Graphing sensibility_3parameters.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "79676cb4",
   "metadata": {},
   "source": [
    "# Prueba de Sensibilidad\n",
    "## Importar librerias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6ada5c1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pruebas de sensibilidad\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#--------Grafico 1: Graficos 2D de HI del componente VS HI del parametro que varía-----------\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import LinearLocator, FormatStrFormatter\n",
    "#from mpl_toolkits.mplot3d import Axes3D\n",
    "#import plotly.graph_objects as go\n",
    "#import plotly.express as px\n",
    "\n",
    "from colour import Color\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc449027",
   "metadata": {},
   "source": [
    "##  1: Modificar parámetros "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8695abbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#----------- Valores a editar por combinación -----------\n",
    "activo ='RC'\n",
    "compo = 'Total'\n",
    "\n",
    "#---MODIFICAR LOS PARAMETROS: --------#\n",
    "\n",
    "param =['Tanque','Ubicación','Inspección']          #Anotar el nombre de los parametros\n",
    "par1,par2,par3 = 0,1,2                             #par1 será el parametro a variar su HI\n",
    "                                                    #par2 varía su peso con par1\n",
    "                                                    #par3 permanece constante\n",
    "    \n",
    "pesos_pre =[0.4,0.35,0.25]                          #Anotar el valor predefinido de los parametro\n",
    "ind_pred = 50.0                                     #Valor predeterminado de indice\n",
    "\n",
    "#------ Definir variación de indices--------\n",
    "y=list(range(0,101))                             #Desde 0 hasta 100 (101-1) con paso de 10         \n",
    "\n",
    "#--------- Variacion de pesos --------------\n",
    "salto, Max = 0.1,0.3\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb4d387c",
   "metadata": {},
   "source": [
    "# Calculo de HI's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "95ddc6ac",
   "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>Tanque</th>\n",
       "      <th>Ubicación</th>\n",
       "      <th>Inspección</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Tanque Ubicación Inspección\n",
       "1    0.1      0.65       0.25\n",
       "2    0.2      0.55       0.25\n",
       "3    0.3      0.45       0.25\n",
       "4    0.4      0.35       0.25\n",
       "5    0.5      0.25       0.25\n",
       "6    0.6      0.15       0.25"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#---Crear variación de los pesos-----\n",
    "xx =list()                                                               #Crear lista de variación\n",
    "\n",
    "for i in range(int(((Max*2)/salto)+1)):                                  #Asigna de valores a la lista \n",
    "    xx.append(float(\"{:.2f}\".format(i*salto-Max)))\n",
    "\n",
    "#-----Crear variación de los pesos-------\n",
    "\n",
    "pesos = pd.DataFrame()                                                   #crear dataframe, nombrar según combinacion\n",
    "for i in range(len(param)):                                              #agregar tabla segun numero de parametros\n",
    "    pesos[param[i]]='' \n",
    "    pesos.at[0,param[i]]= pesos_pre[i]\n",
    "\n",
    "if (pesos[param[par1]][0])<0.3:\n",
    "    for i in range(len(xx)):\n",
    "        xx[i]=xx[i]+0.15\n",
    "    \n",
    "for i in range(len(xx)):\n",
    "    j = float(\"{:.2f}\".format(pesos[param[par1]][0] + xx[i]))            #Hallar peso nuevo\n",
    "    if( j + pesos[param[par3]][0] <= 1):                                 #tener en cuenta que no pase de 1\n",
    "        if (j >= 0):\n",
    "            pesos.at[i+1,param[par1]]=j\n",
    "        elif(j < 0): \n",
    "            pesos.at[i,param[par1]]=0                                   #Igualar a 0 o 1 si se pasa de los limites\n",
    "    else:\n",
    "        nada='nada'\n",
    "\n",
    "#----Asignar los valores del segundo peso que depende de los demás-------\n",
    "    pesos.at[i+1,param[par3]] = pesos[param[par3]][0]\n",
    "    pesos.at[i+1,param[par2]]= 1 - pesos[param[par1]][i+1]- pesos[param[par3]][i+1]\n",
    "pesos = pesos.drop(0, axis=0)                                          #Eliminar el valor del peso predefinido repetido\n",
    "pesos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "873e1f6f",
   "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>HI Tanque</th>\n",
       "      <th>HI Ubicación</th>\n",
       "      <th>HI Inspección</th>\n",
       "      <th>HI Total 1</th>\n",
       "      <th>HI Total 2</th>\n",
       "      <th>HI Total 3</th>\n",
       "      <th>HI Total 4</th>\n",
       "      <th>HI Total 5</th>\n",
       "      <th>HI Total 6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>55.9</td>\n",
       "      <td>60.8</td>\n",
       "      <td>65.7</td>\n",
       "      <td>70.6</td>\n",
       "      <td>75.5</td>\n",
       "      <td>80.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>55.8</td>\n",
       "      <td>60.6</td>\n",
       "      <td>65.4</td>\n",
       "      <td>70.2</td>\n",
       "      <td>75.0</td>\n",
       "      <td>79.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>97.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>55.7</td>\n",
       "      <td>60.4</td>\n",
       "      <td>65.1</td>\n",
       "      <td>69.8</td>\n",
       "      <td>74.5</td>\n",
       "      <td>79.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>96.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>55.6</td>\n",
       "      <td>60.2</td>\n",
       "      <td>64.8</td>\n",
       "      <td>69.4</td>\n",
       "      <td>74.0</td>\n",
       "      <td>78.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>4.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>46.4</td>\n",
       "      <td>41.8</td>\n",
       "      <td>37.2</td>\n",
       "      <td>32.6</td>\n",
       "      <td>28.0</td>\n",
       "      <td>23.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>3.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>46.3</td>\n",
       "      <td>41.6</td>\n",
       "      <td>36.9</td>\n",
       "      <td>32.2</td>\n",
       "      <td>27.5</td>\n",
       "      <td>22.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>46.2</td>\n",
       "      <td>41.4</td>\n",
       "      <td>36.6</td>\n",
       "      <td>31.8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>22.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>46.1</td>\n",
       "      <td>41.2</td>\n",
       "      <td>36.3</td>\n",
       "      <td>31.4</td>\n",
       "      <td>26.5</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    HI Tanque HI Ubicación HI Inspección  HI Total 1  HI Total 2  HI Total 3  \\\n",
       "0       100.0         50.0          50.0        56.0        61.0        66.0   \n",
       "1        99.0         50.0          50.0        55.9        60.8        65.7   \n",
       "2        98.0         50.0          50.0        55.8        60.6        65.4   \n",
       "3        97.0         50.0          50.0        55.7        60.4        65.1   \n",
       "4        96.0         50.0          50.0        55.6        60.2        64.8   \n",
       "..        ...          ...           ...         ...         ...         ...   \n",
       "96        4.0         50.0          50.0        46.4        41.8        37.2   \n",
       "97        3.0         50.0          50.0        46.3        41.6        36.9   \n",
       "98        2.0         50.0          50.0        46.2        41.4        36.6   \n",
       "99        1.0         50.0          50.0        46.1        41.2        36.3   \n",
       "100       0.0         50.0          50.0        46.0        41.0        36.0   \n",
       "\n",
       "     HI Total 4  HI Total 5  HI Total 6  \n",
       "0          71.0        76.0        81.0  \n",
       "1          70.6        75.5        80.4  \n",
       "2          70.2        75.0        79.8  \n",
       "3          69.8        74.5        79.2  \n",
       "4          69.4        74.0        78.6  \n",
       "..          ...         ...         ...  \n",
       "96         32.6        28.0        23.4  \n",
       "97         32.2        27.5        22.8  \n",
       "98         31.8        27.0        22.2  \n",
       "99         31.4        26.5        21.6  \n",
       "100        31.0        26.0        21.0  \n",
       "\n",
       "[101 rows x 9 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#--Crear escenarios: Cada escenario varía el HI del parámetro, y se repite variando su peso\n",
    "\n",
    "#----Crear tabla de HI\n",
    "\n",
    "hi=pd.DataFrame()   \n",
    "for i in range(len(param)):\n",
    "    hi['HI '+param[i]]=''                                      #Crear columnas de HI de cada parámetro\n",
    "\n",
    "for i in range(hi.shape[1]):                                   #Asignar valores predeterminados de indices\n",
    "    for j in range(len(y)):\n",
    "        hi.at[j,'HI '+param[i]]=ind_pred\n",
    "\n",
    "for i in range(hi.shape[0]):                                   #Asignar valores de variación\n",
    "    hi.at[i,'HI '+param[par1]]=float(y[i])                      #Asignar rango de variación\n",
    " \n",
    "#Asignar valores variables del indice al parametro\n",
    "hi['HI '+param[par1]]= hi['HI '+param[par1]].values[::-1]        #Ordenar valores que varían(de 100 a 0)\n",
    "\n",
    "for k in range(pesos.shape[0]):                                #Asignar indice de salud al componente\n",
    "    hi['HI '+compo+' '+str(k+1)]=0.0\n",
    "    for j in range(hi.shape[0]):\n",
    "        for i in range(len(param)):\n",
    "            z = float(hi['HI '+param[i]][j]*pesos[param[i]][k+1])\n",
    "            hi.at[j,'HI '+compo+' '+str(k+1)]+=float(z)\n",
    "        hi.at[j,'HI '+compo+' '+str(k+1)]+=1.0\n",
    "\n",
    "hi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "656eed21",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#--------Grafico 1: Graficos 2D de HI del componente VS HI del parámetro variado-----------\n",
    "red = Color(\"red\")\n",
    "colors = list(red.range_to(Color(\"green\"),pesos.shape[0]))\n",
    "ax = plt.gca()\n",
    "\n",
    "\n",
    "for i in range(pesos.shape[0]):\n",
    "    #hi.plot(kind='scatter',x='HI '+param[par1], y='HI '+compo+' '+str(i+1),ax=ax,color= str(colors[i]),title =title,label=str(pesos3[param[par1][i+1]]))\n",
    "    label = str(pesos[param[par1]][i+1])\n",
    "    title = 'HI '+compo+' VS HI '+param[par1]+' Según varia el peso'\n",
    "    hi.plot(kind='line',x='HI '+param[par1], y='HI '+compo+' '+str(i+1),title=title,color =str(colors[i]),ax=ax,label=label)\n",
    "    plt.xlim(max(hi['HI '+param[par1]])+5, min(hi['HI '+param[par1]])-5)\n",
    "plt.ylabel(\"HI \"+compo)\n",
    "plt.savefig(activo+\"_\"+compo+\"_\"+param[par1].upper()+'_'+param[par2]+'_.png',dpi=200)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}