DOCS: Numpydocs1 (SciTools#5578)

* baseline

* added some noqa.

* api contents ordering to aplhabetical

* remove duplicate note

* updated string to str for rendering in docs

* ensured spaced around colon for listed parameters.

docs/src/conf.py

@@ -15,7 +15,6 @@
 #
 # All configuration values have a default; values that are commented out
 # serve to show the default.
-
 # ----------------------------------------------------------------------------
 
 import datetime
@@ -195,7 +194,7 @@ def _dotv(version):
 todo_include_todos = True
 
 # api generation configuration
-autodoc_member_order = "groupwise"
+autodoc_member_order = "alphabetical"
 autodoc_default_flags = ["show-inheritance"]
 
 # https://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html#confval-autodoc_typehints

lib/iris/config.py

@@ -27,6 +27,7 @@
     The [optional] name of the logger to notify when first imported.
 
 ----------
+
 """
 
 import configparser
@@ -42,41 +43,37 @@ def get_logger(
     name, datefmt=None, fmt=None, level=None, propagate=None, handler=True
 ):
     """
+    Create a custom class for logging.
+
     Create a :class:`logging.Logger` with a :class:`logging.StreamHandler`
     and custom :class:`logging.Formatter`.
 
-    Args:
-
-    * name:
+    Parameters
+    ----------
+    name
         The name of the logger. Typically this is the module filename that
         owns the logger.
-
-    Kwargs:
-
-    * datefmt:
+    datefmt: optional
         The date format string of the :class:`logging.Formatter`.
         Defaults to ``%d-%m-%Y %H:%M:%S``.
-
-    * fmt:
+    fmt: optional
         The additional format string of the :class:`logging.Formatter`.
         This is appended to the default format string
         ``%(asctime)s %(name)s %(levelname)s - %(message)s``.
-
-    * level:
+    level: optional
         The threshold level of the logger. Defaults to ``INFO``.
-
-    * propagate:
+    propagate: optional
         Sets the ``propagate`` attribute of the :class:`logging.Logger`,
         which determines whether events logged to this logger will be
         passed to the handlers of higher level loggers. Defaults to
         ``False``.
-
-    * handler:
+    handler: optional
         Create and attach a :class:`logging.StreamHandler` to the
         logger. Defaults to ``True``.
 
-    Returns:
-        A :class:`logging.Logger`.
+    Returns
+    -------
+    :class:`logging.Logger`.
 
     """
     if level is None:
@@ -118,6 +115,8 @@ def get_logger(
 # Returns simple string options
 def get_option(section, option, default=None):
     """
+    Return the option value for the given section.
+
     Returns the option value for the given section, or the default value
     if the section/option is not present.
 
@@ -131,6 +130,8 @@ def get_option(section, option, default=None):
 # Returns directory path options
 def get_dir_option(section, option, default=None):
     """
+    Return the directory path from the given option and section.
+
     Returns the directory path from the given option and section, or
     returns the given default value if the section/option is not present
     or does not represent a valid directory.
@@ -196,20 +197,19 @@ def __init__(self, conventions_override=None):
         """
         Set up NetCDF processing options for Iris.
 
-        Currently accepted kwargs:
-
-        * conventions_override (bool):
+        Parameters
+        ----------
+        conventions_override : bool, optional
             Define whether the CF Conventions version (e.g. `CF-1.6`) set when
             saving a cube to a NetCDF file should be defined by
-            Iris (the default) or the cube being saved.
-
-            If `False` (the default), specifies that Iris should set the
+            Iris (the default) or the cube being saved.  If `False`
+            (the default), specifies that Iris should set the
             CF Conventions version when saving cubes as NetCDF files.
             If `True`, specifies that the cubes being saved to NetCDF should
             set the CF Conventions version for the saved NetCDF files.
 
-        Example usages:
-
+        Examples
+        --------
         * Specify, for the lifetime of the session, that we want all cubes
           written to NetCDF to define their own CF Conventions versions::
 
@@ -276,6 +276,7 @@ def _defaults_dict(self):
     def context(self, **kwargs):
         """
         Allow temporary modification of the options via a context manager.
+
         Accepted kwargs are the same as can be supplied to the Option.
 
         """

lib/iris/fileformats/netcdf/__init__.py

@@ -3,8 +3,7 @@
 # This file is part of Iris and is released under the BSD license.
 # See LICENSE in the root of the repository for full licensing details.
 """
-Module to support the loading and saving of NetCDF files, also using the CF conventions
-for metadata interpretation.
+Support loading and saving NetCDF files using CF conventions for metadata interpretation.
 
 See : `NetCDF User's Guide <https://docs.unidata.ucar.edu/nug/current/>`_
 and `netCDF4 python module <https://github.com/Unidata/netcdf4-python>`_.

lib/iris/fileformats/netcdf/_dask_locks.py

@@ -5,45 +5,49 @@
 """
 Module containing code to create locks enabling dask workers to co-operate.
 
-This matter is complicated by needing different solutions for different dask scheduler
-types, i.e. local 'threads' scheduler, local 'processes' or distributed.
+This matter is complicated by needing different solutions for different dask
+scheduler types, i.e. local 'threads' scheduler, local 'processes' or
+distributed.
 
-In any case, an "iris.fileformats.netcdf.saver.Saver" object contains a netCDF4.Dataset
-targeting an output file, and creates a Saver.file_write_lock object to serialise
-write-accesses to the file from dask tasks :  All dask-task file writes go via a
-"iris.fileformats.netcdf.saver.NetCDFWriteProxy" object, which also contains a link
-to the Saver.file_write_lock, and uses it to prevent workers from fouling each other.
+In any case, an "iris.fileformats.netcdf.saver.Saver" object contains a
+netCDF4.Dataset targeting an output file, and creates a Saver.file_write_lock
+object to serialise write-accesses to the file from dask tasks :  All dask-task
+file writes go via a "iris.fileformats.netcdf.saver.NetCDFWriteProxy" object,
+which also contains a link to the Saver.file_write_lock, and uses it to prevent
+workers from fouling each other.
 
 For each chunk written, the NetCDFWriteProxy acquires the common per-file lock;
-opens a Dataset on the file; performs a write to the relevant variable; closes the
-Dataset and then releases the lock.  This process is obviously very similar to what the
-NetCDFDataProxy does for reading lazy chunks.
+opens a Dataset on the file; performs a write to the relevant variable; closes
+the Dataset and then releases the lock.  This process is obviously very similar
+to what the NetCDFDataProxy does for reading lazy chunks.
 
-For a threaded scheduler, the Saver.lock is a simple threading.Lock().  The workers
-(threads) execute tasks which contain a NetCDFWriteProxy, as above.  All of those
-contain the common lock, and this is simply **the same object** for all workers, since
-they share an address space.
+For a threaded scheduler, the Saver.lock is a simple threading.Lock().  The
+workers (threads) execute tasks which contain a NetCDFWriteProxy, as above.
+All of those contain the common lock, and this is simply **the same object**
+for all workers, since they share an address space.
 
 For a distributed scheduler, the Saver.lock is a `distributed.Lock()` which is
 identified with the output filepath.  This is distributed to the workers by
-serialising the task function arguments, which will include the NetCDFWriteProxy.
-A worker behaves like a process, though it may execute on a remote machine.  When a
-distributed.Lock is deserialised to reconstruct the worker task, this creates an object
-that communicates with the scheduler.  These objects behave as a single common lock,
-as they all have the same string 'identity', so the scheduler implements inter-process
-communication so that they can mutually exclude each other.
+serialising the task function arguments, which will include the
+NetCDFWriteProxy.  A worker behaves like a process, though it may execute on a
+remote machine.  When a distributed.Lock is deserialised to reconstruct the
+worker task, this creates an object that communicates with the scheduler.
+These objects behave as a single common lock, as they all have the same string
+'identity', so the scheduler implements inter-process communication so that
+they can mutually exclude each other.
 
 It is also *conceivable* that multiple processes could write to the same file in
-parallel, if the operating system supports it.  However, this also requires that the
-libnetcdf C library is built with parallel access option, which is not common.
-With the "ordinary" libnetcdf build, a process which attempts to open for writing a file
-which is _already_ open for writing simply raises an access error.
-In any case, Iris netcdf saver will not support this mode of operation, at present.
+parallel, if the operating system supports it.  However, this also requires
+that the libnetcdf C library is built with parallel access option, which is
+not common.  With the "ordinary" libnetcdf build, a process which attempts to
+open for writing a file which is _already_ open for writing simply raises an
+access error.  In any case, Iris netcdf saver will not support this mode of
+operation, at present.
 
 We don't currently support a local "processes" type scheduler.  If we did, the
-behaviour should be very similar to a distributed scheduler.  It would need to use some
-other serialisable shared-lock solution in place of 'distributed.Lock', which requires
-a distributed scheduler to function.
+behaviour should be very similar to a distributed scheduler.  It would need to
+use some other serialisable shared-lock solution in place of
+'distributed.Lock', which requires a distributed scheduler to function.
 
 """
 import threading
@@ -55,7 +59,7 @@
 
 
 # A dedicated error class, allowing filtering and testing of errors raised here.
-class DaskSchedulerTypeError(ValueError):
+class DaskSchedulerTypeError(ValueError):  # noqa: D101
     pass
 
 
@@ -82,11 +86,13 @@ def get_dask_array_scheduler_type():
 
     Returns one of 'distributed', 'threads' or 'processes'.
     The return value is a valid argument for dask.config.set(scheduler=<type>).
-    This cannot distinguish between distributed local and remote clusters -- both of
-    those simply return 'distributed'.
+    This cannot distinguish between distributed local and remote clusters --
+    both of those simply return 'distributed'.
 
-    NOTE: this takes account of how dask is *currently* configured.  It will be wrong
-    if the config changes before the compute actually occurs.
+    Notes
+    -----
+    This takes account of how dask is *currently* configured.  It will
+    be wrong if the config changes before the compute actually occurs.
 
     """
     if dask_scheduler_is_distributed():
@@ -114,8 +120,12 @@ def get_worker_lock(identity: str):
     """
     Return a mutex Lock which can be shared by multiple Dask workers.
 
-    The type of Lock generated depends on the dask scheduler type, which must therefore
-    be set up before this is called.
+    The type of Lock generated depends on the dask scheduler type, which must
+    therefore be set up before this is called.
+
+    Parameters
+    ----------
+    identity : str
 
     """
     scheduler_type = get_dask_array_scheduler_type()