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‎2.2.5/Matplotlib.pdf

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‎2.2.5/_downloads/001a0398aba5436bf44cc545a5581e7d/axis_direction_demo_step01.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# Axis Direction Demo Step01\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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"import matplotlib.pyplot as plt\nimport mpl_toolkits.axisartist as axisartist\n\n\ndef setup_axes(fig, rect):\n ax = axisartist.Subplot(fig, rect)\n fig.add_axes(ax)\n\n ax.set_ylim(-0.1, 1.5)\n ax.set_yticks([0, 1])\n\n ax.axis[:].set_visible(False)\n\n ax.axis[\"x\"] = ax.new_floating_axis(1, 0.5)\n ax.axis[\"x\"].set_axisline_style(\"->\", size=1.5)\n\n return ax\n\n\nfig = plt.figure(figsize=(3, 2.5))\nfig.subplots_adjust(top=0.8)\nax1 = setup_axes(fig, \"111\")\n\nax1.axis[\"x\"].set_axis_direction(\"left\")\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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}

‎2.2.5/_downloads/005513053d53651f2ffe511840ceece5/simple_axesgrid2.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# Simple Axesgrid2\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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"import matplotlib.pyplot as plt\nfrom mpl_toolkits.axes_grid1 import ImageGrid\n\n\ndef get_demo_image():\n import numpy as np\n from matplotlib.cbook import get_sample_data\n f = get_sample_data(\"axes_grid/bivariate_normal.npy\", asfileobj=False)\n z = np.load(f)\n # z is a numpy array of 15x15\n return z, (-3, 4, -4, 3)\n\nF = plt.figure(1, (5.5, 3.5))\ngrid = ImageGrid(F, 111, # similar to subplot(111)\n nrows_ncols=(1, 3),\n axes_pad=0.1,\n add_all=True,\n label_mode=\"L\",\n )\n\nZ, extent = get_demo_image() # demo image\n\nim1 = Z\nim2 = Z[:, :10]\nim3 = Z[:, 10:]\nvmin, vmax = Z.min(), Z.max()\nfor i, im in enumerate([im1, im2, im3]):\n ax = grid[i]\n ax.imshow(im, origin=\"lower\", vmin=vmin,\n vmax=vmax, interpolation=\"nearest\")\n\nplt.draw()\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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}

‎2.2.5/_downloads/009bf417d97f3090a82134095e2af059/custom_shaded_3d_surface.py

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"""
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=======================================
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Custom hillshading in a 3D surface plot
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=======================================
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Demonstrates using custom hillshading in a 3D surface plot.
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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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# Load and format data
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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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# Set up plot
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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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plt.show()

‎2.2.5/_downloads/00d16fb53a414217c5b2c46c5561d6dd/quadmesh_demo.py

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"""
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=============
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QuadMesh Demo
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=============
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`~.axes.Axes.pcolormesh` uses a `~matplotlib.collections.QuadMesh`,
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a faster generalization of `~.axes.Axes.pcolor`, but with some restrictions.
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This demo illustrates a bug in quadmesh with masked data.
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"""
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import copy
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from matplotlib import cm, colors, pyplot as plt
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import numpy as np
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n = 12
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x = np.linspace(-1.5, 1.5, n)
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y = np.linspace(-1.5, 1.5, n * 2)
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X, Y = np.meshgrid(x, y)
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Qx = np.cos(Y) - np.cos(X)
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Qz = np.sin(Y) + np.sin(X)
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Z = np.sqrt(X**2 + Y**2) / 5
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Z = (Z - Z.min()) / (Z.max() - Z.min())
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# The color array can include masked values.
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Zm = np.ma.masked_where(np.abs(Qz) < 0.5 * np.max(Qz), Z)
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fig, axs = plt.subplots(nrows=1, ncols=3)
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axs[0].pcolormesh(Qx, Qz, Z, shading='gouraud')
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axs[0].set_title('Without masked values')
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# You can control the color of the masked region. We copy the default colormap
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# before modifying it.
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cmap = copy.copy(cm.get_cmap(plt.rcParams['image.cmap']))
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cmap.set_bad('y', 1.0)
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axs[1].pcolormesh(Qx, Qz, Zm, shading='gouraud', cmap=cmap)
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axs[1].set_title('With masked values')
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# Or use the default, which is transparent.
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axs[2].pcolormesh(Qx, Qz, Zm, shading='gouraud')
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axs[2].set_title('With masked values')
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fig.tight_layout()
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plt.show()
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#############################################################################
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#
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# ------------
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#
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# References
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# """"""""""
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#
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# The use of the following functions and methods is shown in this example:
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import matplotlib
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matplotlib.axes.Axes.pcolormesh
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matplotlib.pyplot.pcolormesh

‎2.2.5/_downloads/01584afcaabbf8ed492b7c42fd162b0a/fill_between_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# Filling the area between lines\n\n\nThis example shows how to use ``fill_between`` to color between lines based on\nuser-defined logic.\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\nx = np.arange(0.0, 2, 0.01)\ny1 = np.sin(2 * np.pi * x)\ny2 = 1.2 * np.sin(4 * np.pi * x)"
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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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"fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True)\n\nax1.fill_between(x, 0, y1)\nax1.set_ylabel('between y1 and 0')\n\nax2.fill_between(x, y1, 1)\nax2.set_ylabel('between y1 and 1')\n\nax3.fill_between(x, y1, y2)\nax3.set_ylabel('between y1 and y2')\nax3.set_xlabel('x')"
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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 fill between y1 and y2 where a logical condition is met. Note\nthis is different than calling\n``fill_between(x[where], y1[where], y2[where] ...)``\nbecause of edge effects over multiple contiguous regions.\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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"fig, (ax, ax1) = plt.subplots(2, 1, sharex=True)\nax.plot(x, y1, x, y2, color='black')\nax.fill_between(x, y1, y2, where=y2 >= y1, facecolor='green', interpolate=True)\nax.fill_between(x, y1, y2, where=y2 <= y1, facecolor='red', interpolate=True)\nax.set_title('fill between where')\n\n# Test support for masked arrays.\ny2 = np.ma.masked_greater(y2, 1.0)\nax1.plot(x, y1, x, y2, color='black')\nax1.fill_between(x, y1, y2, where=y2 >= y1,\n facecolor='green', interpolate=True)\nax1.fill_between(x, y1, y2, where=y2 <= y1,\n facecolor='red', interpolate=True)\nax1.set_title('Now regions with y2>1 are masked')"
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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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"This example illustrates a problem; because of the data\ngridding, there are undesired unfilled triangles at the crossover\npoints. A brute-force solution would be to interpolate all\narrays to a very fine grid before plotting.\n\n"
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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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"Use transforms to create axes spans where a certain condition is satisfied:\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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"fig, ax = plt.subplots()\ny = np.sin(4 * np.pi * x)\nax.plot(x, y, color='black')\n\n# use data coordinates for the x-axis and the axes coordinates for the y-axis\nimport matplotlib.transforms as mtransforms\ntrans = mtransforms.blended_transform_factory(ax.transData, ax.transAxes)\ntheta = 0.9\nax.axhline(theta, color='green', lw=2, alpha=0.5)\nax.axhline(-theta, color='red', lw=2, alpha=0.5)\nax.fill_between(x, 0, 1, where=y > theta,\n facecolor='green', alpha=0.5, transform=trans)\nax.fill_between(x, 0, 1, where=y < -theta,\n facecolor='red', alpha=0.5, transform=trans)\n\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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"name": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.1"
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"nbformat_minor": 0
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}

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