From 2edcfc0233ef20269baa9822edcb7664220c0779 Mon Sep 17 00:00:00 2001 From: Brett Naul Date: Wed, 30 Sep 2015 16:58:26 -0700 Subject: [PATCH] Revert "Remove mltsp.TCP module" This reverts commit 0120754283cb34b024fc2dea32e3ec0d3e77a8db. For now, restore TCP module to simplify the refactor pull request. Will remove TCP later after the replacement code has been merged. --- .../Algorithms/Debil/Phot/dotastro220107.dat | 441 + mltsp/TCP/Algorithms/Debil/README | 68 + mltsp/TCP/Algorithms/Debil/debil.c | 2691 + .../Algorithms/Debil/dotastro220107.dat.data | 433 + .../Algorithms/Debil/dotastro220107.dat.fit | 500 + mltsp/TCP/Algorithms/Debil/test.in | 1 + mltsp/TCP/Algorithms/Debil/test.out | 1 + .../Algorithms/EclFeatures/013113-7829.1.xml | 773 + mltsp/TCP/Algorithms/EclFeatures/LC_246.dat | 468 + mltsp/TCP/Algorithms/EclFeatures/README | 34 + .../EclFeatures/eclipse_features.py | 423 + mltsp/TCP/Algorithms/EclFeatures/lc0.dat | 468 + mltsp/TCP/Algorithms/EclFeatures/numc_eigs.py | 143 + mltsp/TCP/Algorithms/EclFeatures/polyfit.c | 963 + mltsp/TCP/Algorithms/EclFeatures/polyfit.h | 36 + mltsp/TCP/Algorithms/EclFeatures/selectp.py | 207 + mltsp/TCP/Algorithms/PTF_SN_classifier.py | 349 + .../Algorithms/SpatialClustering/cluster.py | 529 + .../SpatialClustering/obj_dict.pickle | 3507 + .../SpatialClustering/obj_dict_309.pickle | 73822 ++++++++++++++++ mltsp/TCP/Algorithms/__init__.py | 1 + mltsp/TCP/Algorithms/asas_catalog.R | 543 + mltsp/TCP/Algorithms/class_cv.R | 704 + mltsp/TCP/Algorithms/classify_Deb.R | 365 + .../compare_randforest_classifiers.py | 331 + mltsp/TCP/Algorithms/count_class_names.py | 114 + mltsp/TCP/Algorithms/cp_noise_files.py | 142 + .../Algorithms/debosscher_vosourcexml_copy.sh | 1393 + mltsp/TCP/Algorithms/do_qsoy.py | 28 + ...evaluate_eclipsing_classifs_using_fiteb.py | 1480 + ...e_1_ajax_retrieves_json_from_phpmysql.html | 30 + ...e_1_givenpost_mysqlqueries_returnsjson.php | 55 + .../Algorithms/example_parse_vosourcexml.py | 165 + .../av_lombtest_tcp_source(32data).png | Bin 0 -> 32018 bytes mltsp/TCP/Algorithms/fitcurve/bad_fit.png | Bin 0 -> 36861 bytes mltsp/TCP/Algorithms/fitcurve/compare_fit.png | Bin 0 -> 38011 bytes mltsp/TCP/Algorithms/fitcurve/cv_dotastro.txt | 62 + mltsp/TCP/Algorithms/fitcurve/fitted_cv.png | Bin 0 -> 38011 bytes 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+4857.680320 9.598000 0.034000 +4861.576280 10.265000 0.050000 +4863.635470 9.605000 0.060000 +4869.620060 9.571000 0.061000 +4871.674090 9.611000 0.045000 +4877.594980 9.599000 0.056000 +4880.600930 9.663000 0.043000 +4882.666860 9.606000 0.041000 +5148.774020 9.595000 0.057000 +5154.781480 9.631000 0.049000 +5157.735080 9.575000 0.049000 +5160.745250 9.796000 0.057000 +5163.722960 9.888000 0.053000 +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 0000000000000000000000000000000000000000..7cf80140d6563fff8051b615b02d4c91853b1b21 GIT binary patch literal 32018 zcmeFZg;$pC5#hNDGQcDIpEgAl(gT z9^89>-&tq<&L42rVXeJC(dT{MJLaCb=9+8n03`+Kt5{@MXlQ6xAIM0mprK(8O^m&l75wMC?Y#$Tm*B_s(sLC2e%VS!%N7j{M<4kgdX{*m2^tzT+5^eEYL0PB zqi(Jp+ovt-EzyIzH%TOlyEVU;1>)1)w^8=fzP0E@>K0DRSVt3C|4qt`F8SUs-40DH zWtn_cwtF)vX`j{T2J2*=mAkon)YN|+`Z~K5*EP2!vRW~0u@~1RIOo(UR=&GfQ=l91 zLJM{F<9OF|6kQVeY4*rM!#n#aP)4Uc`+1Uj?(Cl=YwlzI=kd2#xM$C-lMzv$Ju}J6 zbm#Bq&D1>qKGgsJzX%V{U)bt58ag_<-Rjur%IzxcYI|1r?G3qL_sVTr+DR*9)yxTT zajkw9R8|V;vXR`GO`MaImL|Z(ZD{nqXzRK}%&Lux`+JmX)1wPMvElIA{vs~kx&vEe 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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 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zGFzkF67;Snd>Idg^vEeE(+0@Hkv*opyR-4eFm&>e23XN{m6Im64h;30c|R65)8?Jo z?2_~QH^NN}!A%TnJc5dVzhe5+5T7+LLEAs?fs=*hkhDK;%|b2f3>7zO;NElY@UziF zrcy6ku&Z~ybYCx!dX1GwcycaE$zw~I0~r+&46nC2|Ni}d9u5VQjLYiJOn8JnP#Kn#7M$-*0KTq|(W40Gwo{M<$?*s+dU_TalA zgdrHRXXsVdhw{V*K+i8I?^Zx{cypdpB8g3*6fjXj3mF_e9WjzugR~=Pe|>ZlDnhqz zPgn*0zm*x<;9wC|#WGIWG7#geV1Ed&A9x#U6e>1CNYXtO)$WA1AWN>uT2*Y(I^JBM zFNKAlZ<~v~3rI%+Oij9=&p0JL2}USUAsqt1e3Y2UpaicfQc8HVM3hi|e0C+C-VMd} zPKIM!DX8{&AlQZ@ru%tML7YLMqpVGc0)2+QqvK0d1t}s_Nbuet^prd}9cO0in-w$U zb>^C4(y5fg2lf7QLClN#jK}HIUp;8T$fZh3Z3{sib*F~uzNs;U14j@jUk*%45~0v> zsQ261Z3O2NGHBiy+N3fuBBNEOy@&~_&0)Z;}aHaRhbjn(WD)d zIrolLVX9u299^=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 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zfgt4s%dV?MMhhMaEp2V1*nf<$E8f29}|x}8s^Ao&#b(rh4YABHqFX8LXCc&xoQ zPv|{yusy>rTUjN0c3r$idf#`5L`K>Ygj;{~Gm4@!#_LEA_YgNpugSB~L-6(T^TkE? zt8!c@6^~-_LS=hDIf;gfY#m8B{rfu$g^11-J%l1`8Q2-7oDoPe5l-prGsTIksiLBS zuTOJRS^Am+$;WdYJ?VWAXT?IlZg8aSQdEVuYi|i9Y|;$Be*IeDI2>GFUJi1U4DPx% zHb_jHBN=IH>;;E~R%T++)%aMtk>T}>3NDvKGTDqPAd!3|@UHmNO0Pt`{Qu#Ht7I-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 0000000000000000000000000000000000000000..17f9d42fa34d514567f0de3f5885f391e2000ace GIT binary patch literal 24757 zcmdqJcRber`#yY;c4(>y4Jjomp_I{5cJ?NTY?YbaUZiA%tn4i_WRz02%Lb_L@K3WY)|cJ`DU 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zqQBn<1qFFsE`r%lDU`#bqdL16P*6;QxM@&O8bZUuC=W#F=@;J4|Jj;S2%@s~_V#w` zH^)y=QJ#guqpmcDks5M?kh^rAN}Y+5oK8Ft-HuO~toG5?ND+GcH*w(x+D^s1Cn$rGg_zVmr_P|svFE>{X?gZG%_#FG7 zpsXW@B{eM_XLpaAH=bl)-aog5*a6C;q`bVmJxC(giW<(8b#+tB`ciU%S`xA5iHg!w z`t0) 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 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/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, 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zprN$k`x&C5q%x4bRT^Bd7jQV*wH#Vas<9hdb+jBS*8tbsazJ(mUB0NufU9^v1YzMH zKc<|Xdc+I4r~+#K3n*dVJD}TF4+yL@^z?FovL9W;gi;$kg?Sl;W**uzegElC4||Hj z``JG)o8wE|4s2yrIj)D(7nPKl!;6Jb+vV<1Sm2p$aAVW z9Fp9g;OEhR$Q17P<>s_6*bY+_ETf^EQv8<>z^0X%k?}x7mp1^J%%4BYnki{%<{;>Y zZL6!W8XLt>_z_oxs2(aZ{OFx=0KfZx7@Yrh2D& literal 0 HcmV?d00001 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 0000000000000000000000000000000000000000..691394ef9800573395851c7c21621f0610640fb2 GIT binary 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(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 0000000000000000000000000000000000000000..b2dba1ea63c6754f308deb5bcb04381a62e9dc30 GIT binary patch literal 28672 zcmeHQYpf*4Rc-@z9$*q25e$)_JXeeFhVk5)+1+G!g%e?$5Iehz@q)m7cq zUDbVO4MIrnyL)zS*Ex00sZ)=xbL!OWV)}#6UT5#0Ty*gBPRIGc4}AIM7Y^1B|IjBN za26@g^T;bg!u9+-MReH{*PnX6hX|-oR1A-vN#kS@pB_CEFN-aY^6Y3E=2;^Ded+1v zk6w&d`H7eEqZRk^BIPbW%~NlgA3YuW{762;X?EngK@{Y!J4tr^;s@+tfj6%OihT9N z<%gXM^VyV)a4>p|z3Y*e-@Nu1T0U4{u)tt}!2*K?1`7-p7%VVY;7w?OJbBRhF|_+@ 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+#__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], + 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zT<)K5$#d|3lgDtcHxJ)Q>ngzalo#P|k|%KIQ-R;5e5!EgQ-eF76z+93;rA%ske+lNo|+&h9hpE3M9$}@vI zp9$Rg%;8?w0`AuZ(b>!WyP+uj)B58cha1|I8UeOYMJ_)=*9sD<%W`BiZJ zICiuSKGOIJyrZNxz$(Q__1oOcH_j31!nhMY-7&1o{o*=$UKjj+c(P~k+_vFQ z$Is+z;M2XrxRc@8>w|aTonr7YJbpv){C43_&)eB2e0%_W{-)q5{r7`k2QTPGy8%9b zTNod0AO74l?>oZBUHIVL!ArVcH;q3y`1x?Zze^A9_wCpK_rHI-m9B5ckJf5l_*`Cx zr~3EWZiG+u?{LlG@i8I(8l4lH=GFiA-h#*a|3zAwcQ9p3tB@Krj`94Gm&;K#z#XM$e= z&z=q5gHPoXc=AFRzX4vA|B-I$oATK%+}PXj>}A0x@Pgi_Yy&)zM>~c29Y2-t0k7+Q z%02oeFd_+jvp9`w$KCrKE;4qiGU_=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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zcgTTEn`aME_r@ON + + +""" + +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 0000000000000000000000000000000000000000..f745fef1fdeda6f2f916de774fc6c19858aec9e9 GIT binary patch literal 46154 zcmeFZWmJ{j+b@cVqJStUBB6u`2uMp?!~z8A25IS(4jT{wX$e8;?r!N2P`X39yTfx$ z_5Y6jVUKafIPW-ne|Z_tqrh70jybRR#dYiXL{bD7hXe-;3kw$``bY)~>!ddP&v526 z{H9(PhZp`jY5f2rcLu(k&**x<&u1+}m94R`@U_tY#}Wk-46(4TV__cMm$Q#p7_w6f zn*3eAy3Ku8+G6w(L6%yqwwZW~RkS|${`(VbB1WybsdDdvl)1&M<2-{66KYmvbAv@Wt&#lU$K?N{;>yGw<=dCVg|EEK{h6-L zOU7#}@q2&d_r&62ntF*X9X0i%{jJ3g^NPdxxvms(e>~E=U#26SB;i0LJT{5x8fD3& zqfaq1N=l#Brdq=NLjL4_rH2@DaVN@x7y&gwY5d~#qImwCF7*JX^q9?j#zh9%5lh9e?$S_FhXE$MeIz=R(55^eimW 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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 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+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

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