Merge branch 'chreef-analysis' into create_plots

Author: constantinpape

.gitignore

@@ -4,3 +4,4 @@ converted/
 *.egg-info/
 checkpoints/
 logs/
+*.csv

development/prepare_czi_zebrafish_data.py

@@ -0,0 +1,124 @@
+import os
+import subprocess
+from typing import Tuple
+
+import pandas as pd
+
+from ome_zarr.io import parse_url
+from ome_zarr.reader import Reader
+
+import dask.array as da
+
+
+def _get_dasky_data(url):
+    reader = Reader(parse_url(url))  # Prepare a reader.
+    nodes = list(reader())  # Might include multiple stuff
+    image_node = nodes[0]  # First node is expected to be image pixel data.
+
+    dask_data = image_node.data  # Get the daskified data.
+
+    return dask_data
+
+
+def get_zebrahub_data(timepoint: int = 740, view: bool = False) -> Tuple[da.Array, pd.DataFrame]:
+    """Gets the ZebraHub data from https://doi.org/10.1016/j.cell.2024.09.047.
+
+    Args:
+        timepoint: The timepoint where the 3d imaging data will be returned from.
+        view: Whether to view the dask array via napari.
+
+    Returns:
+        The daskified chunky array.
+        And the tracking annotations.
+    """
+    # NOTE: There's more single objective samples for zebrafish available with tracking annotations
+    # https://public.czbiohub.org/royerlab/zebrahub/imaging/single-objective/
+    url = "https://public.czbiohub.org/royerlab/zebrahub/imaging/single-objective/ZSNS001.ome.zarr"
+
+    # Let's get the image data.
+    dask_data = _get_dasky_data(url)
+
+    # Get the lowest resolution (see below on how to access other resolutions)
+    curr_data = dask_data[-1]
+
+    # And strip out the channel dimension (see below for more details)
+    curr_data = curr_data[timepoint, 0]
+
+    # We have tracking annotations here. Let's check them out.
+    tracks_fpath = "ZSNS001_tracks.csv"
+    if not os.path.exists(tracks_fpath):
+        subprocess.run(
+            ["wget", "https://public.czbiohub.org/royerlab/zebrahub/imaging/single-objective/ZSNS001_tracks.csv"]
+        )
+
+    # Load the tracking annotation file.
+    tracks = pd.read_csv("ZSNS001_tracks.csv")  # I think this is on original resolution (?)
+
+    # HACK: Filtering ids based on one time-frame (the most plausible setup we might be opting for)
+    curr_tracks = tracks.loc[tracks["t"] == timepoint]
+
+    if view:
+        import napari
+        napari.view_image(curr_data)
+        napari.run()
+
+    return curr_data, curr_tracks
+
+
+def get_czi_zebrafish_data(
+    neuromast: bool = True, view: bool = False
+) -> da.Array:
+    """Gets the CZI ZebraFish light-sheet microscopy data.
+    NOTE: Currently, we support only the raw data.
+
+    Args:
+        view: Whether to view the dask array via napari.
+
+    Returns:
+        The daskified chunky array.
+    """
+    # NOTE: Let's try for one link first, we can generalize it later.
+
+    if neuromast:
+        # Link for nuclear and membrane labeled zebrafish neuromast.
+        url = "https://public.czbiohub.org/royerlab/ultrack/zebrafish_neuromast.ome.zarr"
+    else:
+        # Link for dense nuclear labeled zebrafish embryo.
+        # NOTE: This data does not have tracking annotations!
+        url = "https://public.czbiohub.org/royerlab/ultrack/zebrafish_embryo.ome.zarr"
+
+    # Let's get the image data
+    dask_data = _get_dasky_data(url)
+
+    # HACK: Try it for one dask array with lowest resolution (there exists four resolutions in this data).
+    # TODO: Control res below, the highest res starts at the first index, lowest at the last index.
+    curr_data = dask_data[-1]
+
+    # We don't care about the over-time information. Let's get the 3d info for now!
+    # I am removing the channel dimension here (OG dimension style: (T, C, Z, Y, X))
+    curr_data = curr_data[:, 0]  # TODO: Parse values in the time or z-dimension to access limited slices?
+
+    # NOTE: The following line of code brings the entire dask array in memory.
+    # curr_data = curr_data.compute()
+
+    if view:
+        import napari
+        napari.view_image(curr_data)
+        napari.run()
+
+    return curr_data
+
+
+def main():
+    # image = get_czi_zebrafish_data(neuromast=True, view=False)
+
+    # Suggested timepoints I like in the developmental cycle:
+    # 740/760: kind of at the end of cycle.
+    # 650: it's a nice development stage which visually surfaces a lot of nucleus.
+
+    image, tracks = get_zebrahub_data(timepoint=740, view=False)
+    print(image.shape)
+
+
+if __name__ == "__main__":
+    main()

environment.yaml

@@ -1,11 +1,9 @@
 name: flamingo
 
 channels:
-  - pytorch
   - conda-forge
 
 dependencies:
-  - cpuonly
   - cluster_tools
   - scikit-image
   - pybdv