def intensity_histogram(image_stack: ImageStack,
sel: Optional[Mapping[Axes, Union[int, tuple]]] = None,
ax=None,
title: Optional[str] = None,
**kwargs) -> None:
"""
Plot the 1-d intensity histogram of linearized image_stack.
Parameters
----------
image_stack : ImageStack
imagestack containing intensities
sel : Optional[Mapping[Axes, Union[int, tuple]]]
Optional, Selector to pass ImageStack.sel that will restrict the histogram construction to
the specified subset of image_stack.
ax :
Axes to plot on. If not passed, defaults to the current axes.
title : Optional[str]
Title to assign the Axes being plotted on.
kwargs :
additional keyword arguments to pass to plt.hist
"""
if ax is None:
ax = plt.gca()
if sel is not None:
image_stack = image_stack.sel(sel)
if title is not None:
ax.set_title(title)
data: np.ndarray = np.ravel(image_stack.xarray)
ax.hist(data, **kwargs)
def imshow_plane(
image_stack: ImageStack,
sel: Optional[Mapping[Axes, Union[int, tuple]]] = None,
ax=None,
title: Optional[str] = None,
**kwargs,
) -> None:
"""
Plot a single plane of an ImageStack. If passed a selection function (sel), the stack will be
subset using :py:meth:`ImageStack.sel`. If ax is passed, the function will be plotted in the
provided axis. Additional kwargs are passed to :py:func:`plt.imshow`
Parameters
----------
image_stack : ImageStack
imagestack from which to extract a 2-d image for plotting
sel : Optional[Mapping[Axes, Union[int, tuple]]]
Optional, but only if image_stack is already of shape (1, 1, 1, y, x). Selector to pass
ImageStack.sel, Selects the (y, x) plane to be plotted.
ax :
Axes to plot on. If not passed, defaults to the current axes.
title : Optional[str]
Title to assign the Axes being plotted on.
kwargs :
additional keyword arguments to pass to plt.imshow
"""
if ax is None:
ax = plt.gca()
if sel is not None:
image_stack = image_stack.sel(sel)
if title is not None:
ax.set_title(title)
# verify imagestack is 2d before trying to plot it
data: xr.DataArray = image_stack.xarray.squeeze()
if set(data.sizes.keys()).intersection({Axes.CH, Axes.ROUND, Axes.ZPLANE}):
raise ValueError(
f"image_stack must be a 2d (x, y) array, not {data.sizes}")
# set imshow default kwargs
if "cmap" not in kwargs:
kwargs["cmap"] = plt.cm.gray
ax.imshow(data, **kwargs)
ax.axis("off")
def run(self,
stack: ImageStack,
verbose: bool = False,
*args) -> TransformsList:
"""
Iterate over the given axes of an ImageStack and learn the translation transform
based off the reference_stack passed into :py:class:`Translation`'s constructor.
Only supports 2d data.
Parameters
----------
stack : ImageStack
Stack to calculate the transforms on.
verbose : bool
if True, report on transformation progress (default = False)
Returns
-------
List[Tuple[Mapping[Axes, int], SimilarityTransform]] :
A list of tuples containing axes of the Imagestack and associated
transform to apply.
"""
transforms = TransformsList()
reference_image = np.squeeze(self.reference_stack.xarray)
for a in stack.axis_labels(self.axes):
target_image = np.squeeze(stack.sel({self.axes: a}).xarray)
if len(target_image.shape) != 2:
raise ValueError(
f"Only axes: {self.axes.value} can have a length > 1, "
f"please use the MaxProj filter.")
shift, error, phasediff = phase_cross_correlation(
reference_image=target_image.data,
moving_image=reference_image.data,
upsample_factor=self.upsampling)
if verbose:
print(f"For {self.axes}: {a}, Shift: {shift}, Error: {error}")
selectors = {self.axes: a}
# reverse shift because SimilarityTransform stores in y,x format
shift = shift[::-1]
transforms.append(selectors, TransformType.SIMILARITY,
SimilarityTransform(translation=shift))
return transforms
def overlay_spot_calls(
image_stack: ImageStack,
intensities: IntensityTable,
sel: Optional[Mapping[Axes, Union[int, tuple]]] = None,
ax=None,
title: Optional[str] = None,
imshow_kwargs: Optional[Mapping[str, Any]] = None,
scatter_kwargs: Optional[Mapping[str, Any]] = None,
) -> None:
"""
Overlays spot calls atop a 2-d image extracted from ImageStack. Manages sub-selection from the
IntensityTable and ImageStack based on provided `sel` parameter.
Parameters
----------
image_stack : ImageStack
imagestack from which to extract a 2-d image for plotting
intensities : IntensityTable
contains spots to overlay on ImageStack.
sel : Optional[Mapping[Axes, Union[int, tuple]]]
Optional, but only if image_stack is already of shape (1, 1, 1, y, x). Selector to pass
ImageStack.sel, Selects the (y, x) plane to be plotted. Will also be used to reduce the
spots from intensities.
ax :
Axes to plot on. If not passed, defaults to the current axes.
title : Optional[str]
Title to assign the Axes being plotted on.
imshow_kwargs : Optional[Mapping[str, Any]]
additional keyword arguments to pass to imshow
scatter_kwargs : Optional[Mapping[str, Any]]
additional keyword arguments to pass to scatter
"""
if ax is None:
ax = plt.gca()
if sel is not None:
image_stack = image_stack.sel(sel)
# subset the intensities if needed
intensity_keys = (Axes.ROUND.value, Axes.CH.value, Axes.ZPLANE)
intensity_sel = {x: sel[x] for x in intensity_keys if x in sel}
if intensity_sel:
intensities = intensities.sel(intensity_sel)
imshow_kwargs = imshow_kwargs if imshow_kwargs else {}
scatter_kwargs = scatter_kwargs if scatter_kwargs else {}
# plot background
imshow_plane(image_stack, sel=sel, ax=ax, title=title, **imshow_kwargs)
# plot spots
plt.scatter(
x=np.asarray(intensities[Axes.X.value]),
y=np.asarray(intensities[Axes.Y.value]),
s=np.asarray(intensities[Features.SPOT_RADIUS]),
c='red',
**scatter_kwargs,
)
# reset the axes limits; scatter often extends them.
ax.set_ylim((0, image_stack.shape[Axes.Y.value]))
ax.set_xlim((0, image_stack.shape[Axes.X.value]))
