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 test_against_numpy_mean(self):
""" Test results against numpy.mean"""
source = [np.random.random((16, 12, 5)) for _ in range(10)]
stack = np.stack(source, axis=-1)
for axis in (0, 1, 2, None):
with self.subTest("axis = {}".format(axis)):
from_numpy = np.mean(stack, axis=axis)
out = last(imean(source, axis=axis))
self.assertSequenceEqual(from_numpy.shape, out.shape)
self.assertTrue(np.allclose(out, from_numpy))