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‎.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: fee21aba0ad35d7353946075a94d4ae9
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tags: 645f666f9bcd5a90fca523b33c5a78b7

‎.nojekyll

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‎_downloads/2dcollections3d.ipynb

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{
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"metadata": {
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"language_info": {
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"codemirror_mode": {
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"version": 3,
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"name": "ipython"
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},
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"name": "python",
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"pygments_lexer": "ipython3",
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"nbconvert_exporter": "python",
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"version": "3.5.2"
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},
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"kernelspec": {
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"display_name": "Python 3",
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"name": "python3",
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"language": "python"
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}
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},
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"cells": [
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{
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"source": [
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"%matplotlib inline"
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],
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"cell_type": "code",
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"execution_count": null
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},
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{
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"source": [
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"\n# Plot 2D data on 3D plot\n\n\nDemonstrates using ax.plot's zdir keyword to plot 2D data on\nselective axes of a 3D plot.\n\n"
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],
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"metadata": {},
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"cell_type": "markdown"
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},
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{
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"source": [
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"from mpl_toolkits.mplot3d import Axes3D\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfig = plt.figure()\nax = fig.gca(projection='3d')\n\n# Plot a sin curve using the x and y axes.\nx = np.linspace(0, 1, 100)\ny = np.sin(x * 2 * np.pi) / 2 + 0.5\nax.plot(x, y, zs=0, zdir='z', label='curve in (x,y)')\n\n# Plot scatterplot data (20 2D points per colour) on the x and z axes.\ncolors = ('r', 'g', 'b', 'k')\n\n# Fixing random state for reproducibility\nnp.random.seed(19680801)\n\nx = np.random.sample(20 * len(colors))\ny = np.random.sample(20 * len(colors))\nc_list = []\nfor c in colors:\n c_list.extend([c] * 20)\n# By using zdir='y', the y value of these points is fixed to the zs value 0\n# and the (x,y) points are plotted on the x and z axes.\nax.scatter(x, y, zs=0, zdir='y', c=c_list, label='points in (x,z)')\n\n# Make legend, set axes limits and labels\nax.legend()\nax.set_xlim(0, 1)\nax.set_ylim(0, 1)\nax.set_zlim(0, 1)\nax.set_xlabel('X')\nax.set_ylabel('Y')\nax.set_zlabel('Z')\n\n# Customize the view angle so it's easier to see that the scatter points lie\n# on the plane y=0\nax.view_init(elev=20., azim=-35)\n\nplt.show()"
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],
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"cell_type": "code",
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"execution_count": null
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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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}

‎_downloads/2dcollections3d.py

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"""
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=======================
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Plot 2D data on 3D plot
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=======================
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Demonstrates using ax.plot's zdir keyword to plot 2D data on
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selective axes of a 3D plot.
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"""
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from mpl_toolkits.mplot3d import Axes3D
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import numpy as np
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import matplotlib.pyplot as plt
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fig = plt.figure()
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ax = fig.gca(projection='3d')
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# Plot a sin curve using the x and y axes.
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x = np.linspace(0, 1, 100)
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y = np.sin(x * 2 * np.pi) / 2 + 0.5
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ax.plot(x, y, zs=0, zdir='z', label='curve in (x,y)')
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# Plot scatterplot data (20 2D points per colour) on the x and z axes.
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colors = ('r', 'g', 'b', 'k')
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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x = np.random.sample(20 * len(colors))
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y = np.random.sample(20 * len(colors))
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c_list = []
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for c in colors:
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c_list.extend([c] * 20)
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# By using zdir='y', the y value of these points is fixed to the zs value 0
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# and the (x,y) points are plotted on the x and z axes.
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ax.scatter(x, y, zs=0, zdir='y', c=c_list, label='points in (x,z)')
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# Make legend, set axes limits and labels
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ax.legend()
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ax.set_xlim(0, 1)
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ax.set_ylim(0, 1)
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ax.set_zlim(0, 1)
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ax.set_xlabel('X')
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ax.set_ylabel('Y')
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ax.set_zlabel('Z')
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# Customize the view angle so it's easier to see that the scatter points lie
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# on the plane y=0
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ax.view_init(elev=20., azim=-35)
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plt.show()

‎_downloads/3D.ipynb

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{
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"metadata": {
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"language_info": {
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"codemirror_mode": {
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"version": 3,
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"name": "ipython"
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},
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"name": "python",
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"pygments_lexer": "ipython3",
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"nbconvert_exporter": "python",
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"version": "3.5.2"
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},
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"kernelspec": {
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"display_name": "Python 3",
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"name": "python3",
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"language": "python"
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}
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},
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"cells": [
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{
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"source": [
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"%matplotlib inline"
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],
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"cell_type": "code",
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"execution_count": null
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},
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{
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"source": [
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"\n# Frontpage 3D example\n\n\nThis example reproduces the frontpage 3D example.\n\n\n"
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],
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"metadata": {},
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"cell_type": "markdown"
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},
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{
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"source": [
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"from mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib import cbook\nfrom matplotlib import cm\nfrom matplotlib.colors import LightSource\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfilename = cbook.get_sample_data('jacksboro_fault_dem.npz', asfileobj=False)\nwith np.load(filename) as dem:\n z = dem['elevation']\n nrows, ncols = z.shape\n x = np.linspace(dem['xmin'], dem['xmax'], ncols)\n y = np.linspace(dem['ymin'], dem['ymax'], nrows)\n x, y = np.meshgrid(x, y)\n\nregion = np.s_[5:50, 5:50]\nx, y, z = x[region], y[region], z[region]\n\nfig, ax = plt.subplots(subplot_kw=dict(projection='3d'))\n\nls = LightSource(270, 45)\n# To use a custom hillshading mode, override the built-in shading and pass\n# in the rgb colors of the shaded surface calculated from \"shade\".\nrgb = ls.shade(z, cmap=cm.gist_earth, vert_exag=0.1, blend_mode='soft')\nsurf = ax.plot_surface(x, y, z, rstride=1, cstride=1, facecolors=rgb,\n linewidth=0, antialiased=False, shade=False)\nax.set_xticks([])\nax.set_yticks([])\nax.set_zticks([])\nfig.savefig(\"surface3d_frontpage.png\", dpi=25) # results in 160x120 px image"
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],
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"cell_type": "code",
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"execution_count": null
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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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}

‎_downloads/3D.py

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"""
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====================
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Frontpage 3D example
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====================
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This example reproduces the frontpage 3D example.
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"""
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from mpl_toolkits.mplot3d import Axes3D
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from matplotlib import cbook
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from matplotlib import cm
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from matplotlib.colors import LightSource
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import matplotlib.pyplot as plt
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import numpy as np
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filename = cbook.get_sample_data('jacksboro_fault_dem.npz', asfileobj=False)
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with np.load(filename) as dem:
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z = dem['elevation']
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nrows, ncols = z.shape
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x = np.linspace(dem['xmin'], dem['xmax'], ncols)
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y = np.linspace(dem['ymin'], dem['ymax'], nrows)
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x, y = np.meshgrid(x, y)
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region = np.s_[5:50, 5:50]
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x, y, z = x[region], y[region], z[region]
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fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
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ls = LightSource(270, 45)
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# To use a custom hillshading mode, override the built-in shading and pass
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# in the rgb colors of the shaded surface calculated from "shade".
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rgb = ls.shade(z, cmap=cm.gist_earth, vert_exag=0.1, blend_mode='soft')
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surf = ax.plot_surface(x, y, z, rstride=1, cstride=1, facecolors=rgb,
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linewidth=0, antialiased=False, shade=False)
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ax.set_xticks([])
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ax.set_yticks([])
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ax.set_zticks([])
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fig.savefig("surface3d_frontpage.png", dpi=25) # results in 160x120 px image

‎_downloads/3d_bars.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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"source": [
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"%matplotlib inline"
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],
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"outputs": []
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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# Demo of 3D bar charts\n\n\nA basic demo of how to plot 3D bars with and without\nshading.\n\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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"source": [
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"import numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\n\n# setup the figure and axes\nfig = plt.figure(figsize=(8, 3))\nax1 = fig.add_subplot(121, projection='3d')\nax2 = fig.add_subplot(122, projection='3d')\n\n# fake data\n_x = np.arange(4)\n_y = np.arange(5)\n_xx, _yy = np.meshgrid(_x, _y)\nx, y = _xx.ravel(), _yy.ravel()\n\ntop = x + y\nbottom = np.zeros_like(top)\nwidth = depth = 1\n\nax1.bar3d(x, y, bottom, width, depth, top, shade=True)\nax1.set_title('Shaded')\n\nax2.bar3d(x, y, bottom, width, depth, top, shade=False)\nax2.set_title('Not Shaded')\n\nplt.show()"
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],
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"outputs": []
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}
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],
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"nbformat_minor": 0,
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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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"version": "3.5.2",
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"nbconvert_exporter": "python",
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"mimetype": "text/x-python",
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"codemirror_mode": {
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"version": 3,
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"name": "ipython"
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},
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"file_extension": ".py",
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"pygments_lexer": "ipython3",
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"name": "python"
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}
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},
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"nbformat": 4
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}

‎_downloads/3d_bars.py

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"""
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=====================
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Demo of 3D bar charts
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=====================
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A basic demo of how to plot 3D bars with and without
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shading.
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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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from mpl_toolkits.mplot3d import Axes3D
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# setup the figure and axes
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fig = plt.figure(figsize=(8, 3))
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ax1 = fig.add_subplot(121, projection='3d')
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ax2 = fig.add_subplot(122, projection='3d')
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# fake data
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_x = np.arange(4)
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_y = np.arange(5)
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_xx, _yy = np.meshgrid(_x, _y)
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x, y = _xx.ravel(), _yy.ravel()
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top = x + y
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bottom = np.zeros_like(top)
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width = depth = 1
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ax1.bar3d(x, y, bottom, width, depth, top, shade=True)
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ax1.set_title('Shaded')
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ax2.bar3d(x, y, bottom, width, depth, top, shade=False)
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ax2.set_title('Not Shaded')
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plt.show()

‎_downloads/accented_text.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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"source": [
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"%matplotlib inline"
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],
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"outputs": []
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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# Using accented text in matplotlib\n\n\nMatplotlib supports accented characters via TeX mathtext or unicode.\n\nUsing mathtext, the following accents are provided: \\hat, \\breve, \\grave, \\bar,\n\\acute, \\tilde, \\vec, \\dot, \\ddot. All of them have the same syntax,\ne.g., to make an overbar you do \\bar{o} or to make an o umlaut you do\n\\ddot{o}. The shortcuts are also provided, e.g.,: \\\"o \\'e \\`e \\~n \\.x\n\\^y\n\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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"source": [
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"from __future__ import unicode_literals\nimport matplotlib.pyplot as plt\n\n# Mathtext demo\nfig, ax = plt.subplots()\nax.plot(range(10))\nax.set_title(r'$\\ddot{o}\\acute{e}\\grave{e}\\hat{O}'\n r'\\breve{i}\\bar{A}\\tilde{n}\\vec{q}$', fontsize=20)\n\n# Shorthand is also supported and curly braces are optional\nax.set_xlabel(r\"\"\"$\\\"o\\ddot o \\'e\\`e\\~n\\.x\\^y$\"\"\", fontsize=20)\nax.text(4, 0.5, r\"$F=m\\ddot{x}$\")\nfig.tight_layout()\n\n# Unicode demo\nfig, ax = plt.subplots()\nax.set_title(\"GISCARD CHAHUT\u00c9 \u00c0 L'ASSEMBL\u00c9E\")\nax.set_xlabel(\"LE COUP DE D\u00c9 DE DE GAULLE\")\nax.set_ylabel('Andr\u00e9 was here!')\nax.text(0.2, 0.8, 'Institut f\u00fcr Festk\u00f6rperphysik', rotation=45)\nax.text(0.4, 0.2, 'AVA (check kerning)')\n\nplt.show()"
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],
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"outputs": []
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}
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],
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"nbformat_minor": 0,
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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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"version": "3.5.2",
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"nbconvert_exporter": "python",
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"mimetype": "text/x-python",
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"codemirror_mode": {
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"version": 3,
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"name": "ipython"
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},
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"file_extension": ".py",
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"pygments_lexer": "ipython3",
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"name": "python"
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}
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},
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"nbformat": 4
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}

‎_downloads/accented_text.py

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# -*- coding: utf-8 -*-
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r"""
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=================================
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Using accented text in matplotlib
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=================================
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Matplotlib supports accented characters via TeX mathtext or unicode.
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Using mathtext, the following accents are provided: \hat, \breve, \grave, \bar,
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\acute, \tilde, \vec, \dot, \ddot. All of them have the same syntax,
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e.g., to make an overbar you do \bar{o} or to make an o umlaut you do
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\ddot{o}. The shortcuts are also provided, e.g.,: \"o \'e \`e \~n \.x
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\^y
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"""
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from __future__ import unicode_literals
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import matplotlib.pyplot as plt
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# Mathtext demo
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fig, ax = plt.subplots()
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ax.plot(range(10))
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ax.set_title(r'$\ddot{o}\acute{e}\grave{e}\hat{O}'
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r'\breve{i}\bar{A}\tilde{n}\vec{q}$', fontsize=20)
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# Shorthand is also supported and curly braces are optional
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ax.set_xlabel(r"""$\"o\ddot o \'e\`e\~n\.x\^y$""", fontsize=20)
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ax.text(4, 0.5, r"$F=m\ddot{x}$")
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fig.tight_layout()
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# Unicode demo
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fig, ax = plt.subplots()
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ax.set_title("GISCARD CHAHUTÉ À L'ASSEMBLÉE")
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ax.set_xlabel("LE COUP DE DÉ DE DE GAULLE")
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ax.set_ylabel('André was here!')
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ax.text(0.2, 0.8, 'Institut für Festkörperphysik', rotation=45)
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ax.text(0.4, 0.2, 'AVA (check kerning)')
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plt.show()

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