forked from kbarbary/sep
-
Notifications
You must be signed in to change notification settings - Fork 0
/
test.py
executable file
·422 lines (327 loc) · 14.5 KB
/
test.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
#!/usr/bin/env py.test
from __future__ import print_function, division
# unicode_literals doesn't play well with numpy dtype field names
import os
import pytest
import numpy as np
from numpy.testing import assert_allclose, assert_equal, assert_approx_equal
import sep
# Try to import any FITS reader
try:
from fitsio import read as getdata
NO_FITS = False
except:
try:
from astropy.io.fits import getdata
NO_FITS = False
except:
NO_FITS = True
IMAGE_FNAME = os.path.join("data", "image.fits")
BACKIMAGE_FNAME = os.path.join("data", "back.fits")
IMAGECAT_FNAME = os.path.join("data", "image.cat")
IMAGECAT_DTYPE = [('number', np.int64),
('x', np.float64),
('y', np.float64),
('xwin', np.float64),
('ywin', np.float64),
('a', np.float64),
('flux_aper', np.float64),
('fluxerr_aper', np.float64),
('kron_radius', np.float64),
('flux_auto', np.float64),
('fluxerr_auto', np.float64),
('flux_radius', np.float64, (3,)),
('flags', np.int64)]
SUPPORTED_IMAGE_DTYPES = [np.float64, np.float32, np.int32]
def assert_allclose_structured(x, y):
"""Assert that two structured arrays are close.
Compares floats relatively and everything else exactly.
"""
assert x.dtype == y.dtype
for name in x.dtype.names:
if np.issubdtype(x.dtype[name], float):
assert_allclose(x[name], y[name])
else:
assert_equal(x[name], y[name])
# If we have a FITS reader, read in the necessary test images
if not NO_FITS:
image_data = getdata(IMAGE_FNAME)
image_refback = getdata(BACKIMAGE_FNAME)
# -----------------------------------------------------------------------------
# Test versus Source Extractor results
@pytest.mark.skipif(NO_FITS, reason="no FITS reader")
def test_vs_sextractor():
data = np.copy(image_data) # make an explicit copy so we can 'subfrom'
bkg = sep.Background(data, bw=64, bh=64, fw=3, fh=3)
# Test that SExtractor background is same as SEP:
bkgarr = bkg.back(dtype=np.float32)
assert_allclose(bkgarr, image_refback, rtol=1.e-5)
# Extract objects
bkg.subfrom(data)
objs = sep.extract(data, 1.5*bkg.globalrms)
objs = np.sort(objs, order=['y'])
# Read SExtractor result
refobjs = np.loadtxt(IMAGECAT_FNAME, dtype=IMAGECAT_DTYPE)
refobjs = np.sort(refobjs, order=['y'])
# Found correct number of sources at the right locations?
assert_allclose(objs['x'], refobjs['x'] - 1., atol=1.e-3)
assert_allclose(objs['y'], refobjs['y'] - 1., atol=1.e-3)
# Test aperture flux
flux, fluxerr, flag = sep.sum_circle(data, objs['x'], objs['y'], 5.,
err=bkg.globalrms)
assert_allclose(flux, refobjs['flux_aper'], rtol=2.e-4)
assert_allclose(fluxerr, refobjs['fluxerr_aper'], rtol=1.0e-5)
# check if the flags work at all (comparison values
assert ((flag & sep.APER_TRUNC) != 0).sum() == 4
assert ((flag & sep.APER_HASMASKED) != 0).sum() == 0
# Test "flux_auto"
kr, flag = sep.kron_radius(data, objs['x'], objs['y'], objs['a'],
objs['b'], objs['theta'], 6.0)
flux, fluxerr, flag = sep.sum_ellipse(data, objs['x'], objs['y'],
objs['a'], objs['b'],
objs['theta'], r=2.5 * kr,
err=bkg.globalrms, subpix=1)
# For some reason, object at index 59 doesn't match. It's very small
# and kron_radius is set to 0.0 in SExtractor, but 0.08 in sep.
# Most of the other values are within 1e-4 except one which is only
# within 0.01. This might be due to a change in SExtractor between
# v2.8.6 (used to generate "truth" catalog) and v2.18.11.
kr[59] = 0.0
flux[59] = 0.0
fluxerr[59] = 0.0
assert_allclose(2.5*kr, refobjs['kron_radius'], rtol=0.01)
assert_allclose(flux, refobjs['flux_auto'], rtol=0.01)
assert_allclose(fluxerr, refobjs['fluxerr_auto'], rtol=0.01)
# Test ellipse representation conversion
cxx, cyy, cxy = sep.ellipse_coeffs(objs['a'], objs['b'], objs['theta'])
assert_allclose(cxx, objs['cxx'], rtol=1.e-4)
assert_allclose(cyy, objs['cyy'], rtol=1.e-4)
assert_allclose(cxy, objs['cxy'], rtol=1.e-4)
a, b, theta = sep.ellipse_axes(objs['cxx'], objs['cyy'], objs['cxy'])
assert_allclose(a, objs['a'], rtol=1.e-4)
assert_allclose(b, objs['b'], rtol=1.e-4)
assert_allclose(theta, objs['theta'], rtol=1.e-4)
#test round trip
cxx, cyy, cxy = sep.ellipse_coeffs(a, b, theta)
assert_allclose(cxx, objs['cxx'], rtol=1.e-4)
assert_allclose(cyy, objs['cyy'], rtol=1.e-4)
assert_allclose(cxy, objs['cxy'], rtol=1.e-4)
# test flux_radius
fr, flags = sep.flux_radius(data, objs['x'], objs['y'], 6.*refobjs['a'],
[0.1, 0.5, 0.6], normflux=refobjs['flux_auto'],
subpix=5)
assert_allclose(fr, refobjs["flux_radius"], rtol=0.04, atol=0.01)
# test winpos
sig = 2. / 2.35 * fr[:, 1] # flux_radius = 0.5
xwin, ywin, flag = sep.winpos(data, objs['x'], objs['y'], sig)
assert_allclose(xwin, refobjs["xwin"] - 1., rtol=0., atol=0.0025)
assert_allclose(ywin, refobjs["ywin"] - 1., rtol=0., atol=0.0025)
# -----------------------------------------------------------------------------
# Background
def test_masked_background():
data = 0.1 * np.ones((6,6))
data[1,1] = 1.
data[4,1] = 1.
data[1,4] = 1.
data[4,4] = 1.
mask = np.zeros((6,6), dtype=np.bool)
# Background array without mask
sky = sep.Background(data, bw=3, bh=3, fw=1, fh=1)
bkg1 = sky.back()
# Background array with all False mask
sky = sep.Background(data, mask=mask, bw=3, bh=3, fw=1, fh=1)
bkg2 = sky.back()
# All False mask should be the same
assert_allclose(bkg1, bkg2)
# Masking high pixels should give a flat background
mask[1, 1] = True
mask[4, 1] = True
mask[1, 4] = True
mask[4, 4] = True
sky = sep.Background(data, mask=mask, bw=3, bh=3, fw=1, fh=1)
assert_approx_equal(sky.globalback, 0.1)
assert_allclose(sky.back(), 0.1 * np.ones((6, 6)))
# -----------------------------------------------------------------------------
# Extract
@pytest.mark.skipif(NO_FITS, reason="no FITS reader")
def test_extract_with_noise_array():
# Get some background-subtracted test data:
data = np.copy(image_data)
bkg = sep.Background(data, bw=64, bh=64, fw=3, fh=3)
bkg.subfrom(data)
# Ensure that extraction with constant noise array gives the expected
# result. We have to use conv=None here because the results are *not*
# the same when convolution is on! This is because the noise map is
# convolved. Near edges, the convolution doesn't adjust for pixels
# off edge boundaries. As a result, the convolved noise map is not
# all ones.
objects = sep.extract(data, 1.5*bkg.globalrms, conv=None)
objects2 = sep.extract(data, 1.5*bkg.globalrms, err=np.ones_like(data),
conv=None)
assert_equal(objects, objects2)
# Less trivial test where thresh is realistic. Still a flat noise map.
noise = bkg.globalrms * np.ones_like(data)
objects2 = sep.extract(data, 1.5, err=noise, conv=None)
assert_equal(objects, objects2)
def test_extract_with_noise_convolution():
"""Test extraction when there is both noise and convolution.
This will use the matched filter implementation, and will handle bad pixels
and edge effects gracefully.
"""
# Start with an empty image where we label the noise as 1 sigma everywhere.
image = np.zeros((20, 20))
error = np.ones((20, 20))
# Add some noise representing bad pixels. We do not want to detect these.
image[17, 3] = 100.
error[17, 3] = 100.
image[10, 0] = 100.
error[10, 0] = 100.
image[17, 17] = 100.
error[17, 17] = 100.
# Add some real point sources that we should find.
image[3, 17] = 10.
image[6, 6] = 2.0
image[7, 6] = 1.0
image[5, 6] = 1.0
image[6, 5] = 1.0
image[6, 7] = 1.0
objects = sep.extract(image, 2.0, minarea=1, err=error, use_matched_filter=True)
objects.sort(order=['x', 'y'])
# Check that we recovered the two correct objects and not the others.
assert len(objects) == 2
assert_approx_equal(objects[0]['x'], 6.)
assert_approx_equal(objects[0]['y'], 6.)
assert_approx_equal(objects[1]['x'], 17.)
assert_approx_equal(objects[1]['y'], 3.)
# -----------------------------------------------------------------------------
# aperture tests
naper = 1000
x = np.random.uniform(200., 800., naper)
y = np.random.uniform(200., 800., naper)
data_shape = (1000, 1000)
def test_aperture_dtypes():
"""Ensure that all supported image dtypes work in sum_circle() and
give the same answer"""
r = 3.
fluxes = []
for dt in SUPPORTED_IMAGE_DTYPES:
data = np.ones(data_shape, dtype=dt)
flux, fluxerr, flag = sep.sum_circle(data, x, y, r)
fluxes.append(flux)
for i in range(1, len(fluxes)):
assert_allclose(fluxes[0], fluxes[i])
def test_apertures_small_ellipse_exact():
"""Regression test for a bug that manifested primarily when x == y."""
data = np.ones(data_shape)
r = 0.3
rtol=1.e-10
flux, fluxerr, flag = sep.sum_ellipse(data, x, x, r, r, 0., subpix=0)
assert_allclose(flux, np.pi*r**2, rtol=rtol)
def test_apertures_all():
"""Test that aperture subpixel sampling works"""
data = np.random.rand(*data_shape)
r = 3.
rtol=1.e-8
for subpix in [0, 1, 5]:
flux_ref, fluxerr_ref, flag_ref = sep.sum_circle(data, x, y, r,
subpix=subpix)
flux, fluxerr, flag = sep.sum_circann(data, x, y, 0., r,
subpix=subpix)
assert_allclose(flux, flux_ref, rtol=rtol)
flux, fluxerr, flag = sep.sum_ellipse(data, x, y, r, r, 0.,
subpix=subpix)
assert_allclose(flux, flux_ref, rtol=rtol)
flux, fluxerr, flag = sep.sum_ellipse(data, x, y, 1., 1., 0., r=r,
subpix=subpix)
assert_allclose(flux, flux_ref, rtol=rtol)
def test_apertures_exact():
"""Test area as measured by exact aperture modes on array of ones"""
theta = np.random.uniform(-np.pi/2., np.pi/2., naper)
ratio = np.random.uniform(0.2, 1.0, naper)
r = 3.
for dt in SUPPORTED_IMAGE_DTYPES:
data = np.ones(data_shape, dtype=dt)
for r in [0.5, 1., 3.]:
flux, fluxerr, flag = sep.sum_circle(data, x, y, r, subpix=0)
assert_allclose(flux, np.pi*r**2)
rout = r*1.1
flux, fluxerr, flag = sep.sum_circann(data, x, y, r, rout,
subpix=0)
assert_allclose(flux, np.pi*(rout**2 - r**2))
flux, fluxerr, flag = sep.sum_ellipse(data, x, y, 1., ratio,
theta, r=r, subpix=0)
assert_allclose(flux, np.pi*ratio*r**2)
rout = r*1.1
flux, fluxerr, flag = sep.sum_ellipann(data, x, y, 1., ratio,
theta, r, rout, subpix=0)
assert_allclose(flux, np.pi*ratio*(rout**2 - r**2))
def test_aperture_bkgann_overlapping():
"""Test bkgann functionality in circular & elliptical apertures."""
# If bkgann overlaps aperture exactly, result should be zero
# (with subpix=1)
data = np.random.rand(*data_shape)
r = 5.
f, _, _ = sep.sum_circle(data, x, y, r, bkgann=(0., r), subpix=1)
assert_allclose(f, 0., rtol=0., atol=1.e-13)
f, _, _ = sep.sum_ellipse(data, x, y, 2., 1., np.pi/4., r=r,
bkgann=(0., r), subpix=1)
assert_allclose(f, 0., rtol=0., atol=1.e-13)
def test_aperture_bkgann_ones():
"""Test bkgann functionality with flat data"""
data = np.ones(data_shape)
r=5.
bkgann=(6., 8.)
# On flat data, result should be zero for any bkgann and subpix
f, fe, _ = sep.sum_circle(data, x, y, r, bkgann=bkgann, gain=1.)
assert_allclose(f, 0., rtol=0., atol=1.e-13)
# for all ones data and no error array, error should be close to
# sqrt(Npix_aper + Npix_ann * (Npix_aper**2 / Npix_ann**2))
aper_area = np.pi * r**2
bkg_area = np.pi * (bkgann[1]**2 - bkgann[0]**2)
expected_error = np.sqrt(aper_area + bkg_area * (aper_area/bkg_area)**2)
assert_allclose(fe, expected_error, rtol=0.1)
f, _, _ = sep.sum_ellipse(data, x, y, 2., 1., np.pi/4., r, bkgann=bkgann)
assert_allclose(f, 0., rtol=0., atol=1.e-13)
def test_mask_ellipse():
arr = np.zeros((20, 20), dtype=np.bool)
# should mask 5 pixels:
sep.mask_ellipse(arr, 10., 10., 1.0, 1.0, 0.0, r=1.001)
assert arr.sum() == 5
# should mask 13 pixels:
sep.mask_ellipse(arr, 10., 10., 1.0, 1.0, 0.0, r=2.001)
assert arr.sum() == 13
def test_flux_radius():
data = np.ones(data_shape)
fluxfrac = [0.2**2, 0.3**2, 0.7**2, 1.]
true_r = [2., 3., 7., 10.]
r, _ = sep.flux_radius(data, x, y, 10.*np.ones_like(x),
[0.2**2, 0.3**2, 0.7**2, 1.], subpix=5)
for i in range(len(fluxfrac)):
assert_allclose(r[:, i], true_r[i], rtol=0.01)
def test_mask_ellipse_dep():
"""Deprecated version of mask_ellipse"""
arr = np.zeros((20, 20), dtype=np.bool)
# should mask 5 pixels:
sep.mask_ellipse(arr, 10., 10., cxx=1.0, cyy=1.0, cxy=0.0, scale=1.001)
assert arr.sum() == 5
# should mask 13 pixels:
sep.mask_ellipse(arr, 10., 10., cxx=1.0, cyy=1.0, cxy=0.0, scale=2.001)
assert arr.sum() == 13
# -----------------------------------------------------------------------------
# General behavior and utilities
def test_byte_order_exception():
"""Test that error about byte order is raised with non-native
byte order input array. This should happen for Background, extract,
and aperture functions."""
data = np.ones((100, 100), dtype=np.float64)
data = data.byteswap(True).newbyteorder()
with pytest.raises(ValueError) as excinfo:
bkg = sep.Background(data)
assert 'byte order' in str(excinfo.value)
def test_set_pixstack():
"""Ensure that setting the pixel stack size works."""
old = sep.get_extract_pixstack()
new = old * 2
sep.set_extract_pixstack(new)
assert new == sep.get_extract_pixstack()
sep.set_extract_pixstack(old)