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dmcdougalldmcdougall
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Merge branch 'master' of github.com:dmcdougall/scipy14-colormaps
2 parents da53ae7 + 925c510 commit 0f7fe0a

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‎perceptions/analysis.py‎

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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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## Get colormaps, from http://matplotlib.org/1.2.1/examples/pylab_examples/show_colormaps.html
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# Get a list of the colormaps in matplotlib. Ignore the ones that end with
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# '_r' because these are simply reversed versions of ones that don't end
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# with '_r'
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maps = sorted(m for m in plt.cm.datad if not m.endswith("_r"))
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nmaps = len(maps) + 1
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# OR, to 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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('Sequential (2)', ['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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ncmaps = len(cmaps)
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x = np.linspace(0.0, 1.0, 100)
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## Sequential colormaps
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fig = plt.figure(figsize=(18,8))
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# loop through maps
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# for i,m in enumerate(maps):
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i = 1
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for cmap_category, cmap_list in cmaps:
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if 'Sequential' not in cmap_category:
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continue
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flag = True
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ax = fig.add_subplot(2, 1, i)
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if i==1:
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ax.set_title('Lightness $L^*$ along colormap index', fontsize=18)
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for j, cmap in enumerate(cmap_list):
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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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# hsv = matplotlib.colors.rgb_to_hsv(rgb).squeeze()
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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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# if cmap=='binary':
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# pdb.set_trace()
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# Plot colormap L values
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if '(2)' in cmap_category:
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ax.scatter(x+j*0.83, lab[0,:,0], c=x, cmap=cmap, s=300, linewidths=0.1)
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ax.set_ylabel('Sequential MatLab', fontsize=18)
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ax.axis([0,11,0,100])
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else:
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ax.scatter(x+j*0.5, lab[0,::-1,0], c=x, cmap=cmap + '_r', s=300, linewidths=0.1)
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ax.set_ylabel('Sequential', fontsize=18)
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ax.axis([0,10.5,0,100])
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ax.get_xaxis().set_ticks([])
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i += 1
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fig.subplots_adjust(left=0.04, right=0.99, bottom=0.05, top=0.93, hspace=0.1)
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fig.show()
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fig.savefig('figures/lightness-sequential.png')
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## Non-sequential colormaps
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fig = plt.figure(figsize=(18,10))
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# loop through maps
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# for i,m in enumerate(maps):
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i = 1
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for cmap_category, cmap_list in cmaps:
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if 'Sequential' in cmap_category:
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continue
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flag = True
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ax = fig.add_subplot(3, 1, i)
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if i==1:
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ax.set_title('Lightness $L^*$ along colormap index', fontsize=18)
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for j, cmap in enumerate(cmap_list):
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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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# hsv = matplotlib.colors.rgb_to_hsv(rgb).squeeze()
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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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if 'Qualitative' in cmap_category:
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ax.scatter(x+j*1.3, lab[0,:,0], c=x, cmap=cmap, s=300, linewidths=0.1)
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ax.axis([0,13,0,100])
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elif 'Diverging' in cmap_category:
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ax.scatter(x+j*1.2, lab[0,::-1,0], c=x, cmap=cmap + '_r', s=300, linewidths=0.1)
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ax.axis([0,13,0,100])
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elif 'Miscellaneous' in cmap_category:
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ax.scatter(x+j*1.5, lab[0,::-1,0], c=x, cmap=cmap + '_r', s=300, linewidths=0.1)
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ax.axis([0,22,0,100])
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ax.get_xaxis().set_ticks([])
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ax.set_ylabel(cmap_category, fontsize=18)
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i += 1
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fig.subplots_adjust(left=0.04, right=0.99, bottom=0.03, top=0.95, hspace=0.1)
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fig.show()
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fig.savefig('figures/lightness-rest.png')
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# can't figure out this part. Curve fitting isn't looking right.
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# ## Sequential colormap L* curve fitting
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# def func(x, a, b, c):
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# return c + a * x ** b
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# # xdata = np.linspace(0, 4, 50)
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# # Loop through sequential colormaps and calculate the curve fit
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# # Then paste the exponent on the plot for each.
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# for j, cmap in enumerate(cmaps[0][1]): # Sequential colormaps
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# # Get rgb values for colormap
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# rgb = cm.get_cmap(cmap + '_r')(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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# popt, pcov = curve_fit(func, x, lab[0,:,0])
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# print cmap, popt[1]
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# # pdb.set_trace()

‎perceptions/figures/Cielab.jpg‎

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‎perceptions/figures/Protanopia-Color-Spectrum.jpg‎

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‎perceptions/figures/albers-bw.pdf‎

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‎perceptions/figures/albers.pdf‎

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‎perceptions/figures/deuteranopia-color-spectrum.jpg‎

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‎perceptions/figures/div.png‎

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‎perceptions/figures/div_deuteranopia.png‎

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‎perceptions/figures/lightness-rest.png‎

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‎perceptions/figures/lightness-sequential.png‎

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