def isnr(images, fill_value=0.0):
"""
Streaming, pixelwise signal-to-noise ratio (SNR).
Parameters
----------
images : iterable of ndarray
These images should represent identical measurements. ``images`` can also be a generator.
fill_value : float, optional
Division-by-zero results will be filled with this value.
Yields
------
snr : `~numpy.ndarray`
Pixelwise signal-to-noise ratio
See Also
--------
snr_from_collection : pixelwise signal-to-noise ratio from a collection of measurements
"""
first, images = peek(images)
snr = np.empty_like(first)
images1, images2 = itercopy(images, 2)
for mean, std in zip(imean(images1), istd(images2)):
valid = std != 0
snr[valid] = mean[valid] / std[valid]
snr[np.logical_not(valid)] = fill_value
yield snr
def mask_from_collection(images, px_thresh=(0, 3e4), std_thresh=None):
"""
Determine binary mask from a set of images. These images should represent identical measurements, e.g. a set
of diffraction patterns before photoexcitation. Pixels are rejected on the following two criteria:
* Pixels with a value above a certain threshold or below zero, for any image in the set, are considered dead;
* Pixels with a cumulative standard deviation above a certain threshold are considered uncertain.
This function operates in constant-memory; it is therefore safe to use on a large collection
of images (>10GB).
Parameters
----------
images : iterable of ndarray
These images should represent identical measurements. ``images`` can also be a generator.
px_thresh : float or iterable, optional
Pixels with a value outside of [``min(px_thresh)``, ``max(px_thresh)``] in any of the images in ``images``
are rejected. If ``px_thresh`` is a single float, it is assumed to be the maximal intensity, and no lower
bound is enforced.
std_thresh : int or float or None, optional
Standard-deviation threshold. If the standard deviation of a pixel exceeds ``std_thresh``,
it is rejected. If None (default), a threshold is not enforced.
Returns
-------
mask : `~numpy.ndarray`, dtype bool
Pixel mask. Pixels where ``mask`` is True are invalid.
Notes
-----
``numpy.inf`` can be used to have a lower pixel value bound but no upper bound. For example, to
reject all negative pixels only, set ``px_thresh = (0, numpy.inf)``.
"""
if isinstance(px_thresh, Iterable):
min_int, max_int = min(px_thresh), max(px_thresh)
else:
min_int, max_int = None, px_thresh
first, images = peek(images)
mask = np.zeros_like(first, dtype=bool) # 0 = False
if std_thresh is not None:
images, images_for_std = itercopy(images)
std_calc = istd(images_for_std)
else:
std_calc = repeat(np.inf)
for image, std in zip(images, std_calc):
mask[image > max_int] = True
if std_thresh is not None:
mask[std > std_thresh] = True
if min_int is not None:
mask[image < min_int] = True
return mask
def test_against_numpy_std(self):
stream = [np.random.random((16, 7, 3)) for _ in range(10)]
stack = np.stack(stream, axis=-1)
with catch_warnings():
simplefilter("ignore")
for axis in (0, 1, 2, None):
for ddof in range(4):
with self.subTest("axis = {}, ddof = {}".format(axis, ddof)):
from_numpy = np.std(stack, axis=axis, ddof=ddof)
from_ivar = last(istd(stream, axis=axis, ddof=ddof))
self.assertSequenceEqual(from_numpy.shape, from_ivar.shape)
self.assertTrue(np.allclose(from_ivar, from_numpy))
def test_against_numpy_nanstd(self):
source = [np.random.random((16, 12, 5)) for _ in range(10)]
for arr in source:
arr[randint(0, 15), randint(0, 11), randint(0, 4)] = np.nan
stack = np.stack(source, axis=-1)
for axis in (0, 1, 2, None):
for ddof in range(4):
with self.subTest("axis = {}, ddof = {}".format(axis, ddof)):
from_numpy = np.nanstd(stack, axis=axis, ddof=ddof)
from_ivar = last(
istd(source, axis=axis, ddof=ddof, ignore_nan=True)
)
self.assertSequenceEqual(from_numpy.shape, from_ivar.shape)
self.assertTrue(np.allclose(from_ivar, from_numpy))
