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@LeslieK
Created June 18, 2014 15:52
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Notes from Anatomy of Matplotlib tutorial on youtube
{
"metadata": {
"name": "AnatomyOfMatplotlib3"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": "%pylab inline",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Populating the interactive namespace from numpy and matplotlib\n"
}
],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": "Artists"
},
{
"cell_type": "raw",
"metadata": {},
"source": "Anything that can be displayed in a Figure is an \"Artist\". There are 2 main classes of Artists:\n1. primitives\n2. containers (ex: Figure, Axes, Collection)\n\nSome graphics primitives: Wedge, Arrow, Line2D, Rectangle, Ellipse, Polygon, ...\n\nContainers are objects like Figure and Axes. Containers are given primitives to draw. The plotting functions mentioned in Part I are convenience functions that generate these primities and place them into the appropriate containers. Most of those functions return artist objects (or a list of artist objects) as well as store them into the appropriate axes container.\n\nPart 2:\nThere is a wide range of properties that can be defined for your plots. These properties are processed and passed down to the primitives, for your convenience. Ultimately, you can override anything you want just by directly setting a property to the object itself."
},
{
"cell_type": "code",
"collapsed": false,
"input": "from matplotlib.collections import PatchCollection\nimport matplotlib.path as mpath\nimport matplotlib.patches as mpatches\nimport matplotlib.lines as mlines\n\nfont = 'sans-serif'\nfig = plt.figure(figsize=(2, 1)) # width x height inches\nax = plt.axes([0, 0, 1, 1]) # [left, bottom, right, top] dims relative to axes",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAALgAAABpCAYAAACJUelNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
"text": "<matplotlib.figure.Figure at 0x7435828>"
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig, ax = plt.subplots(1, 1)\nlines = plt.plot([1, 2, 3, 4], [1, 2, 3, 4], \\\n 'b', [1, 2, 3, 4], [4, 3, 2, 1], 'r')\nlines[0].set(linewidth=5)\nlines[1].set(linewidth=10, alpha=0.7)\nplt.show()",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x6f3f7f0>"
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": "# to see the properties that are set for an artist, use getp()\nfig, ax = plt.subplots(1, 1)\nprint(plt.getp(fig.patch))",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": " aa = False\n agg_filter = None\n alpha = None\n animated = False\n antialiased or aa = False\n axes = None\n bbox = Bbox('array([[ 0., 0.],\\n [ 1., 1.]])')\n children = []\n clip_box = None\n clip_on = True\n clip_path = None\n contains = None\n data_transform = BboxTransformTo(TransformedBbox(Bbox('array([[ 0.,...\n ec = (1.0, 1.0, 1.0, 1.0)\n edgecolor or ec = (1.0, 1.0, 1.0, 1.0)\n extents = Bbox('array([[ 0., 0.],\\n [ 480., 320....\n facecolor or fc = (1.0, 1.0, 1.0, 1.0)\n fc = (1.0, 1.0, 1.0, 1.0)\n figure = Figure(480x320)\n fill = True\n gid = None\n hatch = None\n height = 1\n label = \n linestyle or ls = solid\n linewidth or lw = 0.0\n ls = solid\n lw = 0.0\n patch_transform = CompositeGenericTransform(BboxTransformTo(Bbox('ar...\n path = Path([[ 0. 0.] [ 1. 0.] [ 1. 1.] [ 0. 1.] ...\n path_effects = []\n picker = None\n rasterized = None\n sketch_params = None\n snap = None\n transform = CompositeGenericTransform(CompositeGenericTransfor...\n transformed_clip_path_and_affine = (None, None)\n url = None\n verts = [[ 0. 0.] [ 480. 0.] [ 480. 320.] [ ...\n visible = True\n width = 1\n window_extent = Bbox('array([[ 0., 0.],\\n [ 480., 320....\n x = 0\n xy = (0, 0)\n y = 0\n zorder = 1\nNone\n"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x6e2a668>"
}
],
"prompt_number": 12
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": "Collections"
},
{
"cell_type": "raw",
"metadata": {},
"source": "In addition to the Figure and Axes containers, there is another special type of container called a 'Collection'. A Collection contains a list of primitives of the same kind that should all be treated similarly. For ex, a CircleCollection would have a list of Circle objects all with the same color, size, and edge width. Individual values for artists in the collection can also be set.\n\nCollections simplify the management of the artists by grouping artists by type. All plotting functions return a list of artists or a collection of artists or individual artists. "
},
{
"cell_type": "code",
"collapsed": false,
"input": "from matplotlib.collections import LineCollection\nfig, ax = plt.subplots(1, 1) # returns 2 artists: fig and ax\nlc = LineCollection([\n [(4, 10), (16, 10)],\n [(2, 2), (10, 15), (6, 7)],\n [(14, 3), (1, 1), (3, 5)]\n ]) # make a collection by hand\n# set properties on the collection\nlc.set_color('r')\nlc.set_linewidth(5)\nax.add_collection(lc)\nax.set_xlim(0, 18)\nax.set_ylim(0, 18)\nplt.show()",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x74ce9b0>"
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Set individual properties in a collection\nfig, ax = plt.subplots(1, 1)\nlc = LineCollection([\n [(4, 10), (16, 10)],\n [(2, 2), (10, 15), (6, 7)],\n [(14, 3), (1, 1), (3, 5)]\n ])\n# set unique properties (color, linewidth) to collection elements\nlc.set_color(['r', 'blue', (0.2, 0.9, 0.3)]) # RBG tuple\nlc.set_linewidth([4, 3, 6])\n\n# all other properties remain unchanged and the same for the entire collection\nax.add_collection(lc)\nax.set_xlim(0, 18)\nax.set_ylim(0, 18)\nplt.show()",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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yCoKCgtDcLP9eAe6saevWrUhISEBSUhLWrFnj1Zp6quvEiROYMmUKUlNTMXny\nZJw8edL5QeR8sOlMR0eHiIuLEzU1NaKtrU2kpKSI7777zt3fxiXnz58Xp0+fFkII8csvv4j4+HjF\nazJ75ZVXxMKFC0VmZqbSpQghhMjNzRU7duwQQgjR3t4uDAaDovXU1NSIMWPGiKtXrwohhMjJyRE7\nd+5UpJbDhw+LiooKkZSUZNm3evVq8eKLLwohhCgqKhJr1qxRvKbPP/9cmEwmIYQQa9as8YmahBDi\nxx9/FLNmzRK33HKLaGpqUrymQ4cOiZkzZ4q2tjYhhBAXLlzwak091TVjxgxRWloqhBBi//79QqfT\nOT2G28/EffFCoOHDh2PixIkAgNDQUCQkJODcuXOK1gQA9fX12L9/Px555BGfGNvb2tqKr776Cvn5\n+QCkzz6GDh2qaE1hYWEICQmB0WhER0cHjEYjRo4cqUgt06dPR3h4uN2+vXv3YvHixQCAxYsX4+OP\nP1a8pvT0dAQFSb/aaWlpqK+vV7wmAHjyySfx0ksvebUWM0c1bd++Hc888wxCQkIAAFFRUT5R14gR\nIyx/ezIYDL3+vLu9ifv6hUC1tbU4ffo00tLSlC4FK1euxKZNmyy/cEqrqalBVFQU8vLyMGnSJDz6\n6KMwGo2K1hQREYFVq1Zh1KhRuPnmmzFs2DDMnDlT0ZpsNTY2QqvVAgC0Wi0aGxsVrshecXEx7rvv\nPqXLwJ49exATE4MJEyYoXYrFDz/8gMOHD2Pq1KnQ6XQ4deqU0iUBAIqKiiw/86tXr0ZhYaHT17u9\ne/jyhUCXL1/G3LlzsWXLFoSGhipayyeffILo6Gikpqb6xFk4IC0draiowF/+8hdUVFTgxhtvRFFR\nkaI1VVVV4bXXXkNtbS3OnTuHy5cvY/fu3YrW1BONRuNTP/8bNmzAwIEDsXDhQkXrMBqN2LhxI55/\n/nnLPl/4me/o6EBLSwuOHz+OTZs2IScnR+mSAABLlizB66+/jh9//BGvvvqq5W/GPXF7Ex85ciTq\n6uos23V1dYiJiXH3t3FZe3s75syZg4cffhgPPvig0uWgvLwce/fuxZgxY7BgwQIcOnQIubm5itYU\nExODmJgYTJ48GQAwd+5cVFRUKFrTqVOncNdddyEyMhIDBgxAdnY2ysvLFa3JllarRUNDAwDg/Pnz\niI6OVrgiyc6dO7F//36f+AOvqqoKtbW1SElJwZgxY1BfX4/bb78dFy5cULSumJgYZGdnAwAmT56M\noKAgNDWVtIStAAACBElEQVQ1KVoTIEXSs2fPBiD9Dp44ccLp693exO+44w788MMPqK2tRVtbGz74\n4ANkZWW5+9u4RAiBJUuWIDExEStWrFC0FrONGzeirq4ONTU1eP/99/HHP/4R7777rqI1DR8+HLGx\nsTh79iwA4ODBgxg/fryiNd122204fvw4rly5AiEEDh48iMTEREVrspWVlYVdu3YBAHbt2uUTJwil\npaXYtGkT9uzZg8GDBytdDpKTk9HY2IiamhrU1NQgJiYGFRUViv+B9+CDD+LQoUMAgLNnz6KtrQ2R\nkZGK1gQAY8eORVlZGQDg0KFDiI+Pd/4GT3ziun//fhEfHy/i4uLExo0bPfEtXPLVV18JjUYjUlJS\nxMSJE8XEiRPFZ599pnRZFnq93mdWp/zzn/8Ud9xxh5gwYYKYPXu24qtThBDixRdfFImJiSIpKUnk\n5uZaVhN42/z588WIESNESEiIiImJEcXFxaKpqUncfffdYty4cSI9PV20tLQoWtOOHTvE2LFjxahR\noyw/63/+858VqWngwIGW/062xowZ4/XVKY5qamtrEw8//LBISkoSkyZNEl9++aVXa7Kty/Zn6uTJ\nk2LKlCkiJSVFTJ06VVRUVDg9Bi/2ISJSMd9YFkFERLKwiRMRqRibOBGRirGJExGpGJs4EZGKsYkT\nEakYmzgRkYqxiRMRqdj/Bzf1Rh8dbr0NAAAAAElFTkSuQmCC\n",
"text": "<matplotlib.figure.Figure at 0x71a7358>"
}
],
"prompt_number": 15
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": "mpl_toolkits"
},
{
"cell_type": "raw",
"metadata": {},
"source": "In addition to the core matplotlib library, there are a few additional utilites that are set apart from matplotlib proper, but are often shipped with matplotlib.\n\nBasemap - shipped separately due to size of mapping data\nmplot3d - shipped with matplotlib to provide rudimentary 3D plots\naxes_grid1 - an enhanced SubplotAxes. Very enhanced."
},
{
"cell_type": "code",
"collapsed": false,
"input": "from mpl_toolkits.mplot3d import Axes3D, axes3d\n\nfig, ax = plt.subplots(1, 1, subplot_kw={'projection': '3d'})\nX, Y, Z = axes3d.get_test_data(0.05)\nax.plot_wireframe(X, Y, Z, rstride=10, cstride=10)\n\nplt.show()",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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+eo1TTvFx5506fn9Z0W75cgfjxxcTDluFK/s5iwLf0KE6t99ued2uXKmQn2+k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\neRAu1f4AAAAASUVORK5CYII=\n",
"text": "<matplotlib.figure.Figure at 0x6fcfdd8>"
}
],
"prompt_number": 19
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": "axes_grid1"
},
{
"cell_type": "raw",
"metadata": {},
"source": "One useful feature: automatic allocation of space for colorbars"
},
{
"cell_type": "code",
"collapsed": false,
"input": "from mpl_toolkits.axes_grid1 import AxesGrid\ngrid = AxesGrid(fig, 111, # similar to subplot(111)\n nrows_ncols = (2, 2),\n axes_pad = 0.2,\n share_all=True,\n label_mode = 'L', # similar to 'label_outer'\n cbar_location = 'right',\n cbar_mode='single',\n )\nextent = (-3, 4, -4, 3)\nfor i in range(4):\n im = grid[i].imshow(Z, extent=extent, interpolation='nearest')\n\ngrid.cbar_axes[0].colorbar(im)\nplt.show()",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": "",
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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