{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Minimal example for working with ergometer data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the worklab module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import worklab as wl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Import the data with com.load() or the device specific load function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================\n",
      "Initializing loading for C:\\Users\\rick_\\Development\\worklab2\\example_data\\Esseda_example_LEM.xls ...\n",
      "File identified as Esseda datafile. Attempting to load ...\n",
      "Data loaded!\n",
      "================================================================================\n",
      "\n",
      "Ergometer data is stored in a:  <class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "filename = os.getcwd()\n",
    "filename = os.path.join(os.path.split(filename)[0], 'example_data', 'Esseda_example_LEM.xls')\n",
    "ergo_data = wl.com.load(filename)\n",
    "print(\"Ergometer data is stored in a: \", type(ergo_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['left', 'right'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ergo_data.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ergometer data is a little more simple than measurement wheel data, (but you do have two modules):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>force</th>\n",
       "      <th>speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.788487</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.780378</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.797562</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.792799</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.838543</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time     force  speed\n",
       "0  0.01  0.788487    0.0\n",
       "1  0.02  0.780378    0.0\n",
       "2  0.03  0.797562    0.0\n",
       "3  0.04  0.792799    0.0\n",
       "4  0.05  0.838543    0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ergo_data[\"left\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Processing is identical however:\n",
    "\n",
    "- filter\n",
    "- process\n",
    "- push-by-push"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>force</th>\n",
       "      <th>speed</th>\n",
       "      <th>torque</th>\n",
       "      <th>acc</th>\n",
       "      <th>power</th>\n",
       "      <th>dist</th>\n",
       "      <th>work</th>\n",
       "      <th>uforce</th>\n",
       "      <th>aspeed</th>\n",
       "      <th>angle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.788493</td>\n",
       "      <td>-1.163828e-14</td>\n",
       "      <td>0.244433</td>\n",
       "      <td>-2.799567e-13</td>\n",
       "      <td>-9.176693e-15</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.176693e-17</td>\n",
       "      <td>0.888846</td>\n",
       "      <td>-3.754282e-14</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.793392</td>\n",
       "      <td>-1.443784e-14</td>\n",
       "      <td>0.245951</td>\n",
       "      <td>-2.414086e-13</td>\n",
       "      <td>-1.145486e-14</td>\n",
       "      <td>-1.303806e-16</td>\n",
       "      <td>-1.145486e-16</td>\n",
       "      <td>0.894369</td>\n",
       "      <td>-4.657369e-14</td>\n",
       "      <td>-4.205825e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.802370</td>\n",
       "      <td>-1.646645e-14</td>\n",
       "      <td>0.248735</td>\n",
       "      <td>-1.179231e-13</td>\n",
       "      <td>-1.321219e-14</td>\n",
       "      <td>-2.849020e-16</td>\n",
       "      <td>-1.321219e-16</td>\n",
       "      <td>0.904490</td>\n",
       "      <td>-5.311757e-14</td>\n",
       "      <td>-9.190388e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.812440</td>\n",
       "      <td>-1.679630e-14</td>\n",
       "      <td>0.251857</td>\n",
       "      <td>1.148631e-13</td>\n",
       "      <td>-1.364600e-14</td>\n",
       "      <td>-4.512158e-16</td>\n",
       "      <td>-1.364600e-16</td>\n",
       "      <td>0.915842</td>\n",
       "      <td>-5.418163e-14</td>\n",
       "      <td>-1.455535e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.811675</td>\n",
       "      <td>-1.416918e-14</td>\n",
       "      <td>0.251619</td>\n",
       "      <td>4.896777e-13</td>\n",
       "      <td>-1.150078e-14</td>\n",
       "      <td>-6.060432e-16</td>\n",
       "      <td>-1.150078e-16</td>\n",
       "      <td>0.914979</td>\n",
       "      <td>-4.570705e-14</td>\n",
       "      <td>-1.954978e-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time     force         speed    torque           acc         power  \\\n",
       "0  0.01  0.788493 -1.163828e-14  0.244433 -2.799567e-13 -9.176693e-15   \n",
       "1  0.02  0.793392 -1.443784e-14  0.245951 -2.414086e-13 -1.145486e-14   \n",
       "2  0.03  0.802370 -1.646645e-14  0.248735 -1.179231e-13 -1.321219e-14   \n",
       "3  0.04  0.812440 -1.679630e-14  0.251857  1.148631e-13 -1.364600e-14   \n",
       "4  0.05  0.811675 -1.416918e-14  0.251619  4.896777e-13 -1.150078e-14   \n",
       "\n",
       "           dist          work    uforce        aspeed         angle  \n",
       "0  0.000000e+00 -9.176693e-17  0.888846 -3.754282e-14  0.000000e+00  \n",
       "1 -1.303806e-16 -1.145486e-16  0.894369 -4.657369e-14 -4.205825e-16  \n",
       "2 -2.849020e-16 -1.321219e-16  0.904490 -5.311757e-14 -9.190388e-16  \n",
       "3 -4.512158e-16 -1.364600e-16  0.915842 -5.418163e-14 -1.455535e-15  \n",
       "4 -6.060432e-16 -1.150078e-16  0.914979 -4.570705e-14 -1.954978e-15  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ergo_data = wl.kin.filter_ergo(ergo_data)\n",
    "ergo_data = wl.kin.process_ergo(ergo_data)\n",
    "ergo_data[\"left\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you have almost all parameters that you will ever need:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ergo_data[\"left\"].plot(\"time\", \"torque\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do a christmas tree for a smaller section of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ergo_data[\"left\"] = ergo_data[\"left\"].iloc[:3000, :]\n",
    "ergo_data[\"right\"] = ergo_data[\"right\"].iloc[:3000, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the pushes with the push by push function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n",
      "dict_keys(['left', 'right'])\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stop</th>\n",
       "      <th>start</th>\n",
       "      <th>peak</th>\n",
       "      <th>tstart</th>\n",
       "      <th>tstop</th>\n",
       "      <th>tpeak</th>\n",
       "      <th>cangle</th>\n",
       "      <th>ptime</th>\n",
       "      <th>meanpower</th>\n",
       "      <th>maxpower</th>\n",
       "      <th>meantorque</th>\n",
       "      <th>maxtorque</th>\n",
       "      <th>meanforce</th>\n",
       "      <th>maxforce</th>\n",
       "      <th>work</th>\n",
       "      <th>slope</th>\n",
       "      <th>ctime</th>\n",
       "      <th>reltime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>351</td>\n",
       "      <td>276</td>\n",
       "      <td>311</td>\n",
       "      <td>2.77</td>\n",
       "      <td>3.52</td>\n",
       "      <td>3.12</td>\n",
       "      <td>1.180683</td>\n",
       "      <td>0.75</td>\n",
       "      <td>34.260157</td>\n",
       "      <td>77.243329</td>\n",
       "      <td>20.808663</td>\n",
       "      <td>33.636038</td>\n",
       "      <td>75.667865</td>\n",
       "      <td>122.312864</td>\n",
       "      <td>26.037719</td>\n",
       "      <td>96.102965</td>\n",
       "      <td>1.15</td>\n",
       "      <td>65.217391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>419</td>\n",
       "      <td>391</td>\n",
       "      <td>409</td>\n",
       "      <td>3.92</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.10</td>\n",
       "      <td>1.122666</td>\n",
       "      <td>0.28</td>\n",
       "      <td>67.705997</td>\n",
       "      <td>142.440912</td>\n",
       "      <td>16.666537</td>\n",
       "      <td>34.028533</td>\n",
       "      <td>60.605589</td>\n",
       "      <td>123.740121</td>\n",
       "      <td>19.634739</td>\n",
       "      <td>189.047406</td>\n",
       "      <td>0.77</td>\n",
       "      <td>36.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>474</td>\n",
       "      <td>468</td>\n",
       "      <td>472</td>\n",
       "      <td>4.69</td>\n",
       "      <td>4.75</td>\n",
       "      <td>4.73</td>\n",
       "      <td>0.272526</td>\n",
       "      <td>0.06</td>\n",
       "      <td>15.354861</td>\n",
       "      <td>27.136431</td>\n",
       "      <td>3.381357</td>\n",
       "      <td>5.975240</td>\n",
       "      <td>12.295844</td>\n",
       "      <td>21.728147</td>\n",
       "      <td>1.074840</td>\n",
       "      <td>149.381011</td>\n",
       "      <td>0.12</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>493</td>\n",
       "      <td>480</td>\n",
       "      <td>487</td>\n",
       "      <td>4.81</td>\n",
       "      <td>4.94</td>\n",
       "      <td>4.88</td>\n",
       "      <td>0.599194</td>\n",
       "      <td>0.13</td>\n",
       "      <td>44.567863</td>\n",
       "      <td>79.093543</td>\n",
       "      <td>9.648388</td>\n",
       "      <td>17.036661</td>\n",
       "      <td>35.085049</td>\n",
       "      <td>61.951494</td>\n",
       "      <td>6.239501</td>\n",
       "      <td>243.380868</td>\n",
       "      <td>0.60</td>\n",
       "      <td>21.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>547</td>\n",
       "      <td>540</td>\n",
       "      <td>543</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.48</td>\n",
       "      <td>5.44</td>\n",
       "      <td>0.319896</td>\n",
       "      <td>0.07</td>\n",
       "      <td>31.561252</td>\n",
       "      <td>54.087009</td>\n",
       "      <td>6.901818</td>\n",
       "      <td>11.812804</td>\n",
       "      <td>25.097520</td>\n",
       "      <td>42.955650</td>\n",
       "      <td>2.524900</td>\n",
       "      <td>393.760127</td>\n",
       "      <td>0.12</td>\n",
       "      <td>58.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   stop  start  peak  tstart  tstop  tpeak    cangle  ptime  meanpower  \\\n",
       "0   351    276   311    2.77   3.52   3.12  1.180683   0.75  34.260157   \n",
       "1   419    391   409    3.92   4.20   4.10  1.122666   0.28  67.705997   \n",
       "2   474    468   472    4.69   4.75   4.73  0.272526   0.06  15.354861   \n",
       "3   493    480   487    4.81   4.94   4.88  0.599194   0.13  44.567863   \n",
       "4   547    540   543    5.41   5.48   5.44  0.319896   0.07  31.561252   \n",
       "\n",
       "     maxpower  meantorque  maxtorque  meanforce    maxforce       work  \\\n",
       "0   77.243329   20.808663  33.636038  75.667865  122.312864  26.037719   \n",
       "1  142.440912   16.666537  34.028533  60.605589  123.740121  19.634739   \n",
       "2   27.136431    3.381357   5.975240  12.295844   21.728147   1.074840   \n",
       "3   79.093543    9.648388  17.036661  35.085049   61.951494   6.239501   \n",
       "4   54.087009    6.901818  11.812804  25.097520   42.955650   2.524900   \n",
       "\n",
       "        slope  ctime    reltime  \n",
       "0   96.102965   1.15  65.217391  \n",
       "1  189.047406   0.77  36.363636  \n",
       "2  149.381011   0.12  50.000000  \n",
       "3  243.380868   0.60  21.666667  \n",
       "4  393.760127   0.12  58.333333  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pushes = wl.kin.push_by_push_ergo(ergo_data)\n",
    "print(type(pushes))\n",
    "print(pushes.keys())\n",
    "pushes[\"left\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is all very similar to the measurement wheel, except you don't have access to 3D forces. Similar to the measurement wheel data, all of the above can be achieved with the `auto_process` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wl.plots.plot_ergometer_pushes(ergo_data, pushes);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Very festive! 🎄"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
