{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "source": [ "**Taller de Proyecto de Datos**\n", "\n", "**INFORME FINAL**\n", "\n", "***Persiguiendo el clima: conocimientos y predicciones basados en datos***\n", "\n", "Integrantes:\n", "Daniela Mancilla,\n", "Gabriela Martínez,\n", "Francisca Quijada" ], "metadata": { "id": "tuxwR3blCufU" } }, { "cell_type": "markdown", "source": [ "**INTRODUCCIÓN**\n", "\n", "El clima afecta a Chile de diversas maneras debido a su geografía única. Para entender y abordar estos impactos, es esencial analizar los datos climáticos, ya que su alcance se extiende a múltiples aspectos fundamentales de la sociedad, incluyendo:\n", "\n", "* **Recursos hídricos**: los patrones de precipitación determinan la cantidad de agua que fluye a ríos y embalses.\n", "\n", "* **Agricultura**: las temperaturas extremas, ya sean altas o bajas, pueden dañar los cultivos.\n", "\n", "* **Desastres naturales**: por un lado lluvias intensas pueden provocar inundaciones y deslizamientos de tierra, mientras que condiciones secas y cálidas pueden aumentar la probabilidad de incendios forestales.\n", "\n", "\n", "El comprender mejor los efectos del clima, permite tomar decisiones informadas y desarrollar estrategias para proteger los recursos, fortalecer la agricultura y garantizar una planificación adecuada de medidas de prevención y respuesta ante desastres.\n", "\n", "\n", "\n", "El objetivo general del proyecto es *estudiar y comprender cómo las variables atmosféricas y oceánicas influyen en las condiciones climáticas*. Para ello se proponen responder las siguientes preguntas:\n", "\n", "- ¿Se pueden realizar pronósticos o estimaciones basados en los patrones observados? Se buscará responder preguntas como: ¿cuál será la temperatura?, ¿cuánto lloverá?.\n", "\n", "- ¿Existen patrones complejos entre las variables? El objetivo es dividir los datos climáticos en grupos significativos y buscar relaciones no evidentes entre las variables.\n", "\n", "- ¿Se pueden detectar anomalías? Esto podría ser utilizado como una herramienta para anticipar eventos climáticos extremos.\n", "\n", "Principalmente, el éxito del proyecto se medirá en base a si se pueden responder las preguntas planteadas, para lo cual es esencial analizar los valores de las métricas de cada uno de los modelos utilizados. Adicionalmente, el ser capaz de plantear nuevas preguntas a partir de lo concluido, también aportará a este aspecto." ], "metadata": { "id": "QbzetI7GCkds" } }, { "cell_type": "markdown", "source": [ "**Datos**\n", "\n", "La atmósfera y el océano son sistemas interdependientes, lo que significa que los eventos en uno de ellos desencadenan cambios en el otro. Para comprender con precisión el clima, es esencial analizar datos de ambos.\n", "\n", "Los datos que se utilizarán son registros correspondientes a la ciudad de Valparaíso durante el año 2022.\n", "\n", "\n", "Los archivos iniciales que contienen los datos a analizar son:\n", "- 2 archivos .CSV asociados a los atributos atmosféricos, que fueron obtenidos en https://agrometeorologia.cl/, correspondientes a la Estación Rodelillo, Valparaíso.\n", "- 12 archivos .CSV asociados a los atributos oceánicos, que fueron solicitados a cendoc@shoa.cl, correspondientes al Faro Extremo Molo de Abrigo, Valparaíso.\n", "\n", "En total, luego de unir todos los archivos, se obtiene un dataframe con 8270 registros (filas) y 9 atributos (columnas), donde 7 de ellos son atributos atmosféricos y 2 océanicos. Los registros corresponden a mediciones realizadas cada una hora, durante el año 2022.\n", "\n", "Los atributos del dataframe son:\n", "\n", "- Temperatura del aire (TA) en ºC,\n", "- Humedad relativa (HR) en %,\n", "- Precipitación acumulada (PP) en mm, \n", "- Presión atmosférica (PA) en mbar,\n", "- Velocidad del viento (VV) en km/h,\n", "- Ráfaga de viento (RV) en km/h,\n", "- Dirección del viento (DV) en º,\n", "- Nivel del mar (PRS) en m, y\n", "- Temperatura del agua (TW) en ºC." ], "metadata": { "id": "q9yadkz2lM6u" } }, { "cell_type": "markdown", "source": [ "El dataframe final, luego del preprocesamiento, se ve de la siguiente manera:" ], "metadata": { "id": "UstGejbMplf0" } }, { "cell_type": "code", "source": [ "import pandas as pd\n", "df=pd.read_csv(\"datos2022.csv\", sep=\",\")\n", "df['fecha']= pd.to_datetime(df['fecha'])\n", "df.head(2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 112 }, "id": "t1S-ySBxQdbF", "outputId": "429ce009-d05e-45e9-ff11-a4bd3747778d" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " fecha TA HR PP PA VV RV DV PRS TW\n", "0 2022-01-01 00:00:00 11.2 81.8 0.0 975.0 3.9 14.0 177.0 3.11 15.47\n", "1 2022-01-01 01:00:00 11.0 81.5 0.0 974.0 2.3 8.6 208.0 3.04 14.90" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
fechaTAHRPPPAVVRVDVPRSTW
02022-01-01 00:00:0011.281.80.0975.03.914.0177.03.1115.47
12022-01-01 01:00:0011.081.50.0974.02.38.6208.03.0414.90
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 4 } ] }, { "cell_type": "code", "source": [ "df.info()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "F9wcfhsnQmgt", "outputId": "b62160e3-a652-48d6-fa5a-1532d6241d58" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "RangeIndex: 8270 entries, 0 to 8269\n", "Data columns (total 10 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 fecha 8270 non-null datetime64[ns]\n", " 1 TA 8270 non-null float64 \n", " 2 HR 8270 non-null float64 \n", " 3 PP 8270 non-null float64 \n", " 4 PA 8270 non-null float64 \n", " 5 VV 8270 non-null float64 \n", " 6 RV 8270 non-null float64 \n", " 7 DV 8270 non-null float64 \n", " 8 PRS 8270 non-null float64 \n", " 9 TW 8270 non-null float64 \n", "dtypes: datetime64[ns](1), float64(9)\n", "memory usage: 646.2 KB\n" ] } ] }, { "cell_type": "markdown", "source": [ "**MÉTODO**" ], "metadata": { "id": "HCUG7WLTQWCT" } }, { "cell_type": "markdown", "source": [ "Se comienza realizando un ETL correspondiente a Extraer, Transformar, Cargar." ], "metadata": { "id": "WsC1QP6dp-RD" } }, { "cell_type": "markdown", "source": [ "**Carga de datos atmosféricos**\n", "\n", "Se comienza cargando los datos atmosféricos." ], "metadata": { "id": "yzYInHs5LeQA" } }, { "cell_type": "code", "source": [ "import pandas as pd\n", "df1=pd.read_csv(\"atm1.csv\", sep=\",\")\n", "df2=pd.read_csv(\"atm2.csv\", sep=\",\")" ], "metadata": { "id": "sg5HMFyLBznI" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Se renombran las columnas de acuerdo a los atributos atmosféricos descritos en la sección Datos." ], "metadata": { "id": "OZrIYaXXPG0e" } }, { "cell_type": "code", "source": [ "df1=df1.rename(columns={\"www.agrometeorologia.cl\": \"fecha\",\t\"Unnamed: 1\": \"TA\",\t\"Unnamed: 2\": \"HR\",\t\"Unnamed: 3\": \"PP\",\n", " \"Unnamed: 4\": \"PA\" })\n", "df2=df2.rename(columns={\"www.agrometeorologia.cl\": \"fecha\",\t\"Unnamed: 3\": \"VV\",\t\"Unnamed: 4\": \"RV\",\t\"Unnamed: 5\": \"DV\"})" ], "metadata": { "id": "CI-R4ZRIPQDu" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "A continuación se eliminan algunas filas del comienzo y final, que no registran información relevante a los atributos; y algunas columnas." ], "metadata": { "id": "eI_eD8E2QVd4" } }, { "cell_type": "code", "source": [ "df1=df1.drop(index=[0,1,2,3,4,8765,8766,8767,8768],\n", " columns=[\"Unnamed: 5\",\"Unnamed: 6\",\"Unnamed: 7\",\"Unnamed: 8\",\"Unnamed: 9\",\"Unnamed: 10\"])\n", "df1.head(2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 112 }, "id": "cem6ywepQU4r", "outputId": "c4fd7c0d-aaef-4247-8532-17fae38b9b82" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " fecha TA HR PP PA\n", "5 01-01-2022 00:00 11.2 81.8 0 975\n", "6 01-01-2022 01:00 11 81.5 0 974" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
fechaTAHRPPPA
501-01-2022 00:0011.281.80975
601-01-2022 01:001181.50974
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 14 } ] }, { "cell_type": "code", "source": [ "df2=df2.drop(index=[0,1,2,3,4,8765,8766,8767,8768],\n", " columns=[\"Unnamed: 1\",\"Unnamed: 2\",\"Unnamed: 6\",\"Unnamed: 7\",\"Unnamed: 8\",\"Unnamed: 9\",\"Unnamed: 10\"])\n", "df2.head(2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 112 }, "id": "PdevomnX_QUB", "outputId": "44d17056-8e87-4ccd-dcae-1d76b60ff669" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " fecha VV RV DV\n", "5 01-01-2022 00:00 3.9 14 177\n", "6 01-01-2022 01:00 2.3 8.6 208" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
fechaVVRVDV
501-01-2022 00:003.914177
601-01-2022 01:002.38.6208
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 15 } ] }, { "cell_type": "markdown", "source": [ "Todas las columnas, excepto la fecha, se cambiarán a formato float, ya que todos los datos son numéricos y no necesariamente enteros." ], "metadata": { "id": "w0Tar3PqR3l-" } }, { "cell_type": "code", "source": [ "df1=df1.astype({'TA': 'float','HR': 'float','PP': 'float','PA': 'float' })\n", "df2=df2.astype({'VV': 'float','RV': 'float','DV': 'float'})" ], "metadata": { "id": "hRWhAYHjzOGT" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "La columna fecha se transforma al formato datetime." ], "metadata": { "id": "fT6k0vNzSUSb" } }, { "cell_type": "code", "source": [ "df1['fecha'] = df1['fecha'].apply(lambda x:pd.to_datetime(x,format='%d-%m-%Y %H:%M'))\n", "df2['fecha'] = df2['fecha'].apply(lambda x:pd.to_datetime(x,format='%d-%m-%Y %H:%M'))" ], "metadata": { "id": "MlCXgK9czRdr" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Se realiza un merge, para unir ambos dataframes en uno solo, para ello se utilizará la columna \"fecha\" que es común a ambos." ], "metadata": { "id": "vwEDy6WPAfxN" } }, { "cell_type": "code", "source": [ "df_atm=df1.merge(df2, how='inner', on='fecha')\n", "df_atm.head(2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 112 }, "id": "RL6k2zZLAesS", "outputId": "abeeea6d-d84a-4456-a2c2-b88a2ca86469" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " fecha TA HR PP PA VV RV DV\n", "0 2022-01-01 00:00:00 11.2 81.8 0.0 975.0 3.9 14.0 177.0\n", "1 2022-01-01 01:00:00 11.0 81.5 0.0 974.0 2.3 8.6 208.0" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
fechaTAHRPPPAVVRVDV
02022-01-01 00:00:0011.281.80.0975.03.914.0177.0
12022-01-01 01:00:0011.081.50.0974.02.38.6208.0
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 21 } ] }, { "cell_type": "markdown", "source": [ "**Carga de datos oceánicos**\n", "\n", "A continuación se cargan los archivos asociados a los datos oceánicos." ], "metadata": { "id": "iL-EGy9JAnMw" } }, { "cell_type": "code", "source": [ "ene=pd.read_csv(\"ene.csv\", sep=\";\")\n", "feb=pd.read_csv(\"feb.csv\", sep=\";\")\n", "mar=pd.read_csv(\"mar.csv\", sep=\";\")\n", "abr=pd.read_csv(\"abr.csv\", sep=\";\")\n", "may=pd.read_csv(\"may.csv\", sep=\";\")\n", "jun=pd.read_csv(\"jun.csv\", sep=\";\")\n", "jul=pd.read_csv(\"jul.csv\", sep=\";\")\n", "ago=pd.read_csv(\"ago.csv\", sep=\";\")\n", "sep=pd.read_csv(\"sep.csv\", sep=\";\")\n", "oct=pd.read_csv(\"oct.csv\", sep=\";\")\n", "nov=pd.read_csv(\"nov.csv\", sep=\";\")\n", "dic=pd.read_csv(\"dic.csv\", sep=\";\")" ], "metadata": { "id": "yKDh_z-mCzW6" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ " Como a cada archivo le corresponde un mes distinto haremos una concatenación hacia abajo. Dado que las columnas tienen los mismos nombres." ], "metadata": { "id": "gFIdKM_lDm_h" } }, { "cell_type": "code", "source": [ "df_oce=pd.concat([ene,feb,mar,abr,may,jun,jul,ago,sep,oct,nov,dic])" ], "metadata": { "id": "SS131EMODrdF" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Se eliminan algunas columnas pues corresponden a parámetros que ya se tienen en los datos atmosféricos. Seleccionamos: Sensor PRS: mide el nivel del mar (m). Sensor RAD: mide el nivel del mar (m) y Sensor TW: Temperatura del agua (ºC).\n" ], "metadata": { "id": "HLyTbdBGEa-0" } }, { "cell_type": "code", "source": [ "df_oce=df_oce.drop(columns=[\"TA\",\"BP\",\"RH\",\"BAT\"])\n", "df_oce.head(2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 112 }, "id": "4fwJil9DEFUk", "outputId": "bbd2d1d6-0350-4cfb-8ab9-8312afe9c2ef" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " fechahora PRS RAD TW\n", "0 2022-01-01 00:00:00 3.092 4.345 15.7\n", "1 2022-01-01 00:01:00 3.101 4.342 15.7" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
fechahoraPRSRADTW
02022-01-01 00:00:003.0924.34515.7
12022-01-01 00:01:003.1014.34215.7
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 25 } ] }, { "cell_type": "markdown", "source": [ "Se renombra la columna \"fechahora\" y se cambia su formato, de la misma forma que con los datos atmosféricos." ], "metadata": { "id": "DfaAS8rhFNUh" } }, { "cell_type": "code", "source": [ "df_oce=df_oce.rename(columns={\"fechahora\": \"fecha\"})\n", "df_oce['fecha'] = df_oce['fecha'].apply(lambda x: pd.to_datetime(x,format='%Y-%m-%d %H:%M:%S'))" ], "metadata": { "id": "lWxJ_FgyFWru" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "**Unión de datos oceánicos y atmosféricos**\n", "\n", "Los datos oceánicos están registrados cada un minuto, mientras que los atmosféricos cada una hora. Para unir ambos dataframes, se promediarán los datos oceánicos cada una hora." ], "metadata": { "id": "whIICpt_D-JW" } }, { "cell_type": "code", "source": [ "df_oce_hour=df_oce.resample('60min', on='fecha').mean().round(2)\n", "df_oce_hour.head(2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 143 }, "id": "AYqpz8pHEJ6a", "outputId": "df77566a-9f5a-4a8d-ef96-e1120ac033f6" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " PRS RAD TW\n", "fecha \n", "2022-01-01 00:00:00 3.11 4.35 15.47\n", "2022-01-01 01:00:00 3.04 4.29 14.90" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PRSRADTW
fecha
2022-01-01 00:00:003.114.3515.47
2022-01-01 01:00:003.044.2914.90
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 29 } ] }, { "cell_type": "markdown", "source": [ "Se realiza un merge." ], "metadata": { "id": "RY8CQSufhPzp" } }, { "cell_type": "code", "source": [ "df=df_atm.merge(df_oce_hour, how='inner', on='fecha')\n", "df.head(2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 112 }, "id": "bwftnoujGRCY", "outputId": "0191e431-5a78-49ce-f178-85498faee021" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " fecha TA HR PP PA VV RV DV PRS RAD \\\n", "0 2022-01-01 00:00:00 11.2 81.8 0.0 975.0 3.9 14.0 177.0 3.11 4.35 \n", "1 2022-01-01 01:00:00 11.0 81.5 0.0 974.0 2.3 8.6 208.0 3.04 4.29 \n", "\n", " TW \n", "0 15.47 \n", "1 14.90 " ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
fechaTAHRPPPAVVRVDVPRSRADTW
02022-01-01 00:00:0011.281.80.0975.03.914.0177.03.114.3515.47
12022-01-01 01:00:0011.081.50.0974.02.38.6208.03.044.2914.90
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 30 } ] }, { "cell_type": "markdown", "source": [ "**Análisis de nulos**\n", "\n", "Lo siguiente corresponde a analizar los valores nulos." ], "metadata": { "id": "OXKnhPpxSkcf" } }, { "cell_type": "code", "source": [ "df.isnull().sum()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "aByTxplCeMtq", "outputId": "ea92cea9-86ce-488f-db34-8561fc83f9b4" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "fecha 0\n", "TA 163\n", "HR 146\n", "PP 146\n", "PA 146\n", "VV 146\n", "RV 146\n", "DV 146\n", "PRS 277\n", "RAD 277\n", "TW 277\n", "dtype: int64" ] }, "metadata": {}, "execution_count": 31 } ] }, { "cell_type": "code", "source": [ "import missingno as msno\n", "msno.matrix(df)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 362 }, "id": "BGocC8PGevbv", "outputId": "a820053c-aad7-4bb4-e412-b35bb826bb3c" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 32 }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "Se observa que principalmente existen dos periodos de tiempo donde los datos atmosféricos no están registrados. Mientras que los datos atmosféricos tienen datos faltantes los días finales de cada mes." ], "metadata": { "id": "tuRO7egqe9yF" } }, { "cell_type": "markdown", "source": [ "Como los datos nulos representan menos del 4% de\n", "los datos total, ya que son menos de 300 de un total de aproximadamente 8600, se eliminarán." ], "metadata": { "id": "CQq58nUyibLN" } }, { "cell_type": "code", "source": [ "df = df.dropna(axis=0, how='any')\n", "df.info()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "PXvCW0Vlbw0R", "outputId": "bd8ee9e0-352c-4aef-d8d5-340ee122d7a6" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Int64Index: 8297 entries, 0 to 8736\n", "Data columns (total 11 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 fecha 8297 non-null datetime64[ns]\n", " 1 TA 8297 non-null float64 \n", " 2 HR 8297 non-null float64 \n", " 3 PP 8297 non-null float64 \n", " 4 PA 8297 non-null float64 \n", " 5 VV 8297 non-null float64 \n", " 6 RV 8297 non-null float64 \n", " 7 DV 8297 non-null float64 \n", " 8 PRS 8297 non-null float64 \n", " 9 RAD 8297 non-null float64 \n", " 10 TW 8297 non-null float64 \n", "dtypes: datetime64[ns](1), float64(10)\n", "memory usage: 777.8 KB\n" ] } ] }, { "cell_type": "markdown", "source": [ "Se resetean los índices, para que sea más fácil localizar las filas." ], "metadata": { "id": "tPsRZcM0P4zi" } }, { "cell_type": "code", "source": [ "df=df.reset_index(drop=True)" ], "metadata": { "id": "4D25lSJ_P3hy" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "**Duplicados**\n", "\n", "Se revisa si existen duplicados.\n", "Podrían existir más de un registro para la misma fecha, sin embargo, a continuación se observa que no ocurre." ], "metadata": { "id": "lmO_mVzUe6NL" } }, { "cell_type": "code", "source": [ "masc=(df.loc[:,[\"fecha\"]]).duplicated(keep=False)\n", "df.loc[masc]" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 53 }, "id": "tnKJUp2FgX8v", "outputId": "1eb2766f-6022-4848-edbd-1e468aa70e55" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Empty DataFrame\n", "Columns: [fecha, TA, HR, PP, PA, VV, RV, DV, PRS, RAD, TW]\n", "Index: []" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
fechaTAHRPPPAVVRVDVPRSRADTW
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 35 } ] }, { "cell_type": "code", "source": [ "df= df.drop_duplicates()\n", "df.info()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "m0WjMUnCde05", "outputId": "799e51bc-71cd-4f0d-ea6e-c426c7316f7c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Int64Index: 8297 entries, 0 to 8296\n", "Data columns (total 11 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 fecha 8297 non-null datetime64[ns]\n", " 1 TA 8297 non-null float64 \n", " 2 HR 8297 non-null float64 \n", " 3 PP 8297 non-null float64 \n", " 4 PA 8297 non-null float64 \n", " 5 VV 8297 non-null float64 \n", " 6 RV 8297 non-null float64 \n", " 7 DV 8297 non-null float64 \n", " 8 PRS 8297 non-null float64 \n", " 9 RAD 8297 non-null float64 \n", " 10 TW 8297 non-null float64 \n", "dtypes: datetime64[ns](1), float64(10)\n", "memory usage: 777.8 KB\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Información estadística**\n", "\n", "Se obtiene información estadística resumida, que sirve para detectar posibles errores en los datos, especialmente al observar los mínimos y máximos.De esta manera se analizarán los valores outliers.\n", "\n", "Se puede fijar un threshold, calculando qué tan alejado los valores están medidos en desviaciones estándar (std), de la media (mean)." ], "metadata": { "id": "6PetVwdCifgK" } }, { "cell_type": "code", "source": [ "df.describe().round(2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 300 }, "id": "iH4MIhHaikzw", "outputId": "a0011a98-acc8-49da-dcbc-72121fc9c2f5" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " TA HR PP PA VV RV DV PRS \\\n", "count 8297.00 8297.00 8297.00 8297.00 8297.00 8297.00 8297.00 8297.00 \n", "mean 13.16 76.61 0.04 978.71 5.59 11.69 147.12 2.29 \n", "std 4.65 18.49 0.43 3.04 4.89 7.35 118.62 0.38 \n", "min -33.90 1.70 0.00 970.00 0.00 0.00 0.00 0.12 \n", "25% 9.80 64.20 0.00 977.00 1.50 6.50 0.00 2.00 \n", "50% 12.40 80.80 0.00 978.00 4.40 10.40 156.00 2.27 \n", "75% 15.70 92.20 0.00 980.00 8.50 15.80 265.00 2.57 \n", "max 32.60 100.00 17.00 990.00 34.80 58.70 355.00 3.40 \n", "\n", " RAD TW \n", "count 8297.00 8297.00 \n", "mean 3.59 13.29 \n", "std 0.47 1.31 \n", "min 2.71 10.75 \n", "25% 3.29 12.42 \n", "50% 3.55 12.94 \n", "75% 3.84 13.76 \n", "max 15.95 20.04 " ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TAHRPPPAVVRVDVPRSRADTW
count8297.008297.008297.008297.008297.008297.008297.008297.008297.008297.00
mean13.1676.610.04978.715.5911.69147.122.293.5913.29
std4.6518.490.433.044.897.35118.620.380.471.31
min-33.901.700.00970.000.000.000.000.122.7110.75
25%9.8064.200.00977.001.506.500.002.003.2912.42
50%12.4080.800.00978.004.4010.40156.002.273.5512.94
75%15.7092.200.00980.008.5015.80265.002.573.8413.76
max32.60100.0017.00990.0034.8058.70355.003.4015.9520.04
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 37 } ] }, { "cell_type": "markdown", "source": [ "Los valores que más llaman la atención son la temperatura mínima de -33.9, humedad relativa mínima de 1.7, el nivel del mar \"PRS\" mínimo de 0.12, y el nivel del mar \"RAD\" máximo de 15.95.\n", "\n", "\n", "**Valor atípico leve**\n", "\n", "Siendo $Q_1$ y $Q_3$ el primer y tercer cuartil, y\n", "$IQR$ el rango intercuartil $ Q_{3}-Q_{1}$.\n", "\n", "Un valor atípico leve será aquel que:\n", "\n", "$qQ_3+1.5 IQR$\n", "\n", "**Temperatura**\n", "\n", "Se comienza analizando la temperatura:\n", "\n", "TT(75%)-1.5 IQR =15.7+1.5*5.9=24.55" ], "metadata": { "id": "Rsadsenlix7K" } }, { "cell_type": "code", "source": [ "masc=df[\"TA\"]<0.95\n", "temp=df.loc[masc]\n", "temp" ], "metadata": { "id": "Mu0I7_2Gi6Rf", "colab": { "base_uri": "https://localhost:8080/", "height": 269 }, "outputId": "f48e9a6f-d6eb-4edb-e3e2-d00236d23b53" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " fecha TA HR PP PA VV RV DV PRS RAD \\\n", "145 2022-01-07 05:00:00 -13.3 16.9 0.0 974.0 0.5 5.4 0.0 3.15 4.40 \n", "180 2022-01-08 20:00:00 -33.9 1.7 0.0 977.0 3.3 8.3 282.0 2.49 3.75 \n", "183 2022-01-08 23:00:00 -1.3 3.8 0.0 977.0 0.0 0.0 0.0 2.07 3.35 \n", "189 2022-01-09 05:00:00 -32.2 2.2 0.0 978.0 3.0 8.6 322.0 2.68 3.93 \n", "594 2022-01-26 06:00:00 0.3 20.4 0.0 975.0 4.9 9.0 147.0 2.49 3.75 \n", "615 2022-01-27 03:00:00 0.8 17.1 0.0 975.0 4.4 11.5 121.0 2.05 3.33 \n", "639 2022-01-28 08:00:00 -13.6 15.4 0.0 977.0 6.2 10.4 183.0 2.26 3.53 \n", "\n", " TW \n", "145 12.30 \n", "180 14.91 \n", "183 15.17 \n", "189 15.46 \n", "594 12.02 \n", "615 12.66 \n", "639 11.95 " ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
fechaTAHRPPPAVVRVDVPRSRADTW
1452022-01-07 05:00:00-13.316.90.0974.00.55.40.03.154.4012.30
1802022-01-08 20:00:00-33.91.70.0977.03.38.3282.02.493.7514.91
1832022-01-08 23:00:00-1.33.80.0977.00.00.00.02.073.3515.17
1892022-01-09 05:00:00-32.22.20.0978.03.08.6322.02.683.9315.46
5942022-01-26 06:00:000.320.40.0975.04.99.0147.02.493.7512.02
6152022-01-27 03:00:000.817.10.0975.04.411.5121.02.053.3312.66
6392022-01-28 08:00:00-13.615.40.0977.06.210.4183.02.263.5311.95
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", " \n", " \n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 38 } ] }, { "cell_type": "markdown", "source": [ "Se exploran los datos en torno a los\n", " valores fuera de rango. Para ello se comienza calculando el promedio de la diferencia de grados entre dos registros consecutivos:" ], "metadata": { "id": "aG3ENY7zrZBH" } }, { "cell_type": "code", "source": [ "i=0\n", "suma=0\n", "while i < 8296:\n", " suma=suma+abs(df.iloc[i+1][\"TA\"]-df.iloc[i][\"TA\"])\n", " i=i+1\n", "suma/8296" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "3Cp6FeenouKA", "outputId": "3b56b84b-8d69-428f-ab54-f154ce0ccca9" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.8277603664416638" ] }, "metadata": {}, "execution_count": 39 } ] }, { "cell_type": "markdown", "source": [ "Se analizan los registros extremos comparándolos con sus registros consecutivos. Se consideran erróneos aquellos que tienen diferencias significativas con sus vecinos (se consideró más 3 veces el promedio de la diferencia) y se borran." ], "metadata": { "id": "dGIX39DKrjSs" } }, { "cell_type": "code", "source": [ "df_new=df\n", "for i in range(len(temp)):\n", " indice=temp.index[i]\n", " dif1=abs(df.iloc[indice+1][\"TA\"]-df.iloc[indice][\"TA\"])\n", " dif2=abs(df.iloc[indice-1][\"TA\"]-df.iloc[indice][\"TA\"])\n", " if dif1>3 or dif2>3:\n", " df_new=df_new.drop([indice])" ], "metadata": { "id": "z9HRE9wnmj_N" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "df.info()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qR0vH3CvwmvL", "outputId": "abe7efa7-d03d-4487-f2eb-a851c1c6796f" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Int64Index: 8297 entries, 0 to 8296\n", "Data columns (total 11 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 fecha 8297 non-null datetime64[ns]\n", " 1 TA 8297 non-null float64 \n", " 2 HR 8297 non-null float64 \n", " 3 PP 8297 non-null float64 \n", " 4 PA 8297 non-null float64 \n", " 5 VV 8297 non-null float64 \n", " 6 RV 8297 non-null float64 \n", " 7 DV 8297 non-null float64 \n", " 8 PRS 8297 non-null float64 \n", " 9 RAD 8297 non-null float64 \n", " 10 TW 8297 non-null float64 \n", "dtypes: datetime64[ns](1), float64(10)\n", "memory usage: 1.0 MB\n" ] } ] }, { "cell_type": "code", "source": [ "df_new.info()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "TYzFDCJ0wsAt", "outputId": "c1266ae5-c077-47d2-a396-741a4d27f675" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Int64Index: 8290 entries, 0 to 8296\n", "Data columns (total 11 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 fecha 8290 non-null datetime64[ns]\n", " 1 TA 8290 non-null float64 \n", " 2 HR 8290 non-null float64 \n", " 3 PP 8290 non-null float64 \n", " 4 PA 8290 non-null float64 \n", " 5 VV 8290 non-null float64 \n", " 6 RV 8290 non-null float64 \n", " 7 DV 8290 non-null float64 \n", " 8 PRS 8290 non-null float64 \n", " 9 RAD 8290 non-null float64 \n", " 10 TW 8290 non-null float64 \n", "dtypes: datetime64[ns](1), float64(10)\n", "memory usage: 777.2 KB\n" ] } ] }, { "cell_type": "code", "source": [ "masc=df_new[\"TA\"]<0.95\n", "temp=df_new.loc[masc]\n", "temp" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 89 }, "id": "GvTiEnVPtuYj", "outputId": "8b20040d-9ca2-4da0-bb3c-bc3be082d5bd" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Empty DataFrame\n", "Columns: [fecha, TA, HR, PP, PA, VV, RV, DV, PRS, RAD, TW]\n", "Index: []" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
fechaTAHRPPPAVVRVDVPRSRADTW
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", " \n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 43 } ] }, { "cell_type": "markdown", "source": [ "Se realiza lo mismo con la humedad relativa." ], "metadata": { "id": "TGQ3MgSYTvLX" } }, { "cell_type": "code", "source": [ "masc=df_new[\"HR\"]<20\n", "temp=df_new.loc[masc]\n", "temp" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 394 }, "id": "BogKrVHcsPkQ", "outputId": "ee58d8ad-2a18-4db4-c4cc-e07793902f40" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " fecha TA HR PP PA VV RV DV PRS \\\n", "184 2022-01-09 00:00:00 8.4 7.1 0.0 977.0 0.5 5.0 0.0 1.98 \n", "570 2022-01-25 02:00:00 5.3 15.4 0.0 973.0 3.6 10.1 112.0 2.12 \n", "2059 2022-03-30 10:00:00 24.2 16.4 0.0 975.0 7.1 19.1 198.0 2.23 \n", "2060 2022-03-30 11:00:00 26.6 11.1 0.0 975.0 6.3 17.6 186.0 2.49 \n", "2061 2022-03-30 12:00:00 27.3 9.8 0.0 975.0 14.2 27.4 144.0 2.67 \n", "2062 2022-03-30 13:00:00 26.7 11.2 0.0 975.0 15.8 28.4 129.0 2.74 \n", "2063 2022-03-30 14:00:00 26.0 14.5 0.0 974.0 15.5 32.4 161.0 2.66 \n", "2064 2022-03-30 15:00:00 25.6 14.7 0.0 974.0 18.3 30.2 157.0 2.46 \n", "2065 2022-03-30 16:00:00 23.7 17.7 0.0 974.0 14.7 27.4 152.0 2.21 \n", "3412 2022-05-30 15:00:00 15.8 17.8 0.0 978.0 8.9 15.1 175.0 2.98 \n", "7447 2022-11-21 12:00:00 28.7 15.7 0.0 975.0 12.3 26.6 197.0 2.72 \n", "\n", " RAD TW \n", "184 3.26 15.30 \n", "570 3.39 13.16 \n", "2059 3.50 12.53 \n", "2060 3.76 12.49 \n", "2061 3.93 12.22 \n", "2062 4.00 12.54 \n", "2063 3.93 13.04 \n", "2064 3.74 13.21 \n", "2065 3.49 13.04 \n", "3412 4.26 12.70 \n", "7447 3.98 12.32 " ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
fechaTAHRPPPAVVRVDVPRSRADTW
1842022-01-09 00:00:008.47.10.0977.00.55.00.01.983.2615.30
5702022-01-25 02:00:005.315.40.0973.03.610.1112.02.123.3913.16
20592022-03-30 10:00:0024.216.40.0975.07.119.1198.02.233.5012.53
20602022-03-30 11:00:0026.611.10.0975.06.317.6186.02.493.7612.49
20612022-03-30 12:00:0027.39.80.0975.014.227.4144.02.673.9312.22
20622022-03-30 13:00:0026.711.20.0975.015.828.4129.02.744.0012.54
20632022-03-30 14:00:0026.014.50.0974.015.532.4161.02.663.9313.04
20642022-03-30 15:00:0025.614.70.0974.018.330.2157.02.463.7413.21
20652022-03-30 16:00:0023.717.70.0974.014.727.4152.02.213.4913.04
34122022-05-30 15:00:0015.817.80.0978.08.915.1175.02.984.2612.70
74472022-11-21 12:00:0028.715.70.0975.012.326.6197.02.723.9812.32
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", " \n", " \n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 44 } ] }, { "cell_type": "code", "source": [ "i=0\n", "suma=0\n", "while i < 8296:\n", " suma=suma+abs(df.iloc[i+1][\"HR\"]-df.iloc[i][\"HR\"])\n", " i=i+1\n", "suma/8296" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nO4XIEi9x9Kw", "outputId": "280f8929-2a0d-4af2-d053-05f445277fcf" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "3.6812439729990327" ] }, "metadata": {}, "execution_count": 45 } ] }, { "cell_type": "code", "source": [ "for i in range(len(temp)):\n", " indice=temp.index[i]\n", " dif1=abs(df.iloc[indice+1][\"HR\"]-df.iloc[indice][\"HR\"])\n", " dif2=abs(df.iloc[indice-1][\"HR\"]-df.iloc[indice][\"HR\"])\n", " if dif1>10 or dif2>10:\n", " df_new=df_new.drop([indice])" ], "metadata": { "id": "FgGQj-eSyWyb" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "df=df_new" ], "metadata": { "id": "Rc6e7-4My2O4" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "**Nivel del mar**\n", "\n", "Ahora, se analizan los valores del nivel del mar:\n", "\n", "PRS>PRS(25%)+1.5 IQR=2-1.5*0.57=1.15\n", "\n", "PRS>PRS(75%)+1.5 IQR=2.57+1.5*0.57=3.43\n", "\n", "\n", "RAD>RAD(25%)+1.5 IQR=3.29-1.5*0.56=2.45\n", "\n", "RAD>RAD(75%)+1.5 IQR=3.85+1.5*0.56=4.69\n", "\n", "Ambas variables miden el mismo parámetro, el nivel del mar, pero a distintas profundidades." ], "metadata": { "id": "aIj6o3BhUOYr" } }, { "cell_type": "code", "source": [ "import plotly.express as px\n", "px.line(df, x=\"fecha\", y=df[\"RAD\"]-df[\"PRS\"], height=400,range_y=[1.1,2])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 417 }, "id": "3qSD8atzEHu1", "outputId": "f4847df1-7174-4f57-b832-5685d36ab2f4" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "De este gráfico, se observa que en ciertos registros la diferencia de profundidad es mayor al promedio, que es en torno al 1.25m. Esto indica que claramente hubo un problema con los sensores." ], "metadata": { "id": "MfR9JpRY2-fT" } }, { "cell_type": "markdown", "source": [ "**Radar RAD**\n", "\n", "Se analizan los valores atípicos." ], "metadata": { "id": "hMA0cenz1WIW" } }, { "cell_type": "code", "source": [ "masc3=df[\"RAD\"]<2.45\n", "df.loc[masc3].info()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Zn6RywuhjZZ5", "outputId": "bfad01ce-64bf-417d-82d4-706a4271b1eb" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Int64Index: 0 entries\n", "Data columns (total 11 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 fecha 0 non-null datetime64[ns]\n", " 1 TA 0 non-null float64 \n", " 2 HR 0 non-null float64 \n", " 3 PP 0 non-null float64 \n", " 4 PA 0 non-null float64 \n", " 5 VV 0 non-null float64 \n", " 6 RV 0 non-null float64 \n", " 7 DV 0 non-null float64 \n", " 8 PRS 0 non-null float64 \n", " 9 RAD 0 non-null float64 \n", " 10 TW 0 non-null float64 \n", "dtypes: datetime64[ns](1), float64(10)\n", "memory usage: 0.0 bytes\n" ] } ] }, { "cell_type": "code", "source": [ "masc3=df[\"RAD\"]>4.69\n", "df.loc[masc3].info()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "a4wj265GA_Tj", "outputId": "8b28da0e-7eb6-42d2-cb46-ff11b7094d26" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Int64Index: 53 entries, 1751 to 7646\n", "Data columns (total 11 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 fecha 53 non-null datetime64[ns]\n", " 1 TA 53 non-null float64 \n", " 2 HR 53 non-null float64 \n", " 3 PP 53 non-null float64 \n", " 4 PA 53 non-null float64 \n", " 5 VV 53 non-null float64 \n", " 6 RV 53 non-null float64 \n", " 7 DV 53 non-null float64 \n", " 8 PRS 53 non-null float64 \n", " 9 RAD 53 non-null float64 \n", " 10 TW 53 non-null float64 \n", "dtypes: datetime64[ns](1), float64(10)\n", "memory usage: 5.0 KB\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Radar PRS**\n", "\n", "Se analizan los valores atíopicos." ], "metadata": { "id": "Znv7pk7j1fb4" } }, { "cell_type": "code", "source": [ "masc3=df[\"PRS\"]<1.15\n", "df.loc[masc3].info()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HAyI7qR7AWag", "outputId": "37a72839-ece8-4fb5-91a5-57e879e055aa" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Int64Index: 16 entries, 1752 to 1771\n", "Data columns (total 11 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 fecha 16 non-null datetime64[ns]\n", " 1 TA 16 non-null float64 \n", " 2 HR 16 non-null float64 \n", " 3 PP 16 non-null float64 \n", " 4 PA 16 non-null float64 \n", " 5 VV 16 non-null float64 \n", " 6 RV 16 non-null float64 \n", " 7 DV 16 non-null float64 \n", " 8 PRS 16 non-null float64 \n", " 9 RAD 16 non-null float64 \n", " 10 TW 16 non-null float64 \n", "dtypes: datetime64[ns](1), float64(10)\n", "memory usage: 1.5 KB\n" ] } ] }, { "cell_type": "markdown", "source": [ "Se observa que el sensor \"RAD\" tiene mayores problemas que el sensor \"PRS\", por lo que se considerará sólo el sensor \"PRS\" de aquí en adelante, donde además se borrarán los datos asociados a errores." ], "metadata": { "id": "JlknZs-EEPTC" } }, { "cell_type": "code", "source": [ "px.line(df, x=\"fecha\", y=df[\"PRS\"], height=400,range_y=[1,5])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 417 }, "id": "oDL07tCF1qQh", "outputId": "ec6123b9-bfd5-461e-a6ea-c4b038e27721" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "df=df.loc[df[\"PRS\"]>1.15]\n", "df.shape" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "y4ZTtehQCYEP", "outputId": "64a82df0-00a9-4cdf-ebd4-418b621c26ce" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(8270, 11)" ] }, "metadata": {}, "execution_count": 53 } ] }, { "cell_type": "code", "source": [ "df= df.drop(columns=['RAD'])" ], "metadata": { "id": "j9aYn5jmx4qL" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "df.head(2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 112 }, "id": "7aggnMk22PnQ", "outputId": "7c317552-ae1b-47c1-c02d-6377c1533949" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " fecha TA HR PP PA VV RV DV PRS TW\n", "0 2022-01-01 00:00:00 11.2 81.8 0.0 975.0 3.9 14.0 177.0 3.11 15.47\n", "1 2022-01-01 01:00:00 11.0 81.5 0.0 974.0 2.3 8.6 208.0 3.04 14.90" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
fechaTAHRPPPAVVRVDVPRSTW
02022-01-01 00:00:0011.281.80.0975.03.914.0177.03.1115.47
12022-01-01 01:00:0011.081.50.0974.02.38.6208.03.0414.90
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 55 } ] }, { "cell_type": "markdown", "source": [ "**Guardar datos**\n", "\n", "Finalmente, se guardan los datos en un archivo .CVS." ], "metadata": { "id": "kul5iIwAcmp8" } }, { "cell_type": "code", "source": [ "df.to_csv('datos2022.csv',index=False)" ], "metadata": { "id": "fJ8PZ80mctaH" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "**RESULTADOS**" ], "metadata": { "id": "NRrzwdv6vBDz" } }, { "cell_type": "markdown", "source": [ "Con el objetivo de tener una idea preliminar de cómo se comportan los datos, se extraerá información de las siguientes visualizaciones: gráficos de series temporales, histogramas y diagramas de cajas, y mapas de calor." ], "metadata": { "id": "tc4YUzQ4znnI" } }, { "cell_type": "markdown", "source": [ "Se cargan las librerías que se podrían utilizar para realizar las visualizaciones." ], "metadata": { "id": "2PUdLVbtHatV" } }, { "cell_type": "code", "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import scipy.stats as stats\n", "import plotly.express as px\n", "import plotly.graph_objects as go\n", "from plotly.subplots import make_subplots\n", "import datetime" ], "metadata": { "id": "U4ZvzPfzSUBs" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Se parte leyendo los datos procesados anteriormente." ], "metadata": { "id": "V0gk4aQ7HWec" } }, { "cell_type": "code", "source": [ "df=pd.read_csv(\"datos2022.csv\", sep=\",\")\n", "df['fecha']= pd.to_datetime(df['fecha'])" ], "metadata": { "id": "ma0s0VEZ0FmW" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "**Visualizaciones de la temperatura atmosférica**\n", "\n", "La temperatura atmosférica es crucial ya que sirve como indicador de cambios climáticos. Bajo esta idea, se comenzará mostrando algunas visualizaciones que den cuenta de cómo la temperatura varía a lo largo del año." ], "metadata": { "id": "9-QQCi7UnWh6" } }, { "cell_type": "markdown", "source": [ "**Registros agrupados semanalmente**\n", "\n", "Se pueden realizar agrupamientos diarios ('D'), semanales ('W'), mensuales ('M'), etcétera.\n", "\n", "A continuación se agruparán los datos semanalmente:" ], "metadata": { "id": "k7jha7atnw9s" } }, { "cell_type": "code", "source": [ "df_week=df.resample('W', on='fecha').mean().round(2)\n", "df_week=df_week.reset_index()\n", "df_week_max=df.resample('W', on='fecha').max().round(2)\n", "df_week_max=df_week_max.reset_index()\n", "df_week_min=df.resample('W', on='fecha').min().round(2)\n", "df_week_min=df_week_min.reset_index()" ], "metadata": { "id": "ULrs6LL9n36E" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "**Temperatura promedio, mínima y máxima**" ], "metadata": { "id": "OzOFkQxcokrm" } }, { "cell_type": "code", "source": [ "fig = go.Figure()\n", "\n", "fig.add_trace(go.Scatter( x=df_week_max[\"fecha\"], y=df_week_max[\"TA\"],\n", " mode='lines+markers',\n", " name='máxima'))\n", "fig.add_trace(go.Scatter( x=df_week_min[\"fecha\"], y=df_week_min[\"TA\"],\n", " mode='lines+markers',\n", " name='mínima'))\n", "fig.add_trace(go.Scatter( x=df_week[\"fecha\"], y=df_week[\"TA\"],\n", " mode='lines+markers',\n", " name='promedio'))\n", "fig.update_layout(\n", " title=\"Temperatura ambiente promedio, máxima y mínima semanal\",\n", " xaxis_title=\"Fecha\",\n", " yaxis_title=\"Temperatura (ºC)\",\n", " height=400, width=700, yaxis_range=[0,35])\n", "\n", "fig.show()" ], "metadata": { "id": "wcvp3K79n_DX", "outputId": "da23d029-69e3-401d-a44b-44ea7c1ed00a", "colab": { "base_uri": "https://localhost:8080/", "height": 417 } }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "En este gráfico, el eje x representa la fecha, mientras que el eje y la temperatura ambiente. Se grafican las temperaturas máxima (azul), promedio (verde) y mínima (roja) por semana.\n", "\n", "Se observa que la temperatura máxima ocurrió durante la semana del 18 de diciembre, correspondiente a 32.6ºC, mientras que la temperatura mínima ocurrió durante la semana del 5 de junio, correspondiente a 3.1ºC. En cuanto a la temperatura promedio, su máximo es de 19.7ºC en la semana del 11 de diciembre, mientras que su mínima es de 8ºC en la semana del 17 de julio." ], "metadata": { "id": "Ne6e0nZQoGm5" } }, { "cell_type": "markdown", "source": [ "**Estaciones del año**\n", "\n", "Se añade una nueva columna al dataframe que represente la estación del año." ], "metadata": { "id": "ucO2LbO7oyIj" } }, { "cell_type": "code", "source": [ "def estacion(fecha):\n", " if pd.to_datetime(\"2022-01-01\")<= fecha<= pd.to_datetime(\"2022-03-20\"):\n", " return 'verano'\n", " elif pd.to_datetime(\"2022-03-21\")<= fecha<= pd.to_datetime(\"2022-06-21\"):\n", " return 'otoño'\n", " elif pd.to_datetime(\"2022-06-22\")<= fecha<=pd.to_datetime( \"2022-09-23\"):\n", " return 'invierno'\n", " elif pd.to_datetime(\"2022-09-24\")<= fecha<= pd.to_datetime(\"2022-12-21\"):\n", " return 'primavera'\n", " else:\n", " return 'verano'\n", "\n", "df['estación'] = df['fecha'].map(estacion)\n", "df.head(2)" ], "metadata": { "id": "maJ2PwSWo0KH", "outputId": "e4c82453-e3f9-4825-8b46-e0286f4cd701", "colab": { "base_uri": "https://localhost:8080/", "height": 112 } }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " fecha TA HR PP PA VV RV DV PRS TW \\\n", "0 2022-01-01 00:00:00 11.2 81.8 0.0 975.0 3.9 14.0 177.0 3.11 15.47 \n", "1 2022-01-01 01:00:00 11.0 81.5 0.0 974.0 2.3 8.6 208.0 3.04 14.90 \n", "\n", " estación \n", "0 verano \n", "1 verano " ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
fechaTAHRPPPAVVRVDVPRSTWestación
02022-01-01 00:00:0011.281.80.0975.03.914.0177.03.1115.47verano
12022-01-01 01:00:0011.081.50.0974.02.38.6208.03.0414.90verano
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 64 } ] }, { "cell_type": "code", "source": [ "fig=px.violin(df,\n", " y=\"TA\" , color=\"estación\",\n", " width=700, height=500,\n", " labels = {'TA': 'Temperatura ambiente (ºC)', 'estación': 'Estación del año'},\n", " box=True,\n", " title=\"Temperatura ambiente según la estación del año\",\n", " )\n", "fig.show()" ], "metadata": { "id": "oUiB8feWo8cY", "outputId": "435e71e7-37c9-4578-ec0b-a21621277a78", "colab": { "base_uri": "https://localhost:8080/", "height": 517 } }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "Se observa que, como era de esperar, la mediana mínima se observa en el invierno (9.6ºC) mientras que la máxima en verano (15.3ºC). Los rangos intercuartílicos más grandes corresponden a la primavera y verano, lo que indica que los datos se encuentran más dispersos. Mientras que el menor rango intercuartílico corresponde al invierno. En cuanto a los valores outliers, para el otoño se observa un valor outlier bajo el bigote inferior. En cuanto a valores outliers sobre el bigote superior, se observan para todas las estaciones del año." ], "metadata": { "id": "r_nOMLHPpKAh" } }, { "cell_type": "markdown", "source": [ "**Variación de la temperatura por hora y día**" ], "metadata": { "id": "cTUC0PrXp76Y" } }, { "cell_type": "code", "source": [ "df[\"hour\"]=df.fecha.dt.hour\n", "df[\"date\"]=df.fecha.dt.date\n", "table = df.groupby(['date', 'hour']).mean(numeric_only=True)[['TA']].reset_index()\n", "table = table.pivot(index=\"hour\", columns=\"date\", values=\"TA\")\n", "table.head(2)" ], "metadata": { "id": "V7EVPKzkqA3o", "outputId": "b4209664-4d78-40c2-b5fe-dea9dc57b82a", "colab": { "base_uri": "https://localhost:8080/", "height": 210 } }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "date 2022-01-01 2022-01-02 2022-01-03 2022-01-04 2022-01-05 2022-01-06 \\\n", "hour \n", "0 11.2 12.8 14.0 14.3 12.6 12.7 \n", "1 11.0 12.5 NaN 14.3 12.0 12.6 \n", "\n", "date 2022-01-07 2022-01-08 2022-01-09 2022-01-10 ... 2022-12-22 \\\n", "hour ... \n", "0 11.7 NaN NaN 14.2 ... 15.2 \n", "1 11.6 NaN 13.5 13.7 ... 15.2 \n", "\n", "date 2022-12-23 2022-12-24 2022-12-25 2022-12-26 2022-12-27 2022-12-28 \\\n", "hour \n", "0 16.5 14.2 11.2 14.3 14.1 15.4 \n", "1 15.9 13.9 11.2 14.4 14.0 14.5 \n", "\n", "date 2022-12-29 2022-12-30 2022-12-31 \n", "hour \n", "0 14.5 13.6 14.7 \n", "1 14.0 13.8 NaN \n", "\n", "[2 rows x 360 columns]" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
date2022-01-012022-01-022022-01-032022-01-042022-01-052022-01-062022-01-072022-01-082022-01-092022-01-10...2022-12-222022-12-232022-12-242022-12-252022-12-262022-12-272022-12-282022-12-292022-12-302022-12-31
hour
011.212.814.014.312.612.711.7NaNNaN14.2...15.216.514.211.214.314.115.414.513.614.7
111.012.5NaN14.312.012.611.6NaN13.513.7...15.215.913.911.214.414.014.514.013.8NaN
\n", "

2 rows × 360 columns

\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 67 } ] }, { "cell_type": "code", "source": [ "fig = px.imshow(table,text_auto=True,\n", " color_continuous_scale=\"orrd\",\n", " title=\"Tamperatura ambiente según el día y la hora para el año 2022\",\n", " aspect=\"auto\",\n", " origin=\"lower\",\n", " width=650,height=550)\n", "\n", "fig.update_yaxes(title_text=\"Hora del día\")\n", "fig.update_xaxes(title_text=\"Día del año\")\n", "fig.show()" ], "metadata": { "id": "k7umLmudqFv2", "outputId": "30572f76-a497-43e0-f7d7-208d9626e181", "colab": { "base_uri": "https://localhost:8080/", "height": 567 } }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "En el gráfico, el eje x corresponde al día del año, mientras que el eje y representa la hora del día. La escala de temperatura representa la temperatura atmosférica medida en ºC, donde mientras más fuerte el color representa mayor temperatura, y mientras más débil el color representa menor temperatura." ], "metadata": { "id": "nggzroFdqidN" } }, { "cell_type": "markdown", "source": [ "Se observa que las temperaturas más altas se registran entre noviembre y marzo y dismiuye entre abril y octubre. Esto, coincide con las características climáticas de nuestro país.\n", "\n", "En cuanto a la relación entre la hora y la temperatura, se observa cómo las temperaturas máximas en todo el año se concentra entre las 10 de la mañana hasta las 8 de la tarde, siendo las 3 de la tarde (aproximadamente) donde se concentran las máximas temperaturas del día y las mínimas se concentran en la mañana.\n", "\n", "Los datos NaN corresponden a horas del día que no tienen registro, ya sea porque los sensores no lo registraron o porque hayan sido borrados debido a registros erróneos." ], "metadata": { "id": "hZFq2v-UqPB_" } }, { "cell_type": "markdown", "source": [ "**Precipitación**" ], "metadata": { "id": "lwuCMIu1sBZz" } }, { "cell_type": "code", "source": [ "df_day=df.resample('D', on='fecha').mean().round(2)\n", "df_day=df_day.reset_index()" ], "metadata": { "id": "hlp7Qw27sAwD", "outputId": "38d302e7-c174-4844-8fb9-34fff4c4684e", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ ":1: FutureWarning:\n", "\n", "The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "fig = px.scatter(df_day, x=\"fecha\", y=\"PP\",\n", " labels={'fecha':'Fecha', 'PP': 'Precipitación (mm)'},\n", " width=700,height=400,\n", " title='Precipitación por día',\n", " ).update_traces(mode=\"lines\")\n", "\n", "fig.show()" ], "metadata": { "id": "c4M6rW30sRkC", "outputId": "77712377-cd99-437c-86af-c965a04d0e03", "colab": { "base_uri": "https://localhost:8080/", "height": 417 } }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "Se observa que los días lluviosos se concentran principalmente entre junio y agosto, es decir, en el invierno. En tan sólo 6 días el agua caída supera el 1mm. El día más lluvioso corresponde al 14 de julio con 2mm.\n", "\n" ], "metadata": { "id": "hQsv1C9ks7Uq" } }, { "cell_type": "markdown", "source": [ "**Visualizaciones de más atributos**" ], "metadata": { "id": "jFFHj5yxqAnt" } }, { "cell_type": "markdown", "source": [ "A continuación, se muestra un **mapa de calor** con las correlaciones de todos los atributos de la base de datos, en el cual será posible visualizar, en términos generales, cómo se relacionan las variables entre sí." ], "metadata": { "id": "D-NF_EVsyTAP" } }, { "cell_type": "code", "source": [ "df_sin=df.drop(columns=\"fecha\")\n", "df_sin" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 423 }, "id": "tsrBPsUNXLwU", "outputId": "b6b1a2ea-180e-4e05-c39f-f089ff342f99" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " TA HR PP PA VV RV DV PRS TW\n", "0 11.2 81.8 0.0 975.0 3.9 14.0 177.0 3.11 15.47\n", "1 11.0 81.5 0.0 974.0 2.3 8.6 208.0 3.04 14.90\n", "2 10.4 95.4 0.0 974.0 3.0 8.6 196.0 2.84 13.55\n", "3 9.5 100.0 0.0 973.0 1.6 8.6 155.0 2.57 13.42\n", "4 9.2 100.0 0.0 974.0 1.5 9.4 0.0 2.27 13.34\n", "... ... ... ... ... ... ... ... ... ...\n", "8265 14.8 88.2 0.0 976.0 4.2 7.9 297.0 2.70 16.93\n", "8266 14.7 86.1 0.0 977.0 3.8 7.6 230.0 2.70 16.96\n", "8267 14.8 85.9 0.0 977.0 4.2 8.3 247.0 2.62 16.95\n", "8268 14.8 85.5 0.0 977.0 5.7 8.3 296.0 2.49 16.84\n", "8269 14.7 85.4 0.0 977.0 7.1 10.8 301.0 2.36 16.80\n", "\n", "[8270 rows x 9 columns]" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TAHRPPPAVVRVDVPRSTW
011.281.80.0975.03.914.0177.03.1115.47
111.081.50.0974.02.38.6208.03.0414.90
210.495.40.0974.03.08.6196.02.8413.55
39.5100.00.0973.01.68.6155.02.5713.42
49.2100.00.0974.01.59.40.02.2713.34
..............................
826514.888.20.0976.04.27.9297.02.7016.93
826614.786.10.0977.03.87.6230.02.7016.96
826714.885.90.0977.04.28.3247.02.6216.95
826814.885.50.0977.05.78.3296.02.4916.84
826914.785.40.0977.07.110.8301.02.3616.80
\n", "

8270 rows × 9 columns

\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", " \n", " \n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 28 } ] }, { "cell_type": "code", "source": [ "z=df_sin.corr().round(2)\n", "fig = px.imshow(z,text_auto=True,\n", " zmin=-1, zmax=1,\n", " color_continuous_scale=\"temps\",\n", " title=\"Correlación entre los distintos atributos\",\n", " width=550,height=550)\n", "fig.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 567 }, "id": "PmqhzWEleTH8", "outputId": "9a926bdd-39c0-4f98-f279-46715a31d94a" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "Se observa que, algunas *correlaciones positivas* que se podrían destacar, corresponden a los pares de variables:\n", "- grande (sobre 0.5):\n", " - temperatura ambiente y rapidez del viento,\n", " - temperatura ambiente y ráfaga de viento,\n", " - dirección del viento y rapidez del viento\n", "\n", "- mediana (entre 0.3 y 0.5):\n", " -temperatura del agua y temperatura del mar\n", "\n", "Mientras que, algunas *correlaciones negativas* que se podrían destacar, corresponden a los pares de variables:\n", "- grande (menor a -0.5):\n", " - temperatura ambiente y humedad relativa\n", "- mediana (entre -0.5 y -0.3):\n", " - temperatura ambiente y presión atmosférica,\n", " - humedad relativa y rapidez del viento,\n", " - humedad relativa y ráfaga de viento" ], "metadata": { "id": "f-XCSUwQ8eP5" } }, { "cell_type": "markdown", "source": [ "A continuación, se realizarán algunas visualizaciones de atributos que presenten correlaciones positivas y negativas medianas o altas." ], "metadata": { "id": "HLeGMm8x-mjj" } }, { "cell_type": "markdown", "source": [ "**Temperatura ambiente y rapidez del viento**\n", "\n", "A continuación, se graficarán la temperatura ambiente y rapidez del viento en un gráfico de **serie temporal**." ], "metadata": { "id": "WnnGD_iB_F_X" } }, { "cell_type": "code", "source": [ "fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n", "\n", "fig.add_trace(\n", " go.Scatter( x=df_week[\"fecha\"], y=df_week[\"TA\"], name=\"Temperatura ambiental\",mode='lines+markers'),\n", " secondary_y=False)\n", "\n", "fig.add_trace(\n", " go.Scatter(x=df_week[\"fecha\"], y=df_week[\"VV\"], name=\"Rapidez del viento\",mode='lines+markers'),\n", " secondary_y=True)\n", "\n", "fig.update_layout(\n", " title_text=\"Temperatura ambiental y rapidez del viento durante el año\",height=400, width=800)\n", "\n", "fig.update_xaxes(title_text=\"Fecha\")\n", "fig.update_yaxes(title_text=\"Temperatura ambiental (ºC)\", secondary_y=False)\n", "fig.update_yaxes(title_text=\"Rapidez del viento (km/h)\", secondary_y=True)\n", "\n", "fig.show()" ], "metadata": { "id": "Rq0IFXSD_IiY", "outputId": "c042532b-89a1-4c2a-c8a9-7bd355c09677", "colab": { "base_uri": "https://localhost:8080/", "height": 437 } }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "En el gráfico, el eje x representa la fecha, mientras que el eje y representa la Temperatura ambiental (escala al lado izquierdo, color azul) y Rapidez del viento (escala al lado derecho, color rojo).\n", "\n", "Se observa que en términos generales, ambas variables se comportan similar. Ente los meses de enero a mayo, aproximadamente, ambas variables tienden a disminuir, mientras que entre septiembre y diciembre, tienden a aumentar.\n", "\n", "Por otra parte, entre los meses de junio a septiembre, que coindice con el periodo de invierno, se observan las mayores diferencias en el comportamiento de estos atributos, donde la rapidez del viento se mantiene alta y la temperatura ambiente se mantiene baja.\n", "El valor más alto para la rapidez del viento ocurre la semana del 10 de julio, con un valor de 7.4 km/h, mientras que el valor más bajo de la temperatura ambiental, ocurre en la siguiente semana, la del 17 de julio, con un valor de 8ºC." ], "metadata": { "id": "rGSM3fycBUDO" } }, { "cell_type": "markdown", "source": [ "**Humedad relativa y rapidez del viento**\n", "\n", "La mediana de la humedad relativa es 80.8%, en base a eso, se decidió categorizar a los datos en \"humedad alta\" para humedades superiores a la mediana, y \"humedad baja\" en el caso contrario." ], "metadata": { "id": "Cl5j27TSHaE1" } }, { "cell_type": "code", "source": [ "def sensacion(humedad) -> str:\n", " if humedad<80.80:\n", " return 'Humedad baja'\n", " return 'Humedad alta'\n", "df['humedad'] = df['HR'].map(sensacion)\n", "df.head(3)" ], "metadata": { "id": "NmNvwfahHeNW", "outputId": "624fab41-3ba6-4034-d1cf-a9e64c6f4b80", "colab": { "base_uri": "https://localhost:8080/", "height": 247 } }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " fecha TA HR PP PA VV RV DV PRS TW \\\n", "0 2022-01-01 00:00:00 11.2 81.8 0.0 975.0 3.9 14.0 177.0 3.11 15.47 \n", "1 2022-01-01 01:00:00 11.0 81.5 0.0 974.0 2.3 8.6 208.0 3.04 14.90 \n", "2 2022-01-01 02:00:00 10.4 95.4 0.0 974.0 3.0 8.6 196.0 2.84 13.55 \n", "\n", " estación hour date humedad \n", "0 verano 0 2022-01-01 Humedad alta \n", "1 verano 1 2022-01-01 Humedad alta \n", "2 verano 2 2022-01-01 Humedad alta " ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
fechaTAHRPPPAVVRVDVPRSTWestaciónhourdatehumedad
02022-01-01 00:00:0011.281.80.0975.03.914.0177.03.1115.47verano02022-01-01Humedad alta
12022-01-01 01:00:0011.081.50.0974.02.38.6208.03.0414.90verano12022-01-01Humedad alta
22022-01-01 02:00:0010.495.40.0974.03.08.6196.02.8413.55verano22022-01-01Humedad alta
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 73 } ] }, { "cell_type": "code", "source": [ "fig=px.violin(df,\n", " y=\"VV\" , color=\"humedad\",\n", " width=700, height=500,\n", " labels = {'VV': 'Rapidez del viento (km/h)', 'humedad':''},\n", " box=True,\n", " title=\"Rapidez del viento según el nivel de humedad\",\n", " )\n", "fig.show()" ], "metadata": { "id": "w40UbFUPH1G4", "outputId": "7b3f0451-b62d-4f75-93cf-00073180a88e", "colab": { "base_uri": "https://localhost:8080/", "height": 517 } }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "Se observa que en términos generales, cuando la humedad es alta, la rapidez del viento es menor. Por el contrario, cuando la humedad es baja, la rapidez del tiempo es mayor. En específico, la mediana de la humedad alta es 2.7 km/h, mientras que la mediana de la humedad baja es 6.78 km/h. Estos resultados tienen sentido ya que cuando la humedad es baja, el clima se siente más \"seco\", y la rapidez del viento tiende a secar el aire.\n", "\n", "Adicionalmente, se observa que sobre los bigotes superiores hay un número considerable de valores outliers, mientras que bajo los bigotes inferiores no hay.\n", "\n", "Se observa que el rango intercuartílico es mayor para cuando la humedad es baja, lo que indica una mayor dispersión de estos datos, si se compara con los de humedad alta." ], "metadata": { "id": "CiWdc_64IGOS" } }, { "cell_type": "markdown", "source": [ "**Temperatura ambiente, presión atmosférica y humedad relativa**\n", "\n", "A continuación, se realizará un **mapa de calor** que representará los valores de estos tres atributos.\n", "\n" ], "metadata": { "id": "2eqq44w9DakT" } }, { "cell_type": "code", "source": [ "table = df.groupby(['PA', 'HR']).mean(numeric_only=True)[['TA']].reset_index()\n", "table = table.pivot(index=\"PA\", columns=\"HR\", values=\"TA\")" ], "metadata": { "id": "UBhqZghnb_0x" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "fig = px.imshow(table,text_auto=True,\n", " color_continuous_scale=\"orrd\",\n", " title=\"Tamperatura ambiente según presión y humedad relativa\",\n", " aspect=\"auto\",\n", " width=650,height=550)\n", "\n", "fig.update_yaxes(title_text=\"Presión atmosférica (hPA)\")\n", "fig.update_xaxes(title_text=\"Humedad relativa (%)\")\n", "fig.show()" ], "metadata": { "id": "3TF1oisdDzgH", "outputId": "b33c6d70-c96a-4de6-bf80-9cc4a26f779a", "colab": { "base_uri": "https://localhost:8080/", "height": 567 } }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "En el eje x representa la humedad relativa, el eje y la presión atmosférica y el color la temperatura ambiente.\n", "\n", "Se observa que a medida que disminuye la humedad relativa tiende a aumentar la temperatura ambiente (color más rojo). Adicionalmente, se observa un leve aumento de la presión atmosférica a medida que la humedad relativa disminye (los datos tienden a estar más agrupados en la zona superior).\n", "\n", "Como comentario extra, los valores graficados correspondientes a humedad relativa menor a 20% podrían indicar que los sensores pudieran haberse \"pegado\", en el sentido que muestran la misma temperatura para muchos pares de presión atmosférica y humedad relativa.\n", "\n", "Los datos NaN corresponden a pares de variables de (presión atmosférica, humedad relativa) que no son observados." ], "metadata": { "id": "t9MVgptxEGB6" } }, { "cell_type": "markdown", "source": [ "**Visualizaciones de variables oceanográficas**" ], "metadata": { "id": "Sl9aPQf7t6NH" } }, { "cell_type": "code", "source": [ "df_day=df.resample('D', on='fecha').mean().round(2)\n", "df_day=df_day.reset_index()\n", "fig=px.violin(df_day,\n", " y=\"TW\" ,\n", " width=700, height=400,\n", " labels = {'TW': 'Temperatura del mar (ºC)'},\n", " box=True,\n", " points='all',\n", " title=\"Temperatura del mar\",)\n", "fig.show()" ], "metadata": { "id": "2jumwsNpuEo0", "outputId": "417ad0b9-d68e-492c-9a50-7b14cf6a61cb", "colab": { "base_uri": "https://localhost:8080/", "height": 506 } }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ ":1: FutureWarning:\n", "\n", "The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n", "\n" ] }, { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "Se puede observar que la mediana es 12.94ºC, el primer cuartil corresponde a 12.46ºC y el tercer cuartil a 13.78ºC.\n", "\n", "Se observa que sobre el bigote superior hay un número considerable de valores outliers, mientras que bajo el bigote inferior no hay.\n", "\n", "El que la mediana está más cerca del fondo de la caja, y el bigote sea más corto en el extremo inferior de la caja, indica que la distribución es asimétrica y está sesgada positivamente, así la media se situará sobre la mediana." ], "metadata": { "id": "M0pdIZhwuKoY" } }, { "cell_type": "code", "source": [ "def get_day_moment(hour) -> str:\n", " if 6 <= hour < 18:\n", " return 'Día'\n", " return 'Noche'\n", "df['Momento_día'] = df['fecha'].dt.hour.map(get_day_moment)\n", "df.head(2)" ], "metadata": { "id": "ce-gE9AFuUK7", "outputId": "e0564288-ff57-4e03-e072-a2b6e2f927a9", "colab": { "base_uri": "https://localhost:8080/", "height": 201 } }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " fecha TA HR PP PA VV RV DV PRS TW \\\n", "0 2022-01-01 00:00:00 11.2 81.8 0.0 975.0 3.9 14.0 177.0 3.11 15.47 \n", "1 2022-01-01 01:00:00 11.0 81.5 0.0 974.0 2.3 8.6 208.0 3.04 14.90 \n", "\n", " estación hour date humedad Momento_día \n", "0 verano 0 2022-01-01 Humedad alta Noche \n", "1 verano 1 2022-01-01 Humedad alta Noche " ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
fechaTAHRPPPAVVRVDVPRSTWestaciónhourdatehumedadMomento_día
02022-01-01 00:00:0011.281.80.0975.03.914.0177.03.1115.47verano02022-01-01Humedad altaNoche
12022-01-01 01:00:0011.081.50.0974.02.38.6208.03.0414.90verano12022-01-01Humedad altaNoche
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 79 } ] }, { "cell_type": "code", "source": [ "day=df.loc[df[\"Momento_día\"]==\"Día\"].resample('W', on='fecha').mean().round(2)\n", "day=day.reset_index()\n", "night=df.loc[df[\"Momento_día\"]==\"Noche\"].resample('W', on='fecha').mean().round(2)\n", "night=night.reset_index()\n", "day[\"Momento\"]=\"Día\"\n", "night[\"Momento\"]=\"Noche\"\n", "dia_noche = pd.concat([day, night])\n", "\n", "fig=px.violin(dia_noche,\n", " y=\"TW\" , color=\"Momento\",\n", " width=700, height=500,\n", " labels = {'TW': 'Temperatura del mar (ºC)', 'Momento':''},\n", " box=True,\n", " points='all',\n", " title=\"Temperatura del mar en el día y la noche\",)\n", "fig.show()" ], "metadata": { "id": "8v90ZLvnuQTl", "outputId": "7356b1e5-1f23-4549-e030-0ee1681622e4", "colab": { "base_uri": "https://localhost:8080/", "height": 676 } }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ ":1: FutureWarning:\n", "\n", "The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n", "\n", ":3: FutureWarning:\n", "\n", "The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n", "\n" ] }, { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "Se observa que en términos generales, la temperatura del mar no muestra variaciones significativas entre el día y la noche, ya que la mediana del día es 12.98 ºC, mientras que de noche es 13.04ºC.\n", "\n", "Adicionalmente, la dispersión de los datos es similar en ambos gráficos de violín, dado que los rangos intercuartílicos son similares. En cuanto a los valores outliers, sólo se observan por sobre los bigotes superiores.\n", "\n", "\n" ], "metadata": { "id": "cD62CVA0urjO" } }, { "cell_type": "markdown", "source": [ "**Nivel del mar y presión atmosférica**" ], "metadata": { "id": "By05_YnIS2TS" } }, { "cell_type": "code", "source": [ "fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n", "\n", "fig.add_trace(\n", " go.Scatter( x=df_week[\"fecha\"], y=df_week[\"PA\"], name=\"Presión atmosférica\",mode='lines+markers'),\n", " secondary_y=False)\n", "\n", "fig.add_trace(\n", " go.Scatter(x=df_week[\"fecha\"], y=df_week[\"PRS\"], name=\"Nivel del mar\",mode='lines+markers'),\n", " secondary_y=True)\n", "\n", "fig.update_layout(\n", " title_text=\"Presión atmosférica y nivel del mar\",height=400, width=800)\n", "\n", "fig.update_xaxes(title_text=\"Fecha\")\n", "fig.update_yaxes(title_text=\"Presión atmosférica (hPa)\", secondary_y=False)\n", "fig.update_yaxes(title_text=\"Nivel del mar (m)\", secondary_y=True)\n", "\n", "fig.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 437 }, "id": "akQg7g-9p4Vo", "outputId": "3606f86e-3b8d-45b6-d1df-7fc96f34dc9a" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "En el gráfico, el eje x representa la fecha, mientras que el eje y representa la presión atmosférica (escala al lado izquierdo, color azul) y nivel del mar (escala al lado derecho, color rojo).\n", "\n", "No se observa una relación directa entre ambas variables, dado que en algunos tramos ambas aumentan, mientras que en otros tramos una aumenta mientras la otra disminuye. El nivel del mar máximo se alcanzó en febrero siendo 2.39m, mientras que el mínimo se alcanzó en septiembre con 2.17m. La presión atmosférica máxima se observa en septiembre, con 983 hPA, mientras que la mínima en marzo con 975 hPA." ], "metadata": { "id": "4VgOX2kZummk" } }, { "cell_type": "markdown", "source": [ "**Análisis de datos**" ], "metadata": { "id": "REp5dYxNxmAg" } }, { "cell_type": "markdown", "source": [ "A continuación se utilizarán las distintos algoritmos de clustering y regresión para intentar de responder las preguntas planteadas al inicio del proyecto." ], "metadata": { "id": "CRkHegXm0fuB" } }, { "cell_type": "markdown", "source": [ "**CLUSTERING**" ], "metadata": { "id": "SX1oBw-FvjKy" } }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "cZoA5tqhrtaj" }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "df=pd.read_csv(\"datos2022.csv\", sep=\",\")\n", "df['fecha']= pd.to_datetime(df['fecha'])" ] }, { "cell_type": "markdown", "source": [ "Se elimina la columna fecha, ya que no será utilizada para los análisis posteriores." ], "metadata": { "id": "bVoL3U5V0ung" } }, { "cell_type": "code", "source": [ "df_new=df.drop(columns=['fecha'])\n", "df_new.head(2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 112 }, "id": "Kn35LuZbstha", "outputId": "8f8f1ab3-325c-416f-b235-013b5c3d94c2" }, "execution_count": 2, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " TA HR PP PA VV RV DV PRS TW\n", "0 11.2 81.8 0.0 975.0 3.9 14.0 177.0 3.11 15.47\n", "1 11.0 81.5 0.0 974.0 2.3 8.6 208.0 3.04 14.90" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TAHRPPPAVVRVDVPRSTW
011.281.80.0975.03.914.0177.03.1115.47
111.081.50.0974.02.38.6208.03.0414.90
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 2 } ] }, { "cell_type": "markdown", "source": [ "**K-MEANS**" ], "metadata": { "id": "_Rom18KrN6Ld" } }, { "cell_type": "markdown", "metadata": { "id": "ZH5aquJWjizK" }, "source": [ "Para determinar el número óptimo de grupos al utilizar el algoritmo K-means, se utiliza el método del codo." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "tL2mOfTa3Pds", "colab": { "base_uri": "https://localhost:8080/", "height": 819 }, "outputId": "cd11a593-3c10-4949-adc2-e4940d8f0f66" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ], "source": [ "import matplotlib.pyplot as plt\n", "from sklearn.cluster import KMeans\n", "\n", "sse = []\n", "\n", "clusters = list(range(1, 11))\n", "for k in clusters:\n", " kmeans = KMeans(n_clusters=k, random_state = 10).fit(df_new)\n", " sse.append(kmeans.inertia_)\n", "\n", "plt.plot(clusters, sse, marker=\"o\")\n", "plt.title(\"Método del codo de 1 a 10 clusters\")\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "markdown", "source": [ "Se observa que el punto de inflexión ocurre para k=3, por lo que este será el número de grupos escogido. Adicionalmente, se mide el coeficiente de silhouette para algunos números de grupos:" ], "metadata": { "id": "OZzsOOZi1KTk" } }, { "cell_type": "code", "source": [ "from sklearn.metrics import silhouette_score\n", "random_state = 20\n", "k=2\n", "while k < 6:\n", " kmeans= KMeans(n_clusters=k, n_init=20, max_iter=300, random_state=random_state)\n", " kmeans.fit(df_new)\n", " y_pred = kmeans.predict(df_new)\n", " print(\"Kmeans silhouette para k =\",str(k), silhouette_score(df_new, kmeans.labels_))\n", " k=k+1" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hTpncNeFYP9c", "outputId": "aa5919d2-3db8-4c82-c62a-7041ce1b8555" }, "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Kmeans silhouette para k= 2 0.6148754891283655\n", "Kmeans silhouette para k= 3 0.6921952814682361\n", "Kmeans silhouette para k= 4 0.576422473389696\n", "Kmeans silhouette para k= 5 0.5060160426548631\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "x1lHWlA7kqgT" }, "source": [ "El coeficiente de silhouette también es máximo para k=3, por lo que se propone utilizar 3 clusters. Su valor es de 0.692." ] }, { "cell_type": "code", "source": [ "random_state = 20\n", "kmeans = KMeans(n_clusters=3, n_init=40, max_iter=500, random_state=random_state)\n", "kmeans.fit(df_new)\n", "y_pred = kmeans.predict(df_new)" ], "metadata": { "id": "-mqbW_zjGBAA" }, "execution_count": 7, "outputs": [] }, { "cell_type": "markdown", "source": [ "El tamaño de cada uno de los cluster es:" ], "metadata": { "id": "ELatdgSb-OBK" } }, { "cell_type": "code", "source": [ "counts = np.bincount(y_pred)\n", "print(counts)" ], "metadata": { "id": "h2W8ct0bX2M6", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "f2e63b9f-81a8-46aa-a679-c89fd75a7252" }, "execution_count": 8, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[3121 2283 2866]\n" ] } ] }, { "cell_type": "markdown", "source": [ "Se observa que la cantidad de datos por cluster es del mismo orden de magnitud, bastante similares entre sí." ], "metadata": { "id": "rRCpc9PP0LQL" } }, { "cell_type": "markdown", "metadata": { "id": "TwD2fgUO9uPh" }, "source": [ "A continuación se reduce la dimensionalidad de los datos a 2 y se grafican los datos transformados para tener una representación visual de los clusters." ] }, { "cell_type": "code", "source": [ "from sklearn.decomposition import PCA\n", "reduX = PCA(n_components=2, random_state=0).fit_transform(df_new)\n", "plt.scatter(reduX[:, 0], reduX[:, 1], c=kmeans.labels_)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 430 }, "id": "6HfR0VsGHeRu", "outputId": "0e170ad1-4ed4-4b52-a094-9a4de231f314" }, "execution_count": 9, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "Se distinguen fácilmente la diferencia entre el cluster morado y verde, siendo evidente la \"separación\" entre ellos. Para los cluster amarillo y morado, es menos evidente esta distinción, sin embargo también es posible identificarlos." ], "metadata": { "id": "Ljbr0zhY-nsk" } }, { "cell_type": "markdown", "source": [ "**DATOS ESCALADOS**\n", "\n", "Se comienzan escalando los datos con la función `MinMaxScaler` y se repite el proceso de encontrar el `k` óptimo.\n", "Esta función, escala el conjunto de datos entre 0 y 1, donde los valores mínimo y máximo en el conjunto de datos escalados son 0 y 1 respectivamente.\n", "\n" ], "metadata": { "id": "neW2mkrD2TMp" } }, { "cell_type": "code", "source": [ "from sklearn.preprocessing import MinMaxScaler\n", "scaler1 = MinMaxScaler()\n", "\n", "scaler1.fit(df_new)\n", "scaled1 = scaler1.transform(df_new)\n", "scaled1_df = pd.DataFrame(scaled1, columns=df_new.columns)" ], "metadata": { "id": "Y9kHzv1aHtlM" }, "execution_count": 10, "outputs": [] }, { "cell_type": "code", "source": [ "sse_scaled = []\n", "\n", "clusters = list(range(1, 11)) #range(1,41)\n", "for k in clusters:\n", " kmeans_scaled1 = KMeans(n_clusters=k).fit(scaled1_df)\n", " sse_scaled.append(kmeans_scaled1.inertia_)\n", "\n", "plt.plot(clusters, sse_scaled, marker=\"o\")\n", "plt.title(\"Método del codo de 1 a 10 clusters\")\n", "plt.grid(True)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 819 }, "id": "p8Wwon8_H5KV", "outputId": "e07c2d54-fdf7-482b-d891-fecd65ae24bb" }, "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "En este caso, no es tan directo identificar \"el codo\" de este gráfico.\n", "Para seleccionar un valor para k, se escogerá el que tenga el mayor coeficiente de silhouette." ], "metadata": { "id": "7jBIpw720qCJ" } }, { "cell_type": "code", "source": [ "from sklearn.metrics import silhouette_score\n", "random_state = 20\n", "k=2\n", "while k < 10:\n", " kmeans_scaled1 = KMeans(n_clusters=k, n_init=20, max_iter=300, random_state=random_state)\n", " kmeans_scaled1.fit(scaled1_df)\n", " y_pred = kmeans_scaled1.predict(scaled1_df)\n", " print(\"Kmeans silhouette para k =\",str(k), silhouette_score(scaled1_df, kmeans_scaled1.labels_))\n", " k=k+1" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "CKPRRe5DbLa3", "outputId": "3f0a98e2-74ee-40cc-b2eb-347082a3a189" }, "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Kmeans silhouette para k= 2 0.3056110474421643\n", "Kmeans silhouette para k= 3 0.26975407208793994\n", "Kmeans silhouette para k= 4 0.23001580783439204\n", "Kmeans silhouette para k= 5 0.22936634491403338\n", "Kmeans silhouette para k= 6 0.22222604418100075\n", "Kmeans silhouette para k= 7 0.20824035011917372\n", "Kmeans silhouette para k= 8 0.21450988853093278\n", "Kmeans silhouette para k= 9 0.21177714879512222\n" ] } ] }, { "cell_type": "markdown", "source": [ "En base al coeficiente de silhouette para k=2 igual a 0.305, que es el mayor, se selecciona k=2." ], "metadata": { "id": "7VkiZ6yC0uXn" } }, { "cell_type": "code", "source": [ "random_state = 20\n", "kmeans_scaled1 = KMeans(n_clusters=2, n_init=20, max_iter=300, random_state=random_state)\n", "kmeans_scaled1.fit(scaled1_df)\n", "y_pred = kmeans_scaled1.predict(scaled1_df)\n", "counts = np.bincount(y_pred)\n", "print(counts)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6iHQrdcbIeWZ", "outputId": "7b190ba1-2903-411e-f485-f6e8e4b57f2f" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[4349 3921]\n" ] } ] }, { "cell_type": "markdown", "source": [ "Los dos grupos tienen una cantidad de datos similares." ], "metadata": { "id": "rIYF2NKlAMcl" } }, { "cell_type": "code", "source": [ "reduX = PCA(n_components=2, random_state=0).fit_transform(scaled1_df)\n", "plt.scatter(reduX[:, 0], reduX[:, 1], c=kmeans_scaled1.labels_)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 430 }, "id": "YhA2Q1UqIuOl", "outputId": "b8d04829-b9dc-440e-ff70-0c2272096d17" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "Adicionalmente a los dos grupos graficados, visualmente es posible identificar una \"línea\" en diagonal que podría separar al cluster morado en dos." ], "metadata": { "id": "VbH4q1pb0-lk" } }, { "cell_type": "markdown", "source": [ "A continuación, se escalan los datos con la función \"Standad Scaler\". La estandarización escala cada variable, restando la media y dividiendo por la desviación estándar, para cambiar la distribución para tener una media de cero y una desviación estándar de uno." ], "metadata": { "id": "z-kkrIJYAymL" } }, { "cell_type": "code", "source": [ "from sklearn.preprocessing import StandardScaler\n", "scaler2 = StandardScaler()\n", "\n", "scaler2.fit(df_new)\n", "scaled2 = scaler2.transform(df_new)\n", "scaled2_df = pd.DataFrame(scaled2, columns=df_new.columns)" ], "metadata": { "id": "q3R5nzqeRKj4" }, "execution_count": 14, "outputs": [] }, { "cell_type": "code", "source": [ "sse_scaled = []\n", "\n", "clusters = list(range(1, 11)) #range(1,41)\n", "for k in clusters:\n", " kmeans_scaled2 = KMeans(n_clusters=k).fit(scaled2_df)\n", " sse_scaled.append(kmeans_scaled2.inertia_)\n", "\n", "plt.plot(clusters, sse_scaled, marker=\"o\")\n", "plt.title(\"Método del codo de 1 a 10 clusters\")\n", "plt.grid(True)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 819 }, "id": "OuXUIQl1RTuC", "outputId": "30bf566b-31f0-4b48-ca3c-8e6db3f34a67" }, "execution_count": 15, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "Nuevamente, no es fácilmente identificable el punto de inflexión de la curva, por lo que se calcula el coeficiente de silhouette." ], "metadata": { "id": "MEisDA3kCB1m" } }, { "cell_type": "code", "source": [ "from sklearn.metrics import silhouette_score\n", "random_state = 20\n", "k=2\n", "while k < 7:\n", " kmeans_scaled2 = KMeans(n_clusters=k, n_init=20, max_iter=300, random_state=random_state)\n", " kmeans_scaled2.fit(scaled2_df)\n", " y_pred = kmeans_scaled2.predict(scaled2_df)\n", " print(\"Kmeans silhouette para k =\",str(k), silhouette_score(scaled2_df, kmeans_scaled2.labels_))\n", " k=k+1" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "KmC7Dp_ERbkC", "outputId": "4d6f03ad-ef29-4a66-c411-744cbae44a9c" }, "execution_count": 16, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Kmeans silhouette para k = 2 0.27215772748597616\n", "Kmeans silhouette para k = 3 0.1873573938236654\n", "Kmeans silhouette para k = 4 0.185703573112081\n", "Kmeans silhouette para k = 5 0.1893515048309627\n", "Kmeans silhouette para k = 6 0.16383685754736438\n" ] } ] }, { "cell_type": "markdown", "source": [ "En base al coeficiente de silhouette para k=2 igual a 0.272, que es el mayor, se selecciona k=2." ], "metadata": { "id": "-7Q6FAwjDZIw" } }, { "cell_type": "code", "source": [ "random_state = 20\n", "kmeans_scaled2 = KMeans(n_clusters=2, n_init=20, max_iter=300, random_state=random_state)\n", "kmeans_scaled2.fit(scaled2_df)\n", "y_pred = kmeans_scaled2.predict(scaled2_df)" ], "metadata": { "id": "CDchI4UjYARy" }, "execution_count": 19, "outputs": [] }, { "cell_type": "code", "source": [ "reduX = PCA(n_components=2, random_state=0).fit_transform(scaled2_df)\n", "plt.scatter(reduX[:, 0], reduX[:, 1], c=kmeans_scaled2.labels_)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 430 }, "id": "cVDttNHgTjDA", "outputId": "22059a2a-cffe-4b07-f2b7-c24d0f893d0f" }, "execution_count": 20, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "En este caso, no es fácilmente identificable la \"separación\" entre los cluster. Se observa que los puntos más separados del cluster amarillo (los que están arriba) podrían jugar un rol en esa división." ], "metadata": { "id": "FPq7rylR1LVB" } }, { "cell_type": "markdown", "source": [ "A continuación se graficará el coeficiente de silhouette para el caso que presenta un mayor valor, esto es, para el caso sin escalamiento de los datos." ], "metadata": { "id": "uJnyvWIHCzyQ" } }, { "cell_type": "code", "source": [ "from sklearn.metrics import silhouette_samples\n", "\n", "def plot_silhouette(dataset, model):\n", " use_indices = model.labels_ >= 0\n", " use_labels = model.labels_[use_indices]\n", " use_data = dataset.iloc[use_indices]\n", "\n", " n_clusters = len(np.unique(use_labels))\n", "\n", "\n", " fig, ax1 = plt.subplots()\n", "\n", " silhouette_avg = silhouette_score(use_data, use_labels)\n", " print(f\"The average silhouette_score for {model.__class__.__name__} is : {silhouette_avg}\")\n", " sample_silhouette_values = silhouette_samples(use_data, use_labels)\n", "\n", " y_lower = 10\n", " for i in range(n_clusters):\n", " ith_cluster_silhouette_values = sample_silhouette_values[use_labels == i]\n", " ith_cluster_silhouette_values.sort()\n", " size_cluster_i = ith_cluster_silhouette_values.shape[0]\n", " y_upper = y_lower + size_cluster_i\n", " ax1.fill_betweenx(np.arange(y_lower, y_upper),\n", " 0, ith_cluster_silhouette_values, alpha=0.7)\n", " ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n", " y_lower = y_upper + 10\n", "\n", " ax1.set_title(f\"{model.__class__.__name__}\")\n", " ax1.set_xlabel(\"The silhouette coefficient values\")\n", " ax1.set_ylabel(\"Cluster label\")\n", "\n", " ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n", " ax1.set_yticks([])" ], "metadata": { "id": "WQTCneHFe-vf" }, "execution_count": 21, "outputs": [] }, { "cell_type": "code", "source": [ "plot_silhouette(df_new, kmeans)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 489 }, "id": "OuQfePjTfDLz", "outputId": "87c05537-b3cf-4b9f-f30b-d2a250121cf2" }, "execution_count": 22, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "The average silhouette_score for KMeans is : 0.6921952814682361\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhcAAAHHCAYAAAAMD3r6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABIR0lEQVR4nO3deXwT1f4//tfMZCZrk3Rf05YWKJR9K0KBIltZBFEUFEUWBS+LoFxAvS6AC6gfFZfLBfXeC+gPBAUVL3JRVEDBDSzLFZAri+L3yiKK7NAl8/sjJBDaQlOSTJbX8z7yYDqZzrzbqXdeOefMGUFVVRVEREREfiJqXQARERFFFoYLIiIi8iuGCyIiIvIrhgsiIiLyK4YLIiIi8iuGCyIiIvIrhgsiIiLyK4YLIiIi8iuGCyIiIvIrhgsiIiLyK4YLogg1f/58CIKATZs2ea0/duwYCgoKYDAYsGrVKkybNg2CIEAURfz888+V9nP8+HEYjUYIgoBx48YFq3wiCmMMF0RR5Pjx4+jRowe2bduGd999Fz179vS8p9fr8eabb1b6nnfeeSeYJRJRBGC4IIoSJ06cQHFxMbZs2YJly5ahV69eXu/37t27ynCxaNEi9OnTJ1hlElEEYLggigInT55Ez549UVJSgmXLllUZFgYPHowtW7bg+++/96w7ePAgPv30UwwePLjK/Z47dw5Tp05F3bp1odfr4XA4MGXKFJw7d85ru3nz5qFLly5ISkqCXq9Hfn4+5syZU2l/2dnZuO6667B+/XpP101OTg5ef/11r+3Kysowffp01KtXDwaDAfHx8ejQoQNWr15dm18PEfkZwwVRhDt16hR69eqFjRs34u2338Z1111X5XadOnVCRkYGFi1a5Fm3ZMkSWCyWKsOI0+lEv3798Oyzz6Jv3754+eWX0b9/f8yaNQuDBg3y2nbOnDnIysrCX/7yFzz33HNwOBwYM2YMZs+eXWm/u3fvxk033YTu3bvjueeeQ2xsLIYNG4bt27d7tpk2bRqmT5+Oa6+9Fn/961/x0EMPITMzEyUlJbX9NRGRP6lEFJHmzZunAlCzsrJUWZbV9957r8rtpk6dqgJQf/31V3XSpElq3bp1Pe+1adNGHT58uKqqqgpAHTt2rOe9N954QxVFUf3888+99jd37lwVgLphwwbPutOnT1c6bnFxsZqTk+O1LisrSwWgfvbZZ551hw8fVvV6vfrnP//Zs65Zs2Zqnz59avJrICINsOWCKMIdOnQIBoMBDofjitsOHjwYu3fvxsaNGz3/Vtcl8vbbb6Nhw4Zo0KABjhw54nl16dIFALBmzRrPtkaj0bN87NgxHDlyBEVFRdi7dy+OHTvmtd/8/Hx07NjR83ViYiLy8vKwd+9ezzq73Y7t27fjhx9+qNkvgYiCiuGCKMK98sorUBQFPXv2xK5duy67bYsWLdCgQQMsWrQICxcuREpKiicsXOqHH37A9u3bkZiY6PWqX78+AODw4cOebTds2IBu3brBbDbDbrcjMTERf/nLXwCgUrjIzMysdKzY2FgcPXrU8/Vjjz2GP/74A/Xr10eTJk0wefJkbNu2rWa/ECIKOJ3WBRBRYOXn52PlypXo2rUrunfvjg0bNly2FWPw4MGYM2cOYmJiMGjQIIhi1Z9BnE4nmjRpgueff77K993H2LNnD7p27YoGDRrg+eefh8PhgKIoWLlyJWbNmgWn0+n1fZIkVbk/VVU9y506dcKePXuwfPlyfPTRR/j73/+OWbNmYe7cubjrrrsu+/sgosBjuCCKAgUFBXjvvffQp08fdO/eHZ9//jkSExOr3Hbw4MF49NFHceDAAbzxxhvV7jM3Nxdbt25F165dIQhCtdv961//wrlz5/D+++97tUpc3G1SG3FxcRg+fDiGDx+OkydPolOnTpg2bRrDBVEIYLcIUZTo2rUr3nzzTezevRs9e/bE8ePHq9wuNzcXL7zwAmbOnImCgoJq9zdw4ED873//w2uvvVbpvTNnzuDUqVMALrREXNzycOzYMcybN6/WP8tvv/3m9bXFYkHdunUr3QJLRNpgywVRFLnhhhvw2muvYcSIEejXrx9WrVpV5XYTJky44r6GDBmCt956C3/605+wZs0aFBYWoqKiAt9//z3eeustfPjhh2jdujV69OgBRVHQt29f3H333Th58iRee+01JCUl4cCBA7X6OfLz89G5c2e0atUKcXFx2LRpE5YuXcrpyYlCBMMFUZQZPnw4fv/9d0yaNAk333wzmjVrVqv9iKKI9957D7NmzcLrr7+Od999FyaTCTk5OZgwYYJnYGdeXh6WLl2Khx9+GJMmTUJKSgpGjx6NxMREjBgxolbHHj9+PN5//3189NFHOHfuHLKysvDEE09g8uTJtdofEfmXoF7cVklERER0lTjmgoiIiPyK4YKIiIj8iuGCiIiI/IrhgoiIiPyK4YKIiIj8iuGCiIiI/EqTeS6cTid++eUXxMTEXHbaYCIiIgodqqrixIkTSEtLq/a5Q4BG4eKXX36p0eOfiYiIKPT8/PPPyMjIqPZ9TcJFTEwMAFdxVqtVixKIiCgYTp0C0tJcy7/8ApjN2tZDV+X48eNwOBye63h1NAkX7q4Qq9XKcEFEFMnOP7gOAGC1MlxEiCsNaeCATiIiIvIrhgsiIiLyKz4VlYiIAkenA4YOvbBMUYFnmoiIAkevB+bP17oKCjJ2ixAREZFfseWCiIgCR1WB06ddyyYTwIkTowJbLoiIKHBOnwYsFtfLHTIo4jFcEBERkV8xXBAREZFfMVwQERGRXzFcEBERkV8xXBAREZFfMVwQERGRX0XVPBcVzgqcKT+jdRlERNGj4iwMN/YHAJytOAOUqtrWEyXMsvmKTy4NpKgKF7+c/AVPb3xa6zKIiKLLmHTXvxunaVpGtKhjq4M/t/6zpjWwW4SIiCiCXJN6jdYlMFwQERFFClEQ0SKphdZlMFwQEVHgKGfK8Ndus/HXbrOhnCnTupyIlxGTAZNs0roMhgsiIqJI0SCugdYlAGC4ICIiihiN4xtrXQIAhgsiIqKIIAgCMq2ZWpcBgOGCiIgoImTFZEEnhsYMEwwXREREESDXnqt1CR4MF0RERBEgxZyidQkeodF+QkREEckpCfiuIMuzTIGTYcnQugQPhgsiIgqYckWHuTOu07qMiKdICtIsaVqX4cFuESIiojAXZ4iDJEpal+HBcEFERBTmQmm8BcBwQUREAaScKcNz172C5657hdN/B1CSKUnrErxwzAUREQWU/my51iVEvFALF2y5ICIiCnOp5lStS/DCcEFERBTGZFFGmjl07hQBGC6IiIjCWkZMBmRJ1roMLwwXREREYSzUukQAhgsiIqKwlhETOjNzuvFuESIiChhVFPBD0zTPMvlfKE377cZwQUREAVOm1+HF52/QuoyIFm+M17qEStgtQkREFKZsehusilXrMiphuCAiIgpTKeYUCELodTcxXBARUcAoZ8owc8A/MHPAPzj9dwAkGBO0LqFKHHNBREQBFXPsrNYlRKx4Q+iNtwDYckFERBS20iyhNTOnG8MFERFRmAq1R627MVwQERGFIaPOCLvernUZVWK4ICIiCkOp5lToxNAcOslwQUREFIYcVofWJVQrNCMPERFFBFUU8FP9JM8y+U8oPrDMjeGCiIgCpkyvw//97Waty4hIofhMETd2ixAREYUZURBD8mmobgwXREREYSbBmBCygzkBhgsiIgog+WwZpt/2Oqbf9jrks5z+219CdfIst9CNPUREFPYEFYg/dMKzTP6RZg7tcMGWCyIiojCTagndO0UAhgsiIqKwIkBAXmye1mVcFsMFERFRGEkwJcAkm7Qu47IYLoiIiMJIKM9v4cZwQUREFEZC/U4RgHeLEBFRAKkCcCAr1rNMVy/ZlKx1CVfEcEFERAFTZpDx5D8Ga11GRMm152pdwhWxW4SIiChMJBgTYNPbtC7jihguiIiIwkQdWx2tS6gRdosQEVHAyGfLMGXs2wCAZ2bfjDKDrHFF4S3LmqV1CTUSUi0XM2fORJs2bRATE4OkpCT0798fu3bt0rosIiKqJUEFUn86itSfjnL6bz9IMadoXUKNhFS4WLduHcaOHYuvvvoKq1evRllZGXr06IFTp05pXRoREZHmwuFOESDEukVWrVrl9fX8+fORlJSEb7/9Fp06ddKoKiIiIu3FGeJg19u1LqNGQqrl4lLHjh0DAMTFxWlcCRERkbayrFkQhPCYLCRkw4XT6cS9996LwsJCNG7cWOtyiIiINOWIcWhdQo2FVLfIxcaOHYvvvvsO69ev17oUIiIizYXLYE4gRMPFuHHjsGLFCnz22WfIyAj9B7QQEVHVVAH4LTnGs0y1Fy6DOYEQCxeqquKee+7Bu+++i7Vr16JOnfCYLISIiKpWZpAxdeEdWpcR9gQIiDOEz/jDkAoXY8eOxaJFi7B8+XLExMTg4MGDAACbzQaj0ahxdURERNqIN8ZDlsJnArKQGtA5Z84cHDt2DJ07d0ZqaqrntWTJEq1LIyIi0kyyOXy6RIAQa7lQVU7fRkQUSeRz5bj3vncBAC/MugFl+pC67ISNJGOS1iX4hGeZiIgCRnCqyPrvYc8y1U6OPUfrEnwSUt0iREREVFm6JV3rEnzCcEFERBTCbHobEo2JWpfhE4YLIiKiEFY/tn7YTPvtxnBBREQUwponNde6BJ8xXBAREYUonahDXmye1mX4jHeLEBFRQJ2wGbQuIWzl2HJg0IXf74/hgoiIAqbUKOPBZXdqXUbYCrdbUN3YLUJERBSCJEFCx/SOWpdRKwwXREREIahRQiPY9Daty6gVdosQEVHAyOfKMebBfwEA/jazL6f/9kFRRpHWJdQazzIREQWM4FRRb9svnmWqmZbJLZEXF353ibixW4SIiCiE2PV23JJ3i9ZlXBWGCyIiohAhQMCIxiNgkk1al3JVGC6IiIhCxM15N4ft7acXY7ggIiIKAfnx+eiQ3kHrMvyCAzqJiIg0ZJJNuLHejWiT3AaiEBmf+RkuiIgooM4ZeKmpjiAIGJQ3CK2SW2ldil/xjBMRUcCUGmX8ecXdWpcRkhKMCRiUNwgN4xtqXYrfMVwQEREFWcvklrgl75awvyukOgwXREREQaITdbi5/s1on9YegiBoXU7AMFwQEVHA6ErLcde0VQCAv0/riXIlei87kiDh1ga3om1qW61LCbjoPctERBRwYoWKxt/85FmOZrc1vA0FqQValxEUDBdEREQBZNPbcEveLWiS2ETrUoKG4YKIiCgAMq2ZKM4uRqP4RtCJ0XW5ja6floiIKMBSzam4qf5NYf1U06vFcEFEROQHeXF56JHVA/Vj60f0nSA1wXBBRER0FZJMSbih7g1RNabiShguiIiIasGiWNAhvQOKs4ohS7LW5YQUhgsiIgqYUqOMcR+P1boMv5EECQ3iG6BRfCMUpBTAoDNoXVJIYrggIiK6ApvehiYJTdAtqxsSjAlalxPyGC6IiIiq0TypObo4uqCOrU7UD9L0BcMFEREFjK60HHc89TEA4PUHuoX89N+iICLVnIoWyS3QOrk1WylqKbTPMhERhTWxQkXLz/YAAP6/yV01rqZqNr0NzRKboY6tDpokNOE4Cj9guCAioqhi09vQNKEp0mPSkWXNQoYlg10efsZwQUREEU0QBNSPrY/8+Hw0TWiKBGMCw0SAMVwQEVFEkUUZDqsDyaZkpFvS0SKpBWx6m9ZlRRWGCyIiCnt5cXlom9IWOfYcxBvi2TKhMYYLIiIKK5IgIcWcggZxDVDHVge59lzEKDFal0UXYbggIqKQJggC6tnrIdeei7r2usi150bdI8zDDc8OEREFTKlBh4n/GuVZrol4YzwcMQ6kW9JRz14PSeYkWBVrIMskP2O4ICKiwBEElBorP9RLJ+qQZEpCsikZ2bZsxBviEW+MR6IxkfNMRACGCyIiCghFUpBtzUaDuAaw6W2w6W0wy2akmFMgi3yKaCRjuCAiohqTBAmyJMMiW5BmSYNZNiPRmAizbIYsyjDJJiSbkmHVW6GICoTSUuDuu13f/MorgF6v7Q9AQcFwQUREAACDzgCdqINdb4dVsSLBmABZlBFvjEeKOQWx+ljEGmJ9G0xZXg4sWOBanj2b4SJKMFwQEUUQAQKseitsehsMkgEp5hQYdUboRB0EQYAiKrDIFiiSghglxtPaYJEtHOtAfsNwQUQUAkRBhFFnhFk2Qy/pYdQZIYsyZEmGQTJAr9NDJ+ggCqKnC0ISJdj1ds/X7mVOIEVaY7ggIgoASZBgUSywKlZY9VaYdWYYZSNMOhMMkgFGnRE2vQ1G2Yh4QzxilBiIgqh12UR+wXBBRFFJgACDzgCzbPZ0ESiiApNsgizKMOqMrjEIgg4QABGiZztJlCAJEhRJgSzIEEURkiBBJ+pg0pmgl/SQREnrH5FIMwwXRBTyBAgQBRGSKMEgGSBLMsyyGZLgushLogRZdN3BEGtwDTp0dyuIwoULv0W2wKJYYNQZ2UpAFEAMF0R01dytAFbFCkmUoIgKFEmBTtR5PuFLggSjbIRRZ3StExVPN4EsydCLes/4Ap2og17Sw6AzQBIkjiEgCjMMF0RRRhRExCgxiDfEw6AzeAYRulsAREGEAMFzgXcHBEmUYFWsMOqMsMgWmGTX2AFREHnxp+qZTMDhwxeWKSowXBAFgSi4+utFQfQ00wsQIEsydILOc1F33w0gCIKnK0An6jwXeFEQPZ/s3fsSBRGyJEOEa78QAFmUPd0Hiqi4ZkSUZM96hgEKGkEAEhO1roKCjOGCIpIgCNAJOhh0Bs9FXXD/7/yF1b0sQECMEuO5518SJIii60Jvkk3QCTrPhdl9+5+7r9+gM0AWZa8wIAqip6/fHRSMkhGyxOmOiSg6MFxQrZhkE0w6k+fTtPuTtwjR6xP1xc3sF1/M3Rd494XY0/R+fuCdXtJ7PrW7L+DuEfzuT/6SKMGoM0Iv6b32wWZ6ohBy7hwwcaJr+fnnOUNnlGC4CCPuOf3dF9OLL+LuC7ZO0HmazN2fti++qLub1fU6PfSSHonGRJhkV0gA4NlOEiTPekEQPBd09615vM2OiGqkvBz4299cy888w3ARJRgurkAQBK++bvenaEmUPP3j7pnzLv5ULuL8bXM6A2KUmAtN5hBhVswQIXptL8D1Sduut3sG2YmC6DWgjp/GiYgoHERVuEiXjHgmvv0VtxMFATpBgnRR871P1PP/Vpx/nTt5yQZHfd8nEVE4On32wvJ37wKmMH1+SWIDILmRa4AqXVFUhQvx3HGYdizXugwiouhxtvzC8vZ3AUMYXnYa9gVSGmtdRVjhFHVERETVyWwHNL1F6yrCDsMFERFRVZIaAgUjASkMW1s0xnBBRER0qaSGQKfJgGLWupKwxDhGRESBo0jAC30uLIcDSzKDxVViuCAiosARBSAxjC7SOj3Q8c8MFleJ3SJERERu7cYBsVlaVxH22HJBRESBU14BvPWda3lgY0AXwl0jjW4EHAVaVxER2HJBRESBU64CH+xyvcrVK2+vlZQmQOMBWlcRMWrUcnH8+PEa79Bqtda6GCIioqDTW13dIbzl1G9q9Ju02+1XnAZbVVUIgoCKigq/FEZERBQUjfoDRrvWVUSUGoWLNWvWBLoOIiKi4EtpAtTvpXUVEadG4aKoqCjQdRAREQVXbDbQ/h5A5PBDf6vVb/Tzzz/H7bffjvbt2+N///sfAOCNN97A+vXr/VocERFRwFwzBjDYtK4iIvkcLpYtW4bi4mIYjUaUlJTg3LlzAIBjx45hxowZfi+QiIjI77Lacz6LAPI5XDzxxBOYO3cuXnvtNciy7FlfWFiIkpISvxZHRERhTpGAp4tdr1CZ/tuUALQapnUVEc3n+2527dqFTp06VVpvs9nwxx9/+KMmIiKKFKIAZIRY10OzW9gdEmA+t1ykpKRg9+7dldavX78eOTk5fimKiIgoIBLzgOwOWlcR8XwOFyNHjsSECRPw9ddfQxAE/PLLL1i4cCEmTZqE0aNHB6JGIiIKV+UVwLLvXK9yjedBUsxA4b3AFeZtoqvnc7fIAw88AKfTia5du+L06dPo1KkT9Ho9Jk2ahHvuuScQNRIRUbgqV4F3driW+zTQ9olWzQYDpjgNC4gePp9mQRDw0EMPYfLkydi9ezdOnjyJ/Px8WCyWQNRHRER09RLqA7ldtK4iatQ6QyqKgpiYGMTExDBYEBFRaMu/npNlBZHPv+ny8nI88sgjsNlsyM7ORnZ2Nmw2Gx5++GGUlZUFokYiIqLai68LpLXUuoqo4nPLxT333IN33nkHzzzzDNq1awcA+PLLLzFt2jT89ttvmDNnjt+LJCIiqh0BaD2CrRZB5nO4WLRoERYvXoxevS486KVp06ZwOBy49dZbGS6IiCh0NB4AxOdqXUXU8TnK6fV6ZGdnV1pfp04dKIrij5qIiIiunjkBaHSD1lVEJZ/Dxbhx4/D44497nikCAOfOncOTTz6JcePG+bU4IiIKc4oIPNbN9VKC3DXR6AZA0vLe1+hVo9/6jTfe6PX1xx9/jIyMDDRr1gwAsHXrVpSWlqJr167+r5CIiMKXKAK5GswtIZuAjILgH5cA1DBc2Gzec7APGDDA62uHw+G/ioiIiK5WoxsAg1XrKqJWjcLFvHnzAl0HERFFovIKYNUPruWe9QBdEJ6Mqlg4YZbG2BlFRESBU64Cb25zLXerG5yrTvPBgJ6TO2qpVqd56dKleOutt7B//36UlpZ6vVdSUuKXwoiIiHxmzwRyrtW6iqjn89Ddl156CcOHD0dycjI2b96MgoICxMfHY+/evV5zXxAREQVdk5s5YVYI8PkM/O1vf8Orr76Kl19+GYqiYMqUKVi9ejXGjx+PY8eOBaJGIiKiK7M5gPTWWldBqEW42L9/P9q3bw8AMBqNOHHiBABgyJAhePPNN/1bHRERUU01uYmtFiHC57OQkpKC33//HQCQmZmJr776CgCwb98+qKrq3+qIiIhqIqUp4GirdRV0ns/hokuXLnj//fcBAMOHD8d9992H7t27Y9CgQbjhhqufZvWzzz5D3759kZaWBkEQ8N577131PomIKILpDMA1owFB0LoSOs/nu0VeffVVOJ1OAMDYsWMRHx+PL774Av369cPdd9991QWdOnUKzZo1w4gRIyrNDEpERGFGEYGHOl9YDoRG/QGTBrOAUrV8DheiKEK8qE/rlltuwS233OK3gnr16sW7ToiIIoUoAvlJgdu/PRPI6xO4/VOt1ChcbNu2rcY7bNq0aa2LISIiqjHZCLS5C9Dxidyhpkbhonnz5hAE4YoDNgVBQEVFhV8KIyKiCFDuBD7d41rukgvo/NQ1opiBovuBxDz/7I/8qkbhYt++fYGug4iIIlG5E1iw2bXcqY7/wkWbkQwWIaxG4SIrKyvQdRAREdVMi9uBrHZaV0GXwdlGiIgofDS5GWjYV+sq6ApC7qmoJ0+exO7duz1f79u3D1u2bEFcXBwyMzM1rIyIiDRVpwhoPEDrKqgGQi5cbNq0Cddee+GJdhMnTgQADB06FPPnz9eoKiIi0pTNARSM4kRZYcKncFFRUYENGzagadOmsNvtASmoc+fOnEaciIgukI1Au7GAFHKfh6kaPo25kCQJPXr0wNGjRwNVDxER0QWmeKD740BcHa0rIR/4HAMbN26MvXv3ok4dnmgiIroCWQQmdbiw7BMBaPsnwO7we1kUWD7fLfLEE09g0qRJWLFiBQ4cOIDjx497vYiIiDwkEWiR5npJPl5y8q8HUjnrczjyueWid+/eAIB+/fpBuGhgjaqqnKGTiIj8I60l0Mx/z62i4PI5XKxZsyYQdRARUSQqdwIbfnItF2bVbIZOnR4oGMk7Q8KYz+GiqKgoEHUQEVEkKncCr250Lbd1XDlciDqg/Xg+Qj3M1WqGzs8//xy333472rdvj//9738AgDfeeAPr16/3a3FERBRNBKDwXiCjtdaF0FXyOVwsW7YMxcXFMBqNKCkpwblz5wAAx44dw4wZM/xeIBERRQFBdM1l4WijdSXkB7W6W2Tu3Ll47bXXIMuyZ31hYSFKSkr8WhwREUWJJjcDdTpqXQX5ic9jLnbt2oVOnTpVWm+z2fDHH3/4oyYiIoomzW/jw8gijM8tFykpKV4PFnNbv349cnJy/FIUERFFibxeQH4/3hkSYXwOFyNHjsSECRPw9ddfQxAE/PLLL1i4cCEmTZqE0aNHB6JGIiKKRHG5QMuhWldBAeBzt8gDDzwAp9OJrl274vTp0+jUqRP0ej0mTZqEe+65JxA1EhFRuJJFYHy7C8tuaS1cd4awxSIiCWotH0FaWlqK3bt34+TJk8jPz4fFYqnx9x4/fhw2mw3Hjh2D1WqtzeFr5/e9wKoHg3c8IiLyJslAs1uBvN4MFmGoptdvn7tFRowYgRMnTkBRFOTn56OgoAAWiwWnTp3CiBEjrqpoIiKKYJZkoPezQIM+DBYRzudwsWDBApw5c6bS+jNnzuD111/3S1FERBQhKpzA1z8DO8qBor8AMSlaV0RBUOMxF8ePH4eqqlBVFSdOnIDBYPC8V1FRgZUrVyIpKSkgRRIRUZiS7cBLS13LD/IDaLSocbiw2+0QBAGCIKB+/fqV3hcEAdOnT/drcUREFKaMcUDTgUB8UwB/17oaCrIah4s1a9ZAVVV06dIFy5YtQ1zchYfKKIqCrKwspKWlBaRIIiIKE7LJNSFWg+sAnQKcOqV1RaSBGocL99NQ9+3bh8zMTAgcjENERG46PeC4Bmg9HJCNWldDGvN5QOfOnTuxYcMGz9ezZ89G8+bNMXjwYBw9etSvxRERUQgTdUBaS6DDfcCAfwLtxjBYEIBahIvJkyfj+PHjAID//Oc/mDhxInr37o19+/Zh4sSJfi+QiIhCjKgD6nUH+jwHdL4fyLwGkHyek5EimM9/Dfv27UN+fj4A1+PX+/btixkzZqCkpAS9e/f2e4FERBQqBKBeN6DRjYAp7sqbU9TyOVwoioLTp08DAD7++GPccccdAIC4uDhPiwYREUWQmFQguwNQpwiwJPr2vYoCzJt3YZmigs/hokOHDpg4cSIKCwvxzTffYMmSJQCA//73v8jIyPB7gUREpJGYVNedH7ldaj+jpiwDw4b5tSwKfT6PufjrX/8KnU6HpUuXYs6cOUhPTwcA/Pvf/0bPnj39XiAREQWRpACOAqBgFHDdLKBuV07VTT6r9YPLrgYfXEZEFEJkI5DSFIjLARxtAWuq//ZdXg58+KFrubgY0HHgZzir6fXb57O8f//+y76fmZnp6y6JiCjYJBnI6gAkNXAFikDdQnruHHDdda7lkycZLqKEz2c5Ozv7shNoVVRUXFVBRETkZ5ICWNOA2DqA3QHYMwFrOu/4oIDxOVxs3rzZ6+uysjJs3rwZzz//PJ588km/FUZERLUkiIAtw9XVkd4SiK/nmoqbKEh8DhfNmjWrtK5169ZIS0vD//3f/+HGG2/0S2FERFRDkgwkNwbSWwEJ9V2tFJKsdVUUxfzW+ZWXl4eNGzf6a3dERHQp2QjYs1y3iMakuLo37Fmu7g3e0UEhxOdwcelEWaqq4sCBA5g2bRrq1avnt8KIiKKb4OrasKYCKc2AlMaAKZ4tEhQWfA4Xdru90oBOVVXhcDiwePFivxVGRBQ1RB1gTnC1RMSkue7gSMgDFJPWlRHVis/hYs2aNV5fi6KIxMRE1K1bFzreYkREVJlsAgw2QB8DKBZX94Y+BjAnAsmNXF0bos9zGoYHRQH++tcLyxQVfE4DRUVFgaiDiCg8yUZAZwAMdtdzN2yZQFpz1zp9DKC3Rm5wqAlZBsaO1boKCrIahYv333+/xjvs169frYshIgo5xljAGOfqorAku1ob4uu6BlTq9K4AQUReahQu+vfvX6OdCYLASbSIKDQZ7IAouVoUFJPrX1E+HxAsrkmlDHbXhFN6iytUuFsfeCdG7VVUAJ9/7lru2BGQJG3roaCoUbhwOp2BroOIqHqC5Op+kBTX3RKKBTDHu9YL4vmwYHS95x7P4B7fYLC5XoGa3pou7+xZ4NprXcsnTwJms7b1UFBwBCYR+Y+oc13UdQbXRV8Qzv8rAhAurBMlV1AQda6WA1F2LUs6QIlx3XJpir0QDizJbD0gCiM1Dheffvopxo0bh6+++qrSk9COHTuG9u3bY86cOejUqZPfiyQifxBcF2/Z7PqEL+pcF3tRcl3c3UFAlFwtAu71isk1KNH9PaJ0/nslV/dBTJqrC0E2ul4MAURRr8bh4oUXXsDIkSOrfMSqzWbD3XffjVmzZjFcUJQQXM9qcDfLe14XfTrXGQGD9Xxzve78hfn8SxDOX8B1rguy+6Ltbvb3ek9yHU8Qqv7XfbH3tBaIF/bl1YLAiz4RBUeNw8XWrVvx9NNPV/t+jx498Oyzz/qlKIp2gvdF2n2hleSLmtfh3dTu/pStt7j+dV/MJeVC87v7IuxeFkRA0rua5d0Xdfen+YvDgvt7DLbztxVKF94jIqJKahwuDh06BFmuftpZnU6HX3/91S9FkR8Jkne/98WfbHWGC4PkZOP52+3M1XxCxoVlSfH+NC2IruPolAuj7y8OBhcf3/0J2+tT+vmLvHtaY160iYjCWo3DRXp6Or777jvUrVu3yve3bduG1NRUvxWmKa9BZtJF/dCidzO4+9PxpRdi94XS3XftNagNF76uqmlbsVx0LPF807be+zgXf5q/dFvF3Z8unw8BUTx5DxERaaLG4aJ379545JFH0LNnTxgMBq/3zpw5g6lTp+K6667ze4H+9LuSjtV1n7nyhsH45Kxe8q/7bt/Sq93pyavZARGRX4llpah3z4MAgB+2HoBTDp0pwLPizWicboNZkSo9M4uujqCqqnrlzVzdIi1btoQkSRg3bhzy8vIAAN9//z1mz56NiooKlJSUIDk5+Yr7On78OGw2G44dO1blANFA+em3U3jsXzuCdjwiIgpN/Zqn4bqmaZBEhgpf1PT6XeOWi+TkZHzxxRcYPXo0HnzwQbgziSAIKC4uxuzZs2sULIiIiLQ0qI0DPRqlaF1GRPNpEq2srCysXLkSR48exe7du6GqKurVq4fY2NhA1UdERGFMcFYg68ddAICfsvOgitpN/y1LIoYVZuOanHjNaogWtZqhMzY2Fm3atPF3LUREFGHkslI88vgwAMDouetQqtdmGnadJGByzzzkJlo0OX604a0EREQU8W5smcFgEUQMF0REFNFubJmBYo6xCCo+uIyIiCLWgFYZ6N0kQuZgCiMMF0REFHEkUcDA1g50bZikdSlRieGCiIgizqA2DnRtyOkRtMIxF0REFFGSbQYU1k3QuoyoxpYLIiIKmApJh+XX3+VZDrRUuwFTejaAQdZuPg1iuCAiogCq0Ml4v/+ooBzLrNfhni71YDVU/wRvCg52ixARUdjTSQLGd62HZKvhyhtTwLHlgoiIAkZwOpF6YB8A4EBqHaii/z/TCoKAoe2zUTeJk2SFCoYLIiIKGLnsHB5/+FYAgZv+u6h+AtrncgBnKGG3CBERha14i4KbWzu0LoMuwXBBRERh64522bwzJAQxXBARUVgqbpyCRmlWrcugKjBcEBFR2Emw6NGvWRoEQdC6FKoCwwUREYUVSRRwX/f67A4JYQwXREQUVu7qmIMUG+ezCGW8FZWIiAKmQtJhVc/bPctX69oGSSioE3fV+6HAYrggIqKAqdDJeHvQeL/sSxCAHvl80mk4YLcIERGFhcK6CUji9N5hgS0XREQUMILTibjfDwIAfo9LqfX038k2Awa14WRZ4YLhgoiIAkYuO4dnJvcHUPvpvyVRwO1ts2BSeMkKF+wWISKikCUIwOjOucjnZFlhheGCiIhC1o0tM9AiM1brMshHDBdERBSSOtRLQK/GKVqXQbXAcEFERCEn3qJgUBsHp/cOUwwXREQUUgQBGNGhDgdwhjGGCyIiCin9W6SjQQoHcIYzxkIiIgoYpyjh0y43eZavpG6SBX2apAa6LAowhgsiIgqYclnBwiFTarStSa/DiA51OM4iArBbhIiINCcIAu7tVg/JnN47IrDlgoiIAkdVYTnxBwDgZIzdNVqzCsWNkpGbaAleXRRQDBdERBQwSulZvDihGED103/nJlkwoGVGsEujAGK3CBERaUYUBdzSxgFR5DiLSMJwQUREmrm+eRpy2B0ScRguiIhIE9fkxPO20wjFcEFEREHX3GHH8MJs3nYaoTigk4iIgqpFph2jOuVCJ/HzbaTimSUioqCxmxTc0T4bio6Xn0jGlgsiIgoYpyhhQ2EfAECs1YQ/da0Hq0HWuCoKNIYLIiIKmHJZwT/vmgoAGN0uB5nxJo0romBguxQREQVcUV4iWmXFal0GBQnDBRERBY6qIkVXgVvy48H7QqIHu0WIiChgEqUKPDmkveuLkycBs1nbgigo2HJBREQBIQjA0PbZWpdBGmC4ICKigOiRn4KGqVatyyANMFwQEZHftc6Ow4BWfNJptGK4ICIiv0qy6jG8MBsSn3QatRguiIjIbywGHe7rVh8GWdK6FNJQSIaL2bNnIzs7GwaDAW3btsU333yjdUlERHQFggBM6dkASVaD1qWQxkIuXCxZsgQTJ07E1KlTUVJSgmbNmqG4uBiHDx/WujQiIrqM7vnJSLcbvVdKEnDTTa6XxNaMaBFy4eL555/HyJEjMXz4cOTn52Pu3LkwmUz45z//qXVpRERUjc4NknBDiyoGcBoMwNtvu14GtmhEi5AKF6Wlpfj222/RrVs3zzpRFNGtWzd8+eWXGlZGRETVaZ0dh9vbZvJJp+QRUn8JR44cQUVFBZKTk73WJycn4+DBgxpVRURE1WmbE4eRHetAEHhnCF0QUuGCiIjCR4d6CbirQw500mUuJadOuUZ6CoJrmaJCSIWLhIQESJKEQ4cOea0/dOgQUlJSNKqKiIgu1b5uAoZckwWRc1lQFUIqXCiKglatWuGTTz7xrHM6nfjkk0/Qrl07DSsjIiK3drnxGFGYffkWC4pqIfdU1IkTJ2Lo0KFo3bo1CgoK8MILL+DUqVMYPny41qUREUW9llmxGFHIMRZ0eSEXLgYNGoRff/0Vjz76KA4ePIjmzZtj1apVlQZ5EhFRcMVbFIzsmMOuELqikAsXADBu3DiMGzdO6zKIiOi8eIuC+7rX5+2mVCMhGS6IiCh01Ekw454u9WAzyVqXQmGC4YKIiKqVEWvE+G71YDXUMlhIEtC794VligoMF0REVKUkqx73da9f+2ABuKb8/uAD/xVFYYGdZ0REVInNJGNcl3qwmxStS6EwxHBBREReDLKE8V3qVX7CKVENsVuEiIg86iSYr26MxaVOnQKSklzLhw8DZrN/9kshjeGCiIgAAMk2A0Z2yvFfsHA7fdq/+6OQx3BBRBTlZElE14ZJuKlVBmfeJL9guCAiimJ1Esy47Zos1ElgdwX5D8MFEVEUkiUR3fKTcV3TVBhkzj9B/sVwQUQUZewmBcMLs9E43aZ1KRShGC6IiKJIg9QY3NkhB3Fmzl9BgcNwQUQUJTo3SMLtbTODO2hTFIGiogvLFBUYLoiIIpwoChjQMgM9G6cE/+BGI7B2bfCPS5piuCAiimANU63o0zQVDVOtWpdCUYThgogoAtlMMvo0SUXHeolQdOyOoOBiuCAiiiCCAHSsl4iBrR0wKiFwi+mpU0B2tmv5xx85/XeUYLggIooQqXYD7uqQg+xQmxDryBGtK6AgY7ggIooAfZuloW+zNEgip+8m7TFcEBGFKVkS0TzTjnY58WjmsGtdDpEHwwURUZipk2BGUV4iWmTGwqLn/41T6OFfJRFRmEi1GzDkmmzUT7bw6aUU0hguiIhCXG6SBa2yYtGhbgLMbKmgMMC/UiKiEJVmN6JjvQR0z08O35YKUQRat76wTFGB4YKIKMRkxZvRp2kqWmXFal3K1TMagY0bta6CgozhgogoBAgC0CIzFl0aJHGqbgp7DBdERBrSyyKaZtjRt1ka0u1Grcsh8guGCyKiIDPrdchNtOCanDi0yoqFTorgsQinTwP5+a7lHTsAk0nbeigoGC6IiIJEL4voUDcRN7ZMh0EOged+BIOqAj/9dGGZogLDBRFRgOUkmtE6Ow7X5iXxCaUUFRguiIj8TJZE9GqSghaOWCRZ9dHTSkF0HsMFEZGfCIKAvBQL7miXjWSrQetyiDTDcEFEdBUMioRmGTZkxJrQKM2KrPgQe9w5kQYYLoiIaqFxug3tc+Mj/24PolpguCAiqgGzXoeseBPqJcegaboN2QlsoagRQbhwK2q4TmFOPmO4ICKqht2koFVWLBqnW9Ek3Ra+z/fQkskEbN+udRUUZAwXREQXyYg1IsVmREGdWDRJt/PWUaJaYLggoqiXajegcZoNLTJjkZcSo3U5RGGP4YKIok6CRY+uDZNQLzkGZkVCYoyeXR6Bcvo00KaNa3njRk7/HSUYLogo4gkCkGIzoHVWHNrlxiPRoocoMkwEhaq6niniXqaowHBBRBFJlkRkxZvQItOOlpmxSOKkVkRBw3BBRGEv1W5Amt2InAQzYk0KcpMsiDcr7Oog0gjDBRGFlVS7AdnxZqTYDKiTYEa63Qi7SdG6LCK6CMMFEYW0eIuCNtlxaJ0dh2SrHiaF/7dFFOr4XykRhQy9LCIzzoyMWCPyUmKQFWfiWAmiMMRwQURBZTPKSIzRw2aSkZNgRrLVgFSbERaDDmZF4jiJSCMIQFbWhWWKCgwXRBQQcWYFSVY9UmxGpNsNiDHIrtkvrQYGiGhiMgE//qh1FRRkDBdE5DO9LMKgk2DSS0iOMcBqlGEzysiMNyHFakCsSYFRkbQuk4g0wnBBRF4kUUBukgWOWBPMegl2kwK7UYZeFhFnUhBrViDzEeNEdBkMF0RRxGLQwShL0OtExJn1SLbqkWTVwxFrQpxZgdUoMziQf505A3Tq5Fr+7DPAaNS2HgoKhguiCKLoRMSaFSiSiGSrAQ1TYxBrUpBsNcBukmGQ2VVBQeZ0Aps2XVimqMBwQRSiFJ0ISRQgCgJ0ooBEqx5JMQbE6HUw63WQRNd6m0lGrEmBzSgjwcJZKYlIewwXRAEiiQJsRhmSKEAQBCRb9Yi36GHRSzDKEmTJFR6MsgSrUYZRlmBSJCg6ESZFB0XH7gkiCk8MF0Q1IAg431IgIjFGj1SbAYkxrpYE96BHoyxBJwnQ60ToRBF6ncgnbxJRVGK4oKgmigLsRhlNMmywm1xjFcx6VwuCSdEh1qQg1ixDr+NYBSKimmK4oLAlCIAoCOe7EVxdDXpZgiQKUCQRBlmCURZh0uug14mwGmSYzy/bjDLsJtfXvDuCiMi/GC4oJBhkCUZFQoJFjwSLApOig04SEKPXQS+LkEQROlFAqs0Ak6KDzShzkiaicJGQoHUFFGQMF1SJThKgk0TIooAYgwxRgOcOBEEABAgQBJyfK0GBRa+D1ShDFASI51sTdJKrRUGvE8/f7eDqbjDIkud9nSjAcH7OBd7hQBShzGbg11+1roKCjOEiQgkCoBNFWAyuT/lWg4xYswxFEqHoXK+48zMtioKARIvrQVJmRYKO3QRERHQVGC5ChF4WPZ/iDbJ0fmChDibFdZuiRa+DIokwKq4xBZIowKCTYDG4blnUnR9nYFSk84EBbA0gIiJNMFxcgVFxXehxvrlfgKtVQNGJkCXXRd3dhaA/Hw5Mig5mveSZw8Ci18GouLoD3MHBFQJc3+tuTSAiijhnzgC9ermW//1vTv8dJSIuXJwtq8C5cidUVYVTBcqdTpwrc8KpqogzKyjKS4RFr4NJcbUKmPWSZzyAXucKBHqd6+JvlCV++iciuhpOJ7Bu3YVligoRFy4MsnTZ5yfc0S47eMUQERFFIbbFExERkV8xXBAREZFfMVwQERGRXzFcEBERkV9F3IBOIiIKMSaT1hVQkDFcEBFR4JjNwKlTWldBQcZuESIiIvIrhgsiIiLyK4YLIiIKnLNngT59XK+zZ7WuhoKEYy6IiChwKiqAlSsvLFNUYMsFERER+RXDBREREfkVwwURERH5FcMFERER+RXDBREREfmVJneLqKoKADh+/LgWhyciomC5eHbO48d5x0iYc1+33dfx6mgSLk6cOAEAcDgcWhyeiIi0kJamdQXkJydOnIDNZqv2fUG9UvwIAKfTiV9++QUxMTEQBCFoxz1+/DgcDgd+/vlnWK3WoB2XqsdzEpp4XkIPz0loirbzoqoqTpw4gbS0NIhi9SMrNGm5EEURGRkZWhwaAGC1WqPijyCc8JyEJp6X0MNzEpqi6bxcrsXCjQM6iYiIyK8YLoiIiMivoipc6PV6TJ06FXq9XutS6Dyek9DE8xJ6eE5CE89L1TQZ0ElERESRK6paLoiIiCjwGC6IiIjIrxguiIiIyK8YLoiIiMivIi5czJ49G9nZ2TAYDGjbti2++eaby27/9ttvo0GDBjAYDGjSpAlWrlwZpEqjhy/n5LXXXkPHjh0RGxuL2NhYdOvW7YrnkGrH1/9W3BYvXgxBENC/f//AFhiFfD0nf/zxB8aOHYvU1FTo9XrUr1+f/x/mZ76ekxdeeAF5eXkwGo1wOBy47777cPbs2SBVG0LUCLJ48WJVURT1n//8p7p9+3Z15MiRqt1uVw8dOlTl9hs2bFAlSVKfeeYZdceOHerDDz+syrKs/uc//wly5ZHL13MyePBgdfbs2ermzZvVnTt3qsOGDVNtNpv6//7f/wty5ZHN1/Pitm/fPjU9PV3t2LGjev311wen2Cjh6zk5d+6c2rp1a7V3797q+vXr1X379qlr165Vt2zZEuTKI5ev52ThwoWqXq9XFy5cqO7bt0/98MMP1dTUVPW+++4LcuXai6hwUVBQoI4dO9bzdUVFhZqWlqbOnDmzyu0HDhyo9unTx2td27Zt1bvvvjugdUYTX8/JpcrLy9WYmBh1wYIFgSoxKtXmvJSXl6vt27dX//73v6tDhw5luPAzX8/JnDlz1JycHLW0tDRYJUYdX8/J2LFj1S5dunitmzhxolpYWBjQOkNRxHSLlJaW4ttvv0W3bt0860RRRLdu3fDll19W+T1ffvml1/YAUFxcXO325JvanJNLnT59GmVlZYiLiwtUmVGntuflscceQ1JSEu68885glBlVanNO3n//fbRr1w5jx45FcnIyGjdujBkzZqCCjzT3i9qck/bt2+Pbb7/1dJ3s3bsXK1euRO/evYNScyjR5MFlgXDkyBFUVFQgOTnZa31ycjK+//77Kr/n4MGDVW5/8ODBgNUZTWpzTi51//33Iy0trVIIpNqrzXlZv349/vGPf2DLli1BqDD61Oac7N27F59++iluu+02rFy5Ert378aYMWNQVlaGqVOnBqPsiFabczJ48GAcOXIEHTp0gKqqKC8vx5/+9Cf85S9/CUbJISViWi4o8jz11FNYvHgx3n33XRgMBq3LiVonTpzAkCFD8NprryEhIUHrcug8p9OJpKQkvPrqq2jVqhUGDRqEhx56CHPnztW6tKi1du1azJgxA3/7299QUlKCd955Bx988AEef/xxrUsLuohpuUhISIAkSTh06JDX+kOHDiElJaXK70lJSfFpe/JNbc6J27PPPounnnoKH3/8MZo2bRrIMqOOr+dlz549+PHHH9G3b1/POqfTCQDQ6XTYtWsXcnNzA1t0hKvNfyupqamQZRmSJHnWNWzYEAcPHkRpaSkURQlozZGuNufkkUcewZAhQ3DXXXcBAJo0aYJTp05h1KhReOihhyCK0fN5PmJ+UkVR0KpVK3zyySeedU6nE5988gnatWtX5fe0a9fOa3sAWL16dbXbk29qc04A4JlnnsHjjz+OVatWoXXr1sEoNar4el4aNGiA//znP9iyZYvn1a9fP1x77bXYsmULHA5HMMuPSLX5b6WwsBC7d+/2BD0A+O9//4vU1FQGCz+ozTk5ffp0pQDhDn9qtD3GS+sRpf60ePFiVa/Xq/Pnz1d37Nihjho1SrXb7erBgwdVVVXVIUOGqA888IBn+w0bNqg6nU599tln1Z07d6pTp07lrah+5us5eeqpp1RFUdSlS5eqBw4c8LxOnDih1Y8QkXw9L5fi3SL+5+s52b9/vxoTE6OOGzdO3bVrl7pixQo1KSlJfeKJJ7T6ESKOr+dk6tSpakxMjPrmm2+qe/fuVT/66CM1NzdXHThwoFY/gmYiKlyoqqq+/PLLamZmpqooilpQUKB+9dVXnveKiorUoUOHem3/1ltvqfXr11cVRVEbNWqkfvDBB0GuOPL5ck6ysrJUAJVeU6dODX7hEc7X/1YuxnARGL6eky+++EJt27atqtfr1ZycHPXJJ59Uy8vLg1x1ZPPlnJSVlanTpk1Tc3NzVYPBoDocDnXMmDHq0aNHg1+4xvjIdSIiIvKriBlzQURERKGB4YKIiIj8iuGCiIiI/IrhgoiIiPyK4YKIiIj8iuGCiIiI/IrhgoiIiPyK4YLC0tq1ayEIAv7444+gHXPatGlo3ry55+thw4ahf//+nq87d+6Me++9N2j1RDJVVTFq1CjExcVBEARs2bKlynW+/M61+Jvxp/nz58Nut2tdBlGNMFxQyBEE4bKvadOmaVLXpEmTKj2LJhRVdxENp/CzatUqzJ8/HytWrMCBAwfQuHHjKte98847NX7iZPv27XHgwAHYbDa/1fnjjz96gg4RXRAxT0WlyHHgwAHP8pIlS/Doo49i165dnnUWiwWbNm0Kel0WiwUWiyXox41Ge/bsQWpqKtq3b3/ZdXFxcTXep6IofOIxUZCw5YJCTkpKiudls9kgCILXuosv8N9++y1at24Nk8mE9u3be4UQAFi+fDlatmwJg8GAnJwcTJ8+HeXl5dUee+3atSgoKIDZbIbdbkdhYSF++uknAJW7RaridDoxZcoUxMXFISUlpVIry/79+3H99dfDYrHAarVi4MCBXo90vrSrBQDuvfdedO7c2esYM2fORJ06dWA0GtGsWTMsXboUgOuT9LXXXgsAiI2NhSAIGDZsGIYNG4Z169bhxRdf9LQA/fjjjwCA7777Dr169YLFYkFycjKGDBmCI0eOXPbn3LBhAzp37gyTyYTY2FgUFxfj6NGjAIBz585h/PjxSEpKgsFgQIcOHbBx40av77/cMYcNG4Z77rkH+/fvhyAIyM7OrnIdULk15ty5c7j//vvhcDig1+tRt25d/OMf/wBQdYvO+vXr0bFjRxiNRjgcDowfPx6nTp3yvJ+dnY0ZM2ZgxIgRiImJQWZmJl599VXP+3Xq1AEAtGjRAoIgeJ2ni89XRkYG5syZ47V+8+bNEEXR8/f1/PPPo0mTJjCbzXA4HBgzZgxOnjxZ7Tm42r8VADh69Chuu+02JCYmwmg0ol69epg3b161xySqKYYLCmsPPfQQnnvuOWzatAk6nQ4jRozwvPf555/jjjvuwIQJE7Bjxw688sormD9/Pp588skq91VeXo7+/fujqKgI27Ztw5dffolRo0ZBEIQa17NgwQKYzWZ8/fXXeOaZZ/DYY49h9erVAFz/R3/99dfj999/x7p167B69Wrs3bsXgwYN8ulnnjlzJl5//XXMnTsX27dvx3333Yfbb78d69atg8PhwLJlywAAu3btwoEDB/Diiy/ixRdfRLt27TBy5EgcOHAABw4cgMPhwB9//IEuXbqgRYsW2LRpE1atWoVDhw5h4MCB1R5/y5Yt6Nq1K/Lz8/Hll19i/fr16Nu3LyoqKgAAU6ZMwbJly7BgwQKUlJSgbt26KC4uxu+//w4AVzzmiy++iMceewwZGRk4cOAANm7cWOW6qtxxxx1488038dJLL2Hnzp145ZVXqm1t2rNnD3r27IkBAwZg27ZtWLJkCdavX49x48Z5bffcc8+hdevW2Lx5M8aMGYPRo0d7Quw333wDAPj4449x4MABvPPOO5WOI4oibr31VixatMhr/cKFC1FYWIisrCzPdi+99BK2b9+OBQsW4NNPP8WUKVOqPQ81cbm/FQB45JFHsGPHDvz73//Gzp07MWfOHCQkJFzVMYkARNYj1ynyzJs3T7XZbJXWr1mzRgWgfvzxx551H3zwgQpAPXPmjKqqqtq1a1d1xowZXt/3xhtvqKmpqVUe67ffflMBqGvXrq3y/alTp6rNmjXzfH3pk0GLiorUDh06eH1PmzZt1Pvvv19VVVX96KOPVEmS1P3793ve3759uwpA/eabb6rcp6qq6oQJE9SioiJVVVX17NmzqslkUr/44guvbe6880711ltvVVX1wu/m0icxFhUVqRMmTPBa9/jjj6s9evTwWvfzzz+rANRdu3ZV+Xu49dZb1cLCwirfO3nypCrLsrpw4ULPutLSUjUtLU195plnanzMWbNmqVlZWV7bVLXu4p9p165dKgB19erVVdZ26e/lzjvvVEeNGuW1zeeff66Kouj5G8rKylJvv/12z/tOp1NNSkpS58yZo6qqqu7bt08FoG7evLnKY7pt3rxZFQRB/emnn1RVVdWKigo1PT3ds5+qvP3222p8fLzn60v/W/DH30rfvn3V4cOHX7Z2otrgmAsKa02bNvUsp6amAgAOHz6MzMxMbN26FRs2bPBqqaioqMDZs2dx+vRpmEwmr33FxcVh2LBhKC4uRvfu3dGtWzcMHDjQs19f63HXdPjwYQDAzp074XA44HA4PO/n5+fDbrdj586daNOmzRX3v3v3bpw+fRrdu3f3Wl9aWooWLVrUuE63rVu3Ys2aNVV+ut+zZw/q169faf2WLVtw8803V7m/PXv2oKysDIWFhZ51siyjoKAAO3furPUxa2LLli2QJAlFRUU12n7r1q3Ytm0bFi5c6FmnqiqcTif27duHhg0bAvA+p+4uOvc5ranmzZujYcOGWLRoER544AGsW7cOhw8f9vo9fvzxx5g5cya+//57HD9+HOXl5dX+rdZETf5WRo8ejQEDBqCkpAQ9evRA//79vca0ENUWwwWFNVmWPcvu7gun0wkAOHnyJKZPn44bb7yx0vcZDIYq9zdv3jyMHz8eq1atwpIlS/Dwww9j9erVuOaaa3yux12Tu56aEEURqqp6rSsrK/Msu/vgP/jgA6Snp3ttp9fra3yci/fXt29fPP3005Xeqy5UGY1Gn49ztcesCV/rOnnyJO6++26MHz++0nuZmZme5as9p2633XabJ1wsWrQIPXv2RHx8PADXWJnrrrsOo0ePxpNPPom4uDisX78ed955J0pLS6sMF/74W+nVqxd++uknrFy5EqtXr0bXrl0xduxYPPvssz7/fEQXY7igiNWyZUvs2rULdevW9en7WrRogRYtWuDBBx9Eu3btsGjRohqHi8tp2LAhfv75Z/z888+e1osdO3bgjz/+QH5+PgAgMTER3333ndf3bdmyxXOBy8/Ph16vx/79+6v9hK4oCgB4xkBcvP7SdS1btsSyZcuQnZ0Nna5m/3fQtGlTfPLJJ5g+fXql93Jzc6EoCjZs2OAZS1BWVoaNGzd6Bl7W5pg10aRJEzidTqxbtw7dunW74vYtW7bEjh07fP77uFh1v+uqDB48GA8//DC+/fZbLF26FHPnzvW89+2338LpdOK5556DKLqGwr311luX3Z8//lbc+xk6dCiGDh2Kjh07YvLkyQwXdNU4oJMi1qOPPorXX38d06dPx/bt27Fz504sXrwYDz/8cJXb79u3Dw8++CC+/PJL/PTTT/joo4/www8/eJrHr1a3bt3QpEkT3HbbbSgpKcE333yDO+64A0VFRWjdujUAoEuXLti0aRNef/11/PDDD5g6darXBSQmJgaTJk3CfffdhwULFmDPnj0oKSnByy+/jAULFgAAsrKyIAgCVqxYgV9//dXzCTY7Oxtff/01fvzxRxw5cgROpxNjx47F77//jltvvRUbN27Enj178OGHH2L48OHVXjAffPBBbNy4EWPGjMG2bdvw/fffY86cOThy5AjMZjNGjx6NyZMnY9WqVdixYwdGjhyJ06dP48477wSAWh2zJrKzszF06FCMGDEC7733Hvbt24e1a9dWe5G+//778cUXX2DcuHHYsmULfvjhByxfvrzSgM7LSUpKgtFo9AxKPXbs2GXra9++Pe68805UVFSgX79+nvfq1q2LsrIyvPzyy9i7dy/eeOMNr/BRFX/8rTz66KNYvnw5du/eje3bt2PFihV++3un6MZwQRGruLgYK1aswEcffYQ2bdrgmmuuwaxZszyfqC9lMpnw/fffY8CAAahfvz5GjRqFsWPH4u677/ZLPYIgYPny5YiNjUWnTp3QrVs35OTkYMmSJV41P/LII5gyZQratGmDEydO4I477vDaz+OPP45HHnkEM2fORMOGDdGzZ0988MEHntsi09PTMX36dDzwwANITk72XCwnTZoESZKQn5+PxMRE7N+/H2lpadiwYQMqKirQo0cPNGnSBPfeey/sdrvnE/Sl6tevj48++ghbt25FQUEB2rVrh+XLl3taIZ566ikMGDAAQ4YMQcuWLbF79258+OGHiI2NBYBaHbOm5syZg5tuugljxoxBgwYNMHLkSK9bSy/WtGlTrFu3Dv/973/RsWNHtGjRAo8++ijS0tJqfDydToeXXnoJr7zyCtLS0nD99ddfdvvbbrsNW7duxQ033ODVjdOsWTM8//zzePrpp9G4cWMsXLgQM2fOvOy+/PG3oigKHnzwQTRt2hSdOnWCJElYvHhxjX9+ouoI6qWddkRERERXgS0XRERE5FcMF0RERORXDBdERETkVwwXRERE5FcMF0RERORXDBdERETkVwwXRERE5FcMF0RERORXDBdERETkVwwXRERE5FcMF0RERORXDBdERETkV/8/SGZ+UKpGdPQAAAAASUVORK5CYII=\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "El gráfico muestra que el valor escogido para el número de cluster es una buena elección para los datos proporcionados, debido a que los tres grupos presentan puntuaciones superiores al promedio." ], "metadata": { "id": "6kxJwyWH1Ppz" } }, { "cell_type": "markdown", "source": [ "A continuación, se analizarán cada uno de los tres grupos formados." ], "metadata": { "id": "o6fRGSKjGzGu" } }, { "cell_type": "code", "source": [ "random_state = 20\n", "kmeans = KMeans(n_clusters=3, n_init=20, max_iter=500, random_state=random_state)\n", "kmeans.fit(df_new)\n", "y_pred = kmeans.predict(df_new)" ], "metadata": { "id": "8L-_yun5LFUY" }, "execution_count": 23, "outputs": [] }, { "cell_type": "markdown", "source": [ "Se agrega al dataframe la información del cluster al que pertenece el registro." ], "metadata": { "id": "ktgX-vLhHBMI" } }, { "cell_type": "code", "source": [ "df_new[\"Cluster\"]=y_pred" ], "metadata": { "id": "k37wpCCqpkGi" }, "execution_count": 24, "outputs": [] }, { "cell_type": "code", "source": [ "#Temperatura ambiente\n", "df_new.groupby(['Cluster'])[\"TA\"].describe().round(2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 175 }, "id": "kifSuTmoLPAG", "outputId": "c79f48bb-31ee-46d1-8fa8-6e6c9695bce6" }, "execution_count": 25, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " count mean std min 25% 50% 75% max\n", "Cluster \n", "0 3121.0 14.16 5.21 3.4 10.2 13.2 17.3 32.6\n", "1 2283.0 14.46 4.08 3.6 11.2 13.9 17.3 29.0\n", "2 2866.0 11.08 3.26 3.1 8.8 10.6 13.0 26.7" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countmeanstdmin25%50%75%max
Cluster
03121.014.165.213.410.213.217.332.6
12283.014.464.083.611.213.917.329.0
22866.011.083.263.18.810.613.026.7
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 25 } ] }, { "cell_type": "markdown", "source": [ "La temperatura promedio es parecida entre los cluster 0 y 1, mientras que el cluster 2 se caracteriza por tener una temperatura inferior." ], "metadata": { "id": "l-559FfkbgWV" } }, { "cell_type": "code", "source": [ "#Humedad relativa\n", "df_new.groupby(['Cluster'])[\"HR\"].describe().round(2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 175 }, "id": "U5yXrwMlgCM4", "outputId": "ac19f987-9200-4ebf-b83b-834945cf9dbf" }, "execution_count": 26, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " count mean std min 25% 50% 75% max\n", "Cluster \n", "0 3121.0 69.90 19.46 9.8 54.6 73.6 86.3 100.0\n", "1 2283.0 76.94 17.29 26.5 64.4 78.7 92.2 100.0\n", "2 2866.0 83.89 14.83 20.4 75.9 88.3 95.6 100.0" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countmeanstdmin25%50%75%max
Cluster
03121.069.9019.469.854.673.686.3100.0
12283.076.9417.2926.564.478.792.2100.0
22866.083.8914.8320.475.988.395.6100.0
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 26 } ] }, { "cell_type": "markdown", "source": [ "El promedio de la humedad relativa es distinto en los 3 cluster, habiendo una diferencia de 7% entre el 0 y el 1, y también del 7% entre el 1 y el 2." ], "metadata": { "id": "-CyTCcI0bwPv" } }, { "cell_type": "code", "source": [ "#Precipitaciones\n", "df_new.groupby(['Cluster'])[\"PP\"].describe()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 175 }, "id": "QJ2vp_IyubuK", "outputId": "6b18665c-d035-4763-ee04-78a1f40e4f06" }, "execution_count": 27, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " count mean std min 25% 50% 75% max\n", "Cluster \n", "0 3121.0 0.006825 0.179033 0.0 0.0 0.0 0.0 9.5\n", "1 2283.0 0.098336 0.737446 0.0 0.0 0.0 0.0 17.0\n", "2 2866.0 0.025645 0.231563 0.0 0.0 0.0 0.0 5.1" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countmeanstdmin25%50%75%max
Cluster
03121.00.0068250.1790330.00.00.00.09.5
12283.00.0983360.7374460.00.00.00.017.0
22866.00.0256450.2315630.00.00.00.05.1
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 27 } ] }, { "cell_type": "markdown", "source": [ "Las precipitaciones no se diferencian considerablemente." ], "metadata": { "id": "DKvEygy7cEIX" } }, { "cell_type": "code", "source": [ "#Presión atmosférica\n", "df_new.groupby(['Cluster'])[\"PA\"].describe()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 175 }, "id": "ctWFxNtRueMD", "outputId": "0088ec5b-24b0-4897-9c3e-a936aeda3b06" }, "execution_count": 28, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " count mean std min 25% 50% 75% max\n", "Cluster \n", "0 3121.0 979.079782 3.227443 971.0 977.0 979.0 981.0 990.0\n", "1 2283.0 978.272887 2.525505 971.0 977.0 978.0 980.0 989.0\n", "2 2866.0 978.677599 3.157091 970.0 976.0 978.5 981.0 990.0" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countmeanstdmin25%50%75%max
Cluster
03121.0979.0797823.227443971.0977.0979.0981.0990.0
12283.0978.2728872.525505971.0977.0978.0980.0989.0
22866.0978.6775993.157091970.0976.0978.5981.0990.0
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 28 } ] }, { "cell_type": "markdown", "source": [ "La presión atmosférica tampoco se diferencia considerablemente." ], "metadata": { "id": "eS_b3taicJPF" } }, { "cell_type": "code", "source": [ "#Velocidad del viento\n", "df_new.groupby(['Cluster'])[\"VV\"].describe().round(2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 175 }, "id": "PyUwP6tzuh2I", "outputId": "59f078a7-f2df-4a5b-9b90-871df3f5fea8" }, "execution_count": 29, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " count mean std min 25% 50% 75% max\n", "Cluster \n", "0 3121.0 6.64 4.86 0.0 2.8 5.3 9.6 24.6\n", "1 2283.0 8.53 4.46 0.1 5.4 8.1 11.0 34.8\n", "2 2866.0 2.15 2.76 0.0 0.2 1.2 3.0 29.3" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countmeanstdmin25%50%75%max
Cluster
03121.06.644.860.02.85.39.624.6
12283.08.534.460.15.48.111.034.8
22866.02.152.760.00.21.23.029.3
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 29 } ] }, { "cell_type": "markdown", "source": [ "La velocidad del viento promedio es distinta en los 3 cluster, siendo mayor para el cluster 1 y menor para el cluster 2." ], "metadata": { "id": "IyOq8T4RcNfr" } }, { "cell_type": "code", "source": [ "#Rágafa de viento\n", "df_new.groupby(['Cluster'])[\"RV\"].describe()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 175 }, "id": "dSfoJUnpukse", "outputId": "c955968a-f313-4948-8b0a-ef3914a60e73" }, "execution_count": 30, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " count mean std min 25% 50% 75% max\n", "Cluster \n", "0 3121.0 14.469401 7.338526 1.1 8.6 13.3 19.4 38.5\n", "1 2283.0 14.342970 6.434811 1.4 10.1 13.7 17.3 58.7\n", "2 2866.0 6.581612 4.928230 0.0 3.6 6.1 9.0 51.1" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countmeanstdmin25%50%75%max
Cluster
03121.014.4694017.3385261.18.613.319.438.5
12283.014.3429706.4348111.410.113.717.358.7
22866.06.5816124.9282300.03.66.19.051.1
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 30 } ] }, { "cell_type": "markdown", "source": [ "La ráfaga de viento es menor para el cluster 2, coincidiendo con que este grupo también presenta la velocidad del viento promedio más baja." ], "metadata": { "id": "rSxJ2F5WHrLY" } }, { "cell_type": "code", "source": [ "#Dirección del viento\n", "df_new.groupby(['Cluster'])[\"DV\"].describe().round(2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 175 }, "id": "HqLlhb7VunHP", "outputId": "31ab8cf7-2048-4aff-910a-e1ddeb6e89d5" }, "execution_count": 31, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " count mean std min 25% 50% 75% max\n", "Cluster \n", "0 3121.0 160.64 29.62 83.0 142.0 162.0 181.0 232.0\n", "1 2283.0 302.73 28.29 231.0 282.0 303.0 324.0 355.0\n", "2 2866.0 8.88 20.18 0.0 0.0 0.0 0.0 86.0" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countmeanstdmin25%50%75%max
Cluster
03121.0160.6429.6283.0142.0162.0181.0232.0
12283.0302.7328.29231.0282.0303.0324.0355.0
22866.08.8820.180.00.00.00.086.0
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 31 } ] }, { "cell_type": "markdown", "source": [ "Hay una gran diferencia en la dirección del viento.\n", "El cluster 0 se caracteriza por una dirección aproximadamente sur (160 grados). El cluster 1 se caracteriza por una dirección aproximadamente nor-oeste (300 grados). Y el cluster 2 por una dirección norte (8 grados)." ], "metadata": { "id": "dhrlvHzfcZJl" } }, { "cell_type": "code", "source": [ "#Nivel del mar\n", "df_new.groupby(['Cluster'])[\"PRS\"].describe()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 175 }, "id": "-pcpKWCLuq1K", "outputId": "07f6ba95-29b1-4c18-9409-8e6537266eb5" }, "execution_count": 32, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " count mean std min 25% 50% 75% max\n", "Cluster \n", "0 3121.0 2.282768 0.370058 1.22 1.98 2.26 2.56 3.31\n", "1 2283.0 2.321599 0.369025 1.16 2.04 2.29 2.58 3.40\n", "2 2866.0 2.287313 0.366482 1.19 2.00 2.26 2.56 3.34" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countmeanstdmin25%50%75%max
Cluster
03121.02.2827680.3700581.221.982.262.563.31
12283.02.3215990.3690251.162.042.292.583.40
22866.02.2873130.3664821.192.002.262.563.34
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 32 } ] }, { "cell_type": "markdown", "source": [ "No se observan grandes diferencias. Esto se debe a que este atributo presenta pequeñas variaciones a lo largo del año." ], "metadata": { "id": "F_u1C3S8c8q5" } }, { "cell_type": "code", "source": [ "#Temperatura del océano\n", "df_new.groupby(['Cluster'])[\"TW\"].describe()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 175 }, "id": "jYrKvAHAuss3", "outputId": "efb71706-4953-4953-d405-b42d00a69986" }, "execution_count": 33, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " count mean std min 25% 50% 75% max\n", "Cluster \n", "0 3121.0 13.293682 1.311902 11.16 12.400 12.98 13.8100 18.27\n", "1 2283.0 13.390710 1.329994 10.75 12.445 12.99 14.1550 17.81\n", "2 2866.0 13.190122 1.220973 10.98 12.460 12.90 13.4175 18.26" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countmeanstdmin25%50%75%max
Cluster
03121.013.2936821.31190211.1612.40012.9813.810018.27
12283.013.3907101.32999410.7512.44512.9914.155017.81
22866.013.1901221.22097310.9812.46012.9013.417518.26
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 33 } ] }, { "cell_type": "markdown", "source": [ "No se observan grandes diferencias." ], "metadata": { "id": "xX9JPx1vIXOC" } }, { "cell_type": "markdown", "source": [ "La características que más diferencian a los distintos grupos son:\n", "\n", "* Cluster 0: temperatura alta, humedad baja, viento medio y sur.\n", "\n", "* Cluster 1: temperatura alta, humedad media, viento alto y nor-oeste.\n", "\n", "* Cluster 2: temperatura baja, humedad alta, viento bajo y norte." ], "metadata": { "id": "Ko7fxyETdCuc" } }, { "cell_type": "markdown", "source": [ "Como conclusión final de este método, se destaca que el coeficiente de silhouette alcanza su valor máximo cuando los datos no han sido sometidos a escala. Este resultado nos muestra la influencia del escalamiento, donde la disparidad en el comportamiento y rango de los diversos atributos es clave.\n", "\n", "Tomemos, por ejemplo, las mediciones de temperatura ambiente y temperatura del agua. Mientras que la temperatura ambiente puede variar en un rango de aproximadamente 30ºC, la temperatura del agua abarca solo unos 8ºC. Al aplicar el MinMaxScaler, ambos atributos quedan normalizados en el mismo rango. Sin embargo, esto plantea un dilema, ya que la variación relativa en la temperatura del agua es mucho menor en comparación con la temperatura ambiente.\n", "\n", "Este fenómeno puede tener implicaciones significativas. Si, por ejemplo, la temperatura del agua se mantiene casi constante, con una variación de apenas 1ºC a lo largo del tiempo, la aplicación del MinMaxScaler asigna valores de 0 a 1, equiparándola con la variación mucho mayor de la temperatura ambiente. Esta homogeneización de escalas puede distorsionar la representación de la importancia relativa de las variables." ], "metadata": { "id": "VvZ7b947I5_t" } }, { "cell_type": "markdown", "source": [ "**DBSCAN**" ], "metadata": { "id": "skc4_-MbvoNM" } }, { "cell_type": "code", "source": [ "from sklearn.cluster import DBSCAN\n", "from sklearn import datasets\n", "import matplotlib.pyplot as plt" ], "metadata": { "id": "M66u8WMjvnqV" }, "execution_count": 43, "outputs": [] }, { "cell_type": "markdown", "source": [ "Para utilizar el método DBSCAN, se partirá seleccionando el valor óptimo para el eps, para los datos no escalados, datos escalados con MinMaxScaler y datos escalados con StandardScaler." ], "metadata": { "id": "NUvgqY5oOIix" } }, { "cell_type": "markdown", "source": [ "**Sin escalar**" ], "metadata": { "id": "bbkf5qPkOW38" } }, { "cell_type": "code", "source": [ "from sklearn.neighbors import NearestNeighbors\n", "import numpy as np\n", "\n", "nbrs = NearestNeighbors(n_neighbors=10).fit(df_new) #n_neighbors es minpts\n", "distances, _ = nbrs.kneighbors(df_new)\n", "\n", "distances = np.sort(distances, axis=0)\n", "distances = distances[:,1]\n", "plt.axhline(y=7, color='r', linestyle='--') #Ajustar el valor de y, el cual representa el eps\n", "plt.plot(distances)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 447 }, "id": "rhkz_QzwMCX3", "outputId": "8eea8dd6-aa66-4407-a546-49dfe5f54591" }, "execution_count": 39, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, "metadata": {}, "execution_count": 39 }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**MinMaxScaler**" ], "metadata": { "id": "l2CHOcnwOaTJ" } }, { "cell_type": "code", "source": [ "from sklearn.neighbors import NearestNeighbors\n", "import numpy as np\n", "\n", "nbrs = NearestNeighbors(n_neighbors=10).fit(scaled1_df) #n_neighbors es minpts\n", "distances, _ = nbrs.kneighbors(scaled1_df)\n", "\n", "distances = np.sort(distances, axis=0)\n", "distances = distances[:,1]\n", "plt.axhline(y=0.17, color='r', linestyle='--') #Ajustar el valor de y, el cual representa el eps\n", "plt.plot(distances)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 448 }, "id": "8jKX7xcePkq4", "outputId": "c4a7e5ef-1e72-4f17-8582-81a40d01c6d7" }, "execution_count": 40, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, "metadata": {}, "execution_count": 40 }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**StandardScaler**" ], "metadata": { "id": "HLfg-f09OeM5" } }, { "cell_type": "code", "source": [ "from sklearn.neighbors import NearestNeighbors\n", "import numpy as np\n", "\n", "nbrs = NearestNeighbors(n_neighbors=10).fit(scaled2_df) #n_neighbors es minpts\n", "distances, _ = nbrs.kneighbors(scaled2_df)\n", "\n", "distances = np.sort(distances, axis=0)\n", "distances = distances[:,1]\n", "plt.axhline(y=1.4, color='r', linestyle='--') #Ajustar el valor de y, el cual representa el eps\n", "plt.plot(distances)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 447 }, "id": "iqR0JhAQNEFg", "outputId": "6dd345c9-dbc7-4f39-84f7-875ef2bb5602" }, "execution_count": 41, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, "metadata": {}, "execution_count": 41 }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "En los 3 casos se selecciona min_samples=18 (que corresponde a 2*dimensionalidad)." ], "metadata": { "id": "nKYL-tH_OjK9" } }, { "cell_type": "code", "source": [ "eps = 7\n", "min_samples = 18\n", "dbscan = DBSCAN(eps=eps, min_samples=min_samples).fit(df_new)\n", "\n", "n_clusters_ = len(set(dbscan.labels_)) - (1 if -1 in dbscan.labels_ else 0)\n", "n_noise_ = list(dbscan.labels_).count(-1)\n", "print(\"Estimated number of clusters: %d\" % n_clusters_)\n", "print(\"Estimated number of noise points: %d\" % n_noise_)" ], "metadata": { "id": "3bUbGx2Uv35l", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "ba576431-1524-40f9-ebcd-c550b7b19c6d" }, "execution_count": 51, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Estimated number of clusters: 10\n", "Estimated number of noise points: 2782\n" ] } ] }, { "cell_type": "code", "source": [ "eps = 0.17\n", "min_samples = 18\n", "dbscan_scaled1 = DBSCAN(eps=eps, min_samples=min_samples).fit(scaled1_df)\n", "\n", "n_clusters_ = len(set(dbscan_scaled1.labels_)) - (1 if -1 in dbscan_scaled1.labels_ else 0)\n", "n_noise_ = list(dbscan_scaled1.labels_).count(-1)\n", "print(\"Estimated number of clusters: %d\" % n_clusters_)\n", "print(\"Estimated number of noise points: %d\" % n_noise_)" ], "metadata": { "id": "qxy22bCUPySG", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "60782b2a-1b0d-4431-dee6-92528331c2ac" }, "execution_count": 52, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Estimated number of clusters: 3\n", "Estimated number of noise points: 2859\n" ] } ] }, { "cell_type": "code", "source": [ "eps = 1.4\n", "min_samples = 18\n", "dbscan_scaled2 = DBSCAN(eps=eps, min_samples=min_samples).fit(scaled2_df)\n", "\n", "n_clusters_ = len(set(dbscan_scaled2.labels_)) - (1 if -1 in dbscan_scaled2.labels_ else 0)\n", "n_noise_ = list(dbscan_scaled2.labels_).count(-1)\n", "print(\"Estimated number of clusters: %d\" % n_clusters_)\n", "print(\"Estimated number of noise points: %d\" % n_noise_)" ], "metadata": { "id": "avMFyz3UNiCh", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "11da0047-c450-4db4-8b90-8000b9011af1" }, "execution_count": 53, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Estimated number of clusters: 1\n", "Estimated number of noise points: 263\n" ] } ] }, { "cell_type": "code", "source": [ "from sklearn.decomposition import PCA\n", "reduX = PCA(n_components=2, random_state=0).fit_transform(df_new)\n", "fig, ax = plt.subplots()\n", "sc = ax.scatter(reduX[:, 0], reduX[:, 1], c=dbscan.labels_, cmap='tab10')\n", "ax.legend(*sc.legend_elements(), title='clusters')\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 430 }, "id": "B7qm5PlTv-j_", "outputId": "81ea82e0-94ec-472e-adf4-7a2b11ad3ddc" }, "execution_count": 47, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "from sklearn.decomposition import PCA\n", "reduX = PCA(n_components=2, random_state=0).fit_transform(scaled1_df)\n", "fig, ax = plt.subplots()\n", "sc = ax.scatter(reduX[:, 0], reduX[:, 1], c=dbscan_scaled1.labels_, cmap='tab10')\n", "ax.legend(*sc.legend_elements(), title='clusters')\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 430 }, "id": "sjAYBsCHP6wE", "outputId": "e70b3907-c4d7-4de8-98a5-44a7a2f70c44" }, "execution_count": 48, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "from sklearn.decomposition import PCA\n", "reduX = PCA(n_components=2, random_state=0).fit_transform(scaled2_df)\n", "fig, ax = plt.subplots()\n", "sc = ax.scatter(reduX[:, 0], reduX[:, 1], c=dbscan_scaled2.labels_, cmap='tab10')\n", "ax.legend(*sc.legend_elements(), title='clusters')\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 430 }, "id": "lnAgKf2iNqdH", "outputId": "f7438fec-d055-4f42-f351-ffe6d7e8fbd2" }, "execution_count": 49, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi8AAAGdCAYAAADaPpOnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADDmElEQVR4nOzdd3gc1fXw8e/MbFPvxbIlW+4Nd3Chl2AIkNDBoRMIBEgjgYSEljf8QgJpJJDQAoTQq4GEQILBNPfebclW772spC0z8/6x0tqydlerXjif5xFYu7M7V1tmztx77rmKaZomQgghhBAjhDrUDRBCCCGE6AkJXoQQQggxokjwIoQQQogRRYIXIYQQQowoErwIIYQQYkSR4EUIIYQQI4oEL0IIIYQYUSR4EUIIIcSIYhnqBvQ3wzAoLS0lJiYGRVGGujlCCCGECINpmjQ1NZGRkYGqhu5bGXXBS2lpKZmZmUPdDCGEEEL0QlFREePGjQu5zagLXmJiYgDfHx8bGzvErRFCCCFEOBobG8nMzPSfx0MZdcFLx1BRbGysBC9CCCHECBNOyock7AohhBBiRJHgRQghhBAjigQvQgghhBhRep3z8tlnn/Hwww+zefNmysrKePvttzn//PMB8Hg83H333bz//vscOnSIuLg4zjjjDH7zm9+QkZER9Dnvv/9+fvnLX3a6bdq0aezbt6+3zRRCCCGGhGmaeL1edF0f6qYMG1arFU3T+vw8vQ5enE4nc+fO5frrr+fCCy/sdF9LSwtbtmzhnnvuYe7cudTV1fGDH/yAb3zjG2zatCnk886aNYuPPvrocAMtoy6nWAghxCjndrspKyujpaVlqJsyrCiKwrhx44iOju7T8/Q6Mjj77LM5++yzA94XFxfH//73v063Pfrooxx33HEUFhaSlZUVvEEWC+np6b1tlhBCCDGkDMMgLy8PTdPIyMjAZrNJ0VR8PVFVVVUUFxczZcqUPvXADFq3RkNDA4qiEB8fH3K7nJwcMjIycDgcLF26lAcffDBksCOEEEIMJ263G8MwyMzMJDIycqibM6ykpKSQn5+Px+MZ/sFLW1sbP/3pT1mxYkXI2iuLFy/mueeeY9q0aZSVlfHLX/6SE088kV27dgUtWuNyuXC5XP7fGxsb+739QgghRE91V+L+q6i/eqAGPHjxeDxceumlmKbJ3/72t5DbHjkMNWfOHBYvXsz48eN57bXX+Pa3vx3wMQ8++GCXJF8hhBD9wzRNNhfUkV/TQqzDwolTUoiw9T3hUoi+GNDgpSNwKSgo4OOPP+5xxdv4+HimTp1Kbm5u0G3uuusubr/9dv/vHeWFhRBC9M3G/FrufGMHedVO/21Rdo3vnzaF75w0UfI4xJAZsD6tjsAlJyeHjz76iKSkpB4/R3NzMwcPHmTMmDFBt7Hb7f6lAGRJACGE6B/bi+r51lPrKKhxdrrd6dJ58D/7+POq4BeVou/y8/NRFIVt27YNdVOGpV4HL83NzWzbts3/wubl5bFt2zYKCwvxeDxcfPHFbNq0iRdffBFd1ykvL6e8vBy32+1/jtNPP51HH33U//tPfvITPv30U/Lz81mzZg0XXHABmqaxYsWK3v+FQggheuyhD/ehGyaGGfj+v3ycQ63THfhOMew899xz3U6YGUl6PWy0adMmTj31VP/vHUM311xzDffffz/vvvsuAPPmzev0uE8++YRTTjkFgIMHD1JdXe2/r7i4mBUrVlBTU0NKSgonnHAC69atIyUlpbfNFEII0UOVjW18mVsTchvdNPnXjlKuXjphcBolhgVd11EUZciTkXu991NOOQXTNLv8PPfcc0yYMCHgfaZp+gMX8HWL3X///f7fX3nlFUpLS3G5XBQXF/PKK68wadKkvvx9Qggheqi6ufseFU1RqGpydbudCM0wDB566CEmT56M3W4nKyuL//u//+uyXaCek5UrV3bKO9q+fTunnnoqMTExxMbGsnDhQjZt2sTq1au57rrr/CVLFEXxn3tdLhc/+clPGDt2LFFRUSxevJjVq1d32e+7777LzJkzsdvtFBYWsnr1ao477jiioqKIj4/n+OOPp6CgYCBeooCkfK0QQohOUmLsKECQESMAdMMkLdYxWE0ate666y6eeuop/vjHP3LCCSdQVlbW6yVxrrjiCubPn8/f/vY3NE1j27ZtWK1Wli1bxp/+9Cfuvfde9u/fD+CvcHvbbbexZ88eXnnlFTIyMnj77bc566yz2LlzJ1OmTAF8VfN/+9vf8vTTT5OUlERiYiLz5s3jxhtv5OWXX8btdrNhw4ZBTeCW4EUIIUQnKTF2Tpqawhc5VehBIhirpnLenOBr1YnuNTU18cgjj/Doo49yzTXXADBp0iROOOEE8vPze/x8hYWF3HHHHUyfPh3AH3yAr/K9oiidKtgXFhby7LPPUlhY6F938Cc/+QkffPABzz77LL/+9a8B3wScv/71r8ydOxeA2tpaGhoaOPfcc/2jIzNmzOj5C9AHUkFHCCFEFz89azpWi4oa5GL6x2dOJS7SOriNGmX27t2Ly+Xi9NNP75fnu/3227nhhhv8CyEfPHgw5PY7d+5E13WmTp1KdHS0/+fTTz/t9FibzcacOXP8vycmJnLttdeyfPlyzjvvPB555BHKysr65W8IlwQvQgghupiZEctrNy1lxpjO5ScSI2386vzZ3HSy5CP2VURERNjbqqqKaXbuBvN4PJ1+v//++9m9ezfnnHMOH3/8MTNnzuTtt98O+pzNzc1omsbmzZv9s4e3bdvG3r17eeSRRzq18+ghoWeffZa1a9eybNkyXn31VaZOncq6devC/nv6SoaNhBBCBDRnXDz//v6J7CltpLDWSYzDynHZiVg1ue7tD1OmTCEiIoJVq1Zxww03hNw2JSWFpqYmnE4nUVFRAAFrwEydOpWpU6fyox/9iBUrVvDss89ywQUXYLPZ0HW907bz589H13UqKys58cQTe9z++fPnM3/+fO666y6WLl3KSy+9xJIlS3r8PL0hwYsQQoiQZmbEMjNDCoD2N4fDwU9/+lPuvPNObDYbxx9/PFVVVezevbvLUNLixYuJjIzk5z//Od///vdZv349zz33nP/+1tZW7rjjDi6++GKys7MpLi5m48aNXHTRRQBMmDCB5uZmVq1axdy5c4mMjGTq1KlcccUVXH311fz+979n/vz5VFVVsWrVKubMmcM555wTsN15eXk8+eSTfOMb3yAjI4P9+/eTk5PD1VdfPWCv1dEkfBZCCCGGyD333MOPf/xj7r33XmbMmMFll11GZWVll+0SExN54YUXeP/99znmmGN4+eWXO5Ua0TSNmpoarr76aqZOncqll17K2Wef7V/7b9myZdx8881cdtllpKSk8NBDDwG+4Z+rr76aH//4x0ybNo3zzz+fjRs3kpWVFbTNkZGR7Nu3j4suuoipU6fyne98h1tvvZWbbrqpf1+cEBTz6EG0Ea6xsZG4uDgaGhpkqQAhhBCDrq2tjby8PLKzs3E4ZDr5kUK9Nj05f0vPixBCCCFGFAlehBBCCDGiSPAihBBCiBFFghchhBBCjCgSvAghhBBiRJHgRQghhBAjigQvQgghhBhRJHgRQgghxIgiwYsQQgghRhQJXoQQQohhSDdM1h6s4Z1tJaw9WINuDK+C+J999hnnnXceGRkZKIrCypUrB23fsjCjEEIIMcx8sKuMX763h7KGNv9tY+Ic3HfeTM6aPWYIW3aY0+lk7ty5XH/99Vx44YWDum8JXoQQQohh5INdZXz3hS0c3c9S3tDGd1/Ywt+uXDAsApizzz6bs88+e0j2LcNGQgghxDChGya/fG9Pl8AF8N/2y/f2DLshpMEmwYsQQggxTGzIq+00VHQ0EyhraGNDXu3gNWoYkuBFCCGEGCYqm4IHLr3Zrqd+9rOfoShKyJ99+/YNyL57QnJehBBCiGEiNcbRr9v11I9//GOuvfbakNtMnDhxQPbdExK8CCGEEMPEcdmJjIlzUN7QFjDvRQHS4xwcl504IPtPSUkhJSVlQJ67P8mwkRBCCDFMaKrCfefNBHyBypE6fr/vvJlo6tH3Dr7m5ma2bdvGtm3bAMjLy2Pbtm0UFhYO+L4leBFCCCGGkbNmj+FvVy4gPa7z0FB6nGPYTJMG2LRpE/Pnz2f+/PkA3H777cyfP5977713wPctw0ZCCCHEMHPW7DF8bWY6G/JqqWxqIzXGN1Q0HHpcOpxyyimY5tBM2ZbgRQghhBiGNFVh6aSkoW7GsCTDRkIIIYQYUSR4EUIIIcSIIsGLEEIIIUYUCV6EEEIIMaJI8CKEEEKIEUWCFyGEEEKMKBK8CCGEEGJEkeBFCCGEECOKBC9CCCGEGFEkeBFCCCGGId00+bKuibcr6viyrgl9kErxP/bYY0yYMAGHw8HixYvZsGHDoOy3J2R5ACGEEGKY+XdVPXfnlFDm8vhvG2O38sCUsZyTEj9g+3311Ve5/fbbefzxx1m8eDF/+tOfWL58Ofv37yc1NXXA9ttTve55+eyzzzjvvPPIyMhAURRWrlzZ6X7TNLn33nsZM2YMERERnHHGGeTk5HT7vCMh4hNCCCEGyr+r6rlhV36nwAWg3OXhhl35/LuqfsD2/Yc//IEbb7yR6667jpkzZ/L4448TGRnJM888M2D77I1eBy9Op5O5c+fy2GOPBbz/oYce4s9//jOPP/4469evJyoqiuXLl9PW1hb0OTsivvvuu48tW7Ywd+5cli9fTmVlZW+bKYQQQowYumlyd04JgQaIOm67J6dkQIaQ3G43mzdv5owzzvDfpqoqZ5xxBmvXru33/fVFr4OXs88+mwceeIALLrigy32mafKnP/2Ju+++m29+85vMmTOH559/ntLS0i49NEcaKRGfEEIIMRDW1Td36XE5kgmUujysq2/u931XV1ej6zppaWmdbk9LS6O8vLzf99cXA5Kwm5eXR3l5eafoLS4ujsWLFweN3nob8blcLhobGzv9CCGEECNRpdvbr9uNVgMSvHREaD2J3nob8T344IPExcX5fzIzM/vYeiGEEGJopNrCm0cT7nY9kZycjKZpVFRUdLq9oqKC9PT0ft9fX4z4qdJ33XUXDQ0N/p+ioqKhbpIQQgjRK0vioxljt6IEuV8BMuxWlsRH9/u+bTYbCxcuZNWqVf7bDMNg1apVLF26tN/31xcDErx0RGg9id56G/HZ7XZiY2M7/QghhBAjkaYoPDBlLECXAKbj919NGYumBAtv+ub222/nqaee4h//+Ad79+7lu9/9Lk6nk+uuu25A9tdbAxK8ZGdnk56e3il6a2xsZP369UGjt5EU8QkhhBAD5ZyUeJ6ePYF0u7XT7WPsVp6ePWFA67xcdtll/O53v+Pee+9l3rx5bNu2jQ8++KBLSsdQ6/WgWXNzM7m5uf7f8/Ly2LZtG4mJiWRlZfHDH/6QBx54gClTppCdnc0999xDRkYG559/vv8xp59+OhdccAG33XYb4Iv4rrnmGhYtWsRxxx3Hn/70p2EZ8QkhhBAD6ZyUeM5KjmNdfTOVbi+pNgtL4qMHrMflSLfddpv/vDxc9Tp42bRpE6eeeqr/99tvvx2Aa665hueee44777wTp9PJd77zHerr6znhhBP44IMPcDgc/sccPHiQ6upq/++XXXYZVVVV3HvvvZSXlzNv3rxhGfEJIYQQA01TFI5PiBnqZgxLimkO0mIJg6SxsZG4uDgaGhok/0UIIcSga2trIy8vj+zs7E4X7CL0a9OT8/eIn20khBBCiK8WCV6EEEIIMaJI8CKEEEKIEUWCFyGEEGIAjLKU0n7RX6+JBC9CCCFEP7JaffVZWlpahrglw4/b7QZA07Q+PU//L44ghBBCfIVpmkZ8fDyVlZUAREZGogxCfZbhzjAMqqqqiIyMxGLpW/ghwYsQQgjRzzqWtekIYISPqqpkZWX1OZiT4EUIIYToZ4qiMGbMGFJTU/F4PEPdnGHDZrOhqn3PWJHgRQghhBggmqb1Ob9DdCXBixBCjGINrR52FNdjmjBnXBzxkbahbpIQfSbBixBCjEJtHp0H/r2H1zYW49YNAKyawkULxnHPuTOJssvhX4xcMlVaCCFGGa9ucN2zG3lpfaE/cAHw6CavbSriyqfX4/LqQ9hCIfpGghchhBhlPtxdwdpDNRgB6oEZJmwtque97WWD3zAh+okEL0IIMcq8srEQNcRMVFWBlzcUDl6DhOhnErwIIcQoU1zXGrDXpYNhQkld6+A1SIh+JsGLEEKMMikxdkLVAFPatxFipJLgRQghRpmLF46ju/XvLlk0bnAaI8QAkOBFCCFGmW/MzWB6egxagMQXTVWYkBzFRQskeBEjlwQvQggxyjisGi/duISTp6Z0uW/ZxCRevWmJ1HkRI5p8eoUQYhRKjLLxzLXHkl/tZH1eDaYJx2YnMikleqibJkSfSfAihBCj2ITkKCYkRw11M4ToVzJsJIQQQogRRYIXIYQQQowoErwIIYQQYkSR4EUIIYQQI4oEL0IIIYQYUSR4EUIIIcSIIsGLEEIIIUYUCV6EEEIIMaJI8CKEEEKIEUWCFyGEEEKMKBK8CCGEEGJEkeBFCCGEECOKBC9CCCGEGFEkeBFCCCHEiCLBixBCCCFGFAlehBBCCDGiSPAihBBCiBHFMtQNEEIIMXyZpsnu0kaKaluIi7Ry3IRELJpc94qhJcGLEEKIgDbl1/KLlbvYX97kvy0l2s6dZ03jkkWZQ9gy8VU3oOHzhAkTUBSly8+tt94acPvnnnuuy7YOh2MgmyiEECKArYV1rHhqHTkVTZ1ur2p2cccbO/jnuoIhapkQA9zzsnHjRnRd9/++a9cuvva1r3HJJZcEfUxsbCz79+/3/64oykA2UQghRAD/9++96IaJYQa+/8H393LRgrFE2qQDXwy+Af3UpaSkdPr9N7/5DZMmTeLkk08O+hhFUUhPTx/IZgkhRpmi2hZe2lDIntJGIqwap89I5by5GTis2lA3bUQqrGlhU0FdyG1a3Dr/3V3B+fPHDlKrhDhs0EJmt9vNCy+8wO233x6yN6W5uZnx48djGAYLFizg17/+NbNmzQq6vcvlwuVy+X9vbGzs13YLIYa3f64r4N53dqECugmKAh/sLucP/zvASzcuITs5aqibOOJUNLV1u42mKpQ1dL+dEANh0FLGV65cSX19Pddee23QbaZNm8YzzzzDO++8wwsvvIBhGCxbtozi4uKgj3nwwQeJi4vz/2RmShKZEF8Vn+dUcc/KXZimL3ABMNv/X9nk4qq/r8ejG0PXwBEqJdre7Ta6YZIS0/12QgwExTTNICOa/Wv58uXYbDbee++9sB/j8XiYMWMGK1as4Fe/+lXAbQL1vGRmZtLQ0EBsbGyf2y2EGL6+9dQ61h2qCZqXAfDYtxZwzpwxg9eoUeKbj37BzpKGoK+tw6qy8RdnEOOwDm7DxKjV2NhIXFxcWOfvQel5KSgo4KOPPuKGG27o0eOsVivz588nNzc36DZ2u53Y2NhOP0KI0c+jG6w9GDpw0VSFT/ZXDl6jRpGff31G+6zPwPf/5MxpEriIITMowcuzzz5Lamoq55xzTo8ep+s6O3fuZMwYuWoSQnSmGybddRubpinDRr20eGIS/7juOMbFR3S6PdZh4ZffmMW3T8geopYJMQgJu4Zh8Oyzz3LNNddgsXTe3dVXX83YsWN58MEHAfh//+//sWTJEiZPnkx9fT0PP/wwBQUFPe6xEUKMfg6rRnZyFPnVzqBBjGnCMWPjBrVdo8kJU5L59I5T2ZhfS1FdKwmRVo6fnCyzuMSQG/Dg5aOPPqKwsJDrr7++y32FhYWo6uHOn7q6Om688UbKy8tJSEhg4cKFrFmzhpkzZw50M4UQI9D1J2Rz78pdAe9TALtF5eKF4wa3UaOMqiosnpjE4qFuiBBHGLSE3cHSk4QfIcTIphsm33t5C+/vLEdV8Oe/aKovUeNvVyzgzFlSN0qIkaAn528pjSiEGLE0VeEvKxZw6rRinluTz/7yJmwWleWz0vn2CdnMliEjIUYlCV6EECOapipcsihTFgocYk6Xl5XbSvh0fxVu3WDuuHguPy6TMXER3T9YiB6S4EUIIUSf7Clt5Kq/r6fG6UYBTOCzA1U8+kkuv79kriwhIPrdoFXYFUIIMfo4XV6u+vt66lrcAP6ZX4bpy0m6/bVtbCuqH7L2idFJghchhBiF2jw6mwtq2ZBXS0OrZ8D28/bWEmqc7qDFAhVF4enPDw3Y/sVXkwwbCSGGNdM0WXOwhi9zq9FNk0XjEzlteqp/RpHozKsb/PnjXJ77Mo/GNi8ANk3l4oVjuevrM/q9Ku7q/ZX+oaJAdMPk431S5Vj0LwlehBDDVlFtC9/+x0YOVDRjaQ9Wnvj0EBnxDp6++lhmZkg5hCOZpm+Y5r3tZZ2CCbdu8OrGInaWNPL6zUv7tcic22t0W+lYqhyL/ibDRkKIYcnp8nL5k+s4WOUEwGuYeNvHJioa2ljx1DoqG9uGsonDztpDNbx7VODSQTdhV0kDr24s6td9zsuMJ1QnmKpIlWPR/yR4EUIMS29vLaGkvhU9QDKFbkJTm4d/risYgpYNX69uLOp2OO2l9YX9us/Lj8tCCbZ6I77E3euOl3WQRP+S4EUIMSy9t72UUKdhw4R3tpUOWntGgoKaloDBXgcTKKlv6dd9ZsRH8NBFc1AUOgVOHf9ccWwm586RxXVF/5KcFyHEsNTU5u02l6LZ5R2UtowUSVG2TsskBBIfaev3/V60cBzZKVE8/fkhPt5XiVc3mTMujmuPz+a8OWNC9swI0RsSvAghhqUpadHsr2gK2pOgKjAxJWqQWzW8XbBgLKtCzOxRFbhowcAsVLkgK4G/XrFwQJ5biKPJsJEQYlj61nFZIYdADBOuWjJ+EFs0/C2flc6sjFi0AD0dmqqQFG3nqqXymomRT4IXIUY5j27w9tZiLn1iLcseXMV5f/mCf6zJxznMh1yOy07kysVZAe9TgDNmpHHunIzBbdQwZ9VUXrxhMSdOTQZ8r1NH7sn09Bhev2kpydH2IWmbbpjUt7hp8+hDsn8xuiimaXY3rDyi9GRJbSFGu1a3zrXPbmB9Xq0/F6LjmnxCciSv3rSU1BjHkLYxFNM0+ee6Ap749BAl9a0AJEfbuO74bL5z0kSsmlx/BZNb2cyag9V4dZP5WfHMy4wfktyTpjYPT3x6iBfWF1Df4kFR4LRpqdx62mQWZCUMenvE8NWT87cEL0KMYve+s4sX1hUETODUVIXF2Ym8dOOSwW9YDxmGSUl9K6YJGfEOLBK0jAhNbR4ueXwtByqaOn0GO2YlPX7lQr42M22IWieGm56cv+UIIMQo1dDq4dWNRUFnnuiGr+x+TkXT4DasF1RVITMxkqykSAlcRpA/r8rpEriA77NnGCY/fHUrLe7hPXwphieZbSTEKLW7pAGXN3RZdgVYn1fLlLSYwWnUCFXndPPW1hJyK5uIsFo4a3Y6x05IkCnAIbi8Oi9vCB48m4DTpfOvHWVcuihzUNsmRj4JXoQYrcI4r5qAnH9De2tLMT99cwde3URVFRTgmS/zWDQhgaevXjQgdVNGg4oGV7d1eKyawoHy4d/zJ4Yf6X8VYpQ6Zmwcdkv3X/HF2UmD0JqR6ZN9ldz+2nY8uomJb7ijY32lrYX1fOefmxllaYP9xmHr/rNnmBBh679FIsVXhwQvQoxSMQ4rK47LCrponqYqnDA5mcmp0YPbsBHCqxv88NVtQe/XDZMNebVsKawbvEaNIKkxDuaMiwu5aKNumJw5M33wGiVGDQlehBjFfnb2dJZM9PWsdJxE/FOlkyL50+XzhqRdI8FLGwppaPWE3EZT4INd5YPUopHne6dNCZrzoikKx09K4phxsuK06DnJeRFiFHNYNZ6//jg+3F3ByxsKKaprITnazsULx3H+vLHSZR/CKxu6X33ZMKFViq4F9bWZaTxw/mzue3c3pmmiKL6cIa9hsnBCAn+9UpYTEL0jwYsQo5xFUzlnzhjOkZV9e6SgpvvVl01gqszUCunKJeNZPiudN7cUc7CymSi7hXPmjGHReJmtJXpPghchhAgg2mHB6Q7dq6IqcP78sYPUopErJcbOzSdPGupmiFFEcl6EECKAb84b2+0B8vLjsoh1WAelPUKIwyR4EUKIAK5ZNoFIuyVouZzUGDv3njtzUNskhPCRYSMhxLBnmiZbCut5bWMRRXUtJEXZ+Oa8sZw6PdW/Tk5/GxsfwUs3LubG5zdR0ehCa9+NbsK09BieufZYHFZJeO6O22uwr7wR3TCZmhZDlF1OO6LvZGFGIcSwphsmd76xnTe3lKCpCrphoim+IGLR+ASeue7YAR268egGq/ZWsLWw3lcbZ0oySycmSbJpN3TD5K+f5PL3L/Kob59y7rCqXH5sFneeNY1ImwQxojNZVVqCFyGG1Laiel7ZUMjBqmbiI22cNzeDs2alYwuj4u/RHvkohz9+dCDgfarim477xFWL+tpk0Y9M0+RHr21n5daSLvepCszLjOelG5dIz5XopCfnbwl9hRD9xjRN7nt3N8+vLfD3kqgK/G9PBdPSYnjhhsWkxNjDfr42j87fvzgU9H7DhP/urqCgxsn4pKj++BNEP1h7qCZg4AK+92xLYT1vbC7myiXjB7llo0NNs4t/rivgzc3FNLR6yEyM5IrF47lo4Vjslq9GQCjBixCi3zy3Jp/n1xYAvmEDwF9hNbeqmVte3MzrNy8L+/l2lTTQ2BZ6cT/wBUcA72wrpaHVw6TUKK5cPJ5Tp6WiDlBOjAju1Y1F/uA1EAV4cX2BBC+9kFft5JLH11DrdPu/W3tKG/n52zt5c0sx//z2cV+JIbnR/xcKIQaFbpg88enBkPdvzK9jR3E9c8bFh/Wc3mC15Y/yyKocmtu8dGxdUt/KJ/uqOHfOGB65fP6AJfX2VGl9Ky+uL+DjfVV4DYNjJyRy1ZLxzBgzuoa486udQQMX8BX3K6ptHbwGjRKmafLdFzZT1+LptOxCxz+3Ftbx0Af7uf8bs4akfYNJpkoLIfpFXnUz5Y2ukNtoqsJnB6rCfs5paTFYugk8TMDpOhy4wOFen3/vKOOJz4IHVIPp85wqTv3dav62+iB7yxrJqWjm1Y1FfP2Rz/nHmvyhbl6/SoyyhVyQESAuQurj9NSWwjr2lTcFDQwN09fr1ezqvrdypJPgRQjRL8LpJVHC3K5DQpSNb87LQAsys6fjBBnsKU3g75/n4dWNsPc5EKqaXNz4/CbcutGprbphYgL3vbubjfm1Q9a+/nb+/LFB3xPwvW8XLRw3eA0aJTYX1HUbFLZ6dA5UNA1Og4aQBC9CiH4xISmKKHvoZEGvYTIvM75Hz3vvubOYnBrN0fGLpoDdonV7MK9xuskPY52igfTapiLcXoNgczs1VeHvX+QNbqMG0NmzxzA9PSbgcJ2mKiRE2bh6qeS79JQa5vT8YMH+aCLBixCiXzisGlcsHh80mNAUyEyI4KQpKT163rhIK2/dsoy7zp7O+KRIrJpCSrSN75w8iW8tzgqz3srQVoT4Iqc6ZE+Ebph8mVs9eA0aYDaLyos3LGZJdiLg62np+FxMToni9ZuWkhwd/qwz4XPilJSQnyPwDcdNHzP6FwuVhF0hRL/50RlT2VJQx+aCOuBwyKApClF2jSeuWtSr2T9RdgvfOWkS3zmp8+J+X+ZWd9tjkRhlG/Jp1GYYwdPoqrgFSdF2XrxxCfvKG/kipxrdMFkwPkFWk+6DaekxHD8piXV5tQHzXhTg+uOzvxLTpSV4EUL0mwibxos3Lua1TcX8c20+RbWtRDssXDh/LNcsm0BGfES/7m/ZpCQmpUSRX9MS4mA+Aas2tJ3Mi7OT2JBXG/SqWVMVFk9MHNxGDZLp6bFMTx9ds6mG0p9XzOeKp9ezr7wJVfHle3VMSz9vbga3nTZ5qJs4KKTCrhBiRMurdnLZE2upbnb5g4OO5QPOnp3OX1bMxzLEwUt5QxsnPvQxXj14H8xLNyxm2eTkQW2XGJlcXp0PdpXz9tYSaprdTEiK5PLjslg2aWQvW9GT8/eAfqPvv/9+FEXp9DN9+vSQj3n99deZPn06DoeDY445hvfff38gmyiEGOGyk6P4349O5mdnT2fmmFjGJkRw/ORknrhqIY99a8GQBy4A6XEOHvvWAjRV6ZTE2vHvO8+aJoGLCJvdovHNeWN57rrjeO97J/CXby3g+MnJIzpw6akBHzaaNWsWH3300eEdWoLvcs2aNaxYsYIHH3yQc889l5deeonzzz+fLVu2MHv27IFuqhBihIqLtAbMiRlOzpyVzn9/dBLPry1g1d4KvIbJovEJXHv8BBaOH51DRkIMlAEdNrr//vtZuXIl27ZtC2v7yy67DKfTyb/+9S//bUuWLGHevHk8/vjjYT2HDBsJIYQQI8+wGTYCyMnJISMjg4kTJ3LFFVdQWFgYdNu1a9dyxhlndLpt+fLlrF27NuhjXC4XjY2NnX6EEEIIMXoNaPCyePFinnvuOT744AP+9re/kZeXx4knnkhTU+Dqf+Xl5aSlpXW6LS0tjfLy8qD7ePDBB4mLi/P/ZGZm9uvfIIQQon+ZpkmL2xty/aPu1DrdvLi+gEc/zuHtrcW0uvV+bKEY7gY05+Xss8/2/3vOnDksXryY8ePH89prr/Htb3+7X/Zx1113cfvtt/t/b2xslABGCCGGoYZWD099dogX1xdQ1+LBqimcOyeDW06ZxJS08AqrGYbJHz86wN9WH0Q3TDRVwWuY3G3fxf3nzeKSRXL8/yoY1Dov8fHxTJ06ldzc3ID3p6enU1FR0em2iooK0tPTgz6n3W7HbpdKjUIIMZzVOd1c9Pga8qud/intHt3k3e2l/GdXGS/esDisxOVHVuXwl48Pn0M61spyunTueGMHUXYLXz9mzID8DWL4GNQ5hM3NzRw8eJAxYwJ/sJYuXcqqVas63fa///2PpUuXDkbzhBBCDJAH/7OXgpqWLoX6dMPE7TW49cWt3Q4jNbR6+Nvq4KuEK8BvP9jHQJYvM02TzQW13PvOLr738lZ++8E+8qqdA7Y/EdiA9rz85Cc/4bzzzmP8+PGUlpZy3333oWkaK1asAODqq69m7NixPPjggwD84Ac/4OSTT+b3v/8955xzDq+88gqbNm3iySefHMhmCiGEGECNbR5Wbi0NGpwYJpQ3trF6fyWnz0gLuA3AR3sqcIdYIdwECmpa2F3ayOyxcX1tdhetbp1bXtrMJ/uqsKgKhmmiKAp/W32Qm0+exE/PmvaVqrUylAa056W4uJgVK1Ywbdo0Lr30UpKSkli3bh0pKb6F2QoLCykrK/Nvv2zZMl566SWefPJJ5s6dyxtvvMHKlSulxosQo4hpmn1K1BQjT16VM2TQAWBRFfaVB57M0aG+1dPtKuIAja2enjQvbHe9tYNP91cBvuEqw8T/WX7804P8Y03+gOy3Pxjti3++tL6Qf+8ow+nyDnWT+mRAe15eeeWVkPevXr26y22XXHIJl1xyyQC1SAgxVNYdquHJzw7x6YEqdMNk5phYrjt+AhctGNerxRrFyOGwdr9QoGGa2C2hr6fHJ0Z2u6oyQGZiZLhNC1txXQvvbC8NuYDmY58c5Mol44dFVecjfZ5Txc/e3ElJfav/tkibxq2nTuaWUyaNyN6i4fUKCyFGpVc2FLLiyXX+wAVgX3kjd7yxgx+/vh1DemJGtSmp0YztZlFOwyTkkBHAydNSSIqyEexUqykKSyYmDUjw8sn+KrpbHLyq2cXu0uFVa2xjfi3XPruR0obWTre3uHUe/nA/f14VeALNcCfBixBiQBXXtfDzt3diQqfhoo5/vr21hHe2lwxN48SgUFWF74VY7VhVYPmsNLKTo0I+j1VT+c1Fc1AUugwfaYqCw6py/zdm9keTu3B5dMLpoHB5Qw+P9bdap5s/fXSA43/zMTPv/YDTf7+apz8/RIvbNyz0m//4EpiD9Rg9+kkOdU73ILa4f0jwIoQYUC9vKAx6pQy+k9BzX+YPVnPEELns2Ex+cPoUFHyBhqr48lwAlk5K4g+Xzgvreb42M43nr1/MrIzDCbkKcMKUZN6+9Ximpw/MsjAzxsR2O2SlqQqTUkIHYP2pqLaFsx/5jD+vyqGkvpUWt86hKif/9/5eLvrrGvaUNrC5oC5ku726yfu7yoJvMEwNap0XIcRXz66SRvQQB0/DhD1lw6urXfQ/RVH40demctGCcby2qYjC2hZiIyx8Y+5Yjp2Q0KO8ixOmJHPClBMoqHFS1+JhTJyDtFjHALYelk5MIisxkuK6rtO9wRe4nHPMGJKiB6/u2Pde3kp1s7tTe8z2/xyoaOahD/d3+xyaqlDTPPJ6XiR4EUIMKLtFRVEImehoHWYJjmLgZCVF8pPl0/rlucYnRTE+qV+eqluqqvDot+Zz+ZPrcHmNTkOgmqIwNj6Ce84dmCGrQHaVNLCtqD7o/bpp8nlOdbfP4zVM0uMGNvAbCBK8CNEH+8obeeqzQ/xnVzkuj8Gk1CiuXZbNpYvGDbsZB0PltOmp/HdPRdD7NVXhjG4SNTsYhsnnudW8taWYykYXY+IdXLxwHEsnJo2YGROtbp1/7ywjp6IJh1Vj+ax0ZmYMzFCH6F9zxsXzr++dwOOfHmTl1lLcukFchJUVx2Vx00kTSYiyDVpbthXVoxA6h1g3TGaPjWVvaRN6kKsHh1UdkRWJFXMgSxEOgZ4sqS1EX6zeX8mNz2/qVOuh4/R5yrQUnrx6kfQoAC1uL6c8vJqaZneXA6gCKAq8c+sJHDMudFGxVrfOd/65ic9zqtFUxb+ujW6YLJ+Vxl9WLMDWzVTbofbRngp++Oo2ml1eLKriT2I+ZVoKf1kxnxiHdaibKMJkGCZtXp0IqzYkgfMrGwr52Vs7u93uNxcew/3v7cbtNQIOd/3qm7O4aumE/m9gL/Tk/D28v+lCDFPNLi+3vrgFr9654JrZ/rN6fxXPfpk3ZO0bTiJtFl68YTHJMb6rUlXxBS2qAhZN4S8rFnQbuADc9+4uvsz1dYN3vOYd///vngp++8G+PrXTV/a9jqc+O8TTnx9iTz9Ped1SWMdNL2z2FwfzGoc/O58dqOK7L2wZ0LL2on+pqkKkzTJkPX7HT04OmQgPvlou35w3ljduXsaccfGd7kuPtfO7S+YOm8Clp2TYSIheWLm1BKdbD3q/CTz7ZT43njhx0A5ulY1tvLCugHe3l+J06UxJi+bKJeM5a1b6kBeBm5IWw6d3nMq/d5Sx+kAVHq/BnMw4Ll2USXIYCY6VTW28uaUk6KwJ04QX1hXwgzOmENuL3ouCGie3vLiF3aWN/im4hgmLsxN59FsLSInpexLmX1blgBm4m98w4YvcarYW1bMgK6HP+xKjX2ZiJMtnpfO/PRUBh4QU4NplE4iwacweG8fKW48nt7LJlyjtsDI/KwFtBBeHlOBFiF7YVdLgH7IIpqyhjVV7K8hKimJKavSABjG7SxtY8dQ6mtu8/hN8rdPFmoM1nDtnDI9cPn/ID1QOq8ZFC8dx0cJxPX7s2oM13S4p4PIabM6v49TpqT167jqnm0ufWEt1+4yLI3ezqaCOFU+t41/fOyGsKrHBtLi9rN5fFTI/waIqvL+jTIIXEbaHLpnDtc9sYEthPari++x2HJfOPiadH31taqftJ6fGMDk1Zoha278keBGiF6ya2m2XLcANz28GfBVGf3b29G4riPaGVze44R+bcLbpnU68HdOT/72jjLnj4rnxpIn9vu/BEu5aSJ5u1s8J5KUNhVQ1uQL26uiGSW5lM//aUcbFvQi6OrS69e6KswK+4UghwhXrsPLaTUtZta+St7YUU9XkIjMxksuOzRxRSey9ITkvQvTCKdNS8PagpH1uZTM3/GMT720v7fe2fLS3grKGtqCzCUzg71/kjegS/HPCyIlRFHq1kvCbm4tDFvFSFHh7a3GPn/dIcRFWYh2hrxUN0+y2wqwQR7NoKstnpfPEVYt465bjeeTy+SyblDyqAxeQ4EWIXjllWioTU6LCHorpODf+YuVOXN7guTK9sSm/zl+pNJjyxjbKG9v6db+DaXJqDIuzE4O+3h3TrTO6WT8nkPpuViA2TXpcxMvtNSiua6Gm2QX4TjArFmehhXibVEXp1ZCaGJ2aXV72lDZyqKpZErkDkGEjIXpBUxWev/44rnhqPQW1Lf7x5lBMoLHVy6q9lf1aVyHcZFx1hF+J/eGyeVz8tzVUNLZ1eq1VBTITIvj1Bcf06nmzEiOpa3EHLaKnqQoTwuwRaWrz8JePc3l5fSFN7UNAC7Li+d7pU7jllMms2ltJXpWzUy9Zx2fn/m/MCit5WYxcJfWtrNpbQYtbZ2paNCdPTe0SkNe3uHnow/28ubnYv07S+KRIvn/aFAlujyDBixC9NC4hkv/efhIf7Crnv7srKK1vZWuIipfgq8RZXNfSr+1YOjGJJz87FPR+Bd/MhLTYkX1iHBsfwb+/fyL/WJPPa5uKqGl2kxpjZ8XiLK5cMp64iN7VSPnW4qzQlUoNk8uPzez2eZpdXi59fC0HKpo6LYewraie657dyG8vOoY3b17GHz86wKubimhtn602Y0wsPzh9CmfOSu9V+8Xw1+bRufvtnby5tQRM34WEbpqMiXPwp8vmsXiir0xwY5uHix9f2yXALaxp4cevb6e8sY1bTw2+wOVXiRSpE6KfHKho4sw/ftbtdg9dPIdLF3V/MgyXYZic/vvVFNa1Bk1sfeD82Vy5ZHy/7XMw6IbJpwcq+SKnBsM0WTA+gbNmpfd7ITq31+Cqv69nY35tl94zBTj7mHQe+9aCbnMIHv5wH39bfTBoD5xVU1j/8zNIjLLR5tEpa2gjwqqNyNLsome++8JmPtxd3uWzoSq+5P+3bzmemRmx/O7D/fx1dW7Qz5CiwOd3nsq4hMiBb/QQkCJ1YkTx6AZVTS7/lehINSU12jclOsQ2Nk1l+cz+vcJWVYVnrjuO5Gibr2Jt++0d3dFXLsniisVZ/brPgZZX7eT036/m+uc28fzafF5YV8D3X97Kst+sCtlL0hs2i8o/rj+Oa5dlE2E7PB061mHhe6dP4c+Xz+82cNENkxfXFYZevdcweXOzL/HXYdXITo4aUYGLbpis2lvBox/n8NRnhzhY1TzUTRoRdpU08J9dXQMX8A0Xeg2TRz/JwTRNXlxfEPIzpAKvbepb8vhoIcNGYshUN7t47JNcXt1YRItbR1V8y91/77QpvZo1MtQUReFnZ0/nhn9sCrrmyC2nTiIusv9LwGcnR/HR7Sfz5uZi/rWjjMY2L9Pai9Qdl504omYeNLu8rHhyHVXtya5Hzuqqdbq58un1/PdHJ/UqOTcYh1Xj3vNm8uMzp3KgoglVUZiWHtOltotHN2hx6UQ7LJ1yFRpaPWEl/uZUNvVbm3vD5dXJq3aioDAxJSrs5Ss2F9Ry64tbKW9sQ1MVTNPk/97fy5kz0/jDZfOItsupJJj3tpeGrAmlGyYf7qqgxummrqWbzxBQWOMcgFaOPPKJE0OisqmN8x/7kopGl/9LbZjw0d5KPt5XyfPXL2bppEFaLrYfnT4jjb98az6/eHsXDa0efzKmTVO55dRJ/OD0KQO27xiHlWuPz+ba47MHbB+D4a0txVQ0tgWtRNvq1nl+bQE/O3t6v+87ym5hfoAicbmVTTz6cS7/2lGG1zCJsmtctiiLW06dRHK0HYc1vCCguW1o6ri4vQaPfpzDc2vyaWxvQ1KUjetPyObmkyeFnDWXW9nEFU+vx92ePHrkSXjV3gq+8/wmXrxh8YgKkAdTfYun25pQumniNQwsqhKyBIOqKMT2MrdrtJHgRQyJX/97b6fApYNumJgK/OCVraz52WkjcmXmc+dk8LWZaazaW0lxXQvxkTaWz0wfkB6X0WDtwRqeW5PH1sJ6LFroqsXgO9C/t710QIKXQLYV1bPiyXW4dcPfNqdL5x9r8/nPrjJW3np82DO5hmLGl1c3+M4/N/HpgapOM6pqnG5+9+F+9pc38cjl84IGH39bfRCPbgYu4mfCmoM1rM+rZcnEkXexMRgyEyMwukktjbRpJEXZOXt2Ou/vKg/6HfAaJufNzRiIZo44I+/MIEa8Oqebf+0oC/oFNUyobHKxen/VILes/9gtGl8/ZgzfOWkSly7KlMAliD/+7wArnlrHR3srqWxyUVrfRkWjq9tqtC3uwenBMAyT77+8FZdXDxhoVza5+OV7u8Ou7Lshv2bQ2t7h/V3lvqUJAryoJvDu9lI+y6kO+FjDMHl3e2nIgFJTFd4dgOKLo0V305sVBRaOT6Cm2c0tp07GoioE6ghTFVg2KYlF42X5CJDgRQyB/Bpnt9VpNVXhwBDnB4iB9cm+Sh5ZlQOEX/4ffAfxKWm9W5+l0uXhj/nlfGv7Qa7acYini6to9OrUt7h5bVMRT312kD9tLeC+nGLuzinm1zsLKahvCZpEqRsmH+wqRwESwghQq5rcPPCvPb1qezi8usGWwjrWHKymqsmXM/TiuoKAJ8MOmqrw0vqCgPe5dQOPHvq9MU2Txm7yfb7KxsRF8OMzpwW93zTh85xqlv1mFX9dncvfrlxAYpRvBfYjA5nTpqfy5NWLZHiunQwbiUEXaev+Y2eYJhF9WAhvOKlpdlHjdJMSbSeh/aAkfEsWaO31LnrCMOHqpT2f9v2fqnpu2l2A1zQx8M3K+qimkV8dKEHbWIXu9OJekISp26DW9CVZApwyBuu2WrRaV9D2FNS2cNXSCfxlVU7IXiMTeH1zMXeeNZ34yP77LJimybNf5vPX1bn+BSZVBc6anU5ORXPIGSy6YXKoKnASqN2ikhhlo9YZvMKwoihkJY7Oqbv95dZTJ5McbeORVTmU1geudG2YvnXISuvb+PzOU/n0QBV7y5pwWDW+NjN11Cyo2F8keBGDbkpqNJkJERTVtQbfyIQzBmARw74wTZNP65p4triaHc2tOFSVc1PiuHZsMmMdXU9Eu0oaePjD/Xx2wLeasKLA6dNTufOs6UztZc9BuO3cVlTPlsJ6NAVOmJI84Ae+No+Oy2MQ47CEXfF3U0FtjwMXBfj6MWP4+uzAFYprPV7KXB7iLVqn92Sfs5Ubd+ejm4dngXX834UJ85NQXDpmZPshsSNwAbAoeBYkoayrRG0OPOTjsGrccsok3ttWQl5N6CKEHt33/pwyrWerX4fy0If7+dvqg51uM0z4cHdFt8miChAfpNdIURSuXJzFo58Erz1iGCaXhVHE76vusmOzuGRhJv/3/l6e+SIvaEL65oI6Vu+v4uxjxnBWkM+5kOBFDAFVVfjBGVP5yevbA9+vwDfnjSVzGF3NmabJPTklPF1SjQb+E9tfCyv5e3E1r8ydyHHx0f7tNxfUsuLJ9b4EZP9zwCf7qlhzsIbXb17KrAzfdPCDLW28UFpDTouLaE3l3JR4lifHYQ0zCDhSQY2TW17cwu7SRlTFd4I2TThxSjKPXD7f3x3dXzbk1fLYJ7n+AC0pysZVS8dz00mTOtVMCUQJY13uCKtGq8f3ao+Jc/DtE7K57vjsLgFSfquLBw6W8p+qBv97szA2krsmjuGEhBj+Xlztey0CNkQBC5jWIMM+igKKiXdCNLZd9V3uTo2xMzsjFoumcsNJE/nF27u6/bv6szJoQY2zS+DSQTfMbl9lE9/3LZgbTprI+7vKyat2Bhze+97pUxifJAtKhkNVFT7dXxXy/VcVX+/c2f24hMhoJMGLGBIXLxxHdbOLhz7YB/hmYZj4DrZfm5nGgxf2bp2agfJmRR1Pl/iSGo8spacDbYbBlTvz2Lp0JlEWDdM0ueONHXgNo8vVqm6atHl07nprJ+/edgKP5FfwYF6ZPyDSgJWV9UyNdPD6vEmk2cNP9K11urnk8bXUtHfxH7nvNQdruPLpday89YR+q1D73vZSfvDK1k41bWqcbv68KofV+6t47rpj+e/uCl5YX0B+jZNYh5UL5o/lqqXjSY1xsGxyEqv3VwXNd1EU+OEZk7lwQSaGaZISbQ/Yq5Pf6uLsTQdo9Oqd3putjS1cuu0gf589gQ+qGugmdcMX5QXLJ1AVjPQICBC83HbaZP+suGWTkrvZia/S7rxx8d1uF67XNxWHrCNicrhw4dFbqIpv2YUL5gcPXmIdVt64eSm/+c8+3tpSgrs9OXlsfATfO22y9Lr0UEf9omAMEypG8CKqg0WCFzFkbj55EhfMH8sbm4spqm0hLtLKeXMyhmWBuseLqlCBQHNKDKDRq/NWZR1XZSSzpbAuaA4B+A5OO4ob+Ov+Uh4srQQOB0Qd/z/Y0sZVOw7x4aKpYSfo/XNtAdXNrsBTWg2TPWVN/GdXWcir7HA1tHq44/XtmGbX18T399Vz7p+/oLi+1V/rprHVy2Of5PLCugJeu2kpN5wwkVV7K4Puw2FRuXRRVqc8oWq3l1KXm3iLRlaEb62m+3JKugQugD+v5fZ9Rd33dCgKQVdm7KAqqGp7f5Hpy8u67bTJXHXEsgvZyVGcPDWFL3KrAwYTqgIXLhjXr7lPJfWt3bY92L2GCefNzSCqmyJz8ZE2fnPRHH5+zgwKqluwW1Ump0SHPUQoDkuPc9DY6gn6nmiq0q8FGEcrCV7EkEqLdQz7hcZchsGu5hD5Ofh6TNbXO7kqIzlk4NLBBP5eURs0INKBHc2trGtwMjPKwfOlNbxUVkO128sYu5UrMpK4ckwSUZbDQzNvbikOXVpcgbe3lvRL8LJyawkurxHypFhc3+r/95G3N7Z6+c7zm/j4J6dwzjHp/HtneZD2KjS0ekiIspHX4uL/HSzlw+oG/+s1NyaCWzJT+W9NY9B2mECdy8s0q5Umr4EerNepm5O/AmTYrFywbAK1LW7GJURyycJxAYc2f3/pXC5/ch25lc3+XqmOAG5eZjz3njsz5L56KiHS5gtwe7lM3V9XH+SMmWksCFCc72ixDivHjBt+FxcjyeXHZvL/3gs+40w3TC7rx7XPRisJXkaReo+Xj2oaafTqTIp0cEJCNNpXeFqd1zD5uLaRnBYXkZrK8qRYMgIk1va3GEcYQz02lRI9dL0PDXgqt5ztLhdlbo//pN3c4uL+3FJeLK1l5YLJJFp9X+P6luAzQsB38qxpDr3NkYpqWyiqayEuwsrMMbGdeoD2lTehdVMNNBjdNMmraeGjvRV8FKLnpc1r8NCH+/jJBbM5e/MBmr16p0BvZ1MrN+0JPMXX9wQ6loONaKUtFBi+g52a4sA7KQYzruefg+9OSOOGcSndbpccbefd247nrS0lvL6piKomF2MTIlhxXBbnzsno94Ulvzkvg2e+zAt6f7ClKjpoqsLza/LDCl5E3112bCavbCgit6q5S++cr5ZLMqdO779k7tFKgpdRwDBNfptXzt8KK3Gbpv9glWG38ucZWZyQMHqn2LXoBpVuDzGaRtIRU7C/qGvi1j0FVLi9aPh6N34BrBiTyK+njsOuhn8Csasq82Mi2d7UErCXBHw9Jccn+BJ2T5qaTKRNoyXEQpNxUTaCn7bbn9Mweb+yHuwaRxbq6DjcHWxp4679xTwxewIA4xIi2VsWvBdCUxXGJ3WfBJ1T0cR97+5mzcEa/23jEyO586zpnDPHl0TY12nsmqrwyoYif8n5QDpqqNROjaU5yLBQUK1e7OuqwGOgtL8gCqBWt2GrbsOzMBkjyX54e0VBqXdjxtu65L6owOL4KK7KCL+CbKTNwpVLxg/KSt5zM+M5c2YaH+2tCLhqcXcdMrphsrGgbuAaKDqJtFl49aYl3LNyF//eWeZ/z6yawmXHZnL3OTNDLtcgfCR4GQV+ebCUJ4uqukwBLXd5uHz7Qd6ZP4WFcYFnAzR7dd6qqGNrUwsWReGUxBiWJ8VhGeZfngqXh9/mlfFmRR2u9m//svho7shOJ1JTWbH9kH8absdJzwReKqulzTB5bGb4J5VDLS5iLWrIk2WsReObqb4r10ibhVtPnczDH+4Puv0PT5jI78xmaj0hVtJWFYgI/hXVgX9V1VPh8pBmt/KtxVncvTL4TBfdMLn82NCrS+dWNnHBX9d0WeG7sLaFW1/agtM1h0uPzeTMWWkhr/bD0ezyYtGUkEXQDBM+K6/HiO5ZhWLrvoZOgUsHpX2qtHVnLa6T0zsFKWacFbW8BTPSghnr65lJslq4bmwyt2Wl9ijgHWx/XjGfu1fu4q32ocOOYarMxEhor0MTynD/vo828ZE2/vKtBdzT2Mb24gY0FRZmJUol7h6Q4GWEK21z81RR4Kl3Br6D9W/yynh9Xte8kk9rm/j2rjyadQNL+7Hrn6U1jHfYeGXuJLIj7V0eMxyUuzycvekAlW5Pp6vx9fXNXLw1l2NifGuJBAo2THwzh34wPo2pUY5u97WuvpnLtx/E083lq9Ors66+mdOSYgG45ZRJtLp1/ro6F8A/xKKpCj86YyrXLZtATX45f8yvCBwUmebhZIkQdGB7Uwtn2uO4eOE43thczI7i+i5X4ApwzpwxHD85dO/Br9/fR6tH71J/peO3+9/bzblzx7A4O5H5mfHsKGnoUXVcf7sNEyXG2m31VgCjm0UPu+QNuXTUyragU4QVAJeBWu3CSDniM6AoGCkO7J+Ug6Zw5ux0HrtoJrYRsL6Ww6rxu0vm8pMzp/HJ/kraPDrT0mNYOjGJ336wn6c+Oxh0tpWmKpw+vX9rKjldXrYX1aObJrMy4vp9iv5okRrr4Gszuz8Oia4keBnh3q2sD3m/Dnxe10yV20OK7XBUf8Dpm83ScVL2HnFgK25zc9G2XL5YPIPIYXjg/tn+IsrdXbP1OwKZbU3dJNcq8HZFHT+dGLqOgsswuH5XHm4jcCB0JBO4bW8BW5bOZEtjK2UuN8cdm8G3lmbxnx3lVDW5GBPn4Ly5Gf4D+W2Zqaypa2Zdg9P/HMDhDNcwL4Y78pocVo0Xb1jMg//Zy+ubinG1D8nEOCxcd3w23z9tcsiZS5VNbXyyrzJkfkSLW+c/O8u5aOE4nr5mEdc/t5HtxQ1YVAXT9C3ep6kKy2elhUjEBeJsrE5QsIf4M1UFZmXGs9EeeojKBC5OS+DfVQ20GgbWFm+3L503xYERHeDwp6kYiXa0qjb+t6WU/0xN7ZcE58GSHudgxXGde9euWJzFM1/kYehdE6wVfK/zVb2oWByI22vw+//t5/m1Bf7eO4uq8M15Gdx73iziZEVk0U8keBlgh1pc7Gluxa4qLI2PJtoSfq6AyzB4s6KOF0trKHV5SLVZWDEmiUvSE4jSfM9T4/GiKUq3q5bWeLydgpeniqvQMQOeqHSg1OVhZWUd3xozvFaK3dvcygc1jX16DhVfJdaj7XO28kxxNf+raUQ3TcbabaGHdY5gALUenQVr9lDrPfyYVJuF+6eM5dtph5Mh1+RW88Rnh/gipxodk3Gzk3CPi6RC10E3UStaMdIju+11AbCrCpMi7expbiXeopHhsPHA+cdw51nT2V/ehKoozMqIxRFGjkp5Q1u3pe0tqkJxe2XkpGg7b9+yjAf+vY9XNhb6c3w6PovnzRnDezvK/DVIOoYyzEgLrrmJ4NDQs6LQCp1dgg0FX3XXeIcFpdmDGWUJWoNFVeC+yRn8avJY/lhQwU6lga0EXmgQwDsxBu+U2ODJIJrif97n1uT3e/DS6tbJr3Fi1VQmJkcN+HTjzMRInrpmETc9vwm3frj2kKqARVX56xULyE7ue5E50zS59aUtfLS3otNL6zVMVm4tYW95E2/evKzb4oVHanZ5eWNTEW9vLaG2xc2EpCi+dVwWZ85Kl7yQrzgJXgZIQauLH+8r4ov6Zv9tEarCdzJTuTM7vdtZQE1enUu25bKtqdXfLV7u8rC9qZhniqt5e/5kkmwWMhw2vN0ELhqQZut8xfNeZX23RbtWlvdP8NLo1Wn06iRZLUT0sSfnobyyPrdHN2F8ROchsXcr6/nu7nxQ8L8ulb1Y/ffIwKXjOW7ZU4BpmlyUnsg/1+Zzzzu70Y7YT82uGoydNcxKiSKv2okn1YExtvuTiQKk26wsW7fX3zO0KCaS2U0mX6wvoayhjRiHhQsXjOPbJ2YztpvaEQlB1tox4m14s6Mxkh24VIVnlVZSSmu4fEwif/n4YJfcF8OED3ZXkBpj44mrFvKfnWUcrHISH2klLiuGNxUXZnuA4J0eBypoBU5/15MCJEXbuHDBOJ787BCWOgeeBUkBi8gpwA1jU/iirpl7ckqo9njBNLE5NJQ2vUtQZMRZfYELBA2GPOOjMeJtqA1u9pT3LVA+ktPl5ff/PdAp0MuId/DdUyZz5eKsAV1w7+SpKXz201N5dUMRaw7WYJomSyYlseK4LNJigw9bFNQ42VpYj6oqLMlOJDXEtp/nVPO/PRUB79NN2FvayGubirhm2YRu29vm0Vm1t4L73t1NdbPbPwmhpK6Vz3OqOXV6Ck9cuajfZ26JkUMxzV4WBximGhsbiYuLo6GhgdjY2CFpQ4XLwxkb91Pr8XaZIaEAl6cn8scZoRMnv7engLcq6ro8HnzByGlJsfxzzkTqPF7mfLk7aE6GBkyLctBmmBS0uYjRNC5MS+CfJdV0tw5sms3C9uNnd7NVcFsbW/hdXhkf1zZh4usluDQ9kR9PSCe9B5VjO7gMg8mf7ui23RAgD+IImgLbls3y90QVt7lZsm5Pp3VveiVEhdYkq4W3p45n+R8+63YfekYknmO6n7bacdju9He2fw6sW2rQqn2VPDVVIcqm8drNS5meHvo78c1Hv2BnSYP/6tybEYl3dnyn/JuOE8nyhBg+fXVf0BdNUxWuWTqBe887XNfk4q25nQJ6P7eOVtkGXpPoWBu7z1/ExY+v9efv6GMi8MyM9715R5SMXWBYuPGYsXx3T2HnfZc4sQaohuual4CZGhG8km6H9vdScRu8s3hqp6UfeqPNo3PZE2s7vbZH+s5JE/n512f0aR/9qbKpjTvf2MHq/VX+2zqW7Xjg/NkBi9rd+uIWPthdHrxiMjA1LYYPf3RS0P3qhsmfV+Xw9y/yaHYFv3hQFF9e2R3Lp4f/R4lhryfnbwlbB8BjhZUBAxfwHXdfLq9lT4iiZ1VuD29VBg5cwDes81FNIwWtLhKsFu6dlBFwO619f3ucbeS1utBNqPfq/KOkmnD6FKrdXpq94Q2bHO3z2ia+sSWH1e2BC4DLMHmptIYzNu7jjn1FLF67h3lf7ubqHYdYXdtIqDhaN03uOVAcVuACkGi1dPT+d/GLiRn+wMVrmPzfwdK+By4Q8oRY4/Hy8OaCkEMEmgJxEVa05vD+SpMAAVp7GzyzEzDbd6UbJk63zi0vbgn5GgP89CzfyUABTLvqC1wg4FTtD+uaMDKCT73WDZNXNxXi1Q+30hUsudemoY+LQp8QjZHiwKKp7C49fKLXylqxry7HuqsO7VATln0N2FeXk1zYyi9zS7vue2wUnqmxmB3rOwFGpBZe4AL+bUyrymXbD1HQGrqke3deWFfAjiCBC8CTnx1ib1n/9fJ02FPayC/e3sk3Hv2Cy59YyzNf5NHQGvrz1dTm4dLH1/J5TuehN8OEd7aVcN2zGzq9px2K6lpCJm+bQGlD8OOeaZrc+cZ2HlmVEzJw8W0Lz68poC3MYV0x+kjw0s9M0+SlspqggQf4TlKvlNUGvX9rY0u3QzomsKE90fPGzBT+OD2TMUcNDaXZrV2mT4Mv+AnnRK0DRW3hFzXr4DVMbt1bgG6aXV4HHaj26LxQVkNBm5tyt4dVNY1cvv0Q9+aWBD25PnCwlOdDvGZHmhHl4MNFUzk7OY4jR9czHTYemZ7FLVm+AlCvldcyf81u3q6s79eF8oLZX9/14G6qCkaMFSPKgtcEj25wXnYKSqObYGe6ji9t0DYrCtg1jOTDXfy6YXKoysm6Q6Ffw2WTk3nyqkUkRtvwjos6/HyBmODNCj285XTpNLUdPhEtiI0kVMaDBsyN8QVE1qOGGBXdRCttxXqwCUuhE8VtUGKD8iDDe3p2DK6T0/FOjfUFYlN60ROrgNs0eLq4qvttQ/jnuoJuv3S3vriFXSUNfdrPkR77JJev//lzXtlYxI7iBtbl1fKrf+3h1N+tZl+I4bCXNxRSUBs4EDFM2JBfF7C4YEq0vds0rWCzjqqaXNz7zi7e3FIS+gmO0OTycqCiKeztxegiwUs/azNMmgNclRzJNKHcHW4fQnBHHidWjEli07KZ/HvBFF6ZO5FPj51Gg1fv80m5N7ONPq5tpNLtDTlD5+hgCuCp4mreDjB7qsrt4ckenDx+nJJEbXUrj0zJZNvxs3h3/mRWHTuN9UtmcNmYRABeKq3h+3sLqQqQuBs20+xRSfYo8/B7ZthV3LPjcZ2ajntZKu4T0nCdnI4nK4pHLp/HC4umYFeVLl9QTQnzPTFNzMjDXfumAooGO4q7L0Z2xsw01t11OnNmpoTupVDAiLGG/IxpqkLkEbOFrh6bFPJzoQPfHudb3PD06akhkzJNYI+jm3lgdg09OwYjxYGRFmavy9FtMuHtivoeP664zc2vDpayeO0eDhwTi2tBEnqyPejrlVft5BuPfsFbW4p7vK+j/W9Phb/O0JFBiAk0tLi5+u8bcAXpVX1tY3HIj7WmwBubi7rcfuGCcd0uT3FpgLL3b2wuZumDq/jnusIAjxIiMAle+plDVYjsppiVqvhmoQSzMDYKazcHWQVYfNQ4vKYoLIyL4pTEWAzA2U0Q1d3zT420k9WLcvr7nW0hr66DUYEnirpe0b1fFby7/WiRFW3c9sQGznv0CxY98BF/+GA/expbWVXTyFsVdTh1nVbd4L7c8K/wAgp0dA92xDdN4lSVS8anYERZcM1LxH1yui8p98iEQ7tK06QYfn6gmNPHJvDx4ulcmJbgr8FjUxQuTUvkb+EW2PMaGAk23AuScH0tg9YzxvKI4eTJoko83bygVk1lbJyj+wOEEXyqs6YqfH12OvYjZthNinTw66njfPcfsW3Hc2TarexuaqXc5eHGkyYG7YkzAWxq+GX+jeD5SOFo6eF3aUN9Myet38fjhZUUtLkx7RpGkh3PwmQ8M+ICBjBmezPveH0HBTXdr48VyhOfHgzaC6KbUNnk4v2dgZPfK5tDr2ism1AeYNXjM2elMXdcXMDJCJqqkBbr4FtHTeNek1vNHa9v79UyE9F2C1PTRm/1cBHagAYvDz74IMceeywxMTGkpqZy/vnns39/8KqjAM899xyKonT6cThGThEfRVG4fExi0HwL8NVUuTQ9Mej9STYLl6QnBH1zNODs5DgyQwQW1jCnEQbbygR+nJ2OoviKq3U3FftIUVroarTBGMD2plb/ibXZq/PbQ2Xcn1sSVg+SWtaCvt1X0t4EmsZF8Gykh5/lFvPbQ2XctreQOV/u5t7cYpr6ENh1cuSBOtDJsT3xU99ZS3Z2PO6lKZgpjsDbtt/2XGkNWxqdTIp08OjM8eScOIfty2Zx4MRj+OOMLM5IimVChC10LZP2BFv3se1l8Nufuw6Te3NL+dqm/Zy7+QAL1+zm7E0HeL6kussJ+oyk2JDvo0WBDJcZ8CTpm4arcNtpU7rcd93YZN6cN4lTk2L8gVlHDm6Ry8PvCyqYv2Y3f6it5eqvT+2Ut+L/HNhU3Mcmdw7+Ar4OJrR6Uat7n7OiAlOiOs9OM02TNt0IGFy16AZX7cyjzTA6D5u2v1BGVjR6iFwhgBfX974Xwu012FRQFzLg11SlS05Lh7QYR8jPVrBVj62ayvPfXswZM1O7PH5eZjyv37y0y2raj63ORe1FUKm016YJpwSAGJ0GdKr0p59+yq233sqxxx6L1+vl5z//OWeeeSZ79uwhKir4WHlsbGynIGcgpxAOhFuzUnmlrIaWICf8Y2MjmRMT+uD1qyljOdjiYn2D0z9zpuP/M6Ij+MP00KuOZjtsROngVENfcR4bF8mGhhb/SaTjAvW+SRk0ew1O2bCPfc42VOCUxBhuzUrl+G7WSjorOY67c8ILOAJR8AUu39yaw97mtvACIdPEur/BXw5enxCNd2qc/+6Ok4hTN/hnaW23i9WF19AwPpcuA+v+BvTyVu48UIyiqd3uV1PgqaIqTkxoo9UwmBUdweK4KP/3QFEUfpY9hpuDLUpomiglTt80ZAiYbLvP2eZ/DUpdHrY2tfBMSTVvzpvsXyPqm6kJ/PZQOZUeT5ccLAXfZ+WvS6fwulPlra0lYPpWgtZNk6SUSC742iTW6y48TRqzj/q8H58Qg1VRWFfXTHP79+To/Cx/PZ9T0tGKW1Ab3O1VcO3o6ZH+eixB38uOmVd7G1Ddhq9eTA+XGQDfd+66sb6hrFqXh0cPlfNKdT21Xh2bonBhWjy3ZqUxpb1i88qKOhpCJbqbJvqEaCylgUv266bJ+kM1Ae8Lq71hXGiYpokRJLq5/LgsHvhX6FWPVRS+/ufPMU2TpROTuXJJFhNToomLsPLEVYsoqm1hzcFqvIbJgqwEZozpmm/U6tb5Mrdnf2fHe33y1BR+dMbUHj1WjC4DGrx88MEHnX5/7rnnSE1NZfPmzZx0UvDpcoqikJ6ePpBNG1A1Hi8tIS57Nja2sM/ZyvSo4HU3ojSNN+ZN5t9V9bxYVkNJm4c0m4XLxyRxflp8t+usbCmsx7WvDmYFmXJrmEy0WHl3wVS2NrbwbmUdTV6DCRE2LklP4L7cUlZW1h/O0cC3nMAntU08PC2TK0MsUpfhsHH5mEReKavtUQ+Mim/IzKIq/P5QOft6ELigKOipDhS3gakoeCeFDrAGI3BRKloxrSp6RiRmtIUiM7yZEboJb1fWd8r/mRxp528zxzM7OoJil4eNDU4cqkJb++fMfwI3Aa+JOTaq2yJ3RwcLOc42bt9XyD/mTAQgQlN5YU42F207SL33cM0UBd8Q5SMzMil0e9gzMQLSxqEaMB0LXpvCDrebP1fXQnWt/zHjHTZuzkrluLgofrSvkO3dVEL2s2noE2OCJsF39Noc/Z5m2G3ckpzI1HHjSI628UBRBR+3hF7j52gKcFpiDK15jXzjrb3sKGkAA0xNwTI2End2DK9X1PFOZT1vzJvMwrgoNjQ4/XV8lEY3WnELitMDVhU9PRIj1YEZY8XUFJQgmfkduT6tbp03txTz+qYiKhp9VZovPy6Tb84bG7TXwWHVmJYWzYGK5qCfc9OEheMDHxsuPzaTVzYUcqja2SVpt+Mz8L895f6A9kBFM/9Yk8/vLp3DBfN9Q4KZiZFclhi6HESoRTmDWTQhgWuXZXPWbClS91U3qEXqGhp8mfSJicGHTACam5sZP348hmGwYMECfv3rXzNr1qyA27pcLlyuw13CjY39P92wp/6YH7gs+pF+tr+YlQu6dqkfyaoqnJ+WwPlpQQKQEF7ZUIittBUz0oKeHUOn1dpUBaXZQ0RhM5wM82MjmR97+Mr41bJaVrafOAMl1t65v4hjYyOZFh08+Hpw6jhchsmbFd0niHYwgFuyUnAZBv8sDT1jqxNFAdPEO7Pnr9NAMlMcoPqqHxvJfVsnKrfFxdc2HQi+r45/KEA3awEFowP/bZ+CPz7Czhd1TdywK596r47K4WGbdJuF54+ZyB8KyvlPdWOnmjqb8UCACWomkN/m5mcHilEJe+WDsHU5SZsm9S4PuzwuaqPgwvgYMhod0IPgJc1mYTE2vngrhy9d+pHlZXwzn4qcaOWtuJek4IqwcOPufDYunemLaw0Ty/4GLAVOX7J0+1R8raINI9riG/IKQlXg5Kmp1DrdXP7kWg5UNPuDs8qmNrYW1fPPdQW8dOMSYh2Be5K+fcJE7nxzR8D7FAUibRrnzw9cOTjKbuHVm5byi7d38uHucv/wk0Vtr5Bs0qknriPA+fFr25kxJjZoLaE2j86hKieqCpNSoolxWEiNsVPZFN6QXkKklddvXhbWtmL0G7QidYZh8I1vfIP6+nq++OKLoNutXbuWnJwc5syZQ0NDA7/73e/47LPP2L17N+PGjeuy/f33388vf/nLLrcPZZG6iZ9uD9nzAr58geJT5g1YGy7865dsKawHwIi2oI+LwoiyoHgMtPJW1Ko2oqwae/7fWV0ee+bG/exqbg3Z66HiK7b380kZJAdJPnYbBpM/24G7m09Yx1XqTyak85PsdApaXSxetze8P1T0u99NHcfCuCiWbzqAx+y6hISm+Iru9aYC8aAzTFRVwQAmRdg42Nr91P9Pjp1GvFVj7e5Kbn9tO9CpLl4npgJGoh3PIl8w8sKciTR4vHz/wz1Y9wae9mwqYMTZUNp01LbOIbqigN2i8tkdp/KLlbv4eF9lwCnLmgLnzRvLny6bh2Ga/uUU/PswTX765g5e21Tsv2YBX4+OZlP51vnTiY13kG6zcm5qPLFBli0pa2hle1EDmqrwRU4VL6wr7LJop79NqsKlizJ58MJjOt3u8ur86aMcXlhbQFN7/ZbEKBvfPiEbwzT5w/8OhDVpb1xCBF/89LTuNxQjVk+K1A1az8utt97Krl27QgYuAEuXLmXp0qX+35ctW8aMGTN44okn+NWvftVl+7vuuovbb7/d/3tjYyOZmaHzQQZaGAvl4jV99VAGain6hEjb4Y6WZi/qvq4H0mCLpO1xhg5cwHel/Up5LV/WN/GfRdNItHb9KH1a29Rt4AIwOcLOn2aM9/f+RHQzJCYG1sP55SyKjQwYuED7bJWRELiAr+er/Z/hBC5pVgvTohwYhslv/rPPf3uwb6liglrjQmn1QoSFbY0t3JKZgi2v2beqe5DHaPVuPFNjseY0+YMBBXBYNP5+zSJcXoOP9lQEHfbxmvBWeS071u9lb4sLBTghIZrvZqZyalIsiqLw24vmcNr0VP6xpoA9ZY3YLCoZC1PZ4jB5oq4erd73Xt6VU8zPJ47hpszULvsZExfBmDhfD+vv/7s/aOACvh6Yzw50Lmng0Q2+/dwm1hys7pRAXOt08/CH+zlvzhiOHZ/Axvy60FPuFYVzjgm9kKr4ahmU4OW2227jX//6F5999lnA3pNQrFYr8+fPJzc3N+D9drsdu71vXfL9Ld1upaCb4m4WhaAzkgzTZHVtE5/XNWGYsCguirOS48KeQQTwzfljWbWv67TjDpoCFy4I3G1sVZRu10sCXwBT0ObhNwfLeChAAnF3r0GHJE2jvrSZLytbWDg+gVS7lewIG3lhnGxE/6t0e/l39dAPvw42BbghMwVVUViXXxP2cIYCKM1ejAgL71bWcX5kFGaAdZWOZCqgGHD+4nF8vruSGqcb3TBxWFW+yK0mIy4ieL4K4JkRh5EVzR5nGygKJvBlXTOf1TXzy8kZ3JSZiqIonDV7DGfN9p30ny6u4u6cEv8YW8dK8i7D5L7cUrbm1ZLVbJIYZeO8uRld1sIKp3ekvsXN7/+7n9NnpDEvM553t5XyRe7hWU2mpqCPjURPdYCm8FZjC/93/ASWTkrmzx/nBNyHqoDdqvZ65WuvbvB5TjXF9a0kRFo5bXoqkSFKVYiRYUDfQdM0+d73vsfbb7/N6tWryc7O7vFz6LrOzp07+frXvz4ALQxfrcdLXouLCE1lepQj5PS+G8clc3eAkuVHOjclPuAsqvxWF9/afohDrS4sCigoPFFcRZrNwvNzJvqrj3bnrFnpzEiP4UBlc5duZ01ViHNYuWbphICPXZ4cx3tV3S/c2OGfZTV8NzOF7KjOU9rDrRGzpqqRtQV1vpod+UWkqgqVkos3ZAZlHHkYWhIXxc3tvQ+1zh4Gzu0XFgdaXGxrDDOvxq2zcmMxpmH6eyXqWjw88ekhYhxdD81GrBVvZpRv6ntE+/1HHEM6BqDuyy3l5MSYThMC2nSj20VNV7Y6iV5TgaGb/PaDfVy3bAK/OGemPzF26aQkcqu6Hk+O1OLW+evqg/zl41wWZyfS6vb6e4CNqPZcH5vqb7sea+Nn1dX8fOoY3p99Atc8s5HKJpd/n7phEuOw8vQ1ixiXEN6x70j/3V3Oz9/eSXXz4fcz0qbxozOmcsOJ2SNuJqs4bECDl1tvvZWXXnqJd955h5iYGMrLfYmscXFxRET4vlhXX301Y8eO5cEHHwTg//2//8eSJUuYPHky9fX1PPzwwxQUFHDDDTcMZFODqnR5uD+3hHeOOJmPs1u5PTs96IrL149L4ani6qA9DzZF4c7srl2gzV6dC7bmUtlefdfrnz7iW2fo4q25fHrcdDLCCApsFpUXbljM917eypqDNajtgZBumkxIiuTxKxeSGutgW2ML/yipZkdTCxGayjkp8VyZkcR77TONwjmRmcDSDfuYHe1gXkwkJyTE8PWUOM5IjMGmKLi7u2RzWDDtmv9AXGma7ZemYexciH6gAsfERPp7NzuGSsJhWhSMBN93UgEerKzCOzcRpawFtarNP33/SB3DTXqAKwTdNGlq61yB2zshGu+0uMOJ9yFoCvy9sIrvpyWTGGUjym7h87omGrub3WPTcMXZ0Gp8PU7PfplPhE3zL3545ZLxPL82P+RTmBxO4N1U4EvWN9q/zu5Fyb5k8iMDhva/5deHypg6O5svfnoa/9lVxtqDNRimyaLxiZw3N4MIW+CcnDaPjqYqXZaSAFi9v5KbXtjc5SDW4tb5v/f3YpgmN508KfRrIoatAU3YDRbVPvvss1x77bUAnHLKKUyYMIHnnnsOgB/96Ee89dZblJeXk5CQwMKFC3nggQeYP39+WPvsz1Wlq9wezt50gDKXJ+DMl7uyx/CDCWmB2+HVuWrHIdY3dK6UmWqz8Nwx2SyI7Vrn5tmSan5+oDhowKDhqyHz8yALMQazp7SRL3Kr8Bom8zMTWDIxEUVReCivjD/kV/gTZsF38E2wanw/K41fHyrrPvA4SkfAM8Zm5c8zMnk4r5wN4V6JCtFbHSt6h1jZuztTIu18vngGe5tb+XNBBSuLazAVBevOWtQqV9BY2jMlFn3iUVPzO1albnRj21yD4j4cOJiAaVNQw0kIA/SkwwnB4VLqXdjXV2NRFc6ZM4bpC9P5ZXFFt4+zbqtBqzhcPdemqWy8+wx/ftwbm4u5443tvno+PaiKq6dH4JkbfJapr0xCJO8t7L52i26YvLS+gGe+zCev2okCLJucxM0nT+LEKSmAr9f/rD99xoHK5qDDXQ6ryqa7v0Z0gBWyxdDoyfl70GYbDZb+DF7uzSnh78VVQafsqsCmpTOD9oSYpsnr5XU8X1qNUzeYGuXgp9npTIwMXDH4/C05rG9whuztyHTY2Lh0Zo/+jo62bGlsYVtTCzZVQTfhZwcCr6GiAPEWjY8WTeV3+RW8XB7egohCDDrTxLKvAT3JDpEWTJva9eo+TMlWjewIB5saj/oO6gbW3fVoZa2+lbrbpx4pJnjHR/l6RILtzzBRGtzYNlT7AnsFUBW8E6KxFDvRkx2ggNroQWn0dAmQ3LPiMcZE+g424f5NpolS68a+yZdrorTPiHKFEQDZ1laiNnbu9Vk+K41ffXM2qbG+49ae0kaeW5PHpweqqGgMnRfUEU96ZsX7qgp302uUf9IcHCHW7tINk9te2sIHu3y9+B3vk9ZeHPGB82dz5ZLx7C9vYvmfPuvmr4U/XjbXX5tGDL1hOdtopPEa3a8ODfBqeS0/mtC1oF6TV+e6nXl8Ud+MpX3M94CzjXcr6/nJhHRun5DWpWeqWTe6HaZx6j1fAj7H2cbNu/PZfURV1VBMoM6rc9OeAl6fN5k3KmrxjKoQV4TUh96LQec1sRQ6sRT6ejgNh4Z7SYovgOnhTL5qj061J8CaQpqKZ04i3mwPWolvNWszQkPPiMKM6uYQqiqYCXaMeCuqU0fPiMA73rcmmT4pplNvkdLoxrqjDtV5xEwuuxY8sz8ErepwAUDTBLPGBe0zogIyTZRmL0pj1wVj/7u7gi9yq3n++sUsHJ/AzIxYHrp4Lm6vwdS7/9NtW2wWFW+Y70V3S8m+ubmY/+zqWkerYxbUPe/s4uSpKdQ4u0+2VhWoaZZJASOVBC9B1Hv1bleHVhUoCDIj5rY9BaytbwYOZ/V3eDi/nDEOa5ecmelRDvY6W4MmyqrA1CN6bVp0gzcranmlrJZKt5dxditXZCTxjdR4bKqKaZo8V1LFfblleI4qwR6OTY0tLFq7WwIXMWypla2darCobTr2dVV4JsX4eiy0vg0lHcmMtuCdHt+rx3oWHzUN+cg2tf/fjLbiXpqC0uBBafGiFjn9uTRhM0zwGmglnYdqVcC2s86XMHv0a2GaYIB1d33gWjb4Kv1e9+wG1tx1un+YxWZRSYu1B+x9McE3qyg7Gk+kFd0IsYInvrsmR9qJ0ny5LR7DpLDNhYLC+Aibf7HH59bk++O9YM/zysZCLlnYfbkMw6TLjCoxckjwEkS0pqJBtz0vCQFKdB9wtvFhTfCppgrwp/wKLk9P7DRr6eqMJN4IUZHWAK5tX2Ol2u3lwq25HGg53JtS0uZmbYOTf5TUcGl6AvfmltDai9Vaj1Tj6XlPj+hHQ9ELMhj7M03UWhdKkwdTUzBSHBBghk13tBJnl3Oi0qZj212PubcebJovWfSkflhupD9fl0DPpSqAgplgw0y0Y4wLvv5bJ0eeyb0Gtk01KEdfMQFqnRulzoWZ2HnYWq11YznQ0GW46EiGCY1tXt7ZVsIVi8ez39nGsyXVNC1NweV0o9a40IqcqM1e3+u9NAUzxuarfK0AHUNBQar9mUC5y8O5mw6QZrfyZX0z9e3rQ6XbLNyUmcpNmSnsL28KOWXbMH3DWhOWR7FofAJbCoMvUBnrsHDajK61bcTIIMFLEA5N5espcbxf3RC0J8RrwgUBSvf/r6ZzyfSjmUBhm5vcFhdTj5hevCA2irOTYvlPgMBHAb6eHMd5qfEAfH9vAQdb2vzPxxH729ToZGNjgO7vvhhJQwmjSR+TUIcjpd6NdUctitvAMzUWIyPq8NBIT//W9oUuAxaDM8Bs0yH2qGKMw/317Enb2s/kamUbalUbWlkrSogLlo5lCjrEtho4dtXjdOvdXqipCqw9VEtRio1HCnzJvyZAlBU9woKeGYV1Vz16st0XuAT6W0L8aU26waamFmjqfHu528svD5ayp7kVq0VFD3FBpSr413y659yZXPL4WryGETCA+eU3Z2EPUllYDH9SyjSEH05IR0MJ+CKpwNnJsQFXh27TjbCG212GL9zQTZNf5pYw8bPtAQOXdJuVuydl8MBU30rT2xudfFzbFHKhuj4xza79sgbhVakS/W84n2h7SGn2YNtYDS4D97HJGJlRnXM6Qo0JBKCPi+p2Rr1+dA/GYL6eHX/LAHx3FNN3HDq9ScG2rRZLSUvIwMVUFYyEzgU9narJZZfNwnLKGNpOSsO1KBk9PcKXWHwUw6byYSL8qaDCv86VX/sBzzM7HiO9+6EYm6L0uBLC6xV1zJmfFnJBRsOEM2b4ZoDOzYzntZuXcszYuE7bZCVG8tcrFkii7ggnPS8hzIqO4NV5k7hldwFlbg/aEWuEXJiWwMPTfOOqLbpBmctNpKYyxm5jdkxElzyXo9lVhQkRdtp0g69vPuCrlBnEsbGRfFrbyK8O+grf9fuh98gr0WBXpT2Z7SBEEJa8JjBNjKwozFhr4M9UDz5nRqoDPcnuK9F/1H0mYMZafbNcBsvR36UO/fzdSVBVLh2bxHVjk5kQYefyijbWHaoJ+RhvdnTnJGbDRFcVHi2vBqsCVl+9JU+SHaW6DdvWGpT27lxTAfexybRZQyWuKCF7nI/U0xIM4DvuGZlRsCnwKuKaqpAWa+ecOYdraM3LjOed204gt7KJ4rpWEiJtHDM2DlVWpB7xZKp0GHTT5OOaRvY524jQVM5MiiUrwk6tx8vDeeW8Ulbjzy1JtGqMt9vY3+Ki1Qg8e0jDF/xMiXLwZFEl1WHklYRbMK7HvAZKoxszyuqLzByaBCliYBgm9o9KUUxwnZCKGWnpn8+abmI50IBW7Ox0stXHROCdEe9bDnkwmCa4DLCrh3M7+uu7dNRFxZlJsTw/Z6L/962FdVzy+Bo66tAZURawqZguHWKs6JlRGIn2w89hmIcPKoFO5KaJlt+M9YCvJ9ibHoE3RJ2WwTIl0s7d0Ql8/+WtuL0GiuKrJ6YbJmmxdu45ZyaLJiSSHhe4HIUY3qTOSz8HL4HUebycuzmH/FZXt2PFR4tQFRQTWobLS+8x0IqdmG06xvQQNSuE6AuvgWOVr0R929cyejyVuVseA7XBDSYYcVYIUpV1wLR4sG2txTMrATO+h7OEQjFNlOo21BYdPSvK//38aXY6P5qQzpYGJ8+WVLOutonyJhcezK7Jz0cX8QujUi9eA/sn5SiGiWtREmbS0AcEC2Mj+ffCqdS3uHljczE7ihuobnaRX+2ktMHXe60AJ01N4Z5zZzA5NSb0E4phReq8DKBGr84ntY28VFpDXqsrrC7So/V1BlBApola7UIrbQG3gZ4VhZHqCC8QsaroE3y1JyRwEQNGUzAtim8mjMfw1TDpT1YVI3kIT7B2C+7FKf0flCkKlqIWtKo20E1/Nd/f5pWzz9nGO5X1h6tkO4K8pkdNy0YNIxHcomLEWlDrPZhhzgTTgGSbhYoBWnX8wvYJEvGRNm44cSL/2lHK917a2mks3QQ+z6niG4/V8s4txzMlTQKY0UgSdsOkmya/OVTGnC93cdPuAj6ta+5V4DIgdANLTiOW/Q2o5a1otS5Mi9KzQETp4fZC9JSioI+LwgRfkD0QQXwfZfRltWFN8f30Z/BimuDW8UyOwT0rHrWyFdy+vl4VeKeyHiDsRVQ7PW8YPItTcZ2a7otKwniMCTwzawKJAzCLJ1JVuTT98NBVm0fn52/t9O336PkFJjhdOpe8spnC1vBWBxcjiwQvYbo3p4RHCipoG4YHXDQV79Q43Cek4T4uGSPagqxqKIYjb3Y0ZoSGVtjsqzUwzL5PpX3pMejrFOyjz8Adv1tViLVhZETiWZKK0uoLXvp08dSTixWb5uslC2P7h6eNY2F8NJuXzeTKMYlE9mMgd3VGIjFHBEUf7a2gsc0bNBdQAerKnJz9xR4qXMFr2HSo9Xh5sbSGvxRUsLKijrZuipSKoSXBSxgKWl08U1I9MAmz/cyMs+FenILa7JGpzWL4sWm4F6dgxtuxbqpCaWkPFgJNzx9p+tpz2ezF+kUF2s7aI1ZKVToP9eD7joelP1/PMKawz4y0+6uGR2gav5uexaGT51J+6jyePya7212kWrWQl1xPFFf7q5YDFNW2hpw2Db4AprbJzU278wmW3mmaJr89VMbcL3fzk/1F/CavjJv3FDBnzS7eClE0VAwtCV7C8HZF3ch5oVRft7URqbXPFR3hJwQx+tg1PHMT8SxKxvQY0Oxpn53Tw5O/aYJLHz2fcZuK5vRiprcvaxDs9Qj37+3vYeBunm9Pi4tnS6oD3ndGUiznpsQFLiYILE+K5ReTMkJeIKrAXwsr/b/HR1oxwum5s6qsa3By2baDNHq7Tq/4XX45fyyowGOavmUN2p+y0Wtwy54CPqhq6H4fYtCNmHPyUKpyezuV8R/2VAUz2YFS2dr9tkIMFZsGCXaItvYuT0RRwNa7FaSHJbuGN9WBkWwPvV0PVpfuSWBn7YfX8ZGCCrwBAgpVUXh85gTuzE4n8YglVRIsGrdPSOPp2dl8UtsU8oSkAx/XNvp7UJbPSg/Z82ICRrTFv3jmZ/XNzP5iJ789VIbR/hz1Hi9/KagM+hwK8H+HSoP22oihI7ONwpBut/pXLR0xFAWt1o03fRALdAkxmEbadzIM3qmx/ROMGebhWi4Kh4PDEHk5nn54PSvcXnY1tzIvtutxx6Iq/GhCOrdlpZHbvrTJpEg7NlX177+7FuimL9dHAxKjbHznpIn8dfXBLtt1PI93aufSD24T/lhQQZthcN/ksXxQ3RCyYJ4J5LS42OdsY0a0LOI4nEjPSxguTu+6ftGwZ5qoFa3QNoq61YXoYJi+hN/R0uvSIcra/TaBHPkdN32Bi21zDbY1lWjFTt/U9I7XbIC1GaETXa2qwozoCGZER2BRFD6rbeKO/UUcanF1O2w0M8rhX2Ea4CdnTuOSZVmYSvsoecddFgXPnATfgp8BPFFURZnLTb1HD+skWOsZmKnfovek5yUMY+w2fjghjT/kVwx1U3pGVVDLnBgTpM6BGEUM01cZusmDmWDv/7oqI5Hue03QTbSKVixFTpS29inVexuw7m3ABLzT49DHRw9oU94sr+W4uKhuh9rrPV6u2H6QzU3hDW8bwA2ZKZ1uU1WFh79xDDVjI/hwdwV4DMwIDSM1ovOaWQG8XVHPhAhbWLO2Mh39WHRQ9AsJXsJ0x4R04i0af8yvoO6IpK8YTcWpG8On5ssRPPMSg68fI8QIpdS6sO6px4y14hkGVV+HBQPsn5aHnK2jAGb0wB/yXyirJcFq4eeTMkJud8OuvLACl45VDKZE2tnb3MpbFXWckxKHXT3cZ/L4/ImcY+jsbA4vENIUqHZ7uWFcMgkWrdMxvdN2wJL4aLIiuslDEoNOlgfoIbdhsKHBiVM3mBxp54Czjet25ff7foQQRzFNMEErdqKVtUK9G+/CJIwkuwTogHVLDWp1G0qAI7oJmA4N90lpg/Ja2RSFHcfPIt4aOFja0dTCmZsOhPVcFuXwaFdHIJNo1Xh2djaL4w/3IpW0uTllwz6a9cBryh0t1WZhUWwUUyLtPFJY2WX9OA2wqyr/WjiFmZLvMih6cv6WnJcesqkqJyTEsDw5jkmRDs5KjuPGccmA78MuhBggigIK6JlRuBen4FmWira3Hq3ACVJQDM+MOLCqh/M+2pkKoIJnTsKgBXlu0+R/NY0YpslntU38Ib+cR/Ir2NLoxDRN/hlkSvXRFDqn6XT8s9ajc+m2XA61HK6eO9ZhY+WCKWSFOcRT6fbyYXUDjxRWsiQuimmRnXtXjk+I5t8SuAxb0vPSD0zT5L81jTxVVMWmRufwrMIrxGhjmOA2sK2pwDs9DiMjaqhbNPTadCwHG9FKW1CM9unCaQ68k2IxY3qZDNwLCvCD8Wm8W1nPoVaXL/3E9E13nhpp50BL30v2K8A1Y5P5zdRxnW43TJOXy2q460BJWDOYOp7rjgnpfCMtnjqPTobdyljJcxl0sqr0IAcvR7pmxyE+qm3s+VojQoie6yhUF+bCgV8ZevvilxYFLEPTwR6jqbToBoGzSfpHvEVj34nHoJsmhumbydRhn7OVn+4vZn2DM6znSrBobD9+ln/qthh8Mmw0REzTZJUELkIMHkWRwCUQTfGtMN2XwKWX17UqEK2pNA9w4ALQ4NW5YGsu41ZvJ/PT7ZyxcT+vl9dimibToyK4IzudGC2816DOq5PTDz1CYnDIt74fHVlaWgghvooUwKEoNHe7Zd+ZwLr6Zv/Q0J7mVr63t5A1dc2cnBDN9/YV9ajAqDG6BiJGNel56UeqojAzyiEvqhCiZ9oLy/lL+g+Hk2gvk3t1oCbI1OOBcOQr1ZG2/XJ5LTfvLcRjmmGXsYjRVCZHytT7kULOs/2o0auzIiNpWNZ8EUIMoW6CEcvBJmyflGHZ14Ba0gLO9oquHcn/wyGY6YGR1VrfifDasclEhDnEJIaeDBv1g39V1vNoYQXb2gsuRagKrYbZpW6AEOIrqqMXI9DaQh4DLa8JxQCl0IkaZ8OobsO0axjxNoi0YEZqYP3qFWOwKuAZhIPoiQnR/HB82sDvSPQbCV766M8FFfz6UFmnLqzW9qslyyB98YQQI0SAoRi1qg3FAD3ZjndGPGbkEYdlr+FLIvmK9gg8MGUsPz1QMmDPn2DRSLRa+LyumUmf72RihJ1vj0vm6ozkTjOXxPDz1fxG9JP9zjZ+fagMIOBQkQQuQogujiqop3gM9GQ7ngVJmBFH9a5oSq8CF6XZg1rRilrTdnjoaQRR8fWGXJWRTKZj4OrT1Hl1Dra6/MfvQ60ufpFTwtc27mN3mGsuiaEhPS998EJpNRoM+HRAIcQoclQsYURa0GfF+345umcmWNKs4Vs1XityorZ6Ma0qekYkRpwN64FG1Hr34d1ZFbyTYtGzokbMMgoGvhG2Rq/O8qQ4ng6zIm84VAJfbB5pX4uL0zft5/j4aB6bOZ50++AV+BPhkeClD/Y0t0ngIoTomaNWOzaTe7g2k2Fi3VKDVuPCxDeqRJuBZX9jwM0Vj4l1XwN4DPTJg1+4s7fW1jdz8bZcat3efn3eiZF2csOs57Kuvpnzt+bw0aJpRFu+ejlHw5kMG/VBpKbKCyiE6Jlwe1eCsOQ2otb4Tr4dj1SO+nfAxx1s8lUjHiF0YFdzG6XdBC8KhFWILkpV2Lx0Jl9Lig25+vbRbShodfNKeW2YjxCDRc69ffD1lDiZFi2E6L2e1nTRDbRCZ9CTb3cnZa2sJfx9DQPhBBkq4AxjYU4D3+KNpS5Pj2eBvlxWi9sw+KKuiQ+qGjjgbPPft6aumet25jH9853M+Hwn392dz5YwlyQQvSfDRn1wfmoCv88rp8zt6VJZN5xxVSHEV5jX8A0h9aTnpUXHtKnQqofde+CngNI2cnpeILxSE+H+Ra2GSaXLQ5LV0qPjswkUtrqYt2Y3tZ7De1sUG8mC2EieLK5GUw5XV3+3qp6VlfU8PC2TKzOSwtyL6CnpeemDCE3lzfmTGe/wLaVuUXw/AFGaim2EJMcJIYZAbw4PMVbcJ6XjPj4VPS2iZ481wbSNrLwNFUi1WgjWahVI6kEuystltVycltCjC0sFaNKNToELwJbGFp4s9iUSH3nxqpu+gOeO/UXsP6KHRvQvCV76aHyEnc8XT+f5Y7K5KiOZFWOS+MO0TB6aNg73CKuKKYQYJKYJahi9LkGOIWaUBc+8RLxZUT3arT6mhwHPEDOAFJsFNcDrpCm+InZ3ZKeHPYSwrr6J+bGRxPZg+nmwo3h3AZCqwHP9OEtKdCbDRv1AUxTOTI7jzOQ4/23PyodWfJWYpu+SU1V8P8Oc0uhGK3SiNrhBVdBTHOiZUWAfpJ6JcHplQ138tD/eOy0OrbwVxe07lXY84uhnNwF9QjREjLxD/u4AvRcWYGlcNEVtbn6WE34Ru0/qmrlhdz52VRnwGhe66ZutBODUdVZW1POvqnqavQYzoh1clZHEMTGR/brPwlYX71TWU+fRGeewckFaAgnWkfeeh2N0/lXDQJXLM9RNEGJgmCZqjQulxYupKigtXiylLSguAxMwUh14J8ZgxtmGuqUBKTVt2HbUgcdAaT/bK40eLPnNuBcmYSbYh7aBPaGAnhGJlu87SZp2FVQFpfXwmdlUFfTsaLyTYoaqlf1KA8bYraypb+5VXuG/qxp61PPSF7ppUtDq4sKtuZS4PP4lY7Y2OXm+tIYfjk/jp9npKH1MMfAaJr/IKeb50hpfQWZFwWua3Jdbyv2TM/j2uJT++HOGFcU0R9fYRmNjI3FxcTQ0NBAbO3Q1DZZv3M/2ZqnQKEYXtaYN6656lDbdX2Pk6Kt9s/0fnnmJGKnDbJii43Bn+qYca3nNh9sNoCm4Tk73Ja91nFC8OoSTVxFo3aKB1l6szlLQjJ4RiZ4RCZqCUutCbdMxNRUj2Q6W0ZchMFLWjrO1BxLBAq3HZmRxUXpin/Zxb04xTxVXB309Hp2RxcV93Mdg6Mn5e/R9ooeBKrdHAhcx6ij1Lqyba6B9xkqgGiOArzfDBOuOOt+MmuFEaQ9KVAXv1Dj0cUd02zs09AQbannn6cRKZXgFzYakeq2qYIyJxL0kFT0r2hekKApoiq/ibnpE4MClp1O0h6GR0np3iMBFBf5SWElf+hCq3B7+XhI8cAF4KK8cY4S/30cblODlscceY8KECTgcDhYvXsyGDRtCbv/6668zffp0HA4HxxxzDO+///5gNLPf5EqGuRiFLAcawQxvkowCoJto5cM4iDdNvJNjMRwa7vmJuE5Kw7swGSMzutNmWlUbtHr7/2Q/UAGEafqG7AIFU+37VJpkWHs4MIB9zjZqPL1PwPlvdWOXUh1HK2xzs3uUXVAPePDy6quvcvvtt3PfffexZcsW5s6dy/Lly6msrAy4/Zo1a1ixYgXf/va32bp1K+effz7nn38+u3btGuim9pvqPnwQhRiWXDpanbtns3sVhvdJUlHAruFemoKR7Oh8sj/i34raHrgpSv8GGwPVU6MoIZ9bLW9FPdg0YtY5+irQ+/C5atb1sE7kTcOtF7SPBjx4+cMf/sCNN97Iddddx8yZM3n88ceJjIzkmWeeCbj9I488wllnncUdd9zBjBkz+NWvfsWCBQt49NFHB7qp/SZrAFdBFWIodMxm6bERMPMIixqynd60CCzlrVh31oG3I2em/f9eA21fQ++HxwYigOhmlpKR4hjxcctQN78/9z/GbiXF1vu5M9kR9m4TlxVgQsTwTKDvrQENXtxuN5s3b+aMM844vENV5YwzzmDt2rUBH7N27dpO2wMsX7486PYul4vGxsZOP0NtcqTDX6xOiNHAtKs9zjFQTNBTHQPSnn7VTYBlpjgw7CpaaQu2NRWopS3QqoPTg1bsRKts9c32GS45Bd1FJppvaviwaW8vDFXLVeCeiWOwKEq/BTA3jksJWMcmXKclxpJmswRtjwacnhRDhkOCl7BVV1ej6zppaWmdbk9LS6O8vDzgY8rLy3u0/YMPPkhcXJz/JzMzs38a3wdRFo0rM5KH/OpAiH5j0zBSHf6ZRN0xFTDibZjxw/iAaYRxCmyfQeQ5NgU92Y77+DSMMREQoUGUFT0rGveJaeAxwnu+4UBRMDP6t77IV0WWw8Y30xJ47phsHGrfA5iFsZF8p5tpzPudbbxbWc9HNY20BFjDyaIqPDIjC02hSyViDYi2aNw3aWwfWzr8jPjZRnfddRcNDQ3+n6KioqFuEgA/nziGmdGOLh9ulVHwoouvJO+UWFCVLle95hH/7whuzBgr7vmJwzevwjR9wUZ3AYcJGKavou2CpMNrEXX8Xe29Nvr0OBik2iH9ouNvGCkB1zBR1Obmkm25nJAQzcYlM4nqw3uuAmcnx2EJ0vN3wNnGuZsPcPKGfXxndz5X7jjEMV/u4o/5XWcOnZIYy8r5U1iWcDjZXMFXh6/Bq3Pulhz+72ApTd7Rk485oN+25ORkNE2joqKi0+0VFRWkp6cHfEx6enqPtrfb7cTGxnb6GQ5iLRrvzp/Cz7LHkGH35cBEqipXZCTx99kTsA7TY7oQwZjRVtxLUjASjupNsSl4Ux0YYyLQx0biXpiEe0kKDOd1dBTFl+vi7abHRFVQO1ZiDpYI202C7LDmNcA9ek5oA00H8lrdvF/VwJamFprDWM06GBPf+niBFLS6OG9LDlsbO0/bd+oGv80r577crlWFF8VF8fq8yTw8bRzQOS+nwavz18JKvrElZ9QEMAMavNhsNhYuXMiqVav8txmGwapVq1i6dGnAxyxdurTT9gD/+9//gm4/nEVZNH4wIY0ty2ZRespcDp08h4enZXJ2Sjxvz59C3Ei6UhOC9jV1jkvBdWIa7oVJuBan4DplDN75SXjmJOKdldB15s4wpuU3+4KXQPkfpola2oLlYNPgN2yw2DS0nXUjOv+lN/ry6VSBf1XVk9viCrpgZLi+lhT4YvuP+eU0e/WgKxg8XVxNfmvX+kNNXp17c0pQ6Lr2ko6vN+eP+RVdHjcSDfjZ8/bbb+epp57iH//4B3v37uW73/0uTqeT6667DoCrr76au+66y7/9D37wAz744AN+//vfs2/fPu6//342bdrEbbfdNtBNHVBHJ2SVuTw09CFqF2LImCZmpAUj2eHLaRkhgUogaoMb24ZqFKfXd0PHSdwwUQudWHfVobSNjivVgEwTfVpc/08DH+bi+jALzgCavQaxFq1XyxOA78R7fmo8WRG+pSicus4LpTXcuCufa3ce4vXyupBLL6nAa+W1XW5/q6KONsMMmtCsA/8srcZtjPxzz4CvbXTZZZdRVVXFvffeS3l5OfPmzeODDz7wJ+UWFhaiqodjqGXLlvHSSy9x99138/Of/5wpU6awcuVKZs+ePdBNHVS/yysb6iYI0XMjOFAJxEiPwLKnAduXlZjxNoxoC4puola7UDztix3aRnEPqaJAtPXwv78i6vuY6zM92sHy5Fh+eoCQBeLiNJVG3fD3EiiKb7b96Umx/H56FgA7mlq4fPtBaj2+ei3hhBWKAhUB1s/b72xDUw7P6A+kSTeocnsZO8JnHw3Kwoy33XZb0J6T1atXd7ntkksu4ZJLLhngVg0dp1dnf0uYJceFGI6GYh2fAaBnRqPWuNAq2lDq3aj17k73m/gWPvxKGyXvdX/6VnoiKTYr149N5ukQawpdPy6Fy9ITeLW8jqI2N/FWjQvTEpgfE4miKNR7vFy27SCN7Xko4faHmCak2rrWE4sMMxUh3O2GM1lVegjUjZKEKfEVFuhkZpgjoyjdkUwTz7Q4lIq2LmPopgJYVLzjowM98qtDApcuKt1epgH3TRqLWzf4R1nXIRwV+GNBBQdbXDw2czzWAN+NV8trqffqPa5bowOXBFho8evJcTxaGLh6fUebjo2LIsE68k/9Iz/8GoE8o2C8UQg/w8CyqRrtUNPIy5tQFIiw4J0aQ/t6kv4TiRFpwbUkGRzDeNaUGBI1Hl+O1JZGJ/+tDZzQ3XGUf6+qnocCpAnopsnbFXU9L/4IXJeRxMRIe5f75sdGckJ8dNBEYhP40YS0IPeOLCM//BqBkm3WsMc2hRj2VBXv7ASs22vQJw+PUgU9ZWTH4hofg1LjQnV60ZPtEGUZuPL90psxoun1Lm7bsJO3o3TfcTzE22kCfy+u5ocT0ojSNAzT5ImiKh4vqqTC7e3RfiNUhZszU/lJduDSIYqi8PfZE7huVx5r6p3+Su+6CVZF4XfTMzklcWR+R48mwcsQiLFonJcaz7uV9SNmWXchQrKrGGkRQ92KvlEVzBQHeuiCp30ngcvw1tF7GGRVbqVN58fPbsYzJwE9MiKsodIWw+DLuma+lhTLT/YX8VKAYaZANGBOTAQ3ZqYSoSqcmBBDtCV0T2Cc1cKb8yazubGFf1XV06IbTIl0cHF6wqgYLuowev6SEean2WP4uKYRp25ID4wYckqTB7XGBaaJkWDDjOvhFGhFQR/zFU9sFaNDq+5b/uHoHjLTBBO0bbWYCuhp4QUuHb6/pwAPvkJz4dKBH05IZ3lyXPjtx9cDsyguikVxUT16XHfKGlp5e2sJlY0uUmLsfHNeBuMShuZ7L8HLEJkYaedfC6dy5/4i1jc4h7o54qvKpWPdXotW5/b3AiqAEWPBPS8JItsPEeEMdYzmKcXiK8Oa24ipgndqXOcq0c1erDtr0Zq8mBalx8np9T0IWjrSCq7LSOLMIIXsBpppmqw9VMPWwnpUBfKqnbyxudjXPkXBME1+9+F+rj8hm198fQbqICfrS/AyhKZFOXhnwRQ21js5b2vOUDdHfAVZt9WiNvimBx956FGavdg3VOFalnr4AB4qgBlpibpCBKG0eNEaPFhKWjEiNbBqKG1eFNcRwYfX9C3GaR2YgH2aw84NY1P4VmYSyhAMM+ZWNnHTPzdzsMqJ1h6oHPkNP3Jtpb9/kUeMw8IPz5g6qG2US6Vh4Nj4KP46M2uomyG+glSnByVA3KGYgMtAK+5mXZ9OD5JcDjFyLYjx5WwZDg3P+Chci5LwzE5AHxOBael8qlQArdg5MAtbtnjJe+cQ9/xtPbe+tJX86sHtma9sauPSJ9aRX+P77utm8Iq9HZ749BBOV8+Sj/tKgpdh4sK0RP6zcCpnJsX6M8SzHFYi5HwgBkqzBzyhD0taSZgHTglcxAim4PsqPDEzC++cRPRpcZiJdswEO3pmFO7jU/FOjO50ErfkNfuWjjg6gOlLL6RporUvBGqY8OHucr7x2BccrGru/XP20PNrCqhvcaP3IDBr9eh8nlM9gK3qSoaNhpH5sZE8P2ci4BtvVBSFf1fW8+3d+UPbMDEqWYqdIReoU6Db4EaI0cAEdjW38oN9Rb4P/pHBeHsuh3dKHN6xUVhyG7GUtaJ4DGzrq/BMjcMYc0Tybm8DecMEr4Gl8PAFg26YONt07ntnNy/csLh3z3uU0vpWXt5QyJaCOjRV4aSpKVyyMJO4SF/F3re3FveqQ6mlh9O++0qCl2GqY5zz6ylxxKgKTQPRPSm+OgLkqyhtOqZCwGEjaC/YFiEF2sRXgwm0Gd0kpkdoeOckQmQjloNNKG4D2646lAMNpGfE4JkZR6mu92oGqdKqY91SjeLu/GjdNPkit5qi2hYyE/s2s+fd7aXc/uo2DNP0Byif51TzyKocnrvuOBaOT6ChtXdByOTUwa1ELcNGI4AELqLPOg7IR3Rp6+OiggYu/m0y+3eqpRAjWvv3yDs5FqJ91/4JkVZuPX4iH167mI+WzuCqjCQcPZh5k6Ur2DZVY/uiArUl+NIxhbUtfWr6rpIGfvjKVryG2alnxQScLi/XPruBOqebrMTIkD2yR1MVmJ4ewzFjezadu6+k52WYq3J3XTlUiF4zTP8li5FkR09zoFa0dTlYmYARb5NFCUcS04Q2HSLksD4YzBPSWTlnEnPjozpNE/7ttEzunZRBYZubn+4vZmOjM2jCqwJENnp9NZa6sb2onuMnJ/e6vc9+mefr0Q+Qk2OY0Ozy8vrmIq5YksUv3t4V1nNqqoLdovK7S+YO+qwo6XkZ5jY29i3aFqKTjtVkdd8BzDMnEX1SjK9uRTtTVdDHR+FZlDTwCy3KFOv+oyhY9tajlrfI6zoIXKbJ17fn8veSromqURaNGdERVLg9IWfqmEBegoZ3SgxGN0O0/9lV3qf2rtpXGTIJ1zRh9f4qLl44jmMnJHT71VeAk6ek8M6txzN7kHtdQIKXYU+XY5Doi0AnMUUBi+r7v6rgnRyL65QxtJ2STtup6bhOS8c7Pf5woDOQZJZS/zBNlHo3WpULxWMi644MDhO4J7eEtyrqAt6faLWEHoJx6Rhba9BymlBbdf9zBrKzpIHGtt73xIcze8itG9gtGs9fv5jrj88m8ogifdF2C/b2KeOaqqAo8MmBSp7+Ig+3d/DrxEvwMswtiO3Z+KMQwOGgxemFRnf322sK2DVfQbqOoEWu3kcOr4ltS7XvWNGDSq6i7xTgd3nlmAG+L5ekJwR/oMfAtqEKtarzsG2o473ey6vZOqebqanRIXtTNEVhYZavvRE2jbvPncmmu8/gvdtO4P/On43T7fUHKXp73oxpwmubirh7ZXjDTP1JgpdhbpzDxvLk2KBLnAvRhWGi1LmxbqjC9mUl9g3V4A6eCBiU9IqMDKaJ0uD29bgAWkXbwA/3CT8TONTqIrela97KZemJjI+woQV4O7QiJ/+/vTuPj6q8Fz/+ec6ZPZns+76whH3fFUGsUm21atHWpehFrBZti/6uYjer14q9avXeWnEtatXa21pbta2t0ooWQZFFQNkCBEL2ELIns53z+2OSkJCZyUxIMpnwvF+vvJSZM2eezCQ53/k+3+f7iFZPn0Xz4A1osuKtxHUsZw7WwaomVr70KTMeeJdtx+oDLoHW0blmTs9mqTaTgUlZsfxh23EEvrNCug6//7SU0jMsKA6VDF4iwCNjc8i3mWUGRgpIPdSIYVcdanEjSpMLLdEMUQbw6KhlsnZqJFMr2rr+X9Q7EXWOwen+Olzpes+vMPC14aJBEdyclYzdxxSsejy0zrn/sSA/pKLYL8obuexXm/jnvuqAPwqdU0APXTmZ3MTeqwsrGtrYURo48BEC/rq7IuixDQRZlh4BkkwG/j5jDK9W1PFKxQlK2524NR1Hxy+pSQicMsU/cvW1KaLesc+KU8M9MZ7uH5E8hTEo5a0oFa2Qbx+S4UpDSNcRbR7UylPBqQBMO07gnJaInmAOblPNSNX5vYX5+zMIyLWaetxW1u5k2c5DHG5zdGUJumcvhEML+gPp0olpLJ+fF9KYfvjGbhwuj9+6SQHYTCrnjU3hpnPzmZ7je4qrMYi+L4oQZ1SP0x8yeIkQUQaVldnJrMxO7rqt0e3BretYhGB9+Ql+fbyGMocLFe9W6lKE6wxIOzeA8/cHWghEoxPPuLhut536Xy3dim4coRevTm7Nu9zUraFHGdBjTWG/oA0VUdrC6V3RhFvHuLUWT05Uz5+LMzWcAqFhNJZYVWXlnhIuTIrh6rQE7AaVa3cd5mibdyqp8+3pHkfoJsW7vUAAcVYj9146nkunZKKGMBV4sKqJHaX1AY/RgfU3zmZ2fkLA49JiLRgUgTtA6sWt6eQmDG1PKBm8RLAYw6lKmFU5KazKScGpaRiE4IodxWxtaJFBTCTr7Mlg6GN2V9fR4wN8whYCPckyOGMMN11HPdKM4XATottHTC3KgGtiHHqcOYyDO3OizY16tBm1sg08OnqUAXdOtLcdfcd7rY2NxWk3YNpd3xWz6oBQBdpANhnsZ3bXX63ESHLC7eHf9c1sqm/mFyVV3J2fzr6W9t4HtrkRLg3douLJsiGKmwJmXx77xlQWj00JeTxHgtzMsaS2pc/gJdZq5CuT03lrV4XfFUtWo8olk9NDHueZkDUvI4xJUVCE4Ffjc0k3G3u8wcPjM4oUEiF677Xi6xg1QGamvyJgKtJQ3ITxYGOPwAVAtLgxba1FBLPSapgS9Q5Mm6pRj7V4pxjcOqLBhWn3SYw767w1LR3vuZ4RhXNGIrrS0WAw0YxzbjJ6dGgFnoEH1DE9o+kh1dNEK2JwLjTDJOvSnY43I37fobIeiyyUEw5MW6qxfFCFeXMN5n9VotQ70U0Kuo9vQxEwvzCR80Yn974zCHZLcO97tCW4/MVdS4uItxl7ZX8634KfXT6RKPPQ5kJk8DJCZVpMvDdrLPcUpJNvNRGjKoyxWXhgdCaToq3hHp4UCj9dMUM+x0jj8KAebvJ5lwDQwXCwcUiHNGA0HdP2OvDoPVajdL6LSnU76tGeOw3riWYci9JwnJeGa2bSwAYu3X/+FBHSJyGLqhA9GD2DhmlBsoZ3j6TO0SnVbRg/rUU0nKoJEXgDGkXT0WN6vk+qIrhiehbPL5/Vo3NvKGbmxZMQZQp4jNWoct6Y4IKjjDgrf77tHC6emNYjgBmXFsPzy2dyxfSsfo3zTMhpoxEszmjg9txUbs9N7XF7vEFl1d5jYRqV1C8jMfg4Q91X2PgidFBqHd6+J0PRcG8AKZXeXYsDMRxtxpMXfepnQwgwqjCAMYvfgtgQfh5rXIM0eT2Ml4MLOupcNB3jnvqu23oco4OqwXmpsfzkpgl8VlqPqgjmFSaSYj+zaV6jqrD6S2P4cYD+K99ZVBhStiQzzsovr5nOf7U6OX6yjRiLkZzE8G0fIoOXs9DlqfH85/5SWofpJxfJj6EuUBzmAZNwevosqBDgLXiOtOClwRlwx28B4PCuMMM8CF2gImDKkBYXRBmHVeFuJx3vxVWvaQ8YhHo0nQ8O1GIzqQOevbhuTg4tDjeP/mM/bk3HoIiumpVbzivktvNH9eu8cTYTcbbAWZ2hIIOXs5AiBAvj7fz9ROOIL6QbMTw6PjtdncV0s9pnJagOw/oT+rDlYxdyv4cSpoJci4phVx3uQrs3iOlDrsXICaeb5gH40JZjMXG83Xn6Iq8uCnB1egJ/KDnmLZ4OcC4dKK1rIz12YKfzhRDccl4hV8/M5q1d5ZTXt5NsN/PVKelnnNkZDmTwcpa6MSuZd05EaD3A2SiyEgdDwpNuxbC/we+VUxegJVm8Wx74PKDjE7vTE3gpuj+ajlLTjlrR6s2C2FTcmVHo8We+TFtLsmA45n/FiA7oUQYwDfIPRhDfR9g+AKkKnnw7xoONuKYm9nn4L8flMiM2itcqTnD3geO4gxy4AAxC4NZ1DELwg4J00s1GbvniaMDj1xSkk1Pr4rH9DX0+R8wg7gQeH2XiW/PyBu384SL/JJ6lFsZHc0164CVyUhh17xQ6DNPiw4JJxT0qxuddnRkX92jf9wPe/jhVrR0FqCG+vk4Ppk9qMO2sQ6lqR613olS0Yd5ai3H3yTMuJtWSzGg2g8+VKOC9QLrz7Wffz4VLQy1pwrCvHvVwE7pB4Jqc4M1M+skSCbyZkpmxUahCcG1GElvmjmdebN/LyBXgnPhobslO5sExWexaMIFbc1K4LCWOGzO8AVP30FgV3q+nJ+SSbDJy3dQsDH1k/mxGle1HT9Lq7LsZnHSK0H3tJhXBGhsbiY2NpaGhgZiYAH+4JHRd5/IdxWxpCK1NtSQNG7qOeqwFw6GmHrUFWowR14Q49JgAc/P9CQo7HmPcVotS6/A5HaADnkK738AqWKLVjXFrbVcjMwFddTDu/GhvYHY2BS+dHcU/rPK+Jp1dpHOicBfF+nwtOm95cVI+FybF9rivwuFk2kdfBHxKg4Br0hP577HZPoaj805tA88fr+WzplaMimBpYiwrs5MZ121F59q/7uWZDw77zVB1TrslRZt4+aY5FKWdvdetUK7fctroLCaE4MnxuVy4dT8n3B5Z/yKFLtxZISHw5EbjyYnyFuZ6dO+VwBbEn7b+jvtkO2pt7034uk4LqEebvZmRM6hT0m0GnAtSUCvaUKraEG4NLdqIJzvK20H4bNPRY8aTHYXxwKkpb/VYCxgUv1m2nxSk9wpcABKMBiyKoD1AlkzTvVkb38MRfDk5ji8nxwUc9l1Li/DoOr/+9xFvQvW0+zv/fbLFyXXPfcwHdy3GZpKX5r7IaaOzXIbFxF9njuGCxLM32o944diQrvP5BipwOdPvQQhvbYvVEFzgEurYANrd0O7BtPNkn4G+cOsD0yDPoODJjsI1Mwnn3BTcE+MHPXAxQo+9eEIxKP1culMEWnzPrskCUEuavMGrD0+U1uDQet9nVhSuSksIGF8KAVelndn0uqoIfnTJeLbcs4SiNLvfXxmPDrXNTt76rPyMnu9sIYMXiRyrmd9MLmD7vPH8fkph11yuNMyFawddTYcmF4Yv6hE1bQPTLKx799bhNJOt6Yh6J4btJzBursH8QRWKM7gN9Xw1l/NnYtTwWf0xvVstSKjvRIbZyLZ541mdm8qCuGgWxkcz109tSb/DXh8/H0IDpbZ3O34dOOFy85ca30Wzd+alkWI04m+x+Q8LMkgxD0zjnJQYC4drWwL+eAsB/9pXMyDPN9LJ4EXqkmExcW6CnQl22YE34gzhzrqipAnj4SbU8lZMu08imjo6hw5E0DEY2xycAcO2Wsyf1GKoaUcNMmiBjpVO3TrcxqqK3z+2AmjRNN6cWog5zMu678xL5ZOGFr9LgANRBcyPt5NpMXF3QTqvTxvF/00dxevTRnF7TgrGjve183VIMho4Jy46tJk1XUc94XvKTvhZPmQQsNVPXV+q2chfZozm4uTYHgFMtsXE/xTl8J2c0PcVCsTtb4vnDroOTk9/Xv2zj5xYk3oZL7cPiAxhusjr+XZcQoBbw7C/AdPHNXiSLbjHxUKQe6UMe7oO7R7UOmeffTp6PRTwZNh6LGOuD3BB0oEjbU48QvDezLEs/GRf2OrPTjjd/X5uTYcbM5N63a4KwQ8LM7g1J4W/1zbQ5PaQZzVzfkIMu5pauXj7weCeQNdBA7XUdyCiRfv/2Qv0KT3DYuLZifnUOF2UtDmJUhWKoiwog/D7NTEzht1lDX6TlYqAyVm963Ok3mTmReplmt1GUYhpbHGivfecs9NzakrB6QFHtzbh4ZrykPqv8/3q/KNuUHBPiMeTYUOtafffTyXSdHyfhr0dUw1G4d1Ar6+HdX5FG3CPDf0CdKjVwegoC7+dXBDyYwfKC+Ungjqu+4VDxRvcPTo2m7EB/m4kGA18Mz2Rm7NTuDApFoMimB4bxd35aX0/YUfgYtxxAuHsHQhqNhU9znctkFuHc+Ptfk6rs6m4lm//5lMue/QD7n5uK3/ddIyqRh87Qg+AGxfkB5xlFULwjVk5g/LcI80I+ZgkDSTRsSv1ZdsP0urReqWQVeD03UrUOgfKjhPocWZQBaLFjdLS8SmuY3mnDjjOTQWrimh2gUNDTxo+c/1SH/x8EnWPiYE294jrZKtHGXAnxnqDs6o2jJ/X+z7OpIBbR7eoeLKj8GTZwBD658LOYtdFiTE8OyGPlZ+XnMHoB48CXJQYw7amVgRwXrydr6TE4dF13jvRyNzYKKINwQeyq/PSGGMzs+Jz303fAHBpmD6uQW3ruSpSFQKhQMvEeJ8/nyreTWq/lNR7QYKu6/zX21/w600lqN1a5697v5hfbzrCb1bMZkbuwPbCumxqBv8uruUP246jiFOf7VRFoOs6jy6bQlqs/JsYDNnnRfLrUGs7Py0u593TOvF29iUwCLq6VCod9Q+B6Ao4zksDBOZNVThnDfDOt1L4uDzeTQGHGQX6Vb/RnXq40bsLsA6iuGMX6869EHVoPy8NLGf2vRsEvDtzbI/+INM/+pxyhyvAo4aeKuCSpDiemZgHeHul/L99pfyzrqkrqLAoghVZydyTn+63QZvLo/H6tuMcqGoiK97K+ZPTmbttf8DnNrS6yTveTsXRRjyajhCwpCiV1ReM5vW2Fp4srUYV3lU7nc+aYjLw+rRRjLL1Dgj+tKOM7/9up8/nUgTYLUY233P+gC9b1nWdP+8s54WPSthT1oBBFSwpSuWmc/OZlhM/oM8VaUK5fsvgRfKrzaMxZ8sX1Drdfi8Ad+Slkmsxk2sysuJ/N9Hs8Phu3CW8dQDuUTGYtteiJVr6lVqXhifR4F0WrEcbh80eTP+Zl8rWhlY2nmzqfw2J7l1tZPqkFj0vGldOFEpZK0qbB90o8KTbBmzpsgCWJNp5eGw26WYTd+w9xu+q6uijxnPIKECu1cSb00eTbPLuE3Thp/upcLh8/n24IiWOX43PRXTLiOi6zi/ePcCT7x/qynRAxweieSm0x/j/MKMAPyzM4Fsp8ZxodhJvMxFrO3X89oYWXiiv5YvmdqJVhUtT4liWloDdTxbokv/9kL0VjQGncR66YhLfmC2ncYaKbFInDYg/V5+kOkDLahX4rLGV+XHR/HdJFSfHxWLcWYfHZkBPtoACosmFqHUgDALh8GDaUo0n3+5tKiaNGLrdeOrjrif8uzinGA18Py8NVQg2nGhgzf7jlPYniyEEerwZjArK0WZEsgV9VAw9zjRA/W504F91TVyy7SD/mDmW5ZlJ/Lay7ozPOxAsimBVTgors5KJM3ovG88er/EbuAD8sbqem7KSeyy9/vk7+3hq4+Fex+qAvqUaZV4Kmt13AKMIuCotHrvJiN3S+5jpsVE9nut0mqbzwcEaNh6owenW+Lw88N5uqhB8fKROBi/DlAxeJL/+VdcUcMdYT8cxG+qaUAEt1YpjcZq3cLOzlaTSsfFdswuhKt6L3AirjZDo+Z6GMfPS+cwPjc2izuVmxZ4SPmloOfOVCaoAF1h2nGDMgkwO2KC18yP7AK5K8ehQ5XBx054jHGp1DMiqI181aqHevzwzif/MT+9x+yvlJ/qcklt7uILfTxsFQF2Lk2c+6B24dNFB3VsPs5N7nLdz6m/tmCySTf2bZi6ta+XGF7ZSXN2MoaO+pE/iDHrRSINuUD4elZSUsGLFCvLz87FarRQWFnLvvffidAbuOLlo0SKEED2+brnllsEYohQEt673+cez849M1x+/zhUnQpy6oJlUSLB40+sycBn5wriSLMdi4oVJ+SxJjOHrOw+xraO/h6+LbNB//Fxa10o5za1T+VkNrlC/vW6bbCo17ahHm1GPt/RcgdfBA2xpaKHGNTAb9WnAV5JjsXT87hk6LspGIfjPvDSuSo0PGG96gGWpPWsxdF0Panzbm1q7AoU/7SgLvNIGUE86mdCq9+jUOzXGxm8m5XN9Ru9l2MFod3m49rmPOVLr/Vlwa3pQU3EeTWduoWzYOVwNSuZl3759aJrG008/zahRo9izZw8rV66kpaWFRx55JOBjV65cyf3339/1b5vNNhhDlIIwymru+yBJOt0Q9p/JNRt5bFwO9W4PqSYj02NsCCH4Y9VJ9rf4X+6qAosT7bx3oinwE+g6amlLj265TclmPKEGZx19ccybqhHtp1bMGAR4sqO89V8hBPahFCIbheDhsdkYheCtmnoqHC6SjAa+khJHgtFASZuDt2sbaPVovTIwArgsJY6J9p5/h4UQfe4LBNDi0TjucJFtMXH8ZGtQ482qcfH2l6dQ7XRjUUS/sy2d3t5VwbG64J67kyLAoCr8a181Te0uls3MJsbHVJUUPoMSvCxdupSlS5d2/bugoID9+/ezbt26PoMXm81GWloQ6/6lQWdTh9/qESnMNH3Qs2d2RXCpLYpXmpr6DIQm2G28UlFHlKpwcXJsV0O5P1WdDHiB9wB7mtp4rCibHx4oo9XH3jfoOtS2Yyg+VRuhAFHJNtr6842pAl3TEXSbjtA7NhbUdNwT+l5pogAXJcWgCMGhVgeNLjcnXB4cvlrme0/P/aMzie+oU/lmeu9MQp7VzJ+nj2bVF0fZ2y3gMwi4PiOR+0Zl+hxLkc3CzqbWPt+jzn2F0mODa36ZHWfDrChk+9kQMVTv7KlAiNCSgZoOTrfG3/ZU8rc9lTz4l73cd9kErpubNyBjks7ckNW8NDQ0kJDQ95r5V155hZdffpm0tDS++tWv8uMf/zhg9sXhcOBwnGoX3dgYuAhLCl67pvU5Hy6dZYLujx+4iNVnLZWug1unVdf544aDiGkJ3qlGX+fpiFLeqW3wBgMCXio/weRoK69MKeCky9NnZqLRo/HN9ETmxEax9NMDNHbvgttxpRMWg/cq3jFPpAHmshaUsXa0UDNMQniXVJ/WZE0A6vFWPPl29D42ldQAh6bz6pSejewqHE7uLy7nzZr6rimRQpuZu/LTuTQlrs+hjY+28s9ZY9nR2MrelnYsimBRQgyJAZYJ26rbwRb4NYhSFTLN3iDksqkZPPjXvX1ORV83L7fP8QK0OT28vaucLyoaMRkULhiXyszc+B6rmwCaHZ4+AxdFQG6ijdK6Ntw+skkeHX70p89JiDJz8aR0H2eQhtqQBC/FxcX88pe/7DPrcs0115Cbm0tGRga7du3i7rvvZv/+/fzxj3/0+5i1a9dy3333DfSQJbzNneQuG8PMQO7k3B/BPHcQY9TpNvWh6SjHmjEcbUFp93Tdb/zsJM75yd6Gb93P1+38WvcTAp+3tPGtXUcYazOzvanFb22DAPI6Ptnfsa+UltMzLx3n16MMuIrievQwqjna5H3moriA36NPPrrDdlIqWvEUBl4eqoDPBnDpZhPrJuTxM5ebY21Oog0KhVZzrwt5IEKIPlfsdKpvdbJrcxksTOsoovEVYOpclhCLtaN+JSXGwvXzcnlps/9mdBeNT2VMqu9uuN29v7+a23+7g6Z2d1cvmac3HmZadhzPLp9JUvSpKe+xqdFsLanrsTS7O0XAuPQYrp6VzU/+/HnA5/2vt7/gyxPTQnpdpcERUsHumjVrehXUnv61b9++Ho8pKytj6dKlLFu2jJUrVwY8/80338xFF13EpEmTuPbaa3nppZd44403OHTokN/H3HPPPTQ0NHR9lZaWhvItSQFcmhKHKcAUgQpYZQHu0BriP5qxBpXbcpKDX3URZG5e5VTgYtxZh3F/I6L9VI5PB5R2D6bNNSjV7T3PG2DnaY8OO5pamRJj67Moc3lmEvta2tjS4D/IQRFoaVZvF91uDKUtqKdvhxFIR78Ypd1PHlOAcGokGtWAf5Q14JJk//2REowGpsbYGGWzDOoFdsexetxu73uHRs+dxTsKtkWTi1nOnmO479IJrDgnr9fMowC+Pj2TJ6+b0edz7ylr4KaXPqXZ4S0Ydmt6V7ZkZ2k9X/rFRt7fX43Wcds1c3L9Bi50DH35vDzWbyrp87krGtoprevXpKE0wELKvNx5553ccMMNAY8pKDiVziwvL2fx4sXMnz+fZ555JuTBzZkzB/BmbgoLC30eYzabMZtlYelgiDGo/KQwgx8eLOt1nwpYVIU781K5/1DF0A9OGhLL0uIBgUEIXH0FJqfvfeSDVRHcmpPC/5ZUAaCWtaLUtPcKjjr/rbR5MO2s8+4tZFXRdXDNC7zTr0FASZuT5RmJvOhjrx4FmBUbxTfSE3i9MnBXaO8DBFqMEbW2227GGqQ1eihLVDB01FN4AyAfWafOvZIONvh/CmDlmHSWTcvlS1v349b13ttyCMizmPlyUvibO2od35Na50Bsqcadb0dLs3rTGA7NG9wdbcaUldHjcUIIfvyVCdzxpbH8/tNSDtW0kBVv5bq5OUSZgyuIfXrjIb8L2nTgZKuLG9ZvZXx6DC/cOIuxaXZWXzCGx9470Kv2RQCLipK5YnomP/vr3qCev2mYdT0+W4UUvCQnJ5OcnBzUsWVlZSxevJgZM2awfv16FCX0Vdk7d+4EID1dzjGGy4qsZKJVlYeOVFDR7Zd2ZmwUD43JYrTNwpNHq6l1y8qYkeifJ5r4ZnpCcKtr+qhxeW5CHucl2Ik2qLxf18TOxlbUY81BjUM4NYRTQ7P2XUSu69CmaTw0JotCm5knj1VT2dFsMVpVuD4jkbvy0zErSvC1xz6+/dXZKaQUxvGP2kbaNY1xURa2nGzi3brOLQRE13/VkiaUk/5bRahCcOvcPBKjzLwyuYAVn5fQ6PZg6DiFW4dRRiPXY2X9h0dIi7Vw4fg0rGHaDHNyVlzX3jxKsxvT7pPou092zQV2vqwzcn0XIUeZDdywID/k59U0nb/tqQyYSem0v6qJ5b/+hL9891y+d8Fo8pJsPPX+IfZWet+fVLuZGxbkc9O5+RhUhXibkYa2vgOTrDi5AnY4GJSal7KyMhYtWkRubi6PPPIINTU1Xfd1riQqKytjyZIlvPTSS8yePZtDhw7x6quvcvHFF5OYmMiuXbtYvXo1CxcuZPLkyYMxTClIV6cn8PW0eHY0ttLYsZ19ge1Utuvx8bks33V4ZBT2BpE9OJscbnOQZTaeUe2TKuCixFgu6VY4ujIrmVu/OIpodofUCMyTYumzpsaDtwBVEYKbs1NYkZVMcasDj66TbzV31WAAnBNvD9iI0XtCDaW+d+AxNtXOrKRYLuyWCSluc6AK0et3wZNqxXC4Gd2t9Vh23enupUUkdtRpnJtgZ+f8CbxZfZLPmtowAs7Djbyx4SgPuDXv+XWdKLPKT74ynqvDsAtxst3MpVMyeOuziq7AVkBXEZKqCM4ZlURe0sB20u4+RdQXj6azt7KJjQdrWDw2hcumZnLplAxOtrpwezSSos0o3aLXG+bn8dO3vgh4zkmZMT22JJDCZ1Ca1L377rsUFxezYcMGsrKySE9P7/rq5HK52L9/P62t3vX3JpOJ9957jwsvvJCioiLuvPNOrrzySt56663BGKIUIlUIZsZGcX5iTI/ABeCCxBj+MG0Us2Mi7BNJZ+5Z10/N2be4EScdgR8XDmHcgmxTfXDZEV8EgA635fSc6vlaShzfSEsIqRuvqgi0rCjoI9xRgCu7NVVThWBslIXx0dYegQtAlsXEpSlx+M1f6DrqsRbEaUUxVqPKTB9ZhTaP5jsQshpwzk1GSzT3uD891sJ/f30yKxf2XD1kUxW+kZ7I2jFZ5FQ5ee39Izjc3sigM1hocXi4+/Xd/GlH72ndoXDfZRMpSvcW13Z/RwSQk2DjkWVTBvw5jaogMTr4JdSqIvjH51WnxiYECVEmUmIsPQIXgKtmZZMa478EQRXw+NXTQh+0NCjkxozSgLpyx0E217dEziolXUdUt6O4NG/tRU07RBlwLkgN98h6GYgdkkNlFIJRNnOP/h99EdA1pWBSBE+My+WrPpbr6rrO11/6hG37agOmPiZmxBBlNjAqJZpX4zXa+viTVWA18dHc8UGPt8nt4drPDvNJY8up1gAd/WyUqjaMn9X1ypZcPCmNJ6/tXVz6PyVV/PxIhf/3qaNwN/3zBu758jiumJ6FGmDuqs3pYebP3qXFRyfeTmmxFjbdfX7A8wyWdpeH17cf57VPSimvbyPZbuaqmdlcNSubaPPAJvY1TWfNH3fxf58eD/oxqoBLp2by2NVTgzq+urGd5es/YW9Fz+aFSdEm1t84i0mZcSGMWAqV3JhRCpvVeWls2ul/ddiwIwTGI80oDaemBZz5fS/VHEoToy3UuzyUB1EoGK0qzI2LxqoIVCFodnvYXN9MS5Cp9tNdnhLnbUQWgmhV4eLkOCbZrXw9Nb5rI7/TCSF44MJxfPXAv/F4em9FoSqCzDgrr39nPuaO5cHvbv6CY+3+a0cUoCgquGZonewGlT9OG8V7Jxr5v8o69pxo5nhVk7eY+ITDZ57ntsWjfZ7rm+kJPFxSEbANvlrVTn2ri3vf/JxZeQkBp1Y2HqgOGLgAVDa0s+1oHbPzh76VvcWocu2cXK6dE1xvljPx/L+PhBS4gDcmDmbpdaeUGAt/+95C9lY0smFfFe0ujUVjkpmZ13ePMmlohXfrV2nEOSfezs/HZEXWhmYuDb2zMDIvGi09tIvfYPpmWjwXJMZSHmD3XoBRVhM/H5PFZ/Mn8PLkAp6dmM9TE/J4eUoh6ybk9eu5jQJ+VJjBjJiokPZaTDEZ+Z9xOdzUbQdif8alx/Ds9TOxmlRvszZFdPXtyEmw8cpNc7oCF4Br0hP6XEp8VVroFxqDIliaHMuvJ+XzwYLxzG8AY53vwOXbCwsYn+H7U2GK2cjDY7O92afud3QuH65zoB5rRtPB4dZ49sMAGxUC9a3BrWz5zsvbeXnL0eA2HIxAbk/fr5UvihAsm5kV8uPGpcdw2+LR/L8Lx8rAZZiSmRdpwC3PTOK8BDs37ylhV/Mw7omg6+D2Bi56mhV3dhR6/PBadv/bysBt7sG7NHhJYizLM31vXGftx0o/AdySnUKK2cgNWUn8trIuqMepwrtnUCgWF6Xw8Q+W8Ked5ew+Xo9RVVg8NoXFRSm9pkKWZybxUvkJqhyuXkWxKjAjJooLEs9suthiVPnNijn8z4aDvLLlKE0d/USy463cumgU35ydHfDx30xPJNti4raPD1HZ+ePk8GA46l0+3DkF5dF0/ryznJ9dPsnvubLig6sjq21x8qM/7eFAVRP3XzYxqMdEkiO1LVQ3BV+LpgqBpuusvWJSj4Z10sghgxdpUORZzfxqfC4LP9nXZzvwsBECjCquc4ZffUt3fdW5eHQ47vA/lTIrNopYg0pDkMvZBd4ePzdne9siTLHb+FFBOg8crgiq7uZGP0FUIHaLkevn5gKBpx/ijQbenD6a2744ypaOHaM7x/yVlDgeGZuNruv8bXclO0vrURXBuaOTmVuQEFLTNqtJZc2Xi/j+BaM5frIVg6KQk2DrVeTpzznxdqZXuthwoNpbAOTWfWZxWhxudF33O7Z5hYmkx1qobGgP6vfopc1HuXRKxojLFoS6EebcwgRWLRrF/FH924laGv5k8CINmtFRFn5cmMH9h8rDPZQRTRXezqr+WFSF23NSeOBwcM0EdeA3k/N77OZ7W24qE6KtPFVaw79PNvXOenRce58cn0uhzRLidxCaLIuJP00fzb6WNrY3tKIKwTnx0WRaTOwsrefmlz6lusnRNf305PuHGJdu5/nls8iIC21K0GJUGZXSvxqovMQoDAg8bv8X3sw4a8CgSlUEa6+YxH+8sBUBAWtpOo9/5eNjIy54yU+KIsZioLHdHfC4p66bznljUsLW/0YaOjJ4kQbVd3JSiFYV7j5wfPhmYCKcW++5NNiXVTkpnHC5WVdaE/C4Tq2e3vmVxYkxLO6Ykjnc6uCFslo+POldlbEw3s4NmUnk2/qXom93efj755XsrWjCYlT40vhUJmQE7iRbFGXtUZx7/GQr1z63hTanN7Tq3g/kQFUz1zy7hXe+vxCLcWgubN+Ync2vNx3xe78i4Nq5fRe6Lhqbwss3zWHtX/exu8x/l17wTkUdqGoKeEwkMhtUrp+Xy7r3D/kM4FRFkB1v5cLxaUFnx6TIJoMXadB9KzOJPKuZqz6LoFVIg2xclIWLEmOYG2/nG2fwuijAogQ7c/rYTE8Iwb2jMkk0GoLKwDj6+IhfYDNz/+jMUIbq18YDNXz3tztoaHNhUAQ68Ph7B1k4Opknrp1GjCW4pmDrN5XQ7tJ8Xtw8mk7JiVb+squCK2eEXsDZH2NS7dxyXiFPbez9/qpCUJRuZ/n84FbpzC9M4q3bz2HOg+9R1ei/9kPgnYIbib67ZDSflTbw7+LarqX44A0CYy1GnvnWTBm4nEXkaiNpSCxMsDMz0prYDQIBXJUWz79mF7GmMIOF8dFkBrmniy9Xpsbz3MT8oOs5FiX0PQUi8AZXQ2FPWQMrXtxKY7t3VY1b07tav28qrmXli58GvYLmzc/KA7aNVwS8vWtopzDvXjqWn10+kcxu01VWozeL8Ltvz8NmCu3z45XTswJuaaADX50yMrdTMRtU1t84i4e/PpnJWXHEWY1kx1u57fzRvLP63JCWREuRT2ZepCHz8zFZLPn0QLiHETYGATdkJHHvqFMZC0UI7h+dyU17SkKaVotSFf45cyy5IU7TTLTbmGa3squpzed2DireACfHOjQrNJ74V7HfTfY8us7HR+r45Egdcwr67mHS6ghcD6Hp0NRHzcRAE0Jw7Zxcvjkrh8O1LTjdGnlJtpCDlk7fmpfHy1uO0uLw9CpiVRVBWoyFy6cNTEZsODKqCstmZrNsZuAVX9LIJzMv0pCZYLdxUWJMZPWAGSAL4qL5bP5EHhiThfG0j86XJMfx7IQ80kzBZWAE3hqWUAOXTr8cn0usUe3VEl/lVJ+SoeB0a7z7eVXAbIlBEfxld3CFxgXJ0QGzEqoiGJ0aHeowB4SiCEalRDM+I6bfgQt4u+m+unIuKR1t7A3d+uIUJEXx2s1zz+j8khQp5E+5NKQeLcrhsu0HOdzmOGsKeFXguQm5xAe4qHwlJY4vJcbwzPEajrQ5MArBHyvraNX0rgxJ5zLl8xPs3J4T2vLuzqkXIQSjbBbenTmWdaXVvFZRR7NHI9agcl1GIrdkJ/dYZTSY2t29swen03Wd5iCzJdfPzeWu13f5vd+j6Vwze/A7wQ62iZmxfHjXYjbsq2bb0ZMowrsJ4oJRiSEtB5ekSCaDF2lIJZkM/G3mGNYfr+Wl8loqnS6EDkObzB9aL07KJ76PgOAvNfX84MBxqpynXolYVeG8hGj2t7TT7NEotJm5MTOJy1Piuz5tB6LrOn//vJLn/32E7UfrEcLbN2TluQUsHJPMA6Oz+K9Rmbh0HVM/GtmdqWiTgXibkZMBusjqeJfJBuPy6Zm8vaucD4tre0xDde4affPCAiZlBV7BFCkMqsJFE9K4aEJauIciSWEhN2aUwu6hQ+U8fqw63MMYFHkWI5vnjg/4ifjvtQ3csNu7pNbXL+MvirK5Jj30fWvW/nUvT39wuMfKDFUReDSdH148rtdOxuHw6D/286t/FfvtX6II+GjNEtJigysgdro11r1/iBc3l1DX4m3cl5to49bzCrl6VrbMTEjSMBbK9VsGL1LY1ThdTNn0+aDumJxqNFDlGuJiTWDtmCxuCNBxVtd15m7Zy7F2p99ptDiDymcLJmAOITvy4cEarn/+k4DH/PW75/rdo2eoNLW7+Pq6zRRXN/eYQhLCW8T7o0vGcdO5oQdZLo9GRX07qirIiLXIoEWSIkAo129ZsCuFXbLJyCNFg9d7Y05sFB/PG887M8ZwfUbigBUMdz+P4uP/L0uJ41sZgTMmOxpbORogcAGod3vYWBda47EXPirptS9Qd6oi+M2WoyGdczDYLUb+75Z5XD8vF1u3rqhjUqL51TXT+xW4gHdVSk6irc8OtpIkRSZZ8yINC9ekJ/HksWqKW/3v0dMfCrBufC4WVWFqjI0Cq4mPTjZxqC305+msnfhOdjJjoiy0eDTGRllINhp47ngtf6o+SatHw6QIsiwmZsXYcGoaFtV/R9eaILNBNc7Qska7ShsCruLxaDo7jp0M6ZyDJdZq5KeXTuDupUWUN7RhMaoyWyJJUkAy8yING9/LHfjiQw04f+s+/lHbwO8rTjD5o8+DDlxOj+w7Q4E2j8bVaQmsyErmnHg7hTYLDW4PTR4NDWjTdA62OvhhcTljP9zDi8dr/T5HWpAN6tJDbGRnNPR94R+qNvnBsppUCpOjZbZEkqQ+ycyLNGxckRrPfcVl1Lr8736cYlSpdXlCqo+pd2t8a7f/PWb88TeK9eUnmBoTxdXp3s3vHi2p5M2aeqB3wa1D17n74HGqXS7+M79359PJ0VZG28wUt/pfOp5sNLAw3n/30INVTbzwUQnv76/Bo+nMzk9ganYcVY3+e6goAr40fnjvpi1JkuSPzLxIw4YqBP+aXUS6n34o385K5q6CjEEt7O3OXzChAE+VeldHtXk0njve92aHj5ZUsbe5revfHl3nrep6rv7sEA1uj8/n6sw9/GxMlt+l0W/vKmfp/3zIa1tLKatvo7Kxnb/sruCvuytBx2d9jyLAZjJw9SzZpVSSpMgkMy/SsJJsMrJjwUQ2nGjk2eM1NLs9jIuycldBGskmIy1uD/cVl9HSMUUTDhqwt6WdFo+Hvc3tNPnYgfl0CvCb8hM8OCYLh6Zx4+4j/LOuCRX/GZ4si4mfjsrgkuQ4n/eX1rXyvdd29squdP7bo+uYVIFL072RWEfRTrTZwAv/MZuk6KHZAkCSJGmgyeBFGpaWJMawJLH3Urkog8rzE/O5btdhPOh4wrjQXyD67BDbSQP2t7QD8PCRSt7vWD10euCiAnlWM4+Py2FGjA0lQO3HKx8f870pUOe5FMHCMcnMzk/g0xJvJ9b5oxK5fFrmiN15WJKks4MMXqSIszDBznuzxvLs8Rreqq6n2ePBPYBBTOeqIn8UYGK0FZuqMC7ailkROAKs7Ok8Z7Sq0OrReKGs1m/WyAMcanNgUkTAwAVgy+ETAYM3j6az/Wg9zy2fxc0LA55KkiQposiaFykijYmy8N9jsnhoTBaxhoFdNXNFShyJRkOvjQs7acCtOSkAxBhUvhlE91sd7/5F+1raaO5jmkkBNp9s7vOcQewQgFy0I0nSSCSDFyki6brOjw+WccsXR6kLsDopWAYB58ZH848ZY/jVhDx+O6UAu0H12Xzu1uxkvpYS13X7jwvSmRDlv329dyrIxFf91K701zmjk/vcRfnc0f67+0qSJEUqGbxIEWlDXRPPlXn7pwzEjJFbh00nm7lm12EOtbYz2W7j33PGcVd+GqkmQ9eOzgC/rajj4ZJKnJr3liiDyl9mjOHW7OQe2ZrOX65RURb+MHUUFlWhKMpKtBr4104D5sZF9znma2bnYFQVvx2DNU3nxgX5fZ5HkiQp0sjgRYpIvz5egzrAUyIacNLlZuWeEnRdJ8Go8kVzO9VOd48alXq3h8dKqlixp6SrYNeiKtw7KpNDCyfzxLgcbspK4ubsZH43pZB/zRpLlsUEgE1VuCEzye8vngpMj7ExNcbW53jTYi08df0MjKrSIwOjKgIhYO0Vk5iSHdefl0KSJGlYkxszShFp/L93D8h0kT9vTR/NSZe7z+Z2T0/I5bKU+JDO7WupdGfskW0x8ca0UWR2BDvBKKtv45UtR9l4oAa3R2duQQLXzc1ldKr/xnaSJEnDTSjXb7naSIpIJqHgv0PKmVGAbQ0tfFTfjCrwu6JHBX5TdiLk4MWsKPxmcgF/qWng5fJajrQ5STSqLEtL4Oq0BKJDLEDOjLNy19Ii7lpaFNLjJEmSIpUMXqSI9OXkWF4qrx2UPi86YFAExa2OwEuRgcNtjn49hyoEl6bEcWm3wl9JkiQpOLLmRYpIN2UloSD8FqueCR1YGG8nzqj2ef6BXqYtSZIk9U0GL1JEKrRZeHFSPpZgmp2EQAUWJdgZE2XhitTA00ECuLKPYyRJkqSBJ4MXKWKdnxjD9vkT+F5uStCPUYFMk4HLO6ZrOlcsdf4ijI+28uT4XACuTksg02z0uapJFZBqMnBtRt8N6iRJkqSBJWtepIgWbzRwT0EGTk1nXan/3Z07W/6nmY38Yeoo8qwmrs9I4tWKExxpc5BkNHBFWjwXJ8Vh7Mjm2A0qb0wfzX/sPsLu5jZvD5eOAt7RNgvrJ+YTb5S/QpIkSUNNLpWWRgRd13niWDX/e7Sqxy7PmWYjBVYziSYDX0qM4SspcZiV0BKOuq6ztWP1kQ7MiY1mXlwUQvbelyRJGjChXL9l8CKNKG0ejS31zbRqGmNsFkYHaNsvSZIkDR+yz4t01rKqCosTZdAqSZI0ksmCXUmSJEmSIooMXiRJkiRJiigyeJEkSZIkKaLI4EWSJEmSpIgyaMFLXl4eQogeXw899FDAx7S3t7Nq1SoSExOJjo7myiuvpKqqarCGKEmSJElSBBrUzMv9999PRUVF19ftt98e8PjVq1fz1ltv8fvf/56NGzdSXl7OFVdcMZhDlCRJkiQpwgzqUmm73U5aWlpQxzY0NPD888/z6quvcv755wOwfv16xo0bx5YtW5g7d+5gDlWSJEmSpAgxqJmXhx56iMTERKZNm8bDDz+M2+32e+y2bdtwuVxccMEFXbcVFRWRk5PD5s2b/T7O4XDQ2NjY40uSJEmSpJFr0DIv3/3ud5k+fToJCQl89NFH3HPPPVRUVPCLX/zC5/GVlZWYTCbi4uJ63J6amkplZaXf51m7di333XffQA5dkiRJkqRhLKTgZc2aNfz85z8PeMzevXspKirijjvu6Lpt8uTJmEwmvv3tb7N27VrMZnP/RuvDPffc0+O5GhoayMnJkRkYSZIkSYogndftYHYtCil4ufPOO7nhhhsCHlNQUODz9jlz5uB2uykpKWHs2LG97k9LS8PpdFJfX98j+1JVVRWwbsZsNvcIhjq/+ezs7IDjlCRJkiRp+GlqaiI2NjbgMSEFL8nJySQnJ/drMDt37kRRFFJSUnzeP2PGDIxGIxs2bODKK68EYP/+/Rw7dox58+YF/TwZGRmUlpZit9vlrr+DpLGxkezsbEpLS+Xml0NAvt5DS77eQ0u+3kNrOL/euq7T1NRERkZGn8cOSs3L5s2b+fjjj1m8eDF2u53NmzezevVqrrvuOuLj4wEoKytjyZIlvPTSS8yePZvY2FhWrFjBHXfcQUJCAjExMdx+++3MmzcvpJVGiqKQlZU1GN+WdJqYmJhh98M/ksnXe2jJ13toydd7aA3X17uvjEunQQlezGYzr732Gj/96U9xOBzk5+ezevXqHrUpLpeL/fv309ra2nXbY489hqIoXHnllTgcDi666CKefPLJwRiiJEmSJEkRSujBVMZIUjeNjY3ExsbS0NAwLCP3kUa+3kNLvt5DS77eQ2ukvN5ybyMpZGazmXvvvXdAV41J/snXe2jJ13toydd7aI2U11tmXiRJkiRJiigy8yJJkiRJUkSRwYskSZIkSRFFBi+SJEmSJEUUGbxIkiRJkhRRZPAi9VtJSQkrVqwgPz8fq9VKYWEh9957L06nM9xDGzF+9atfkZeXh8ViYc6cOXzyySfhHtKItHbtWmbNmoXdbiclJYWvfe1r7N+/P9zDOms89NBDCCH4/ve/H+6hjFhlZWVcd911JCYmYrVamTRpEp9++mm4h9VvMniR+m3fvn1omsbTTz/N559/zmOPPcZTTz3FD37wg3APbUT43e9+xx133MG9997L9u3bmTJlChdddBHV1dXhHtqIs3HjRlatWsWWLVt49913cblcXHjhhbS0tIR7aCPe1q1befrpp5k8eXK4hzJinTx5kgULFmA0Gvnb3/7GF198waOPPtrV8T4SyaXS0oB6+OGHWbduHYcPHw73UCLenDlzmDVrFk888QQAmqaRnZ3N7bffzpo1a8I8upGtpqaGlJQUNm7cyMKFC8M9nBGrubmZ6dOn8+STT/LAAw8wdepUHn/88XAPa8RZs2YNmzZt4sMPPwz3UAaMzLxIA6qhoYGEhIRwDyPiOZ1Otm3bxgUXXNB1m6IoXHDBBWzevDmMIzs7NDQ0AMif5UG2atUqLrnkkh4/59LAe/PNN5k5cybLli0jJSWFadOm8eyzz4Z7WGdEBi/SgCkuLuaXv/wl3/72t8M9lIhXW1uLx+MhNTW1x+2pqalUVlaGaVRnB03T+P73v8+CBQuYOHFiuIczYr322mts376dtWvXhnsoI97hw4dZt24do0eP5u9//zu33nor3/3ud3nxxRfDPbR+k8GL1MuaNWsQQgT82rdvX4/HlJWVsXTpUpYtW8bKlSvDNHJJOnOrVq1iz549vPbaa+EeyohVWlrK9773PV555RUsFku4hzPiaZrG9OnTefDBB5k2bRo333wzK1eu5Kmnngr30PptUHaVliLbnXfeyQ033BDwmIKCgq7/Ly8vZ/HixcyfP59nnnlmkEd3dkhKSkJVVaqqqnrcXlVVRVpaWphGNfLddtttvP3223zwwQdkZWWFezgj1rZt26iurmb69Oldt3k8Hj744AOeeOIJHA4HqqqGcYQjS3p6OuPHj+9x27hx43j99dfDNKIzJ4MXqZfk5GSSk5ODOrasrIzFixczY8YM1q9fj6LIZN5AMJlMzJgxgw0bNvC1r30N8H562rBhA7fddlt4BzcC6brO7bffzhtvvMH7779Pfn5+uIc0oi1ZsoTdu3f3uO3GG2+kqKiIu+++WwYuA2zBggW9lv4fOHCA3NzcMI3ozMngReq3srIyFi1aRG5uLo888gg1NTVd98nswJm74447WL58OTNnzmT27Nk8/vjjtLS0cOONN4Z7aCPOqlWrePXVV/nzn/+M3W7vqiuKjY3FarWGeXQjj91u71VPFBUVRWJioqwzGgSrV69m/vz5PPjgg1x11VV88sknPPPMMxGdKZfBi9Rv7777LsXFxRQXF/dKscsV+Gfu6quvpqamhp/85CdUVlYydepU3nnnnV5FvNKZW7duHQCLFi3qcfv69ev7nEKVpOFu1qxZvPHGG9xzzz3cf//95Ofn8/jjj3PttdeGe2j9Jvu8SJIkSZIUUWSBgiRJkiRJEUUGL5IkSZIkRRQZvEiSJEmSFFFk8CJJkiRJUkSRwYskSZIkSRFFBi+SJEmSJEUUGbxIkiRJkhRRZPAiSZIkSVJEkcGLJEmSJEkRRQYvkiRJkiRFFBm8SJIkSZIUUWTwIkmSJElSRPn/FhwOtx1SoVQAAAAASUVORK5CYII=\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "En estas 3 siatuaciones, se observa que el número de ouliers identificados por el método DBSAN son 2782 (sin escalado), 2859 (MinMaxScaler) y 263 (StandardScaler). Adicionalmente, para el escalado con StandardScaler solo se forma un grupo, por lo que seleccionaremos analizar los outliers en este caso." ], "metadata": { "id": "fagbY1Xd128m" } }, { "cell_type": "code", "source": [ "y_pred=dbscan_scaled2.labels_\n", "df_new[\"Cluster\"]=y_pred\n", "df_new.head(2)" ], "metadata": { "id": "8A9AhzX7TjM7", "colab": { "base_uri": "https://localhost:8080/", "height": 112 }, "outputId": "14a93d0c-df85-4a1c-f424-532830cf9ef1" }, "execution_count": 56, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " TA HR PP PA VV RV DV PRS TW Cluster\n", "0 11.2 81.8 0.0 975.0 3.9 14.0 177.0 3.11 15.47 0\n", "1 11.0 81.5 0.0 974.0 2.3 8.6 208.0 3.04 14.90 0" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TAHRPPPAVVRVDVPRSTWCluster
011.281.80.0975.03.914.0177.03.1115.470
111.081.50.0974.02.38.6208.03.0414.900
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 56 } ] }, { "cell_type": "code", "source": [ "#Temperatura ambiente\n", "df_new.groupby(['Cluster'])[\"TA\"].describe()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 143 }, "id": "w1yBKzyNT8F4", "outputId": "a14837b7-317e-4d11-8a3b-7c9979df3597" }, "execution_count": 58, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " count mean std min 25% 50% 75% max\n", "Cluster \n", "-1 263.0 13.778707 6.469972 5.7 9.5 11.6 17.25 32.6\n", " 0 8007.0 13.158499 4.491293 3.1 9.8 12.4 15.60 31.3" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countmeanstdmin25%50%75%max
Cluster
-1263.013.7787076.4699725.79.511.617.2532.6
08007.013.1584994.4912933.19.812.415.6031.3
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 58 } ] }, { "cell_type": "code", "source": [ "#Humedad relativa\n", "df_new.groupby(['Cluster'])[\"HR\"].describe()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 143 }, "id": "sU9ND_kveQXn", "outputId": "6442d46b-59ba-4703-8c39-3ee64bdb08f0" }, "execution_count": 59, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " count mean std min 25% 50% 75% max\n", "Cluster \n", "-1 263.0 74.532700 26.770212 9.8 46.55 89.9 96.9 100.0\n", " 0 8007.0 76.760972 18.012040 14.5 64.50 80.6 92.0 100.0" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countmeanstdmin25%50%75%max
Cluster
-1263.074.53270026.7702129.846.5589.996.9100.0
08007.076.76097218.01204014.564.5080.692.0100.0
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 59 } ] }, { "cell_type": "code", "source": [ "#Precipitación\n", "df_new.groupby(['Cluster'])[\"PP\"].describe()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 143 }, "id": "1ceVLC2beUg1", "outputId": "3a76d83e-815a-47b2-abdd-282eab7f28a9" }, "execution_count": 60, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " count mean std min 25% 50% 75% max\n", "Cluster \n", "-1 263.0 1.077567 2.144403 0.0 0.0 0.0 1.1 17.0\n", " 0 8007.0 0.004484 0.032409 0.0 0.0 0.0 0.0 0.6" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countmeanstdmin25%50%75%max
Cluster
-1263.01.0775672.1444030.00.00.01.117.0
08007.00.0044840.0324090.00.00.00.00.6
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 60 } ] }, { "cell_type": "code", "source": [ "#Presión atmosférica\n", "df_new.groupby(['Cluster'])[\"PA\"].describe()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 143 }, "id": "hODMYD_XeWqM", "outputId": "49e8dff0-117a-4b66-d6ca-566c93811930" }, "execution_count": 61, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " count mean std min 25% 50% 75% max\n", "Cluster \n", "-1 263.0 977.608365 3.866405 971.0 975.0 977.0 979.0 990.0\n", " 0 8007.0 978.754090 3.003554 970.0 977.0 979.0 980.0 990.0" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countmeanstdmin25%50%75%max
Cluster
-1263.0977.6083653.866405971.0975.0977.0979.0990.0
08007.0978.7540903.003554970.0977.0979.0980.0990.0
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 61 } ] }, { "cell_type": "code", "source": [ "#Temperatura del agua\n", "df_new.groupby(['Cluster'])[\"TW\"].describe()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 143 }, "id": "1Qxucg8QeZhd", "outputId": "99bee6fc-fce8-4bf7-abc7-d4e5bca98a9f" }, "execution_count": 62, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " count mean std min 25% 50% 75% max\n", "Cluster \n", "-1 263.0 13.445970 1.545442 10.75 12.66 12.97 13.685 18.14\n", " 0 8007.0 13.279277 1.278981 10.76 12.42 12.94 13.745 18.27" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countmeanstdmin25%50%75%max
Cluster
-1263.013.4459701.54544210.7512.6612.9713.68518.14
08007.013.2792771.27898110.7612.4212.9413.74518.27
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 62 } ] }, { "cell_type": "code", "source": [ "#Velocidad del viento\n", "df_new.groupby(['Cluster'])[\"VV\"].describe()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 143 }, "id": "M3-WbX8TfZdp", "outputId": "9733403a-9e0d-4e74-8359-9972fe917a8b" }, "execution_count": 63, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " count mean std min 25% 50% 75% max\n", "Cluster \n", "-1 263.0 12.600760 7.531871 0.0 6.8 12.1 17.4 34.8\n", " 0 8007.0 5.372711 4.605777 0.0 1.5 4.3 8.3 23.8" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countmeanstdmin25%50%75%max
Cluster
-1263.012.6007607.5318710.06.812.117.434.8
08007.05.3727114.6057770.01.54.38.323.8
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 63 } ] }, { "cell_type": "code", "source": [ "#Ráfaga de viento\n", "df_new.groupby(['Cluster'])[\"RV\"].describe()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 143 }, "id": "xStgp6rzfAOG", "outputId": "93e1ad66-af85-4ce7-b6fb-d923c089b4b3" }, "execution_count": 64, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " count mean std min 25% 50% 75% max\n", "Cluster \n", "-1 263.0 22.612167 11.383512 0.0 14.2 22.0 29.2 58.7\n", " 0 8007.0 11.342563 6.896460 0.0 6.5 10.1 15.5 39.2" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countmeanstdmin25%50%75%max
Cluster
-1263.022.61216711.3835120.014.222.029.258.7
08007.011.3425636.8964600.06.510.115.539.2
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 64 } ] }, { "cell_type": "code", "source": [ "#Nivel del mar\n", "df_new.groupby(['Cluster'])[\"PRS\"].describe()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 143 }, "id": "b_UifvjhfAGG", "outputId": "56a828f8-80d2-4511-9eb6-2f1f054efab0" }, "execution_count": 65, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " count mean std min 25% 50% 75% max\n", "Cluster \n", "-1 263.0 2.373422 0.443023 1.16 2.03 2.37 2.695 3.40\n", " 0 8007.0 2.292489 0.365920 1.42 2.00 2.27 2.560 3.38" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countmeanstdmin25%50%75%max
Cluster
-1263.02.3734220.4430231.162.032.372.6953.40
08007.02.2924890.3659201.422.002.272.5603.38
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 65 } ] }, { "cell_type": "code", "source": [ "#Dirección del viento\n", "df_new.groupby(['Cluster'])[\"DV\"].describe()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 143 }, "id": "bGmQqADcUY1Q", "outputId": "afa76e3f-c417-4a1d-ed75-afeb6634f81c" }, "execution_count": 66, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " count mean std min 25% 50% 75% max\n", "Cluster \n", "-1 263.0 188.045627 137.099026 0.0 30.0 181.0 329.0 354.0\n", " 0 8007.0 145.929437 117.735961 0.0 0.0 156.0 261.0 355.0" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countmeanstdmin25%50%75%max
Cluster
-1263.0188.045627137.0990260.030.0181.0329.0354.0
08007.0145.929437117.7359610.00.0156.0261.0355.0
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 66 } ] }, { "cell_type": "markdown", "source": [ "Al analizar las tablas anteriores, se destaca una diferencia significativa entre los outliers y el grupo principal, especialmente en:\n", "\n", "* Precipitación promedio (1mm vs 0mm)\n", "\n", "* Velocidad del viento promedio (12.6 km/h vs 5.4 km/h)\n", "\n", "* Ráfaga de viento promedio (22.6 km/h vs 11.3 km/h)\n", "\n", "Estos resultados indican que el modelo está identificando predominantemente como outliers aquellos días con precipitaciones. Esta observación cobra sentido debido a que las condiciones climáticas en la ciudad de Valparaíso experimentan variaciones mínimas a lo largo del año, y los días con precipitaciones significativas son escasos. En consecuencia, el modelo parece destacar estos eventos climáticos poco frecuentes como puntos atípicos.\n", "\n" ], "metadata": { "id": "RK_f8Zjh18PI" } }, { "cell_type": "markdown", "source": [ "**REGRESIÓN**" ], "metadata": { "id": "KaV1DZldvqKI" } }, { "cell_type": "markdown", "source": [ "A continuación se aplicarán métodos de regresión para analizar los datos." ], "metadata": { "id": "BCmFfb3VUXmP" } }, { "cell_type": "markdown", "source": [ "**Predicción de la temperatura ambiente**" ], "metadata": { "id": "OjnZQkhqpsvk" } }, { "cell_type": "markdown", "source": [ "**REGRESIÓN LINEAL**" ], "metadata": { "id": "cxUZnwj2tdSr" } }, { "cell_type": "markdown", "source": [ "Se comienza aplicando una regresión lineal." ], "metadata": { "id": "v-mAMW32gjKn" } }, { "cell_type": "code", "source": [ "import numpy as np\n", "import pandas as pd\n", "pd.set_option('display.max_columns', None)" ], "metadata": { "id": "YH3jdK_JkoO9" }, "execution_count": 79, "outputs": [] }, { "cell_type": "markdown", "source": [ "El primer paso corresponde a cargar los datos a utilizar." ], "metadata": { "id": "fwfjwxaHtvi2" } }, { "cell_type": "code", "source": [ "df=pd.read_csv(\"datos2022.csv\", sep=\",\")\n", "df['fecha']= pd.to_datetime(df['fecha'])" ], "metadata": { "id": "cd5Q2Gdhknrt" }, "execution_count": 80, "outputs": [] }, { "cell_type": "code", "source": [ "df.head(2)" ], "metadata": { "id": "4KLp0QKNlGWJ", "colab": { "base_uri": "https://localhost:8080/", "height": 112 }, "outputId": "a649be89-5388-43bf-8e41-6474dba4267e" }, "execution_count": 81, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " fecha TA HR PP PA VV RV DV PRS TW\n", "0 2022-01-01 00:00:00 11.2 81.8 0.0 975.0 3.9 14.0 177.0 3.11 15.47\n", "1 2022-01-01 01:00:00 11.0 81.5 0.0 974.0 2.3 8.6 208.0 3.04 14.90" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
fechaTAHRPPPAVVRVDVPRSTW
02022-01-01 00:00:0011.281.80.0975.03.914.0177.03.1115.47
12022-01-01 01:00:0011.081.50.0974.02.38.6208.03.0414.90
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 81 } ] }, { "cell_type": "markdown", "source": [ "A continuación se selecciona la variable de respuesta (y) y los atributos (X) a utilizar.\n", "\n", "En este caso elegimos y como la temperatura ambiente (TA), y los demás atributos corresponderán a las demás columnas del dataframe, excepto la fecha.\n", "\n" ], "metadata": { "id": "HgsQqvchto8Q" } }, { "cell_type": "code", "source": [ "y = df.pop('TA')\n", "X=np.array(df.iloc[:,1:] )" ], "metadata": { "id": "P0WecPpScywZ" }, "execution_count": 82, "outputs": [] }, { "cell_type": "markdown", "source": [ "Se dividen los datos en datos de entrenamiento y de validación, en este caso, se escoge un 70% y 30% respectivamente." ], "metadata": { "id": "QADvwk7TuOOR" } }, { "cell_type": "code", "source": [ "from sklearn.model_selection import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X, y, train_size = 0.7, test_size = 0.3, random_state = 5)" ], "metadata": { "id": "zOebSYFhtlvE" }, "execution_count": 83, "outputs": [] }, { "cell_type": "markdown", "source": [ "Se escoge el algoritmo a utilizar, en este caso Regresión Lineal, y se entrena el modelo con los datos de entrenamiento." ], "metadata": { "id": "j3-4SGDYunic" } }, { "cell_type": "code", "source": [ "from sklearn.linear_model import LinearRegression\n", "\n", "lm = LinearRegression()\n", "lm.fit(X_train,y_train)" ], "metadata": { "id": "T1vpI9Yrs9Kz", "colab": { "base_uri": "https://localhost:8080/", "height": 75 }, "outputId": "a3fdd038-f0b0-413c-b304-01c8fc13c608" }, "execution_count": 84, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "LinearRegression()" ], "text/html": [ "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ] }, "metadata": {}, "execution_count": 84 } ] }, { "cell_type": "markdown", "source": [ "Se evalúa la métrica correspondiente al coeficiente de determinación, utilizando los datos de entrenamiento." ], "metadata": { "id": "k_eMf-xA2TgE" } }, { "cell_type": "code", "source": [ "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n", "print(\"R^2:\", lm.score(X_train, y_train).round(3))" ], "metadata": { "id": "GRfzoB4_vwmB", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "2849b81c-2302-4164-9b17-ca8b598835c5" }, "execution_count": 85, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "R^2: 0.775\n" ] } ] }, { "cell_type": "markdown", "source": [ "A continuación, se predicen los valores de la variable de respuesta con los datos de validación." ], "metadata": { "id": "Ww4woYRd3UzT" } }, { "cell_type": "code", "source": [ "y_pred = lm.predict(X_test)" ], "metadata": { "id": "vxBR_eQU-F8n" }, "execution_count": 86, "outputs": [] }, { "cell_type": "markdown", "source": [ "Se evalúa la métrica con los datos de validación:" ], "metadata": { "id": "Zm4J_TbC-rA4" } }, { "cell_type": "code", "source": [ "print(\"R^2:\", r2_score(y_test, y_pred).round(3))" ], "metadata": { "id": "G-wPWA5Y-tea", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "9c8824d9-1045-46d9-c632-691b05760221" }, "execution_count": 87, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "R^2: 0.769\n" ] } ] }, { "cell_type": "markdown", "source": [ "Esto significa que aproximadamente el 77% de la variabilidad en la variable dependiente puede ser explicada por el modelo de regresión. En otras palabras, el 77% de las fluctuaciones de le temperatura ambiente son capturadas por las variables independientes incluidas en el modelo.\n", "\n", "\n" ], "metadata": { "id": "mSiod2wygV_x" } }, { "cell_type": "code", "source": [ "print(\"Error absoluto medio: %.2f\" % mean_absolute_error(y_test, y_pred), \"ºC\")" ], "metadata": { "id": "Jwcxae7P-wnv", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "7bdaa13c-f0e4-4c94-a7fe-f02de855ecd8" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Error absoluto medio: 1.71 ºC\n" ] } ] }, { "cell_type": "markdown", "source": [ "Este valor indica que, en promedio, las predicciones del modelo tienen un error absoluto de aproximadamente 1.71 grados Celsius con respecto a los valores reales. En otras palabras, las predicciones tienden a desviarse, en promedio, alrededor de 1.71°C de los valores observados." ], "metadata": { "id": "iV-67SvLhQqL" } }, { "cell_type": "markdown", "source": [ "**DATOS ESCALADOS**" ], "metadata": { "id": "xnBcEOdz7Jqn" } }, { "cell_type": "markdown", "source": [ "**MinMaxScaler**" ], "metadata": { "id": "FshOmEr7v-1x" } }, { "cell_type": "markdown", "source": [ "A continuación se estudiará el efecto de escalar los datos, para lo cual se utilizará la función MinMaxScaler." ], "metadata": { "id": "uf83FBWchtuo" } }, { "cell_type": "code", "source": [ "from sklearn.preprocessing import MinMaxScaler\n", "scaler = MinMaxScaler()" ], "metadata": { "id": "o3cHPvSxhk96" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Se escalan las variables independientes (X)." ], "metadata": { "id": "SrFqisLNh0mt" } }, { "cell_type": "code", "source": [ "scaler.fit(X)\n", "X_scaled1=scaler.transform(X)" ], "metadata": { "id": "CAx3IDNMgzgm" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Se repite lo anteriormente realizado." ], "metadata": { "id": "k0O8X-sNiWFz" } }, { "cell_type": "code", "source": [ "X_train, X_test, y_train, y_test = train_test_split(\n", " X_scaled1, y, train_size = 0.7, test_size = 0.3, random_state = 5)\n", "lm = LinearRegression()\n", "lm.fit(X_train,y_train)" ], "metadata": { "id": "ZNmMOoluiTST", "colab": { "base_uri": "https://localhost:8080/", "height": 75 }, "outputId": "5e927540-d9a6-4402-ab05-773afb9805d6" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "LinearRegression()" ], "text/html": [ "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ] }, "metadata": {}, "execution_count": 14 } ] }, { "cell_type": "code", "source": [ "print(\"R^2:\", lm.score(X_train, y_train).round(3))" ], "metadata": { "id": "QYzEbBdoios7", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "e6b73720-2e77-4b73-9739-2d221ab1744e" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "R^2: 0.775\n" ] } ] }, { "cell_type": "code", "source": [ "y_pred=lm.predict(X_test)\n", "print(\"R^2:\", r2_score(y_test, y_pred).round(3))" ], "metadata": { "id": "9sFzU0I2ip-i", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "f2d311a1-0c64-44bf-f8f0-b3495e289dc8" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "R^2: 0.769\n" ] } ] }, { "cell_type": "code", "source": [ "print(\"Error absoluto medio: %.2f\" % mean_absolute_error(y_test, y_pred), \"ºC\")" ], "metadata": { "id": "Xj6DDjDxi3ol", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "0380b2f1-87b5-4069-e4d0-1a49ee974d0b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Error absoluto medio: 1.71 ºC\n" ] } ] }, { "cell_type": "markdown", "source": [ "Dado que estamos utilizando una regresión lineal, el escalado de datos no influye en el coeficiente de determinación ni en el error absoluto medio. Sin embargo, los coeficientes asociados con cada una de las variables independientes sí experimentan cambios. Cuando aplicamos escalamiento a los datos, la interpretación de cuál de estas variables tiene un impacto mayor en la variable de respuesta se simplifica, facilitando la comprensión de su contribución relativa." ], "metadata": { "id": "YwoP9_Htg6nr" } }, { "cell_type": "code", "source": [ "print(\"Intercepto en y:\")\n", "lm.intercept_.round(2)" ], "metadata": { "id": "YKub0m6adZ-s", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "0a11227a-af48-4cf2-f954-022f76be06bb" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Intercepto en y:\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "22.69" ] }, "metadata": {}, "execution_count": 18 } ] }, { "cell_type": "markdown", "source": [ "Este valor indica el valor esperado de la variable dependiente cuando todos los atributos son cero.\n", "\n" ], "metadata": { "id": "EivIzLeymUYM" } }, { "cell_type": "code", "source": [ "print(\"Coeficientes de cada atributo:\")\n", "lm.coef_.round(2)" ], "metadata": { "id": "2MamH6i-1blm", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "10d7cc74-e1f5-4895-cd16-c2a73aedca60" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Coeficientes de cada atributo:\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "array([-13.87, -8.49, -5.46, 11.1 , -4.01, 0.65, 0.05, 5.64])" ] }, "metadata": {}, "execution_count": 19 } ] }, { "cell_type": "markdown", "source": [ "Cada valor en nuestros resultados representa cuánto se espera que la variable dependiente cambie por cada unidad de cambio en el atributo correspondiente, manteniendo constantes todos los demás atributos.\n", "\n", "Para ilustrar, si incrementamos el primer atributo en una unidad, anticipamos una disminución de aproximadamente 13.87 unidades en la variable dependiente.\n", "\n", "La consideración del signo y la magnitud de los coeficientes es esencial. El signo nos indica la dirección de la relación (si es positiva o negativa), mientras que la magnitud nos proporciona información sobre la fuerza de esa relación.\n", "\n", "A pesar de que estamos utilizando un modelo lineal y las métricas evaluadas no han cambiado, el escalado de los datos juega un papel crucial al ofrecer una interpretación más clara de los coeficientes asociados con cada variable independiente. Esto simplifica la identificación de qué variable tiene un impacto mayor o menor en la variable de respuesta.\n", "\n", "Especialmente, al observar los valores más bajos, 0.65 y 0.05, correspondientes a DV (dirección del viento) y PRS (nivel del mar) respectivamente, se destaca que estas variables tienen una influencia relativamente menor en la variable de respuesta en comparación con otros atributos." ], "metadata": { "id": "5sJTqZQMmZUZ" } }, { "cell_type": "markdown", "source": [ "A continuación se grafican los resultados." ], "metadata": { "id": "BkEqjGdIhwrP" } }, { "cell_type": "code", "source": [ "import matplotlib.pyplot as plt\n", "from sklearn.metrics import PredictionErrorDisplay\n", "\n", "fig, axs = plt.subplots(ncols=2, figsize=(8, 4))\n", "\n", "PredictionErrorDisplay.from_predictions(\n", " y_true = y_test,\n", " y_pred = y_pred,\n", " kind=\"actual_vs_predicted\",\n", " subsample=500,\n", " ax=axs[0],\n", " random_state=0,)\n", "axs[0].set_title(\"Valores reales vs predichos\")\n", "\n", "PredictionErrorDisplay.from_predictions(\n", " y_true = y_test,\n", " y_pred = y_pred,\n", " kind=\"residual_vs_predicted\",\n", " subsample=500,\n", " ax=axs[1],\n", " random_state=0,)\n", "axs[1].set_title(\"Valores residuales vs predichos \")\n", "\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "id": "stqxsutbnAKI", "colab": { "base_uri": "https://localhost:8080/", "height": 407 }, "outputId": "64897595-a61d-4fde-c980-303c01b3c40c" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "En estos gráficos, se seleccionó aleatoriamente una muestra de 500 datos. En el gráfico izquierdo, se representa la comparación entre los valores reales y los predichos, mientras que en el gráfico derecho se muestran los residuos, que son las diferencias entre las predicciones y los valores reales.\n", "\n", "Se destaca que el valor absoluto máximo de los residuos es aproximadamente 6ºC. No obstante, es evidente que la mayoría de los datos se sitúa a una distancia inferior a 2ºC de la línea horizontal central en el gráfico de residuos." ], "metadata": { "id": "8ZgHnUbgkkic" } }, { "cell_type": "markdown", "source": [ "**ELIMINACIÓN RECURSIVA DE CARACTERÍSTICAS**" ], "metadata": { "id": "0_2p2HM1nHMv" } }, { "cell_type": "markdown", "source": [ "Adicionalmente, se utilizará la técnica **Recursive Feature Elimination (RFE)**, que trata de seleccionar la mejor combinación de variables que mejore la predicción." ], "metadata": { "id": "QraMO2kt3HZc" } }, { "cell_type": "markdown", "source": [ "**Sin escalar**" ], "metadata": { "id": "GKkZJZ1DxGEj" } }, { "cell_type": "code", "source": [ "X_train, X_test, y_train, y_test = train_test_split(\n", " X, y, train_size = 0.7, test_size = 0.3, random_state = 5)" ], "metadata": { "id": "4GfkGafswx04" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from sklearn.feature_selection import RFE\n", "lm_rfe = LinearRegression()\n", "\n", "k=1\n", "while k<=8:\n", " rfe = RFE(lm_rfe, n_features_to_select=k, step=1)\n", " rfe = rfe.fit(X_train, y_train)\n", " print(\"R^2 para k=\", str(k),\":\", round(rfe.score(X_train, y_train),3))\n", " k=k+1" ], "metadata": { "id": "DEr0OX-U3GiE", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "4d62734b-5cfc-4bcc-93f6-f23879587296" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "R^2 para k= 1 : 0.124\n", "R^2 para k= 2 : 0.128\n", "R^2 para k= 3 : 0.441\n", "R^2 para k= 4 : 0.494\n", "R^2 para k= 5 : 0.772\n", "R^2 para k= 6 : 0.774\n", "R^2 para k= 7 : 0.774\n", "R^2 para k= 8 : 0.775\n" ] } ] }, { "cell_type": "markdown", "source": [ "Se observa que la diferencia entre k=5 y k=8 es menos de 0.01, por lo que se puede seleccionar una cantidad de variables igual a k=5 para hacer más sencillo el modelo." ], "metadata": { "id": "Ptd5XSLixloi" } }, { "cell_type": "code", "source": [ "lm_rfe = LinearRegression()\n", "rfe = RFE(lm_rfe, n_features_to_select=5, step=1)\n", "rfe = rfe.fit(X_train, y_train)\n", "print(\"R^2:\", round(rfe.score(X_train, y_train),3))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_SIOu3Q9yKXs", "outputId": "cd9619de-7a78-4631-9dc8-eb4f273d809d" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "R^2: 0.772\n" ] } ] }, { "cell_type": "code", "source": [ "[col for col, b in zip(df.loc[:, df.columns != 'fecha'], rfe.support_) if b]" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "obb0NSEAyQwk", "outputId": "67d35863-c148-44ec-a420-55bbbfd2a3fa" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "['HR', 'PP', 'PA', 'VV', 'TW']" ] }, "metadata": {}, "execution_count": 30 } ] }, { "cell_type": "markdown", "source": [ "El resultado anterior nos muestra que los atributos seleccionados serían:\n", "HR (humedad relativa), PP (precipitaciones), PA (presión atmosférica), VV (velocidad del viento) y TW (temperatura del agua)." ], "metadata": { "id": "DaPV4HDuyTDk" } }, { "cell_type": "markdown", "source": [ "**Con escalado**" ], "metadata": { "id": "8WHoEMDrxIz2" } }, { "cell_type": "code", "source": [ "X_train, X_test, y_train, y_test = train_test_split(\n", " X_scaled1, y, train_size = 0.7, test_size = 0.3, random_state = 5)" ], "metadata": { "id": "-heny633xMUt" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from sklearn.feature_selection import RFE\n", "lm_rfe = LinearRegression()\n", "\n", "k=1\n", "while k<=8:\n", " rfe = RFE(lm_rfe, n_features_to_select=k, step=1)\n", " rfe = rfe.fit(X_train, y_train)\n", " print(\"R^2 para k=\", str(k),\":\", round(rfe.score(X_train, y_train),3))\n", " k=k+1" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "jpPi0ScNxPIy", "outputId": "125366e7-1d2a-469e-c275-9674b3e3f118" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "R^2 para k= 1 : 0.6\n", "R^2 para k= 2 : 0.6\n", "R^2 para k= 3 : 0.667\n", "R^2 para k= 4 : 0.742\n", "R^2 para k= 5 : 0.772\n", "R^2 para k= 6 : 0.774\n", "R^2 para k= 7 : 0.775\n", "R^2 para k= 8 : 0.775\n" ] } ] }, { "cell_type": "markdown", "source": [ "Con el escalado MinMax se obtiene un buen rendimiento para k=4.\n", "\n" ], "metadata": { "id": "I2ab5MGsylQp" } }, { "cell_type": "code", "source": [ "lm_rfe = LinearRegression()\n", "rfe = RFE(lm_rfe, n_features_to_select=4, step=1)\n", "rfe = rfe.fit(X_train, y_train)\n", "print(\"R^2:\", round(rfe.score(X_train, y_train),3))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "3tjY0JPsyqjd", "outputId": "e37a7b32-8f3a-4f2c-fcd0-1a786641f7aa" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "R^2: 0.742\n" ] } ] }, { "cell_type": "code", "source": [ "[col for col, b in zip(df.loc[:, df.columns != 'fecha'], rfe.support_) if b]" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "x0nyGvZlyvgU", "outputId": "db5680d6-68c2-426f-9377-08d4171828cb" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "['HR', 'PP', 'VV', 'TW']" ] }, "metadata": {}, "execution_count": 34 } ] }, { "cell_type": "markdown", "source": [ "Las variables a utilizar serían humedad relativa, presión atmosférica, velocidad del viento y temperatura del agua." ], "metadata": { "id": "y_u9gSSvy7f6" } }, { "cell_type": "markdown", "source": [ "Si bien los resultados no cambian significativamente con respecto a usar todas las variables, se simplifica el modelo al tener una menor cantidad de ellas." ], "metadata": { "id": "_a4IgdFsgB_O" } }, { "cell_type": "markdown", "source": [ "**REGRESIÓN POLINOMIAL**" ], "metadata": { "id": "lpqyoAy20j5-" } }, { "cell_type": "markdown", "source": [ "Se comenzará evaluando el coeficiente de determinación para distintos grados del polinomio, y se escogerá el que sea mayor." ], "metadata": { "id": "HJkFPaabjZ_r" } }, { "cell_type": "markdown", "source": [ "**Sin escalado**" ], "metadata": { "id": "EqAJrFeu0Ar0" } }, { "cell_type": "code", "source": [ "X_train, X_test, y_train, y_test = train_test_split(\n", " X, y, train_size = 0.7, test_size = 0.3, random_state = 5)" ], "metadata": { "id": "UjN0i7lI0F-z" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from sklearn.pipeline import make_pipeline\n", "from sklearn.preprocessing import PolynomialFeatures\n", "degrees = [1, 2, 3, 4, 5]\n", "\n", "for i, degree in enumerate(degrees):\n", " X_train_poly = PolynomialFeatures(degree=degree).fit_transform(X_train)\n", " model = LinearRegression()\n", " model.fit(X_train_poly, y_train)\n", " X_test_poly = PolynomialFeatures(degree=degree).fit_transform(X_test)\n", " y_test_pred = model.predict(X_test_poly)\n", " print(\"Grado igual a\", degree, \", R^2 igual a\", r2_score(y_test, y_test_pred).round(3))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "UyO0TN72NFMh", "outputId": "b752e84d-04ad-499c-9dd2-252e4098a531" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Grado igual a 1 , R^2 igual a 0.769\n", "Grado igual a 2 , R^2 igual a 0.806\n", "Grado igual a 3 , R^2 igual a 0.801\n", "Grado igual a 4 , R^2 igual a -13.199\n", "Grado igual a 5 , R^2 igual a -20033.402\n" ] } ] }, { "cell_type": "markdown", "source": [ "Dado el resultado anterior, se escoge un polinomio de grado igual a 2, es decir cuadrático. A continuación se entrena el modelo y se evalúa." ], "metadata": { "id": "bWdJIpcljjU4" } }, { "cell_type": "code", "source": [ "from sklearn.preprocessing import PolynomialFeatures\n", "X_train_poly = PolynomialFeatures(degree=2).fit_transform(X_train)" ], "metadata": { "id": "AppOyr0afzj-" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "lm_poly = LinearRegression()\n", "lm_poly.fit(X_train_poly, y_train)" ], "metadata": { "id": "ouhqCPaNGjTQ", "colab": { "base_uri": "https://localhost:8080/", "height": 75 }, "outputId": "71cbaa50-787f-4fa4-a2fb-51d2d5d5e286" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "LinearRegression()" ], "text/html": [ "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ] }, "metadata": {}, "execution_count": 49 } ] }, { "cell_type": "markdown", "source": [ "Evaluando el coeficiente de determinación en los datos de entrenamiento:" ], "metadata": { "id": "1vQHsEPcNhsD" } }, { "cell_type": "code", "source": [ "print(\"R^2:\", lm_poly.score(X_train_poly, y_train).round(3))" ], "metadata": { "id": "YxeJGxK3HAkj", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "eaf0d14c-aa44-427f-867a-61dbe8891f59" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "R^2: 0.81\n" ] } ] }, { "cell_type": "markdown", "source": [ "Evaluando el coeficiente de determinación en los datos de validación:" ], "metadata": { "id": "YghK3TZjNmT0" } }, { "cell_type": "code", "source": [ "X_test_poly = PolynomialFeatures(degree=2).fit_transform(X_test)\n", "y_pred_poly = lm_poly.predict(X_test_poly)\n", "r2_score(y_test, y_pred_poly).round(3)" ], "metadata": { "id": "rNgNDKDCHIH4", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "94038c69-b8b7-4f24-866f-e5b4b38923e2" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.806" ] }, "metadata": {}, "execution_count": 56 } ] }, { "cell_type": "code", "source": [ "print(\"Error absoluto medio: %.2f\" % mean_absolute_error(y_test, y_pred_poly), \"ºC\")" ], "metadata": { "id": "p_0cJO_1044p", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "71f95fe3-af5d-4e49-93b3-4916f54fd3d3" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Error absoluto medio: 1.57 ºC\n" ] } ] }, { "cell_type": "code", "source": [ "fig, axs = plt.subplots(ncols=2, figsize=(8, 4))\n", "PredictionErrorDisplay.from_predictions(\n", " y_true = y_test,\n", " y_pred = y_pred_poly,\n", " kind=\"actual_vs_predicted\",\n", " subsample=500,\n", " ax=axs[0],\n", " random_state=0,\n", ")\n", "axs[0].set_title(\"Actual vs. Predicted values Poly\")\n", "PredictionErrorDisplay.from_predictions(\n", " y_true = y_test,\n", " y_pred = y_pred_poly,\n", " kind=\"residual_vs_predicted\",\n", " subsample=500,\n", " ax=axs[1],\n", " random_state=0,\n", ")\n", "axs[1].set_title(\"Residuals vs. Predicted Values Poly\")\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "id": "paMTtvNBHrNC", "colab": { "base_uri": "https://localhost:8080/", "height": 407 }, "outputId": "1271a693-c14d-4d84-9c9a-bb5cfac24bc7" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "Se puede observar que la regresión polinomial predice los datos de mejor manera que la regresión lineal, ya que el coeficiente de correlación para la regresión polinomial (0.81) es mayor que el obtenido en la regresión lineal (0.77).\n", "Además, se evidencia lo mismo al obtenerse un error absoluto medio menor en el caso de la regresión polinomial (1,57°C) respecto a la regresión lineal (1,71°C).\n", "El valor del coeficiente de determinación sugiere una buena capacidad de predicción del modelo." ], "metadata": { "id": "jlDIftiej0Tr" } }, { "cell_type": "markdown", "source": [ "**CROSS VALIDATION**" ], "metadata": { "id": "__E7jgHRlKWR" } }, { "cell_type": "markdown", "source": [ "A continuación se utilizará el metodo de validación cruzada para varios métodos." ], "metadata": { "id": "rVLuiktulGhc" } }, { "cell_type": "code", "source": [ "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import cross_val_predict\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.linear_model import RidgeCV\n", "from sklearn.linear_model import LassoCV\n", "from sklearn.tree import DecisionTreeRegressor\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.ensemble import GradientBoostingRegressor\n", "from sklearn.ensemble import ExtraTreesRegressor\n", "from sklearn.ensemble import AdaBoostRegressor\n", "from sklearn.svm import SVR" ], "metadata": { "id": "sAYNhCTJLqz9" }, "execution_count": 73, "outputs": [] }, { "cell_type": "markdown", "source": [ "Se escoge un k=10 para el cross validation." ], "metadata": { "id": "t6JQNdxRO8p8" } }, { "cell_type": "code", "source": [ "from sklearn.model_selection import cross_val_score\n", "from sklearn import svm\n", "from sklearn.svm import SVC\n", "\n", "# Se crea una lista con los modelos\n", "models = [\n", " LinearRegression(),\n", " RidgeCV(),\n", " LassoCV(),\n", " DecisionTreeRegressor(),\n", " RandomForestRegressor(),\n", " GradientBoostingRegressor(),\n", " SVR(),\n", " AdaBoostRegressor(),\n", " ExtraTreesRegressor()]\n", "\n", "# Se realiza una validación cruzada para cada modelo.\n", "for model in models:\n", " scores = cross_val_score(model, X, y, cv=10)\n", " print(f\"Model: {model.__class__.__name__}, Mean score: {np.mean(scores).round(3)}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ZPhxCbRm2wn5", "outputId": "ba6e972d-0748-47a7-dee1-c9949e31b723" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model: LinearRegression, Mean score: 0.562\n", "Model: RidgeCV, Mean score: 0.562\n", "Model: LassoCV, Mean score: 0.55\n", "Model: DecisionTreeRegressor, Mean score: 0.263\n", "Model: RandomForestRegressor, Mean score: 0.584\n", "Model: GradientBoostingRegressor, Mean score: 0.617\n", "Model: SVR, Mean score: 0.197\n", "Model: AdaBoostRegressor, Mean score: 0.552\n", "Model: ExtraTreesRegressor, Mean score: 0.592\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Polynomial Regression**\n", "\n", "Se escoge el grado igual a 2, de acuerdo a lo discutido previamente en el documento." ], "metadata": { "id": "tCr6QM6NwxKk" } }, { "cell_type": "code", "source": [ "from sklearn.preprocessing import PolynomialFeatures\n", "poly_features = PolynomialFeatures(degree=2)\n", "X_poly = poly_features.fit_transform(X)\n", "poly = LinearRegression()\n", "np.mean(cross_val_score(poly, X_poly, y, cv=10)).round(3)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hSCEO4yUxAgr", "outputId": "80fbe843-f8ea-4b39-e2f5-a42bb91640bd" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.608" ] }, "metadata": {}, "execution_count": 80 } ] }, { "cell_type": "markdown", "source": [ "**K-Neighbors Regressor**" ], "metadata": { "id": "v3J7Q-b0xJ9d" } }, { "cell_type": "code", "source": [ "from sklearn.neighbors import KNeighborsRegressor\n", "cv_score=[]\n", "for i in range(1,10):\n", " knn = KNeighborsRegressor(n_neighbors= i)\n", " cv_score.append(np.mean(cross_val_score(knn,X,y,cv=10)))\n", "x = range(1,10)\n", "plt.scatter(x,cv_score)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 447 }, "id": "2FtAn9AAxMtZ", "outputId": "0726f814-03c0-4b6d-97e9-5f86d6c3df94" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 78 }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "Al evaluar diferentes modelos de regresión en función del score obtenido en el cross validation, se observa que el modelo GradientBoostingRegressor obtiene el mejor resultado igual a 0.617." ], "metadata": { "id": "t662zJc5zWJT" } }, { "cell_type": "markdown", "source": [ "**GradientBoostingRegressor + GridSearchCV**" ], "metadata": { "id": "o7DEOSdg6rrC" } }, { "cell_type": "markdown", "source": [ "Aunque la estrategia óptima sería aplicar GridSearch a todos los métodos antes de realizar la validación cruzada, en esta ocasión se optó por emplearlo únicamente en el algoritmo que mostró los mejores resultados. Esta elección se basa en la consideración del tiempo de cálculo, ya que aplicar GridSearch a todos los métodos es computacionalmente costoso." ], "metadata": { "id": "rbBKpyw6jcuI" } }, { "cell_type": "code", "source": [ "from sklearn.model_selection import GridSearchCV" ], "metadata": { "id": "LohYvQMZ637O" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Se escogen algunos hiperparámetros a optimizar del método." ], "metadata": { "id": "9L1ESILOj23X" } }, { "cell_type": "code", "source": [ "model = GradientBoostingRegressor()\n", "grid = dict()\n", "grid['n_estimators'] = [50, 100, 500]\n", "grid['learning_rate'] = [0.01, 0.1, 1.0]\n", "grid['subsample'] = [0.5, 0.7, 1.0]\n", "grid['max_depth'] = [3, 5, 7]" ], "metadata": { "id": "oDvQDxuO7iyn" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=10)\n", "grid_result = grid_search.fit(X, y)" ], "metadata": { "id": "wjPHRu_-70WM" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "grid_result.best_score_" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "eD-GuKC4D14U", "outputId": "443d6ffa-c0e7-4093-d513-fc9423bad517" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.617930658523954" ] }, "metadata": {}, "execution_count": 84 } ] }, { "cell_type": "markdown", "source": [ "Los mejores parámetros son:" ], "metadata": { "id": "xzdHH2-2j69B" } }, { "cell_type": "code", "source": [ "grid_result.best_params_" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qiAuY5hz_xqF", "outputId": "e936f8b2-0538-4ba6-8ddb-5b694c617fc1" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 100, 'subsample': 0.5}" ] }, "metadata": {}, "execution_count": 85 } ] }, { "cell_type": "markdown", "source": [ "Ahora se entrena el modelo con esta selección de parámetros." ], "metadata": { "id": "hS13ulWTj9Hz" } }, { "cell_type": "code", "source": [ "model = GradientBoostingRegressor(learning_rate= 0.1, max_depth= 3, n_estimators= 100, subsample= 0.5)" ], "metadata": { "id": "6VmTKcrBD55V" }, "execution_count": 88, "outputs": [] }, { "cell_type": "code", "source": [ "X_train, X_test, y_train, y_test = train_test_split(\n", " X, y, train_size = 0.7, test_size = 0.3, random_state = 5)" ], "metadata": { "id": "t4QpssuPEL4_" }, "execution_count": 89, "outputs": [] }, { "cell_type": "markdown", "source": [ "Se aplica la eliminación de características recursivas." ], "metadata": { "id": "huuGjdR6kBqs" } }, { "cell_type": "code", "source": [ "model.fit(X_train,y_train)\n", "print(\"R^2:\", round(model.score(X_train, y_train),3))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HMCH2A_rE278", "outputId": "cda7a041-89f0-4553-d43a-3666c85ace5b" }, "execution_count": 90, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "R^2: 0.847\n" ] } ] }, { "cell_type": "code", "source": [ "y_pred=model.predict(X_test)\n", "print(\"R^2:\", r2_score(y_test, y_pred).round(3))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qUyv43teFFAB", "outputId": "3ff4245d-a7f4-403c-f4d6-4b7901522e68" }, "execution_count": 92, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "R^2: 0.818\n" ] } ] }, { "cell_type": "code", "source": [ "print(\"Error absoluto medio: %.2f\" % mean_absolute_error(y_test, y_pred), \"ºC\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Mda3cE74X0nF", "outputId": "25ef451c-ba80-400b-9b6a-0a3c7eb1dbce" }, "execution_count": 93, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Error absoluto medio: 1.50 ºC\n" ] } ] }, { "cell_type": "code", "source": [ "import matplotlib.pyplot as plt\n", "from sklearn.metrics import PredictionErrorDisplay\n", "\n", "fig, axs = plt.subplots(ncols=2, figsize=(8, 4))\n", "PredictionErrorDisplay.from_predictions(\n", " y_true = y_test,\n", " y_pred = y_pred,\n", " kind=\"actual_vs_predicted\",\n", " subsample=500,\n", " ax=axs[0],\n", " random_state=0,\n", ")\n", "axs[0].set_title(\"Actual vs. Predicted values\")\n", "PredictionErrorDisplay.from_predictions(\n", " y_true = y_test,\n", " y_pred = y_pred,\n", " kind=\"residual_vs_predicted\",\n", " subsample=500,\n", " ax=axs[1],\n", " random_state=0,\n", ")\n", "axs[1].set_title(\"Residuals vs. Predicted Values\")\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "id": "2Kq5GQvGFJqQ", "outputId": "661566e5-77d1-422d-8912-60ebd142f32a", "colab": { "base_uri": "https://localhost:8080/", "height": 407 } }, "execution_count": 94, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "Se puede observar este algoritmo predice los datos de mejor manera que la regresión lineal y polinomial, ya que el coeficiente de correlación es mayor (0.818) y el error absoluto medio es menor (1,50°C).\n", "El valor del coeficiente de determinación sugiere una buena capacidad de predicción del modelo." ], "metadata": { "id": "-q7oP0dnlbyB" } }, { "cell_type": "markdown", "source": [ "**Predicción de las precipitaciones**" ], "metadata": { "id": "luRO63tKpzW2" } }, { "cell_type": "markdown", "source": [ "Se repite un procedimiento similar al realizado con la temperatura ambiente, pero ahora para predecir las precipitaciones." ], "metadata": { "id": "Gk8doOPUqbSO" } }, { "cell_type": "code", "source": [ "df=pd.read_csv(\"datos2022.csv\", sep=\",\")\n", "df['fecha']= pd.to_datetime(df['fecha'])\n", "y = df.pop('PP')\n", "X=np.array(df.iloc[:,1:] )" ], "metadata": { "id": "xoK5bBiOqh-I" }, "execution_count": 97, "outputs": [] }, { "cell_type": "markdown", "source": [ "Se dividen los datos en datos de entrenamiento y de validación, en este caso, se escoge un 70% y 30% respectivamente." ], "metadata": { "id": "kc5FjeqtqrJ3" } }, { "cell_type": "code", "source": [ "from sklearn.model_selection import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X, y, train_size = 0.7, test_size = 0.3, random_state = 5)" ], "metadata": { "id": "NBty5Dpkqu2r" }, "execution_count": 98, "outputs": [] }, { "cell_type": "code", "source": [ "from sklearn.linear_model import LinearRegression\n", "lm = LinearRegression()\n", "lm.fit(X_train,y_train)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 75 }, "id": "72Je7l9bqxU-", "outputId": "bd1fdd45-20f6-4bc7-b58f-433be5265842" }, "execution_count": 100, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "LinearRegression()" ], "text/html": [ "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ] }, "metadata": {}, "execution_count": 100 } ] }, { "cell_type": "code", "source": [ "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n", "print(\"R^2:\", lm.score(X_train, y_train).round(2))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "tKFQtuRRqzg0", "outputId": "37540ae2-6fb9-4ba6-fef0-b6702a143cef" }, "execution_count": 101, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "R^2: 0.08\n" ] } ] }, { "cell_type": "code", "source": [ "y_pred = lm.predict(X_test)\n", "print(\"R^2:\", r2_score(y_test, y_pred).round(2))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "s5_x56Taq3vz", "outputId": "70f5460f-50c7-4795-dbce-4275751dd936" }, "execution_count": 103, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "R^2: 0.06\n" ] } ] }, { "cell_type": "markdown", "source": [ "El coeficiente de determinación de 0.06 sugiere que el modelo de regresión no está explicando una proporción significativa de la variabilidad en los datos. Esto sugiere que el modelo no está capturando adecuadamente la relación entre las variables independientes y la variable dependiente.\n", "\n", "Es posible que el modelo seleccionado no sea el más apropiado para describir la relación en los datos. Es probable que existan otras variables relevantes que no están siendo consideradas en el modelo, como por ejemplo, la nubosidad.\n", "Este y otros factores importantes que no se están teniendo en cuenta, afectan la capacidad del modelo para explicar la variabilidad." ], "metadata": { "id": "Yo952SnLrvdK" } }, { "cell_type": "code", "source": [ "import matplotlib.pyplot as plt\n", "from sklearn.metrics import PredictionErrorDisplay\n", "\n", "fig, axs = plt.subplots(ncols=2, figsize=(8, 4))\n", "\n", "PredictionErrorDisplay.from_predictions(\n", " y_true = y_test,\n", " y_pred = y_pred,\n", " kind=\"actual_vs_predicted\",\n", " subsample=500,\n", " ax=axs[0],\n", " random_state=0,)\n", "axs[0].set_title(\"Valores reales vs predichos\")\n", "\n", "PredictionErrorDisplay.from_predictions(\n", " y_true = y_test,\n", " y_pred = y_pred,\n", " kind=\"residual_vs_predicted\",\n", " subsample=500,\n", " ax=axs[1],\n", " random_state=0,)\n", "axs[1].set_title(\"Valores residuales vs predichos \")\n", "\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 407 }, "id": "5HaCk977rBBh", "outputId": "498530a8-0398-4f65-9250-7453cc5fe713" }, "execution_count": 104, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "De la representación visual, se observa que el modelo asigna precipitaciones en días sin lluvia (incluso cantidades negativas). Esto genera la línea horizontal que se observa en el gráfico de la izquierda, en lugar de una recta con pendiente 1. Algunas posibles razones son:\n", "\n", "* Desbalance de datos: debido a la mayoría de los días del conjunto de datos no presentan lluvia. Por esta razón, podría ser importante revisar y equilibrar el conjunto de datos, asegurándonos que haya una representación equitativa de días lluviosos y días secos, para evitar sesgos.\n", "\n", "* Sensibilidad a atributos: Es posible que el modelo esté dando demasiada importancia a ciertos atributos o características que no son relevantes para la predicción de la lluvia.\n", "\n", "* Modelo demasiado simple: Un modelo de regresión lineal simple puede no capturar adecuadamente la complejidad de los datos climáticos. Es necesario experimentar modelos más complejos que puedan capturar relaciones no lineales entre las variables.\n", "\n", "* Características climáticas no consideradas: Puede haber variables o características climáticas importantes que no se están teniendo en cuenta en el modelo actual." ], "metadata": { "id": "IDgAImUbs-jf" } }, { "cell_type": "markdown", "source": [ "A continuación, se explora la regresión polinomial:" ], "metadata": { "id": "A0TER3OWuo7Q" } }, { "cell_type": "code", "source": [ "from sklearn.pipeline import make_pipeline\n", "from sklearn.preprocessing import PolynomialFeatures\n", "degrees = [1, 2, 3, 4, 5]\n", "\n", "for i, degree in enumerate(degrees):\n", " X_train_poly = PolynomialFeatures(degree=degree).fit_transform(X_train)\n", " model = LinearRegression()\n", " model.fit(X_train_poly, y_train)\n", " X_test_poly = PolynomialFeatures(degree=degree).fit_transform(X_test)\n", " y_test_pred = model.predict(X_test_poly)\n", " print(\"Grado igual a\", degree, \", R^2 igual a\", r2_score(y_test, y_test_pred).round(3))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nzPChnibuuJm", "outputId": "2edbe685-b444-422d-c09b-0fb18c513420" }, "execution_count": 107, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Grado igual a 1 , R^2 igual a 0.057\n", "Grado igual a 2 , R^2 igual a 0.177\n", "Grado igual a 3 , R^2 igual a 0.172\n", "Grado igual a 4 , R^2 igual a -0.305\n", "Grado igual a 5 , R^2 igual a -13.525\n" ] } ] }, { "cell_type": "markdown", "source": [ "Se observa que se obtiene un mejor coeficiente de determinación para el grado 2, correspondiente a 0.17, sin embargo, sigue siendo deficiente." ], "metadata": { "id": "J9cPZY9ruwhv" } }, { "cell_type": "markdown", "source": [ "\n", "\n", "Finalmente, se utilizará el método de validación cruzada, para explorar el rendimiento de distintos modelos." ], "metadata": { "id": "6cH1U2ZnuWl7" } }, { "cell_type": "code", "source": [ "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import cross_val_predict\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.linear_model import RidgeCV\n", "from sklearn.linear_model import LassoCV\n", "from sklearn.tree import DecisionTreeRegressor\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.ensemble import GradientBoostingRegressor\n", "from sklearn.ensemble import ExtraTreesRegressor\n", "from sklearn.ensemble import AdaBoostRegressor\n", "from sklearn.svm import SVR" ], "metadata": { "id": "zHuvuap3rQJB" }, "execution_count": 105, "outputs": [] }, { "cell_type": "code", "source": [ "from sklearn.model_selection import cross_val_score\n", "from sklearn import svm\n", "from sklearn.svm import SVC\n", "\n", "# Se crea una lista con los modelos\n", "models = [\n", " LinearRegression(),\n", " RidgeCV(),\n", " LassoCV(),\n", " DecisionTreeRegressor(),\n", " RandomForestRegressor(),\n", " GradientBoostingRegressor(),\n", " SVR(),\n", " AdaBoostRegressor(),\n", " ExtraTreesRegressor()]\n", "\n", "# Se realiza una validación cruzada para cada modelo.\n", "for model in models:\n", " scores = cross_val_score(model, X, y, cv=5)\n", " print(f\"Model: {model.__class__.__name__}, Mean score: {np.mean(scores).round(3)}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cHsI55VxrTWO", "outputId": "33922cb3-dddc-4ed4-cae5-393283a25435" }, "execution_count": 106, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model: LinearRegression, Mean score: -21.284\n", "Model: RidgeCV, Mean score: -21.276\n", "Model: LassoCV, Mean score: -20.266\n", "Model: DecisionTreeRegressor, Mean score: -28.11\n", "Model: RandomForestRegressor, Mean score: -69.762\n", "Model: GradientBoostingRegressor, Mean score: -21.291\n", "Model: SVR, Mean score: -14.364\n", "Model: AdaBoostRegressor, Mean score: -555.646\n", "Model: ExtraTreesRegressor, Mean score: -21.047\n" ] } ] }, { "cell_type": "markdown", "source": [ "Se observa que todos los coeficientes resultan ser negativos, esto indica que los modelos de regresión no se ajustan bien a los datos en absoluto.\n", "\n", "R2 mide la proporción de la variabilidad en la variable dependiente que es explicada por el modelo. Un valor negativo sugiere que el modelo no es útil y, de hecho, está haciendo predicciones peores que simplemente utilizar la media de la variable dependiente." ], "metadata": { "id": "wHjFPLTUu58G" } }, { "cell_type": "markdown", "source": [ "\n", "\n", "**Discusión y conclusiones**\n", "\n", "\n", "A lo largo del desarrollo del proyecto, hemos explorado diversos métodos de análisis de datos para seleccionar aquel que mejor predice la información disponible. Este proceso nos ha permitido profundizar en nuestro entendimiento sobre las condiciones climáticas, especialmente en la ciudad de Valparaíso.\n", "\n", "\n", "Durante el análisis de los registros climáticos del año 2022, logramos caracterizar parcialmente el clima predominante en la zona. Si bien los modelos demostraron una capacidad aceptable para predecir la temperatura (R2 = 0.82), no obtuvieron el mismo éxito con las precipitaciones (R2 = 0.18). Esta discrepancia podría ser atribuible a la escasez de días lluviosos en la zona, generando un desbalance en los datos. Para mejorar la predicción, se sugiere:\n", "\n", "* considerar atributos adicionales como la nubosidad y la radiación solar, y\n", "\n", "* ampliar el intervalo de tiempo a por ejemplo 10 años.\n", "\n", "Además, reconocemos que las condiciones climáticas no son un fenómeno exclusivamente local, lo que podría influir en la calidad predictiva de nuestros modelos.\n", "\n", "Adicionalmente, la identificación de patrones climáticos mediante técnicas de agrupación como:\n", "\n", "* DBSCAN reveló que los datos identificados como anómalos correspondían principalmente a los días con precipitaciones, y\n", "\n", "* K-means reveló la identificación de tres grupos distintos.\n", "\n", "Es importante destacar que el escalado de los datos no mostró mejoras significativas en el rendimiento de K-means, ya que el coeficiente de silhouette disminuía.\n", "\n", "\n", "Para mejorar y expandir el modelo se propone:\n", "\n", "* la incorporación del atributo de fecha para predecir comportamientos futuros, utilizando datos históricos hasta la fecha n - 1 para prever los datos en la fecha n,\n", "\n", "* la representación de la dirección del viento en grados (de 0 a 360) puede no ser la medida más adecuada, ya que implica dos dimensiones. Sería preferible emplear dos valores distintos: uno para describir la orientación norte-sur y otro para la orientación este-oeste. Esta mejor representación podría lograrse mediante las funciones seno y coseno aplicadas al grado sexagesimal correspondiente, proporcionando una visión más precisa de la dirección del viento. Esta modificación podría enriquecer el análisis y mejorar la interpretación de la influencia de la dirección del viento, y\n", "\n", "* utilizar una selección parcial de atributos para luego aplicar las técnicas de clustering.\n", "\n", "\n", "En resumen, aunque los objetivos se cumplieron parcialmente y se lograron resultados relativamente exitosos para la temperatura ambiental, se reconoce la necesidad de ajustar y ampliar nuestros enfoques para mejorar las predicciones, especialmente en lo que respecta a las precipitaciones." ], "metadata": { "id": "9bb_FWiErCKe" } } ] }