Add neuromast data

Author: anwai98

development/prepare_czi_zebrafish_data.py

@@ -4,7 +4,9 @@
 import dask.array as da
 
 
-def get_czi_zebrafish_data(view: bool = False) -> da.Array:
+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.
 
@@ -15,20 +17,29 @@ def get_czi_zebrafish_data(view: bool = False) -> da.Array:
         The daskified chunky array.
     """
     # NOTE: Let's try for one link first, we can generalize it later.
-    url = "https://public.czbiohub.org/royerlab/ultrack/zebrafish_embryo.ome.zarr"
 
+    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"
+
+    # First, let's get the image data
     reader = Reader(parse_url(url))  # Prepare a reader.
     nodes = list(reader())  # Might include multiple stuff
     image_node = nodes[0]  # First node is expecte to be image pixel data.
 
     dask_data = image_node.data  # Get the daskified data.
 
     # HACK: Try it for one dask array with lowest resolution (there exists four resolutions in this data).
-    curr_data = dask_data[-1]  # TODO: Control dimensions from here, the highest res starts at the first index.
+    # 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 parse limited slices?
+    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()
@@ -42,7 +53,7 @@ def get_czi_zebrafish_data(view: bool = False) -> da.Array:
 
 
 def main():
-    image = get_czi_zebrafish_data(view=False)
+    image = get_czi_zebrafish_data(neuromast=True, view=False)
     print(image.shape)