Thursday Feb 19 2015 - intro to Pyndamics - orig.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:5cd23e5b9817171231ab66f5d3faec43fc45d4312154934d262c269e995289e4"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pyndamics import *\n",
      "import pandas  # for data stuff"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim=Simulation()\n",
      "sim.add(\"y'=constant*y\",0.01,plot=True)\n",
      "sim.params(constant=.4)\n",
      "sim.run(0,10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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eXz+Aea6OMd4EnAnsAr65v9eQJElqNpMnw7JlpbE3rbaWamwjuRZ4AdgGvAx4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\n/wHT/83NHVbErQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b69cd90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x10ba80a10>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data=pandas.read_excel('data/s009.xls',skiprows=[0,1])\n",
      "data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>day</th>\n",
        "      <th>height (cm)</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  0</td>\n",
        "      <td>  0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  7</td>\n",
        "      <td> 17.93</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 14</td>\n",
        "      <td> 36.36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 21</td>\n",
        "      <td> 67.76</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 28</td>\n",
        "      <td> 98.10</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "   day  height (cm)\n",
        "0    0         0.00\n",
        "1    7        17.93\n",
        "2   14        36.36\n",
        "3   21        67.76\n",
        "4   28        98.10"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t,y=data['day'],data['height (cm)']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim=Simulation()\n",
      "sim.add(\"y'=constant\",0,plot=True)\n",
      "sim.params(constant=1)\n",
      "sim.add_data(t=t,y=y,plot=True)\n",
      "sim.run(0,90)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GThTR1plVjjnlmFOeWeWYU55Z5ZhT+3X0ajmllPOoVHKmu98a4FXT3GclsHKr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\nb2ZmZmY2JP4f/OnCIO/Y+6sAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b801650>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x10c383f50>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim.mse('y')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "16474.867853846154"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
}