def _measure_spot_intensities(
self, stack: ImageStack,
spot_attributes: pd.DataFrame) -> IntensityTable:
n_ch = stack.shape[Indices.CH]
n_hyb = stack.shape[Indices.HYB]
spot_attribute_index = dataframe_to_multiindex(spot_attributes)
intensity_table = IntensityTable.empty_intensity_table(
spot_attribute_index, n_ch, n_hyb)
indices = product(range(n_ch), range(n_hyb))
for c, h in indices:
image, _ = stack.get_slice({Indices.CH: c, Indices.HYB: h})
blob_intensities: pd.Series = self._measure_blob_intensity(
image, spot_attributes, self.measurement_function)
intensity_table[:, c, h] = blob_intensities
return intensity_table
def register(self, image: ImageStack):
# TODO: (ambrosejcarr) is this the appropriate way of dealing with Z in registration?
mp = image.max_proj(Indices.CH, Indices.Z)
reference_image = self.reference_stack.max_proj(
Indices.HYB, Indices.CH, Indices.Z)
for h in range(image.num_hybs):
# compute shift between maximum projection (across channels) and dots, for each hyb round
# TODO: make the max projection array ignorant of axes ordering.
shift, error = compute_shift(mp[h, :, :], reference_image,
self.upsampling)
print("For hyb: {}, Shift: {}, Error: {}".format(h, shift, error))
for c in range(image.num_chs):
for z in range(image.num_zlayers):
# apply shift to all zlayers, channels, and hyb rounds
indices = {Indices.HYB: h, Indices.CH: c, Indices.Z: z}
data, axes = image.get_slice(indices=indices)
assert len(axes) == 0
result = shift_im(data, shift)
image.set_slice(indices=indices, data=result)
return image