docs: 📝 Add dummy example for run_blockwise()

Also fixed docstrings for blockwise worker functions.

dacapo/blockwise/argmax_worker.py

@@ -32,12 +32,6 @@ def cli(log_level):
 
     Args:
         log_level (str): The log level to use.
-    Raises:
-        NotImplementedError: If the method is not implemented in the derived class.
-    Examples:
-        >>> cli(log_level="INFO")
-    Note:
-        The method is implemented in the class.
     """
     logging.basicConfig(level=getattr(logging, log_level.upper()))
 
@@ -69,12 +63,6 @@ def start_worker(
         input_dataset (str): The input dataset.
         output_container (Path | str): The output container.
         output_dataset (str): The output dataset.
-    Raises:
-        NotImplementedError: If the method is not implemented in the derived class.
-    Examples:
-        >>> start_worker(input_container="input_container", input_dataset="input_dataset", output_container="output_container", output_dataset="output_dataset")
-    Note:
-        The method is implemented in the class.
     """
     # get arrays
     input_array_identifier = LocalArrayIdentifier(Path(input_container), input_dataset)
@@ -119,13 +107,7 @@ def spawn_worker(
         raw_array (Array): The raw data to predict on.
         prediction_array_identifier (LocalArrayIdentifier): The identifier of the prediction array.
     Returns:
-        The worker to run.
-    Raises:
-        NotImplementedError: If the method is not implemented in the derived class.
-    Examples:
-        >>> spawn_worker(model, raw_array, prediction_array_identifier)
-    Note:
-        The method is implemented in the class.
+        Callable: The function to run the worker.
     """
     compute_context = create_compute_context()
     if not compute_context.distribute_workers:
@@ -156,15 +138,7 @@ def spawn_worker(
     def run_worker():
         """
         Run the worker in the given compute context.
-
-        Raises:
-            NotImplementedError: If the method is not implemented in the derived class.
-        Examples:
-            >>> run_worker()
-        Note:
-            The method is implemented in the class.
         """
-        # Run the worker in the given compute context
         compute_context.execute(command)
 
     return run_worker

dacapo/blockwise/blockwise_task.py

@@ -25,8 +25,6 @@ class DaCapoBlockwiseTask(Task):
     Methods:
         __init__:
             Initialize the task.
-    Note:
-        The method is implemented in the class.
     """
 
     def __init__(
@@ -58,12 +56,6 @@ def __init__(
             upstream_tasks: The upstream tasks.
             *args: Additional positional arguments to pass to ``worker_function``.
             **kwargs: Additional keyword arguments to pass to ``worker_function``.
-        Raises:
-            ValueError: If the worker file is not a valid path.
-        Examples:
-            >>> DaCapoBlockwiseTask(worker_file="worker_file", total_roi=Roi, read_roi=Roi, write_roi=Roi, num_workers=16, max_retries=2, timeout=None, upstream_tasks=None)
-        Note:
-            The method is implemented in the class.
         """
         # Load worker functions
         worker_name = Path(worker_file).stem

dacapo/blockwise/predict_worker.py

@@ -43,12 +43,6 @@ def cli(log_level):
 
     Args:
         log_level (str): The log level to use for logging.
-    Raises:
-        NotImplementedError: If the method is not implemented in the derived class.
-    Examples:
-        >>> cli(log_level="INFO")
-    Note:
-        The method is implemented in the class.
     """
     logging.basicConfig(level=getattr(logging, log_level.upper()))
 
@@ -94,13 +88,6 @@ def start_worker(
         input_dataset (str): The input dataset.
         output_container (Path | str): The output container.
         output_dataset (str): The output dataset.
-    Raises:
-        NotImplementedError: If the method is not implemented in the derived class.
-    Examples:
-        >>> start_worker(run_name="run", iteration=0, input_container="input", input_dataset="input", output_container="output", output_dataset="output")
-    Note:
-        The method is implemented in the class.
-
     """
     compute_context = create_compute_context()
     device = compute_context.device
@@ -233,12 +220,8 @@ def spawn_worker(
         iteration (int or None): The training iteration of the model to use for prediction.
         input_array_identifier (LocalArrayIdentifier): The raw data to predict on.
         output_array_identifier (LocalArrayIdentifier): The identifier of the prediction array.
-    Raises:
-        NotImplementedError: If the method is not implemented in the derived class.
-    Examples:
-        >>> spawn_worker(run_name="run", iteration=0, input_array_identifier=LocalArrayIdentifier(Path("input"), "input"), output_array_identifier=LocalArrayIdentifier(Path("output"), "output"))
-    Note:
-        The method is implemented in the class.
+    Returns:
+        Callable: The function to run the worker.
     """
     compute_context = create_compute_context()
     if not compute_context.distribute_workers:
@@ -277,15 +260,7 @@ def spawn_worker(
     def run_worker():
         """
         Run the worker in the given compute context.
-
-        Raises:
-            NotImplementedError: If the method is not implemented in the derived class.
-        Examples:
-            >>> run_worker()
-        Note:
-            The method is implemented in the class.
         """
-        # Run the worker in the given compute context
         print("Running worker with command: ", command)
         compute_context.execute(command)
 

dacapo/blockwise/relabel_worker.py

@@ -29,12 +29,6 @@ def cli(log_level):
 
     Args:
         log_level (str): The log level to use.
-    Raises:
-        NotImplementedError: If the method is not implemented in the derived class.
-    Examples:
-        >>> cli(log_level="INFO")
-    Note:
-        The method is implemented in the class.
     """
     logging.basicConfig(level=getattr(logging, log_level.upper()))
 
@@ -63,12 +57,6 @@ def start_worker(
         output_container (str): The output container
         output_dataset (str): The output dataset
         tmpdir (str): The temporary directory
-    Raises:
-        NotImplementedError: If the method is not implemented in the derived class.
-    Examples:
-        >>> start_worker(output_container="output_container", output_dataset="output_dataset", tmpdir="tmpdir")
-    Note:
-        The method is implemented in the class.
     """
     client = daisy.Client()
     array_out = open_ds(output_container, output_dataset, mode="a")
@@ -108,12 +96,6 @@ def relabel_in_block(array_out, old_values, new_values, block):
         old_values (np.ndarray): The old values
         new_values (np.ndarray): The new values
         block (daisy.Block): The block
-    Raises:
-        NotImplementedError: If the method is not implemented in the derived class.
-    Examples:
-        >>> relabel_in_block(array_out, old_values, new_values, block)
-    Note:
-        The method is implemented in the class.
     """
     a = array_out.to_ndarray(block.write_roi)
     # DGA: had to add in flatten and reshape since remap (in particular indices) didn't seem to work with ndarrays for the input
@@ -131,12 +113,6 @@ def find_components(nodes, edges):
         edges (np.ndarray): The edges
     Returns:
         List[int]: The components
-    Raises:
-        NotImplementedError: If the method is not implemented in the derived class.
-    Examples:
-        >>> find_components(nodes, edges)
-    Note:
-        The method is implemented in the class.
     """
     # scipy
     disjoint_set = DisjointSet(nodes)
@@ -153,12 +129,6 @@ def read_cross_block_merges(tmpdir):
         tmpdir (str): The temporary directory
     Returns:
         Tuple[np.ndarray, np.ndarray]: The nodes and edges
-    Raises:
-        NotImplementedError: If the method is not implemented in the derived class.
-    Examples:
-        >>> read_cross_block_merges(tmpdir)
-    Note:
-        The method is implemented in the class.
     """
     block_files = glob(os.path.join(tmpdir, "block_*.npz"))
 
@@ -186,12 +156,6 @@ def spawn_worker(
         tmpdir (str): The temporary directory
     Returns:
         Callable: The function to run the worker
-    Raises:
-        NotImplementedError: If the method is not implemented in the derived class.
-    Examples:
-        >>> spawn_worker(output_array_identifier, tmpdir)
-    Note:
-        The method is implemented in the class.
     """
     compute_context = create_compute_context()
 
@@ -220,15 +184,7 @@ def spawn_worker(
     def run_worker():
         """
         Run the worker in the given compute context.
-
-        Raises:
-            NotImplementedError: If the method is not implemented in the derived class.
-        Examples:
-            >>> run_worker()
-        Note:
-            The method is implemented in the class.
         """
-        # Run the worker in the given compute context
         compute_context.execute(command)
 
     return run_worker

dacapo/blockwise/scheduler.py

@@ -54,12 +54,8 @@ def run_blockwise(
             Additional keyword arguments to pass to ``worker_function``.
     Returns:
         ``Bool``.
-    Raises:
-        NotImplementedError: If the method is not implemented in the derived class.
     Examples:
         >>> run_blockwise(worker_file, total_roi, read_roi, write_roi, num_workers, max_retries, timeout, upstream_tasks)
-    Note:
-        The method is implemented in the class.
 
     """
 
@@ -127,12 +123,8 @@ def segment_blockwise(
             Additional keyword arguments to pass to ``worker_function``.
     Returns:
             ``Bool``.
-    Raises:
-        NotImplementedError: If the method is not implemented in the derived class.
     Examples:
         >>> segment_blockwise(segment_function_file, context, total_roi, read_roi, write_roi, num_workers, max_retries, timeout, upstream_tasks)
-    Note:
-        The method is implemented in the class.
     """
     options = Options.instance()
     if not options.runs_base_dir.exists():

dacapo/blockwise/segment_worker.py

@@ -31,12 +31,6 @@ def cli(log_level):
 
     Args:
         log_level (str): The log level to use.
-    Raises:
-        NotImplementedError: If the method is not implemented in the derived class.
-    Examples:
-        >>> cli(log_level="INFO")
-    Note:
-        The method is implemented in the class.
     """
     logging.basicConfig(level=getattr(logging, log_level.upper()))
 
@@ -73,12 +67,6 @@ def start_worker(
         tmpdir (str): The temporary directory.
         function_path (str): The path to the segment function.
         return_io_loop (bool): Whether to return the io loop or run it.
-    Raises:
-        NotImplementedError: If the method is not implemented in the derived class.
-    Examples:
-        >>> start_worker(input_container="input_container", input_dataset="input_dataset", output_container="output_container", output_dataset="output_dataset", tmpdir="tmpdir", function_path="function_path")
-    Note:
-        The method is implemented in the class.
     """
 
     print("Starting worker")
@@ -218,12 +206,8 @@ def spawn_worker(
         model (Model): The model to use for prediction.
         raw_array (Array): The raw data to predict on.
         prediction_array_identifier (LocalArrayIdentifier): The identifier of the prediction array.
-    Raises:
-        NotImplementedError: If the method is not implemented in the derived class.
-    Examples:
-        >>> spawn_worker(input_array_identifier="input_array_identifier", output_array_identifier="output_array_identifier", tmpdir="tmpdir", function_path="function_path")
-    Note:
-        The method is implemented in the class.
+    Returns:
+        Callable: The function to run the worker.
     """
     compute_context = create_compute_context()
     if not compute_context.distribute_workers:
@@ -260,15 +244,7 @@ def spawn_worker(
     def run_worker():
         """
         Run the worker in the given compute context.
-
-        Raises:
-            NotImplementedError: If the method is not implemented in the derived class.
-        Examples:
-            >>> run_worker()
-        Note:
-            The method is implemented in the class.
         """
-        # Run the worker in the given compute context
         compute_context.execute(command)
 
     return run_worker

dacapo/blockwise/threshold_worker.py

@@ -27,18 +27,6 @@
     default="INFO",
 )
 def cli(log_level):
-    """
-    CLI for running the threshold worker.
-
-    Args:
-        log_level (str): The log level to use.
-    Raises:
-        NotImplementedError: If the method is not implemented in the derived class.
-    Examples:
-        >>> cli(log_level="INFO")
-    Note:
-        The method is implemented in the class.
-    """
     logging.basicConfig(level=getattr(logging, log_level.upper()))
 
 
@@ -72,12 +60,6 @@ def start_worker(
         output_container (Path | str): The output container.
         output_dataset (str): The output dataset.
         threshold (float): The threshold.
-    Raises:
-        NotImplementedError: If the method is not implemented in the derived class.
-    Examples:
-        >>> start_worker(input_container="input", input_dataset="input", output_container="output", output_dataset="output", threshold=0.0)
-    Note:
-        The method is implemented in the class.
 
     """
     # get arrays
@@ -119,19 +101,11 @@ def spawn_worker(
     Spawn a worker to predict on a given dataset.
 
     Args:
-        model (Model): The model to use for prediction.
-        raw_array (Array): The raw data to predict on.
-        prediction_array_identifier (LocalArrayIdentifier): The identifier of the prediction array.
-        block_shape (Tuple[int]): The shape of the blocks.
-        halo (Tuple[int]): The halo to use.
+        input_array_identifier (LocalArrayIdentifier): The raw data to predict on.
+        output_array_identifier (LocalArrayIdentifier): The identifier of the prediction array.
+        threshold (float): The threshold.
     Returns:
         Callable: The function to run the worker.
-    Raises:
-        NotImplementedError: If the method is not implemented in the derived class.
-    Examples:
-        >>> spawn_worker(model, raw_array, prediction_array_identifier, block_shape, halo)
-    Note:
-        The method is implemented in the class.
     """
     compute_context = create_compute_context()
     if not compute_context.distribute_workers:
@@ -145,7 +119,6 @@ def spawn_worker(
 
     # Make the command for the worker to run
     command = [
-        # "python",
         sys.executable,
         path,
         "start-worker",
@@ -164,15 +137,7 @@ def spawn_worker(
     def run_worker():
         """
         Run the worker in the given compute context.
-
-        Raises:
-            NotImplementedError: If the method is not implemented in the derived class.
-        Examples:
-            >>> run_worker()
-        Note:
-            The method is implemented in the class.
         """
-        # Run the worker in the given compute context
         compute_context.execute(command)
 
     return run_worker