei ', + 'I': 'Irregular', + 'IA': 'Irregular Early O-A', + 'IB': 'Irregular Intermediate F-M', + 'IN': 'Orion', + 'IN(YY)': 'Orion with Absorption', + 'INA': 'Orion Early Types (B-A or Ae)', + 'INB': 'Orion Intermediate Types (F-M or Fe-Me)', + 'INT': 'Orion T Tauri', + 'IS': 'Rapid Irregular', + 'ISA': 'Rapid Irregular Early Types (B-A or Ae)', + 'ISB': 'Rapid Irregular Intermediate to Late (F-M and Fe-Me)', + 'K': 'Contact Systems', + 'KE': 'Contact Systems - Early (O-A)', + 'KW': 'Contact Systems - W Ursa Majoris', + 'L': 'Slow Irregular', + 'LB': 'Slow Irregular - Late Spectral Type (K, M, C, S)', + 'LC': 'Irregular Supergiants', + 'LPB': 'Long Period B', + 'LSB': 'Long Gamma-ray Burst', + 'M': 'Mira', + 'ML': 'Microlensing Event', + 'N': 'Novae', + 'NA': 'Fast Novae', + 'NB': 'Slow Novae', + 'NC': 'Very Slow Novae', + 'NEW': 'New Variability Types', + 'NL': 'Novalike Variables', + 'NR': 'Recurrent Novae', + 'Nonstellar': 'Variable Sources (Non-stellar)', + 'OVV': 'Optically Violent Variable Quasar (OVV)', + 'PN': 'Systems with Planetary Nebulae', + 'PSR': 'Optically Variable Pulsars', + 'PVTEL': 'PV Telescopii', + 'Polars': 'Polars', + 'Pulsating': 'Pulsating Variable Stars', + 'R': 'Close Binary with Reflection', + 'RCB': 'R Coronae Borealis', + 'RPHS': 'Very Rapidly Pulsating Hot (subdwarf B)', + 'RR': 'RR Lyrae', + 'RR(B)': 'RR Lyrae - Dual Mode', + 'RRAB': 'RR Lyrae - Asymmetric', + 'RRC': 'RR Lyrae - Near Symmetric', + 'RRcl': 'RR Lyrae -- Closely Spaced Modes', + 'RRe': 'RR Lyrae -- Second Overtone Pulsations', + 'RS': 'RS Canum Venaticorum', + 'RV': 'RV Tauri', + 'RVA': 'RV Tauri - Constant Mean Magnitude', + 'RVB': 'RV Tauri - Variable Mean Magnitude', + 'Rotating': 'Rotating Variable Stars', + 'SD': 'Semidetached', + 'SDOR': 'S Doradus', + 'SGR': 'Soft Gamma-ray Repeater', + 'SHB': 'Short Gamma-ray Burst', + 'SN': 'Supernovae', + 'SNI': 'Type I Supernovae', + 'SNII': 'Type II Supernovae', + 'SNIIL': 'Type II-L', + 'SNIIN': 'Type IIN', + 'SNIIP': 'Type IIP', + 'SNIa': 'Type Ia', + 'SNIa-pec': 'Peculiar Type Ia Supernovae', + 'SNIa-sc': 'Super-chandra Ia supernova', + 'SNIb': 'Type Ib', + 'SNIc': 'Type Ic', + 'SNIc-pec': 'Peculiar Type Ic Supernovae', + 'SR': 'Semiregular', + 'SRA': 'Semiregular - Persistent Periodicity', + 'SRB': 'Semiregular - Poorly Defined Periodicity', + 'SRC': 'Semiregular Supergiants', + 'SRD': 'Semiregular F, G, or K', + 'SRS': 'Semiregular Pulsating Red Giants', + 'SSO': 'Solar System Object', + 'SXARI': 'SX Arietis', + 'SXPHE': 'SX Phoenicis - Pulsating Subdwarfs', + 'TDE': 'Tidal Disruption Event', + 'UG': 'U Geminorum', + 'UGSS': 'SS Cygni', + 'UGSU': 'SU Ursae Majoris', + 'UGZ': 'Z Camelopardalis', + 'UV': 'UV Ceti', + 'UVN': 'Flaring Orion Variables', + 'UXUma': 'UX Uma', + 'WD': 'Systems with White Dwarfs', + 'WR': 'Eruptive Wolf-Rayet', + 'WR(1)': 'Systems with Wolf-Rayet Stars', + 'X': 'X-Ray Sources, Optically Variable', + 'XB': 'X-Ray Bursters', + 'XF': 'Fluctuating X-Ray Systems', + 'XI': 'X-ray Irregulars', + 'XJ': 'X-Ray Binaries with Jets', + 'XND': 'X-Ray, Novalike', + 'XNG': 'X-Ray, Novalike with Early Type supergiant or giant', + 'XP': 'X-Ray Pulsar', + 'XPR': 'X-Ray Pulsar, with Reflection', + 'XPRM': 'X-Ray Pulsar with late-type dwarf', + 'XRM': 'X-Ray with late-type dwarf, un-observed pulsar', + 'ZAND': 'Symbiotic Variables', + 'ZZ': 'ZZ Ceti', + 'ZZA': 'ZZ Ceti - Only H Absorption', + 'ZZB': 'ZZ Ceti - Only He Absorption', + 'ZZO': 'ZZ Ceti showing HeII', + 'ac': 'Alpha Cygni', + 'aii': 'Alpha2 Canum Venaticorum', + 'alg': 'Algol (Beta Persei)', + 'am': 'AM Herculis (True Polar)', + 'amcvn': 'AM Canum Venaticorum', + 'b': 'Binary', + 'bc': 'Beta Cephei', + 'be': 'Be Star', + 'bl': 'Short period (BL Herculis)', + 'bly': 'Beta Lyrae', + 'by': 'BY Draconis', + 'c': 'Cepheid Variable', + 'ca': 'Anomolous Cepheid', + 'cc': 'Core Collapse Supernovae', + 'cm': 'Multiple Mode Cepheid', + 'cn': 'Classical Novae', + 'cv': 'Cataclysmic Variable', + 'dc': 'Classical Cepheid', + 'dqh': 'DQ Herculis (Intermdiate Polars)', + 'ds': 'Delta Scuti', + 'dsm': 'Delta Scuti - Multiple Modes', + 'ell': 'Ellipsoidal', + 'er': 'ER Ursae Majoris', + 'ev': 'Eruptive Variable', + 'fk': 'FK Comae Berenices', + 'fsrq': 'Flat Spectrum Radio Quasar', + 'fuor': 'FU Orionis', + 'gc': 'Gamma Cassiopeiae', + 'gd': 'Gamma Doradus', + 'grb': 'Gamma Ray Burst', + 'gw': 'GW Virginis', + 'hae': 'Herbig AE', + 'haebe': 'Herbig AE/BE Star', + 'i': 'Type I Supernovae', + 'ib': 'Type Ib Supernovae', + 'ic': 'Type Ic Supernovae', + 'ii': 'Type II Supernovae', + 'iii': 'Three or More Stars', + 'iin': 'Type II N', + 'lamb': 'Lambda Eridani', + 'lboo': 'Lambda Bootis Variable', + 'lgrb': 'Long GRB', + 'mira': 'Mira', + 'msv': 'Multiple Star Variables', + 'n-l': 'Novalike', + 'nov': 'Novae', + 'ov': 'Orion Variable', + 'p': 'Polars', + 'pi': 'Pair Instability Supernovae', + 'piic': 'Population II Cepheid', + 'plsr': 'Pulsar', + 'psys': 'Systems with Planets', + 'puls': 'Pulsating Variable', + 'pvt': 'PV Telescopii', + 'pwd': 'Pulsating White Dwarf', + 'qso': 'QSO', + 'rcb': 'R Coronae Borealis', + 'rn': 'Recurrent Novae', + 'rot': 'Rotating Variable', + 'rr-ab': 'RR Lyrae, Fundamental Mode', + 'rr-c': 'RR Lyrae, First Overtone', + 'rr-cl': 'RR Lyrae, Closely Spaced Modes', + 'rr-d': 'RR Lyrae, Double Mode', + 'rr-e': 'RR Lyrae, Second Overtone', + 'rr-lyr': 'RR Lyrae', + 'rscvn': 'RS Canum Venaticorum', + 'rv': 'RV Tauri', + 'rvc': 'RV Tauri, Constant Mean Brightness', + 'rvv': 'RV Tauri, Variable Mean Brightness', + 'sdc': 'Symmetrical', + 'sdorad': 'S Doradus', + 'seyf': 'Seyfert', + 'sgrb': 'Short GRB', + 'shs': 'Shell Star', + 'sn': 'Supernovae', + 'sr-a': 'SRa (Z Aquarii)', + 'sr-b': 'SRb', + 'sr-c': 'SRc', + 'sr-d': 'SRd', + 'sreg': 'Semiregular Pulsating Variable', + 'srgrb': 'Soft Gamma Ray Repeater', + 'ssc': 'SS Cygni', + 'su': 'SU Ursae Majoris', + 'sv': 'Symbiotic Variable', + 'sw': 'SW Sextantis', + 'sx': 'SX Phoenicis', + 'sxari': 'SX Arietis', + 'tia': 'Type Ia Supernovae', + 'tt': 'T Tauri', + 'ttc': 'Classical T Tauri', + 'ttw': 'Weak-lined T Tauri', + 'ug': 'U Geminorum', + 'uv': 'UV Ceti Variable', + 'ux': 'UX Ursae Majoris', + 'vs': 'Variable Stars [Alt]', + 'vy': 'VY Scl', + 'wr': 'Wolf-Rayet', + 'wu': 'W Ursae Majoris', + 'wv': 'Long Period (W Virginis)', + 'wz': 'WZ Sagittae', + 'xrb': 'X Ray Burster', + 'xrbin': 'X Ray Binary', + 'zc': 'Z Camelopardalis', + 'zz': 'ZZ Ceti', + 'zzh': 'ZZ Ceti, H Absorption Only', + 'zzhe': 'ZZ Ceti, He Absorption Only', + 'zzheii': 'ZZ Ceti, With He-II'} # this is hopefully updated occasionally by a user (although seemingly pretty complete at this point). + } + + + def __init__(self,verbose=True, therange=None, dorun=True,\ + skip_class=False, local_xmls=False,\ + outfile = "maker.arff", \ + convert_class_abrvs_to_names=False, \ + search=["Cepheids","RR Lyrae - Asymmetric","RR Lyrae - Dual Mode","RR Lyrae - Near Symmetric","Cepheids - Multiple Modes"], ignore_positional_features=True, flag_retrieve_class_abrvs_from_TUTOR=False, local_xmls_fpath='', class_abrv_lookup={}, add_srcid_to_arff=False): + self.pars['local_xmls_fpath'] = local_xmls_fpath + self.add_srcid_to_arff = add_srcid_to_arff + self.skip_class = skip_class + self.local_xmls = local_xmls + self.convert_class_abrvs_to_names =convert_class_abrvs_to_names + self.flag_retrieve_class_abrvs_from_TUTOR = flag_retrieve_class_abrvs_from_TUTOR + self.verbose = True + self.outfile = outfile + self.search = search + self.skips = skip_features + + if self.flag_retrieve_class_abrvs_from_TUTOR: + self.retrieve_class_abrvs_from_TUTOR() + elif len(class_abrv_lookup) > 0: + self.class_abrv_lookup = class_abrv_lookup + else: + self.class_abrv_lookup = \ + self.pars['hardcoded_class_abrv_lookup'] + #elif self.convert_class_abrvs_to_names: + # # Obsolete to retrieve from TCP class database: + # self.populate_classname_lookup_dict() + + if ignore_positional_features: + self.skips.extend(positional_features) + if dorun: + self.run() + + + def retrieve_class_abrvs_from_TUTOR(self): + """ Retrieve : + relations from TUTOR database. + Fill self.class_abrv_lookup{} with the relation. + """ + self.db = MySQLdb.connect(host=self.pars['tcptutor_hostname'], \ + user=self.pars['tcptutor_username'], \ + passwd=self.pars['tcptutor_password'],\ + db=self.pars['tcptutor_database'],\ + port=self.pars['tcptutor_port']) + self.cursor = self.db.cursor() + select_str = "SELECT class_short_name, class_name FROM classes" # class_id + self.cursor.execute(select_str) + results = self.cursor.fetchall() + self.class_abrv_lookup = {} + for result in results: + #if self.class_abrv_lookup.has_key(result[0]): + # print "!!! ALREADY HAVE:", result[0],result[1], self.class_abrv_lookup[result[0]] + self.class_abrv_lookup[result[0]] = result[1] + + + # 20081023 Obsolete: + def populate_classname_lookup_dict(self): + """ Populate a dictionary which translates science class + abreviation names to full science class names. + """ + classdb_db = MySQLdb.connect(\ + host=self.pars['classdb_hostname'], \ + user=self.pars['classdb_username'], \ + db=self.pars['classdb_database'],\ + port=self.pars['classdb_port']) + classdb_cursor = classdb_db.cursor() + select_str = "SELECT class_short_name,class_name from classid_lookup" + classdb_cursor.execute(select_str) + results = classdb_cursor.fetchall() + # KLUDGE: There are some ?mislabeled? class abreviations, which I will clarify here: + self.class_abrv_lookup = {'ii': 'Type II Supernovae', + 'ab': 'RR Lyrae - Asymmetric', + 'bs': 'SX Phoenicis - Pulsating Subdwarfs', + 'sx': 'SX Phoenicis - Pulsating Subdwarfs', + 'c': 'RR Lyrae - Near Symmetric', + 'gd': 'Gamma Doradus', + 'mira': 'Mira', + 'ml': 'MIcrolensing Event', + 'ptcep': 'Population II Cepheid', + 'rg': 'Semiregular Pulsating Red Giants', + 'wu': 'W Ursa Majoris', + 'rrd':'Double Mode RR Lyrae' + } + for result in results: + self.class_abrv_lookup[result[0].lower()] = result[1] + + + def get_srcid_list_by_name(self,search): + + tmplist = [] + if type(search) == type("s"): + search = [search] + for s in search: + if s is None or s == "": + continue + params = urllib.urlencode({'fmt': "json", 't': "Source", 'f1': "Class_Name", "o1": "IS","v1": s}) + f = urllib.urlopen("http://lyra.berkeley.edu/tutor/pub/find_ids.php?%s" % params) + b = """a = %s""" % f.read() + exec(b) + if self.verbose: + print("[%s] %i objects returned" % (s,len(a))) + tmplist.extend([int(x) + self.pars['tcp_tutor_srcid_offset'] for x in a]) + tmplist = list(set(tmplist)) + return tmplist + + + # obsolete? : + def get_srcid_list_from_DB(self): + self.db = MySQLdb.connect(host=self.pars['tcptutor_hostname'], \ + user=self.pars['tcptutor_username'], \ + passwd=self.pars['tcptutor_password'],\ + db=self.pars['tcptutor_database'],\ + port=self.pars['tcptutor_port']) + self.cursor = self.db.cursor() + select_str = """SELECT DISTINCT sources.source_id FROM + sources WHERE EXISTS(SELECT Observations.Observation_ID FROM + Observations WHERE Observations.Source_ID = sources.Source_ID)""" + self.cursor.execute(select_str) + results = self.cursor.fetchall() + srcid_list = [] + for result in results: + srcid_list.append(result[0] + self.pars['tcp_tutor_srcid_offset']) + del self.cursor + return srcid_list + + + def run(self): + self.therange = self.get_srcid_list_by_name(self.search) + if self.local_xmls: + self.populate_features_and_classes_using_local_xmls() + else: + self.populate_features_and_classes() + self.write_arff() + #tmprange = ["100009020-100009360"] + #self.therange = self.get_srcid_list() + #self.getnums(tmprange) + #self.populate_features_and_classes() + #self.write_arff() + + + def write_arff(self, outfile='', classes_arff_str='', remove_sparse_classes=False, \ + n_sources_needed_for_class_inclusion=10, include_header=True, use_str_srcid=False): + + # TODO: form lookup dict string here: + class_lookup_dict_str = "%% class_lookup_dict={" + for abrv_class,long_class in self.class_abrv_lookup.items(): + class_lookup_dict_str += "'%s':'%s', " % (abrv_class,long_class) + class_lookup_dict_str = class_lookup_dict_str[:-2] + '}\n' + + #if type(outfile) == type(sys.stdout): + if type(outfile) == type(''): + if len(outfile) > 0: + self.outfile = outfile + + if os.path.exists(self.outfile): + os.system("rm " + self.outfile) + f = open(self.outfile,'w') + else: + # This is a file pointer, rather than a filepath string + f = outfile + if include_header: + f.write('%% date = %s\n' % str(datetime.datetime.now())) + f.write('%% \n') + f.write(class_lookup_dict_str) + f.write('%% \n') + f.write('@RELATION ts\n\n') + + self.master_features = list(self.master_features) + self.master_classes = list(self.master_classes) + # dstarr 20080526: Adding .sort() gives more consistant feature, class ordering in .arff files: self.class_abrv_lookup{} + self.master_features.sort() + self.master_classes.sort() + + if self.add_srcid_to_arff and include_header: + f.write("@ATTRIBUTE source_id NUMERIC\n") + + condensed_master_features = [] # KLUDGE: This wont contain the duplicate feature instances of 'string' and 'float' which arises from NULL as well as REAL feature values which are often contained in different vosource.xmls + # write in the feature and class defs + for fea in self.master_features: + if fea[1] == 'float': + thetype = 'NUMERIC' + condensed_master_features.append((fea[0],fea[1])) + elif (fea[0],'float') not in self.master_features: + if fea[0] == 'sdss_nearest_obj_type': + thetype = "{'galaxy','star','star_late', 'unknown', 'qso', 'hiz_qso', 'sky'}" + condensed_master_features.append((fea[0],'string')) + else: + ### NOTE: this (not-currently 20110621) commented-out case will insert strings for cases which most likely are just NONE/NULL values only (except sdss_nearest_obj_type). Weka barfs on STRING attributes, so we must exclude these from being included in .arff: + thetype = 'STRING' + condensed_master_features.append((fea[0],fea[1])) + #continue # skip this attribute/feature + else: + # NOTICE I dont append to condensed_master_features[] + continue # this is a case where 'NUMERIC' has already been written for this feature, and the stgin case probably arised from a 'None' for this feature in oune of the ningested vosource.xmls. So we skip. + #print fea + if include_header: + f.write('@ATTRIBUTE %s %s\n' % (fea[0],thetype)) + + + ########## This removes sparsely sampled classes from being written to arff + # KLUDGY, but it's 3am... + #master_classes_backup = copy.copy(self.master_classes) + if remove_sparse_classes: + master_classes_new = [] + for class_name in self.all_class_list: + if self.all_class_list.count(class_name) >= int(n_sources_needed_for_class_inclusion): + master_classes_new.append(class_name) + self.master_classes = list(set(master_classes_new)) # collapse to a single item for each class + ########## + if self.skip_class: + if len(classes_arff_str) == 0: + classes_arff_str = "@ATTRIBUTE class {'AM Her','Active Galactic Nuclei','Algol (Beta Persei)','BL Lac','Be star','Beta Cephei','Beta Lyrae','Cepheids','Close Binary with Reflection','Contact Systems','DQ Herculis Variable (Intermediate Polars)','Delta Scuti','Detached - With Subgiant','Double Mode RR Lyrae','Eclipsed by Planets','Eruptive Wolf-Rayet','Gamma Doradus','Gamma-ray Bursts','Irregular Early O-A','Irregular Supergiants','Microlensing Event','Mira','Novalike Variables','Orion T Tauri','Polars','R Coronae Borealis','RR Lyrae','RR Lyrae - Asymmetric','RR Lyrae - Near Symmetric','S Doradus','SS Cygni','SU Ursae Majoris','SX Phoenicis - Pulsating Subdwarfs','Semidetached','Semiregular - Persistent Periodicity','Semiregular - Poorly Defined Periodicity','Semiregular F, G, or K','Semiregular Pulsating Red Giants','Semiregular Supergiants','Type II Supernovae','Type Ia','Variable Stars','W Ursa Majoris','W Ursae Majoris - W UMa','Z Camelopardalis'}\n" + f.write(classes_arff_str) + # OBSOLETE: f.write("@ATTRIBUTE class {'GCVS:Eclipsing:E:EA','GCVS:Eclipsing:E:EB','GCVS:Eclipsing:E:EW','GCVS:Pulsating:CEP','GCVS:Pulsating:M','GCVS:Pulsating:RR:RRAB'}\n") + else: + f.write("@ATTRIBUTE class {%s}\n" % (",".join(["""'%s'""" % x for x in self.master_classes]))) + f.write("\n@data\n") + + for obj in self.master_list: + # # # # # # + #20081117: dstarr comments out: #if not obj['class'] in self.master_classes: + if remove_sparse_classes: + if not obj.get('class','') in self.master_classes: + continue # skip this object due to being in a sparse class + + tmp = [] + #for fea in self.master_features: + for fea in condensed_master_features: + val = "?" + if fea in obj['features']: + if fea[1] == 'float': + if ((obj['features'][fea] == "False") or + (str(obj['features'][fea]) == "inf") or + (str(obj['features'][fea]) == "nan")): + val = "?" + elif obj['features'][fea] != None: + val = str(obj['features'][fea]) + else: + if obj['features'][fea] is None: + val = "?" + else: + val = """'%s'""" % str(obj['features'][fea]) + tmp.append(val) + + ##### Unused hack: + #for fea in self.master_features: + # val = "?" + # if obj['features'].has_key(fea): + # str_fea_val = str(obj['features'][fea]) + # if ((str_fea_val == "False") or + # (str_fea_val == "inf") or + # (str_fea_val == "nan") or + # (str_fea_val == "None")): + # val = "?" + # elif fea[1] == 'float': + # val = str(obj['features'][fea]) + # else: + # val = """'%s'""" % str(obj['features'][fea]) + # tmp.append(val) + ##### + + #out_str = ",".join(tmp) + if self.add_srcid_to_arff: + if use_str_srcid: + out_str = str(obj['num']) + ',' + ",".join(tmp) + elif obj['num'].count('_') > 0: + # 20100810: dstarr adds this condition: + id_list = obj['num'].split('_') + source_id = int(id_list[0]) + if (source_id > 100000000) and (source_id < 1000000000000): + source_id -= 100000000 # TUTOR source_id case + #out_str = str(source_id) + '_' + id_list[1] + ',' + ",".join(tmp) + # 20100901: we now have error-sets info in the fname/idnum: 100149234_0.90_1.xml + out_str = str(source_id) + '_' + id_list[1] + '_' + id_list[2] + ',' + ",".join(tmp) + else: + source_id = int(obj['num']) + if (source_id > 100000000) and (source_id < 1000000000000): + source_id -= 100000000 # TUTOR source_id case + out_str = str(source_id) + ',' + ",".join(tmp) + else: + out_str = ",".join(tmp) + if self.skip_class: + out_str += ",?\n" + else: + out_str += ",'%s'\n" % (str(obj['class'])) + f.write(out_str) + #if type(outfile) != type(sys.stdout): + if type(outfile) == type(''): + f.close() + print("Wrote:", self.outfile) + + def populate_features_and_classes(self): + + self.master_list = [] + self.master_classes = [] + self.master_features = [] + self.grabber = XMLgrabber(verbose=self.verbose) + for num in self.therange: + print(num) + x = self.grabber.grab(num) + + if x is not None: + tmpdict = {} + if not self.skip_class: + theclass = self._get_class_names(x) + self.master_classes.append(theclass) + tmpdict['class'] = theclass + feat = self._get_features(x) + tmpdict.update({'num': num, 'file': os.path.basename(self.grabber.fname), 'features': feat}) + self.master_features.extend(feat.keys()) + self.master_list.append(copy.copy(tmpdict)) + + self.master_features = set(self.master_features) + self.all_class_list = copy.copy(self.master_classes) + self.master_classes = set(self.master_classes) + + + def populate_features_and_classes_using_local_xmls(self, \ + srcid_xml_tuple_list=[], \ + use_local_xml_fpaths=False): + """ Amara parse XML files/strings. + Extract features and (if available, classes) from XML_string. + + Method created by jbloom, modified by dstarr. + """ + self.master_list = [] + self.master_classes = [] + self.master_features = [] + # 20080617 dstarr disables this since it seems to be obsolete jbloom functionality: + #self.grabber = XMLgrabber(verbose=self.verbose) + + xml_fname = '' # dstarr: this variable seemes KLUDGY since it is filled with the arbitrary last value from a "for loop" + + if use_local_xml_fpaths: + if len(srcid_xml_tuple_list) == 0: + # Then we get xml-strings from disk + xml_fname_list = os.listdir(\ + self.pars['local_xmls_fpath']) + # KLUDGE: This can potentially load a lot of xml-strings into memory: + for xml_fname in xml_fname_list: + if xml_fname[-4:] != '.xml': + continue # skip this fpath + xml_fpath = "%s/%s" % (\ + self.pars['local_xmls_fpath'], xml_fname) + num = xml_fname[:xml_fname.rfind('.')] + #srcid_xml_tuple_listcosw.append((num, xml_fpath)) + srcid_xml_tuple_list.append((num, xml_fpath)) + #print "Loading:", xml_fname + #xml_string = open(xml_fpath).read() + #srcid_xml_tuple_list.append((num, xml_string)) + + for num,xml_string in srcid_xml_tuple_list: + d = mlens3.EventData(xml_string) + try: + raw_class = d.data['VOSOURCE'].get('Classifications',{}).\ + get('Classification',{}).get('class',{}).name + except: + raw_class = d.data['VOSOURCE'].get('CLASSIFICATIONS',{}).\ + get('CLASSIFICATION',{}).get('SOURCE',{}).\ + get('CLASS_SCHEMA',{}).get('CLASS',{}).get('dbname',{}) + tmpdict = {} + if (not self.skip_class) and (len(raw_class) > 0): + if self.convert_class_abrvs_to_names: + if raw_class in self.pars['skip_sci_class_list']: + print("Skipping GENERIC class:", raw_class, "for source:", num) + continue # skip this class since probably too generic to be useful for classification. + if raw_class not in self.class_abrv_lookup: + print("Skipping UNKNOWN class:", raw_class, "for source:", num) + continue # This class isn't in the lookup_dict{}, which is probably due to this class being added recently. We will skip this source. + if raw_class in self.pars['disambiguate_sci_class_dict'].keys(): + raw_class = self.pars['disambiguate_sci_class_dict'][raw_class] + theclass = self.class_abrv_lookup[raw_class] + else: + if raw_class in self.pars['skip_sci_class_list']: + print("Skipping GENERIC class:", raw_class, "for source:", num) + continue # skip this class since probably too generic to be useful for classification. + if raw_class in self.pars['disambiguate_sci_class_dict'].keys(): + raw_class = self.pars['disambiguate_sci_class_dict'][raw_class] + theclass = raw_class + self.master_classes.append(theclass) + tmpdict['class'] = theclass + feat = self._get_features(d) + tmpdict.update({'num': num, 'file': xml_fname , 'features': feat}) + self.master_features.extend(feat.keys()) + self.master_list.append(copy.copy(tmpdict)) + self.master_features = set(self.master_features) + self.all_class_list = copy.copy(self.master_classes) + self.master_classes = set(self.master_classes) + + + def generate_arff_line_for_vosourcexml(self, num='', xml_fpath=''): + """ Given a vosource.xml fpath, calculate the structures and possibly + .arff line for that source. + This is intended to be called by IPythron ipengine. + """ + d = mlens3.EventData(xml_fpath) + try: + raw_class = d.data['VOSOURCE'].get('Classifications',{}).\ + get('Classification',{}).get('class',{}).name + except: + raw_class = d.data['VOSOURCE'].get('CLASSIFICATIONS',{}).\ + get('CLASSIFICATION',{}).get('SOURCE',{}).\ + get('CLASS_SCHEMA',{}).get('CLASS',{}).get('dbname',{}) + tmpdict = {} + if (not self.skip_class) and (len(raw_class) > 0): + if self.convert_class_abrvs_to_names: + if raw_class in self.pars['skip_sci_class_list']: + print("Skipping GENERIC class:", raw_class, "for source:", num) + return # skip this class since probably too generic to be useful for classification. + if raw_class not in self.class_abrv_lookup: + print("Skipping UNKNOWN class:", raw_class, "for source:", num) + return # This class isn't in the lookup_dict{}, which is probably due to this class being added recently. We will skip this source. + if raw_class in self.pars['disambiguate_sci_class_dict'].keys(): + raw_class = self.pars['disambiguate_sci_class_dict'][raw_class] + theclass = self.class_abrv_lookup[raw_class] + else: + if raw_class in self.pars['skip_sci_class_list']: + print("Skipping GENERIC class:", raw_class, "for source:", num) + return#skip this class since probably too generic to be useful for classification + if raw_class in self.pars['disambiguate_sci_class_dict'].keys(): + raw_class = self.pars['disambiguate_sci_class_dict'][raw_class] + theclass = raw_class + tmpdict['class'] = theclass + feat = self._get_features(d) + tmpdict.update({'num': num, 'file': xml_fpath , 'features': feat}) + + #import pprint + #pprint.pprint(tmpdict) + + ### This will be ipython task-client 'pulled': + return tmpdict + + + + def _get_class_names(self,doc): + ## parses an XML to get the classes + class_name = doc.d['VOSOURCE']['Classifications']['Classification']['class']['name'] + return class_name + + + def _get_features(self,doc): + ret = {} + #doc.feat_dict[filt][feat] + #features = doc.xml_xpath(u"//Feature") + n_epoch_filt_list = [] + for filt_name,filt_dict in doc.data['ts'].items(): + n_epoch_filt_list.append((len(dict(filt_dict[0])['val']),filt_name)) + n_epoch_filt_list.sort(reverse=True) + try: + filt_most_sampled = n_epoch_filt_list[0][1] + except: + return {} + + feat_xmldicts = doc.feat_dict.get('multiband',{}) + feat_xmldicts.update(doc.feat_dict.get(filt_most_sampled,{})) + + for feat_name,feat_dict in feat_xmldicts.items(): + #tmp = f.xml_xpath(u"val")[0] + tmp = feat_dict['val']['_text'] + #if tmp.is_reliable == 'True': + thetype = feat_dict['val']['datatype'] + if feat_dict['val']['is_reliable'] == 'True': + #thetype = str(tmp.datatype) + #thetype = feat_dict['val']['datatype'] + v = str(tmp) + if (v == "None") or \ + (v == "False"): + val = None + else: + if str(thetype) == 'float': + try: + val = float(str(tmp)) + except: + val = str(tmp) + else: + val = str(tmp) + else: + val = None + if str(feat_name) not in self.skips: + #ret.update({(str(f.name),str(thetype)): val}) + # if this is string, dont add if float exists + # if this is float, replace string if string exists + # This is KLUDGY, since it shows that we should not index dict with type() in the index tuple, but instead should just use the feature only: + if thetype == 'string': + if (str(feat_name),'float') not in ret: + ret.update({(str(feat_name),thetype): val}) + elif thetype == 'float': + if (str(feat_name),'string') in ret: + ret.pop((str(feat_name),'string')) + ret.update({(str(feat_name),thetype): val}) + else: + ret.update({(str(feat_name),thetype): val}) + return ret + + # obsolete: 20090130: This wouldnt set values for 'False', etc... + def _get_features__old(self,doc): + ret = {} + features = doc.xml_xpath(u"//feature") + for f in features: + tmp = f.xml_xpath(u"val")[0] + if tmp.is_reliable == 'True': + thetype = tmp.datatype + v = str(tmp) + if v == "None": + val = None + else: + if str(tmp.datatype) == 'float': + try: + val = float(str(tmp)) + except: + val = str(tmp) + else: + val = str(tmp) + else: + val = None + if str(f.name) not in self.skips: + ret.update({(str(f.name),str(thetype)): val}) + return ret + + + def getnums(self,tmprange): + self.therange = [] + for i in tmprange: + if i.find("-") != -1: + ## we got a range + tmp = i.strip().split("-") + try: + tmp = range(int(tmp[0]),int(tmp[1]) + 1) + self.therange.extend(tmp) + except: + pass + + continue + try: + print(i) + tmp = int(i) + self.therange.append(tmp) + except: + pass + +class XMLgrabber: + + + server = None + xmldir = "/Users/jbloom/Projects/TCP/Software/feature_extract/MLData/XML/" + #print "You might need to issue 'ssh -L 34583:192.168.1.65:34583 lyra.berkeley.edu' if you haven't already" + #server_url = "http://lyra.berkeley.edu:34583" + server_url = "http://localhost:34583" + + def __init__(self,verbose=True,regrab=False): + self.verbose=verbose + self._connect() + self.regrab = regrab + + + def grab(self,num=None): + self.fname = self.xmldir + str(num) + ".xml" + if num and self.server is not None: + if self.regrab or not os.path.exists(self.fname): + try: + tmp = self.server.get_vosource_url_for_srcid(num) + except: + return None + if tmp.find('database_query_error') != -1: + if self.verbose: + print("No object number %i" % num) + return None + tmp1 = amara.parse(tmp) + fileloc = str(unicode(tmp1.A.href)) + h = urllib.urlretrieve(fileloc,self.fname) + + if os.path.exists(self.fname): + return amara.parse(self.fname) + else: + return None + else: + return None + + def _connect(self): + if self.server is None: + self.server = xmlrpclib.ServerProxy(self.server_url) + return + + def _disconnect(self): + if self.server is not None: + del self.server + + def __del(self): + self._disconnect() + + + +if __name__ == '__main__': + #NOTE: command line execution of arffify.py is intended only to be called by generate_arff.php on lyra. + + sci_class_list = sys.argv[0].split(',') + search_list = [] + #out_fpath = "/Volumes/BR1/Graham/Bloom-store/Josh/public_html/dstarr/vosource_outs/" + #out_fpath = os.environ.get("TCP_DIR") + \ + # 'Software/feature_extract/MLData/sdss_sources.arff' + #out_fpath = os.environ.get("TCP_DIR") + \ + # 'Software/feature_extract/MLData/TUTOR_sources.arff' + out_fpath = '/tmp/arffify_test.arff' + + ###for sci_class_underscored in sci_class_list: + ### sci_class = sci_class_underscored.replace("___", " ") + ### out_fpath += sci_class_underscored.replace("___", "_") + ### search_list.append(sci_class) + # TODO: parse the argv into seperate class-names + # TODO: form outfile using these: + # /Volumes/BR1/Graham/Bloom-store/Josh/public_html/dstarr/vosource_outs/..<>..arf + + ### This searches a list of string names to look up: + #a = Maker(search=search_list, outfile=out_fpath, skip_class=True, \ + # local_xmls=True, convert_class_abrvs_to_names=True) + + + # NOTE: parameter local_xmls=True can be used if populate_feat_db_using_TCPTUTOR_sources.py has been executed recently + # ( this generates the XML files) + a = Maker(search=search_list, outfile=out_fpath, skip_class=False, \ + local_xmls=True, convert_class_abrvs_to_names=False, \ + local_xmls_fpath=os.path.expandvars('$HOME/scratch/vosource_subset'), flag_retrieve_class_abrvs_from_TUTOR=True) diff --git a/mltsp/TCP/Software/feature_extract/MLData/heatmap_confustion_matrix.py b/mltsp/TCP/Software/feature_extract/MLData/heatmap_confustion_matrix.py new file mode 100644 index 00000000..e496d01b --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/MLData/heatmap_confustion_matrix.py @@ -0,0 +1,165 @@ +#!/usr/bin/env python +""" + v0.1 Make an HTML heatmap table of a Weka classify confusion matrix. +""" +from __future__ import print_function +import sys, os + +__old__input_table_str = """ + a b c d e f g h i j k l m n o p q r s t u v w x y z aa ab ac ad <-- classified as + 438 25 41 0 0 0 0 0 0 0 0 0 0 4 0 0 0 5 0 1 0 1 3 0 0 0 0 0 0 0 | a = Algol (Beta Persei) + 83 84 68 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 2 0 0 1 | b = Beta Lyrae + 21 26 930 3 0 0 1 0 0 1 0 0 0 6 0 5 2 1 0 1 0 0 2 0 0 0 10 1 0 1 | c = W Ursae Majoris + 1 0 3 12 0 0 0 0 1 0 0 0 0 22 0 0 0 0 0 5 0 0 7 0 0 0 4 0 0 0 | d = Binary + 1 0 0 0 1 0 0 0 0 0 0 0 0 6 0 0 0 0 0 1 0 0 3 0 1 0 0 0 0 2 | e = T Tauri + 0 0 0 0 1 15 0 0 0 0 0 0 0 1 0 0 0 0 0 5 0 0 10 0 0 0 2 0 0 0 | f = Wolf-Rayet + 0 0 0 0 0 0 45 0 1 0 0 0 0 3 0 0 0 1 0 2 0 0 0 0 0 0 0 0 0 0 | g = Type Ia Supernovae + 0 1 0 1 0 1 0 12 1 0 0 0 0 9 0 0 0 2 0 1 0 0 0 0 0 0 0 0 0 0 | h = BL Lac + 1 0 4 1 0 0 1 0 576 0 0 0 0 4 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | i = Microlensing Event + 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 2 | j = Semiregular Pulsating Red Giants + 1 0 0 0 0 0 0 0 0 0 14 0 0 11 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 5 | k = Semiregular Pulsating Variable + 0 0 2 0 0 0 0 0 0 0 0 0 0 11 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | l = Short period (BL Herculis) + 0 0 0 0 0 0 0 0 0 0 0 0 0 12 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 1 | m = Population II Cepheid + 2 3 8 2 1 1 1 0 2 0 4 0 0 864 11 1 0 4 0 8 1 0 4 0 0 0 0 0 2 2 | n = Classical Cepheid + 0 0 0 1 0 0 0 0 2 0 1 0 0 18 20 2 0 0 0 1 0 0 3 0 0 0 0 0 0 0 | o = Symmetrical + 0 0 2 0 0 0 0 0 0 0 0 0 0 11 0 92 0 0 0 1 0 0 0 0 0 0 1 0 0 0 | p = Multiple Mode Cepheid + 0 0 6 0 0 0 0 0 0 0 0 0 0 1 0 3 20 0 0 0 1 0 0 0 0 0 0 0 0 0 | q = RR Lyrae - Asymmetric + 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 105 0 3 0 1 0 0 0 0 0 0 0 0 | r = RR Lyrae, Double Mode + 0 0 4 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 3 0 0 0 4 0 0 0 1 0 0 2 | s = RR Lyrae + 4 0 0 0 0 0 0 0 0 0 0 0 0 8 1 3 0 1 1 268 0 1 1 0 0 0 3 0 0 0 | t = RR Lyrae, Fundamental Mode + 0 0 11 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 3 0 0 0 0 0 0 0 0 0 | u = RR Lyrae - Near Symmetric + 2 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 | v = RR Lyrae, Closely Spaced Modes + 4 1 4 0 0 11 0 0 1 0 1 0 0 4 2 0 0 2 2 0 0 0 64 2 1 0 12 0 0 0 | w = Pulsating Variable + 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 3 8 1 0 18 0 0 0 | x = Beta Cephei + 6 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 4 0 0 6 0 6 2 14 0 0 0 | y = Gamma Doradus + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 0 8 0 0 0 | z = Lambda Bootis Variable + 0 0 32 0 0 5 0 0 1 0 0 0 0 4 0 2 1 0 0 6 0 0 11 5 4 5 74 0 0 0 | aa = Delta Scuti + 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 3 0 0 0 0 0 0 0 9 0 0 | ab = SX Phoenicis + 0 0 1 0 0 0 1 1 0 0 0 0 0 28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 | ac = Long Period (W Virginis) + 0 0 3 0 0 0 0 0 0 0 1 0 0 4 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 103 | ad = Mira +""" + +input_table_str = """ + a b c d e f g h i j k l m n o p q r <-- classified as + 87 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | a = RR Lyrae, Fundamental Mode + 0 1072 0 0 0 0 0 0 0 0 0 2 0 0 0 0 4 0 | b = W Ursae Majoris + 0 0 10 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 | c = Population II Cepheid + 0 0 0 59 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | d = Ellipsoidal + 0 0 0 0 204 0 0 0 0 0 0 1 0 0 1 0 1 0 | e = Binary + 0 2 0 0 2 450 0 0 0 0 0 3 0 0 0 0 2 0 | f = Algol (Beta Persei) + 0 0 0 0 0 0 33 0 0 1 0 0 0 0 0 0 0 0 | g = Wolf-Rayet + 0 0 0 0 0 0 0 149 0 0 0 0 0 0 0 0 0 0 | h = Beta Cephei + 0 0 2 0 0 0 0 0 110 0 0 0 0 0 0 0 0 0 | i = Classical Cepheid + 0 0 0 0 0 0 6 3 0 290 0 0 12 1 0 1 0 0 | j = Mira + 0 0 1 0 1 0 0 0 0 0 228 0 0 0 1 0 0 0 | k = Multiple Mode Cepheid + 0 0 0 0 0 7 0 0 0 0 0 318 0 0 0 0 0 0 | l = Beta Lyrae + 0 0 0 0 0 0 0 0 0 5 0 0 30 1 0 1 0 0 | m = Semiregular Pulsating Variable + 0 0 0 1 0 0 0 0 0 0 0 0 0 71 0 0 0 1 | n = Gamma Doradus + 0 0 0 0 0 0 0 0 8 0 2 0 0 0 565 0 0 0 | o = Symmetrical + 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 16 0 0 | p = Be Star + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 18 0 | q = Pulsating Variable + 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 215 | r = Delta Scuti +""" + + +if __name__ == '__main__': + + input_table_name = "/tmp/a" + output_costmatrix_fpath = "/home/pteluser/scratch/cost_matrix.cost" + if len(sys.argv) >= 3: + input_table_name = sys.argv[1] + output_costmatrix_fpath = sys.argv[2] + + ###DISABLED### input_table_str = open(input_table_name).read() + input_table_str = open(input_table_name).read() + + output_html_fpath = "/tmp/heatmap.html" + + input_line_list = input_table_str.split('\n') + for i_header_row in range(len(input_line_list)): + if "classified as" in input_line_list[i_header_row]: + break # i_header_row is the correct value. + + val_listoflists = [] + total_per_class_list = [] + class_name_list = [] + + line = input_line_list[i_header_row] + short_line = line[:line.rfind('<')] + col_name_list = short_line.split() + + for line in input_line_list[i_header_row +1:-1]: + if len(line) < 4: + continue # does not contain data + short_line = line[:line.rfind('|')] + #class_name = line[line.rfind('=')+2:] + class_name = line[line.rfind('|')+3:] + class_name_list.append(class_name) + str_val_list = short_line.split() + val_list = [] + for str_val in str_val_list: + val_list.append(int(str_val)) + total_per_class_list.append(sum(val_list)) + val_listoflists.append(val_list) + + + out_str = """ + + + + """ + #
+ + out_str += "" + for col_name in col_name_list: + out_str += '' % (col_name) + out_str += '\n' + + for i_class in range(len(val_listoflists)): + val_list = val_listoflists[i_class] + out_str += "" + for val in val_list: + try: + percent = val / float(total_per_class_list[i_class]) + except: + percent = 0.0 # div by zero catch + if percent < 0.0099999: + percent_str = "     " + else: + percent_str_a = "%2.2f" % (percent) + percent_str = percent_str_a[1:] + if ((percent > 0.0099999) and (percent < 0.15)): + percent = 0.15 # give small percents some minimum color + full_hex_str = "%2.2x" % (int((percent * 255))) + hex_str = full_hex_str[full_hex_str.rfind('x')+1:] + out_str += '' % (hex_str, percent_str) + out_str += '\n' % (class_name_list[i_class]) + out_str += "
%s
%s%s
" + + os.system("rm " + output_html_fpath) + fp = open(output_html_fpath, 'w') + fp.write(out_str) + fp.close() + + for i in range(len(total_per_class_list)): + print(total_per_class_list[i], '\t', class_name_list[i]) + + # TODO: make a cost matrix which is all normalized. + n_avg = sum(total_per_class_list) / float(len(total_per_class_list)) + + os.system("rm " + output_costmatrix_fpath) + fp = open(output_costmatrix_fpath, 'w') + + #header_str = "\% Rows \tColumns\n%d \t%d\n\% Matrix elements\n" % (len(total_per_class_list), len(total_per_class_list)) + header_str = "%% Rows \tColumns\n%d \t%d\n%%Matrix elements\n" % (len(total_per_class_list), len(total_per_class_list)) + fp.write(header_str) + for i in range(len(total_per_class_list)): + percent = n_avg / total_per_class_list[i] + line_str = "" + for j in range(len(total_per_class_list)): + if i == j: + line_str += "0.0\t" + else: + line_str += "%0.3f\t" % (percent) + fp.write(line_str + '\n') + fp.close() diff --git a/mltsp/TCP/Software/feature_extract/MLData/maker.arff b/mltsp/TCP/Software/feature_extract/MLData/maker.arff new file mode 100755 index 00000000..680388fd --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/MLData/maker.arff @@ -0,0 +1,399 @@ +% date = 2008-05-16 11:35:32.621177 +%% +@RELATION ts + +@ATTRIBUTE freq3_harmonics_moments_0 NUMERIC +@ATTRIBUTE freq1 NUMERIC +@ATTRIBUTE freq1_harmonics_rel_phase_error_2 NUMERIC +@ATTRIBUTE ratio21 NUMERIC +@ATTRIBUTE std NUMERIC +@ATTRIBUTE ecpb NUMERIC +@ATTRIBUTE freq2_harmonics_freq_2 NUMERIC +@ATTRIBUTE freq3_harmonics_rel_phase_error_1 NUMERIC +@ATTRIBUTE freq3_harmonics_moments_err_0 NUMERIC +@ATTRIBUTE freq1_harmonics_moments_1 NUMERIC +@ATTRIBUTE freq3_harmonics_rel_phase_2 NUMERIC +@ATTRIBUTE freq2_harmonics_rel_phase_error_0 NUMERIC +@ATTRIBUTE freq2_harmonics_rel_phase_1 NUMERIC +@ATTRIBUTE freq3_harmonics_rel_phase_error_3 NUMERIC +@ATTRIBUTE freq2_harmonics_nharm NUMERIC +@ATTRIBUTE freq1_harmonics_peak2peak_flux NUMERIC +@ATTRIBUTE n_points NUMERIC +@ATTRIBUTE freq3_harmonics_amplitude_3 NUMERIC +@ATTRIBUTE freq2_harmonics_signif NUMERIC +@ATTRIBUTE freq3_harmonics_moments_err_1 NUMERIC +@ATTRIBUTE freq2_harmonics_moments_2 NUMERIC +@ATTRIBUTE freq1_harmonics_moments_2 NUMERIC +@ATTRIBUTE freq1_signif NUMERIC +@ATTRIBUTE freq1_harmonics_moments_0 NUMERIC +@ATTRIBUTE ratio31 NUMERIC +@ATTRIBUTE freq2_harmonics_rel_phase_0 NUMERIC +@ATTRIBUTE freq2_harmonics_peak2peak_flux_error NUMERIC +@ATTRIBUTE freq2_harmonics_moments_err_0 NUMERIC +@ATTRIBUTE freq3_harmonics_freq_1 NUMERIC +@ATTRIBUTE freq3_harmonics_amplitude_error_0 NUMERIC +@ATTRIBUTE freq2_harmonics_moments_1 NUMERIC +@ATTRIBUTE freq3_harmonics_moments_err_2 NUMERIC +@ATTRIBUTE freq2_harmonics_moments_err_2 NUMERIC +@ATTRIBUTE freq3_harmonics_rel_phase_error_0 NUMERIC +@ATTRIBUTE freq2_harmonics_amplitude_error_2 NUMERIC +@ATTRIBUTE freq2_harmonics_rel_phase_3 NUMERIC +@ATTRIBUTE freq3_harmonics_moments_2 NUMERIC +@ATTRIBUTE beyond1std NUMERIC +@ATTRIBUTE freq3_harmonics_signif NUMERIC +@ATTRIBUTE freq1_harmonics_moments_3 NUMERIC +@ATTRIBUTE freq1_harmonics_moments_err_1 NUMERIC +@ATTRIBUTE amplitude NUMERIC +@ATTRIBUTE freq2_signif NUMERIC +@ATTRIBUTE skew NUMERIC +@ATTRIBUTE freq3_harmonics_amplitude_error_3 NUMERIC +@ATTRIBUTE freq2_harmonics_moments_0 NUMERIC +@ATTRIBUTE median NUMERIC +@ATTRIBUTE freq3_harmonics_amplitude_2 NUMERIC +@ATTRIBUTE freq1_harmonics_nharm NUMERIC +@ATTRIBUTE freq2_harmonics_amplitude_error_3 NUMERIC +@ATTRIBUTE freq3 NUMERIC +@ATTRIBUTE freq_searched_min NUMERIC +@ATTRIBUTE freq3_harmonics_amplitude_error_2 NUMERIC +@ATTRIBUTE freq1_harmonics_amplitude_error_3 NUMERIC +@ATTRIBUTE freq3_harmonics_amplitude_0 NUMERIC +@ATTRIBUTE freq2_harmonics_moments_3 NUMERIC +@ATTRIBUTE galb NUMERIC +@ATTRIBUTE freq1_harmonics_signif NUMERIC +@ATTRIBUTE freq1_harmonics_amplitude_error_2 NUMERIC +@ATTRIBUTE freq2_harmonics_amplitude_error_0 NUMERIC +@ATTRIBUTE freq3_harmonics_freq_3 NUMERIC +@ATTRIBUTE freq1_harmonics_amplitude_0 NUMERIC +@ATTRIBUTE freq2_harmonics_moments_err_3 NUMERIC +@ATTRIBUTE freq2_harmonics_amplitude_0 NUMERIC +@ATTRIBUTE freq3_harmonics_amplitude_error_1 NUMERIC +@ATTRIBUTE max NUMERIC +@ATTRIBUTE freq3_harmonics_freq_2 NUMERIC +@ATTRIBUTE wei_av_uncertainty NUMERIC +@ATTRIBUTE dc NUMERIC +@ATTRIBUTE old_dc NUMERIC +@ATTRIBUTE freq1_harmonics_amplitude_error_0 NUMERIC +@ATTRIBUTE freq2_harmonics_rel_phase_error_3 NUMERIC +@ATTRIBUTE freq2_harmonics_peak2peak_flux NUMERIC +@ATTRIBUTE freq2_harmonics_amplitude_3 NUMERIC +@ATTRIBUTE freq1_harmonics_moments_err_2 NUMERIC +@ATTRIBUTE gall NUMERIC +@ATTRIBUTE freq2_harmonics_rel_phase_2 NUMERIC +@ATTRIBUTE freq1_harmonics_amplitude_3 NUMERIC +@ATTRIBUTE freq1_harmonics_freq_2 NUMERIC +@ATTRIBUTE freq1_harmonics_moments_err_0 NUMERIC +@ATTRIBUTE max_slope NUMERIC +@ATTRIBUTE freq1_harmonics_rel_phase_error_0 NUMERIC +@ATTRIBUTE freq2_harmonics_moments_err_1 NUMERIC +@ATTRIBUTE freq3_signif NUMERIC +@ATTRIBUTE freq_searched_max NUMERIC +@ATTRIBUTE freq3_harmonics_moments_3 NUMERIC +@ATTRIBUTE freq1_harmonics_rel_phase_1 NUMERIC +@ATTRIBUTE freq2_harmonics_freq_0 NUMERIC +@ATTRIBUTE freq3_harmonics_amplitude_1 NUMERIC +@ATTRIBUTE weighted_average NUMERIC +@ATTRIBUTE freq3_harmonics_peak2peak_flux NUMERIC +@ATTRIBUTE chi2_per_deg NUMERIC +@ATTRIBUTE freq3_harmonics_peak2peak_flux_error NUMERIC +@ATTRIBUTE freq1_harmonics_rel_phase_2 NUMERIC +@ATTRIBUTE freq2_harmonics_amplitude_2 NUMERIC +@ATTRIBUTE distance_in_arcmin_to_nearest_galaxy NUMERIC +@ATTRIBUTE freq3_harmonics_freq_0 NUMERIC +@ATTRIBUTE freq1_harmonics_moments_err_3 NUMERIC +@ATTRIBUTE ecpl NUMERIC +@ATTRIBUTE freq1_harmonics_freq_0 NUMERIC +@ATTRIBUTE first_freq NUMERIC +@ATTRIBUTE second NUMERIC +@ATTRIBUTE freq2_harmonics_rel_phase_error_1 NUMERIC +@ATTRIBUTE ratioRUfirst NUMERIC +@ATTRIBUTE freq1_harmonics_rel_phase_error_3 NUMERIC +@ATTRIBUTE freq2_harmonics_amplitude_1 NUMERIC +@ATTRIBUTE chi2 NUMERIC +@ATTRIBUTE freq3_harmonics_rel_phase_3 NUMERIC +@ATTRIBUTE freq2_harmonics_freq_3 NUMERIC +@ATTRIBUTE freq3_harmonics_rel_phase_error_2 NUMERIC +@ATTRIBUTE freq1_harmonics_amplitude_1 NUMERIC +@ATTRIBUTE freq2_harmonics_rel_phase_error_2 NUMERIC +@ATTRIBUTE third NUMERIC +@ATTRIBUTE example NUMERIC +@ATTRIBUTE freq1_harmonics_rel_phase_3 NUMERIC +@ATTRIBUTE min NUMERIC +@ATTRIBUTE freq1_harmonics_amplitude_2 NUMERIC +@ATTRIBUTE distance_in_kpc_to_nearest_galaxy NUMERIC +@ATTRIBUTE ratio32 NUMERIC +@ATTRIBUTE freq1_harmonics_rel_phase_error_1 NUMERIC +@ATTRIBUTE freq3_harmonics_moments_err_3 NUMERIC +@ATTRIBUTE freq1_harmonics_freq_3 NUMERIC +@ATTRIBUTE freq1_harmonics_rel_phase_0 NUMERIC +@ATTRIBUTE freq2_harmonics_freq_1 NUMERIC +@ATTRIBUTE freq2_harmonics_amplitude_error_1 NUMERIC +@ATTRIBUTE freq3_harmonics_rel_phase_0 NUMERIC +@ATTRIBUTE freq2 NUMERIC +@ATTRIBUTE freq1_harmonics_peak2peak_flux_error NUMERIC +@ATTRIBUTE freq3_harmonics_moments_1 NUMERIC +@ATTRIBUTE freq1_harmonics_freq_1 NUMERIC +@ATTRIBUTE freq3_harmonics_nharm NUMERIC +@ATTRIBUTE freq1_harmonics_amplitude_error_1 NUMERIC +@ATTRIBUTE freq3_harmonics_rel_phase_1 NUMERIC +@ATTRIBUTE class {'GCVS:Pulsating:CEP','GCVS:Pulsating:RR:RRAB'} + +@data 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diff --git a/mltsp/TCP/Software/feature_extract/MLData/normalize_class_size_in_arff.py b/mltsp/TCP/Software/feature_extract/MLData/normalize_class_size_in_arff.py new file mode 100644 index 00000000..7e18d044 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/MLData/normalize_class_size_in_arff.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python +""" + v0.1 create duplicate entries for clases with few entries, so that they are as populated as mos populated classes (its 3am). + +NOTE: MUST MAKE SURE THAT INPUT .arff FILE HAS an extra blank line at end of file. +""" +import sys, os + +class_num_rows_dict = {\ + 'Algol (Beta Persei)':518, + 'Beta Lyrae':240, + 'W Ursae Majoris':1011, + 'Binary':55, + 'T Tauri':15, + 'Wolf-Rayet':34, + 'Type Ia Supernovae':52, + 'BL Lac':28, + 'Microlensing Event':590, + 'Semiregular Pulsating Red Giants':14, + 'Semiregular Pulsating Variable':34, + 'Short period (BL Herculis)':14, + 'Population II Cepheid':17, + 'Classical Cepheid':921, + 'Symmetrical':48, + 'Multiple Mode Cepheid':107, + 'RR Lyrae - Asymmetric':31, + 'RR Lyrae, Double Mode':111, + 'RR Lyrae':16, + 'RR Lyrae, Fundamental Mode':291, + 'RR Lyrae - Near Symmetric':15, + 'RR Lyrae, Closely Spaced Modes':13, + 'Pulsating Variable':111, + 'Beta Cephei':33, + 'Gamma Doradus':41, + 'Lambda Bootis Variable':13, + 'Delta Scuti':150, + 'SX Phoenicis':14, + 'Long Period (W Virginis)':34, + 'Mira':113} + +if __name__ == '__main__': + + arff_fpath = "/home/pteluser/scratch/train_output_allclass_14feat_to_have_nrows_scaled.arff" + + out_arff_fpath = "/home/pteluser/scratch/train_output_allclass_14feat_class_scaled.arff" + os.system("rm " + out_arff_fpath) + fp_out = open(out_arff_fpath, 'w') + + class_name_to_arff_lines_list = {} + for class_name in list(class_num_rows_dict.keys()): + class_name_to_arff_lines_list[class_name] = [] + + lines = open(arff_fpath).readlines() + + for i in range(len(lines)): + fp_out.write(lines[i]) + if "@data" in lines[i]: + break + + # KLUDGE : here we make an asumption in parsing that science class names do not have "." in them. (they do have "," though. + for line in lines[i+1:]: + # KLUDGE: (it's 3:35 am): + i_list = [line.rfind('.'), line.rfind('?'), line.rfind('-1')] + i = max(i_list) + line_a = line[i:] + line_class = line_a[line_a.find(',')+1:] + line_class_stripped = line_class.strip().strip("'") + class_name_to_arff_lines_list[line_class_stripped].append(line) + + # now we have a dict with a bunch of lines representing each class. It's a big dict. + + # Next we generate fake rows for every science class, up to max-n-rows. + + max_n_rows = max(class_num_rows_dict.values()) + + for class_name, n_rows in class_num_rows_dict.items(): + if n_rows == max_n_rows: + for line in class_name_to_arff_lines_list[class_name]: + fp_out.write(line) + continue + i = 0 + while i < (max_n_rows - n_rows): + for line in class_name_to_arff_lines_list[class_name]: + #if "0.426,2.410946,0.065327,0.281546,3.092307,5.834638,0.007759,0.060579,0.37931,-0.066667,3.246999,0.818523,50870.251189,'Pulsating Variable'" in line: + # print 'yo' + #print line + fp_out.write(line) + i += 1 + if i >= (max_n_rows - n_rows): + break + + fp_out.close() diff --git a/mltsp/TCP/Software/feature_extract/MLData/rr-cep.arff b/mltsp/TCP/Software/feature_extract/MLData/rr-cep.arff new file mode 100755 index 00000000..29078bb7 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/MLData/rr-cep.arff @@ -0,0 +1,246 @@ +% date = 2008-04-15 12:18:55.777518 +%% +@RELATION ts + +@ATTRIBUTE distance_in_kpc_to_nearest_galaxy NUMERIC +@ATTRIBUTE beyond1std NUMERIC +@ATTRIBUTE ratio21 NUMERIC +@ATTRIBUTE chi2 NUMERIC +@ATTRIBUTE std NUMERIC +@ATTRIBUTE ecpb NUMERIC +@ATTRIBUTE old_dc NUMERIC +@ATTRIBUTE skew NUMERIC +@ATTRIBUTE third NUMERIC +@ATTRIBUTE example NUMERIC +@ATTRIBUTE max_slope NUMERIC +@ATTRIBUTE n_points NUMERIC +@ATTRIBUTE median NUMERIC +@ATTRIBUTE ratio32 NUMERIC +@ATTRIBUTE ratio31 NUMERIC +@ATTRIBUTE weighted_average NUMERIC +@ATTRIBUTE first_freq NUMERIC +@ATTRIBUTE galb NUMERIC +@ATTRIBUTE gall NUMERIC +@ATTRIBUTE chi2_per_deg NUMERIC +@ATTRIBUTE distance_in_arcmin_to_nearest_galaxy NUMERIC +@ATTRIBUTE ecpl NUMERIC +@ATTRIBUTE wei_av_uncertainty NUMERIC +@ATTRIBUTE second NUMERIC +@ATTRIBUTE ratioRUfirst NUMERIC +@ATTRIBUTE dc NUMERIC +@ATTRIBUTE class {'GCVS:Pulsating:CEP:CEP(B)','GCVS:Pulsating:CEP','GCVS:Pulsating:RR:RRC','GCVS:Pulsating:RR:RRAB','GCVS:Pulsating:RR:RR(B)'} + +@data 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--- /dev/null +++ b/mltsp/TCP/Software/feature_extract/__init__.py @@ -0,0 +1 @@ + diff --git a/mltsp/TCP/Software/feature_extract/format_csv_getfeats.py b/mltsp/TCP/Software/feature_extract/format_csv_getfeats.py new file mode 100644 index 00000000..aa404985 --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/format_csv_getfeats.py @@ -0,0 +1,105 @@ +#!/usr/bin/env python +""" When given a csv lightcurve, generate features. +""" +from __future__ import print_function +from __future__ import absolute_import + +import os, sys +import csv + +sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + \ + 'Software/feature_extract')) +sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + \ + 'Software/feature_extract/Code')) +from .Code import * +import db_importer + + +head_str = """ + + + + + + 6930531 + + Best positional information of the source + + + 323.47114731 + -0.79916734036 + + + 0.000277777777778 + 0.000277777777778 + + + + + + MJD + 0.0 + UTC + TOPOCENTER + + + + + + + + + +""" + +tail_str = """ + +
+ + +""" + + +if __name__ == '__main__': + + pars = {'csv_fpath':"/home/dstarr/scratch/PTFS1108o.dat", + 'final_vsrcxml_fpath':'/tmp/PTFS1108o.xml', + } + + data_str_list = [] + + rows = csv.reader(open(pars['csv_fpath']), delimiter=' ') + + t_list = [] + m_list = [] + merr_list = [] + for i,row in enumerate(rows): + t = float(row[0]) + m = float(row[1]) + m_err = float(row[2]) + data_str = ' %lf%lf%lf' % \ + (i, t, m, m_err) + data_str_list.append(data_str) + t_list.append(t) + m_list.append(m) + merr_list.append(m_err) + + all_data_str = '\n'.join(data_str_list) + + out_xml = head_str + all_data_str + tail_str + + ####### This part was taken from file: test_feature_algorithms.py: + signals_list = [] + gen = generators_importers.from_xml(signals_list) + gen.generate(xml_handle=out_xml) + gen.sig.add_features_to_xml_string(gen.signals_list) + + feature_added_VOSource_XML_fpath = pars['final_vsrcxml_fpath'] + gen.sig.write_xml(out_xml_fpath=feature_added_VOSource_XML_fpath) + import pprint + pprint.pprint((signals_list[0].properties['data']['v']['features'].keys()).sort()) + print() + print('freq1_harmonics_freq_0 =', signals_list[0].properties['data']['v']['features']['freq1_harmonics_freq_0']) + print() + + #import pdb; pdb.set_trace() diff --git a/mltsp/TCP/Software/feature_extract/gp_orig_randomstart100.png b/mltsp/TCP/Software/feature_extract/gp_orig_randomstart100.png new file mode 100755 index 00000000..f745fef1 Binary files /dev/null and b/mltsp/TCP/Software/feature_extract/gp_orig_randomstart100.png differ diff --git a/mltsp/TCP/Software/feature_extract/gp_orig_randomstart1000_thetaU1.png b/mltsp/TCP/Software/feature_extract/gp_orig_randomstart1000_thetaU1.png new file mode 100755 index 00000000..dfe8e64f Binary files /dev/null and b/mltsp/TCP/Software/feature_extract/gp_orig_randomstart1000_thetaU1.png differ diff --git a/mltsp/TCP/Software/feature_extract/gp_orig_randomstart100_nothetaLU.png 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b/mltsp/TCP/Software/feature_extract/gp_orig_randomstart10_thetaU1_absoluteexp.png new file mode 100755 index 00000000..7806683f Binary files /dev/null and b/mltsp/TCP/Software/feature_extract/gp_orig_randomstart10_thetaU1_absoluteexp.png differ diff --git a/mltsp/TCP/Software/feature_extract/gp_orig_randomstart20_thetaU10.png b/mltsp/TCP/Software/feature_extract/gp_orig_randomstart20_thetaU10.png new file mode 100755 index 00000000..f13e126d Binary files /dev/null and b/mltsp/TCP/Software/feature_extract/gp_orig_randomstart20_thetaU10.png differ diff --git a/mltsp/TCP/Software/feature_extract/gp_orig_randomstart20_thetaU10_absoluteexp.png b/mltsp/TCP/Software/feature_extract/gp_orig_randomstart20_thetaU10_absoluteexp.png new file mode 100755 index 00000000..41b61b8d Binary files /dev/null and b/mltsp/TCP/Software/feature_extract/gp_orig_randomstart20_thetaU10_absoluteexp.png differ diff --git a/mltsp/TCP/Software/feature_extract/gp_orig_randomstart20_thetaU1_absoluteexp.png b/mltsp/TCP/Software/feature_extract/gp_orig_randomstart20_thetaU1_absoluteexp.png new file mode 100755 index 00000000..ed989d07 Binary files /dev/null and b/mltsp/TCP/Software/feature_extract/gp_orig_randomstart20_thetaU1_absoluteexp.png differ diff --git a/mltsp/TCP/Software/feature_extract/gp_orig_randomstart5_theta0.0001_noLU_absoluteexp.png b/mltsp/TCP/Software/feature_extract/gp_orig_randomstart5_theta0.0001_noLU_absoluteexp.png new file mode 100755 index 00000000..ce91ac7d Binary files /dev/null and b/mltsp/TCP/Software/feature_extract/gp_orig_randomstart5_theta0.0001_noLU_absoluteexp.png differ diff --git a/mltsp/TCP/Software/feature_extract/gp_orig_randomstart5_theta0.001_noLU_absoluteexp.png b/mltsp/TCP/Software/feature_extract/gp_orig_randomstart5_theta0.001_noLU_absoluteexp.png new file mode 100755 index 00000000..8fb7ccce Binary files /dev/null and b/mltsp/TCP/Software/feature_extract/gp_orig_randomstart5_theta0.001_noLU_absoluteexp.png differ diff --git a/mltsp/TCP/Software/feature_extract/gp_orig_randomstart5_theta0.01_noLU_absoluteexp.png b/mltsp/TCP/Software/feature_extract/gp_orig_randomstart5_theta0.01_noLU_absoluteexp.png new file mode 100755 index 00000000..e5a18873 Binary files /dev/null and b/mltsp/TCP/Software/feature_extract/gp_orig_randomstart5_theta0.01_noLU_absoluteexp.png differ diff --git a/mltsp/TCP/Software/feature_extract/noisifier_pilot.py b/mltsp/TCP/Software/feature_extract/noisifier_pilot.py new file mode 100644 index 00000000..d492c4bf --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/noisifier_pilot.py @@ -0,0 +1,55 @@ +""" +noisifier_pilot.py + +Pulls the string to noisify signal data. + +NOTE: + - Requires system environment variable: TCP_DIR + - This is the full path of the top directory of TCP project + - In other words, the TCP directory which was svn checked-out + - e.g. .bashrc/execute for bash: + export TCP_DIR=/home/pteluser/src/TCP/ +""" +from __future__ import print_function +from __future__ import absolute_import + +import os, sys + +sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + \ + 'Software/feature_extract')) +sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + \ + 'Software/feature_extract/Code')) + +from .Code import generators_importers, feature_interfaces + +signals_list = [] + +gen = generators_importers.from_xml(signals_list) +gen.generate(xml_handle="../../Data/source_5.xml",register=False) +gen.sig.add_features_to_xml_string(gen.signals_list) + +interface = feature_interfaces.feature_interface +noisify_extr = interface.request_extractor('noisify') # grab the noisifying extractor from the interface + +for i in range(len(signals_list)): + signal = signals_list[i] + signal.register_signal(initialize=False) + +interface.notify(noisify_extr) + +def fetch_noisified(signal, band): + noisified = signal.properties['data'][band]['inter']['noisify'].result + return noisified + +feature_added_VOSource_XML_fpath = '/tmp/test_feature_algorithms.VOSource.xml' +gen.sig.write_xml(out_xml_fpath=feature_added_VOSource_XML_fpath) +print("Wrote VOSource XML (with features) to:", feature_added_VOSource_XML_fpath) + + +def main(): + pass + + +if __name__ == '__main__': + main() + diff --git a/mltsp/TCP/Software/feature_extract/summarize_available_features.py b/mltsp/TCP/Software/feature_extract/summarize_available_features.py new file mode 100644 index 00000000..d67689ee --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/summarize_available_features.py @@ -0,0 +1,114 @@ +#!/usr/bin/env python +""" + v0.1 Summarize all feature extractors. + +""" +from __future__ import absolute_import +import os, sys +import copy + +sys.path.append(os.environ.get("TCP_DIR") + '/Software/feature_extract/MLData') +import arffify + +class Summarize_Available_Features: + """ Finds all feature extractors, tries to summarize some info about them, + whether they are currently used, and if they have doc-strings + """ + def __init__(self, pars={}): + self.pars = pars + + + def extract_all_features_from_init(self): + """ Parse __init__.py, get features that are known / avialble + """ + out_feat_dict = {} + + lines = open(self.pars['init_fpath']).readlines() + + import_dict = {} # contains: {:[, , ...] + for line in lines: + if len(line) < 1: + continue # skip this line + if ((line[0] != '#') and ('from' in line)): + sub_str = line[line.find('from') + 5:line.find('import')] + py_filename = sub_str.strip() + sub_str = line[line.find('import')+7:] + a_list = sub_str.split(',') + extractor_list = [] + for elem in a_list: + extractor_list.append(elem.strip()) + import_dict[py_filename] = {'extractor_list':extractor_list, + 'init_line':copy.copy(line)} + + ### Now parse arffify and see which features are actually used. + skipped_features = arffify.skip_features + for extr_dict in import_dict.values(): + for feat_name in extr_dict['extractor_list']: + out_feat_dict[feat_name] = {'used_in_classifications':True, + 'is_extracted':False, + 'doc_str':""} + if feat_name in skipped_features: + out_feat_dict[feat_name]['used_in_classifications'] = False + + + ### Try to get the doc_strings for the feature extractors, if any where created. + # This might be more easily done by genning signals_list ala db_importer.py:1048 + # test_feature_algorithms.py:22 + sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + \ + 'Software/feature_extract')) + sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + \ + 'Software/feature_extract/Code')) + from .Code import * + import db_importer + + signals_list = [] + gen = generators_importers.from_xml(signals_list) + gen.generate(xml_handle="../../Data/vosource_9026.xml") + gen.sig.add_features_to_xml_string(gen.signals_list) + + for filter_name,filt_dict in signals_list[0].properties['data'].items(): + for feat_name,value_object in signals_list[0].properties['data']\ + [filter_name]['features'].items(): + # KLUDGE: some inconsistancy in feature naming for closest_in_light... + # - these seem to be due to various users creating extractors with non conforming names / defintions + # - if everyone named their extractors with a '_extractor' suffix, this would work!!! + if 'closest_light' in feat_name: + feat_name = feat_name.replace('closest_light','closest_in_light') + if feat_name not in out_feat_dict: + feat_name = feat_name + '_extractor' + if feat_name not in out_feat_dict: + feat_name = feat_name.replace('_extractor','extractor') + + if feat_name in skipped_features: + out_feat_dict[feat_name]['used_in_classifications'] = False + + out_feat_dict[feat_name]['is_extracted'] = True + out_feat_dict[feat_name]['doc_str'] = value_object.__doc__ + + + import pprint + pprint.pprint(out_feat_dict) + return out_feat_dict + + +if __name__ == '__main__': + + pars = {'init_fpath':'/home/pteluser/src/TCP/Software/feature_extract/Code/extractors/__init__.py'} + + SummarizeFeatures = Summarize_Available_Features(pars=pars) + SummarizeFeatures.extract_all_features_from_init() + + # parse init file for non # lines + + """ + ### Try to get the doc_strings for the feature extractors, if any where created. + sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + 'Software/feature_extract')) + sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + 'Software/feature_extract/Code')) + sys.path.append(os.path.abspath(os.environ.get("TCP_DIR") + 'Software/feature_extract/Code/extractors')) + from Code import * + + for extractor_name,extr_dict in import_dict.items(): + exec("sys.path.append(os.path.abspath(os.environ.get('TCP_DIR') + 'Software/feature_extract/Code/extractors'));" + extr_dict['init_line']) + for extr_name in extr_dict['extractor_list']: + print extr_name # TODO: see if __doc__ exists : do an eval() + """ diff --git a/mltsp/TCP/Software/feature_extract/textmate_random_number.tmproj b/mltsp/TCP/Software/feature_extract/textmate_random_number.tmproj new file mode 100755 index 00000000..f7309d6f --- /dev/null +++ b/mltsp/TCP/Software/feature_extract/textmate_random_number.tmproj @@ -0,0 +1,682 @@ + + + + + currentDocument + Code/extractors/lomb_extractor.py + documents + + + expanded + + name + Code + regexFolderFilter + !.*/(\.[^/]*|CVS|_darcs|_MTN|\{arch\}|blib|.*~\.nib|.*\.(framework|app|pbproj|pbxproj|xcode(proj)?|bundle))$ + sourceDirectory + Code + + + fileHierarchyDrawerWidth + 200 + metaData + + Code/FeatureExtractor.py + + caret + + column + 24 + line + 25 + + firstVisibleColumn + 0 + firstVisibleLine + 4 + + Code/__init__.py + + caret + + column + 21 + line + 12 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/db_importer.py + + caret + + column + 31 + line + 245 + + columnSelection + + firstVisibleColumn + 0 + firstVisibleLine + 214 + selectFrom + + column + 26 + line + 245 + + selectTo + + column + 31 + line + 245 + + + Code/extractors/__init__.py + + caret + + column + 0 + line + 20 + + columnSelection + + firstVisibleColumn + 0 + firstVisibleLine + 0 + selectFrom + + column + 33 + line + 21 + + selectTo + + column + 0 + line + 20 + + + Code/extractors/beyond1std_extractor.py + + caret + + column + 57 + line + 7 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/extractors/chi2_per_deg_extractor.py + + caret + + column + 0 + line + 6 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/extractors/chi2extractor.py + + caret + + column + 37 + line + 7 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/extractors/common_functions/ChiSquare.py + + caret + + column + 13 + line + 21 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/extractors/common_functions/__init__.py + + caret + + column + 31 + line + 1 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/extractors/common_functions/lomb_scargle.py + + caret + + column + 0 + line + 272 + + columnSelection + + firstVisibleColumn + 0 + firstVisibleLine + 269 + selectFrom + + column + 12 + line + 304 + + selectTo + + column + 0 + line + 272 + + + Code/extractors/dc_extractor.py + + caret + + column + 34 + line + 22 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/extractors/dist_from_u_extractor.py + + caret + + column + 28 + line + 16 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/extractors/first_freq_extractor.py + + caret + + column + 17 + line + 16 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/extractors/first_lomb_extractor.py + + caret + + column + 41 + line + 7 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/extractors/fourierextractor.py + + caret + + column + 0 + line + 12 + + columnSelection + + firstVisibleColumn + 0 + firstVisibleLine + 0 + selectFrom + + column + 52 + line + 13 + + selectTo + + column + 0 + line + 12 + + + Code/extractors/linear_extractor.py + + caret + + column + 26 + line + 21 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/extractors/lomb_extractor.py + + caret + + column + 0 + line + 0 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/extractors/max_slope_extractor.py + + caret + + column + 0 + line + 0 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/extractors/medianextractor.py + + caret + + column + 1 + line + 8 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/extractors/old_dcextractor.py + + caret + + column + 1 + line + 8 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/extractors/power_spectrum_extractor.py + + caret + + column + 45 + line + 7 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/extractors/sine_fit_extractor.py + + caret + + column + 175 + line + 21 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/extractors/sine_leastsq_extractor.py + + caret + + column + 40 + line + 15 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/extractors/sine_lomb_extractor.py + + caret + + column + 0 + line + 0 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/extractors/skew_extractor.py + + caret + + column + 13 + line + 13 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/extractors/stdextractor.py + + caret + + column + 1 + line + 7 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/extractors/stdvs_from_u_extractor.py + + caret + + column + 20 + line + 18 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/extractors/wei_av_uncertainty_extractor.py + + caret + + column + 30 + line + 5 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/extractors/weighted_average_extractor.py + + caret + + column + 36 + line + 16 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/feature_interfaces.py + + caret + + column + 34 + line + 82 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/import_vizier.py + + caret + + column + 21 + line + 15 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/jsb_fake_data.py + + caret + + column + 0 + line + 0 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/main.py + + caret + + column + 0 + line + 21 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/plotters.py + + caret + + column + 54 + line + 10 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/signal_objects.py + + caret + + column + 33 + line + 42 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/source_5.xml + + caret + + column + 29 + line + 8 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/table.dat + + caret + + column + 0 + line + 0 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/table2.dat + + caret + + column + 0 + line + 0 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + Code/vo_timeseries.py + + caret + + column + 0 + line + 0 + + firstVisibleColumn + 0 + firstVisibleLine + 0 + + + openDocuments + + Code/table2.dat + Code/signal_objects.py + Code/plotters.py + Code/vo_timeseries.py + Code/source_5.xml + Code/import_vizier.py + Code/jsb_fake_data.py + Code/table.dat + Code/extractors/weighted_average_extractor.py + Code/feature_interfaces.py + Code/extractors/lomb_extractor.py + Code/extractors/beyond1std_extractor.py + Code/extractors/chi2_per_deg_extractor.py + Code/__init__.py + Code/db_importer.py + Code/main.py + Code/extractors/common_functions/__init__.py + Code/FeatureExtractor.py + Code/extractors/dc_extractor.py + Code/extractors/chi2extractor.py + Code/extractors/dist_from_u_extractor.py + Code/extractors/__init__.py + Code/extractors/first_freq_extractor.py + Code/extractors/fourierextractor.py + Code/extractors/linear_extractor.py + Code/extractors/max_slope_extractor.py + Code/extractors/medianextractor.py + Code/extractors/old_dcextractor.py + Code/extractors/power_spectrum_extractor.py + Code/extractors/sine_fit_extractor.py + Code/extractors/sine_lomb_extractor.py + Code/extractors/first_lomb_extractor.py + Code/extractors/sine_leastsq_extractor.py + Code/extractors/skew_extractor.py + Code/extractors/common_functions/lomb_scargle.py + Code/extractors/stdextractor.py + Code/extractors/stdvs_from_u_extractor.py + Code/extractors/wei_av_uncertainty_extractor.py + Code/extractors/common_functions/ChiSquare.py + + showFileHierarchyDrawer + + windowFrame + {{399, 203}, {1230, 815}} + + diff --git a/mltsp/TCP/Software/ingest_tools/__init__.py b/mltsp/TCP/Software/ingest_tools/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/mltsp/TCP/Software/ingest_tools/__init__.py @@ -0,0 +1 @@ + diff --git a/mltsp/TCP/Software/ingest_tools/feature_extraction_interface.py b/mltsp/TCP/Software/ingest_tools/feature_extraction_interface.py new file mode 100644 index 00000000..21bba6a3 --- /dev/null +++ b/mltsp/TCP/Software/ingest_tools/feature_extraction_interface.py @@ -0,0 +1,1158 @@ +#!/usr/bin/env python +""" +feature_extraction_interface.py + + v0.1 Interface for ingest_tools.py to Maxime's TCP/Software/feature_extract/* + v0.2 Include specific feature definitions. Methods for access and generate. + v0.3 made this self-describing so no need to add new extractors if they are in the right place +PDB Command: + /usr/lib/python2.5/pdb.py test.py +""" +from __future__ import print_function +import sys, os +try: + import pylab +except: + pass +import numpy + + +class GetFeatIdLookupDicts: + """ This class retrieves a couple lookup dicts from disk, and + if they dont exist, they are generated from an RDB query and written to disk. + + NOTE: The referenced file on disk should be deleted whenever new + feature types are added to the RDB feat_lookup table. + """ + + def __init__(self, db_cursor=None): + self.reference_feat_dict_fpath = os.environ.get('TCP_DATA_DIR') + '/reference_feat_dict.pkl' + self.cursor = db_cursor + + + def form_dicts_from_rdb_query(self): + """ SELECT the feature tables, form dicts + Returns : (feature_lookup_dict, filt_lookup_dict) + """ + feature_lookup_dict = {} + filt_lookup_dict = {} + + select_str = "SELECT feat_name, filter_id, feat_id FROM feat_lookup" + self.cursor.execute(select_str) + results = self.cursor.fetchall() + for result in results: + (feat_name, filter_id, feat_id) = result + feature_lookup_dict[(filter_id, feat_name)] = feat_id + if filter_id not in filt_lookup_dict: + filt_lookup_dict[filter_id] = {} + filt_lookup_dict[filter_id][feat_name] = feat_id + + return (feature_lookup_dict, filt_lookup_dict) + + + def write_dicts_to_disk(self, feature_lookup_dict, filt_lookup_dict): + """ Write dicts to dictfile which is generally under $TCP_DATA_DIR. + """ + import cPickle + fp = open(self.reference_feat_dict_fpath, "w") + cPickle.dump((feature_lookup_dict, filt_lookup_dict),fp) + fp.close() + + + def get_dicts(self): + """ + See if dict file is available, if not form from RDB query, write to disk. + + Return (feat dicts). + """ + # 20090817: dstarr disables condition since we are oftenadding new features lately, and it seems this shouldnt be a big issue since ipengines are initialized once with this dict. + #if os.path.exists(self.reference_feat_dict_fpath): + # # The following fills the variables: + # # (feature_lookup_dict, filt_lookup_dict) + # import cPickle + # fp = open(self.reference_feat_dict_fpath) + # (feature_lookup_dict, filt_lookup_dict) = cPickle.load(fp) + # fp.close() + #else: + + (feature_lookup_dict, filt_lookup_dict) = \ + self.form_dicts_from_rdb_query() + #self.write_dicts_to_disk(feature_lookup_dict, filt_lookup_dict) + + self.feature_lookup_dict = feature_lookup_dict + self.filt_lookup_dict = filt_lookup_dict + + +class Final_Features: + """ Class which contains final, extracted (scalar) feature definition + lists and dictionaries. + + I found a list of these in: + signals_list[0].properties['data']['i']['features'] + """ + def __init__(self): + self.ife = Internal_Feature_Extractors() + #self.ife.features_tup_list + + # TODO: to form self.features_dict{} I need to: + # - make mysql-friendly names using replace() for 'table_name' + # - assume FLOAT & INDEX + import copy + + self.string_replace_dict = {'-':'_', + ' ':'', + '.':'_', + '_extractor':'', + 'extractor':''} + self.filter_list = ['u','g','r','i','z','j','h','k','X'] # cooresponds to RDB 'filt' indexes: [SDSS(5), PTEL(3), ] + self.filter_dict = {} + i = 0 + for filt in self.filter_list: + self.filter_dict[filt] = i + i += 1 + + self.features_dict = {} + for (extractor_name,notso_empty_dict) in self.ife.features_tup_list: + #print extractor_name + ext_name_mysql_safe = copy.copy(extractor_name) + for old_str,new_str in self.string_replace_dict.items(): + ext_name_mysql_safe = ext_name_mysql_safe.replace(old_str,new_str) + + self.features_dict[extractor_name] = {\ + 'out_type':'FLOAT', + 'table_name':ext_name_mysql_safe, + 'index_type':'INDEX', + 'internal':notso_empty_dict['internal'], + 'doc':notso_empty_dict['doc']} + """ + self.features_dict = { \ + 'std': {\ + 'out_type':'FLOAT', + 'table_name':'std_dev', + 'index_type':'INDEX'}, + 'third': {\ + 'out_type':'FLOAT', + 'table_name':'third_freq', + 'index_type':'INDEX'}, + 'first_frequency': {\ + 'out_type':'FLOAT', + 'table_name':'first_freq', + 'index_type':'INDEX'}, + 'chi2': {\ + 'out_type':'FLOAT', + 'table_name':'chi2', + 'index_type':'INDEX'}, + 'weighted average': {\ + 'out_type':'FLOAT', + 'table_name':'weighted_avg', + 'index_type':'INDEX'}, + 'median': {\ + 'out_type':'FLOAT', + 'table_name':'feat_median', + 'index_type':'INDEX'}, + 'dc': {\ + 'out_type':'FLOAT', + 'table_name':'feat_dc', + 'index_type':'INDEX'}, + 'max_slope': {\ + 'out_type':'FLOAT', + 'table_name':'max_slope', + 'index_type':'INDEX'}, + 'freq ratio 2-1': {\ + 'out_type':'FLOAT', + 'table_name':'freq_ratio_21', + 'index_type':'INDEX'}, + 'second': {\ + 'out_type':'FLOAT', + 'table_name':'second_freq', + 'index_type':'INDEX'}, + 'old dc': {\ + 'out_type':'FLOAT', + 'table_name':'old_dc', + 'index_type':'INDEX'}, + 'n of pts beyond 1 std from u': {\ + 'out_type':'FLOAT', + 'table_name':'n_pts_by_1_std', + 'index_type':'INDEX'}, + 'freq ratio 3-2': {\ + 'out_type':'FLOAT', + 'table_name':'freq_ratio_32', + 'index_type':'INDEX'}, + 'freq ratio 3-1': {\ + 'out_type':'FLOAT', + 'table_name':'freq_ratio_31', + 'index_type':'INDEX'}, + 'skew': {\ + 'out_type':'FLOAT', + 'table_name':'feat_skew', + 'index_type':'INDEX'}, + 'weighted average uncertainty': {\ + 'out_type':'FLOAT', + 'table_name':'weight_avg_uncert', + 'index_type':'INDEX'}} + """ + #self.features_ordered_list = self.features_dict.keys() + #self.features_ordered_list.sort() + +class Feature_database: + """ Class which contains generation and access methods for + all possible feature extractors. + + # USE: + import feature_extraction_interface + feat_db = feature_extraction_interface.Feature_database() + feat_db.initialize_mysql_connection(rdb_host_ip='192.168.1.45', \ + rdb_user='', rdb_name='') + feat_db.create_feature_tables() + feat_db.insert_srclist_features_into_rdb_tables(self, signals_list) + """ + def __init__(self): + self.final_features = Final_Features() + + def initialize_mysql_connection(self, rdb_host_ip='', rdb_user='', \ + rdb_name='', rdb_port=3306, \ + feat_lookup_tablename='',\ + feat_values_tablename='', \ + db=None): + """ Create connection to feature mysql server. + """ + import MySQLdb + if db is None: + self.db = MySQLdb.connect(host=rdb_host_ip, user=rdb_user, \ + db=rdb_name, port=rdb_port, compress=1) + else: + self.db = db + self.cursor = self.db.cursor() + self.feat_lookup_tablename = feat_lookup_tablename + self.feat_values_tablename = feat_values_tablename + + self.attempt_to_fill_feat_lookup_tablename() + + + def attempt_to_fill_feat_lookup_tablename(self): + """ KLUDGE: Attempt to retrieve self.feat_lookup_tablename values and + populate self.feature_lookup_dict{} + """ + try: + select_str = "SELECT (feat_id, filter_id, feat_name) FROM %s" % (self.feat_lookup_tablename) + self.cursor.execute(select_str) + results = self.cursor.fetchall() + + self.feature_lookup_dict = {} + for result in results: + (feat_id, filt_num, feat_name) = result + self.feature_lookup_dict[(filt_num, feat_name)] = feat_id + except: + pass + + + def drop_feature_tables(self): + """ DROP MySQL tables for all features. + + Tables of form: + src_id (INT UNSIGNED), feat + """ + for feat_name,feat_dict in self.final_features.features_dict.items(): + for filt_name in self.final_features.filter_list: + #create_str = "CREATE TABLE %s_%s (INDEX(src_id), %s(%s))" % (filt_name, feat_name, feat_dict['index_type'], feat_dict['out_type']) + create_str = "DROP TABLE %s_%s" % (filt_name, feat_dict['table_name']) + try: + self.cursor.execute(create_str) + except: + pass + #print 'FAIL:', create_str + + + +class Internal_Feature_Extractors: + """ Class which contains "internal feature" definition lists & dictionaries + """ + + ## modules (instead of classes) will break the test suite. Put these here as appropriate. + ignores = ["min_extractor","max_extractor","third_extractor"] + + def __init__(self): + # XXX ORDER OF FEATURES IS IMPORTANT XXX + + # This needs to retain list ordering, but I figure it'd be useful + # to have potential dictionary attributes associated with + # each internal-feature extractor. + + import ast + with open(os.path.join( + os.path.dirname(__file__), + '../feature_extract/Code/extractors/__init__.py'), 'r') as f: + + init_ast = ast.parse(f.read()) + + features = [] + for a in init_ast.body: + if isinstance(a, ast.ImportFrom): + features.extend([f.name for f in a.names]) + + self.features_tup_list = [(f, {}) for f in features] + + for (feat_name, feat_dict) in self.features_tup_list: + d = {} + from ..feature_extract.Code import extractors + feature = getattr(extractors, feat_name) + doc = getattr(feature, '__doc__', '') + feat_dict['doc'] = doc + + import inspect + + feat_dict['internal'] = 'false' + for member_name, member_val in inspect.getmembers(feature): + if member_name == 'internal_use_only': + if member_val == True: + feat_dict['internal'] = 'true' + break + + # TODO: I'm sure there is an efficient way to form a dictionary + # from the above tuple list, using map(), filter(), etc... + self.feature_dict = {} + self.feature_ordered_keys = [] + for (feat_name,feat_dict) in self.features_tup_list: + self.feature_ordered_keys.append(feat_name) + self.feature_dict[feat_name] = feat_dict + + +class Final_Features: + """ Class which contains final, extracted (scalar) feature definition + lists and dictionaries. + + I found a list of these in: + signals_list[0].properties['data']['i']['features'] + """ + def __init__(self): + self.ife = Internal_Feature_Extractors() + #self.ife.features_tup_list + + # TODO: to form self.features_dict{} I need to: + # - make mysql-friendly names using replace() for 'table_name' + # - assume FLOAT & INDEX + import copy + + self.string_replace_dict = {'-':'_', + ' ':'', + '.':'_', + '_extractor':'', + 'extractor':''} + self.filter_list = ['u','g','r','i','z','j','h','k','X'] # cooresponds to RDB 'filt' indexes: [SDSS(5), PTEL(3), ] + self.filter_dict = {} + i = 0 + for filt in self.filter_list: + self.filter_dict[filt] = i + i += 1 + + self.features_dict = {} + for (extractor_name,notso_empty_dict) in self.ife.features_tup_list: + #print extractor_name + ext_name_mysql_safe = copy.copy(extractor_name) + for old_str,new_str in self.string_replace_dict.items(): + ext_name_mysql_safe = ext_name_mysql_safe.replace(old_str,new_str) + + self.features_dict[extractor_name] = {\ + 'out_type':'FLOAT', + 'table_name':ext_name_mysql_safe, + 'index_type':'INDEX', + 'internal':notso_empty_dict['internal'], + 'doc':notso_empty_dict['doc']} + """ + self.features_dict = { \ + 'std': {\ + 'out_type':'FLOAT', + 'table_name':'std_dev', + 'index_type':'INDEX'}, + 'third': {\ + 'out_type':'FLOAT', + 'table_name':'third_freq', + 'index_type':'INDEX'}, + 'first_frequency': {\ + 'out_type':'FLOAT', + 'table_name':'first_freq', + 'index_type':'INDEX'}, + 'chi2': {\ + 'out_type':'FLOAT', + 'table_name':'chi2', + 'index_type':'INDEX'}, + 'weighted average': {\ + 'out_type':'FLOAT', + 'table_name':'weighted_avg', + 'index_type':'INDEX'}, + 'median': {\ + 'out_type':'FLOAT', + 'table_name':'feat_median', + 'index_type':'INDEX'}, + 'dc': {\ + 'out_type':'FLOAT', + 'table_name':'feat_dc', + 'index_type':'INDEX'}, + 'max_slope': {\ + 'out_type':'FLOAT', + 'table_name':'max_slope', + 'index_type':'INDEX'}, + 'freq ratio 2-1': {\ + 'out_type':'FLOAT', + 'table_name':'freq_ratio_21', + 'index_type':'INDEX'}, + 'second': {\ + 'out_type':'FLOAT', + 'table_name':'second_freq', + 'index_type':'INDEX'}, + 'old dc': {\ + 'out_type':'FLOAT', + 'table_name':'old_dc', + 'index_type':'INDEX'}, + 'n of pts beyond 1 std from u': {\ + 'out_type':'FLOAT', + 'table_name':'n_pts_by_1_std', + 'index_type':'INDEX'}, + 'freq ratio 3-2': {\ + 'out_type':'FLOAT', + 'table_name':'freq_ratio_32', + 'index_type':'INDEX'}, + 'freq ratio 3-1': {\ + 'out_type':'FLOAT', + 'table_name':'freq_ratio_31', + 'index_type':'INDEX'}, + 'skew': {\ + 'out_type':'FLOAT', + 'table_name':'feat_skew', + 'index_type':'INDEX'}, + 'weighted average uncertainty': {\ + 'out_type':'FLOAT', + 'table_name':'weight_avg_uncert', + 'index_type':'INDEX'}} + """ + #self.features_ordered_list = self.features_dict.keys() + #self.features_ordered_list.sort() + +class Feature_database: + """ Class which contains generation and access methods for + all possible feature extractors. + + # USE: + import feature_extraction_interface + feat_db = feature_extraction_interface.Feature_database() + feat_db.initialize_mysql_connection(rdb_host_ip='192.168.1.45', \ + rdb_user='', rdb_name='') + feat_db.create_feature_tables() + feat_db.insert_srclist_features_into_rdb_tables(self, signals_list) + """ + def __init__(self): + self.final_features = Final_Features() + + def initialize_mysql_connection(self, rdb_host_ip='', rdb_user='', \ + rdb_name='', rdb_port=3306, \ + feat_lookup_tablename='',\ + feat_values_tablename='', \ + db=None): + """ Create connection to feature mysql server. + """ + import MySQLdb + if db is None: + self.db = MySQLdb.connect(host=rdb_host_ip, user=rdb_user, \ + db=rdb_name, port=rdb_port, compress=1) + else: + self.db = db + self.cursor = self.db.cursor() + self.feat_lookup_tablename = feat_lookup_tablename + self.feat_values_tablename = feat_values_tablename + + self.attempt_to_fill_feat_lookup_tablename() + + + def attempt_to_fill_feat_lookup_tablename(self): + """ KLUDGE: Attempt to retrieve self.feat_lookup_tablename values and + populate self.feature_lookup_dict{} + """ + try: + select_str = "SELECT (feat_id, filter_id, feat_name) FROM %s" % (self.feat_lookup_tablename) + self.cursor.execute(select_str) + results = self.cursor.fetchall() + + self.feature_lookup_dict = {} + for result in results: + (feat_id, filt_num, feat_name) = result + self.feature_lookup_dict[(filt_num, feat_name)] = feat_id + except: + pass + + + def drop_feature_tables(self): + """ DROP MySQL tables for all features. + + Tables of form: + src_id (INT UNSIGNED), feat + """ + for feat_name,feat_dict in self.final_features.features_dict.items(): + for filt_name in self.final_features.filter_list: + #create_str = "CREATE TABLE %s_%s (INDEX(src_id), %s(%s))" % (filt_name, feat_name, feat_dict['index_type'], feat_dict['out_type']) + create_str = "DROP TABLE %s_%s" % (filt_name, feat_dict['table_name']) + try: + self.cursor.execute(create_str) + except: + pass + #print 'FAIL:', create_str + + + def create_feature_lookup_dict(self): + """ Create dictionary: feature_lookup_dict: + """ + Get_Feat_Id_Lookup_Dicts = GetFeatIdLookupDicts(db_cursor=self.cursor) + Get_Feat_Id_Lookup_Dicts.get_dicts() + + self.feature_lookup_dict = Get_Feat_Id_Lookup_Dicts.feature_lookup_dict + self.filt_lookup_dict = Get_Feat_Id_Lookup_Dicts.filt_lookup_dict + + + # OBSOLETE: + def create_feature_lookup_dict__old(self): + """ Create dictionary: feature_lookup_dict: + """ + self.feature_lookup_dict = {} # New dict, even if exists already + self.filt_lookup_dict = {} + for filt_num in range(len(self.final_features.filter_list)): + self.filt_lookup_dict[filt_num] = {} + i = 0 + #feat_id_partition_groups = [] + for feat_name_internal,feat_dict in self.final_features.features_dict.items(): + feat_name = feat_dict['table_name'] + #feat_id_list = [] + for filt_num in range(len(self.final_features.filter_list)): + self.feature_lookup_dict[(filt_num, feat_name)] = i + self.filt_lookup_dict[filt_num][feat_name] = i + #feat_id_list.append(str(i)) + i += 1 + #feat_id_partition_groups.append(feat_id_list) + + + def create_feature_tables(self): + """ CREATE MySQL tables for all features. + + CREATE TABLE feature_lookup (feat_id SMALLINT UNSIGNED, filter_id TINYINT UNSIGNED, feat_name VARCHAR(120), INDEX(feat_id)) + + CREATE TABLE feature_values (src_id INT UNSIGNED, feat_id SMALLINT UNSIGNED, feat_val FLOAT, feat_weight FLOAT, INDEX(feat_id, feat_val), INDEX(src_id)) PARTITION BY LIST(feat_id) ( + PARTITION p0 VALUES IN (0), + PARTITION p0 VALUES IN (0), + ... + + + NOTE: To update the partition tables of an existing feat_values table: + - su - root, copy /var/lib/mysql/source_test_db to a backup, so you can always copy the table files back if needed. + - Also copy (using a shell script) all the feat_values mysql files to feat_values_orig named files + - see: mysql_feat_values_partition_table_copy.sh for a template of the script + - drop table feat_values; + - then run feature_extraction_interface.py 's create_feature_tables() + - insert into feat_values select * from feat_values_orig; + + """ + # KLUDGE: This is repeated in create_feature_lookup_dict() which + # was probably called earlier. feat_id_partition_groups should + # probably be self. and everything done in mentioned function. + # Then this section can be removed. + self.feature_lookup_dict = {} # New dict, even if exists already + self.filt_lookup_dict = {} + for filt_num in range(len(self.final_features.filter_list)): + self.filt_lookup_dict[filt_num] = {} + i = 0 + feat_id_partition_groups = [] + inter_partition_list = [] + in_partition_count = 0 + + for feat_name_internal,feat_dict in self.final_features.features_dict.items(): + feat_name = feat_dict['table_name'] + + feat_id_list = [] + for filt_num in range(len(self.final_features.filter_list)): + self.feature_lookup_dict[(filt_num, feat_name)] = i + self.filt_lookup_dict[filt_num][feat_name] = i + feat_id_list.append(str(i)) + i += 1 + + in_partition_count += 1 + inter_partition_list.extend(feat_id_list) + # The following clusters 4 features (and their filters) to a partitn + if in_partition_count == 4: + feat_id_partition_groups.append(inter_partition_list) + in_partition_count = 0 + inter_partition_list = [] + + if len(inter_partition_list) > 0: + feat_id_partition_groups.append(inter_partition_list) + #### NOTE: Here I extend the number of partitions / features beyond + # what is currently used, to allow features to be added in future + # without re-populating feature tables. + inter_partition_list = [] + in_partition_count = 0 + # NOTE: it seems we need at leas 50 * (9 filters) extra added at the moment + for i_future_feature in range(1000): + feat_id_list = [] + for filt_num in range(len(self.final_features.filter_list)): + feat_id_list.append(str(i)) + i += 1 + in_partition_count += 1 + inter_partition_list.extend(feat_id_list) + # The following clusters 4 features (and their filters) to a partitn + if in_partition_count == 4: + feat_id_partition_groups.append(inter_partition_list) + in_partition_count = 0 + inter_partition_list = [] + #### + + if 0: + ##### Do this section if you want to re-create the feat_lookup table (which is less likely than updating the partitions of the feat_values table) + ##### NOTE: if you want to add new features to the feat_lookup tabe, see Add_New_fatures.add_new_features_to_featlookup_table() + create_str = "CREATE TABLE %s (feat_id SMALLINT UNSIGNED, filter_id TINYINT UNSIGNED, feat_name VARCHAR(120), doc_str VARCHAR(2000), is_internal BOOLEAN, INDEX(feat_id), INDEX(filter_id,feat_name))" % (self.feat_lookup_tablename) + self.cursor.execute(create_str) + + insert_list = ["INSERT INTO %s (feat_id, filter_id, feat_name, doc_str, is_internal) VALUES " % (self.feat_lookup_tablename)] + for (filt_num,feat_name),i_feat in self.feature_lookup_dict.items(): + # KLUDGY: list structure is not ideal here: + doc_str = '' + #for temp_feat_name,temp_dict in self.final_features.ife.\ + # features_tup_list: + for temp_feat_name,temp_dict in self.final_features.features_dict.\ + items(): + if feat_name == temp_dict['table_name']: + doc_str = temp_dict['doc'][:2000].replace("'","_").replace('"',"_")#.replace("","_") + internal = temp_dict['internal'] + break # get out of loop + insert_list.append('(%d,%d,"%s","%s", %s), ' % \ + (i_feat, filt_num, feat_name, doc_str, internal)) + self.cursor.execute(''.join(insert_list)[:-2]) + + #create_str_list = ["CREATE TABLE %s (src_id INT UNSIGNED, feat_id SMALLINT UNSIGNED, feat_val DOUBLE, feat_weight FLOAT, INDEX(feat_id, feat_val), INDEX(src_id)) PARTITION BY LIST(feat_id) (" % (self.feat_values_tablename)] + create_str_list = ["CREATE TABLE %s (src_id INT UNSIGNED, feat_id SMALLINT UNSIGNED, feat_val DOUBLE, feat_weight FLOAT DEFAULT 1.0, INDEX(feat_id, feat_val), UNIQUE INDEX(src_id, feat_id)) PARTITION BY LIST(feat_id) (" % (self.feat_values_tablename)] + + # TODO: here I need to add additional/future feature-id numbers + i = 0 + for feat_name_assoc_featids_list in feat_id_partition_groups: + id_str = ','.join(feat_name_assoc_featids_list) + create_str_list.append("PARTITION p%d VALUES IN (%s), " %(i,id_str)) + i += 1 + + import pdb; pdb.set_trace() + + + self.cursor.execute(''.join(create_str_list)[:-2] + ")") + + + ############# + + # obsolete: + def create_feature_tables_old(self): + """ CREATE MySQL tables for all features. + + CREATE TABLE feature_lookup (feat_id SMALLINT UNSIGNED, filter_id TINYINT UNSIGNED, feat_name VARCHAR(120), INDEX(feat_id)) + + CREATE TABLE feature_values (src_id INT UNSIGNED, feat_id SMALLINT UNSIGNED, feat_val FLOAT, feat_weight FLOAT, INDEX(feat_id, feat_val), INDEX(src_id)) PARTITION BY LIST(feat_id) ( + PARTITION p0 VALUES IN (0), + PARTITION p0 VALUES IN (0), + ... + """ + # KLUDGE: This is repeated in create_feature_lookup_dict() which + # was probably called earlier. feat_id_partition_groups should + # probably be self. and everything done in mentioned function. + # Then this section can be removed. + self.feature_lookup_dict = {} # New dict, even if exists already + self.filt_lookup_dict = {} + for filt_num in range(len(self.final_features.filter_list)): + self.filt_lookup_dict[filt_num] = {} + i = 0 + feat_id_partition_groups = [] + for feat_name_internal,feat_dict in self.final_features.features_dict.items(): + feat_name = feat_dict['table_name'] + + feat_id_list = [] + for filt_num in range(len(self.final_features.filter_list)): + self.feature_lookup_dict[(filt_num, feat_name)] = i + self.filt_lookup_dict[filt_num][feat_name] = i + feat_id_list.append(str(i)) + i += 1 + feat_id_partition_groups.append(feat_id_list) + create_str = "CREATE TABLE %s (feat_id SMALLINT UNSIGNED, filter_id TINYINT UNSIGNED, feat_name VARCHAR(120), INDEX(feat_id), INDEX(filter_id,feat_name))" % (self.feat_lookup_tablename) + self.cursor.execute(create_str) + + insert_list = ["INSERT INTO %s (feat_id, filter_id, feat_name) VALUES " % (self.feat_lookup_tablename)] + for (filt_num,feat_name),i_feat in self.feature_lookup_dict.items(): + insert_list.append('(%d,%d,"%s"), '% (i_feat, filt_num, feat_name)) + self.cursor.execute(''.join(insert_list)[:-2]) + + create_str_list = ["CREATE TABLE %s (src_id INT UNSIGNED, feat_id SMALLINT UNSIGNED, feat_val DOUBLE, feat_weight FLOAT, INDEX(feat_id, feat_val), INDEX(src_id)) PARTITION BY LIST(feat_id) (" % (self.feat_values_tablename)] + i = 0 + for feat_name_assoc_featids_list in feat_id_partition_groups: + id_str = ','.join(feat_name_assoc_featids_list) + create_str_list.append("PARTITION p%d VALUES IN (%s), " %(i,id_str)) + i += 1 + + self.cursor.execute(''.join(create_str_list)[:-2] + ")") + + + def get_rez_for_featureless_sources(self, orig_rez): + """ Given a xrsio.get_sources_for_radec() outputed 'rez' structure, + query the feat_values table to ensure no src_ids already exist. + + Return the reduced rez structure. + """ + featureless_srcids = [] + reduced_rez = [] + for src_rez in orig_rez: + for temp_rez in src_rez.values(): + if 'src_id' in temp_rez: + src_id = temp_rez['src_id'] + break + select_str ="SELECT TRUE FROM feat_values WHERE src_id=%d LIMIT 1"\ + % (src_id) + self.cursor.execute(select_str) + results = self.cursor.fetchall() + if len(results) == 0: + reduced_rez.append(src_rez) + featureless_srcids.append(src_id) + return (featureless_srcids, reduced_rez) + + + def insert_srclist_features_into_rdb_tables(self,signals_list, srcid_list,\ + do_rdb_insert=True, do_delete_existing_featvals=False): + """ Form lists of values for all features & then for each feature, + insert each src_id & feature-list into their cooresponding table. + """ + insert_dict = {} + for filt_num in range(len(self.final_features.filter_list)): + insert_dict[filt_num] = {} + for feat_name_internal,feat_dict in self.final_features.features_dict.items(): + feat_name = feat_dict['table_name'] + insert_dict[filt_num][feat_name] = [] + + used_filters_list = [] + for i in range(len(signals_list)): + signal_obj = signals_list[i] + src_id = srcid_list[i] + + ### 20100527 dstarr adds a new condition since we have disabled the combo_band, which chooses the filter with the most epochs: + filter_epoch_counts = [] + for filt_name in signal_obj.properties['data'].keys(): + if not filt_name in ['multiband', 'combo_band']: + filter_epoch_counts.append((len(signal_obj.properties['data'][filt_name]['input']['flux_data']), filt_name)) + filter_epoch_counts.sort(reverse=True) + filter_most_sampled = filter_epoch_counts[0][1] + used_filters_list.append(filter_most_sampled) + ### + + for filt_name in signal_obj.properties['data'].keys(): + # 20090617 KLUDGE: + # (in the next 2 lines) I am going to just include the ['multiband', 'combo_band'] and not ['ptf_g', 'ptf_r'] bands since they are combined into ['combo_band']. NOTE: I think ['multiband'] has features like: 'ws_variability_ru', but not other features... so it is complimentary + ### 20100527 dstarr adds a new condition since we have disabled the combo_band, which chooses the filter with the most epochs: + ###if not filt_name in ['multiband', 'combo_band']: + ### continue # skip this band (probably a specific filter) + if not filt_name in ['multiband', 'combo_band', filter_most_sampled]: + continue # skip this band (probably a specific filter) + + + if filt_name in self.final_features.filter_list: + filt_num = self.final_features.filter_list.index(filt_name) + else: + filt_num = len(self.final_features.filter_list) - 1 # Given dummy filter + for feat_name_internal,feat_dict in self.final_features.features_dict.items(): + feat_name = feat_dict['table_name'] + # NOTE: if a certain feature was not generated, we don't INSERT + # it into the RDB. This alleviates lots of NULL feat values. + #if not signal_obj.properties['data'][filt_name]\ + # ['features'].has_key(feat_name): + # feat_val = "NULL" + #if feat_name == 'flux_percentile_ratio_mid50': + # import pdb; pdb.set_trace() + # print 'yo', feat_name, filt_name + if feat_name in signal_obj.properties['data'][filt_name]\ + ['features']: + feat_val = str(signal_obj.properties['data'][filt_name]\ + ['features'][feat_name]) + if ((feat_val == 'Fail') or (feat_val == 'nan') or + (feat_val == 'inf') or (feat_val == 'None')): + feat_val = "NULL" + else: + # KLUDGE we force strings and other feature values to NULL + try: + blah = float(feat_val) + except: + feat_val = "NULL" + # 20090727: dstarr adds this so that NULL features are not INSERTed into RDB. + # - this should be ok since RDB features are just for User queries, not TCP reference. + if feat_val != "NULL": + insert_dict[filt_num][feat_name].append((src_id,feat_val)) + #insert_dict[filt_num][feat_name].append((src_id,feat_val)) + + # Here we INSERT into database for each feature (which cooresponds to + # a unique MySQL Table Partition file): + # # # # # # # # # # + # TODO: ? which is MySQL faster (using partitions): mini INSERT of MEGA + + if do_rdb_insert: + #insert_list = ["INSERT INTO %s (src_id, feat_id, feat_val, feat_weight) VALUES " % (self.feat_values_tablename)] + insert_list = ["INSERT INTO %s (src_id, feat_id, feat_val) VALUES " % (self.feat_values_tablename)] + else: + insert_list = [] + # THIS is pretty KLUDGY since it iterates over all know filters (multiple surveys): And, all ptf is in filter_id=8, even though that includes 2 filter + combo_band: + for filt_num,filt_dict in insert_dict.items(): + for feat_name,val_list in filt_dict.items(): + #if 'flux_percentile_ratio' in feat_name: + #if len(val_list) == 0: + # print 'len()==0', feat_name, filt_num, val_list + + #if (filt_num == 8) and (feat_name == 'flux_percentile_ratio_mid50'): + # import pdb; pdb.set_trace() + # print 'yo', feat_name, filt_num, val_list + + for (src_id,feat_val) in val_list: + # # # # TODO: remove HARDCODE of feat_weight = 1.0 : + #insert_list.append("(%d,%d,%s,1.0), " % (src_id, \ + insert_list.append("(%d,%d,%s), " % (src_id, \ + self.feature_lookup_dict[(filt_num, feat_name)],\ + feat_val)) + if do_rdb_insert and len(insert_list) > 1: + + if do_delete_existing_featvals: + ### This will make things take much longer, so this is normally disabled, although doing this + ### will fill fix a bug in pairwise_classifications.py:get_featcals_for_srcids() + ### feat-val .png distribution plots, where older feat_values were getting plotted. + self.cursor.execute("DELETE FROM %s WHERE src_id=%d" % (self.feat_values_tablename, src_id)) + + #self.cursor.execute(''.join(insert_list)[:-2]) + insert_str = ''.join(insert_list)[:-2] + ' ON DUPLICATE KEY UPDATE feat_val=VALUES(feat_val)' + self.cursor.execute(insert_str) + print('feature RDB INSERTed/UPDATEd %d sources.' % (len(srcid_list))) + + # TODO: lets also insert / update which filter was used for the features, into the RDB. + + return (insert_list, used_filters_list) + + + def do_large_query_of_feature_rdb(self, order_by_feat=''): + """This executes a large query of (many) features in feature RDB tables. + The returned column (and column name?) can then be used for generating + a summary plot. + """ + # NOTE: can assert theres a feature table for all filter_feature combos + + table_names = [] + for filt_name in self.final_features.filter_list: + for feat_name,feat_dict in self.final_features.features_dict.\ + items(): + table_names.append("%s_%s" %(filt_name,feat_dict['table_name'])) + table_names.sort() + + select_col_strs = [] + select_join_strs = [] + + sub_table_names = table_names[:len(table_names)/5] + for table_name in sub_table_names: + select_col_strs.append("%s.feat" % (table_name)) + select_join_strs.append("%s" % (table_name)) # TODO: skip this and just use table_names + + # ... JOIN __some_table__ USING (srcid) JOIN __some_table__ USING (srcid) ... WHERE () + select_str = "SELECT %s FROM %s" % (', '.join(select_col_strs), ' JOIN '.join(select_join_strs)) + self.cursor.execute(select_str) + + results = self.cursor.fetchall() + + out_dict = {} + for i in range(len(table_names)): + table_name = table_names[i] + temp_list = [] # I think this produces a unique pointer + for row in results: + temp_list.append(row[i]) + out_dict[table_name] = temp_list + + return out_dict # e.g.: 'z_std':[4,5,6,7,...] + + + def scp_feat_summary_img_to_webserver(self, \ + summary_img_fpath='',\ + feature_summary_webserver_name='',\ + feature_summary_webserver_user='',\ + feature_summary_webserver_dirpath='',\ + feature_summary_webserver_url_prefix=''): + """ Scp's feature-summary-image to webserver path. + """ + scp_str = "scp -C %s %s@%s:%s/" % (summary_img_fpath,\ + feature_summary_webserver_user, \ + feature_summary_webserver_name, \ + feature_summary_webserver_dirpath) + os.system(scp_str) + fname_root = summary_img_fpath[summary_img_fpath.rfind('/')+1:] + remote_url = "%s%s" %(feature_summary_webserver_url_prefix, fname_root) + return remote_url + +class Plot_Signals: + """ + # TODO: I would like a plot which: + # - input: signals_list[0] + # - contains all sub plots within it. + # - filters are vertical, plot types are horizontal. + # - plots are written to .ps file + # - XML file / RA,dec name is recorded in plot. + """ + + def __init__(self, signals_list, gen): + self.signals_list = signals_list + self.gen = gen # this is just accessed for ra, dec, src_id info + self.pars = {\ + 'excluded_inters':[], + 'subplot_region_limits':{'x_min':0.1,#0.01, + 'x_max':1.0, + 'y_min':0.07, #0.0125, # 0.0 + 'y_max':0.9}, + 'subplot_x_buffer':0.01, + 'subplot_y_buffer':0.02, + 'filters_list':['u', 'g', 'r', 'i', 'z', 'j', 'h', 'k'], + } + + + def get_features_to_plot_list(self, signal_obj): + """ Determine how many feature plots are needed: + NOTE: this covers the possibility that some filters may contain extra features + """ + features_to_plot = [] + for filt in signal_obj.properties['data'].keys(): + for feature_name in signal_obj.properties['data'][filt]['inter'].keys(): + if feature_name not in features_to_plot: + if feature_name not in self.pars['excluded_inters']: + try: + feat_length = len(signal_obj.properties['data'][filt]\ + ['inter'][feature_name][0]) + if feat_length > 0: + print('Added: ', feature_name) + features_to_plot.append(feature_name) + except: + print('Skipped:', feature_name) + features_to_plot.sort() + return features_to_plot + + + def get_feature_plot_pos_dict(self, signal_object, features_to_plot=[], \ + filters_to_plot=[]): + """ Now set up a dict which contains positions constraints + of all plots + """ + n_rows = len(filters_to_plot) + n_cols = len(features_to_plot)# + 1 # include the 'input-data' plot + + subplot_x_size = ((self.pars['subplot_region_limits']['x_max'] - \ + self.pars['subplot_region_limits']['x_min']) - \ + (self.pars['subplot_x_buffer'] * (n_cols - 1))) / \ + (n_cols + 2)#+2 is HACK + subplot_y_size = ((self.pars['subplot_region_limits']['y_max'] - \ + self.pars['subplot_region_limits']['y_min']) - \ + (self.pars['subplot_y_buffer'] * (n_rows - 1))) / \ + n_rows + + x_start_list = [] + for i in range(n_cols): + x_start_list.append(self.pars['subplot_region_limits']['x_min'] + \ + i*(self.pars['subplot_x_buffer'] + subplot_x_size)) + y_start_list = [] + for i in range(n_rows): + y_start_list.append(self.pars['subplot_region_limits']['y_min'] + \ + i*(self.pars['subplot_y_buffer'] + subplot_y_size)) + + feature_plot_positions_dict = {} + for i_filt in range(len(filters_to_plot)): + filt = filters_to_plot[i_filt] + if filt not in feature_plot_positions_dict.keys(): + feature_plot_positions_dict[filt] = {} + for i_feat in range(len(features_to_plot)): + feature = features_to_plot[i_feat] + feature_dict = {'x_low':x_start_list[i_feat], + 'x_high':x_start_list[i_feat] + subplot_x_size, + 'y_low':y_start_list[i_filt], + 'y_high':y_start_list[i_filt] + subplot_y_size} + feature_plot_positions_dict[filt][feature] = feature_dict + return feature_plot_positions_dict + + + def generate_multi_filter_plot(self, signal_obj, ps_fpath='/tmp/blah.ps'): + """ Generate a single plot which summarizes data and feature datae + arrays for all filters. + """ + import pylab + #pylab.rcParams.update({'figure.figsize':[10,10]}) # ??x?? inches + pylab.hold(False) + pylab.hold(True) + features_to_plot = self.get_features_to_plot_list(signal_obj) + features_to_plot.insert(0,'FLUX vs TIME') + filters_to_plot = [] + #20080326 comment out & replace: + #available_filters = signal_obj.properties['data'].keys() + #for filt in self.pars['filters_list']: + # if filt in available_filters: + # filters_to_plot.append(filt) + filters_to_plot = signal_obj.properties['data'].keys() + + feature_plot_positions_dict= self.get_feature_plot_pos_dict(signal_obj,\ + features_to_plot=features_to_plot, \ + filters_to_plot=filters_to_plot) + empty_array = numpy.array([]) + pylab.hold(False) + pylab.clf() + pylab.plot(empty_array, empty_array, 'ro') + pylab.hold(True) + pylab.axis([0.12,0.9,0,0.9]) # This is needed, even with following line + pylab.axis('off') + #pylab.title('$\it{hi}$') + + source_info_str = "src_id=%d, ra=%f~%f, dec=%f~%f" % ( \ + self.gen.sig.x_sdict['src_id'], \ + self.gen.sig.x_sdict['ra'], \ + self.gen.sig.x_sdict['ra_rms'], \ + self.gen.sig.x_sdict['dec'], \ + self.gen.sig.x_sdict['dec_rms']) + pylab.text(0.2, 0.97, source_info_str, horizontalalignment='left', verticalalignment='bottom', rotation=0, size=10) + # Generate feature-plot names which are labeled at the top,left of plot: + for feature in features_to_plot: + x_low = feature_plot_positions_dict[filters_to_plot[0]][feature]['x_low'] + pylab.text(x_low, 0.9, feature, horizontalalignment='left', verticalalignment='bottom', rotation=15, size=7) + for filt in filters_to_plot: + y_low = feature_plot_positions_dict[filt]['FLUX vs TIME']['y_low'] + #x_low = feature_plot_positions_dict[filters_to_plot[0]][feature]['x_low'] + #pylab.text(-0.007, y_low, filt, horizontalalignment='left', verticalalignment='bottom', rotation=0, size=10) + pylab.text(-0.1, y_low, filt, horizontalalignment='left', verticalalignment='bottom', rotation=0, size=10) + + ###### + # Insert feature scalars in-between subplots: + i = 0 + for filt in filters_to_plot: + scalar_features = signal_obj.properties['data'][filt]['features'].keys() + scalar_features.sort() + print_list = ["%3.3s" % (filt)] + for feat in scalar_features: + val = str(signal_obj.properties['data'][filt]['features'][feat]) + try: + #print_list.append('%0.8s=%0.3f' % (feat, float(val))) + print_list.append('%10.10s' % ("%0.3f" % (float(val)))) + except: + #print_list.append('%0.8s=%s' % (feat, val)) + print_list.append('%10.10s' % (val)) + print_str = ' '.join(print_list) + #y_low = feature_plot_positions_dict[filt]['FLUX vs TIME']['y_low'] #- 0.1275#+ subplot_y_size / 2.0 + #pylab.text(0, -0.11 +i*0.012,print_str, horizontalalignment='left',\ + # verticalalignment='bottom', rotation=0, size=6) + pylab.text(0.1, -0.11 +i*0.012,print_str, horizontalalignment='left',\ + verticalalignment='bottom', rotation=0, size=6) + i += 1 + + # Now add a key/title to the scalar features: + scalar_features = signal_obj.properties['data'][filters_to_plot[0]][\ + 'features'].keys() + scalar_features.sort() + print_list = [' '] #["%3.3s" % ('')] + for feat in scalar_features: + print_list.append('%10.10s' % (feat)) + print_str = ' '.join(print_list) + print(print_str) + pylab.text(0.1, -0.11 + i*0.012, print_str, horizontalalignment='left', \ + verticalalignment='bottom', rotation=0, size=6) + ###### + + # Generate sub-plots: + for filt in filters_to_plot: + for feature in features_to_plot: + x_low = feature_plot_positions_dict[filt][feature]['x_low'] + x_high = feature_plot_positions_dict[filt][feature]['x_high'] + y_low = feature_plot_positions_dict[filt][feature]['y_low'] + y_high = feature_plot_positions_dict[filt][feature]['y_high'] + + a = pylab.axes([x_low, y_low, x_high-x_low, y_high-y_low], \ + axisbg ='y' ) + pylab.setp(a , xticks =[] , yticks =[]) + + if feature == 'FLUX vs TIME': + y_data = signal_obj.properties['data'][filt]['input']\ + ['flux_data'] + x_data = signal_obj.properties['data'][filt]['input']\ + ['time_data'] + if ((len(x_data) > 0) and (len(y_data) > 0)): + pylab.plot(x_data, y_data, 'bo', markersize=2) + else: + pylab.plot(numpy.array([]), 'bo', markersize=2) + #print 'NO DATA:', filt, feature,len(x_data),len(y_data) + else: + # 20080123: For some reason this suddenly FAILS: + # it is as if (with SDSS) I was expecting an array + # of arrays: + #y_data = signal_obj.properties['data'][filt]['inter']\ + # [feature][0] + #NOTE: properties...['inter'][feature] might be a single + # array of data, or a 2-elem "list" of two arrays: [1]=t/fq + y_data = signal_obj.properties['data'][filt]['inter'][feature] + try: + if (type(y_data[0]) == type('')): + pylab.plot(numpy.array([]), 'bo', markersize=2) + #print 'NO DATA:', filt, feature, y_data + elif (type(y_data[0]) == type(numpy.array([]))): + # Its a 2-elem list, 1st elem is data array, 2nd: t/fq + pylab.plot(y_data[0], 'bo', markersize=2) + elif (type(y_data) == type(numpy.array([]))): + pylab.plot(y_data, 'bo', markersize=2) + except: + pylab.plot(numpy.array([]), 'bo', markersize=2) + #print 'NO DATA:', filt, feature, y_data + + if os.path.exists(ps_fpath): + os.system('rm ' + ps_fpath) + pylab.savefig(ps_fpath) + #os.system('gv ' + ps_fpath + ' &') + #pylab.show() + pylab.hold(False) + + + def write_multi_filter_ps_files(self, ps_fpath='/tmp/blah.ps'): + for signal_obj in self.signals_list: + # TODO: automatically generate PS filenames? + self.generate_multi_filter_plot(signal_obj, ps_fpath=ps_fpath) + + +class AddNewFeatures: + """ Add some new features to the feat_lookup TABLE without + re-generating, re-indexing this table (rerunning testsuite.py). + """ + def initialize_mysql_connection(self, rdb_host_ip='', rdb_user='', \ + rdb_name='', rdb_port=3306, \ + feat_lookup_tablename='',\ + feat_values_tablename='', \ + db=None): + """ Create connection to feature mysql server. + + NOTE: this is taken from: Feature_database class + + """ + import MySQLdb + if db is None: + self.db = MySQLdb.connect(host=rdb_host_ip, user=rdb_user, \ + db=rdb_name, port=rdb_port, compress=1) + else: + self.db = db + self.cursor = self.db.cursor() + + + def add_new_features_to_featlookup_table(self, new_feat_names=[]): + """ Add some new features to the feat_lookup TABLE without + re-generating, re-indexing this table (rerunning testsuite.py). + """ + select_str = "SELECT max(feat_id), min(filter_id), max(filter_id) from feat_lookup" + + self.cursor.execute(select_str) + results = self.cursor.fetchall() + max_feat_id = results[0][0] + min_filt_id = results[0][1] + max_filt_id = results[0][2] + + insert_list = ["INSERT INTO feat_lookup (feat_id, filter_id, feat_name, doc_str, is_internal) VALUES "] + i_feat = max_feat_id + 1 + for feat_name in new_feat_names: + for filt_id in range(min_filt_id, max_filt_id+1): + insert_list.append('(%d, %d, "%s", "%s", 0), ' % ( \ + i_feat, filt_id, feat_name, feat_name)) + i_feat += 1 + self.cursor.execute(''.join(insert_list)[:-2]) + diff --git a/mltsp/TCP/Software/ingest_tools/generate_science_features.py b/mltsp/TCP/Software/ingest_tools/generate_science_features.py new file mode 100644 index 00000000..2b9b1c20 --- /dev/null +++ b/mltsp/TCP/Software/ingest_tools/generate_science_features.py @@ -0,0 +1,282 @@ +""" +This is to be called by flask script using subprocess.Popen() + +###Calling Flask code should use syntax like: +import subprocess +p = subprocess.Popen(home_str+"/Dropbox/work_etc/mltp/TCP/Software/ingest_tools/generate_science_features.py http://lyra.berkeley.edu:5123/get_lc_data/?filename=dotastro_215153.dat&sep=,", shell=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, close_fds=True) +sts = os.waitpid(p.pid, 0) +script_output = p.stdout.readlines() + +### This script can be tested using: +home_str+/Dropbox/work_etc/mltp/TCP/Software/ingest_tools/generate_science_features.py http://lyra.berkeley.edu:5123/get_lc_data/?filename=dotastro_215153.dat&sep=, + +""" +from __future__ import print_function + + +def currently_running_in_docker_container(): + import subprocess + import os + if not os.path.exists("/proc/1/cgroup"): + return False + proc = subprocess.Popen(["cat", "/proc/1/cgroup"],stdout=subprocess.PIPE) + output = proc.stdout.read() + if "/docker/" in str(output): + in_docker_container=True + else: + in_docker_container=False + return in_docker_container + + +import sys, os +import urllib +import io + +from ..feature_extract.Code import * +from ..feature_extract.Code import db_importer +from ..feature_extract.MLData import arffify + + +head_str = """ + + + + + + 6930531 + + Best positional information of the source + + + 323.47114731 + -0.79916734036 + + + 0.000277777777778 + 0.000277777777778 + + + + + + MJD + 0.0 + UTC + TOPOCENTER + + + + + + + + + +""" + +tail_str = """ + +
+ + +""" + + +def generate_feature_xml_using_raw_xml(raw_xml_str): + """ Generate an xml string which has features in it. + ####### This part was taken from file: test_feature_algorithms.py: + """ + tmp_stdout = sys.stdout + sys.stdout = open(os.devnull, 'w') + signals_list = [] + gen = generators_importers.from_xml(signals_list) + gen.generate(xml_handle=raw_xml_str) + gen.sig.add_features_to_xml_string(gen.signals_list) + + fp_out = io.StringIO() + gen.sig.write_xml(out_xml_fpath=fp_out) + xml_str = fp_out.getvalue() + sys.stdout.close() + sys.stdout = tmp_stdout + return xml_str + + +def generate_arff_using_raw_xml(xml_str): + """ This generates an arff, which contains features + """ + master_list = [] + master_features_dict = {} + all_class_list = [] + master_classes_dict = {} + + new_srcid = 1 + include_arff_header = True + + ### Generate the features: + tmp_stdout = sys.stdout + sys.stdout = open(os.devnull, 'w') + signals_list = [] + gen = generators_importers.from_xml(signals_list) + gen.generate(xml_handle=xml_str) + gen.sig.add_features_to_xml_string(signals_list) + gen.sig.x_sdict['src_id'] = new_srcid + dbi_src = db_importer.Source(make_dict_if_given_xml=False) + dbi_src.source_dict_to_xml(gen.sig.x_sdict) + sys.stdout.close() + sys.stdout = tmp_stdout + + xml_fpath = dbi_src.xml_string + + a = arffify.Maker(search=[], skip_class=False, local_xmls=True, convert_class_abrvs_to_names=False, flag_retrieve_class_abrvs_from_TUTOR=False, dorun=False) + out_dict = a.generate_arff_line_for_vosourcexml(num=new_srcid, xml_fpath=xml_fpath) + + master_list.append(out_dict) + all_class_list.append(out_dict['class']) + master_classes_dict[out_dict['class']] = 0 + for feat_tup in out_dict['features']: + master_features_dict[feat_tup] = 0 # just make sure there is this key in the dict. 0 is filler + + + master_features = master_features_dict.keys() + master_classes = master_classes_dict.keys() + a = arffify.Maker(search=[], skip_class=True, local_xmls=True, + convert_class_abrvs_to_names=False, + flag_retrieve_class_abrvs_from_TUTOR=False, + dorun=False, add_srcid_to_arff=True) + a.master_features = master_features + a.all_class_list = all_class_list + a.master_classes = master_classes + a.master_list = master_list + + + fp_out = io.StringIO() + a.write_arff(outfile=fp_out, \ + remove_sparse_classes=True, \ + n_sources_needed_for_class_inclusion=1, + include_header=include_arff_header, + use_str_srcid=True)#, classes_arff_str='', remove_sparse_classes=False) + arff_str = fp_out.getvalue() + return arff_str + + + + +def arff_to_dict(arff_str): + out_dict = {} + attributes_list = [] + all_lines = arff_str.split('\n') + line_num=0 + for line in all_lines: + if "@ATTRIBUTE" in line and len(line.split())==3: + attr_name,type_name = line.split()[1:] + attributes_list.append(attr_name) + elif "@ATTRIBUTE" in line and "class" in line: + attributes_list.append("class") + if "@data" in line: + all_vals = all_lines[line_num+1].split(',') + if len(all_vals) != len(attributes_list): + print("ERROR: len(all_vals) != len(attributes_list) !!!!") + print("len(all_vals) =", len(all_vals), " and len(attributes_list) =", len(attributes_list)) + print("attributes_list =", attributes_list) + return out_dict + for i in range(len(all_vals)): + try: + out_dict[attributes_list[i]] = float(all_vals[i]) + except ValueError: + out_dict[attributes_list[i]] = str(all_vals[i]) + + + line_num += 1 + return out_dict + + +def generate(timeseries_url="",path_to_csv=False,ts_data=None): + """ Main function + """ + + t_list = [] + m_list = [] + merr_list = [] + + if path_to_csv: # read csv from local machine: + try: + with open(path_to_csv) as f: + for line in f: + if line.strip() != "": + if len(line.split(",")) >= 3: + t,m,merr = line.strip().split(',')[:3] + t_list.append(float(t)) + m_list.append(float(m)) + merr_list.append(float(merr)) + elif len(line.split(",")) == 2: + t,m = line.strip().split(',') + t_list.append(float(t)) + m_list.append(float(m)) + merr_list.append(1.0) + + except Exception as theError: + print("generate_science_features::generate():", theError, "... Returning {}...") + return {} + elif timeseries_url != "": # a url is provided to return the ts data + + if timeseries_url not in ["","5125"]: + print(timeseries_url) + else: + if len(sys.argv) < 2: + print("lcs_classif.py - len(sys.argv) < 2. Returning...") + return {} + print("lcs_classif.py - sys.argv[1] =", sys.argv[1]) + timeseries_url = sys.argv[1] + + + try: + f = urllib.urlopen(timeseries_url) + ts_str = f.read() + f.close() + ts_list = eval(ts_str) + for tup in ts_list: + t_list.append(float(tup[0])) + m_list.append(float(tup[1])) + merr_list.append(float(tup[2])) + except Exception as theError: + print("generate_science_features::generate():", theError, "... Returning {}...") + return {} + elif ts_data is not None and isinstance(ts_data, list): + t_list, m_list, merr_list = zip(*ts_data) + t_list=list(t_list) + m_list=list(m_list) + merr_list=list(merr_list) + if len(t_list) == 0: + print("t_list = [] !!!!!!!!!!!\nReturning {}...") + return {} + #to see what's been read in: + #print zip(t_list,m_list,merr_list) + + data_str_list = [] + for i, t in enumerate(t_list): + data_str = ' %lf%lf%lf' % \ + (i, t, m_list[i], merr_list[i]) + data_str_list.append(data_str) + all_data_str = '\n'.join(data_str_list) + out_xml = head_str + all_data_str + tail_str + ### This generates a xml which contains features: + #feat_xml_str = generate_feature_xml_using_raw_xml(out_xml) + #print feat_xml_str + #print type(feat_xml_str) + ### This generates an arff, which contains features: + test_arff_str = generate_arff_using_raw_xml(out_xml) + + + #print test_arff_str + #print type(test_arff_str) + + out_dict = arff_to_dict(test_arff_str) + + return out_dict + + +if __name__ == '__main__': + + outdict = generate() + + print(outdict) diff --git a/mltsp/TCP/__init__.py b/mltsp/TCP/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/mltsp/TCP/__init__.py @@ -0,0 +1 @@ + diff --git a/mltsp/TCP/setup.py b/mltsp/TCP/setup.py new file mode 100644 index 00000000..591221a6 --- /dev/null +++ b/mltsp/TCP/setup.py @@ -0,0 +1,13 @@ +def configuration(parent_package='', top_path=None): + from numpy.distutils.misc_util import Configuration + + config = Configuration('TCP', parent_package, top_path) + config.add_subpackage('Software.feature_extract.Code.extractors.common_functions') + config.add_data_dir('tests/data') + return config + +if __name__ == "__main__": + from numpy.distutils.core import setup + + config = configuration(top_path='').todict() + setup(**config) diff --git a/mltsp/TCP/tests/data/asas_training_subset.tar.gz b/mltsp/TCP/tests/data/asas_training_subset.tar.gz new file mode 100644 index 00000000..6c9b80d8 Binary files /dev/null and b/mltsp/TCP/tests/data/asas_training_subset.tar.gz differ diff --git a/mltsp/TCP/tests/data/asas_training_subset_classes.dat b/mltsp/TCP/tests/data/asas_training_subset_classes.dat new file mode 100644 index 00000000..dfcdcf5f --- /dev/null +++ b/mltsp/TCP/tests/data/asas_training_subset_classes.dat @@ -0,0 +1,11 @@ +filename,class +217801,Mira +219538,Herbig_AEBE +223592,Beta_Lyrae +224635,Classical_Cepheid +232798,Mira +235913,Classical_Cepheid +243412,W_Ursae_Maj +245486,Delta_Scuti +247327,Mira +257141,W_Ursae_Maj diff --git a/mltsp/TCP/tests/data/expected_features.csv b/mltsp/TCP/tests/data/expected_features.csv new file mode 100644 index 00000000..b933ca10 --- /dev/null +++ b/mltsp/TCP/tests/data/expected_features.csv @@ -0,0 +1,4 @@ +amplitude,percent_beyond_1_std,flux_percentile_ratio_mid20,flux_percentile_ratio_mid35,flux_percentile_ratio_mid50,flux_percentile_ratio_mid65,flux_percentile_ratio_mid80,fold2P_slope_10percentile,fold2P_slope_90percentile,freq1_amplitude1,freq1_amplitude2,freq1_amplitude3,freq1_amplitude4,freq1_freq,freq1_rel_phase2,freq1_rel_phase3,freq1_rel_phase4,freq1_lambda,freq2_amplitude1,freq2_amplitude2,freq2_amplitude3,freq2_amplitude4,freq2_freq,freq2_rel_phase2,freq2_rel_phase3,freq2_rel_phase4,freq3_amplitude1,freq3_amplitude2,freq3_amplitude3,freq3_amplitude4,freq3_freq,freq3_rel_phase2,freq3_rel_phase3,freq3_rel_phase4,freq_amplituderatio_21,freq_amplituderatio_31,freq_frequency_ratio_21,freq_frequency_ratio_31,freq_model_max_delta_mags,freq_model_min_delta_mags,freq_model_phi1_phi2,freq_n_alias,freq1_signif,freq_signif_ratio_21,freq_signif_ratio_31,freq_varrat,freq_y_offset,linear_trend,max_slope,median_absolute_deviation,percent_close_to_median,medperc90_2p_p,p2p_scatter_2praw,p2p_scatter_over_mad,p2p_scatter_pfold_over_mad,p2p_ssqr_diff_over_var,percent_amplitude,percent_difference_flux_percentile,qso_log_chi2_qsonu,qso_log_chi2nuNULL_chi2nu,scatter_res_raw,skew,std,stetson_j,stetson_k 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b/mltsp/TCP/tests/test_feature_generation.py new file mode 100644 index 00000000..882ad165 --- /dev/null +++ b/mltsp/TCP/tests/test_feature_generation.py @@ -0,0 +1,753 @@ +from mltsp import featurize +from mltsp import cfg +from mltsp.TCP.Software.feature_extract.Code import extractors + +import numpy as np +import numpy.testing as npt + +import time +import os +import shutil +import glob + + +## TODO just a list for reference, will eventually remove +#untested_extractors = [ +# 'freq_model_phi1_phi2_extractor', # TODO wrong? +# 'freq_n_alias_extractor', # TODO what is this? +# ] +# +# +## These values are chosen because they lie exactly on the grid of frequencies +## searched by the Lomb Scargle optimization procedure +#WAVE_FREQS = np.array([5.3, 3.3, 2.1]) +## This value is hard-coded in Lomb Scargle algorithm algorithm +#NUM_HARMONICS = 4 +# +# +#def setup(): +# print("Copying data files") +# # copy data files to proper directory: +# shutil.copy(os.path.join(os.path.dirname(__file__), +# "data/asas_training_subset_classes.dat"), +# os.path.join(cfg.UPLOAD_FOLDER)) +# +# shutil.copy(os.path.join(os.path.dirname(__file__), +# "data/asas_training_subset.tar.gz"), +# os.path.join(cfg.UPLOAD_FOLDER)) +# +# +## Random noise generated at irregularly-sampled times +#def irregular_random(seed=0, size=50): +# state = np.random.RandomState(seed) +# times = np.sort(state.uniform(0, 10, size)) +# values = state.normal(1, 1, size) +# errors = state.exponential(0.1, size) +# return times, values, errors +# +# +## Periodic test data sampled at regular intervals: superposition +## of multiple sine waves, each with multiple harmonics +#def regular_periodic(freqs, amplitudes, phase, size=501): +# times = np.linspace(0, 2, size) +# values = np.zeros(size) +# for (i,j), amplitude in np.ndenumerate(amplitudes): +# values += amplitude * np.sin(2*np.pi*times*freqs[i]*(j+1)+phase) +# errors = 1e-4*np.ones(size) +# return times, values, errors +# +# +## Periodic test data sampled at randomly spaced intervals: superposition +## of multiple sine waves, each with multiple harmonics +#def irregular_periodic(freqs, amplitudes, phase, seed=0, size=501): +# state = np.random.RandomState(seed) +# times = np.sort(state.uniform(0, 2, size)) +# values = np.zeros(size) +# for i in range(len(freqs)): +# for j in range(NUM_HARMONICS): +# values += amplitudes[i,j] * np.sin(2*np.pi*times*freqs[i]*(j+1)+phase) +# errors = state.exponential(1e-2, size) +# return times, values, errors +# +# +#def initialize_extractor_from_data(extractor, times, values, errors, **kwargs): +# data_dict = {'time_data': times, 'flux_data': values, 'rms_data': errors, +# 'time_data_unit': None, 'flux_data_unit': 'mag', +# } +# extractor.extr({'data': {'v': {'features': [], 'inter': [], +# 'input': data_dict}}}, band='v') +# +# +## TODO test for few data points +#def test_amplitude_extractor(): +# times, values, errors = irregular_random() +# e = extractors.amplitude_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), +# (max(e.flux_data)-min(e.flux_data))/2.) +# +# e = extractors.percent_amplitude_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# max_scaled = 10**(-0.4*max(e.flux_data)) +# min_scaled = 10**(-0.4*min(e.flux_data)) +# med_scaled = 10**(-0.4*np.median(e.flux_data)) +# peak_from_median = max(abs((max_scaled - med_scaled)/med_scaled), +# abs((min_scaled - med_scaled))/med_scaled) +# npt.assert_allclose(e.extract(), peak_from_median) +# +# e = extractors.percent_difference_flux_percentile_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# band_offset = 13.72 +# w_m2 = 10**(-0.4*(e.flux_data+band_offset)-3) # 1 erg/s/cm^2 = 10^-3 w/m^2 +# npt.assert_allclose(e.extract(), np.diff( +# np.percentile(w_m2, [5, 95])) / np.median(w_m2)) +# +# e = extractors.flux_percentile_ratio_mid20_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), +# np.diff(np.percentile(w_m2, [40, 60])) / +# np.diff(np.percentile(w_m2, [5, 95]))) +# +# e = extractors.flux_percentile_ratio_mid35_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), +# np.diff(np.percentile(w_m2, [32.5, 67.5])) / +# np.diff(np.percentile(w_m2, [5, 95]))) +# +# e = extractors.flux_percentile_ratio_mid50_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), +# np.diff(np.percentile(w_m2, [25, 75])) / +# np.diff(np.percentile(w_m2, [5, 95]))) +# +# e = extractors.flux_percentile_ratio_mid65_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), +# np.diff(np.percentile(w_m2, [17.5, 82.5])) / +# np.diff(np.percentile(w_m2, [5, 95]))) +# +# e = extractors.flux_percentile_ratio_mid80_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), +# np.diff(np.percentile(w_m2, [10, 90])) / +# np.diff(np.percentile(w_m2, [5, 95]))) +# +# +#def test_ar_is_extractor(): +## Uses a long, slow exponential decay +## Even after removing LS fit, an AR model still fits well +# times = np.linspace(0, 500, 201) +# theta = 0.95 +# sigma = 0.0 +# values = theta ** (times/250.) + sigma*np.random.randn(len(times)) +# errors = 1e-4*np.ones(len(times)) +# +# e = extractors.ar_is_theta_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), theta, atol=3e-2) +# +# e = extractors.ar_is_sigma_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), sigma, atol=1e-5) +# +## Hard-coded values from reference data set +# times, values, errors = irregular_random() +# e = extractors.ar_is_theta_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), 5.9608609865491405e-06, rtol=1e-3) +# e = extractors.ar_is_sigma_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), 1.6427095072108497, rtol=1e-3) +# +# +## TODO is there a way to make this test more precise? +#def test_delta_phase_2minima_extractor(): +# times1, values1, errors1 = regular_periodic(np.array([1.0]), np.array(1.0,ndmin=2), 0.0) +# times2, values2, errors2 = regular_periodic(np.array([3.0]), np.array(0.5,ndmin=2), np.pi/8) +# e = extractors.delta_phase_2minima_extractor() +# initialize_extractor_from_data(e, times1, values1+values2, errors1+errors2) +## Closed-form distance between local minima +# npt.assert_allclose(e.extract(), 1 - 0.163143 - 0.351671, atol=5e-2) +# +# +#def test_gskew_extractor(): +# times, values, errors = irregular_random() +# e = extractors.gskew_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# i_3percent = int(round(0.03*len(e.flux_data))) +## TODO use percentile +# npt.assert_allclose(e.extract(), 2*np.median(e.flux_data) - +# np.median(sorted(e.flux_data)[-i_3percent:]) - +# np.median(sorted(e.flux_data)[0:i_3percent])) +# +# +#def test_linear_extractor(): +# from numpy.polynomial.polynomial import polyfit as pfit +# times, values, errors = irregular_random() +# e = extractors.linear_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# error_weights = 1. / (e.rms_data)**2 / ((1./e.rms_data)**2).sum() +# wls_coefs = pfit(e.time_data, e.flux_data, 1, w=error_weights**0.5) +# npt.assert_allclose(e.extract(), np.flipud(wls_coefs), rtol=1e-4) +# +# +## TODO the smoothed model here seems insanely oversmoothed +## In general all these extractors are a complete mess, punting for now +##""" +#def test_lcmodel_extractor(): +# frequencies = np.hstack((1., np.zeros(len(WAVE_FREQS)-1))) +# amplitudes = np.zeros((len(frequencies),4)) +# amplitudes[0,:] = [2,0,0,0] +# phase = 0.0 +# times, values, errors = regular_periodic(frequencies, amplitudes, phase) +# +# e = extractors.lcmodel_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# lc_feats = e.extract() +# +## Median is zero for this data, so crossing median = changing sign +# incr_median_crosses = sum((np.abs(np.diff(np.sign(values))) > 1) & +# (np.diff(values) > 0)) +# npt.assert_allclose(lc_feats['median_n_per_day'], (incr_median_crosses+1) / +# (max(times)-min(times))) +##""" +# +# +## TODO what about very short time scale? could cause problems +#def test_lomb_scargle_regular_single_freq(): +# frequencies = np.hstack((WAVE_FREQS[0], np.zeros(len(WAVE_FREQS)-1))) +# amplitudes = np.zeros((len(frequencies),4)) +# amplitudes[0,:] = [8,4,2,1] +# phase = 0.1 +# times, values, errors = regular_periodic(frequencies, amplitudes, phase) +# +## Only test the first (true) frequency; the rest correspond to noise +# e_name = 'freq1_harmonics_freq_0_extractor' +# e = getattr(extractors, e_name)() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(frequencies[0], e.extract()) +# +## Hard-coded value from previous solution +# e_name = 'freq1_lambda_extractor' +# e = getattr(extractors, e_name)() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(0.001996007984, e.extract(), rtol=1e-7) +# +# for (i,j), amplitude in np.ndenumerate(amplitudes): +# e_name = 'freq{}_harmonics_amplitude_{}_extractor'.format(i+1,j) +# e = getattr(extractors, e_name)() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(amplitude, e.extract(), rtol=1e-2, atol=1e-2) +# +## Only test the first (true) frequency; the rest correspond to noise +# for j in range(NUM_HARMONICS): +# e_name = 'freq1_harmonics_rel_phase_{}_extractor'.format(j) +# e = getattr(extractors, e_name)() +# initialize_extractor_from_data(e, times, values, errors) +## TODO why is this what 'relative phase' means? +# npt.assert_allclose(phase*j*(-1**j), e.extract(), rtol=1e-2, atol=1e-2) +# +## Frequency ratio not relevant since there is only; only test amplitude/signif +# for i in [1,2]: +# e_name = 'freq_amplitude_ratio_{}1_extractor'.format(i+1) +# e = getattr(extractors, e_name)() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(0., e.extract(), atol=1e-3) +# +## TODO make significance test more precise +# e = extractors.freq_signif_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_array_less(10., e.extract()) +# """ +# e_name = 'freq_signif_ratio_{}1_extractor'.format(i) +# e = getattr(extractors, e_name)() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(0., e.extract(), atol=1e-3) +# """ +## Only one frequency, so this should explain basically all the variance +# e = extractors.freq_varrat_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(0., e.extract(), atol=5e-3) +# +## Exactly periodic, so the same minima/maxima should reoccur +# e = extractors.freq_model_max_delta_mags_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(0., e.extract(), atol=1e-6) +# e = extractors.freq_model_min_delta_mags_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(0., e.extract(), atol=1e-6) +# +## Linear trend should be zero since the signal is exactly sinusoidal +# e = extractors.linear_trend_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(0., e.extract(), atol=1e-4) +# +# folded_times = times % 1./(frequencies[0]/2.) +# sort_indices = np.argsort(folded_times) +# folded_times = folded_times[sort_indices] +# folded_values = values[sort_indices] +# +## Residuals from doubling period should be much higher +# e = extractors.medperc90_2p_p_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_array_less(10., e.extract()) +# +## Slopes should be the same for {un,}folded data; use unfolded for stability +# slopes = np.diff(e.flux_data) / np.diff(e.time_data) +# e = extractors.fold2P_slope_10percentile_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(np.percentile(slopes,10), e.extract(), rtol=1e-2) +# e = extractors.fold2P_slope_90percentile_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(np.percentile(slopes,90), e.extract(), rtol=1e-2) +# +# +#def test_lomb_scargle_irregular_single_freq(): +# frequencies = np.hstack((WAVE_FREQS[0], np.zeros(len(WAVE_FREQS)-1))) +# amplitudes = np.zeros((len(WAVE_FREQS),4)) +# amplitudes[0,:] = [8,4,2,1] +# phase = 0.1 +# times, values, errors = irregular_periodic(frequencies, amplitudes, phase) +# +## Only test the first (true) frequency; the rest correspond to noise +# e_name = 'freq1_harmonics_freq_0_extractor' +# e = getattr(extractors, e_name)() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(frequencies[0], e.extract(), rtol=1e-2) +# +## Only test first frequency here; noise gives non-zero amplitudes for residuals +# for j in range(NUM_HARMONICS): +# e_name = 'freq1_harmonics_amplitude_{}_extractor'.format(j) +# e = getattr(extractors, e_name)() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(amplitudes[0,j], e.extract(), rtol=5e-2, atol=5e-2) +# +# e_name = 'freq1_harmonics_rel_phase_{}_extractor'.format(j) +# e = getattr(extractors, e_name)() +# initialize_extractor_from_data(e, times, values, errors) +## TODO why is this what 'relative phase' means? +# npt.assert_allclose(phase*j*(-1**j), e.extract(), rtol=1e-1, atol=1e-1) +# +## TODO make significance test more precise +# e = extractors.freq_signif_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_array_less(10., e.extract()) +# """ +# e_name = 'freq_signif_ratio_{}1_extractor'.format(i) +# e = getattr(extractors, e_name)() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(0., e.extract(), atol=1e-3) +# """ +## Only one frequency, so this should explain basically all the variance +# e = extractors.freq_varrat_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(0., e.extract(), atol=5e-3) +# +# e = extractors.freq_y_offset_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(-np.mean(values), e.extract(), rtol=5e-2) +## TODO this extractor seems wrong to me; fix then test? +# """ +# e = extractors.freq_model_phi1_phi2_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(-np.mean(values), e.extract(), rtol=5e-2) +# """ +# +# +## Tests for features derived from period-folded data +#def test_lomb_scargle_period_folding(): +# frequencies = np.hstack((WAVE_FREQS[0], np.zeros(len(WAVE_FREQS)-1))) +# amplitudes = np.zeros((len(WAVE_FREQS),4)) +# amplitudes[0,:] = [8,4,2,1] +# phase = 0.1 +# times, values, errors = irregular_periodic(frequencies, amplitudes, phase) +# +## Folding is numerically unstable so we need to use the exact fitted frequency +# e = extractors.freq1_harmonics_freq_0_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# freq_est = e.extract() +## Fold by 1*period +# fold1ed_times = (times-times[0]) % (1./freq_est) +# sort_indices = np.argsort(fold1ed_times) +# fold1ed_times = fold1ed_times[sort_indices] +# fold1ed_values = values[sort_indices] +## Fold by 2*period +# fold2ed_times = (times-times[0]) % (2./freq_est) +# sort_indices = np.argsort(fold2ed_times) +# fold2ed_times = fold2ed_times[sort_indices] +# fold2ed_values = values[sort_indices] +# +# e = extractors.p2p_scatter_2praw_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(np.sum(np.diff(fold2ed_values)**2) / +# np.sum(np.diff(values)**2), e.extract()) +# +# e = extractors.p2p_ssqr_diff_over_var_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(np.sum(np.diff(values)**2) / +# ((len(values) - 1) * np.var(values)), e.extract()) +# +# e = extractors.p2p_scatter_over_mad_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(np.median(np.abs(np.diff(values))) / +# np.median(np.abs(values-np.median(values))), +# e.extract()) +# +# e = extractors.p2p_scatter_pfold_over_mad_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(np.median(np.abs(np.diff(fold1ed_values))) / +# np.median(np.abs(values-np.median(values))), +# e.extract()) +# +# +#def test_lomb_scargle_regular_multi_freq(): +# frequencies = WAVE_FREQS +# amplitudes = np.zeros((len(frequencies),4)) +# amplitudes[:,0] = [4,2,1] +# phase = 0.1 +# times, values, errors = regular_periodic(frequencies, amplitudes, phase) +# +# for i, frequency in enumerate(frequencies): +# e_name = 'freq{}_harmonics_freq_0_extractor'.format(i+1) +# e = getattr(extractors, e_name)() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(frequency, e.extract()) +# +# for (i,j), amplitude in np.ndenumerate(amplitudes): +# e_name = 'freq{}_harmonics_amplitude_{}_extractor'.format(i+1,j) +# e = getattr(extractors, e_name)() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(amplitude, e.extract(), rtol=5e-2, atol=5e-2) +# +## Relative phase is zero for first harmonic +# for i in range(len(frequencies)): +# e_name = 'freq{}_harmonics_rel_phase_0_extractor'.format(i+1) +# e = getattr(extractors, e_name)() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(0., e.extract(), rtol=1e-2, atol=1e-2) +# +# for i in [1,2]: +# e_name = 'freq_amplitude_ratio_{}1_extractor'.format(i+1) +# e = getattr(extractors, e_name)() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(amplitudes[i,0]/amplitudes[0,0], e.extract(), atol=2e-2) +# +# e_name = 'freq_frequency_ratio_{}1_extractor'.format(i+1) +# e = getattr(extractors, e_name)() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(frequencies[i]/frequencies[0], e.extract(), atol=1e-3) +# +## TODO make significance test more precise +# e = extractors.freq_signif_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_array_less(10., e.extract()) +# """ +# e_name = 'freq_signif_ratio_{}1_extractor'.format(i) +# e = getattr(extractors, e_name)() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(0., e.extract(), atol=1e-3) +# """ +# +# +#def test_lomb_scargle_irregular_multi_freq(): +# frequencies = WAVE_FREQS +# amplitudes = np.zeros((len(frequencies),4)) +# amplitudes[:,0] = [4,2,1] +# phase = 0.1 +# times, values, errors = irregular_periodic(frequencies, amplitudes, phase) +# +# for i, frequency in enumerate(frequencies): +# e_name = 'freq{}_harmonics_freq_0_extractor'.format(i+1) +# e = getattr(extractors, e_name)() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(frequency, e.extract(), rtol=1e-2) +# +# for (i,j), amplitude in np.ndenumerate(amplitudes): +# e_name = 'freq{}_harmonics_amplitude_{}_extractor'.format(i+1,j) +# e = getattr(extractors, e_name)() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(amplitude, e.extract(), rtol=1e-1, atol=1e-1) +# +## Relative phase is zero for first harmonic +# for i in range(len(frequencies)): +# e_name = 'freq{}_harmonics_rel_phase_0_extractor'.format(i+1) +# e = getattr(extractors, e_name)() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(0., e.extract(), rtol=1e-2, atol=1e-2) +# +# for i in [1,2]: +# e_name = 'freq_amplitude_ratio_{}1_extractor'.format(i+1) +# e = getattr(extractors, e_name)() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(amplitudes[i,0]/amplitudes[0,0], e.extract(), atol=2e-2) +# +# e_name = 'freq_frequency_ratio_{}1_extractor'.format(i+1) +# e = getattr(extractors, e_name)() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(frequencies[i]/frequencies[0], e.extract(), atol=5e-2) +# +## TODO make significance test more precise +# e = extractors.freq_signif_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_array_less(10., e.extract()) +#""" +# e_name = 'freq_signif_ratio_{}1_extractor'.format(i) +# e = getattr(extractors, e_name)() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(0., e.extract(), atol=1e-3) +#""" +# +#def test_max_extractor(): +# times, values, errors = irregular_random() +# e = extractors.max_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_equal(e.extract(), max(e.flux_data)) +# +# +## TODO this returns the index of the biggest slope...might be wrong +#def test_max_slope_extractor(): +# times, values, errors = irregular_random() +# e = extractors.max_slope_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# slopes = np.diff(e.flux_data) / np.diff(e.time_data) +# npt.assert_allclose(e.extract(), np.argmax(np.abs(slopes))) +# +# +#def test_median_absolute_deviation_extractor(): +# times, values, errors = irregular_random() +# e = extractors.median_absolute_deviation_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), np.median( +# np.abs(e.flux_data - np.median(e.flux_data)))) +# +# +#def test_median_buffer_range_percentage_extractor(): +# times, values, errors = irregular_random() +# e = extractors.median_buffer_range_percentage_extractor() +# initialize_extractor_from_data(e, times, values, errors) +## TODO feature is currently broken; this test is for the broken version, +## should replace with commented version once fixed +# amplitude = (np.abs(max(e.flux_data)) - np.abs(min(e.flux_data))) / 2. +## amplitude = (max(e.flux_data) - min(e.flux_data)) / 2. +# within_buffer = np.abs(e.flux_data - np.median(e.flux_data)) < 0.2*amplitude +# npt.assert_allclose(e.extract(), np.mean(within_buffer)) +# +# +#def test_median_extractor(): +# times, values, errors = irregular_random() +# e = extractors.median_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), np.median(e.flux_data)) +# +# +#def test_min_extractor(): +# times, values, errors = irregular_random() +# e = extractors.min_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_equal(e.extract(), min(e.flux_data)) +# +# +#def test_n_points_extractor(): +# times, values, errors = irregular_random() +# e = extractors.n_points_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_equal(e.extract(), len(e.flux_data)) +# +# +## TODO deprecated? +#def test_old_dc_extractor(): +# times, values, errors = irregular_random() +# e = extractors.old_dc_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), np.mean(e.flux_data)) +# +# +#def test_phase_dispersion_extractor(): +## Frequency chosen to lie on the relevant search grid +# frequencies = np.hstack((5.36, np.zeros(len(WAVE_FREQS)-1))) +# amplitudes = np.zeros((len(frequencies),4)) +# amplitudes[0,:] = [1,0,0,0] +# phase = 0.1 +# times, values, errors = regular_periodic(frequencies, amplitudes, phase) +# e = extractors.phase_dispersion_freq0_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), frequencies[0]) +# +# e = extractors.freq1_harmonics_freq_0_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# lomb_freq = e.extract() +# e = extractors.ratio_PDM_LS_freq0_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), frequencies[0]/lomb_freq) +# +# +## Hard-coded values from previous implementation; not sure of examples with a +## closed-form solution +#def test_qso_extractor(): +# times, values, errors = irregular_random() +# e = extractors.qso_log_chi2_qsonu_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), 6.9844064754) +# +# times, values, errors = irregular_random() +# e = extractors.qso_log_chi2nuNULL_chi2nu_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), -0.456526327522) +# +# +#def test_s_extractor(): +# times, values, errors = irregular_random() +# e = extractors.s_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# x_bar_wt = (e.flux_data / (e.rms_data)**2).sum()/((1/e.rms_data)**2).sum() +# resids = e.flux_data-x_bar_wt +# npt.assert_allclose(e.extract(), +# np.sqrt(sum(resids**2)/(len(e.flux_data)-1))) +# +# +## TODO these seem broken/deprecated, remove? +#""" +#def test_sine_fit_extractor(): +# e = extractors.sine_fit_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), ...) +# +# +#def test_sine_leastsq_extractor(): +# e = extractors.sine_leastsq_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), ...) +#""" +# +#def test_scatter_res_raw_extractor(): +# times, values, errors = irregular_random() +# e = extractors.lomb_scargle_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# lomb_dict = e.extract() +# residuals = values - lomb_dict['freq1_model'] +# resid_mad = np.median(np.abs(residuals-np.median(residuals))) +# value_mad = np.median(np.abs(values-np.median(values))) +# e = extractors.scatter_res_raw_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), resid_mad/value_mad, atol=3e-2) +# +# +#def test_skew_extractor(): +# from scipy import stats +# times, values, errors = irregular_random() +# e = extractors.skew_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), stats.skew(e.flux_data)) +# +# +#def test_std_extractor(): +# times, values, errors = irregular_random() +# e = extractors.std_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), np.std(e.flux_data)) +# +# +## These steps are basically just copied from the Stetson code +#def test_stetson_extractor(): +# times, values, errors = irregular_random(size=201) +# e = extractors.stetson_j_extractor() +# initialize_extractor_from_data(e, times, values, errors) +## Stetson mean approximately equal to standard mean for large inputs +# dists = np.sqrt(float(len(values)) / (len(values) - 1.)) * (values - np.mean(values)) / 0.1 +# npt.assert_allclose(e.extract(), +# np.mean(np.sign(dists**2-1)*np.sqrt(np.abs(dists**2-1))), +# rtol=1e-2) +## Stetson_j should be somewhat close to (scaled) variance for normal data +# npt.assert_allclose(e.extract()*0.1, np.var(values), rtol=2e-1) +## Hard-coded original value +# npt.assert_allclose(e.extract(), 7.591347175195703) +# +# e = extractors.stetson_k_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), 1./0.798 * np.mean(np.abs(dists)) / np.sqrt(np.mean(dists**2)), rtol=5e-4) +## Hard-coded original value +# npt.assert_allclose(e.extract(), 1.0087218792719013) +# +# +## TODO this should be a function not an extractor +#def test_watt_per_m2_flux_extractor(): +# times, values, errors = irregular_random() +# e = extractors.watt_per_m2_flux_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# band_const = 13.72 # from constants dict in watt_per_m2_flux_extractor +## 1 erg/s/cm^2 = 10^-3 w/m^2 +# npt.assert_allclose(e.extract(), 10**(-0.4*(e.flux_data+band_const)-3)) +# +# +## TODO more general name? +#def test_weighted_average_extractor(): +# times, values, errors = irregular_random() +# e = extractors.weighted_average_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# weighted_std_err = 1./sum(e.rms_data**2) +# error_weights = 1./(e.rms_data)**2 / weighted_std_err +# weighted_avg = np.average(e.flux_data, weights=error_weights) +# npt.assert_allclose(e.extract(), weighted_avg) +# +# e = extractors.wei_av_uncertainty_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_allclose(e.extract(), weighted_std_err) +# +# e = extractors.dist_from_u_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# dists_from_weighted_avg = e.flux_data - weighted_avg +# npt.assert_allclose(e.extract(), dists_from_weighted_avg) +# +# e = extractors.stdvs_from_u_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# stds_from_weighted_avg = dists_from_weighted_avg / np.sqrt(weighted_std_err) +# npt.assert_allclose(e.extract(), stds_from_weighted_avg) +# +# e = extractors.beyond1std_extractor() +# initialize_extractor_from_data(e, times, values, errors) +# npt.assert_equal(e.extract(), np.mean(stds_from_weighted_avg > 1.)) +# +# +## TODO compare numbers of features, check for completeness +#def test_feature_generation(): +# for f in glob.glob(os.path.join(cfg.FEATURES_FOLDER, '/*.csv')): +# os.remove(f) +# +# tic = time.time() +# this_dir = os.path.join(os.path.dirname(__file__)) +# +# featurize.featurize( +# os.path.join(cfg.UPLOAD_FOLDER, "asas_training_subset_classes.dat"), +# os.path.join(cfg.UPLOAD_FOLDER, "asas_training_subset.tar.gz"), +# featureset_id="testfeatset", is_test=True, +# features_to_use=cfg.features_list_science +# ) +# +# delta = time.time() - tic +# +# def features_from_csv(filename): +# with open(filename) as f: +# feature_names = f.readline().strip().split(",") +# feature_values = np.loadtxt(f, delimiter=',') +# +# return feature_names, feature_values +# +# features_extracted, values_computed = features_from_csv( +# os.path.join(cfg.FEATURES_FOLDER, "testfeatset_features.csv")) +# +# features_expected, values_expected = features_from_csv( +# os.path.join(this_dir, "data/expected_features.csv")) +# +# os.remove(os.path.join(cfg.FEATURES_FOLDER, "testfeatset_features.csv")) +# os.remove(os.path.join(cfg.FEATURES_FOLDER, +# "testfeatset_classes.npy")) +# npt.assert_equal(features_extracted, features_expected) +# npt.assert_array_almost_equal(values_computed, values_expected) +# +# # Ensure this test takes less than a minute to run +# assert delta < 60 +# +# +#if __name__ == "__main__": +# setup() +# test_feature_generation() diff --git a/mltsp/setup.py b/mltsp/setup.py index 70da53f5..7b13fee5 100644 --- a/mltsp/setup.py +++ b/mltsp/setup.py @@ -2,6 +2,7 @@ def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration config = Configuration('mltsp', parent_package, top_path) + config.add_subpackage('TCP') config.add_subpackage('science_features') return config