Concatenate with OME-Zarr v0.5 and sharding

biahub/concatenate.py

@@ -238,22 +238,32 @@ def calculate_cropped_size(
 def concatenate(
     settings: ConcatenateSettings,
     output_dirpath: Path,
-    sbatch_filepath: str = None,
+    sbatch_filepath: str | None = None,
     local: bool = False,
+    block: bool = False,
     monitor: bool = True,
 ):
-    """
-    Concatenate datasets (with optional cropping)
-
-    >> biahub concatenate -c ./concat.yml -o ./output_concat.zarr -j 8
+    """Concatenate datasets (with optional cropping).
+
+    Parameters
+    ----------
+    settings : ConcatenateSettings
+        Configuration settings for concatenation
+    output_dirpath : Path
+        Path to the output dataset
+    sbatch_filepath : str | None, optional
+        Path to the SLURM batch file, by default None
+    local : bool, optional
+        Whether to run locally or on a cluster, by default False
+    block : bool, optional
+        Whether to block until all the jobs are complete,
+        by default False
+    monitor : bool, optional
+        Whether to monitor the jobs, by default True
     """
     slurm_out_path = output_dirpath.parent / "slurm_output"
 
-    slicing_params = [
-        settings.Z_slice,
-        settings.Y_slice,
-        settings.X_slice,
-    ]
+    slicing_params = [settings.Z_slice, settings.Y_slice, settings.X_slice]
     (
         all_data_paths,
         all_channel_names,
@@ -334,11 +344,12 @@ def concatenate(
         chunk_size = [1] + list(settings.chunks_czyx)
     else:
         chunk_size = settings.chunks_czyx
-
     # Logic for creation of zarr and metadata
     output_metadata = {
         "shape": (len(input_time_indices), len(all_channel_names)) + tuple(cropped_shape_zyx),
         "chunks": chunk_size,
+        "shards_ratio": settings.shards_ratio,
+        "version": settings.output_ome_zarr_version,
         "scale": (1,) * 2 + tuple(output_voxel_size),
         "channel_names": all_channel_names,
         "dtype": dtype,
@@ -352,8 +363,9 @@ def concatenate(
     )
 
     # Estimate resources
+    batch_size = settings.shards_ratio[0] if settings.shards_ratio else 1
     num_cpus, gb_ram_per_cpu = estimate_resources(
-        shape=[T, C, Z, Y, X], ram_multiplier=16, max_num_cpus=16
+        shape=(T // batch_size, C, Z, Y, X), ram_multiplier=4 * batch_size, max_num_cpus=16
     )
     # Prepare SLURM arguments
     slurm_args = {
@@ -380,8 +392,9 @@ def concatenate(
     executor = submitit.AutoExecutor(folder=slurm_out_path, cluster=cluster)
     executor.update_parameters(**slurm_args)
 
-    click.echo("Submitting SLURM jobs...")
+    click.echo(f"Submitting {cluster} jobs...")
     jobs = []
+
     with submitit.helpers.clean_env(), executor.batch():
         for i, (
             input_position_path,
@@ -424,6 +437,9 @@ def concatenate(
     with log_path.open("w") as log_file:
         log_file.write("\n".join(job_ids))
 
+    if block:
+        _ = [job.result() for job in jobs]
+
     if monitor:
         monitor_jobs(jobs, all_data_paths)
 
@@ -437,21 +453,22 @@ def concatenate(
 def concatenate_cli(
     config_filepath: Path,
     output_dirpath: str,
-    sbatch_filepath: str = None,
+    sbatch_filepath: str | None = None,
     local: bool = False,
     monitor: bool = True,
 ):
     """
     Concatenate datasets (with optional cropping)
 
-    >> biahub concatenate -c ./concat.yml -o ./output_concat.zarr -j 8
+    >> biahub concatenate -c ./concat.yml -o ./output_concat.zarr
     """
 
     concatenate(
         settings=yaml_to_model(config_filepath, ConcatenateSettings),
         output_dirpath=Path(output_dirpath),
         sbatch_filepath=sbatch_filepath,
         local=local,
+        block=False,
         monitor=monitor,
     )
 

biahub/settings.py

@@ -375,7 +375,9 @@ class ConcatenateSettings(MyBaseModel):
     Y_slice: Union[list, list[Union[list, Literal["all"]]], Literal["all"]] = "all"
     Z_slice: Union[list, list[Union[list, Literal["all"]]], Literal["all"]] = "all"
     chunks_czyx: Union[Literal[None], list[int]] = None
+    shards_ratio: list[int] | None = None
     ensure_unique_positions: Optional[bool] = False
+    output_ome_zarr_version: Literal["0.4", "0.5"] = "0.4"
 
     @field_validator("concat_data_paths")
     @classmethod

biahub/tests/test_concatenate.py

@@ -1,3 +1,6 @@
+import numpy as np
+import pytest
+
 from iohub import open_ome_zarr
 
 from biahub.concatenate import concatenate
@@ -204,7 +207,13 @@ def test_concatenate_with_cropping(create_custom_plate, tmp_path, sbatch_file):
     assert output_X == x_end - x_start
 
 
-def test_concatenate_with_custom_chunks(create_custom_plate, tmp_path, sbatch_file):
+@pytest.mark.parametrize(
+    ["version", "shards_ratio_time"],
+    [["0.4", 1], ["0.5", None], ["0.5", 1], ["0.5", 2], ["0.5", 5]],
+)
+def test_concatenate_with_custom_chunks(
+    create_custom_plate, tmp_path, sbatch_file, version, shards_ratio_time
+):
     """
     Test concatenating with custom chunk sizes
     """
@@ -227,13 +236,22 @@ def test_concatenate_with_custom_chunks(create_custom_plate, tmp_path, sbatch_fi
     )
 
     # Define custom chunk sizes
-    custom_chunks = [1, 2, 4, 3]  # [C, Z, Y, X]
+    chunks = [1, 1, 2, 4, 3]  # [C, Z, Y, X]
+    if version == "0.5":
+        if shards_ratio_time is None:
+            shards_ratio = None
+        else:
+            shards_ratio = [shards_ratio_time, 1, 1, 2, 2]
+    elif version == "0.4":
+        shards_ratio = None
 
     settings = ConcatenateSettings(
         concat_data_paths=[str(plate_1_path) + "/*/*/*", str(plate_2_path) + "/*/*/*"],
         channel_names=['all', 'all'],
         time_indices='all',
-        chunks_czyx=custom_chunks,
+        chunks_czyx=chunks[1:],
+        shards_ratio=shards_ratio,
+        output_ome_zarr_version=version,
     )
 
     output_path = tmp_path / "output.zarr"
@@ -242,10 +260,21 @@ def test_concatenate_with_custom_chunks(create_custom_plate, tmp_path, sbatch_fi
         output_dirpath=output_path,
         sbatch_filepath=sbatch_file,
         local=True,
+        monitor=False,
+        block=True,
     )
 
-    # We can't easily check the chunks directly, but we can verify the operation completed successfully
     output_plate = open_ome_zarr(output_path)
+    for pos_name, pos in output_plate.positions():
+        assert pos.data.chunks == tuple(chunks)
+        if version == "0.5" and shards_ratio is not None:
+            assert pos.data.shards == tuple(c * s for c, s in zip(chunks, shards_ratio))
+        np.testing.assert_array_equal(
+            pos.data.numpy(),
+            np.concatenate(
+                [plate_1[pos_name].data.numpy(), plate_2[pos_name].data.numpy()], axis=1
+            ),
+        )
 
     # Check that the output plate has all the channels from the input plates
     output_channels = output_plate.channel_names

biahub/track.py

@@ -36,6 +36,8 @@
 )
 from biahub.settings import ProcessingInputChannel, TrackingSettings
 
+# Lazy imports for ultrack - imported only when needed in specific functions
+
 
 def mem_nuc_contour(nuclei_prediction: ArrayLike, membrane_prediction: ArrayLike) -> ArrayLike:
     """

pyproject.toml

@@ -20,7 +20,7 @@ classifiers = [
 
 # list package dependencies here
 dependencies = [
-  "iohub>=0.2,<0.3",
+  "iohub>=0.3.0a2,<0.4",
   "matplotlib",
   "napari",
   "PyQt6",
@@ -53,9 +53,11 @@ segment = ["cellpose"]
 
 track = ["ultrack>=0.6.3"]
 
+shard = ["tensorstore"]
+
 build = ["build", "twine"]
 
-all = ["biahub[segment,track,build]"]
+all = ["biahub[segment,track,shard,build]"]
 
 dev = [
   "biahub[all]",

settings/example_concatenate_settings.yml

@@ -4,8 +4,8 @@
 # List of paths to concatenate - can use glob patterns
 # Each path will be treated as a separate input dataset
 concat_data_paths:
-  - "/path/to/data1.zarr/*/*/*"  # First dataset
-  - "/path/to/data2.zarr/*/*/*"  # Second dataset
+  - "/path/to/data1.zarr/*/*/*" # First dataset
+  - "/path/to/data2.zarr/*/*/*" # Second dataset
   # - "/path/to/data3.zarr/A/1/0"  # You can also specify exact positions
 
 # Time indices to include in the output
@@ -22,8 +22,8 @@ time_indices: "all"
 #   - For multiple datasets, specify channels for each:
 #     [["DAPI"], ["GFP", "RFP"]] - Take DAPI from first dataset, GFP and RFP from second
 channel_names:
-  - "all"  # Include all channels from first dataset
-  - "all"  # Include all channels from second dataset
+  - "all" # Include all channels from first dataset
+  - "all" # Include all channels from second dataset
 
 # Spatial cropping options for X dimension
 # Options:
@@ -55,12 +55,23 @@ Z_slice: "all"
 #   - [1, 10, 100, 100]: Specify custom chunk sizes
 chunks_czyx: null
 
+# Number of chunks in a shard for each dimension [T, C, Z, Y, X]
+# Options:
+#   - null: No sharding
+#   - [1, 1, 4, 8, 8]: Specify custom shards ratio
+shards_ratio: null
+
+# Version of the OME-Zarr format to use for the output
+# Options:
+#   - "0.4" (default)
+#   - "0.5"
+output_ome_zarr_version: "0.4"
+
 # Whether to ensure unique position names in the output
 # Options:
 #   - false or null: Positions with the same name will overwrite each other
 #   - true: Ensure unique position names by adding suffixes (e.g., A/1d1/0)
 ensure_unique_positions: null
-
 # EXAMPLE USE CASES:
 
 # 1. Basic concatenation of all data: