diff --git a/.idea/PythonProject.iml b/.idea/PythonProject.iml
index d8b3f6c..f571432 100644
--- a/.idea/PythonProject.iml
+++ b/.idea/PythonProject.iml
@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="Python 3.13" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.12" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>
\ No newline at end of file
diff --git a/GUI/pantalla.py b/GUI/pantalla.py
new file mode 100644
index 0000000..08386ad
--- /dev/null
+++ b/GUI/pantalla.py
@@ -0,0 +1,62 @@
+import pygame
+
+# Inicializar pygame
+pygame.init()
+
+# Configuración de la pantalla
+ANCHO, ALTO = 600, 400  # Puedes cambiar el tamaño
+TERCIO_ANCHO = ANCHO // 3
+
+screen = pygame.display.set_mode((ANCHO, ALTO))
+pygame.display.set_caption("Cambio de colores con teclas")
+
+# Fuente para el texto
+font = pygame.font.Font(None, 36)
+
+
+# Variables para controlar la iluminación
+i_presionado = False
+d_presionado = False
+espacio_presionado = False
+
+# Bucle principal
+running = True
+while running:
+    for event in pygame.event.get():
+        if event.type == pygame.QUIT:
+            running = False
+        elif event.type == pygame.KEYDOWN:
+            if event.key == pygame.K_i:
+                i_presionado = True
+            elif event.key == pygame.K_d:
+                d_presionado = True
+            elif event.key == pygame.K_SPACE:
+                espacio_presionado = True
+        elif event.type == pygame.KEYUP:
+            if event.key == pygame.K_i:
+                i_presionado = False
+            elif event.key == pygame.K_d:
+                d_presionado = False
+            elif event.key == pygame.K_SPACE:
+                espacio_presionado = False
+
+    # Fondo en blanco
+    screen.fill((255, 255, 255))
+
+    # Dibujar los rectángulos si se presionan teclas
+    if i_presionado:
+        pygame.draw.rect(screen, (255, 0, 0), (0, 0, TERCIO_ANCHO, ALTO))
+        text_surface = font.render("Izquierda", True, (0, 0, 0))
+        screen.blit(text_surface, (TERCIO_ANCHO // 2 - text_surface.get_width() // 2, 20))
+    if d_presionado:
+        pygame.draw.rect(screen, (0, 255, 0), (2 * TERCIO_ANCHO, 0, TERCIO_ANCHO, ALTO))
+        text_surface = font.render("Derecha", True, (0, 0, 0))
+        screen.blit(text_surface, (2 * TERCIO_ANCHO + TERCIO_ANCHO // 2 - text_surface.get_width() // 2, 20))
+    # Mostrar texto solo cuando se presiona espacio
+    if espacio_presionado:
+        text_surface = font.render("None", True, (0, 0, 0))
+        screen.blit(text_surface, (ANCHO // 2 - text_surface.get_width() // 2, ALTO // 2))
+
+    pygame.display.flip()
+
+pygame.quit()
\ No newline at end of file
diff --git a/TestEtiquetado.ipynb b/TestEtiquetado.ipynb
index abf76c7..9786a29 100644
--- a/TestEtiquetado.ipynb
+++ b/TestEtiquetado.ipynb
@@ -2,88 +2,86 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
    "id": "5fe31b7f-2243-4587-9740-46164846cff3",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-02-13T20:09:41.300262Z",
-     "start_time": "2025-02-13T20:09:41.297886Z"
-    },
     "collapsed": true,
     "jupyter": {
      "outputs_hidden": true
+    },
+    "ExecuteTime": {
+     "end_time": "2025-03-02T19:13:55.436438Z",
+     "start_time": "2025-03-02T19:13:46.916154Z"
     }
    },
-   "outputs": [],
    "source": [
     "import os\n",
     "import matplotlib\n",
     "import pandas as pd\n",
     "from datetime import datetime, timezone\n",
     "import zoneinfo\n"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 1
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
    "id": "cbb445af-6351-43b0-8142-f9aa35388e6e",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-02-13T20:09:41.308855Z",
-     "start_time": "2025-02-13T20:09:41.306360Z"
-    },
     "collapsed": true,
     "jupyter": {
      "outputs_hidden": true
+    },
+    "ExecuteTime": {
+     "end_time": "2025-03-02T19:13:55.474641Z",
+     "start_time": "2025-03-02T19:13:55.449280Z"
     }
    },
-   "outputs": [],
    "source": [
     "logs_path = \"join/mergedLog_output.txt\"\n",
     "openbci_path = \"join/mergedData_output.txt\"\n",
     "output_csv_path = \"OpenBCI_with_labels.csv\"\n",
     "label_name = \"Movement\"\n",
     "timestamp_col=\"Timestamp\""
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 2
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
    "id": "f613abe9-8d5b-444c-af3c-b0ab826ed3f4",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-02-13T20:09:43.026443Z",
-     "start_time": "2025-02-13T20:09:41.323419Z"
-    },
     "collapsed": true,
     "jupyter": {
      "outputs_hidden": true
+    },
+    "ExecuteTime": {
+     "end_time": "2025-03-02T19:13:57.903721Z",
+     "start_time": "2025-03-02T19:13:55.482610Z"
     }
    },
-   "outputs": [],
    "source": [
     "# Leer los logs y extraer tiempos y etiquetas\n",
     "with open(logs_path, \"r\") as logs_file:\n",
     "   logs_data = [log.strip().split(\",\") for log in logs_file.readlines()]\n",
     "with open(openbci_path, \"r\") as openbci_file:\n",
     "    lineas = [linea.strip().split(\",\") for linea in openbci_file.readlines() if not linea.strip().startswith('%')]"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 3
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
    "id": "d8eba4ab-63ee-40f5-b30d-f8ae58d5aa79",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-02-13T20:09:43.043659Z",
-     "start_time": "2025-02-13T20:09:43.039923Z"
-    },
     "collapsed": true,
     "jupyter": {
      "outputs_hidden": true
+    },
+    "ExecuteTime": {
+     "end_time": "2025-03-02T19:13:58.869318Z",
+     "start_time": "2025-03-02T19:13:58.856580Z"
     }
    },
-   "outputs": [],
    "source": [
     "# Utility functions\n",
     "def remove_columns(df, columns):\n",
@@ -104,25 +102,90 @@
     "    else:\n",
     "        data.loc[mask, label_name] = label.strip()\n",
     "        return True"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 4
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
    "id": "1ecf8906-828c-4e13-a267-2b96f61e2c2e",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-02-13T20:09:46.253744Z",
-     "start_time": "2025-02-13T20:09:43.057864Z"
-    },
     "collapsed": true,
     "jupyter": {
      "outputs_hidden": true
+    },
+    "ExecuteTime": {
+     "end_time": "2025-03-02T19:14:07.618668Z",
+     "start_time": "2025-03-02T19:13:58.903011Z"
     }
    },
+   "source": [
+    "#Convertir los datos en un DataFrame\n",
+    "columns = \"Sample Index, EXG Channel 0, EXG Channel 1, EXG Channel 2, EXG Channel 3, EXG Channel 4, EXG Channel 5, EXG Channel 6, EXG Channel 7, EXG Channel 8, EXG Channel 9, EXG Channel 10, EXG Channel 11, EXG Channel 12, EXG Channel 13, EXG Channel 14, EXG Channel 15, Accel Channel 0, Accel Channel 1, Accel Channel 2, Not_Used1, Digital Channel 0 (D11), Digital Channel 1 (D12), Digital Channel 2 (D13), Digital Channel 3 (D17), Not_Used2, Digital Channel 4 (D18), Analog Channel 0, Analog Channel 1, Analog Channel 2, Timestamp, Marker Channel, Timestamp (Formatted)\".split(\",\")\n",
+    "columns = [column.strip() for column in columns]\n",
+    "types = {}\n",
+    "\n",
+    "data_df = pd.DataFrame(lineas, columns=columns)\n",
+    "exceptions = [\"Timestamp (Formatted)\"]\n",
+    "\n",
+    "for column in data_df.columns:\n",
+    "    if column in exceptions:\n",
+    "        continue\n",
+    "    data_df[column] = pd.to_numeric(data_df[column], errors='coerce')\n",
+    "remove_columns(data_df, [\"Not_Used1\", \"Not_Used2\", \"Timestamp (Formatted)\", \"Sample Index\"])\n",
+    "data_df[timestamp_col] = pd.to_datetime(data_df[timestamp_col], unit=\"s\", utc=True).dt.tz_convert(\"Europe/Madrid\")\n",
+    "# Create the label column\n",
+    "data_df[label_name]= \"\"\n",
+    "data_df.head()"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "   EXG Channel 0  EXG Channel 1  EXG Channel 2  EXG Channel 3  EXG Channel 4  \\\n",
+       "0   19162.754019   10400.132585  -26819.835820  -10368.437811  -17614.694549   \n",
+       "1   38187.821291   20561.995573  -53613.967134  -20684.863113  -36268.231126   \n",
+       "2   37916.314652   20446.347647  -53464.702185  -20528.311494  -36279.362295   \n",
+       "3   38343.098860   20722.995189  -53693.628751  -20771.207901  -35548.817879   \n",
+       "4   37910.905529   20389.820086  -53463.472839  -20529.742006  -36741.171687   \n",
+       "\n",
+       "   EXG Channel 5  EXG Channel 6  EXG Channel 7  EXG Channel 8  EXG Channel 9  \\\n",
+       "0     175.684711  -23469.398733  -13855.980498  -20491.900503  -22635.343389   \n",
+       "1     361.517115  -46906.543899  -27692.805552    4671.916923  -32254.126043   \n",
+       "2    -755.690128  -46767.136069  -27537.952666    4684.500955  -32010.313214   \n",
+       "3     628.978089  -47001.941145  -27767.415675    4760.072203  -32191.876434   \n",
+       "4    -392.630743  -46768.946560  -27551.766044    4555.129058  -32183.382771   \n",
+       "\n",
+       "   ...  Digital Channel 1 (D12)  Digital Channel 2 (D13)  \\\n",
+       "0  ...                    112.0                     15.5   \n",
+       "1  ...                      0.0                      0.0   \n",
+       "2  ...                      0.0                      0.0   \n",
+       "3  ...                      0.0                      0.0   \n",
+       "4  ...                    104.0                     15.5   \n",
+       "\n",
+       "   Digital Channel 3 (D17)  Digital Channel 4 (D18)  Analog Channel 0  \\\n",
+       "0                    120.0                     16.0               0.0   \n",
+       "1                      0.0                      0.0               0.0   \n",
+       "2                      0.0                      0.0               0.0   \n",
+       "3                      0.0                      0.0               0.0   \n",
+       "4                    112.0                    120.0               0.0   \n",
+       "\n",
+       "   Analog Channel 1  Analog Channel 2                           Timestamp  \\\n",
+       "0               0.0               0.0 2025-01-27 17:31:42.443204641+01:00   \n",
+       "1               0.0               0.0 2025-01-27 17:31:42.443370104+01:00   \n",
+       "2               0.0               0.0 2025-01-27 17:31:42.443432808+01:00   \n",
+       "3               0.0               0.0 2025-01-27 17:31:42.443464756+01:00   \n",
+       "4               0.0               0.0 2025-01-27 17:31:42.443489552+01:00   \n",
+       "\n",
+       "   Marker Channel  Movement  \n",
+       "0             0.0            \n",
+       "1             0.0            \n",
+       "2             0.0            \n",
+       "3             0.0            \n",
+       "4             0.0            \n",
+       "\n",
+       "[5 rows x 30 columns]"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -290,51 +353,6 @@
        "</table>\n",
        "<p>5 rows × 30 columns</p>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "   EXG Channel 0  EXG Channel 1  EXG Channel 2  EXG Channel 3  EXG Channel 4  \\\n",
-       "0   19162.754019   10400.132585  -26819.835820  -10368.437811  -17614.694549   \n",
-       "1   38187.821291   20561.995573  -53613.967134  -20684.863113  -36268.231126   \n",
-       "2   37916.314652   20446.347647  -53464.702185  -20528.311494  -36279.362295   \n",
-       "3   38343.098860   20722.995189  -53693.628751  -20771.207901  -35548.817879   \n",
-       "4   37910.905529   20389.820086  -53463.472839  -20529.742006  -36741.171687   \n",
-       "\n",
-       "   EXG Channel 5  EXG Channel 6  EXG Channel 7  EXG Channel 8  EXG Channel 9  \\\n",
-       "0     175.684711  -23469.398733  -13855.980498  -20491.900503  -22635.343389   \n",
-       "1     361.517115  -46906.543899  -27692.805552    4671.916923  -32254.126043   \n",
-       "2    -755.690128  -46767.136069  -27537.952666    4684.500955  -32010.313214   \n",
-       "3     628.978089  -47001.941145  -27767.415675    4760.072203  -32191.876434   \n",
-       "4    -392.630743  -46768.946560  -27551.766044    4555.129058  -32183.382771   \n",
-       "\n",
-       "   ...  Digital Channel 1 (D12)  Digital Channel 2 (D13)  \\\n",
-       "0  ...                    112.0                     15.5   \n",
-       "1  ...                      0.0                      0.0   \n",
-       "2  ...                      0.0                      0.0   \n",
-       "3  ...                      0.0                      0.0   \n",
-       "4  ...                    104.0                     15.5   \n",
-       "\n",
-       "   Digital Channel 3 (D17)  Digital Channel 4 (D18)  Analog Channel 0  \\\n",
-       "0                    120.0                     16.0               0.0   \n",
-       "1                      0.0                      0.0               0.0   \n",
-       "2                      0.0                      0.0               0.0   \n",
-       "3                      0.0                      0.0               0.0   \n",
-       "4                    112.0                    120.0               0.0   \n",
-       "\n",
-       "   Analog Channel 1  Analog Channel 2                           Timestamp  \\\n",
-       "0               0.0               0.0 2025-01-27 17:31:42.443204641+01:00   \n",
-       "1               0.0               0.0 2025-01-27 17:31:42.443370104+01:00   \n",
-       "2               0.0               0.0 2025-01-27 17:31:42.443432808+01:00   \n",
-       "3               0.0               0.0 2025-01-27 17:31:42.443464756+01:00   \n",
-       "4               0.0               0.0 2025-01-27 17:31:42.443489552+01:00   \n",
-       "\n",
-       "   Marker Channel  Movement  \n",
-       "0             0.0            \n",
-       "1             0.0            \n",
-       "2             0.0            \n",
-       "3             0.0            \n",
-       "4             0.0            \n",
-       "\n",
-       "[5 rows x 30 columns]"
       ]
      },
      "execution_count": 5,
@@ -342,43 +360,40 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "#Convertir los datos en un DataFrame\n",
-    "columns = \"Sample Index, EXG Channel 0, EXG Channel 1, EXG Channel 2, EXG Channel 3, EXG Channel 4, EXG Channel 5, EXG Channel 6, EXG Channel 7, EXG Channel 8, EXG Channel 9, EXG Channel 10, EXG Channel 11, EXG Channel 12, EXG Channel 13, EXG Channel 14, EXG Channel 15, Accel Channel 0, Accel Channel 1, Accel Channel 2, Not_Used1, Digital Channel 0 (D11), Digital Channel 1 (D12), Digital Channel 2 (D13), Digital Channel 3 (D17), Not_Used2, Digital Channel 4 (D18), Analog Channel 0, Analog Channel 1, Analog Channel 2, Timestamp, Marker Channel, Timestamp (Formatted)\".split(\",\")\n",
-    "columns = [column.strip() for column in columns]\n",
-    "types = {}\n",
-    "\n",
-    "data_df = pd.DataFrame(lineas, columns=columns)\n",
-    "exceptions = [\"Timestamp (Formatted)\"]\n",
-    "\n",
-    "for column in data_df.columns:\n",
-    "    if column in exceptions:\n",
-    "        continue\n",
-    "    data_df[column] = pd.to_numeric(data_df[column], errors='coerce')\n",
-    "remove_columns(data_df, [\"Not_Used1\", \"Not_Used2\", \"Timestamp (Formatted)\", \"Sample Index\"])\n",
-    "data_df[timestamp_col] = pd.to_datetime(data_df[timestamp_col], unit=\"s\", utc=True).dt.tz_convert(\"Europe/Madrid\")\n",
-    "# Create the label column\n",
-    "data_df[label_name]= \"\"\n",
-    "data_df.head()"
-   ]
+   "execution_count": 5
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
    "id": "7f4bf8f8-99dd-4f0a-aa20-aa09643d9553",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-02-13T20:12:07.148168Z",
-     "start_time": "2025-02-13T20:12:07.142272Z"
-    },
     "collapsed": true,
     "jupyter": {
      "outputs_hidden": true
+    },
+    "ExecuteTime": {
+     "end_time": "2025-03-02T19:14:07.774264Z",
+     "start_time": "2025-03-02T19:14:07.736637Z"
     }
    },
+   "source": [
+    "# Convertir los logs en un DataFrame\n",
+    "log_df = pd.DataFrame(logs_data, columns=[\"Timestamp\", \"Label\"])\n",
+    "#log_df[timestamp_col] = pd.to_datetime(log_df[timestamp_col], errors=\"coerce\", utc=True)\n",
+    "log_df[timestamp_col] = log_df[timestamp_col].astype(str)\n",
+    "\n",
+    "log_df.head()"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "               Timestamp  Label\n",
+       "0  2025-01-27 17:31:40.0      i\n",
+       "1  2025-01-27 17:32:13.0   none\n",
+       "2  2025-01-27 17:33:08.0   none\n",
+       "3  2025-01-27 17:50:46.0      c\n",
+       "4  2025-01-27 17:51:11.0      i"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -431,14 +446,6 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "               Timestamp  Label\n",
-       "0  2025-01-27 17:31:40.0      i\n",
-       "1  2025-01-27 17:32:13.0   none\n",
-       "2  2025-01-27 17:33:08.0   none\n",
-       "3  2025-01-27 17:50:46.0      c\n",
-       "4  2025-01-27 17:51:11.0      i"
       ]
      },
      "execution_count": 6,
@@ -446,20 +453,20 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "# Convertir los logs en un DataFrame\n",
-    "log_df = pd.DataFrame(logs_data, columns=[\"Timestamp\", \"Label\"])\n",
-    "#log_df[timestamp_col] = pd.to_datetime(log_df[timestamp_col], errors=\"coerce\", utc=True)\n",
-    "log_df[timestamp_col] = log_df[timestamp_col].astype(str)\n",
-    "\n",
-    "log_df.head()"
-   ]
+   "execution_count": 6
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
    "id": "b46901ba",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-02T19:14:07.881015Z",
+     "start_time": "2025-03-02T19:14:07.862014Z"
+    }
+   },
+   "source": [
+    "log_df.dtypes"
+   ],
    "outputs": [
     {
      "data": {
@@ -474,25 +481,21 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "log_df.dtypes"
-   ]
+   "execution_count": 7
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
    "id": "0c8715db-9090-4fd6-9ade-9796c268b0b5",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-02-13T20:09:46.288560Z",
-     "start_time": "2025-02-13T20:09:46.285065Z"
-    },
     "collapsed": true,
     "jupyter": {
      "outputs_hidden": true
+    },
+    "ExecuteTime": {
+     "end_time": "2025-03-02T19:14:08.125032Z",
+     "start_time": "2025-03-02T19:14:08.118581Z"
     }
    },
-   "outputs": [],
    "source": [
     "def test(data, start_time, end_time):\n",
     "    mask = (data[timestamp_col] >= start_time) & (data[\"Timestamp\"] < end_time)\n",
@@ -500,32 +503,23 @@
     "    print(\"xdata=\")\n",
     "    print(xdata.empty)\n",
     "    print(data.loc[mask,timestamp_col])"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 8
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
    "id": "90095eb1-91fb-4159-ae70-0ff42cfb2b4b",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-02-13T20:09:46.444389Z",
-     "start_time": "2025-02-13T20:09:46.316312Z"
-    },
     "collapsed": true,
     "jupyter": {
      "outputs_hidden": true
+    },
+    "ExecuteTime": {
+     "end_time": "2025-03-02T19:14:09.127121Z",
+     "start_time": "2025-03-02T19:14:08.512800Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "*Error, no hay registros en el intervalo [2025-01-27 18:35:34.6, 2025-01-27 18:35:35.5)\n",
-      "There were 1 errors in 79 intervals\n"
-     ]
-    }
-   ],
    "source": [
     "errors = 0\n",
     "for i in range(len(log_df) - 1):\n",
@@ -538,13 +532,31 @@
     "    #test(data_df, start_time, end_time)\n",
     "    #break\n",
     "print(f\"There were {errors} errors in {len(log_df)} intervals\")"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "*Error, no hay registros en el intervalo [2025-01-27 18:35:34.6, 2025-01-27 18:35:35.5)\n",
+      "There were 1 errors in 79 intervals\n"
+     ]
+    }
+   ],
+   "execution_count": 9
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
    "id": "d4e2a7aa",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-02T19:14:09.252955Z",
+     "start_time": "2025-03-02T19:14:09.220421Z"
+    }
+   },
+   "source": [
+    "data_df.value_counts(label_name)"
+   ],
    "outputs": [
     {
      "data": {
@@ -563,15 +575,26 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "data_df.value_counts(label_name)"
-   ]
+   "execution_count": 10
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
    "id": "1dfbc859",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-02T19:14:09.887517Z",
+     "start_time": "2025-03-02T19:14:09.417859Z"
+    }
+   },
+   "source": [
+    "# Clean dataset\n",
+    "data_df.dropna(inplace=True)\n",
+    "data_df = data_df[data_df.Movement != \"\"] # Remove empty labels\n",
+    "data_df = data_df[data_df.Movement != \"c\"] # Remove calibration\n",
+    "data_df.reset_index(drop=True, inplace=True)\n",
+    "data_df[\"Movement\"] = data_df[\"Movement\"].astype(\"category\")\n",
+    "data_df.value_counts(label_name)"
+   ],
    "outputs": [
     {
      "data": {
@@ -588,165 +611,32 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "# Clean dataset\n",
-    "data_df.dropna(inplace=True)\n",
-    "data_df = data_df[data_df.Movement != \"\"] # Remove empty labels\n",
-    "data_df = data_df[data_df.Movement != \"c\"] # Remove calibration\n",
-    "data_df.reset_index(drop=True, inplace=True)\n",
-    "data_df[\"Movement\"] = data_df[\"Movement\"].astype(\"category\")\n",
-    "data_df.value_counts(label_name)"
-   ]
+   "execution_count": 11
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
    "id": "aa4aa1a2",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-02T19:14:18.221695Z",
+     "start_time": "2025-03-02T19:14:09.977021Z"
+    }
+   },
    "source": [
     "data_df.to_csv(os.path.join(\"csv\", \"openbci.csv\"), index=False)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 12
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "ef4a6478",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "20d636799a67496f8cdf8619162fef24",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/rmontanana/miniconda3/lib/python3.11/site-packages/ydata_profiling/model/pandas/describe_date_pandas.py:57: UserWarning: Discarding nonzero nanoseconds in conversion.\n",
-      "  \"min\": pd.Timestamp.to_pydatetime(series.min()),\n",
-      "/home/rmontanana/miniconda3/lib/python3.11/site-packages/ydata_profiling/model/pandas/describe_date_pandas.py:58: UserWarning: Discarding nonzero nanoseconds in conversion.\n",
-      "  \"max\": pd.Timestamp.to_pydatetime(series.max()),\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "35e53f0f76e14e7f923b1055285670d3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6bbac27856134eef912956682f6cf5f3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d2697cde02024e359b2e9df92b78179a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ac17608271ce4757b0156753b390fc28",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/rmontanana/miniconda3/lib/python3.11/site-packages/ydata_profiling/model/pandas/describe_date_pandas.py:57: UserWarning: Discarding nonzero nanoseconds in conversion.\n",
-      "  \"min\": pd.Timestamp.to_pydatetime(series.min()),\n",
-      "/home/rmontanana/miniconda3/lib/python3.11/site-packages/ydata_profiling/model/pandas/describe_date_pandas.py:58: UserWarning: Discarding nonzero nanoseconds in conversion.\n",
-      "  \"max\": pd.Timestamp.to_pydatetime(series.max()),\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f4a5bd4acb4c406492ad4fab3aed97cd",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e8487fd4f12a493497e4b86b567149d8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2c62e1d308d9498f8bca16b58f44204b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-02T19:16:52.896034Z",
+     "start_time": "2025-03-02T19:14:18.274675Z"
     }
-   ],
+   },
    "source": [
     "# Generate analysis info into html folder\n",
     "from ydata_profiling import ProfileReport\n",
@@ -768,52 +658,103 @@
     "    )\n",
     "report.to_file(os.path.join(\"html\", \"OpenBCI_report.html\"))\n",
     "\n"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\lucia\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "Summarize dataset:  80%|████████  | 28/35 [00:06<00:01,  6.63it/s, Describe variable:Movement]               C:\\Users\\lucia\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ydata_profiling\\model\\pandas\\describe_date_pandas.py:57: UserWarning: Discarding nonzero nanoseconds in conversion.\n",
+      "  \"min\": pd.Timestamp.to_pydatetime(series.min()),\n",
+      "C:\\Users\\lucia\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ydata_profiling\\model\\pandas\\describe_date_pandas.py:58: UserWarning: Discarding nonzero nanoseconds in conversion.\n",
+      "  \"max\": pd.Timestamp.to_pydatetime(series.max()),\n",
+      "Summarize dataset: 100%|██████████| 613/613 [01:43<00:00,  5.91it/s, Completed]                                               \n",
+      "Generate report structure: 100%|██████████| 1/1 [00:09<00:00,  9.54s/it]\n",
+      "Render HTML: 100%|██████████| 1/1 [00:17<00:00, 17.93s/it]\n",
+      "Export report to file: 100%|██████████| 1/1 [00:00<00:00,  5.79it/s]\n"
+     ]
+    }
+   ],
+   "execution_count": 13
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "09039cdf",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-02T19:24:14.038282Z",
+     "start_time": "2025-03-02T19:24:13.189122Z"
+    }
+   },
+   "source": [
+    "import  sweetviz as sv\n",
+    "report = sv.analyze(data_df)\n",
+    "report.show_html(os.path.join(\"html\", \"OpenBCI_report_sv.html\"), open_browser=False, layout=\"widescreen\")"
+   ],
    "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "985c493925d6403684ddaba1b2280a5e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "                                             |          | [  0%]   00:00 -> (? left)"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Feature: EXG Channel 0                       |▎         | [  3%]   00:00 -> (00:12 left)"
+     ]
     },
+    {
+     "ename": "AttributeError",
+     "evalue": "module 'numpy' has no attribute 'VisibleDeprecationWarning'",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mAttributeError\u001B[0m                            Traceback (most recent call last)",
+      "Cell \u001B[1;32mIn[20], line 2\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m\u001B[38;5;250m  \u001B[39m\u001B[38;5;21;01msweetviz\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mas\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21;01msv\u001B[39;00m\n\u001B[1;32m----> 2\u001B[0m report \u001B[38;5;241m=\u001B[39m \u001B[43msv\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43manalyze\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdata_df\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m      3\u001B[0m report\u001B[38;5;241m.\u001B[39mshow_html(os\u001B[38;5;241m.\u001B[39mpath\u001B[38;5;241m.\u001B[39mjoin(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mhtml\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mOpenBCI_report_sv.html\u001B[39m\u001B[38;5;124m\"\u001B[39m), open_browser\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m, layout\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mwidescreen\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
+      "File \u001B[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\sweetviz\\sv_public.py:12\u001B[0m, in \u001B[0;36manalyze\u001B[1;34m(source, target_feat, feat_cfg, pairwise_analysis)\u001B[0m\n\u001B[0;32m      8\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21manalyze\u001B[39m(source: Union[pd\u001B[38;5;241m.\u001B[39mDataFrame, Tuple[pd\u001B[38;5;241m.\u001B[39mDataFrame, \u001B[38;5;28mstr\u001B[39m]],\n\u001B[0;32m      9\u001B[0m             target_feat: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m     10\u001B[0m             feat_cfg: FeatureConfig \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m     11\u001B[0m             pairwise_analysis: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mauto\u001B[39m\u001B[38;5;124m'\u001B[39m):\n\u001B[1;32m---> 12\u001B[0m     report \u001B[38;5;241m=\u001B[39m \u001B[43msweetviz\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mDataframeReport\u001B[49m\u001B[43m(\u001B[49m\u001B[43msource\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtarget_feat\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[0;32m     13\u001B[0m \u001B[43m                                      \u001B[49m\u001B[43mpairwise_analysis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfeat_cfg\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     14\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m report\n",
+      "File \u001B[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\sweetviz\\dataframe_report.py:277\u001B[0m, in \u001B[0;36mDataframeReport.__init__\u001B[1;34m(self, source, target_feature_name, compare, pairwise_analysis, fc, verbosity)\u001B[0m\n\u001B[0;32m    274\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m f \u001B[38;5;129;01min\u001B[39;00m features_to_process:\n\u001B[0;32m    275\u001B[0m     \u001B[38;5;66;03m# start = time.perf_counter()\u001B[39;00m\n\u001B[0;32m    276\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mprogress_bar\u001B[38;5;241m.\u001B[39mset_description_str(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mFeature: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mf\u001B[38;5;241m.\u001B[39msource\u001B[38;5;241m.\u001B[39mname\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m--> 277\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_features[f\u001B[38;5;241m.\u001B[39msource\u001B[38;5;241m.\u001B[39mname] \u001B[38;5;241m=\u001B[39m \u001B[43msa\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43manalyze_feature_to_dictionary\u001B[49m\u001B[43m(\u001B[49m\u001B[43mf\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    278\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mprogress_bar\u001B[38;5;241m.\u001B[39mupdate(\u001B[38;5;241m1\u001B[39m)\n\u001B[0;32m    279\u001B[0m     \u001B[38;5;66;03m# print(f\"DONE FEATURE------> {f.source.name}\"\u001B[39;00m\n\u001B[0;32m    280\u001B[0m     \u001B[38;5;66;03m#       f\" {(time.perf_counter() - start):.2f}   {self._features[f.source.name]['type']}\")\u001B[39;00m\n\u001B[0;32m    281\u001B[0m \u001B[38;5;66;03m# self.progress_bar.set_description_str('[FEATURES DONE]')\u001B[39;00m\n\u001B[0;32m    282\u001B[0m \u001B[38;5;66;03m# self.progress_bar.close()\u001B[39;00m\n\u001B[0;32m    283\u001B[0m \n\u001B[0;32m    284\u001B[0m \u001B[38;5;66;03m# Wrap up summary\u001B[39;00m\n",
+      "File \u001B[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\sweetviz\\series_analyzer.py:142\u001B[0m, in \u001B[0;36manalyze_feature_to_dictionary\u001B[1;34m(to_process)\u001B[0m\n\u001B[0;32m    140\u001B[0m \u001B[38;5;66;03m# Perform full analysis on source/compare/target\u001B[39;00m\n\u001B[0;32m    141\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m returned_feature_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtype\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m==\u001B[39m FeatureType\u001B[38;5;241m.\u001B[39mTYPE_NUM:\n\u001B[1;32m--> 142\u001B[0m     \u001B[43msweetviz\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mseries_analyzer_numeric\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43manalyze\u001B[49m\u001B[43m(\u001B[49m\u001B[43mto_process\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreturned_feature_dict\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    143\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m returned_feature_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtype\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m==\u001B[39m FeatureType\u001B[38;5;241m.\u001B[39mTYPE_CAT:\n\u001B[0;32m    144\u001B[0m     sweetviz\u001B[38;5;241m.\u001B[39mseries_analyzer_cat\u001B[38;5;241m.\u001B[39manalyze(to_process, returned_feature_dict)\n",
+      "File \u001B[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\sweetviz\\series_analyzer_numeric.py:102\u001B[0m, in \u001B[0;36manalyze\u001B[1;34m(to_process, feature_dict)\u001B[0m\n\u001B[0;32m     98\u001B[0m     do_stats_numeric(to_process\u001B[38;5;241m.\u001B[39mcompare, compare_dict)\n\u001B[0;32m    100\u001B[0m do_detail_numeric(to_process\u001B[38;5;241m.\u001B[39msource, to_process\u001B[38;5;241m.\u001B[39msource_counts, to_process\u001B[38;5;241m.\u001B[39mcompare_counts, feature_dict)\n\u001B[1;32m--> 102\u001B[0m feature_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mminigraph\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[43mGraphNumeric\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mmini\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mto_process\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    103\u001B[0m feature_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdetail_graphs\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m()\n\u001B[0;32m    104\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m num_bins \u001B[38;5;129;01min\u001B[39;00m [\u001B[38;5;241m0\u001B[39m, \u001B[38;5;241m5\u001B[39m, \u001B[38;5;241m15\u001B[39m, \u001B[38;5;241m30\u001B[39m]:\n",
+      "File \u001B[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\sweetviz\\graph_numeric.py:71\u001B[0m, in \u001B[0;36mGraphNumeric.__init__\u001B[1;34m(self, which_graph, to_process)\u001B[0m\n\u001B[0;32m     67\u001B[0m     normalizing_weights \u001B[38;5;241m=\u001B[39m norm_source\n\u001B[0;32m     69\u001B[0m gap_percent \u001B[38;5;241m=\u001B[39m config[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mGraphs\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mgetfloat(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124msummary_graph_categorical_gap\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m---> 71\u001B[0m warnings\u001B[38;5;241m.\u001B[39mfilterwarnings(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mignore\u001B[39m\u001B[38;5;124m'\u001B[39m, category\u001B[38;5;241m=\u001B[39m\u001B[43mnp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mVisibleDeprecationWarning\u001B[49m)\n\u001B[0;32m     72\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhist_specs \u001B[38;5;241m=\u001B[39m axs\u001B[38;5;241m.\u001B[39mhist(plot_data, weights \u001B[38;5;241m=\u001B[39m normalizing_weights, bins\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mnum_bins, \\\n\u001B[0;32m     73\u001B[0m                            rwidth \u001B[38;5;241m=\u001B[39m (\u001B[38;5;241m100.0\u001B[39m \u001B[38;5;241m-\u001B[39m gap_percent) \u001B[38;5;241m/\u001B[39m \u001B[38;5;241m100.0\u001B[39m)\n\u001B[0;32m     74\u001B[0m warnings\u001B[38;5;241m.\u001B[39mfilterwarnings(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124monce\u001B[39m\u001B[38;5;124m'\u001B[39m, category\u001B[38;5;241m=\u001B[39mnp\u001B[38;5;241m.\u001B[39mVisibleDeprecationWarning)\n",
+      "File \u001B[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\numpy\\__init__.py:427\u001B[0m, in \u001B[0;36m__getattr__\u001B[1;34m(attr)\u001B[0m\n\u001B[0;32m    424\u001B[0m     \u001B[38;5;28;01mimport\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21;01mnumpy\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mchar\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mas\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21;01mchar\u001B[39;00m\n\u001B[0;32m    425\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m char\u001B[38;5;241m.\u001B[39mchararray\n\u001B[1;32m--> 427\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mAttributeError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmodule \u001B[39m\u001B[38;5;132;01m{!r}\u001B[39;00m\u001B[38;5;124m has no attribute \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    428\u001B[0m                      \u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{!r}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mformat(\u001B[38;5;18m__name__\u001B[39m, attr))\n",
+      "\u001B[1;31mAttributeError\u001B[0m: module 'numpy' has no attribute 'VisibleDeprecationWarning'"
+     ]
+    }
+   ],
+   "execution_count": 20
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-02T19:19:56.383373Z",
+     "start_time": "2025-03-02T19:19:54.731156Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Clean the dataset\n",
+    "dataset = pd.read_csv(os.path.join(\"csv\", 'openbci.csv'))\n",
+    "\n",
+    "print(\"Column Timestamp is not useful, dropping it\")\n",
+    "dataset = dataset.drop(columns=['Timestamp'])\n",
+    "for column in dataset.columns:\n",
+    "    if dataset[column].unique().size == 1:\n",
+    "        print(f\"Column {column} has only one unique value, dropping it\")\n",
+    "        dataset = dataset.drop(columns=[column])"
+   ],
+   "id": "a84eb1933bd93977",
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Report html/OpenBCI_report_sv.html was generated.\n"
-     ]
-    },
-    {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
-      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
-      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
-      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+      "Column Timestamp is not useful, dropping it\n",
+      "Column Analog Channel 0 has only one unique value, dropping it\n",
+      "Column Analog Channel 1 has only one unique value, dropping it\n",
+      "Column Analog Channel 2 has only one unique value, dropping it\n",
+      "Column Marker Channel has only one unique value, dropping it\n"
      ]
     }
    ],
-   "source": [
-    "import sweetviz as sv\n",
-    "report = sv.analyze(data_df)\n",
-    "report.show_html(os.path.join(\"html\", \"OpenBCI_report_sv.html\"), open_browser=False, layout=\"widescreen\")"
-   ]
+   "execution_count": 16
   }
  ],
  "metadata": {
diff --git a/classifier.ipynb b/classifier.ipynb
index fa91c7e..76dc8ec 100644
--- a/classifier.ipynb
+++ b/classifier.ipynb
@@ -17,6 +17,57 @@
     "estimators = 100"
    ]
   },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "# UDP Receiver"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "import socket\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "import os\n",
+    "\n",
+    "UDP_IP = \"0.0.0.0\"\n",
+    "UDP_PORT = 5005\n",
+    "BUFFER_SIZE = 1024\n",
+    "\n",
+    "dataset = pd.read_csv(os.path.join(\"csv\", 'openbci.csv'))\n",
+    "\n",
+    "X = dataset.iloc[:, :-1].values\n",
+    "y = dataset.iloc[:, -1].values\n",
+    "\n",
+    "modelo = RandomForestClassifier(n_estimators=100, random_state=19)\n",
+    "modelo.fit(X, y)\n",
+    "\n",
+    "columnas = dataset.columns[:-1]\n",
+    "\n",
+    "sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n",
+    "sock.bind((UDP_IP, UDP_PORT))\n",
+    "\n",
+    "print(f\"🔄 Servidor UDP en {UDP_IP}:{UDP_PORT}, esperando datos...\")\n",
+    "\n",
+    "while True:\n",
+    "    try:\n",
+    "        data, addr = sock.recvfrom(BUFFER_SIZE)\n",
+    "        decoded_data = data.decode(\"utf-8\").strip()\n",
+    "\n",
+    "        values = np.array([float(x) for x in decoded_data.split(\",\")]).reshape(1, -1)\n",
+    "\n",
+    "        df = pd.DataFrame(values, columns=columnas)\n",
+    "\n",
+    "        prediccion = modelo.predict(df)[0]\n",
+    "\n",
+    "    except Exception as e:\n",
+    "        print(f\"❌ Error al procesar el dato: {e}\")\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -53,13 +104,7 @@
    ],
    "source": [
     "dataset = pd.read_csv(os.path.join(\"csv\", 'openbci.csv'))\n",
-    "# Clean the dataset\n",
-    "print(\"Column Timestamp is not useful, dropping it\")\n",
-    "dataset = dataset.drop(columns=['Timestamp'])\n",
-    "for column in dataset.columns:\n",
-    "    if dataset[column].unique().size == 1:\n",
-    "        print(f\"Column {column} has only one unique value, dropping it\")\n",
-    "        dataset = dataset.drop(columns=[column])\n",
+    "\n",
     "print(\"-\"*70)\n",
     "# Split the dataset\n",
     "X = dataset.iloc[:, :-1].values\n",
@@ -81,23 +126,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 0.9947342176450832 (+/- 0.0004227947973763874)\n"
-     ]
+   "metadata": {
+    "jupyter": {
+     "is_executing": true
     }
-   ],
+   },
    "source": [
     "clf = RandomForestClassifier(n_estimators=estimators, n_jobs=-1, random_state=random_state)\n",
     "cv = StratifiedKFold(n_splits=splits, shuffle=True, random_state=random_state)\n",
     "scores = cross_validate(clf, X, y, scoring=\"accuracy\", cv=cv, n_jobs=-1, return_train_score=True)\n",
     "print(f\"Accuracy: {np.mean(scores['test_score'])} (+/- {np.std(scores['test_score'])})\")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -131,6 +172,7 @@
    "source": [
     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=random_state, stratify=y)\n",
     "clf.fit(X_train, y_train)\n",
+    "\n",
     "y_pred = clf.predict(X_test)\n",
     "print(classification_report(y_test, y_pred, digits=7))"
    ]
diff --git a/graficas.py b/graficas.py
new file mode 100644
index 0000000..e98b6d2
--- /dev/null
+++ b/graficas.py
@@ -0,0 +1,23 @@
+import matplotlib.pyplot as plt
+
+# Datos de número de esferas y tiempos de ejecución
+esferas = [2, 3, 4, 5, 6, 7, 8, 9, 10]
+tiempo_sin_opt = [0.000352, 0.000665, 0.001057, 0.001575, 0.002254, 0.003026, 0.003879, 0.004943, 0.006069]
+tiempo_con_opt = [0.000415, 0.000782, 0.001290, 0.001933, 0.002772, 0.003041, 0.004010, 0.005100, 0.006061]
+
+# Crear la gráfica
+plt.figure(figsize=(8, 5))
+plt.plot(esferas, tiempo_sin_opt, marker="o", linestyle="-", label="Tiempo sin optimizar")
+plt.plot(esferas, tiempo_con_opt, marker="s", linestyle="--", label="Tiempo optimizado (-O2)")
+
+plt.xlabel("Número de Esferas")
+plt.ylabel("Tiempo de ejecución (s)")
+plt.title("Comparación de Tiempos de Ejecución con y sin Optimización")
+plt.legend()
+plt.grid(True)
+
+# Guardar la imagen en el mismo directorio
+plt.savefig("comparacion_tiempos.png")
+
+# Mostrar la gráfica
+plt.show()