{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from __future__ import division, print_function\n",
"%matplotlib inline\n",
"import sys\n",
"sys.path.insert(0,'..') # allow us to format the book\n",
"sys.path.insert(0,'../code') \n",
"# use same formatting as rest of book so that the plots are\n",
"# consistant with that look and feel.\n",
"import book_format\n",
"book_format.load_style(directory='..')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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igxZy5Q6RmdnZ2GNp3P1Ii81Ec3s98su/QlF1rtHtsvoHenCi5K84UfJX+HuF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\nAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from gif_animate import animate\n",
"from stats import gaussian\n",
"import matplotlib.pylab as pylab\n",
"from matplotlib import animation\n",
"import matplotlib\n",
"\n",
"def plt_g (mu, variance):\n",
" xs = np.arange(2,8,0.05)\n",
" ys = [gaussian (x, mu, variance) for x in xs]\n",
" plt.plot (xs, ys)\n",
" plt.ylim((0,1))\n",
"\n",
"\n",
"mu = 2\n",
"sigma = 0.6\n",
"def ganimate(frame):\n",
" global mu, sigma\n",
" if frame < 25:\n",
" mu += .2\n",
" elif frame == 25:\n",
" mu = 5\n",
" elif frame < 37:\n",
" sigma -= 0.05\n",
" else:\n",
" sigma += 0.05\n",
"\n",
" plt.cla()\n",
" plt_g(mu,sigma)\n",
"\n",
"animate('04_gaussian_animate.gif', ganimate, \n",
" frames=80, interval=50, figsize=(6,4))"
]
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