-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathPSF_Photometry.py
339 lines (210 loc) · 11.7 KB
/
PSF_Photometry.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
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 16 14:01:10 2020
@author: Bill Kamtchou
"""
from astropy import units as u
from astropy.coordinates import SkyCoord
from astropy.io import fits
from astropy.modeling.fitting import LevMarLSQFitter
from astropy.nddata import NDData
from astropy.stats import gaussian_sigma_to_fwhm
from astropy.table import Table
from astropy.visualization import simple_norm
from astropy.wcs import WCS
from Background_Reduction import Reduced_Image_Data, Std, R_Band_Files, I_Band_Files, Z_Band_Files, G_Band_Files, U_Band_Files
import csv
import matplotlib.pyplot as plt
from numpy import mean
import numpy as np
from scipy import spatial
from scipy.stats import zscore
from standard_star_file import std_star_file_processing
import statistics
from photutils import DAOStarFinder
from photutils.psf import IterativelySubtractedPSFPhotometry, DAOGroup
from photutils.psf import extract_stars
from photutils import EPSFBuilder
from photutils import find_peaks
RA, DEC, Position, Umag, Err_Umag, Gmag, Err_Gmag, Rmag, Err_Rmag, Imag, Err_Imag, Zmag, Err_Zmag = std_star_file_processing('Standard_Star_File.csv')
def psf_main(Band, Band_Mag, Band_Mag_Err, image_num, Letter):
PSF_Mags_1, PSF_Mags_Err_1, Flux, Flux_Err, X_0, Y_0 = do_psf(image_num, Letter)
catalog_matches, c_matches, Cata_RA, Cata_DEC, WX, WY = source_pos_comparison(Band, X_0, X_0, Y_0, RA, DEC, image_num, Letter)
Mag_Cat, Mag_Err_Cat, Mag_WCS, Mag_Err_WCS, WCS_RA_DEC_PSF_, Source_Matching_PSF = cross_correlation(Position, Band_Mag, Band_Mag_Err, PSF_Mags_1, PSF_Mags_Err_1, WX, WY, Cata_RA, Cata_DEC, image_num)
write_out_data(image_num, WX, WY, Flux, Flux_Err, PSF_Mags_1, PSF_Mags_Err_1, Letter)
slope_, intercept_, slope_error_, intercept_error_ = psf_calibration_stats(Mag_WCS, Mag_Err_WCS, Mag_Cat, Mag_Err_Cat, Letter)
return slope_, intercept_, slope_error_, intercept_error_, PSF_Mags_1, PSF_Mags_Err_1, WCS_RA_DEC_PSF_
def build_epsf(image_num):
size = 7
hsize = (size - 1) / 2
peaks_tbl = find_peaks(Reduced_Image_Data[image_num], threshold = 750.)
x = peaks_tbl['x_peak']
y = peaks_tbl['y_peak']
mask = ((x > hsize) & (x < (Reduced_Image_Data[image_num].shape[1] -1 - hsize)) & (y > hsize) & (y < (Reduced_Image_Data[image_num].shape[0] -1 - hsize)))
stars_tbl = Table()
stars_tbl['x'] = x[mask]
stars_tbl['y'] = y[mask]
nddata = NDData(data = Reduced_Image_Data[image_num])
stars = extract_stars(nddata, stars_tbl, size = 10)
#nrows = 5
#ncols = 5
#fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize = (15, 15), squeeze = True)
#ax = ax.ravel()
#for i in range(nrows*ncols):
#norm = simple_norm(stars[i], 'log', percent = 99.)
#ax[i].imshow(stars[i], norm = norm, origin = 'lower', cmap = 'gray')
epsf_builder = EPSFBuilder(oversampling = 8, maxiters = 20, progress_bar = False)
epsf, fitted_stars = epsf_builder(stars)
#norm = simple_norm(epsf.data, 'log', percent = 99.)
#plt.imshow(epsf.data, norm = norm, origin = 'lower', cmap = 'viridis')
#plt.colorbar()
return epsf
def do_psf(image_num, Letter):
epsf = build_epsf(image_num)
daofind = DAOStarFinder(fwhm = 8, threshold = 3. * Std[image_num])
sigma_psf = 2.0
daogroup = DAOGroup(2.0*sigma_psf*gaussian_sigma_to_fwhm)
photometry = IterativelySubtractedPSFPhotometry(finder = daofind,
group_maker = daogroup,
bkg_estimator = None,
psf_model = epsf,
fitter = LevMarLSQFitter(),
aperture_radius = 5,
niters = 1, fitshape = (11,11))
result_tab = photometry(image = Reduced_Image_Data[image_num])
#residual_image = photometry.get_residual_image()
X_0 = result_tab['x_fit']
Y_0 = result_tab['y_fit']
Flux = result_tab['flux_fit']
Flux_Err = np.sqrt(np.array(Flux))
PSF_Mags_1 = -2.5*np.log10(np.array(Flux)) # Convert aperture sums to magnitudes
PSF_Mags_Err_1 = ((-2.5*Flux_Err)/(np.log10(10)*Flux))
return PSF_Mags_1, PSF_Mags_Err_1, Flux, Flux_Err, X_0, Y_0
def source_pos_comparison(List, coord_loop_len, X_coord, Y_coord, RA, DEC, image_num, Letter):
Cata_RA = []
Cata_DEC = []
header = fits.getheader(f'RAW_DATA/{List[image_num]}')
w = WCS(header)
WX, WY = w.wcs_pix2world(np.array(X_coord), np.array(Y_coord), 0)
c = SkyCoord(RA*u.deg, DEC*u.deg)
catalog = SkyCoord(WX*u.deg, WY*u.deg)
max_sep = 1 * u.arcsec
idx, d2d, d3d = c.match_to_catalog_sky(catalog)
sep_constraint = d2d < max_sep
c_matches = c[sep_constraint]
catalog_matches = catalog[idx[sep_constraint]]
Cata_List = list(catalog_matches)
for m in range(len(catalog_matches)):
Cata_Val = str(Cata_List[m])[40 : -3]
Cata_Array = [float(idx) for idx in Cata_Val.split(', ')]
Cata_RA_ta = Cata_Array[0]
Cata_DEC_ta = Cata_Array[1]
Cata_RA.append(Cata_RA_ta)
Cata_DEC.append(Cata_DEC_ta)
with open(f'CATALOGS/' + f'PSF_{Letter}_{image_num}_my_catalog.tsv', 'w') as out_file:
tsv_writer = csv.writer(out_file, delimiter = '\t')
for n in range(len(Cata_RA)):
tsv_writer.writerow([Cata_RA[n], Cata_DEC[n]])
return catalog_matches, c_matches, Cata_RA, Cata_DEC, WX, WY
def cross_correlation(Position, Band, Band_Err, Mags, Mag_Err, WX, WY, Cata_RA, Cata_DEC, image_num):
Pos_Mag_Idx = []
WCS_Mag_Idx = []
Mag_Cat = []
Mag_Err_Cat = []
Mag_WCS = []
Mag_Err_WCS = []
Cata_RA_DEC = [(Cata_RA[i], Cata_DEC[i]) for i in range(0, len(Cata_RA))]
WCS_RA_DEC_PSF = [(WX[i], WY[i]) for i in range(0, len(WX))]
Tree_Cat = spatial.KDTree(Position)
for i in range(len(Cata_RA_DEC)):
Idx = Tree_Cat.query(Cata_RA_DEC[i])
Pos_Mag_Idx.append(Idx[1])
for j in range(len(Pos_Mag_Idx)):
Mag_Cat.append(Band[Pos_Mag_Idx[j]])
Mag_Err_Cat.append(Band_Err[Pos_Mag_Idx[j]])
Tree_WCS = spatial.KDTree(WCS_RA_DEC_PSF)
for k in range(len(Cata_RA_DEC)):
Idx_WCS = Tree_WCS.query(Cata_RA_DEC[k])
WCS_Mag_Idx.append(Idx_WCS[1])
for l in range(len(WCS_Mag_Idx)):
Mag_WCS.append(Mags[WCS_Mag_Idx[l]])
for m in range(len(WCS_Mag_Idx)):
Mag_Err_WCS.append(Mag_Err[WCS_Mag_Idx[m]])
Source_Matching_PSF = [(WCS_Mag_Idx[i], Pos_Mag_Idx[i]) for i in range(0, len(WCS_Mag_Idx))]
return np.array(Mag_Cat), np.array(Mag_Err_Cat), np.array(Mag_WCS), np.array(Mag_Err_WCS), WCS_RA_DEC_PSF, Source_Matching_PSF
def psf_calibration_stats(Mag_WCS, Mag_Err_WCS, Mag_Cat, Mag_Err_Cat, Letter):
Mag_Cat = Mag_Cat[np.logical_not(np.isnan(Mag_WCS))]
Mag_Err_Cat = Mag_Err_Cat[np.logical_not(np.isnan(Mag_WCS))]
Mag_Err_WCS = Mag_Err_WCS[np.logical_not(np.isnan(np.array(Mag_WCS)))]
Mag_WCS = Mag_WCS[np.logical_not(np.isnan(np.array(Mag_WCS)))]
indices = np.where(np.absolute(zscore(Mag_WCS)) > 5)[0]
indices_filter = [i for i,n in enumerate(Mag_WCS) if i not in indices]
PSF_Mags = Mag_WCS[indices_filter]
PSF_Mags_Err = Mag_Err_WCS[indices_filter]
Mag_Cat = Mag_Cat[indices_filter]
Mag_Err_Cat = Mag_Err_Cat[indices_filter]
plt.figure(1)
plt.plot(PSF_Mags, Mag_Cat, 'o')
plt.errorbar(PSF_Mags, Mag_Cat, yerr = np.array(Mag_Err_Cat), xerr = np.array(PSF_Mags_Err), linestyle = 'None')
linear_coeff, cov = np.polyfit(PSF_Mags, Mag_Cat, 1, w = 1/np.array(Mag_Err_Cat), cov = True)
slope_ = linear_coeff[0]
intercept_ = linear_coeff[1]
fit_error = np.sqrt(np.diagonal(cov))
slope_error_ = fit_error[0]
intercept_error_ = fit_error[1]
x_new = np.linspace(-15, -7.5, 5000)
y_new = np.poly1d(linear_coeff)
plt.plot(x_new, y_new(x_new), label = 'Line of Best Fit')
plt.xlabel('Instrumental Magnitude')
plt.ylabel('Standard Star File Magnitude')
plt.title(f'Photometric Calibration Plot Using {Letter} Band Files (PSF)')
plt.legend(loc = 'best')
Calibrated_Mag = slope_ * PSF_Mags + intercept_
Mag_Diff = (Mag_Cat - Calibrated_Mag)*10
Mean_Diff = mean(Mag_Diff)
Median_Diff = statistics.median(Mag_Diff)
Stdev_Diff = statistics.stdev(Mag_Diff, Mean_Diff)
plt.figure(2)
plt.hist(Mag_Diff, bins = 'auto', facecolor = 'blue', edgecolor = 'k', alpha = 0.75)
plt.xlabel('Percentage [%]')
plt.ylabel('Number')
plt.title('Histogram of Percentage Differnce in Calibrated Stars vs Standard Star Magnitudes (PSF)')
plt.text(1, 15, f'$\mu$ = {Mean_Diff} % \n \n $\sigma$ = {Stdev_Diff} % \n \n Median = {Median_Diff} % ')
plt.axvline(Median_Diff, color = 'k', linestyle = 'dashed', linewidth = 2)
plt.show()
return slope_, intercept_, slope_error_, intercept_error_
def write_out_data(image_num, WX, WY, Flux, Flux_Err, Mags, Mag_Err, Letter):
with open(f'FIT_DATA/PSF_{Letter}_Data_{image_num}.csv', 'w') as out_file:
csv_writer = csv.writer(out_file, delimiter = ',')
csv_writer.writerow(['Label', 'RA', 'DEC', 'Flux', 'Flux_Err', 'Mag', 'Err_Mag'])
for n in range(len(Mags)):
csv_writer.writerow([f'Star{n}', WX[n], WY[n], Flux[n], Flux_Err[n], Mags[n], Mag_Err[n]])
return
def main_psf_data(Band, SSF_Mag, SSF_Mag_Err, Letter):
PSF_Slope = []
PSF_Intercept = []
PSF_Slope_Err = []
PSF_Intercept_Err = []
PSF_Mags_1_ = []
PSF_Mags_Err_1_ = []
WCS_RA_DEC_PSF_ = []
for i in range(len(Band)):
slope_, intercept_, slope_error_, intercept_error_, PSF_Mags_1, PSF_Mags_Err_1, WCS_RA_DEC_PSF = psf_main(Band, SSF_Mag, SSF_Mag_Err, i, Letter)
PSF_Slope.append(slope_)
PSF_Intercept.append(intercept_)
PSF_Slope_Err.append(slope_error_)
PSF_Intercept_Err.append(intercept_error_)
PSF_Mags_1_.append(PSF_Mags_1)
PSF_Mags_Err_1_.append(PSF_Mags_Err_1)
WCS_RA_DEC_PSF_.append(WCS_RA_DEC_PSF)
print('PSF Photometry Completed')
return PSF_Slope, PSF_Intercept, PSF_Slope_Err, PSF_Intercept_Err, PSF_Mags_1_, PSF_Mags_Err_1_, WCS_RA_DEC_PSF_
PSF_Slope, PSF_Intercept, PSF_Slope_Err, PSF_Intercept_Err, PSF_Mags_1_, PSF_Mags_Err_1_, WCS_RA_DEC_PSF_ = main_psf_data(Z_Band_Files, Zmag, Err_Zmag, 'Z')
##################################################################
# This script performs PSF photometry on a set of exposure
# fits files by calulating an ePSF for each exposure This sciprt
# requires the background subtracted exposures, the standard star
# magnitudes for the band your running. The code is quite automatic,
# you can vary the aperture radius, threshold and FWHM on the
# starfinder argument on line 96 at your discretion.
##################################################################