Added new example

matplotlib · alvarosg · Oct 17, 2016 · Oct 18, 2016 · Oct 18, 2016 · Oct 18, 2016
commit 63dab61f6a0a4398c8eeaef7960feb96baf9e8ea
diff --git a/examples/colormap_normalization/colormap_normalizations_funcnorm.py b/examples/colormap_normalization/colormap_normalizations_funcnorm.py
@@ -0,0 +1,86 @@
+"""
+=====================================================================
+Examples of normalization using  :class:`~matplotlib.colors.FuncNorm`
+=====================================================================
+
+This is an example on how to perform a normalization using an arbitrary
+function with :class:`~matplotlib.colors.FuncNorm`. A logarithm normalization
+and a square root normalization will be use as examples.
+
+"""
+
+import matplotlib.cm as cm
+import matplotlib.colors as colors
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+
+def main():
+    fig, ((ax11, ax12),
+          (ax21, ax22),
+          (ax31, ax32)) = plt.subplots(3, 2, gridspec_kw={
+                        'width_ratios': [1, 2]}, figsize=plt.figaspect(0.6))
+
+    cax = make_plot(None, 'Regular linear scale', fig, ax11, ax12)
+    fig.colorbar(cax, format='%.3g', ax=ax12)
+
+    # Example of logarithm normalization using FuncNorm
+    norm = colors.FuncNorm(f=np.log10,
+                           finv=lambda x: 10.**(x), vmin=0.01)
+    cax = make_plot(norm, 'Log normalization using FuncNorm', fig, ax21, ax22)
+    fig.colorbar(cax, format='%.3g', ticks=cax.norm.ticks(5), ax=ax22)
+    # The same can be achieved with
+    # norm = colors.FuncNorm(f='log10', vmin=0.01)
+
+    # Example of root normalization using FuncNorm
+    norm = colors.FuncNorm(f=lambda x: x**0.5,
+                           finv=lambda x: x**2, vmin=0.0)
+    cax = make_plot(norm, 'Root normalization using FuncNorm', fig, ax31, ax32)
+    fig.colorbar(cax, format='%.3g', ticks=cax.norm.ticks(5), ax=ax32)
+    # The same can be achieved with
+    # norm = colors.FuncNorm(f='sqrt', vmin=0.0)
+    # or with
+    # norm = colors.FuncNorm(f='root{2}', vmin=0.)
+
+    fig.subplots_adjust(hspace=0.4, wspace=0.15)
+    plt.show()
+
+
+def make_plot(norm, label, fig, ax1, ax2):
+    X, Y, data = get_data()
+    cax = ax2.imshow(data, cmap=cm.gray, norm=norm)
+
+    d_values = np.linspace(cax.norm.vmin, cax.norm.vmax, 100)
+    cm_values = cax.norm(d_values)
+    ax1.plot(d_values, cm_values)
+    ax1.set_xlabel('Data values')
+    ax1.set_ylabel('Colormap values')
+    ax2.set_title(label)
+    ax2.axes.get_xaxis().set_ticks([])
+    ax2.axes.get_yaxis().set_ticks([])
+    return cax
+
+
+def get_data(_cache=[]):
+    if len(_cache) > 0:
+        return _cache[0]
+    x = np.linspace(0, 1, 300)
+    y = np.linspace(-1, 1, 90)
+    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)
+    N = 15
+    for x in np.linspace(0., 1, N):
+        data += gauss2d(X, Y, x, x, 0, 0.25/N, 0.25)
+
+    data = data - data.min()
+    data = data / data.max()
+    _cache.append((X, Y, data))
+
+    return _cache[0]
+
+main()