{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Uso de la API del Ayuntamiento de Arganda del Rey sobre la concentración de ozono en el municipio" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El Ayuntamiento de Arganda del Rey dispone de un [portal de datos abiertos](https://datosabiertos.ayto-arganda.es/). De los datos disponibles, usaremos los publicados sobre la concentración de ozono en el municipio. Por un lado, los datos agregados de los años 2006-2016, y posteriormente los datos por mes de 2014, 2015 y 2016, para comparar sus valores." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Instalación de la librería necesaria\n", "Usaremos la librería *Pandas* para trabajar los datos. La instalamos a través de `pip`, el gestor de paquetes de Python. Como es un comando que ejecutamos en la terminal, ponemos un `!` al inicio para que Jupyter lo interprete como tal." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (1.3.1)\r\n", "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (1.21.1)\r\n", "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2.8.1)\r\n", "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2020.4)\r\n", "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7.3->pandas) (1.14.0)\r\n" ] } ], "source": [ "!pip install pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Importar *Pandas* al proyecto" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Análisis de la concentración de ozono anual entre 2006 y 2016" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Importamos la base de datos con la que vamos a trabajar" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "url = 'https://datosabiertos.ayto-arganda.es/dataset/b7ce7868-f873-402f-951f-ec7e5ce8b854/resource/5c34a69d-b313-4b06-8097-e1dfb81ca3ad/download/convertcsv-108.json'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creamos el dataframe" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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AñoOzono - ug
02006{'m3': 60.75}
12007{'m3': 48.16}
22008{'m3': 45.08}
32009{'m3': 35.66}
42010{'m3': 43.75}
52011{'m3': 56.16}
62012{'m3': 59.75}
72013{'m3': 60.72}
82014{'m3': 56.66}
92015{'m3': 52.83}
102016{'m3': 54.5}
\n", "
" ], "text/plain": [ " Año Ozono - ug\n", "0 2006 {'m3': 60.75}\n", "1 2007 {'m3': 48.16}\n", "2 2008 {'m3': 45.08}\n", "3 2009 {'m3': 35.66}\n", "4 2010 {'m3': 43.75}\n", "5 2011 {'m3': 56.16}\n", "6 2012 {'m3': 59.75}\n", "7 2013 {'m3': 60.72}\n", "8 2014 {'m3': 56.66}\n", "9 2015 {'m3': 52.83}\n", "10 2016 {'m3': 54.5}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_json(url)\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Limpiamos las variables\n", "Desde el ayuntamiento no han codificado la variable de microgramos de ozono como numérica, sino que han indicado dicha concentración por metro cúbico indicando tanto la medida (m3) como el dato, por lo que Python no sabrá interpretarlo a la hora de hacer un gráfico. Por ello, reasignamos los valores manteniendo únicamente el valor numérico. Lo hacemos a través de `df.at[]`, donde, entre corchetes, indicamos primero el número de fila y luego el nombre de la columna, para posteriormente indicar el valor que queremos asignar." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "df.at[0, 'Ozono - ug']=60.75\n", "df.at[1, 'Ozono - ug']=48.16\n", "df.at[2, 'Ozono - ug']=45.08\n", "df.at[3, 'Ozono - ug']=35.66\n", "df.at[4, 'Ozono - ug']=43.75\n", "df.at[5, 'Ozono - ug']=56.16\n", "df.at[6, 'Ozono - ug']=59.75\n", "df.at[7, 'Ozono - ug']=60.72\n", "df.at[8, 'Ozono - ug']=56.66\n", "df.at[9, 'Ozono - ug']=52.83\n", "df.at[10, 'Ozono - ug']=54.50" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora, comprobamos que se ha recodificado bien mostrando el dataframe con `df`." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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AñoOzono - ug
0200660.75
1200748.16
2200845.08
3200935.66
4201043.75
5201156.16
6201259.75
7201360.72
8201456.66
9201552.83
10201654.5
\n", "
" ], "text/plain": [ " Año Ozono - ug\n", "0 2006 60.75\n", "1 2007 48.16\n", "2 2008 45.08\n", "3 2009 35.66\n", "4 2010 43.75\n", "5 2011 56.16\n", "6 2012 59.75\n", "7 2013 60.72\n", "8 2014 56.66\n", "9 2015 52.83\n", "10 2016 54.5" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si queremos explorar más en profundidad algunos datos de la tabla, podemos ver datos concretos, solo los primeros, los últimos... Lo hacemos con `df.head()` para ver X valores empezando por la cabecera, con `df.tail()` por el final o con `df.info()` para ver datos generales." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AñoOzono - ug
0200660.75
1200748.16
\n", "
" ], "text/plain": [ " Año Ozono - ug\n", "0 2006 60.75\n", "1 2007 48.16" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(2)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "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", "
AñoOzono - ug
8201456.66
9201552.83
10201654.5
\n", "
" ], "text/plain": [ " Año Ozono - ug\n", "8 2014 56.66\n", "9 2015 52.83\n", "10 2016 54.5" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.tail(3)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 11 entries, 0 to 10\n", "Data columns (total 2 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Año 11 non-null int64 \n", " 1 Ozono - ug 11 non-null object\n", "dtypes: int64(1), object(1)\n", "memory usage: 304.0+ bytes\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualizando la concentración de ozono en Arganda del Rey (2006-2016)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Una vez hemos limpiado las variables y explorado el dataframe, vamos a mostrar visualmente estos datos, para así hacernos una idea de la evolución de la concentración de ozono, medida por metro cúbico, en el municipio de Arganda del Rey en los años comprendidos entre 2006 y 2016, ambos inclusive. \n", "Añadimos el valor `ylabel=\"m3\"` para ofrecer la medida de los valores del eje de las y, que muestran la concentración por m3 de Ozono." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.set_index('Año')['Ozono - ug'].plot(title=\"Concentración de ozono en Arganda del Rey\",ylabel=\"m3\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Análisis por año: 2014, 2015 y 2016\n", "Ahora, pasaremos a analizar los datos mes a mes de tres años diferentes (2014, 2015 y 2016), para posteriormente hacer una comparación entre ellos. Seguimos usando el [portal de datos abiertos de Arganda del Rey](https://datosabiertos.ayto-arganda.es/) para obtener las bases de datos." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Año 2014\n", "Tal y como realizamos con la base de datos de 2006 a 2016, primero vamos a importar, a través de la API de Arganda del Rey, la base de datos correspondiente al primer año de los que vamos a analizar, 2014." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "url_14 = 'https://datosabiertos.ayto-arganda.es/dataset/1cf5b526-a132-4240-b1eb-63c110284136/resource/844f00c7-f002-46f2-b4b0-e1eb578dd2a6/download/convertcsv-107.json'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creamos el dataframe a partir de la variable `url_14` y vemos sus datos." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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001| 2014{'m3': 45}
102| 2014{'m3': 53}
203| 2014{'m3': 63}
304| 2014{'m3': 65}
405| 2014{'m3': 79}
506| 2014{'m3': 71}
607| 2014{'m3': 80}
708| 2014{'m3': 75}
809| 2014{'m3': 58}
910| 2014{'m3': 37}
1011| 2014{'m3': 35}
1112| 2014{'m3': 19}
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" ], "text/plain": [ " Mes Ozono ug\n", "0 01| 2014 {'m3': 45}\n", "1 02| 2014 {'m3': 53}\n", "2 03| 2014 {'m3': 63}\n", "3 04| 2014 {'m3': 65}\n", "4 05| 2014 {'m3': 79}\n", "5 06| 2014 {'m3': 71}\n", "6 07| 2014 {'m3': 80}\n", "7 08| 2014 {'m3': 75}\n", "8 09| 2014 {'m3': 58}\n", "9 10| 2014 {'m3': 37}\n", "10 11| 2014 {'m3': 35}\n", "11 12| 2014 {'m3': 19}" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_14 = pd.read_json(url_14)\n", "df_14" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 12 entries, 0 to 11\n", "Data columns (total 2 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Mes 12 non-null object\n", " 1 Ozono ug 12 non-null object\n", "dtypes: object(2)\n", "memory usage: 320.0+ bytes\n" ] } ], "source": [ "df_14.info()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Mes Ozono ug\n", "count 12 12\n", "unique 12 12\n", "top 01| 2014 {'m3': 45}\n", "freq 1 1" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_14.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Limpiamos los datos \n", "De nuevo, tal y como sucedió con la base de datos de 2006 a 2016, los valores de la concentración de ozono no son numéricos, como tampoco lo son los de los meses, que vienen codificados como `mm|aaaa`. Vamos a transformarlos a solo `mm`, pues posteriormente, cuando los mostremos, el año del que se traten los datos ya vendrá con un color diferenciado." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Primero recodificamos los datos de la concentración de ozono..." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "df_14.at[0, 'Ozono ug']=45\n", "df_14.at[1, 'Ozono ug']=53\n", "df_14.at[2, 'Ozono ug']=63\n", "df_14.at[3, 'Ozono ug']=65\n", "df_14.at[4, 'Ozono ug']=79\n", "df_14.at[5, 'Ozono ug']=71\n", "df_14.at[6, 'Ozono ug']=80\n", "df_14.at[7, 'Ozono ug']=75\n", "df_14.at[8, 'Ozono ug']=58\n", "df_14.at[9, 'Ozono ug']=37\n", "df_14.at[10, 'Ozono ug']=35\n", "df_14.at[11, 'Ozono ug']=19" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Mes Ozono ug\n", "0 01| 2014 45\n", "1 02| 2014 53\n", "2 03| 2014 63\n", "3 04| 2014 65\n", "4 05| 2014 79\n", "5 06| 2014 71\n", "6 07| 2014 80\n", "7 08| 2014 75\n", "8 09| 2014 58\n", "9 10| 2014 37\n", "10 11| 2014 35\n", "11 12| 2014 19" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_14" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... y luego los de los meses." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "df_14.at[0, 'Mes']=1\n", "df_14.at[1, 'Mes']=2\n", "df_14.at[2, 'Mes']=3\n", "df_14.at[3, 'Mes']=4\n", "df_14.at[4, 'Mes']=5\n", "df_14.at[5, 'Mes']=6\n", "df_14.at[6, 'Mes']=7\n", "df_14.at[7, 'Mes']=8\n", "df_14.at[8, 'Mes']=9\n", "df_14.at[9, 'Mes']=10\n", "df_14.at[10, 'Mes']=11\n", "df_14.at[11, 'Mes']=12" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Mes Ozono ug\n", "0 1 45\n", "1 2 53\n", "2 3 63\n", "3 4 65\n", "4 5 79\n", "5 6 71\n", "6 7 80\n", "7 8 75\n", "8 9 58\n", "9 10 37\n", "10 11 35\n", "11 12 19" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_14" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Generamos el gráfico del año 2014" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Una vez que tenemos los datos listos y recodificamos, vamos a mostrarlos gráficamente. Para ello, fijamos los ejes, el título del gráfico y la etiqueta del eje de las y, para especificar que es la concentración por metro cúbico." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_14.set_index('Mes')['Ozono ug'].plot(title=\"Concentración de ozono\", ylabel=\"m3\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Año 2015\n", "De igual manera que con el año 2014, importamos la base de datos de los registros de concentración de ozono en Arganda del Rey en 2015." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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MesOzono ug
001|2015{'m3': 26}
102|2015{'m3': 48}
203|2015{'m3': 46}
304|2015{'m3': 64}
405|2015{'m3': 71}
506|2015{'m3': 86}
607|2015{'m3': 79}
708|2015{'m3': 72}
809|2015{'m3': 61}
910|2015{'m3': 41}
1011|2015{'m3': 25}
1112|2015{'m3': 15}
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" ], "text/plain": [ " Mes Ozono ug\n", "0 01|2015 {'m3': 26}\n", "1 02|2015 {'m3': 48}\n", "2 03|2015 {'m3': 46}\n", "3 04|2015 {'m3': 64}\n", "4 05|2015 {'m3': 71}\n", "5 06|2015 {'m3': 86}\n", "6 07|2015 {'m3': 79}\n", "7 08|2015 {'m3': 72}\n", "8 09|2015 {'m3': 61}\n", "9 10|2015 {'m3': 41}\n", "10 11|2015 {'m3': 25}\n", "11 12|2015 {'m3': 15}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "url_15 = 'https://datosabiertos.ayto-arganda.es/dataset/bd39d02d-10c0-4d6d-a0dc-cff1b77692a6/resource/566164ff-9757-4b50-91d9-d8b83bca7c6f/download/convertcsv-106.json'\n", "df_15 = pd.read_json(url_15)\n", "df_15" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 12 entries, 0 to 11\n", "Data columns (total 2 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Mes 12 non-null object\n", " 1 Ozono ug 12 non-null object\n", "dtypes: object(2)\n", "memory usage: 320.0+ bytes\n" ] } ], "source": [ "df_15.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Limpiamos y recodificamos los valores\n", "De la misma forma, siguen sin estar codificados como numéricos los valores, así que tenemos que limpiar las variables y recodificarlos como tal para poder hacer el gráfico." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Primero, la concentración de ozono." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "df_15.at[0, 'Ozono ug']=26\n", "df_15.at[1, 'Ozono ug']=48\n", "df_15.at[2, 'Ozono ug']=46\n", "df_15.at[3, 'Ozono ug']=64\n", "df_15.at[4, 'Ozono ug']=71\n", "df_15.at[5, 'Ozono ug']=86\n", "df_15.at[6, 'Ozono ug']=79\n", "df_15.at[7, 'Ozono ug']=72\n", "df_15.at[8, 'Ozono ug']=61\n", "df_15.at[9, 'Ozono ug']=41\n", "df_15.at[10, 'Ozono ug']=25\n", "df_15.at[11, 'Ozono ug']=15" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Luego, los meses del año como valor numérico." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "df_15.at[0, 'Mes']=1\n", "df_15.at[1, 'Mes']=2\n", "df_15.at[2, 'Mes']=3\n", "df_15.at[3, 'Mes']=4\n", "df_15.at[4, 'Mes']=5\n", "df_15.at[5, 'Mes']=6\n", "df_15.at[6, 'Mes']=7\n", "df_15.at[7, 'Mes']=8\n", "df_15.at[8, 'Mes']=9\n", "df_15.at[9, 'Mes']=10\n", "df_15.at[10, 'Mes']=11\n", "df_15.at[11, 'Mes']=12" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Generamos el gráfico para los valores de 2015" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Al igual que hicimos anteriormente, especificamos, a través de `ylabel=\"m3\"`, la leyenda del eje de las y." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_15.set_index('Mes')['Ozono ug'].plot(title=\"Concentración de ozono\", ylabel=\"m3\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Año 2016\n", "De la misma manera, repetimos el procedimiento que hemos hecho con los años anteriores para 2016." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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MesOzono ug
001| 2016{'m3': 30}
102| 2016{'m3': 48}
203| 2016{'m3': 57}
304| 2016{'m3': 65}
405| 2016{'m3': 70}
506| 2016{'m3': 73}
607| 2016{'m3': 85}
708| 2016{'m3': 77}
809| 2016{'m3': 59}
910| 2016{'m3': 39}
1011| 2016{'m3': 31}
1112| 2016{'m3': 20}
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" ], "text/plain": [ " Mes Ozono ug\n", "0 01| 2016 {'m3': 30}\n", "1 02| 2016 {'m3': 48}\n", "2 03| 2016 {'m3': 57}\n", "3 04| 2016 {'m3': 65}\n", "4 05| 2016 {'m3': 70}\n", "5 06| 2016 {'m3': 73}\n", "6 07| 2016 {'m3': 85}\n", "7 08| 2016 {'m3': 77}\n", "8 09| 2016 {'m3': 59}\n", "9 10| 2016 {'m3': 39}\n", "10 11| 2016 {'m3': 31}\n", "11 12| 2016 {'m3': 20}" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "url_16 = 'https://datosabiertos.ayto-arganda.es/dataset/1b6a99e0-1588-4e8e-aa93-f81c007ef424/resource/9e7635c0-0e0d-4884-8ce0-6143e11be9a8/download/convertcsv-105.json'\n", "df_16 = pd.read_json(url_16)\n", "df_16" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 12 entries, 0 to 11\n", "Data columns (total 2 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Mes 12 non-null object\n", " 1 Ozono ug 12 non-null object\n", "dtypes: object(2)\n", "memory usage: 320.0+ bytes\n" ] } ], "source": [ "df_16.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Limpiamos los datos\n", "Como sucedió con los años anteriores, tenemos que limpiar las variables y recodificar los datos para convertirlos en numéricos para poder visualizarlos en un gráfico." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recodificamos los valores de la concentración de ozono:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "df_16.at[0, 'Ozono ug']=30\n", "df_16.at[1, 'Ozono ug']=48\n", "df_16.at[2, 'Ozono ug']=57\n", "df_16.at[3, 'Ozono ug']=65\n", "df_16.at[4, 'Ozono ug']=70\n", "df_16.at[5, 'Ozono ug']=73\n", "df_16.at[6, 'Ozono ug']=85\n", "df_16.at[7, 'Ozono ug']=77\n", "df_16.at[8, 'Ozono ug']=59\n", "df_16.at[9, 'Ozono ug']=39\n", "df_16.at[10, 'Ozono ug']=31\n", "df_16.at[11, 'Ozono ug']=20" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hacemos lo propio con los meses del año." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "df_16.at[0, 'Mes']=1\n", "df_16.at[1, 'Mes']=2\n", "df_16.at[2, 'Mes']=3\n", "df_16.at[3, 'Mes']=4\n", "df_16.at[4, 'Mes']=5\n", "df_16.at[5, 'Mes']=6\n", "df_16.at[6, 'Mes']=7\n", "df_16.at[7, 'Mes']=8\n", "df_16.at[8, 'Mes']=9\n", "df_16.at[9, 'Mes']=10\n", "df_16.at[10, 'Mes']=11\n", "df_16.at[11, 'Mes']=12" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Mes Ozono ug\n", "0 1 30\n", "1 2 48\n", "2 3 57\n", "3 4 65\n", "4 5 70\n", "5 6 73\n", "6 7 85\n", "7 8 77\n", "8 9 59\n", "9 10 39\n", "10 11 31\n", "11 12 20" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_16" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Visualizamos los datos de 2016\n", "Una vez recodificados como numéricos los valores, mostramos el gráfico tal y como lo hemos hecho las veces anteriores, añadiendo `ylabel=\"m3\"` a la leyenda del eje de las y." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_16.set_index('Mes')['Ozono ug'].plot(title=\"Concentración de ozono\", ylabel=\"m3\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparación unificada de los años 2014, 2015 y 2016\n", "Una vez que hemos visto estos tres años por separado, ahora vamos a unificarlos en un solo gráfico para ver, de forma más visual y sencilla, la evolución de la concentración de ozono en Arganda del Rey en 2014, 2015 y 2016." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Primero, creamos las nuevas variables, fijando ya los valores que hemos recodificado." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "ozono_14 = df_14.set_index('Mes')['Ozono ug']\n", "ozono_15 = df_15.set_index('Mes')['Ozono ug']\n", "ozono_16 = df_16.set_index('Mes')['Ozono ug']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A través de la concatenación, disponible en la librería *Pandas*, creamos una nueva variable que unifique los tres años." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Ozono ug Ozono ug Ozono ug\n", "Mes \n", "1 45 26 30\n", "2 53 48 48\n", "3 63 46 57\n", "4 65 64 65\n", "5 79 71 70\n", "6 71 86 73\n", "7 80 79 85\n", "8 75 72 77\n", "9 58 61 59\n", "10 37 41 39\n", "11 35 25 31\n", "12 19 15 20" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vs = pd.concat([ozono_14, ozono_15, ozono_16],axis=1)\n", "vs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cambiamos el nombre de las columnas para que muestre el año al que pertenecen los datos." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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201420152016
Mes
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" ], "text/plain": [ " 2014 2015 2016\n", "Mes \n", "1 45 26 30\n", "2 53 48 48\n", "3 63 46 57\n", "4 65 64 65\n", "5 79 71 70\n", "6 71 86 73\n", "7 80 79 85\n", "8 75 72 77\n", "9 58 61 59\n", "10 37 41 39\n", "11 35 25 31\n", "12 19 15 20" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vs.columns = ['2014', '2015', '2016']\n", "vs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualización conjunta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora sí, mostramos el gráfico con todos los datos de los tres años unificados en uno solo, siguiendo la misma fórmula que hemos empleado en los gráficoso anteriores de añadir `ylabel=\"m3\"` para la leyenda del eje de las y. No hace falta especificar `set_index` porque ya lo hicimos cuando unificamos las tres bases de datos al crear las variables de ozono por año para luego crear la concatenación en `vs`." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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e8Mdo+HWIdgI4hy6EXeDD/R/i7eDNV42+IuBBAH029eHcg3M5LlvJmx49ekSvXr2YPn06NjY2T80TQmTYG2fTpk04OTlRu3ZtvcalavxK4RMdqtXoy9SChmPTXsauLAz5Cw7Ogp1fwe2j0H0eVGqRrU2Gxoby1s63sLGwYUbLGThYOVC1RFXG7RrHkK1D+KzBZ3Sp1CUHO6WkJSs1c31LTEykV69eDBgwgJ49ewJQqlQp7t69S+nSpbl79y5OTk4vLOPAgQNs3LiRLVu2EBcXR2RkJAMHDmTVqlU5ik3V+JXCZ8v7EBcJ3eaC6QvqPiam0PhtGP4PFLGGn7rDto8gMWtDASQkJ/DOrncIjwtnZsuZOFg5AOBR0oO1ndfi7ejNR/s/4vtj35OUok4qFwRSSoYNG4aHhwfjx4//b3rXrl1ZsWIFACtWrKBbt24vLOfbb78lMDCQGzdusHbtWlq2bJnjpP9fgHn9Ubt2bakoenHuDyk/s5Fyz/dZWy8+WsrN72nrzmkgZfDZTK2WkpIiP973sayxvIbcdn1bmsskJCfIyUcmyxrLa8hh24bJsNiwrMWmPCUgIMDYIch9+/ZJQHp5ecmaNWvKmjVrys2bN8vQ0FDZsmVLWblyZdmqVSv54MEDKaWUd+/elS4uLtLa2lra2tpKFxcXGRER8VSZu3btkp06dUp3m2ntN+An08ip+eKeu76+vlLdiEXJsegHMLceWJeG13eCaeYGtHrK5R3wxxiIC4fWk6DeaDBJ/4fzinMrmOI3hTE1xzDaZ/QLi/7jyh98eehLHIs6MqPFDNzt3bMen8L58+f12vUxv0hrv4UQx6WUvs8uq5p6lMJj2wcQ+1Brq89O0geo0gbGHILKbeDvj2BVD4i8k+aiewP38qPfj7Qt15aRNUdmWHT3yt1Z3n45iSmJDNwykG3Xt2UvRkXJgEr8SuFwfhP4/wpNJ4BzjZyVVcwB+q7W+v7fPgpzG8C5P55a5Gr4VSbsnUA1+2p81fgrTETmvmpejl780vkXPEp68P7e95l2fBrJKck5i1dRnqESv1LwxYTBpneglBc0GZ/x8pkhBNQeCqP2Q8lKWpfPP8ZAXCThceG8+e+bWJlZMbPlTKzMrLJUtIOVA0vaLqF31d4sPbuUN/59g4j4CP3ErSioxK8UBts+hNgw6D43+0086SlZCV77W/slcXoNifMbMf7v4dyPuc+MFjNwLuacrWLNTc35tMGn/K/B/zgSfIR+m/tx5eEV/cauFFoq8SsF28VtcGYtNB6vXZBlCKbm0PJj5NCtfFsUjoVfZJJdLbztc36C8ZWqr7Cs3TJik2Lpv6U//9z8Rw8BK4WdQRO/EOIdIcQ5IcRZIcQaIYSlEKKCEOKIEOKKEOIXIYSFIWNQCrHYh7DpbXCqDk3fN/jm1sZe59ciMKyIG12O/wpL2kJozmvpPk4+rO20lsp2lXln9zvMPjmbFJmih4iVwspgiV8I4QKMBXyllDUAU6Av8B0wTUpZGXgIDDNUDEoh9/fH2kBs3eeAmWHrF4fuHOK7o9/R3LU5Y/tsgldWwMPrsKAJ+C3Thn/OgVLFSrGs/TK6V+7OgjMLGLtzLFEJ+hu7RdEvfQ7LXL58eby8vPDx8cHX97memdli6KYeM8BKCGEGFAXuAi2B33TzVwDdDRyDUhhd3gGnVmtX3papZdBN3Yy8ybt73qWCbQUmN52s9eCp3h1GHwS3utqvjrX9ITqUlBTJlL8vMnbNSS4EZ23wsCKmRfii4Rd8VO8jDgQdoP/m/lyPMMwIokrO6HtY5l27dnHq1Cn0dT2TwRK/lDIImALcQkv4EcBxIFxK+fi69EDAJa31hRAjhBB+Qgi/kJAQQ4WpFERxEbBxLDhWg2YfGHRTkQmRvPnvm5gJM2a1nEUx82JPZtqUgYEboP1kuPIvcm4DFiyZz+xdV/j7XDDtp+9jzOrjXAzOfM1dCEG/av1Y2HYhEfER9N/cnz239xhgz5Sc0OewzIZgsEHahBAlgG5ABSAc+BVon9n1pZQLgYWgXblrgBCVPExKmf37pv79MTwKhr6rwKyIfgNLJSkliQl7JhD4KJBFbRbhau36/EImJlB/NDEujQhdOZjRQRNpXLE3bn1+YMmReyw7cIOtZ4Pp6FWaca2qULWUdaa2Xce5Dr90/oVxu8bx1s63eMPnDV73fj3T1wsUGlsnQrC/fst09oIOkzO9eE6HZRZC0LZtW4QQjBw5khEjRmQ79McM+SlpDVyXUoZIKROB9UAjwE7X9APgCgQZMAYlH7obEUuT73fx0QZ/4pOyePHSlX/g5E/aqJsu+h3K9lk/+v3IgTsH+LT+p/g6p9/2Gvoonj5/RNIuehIXKw7B68467H5qw7s1Ytn/QQveaF6Z3Rfu0276Xt78+QRX7mfuF0Dp4qVZ0WEFHSt2ZPap2by7+12iE9V9g/OSnA7LDLB//35OnDjB1q1bmTNnDnv37s1xXIYclvkWUF8IURSIBVoBfsAu4GVgLTAE+NOAMSj5TEqKZPwvp7kfGc/PR25x4W4k8wfWxsnGMuOV4yJh4zhwqKqNs29Av1/6nVXnVzHQYyA9q/RMd7lbD2IYvPQIwZFxzB3cAPdq3eFaT9gwGha3xq7lx7zXZizDGldg0b5rrDh4g83+d+niXYaxrapQ2an4C+OwMrPi28bf4mHvwdTjUxm4ZSAzW8zEzcZNz3ucT2WhZq5v+hiWGcDFRWsNd3JyokePHhw9epSmTZvmKDZDtvEfQTuJewLw121rIfABMF4IcQUoCSwxVAxK/rNo3zUOXXvAV91rMHfAS1wIjqLzrP2cuPUw45V3fApRd7Thls0zcaDIJr9gP7468hWNyjTiXd93013u3J0Ies47yMOYRFYPr0/LaqW0GRWba7d5rNYJ/pkEK7pQIjGYCe2rse+DloxsWol/zt+j7bQ9vL32JFdDHr0wHiEEQ6oPYX7r+YTEhtBncx8OBB3Q3w4rWSb1NCxzdHT0f3feio6OZvv27dSokcMhRx4HmNcfaljmwsE/MFxW/mizHLnST6akpEgppTx/N0I2/u5fWeWjLfKXo7fSX/nKTm3I5L8/NmiMtyNvyyZrmsjO6zvLiPiIdJc7cCVEVv/fNln/m3/kpeDItBdKSZHy5M9Sfu0i5TduUp5e99+s0Kg4+c2WAFntk62ywsRN8u21J+XV+1EZxncr8pbs8WcP6b3CWy71X/rf+1iYFKRhma9evSq9vb2lt7e39PT0lF999VW621TDMiv5TmxCMp1n7eNRfBLbxjWlRLEn/e7DYxJ4a81J9l0OZXCDcnza2RNz01Q/VuOjYG5Dra/+qP1gnrWxcTIrOjGagVsGcj/mPj93+plyNuXSXG6L/13eXnuKciWLsnJYXUrbZhDPwxuwfiTcPgw1XoZOP2r3/0U7P7Bw7zVWHrpBQlIK3Wu58FbLKlRwKJZucTGJMXx64FO239xOh/Id+LzR51keLyg/U8MyP6GGZVbytK+3BHA1JJqpvX2eSvoAdkUtWDa0DiOaVmTloZsMWHSE0EfxTxbY8RlE3NY18RgmwSWnJDNx70SuR1xnSrMp6Sb9nw7d4I2fT+DlasuvoxpknPQBSpSHoZuh5ScQ8AfMawTX9wHgULwIH3X0YN+ElgxrXIEt/ndpPXUP7647zc0HaZ/ILWpelCnNpjDupXFsu7GNwVsHExwdnM09VwoilfgVo/sn4B6rDt/i9SYVaFTZIc1lzExN+KijBzP6+nA6MJwus/bjHxgB1/eC3xKoPwbK1jNYjLNOzmJ34G4+qPsBDco0eG6+lJKp2y/y6Z/naOnuxKph9bArmoWrhU3NtGElhm3Xzk+s6AI7/gdJ2gHO0boIH3fyZO+EFgxtWJ5NZ+7Q8sc9vP/raW49iHmuOCEEw72GM6fVHG5G3uTrw19ne9+VgkclfsWo7kfFMeH3M3iUtuG9dhnfcaqbjwu/j26IiRAMnr+T6F9Hg31FrbZsIH9d/YslZ7Vhkvu6931uflJyCh9tOMvMnVfo7evKgkG1sbIwzd7GXGrDyL3g+yocmAGLW8H9C//NdrK25NPOnuz7oAVDGpRn4+k7tPxxNx/8dobbYc8fAJq4NmGE9wh2B+7mWPCx7MWkFDgq8StGI6Xk/V/PEB2fxMy+PhQxy1yyrOFiy8Y3G/Gt7XqsooNY7vg+SaaG6cVzOuQ0kw5Ooo5zHSbWm/hcv+u4xGTGrD7BmqO3eKNFJb7r5Y2ZaQ6/VhbFoPM06LcWIu/CwmZwZOFT4/04WVvyvy6e7JvQgoH1y7HhVBAtpuxm4u/PHwAGegzEuZgzU/ymqMHdFEAlfsWIVhy8wZ5LIXzSyYMqmbxi9bGSocdoH72Ro04vM+m0LYOXHiUsOkGv8QVHBzNu5zicijoxtdlUzE2evpw+IjaRwUuOsuP8PT7r4sn77apl/2rjtLh30G7zWKEZbH0fVr8MUU+31TvZWDKpa3X2vt+CAfXKsv6EdgD4cL0/QeGxAFiaWTK21lgCHgSw9fpW/cWn5Fsq8StGcTE4im+2XqBlNScG1k/7RGm6EmLgzzehRHnqvz6DH172xu/mQ7rO3k/AnawNfJaemMQYxu4cS1xyHLNbzcbO0u6p+fci4+iz4BAnbz9kZt9avNqogl62+5ziTtD/F62nz40DMK8hXNj83GLOtpZ83q0GeyY0p3+9svx+PJDmP+zi4w3aAaBTxU542Hsw48QM4pPj09iQUpioxK/kurjEZMatPYmNpRnf9fLOei1555fakMddZ4NFMV7xdWPdyAYkJUt6zjvAX6fTvvl5ZqXIFD458AkXwi7wfdPvqWRX6an5V0Me0XPuQW6HxbBsaF261CyTo+1lSAioM1xr+7d11Ub63PgWxD9/YVdpWyu+6FaD3e83p08dN9b53ab5D7vY6n+Pd33f5W70XX4+/7Nh41X0OixzeHg4L7/8MtWqVcPDw4NDhw7lOD6V+JVc98PfF7kQHMUPL9fE0TqLg6jdPASH52mJsEKT/yb7uNmx8a1GVC9jy1trTjJ56wWSU7J3jcqC0wvYcXMH7/q+S1PXpy+NP3U7nJfnHSQ+KZm1IxrQuEravZAMwrEqDPtHu5vYiZ+0sf4D076+pYydFV9192L3+y3wLG3DZxvP4lHiJZq4NGHRmUWEx4XnXtyFkD6HZR43bhzt27fnwoULnD59Wi/XKKjEr+SqvZdCWLL/OkMalKNFtYzHKXlKQgz8+QbYuUHrz5+b7WRtyZrX69O/Xlnm77nKq8uPERGTmKVN/H3jb+aenku3St0Y7Dn4qXm7L96n38LDWFua89uohni52mYtfn0ws4DWn8GrWyA5SbvL1+7vtOdpcLGz4useXjyITmDWv5cZX3s80UnRLDizIJcDL1z0NSxzREQEe/fuZdgw7X5VFhYW2NnZ5Tg+Qw7SpihPCYtO4L1fT1PFqTgfdsxGrWXX1xB2FQZvhCJpD15mYWbCNz28qFHGls82nqXrnP0sGuybqeGOAx4E8Mn+T/Bx9OF/Df73VBPU+hOBTPjtDFVLWbP8tTo4WRtuLKBMKdcQRu+HLe/D7m+0UUl7LtC6tj6jhostfeu4sezADfrUaUqPyj1Ye3Et/av1L/CDuX139DsuhF3IeMEsqGZfjQ/qZv4+DzkZlvn69es4Ojry6quvcvr0aWrXrs2MGTMoViz9K7czQ9X4lVwhpeSD388QHpPI9L4+WJpnsZ/7rSNwaA7UfhUqNstw8f71yrJ2RH1iEpLpMecA286++MrVkJgQxu4cSwnLEkxrMQ0L0ycXXy3ae43x605Tp7w9v4ysb/yk/5ilLfRcCL2WQOhFmN8ETq5K8zaP77V1x8rClC82BTCm5hjMTcyZfmJ67sdcyOR0WOakpCROnDjB6NGjOXnyJMWKFfuveSgnVI1fyRVrj91mR8A9PunkQfUyWWwiSYzVmnhsXaHNF5lerXY5e/56szEjVx1n1KrjjG1VhbdbVcHE5OkvW2hsKG/vepvIhEh+6vATDlZau31KimTytgss3HuNTl6lmdqnZqavNchVXi9D2fqwYZT2Pl3aBl1mQlH7/xYpWbwI77SuyhebAjhzsxxDqw9l3ul5nLp/Ch8nH+PFbmBZqZnrmz6GZXZ1dcXV1ZV69bSr0l9++WW9JH5V41cM7lrII774K4DGlR14LTvdHnd/Cw8uQ9eZYGmT8fKpONta8suI+rxS25WZ/15mxE9+RMYmcOnhJRadWcSALQNoua4l/qH+fNv4W9zttauHE5NTeO/X0yzce43BDcoxs1+tvJn0H7N11ZrA2nwJF7fB3AZw5d+nFhnUoBxVnIrzxaYA+lYdhIOVAz/6/Uh+GKgxv5F6GpbZ2dkZNzc3Ll68CMC///6Lp6dnjuNTo3MqBpWQlEKveQe5/TCGbeOa4mybxWaSQD9Y0gZqDYSus3IQRwKTd29mzdltWNhcIMU0DIAaJWvQzK0Zrcu2pnKJygDEJCQxZvUJdl8M4d02VXmzZWX9XphlaHfPwPrXIeQC1BsNrSf9d3+C/ZdDGbjkCO+3c6eUyyk+P/Q505pPo3W51saNWY/ywuic+/fvp0mTJnh5eWFiotWvv/nmG+rVq0fv3r25desW5cqVY926ddjb2xMcHIyvry+RkZGYmJhQvHhxAgICsLGx4dSpUwwfPpyEhAQqVqzIsmXLKFGixHPbzMronCrxKwb1/bYLzN19lfkDa9O+hnPWVk6M04YriI/SrmC1zFoTUXhcOPuC9rHr9i4O3jlIdGI05iYWJD2qgoz24Iu2L9PDu/pT64RFJ/Dq8mP4B4bzTQ8v+tYtm7WY84rEWO0mL0fmg6MH9PkJHKoAMPInP/ZdDmXHO415Y89AElMS+aPbH5ib5s6Nvg0tLyR+Y8hK4ldt/IrBHL72gHl7rtK3jlvWkz7Anu+0WuuA3zOV9KWUXI+8zu7bu9lzew+nQk6RIlNwtHKkffn2NHdrTr3S9Qh7BCNW+jF+zQ2CQi14o4VWow98GMPgpUcJehjL/IG1aVs9GzHnFeZW0OE7qNJGG+t/ZTcYtgNsXfikkyetpu7hh78vM77xeN749w3WXVrHAI8Bxo5aySUq8SsGERGbyPhfTlHOviifds5Gm2TQCW10Sp+BUCX9ZojElERO3jvJ7kAt2d+KugVoXe5e93qd5m7N8SzpiYl4cjrLxQ5+G9WQD9efYcr2S5y7E8nrTSsyetVxYhOS+WlYPepWsE9ni/lM5dYwaAMs6wiresFrW3GzL8HIphWZtfMKA+rVp55zPeafnk/XSl2xtsjamElK/qQSv6J3Uko+3uDP/ah4fh/dkGJFsvgxC7+l9VAp7gTtnh9HPjIhkv2B+9kduJv9QfuJSojC3MScuqXrMshzEM1cm1G6eOkXbsLKwpRpfXyo4WLLN1vOs/VsMKVsivDrqIa4Oxew5FfaG/r9rCX+Nf1g0AZGN6/Eb8cD+XxTAJP7vkO/LX1Z7L+Yd2q/Y+xo9UJKmb/Oy+RQVpvsDZb4hRDuwC+pJlUE/ges1E0vD9wAekspM3EnbSW/2HAyiE1n7vJ+O3dqutllbeUzv8Lmd0GmQN9V/92C8FbkLa0JJ3APJ+6dIEkmYW9pT0u3lrRwa0GDMg0oal40S5sSQjC8SUWqOduw9tgtJnaohmuJrJWRb1RoCj0Xwa9D4bfXKNr7Jz7q6MFba07if70cXSp2YVXAKvq6983woJnXWVpa8uDBA0qWLFkokr+UkgcPHmBpmfmOE7lyclcIYQoEAfWAN4AwKeVkIcREoISU8oWdbdXJ3fzjdlgMHWbsw7O0DWtG1MfUJJNfvNhwLeGf/Q3c6pHcYx6nEyPYHbib3bd3cz3iOgCV7SrTzLUZzd2a4+XghalJHu5imRcdXQRb3oOXBiM7z6DPoiNcuf+INaPd6b+1B+3Kt+ObJt8YO8ocSUxMJDAwkLi4OGOHkmssLS1xdXV9bsgHY5/cbQVclVLeFEJ0A5rrpq8AdgPGu8pC0Zuk5BTe/uUUApjap2bmk/71fVrTTtRd4ppN5FfHMiz753VCYkMwE2bUdq5NH/c+NHVtipt1wR5iwODqvg6P7sHeHxDFnfmsy5t0mbWftYeiGOg5kKVnlzLQcyCeJXPeV9xYzM3NqVDBQMNkFxC5lfj7Amt0z0tJKe/qngcDpdJaQQgxAhgBULZsPu1SV8jM2XWV4zcfMqOvT+aaTJLitfF3Dswkzr4Cv7V5nyWB2wm9FUo953pMqDuBRmUaqROO+tbiY13y/57qxZ3oX68+Kw/d5LcxfVl/eT1T/aayqO2iQtFMUlgZvKlHCGEB3AGqSynvCSHCpZR2qeY/lFI+fzVCKqqpJ+87ceshr8w/RNeaZZjWxyfjFe5fgPXDib/nz2+erVgiHxISG0pd57qMrjkaX+fnfp0q+pScBOsGwcWtPOq6mEZ/2VDDxYaODa8y+dhk5rSa89yQ1Er+k15TT24M2dABOCGlfDwM3T0hRGldUKWB+7kQg2JAj+KTeHvtKZxtLPm8W/UXLywlHFlI/MJm/Jx4n45VajA55jLlbMqztN1SlrRbopJ+bjA10wZ3c6tL8c2j+d43ggNXHmCb1JSy1mWZ6jeVpJS0h3pW8r/cSPz9eNLMA7ARGKJ7PgT4MxdiUAxo0sZzBD6MYXpfH2wsX3D1Z9Q9Elb1ZO3+z+noVoZvbYrgWqIyS9ouYVn7ZdRxrpN7QStgUVS7obt9RdqeeYcOjqFM3nqZN3zGcTXiKn9c+cPYESoGYtDEL4QoBrQB1qeaPBloI4S4DLTWvVbyqc1n7vLb8UDebFGZOuXTv+gpIeBP1i1vQqeEi3ztYI+LoxeL2i5iefvl1C1dNxcjVp5S1B4G/o4oYsOMpC8h/CaXr5WnllMt5pyaQ0xijLEjVAzAoIlfShktpSwppYxINe2BlLKVlLKKlLK1lDLMkDEohnMnPJYP15+hppsdb7WqkuYyiTEPWfdrLzodmsiXNkVwdvRkYZuFrOiwgvql66sTiHmBrSsMXI+FTOT34lNYu+ckg93fIDQ2lOXnlhs7OsUA1LDMSrYkp0jGrztFUopkRh8fzE2f/iglJify25Ef6fxLU76MuYSTlRMLWs5mZZdfaVCmgUr4eY1TNei/DqeUUBaYfMeWg0m0LdeW5eeWExITYuzoFD1TiV/JlkX7rnH4WhiTulanvMOT28AlpiTy+4V1dFnThM8vLMchRTLf6y1W9dtNQ7dmKuHnZWXrIXovp4a4QbeLE2lecgCJKYnMOTXH2JEpeqYSv5JlZ4Mi+HH7RTp6OfNKbVdAS/gbLm+gy+8dmHTkS0rEhDHX0p1V/fbR6KURKuHnF+4dSO40lWamZyi68XP6VO3DhisbuPLwirEjU/RIDdKmZElsQjJj156kZLEifNPDi2SZzF9X/mLhmYUEPgqkekISH0XF06T1dwjvV4wdrpIN5nWGcvHGdVqdm06x82XYaFGMqcenMrf1XGOHpuiJSvxKlny1OYDrodGsfM2XPXe2suDMAm5H3cZDWDE7+D5NS/kiXpsPdmpohfysaq/P+Pv6Vdrd+JmhL73MrKB9HL57mPql6xs7NEUPVFOPkmk7Au6x+sh1Wte5zWT/1/jkwCcUT5HMjEzilxvXaNb4I8TgjSrpFwDCxIRyA2exObkeQ07+RhkLW6b6TSVFphg7NEUPVOJXMuVuZDTvb11KiaozOBw1h6Jmlsyw9eWXswdpIawRr++ERuNAjZZZYFQrU4LjL03meLInbwXd4HzYeTZf22zssBQ9UE09ynOklNx8EIN/UAT+QRGcCLzBRTkbHG5R3royb1caSIsDCzC5/w/UHQltPtdu9acUOGPb1aDLmQmsiPscz4R4Zh6bQptybbA0y/zY70reoxJ/ISel5FaYLskHaon+bFAEkXHaOC1Fit/CymUVpiYJvOb+EW8mh2KycQJYlYABv2n3dFUKLLuiFoxq/xJ9NrzH1GLfMsYijFXHZzK83gRjh6bkQK7ciCWn1Oic+iGlJPBhLP5BEZwJjMA/KJyzQZFExCYCYGFqQrXS1ni52OLlYss9drP84jTKFCvDjLofU/nfyXB9D7h3gq4zoZiDkfdIyQ3JKZIus/ZT/NENnBy/4biFGZs7/4q9QzVjh6ZkwNg3YlFy2eMkf1bXXPP4ER6jJXlzU0E1Zxs6epXG21VL9FVLWWNhZkJiciLfHv2WXy/9SiOXRnzXaDK2K7tC6BXoOgtqDQLVL7/QMDURTOpand4LInmn6jvsj57Jgj8G8OHAnWBpa+zwlGxQib8AkFJyJyIO/8Dw/2rzZ4MieKhL8mYmAndna9pXd8bL1RZvFzuqOheniNnzJ2JDY0MZv3s8J++f5LUarzG21lhM/X+Fu6e1e7Z6987t3VPygLoV7OlSswxzT5vQq14D1j04RL+1vSk/cCOYFTF2eEoWqcSfT4VFJ7D84A1O39aSfVh0AqDVzqqWsqatpzM1XG3xdrHF3dkaS/OMe9v4h/jz9u63iYyP5IemP9C+QntIjIV/v4TSPlDjZQPvlZKXfdihGv8E3ONeeD8sTE8wI+oK09a/Di8vU7258hmV+POhB4/iGbD4CJfuRVG1lDWtqjnh7WpLDRdbPErbZCrJP+uPK3/w5aEvcSzqyKqOq3C3d9dmHJ4HkYHQYz6YqN6/hVkZOyveaFGJKdsv0b/dAP66tYyTV7dSa+sH0PEH1fyXj6jEn8+ERScwYPERrodG89OwejSqnLMTrIkpifzo9yOrz6+mnnM9fmj2AyUsdXfCjA6F/dOgageo0EQP0Sv53fAmFfnF7zZHT9XA0cWRKeVKsOrYIoR1KWj6vrHDUzJJVeHykYepkv6SIXVynPTD4sIYuWMkq8+vZpDnIOa3mf8k6QPs+R4SorV++ooCWJqb8kknT67cS6SWdV/OJD5ku0dr2PkVnFhp7PCUTFI1/nzicdK/GvKIxYN9aVwlZ0n//IPzjNs1jgexD/im8Td0qdTl6QVCr4DfEnhpMDi652hbSsHS1rMUTao4sP2ICRVrVmZ6SgwtK7XC/K9xUNQBqnU0dohKBlSNPx8Ij0lg4JIjXAl5xKLBvjSt6pij8jZf28zgrYNJkSms7LDy+aQP8O8kMLOE5h/maFtKwSOE4H+dPYlJkDgk9iLwUSBra3bUOgD89ircOmzsEJUMGPqeu3ZCiN+EEBeEEOeFEA2EEPZCiB1CiMu6vyUyLqnwepz0L99/xMJBtWmWg6SflJLEj34/MnHfRDxLerK281qqO1R/fsFbh+H8X9rYO9alchC9UlBVKWXNkAbl2XmyBF72dVgQsJzIV5aBjQv83Aeigo0dovIChq7xzwC2SSmrATWB88BE4F8pZRXgX91rJQ0RMYkMWnKUS8GPWDCoNs3dnbJfVnwEY/4Zw/Jzy+nr3pfFbRfjYJVGc5GUsP0TKO4MDd7IQfRKQTeudRXsi1oQGdSOyPhIFl9dD/3XQXwU7J9u7PCUFzBY4hdC2AJNgSUAUsoEKWU40A1YoVtsBdDdUDHkZxGxiQxaeoSLwVEsGFSbFjlI+pceXqLvpr743fNjUoNJfFz/Y8xNzdNeOOAPCDwGLT8Bi2JpL6MogK2VOe+3c+fsjeL4lGjNqvOrCLK0gpp94fgyVevPwwxZ468AhADLhBAnhRCLhRDFgFJSyru6ZYIB1ZbwjIjYRAYvOcL5u5HMG/gSLaplP+lvv7GdgVsGEp8cz9J2S+lVtVf6CyclwD+TwKk6+PTP9jaVwuMVXze8XGy5ENAIE2HKzBMzocm7kJwIB2YaOzwlHYZM/GbAS8A8KWUtIJpnmnWkNkJcmqPECSFGCCH8hBB+ISEhBgwzb4mMS2Tw0qME3I1k3oDatPLI3nExOSWZmSdm8u6ed6lSogprO6/Fx8nnxSsdWwwPb0CbL9SVmEqmaOP4eHI/3JKqlh3Ycn0L52QcePcBv6Xw6L6xQ1TSYMjEHwgESimP6F7/hnYguCeEKA2g+5vmJ0NKuVBK6Sul9HV0zFkvlvwiMi6RwUuOEnAngrkDatPaM3tJPzIhkrG7xrLIfxE9q/RkWbtlOBXN4FdDbDjs/R4qtoDKrbK1XaVwql3Onh61XPA7XQtbixJM8ZuCbPIuJMfDgRnGDk9Jg8ESv5QyGLgthHjcCbwVEABsBIbopg0B/jRUDPlJVFwiQ5Ye5WxQBHP6v0SbbCb9a+HXGLB5AAeDDvJJvU+Y1GASFqYWGa+470ct+bf9Ul16r2TZxA7VMBNWlEjojN89Pw7G3wOv3nBsiar150GG7tXzFrBaCHEG8AG+ASYDbYQQl4HWuteF2uOk7x8Ywez+L9G2unO2ytl1axf9t/QnMiGSxe0W06daH0RmkvjDm3BkPtTsB85e2dq2UriVsrHkzZaV8T/vTgkLJ+adnods8p5W6z+o2vrzGoMmfinlKV1zjbeUsruU8qGU8oGUspWUsoqUsrWUMsyQMeR1j+KTGLrsGGcCI5jdvxbta2Q96afIFOadnsfYXWMpZ1OOXzr/Qu1StTNfwM4vQZhoPXkUJZuGNa5AuZI2JIa14HTIaQ4lhmgjuh5bAo8Kz3m6/EBduWtEj+KTGLr0KKduhzOrXy3a1yid5TKiE6N5Z9c7zD01ly4Vu7Ci/Qqci2Xh4BF0Avx/1frs27pkefuK8lgRM1M+7ujB3UAvbMwcmHdKV+tPioNDs4wdnpKKSvxGEh2fxKvLjnJSl/Q7eGU96d+MvMmAzQPYE7iHD+p8wNeNv87aTbClhO2fauOrNHo7y9tXlGe18SxFtVIlkOGtOBVyisNJYVCjFxxdrI32quQJKvEbgZb0j3HiVjgz+9aiYzaS/pG7R+i3uR8P4h4wv818BnoOzFx7fmqXtsHN/dB8IljaZDkGRXmWEILRzStxJ9ALW3NH5p+er9X6E2PgoKr15xUq8eeymIQkXl1+DL+bYUzv40Mn76wn/W03tjHqn1GUKlqKNZ3WUL90/awHkpwEO/4HJStD7aFZX19R0tHJqzRlS9hgFtWSE/dPcDQ5Amr0hKOLIPqBscNTUIk/V8UkJPHa8mP43Qhjet9adKlZJstlrD6/mgl7JuDt4M3y9stxtXbNXjAnVkDoJe1irfSGb1CUbDAzNWFUs0rcuFEDWwsH5p6ai2zyvlbrPzTb2OEpqMSfa2ITkhm23I+j18OY1seHrllM+lJKZp6YyeSjk2nu1pwFbRZgW8Q2e8HER8Hub6FsQ3BXY6cr+tertgtOxYtTNKY1J+6f4FhKFFTvAUcXQkyh7siXJ6jEnwtiE5IZtuIYR64/YGpvH7r5ZK33TFJKEpMOTWKR/yJ6VenF1OZTs3YS91kHZkB0CLT9Sl2spRhEETNThjepwKUr1bGzKMm80/Og2QTtjm6H5hg7vEJPJX4Di0tMZvjKYxy69oAfe9eke62sJf3YpFje2f0O6y+vZ6T3SD5r8BlmJjm4cVrkHTg4W+tp4ZqFvv6KkkX965XD1rIodgnt8Lvnx7GUR+DZDY4sULV+I1OJ34DiEpMZvsKPg1cfMOXlmvSolbX2+Ij4CEbuGMme23v4qN5HvFnrzaz33HnWrq9BJkOr/+WsHEXJQPEiZgxpUA7/Cx7YFSnJ/NPzdbX+KDg819jhFWoq8RtIXGIyr6/048DVUH54uSa9amct6QdHBzN021DOhp7lh2Y/0K9av5wHFXwWTq6GuiOgRPmcl6coGRjaqAJWZpY4pbTnaPBR/GQseHTVav2xD40dXqGlEr8BxCUmM+Kn4+y/Esr3vbx5OYtJ/1r4NQZtHcTd6LvMaz2PduXb6SewHf8DS1ttvHRFyQX2xSzoW9eNU+c8KFHEXlfr/wDiI+HwPGOHV2ipxK9ncYnJjPzpOPsuh/BdT29e8XXL0vqnQ04zeNtgEpMTWdZuGfVK19NPYFf+hav/QtP3oai9fspUlEx4vUlFTDDHVXTiSPARTogE8OgCh+drI8IquU4lfj2KT0pm1Krj7LkUwuSeXvSuk7WkvzdwL8P/Ho6NhQ0/dfwJj5Ie+gksJVmr7duVg7qv66dMRcmkMnZW9KjlwvGz7pQo8riHzwcQH6Fq/UaiEr+eRMcnMeqn4+y+qCX9PnXKZmn9P6/8ydidY6lgW4GVHVbiZp21g8YLnV4L985C68/ArIj+ylWUTBrZrBIJSWZUNO/E4buHOWmSBNU6a4lf1fpznUr8enDzQTQ95x5kz6UQvu3pRd+6mU/6UkqWnl3KJwc+wdfZl2Xtl+Fg5aC/4BJiYOdX4FIbqvfUX7mKkgWVHIvToYYzx/3dsStSgnmndP364yO0E71KrlKJP4f2Xgqh6+wD3IuKY+Vr9eiXhaSfIlP4we8Hph2fRvvy7Znbai7FzIvpN8DDcyDqjrpYSzG6Mc0rExVnSjWrLhy6e4hTphLcO2mf0bgIY4dXqGQ58Qsh1JlBtJr6wr1XGbrsKKVtLdn4RmMaV8l8TT0xOZEP933ITwE/McBjAN81/S5zt0jMikchsH+G9pO6XEP9lq0oWVTDxZYmVRw4cdYDuyIlnvTrj4uAIwuNHV6h8sLEL4RoJIQ4L4Q4J4SoJ4TYARwTQtwWQjTIpRjznNiEZMatPcU3Wy7QoUZp1o9pSNmSRTO9fkxiDG/ufJMt17cw7qVxfFDnA0yEAX587ZmsDYzVepL+y1aUbHijRWUeRAm8rbtx4M4BTpsLqNpBG7wtLtLY4RUaGWWbaUBvYDiwGfhcSlkJ6AZMMXBseVLgwxh6zTvIX2fuMKG9O7P716KoReaHUAiLC2PY38M4cvcIXzT8guFew3N+NW5aQi6B3zLwfRUcqui/fEXJhnoV7HmprB0nz3pqbf2n50HzDyAuHI6qtv7cklHiN5dS+kspDwEhUsr9AFLKE4BVRoULIW4IIfyFEKeEEH66afZCiB1CiMu6vyVyvBe55ODVULrOPsDthzEsHVqHMc0rZylpB0YFMnjrYC6HX2Z6i+n0qNLDcMH+MwnMi0KziYbbhqJkkRCCMc0rc+dhCr523TkQdAB/czOo0k4bvC0+ytghFgoZJf7U8z98Zl5mG6RbSCl9pJS+utcTgX+llFWAf3Wv8zQpJcsOXGfQkqPYF7Ng45uNaeHulKUyLoZdZNDWQTyMe8jitotp7tbcMMEC3DgAFzdD47ehuKPhtqMo2dCymhPupaw5E1AdWwvbJ7X+2IfasM2KwWWU+D8VQhQFkFL+8XiiEKIisCKb2+yWat0VQPdslpMr4hKTee/XM3z+VwAtqzmxYUxDKjhkrefNseBjDN02FFNhysoOK/Fx8jFMsAApKbD9E7AuA/XHGG47ipJNJiba7Rmv3k+ikWMv9gXt42yRIlClrTZyrKr1G9wLE7+UcqOUMkYI4SuE2CCEOCGEOAP8CQzKRPkS2C6EOC6EGKGbVkpKeVf3PBgoldaKQogRQgg/IYRfSEhIJndHv+5GxNJnwSF+PxHI262rsGBgbawts3a3qn9u/sOoHaNwKurEqo6rqGRXyUDR6pxbD3dOQKtPwSLzJ5wVJTd19i6Nm70VARe9sbWw1fXwmQixYdotGhWDymxXktXAMqAX0AXorPubkcZSypeADsAbQoimqWdKKSXaweE5UsqFUkpfKaWvo2PuN1ccuxFGl1n7uXL/EQsH1ebt1lUxMcnaSdh1F9cxfvd4PEp6sLLDSpyLORsoWp2kePj3cyjlBd59DLstRckBM1MTRjSthP/tOJo7v8yewD2cs7SEyq21m7LHPzJ2iAVaZhN/iK72f11KefPxI6OVpJRBur/3gQ1AXeCeEKI0gO7v/WzGbjCrDt+k38LDWFua88cbjWhbPWsJW0rJ3FNz+fLwlzR1bcqitouyf5vErDi6EMJvQdsvwMTU8NtTlBx4pbYrDsWLcPWqDzYWNk/X+o8tNnZ4BVpmE/9nQojFQoh+Qoiejx8vWkEIUUwIYf34OdAWOAtsBIboFhuC1myUJ8QnJfPh+jN88sdZGldx4I83GlGllHWWykhOSebLw18y7/Q8ulfuzvQW07Eyy7ADVM7FhMHeH7QaU6WWht+eouSQpbl2e8ZDV6Jp6/oKuwN3E1C0GFRqpdX6E6KNHWKBldnE/yrgA7RHa+J53NzzIqWA/UKI08BRYLOUchswGWgjhLgMtNa9Nrr7kXH0W3iYNUdv80aLSiwZUgdbq6y158cnx/Penvf49dKvDPcazhcNv8jZbRKzYt+P2kmxNl/kzvYURQ8G1CuLjaUZt6/XxtrCWqv1N58IMaFwbImxwyuwMpuV6kgp3bNSsJTyGlAzjekPgFZZKcvQTtx6yKifjhMVl8Sc/i/Rybt0lstISopnzLo2HE18yAdlWjPQtS3IFBC50OQSdl0b6MqnP5SqbvjtKYqeWFuaM7hBeebsvsLwzn1Ye2Ux52uOxqNiCzgwA+oMAws9j1+lZLrGf1AI4WnQSIxk3bHb9F1wmCLmJqwf0zBbSR9g/f4vOJr4kM8fRDDwwFKYWx++dYMl7WDrRG1o5JCL2tj4+vbvF2BqDi0+1n/ZimJgrzYqTxEzE+4H1cHa/Jlav99SY4dXIGW2xl8fOCWEuA7EAwKtU463wSIzsMTkFL7cFMDKQzdpUsWBWf1qYVc0e4OkRSdGM/fGJl5Khh5vXYaH1+HOSbh7Svt7fDkcidUWtigOzt5QphaU8dH+2lcCk2yO1RPop3XhbDoBbMpkrwxFMaKSxYvQt05ZVh2+yfCufVh9cTEXfcbgXqGZVuv3Haa6JutZZhN/e4NGkctCH8UzZvUJjl4PY0TTikxo546ZafYHSVt+8GseiBRmVnoFYV4EnKppDx/dDdKTkyD00pMDwZ1T4LcEkuK0+RbWULrmkwNBaR+wr5jxwUBK7WKtYo7QaGy241cUY3u9aUVWHb5JZHB9ipuvZf7p+UxrPhGWdYDjy6DBG8YOsUDJVOLPTNfN/MI/MIKRP/nxIDqBGX196ObjkqPy7sfcZ8WNTbSPTcS7YTo3MTc1g1Ke2sOnvzYtOQlCLz45ENw5qV24khyvzS9i8+RgUNpH98ug4tNj6l/YDLcOQedpUCRrvY8UJS9xsbOiey0X1h+/w/Au/VhxfhEXa47CvUJTXa3/NTDPhd5xhUQudTnJGzacDGTi7/44FC/C76MbUsMl533r5xz+lkSZwtgKXbN2EsrUTDsRW6o61BqoTUtOhJALTw4Ed09p45T/dzCwhTI1nxwIdn0NDu5Qa3CO90NRjG1Us0r8fiKQ+AeNKG6+hgVnFjC12URY3lEbabaBGoJEXwpF4k9KTmHy1gss3n+d+hXtmdP/JUoWz/m9Zy89vMQft/9hQFQMbr3G5zxQU3Nw9tIeL+lGxEhOhPvnnz5ncGQ+JCdo8/ut1Q4iipLPVXYqTjtPZ9YeCWVop74sD1jMpZqjqFq+CRyYrg0xrmr9elHgM8bD6ATeXHOCA1ceMLRheT7u5IF5DtrzU5t69DuKpaQw0rU1WBtoOAZTcyjtrT0eX/eWlAD3A7QrHCu2MMx2FcUIxrSoxLZzwZhGNaOY+RoWnF7Aj80+gBWd4fgKqD/K2CEWCAX6nrsBdyLpMns/x2485IeXvZnUtbrekv7BOwc5EHyUkQ8jsG30jl7KzDQzC63tv1JLdR9dpUDxdrWjcWUHVh0MpXfVvuy4uYPLds5QrpFW60+MM3aIBUKBTfxSSib9dY6kZMm6kQ14xddNb2UnpyQz9dgUXJJT6OdYF5w89Fa2ohR2Y1pUIvRRPMXjW2FlZsWCMwu0fv1Rd+HESmOHVyAU2MQvhGBGXx82vtUIHzc7vZa96domLoZfZtyDMCxUN0pF0asGFUvi42bHTwdC6Ovej+03tnPVrgyUbQj7p6pavx4U2MQPUNrWCidrS72WGZsUy6yTs6iRLGhvXREqNNNr+YpS2Gm3Z6zE7bBYnGRbLM0sWXBmoXaXrqi7cPInY4eY7xXoxG8IqwJWcS/mHu/eD0Y0HKfa2BXFAFp7lKKKU3FW7g+lr3s/tt3YxrUSbuBWH/ZP0+49oWSbSvxZ8CD2AUvOLqGFtMLXwgGqG/Bm6YpSiD2+PePFe1FUNO+o1fr9dbX+yCBV688hlfizYP7p+cQlxfJO0FWoN0rraqkoikF0qVkGFzsrVhwIpa97X7Ze38q1kuXBrR7sU7X+nFCJP5OuR1zn10u/8rKpAxWEFdQekvFKiqJkm7mpCSObVeTkrXBqFO+GpZklC/0XQrMPIDIQTq4ydoj5lkr8mTT9+HQsTS0Yfe20lvQtc+FWiopSyPX2dcOhuAU/HQilj3sftl7fyg3HiuBaR9fWn2DsEPMllfgz4fi94+y8vZPXLMtRMiVFa+ZRFMXgLM1Nea1xBfZdDqVeiZ5YmFiw8Mwi7d68Ebfh1Gpjh5gvqcSfASklP/r9iJOVI4MuHYIaPcFOfxeDKYryYgPrl8O6iBk/Hw6jt3tvNl/fzE2nyuBSG/ZNVbX+bFCJPwN/3/gb/1B/3rL2xCo+Chq8aeyQFKVQsbE0Z1CDcmw9G0yL0r21Wr//Imj+IUTcgtNrjB1ivmPwxC+EMBVCnBRCbNK9riCEOCKEuCKE+EUIkb3bXuWChOQEpp+YTlW7KnQ5vxPKN9HGyFEUJVe91rgCFqYmrDsczivur7D52mZuOVXVav17voP4R8YOMV/JjRr/OOB8qtffAdOklJWBh8CwXIghW9ZcWEPQoyDeLVkX08ggaPiWsUNSlELJoXgR+tRxY8PJIDq69cPMxEyr9bf7VuvXv/tbY4eYrxg08QshXIFOwGLdawG0BH7TLbIC6G7IGLIrIj6ChWcW0qhMQxqe3aTd8KRyG2OHpSiF1oimFZESfj8WyStVX2HTtU3ctisDtYfC4Xlw94yxQ8w3DF3jnw5MAFJ0r0sC4VLKJN3rQCDNex8KIUYIIfyEEH4hISEGDvN5i84sIiohinecm0KwPzR8M/s3RFcUJcdcSxSlq08Z1h69TY+KAzEVplq//taToKg9bHobUpKNHWa+YLBMJoToDNyXUh7PzvpSyoVSSl8ppa+jo6Oeo3uxwKhAfr7wM90rd8f99AbtZuZevXM1BkVRnje6WSViE5PZdOIRr7i/wl9X/+J20iNo9w0EHQe/pcYOMV8wZBW2EdBVCHEDWIvWxDMDsBNCPL7zlysQZMAYsmXmiZmYClPecG0LV3ZA3ZFgrt9RPhVFyboqpaxp61mK5Qdv0KfqYEyFKTNPzCS5ek+o2Bz+/QIi7xo7zDzPYIlfSvmhlNJVSlke6AvslFIOAHYBL+sWGwL8aagYssM/xJ+tN7YyuPpgSp1cA2ZWUCfPnn9WlEJnTIvKRMYlseNMLMO8hrHtxjbG7h5HZNsvtPF7/v7Q2CHmecZotP4AGC+EuILW5r/ECDGkSUrJFL8p2Fva81q5TuC/DmoN0NoPFUXJE3zc7GhYqSSL913nVc8RfFLvEw4GHWTAoU+4Vn84nNsAl/8xdph5Wq4kfinlbillZ93za1LKulLKylLKV6SUeWaIvV23d3Hi/gne8HmDYidXQXIi1B9j7LAURXnGmOaVuR8Vz/qTQfSp1ofF7RYTmRBJ//s72VWqImweDwkxxg4zz1LdVHQSUxKZdnwaFWwr0LNcOzi2BDw6Q8lKxg5NUZRnNKpckpqutizYc42k5BRql6rNL51/oZxtOcYWTWIeD0nZ872xw8yzVOLX+f3S79yIvMH42uMxO/0LxIVDA3XBlqLkRUIIRjevzK2wGDb7aydznYs5s6L9CrpU7MLcEna8c2UV0XdOGDnSvElIKY0dQ4Z8fX2ln5+fwcp/lPCIThs6UdG2IkvbLELMegmKl4LhOwy2TUVRciYlRdJ2+l6uh0ZTxak43q62eLnaUaOMDadC1zLjzBzKY86M7uspZ1fB2OEahRDiuJTS99npZmktXNgsPbuUsLgw5raai7iwCcJvQtsvjR2WoigvYGIiWDLEl3V+t/EPimRHwD3W+QUCYGZSjh6OjTlsu5dX/nyZN2t+TZ8abShiZmrkqPOGQl/jD44OpvOGzrQq24rvmkyGxa0hJhTeOgEm6kOiKPmFlJKg8Fj8AyPwD4rAPzCcAcFjWeAUxRULMxJD21OpSBe8XezwcrHD29WWqqWssTAruC3eqsafjtknZ5MiUxj70li4fQSC/KDjFJX0FSWfEULgWqIoriWK0sGrNADy/gqaLmjCBFcP9jhuIyIllE3+PVhzVPt+W5iaUK20NTVcbPF2saWGiy3uztaYmxbcgwEU8sR/MewiG69uZGj1obgUd4FNE8DSDnz6Gzs0RVH0QDh5ULThWGbt+5HlLccy/cZGqtaKZILPZELCi+l+GUTw1+k7/HzkFgAWZiZ4OFvj5WqLl4stXi52VClVvEAdDAp1U8+I7SMICAtgS88t2ESFwKza0ORdaPWp3relKIqRJMbC3PpgYsaBbj/y/oGPMBEm/ND0BxqUaQBoJ4pvhcVoB4KgCM4EhnMuKJKoeG08SQszEzxL2+DtasvIZpVwsbMy5h5lmmrqecaBoAMcunuICXUmYGNhA4cmgak51B1h7NAURdEncyvo9COs6kWja0dY22kt43aNY9Q/oxhfezyDPQdjYiIo71CM8g7F6FKzDKAdDG48iP7vV4F/UAS/HLuNf1AEv49qiImJMPKOZV/B+e2SBckpyUzxm4KbtRt93ftC9AM49TN49wbrUsYOT1EUfavcGqr3hH0/UjYxkVUdV9HSrSVT/Kbw4f4PiUuKe24VExNBRcfidPNx4ZPOnvwysgHf9PDi5K1wfj56ywg7oT+FMvFvvLqRK+FXGPfSOMxNzcFvCSTFqvvpKkpB1v5bMCsCm96hmFlRfmz+I2/6vMmWa1sYvHUwdx9lPKpnz5dcaFCxJN9tu8D9qOcPFvlFoUv8MYkxzD45G29Hb9qWawuJcXB0IVRpC04exg5PURRDsXaGVv+D63vA/1dMhAkja45kZsuZ3I66Td/NfTkWfOyFRQgh+KpHDeITU/hq0/kXLpuXFbrEvzJgJfdj7/Oe73sIIeDMLxAdomr7ilIY+L6m3aD9748g9iEAzd2as7rTamwsbBixfQQ/n/+ZF3V6qeRYnFHNK7Hx9B32Xsr9uwPqQ6FK/KGxoSw7u4zWZVtTy6kWpKTAodng7A0Vmho7PEVRDM3EFDpPh5gw+GfSf5Mr2lbk504/08ilEd8e/ZbPDn5GfHL6AwePaV6JCg7F+PTPs8Ql5r/bPRaqxD/v1DwSkhN4u/bb2oTL2yH0EjR8C0T+PUOvKEoWlPaG+qPh+HK4deS/ydYW1sxsOZOR3iPZcGUDr217jXvR99IswtLclK+71+Dmgxjm7LqSS4HrT6FJ/NfCr/H75d/p7d6bcjbltImHZoONC1TvYdzgFEXJXc0/BBtX7QbtyYn/TTYRJrxZ602mNZ/G5fDL9N3cl1P3T6VZRMPKDvSo5cL8PVe5cj8qd+LWk0KT+Kcdn4aVmRWjao7SJtw5CTf2aUd+U3PjBqcoSu4qUhw6fg/3A+DQnOdmty7XmtUdV2NlZsWrf7/Kb5d+S7OYjzt5UNTCjI83nH3heYG8plAk/mPBx9gduJthXsMoYVlCm3hwNlhYw0uDjRucoijGUa0TuHeC3ZPh4c3nZlcpUYU1ndZQz7kenx/6nC8PfUliql8HAA7FizCxQzWOXA/jt+OBuRV5jhX4xJ8iU5jiNwXnYs4M9BioTQy/pd2Xs/YQsLQ1boCKohhPx+9BmMCW9yGNGrttEVvmtJrDazVeY92ldQzbPowHsQ+eWqaPrxu+5UrwzZbzhEUn5FbkOWKwxC+EsBRCHBVCnBZCnBNCfK6bXkEIcUQIcUUI8YsQwsJQMQBsvb6VgAcBjK01FkszS23i4fnaydz6ow25aUVR8jpbV2jxEVz+G85vTHMRUxNT3qn9Dj80/YHzD84z6p9RPEp49N98ExPB1z28iIpL4tst+aNvvyFr/PFASyllTcAHaC+EqA98B0yTUlYGHgLDDBZAcjwzT8zEw96DThU7aRNjw+HECu2Erq2roTatKEp+UW8UOHvB1g8gLjLdxdpXaM/0FtO58vAK43aNIyH5Se3e3dma4U0q8uvxQI5ce5BuGXmFwRK/1Dw+LJrrHhJoCTw+U7IC6G6oGH4+/zN3ou/wru+7mAjdrp5YAQmP1AVbiqJoTM2g8wyICoZdX79w0UYujfiy8ZccDT7Kh/s+JDnlSR/+ca2q4FrCio//OEtCUoqho84Rg7bxCyFMhRCngPvADuAqEC6lTNItEgi4pLPuCCGEnxDCLyQke1fHnQ87TxOXJtQrXU+bkJSgNfNUaAplfLJVpqIoBZBrbagzTBu+5c7JFy7auWJn3vN9j+03t/Pdse/+681jZWHKl91qcOX+IxbuvZobUWebQRO/lDJZSukDuAJ1gWpZWHehlNJXSunr6OiYre1/3/R7pjaf+mTCuQ0QdQcavJWt8hRFKcBa/Q+KOcJfb0PKi6/GHVJ9CEOrD2XNhTUs9l/83/QW1Zzo6OXMrJ1XuPkg2sABZ1+u9OqRUoYDu4AGgJ0Q4vF9AFyBIENu+78TulLCwVngWE0bolVRFCU1S1ttBM+7p+DoogwXf6f2O3Su2JmZJ2ey/vL6/6Z/1qU65qYmfPJH3u3bb8hePY5CCDvdcyugDXAe7QDwsm6xIcCfhorhKdd2wz1/aPAGmBT4XqyKomRH9Z5QqRXs/Aoi77xwURNhwheNvqBRmUZ8fuhzdt/eDUApG0vea1uVfZdD2Xj6xWUYiyEzYGlglxDiDHAM2CGl3AR8AIwXQlwBSgJLDBjDE4dmQzEn8OqdK5tTFCUfEkK7W1dKotbLJwPmJuZMbT4VT3tP3tvz3n/DOwxqUB5vV1u+3HSeiNjEFxdiBIbs1XNGSllLSuktpawhpfxCN/2alLKulLKylPIVKWX6Q+Dpy70AuPIP1BsB5pYG35yiKPmYfQVo+r7Wr//S3xkuXtS8KHNaz8G5mDNv/PsGV8OvYmoi+KaHF2HR8Xy/7UIuBJ01haPN49BsMLMCX4NdMqAoSkHScKx2PnDze5CQ8Ulae0t75reej4WpBSN3jCQ4OpgaLrYMaVien4/e4sSth7kQdOYV/MQfFQxn1kGtgVDU3tjRKIqSH5hZQOdpEHEL9nyXqVVcrV2Z33o+0YnRjNoxioj4CN5t604pa0s+Wu9PYnLe6dtf8BP/kQWQkgQNxhg7EkVR8pNyDbUK46E5cO9cplZxt3dnZsuZ3Iq6xVs738LUNJFJXT25EBzFsgPXDRxw5hXsxB//CPyWgkdnsK9o7GgURclv2nypdfP8623tjn2ZUMe5DpObTObU/VNM2DOBVh4OtKrmxLQdlwl8GGPYeDOpYCf+U6shLlxrr1MURcmqovbQ9isIPKoN95JJbcu35aN6H7E7cDdfHfmKSV09AZi08Vye6NtfsBP/xS3gVg/c6ho7EkVR8qua/aB8E/jnM3h0P9Or9a3WlxHeI1h/eT1/3FzK262r8M/5+/x9Lu3bOeamgp34B66H3iuNHYWiKPmZENBpKiTEwN8fZ2nVN33epFeVXiw8s5Bijkeo5mzNpI3neBSflPHKBlSwE7+JKVg7GzsKRVHyO8eq0Pgd8F8HF7dlejUhBJ/U/4Tmbs35/thkujUK5V5UHFO3XzJgsBkr2IlfURRFX5q8C6W84NehcG1PplczMzHjh6Y/4OPkw6ILX9HmpSiWH7zO2aAIw8WaAZX4FUVRMsPcEgb/oV3Z+3MfuL4306tamlkyq+UsylqX5UzCDOzsQvhogz/JKcY50asSv6IoSmYVc4DBG6FEeVjdG67vy/SqtkVsmd9mPtZFilPEdSn+966z6vDzN3nPDSrxK4qiZEVxRxjyF5QoBz/3hhv7M72qczFnFrRegIlJMvaVVvDDjuPci4wzYLBpU4lfURQlqx4nf1s3WP0K3DiQ6VUr2lVkTqs5YBqOdF7CZxtPGDDQtKnEryiKkh3FnXTJ31VL/jcPZnpVHycffmw+BVPLIPZETGHHeYPej+o5KvEriqJkl3UpLfnblIFVL8PNQ5letblbcz6p/z/Mil/mgz0fEx2fe+P2q8SvKIqSE9bOMHQT2JSG1S/DrSOZXrW3ey+6lxtOotVxXt34mQGDfJpK/IqiKDll7QxDNkHxUrCqF9w+mulVv2g2FjfTNpyP+YsfDi8wYJBPqMSvKIqiDzaltZp/cSf4qSfcPpap1YQQrOj+NSK6Jisvzmbjlb8MHKhK/IqiKPpjU0aX/B1hVU8I9MvUao7FrZhY+wuSoivx6YFP2R+U+S6i2WGwxC+EcBNC7BJCBAghzgkhxumm2wshdgghLuv+ljBUDIqiKLnOpozW7FO0JPzUAwKPZ2q1fnUr4GkylpT4UryzazxnQ88aLERD1viTgHellJ5AfeANIYQnMBH4V0pZBfhX91pRFKXgsHXRav5F7bXkH5Rx8hdCMLlHHeICX0OkFGfMP2O4EXHDIOEZLPFLKe9KKU/onkcB5wEXoBvw+I4GK4DuhopBURTFaGxdtZq/lR2s7AFBGV+oVaWUNSMa1STk8hCSUiSj/hlFSEyI3kPLlTZ+IUR5oBZwBCglpbyrmxUMlEpnnRFCCD8hhF9IiP53XFEUxeDs3LSav5Ut/NQd7pzMcJW3WlbBzbospvdfx9bClsQU/ffvN3jiF0IUB34H3pZSRqaeJ7V7kKU5PJ2UcqGU0ldK6evo6GjoMBVFUQzDriwM3azdu3dlN7hz6oWLW5qb8mX3GtwOLkl9yy8oU7yM3kMyaOIXQpijJf3VUsr1usn3hBCldfNLA5m/l5miKEp+ZFdWa/YpYqMl/7unX7h4s6qOdPYuzbzd17gW8kjv4RiyV48AlgDnpZRTU83aCAzRPR8C/GmoGBRFUfKMEuW0Zp8i1rrkf+aFi/+vsyf96rphX8xC76EIQ93xXQjRGNgH+AMpuskfobXzrwPKAjeB3lLKsBeV5evrK/38MtcfVlEUJU8Luw7LO0NitDbOj7OXwTYlhDgupfR9brqhEr8+qcSvKEqBEnZNl/xjYchGgyX/9BK/unJXURQlt9lX1Jp9zCxhRVcINtzFWmlRiV9RFMUYUif/lV3h3rlc27RK/IqiKMZSspKW/E0tYEUXuBeQK5tViV9RFMWYSlbSunqamGvJ//55g29SJX5FURRjc6is1fxNzHTJ/4JBN6cSv6IoSl7gUEXr3ilMtOQfctFgm1KJX1EUJa9wrKo1+4DW3TPkkkE2oxK/oihKXuJYVWv2QcKKzhB6Re+bUIlfURQlr3F012r+pWpoY/rrmZneS1QURVFyzqkaDFqf8XLZoGr8iqIohYxK/IqiKIWMSvyKoiiFjEr8iqIohYxK/IqiKIWMSvyKoiiFjEr8iqIohYxK/IqiKIVMvrj1ohAiBO3+vPmBAxBq7CAMRO1b/lWQ90/tW/rKSSkdn52YLxJ/fiKE8EvrHpcFgdq3/Ksg75/at6xTTT2KoiiFjEr8iqIohYxK/Pq30NgBGJDat/yrIO+f2rcsUm38iqIohYyq8SuKohQyKvEriqIUMirx64EQwk0IsUsIESCEOCeEGGfsmPRNCGEqhDgphNhk7Fj0TQhhJ4T4TQhxQQhxXgjRwNgx6YsQ4h3dZ/KsEGKNEMLS2DHlhBBiqRDivhDibKpp9kKIHUKIy7q/JYwZY3als28/6D6XZ4QQG4QQdvrYlkr8+pEEvCul9ATqA28IITyNHJO+jQPOGzsIA5kBbJNSVgNqUkD2UwjhAowFfKWUNQBToK9xo8qx5UD7Z6ZNBP6VUlYB/tW9zo+W8/y+7QBqSCm9gUvAh/rYkEr8eiClvCulPKF7HoWWOFyMG5X+CCFcgU7AYmPHom9CCFugKbAEQEqZIKUMN2pQ+mUGWAkhzICiwB0jx5MjUsq9QNgzk7sBK3TPVwDdczMmfUlr36SU26WUSbqXhwFXfWxLJX49E0KUB2oBR4wcij5NByYAKUaOwxAqACHAMl1T1mIhRDFjB6UPUsogYApwC7gLREgptxs3KoMoJaW8q3seDJQyZjAG9BqwVR8FqcSvR0KI4sDvwNtSykhjx6MPQojOwH0p5XFjx2IgZsBLwDwpZS0gmvzbVPAUXVt3N7SDWxmgmBBioHGjMiyp9U8vcH3UhRAfozUpr9ZHeSrx64kQwhwt6a+WUq43djx61AjoKoS4AawFWgohVhk3JL0KBAKllI9/of2GdiAoCFoD16WUIVLKRGA90NDIMRnCPSFEaQDd3/tGjkevhBBDgc7AAKmnC69U4tcDIYRAayM+L6Wcaux49ElK+aGU0lVKWR7txOBOKWWBqTVKKYOB20IId92kVkCAEUPSp1tAfSFEUd1ntBUF5MT1MzYCQ3TPhwB/GjEWvRJCtEdrZu0qpYzRV7kq8etHI2AQWm34lO7R0dhBKZn2FrBaCHEG8AG+MW44+qH7FfMbcALwR/u+5+vhDYQQa4BDgLsQIlAIMQyYDLQRQlxG+5Uz2ZgxZlc6+zYbsAZ26PLKfL1sSw3ZoCiKUrioGr+iKEohoxK/oihKIaMSv6IoSiGjEr+iKEohoxK/oihKIaMSv6KkQwghU1+sJoQwE0KEFMQRSpXCRSV+RUlfNFBDCGGle90GCDJiPIqiFyrxK8qLbUEbmRSgH7Dm8QwhRDHdGOpHdQO8ddNNr66bdko3jnoVI8StKOlSiV9RXmwt0Fd3AxNvnh519WO0ISzqAi2AH3Qje44CZkgpfQBftPGAFCXPMDN2AIqSl0kpz+iG2u6HVvtPrS3aAHbv6V5bAmXRLrv/WHcfg/VSysu5Fa+iZIZK/IqSsY1o49o3B0qmmi6AXlLKi88sf14IcQStiWiLEGKklHJnrkSqKJmgmnoUJWNLgc+llP7PTP8beEs38iVCiFq6vxWBa1LKmWgjRXrnZrCKkhGV+BUlA1LKQF0Sf9aXgDlwRghxTvcaoDdwVghxCqgBrMyVQBUlk9TonIqiKIWMqvEriqIUMirxK4qiFDIq8SuKohQyKvEriqIUMirxK4qiFDIq8SuKohQyKvEriqIUMv8HsgXSyKaj4RgAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "vs.plot(title=\"Concentración de ozono (m3) en 2014, 2015 y 2016\", ylabel=\"m3\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternativa por áreas:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "vs.plot(title=\"Concentración de ozono (m3) en 2014, 2015 y 2016\", kind='area')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exportamos los datos como .csv y el gráfico como .png" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora, para facilitar el uso de los datos que hemos tratado y recodificado, vamos a exportarlo como archivo separado por comas (`.csv`), a través de `to_csv()`, donde en los paréntesis especificamos el nombre del archivo." ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "vs.to_csv('practica-4.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Verificamos que lo hemos exportado haciendo un `!ls` para ver los archivos de nuestra carpeta de JupyterHub." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "api-pandas-folium.ipynb python-api-covid19-pandas-Copy1.ipynb\r\n", "esvsit.csv\t\t python-api-covid19-pandas.ipynb\r\n", "esvsit.png\t\t python-api-ozono-arganda-pandas.ipynb\r\n", "ozono-arganda-14-15-16.csv python-pruebas.ipynb\r\n", "ozono-arganda-14-15-16.png Shared_Resources\r\n", "practica-4.csv\t\t Untitled1.ipynb\r\n", "practica-4.png\t\t Untitled.ipynb\r\n", "pruebas-r.ipynb\r\n" ] } ], "source": [ "!ls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para exportar el gráfico como `.png`, tenemos que importar la librería `matplotlib`, concretamente dentro de esta, `pyplot`, y lo importamos como `plt`. Luego, volvemos a mostrar el gráfico para decirle a `pyplot` cuál es el gráfico que queremos guardar como `.png`. Luego, lo guardamos a través de `plt.savefig()`, y entre los paréntesis especificamos el nombre del archivo." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": 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e8Mdo+HWIdgI4hy6EXeDD/R/i7eDNV42+IuBBAH029eHcg3M5LlvJmx49ekSvXr2YPn06NjY2T80TQmTYG2fTpk04OTlRu3ZtvcalavxK4RMdqtXoy9SChmPTXsauLAz5Cw7Ogp1fwe2j0H0eVGqRrU2Gxoby1s63sLGwYUbLGThYOVC1RFXG7RrHkK1D+KzBZ3Sp1CUHO6WkJSs1c31LTEykV69eDBgwgJ49ewJQqlQp7t69S+nSpbl79y5OTk4vLOPAgQNs3LiRLVu2EBcXR2RkJAMHDmTVqlU5ik3V+JXCZ8v7EBcJ3eaC6QvqPiam0PhtGP4PFLGGn7rDto8gMWtDASQkJ/DOrncIjwtnZsuZOFg5AOBR0oO1ndfi7ejNR/s/4vtj35OUok4qFwRSSoYNG4aHhwfjx4//b3rXrl1ZsWIFACtWrKBbt24vLOfbb78lMDCQGzdusHbtWlq2bJnjpP9fgHn9Ubt2bakoenHuDyk/s5Fyz/dZWy8+WsrN72nrzmkgZfDZTK2WkpIiP973sayxvIbcdn1bmsskJCfIyUcmyxrLa8hh24bJsNiwrMWmPCUgIMDYIch9+/ZJQHp5ecmaNWvKmjVrys2bN8vQ0FDZsmVLWblyZdmqVSv54MEDKaWUd+/elS4uLtLa2lra2tpKFxcXGRER8VSZu3btkp06dUp3m2ntN+An08ip+eKeu76+vlLdiEXJsegHMLceWJeG13eCaeYGtHrK5R3wxxiIC4fWk6DeaDBJ/4fzinMrmOI3hTE1xzDaZ/QLi/7jyh98eehLHIs6MqPFDNzt3bMen8L58+f12vUxv0hrv4UQx6WUvs8uq5p6lMJj2wcQ+1Brq89O0geo0gbGHILKbeDvj2BVD4i8k+aiewP38qPfj7Qt15aRNUdmWHT3yt1Z3n45iSmJDNwykG3Xt2UvRkXJgEr8SuFwfhP4/wpNJ4BzjZyVVcwB+q7W+v7fPgpzG8C5P55a5Gr4VSbsnUA1+2p81fgrTETmvmpejl780vkXPEp68P7e95l2fBrJKck5i1dRnqESv1LwxYTBpneglBc0GZ/x8pkhBNQeCqP2Q8lKWpfPP8ZAXCThceG8+e+bWJlZMbPlTKzMrLJUtIOVA0vaLqF31d4sPbuUN/59g4j4CP3ErSioxK8UBts+hNgw6D43+0086SlZCV77W/slcXoNifMbMf7v4dyPuc+MFjNwLuacrWLNTc35tMGn/K/B/zgSfIR+m/tx5eEV/cauFFoq8SsF28VtcGYtNB6vXZBlCKbm0PJj5NCtfFsUjoVfZJJdLbztc36C8ZWqr7Cs3TJik2Lpv6U//9z8Rw8BK4WdQRO/EOIdIcQ5IcRZIcQaIYSlEKKCEOKIEOKKEOIXIYSFIWNQCrHYh7DpbXCqDk3fN/jm1sZe59ciMKyIG12O/wpL2kJozmvpPk4+rO20lsp2lXln9zvMPjmbFJmih4iVwspgiV8I4QKMBXyllDUAU6Av8B0wTUpZGXgIDDNUDEoh9/fH2kBs3eeAmWHrF4fuHOK7o9/R3LU5Y/tsgldWwMPrsKAJ+C3Thn/OgVLFSrGs/TK6V+7OgjMLGLtzLFEJ+hu7RdEvfQ7LXL58eby8vPDx8cHX97memdli6KYeM8BKCGEGFAXuAi2B33TzVwDdDRyDUhhd3gGnVmtX3papZdBN3Yy8ybt73qWCbQUmN52s9eCp3h1GHwS3utqvjrX9ITqUlBTJlL8vMnbNSS4EZ23wsCKmRfii4Rd8VO8jDgQdoP/m/lyPMMwIokrO6HtY5l27dnHq1Cn0dT2TwRK/lDIImALcQkv4EcBxIFxK+fi69EDAJa31hRAjhBB+Qgi/kJAQQ4WpFERxEbBxLDhWg2YfGHRTkQmRvPnvm5gJM2a1nEUx82JPZtqUgYEboP1kuPIvcm4DFiyZz+xdV/j7XDDtp+9jzOrjXAzOfM1dCEG/av1Y2HYhEfER9N/cnz239xhgz5Sc0OewzIZgsEHahBAlgG5ABSAc+BVon9n1pZQLgYWgXblrgBCVPExKmf37pv79MTwKhr6rwKyIfgNLJSkliQl7JhD4KJBFbRbhau36/EImJlB/NDEujQhdOZjRQRNpXLE3bn1+YMmReyw7cIOtZ4Pp6FWaca2qULWUdaa2Xce5Dr90/oVxu8bx1s63eMPnDV73fj3T1wsUGlsnQrC/fst09oIOkzO9eE6HZRZC0LZtW4QQjBw5khEjRmQ79McM+SlpDVyXUoZIKROB9UAjwE7X9APgCgQZMAYlH7obEUuT73fx0QZ/4pOyePHSlX/g5E/aqJsu+h3K9lk/+v3IgTsH+LT+p/g6p9/2Gvoonj5/RNIuehIXKw7B68467H5qw7s1Ytn/QQveaF6Z3Rfu0276Xt78+QRX7mfuF0Dp4qVZ0WEFHSt2ZPap2by7+12iE9V9g/OSnA7LDLB//35OnDjB1q1bmTNnDnv37s1xXIYclvkWUF8IURSIBVoBfsAu4GVgLTAE+NOAMSj5TEqKZPwvp7kfGc/PR25x4W4k8wfWxsnGMuOV4yJh4zhwqKqNs29Av1/6nVXnVzHQYyA9q/RMd7lbD2IYvPQIwZFxzB3cAPdq3eFaT9gwGha3xq7lx7zXZizDGldg0b5rrDh4g83+d+niXYaxrapQ2an4C+OwMrPi28bf4mHvwdTjUxm4ZSAzW8zEzcZNz3ucT2WhZq5v+hiWGcDFRWsNd3JyokePHhw9epSmTZvmKDZDtvEfQTuJewLw121rIfABMF4IcQUoCSwxVAxK/rNo3zUOXXvAV91rMHfAS1wIjqLzrP2cuPUw45V3fApRd7Thls0zcaDIJr9gP7468hWNyjTiXd93013u3J0Ies47yMOYRFYPr0/LaqW0GRWba7d5rNYJ/pkEK7pQIjGYCe2rse+DloxsWol/zt+j7bQ9vL32JFdDHr0wHiEEQ6oPYX7r+YTEhtBncx8OBB3Q3w4rWSb1NCxzdHT0f3feio6OZvv27dSokcMhRx4HmNcfaljmwsE/MFxW/mizHLnST6akpEgppTx/N0I2/u5fWeWjLfKXo7fSX/nKTm3I5L8/NmiMtyNvyyZrmsjO6zvLiPiIdJc7cCVEVv/fNln/m3/kpeDItBdKSZHy5M9Sfu0i5TduUp5e99+s0Kg4+c2WAFntk62ywsRN8u21J+XV+1EZxncr8pbs8WcP6b3CWy71X/rf+1iYFKRhma9evSq9vb2lt7e39PT0lF999VW621TDMiv5TmxCMp1n7eNRfBLbxjWlRLEn/e7DYxJ4a81J9l0OZXCDcnza2RNz01Q/VuOjYG5Dra/+qP1gnrWxcTIrOjGagVsGcj/mPj93+plyNuXSXG6L/13eXnuKciWLsnJYXUrbZhDPwxuwfiTcPgw1XoZOP2r3/0U7P7Bw7zVWHrpBQlIK3Wu58FbLKlRwKJZucTGJMXx64FO239xOh/Id+LzR51keLyg/U8MyP6GGZVbytK+3BHA1JJqpvX2eSvoAdkUtWDa0DiOaVmTloZsMWHSE0EfxTxbY8RlE3NY18RgmwSWnJDNx70SuR1xnSrMp6Sb9nw7d4I2fT+DlasuvoxpknPQBSpSHoZuh5ScQ8AfMawTX9wHgULwIH3X0YN+ElgxrXIEt/ndpPXUP7647zc0HaZ/ILWpelCnNpjDupXFsu7GNwVsHExwdnM09VwoilfgVo/sn4B6rDt/i9SYVaFTZIc1lzExN+KijBzP6+nA6MJwus/bjHxgB1/eC3xKoPwbK1jNYjLNOzmJ34G4+qPsBDco0eG6+lJKp2y/y6Z/naOnuxKph9bArmoWrhU3NtGElhm3Xzk+s6AI7/gdJ2gHO0boIH3fyZO+EFgxtWJ5NZ+7Q8sc9vP/raW49iHmuOCEEw72GM6fVHG5G3uTrw19ne9+VgkclfsWo7kfFMeH3M3iUtuG9dhnfcaqbjwu/j26IiRAMnr+T6F9Hg31FrbZsIH9d/YslZ7Vhkvu6931uflJyCh9tOMvMnVfo7evKgkG1sbIwzd7GXGrDyL3g+yocmAGLW8H9C//NdrK25NPOnuz7oAVDGpRn4+k7tPxxNx/8dobbYc8fAJq4NmGE9wh2B+7mWPCx7MWkFDgq8StGI6Xk/V/PEB2fxMy+PhQxy1yyrOFiy8Y3G/Gt7XqsooNY7vg+SaaG6cVzOuQ0kw5Ooo5zHSbWm/hcv+u4xGTGrD7BmqO3eKNFJb7r5Y2ZaQ6/VhbFoPM06LcWIu/CwmZwZOFT4/04WVvyvy6e7JvQgoH1y7HhVBAtpuxm4u/PHwAGegzEuZgzU/ymqMHdFEAlfsWIVhy8wZ5LIXzSyYMqmbxi9bGSocdoH72Ro04vM+m0LYOXHiUsOkGv8QVHBzNu5zicijoxtdlUzE2evpw+IjaRwUuOsuP8PT7r4sn77apl/2rjtLh30G7zWKEZbH0fVr8MUU+31TvZWDKpa3X2vt+CAfXKsv6EdgD4cL0/QeGxAFiaWTK21lgCHgSw9fpW/cWn5Fsq8StGcTE4im+2XqBlNScG1k/7RGm6EmLgzzehRHnqvz6DH172xu/mQ7rO3k/AnawNfJaemMQYxu4cS1xyHLNbzcbO0u6p+fci4+iz4BAnbz9kZt9avNqogl62+5ziTtD/F62nz40DMK8hXNj83GLOtpZ83q0GeyY0p3+9svx+PJDmP+zi4w3aAaBTxU542Hsw48QM4pPj09iQUpioxK/kurjEZMatPYmNpRnf9fLOei1555fakMddZ4NFMV7xdWPdyAYkJUt6zjvAX6fTvvl5ZqXIFD458AkXwi7wfdPvqWRX6an5V0Me0XPuQW6HxbBsaF261CyTo+1lSAioM1xr+7d11Ub63PgWxD9/YVdpWyu+6FaD3e83p08dN9b53ab5D7vY6n+Pd33f5W70XX4+/7Nh41X0OixzeHg4L7/8MtWqVcPDw4NDhw7lOD6V+JVc98PfF7kQHMUPL9fE0TqLg6jdPASH52mJsEKT/yb7uNmx8a1GVC9jy1trTjJ56wWSU7J3jcqC0wvYcXMH7/q+S1PXpy+NP3U7nJfnHSQ+KZm1IxrQuEravZAMwrEqDPtHu5vYiZ+0sf4D076+pYydFV9192L3+y3wLG3DZxvP4lHiJZq4NGHRmUWEx4XnXtyFkD6HZR43bhzt27fnwoULnD59Wi/XKKjEr+SqvZdCWLL/OkMalKNFtYzHKXlKQgz8+QbYuUHrz5+b7WRtyZrX69O/Xlnm77nKq8uPERGTmKVN/H3jb+aenku3St0Y7Dn4qXm7L96n38LDWFua89uohni52mYtfn0ws4DWn8GrWyA5SbvL1+7vtOdpcLGz4useXjyITmDWv5cZX3s80UnRLDizIJcDL1z0NSxzREQEe/fuZdgw7X5VFhYW2NnZ5Tg+Qw7SpihPCYtO4L1fT1PFqTgfdsxGrWXX1xB2FQZvhCJpD15mYWbCNz28qFHGls82nqXrnP0sGuybqeGOAx4E8Mn+T/Bx9OF/Df73VBPU+hOBTPjtDFVLWbP8tTo4WRtuLKBMKdcQRu+HLe/D7m+0UUl7LtC6tj6jhostfeu4sezADfrUaUqPyj1Ye3Et/av1L/CDuX139DsuhF3IeMEsqGZfjQ/qZv4+DzkZlvn69es4Ojry6quvcvr0aWrXrs2MGTMoViz9K7czQ9X4lVwhpeSD388QHpPI9L4+WJpnsZ/7rSNwaA7UfhUqNstw8f71yrJ2RH1iEpLpMecA286++MrVkJgQxu4cSwnLEkxrMQ0L0ycXXy3ae43x605Tp7w9v4ysb/yk/5ilLfRcCL2WQOhFmN8ETq5K8zaP77V1x8rClC82BTCm5hjMTcyZfmJ67sdcyOR0WOakpCROnDjB6NGjOXnyJMWKFfuveSgnVI1fyRVrj91mR8A9PunkQfUyWWwiSYzVmnhsXaHNF5lerXY5e/56szEjVx1n1KrjjG1VhbdbVcHE5OkvW2hsKG/vepvIhEh+6vATDlZau31KimTytgss3HuNTl6lmdqnZqavNchVXi9D2fqwYZT2Pl3aBl1mQlH7/xYpWbwI77SuyhebAjhzsxxDqw9l3ul5nLp/Ch8nH+PFbmBZqZnrmz6GZXZ1dcXV1ZV69bSr0l9++WW9JH5V41cM7lrII774K4DGlR14LTvdHnd/Cw8uQ9eZYGmT8fKpONta8suI+rxS25WZ/15mxE9+RMYmcOnhJRadWcSALQNoua4l/qH+fNv4W9zttauHE5NTeO/X0yzce43BDcoxs1+tvJn0H7N11ZrA2nwJF7fB3AZw5d+nFhnUoBxVnIrzxaYA+lYdhIOVAz/6/Uh+GKgxv5F6GpbZ2dkZNzc3Ll68CMC///6Lp6dnjuNTo3MqBpWQlEKveQe5/TCGbeOa4mybxWaSQD9Y0gZqDYSus3IQRwKTd29mzdltWNhcIMU0DIAaJWvQzK0Zrcu2pnKJygDEJCQxZvUJdl8M4d02VXmzZWX9XphlaHfPwPrXIeQC1BsNrSf9d3+C/ZdDGbjkCO+3c6eUyyk+P/Q505pPo3W51saNWY/ywuic+/fvp0mTJnh5eWFiotWvv/nmG+rVq0fv3r25desW5cqVY926ddjb2xMcHIyvry+RkZGYmJhQvHhxAgICsLGx4dSpUwwfPpyEhAQqVqzIsmXLKFGixHPbzMronCrxKwb1/bYLzN19lfkDa9O+hnPWVk6M04YriI/SrmC1zFoTUXhcOPuC9rHr9i4O3jlIdGI05iYWJD2qgoz24Iu2L9PDu/pT64RFJ/Dq8mP4B4bzTQ8v+tYtm7WY84rEWO0mL0fmg6MH9PkJHKoAMPInP/ZdDmXHO415Y89AElMS+aPbH5ib5s6Nvg0tLyR+Y8hK4ldt/IrBHL72gHl7rtK3jlvWkz7Anu+0WuuA3zOV9KWUXI+8zu7bu9lzew+nQk6RIlNwtHKkffn2NHdrTr3S9Qh7BCNW+jF+zQ2CQi14o4VWow98GMPgpUcJehjL/IG1aVs9GzHnFeZW0OE7qNJGG+t/ZTcYtgNsXfikkyetpu7hh78vM77xeN749w3WXVrHAI8Bxo5aySUq8SsGERGbyPhfTlHOviifds5Gm2TQCW10Sp+BUCX9ZojElERO3jvJ7kAt2d+KugVoXe5e93qd5m7N8SzpiYl4cjrLxQ5+G9WQD9efYcr2S5y7E8nrTSsyetVxYhOS+WlYPepWsE9ni/lM5dYwaAMs6wiresFrW3GzL8HIphWZtfMKA+rVp55zPeafnk/XSl2xtsjamElK/qQSv6J3Uko+3uDP/ah4fh/dkGJFsvgxC7+l9VAp7gTtnh9HPjIhkv2B+9kduJv9QfuJSojC3MScuqXrMshzEM1cm1G6eOkXbsLKwpRpfXyo4WLLN1vOs/VsMKVsivDrqIa4Oxew5FfaG/r9rCX+Nf1g0AZGN6/Eb8cD+XxTAJP7vkO/LX1Z7L+Yd2q/Y+xo9UJKmb/Oy+RQVpvsDZb4hRDuwC+pJlUE/ges1E0vD9wAekspM3EnbSW/2HAyiE1n7vJ+O3dqutllbeUzv8Lmd0GmQN9V/92C8FbkLa0JJ3APJ+6dIEkmYW9pT0u3lrRwa0GDMg0oal40S5sSQjC8SUWqOduw9tgtJnaohmuJrJWRb1RoCj0Xwa9D4bfXKNr7Jz7q6MFba07if70cXSp2YVXAKvq6983woJnXWVpa8uDBA0qWLFkokr+UkgcPHmBpmfmOE7lyclcIYQoEAfWAN4AwKeVkIcREoISU8oWdbdXJ3fzjdlgMHWbsw7O0DWtG1MfUJJNfvNhwLeGf/Q3c6pHcYx6nEyPYHbib3bd3cz3iOgCV7SrTzLUZzd2a4+XghalJHu5imRcdXQRb3oOXBiM7z6DPoiNcuf+INaPd6b+1B+3Kt+ObJt8YO8ocSUxMJDAwkLi4OGOHkmssLS1xdXV9bsgHY5/cbQVclVLeFEJ0A5rrpq8AdgPGu8pC0Zuk5BTe/uUUApjap2bmk/71fVrTTtRd4ppN5FfHMiz753VCYkMwE2bUdq5NH/c+NHVtipt1wR5iwODqvg6P7sHeHxDFnfmsy5t0mbWftYeiGOg5kKVnlzLQcyCeJXPeV9xYzM3NqVDBQMNkFxC5lfj7Amt0z0tJKe/qngcDpdJaQQgxAhgBULZsPu1SV8jM2XWV4zcfMqOvT+aaTJLitfF3Dswkzr4Cv7V5nyWB2wm9FUo953pMqDuBRmUaqROO+tbiY13y/57qxZ3oX68+Kw/d5LcxfVl/eT1T/aayqO2iQtFMUlgZvKlHCGEB3AGqSynvCSHCpZR2qeY/lFI+fzVCKqqpJ+87ceshr8w/RNeaZZjWxyfjFe5fgPXDib/nz2+erVgiHxISG0pd57qMrjkaX+fnfp0q+pScBOsGwcWtPOq6mEZ/2VDDxYaODa8y+dhk5rSa89yQ1Er+k15TT24M2dABOCGlfDwM3T0hRGldUKWB+7kQg2JAj+KTeHvtKZxtLPm8W/UXLywlHFlI/MJm/Jx4n45VajA55jLlbMqztN1SlrRbopJ+bjA10wZ3c6tL8c2j+d43ggNXHmCb1JSy1mWZ6jeVpJS0h3pW8r/cSPz9eNLMA7ARGKJ7PgT4MxdiUAxo0sZzBD6MYXpfH2wsX3D1Z9Q9Elb1ZO3+z+noVoZvbYrgWqIyS9ouYVn7ZdRxrpN7QStgUVS7obt9RdqeeYcOjqFM3nqZN3zGcTXiKn9c+cPYESoGYtDEL4QoBrQB1qeaPBloI4S4DLTWvVbyqc1n7vLb8UDebFGZOuXTv+gpIeBP1i1vQqeEi3ztYI+LoxeL2i5iefvl1C1dNxcjVp5S1B4G/o4oYsOMpC8h/CaXr5WnllMt5pyaQ0xijLEjVAzAoIlfShktpSwppYxINe2BlLKVlLKKlLK1lDLMkDEohnMnPJYP15+hppsdb7WqkuYyiTEPWfdrLzodmsiXNkVwdvRkYZuFrOiwgvql66sTiHmBrSsMXI+FTOT34lNYu+ckg93fIDQ2lOXnlhs7OsUA1LDMSrYkp0jGrztFUopkRh8fzE2f/iglJify25Ef6fxLU76MuYSTlRMLWs5mZZdfaVCmgUr4eY1TNei/DqeUUBaYfMeWg0m0LdeW5eeWExITYuzoFD1TiV/JlkX7rnH4WhiTulanvMOT28AlpiTy+4V1dFnThM8vLMchRTLf6y1W9dtNQ7dmKuHnZWXrIXovp4a4QbeLE2lecgCJKYnMOTXH2JEpeqYSv5JlZ4Mi+HH7RTp6OfNKbVdAS/gbLm+gy+8dmHTkS0rEhDHX0p1V/fbR6KURKuHnF+4dSO40lWamZyi68XP6VO3DhisbuPLwirEjU/RIDdKmZElsQjJj156kZLEifNPDi2SZzF9X/mLhmYUEPgqkekISH0XF06T1dwjvV4wdrpIN5nWGcvHGdVqdm06x82XYaFGMqcenMrf1XGOHpuiJSvxKlny1OYDrodGsfM2XPXe2suDMAm5H3cZDWDE7+D5NS/kiXpsPdmpohfysaq/P+Pv6Vdrd+JmhL73MrKB9HL57mPql6xs7NEUPVFOPkmk7Au6x+sh1Wte5zWT/1/jkwCcUT5HMjEzilxvXaNb4I8TgjSrpFwDCxIRyA2exObkeQ07+RhkLW6b6TSVFphg7NEUPVOJXMuVuZDTvb11KiaozOBw1h6Jmlsyw9eWXswdpIawRr++ERuNAjZZZYFQrU4LjL03meLInbwXd4HzYeTZf22zssBQ9UE09ynOklNx8EIN/UAT+QRGcCLzBRTkbHG5R3royb1caSIsDCzC5/w/UHQltPtdu9acUOGPb1aDLmQmsiPscz4R4Zh6bQptybbA0y/zY70reoxJ/ISel5FaYLskHaon+bFAEkXHaOC1Fit/CymUVpiYJvOb+EW8mh2KycQJYlYABv2n3dFUKLLuiFoxq/xJ9NrzH1GLfMsYijFXHZzK83gRjh6bkQK7ciCWn1Oic+iGlJPBhLP5BEZwJjMA/KJyzQZFExCYCYGFqQrXS1ni52OLlYss9drP84jTKFCvDjLofU/nfyXB9D7h3gq4zoZiDkfdIyQ3JKZIus/ZT/NENnBy/4biFGZs7/4q9QzVjh6ZkwNg3YlFy2eMkf1bXXPP4ER6jJXlzU0E1Zxs6epXG21VL9FVLWWNhZkJiciLfHv2WXy/9SiOXRnzXaDK2K7tC6BXoOgtqDQLVL7/QMDURTOpand4LInmn6jvsj57Jgj8G8OHAnWBpa+zwlGxQib8AkFJyJyIO/8Dw/2rzZ4MieKhL8mYmAndna9pXd8bL1RZvFzuqOheniNnzJ2JDY0MZv3s8J++f5LUarzG21lhM/X+Fu6e1e7Z6987t3VPygLoV7OlSswxzT5vQq14D1j04RL+1vSk/cCOYFTF2eEoWqcSfT4VFJ7D84A1O39aSfVh0AqDVzqqWsqatpzM1XG3xdrHF3dkaS/OMe9v4h/jz9u63iYyP5IemP9C+QntIjIV/v4TSPlDjZQPvlZKXfdihGv8E3ONeeD8sTE8wI+oK09a/Di8vU7258hmV+POhB4/iGbD4CJfuRVG1lDWtqjnh7WpLDRdbPErbZCrJP+uPK3/w5aEvcSzqyKqOq3C3d9dmHJ4HkYHQYz6YqN6/hVkZOyveaFGJKdsv0b/dAP66tYyTV7dSa+sH0PEH1fyXj6jEn8+ERScwYPERrodG89OwejSqnLMTrIkpifzo9yOrz6+mnnM9fmj2AyUsdXfCjA6F/dOgageo0EQP0Sv53fAmFfnF7zZHT9XA0cWRKeVKsOrYIoR1KWj6vrHDUzJJVeHykYepkv6SIXVynPTD4sIYuWMkq8+vZpDnIOa3mf8k6QPs+R4SorV++ooCWJqb8kknT67cS6SWdV/OJD5ku0dr2PkVnFhp7PCUTFI1/nzicdK/GvKIxYN9aVwlZ0n//IPzjNs1jgexD/im8Td0qdTl6QVCr4DfEnhpMDi652hbSsHS1rMUTao4sP2ICRVrVmZ6SgwtK7XC/K9xUNQBqnU0dohKBlSNPx8Ij0lg4JIjXAl5xKLBvjSt6pij8jZf28zgrYNJkSms7LDy+aQP8O8kMLOE5h/maFtKwSOE4H+dPYlJkDgk9iLwUSBra3bUOgD89ircOmzsEJUMGPqeu3ZCiN+EEBeEEOeFEA2EEPZCiB1CiMu6vyUyLqnwepz0L99/xMJBtWmWg6SflJLEj34/MnHfRDxLerK281qqO1R/fsFbh+H8X9rYO9alchC9UlBVKWXNkAbl2XmyBF72dVgQsJzIV5aBjQv83Aeigo0dovIChq7xzwC2SSmrATWB88BE4F8pZRXgX91rJQ0RMYkMWnKUS8GPWDCoNs3dnbJfVnwEY/4Zw/Jzy+nr3pfFbRfjYJVGc5GUsP0TKO4MDd7IQfRKQTeudRXsi1oQGdSOyPhIFl9dD/3XQXwU7J9u7PCUFzBY4hdC2AJNgSUAUsoEKWU40A1YoVtsBdDdUDHkZxGxiQxaeoSLwVEsGFSbFjlI+pceXqLvpr743fNjUoNJfFz/Y8xNzdNeOOAPCDwGLT8Bi2JpL6MogK2VOe+3c+fsjeL4lGjNqvOrCLK0gpp94fgyVevPwwxZ468AhADLhBAnhRCLhRDFgFJSyru6ZYIB1ZbwjIjYRAYvOcL5u5HMG/gSLaplP+lvv7GdgVsGEp8cz9J2S+lVtVf6CyclwD+TwKk6+PTP9jaVwuMVXze8XGy5ENAIE2HKzBMzocm7kJwIB2YaOzwlHYZM/GbAS8A8KWUtIJpnmnWkNkJcmqPECSFGCCH8hBB+ISEhBgwzb4mMS2Tw0qME3I1k3oDatPLI3nExOSWZmSdm8u6ed6lSogprO6/Fx8nnxSsdWwwPb0CbL9SVmEqmaOP4eHI/3JKqlh3Ycn0L52QcePcBv6Xw6L6xQ1TSYMjEHwgESimP6F7/hnYguCeEKA2g+5vmJ0NKuVBK6Sul9HV0zFkvlvwiMi6RwUuOEnAngrkDatPaM3tJPzIhkrG7xrLIfxE9q/RkWbtlOBXN4FdDbDjs/R4qtoDKrbK1XaVwql3Onh61XPA7XQtbixJM8ZuCbPIuJMfDgRnGDk9Jg8ESv5QyGLgthHjcCbwVEABsBIbopg0B/jRUDPlJVFwiQ5Ye5WxQBHP6v0SbbCb9a+HXGLB5AAeDDvJJvU+Y1GASFqYWGa+470ct+bf9Ul16r2TZxA7VMBNWlEjojN89Pw7G3wOv3nBsiar150GG7tXzFrBaCHEG8AG+ASYDbYQQl4HWuteF2uOk7x8Ywez+L9G2unO2ytl1axf9t/QnMiGSxe0W06daH0RmkvjDm3BkPtTsB85e2dq2UriVsrHkzZaV8T/vTgkLJ+adnods8p5W6z+o2vrzGoMmfinlKV1zjbeUsruU8qGU8oGUspWUsoqUsrWUMsyQMeR1j+KTGLrsGGcCI5jdvxbta2Q96afIFOadnsfYXWMpZ1OOXzr/Qu1StTNfwM4vQZhoPXkUJZuGNa5AuZI2JIa14HTIaQ4lhmgjuh5bAo8Kz3m6/EBduWtEj+KTGLr0KKduhzOrXy3a1yid5TKiE6N5Z9c7zD01ly4Vu7Ci/Qqci2Xh4BF0Avx/1frs27pkefuK8lgRM1M+7ujB3UAvbMwcmHdKV+tPioNDs4wdnpKKSvxGEh2fxKvLjnJSl/Q7eGU96d+MvMmAzQPYE7iHD+p8wNeNv87aTbClhO2fauOrNHo7y9tXlGe18SxFtVIlkOGtOBVyisNJYVCjFxxdrI32quQJKvEbgZb0j3HiVjgz+9aiYzaS/pG7R+i3uR8P4h4wv818BnoOzFx7fmqXtsHN/dB8IljaZDkGRXmWEILRzStxJ9ALW3NH5p+er9X6E2PgoKr15xUq8eeymIQkXl1+DL+bYUzv40Mn76wn/W03tjHqn1GUKlqKNZ3WUL90/awHkpwEO/4HJStD7aFZX19R0tHJqzRlS9hgFtWSE/dPcDQ5Amr0hKOLIPqBscNTUIk/V8UkJPHa8mP43Qhjet9adKlZJstlrD6/mgl7JuDt4M3y9stxtXbNXjAnVkDoJe1irfSGb1CUbDAzNWFUs0rcuFEDWwsH5p6ai2zyvlbrPzTb2OEpqMSfa2ITkhm23I+j18OY1seHrllM+lJKZp6YyeSjk2nu1pwFbRZgW8Q2e8HER8Hub6FsQ3BXY6cr+tertgtOxYtTNKY1J+6f4FhKFFTvAUcXQkyh7siXJ6jEnwtiE5IZtuIYR64/YGpvH7r5ZK33TFJKEpMOTWKR/yJ6VenF1OZTs3YS91kHZkB0CLT9Sl2spRhEETNThjepwKUr1bGzKMm80/Og2QTtjm6H5hg7vEJPJX4Di0tMZvjKYxy69oAfe9eke62sJf3YpFje2f0O6y+vZ6T3SD5r8BlmJjm4cVrkHTg4W+tp4ZqFvv6KkkX965XD1rIodgnt8Lvnx7GUR+DZDY4sULV+I1OJ34DiEpMZvsKPg1cfMOXlmvSolbX2+Ij4CEbuGMme23v4qN5HvFnrzaz33HnWrq9BJkOr/+WsHEXJQPEiZgxpUA7/Cx7YFSnJ/NPzdbX+KDg819jhFWoq8RtIXGIyr6/048DVUH54uSa9amct6QdHBzN021DOhp7lh2Y/0K9av5wHFXwWTq6GuiOgRPmcl6coGRjaqAJWZpY4pbTnaPBR/GQseHTVav2xD40dXqGlEr8BxCUmM+Kn4+y/Esr3vbx5OYtJ/1r4NQZtHcTd6LvMaz2PduXb6SewHf8DS1ttvHRFyQX2xSzoW9eNU+c8KFHEXlfr/wDiI+HwPGOHV2ipxK9ncYnJjPzpOPsuh/BdT29e8XXL0vqnQ04zeNtgEpMTWdZuGfVK19NPYFf+hav/QtP3oai9fspUlEx4vUlFTDDHVXTiSPARTogE8OgCh+drI8IquU4lfj2KT0pm1Krj7LkUwuSeXvSuk7WkvzdwL8P/Ho6NhQ0/dfwJj5Ie+gksJVmr7duVg7qv66dMRcmkMnZW9KjlwvGz7pQo8riHzwcQH6Fq/UaiEr+eRMcnMeqn4+y+qCX9PnXKZmn9P6/8ydidY6lgW4GVHVbiZp21g8YLnV4L985C68/ArIj+ylWUTBrZrBIJSWZUNO/E4buHOWmSBNU6a4lf1fpznUr8enDzQTQ95x5kz6UQvu3pRd+6mU/6UkqWnl3KJwc+wdfZl2Xtl+Fg5aC/4BJiYOdX4FIbqvfUX7mKkgWVHIvToYYzx/3dsStSgnmndP364yO0E71KrlKJP4f2Xgqh6+wD3IuKY+Vr9eiXhaSfIlP4we8Hph2fRvvy7Znbai7FzIvpN8DDcyDqjrpYSzG6Mc0rExVnSjWrLhy6e4hTphLcO2mf0bgIY4dXqGQ58Qsh1JlBtJr6wr1XGbrsKKVtLdn4RmMaV8l8TT0xOZEP933ITwE/McBjAN81/S5zt0jMikchsH+G9pO6XEP9lq0oWVTDxZYmVRw4cdYDuyIlnvTrj4uAIwuNHV6h8sLEL4RoJIQ4L4Q4J4SoJ4TYARwTQtwWQjTIpRjznNiEZMatPcU3Wy7QoUZp1o9pSNmSRTO9fkxiDG/ufJMt17cw7qVxfFDnA0yEAX587ZmsDYzVepL+y1aUbHijRWUeRAm8rbtx4M4BTpsLqNpBG7wtLtLY4RUaGWWbaUBvYDiwGfhcSlkJ6AZMMXBseVLgwxh6zTvIX2fuMKG9O7P716KoReaHUAiLC2PY38M4cvcIXzT8guFew3N+NW5aQi6B3zLwfRUcqui/fEXJhnoV7HmprB0nz3pqbf2n50HzDyAuHI6qtv7cklHiN5dS+kspDwEhUsr9AFLKE4BVRoULIW4IIfyFEKeEEH66afZCiB1CiMu6vyVyvBe55ODVULrOPsDthzEsHVqHMc0rZylpB0YFMnjrYC6HX2Z6i+n0qNLDcMH+MwnMi0KziYbbhqJkkRCCMc0rc+dhCr523TkQdAB/czOo0k4bvC0+ytghFgoZJf7U8z98Zl5mG6RbSCl9pJS+utcTgX+llFWAf3Wv8zQpJcsOXGfQkqPYF7Ng45uNaeHulKUyLoZdZNDWQTyMe8jitotp7tbcMMEC3DgAFzdD47ehuKPhtqMo2dCymhPupaw5E1AdWwvbJ7X+2IfasM2KwWWU+D8VQhQFkFL+8XiiEKIisCKb2+yWat0VQPdslpMr4hKTee/XM3z+VwAtqzmxYUxDKjhkrefNseBjDN02FFNhysoOK/Fx8jFMsAApKbD9E7AuA/XHGG47ipJNJiba7Rmv3k+ikWMv9gXt42yRIlClrTZyrKr1G9wLE7+UcqOUMkYI4SuE2CCEOCGEOAP8CQzKRPkS2C6EOC6EGKGbVkpKeVf3PBgoldaKQogRQgg/IYRfSEhIJndHv+5GxNJnwSF+PxHI262rsGBgbawts3a3qn9u/sOoHaNwKurEqo6rqGRXyUDR6pxbD3dOQKtPwSLzJ5wVJTd19i6Nm70VARe9sbWw1fXwmQixYdotGhWDymxXktXAMqAX0AXorPubkcZSypeADsAbQoimqWdKKSXaweE5UsqFUkpfKaWvo2PuN1ccuxFGl1n7uXL/EQsH1ebt1lUxMcnaSdh1F9cxfvd4PEp6sLLDSpyLORsoWp2kePj3cyjlBd59DLstRckBM1MTRjSthP/tOJo7v8yewD2cs7SEyq21m7LHPzJ2iAVaZhN/iK72f11KefPxI6OVpJRBur/3gQ1AXeCeEKI0gO7v/WzGbjCrDt+k38LDWFua88cbjWhbPWsJW0rJ3FNz+fLwlzR1bcqitouyf5vErDi6EMJvQdsvwMTU8NtTlBx4pbYrDsWLcPWqDzYWNk/X+o8tNnZ4BVpmE/9nQojFQoh+Qoiejx8vWkEIUUwIYf34OdAWOAtsBIboFhuC1myUJ8QnJfPh+jN88sdZGldx4I83GlGllHWWykhOSebLw18y7/Q8ulfuzvQW07Eyy7ADVM7FhMHeH7QaU6WWht+eouSQpbl2e8ZDV6Jp6/oKuwN3E1C0GFRqpdX6E6KNHWKBldnE/yrgA7RHa+J53NzzIqWA/UKI08BRYLOUchswGWgjhLgMtNa9Nrr7kXH0W3iYNUdv80aLSiwZUgdbq6y158cnx/Penvf49dKvDPcazhcNv8jZbRKzYt+P2kmxNl/kzvYURQ8G1CuLjaUZt6/XxtrCWqv1N58IMaFwbImxwyuwMpuV6kgp3bNSsJTyGlAzjekPgFZZKcvQTtx6yKifjhMVl8Sc/i/Rybt0lstISopnzLo2HE18yAdlWjPQtS3IFBC50OQSdl0b6MqnP5SqbvjtKYqeWFuaM7hBeebsvsLwzn1Ye2Ux52uOxqNiCzgwA+oMAws9j1+lZLrGf1AI4WnQSIxk3bHb9F1wmCLmJqwf0zBbSR9g/f4vOJr4kM8fRDDwwFKYWx++dYMl7WDrRG1o5JCL2tj4+vbvF2BqDi0+1n/ZimJgrzYqTxEzE+4H1cHa/Jlav99SY4dXIGW2xl8fOCWEuA7EAwKtU463wSIzsMTkFL7cFMDKQzdpUsWBWf1qYVc0e4OkRSdGM/fGJl5Khh5vXYaH1+HOSbh7Svt7fDkcidUWtigOzt5QphaU8dH+2lcCk2yO1RPop3XhbDoBbMpkrwxFMaKSxYvQt05ZVh2+yfCufVh9cTEXfcbgXqGZVuv3Haa6JutZZhN/e4NGkctCH8UzZvUJjl4PY0TTikxo546ZafYHSVt+8GseiBRmVnoFYV4EnKppDx/dDdKTkyD00pMDwZ1T4LcEkuK0+RbWULrmkwNBaR+wr5jxwUBK7WKtYo7QaGy241cUY3u9aUVWHb5JZHB9ipuvZf7p+UxrPhGWdYDjy6DBG8YOsUDJVOLPTNfN/MI/MIKRP/nxIDqBGX196ObjkqPy7sfcZ8WNTbSPTcS7YTo3MTc1g1Ke2sOnvzYtOQlCLz45ENw5qV24khyvzS9i8+RgUNpH98ug4tNj6l/YDLcOQedpUCRrvY8UJS9xsbOiey0X1h+/w/Au/VhxfhEXa47CvUJTXa3/NTDPhd5xhUQudTnJGzacDGTi7/44FC/C76MbUsMl533r5xz+lkSZwtgKXbN2EsrUTDsRW6o61BqoTUtOhJALTw4Ed09p45T/dzCwhTI1nxwIdn0NDu5Qa3CO90NRjG1Us0r8fiKQ+AeNKG6+hgVnFjC12URY3lEbabaBGoJEXwpF4k9KTmHy1gss3n+d+hXtmdP/JUoWz/m9Zy89vMQft/9hQFQMbr3G5zxQU3Nw9tIeL+lGxEhOhPvnnz5ncGQ+JCdo8/ut1Q4iipLPVXYqTjtPZ9YeCWVop74sD1jMpZqjqFq+CRyYrg0xrmr9elHgM8bD6ATeXHOCA1ceMLRheT7u5IF5DtrzU5t69DuKpaQw0rU1WBtoOAZTcyjtrT0eX/eWlAD3A7QrHCu2MMx2FcUIxrSoxLZzwZhGNaOY+RoWnF7Aj80+gBWd4fgKqD/K2CEWCAX6nrsBdyLpMns/x2485IeXvZnUtbrekv7BOwc5EHyUkQ8jsG30jl7KzDQzC63tv1JLdR9dpUDxdrWjcWUHVh0MpXfVvuy4uYPLds5QrpFW60+MM3aIBUKBTfxSSib9dY6kZMm6kQ14xddNb2UnpyQz9dgUXJJT6OdYF5w89Fa2ohR2Y1pUIvRRPMXjW2FlZsWCMwu0fv1Rd+HESmOHVyAU2MQvhGBGXx82vtUIHzc7vZa96domLoZfZtyDMCxUN0pF0asGFUvi42bHTwdC6Ovej+03tnPVrgyUbQj7p6pavx4U2MQPUNrWCidrS72WGZsUy6yTs6iRLGhvXREqNNNr+YpS2Gm3Z6zE7bBYnGRbLM0sWXBmoXaXrqi7cPInY4eY7xXoxG8IqwJWcS/mHu/eD0Y0HKfa2BXFAFp7lKKKU3FW7g+lr3s/tt3YxrUSbuBWH/ZP0+49oWSbSvxZ8CD2AUvOLqGFtMLXwgGqG/Bm6YpSiD2+PePFe1FUNO+o1fr9dbX+yCBV688hlfizYP7p+cQlxfJO0FWoN0rraqkoikF0qVkGFzsrVhwIpa97X7Ze38q1kuXBrR7sU7X+nFCJP5OuR1zn10u/8rKpAxWEFdQekvFKiqJkm7mpCSObVeTkrXBqFO+GpZklC/0XQrMPIDIQTq4ydoj5lkr8mTT9+HQsTS0Yfe20lvQtc+FWiopSyPX2dcOhuAU/HQilj3sftl7fyg3HiuBaR9fWn2DsEPMllfgz4fi94+y8vZPXLMtRMiVFa+ZRFMXgLM1Nea1xBfZdDqVeiZ5YmFiw8Mwi7d68Ebfh1Gpjh5gvqcSfASklP/r9iJOVI4MuHYIaPcFOfxeDKYryYgPrl8O6iBk/Hw6jt3tvNl/fzE2nyuBSG/ZNVbX+bFCJPwN/3/gb/1B/3rL2xCo+Chq8aeyQFKVQsbE0Z1CDcmw9G0yL0r21Wr//Imj+IUTcgtNrjB1ivmPwxC+EMBVCnBRCbNK9riCEOCKEuCKE+EUIkb3bXuWChOQEpp+YTlW7KnQ5vxPKN9HGyFEUJVe91rgCFqYmrDsczivur7D52mZuOVXVav17voP4R8YOMV/JjRr/OOB8qtffAdOklJWBh8CwXIghW9ZcWEPQoyDeLVkX08ggaPiWsUNSlELJoXgR+tRxY8PJIDq69cPMxEyr9bf7VuvXv/tbY4eYrxg08QshXIFOwGLdawG0BH7TLbIC6G7IGLIrIj6ChWcW0qhMQxqe3aTd8KRyG2OHpSiF1oimFZESfj8WyStVX2HTtU3ctisDtYfC4Xlw94yxQ8w3DF3jnw5MAFJ0r0sC4VLKJN3rQCDNex8KIUYIIfyEEH4hISEGDvN5i84sIiohinecm0KwPzR8M/s3RFcUJcdcSxSlq08Z1h69TY+KAzEVplq//taToKg9bHobUpKNHWa+YLBMJoToDNyXUh7PzvpSyoVSSl8ppa+jo6Oeo3uxwKhAfr7wM90rd8f99AbtZuZevXM1BkVRnje6WSViE5PZdOIRr7i/wl9X/+J20iNo9w0EHQe/pcYOMV8wZBW2EdBVCHEDWIvWxDMDsBNCPL7zlysQZMAYsmXmiZmYClPecG0LV3ZA3ZFgrt9RPhVFyboqpaxp61mK5Qdv0KfqYEyFKTNPzCS5ek+o2Bz+/QIi7xo7zDzPYIlfSvmhlNJVSlke6AvslFIOAHYBL+sWGwL8aagYssM/xJ+tN7YyuPpgSp1cA2ZWUCfPnn9WlEJnTIvKRMYlseNMLMO8hrHtxjbG7h5HZNsvtPF7/v7Q2CHmecZotP4AGC+EuILW5r/ECDGkSUrJFL8p2Fva81q5TuC/DmoN0NoPFUXJE3zc7GhYqSSL913nVc8RfFLvEw4GHWTAoU+4Vn84nNsAl/8xdph5Wq4kfinlbillZ93za1LKulLKylLKV6SUeWaIvV23d3Hi/gne8HmDYidXQXIi1B9j7LAURXnGmOaVuR8Vz/qTQfSp1ofF7RYTmRBJ//s72VWqImweDwkxxg4zz1LdVHQSUxKZdnwaFWwr0LNcOzi2BDw6Q8lKxg5NUZRnNKpckpqutizYc42k5BRql6rNL51/oZxtOcYWTWIeD0nZ872xw8yzVOLX+f3S79yIvMH42uMxO/0LxIVDA3XBlqLkRUIIRjevzK2wGDb7aydznYs5s6L9CrpU7MLcEna8c2UV0XdOGDnSvElIKY0dQ4Z8fX2ln5+fwcp/lPCIThs6UdG2IkvbLELMegmKl4LhOwy2TUVRciYlRdJ2+l6uh0ZTxak43q62eLnaUaOMDadC1zLjzBzKY86M7uspZ1fB2OEahRDiuJTS99npZmktXNgsPbuUsLgw5raai7iwCcJvQtsvjR2WoigvYGIiWDLEl3V+t/EPimRHwD3W+QUCYGZSjh6OjTlsu5dX/nyZN2t+TZ8abShiZmrkqPOGQl/jD44OpvOGzrQq24rvmkyGxa0hJhTeOgEm6kOiKPmFlJKg8Fj8AyPwD4rAPzCcAcFjWeAUxRULMxJD21OpSBe8XezwcrHD29WWqqWssTAruC3eqsafjtknZ5MiUxj70li4fQSC/KDjFJX0FSWfEULgWqIoriWK0sGrNADy/gqaLmjCBFcP9jhuIyIllE3+PVhzVPt+W5iaUK20NTVcbPF2saWGiy3uztaYmxbcgwEU8sR/MewiG69uZGj1obgUd4FNE8DSDnz6Gzs0RVH0QDh5ULThWGbt+5HlLccy/cZGqtaKZILPZELCi+l+GUTw1+k7/HzkFgAWZiZ4OFvj5WqLl4stXi52VClVvEAdDAp1U8+I7SMICAtgS88t2ESFwKza0ORdaPWp3relKIqRJMbC3PpgYsaBbj/y/oGPMBEm/ND0BxqUaQBoJ4pvhcVoB4KgCM4EhnMuKJKoeG08SQszEzxL2+DtasvIZpVwsbMy5h5lmmrqecaBoAMcunuICXUmYGNhA4cmgak51B1h7NAURdEncyvo9COs6kWja0dY22kt43aNY9Q/oxhfezyDPQdjYiIo71CM8g7F6FKzDKAdDG48iP7vV4F/UAS/HLuNf1AEv49qiImJMPKOZV/B+e2SBckpyUzxm4KbtRt93ftC9AM49TN49wbrUsYOT1EUfavcGqr3hH0/UjYxkVUdV9HSrSVT/Kbw4f4PiUuKe24VExNBRcfidPNx4ZPOnvwysgHf9PDi5K1wfj56ywg7oT+FMvFvvLqRK+FXGPfSOMxNzcFvCSTFqvvpKkpB1v5bMCsCm96hmFlRfmz+I2/6vMmWa1sYvHUwdx9lPKpnz5dcaFCxJN9tu8D9qOcPFvlFoUv8MYkxzD45G29Hb9qWawuJcXB0IVRpC04exg5PURRDsXaGVv+D63vA/1dMhAkja45kZsuZ3I66Td/NfTkWfOyFRQgh+KpHDeITU/hq0/kXLpuXFbrEvzJgJfdj7/Oe73sIIeDMLxAdomr7ilIY+L6m3aD9748g9iEAzd2as7rTamwsbBixfQQ/n/+ZF3V6qeRYnFHNK7Hx9B32Xsr9uwPqQ6FK/KGxoSw7u4zWZVtTy6kWpKTAodng7A0Vmho7PEVRDM3EFDpPh5gw+GfSf5Mr2lbk504/08ilEd8e/ZbPDn5GfHL6AwePaV6JCg7F+PTPs8Ql5r/bPRaqxD/v1DwSkhN4u/bb2oTL2yH0EjR8C0T+PUOvKEoWlPaG+qPh+HK4deS/ydYW1sxsOZOR3iPZcGUDr217jXvR99IswtLclK+71+Dmgxjm7LqSS4HrT6FJ/NfCr/H75d/p7d6bcjbltImHZoONC1TvYdzgFEXJXc0/BBtX7QbtyYn/TTYRJrxZ602mNZ/G5fDL9N3cl1P3T6VZRMPKDvSo5cL8PVe5cj8qd+LWk0KT+Kcdn4aVmRWjao7SJtw5CTf2aUd+U3PjBqcoSu4qUhw6fg/3A+DQnOdmty7XmtUdV2NlZsWrf7/Kb5d+S7OYjzt5UNTCjI83nH3heYG8plAk/mPBx9gduJthXsMoYVlCm3hwNlhYw0uDjRucoijGUa0TuHeC3ZPh4c3nZlcpUYU1ndZQz7kenx/6nC8PfUliql8HAA7FizCxQzWOXA/jt+OBuRV5jhX4xJ8iU5jiNwXnYs4M9BioTQy/pd2Xs/YQsLQ1boCKohhPx+9BmMCW9yGNGrttEVvmtJrDazVeY92ldQzbPowHsQ+eWqaPrxu+5UrwzZbzhEUn5FbkOWKwxC+EsBRCHBVCnBZCnBNCfK6bXkEIcUQIcUUI8YsQwsJQMQBsvb6VgAcBjK01FkszS23i4fnaydz6ow25aUVR8jpbV2jxEVz+G85vTHMRUxNT3qn9Dj80/YHzD84z6p9RPEp49N98ExPB1z28iIpL4tst+aNvvyFr/PFASyllTcAHaC+EqA98B0yTUlYGHgLDDBZAcjwzT8zEw96DThU7aRNjw+HECu2Erq2roTatKEp+UW8UOHvB1g8gLjLdxdpXaM/0FtO58vAK43aNIyH5Se3e3dma4U0q8uvxQI5ce5BuGXmFwRK/1Dw+LJrrHhJoCTw+U7IC6G6oGH4+/zN3ou/wru+7mAjdrp5YAQmP1AVbiqJoTM2g8wyICoZdX79w0UYujfiy8ZccDT7Kh/s+JDnlSR/+ca2q4FrCio//OEtCUoqho84Rg7bxCyFMhRCngPvADuAqEC6lTNItEgi4pLPuCCGEnxDCLyQke1fHnQ87TxOXJtQrXU+bkJSgNfNUaAplfLJVpqIoBZBrbagzTBu+5c7JFy7auWJn3vN9j+03t/Pdse/+681jZWHKl91qcOX+IxbuvZobUWebQRO/lDJZSukDuAJ1gWpZWHehlNJXSunr6OiYre1/3/R7pjaf+mTCuQ0QdQcavJWt8hRFKcBa/Q+KOcJfb0PKi6/GHVJ9CEOrD2XNhTUs9l/83/QW1Zzo6OXMrJ1XuPkg2sABZ1+u9OqRUoYDu4AGgJ0Q4vF9AFyBIENu+78TulLCwVngWE0bolVRFCU1S1ttBM+7p+DoogwXf6f2O3Su2JmZJ2ey/vL6/6Z/1qU65qYmfPJH3u3bb8hePY5CCDvdcyugDXAe7QDwsm6xIcCfhorhKdd2wz1/aPAGmBT4XqyKomRH9Z5QqRXs/Aoi77xwURNhwheNvqBRmUZ8fuhzdt/eDUApG0vea1uVfZdD2Xj6xWUYiyEzYGlglxDiDHAM2CGl3AR8AIwXQlwBSgJLDBjDE4dmQzEn8OqdK5tTFCUfEkK7W1dKotbLJwPmJuZMbT4VT3tP3tvz3n/DOwxqUB5vV1u+3HSeiNjEFxdiBIbs1XNGSllLSuktpawhpfxCN/2alLKulLKylPIVKWX6Q+Dpy70AuPIP1BsB5pYG35yiKPmYfQVo+r7Wr//S3xkuXtS8KHNaz8G5mDNv/PsGV8OvYmoi+KaHF2HR8Xy/7UIuBJ01haPN49BsMLMCX4NdMqAoSkHScKx2PnDze5CQ8Ulae0t75reej4WpBSN3jCQ4OpgaLrYMaVien4/e4sSth7kQdOYV/MQfFQxn1kGtgVDU3tjRKIqSH5hZQOdpEHEL9nyXqVVcrV2Z33o+0YnRjNoxioj4CN5t604pa0s+Wu9PYnLe6dtf8BP/kQWQkgQNxhg7EkVR8pNyDbUK46E5cO9cplZxt3dnZsuZ3Iq6xVs738LUNJFJXT25EBzFsgPXDRxw5hXsxB//CPyWgkdnsK9o7GgURclv2nypdfP8623tjn2ZUMe5DpObTObU/VNM2DOBVh4OtKrmxLQdlwl8GGPYeDOpYCf+U6shLlxrr1MURcmqovbQ9isIPKoN95JJbcu35aN6H7E7cDdfHfmKSV09AZi08Vye6NtfsBP/xS3gVg/c6ho7EkVR8qua/aB8E/jnM3h0P9Or9a3WlxHeI1h/eT1/3FzK262r8M/5+/x9Lu3bOeamgp34B66H3iuNHYWiKPmZENBpKiTEwN8fZ2nVN33epFeVXiw8s5Bijkeo5mzNpI3neBSflPHKBlSwE7+JKVg7GzsKRVHyO8eq0Pgd8F8HF7dlejUhBJ/U/4Tmbs35/thkujUK5V5UHFO3XzJgsBkr2IlfURRFX5q8C6W84NehcG1PplczMzHjh6Y/4OPkw6ILX9HmpSiWH7zO2aAIw8WaAZX4FUVRMsPcEgb/oV3Z+3MfuL4306tamlkyq+UsylqX5UzCDOzsQvhogz/JKcY50asSv6IoSmYVc4DBG6FEeVjdG67vy/SqtkVsmd9mPtZFilPEdSn+966z6vDzN3nPDSrxK4qiZEVxRxjyF5QoBz/3hhv7M72qczFnFrRegIlJMvaVVvDDjuPci4wzYLBpU4lfURQlqx4nf1s3WP0K3DiQ6VUr2lVkTqs5YBqOdF7CZxtPGDDQtKnEryiKkh3FnXTJ31VL/jcPZnpVHycffmw+BVPLIPZETGHHeYPej+o5KvEriqJkl3UpLfnblIFVL8PNQ5letblbcz6p/z/Mil/mgz0fEx2fe+P2q8SvKIqSE9bOMHQT2JSG1S/DrSOZXrW3ey+6lxtOotVxXt34mQGDfJpK/IqiKDll7QxDNkHxUrCqF9w+mulVv2g2FjfTNpyP+YsfDi8wYJBPqMSvKIqiDzaltZp/cSf4qSfcPpap1YQQrOj+NSK6Jisvzmbjlb8MHKhK/IqiKPpjU0aX/B1hVU8I9MvUao7FrZhY+wuSoivx6YFP2R+U+S6i2WGwxC+EcBNC7BJCBAghzgkhxumm2wshdgghLuv+ljBUDIqiKLnOpozW7FO0JPzUAwKPZ2q1fnUr4GkylpT4UryzazxnQ88aLERD1viTgHellJ5AfeANIYQnMBH4V0pZBfhX91pRFKXgsHXRav5F7bXkH5Rx8hdCMLlHHeICX0OkFGfMP2O4EXHDIOEZLPFLKe9KKU/onkcB5wEXoBvw+I4GK4DuhopBURTFaGxdtZq/lR2s7AFBGV+oVaWUNSMa1STk8hCSUiSj/hlFSEyI3kPLlTZ+IUR5oBZwBCglpbyrmxUMlEpnnRFCCD8hhF9IiP53XFEUxeDs3LSav5Ut/NQd7pzMcJW3WlbBzbospvdfx9bClsQU/ffvN3jiF0IUB34H3pZSRqaeJ7V7kKU5PJ2UcqGU0ldK6evo6GjoMBVFUQzDriwM3azdu3dlN7hz6oWLW5qb8mX3GtwOLkl9yy8oU7yM3kMyaOIXQpijJf3VUsr1usn3hBCldfNLA5m/l5miKEp+ZFdWa/YpYqMl/7unX7h4s6qOdPYuzbzd17gW8kjv4RiyV48AlgDnpZRTU83aCAzRPR8C/GmoGBRFUfKMEuW0Zp8i1rrkf+aFi/+vsyf96rphX8xC76EIQ93xXQjRGNgH+AMpuskfobXzrwPKAjeB3lLKsBeV5evrK/38MtcfVlEUJU8Luw7LO0NitDbOj7OXwTYlhDgupfR9brqhEr8+qcSvKEqBEnZNl/xjYchGgyX/9BK/unJXURQlt9lX1Jp9zCxhRVcINtzFWmlRiV9RFMUYUif/lV3h3rlc27RK/IqiKMZSspKW/E0tYEUXuBeQK5tViV9RFMWYSlbSunqamGvJ//55g29SJX5FURRjc6is1fxNzHTJ/4JBN6cSv6IoSl7gUEXr3ilMtOQfctFgm1KJX1EUJa9wrKo1+4DW3TPkkkE2oxK/oihKXuJYVWv2QcKKzhB6Re+bUIlfURQlr3F012r+pWpoY/rrmZneS1QURVFyzqkaDFqf8XLZoGr8iqIohYxK/IqiKIWMSvyKoiiFjEr8iqIohYxK/IqiKIWMSvyKoiiFjEr8iqIohYxK/IqiKIVMvrj1ohAiBO3+vPmBAxBq7CAMRO1b/lWQ90/tW/rKSSkdn52YLxJ/fiKE8EvrHpcFgdq3/Ksg75/at6xTTT2KoiiFjEr8iqIohYxK/Pq30NgBGJDat/yrIO+f2rcsUm38iqIohYyq8SuKohQyKvEriqIUMirx64EQwk0IsUsIESCEOCeEGGfsmPRNCGEqhDgphNhk7Fj0TQhhJ4T4TQhxQQhxXgjRwNgx6YsQ4h3dZ/KsEGKNEMLS2DHlhBBiqRDivhDibKpp9kKIHUKIy7q/JYwZY3als28/6D6XZ4QQG4QQdvrYlkr8+pEEvCul9ATqA28IITyNHJO+jQPOGzsIA5kBbJNSVgNqUkD2UwjhAowFfKWUNQBToK9xo8qx5UD7Z6ZNBP6VUlYB/tW9zo+W8/y+7QBqSCm9gUvAh/rYkEr8eiClvCulPKF7HoWWOFyMG5X+CCFcgU7AYmPHom9CCFugKbAEQEqZIKUMN2pQ+mUGWAkhzICiwB0jx5MjUsq9QNgzk7sBK3TPVwDdczMmfUlr36SU26WUSbqXhwFXfWxLJX49E0KUB2oBR4wcij5NByYAKUaOwxAqACHAMl1T1mIhRDFjB6UPUsogYApwC7gLREgptxs3KoMoJaW8q3seDJQyZjAG9BqwVR8FqcSvR0KI4sDvwNtSykhjx6MPQojOwH0p5XFjx2IgZsBLwDwpZS0gmvzbVPAUXVt3N7SDWxmgmBBioHGjMiyp9U8vcH3UhRAfozUpr9ZHeSrx64kQwhwt6a+WUq43djx61AjoKoS4AawFWgohVhk3JL0KBAKllI9/of2GdiAoCFoD16WUIVLKRGA90NDIMRnCPSFEaQDd3/tGjkevhBBDgc7AAKmnC69U4tcDIYRAayM+L6Wcaux49ElK+aGU0lVKWR7txOBOKWWBqTVKKYOB20IId92kVkCAEUPSp1tAfSFEUd1ntBUF5MT1MzYCQ3TPhwB/GjEWvRJCtEdrZu0qpYzRV7kq8etHI2AQWm34lO7R0dhBKZn2FrBaCHEG8AG+MW44+qH7FfMbcALwR/u+5+vhDYQQa4BDgLsQIlAIMQyYDLQRQlxG+5Uz2ZgxZlc6+zYbsAZ26PLKfL1sSw3ZoCiKUrioGr+iKEohoxK/oihKIaMSv6IoSiGjEr+iKEohoxK/oihKIaMSv6KkQwghU1+sJoQwE0KEFMQRSpXCRSV+RUlfNFBDCGGle90GCDJiPIqiFyrxK8qLbUEbmRSgH7Dm8QwhRDHdGOpHdQO8ddNNr66bdko3jnoVI8StKOlSiV9RXmwt0Fd3AxNvnh519WO0ISzqAi2AH3Qje44CZkgpfQBftPGAFCXPMDN2AIqSl0kpz+iG2u6HVvtPrS3aAHbv6V5bAmXRLrv/WHcfg/VSysu5Fa+iZIZK/IqSsY1o49o3B0qmmi6AXlLKi88sf14IcQStiWiLEGKklHJnrkSqKJmgmnoUJWNLgc+llP7PTP8beEs38iVCiFq6vxWBa1LKmWgjRXrnZrCKkhGV+BUlA1LKQF0Sf9aXgDlwRghxTvcaoDdwVghxCqgBrMyVQBUlk9TonIqiKIWMqvEriqIUMirxK4qiFDIq8SuKohQyKvEriqIUMirxK4qiFDIq8SuKohQyKvEriqIUMv8HsgXSyKaj4RgAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "vs.plot(title=\"Concentración de ozono (m3) en 2014, 2015 y 2016\", ylabel=\"m3\")\n", "plt.savefig('practica-4.png')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "api-pandas-folium.ipynb python-api-covid19-pandas-Copy1.ipynb\r\n", "esvsit.csv\t\t python-api-covid19-pandas.ipynb\r\n", "esvsit.png\t\t python-api-ozono-arganda-pandas.ipynb\r\n", "ozono-arganda-14-15-16.csv python-pruebas.ipynb\r\n", "ozono-arganda-14-15-16.png Shared_Resources\r\n", "practica-4.png\t\t Untitled1.ipynb\r\n", "pruebas-r.ipynb\t\t Untitled.ipynb\r\n" ] } ], "source": [ "!ls" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 4 }