Add typing to plot methods

ci/requirements/min-all-deps.yml

@@ -3,10 +3,10 @@ channels:
   - conda-forge
   - nodefaults
 dependencies:
-  # MINIMUM VERSIONS POLICY: see doc/installing.rst
+  # MINIMUM VERSIONS POLICY: see doc/user-guide/installing.rst
   # Run ci/min_deps_check.py to verify that this file respects the policy.
   # When upgrading python, numpy, or pandas, must also change
-  # doc/installing.rst and setup.py.
+  # doc/user-guide/installing.rst, doc/user-guide/plotting.rst and setup.py.
   - python=3.8
   - boto3=1.18
   - bottleneck=1.3

doc/api-hidden.rst

@@ -330,11 +330,6 @@
    plot.scatter
    plot.surface
 
-   plot.FacetGrid.map_dataarray
-   plot.FacetGrid.set_titles
-   plot.FacetGrid.set_ticks
-   plot.FacetGrid.map
-
    CFTimeIndex.all
    CFTimeIndex.any
    CFTimeIndex.append

doc/api.rst

@@ -703,6 +703,7 @@ DataArray
    DataArray.plot.line
    DataArray.plot.pcolormesh
    DataArray.plot.step
+   DataArray.plot.scatter
    DataArray.plot.surface
 
 
@@ -719,6 +720,7 @@ Faceting
    plot.FacetGrid.map_dataarray
    plot.FacetGrid.map_dataarray_line
    plot.FacetGrid.map_dataset
+   plot.FacetGrid.map_plot1d
    plot.FacetGrid.set_axis_labels
    plot.FacetGrid.set_ticks
    plot.FacetGrid.set_titles

doc/user-guide/plotting.rst

@@ -27,7 +27,7 @@ Matplotlib must be installed before xarray can plot.
 
 To use xarray's plotting capabilities with time coordinates containing
 ``cftime.datetime`` objects
-`nc-time-axis <https://github.com/SciTools/nc-time-axis>`_ v1.2.0 or later
+`nc-time-axis <https://github.com/SciTools/nc-time-axis>`_ v1.3.0 or later
 needs to be installed.
 
 For more extensive plotting applications consider the following projects:
@@ -106,7 +106,13 @@ The simplest way to make a plot is to call the :py:func:`DataArray.plot()` metho
     @savefig plotting_1d_simple.png width=4in
     air1d.plot()
 
-Xarray uses the coordinate name along with metadata ``attrs.long_name``, ``attrs.standard_name``, ``DataArray.name`` and ``attrs.units`` (if available) to label the axes. The names ``long_name``, ``standard_name`` and ``units`` are copied from the `CF-conventions spec <https://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/build/ch03s03.html>`_. When choosing names, the order of precedence is ``long_name``, ``standard_name`` and finally ``DataArray.name``. The y-axis label in the above plot was constructed from the ``long_name`` and ``units`` attributes of ``air1d``.
+Xarray uses the coordinate name along with metadata ``attrs.long_name``,
+``attrs.standard_name``, ``DataArray.name`` and ``attrs.units`` (if available)
+to label the axes.
+The names ``long_name``, ``standard_name`` and ``units`` are copied from the
+`CF-conventions spec <https://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/build/ch03s03.html>`_.
+When choosing names, the order of precedence is ``long_name``, ``standard_name`` and finally ``DataArray.name``.
+The y-axis label in the above plot was constructed from the ``long_name`` and ``units`` attributes of ``air1d``.
 
 .. ipython:: python
 
@@ -340,7 +346,10 @@ The keyword arguments ``xincrease`` and ``yincrease`` let you control the axes d
         y="lat", hue="lon", xincrease=False, yincrease=False
     )
 
-In addition, one can use ``xscale, yscale`` to set axes scaling; ``xticks, yticks`` to set axes ticks and ``xlim, ylim`` to set axes limits. These accept the same values as the matplotlib methods ``Axes.set_(x,y)scale()``, ``Axes.set_(x,y)ticks()``, ``Axes.set_(x,y)lim()`` respectively.
+In addition, one can use ``xscale, yscale`` to set axes scaling;
+``xticks, yticks`` to set axes ticks and ``xlim, ylim`` to set axes limits.
+These accept the same values as the matplotlib methods ``Axes.set_(x,y)scale()``,
+``Axes.set_(x,y)ticks()``, ``Axes.set_(x,y)lim()`` respectively.
 
 
 Two Dimensions
@@ -350,7 +359,8 @@ Two Dimensions
  Simple Example
 ================
 
-The default method :py:meth:`DataArray.plot` calls :py:func:`xarray.plot.pcolormesh` by default when the data is two-dimensional.
+The default method :py:meth:`DataArray.plot` calls :py:func:`xarray.plot.pcolormesh`
+by default when the data is two-dimensional.
 
 .. ipython:: python
     :okwarning:
@@ -585,7 +595,10 @@ Faceting here refers to splitting an array along one or two dimensions and
 plotting each group.
 Xarray's basic plotting is useful for plotting two dimensional arrays. What
 about three or four dimensional arrays? That's where facets become helpful.
-The general approach to plotting here is called “small multiples”, where the same kind of plot is repeated multiple times, and the specific use of small multiples to display the same relationship conditioned on one or more other variables is often called a “trellis plot”.
+The general approach to plotting here is called “small multiples”, where the
+same kind of plot is repeated multiple times, and the specific use of small
+multiples to display the same relationship conditioned on one or more other
+variables is often called a “trellis plot”.
 
 Consider the temperature data set. There are 4 observations per day for two
 years which makes for 2920 values along the time dimension.
@@ -670,8 +683,8 @@ Faceted plotting supports other arguments common to xarray 2d plots.
 
     @savefig plot_facet_robust.png
     g = hasoutliers.plot.pcolormesh(
-        "lon",
-        "lat",
+        x="lon",
+        y="lat",
         col="time",
         col_wrap=3,
         robust=True,
@@ -711,7 +724,7 @@ they have been plotted.
 .. ipython:: python
     :okwarning:
 
-    g = t.plot.imshow("lon", "lat", col="time", col_wrap=3, robust=True)
+    g = t.plot.imshow(x="lon", y="lat", col="time", col_wrap=3, robust=True)
 
     for i, ax in enumerate(g.axes.flat):
         ax.set_title("Air Temperature %d" % i)
@@ -727,7 +740,8 @@ they have been plotted.
 axis labels, axis ticks and plot titles. See :py:meth:`~xarray.plot.FacetGrid.set_titles`,
 :py:meth:`~xarray.plot.FacetGrid.set_xlabels`, :py:meth:`~xarray.plot.FacetGrid.set_ylabels` and
 :py:meth:`~xarray.plot.FacetGrid.set_ticks` for more information.
-Plotting functions can be applied to each subset of the data by calling :py:meth:`~xarray.plot.FacetGrid.map_dataarray` or to each subplot by calling :py:meth:`~xarray.plot.FacetGrid.map`.
+Plotting functions can be applied to each subset of the data by calling
+:py:meth:`~xarray.plot.FacetGrid.map_dataarray` or to each subplot by calling :py:meth:`~xarray.plot.FacetGrid.map`.
 
 TODO: add an example of using the ``map`` method to plot dataset variables
 (e.g., with ``plt.quiver``).
@@ -777,7 +791,8 @@ Additionally, the boolean kwarg ``add_guide`` can be used to prevent the display
     @savefig ds_discrete_legend_hue_scatter.png
     ds.plot.scatter(x="A", y="B", hue="w", hue_style="discrete")
 
-The ``markersize`` kwarg lets you vary the point's size by variable value. You can additionally pass ``size_norm`` to control how the variable's values are mapped to point sizes.
+The ``markersize`` kwarg lets you vary the point's size by variable value.
+You can additionally pass ``size_norm`` to control how the variable's values are mapped to point sizes.
 
 .. ipython:: python
     :okwarning:
@@ -794,7 +809,8 @@ Faceting is also possible
     ds.plot.scatter(x="A", y="B", col="x", row="z", hue="w", hue_style="discrete")
 
 
-For more advanced scatter plots, we recommend converting the relevant data variables to a pandas DataFrame and using the extensive plotting capabilities of ``seaborn``.
+For more advanced scatter plots, we recommend converting the relevant data variables
+to a pandas DataFrame and using the extensive plotting capabilities of ``seaborn``.
 
 Quiver
 ~~~~~~
@@ -816,7 +832,8 @@ where ``u`` and ``v`` denote the x and y direction components of the arrow vecto
     @savefig ds_facet_quiver.png
     ds.plot.quiver(x="x", y="y", u="A", v="B", col="w", row="z", scale=4)
 
-``scale`` is required for faceted quiver plots. The scale determines the number of data units per arrow length unit, i.e. a smaller scale parameter makes the arrow longer.
+``scale`` is required for faceted quiver plots.
+The scale determines the number of data units per arrow length unit, i.e. a smaller scale parameter makes the arrow longer.
 
 Streamplot
 ~~~~~~~~~~
@@ -830,7 +847,8 @@ Visualizing vector fields is also supported with streamline plots:
     ds.isel(w=1, z=1).plot.streamplot(x="x", y="y", u="A", v="B")
 
 
-where ``u`` and ``v`` denote the x and y direction components of the vectors tangent to the streamlines. Again, faceting is also possible:
+where ``u`` and ``v`` denote the x and y direction components of the vectors tangent to the streamlines.
+Again, faceting is also possible:
 
 .. ipython:: python
     :okwarning:
@@ -983,7 +1001,7 @@ instead of the default ones:
     )
 
     @savefig plotting_example_2d_irreg.png width=4in
-    da.plot.pcolormesh("lon", "lat")
+    da.plot.pcolormesh(x="lon", y="lat")
 
 Note that in this case, xarray still follows the pixel centered convention.
 This might be undesirable in some cases, for example when your data is defined
@@ -996,7 +1014,7 @@ this convention when plotting on a map:
     import cartopy.crs as ccrs
 
     ax = plt.subplot(projection=ccrs.PlateCarree())
-    da.plot.pcolormesh("lon", "lat", ax=ax)
+    da.plot.pcolormesh(x="lon", y="lat", ax=ax)
     ax.scatter(lon, lat, transform=ccrs.PlateCarree())
     ax.coastlines()
     @savefig plotting_example_2d_irreg_map.png width=4in
@@ -1009,7 +1027,7 @@ You can however decide to infer the cell boundaries and use the
     :okwarning:
 
     ax = plt.subplot(projection=ccrs.PlateCarree())
-    da.plot.pcolormesh("lon", "lat", ax=ax, infer_intervals=True)
+    da.plot.pcolormesh(x="lon", y="lat", ax=ax, infer_intervals=True)
     ax.scatter(lon, lat, transform=ccrs.PlateCarree())
     ax.coastlines()
     @savefig plotting_example_2d_irreg_map_infer.png width=4in

doc/whats-new.rst

@@ -23,14 +23,22 @@ v2022.10.1 (unreleased)
 New Features
 ~~~~~~~~~~~~
 
+- Add static typing to plot accessors (:issue:`6949`, :pull:`7052`).
+  By `Michael Niklas <https://github.com/headtr1ck>`_.
 
 Breaking changes
 ~~~~~~~~~~~~~~~~
 
+- Many arguments of plotmethods have been made keyword-only.
+- ``xarray.plot.plot`` module renamed to ``xarray.plot.dataarray_plot`` to prevent
+  shadowing of the ``plot`` method. (:issue:`6949`, :pull:`7052`).
+  By `Michael Niklas <https://github.com/headtr1ck>`_.
 
 Deprecations
 ~~~~~~~~~~~~
 
+- Positional arguments for all plot methods have been deprecated (:issue:`6949`, :pull:`7052`).
+  By `Michael Niklas <https://github.com/headtr1ck>`_.
 
 Bug fixes
 ~~~~~~~~~
@@ -64,8 +72,8 @@ New Features
   the z argument. (:pull:`6778`)
   By `Jimmy Westling <https://github.com/illviljan>`_.
 - Include the variable name in the error message when CF decoding fails to allow
-  for easier identification of problematic variables (:issue:`7145`,
-  :pull:`7147`). By `Spencer Clark <https://github.com/spencerkclark>`_.
+  for easier identification of problematic variables (:issue:`7145`, :pull:`7147`).
+  By `Spencer Clark <https://github.com/spencerkclark>`_.
 
 Breaking changes
 ~~~~~~~~~~~~~~~~

pyproject.toml

@@ -48,6 +48,7 @@ module = [
   "importlib_metadata.*",
   "iris.*",
   "matplotlib.*",
+  "mpl_toolkits.*",
   "Nio.*",
   "nc_time_axis.*",
   "numbagg.*",

setup.cfg

@@ -165,7 +165,6 @@ float_to_top = true
 default_section = THIRDPARTY
 known_first_party = xarray
 
-
 [aliases]
 test = pytest
 

xarray/core/alignment.py

@@ -38,7 +38,7 @@
 if TYPE_CHECKING:
     from .dataarray import DataArray
     from .dataset import Dataset
-    from .types import JoinOptions, T_DataArray, T_DataArrayOrSet, T_Dataset
+    from .types import JoinOptions, T_DataArray, T_Dataset, T_DataWithCoords
 
 DataAlignable = TypeVar("DataAlignable", bound=DataWithCoords)
 
@@ -944,8 +944,8 @@ def _get_broadcast_dims_map_common_coords(args, exclude):
 
 
 def _broadcast_helper(
-    arg: T_DataArrayOrSet, exclude, dims_map, common_coords
-) -> T_DataArrayOrSet:
+    arg: T_DataWithCoords, exclude, dims_map, common_coords
+) -> T_DataWithCoords:
 
     from .dataarray import DataArray
     from .dataset import Dataset
@@ -976,14 +976,16 @@ def _broadcast_dataset(ds: T_Dataset) -> T_Dataset:
 
     # remove casts once https://github.com/python/mypy/issues/12800 is resolved
     if isinstance(arg, DataArray):
-        return cast("T_DataArrayOrSet", _broadcast_array(arg))
+        return cast("T_DataWithCoords", _broadcast_array(arg))
     elif isinstance(arg, Dataset):
-        return cast("T_DataArrayOrSet", _broadcast_dataset(arg))
+        return cast("T_DataWithCoords", _broadcast_dataset(arg))
     else:
         raise ValueError("all input must be Dataset or DataArray objects")
 
 
-def broadcast(*args, exclude=None):
+# TODO: this typing is too restrictive since it cannot deal with mixed
+# DataArray and Dataset types...? Is this a problem?
+def broadcast(*args: T_DataWithCoords, exclude=None) -> tuple[T_DataWithCoords, ...]:
     """Explicitly broadcast any number of DataArray or Dataset objects against
     one another.
 

xarray/core/dataarray.py

@@ -22,7 +22,7 @@
 
 from ..coding.calendar_ops import convert_calendar, interp_calendar
 from ..coding.cftimeindex import CFTimeIndex
-from ..plot.plot import _PlotMethods
+from ..plot.accessor import DataArrayPlotAccessor
 from ..plot.utils import _get_units_from_attrs
 from . import alignment, computation, dtypes, indexing, ops, utils
 from ._reductions import DataArrayReductions
@@ -4189,7 +4189,7 @@ def _inplace_binary_op(self: T_DataArray, other: Any, f: Callable) -> T_DataArra
     def _copy_attrs_from(self, other: DataArray | Dataset | Variable) -> None:
         self.attrs = other.attrs
 
-    plot = utils.UncachedAccessor(_PlotMethods)
+    plot = utils.UncachedAccessor(DataArrayPlotAccessor)
 
     def _title_for_slice(self, truncate: int = 50) -> str:
         """

xarray/core/dataset.py

@@ -35,7 +35,7 @@
 
 from ..coding.calendar_ops import convert_calendar, interp_calendar
 from ..coding.cftimeindex import CFTimeIndex, _parse_array_of_cftime_strings
-from ..plot.dataset_plot import _Dataset_PlotMethods
+from ..plot.accessor import DatasetPlotAccessor
 from . import alignment
 from . import dtypes as xrdtypes
 from . import duck_array_ops, formatting, formatting_html, ops, utils
@@ -7483,7 +7483,7 @@ def imag(self: T_Dataset) -> T_Dataset:
         """
         return self.map(lambda x: x.imag, keep_attrs=True)
 
-    plot = utils.UncachedAccessor(_Dataset_PlotMethods)
+    plot = utils.UncachedAccessor(DatasetPlotAccessor)
 
     def filter_by_attrs(self: T_Dataset, **kwargs) -> T_Dataset:
         """Returns a ``Dataset`` with variables that match specific conditions.
@@ -8575,7 +8575,9 @@ def curvefit(
             or not isinstance(coords, Iterable)
         ):
             coords = [coords]
-        coords_ = [self[coord] if isinstance(coord, str) else coord for coord in coords]
+        coords_: Sequence[DataArray] = [
+            self[coord] if isinstance(coord, str) else coord for coord in coords
+        ]
 
         # Determine whether any coords are dims on self
         for coord in coords_:

xarray/core/pycompat.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 from importlib import import_module
+from typing import Any, Literal
 
 import numpy as np
 from packaging.version import Version
@@ -9,6 +10,8 @@
 
 integer_types = (int, np.integer)
 
+ModType = Literal["dask", "pint", "cupy", "sparse"]
+
 
 class DuckArrayModule:
     """
@@ -18,7 +21,12 @@ class DuckArrayModule:
     https://github.com/pydata/xarray/pull/5561#discussion_r664815718
     """
 
-    def __init__(self, mod):
+    module: ModType | None
+    version: Version
+    type: tuple[type[Any]]  # TODO: improve this? maybe Generic
+    available: bool
+
+    def __init__(self, mod: ModType) -> None:
         try:
             duck_array_module = import_module(mod)
             duck_array_version = Version(duck_array_module.__version__)

xarray/core/types.py

@@ -162,7 +162,10 @@ def dtype(self) -> np.dtype:
 CoarsenBoundaryOptions = Literal["exact", "trim", "pad"]
 SideOptions = Literal["left", "right"]
 
+ScaleOptions = Literal["linear", "symlog", "log", "logit", None]
 HueStyleOptions = Literal["continuous", "discrete", None]
+AspectOptions = Union[Literal["auto", "equal"], float, None]
+ExtendOptions = Literal["neither", "both", "min", "max", None]
 
 # TODO: Wait until mypy supports recursive objects in combination with typevars
 _T = TypeVar("_T")