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Commitdbd81bd

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Add docs for 3.7.1
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‎3.7.1/Matplotlib.pdf

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{
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"cells": [
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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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"%matplotlib inline"
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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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"\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":"3.11.1"
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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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}
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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()
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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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importnumpyasnp
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importmatplotlib.cmascm
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importmatplotlib.pyplotasplt
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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')
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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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importmatplotlib.pyplotasplt
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importnumpyasnp
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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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fori,imginenumerate(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)
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{
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"cells": [
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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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"%matplotlib inline"
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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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"\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":"3.11.1"
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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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}

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