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update docs for 3.2.0 pt2
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‎3.2.0/Matplotlib.pdf

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‎3.2.0/_downloads/0017d0c84e03d6c5e463e23d8e570671/line_collection.ipynb

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"source": [
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"%matplotlib inline"
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]
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"cell_type": "markdown",
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"metadata": {},
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"\n# Line Collection\n\n\nPlotting lines with Matplotlib.\n\n:class:`~matplotlib.collections.LineCollection` allows one to plot multiple\nlines on a figure. Below we show off some of its properties.\n"
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"import matplotlib.pyplot as plt\nfrom matplotlib.collections import LineCollection\nfrom matplotlib import colors as mcolors\n\nimport numpy as np\n\n# In order to efficiently plot many lines in a single set of axes,\n# Matplotlib has the ability to add the lines all at once. Here is a\n# simple example showing how it is done.\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.\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)\n# where onoffseq is an even length tuple of on and off ink in points.\n# If linestyle is omitted, 'solid' is used\n# See :class:`matplotlib.collections.LineCollection` for more information\ncolors = [mcolors.to_rgba(c)\n for c in 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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"In order to efficiently plot many lines in a single set of axes,\nMatplotlib has the ability to add the lines all at once. Here is a\nsimple example showing how it is done.\n\n"
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"N = 50\nx = np.arange(N)\n# Here are many sets of y to plot vs. x\nys = [x + i for i in x]\n\n# We need to set the plot limits, they will not autoscale\nfig, ax = plt.subplots()\nax.set_xlim(np.min(x), np.max(x))\nax.set_ylim(np.min(ys), np.max(ys))\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)\n# where onoffseq is an even length tuple of on and off ink in points.\n# If linestyle is omitted, 'solid' is used\n# See :class:`matplotlib.collections.LineCollection` for more information\n\n# Make a sequence of (x, y) pairs.\nline_segments = LineCollection([np.column_stack([x, y]) for y in ys],\n linewidths=(0.5, 1, 1.5, 2),\n linestyles='solid')\nline_segments.set_array(x)\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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"cell_type": "markdown",
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"metadata": {},
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"------------\n\nReferences\n\"\"\"\"\"\"\"\"\"\"\n\nThe use of the following functions, methods, classes and modules is shown\nin this example:\n\n"
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]
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"import matplotlib\nmatplotlib.collections\nmatplotlib.collections.LineCollection\nmatplotlib.cm.ScalarMappable.set_array\nmatplotlib.axes.Axes.add_collection\nmatplotlib.figure.Figure.colorbar\nmatplotlib.pyplot.colorbar\nmatplotlib.pyplot.sci"
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‎3.2.0/_downloads/003118d8eb1e7df55671bcdf18bfac3c/sample_plots.ipynb

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"execution_count": null,
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"metadata": {
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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# Sample plots in Matplotlib\n\n\nHere you'll find a host of example plots with the code that\ngenerated them.\n\n\nLine Plot\n=========\n\nHere's how to create a line plot with text labels using\n:func:`~matplotlib.pyplot.plot`.\n\n.. figure:: ../../gallery/lines_bars_and_markers/images/sphx_glr_simple_plot_001.png\n :target: ../../gallery/lines_bars_and_markers/simple_plot.html\n :align: center\n :scale: 50\n\n Simple Plot\n\n\nMultiple subplots in one figure\n===============================\n\nMultiple axes (i.e. subplots) are created with the\n:func:`~matplotlib.pyplot.subplot` function:\n\n.. figure:: ../../gallery/subplots_axes_and_figures/images/sphx_glr_subplot_001.png\n :target: ../../gallery/subplots_axes_and_figures/subplot.html\n :align: center\n :scale: 50\n\n Subplot\n\n\nImages\n======\n\nMatplotlib can display images (assuming equally spaced\nhorizontal dimensions) using the :func:`~matplotlib.pyplot.imshow` function.\n\n.. figure:: ../../gallery/images_contours_and_fields/images/sphx_glr_image_demo_003.png\n :target: ../../gallery/images_contours_and_fields/image_demo.html\n :align: center\n :scale: 50\n\n Example of using :func:`~matplotlib.pyplot.imshow` to display a CT scan\n\n\n\nContouring and pseudocolor\n==========================\n\nThe :func:`~matplotlib.pyplot.pcolormesh` function can make a colored\nrepresentation of a two-dimensional array, even if the horizontal dimensions\nare unevenly spaced. The\n:func:`~matplotlib.pyplot.contour` function is another way to represent\nthe same data:\n\n.. figure:: ../../gallery/images_contours_and_fields/images/sphx_glr_pcolormesh_levels_001.png\n :target: ../../gallery/images_contours_and_fields/pcolormesh_levels.html\n :align: center\n :scale: 50\n\n Example comparing :func:`~matplotlib.pyplot.pcolormesh` and :func:`~matplotlib.pyplot.contour` for plotting two-dimensional data\n\n\nHistograms\n==========\n\nThe :func:`~matplotlib.pyplot.hist` function automatically generates\nhistograms and returns the bin counts or probabilities:\n\n.. figure:: ../../gallery/statistics/images/sphx_glr_histogram_features_001.png\n :target: ../../gallery/statistics/histogram_features.html\n :align: center\n :scale: 50\n\n Histogram Features\n\n\n\nPaths\n=====\n\nYou can add arbitrary paths in Matplotlib using the\n:mod:`matplotlib.path` module:\n\n.. figure:: ../../gallery/shapes_and_collections/images/sphx_glr_path_patch_001.png\n :target: ../../gallery/shapes_and_collections/path_patch.html\n :align: center\n :scale: 50\n\n Path Patch\n\n\nThree-dimensional plotting\n==========================\n\nThe mplot3d toolkit (see `toolkit_mplot3d-tutorial` and\n`mplot3d-examples-index`) has support for simple 3d graphs\nincluding surface, wireframe, scatter, and bar charts.\n\n.. figure:: ../../gallery/mplot3d/images/sphx_glr_surface3d_001.png\n :target: ../../gallery/mplot3d/surface3d.html\n :align: center\n :scale: 50\n\n Surface3d\n\nThanks to John Porter, Jonathon Taylor, Reinier Heeres, and Ben Root for\nthe `.mplot3d` toolkit. This toolkit is included with all standard Matplotlib\ninstalls.\n\n\n\nStreamplot\n==========\n\nThe :meth:`~matplotlib.pyplot.streamplot` function plots the streamlines of\na vector field. In addition to simply plotting the streamlines, it allows you\nto map the colors and/or line widths of streamlines to a separate parameter,\nsuch as the speed or local intensity of the vector field.\n\n.. figure:: ../../gallery/images_contours_and_fields/images/sphx_glr_plot_streamplot_001.png\n :target: ../../gallery/images_contours_and_fields/plot_streamplot.html\n :align: center\n :scale: 50\n\n Streamplot with various plotting options.\n\nThis feature complements the :meth:`~matplotlib.pyplot.quiver` function for\nplotting vector fields. Thanks to Tom Flannaghan and Tony Yu for adding the\nstreamplot function.\n\n\nEllipses\n========\n\nIn support of the `Phoenix <http://www.jpl.nasa.gov/news/phoenix/main.php>`_\nmission to Mars (which used Matplotlib to display ground tracking of\nspacecraft), Michael Droettboom built on work by Charlie Moad to provide\nan extremely accurate 8-spline approximation to elliptical arcs (see\n:class:`~matplotlib.patches.Arc`), which are insensitive to zoom level.\n\n.. figure:: ../../gallery/shapes_and_collections/images/sphx_glr_ellipse_demo_001.png\n :target: ../../gallery/shapes_and_collections/ellipse_demo.html\n :align: center\n :scale: 50\n\n Ellipse Demo\n\n\nBar charts\n==========\n\nUse the :func:`~matplotlib.pyplot.bar` function to make bar charts, which\nincludes customizations such as error bars:\n\n.. figure:: ../../gallery/statistics/images/sphx_glr_barchart_demo_001.png\n :target: ../../gallery/statistics/barchart_demo.html\n :align: center\n :scale: 50\n\n Barchart Demo\n\nYou can also create stacked bars\n(`bar_stacked.py <../../gallery/lines_bars_and_markers/bar_stacked.html>`_),\nor horizontal bar charts\n(`barh.py <../../gallery/lines_bars_and_markers/barh.html>`_).\n\n\n\nPie charts\n==========\n\nThe :func:`~matplotlib.pyplot.pie` function allows you to create pie\ncharts. Optional features include auto-labeling the percentage of area,\nexploding one or more wedges from the center of the pie, and a shadow effect.\nTake a close look at the attached code, which generates this figure in just\na few lines of code.\n\n.. figure:: ../../gallery/pie_and_polar_charts/images/sphx_glr_pie_features_001.png\n :target: ../../gallery/pie_and_polar_charts/pie_features.html\n :align: center\n :scale: 50\n\n Pie Features\n\n\nTables\n======\n\nThe :func:`~matplotlib.pyplot.table` function adds a text table\nto an axes.\n\n.. figure:: ../../gallery/misc/images/sphx_glr_table_demo_001.png\n :target: ../../gallery/misc/table_demo.html\n :align: center\n :scale: 50\n\n Table Demo\n\n\n\n\nScatter plots\n=============\n\nThe :func:`~matplotlib.pyplot.scatter` function makes a scatter plot\nwith (optional) size and color arguments. This example plots changes\nin Google's stock price, with marker sizes reflecting the\ntrading volume and colors varying with time. Here, the\nalpha attribute is used to make semitransparent circle markers.\n\n.. figure:: ../../gallery/lines_bars_and_markers/images/sphx_glr_scatter_demo2_001.png\n :target: ../../gallery/lines_bars_and_markers/scatter_demo2.html\n :align: center\n :scale: 50\n\n Scatter Demo2\n\n\n\nGUI widgets\n===========\n\nMatplotlib has basic GUI widgets that are independent of the graphical\nuser interface you are using, allowing you to write cross GUI figures\nand widgets. See :mod:`matplotlib.widgets` and the\n`widget examples <../../gallery/index.html>`_.\n\n.. figure:: ../../gallery/widgets/images/sphx_glr_slider_demo_001.png\n :target: ../../gallery/widgets/slider_demo.html\n :align: center\n :scale: 50\n\n Slider and radio-button GUI.\n\n\n\nFilled curves\n=============\n\nThe :func:`~matplotlib.pyplot.fill` function lets you\nplot filled curves and polygons:\n\n.. figure:: ../../gallery/lines_bars_and_markers/images/sphx_glr_fill_001.png\n :target: ../../gallery/lines_bars_and_markers/fill.html\n :align: center\n :scale: 50\n\n Fill\n\nThanks to Andrew Straw for adding this function.\n\n\nDate handling\n=============\n\nYou can plot timeseries data with major and minor ticks and custom\ntick formatters for both.\n\n.. figure:: ../../gallery/text_labels_and_annotations/images/sphx_glr_date_001.png\n :target: ../../gallery/text_labels_and_annotations/date.html\n :align: center\n :scale: 50\n\n Date\n\nSee :mod:`matplotlib.ticker` and :mod:`matplotlib.dates` for details and usage.\n\n\n\nLog plots\n=========\n\nThe :func:`~matplotlib.pyplot.semilogx`,\n:func:`~matplotlib.pyplot.semilogy` and\n:func:`~matplotlib.pyplot.loglog` functions simplify the creation of\nlogarithmic plots.\n\n.. figure:: ../../gallery/scales/images/sphx_glr_log_demo_001.png\n :target: ../../gallery/scales/log_demo.html\n :align: center\n :scale: 50\n\n Log Demo\n\nThanks to Andrew Straw, Darren Dale and Gregory Lielens for contributions\nlog-scaling infrastructure.\n\n\nPolar plots\n===========\n\nThe :func:`~matplotlib.pyplot.polar` function generates polar plots.\n\n.. figure:: ../../gallery/pie_and_polar_charts/images/sphx_glr_polar_demo_001.png\n :target: ../../gallery/pie_and_polar_charts/polar_demo.html\n :align: center\n :scale: 50\n\n Polar Demo\n\n\n\nLegends\n=======\n\nThe :func:`~matplotlib.pyplot.legend` function automatically\ngenerates figure legends, with MATLAB-compatible legend-placement\nfunctions.\n\n.. figure:: ../../gallery/text_labels_and_annotations/images/sphx_glr_legend_001.png\n :target: ../../gallery/text_labels_and_annotations/legend.html\n :align: center\n :scale: 50\n\n Legend\n\nThanks to Charles Twardy for input on the legend function.\n\n\nTeX-notation for text objects\n=============================\n\nBelow is a sampling of the many TeX expressions now supported by Matplotlib's\ninternal mathtext engine. The mathtext module provides TeX style mathematical\nexpressions using `FreeType <https://www.freetype.org/>`_\nand the DejaVu, BaKoMa computer modern, or `STIX <http://www.stixfonts.org>`_\nfonts. See the :mod:`matplotlib.mathtext` module for additional details.\n\n.. figure:: ../../gallery/text_labels_and_annotations/images/sphx_glr_mathtext_examples_001.png\n :target: ../../gallery/text_labels_and_annotations/mathtext_examples.html\n :align: center\n :scale: 50\n\n Mathtext Examples\n\nMatplotlib's mathtext infrastructure is an independent implementation and\ndoes not require TeX or any external packages installed on your computer. See\nthe tutorial at :doc:`/tutorials/text/mathtext`.\n\n\n\nNative TeX rendering\n====================\n\nAlthough Matplotlib's internal math rendering engine is quite\npowerful, sometimes you need TeX. Matplotlib supports external TeX\nrendering of strings with the *usetex* option.\n\n.. figure:: ../../gallery/text_labels_and_annotations/images/sphx_glr_tex_demo_001.png\n :target: ../../gallery/text_labels_and_annotations/tex_demo.html\n :align: center\n :scale: 50\n\n Tex Demo\n\n\nEEG GUI\n=======\n\nYou can embed Matplotlib into pygtk, wx, Tk, or Qt applications.\nHere is a screenshot of an EEG viewer called `pbrain\n<https://github.com/nipy/pbrain>`__.\n\n![](../../_static/eeg_small.png)\n\n\nThe lower axes uses :func:`~matplotlib.pyplot.specgram`\nto plot the spectrogram of one of the EEG channels.\n\nFor examples of how to embed Matplotlib in different toolkits, see:\n\n * :doc:`/gallery/user_interfaces/embedding_in_gtk3_sgskip`\n * :doc:`/gallery/user_interfaces/embedding_in_wx2_sgskip`\n * :doc:`/gallery/user_interfaces/mpl_with_glade3_sgskip`\n * :doc:`/gallery/user_interfaces/embedding_in_qt_sgskip`\n * :doc:`/gallery/user_interfaces/embedding_in_tk_sgskip`\n\nXKCD-style sketch plots\n=======================\n\nJust for fun, Matplotlib supports plotting in the style of `xkcd\n<https://www.xkcd.com/>`_.\n\n.. figure:: ../../gallery/showcase/images/sphx_glr_xkcd_001.png\n :target: ../../gallery/showcase/xkcd.html\n :align: center\n :scale: 50\n\n xkcd\n\nSubplot example\n===============\n\nMany plot types can be combined in one figure to create\npowerful and flexible representations of data.\n"
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"import matplotlib.pyplot as plt\nimport numpy as np\n\nnp.random.seed(19680801)\ndata = np.random.randn(2, 100)\n\nfig, axs = plt.subplots(2, 2, figsize=(5, 5))\naxs[0, 0].hist(data[0])\naxs[1, 0].scatter(data[0], data[1])\naxs[0, 1].plot(data[0], data[1])\naxs[1, 1].hist2d(data[0], data[1])\n\nplt.show()"
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‎3.2.0/_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.pyplot as plt
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###############################################################################
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f = plt.figure()
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s = plt.scatter([1, 2, 3], [4, 5, 6])
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s.set_urls(['http://www.bbc.co.uk/news', 'http://www.google.com', None])
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f.savefig('scatter.svg')
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###############################################################################
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f = 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('http://www.google.com')
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f.savefig('image.svg')

‎3.2.0/_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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"""
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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 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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# Note that using time.sleep does *not* work here!
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plt.pause(0.1)

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