Coverage for shear_meas_test / test_shear_meas.py: 8%
255 statements
« prev ^ index » next coverage.py v7.13.4, created at 2026-03-02 01:40 -0800
1import time
2import copy
3import numpy as np
4import ngmix
5import galsim
6import metadetect
7from esutil.pbar import PBar
8import joblib
10import pytest
13TEST_METADETECT_CONFIG = {
14 "model": "wmom",
16 'weight': {
17 'fwhm': 1.2, # arcsec
18 },
20 'metacal': {
21 'psf': 'fitgauss',
22 'types': ['noshear', '1p', '1m', '2p', '2m'],
23 },
25 'sx': {
26 # in sky sigma
27 # DETECT_THRESH
28 'detect_thresh': 0.8,
30 # Minimum contrast parameter for deblending
31 # DEBLEND_MINCONT
32 'deblend_cont': 0.00001,
34 # minimum number of pixels above threshold
35 # DETECT_MINAREA: 6
36 'minarea': 4,
38 'filter_type': 'conv',
40 # 7x7 convolution mask of a gaussian PSF with FWHM = 3.0 pixels.
41 'filter_kernel': [
42 [0.004963, 0.021388, 0.051328, 0.068707, 0.051328, 0.021388, 0.004963], # noqa
43 [0.021388, 0.092163, 0.221178, 0.296069, 0.221178, 0.092163, 0.021388], # noqa
44 [0.051328, 0.221178, 0.530797, 0.710525, 0.530797, 0.221178, 0.051328], # noqa
45 [0.068707, 0.296069, 0.710525, 0.951108, 0.710525, 0.296069, 0.068707], # noqa
46 [0.051328, 0.221178, 0.530797, 0.710525, 0.530797, 0.221178, 0.051328], # noqa
47 [0.021388, 0.092163, 0.221178, 0.296069, 0.221178, 0.092163, 0.021388], # noqa
48 [0.004963, 0.021388, 0.051328, 0.068707, 0.051328, 0.021388, 0.004963], # noqa
49 ]
50 },
52 'meds': {
53 'min_box_size': 32,
54 'max_box_size': 32,
56 'box_type': 'iso_radius',
58 'rad_min': 4,
59 'rad_fac': 2,
60 'box_padding': 2,
61 },
63 # check for an edge hit
64 'bmask_flags': 2**30,
66 'nodet_flags': 2**0,
67}
70def make_sim(
71 *,
72 seed,
73 g1,
74 g2,
75 dim=251,
76 buff=34,
77 scale=0.25,
78 dens=100,
79 ngrid=7,
80 snr=1e6,
81):
82 rng = np.random.RandomState(seed=seed)
84 half_loc = (dim-buff*2)*scale/2
86 if ngrid is None:
87 area_arcmin2 = ((dim - buff*2)*scale/60)**2
88 nobj = int(dens * area_arcmin2)
89 x = rng.uniform(low=-half_loc, high=half_loc, size=nobj)
90 y = rng.uniform(low=-half_loc, high=half_loc, size=nobj)
91 else:
92 half_ngrid = (ngrid-1)/2
93 x, y = np.meshgrid(np.arange(ngrid), np.arange(ngrid))
94 x = (x.ravel() - half_ngrid)/half_ngrid * half_loc
95 y = (y.ravel() - half_ngrid)/half_ngrid * half_loc
96 nobj = x.shape[0]
98 cen = (dim-1)/2
99 psf_dim = 53
100 psf_cen = (psf_dim-1)/2
102 psf = galsim.Gaussian(fwhm=0.9)
103 gals = []
104 for ind in range(nobj):
105 u, v = rng.uniform(low=-scale, high=scale, size=2)
106 u += x[ind]
107 v += y[ind]
108 gals.append(galsim.Exponential(half_light_radius=0.5).shift(u, v))
109 gals = galsim.Add(gals)
110 gals = gals.shear(g1=g1, g2=g2)
111 gals = galsim.Convolve([gals, psf])
113 im = gals.drawImage(nx=dim, ny=dim, scale=scale).array
114 psf_im = psf.drawImage(nx=psf_dim, ny=psf_dim, scale=scale).array
116 nse = (
117 np.sqrt(np.sum(
118 galsim.Convolve([
119 psf,
120 galsim.Exponential(half_light_radius=0.5),
121 ]).drawImage(scale=0.25).array**2)
122 )
123 / snr
124 )
126 im += rng.normal(size=im.shape, scale=nse)
127 wgt = np.ones_like(im) / nse**2
128 jac = ngmix.DiagonalJacobian(scale=scale, row=cen, col=cen)
129 psf_jac = ngmix.DiagonalJacobian(scale=scale, row=psf_cen, col=psf_cen)
131 obs = ngmix.Observation(
132 image=im,
133 weight=wgt,
134 jacobian=jac,
135 ormask=np.zeros_like(im, dtype=np.int32),
136 bmask=np.zeros_like(im, dtype=np.int32),
137 psf=ngmix.Observation(
138 image=psf_im,
139 jacobian=psf_jac,
140 ),
141 )
142 mbobs = ngmix.MultiBandObsList()
143 obslist = ngmix.ObsList()
144 obslist.append(obs)
145 mbobs.append(obslist)
146 return mbobs
149def make_sim_color(
150 *,
151 seed,
152 g1,
153 g2,
154 dim=251,
155 buff=34,
156 scale=0.25,
157 dens=100,
158 ngrid=7,
159 snr=1e6,
160):
161 rng = np.random.RandomState(seed=seed)
163 half_loc = (dim-buff*2)*scale/2
165 if ngrid is None:
166 area_arcmin2 = ((dim - buff*2)*scale/60)**2
167 nobj = int(dens * area_arcmin2)
168 x = rng.uniform(low=-half_loc, high=half_loc, size=nobj)
169 y = rng.uniform(low=-half_loc, high=half_loc, size=nobj)
170 else:
171 half_ngrid = (ngrid-1)/2
172 x, y = np.meshgrid(np.arange(ngrid), np.arange(ngrid))
173 x = (x.ravel() - half_ngrid)/half_ngrid * half_loc
174 y = (y.ravel() - half_ngrid)/half_ngrid * half_loc
175 nobj = x.shape[0]
177 cen = (dim-1)/2
178 psf_dim = 53
179 psf_cen = (psf_dim-1)/2
180 us = rng.uniform(low=-scale, high=scale, size=nobj)
181 vs = rng.uniform(low=-scale, high=scale, size=nobj)
182 colors = rng.randint(low=0, high=2, size=nobj)
184 mbobs = ngmix.MultiBandObsList()
185 base_psf = galsim.Gaussian(fwhm=0.9)
186 # this dict holds the PSFs as a function of color (c0 or c1) and band (index 0 or 1)
187 psfs = {
188 "c0": [galsim.Gaussian(fwhm=0.83), galsim.Gaussian(fwhm=0.96)],
189 "c1": [galsim.Gaussian(fwhm=0.88), galsim.Gaussian(fwhm=1.03)],
190 }
191 for band in range(2):
192 # we render all objects with the same color at once and so loop through the
193 # colors here
194 for color in range(2):
195 gals = []
196 psf = psfs["c%d" % color][band]
197 for ind in range(nobj):
198 # we only render objects at this color
199 if colors[ind] != color:
200 continue
202 # the object should be brighter in one band than the other
203 # brighter in band 0 is c0 and thew opposite is c1
204 if colors[ind] == band:
205 flux = 0.8
206 else:
207 flux = 0.2
209 u = us[ind] + x[ind]
210 v = vs[ind] + y[ind]
211 gals.append(
212 flux * galsim.Exponential(half_light_radius=0.5).shift(u, v)
213 )
214 gals = galsim.Add(gals)
215 gals = gals.shear(g1=g1, g2=g2)
216 gals = galsim.Convolve([gals, psf])
218 if color == 0:
219 im = gals.drawImage(nx=dim, ny=dim, scale=scale).array
220 else:
221 im += gals.drawImage(nx=dim, ny=dim, scale=scale).array
223 psf_im = base_psf.drawImage(nx=psf_dim, ny=psf_dim, scale=scale).array
225 nse = (
226 np.sqrt(np.sum(
227 galsim.Convolve([
228 base_psf,
229 galsim.Exponential(half_light_radius=0.5),
230 ]).drawImage(scale=0.25).array**2)
231 )
232 / snr
233 )
235 im += rng.normal(size=im.shape, scale=nse)
236 wgt = np.ones_like(im) / nse**2
237 jac = ngmix.DiagonalJacobian(scale=scale, row=cen, col=cen)
238 psf_jac = ngmix.DiagonalJacobian(scale=scale, row=psf_cen, col=psf_cen)
240 obs = ngmix.Observation(
241 image=im,
242 weight=wgt,
243 jacobian=jac,
244 ormask=np.zeros_like(im, dtype=np.int32),
245 bmask=np.zeros_like(im, dtype=np.int32),
246 psf=ngmix.Observation(
247 image=psf_im,
248 jacobian=psf_jac,
249 ),
250 )
251 obslist = ngmix.ObsList()
252 obslist.append(obs)
253 mbobs.append(obslist)
255 # these are the observations with the correct PSFs for a given color
256 color_dep_mbobs = {
257 "c0": mbobs.copy(),
258 "c1": mbobs.copy(),
259 }
260 for color in range(2):
261 for band in range(2):
262 psf = psfs["c%d" % color][band]
263 psf_im = psf.drawImage(nx=psf_dim, ny=psf_dim, scale=scale).array
264 psf_jac = ngmix.DiagonalJacobian(scale=scale, row=psf_cen, col=psf_cen)
266 psf = ngmix.Observation(
267 image=psf_im,
268 jacobian=psf_jac,
269 )
270 color_dep_mbobs["c%d" % color][band][0].psf = psf
272 return mbobs, color_dep_mbobs
275def _shear_cuts(arr, model):
276 if model == "wmom":
277 tmin = 1.2
278 else:
279 tmin = 0.5
280 msk = (
281 (arr[f'{model}_flags'] == 0)
282 & (arr[f'{model}_s2n'] > 10)
283 & (arr[f'{model}_T_ratio'] > tmin)
284 )
285 return msk
288def _meas_shear_data(res, model):
289 msk = _shear_cuts(res['noshear'], model)
290 g1 = np.mean(res['noshear'][f'{model}_g'][msk, 0])
291 g2 = np.mean(res['noshear'][f'{model}_g'][msk, 1])
293 msk = _shear_cuts(res['1p'], model)
294 g1_1p = np.mean(res['1p'][f'{model}_g'][msk, 0])
295 msk = _shear_cuts(res['1m'], model)
296 g1_1m = np.mean(res['1m'][f'{model}_g'][msk, 0])
297 R11 = (g1_1p - g1_1m) / 0.02
299 msk = _shear_cuts(res['2p'], model)
300 g2_2p = np.mean(res['2p'][f'{model}_g'][msk, 1])
301 msk = _shear_cuts(res['2m'], model)
302 g2_2m = np.mean(res['2m'][f'{model}_g'][msk, 1])
303 R22 = (g2_2p - g2_2m) / 0.02
305 dt = [
306 ('g1', 'f8'),
307 ('g2', 'f8'),
308 ('R11', 'f8'),
309 ('R22', 'f8')]
310 return np.array([(g1, g2, R11, R22)], dtype=dt)
313def _bootstrap_stat(d1, d2, func, seed, nboot=500):
314 dim = d1.shape[0]
315 rng = np.random.RandomState(seed=seed)
316 stats = []
317 for _ in range(nboot):
318 ind = rng.choice(dim, size=dim, replace=True)
319 stats.append(func(d1[ind], d2[ind]))
320 return stats
323def meas_m_c_cancel(pres, mres):
324 x = np.mean(pres['g1'] - mres['g1'])/2
325 y = np.mean(pres['R11'] + mres['R11'])/2
326 m = x/y/0.02 - 1
328 x = np.mean(pres['g2'] + mres['g2'])/2
329 y = np.mean(pres['R22'] + mres['R22'])/2
330 c = x/y
332 return m, c
335def boostrap_m_c(pres, mres):
336 m, c = meas_m_c_cancel(pres, mres)
337 bdata = _bootstrap_stat(pres, mres, meas_m_c_cancel, 14324, nboot=500)
338 merr, cerr = np.std(bdata, axis=0)
339 return m, merr, c, cerr
342def run_sim(seed, mdet_seed, model, **kwargs):
343 mbobs_p = make_sim(seed=seed, g1=0.02, g2=0.0, **kwargs)
344 cfg = copy.deepcopy(TEST_METADETECT_CONFIG)
345 cfg["model"] = model
346 _pres = metadetect.do_metadetect(
347 copy.deepcopy(cfg),
348 mbobs_p,
349 np.random.RandomState(seed=mdet_seed)
350 )
351 if _pres is None:
352 return None
354 mbobs_m = make_sim(seed=seed, g1=-0.02, g2=0.0, **kwargs)
355 _mres = metadetect.do_metadetect(
356 copy.deepcopy(cfg),
357 mbobs_m,
358 np.random.RandomState(seed=mdet_seed)
359 )
360 if _mres is None:
361 return None
363 return _meas_shear_data(_pres, model), _meas_shear_data(_mres, model)
366def run_sim_color(seed, mdet_seed, model, **kwargs):
367 # if an object is bright in band 0 it is c0, otherwise it is c1
368 def _color_key_func(fluxes):
369 if fluxes[0] > fluxes[1]:
370 return "c0"
371 else:
372 return "c1"
374 mbobs_p, color_dep_mbobs_p = make_sim_color(seed=seed, g1=0.02, g2=0.0, **kwargs)
375 cfg = copy.deepcopy(TEST_METADETECT_CONFIG)
376 cfg["model"] = model
377 _pres = metadetect.do_metadetect(
378 copy.deepcopy(cfg),
379 mbobs_p,
380 np.random.RandomState(seed=mdet_seed),
381 color_dep_mbobs=color_dep_mbobs_p,
382 color_key_func=_color_key_func,
383 )
384 if _pres is None:
385 return None
387 mbobs_m, color_dep_mbobs_m = make_sim_color(seed=seed, g1=-0.02, g2=0.0, **kwargs)
388 _mres = metadetect.do_metadetect(
389 copy.deepcopy(cfg),
390 mbobs_m,
391 np.random.RandomState(seed=mdet_seed),
392 color_dep_mbobs=color_dep_mbobs_m,
393 color_key_func=_color_key_func,
394 )
395 if _mres is None:
396 return None
398 return _meas_shear_data(_pres, model), _meas_shear_data(_mres, model)
401@pytest.mark.parametrize(
402 'model,snr,ngrid,ntrial', [
403 ("wmom", 1e6, 7, 64),
404 ("pgauss", 1e6, 7, 64),
405 ]
406)
407def test_shear_meas_color(model, snr, ngrid, ntrial):
408 pytest.importorskip("sxdes")
410 nsub = max(ntrial // 100, 8)
411 nitr = ntrial // nsub
412 rng = np.random.RandomState(seed=116)
413 seeds = rng.randint(low=1, high=2**29, size=ntrial)
414 mdet_seeds = rng.randint(low=1, high=2**29, size=ntrial)
416 tm0 = time.time()
418 print("")
420 pres = []
421 mres = []
422 loc = 0
423 with joblib.Parallel(n_jobs=-1, verbose=100, backend='loky') as par:
424 for itr in PBar(range(nitr)):
425 jobs = [
426 joblib.delayed(run_sim_color)(
427 seeds[loc+i], mdet_seeds[loc+i], model, snr=snr, ngrid=ngrid,
428 )
429 for i in range(nsub)
430 ]
431 print("\n", end="", flush=True)
432 outputs = par(jobs)
434 for out in outputs:
435 if out is None:
436 continue
437 pres.append(out[0])
438 mres.append(out[1])
439 loc += nsub
441 m, merr, c, cerr = boostrap_m_c(
442 np.concatenate(pres),
443 np.concatenate(mres),
444 )
445 print(
446 (
447 "\n"
448 "nsims: %d\n"
449 "m [1e-3, 3sigma]: %s +/- %s\n"
450 "c [1e-5, 3sigma]: %s +/- %s\n"
451 "\n"
452 ) % (
453 len(pres),
454 m/1e-3,
455 3*merr/1e-3,
456 c/1e-5,
457 3*cerr/1e-5,
458 ),
459 flush=True,
460 )
462 total_time = time.time()-tm0
463 print("time per:", total_time/ntrial, flush=True)
465 pres = np.concatenate(pres)
466 mres = np.concatenate(mres)
467 m, merr, c, cerr = boostrap_m_c(pres, mres)
469 print(
470 (
471 "m [1e-3, 3sigma]: %s +/- %s"
472 "\nc [1e-5, 3sigma]: %s +/- %s"
473 ) % (
474 m/1e-3,
475 3*merr/1e-3,
476 c/1e-5,
477 3*cerr/1e-5,
478 ),
479 flush=True,
480 )
482 assert np.abs(m) < max(1e-3, 3*merr)
483 assert np.abs(c) < 3*cerr
486@pytest.mark.parametrize(
487 'model,snr,ngrid,ntrial', [
488 ("wmom", 1e6, 7, 64),
489 ("ksigma", 1e6, 7, 64),
490 ("pgauss", 1e6, 7, 64),
491 ("am", 1e6, 7, 64),
492 ("gauss", 1e6, 7, 64),
493 # this test takes ~3 hours on github actions so we only run it once and not
494 # with color
495 ("wmom", 1e6, None, 9500),
496 ]
497)
498def test_shear_meas_simple(model, snr, ngrid, ntrial):
499 pytest.importorskip("sxdes")
501 nsub = max(ntrial // 128, 8)
502 nitr = ntrial // nsub
503 rng = np.random.RandomState(seed=116)
504 seeds = rng.randint(low=1, high=2**29, size=ntrial)
505 mdet_seeds = rng.randint(low=1, high=2**29, size=ntrial)
507 tm0 = time.time()
509 print("")
511 pres = []
512 mres = []
513 loc = 0
514 with joblib.Parallel(n_jobs=-1, verbose=100, backend='loky') as par:
515 for itr in PBar(range(nitr)):
516 jobs = [
517 joblib.delayed(run_sim)(
518 seeds[loc+i], mdet_seeds[loc+i], model, snr=snr, ngrid=ngrid,
519 )
520 for i in range(nsub)
521 ]
522 print("\n", end="", flush=True)
523 outputs = par(jobs)
525 for out in outputs:
526 if out is None:
527 continue
528 pres.append(out[0])
529 mres.append(out[1])
530 loc += nsub
532 m, merr, c, cerr = boostrap_m_c(
533 np.concatenate(pres),
534 np.concatenate(mres),
535 )
536 print(
537 (
538 "\n"
539 "nsims: %d\n"
540 "m [1e-3, 3sigma]: %s +/- %s\n"
541 "c [1e-5, 3sigma]: %s +/- %s\n"
542 "\n"
543 ) % (
544 len(pres),
545 m/1e-3,
546 3*merr/1e-3,
547 c/1e-5,
548 3*cerr/1e-5,
549 ),
550 flush=True,
551 )
553 total_time = time.time()-tm0
554 print("time per:", total_time/ntrial, flush=True)
556 pres = np.concatenate(pres)
557 mres = np.concatenate(mres)
558 m, merr, c, cerr = boostrap_m_c(pres, mres)
560 print(
561 (
562 "m [1e-3, 3sigma]: %s +/- %s"
563 "\nc [1e-5, 3sigma]: %s +/- %s"
564 ) % (
565 m/1e-3,
566 3*merr/1e-3,
567 c/1e-5,
568 3*cerr/1e-5,
569 ),
570 flush=True,
571 )
573 assert np.abs(m) < max(1e-3, 3*merr)
574 assert np.abs(c) < 3*cerr
577@pytest.mark.parametrize(
578 'model,snr,ngrid', [
579 ("wmom", 1e6, 7),
580 ("ksigma", 1e6, 7),
581 ("pgauss", 1e6, 7),
582 ]
583)
584def test_shear_meas_timing(model, snr, ngrid):
585 pytest.importorskip("sxdes")
587 rng = np.random.RandomState(seed=116)
588 seeds = rng.randint(low=1, high=2**29, size=1)
589 mdet_seeds = rng.randint(low=1, high=2**29, size=1)
591 run_sim(
592 seeds[0], mdet_seeds[0], model, snr=snr, ngrid=ngrid,
593 )