now with scripts actually included!

matplotlib · tacaswell · Jul 29, 2014 · Jul 12, 2014 · Jul 12, 2014 · Jul 18, 2014
commit 52d585a44cf40f93bea18f25285c638f5b288b62
diff --git a/doc/users/plotting/colormaps/Lfunction.py b/doc/users/plotting/colormaps/Lfunction.py
@@ -0,0 +1,171 @@
+'''
+Recreate Josef Albers plot illustrating the Weber-Fechner law and illustrate 
+with the binary matplotlib colormap, too. Trying to show the difference between 
+adding blackness to a color at different rates.
+'''
+
+import numpy as np
+import matplotlib.pyplot as plt
+from skimage import io, color
+import pdb
+import matplotlib as mpl
+from mpl_toolkits.mplot3d import Axes3D
+from matplotlib import cm, colors
+
+
+mpl.rcParams.update({'font.size': 20})
+mpl.rcParams['font.sans-serif'] = 'Arev Sans, Bitstream Vera Sans, Lucida Grande, Verdana, Geneva, Lucid, Helvetica, Avant Garde, sans-serif'
+mpl.rcParams['mathtext.fontset'] = 'custom'
+mpl.rcParams['mathtext.cal'] = 'cursive'
+mpl.rcParams['mathtext.rm'] = 'sans'
+mpl.rcParams['mathtext.tt'] = 'monospace'
+mpl.rcParams['mathtext.it'] = 'sans:italic'
+mpl.rcParams['mathtext.bf'] = 'sans:bold'
+mpl.rcParams['mathtext.sf'] = 'sans'
+mpl.rcParams['mathtext.fallback_to_cm'] = 'True'
+
+
+### Red, original Albers plot
+
+nrows = 5
+
+# Start with red
+red = np.array([np.hstack([np.ones((nrows,1)), np.zeros((nrows,2))])])
+
+# Get basic red in LAB
+lab_add = color.rgb2lab(red)
+lab_geometric = lab_add.copy()
+
+# Alter successive rows with more black
+k = 1
+for i in xrange(red.shape[1]):
+    # more blackness is closer to 0 than one, and in first column of LAB
+    lab_add[0,i,0] = lab_add[0,i,0] - 10*i
+    print i,k
+    if i != 0:
+        lab_geometric[0,i,0] = lab_geometric[0,i,0] - 10*k
+        k *= 2
+
+# Change LAB back to RGB for plotting
+rgb_add = red.copy() # only change red values
+temp = color.lab2rgb(lab_add)
+rgb_add[0,:,0] = temp[0,:,0]
+rgb_geometric = red.copy() # only change red values
+temp = color.lab2rgb(lab_geometric)
+rgb_geometric[0,:,0] = temp[0,:,0]
+
+fig = plt.figure()
+k = 1
+for i in xrange(red.shape[1]):
+
+    # LHS: additive
+    ax1 = fig.add_subplot(nrows,2,i*2+1, axisbg=tuple(rgb_add[0,i,:]))
+    print tuple(lab_add[0,i,:])#, tuple(rgb_add[0,i,:])
+
+    # RHS: multiplicative
+    ax2 = fig.add_subplot(nrows,2,i*2+2, axisbg=tuple(rgb_geometric[0,i,:]))
+    print tuple(lab_geometric[0,i,:])#, tuple(rgb_geometric[0,i,:])
+
+    # ylabels
+    if i!=0:
+        ax1.set_ylabel(str(1*i))
+        ax2.set_ylabel(str(k))
+        k *= 2
+
+    # Turn off ticks
+    ax1.get_xaxis().set_ticks([])
+    ax2.get_xaxis().set_ticks([])
+    ax1.get_yaxis().set_ticks([])
+    ax2.get_yaxis().set_ticks([])
+
+    # Turn off black edges
+    ax1.spines['right'].set_visible(False)
+    ax1.spines['top'].set_visible(False)
+    ax1.spines['bottom'].set_visible(False)
+    ax1.spines['left'].set_visible(False)
+    ax2.spines['right'].set_visible(False)
+    ax2.spines['top'].set_visible(False)
+    ax2.spines['bottom'].set_visible(False)
+    ax2.spines['left'].set_visible(False)
+
+
+# common ylabel
+ax1.text(-0.3, 3.8, 'Additional Parts Black', 
+            rotation=90, transform=ax1.transAxes)
+
+
+fig.subplots_adjust(hspace=0.0)
+plt.show()
+
+
+### Albers plot with linear scale black and white
+
+nrows = 5
+ncols = 2
+
+x = np.linspace(0.0, 1.0, 100)
+cmap = 'binary'
+
+# Get binary colormap entries for full 100 entries
+rgb = cm.get_cmap(cmap)(x)[np.newaxis,:,:3]
+
+# Sample 100-entry rgb additively and geometrically
+rgb_add = np.empty((1,nrows,3))
+rgb_geometric = np.empty((1,nrows,3))
+
+k = 1
+di = 8
+I0 = 5
+for i in xrange(nrows):
+    # Do more blackness via increasing indices
+    rgb_add[:,i,:] = rgb[:,i*di+I0,:]
+
+    if i != 0:
+        print i*di+I0, di*k+I0, (I0**(1./3)+i*di**(1./3))**3
+        rgb_geometric[:,i,:] = rgb[:,I0+di*k,:]
+        k *= 2
+    elif i==0:
+        print i*di+I0, I0, (I0**(1./3)+i*di**(1./3))**3
+        rgb_geometric[:,i,:] = rgb[:,I0,:]
+
+lab_add = color.rgb2lab(rgb_add)
+lab_geometric = color.rgb2lab(rgb_geometric)
+
+fig = plt.figure()
+k = 1
+for i in xrange(nrows):
+
+    # LHS: additive
+    ax1 = fig.add_subplot(nrows,ncols,i*2+1, axisbg=tuple(rgb_add[0,i,:]))
+
+    # middle: multiplicative
+    ax2 = fig.add_subplot(nrows,ncols,i*2+2, axisbg=tuple(rgb_geometric[0,i,:]))
+
+    # ylabels
+    if i!=0:
+        ax1.set_ylabel(str(1*i))
+        ax2.set_ylabel(str(k))
+        k *= 2
+
+    # Turn off ticks
+    ax1.get_xaxis().set_ticks([])
+    ax2.get_xaxis().set_ticks([])
+    ax1.get_yaxis().set_ticks([])
+    ax2.get_yaxis().set_ticks([])
+
+    # Turn off black edges
+    ax1.spines['right'].set_visible(False)
+    ax1.spines['top'].set_visible(False)
+    ax1.spines['bottom'].set_visible(False)
+    ax1.spines['left'].set_visible(False)
+    ax2.spines['right'].set_visible(False)
+    ax2.spines['top'].set_visible(False)
+    ax2.spines['bottom'].set_visible(False)
+    ax2.spines['left'].set_visible(False)
+
+# common ylabel
+ax1.text(-0.3, 4.0, 'Steps through map indices', 
+            rotation=90, transform=ax1.transAxes)
+
+fig.subplots_adjust(hspace=0.0)
+plt.show()
diff --git a/doc/users/plotting/colormaps/grayscale.py b/doc/users/plotting/colormaps/grayscale.py
@@ -0,0 +1,81 @@
+'''
+Show what matplotlib colormaps look like in grayscale.
+Uses lightness L* as a proxy for grayscale value.
+'''
+
+from skimage import io, color
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib import cm
+import matplotlib as mpl
+import pdb
+from scipy.optimize import curve_fit
+
+mpl.rcParams.update({'font.size': 14})
+mpl.rcParams['font.sans-serif'] = 'Arev Sans, Bitstream Vera Sans, Lucida Grande, Verdana, Geneva, Lucid, Helvetica, Avant Garde, sans-serif'
+mpl.rcParams['mathtext.fontset'] = 'custom'
+mpl.rcParams['mathtext.cal'] = 'cursive'
+mpl.rcParams['mathtext.rm'] = 'sans'
+mpl.rcParams['mathtext.tt'] = 'monospace'
+mpl.rcParams['mathtext.it'] = 'sans:italic'
+mpl.rcParams['mathtext.bf'] = 'sans:bold'
+mpl.rcParams['mathtext.sf'] = 'sans'
+mpl.rcParams['mathtext.fallback_to_cm'] = 'True'
+
+# Have colormaps separated into categories: http://matplotlib.org/examples/color/colormaps_reference.html
+
+cmaps = [('Sequential',     ['binary', 'Blues', 'BuGn', 'BuPu', 'gist_yarg',
+                             'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd',
+                             'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu',
+                             'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']),
+         ('Sequential2', ['afmhot', 'autumn', 'bone', 'cool', 'copper',
+                             'gist_gray', 'gist_heat', 'gray', 'hot', 'pink',
+                             'spring', 'summer', 'winter']),
+         ('Diverging',      ['BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr',
+                             'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'seismic']),
+         ('Qualitative',    ['Accent', 'Dark2', 'hsv', 'Paired', 'Pastel1',
+                             'Pastel2', 'Set1', 'Set2', 'Set3', 'spectral']),
+         ('Miscellaneous',  ['gist_earth', 'gist_ncar', 'gist_rainbow',
+                             'gist_stern', 'jet', 'brg', 'CMRmap', 'cubehelix',
+                             'gnuplot', 'gnuplot2', 'ocean', 'rainbow',
+                             'terrain', 'flag', 'prism'])]
+
+# indices to step through colormap        
+x = np.linspace(0.0, 1.0, 100)
+
+nrows = max(len(cmap_list) for cmap_category, cmap_list in cmaps)
+gradient = np.linspace(0, 1, 256)
+gradient = np.vstack((gradient, gradient))
+
+def plot_color_gradients(cmap_category, cmap_list):
+    fig, axes = plt.subplots(nrows=nrows, ncols=2)
+    fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99, wspace=0.05)
+    fig.suptitle(cmap_category + ' colormaps', fontsize=14, y=1.0, x=0.6)
+
+    for ax, name in zip(axes, cmap_list):
+
+        # Get rgb values for colormap
+        rgb = cm.get_cmap(plt.get_cmap(name))(x)[np.newaxis,:,:3]
+
+        # Get colormap in CIE LAB. We want the L here.
+        lab = color.rgb2lab(rgb)
+        L = lab[0,:,0]
+        L = np.float32(np.vstack((L, L, L)))
+
+        ax[0].imshow(gradient, aspect='auto', cmap=plt.get_cmap(name))
+        ax[1].imshow(L, aspect='auto', cmap='binary_r', vmin=0., vmax=100.)
+        pos = list(ax[0].get_position().bounds)
+        x_text = pos[0] - 0.01
+        y_text = pos[1] + pos[3]/2.
+        fig.text(x_text, y_text, name, va='center', ha='right', fontsize=10)
+
+    # Turn off *all* ticks & spines, not just the ones with colormaps.
+    for ax in axes:
+        ax[0].set_axis_off()
+        ax[1].set_axis_off()
+
+
+for cmap_category, cmap_list in cmaps:
+
+    plot_color_gradients(cmap_category, cmap_list)
+
diff --git a/doc/users/plotting/colormaps/lightness.py b/doc/users/plotting/colormaps/lightness.py
@@ -0,0 +1,131 @@
+'''
+For each colormap, plot the lightness parameter L* from CIELAB colorspace along the y axis vs index through the colormap. Colormaps are examined in categories as in the original matplotlib gallery of colormaps.
+'''
+
+from skimage import io, color
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib import cm
+import matplotlib as mpl
+import pdb
+from scipy.optimize import curve_fit
+
+mpl.rcParams.update({'font.size': 14})
+mpl.rcParams['font.sans-serif'] = 'Arev Sans, Bitstream Vera Sans, Lucida Grande, Verdana, Geneva, Lucid, Helvetica, Avant Garde, sans-serif'
+mpl.rcParams['mathtext.fontset'] = 'custom'
+mpl.rcParams['mathtext.cal'] = 'cursive'
+mpl.rcParams['mathtext.rm'] = 'sans'
+mpl.rcParams['mathtext.tt'] = 'monospace'
+mpl.rcParams['mathtext.it'] = 'sans:italic'
+mpl.rcParams['mathtext.bf'] = 'sans:bold'
+mpl.rcParams['mathtext.sf'] = 'sans'
+mpl.rcParams['mathtext.fallback_to_cm'] = 'True'
+
+# Have colormaps separated into categories: http://matplotlib.org/examples/color/colormaps_reference.html
+
+cmaps = [('Sequential',     ['binary', 'Blues', 'BuGn', 'BuPu', 'gist_yarg',
+                             'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd',
+                             'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu',
+                             'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']),
+         ('Sequential2', ['afmhot', 'autumn', 'bone', 'cool', 'copper',
+                             'gist_gray', 'gist_heat', 'gray', 'hot', 'pink',
+                             'spring', 'summer', 'winter']),
+         ('Diverging',      ['BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr',
+                             'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'seismic']),
+         ('Qualitative',    ['Accent', 'Dark2', 'hsv', 'Paired', 'Pastel1',
+                             'Pastel2', 'Set1', 'Set2', 'Set3', 'spectral']),
+         ('Miscellaneous',  ['gist_earth', 'gist_ncar', 'gist_rainbow',
+                             'gist_stern', 'jet', 'brg', 'CMRmap', 'cubehelix',
+                             'gnuplot', 'gnuplot2', 'ocean', 'rainbow',
+                             'terrain', 'flag', 'prism'])]
+
+# indices to step through colormap        
+x = np.linspace(0.0, 1.0, 100)
+
+# Do plot
+for cmap_category, cmap_list in cmaps:
+
+    # Do subplots so that colormaps have enough space. 5 per subplot?
+    dsub = 5 # number of colormaps per subplot
+    if cmap_category == 'Diverging': # because has 13 colormaps
+        dsub = 6
+    elif cmap_category == 'Sequential2': 
+        dsub = 7
+    elif cmap_category == 'Sequential': 
+        dsub = 7
+    nsubplots = int(np.ceil(len(cmap_list)/float(dsub)))
+
+    fig = plt.figure(figsize=(11.5,4*nsubplots))
+
+    for i, subplot in enumerate(xrange(nsubplots)):
+
+        locs = [] # locations for text labels
+
+        ax = fig.add_subplot(nsubplots, 1, i+1)
+        # pdb.set_trace()
+
+        for j, cmap in enumerate(cmap_list[i*dsub:(i+1)*dsub]):
+
+            # Get rgb values for colormap
+            rgb = cm.get_cmap(cmap)(x)[np.newaxis,:,:3]
+
+            # Get colormap in CIE LAB. We want the L here.
+            lab = color.rgb2lab(rgb)
+
+            # Plot colormap L values
+            # Do separately for each category so each plot can be pretty
+            # to make scatter markers change color along plot: http://stackoverflow.com/questions/8202605/matplotlib-scatterplot-colour-as-a-function-of-a-third-variable
+            if cmap_category=='Sequential':
+                dc = 0.6 # spacing between colormaps
+                ax.scatter(x+j*dc, lab[0,::-1,0], c=x, cmap=cmap + '_r', s=300, linewidths=0.)
+                if i==2:
+                    ax.axis([-0.1,4.1,0,100])
+                else:
+                    ax.axis([-0.1,4.7,0,100])
+                locs.append(x[-1]+j*dc) # store locations for colormap labels
+
+            elif cmap_category=='Sequential2':
+                dc = 1.15
+                ax.scatter(x+j*dc, lab[0,:,0], c=x, cmap=cmap, s=300, linewidths=0.)
+                if i==0:
+                    ax.axis([-0.1,8.1,0,100])
+                else:
+                    ax.axis([-0.1,7.0,0,100])
+                locs.append(x[-1]+j*dc) # store locations for colormap labels
+
+            elif cmap_category=='Diverging':
+                dc = 1.2
+                ax.scatter(x+j*dc, lab[0,:,0], c=x, cmap=cmap, s=300, linewidths=0.)
+                if i==0:
+                    ax.axis([-0.1,7.1,0,100])
+                else:
+                    ax.axis([-0.1,6,0,100])
+                locs.append(x[int(x.size/2.)]+j*dc) # store locations for colormap labels
+
+            elif cmap_category=='Qualitative':
+                dc = 1.3
+                ax.scatter(x+j*dc, lab[0,:,0], c=x, cmap=cmap, s=300, linewidths=0.)
+                ax.axis([-0.1,6.3,0,100])
+                locs.append(x[int(x.size/2.)]+j*dc) # store locations for colormap labels
+
+            elif cmap_category=='Miscellaneous':
+                dc = 1.25
+                ax.scatter(x+j*dc, lab[0,:,0], c=x, cmap=cmap, s=300, linewidths=0.)
+                ax.axis([-0.1,6.1,0,100])
+                locs.append(x[int(x.size/2.)]+j*dc) # store locations for colormap labels
+
+            # Set up labels for colormaps
+            ax.xaxis.set_ticks_position('top')
+            ticker = mpl.ticker.FixedLocator(locs)
+            ax.xaxis.set_major_locator(ticker)
+            formatter = mpl.ticker.FixedFormatter(cmap_list[i*dsub:(i+1)*dsub])
+            ax.xaxis.set_major_formatter(formatter)
+            labels = ax.get_xticklabels()
+            for label in labels:
+                label.set_rotation(60)
+
+    ax.set_xlabel(cmap_category + ' colormaps', fontsize=22)
+    fig.text(-0.005, 0.55, 'Lightness $L^*$', fontsize=18, transform=fig.transFigure, rotation=90)
+
+    fig.tight_layout(h_pad=0.05)
+    plt.show