From e7b3310c996498905202b21d7f8d95e958f33c13 Mon Sep 17 00:00:00 2001 From: Paul Ivanov Date: Mon, 30 Jan 2012 10:37:26 -0800 Subject: [PATCH] cleared out elaborate example and added to it --- elaborate_example.ipynb | 718 +++++++++++----------------------------- 1 file changed, 198 insertions(+), 520 deletions(-) diff --git a/elaborate_example.ipynb b/elaborate_example.ipynb index d612fb2..ec80036 100644 --- a/elaborate_example.ipynb +++ b/elaborate_example.ipynb @@ -9,11 +9,10 @@ { "cell_type": "markdown", "source": [ - "Elaborate Matplotlib Plots: An XKCD Re-enactment", - "------------------", + "# Elaborate Matplotlib Plots: ", + "An [XKCD](http://xkcd.com/231) Re-enactment by [Paul Ivanov](http://pirsquared.org)", "", - "![cat proximity](http://imgs.xkcd.com/comics/cat_proximity.png)", - "" + "![cat proximity](http://imgs.xkcd.com/comics/cat_proximity.png)" ] }, { @@ -24,28 +23,8 @@ "# xkcd.com/231" ], "language": "python", - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "", - "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].", - "For more information, type 'help(pylab)'." - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "ax = plt.gca() # gca = get current axes" - ], - "language": "python", "outputs": [], - "prompt_number": 10 + "prompt_number": 1 }, { "cell_type": "code", @@ -59,94 +38,51 @@ "plt.plot(x,inan)" ], "language": "python", - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 6, - "text": [ - "[]" - ] - }, - { - "output_type": "display_data", - "png": 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Y+uJSuaOU4Z0VEVUprVuLae5duojXa9bwLqsy4jPisf3CdrzXXv8fWa8MlYvVvHnz0Lx5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\nxMRovy8WKyIiUomxMTB6NLB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- } - ], - "prompt_number": 6 + "outputs": [], + "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ + "# Will Smith's movie that nobody saw...", "plt.plot(x,intel, label=\"INTELLIGENCE\")", "plt.plot(x,inan, label=\"INANITY OF STATEMENTS\")", + "", "plt.legend()" ], "language": "python", - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 8, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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8Vu81rUOxOklWQogir1gxWLkSypZV52Ldv691RI+LTozmw60fMqPdDHR2Re+/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\nUV7nsC5evJiePXtaMcLcGXMNAQEB3L9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- } - ], - "prompt_number": 8 + "outputs": [], + "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ + "# legend? detour", "plt.plot(x,intel, label=\"INTELLIGENCE\")", "plt.plot(x,inan, label=\"INANITY OF STATEMENTS\")", + "", "plt.legend(loc='best')" ], "language": "python", - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 9, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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6moiSQa/oGfTLIBpWasiLjXP+br36KlSsCK+9plFwRZViIXlVPXHiREs1WWgB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\nEMIiMvWZLDi0gMlbJxPoE8i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- } - ], - "prompt_number": 9 + "outputs": [], + "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ - "#legend detour", + "# Label our abcissa - stay tuned for ordinates via twinx()", "plt.plot(x,intel, label=\"INTELLIGENCE\")", "plt.plot(x,inan, label=\"INANITY OF STATEMENTS\")", "plt.legend(loc='best')", + "", "plt.xlabel(\"HUMAN PROXIMITY TO CAT\")" ], "language": "python", - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 10, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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KisryNRFFg0bR0O/3ftQpV4eh9TL+bo0aBWXLwvvvmyi4gkoxkOyqnjBhgqGa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\n9OjRDM/56quvlOHDhyuKoijr1q1TOnb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- } - ], - "prompt_number": 10 + "outputs": [], + "prompt_number": 5 }, { "cell_type": "code", @@ -157,25 +93,13 @@ "plt.plot(x,inan, label=\"INANITY OF STATEMENTS\")", "plt.legend(loc='best')", "plt.xlabel(\"HUMAN PROXIMITY TO CAT\")", + "", "plt.yticks([])", "plt.xticks([0,.8],['FAR','NEAR'])" ], "language": "python", - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 18, - "text": [ - "([, ],", - " )" - ] - }, - { - "output_type": "display_data", - "png": 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sa68EGG8K+uyWt9+/fz8RERH6X/C9evVi7NixhIWF4evra/De8ePH4+Xlxd9/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\nGgAQHh5OkyZNDN6TsS5PVFQUFhYWTJs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- } - ], - "prompt_number": 18 + "outputs": [], + "prompt_number": 6 }, { "cell_type": "code", @@ -189,18 +113,14 @@ "plt.yticks([])", "plt.xticks([0,.8],['FAR','NEAR'])", "ax = plt.gca()", + "", "ax.spines['top'].set_visible(False)", "ax.spines['right'].set_visible(False)", "l.draw_frame(False)" ], "language": "python", - "outputs": [ - { - "output_type": "display_data", - "png": 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PyZIlMzRJAqSmphIVFUXJkiWB3E1N//777/P++++TlpbG+vXrCQgIwNfXl44d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\nXAAAAABJRU5ErkJggg==\n" - } - ], - "prompt_number": 21 + "outputs": [], + "prompt_number": 7 }, { "cell_type": "code", @@ -217,17 +137,12 @@ "ax.spines['top'].set_visible(False)", "ax.spines['right'].set_visible(False)", "l.draw_frame(False)", - "ax.xaxis.tick_bottom()", - "" + "", + "ax.xaxis.tick_bottom()" ], "language": "python", - "outputs": [ - { - "output_type": "display_data", - "png": 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ioqIoWbIkkLul6d9//33ef/990tLS2LBhAwEBAfj6+tKxY8csX5OQkMDq1atZ\nsGABAH5+fnh5ebF8+XKGDx/+wmP27Nkz038ApDMzM6Nv376M/ef/+KlTp2boRZmZmfHDDz/wwQcf\nZHusl/0d+fv74+/vD6inBbt27cqECROYNm1art5HFG1bLm7hmwPfcGjAoRzNpG5mBr/8An5+0KaN\nOo+gyD3Nwio/Qya/ZLW8/aJFi1AURT8q7Nn9z1/ncnNzY8SIEYwbNy7P9eTktN3rr7/OpEmTSEpK\nMgi2rVu3YmZmRuPGLz/bs06n45133qFu3bqcPXs227Bat24dsbGxDBw4UD8SMCYmhkWLFr0wrHI6\nC0qzZs24c+cOOp2Opk2bcvklb2R5/neUm+te6fz8/OjWrRshISEvVYMomv6M+JN+G/qxqdcmvJy9\ncvw6V1d1SZFeveDkSShVquBqNFVyGvA5zy9vn5iYSFBQEL/88gshISH69sMPP7B8+XKD4dTpRo4c\nyeHDhzl37lyB19unTx/c3d3p3bu3voe1f/9+Bg8ezKRJk3BwcMjV+y1dupSdO3eSlJREWloav//+\nO6dPn6ZBg4xznD37R37RokUMGDCA0NBQ/c/o4MGDhISEEBoamu0xcxMWmzZtYuPGjXl+r2d/R1ld\nb3jWoUOHWLJkif6/i/Pnz7Np0yYaNmyYs8JFkXfpwSXeDnyb+W/Pf6klP1q0UFcZ7tcPZIa63Cv2\nYfWi5e3Xr1+Pvb09ffv2xcPDQ9/69+9PSkoKO3bsyPAeDg4OjBkzhuj0KZgzOc7L1JaZ9GXpS5Ys\nia+vLw4ODgwePJjJkyczatSoXB0PwNbWlvHjx+Ph4UGJEiUYMWIE06ZNy3ANKb0+gIiICP744w9G\njBhh8DOqV68ebdu2zfb0Xvr7rFy50uD+KEdHR/31tmd/BjVr1qRGjRoZakg3dOhQg/d5Nkxe9Dt6\n9nnPv6+joyPLly/H29sbe3t7WrduzZtvvsn48eOz/WzCNNx9dJe2y9oyqeUk3q729ku/z4QJ8OAB\nzJuXj8UVEzKRrRBCZCMuKY6Wi1rSsWpHJracmOf3O3cOmjeHI0egcuW811dcSFgJIUQWElMS6bii\nIxWdKzKvw7xcnx3JyowZsGYN7N0L5ub58pYmr9ifBhRCiMwkpyYTsDoAFxsXfmr/U74FFcDw4WBh\nAdnMQyCeIz0rIYR4TpqSRp91fYhJjGFdz3VYmVu9+EW5dO0avPoq7Nmj3jwssic9KyGEeIaiKHy0\n9SMiYiOoEAxuAAAcKElEQVRY3X11gQQVQMWK8NVX6ujAlJQCOYRJkbASQoh/KIrCxzs/5sStE2zq\ntQlbS9sCPd7//A84OanXsET25DSgEEKgBtXonaPZE7aHXX124WLrUijHvXpVPR14+DBUqVIohyyS\npGclhCj2FEVhzK4x/HHtD3b22VloQQXqvIGffabOISj/7s+ahJUQolhTFIVPdn3C7qu72dV3F662\nri9+UT4bPhzi4yGLlYAEchpQCFGMKYrCqN9HsSdsD7v77tYkqNKdPg2tWsGpU1C2rGZlGC0JKyFE\nsZSalsqQrUMIuRPCtt7bCvXUX1a++AJCQ2HtWq0rMT4SVkKIYiclLYV+6/sRERfBxoCNOFjnbsLn\ngpKYCLVrw6xZ8NZbWldjXCSshBDFSlJKEr3W9CIxJZE1PdYU+PD03Pr9dxg0SO1h2dlpXY3xkAEW\nQohiIzYplreWv4W5zpz1AeuNLqhAXaCxQQP4+mutKzEu0rMSQhQLdx7dod2ydrxW7jVmt52Nuc54\nZ5CNiABfXzh4EKpV07oa4yA9KyGEybscdZmmvzWlS/UuzGk3x6iDCtTRgOPGwUcfyb1X6SSshBAm\n7VjEMZovaM7YpmMZ32J8vs6eXpCGDoXISFi5UutKjIOcBhRCmKw1Z9cweMtg5r89n47VOmpdTq4d\nOAC9esH582Bvr3U12pKwEkKYHEVR+P7Q98w+NpuNARupW7qu1iW9tF69oGpVmDRJ60q0JWElhDAp\nyanJDN02lKM3j7L53c14OnpqXVKeXL8OdevCX39BhQpaV6MduWYlhDAZ9x/f5/Ulr3Mr7hb7++8v\n8kEFUL48DBsGY8ZoXYm2JKyEECYh5E4Ir/76Kv7l/dkQsMFoZqXID6NHw5EjsG+f1pVoR04DCiGK\nvNVnVzN4y2DmtJtDT5+eWpdTIIKC1BuFjx8Hc+MeeV8gJKyEEEVWSloKn+7+lFVnVrG251rqla6n\ndUkFRlGgRQt4/30YMEDragqfhJUQoki68+gOPVf3xNbClmVdluFm56Z1SQXu2DHo0gUuXCh+Q9nl\nmpUQosjZH76fBj83oJVXK7a8u6VYBBXAq69C06Ywc6bWlRQ+6VkJIYqM1LRUvj7wNXOOzWFh54W0\n9W6rdUmF7soVaNQIzp0Dd3etqyk8ElZCiCLhdtxt3lv3HqlpqSzrsoyyjsV3Od3hw9VrWLNna11J\n4ZHTgEIIo7f98nbq/VyP5uWbs7vv7mIdVKBOcrt8OVy+rHUlhUd6VkIIoxWfHM8nuz5h44WNLO68\nmBZeLbQuyWh8/TWcPKkOaS8OpGclhDBKf93+i/o/1ycqIYqQQSESVM8ZPhwOHVJHCBYH0rMSQhiV\n5NRkph6cyqyjs5jVdha9avfSuiSjNW8erF0LO3ZoXUnBk7ASQhiN03dP029DP9zt3Pml4y+Ucyqn\ndUlG7ckTqF4dFi6E5s21rqZgyWlAIYTmklOTmbJvCq0Wt2JIgyFs671NgioHrKxgwgR1wIWp9xmk\nZyWE0NSfEX/yv5v+l1dKvCK9qZeQkgI+Puow9jZttK6m4EhYCSE0EZcUx/g94wkMDWRam2m8W/vd\nIrPkvLEJCoLvv4ejR8FUf4RyGlAIUagURWHD+Q34/OTDw6SHnBlyht51ektQ5UG3bpCUBJs2aV1J\nwZGelRCi0FyJusKw7cO4Gn2VH9/6kVYVW2ldksnYuBHGj1fvvdKZYDfEBD+SEMLYxCfHMyF4Ao1+\nbUSLCi0IGRQiQZXPOnZUB1ysX691JQVDwkoIUWAURSEwNJAaP9bg3P1znBx4kjFNx2BlbqV1aSbH\nzEwdFThlimmODJTTgEKIAnH81nFGbB9BfHI8M9vOpHkFE78RyAgoCvj5wX/+Ax06aF1N/pKwEkLk\nq7CYMMb9MY7d13Yz5V9T6OfXD3NdMVyHXSOrVqkjA48cMa2RgXIaUAiRL6ISovj494+p/3N9vF29\nuTj0IgPqDZCgKmRdu0JcHOzcqXUl+UvCSgiRJ4+ePOI/+/5DtTnVePTkEaGDQ5nYciIO1g5al1Ys\n6XTw2Wfw5Zemde1KwkoI8VISUxKZeWQm3rO9Cb0fysEPDjK3w1xKO5TWurRiLyAAbt+GvXu1riT/\nyDUrIUSuJKYk8utfv/LtwW+pV7oeX/7rS+qUqqN1WeI5v/0Gy5bB7t1aV5I/JKyEEDmSkJzAL3/9\nwtSDU6lbui5fNP+ChmUbal2WyEJyMlSuDGvWQEMT+DVJWAkhshWbFMtPf/7EzKMzebXsq3zR/Avq\nl6mvdVkiB2bMgMOHTWM1YQkrIUSm7j2+x6yjs5h3fB5ver/J2KZjqV2qttZliVyIi4OKFdUJbitX\n1rqavJEBFkIIA+cjz/Phpg+pNqcaUQlRHPvfYyzrskyCqghycICBA2H6dK0ryTvpWQkhUBSFPWF7\nmH54On/e+pMhDYYwpOEQ3O3dtS5N5NGdO1CzJly4AO5F+NcpYSVEMRafHM/Sv5cy++hsFBSGNxpO\nnzp9sLW01bo0kY8GDoRXXoFJk7Su5OVJWAlRDF16cIm5J+ayOGQxr5V7jWGvDqNVxVayppSJungR\n/P3h2jWwt9e6mpcjYSVEMZGcmsymi5v46fhPhNwJoX/d/gysP5BKLpW0Lk0Ugq5doWVL+Pe/ta7k\n5UhYCWHiLj24xPyT81kUsghvV28G1R9Et5rdsLaw1ro0UYiOHIF334VLl8C8CE7XaKF1AUKI/Pfo\nySNWn13NglMLOB95nr6+fdnz/h6ql6yudWlCI40bQ6lSsGkTdO6sdTW5Jz0rIUxEmpJGcFgwi0IW\nsfHCRpqVb8b7vu/TsVpHWexQABAYCHPnQnCw1pXknoSVEEWYoiiE3A1h2ellrDi9And7d973fZ93\na7+Lh72H1uUJI5OcrN4kvHmzukhjUSJhJUQRdCHyAivPrGTlmZXEJ8fzbu136V27NzXda2pdmjBy\nX30Fly+rE90WJRJWQhQRFyIvsObcGoLOBHHv8T261+pOz1o9aezZGJ2ZTEYjciYyEqpUUW8S9ihC\nnW8JKyGMlKIo/H33b9adX8fqs6uJSoiiS40udK/ZHf/y/rICr3hp//u/UL48jB+vdSU5J2ElhBFJ\nSUthf/h+NlzYwIYLGzDDjM7VO9OtZjfpQYl8c/o0vPkmhIWBVREZeyNhJYTGHsQ/YPvl7Wy+tJkd\nl3dQ0aUinat1pnP1zvh4+MisEqJAtG4NH3wAvXtrXUnOSFgJUcjSlDRO3DrB9svb2XZ5G6H3QmlV\nsRUdqnbgrSpvUcahjNYlimJg40Z1sMWRI1pXkjMSVkIUgpuxN9l5ZSc7r+5k19VduNu709a7LW0r\nt6VZhWbYWNhoXaIoZlJT1WHsGzZA3bpaV/NiElZCFICohCiCw4L549of7L62m/uP79O6UmvaVGrD\nG5XfoLxTea1LFIIpU+D6dfj5Z60reTEJKyHyQVRCFPvC97E3fC/BYcFcibpC0/JNaeXVilYVW1G3\ndF0ZHCGMzp07UKOGOtDCyUnrarInYSXES7gZe5P94fs5cOMA+8P3ExYTRpNyTWhRoQUtKrSgYdmG\nMsWRKBJ69IDmzWHoUK0ryZ6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- } - ], - "prompt_number": 23 + "outputs": [], + "prompt_number": 8 }, { "cell_type": "code", @@ -236,129 +151,71 @@ "# no more legend", "plt.plot(x,intel, label=\"INTELLIGENCE\")", "plt.plot(x,inan, label=\"INANITY OF STATEMENTS\")", - "", "plt.xlabel(\"HUMAN PROXIMITY TO CAT\")", "plt.yticks([])", "plt.xticks([0,.8],['FAR','NEAR'])", "ax = plt.gca()", "ax.spines['top'].set_visible(False)", "ax.spines['right'].set_visible(False)", - "", "ax.xaxis.tick_bottom()", "", "# point out ax.transAxes vs ax.transData ", + "# this is a good place to show how to use a ", + "# **kw dict (time permitting)", + "noarrow_kwargs=dict(textcoords='offset points',", + " arrowprops=dict(width=1.0, headwidth=1.0, shrink=0))", + "plt.annotate(\"\\nINTELLIGENCE\", (x[40],intel[40]), (-10,-40), ", + " ha='center', **noarrow_kwargs)", "", - "plt.annotate(\"\\nINTELLIGENCE\", (x[40],intel[40]), (-10,-40), textcoords='offset points',", - " arrowprops=dict(width=1.0, headwidth=1.0, shrink=0),", - " ha='center'", - " )", - "", - "plt.annotate(\"\\nINANITY OF\\nSTATEMENTS\", (x[60],inan[60]), (10,-45), textcoords='offset points',", - " arrowprops=dict(width=1.0, headwidth=1.0, shrink=0),", - " )" + "plt.annotate(\"\\nINANITY OF\\nSTATEMENTS\", (x[60],inan[60]), (10,-45),", + " **noarrow_kwargs)" ], "language": "python", - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 86, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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kAnfpEjRuDKGh4OCgdTUFR8JKCFEo3Iq7Rb8N/UhJTWFFtxVULFF0b6c7cqT6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\npx9PnDgRb29vADZu3Ejbtm3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- } - ], - "prompt_number": 86 + "outputs": [], + "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ - "l, = plt.plot(0,1)", "plt.annotate(\"YOU'RE A KITTY!\", xy=(0, -1), xycoords='data',", - " xytext=(-30, 20), textcoords='offset points',", - " size=10,", - " va='center',", - " bbox=dict(boxstyle=\"round\", fc=(1.0, 1.0, 1.0), ec=(0, 0, 0)),", - " arrowprops=dict(arrowstyle=\"wedge,tail_width=1.\",", - " fc=(1.0, 1.0, 1.0), ec=(0, 0, 0),", - " patchA=None,", - " patchB=None,", - "", - " relpos=(0.5, 0.4),", - " connectionstyle=\"arc3,rad=-0.0\"),", - " )", - "plt.xlim(-2,2)", - "plt.ylim(-2,2)" + " xytext=(-30, 20), textcoords='offset points',", + " size=10, va='center',", + " bbox=dict(boxstyle=\"round\", fc=(1.0, 1.0, 1.0), ec=(0, 0, 0)),", + " arrowprops=dict(arrowstyle=\"wedge,tail_width=1.\",", + " fc=(1.0, 1.0, 1.0), ec=(0, 0, 0),", + " patchA=None, patchB=None,", + " relpos=(0.5, 0.4), connectionstyle=\"arc3,rad=-0.0\"),", + " )", + "plt.xlim(-2,2);", + "plt.ylim(-2,2);", + "kitty_kwargs = dict( xy=(30, 10), xycoords='data',", + " xytext=(-30, 20), textcoords='offset points',", + " size=10, va='center',", + " bbox=dict(boxstyle=\"round\", fc=(1.0, 1.0, 1.0), ec=(0, 0, 0)),", + " arrowprops=dict(arrowstyle=\"wedge,tail_width=1.\",", + " fc=(1.0, 1.0, 1.0), ec=(0, 0, 0),", + " patchA=None, patchB=None,", + " relpos=(0.5, 0.4), connectionstyle=\"arc3,rad=-0.0\"),", + " zorder=10", + " )" ], "language": "python", - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 129, - "text": [ - "(-2, 2)" - ] - }, - { - "output_type": "display_data", - "png": 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- } - ], - "prompt_number": 129 + "outputs": [], + "prompt_number": 10 }, { "cell_type": "code", - "collapsed": false, + "collapsed": true, "input": [ - "!wget http://imgs.xkcd.com/comics/cat_proximity.png" + "#!wget http://imgs.xkcd.com/comics/cat_proximity.png", + "# The above command will not work on OS X and Windows, ", + "# I'm calling out to an external program to download", + "# the image. You should have it in the repo. " ], "language": "python", - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "--2012-01-29 21:46:05-- http://imgs.xkcd.com/comics/cat_proximity.png", - "Resolving imgs.xkcd.com... " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "208.122.62.226", - "Connecting to imgs.xkcd.com|208.122.62.226|:80... connected.", - "HTTP request sent, awaiting response... 200 OK", - "Length: 16573 (16K) [image/png]", - "Saving to: `cat_proximity.png'", - "", - "", - " 0% [ ] 0 --.-K/s " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "", - "100%[======================================>] 16,573 --.-K/s in 0.05s ", - "" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "2012-01-29 21:46:05 (312 KB/s) - `cat_proximity.png' saved [16573/16573]", - "" - ] - } - ], - "prompt_number": 130 + "outputs": [], + "prompt_number": 11 }, { "cell_type": "code", @@ -368,7 +225,7 @@ ], "language": "python", "outputs": [], - "prompt_number": 131 + "prompt_number": 12 }, { "cell_type": "code", @@ -377,43 +234,21 @@ "plt.imshow(im[0:40,0:40])" ], "language": "python", - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 134, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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- } - ], - "prompt_number": 134 + "outputs": [], + "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ + "# By default, we get the sadly ubiquitous \"Jet\" colormap,", + "# Let's change it to someting more suitable for us...", "plt.gray()", "plt.imshow(im[0:100,0:100])" ], "language": "python", - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 141, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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- } - ], - "prompt_number": 141 + "outputs": [], + "prompt_number": 14 }, { "cell_type": "code", @@ -422,20 +257,15 @@ "ax_im = plt.imshow(im[-100:-20,40:100])" ], "language": "python", - "outputs": [ - { - "output_type": "display_data", - "png": 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- } - ], - "prompt_number": 215 + "outputs": [], + "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.gcf()", - "fig.subplotpars.bottom=.35", + "fig.subplotpars.bottom=.35 # can set this for all figures via rcParams", "ax= plt.subplot(1,1,1) #matlab-style initialization", "ax2 = fig.add_axes([0.0, 0.0, .3, 0.3])", "", @@ -447,36 +277,11 @@ "ax4 = fig.add_axes([0.6, .0, .45, 0.3])", "ax_im = plt.imshow(im[-100:-20:,320:420])", "", - "plt.annotate(\"YOU'RE A KITTY!\", xy=(30, 10), xycoords='data',", - " xytext=(-30, 20), textcoords='offset points',", - " size=10,", - " va='center',", - " bbox=dict(boxstyle=\"round\", fc=(1.0, 1.0, 1.0), ec=(0, 0, 0)),", - " arrowprops=dict(arrowstyle=\"wedge,tail_width=1.\",", - " fc=(1.0, 1.0, 1.0), ec=(0, 0, 0),", - " patchA=None,", - " patchB=None,", - "", - " relpos=(0.5, 0.4),", - " connectionstyle=\"arc3,rad=-0.0\"),", - " )", - "" + "plt.annotate(\"YOU'RE A KITTY!\", **kitty_kwargs)" ], "language": "python", - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 214, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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TJ06kiRMn1ngkVZPTp0/T6dOnyc7OjoyMjGjPnj306NEj6tKlC7m6utL9+/e1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\ndKm1jampKQICAvDbb7/h1KlTNV4bE1t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- } - ], - "prompt_number": 214 + "outputs": [], + "prompt_number": 16 }, { "cell_type": "code", @@ -499,65 +304,32 @@ "", "ax.xaxis.tick_bottom()", "", - "# point out ax.transAxes vs ax.transData ", - "", - "plt.annotate(\"\\nINTELLIGENCE\", (x[40],intel[40]), (-10,-40), textcoords='offset points',", - " arrowprops=dict(width=1.0, headwidth=1.0, shrink=0),", - " ha='center'", - " )", + "plt.annotate(\"\\nINTELLIGENCE\", (x[40],intel[40]), (-10,-40),", + " ha='center', **noarrow_kwargs)", "", - "plt.annotate(\"\\nINANITY OF\\nSTATEMENTS\", (x[60],inan[60]), (10,-45), textcoords='offset points',", - " arrowprops=dict(width=1.0, headwidth=1.0, shrink=0),", - " )", + "plt.annotate(\"\\nINANITY OF\\nSTATEMENTS\", (x[60],inan[60]), (10,-45),", + " **noarrow_kwargs)", "", "ax2 = fig.add_axes([0.0, 0.0, .3, 0.3])", - "", "ax_im = plt.imshow(im[-100:-20,40:100])", "", "ax3 = fig.add_axes([0.3, .0, .3, 0.3])", - "", "ax_im = plt.imshow(im[-100:-20:,180:240])", "", "ax4 = fig.add_axes([0.6, .0, .45, 0.3])", - "", "ax_im = plt.imshow(im[-100:-20:,320:420])", "", - "plt.annotate(\"YOU'RE A KITTY!\", xy=(30, 10), xycoords='data',", - " xytext=(-30, 20), textcoords='offset points',", - " size=10,", - " va='center',", - " bbox=dict(boxstyle=\"round\", fc=(1.0, 1.0, 1.0), ec=(0, 0, 0)),", - " arrowprops=dict(arrowstyle=\"wedge,tail_width=1.\",", - " fc=(1.0, 1.0, 1.0), ec=(0, 0, 0),", - " patchA=None,", - " patchB=None,", - "", - " relpos=(0.5, 0.4),", - " connectionstyle=\"arc3,rad=-0.0\"),", - " )" + "plt.annotate(\"YOU'RE A KITTY!\", **kitty_kwargs)" ], "language": "python", - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 225, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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NGmxByYoQJcUYw+vU1wiLCUNYTBgux1xGdHI0vKy80KlRJ3S06YhW9VuVOnIv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\nKiJTU1OysbFps62gqampV7Zwfb/6nLuljqiurkZ5eTnz+gs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- } - ], - "prompt_number": 225 + "outputs": [], + "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "# putting it all together", - "# no more legend", "fig = plt.gcf()", "fig.subplotpars.bottom=.35", "ax= plt.subplot(1,1,1) #matlab-style initialization", @@ -570,74 +342,44 @@ "ax = plt.gca()", "ax.spines['top'].set_visible(False)", "ax.spines['right'].set_visible(False)", - "", "ax.xaxis.tick_bottom()", - "", - "# point out ax.transAxes vs ax.transData ", - "", - "plt.annotate(\"\\nINTELLIGENCE\", (x[40],intel[40]), (-10,-40), textcoords='offset points',", - " arrowprops=dict(width=1.0, headwidth=1.0, shrink=0),", - " ha='center'", - " )", - "", - "plt.annotate(\"\\nINANITY OF\\nSTATEMENTS\", (x[60],inan[60]), (10,-45), textcoords='offset points',", - " arrowprops=dict(width=1.0, headwidth=1.0, shrink=0),", - " )", + "plt.annotate(\"\\nINTELLIGENCE\", (x[40],intel[40]), (-10,-40),", + " ha='center', **noarrow_kwargs)", + "plt.annotate(\"\\nINANITY OF\\nSTATEMENTS\", (x[60],inan[60]), (10,-45),", + " **noarrow_kwargs)", "", "ax2 = fig.add_axes([0.0, 0.0, .3, 0.3])", - "", "ax_im = plt.imshow(im[-100:-20,40:100])", "", "ax3 = fig.add_axes([0.3, .0, .3, 0.3])", - "", "ax_im = plt.imshow(im[-100:-20:,180:240])", "", "ax4 = fig.add_axes([0.6, .0, .45, 0.3])", - "", "ax_im = plt.imshow(im[-100:-20:,320:420])", "", - "plt.annotate(\"YOU'RE A KITTY!\", xy=(30, 10), xycoords='data',", - " xytext=(-30, 20), textcoords='offset points',", - " size=10,", - " va='center',", - " bbox=dict(boxstyle=\"round\", fc=(1.0, 1.0, 1.0), ec=(0, 0, 0)),", - " arrowprops=dict(arrowstyle=\"wedge,tail_width=1.\",", - " fc=(1.0, 1.0, 1.0), ec=(0, 0, 0),", - " patchA=None,", - " patchB=None,", - "", - " relpos=(0.5, 0.4),", - " connectionstyle=\"arc3,rad=-0.0\"),", - " )" + "plt.annotate(\"YOU'RE A KITTY!\", **kitty_kwargs)" ], "language": "python", - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 227, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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NGmxByYoQJcUYw+vU1wiLCUNYTBgux1xGdHI0vKy80KlRJ3S06YhW9VuVOnIv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\nKiJTU1OysbFps62gqampV7Zwfb/6nLuljqiurkZ5eTnz+gs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- } - ], - "prompt_number": 227 + "outputs": [], + "prompt_number": 18 }, { "cell_type": "code", - "collapsed": false, + "collapsed": true, "input": [ "def cleanup_axes(ax):", + " \"Remove crud around axes - no ticks, no spines\"", + " # Note: we can get this behavior by passing", + " # frame_on=False,yticks=[],xticks=[] at the creation of ax", + " # this is here to demonstrate how you can adjust your plots ", + " # after they've been create", " ax.set_frame_on(False)", " ax.set_yticks([])", " ax.set_xticks([])" ], "language": "python", "outputs": [], - "prompt_number": 228 + "prompt_number": 19 }, { "cell_type": "code", @@ -660,151 +402,67 @@ "", "ax.xaxis.tick_bottom()", "", - "# point out ax.transAxes vs ax.transData ", - "", - "plt.annotate(\"\\nINTELLIGENCE\", (x[40],intel[40]), (-10,-40), textcoords='offset points',", - " arrowprops=dict(width=1.0, headwidth=1.0, shrink=0),", - " ha='center'", - " )", - "", - "plt.annotate(\"\\nINANITY OF\\nSTATEMENTS\", (x[60],inan[60]), (10,-45), textcoords='offset points',", - " arrowprops=dict(width=1.0, headwidth=1.0, shrink=0),", - " )", + "plt.annotate(\"\\nINTELLIGENCE\", (x[40],intel[40]), (-10,-40),", + " ha='center', **noarrow_kwargs)", + "plt.annotate(\"\\nINANITY OF\\nSTATEMENTS\", (x[60],inan[60]), (10,-45),", + " **noarrow_kwargs)", "", "ax2 = fig.add_axes([0.0, 0.0, .3, 0.25])", - "", "ax_im = plt.imshow(im[-100:-20,40:100])", "", "ax3 = fig.add_axes([0.3, .0, .3, 0.25])", - "", "ax_im = plt.imshow(im[-100:-20:,180:240])", "", "ax4 = fig.add_axes([0.6, .0, .45, 0.25])", - "", "ax_im = plt.imshow(im[-100:-20:,320:420])", "", - "plt.annotate(\"YOU'RE A KITTY!\", xy=(30, 10), xycoords='data',", - " xytext=(-30, 20), textcoords='offset points',", - " size=10,", - " va='center',", - " bbox=dict(boxstyle=\"round\", fc=(1.0, 1.0, 1.0), ec=(0, 0, 0)),", - " arrowprops=dict(arrowstyle=\"wedge,tail_width=1.\",", - " fc=(1.0, 1.0, 1.0), ec=(0, 0, 0),", - " patchA=None,", - " patchB=None,", - "", - " relpos=(0.5, 0.4),", - " connectionstyle=\"arc3,rad=-0.0\"),", - " )", + "plt.annotate(\"YOU'RE A KITTY!\", **kitty_kwargs)", + "", "[cleanup_axes(ax) for ax in ax2, ax3, ax4]" ], "language": "python", - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 237, - "text": [ - "[None, None, None]" - ] - }, - { - "output_type": "display_data", - "png": 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DmDFjuHfvnn7bs0GVU4iOHj2apUuXkpycDECbNm24ffs2v/zyi36fs2fPcuTIkWyPI9Iz\nMYGePZVrWoMHK/dt9egBFy6oXZnITrI2mcUnFhfp4eppSVgVU2nDKO3rzz77jOvXr2e7f6q016x8\nfHxISkpCo9Hw448/4uLigouLC5UqVeLGjRuAMrghdfuYMWPQaDT64/r6+vL+++/Tpk0bvL29eeWV\nV0hKSqJNmzb686W9ZvXGG29kqK1s2bL06NGDxMREAMzMzNi9ezfbtm3D3d0dT09Pxo4di5OTE5Dx\nmlXaVpnIyNQUBg2CixeVpxc3aaLcXJzm7wthQDaHbsallAs+FTJ2nRdFMt2SEOK53L8P06aBn5/y\nJOPhw8HCQu2qRKpGKxvxyWuf0L1Wd7VLyRfSshJCPJeyZWHRImUqp4MHoU4d2LFD7aoEwB/X/iAy\nNpIuNbqoXUq+kZaVECJf7NkDH30EL78MCxYos70LdfTa0IsWVVow/JXhapeSb6RlJYTIF23bKoMw\nGjeGV1+FyZMhPl7tqoqfKw+vEBgeyECvgWqXkq8krIQQ+aZECRg/HoKDITQUPDxg/361qypeFh5f\nyHs+71HSomTOOxch0g0ohCgwO3YoAy+aNlVmxHBwULsi4/Yw/iHVFlXj7AdneanUS2qXk6+kZSWE\nKDCdOsE//0D58uDuDr/8IlM3FaTlJ5fT8eWORhdUIC0rIUQhCQpS7tOqUgUWL4aXjO/3qaoSUxKp\nurAq2/tux7tC1vNsFlXSshJCFIp69ZSpm3x8nj4IUv7OzT8/n/mZ2g61jTKoQFpWQggVnD4NAwZA\n5cqwbBn8N6mIeE4p2hRqfV+LpZ2W0tK1pdrlFAhpWQkhCp2nJxw/rtxI7OUFW7aoXVHRtil0E2Ws\nytCiSgu1Sykw0rISQqjq6FGlldWsmTIjRknjGnFd4HQ6HfWW1WNK8yl0rdlV7XIKjLSshBCqatwY\nTp1SXterp9yjJXJv7797SUxJpHONzmqXUqAkrIQQqitZEv7v/2DqVGUmjAULZPBFbn155EvGNxmP\nica4f50b93cnhChS+vaFP/+EdeugSxd48EDtigzbsWvHiHgUQR/3PmqXUuAkrIQQBqVqVWUm9+rV\nnw53F5n78siXjHttHGYmZmqXUuAkrIQQBsfcXJmeae5caN8efvhBugWfdfLWSYJuBTHIa5DapRQK\nGQ0ohDBoly5Br17KpLjLloGVldoVGYau67ryuuvrjHh1hNqlFAppWQkhDFr16nDsGKSkQJMmcPWq\n2hWp7+Stk5y4eYL/1fuf2qUUGgkrIYTBs7ZWJsF96y3lWVmHDqldkbqmBk5lfOPxWJpZql1KoZGw\nEkIUCRoNjBkDa9aAry8sWaJ2ReoIuhnEyVsni1WrCuSalRCiCLp8WXn8SPv2yiAMU1O1Kyo8Xfy6\n8Ea1N4zqkfW5IS0rIUSR4+amXMc6exa6doWYGLUrKhyprar3fN5Tu5RCJ2ElhCiS7O1h1y7luViN\nG8O1a2pXVPCmBE5hfJPida0qlYSVEKLIMjdXrl31768E1tmzaldUcI5cPULI3RD+51O8rlWlMv7b\nnoUQRk2jgbFjlRbW66/D+vXQooXaVeUvnU7H+P3jmd5yOiXMSqhdjiqkZSWEMAp9+4KfnzJScP16\ntavJXzsv7STqSRT96vZTuxTVSMtKCGE0WrWC/fuhQwe4fx+GDVO7ohen1WmZeHAis1rNwtSkGA17\nfIaElRDCqHh4wO+/Q5s2EBUFEyYoXYVF1bqQdVibW9OlRhe1S1GVhJUQwuhUrQpHjjwNrDlzimZg\nJaYk8vlvn7Oyy0o0RfEbyEdyzUoIYZQqVFCmZTp0CIYOBa1W7YrybsXJFVQvU50WVVqoXYrqZAYL\nIYRRi4lRZrtwdYWVK4vObBfRCdHU+K4Gu/rtwqu8l9rlqE5aVkIIo2ZrCzt3KrO1DxqkzN5eFHx5\n5Evau7WXoPqPtKyEEMVCXJwyNZOjI6xeDWYGfMU+IiqCesvqceaDM1S0rah2OQZBWlZCiGLB2hq2\nbYN79+Cddwy7hTXhwARGvDJCgioNCSshRLFhZQVbtyqB9e67hjno4vj14/we8TtjXxurdikGRcJK\nCFGsWFrCli1w5Qp8+CEY0lUMnU7H6L2jmdlqJjYWNmqXY1AkrIQQxY6NDezYAcHBMHq04QSW/zl/\n4pLi6O/ZX+1SDI6ElRCiWCpVSnnESGAgTJ6sdjUQmxjLuH3jWNB2ASYa+dX8LAMeDyOEEAXL3h72\n7oUmTaBcORg5Ur1aZh+dTWOXxjSv0ly9IgyYhJUQolhzcEgfWP1UmNj88oPLLP57MaffP134Jy8i\nJKyEEMVe5cqwe7cya3vZstCuXeGe/+M9H/NJ4094qdRLhXviIkQ6RoUQAqhTBzZvVp46fPx44Z03\n4GIAF+9fZFTDUYV30iJIwkoIIf7z2muwahV06wb//lvw53uS/ISRu0eyqN0iLEwtCv6ERZiElRBC\npNGxozI6sEMHePCgYM/19dGvqetUl7ZubQv2REZA5gYUQohMjBundAfu2wclSuT/8S/ev8hrK18j\neGgwLnYu+X8CIyNhJYQQmdBqwdcXLCzg55/BJB/7oXQ6Ha3XtKZLjS5yrSqXpBtQCCEyYWICP/0E\nYWEwbVr+HvunMz8RnRDNiFdG5O+BjZgMXRdCiCxYWSnzCL7yCri7Q+/eL37Me3H3+GTfJwS8FYCp\nSRF5EqQBkG5AIYTIQXAwvPGGcvOwt/eLHWvQ1kGUtizN/Lbz86e4YkJaVkIIkQNvb/jhB2VI+19/\ngZPT8x3nwJUDHLhygHMfnsvfAosBuWYlhBC50Ls3DBgAPXpAQkLeP/848THvbX+PpZ2WUtKiZP4X\naOSkG1AIIXJJq4WePaF8eVi8OG+fHb5zOLFJsazquqpgijNy0rISQohcMjGB1avh4EFYsyb3nwsM\nD2TL+S3Me2NewRVn5OSalRBC5EGpUrBpE7RoAR4e4OWV/f6xibG8u+1dlnRagr2VfaHUaIykZSWE\nEHlUpw4sWqR0CT58mP2+Ew9O5DWX1+j0cqfCKc5IyTUrIYR4TiNHwpUrsHVr5jNcBIYH0m9TP85+\ncJYyVmUKv0AjIi0rIYR4Tl9/rUx2O3duxveinkQxYMsAVnReIUGVD6RlJYQQL+DqVWjQQGldNWz4\ndPvbm97GztKO7zt8r15xRkRaVkII8QIqVYKlS6Fv36fXr9aHrOfEzRN83eZrdYszItKyEkKIfDBi\nBNy8CQv+7zr1l9cj4K0A6lesr3ZZRkPCSggh8sGTJ9CwkZaE3m3p16Q5k5pNUrskoyLdgEIIkQ8s\nLeH1KV9xOSKeTqXHq12O0ZGwEkKIfHDs2jF+ujyfL3z8GPCO2XPNHyiyJmElhBAv6GH8Q/pu7Mvy\nzssZO8SFqlXh88/Vrsq4yDUrIYR4ATqdjp4beuJi58LCdgsBiIwET0/w84PmzVUu0EhIy0oIIV7A\nD3//QMSjCL56/Sv9NgcHWL4c+veHR49ULM6ISMtKCCGe0983/qbD2g78MfgPqpetnuH9Dz6Ax4/h\np59UKM7ISMtKCCGew724e/Ty78WyTssyDSpQpmH6809ldgvxYqRlJYQQeZSiTaHdL+3wqeDDnNfn\nZLvv778rs1ucPQtlZIrA5yZhJYQQeTTp4CT+uPYHe9/Zi5lJzo8F/Ogj5drV6tWFUJyRkm5AIYTI\ng20XtrH69GrW9VqXq6AC+PJLOHwYAgIKuDgjJmElhBC5FBoZynvb3mNDrw042jjm+nM2NrByJbz/\nPkRFFWCBRky6AYUQIhcexj/klRWvMLHJRAZ5D3quYwwbpswh+H//l8/FFQMSVkIIkYNkbTIdfumA\nu6M789rOe+7jxMRAnTqwZg20aJF/9RUH0g0ohBA5GLt3LBqNhq/afJXzztmwtYVvv4WhQ5UWlsg9\nCSshhMjGypMr2XlpJ+t65n5ARXa6dlVaV7Nn50NxxYh0AwohRBb2X9lPv039+H3g79QoVyPfjnv9\nOnh5wZEjULNmvh3WqEnLSgghMhFyN4S3Nr6Ff2//fA0qAGdnmDxZGR0of+vnjoSVEEI841bMLTqu\n7ciCdgtoVrlZgZzjww8hNhZWrSqQwxsd6QYUQog0Hic+pvmPzelRswefNfusQM918iR06AChoWBv\nX6CnKvIkrIQQ4j9JKUl0XdeVCrYVWNF5BRqNpsDPOWwYmJoqowRF1iSshBAC0Oq0DNgygKgnUWx+\nc3O+jPzLjQcPoFYt2LtXeWCjyJxcsxJCFHs6nY4xe8cQHhXO+l7rCy2oQJmJffp0GD5cBltkR8JK\nCFHszTk6hwNXDrCtzzasza0L/fzvvQdxcbB2baGfusiQbkAhRLG2LGgZXx75kqODj1LRtqJqdRw7\nBr17K4MtbG1VK8NgSctKCFFsrTm9hhm/z2DfO/tUDSqARo2gTRuYOVPVMgyWtKyEEMWS/z/+jNw9\nkgP9D1DLoZba5QBw6xbUrQt//w2urmpXY1ikZSWEKHa2XdjG8F3D2dVvl8EEFUCFCjByJEyYoHYl\nhkdaVkKIYmXXpV0M2DKAgLcCaPBSA7XLySAuDmrUgA0blK5BoZCWlRCi2NhxcQcDtgxga5+tBhlU\nANbWMGsWjB4tQ9nTkrASQhQLW89vZfDWwex4aweNXAy7yfL225CYqLSuhEK6AYUQRm9z6GbeD3if\ngLcCqF+xvtrl5MqhQzBwoDKU3dJS7WrUJy0rIYRR8zvrxwcBH7Cr364iE1QAzZsr0y/JnIEKaVkJ\nIYzWkhNLmPH7DPa8vQd3R3e1y8mz0FAltC5ehNKl1a5GXRJWQgijNOfIHJYGLWXfO/uoVqaa2uU8\nt8GDoWJFuVlYwkoIYVR0Oh0TD05k6/mt7HtnHy+Vekntkl7I1avg7Q3//APly6tdjXokrIQQRiMp\nJYkhO4bwz91/2NlvJ+Wsy6ldUr4YPRqSkor39SsJKyGEUXic+JheG3phamLKhl4bsLGwUbukfBMZ\nqTzz6q+/oGpVtatRh4wGFEIUebcf36b5j81xsXNha5+tRhVUAA4OMGIETJmidiXqkZaVEKJIOxd5\njk5rOzHAcwCTm08ulEfRqyEmBqpXh337lMluixsJKyFEkbX33728velt5r4xl/6e/dUup8DNmwd/\n/AG//qp2JYVPwkoIUSQt/nsx0w5Nw7+3P00rN1W7nEIRFwfVqsGePeDhoXY1hUvCSghRpCSlJDFm\n7xj2XdnHjr47ivQ9VM+juLauJKyEEEVGZGwkvr/6YmlmiV9PP0pbFr9pHVJbV7t3K9MxFRcyGlAI\nUSQE3wqmwfIGNHJuxI6+O4plUIHyCJFPPoHp09WupHBJy0oIYfB+OfMLo/aM4ocOP9C7Tm+1y1Fd\ncWxdSVgJIQzWk+QnfLznYw5cOcCvvr/i4VTMRhVkY/58OHIENm5Uu5LCIWElhDBIYQ/D6OXfC9fS\nrqzsshI7Szu1SzIocXHg5qa0rorDyEC5ZiWEMDhbz2+l4cqG9Pfoj39vfwmqTFhbw8cfw+zZaldS\nOKRlJYQwGPFJ8YzbN46ASwH49fSjoXNDtUsyaDEx4OoKf/6ptLKMmbSshBAG4VzkOV5d8SqRcZEE\nDw2WoMoFW1v44AP4+mu1Kyl40rISQqhKp9Ox+MRipgROYc7rcxjkNcho5/crCJGRUKMGhIQoD2k0\nVhJWQgjV3Iy5yeCtg3kQ/4Cfuv9EjXI11C6pSBo5EiwsjLuFJd2AQghVrA9Zj/dSbxo5N+Lo4KMS\nVC9gzBhYuRIePFC7koIjLSshRKG6G3uXEbtGcPr2aX7q/hMNXmqgdklGYdAgZbDF5MlqV1IwpGUl\nhCgUOp0Ov7N+eCz2oErpKgQPDZagykeffgrffQexsWpXUjCkZSWEKHA3om/w4c4PufzgMqu6rpKQ\nKiDdu0ObNjBsmNqV5D9pWQkhCkyKNoVvj3+L11IvPMt7EjQkSIKqAI0erUzDpNWqXUn+M1O7ACGE\ncTp1+xRDtg/BytyKw4MOU7NcTbVLMnpNmoC9PWzfDl27ql1N/pKWlRAiX0U9ieKjXR/R9ue2vF//\nfQIHBEpQFRKNRmldzZundiX5T8JKCJEvtDotq4JXUev7WiSkJHBu2DkGew+WG3wLWa9eEB4OJ06o\nXUn+kgGMuiz9AAAgAElEQVQWQogXdvz6cUbtGYVWp+X7Dt9Tv2J9tUsq1r75BoKCYO1atSvJPxJW\nQojndu3RNcYfGE9geCAzW85kgNcATDTSYaO2R4+galU4dQpcXNSuJn/IvyohRJ7FJMQw+bfJeC31\noqp9VS4Mv8Ag70ESVAbCzg4GDIBvv1W7kvwjLSshRK4lpiSyLGgZM3+fSZtqbZjVahaV7CqpXZbI\nREQE+Pgo65Il1a7mxcmfQUKIHGl1WtaFrKP297UJuBTAnrf38FP3nySoDFjlytC8Ofzyi9qV5A9p\nWQkhsqTT6dh6YSuTf5uMtbk1X7T+glaurdQuS+TSwYPKjOxnzijD2osyuSlYCJGBTqdj1+VdTAmc\nQlJKEl+0/oKO1TvKMPQipmVLSEmB339XWllFmbSshBB6Op2O7Re3M/3QdBJSEpjcbDI9a/eUgRNF\n2A8/KC2sX39Vu5IXI2ElhCBFm8Km0E3MOjwLjUbD580+p1vNbhJSRiAmRrl+dfp00R7GLmElRDGW\nkJzAmtNr+OqPryhnXY4JTSbQ+eXO0t1nZD76CEqVgpkz1a7k+UlYCVEMPYx/yLKgZSz6axGeTp6M\nbzKeppWaSkgZqfPnlWtWV69CiRJqV/N8pI0vRDFy+cFlRuwaQbVF1Th37xw739rJzn47aVa5mQSV\nEatZEzw9wd9f7Uqen4SVEEZOp9Ox7999dPHrQsMVDbG1sCVkWAiru63Gs7yn2uWJQjJ8uPIk4aJK\nugGFMFIxCTGsClrFkuAlmJqY8tErH9HPox/W5tZqlyZUkJwMrq6wY4fSyipqJKyEMDKnbp9iyYkl\nrP9nPdYbK9PUuxW//PA1pqamapcmVDZ1Kty7VzRbWBJWQhiBmIQY1v+znuUnl3Mr5hZD6g1hsPdg\nTh4KpU+fQP73P3vmzx+tdplCZVevgpcXXL8O1kWsgS1hJUQRpdPpOHb9GCuDV7IpdBMtqrTgXe93\nae/WHlMTU/0+3bp9yuHDDqxc6U337q+rXLVQW6dOygMaBw5Uu5K8kbASooiJiIpgzek1rDmzBjMT\nMwZ7Daa/Z3+cSjplun9sbCyNG39KTIwNv/02gkqVnAu5YmFItm2D2bPhjz/UriRvJKyEKAIexj/k\n13O/8svZXwi5G8Kb7m8ywHMADSo2yNWQ85CQC7Rv/zPOzlEcOvQNFhYWhVC1METJycqMFnv2gLu7\n2tXknoSVEAYqNjGWgEsBrD27lt/Cf6NN1Tb0q9uPDtU7UMIs73d2Llv2K+PGXadbt0esXj2lACoW\nRcXkycrThBcuVLuS3JOwEsKAxCfFs/PSTjac28Duy7tp6NyQPnX60KNWD+ws7V74+AMHTmPTJjvm\nznVmyJBe+VCxKIoiIqBePbh2Days1K4mdySshFBZdEI0ARcD2HR+E3v/3Uv9ivV5s86b9KjVg3LW\n5fL1XImJiTRvPprr1+3ZubMfdevWzNfji6KjQwfo2xfeeUftSnJHwkoIFdyKucX2i9vZemErhyMO\n07RyU3rU7EHXml3zPaCedfXqdVq1+paSJeM4enQ2NjY2BXo+YZg2b4b585VnXRUFElZCFAKdTsfp\nO6cJuBjA9ovbuXD/Au3c2tG1Rlfau7XPly6+vNi8eT/vvhtM06aRbNkyR+YFLIaSksDZGY4eBTc3\ntavJmYSVEAUkOiGag2EH2XV5FwEXA7A0s6TTy53oWL0jzas0x8JU3RF5H3/8DcuXl2D8eEsmTXpP\n1VqEOj7+GGxtYfp0tSvJmYSVEPlEq9MSfCuYfVf2sfvyboJuBdHIuRHt3NrRsXpHXi77skG1YLRa\nLW3afExIiAP+/m1p1qyB2iWJQnb6NHTpAmFhYGLg05pLWAnxnHQ6HVceXuFg2EH2XdnHwbCDONg4\n0KZqG9q5taN55ebYWBj29aD79x/QtOl0tNpYTp/+jhJF9WFH4rl5e8M330CrVmpXkj0ztQsQoiiJ\niIrgUMQhfgv/jYNhB0lKSaKla0s6VO/AvLbzcC5VtGaHKFu2DMuXv8Pu3cflRuFiauBAWLXK8MNK\nWlZCZEGn03HpwSUORxzm96u/cyj8EPHJ8TSr3IxWVVrRyrWVwXXtCZFXkZFQvboyyW2pUmpXkzUJ\nKyH+8yT5CSdvneTYtWMcvXaUI1ePYGVuRZNKTWhaqSnNKzenZrmaEk7C6HTvrkxw++67aleSNQkr\nUSzpdDrCo8I5fuO4slw/zuk7p6lZriavubzGa86v0bhSYyrZVVK7VCEK3LZt8PXXcPiw2pVkzcDH\nfwiRP+7G3iXgYgDTAqfRaW0nyn9Tnsb/15j1/6zHycaJWa1mcXfsXYKGBPFt+2/pW7dvsQuqkiVL\nAhAeHo6JiQnfpXlC3/Dhw1m9erX+6+TkZBwcHJgwYUK6Y7Ro0YIGDZ6OKjxx4gQtW7YEIDAwkM6d\nO/Pjjz/i7e2Nt7c3FhYWeHh44O3tja+vLzVq1ODJkyf6z3fs2JH169dnqPXIkSM0atQIb29vPDw8\nWL58uf69qVOn4uzsrD/HxIkTX/AnY/zat4eLF+HSJbUryZoMsBBGRafTcT36OsG3gwm+FczJ2yc5\neeskjxMfU79ifRpUbMBg78Es7rgY51LO0qWXRtqfhaOjI4sWLWLo0KGYm5tn+Dnt27ePevXqsXHj\nRr788st070VGRrJ7927atWuX6XkGDhzIwP8epuTq6kpgYCBlypQBYMKECcyaNYsZM2awZcsWUlJS\nePPNN9N9/sqVK/Tr14/du3dTq1YtHj16RPv27bGzs8PX1xeNRsPo0aMZPVoeNplb5ubQrx+sXg0z\nZ6pdTeYkrESRlZCcQOi9UM7cOcOZO2c4dfsUp26fwtTEFK/yXtSrUI93PN5hftv5uJZ2lWDKAwcH\nB5o0acLq1at5772MNwyvW7eODz74gMWLF3Ps2DEaNWoEKIE3duxYZs2alWVYZWfy5Ml4e3vTs2dP\nJkyYwI4dOzLss2TJEoYOHUqtWrUAsLOz46uvvmLs2LH4+voCyKWJ5/D228pDGWfMAEP8X0XCShi8\nZG0yVx5e4Z+7/xByN4SQyBBC7oZw5eEV3Mq44eHkQV3Huox9bSxe5b0oX7K82iUbhU8++YT27dsz\nePDgdNufPHnCwYMHWb58Offv38fPz08fVgCNGjVi8+bNBAYGYmtrm6dzWllZMXfuXJo1a8bYsWOp\nVq1ahn1CQkIYNWpUum316tXj7NmzgBJU8+fP5+effwbgq6++ok2bNnmqozjy9oYSJeDYMXjtNbWr\nyUjCShiM+KR4Lt6/yPl75zl/7zyh90I5F3mOSw8uUaFkBWo71KauU126vNyFCU0mUKtcred6rpPI\nHVdXV1599VXWrl2bbvuOHTto0aIFFhYWdOvWjalTp7Jw4cJ0LddJkyYxc+ZM5syZk+fzdurUCXt7\ne4YNG5blPpm1nFK3STfg89FolK7AtWslrIQgWZtMRFQElx9c5tKDS1y4f4GL9y9y4d4F7sTeoap9\nVWqWq0nNcjXpWL0jnzT+hBplaxj8TBDGauLEifTq1YvmzZvrt/n5+XH06FFcXV0BePDgAQcOHOD1\n118HlLBo2bIlkyZN4s8//3yu85qYmGCSxfw/devWJSgoiLZt2+q3BQUF4enpqf9augGfz1tvQcOG\nymzs5uZqV5OehJXId7GJsYRFhRH2MIx/H/6rLA+UdURUBE4lnahepjrVy1anRtkatHdrT42yNahc\nujJmJvJP0pDUqFGD2rVrs337dl555RWio6M5cuQI169fx/y/32Y//vgjfn5++rBKNWnSJIYOHYpb\nPk/pPWTIEFq3bk2PHj2oWbMmjx49Yvz48Rm6BkXeVa0K1arBvn3K864MifxmEHkWmxhLxKMIrj66\nSkRUBOGPwgmPCifsYRhhUWFEJ0RTpXQVXEu7Uq1MNarZV+ONqm9QrUw1qtpXxdLMUu1vQWQibTde\n2tefffYZPj4+AGzZsoXWrVvrgwqgS5cufPrppyQmJqY7Xvv27XF0dEx3zGcHuWQ16CW7wTDVqlXj\np59+YuDAgTx58oSUlBSGDx+ebtSgDKZ5fqldgYYWVnJTsEgnPimeGzE3uB59Xb9ci77GtUfXuBZ9\njauPrhKfFE8lu0q42LlQpXQVqthVUdalq+Bq70r5kuUx0cgtfEIURXfvwssvw40bYEjP5ZSwKibi\nkuK48/gOtx7f4lbMLWX93+ubMTe5EXODmzE3iU2MpaJtRZxLOfNSqZdwLuWMSykXJZz+W5ezLid/\nuQphxNq3h/79lcfeGwoJqyIqRZvCg/gHRMZFEhkbSWRcJHdj7xIZG8md2Dvcjb3Lndg73Hl8hzux\nd0hITqB8yfKUL1meCrYVqFCyAhVtK+rXL5V6iYq2FSlrVVaCSIhi7uefYd06yOQ2N9VIWKlMp9MR\nkxjDw/iHPIh/wMMnD3kY/5D78fd5EP+AB/EPuB9/n3tx97gXd4/7ccrrqCdRlLYsTTnrcjjYOOBo\n44iDtQMONg442TjhZOOEo40jTiWdKF+yPHYl7CSEhBC58vix8sj7y5ehXDm1q1FIWL2ApJQkYhJj\niEmIISYxhuiEaGISlHV0QjSPEh7p14+ePCLqSRSPEpR16vLoySOszK0oY1UGe0t77K3ssbe0p6x1\nWcpYlaGslbIuZ12OslZlKWtdlnLW5ShjVUZGzol89fnnn7NhwwYsLZUBMGXLluXhw4c8fvyYyMhI\n/VD1xYsX07BhQ7y8vKhVqxZ+fn6sWrWKRYsWAfDPP/9Qs2ZNTE1NadeuHTVq1GDcuHE4Oz991pef\nnx+WlpZUrVqVzz77jBkzZgBw7949KlSowPvvv8+3337L1KlTWbFiBQ4ODvrPBgYGEhwcTKtWrdi2\nbRudOnUCoHPnzowdO5aFCxcSFhaWoe4ffviBGzduMHXqVExNTUlOTmbEiBG8//77Bf/DLYL69oVm\nzeCDD9SuRGF0v+10Oh1J2iQSkhNISEngSfKTdEt8UryyTo4nPimeuKQ4/evYpFjikuKIS4ojNimW\n2MRY/bbYxFgeJz7WLzGJMaRoU7AtYUtJi5KUKlEKWwtbZV3CFrsSdthZ2lGqRCmcbZ1xd3DHztKO\n0palsSthh72VPaUtS1PasrSEjlBdYGAgBw4cICQkBHNzc6Kjo4mPj8fJyYlDhw4xd+5ctm/frt8/\nNDQUS0tLjh8/TlxcHIMGDWLQoEFAxvn+Vq9eTd++ffVhlio8PBxXV1d27typDyt/f3/c3d31vQDZ\n3eDr7OzMrFmz9GGVuv+mTZsAMtQdFxdH9+7dOXnyJBUrViQlJYXw8PB8+gkanz59YMECIwurKYFT\nACUodOj0a61Oi06nrJ9dUnQpaHVakrXJpGhTSNGl6F8na5NJ1iaTpE0iKSWJJG2S8nVKEokpiSRp\n/1unJJGQkkBiSiKJKYkkJCuvTU1MKWFaAkszSyzNLClhVoISpiWwMrfCyswKSzNL/Wsrcyusza2x\nMrPCxsIGuxJ2VChZAWtza2wsbLAxt9GvbUvY6r+2tbDF0sxSutaEUYiMjMTBwUE/JL1UqVKU+u9J\nfJn1nPj5+dG3b19CQ0PZunUrfXO4Ep9V74u1tTW1atUiKCiIevXqsWHDBnx9fbl582a2n9VoNHh6\nepKcnMz+/fsz3OOV2eceP36MTqejbNmyAJiammY6nZNQtG2rPEX49m0obwAzmOVLWJmkPmlE89+9\nFGgw0Zig0Sjr1EWDBlMT03TbzEzMMNWYKmsTZZ12MTcxx9zUHHMTc8xMzLAwtcDC1AJzU3P96xKm\nJTA3NaeEaQlKmJWQYdNC5FHbtm2ZPn06tWrVokWLFvTq1YvWrVtnuf+GDRv47bffCA0NZcGCBdmG\nlU6nY/369Rw5cgRQfkccO3ZM/36fPn1Yt24dTk5OmJqaUrFiRX1YPTvPX5kyZThw4IA+iCZOnMjn\nn3+eaVg9y9HRkbZt21KlShVatmxJu3bt6NevH6ampjn/gIohS0vo2BE2bYJsZr4qNPnTsmoxJT8O\nI4RQSalSpTh16hSHDh3i999/5+2332bGjBmZzrh+4sQJHBwcqFChAo6OjgwcOJCHDx9ib2+f6bE1\nGg19+vTJ0A2Yqm3btkyaNAknJ6cMjwPJaZ6/pk2bAnD06FEg52mW1qxZw+nTp/ntt99YsGABe/fu\n1QehyKh3b6Ur0BDCSpogQghA6RZr1aoVU6dO5bvvvmPjxo2Z7ufn50doaCiurq64ubkRHR2d5b6p\nsgsRc3Nz6tWrx7x58+jdu3eGfXMKoLQDNHLD09OTUaNGsW/fvhzrLu7atoVTp5SuQLVJWAkhuHTp\nUrrBBsHBwbi4uGTYT6vV4u/vT0hICGFhYYSFhbFlyxb8/PyyPHZuRguPGTOGOXPmULp06Tx/tk2b\nNkRFRXHmzJlsryHHxsbyxx9/6L/O6nsUT6XtClSbDEMTQhATE8OwYcOIjY0lOTkZNzc3VqxYAaSf\n0+/w4cM4OztTPs0V96ZNm3Lu3Dnu3LmDk5NTpvP/pb1mBcrw9/Lly+v3rV27NrVr185wPo1Gk+6a\nFSjzEz47z+Bnn31Gt27dMpw37T46nY7p06dz9epVTE1NsbKyYs2aNc//QysmfH1h3jz1uwLlPish\nhBBZevIEKlSA0FB1RwVKN6AQQogsWVpCp06g9uU9CSshhBDZ6t0b/P3VrUG6AYUQQmQrtSvw3Dll\nrQZpWQkhhMhW6qjAzZvVq0HCSgghRI66d4ctW9Q7v3QDCiGEyFFsrNIFGBEBWUxWUqCkZSWEECJH\nNjbQogXs3KnO+SWshBBC5Er37updt5JuQCGEELkSGQlubspcgVZWhXtuaVkJIYTIFQcH8PaG/fsL\n/9wSVkIIIXKtWzd1ugKlG1AIIUSuhYdDgwZw6xaYFeJU6NKyEkIIkWtVqoCzM/z3vMssBQYG5ut5\nJayEEELkSW5uEJawEkIIoarU61aFeQVIwkoIIUSe1K0LpqZw5kzhnTNfBlgIIYQQz8rPwXcvPJZD\nRgIKIYQoaNINKIQQwuBJWAkhhDB42YaVqakp3t7e+uXq1asALFiwACsrK6Kjo/X7BgYGYmdnh7e3\nN25ubowdO7ZgKxdCCFHgTExM0v0+nzt3LtOmTQNg6tSpODs7p8uJR48e6fcdNWoUzs7O6S4X/fjj\njzg4OOiz4ptvvsldHdm9aW1tTXBwsH6pVKkSAH5+frRp04ZNmzal279Zs2YEBwcTEhJCQEAAx44d\ny1URQgghDJOFhQWbN2/m/v37QPpBdRqNhtGjR6fLCTs7OwC0Wi3btm2jdu3aHDp0KN1n+vbtS3Bw\nMH/99RezZ8/m5s2bOdaR527Af//9l6SkJCZOnIifn1+m+1haWuLl5aVviQkhhCiazM3NGTJkCPPn\nz8/0/awG2QUGBuLp6cngwYMzZEXqZ8qUKYObmxvXrl3LsY5swyo+Pl7ftOvZsycA69atw9fXl4YN\nG3L58mXu3r2b4XMPHjzgr7/+wt3dPccChBBCGLZhw4bxyy+/pLv0A0rozJ8/X58TrVu31r/n5+fH\nm2++SefOndm5cycpKSkZjhsREcGVK1eoXr16jjVkG1ZWVlb6pt3GjRsBJax69+4NQLdu3fD399fv\nf/jwYby8vHBxcaFbt27UqVMnxwKEEEIYNltbW/r378+iRYvSbX+2G/DAgQMAJCYmsmvXLjp37oyN\njQ2vvvoqu3fvBpSAW79+PZ6enrz88stMmjSJMmXK5FhDnroBz549y6VLl3j99ddxdXVl3bp16Zp3\nTZs25dSpU5w7d47NmzfnqmknhBDC8I0aNYqVK1cSGxubbntm3YB79uwhKioKd3d3XF1dOXz4cLqs\n6NOnD6dPn+bYsWMsXLiQx48f53j+PIWVn58f06ZNIywsjLCwMG7cuMHNmzczXJuqXLkyH330ETNm\nzMjL4YUQQhgoe3t7fH19WblypX6QRVbXq/z8/Fi5cqU+K8LCwti3bx/x8fHpPufj40OnTp0ytNgy\nk21YPTuV0vr16+nevXu6bd27d2fdunVoNJp0+7///vvs3r2b69ev51iEEEIIw5T29/qYMWO4d+9e\nuvfSXrPy8fEhNDSUPXv20LFjR/1+1tbWNGnShO3bt2fIik8//ZTFixcTFxeXfR0vOjegEEIIUdBk\nBgshhBAGT8JKCCGEwZOwEkIIYfAkrIQQQhg8CSvxwkqWLJnu6x9//JERI0YAMHDgQP0N5c/uHx4e\njomJCZ9//rn+vXv37mFubq7/fCovLy/69u2bbtvAgQNxdnYmMTFR/1lXV9dMa0ydlLlmzZp07dqV\nmJiYDNs7duyYbhLOf/75h1atWuHp6Ym7uzszZ84E4Nq1a1StWpWHDx8C8PDhQ6pWrcrVq1cJDw+n\nbt26gDLdjImJCStXrtQf89SpU5iYmDBv3rx0P58ePXrg7e1N9erVKV26tH50VatWrRg/frz+8xER\nEVSrVi3dTALDhw/H29ubOnXqYG1trf9s6tydM2fOxN3dHU9PT1q3bs25c+cy/RklJSUxfvx43N3d\n8fLyokGDBmzfvj1D7Xv27AGUkcCZ1fznn39menwhXoSElXhhz97i8OxEl9m97+rqys6dO/Vf+/v7\n4+7unm6f0NBQLC0tOX78eIbhrWZmZvzf//1fjjWmTsp8/vx5bG1t+f777zNsd3Bw4IcffgAgKiqK\nTp06MX36dE6fPk1QUBBBQUF89dVXuLi48MEHH+hDZPz48QwdOlQ/0XNa7u7ubNiwQf+1n58fnp6e\nGX4+mzZtIjg4mBUrVtC0aVP9jAA7d+5ky5YtnD9/HoCRI0cyc+ZMSpUqpT/Gd999p9+3WrVq+s/2\n6NGD2bNnExwczMmTJzl9+jRTpkzJEMqpRo0aRXR0NKdPn+bUqVMEBATw5MmTdLV36tRJf3Pn5s2b\nM625YcOGOf73ECKvJKxEvnv2bojs7o6wtramVq1aBAUFAbBhwwZ8fX3TfcbPz4++ffvyxhtvsHXr\nVv12jUbDyJEjmT9/PlqtNtf1NWnShLCwsAzbGzZsSEREBABr166lTZs2NGnSBIASJUrw7bff6ifz\n/Pjjj/nzzz9ZsGABf/zxR5aPxKlcuTIJCQncvXsXnU7Hnj17aN++fbrvL6vXoEwKPX/+fD788EN2\n7txJbGxshhZmVp/V6XQsXLiQRYsWYWFhAShPRmjdunWGiUWjo6NZv349CxYswNTUFABHR0f91Go6\nnY5NmzaxZMkSDh48SEJCQpbnFaIgSFiJF5Z2wmNvb2+mTJmSoTWVnT59+rBu3TquX7+OqakpFStW\nTPd+aoD5+vpm+CVbqVIlmjRpwpo1a3J1zuTkZHbt2kXt2rXTbU9JSWHfvn14eHgAEBISQv369dPt\nk/pcnnv37mFmZsZXX33F6NGj0/2Cz0yvXr3w9/fn2LFj+Pj4UKJEiRzrTKt9+/bY29szcOBAfcsv\nN1InmX7ppZfSba9fvz5nz55Nty00NJTq1avrQ+1Zf/zxB9WqVaNixYq0aNGCgICAPH0PQrwoCSvx\nwtJOeBwcHMz06dP1f21nFiDPbmvbti379u1j3bp1vPnmm+neO3HiBA4ODlSoUIHmzZtz6tQpoqKi\n0h1rwoQJfP3119m2rlID1cvLiwoVKjBs2LB02ytUqMC1a9d4//339Z/JqsWQun3Xrl1UrFgxwy/+\nZ/Xu3ZsNGzboW4jP48MPP6RBgwa5mp06JzqdLs+tIT8/P30rq3fv3lk+HkiIgiJhJfJd2l+ENjY2\n+sEMoHQ3PTsgw9zcnHr16jFv3jx69+6doQswNDQUV1dX3NzciI6O5tdff033eTc3N7y8vFi/fn2W\nNaUGakhICEuWLMHc3Dzd9oiICKytrfXdjHXr1tV3Taa6fv06Go0GBwcHTp06xf79+zl27Bjz58/n\n9u3bWZ7byckJCwsL9u/fr3+EQl5anqn7m5jk7X9XR0dHdDodN27cSLc9KCgo3XUzgJo1a3Lp0qV0\n3XupUlJS2LhxI9OmTcPV1ZURI0awZ8+eXE0+KkR+kbASBap58+b4+/vrWz2//PILLVu2zLDfmDFj\nmDNnDqVLl9Zv02q1+Pv7ExISop8Mc8uWLen+qk8Nts8++4y5c+c+d51WVlYsWLCAzz77DJ1OR58+\nfdi7dy9Hjx4FICEhgZEjRzJq1Ch0Oh0ffPABCxcuxMXFhXHjxmV5zSrV9OnTmTNnjj5wCuM6T+o1\nvZEjR+pHTB4+fJj9+/dnaOHZ2dnh6+vLmDFjSE5OBpTRlb/++isHDhzA09OTq1evEhYWRnh4OD16\n9GDz5s0F/j0IkUrCSrywzEb7pW7r1asXHh4euLu74+HhQUBAAAsWLMjw2dq1a/POO++k+/yRI0dw\ndnamfPny+v2bNm1KaGioviWT9vP16tXLssWSm+1eXl64ubmxYcMG7O3t2bFjB59//jmenp74+Pjg\n5eXFp59+yvLly6lSpYq+lTRs2DBCQ0M5fPhwhmOmvm7UqBFdunTJ9Lw5jZ7MbntO3+eECRPw8PDA\nx8cHT09PpkyZQkBAQLrRhKkWLlyIjY0Nnp6eeHl50aFDB6ysrFi3bh09evRIt2/Pnj1Zt25dnmoT\n4kXIRLZCCCEMnrSshBBCGDwJKyGEEAZPwkoIIYTBM1O7ACGESExMJDQ0lDt37qhdSpGl0WioWLEi\nNWrUwMzM+H61ywALIYSqYmJiGD9+PK1ataJ8+fIysvA5abVarl+/zrFjx/jqq6/yPFOKoZOwEkKo\natSoUUyaNIly5cqpXYpRiIiIYNmyZcyaNUvtUvKVXLMSQqgmKSmJMmXKSFDlo8qVKxtlN6CElRBC\nNbGxseluUNbpdDRt2pTdu3frt/n7+9O+fXuWLVuGh4cH3t7eNGrUSD+7CECVKlV48OCB/uvAwEA6\nd+4MKM9XmzZtGgBTp07F2dkZLy8vXn75ZdauXav/zMCBA6latap+QubUGfczM2rUKP3ExplJe36A\nScyQCr0AACAASURBVJMm0aFDBxITE2nRogVBQUE0bNgQb29vKleujKOjI97e3nh4eGBvb6+fr9LZ\n2Vk/p2Xjxo0z/bmkfl/ffPON/j1j7Eo1vvgVQhRZGo2GJUuW0Lt3b1q2bElSUhKfffYZAwcO5Mcf\nf+TIkSOUKlWKc+fO0b59ewIDA3F1dc32l/OzM4SMHj2a0aNHc/nyZRo0aICvry9mZmZoNBrmzp2b\nYbaOZ2m1WrZt20bt2rU5dOgQLVq0yHb/mTNncuzYMXbu3ImFhYV+xo/Uh1SuXr2aoKAgFi1alO5z\n06ZNw9bWltGjRwPKw0Cf/bmkPgjTGMPpWRJWQgiDUqdOHTp37szs2bOJi4ujf//+bN26lfnz5+tb\nYbVr1+Z///sfS5cuZfbs2dkez8rKCltbW/3Xqa0hNzc3LC0tuX//Pk5OTuney05gYCCenp68+eab\n+Pn5ZRpWqeHxzTffsGfPHvbs2ZPlgIfsZsFPuz315zJnzhweP37MgAEDsnwytjGSsBJCGJwpU6bg\n7e2NpaUlf//9N1988UWG54vVr1+fb7/9Nsdj+fr6Zro9KCgIU1NTHB0dASUYxo0bx8yZMwHlKc8/\n/fRThs/5+fnx5ptv0rlzZ8aNG0dKSkqG55npdDqOHDnChQsXOHnyJNbW1lnWl5dWUdqfy4kTJ3L9\nOWMgYSWEMDjW1tb06dMHW1vbLB8ImbZFkpvnpqV+Zv78+axatYrz58+zadMm/X656QZMTExk165d\nLFiwABsbG1599VV2795Nx44dM5y7evXqREVFsXfv3hy7FnMr7c8l9TE3ac9pzGSAhRDCIJmYmOgf\nqVK3bt0MLYm0z+WysbEhOjpa/15mz02Dp9esQkJC2Lp1K1OnTs1TTXv27CEqKgp3d3dcXV05fPhw\npg+i1Ol0ODk5ERAQwKhRowgMDMzTebJjYmKSIZimTJmiv7ZlrCSshBAGK7XlNGrUKD755BN9IIWG\nhrJ8+XKGDBkCKM9NSw0NrVaLn59fps9NS3vMjh07UqlSpXQjAnO6ZuXn58fKlSv1z1cLCwtj3759\nxMfHZ7p/9erV2bRpE2+//TanT5/Oth6RPQkrIYSqsvtlndqC6Nu3L2+//TZNmjTBy8uLQYMG8csv\nv+gHGMycOZO///6bunXrUrduXRwdHXnvvfeyPSbA5MmT+eKLL/Q1jBs3Tj903cfHh6SkJP2+cXFx\n7NmzJ12Xn7W1NU2aNGHHjh0ZzpF6nvr167Nq1Sq6dOnClStXMq3nRZ7DBrB06dJ019eMMQBlBgsh\nhGqSk5P54osvmDx5stqlGJUpU6bo7y0zFtKyEkKoxszMjEePHnHr1i21SzEaly5dUruEAiEtKyGE\nquLi4vj000/x8fGhYsWKqoxqS0lJSTey8Nmh6GlptVoA/eAPQ6HVarl27Rrnz59nzpw5RjflkoSV\nEEJ1KSkpXL16VbUW1owZM4iJiSEpKYlGjRpleW/W7t270Wg0tG3btpArzJlGo+Gll17CxcXFKIex\nS1gJIYq9FStWMHfuXBISEli9ejXNmjXLsM/u3bv5999/+fDDD1WoUBhWO1YIIVTQuHFj7ty5g6mp\nKd7e3hne//333zlz5owElYqMq1NTCCGeQ61atXBzc8PW1jbdPIKg3Hy8f///t3ffgTWe7QPHvyKE\n1whCNUGNKrFq1ghNrajxpkbt1t6KqtHWfhtVxWsEfVGjaFFFQyRWiURi75qxKbUiCYnIkDy/P57f\nczchIuQk55zk+vzTk+ScJ3clOddz3/d1X9fOTJddZ20kWAkhxP97tq9WcHAww4YNIzAwMFPuA1kT\nCVZCCAE0bNiQXLlyqY9v3rxJx44d8fT0tLjMv6xIgpUQQqC3HSlevDgAoaGh9O7dmypVqry0X5XI\nGHK7IIQQ6Idpa9asSWRkJC1atKBw4cKZ7qySNZOfhBBCoFfTyJMnDz179uTRo0dMnjzZ3EMSiWT6\nc1axsbGcPn1atQtwcnJKtnWAsHzx8fFcvnyZS5cuAXoH2NKlS/PWW28BlldRQJhPQkICf//9N46O\njgApVqQwjBs3jidPnjBs2DBKliwpCRUWJlMGq4SEBNauXQuAh4cH58+fV19zcnKiR48euLm5AVCt\nWjUKFixolnGK1Dlw4AAAEyZMYOfOnc99vVOnTgDMnTtXdX0VWVtwcDBffPEF69evB0ixUy/A48eP\nqVatGuvXr6d06dKEhYWpm6JixYpRvnx5CV5mJreiQogsT9M01q5di7Ozs7mHIl4g082swsPD6dq1\nK1u3bgXg448/xtXVlfDwcADmzZtHSEiIen7NmjVZuXIloGcDCcsQEREBwNSpU5k6dSoAlSpVolu3\nbpQvXx7QlwVPnjzJokWLAHjy5AmTJk0CoE+fPpw6dYq6desCPNcCXGRuq1evZsyYMZw5cwbghUv/\nJ06cAGD27Nmqm29ERARPnz4lOjoagLi4ONzd3ZkxYwaA+v17kcuXL+Pr6wvAkCFDZHnaRDJNsIqK\nigKgZcuWXL9+HW9vb0Bvh51YdHQ0169fx9PTE4Bly5axZMkSANzc3ChatGgGjlok5+nTp6rL6+XL\nl5k7dy4Abdu2TXbv4d69ewBMnz5d/Vzt7e158OAB8+bNA/Q3DZF1rFixghEjRqhg9eabbyb5+oMH\nDxgyZAi//vorAKVKlaJbt26Aft4qe/bs5M6dG4CTJ08ya9YstZ0wYMAApkyZgoODQ7Lfe9CgQWrp\n+vDhw5JRaCKZIlglJCTQokULQD/It2fPnhf+IhkCAgIA/RfTmFHNnDmT5s2bp+9gxUsFBATQuHFj\nALZs2fJKFa5Pnz4NwNixY/Hx8VEb7AcPHlRnaETmd+TIERo0aMC2bdsA1FmpjRs3AtC9e3eKFCnC\n4sWL1ddTmgFFR0cze/ZsAMaPH89bb73FqlWrAHBxcQHgjz/+AKBZs2bq5si4WRJpJ/NTIYQQlk/L\nBNauXasBGqB5eXm90mvHjBmjXjtmzBgtNDQ0nUYpUqt79+5a06ZNtaZNm6bpOkeOHNFy5Mih5ciR\nQ+vXr5+JRiesQUREhObg4KBNmDBBmzBhgqZpmjZ58mTNxsZGs7Gx0T799FMtJCTkta69b98+rUSJ\nEpqtra1ma2urbdq0SQsNDdXKli2rlS1bVqtUqZJ29+5d7e7du6b8X8ryrHoZ0OjYWb9+fcLCwgB9\nfdnOzi7V17h58yZVq1YFIH/+/Pj7+1OyZEnTD1akysOHD3F2dmbmzJkAdO3aNU3X69GjBwArV67k\n8OHD1KpVK81jFNahffv2KmW9ffv2tG7dmqZNmwKwefPmJHUAX9W9e/do1aoVAPfv36dcuXJqGfDg\nwYPUrl07jaMXz7LqYOXj4wOAu7u72lgfNmzYK1/Hw8MDgEmTJrFixQo6d+4MQM6cOU00UpFaR44c\noXXr1hw8eBAgzftM586dA/RMz169erFs2bI0j1FYh/nz5zNlyhRAzwaMjY1l//79gH7eMq1OnjwJ\nwAcffMDDhw9Vd2HjjKcwLdmzEkIIYfGsOqdy+/btgH6GpnXr1q99nX79+gEwefJkAgMD1d28kZEm\nMs6OHTtwdHRUWXxpVaFCBQD69+/Ppk2biIyMBF587kZkHq6urowYMQKAO3fusGHDBpPMqAzG9kGH\nDh1YsmQJ9erVM9m1xfOsdmb15MkT/Pz88PPzo0KFChQrVoxixYq91rUcHBxwcHCgTJkyHDhwgDfe\neEPK9mQwTdPQNA0vLy9y585N9uzZU1XPLbUGDRpEVFQU586dU0uDInNLfLbKzc2Ndu3apcv3MW52\nw8PDVfEBYXpWG6yEEEJkHVa7DHj16lXOnj0LwOeff56mU+JGIkWpUqXYv3+/zKrMwMjziYuLo0CB\nAia/ftmyZXF0dOTvv/82+bWFZfLy8iIuLg7QV2I0TUuXYrTVqlWjVKlSMmNPZ1YbrGJiYtTjwoUL\nm+Sab7zxBhEREap0k8g4RvWA3Llzm+znmVj27NmlPmAWc+rUKfX4zJkz3Llzx2R7oYnlzJmT6tWr\nc+zYMUC/4ZLfNdOz2mXAxG0/TFVGx9gjsbW1lXpeZlK0aNEkhYZNJVu2bGTLlo2YmJgkNzoi87pw\n4QJ2dnbY2dnx6NEjVXopPTg7O3Pp0iUuXbok+1bpxGqDlRBCiKzDaqcPd+7cUY/v379vkmsa69lG\nawCR8RwdHdm4caP6GaSlykBimqaRkJAgyzNZxMOHDzlw4ACffvopAKGhoaxbt45evXqly/e7cuUK\nefLkAf7ZfxWmZbXBqkSJEupx4rXptDCWh0yZMi1eTcGCBblz547q0lq5cmWTXDcuLo7IyEiTBT9h\n2e7du0dERITqqFCmTBk+/vhjDh06BGDSckghISFs3rxZnfWUBK30YbXBqmbNmuqNJyAgQCVFvKx9\ndUqMO6InT56kfYDitRhvIkYjPFMFq2vXrvH48WPpBJtFGO8HsbGxADRt2pSEhAT1e2XKYOXv709U\nVBRNmjQx2TXF82TPSgghhMWz2plV6dKlVUO1bdu2qc6caSmRFBUVhZ2dHUWKFDHFEMVrqF27NjY2\nNqpIsak6/G7YsIEiRYpIA8YsIn/+/ADqXF3evHlp2LChWl42BWNfdcqUKVSuXJkuXbqY7NrieVYb\nrEAv+w96sBo3bhyg15bLly/fK13HSDUNDAykYcOGEqzMyMnJiQ8//JCtW7cC+s/WFN2bd+zYQaNG\njSTBIosoWrQohQsX5tq1a+pzTZo0YdeuXSb7HrNmzQLgxIkT7N69O01bECIVzNRHyyQeP36sPX78\nWKtVq1aSBoqvysvLS/Py8tIAbcmSJekwUvEq/Pz81M+zXLly2l9//aX99ddfr3WtixcvahcvXtQK\nFCigbd682cQjFZasRYsWmr29vWZvb6/Fx8drW7Zs0RwdHTVHR0ctLCwsTde+ceOGVqBAAa1AgQJa\n+/btTTRikRLZsxJCCGHxrLr5oiEoKIgPPvhAfbx27Vq1RJgaHTp0AGDr1q1cunQpSbVmYR4DBw4E\nYNGiRarb7/Lly1/5Okan4Rs3bhAYGJguteGEZZo/fz5Dhw4F9GXg2rVrqyMv69at48MPP3zta3fp\n0gVvb28Ajh07Rvny5dM+YJEyc0/tTGXGjBnajBkzNEDLlSuX5u3trXl7e7/0devXr1dLTsOHD8+A\nkYrUuHHjhnbjxg2tVKlS6uczefJkLSYmJtXX2LFjh1qq2bt3bzqOVliiy5cva3Z2dpqdnZ3WpUsX\n7enTp1rx4sW14sWLaxMmTHjt6y5cuFADtKlTp2pTp0414YhFSmQZUAghhMXLFMuAiXl4eDBp0iTV\n9mPatGkMGTLkucK0S5cuBfTU6Bo1agCwZcsW7O3tM3bAIkWHDh2iW7dugF6YdNSoUUycOBFAZX0m\nJCSo/96+fRuAFStW4OHhwciRIwGYOnVqRg9dWID//Oc/AHzzzTf89NNPnDx5EoAjR44QGBj4Stfy\n9PQEYPjw4TRv3hwvLy/AdCXBRMoyXbACfZ9izZo16uMGDRowYMAAAOzt7Zk7dy47d+4E9P2qxYsX\nq68Jy2O8wXTv3p0///xTlbNxdnbmjTfe4OHDh4Beif/WrVuAHrh69erFwoULgX96lomsxahk0bhx\nYw4dOkSVKlUAuH79Onv27OHdd99N1XU2btxI27ZtAf39ZMOGDVJWKYNlumD16NEjqlevru6wS5cu\nzYULF3j69Kl6TtGiRRkzZgygtzuXNzLrMHnyZDWrArCzs6NEiRKqwV6JEiWoVKkSoG+Au7q6SkKF\nAODs2bP079+fvXv3qs+1adOGDRs2AP/0U0vOli1baN++PdWrVwf0BC45XJ7xZM9KCCGExcs0Mytj\n32LUqFHMnj1bfd7Dw4PWrVurPYtixYoxatQoSU+3IkY1i5YtWyb5fI8ePVi+fLmqlm9nZ5fhYxPW\nZfDgwQAsWLAAQBWf7devH9WqVQPgrbfe4sGDB8ydOxeAGTNmUKZMGbV1ULp06YwetiCTBKvo6Gh6\n9+4NkGSvCvRfvCNHjkgJJStm9CB69pxVzpw58fb2TtN5GZE1jRkzhu+//z7J54ztAHt7e6Kjo4mI\niACgbt26LF682GQdAMTryRTBKvEvXqFChQgLC1Nr0PHx8cycOZMRI0aYc4jiNV29elW1cwgJCcHG\nxka1+Th79izOzs4EBQUB4ODgYLZxCusSGBiIq6srZcuWBVBJOqA3c3V2dmb69OkANGvWTGbtFkD2\nrIQQQlg8q55ZGW0k3N3d6dSpEwD16tVj+PDh9O3bF4D169eTN29ejhw5AuiZgMJ6fPXVVypjK2/e\nvFy7do2jR48CMHv2bH744QdVUsfYYxDiZSIjI6lSpYqqyl6gQAFVNb1q1aosX75cUtMtjFUHK6N3\n1YEDB7h58yag9y3q37+/2gz19fVl9uzZ6nDgpEmTzDJW8WquX78OQKVKlRg/fjygd2SNjIxUy37h\n4eHUq1ePixcvArBnzx5cXFzMM2BhdXr37s1PP/2kPjbS0Y8cOSI3tRbIapcBt27dyu7du9m9ezdf\nfvklhQoVolChQupQaI4cOciRIwejRo0if/78LFmyhCVLlqjzV8KyrV+/nvXr15MzZ07atGlDmzZt\nuHTpUpJMrAIFCjBp0iTi4+OJj4/nv//9rxlHLKxNhw4dsLGxUfvbQ4cOZejQoRKoLJTVBishhBBZ\nh9V2Cp4zZ45aUx41apT6fHx8fJLnOTk5MXjwYJUtuGHDBpO1Shfpx6i71rNnT5X9Z7QoT6xz584q\npd3Ly4vdu3cD0KhRo4wZqLBazZo1U3VBr1+/zqBBg8w8IpESqwxWO3fuZMeOHXz++eeAvvFuePDg\nAUCS/jKDBg1SRSg9PT3p3LkzhQsXzsARi1dx/vx5/vzzTwAmTJigSmXFxcUlWzR02LBhAGzfvp0Z\nM2YAEqzEy8XExKi90dq1a6vCyMIyyTKgEEIIi2dVMyvjRPkXX3yBg4ODyvBL7NatW+TJk0eloYJe\nxcJoM/Hjjz+yfPnyJEuHwrJs2rRJVcBv2LChyvR8+vQpJUuWfO75LVq0APTZlFGaKSAgIEn3aCGe\ndffuXe7fvw+gitQKy2VVM6vVq1ezevVqTp8+zciRIylQoAAFChRI8pxHjx5RpEgRcubMmaSa+ief\nfMInn3wC6CWZnj59mqQSu7AcPj4+uLu74+7ujp2dHZGRkURGRgIQGhr63POzZctGtmzZ1Nk60LMJ\nhUjJnTt31GOpom75rGZmFR8frzbSc+XKpXrLGIxCthERERQ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- } - ], - "prompt_number": 237 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# TODO: I shold redo the above images with plt.figimage() - see ", - "%loadpy http://matplotlib.sourceforge.net/mpl_examples/pylab_examples/figimage_demo.py", - "# but i think IPython clips them out" - ], - "language": "python", "outputs": [], - "prompt_number": 242 + "prompt_number": 20 }, { "cell_type": "code", "collapsed": true, "input": [ - "\"\"\"", - "This illustrates placing images directly in the figure, with no axes.", - "", - "\"\"\"", - "import numpy as np", - "import matplotlib", - "import matplotlib.cm as cm", - "import matplotlib.pyplot as plt", - "", - "", - "fig = plt.figure()", - "Z = np.arange(10000.0)", - "Z.shape = 100,100", - "Z[:,50:] = 1.", - "", - "im1 = plt.figimage(Z, xo=50, yo=0, cmap=cm.jet, origin='lower')", - "im2 = plt.figimage(Z, xo=100, yo=100, alpha=.8, cmap=cm.jet, origin='lower')", - "", - "plt.show()", - "", - "" - ], - "language": "python", - "outputs": [], - "prompt_number": 243 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\"\"\"", - "This illustrates placing images directly in the figure, with no axes.", - "", - "\"\"\"", - "import numpy as np", - "import matplotlib", - "import matplotlib.cm as cm", - "import matplotlib.pyplot as plt", - "", - "", - "fig = plt.figure()", - "Z = np.arange(10000.0)", - "Z.shape = 100,100", - "Z[:,50:] = 1.", - "", - "im1 = plt.figimage(Z, xo=50, yo=0, cmap=cm.jet, origin='lower')", - "im2 = plt.figimage(Z, xo=100, yo=100, alpha=.8, cmap=cm.jet, origin='lower')", - "plt.show()", - "", - "" + "# One could redo the above images with plt.figimage() - see figimage_demo", + "# %loadpy http://matplotlib.sourceforge.net/mpl_examples/pylab_examples/figimage_demo.py", + "# but i think IPython clips them out right now. TODO: file this bug" ], "language": "python", "outputs": [], - "prompt_number": 245 + "prompt_number": 21 }, { "cell_type": "markdown", "source": [ - "# TODO: ", - " - mention or demonstrate twinx(), since intelligene and inanity are of different units", + "# MISC (time permitting): ", + " - mention or demonstrate twinx(), since intelligene and inanity are of different units (done in supplement below)", " - sharex and sharey that can be passed to subplot/subplots", " - if fernando hasn't already, poke around the ax internals", - " - we showed how to get rid of and set ticks manually, we can also do this programmatically via", + " - e.g. `ax.get_frame().set_color('red')`", + " - talk about the `rcParams` way of setting these, ", + " including in one's ``~/.matplotlib/matplotlibrc``", + " - we showed how to get rid of and set ticks manually, ", + " - we can also do this programmatically via", " locators and formatters on each axis", - "- potential homework, or other example: ", - "- ", + " - e.g. `plot_date` does this for us", + " - potential homework, or other example with a pointer to ", + " [broken_axis](http://matplotlib.sourceforge.net/examples/pylab_examples/broken_axis.html) example: ", + " ", "![One-sided](http://imgs.xkcd.com/comics/one-sided.png), ", - " with a pointer to [broken_axis](http://matplotlib.sourceforge.net/examples/pylab_examples/broken_axis.html) example" + "" ] }, { "cell_type": "code", "collapsed": false, "input": [ - "# scatter plot point of no return", + "# A few more, bonus, things...", + "# scatter plot and vertical line point of no return (try zooming in/out)", "plt.plot(x,intel)", "plt.plot(x,inan)", "", @@ -817,42 +475,25 @@ "", "ax.xaxis.tick_bottom()", "", - "# point out ax.transAxes vs ax.transData ", - "", - "plt.annotate(\"\\nINTELLIGENCE\", (x[40],intel[40]), (-10,-40), textcoords='offset points',", - " arrowprops=dict(width=1.0, headwidth=1.0, shrink=0),", - " ha='center'", - " )", - "", - "plt.annotate(\"\\nINANITY OF\\nSTATEMENTS\", (x[60],inan[60]), (10,-45), textcoords='offset points',", - " arrowprops=dict(width=1.0, headwidth=1.0, shrink=0),", - " )", + "plt.annotate(\"\\nINTELLIGENCE\", (x[40],intel[40]), (-10,-40),", + " ha='center', **noarrow_kwargs)", + "plt.annotate(\"\\nINANITY OF\\nSTATEMENTS\", (x[60],inan[60]), (10,-45),", + " **noarrow_kwargs)", "", "plt.scatter(x[:51:10],intel[:51:10],c='r', marker='*',s=150, label='Smart enough to save yourself')", "plt.axvline(x[50], color='r', linestyle='--', label='point of no return')", "plt.legend(loc='best')" ], "language": "python", - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 275, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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JyXbJBWDt2rXs3LmTmzdvEhgYyNixYwHYsmUL06ZNY/v27QQHB1OhQgU6duyY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\nzs5MbW0twzAM09LSwty8edPCNzM4RUUxTHz8i+0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f7amYOl8VAAAAAElFTkSuQmCC\n" - } - ], - "prompt_number": 275 + "outputs": [], + "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ - "# scatter plot point of no return", + "# axvspan is similar to axvline, but fills a rectangle. Both have horizontal analogues:", + "# They'r'e called axhspan and axhline.", "plt.plot(x,intel)", "plt.plot(x,inan)", "", @@ -865,45 +506,82 @@ "", "ax.xaxis.tick_bottom()", "", - "# point out ax.transAxes vs ax.transData ", + "plt.annotate(\"\\nINTELLIGENCE\", (x[40],intel[40]), (-10,-40),", + " ha='center', **noarrow_kwargs)", + "plt.annotate(\"\\nINANITY OF\\nSTATEMENTS\", (x[60],inan[60]), (10,-45),", + " **noarrow_kwargs)", "", - "plt.annotate(\"\\nINTELLIGENCE\", (x[40],intel[40]), (-10,-40), textcoords='offset points',", - " arrowprops=dict(width=1.0, headwidth=1.0, shrink=0),", - " ha='center'", - " )", + "plt.scatter(x[:51:10],intel[:51:10],c='r', marker='*',s=150, ", + " label='Smart enough to save yourself')", + "plt.axvline(x[50], color='r', linestyle='--', ", + " label='point of no return')", "", - "plt.annotate(\"\\nINANITY OF\\nSTATEMENTS\", (x[60],inan[60]), (10,-45), textcoords='offset points',", - " arrowprops=dict(width=1.0, headwidth=1.0, shrink=0),", - " )", - "", - "plt.scatter(x[:51:10],intel[:51:10],c='r', marker='*',s=150, label='Smart enough to save yourself')", - "plt.axvline(x[50], color='r', linestyle='--', label='point of no return')", - "", - "plt.axvspan(x[80],x[-1], color='r', label='Toxoplasmosis danger zone!', alpha=.1)", + "plt.axvspan(x[80],x[-1], color='r', ", + " label='Toxoplasmosis danger zone!', alpha=.1)", "plt.legend(loc='best')" ], "language": "python", - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 276, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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tIyIigpJv1opmFL8g5BRzczh+XNqTbOBAzV9seSfEm82+e/ix3kzNBpIHFLjk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\nCCFaqDatq3isZzk79937hLYSXIRorAf0hr54OEX+MTR2r8m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- } + "outputs": [], + "prompt_number": 23 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# twinx example - share the xaxis among two axes which are overlaid.", + "# panning horizontally in one will pan in both axes. There's a twiny() ", + "# equivalent for doing the same to the Y axis. See sharex= and sharey=", + "# keyword arguments to plt.subplot and subplots", + "# IQ should be normalized to 100", + "new_intel = intel / intel.max() * 100", + "# inanity is exponentially increasing", + "new_inan = np.exp(y)", + "ax = plt.gca()", + "ax.plot(x,new_intel)", + "ax2 = ax.twinx()", + "ax2.semilogy(x,new_inan)" ], - "prompt_number": 276 + "language": "python", + "outputs": [], + "prompt_number": 24 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# more clearly mark the two x axes", + "ax = plt.gca()", + "ax.plot(x,new_intel)", + "ax2 = ax.twinx()", + "ax2.semilogy(x,new_inan, 'r')", + "ax.set_ylabel(\"IQ POINTS\")", + "# the units of INANITY is Twits: see Monty Python's Twit Race and Twit Olympics", + "# http://www.youtube.com/watch?v=rCyr1ugzxXM", + "ax2.set_ylabel(\"TWITS\")" + ], + "language": "python", + "outputs": [], + "prompt_number": 25 + }, + { + "cell_type": "code", + "collapsed": true, + "input": [ + "# Challenge: poke around to try to set the ylabels ", + "# (named and numerical) of ax2 to be red.", + "# Hint: start with ax2.get_yt" + ], + "language": "python", + "outputs": [], + "prompt_number": 26 }, { "cell_type": "code", "collapsed": true, "input": [], "language": "python", - "outputs": [] + "outputs": [], + "prompt_number": 26 } ] }