From 9fb4c545c3be5338427ee87c1312b3812c2445a0 Mon Sep 17 00:00:00 2001
From: arturos <arturos@cern.ch>
Date: Wed, 17 Mar 2021 03:08:44 +0100
Subject: [PATCH] subida del proyecto final

---
 codigo/VACF_liso_Hr-10-500.root               |  Bin 0 -> 13326 bytes
 codigo/animacion.gif                          |  Bin 0 -> 179865 bytes
 codigo/codigo_proyecto_final.ipynb            | 2564 +++++++++++++++++
 .../conversion_de_archivos_csv_a_ROOT.ipynb   |   74 +
 codigo/convertir_csv_a_root-2do.ipynb         |   74 +
 codigo/fdr.png                                |  Bin 0 -> 15160 bytes
 codigo/fdr_evolucion.png                      |  Bin 0 -> 62372 bytes
 codigo/imagen.png                             |  Bin 0 -> 19182 bytes
 codigo/pairplot_para_fdr.ipynb                |  376 +++
 codigo/previsualizacion_dataset.ipynb         | 2564 +++++++++++++++++
 codigo/vacf.png                               |  Bin 0 -> 10577 bytes
 11 files changed, 5652 insertions(+)
 create mode 100644 codigo/VACF_liso_Hr-10-500.root
 create mode 100644 codigo/animacion.gif
 create mode 100644 codigo/codigo_proyecto_final.ipynb
 create mode 100644 codigo/conversion_de_archivos_csv_a_ROOT.ipynb
 create mode 100644 codigo/convertir_csv_a_root-2do.ipynb
 create mode 100644 codigo/fdr.png
 create mode 100644 codigo/fdr_evolucion.png
 create mode 100644 codigo/imagen.png
 create mode 100644 codigo/pairplot_para_fdr.ipynb
 create mode 100644 codigo/previsualizacion_dataset.ipynb
 create mode 100644 codigo/vacf.png

diff --git a/codigo/VACF_liso_Hr-10-500.root b/codigo/VACF_liso_Hr-10-500.root
new file mode 100644
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diff --git a/codigo/codigo_proyecto_final.ipynb b/codigo/codigo_proyecto_final.ipynb
new file mode 100644
index 0000000..50c2cd6
--- /dev/null
+++ b/codigo/codigo_proyecto_final.ipynb
@@ -0,0 +1,2564 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from seaborn import lmplot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>t</th>\n",
+       "      <th>vacf</th>\n",
+       "      <th>vacf_2</th>\n",
+       "      <th>vacf_3</th>\n",
+       "      <th>vacf_4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.893155</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.005954</td>\n",
+       "      <td>0.894384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.867854</td>\n",
+       "      <td>0.000177</td>\n",
+       "      <td>0.011740</td>\n",
+       "      <td>0.874035</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.821965</td>\n",
+       "      <td>0.000701</td>\n",
+       "      <td>0.017220</td>\n",
+       "      <td>0.832205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.758831</td>\n",
+       "      <td>0.001553</td>\n",
+       "      <td>0.022279</td>\n",
+       "      <td>0.773162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.682706</td>\n",
+       "      <td>0.002707</td>\n",
+       "      <td>0.026830</td>\n",
+       "      <td>0.700901</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>194</th>\n",
+       "      <td>1.94</td>\n",
+       "      <td>-0.000913</td>\n",
+       "      <td>0.424637</td>\n",
+       "      <td>0.039358</td>\n",
+       "      <td>0.004982</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>1.95</td>\n",
+       "      <td>-0.000803</td>\n",
+       "      <td>0.426829</td>\n",
+       "      <td>0.039352</td>\n",
+       "      <td>0.004958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>1.96</td>\n",
+       "      <td>-0.000885</td>\n",
+       "      <td>0.429018</td>\n",
+       "      <td>0.039346</td>\n",
+       "      <td>0.004928</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>1.97</td>\n",
+       "      <td>-0.000768</td>\n",
+       "      <td>0.431205</td>\n",
+       "      <td>0.039341</td>\n",
+       "      <td>0.004659</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>1.98</td>\n",
+       "      <td>-0.000709</td>\n",
+       "      <td>0.433389</td>\n",
+       "      <td>0.039336</td>\n",
+       "      <td>0.004458</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>199 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        t      vacf    vacf_2    vacf_3    vacf_4\n",
+       "0    0.00  0.893155  0.000000  0.005954  0.894384\n",
+       "1    0.01  0.867854  0.000177  0.011740  0.874035\n",
+       "2    0.02  0.821965  0.000701  0.017220  0.832205\n",
+       "3    0.03  0.758831  0.001553  0.022279  0.773162\n",
+       "4    0.04  0.682706  0.002707  0.026830  0.700901\n",
+       "..    ...       ...       ...       ...       ...\n",
+       "194  1.94 -0.000913  0.424637  0.039358  0.004982\n",
+       "195  1.95 -0.000803  0.426829  0.039352  0.004958\n",
+       "196  1.96 -0.000885  0.429018  0.039346  0.004928\n",
+       "197  1.97 -0.000768  0.431205  0.039341  0.004659\n",
+       "198  1.98 -0.000709  0.433389  0.039336  0.004458\n",
+       "\n",
+       "[199 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#carguemos el dataframe\n",
+    "\n",
+    "file = '/home/student/ejercicios-clase-08-datos/data-used/VACF_liso_Hr-10-500.csv'\n",
+    "df = pd.read_csv(file)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>t</th>\n",
+       "      <th>vacf</th>\n",
+       "      <th>vacf_2</th>\n",
+       "      <th>vacf_3</th>\n",
+       "      <th>vacf_4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.893155</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.005954</td>\n",
+       "      <td>0.894384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.867854</td>\n",
+       "      <td>0.000177</td>\n",
+       "      <td>0.011740</td>\n",
+       "      <td>0.874035</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.821965</td>\n",
+       "      <td>0.000701</td>\n",
+       "      <td>0.017220</td>\n",
+       "      <td>0.832205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.758831</td>\n",
+       "      <td>0.001553</td>\n",
+       "      <td>0.022279</td>\n",
+       "      <td>0.773162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.682706</td>\n",
+       "      <td>0.002707</td>\n",
+       "      <td>0.026830</td>\n",
+       "      <td>0.700901</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      t      vacf    vacf_2    vacf_3    vacf_4\n",
+       "0  0.00  0.893155  0.000000  0.005954  0.894384\n",
+       "1  0.01  0.867854  0.000177  0.011740  0.874035\n",
+       "2  0.02  0.821965  0.000701  0.017220  0.832205\n",
+       "3  0.03  0.758831  0.001553  0.022279  0.773162\n",
+       "4  0.04  0.682706  0.002707  0.026830  0.700901"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#visualicemos la data de forma general, solo los 5 primeros elementos y los nombres de las columnas\n",
+    "\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.- Una vez que tenemos el archivo de nuestros datos disponibles, visualicemos y exploremos la composición de la data que tenemos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(199, 5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#tengo manera de saber cuanto registros tengo?\n",
+    "\n",
+    "print(df.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-  Puede ocurrir que al pre-visualizar los datos, alguna columna tenga valores en `NaN`, este valor\n",
+    "   se traduce en python como un `None` y en humano como un valor nulo. Así que sería de gran utilidad saber que registros por columna tienen los datos con valores nulos para poder limpiarlos o interpretarlos, ya sea el caso.\n",
+    "   Una manera de realizar esta exploración es usando el método `count` (aunque para nuestra data, no se cuenta ningún `NaN`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<bound method DataFrame.count of         t      vacf    vacf_2    vacf_3    vacf_4\n",
+      "0    0.00  0.893155  0.000000  0.005954  0.894384\n",
+      "1    0.01  0.867854  0.000177  0.011740  0.874035\n",
+      "2    0.02  0.821965  0.000701  0.017220  0.832205\n",
+      "3    0.03  0.758831  0.001553  0.022279  0.773162\n",
+      "4    0.04  0.682706  0.002707  0.026830  0.700901\n",
+      "..    ...       ...       ...       ...       ...\n",
+      "194  1.94 -0.000913  0.424637  0.039358  0.004982\n",
+      "195  1.95 -0.000803  0.426829  0.039352  0.004958\n",
+      "196  1.96 -0.000885  0.429018  0.039346  0.004928\n",
+      "197  1.97 -0.000768  0.431205  0.039341  0.004659\n",
+      "198  1.98 -0.000709  0.433389  0.039336  0.004458\n",
+      "\n",
+      "[199 rows x 5 columns]>\n"
+     ]
+    }
+   ],
+   "source": [
+    "#En la previsualizacion de los datos, revisemos la presencia de algún valor NaN\n",
+    "\n",
+    "print(df.count)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Otra forma de saber la cuenta de valores nulos, es contarlos por columna, ya que con el `data.count()` lo que estoy obteniendo en realidad es la cuenta de datos no-nulos y esto lo conseguimos iterando sobre la lista de columnas preguntando a cada uno por el método `isnull()` y obteniendo la suma con `sum()`. En la siguiente celda pordemos ver la salida de este procedimiento:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "valores nulos en <t>: <bound method Series.sum of 0      False\n",
+      "1      False\n",
+      "2      False\n",
+      "3      False\n",
+      "4      False\n",
+      "       ...  \n",
+      "194    False\n",
+      "195    False\n",
+      "196    False\n",
+      "197    False\n",
+      "198    False\n",
+      "Name: t, Length: 199, dtype: bool>\n",
+      "valores nulos en <vacf>: <bound method Series.sum of 0      False\n",
+      "1      False\n",
+      "2      False\n",
+      "3      False\n",
+      "4      False\n",
+      "       ...  \n",
+      "194    False\n",
+      "195    False\n",
+      "196    False\n",
+      "197    False\n",
+      "198    False\n",
+      "Name: vacf, Length: 199, dtype: bool>\n",
+      "valores nulos en <vacf_2>: <bound method Series.sum of 0      False\n",
+      "1      False\n",
+      "2      False\n",
+      "3      False\n",
+      "4      False\n",
+      "       ...  \n",
+      "194    False\n",
+      "195    False\n",
+      "196    False\n",
+      "197    False\n",
+      "198    False\n",
+      "Name: vacf_2, Length: 199, dtype: bool>\n",
+      "valores nulos en <vacf_3>: <bound method Series.sum of 0      False\n",
+      "1      False\n",
+      "2      False\n",
+      "3      False\n",
+      "4      False\n",
+      "       ...  \n",
+      "194    False\n",
+      "195    False\n",
+      "196    False\n",
+      "197    False\n",
+      "198    False\n",
+      "Name: vacf_3, Length: 199, dtype: bool>\n",
+      "valores nulos en <vacf_4>: <bound method Series.sum of 0      False\n",
+      "1      False\n",
+      "2      False\n",
+      "3      False\n",
+      "4      False\n",
+      "       ...  \n",
+      "194    False\n",
+      "195    False\n",
+      "196    False\n",
+      "197    False\n",
+      "198    False\n",
+      "Name: vacf_4, Length: 199, dtype: bool>\n"
+     ]
+    }
+   ],
+   "source": [
+    "col_names = df.columns.tolist()\n",
+    "for column in col_names:\n",
+    "    print(\"valores nulos en <{0}>: {1}\".format(column,df[column].isnull().sum))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Exploremos los datos visualizandolos por columnas, lo cual podemos hacerlo con `.columns`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['t', 'vacf', 'vacf_2', 'vacf_3', 'vacf_4'], dtype='object')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#visualicemos solo las columnas\n",
+    "\n",
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Este método nos sirve para visualizar alguna columna en especial, por ejemplo, si quiero explorar la segunda columna de nuestra data, obtendremos la numeración del registro por fila en la primera columna y los valores correspondientes para la *VACF*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0.893155\n",
+       "1    0.867854\n",
+       "2    0.821965\n",
+       "3    0.758831\n",
+       "4    0.682706\n",
+       "Name: vacf, dtype: float64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Me interesa visualizar una columna en especial, la vacf(t)\n",
+    "\n",
+    "columna = df[\"vacf\"]\n",
+    "columna.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2- Ahora intentemos explorar detalles de nuestros datos: \n",
+    "\n",
+    "Aquí se muestra el poder de python para el análisis de datos!... Observe la facilidad de obtener información de los principales indicadores estadísticos sobre nuestro dataset en una sola línea con el método `.describe`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method NDFrame.describe of         t      vacf    vacf_2    vacf_3    vacf_4\n",
+       "0    0.00  0.893155  0.000000  0.005954  0.894384\n",
+       "1    0.01  0.867854  0.000177  0.011740  0.874035\n",
+       "2    0.02  0.821965  0.000701  0.017220  0.832205\n",
+       "3    0.03  0.758831  0.001553  0.022279  0.773162\n",
+       "4    0.04  0.682706  0.002707  0.026830  0.700901\n",
+       "..    ...       ...       ...       ...       ...\n",
+       "194  1.94 -0.000913  0.424637  0.039358  0.004982\n",
+       "195  1.95 -0.000803  0.426829  0.039352  0.004958\n",
+       "196  1.96 -0.000885  0.429018  0.039346  0.004928\n",
+       "197  1.97 -0.000768  0.431205  0.039341  0.004659\n",
+       "198  1.98 -0.000709  0.433389  0.039336  0.004458\n",
+       "\n",
+       "[199 rows x 5 columns]>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspeccionemos a mayor profundidad nuestra data: podemos obtener información de los principales indicadores\n",
+    "# estadísticos sobre la data set\n",
+    "\n",
+    "df.describe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Para explorar las características principales de nuestros datos de manera detallada, usamos el método `.info()` o viendo los tipos de valores de los que disponemos usando `dtypes` combinada con un operador lógico. Estos dos procedimientos nos describen los tipos de objetos que tenemos en nuestra dataset y tener una visión más clara del procesamiento que podemos realizar a la misma."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 199 entries, 0 to 198\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column  Non-Null Count  Dtype  \n",
+      "---  ------  --------------  -----  \n",
+      " 0   t       199 non-null    float64\n",
+      " 1   vacf    199 non-null    float64\n",
+      " 2   vacf_2  199 non-null    float64\n",
+      " 3   vacf_3  199 non-null    float64\n",
+      " 4   vacf_4  199 non-null    float64\n",
+      "dtypes: float64(5)\n",
+      "memory usage: 7.9 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "t         True\n",
+       "vacf      True\n",
+       "vacf_2    True\n",
+       "vacf_3    True\n",
+       "vacf_4    True\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# columnas numericas y columnas de texto\n",
+    "df.dtypes == float"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "t         False\n",
+       "vacf      False\n",
+       "vacf_2    False\n",
+       "vacf_3    False\n",
+       "vacf_4    False\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes == object"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "De hecho, podemos explorar cuantos valores nulos tenemos por cada una de las variables (columnas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "t         0\n",
+       "vacf      0\n",
+       "vacf_2    0\n",
+       "vacf_3    0\n",
+       "vacf_4    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# veamos cuantos valores nulos hay por cada variable\n",
+    "\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3- Busquemos relaciones entre nuestras variables:\n",
+    "\n",
+    "En esta sección del proyecto, mostramos cómo configurar y ejecutar *gráficos de pares* en Python utilizando la biblioteca de visualización `seaborn`. Siendo más específico, se muestra cómo crear un gráfico de pares predeterminado para examinar nuestros datos y cómo personalizar la visualización para obtener información más profunda. Gracias a este curso he conocido esta manera de trabajar con los datos:\n",
+    "\n",
+    "Estoy sorprendido que una simple línea de código nos proporcione toda esta información!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- El diagrama de pares se basa en dos figuras básicas, el histograma y el diagrama de dispersión. El histograma en la diagonal nos permite ver la distribución de una sola variable, mientras que los diagramas de dispersión en los triángulos superior e inferior muestran la relación (o falta de ella) entre dos variables. Por ejemplo, el gráfico más a la izquierda en la segunda fila muestra el gráfico de dispersión de *VACF* versus tiempo."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x7f538e3454a8>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x900 with 30 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(df)\n",
+    "\n",
+    "#plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Los diagramas de pares son una herramienta poderosa para explorar rápidamente distribuciones y relaciones en un conjunto de datos. `SEABORN` nos proporciona un método predeterminado simple para hacer graficas de pares de variables que se pueden personalizar. En un proyecto de análisis de datos, una parte importante del valor proviena de la visualización de los datos. Un diagrama de pares nos proporciona este primer vistazo completo de nuestros datos y es un excelente punto de partida en el análisis de datos."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4- Ahora inspeccionemos los datos de la función de autocorrelación de velocidades *(vacf(t))*\n",
+    "\n",
+    "El método `.unique()` nos muestra los valores almacenad en la columna de nuestro dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 8.93154621e-01,  8.67853999e-01,  8.21965277e-01,  7.58830547e-01,\n",
+       "        6.82705879e-01,  5.99214256e-01,  5.12496531e-01,  4.26708788e-01,\n",
+       "        3.45072299e-01,  2.69724578e-01,  2.02340394e-01,  1.44143075e-01,\n",
+       "        9.50296447e-02,  5.50241806e-02,  2.31665950e-02, -9.18336620e-04,\n",
+       "       -1.86049007e-02, -3.06770168e-02, -3.79997827e-02, -4.15365249e-02,\n",
+       "       -4.23383266e-02, -4.11057547e-02, -3.85393314e-02, -3.52143683e-02,\n",
+       "       -3.14373672e-02, -2.74279676e-02, -2.36809831e-02, -2.02437267e-02,\n",
+       "       -1.71742495e-02, -1.48698576e-02, -1.29330419e-02, -1.18340570e-02,\n",
+       "       -1.10314433e-02, -1.04457177e-02, -1.01393731e-02, -1.03211133e-02,\n",
+       "       -1.06234215e-02, -1.07802572e-02, -1.10281892e-02, -1.10716727e-02,\n",
+       "       -1.11191701e-02, -1.12334173e-02, -1.11250076e-02, -1.07489433e-02,\n",
+       "       -1.04600526e-02, -1.03032459e-02, -9.96753480e-03, -9.46925581e-03,\n",
+       "       -9.13583953e-03, -8.54541920e-03, -7.87504204e-03, -7.39814481e-03,\n",
+       "       -6.95338519e-03, -6.47809729e-03, -5.87645266e-03, -5.16474200e-03,\n",
+       "       -4.80043422e-03, -4.64318646e-03, -4.29089228e-03, -4.09926428e-03,\n",
+       "       -4.04093787e-03, -3.86099005e-03, -3.77666252e-03, -3.56659852e-03,\n",
+       "       -3.36388382e-03, -2.98618292e-03, -2.89732125e-03, -2.91161449e-03,\n",
+       "       -2.71109212e-03, -2.64524529e-03, -2.53665051e-03, -2.60184612e-03,\n",
+       "       -2.72347359e-03, -2.72307545e-03, -2.75016972e-03, -2.73831910e-03,\n",
+       "       -2.67977756e-03, -2.54126010e-03, -2.70556612e-03, -2.93237646e-03,\n",
+       "       -2.85940222e-03, -2.75847316e-03, -2.55324272e-03, -2.43231817e-03,\n",
+       "       -2.22090632e-03, -1.81037607e-03, -1.66162930e-03, -1.33774348e-03,\n",
+       "       -1.03217398e-03, -5.66349074e-04, -2.74554506e-04, -5.68616888e-05,\n",
+       "       -4.33644163e-06,  5.21896218e-05, -1.01174715e-04, -2.90779426e-04,\n",
+       "       -3.62070772e-04, -4.50906868e-04, -6.24679378e-04, -6.60558580e-04,\n",
+       "       -7.15219358e-04, -6.02265471e-04, -5.96747268e-04, -4.89137834e-04,\n",
+       "       -3.73787334e-04, -3.35799938e-04, -1.71133324e-05,  3.81512291e-05,\n",
+       "        1.75224952e-04,  2.74340564e-04,  2.92499433e-04,  5.11027640e-04,\n",
+       "        5.76491526e-04,  4.80154820e-04,  4.12027701e-04,  3.34814598e-04,\n",
+       "        3.94699513e-04,  8.88253999e-05, -1.27831736e-04, -2.92120531e-04,\n",
+       "       -2.59702938e-04, -1.47469589e-04, -2.97198887e-04, -4.53291228e-04,\n",
+       "       -5.23567956e-04, -5.18088520e-04, -7.21369695e-04, -6.01590495e-04,\n",
+       "       -6.43687206e-04, -3.95731739e-04, -3.52889416e-04, -4.81653406e-04,\n",
+       "       -6.77651318e-04, -6.92399044e-04, -7.43436685e-04, -9.58102290e-04,\n",
+       "       -1.17542152e-03, -1.01089594e-03, -8.60720582e-04, -7.21602875e-04,\n",
+       "       -6.49456983e-04, -5.52507583e-04, -3.34334822e-04, -7.83924770e-05,\n",
+       "        1.06010542e-04,  5.89528609e-05,  1.48837338e-04, -4.80122690e-05,\n",
+       "       -8.75702754e-05, -1.15438765e-04, -2.85895134e-04, -4.50302789e-04,\n",
+       "       -3.51646973e-04, -2.38260647e-04, -2.42416674e-04, -1.83340962e-04,\n",
+       "       -1.50821346e-04, -1.66885860e-04, -3.07395501e-04, -4.77931811e-04,\n",
+       "       -5.23011549e-04, -3.56306729e-04, -1.00346508e-04,  9.04311746e-05,\n",
+       "        5.27369921e-05,  1.09636658e-05, -3.95643983e-05,  4.34031572e-05,\n",
+       "        6.19495986e-05,  2.03832184e-04,  2.06827404e-04,  2.27460230e-04,\n",
+       "        2.41240807e-04,  3.17600294e-04,  3.92820017e-04,  3.48903006e-04,\n",
+       "        2.48353666e-04,  2.90645345e-04,  3.54456497e-05,  1.41456272e-04,\n",
+       "        1.91575100e-06, -1.05931562e-04, -1.12848858e-04, -1.59171730e-04,\n",
+       "       -3.88738437e-04, -3.07661714e-04, -2.84524198e-04, -3.20903375e-04,\n",
+       "       -3.85275809e-04, -3.96396877e-04, -4.21230390e-04, -5.73414552e-04,\n",
+       "       -6.10906398e-04, -7.30710512e-04, -9.13490599e-04, -8.02771014e-04,\n",
+       "       -8.85016285e-04, -7.67805497e-04, -7.08730426e-04])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#pd.unique nos dice los distintos valores presentes en la columna vacf(t)\n",
+    "pd.unique(df[\"vacf\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Trabajemos con esta columna, dado que queremos calcular estadísticas de datos agrupados por subconjuntos o atributos.\n",
+    "Por ejemplo, mostremos alguna estadística básica de todos los datos en la columna usando el comando `.describe()`. Note la salida que nos devuelve:\n",
+    "\n",
+    "- conteo de datos\n",
+    "- la media\n",
+    "- desviación standard\n",
+    "- valor mínimo\n",
+    "- valor máximo\n",
+    "- nombre y tipo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    199.000000\n",
+       "mean       0.029651\n",
+       "std        0.145300\n",
+       "min       -0.042338\n",
+       "25%       -0.002959\n",
+       "50%       -0.000566\n",
+       "75%       -0.000044\n",
+       "max        0.893155\n",
+       "Name: vacf, dtype: float64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# En algunas ocasiones, esta es una de ellas, queremos calcular estadísticas de datos\n",
+    "# agrupados por subconjuntos o atributos de nuestros datos.\n",
+    "\n",
+    "df[\"vacf\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "También podemos extraer un de las métricas que nos interese:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.042338326600000004\n",
+      "0.893154621\n",
+      "0.02965061882257874\n",
+      "0.14529958318959033\n",
+      "199\n"
+     ]
+    }
+   ],
+   "source": [
+    "# otra manera de hacer la misma estadística:\n",
+    "\n",
+    "print(df[\"vacf\"].min())\n",
+    "print(df[\"vacf\"].max())\n",
+    "print(df[\"vacf\"].mean())\n",
+    "print(df[\"vacf\"].std())\n",
+    "print(df[\"vacf\"].count())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      30.122630\n",
+       "1      29.269338\n",
+       "2      27.721690\n",
+       "3      25.592402\n",
+       "4      23.025013\n",
+       "         ...    \n",
+       "194    -0.030808\n",
+       "195    -0.027074\n",
+       "196    -0.029848\n",
+       "197    -0.025895\n",
+       "198    -0.023903\n",
+       "Name: vacf, Length: 199, dtype: float64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Si queremos, podemos hacer operaciones sobre una columna de nuestra data. Como por ejemplo\n",
+    "# multiplicar todos los valores por 2. Un uso más útil podría ser normalizar los datos con\n",
+    "# la media, área o algún otro valor calculado de nuestra data\n",
+    "\n",
+    "df[\"vacf\"]/df[\"vacf\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Grafiquemos los datos usando pandas\n",
+    "\n",
+    "Uno de los gráficos que podemos construír es el gráfico de dispersión. Mediante el cual podemos ver la relación entre dos variables, como en este caso: tiempo *(t)* vs la función de autocorrelación de velocidades *(vacf)*. Este tipo de gráfico puede obtenerse mediante el método ´lmplot´ al que se le indicará la característica para cada uno de los ejes y el conjunto de datos. Como se muestra a continuación."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from seaborn import load_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/student/.local/lib/python3.6/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
+      "  FutureWarning\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f5384823cc0>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lmplot(\"t\",\"vacf\",data=df, fit_reg=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from seaborn import kdeplot\n",
+    "from seaborn import distplot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/student/.local/lib/python3.6/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='t', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(df[\"t\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/student/.local/lib/python3.6/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='vacf', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(df[\"vacf\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "La gráfica de los diagramas a pares son una gran herramienta para visualizar la dependencia y distribución de la data, pero para una mejor interpretación del significado de nuestros histogramas podemos usar `jointplot` y visualizar las densidades de las columnas del dataframe que nos insterese. A continuación presentamos la gráfica generada con este método y los gráficos de densidad de las variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "prueba = sns.jointplot(x=\"t\",y=\"vacf\",data=df)\n",
+    "\n",
+    "prueba.fig.set_size_inches(8,8)\n",
+    "\n",
+    "plt.grid()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  5- Grafica de la Función de Autocorrelación de Velocidades a partir de nuestros datos\n",
+    "\n",
+    "\n",
+    "Usamos dos maneras diferentes de construír la gráfica explotando recursos de python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "import pylab as pl\n",
+    "import csv\n",
+    "\n",
+    "entrada = open(\"/home/student/ejercicios-clase-08-datos/data-used/VACF_liso_Hr-10-500.csv\")\n",
+    "\n",
+    "tabla = []\n",
+    "\n",
+    "for fila in csv.reader(entrada):\n",
+    "    tabla.append(fila)\n",
+    "entrada.close()\n",
+    "\n",
+    "x = [0]\n",
+    "y = [0.893155]\n",
+    "\n",
+    "for fila in range(1, len(tabla)):\n",
+    "    x.append(float(tabla[fila][0]))\n",
+    "    y.append(float(tabla[fila][1]))\n",
+    "    \n",
+    "pl.figure(figsize =(10,10))\n",
+    "\n",
+    "pl.plot(x,y)\n",
+    "pl.xlabel(\"tiempo\")\n",
+    "pl.ylabel(\"VACF\")\n",
+    "pl.grid()\n",
+    "pl.legend([\"vacf(t)\"])\n",
+    "pl.title(\"Función de Autocorrelación de Velocidades\")\n",
+    "pl.savefig(\"imagen.png\")\n",
+    "pl.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Función de autocorrelación de velocidades')"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#FDR_cilindro_liso_Hr-10.csv\n",
+    "\n",
+    "file = '/home/student/ejercicios-clase-08-datos/data-used/VACF_liso_Hr-10-500.csv'\n",
+    "\n",
+    "dataframe0 = pd.read_csv(file)\n",
+    "\n",
+    "x = dataframe0[\"t\"]\n",
+    "y = dataframe0[\"vacf\"]\n",
+    "\n",
+    "plt.figure(figsize =(8,8))\n",
+    "\n",
+    "#plt.scatter(x,y, marker = \"+\")\n",
+    "pl.plot(x,y, \"r.-\")\n",
+    "plt.savefig(\"vacf.png\")\n",
+    "\n",
+    "pl.xlabel(\"tiempo\")\n",
+    "pl.ylabel(\"VACF\")\n",
+    "pl.grid()\n",
+    "pl.legend([\"VACF(t)\"])\n",
+    "pl.title(\"Función de autocorrelación de velocidades\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  6- Gráfica de la Función de distribución radial\n",
+    "\n",
+    "- Partiremos inicialmente mostrando el gráfico de la Función de Distribución Radial *(FDR)* correspondiente a un cilindro con una superficie lisa. Esta función nos muestra la estructura y organización de las partículas del fluido confinadas dentro del cilindro con un radio de 10 diámetros moleculares. Los máximos de la función nos están indicando la distancia en la cual las partículas tienden a acumularse, dicho de una manera más formal: los máximos de probabilidad en la que conseguiremos distribuidas las partículas.\n",
+    "\n",
+    "- Veremos el tratamiento y la visualización estadística de la distribución de los puntos de data en histogramas individuales para cada variable y luego la gráfica que nos resume ambos histogramas. \n",
+    "\n",
+    "- En la siguiente celda construimos a partir de la data la gráfica de la evolución de la estructura del fluido a medida que aumentamos la cantidad de obstáculos en las paredes del cilindro."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Función de distribución radial')"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#FDR_cilindro_liso_Hr-10.csv\n",
+    "\n",
+    "file = '/home/student/ejercicios-clase-08-datos/data-used/FDR_cilindro_liso_Hr-10.csv'\n",
+    "\n",
+    "dataframe0 = pd.read_csv(file)\n",
+    "\n",
+    "x = dataframe0[\"r\"]\n",
+    "y = dataframe0[\"g(r)\"]\n",
+    "\n",
+    "plt.figure(figsize =(8,8))\n",
+    "\n",
+    "#plt.scatter(x,y, marker = \"+\")\n",
+    "pl.plot(x,y, \"r.-\")\n",
+    "plt.savefig(\"fdr.png\")\n",
+    "\n",
+    "pl.xlabel(\"Distancia\")\n",
+    "pl.ylabel(\"FDR(r)\")\n",
+    "pl.grid()\n",
+    "pl.legend([\"FDR(r)\"])\n",
+    "pl.title(\"Función de distribución radial\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Al igual que con la data correspondiente a la Función de Autocorrelación de Velocidades, también podemos visualizar el perfil estadístico básico de la data que genera nuestra Función de Distribución Radial. A continuación se muestra los perfiles de densidad en cada uno de los ejes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/student/.local/lib/python3.6/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='r', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(dataframe0[\"r\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/student/.local/lib/python3.6/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='g(r)', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(dataframe0[\"g(r)\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#prueba = sns.jointplot(x=dataframe0[\"r\"],y=dataframe0[\"g(r)\"],data=df,kind=\"reg\",line_kws={\"color\":\"red\"},scatter_kws={\"alpha\":0.33})\n",
+    "\n",
+    "prueba = sns.jointplot(x=dataframe0[\"r\"],y=dataframe0[\"g(r)\"],data=df)\n",
+    "\n",
+    "print(\"\")\n",
+    "\n",
+    "print(\"\")\n",
+    "\n",
+    "prueba.fig.set_size_inches(8,8)\n",
+    "\n",
+    "pl.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      r    g_liso       g_2        g3        g5       g12       g15       g20\n",
+      "0  0.03  0.749559  1.067157  1.060624  1.086467  0.927405  1.010291  0.944136\n",
+      "1  0.05  0.820106  0.960441  0.954561  1.231928  1.008123  1.000188  0.825305\n",
+      "2  0.07  0.831444  0.961748  0.955860  1.173692  1.010822  0.962181  0.815073\n",
+      "3  0.09  1.087596  1.036667  1.030320  1.198206  1.022817  0.944809  0.860936\n",
+      "4  0.11  0.889851  1.129246  1.122333  1.048102  0.947701  0.910792  0.813170\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Disponemos del archivo de data que almacena todas las funciones de distribución radial clasificadas por filas y columnas,\n",
+    "# esto nos permitirá construír el código para graficar la evolución de la función de distribución radial del fluido confinado\n",
+    "# dentro de un cilindro\n",
+    "\n",
+    "dataframe7=pd.read_csv(\"/home/student/ejercicios-clase-08-datos/data-used/FDR_evolucion_general.csv\")\n",
+    "print(dataframe7.head())\n",
+    "\n",
+    "plt.figure(figsize =(10,10))\n",
+    "ax = plt.gca()\n",
+    "\n",
+    "dataframe7.plot(kind=\"line\",x=\"r\",y=\"g_liso\",ax=ax, label=\"Cilindro liso\")\n",
+    "dataframe7.plot(kind=\"line\",x=\"r\",y=\"g_2\",ax=ax, label=\"Cilindro con 2 dientes\")\n",
+    "dataframe7.plot(kind=\"line\",x=\"r\",y=\"g3\",ax=ax, label =\"Cilindro con 3 dientes\")\n",
+    "dataframe7.plot(kind=\"line\",x=\"r\",y=\"g5\",ax=ax,label =\"Cilindro con 5 dientes\")\n",
+    "dataframe7.plot(kind=\"line\",x=\"r\",y=\"g12\",ax=ax, label =\"Cilindro con 12 dientes\")\n",
+    "dataframe7.plot(kind=\"line\",x=\"r\",y=\"g15\",ax=ax, label =\"Cilindro con 15 dientes\")\n",
+    "dataframe7.plot(kind=\"line\",x=\"r\",y=\"g20\",ax=ax, label =\"Cilindro con 20 dientes\")\n",
+    "\n",
+    "pl.xlabel(\"r\")\n",
+    "pl.ylabel(\"FDR(r)\")\n",
+    "pl.grid()\n",
+    "#pl.legend([\"FDR(r)\"])\n",
+    "pl.title(\"Evolución de la función de distribución radial\")\n",
+    "\n",
+    "pl.savefig(\"fdr_evolucion.png\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Presentar la evolución de la función de distribución radial en forma de gráfica *(estática)*, tiende a ser confusa, por lo que a continuación aplicaremos lo aprendido en las clases del ***Módulo de Ciencia de Datos** mostrando una representación *dinámica* de la variación de la Función de Distribución Radial a medida que cambian las características del cilindro que contiene el fluido."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7- Intentemos una animación"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import animation\n",
+    "from matplotlib.animation import FuncAnimation\n",
+    "#from Tkinter import *\n",
+    "from IPython.display import HTML\n",
+    "%matplotlib notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A nosostros como humanos se nos hace más sencillo observar el comportamiento de la data mediante algún tipo de representación visual, esto nos permite explicar el fenómeno que registra nuestra data, pero en muchas ocasiones las imágenes estáticas no lo lo muestran. Es aquí donde las animaciones comienzan a tener sentido y demostrar su valor en la visualización de nuestros datos!\n",
+    "\n",
+    "Lo que haremos para la animación será tomar las gráficas de cada columna de datos de interés y grafiquémoslas de manera consecutiva dentro de un mismo marco de ejes coordenados. Por lo que debemos definir los datos y juntarlos en una lista que podamos manejar y graficar echando mano de la función `animate` y `FuncAnimation`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     primera_columna  segunda_columna\n",
+      "0               0.03         0.944136\n",
+      "1               0.05         0.825305\n",
+      "2               0.07         0.815073\n",
+      "3               0.09         0.860936\n",
+      "4               0.11         0.813170\n",
+      "..               ...              ...\n",
+      "495             9.93         0.000000\n",
+      "496             9.95         0.000000\n",
+      "497             9.97         0.000000\n",
+      "498             9.99         0.000000\n",
+      "499            10.01         0.000000\n",
+      "\n",
+      "[500 rows x 2 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#                 r    g_liso       g_2        g3        g5       g12       g15       g20\n",
+    "\n",
+    "#dataframe7=pd.read_csv(\"/home/student/ejercicios-clase-08-datos/data-used/FDR_evolucion_general.csv\")\n",
+    "\n",
+    "#f0 = dataframe7[\"r\"], dataframe7[\"g_liso\"]\n",
+    "\n",
+    "f0 = pd.DataFrame({\"primera_columna\":dataframe7[\"r\"], \"segunda_columna\": dataframe7[\"g_liso\"]})\n",
+    "f1 = pd.DataFrame({\"primera_columna\":dataframe7[\"r\"], \"segunda_columna\": dataframe7[\"g_2\"]})\n",
+    "f2 = pd.DataFrame({\"primera_columna\":dataframe7[\"r\"], \"segunda_columna\": dataframe7[\"g3\"]})\n",
+    "f3 = pd.DataFrame({\"primera_columna\":dataframe7[\"r\"], \"segunda_columna\": dataframe7[\"g5\"]})\n",
+    "f4 = pd.DataFrame({\"primera_columna\":dataframe7[\"r\"], \"segunda_columna\": dataframe7[\"g12\"]})\n",
+    "f5 = pd.DataFrame({\"primera_columna\":dataframe7[\"r\"], \"segunda_columna\": dataframe7[\"g15\"]})\n",
+    "f6 = pd.DataFrame({\"primera_columna\":dataframe7[\"r\"], \"segunda_columna\": dataframe7[\"g20\"]})\n",
+    "\n",
+    "print(f6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Crearemos una variable global que almacene todos los dataframes que escogimos\n",
+    "global mylist\n",
+    "mylist=[f0,f1,f2,f3,f4,f5,f6]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "/* global mpl */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function () {\n",
+       "    if (typeof WebSocket !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert(\n",
+       "            'Your browser does not have WebSocket support. ' +\n",
+       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "                'Firefox 4 and 5 are also supported but you ' +\n",
+       "                'have to enable WebSockets in about:config.'\n",
+       "        );\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById('mpl-warnings');\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent =\n",
+       "                'This browser does not support binary websocket messages. ' +\n",
+       "                'Performance may be slow.';\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = document.createElement('div');\n",
+       "    this.root.setAttribute('style', 'display: inline-block');\n",
+       "    this._root_extra_style(this.root);\n",
+       "\n",
+       "    parent_element.appendChild(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen = function () {\n",
+       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
+       "        fig.send_message('send_image_mode', {});\n",
+       "        if (fig.ratio !== 1) {\n",
+       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
+       "        }\n",
+       "        fig.send_message('refresh', {});\n",
+       "    };\n",
+       "\n",
+       "    this.imageObj.onload = function () {\n",
+       "        if (fig.image_mode === 'full') {\n",
+       "            // Full images could contain transparency (where diff images\n",
+       "            // almost always do), so we need to clear the canvas so that\n",
+       "            // there is no ghosting.\n",
+       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "        }\n",
+       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "    };\n",
+       "\n",
+       "    this.imageObj.onunload = function () {\n",
+       "        fig.ws.close();\n",
+       "    };\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function () {\n",
+       "    var titlebar = document.createElement('div');\n",
+       "    titlebar.classList =\n",
+       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
+       "    var titletext = document.createElement('div');\n",
+       "    titletext.classList = 'ui-dialog-title';\n",
+       "    titletext.setAttribute(\n",
+       "        'style',\n",
+       "        'width: 100%; text-align: center; padding: 3px;'\n",
+       "    );\n",
+       "    titlebar.appendChild(titletext);\n",
+       "    this.root.appendChild(titlebar);\n",
+       "    this.header = titletext;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function () {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
+       "    canvas_div.setAttribute(\n",
+       "        'style',\n",
+       "        'border: 1px solid #ddd;' +\n",
+       "            'box-sizing: content-box;' +\n",
+       "            'clear: both;' +\n",
+       "            'min-height: 1px;' +\n",
+       "            'min-width: 1px;' +\n",
+       "            'outline: 0;' +\n",
+       "            'overflow: hidden;' +\n",
+       "            'position: relative;' +\n",
+       "            'resize: both;'\n",
+       "    );\n",
+       "\n",
+       "    function on_keyboard_event_closure(name) {\n",
+       "        return function (event) {\n",
+       "            return fig.key_event(event, name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.addEventListener(\n",
+       "        'keydown',\n",
+       "        on_keyboard_event_closure('key_press')\n",
+       "    );\n",
+       "    canvas_div.addEventListener(\n",
+       "        'keyup',\n",
+       "        on_keyboard_event_closure('key_release')\n",
+       "    );\n",
+       "\n",
+       "    this._canvas_extra_style(canvas_div);\n",
+       "    this.root.appendChild(canvas_div);\n",
+       "\n",
+       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
+       "    canvas.classList.add('mpl-canvas');\n",
+       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
+       "\n",
+       "    this.context = canvas.getContext('2d');\n",
+       "\n",
+       "    var backingStore =\n",
+       "        this.context.backingStorePixelRatio ||\n",
+       "        this.context.webkitBackingStorePixelRatio ||\n",
+       "        this.context.mozBackingStorePixelRatio ||\n",
+       "        this.context.msBackingStorePixelRatio ||\n",
+       "        this.context.oBackingStorePixelRatio ||\n",
+       "        this.context.backingStorePixelRatio ||\n",
+       "        1;\n",
+       "\n",
+       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
+       "        'canvas'\n",
+       "    ));\n",
+       "    rubberband_canvas.setAttribute(\n",
+       "        'style',\n",
+       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
+       "    );\n",
+       "\n",
+       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
+       "    if (this.ResizeObserver === undefined) {\n",
+       "        if (window.ResizeObserver !== undefined) {\n",
+       "            this.ResizeObserver = window.ResizeObserver;\n",
+       "        } else {\n",
+       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
+       "            this.ResizeObserver = obs.ResizeObserver;\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
+       "        var nentries = entries.length;\n",
+       "        for (var i = 0; i < nentries; i++) {\n",
+       "            var entry = entries[i];\n",
+       "            var width, height;\n",
+       "            if (entry.contentBoxSize) {\n",
+       "                if (entry.contentBoxSize instanceof Array) {\n",
+       "                    // Chrome 84 implements new version of spec.\n",
+       "                    width = entry.contentBoxSize[0].inlineSize;\n",
+       "                    height = entry.contentBoxSize[0].blockSize;\n",
+       "                } else {\n",
+       "                    // Firefox implements old version of spec.\n",
+       "                    width = entry.contentBoxSize.inlineSize;\n",
+       "                    height = entry.contentBoxSize.blockSize;\n",
+       "                }\n",
+       "            } else {\n",
+       "                // Chrome <84 implements even older version of spec.\n",
+       "                width = entry.contentRect.width;\n",
+       "                height = entry.contentRect.height;\n",
+       "            }\n",
+       "\n",
+       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
+       "            // the canvas container.\n",
+       "            if (entry.devicePixelContentBoxSize) {\n",
+       "                // Chrome 84 implements new version of spec.\n",
+       "                canvas.setAttribute(\n",
+       "                    'width',\n",
+       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
+       "                );\n",
+       "                canvas.setAttribute(\n",
+       "                    'height',\n",
+       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
+       "                );\n",
+       "            } else {\n",
+       "                canvas.setAttribute('width', width * fig.ratio);\n",
+       "                canvas.setAttribute('height', height * fig.ratio);\n",
+       "            }\n",
+       "            canvas.setAttribute(\n",
+       "                'style',\n",
+       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
+       "            );\n",
+       "\n",
+       "            rubberband_canvas.setAttribute('width', width);\n",
+       "            rubberband_canvas.setAttribute('height', height);\n",
+       "\n",
+       "            // And update the size in Python. We ignore the initial 0/0 size\n",
+       "            // that occurs as the element is placed into the DOM, which should\n",
+       "            // otherwise not happen due to the minimum size styling.\n",
+       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
+       "                fig.request_resize(width, height);\n",
+       "            }\n",
+       "        }\n",
+       "    });\n",
+       "    this.resizeObserverInstance.observe(canvas_div);\n",
+       "\n",
+       "    function on_mouse_event_closure(name) {\n",
+       "        return function (event) {\n",
+       "            return fig.mouse_event(event, name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mousedown',\n",
+       "        on_mouse_event_closure('button_press')\n",
+       "    );\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mouseup',\n",
+       "        on_mouse_event_closure('button_release')\n",
+       "    );\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mousemove',\n",
+       "        on_mouse_event_closure('motion_notify')\n",
+       "    );\n",
+       "\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mouseenter',\n",
+       "        on_mouse_event_closure('figure_enter')\n",
+       "    );\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mouseleave',\n",
+       "        on_mouse_event_closure('figure_leave')\n",
+       "    );\n",
+       "\n",
+       "    canvas_div.addEventListener('wheel', function (event) {\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        on_mouse_event_closure('scroll')(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.appendChild(canvas);\n",
+       "    canvas_div.appendChild(rubberband_canvas);\n",
+       "\n",
+       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
+       "    this.rubberband_context.strokeStyle = '#000000';\n",
+       "\n",
+       "    this._resize_canvas = function (width, height, forward) {\n",
+       "        if (forward) {\n",
+       "            canvas_div.style.width = width + 'px';\n",
+       "            canvas_div.style.height = height + 'px';\n",
+       "        }\n",
+       "    };\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
+       "        event.preventDefault();\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus() {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function () {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var toolbar = document.createElement('div');\n",
+       "    toolbar.classList = 'mpl-toolbar';\n",
+       "    this.root.appendChild(toolbar);\n",
+       "\n",
+       "    function on_click_closure(name) {\n",
+       "        return function (_event) {\n",
+       "            return fig.toolbar_button_onclick(name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    function on_mouseover_closure(tooltip) {\n",
+       "        return function (event) {\n",
+       "            if (!event.currentTarget.disabled) {\n",
+       "                return fig.toolbar_button_onmouseover(tooltip);\n",
+       "            }\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    fig.buttons = {};\n",
+       "    var buttonGroup = document.createElement('div');\n",
+       "    buttonGroup.classList = 'mpl-button-group';\n",
+       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            /* Instead of a spacer, we start a new button group. */\n",
+       "            if (buttonGroup.hasChildNodes()) {\n",
+       "                toolbar.appendChild(buttonGroup);\n",
+       "            }\n",
+       "            buttonGroup = document.createElement('div');\n",
+       "            buttonGroup.classList = 'mpl-button-group';\n",
+       "            continue;\n",
+       "        }\n",
+       "\n",
+       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
+       "        button.classList = 'mpl-widget';\n",
+       "        button.setAttribute('role', 'button');\n",
+       "        button.setAttribute('aria-disabled', 'false');\n",
+       "        button.addEventListener('click', on_click_closure(method_name));\n",
+       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
+       "\n",
+       "        var icon_img = document.createElement('img');\n",
+       "        icon_img.src = '_images/' + image + '.png';\n",
+       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
+       "        icon_img.alt = tooltip;\n",
+       "        button.appendChild(icon_img);\n",
+       "\n",
+       "        buttonGroup.appendChild(button);\n",
+       "    }\n",
+       "\n",
+       "    if (buttonGroup.hasChildNodes()) {\n",
+       "        toolbar.appendChild(buttonGroup);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker = document.createElement('select');\n",
+       "    fmt_picker.classList = 'mpl-widget';\n",
+       "    toolbar.appendChild(fmt_picker);\n",
+       "    this.format_dropdown = fmt_picker;\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = document.createElement('option');\n",
+       "        option.selected = fmt === mpl.default_extension;\n",
+       "        option.innerHTML = fmt;\n",
+       "        fmt_picker.appendChild(option);\n",
+       "    }\n",
+       "\n",
+       "    var status_bar = document.createElement('span');\n",
+       "    status_bar.classList = 'mpl-message';\n",
+       "    toolbar.appendChild(status_bar);\n",
+       "    this.message = status_bar;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function (type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function () {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
+       "        fig.send_message('refresh', {});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
+       "    var x0 = msg['x0'] / fig.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
+       "    var x1 = msg['x1'] / fig.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0,\n",
+       "        0,\n",
+       "        fig.canvas.width / fig.ratio,\n",
+       "        fig.canvas.height / fig.ratio\n",
+       "    );\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch (cursor) {\n",
+       "        case 0:\n",
+       "            cursor = 'pointer';\n",
+       "            break;\n",
+       "        case 1:\n",
+       "            cursor = 'default';\n",
+       "            break;\n",
+       "        case 2:\n",
+       "            cursor = 'crosshair';\n",
+       "            break;\n",
+       "        case 3:\n",
+       "            cursor = 'move';\n",
+       "            break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
+       "    for (var key in msg) {\n",
+       "        if (!(key in fig.buttons)) {\n",
+       "            continue;\n",
+       "        }\n",
+       "        fig.buttons[key].disabled = !msg[key];\n",
+       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
+       "    if (msg['mode'] === 'PAN') {\n",
+       "        fig.buttons['Pan'].classList.add('active');\n",
+       "        fig.buttons['Zoom'].classList.remove('active');\n",
+       "    } else if (msg['mode'] === 'ZOOM') {\n",
+       "        fig.buttons['Pan'].classList.remove('active');\n",
+       "        fig.buttons['Zoom'].classList.add('active');\n",
+       "    } else {\n",
+       "        fig.buttons['Pan'].classList.remove('active');\n",
+       "        fig.buttons['Zoom'].classList.remove('active');\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function () {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message('ack', {});\n",
+       "};\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = 'image/png';\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src\n",
+       "                );\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data\n",
+       "            );\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        } else if (\n",
+       "            typeof evt.data === 'string' &&\n",
+       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
+       "        ) {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig['handle_' + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\n",
+       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
+       "                msg\n",
+       "            );\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\n",
+       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
+       "                    e,\n",
+       "                    e.stack,\n",
+       "                    msg\n",
+       "                );\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "};\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function (e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e) {\n",
+       "        e = window.event;\n",
+       "    }\n",
+       "    if (e.target) {\n",
+       "        targ = e.target;\n",
+       "    } else if (e.srcElement) {\n",
+       "        targ = e.srcElement;\n",
+       "    }\n",
+       "    if (targ.nodeType === 3) {\n",
+       "        // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "    }\n",
+       "\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    var boundingRect = targ.getBoundingClientRect();\n",
+       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
+       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
+       "\n",
+       "    return { x: x, y: y };\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys(original) {\n",
+       "    return Object.keys(original).reduce(function (obj, key) {\n",
+       "        if (typeof original[key] !== 'object') {\n",
+       "            obj[key] = original[key];\n",
+       "        }\n",
+       "        return obj;\n",
+       "    }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event);\n",
+       "\n",
+       "    if (name === 'button_press') {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * this.ratio;\n",
+       "    var y = canvas_pos.y * this.ratio;\n",
+       "\n",
+       "    this.send_message(name, {\n",
+       "        x: x,\n",
+       "        y: y,\n",
+       "        button: event.button,\n",
+       "        step: event.step,\n",
+       "        guiEvent: simpleKeys(event),\n",
+       "    });\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function (event, name) {\n",
+       "    // Prevent repeat events\n",
+       "    if (name === 'key_press') {\n",
+       "        if (event.which === this._key) {\n",
+       "            return;\n",
+       "        } else {\n",
+       "            this._key = event.which;\n",
+       "        }\n",
+       "    }\n",
+       "    if (name === 'key_release') {\n",
+       "        this._key = null;\n",
+       "    }\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which !== 17) {\n",
+       "        value += 'ctrl+';\n",
+       "    }\n",
+       "    if (event.altKey && event.which !== 18) {\n",
+       "        value += 'alt+';\n",
+       "    }\n",
+       "    if (event.shiftKey && event.which !== 16) {\n",
+       "        value += 'shift+';\n",
+       "    }\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
+       "    return false;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
+       "    if (name === 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message('toolbar_button', { name: name });\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "\n",
+       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
+       "// prettier-ignore\n",
+       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";/* global mpl */\n",
+       "\n",
+       "var comm_websocket_adapter = function (comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function () {\n",
+       "        comm.close();\n",
+       "    };\n",
+       "    ws.send = function (m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function (msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data']);\n",
+       "    });\n",
+       "    return ws;\n",
+       "};\n",
+       "\n",
+       "mpl.mpl_figure_comm = function (comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = document.getElementById(id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm);\n",
+       "\n",
+       "    function ondownload(figure, _format) {\n",
+       "        window.open(figure.canvas.toDataURL());\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element;\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error('Failed to find cell for figure', id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "    fig.cell_info[0].output_area.element.on(\n",
+       "        'cleared',\n",
+       "        { fig: fig },\n",
+       "        fig._remove_fig_handler\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
+       "    var width = fig.canvas.width / fig.ratio;\n",
+       "    fig.cell_info[0].output_area.element.off(\n",
+       "        'cleared',\n",
+       "        fig._remove_fig_handler\n",
+       "    );\n",
+       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable();\n",
+       "    fig.parent_element.innerHTML =\n",
+       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "    fig.close_ws(fig, msg);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width / this.ratio;\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] =\n",
+       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function () {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message('ack', {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () {\n",
+       "        fig.push_to_output();\n",
+       "    }, 1000);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function () {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var toolbar = document.createElement('div');\n",
+       "    toolbar.classList = 'btn-toolbar';\n",
+       "    this.root.appendChild(toolbar);\n",
+       "\n",
+       "    function on_click_closure(name) {\n",
+       "        return function (_event) {\n",
+       "            return fig.toolbar_button_onclick(name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    function on_mouseover_closure(tooltip) {\n",
+       "        return function (event) {\n",
+       "            if (!event.currentTarget.disabled) {\n",
+       "                return fig.toolbar_button_onmouseover(tooltip);\n",
+       "            }\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    fig.buttons = {};\n",
+       "    var buttonGroup = document.createElement('div');\n",
+       "    buttonGroup.classList = 'btn-group';\n",
+       "    var button;\n",
+       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            /* Instead of a spacer, we start a new button group. */\n",
+       "            if (buttonGroup.hasChildNodes()) {\n",
+       "                toolbar.appendChild(buttonGroup);\n",
+       "            }\n",
+       "            buttonGroup = document.createElement('div');\n",
+       "            buttonGroup.classList = 'btn-group';\n",
+       "            continue;\n",
+       "        }\n",
+       "\n",
+       "        button = fig.buttons[name] = document.createElement('button');\n",
+       "        button.classList = 'btn btn-default';\n",
+       "        button.href = '#';\n",
+       "        button.title = name;\n",
+       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
+       "        button.addEventListener('click', on_click_closure(method_name));\n",
+       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
+       "        buttonGroup.appendChild(button);\n",
+       "    }\n",
+       "\n",
+       "    if (buttonGroup.hasChildNodes()) {\n",
+       "        toolbar.appendChild(buttonGroup);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = document.createElement('span');\n",
+       "    status_bar.classList = 'mpl-message pull-right';\n",
+       "    toolbar.appendChild(status_bar);\n",
+       "    this.message = status_bar;\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = document.createElement('div');\n",
+       "    buttongrp.classList = 'btn-group inline pull-right';\n",
+       "    button = document.createElement('button');\n",
+       "    button.classList = 'btn btn-mini btn-primary';\n",
+       "    button.href = '#';\n",
+       "    button.title = 'Stop Interaction';\n",
+       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
+       "    button.addEventListener('click', function (_evt) {\n",
+       "        fig.handle_close(fig, {});\n",
+       "    });\n",
+       "    button.addEventListener(\n",
+       "        'mouseover',\n",
+       "        on_mouseover_closure('Stop Interaction')\n",
+       "    );\n",
+       "    buttongrp.appendChild(button);\n",
+       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
+       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
+       "    var fig = event.data.fig;\n",
+       "    if (event.target !== this) {\n",
+       "        // Ignore bubbled events from children.\n",
+       "        return;\n",
+       "    }\n",
+       "    fig.close_ws(fig, {});\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function (el) {\n",
+       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.setAttribute('tabindex', 0);\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    } else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager) {\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "    }\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which === 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "};\n",
+       "\n",
+       "mpl.find_output_cell = function (html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i = 0; i < ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code') {\n",
+       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] === html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel !== null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target(\n",
+       "        'matplotlib',\n",
+       "        mpl.mpl_figure_comm\n",
+       "    );\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Configuramos la figura, los ejes, y la gráfica que queremos animar\n",
+    "fig = plt.figure()\n",
+    "ax = plt.axes(xlim=(0, 11), ylim=(0, 6))\n",
+    "line, = ax.plot([], [], lw=2)\n",
+    "\n",
+    "# función inicialización: grafica el fondo de cada frame\n",
+    "def init():\n",
+    "    line.set_data([], [])\n",
+    "    return line,\n",
+    "\n",
+    "# función animation para la lista de los dataframes\n",
+    "def animate(i):\n",
+    "    line.set_data(mylist[i]['primera_columna'], mylist[i]['segunda_columna'])\n",
+    "    return line,\n",
+    "\n",
+    "# Animamos usando FuncAnimation, en intervalos de 300 ms\n",
+    "# declaramos el número de frames de la lista \n",
+    "anim = animation.FuncAnimation(fig, animate, frames=len(mylist), init_func=init, interval=300, blit=True)\n",
+    "\n",
+    "writergif = animation.PillowWriter(fps=1000)\n",
+    "anim.save(\"animacion.gif\",writer=writergif)\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "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": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/codigo/conversion_de_archivos_csv_a_ROOT.ipynb b/codigo/conversion_de_archivos_csv_a_ROOT.ipynb
new file mode 100644
index 0000000..22f6591
--- /dev/null
+++ b/codigo/conversion_de_archivos_csv_a_ROOT.ipynb
@@ -0,0 +1,74 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conversión de archivos csv a ROOT\n",
+    "\n",
+    "Este pequeño programa nos permite convertir archivos csv a ROOT usando comandos C++, la utilidad de estos archivos se basa en la forma en que almacena la data y directorios. Están diseñados para almacenar un gran volumen de información en menor espacio optimizando el tratamiento de la data cuando son archivos muy grandes, en física de altas energías por ejemplo.\n",
+    "\n",
+    "Una vez tenemos el archivo convertido a ROOT podemos realizar los tramientos estadísticos en C++ o escoger un camino alternativo procesandolos con UPROOT."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%jsroot on"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#include \"Riostream.h\"\n",
+    "#include \"TString.h\"\n",
+    "#include \"TFile.h\"\n",
+    "#include \"TTree.h\"\n",
+    "#include \"TSystem.h\"\n",
+    "#include <stdio.h>\n",
+    "#include <stdlib.h>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TString dir = gSystem->UnixPathName(\"/home/student/ejercicios-clase-08-datos/data-used/FDR_cilindro_liso_Hr-10.csv\");\n",
+    "dir.ReplaceAll(\"FDR_cilindro_liso_Hr-10.C\",\"\");\n",
+    "dir.ReplaceAll(\"/./\",\"/\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TFile *f = new TFile(\"/home/student/ejercicios-clase-08-datos/data-used//FDR_cilindro_liso_Hr-10.root\",\"RECREATE\");"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ROOT C++",
+   "language": "c++",
+   "name": "root"
+  },
+  "language_info": {
+   "codemirror_mode": "text/x-c++src",
+   "file_extension": ".C",
+   "mimetype": " text/x-c++src",
+   "name": "c++"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/codigo/convertir_csv_a_root-2do.ipynb b/codigo/convertir_csv_a_root-2do.ipynb
new file mode 100644
index 0000000..f06d196
--- /dev/null
+++ b/codigo/convertir_csv_a_root-2do.ipynb
@@ -0,0 +1,74 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conversión de archivos csv a ROOT\n",
+    "\n",
+    "Este pequeño programa nos permite convertir archivos csv a ROOT usando comandos C++, la utilidad de estos archivos se basa en la forma en que almacena la data y directorios. Están diseñados para almacenar un gran volumen de información en menor espacio optimizando el tratamiento de la data cuando son archivos muy grandes, en física de altas energías por ejemplo.\n",
+    "\n",
+    "Una vez tenemos el archivo convertido a ROOT podemos realizar los tramientos estadísticos en C++ o escoger un camino alternativo trabaja"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%jsroot on"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#include \"Riostream.h\"\n",
+    "#include \"TString.h\"\n",
+    "#include \"TFile.h\"\n",
+    "#include \"TTree.h\"\n",
+    "#include \"TSystem.h\"\n",
+    "#include <stdio.h>\n",
+    "#include <stdlib.h>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TString dir = gSystem->UnixPathName(\"/home/student/ejercicios-clase-08-datos/data-used/FDR_cilindro_liso_Hr-10.csv\");\n",
+    "dir.ReplaceAll(\"FDR_cilindro_liso_Hr-10.C\",\"\");\n",
+    "dir.ReplaceAll(\"/./\",\"/\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TFile *f = new TFile(\"/home/student/ejercicios-clase-08-datos/data-used//FDR_cilindro_liso_Hr-10.root\",\"RECREATE\");"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ROOT C++",
+   "language": "c++",
+   "name": "root"
+  },
+  "language_info": {
+   "codemirror_mode": "text/x-c++src",
+   "file_extension": ".C",
+   "mimetype": " text/x-c++src",
+   "name": "c++"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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diff --git a/codigo/pairplot_para_fdr.ipynb b/codigo/pairplot_para_fdr.ipynb
new file mode 100644
index 0000000..1cbba88
--- /dev/null
+++ b/codigo/pairplot_para_fdr.ipynb
@@ -0,0 +1,376 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from seaborn import lmplot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>r</th>\n",
+       "      <th>g(r)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.749559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.820106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.07</td>\n",
+       "      <td>0.831444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.09</td>\n",
+       "      <td>1.087596</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.11</td>\n",
+       "      <td>0.889851</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>495</th>\n",
+       "      <td>9.93</td>\n",
+       "      <td>3.514682</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>496</th>\n",
+       "      <td>9.95</td>\n",
+       "      <td>4.027678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>497</th>\n",
+       "      <td>9.97</td>\n",
+       "      <td>4.659614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498</th>\n",
+       "      <td>9.99</td>\n",
+       "      <td>5.603222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>10.01</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>500 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         r      g(r)\n",
+       "0     0.03  0.749559\n",
+       "1     0.05  0.820106\n",
+       "2     0.07  0.831444\n",
+       "3     0.09  1.087596\n",
+       "4     0.11  0.889851\n",
+       "..     ...       ...\n",
+       "495   9.93  3.514682\n",
+       "496   9.95  4.027678\n",
+       "497   9.97  4.659614\n",
+       "498   9.99  5.603222\n",
+       "499  10.01  0.000000\n",
+       "\n",
+       "[500 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file = '/home/student/ejercicios-clase-08-datos/data-used/FDR_cilindro_liso_Hr-10.csv'\n",
+    "df = pd.read_csv(file)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(500, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<bound method DataFrame.count of          r      g(r)\n",
+      "0     0.03  0.749559\n",
+      "1     0.05  0.820106\n",
+      "2     0.07  0.831444\n",
+      "3     0.09  1.087596\n",
+      "4     0.11  0.889851\n",
+      "..     ...       ...\n",
+      "495   9.93  3.514682\n",
+      "496   9.95  4.027678\n",
+      "497   9.97  4.659614\n",
+      "498   9.99  5.603222\n",
+      "499  10.01  0.000000\n",
+      "\n",
+      "[500 rows x 2 columns]>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['r', 'g(r)'], dtype='object')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(df)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/student/.local/lib/python3.6/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='g(r)', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(df[\"g(r)\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/student/.local/lib/python3.6/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='r', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(df[\"r\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.05, 'Distancia vs Probabilidad de encontrar las partículas')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "prueba = sns.jointplot(x=\"r\",y=\"g(r)\",data=df,kind=\"reg\",line_kws={\"color\":\"red\"},scatter_kws={\"alpha\":0.33})\n",
+    "\n",
+    "print(\"\")\n",
+    "\n",
+    "print(\"\")\n",
+    "\n",
+    "prueba.fig.set_size_inches(8,8)\n",
+    "\n",
+    "prueba.fig.suptitle(\"Distancia vs Probabilidad de encontrar las partículas\",fontsize=16, weight=\"bold\",y=1.05)"
+   ]
+  }
+ ],
+ "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": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/codigo/previsualizacion_dataset.ipynb b/codigo/previsualizacion_dataset.ipynb
new file mode 100644
index 0000000..dcae0cd
--- /dev/null
+++ b/codigo/previsualizacion_dataset.ipynb
@@ -0,0 +1,2564 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from seaborn import lmplot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>t</th>\n",
+       "      <th>vacf</th>\n",
+       "      <th>vacf_2</th>\n",
+       "      <th>vacf_3</th>\n",
+       "      <th>vacf_4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.893155</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.005954</td>\n",
+       "      <td>0.894384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.867854</td>\n",
+       "      <td>0.000177</td>\n",
+       "      <td>0.011740</td>\n",
+       "      <td>0.874035</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.821965</td>\n",
+       "      <td>0.000701</td>\n",
+       "      <td>0.017220</td>\n",
+       "      <td>0.832205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.758831</td>\n",
+       "      <td>0.001553</td>\n",
+       "      <td>0.022279</td>\n",
+       "      <td>0.773162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.682706</td>\n",
+       "      <td>0.002707</td>\n",
+       "      <td>0.026830</td>\n",
+       "      <td>0.700901</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>194</th>\n",
+       "      <td>1.94</td>\n",
+       "      <td>-0.000913</td>\n",
+       "      <td>0.424637</td>\n",
+       "      <td>0.039358</td>\n",
+       "      <td>0.004982</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>1.95</td>\n",
+       "      <td>-0.000803</td>\n",
+       "      <td>0.426829</td>\n",
+       "      <td>0.039352</td>\n",
+       "      <td>0.004958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>1.96</td>\n",
+       "      <td>-0.000885</td>\n",
+       "      <td>0.429018</td>\n",
+       "      <td>0.039346</td>\n",
+       "      <td>0.004928</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>1.97</td>\n",
+       "      <td>-0.000768</td>\n",
+       "      <td>0.431205</td>\n",
+       "      <td>0.039341</td>\n",
+       "      <td>0.004659</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>1.98</td>\n",
+       "      <td>-0.000709</td>\n",
+       "      <td>0.433389</td>\n",
+       "      <td>0.039336</td>\n",
+       "      <td>0.004458</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>199 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        t      vacf    vacf_2    vacf_3    vacf_4\n",
+       "0    0.00  0.893155  0.000000  0.005954  0.894384\n",
+       "1    0.01  0.867854  0.000177  0.011740  0.874035\n",
+       "2    0.02  0.821965  0.000701  0.017220  0.832205\n",
+       "3    0.03  0.758831  0.001553  0.022279  0.773162\n",
+       "4    0.04  0.682706  0.002707  0.026830  0.700901\n",
+       "..    ...       ...       ...       ...       ...\n",
+       "194  1.94 -0.000913  0.424637  0.039358  0.004982\n",
+       "195  1.95 -0.000803  0.426829  0.039352  0.004958\n",
+       "196  1.96 -0.000885  0.429018  0.039346  0.004928\n",
+       "197  1.97 -0.000768  0.431205  0.039341  0.004659\n",
+       "198  1.98 -0.000709  0.433389  0.039336  0.004458\n",
+       "\n",
+       "[199 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#carguemos el dataframe\n",
+    "\n",
+    "file = '/home/student/ejercicios-clase-08-datos/data-used/VACF_liso_Hr-10-500.csv'\n",
+    "df = pd.read_csv(file)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>t</th>\n",
+       "      <th>vacf</th>\n",
+       "      <th>vacf_2</th>\n",
+       "      <th>vacf_3</th>\n",
+       "      <th>vacf_4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.893155</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.005954</td>\n",
+       "      <td>0.894384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.867854</td>\n",
+       "      <td>0.000177</td>\n",
+       "      <td>0.011740</td>\n",
+       "      <td>0.874035</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.821965</td>\n",
+       "      <td>0.000701</td>\n",
+       "      <td>0.017220</td>\n",
+       "      <td>0.832205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.758831</td>\n",
+       "      <td>0.001553</td>\n",
+       "      <td>0.022279</td>\n",
+       "      <td>0.773162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.682706</td>\n",
+       "      <td>0.002707</td>\n",
+       "      <td>0.026830</td>\n",
+       "      <td>0.700901</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      t      vacf    vacf_2    vacf_3    vacf_4\n",
+       "0  0.00  0.893155  0.000000  0.005954  0.894384\n",
+       "1  0.01  0.867854  0.000177  0.011740  0.874035\n",
+       "2  0.02  0.821965  0.000701  0.017220  0.832205\n",
+       "3  0.03  0.758831  0.001553  0.022279  0.773162\n",
+       "4  0.04  0.682706  0.002707  0.026830  0.700901"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#visualicemos la data de forma general, solo los 5 primeros elementos y los nombres de las columnas\n",
+    "\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.- Una vez que tenemos el archivo de nuestros datos disponibles, visualicemos y exploremos la composición de la data que tenemos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(199, 5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#tengo manera de saber cuanto registros tengo?\n",
+    "\n",
+    "print(df.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-  Puede ocurrir que al pre-visualizar los datos, alguna columna tenga valores en `NaN`, este valor\n",
+    "   se traduce en python como un `None` y en humano como un valor nulo. Así que sería de gran utilidad saber que registros por columna tienen los datos con valores nulos para poder limpiarlos o interpretarlos, ya sea el caso.\n",
+    "   Una manera de realizar esta exploración es usando el método `count` (aunque para nuestra data, no se cuenta ningún `NaN`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<bound method DataFrame.count of         t      vacf    vacf_2    vacf_3    vacf_4\n",
+      "0    0.00  0.893155  0.000000  0.005954  0.894384\n",
+      "1    0.01  0.867854  0.000177  0.011740  0.874035\n",
+      "2    0.02  0.821965  0.000701  0.017220  0.832205\n",
+      "3    0.03  0.758831  0.001553  0.022279  0.773162\n",
+      "4    0.04  0.682706  0.002707  0.026830  0.700901\n",
+      "..    ...       ...       ...       ...       ...\n",
+      "194  1.94 -0.000913  0.424637  0.039358  0.004982\n",
+      "195  1.95 -0.000803  0.426829  0.039352  0.004958\n",
+      "196  1.96 -0.000885  0.429018  0.039346  0.004928\n",
+      "197  1.97 -0.000768  0.431205  0.039341  0.004659\n",
+      "198  1.98 -0.000709  0.433389  0.039336  0.004458\n",
+      "\n",
+      "[199 rows x 5 columns]>\n"
+     ]
+    }
+   ],
+   "source": [
+    "#En la previsualizacion de los datos, revisemos la presencia de algún valor NaN\n",
+    "\n",
+    "print(df.count)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Otra forma de saber la cuenta de valores nulos, es contarlos por columna, ya que con el `data.count()` lo que estoy obteniendo en realidad es la cuenta de datos no-nulos y esto lo conseguimos iterando sobre la lista de columnas preguntando a cada uno por el método `isnull()` y obteniendo la suma con `sum()`. En la siguiente celda pordemos ver la salida de este procedimiento:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "valores nulos en <t>: <bound method Series.sum of 0      False\n",
+      "1      False\n",
+      "2      False\n",
+      "3      False\n",
+      "4      False\n",
+      "       ...  \n",
+      "194    False\n",
+      "195    False\n",
+      "196    False\n",
+      "197    False\n",
+      "198    False\n",
+      "Name: t, Length: 199, dtype: bool>\n",
+      "valores nulos en <vacf>: <bound method Series.sum of 0      False\n",
+      "1      False\n",
+      "2      False\n",
+      "3      False\n",
+      "4      False\n",
+      "       ...  \n",
+      "194    False\n",
+      "195    False\n",
+      "196    False\n",
+      "197    False\n",
+      "198    False\n",
+      "Name: vacf, Length: 199, dtype: bool>\n",
+      "valores nulos en <vacf_2>: <bound method Series.sum of 0      False\n",
+      "1      False\n",
+      "2      False\n",
+      "3      False\n",
+      "4      False\n",
+      "       ...  \n",
+      "194    False\n",
+      "195    False\n",
+      "196    False\n",
+      "197    False\n",
+      "198    False\n",
+      "Name: vacf_2, Length: 199, dtype: bool>\n",
+      "valores nulos en <vacf_3>: <bound method Series.sum of 0      False\n",
+      "1      False\n",
+      "2      False\n",
+      "3      False\n",
+      "4      False\n",
+      "       ...  \n",
+      "194    False\n",
+      "195    False\n",
+      "196    False\n",
+      "197    False\n",
+      "198    False\n",
+      "Name: vacf_3, Length: 199, dtype: bool>\n",
+      "valores nulos en <vacf_4>: <bound method Series.sum of 0      False\n",
+      "1      False\n",
+      "2      False\n",
+      "3      False\n",
+      "4      False\n",
+      "       ...  \n",
+      "194    False\n",
+      "195    False\n",
+      "196    False\n",
+      "197    False\n",
+      "198    False\n",
+      "Name: vacf_4, Length: 199, dtype: bool>\n"
+     ]
+    }
+   ],
+   "source": [
+    "col_names = df.columns.tolist()\n",
+    "for column in col_names:\n",
+    "    print(\"valores nulos en <{0}>: {1}\".format(column,df[column].isnull().sum))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Exploremos los datos visualizandolos por columnas, lo cual podemos hacerlo con `.columns`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['t', 'vacf', 'vacf_2', 'vacf_3', 'vacf_4'], dtype='object')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#visualicemos solo las columnas\n",
+    "\n",
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Este método nos sirve para visualizar alguna columna en especial, por ejemplo, si quiero explorar la segunda columna de nuestra data, obtendremos la numeración del registro por fila en la primera columna y los valores correspondientes para la *VACF*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0.893155\n",
+       "1    0.867854\n",
+       "2    0.821965\n",
+       "3    0.758831\n",
+       "4    0.682706\n",
+       "Name: vacf, dtype: float64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Me interesa visualizar una columna en especial, la vacf(t)\n",
+    "\n",
+    "columna = df[\"vacf\"]\n",
+    "columna.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2- Ahora intentemos explorar detalles de nuestros datos: \n",
+    "\n",
+    "Aquí se muestra el poder de python para el análisis de datos!... Observe la facilidad de obtener información de los principales indicadores estadísticos sobre nuestro dataset en una sola línea con el método `.describe`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method NDFrame.describe of         t      vacf    vacf_2    vacf_3    vacf_4\n",
+       "0    0.00  0.893155  0.000000  0.005954  0.894384\n",
+       "1    0.01  0.867854  0.000177  0.011740  0.874035\n",
+       "2    0.02  0.821965  0.000701  0.017220  0.832205\n",
+       "3    0.03  0.758831  0.001553  0.022279  0.773162\n",
+       "4    0.04  0.682706  0.002707  0.026830  0.700901\n",
+       "..    ...       ...       ...       ...       ...\n",
+       "194  1.94 -0.000913  0.424637  0.039358  0.004982\n",
+       "195  1.95 -0.000803  0.426829  0.039352  0.004958\n",
+       "196  1.96 -0.000885  0.429018  0.039346  0.004928\n",
+       "197  1.97 -0.000768  0.431205  0.039341  0.004659\n",
+       "198  1.98 -0.000709  0.433389  0.039336  0.004458\n",
+       "\n",
+       "[199 rows x 5 columns]>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspeccionemos a mayor profundidad nuestra data: podemos obtener información de los principales indicadores\n",
+    "# estadísticos sobre la data set\n",
+    "\n",
+    "df.describe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Para explorar las características principales de nuestros datos de manera detallada, usamos el método `.info()` o viendo los tipos de valores de los que disponemos usando `dtypes` combinada con un operador lógico. Estos dos procedimientos nos describen los tipos de objetos que tenemos en nuestra dataset y tener una visión más clara del procesamiento que podemos realizar a la misma."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 199 entries, 0 to 198\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column  Non-Null Count  Dtype  \n",
+      "---  ------  --------------  -----  \n",
+      " 0   t       199 non-null    float64\n",
+      " 1   vacf    199 non-null    float64\n",
+      " 2   vacf_2  199 non-null    float64\n",
+      " 3   vacf_3  199 non-null    float64\n",
+      " 4   vacf_4  199 non-null    float64\n",
+      "dtypes: float64(5)\n",
+      "memory usage: 7.9 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "t         True\n",
+       "vacf      True\n",
+       "vacf_2    True\n",
+       "vacf_3    True\n",
+       "vacf_4    True\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# columnas numericas y columnas de texto\n",
+    "df.dtypes == float"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "t         False\n",
+       "vacf      False\n",
+       "vacf_2    False\n",
+       "vacf_3    False\n",
+       "vacf_4    False\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes == object"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "De hecho, podemos explorar cuantos valores nulos tenemos por cada una de las variables (columnas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "t         0\n",
+       "vacf      0\n",
+       "vacf_2    0\n",
+       "vacf_3    0\n",
+       "vacf_4    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# veamos cuantos valores nulos hay por cada variable\n",
+    "\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3- Busquemos relaciones entre nuestras variables:\n",
+    "\n",
+    "En esta sección del proyecto, mostramos cómo configurar y ejecutar *gráficos de pares* en Python utilizando la biblioteca de visualización `seaborn`. Siendo más específico, se muestra cómo crear un gráfico de pares predeterminado para examinar nuestros datos y cómo personalizar la visualización para obtener información más profunda. Gracias a este curso he conocido esta manera de trabajar con los datos:\n",
+    "\n",
+    "Estoy sorprendido que una simple línea de código nos proporcione toda esta información!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- El diagrama de pares se basa en dos figuras básicas, el histograma y el diagrama de dispersión. El histograma en la diagonal nos permite ver la distribución de una sola variable, mientras que los diagramas de dispersión en los triángulos superior e inferior muestran la relación (o falta de ella) entre dos variables. Por ejemplo, el gráfico más a la izquierda en la segunda fila muestra el gráfico de dispersión de *VACF* versus tiempo."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x7f538e3454a8>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x900 with 30 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(df)\n",
+    "\n",
+    "#plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Los diagramas de pares son una herramienta poderosa para explorar rápidamente distribuciones y relaciones en un conjunto de datos. `SEABORN` nos proporciona un método predeterminado simple para hacer graficas de pares de variables que se pueden personalizar. En un proyecto de análisis de datos, una parte importante del valor proviena de la visualización de los datos. Un diagrama de pares nos proporciona este primer vistazo completo de nuestros datos y es un excelente punto de partida en el análisis de datos."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4- Ahora inspeccionemos los datos de la función de autocorrelación de velocidades *(vacf(t))*\n",
+    "\n",
+    "El método `.unique()` nos muestra los valores almacenad en la columna de nuestro dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 8.93154621e-01,  8.67853999e-01,  8.21965277e-01,  7.58830547e-01,\n",
+       "        6.82705879e-01,  5.99214256e-01,  5.12496531e-01,  4.26708788e-01,\n",
+       "        3.45072299e-01,  2.69724578e-01,  2.02340394e-01,  1.44143075e-01,\n",
+       "        9.50296447e-02,  5.50241806e-02,  2.31665950e-02, -9.18336620e-04,\n",
+       "       -1.86049007e-02, -3.06770168e-02, -3.79997827e-02, -4.15365249e-02,\n",
+       "       -4.23383266e-02, -4.11057547e-02, -3.85393314e-02, -3.52143683e-02,\n",
+       "       -3.14373672e-02, -2.74279676e-02, -2.36809831e-02, -2.02437267e-02,\n",
+       "       -1.71742495e-02, -1.48698576e-02, -1.29330419e-02, -1.18340570e-02,\n",
+       "       -1.10314433e-02, -1.04457177e-02, -1.01393731e-02, -1.03211133e-02,\n",
+       "       -1.06234215e-02, -1.07802572e-02, -1.10281892e-02, -1.10716727e-02,\n",
+       "       -1.11191701e-02, -1.12334173e-02, -1.11250076e-02, -1.07489433e-02,\n",
+       "       -1.04600526e-02, -1.03032459e-02, -9.96753480e-03, -9.46925581e-03,\n",
+       "       -9.13583953e-03, -8.54541920e-03, -7.87504204e-03, -7.39814481e-03,\n",
+       "       -6.95338519e-03, -6.47809729e-03, -5.87645266e-03, -5.16474200e-03,\n",
+       "       -4.80043422e-03, -4.64318646e-03, -4.29089228e-03, -4.09926428e-03,\n",
+       "       -4.04093787e-03, -3.86099005e-03, -3.77666252e-03, -3.56659852e-03,\n",
+       "       -3.36388382e-03, -2.98618292e-03, -2.89732125e-03, -2.91161449e-03,\n",
+       "       -2.71109212e-03, -2.64524529e-03, -2.53665051e-03, -2.60184612e-03,\n",
+       "       -2.72347359e-03, -2.72307545e-03, -2.75016972e-03, -2.73831910e-03,\n",
+       "       -2.67977756e-03, -2.54126010e-03, -2.70556612e-03, -2.93237646e-03,\n",
+       "       -2.85940222e-03, -2.75847316e-03, -2.55324272e-03, -2.43231817e-03,\n",
+       "       -2.22090632e-03, -1.81037607e-03, -1.66162930e-03, -1.33774348e-03,\n",
+       "       -1.03217398e-03, -5.66349074e-04, -2.74554506e-04, -5.68616888e-05,\n",
+       "       -4.33644163e-06,  5.21896218e-05, -1.01174715e-04, -2.90779426e-04,\n",
+       "       -3.62070772e-04, -4.50906868e-04, -6.24679378e-04, -6.60558580e-04,\n",
+       "       -7.15219358e-04, -6.02265471e-04, -5.96747268e-04, -4.89137834e-04,\n",
+       "       -3.73787334e-04, -3.35799938e-04, -1.71133324e-05,  3.81512291e-05,\n",
+       "        1.75224952e-04,  2.74340564e-04,  2.92499433e-04,  5.11027640e-04,\n",
+       "        5.76491526e-04,  4.80154820e-04,  4.12027701e-04,  3.34814598e-04,\n",
+       "        3.94699513e-04,  8.88253999e-05, -1.27831736e-04, -2.92120531e-04,\n",
+       "       -2.59702938e-04, -1.47469589e-04, -2.97198887e-04, -4.53291228e-04,\n",
+       "       -5.23567956e-04, -5.18088520e-04, -7.21369695e-04, -6.01590495e-04,\n",
+       "       -6.43687206e-04, -3.95731739e-04, -3.52889416e-04, -4.81653406e-04,\n",
+       "       -6.77651318e-04, -6.92399044e-04, -7.43436685e-04, -9.58102290e-04,\n",
+       "       -1.17542152e-03, -1.01089594e-03, -8.60720582e-04, -7.21602875e-04,\n",
+       "       -6.49456983e-04, -5.52507583e-04, -3.34334822e-04, -7.83924770e-05,\n",
+       "        1.06010542e-04,  5.89528609e-05,  1.48837338e-04, -4.80122690e-05,\n",
+       "       -8.75702754e-05, -1.15438765e-04, -2.85895134e-04, -4.50302789e-04,\n",
+       "       -3.51646973e-04, -2.38260647e-04, -2.42416674e-04, -1.83340962e-04,\n",
+       "       -1.50821346e-04, -1.66885860e-04, -3.07395501e-04, -4.77931811e-04,\n",
+       "       -5.23011549e-04, -3.56306729e-04, -1.00346508e-04,  9.04311746e-05,\n",
+       "        5.27369921e-05,  1.09636658e-05, -3.95643983e-05,  4.34031572e-05,\n",
+       "        6.19495986e-05,  2.03832184e-04,  2.06827404e-04,  2.27460230e-04,\n",
+       "        2.41240807e-04,  3.17600294e-04,  3.92820017e-04,  3.48903006e-04,\n",
+       "        2.48353666e-04,  2.90645345e-04,  3.54456497e-05,  1.41456272e-04,\n",
+       "        1.91575100e-06, -1.05931562e-04, -1.12848858e-04, -1.59171730e-04,\n",
+       "       -3.88738437e-04, -3.07661714e-04, -2.84524198e-04, -3.20903375e-04,\n",
+       "       -3.85275809e-04, -3.96396877e-04, -4.21230390e-04, -5.73414552e-04,\n",
+       "       -6.10906398e-04, -7.30710512e-04, -9.13490599e-04, -8.02771014e-04,\n",
+       "       -8.85016285e-04, -7.67805497e-04, -7.08730426e-04])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#pd.unique nos dice los distintos valores presentes en la columna vacf(t)\n",
+    "pd.unique(df[\"vacf\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Trabajemos con esta columna, dado que queremos calcular estadísticas de datos agrupados por subconjuntos o atributos.\n",
+    "Por ejemplo, mostremos alguna estadística básica de todos los datos en la columna usando el comando `.describe()`. Note la salida que nos devuelve:\n",
+    "\n",
+    "- conteo de datos\n",
+    "- la media\n",
+    "- desviación standard\n",
+    "- valor mínimo\n",
+    "- valor máximo\n",
+    "- nombre y tipo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    199.000000\n",
+       "mean       0.029651\n",
+       "std        0.145300\n",
+       "min       -0.042338\n",
+       "25%       -0.002959\n",
+       "50%       -0.000566\n",
+       "75%       -0.000044\n",
+       "max        0.893155\n",
+       "Name: vacf, dtype: float64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# En algunas ocasiones, esta es una de ellas, queremos calcular estadísticas de datos\n",
+    "# agrupados por subconjuntos o atributos de nuestros datos.\n",
+    "\n",
+    "df[\"vacf\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "También podemos extraer un de las métricas que nos interese:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.042338326600000004\n",
+      "0.893154621\n",
+      "0.02965061882257874\n",
+      "0.14529958318959033\n",
+      "199\n"
+     ]
+    }
+   ],
+   "source": [
+    "# otra manera de hacer la misma estadística:\n",
+    "\n",
+    "print(df[\"vacf\"].min())\n",
+    "print(df[\"vacf\"].max())\n",
+    "print(df[\"vacf\"].mean())\n",
+    "print(df[\"vacf\"].std())\n",
+    "print(df[\"vacf\"].count())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      30.122630\n",
+       "1      29.269338\n",
+       "2      27.721690\n",
+       "3      25.592402\n",
+       "4      23.025013\n",
+       "         ...    \n",
+       "194    -0.030808\n",
+       "195    -0.027074\n",
+       "196    -0.029848\n",
+       "197    -0.025895\n",
+       "198    -0.023903\n",
+       "Name: vacf, Length: 199, dtype: float64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Si queremos, podemos hacer operaciones sobre una columna de nuestra data. Como por ejemplo\n",
+    "# multiplicar todos los valores por 2. Un uso más útil podría ser normalizar los datos con\n",
+    "# la media, área o algún otro valor calculado de nuestra data\n",
+    "\n",
+    "df[\"vacf\"]/df[\"vacf\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Grafiquemos los datos usando pandas\n",
+    "\n",
+    "Uno de los gráficos que podemos construír es el gráfico de dispersión. Mediante el cual podemos ver la relación entre dos variables, como en este caso: tiempo *(t)* vs la función de autocorrelación de velocidades *(vacf)*. Este tipo de gráfico puede obtenerse mediante el método ´lmplot´ al que se le indicará la característica para cada uno de los ejes y el conjunto de datos. Como se muestra a continuación."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from seaborn import load_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/student/.local/lib/python3.6/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
+      "  FutureWarning\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f5384823cc0>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lmplot(\"t\",\"vacf\",data=df, fit_reg=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from seaborn import kdeplot\n",
+    "from seaborn import distplot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/student/.local/lib/python3.6/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='t', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(df[\"t\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/student/.local/lib/python3.6/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='vacf', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(df[\"vacf\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "La gráfica de los diagramas a pares son una gran herramienta para visualizar la dependencia y distribución de la data, pero para una mejor interpretación del significado de nuestros histogramas podemos usar `jointplot` y visualizar las densidades de las columnas del dataframe que nos insterese. A continuación presentamos la gráfica generada con este método y los gráficos de densidad de las variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "prueba = sns.jointplot(x=\"t\",y=\"vacf\",data=df)\n",
+    "\n",
+    "prueba.fig.set_size_inches(8,8)\n",
+    "\n",
+    "plt.grid()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  5- Grafica de la Función de Autocorrelación de Velocidades a partir de nuestros datos\n",
+    "\n",
+    "\n",
+    "Usamos dos maneras diferentes de construír la gráfica explotando recursos de python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "import pylab as pl\n",
+    "import csv\n",
+    "\n",
+    "entrada = open(\"/home/student/ejercicios-clase-08-datos/data-used/VACF_liso_Hr-10-500.csv\")\n",
+    "\n",
+    "tabla = []\n",
+    "\n",
+    "for fila in csv.reader(entrada):\n",
+    "    tabla.append(fila)\n",
+    "entrada.close()\n",
+    "\n",
+    "x = [0]\n",
+    "y = [0.893155]\n",
+    "\n",
+    "for fila in range(1, len(tabla)):\n",
+    "    x.append(float(tabla[fila][0]))\n",
+    "    y.append(float(tabla[fila][1]))\n",
+    "    \n",
+    "pl.figure(figsize =(10,10))\n",
+    "\n",
+    "pl.plot(x,y)\n",
+    "pl.xlabel(\"tiempo\")\n",
+    "pl.ylabel(\"VACF\")\n",
+    "pl.grid()\n",
+    "pl.legend([\"vacf(t)\"])\n",
+    "pl.title(\"Función de Autocorrelación de Velocidades\")\n",
+    "pl.savefig(\"imagen.png\")\n",
+    "pl.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Función de autocorrelación de velocidades')"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#FDR_cilindro_liso_Hr-10.csv\n",
+    "\n",
+    "file = '/home/student/ejercicios-clase-08-datos/data-used/VACF_liso_Hr-10-500.csv'\n",
+    "\n",
+    "dataframe0 = pd.read_csv(file)\n",
+    "\n",
+    "x = dataframe0[\"t\"]\n",
+    "y = dataframe0[\"vacf\"]\n",
+    "\n",
+    "plt.figure(figsize =(8,8))\n",
+    "\n",
+    "#plt.scatter(x,y, marker = \"+\")\n",
+    "pl.plot(x,y, \"r.-\")\n",
+    "plt.savefig(\"vacf.png\")\n",
+    "\n",
+    "pl.xlabel(\"tiempo\")\n",
+    "pl.ylabel(\"VACF\")\n",
+    "pl.grid()\n",
+    "pl.legend([\"VACF(t)\"])\n",
+    "pl.title(\"Función de autocorrelación de velocidades\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  6- Gráfica de la Función de distribución radial\n",
+    "\n",
+    "- Partiremos inicialmente mostrando el gráfico de la Función de Distribución Radial *(FDR)* correspondiente a un cilindro con una superficie lisa. Esta función nos muestra la estructura y organización de las partículas del fluido confinadas dentro del cilindro con un radio de 10 diámetros moleculares. Los máximos de la función nos están indicando la distancia en la cual las partículas tienden a acumularse, dicho de una manera más formal: los máximos de probabilidad en la que conseguiremos distribuidas las partículas.\n",
+    "\n",
+    "- Veremos el tratamiento y la visualización estadística de la distribución de los puntos de data en histogramas individuales para cada variable y luego la gráfica que nos resume ambos histogramas. \n",
+    "\n",
+    "- En la siguiente celda construimos a partir de la data la gráfica de la evolución de la estructura del fluido a medida que aumentamos la cantidad de obstáculos en las paredes del cilindro."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Función de distribución radial')"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#FDR_cilindro_liso_Hr-10.csv\n",
+    "\n",
+    "file = '/home/student/ejercicios-clase-08-datos/data-used/FDR_cilindro_liso_Hr-10.csv'\n",
+    "\n",
+    "dataframe0 = pd.read_csv(file)\n",
+    "\n",
+    "x = dataframe0[\"r\"]\n",
+    "y = dataframe0[\"g(r)\"]\n",
+    "\n",
+    "plt.figure(figsize =(8,8))\n",
+    "\n",
+    "#plt.scatter(x,y, marker = \"+\")\n",
+    "pl.plot(x,y, \"r.-\")\n",
+    "plt.savefig(\"fdr.png\")\n",
+    "\n",
+    "pl.xlabel(\"Distancia\")\n",
+    "pl.ylabel(\"FDR(r)\")\n",
+    "pl.grid()\n",
+    "pl.legend([\"FDR(r)\"])\n",
+    "pl.title(\"Función de distribución radial\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Al igual que con la data correspondiente a la Función de Autocorrelación de Velocidades, también podemos visualizar el perfil estadístico básico de la data que genera nuestra Función de Distribución Radial. A continuación se muestra los perfiles de densidad en cada uno de los ejes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/student/.local/lib/python3.6/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='r', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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cq36IS6ACkQKOdw2RZlBVkht0FJF5VVueR0v3EKGwJsxdDBWIFNDcNcTS4lyyMvTfLamltiyP8ZBzum8k6ChJSWeMBS7sTkvPMDVlunqQ1DO5rL3uMHdxVCAWuPb+UcYmwlTr/g+SgkrzMsnLSqelS0t/XwwViAWuJfrJSTcIklRkFrk5lq4gLo4KxALX3DVMTmYa5QVZQUcRCUR1WS7t/aOMjGvC3GzFtUCY2a1mdtDMGszsvhivZ5vZN6Kvv2BmdVNeW29mz5nZfjPbZ2Y58cy6ULV0D1FdqglykrpqSvNw4ITuMDdrMyoQZvYtM3u7mc24oJhZOvA54DZgHXC3ma2bttsHgW53XwV8BvhU9NgM4OvAb7r75cCbgPGZfm+JGJsIc6pvhOpSdVBL6pp8/7dowtyszfSE/3ngvcBhM/ukma2dwTFbgAZ3b3T3MeAhYOu0fbYCD0YfPwLcbGYGvAXY6+57ANy90911fThLJ3uHCbv6HyS15WVlUJ6fRXO3riBma0YFwt2fcPf3AZuAY8ATZvZTM/uAmWWe47AqoHnK85botpj7uPsE0AuUA2sAN7PHzGyXmf1prG9gZvea2Q4z29He3j6THyWlNEc/MekKQlJdTVnemQEbMnOzaTIqB34V+BDwEvBZIgXjh3HIlQHcCLwv+ue7zOzm6Tu5+wPuvtndN1dWVsYhRnJr7h6mJDeTwpxz1XCR1FBdmkvfyAS9w2qpno2Z9kF8G3gayAPe4e53uPs33P13gYJzHHYCqJnyvDq6LeY+0X6HYqCTyNXGU+7e4e5DwKNEipHMQkv3ENW6/4PImXlAzeqHmJWZXkH8s7uvc/e/dveTEBmBBODum89xzHZgtZnVm1kWcBewbdo+24B7oo/vBJ70yF3GHwOuNLO8aOF4I/DKjH8qYWB0gu6hcWrUvCTC0uIc0s1oUT/ErMy0QPxljG3Pne+AaJ/Ch4mc7A8AD7v7fjP7hJndEd3tS0C5mTUAHwHuix7bDXyaSJHZDexy9+/NMKvwswlymkEtApnpaSwpzlE/xCxlnO9FM1tCpCM518w2ApOD6YuINDedl7s/SqR5aOq2j055PAK8+xzHfp3IUFe5CM1dw1rBVWSKmrJcdjX1EHbXvKAZOm+BAN5KpGO6msgn+kn9wJ/FKZPMgZbuIRYX5WgFV5Go6tI8nm/sor1/lMVFmnc7E+ctEO7+IPCgmf2iu39znjLJJXJ3WrqHuaKqKOgoIgmjZkpHtQrEzFyoien90aaeOjP7yPTX3f3TMQ6TgHUOjjE8HtIEOZEpyguyyMlMo6V7mM11QadJDhdqYpq8R+W5hrJKAvrZBDkVCJFJaWZUl2hl19m4UBPTF6J/fnx+4shcaOkeJis9jUVF2UFHEUko1WW5PHWonbGJcNBRksJMJ8r9jZkVmVmmmf3IzNrN7P3xDicXp7l7iKrSXI3UEJmmpjSPsEOrVnadkZkOcXmLu/cBtxNZi2kV8CfxCiUXbyIU5mTviCbIicRwZmVXNTPNyEwLxGRT1NuB/3D33jjlkUt0qm+EUNipUv+DyGsU5mRSkpuplV1n6EKd1JO+a2avAsPAb5lZJTASv1hysSbf+LqCEImtujRXVxAzNNPlvu8DbgA2u/s4MMhr7+0gCeBE9xAF2RkU52oFV5FYqkvz6B4ap2twLOgoCW+mVxAAlxGZDzH1mK/OcR65RM3dw1SX5mLqoBaJabIfYk9LDz+3dlHAaRLbTEcxfQ34OyL3Zrgm+nWuVVwlIP0j43T0j2r+g8h5VJXkYsCe5p6goyS8mV5BbAbWRZfilgS170Qvju4gJ3I+2ZnpVBZms7dFY20uZKajmF4GlsQziFy6Pc2RN7wKhMj5VZfmsae5B33mPb+ZXkFUAK+Y2YvA6ORGd7/j3IfIfNvb0kNZfhZ5WbPpWhJJPdWluexq6uZEz7CaZM9jpmeSj8UzhMyNPc09unoQmYEzHdXNvSoQ5zHTYa4/ITKDOjP6eDuwK465ZJba+kdo7R3Rm11kBpYU55CVnsbelp6goyS0mY5i+nXgEeAL0U1VwHfilEkuwt5o/4MmyIlcWEZaGq9bVsRujWQ6r5l2Uv8O8HqgD8DdDwMaQJxA9rb0kJ5mLC1WgRCZiQ3Vxbx8opdQWB3V5zLTAjHq7memHUYny+lfNYHsbullzeJC3WJUZIY2VJcwOBbiSPtA0FES1kzPJj8xsz8Dcs3sFuA/gP+KXyyZDXdnb0sPG6qLg44ikjQ21ER+X9TMdG4zLRD3Ae3APuA3gEeB/xWvUDI7TV1D9AyNs6GmJOgoIkljRUUBBdkZ6qg+jxkNc3X3sJl9B/iOu7fHN5LM1uQnoPXVxWcmy4nI+aWlGVdW6XfmfM57BWERHzOzDuAgcDB6N7mPzk88mYm9Lb3kZKaxZnFh0FFEksqGmhJePdXHyHgo6CgJ6UJNTH9IZPTSNe5e5u5lwLXA683sD+OeTmZkT3MPVywrJjNdHdQis3FVTTHjIefAyb6goySkC51Rfhm4292PTm5w90bg/cCvxDOYzMxEKMzLrb2sry4JOopI0pn8vdHCfbFdqEBkunvH9I3RfgjdkSYBHDo9wMh4+MyIDBGZuaXFOVQWZmvp73O4UIE43y2XdDumBDA5AmODriBEZs3M2FBdzB6NZIrpQqOYNphZrMY5A3LikEdmaU9LD8W5mSwv1xpMIhdjQ3UJTxxoo29knKIcNYxMdd4rCHdPd/eiGF+F7q5/yQSwp7mX9dXFusWoyEVaH50/9LL6IV5Dw16S2PBYiIOn+7lKE+RELtrkCgS71cz0GioQSWx/a2ShMY1gErl4JXlZLC/PU0d1DHEtEGZ2q5kdNLMGM7svxuvZZvaN6OsvmFndtNdrzWzAzP44njmT1Z7oJbHWYBK5NBuqSzTUNYa4FQgzSwc+B9wGrAPuNrN103b7INDt7quAzwCfmvb6p4HvxytjstvT3MPS4hwWFWm8gMilWF9dzMneEdr6RoKOklDieQWxBWhw98boUuEPAVun7bMVeDD6+BHgZov2tprZO4GjwP44ZkxqkRVcS4KOIZL0Jvvx9ugq4izxLBBVQPOU5y3RbTH3cfcJoBcoN7MC4H8AHz/fNzCze81sh5ntaG9PrTUEe4bGONY5xHpNkBO5ZJcvKyY9zdQPMU2idlJ/DPiMu5/3Th7u/oC7b3b3zZWVlfOTLEHsPdP/UBJsEJEFIDcrnTWLCzVhbpoZLfd9kU4ANVOeV0e3xdqnJXqXumKgk8iCgHea2d8AJUDYzEbc/R/jmDep7GrqxizSdioil+6qmhK+u7eVcNhJS9O8IojvFcR2YLWZ1ZtZFnAXsG3aPtuAe6KP7wSe9Iib3L3O3euAfwD+SsXhbLuaeli7uJBCzfwUmRObakvoH5mgQbcgPSNuBSLap/Bh4DHgAPCwu+83s0+Y2R3R3b5EpM+hAfgIkTvXyQWEw85LTd1srC0NOorIgrFpeeT36aWm7oCTJI54NjHh7o8SuT3p1G0fnfJ4BHj3Bf6Oj8UlXBI70j5A/8gEm2pLgo4ismCsqMinODeTXcd7eM81tUHHSQiJ2kkt5/FSUw+AriBE5pCZsbG2hF26gjhDBSIJ7Wrqpjg3kxUV+UFHEVlQNtWWcrhtgN7h8aCjJAQViCS0q6mbjbUlGmkhMsc2Ra/Kd2s+BKACkXT6RsY53DZw5o0sInNnQ00xZuqonqQCkWR2N/XgjgqESBwU5mSydnEhu6L9fKlOBSLJTE6Q0z2oReJjY20pLzV1Ew570FECpwKRZHY19bBmkSbIicTLxuiEuSOaMKcCkUzCYWd3UzeblpcEHUVkwZpsvtVwVxWIpNLYMUDfyITmP4jE0eSEuZfUD6ECkUx2Ho98olEHtUj8pKVFJsxN/r6lMhWIJPLi0W7K8rNYWakJciLxdE1dGYfbBugaHAs6SqBUIJLIi8c6uaaulOhN90QkTrbUlwGw/VhXwEmCpQKRJE72DtPcNcyW+vKgo4gseOuri8nKSGP7URUISQIvRt+o10Y/2YhI/GRnpLOxpoQXdQUhyeDFo10UZGfwuqVFQUcRSQnX1pfx8oleBkYngo4SGBWIJLH9WBdXLy8lXQv0icyLa+rLCDvsSuHRTCoQSaBrcIxDpwfOdJyJSPxtqo18IHsxhfshVCCSwORICvU/iMyf/OwMrqgqVoGQxPbi0S6yMtK4sloL9InMpy11pexu7mFkPBR0lECoQCSB7ce62FhTQnZGetBRRFLKlvpyxkJh9rb0Bh0lECoQCW5gdIKXT/SqeUkkANfURZa1efFoZ8BJgqECkeB2Hu8m7GiCnEgASvKyuGxJIS+kaD+ECkSCe+5IJxnRxcNEZP5tqS9j5/FuxibCQUeZdyoQCe6ZhnY21ZaSn50RdBSRlPT6VRUMjYVS8v4QKhAJrHNglP2tfdy0uiLoKCIp6/qV5aSnGc8c7gg6yrxTgUhgzx7pxB1uVIEQCUxRTiZX1ZTw9OH2oKPMOxWIBPbM4XaKcjJYX10SdBSRlHbjqgr2nuilZyi17g+hApGg3J1nDndww8oKrb8kErCbVlfgDj89klrDXVUgEtSR9kFae0e4aY2al0SCtqGmhMLsjJRrZlKBSFDPRN+IN62qDDiJiGSmp3HdynKePtyBuwcdZ96oQCSoZxo6qC3Lo7Y8L+goIkKkmamle5jjnUNBR5k3KhAJaDwU5rkjnRq9JJJAblwV+X18uiF1hrvGtUCY2a1mdtDMGszsvhivZ5vZN6Kvv2BmddHtt5jZTjPbF/3z5+OZM9G81NTD4FiIm1apQIgkivqKfKpKcnn6UOr0Q8StQJhZOvA54DZgHXC3ma2bttsHgW53XwV8BvhUdHsH8A53vxK4B/havHImoqcPt5NmcMNKFQiRRGFm3LS6gueOdDIeSo1lN+J5BbEFaHD3RncfAx4Ctk7bZyvwYPTxI8DNZmbu/pK7t0a37wdyzSw7jlkTyhMH2rh6eSnFeZlBRxGRKd60dhH9oxNsT5HF++JZIKqA5inPW6LbYu7j7hNALzB92dJfBHa5++j0b2Bm95rZDjPb0d6+MC77TvQMc+BkHze/bnHQUURkmptWV5CVnsYTB9qCjjIvErqT2swuJ9Ls9BuxXnf3B9x9s7tvrqxcGMNBnzxwGoA3q0CIJJz87AxuWFXOj149nRLDXeNZIE4ANVOeV0e3xdzHzDKAYqAz+rwa+DbwK+5+JI45E8oTB9qoK89jZWV+0FFEJIabX7eY451DHGkfCDpK3MWzQGwHVptZvZllAXcB26bts41IJzTAncCT7u5mVgJ8D7jP3Z+NY8aEMjA6wXNHOrn5dYsx0/IaIono5ssWAfD4K6cDThJ/cSsQ0T6FDwOPAQeAh919v5l9wszuiO72JaDczBqAjwCTQ2E/DKwCPmpmu6Nfi+KVNVE8+WobY6Ewb718SdBRROQclpXksqG6mMdePhV0lLiL611o3P1R4NFp2z465fEI8O4Yx/0l8JfxzJaIvr/vJJWF2Vy9vDToKCJyHrdduZRPfv9VWrqHqC5duKsdJHQndSoZGpvgvw+2cevlS7R6q0iCu+2KyFX+Dxb4VYQKRIL48cF2RsbDZ954IpK4lpfns25pEY/uOxl0lLhSgUgQ393bSnl+Flvqy4KOIiIz8Pb1S9nV1ENL98JdvE8FIgH0jYzzxIE2bl+/lIx0/ZeIJIN3rF8GwLY9rRfYM3npbJQAfrDvFGMTYd65cfpEcxFJVLXleVy9vJTvvHRiwU6aU4FIAN/ZfYK68jyuqikJOoqIzMI7N1Zx6PQAB072Bx0lLlQgAtbaM8xzjZ1svapKk+NEksztVy4lM9341q6WoKPEhQpEwB7eEVnP8M6rqwNOIiKzVZqfxZtft5hv7mphdCIUdJw5pwIRoFDYeXh7MzeuqqCmbOFOthFZyO7aUkv30DiP7194S2+oQAToqUPttPaOcPeW2qCjiMhFumlVBVUluTy0vSnoKHNOBSJA//rCcSoKsrS0t0gSS0sz7rqmhmcbOhfcCq8qEAE52jHIj15t473XLicrQ/8NIsnsri21ZKWn8ZVnjwUdZU7pzBSQrzx7lMy0NN5/nZqXRJJdZWE2W69axiM7W+gZGgs6zpxRgQhA79A4/7GzhXdsWMaiwpyg44jIHPjA6+sZHg/xby8unL4IFYgAfPnZowyNhfj1N9QHHUVE5si6ZUXctLqCLz9zlOGxhTHkVQVinvWNjPPlZ4/y1ssXc9mSoqDjiMgc+r2bV9MxMLZgriJUIObZV549Rv/IBL/786uDjiIic+yaujKuX1HOP/3kCCPjyX8VoQIxjzoGRnngqUZuWbeYK6qKg44jInHwh7esob1/lC89czToKJdMBWIefeaHhxgZD3HfbZcFHUVE4mRLfRm3rFvM5/+7gfb+0aDjXBIViHny6qk+HtrezPuurWVlZUHQcUQkju6/7TJGJ8L8/eMHg45ySVQg5kEo7Nz3zX0U52byB29eE3QcEYmzFZUF/NqN9Ty0vZkXGjuDjnPRVCDmwdeeO8bu5h4+evs6SvOzgo4jIvPgD968murSXO7/9r6k7bBWgYizQ6f7+eQPXuWNayrZetWyoOOIyDzJy8rgr951JY3tg3zy+68GHeeiqEDE0ch4iN/9t5coyM7gb9+9XjcEEkkxb1hTyQdvrOcrPz3G4/tPBR1n1lQg4iQcdv7oP/ZwqK2fv/+lq7SkhkiK+tNb13JlVTF/9PAeDp1OrluTqkDEyd89fpDv7T3JfbdexhvXVAYdR0QCkp2Rzhd++Wpys9L5wL9sp61vJOhIM6YCEQf/90eH+fyPj3D3llrufcOKoOOISMCWleTyxXs20z00xt3//Dxt/clRJFQg5lA47Pz1owf49A8P8QubqvjLd16hfgcRAWB9dQlf+cAWTvaO8J4vPM+xjsGgI12QCsQc6R0a5ze/vpMvPNXI+6+r5W/v3EB6moqDiPzMlvoyvvbBLfQMjfHOzz/LTw61Bx3pvFQg5sBPDrVz22ef4slX2/jft6/jL7ZeoeIgIjFdvbyMb//261lUmM09X36Rj23bz8DoRNCxYsoIOkAye/VUH59+/BCPv3KaFRX5fPO3bmBDTUnQsUQkwdVV5LPtwzfyV48e4MHnjvH9l0/yB29ewy9uqk6oWxCrQMzS0NgEP3j5FI/sbOGnRzopzM7gT966lg/dVE92RnrQ8UQkSeRkpvOJrVfwro1VfPy/XuH+b+3jH544xLs2VnPn1VWsWlQYdMT4FggzuxX4LJAOfNHdPznt9Wzgq8DVQCfwHnc/Fn3tfuCDQAj4PXd/LJ5Zz2U8FObgqX6eb+zkhaNd/LShg8GxELVleXzkljXcc30dxXmZQUQTkQVgY20p3/7tG/jxoXa+/txx/vnpRv7pJ0dYt7SIG1aWs6W+jM11ZZQFsExP3AqEmaUDnwNuAVqA7Wa2zd1fmbLbB4Fud19lZncBnwLeY2brgLuAy4FlwBNmtsbd53xBk4HRCV5p7aNzYJSOwTG6BsboHByltWeYxvZBjncNEQo7AHXledxxVRXv2ljFNXWlGqEkInPCzPi5tYv4ubWLaOsfYdvuVn74ymm++vxxvhi9r0RZfhYrK/NZXp5PRUE2FQVZlOVnUV6QTU1pLivisEp0PK8gtgAN7t4IYGYPAVuBqQViK/Cx6ONHgH+0yFl3K/CQu48CR82sIfr3PTfXIRvaBvilL5z91xbnZrKkKIe1Swp525VLWbOkkC11ZSwp1mxoEYmvRYU5fOimFXzophWMToTY29LL7qYeGjsGONI2yNOH2+kaHGM85GeOefv6pXzuvZvmPEs8C0QV0DzleQtw7bn2cfcJM+sFyqPbn592bNX0b2Bm9wL3Rp8OmNmcLb6+N/bmCqBjrr5HnCR6xkTPB8o4FxI9H+9LgozMMOPngc+/76K/x/JzvZDUndTu/gDwwHx9PzPb4e6b5+v7XYxEz5jo+UAZ50Ki5wNlnIl4jqc6AdRMeV4d3RZzHzPLAIqJdFbP5FgREYmjeBaI7cBqM6s3sywinc7bpu2zDbgn+vhO4El39+j2u8ws28zqgdXAi3HMKiIi08StiSnap/Bh4DEiw1y/7O77zewTwA533wZ8CfhatBO6i0gRIbrfw0Q6tCeA34nHCKaLMG/NWZcg0TMmej5QxrmQ6PlAGS/IIh/YRUREzpY4c7pFRCShqECIiEhMKhCzZGZ/a2avmtleM/u2mZUEnQkiy5qY2UEzazCz+4LOM52Z1ZjZf5vZK2a238x+P+hMsZhZupm9ZGbfDTpLLGZWYmaPRN+DB8zs+qAzTWdmfxj9P37ZzP7dzAKfYWpmXzazNjN7ecq2MjP7oZkdjv5ZmmD5Aj/XqEDM3g+BK9x9PXAIuD/gPFOXNbkNWAfcHV2uJJFMAH/k7uuA64DfScCMAL8PHAg6xHl8FviBu18GbCDBsppZFfB7wGZ3v4LIAJW7gk0FwFeAW6dtuw/4kbuvBn4UfR6Ur/DafIGfa1QgZsndH3f3ycXbnycyRyNoZ5Y1cfcxYHJZk4Th7ifdfVf0cT+RE9trZscHycyqgbcDXww6SyxmVgy8gcjoP9x9zN17Ag0VWwaQG53blAe0BpwHd3+KyEjJqbYCD0YfPwi8cz4zTRUrXyKca1QgLs2vAd8POgSxlzVJqJPvVGZWB2wEXgg4ynT/APwpEA44x7nUA+3Av0Sbwb5oZvlBh5rK3U8Afwc0ASeBXnd/PNhU57TY3U9GH58CFgcZ5gICOdeoQMRgZk9E20+nf22dss//JNJs8q/BJU0+ZlYAfBP4A3fvCzrPJDO7HWhz951BZzmPDGAT8P/cfSMwSLDNIq8RbcffSqSYLQPyzez9waa6sOgE3YQc8x/kuSap12KKF3d/8/leN7NfBW4HbvbEmEiSFEuTmFkmkeLwr+7+raDzTPN64A4zexuQAxSZ2dfdPZFObi1Ai7tPXnk9QoIVCODNwFF3bwcws28BNwBfDzRVbKfNbKm7nzSzpUBb0IGmC/pcoyuIWYreBOlPgTvcfSjoPFEzWdYkUNFl3L8EHHD3TwedZzp3v9/dq929jsi/35MJVhxw91NAs5mtjW66mbOXz08ETcB1ZpYX/T+/mQTrSJ9i6lI/9wD/GWCW10iEc41mUs9SdFmQbCKLCgI87+6/GWAkAKKffP+Bny1r8n+CTXQ2M7sReBrYx8/a+P/M3R8NLlVsZvYm4I/d/faAo7yGmV1FpBM9C2gEPuDu3YGGmsbMPg68h0izyEvAh6L3dgky078DbyKyfPZp4M+B7wAPA7XAceCX3H16R3aQ+e4n4HONCoSIiMSkJiYREYlJBUJERGJSgRARkZhUIEREJCYVCBERiUkFQkREYlKBEJlHFqHfO0kKeqOKxJmZ1UXv1fFV4GXOXhZFJGFpopxInEVXr20EbnD35wOOIzJjuoIQmR/HVRwk2ahAiMyPwaADiMyWCoSIiMSkAiEiIjGpk1pERGLSFYSIiMSkAiEiIjGpQIiISEwqECIiEpMKhIiIxKQCISIiMalAiIhITP8fnwQItf5jee8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(dataframe0[\"r\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/student/.local/lib/python3.6/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='g(r)', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(dataframe0[\"g(r)\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#prueba = sns.jointplot(x=dataframe0[\"r\"],y=dataframe0[\"g(r)\"],data=df,kind=\"reg\",line_kws={\"color\":\"red\"},scatter_kws={\"alpha\":0.33})\n",
+    "\n",
+    "prueba = sns.jointplot(x=dataframe0[\"r\"],y=dataframe0[\"g(r)\"],data=df)\n",
+    "\n",
+    "print(\"\")\n",
+    "\n",
+    "print(\"\")\n",
+    "\n",
+    "prueba.fig.set_size_inches(8,8)\n",
+    "\n",
+    "pl.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      r    g_liso       g_2        g3        g5       g12       g15       g20\n",
+      "0  0.03  0.749559  1.067157  1.060624  1.086467  0.927405  1.010291  0.944136\n",
+      "1  0.05  0.820106  0.960441  0.954561  1.231928  1.008123  1.000188  0.825305\n",
+      "2  0.07  0.831444  0.961748  0.955860  1.173692  1.010822  0.962181  0.815073\n",
+      "3  0.09  1.087596  1.036667  1.030320  1.198206  1.022817  0.944809  0.860936\n",
+      "4  0.11  0.889851  1.129246  1.122333  1.048102  0.947701  0.910792  0.813170\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Disponemos del archivo de data que almacena todas las funciones de distribución radial clasificadas por filas y columnas,\n",
+    "# esto nos permitirá construír el código para graficar la evolución de la función de distribución radial del fluido confinado\n",
+    "# dentro de un cilindro\n",
+    "\n",
+    "dataframe7=pd.read_csv(\"/home/student/ejercicios-clase-08-datos/data-used/FDR_evolucion_general.csv\")\n",
+    "print(dataframe7.head())\n",
+    "\n",
+    "plt.figure(figsize =(10,10))\n",
+    "ax = plt.gca()\n",
+    "\n",
+    "dataframe7.plot(kind=\"line\",x=\"r\",y=\"g_liso\",ax=ax, label=\"Cilindro liso\")\n",
+    "dataframe7.plot(kind=\"line\",x=\"r\",y=\"g_2\",ax=ax, label=\"Cilindro con 2 dientes\")\n",
+    "dataframe7.plot(kind=\"line\",x=\"r\",y=\"g3\",ax=ax, label =\"Cilindro con 3 dientes\")\n",
+    "dataframe7.plot(kind=\"line\",x=\"r\",y=\"g5\",ax=ax,label =\"Cilindro con 5 dientes\")\n",
+    "dataframe7.plot(kind=\"line\",x=\"r\",y=\"g12\",ax=ax, label =\"Cilindro con 12 dientes\")\n",
+    "dataframe7.plot(kind=\"line\",x=\"r\",y=\"g15\",ax=ax, label =\"Cilindro con 15 dientes\")\n",
+    "dataframe7.plot(kind=\"line\",x=\"r\",y=\"g20\",ax=ax, label =\"Cilindro con 20 dientes\")\n",
+    "\n",
+    "pl.xlabel(\"r\")\n",
+    "pl.ylabel(\"FDR(r)\")\n",
+    "pl.grid()\n",
+    "#pl.legend([\"FDR(r)\"])\n",
+    "pl.title(\"Evolución de la función de distribución radial\")\n",
+    "\n",
+    "pl.savefig(\"fdr_evolucion.png\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Presentar la evolución de la función de distribución radial en forma de gráfica *(estática)*, tiende a ser confusa, por lo que a continuación aplicaremos lo aprendido en las clases del ***Módulo de Ciencia de Datos** mostrando una representación *dinámica* de la variación de la Función de Distribución Radial a medida que cambian las características del cilindro que contiene el fluido."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7- Intentemos una animación"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import animation\n",
+    "from matplotlib.animation import FuncAnimation\n",
+    "#from Tkinter import *\n",
+    "from IPython.display import HTML\n",
+    "%matplotlib notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A nosostros como humanos se nos hace más sencillo observar el comportamiento de la data mediante algún tipo de representación visual, esto nos permite explicar el fenómeno que registra nuestra data, pero en muchas ocasiones las imágenes estáticas no lo lo muestran. Es aquí donde las animaciones comienzan a tener sentido y demostrar su valor en la visualización de nuestros datos!\n",
+    "\n",
+    "Lo que haremos para la animación será tomar las gráficas de cada columna de datos de interés y grafiquémoslas de manera consecutiva dentro de un mismo marco de ejes coordenados. Por lo que debemos definir los datos y juntarlos en una lista que podamos manejar y graficar echando mano de la función `animate` y `FuncAnimation`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     primera_columna  segunda_columna\n",
+      "0               0.03         0.944136\n",
+      "1               0.05         0.825305\n",
+      "2               0.07         0.815073\n",
+      "3               0.09         0.860936\n",
+      "4               0.11         0.813170\n",
+      "..               ...              ...\n",
+      "495             9.93         0.000000\n",
+      "496             9.95         0.000000\n",
+      "497             9.97         0.000000\n",
+      "498             9.99         0.000000\n",
+      "499            10.01         0.000000\n",
+      "\n",
+      "[500 rows x 2 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#                 r    g_liso       g_2        g3        g5       g12       g15       g20\n",
+    "\n",
+    "#dataframe7=pd.read_csv(\"/home/student/ejercicios-clase-08-datos/data-used/FDR_evolucion_general.csv\")\n",
+    "\n",
+    "#f0 = dataframe7[\"r\"], dataframe7[\"g_liso\"]\n",
+    "\n",
+    "f0 = pd.DataFrame({\"primera_columna\":dataframe7[\"r\"], \"segunda_columna\": dataframe7[\"g_liso\"]})\n",
+    "f1 = pd.DataFrame({\"primera_columna\":dataframe7[\"r\"], \"segunda_columna\": dataframe7[\"g_2\"]})\n",
+    "f2 = pd.DataFrame({\"primera_columna\":dataframe7[\"r\"], \"segunda_columna\": dataframe7[\"g3\"]})\n",
+    "f3 = pd.DataFrame({\"primera_columna\":dataframe7[\"r\"], \"segunda_columna\": dataframe7[\"g5\"]})\n",
+    "f4 = pd.DataFrame({\"primera_columna\":dataframe7[\"r\"], \"segunda_columna\": dataframe7[\"g12\"]})\n",
+    "f5 = pd.DataFrame({\"primera_columna\":dataframe7[\"r\"], \"segunda_columna\": dataframe7[\"g15\"]})\n",
+    "f6 = pd.DataFrame({\"primera_columna\":dataframe7[\"r\"], \"segunda_columna\": dataframe7[\"g20\"]})\n",
+    "\n",
+    "print(f6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Crearemos una variable global que almacene todos los dataframes que escogimos\n",
+    "global mylist\n",
+    "mylist=[f0,f1,f2,f3,f4,f5,f6]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "/* global mpl */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function () {\n",
+       "    if (typeof WebSocket !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert(\n",
+       "            'Your browser does not have WebSocket support. ' +\n",
+       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "                'Firefox 4 and 5 are also supported but you ' +\n",
+       "                'have to enable WebSockets in about:config.'\n",
+       "        );\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById('mpl-warnings');\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent =\n",
+       "                'This browser does not support binary websocket messages. ' +\n",
+       "                'Performance may be slow.';\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = document.createElement('div');\n",
+       "    this.root.setAttribute('style', 'display: inline-block');\n",
+       "    this._root_extra_style(this.root);\n",
+       "\n",
+       "    parent_element.appendChild(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen = function () {\n",
+       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
+       "        fig.send_message('send_image_mode', {});\n",
+       "        if (fig.ratio !== 1) {\n",
+       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
+       "        }\n",
+       "        fig.send_message('refresh', {});\n",
+       "    };\n",
+       "\n",
+       "    this.imageObj.onload = function () {\n",
+       "        if (fig.image_mode === 'full') {\n",
+       "            // Full images could contain transparency (where diff images\n",
+       "            // almost always do), so we need to clear the canvas so that\n",
+       "            // there is no ghosting.\n",
+       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "        }\n",
+       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "    };\n",
+       "\n",
+       "    this.imageObj.onunload = function () {\n",
+       "        fig.ws.close();\n",
+       "    };\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function () {\n",
+       "    var titlebar = document.createElement('div');\n",
+       "    titlebar.classList =\n",
+       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
+       "    var titletext = document.createElement('div');\n",
+       "    titletext.classList = 'ui-dialog-title';\n",
+       "    titletext.setAttribute(\n",
+       "        'style',\n",
+       "        'width: 100%; text-align: center; padding: 3px;'\n",
+       "    );\n",
+       "    titlebar.appendChild(titletext);\n",
+       "    this.root.appendChild(titlebar);\n",
+       "    this.header = titletext;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function () {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
+       "    canvas_div.setAttribute(\n",
+       "        'style',\n",
+       "        'border: 1px solid #ddd;' +\n",
+       "            'box-sizing: content-box;' +\n",
+       "            'clear: both;' +\n",
+       "            'min-height: 1px;' +\n",
+       "            'min-width: 1px;' +\n",
+       "            'outline: 0;' +\n",
+       "            'overflow: hidden;' +\n",
+       "            'position: relative;' +\n",
+       "            'resize: both;'\n",
+       "    );\n",
+       "\n",
+       "    function on_keyboard_event_closure(name) {\n",
+       "        return function (event) {\n",
+       "            return fig.key_event(event, name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.addEventListener(\n",
+       "        'keydown',\n",
+       "        on_keyboard_event_closure('key_press')\n",
+       "    );\n",
+       "    canvas_div.addEventListener(\n",
+       "        'keyup',\n",
+       "        on_keyboard_event_closure('key_release')\n",
+       "    );\n",
+       "\n",
+       "    this._canvas_extra_style(canvas_div);\n",
+       "    this.root.appendChild(canvas_div);\n",
+       "\n",
+       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
+       "    canvas.classList.add('mpl-canvas');\n",
+       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
+       "\n",
+       "    this.context = canvas.getContext('2d');\n",
+       "\n",
+       "    var backingStore =\n",
+       "        this.context.backingStorePixelRatio ||\n",
+       "        this.context.webkitBackingStorePixelRatio ||\n",
+       "        this.context.mozBackingStorePixelRatio ||\n",
+       "        this.context.msBackingStorePixelRatio ||\n",
+       "        this.context.oBackingStorePixelRatio ||\n",
+       "        this.context.backingStorePixelRatio ||\n",
+       "        1;\n",
+       "\n",
+       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
+       "        'canvas'\n",
+       "    ));\n",
+       "    rubberband_canvas.setAttribute(\n",
+       "        'style',\n",
+       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
+       "    );\n",
+       "\n",
+       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
+       "    if (this.ResizeObserver === undefined) {\n",
+       "        if (window.ResizeObserver !== undefined) {\n",
+       "            this.ResizeObserver = window.ResizeObserver;\n",
+       "        } else {\n",
+       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
+       "            this.ResizeObserver = obs.ResizeObserver;\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
+       "        var nentries = entries.length;\n",
+       "        for (var i = 0; i < nentries; i++) {\n",
+       "            var entry = entries[i];\n",
+       "            var width, height;\n",
+       "            if (entry.contentBoxSize) {\n",
+       "                if (entry.contentBoxSize instanceof Array) {\n",
+       "                    // Chrome 84 implements new version of spec.\n",
+       "                    width = entry.contentBoxSize[0].inlineSize;\n",
+       "                    height = entry.contentBoxSize[0].blockSize;\n",
+       "                } else {\n",
+       "                    // Firefox implements old version of spec.\n",
+       "                    width = entry.contentBoxSize.inlineSize;\n",
+       "                    height = entry.contentBoxSize.blockSize;\n",
+       "                }\n",
+       "            } else {\n",
+       "                // Chrome <84 implements even older version of spec.\n",
+       "                width = entry.contentRect.width;\n",
+       "                height = entry.contentRect.height;\n",
+       "            }\n",
+       "\n",
+       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
+       "            // the canvas container.\n",
+       "            if (entry.devicePixelContentBoxSize) {\n",
+       "                // Chrome 84 implements new version of spec.\n",
+       "                canvas.setAttribute(\n",
+       "                    'width',\n",
+       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
+       "                );\n",
+       "                canvas.setAttribute(\n",
+       "                    'height',\n",
+       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
+       "                );\n",
+       "            } else {\n",
+       "                canvas.setAttribute('width', width * fig.ratio);\n",
+       "                canvas.setAttribute('height', height * fig.ratio);\n",
+       "            }\n",
+       "            canvas.setAttribute(\n",
+       "                'style',\n",
+       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
+       "            );\n",
+       "\n",
+       "            rubberband_canvas.setAttribute('width', width);\n",
+       "            rubberband_canvas.setAttribute('height', height);\n",
+       "\n",
+       "            // And update the size in Python. We ignore the initial 0/0 size\n",
+       "            // that occurs as the element is placed into the DOM, which should\n",
+       "            // otherwise not happen due to the minimum size styling.\n",
+       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
+       "                fig.request_resize(width, height);\n",
+       "            }\n",
+       "        }\n",
+       "    });\n",
+       "    this.resizeObserverInstance.observe(canvas_div);\n",
+       "\n",
+       "    function on_mouse_event_closure(name) {\n",
+       "        return function (event) {\n",
+       "            return fig.mouse_event(event, name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mousedown',\n",
+       "        on_mouse_event_closure('button_press')\n",
+       "    );\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mouseup',\n",
+       "        on_mouse_event_closure('button_release')\n",
+       "    );\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mousemove',\n",
+       "        on_mouse_event_closure('motion_notify')\n",
+       "    );\n",
+       "\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mouseenter',\n",
+       "        on_mouse_event_closure('figure_enter')\n",
+       "    );\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mouseleave',\n",
+       "        on_mouse_event_closure('figure_leave')\n",
+       "    );\n",
+       "\n",
+       "    canvas_div.addEventListener('wheel', function (event) {\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        on_mouse_event_closure('scroll')(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.appendChild(canvas);\n",
+       "    canvas_div.appendChild(rubberband_canvas);\n",
+       "\n",
+       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
+       "    this.rubberband_context.strokeStyle = '#000000';\n",
+       "\n",
+       "    this._resize_canvas = function (width, height, forward) {\n",
+       "        if (forward) {\n",
+       "            canvas_div.style.width = width + 'px';\n",
+       "            canvas_div.style.height = height + 'px';\n",
+       "        }\n",
+       "    };\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
+       "        event.preventDefault();\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus() {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function () {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var toolbar = document.createElement('div');\n",
+       "    toolbar.classList = 'mpl-toolbar';\n",
+       "    this.root.appendChild(toolbar);\n",
+       "\n",
+       "    function on_click_closure(name) {\n",
+       "        return function (_event) {\n",
+       "            return fig.toolbar_button_onclick(name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    function on_mouseover_closure(tooltip) {\n",
+       "        return function (event) {\n",
+       "            if (!event.currentTarget.disabled) {\n",
+       "                return fig.toolbar_button_onmouseover(tooltip);\n",
+       "            }\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    fig.buttons = {};\n",
+       "    var buttonGroup = document.createElement('div');\n",
+       "    buttonGroup.classList = 'mpl-button-group';\n",
+       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            /* Instead of a spacer, we start a new button group. */\n",
+       "            if (buttonGroup.hasChildNodes()) {\n",
+       "                toolbar.appendChild(buttonGroup);\n",
+       "            }\n",
+       "            buttonGroup = document.createElement('div');\n",
+       "            buttonGroup.classList = 'mpl-button-group';\n",
+       "            continue;\n",
+       "        }\n",
+       "\n",
+       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
+       "        button.classList = 'mpl-widget';\n",
+       "        button.setAttribute('role', 'button');\n",
+       "        button.setAttribute('aria-disabled', 'false');\n",
+       "        button.addEventListener('click', on_click_closure(method_name));\n",
+       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
+       "\n",
+       "        var icon_img = document.createElement('img');\n",
+       "        icon_img.src = '_images/' + image + '.png';\n",
+       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
+       "        icon_img.alt = tooltip;\n",
+       "        button.appendChild(icon_img);\n",
+       "\n",
+       "        buttonGroup.appendChild(button);\n",
+       "    }\n",
+       "\n",
+       "    if (buttonGroup.hasChildNodes()) {\n",
+       "        toolbar.appendChild(buttonGroup);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker = document.createElement('select');\n",
+       "    fmt_picker.classList = 'mpl-widget';\n",
+       "    toolbar.appendChild(fmt_picker);\n",
+       "    this.format_dropdown = fmt_picker;\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = document.createElement('option');\n",
+       "        option.selected = fmt === mpl.default_extension;\n",
+       "        option.innerHTML = fmt;\n",
+       "        fmt_picker.appendChild(option);\n",
+       "    }\n",
+       "\n",
+       "    var status_bar = document.createElement('span');\n",
+       "    status_bar.classList = 'mpl-message';\n",
+       "    toolbar.appendChild(status_bar);\n",
+       "    this.message = status_bar;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function (type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function () {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
+       "        fig.send_message('refresh', {});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
+       "    var x0 = msg['x0'] / fig.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
+       "    var x1 = msg['x1'] / fig.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0,\n",
+       "        0,\n",
+       "        fig.canvas.width / fig.ratio,\n",
+       "        fig.canvas.height / fig.ratio\n",
+       "    );\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch (cursor) {\n",
+       "        case 0:\n",
+       "            cursor = 'pointer';\n",
+       "            break;\n",
+       "        case 1:\n",
+       "            cursor = 'default';\n",
+       "            break;\n",
+       "        case 2:\n",
+       "            cursor = 'crosshair';\n",
+       "            break;\n",
+       "        case 3:\n",
+       "            cursor = 'move';\n",
+       "            break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
+       "    for (var key in msg) {\n",
+       "        if (!(key in fig.buttons)) {\n",
+       "            continue;\n",
+       "        }\n",
+       "        fig.buttons[key].disabled = !msg[key];\n",
+       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
+       "    if (msg['mode'] === 'PAN') {\n",
+       "        fig.buttons['Pan'].classList.add('active');\n",
+       "        fig.buttons['Zoom'].classList.remove('active');\n",
+       "    } else if (msg['mode'] === 'ZOOM') {\n",
+       "        fig.buttons['Pan'].classList.remove('active');\n",
+       "        fig.buttons['Zoom'].classList.add('active');\n",
+       "    } else {\n",
+       "        fig.buttons['Pan'].classList.remove('active');\n",
+       "        fig.buttons['Zoom'].classList.remove('active');\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function () {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message('ack', {});\n",
+       "};\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = 'image/png';\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src\n",
+       "                );\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data\n",
+       "            );\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        } else if (\n",
+       "            typeof evt.data === 'string' &&\n",
+       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
+       "        ) {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig['handle_' + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\n",
+       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
+       "                msg\n",
+       "            );\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\n",
+       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
+       "                    e,\n",
+       "                    e.stack,\n",
+       "                    msg\n",
+       "                );\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "};\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function (e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e) {\n",
+       "        e = window.event;\n",
+       "    }\n",
+       "    if (e.target) {\n",
+       "        targ = e.target;\n",
+       "    } else if (e.srcElement) {\n",
+       "        targ = e.srcElement;\n",
+       "    }\n",
+       "    if (targ.nodeType === 3) {\n",
+       "        // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "    }\n",
+       "\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    var boundingRect = targ.getBoundingClientRect();\n",
+       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
+       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
+       "\n",
+       "    return { x: x, y: y };\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys(original) {\n",
+       "    return Object.keys(original).reduce(function (obj, key) {\n",
+       "        if (typeof original[key] !== 'object') {\n",
+       "            obj[key] = original[key];\n",
+       "        }\n",
+       "        return obj;\n",
+       "    }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event);\n",
+       "\n",
+       "    if (name === 'button_press') {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * this.ratio;\n",
+       "    var y = canvas_pos.y * this.ratio;\n",
+       "\n",
+       "    this.send_message(name, {\n",
+       "        x: x,\n",
+       "        y: y,\n",
+       "        button: event.button,\n",
+       "        step: event.step,\n",
+       "        guiEvent: simpleKeys(event),\n",
+       "    });\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function (event, name) {\n",
+       "    // Prevent repeat events\n",
+       "    if (name === 'key_press') {\n",
+       "        if (event.which === this._key) {\n",
+       "            return;\n",
+       "        } else {\n",
+       "            this._key = event.which;\n",
+       "        }\n",
+       "    }\n",
+       "    if (name === 'key_release') {\n",
+       "        this._key = null;\n",
+       "    }\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which !== 17) {\n",
+       "        value += 'ctrl+';\n",
+       "    }\n",
+       "    if (event.altKey && event.which !== 18) {\n",
+       "        value += 'alt+';\n",
+       "    }\n",
+       "    if (event.shiftKey && event.which !== 16) {\n",
+       "        value += 'shift+';\n",
+       "    }\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
+       "    return false;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
+       "    if (name === 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message('toolbar_button', { name: name });\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "\n",
+       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
+       "// prettier-ignore\n",
+       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";/* global mpl */\n",
+       "\n",
+       "var comm_websocket_adapter = function (comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function () {\n",
+       "        comm.close();\n",
+       "    };\n",
+       "    ws.send = function (m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function (msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data']);\n",
+       "    });\n",
+       "    return ws;\n",
+       "};\n",
+       "\n",
+       "mpl.mpl_figure_comm = function (comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = document.getElementById(id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm);\n",
+       "\n",
+       "    function ondownload(figure, _format) {\n",
+       "        window.open(figure.canvas.toDataURL());\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element;\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error('Failed to find cell for figure', id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "    fig.cell_info[0].output_area.element.on(\n",
+       "        'cleared',\n",
+       "        { fig: fig },\n",
+       "        fig._remove_fig_handler\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
+       "    var width = fig.canvas.width / fig.ratio;\n",
+       "    fig.cell_info[0].output_area.element.off(\n",
+       "        'cleared',\n",
+       "        fig._remove_fig_handler\n",
+       "    );\n",
+       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable();\n",
+       "    fig.parent_element.innerHTML =\n",
+       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "    fig.close_ws(fig, msg);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width / this.ratio;\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] =\n",
+       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function () {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message('ack', {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () {\n",
+       "        fig.push_to_output();\n",
+       "    }, 1000);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function () {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var toolbar = document.createElement('div');\n",
+       "    toolbar.classList = 'btn-toolbar';\n",
+       "    this.root.appendChild(toolbar);\n",
+       "\n",
+       "    function on_click_closure(name) {\n",
+       "        return function (_event) {\n",
+       "            return fig.toolbar_button_onclick(name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    function on_mouseover_closure(tooltip) {\n",
+       "        return function (event) {\n",
+       "            if (!event.currentTarget.disabled) {\n",
+       "                return fig.toolbar_button_onmouseover(tooltip);\n",
+       "            }\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    fig.buttons = {};\n",
+       "    var buttonGroup = document.createElement('div');\n",
+       "    buttonGroup.classList = 'btn-group';\n",
+       "    var button;\n",
+       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            /* Instead of a spacer, we start a new button group. */\n",
+       "            if (buttonGroup.hasChildNodes()) {\n",
+       "                toolbar.appendChild(buttonGroup);\n",
+       "            }\n",
+       "            buttonGroup = document.createElement('div');\n",
+       "            buttonGroup.classList = 'btn-group';\n",
+       "            continue;\n",
+       "        }\n",
+       "\n",
+       "        button = fig.buttons[name] = document.createElement('button');\n",
+       "        button.classList = 'btn btn-default';\n",
+       "        button.href = '#';\n",
+       "        button.title = name;\n",
+       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
+       "        button.addEventListener('click', on_click_closure(method_name));\n",
+       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
+       "        buttonGroup.appendChild(button);\n",
+       "    }\n",
+       "\n",
+       "    if (buttonGroup.hasChildNodes()) {\n",
+       "        toolbar.appendChild(buttonGroup);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = document.createElement('span');\n",
+       "    status_bar.classList = 'mpl-message pull-right';\n",
+       "    toolbar.appendChild(status_bar);\n",
+       "    this.message = status_bar;\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = document.createElement('div');\n",
+       "    buttongrp.classList = 'btn-group inline pull-right';\n",
+       "    button = document.createElement('button');\n",
+       "    button.classList = 'btn btn-mini btn-primary';\n",
+       "    button.href = '#';\n",
+       "    button.title = 'Stop Interaction';\n",
+       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
+       "    button.addEventListener('click', function (_evt) {\n",
+       "        fig.handle_close(fig, {});\n",
+       "    });\n",
+       "    button.addEventListener(\n",
+       "        'mouseover',\n",
+       "        on_mouseover_closure('Stop Interaction')\n",
+       "    );\n",
+       "    buttongrp.appendChild(button);\n",
+       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
+       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
+       "    var fig = event.data.fig;\n",
+       "    if (event.target !== this) {\n",
+       "        // Ignore bubbled events from children.\n",
+       "        return;\n",
+       "    }\n",
+       "    fig.close_ws(fig, {});\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function (el) {\n",
+       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.setAttribute('tabindex', 0);\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    } else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager) {\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "    }\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which === 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "};\n",
+       "\n",
+       "mpl.find_output_cell = function (html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i = 0; i < ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code') {\n",
+       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] === html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel !== null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target(\n",
+       "        'matplotlib',\n",
+       "        mpl.mpl_figure_comm\n",
+       "    );\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Configuramos la figura, los ejes, y la gráfica que queremos animar\n",
+    "fig = plt.figure()\n",
+    "ax = plt.axes(xlim=(0, 11), ylim=(0, 6))\n",
+    "line, = ax.plot([], [], lw=2)\n",
+    "\n",
+    "# función inicialización: grafica el fondo de cada frame\n",
+    "def init():\n",
+    "    line.set_data([], [])\n",
+    "    return line,\n",
+    "\n",
+    "# función animation para la lista de los dataframes\n",
+    "def animate(i):\n",
+    "    line.set_data(mylist[i]['primera_columna'], mylist[i]['segunda_columna'])\n",
+    "    return line,\n",
+    "\n",
+    "# Animamos usando FuncAnimation, en intervalos de 300 ms\n",
+    "# declaramos el set number of frames to the length of your list of dataframes\n",
+    "anim = animation.FuncAnimation(fig, animate, frames=len(mylist), init_func=init, interval=300, blit=True)\n",
+    "\n",
+    "writergif = animation.PillowWriter(fps=1000)\n",
+    "anim.save(\"animacion.gif\",writer=writergif)\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "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": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/codigo/vacf.png b/codigo/vacf.png
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literal 0
HcmV?d00001

-- 
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