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update docs for 2.2.4
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‎2.2.4/Matplotlib.pdf

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‎2.2.4/_downloads/00092e8501d7b746e7fd736473feb0f6/scatter_star_poly.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# Scatter Star Poly\n\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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"outputs": [],
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"source": [
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"import numpy as np\nimport matplotlib.pyplot as plt\n\n# Fixing random state for reproducibility\nnp.random.seed(19680801)\n\n\nx = np.random.rand(10)\ny = np.random.rand(10)\nz = np.sqrt(x**2 + y**2)\n\nplt.subplot(321)\nplt.scatter(x, y, s=80, c=z, marker=\">\")\n\nplt.subplot(322)\nplt.scatter(x, y, s=80, c=z, marker=(5, 0))\n\nverts = np.array([[-1, -1], [1, -1], [1, 1], [-1, -1]])\nplt.subplot(323)\nplt.scatter(x, y, s=80, c=z, marker=(verts, 0))\n# equivalent:\n# plt.scatter(x, y, s=80, c=z, marker=None, verts=verts)\n\nplt.subplot(324)\nplt.scatter(x, y, s=80, c=z, marker=(5, 1))\n\nplt.subplot(325)\nplt.scatter(x, y, s=80, c=z, marker='+')\n\nplt.subplot(326)\nplt.scatter(x, y, s=80, c=z, marker=(5, 2))\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": "3.7.2"
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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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}

‎2.2.4/_downloads/0019310e9444a709ad98db6ee976416e/errorbar_features.py

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"""
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=======================================
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Different ways of specifying error bars
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=======================================
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Errors can be specified as a constant value (as shown in
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`errorbar_demo.py`). However, this example demonstrates
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how they vary by specifying arrays of error values.
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If the raw ``x`` and ``y`` data have length N, there are two options:
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Array of shape (N,):
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Error varies for each point, but the error values are
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symmetric (i.e. the lower and upper values are equal).
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Array of shape (2, N):
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Error varies for each point, and the lower and upper limits
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(in that order) are different (asymmetric case)
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In addition, this example demonstrates how to use log
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scale with error bars.
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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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# example data
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x = np.arange(0.1, 4, 0.5)
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y = np.exp(-x)
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# example error bar values that vary with x-position
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error = 0.1 + 0.2 * x
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fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True)
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ax0.errorbar(x, y, yerr=error, fmt='-o')
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ax0.set_title('variable, symmetric error')
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# error bar values w/ different -/+ errors that
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# also vary with the x-position
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lower_error = 0.4 * error
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upper_error = error
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asymmetric_error = [lower_error, upper_error]
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ax1.errorbar(x, y, xerr=asymmetric_error, fmt='o')
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ax1.set_title('variable, asymmetric error')
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ax1.set_yscale('log')
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plt.show()

‎2.2.4/_downloads/002dd88353c81f8ead8c49d917e79775/agg_buffer.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# Agg Buffer\n\n\nUse backend agg to access the figure canvas as an RGB string and then\nconvert it to an array and pass it to Pillow for rendering.\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 numpy as np\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_agg import FigureCanvasAgg\n\n\ntry:\n from PIL import Image\nexcept ImportError:\n raise SystemExit(\"Pillow must be installed to run this example\")\n\nplt.plot([1, 2, 3])\n\ncanvas = plt.get_current_fig_manager().canvas\n\nagg = canvas.switch_backends(FigureCanvasAgg)\nagg.draw()\ns = agg.tostring_rgb()\n\n# get the width and the height to resize the matrix\nl, b, w, h = agg.figure.bbox.bounds\nw, h = int(w), int(h)\n\nX = np.fromstring(s, np.uint8).reshape((h, w, 3))\n\ntry:\n im = Image.fromstring(\"RGB\", (w, h), s)\nexcept Exception:\n im = Image.frombytes(\"RGB\", (w, h), s)\n\n# Uncomment this line to display the image using ImageMagick's\n# `display` tool.\n# im.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": "3.7.2"
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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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}

‎2.2.4/_downloads/0030ea2ba073910bfaeee3324b663fd9/simple_anim.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# Animated line plot\n\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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"outputs": [],
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"source": [
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"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\n\nfig, ax = plt.subplots()\n\nx = np.arange(0, 2*np.pi, 0.01)\nline, = ax.plot(x, np.sin(x))\n\n\ndef init(): # only required for blitting to give a clean slate.\n line.set_ydata([np.nan] * len(x))\n return line,\n\n\ndef animate(i):\n line.set_ydata(np.sin(x + i / 100)) # update the data.\n return line,\n\n\nani = animation.FuncAnimation(\n fig, animate, init_func=init, interval=2, blit=True, save_count=50)\n\n# To save the animation, use e.g.\n#\n# ani.save(\"movie.mp4\")\n#\n# or\n#\n# from matplotlib.animation import FFMpegWriter\n# writer = FFMpegWriter(fps=15, metadata=dict(artist='Me'), bitrate=1800)\n# ani.save(\"movie.mp4\", writer=writer)\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": "3.7.2"
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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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}

‎2.2.4/_downloads/01d2d618b11a1582ce766e85ecc828ee/fancytextbox_demo.py

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"""
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=================
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Fancytextbox Demo
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=================
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"""
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import matplotlib.pyplot as plt
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plt.text(0.6, 0.5, "test", size=50, rotation=30.,
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ha="center", va="center",
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bbox=dict(boxstyle="round",
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ec=(1., 0.5, 0.5),
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fc=(1., 0.8, 0.8),
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)
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)
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plt.text(0.5, 0.4, "test", size=50, rotation=-30.,
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ha="right", va="top",
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bbox=dict(boxstyle="square",
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ec=(1., 0.5, 0.5),
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fc=(1., 0.8, 0.8),
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)
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)
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plt.draw()
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plt.show()

‎2.2.4/_downloads/01d7fb1e5f050fc8d9c0397ba962a086/demo_tight_layout.py

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"""
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=================
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Demo Tight Layout
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=================
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"""
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import matplotlib.pyplot as plt
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import itertools
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import warnings
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fontsizes = itertools.cycle([8, 16, 24, 32])
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def example_plot(ax):
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ax.plot([1, 2])
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ax.set_xlabel('x-label', fontsize=next(fontsizes))
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ax.set_ylabel('y-label', fontsize=next(fontsizes))
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ax.set_title('Title', fontsize=next(fontsizes))
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###############################################################################
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fig, ax = plt.subplots()
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example_plot(ax)
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plt.tight_layout()
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###############################################################################
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fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2)
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example_plot(ax1)
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example_plot(ax2)
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example_plot(ax3)
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example_plot(ax4)
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plt.tight_layout()
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###############################################################################
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fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1)
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example_plot(ax1)
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example_plot(ax2)
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plt.tight_layout()
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###############################################################################
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fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2)
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example_plot(ax1)
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example_plot(ax2)
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plt.tight_layout()
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###############################################################################
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fig, axes = plt.subplots(nrows=3, ncols=3)
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for row in axes:
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for ax in row:
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example_plot(ax)
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plt.tight_layout()
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###############################################################################
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fig = plt.figure()
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ax1 = plt.subplot(221)
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ax2 = plt.subplot(223)
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ax3 = plt.subplot(122)
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example_plot(ax1)
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example_plot(ax2)
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example_plot(ax3)
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plt.tight_layout()
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###############################################################################
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fig = plt.figure()
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ax1 = plt.subplot2grid((3, 3), (0, 0))
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ax2 = plt.subplot2grid((3, 3), (0, 1), colspan=2)
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ax3 = plt.subplot2grid((3, 3), (1, 0), colspan=2, rowspan=2)
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ax4 = plt.subplot2grid((3, 3), (1, 2), rowspan=2)
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example_plot(ax1)
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example_plot(ax2)
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example_plot(ax3)
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example_plot(ax4)
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plt.tight_layout()
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plt.show()
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###############################################################################
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fig = plt.figure()
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import matplotlib.gridspec as gridspec
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gs1 = gridspec.GridSpec(3, 1)
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ax1 = fig.add_subplot(gs1[0])
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ax2 = fig.add_subplot(gs1[1])
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ax3 = fig.add_subplot(gs1[2])
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example_plot(ax1)
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example_plot(ax2)
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example_plot(ax3)
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with warnings.catch_warnings():
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warnings.simplefilter("ignore", UserWarning)
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# This raises warnings since tight layout cannot
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# handle gridspec automatically. We are going to
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# do that manually so we can filter the warning.
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gs1.tight_layout(fig, rect=[None, None, 0.45, None])
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gs2 = gridspec.GridSpec(2, 1)
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ax4 = fig.add_subplot(gs2[0])
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ax5 = fig.add_subplot(gs2[1])
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example_plot(ax4)
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example_plot(ax5)
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with warnings.catch_warnings():
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# This raises warnings since tight layout cannot
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# handle gridspec automatically. We are going to
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# do that manually so we can filter the warning.
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warnings.simplefilter("ignore", UserWarning)
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gs2.tight_layout(fig, rect=[0.45, None, None, None])
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# now match the top and bottom of two gridspecs.
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top = min(gs1.top, gs2.top)
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bottom = max(gs1.bottom, gs2.bottom)
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gs1.update(top=top, bottom=bottom)
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gs2.update(top=top, bottom=bottom)
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plt.show()

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