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    <p>There is an illustrative example of <a href="http://matplotlib.org/examples/pylab_examples/custom_cmap.html" rel="noreferrer">how to create custom colormaps here</a>. The docstring is essential for understanding the meaning of <code>cdict</code>. Once you get that under your belt, you might use a <code>cdict</code> like this:</p> <pre><code>cdict = {'red': ((0.0, 1.0, 1.0), (0.1, 1.0, 1.0), # red (0.4, 1.0, 1.0), # violet (1.0, 0.0, 0.0)), # blue 'green': ((0.0, 0.0, 0.0), (1.0, 0.0, 0.0)), 'blue': ((0.0, 0.0, 0.0), (0.1, 0.0, 0.0), # red (0.4, 1.0, 1.0), # violet (1.0, 1.0, 0.0)) # blue } </code></pre> <p>Although the <code>cdict</code> format gives you a lot of flexibility, I find for simple gradients its format is rather unintuitive. Here is a utility function to help generate simple LinearSegmentedColormaps:</p> <pre><code>import numpy as np import matplotlib.pyplot as plt import matplotlib.colors as mcolors def make_colormap(seq): """Return a LinearSegmentedColormap seq: a sequence of floats and RGB-tuples. The floats should be increasing and in the interval (0,1). """ seq = [(None,) * 3, 0.0] + list(seq) + [1.0, (None,) * 3] cdict = {'red': [], 'green': [], 'blue': []} for i, item in enumerate(seq): if isinstance(item, float): r1, g1, b1 = seq[i - 1] r2, g2, b2 = seq[i + 1] cdict['red'].append([item, r1, r2]) cdict['green'].append([item, g1, g2]) cdict['blue'].append([item, b1, b2]) return mcolors.LinearSegmentedColormap('CustomMap', cdict) c = mcolors.ColorConverter().to_rgb rvb = make_colormap( [c('red'), c('violet'), 0.33, c('violet'), c('blue'), 0.66, c('blue')]) N = 1000 array_dg = np.random.uniform(0, 10, size=(N, 2)) colors = np.random.uniform(-2, 2, size=(N,)) plt.scatter(array_dg[:, 0], array_dg[:, 1], c=colors, cmap=rvb) plt.colorbar() plt.show() </code></pre> <p><img src="https://i.stack.imgur.com/3ULpI.png" alt="enter image description here"></p> <hr> <p>By the way, the <code>for-loop</code></p> <pre><code>for i in range(0, len(array_dg)): plt.plot(array_dg[i], markers.next(),alpha=alpha[i], c=colors.next()) </code></pre> <p>plots one point for every call to <code>plt.plot</code>. This will work for a small number of points, but will become extremely slow for many points. <code>plt.plot</code> can only draw in one color, but <code>plt.scatter</code> can assign a different color to each dot. Thus, <code>plt.scatter</code> is the way to go.</p>
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    1. CONow I got a problem. I also would like to get a different marker symbol according to the color (I have 13 different colors). But the scatter plot allows only one marker per plot, or do I miss something?
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    2. COIn that case you will need to call `plt.scatter` (or `plt.plot`) once for each color/marker combination.
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