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        "\n# Style sheets reference\n\nThis script demonstrates the different available style sheets on a\ncommon set of example plots: scatter plot, image, bar graph, patches,\nline plot and histogram.\n\nAny of these style sheets can be imported (i.e. activated) by its name.\nFor example for the ggplot style:\n\n>>> plt.style.use('ggplot')\n\nThe names of the available style sheets can be found\nin the list `matplotlib.style.available`\n(they are also printed in the corner of each plot below).\n\nSee more details in `Customizing Matplotlib\nusing style sheets<customizing-with-style-sheets>`.\n"
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        "import matplotlib.pyplot as plt\nimport numpy as np\n\nimport matplotlib.colors as mcolors\nfrom matplotlib.patches import Rectangle\n\n# Fixing random state for reproducibility\nnp.random.seed(19680801)\n\n\ndef plot_scatter(ax, prng, nb_samples=100):\n    \"\"\"Scatter plot.\"\"\"\n    for mu, sigma, marker in [(-.5, 0.75, 'o'), (0.75, 1., 's')]:\n        x, y = prng.normal(loc=mu, scale=sigma, size=(2, nb_samples))\n        ax.plot(x, y, ls='none', marker=marker)\n    ax.set_xlabel('X-label')\n    ax.set_title('Axes title')\n    return ax\n\n\ndef plot_colored_lines(ax):\n    \"\"\"Plot lines with colors following the style color cycle.\"\"\"\n    t = np.linspace(-10, 10, 100)\n\n    def sigmoid(t, t0):\n        return 1 / (1 + np.exp(-(t - t0)))\n\n    nb_colors = len(plt.rcParams['axes.prop_cycle'])\n    shifts = np.linspace(-5, 5, nb_colors)\n    amplitudes = np.linspace(1, 1.5, nb_colors)\n    for t0, a in zip(shifts, amplitudes):\n        ax.plot(t, a * sigmoid(t, t0), '-')\n    ax.set_xlim(-10, 10)\n    return ax\n\n\ndef plot_bar_graphs(ax, prng, min_value=5, max_value=25, nb_samples=5):\n    \"\"\"Plot two bar graphs side by side, with letters as x-tick labels.\"\"\"\n    x = np.arange(nb_samples)\n    ya, yb = prng.randint(min_value, max_value, size=(2, nb_samples))\n    width = 0.25\n    ax.bar(x, ya, width)\n    ax.bar(x + width, yb, width, color='C2')\n    ax.set_xticks(x + width, labels=['a', 'b', 'c', 'd', 'e'])\n    return ax\n\n\ndef plot_colored_circles(ax, prng, nb_samples=15):\n    \"\"\"\n    Plot circle patches.\n\n    NB: draws a fixed amount of samples, rather than using the length of\n    the color cycle, because different styles may have different numbers\n    of colors.\n    \"\"\"\n    for sty_dict, j in zip(plt.rcParams['axes.prop_cycle'](),\n                           range(nb_samples)):\n        ax.add_patch(plt.Circle(prng.normal(scale=3, size=2),\n                                radius=1.0, color=sty_dict['color']))\n    ax.grid(visible=True)\n\n    # Add title for enabling grid\n    plt.title('ax.grid(True)', family='monospace', fontsize='small')\n\n    ax.set_xlim([-4, 8])\n    ax.set_ylim([-5, 6])\n    ax.set_aspect('equal', adjustable='box')  # to plot circles as circles\n    return ax\n\n\ndef plot_image_and_patch(ax, prng, size=(20, 20)):\n    \"\"\"Plot an image with random values and superimpose a circular patch.\"\"\"\n    values = prng.random_sample(size=size)\n    ax.imshow(values, interpolation='none')\n    c = plt.Circle((5, 5), radius=5, label='patch')\n    ax.add_patch(c)\n    # Remove ticks\n    ax.set_xticks([])\n    ax.set_yticks([])\n\n\ndef plot_histograms(ax, prng, nb_samples=10000):\n    \"\"\"Plot 4 histograms and a text annotation.\"\"\"\n    params = ((10, 10), (4, 12), (50, 12), (6, 55))\n    for a, b in params:\n        values = prng.beta(a, b, size=nb_samples)\n        ax.hist(values, histtype=\"stepfilled\", bins=30,\n                alpha=0.8, density=True)\n\n    # Add a small annotation.\n    ax.annotate('Annotation', xy=(0.25, 4.25),\n                xytext=(0.9, 0.9), textcoords=ax.transAxes,\n                va=\"top\", ha=\"right\",\n                bbox=dict(boxstyle=\"round\", alpha=0.2),\n                arrowprops=dict(\n                          arrowstyle=\"->\",\n                          connectionstyle=\"angle,angleA=-95,angleB=35,rad=10\"),\n                )\n    return ax\n\n\ndef plot_figure(style_label=\"\"):\n    \"\"\"Setup and plot the demonstration figure with a given style.\"\"\"\n    # Use a dedicated RandomState instance to draw the same \"random\" values\n    # across the different figures.\n    prng = np.random.RandomState(96917002)\n\n    fig, axs = plt.subplots(ncols=6, nrows=1, num=style_label,\n                            figsize=(14.8, 2.8), layout='constrained')\n\n    # make a suptitle, in the same style for all subfigures,\n    # except those with dark backgrounds, which get a lighter color:\n    background_color = mcolors.rgb_to_hsv(\n        mcolors.to_rgb(plt.rcParams['figure.facecolor']))[2]\n    if background_color < 0.5:\n        title_color = [0.8, 0.8, 1]\n    else:\n        title_color = np.array([19, 6, 84]) / 256\n    fig.suptitle(style_label, x=0.01, ha='left', color=title_color,\n                 fontsize=14, fontfamily='DejaVu Sans', fontweight='normal')\n\n    plot_scatter(axs[0], prng)\n    plot_image_and_patch(axs[1], prng)\n    plot_bar_graphs(axs[2], prng)\n    plot_colored_lines(axs[3])\n    plot_histograms(axs[4], prng)\n    plot_colored_circles(axs[5], prng)\n\n    # add divider\n    rec = Rectangle((1 + 0.025, -2), 0.05, 16,\n                    clip_on=False, color='gray')\n\n    axs[4].add_artist(rec)\n\nif __name__ == \"__main__\":\n\n    # Set up a list of all available styles, in alphabetical order but\n    # the `default` and `classic` ones, which will be forced resp. in\n    # first and second position.\n    # styles with leading underscores are for internal use such as testing\n    # and plot types gallery. These are excluded here.\n    style_list = ['default', 'classic'] + sorted(\n        style for style in plt.style.available\n        if style != 'classic' and not style.startswith('_'))\n\n    # Plot a demonstration figure for every available style sheet.\n    for style_label in style_list:\n        with plt.rc_context({\"figure.max_open_warning\": len(style_list)}):\n            with plt.style.context(style_label):\n                plot_figure(style_label=style_label)\n\n    plt.show()"
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