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@john-science
Last active September 20, 2017 17:25
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Plot Weights using `fill_between`.
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{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\" A quick matplotlib example of using `fill_between`.\n",
"\"\"\"\n",
"from datetime import datetime, timedelta\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use('ggplot')\n",
"%matplotlib inline\n",
"\n",
"\n",
"def file_plot(x, y):\n",
" \"\"\" A simple, layered example of fill between, plotting someone's weight,\n",
" but clearly displaying the systematic errors of the measurement.\n",
" \"\"\"\n",
" fig, ax = plt.subplots()\n",
" y_bottom = y - 1\n",
" y_top = y + 1\n",
" ax.fill_between(x, y_bottom, y_top, edgecolor='', color='blue', alpha=0.5)\n",
" y_bottom = y - 0.5\n",
" y_top = y + 0.5\n",
" ax.fill_between(x, y_bottom, y_top, edgecolor='', color='blue')\n",
" plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\" This is just some sample body weight data. \"\"\"\n",
"data = \"\"\"Date,Weight\n",
"2017-09-01-06-10,161.8\n",
"2017-09-05-06-10,161.2\n",
"2017-09-06-05-59,161.2\n",
"2017-09-07-05-49,160\n",
"2017-09-08-05-53,160\n",
"2017-09-11-05-49,163.8\n",
"2017-09-12-05-49,162\n",
"2017-09-13-05-07,161\n",
"2017-09-14-05-11,159\n",
"2017-09-15-06-05,159\n",
"2017-09-18-06-02,158\n",
"2017-09-19-06-05,158\"\"\".split('\\n')\n",
"\n",
"\n",
"def read_data(lines):\n",
" dates = []\n",
" weights = []\n",
" \n",
" for line in lines[1:]:\n",
" date, weight = line.rstrip().split(',')\n",
" dates.append(datetime.strptime(date, '%Y-%m-%d-%M-%S'))\n",
" weights.append(float(weight))\n",
"\n",
" return np.array(dates), np.array(weights)\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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0U6cCJc2faEQbN44r2LHyYx9z57XGQvtiN3R0DHH4cO6PbzhcenlMS2PKDaUm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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fecc8f7c410>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dates, weights = read_data(data)\n",
"\n",
"file_plot(dates, weights)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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