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kthyngkthyng
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now with scripts actually included!
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‎doc/users/plotting/colormaps/Lfunction.py

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'''
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Recreate Josef Albers plot illustrating the Weber-Fechner law and illustrate
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with the binary matplotlib colormap, too. Trying to show the difference between
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adding blackness to a color at different rates.
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'''
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import numpy as np
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import matplotlib.pyplot as plt
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from skimage import io, color
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import pdb
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import matplotlib as mpl
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from mpl_toolkits.mplot3d import Axes3D
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from matplotlib import cm, colors
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mpl.rcParams.update({'font.size': 20})
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mpl.rcParams['font.sans-serif'] = 'Arev Sans, Bitstream Vera Sans, Lucida Grande, Verdana, Geneva, Lucid, Helvetica, Avant Garde, sans-serif'
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mpl.rcParams['mathtext.fontset'] = 'custom'
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mpl.rcParams['mathtext.cal'] = 'cursive'
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mpl.rcParams['mathtext.rm'] = 'sans'
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mpl.rcParams['mathtext.tt'] = 'monospace'
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mpl.rcParams['mathtext.it'] = 'sans:italic'
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mpl.rcParams['mathtext.bf'] = 'sans:bold'
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mpl.rcParams['mathtext.sf'] = 'sans'
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mpl.rcParams['mathtext.fallback_to_cm'] = 'True'
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### Red, original Albers plot
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nrows = 5
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# Start with red
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red = np.array([np.hstack([np.ones((nrows,1)), np.zeros((nrows,2))])])
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# Get basic red in LAB
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lab_add = color.rgb2lab(red)
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lab_geometric = lab_add.copy()
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# Alter successive rows with more black
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k = 1
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for i in xrange(red.shape[1]):
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# more blackness is closer to 0 than one, and in first column of LAB
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lab_add[0,i,0] = lab_add[0,i,0] - 10*i
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print i,k
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if i != 0:
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lab_geometric[0,i,0] = lab_geometric[0,i,0] - 10*k
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k *= 2
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# Change LAB back to RGB for plotting
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rgb_add = red.copy() # only change red values
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temp = color.lab2rgb(lab_add)
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rgb_add[0,:,0] = temp[0,:,0]
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rgb_geometric = red.copy() # only change red values
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temp = color.lab2rgb(lab_geometric)
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rgb_geometric[0,:,0] = temp[0,:,0]
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fig = plt.figure()
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k = 1
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for i in xrange(red.shape[1]):
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# LHS: additive
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ax1 = fig.add_subplot(nrows,2,i*2+1, axisbg=tuple(rgb_add[0,i,:]))
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print tuple(lab_add[0,i,:])#, tuple(rgb_add[0,i,:])
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# RHS: multiplicative
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ax2 = fig.add_subplot(nrows,2,i*2+2, axisbg=tuple(rgb_geometric[0,i,:]))
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print tuple(lab_geometric[0,i,:])#, tuple(rgb_geometric[0,i,:])
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# ylabels
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if i!=0:
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ax1.set_ylabel(str(1*i))
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ax2.set_ylabel(str(k))
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k *= 2
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# Turn off ticks
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ax1.get_xaxis().set_ticks([])
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ax2.get_xaxis().set_ticks([])
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ax1.get_yaxis().set_ticks([])
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ax2.get_yaxis().set_ticks([])
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# Turn off black edges
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ax1.spines['right'].set_visible(False)
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ax1.spines['top'].set_visible(False)
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ax1.spines['bottom'].set_visible(False)
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ax1.spines['left'].set_visible(False)
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ax2.spines['right'].set_visible(False)
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ax2.spines['top'].set_visible(False)
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ax2.spines['bottom'].set_visible(False)
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ax2.spines['left'].set_visible(False)
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# common ylabel
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ax1.text(-0.3, 3.8, 'Additional Parts Black',
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rotation=90, transform=ax1.transAxes)
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fig.subplots_adjust(hspace=0.0)
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plt.show()
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### Albers plot with linear scale black and white
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nrows = 5
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ncols = 2
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x = np.linspace(0.0, 1.0, 100)
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cmap = 'binary'
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# Get binary colormap entries for full 100 entries
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rgb = cm.get_cmap(cmap)(x)[np.newaxis,:,:3]
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# Sample 100-entry rgb additively and geometrically
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rgb_add = np.empty((1,nrows,3))
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rgb_geometric = np.empty((1,nrows,3))
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k = 1
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di = 8
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I0 = 5
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for i in xrange(nrows):
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# Do more blackness via increasing indices
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rgb_add[:,i,:] = rgb[:,i*di+I0,:]
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if i != 0:
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print i*di+I0, di*k+I0, (I0**(1./3)+i*di**(1./3))**3
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rgb_geometric[:,i,:] = rgb[:,I0+di*k,:]
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k *= 2
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elif i==0:
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print i*di+I0, I0, (I0**(1./3)+i*di**(1./3))**3
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rgb_geometric[:,i,:] = rgb[:,I0,:]
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lab_add = color.rgb2lab(rgb_add)
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lab_geometric = color.rgb2lab(rgb_geometric)
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fig = plt.figure()
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k = 1
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for i in xrange(nrows):
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# LHS: additive
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ax1 = fig.add_subplot(nrows,ncols,i*2+1, axisbg=tuple(rgb_add[0,i,:]))
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# middle: multiplicative
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ax2 = fig.add_subplot(nrows,ncols,i*2+2, axisbg=tuple(rgb_geometric[0,i,:]))
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# ylabels
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if i!=0:
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ax1.set_ylabel(str(1*i))
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ax2.set_ylabel(str(k))
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k *= 2
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# Turn off ticks
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ax1.get_xaxis().set_ticks([])
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ax2.get_xaxis().set_ticks([])
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ax1.get_yaxis().set_ticks([])
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ax2.get_yaxis().set_ticks([])
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# Turn off black edges
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ax1.spines['right'].set_visible(False)
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ax1.spines['top'].set_visible(False)
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ax1.spines['bottom'].set_visible(False)
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ax1.spines['left'].set_visible(False)
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ax2.spines['right'].set_visible(False)
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ax2.spines['top'].set_visible(False)
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ax2.spines['bottom'].set_visible(False)
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ax2.spines['left'].set_visible(False)
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# common ylabel
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ax1.text(-0.3, 4.0, 'Steps through map indices',
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rotation=90, transform=ax1.transAxes)
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fig.subplots_adjust(hspace=0.0)
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plt.show()

‎doc/users/plotting/colormaps/grayscale.py

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'''
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Show what matplotlib colormaps look like in grayscale.
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Uses lightness L* as a proxy for grayscale value.
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'''
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from skimage import io, color
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib import cm
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import matplotlib as mpl
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import pdb
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from scipy.optimize import curve_fit
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mpl.rcParams.update({'font.size': 14})
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mpl.rcParams['font.sans-serif'] = 'Arev Sans, Bitstream Vera Sans, Lucida Grande, Verdana, Geneva, Lucid, Helvetica, Avant Garde, sans-serif'
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mpl.rcParams['mathtext.fontset'] = 'custom'
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mpl.rcParams['mathtext.cal'] = 'cursive'
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mpl.rcParams['mathtext.rm'] = 'sans'
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mpl.rcParams['mathtext.tt'] = 'monospace'
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mpl.rcParams['mathtext.it'] = 'sans:italic'
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mpl.rcParams['mathtext.bf'] = 'sans:bold'
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mpl.rcParams['mathtext.sf'] = 'sans'
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mpl.rcParams['mathtext.fallback_to_cm'] = 'True'
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# Have colormaps separated into categories: http://matplotlib.org/examples/color/colormaps_reference.html
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cmaps = [('Sequential', ['binary', 'Blues', 'BuGn', 'BuPu', 'gist_yarg',
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'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd',
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'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu',
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'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']),
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('Sequential2', ['afmhot', 'autumn', 'bone', 'cool', 'copper',
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'gist_gray', 'gist_heat', 'gray', 'hot', 'pink',
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'spring', 'summer', 'winter']),
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('Diverging', ['BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr',
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'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'seismic']),
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('Qualitative', ['Accent', 'Dark2', 'hsv', 'Paired', 'Pastel1',
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'Pastel2', 'Set1', 'Set2', 'Set3', 'spectral']),
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('Miscellaneous', ['gist_earth', 'gist_ncar', 'gist_rainbow',
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'gist_stern', 'jet', 'brg', 'CMRmap', 'cubehelix',
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'gnuplot', 'gnuplot2', 'ocean', 'rainbow',
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'terrain', 'flag', 'prism'])]
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# indices to step through colormap
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x = np.linspace(0.0, 1.0, 100)
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nrows = max(len(cmap_list) for cmap_category, cmap_list in cmaps)
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gradient = np.linspace(0, 1, 256)
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gradient = np.vstack((gradient, gradient))
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def plot_color_gradients(cmap_category, cmap_list):
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fig, axes = plt.subplots(nrows=nrows, ncols=2)
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fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99, wspace=0.05)
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fig.suptitle(cmap_category + ' colormaps', fontsize=14, y=1.0, x=0.6)
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for ax, name in zip(axes, cmap_list):
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# Get rgb values for colormap
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rgb = cm.get_cmap(plt.get_cmap(name))(x)[np.newaxis,:,:3]
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# Get colormap in CIE LAB. We want the L here.
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lab = color.rgb2lab(rgb)
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L = lab[0,:,0]
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L = np.float32(np.vstack((L, L, L)))
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ax[0].imshow(gradient, aspect='auto', cmap=plt.get_cmap(name))
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ax[1].imshow(L, aspect='auto', cmap='binary_r', vmin=0., vmax=100.)
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pos = list(ax[0].get_position().bounds)
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x_text = pos[0] - 0.01
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y_text = pos[1] + pos[3]/2.
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fig.text(x_text, y_text, name, va='center', ha='right', fontsize=10)
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# Turn off *all* ticks & spines, not just the ones with colormaps.
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for ax in axes:
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ax[0].set_axis_off()
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ax[1].set_axis_off()
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for cmap_category, cmap_list in cmaps:
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plot_color_gradients(cmap_category, cmap_list)
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‎doc/users/plotting/colormaps/lightness.py

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'''
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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.
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'''
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from skimage import io, color
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib import cm
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import matplotlib as mpl
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import pdb
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from scipy.optimize import curve_fit
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mpl.rcParams.update({'font.size': 14})
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mpl.rcParams['font.sans-serif'] = 'Arev Sans, Bitstream Vera Sans, Lucida Grande, Verdana, Geneva, Lucid, Helvetica, Avant Garde, sans-serif'
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mpl.rcParams['mathtext.fontset'] = 'custom'
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mpl.rcParams['mathtext.cal'] = 'cursive'
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mpl.rcParams['mathtext.rm'] = 'sans'
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mpl.rcParams['mathtext.tt'] = 'monospace'
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mpl.rcParams['mathtext.it'] = 'sans:italic'
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mpl.rcParams['mathtext.bf'] = 'sans:bold'
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mpl.rcParams['mathtext.sf'] = 'sans'
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mpl.rcParams['mathtext.fallback_to_cm'] = 'True'
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# Have colormaps separated into categories: http://matplotlib.org/examples/color/colormaps_reference.html
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cmaps = [('Sequential', ['binary', 'Blues', 'BuGn', 'BuPu', 'gist_yarg',
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'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd',
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'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu',
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'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']),
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('Sequential2', ['afmhot', 'autumn', 'bone', 'cool', 'copper',
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'gist_gray', 'gist_heat', 'gray', 'hot', 'pink',
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'spring', 'summer', 'winter']),
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('Diverging', ['BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr',
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'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'seismic']),
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('Qualitative', ['Accent', 'Dark2', 'hsv', 'Paired', 'Pastel1',
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'Pastel2', 'Set1', 'Set2', 'Set3', 'spectral']),
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('Miscellaneous', ['gist_earth', 'gist_ncar', 'gist_rainbow',
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'gist_stern', 'jet', 'brg', 'CMRmap', 'cubehelix',
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'gnuplot', 'gnuplot2', 'ocean', 'rainbow',
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'terrain', 'flag', 'prism'])]
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# indices to step through colormap
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x = np.linspace(0.0, 1.0, 100)
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# Do plot
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for cmap_category, cmap_list in cmaps:
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# Do subplots so that colormaps have enough space. 5 per subplot?
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dsub = 5 # number of colormaps per subplot
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if cmap_category == 'Diverging': # because has 13 colormaps
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dsub = 6
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elif cmap_category == 'Sequential2':
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dsub = 7
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elif cmap_category == 'Sequential':
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dsub = 7
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nsubplots = int(np.ceil(len(cmap_list)/float(dsub)))
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fig = plt.figure(figsize=(11.5,4*nsubplots))
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for i, subplot in enumerate(xrange(nsubplots)):
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locs = [] # locations for text labels
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ax = fig.add_subplot(nsubplots, 1, i+1)
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# pdb.set_trace()
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for j, cmap in enumerate(cmap_list[i*dsub:(i+1)*dsub]):
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# Get rgb values for colormap
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rgb = cm.get_cmap(cmap)(x)[np.newaxis,:,:3]
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# Get colormap in CIE LAB. We want the L here.
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lab = color.rgb2lab(rgb)
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# Plot colormap L values
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# Do separately for each category so each plot can be pretty
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# to make scatter markers change color along plot: http://stackoverflow.com/questions/8202605/matplotlib-scatterplot-colour-as-a-function-of-a-third-variable
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if cmap_category=='Sequential':
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dc = 0.6 # spacing between colormaps
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ax.scatter(x+j*dc, lab[0,::-1,0], c=x, cmap=cmap + '_r', s=300, linewidths=0.)
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if i==2:
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ax.axis([-0.1,4.1,0,100])
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else:
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ax.axis([-0.1,4.7,0,100])
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locs.append(x[-1]+j*dc) # store locations for colormap labels
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elif cmap_category=='Sequential2':
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dc = 1.15
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ax.scatter(x+j*dc, lab[0,:,0], c=x, cmap=cmap, s=300, linewidths=0.)
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if i==0:
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ax.axis([-0.1,8.1,0,100])
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else:
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ax.axis([-0.1,7.0,0,100])
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locs.append(x[-1]+j*dc) # store locations for colormap labels
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elif cmap_category=='Diverging':
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dc = 1.2
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ax.scatter(x+j*dc, lab[0,:,0], c=x, cmap=cmap, s=300, linewidths=0.)
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if i==0:
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ax.axis([-0.1,7.1,0,100])
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else:
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ax.axis([-0.1,6,0,100])
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locs.append(x[int(x.size/2.)]+j*dc) # store locations for colormap labels
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elif cmap_category=='Qualitative':
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dc = 1.3
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ax.scatter(x+j*dc, lab[0,:,0], c=x, cmap=cmap, s=300, linewidths=0.)
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ax.axis([-0.1,6.3,0,100])
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locs.append(x[int(x.size/2.)]+j*dc) # store locations for colormap labels
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elif cmap_category=='Miscellaneous':
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dc = 1.25
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ax.scatter(x+j*dc, lab[0,:,0], c=x, cmap=cmap, s=300, linewidths=0.)
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ax.axis([-0.1,6.1,0,100])
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locs.append(x[int(x.size/2.)]+j*dc) # store locations for colormap labels
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# Set up labels for colormaps
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ax.xaxis.set_ticks_position('top')
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ticker = mpl.ticker.FixedLocator(locs)
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ax.xaxis.set_major_locator(ticker)
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formatter = mpl.ticker.FixedFormatter(cmap_list[i*dsub:(i+1)*dsub])
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ax.xaxis.set_major_formatter(formatter)
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labels = ax.get_xticklabels()
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for label in labels:
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label.set_rotation(60)
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ax.set_xlabel(cmap_category + ' colormaps', fontsize=22)
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fig.text(-0.005, 0.55, 'Lightness $L^*$', fontsize=18, transform=fig.transFigure, rotation=90)
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fig.tight_layout(h_pad=0.05)
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plt.show

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