optimize_edar_effluent.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from src.GenerationAttenuationOptimizer import GenerationAttenuationOptimizer\n",
    "from pathlib import Path\n",
    "import subprocess\n",
    "import os\n",
    "import pandas as pd\n",
    "from sqlalchemy import create_engine\n",
    "import json\n",
    "import re\n",
    "#Import cmath Library\n",
    "import cmath "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine('postgresql://traca_user:EdificiH2O!@217.61.208.188:5432/traca_1')\n",
    "contaminant = 'Ciprofloxacina'\n",
    "conca = 'llobregat'\n",
    "cwd = Path.cwd()\n",
    "compound_generator_path = Path().resolve().parent / 'traca' / 'traca'/ 'inputs compound generator'\n",
    "recall_points_path = compound_generator_path / 'inputs' / 'recall_points.xlsx'\n",
    "removal_rate_path = compound_generator_path / 'inputs' / 'atenuacions_generacions.xlsx'\n",
    "removal_rate_path_updated = cwd / 'data' / 'atenuacions_generacions_2.xlsx'\n",
    "\n",
    "#txtinout_path = Path().resolve() / 'data' / 'txtinouts' / f'Txtinout_{conca}'\n",
    "#channels_geom_path = cwd / 'data' / 'rivs1' / 'canals_tot_ci.shp'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_default = {\n",
    "    'Venlafaxina': {\n",
    "        'UF': 0,\n",
    "        'OTHER': 0,\n",
    "        'Primari': 11.6,\n",
    "        'C': 28,\n",
    "        'CN': 45,\n",
    "        'CNP': 28,\n",
    "        'coef': 0.960506475055147\n",
    "    },\n",
    "    'Ciprofloxacina': {\n",
    "        'UF': 0,\n",
    "        'OTHER': 0,\n",
    "        'Primari': 30,\n",
    "        'C': 83.7,\n",
    "        'CN': 83.7,\n",
    "        'CNP': 83.7,\n",
    "        'coef': 0.22\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Actualitzar nous parametres amb resultats calibracio\n",
    "params_calibrated = {\n",
    "    'llobregat':  {\n",
    "        'Venlafaxina': {\n",
    "            'UF': 0.994981062768469,\n",
    "            'OTHER': 0.8808706791963803,\n",
    "            'Primari': 13.647398565470695,\n",
    "            'C': 36.39989588484838,\n",
    "            'CN': 55.84263851515217,\n",
    "            'CNP': 22.361249661542463,\n",
    "            'coef': 3.3781068007046515e-05\n",
    "        },\n",
    "        'Ciprofloxacina': {\n",
    "            'UF': 0.028119221524804092,\n",
    "            'OTHER': 0.46782972446135795,\n",
    "            'Primari': 25.97240221136915,\n",
    "            'C': 99.99101340844878,\n",
    "            'CN': 99.97761513754693,\n",
    "            'CNP': 69.65039530032158,\n",
    "            'coef': 0.0016681684496645895\n",
    "        },\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#overwrite atenuacions_generacions.xlsx\n",
    "df = pd.read_excel(removal_rate_path)\n",
    "params_conca_contaminant = params_calibrated[conca][contaminant]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joans\\AppData\\Local\\Temp\\ipykernel_12048\\3159196237.py:2: FutureWarning:\n",
      "\n",
      "Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '0.46782972446135795' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for key in params_calibrated[conca][contaminant].keys():\n",
    "    df.loc[df['contaminant'] == contaminant, key] = params_conca_contaminant[key]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel(removal_rate_path_updated, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "recall_points_df = pd.read_excel(recall_points_path)\n",
    "wwtp_conca = recall_points_df[recall_points_df['conca'] == conca]['edar_code'].dropna().unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cod_eu</th>\n",
       "      <th>cod_aca</th>\n",
       "      <th>nom_edar</th>\n",
       "      <th>fecha</th>\n",
       "      <th>Unit</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ES9080010001010E</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2011-10-13</td>\n",
       "      <td>mg/L</td>\n",
       "      <td>Ciprofloxacina</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ES9081130006010E</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2010-10-23</td>\n",
       "      <td>mg/L</td>\n",
       "      <td>Ciprofloxacina</td>\n",
       "      <td>0.000186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ES9081130006010E</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2011-10-13</td>\n",
       "      <td>mg/L</td>\n",
       "      <td>Ciprofloxacina</td>\n",
       "      <td>0.000010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ES9083020001010E</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2010-10-23</td>\n",
       "      <td>mg/L</td>\n",
       "      <td>Ciprofloxacina</td>\n",
       "      <td>0.000361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ES9083020001010E</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2011-10-18</td>\n",
       "      <td>mg/L</td>\n",
       "      <td>Ciprofloxacina</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             cod_eu cod_aca nom_edar      fecha  Unit  Substance name  \\\n",
       "0  ES9080010001010E    None     None 2011-10-13  mg/L  Ciprofloxacina   \n",
       "1  ES9081130006010E    None     None 2010-10-23  mg/L  Ciprofloxacina   \n",
       "2  ES9081130006010E    None     None 2011-10-13  mg/L  Ciprofloxacina   \n",
       "3  ES9083020001010E    None     None 2010-10-23  mg/L  Ciprofloxacina   \n",
       "4  ES9083020001010E    None     None 2011-10-18  mg/L  Ciprofloxacina   \n",
       "\n",
       "      Value  \n",
       "0  0.000013  \n",
       "1  0.000186  \n",
       "2  0.000010  \n",
       "3  0.000361  \n",
       "4  0.000013  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "observacions = pd.read_sql(f\"SELECT * FROM edars_effluent where \\\"Substance name\\\" = '{contaminant}'\", engine)\n",
    "observacions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cod_eu</th>\n",
       "      <th>cod_aca</th>\n",
       "      <th>nom_edar</th>\n",
       "      <th>fecha</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Substance name</th>\n",
       "      <th>Value</th>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2011-10-13</td>\n",
       "      <td>mg/L</td>\n",
       "      <td>Ciprofloxacina</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ES9081130006010E</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2010-10-23</td>\n",
       "      <td>mg/L</td>\n",
       "      <td>Ciprofloxacina</td>\n",
       "      <td>0.000186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ES9081130006010E</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2011-10-13</td>\n",
       "      <td>mg/L</td>\n",
       "      <td>Ciprofloxacina</td>\n",
       "      <td>0.000010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ES9083020001010E</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2010-10-23</td>\n",
       "      <td>mg/L</td>\n",
       "      <td>Ciprofloxacina</td>\n",
       "      <td>0.000361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ES9083020001010E</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2011-10-18</td>\n",
       "      <td>mg/L</td>\n",
       "      <td>Ciprofloxacina</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             cod_eu cod_aca nom_edar      fecha  Unit  Substance name  \\\n",
       "0  ES9080010001010E    None     None 2011-10-13  mg/L  Ciprofloxacina   \n",
       "1  ES9081130006010E    None     None 2010-10-23  mg/L  Ciprofloxacina   \n",
       "2  ES9081130006010E    None     None 2011-10-13  mg/L  Ciprofloxacina   \n",
       "3  ES9083020001010E    None     None 2010-10-23  mg/L  Ciprofloxacina   \n",
       "4  ES9083020001010E    None     None 2011-10-18  mg/L  Ciprofloxacina   \n",
       "\n",
       "      Value  \n",
       "0  0.000013  \n",
       "1  0.000186  \n",
       "2  0.000010  \n",
       "3  0.000361  \n",
       "4  0.000013  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "observacions_wwtp_conca = observacions[observacions['cod_eu'].isin(wwtp_conca)]\n",
    "observacions_wwtp_conca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for each row in observacions_wwtp_conca, apply functionm\n",
    "def get_wwtp_data(row, wwtp_json):\n",
    "    wwtp_data = wwtp_json[row['cod_eu']]\n",
    "    prediction = wwtp_data['compounds_effluent'][row['Substance name']]  #kg\n",
    "    flow = wwtp_data['compounds_effluent']['q']  #m3/d\n",
    "    concentration = 1000 * prediction / flow  #g/m3\n",
    "    return concentration\n",
    "\n",
    "def get_and_treat_edar_data(conca, removal_rate_path, contaminant, observacions_wwtp_conca):\n",
    "    os.chdir(compound_generator_path)\n",
    "    result = subprocess.run(['python', 'dades_efluent.py', conca, removal_rate_path, contaminant], capture_output=True, text=True, check=True)\n",
    "    os.chdir(cwd)\n",
    "\n",
    "    result_str = result.stdout\n",
    "\n",
    "    result_str = result.stdout\n",
    "\n",
    "    result_str = result_str.replace(\"'\", '\"').replace(\"\\n\", '')\n",
    "\n",
    "    # Define the regex pattern\n",
    "    pattern = r'([A-Z])\"([A-Z])'\n",
    "\n",
    "    # Replace the matches with the desired format\n",
    "    result_str = re.sub(pattern, r\"\\1'\\2\", result_str)\n",
    "\n",
    "    pattern = r', ([A-Z])\"'\n",
    "\n",
    "    # Replace the matches with the desired format\n",
    "    result_str = re.sub(pattern, r\", \\1'\", result_str)\n",
    "\n",
    "    wwtp_json = json.loads(result_str)\n",
    "\n",
    "    return  observacions_wwtp_conca.apply(get_wwtp_data, wwtp_json = wwtp_json, axis=1) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "observacions_wwtp_conca['prediction'] = get_and_treat_edar_data(conca, removal_rate_path_updated, contaminant, observacions_wwtp_conca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "observacions_wwtp_conca['prediction_default'] = get_and_treat_edar_data(conca, removal_rate_path, contaminant, observacions_wwtp_conca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>cod_eu</th>\n",
       "      <th>cod_aca</th>\n",
       "      <th>nom_edar</th>\n",
       "      <th>fecha</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Substance name</th>\n",
       "      <th>Value</th>\n",
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       "      <th>0</th>\n",
       "      <td>ES9080010001010E</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2011-10-13</td>\n",
       "      <td>mg/L</td>\n",
       "      <td>Ciprofloxacina</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.001162</td>\n",
       "      <td>0.081401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ES9081130006010E</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2010-10-23</td>\n",
       "      <td>mg/L</td>\n",
       "      <td>Ciprofloxacina</td>\n",
       "      <td>0.000186</td>\n",
       "      <td>0.001549</td>\n",
       "      <td>0.108455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ES9081130006010E</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2011-10-13</td>\n",
       "      <td>mg/L</td>\n",
       "      <td>Ciprofloxacina</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.001549</td>\n",
       "      <td>0.108455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ES9083020001010E</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2010-10-23</td>\n",
       "      <td>mg/L</td>\n",
       "      <td>Ciprofloxacina</td>\n",
       "      <td>0.000361</td>\n",
       "      <td>0.001143</td>\n",
       "      <td>0.080070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ES9083020001010E</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2011-10-18</td>\n",
       "      <td>mg/L</td>\n",
       "      <td>Ciprofloxacina</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.001143</td>\n",
       "      <td>0.080070</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             cod_eu cod_aca nom_edar      fecha  Unit  Substance name  \\\n",
       "0  ES9080010001010E    None     None 2011-10-13  mg/L  Ciprofloxacina   \n",
       "1  ES9081130006010E    None     None 2010-10-23  mg/L  Ciprofloxacina   \n",
       "2  ES9081130006010E    None     None 2011-10-13  mg/L  Ciprofloxacina   \n",
       "3  ES9083020001010E    None     None 2010-10-23  mg/L  Ciprofloxacina   \n",
       "4  ES9083020001010E    None     None 2011-10-18  mg/L  Ciprofloxacina   \n",
       "\n",
       "      Value  prediction  prediction_default  \n",
       "0  0.000013    0.001162            0.081401  \n",
       "1  0.000186    0.001549            0.108455  \n",
       "2  0.000010    0.001549            0.108455  \n",
       "3  0.000361    0.001143            0.080070  \n",
       "4  0.000013    0.001143            0.080070  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "observacions_wwtp_conca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert from mg/L to ng/L for representation\n",
    "observacions_wwtp_conca['prediction'] = observacions_wwtp_conca['prediction'] * 1e6\n",
    "observacions_wwtp_conca['prediction_default'] = observacions_wwtp_conca['prediction_default'] * 1e6\n",
    "observacions_wwtp_conca['Value'] = observacions_wwtp_conca['Value'] * 1e6\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
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   "source": [
    "#plot\n",
    "\n",
    "import plotly.graph_objs as go\n",
    "import plotly.express as px\n",
    "\n",
    "min_value = observacions_wwtp_conca['Value'].min()\n",
    "max_value = observacions_wwtp_conca['Value'].max()\n",
    "\n",
    "color_map = {\n",
    "    'prediction': '#0d920d',  # Color for 'prediction' column\n",
    "    'prediction_default': '#fe7c09'  # Color for 'prediction_default' column\n",
    "}\n",
    "\n",
    "\n",
    "# Scatter plot\n",
    "fig = px.scatter(observacions_wwtp_conca, x='Value', y=['prediction', 'prediction_default'], \n",
    "                 color_discrete_map=color_map,\n",
    "                 labels={'Value': 'Actual Value', 'variable': 'Prediction Type', 'value_0': 'Calibrated Prediction', 'value_1': 'Default Prediction'})\n",
    "\n",
    "# Add x=y line\n",
    "fig.add_trace(\n",
    "    go.Scatter(x=[min_value, max_value], y=[min_value, max_value],  line=dict(width=1, dash='dot', color='black'), marker=dict(opacity=0), name='x=y')\n",
    ")\n",
    "\n",
    "fig.update_traces(marker=dict(size=9,\n",
    "                    line=dict(width=1)),\n",
    "                    selector=dict(mode='markers'))\n",
    "\n",
    "# Change the legend labels\n",
    "fig.update_traces(name='Calibrated Prediction', selector=dict(name='prediction'))\n",
    "fig.update_traces(name='Default Prediction', selector=dict(name='prediction_default'))\n",
    "\n",
    "\n",
    "fig.update_yaxes(type=\"log\")\n",
    "\n",
    "\n",
    "fig.update_layout(\n",
    "    showlegend=True,\n",
    "    plot_bgcolor=\"white\",\n",
    "    legend_title_text='',\n",
    "    legend=dict(\n",
    "        orientation='h',  # horizontal legend\n",
    "        yanchor='bottom',  # anchor the legend to the bottom\n",
    "        y=1.15,  # position the legend just above the bottom\n",
    "        xanchor='right',  # anchor the legend to the right\n",
    "        x=1  # position the legend at the far right\n",
    "    )\n",
    "    )\n",
    "        \n",
    "fig.update_xaxes(\n",
    "    mirror=True,\n",
    "    ticks='outside',\n",
    "    showline=True,\n",
    "    linecolor='black',\n",
    "    gridcolor='lightgrey',\n",
    "    title=\"Observation (ng/l)\"\n",
    ")\n",
    "\n",
    "fig.update_yaxes(\n",
    "    mirror=True,\n",
    "    ticks='outside',\n",
    "    showline=True,\n",
    "    linecolor='black',\n",
    "    gridcolor='lightgrey',\n",
    "    title=\"Prediction (ng/l)\"\n",
    ")\n",
    "\n",
    "fig.write_image(f'C:\\\\Users\\\\joans\\\\OneDrive\\\\Escriptori\\\\icra\\\\traca_contaminacio\\\\traca_contaminacio\\\\images\\\\{contaminant}_{conca}_effluent.pdf', width=800, height=500)\n",
    "\n",
    "fig.show()"
   ]
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