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Add docs for 3.7.5
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‎3.7.5/Matplotlib.pdf

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‎3.7.5/_downloads/0017d0c84e03d6c5e463e23d8e570671/line_collection.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n# Plotting multiple lines with a LineCollection\n\nMatplotlib can efficiently draw multiple lines at once using a\n`~.LineCollection`, as showcased below.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\nfrom matplotlib.collections import LineCollection\n\nimport numpy as np\n\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()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"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()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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".. 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"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

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

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

‎3.7.5/_downloads/00acb51f759431356d920d5a71341b06/whats_new_3-6-0-7.png

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‎3.7.5/_downloads/00c94fd2ec27d6cef4b040acefbf730d/hyperlinks_sgskip.py

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

‎3.7.5/_downloads/00f7f20b47c4afdb8cf00a246bc6af53/animation_demo.py

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

‎3.7.5/_downloads/00fb9b6278ae3996d0eda53a7198ae42/hatch_style_reference.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\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"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\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)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Hatching patterns can be repeated to increase the density.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"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)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Hatching patterns can be combined to create additional patterns.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"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)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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".. 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"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

‎3.7.5/_downloads/01526d1bc6f0260beff6382b79ae89ea/tricontour_demo.py

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"""
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===============
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Tricontour Demo
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===============
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Contour plots of unstructured triangular grids.
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"""
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import matplotlib.pyplot as plt
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import matplotlib.tri as tri
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import numpy as np
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###############################################################################
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# Creating a Triangulation without specifying the triangles results in the
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# Delaunay triangulation of the points.
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# First create the x and y coordinates of the points.
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n_angles = 48
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n_radii = 8
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min_radius = 0.25
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radii = np.linspace(min_radius, 0.95, n_radii)
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angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
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angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
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angles[:, 1::2] += np.pi / n_angles
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x = (radii * np.cos(angles)).flatten()
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y = (radii * np.sin(angles)).flatten()
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z = (np.cos(radii) * np.cos(3 * angles)).flatten()
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# Create the Triangulation; no triangles so Delaunay triangulation created.
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triang = tri.Triangulation(x, y)
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# Mask off unwanted triangles.
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triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
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y[triang.triangles].mean(axis=1))
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< min_radius)
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###############################################################################
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# pcolor plot.
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fig1, ax1 = plt.subplots()
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ax1.set_aspect('equal')
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tcf = ax1.tricontourf(triang, z)
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fig1.colorbar(tcf)
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ax1.tricontour(triang, z, colors='k')
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ax1.set_title('Contour plot of Delaunay triangulation')
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###############################################################################
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# You could also specify hatching patterns along with different cmaps.
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fig2, ax2 = plt.subplots()
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ax2.set_aspect("equal")
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tcf = ax2.tricontourf(
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triang,
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z,
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hatches=["*", "-", "/", "//", "\\", None],
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cmap="cividis"
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)
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fig2.colorbar(tcf)
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ax2.tricontour(triang, z, linestyles="solid", colors="k", linewidths=2.0)
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ax2.set_title("Hatched Contour plot of Delaunay triangulation")
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###############################################################################
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# You could also generate hatching patterns labeled with no color.
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fig3, ax3 = plt.subplots()
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n_levels = 7
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tcf = ax3.tricontourf(
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triang,
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z,
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n_levels,
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colors="none",
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hatches=[".", "/", "\\", None, "\\\\", "*"],
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)
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ax3.tricontour(triang, z, n_levels, colors="black", linestyles="-")
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# create a legend for the contour set
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artists, labels = tcf.legend_elements(str_format="{:2.1f}".format)
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ax3.legend(artists, labels, handleheight=2, framealpha=1)
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###############################################################################
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# You can specify your own triangulation rather than perform a Delaunay
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# triangulation of the points, where each triangle is given by the indices of
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# the three points that make up the triangle, ordered in either a clockwise or
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# anticlockwise manner.
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xy = np.asarray([
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[-0.101, 0.872], [-0.080, 0.883], [-0.069, 0.888], [-0.054, 0.890],
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[-0.045, 0.897], [-0.057, 0.895], [-0.073, 0.900], [-0.087, 0.898],
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[-0.090, 0.904], [-0.069, 0.907], [-0.069, 0.921], [-0.080, 0.919],
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[-0.073, 0.928], [-0.052, 0.930], [-0.048, 0.942], [-0.062, 0.949],
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[-0.054, 0.958], [-0.069, 0.954], [-0.087, 0.952], [-0.087, 0.959],
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[-0.080, 0.966], [-0.085, 0.973], [-0.087, 0.965], [-0.097, 0.965],
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[-0.097, 0.975], [-0.092, 0.984], [-0.101, 0.980], [-0.108, 0.980],
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[-0.104, 0.987], [-0.102, 0.993], [-0.115, 1.001], [-0.099, 0.996],
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[-0.101, 1.007], [-0.090, 1.010], [-0.087, 1.021], [-0.069, 1.021],
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[-0.052, 1.022], [-0.052, 1.017], [-0.069, 1.010], [-0.064, 1.005],
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[-0.048, 1.005], [-0.031, 1.005], [-0.031, 0.996], [-0.040, 0.987],
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[-0.045, 0.980], [-0.052, 0.975], [-0.040, 0.973], [-0.026, 0.968],
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[-0.020, 0.954], [-0.006, 0.947], [ 0.003, 0.935], [ 0.006, 0.926],
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[ 0.005, 0.921], [ 0.022, 0.923], [ 0.033, 0.912], [ 0.029, 0.905],
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[ 0.017, 0.900], [ 0.012, 0.895], [ 0.027, 0.893], [ 0.019, 0.886],
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[ 0.001, 0.883], [-0.012, 0.884], [-0.029, 0.883], [-0.038, 0.879],
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[-0.057, 0.881], [-0.062, 0.876], [-0.078, 0.876], [-0.087, 0.872],
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[-0.030, 0.907], [-0.007, 0.905], [-0.057, 0.916], [-0.025, 0.933],
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[-0.077, 0.990], [-0.059, 0.993]])
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x = np.degrees(xy[:, 0])
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y = np.degrees(xy[:, 1])
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x0 = -5
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y0 = 52
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z = np.exp(-0.01 * ((x - x0) ** 2 + (y - y0) ** 2))
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triangles = np.asarray([
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[67, 66, 1], [65, 2, 66], [ 1, 66, 2], [64, 2, 65], [63, 3, 64],
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[60, 59, 57], [ 2, 64, 3], [ 3, 63, 4], [ 0, 67, 1], [62, 4, 63],
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[57, 59, 56], [59, 58, 56], [61, 60, 69], [57, 69, 60], [ 4, 62, 68],
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[ 6, 5, 9], [61, 68, 62], [69, 68, 61], [ 9, 5, 70], [ 6, 8, 7],
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[ 4, 70, 5], [ 8, 6, 9], [56, 69, 57], [69, 56, 52], [70, 10, 9],
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[54, 53, 55], [56, 55, 53], [68, 70, 4], [52, 56, 53], [11, 10, 12],
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[69, 71, 68], [68, 13, 70], [10, 70, 13], [51, 50, 52], [13, 68, 71],
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[52, 71, 69], [12, 10, 13], [71, 52, 50], [71, 14, 13], [50, 49, 71],
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[49, 48, 71], [14, 16, 15], [14, 71, 48], [17, 19, 18], [17, 20, 19],
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[48, 16, 14], [48, 47, 16], [47, 46, 16], [16, 46, 45], [23, 22, 24],
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[21, 24, 22], [17, 16, 45], [20, 17, 45], [21, 25, 24], [27, 26, 28],
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[20, 72, 21], [25, 21, 72], [45, 72, 20], [25, 28, 26], [44, 73, 45],
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[72, 45, 73], [28, 25, 29], [29, 25, 31], [43, 73, 44], [73, 43, 40],
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[72, 73, 39], [72, 31, 25], [42, 40, 43], [31, 30, 29], [39, 73, 40],
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[42, 41, 40], [72, 33, 31], [32, 31, 33], [39, 38, 72], [33, 72, 38],
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[33, 38, 34], [37, 35, 38], [34, 38, 35], [35, 37, 36]])
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###############################################################################
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# Rather than create a Triangulation object, can simply pass x, y and triangles
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# arrays to tripcolor directly. It would be better to use a Triangulation
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# object if the same triangulation was to be used more than once to save
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# duplicated calculations.
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fig4, ax4 = plt.subplots()
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ax4.set_aspect('equal')
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tcf = ax4.tricontourf(x, y, triangles, z)
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fig4.colorbar(tcf)
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ax4.set_title('Contour plot of user-specified triangulation')
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ax4.set_xlabel('Longitude (degrees)')
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ax4.set_ylabel('Latitude (degrees)')
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plt.show()
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#############################################################################
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#
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# .. admonition:: References
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#
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# The use of the following functions, methods, classes and modules is shown
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# in this example:
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#
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# - `matplotlib.axes.Axes.tricontourf` / `matplotlib.pyplot.tricontourf`
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# - `matplotlib.tri.Triangulation`
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# - `matplotlib.figure.Figure.colorbar` / `matplotlib.pyplot.colorbar`
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# - `matplotlib.axes.Axes.legend` / `matplotlib.pyplot.legend`
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# - `matplotlib.contour.ContourSet.legend_elements`

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