matplotlib
diff --git a/‎examples/images_contours_and_fields/bivariate_demo.py
Lines changed: 38 additions & 0 deletions b/‎examples/images_contours_and_fields/bivariate_demo.py
Lines changed: 38 additions & 0 deletions
diff --git a/‎lib/matplotlib/axes/_axes.py
Lines changed: 4 additions & 4 deletions b/‎lib/matplotlib/axes/_axes.py
Lines changed: 4 additions & 4 deletions
diff --git a/‎lib/matplotlib/mpl-data/sample_data/air_temperature.npy
82.2 KB b/‎lib/matplotlib/mpl-data/sample_data/air_temperature.npy
82.2 KB
diff --git a/‎lib/matplotlib/mpl-data/sample_data/surface_pressure.npy
82.2 KB b/‎lib/matplotlib/mpl-data/sample_data/surface_pressure.npy
82.2 KB
@@ -0,0 +1,38 @@
+"""
+===========
+Bivariate Demo
+===========
+
+Plotting bivariate data.
+
+imshow, pcolor, pcolormesh, pcolorfast allows you to plot bivariate data
+using a bivaraite colormap.
+
+In this example we use imshow to plot air temperature with surface pressure
+alongwith a color square.
+"""
+import matplotlib.colors as colors
+from matplotlib.cbook import get_sample_data
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+###############################################################################
+# Bivariate plotting demo
+# -----------------------
+
+air_temp = np.load(get_sample_data('air_temperature.npy'))
+surf_pres = np.load(get_sample_data('surface_pressure.npy'))
+
+fig, ax = plt.subplots()
+
+bivariate = [air_temp, surf_pres]
+
+cax = ax.imshow(bivariate, norm=colors.BivariateNorm(),
+                cmap=colors.BivariateColormap())
+
+# if input data is bivariate then colorbar automatically draws colorsquare
+# instead of colorbar
+cbar = fig.colorbar(cax, xlabel='air_temp', ylabel='surf_pres')
+
+plt.show()
@@ -5081,8 +5081,8 @@ def imshow(self, X, cmap=None, norm=None, aspect=None,
             on the Agg, ps and pdf backends. Other backends will fall back to
             'nearest'.
 
-        norm : `~matplotlib.colors.Normalize`, `matplotlib.colors.BivariateNorm`
-            optional, default: None
+        norm : `~matplotlib.colors.Normalize`,
+            `matplotlib.colors.BivariateNorm` optional, default: None
             A `~matplotlib.colors.Normalize` instance is used to scale
             a 2-D float `X` input to the (0, 1) range for input to the
             `cmap`. If `norm` is None, use the default func:`normalize`.
@@ -5744,8 +5744,8 @@ def pcolorfast(self, *args, **kwargs):
 
           *cmap*: [ *None* | Colormap ]
             A :class:`matplotlib.colors.Colormap` or
-            :class:`matplotlib.colors.BivariateColormap` instance from cm. If *None*,
-            use rc settings.
+            :class:`matplotlib.colors.BivariateColormap` instance from cm.
+            If *None*, use rc settings.
 
           *norm*: [ *None* | Normalize ]
             A :class:`matplotlib.colors.Normalize` or