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‎3.1.3/Matplotlib.pdf

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‎3.1.3/_downloads/000ad3317b78df156c632a5c653d11ba/multiple_figs_demo.ipynb

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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# Multiple Figs Demo\n\n\nWorking with multiple figure windows and subplots\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\nt = np.arange(0.0, 2.0, 0.01)\ns1 = np.sin(2*np.pi*t)\ns2 = np.sin(4*np.pi*t)"
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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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"Create figure 1\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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"plt.figure(1)\nplt.subplot(211)\nplt.plot(t, s1)\nplt.subplot(212)\nplt.plot(t, 2*s1)"
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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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"Create figure 2\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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"plt.figure(2)\nplt.plot(t, s2)"
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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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"Now switch back to figure 1 and make some changes\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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"plt.figure(1)\nplt.subplot(211)\nplt.plot(t, s2, 's')\nax = plt.gca()\nax.set_xticklabels([])\n\nplt.show()"
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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": "3.8.1"
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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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}

‎3.1.3/_downloads/00203bf5bb8c7d15b41d4ce0fe1d4050/contour3d_2.py

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'''
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============================================================================
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Demonstrates plotting contour (level) curves in 3D using the extend3d option
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============================================================================
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This modification of the contour3d_demo example uses extend3d=True to
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extend the curves vertically into 'ribbons'.
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'''
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from mpl_toolkits.mplot3d import axes3d
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import matplotlib.pyplot as plt
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from matplotlib import cm
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fig = plt.figure()
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ax = fig.gca(projection='3d')
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X, Y, Z = axes3d.get_test_data(0.05)
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cset = ax.contour(X, Y, Z, extend3d=True, cmap=cm.coolwarm)
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ax.clabel(cset, fontsize=9, inline=1)
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plt.show()

‎3.1.3/_downloads/00d34761f9dea7e4fbb49d34f3490352/tutorials_python.zip

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‎3.1.3/_downloads/00fcec53627838553c8e3bedabcc4c2a/customizing.ipynb

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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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"\nCustomizing Matplotlib with style sheets and rcParams\n=====================================================\n\nTips for customizing the properties and default styles of Matplotlib.\n\nUsing style sheets\n------------------\n\nThe ``style`` package adds support for easy-to-switch plotting \"styles\" with\nthe same parameters as a\n`matplotlib rc <customizing-with-matplotlibrc-files>` file (which is read\nat startup to configure matplotlib).\n\nThere are a number of pre-defined styles `provided by Matplotlib`_. For\nexample, there's a pre-defined style called \"ggplot\", which emulates the\naesthetics of ggplot_ (a popular plotting package for R_). To use this style,\njust add:\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 numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nplt.style.use('ggplot')\ndata = np.random.randn(50)"
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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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"To list all available styles, use:\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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"print(plt.style.available)"
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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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"Defining your own style\n-----------------------\n\nYou can create custom styles and use them by calling ``style.use`` with the\npath or URL to the style sheet. Additionally, if you add your\n``<style-name>.mplstyle`` file to ``mpl_configdir/stylelib``, you can reuse\nyour custom style sheet with a call to ``style.use(<style-name>)``. By default\n``mpl_configdir`` should be ``~/.config/matplotlib``, but you can check where\nyours is with ``matplotlib.get_configdir()``; you may need to create this\ndirectory. You also can change the directory where matplotlib looks for\nthe stylelib/ folder by setting the MPLCONFIGDIR environment variable,\nsee `locating-matplotlib-config-dir`.\n\nNote that a custom style sheet in ``mpl_configdir/stylelib`` will\noverride a style sheet defined by matplotlib if the styles have the same name.\n\nFor example, you might want to create\n``mpl_configdir/stylelib/presentation.mplstyle`` with the following::\n\n axes.titlesize : 24\n axes.labelsize : 20\n lines.linewidth : 3\n lines.markersize : 10\n xtick.labelsize : 16\n ytick.labelsize : 16\n\nThen, when you want to adapt a plot designed for a paper to one that looks\ngood in a presentation, you can just add::\n\n >>> import matplotlib.pyplot as plt\n >>> plt.style.use('presentation')\n\n\nComposing styles\n----------------\n\nStyle sheets are designed to be composed together. So you can have a style\nsheet that customizes colors and a separate style sheet that alters element\nsizes for presentations. These styles can easily be combined by passing\na list of styles::\n\n >>> import matplotlib.pyplot as plt\n >>> plt.style.use(['dark_background', 'presentation'])\n\nNote that styles further to the right will overwrite values that are already\ndefined by styles on the left.\n\n\nTemporary styling\n-----------------\n\nIf you only want to use a style for a specific block of code but don't want\nto change the global styling, the style package provides a context manager\nfor limiting your changes to a specific scope. To isolate your styling\nchanges, you can write something like the following:\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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"with plt.style.context('dark_background'):\n plt.plot(np.sin(np.linspace(0, 2 * np.pi)), 'r-o')\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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"\nmatplotlib rcParams\n===================\n\n\nDynamic rc settings\n-------------------\n\nYou can also dynamically change the default rc settings in a python script or\ninteractively from the python shell. All of the rc settings are stored in a\ndictionary-like variable called :data:`matplotlib.rcParams`, which is global to\nthe matplotlib package. rcParams can be modified directly, for example:\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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"mpl.rcParams['lines.linewidth'] = 2\nmpl.rcParams['lines.color'] = 'r'\nplt.plot(data)"
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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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"Matplotlib also provides a couple of convenience functions for modifying rc\nsettings. The :func:`matplotlib.rc` command can be used to modify multiple\nsettings in a single group at once, using keyword arguments:\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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"mpl.rc('lines', linewidth=4, color='g')\nplt.plot(data)"
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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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"The :func:`matplotlib.rcdefaults` command will restore the standard matplotlib\ndefault settings.\n\nThere is some degree of validation when setting the values of rcParams, see\n:mod:`matplotlib.rcsetup` for details.\n\n\nThe :file:`matplotlibrc` file\n-----------------------------\n\nmatplotlib uses :file:`matplotlibrc` configuration files to customize all kinds\nof properties, which we call `rc settings` or `rc parameters`. You can control\nthe defaults of almost every property in matplotlib: figure size and dpi, line\nwidth, color and style, axes, axis and grid properties, text and font\nproperties and so on. matplotlib looks for :file:`matplotlibrc` in four\nlocations, in the following order:\n\n1. :file:`matplotlibrc` in the current working directory, usually used for\n specific customizations that you do not want to apply elsewhere.\n\n2. :file:`$MATPLOTLIBRC` if it is a file, else :file:`$MATPLOTLIBRC/matplotlibrc`.\n\n3. It next looks in a user-specific place, depending on your platform:\n\n - On Linux and FreeBSD, it looks in :file:`.config/matplotlib/matplotlibrc`\n (or `$XDG_CONFIG_HOME/matplotlib/matplotlibrc`) if you've customized\n your environment.\n\n - On other platforms, it looks in :file:`.matplotlib/matplotlibrc`.\n\n See `locating-matplotlib-config-dir`.\n\n4. :file:`{INSTALL}/matplotlib/mpl-data/matplotlibrc`, where\n :file:`{INSTALL}` is something like\n :file:`/usr/lib/python3.7/site-packages` on Linux, and maybe\n :file:`C:\\\\Python37\\\\Lib\\\\site-packages` on Windows. Every time you\n install matplotlib, this file will be overwritten, so if you want\n your customizations to be saved, please move this file to your\n user-specific matplotlib directory.\n\nOnce a :file:`matplotlibrc` file has been found, it will *not* search any of\nthe other paths.\n\nTo display where the currently active :file:`matplotlibrc` file was\nloaded from, one can do the following::\n\n >>> import matplotlib\n >>> matplotlib.matplotlib_fname()\n '/home/foo/.config/matplotlib/matplotlibrc'\n\nSee below for a sample `matplotlibrc file<matplotlibrc-sample>`.\n\n\nA sample matplotlibrc file\n~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. literalinclude:: ../../../matplotlibrc.template\n\n\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": "3.8.1"
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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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}

‎3.1.3/_downloads/0103ebb71db6da20165275a527c4ab5b/geo_demo.ipynb

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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# Geographic Projections\n\n\nThis shows 4 possible projections using subplot. Matplotlib also\nsupports `Basemaps Toolkit <https://matplotlib.org/basemap>`_ and\n`Cartopy <http://scitools.org.uk/cartopy>`_ for geographic projections.\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"
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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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"plt.figure()\nplt.subplot(111, projection=\"aitoff\")\nplt.title(\"Aitoff\")\nplt.grid(True)"
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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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"plt.figure()\nplt.subplot(111, projection=\"hammer\")\nplt.title(\"Hammer\")\nplt.grid(True)"
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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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"plt.figure()\nplt.subplot(111, projection=\"lambert\")\nplt.title(\"Lambert\")\nplt.grid(True)"
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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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"plt.figure()\nplt.subplot(111, projection=\"mollweide\")\nplt.title(\"Mollweide\")\nplt.grid(True)\n\nplt.show()"
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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": "3.8.1"
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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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}

‎3.1.3/_downloads/012cbb8895aa47b0d33b5fd2a220cbb8/subplots_adjust.py

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"""
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===============
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Subplots Adjust
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===============
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Adjusting the spacing of margins and subplots using
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:func:`~matplotlib.pyplot.subplots_adjust`.
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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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# Fixing random state for reproducibility
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np.random.seed(19680801)
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plt.subplot(211)
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plt.imshow(np.random.random((100, 100)), cmap=plt.cm.BuPu_r)
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plt.subplot(212)
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plt.imshow(np.random.random((100, 100)), cmap=plt.cm.BuPu_r)
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plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
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cax = plt.axes([0.85, 0.1, 0.075, 0.8])
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plt.colorbar(cax=cax)
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plt.show()

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