def it_returns_3_for_rho_one_half():
im = imops.gauss2_rho_form(10,
1.0,
1.0,
8,
8,
rho=0.5,
const=0,
mea=17)
ratio = imops.distribution_aspect_ratio(im)
assert (ratio - 3.0)**2 < 0.001**2
def it_returns_2_for_x_ellipse():
im = imops.gauss2_rho_form(10,
1.0,
1.5,
8,
8,
rho=0.0,
const=0,
mea=17)
ratio = imops.distribution_aspect_ratio(im)
assert (ratio - 2.25)**2 < 0.01**2
def it_returns_1_for_circluar():
im = imops.gauss2_rho_form(10,
1.0,
1.0,
8,
8,
rho=0.0,
const=0,
mea=17)
ratio = imops.distribution_aspect_ratio(im)
assert (ratio - 1.0)**2 < 0.001**2
def _psf_accumulate(im,
locs,
mea,
keep_dist=8,
threshold_abs=None,
return_reasons=True):
"""
Given a single im, typically a regional sub-image, accumulate
PSF evidence from each locs that meets a set of criteria.
Any one image may not produce enough (or any) candidate spots and it
is therefore expected that this function is called over a large number
of fields to get sufficient samples.
Arguments:
im: Expected to be a single field, channel, cycle (BG already removed).
locs: array (n, 2) in coordinates of im. Expected to be well-separated
mea: The peak_measure (must be odd)
threshold_abs: The average pixel brightness to accept the peak
keep_dist: Pixels distance to determine crowding
Returns:
psf: ndarray (mea, mea) image
reason_counts: An array of masks of why peaks were accepted/rejected
See PSFEstimateMaskFields for the columns
"""
from scipy.spatial.distance import cdist # Defer slow import
n_locs = len(locs)
dist = cdist(locs, locs, metric="euclidean")
dist[dist == 0.0] = np.nan
if not np.all(np.isnan(dist)):
closest_dist = np.nanmin(dist, axis=1)
else:
closest_dist = np.zeros(n_locs)
# Aligned peaks will accumulate into this psf matrix
dim = (mea, mea)
dim2 = (mea + 2, mea + 2)
psf = np.zeros(dim)
n_reason_mask_fields = len(PSFEstimateMaskFields)
reason_masks = np.zeros((n_locs, n_reason_mask_fields))
for i, (loc, closest_neighbor_dist) in enumerate(zip(locs, closest_dist)):
reason_masks[i, PSFEstimateMaskFields.considered] = 1
# EXTRACT a peak with extra pixels around the edges (dim2 not dim)
peak_im = imops.crop(im, off=YX(loc), dim=HW(dim2), center=True)
if peak_im.shape != dim2:
# Skip near edges
reason_masks[i, PSFEstimateMaskFields.skipped_near_edges] = 1
continue
if closest_neighbor_dist < keep_dist:
reason_masks[i, PSFEstimateMaskFields.skipped_too_crowded] = 1
continue
if np.any(np.isnan(peak_im)):
reason_masks[i, PSFEstimateMaskFields.skipped_has_nan] = 1
continue
# Sub-pixel align the peak to the center
assert not np.any(np.isnan(peak_im))
centered_peak_im = sub_pixel_center(peak_im.astype(np.float64))
# Removing ckipping as the noise should cancel out
# centered_peak_im = np.clip(centered_peak_im, a_min=0.0, a_max=None)
peak_max = np.max(centered_peak_im)
if peak_max == 0.0:
reason_masks[i, PSFEstimateMaskFields.skipped_empty] = 1
continue
if threshold_abs is not None and peak_max < threshold_abs:
# Reject spots that are not active
reason_masks[i, PSFEstimateMaskFields.skipped_too_dark] = 1
continue
r = imops.distribution_aspect_ratio(centered_peak_im)
if r > 2.0:
reason_masks[i, PSFEstimateMaskFields.skipped_too_oval] = 1
continue
# TRIM off the extra now
centered_peak_im = centered_peak_im[1:-1, 1:-1]
psf += centered_peak_im / np.sum(centered_peak_im)
reason_masks[i, PSFEstimateMaskFields.accepted] = 1
n_accepted = np.sum(reason_masks[:, PSFEstimateMaskFields.accepted])
if n_accepted > 0:
psf /= np.sum(psf)
if return_reasons:
return psf, reason_masks
return psf