matplotlib · alvarosg · Oct 17, 2016 · Oct 18, 2016 · Oct 18, 2016 · Oct 18, 2016
diff --git a/examples/colormap_normalization/colormap_normalizations_funcnorm.py b/examples/colormap_normalization/colormap_normalizations_funcnorm.py
@@ -0,0 +1,87 @@
+"""
+=====================================================================
+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, 3.5]}, figsize=plt.figaspect(0.6))
+
+    cax = make_plot(None, 'Regular linear scale', fig, ax11, ax12)
+    fig.colorbar(cax, format='%.3g', ax=ax12, ticks=np.linspace(0, 1, 6))
+
+    # Example of logarithm normalization using FuncNorm
+    norm = colors.FuncNorm(f='log10', vmin=0.01)
+    cax = make_plot(norm, 'Log normalization', 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=np.log10,
+    #                        finv=lambda x: 10.**(x), vmin=0.01)
+
+    # Example of root normalization using FuncNorm
+    norm = colors.FuncNorm(f='sqrt', vmin=0.0)
+    cax = make_plot(norm, 'Root normalization', 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='root{2}', vmin=0.)
+    # or with
+    # norm = colors.FuncNorm(f=lambda x: x**0.5,
+    #                        finv=lambda x: x**2, vmin=0.0)
+
+    fig.subplots_adjust(hspace=0.4, wspace=0.15)
+    fig.suptitle('Normalization with FuncNorm')
+    plt.show()
+
+
+def make_plot(norm, label, fig, ax1, ax2):
+    X, Y, data = get_data()
+    cax = ax2.imshow(data, cmap=cm.afmhot, 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()
diff --git a/examples/colormap_normalization/colormap_normalizations_mirrorpiecewisenorm.py b/examples/colormap_normalization/colormap_normalizations_mirrorpiecewisenorm.py
@@ -0,0 +1,95 @@
+"""
+================================================================================
+Examples of normalization using :class:`~matplotlib.colors.MirrorPiecewiseNorm`
+================================================================================
+
+This is an example on how to perform a normalization for positive
+and negative data around zero independently using
+class:`~matplotlib.colors.MirrorPiecewiseNorm`.
+
+"""
+
+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, 3.5]}, figsize=plt.figaspect(0.6))
+
+    cax = make_plot(None, 'Regular linear scale', fig, ax11, ax12)
+    fig.colorbar(cax, format='%.3g', ax=ax12, ticks=np.linspace(-1, 1, 5))
+
+    # Example of symmetric root normalization using MirrorPiecewiseNorm
+    norm = colors.MirrorPiecewiseNorm(fpos='cbrt')
+    cax = make_plot(norm, 'Symmetric cubic root normalization around zero',
+                    fig, ax21, ax22)
+    fig.colorbar(cax, format='%.3g', ticks=cax.norm.ticks(5), ax=ax22)
+    # The same can be achieved with
+    # norm = colors.MirrorPiecewiseNorm(fpos='root{3}')
+    # or with
+    # norm = colors.MirrorPiecewiseNorm(fpos=lambda x: x**(1 / 3.),
+    #                                   fposinv=lambda x: x**3)
+
+    # Example of asymmetric root normalization using MirrorPiecewiseNorm
+    norm = colors.MirrorPiecewiseNorm(fpos='cbrt', fneg='linear')
+    cax = make_plot(norm, 'Cubic root normalization above zero\n'
+                          'and linear below zero',
+                    fig, ax31, ax32)
+    fig.colorbar(cax, format='%.3g', ticks=cax.norm.ticks(5), ax=ax32)
+    # The same can be achieved with
+    # norm = colors.MirrorPiecewiseNorm(fpos='root{3}', fneg='linear')
+    # or with
+    # norm = colors.MirrorPiecewiseNorm(fpos=lambda x: x**(1 / 3.),
+    #                                   fposinv=lambda x: x**3,
+    #                                   fneg=lambda x: x,
+    #                                   fneginv=lambda x: x)
+
+    fig.subplots_adjust(hspace=0.4, wspace=0.15)
+    fig.suptitle('Normalization with MirrorPiecewiseNorm')
+    plt.show()
+
+
+def make_plot(norm, label, fig, ax1, ax2):
+    X, Y, data = get_data()
+    cax = ax2.imshow(data, cmap=cm.seismic, 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, -0.5, 0.25 / N, 0.15)
+        data -= gauss2d(X, Y, x, x, 0.5, 0.25 / N, 0.15)
+
+    data[data > 0] = data[data > 0] / data.max()
+    data[data < 0] = data[data < 0] / -data.min()
+    _cache.append((X, Y, data))
+
+    return _cache[0]
+
+main()
diff --git a/examples/colormap_normalization/colormap_normalizations_mirrorrootnorm.py b/examples/colormap_normalization/colormap_normalizations_mirrorrootnorm.py
@@ -0,0 +1,83 @@
+"""
+================================================================================
+Examples of normalization using :class:`~matplotlib.colors.MirrorRootNorm`
+================================================================================
+
+This is an example on how to perform a root normalization for positive
+and negative data around zero independently using
+class:`~matplotlib.colors.MirrorRootNorm`.
+
+"""
+
+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, 3.5]}, figsize=plt.figaspect(0.6))
+
+    cax = make_plot(None, 'Regular linear scale', fig, ax11, ax12)
+    fig.colorbar(cax, format='%.3g', ax=ax12, ticks=np.linspace(-1, 1, 5))
+
+    # Example of symmetric root normalization using MirrorRootNorm
+    norm = colors.MirrorRootNorm(orderpos=2)
+    cax = make_plot(norm, 'Symmetric cubic root normalization around zero',
+                    fig, ax21, ax22)
+    fig.colorbar(cax, format='%.3g', ticks=cax.norm.ticks(5), ax=ax22)
+
+    # Example of asymmetric root normalization using MirrorRootNorm
+    norm = colors.MirrorRootNorm(orderpos=2, orderneg=4)
+    cax = make_plot(norm, 'Square root normalization above zero\n'
+                          'and quartic root below zero',
+                    fig, ax31, ax32)
+    fig.colorbar(cax, format='%.3g', ticks=cax.norm.ticks(5), ax=ax32)
+
+    fig.subplots_adjust(hspace=0.4, wspace=0.15)
+    fig.suptitle('Normalization with MirrorRootNorm')
+    plt.show()
+
+
+def make_plot(norm, label, fig, ax1, ax2):
+    X, Y, data = get_data()
+    cax = ax2.imshow(data, cmap=cm.seismic, 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, -0.5, 0.25 / N, 0.15)
+        data -= gauss2d(X, Y, x, x, 0.5, 0.25 / N, 0.15)
+
+    data[data > 0] = data[data > 0] / data.max()
+    data[data < 0] = data[data < 0] / -data.min()
+    _cache.append((X, Y, data))
+
+    return _cache[0]
+
+main()
diff --git a/examples/colormap_normalization/colormap_normalizations_piecewisenorm.py b/examples/colormap_normalization/colormap_normalizations_piecewisenorm.py
@@ -0,0 +1,93 @@
+"""
+=========================================================================
+Examples of normalization using :class:`~matplotlib.colors.PiecewiseNorm`
+=========================================================================
+
+This is an example on how to perform a normalization defined by intervals
+using class:`~matplotlib.colors.PiecewiseNorm`.
+
+"""
+
+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)) = plt.subplots(2, 2, gridspec_kw={
+              'width_ratios': [1, 3]}, figsize=plt.figaspect(0.6))
+
+    cax = make_plot(None, 'Regular linear scale', fig, ax11, ax12)
+    fig.colorbar(cax, format='%.3g', ax=ax12, ticks=np.linspace(0, 1, 6))
+
+    # Example of amplification of features above 0.2 and 0.6
+    norm = colors.PiecewiseNorm(flist=['linear', 'root{4}', 'linear',
+                                       'root{4}', 'linear'],
+                                refpoints_cm=[0.2, 0.4, 0.6, 0.8],
+                                refpoints_data=[0.2, 0.4, 0.6, 0.8])
+    cax = make_plot(norm, 'Amplification of features above 0.2 and 0.6',
+                    fig, ax21, ax22)
+    fig.colorbar(cax, format='%.3g', ticks=cax.norm.ticks(11), ax=ax22)
+    # The same can be achieved with
+    # norm = colors.PiecewiseNorm(flist=[lambda x: x,
+    #                                    lambda x: x**(1. / 4),
+    #                                    lambda x: x,
+    #                                    lambda x: x**(1. / 4),
+    #                                    lambda x: x],
+    #                             finvlist=[lambda x: x,
+    #                                       lambda x: x**4,
+    #                                       lambda x: x,
+    #                                       lambda x: x**4,
+    #                                       lambda x: x],
+    #                             refpoints_cm=[0.2, 0.4, 0.6, 0.8],
+    #                             refpoints_data=[0.2, 0.4, 0.6, 0.8])
+
+    fig.subplots_adjust(hspace=0.4, wspace=0.15)
+    fig.suptitle('Normalization with PiecewiseNorm')
+    plt.show()
+
+
+def make_plot(norm, label, fig, ax1, ax2):
+    X, Y, data = get_data()
+    cax = ax2.imshow(data, cmap=cm.gist_heat, norm=norm)
+
+    d_values = np.linspace(cax.norm.vmin, cax.norm.vmax, 300)
+    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, 301)[:-1]
+    y = np.linspace(-1, 1, 120)
+    X, Y = np.meshgrid(x, y)
+
+    data = np.zeros(X.shape)
+
+    def supergauss2d(o, x, y, a0, x0, y0, wx, wy):
+        x_ax = ((x - x0) / wx)**2
+        y_ax = ((y - y0) / wy)**2
+        return a0 * np.exp(-(x_ax + y_ax)**o)
+    N = 6
+
+    data += np.floor(X * (N)) / (N - 1)
+
+    for x in np.linspace(0., 1, N + 1)[0:-1]:
+        data += supergauss2d(3, X, Y, 0.05, x + 0.5 / N, -0.5, 0.25 / N, 0.15)
+        data -= supergauss2d(3, X, Y, 0.05, x + 0.5 / N, 0.5, 0.25 / N, 0.15)
+
+    data = np.clip(data, 0, 1)
+    _cache.append((X, Y, data))
+    return _cache[0]
+
+main()