Refactor API for NGFF datasets

.github/workflows/pr.yml

@@ -51,8 +51,9 @@ jobs:
           python -m pip install --upgrade pip
           pip install flake8
       - name: Check code with Flake8
+        # E203 conflicts with black
         run: |
-          flake8 iohub
+          flake8 iohub --extend-ignore=E203
 
   isort:
     name: isort Check
@@ -72,7 +73,7 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           pip install isort
-      - name: Check code with Flake8
+      - name: Check code with isort
         run: |
           isort --check iohub
 

examples/hcs_zarr.py

@@ -0,0 +1,41 @@
+# %%
+# This script writes a high content screening (HCS) OME-Zarr dataset
+# with a single FOV and a single scaling level per well,
+# and adds an extra well-position to an existing dataset.
+# It can be run as a plain Python script, or as interactive cells in some IDEs.
+
+import numpy as np
+
+from iohub.ngff import HCSZarr
+
+# %%
+# Write 5D data to multiple wells
+
+position_list = (
+    ("A", "1", "0"),
+    ("H", 12, 0),
+    ("Control", "Blank", 0),
+)
+
+with HCSZarr.open(
+    "hcs_wrong.zarr", mode="a", channel_names=["DAPI", "GFP"]
+) as dataset:
+    for row, col, fov in position_list:
+        position = dataset.create_position(row, col, fov)
+        position[0] = np.random.randint(
+            0, np.iinfo(np.uint16).max, size=(5, 3, 3, 32, 32), dtype=np.uint16
+        )
+
+# %%
+# Append a channel to all the positions
+
+with HCSZarr.open("hcs.zarr", mode="r+") as dataset:
+    for name, position in dataset.positions():
+        print(name)
+        position.append_channel("New", resize_arrays=True)
+        position[0][:, 2] = np.random.randint(
+            0, np.iinfo(np.uint16).max, size=(5, 3, 32, 32), dtype=np.uint16
+        )
+
+# %%
+# Try viewing the images with napari-ome-zarr

examples/ome_zarr.py

@@ -0,0 +1,62 @@
+# %%
+# This script shows how to create a single-FOV, single-scale OME-Zarr dataset,
+# read data in read-only mode,
+# append an extra time point to an existing dataset,
+# and adding a new channel to an existing dataset.
+# It can be run as a plain Python script,
+# or as interactive cells in some IDEs.
+
+import numpy as np
+
+from iohub.ngff import OMEZarr
+
+# %%
+# Write 5D data to a new Zarr store
+
+tczyx = np.random.randint(
+    0, np.iinfo(np.uint16).max, size=(5, 2, 3, 32, 32), dtype=np.uint16
+)
+
+with OMEZarr.open(
+    "ome.zarr", mode="a", channel_names=["DAPI", "GFP"]
+) as dataset:
+    dataset[0] = tczyx
+
+# %%
+# Opening in read-only mode prevents writing
+
+with OMEZarr.open("ome.zarr", mode="r") as dataset:
+    img = dataset[0]
+    print(img.numpy())
+    try:
+        img[0, 0, 0, 0, 0] = 0
+    except Exception as e:
+        print(f"Writing was rejected: {e}")
+
+# %%
+# Append a new timepoint to an existing dataset
+
+new_1czyx = np.random.randint(
+    0, np.iinfo(np.uint16).max, size=(1, 2, 3, 32, 32), dtype=np.uint16
+)
+
+with OMEZarr.open("ome.zarr", mode="r+") as dataset:
+    img = dataset[0]
+    print(img.shape)
+    img.append(new_1czyx, axis=0)
+    print(img.shape)
+
+# %%
+# Add a new channel and write a Z-stack
+
+new_zyx = np.random.randint(
+    0, np.iinfo(np.uint16).max, size=(3, 32, 32), dtype=np.uint16
+)
+
+dataset = OMEZarr.open("ome.zarr", mode="r+")
+dataset.append_channel("New", resize_arrays=True)
+dataset[0][0, 2] = new_zyx
+dataset.close()
+
+# %%
+# Try viewing the images with napari-ome-zarr

iohub/lf_utils.py

@@ -12,7 +12,8 @@ def channel_display_settings(
     clim: Tuple[float, float, float, float] = None,
     first_chan: bool = False,
 ):
-    """This will create a dictionary used for OME-zarr metadata.  Allows custom contrast limits and channel
+    """This will create a dictionary used for OME-zarr metadata.
+    Allows custom contrast limits and channel.
     names for display. Defaults everything to grayscale.
 
     Parameters
@@ -22,7 +23,9 @@ def channel_display_settings(
     clim : Tuple[float, float, float, float], optional
         Contrast limits (start, end, min, max)
     first_chan : bool, optional
-        Whether or not this is the first channel of the dataset (display will be set to active), by default False
+        Whether or not this is the first channel of the dataset
+        (display will be set to active),
+        by default False
 
     Returns
     -------