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🔖 Publish docs for matplotlib v3.8.1 and scikit-learn v1.3.2.
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‎README.md‎

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| Docs | Version | Release Page |
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| ------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
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| PyTorch | [![torch version](https://img.shields.io/badge/torch_version-v2.1.0-282828.svg?labelColor=4F4F4F&logo=PyTorch)](https://pytorch.org/blog/pytorch-2-1/) | [Link](https://github.com/unknownue/PyTorch.docs/releases/tag/v2.1.0) |
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| torchvision | [![torchvision version](https://img.shields.io/badge/torchvision_version-v0.16.0-282828.svg?labelColor=4F4F4FF&logo=PyTorch)](https://github.com/pytorch/vision/releases/tag/v0.16.0) | [Link](https://github.com/unknownue/PyTorch.docs/releases/tag/v1.11.0) |
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| Numpy | [![numpy version](https://badgen.net/badge/NumPy%20version/v1.26.0/black?icon=dockbit)](https://numpy.org/doc/1.26/release.html) | [Link](https://numpy.org/doc/1.26/numpy-html.zip) |
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| Scikit-learn | [![scikit-learn version](https://badgen.net/badge/Scikit-learn%20version/v0.22/black?icon=libraries)](https://github.com/scikit-learn/scikit-learn/releases/tag/0.23.2) | [Link](https://github.com/unknownue/PyTorch.docs/releases/tag/v1.7.1) |
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| Matplotlib | [![matplotlib version](https://badgen.net/badge/Matplotlib%20version/v3.3.1/black?icon=graphql)](https://github.com/matplotlib/matplotlib/releases/tag/v3.3.1) | [Link](https://github.com/unknownue/PyTorch.docs/releases/tag/v1.6.1) |
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| torchvision | [![torchvision version](https://img.shields.io/badge/torchvision_version-v0.16.0-282828.svg?labelColor=4F4F4FF&logo=PyTorch)](https://github.com/pytorch/vision/releases/tag/v0.16.0) | [Link](https://github.com/unknownue/PyTorch.docs/releases/tag/v2.1.0) |
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| Numpy | [![numpy version](https://badgen.net/badge/NumPy%20version/v1.26.0/black?icon=dockbit)](https://numpy.org/doc/1.26/release.html) | [Link](https://numpy.org/doc/1.26/numpy-html.zip) |
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| Scikit-learn | [![scikit-learn version](https://badgen.net/badge/Scikit-learn%20version/v1.3.2/black?icon=libraries)](https://github.com/scikit-learn/scikit-learn/releases/tag/1.3.2) | [Link](https://github.com/unknownue/PyTorch.docs/releases/tag/v2.1.1) |
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| Matplotlib | [![matplotlib version](https://badgen.net/badge/Matplotlib%20version/v3.8.1/black?icon=graphql)](https://github.com/matplotlib/matplotlib/releases/tag/v3.8.1) | [Link](https://github.com/unknownue/PyTorch.docs/releases/tag/v2.1.1) |
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## How to use
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matplotlib/.buildinfo renamed to mathplotlib/.buildinfo

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# Sphinx build info version 1
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# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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config: 3fd472b8eebc56647ac771f4a7bc3924
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config: d417b87a3fb2855ee1fc75d9ca941351
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tags: 645f666f9bcd5a90fca523b33c5a78b7

‎mathplotlib/_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\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()"
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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": "3.11.0"
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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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}

‎mathplotlib/_downloads/008c73d5ac1c4af82c0b90fde922ebb9/stairs.py‎

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

‎mathplotlib/_downloads/008dacd6028779838d22c9578023d145/api_interfaces-7.2x.png‎

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‎mathplotlib/_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()

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

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‎mathplotlib/_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 matplotlib.pyplot as plt
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import numpy as np
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import matplotlib.cm as cm
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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')

‎mathplotlib/_downloads/00e4e17ebcbe969045713af499723634/whats_new_1-0-1.2x.png‎

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matplotlib/_downloads/00f7f20b47c4afdb8cf00a246bc6af53/animation_demo.py renamed to mathplotlib/_downloads/00f7f20b47c4afdb8cf00a246bc6af53/animation_demo.py

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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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fig, ax = plt.subplots()
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for i in range(len(data)):
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ax.cla()
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ax.imshow(data[i])
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ax.set_title("frame {}".format(i))
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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)

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