From ce10e3358d82715664e43c2924ba1f1858f7ca2e Mon Sep 17 00:00:00 2001
From: pswain <peter.swain@ed.ac.uk>
Date: Fri, 2 Aug 2024 10:54:01 +0100
Subject: [PATCH] change: added typing; corrected docs
---
src/wela/imageviewer.py | 100 +++++++++++++++++++++++++---------------
1 file changed, 62 insertions(+), 38 deletions(-)
diff --git a/src/wela/imageviewer.py b/src/wela/imageviewer.py
index a2bfd5f..bed51f6 100644
--- a/src/wela/imageviewer.py
+++ b/src/wela/imageviewer.py
@@ -9,7 +9,10 @@ except ModuleNotFoundError:
"Napari cannot be imported.\nRun",
' python -m pip install "napari[all]"',
)
+from typing import Any, Dict, List
+
import numpy as np
+import numpy.typing as npt
from agora.io.cells import Cells
from aliby.io.image import dispatch_image
from aliby.io.omero import Dataset
@@ -47,7 +50,7 @@ class ImageViewer:
print(f" Trouble loading {image_file}.")
@classmethod
- def remote(cls, h5file: str, server_info: dict, omero_id: int):
+ def remote(cls, h5file: str, server_info: Dict, omero_id: int):
"""View images from OMERO."""
iv = cls(h5file)
with h5py.File(iv.h5file_path, "r") as f:
@@ -69,7 +72,9 @@ class ImageViewer:
iv.cells = Cells.from_source(iv.h5file_path)
return iv
- def get_all_traps_with_cells(self, tpt_end, tpt_start=0, display=True):
+ def get_all_traps_with_cells(
+ self, tpt_end: int, tpt_start: int = 0, display: bool = True
+ ):
"""List traps with cells."""
cells = self.cells
tpts = range(tpt_start, tpt_end)
@@ -84,7 +89,9 @@ class ImageViewer:
print(f"Traps with cells {list(traps_with_cells)}")
return traps_with_cells
- def sample_traps_with_cells(self, no_cells, tpt_end, tpt_start=0):
+ def sample_traps_with_cells(
+ self, no_cells: int, tpt_end: int, tpt_start: int = 0
+ ):
"""Sample some traps that have cells."""
traps_with_cells = self.get_all_traps_with_cells(
tpt_end, tpt_start, display=False
@@ -96,7 +103,12 @@ class ImageViewer:
)
return samples
- def get_tiles(self, trap_id, tps, channels_to_skip=None, cell_only=True):
+ def get_tiles(
+ self,
+ trap_id: int,
+ tps: List[int],
+ channels_to_skip: List[str] = None,
+ ):
"""Get dict of tiles with channel indices as keys."""
tiles_dict = {}
if channels_to_skip is None:
@@ -132,7 +144,7 @@ class ImageViewer:
tiles_dict[ch] = new_tiles
return tiles_dict
- def get_outlines(self, trap_id, tps):
+ def get_outlines(self, trap_id: int, tps: List[int]):
"""Get uniquely labelled outlines for each cell time point."""
# get outlines for each time point
outlines = [
@@ -161,14 +173,14 @@ class ImageViewer:
def get_all_tiles(
self,
- tps,
- channel_index,
- z=0,
+ tps: List[int],
+ channel_index: str,
+ z: int = 0,
):
"""
Get dict with time points as keys and all available tiles as values.
- We assume only a single channel.
+ Assume only a single channel.
"""
z = z or self.tiler.ref_z
ch_tps = [(channel_index, tp) for tp in tps]
@@ -181,7 +193,9 @@ class ImageViewer:
tile_dict = {tp: self.full[(ch, tp)] for ch, tp in ch_tps}
return tile_dict
- def get_data_for_viewing(self, trap_id, tps, channels_to_skip):
+ def get_data_for_viewing(
+ self, trap_id: int, tps: List[int], channels_to_skip: List[str]
+ ):
"""
Get images and outlines as multidimensional arrays for Napari.
@@ -217,11 +231,11 @@ class ImageViewer:
ts_labels[tp_index, 0, ...] = outlines[tp_index]
return ts_images, ts_labels, channels
- def concat(self, arrangement, image_dict, axis):
+ def concat(self, arrangement: List[int], image_dict: Dict, axis: int):
"""
Concat dict of images into one image array.
- Following the vertical layout in arrangment.
+ Follow the vertical layout in arrangment.
"""
# concatenate vertically into a list
images_v = [
@@ -239,23 +253,22 @@ class ImageViewer:
images = images_v[0]
return images
- def combine_tiles(self, ts_images_dict, ts_labels_dict, no_vertical_tiles):
- """Combine tiles into one image first vertically then horizontally."""
+ def combine_tiles(
+ self, ts_images_dict: Dict, ts_labels_dict: Dict, no_rows: int
+ ):
+ """Combine tiles into one image first into rows then columns."""
no_tiles = len(ts_images_dict)
trap_ids = list(ts_images_dict.keys())
# find how tiles will be arranged in the concatenated image
- if no_tiles < no_vertical_tiles:
+ if no_tiles < no_rows:
arrangement = [trap_ids]
else:
arrangement = [
- trap_ids[i : min(i + no_vertical_tiles, no_tiles)]
+ trap_ids[i : min(i + no_rows, no_tiles)]
for i in range(
0,
- int(
- np.floor(no_tiles / no_vertical_tiles)
- * no_vertical_tiles
- ),
- no_vertical_tiles,
+ int(np.floor(no_tiles / no_rows) * no_rows),
+ no_rows,
)
]
if no_tiles > np.array(arrangement).size:
@@ -271,26 +284,30 @@ class ImageViewer:
def view(
self,
- trap_ids,
- tpt_end=10,
- tpt_start=0,
- channels_to_skip=None,
- no_vertical_tiles=3,
+ trap_ids: List[int],
+ tpt_start: int = 0,
+ tpt_end: int = 10,
+ channels_to_skip: List[str] = None,
+ no_rows: int = 2,
):
"""
- Use Napari to view all channels and outlines for a particular trap.
+ Use Napari to view all channels and outlines for particular traps.
Fluorescence channels will not be immediately visible.
+ Concatenating traps into one image can become slow for multiple traps.
Parameters
----------
- trap_id: int
- The trap to be viewed.
- tps: int or array of ints
- Either the last time point to be viewed or a rage of time points
- to view.
- If None, all time points will be viewed, but gathering the images
- will be slow.
+ trap_ids: list of int
+ The traps to be viewed.
+ tpt_start: int
+ The index for the initial time point to view.
+ tpt_end: int
+ The index for the final time point.
+ channels_to_skip: list of str
+ Channels to ignore, such as "cy5".
+ no_rows: int
+ The number of rows of traps in the final concatenated image.
"""
tps = np.arange(tpt_start, tpt_end + 1)
if isinstance(trap_ids, int):
@@ -302,13 +319,18 @@ class ImageViewer:
)
# combine tiles
ts_images, ts_labels = self.combine_tiles(
- ts_images_dict, ts_labels_dict, no_vertical_tiles
+ ts_images_dict, ts_labels_dict, no_rows
)
# launch napari
self.launch_napari(ts_images, ts_labels, channels)
- def launch_napari(self, ts_images, ts_labels, channels):
- """Use Napari to see the images and outlines."""
+ def launch_napari(
+ self,
+ ts_images: npt.NDArray[Any],
+ ts_labels: npt.NDArray[Any],
+ channels: List[str],
+ ):
+ """Call Napari viewer."""
viewer = napari.Viewer()
viewer.add_image(
ts_images[:, channels.index("Brightfield"), ...],
@@ -328,7 +350,9 @@ class ImageViewer:
####
-def colormap(channel):
+
+
+def colormap(channel: str):
"""Find default colormap."""
if "GFP" in channel:
colormap = "green"
--
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