Update docs to mention dask support

TomasTorsvik · May 3, 2019 · a7cab29 · a7cab29
1 parent e95c3d2
commit a7cab29
Show file tree

Hide file tree

Showing 5 changed files with 26 additions and 8 deletions.
diff --git a/doc/changelog.rst b/doc/changelog.rst
@@ -5,6 +5,15 @@ Changelog
 
 Source code downloads for released versions can be downloaded from `Github <https://github.com/ajdawson/eofs/releases>`_.
 
+v1.4
+----
+
+:Release: v1.4.0
+:Date: 2019
+
+* Add support for dask arrays [:issue:`107`, :pr:`109`], thanks to `@ScottWales <https://github.com/ScottWales>`_.
+
+
 v1.3
 ----
 

diff --git a/doc/conf.py b/doc/conf.py
@@ -193,6 +193,7 @@
         'iris': ('http://scitools.org.uk/iris/docs/latest', None),
         'numpy': ('http://docs.scipy.org/doc/numpy', None),
         'xarray': ('http://xarray.pydata.org/en/stable', None),
+        'dask': ('https://docs.dask.org/en/latest', None),
 }
 
 # -- Options for LaTeX output --------------------------------------------------

diff --git a/doc/index.rst b/doc/index.rst
@@ -50,7 +50,7 @@ You can also check out the source code for the development version from the `git
 Getting started
 ---------------
 
-`eofs` provides three interfaces for EOF analysis: one for analysing data contained in `numpy` arrays or masked arrays, suitable for any data set; and two for meta-data aware EOF analysis, suitable for analysing data read from self-describing files, using either the `cdms2`, `iris`, or `xarray` packages.
+`eofs` provides various interfaces for EOF analysis: a *standard* interface for analysing data contained in either `numpy` arrays or `dask` arrays, suitable for any data set; and meta-data interfaces suitable for analysing data read from self-describing files, using the `cdms2`, `iris`, or `xarray` packages.
 All the interfaces support the same set of operations.
 
 Regardless of which interface you use, the basic usage is the same.
@@ -80,10 +80,11 @@ Requirements
 ------------
 
 This package requires as a minimum that you have numpy_ available, and requires setuptools_ for installation.
+The dask_ package is an optional dependency that will be used if `dask.array` is available and `dask` arrays are provided to the solver.
 The `eofs.cdms` meta-data enabled interface can only be used if the `cdms2` module is available.
 This module is distributed as part of the UV-CDAT_ project.
 The `eofs.iris` meta-data enabled interface can only be used if the iris_ package is available at version 1.2 or higher.
-The `eofs.xarray` meta-data enabled interface can only be used if the xarray_ package (or earlier xray package) is installed.
+The `eofs.xarray` meta-data enabled interface can only be used if the xarray_ package is installed.
 
 
 Citation
@@ -103,6 +104,8 @@ If you would like to contribute code or documentation please see the :doc:`devgu
 
 .. _UV-CDAT: http://uv-cdat.llnl.gov
 
+.. _dask: https://dask.org
+
 .. _iris: http://scitools.org.uk/iris
 
 .. _xarray: http://xarray.pydata.org

diff --git a/doc/userguide/interfaces.rst b/doc/userguide/interfaces.rst
@@ -14,7 +14,9 @@ cdms      Data read from a self-describing data format stored in a `cdms2`
           variable.
 iris      Data read from a self-describing data format stored in an `iris`
           cube.
-standard  Other data, stored in a `numpy.ndarray` or a `numpy.ma.MaskedArray`.
+xarray    Data stored in an `xarray.DataArray`.
+standard  Other data, stored in a `numpy.ndarray`, a `numpy.ma.MaskedArray`,
+          or a `dask.array.Array`.
 ========= =====================================================================
 
 
@@ -47,7 +49,7 @@ The xarray interface works with `~xarray.DataArray` objects, which are the data
 Standard interface
 ------------------
 
-The standard interface works with `numpy` arrays, which makes the standard interface the general purpose interface. Any data that can be stored in a `numpy.ndarray` or `numpy.ma.MaskedArray` can be analysed with the `eofs.standard.Eof` solver interface.
+The standard interface works with numpy_ arrays or dask_ arrays, which makes the standard interface the general purpose interface. Any data that can be stored in a `numpy.ndarray`, `numpy.ma.MaskedArray` or `dask.array.Array` can be analysed with the `eofs.standard.Eof` solver interface.
 
 
 .. _iris: http://scitools.org.uk/iris
@@ -56,3 +58,6 @@ The standard interface works with `numpy` arrays, which makes the standard inter
 
 .. _xarray: http://xarray.pydata.org
 
+.. _dask: https://dask.org
+
+.. _numpy: https://numpy.org
diff --git a/lib/eofs/standard.py b/lib/eofs/standard.py
@@ -44,10 +44,10 @@ def __init__(self, dataset, weights=None, center=True, ddof=1):
         **Arguments:**
 
         *dataset*
-            A `numpy.ndarray` or `numpy.ma.MaskedArray` with two or more
-            dimensions containing the data to be analysed. The first
-            dimension is assumed to represent time. Missing values are
-            permitted, either in the form of a masked array, or
+            A `numpy.ndarray`, `numpy.ma.MaskedArray` or `dask.array.Array`
+            with two or more dimensions containing the data to be analysed.
+            The first dimension is assumed to represent time. Missing
+            values are permitted, either in the form of a masked array, or
             `numpy.nan` values. Missing values must be constant with time
             (e.g., values of an oceanographic field over land).