Merge pull request #1 from jklymak/jklymak-colormap-normalization

jklymak · jklymak · commit 6188d7151d37 · 2015-06-06T20:51:59.000-07:00
Create colormap_normalizations.py
diff --git a/examples/color/colormap_normalizations.py b/examples/color/colormap_normalizations.py
@@ -0,0 +1,133 @@
+"""
+Demonstration of using norm to map colormaps onto data in non-linear ways.
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.colors as colors
+from matplotlib.mlab import bivariate_normal
+
+'''
+Lognorm: Instead of pcolor log10(Z1) you can have colorbars that have
+the exponential labels using a norm.
+'''
+N = 100
+X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
+
+# A low hump with a spike coming out of the top right.  Needs to have
+# z/colour axis on a log scale so we see both hump and spike.  linear
+# scale only shows the spike.
+Z1 = bivariate_normal(X, Y, 0.1, 0.2, 1.0, 1.0) + 0.1 * bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
+
+fig,ax=plt.subplots(2,1)
+
+pcm=ax[0].pcolor(X, Y, Z1, norm=colors.LogNorm(vmin=Z1.min(), vmax=Z1.max()), cmap='PuBu_r')
+fig.colorbar(pcm,ax=ax[0],extend='max')
+
+pcm=ax[1].pcolor(X, Y, Z1, cmap='PuBu_r')
+fig.colorbar(pcm,ax=ax[1],extend='max')
+fig.show()
+
+
+'''
+PowerNorm: Here a power-law trend in X partially obscures a rectified
+sine wave in Y. We can remove the power law using a PowerNorm.
+'''
+X, Y = np.mgrid[0:3:complex(0, N), 0:2:complex(0, N)]
+Z1 = (1+np.sin(Y*10.))*X**(2.)
+
+fig,ax=plt.subplots(2,1)
+
+pcm=ax[0].pcolormesh(X, Y, Z1, norm=colors.PowerNorm(gamma=1./2.), cmap='PuBu_r')
+fig.colorbar(pcm,ax=ax[0],extend='max')
+
+pcm=ax[1].pcolormesh(X, Y, Z1, cmap='PuBu_r')
+fig.colorbar(pcm,ax=ax[1],extend='max')
+fig.show()
+
+
+'''
+SymLogNorm: two humps, one negative and one positive, The positive
+with 5-times the amplitude. Linearly, you cannot see detail in the
+negative hump.  Here we logarithmically scale the positive and
+negative data separately.
+
+Note that colorbar labels do not come out looking very good.
+'''
+
+X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
+Z1 = (bivariate_normal(X, Y, 1.,1., 1.0, 1.0))**2  \
+      - 0.4 * (bivariate_normal(X, Y, 1.0, 1.0, -1.0, 0.0))**2
+Z1 = Z1/0.03
+
+fig,ax=plt.subplots(2,1)
+
+pcm=ax[0].pcolormesh(X, Y, Z1, 
+                 norm=colors.SymLogNorm(linthresh=0.03,linscale=0.03,
+                                        vmin=-1.0,vmax=1.0), 
+                 cmap='RdBu_r')
+fig.colorbar(pcm,ax=ax[0],extend='both')
+
+pcm=ax[1].pcolormesh(X, Y, Z1, cmap='RdBu_r',vmin=-np.max(Z1))
+fig.colorbar(pcm,ax=ax[1],extend='both')
+fig.show()
+
+
+'''
+Custom Norm: An example with a customized normalization.  This one
+uses the example above, and normalizes the negative data differently
+from the positive.
+'''
+X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
+Z1 = (bivariate_normal(X, Y, 1.,1., 1.0, 1.0))**2  \
+      - 0.4 * (bivariate_normal(X, Y, 1.0, 1.0, -1.0, 0.0))**2
+Z1 = Z1/0.03
+
+# Example of making your own norm.  Also see matplotlib.colors.
+# From Joe Kington: This one gives two different linear ramps:
+class MidpointNormalize(colors.Normalize):
+    def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
+        self.midpoint = midpoint
+        colors.Normalize.__init__(self, vmin, vmax, clip)
+    def __call__(self, value, clip=None):
+        # I'm ignoring masked values and all kinds of edge cases to make a
+        # simple example...
+        x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
+        return np.ma.masked_array(np.interp(value, x, y))
+#####
+fig,ax=plt.subplots(2,1)
+
+pcm=ax[0].pcolormesh(X, Y, Z1, 
+                 norm=MidpointNormalize(midpoint=0.), 
+                 cmap='RdBu_r')
+fig.colorbar(pcm,ax=ax[0],extend='both')
+
+pcm=ax[1].pcolormesh(X, Y, Z1, cmap='RdBu_r',vmin=-np.max(Z1))
+fig.colorbar(pcm,ax=ax[1],extend='both')
+fig.show()
+
+'''
+BoundaryNorm: For this one you provide the boundaries for your colors,
+and the Norm puts the first color in between the first pair, the
+second color between the second pair, etc.
+'''
+
+fig,ax=plt.subplots(3,1,figsize=(8,8))
+ax=ax.flatten()
+# even bounds gives a contour-like effect
+bounds= np.linspace(-1,1,10)
+pcm=ax[0].pcolormesh(X, Y, Z1, 
+                 norm=colors.BoundaryNorm(boundaries=bounds,ncolors=256), 
+                 cmap='RdBu_r')
+fig.colorbar(pcm,ax=ax[0],extend='both',orientation='vertical')
+
+# uneven bounds changes the colormapping:
+bounds = np.array([-0.25,-0.125,0,0.5,1])
+pcm=ax[1].pcolormesh(X, Y, Z1, 
+                 norm=colors.BoundaryNorm(boundaries=bounds,ncolors=256), 
+                 cmap='RdBu_r')
+fig.colorbar(pcm,ax=ax[1],extend='both',orientation='vertical')
+
+pcm=ax[2].pcolormesh(X, Y, Z1, cmap='RdBu_r',vmin=-np.max(Z1))
+fig.colorbar(pcm,ax=ax[2],extend='both',orientation='vertical')
+fig.show()
