-
Notifications
You must be signed in to change notification settings - Fork 0
/
WL_Script.py
485 lines (397 loc) · 23.4 KB
/
WL_Script.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
# Name: WL_Script.py
#
# Weak-Lensing "Study of Systematics and Classification of Compact Objects" Program I
#
# Type: python script
#
# Description: Central script that develops the whole process of reading images, filtering into galaxies and stars, correcting sizes and shapes, correcting PSF annisotropies, and re-classify compact objects into galaxies to obtain a final catalogue
#
# Returns: FITS image - mass-density map
# Catalogs
# Plots
# FITS image - trial from Source Extractor
#
__author__ = "Guadalupe Canas Herrera"
__copyright__ = "Copyright (C) 2015 G. Canas Herrera"
__license__ = "Public Domain"
__version__ = "4.0.0"
__maintainer__ = "Guadalupe Canas"
__email__ = "[email protected]"
# Improvements: more automatic ---> only needs the name of the picture, the catalogue (in case you have it) and the BAND you want to analize
# Old CatalogPlotter3.py has been splitted in two: WL_Script.py and WL_Utils.py
# Also call: WL_utils.py, WL_filter_mag_gal.py - WL_ellip_fitter.py (written by Guadalupe Canas Herrera)
# Also call 2: Source Extractor (by Emmanuel Bertin V2.3.2), sex2fiat (by DAVID WITTMAN v1.2), fiatfilter (by DAVID WITTMAN v1.2), ellipto (by DAVID WITTMAN v1.2), dlscombine (by DAVID WITTMAN v1.2 and modified by GUADALUPE CANAS)
#
# DLSCOMBINE CORRECTS PSF: it has a dependence in fiat.c, fiat.h, dlscombine_utils.c, dlscombine.c, dlscombine.h
# Guadalupe Canas Herrera modified fiat.c, dlscombine_utils.c, dlscombine.h
#
import matplotlib.pyplot as plt #Works for making python behave like matlab
#import sextutils as sex #Program used to read the original catalog
import numpy as np #Maths arrays and more
import numpy.ma as ma #Masking arrays
import sys #Strings inputs
import math #mathematical functions
import subprocess #calling to the terminal
from astropy.modeling import models, fitting #Package for fitting Legendre Polynomials
import warnings #Advices
from mpl_toolkits.mplot3d import Axes3D #Plotting in 3D
import WL_ellip_fitter as ellip_fit #Ellipticity fitting
from WL_Utils import sex_caller, sex_caller_corrected, ellipto_caller, dlscombine_pol_caller, dlscombine_leg_caller, ds9_caller, plotter, ellipticity, specfile, stars_maker, galaxies_maker, specfile_r, specfile_z
from WL_filter_mag_gal import filter_mag #Filtering final catalog of galaxies a function of magnitudes and call fiatmap
import seaborn as sns
import matplotlib.pylab as P #histograms
from Class_CrossMatching import CrossMatching
from Class_CatalogReader import CatalogReader
############################### BEGIN SCRIPT ###############################
# (1): We define the ending of the input/output files
type_fits = ".fits"
type_cat = ".cat"
type_fcat = ".fcat"
type_good = "_good.fcat"
type_galaxies = "_galaxies.fcat"
type_stars = "_stars.fcat"
type_ellipto_galaxies = "_ellipto_galaxies.fcat"
type_ellipto_stars = "_ellipto_stars.fcat"
type_shapes_galaxies = "_shapes_galaxies.fcat"
type_shapes_stars = "_shapes_stars.fcat"
type_match = "_match.fcat"
def main():
sns.set(style="white", palette="muted", color_codes=True)
print("Welcome to the Weak-Lensing Script, here to help you analizing Subaru images in search of galaxy clusters")
print("")
array_file_name = []
# (1): Ask the number of image that did the cross-matching process.
question = int(raw_input("Please, tell me how many pictures did the cross-matching: "))
cont = 0
BEFORE_NAME = ''
FILE_NAME = ''
#print FILE_NAME
FILE_NAME_CORRECTED= ''
while cont < question:
# (2): We need to read the image and band. We ask in screen the image of the region of the sky.
filter =raw_input("Introduce the name of the filter: ")
fits = raw_input("Please, introduce the name of the fits image you want to read or directly the catalogue: ")
#Save the name of the .fits and .cat in a string:
BEFORE_NAME = fits.find('.')
FILE_NAME = fits[:BEFORE_NAME]
#print FILE_NAME
FILE_NAME_CORRECTED='{}_corrected'.format(FILE_NAME)
if fits.endswith(type_fits):
#(3) STEP: Call Source Extractor
print("Let me call Source Extractor (called sex by friends). It will obtain the celestial objects. When it finishes I will show you the trial image")
print("")
catalog_name = sex_caller(fits, FILE_NAME)
#Show results of trial.fits
#subprocess.call('./ds9 {}_trial.fits'.format(FILE_NAME), shell=True)
#(4): Transform Source Extractor catalog into FIAT FORMAT
print("I'm transforming the catalog into a FIAT 1.0 format")
print("")
catalog_name_fiat= '{}.fcat'.format(FILE_NAME)
transform_into_fiat='perl sex2fiat.pl {}>{}'.format(catalog_name, catalog_name_fiat)
subprocess.call(transform_into_fiat, shell=True)
if fits.endswith(type_fcat):
catalog_name_fiat = fits
fits = raw_input("Please, introduce the name of the fits image: ")
#(5): Read the FIAT Catalog
FWHM_max_stars=0
names = ["number", "flux_iso", "fluxerr_iso", "mag_iso", "magger_iso", "mag_aper_1", "magerr_aper_1", "mag", "magger", "flux_max", "isoarea", "x", "y", "ra", "dec", "ixx", "iyy", "ixy", "ixxWIN", "iyyWIN", "ixyWIN", "A", "B", "theta", "enlogation", "ellipticity", "FWHM", "flags", "class_star"]
fcat = np.genfromtxt(catalog_name_fiat, names=names)
P.figure()
P.hist(fcat['class_star'], 50, normed=1, histtype='stepfilled')
P.show()
#Let's fix the ellipcity + and - for all celestial objects
#(6): plot FWHM vs mag_iso
print("I'm ploting MAG_ISO vs. FWHM")
magnitude1='mag_iso'
magnitude2='FWHM'
plotter(fcat, magnitude1, magnitude2, 2, '$mag(iso)$', '$FWHM/pixels$')
plt.show()
print("Do you want to fix axis limits? Please answer with y or n")
answer=raw_input()
if answer== "y":
xmin=float(raw_input("X min: "))
xmax=float(raw_input("X max: "))
ymin=float(raw_input("Y min: "))
ymax=float(raw_input("Y max: "))
#Fix limits
plotter(catalog_name, magnitude1, magnitude2, 3)
plt.xlim(xmin,xmax)
plt.ylim(ymin,ymax)
plt.show(block=False)
elif answer == "n":
plt.show(block=False)
else:
plt.show(block=False)
# (7): Obtaining a GOOD CATALOG without blank spaces and filter saturate objects
print("This catalog is not the good one. I'll show you why")
print("")
magnitude_x="x"
magnitude_y="y"
plotter(fcat, magnitude_x, magnitude_y, 4, '$x/pixels$', '$y/pixels$')
plt.show(block=False)
print("Please, introduce the values you prefer to bound x and y")
xmin_good=float(raw_input("X min: "))
xmax_good=float(raw_input("X max: "))
ymin_good=float(raw_input("Y min: "))
ymax_good=float(raw_input("Y max: "))
catalog_name_good= '{}{}'.format(FILE_NAME, type_good)
terminal_good= 'perl fiatfilter.pl "x>{} && x<{} && y>{} && y<{} && FLUX_ISO<3000000" {}>{}'.format(xmin_good, xmax_good, ymin_good, ymax_good, catalog_name_fiat, catalog_name_good)
subprocess.call(terminal_good, shell=True)
print("Wait a moment, I'm showing you the results in a sec")
fcat_good = np.genfromtxt(catalog_name_good, names=names)
print np.amax(fcat_good['flux_iso'])
plotter(fcat_good, 'x', 'y', 5, '$x/pixels$', '$y/pixels$')
plt.show(block=False)
ellipticity(fcat_good, 1)
plt.show(block=False)
plotter(fcat_good, magnitude1, magnitude2, 2, '$mag(iso)$', '$FWHM/pixels$')
plt.show(block=False)
#(8.1.): Creating STARS CATALOG
print("Let's obtain only a FIAT catalog that contains stars. We need to bound. Have a look to the FWHM vs Mag_ISO plot")
mag_iso_min_stars=float(raw_input("Enter the minimum value for mag_iso: "))
mag_iso_max_stars=float(raw_input("Enter the maximum value for mag_iso: "))
FWHM_min_stars=float(raw_input("Enter the minimum value for FWHM: "))
FWHM_max_stars=float(raw_input("Enter the maximum value for FWHM: "))
catalog_name_stars= '{}{}'.format(FILE_NAME, type_stars)
#Creamos un string para que lo ponga en la terminal
terminal_stars= 'perl fiatfilter.pl "MAG_ISO>{} && MAG_ISO<{} && FWHM>{} && FWHM<{} && CLASS_STAR>0.9 && FLUX_ISO<3000000" {}>{}'.format(mag_iso_min_stars, mag_iso_max_stars, FWHM_min_stars, FWHM_max_stars, catalog_name_good, catalog_name_stars)
subprocess.call(terminal_stars, shell=True)
fcat_stars=np.genfromtxt(catalog_name_stars, names=names)
ellipticity(fcat_stars, 6)
plt.show(block=False)
#(8.2.): Checking STARS CATALOG with Source Extractor Neural Network Output
P.figure()
P.hist(fcat_stars['class_star'], 50, normed=1, histtype='stepfilled')
P.show(block=False)
#(9.1.): Creating GALAXIES CATALOG
print("Let's obtain only a FIAT catalog that contains galaxies. We need to bound. Have a look to the FWHM vs Mag_ISO plot")
print("")
print("First, I'm going to perform a linear fit. Tell me the values of mag_iso")
mag_iso_min_galaxies=float(raw_input("Enter the minimum value for mag_iso: "))
mag_iso_max_galaxies=float(raw_input("Enter the maximum value for mag_iso: "))
catalog_name_fit='{}_fit{}'.format(FILE_NAME, type_galaxies)
#Creamos un string para que lo ponga en la terminal
terminal_fit= 'perl fiatfilter.pl -v "MAG_ISO>{} && MAG_ISO<{}" {}>{}'.format(mag_iso_min_galaxies, mag_iso_max_galaxies, catalog_name_good, catalog_name_fit)
subprocess.call(terminal_fit, shell=True)
fcat_fit = np.genfromtxt(catalog_name_fit, names=names)
fit=np.polyfit(fcat_fit['mag_iso'], fcat_fit['FWHM'], 1)
#Save in variables the values of the fitting
m=fit[0]
n=fit[1]
print 'The value of the y-intercep n={} and the value of the slope m={}'.format(n,m)
# Once you have the values of the fitting we can obtain the catalog of galaxies
catalog_name_galaxies= '{}{}'.format(FILE_NAME, type_galaxies)
#terminal_galaxies= 'perl fiatfilter.pl -v "FWHM>{}*MAG_ISO+{} && FWHM>{} && CLASS_STAR<0.1 && FLUX_ISO<3000000" {}>{}'.format(m, n, FWHM_max_stars, catalog_name_good, catalog_name_galaxies)
terminal_galaxies= 'perl fiatfilter.pl -v "FWHM>{}*MAG_ISO+{} && FWHM>{} && FLUX_ISO<3000000" {}>{}'.format(m, n, FWHM_max_stars, catalog_name_good, catalog_name_galaxies)
subprocess.call(terminal_galaxies, shell=True)
fcat_galaxies=np.genfromtxt(catalog_name_galaxies, names=names)
#subprocess.call('./fiatreview {} {}'.format(fits, catalog_name_galaxies), shell=True)
magnitude1='mag_iso'
magnitude2='FWHM'
plotter(fcat_good, magnitude1, magnitude2, 2, '$mag(iso)$', '$FWHM/pixels$')
mag_th= np.linspace(1, 30, 1000)
p = np.poly1d(fit)
plt.plot(mag_th, p(mag_th), 'b-')
plt.show()
ellipticity(fcat_galaxies, 9)
plt.show()
#(9.2.): Checking GALAXIES CATALOG with Source Extractor Neural Network Output
P.figure()
P.hist(fcat_galaxies['class_star'], 50, normed=1, histtype='stepfilled')
P.show(block=False)
# (***) CHECKING FOR STARS // GALAXIES DIVISION
weights_stars=np.ones_like(fcat_stars['class_star'])/len(fcat_stars['class_star'])
weights_galaxies=np.ones_like(fcat_galaxies['class_star'])/len(fcat_galaxies['class_star'])
weights_all = np.ones_like(fcat_good['class_star'])/len(fcat_good['class_star'])
plt.figure()
plt.hist(fcat_stars['class_star'], weights = weights_stars, bins= 5, histtype='stepfilled', label ='stars')
plt.hist(fcat_galaxies['class_star'], weights = weights_galaxies, bins= 5, histtype='stepfilled', label ='galaxies')
plt.legend(loc='upper right')
plt.xlabel('$class_{star}$', labelpad=20, fontsize=20)
plt.ylabel('$Frequency$', fontsize=20)
plt.ylim(0,0.6)
plt.show()
plt.hist(fcat_good['class_star'], color= 'r', weights = weights_all, bins=50, histtype='stepfilled', label ='all')
plt.legend(loc='upper right')
plt.xlabel('$class_{star}$', labelpad=20, fontsize=20)
plt.ylabel('$Frequency$', fontsize=20)
plt.ylim(0,0.6)
plt.show()
plt.show()
#(10): Calling Ellipto to recalculate shapes and ellipticities: ELLIPTO CATALOG
print("")
print("Now it is necessary to call ellipto in order to obtain in a proper way sizes and shapes both for galaxies and stars")
print("")
print("STARS")
print("")
catalog_name_ellipto_stars='{}{}'.format(FILE_NAME, type_ellipto_stars)
ellipto_caller(catalog_name_stars, fits, catalog_name_ellipto_stars)
print("GALAXIES")
catalog_name_ellipto_galaxies='{}{}'.format(FILE_NAME, type_ellipto_galaxies)
ellipto_caller(catalog_name_galaxies, fits, catalog_name_ellipto_galaxies)
print("DONE")
print("")
#(11): Now we clasify the catalogs obtained with ellipto forcing fiat filter: SHAPES CATALOG
print("Filtering good obtained celestial object from ellipto using fiatfilter...")
print("")
print("STARS")
catalog_name_shapes_stars='{}{}'.format(FILE_NAME, type_shapes_stars)
fiatfilter_errcode_stars='perl fiatfilter.pl -v "errcode<2" {}>{}'.format(catalog_name_ellipto_stars, catalog_name_shapes_stars)
subprocess.call(fiatfilter_errcode_stars, shell=True)
print("")
print("GALAXIES")
print("")
catalog_name_shapes_galaxies='{}{}'.format(FILE_NAME, type_shapes_galaxies)
fiatfilter_errcode_galaxies='perl fiatfilter.pl -v "errcode<2" {}>{}'.format(catalog_name_ellipto_galaxies, catalog_name_shapes_galaxies)
subprocess.call(fiatfilter_errcode_galaxies, shell=True)
print("DONE")
print("")
#(12): Recalculating ellipticities for stars
print("I'm recalculating ellipticities of the new star set after being corrected by ellipto")
names_ellipto = ["x", "y", "mag_iso", "median", "ixx", "iyy", "ixy", "a_input", "b_input", "theta", "ellipticity", "errcode", "sigsky", "size", "flux", "mean_rho_4th", "sigma_e", "wander"]
fiat_shapes_stars= np.genfromtxt(catalog_name_shapes_stars, names=names_ellipto)
ellipticity(fiat_shapes_stars, 15)
plt.show()
print "Show ellipticy as a function of x and y"
plotter(fiat_shapes_stars, 'x', 'ellipticity', 2, '$x/pixels$', '$\epsilon$')
plt.show()
plotter(fiat_shapes_stars, 'y', 'ellipticity', 2, '$y/pixels$', '$\epsilon$')
plt.show()
fiat_shapes_galaxies= np.genfromtxt(catalog_name_shapes_galaxies, names=names_ellipto)
ellipticity(fiat_shapes_galaxies, 15)
plt.show(block=False)
#(13): STARS--> you obtain two fitting both for ellip_1 and ellip_2
print("")
print("I'm performing a fitting of those ellipticities e_1 and e_2: both a simple 2D polynomial fitting and a 2D Legendre Polynomial fitting")
print("")
dlscombine_file_pol=''
dlscombine_file_leg=''
#Let's call the function fit_Polynomial from ellip_fitting3.py
fitting_file_ellip_pol=ellip_fit.fit_Polynomial(FILE_NAME, fiat_shapes_stars)
#Create file read by dlscombine
dlscombine_file_pol=specfile(fits, fitting_file_ellip_pol, FILE_NAME)
print("")
#Let's call the function fit_Legendre from ellip_fitting3.py
fitting_file_ellip_leg=ellip_fit.fit_Legendre(FILE_NAME, fiat_shapes_stars)
#Create file read by dlscombine
if filter=='r':
dlscombine_file_leg=specfile_r(fits, fitting_file_ellip_leg, FILE_NAME)
if filter=='z':
dlscombine_file_leg=specfile_z(fits, fitting_file_ellip_leg, FILE_NAME)
#(14): Let's call DLSCOMBINE to correct PSF anisotropies
print("I'm correcting PSF anisotropies using dlscombine: BOTH FOR POL AND LEG FITTING")
print("")
fits_pol='{}_corrected_pol.fits'.format(FILE_NAME, FILE_NAME)
dlscombine_call_pol='./dlscombine_pol {} {}'.format(dlscombine_file_pol, fits_pol)
subprocess.call(dlscombine_call_pol, shell=True)
fits_leg='{}_corrected_leg.fits'.format(FILE_NAME, FILE_NAME)
dlscombine_call_leg='./dlscombine_leg {} {}'.format(dlscombine_file_leg, fits_leg)
subprocess.call(dlscombine_call_leg, shell=True)
#(15): Call again Source Extractor only for the Legendre Polynomial fitting
print("I'm calling again SExtractor to obtain a new catalog from the corrected picture (only from the leg fitting)")
print("")
catalog_name_corrected=sex_caller_corrected(fits_leg, FILE_NAME)
#(16): Transform .cat into .fcat (FIAT) for the corrected catalog
catalog_name_fiat_corrected='{}_corrected.fcat'.format(FILE_NAME)
transform_into_fiat_corrected='perl sex2fiat.pl {}>{}'.format(catalog_name_corrected, catalog_name_fiat_corrected)
subprocess.call(transform_into_fiat_corrected, shell=True)
print("")
array_file_name.append(catalog_name_fiat_corrected)
cont = cont + 1
NAME_1= array_file_name[0]
NAME_2= array_file_name[1]
BEFORE_NAME_1 = NAME_1.find('.')
FILE_NAME_1 = NAME_1[:BEFORE_NAME]
BEFORE_NAME_2 = NAME_2.find('.')
FILE_NAME_2 = NAME_2[:BEFORE_NAME]
#CROSS-MATCHING
catag_r = CatalogReader(array_file_name[0])
catag_r.read()
catag_z = CatalogReader(array_file_name[1])
catag_z.read()
crossmatching = CrossMatching(catag_r.fcat, catag_z.fcat)
crossmatching.kdtree(n=1*1e-06)
crossmatching.catalog_writter('2CM_{}'.format(FILE_NAME_1), compare = '1to2')
print '\n'
crossmatching.catalog_writter('2CM_{}'.format(FILE_NAME_2), compare = '2to1')
FILE_NAME_FINAL = raw_input("Please, tell me the FINAL name: ")
if crossmatching.cont1to2<crossmatching.cont2to1:
catag_final_1 = CatalogReader('2CM_{}{}'.format(FILE_NAME_1, type_fcat))
catag_final_1.read()
catag_final_2 = CatalogReader('2CM_{}{}'.format(FILE_NAME_2, type_fcat))
catag_final_2.read()
crossmatching_final = CrossMatching(catag_final_1.fcat, catag_final_2.fcat)
crossmatching_final.kdtree(n=1*1e-06)
crossmatching.catalog_writter('{}'.format(FILE_NAME_FINAL), compare = '1to2')
if crossmatching.cont1to2>crossmatching.cont2to1:
catag_final_1 = CatalogReader('2CM_{}{}'.format(FILE_NAME_1, type_fcat))
catag_final_1.read()
catag_final_2 = CatalogReader('2CM_{}{}'.format(FILE_NAME_2, type_fcat))
catag_final_2.read()
crossmatching_final = CrossMatching(catag_final_1.fcat, catag_final_2.fcat)
crossmatching_final.kdtree(n=1*1e-06)
crossmatching.catalog_writter('{}'.format(FILE_NAME_FINAL), compare = '2to1')
if crossmatching.cont1to2==crossmatching.cont2to1:
catag_final_1 = CatalogReader('2CM_{}{}'.format(FILE_NAME_1, type_fcat))
catag_final_1.read()
catag_final_2 = CatalogReader('2CM_{}{}'.format(FILE_NAME_2, type_fcat))
catag_final_2.read()
crossmatching_final = CrossMatching(catag_final_1.fcat, catag_final_2.fcat)
crossmatching_final.kdtree(n=1*1e-06)
crossmatching.catalog_writter('{}'.format(FILE_NAME_FINAL), compare = '1to2')
catalog_name_fiat_corrected_final = '{}{}'.format(FILE_NAME_FINAL, fcat)
#(17): Transform again tshe corrected catalog into a GOOD catalog
catalog_name_corrected_good= '{}{}'.format(FILE_NAME_FINAL, type_good)
terminal_corrected_good= 'perl fiatfilter.pl "x>{} && x<{} && y>{} && y<{}" {}>{}'.format(xmin_good, xmax_good, ymin_good, ymax_good, catalog_name_fiat_corrected_final, catalog_name_corrected_good)
subprocess.call(terminal_corrected_good, shell=True)
FILE_NAME_CORRECTED='{}_corrected'.format(FILE_NAME_FINAL)
#(18): STARS CATALOG again...
print("Now we need to repeat the classification to obtain only galaxies and stars as we did before")
print("")
print("Let me show you again the FWHM vs MAG plot \n")
print("")
fcat_corrected=np.genfromtxt(catalog_name_corrected_good, names=names)
plotter(fcat_corrected, 'mag_iso', 'FWHM', 3, '$mag(iso)$', '$FWHM$')
plt.show(block=False)
print("First stars...")
print("")
catalog_name_fiat_corrected_stars=''
catalog_name_fiat_corrected_stars, FWHM_max_stars=stars_maker(catalog_name_corrected_good, FILE_NAME_CORRECTED)
fcat_stars_corrected=np.genfromtxt(catalog_name_fiat_corrected_stars, names=names)
ellipticity(fcat_stars_corrected, 20)
plt.show(block=False)
#(19): GALAXIES CATALOG again...
print("")
print("Second galaxies...")
print("")
catalog_name_fiat_corrected_galaxies=galaxies_maker(catalog_name_corrected_good, FILE_NAME_CORRECTED, FWHM_max_stars)
fcat_galaxies_corrected=np.genfromtxt(catalog_name_fiat_corrected_galaxies, names=names)
# (***) CHECKING FOR STARS // GALAXIES DIVISION
weights_stars=np.ones_like(fcat_stars_corrected['class_star'])/len(fcat_stars_corrected['class_star'])
weights_galaxies=np.ones_like(fcat_galaxies_corrected['class_star'])/len(fcat_galaxies_corrected['class_star'])
weights_all = np.ones_like(fcat_corrected['class_star'])/len(fcat_corrected['class_star'])
plt.figure()
plt.hist(fcat_stars_corrected['class_star'], weights = weights_stars, bins= 10, histtype='stepfilled', label ='stars')
plt.hist(fcat_galaxies_corrected['class_star'], weights = weights_galaxies, bins= 15, histtype='stepfilled', label ='galaxies')
plt.legend(loc='upper right')
plt.xlabel('$class_{star}$', labelpad=20, fontsize=20)
plt.ylabel('$Frequency$', fontsize=20)
plt.show()
plt.hist(fcat_corrected['class_star'], color= 'r', weights = weights_all, bins=50, histtype='stepfilled', label ='all')
plt.legend(loc='upper right')
plt.xlabel('$class_{star}$', labelpad=20, fontsize=20)
plt.ylabel('$Frequency$', fontsize=20)
plt.show()
#(20): ELLIPTO CATALOG and SHAPES CATALOG (only galaxies) again...
catalog_name_ellipto_stars_corrected='{}{}'.format(FILE_NAME_CORRECTED, type_ellipto_stars)
ellipto_caller(catalog_name_fiat_corrected_stars, fits, catalog_name_ellipto_stars_corrected)
catalog_name_ellipto_galaxies_corrected='{}{}'.format(FILE_NAME_CORRECTED, type_ellipto_galaxies)
ellipto_caller(catalog_name_fiat_corrected_galaxies, fits, catalog_name_ellipto_galaxies_corrected)
catalog_name_shapes_galaxies_corrected='{}{}'.format(FILE_NAME_CORRECTED, type_shapes_galaxies)
fiatfilter_errcode_galaxies_corrected='perl fiatfilter.pl -v "errcode<2" {}>{}'.format(catalog_name_ellipto_galaxies_corrected, catalog_name_shapes_galaxies_corrected)
subprocess.call(fiatfilter_errcode_galaxies_corrected, shell=True)
catalog_name_shapes_stars_corrected='{}{}'.format(FILE_NAME_CORRECTED, type_shapes_stars)
fiatfilter_errcode_stars_corrected='perl fiatfilter.pl -v "errcode<2" {}>{}'.format(catalog_name_ellipto_stars_corrected, catalog_name_shapes_stars_corrected)
subprocess.call(fiatfilter_errcode_stars_corrected, shell=True)
if __name__ == "__main__":
main()