Results_processing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.graph_objects as go\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = r'./data_results\\Customer_tol01_largefx_12V.xlsx'\n",
    "def recup_variable(file):\n",
    "    a = pd.read_excel(file)\n",
    "    data_variable = {}\n",
    "    for var in a[(a[\"Type\"]=='variable')&(a[\"Count\"]>1)]['Name']:\n",
    "        data_variable[var] = pd.read_excel(file,sheet_name=var,header=2)\n",
    "    return data_variable\n",
    "def recup_parameter(file):\n",
    "    a = pd.read_excel(file)\n",
    "    data_variable = {}\n",
    "    for var in a[(a[\"Type\"]=='parameter')&(a[\"Count\"]>1)]['Name']:\n",
    "        data_variable[var] = pd.read_excel(file,sheet_name=var,header=2)\n",
    "    return data_variable\n",
    "def recup_set(file):\n",
    "    a = pd.read_excel(file)\n",
    "    data_variable = {}\n",
    "    for var in a[(a[\"Type\"]=='set')&(a[\"Count\"]>1)]['Name']:\n",
    "        data_variable[var] = pd.read_excel(file,sheet_name=var,header=2)\n",
    "    return data_variable\n",
    "data_variable = recup_variable(file)\n",
    "data_parameter = recup_parameter(file)\n",
    "data_set = recup_set(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>c</th>\n",
       "      <th>degree</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>small</td>\n",
       "      <td>0</td>\n",
       "      <td>152.324500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>small</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.001039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>large</td>\n",
       "      <td>0</td>\n",
       "      <td>178.351600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>large</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.000370</td>\n",
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       "       c  degree       Value\n",
       "0  small       0  152.324500\n",
       "1  small       2   -0.001039\n",
       "2  large       0  178.351600\n",
       "3  large       2   -0.000370"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_parameter['psi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "271.7795010837408"
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     "execution_count": 29,
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   "source": [
    "data_variable['Qpompe']['Value'].max()"
   ]
  },
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   "cell_type": "code",
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           "aaxis": {
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           "baxis": {
            "gridcolor": "white",
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           "caxis": {
            "gridcolor": "white",
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           "x": 0.05
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          "xaxis": {
           "automargin": true,
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           "ticks": "",
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           "automargin": true,
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "data_parameter['psi']\n",
    "Qmax = int(data_variable['Qpompe']['Value'].max()*1.1)\n",
    "X = np.linspace(0,Qmax,Qmax)\n",
    "Y = data_parameter['psi'][data_parameter['psi']['degree']==2]['Value'].iloc[0]*X**2 + data_parameter['psi'][data_parameter['psi']['degree']==0]['Value'].iloc[0]\n",
    "color_map = {\n",
    "    'small' : 'blue',\n",
    "    'large' : 'red'\n",
    "}\n",
    "fig = go.Figure()\n",
    "for c in data_variable['Qpompe']['c'].unique():\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "            x = X,\n",
    "            y = data_parameter['psi'][(data_parameter['psi']['degree']==2)&(data_parameter['psi']['c']==c)]['Value'].iloc[0]*X**2 + data_parameter['psi'][(data_parameter['psi']['degree']==0)&(data_parameter['psi']['c']==c)]['Value'].iloc[0],\n",
    "            marker_color = color_map[c],\n",
    "            name = f'Courbe de gain de charge théorique {c}'\n",
    "        )\n",
    "    )\n",
    "for c in data_variable['Qpompe']['c'].unique():\n",
    "    for d in data_variable['Qpompe']['d'].unique():\n",
    "        fig.add_trace(\n",
    "            go.Scatter(\n",
    "                x               =   data_variable['Qpompe'][(data_variable['Qpompe']['c']==c)&(data_variable['Qpompe']['d']==d)]['Value'],\n",
    "                y               =   data_variable['Charge'][data_variable['Charge']['n']=='s']['Value'],\n",
    "                marker_color    =   color_map[c],\n",
    "                name            =   f\"Débit {c},{d}\",\n",
    "                # hoverinfo       =   data_variable['Qpompe'][(data_variable['Qpompe']['c']==c)&(data_variable['Qpompe']['d']==d)]['t'],\n",
    "                mode='markers'\n",
    "            )\n",
    "        )\n",
    "fig.update_layout(\n",
    "    height= 800,\n",
    "    hovermode='x'\n",
    ")\n",
    "# fig.update_xaxes(range=[0,data_variable['Qpompe']['Value'].max()*1.1])\n",
    "# fig.update_yaxes(range=[data_variable['Charge'][data_variable['Charge']['n']=='s']['Value'].min()*0.9,data_variable['Charge'][data_variable['Charge']['n']=='s']['Value'].max()*1.1])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     152.048130\n",
       "1     152.047536\n",
       "2     152.047333\n",
       "3     152.047255\n",
       "4     152.047160\n",
       "5     152.052122\n",
       "6     152.063275\n",
       "7     152.079546\n",
       "8     145.840219\n",
       "9     147.132310\n",
       "10    151.143323\n",
       "11    151.125791\n",
       "12    151.106413\n",
       "13    151.091649\n",
       "14    151.077808\n",
       "15    151.065812\n",
       "16    151.053816\n",
       "17    151.047818\n",
       "18    151.036902\n",
       "19    151.029520\n",
       "20    151.021884\n",
       "21    151.035698\n",
       "22    151.031841\n",
       "23    151.025495\n",
       "Name: Value, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_variable['Charge'][data_variable['Charge']['n']=='s']['Value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120    178.351970\n",
       "121    178.679993\n",
       "122    204.655853\n",
       "123    179.462034\n",
       "124    179.053045\n",
       "125    181.216839\n",
       "126    204.639925\n",
       "127    184.833057\n",
       "128    178.351970\n",
       "129    178.387071\n",
       "130    178.463949\n",
       "131    185.022948\n",
       "132    178.521298\n",
       "133    178.351970\n",
       "134    178.353000\n",
       "135    179.685856\n",
       "136    178.748250\n",
       "137    180.027517\n",
       "138    178.351970\n",
       "139    178.448285\n",
       "140    179.825298\n",
       "141    178.757119\n",
       "142    179.153170\n",
       "143    179.997128\n",
       "Name: Value, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_parameter['psi'].query(f\"c=='{c}' & degree==0\")['Value'].iloc[0] - data_parameter['psi'].query(f\"c=='{c}' & degree==2\")['Value'].iloc[0]*data_variable['Qpompe'].query(f\"c=='{c}' & d=='{d}'\")['Value']**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "152.3245\n",
      "152.3245\n",
      "178.3516\n",
      "178.3516\n",
      "178.3516\n",
      "178.3516\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('small', 'p1'),\n",
       " ('small', 'p2'),\n",
       " ('large', 'p1'),\n",
       " ('large', 'p2'),\n",
       " ('large', 'p3'),\n",
       " ('large', 'p4')]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pompe = []\n",
    "débit_pompe = {}\n",
    "for c in data_variable['Qpompe']['c'].unique():\n",
    "    for d in data_variable['Qpompe'][data_variable['Qpompe']['c']==c]['d'].unique():\n",
    "        print(data_parameter['psi'].query(f\"c=='{c}' & degree==0\")['Value'].iloc[0])\n",
    "        débit_pompe[(c,d)] = (data_variable['Charge'][data_variable['Charge']['n']=='s']['Value'].to_numpy()/(data_parameter['psi'].query(f\"c=='{c}' & degree==0\")['Value'].iloc[0] - data_parameter['psi'].query(f\"c=='{c}' & degree==2\")['Value'].iloc[0]*data_variable['Qpompe'].query(f\"c=='{c}' & d=='{d}'\")['Value'].to_numpy()**2) - 1)\n",
    "\n",
    "liste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_map   =   {\n",
    "        'p1'    :   'red',\n",
    "        'p2'    :   'blue',\n",
    "        'p3'    :   'green',\n",
    "        'p4'    :   'magenta',\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.25705173, -0.25705679, -0.17648578, -0.25705918, -0.25705999,\n",
       "       -0.25701773, -0.15699321, -0.25678414, -0.18228983, -0.251365  ,\n",
       "       -0.26472362, -0.26487161, -0.15276286, -0.20653741, -0.20778481,\n",
       "       -0.2653777 , -0.26547888, -0.18978183, -0.21515755, -0.26568378,\n",
       "       -0.16162525, -0.26563168, -0.26566421, -0.26212256])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "débit_pompe[('large','p1')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
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   "source": [
    "fig = go.Figure()\n",
    "for pompe in débit_pompe:\n",
    "    fig.add_trace(go.Box(y=débit_pompe[pompe], name=f\"{pompe}\"))\n",
    "fig.update_layout(title = \"Ecart relatif sur la charge des pompes induit par la relaxation convexe\")\n",
    "fig.show()"
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  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Optimisation du problème 4WT. En imposant que toutes les pompes soient allumées la nuit et que la pompe 1 soit tout le temps allumée. Le temps de calcul est de quelques secondes. La solution est réalisable. L'écart relatif de avec la solution optimale est de moins de 10E-4.\""
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "file = './data_results/4WT_all_fx.xlsx'\n",
    "a = pd.read_excel(file,sheet_name='comments')\n",
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       "     r    t   Value\n",
       "24  r2   t1   44.83\n",
       "25  r2   t2   18.00\n",
       "26  r2   t8   45.00\n",
       "27  r2   t9   51.67\n",
       "28  r2  t13   15.17\n",
       "29  r2  t14  103.83\n",
       "30  r2  t15   34.83\n",
       "31  r2  t16   43.83\n",
       "32  r2  t17   54.83\n",
       "33  r2  t18   51.17\n",
       "34  r2  t19   53.50\n",
       "35  r2  t20    0.67\n",
       "36  r2  t23    1.50\n",
       "37  r2  t24   53.50"
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>t6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>t7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>t8</td>\n",
       "      <td>r2</td>\n",
       "      <td>45.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>t9</td>\n",
       "      <td>r2</td>\n",
       "      <td>51.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>t10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>t11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>t12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>t13</td>\n",
       "      <td>r2</td>\n",
       "      <td>15.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>t14</td>\n",
       "      <td>r2</td>\n",
       "      <td>103.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>t15</td>\n",
       "      <td>r2</td>\n",
       "      <td>34.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>t16</td>\n",
       "      <td>r2</td>\n",
       "      <td>43.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>t17</td>\n",
       "      <td>r2</td>\n",
       "      <td>54.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>t18</td>\n",
       "      <td>r2</td>\n",
       "      <td>51.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>t19</td>\n",
       "      <td>r2</td>\n",
       "      <td>53.50</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>t20</td>\n",
       "      <td>r2</td>\n",
       "      <td>0.67</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>t21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>t22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>t23</td>\n",
       "      <td>r2</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>t24</td>\n",
       "      <td>r2</td>\n",
       "      <td>53.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      t    r   Value\n",
       "0    t1   r2   44.83\n",
       "1    t2   r2   18.00\n",
       "2    t3  NaN     NaN\n",
       "3    t4  NaN     NaN\n",
       "4    t5  NaN     NaN\n",
       "5    t6  NaN     NaN\n",
       "6    t7  NaN     NaN\n",
       "7    t8   r2   45.00\n",
       "8    t9   r2   51.67\n",
       "9   t10  NaN     NaN\n",
       "10  t11  NaN     NaN\n",
       "11  t12  NaN     NaN\n",
       "12  t13   r2   15.17\n",
       "13  t14   r2  103.83\n",
       "14  t15   r2   34.83\n",
       "15  t16   r2   43.83\n",
       "16  t17   r2   54.83\n",
       "17  t18   r2   51.17\n",
       "18  t19   r2   53.50\n",
       "19  t20   r2    0.67\n",
       "20  t21  NaN     NaN\n",
       "21  t22  NaN     NaN\n",
       "22  t23   r2    1.50\n",
       "23  t24   r2   53.50"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "n = 'r2'\n",
    "def path_to_n(n,data_set):\n",
    "    path = [n]\n",
    "    while n !='s':\n",
    "        n = data_set['l'][data_set['l']['n.1']==n]['n'].iloc[0]\n",
    "        path.append(n)\n",
    "    return path\n",
    "\n",
    "fig = go.Figure()\n",
    "path = path_to_n(n,data_set)\n",
    "fig.add_trace(\n",
    "    go.Scatter(\n",
    "        x = path,\n",
    "        y = pd.concat([pd.DataFrame([['s',0,0]],columns=data_parameter['height'].columns),data_parameter['height'][data_parameter['height']['n'].isin(path)]])['Value'],\n",
    "        name = 'hauteur'\n",
    "    )\n",
    ")\n",
    "for t in data_set['t']['dim1']:\n",
    "    if t == 't1':\n",
    "        fig.add_trace(\n",
    "        go.Scatter(\n",
    "            x = path,\n",
    "            y = data_variable['Charge'][(data_variable['Charge']['n'].isin(path))&(data_variable['Charge']['t']=='t1')]['Value'],\n",
    "            name = t,\n",
    "            # visible='legendonly'\n",
    "        )\n",
    "        )\n",
    "    else:\n",
    "        fig.add_trace(\n",
    "        go.Scatter(\n",
    "            x = path,\n",
    "            y = data_variable['Charge'][(data_variable['Charge']['n'].isin(path))&(data_variable['Charge']['t']==t)]['Value'],\n",
    "            name = t,\n",
    "            visible='legendonly'\n",
    "        )\n",
    "        )\n",
    "fig.update_layout(\n",
    "    hovermode='x',\n",
    "    # width=400\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
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   "source": [
    "liste_reservoir = data_set['r']['n']\n",
    "\n",
    "\n",
    "for i,reservoir in enumerate(liste_reservoir):\n",
    "    fig = go.Figure()\n",
    "    demand = pd.merge(data_set['t'][['dim1']].rename(columns={'dim1':'t'}),data_parameter['demand'][data_parameter['demand']['r']==reservoir],on=['t'],how='left')\n",
    "    demand['Value'] = demand['Value'].fillna(0)\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "            x = demand['t'],\n",
    "            y = -demand['Value'],\n",
    "            stackgroup='one',\n",
    "            name = 'Demande'\n",
    "        )\n",
    "    )\n",
    "\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "            x = data_set['t']['dim1'],\n",
    "            y = sum(data_variable['Qpipe'][(data_variable['Qpipe']['n.1']==reservoir)&(data_variable['Qpipe']['n']==n)]['Value'] for n in data_set['l'][data_set['l']['n.1']==reservoir]['n']),\n",
    "            stackgroup='two',\n",
    "            name = 'Approvisionnement'\n",
    "        )\n",
    "    )\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "            x = data_set['t']['dim1'],\n",
    "            y = data_variable['v'][data_variable['v']['n']==reservoir]['Value']-data_variable['v'][data_variable['v']['n']==reservoir]['Lowerbound'],\n",
    "            name = 'Volume - Volume min'\n",
    "        )\n",
    "    )\n",
    "    fig.update_layout(\n",
    "        hovermode='x',\n",
    "        legend=dict(\n",
    "                orientation=\"h\",\n",
    "                yanchor=\"bottom\",\n",
    "                y=1.02,\n",
    "                xanchor=\"right\",\n",
    "                x=1\n",
    "                ),\n",
    "        title = reservoir\n",
    "    )\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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           "style": "light"
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           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
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          },
          "scene": {
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            "gridcolor": "white",
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            "ticks": ""
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           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
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           "zerolinewidth": 2
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        },
        "title": {
         "text": "Etat du réseau de distribution d'eau - Coût : 2 €"
        },
        "yaxis": {
         "title": {
          "text": "m3"
         }
        },
        "yaxis2": {
         "anchor": "free",
         "overlaying": "y",
         "position": 1,
         "side": "right",
         "title": {
          "text": "kW"
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        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "Z = 2\n",
    "fig = go.Figure()\n",
    "for r in data_variable['v']['n'].unique():\n",
    "    fig.add_trace(\n",
    "        go.Bar(\n",
    "            x   =  data_variable['v'][data_variable['v']['n']==r]['t'],\n",
    "            y   =  data_variable['v'][data_variable['v']['n']==r]['Value'],\n",
    "            name=   r\n",
    "        )\n",
    "    )\n",
    "for c in data_variable['Ppompe']['c'].unique():\n",
    "    for d in data_variable['Ppompe']['d'].unique():\n",
    "        fig.add_trace(\n",
    "            go.Scatter(\n",
    "                x   =   data_variable['Ppompe'][(data_variable['Ppompe']['c']==c)&(data_variable['Ppompe']['d']==d)]['t'],\n",
    "                y   =   data_variable['Ppompe'][(data_variable['Ppompe']['c']==c)&(data_variable['Ppompe']['d']==d)]['Value'],\n",
    "                yaxis=\"y2\",\n",
    "                name=   f\"({c},{d})\"\n",
    "            )\n",
    "        )\n",
    "fig.update_layout(\n",
    "        legend=dict(\n",
    "            orientation=\"h\",\n",
    "            yanchor=\"bottom\",\n",
    "            y=1.02,\n",
    "            xanchor=\"right\",\n",
    "            x=1\n",
    "            ),\n",
    "        hovermode='x',\n",
    "        yaxis=dict(\n",
    "            title=\"m3\" ,\n",
    "            # range=[0,df[\"Consommation (MW)\"].max()*1.1]\n",
    "            ),\n",
    "        yaxis2=dict(title=\"kW\",\n",
    "        # range=[df[\"Coût MWh\"].min()*0.95,df[\"Coût MWh\"].max()*1.1],\n",
    "        anchor=\"free\",\n",
    "        overlaying=\"y\",\n",
    "        side=\"right\",\n",
    "        position=1\n",
    "        ),\n",
    "        title = f\"Etat du réseau de distribution d'eau - Coût : {Z} €\"\n",
    "    )\n",
    "fig.show()\n",
    "# fig.write_html(\"result.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4WT_fx_night.xlsx'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[file for file in os.listdir('./data_results') if file.endswith('.xlsx')][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     57.635219\n",
       "1     54.299841\n",
       "2     54.558968\n",
       "3     54.949131\n",
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       "5     55.807872\n",
       "6     56.265675\n",
       "7     56.385046\n",
       "8     53.760826\n",
       "9     60.929469\n",
       "10    53.366659\n",
       "11    58.832858\n",
       "12    61.051633\n",
       "13    60.923149\n",
       "14    55.287573\n",
       "15    60.908369\n",
       "16    60.909363\n",
       "17    63.073191\n",
       "18    63.073191\n",
       "19    63.073191\n",
       "20    63.073191\n",
       "21    58.391783\n",
       "22    63.073191\n",
       "23    59.646449\n",
       "Name: Value, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_variable['Charge'][data_variable['Charge']['n']=='s']['Value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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     "metadata": {},
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    "color_map   =   {\n",
    "    'p1'    :   'red',\n",
    "    'p2'    :   'blue',\n",
    "    'p3'    :   'green'\n",
    "}\n",
    "color_map_light   =   {\n",
    "    'p1'    :   'lightcoral',\n",
    "    'p2'    :   'lightblue',\n",
    "    'p3'    :   'lightgreen'\n",
    "}\n",
    "fig = go.Figure()\n",
    "for c in data_variable['Son']['c'].unique():\n",
    "    for d in data_variable['Son']['d'].unique():\n",
    "        fig.add_trace(\n",
    "            go.Bar(\n",
    "                x               =   data_variable['Son'][(data_variable['Son']['c']==c)&(data_variable['Son']['d']==d)]['t'],\n",
    "                y               =   10*data_variable['Son'][(data_variable['Son']['c']==c)&(data_variable['Son']['d']==d)]['Value'],\n",
    "                marker_color    =   color_map_light[d],\n",
    "                name            =   f\"Statut {c},{d}\"\n",
    "            )\n",
    "        )\n",
    "fig.update_layout(\n",
    "    barmode=\"stack\"\n",
    ")\n",
    "fig.add_trace(\n",
    "    go.Scatter(\n",
    "        x               =   data_variable['Gpompe']['t'],\n",
    "        y               =   data_variable['Gpompe']['Value'],\n",
    "        marker_color    =   'black',\n",
    "        line_dash       =   'dash',\n",
    "        name            =   f\"Gain Charge {c},{d}\"\n",
    "    )\n",
    ")\n",
    "for c in data_variable['Qpompe']['c'].unique():\n",
    "    for d in data_variable['Qpompe']['d'].unique():\n",
    "        fig.add_trace(\n",
    "            go.Scatter(\n",
    "                x               =   data_variable['Qpompe'][(data_variable['Qpompe']['c']==c)&(data_variable['Qpompe']['d']==d)]['t'],\n",
    "                y               =   data_variable['Qpompe'][(data_variable['Qpompe']['c']==c)&(data_variable['Qpompe']['d']==d)]['Value'],\n",
    "                marker_color    =   color_map[d],\n",
    "                name            =   f\"Débit {c},{d}\"\n",
    "            )\n",
    "        )\n",
    "fig.update_layout(\n",
    "    hovermode='x',\n",
    "    title = f\"Etat des pompes du réseau de distribution d'eau - Coût : {Z} € \"\n",
    "    )\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   ],
   "source": [
    "fig = go.Figure()\n",
    "for n in data_variable['Charge']['n'].unique():\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "            x = data_variable['Charge'][data_variable['Charge']['n']==n]['t'],\n",
    "            y = data_variable['Charge'][data_variable['Charge']['n']==n]['Value'],\n",
    "            name = n\n",
    "        )\n",
    "    )\n",
    "fig.update_layout(\n",
    "    legend=dict(\n",
    "            orientation=\"h\",\n",
    "            yanchor=\"bottom\",\n",
    "            y=1.02,\n",
    "            xanchor=\"right\",\n",
    "            x=1\n",
    "            ),\n",
    "    hovermode='x',\n",
    "    title = f\"Charge dans le réseau de distribution d'eau - Coût : {Z} € \",\n",
    "    yaxis_title = \"Charge (m)\"\n",
    ")\n",
    "fig.show()"
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