matplotlib
diff --git a/‎doc/users/plotting/examples/colormap_normalizations_arbitrarynorm.py
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diff --git a/‎doc/users/plotting/examples/colormap_normalizations_piecewisenorm.py
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diff --git a/‎doc/users/plotting/examples/colormap_normalizations_rootnorm.py
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+"""
+============================================
+Examples of arbitrary colormap normalization
+============================================
+
+Here I plot an image array with data spanning for a large dynamic range,
+using different normalizations. Look at how each of them enhances
+different features.
+
+"""
+
+import ArbitraryNorm as colors
+
+import numpy as np
+# import matplotlib.colors as colors
+import matplotlib.pyplot as plt
+import matplotlib.cm as cm
+
+# Creating some toy data
+xmax = 16 * np.pi
+x = np.linspace(0, xmax, 1024)
+y = np.linspace(-2, 2, 512)
+X, Y = np.meshgrid(x, y)
+
+data = np.zeros(X.shape)
+
+
+def gauss2d(x, y, a0, x0, y0, wx, wy):
+    return a0 * np.exp(-(x - x0)**2 / wx**2 - (y - y0)**2 / wy**2)
+
+maskY = (Y > -1) * (Y <= 0)
+N = 31
+for i in range(N):
+    maskX = (X > (i * (xmax / N))) * (X <= ((i + 1) * (xmax / N)))
+    mask = maskX * maskY
+    data[mask] += gauss2d(X[mask], Y[mask], 2. * i / (N - 1), (i + 0.5) *
+                          (xmax / N), -0.25, xmax / (3 * N), 0.07)
+    data[mask] -= gauss2d(X[mask], Y[mask], 1. * i / (N - 1), (i + 0.5) *
+                          (xmax / N), -0.75, xmax / (3 * N), 0.07)
+
+maskY = (Y > 0) * (Y <= 1)
+data[maskY] = np.cos(X[maskY]) * Y[maskY]**2
+
+N = 61
+maskY = (Y > 1) * (Y <= 2.)
+for i, val in enumerate(np.linspace(-1, 1, N)):
+    if val < 0:
+        aux = val
+    if val > 0:
+        aux = val * 2
+
+    maskX = (X > (i * (xmax / N))) * (X <= ((i + 1) * (xmax / N)))
+    data[maskX * maskY] = aux
+
+N = 11
+maskY = (Y <= -1)
+for i, val in enumerate(np.linspace(-1, 1, N)):
+    if val < 0:
+        factor = 1
+    if val >= 0:
+        factor = 2
+    maskX = (X > (i * (xmax / N))) * (X <= ((i + 1) * (xmax / N)))
+    mask = maskX * maskY
+    data[mask] = val * factor
+
+    if i != N - 1:
+        data[mask] += gauss2d(X[mask], Y[mask], 0.03 * factor, (i + 0.5) *
+                              (xmax / N), -1.25, xmax / (3 * N), 0.07)
+    if i != 0:
+        data[mask] -= gauss2d(X[mask], Y[mask], 0.1 * factor, (i + 0.5) *
+                              (xmax / N), -1.75, xmax / (3 * N), 0.07)
+
+
+cmap = cm.spectral
+
+
+def makePlot(norm, label=''):
+    fig, ax = plt.subplots()
+    cax = ax.pcolormesh(x, y, data, cmap=cmap, norm=norm)
+    ax.set_title(label)
+    ax.set_xlim(0, xmax)
+    ax.set_ylim(-2, 2)
+    if norm:
+        ticks = norm.ticks()
+    else:
+        ticks = None
+    cbar = fig.colorbar(cax, format='%.3g', ticks=ticks)
+
+
+makePlot(None, 'Regular linear scale')
+
+# Example of logarithm normalization using FuncNorm
+norm = colors.FuncNorm(f=lambda x: np.log10(x),
+                       finv=lambda x: 10.**(x), vmin=0.01, vmax=2)
+makePlot(norm, "Log normalization using FuncNorm")
+# The same can be achived with
+# norm = colors.FuncNorm(f='log',vmin=0.01,vmax=2)
+
+# Example of root normalization using FuncNorm
+norm = colors.FuncNorm(f='sqrt', vmin=0.0, vmax=2)
+makePlot(norm, "Root normalization using FuncNorm")
+
+# Performing a symmetric amplification of the features around 0
+norm = colors.MirrorPiecewiseNorm(fpos='crt')
+makePlot(norm, "Amplified features symetrically around \n"
+               "0 with MirrorPiecewiseNorm")
+
+
+# Amplifying features near 0.6 with MirrorPiecewiseNorm
+norm = colors.MirrorPiecewiseNorm(fpos='crt', fneg='crt',
+                                  center_cm=0.35,
+                                  center_data=0.6)
+makePlot(norm, "Amplifying positive and negative features\n"
+               "standing on 0.6 with MirrorPiecewiseNorm")
+
+# Amplifying features near both -0.4 and near 1.2 with PiecewiseNorm
+norm = colors.PiecewiseNorm(flist=['cubic', 'crt', 'cubic', 'crt'],
+                            refpoints_cm=[0.25, 0.5, 0.75],
+                            refpoints_data=[-0.4, 1, 1.2])
+makePlot(norm, "Amplifying positive and negative features standing\n"
+               " on -0.4 and on 1.2 with PiecewiseNorm")
+
+# Amplifying features near both -0.4 and near 1.2 with PiecewiseNorm
+norm = colors.PiecewiseNorm(flist=['linear', 'crt', 'crt'],
+                            refpoints_cm=[0.2, 0.6],
+                            refpoints_data=[-0.6, 1.2])
+makePlot(norm, "Amplifying only positive features standing on -0.6\n"
+               " and on 1.2 with PiecewiseNorm")
+
+
+plt.show()