matplotlib
diff --git a/‎examples/images_contours_and_fields/irregulardatagrid.py
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+105Lines changed: 105 additions & 0 deletions b/‎examples/images_contours_and_fields/irregulardatagrid.py
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diff --git a/‎lib/matplotlib/mlab.py
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+1-1Lines changed: 1 addition & 1 deletion b/‎lib/matplotlib/mlab.py
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+1-1Lines changed: 1 addition & 1 deletion
@@ -0,0 +1,105 @@
+"""
+=======================================
+Contour plot of irregularly spaced data
+=======================================
+
+Comparison of a contour plot of irregularly spaced data interpolated
+on a regular grid versus a tricontour plot for an unstructured triangular grid.
+
+Since :meth:`~.axes.Axes.contour` and :meth:`~.axes.Axes.contourf` expect the
+data to live on a regular grid, plotting a contour plot of irregularly spaced
+data requires different methods. The two options are:
+
+* Interpolate the data to a regular grid first. This can be done with on-borad
+  means, e.g. via `~.tri.LinearTriInterpolator` or using external functionality
+  e.g. via `scipy.interpolate.griddata`. Then plot the
+  interpolated data with the usual :meth:`~.axes.Axes.contour`.
+* Directly use :meth:`~.axes.Axes.tricontour` or
+  :meth:`~.axes.Axes.tricontourf` which will perform a triangulation
+  internally.
+
+This example shows both methods in action.
+"""
+
+import matplotlib.pyplot as plt
+import matplotlib.tri as tri
+import numpy as np
+
+np.random.seed(19680801)
+npts = 200
+ngridx = 100
+ngridy = 200
+x = np.random.uniform(-2, 2, npts)
+y = np.random.uniform(-2, 2, npts)
+z = x * np.exp(-x**2 - y**2)
+
+fig, (ax1, ax2) = plt.subplots(nrows=2)
+
+# -----------------------
+# Interpolation on a grid
+# -----------------------
+# A contour plot of irregularly spaced data coordinates
+# via interpolation on a grid.
+
+# Create grid values first.
+xi = np.linspace(-2.1, 2.1, ngridx)
+yi = np.linspace(-2.1, 2.1, ngridy)
+
+# Perform linear interpolation of the data (x,y)
+# on a grid defined by (xi,yi)
+triang = tri.Triangulation(x, y)
+interpolator = tri.LinearTriInterpolator(triang, z)
+Xi, Yi = np.meshgrid(xi, yi)
+zi = interpolator(Xi, Yi)
+
+# Note that scipy.interpolate provides means to interpolate data on a grid
+# as well. The following would be an alternative to the four lines above:
+#from scipy.interpolate import griddata
+#zi = griddata((x, y), z, (xi[None,:], yi[:,None]), method='linear')
+
+
+ax1.contour(xi, yi, zi, 14, linewidths=0.5, colors='k')
+cntr1 = ax1.contourf(xi, yi, zi, 14, cmap="RdBu_r")
+
+fig.colorbar(cntr1, ax=ax1)
+ax1.plot(x, y, 'ko', ms=3)
+ax1.axis((-2, 2, -2, 2))
+ax1.set_title('grid and contour (%d points, %d grid points)' %
+              (npts, ngridx * ngridy))
+
+
+# ----------
+# Tricontour
+# ----------
+# Directly supply the unordered, irregularly spaced coordinates
+# to tricontour.
+
+ax2.tricontour(x, y, z, 14, linewidths=0.5, colors='k')
+cntr2 = ax2.tricontourf(x, y, z, 14, cmap="RdBu_r")
+
+fig.colorbar(cntr2, ax=ax2)
+ax2.plot(x, y, 'ko', ms=3)
+ax2.axis((-2, 2, -2, 2))
+ax2.set_title('tricontour (%d points)' % npts)
+
+plt.subplots_adjust(hspace=0.5)
+plt.show()
+
+#############################################################################
+#
+# ------------
+#
+# References
+# """"""""""
+#
+# The use of the following functions and methods is shown in this example:
+
+import matplotlib
+matplotlib.axes.Axes.contour
+matplotlib.pyplot.contour
+matplotlib.axes.Axes.contourf
+matplotlib.pyplot.contourf
+matplotlib.axes.Axes.tricontour
+matplotlib.pyplot.tricontour
+matplotlib.axes.Axes.tricontourf
+matplotlib.pyplot.tricontourf
@@ -3312,7 +3312,7 @@ def newfunc(val, mask, mval):
         fh.close()
 
 
-@cbook.deprecated('2.2')
+@cbook.deprecated('2.2', alternative='scipy.interpolate.griddata')
 def griddata(x, y, z, xi, yi, interp='nn'):
     """
     Interpolates from a nonuniformly spaced grid to some other grid.