sandbox.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sandbox\n",
    "For playing around during development."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/houghton/anaconda/lib/python3.6/site-packages/xarray/core/formatting.py:16: FutureWarning: The pandas.tslib module is deprecated and will be removed in a future version.\n",
      "  from pandas.tslib import OutOfBoundsDatetime\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7.11\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pysd\n",
    "print(pysd.__version__)\n",
    "#print pysd.__file__\n",
    "import pandas as pd\n",
    "#import os\n",
    "# import xarray as xr\n",
    "import numpy as np\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "1\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "l = [1, 2, 3]\n",
    "\n",
    "while l:\n",
    "    print(l[0])\n",
    "    l.pop()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l = set([1, 2, 'burnside'])\n",
    "j = set(['hi', 'bob'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2, 'burnside'}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.update(j)\n",
    "l.difference_update(j)\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "l.remove('burnside')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2, 'burnside'}\n"
     ]
    }
   ],
   "source": [
    "if l:\n",
    "    print(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = pd.Series(np.arange(0,40,1))\n",
    "# y = pd.Series(index=range(0,40))\n",
    "# y[:10] = x[:10]**2 \n",
    "# y[10:30] = -1*(x[10:30]-20)**2 + 2*x[10]**2\n",
    "# y[30:40] = (x[30:40]-40)**2\n",
    "\n",
    "y = -1*np.cos(x*2*np.pi/40)\n",
    "plt.plot(x, y)\n",
    "\n",
    "d1 = y.diff()\n",
    "d2 = d1.diff()\n",
    "inc_growth = (d1 > 0) & (d2 > 0)\n",
    "dec_growth = (d1 > 0) & (d2 < 0)\n",
    "inc_decline = (d1 < 0) & (d2 < 0)\n",
    "dec_decline = (d1 < 0) & (d2 > 0)\n",
    "\n",
    "(inc_growth[inc_growth].index.max() < \n",
    " dec_growth[dec_growth].index.min() <\n",
    " dec_growth[dec_growth].index.max() <\n",
    " inc_decline[inc_decline].index.min() <\n",
    " inc_decline[inc_decline].index.max() <\n",
    " dec_decline[dec_decline].index.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = pysd.read_vensim('tests/test-models/samples/teacup/teacup.mdl')\n",
    "doc = model.doc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Room Temperature</th>\n",
       "      <th>Characteristic Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-366.160321</td>\n",
       "      <td>16.488710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>94.430933</td>\n",
       "      <td>38.938432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-176.090179</td>\n",
       "      <td>2.204597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1409.317356</td>\n",
       "      <td>3.426968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-416.883537</td>\n",
       "      <td>6.072245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>664.603176</td>\n",
       "      <td>0.747561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-237.021635</td>\n",
       "      <td>9.802032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>247.807415</td>\n",
       "      <td>15.158609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-276.607301</td>\n",
       "      <td>3.798534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-46.465066</td>\n",
       "      <td>7.966876</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Room Temperature  Characteristic Time\n",
       "0       -366.160321            16.488710\n",
       "1         94.430933            38.938432\n",
       "2       -176.090179             2.204597\n",
       "3       1409.317356             3.426968\n",
       "4       -416.883537             6.072245\n",
       "5        664.603176             0.747561\n",
       "6       -237.021635             9.802032\n",
       "7        247.807415            15.158609\n",
       "8       -276.607301             3.798534\n",
       "9        -46.465066             7.966876"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pysd.testing.sample_pspace(model, samples=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Real Name</th>\n",
       "      <th>Py Name</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Type</th>\n",
       "      <th>Comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Characteristic Time</td>\n",
       "      <td>characteristic_time</td>\n",
       "      <td>Minutes [0,?]</td>\n",
       "      <td>constant</td>\n",
       "      <td>How long will it take the teacup to cool 1/e o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FINAL TIME</td>\n",
       "      <td>final_time</td>\n",
       "      <td>Minute</td>\n",
       "      <td>constant</td>\n",
       "      <td>The final time for the simulation.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Heat Loss to Room</td>\n",
       "      <td>heat_loss_to_room</td>\n",
       "      <td>Degrees Fahrenheit/Minute</td>\n",
       "      <td>component</td>\n",
       "      <td>This is the rate at which heat flows from the ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>INITIAL TIME</td>\n",
       "      <td>initial_time</td>\n",
       "      <td>Minute</td>\n",
       "      <td>constant</td>\n",
       "      <td>The initial time for the simulation.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Room Temperature</td>\n",
       "      <td>room_temperature</td>\n",
       "      <td>Degrees Fahrenheit [-459.67,?]</td>\n",
       "      <td>constant</td>\n",
       "      <td>Put in a check to ensure the room temperature ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SAVEPER</td>\n",
       "      <td>saveper</td>\n",
       "      <td>Minute [0,?]</td>\n",
       "      <td>component</td>\n",
       "      <td>The frequency with which output is stored.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TIME STEP</td>\n",
       "      <td>time_step</td>\n",
       "      <td>Minute [0,?]</td>\n",
       "      <td>constant</td>\n",
       "      <td>The time step for the simulation.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Teacup Temperature</td>\n",
       "      <td>teacup_temperature</td>\n",
       "      <td>Degrees Fahrenheit [32,212]</td>\n",
       "      <td>component</td>\n",
       "      <td>The model is only valid for the liquid phase o...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Real Name              Py Name                            Unit  \\\n",
       "0  Characteristic Time  characteristic_time                   Minutes [0,?]   \n",
       "4           FINAL TIME           final_time                          Minute   \n",
       "1    Heat Loss to Room    heat_loss_to_room       Degrees Fahrenheit/Minute   \n",
       "5         INITIAL TIME         initial_time                          Minute   \n",
       "2     Room Temperature     room_temperature  Degrees Fahrenheit [-459.67,?]   \n",
       "6              SAVEPER              saveper                    Minute [0,?]   \n",
       "7            TIME STEP            time_step                    Minute [0,?]   \n",
       "3   Teacup Temperature   teacup_temperature     Degrees Fahrenheit [32,212]   \n",
       "\n",
       "        Type                                            Comment  \n",
       "0   constant  How long will it take the teacup to cool 1/e o...  \n",
       "4   constant                 The final time for the simulation.  \n",
       "1  component  This is the rate at which heat flows from the ...  \n",
       "5   constant               The initial time for the simulation.  \n",
       "2   constant  Put in a check to ensure the room temperature ...  \n",
       "6  component         The frequency with which output is stored.  \n",
       "7   constant                  The time step for the simulation.  \n",
       "3  component  The model is only valid for the liquid phase o...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Characteristic Time', 'Room Temperature'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(doc[doc['Type'] == 'constant']['Real Name']) - {'FINAL TIME', 'INITIAL TIME', 'TIME STEP'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not broadcast input array from shape (10) into shape (50000)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-160-e972c553e028>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistributions\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0muniform\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtruncnorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0munit_lhd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpyDOE\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlhs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msamples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mlhd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mppf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munit_lhd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mlhd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not broadcast input array from shape (10) into shape (50000)"
     ]
    }
   ],
   "source": [
    "import pyDOE\n",
    "from scipy.stats.distributions import uniform, expon, truncnorm, norm\n",
    "unit_lhd = pyDOE.lhs(2, samples=10, criterion='c')\n",
    "lhd[:,0] = expon(loc=0, scale=4).ppf(unit_lhd[:,0])\n",
    "lhd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan])"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expon(loc=-1, scale=-1).ppf(unit_lhd[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.13396347,  0.75099   ],\n",
       "       [ 2.10951415,  0.51567   ],\n",
       "       [ 4.38609788,  0.58637   ],\n",
       "       ..., \n",
       "       [ 0.0576535 ,  0.70835   ],\n",
       "       [ 2.52211701,  0.32877   ],\n",
       "       [ 1.63385841,  0.44381   ]])"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lhd = pyDOE.lhs(2, samples=50000, criterion='c')\n",
    "lhd[:,0] = expon(loc=0, scale=4).ppf(lhd[:,0])\n",
    "lhd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isinf(-np.inf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hi\n",
       "0   5\n",
       "1   5\n",
       "2   5\n",
       "3   5\n",
       "4   5\n",
       "5   5\n",
       "6   5\n",
       "7   5\n",
       "8   5\n",
       "9   5"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.DataFrame(index=range(10))\n",
    "a['hi'] = 5\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Unbounded Variable Value Above 0',\n",
       "       'Unbounded Variable Value Below 0', 'Both Bounds Above 0',\n",
       "       'Both Bounds Below 0', 'Both Bounds Identical',\n",
       "       'Unbounded Variable Value 0', 'Lower Bound 0',\n",
       "       'Lower Bound Above 0', 'Lower Bound Below 0', 'Upper Bound 0',\n",
       "       'Upper Bound Above 0', 'Upper Bound Below 0', 'Outflow',\n",
       "       'Lower Bounded Stock', 'Broken Upper Bounded Variable',\n",
       "       'FINAL TIME', 'INITIAL TIME', 'SAVEPER', 'TIME STEP'], dtype=object)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = pysd.read_vensim('tests/test-models/tests/variable_ranges/test_variable_ranges.mdl')\n",
    "model.doc()['Real Name'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(columns=['Unbounded Variable Value Above 0',\n",
    "       'Unbounded Variable Value Below 0', 'Both Bounds Above 0',\n",
    "       'Both Bounds Below 0', 'Both Bounds Identical',\n",
    "       'Unbounded Variable Value 0', 'Lower Bound 0',\n",
    "       'Lower Bound Above 0', 'Lower Bound Below 0', 'Upper Bound 0',\n",
    "       'Upper Bound Above 0', 'Upper Bound Below 0'], index=range(10), data=0)\n",
    "for i, row in df.iterrows():\n",
    "    a = row\n",
    "    break\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for row in df.itertuples():\n",
    "    a = row\n",
    "    break\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import scipy.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KstestResult(statistic=0.99999999999999978, pvalue=0.0)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = scipy.random.normal(loc=10, size=100)\n",
    "scipy.stats.kstest(rvs=data, cdf='norm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = scipy.random.normal(loc=10, size=50000)\n",
    "a = scipy.stats.kstest(rvs=data, cdf='uniform', args=(10,100))\n",
    "a.pvalue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If there are two ends defined, use a uniform distribution between them\n",
    "If only one end is defined, use exponential distribution with current location set as mean, other end as location\n",
    "If neither end is defined, use normal distribution with current value as mean, magnitude of current value as scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/houghton/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:9: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n"
     ]
    }
   ],
   "source": [
    "def sample_pspace(model=None, bounds=None, param_list=None, samples=100):\n",
    "    \"\"\"\n",
    "    In iterator over various locations in the parameter space,\n",
    "    useful for\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    model\n",
    "    bounds\n",
    "    param_list\n",
    "    samples: int\n",
    "        How many samples to include in the iterator?\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    params: dictionary\n",
    "        dictionary of 'variable':'value' pairs\n",
    "    \"\"\"\n",
    "    if isinstance(bounds, pd.DataFrame):\n",
    "        bounds = bounds.set_index('Real Name')\n",
    "    elif bounds is None:\n",
    "        \n",
    "    elif isinstance(bounds, str):\n",
    "        if bounds.split('.')[-1] in ['xls', 'xlsx']:\n",
    "            bounds = pd.read_excel(bounds, sheetname='Bounds', index_col='Real Name')\n",
    "        elif bounds.split('.')[-1] == 'csv':\n",
    "            bounds = pd.read_csv(bounds, index_col='Real Name')\n",
    "        elif bounds.split('.')[-1] == 'tab':\n",
    "            bounds = pd.read_csv(bounds, sep='\\t', index_col='Real Name')\n",
    "        else:\n",
    "            raise ValueError('Unknown file type: bounds')\n",
    "    else:\n",
    "        raise ValueError('Unknown type: bounds')\n",
    "\n",
    "    if param_list is None:\n",
    "        param_list = list(bounds.index)\n",
    "\n",
    "    pyDOE.lhs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = pysd.read_vensim('tests/test-models/samples/pendulum/Single_Pendulum.mdl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d860d30>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dU67nxjw4ODg4OJoNFzw4ODg4OJoNFzw4WowHDx4gMTERXbt2RUREBJ577jnc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\n4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LKyMiC8ziN2nHptiBV9IR5ucruxbid2bHbHYhfpLhJMF24/uVvDVqn4RcR1cfI8jwTI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\nCgoKPM/Dw+DE4sWLefzxx2lvb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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ca38630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = model.run()\n",
    "res.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/houghton/anaconda/lib/python3.6/site-packages/numpy/core/numeric.py:531: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11da67cc0>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wRwzxF57nHbZmlr/OeTNzrb+YgT/CovM68m6CmRmdcx34086Bb2bNYPGcfLOokIE/8p6g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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d9e8080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_series = res['Angular Position']\n",
    "#def is_periodic(t_series):\n",
    "fft = np.fft.fft(t_series)\n",
    "plt.plot(fft)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 32, 158]), array([ 1.6,  7.9]), array([ 0.9995736 ,  0.99894134]))"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cX/4DH/vJgNsG+Mpo91WBo7QwjWe2n+JgXRuLp2nRfji6PtL4DKwzfHZZvtXh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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dcb9470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import signal\n",
    "xs = np.arange(0, 4*np.pi, 0.05)\n",
    "data = np.sin(xs)\n",
    "plt.plot(xs, data)\n",
    "peakind = signal.find_peaks_cwt(data, np.arange(1,10))\n",
    "peakind, xs[peakind], data[peakind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peakind = signal.find_peaks_cwt(t_series, np.arange(1,10))\n",
    "multiple_peaks = len(peakind) > 1\n",
    "multiple_peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segment = int(np.mean(np.diff(peakind))/8)\n",
    "segment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measure = np.mean([t_series.autocorr(i) for i in range(1,segment)])\n",
    "measure > .75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.DataFrame(np.ones((3,3)))\n",
    "a['hi'] = 3\n",
    "a\n",
    "(a.index==2).argmax()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Real Name</th>\n",
       "      <th>Py Name</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Characteristic Time</td>\n",
       "      <td>characteristic_time</td>\n",
       "      <td>Minutes</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Heat Loss to Room</td>\n",
       "      <td>heat_loss_to_room</td>\n",
       "      <td>Degrees/Minute</td>\n",
       "      <td>This is the rate at which heat flows from the ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Room Temperature</td>\n",
       "      <td>room_temperature</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Teacup Temperature</td>\n",
       "      <td>teacup_temperature</td>\n",
       "      <td>Degrees</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FINAL TIME</td>\n",
       "      <td>final_time</td>\n",
       "      <td>Minute</td>\n",
       "      <td>The final time for the simulation.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>INITIAL TIME</td>\n",
       "      <td>initial_time</td>\n",
       "      <td>Minute</td>\n",
       "      <td>The initial time for the simulation.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SAVEPER</td>\n",
       "      <td>saveper</td>\n",
       "      <td>Minute [0,?]</td>\n",
       "      <td>The frequency with which output is stored.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TIME STEP</td>\n",
       "      <td>time_step</td>\n",
       "      <td>Minute [0,?]</td>\n",
       "      <td>The time step for the simulation.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Real Name              Py Name                Unit  \\\n",
       "0  Characteristic Time  characteristic_time             Minutes   \n",
       "1    Heat Loss to Room    heat_loss_to_room      Degrees/Minute   \n",
       "2     Room Temperature     room_temperature                       \n",
       "3   Teacup Temperature   teacup_temperature             Degrees   \n",
       "4           FINAL TIME           final_time              Minute   \n",
       "5         INITIAL TIME         initial_time              Minute   \n",
       "6              SAVEPER              saveper        Minute [0,?]   \n",
       "7            TIME STEP            time_step        Minute [0,?]   \n",
       "\n",
       "                                             Comment  \n",
       "0                                                     \n",
       "1  This is the rate at which heat flows from the ...  \n",
       "2                                                     \n",
       "3                                                     \n",
       "4                 The final time for the simulation.  \n",
       "5               The initial time for the simulation.  \n",
       "6         The frequency with which output is stored.  \n",
       "7                  The time step for the simulation.  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = pysd.read_vensim('tests/test-models/samples/teacup/teacup.mdl')\n",
    "model.doc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Model' object has no attribute 'reload'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-18127db6e222>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mresult1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'Room Temperature'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mresult2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mresult3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Model' object has no attribute 'reload'"
     ]
    }
   ],
   "source": [
    "model = pysd.read_vensim('tests/test-models/samples/teacup/teacup.mdl')\n",
    "result0 = model.run()\n",
    "result1 = model.run(params={'Room Temperature': 1000})\n",
    "result2 = model.run()\n",
    "model.reload()\n",
    "result3 = model.run()\n",
    "\n",
    "self.assertTrue((result0 == result3).all().all())\n",
    "self.assertFalse((result0 == result1).all().all())\n",
    "self.assertTrue((result1 == result2).all().all())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <td>8.236564</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>2.875</td>\n",
       "      <td>0.125</td>\n",
       "      <td>152.365641</td>\n",
       "      <td>2.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.000</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>8.133607</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>3.000</td>\n",
       "      <td>0.125</td>\n",
       "      <td>151.336071</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.125</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>8.031937</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>3.125</td>\n",
       "      <td>0.125</td>\n",
       "      <td>150.319370</td>\n",
       "      <td>3.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.250</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>7.931538</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>3.250</td>\n",
       "      <td>0.125</td>\n",
       "      <td>149.315378</td>\n",
       "      <td>3.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.375</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>7.832394</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>3.375</td>\n",
       "      <td>0.125</td>\n",
       "      <td>148.323936</td>\n",
       "      <td>3.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.500</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>7.734489</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>3.500</td>\n",
       "      <td>0.125</td>\n",
       "      <td>147.344886</td>\n",
       "      <td>3.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.625</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>7.637808</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>3.625</td>\n",
       "      <td>0.125</td>\n",
       "      <td>146.378075</td>\n",
       "      <td>3.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26.375</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.773966</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>26.375</td>\n",
       "      <td>0.125</td>\n",
       "      <td>77.739657</td>\n",
       "      <td>26.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26.500</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.764291</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>26.500</td>\n",
       "      <td>0.125</td>\n",
       "      <td>77.642911</td>\n",
       "      <td>26.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26.625</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.754737</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>26.625</td>\n",
       "      <td>0.125</td>\n",
       "      <td>77.547375</td>\n",
       "      <td>26.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26.750</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.745303</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>26.750</td>\n",
       "      <td>0.125</td>\n",
       "      <td>77.453032</td>\n",
       "      <td>26.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26.875</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.735987</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>26.875</td>\n",
       "      <td>0.125</td>\n",
       "      <td>77.359869</td>\n",
       "      <td>26.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27.000</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.726787</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>27.000</td>\n",
       "      <td>0.125</td>\n",
       "      <td>77.267871</td>\n",
       "      <td>27.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27.125</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.717702</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>27.125</td>\n",
       "      <td>0.125</td>\n",
       "      <td>77.177023</td>\n",
       "      <td>27.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27.250</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.708731</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>27.250</td>\n",
       "      <td>0.125</td>\n",
       "      <td>77.087310</td>\n",
       "      <td>27.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27.375</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.699872</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>27.375</td>\n",
       "      <td>0.125</td>\n",
       "      <td>76.998719</td>\n",
       "      <td>27.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27.500</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.691123</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>27.500</td>\n",
       "      <td>0.125</td>\n",
       "      <td>76.911235</td>\n",
       "      <td>27.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27.625</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.682484</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>27.625</td>\n",
       "      <td>0.125</td>\n",
       "      <td>76.824844</td>\n",
       "      <td>27.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27.750</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.673953</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>27.750</td>\n",
       "      <td>0.125</td>\n",
       "      <td>76.739534</td>\n",
       "      <td>27.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27.875</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.665529</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>27.875</td>\n",
       "      <td>0.125</td>\n",
       "      <td>76.655289</td>\n",
       "      <td>27.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28.000</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.657210</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>28.000</td>\n",
       "      <td>0.125</td>\n",
       "      <td>76.572098</td>\n",
       "      <td>28.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28.125</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.648995</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>28.125</td>\n",
       "      <td>0.125</td>\n",
       "      <td>76.489947</td>\n",
       "      <td>28.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28.250</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.640882</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>28.250</td>\n",
       "      <td>0.125</td>\n",
       "      <td>76.408823</td>\n",
       "      <td>28.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28.375</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.632871</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>28.375</td>\n",
       "      <td>0.125</td>\n",
       "      <td>76.328712</td>\n",
       "      <td>28.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28.500</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.624960</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>28.500</td>\n",
       "      <td>0.125</td>\n",
       "      <td>76.249604</td>\n",
       "      <td>28.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28.625</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.617148</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>28.625</td>\n",
       "      <td>0.125</td>\n",
       "      <td>76.171483</td>\n",
       "      <td>28.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28.750</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.609434</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>28.750</td>\n",
       "      <td>0.125</td>\n",
       "      <td>76.094340</td>\n",
       "      <td>28.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28.875</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.601816</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>28.875</td>\n",
       "      <td>0.125</td>\n",
       "      <td>76.018161</td>\n",
       "      <td>28.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29.000</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.594293</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>29.000</td>\n",
       "      <td>0.125</td>\n",
       "      <td>75.942934</td>\n",
       "      <td>29.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29.125</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.586865</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>29.125</td>\n",
       "      <td>0.125</td>\n",
       "      <td>75.868647</td>\n",
       "      <td>29.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29.250</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.579529</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>29.250</td>\n",
       "      <td>0.125</td>\n",
       "      <td>75.795289</td>\n",
       "      <td>29.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29.375</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.572285</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>29.375</td>\n",
       "      <td>0.125</td>\n",
       "      <td>75.722848</td>\n",
       "      <td>29.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29.500</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.565131</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>29.500</td>\n",
       "      <td>0.125</td>\n",
       "      <td>75.651312</td>\n",
       "      <td>29.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29.625</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.558067</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>29.625</td>\n",
       "      <td>0.125</td>\n",
       "      <td>75.580671</td>\n",
       "      <td>29.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29.750</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.551091</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>29.750</td>\n",
       "      <td>0.125</td>\n",
       "      <td>75.510912</td>\n",
       "      <td>29.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29.875</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.544203</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>29.875</td>\n",
       "      <td>0.125</td>\n",
       "      <td>75.442026</td>\n",
       "      <td>29.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.000</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.537400</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.125</td>\n",
       "      <td>30.000</td>\n",
       "      <td>0.125</td>\n",
       "      <td>75.374001</td>\n",
       "      <td>30.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>241 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Characteristic Time  FINAL TIME  Heat Loss to Room  INITIAL TIME  \\\n",
       "0.000                    10          30          11.000000             0   \n",
       "0.125                    10          30          10.862500             0   \n",
       "0.250                    10          30          10.726719             0   \n",
       "0.375                    10          30          10.592635             0   \n",
       "0.500                    10          30          10.460227             0   \n",
       "0.625                    10          30          10.329474             0   \n",
       "0.750                    10          30          10.200356             0   \n",
       "0.875                    10          30          10.072851             0   \n",
       "1.000                    10          30           9.946940             0   \n",
       "1.125                    10          30           9.822604             0   \n",
       "1.250                    10          30           9.699821             0   \n",
       "1.375                    10          30           9.578573             0   \n",
       "1.500                    10          30           9.458841             0   \n",
       "1.625                    10          30           9.340606             0   \n",
       "1.750                    10          30           9.223848             0   \n",
       "1.875                    10          30           9.108550             0   \n",
       "2.000                    10          30           8.994693             0   \n",
       "2.125                    10          30           8.882260             0   \n",
       "2.250                    10          30           8.771231             0   \n",
       "2.375                    10          30           8.661591             0   \n",
       "2.500                    10          30           8.553321             0   \n",
       "2.625                    10          30           8.446404             0   \n",
       "2.750                    10          30           8.340824             0   \n",
       "2.875                    10          30           8.236564             0   \n",
       "3.000                    10          30           8.133607             0   \n",
       "3.125                    10          30           8.031937             0   \n",
       "3.250                    10          30           7.931538             0   \n",
       "3.375                    10          30           7.832394             0   \n",
       "3.500                    10          30           7.734489             0   \n",
       "3.625                    10          30           7.637808             0   \n",
       "...                     ...         ...                ...           ...   \n",
       "26.375                   10          30           0.773966             0   \n",
       "26.500                   10          30           0.764291             0   \n",
       "26.625                   10          30           0.754737             0   \n",
       "26.750                   10          30           0.745303             0   \n",
       "26.875                   10          30           0.735987             0   \n",
       "27.000                   10          30           0.726787             0   \n",
       "27.125                   10          30           0.717702             0   \n",
       "27.250                   10          30           0.708731             0   \n",
       "27.375                   10          30           0.699872             0   \n",
       "27.500                   10          30           0.691123             0   \n",
       "27.625                   10          30           0.682484             0   \n",
       "27.750                   10          30           0.673953             0   \n",
       "27.875                   10          30           0.665529             0   \n",
       "28.000                   10          30           0.657210             0   \n",
       "28.125                   10          30           0.648995             0   \n",
       "28.250                   10          30           0.640882             0   \n",
       "28.375                   10          30           0.632871             0   \n",
       "28.500                   10          30           0.624960             0   \n",
       "28.625                   10          30           0.617148             0   \n",
       "28.750                   10          30           0.609434             0   \n",
       "28.875                   10          30           0.601816             0   \n",
       "29.000                   10          30           0.594293             0   \n",
       "29.125                   10          30           0.586865             0   \n",
       "29.250                   10          30           0.579529             0   \n",
       "29.375                   10          30           0.572285             0   \n",
       "29.500                   10          30           0.565131             0   \n",
       "29.625                   10          30           0.558067             0   \n",
       "29.750                   10          30           0.551091             0   \n",
       "29.875                   10          30           0.544203             0   \n",
       "30.000                   10          30           0.537400             0   \n",
       "\n",
       "        Room Temperature  SAVEPER    TIME  TIME STEP  Teacup Temperature  \\\n",
       "0.000                 70    0.125   0.000      0.125          180.000000   \n",
       "0.125                 70    0.125   0.125      0.125          178.625000   \n",
       "0.250                 70    0.125   0.250      0.125          177.267188   \n",
       "0.375                 70    0.125   0.375      0.125          175.926348   \n",
       "0.500                 70    0.125   0.500      0.125          174.602268   \n",
       "0.625                 70    0.125   0.625      0.125          173.294740   \n",
       "0.750                 70    0.125   0.750      0.125          172.003556   \n",
       "0.875                 70    0.125   0.875      0.125          170.728511   \n",
       "1.000                 70    0.125   1.000      0.125          169.469405   \n",
       "1.125                 70    0.125   1.125      0.125          168.226037   \n",
       "1.250                 70    0.125   1.250      0.125          166.998212   \n",
       "1.375                 70    0.125   1.375      0.125          165.785734   \n",
       "1.500                 70    0.125   1.500      0.125          164.588413   \n",
       "1.625                 70    0.125   1.625      0.125          163.406057   \n",
       "1.750                 70    0.125   1.750      0.125          162.238482   \n",
       "1.875                 70    0.125   1.875      0.125          161.085501   \n",
       "2.000                 70    0.125   2.000      0.125          159.946932   \n",
       "2.125                 70    0.125   2.125      0.125          158.822595   \n",
       "2.250                 70    0.125   2.250      0.125          157.712313   \n",
       "2.375                 70    0.125   2.375      0.125          156.615909   \n",
       "2.500                 70    0.125   2.500      0.125          155.533210   \n",
       "2.625                 70    0.125   2.625      0.125          154.464045   \n",
       "2.750                 70    0.125   2.750      0.125          153.408244   \n",
       "2.875                 70    0.125   2.875      0.125          152.365641   \n",
       "3.000                 70    0.125   3.000      0.125          151.336071   \n",
       "3.125                 70    0.125   3.125      0.125          150.319370   \n",
       "3.250                 70    0.125   3.250      0.125          149.315378   \n",
       "3.375                 70    0.125   3.375      0.125          148.323936   \n",
       "3.500                 70    0.125   3.500      0.125          147.344886   \n",
       "3.625                 70    0.125   3.625      0.125          146.378075   \n",
       "...                  ...      ...     ...        ...                 ...   \n",
       "26.375                70    0.125  26.375      0.125           77.739657   \n",
       "26.500                70    0.125  26.500      0.125           77.642911   \n",
       "26.625                70    0.125  26.625      0.125           77.547375   \n",
       "26.750                70    0.125  26.750      0.125           77.453032   \n",
       "26.875                70    0.125  26.875      0.125           77.359869   \n",
       "27.000                70    0.125  27.000      0.125           77.267871   \n",
       "27.125                70    0.125  27.125      0.125           77.177023   \n",
       "27.250                70    0.125  27.250      0.125           77.087310   \n",
       "27.375                70    0.125  27.375      0.125           76.998719   \n",
       "27.500                70    0.125  27.500      0.125           76.911235   \n",
       "27.625                70    0.125  27.625      0.125           76.824844   \n",
       "27.750                70    0.125  27.750      0.125           76.739534   \n",
       "27.875                70    0.125  27.875      0.125           76.655289   \n",
       "28.000                70    0.125  28.000      0.125           76.572098   \n",
       "28.125                70    0.125  28.125      0.125           76.489947   \n",
       "28.250                70    0.125  28.250      0.125           76.408823   \n",
       "28.375                70    0.125  28.375      0.125           76.328712   \n",
       "28.500                70    0.125  28.500      0.125           76.249604   \n",
       "28.625                70    0.125  28.625      0.125           76.171483   \n",
       "28.750                70    0.125  28.750      0.125           76.094340   \n",
       "28.875                70    0.125  28.875      0.125           76.018161   \n",
       "29.000                70    0.125  29.000      0.125           75.942934   \n",
       "29.125                70    0.125  29.125      0.125           75.868647   \n",
       "29.250                70    0.125  29.250      0.125           75.795289   \n",
       "29.375                70    0.125  29.375      0.125           75.722848   \n",
       "29.500                70    0.125  29.500      0.125           75.651312   \n",
       "29.625                70    0.125  29.625      0.125           75.580671   \n",
       "29.750                70    0.125  29.750      0.125           75.510912   \n",
       "29.875                70    0.125  29.875      0.125           75.442026   \n",
       "30.000                70    0.125  30.000      0.125           75.374001   \n",
       "\n",
       "          Time  \n",
       "0.000    0.000  \n",
       "0.125    0.125  \n",
       "0.250    0.250  \n",
       "0.375    0.375  \n",
       "0.500    0.500  \n",
       "0.625    0.625  \n",
       "0.750    0.750  \n",
       "0.875    0.875  \n",
       "1.000    1.000  \n",
       "1.125    1.125  \n",
       "1.250    1.250  \n",
       "1.375    1.375  \n",
       "1.500    1.500  \n",
       "1.625    1.625  \n",
       "1.750    1.750  \n",
       "1.875    1.875  \n",
       "2.000    2.000  \n",
       "2.125    2.125  \n",
       "2.250    2.250  \n",
       "2.375    2.375  \n",
       "2.500    2.500  \n",
       "2.625    2.625  \n",
       "2.750    2.750  \n",
       "2.875    2.875  \n",
       "3.000    3.000  \n",
       "3.125    3.125  \n",
       "3.250    3.250  \n",
       "3.375    3.375  \n",
       "3.500    3.500  \n",
       "3.625    3.625  \n",
       "...        ...  \n",
       "26.375  26.375  \n",
       "26.500  26.500  \n",
       "26.625  26.625  \n",
       "26.750  26.750  \n",
       "26.875  26.875  \n",
       "27.000  27.000  \n",
       "27.125  27.125  \n",
       "27.250  27.250  \n",
       "27.375  27.375  \n",
       "27.500  27.500  \n",
       "27.625  27.625  \n",
       "27.750  27.750  \n",
       "27.875  27.875  \n",
       "28.000  28.000  \n",
       "28.125  28.125  \n",
       "28.250  28.250  \n",
       "28.375  28.375  \n",
       "28.500  28.500  \n",
       "28.625  28.625  \n",
       "28.750  28.750  \n",
       "28.875  28.875  \n",
       "29.000  29.000  \n",
       "29.125  29.125  \n",
       "29.250  29.250  \n",
       "29.375  29.375  \n",
       "29.500  29.500  \n",
       "29.625  29.625  \n",
       "29.750  29.750  \n",
       "29.875  29.875  \n",
       "30.000  30.000  \n",
       "\n",
       "[241 rows x 10 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d = {'a':1, 'b':2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a 1\n",
      "b 2\n"
     ]
    }
   ],
   "source": [
    "for key, val in d.items():\n",
    "    print(key, val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1499715812.290142"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import time\n",
    "time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class MyObj(object):\n",
    "    def __init__(self):\n",
    "        self.inittime = time.time()\n",
    "        \n",
    "    def reload_self(self):\n",
    "        self.__init__()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1499715987.2452679"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = MyObj()\n",
    "a.inittime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1499715988.093183"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.reload_self()\n",
    "a.inittime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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