forked from mmaus96/Lens_Modeling_Auto
-
Notifications
You must be signed in to change notification settings - Fork 0
/
remake_modelPlots.py
300 lines (233 loc) · 11.8 KB
/
remake_modelPlots.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
import sys
if sys.platform[:5] == 'linux':
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import re
import os
from os import walk
from os import listdir
from os.path import isfile, join, isdir, exists
import time
import numpy as np
import pandas as pd
from Lens_Modeling_Auto.auto_modeling_functions import openFITS
from Lens_Modeling_Auto.auto_modeling_functions import find_components
from Lens_Modeling_Auto.auto_modeling_functions import calcBackgroundRMS
from Lens_Modeling_Auto.auto_modeling_functions import prepareData
from Lens_Modeling_Auto.auto_modeling_functions import prepareFit
from Lens_Modeling_Auto.auto_modeling_functions import find_components
from Lens_Modeling_Auto.auto_modeling_functions import mask_for_sat
from Lens_Modeling_Auto.auto_modeling_functions import mask_for_lens_gal
from Lens_Modeling_Auto.auto_modeling_functions import df_2_kwargs_results
from Lens_Modeling_Auto.plot_functions import make_modelPlots
from functools import reduce
from matplotlib.colors import SymLogNorm
import re
from matplotlib.patches import Circle
from copy import deepcopy
from lenstronomy.Plots.model_plot import ModelPlot
from lenstronomy.Analysis.image_reconstruction import ModelBand
class HiddenPrints:
def __enter__(self):
self._original_stdout = sys.stdout
sys.stdout = open(os.devnull, 'w')
def __exit__(self, exc_type, exc_val, exc_tb):
sys.stdout.close()
sys.stdout = self._original_stdout
# path = '/Users/markmaus/Desktop/Physics_EPFL/Specialization_Project/CFIS_lenses/'
# csv_paths = [path + 'results_new_priors/']
# results_path = path + 'results_new_priors'
# path = '/Users/markmaus/Desktop/Physics_EPFL/Specialization_Project/ringcatalog/'
# csv_paths = [path + 'results_new_priors/']
# results_path = path + 'results_new_priors'
path = '/Users/markmaus/Desktop/Physics_EPFL/Specialization_Project/lens_candidates/Sure_Lens/'
csv_paths = [path + 'results_new_priors/']
results_path = path + 'results_new_priors'
modelPlot_path = results_path + '/modelPlot_results_remake'
im_path = path + 'data/'
psf_path = path + 'psf/'
noise_path = path + 'psf/'
noise_type = 'EXPTIME'
# im_path = path + 'data/'
# psf_path = path + 'psf/'
# noise_path = path + 'rms/'
# noise_type = 'NOISE_MAP'
if not exists(results_path):
os.mkdir(results_path)
if not exists(modelPlot_path):
os.mkdir(modelPlot_path)
#Folder names for data, psf, noise map, original image [TO DO BY USER]
band_list = ['g','r','i']
obj_name_location = 0
deltaPix = 0.27
psf_upsample_factor = 1
# band_list = ['r']
# obj_name_location = 1
# deltaPix = 0.1857
# psf_upsample_factor = 2
zeroPt = 30
# numCores = 1
includeShear = True
use_mask = True
#Make dataframes from csv files
df_list = []
for i,x in enumerate(csv_paths):
df = pd.read_csv(x + 'full_results.csv',delimiter =',')
df_list.append(df.loc[1:,:])
df_final = pd.concat(df_list,axis=0,ignore_index=True)
df_final['Unnamed: 0'] = df_final['Unnamed: 0.1']
df_final = df_final.drop('Unnamed: 0.1',axis=1)
obj_names = []
im_num = []
for j in range(len(df_final)):
fn = df_final.loc[j,'Unnamed: 1']
im_num.append(df_final.loc[j,'Unnamed: 0'])
obj_names.append(re.findall('\d+', fn)[obj_name_location])
kwargs_result = df_2_kwargs_results(df = df_final,band_list = band_list,ignore_1st_line = False,includeShear = True)
for k,x in enumerate(kwargs_result):
print('Object: {}'.format(obj_names[k]))
print('Lens: {}'.format(x['kwargs_lens']))
print('Source: {}'.format(x['kwargs_source']))
print('Lens Light: {}'.format(x['kwargs_lens_light']))
print('\n')
im_files = [f for f in listdir(im_path) if isfile('/'.join([im_path,f]))]
psf_files,noise_files = [],[]
psf_files_dict, noise_files_dict = {},{}
for b in band_list:
psf_files.append([f for f in listdir(psf_path + '/' + b) if isfile('/'.join([psf_path + '/' + b,f]))])
noise_files.append([f for f in listdir(noise_path + '/' + b) if isfile('/'.join([noise_path + '/' + b,f]))])
psf_files_dict[b] = [f for f in listdir(psf_path + '/' + b) if isfile('/'.join([psf_path + '/' + b,f]))]
noise_files_dict[b] = [f for f in listdir(noise_path + '/' + b) if isfile('/'.join([noise_path + '/' + b,f]))]
data_pairs_dicts = []
for i,obj in enumerate(obj_names):
for x in im_files:
if obj in x: im = x
psf = {}
for b in band_list:
#psf[b] = []
for i,file in enumerate(psf_files_dict[b]):
if obj in file: psf[b] = '/'.join([b,file])
noise = {}
for b in band_list:
#noise[b] = []
for i,file in enumerate(noise_files_dict[b]):
if obj in file: noise[b]= '/'.join([b,file])
data_pairs_dicts.append({'image_data': im , 'psf': psf , 'noise_map': noise, 'noise_type': noise_type, 'object_ID': obj})
# for l,b in enumerate(band_list):
# f, axes = plt.subplots(int(len(data_pairs_dicts) / 6) + 1, 6, figsize=(20,20), sharex=False, sharey=False)
# f.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.5, hspace=0.)
#f, axes = plt.subplots(1, 2, figsize=(20,20), sharex=False, sharey=False)
# axes = axes.ravel()
band_path = modelPlot_path + '/' + b
if not exists(band_path):
os.mkdir(band_path)
for it in range(len(data_pairs_dicts)):
# it = 0
data,hdr = openFITS(im_path + '/' + data_pairs_dicts[it]['image_data'])
psf, psf_hdr = [],[]
noise_map,noise_hdr = [],[]
for z in band_list:
d,h = openFITS(psf_path + '/' + data_pairs_dicts[it]['psf'][z])
# psf.extend(d)
# psf_hdr.extend(h)
psf.append(d)
psf_hdr.append(h)
d2,h2 = openFITS(noise_path + '/' + data_pairs_dicts[it]['noise_map'][z])
# noise_map.extend(d2)
# noise_hdr.extend(h2)
noise_map.append(d2)
noise_hdr.append(h2)
data_dict = {'image_data': [], 'image_hdr': [],
'psf': psf, 'psf_hdr': psf_hdr,
'noise_map': noise_map, 'noise_hdr': noise_hdr}
for i,y in enumerate(band_list):
# data_dict['image_data'].append(data[i])
# data_dict['image_hdr'].append(hdr[0])
data_dict['image_data'].append(data[0][i])
data_dict['image_hdr'].append(hdr[0])
# print('calculating background values')
# print('\n')
with HiddenPrints():
background_rms = calcBackgroundRMS(data_dict['image_data']) #calculate rms background
# print('\n')
lens_info = []
for i,x in enumerate(data_dict['image_data']):
lens_info.append({'deltaPix': deltaPix ,
'numPix': len(x),
'background_rms': background_rms[i],
'psf_type': 'PIXEL',
'psf_upsample_factor': psf_upsample_factor})
if noise_type == 'EXPTIME':
lens_info[i]['exposure_time'] = data_dict['noise_hdr'][i][0]['EXPTIME']
lens_info[i]['noise_map'] = None
else:
lens_info[i]['exposure_time'] = None
lens_info[i]['noise_map'] = data_dict['noise_map'][i]
kwargs_data, kwargs_psf = prepareData(lens_info,data_dict['image_data'],
data_dict['psf'])
#Model Lists
if includeShear == True:
lens_model_list = ['SIE','SHEAR']
else:
lens_model_list = ['SIE']
source_model_list = ['SERSIC_ELLIPSE']
lens_light_model_list = ['SERSIC_ELLIPSE']
gal_mask_list = []
mask_list = []
for data in kwargs_data:
gal_mask_list.append(mask_for_lens_gal(data['image_data'],deltaPix))
if use_mask:
mask_list.append(mask_for_sat(data['image_data'],deltaPix))
else: mask_list = None
#prepare fitting kwargs
kwargs_likelihood, kwargs_model, kwargs_data_joint, multi_band_list,kwargs_constraints = prepareFit(kwargs_data,
kwargs_psf,
lens_model_list, source_model_list,
lens_light_model_list,
image_mask_list = mask_list)
# with HiddenPrints():
# modelPlot = ModelPlot(multi_band_list, kwargs_model, kwargs_result[it],
# arrow_size=0.02, cmap_string="gist_heat",
# likelihood_mask_list= mask_list)
# #Calculate Chi^2
# n_data = modelPlot._imageModel.num_data_evaluate
# logL = modelPlot._imageModel.likelihood_data_given_model(source_marg=False, linear_prior=None, **kwargs_result[it])
# red_X_squared = np.abs(logL * 2.0 / n_data)
# model, error_map, cov_param, param = modelPlot._imageModel.image_linear_solve(inv_bool=True, **kwargs_result[it])
# with HiddenPrints():
# model_band = ModelBand(multi_band_list, kwargs_model, model[l], error_map[l], cov_param[l],
# param[l], deepcopy(kwargs_result[it]),
# image_likelihood_mask_list=mask_list, band_index=l)
red_X_squared = make_modelPlots(multi_band_list,kwargs_model,kwargs_result[it],
kwargs_data,kwargs_psf, lens_info,
lens_model_list,source_model_list,lens_light_model_list,
mask_list,band_list,modelPlot_path,it+1,data_pairs_dicts[it]['object_ID'])
#print(model_band._reduced_x2)
# modelPlot.normalized_residual_plot(ax=axes[it], v_min=-6, v_max=6,
# text='$ID:$ {} \n $ \chi^2 $ (all): {:.4f} \n $\chi^2$({} band){:.4f}'
# .format(obj_names[it], red_X_squared,band_list[l],
# model_band._reduced_x2),font_size=10,band_index=l)
# f, axes = plt.subplots(4, 3, figsize=(20,20), sharex=False, sharey=False)
# # band_path = modelPlot_path + '/' + b
# # if not exists(band_path):
# # os.mkdir(band_path)
# modelPlot.data_plot(ax=axes[0,0],band_index=l)
# modelPlot.model_plot(ax=axes[0,1],band_index=l)
# modelPlot.normalized_residual_plot(ax=axes[0,2], v_min=-6, v_max=6,band_index=l)
# modelPlot.source_plot(ax=axes[1, 0], deltaPix_source=0.01, numPix=100,band_index=l)
# modelPlot.convergence_plot(ax=axes[1, 1], v_max=1,band_index=l)
# modelPlot.magnification_plot(ax=axes[1, 2],band_index=l)
# modelPlot.decomposition_plot(ax=axes[2,0], text='Lens light', lens_light_add=True, unconvolved=True,band_index=l)
# modelPlot.decomposition_plot(ax=axes[3,0], text='Lens light convolved', lens_light_add=True,band_index=l)
# modelPlot.decomposition_plot(ax=axes[2,1], text='Source light', source_add=True, unconvolved=True,band_index=l)
# modelPlot.decomposition_plot(ax=axes[3,1], text='Source light convolved', source_add=True,band_index=l)
# modelPlot.decomposition_plot(ax=axes[2,2], text='All components', source_add=True, lens_light_add=True, unconvolved=True,band_index=l)
# modelPlot.decomposition_plot(ax=axes[3,2], text='All components convolved', source_add=True, lens_light_add=True, point_source_add=True,band_index=l)
# f.tight_layout()
# f.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0., hspace=0.05)
# f.suptitle('$ID:$ {} \n $ \chi^2 $ (all): {:.4f} \n $\chi^2$({} band){:.4f}'
# .format(obj_names[it], red_X_squared,band_list[l],
# model_band._reduced_x2),fontsize=30)
# f.savefig(band_path + '/{}-{}.png'.format(im_num[it],obj_names[it]),dpi = 200)
# plt.close(f)