def channel_labels(self) -> Series:
"""Return a Series with a string for each channel in the ROIs stack."""
if self._channel_labels is not None:
return self._channel_labels
sample = cast(self.sample)
# read channel labels specifically for ROI
channel_labels_file = Path(self.channel_labels_file
or sample.root_dir / self.stacks_dir /
(self.name + "_full.csv"))
if not channel_labels_file.exists():
msg = ("`channel_labels` was not given upon initialization "
f"and '{channel_labels_file}' could not be found!")
raise FileNotFoundError(msg)
# self._channel_labels = pd.read_csv(channel_labels_file, header=None, squeeze=True)
preview = pd.read_csv(channel_labels_file, header=None, squeeze=True)
if isinstance(preview, pd.Series):
order = preview.to_frame(
name="ChannelName").set_index("ChannelName")
# read reference
ref: DataFrame = cast(sample.panel_metadata)
ref = ref.loc[ref["AcquisitionID"].isin(
[self.roi_number, str(self.roi_number)])]
self._channel_labels = (order.join(ref.reset_index().set_index(
"ChannelName"))["index"].reset_index(
drop=True).rename("channel"))
else:
preview = preview.dropna().set_index(0).squeeze().rename("channel")
preview.index = preview.index.astype(int)
self._channel_labels = preview
return self._channel_labels
def read_image_from_file(file: Path, equalize: bool = False) -> Array:
"""
Read images from a tiff or hdf5 file into a numpy array.
Channels, if existing will be in first array dimension.
If `equalize` is :obj:`True`, convert to float type bounded at [0, 1].
"""
if not file.exists():
raise FileNotFoundError(f"Could not find file: '{file}")
# if str(file).endswith("_mask.tiff"):
# arr = tifffile.imread(file) > 0
if file.endswith(".ome.tiff"):
arr = tifffile.imread(str(file), is_ome=True)
elif file.endswith(".tiff"):
arr = tifffile.imread(str(file))
elif file.endswith(".h5"):
with h5py.File(file, "r") as __f:
arr = np.asarray(__f[list(__f.keys())[0]])
if len(arr.shape) == 3:
if min(arr.shape) == arr.shape[-1]:
arr = np.moveaxis(arr, -1, 0)
if equalize:
arr = eq(arr)
return arr