garmin.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_excel(r'C:\\Users\\bbarthelet\\Documents\\gower.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.to_json('output.json', orient='records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from datetime import timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python27\\lib\\site-packages\\ipykernel\\__main__.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "df['td'] = df['Cumulative Time '].apply(lambda x : timedelta(hours=x.hour,minutes=x.minute, seconds=x.second))\n",
    "df['dc']=df['Distance '].cumsum()\n",
    "df['dtd']=df['td']-df['td'].shift()\n",
    "df['dtd'][0]= df['td'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['rm5'] = pd.rolling_mean(df['dtd'], window=5)\n",
    "df['rm10'] = pd.rolling_mean(df['dtd'], window=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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0EFC+fOoqVkMR9Zo1xZgDB6RQVz7HcuC9AXdmwHhc1JOTgTZtZLJaUXwNZsl+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe5bda90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for r in [1, 5, 10 ]:\n",
    "    plt.plot(df['dc'], pd.rolling_mean(df['dtd'], window=r))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Splits                        int64\n",
       "Time                         object\n",
       "Cumulative Time              object\n",
       "Moving Time                  object\n",
       "Distance                    float64\n",
       "Elev Gain                     int64\n",
       "Elev Loss                     int64\n",
       "td                  timedelta64[ns]\n",
       "dc                          float64\n",
       "dtd                 timedelta64[ns]\n",
       "rm5                         float64\n",
       "rm10                        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "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": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}

output.json

[{"Splits ":1,"Time ":"07:53:00","Cumulative Time ":"00:07:53","Moving Time ":"07:52:00","Distance ":1.0,"Elev Gain ":103,"Elev Loss":5},{"Splits ":2,"Time ":"05:50:00","Cumulative Time ":"00:13:43","Moving Time ":"05:49:00","Distance ":1.0,"Elev Gain ":38,"Elev Loss":26},{"Splits ":3,"Time ":"05:20:00","Cumulative Time ":"00:19:03","Moving Time ":"05:19:00","Distance ":1.0,"Elev Gain ":26,"Elev Loss":50},{"Splits ":4,"Time ":"05:42:00","Cumulative Time ":"00:24:45","Moving Time ":"05:42:00","Distance ":1.0,"Elev Gain ":0,"Elev Loss":143},{"Splits ":5,"Time ":"05:32:00","Cumulative Time ":"00:30:17","Moving Time ":"05:31:00","Distance ":1.0,"Elev Gain ":3,"Elev Loss":6},{"Splits ":6,"Time ":"05:34:00","Cumulative Time ":"00:35:51","Moving Time ":"05:33:00","Distance ":1.0,"Elev Gain ":2,"Elev Loss":1},{"Splits ":7,"Time ":"06:28:00","Cumulative Time ":"00:42:19","Moving Time ":"06:26:00","Distance ":1.0,"Elev Gain ":54,"Elev Loss":7},{"Splits ":8,"Time ":"05:19:00","Cumulative Time 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