def it_skips_too_oval():
"""It doesn't have to work perfectly every time, but pass once in a 10 try loop"""
n_fails = 0
for i in range(10):
failed = 0
locs = [[64, 54], [64, 70]]
with synth.Synth(overwrite=True, dim=(128, 128)) as s:
peaks = synth.PeaksModelGaussianCircular(
n_peaks=len(locs)
).amps_constant(val=4_000)
peaks.std_x = [1.0, 1.0]
peaks.std_y = [1.0, 2.0]
synth.CameraModel(bias=bg_mean, std=bg_std)
peaks.locs = locs
im = s.render_chcy()[0, 0]
im = im - bg_mean
psf, reasons = worker._psf_estimate(
im, peaks.locs, mea=17, return_reasons=True
)
for loc, reason in zip(peaks.locs, reasons):
if loc[1] < 64:
if reason[worker.PSFEstimateMaskFields.skipped_too_oval] != 0:
failed += 1
else:
if reason[worker.PSFEstimateMaskFields.skipped_too_oval] != 1:
failed += 1
if failed != 0:
n_fails += 1
assert n_fails < 3
def it_skips_near_edges():
peaks, im, std = _make_image(0.0)
psf, reasons = worker._psf_estimate(im, peaks.locs, mea=17, return_reasons=True)
for loc, reason in zip(peaks.locs, reasons):
skipped = reason[worker.PSFEstimateMaskFields.skipped_near_edges]
if loc[0] < 5 or loc[0] > 1024 - 5 or loc[1] < 5 or loc[1] > 1024 - 5:
assert skipped == 1
def it_skips_nans():
peaks, im = _make_image_n_locs([[64, 50], [64, 70]])
im[64, 50] = np.nan
psf, reasons = worker._psf_estimate(im, peaks.locs, mea=17, return_reasons=True)
for loc, reason in zip(peaks.locs, reasons):
if loc[1] < 64:
assert reason[worker.PSFEstimateMaskFields.skipped_has_nan] == 1
else:
assert reason[worker.PSFEstimateMaskFields.skipped_has_nan] == 0
def it_skips_darks():
peaks, im = _make_image_n_locs([[64, 54]])
psf, reasons = worker._psf_estimate(
im, peaks.locs, mea=17, threshold_abs=bg_std * 3, return_reasons=True
)
peaks.locs = [[64, 54], [64, 80]]
for loc, reason in zip(peaks.locs, reasons):
if loc[1] < 64:
assert reason[worker.PSFEstimateMaskFields.skipped_too_dark] == 0
else:
assert reason[worker.PSFEstimateMaskFields.skipped_too_dark] == 1
def it_extracts_a_clean_psf_with_subpixel_alignment():
# Sweep over various psf z depths
# Generate a seris of small synth images (representing a region over many fields)
# Pass in the locs directly from the synth image maker (bypassing peak finder)
# Check that we get back a good approximation of the PSF
for i, depth in enumerate(np.linspace(-0.25, 0.25, 4)):
peaks, im, expected_std = _make_image(depth)
psf, reasons = worker._psf_estimate(
im, peaks.locs, mea=17, return_reasons=True
)
fit_params, _ = imops.fit_gauss2(psf)
got = np.array(fit_params)
expected = np.array([np.nan, expected_std, expected_std, 8, 8, 0, 0, 17])
assert np.all((got[1:] - expected[1:]) ** 2 < 0.15 ** 2)
def it_does_not_return_reasons_if_requested():
im = np.zeros((100, 100))
psf = worker._psf_estimate(im, [[64, 64]], mea=17, return_reasons=False)
assert psf.shape == (17, 17)
def it_returns_reason_by_default():
im = np.zeros((100, 100))
psf, reasons = worker._psf_estimate(im, [[64, 64]], mea=17)
assert psf.shape == (17, 17) and reasons.shape == (1, 8)
def it_normalizes():
peaks, im, std = _make_image(0.0, 100)
psf = worker._psf_estimate(im, peaks.locs, mea=17, return_reasons=False)
assert utils.np_within(np.sum(psf), 1.0, 0.001)
def it_does_not_skip_contentions():
peaks, im = _make_image_n_locs([[64, 50], [64, 60]])
psf, reasons = worker._psf_estimate(im, peaks.locs, mea=17, return_reasons=True)
for loc, reason in zip(peaks.locs, reasons):
assert reason[worker.PSFEstimateMaskFields.skipped_too_crowded] == 0