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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: 2a5ce83f1f9fffd802d50424354d1359 | ||
tags: 526838a1027dd1fd771fd21a9668211e |
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_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 | ||
} |
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""" | ||
============== | ||
stairs(values) | ||
============== | ||
See `~matplotlib.axes.Axes.stairs`. | ||
""" | ||
import matplotlib.pyplot as plt | ||
import numpy as np | ||
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plt.style.use('_mpl-gallery') | ||
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# make data | ||
y = [4.8, 5.5, 3.5, 4.6, 6.5, 6.6, 2.6, 3.0] | ||
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# plot | ||
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), | ||
ylim=(0, 8), yticks=np.arange(1, 8)) | ||
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plt.show() |
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_downloads/00aacc25eb4ed2eaffa3c821d5562b6d/whats_new_3-6-0-5.py
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x = np.linspace(1., 3., 10) | ||
y = x**3 | ||
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fig, ax = plt.subplots() | ||
ax.plot(x, y, linestyle='--', color='orange', gapcolor='blue', | ||
linewidth=3, label='a striped line') | ||
ax.legend() |
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_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. | ||
""" | ||
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import matplotlib.pyplot as plt | ||
import numpy as np | ||
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import matplotlib.cm as cm | ||
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# %% | ||
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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') | ||
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# %% | ||
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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 | ||
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im = plt.imshow(Z, interpolation='bilinear', cmap=cm.gray, | ||
origin='lower', extent=(-3, 3, -3, 3)) | ||
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im.set_url('https://www.google.com/') | ||
fig.savefig('image.svg') |
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_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`. | ||
""" | ||
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import matplotlib.pyplot as plt | ||
import numpy as np | ||
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np.random.seed(19680801) | ||
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): | ||
ax.clear() | ||
ax.imshow(img) | ||
ax.set_title(f"frame {i}") | ||
# Note that using time.sleep does *not* work here! | ||
plt.pause(0.1) |
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_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 | ||
} |
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