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HotSAP_spec_stellarParam.py
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HotSAP_spec_stellarParam.py
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#! /usr/bin/env python
import SRP
import sys
import os
import pyfits
import numpy as np
import pandas as pd
import subprocess
import threading
import shlex
import pickle
import matplotlib.pyplot as plt
from scipy.signal import medfilt
from scipy.signal import wiener
from scipy.signal import butter
from scipy.optimize import curve_fit
import scipy.ndimage.filters as filt
from scipy.interpolate import interp1d
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from scipy.interpolate import interp2d
from scipy.interpolate import bisplrep
from scipy.interpolate import bisplev
from scipy.interpolate import SmoothBivariateSpline as sbs
from scipy.integrate import simps
from scipy import optimize
from matplotlib.patches import Ellipse
import time
import datetime
from astroquery.simbad import Simbad
import astropy.units as u
from astropy import coordinates
import astropy.time.core
import astropy
import glob
import shutil
import csv
if __name__ == "__main__":
###########################################################################################################################################################
# HOT Stellar Abundances Pipeline
data_dir = 'Data/'
output_dir = 'pipe_output/'
cntnorm_dir = output_dir+'Continuum_Normalized/'
plot_dir = output_dir+'Stellar_Parameter_Plots/'
ew_dir = output_dir+'Metal_Line_EW/'
bal_dir = output_dir+'Balmer_Line/'
cal_dir = output_dir+'Calibrations/'
temp_dir = output_dir+'Temp/'
###########################################################################################################################################################
#Make directories if not present
if not os.path.exists(output_dir):
os.makedirs(output_dir)
if not os.path.exists(cntnorm_dir):
os.makedirs(cntnorm_dir)
if not os.path.exists(plot_dir):
os.makedirs(plot_dir)
if not os.path.exists(ew_dir):
os.makedirs(ew_dir)
if not os.path.exists(bal_dir):
os.makedirs(bal_dir)
if not os.path.exists(cal_dir):
os.makedirs(cal_dir)
if not os.path.exists(temp_dir):
os.makedirs(temp_dir)
files = os.listdir(data_dir+'.')
files = np.append(files,os.listdir(cal_dir+'.'))
files = np.append(files,os.listdir(temp_dir+'.'))
flats=[]
dark=[]
lamp=[]
obj=[]
other=[]
names=[]
timestamp = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d-%H:%M:%S')
#read in SED temperature data
#print "Importing DEBRIS"
#csv_data = open('../DEBRIS/SEDs-fluxes-24Apr14-v7.0.csv','r')
#data = list(csv.reader(csv_data))
#debris = dict( zip(data[0] , SRP.get_mulList(*data[1:]) ))
#sed_teff = debris["teff"]
#star_ids = debris["ID"]
#catalog = pickle.load(open("../DEBRIS/DEBRIS_astropy_catalog.pickle","r"))
#pipe_table=[]
#mark bad files
#files = list(set(files)-set(["dl60232.fits","dl60233.fits","dl60234.fits"]))
#data sorting
#sort files by basic types and target names
for i in files:
if ('.fits' in i):
header=pyfits.getheader(data_dir+i)
try:
typ = header['IMAGETYP']
except KeyError:
typ = "other"
if (typ == "other"):
try:
typ = header['EXPTYPE']
except KeyError:
typ = "other"
typ = typ.lower()
if 'flat' in typ:
flats.append(i)
elif 'dark' in typ:
dark.append(i)
elif 'comp' in typ:
lamp.append(i)
elif 'object' in typ:
obj.append(i)
try:
name = header['OBJECT']
except (KeyError):
ra = header['RA']
dec = header['DEC']
c = coordinates.SkyCoord(ra+' '+dec,frame='fk4',unit=(u.hourangle, u.deg), obstime="J2000")
resultTable = Simbad.query_region(c,radius=5*u.arcminute)
simbad_star_name = resultTable["MAIN_ID"][0]
resultTable2 = Simbad.query_objectids(simbad_star_name)
for id_name in resultTable2:
if 'HR ' in id_name[0]:
name = id_name[0]
names.append(name)
elif True:
other.append(i)
#sort object types
names_u=list(set(names))
targets={}
for i in names_u:
fn=[]
for v,j in enumerate(names):
if i == j:
fn.append(obj[v])
targets[i.replace(" ","")]=fn
#clean out the idiotic yet somehow nessacary database
if (os.path.isdir('./database')):
files_database = os.listdir('./database')
for i in files_database:
os.remove('./database/'+i)
print "Objects in data set: "
for t,v in targets.iteritems():
print t
#target processing
#extract spectrum from each star
beastmode=raw_input('Beast Mode ? (y/n): ').lower()
beast = beastmode in "y"
findstar=raw_input('Looking for a star in particular ? (star catalog id): ').lower()
targets_new = dict(targets)
if findstar == "n":
print "Looping through all stars"
else:
for i,v in targets.iteritems():
if (findstar in i.lower()):
print 'Found ',i
else:
del targets_new[i]
start = time.time()
for i,v in targets_new.iteritems():
#print v
v=np.array(v)
if (str(i) in [] ):
print "Skipping: "+str(i)
continue
#spectral analysis
if(beast):
proc2 = "y"
else:
proc2=raw_input('Proceed with analysis of spectrum '+i+'? (y/n): ').lower()
if proc2 in "y":
star = str(i)
star = star.replace('(','_')
star = star.replace(')','')
star_file = star.replace('*','')+'.fits'
str_star = star_file.replace(".fits","_cntnorm_legendre.fits")
star_hdu1 = pyfits.open(cntnorm_dir+str_star.replace("[0]",""))
star_data1 = star_hdu1[0].data
star_hdr1 = star_hdu1[0].header
star_hdu1.close()
if (np.shape(star_data1)[0] > 2):
rvcorr_lamb = star_data1[0:,0]
flux = star_data1[0:,1]
else:
rvcorr_lamb = star_data1[0,0:]
flux = star_data1[1,0:]
str_star = star_file.replace(".fits","_cntnorm_twod.fits")
star_hdu2 = pyfits.open(cntnorm_dir+str_star.replace("[0]",""))
star_data2 = star_hdu2[0].data
star_hdr2 = star_hdu2[0].header
star_hdu2.close()
if (np.shape(star_data2)[0] > 2):
rvcorr_lamb_balmer = star_data2[0:,0]
flux_balmer = star_data2[0:,1]
else:
rvcorr_lamb_balmer = star_data2[0,0:]
flux_balmer = star_data2[1,0:]
#find metal lines
#Venn 1990
linelist = [
# Mg I
["Mg1",4702.991,4.346,-0.67],
["Mg1",5183.604,2.717,-0.18],
["Mg1",5711.088,4.346,-1.83],
["Mg1",5528.405,4.346,-0.62],
# Mg II
["Mg2",4242.448,11.56,-1.07],
["Mg2",4242.542,11.56,-1.23],
["Mg2",4427.994,9.995,-1.21],
["Mg2",4481.126,8.863,0.74],
["Mg2",4481.150,8.863,-0.56],
["Mg2",4481.325,8.864,0.59],
# Fe I
["Fe1",3920.26,0.120,-1.75],
#["Fe1",3930.30,0.090,-1.59],
["Fe1",3997.39,2.730,-0.39],
["Fe1",4005.25,1.560,-0.61],
["Fe1",4045.82,1.480,0.28],
["Fe1",4063.60,1.560,-0.61],
["Fe1",4071.74,1.610,-0.02],
["Fe1",4202.03,1.480,-0.71],
["Fe1",4235.94,2.430,-0.34],
["Fe1",4260.48,2.400,0.02],
["Fe1",4282.41,2.180,-0.81],
["Fe1",4466.55,2.830,-0.59],
# Fe II
["Fe2",3922.91,7.516,-1.20],
["Fe2",4233.17,2.580,-2.00],
["Fe2",4472.92,2.860,-2.21],
["Fe2",4508.28,2.860,-2.21],
["Fe2",4515.34,2.840,-2.48],
["Fe2",4520.23,2.810,-2.60],
["Fe2",4522.63,2.840,-2.03],
["Fe2",4541.52,2.860,-3.05],
["Fe2",4576.33,2.840,-3.04],
["Fe2",4582.84,2.840,-3.10],
["Fe2",4583.83,2.810,-2.02],
["Fe2",4629.34,2.810,-2.37]
];
linelist = np.array(linelist)
#get EW measurements
EW_data = np.array([0,0,0])
dw = 5
sz = np.shape(linelist)[0]
wlist = (linelist[0:,1]).astype(np.float)
eVlist = (linelist[0:,2]).astype(np.float)
for tag,l in enumerate(linelist):
w = np.float(l[1])
# boundary exception
#if (np.abs(tag - np.floor(sz/2)) < np.floor(sz/2)):
bid = np.where( (wlist > (w-dw)) & (wlist < (w+dw)) )
if (np.size(bid) == 1):
mtype = "singlet"
elif (np.size(bid) == 2):
mtype = "doublet"
elif (np.size(bid) == 3):
mtype = "triplet"
#sys.exit()
ids = np.where((rvcorr_lamb < w+dw) & (rvcorr_lamb > w-dw))
number = np.shape(ids)[1]
#weights = np.sqrt(np.abs( np.arange(number)-np.floor(number/2) )+1)/number
#need to get fancy to renormalize the metal lines so that gaussian fit works
linewave = rvcorr_lamb[ids]
lineflux = flux[ids]
#recenter around nearby line
toff = SRP.find_line(linewave,lineflux,w,4)-w
w2 = w+toff
ids = np.where((rvcorr_lamb < (w2+dw)) & (rvcorr_lamb > (w2-dw)))
linewave = rvcorr_lamb[ids]
lineflux = flux[ids]
#midwave = np.int(np.shape(linewave)[0]/2)
#res = np.abs(linewave[midwave]-linewave[midwave+1])
tab_p_prime = lineflux-np.roll((lineflux ),3)
tab_m_prime = np.roll((lineflux),-3)-lineflux
tab_p_prime2 = lineflux-np.roll((lineflux),7)
tab_m_prime2 = np.roll((lineflux),-7)-lineflux
condition = (tab_p_prime > 0) & (tab_m_prime < 0) & (tab_p_prime2 > 0) & (tab_m_prime2 < 0)
high_points = lineflux[condition]
hpts_wave = linewave[condition]
#no highpoints found then skip
if (np.size(hpts_wave) == 0):
break
fit = np.polyfit(hpts_wave,high_points,1)
norm = linewave*fit[0]+fit[1]
lineflux = lineflux/norm
high_points = high_points/norm[condition]
#iterate local fit
m_high_points = np.median(high_points)
cond = np.abs(lineflux-m_high_points) < 0.01
upper_high_points = lineflux[cond]
hpts_wave = linewave[cond]
#no highpoints found then skip
if (np.size(hpts_wave) == 0):
break
fit = np.polyfit(hpts_wave,upper_high_points,1)
norm = linewave*fit[0]+fit[1]
lineflux = lineflux/norm
high_points = upper_high_points/norm[cond]
red_hpts_wave = hpts_wave[hpts_wave > w2]
blu_hpts_wave = hpts_wave[hpts_wave < w2]
inner_red = np.min(np.append(red_hpts_wave,np.max(linewave)))
inner_blu = np.max(np.append(blu_hpts_wave,np.min(linewave)))
f_min_red = lineflux[linewave == inner_red]
f_min_blue = lineflux[linewave == inner_blu]
red_upper_high_points = upper_high_points[hpts_wave > w2]
blu_upper_high_points = upper_high_points[hpts_wave < w2]
high_points = np.append( blu_upper_high_points[blu_upper_high_points > f_min_blue],red_upper_high_points[red_upper_high_points > f_min_red] )
hpts_wave = np.append( blu_hpts_wave[blu_upper_high_points > f_min_blue],red_hpts_wave[red_upper_high_points > f_min_red] )
if (np.size(hpts_wave) > 4):
fixrange = [np.min(hpts_wave),np.max(hpts_wave)]
if (np.shape(red_upper_high_points)[0] < 1):
fixrange[1] = np.max(linewave)
if (np.shape(blu_upper_high_points)[0] < 1):
fixrange[0] = np.min(linewave)
else:
fixrange = [np.min(linewave),np.max(linewave)]
lineflux2 = lineflux[(linewave > fixrange[0]) & (linewave < fixrange[1])]
linewave2 = linewave[(linewave > fixrange[0]) & (linewave < fixrange[1])]
lineflux2 = pd.rolling_mean(lineflux2,window=10,min_periods=3,center=True)
#do Gaussian fit to get EW
model = np.ones(np.size(linewave2))
try:
if (mtype == "singlet"):
popt = [-0.05, np.mean(linewave2), 1, 1]
#force gaussian fit to be practical
# allow it to be impractical to weed out bad lines?
bounds = ([-1,-np.inf,0,1.0],[0,np.inf,dw/2,1.4])
popt, pcov = curve_fit(SRP.gauss_function, linewave2, lineflux2, p0 = popt, bounds = bounds)
err = np.diagonal(pcov)
model = SRP.gauss_function(linewave2, *popt)
if (mtype == "doublet"):
del_wav = np.abs( np.diff(wlist[bid]) )[0]
del_eV = eVlist[bid]
#print del_eV
del_eV = (np.sum(del_eV)/del_eV)[0]
#print del_eV
w = np.mean(linewave2)
toff = SRP.find_line(linewave2,lineflux2,w,3)-w
popt = [(-0.2/del_eV), w+toff, 0.9, 1, (del_eV-1), del_wav]
#x, a, m, sigma, x0, a_2, dm_2
bounds = ([-1,np.min(linewave2),0,1.0,(del_eV-1)*0.9,del_wav*0.8],[-0.01,np.max(linewave2),1.5,2.0,(del_eV-1)*1.1,del_wav*1.2])
popt, pcov = curve_fit(SRP.gauss_function_2, linewave2, lineflux2, p0 = popt, bounds = bounds)
err = np.diagonal(pcov)
model = SRP.gauss_function_2(linewave2, *popt)
if (mtype == "triplet"):
del_wav = np.diff(np.abs(wlist[bid]))
popt = [-0.1, np.mean(linewave2), 1, 1, -0.05, del_wav[0], -0.05, del_wav[1]]
bounds = ([-1,-np.inf,0,1.0,-1,del_wav[0]*0.5,-1,del_wav[1]*0.9],[0,np.inf,dw/2,1.4,0,del_wav[0]*1.5,0,del_wav[1]*1.1])
popt, pcov = curve_fit(SRP.gauss_function_3, linewave2, lineflux2, p0 = popt, bounds = bounds)
err = np.diagonal(pcov)
model = SRP.gauss_function_3(linewave2, *popt)
except (TypeError,IndexError,ValueError,OSError):
continue
EW = np.abs(popt[0]*np.sqrt(2*np.pi*(popt[2]*1000)**2))#in milli-Angstroms
EWerr = (2*np.pi)**2*popt[0]*popt[2]*err[0]*err[2]
goodnessoffit = np.sum(err)
EW_data = np.vstack((EW_data,[EW,EWerr,goodnessoffit]))
plt.clf()
plt.axvline(fixrange[0],0,2,color='k')
plt.axvline(fixrange[1],0,2,color='k')
plt.axvline(l[1],0,2,color='k',ls='--')
plt.plot(linewave, lineflux)
plt.plot(linewave2, model)
if (mtype == "doublet"):
print w,popt
EW = np.abs(popt[0]*np.sqrt(2*np.pi*(popt[2]*1000)**2))
EW = np.abs(popt[0]*np.sqrt(2*np.pi*(popt[2]*1000)**2))
plt.plot(linewave2,SRP.gauss_function(linewave2,popt[0],popt[1],popt[2],popt[3]),'g--')
plt.plot(linewave2,SRP.gauss_function(linewave2,(popt[0]*popt[4]),(popt[1]+popt[5]),popt[2],popt[3]),'g--')
#sys.exit()
plt.plot(hpts_wave, high_points, 'ro')
plt.plot(linewave, norm, 'c-.')
plt.xlim([w2-dw,w2+dw])
plt.title(str(l[0])+" : "+str(w))
star_dir = ew_dir+star.replace('*','')+'/'
if not os.path.exists(star_dir):
os.makedirs(star_dir)
plt.savefig(star_dir+str(l[0])+'_'+str(l[1])+'_'+str(tag)+'.pdf')
#plt.pause(1)
#sys.exit()
EW_data = EW_data[1:,0:]
#cut out bad fits by relative goodness of fits
#cut out fits or low EW and likely bad
med = np.median(EW_data[0:,2])
ids = np.where((EW_data[0:,2] < 3*med) & (EW_data[0:,0] > 10))
linelist = np.array(linelist)
# assuming element is a string and converting to a number for larger array output
output = np.array([0,0,0,0,0,0,0],dtype=np.float)
for m in ids[0]:
output = np.vstack((output,[np.float(SRP.elementnumber(linelist[m,0])),np.float(linelist[m,1]),np.float(linelist[m,2]),np.float(linelist[m,3]),EW_data[m,0],EW_data[m,1],EW_data[m,2]]))
output = output[1:,0:]
#sys.exit()
#find triplets and doublets to distribute EW values
#lines = output[0:,1]
#for e,o in enumerate(output):
#line_w = o[1]
#ids = np.where(np.abs(lines-line_w)<dw)
#num = np.shape(ids)[1]
#print num
#if (num > 1):
#output[e,4] = output[e,4]/num
#prep for pyBLAMER 9
if ('*' in star):
star = star.replace("*","")
moogfile = SRP.writetomoog(temp_dir+star+"_temp",output)
#znew3,x,y = pybalmer9(moogfile,[rvcorr_lamb,flux],star)
#def pybalmer9(linelist,data,star):
data = [rvcorr_lamb_balmer,flux_balmer]
#main code balmer 9
interp_x = 29
interp_y = 11
interp_kind = 'linear'
dats_dir = os.listdir('./DATS/')
datfiles_mmod = []
for x in dats_dir:
if 'mmod' in x:
#print x
datfiles_mmod.append([x])
# model and match blamer lines
dats_dir = os.listdir('./DATS/')
datfiles_kmod = []
for x in dats_dir:
if 'kmod' in x:
#print x
datfiles_kmod.append([x])
models_dist = []
star_dir = bal_dir+star.replace('*','')+'/'
if not os.path.exists(star_dir):
os.makedirs(star_dir)
first = 0
for x in datfiles_kmod:
SRP.execute_balmer9('./DATS/'+str(x[0]))
chisqr,beta_line,gamma_line,delta_line = SRP.chisqr_balmer9(data)
modelname = str(x[0])
modelname = modelname.replace("['","")
modelname = modelname.replace("']","")
if (('g450' in modelname) or ('T9000' in modelname)):
plt.clf()
plt.title("H Beta")
plt.plot(beta_line[0],beta_line[2],'r')
plt.plot(beta_line[0],beta_line[1],'b')
plt.plot(beta_line[0][beta_line[3]],beta_line[1][beta_line[3]],'go')
plt.xlim([4800,4920])
plt.ylabel("Normalized Intensity")
plt.xlabel("Wavelength (Angs)")
plt.savefig(star_dir+'hbeta_'+str(x)+'.pdf')
plt.clf()
plt.title("H Gamma")
plt.plot(gamma_line[0],gamma_line[2],'r')
plt.plot(gamma_line[0],gamma_line[1],'b')
plt.plot(gamma_line[0][gamma_line[3]],gamma_line[1][gamma_line[3]],'go')
plt.xlim([4280,4400])
plt.ylabel("Normalized Intensity")
plt.xlabel("Wavelength (Angs)")
plt.savefig(star_dir+'hgamma_'+str(x)+'.pdf')
plt.clf()
plt.title("H Delta")
plt.plot(delta_line[0],delta_line[2],'r')
plt.plot(delta_line[0],delta_line[1],'b')
plt.plot(delta_line[0][delta_line[3]],delta_line[1][delta_line[3]],'go')
plt.xlim([4040,4160])
plt.ylabel("Normalized Intensity")
plt.xlabel("Wavelength (Angs)")
plt.savefig(star_dir+'hdelta_'+str(x)+'.pdf')
f=open('./auto.dat','r')
model_info = f.readline()
model_info = filter(None, model_info.split(" "))
teff = model_info[1]
logg = model_info[3]
if (first == 0):
chi_stop = np.array([True,True,True],dtype=bool)
# ignore a line if there is a large discontinuity
beta_line_med = medfilt(beta_line[1],kernel_size=41)
if ( np.max((beta_line_med - np.roll(beta_line_med,-1))) > 0.1):
chi_stop[0] = False
delta_line_med = medfilt(delta_line[1],kernel_size=41)
if ( np.max((delta_line_med - np.roll(delta_line_med,-1))) > 0.1):
chi_stop[1] = False
gamma_line_med = medfilt(gamma_line[1],kernel_size=41)
if ( np.max((gamma_line_med - np.roll(gamma_line_med,-1))) > 0.1):
chi_stop[2] = False
first = 1
if (np.sum(chi_stop) == 0):
chi_stop = np.array([True,True,True],dtype=bool)
update = [float(teff),float(logg),float(np.sum(chisqr[chi_stop]))]
#print update
if not np.isnan(np.sum(chisqr[chi_stop])):
models_dist.append(update)
tst2=np.array(models_dist)
x = np.linspace(min(tst2[:,0]),max(tst2[:,0]),interp_x)
y = np.linspace(min(tst2[:,1]),max(tst2[:,1]),interp_y)
znew2 = np.zeros(shape=(interp_x,interp_y), dtype=np.float)
for m,i in enumerate(x):
for n,j in enumerate(y):
id1 = np.in1d(tst2[:,0],i)
id2 = np.in1d(tst2[:,1],j)
match = id1*id2
if np.sum(match) > 0:
znew2[m,n] = 1.0/tst2[match,2]
#mask balmer line fits
#mask = np.ones(np.shape(znew2))
#znew2 = znew2*mask
mv = np.nanmax(znew2[0:,0:])
for m,i in enumerate(x):
for n,j in enumerate(y):
znew2[m,n] = znew2[m,n]/mv
fig = plt.figure()
p = plt.pcolor(x,y,np.transpose(znew2),vmax=1.0,vmin=0.0)
plt.colorbar()
plt.ylabel('log(g)')
plt.xlabel(r'$T_{eff}$')
plt.title('Balmer Line Fitting')
plt.savefig(plot_dir+star+"_blf.pdf")
# end balmer line matching
# compare abundaces
models_dist2 = []
for x in datfiles_mmod:
SRP.create_moog_file('./DATS/'+str(x[0]),moogfile)
key,a = SRP.execute_moog(ew_dir,star)
while (np.size(a) < 6):
a=np.append(a,[0.0])
if not np.isnan(np.sum(a)):
#print a
mg = (np.abs((a[key == 'MgI']-a[key == 'MgII'])))
fe = (np.abs((a[key == 'FeI']-a[key == 'FeII'])))
try:
mg = float(mg[0])
except (IndexError):
mg = 99
try:
fe = float(fe[0])
except (IndexError):
fe = 99
update = [float(a[0]),float(a[1]),fe,mg]
#print update
models_dist2.append(update)
tst1=np.array(models_dist2)
print np.array(sorted(models_dist2, key =lambda models_dist2: models_dist2[0]))
znew = np.zeros(shape=(interp_x,interp_y), dtype=np.float)
x = np.linspace(min(tst2[:,0]),max(tst2[:,0]),interp_x)
y = np.linspace(min(tst2[:,1]),max(tst2[:,1]),interp_y)
for m,i in enumerate(x):
for n,j in enumerate(y):
#znew[m,n] = 1/tck.ev(i, j)
id1 = np.in1d(tst1[:,0],i)
id2 = np.in1d(tst1[:,1],j)
match = id1*id2
if np.sum(match) > 0:
znew[m,n] = tst1[match,2]+1
znew = 1/znew
znew[znew == np.inf] = 0.0
znew = znew/np.max(znew)
#znew = filt.median_filter(znew,size=(3,3),mode="nearest")
teff_1 = []
logg_1 = []
for i,logg in enumerate(y):
min_id = np.where(znew[:,i] == np.nanmax(znew[:,i]))
min_v = np.mean(x[min_id])
#print min_v
logg_1 = np.append(logg_1,logg)
teff_1 = np.append(teff_1,min_v)
#print 1.0/(tst1[:,2]+1.0)
fig = plt.figure()
p = plt.pcolor(x,y,np.transpose(znew),vmax=1.0, vmin=0.0)
plt.colorbar()
plt.ylabel('log(g)')
plt.xlabel(r'$T_{eff}$')
plt.title('Abundance Comparison')
plt.savefig(plot_dir+star+"_abund.pdf")
#end abundance matching
fig = plt.figure()
teff_2 = np.array([])
logg_2 = np.array([])
for i,logg in enumerate(y):
max_id = np.where(znew2[:,i] == np.nanmax(znew2[:,i]))
min_v = np.mean(x[max_id])
logg_2 = np.append(logg_2,logg)
teff_2 = np.append(teff_2,min_v)
plt.plot(teff_2,logg_2,'b',label="Balmer Line")
plt.plot(teff_1,logg_1,'g',label="Double Ionzied Species")
plt.ylabel('log(g)')
plt.xlabel(r'$T_{eff}$')
#clean up nans
teff_1 = np.nan_to_num(teff_1)
teff_2 = np.nan_to_num(teff_2)
fit1 = np.polyfit(teff_1,logg_1,1)
fit2 = np.polyfit(teff_2,logg_2,1)
plt.plot(teff_1,np.array(teff_1)*fit1[0]+fit1[1],'-r')
plt.plot(teff_2,np.array(teff_2)*fit2[0]+fit2[1],'-r')
plt.xlim([min(x),max(x)])
plt.ylim([min(y),max(y)])
plt.legend(loc=4)
plt.savefig(plot_dir+star+"_trendlines.pdf")
x = (fit1[1]-fit2[1])/(fit2[0]-fit1[0])
y = fit2[0]*x+fit2[1]
#print x,y
match_model_dist = []
for t1 in tst1:
for t2 in tst2:
if ((t1[0] == t2[0]) & (t1[1] == t2[1])):
update = [t1[0],t2[1],t2[2],t1[2],t1[3]]
#print update
match_model_dist.append(update)
tst3=np.array(match_model_dist)
stat = ( tst3[:,2]/sum(tst3[:,2]) )+( tst3[:,3]/sum(tst3[:,3]) )
min_stat = np.where(stat == min(stat))
#print tst3[min_stat,0:2][0][0]
x = np.linspace(min(tst1[:,0]),max(tst1[:,0]),interp_x)
y = np.linspace(min(tst1[:,1]),max(tst1[:,1]),interp_y)
znew3 = (znew2)*(znew)
#end blamer 9
fig = plt.figure()
ax = fig.add_subplot(111)
sz = np.shape(znew3)
p = plt.pcolor(x,y,np.transpose(znew3),vmax=1.0,vmin=0.0)
plt.colorbar()
plt.ylabel('log(g)')
plt.xlabel(r'$T_{eff}$')
plt.title('Combined Prob')
znew3[np.isnan(znew3)] = 0
#znew3 = np.reshape(znew3, (np.size(x),np.size(y)) )
#print np.shape(znew3)
gridx,gridy = np.meshgrid(x,y)
#popt = (1.0,np.median(x),np.median(y),500,1.0,0.0,0.0)
#popt, pcov = curve_fit(gauss_function_2d, (x,y), znew3, p0 = popt, bounds = bounds)
maxid = np.where(znew3 == np.max(znew3))
p = SRP.heat_map_fit(znew3,x,y,gridx,gridy)
e = Ellipse(xy = (p[1],p[2]), width = p[3]/2, height = p[4]/2, angle=0, linewidth=2, fill=False)
ax.add_artist(e)
plt.plot(p[1],p[2],'k*')
plt.savefig(plot_dir+star+"_cbp.pdf")
star_param = np.array([])
try:
f = open( output_dir+"pipe_table_"+timestamp+".pickle", "rb" )
star_param = pickle.load(f)
f.close()
except IOError:
star_param = np.array([])
# Get header information
#ra = star_hdr['RA']
#dec = star_hdr['DEC']
#print star
s = star
s = s.replace('hd','HD ')
s = s.replace('Debris','')
s = s.replace('Debrois','')
s = s.replace('DEebris','')
s = s.replace('-Supergiant','')
s = s.replace('SRd','')
s = s.replace('-Supergiant','')
s = s.replace('O','')
s = s.replace('Her',' Her')
s = s.replace('KV','')
s = s.replace('_North','')
s = s.replace('_South','')
resultTable2 = Simbad.query_object(s)
try:
ra = (resultTable2["RA"][0]).replace(' ',':')
dec = (resultTable2["DEC"][0]).replace(' ',':')
except:
ra = "00:00:00.00"
dec = "+00:00:00.00"
#bias by debris survey
#c = coordinates.SkyCoord(ra+' '+dec,frame='fk4',unit=(u.hourangle, u.deg), obstime="J2000")
#id,d2d,d3d = coordinates.match_coordinates_sky(c,catalog)
#print id,d2d,d3d
#debris data
#debris_teff = np.float(sed_teff[id])
tstrom = SRP.strom_phot(s)
#znew4 = np.zeros(shape=(interp_x,interp_y), dtype=np.float)
#znew4[0:,0:] = 0.25
#dteff=x[1]-x[0]
#tempid = (x > debris_teff-2*dteff) & (x < debris_teff+2*dteff)
#znew4[tempid,0:] = 0.5
#tempid = (x > debris_teff-dteff) & (x < debris_teff+dteff)
#znew4[tempid,0:] = 1.0
znew5 = np.zeros(shape=(interp_x,interp_y), dtype=np.float)
znew5[0:,0:] = 0.25
dteff=x[1]-x[0]
tempid = (x > tstrom-2*dteff) & (x < tstrom+2*dteff)
znew5[tempid,0:] = 0.5
tempid = (x > tstrom-dteff) & (x < tstrom+dteff)
znew5[tempid,0:] = 1.0
#znew_debris = znew3 * znew4
znew_strom = znew3 * znew5
#p_debris = SRP.heat_map_fit(znew_debris,x,y,gridx,gridy)
p_debris = [0,0,0,0,0]
#fig = plt.figure()
#ax = fig.add_subplot(111)
#e = Ellipse(xy = (p_debris[1],p_debris[2]), width = p_debris[3]/2, height = p_debris[4]/2, angle=0, linewidth=2, fill=False)
#ax.add_artist(e)
#plt.plot(p_debris[1],p_debris[2],'k*')
#ptc = plt.pcolor(x,y,np.transpose(znew_debris),vmax=np.max(znew_debris),vmin=0.0)
#plt.ylabel('log(g)')
#plt.xlabel(r'$T_{eff}$')
#plt.title('DEBRIS & Spec')
#plt.savefig(plot_dir+star+"_debris.pdf")
p_strom = SRP.heat_map_fit(znew_strom,x,y,gridx,gridy)
fig = plt.figure()
ax = fig.add_subplot(111)
e = Ellipse(xy = (p_strom[1],p_strom[2]), width = p_strom[3]/2, height = p_strom[4]/2, angle=0, linewidth=2, fill=False)
ax.add_artist(e)
plt.plot(p_strom[1],p_strom[2],'k*')
ptc = plt.pcolor(x,y,np.transpose(znew_strom),vmax=np.max(znew_strom),vmin=0.0)
plt.ylabel('log(g)')
plt.xlabel(r'$T_{eff}$')
plt.title('DEBRIS & Spec')
plt.savefig(plot_dir+star+"_strom.pdf")
ind = (-znew_strom).argpartition(3,axis=None)[:3]
xid,yid = np.unravel_index(ind, znew5.shape)
teff_max = np.mean(x[xid])
logg_max = np.mean(y[yid])
try:
star_param = np.vstack((star_param,[ [star,p[1],p[3]/2,p[2],p[4]/2,p_debris[1],p_debris[3]/2,p_debris[2],p_debris[4]/2,p_strom[1],p_strom[3]/2,p_strom[2],p_strom[4]/2,teff_max,logg_max] ] ))
except ValueError:
star_param = [[star,p[1],p[3]/2,p[2],p[4]/2,p_debris[1],p_debris[3]/2,p_debris[2],p_debris[4]/2,p_strom[1],p_strom[3]/2,p_strom[2],p_strom[4]/2,teff_max,logg_max]]
f = open(output_dir+"pipe_table_"+timestamp+".pickle", "wb")
pickle.dump(star_param, f)
f.close()
elif proc in "n":
print "Skipping star"
elif proc in "b":
break
else:
print "Bugged input"
end = time.time()
print "elapsed time for star: ",np.int(end-start)," sec"
#clean up
#os.remove('master_dark.fits')