process-emprunte-missions-cat.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Type</th>\n",
       "      <th>Mode de deplacement</th>\n",
       "      <th>N</th>\n",
       "      <th>Statut</th>\n",
       "      <th>Nombre de personne</th>\n",
       "      <th>Motif</th>\n",
       "      <th>Aller / Retour</th>\n",
       "      <th>Distance (km)</th>\n",
       "      <th>em</th>\n",
       "      <th>Incertitude (kg eCO2)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1,2,99,515,868,1015,1370,1639</td>\n",
       "      <td>MX</td>\n",
       "      <td>plane</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OUI</td>\n",
       "      <td>79500</td>\n",
       "      <td>6591</td>\n",
       "      <td>659</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1,91,280,838,871,873,962,968,988,1190,1239,135...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>train</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OUI</td>\n",
       "      <td>21405</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>MX</td>\n",
       "      <td>plane</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OUI</td>\n",
       "      <td>9511</td>\n",
       "      <td>788</td>\n",
       "      <td>79</td>\n",
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       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>plane</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OUI</td>\n",
       "      <td>875</td>\n",
       "      <td>124</td>\n",
       "      <td>12</td>\n",
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       "      <th>4</th>\n",
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       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OUI</td>\n",
       "      <td>1691</td>\n",
       "      <td>239</td>\n",
       "      <td>24</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>230</td>\n",
       "      <td>MX</td>\n",
       "      <td>plane</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OUI</td>\n",
       "      <td>2169</td>\n",
       "      <td>222</td>\n",
       "      <td>22</td>\n",
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       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>231</td>\n",
       "      <td>NaN</td>\n",
       "      <td>car</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OUI</td>\n",
       "      <td>291</td>\n",
       "      <td>68</td>\n",
       "      <td>41</td>\n",
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       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>232</td>\n",
       "      <td>NaN</td>\n",
       "      <td>car</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OUI</td>\n",
       "      <td>291</td>\n",
       "      <td>68</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>233</td>\n",
       "      <td>NaN</td>\n",
       "      <td>train</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OUI</td>\n",
       "      <td>2008</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>235</td>\n",
       "      <td>NaN</td>\n",
       "      <td>train</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OUI</td>\n",
       "      <td>599</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "<p>608 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    ID Type  \\\n",
       "0                        1,2,99,515,868,1015,1370,1639   MX   \n",
       "1    1,91,280,838,871,873,962,968,988,1190,1239,135...  NaN   \n",
       "2                                                    2   MX   \n",
       "3                                                    2  NaN   \n",
       "4                                                    3  NaN   \n",
       "..                                                 ...  ...   \n",
       "603                                                230   MX   \n",
       "604                                                231  NaN   \n",
       "605                                                232  NaN   \n",
       "606                                                233  NaN   \n",
       "607                                                235  NaN   \n",
       "\n",
       "    Mode de deplacement   N  Statut  Nombre de personne  Motif Aller / Retour  \\\n",
       "0                 plane   8     NaN                   1    NaN            OUI   \n",
       "1                 train  15     NaN                   1    NaN            OUI   \n",
       "2                 plane   1     NaN                   1    NaN            OUI   \n",
       "3                 plane   1     NaN                   1    NaN            OUI   \n",
       "4                 plane   1     NaN                   1    NaN            OUI   \n",
       "..                  ...  ..     ...                 ...    ...            ...   \n",
       "603               plane   1     NaN                   1    NaN            OUI   \n",
       "604                 car   1     NaN                   1    NaN            OUI   \n",
       "605                 car   1     NaN                   1    NaN            OUI   \n",
       "606               train   1     NaN                   1    NaN            OUI   \n",
       "607               train   1     NaN                   1    NaN            OUI   \n",
       "\n",
       "     Distance (km)    em  Incertitude (kg eCO2)  \n",
       "0            79500  6591                    659  \n",
       "1            21405    64                     13  \n",
       "2             9511   788                     79  \n",
       "3              875   124                     12  \n",
       "4             1691   239                     24  \n",
       "..             ...   ...                    ...  \n",
       "603           2169   222                     22  \n",
       "604            291    68                     41  \n",
       "605            291    68                     41  \n",
       "606           2008     6                      1  \n",
       "607            599     2                      0  \n",
       "\n",
       "[608 rows x 11 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data = pd.read_csv('empreinteCarboneMissions_2022121.tsv', sep='\\t')\n",
    "data = data.rename(columns={'Quantité' : 'N', 'Emission (kg eCO2)': 'em'})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "54c6155c",
   "metadata": {},
   "outputs": [],
   "source": [
    "em = data['em']  # emission is the total. \n",
    "N = data['N']\n",
    "em = em / N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f8cd9ebc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dmin = float(em.min())\n",
    "dmin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8776d1d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2859.0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dmax = float(em.max())\n",
    "dmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6bc37b9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3000"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dmax = 3000\n",
    "dmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "62574896",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,  100,  200,  300,  400,  500,  600,  700,  800,  900, 1000,\n",
       "       1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100,\n",
       "       2200, 2300, 2400, 2500, 2600, 2700, 2800, 2900, 3000])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "step = 100\n",
    "bins = np.arange(0, dmax + step, step)\n",
    "nbins = len(bins)\n",
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "231ecfce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dl = np.diff(bins)[0]\n",
    "dl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "059f579b",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = np.digitize(em, bins, right=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "27e85bce",
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs = np.zeros((len(bins) - 1), dtype=float)\n",
    "for i in np.unique(index):\n",
    "    iok = np.nonzero(index == i)[0]\n",
    "    outputs[i - 1] = N[iok].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "f0afa35c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.size'] = 16\n",
    "plt.figure(figsize=(16, 8), facecolor='white')\n",
    "plt.bar(bins[slice(0, nbins - 1)], outputs, width=dl, align='edge', color='steelblue', edgecolor='k')\n",
    "plt.xlabel('Emission (kg eCO2)')\n",
    "plt.ylabel('Nombre de missions')\n",
    "plt.xlim(0, dmax)\n",
    "plt.savefig('number_missions_eco2.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "24ec9c45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "542.146"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "em = data['em'] / data['N'] * 1e-3  # conversion of emissions into tons. this gives the emission by individual mission\n",
    "intem = data['em'] * 1e-3 # emissions seems to be for all missions\n",
    "emtot = intem.sum()  # total emmissions in tons.\n",
    "emtot  # labo 1.5's total is 542.136, small difference..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "126b7ab2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold = 10 tons: reduction = 0.00% of total\n",
      "Threshold = 8 tons: reduction = 0.00% of total\n",
      "Threshold = 6 tons: reduction = 0.00% of total\n",
      "Threshold = 5 tons: reduction = 0.00% of total\n",
      "Threshold = 2 tons: reduction = 7.31% of total\n"
     ]
    }
   ],
   "source": [
    "threslist = [10, 8, 6, 5, 2]  # list of thresholds\n",
    "for thres  in threslist:\n",
    "    test = (em > thres)  # true if emissions exceeds the threshold    \n",
    "    remem = intem[test].sum()  # integrates the total emissions \n",
    "    perc = (remem / emtot) * 100\n",
    "    print('Threshold = %d tons: reduction = %.2f' %(thres, perc) + '% of total')"
   ]
  }
 ],
 "metadata": {
  "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}