Docs build of d908b4f561c895f0522bbba39a88fda4824d3c5a

.buildinfo

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 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: fee21aba0ad35d7353946075a94d4ae9
-tags: 645f666f9bcd5a90fca523b33c5a78b7
+config: 0d50caa96d5b284a996b1f358b3aa50a
+tags: 526838a1027dd1fd771fd21a9668211e

_downloads/0017d0c84e03d6c5e463e23d8e570671/line_collection.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Plotting multiple lines with a LineCollection\n\nMatplotlib can efficiently draw multiple lines at once using a\n`~.LineCollection`, as showcased below.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom matplotlib.collections import LineCollection\n\nx = np.arange(100)\n# Here are many sets of y to plot vs. x\nys = x[:50, np.newaxis] + x[np.newaxis, :]\n\nsegs = np.zeros((50, 100, 2))\nsegs[:, :, 1] = ys\nsegs[:, :, 0] = x\n\n# Mask some values to test masked array support:\nsegs = np.ma.masked_where((segs > 50) & (segs < 60), segs)\n\n# We need to set the plot limits, they will not autoscale\nfig, ax = plt.subplots()\nax.set_xlim(x.min(), x.max())\nax.set_ylim(ys.min(), ys.max())\n\n# *colors* is sequence of rgba tuples.\n# *linestyle* is a string or dash tuple. Legal string values are\n# solid|dashed|dashdot|dotted.  The dash tuple is (offset, onoffseq) where\n# onoffseq is an even length tuple of on and off ink in points.  If linestyle\n# is omitted, 'solid' is used.\n# See `matplotlib.collections.LineCollection` for more information.\ncolors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n\nline_segments = LineCollection(segs, linewidths=(0.5, 1, 1.5, 2),\n                               colors=colors, linestyle='solid')\nax.add_collection(line_segments)\nax.set_title('Line collection with masked arrays')\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In the following example, instead of passing a list of colors\n(``colors=colors``), we pass an array of values (``array=x``) that get\ncolormapped.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "N = 50\nx = np.arange(N)\nys = [x + i for i in x]  # Many sets of y to plot vs. x\nsegs = [np.column_stack([x, y]) for y in ys]\n\nfig, ax = plt.subplots()\nax.set_xlim(np.min(x), np.max(x))\nax.set_ylim(np.min(ys), np.max(ys))\n\nline_segments = LineCollection(segs, array=x,\n                               linewidths=(0.5, 1, 1.5, 2),\n                               linestyles='solid')\nax.add_collection(line_segments)\naxcb = fig.colorbar(line_segments)\naxcb.set_label('Line Number')\nax.set_title('Line Collection with mapped colors')\nplt.sci(line_segments)  # This allows interactive changing of the colormap.\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        ".. admonition:: References\n\n   The use of the following functions, methods, classes and modules is shown\n   in this example:\n\n   - `matplotlib.collections`\n   - `matplotlib.collections.LineCollection`\n   - `matplotlib.cm.ScalarMappable.set_array`\n   - `matplotlib.axes.Axes.add_collection`\n   - `matplotlib.figure.Figure.colorbar` / `matplotlib.pyplot.colorbar`\n   - `matplotlib.pyplot.sci`\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.18"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

_downloads/008c73d5ac1c4af82c0b90fde922ebb9/stairs.py

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+"""
+==============
+stairs(values)
+==============
+
+See `~matplotlib.axes.Axes.stairs`.
+"""
+import matplotlib.pyplot as plt
+import numpy as np
+
+plt.style.use('_mpl-gallery')
+
+# make data
+y = [4.8, 5.5, 3.5, 4.6, 6.5, 6.6, 2.6, 3.0]
+
+# plot
+fig, ax = plt.subplots()
+
+ax.stairs(y, linewidth=2.5)
+
+ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
+       ylim=(0, 8), yticks=np.arange(1, 8))
+
+plt.show()

_downloads/00aacc25eb4ed2eaffa3c821d5562b6d/whats_new_3-6-0-5.py

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+x = np.linspace(1., 3., 10)
+y = x**3
+
+fig, ax = plt.subplots()
+ax.plot(x, y, linestyle='--', color='orange', gapcolor='blue',
+        linewidth=3, label='a striped line')
+ax.legend()

_downloads/00c94fd2ec27d6cef4b040acefbf730d/hyperlinks_sgskip.py

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+"""
+==========
+Hyperlinks
+==========
+
+This example demonstrates how to set a hyperlinks on various kinds of elements.
+
+This currently only works with the SVG backend.
+
+"""
+
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+import matplotlib.cm as cm
+
+# %%
+
+fig = plt.figure()
+s = plt.scatter([1, 2, 3], [4, 5, 6])
+s.set_urls(['https://www.bbc.com/news', 'https://www.google.com/', None])
+fig.savefig('scatter.svg')
+
+# %%
+
+fig = plt.figure()
+delta = 0.025
+x = y = np.arange(-3.0, 3.0, delta)
+X, Y = np.meshgrid(x, y)
+Z1 = np.exp(-X**2 - Y**2)
+Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
+Z = (Z1 - Z2) * 2
+
+im = plt.imshow(Z, interpolation='bilinear', cmap=cm.gray,
+                origin='lower', extent=(-3, 3, -3, 3))
+
+im.set_url('https://www.google.com/')
+fig.savefig('image.svg')

_downloads/00f7f20b47c4afdb8cf00a246bc6af53/animation_demo.py

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+"""
+================
+pyplot animation
+================
+
+Generating an animation by calling `~.pyplot.pause` between plotting commands.
+
+The method shown here is only suitable for simple, low-performance use.  For
+more demanding applications, look at the :mod:`.animation` module and the
+examples that use it.
+
+Note that calling `time.sleep` instead of `~.pyplot.pause` would *not* work.
+
+Output generated via `matplotlib.animation.Animation.to_jshtml`.
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(19680801)
+data = np.random.random((50, 50, 50))
+
+fig, ax = plt.subplots()
+
+for i, img in enumerate(data):
+    ax.clear()
+    ax.imshow(img)
+    ax.set_title(f"frame {i}")
+    # Note that using time.sleep does *not* work here!
+    plt.pause(0.1)

_downloads/00fb9b6278ae3996d0eda53a7198ae42/hatch_style_reference.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Hatch style reference\n\nHatches can be added to most polygons in Matplotlib, including `~.Axes.bar`,\n`~.Axes.fill_between`, `~.Axes.contourf`, and children of `~.patches.Polygon`.\nThey are currently supported in the PS, PDF, SVG, OSX, and Agg backends. The WX\nand Cairo backends do not currently support hatching.\n\nSee also :doc:`/gallery/images_contours_and_fields/contourf_hatching` for\nan example using `~.Axes.contourf`, and\n:doc:`/gallery/shapes_and_collections/hatch_demo` for more usage examples.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n\nfrom matplotlib.patches import Rectangle\n\nfig, axs = plt.subplots(2, 5, layout='constrained', figsize=(6.4, 3.2))\n\nhatches = ['/', '\\\\', '|', '-', '+', 'x', 'o', 'O', '.', '*']\n\n\ndef hatches_plot(ax, h):\n    ax.add_patch(Rectangle((0, 0), 2, 2, fill=False, hatch=h))\n    ax.text(1, -0.5, f\"' {h} '\", size=15, ha=\"center\")\n    ax.axis('equal')\n    ax.axis('off')\n\nfor ax, h in zip(axs.flat, hatches):\n    hatches_plot(ax, h)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Hatching patterns can be repeated to increase the density.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, axs = plt.subplots(2, 5, layout='constrained', figsize=(6.4, 3.2))\n\nhatches = ['//', '\\\\\\\\', '||', '--', '++', 'xx', 'oo', 'OO', '..', '**']\n\nfor ax, h in zip(axs.flat, hatches):\n    hatches_plot(ax, h)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Hatching patterns can be combined to create additional patterns.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, axs = plt.subplots(2, 5, layout='constrained', figsize=(6.4, 3.2))\n\nhatches = ['/o', '\\\\|', '|*', '-\\\\', '+o', 'x*', 'o-', 'O|', 'O.', '*-']\n\nfor ax, h in zip(axs.flat, hatches):\n    hatches_plot(ax, h)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        ".. admonition:: References\n\n   The use of the following functions, methods, classes and modules is shown\n   in this example:\n\n   - `matplotlib.patches`\n   - `matplotlib.patches.Rectangle`\n   - `matplotlib.axes.Axes.add_patch`\n   - `matplotlib.axes.Axes.text`\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.18"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}