|
| 1 | +""" |
| 2 | +====================== |
| 3 | +Style sheets reference |
| 4 | +====================== |
| 5 | +
|
| 6 | +This script demonstrates the different available style sheets on a |
| 7 | +common set of example plots: scatter plot, image, bar graph, patches, |
| 8 | +line plot and histogram, |
| 9 | +
|
| 10 | +""" |
| 11 | + |
| 12 | +import numpy as np |
| 13 | +import matplotlib.pyplot as plt |
| 14 | + |
| 15 | + |
| 16 | +def plot_scatter(ax, prng, nb_samples=100): |
| 17 | + """Scatter plot. |
| 18 | + """ |
| 19 | + for mu, sigma, marker in [(-.5, 0.75, 'o'), (0.75, 1., 's')]: |
| 20 | + x, y = prng.normal(loc=mu, scale=sigma, size=(2, nb_samples)) |
| 21 | + ax.plot(x, y, ls='none', marker=marker) |
| 22 | + ax.set_xlabel('X-label') |
| 23 | + return ax |
| 24 | + |
| 25 | + |
| 26 | +def plot_colored_sinusoidal_lines(ax): |
| 27 | + """Plot sinusoidal lines with colors following the style color cycle. |
| 28 | + """ |
| 29 | + L = 2 * np.pi |
| 30 | + x = np.linspace(0, L) |
| 31 | + nb_colors = len(plt.rcParams['axes.prop_cycle']) |
| 32 | + shift = np.linspace(0, L, nb_colors, endpoint=False) |
| 33 | + for s in shift: |
| 34 | + ax.plot(x, np.sin(x + s), '-') |
| 35 | + ax.set_xlim([x[0], x[-1]]) |
| 36 | + return ax |
| 37 | + |
| 38 | + |
| 39 | +def plot_bar_graphs(ax, prng, min_value=5, max_value=25, nb_samples=5): |
| 40 | + """Plot two bar graphs side by side, with letters as x-tick labels. |
| 41 | + """ |
| 42 | + x = np.arange(nb_samples) |
| 43 | + ya, yb = prng.randint(min_value, max_value, size=(2, nb_samples)) |
| 44 | + width = 0.25 |
| 45 | + ax.bar(x, ya, width) |
| 46 | + ax.bar(x + width, yb, width, color='C2') |
| 47 | + ax.set_xticks(x + width) |
| 48 | + ax.set_xticklabels(['a', 'b', 'c', 'd', 'e']) |
| 49 | + return ax |
| 50 | + |
| 51 | + |
| 52 | +def plot_colored_circles(ax, prng, nb_samples=15): |
| 53 | + """Plot circle patches. |
| 54 | +
|
| 55 | + NB: draws a fixed amount of samples, rather than using the length of |
| 56 | + the color cycle, because different styles may have different numbers |
| 57 | + of colors. |
| 58 | + """ |
| 59 | + for sty_dict, j in zip(plt.rcParams['axes.prop_cycle'], range(nb_samples)): |
| 60 | + ax.add_patch(plt.Circle(prng.normal(scale=3, size=2), |
| 61 | + radius=1.0, color=sty_dict['color'])) |
| 62 | + # Force the limits to be the same across the styles (because different |
| 63 | + # styles may have different numbers of available colors). |
| 64 | + ax.set_xlim([-4, 8]) |
| 65 | + ax.set_ylim([-5, 6]) |
| 66 | + ax.set_aspect('equal', adjustable='box') # to plot circles as circles |
| 67 | + return ax |
| 68 | + |
| 69 | + |
| 70 | +def plot_image_and_patch(ax, prng, size=(20, 20)): |
| 71 | + """Plot an image with random values and superimpose a circular patch. |
| 72 | + """ |
| 73 | + values = prng.random_sample(size=size) |
| 74 | + ax.imshow(values, interpolation='none') |
| 75 | + c = plt.Circle((5, 5), radius=5, label='patch') |
| 76 | + ax.add_patch(c) |
| 77 | + # Remove ticks |
| 78 | + ax.set_xticks([]) |
| 79 | + ax.set_yticks([]) |
| 80 | + |
| 81 | + |
| 82 | +def plot_histograms(ax, prng, nb_samples=10000): |
| 83 | + """Plot 4 histograms and a text annotation. |
| 84 | + """ |
| 85 | + params = ((10, 10), (4, 12), (50, 12), (6, 55)) |
| 86 | + for a, b in params: |
| 87 | + values = prng.beta(a, b, size=nb_samples) |
| 88 | + ax.hist(values, histtype="stepfilled", bins=30, alpha=0.8, normed=True) |
| 89 | + # Add a small annotation. |
| 90 | + ax.annotate('Annotation', xy=(0.25, 4.25), xycoords='data', |
| 91 | + xytext=(0.9, 0.9), textcoords='axes fraction', |
| 92 | + va="top", ha="right", |
| 93 | + bbox=dict(boxstyle="round", alpha=0.2), |
| 94 | + arrowprops=dict( |
| 95 | + arrowstyle="->", |
| 96 | + connectionstyle="angle,angleA=-95,angleB=35,rad=10"), |
| 97 | + ) |
| 98 | + return ax |
| 99 | + |
| 100 | + |
| 101 | +def plot_figure(style_label=""): |
| 102 | + """Setup and plot the demonstration figure with a given style. |
| 103 | + """ |
| 104 | + # Use a dedicated RandomState instance to draw the same "random" values |
| 105 | + # across the different figures. |
| 106 | + prng = np.random.RandomState(96917002) |
| 107 | + |
| 108 | + # Tweak the figure size to be better suited for a row of numerous plots: |
| 109 | + # double the width and halve the height. NB: use relative changes because |
| 110 | + # some styles may have a figure size different from the default one. |
| 111 | + (fig_width, fig_height) = plt.rcParams['figure.figsize'] |
| 112 | + fig_size = [fig_width * 2, fig_height / 2] |
| 113 | + |
| 114 | + fig, axes = plt.subplots(ncols=6, nrows=1, num=style_label, |
| 115 | + figsize=fig_size, squeeze=True) |
| 116 | + axes[0].set_ylabel(style_label) |
| 117 | + |
| 118 | + plot_scatter(axes[0], prng) |
| 119 | + plot_image_and_patch(axes[1], prng) |
| 120 | + plot_bar_graphs(axes[2], prng) |
| 121 | + plot_colored_circles(axes[3], prng) |
| 122 | + plot_colored_sinusoidal_lines(axes[4]) |
| 123 | + plot_histograms(axes[5], prng) |
| 124 | + |
| 125 | + fig.tight_layout() |
| 126 | + |
| 127 | + return fig |
| 128 | + |
| 129 | + |
| 130 | +if __name__ == "__main__": |
| 131 | + |
| 132 | + # Setup a list of all available styles, in alphabetical order but |
| 133 | + # the `default` and `classic` ones, which will be forced resp. in |
| 134 | + # first and second position. |
| 135 | + style_list = list(plt.style.available) # *new* list: avoids side effects. |
| 136 | + style_list.remove('classic') # `classic` is in the list: first remove it. |
| 137 | + style_list.sort() |
| 138 | + style_list.insert(0, u'default') |
| 139 | + style_list.insert(1, u'classic') |
| 140 | + |
| 141 | + # Plot a demonstration figure for every available style sheet. |
| 142 | + for style_label in style_list: |
| 143 | + with plt.style.context(style_label): |
| 144 | + fig = plot_figure(style_label=style_label) |
| 145 | + |
| 146 | + plt.show() |
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