Plotting with Matplotlib.ipynb
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MinRK
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r11536 | { | |
"metadata": { | |||
Brian E. Granger
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r16108 | "name": "", | |
"signature": "sha256:74dbf5caa25c937be70dfe2ab509783a01f4a2044850d7044e729300a8c3644d" | |||
MinRK
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r11536 | }, | |
"nbformat": 3, | |||
"nbformat_minor": 0, | |||
"worksheets": [ | |||
{ | |||
"cells": [ | |||
{ | |||
"cell_type": "heading", | |||
"level": 1, | |||
"metadata": {}, | |||
"source": [ | |||
"Plotting with Matplotlib" | |||
] | |||
}, | |||
{ | |||
"cell_type": "markdown", | |||
"metadata": {}, | |||
"source": [ | |||
"IPython works with the [Matplotlib](http://matplotlib.org/) plotting library, which integrates Matplotlib with IPython's display system and event loop handling." | |||
] | |||
}, | |||
{ | |||
"cell_type": "heading", | |||
"level": 2, | |||
"metadata": {}, | |||
"source": [ | |||
"matplotlib mode" | |||
] | |||
}, | |||
{ | |||
"cell_type": "markdown", | |||
"metadata": {}, | |||
"source": [ | |||
"To make plots using Matplotlib, you must first enable IPython's matplotlib mode.\n", | |||
"\n", | |||
"To do this, run the `%matplotlib` magic command to enable plotting in the current Notebook.\n", | |||
"\n", | |||
"This magic takes an optional argument that specifies which Matplotlib backend should be used. Most of the time, in the Notebook, you will want to use the `inline` backend, which will embed plots inside the Notebook:" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"collapsed": false, | |||
"input": [ | |||
"%matplotlib inline" | |||
], | |||
"language": "python", | |||
"metadata": {}, | |||
"outputs": [], | |||
"prompt_number": 1 | |||
}, | |||
{ | |||
"cell_type": "markdown", | |||
"metadata": {}, | |||
"source": [ | |||
"You can also use Matplotlib GUI backends in the Notebook, such as the Qt backend (`%matplotlib qt`). This will use Matplotlib's interactive Qt UI in a floating window to the side of your browser. Of course, this only works if your browser is running on the same system as the Notebook Server. You can always call the `display` function to paste figures into the Notebook document." | |||
] | |||
}, | |||
{ | |||
"cell_type": "heading", | |||
"level": 2, | |||
"metadata": {}, | |||
"source": [ | |||
"Making a simple plot" | |||
] | |||
}, | |||
{ | |||
"cell_type": "markdown", | |||
"metadata": {}, | |||
"source": [ | |||
"With matplotlib enabled, plotting should just work." | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"collapsed": false, | |||
"input": [ | |||
"import matplotlib.pyplot as plt\n", | |||
"import numpy as np" | |||
], | |||
"language": "python", | |||
"metadata": {}, | |||
"outputs": [], | |||
"prompt_number": 2 | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"collapsed": false, | |||
"input": [ | |||
"x = np.linspace(0, 3*np.pi, 500)\n", | |||
"plt.plot(x, np.sin(x**2))\n", | |||
"plt.title('A simple chirp');" | |||
], | |||
"language": "python", | |||
"metadata": {}, | |||
"outputs": [ | |||
{ | |||
"metadata": {}, | |||
"output_type": "display_data", | |||
Brian E. Granger
|
r16108 | "png": 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GRkJ8jthyKEhHewcgpK/qQBOKsOwdPxsG0BfIFbF3VHn6UeXpu9k7LIodcK+9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\nCcy6efRRKX1ZCJP+d7/7XbzyyiuYO3c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nJ/H0L7ooujlR2FNK3303HMtp7lyy0Bw8SHYt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\n", | |
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r11536 | "text": [ | |
Brian E. Granger
|
r16108 | "<matplotlib.figure.Figure at 0x1084796d0>" | |
MinRK
|
r11536 | ] | |
} | |||
], | |||
"prompt_number": 3 | |||
}, | |||
{ | |||
"cell_type": "markdown", | |||
"metadata": {}, | |||
"source": [ | |||
"These images can be resized by dragging the handle in the lower right corner. Double clicking will return them to their original size." | |||
] | |||
}, | |||
{ | |||
"cell_type": "markdown", | |||
"metadata": {}, | |||
"source": [ | |||
"One thing to be aware of is that by default, the `Figure` object is cleared at the end of each cell, so you will need to issue all plotting commands for a single figure in a single cell." | |||
] | |||
}, | |||
{ | |||
"cell_type": "heading", | |||
"level": 2, | |||
"metadata": {}, | |||
"source": [ | |||
"Loading Matplotlib demos with %load" | |||
] | |||
}, | |||
{ | |||
"cell_type": "markdown", | |||
"metadata": {}, | |||
"source": [ | |||
"IPython's `%load` magic can be used to load any Matplotlib demo by its URL:" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"collapsed": false, | |||
"input": [ | |||
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r15188 | "%load http://matplotlib.org/mpl_examples/showcase/integral_demo.py" | |
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r11536 | ], | |
"language": "python", | |||
"metadata": {}, | |||
"outputs": [], | |||
"prompt_number": 4 | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"collapsed": false, | |||
"input": [ | |||
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r15188 | "\"\"\"\n", | |
"Plot demonstrating the integral as the area under a curve.\n", | |||
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r11536 | "\n", | |
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r15188 | "Although this is a simple example, it demonstrates some important tweaks:\n", | |
"\n", | |||
" * A simple line plot with custom color and line width.\n", | |||
" * A shaded region created using a Polygon patch.\n", | |||
" * A text label with mathtext rendering.\n", | |||
" * figtext calls to label the x- and y-axes.\n", | |||
" * Use of axis spines to hide the top and right spines.\n", | |||
" * Custom tick placement and labels.\n", | |||
"\"\"\"\n", | |||
"import numpy as np\n", | |||
"import matplotlib.pyplot as plt\n", | |||
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r11536 | "from matplotlib.patches import Polygon\n", | |
"\n", | |||
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r15188 | "\n", | |
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r11536 | "def func(x):\n", | |
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r15188 | " return (x - 3) * (x - 5) * (x - 7) + 85\n", | |
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r11536 | "\n", | |
"\n", | |||
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r15188 | "a, b = 2, 9 # integral limits\n", | |
"x = np.linspace(0, 10)\n", | |||
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r11536 | "y = func(x)\n", | |
"\n", | |||
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r15188 | "fig, ax = plt.subplots()\n", | |
"plt.plot(x, y, 'r', linewidth=2)\n", | |||
"plt.ylim(ymin=0)\n", | |||
"\n", | |||
"# Make the shaded region\n", | |||
"ix = np.linspace(a, b)\n", | |||
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r11536 | "iy = func(ix)\n", | |
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r15188 | "verts = [(a, 0)] + list(zip(ix, iy)) + [(b, 0)]\n", | |
"poly = Polygon(verts, facecolor='0.9', edgecolor='0.5')\n", | |||
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r11536 | "ax.add_patch(poly)\n", | |
"\n", | |||
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r15188 | "plt.text(0.5 * (a + b), 30, r\"$\\int_a^b f(x)\\mathrm{d}x$\",\n", | |
" horizontalalignment='center', fontsize=20)\n", | |||
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r11536 | "\n", | |
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r15188 | "plt.figtext(0.9, 0.05, '$x$')\n", | |
"plt.figtext(0.1, 0.9, '$y$')\n", | |||
"\n", | |||
"ax.spines['right'].set_visible(False)\n", | |||
"ax.spines['top'].set_visible(False)\n", | |||
"ax.xaxis.set_ticks_position('bottom')\n", | |||
"\n", | |||
"ax.set_xticks((a, b))\n", | |||
"ax.set_xticklabels(('$a$', '$b$'))\n", | |||
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|
r11536 | "ax.set_yticks([])\n", | |
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|
r15188 | "\n", | |
"plt.show()\n" | |||
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|
r11536 | ], | |
"language": "python", | |||
"metadata": {}, | |||
"outputs": [ | |||
{ | |||
Brian E. Granger
|
r16108 | "metadata": {}, | |
MinRK
|
r11536 | "output_type": "display_data", | |
Brian E. Granger
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r16108 | "png": 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J1tUp5VJFvnsjmM8Ha9bg/egj3B9/TNzOndiA4pUkCho3xp2SAm3b4r7yStyXXgrx8VZW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\niISh/weZPyRnS1m/IAAAAABJRU5ErkJggg==\n", | |
MinRK
|
r11536 | "text": [ | |
Brian E. Granger
|
r16108 | "<matplotlib.figure.Figure at 0x10848fb10>" | |
MinRK
|
r11536 | ] | |
} | |||
], | |||
"prompt_number": 5 | |||
} | |||
], | |||
"metadata": {} | |||
} | |||
] | |||
} |