Single matplotlib import (pydata#5794)

asv_bench/benchmarks/import_xarray.py

@@ -0,0 +1,9 @@
+class ImportXarray:
+    def setup(self, *args, **kwargs):
+        def import_xr():
+            import xarray  # noqa: F401
+
+        self._import_xr = import_xr
+
+    def time_import_xarray(self):
+        self._import_xr()

xarray/plot/dataset_plot.py

@@ -12,6 +12,7 @@
     _process_cmap_cbar_kwargs,
     get_axis,
     label_from_attrs,
+    plt,
 )
 
 # copied from seaborn
@@ -134,8 +135,7 @@ def _infer_scatter_data(ds, x, y, hue, markersize, size_norm, size_mapping=None)
 
 # copied from seaborn
 def _parse_size(data, norm):
-
-    import matplotlib as mpl
+    mpl = plt.matplotlib
 
     if data is None:
         return None
@@ -544,8 +544,6 @@ def quiver(ds, x, y, ax, u, v, **kwargs):
 
     Wraps :py:func:`matplotlib:matplotlib.pyplot.quiver`.
     """
-    import matplotlib as mpl
-
     if x is None or y is None or u is None or v is None:
         raise ValueError("Must specify x, y, u, v for quiver plots.")
 
@@ -560,7 +558,7 @@ def quiver(ds, x, y, ax, u, v, **kwargs):
 
         # TODO: Fix this by always returning a norm with vmin, vmax in cmap_params
         if not cmap_params["norm"]:
-            cmap_params["norm"] = mpl.colors.Normalize(
+            cmap_params["norm"] = plt.Normalize(
                 cmap_params.pop("vmin"), cmap_params.pop("vmax")
             )
 
@@ -576,8 +574,6 @@ def streamplot(ds, x, y, ax, u, v, **kwargs):
 
     Wraps :py:func:`matplotlib:matplotlib.pyplot.streamplot`.
     """
-    import matplotlib as mpl
-
     if x is None or y is None or u is None or v is None:
         raise ValueError("Must specify x, y, u, v for streamplot plots.")
 
@@ -613,7 +609,7 @@ def streamplot(ds, x, y, ax, u, v, **kwargs):
 
         # TODO: Fix this by always returning a norm with vmin, vmax in cmap_params
         if not cmap_params["norm"]:
-            cmap_params["norm"] = mpl.colors.Normalize(
+            cmap_params["norm"] = plt.Normalize(
                 cmap_params.pop("vmin"), cmap_params.pop("vmax")
             )
 

xarray/plot/facetgrid.py

@@ -9,8 +9,8 @@
     _get_nice_quiver_magnitude,
     _infer_xy_labels,
     _process_cmap_cbar_kwargs,
-    import_matplotlib_pyplot,
     label_from_attrs,
+    plt,
 )
 
 # Overrides axes.labelsize, xtick.major.size, ytick.major.size
@@ -116,8 +116,6 @@ def __init__(
 
         """
 
-        plt = import_matplotlib_pyplot()
-
         # Handle corner case of nonunique coordinates
         rep_col = col is not None and not data[col].to_index().is_unique
         rep_row = row is not None and not data[row].to_index().is_unique
@@ -519,10 +517,8 @@ def set_titles(self, template="{coord} = {value}", maxchar=30, size=None, **kwar
         self: FacetGrid object
 
         """
-        import matplotlib as mpl
-
         if size is None:
-            size = mpl.rcParams["axes.labelsize"]
+            size = plt.rcParams["axes.labelsize"]
 
         nicetitle = functools.partial(_nicetitle, maxchar=maxchar, template=template)
 
@@ -619,8 +615,6 @@ def map(self, func, *args, **kwargs):
         self : FacetGrid object
 
         """
-        plt = import_matplotlib_pyplot()
-
         for ax, namedict in zip(self.axes.flat, self.name_dicts.flat):
             if namedict is not None:
                 data = self.data.loc[namedict]

xarray/plot/plot.py

@@ -29,9 +29,9 @@
     _resolve_intervals_2dplot,
     _update_axes,
     get_axis,
-    import_matplotlib_pyplot,
     label_from_attrs,
     legend_elements,
+    plt,
 )
 
 # copied from seaborn
@@ -83,8 +83,6 @@ def _parse_size(data, norm, width):
 
     If the data is categorical, normalize it to numbers.
     """
-    plt = import_matplotlib_pyplot()
-
     if data is None:
         return None
 
@@ -682,8 +680,6 @@ def scatter(
     **kwargs : optional
         Additional keyword arguments to matplotlib
     """
-    plt = import_matplotlib_pyplot()
-
     # Handle facetgrids first
     if row or col:
         allargs = locals().copy()
@@ -1111,8 +1107,6 @@ def newplotfunc(
             allargs["plotfunc"] = globals()[plotfunc.__name__]
             return _easy_facetgrid(darray, kind="dataarray", **allargs)
 
-        plt = import_matplotlib_pyplot()
-
         if (
             plotfunc.__name__ == "surface"
             and not kwargs.get("_is_facetgrid", False)

xarray/plot/utils.py

@@ -47,6 +47,12 @@ def import_matplotlib_pyplot():
     return plt
 
 
+try:
+    plt = import_matplotlib_pyplot()
+except ImportError:
+    plt = None
+
+
 def _determine_extend(calc_data, vmin, vmax):
     extend_min = calc_data.min() < vmin
     extend_max = calc_data.max() > vmax
@@ -64,7 +70,7 @@ def _build_discrete_cmap(cmap, levels, extend, filled):
     """
     Build a discrete colormap and normalization of the data.
     """
-    import matplotlib as mpl
+    mpl = plt.matplotlib
 
     if len(levels) == 1:
         levels = [levels[0], levels[0]]
@@ -115,8 +121,7 @@ def _build_discrete_cmap(cmap, levels, extend, filled):
 
 
 def _color_palette(cmap, n_colors):
-    import matplotlib.pyplot as plt
-    from matplotlib.colors import ListedColormap
+    ListedColormap = plt.matplotlib.colors.ListedColormap
 
     colors_i = np.linspace(0, 1.0, n_colors)
     if isinstance(cmap, (list, tuple)):
@@ -177,7 +182,7 @@ def _determine_cmap_params(
     cmap_params : dict
         Use depends on the type of the plotting function
     """
-    import matplotlib as mpl
+    mpl = plt.matplotlib
 
     if isinstance(levels, Iterable):
         levels = sorted(levels)
@@ -285,13 +290,13 @@ def _determine_cmap_params(
                 levels = np.asarray([(vmin + vmax) / 2])
             else:
                 # N in MaxNLocator refers to bins, not ticks
-                ticker = mpl.ticker.MaxNLocator(levels - 1)
+                ticker = plt.MaxNLocator(levels - 1)
                 levels = ticker.tick_values(vmin, vmax)
         vmin, vmax = levels[0], levels[-1]
 
     # GH3734
     if vmin == vmax:
-        vmin, vmax = mpl.ticker.LinearLocator(2).tick_values(vmin, vmax)
+        vmin, vmax = plt.LinearLocator(2).tick_values(vmin, vmax)
 
     if extend is None:
         extend = _determine_extend(calc_data, vmin, vmax)
@@ -421,10 +426,7 @@ def _assert_valid_xy(darray, xy, name):
 
 
 def get_axis(figsize=None, size=None, aspect=None, ax=None, **kwargs):
-    try:
-        import matplotlib as mpl
-        import matplotlib.pyplot as plt
-    except ImportError:
+    if plt is None:
         raise ImportError("matplotlib is required for plot.utils.get_axis")
 
     if figsize is not None:
@@ -437,7 +439,7 @@ def get_axis(figsize=None, size=None, aspect=None, ax=None, **kwargs):
         if ax is not None:
             raise ValueError("cannot provide both `size` and `ax` arguments")
         if aspect is None:
-            width, height = mpl.rcParams["figure.figsize"]
+            width, height = plt.rcParams["figure.figsize"]
             aspect = width / height
         figsize = (size * aspect, size)
         _, ax = plt.subplots(figsize=figsize)
@@ -454,9 +456,6 @@ def get_axis(figsize=None, size=None, aspect=None, ax=None, **kwargs):
 
 
 def _maybe_gca(**kwargs):
-
-    import matplotlib.pyplot as plt
-
     # can call gcf unconditionally: either it exists or would be created by plt.axes
     f = plt.gcf()
 
@@ -912,9 +911,7 @@ def _process_cmap_cbar_kwargs(
 
 
 def _get_nice_quiver_magnitude(u, v):
-    import matplotlib as mpl
-
-    ticker = mpl.ticker.MaxNLocator(3)
+    ticker = plt.MaxNLocator(3)
     mean = np.mean(np.hypot(u.to_numpy(), v.to_numpy()))
     magnitude = ticker.tick_values(0, mean)[-2]
     return magnitude
@@ -989,7 +986,7 @@ def legend_elements(
     """
     import warnings
 
-    import matplotlib as mpl
+    mpl = plt.matplotlib
 
     mlines = mpl.lines
 
@@ -1126,7 +1123,6 @@ def _legend_add_subtitle(handles, labels, text, func):
 
 def _adjust_legend_subtitles(legend):
     """Make invisible-handle "subtitles" entries look more like titles."""
-    plt = import_matplotlib_pyplot()
 
     # Legend title not in rcParams until 3.0
     font_size = plt.rcParams.get("legend.title_fontsize", None)