def test_empty_intensity_table():
x = [1, 2]
y = [2, 3]
z = [1, 1]
r = [1, 1]
spot_attributes = pd.MultiIndex.from_arrays([x, y, z, r],
names=('x', 'y', 'z', 'r'))
empty = IntensityTable.empty_intensity_table(spot_attributes, 2, 2)
assert empty.shape == (2, 2, 2)
assert np.sum(empty.values) == 0
def measure_spot_intensities(
data_image: ImageStack,
spot_attributes: SpotAttributes,
measurement_function: Callable[[Sequence], Number],
radius_is_gyration: bool = False,
) -> IntensityTable:
"""given spots found from a reference image, find those spots across a data_image
Parameters
----------
data_image : ImageStack
ImageStack containing multiple volumes for which spots' intensities must be calculated
spot_attributes : pd.Dataframe
Locations and radii of spots
measurement_function : Callable[[Sequence], Number])
Function to apply over the spot volumes to identify the intensity (e.g. max, mean, ...)
radius_is_gyration : bool
if True, indicates that the radius corresponds to radius of gyration, which is a function of
spot intensity, but typically is a smaller unit than the sigma generated by blob_log.
In this case, the spot's bounding box is rounded up instead of down when measuring
intensity. (default False)
Returns
-------
IntensityTable :
3d tensor of (spot, channel, round) information for each coded spot
"""
# determine the shape of the intensity table
n_ch = data_image.shape[Indices.CH]
n_round = data_image.shape[Indices.ROUND]
# construct the empty intensity table
intensity_table = IntensityTable.empty_intensity_table(
spot_attributes=spot_attributes,
n_ch=n_ch,
n_round=n_round,
)
# fill the intensity table
indices = product(range(n_ch), range(n_round))
for c, r in indices:
image, _ = data_image.get_slice({Indices.CH: c, Indices.ROUND: r})
blob_intensities: pd.Series = measure_spot_intensity(
image,
spot_attributes,
measurement_function,
radius_is_gyration=radius_is_gyration)
intensity_table[:, c, r] = blob_intensities
return intensity_table
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 concatenate_spot_attributes_to_intensities(
spot_attributes: Sequence[Tuple[SpotAttributes, Dict[Indices, int]]]
) -> IntensityTable:
"""
Merge multiple spot attributes frames into a single IntensityTable without merging across
channels and imaging rounds
Parameters
----------
spot_attributes : Sequence[Tuple[SpotAttributes, Dict[Indices, int]]]
A sequence of SpotAttribute objects and the Indices (channel, round) that each object is
associated with.
Returns
-------
IntensityTable :
concatenated input SpotAttributes, converted to an IntensityTable object
"""
n_ch: int = max(inds[Indices.CH] for _, inds in spot_attributes) + 1
n_round: int = max(inds[Indices.ROUND] for _, inds in spot_attributes) + 1
all_spots = pd.concat([sa.data for sa, inds in spot_attributes])
# this drop call ensures only x, y, z, radius, and quality, are passed to the IntensityTable
features_coordinates = all_spots.drop(['spot_id', 'intensity'], axis=1)
intensity_table = IntensityTable.empty_intensity_table(
SpotAttributes(features_coordinates),
n_ch,
n_round,
)
i = 0
for attrs, inds in spot_attributes:
for _, row in attrs.data.iterrows():
intensity_table[i, inds[Indices.CH],
inds[Indices.ROUND]] = row['intensity']
i += 1
return intensity_table