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prototype figures #11

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69 changes: 69 additions & 0 deletions examples/example_figures.ipynb
Original file line number Diff line number Diff line change
@@ -0,0 +1,69 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"N = 5\n",
"menMeans = (20, 35, 30, 35, 27)\n",
"womenMeans = (25, 32, 34, 20, 25)\n",
"menStd = (2, 3, 4, 1, 2)\n",
"womenStd = (3, 5, 2, 3, 3)\n",
"ind = np.arange(N) # the x locations for the groups\n",
"width = 0.35 # the width of the bars: can also be len(x) sequence\n",
"\n",
"p1 = plt.bar(ind, menMeans, width, yerr=menStd)\n",
"p2 = plt.bar(ind, womenMeans, width,\n",
" bottom=menMeans, yerr=womenStd)\n",
"\n",
"plt.ylabel('Scores')\n",
"plt.title('Scores by group and gender')\n",
"plt.xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5'))\n",
"plt.yticks(np.arange(0, 81, 10))\n",
"plt.legend((p1[0], p2[0]), ('Men', 'Women'))\n",
"\n",
"plt.show()"
]
}
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