Chunk Control documentation

docs/src/techpapers/index.rst

@@ -11,3 +11,4 @@ Extra information on specific technical issues.
 
    um_files_loading.rst
    missing_data_handling.rst
+   netcdf_io.rst

docs/src/techpapers/netcdf_io.rst

@@ -0,0 +1,139 @@
+.. _netcdf_io:
+
+.. testsetup:: chunk_control
+
+    import iris
+    from iris.fileformats.netcdf.loader import CHUNK_CONTROL
+
+    from pathlib import Path
+    import dask
+    import shutil
+    import tempfile
+
+    dask.config.set({'array.chunk-size': '250B'})
+
+    tmp_dir = Path(tempfile.mkdtemp())
+    tmp_filepath = tmp_dir / "tmp.nc"
+
+    cube = iris.load(iris.sample_data_path("E1_north_america.nc"))[0]
+    iris.save(cube, tmp_filepath, chunksizes=(120, 37, 49))
+
+
+.. testcleanup:: chunk_control
+
+    dask.config.set({'array.chunk-size': '250MiB'})
+    shutil.rmtree(tmp_dir)
+
+
+=============================
+NetCDF I/O Handling in Iris
+=============================
+
+This document provides a basic account of how Iris loads and saves NetCDF files.
+
+.. admonition:: Under Construction
+
+    This document is still a work in progress, so might include blank or unfinished sections,
+    watch this space!
+
+
+Chunk Control
+--------------
+
+Default Chunking
+^^^^^^^^^^^^^^^^
+
+Chunks are, by default, optimised by Iris on load. This will automatically
+decide the best chunksize for your data without any user input. This is
+calculated based on a number of factors, including:
+
+    - File Variable Chunking
+    - Full Variable Shape
+    - Dask Default Chunksize
+    - Dimension Order: Earlier (outer) dimensions will be prioritised to be split over later (inner) dimensions.
+
+.. doctest:: chunk_control
+
+    >>> cube = iris.load_cube(tmp_filepath)
+    >>>
+    >>> print(cube.shape)
+    >>> print(cube.core_data().chunksize)
+    (240, 37, 49)
+
+For more user control, functionality was updated in :pull:`5588`, with the
+creation of the :data:`iris.fileformats.netcdf.loader.CHUNK_CONTROL` class.
+
+Custom Chunking: Set
+^^^^^^^^^^^^^^^^^^^^
+
+There are three context manangers within :data:`iris.fileformats.netcdf.loader.CHUNK_CONTROL`. The most basic is
+:meth:`iris.fileformats.netcdf.loader.CHUNK_CONTROL.set`. This allows you to specify the chunksize for each dimension,
+and to specify a `var_name` specifically to change.
+
+Using ``-1`` in place of a chunksize will ensure the chunksize stays the same
+as the shape, i.e. no optimisation occurs on that dimension.
+
+.. doctest:: chunk_control
+
+    >>> with CHUNK_CONTROL.set("air_temperature", time=180, latitude=-1, longitude=25):
+    ...     cube = iris.load_cube(tmp_filepath)
+    >>>
+    >>> print(cube.core_data().chunksize)
+    (180, 37, 25)
+
+Note that ``var_name`` is optional, and that you don't need to specify every dimension:
+
+.. doctest:: chunk_control
+
+    >>> with CHUNK_CONTROL.set(longitude=25):
+    ...     cube = iris.load_cube(tmp_filepath)
+    >>>
+    >>> print(cube.core_data().chunksize)
+    (240, 37, 25)
+
+Custom Chunking: From File
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The second context manager is :meth:`iris.fileformats.netcdf.loader.CHUNK_CONTROL.from_file`.
+This takes chunksizes as defined in the NetCDF file. Any dimensions without specified chunks
+will default to Iris optimisation.
+
+.. doctest:: chunk_control
+
+    >>> with CHUNK_CONTROL.from_file():
+    ...     cube = iris.load_cube(tmp_filepath)
+    >>>
+    >>> print(cube.core_data().chunksize)
+    (120, 37, 49)
+
+Custom Chunking: As Dask
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+The final context manager, :meth:`iris.fileformats.netcdf.loader.CHUNK_CONTROL.as_dask`, bypasses
+Iris' optimisation all together, and will take its chunksizes from Dask's behaviour.
+
+.. doctest:: chunk_control
+
+    >>> with CHUNK_CONTROL.as_dask():
+    ...    cube = iris.load_cube(tmp_filepath)
+    >>>
+    >>> print(cube.core_data().chunksize)
+    (240, 37, 49)
+
+
+Split Attributes
+-----------------
+
+TBC
+
+
+Deferred Saving
+----------------
+
+TBC
+
+
+Guess Axis
+-----------
+
+TBC

lib/iris/fileformats/netcdf/loader.py

@@ -711,26 +711,26 @@ def set(
 
         Parameters
         ----------
-        var_names : str or list of str
+        var_names : str or list of str, default=None
             apply the ``dimension_chunksizes`` controls only to these variables,
             or when building cubes from these data variables.
-            If None (the default), settings apply to all loaded variables.
-        dimension_chunksizes : dict: str --> int
+            If None, settings apply to all loaded variables.
+        dimension_chunksizes : dict of {str: int}
             Kwargs specifying chunksizes for dimensions of file variables.
             Each key-value pair defines a chunksize for a named file
-            dimension, e.g. {'time': 10, 'model_levels':1}.
+            dimension, e.g. ``{'time': 10, 'model_levels':1}``.
+            Values of ``-1`` will lock the chunk to the size of its shape.
 
         Notes
         -----
         This function acts as a contextmanager, for use in a 'with' block.
 
-        Example:
-
-        #todo
-        # >>> from iris.fileformats.netcdf.loader import CHUNK_CONTROL
-        # >>> from iris import sample_data_path
-        # >>> with CHUNK_CONTROL.set('var1', model_level=1, time=50):
-        # ...     cubes = iris.load(sample_data_path("toa_brightness_stereographic.nc"))
+        >>> import iris
+        >>> from iris.fileformats.netcdf.loader import CHUNK_CONTROL
+        >>> with CHUNK_CONTROL.set("air_temperature", time=180, latitude=-1):
+        ...     cube = iris.load(iris.sample_data_path("E1_north_america.nc"))[0]
+        >>>
+        >>> print(cube.core_data().chunksize)
 
         When ``var_names`` is present, the chunksize adjustments are applied
         only to the selected variables.  However, for a CF data variable, this