diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 00000000..a47b4c85 --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,60 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + + { + "name": "Python Debugger: Current File", + "type": "debugpy", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal" + }, + { + "name": "Python Script Debugging", + "type": "debugpy", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal" + }, + { + "name": "Jupyter Notebook Debugging", + "type": "debugpy", + "request": "launch", + "program": "${workspaceFolder}/mi_notebook.ipynb", + "console": "integratedTerminal" + }, + { + "name": "Custom Task", + "type": "debugpy", + "request": "launch", "program": "${workspaceFolder}/custom_script.py", + "args": ["--custom-arg", "value"], + "console": "integratedTerminal" + }, + { + "name": "🚀 Debug ADRpy automatizado", + "type": "debugpy", + "request": "launch", + "program": "${workspaceFolder}/ADRpy/analisis/main.py", + "console": "integratedTerminal", + "justMyCode": false, + "args": [ + "--ruta_archivo", "${workspaceFolder}/ADRpy/analisis/Data/Datos_aeronaves.xlsx", + "--archivo_destino", "${workspaceFolder}/ADRpy/analisis/Results/archivo_salida.xlsx", + "--debug_mode", + "--umbral_heat_map", "0.7", + "--nivel_confianza_min_similitud", "0.5", + "--rango_min", "0.85", + "--rango_max", "1.15", + "--parametros", "3, 4, 5, 6, 7, 8, 11, 12, 13, 14, 15, 18, 19, 20, 25", + "--columna", "Stalker XE", + "--umbral_correlacion", "0.5", + "--nivel_confianza_min_correlacion", "0.5" + ] + + } + + ] +} \ No newline at end of file diff --git a/ADRpy/analisis/Agents.md b/ADRpy/analisis/Agents.md new file mode 100644 index 00000000..29ad70eb --- /dev/null +++ b/ADRpy/analisis/Agents.md @@ -0,0 +1,121 @@ +# Archivo agents.md para Codex - Script ADRpy de Imputación por Correlación + +## Contexto del proyecto + +El script pertenece al proyecto ADRpy, enfocado en la imputación de datos faltantes en un DataFrame con información técnica aeronáutica. El objetivo específico es imputar valores faltantes utilizando modelos predictivos basados en correlaciones estadísticas. + +## Objetivo del script + +* Automatizar la imputación de valores faltantes mediante regresión lineal y polinómica (grado 2). +* Evaluar todas las combinaciones posibles de hasta 2 predictores, considerando métricas como MAPE, R², coeficiente Corr, y Confianza. +* Seleccionar automáticamente el mejor modelo para imputar, priorizando la precisión, robustez estadística y capacidad de generalización (evaluada con LOOCV). + +## Entradas necesarias + +* importar excel con nombre datos aeronaves dentro del mismo directorio y que use la pestaña que se llama (data\_frame\_prueba) +* manejar errores comunes y como encabezados con caracteres o espacios raros, y detectar las celdas vacias manejando diferentes opciones en excel las celdas vacias estan escritas con "nan" + +## Salidas esperadas + +* DataFrame actualizado con valores imputados. +* Reporte detallado con: + + * Modelo usado (ecuación final). + * Métricas: MAPE, R², Corr, Confianza y Correlación final. + * Advertencias explícitas en caso de extrapolaciones (valores fuera del rango de entrenamiento). + +## Flujo lógico principal detallado + +### Paso 1: Detección de celda objetivo + +* Identificar la celda faltante a imputar. + +### Paso 2: Filtrado por familia + +* Filtrar por tipo de misión de la aeronave objetivo. +* Si este filtro deja menos de 5 muestras completas para entrenar, relajar el filtro y usar dataset completo, registrando advertencia explícita. + +### Paso 3: Selección inicial de predictores + +* En esta etapa realizamos dos verificaciones, por un lugar garantizamos la existencia de valores en la aeronave objetivo para utilizarlos como valor de entrada en la ecuación de regresión que va a resultar de entrenar el modelo. +* En segundo lugar, validamos que los valores de estos parámetros se encuentren dentro del dominio de entrenamiento del modelo (rango ±15%) de valores del parámetro predictor. o sea cada parametro disponible en la aeronave se debe chequear que se encuentre dentro del rango aceptable dentro de ese mismo parametro comparandolo con los valores minimos y maximos de ese parametro sin ser la aeronave que se está evaluando. + +### Paso 4: Generación de combinaciones + +* Crear todas las combinaciones posibles de hasta 2 predictores disponibles. + +### Paso 5: Evaluación de cantidad de datos + +* Para cada combinación, filtrar y asegurar la existencia mínima de datos válidos necesarios según la cantidad de coeficientes del modelo. + +### Paso 6: Entrenamiento del modelo + +* Entrenar modelos (lineales y polinómicos de grado 2). +* Aplicar normalización en caso de regresión polinómica. +* Calcular métricas iniciales para filtrar modelos: + + * Coeficiente Corr: + $Corr = 0.6 \times \frac{R^2}{0.7} + 0.4 \times \left(1 - \frac{MAPE}{15}\right)$ + * Confianza final (tras aplicar Corr) +* Manejar errores posibles por matrices indefinidas brindando las advertencias correspondientes. + +### Paso 7: Filtrado y selección de los mejores modelos + +* Filtrar modelos según MAPE y R² mínimos establecidos. +* Seleccionar los mejores 2 modelos de cada tipo basándose en Confianza final. + +### Paso 8: Validación cruzada (LOOCV) + +* Aplicar Leave-One-Out Cross-Validation para evaluar capacidad de generalización de los modelos seleccionados. +* Seleccionar el modelo con mejor desempeño generalizado (menor error en LOOCV). + +### Paso 9: Imputación final + +* Imputar el valor faltante utilizando el mejor modelo seleccionado. +* Registrar advertencias en caso de extrapolación fuera del dominio entrenado. + +### Paso 10: Generación del reporte + +* Crear un DataFrame con un resumen detallado de imputaciones realizadas. +* Registrar métricas detalladas: + + * El valor imputado + * El valor de confianza (calculado tras validación) + * El número de iteración (en verdad esto se agrega en la función del Loop) + * El coeficiente Corr del modelo original + * El número de observaciones k usado para la función + * El tipo de modelo utilizado (lineal / polinómico) + * La ecuación del modelo entrenado + * La cantidad y nombres de predictores involucrados + * El factor de penalización aplicado (k) + * Cualquier advertencia relevante (ej. extrapolación, uso de datos sin filtrar, validación inestable) + * El estado de validez del modelo final (válido / parcialmente válido) + * Comparación entre coeficientes MAE y R2 de modelo entrenado con todos los datos vs. validación LOOCV    + +## Consideraciones técnicas importantes + +* Aplicar normalización a predictores al entrenar modelos polinómicos. +* Calcular número de condición para evaluar estabilidad numérica. +* Penalización en la confianza final por baja cantidad de datos: corr x f(k) (con función empírica definida según cantidad de datos disponibles). +* f (k) **=** 0.00002281 **\*** (k/**2)**\*\***5 **************************************************************************************************************************************************************************************************************-************************************************************************************************************************************************************************************************************** 0.00024 **************************************************************************************************************************************************************************************************************\*************************************************************************************************************************************************************************************************************** (k**/**2)**\*\***4 **************************************************************************************************************************************************************************************************************-************************************************************************************************************************************************************************************************************** 0.0036 **************************************************************************************************************************************************************************************************************\*************************************************************************************************************************************************************************************************************** (k**/**2)**\*\***3 **************************************************************************************************************************************************************************************************************+************************************************************************************************************************************************************************************************************** 0.046 **************************************************************************************************************************************************************************************************************\*************************************************************************************************************************************************************************************************************** (k**/**2)**\*\***2 **************************************************************************************************************************************************************************************************************+************************************************************************************************************************************************************************************************************** 0.0095 **************************************************************************************************************************************************************************************************************\*************************************************************************************************************************************************************************************************************** (k**/\*\*2) **+** 0.024 +* Validar dominio estricto: valores fuera del rango entrenado (±15%) deben generar advertencias explícitas. + +## Estructura del código sugerida + +dentro de una capeta que sea especificamente (imputación\_correlacion) hacer la modularización de la función adrpy/analisis/modulos/(aqui carpeta)/aqui modulos + +* Claridad modular con funciones específicas: + + * `cargar_y_validar_datos()` + * `filtrar_por_familia()` + * `seleccionar_predictores_validos()` + * `generar_combinaciones()` + * `entrenar_modelos_y_evaluar()` + * `filtrar_mejores_modelos()` + * `validar_con_loocv()` + * `imputar_valores()` + * `generar_reporte_final()` + +## Paquetes necesarios + +* `pandas`, `numpy`, `scikit-learn`, `matplotlib` (opcional). diff --git a/ADRpy/analisis/Data/Datos_aeronaves - Copy.xlsx b/ADRpy/analisis/Data/Datos_aeronaves - Copy.xlsx new file mode 100644 index 00000000..2797f899 Binary files /dev/null and b/ADRpy/analisis/Data/Datos_aeronaves - Copy.xlsx differ diff --git a/ADRpy/analisis/Data/Datos_aeronaves(vieja).xlsx b/ADRpy/analisis/Data/Datos_aeronaves(vieja).xlsx new file mode 100644 index 00000000..7ff55dab Binary files /dev/null and b/ADRpy/analisis/Data/Datos_aeronaves(vieja).xlsx differ diff --git a/ADRpy/analisis/Data/Datos_aeronaves.xlsx b/ADRpy/analisis/Data/Datos_aeronaves.xlsx new file mode 100644 index 00000000..b9bf7c16 Binary files /dev/null and b/ADRpy/analisis/Data/Datos_aeronaves.xlsx differ diff --git a/ADRpy/analisis/Data/Datos_aeronaves_completo.xlsx b/ADRpy/analisis/Data/Datos_aeronaves_completo.xlsx new file mode 100644 index 00000000..ee31f344 Binary files /dev/null and b/ADRpy/analisis/Data/Datos_aeronaves_completo.xlsx differ diff --git "a/ADRpy/analisis/Diagrama sin t\303\255tulo.drawio" "b/ADRpy/analisis/Diagrama sin t\303\255tulo.drawio" new file mode 100644 index 00000000..ace76389 --- /dev/null +++ "b/ADRpy/analisis/Diagrama sin t\303\255tulo.drawio" @@ -0,0 +1,398 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/ADRpy/analisis/Modulos/config_and_loading.py b/ADRpy/analisis/Modulos/config_and_loading.py new file mode 100644 index 00000000..85ba13c7 --- /dev/null +++ b/ADRpy/analisis/Modulos/config_and_loading.py @@ -0,0 +1,105 @@ +import pandas as pd +import tkinter as tk +from tkinter import simpledialog, messagebox +import sys +import unicodedata + +def configurar_entorno(max_rows=20, max_columns=10): + """ + Configura el entorno para mostrar más datos en la consola. + :param max_rows: Número máximo de filas para mostrar en consola. + :param max_columns: Número máximo de columnas para mostrar en consola. + """ + pd.set_option("display.max_rows", max_rows) + pd.set_option("display.max_columns", max_columns) + + +def cargar_datos(ruta_archivo=None): + """ + Carga los datos desde un archivo Excel y realiza validaciones. + Devuelve el DataFrame cargado y la ruta utilizada. + """ +# Detectar si se está ejecutando en modo debug + modo_debug = "--debug_mode" in sys.argv + + if not ruta_archivo: + if modo_debug: + ruta_archivo = r"C:\Users\delpi\OneDrive\Tesis\ADRpy-VTOL\ADRpy\analisis\Data\Datos_aeronaves.xlsx" + print(f"DEBUG MODE ACTIVADO: usando ruta predeterminada: {ruta_archivo}") + else: + ruta_archivo = input( + r"Ingrese la ruta del archivo Excel original (o presione Enter para usar la predeterminada): " + ).strip() or r"C:\Users\delpi\OneDrive\Tesis\ADRpy-VTOL\ADRpy\analisis\Data\Datos_aeronaves.xlsx" + + print(f"DEBUG: ruta_archivo antes de validar: '{ruta_archivo}'") + + # Validar el formato del archivo + if not ruta_archivo.endswith((".xlsx", ".xlsm")): + raise ValueError( + "El archivo debe estar en formato .xlsx o .xlsm compatible con openpyxl." + ) + + # Mostrar mensaje de carga + print(f"=== Cargando datos desde el archivo: {ruta_archivo} ===") + + try: + # Cargar el archivo con encabezado e índice configurados + df = pd.read_excel(ruta_archivo, header=0, index_col=0) + + # Validaciones adicionales + if df.empty: + raise ValueError("El archivo cargado está vacío.") + + # Manejar índices nulos + if df.index.isnull().any(): + print( + "Advertencia: Índices nulos encontrados. Reemplazando por 'indice_desconocido'." + ) + df.index = df.index.fillna("indice_desconocido") + + # Manejar columnas nulas + if df.columns.isnull().any(): + print( + "Advertencia: Columnas nulas encontradas. Reemplazando por 'columna_desconocida'." + ) + df.columns = df.columns.fillna("columna_desconocida") + + # Mostrar información básica del DataFrame cargado + print("\n=== Resumen inicial del DataFrame cargado ===") + print(df.info()) + # print("\n=== Vista previa de índices y columnas ===") + # print(f"Primeros índices: {df.index.tolist()[:10]}") + # print(f"Primeras columnas: {df.columns.tolist()[:10]}") + + return df, ruta_archivo + except FileNotFoundError: + raise ValueError("Error: Archivo no encontrado.") + except Exception as e: + raise ValueError(f"Error al cargar el archivo: {e}") + + + + +def normalizar_encabezados(df): + """ + Normaliza los encabezados del DataFrame: + - Elimina espacios al inicio y al final. + - Convierte a minúsculas. + - Elimina caracteres especiales o no ASCII. + :param df: DataFrame a normalizar. + :return: DataFrame con encabezados normalizados. + """ + def normalizar(texto): + if isinstance(texto, str): + # Eliminar caracteres no ASCII y convertir a minúsculas + texto = ''.join( + c for c in unicodedata.normalize('NFD', texto) + if unicodedata.category(c) != 'Mn' + ) + return texto.strip().lower() # Eliminar espacios y convertir a minúsculas + return texto + + # Normalizar columnas e índices + df.columns = [normalizar(col) for col in df.columns] + df.index = [normalizar(idx) for idx in df.index] + return df \ No newline at end of file diff --git a/ADRpy/analisis/Modulos/correlation_analysis.py b/ADRpy/analisis/Modulos/correlation_analysis.py new file mode 100644 index 00000000..03da95ab --- /dev/null +++ b/ADRpy/analisis/Modulos/correlation_analysis.py @@ -0,0 +1,133 @@ +import pandas as pd +import seaborn as sns +import matplotlib.pyplot as plt +import numpy as np +from .html_utils import convertir_a_html +from .user_interaction import solicitar_umbral + + +def calcular_correlaciones_y_generar_heatmap_con_resumen(df_procesado, parametros_seleccionados, umbral_heat_map=None, devolver_tabla=False): + valor_por_defecto = 0.7 + """ + Calcula las correlaciones completas y filtradas entre variables seleccionadas, + genera tablas en HTML con un resumen agregado, y crea un heatmap. + :param df_procesado: DataFrame procesado con los datos completos. + :param parametros_seleccionados: Lista de variables a incluir en los cálculos y visualización. + :param valor_por_defecto: Umbral predeterminado para correlaciones significativas. + :param devolver_tabla: Si True, retorna la tabla completa de correlaciones. + :return: Tabla completa de correlaciones (opcional). + """ + + def agregar_resumen_a_tabla(tabla, titulo): + """ + Agrega un resumen al final de una tabla HTML indicando: + - Cantidad total de valores. + - Cantidad de valores numéricos. + - Cantidad de valores NaN. + """ + total_valores = tabla.size + valores_numericos = tabla.count().sum() + valores_nan = total_valores - valores_numericos + + resumen = pd.DataFrame( + { + "Resumen": ["Total de valores", "Valores numéricos", "Valores NaN"], + "Cantidad": [total_valores, valores_numericos, valores_nan], + } + ) + + convertir_a_html(tabla, titulo=titulo, mostrar=True) + convertir_a_html(resumen, titulo="Resumen de la Tabla", mostrar=True) + + try: + # === Paso 1: Obtener umbral_heat_map desde el argumento o pedirlo al usuario === + if umbral_heat_map is None: + umbral_heat_map = solicitar_umbral(valor_por_defecto) + + print(f"\nUmbral seleccionado para correlaciones significativas: {umbral_heat_map}") + + # === Validación de parámetros seleccionados === + parametros_no_encontrados = [ + v for v in parametros_seleccionados if v not in df_procesado.columns + ] + if parametros_no_encontrados: + raise ValueError( + f"Los siguientes parámetros no se encontraron en los datos procesados: {', '.join(parametros_no_encontrados)}" + ) + + # === Tabla completa (sin filtrar) === + print("\n=== Cálculo de tabla completa ===") + tabla_completa = df_procesado.corr() + agregar_resumen_a_tabla( + tabla_completa.round(3), + "Tabla de Correlaciones con todos los parametros(tabla_completa)", + ) + + # Filtrar correlaciones por el umbral_heat_map + tabla_completa_significativa = tabla_completa[ + (tabla_completa.abs() >= umbral_heat_map) & (tabla_completa != 1) + ] + # agregar_resumen_a_tabla(tabla_completa_significativa.round(3), f"Tabla de Correlaciones Significativas (Umbral >= {umbral_heat_map})") + + # === Filtrar datos seleccionados === + print("\n=== Filtrando datos seleccionados ===") + + df_filtrado = df_procesado[parametros_seleccionados] + + # Tabla filtrada + print("\n=== Cálculo de correlaciones filtradas ===") + tabla_filtrada = df_filtrado.corr() + agregar_resumen_a_tabla( + tabla_filtrada.round(3), + "Tabla de Correlaciones Filtradas por parametros seleccionados (Para Heatmap)", + ) + + # Filtrar correlaciones por el umbral_heat_map para la tabla filtrada + tabla_filtrada_significativa = tabla_filtrada[ + (tabla_filtrada.abs() >= umbral_heat_map) & (tabla_filtrada != 1) + ] + agregar_resumen_a_tabla( + tabla_filtrada_significativa.round(3), + f"Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= {umbral_heat_map})", + ) + + # Preparar datos para el heatmap + print("\n=== Preparando datos para el heatmap ===") + heatmap_data = df_filtrado.dropna( + thresh=2 + ) # Excluir variables con menos de 2 valores válidos + heatmap_correlaciones = heatmap_data.corr() + + # Generar heatmap + print("\n=== Generando heatmap ===") + plt.figure(figsize=(12, 10)) + cmap = sns.diverging_palette(10, 145, s=80, l=55, n=9, as_cmap=True) + sns.heatmap( + heatmap_correlaciones, + annot=True, + cmap=cmap, + center=0, + linewidths=0.5, + vmin=-1, + vmax=1, + ) + plt.title( + f"Heatmap de Correlaciones de Variables Seleccionadas (Umbral >= {umbral_heat_map})" + ) + plt.show() + + except ValueError as e: + print(f"Error: {e}. Por favor verifica los parámetros seleccionados.") + tabla_completa = None # Asegurar que la variable esté definida + except KeyError as e: + print( + f"Error: {e}. Asegúrate de que las variables seleccionadas existen en los datos." + ) + tabla_completa = None # Asegurar que la variable esté definida + + if devolver_tabla: + return tabla_completa + else: + return None + + diff --git a/ADRpy/analisis/Modulos/correlation_imputation.py b/ADRpy/analisis/Modulos/correlation_imputation.py new file mode 100644 index 00000000..b0832a74 --- /dev/null +++ b/ADRpy/analisis/Modulos/correlation_imputation.py @@ -0,0 +1,234 @@ +import pandas as pd +import numpy as np +from sklearn.linear_model import LinearRegression +from .html_utils import convertir_a_html +from .correlation_analysis import calcular_correlaciones_y_generar_heatmap_con_resumen + + + + +def Imputacion_por_correlacion( + df_correlacion, + parametros_preseleccionados, + tabla_completa, + parametros_seleccionados, + min_datos_validos=5, + max_lineas_consola=250, + umbral_correlacion=0.7, + nivel_confianza_min_correlacion=0.5, +): + # Lógica de la función + """ + Imputa valores faltantes en un dataframe basado en correlaciones significativas entre parámetros. + + Parámetros: + df_procesado (pd.DataFrame): DataFrame con los datos a procesar. + parametros_preseleccionados (list): Lista de parámetros a imputar. + umbral_correlacion (float): Valor mínimo absoluto de correlación para considerar como significativa. + min_datos_validos (int): Mínimo número de datos válidos requeridos por parámetro para ser incluido. + max_lineas_consola (int): Máximo número de líneas a imprimir en la consola. + + Retorna: + pd.DataFrame: DataFrame con valores imputados. + """ + # Cargar datos simulados + df = df_correlacion.copy() + + if tabla_completa is None or tabla_completa.empty: + raise ValueError("La tabla de correlaciones completa no fue proporcionada o está vacía.") + + # Mostrar df en formato HTML + print("\n=== DataFrame inicial ===") + convertir_a_html(df, titulo="DataFrame antes de realizar imputacion por correlacion (df_procesado.copy())", mostrar=True) + #print("Parámetros disponibles en el índice del DataFrame:") + #print(df.index.tolist()) + + # Convertir todo a numérico + print("\n=== Convertir todo a numérico ===") + df = df.apply(pd.to_numeric, errors='coerce') # Forzar datos no numéricos a NaN + + #afasfasfasfasf no se que hace + parametros_validos = df.index[df.notna().sum(axis=1) >= 5].tolist() + df = df.loc[parametros_validos] + print(type(parametros_validos)) + + + # === PASO 1: CÁLCULO DE CORRELACIONES === + print("\n=== PASO 1: CÁLCULO DE CORRELACIONES ENTRE PARÁMETROS ===") + tabla_completa = tabla_completa = calcular_correlaciones_y_generar_heatmap_con_resumen( + df, + parametros_seleccionados, + umbral_heat_map=umbral_correlacion, + devolver_tabla=True +) + + correlaciones = tabla_completa.copy() + indices_validos = df.index + + # Filtrar correlaciones para que coincidan los índices y las columnas con parámetros válidos + correlaciones_filtradas = correlaciones.loc[ + indices_validos.intersection(correlaciones.index), # Filtra filas válidas + indices_validos.intersection(correlaciones.columns) # Filtra columnas válidas + ] + + correlaciones_aceptables = correlaciones_filtradas[(correlaciones_filtradas.abs() >= 0.7) & (correlaciones_filtradas.abs() < 1.0)] + + # Mostrar tabla de correlaciones + #convertir_a_html(correlaciones, titulo="Tabla de Correlaciones", mostrar=True) + #print("Parámetros disponibles en el índice del DataFrame:") + #print(correlaciones.index.tolist()) + + # Mostrar correlaciones aceptables en HTML + convertir_a_html( + datos_procesados=correlaciones_aceptables, + titulo="Tabla de correlaciones con filtro de umbral de correlación", + mostrar=True + ) + #print("Parámetros disponibles en el índice del DataFrame:") + #print(correlaciones_aceptables.index.tolist()) + + # === PASO 2: IMPUTACIÓN === + print("\n=== PASO 2: IMPUTACIÓN DE VALORES ===") + valores_imputados = 0 + lineas_impresas = 0 + MAX_LINEAS_CONSOLA = 40000000 + + def evaluar_confianza(puntaje): + """Evalúa el nivel de confianza basado en el puntaje.""" + if puntaje >= 0.9: + return "Confianza Muy Alta" + elif puntaje >= 0.75: + return "Confianza Alta" + elif puntaje >= 0.6: + return "Confianza Media" + elif puntaje >= 0.4: + return "Confianza Baja" + else: + return "Confianza Muy Baja" + + + #Declara una variable para crear una lista para registrar las imputaciones + reporte_imputaciones = [] + + + + for parametro in parametros_preseleccionados: + if parametro not in correlaciones_aceptables.columns: # Cambiar lógica para trabajar con columnas + print(f"\n=== {parametro}: Sin correlaciones significativas (|r| < 0.7) ===") + continue + + valores_faltantes = df[parametro][df[parametro].isna()].index.tolist() # Ajustar para trabajar con columnas + if not valores_faltantes: + print(f"\n=== {parametro}: No hay valores faltantes para imputar. ===") + continue + + print(f"\n=== Imputación para el parámetro: **{parametro}** ===") + for aeronave in valores_faltantes: + if lineas_impresas >= MAX_LINEAS_CONSOLA: + print("\n--- Límite de impresión alcanzado. ---") + break + + print(f"\n--- Imputación para aeronave: **{aeronave}** ---") + valores_predichos = [] + + correlaciones_parametro = correlaciones_aceptables[parametro].dropna() # Ajustar para trabajar con columnas + + for parametro_correlacionado, correlacion in correlaciones_parametro.items(): + datos_validos = df[[parametro, parametro_correlacionado]].dropna(axis=0) # Ajustar para trabajar con filas + + if datos_validos.shape[0] < 5: # Cambiar a filas + continue + + # Evitar duplicados + datos_validos = datos_validos.drop_duplicates() + + X = datos_validos[parametro_correlacionado].values.reshape(-1, 1) + y = datos_validos[parametro].values + + # Entrenar modelo de regresión + modelo = LinearRegression().fit(X, y) + r2 = modelo.score(X, y) + desviacion_std = np.std(y - modelo.predict(X)) + varianza = np.var(y - modelo.predict(X)) + incertidumbre = desviacion_std / np.sqrt(len(y)) + puntaje_confianza = 0.4 * r2 + 0.3 * (1 - incertidumbre) + 0.2 * (1 - desviacion_std) + 0.1 * (1 - varianza) + nivel_confianza = evaluar_confianza(puntaje_confianza) + + if pd.notna(df.at[aeronave, parametro_correlacionado]): # Ajustar para trabajar con filas + valor_imputado = modelo.predict(np.array([[df.at[aeronave, parametro_correlacionado]]]))[0] # Convertir a numpy.ndarray + valores_predichos.append( + (parametro_correlacionado, round(valor_imputado, 3), round(r2, 3), round(desviacion_std, 3)) + ) + + # Detalle de datos usados + print(f"\n--- Correlación: {parametro_correlacionado} (r = {round(correlacion, 3)}) ---") + print(f"Aeronaves utilizadas: {datos_validos.index.tolist()}") + print(f"Valores para {parametro_correlacionado}: {X.flatten().round(3).tolist()}") + print(f"Valores para {parametro}: {y.round(3).tolist()}") + print(f"Ecuación de regresión: y = {round(modelo.coef_[0], 3)}x + {round(modelo.intercept_, 3)}") + print(f"Valor del parámetro correlacionado para la aeronave: {round(df.at[aeronave, parametro_correlacionado], 3)}") + print(f"Predicción obtenida: {round(valor_imputado, 3)}") + print(f"\tR²: {r2}, Desviación Estándar: {desviacion_std}, Varianza: {varianza}, Incertidumbre: {incertidumbre}") + print(f"\tNivel de confianza: {nivel_confianza}") + lineas_impresas += 1 + + if valores_predichos: + valor_final = np.median([pred[1] for pred in valores_predichos]) + df.at[aeronave, parametro] = round(valor_final, 3) # Ajustar para trabajar con filas + valores_imputados += 1 + print(f"Valores imputados: {[f'{pred[0]}: {pred[1]}' for pred in valores_predichos]}") + print(f"**Mediana calculada:** {round(valor_final, 3)}") + + # Registro correcto + reporte_imputaciones.append({ + "Aeronave": aeronave, + "Parámetro": parametro, + "Valor Imputado": valor_final, + "Nivel de Confianza": puntaje_confianza + }) + else: + info_imposible = pd.DataFrame([{ + "Mensaje": f"No se pudo imputar el parámetro '{parametro}' para la aeronave '{aeronave}'." + }]) + convertir_a_html(info_imposible, titulo="Imputación no Exitosa", mostrar=True) + + lineas_impresas += 1 + + + # Filtro y generación del reporte final + df_reporte = pd.DataFrame(reporte_imputaciones) + #print("Contenido de reporte_imputaciones:", reporte_imputaciones) + #print("Columnas de df_reporte:", df_reporte.columns) + #print("Contenido inicial de df_reporte:\n", df_reporte.head()) + if "Nivel de Confianza" in df_reporte.columns: + df_reporte = df_reporte[df_reporte["Nivel de Confianza"] >= nivel_confianza_min_correlacion] + else: + print("La columna 'Nivel de Confianza' no está presente en df_reporte.") + # Maneja el caso sin filtro, por ejemplo: + return df, [] + + + # Resumen de imputaciones + resumen_imputaciones = df_reporte.groupby("Aeronave").size().reset_index(name="Cantidad de Valores Imputados") + total_imputaciones = resumen_imputaciones["Cantidad de Valores Imputados"].sum() + resumen_imputaciones.loc["Total"] = ["Total", total_imputaciones] + + # Mostrar reportes (HTML opcional) + convertir_a_html(df_reporte, titulo="Reporte Final de Imputaciones", mostrar=True) + convertir_a_html(resumen_imputaciones, titulo="Resumen de Imputaciones", mostrar=True) + + # Validar si se realizaron imputaciones + if not reporte_imputaciones: + print("No se realizaron imputaciones con éxito.") + return df, [] + + # Validar si el DataFrame está vacío (seguridad adicional) + if df.empty: + print("El DataFrame de resultados está vacío.") + return df, [] + + # Opcional: convertir reporte_imputaciones a DataFrame si necesario + #df_reporte_final = pd.DataFrame(reporte_imputaciones) + + # Retornar el DataFrame procesado y la lista de imputaciones + return df, reporte_imputaciones \ No newline at end of file diff --git a/ADRpy/analisis/Modulos/data_processing.py b/ADRpy/analisis/Modulos/data_processing.py new file mode 100644 index 00000000..e94c653c --- /dev/null +++ b/ADRpy/analisis/Modulos/data_processing.py @@ -0,0 +1,201 @@ +import pandas as pd +import numpy as np +import tkinter as tk +from tkinter import simpledialog +from Modulos.html_utils import convertir_a_html + + +def procesar_datos_y_manejar_duplicados(df, respuesta_global=None): + """ + Limpia un DataFrame preservando la estructura original y maneja duplicados en índices y columnas. + Incluye interacción para gestionar duplicados según las elecciones del usuario. + :param df: DataFrame a procesar. + :return: DataFrame limpio y procesado. + """ + try: + print("=== Inicio del procesamiento de datos ===") + + # Paso 1: Limpieza inicial de encabezados + df.columns = df.columns.str.strip().str.replace("\xa0", " ", regex=True) + df.index = df.index.astype(str).str.strip().str.replace("\xa0", " ", regex=True) + + # Paso 2: Eliminar filas y columnas completamente vacías + df.dropna(how="all", inplace=True) # Filas vacías + df.dropna(how="all", axis=1, inplace=True) # Columnas vacías + + # Paso 3: Manejo de duplicados + print("\n=== Comprobación de duplicados ===") + duplicados_fila = df.index[df.index.duplicated()].tolist() + duplicados_columna = df.columns[df.columns.duplicated()].tolist() + + if not duplicados_fila and not duplicados_columna: + print("No se encontraron duplicados en índices o columnas.") + else: + print(f"Índices duplicados: {duplicados_fila}") + print(f"Columnas duplicadas: {duplicados_columna}") + + # Crear ventana emergente para interacción + if respuesta_global is None: + root = tk.Tk() + root.withdraw() + respuesta_global = simpledialog.askstring( + "Manejo global de duplicados", + "Se encontraron duplicados. ¿Deseas aplicar una acción global a todos?\n" + "[1] Eliminar todos los duplicados\n" + "[2] Conservar el primero en todos\n" + "[3] Conservar el último en todos\n" + "[4] Procesar duplicados uno por uno", + ) + + # Aplicar acción global si corresponde + if respuesta_global in ["1", "2", "3"]: + if respuesta_global == "1": + print("Eliminando todos los duplicados...") + if duplicados_fila: + df = df.loc[~df.index.duplicated(keep=False)] + if duplicados_columna: + df = df.loc[:, ~df.columns.duplicated(keep=False)] + + elif respuesta_global == "2": + print("Conservando el primero en todos los duplicados...") + if duplicados_fila: + df = df.loc[~df.index.duplicated(keep="first")] + if duplicados_columna: + df = df.loc[:, ~df.columns.duplicated(keep="first")] + + elif respuesta_global == "3": + print("Conservando el último en todos los duplicados...") + if duplicados_fila: + df = df.loc[~df.index.duplicated(keep="last")] + if duplicados_columna: + df = df.loc[:, ~df.columns.duplicated(keep="last")] + else: + # Procesar duplicados uno por uno si respuesta_global es '4' + for duplicado in duplicados_fila + duplicados_columna: + tipo = "Índice" if duplicado in duplicados_fila else "Columna" + respuesta = simpledialog.askstring( + "Duplicado encontrado", + f"{tipo} duplicado '{duplicado}' encontrado. Opciones:\n" + "[1] Eliminar\n" + "[2] Conservar el primero\n" + "[3] Conservar el último", + ) + # Realizar la acción según la elección del usuario + if respuesta == "1": + if tipo == "Índice": + df = df[df.index != duplicado] + else: + df = df.loc[:, df.columns != duplicado] + elif respuesta == "2": + if tipo == "Índice": + df = df.loc[~df.index.duplicated(keep="first")] + else: + df = df.loc[:, ~df.columns.duplicated(keep="first")] + elif respuesta == "3": + if tipo == "Índice": + df = df.loc[~df.index.duplicated(keep="last")] + else: + df = df.loc[:, ~df.columns.duplicated(keep="last")] + + # Paso 4: Convertir valores internos a numéricos + print("\n=== Convirtiendo valores a numéricos donde sea posible ===") + for col in df.columns: + try: + df.loc[:, col] = pd.to_numeric(df[col], errors="coerce") + except Exception as e: + print( + f"Advertencia: No se pudo convertir la columna '{col}' a numérico. Error: {e}" + ) + + print("=== Procesamiento completado ===") + return df + + except Exception as e: + raise ValueError(f"Error durante el procesamiento y manejo de duplicados: {e}") + + +def mostrar_celdas_faltantes_con_seleccion(df, fila_seleccionada=None, debug_mode=False): + """ + Muestra las celdas faltantes de una fila específica elegida por el usuario o automáticamente. + + :param df: DataFrame a analizar. + :param fila_seleccionada: Nombre de la fila a analizar. Si None, se pedirá al usuario o se usará el modo automático. + :param debug_mode: Si True, selecciona automáticamente la primera fila con datos faltantes si no se pasa ninguna. + :return: DataFrame con las celdas faltantes de la fila seleccionada. + """ + aeronaves_con_nulos = df.index[df.isnull().any(axis=1)].tolist() + + if not aeronaves_con_nulos: + print("✅ No hay filas con valores faltantes.") + return pd.DataFrame() + + if debug_mode and not fila_seleccionada: + fila_seleccionada = aeronaves_con_nulos[0] + print(f"[DEBUG] Seleccionando automáticamente la primera fila con nulos: '{fila_seleccionada}'") + + elif not fila_seleccionada: + print("\n=== Filas con datos faltantes ===") + for i, fila in enumerate(aeronaves_con_nulos, start=1): + print(f"{i}. {fila}") + + seleccion = input("Selecciona el número de la fila a analizar (presiona Enter para seleccionar la primera): ").strip() + + if not seleccion.isdigit(): + print("🔁 Entrada inválida o vacía. Seleccionando la primera fila por defecto.") + fila_seleccionada = aeronaves_con_nulos[0] + else: + seleccion = int(seleccion) - 1 + if 0 <= seleccion < len(aeronaves_con_nulos): + fila_seleccionada = aeronaves_con_nulos[seleccion] + else: + print("🔁 Número fuera de rango. Seleccionando la primera fila por defecto.") + fila_seleccionada = aeronaves_con_nulos[0] + + print(f"\n=== Analizando celdas faltantes en la fila: '{fila_seleccionada}' ===") + celdas_faltantes = df.loc[fila_seleccionada][df.loc[fila_seleccionada].isnull()] + + return celdas_faltantes + + +def generar_resumen_faltantes( + df, titulo="Resumen de Valores Faltantes por Fila", ancho="50%", alto="300px" +): + """ + Genera un resumen de los valores faltantes por fila en un DataFrame. + También genera una tabla HTML con la sumatoria total de los valores faltantes de todas las filas. + + :param df: DataFrame a analizar. + :param titulo: Título opcional para mostrar en la tabla HTML. + :param ancho: Ancho del contenedor HTML. + :param alto: Alto del contenedor HTML. + :return: Tuple con dos DataFrames: resumen de valores faltantes por fila y sumatoria total. + """ + # Calcular la cantidad de valores faltantes por fila + faltantes_por_fila = df.isnull().sum(axis=1) + + # Crear un DataFrame con el resumen por fila + resumen_faltantes = faltantes_por_fila.reset_index() + resumen_faltantes.columns = ["Fila", "Valores Faltantes"] + + # Calcular la sumatoria total de los valores faltantes + total_faltantes = faltantes_por_fila.sum() + resumen_total = pd.DataFrame( + {"Resumen": ["Total de Valores Faltantes"], "Cantidad": [total_faltantes]} + ) + + # Mostrar el resumen por fila como una tabla HTML + convertir_a_html( + resumen_faltantes, titulo=titulo, ancho=ancho, alto=alto, mostrar=True + ) + + # Mostrar la sumatoria total como una tabla HTML + convertir_a_html( + resumen_total, + titulo="Sumatoria Total de Valores Faltantes", + ancho=ancho, + alto="100px", + mostrar=True, + ) + + # Retornar ambos DataFrames para su posible uso posterior + return resumen_faltantes, resumen_total diff --git a/ADRpy/analisis/Modulos/excel_export.py b/ADRpy/analisis/Modulos/excel_export.py new file mode 100644 index 00000000..2a1239ba --- /dev/null +++ b/ADRpy/analisis/Modulos/excel_export.py @@ -0,0 +1,75 @@ +import pandas as pd +from openpyxl import load_workbook +from openpyxl.styles import PatternFill +from openpyxl.comments import Comment + + + + +def exportar_excel_con_imputaciones(archivo_origen, df_procesado, resumen_imputaciones, archivo_destino= r"C:\Users\delpi\OneDrive\Tesis\ADRpy-VTOL\ADRpy\analisis\Results\Datos_imputados.xlsx"): + """ + Exporta el DataFrame procesado a un archivo Excel manteniendo el formato original. + Agrega colores y comentarios a las celdas imputadas por similitud y correlación. + + :param archivo_origen: Ruta del archivo Excel original. + :param archivo_destino: Ruta del archivo Excel de salida. + :param df_procesado: DataFrame con las imputaciones realizadas. + :param resumen_imputaciones: Lista de diccionarios con detalles de imputaciones. + """ + try: + # Asegurarse de que 'resumen_imputaciones' sea una lista de diccionarios + if isinstance(resumen_imputaciones, pd.DataFrame): + resumen_imputaciones = resumen_imputaciones.to_dict('records') + + # Manejar el caso donde no haya imputaciones + if not resumen_imputaciones: + print("No hay imputaciones para exportar.") + return + + print(f"=== Exportando datos al archivo: {archivo_destino} ===") + wb = load_workbook(archivo_origen) + ws = wb.active + + color_similitud = PatternFill(start_color="FFFF00", end_color="FFFF00", fill_type="solid") + color_correlacion = PatternFill(start_color="00FF00", end_color="00FF00", fill_type="solid") + + imputaciones_por_celda = { + (registro["Parámetro"], registro["Aeronave"]): registro + for registro in resumen_imputaciones + } + + # Recorrer las celdas del archivo original y actualizar según las imputaciones + for fila in ws.iter_rows(min_row=2, min_col=2): # Ajustar filas/columnas según tu estructura + for celda in fila: + if celda and celda.column and celda.row: # Validar que la celda y sus propiedades no sean None + parametro = ws.cell(row=1, column=celda.column).value # Obtener nombre del parámetro + aeronave = ws.cell(row=celda.row, column=1).value # Obtener nombre de la aeronave + + if (parametro, aeronave) in imputaciones_por_celda: + registro = imputaciones_por_celda[(parametro, aeronave)] + valor_imputado = df_procesado.at[aeronave, parametro] # Acceder usando filas como aeronaves y columnas como parámetros + + # Actualizar el valor en la celda + celda.value = valor_imputado + + # Asignar color según el tipo de imputación + if registro["Método"] == "Similitud": + celda.fill = color_similitud + elif registro["Método"] == "Correlación": + celda.fill = color_correlacion + + # Agregar comentario con el nivel de confianza y la iteración + comentario = ( + f"Nivel de confianza: {registro['Nivel de Confianza']*100:.2f}%\n" + f"Iteración: {registro['Iteración']}" + ) + celda.comment = Comment(comentario, "Sistema") + + # Guardar el archivo con las imputaciones + wb.save(archivo_destino) + print(f"Exportación completada. El archivo se guardó como '{archivo_destino}'.") + + except FileNotFoundError: + print(f"Error: El archivo '{archivo_origen}' o {archivo_destino} no fue encontrado.") + except Exception as e: + print(f"Error al procesar el archivo: {e}") diff --git a/ADRpy/analisis/Modulos/html_utils.py b/ADRpy/analisis/Modulos/html_utils.py new file mode 100644 index 00000000..293ebdfc --- /dev/null +++ b/ADRpy/analisis/Modulos/html_utils.py @@ -0,0 +1,71 @@ +import pandas as pd +import numpy as np +from IPython.display import display, HTML + + + +def convertir_a_html(datos_procesados, titulo="", ancho="100%", alto="400px", mostrar=True): + """ + Convierte un DataFrame o Series a una tabla HTML, redondeando números a 3 cifras significativas. + :param datos_procesados: DataFrame o Series a transformar. + :param titulo: Título opcional para mostrar en la tabla. + :param ancho: Ancho del contenedor HTML. + :param alto: Alto del contenedor HTML. + :param mostrar: Si True, muestra la tabla directamente; si False, devuelve el HTML. + """ + + # Asegurarse de que sea un DataFrame + if isinstance(datos_procesados, pd.Series): + datos_procesados = datos_procesados.to_frame(name="Valores") + datos_procesados.index.name = "Índice" + + # Redondear números a 3 cifras significativas sin notación científica + datos_procesados = datos_procesados.apply( + lambda col: col.map( + lambda x: f"{x:.3f}" if isinstance(x, (int, float)) else x + ) if col.dtypes in [np.float64, np.int64] else col + ) + + # Estilo CSS modificado + estilo_scroll = f""" + + """ + # Generar el HTML con el título y la tabla + tabla_html = estilo_scroll + f"

{titulo}

{datos_procesados.to_html()}
" + + # Mostrar o devolver el HTML + if mostrar: + from IPython.display import display, HTML + display(HTML(tabla_html)) # Muestra directamente + else: + return tabla_html # Devuelve el HTML diff --git a/ADRpy/analisis/Modulos/imputacion_correlacion/__init__.py b/ADRpy/analisis/Modulos/imputacion_correlacion/__init__.py new file mode 100644 index 00000000..0572c0b9 --- /dev/null +++ b/ADRpy/analisis/Modulos/imputacion_correlacion/__init__.py @@ -0,0 +1 @@ +from .imputacion_correlacion import imputacion_correlacion diff --git a/ADRpy/analisis/Modulos/imputacion_correlacion/imputacion_correlacion.py b/ADRpy/analisis/Modulos/imputacion_correlacion/imputacion_correlacion.py new file mode 100644 index 00000000..4c5df757 --- /dev/null +++ b/ADRpy/analisis/Modulos/imputacion_correlacion/imputacion_correlacion.py @@ -0,0 +1,208 @@ +import pandas as pd +import numpy as np +from itertools import combinations +from collections import defaultdict +from sklearn.linear_model import LinearRegression +from sklearn.preprocessing import PolynomialFeatures +from sklearn.model_selection import LeaveOneOut +from sklearn.metrics import mean_absolute_percentage_error, r2_score + + +def penalizacion_por_k(k: int) -> float: + """Return penalization factor based on sample size.""" + if k > 10: + return 1.0 + return max( + 0, + min( + 1, + 0.00002281 * k ** 5 + - 0.00024 * k ** 4 + - 0.0036 * k ** 3 + + 0.046 * k ** 2 + + 0.0095 * k + + 0.024, + ), + ) + + +def cargar_y_validar_datos(path: str) -> pd.DataFrame: + """Load Excel data from the given path using sheet 'data_frame_prueba'.""" + try: + df = pd.read_excel(path, sheet_name="data_frame_prueba") + except FileNotFoundError: + raise FileNotFoundError(f"Archivo no encontrado: {path}") + except ValueError: + raise ValueError( + "No se pudo leer la hoja 'data_frame_prueba'. Verifique el archivo" + ) + df = df.rename(columns=lambda c: str(c).strip()) + df.replace("nan", np.nan, inplace=True) + return df + + +def seleccionar_predictores_validos(df: pd.DataFrame, objetivo: str, rango: float = 0.15) -> list: + """Return numeric predictors with enough data and within ±15% range.""" + numericas = df.select_dtypes(include=[np.number]).columns + filas_obj = df[df[objetivo].isna()].index + candidatos = [] + for col in numericas: + if col == objetivo or df[col].notna().sum() < 5: + continue + valido = True + vals_no_nan = df[col].dropna() + if vals_no_nan.empty: + continue + mn, mx = vals_no_nan.min(), vals_no_nan.max() + rango_min, rango_max = mn * (1 - rango), mx * (1 + rango) + for idx in filas_obj: + val = df.at[idx, col] + if pd.isna(val) or not (rango_min <= val <= rango_max): + valido = False + break + if valido: + candidatos.append(col) + return candidatos + + +def generar_combinaciones(predictores: list) -> list: + combos = [] + for r in (1, 2): + combos.extend(list(combinations(predictores, r))) + return combos + + +def entrenar_modelo( + df: pd.DataFrame, objetivo: str, predictores: tuple, poly: bool +) -> dict | None: + """Train linear or polynomial model and compute metrics.""" + df_train = df.dropna(subset=[objetivo, *predictores]) + if len(df_train) < len(predictores) + 1: + return None + X = df_train[list(predictores)] + y = df_train[objetivo] + if poly: + pf = PolynomialFeatures(degree=2, include_bias=False) + X_trans = pf.fit_transform(X) + else: + pf = None + X_trans = X + modelo = LinearRegression().fit(X_trans, y) + pred = modelo.predict(X_trans) + mape = mean_absolute_percentage_error(y, pred) * 100 + r2 = r2_score(y, pred) + corr = 0.6 * (r2 / 0.7) + 0.4 * (1 - mape / 15) + confianza = corr * penalizacion_por_k(len(df_train)) + tipo = ("poly" if poly else "linear") + f"-{len(predictores)}" + return { + "predictores": predictores, + "modelo": modelo, + "pf": pf, + "mape": mape, + "r2": r2, + "corr": corr, + "confianza": confianza, + "tipo": tipo, + "n": len(df_train), + } + + +def filtrar_mejores_modelos(modelos: list, top: int = 2) -> list: + """Return top models per type based on confianza.""" + modelos = [m for m in modelos if m is not None and m["mape"] <= 15 and m["r2"] >= 0.7] + grupos: defaultdict[str, list] = defaultdict(list) + for m in modelos: + grupos[m["tipo"]].append(m) + mejores = [] + for lst in grupos.values(): + lst.sort(key=lambda x: x["confianza"], reverse=True) + mejores.extend(lst[:top]) + return mejores + + +def validar_con_loocv(df: pd.DataFrame, objetivo: str, info: dict) -> tuple: + """Return MAE and R2 from LOOCV validation.""" + df_train = df.dropna(subset=[objetivo, *info["predictores"]]) + if df_train.empty: + return np.inf, -np.inf + X = df_train[list(info["predictores"])] + y = df_train[objetivo] + if info["pf"] is not None: + X = info["pf"].fit_transform(X) + X_vals = X + else: + X_vals = X.values + loo = LeaveOneOut() + preds = np.zeros(len(y)) + for i, (train_idx, test_idx) in enumerate(loo.split(X_vals)): + m = LinearRegression().fit(X_vals[train_idx], y.iloc[train_idx]) + preds[i] = m.predict(X_vals[test_idx])[0] + mae = float(np.mean(np.abs(y - preds))) + r2 = float(r2_score(y, preds)) + return mae, r2 + + +def imputar_valores(df: pd.DataFrame, objetivo: str, info: dict): + df_res = df.copy() + faltantes = df_res[df_res[objetivo].isna()].index + imputaciones = [] + if not len(faltantes): + return df_res, imputaciones + X_pred = df_res.loc[faltantes, list(info["predictores"])] + if info["pf"] is not None: + X_pred = info["pf"].transform(X_pred) + valores = info["modelo"].predict(X_pred) + df_res.loc[faltantes, objetivo] = valores + vals_no_nan = df[list(info["predictores"])] + advert = "" + mn = vals_no_nan.min() + mx = vals_no_nan.max() + for idx, val in zip(faltantes, valores): + advert = "" + for col in info["predictores"]: + v = df.at[idx, col] + if pd.isna(v) or not (mn[col] * 0.85 <= v <= mx[col] * 1.15): + advert = "Extrapolacion" + break + imputaciones.append( + { + "Fila": idx, + "Parametro": objetivo, + "Valor imputado": val, + "Confianza": info["confianza"], + "Corr": info["corr"], + "k": info["n"], + "Tipo Modelo": info["tipo"], + "Predictores": ",".join(info["predictores"]), + "Penalizacion_k": penalizacion_por_k(info["n"]), + "Advertencia": advert, + } + ) + return df_res, imputaciones + + +def generar_reporte_final(registros: list) -> pd.DataFrame: + return pd.DataFrame(registros) + + +def imputacion_correlacion(path: str = "ADRpy/analisis/Data/Datos_aeronaves.xlsx"): + df = cargar_y_validar_datos(path) + reporte = [] + for objetivo in [c for c in df.columns if df[c].isna().any()]: + predictores = seleccionar_predictores_validos(df, objetivo) + if not predictores: + continue + modelos = [] + for combo in generar_combinaciones(predictores): + for poly in (False, True): + modelos.append(entrenar_modelo(df, objetivo, combo, poly)) + mejores = filtrar_mejores_modelos(modelos) + if not mejores: + continue + mejor = min( + ((m, validar_con_loocv(df, objetivo, m)) for m in mejores), + key=lambda t: t[1][0], + )[0] + df, imps = imputar_valores(df, objetivo, mejor) + reporte.extend(imps) + return df, generar_reporte_final(reporte) diff --git a/ADRpy/analisis/Modulos/imputacion_similitud_flexible.py b/ADRpy/analisis/Modulos/imputacion_similitud_flexible.py new file mode 100644 index 00000000..79b4f84d --- /dev/null +++ b/ADRpy/analisis/Modulos/imputacion_similitud_flexible.py @@ -0,0 +1,242 @@ +""" +imputacion_similitud_flexible.py +-------------------------------- +Implementa la lógica de K‑NN con 3 ejes obligatorios (físico, geométrico, prestacional) +y filtrado progresivo de familia (F0‑F3). Diseñado para integrarse sin romper +los nombres ni los flujos que ya existen en tu proyecto ADRpy. + +Uso rápido: + python imputacion_similitud_flexible.py --ruta_excel Datos_aeronaves.xlsx \ + --aeronave "Stalker XE" \ + --parametro "Velocidad a la que se realiza el crucero (KTAS)" +""" + +import argparse +import pandas as pd +import numpy as np +# ------------------------ HELPERS ------------------------ + +def imprimir(msg, bold=False): + prefix = "\033[1m" if bold else "" + suffix = "\033[0m" if bold else "" + print(f"{prefix}{msg}{suffix}") +# ------------------------------------------------------------------ # +# CONFIGURACIÓN DE BLOQUES Y CAPAS DE FAMILIA +# ------------------------------------------------------------------ # + +def configurar_similitud(): + """ + Devuelve: + - bloques_rasgos: diccionario de ejes y parámetros + - filas_familia: atributos para clasificación familiar + - capas_familia: filtros progresivos de familia F0, F1, F2 + """ + bloques_rasgos = { + "fisico": ["Peso máximo al despegue (MTOW)", "Peso vacío"], + "geom": ["Área del ala", "envergadura", "Longitud del fuselaje", "Relación de aspecto del ala"], + "prest": [ + "Potencia específica (P/W)", "Autonomía de la aeronave", + "Alcance de la aeronave", "Velocidad a la que se realiza el crucero (KTAS)", + "Velocidad máxima (KIAS)", + ], + } + filas_familia = [ + "Misión", "Despegue", "Propulsión vertical", "Propulsión horizontal", + "Cantidad de motores propulsión vertical", "Cantidad de motores propulsión horizontal", + ] + capas_familia = [ + {attr: "equals" for attr in filas_familia}, + {attr: "equals" for attr in filas_familia[:4]}, + {attr: "equals" for attr in filas_familia[:2]}, + ] + return bloques_rasgos, filas_familia, capas_familia + +# ------------------------ FUNCIÓN PRINCIPAL ------------------------ + +def penalizacion_por_k(k): + """ + Calcula la penalización por cantidad de vecinos (k) usando una ecuación polinomial. + Para k > 10, la penalización se fija en 1.0 (confianza máxima). + """ + if k > 10: + return 1.0 + return max(0, min(1, 0.00002281 * k**5 - 0.00024 * k**4 - 0.0036 * k**3 + 0.046 * k**2 + 0.0095 * k + 0.024)) + +def imputar_por_similitud( + df_parametros: pd.DataFrame, + df_atributos: pd.DataFrame, + aeronave_obj: str, + parametro_objetivo: str, + bloques_rasgos: dict, + capas_familia: list +): + imprimir(f"\n=== Imputación por similitud: {aeronave_obj} - {parametro_objetivo} ===", True) + + # Validaciones + if parametro_objetivo not in df_parametros.columns: + imprimir(f"⚠️ Parámetro '{parametro_objetivo}' no encontrado.", True) + return None + if aeronave_obj not in df_parametros.index: + imprimir(f"⚠️ Aeronave '{aeronave_obj}' no encontrada.", True) + return None + + # Iteración por capas de familia + for capa_idx, criterios in enumerate(capas_familia): + familia = f"F{capa_idx}" + imprimir(f"\n--- Capa {familia}: criterios {list(criterios.keys())} ---", True) + # Filtrar familia + mask = np.ones(df_parametros.shape[0], dtype=bool) + for fila, modo in criterios.items(): + val = df_atributos.loc[aeronave_obj, fila] + mask &= (df_atributos[fila] == val).values + df_familia = df_parametros.loc[mask, :] + if df_familia.shape[0] == 0: + imprimir(f"❌ Sin drones en {familia}. Relajando...", True) + continue + # —> Validar que haya vecinos con el parámetro objetivo + rows_validas = df_familia.index[df_familia[parametro_objetivo].notna()] + if len(rows_validas) == 0: + imprimir(f"❌ Ningún dron en {familia} tiene '{parametro_objetivo}'.", True) + continue + + # Parámetros MTOW y filtro ±20% + mtow_obj = df_familia.loc[aeronave_obj, "Peso máximo al despegue (MTOW)"] + mtow_vec = df_familia.loc[rows_validas, "Peso máximo al despegue (MTOW)"].values + delta_mtow = np.abs(mtow_vec - mtow_obj) / mtow_obj * 100 + mask_mtow = delta_mtow <= 20 + rows_filtrados = rows_validas[mask_mtow] + if len(rows_filtrados) == 0: + imprimir(f"❌ Sin vecinos ±20% MTOW en {familia}.", True) + continue + + # Cálculo de MTOW_score + d = delta_mtow[mask_mtow] + g = -0.002*d**4 + 0.041*d**3 - 0.28135*d**2 + 0.23*d + 99.94 + mtow_scores = g / 100.0 + + # Nueva lógica para calcular los bonos geométricos y prestacionales + + def calcular_bono(tipo): + parametros = bloques_rasgos[tipo] # Obtener los parámetros geométricos o prestacionales + bono_total = 0 # Inicializar el bono total + + for parametro in parametros: + try: + # Valores de la aeronave objetivo y los vecinos + valor_objetivo = df_parametros.loc[aeronave_obj, parametro] + valores_vecinos = df_familia.loc[rows_filtrados, parametro].values + + # Si el valor de la aeronave objetivo es NaN, el bono es 0 + if pd.isna(valor_objetivo): + imprimir(f"⚠️ Parámetro '{parametro}' no tiene valor en la aeronave objetivo. Bono = 0.") + continue + + # Calcular las diferencias relativas para los vecinos válidos + diferencias = np.abs(valores_vecinos - valor_objetivo) / valor_objetivo * 100 + + for d, vecino in zip(diferencias, valores_vecinos): + if pd.isna(d): + imprimir(f"⚠️ Diferencia NaN para el parámetro '{parametro}'. Vecino: {vecino}. Bono = 0.") + continue + + # Ajustar la diferencia relativa según el rango + if d > 40: + g = -100 # Máximo bono negativo + elif d > 20: + # Recalcular la diferencia relativa en el rango 20% a 40% + d_ajustada = d - 20 + g = -0.002 * d_ajustada**4 + 0.041 * d_ajustada**3 - 0.28135 * d_ajustada**2 + 0.23 * d_ajustada + 99.94 + g = -g # Cambiar el signo a negativo + else: + # Rango de 1% a 20% + g = -0.002 * d**4 + 0.041 * d**3 - 0.28135 * d**2 + 0.23 * d + 99.94 + + # Calcular el bono para este parámetro y vecino + bono_parametro = (g / 100) * 0.05 + bono_total += bono_parametro + + # Imprimir detalles para depuración + imprimir(f" Parámetro: {parametro}, Vecino: {vecino}, Objetivo: {valor_objetivo}, d: {d:.2f}%, g: {g:.2f}, Bono: {bono_parametro:.5f}") + + except KeyError: + imprimir(f"⚠️ Parámetro '{parametro}' no encontrado en los datos. Ignorando.") + continue + + imprimir(f" Bono total para '{tipo}': {bono_total:.5f}") + return bono_total + + # Calcular los bonos geométricos y prestacionales + bonus_geom = calcular_bono("geom") + bonus_prest = calcular_bono("prest") + imprimir(f" Bono geométrico: {bonus_geom:.3f}") + imprimir(f" Bono prestacional: {bonus_prest:.3f}") + + # Score de familia + family_scores = {0: 0.95, 1: 0.825, 2: 0.70} + fam_score = family_scores[capa_idx] + sim_i = fam_score * mtow_scores + bonus_geom + bonus_prest + + # Mostrar similitudes + for nbr, s in zip(rows_filtrados, sim_i): + imprimir(f" vecino '{nbr}' → sim_i: {s:.3f}") + + # Filtrar por umbral + umbral = 0.0 + mask_sim = sim_i >= umbral + vecinos_val = rows_filtrados[mask_sim] + sim_vals = sim_i[mask_sim] + if len(vecinos_val) == 0 or sim_vals.sum() < 1e-6: + imprimir(f"❌ Sin vecinos ≥{umbral} en {familia}.", True) + continue + + # Imputación y confianza + y = df_familia.loc[vecinos_val, parametro_objetivo].values + valor_imp = np.dot(sim_vals, y) / sim_vals.sum() + + # Cálculo de métricas estadísticas + if len(y) > 1: + media_y = np.mean(y) + dispersion = np.std(y, ddof=0) + cv = dispersion / media_y if media_y != 0 else 0 # Coeficiente de variación + else: + media_y = y[0] if len(y) == 1 else 0 # Si hay un solo valor, usarlo; si no hay valores, asignar 0 + cv = 1 # Penalización máxima para k=1 + dispersion = 0 + + # Penalización por cantidad de datos usados + penalizacion_k = penalizacion_por_k(len(vecinos_val)) + # Penalización por la calidad de los datos + confianza_cv = max(0,1-(cv/0.5)) #cuando la desviacion estandar es igual al 50% de la media entonces confianza 0 # Dispersión de los valores + + # Confianza final combinada + beta = 0.7 # Peso para cantidad de datos usados + pesos = sim_vals / sim_vals.sum() # Normalizar los pesos + promedio_sim_i = np.dot(pesos, sim_vals) + confianza_datos = beta * penalizacion_k + (1 - beta) * (confianza_cv) # Confianza basada en K y CV + confianza_final = promedio_sim_i*confianza_datos # Confianza final = promedio de confianza de vecinos * confianza de datos + + # Mostrar detalles del cálculo de confianza final + imprimir("\nDetalles del cálculo de confianza:") + + imprimir(f" Confianza que tan similares son los vecinos (familia x Mtow + Bonos): {[f'{s:.3f}' for s in sim_i]}") + imprimir(f" Promedio ponderado de confianza de similitud de aeronaves: {promedio_sim_i:.3f}") + imprimir(f" Media de valores (y): {media_y:.3f}") + imprimir(f" Coeficiente de variación (CV): {confianza_cv:.3f}") + imprimir(f" Dispersión: {dispersion:.3f}") + imprimir(f" Penalización por cantidad de vecinos (k): {penalizacion_k:.3f}") + imprimir(f" Confianza en base a la calidad y cantidad de datos: {confianza_datos:.3f}") + imprimir(f" Confianza final: {confianza_final*100:.3f}%") + + + # Retornar resultados + imprimir(f"✅ Valor imputado: {valor_imp:.3f} (conf {confianza_final:.3f}, datos {len(vecinos_val)}, familia {familia})") + + return { + "valor": valor_imp, + "confianza": confianza_final, + "num_vecinos": len(vecinos_val), + "familia": familia + } + + imprimir("⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...", True) + return None diff --git a/ADRpy/analisis/Modulos/imputation_loop.py b/ADRpy/analisis/Modulos/imputation_loop.py new file mode 100644 index 00000000..682c36ec --- /dev/null +++ b/ADRpy/analisis/Modulos/imputation_loop.py @@ -0,0 +1,255 @@ +import pandas as pd +from Modulos.imputacion_similitud_flexible import * +from .correlation_imputation import Imputacion_por_correlacion +from .html_utils import convertir_a_html +from .data_processing import generar_resumen_faltantes + +def bucle_imputacion_similitud_correlacion( + df_parametros, + df_atributos, + parametros_preseleccionados, + bloques_rasgos, + capas_familia, + df_procesado, + df_filtrado, + tabla_completa, + parametros_seleccionados, + umbral_correlacion=0.7, + rango_min=0.85, + rango_max=1.15, + nivel_confianza_min_similitud=0.7, + max_iteraciones=10, + reduccion_confianza=0.05, + nivel_confianza_min_correlacion=None, + debug_mode=False +): + + """ + Realiza un bucle alternando imputaciones por similitud y correlación, consolidando los resultados. + Ahora se evita actualizar los DataFrames inmediatamente, y se eligen las imputaciones finales + al final de cada iteración. + + Retorna: + df_procesado_base (pd.DataFrame): DataFrame con imputaciones realizadas. + df_resumen (pd.DataFrame): Detalle consolidado de imputaciones realizadas. + """ + + df_procesado_base = df_procesado.copy() # Copia base del DataFrame original + df_filtrado_base = df_filtrado.copy() # Copia base del DataFrame original + + convertir_a_html( + df_procesado_base, + titulo="df_procesado_base", + ancho="100%", + alto="400px", + mostrar=True, + ) + convertir_a_html( + df_filtrado_base, + titulo="df_filtrado_base", + ancho="100%", + alto="400px", + mostrar=True, + ) + resumen_imputaciones = ( + [] + ) # Lista para consolidar detalles de todas las imputaciones finales + + # Configuración inicial para imputaciones por similitud + print("\n=== Configuración Inicial ===") + + from Modulos.user_interaction import ( + solicitar_rango_min, + solicitar_rango_max, + solicitar_confianza_min_similitud, + ) + + rango_min = rango_min if debug_mode else solicitar_rango_min() + rango_max = rango_max if debug_mode else solicitar_rango_max() + nivel_confianza_min_similitud = nivel_confianza_min_similitud if debug_mode else solicitar_confianza_min_similitud() + + + print( + f"\nValores configurados: Rango MTOW [{rango_min*100:.0f}% - {rango_max*100:.0f}%], Confianza Mínima: {nivel_confianza_min_similitud:.2f}" + ) + + # Configuración inicial para imputaciones por correlación + from Modulos.user_interaction import ( + solicitar_umbral_correlacion, + solicitar_confianza_min_correlacion, + ) + + umbral_correlacion = ( + umbral_correlacion if debug_mode and umbral_correlacion is not None else solicitar_umbral_correlacion() + ) + nivel_confianza_min_correlacion = ( + nivel_confianza_min_correlacion if debug_mode and nivel_confianza_min_correlacion is not None + else solicitar_confianza_min_correlacion() + ) + + # Definir valores predeterminados para correlación + min_datos_validos = 5 # Cantidad mínima de datos requeridos por parámetro + max_lineas_consola = 40000000 + + for iteracion in range(1, max_iteraciones + 1): + print("\n" + "=" * 80) + print(f"\033[1m=== INICIO DE ITERACIÓN {iteracion} ===\033[0m") + print("=" * 80) + + print(f"\n=== Iteración {iteracion}: Resumen antes de imputaciones ===") + resumen_antes, total_faltantes_antes = generar_resumen_faltantes( + df_procesado_base, + titulo=f"Resumen de Valores Faltantes Antes de Iteración {iteracion}", + ) + + # Crear copias independientes para cada método + df_similitud = df_procesado_base.copy() + df_correlacion = df_procesado_base.copy() + + # Imputación por similitud (no actualiza todavía) + print("\n" + "-" * 80) + print(f"\033[1m*** IMPUTACIÓN POR SIMILITUD - ITERACIÓN {iteracion} ***\033[0m") + print("-" * 80) + df_similitud_resultado = df_similitud.copy() + reporte_similitud = [] + + for parametro in parametros_preseleccionados: + for aeronave in df_similitud_resultado.index: # Acceder usando filas como aeronaves y columnas como parámetros + if pd.isna(df_similitud_resultado.at[aeronave, parametro]): + resultado = imputar_por_similitud( + df_parametros=df_parametros, + df_atributos=df_atributos, + aeronave_obj=aeronave, + parametro_objetivo=parametro, + bloques_rasgos=bloques_rasgos, + capas_familia=capas_familia + ) + + if resultado is not None: + df_similitud_resultado.at[aeronave, parametro] = resultado["valor"] # Corregir lógica para asignar valores + reporte_similitud.append({ + "Aeronave": aeronave, + "Parámetro": parametro, + "Valor Imputado": resultado["valor"], + "Nivel de Confianza": resultado["confianza"] + }) + + if reporte_similitud and len(reporte_similitud) > 0: + print("\033[1m>>> RESULTADOS DE IMPUTACIÓN POR SIMILITUD\033[0m") + # Se guardan las imputaciones de similitud, pero NO se actualiza el DataFrame aún. + # Se agregan la iteración y método aquí. + for registro in reporte_similitud: + registro["Iteración"] = iteracion + registro["Método"] = "Similitud" + registro["Nivel de Confianza"] *= (1 - reduccion_confianza) ** ( + iteracion - 1 + ) + else: + print( + "\033[1mNo se realizaron imputaciones por similitud en esta iteración.\033[0m" + ) + + # Imputación por correlación (no actualiza todavía) + print("\n" + "-" * 80) + print( + f"\033[1m*** IMPUTACIÓN POR CORRELACIÓN - ITERACIÓN {iteracion} ***\033[0m" + ) + print("-" * 80) + df_correlacion_final, reporte_correlacion = Imputacion_por_correlacion( + df_correlacion, + parametros_preseleccionados, + tabla_completa, + parametros_seleccionados, + min_datos_validos=min_datos_validos, + max_lineas_consola=max_lineas_consola, + umbral_correlacion=umbral_correlacion, + nivel_confianza_min_correlacion=nivel_confianza_min_correlacion, + ) + + if reporte_correlacion and len(reporte_correlacion) > 0: + print("\033[1m>>> RESULTADOS DE IMPUTACIÓN POR CORRELACIÓN\033[0m") + for registro in reporte_correlacion: + registro["Iteración"] = iteracion + registro["Método"] = "Correlación" + registro["Nivel de Confianza"] *= (1 - reduccion_confianza) ** ( + iteracion - 1 + ) + else: + print( + "\033[1mNo se realizaron imputaciones por correlación en esta iteración.\033[0m" + ) + + # Combinar las imputaciones de similitud y correlación + imputaciones_candidatas = {} + + def registrar_imputacion(regs): + for reg in regs: + parametro = reg["Parámetro"] + aeronave = reg["Aeronave"] + key = (parametro, aeronave) + if key not in imputaciones_candidatas: + imputaciones_candidatas[key] = [] + imputaciones_candidatas[key].append(reg) + + if reporte_similitud and len(reporte_similitud) > 0: + registrar_imputacion(reporte_similitud) + if reporte_correlacion and len(reporte_correlacion) > 0: + registrar_imputacion(reporte_correlacion) + + # Seleccionar las mejores imputaciones por celda (la de mayor confianza) + imputaciones_finales = [] + for key, candidatos in imputaciones_candidatas.items(): + parametro, aeronave = key + # Si ya hay un valor en df_procesado_base, no imputar. + if not pd.isna(df_procesado_base.at[aeronave, parametro]): # Cambiar lógica para trabajar con filas como aeronaves y columnas como parámetros + continue + # Escoger la de mayor confianza + mejor = max(candidatos, key=lambda x: x["Nivel de Confianza"]) + imputaciones_finales.append(mejor) + + # Ahora sí, aplicar las imputaciones finales al DataFrame base + for imp in imputaciones_finales: + parametro = imp["Parámetro"] + aeronave = imp["Aeronave"] + valor = imp["Valor Imputado"] + metodo = imp["Método"] + df_procesado_base.at[aeronave, parametro] = valor # Corregir lógica para asignar valores + df_filtrado_base.at[aeronave, parametro] = valor # Corregir lógica para asignar valores + resumen_imputaciones.append(imp) + print( + f"Imputación final aplicada: {parametro} - {aeronave} = {valor} ({metodo})" + ) + + print(f"\n=== Iteración {iteracion}: Resumen después de imputaciones ===") + resumen_despues, total_faltantes_despues = generar_resumen_faltantes( + df_filtrado_base, + titulo=f"Resumen de Valores Faltantes Después de Iteración {iteracion}", + ) + + # Verificar condición de salida + no_similitud = reporte_similitud is None or len(reporte_similitud) == 0 + no_correlacion = reporte_correlacion is None or len(reporte_correlacion) == 0 + if no_similitud and no_correlacion: + print("\033[1mNo se realizaron nuevas imputaciones. Finalizando...\033[0m") + # Retornar resultados actuales antes de salir + return df_procesado_base, pd.DataFrame(resumen_imputaciones) + + print("\n" + "=" * 80) + print(f"\033[1m=== FIN DE ITERACIÓN {iteracion} ===\033[0m") + print("=" * 80) + + # Si se terminan las iteraciones sin break: + df_resumen = pd.DataFrame(resumen_imputaciones) + print("\n" + "=" * 80) + print("\033[1m=== RESUMEN FINAL ===\033[0m") + print("=" * 80) + + convertir_a_html( + df_procesado_base, titulo="DataFrame Procesado Final (df_procesado_base)" + ) + convertir_a_html(df_resumen, titulo="Resumen Final de Imputaciones (df_resumen)") + + print(f"\033[1mTotal de iteraciones realizadas: {iteracion}\033[0m") + print(f"\033[1mTotal de valores imputados: {len(resumen_imputaciones)}\033[0m") + + return df_procesado_base, df_resumen diff --git a/ADRpy/analisis/Modulos/imputation_utils.py b/ADRpy/analisis/Modulos/imputation_utils.py new file mode 100644 index 00000000..a141d073 --- /dev/null +++ b/ADRpy/analisis/Modulos/imputation_utils.py @@ -0,0 +1,34 @@ +import pandas as pd +from .html_utils import convertir_a_html + +def imprimir_detalles_imputacion(numero_valor_imputado, parametro, aeronave, mtow_actual, rango_min, rango_max, candidatas_validas, detalles_ajustes, valores_ajustados, valor_imputado, confianza, calculos_confianza): + """ + Imprime un resumen claro y organizado del proceso de imputación en consola. + """ + negrita = "\033[1m" + reset = "\033[0m" + + print(f"\n{negrita}======================== DETALLE DE CÁLCULO DE IMPUTACIÓN #{numero_valor_imputado} ========================{reset}") + print(f"{negrita}Parámetro:{reset} {parametro}") + print(f"{negrita}Aeronave a imputar:{reset} {aeronave}") + print(f"{negrita}MTOW actual:{reset} {mtow_actual} kg") + print(f"{negrita}Rango Similitud:{reset} {rango_min*100:.0f}% - {rango_max*100:.0f}%") + print(f"{negrita}Candidatas dentro del rango:{reset} {', '.join(candidatas_validas.index)}") + + print("\nAeronaves Válidas para el Cálculo:") + print("-----------------------------------------------------------------------------------------------") + print("Aeronave | MTOW Candidata | Rel. MTOW (Candidata/Actual) | Ajuste Individual | Valor Original | Valor Ajustado") + print("------------|----------------|------------------------------|-------------------|----------------|---------------") + for detalle in detalles_ajustes: + print(f"{detalle['Aeronave']:12}| {detalle['MTOW Candidata']:14}| {detalle['Relación MTOW']:<30.3f}| {detalle['Ajuste Individual']:<19.4f}| {detalle['Valor Original']:<16.2f}| {detalle['Valor Ajustado']:<13.2f}") + print("-----------------------------------------------------------------------------------------------") + + print("\nCálculo del Valor Final:") + print(f"{negrita}- Se tomó la mediana de los valores ajustados {valores_ajustados} = {valor_imputado:.2f}{reset}") + print(f"{negrita}- Nivel de Confianza calculado:{reset} {confianza:.2f}") + print(f"{negrita}- Valor Imputado Final:{reset} {valor_imputado:.2f}") + + print("\nDetalle del Cálculo de Confianza:") + for key, value in calculos_confianza.items(): + print(f"{negrita}- {key}:{reset} {value:.2f}") + print(f"{negrita}============================================================================================{reset}\n") diff --git a/ADRpy/analisis/Modulos/similarity_imputation.py b/ADRpy/analisis/Modulos/similarity_imputation.py new file mode 100644 index 00000000..960468e1 --- /dev/null +++ b/ADRpy/analisis/Modulos/similarity_imputation.py @@ -0,0 +1,265 @@ +import pandas as pd +import numpy as np +from sklearn.metrics import r2_score +from .html_utils import convertir_a_html +from .imputation_utils import imprimir_detalles_imputacion + + + + + +def imputacion_similitud_con_rango(df_filtrado, df_procesado, rango_min=0.85, rango_max=1.15, nivel_confianza_min_similitud=0.7): + + """ + Ajusta el rango de similitud e imputa valores faltantes en los parámetros de df_filtrado. + Genera un reporte final en HTML con un resumen agregado, filtrando por nivel de confianza. + :param df_filtrado: DataFrame con los parámetros a imputar. + :param df_procesado: DataFrame procesado con todos los datos. + :return: Nuevo DataFrame con los valores imputados. + """ + print(f"Rango mínimo: {rango_min}, Rango máximo: {rango_max}, Confianza mínima: {nivel_confianza_min_similitud}") + + # Crear una copia para mantener intacto el DataFrame original + df_resultado_por_similitud = df_filtrado.copy() + + # Función interna para imputar por similitud + def imputar_por_similitud(datos, parametro, aeronave, rango_min, rango_max, numero_valor_imputado): + + """ + Imputa un valor faltante basado en la similitud dentro del rango de MTOW. + Detalla el proceso con mensajes informativos y utiliza la mediana para calcular el valor imputado. + :param datos: DataFrame con los datos originales. + :param parametro: Parámetro a imputar. + :param aeronave: Aeronave con valor faltante. + :param rango_min: Rango mínimo de similitud. + :param rango_max: Rango máximo de similitud. + :return: Tuple (valor imputado, nivel de confianza) o (None, None) si no es posible imputar. + """ + + if df_filtrado.isna().all().all(): + print("Todos los valores en 'df_filtrado' están vacíos. No se puede proceder con la imputación.") + return df_filtrado, [] + + if df_procesado.isna().all().all(): + print("Todos los valores en 'df_procesado' están vacíos. No se puede proceder con la imputación.") + return df_filtrado, [] + + try: + # Verificar si MTOW está presente + if "Peso máximo al despegue (MTOW)" not in datos.index: + print(f"Advertencia: 'Peso máximo al despegue (MTOW)' no está en los datos para la aeronave '{aeronave}'.") + return None, None + + # Obtener el valor actual de MTOW + mtow_actual = datos.loc["Peso máximo al despegue (MTOW)", aeronave] + + # Validar si MTOW es válido + if pd.isna(mtow_actual): + print(f"MTOW faltante para la aeronave '{aeronave}'. Imputación no es posible.") + return None, None + + # Filtrar candidatas dentro del rango ajustado + candidatas_mtow = datos.loc[ + :, (datos.loc["Peso máximo al despegue (MTOW)"] >= rango_min * mtow_actual) & + (datos.loc["Peso máximo al despegue (MTOW)"] <= rango_max * mtow_actual) + ] + + if candidatas_mtow.empty: + print(f"No hay candidatos dentro del rango de {rango_min*100:.0f}% - {rango_max*100:.0f}%.") + return None, None + + # Excluir la misma aeronave de las candidatas + candidatas_mtow = candidatas_mtow.loc[:, candidatas_mtow.columns != aeronave] + + # Verificar que las candidatas tengan valores válidos en el parámetro a imputar + candidatas_validas = candidatas_mtow.loc[parametro].dropna() + + if candidatas_validas.empty: + print(f"Razón: Ninguna aeronave se encuentra dentro del rango MTOW de '{aeronave}'para el parametro '{parametro}'.") + return None, None + + + # Calcular los valores ajustados individualmente en función del MTOW de cada candidata + valores_ajustados = [] + mtow_candidatos = [] # Nueva lista para almacenar los valores de MTOW de los candidatos + detalles_ajustes = [] + + for candidata in candidatas_validas.index: + mtow_candidata = datos.loc["Peso máximo al despegue (MTOW)", candidata] + relacion_mtow_individual = mtow_candidata / mtow_actual + ajuste_individual = (relacion_mtow_individual - 1) / 4 + + valor_candidata = candidatas_validas[candidata] + valor_ajustado = valor_candidata * (1 + ajuste_individual) + + # Guardar en las listas correspondientes + valores_ajustados.append(valor_ajustado) + mtow_candidatos.append(mtow_candidata) + + + # Registrar detalle del ajuste + detalles_ajustes.append({ + "Aeronave": candidata, + "MTOW Candidata": mtow_candidata, + "Relación MTOW": relacion_mtow_individual, + "Ajuste Individual": ajuste_individual, + "Valor Original": valor_candidata, + "Valor Ajustado": valor_ajustado + }) + + + # Usar la mediana de los valores ajustados como valor final imputado + valor_imputado = np.median(valores_ajustados) + + # Calcular la confianza directamente + cantidad_minima = 3 + penalizacion_candidatos = 1 - (cantidad_minima - len(valores_ajustados)) / (3 * cantidad_minima) + penalizacion_candidatos = max(0, penalizacion_candidatos) # Evitar valores negativos + + # Cantidad ponderada basada en calidad + pesos_mtow = np.exp(-np.abs(np.array(mtow_candidatos) - mtow_actual) / mtow_actual) + cantidad_ponderada = np.sum(pesos_mtow) / len(mtow_candidatos) + + # Evaluación de R^2 con normalización + if len(candidatas_validas) > 1: + try: + r2 = r2_score(candidatas_validas.tolist(), valores_ajustados) + ponderacion_modelo = r2 + except ValueError: + print("No se puede calcular R^2: insuficientes datos válidos.") + r2 = None + else: + r2 = None + + # Usar dispersión como respaldo cuando R^2 no se puede calcular + if r2 is None: + dispersion = np.std(valores_ajustados) if len(valores_ajustados) > 1 else 1.0 + ponderacion_modelo = 1 / (1 + dispersion) + + # Validar ponderación del modelo + if ponderacion_modelo is not None and (ponderacion_modelo < 0 or ponderacion_modelo > 1): + print(f"Advertencia: Ponderación del modelo ({ponderacion_modelo:.3f}) fuera de rango. Revisar lógica previa.") + ponderacion_modelo = max(0, min(ponderacion_modelo, 1)) # Forzar a rango válido + + + # Confianza final combinada + peso_candidatos = 0.6 + peso_modelo = 0.4 + confianza_base = ( + peso_candidatos * cantidad_ponderada + + peso_modelo * ponderacion_modelo + ) + + # Aplicar penalización por pocos candidatos + confianza = confianza_base * penalizacion_candidatos + + # Asegurar valores entre 0 y 1 + confianza = min(1.0, max(0.0, confianza)) + + # Detalles del cálculo de confianza + calculos_confianza = { + "Penalización por pocos candidatos": penalizacion_candidatos, + "Cantidad Ponderada (basada en MTOW)": cantidad_ponderada, + "Ponderación del modelo (R² o dispersión)": ponderacion_modelo, + "Confianza Base": confianza_base, + "Confianza Final (tras penalización)": confianza + } + + imprimir_detalles_imputacion( + numero_valor_imputado=numero_valor_imputado, + parametro=parametro, + aeronave=aeronave, + mtow_actual=mtow_actual, + rango_min=rango_min, + rango_max=rango_max, + candidatas_validas=candidatas_validas, + detalles_ajustes=detalles_ajustes, + valores_ajustados=valores_ajustados, + valor_imputado=valor_imputado, + confianza=confianza, + calculos_confianza=calculos_confianza + ) + + + return valor_imputado, confianza + + + except Exception as e: + print(f"Error durante la imputación: {e}") + return None, None + + + + + try: + # Mostrar df_resultado_por_similitud en HTML para verificar valores iniciales + print("\n=== Verificación de 'df_resultado_por_similitud' ===") + convertir_a_html(df_resultado_por_similitud, titulo="Datos Filtrados por aeronaves seleccionadas antes de imputar(df_resultado_por_similitud)", mostrar=True) + + # Reporte de imputaciones + reporte_imputaciones = [] + numero_valor_imputado = 0 + + for parametro in df_resultado_por_similitud.index: + for aeronave in df_resultado_por_similitud.columns: + if pd.isna(df_resultado_por_similitud.loc[parametro, aeronave]): + # Realizar imputación + valor_imputado, confianza = imputar_por_similitud( + df_procesado, parametro, aeronave, rango_min, rango_max, numero_valor_imputado + ) + + # Verificar si se realizó una imputación válida + if valor_imputado is not None and confianza is not None and confianza >= nivel_confianza_min_similitud: + # Incrementar el contador SOLO aquí + numero_valor_imputado += 1 + # Asignar el valor imputado al DataFrame + df_resultado_por_similitud.loc[parametro, aeronave] = valor_imputado + # Registrar la imputación en el resumen + reporte_imputaciones.append({ + "Aeronave": aeronave, + "Parámetro": parametro, + "Valor Imputado": valor_imputado, + "Nivel de Confianza": confianza + }) + + elif confianza is not None and confianza < nivel_confianza_min_similitud: + print(f"Imputación descartada por baja confianza: {confianza:.3f} < {nivel_confianza_min_similitud}.") + else: + print(f"No se pudo imputar: {parametro} para {aeronave}.") + + + + # Generar reporte final en HTML con filtro de confianza + print("\n=== Generando reporte final ===") + if reporte_imputaciones: + df_reporte = pd.DataFrame(reporte_imputaciones) + #print("Contenido de reporte_imputaciones:", reporte_imputaciones) + df_reporte = df_reporte[df_reporte["Nivel de Confianza"] >= nivel_confianza_min_similitud] + convertir_a_html(df_reporte, titulo="Reporte Final de Imputaciones", mostrar=True) + else: + print("No se realizaron imputaciones con el nivel de confianza aceptable.") + + + except Exception as e: + print(f"Error durante la imputación: {e}") + + # Generar DataFrame con valores imputados + if not reporte_imputaciones: + print("No se realizaron imputaciones con éxito.") + return df_filtrado, [] + + + # Convertir lista de diccionarios a DataFrame para exportación final + df_reporte_final = pd.DataFrame(reporte_imputaciones) + + # Asegurar estructura del DataFrame imputado + df_resultado_final = df_resultado_por_similitud.copy() + + # Validar resultados + if df_resultado_final.empty: + print("El DataFrame de resultados está vacío.") + return df_filtrado, [] + + # Retornar DataFrame imputado y lista de diccionarios + return df_resultado_final, reporte_imputaciones + diff --git a/ADRpy/analisis/Modulos/user_interaction.py b/ADRpy/analisis/Modulos/user_interaction.py new file mode 100644 index 00000000..52705dce --- /dev/null +++ b/ADRpy/analisis/Modulos/user_interaction.py @@ -0,0 +1,117 @@ +def seleccionar_parametros_por_indices(parametros_disponibles, parametros_preseleccionados, entrada_indices=None): + print("\n=== Selección de Parámetros ===") + print("Parámetros disponibles:") + for i, parametro in enumerate(parametros_disponibles, 1): + print(f"{i}. {parametro}") + + print(f"\nPreseleccionados: {', '.join(str(parametros_disponibles.index(p) + 1) for p in parametros_preseleccionados if p in parametros_disponibles)}") + + if entrada_indices is None: + indices = input("Ingresa los números separados por coma (o presiona Enter para usar los preseleccionados): ") + else: + indices = entrada_indices + + if not indices.strip(): + seleccionados = parametros_preseleccionados + else: + try: + indices = [int(i.strip()) - 1 for i in indices.split(",")] + seleccionados = [parametros_disponibles[i] for i in indices] + except Exception as e: + print(f"Error al interpretar los índices: {e}") + seleccionados = parametros_preseleccionados + + print("Parámetros seleccionados después de filtrar:") + print(seleccionados) + return seleccionados + + +def solicitar_umbral(valor_por_defecto=0.7, umbral_manual=None): + """ + Solicita al usuario ingresar un umbral para las correlaciones significativas. + Si el usuario no proporciona un valor válido, se usa el valor por defecto. + :param valor_por_defecto: Valor predeterminado del umbral si el usuario no ingresa ninguno. + :return: Umbral de correlación como flotante. + """ + if umbral_manual is not None: + if not (0 < umbral_manual < 1): + print(f"⚠️ El umbral_manual ({umbral_manual}) está fuera de rango. Se usará el valor por defecto ({valor_por_defecto})") + return valor_por_defecto + return umbral_manual + + try: + umbral = float(input(f"Ingrese el umbral mínimo de correlación significativa (por defecto {valor_por_defecto}): ") or valor_por_defecto) + if not (0 < umbral < 1): + raise ValueError("El umbral debe estar entre 0 y 1.") + return umbral + except ValueError as e: + print(f"Valor inválido: {e}. Se usará el umbral por defecto de {valor_por_defecto}.") + return valor_por_defecto + +def solicitar_rango_min(valor_por_defecto=0.85, valor=None): + try: + if valor is None: + valor = input(f"Ingrese el rango mínimo de MTOW (0-2, predeterminado {valor_por_defecto * 100:.0f}): ") + valor = float(valor) / 100 if valor else valor_por_defecto + if not (0.01 <= valor <= 2.00): + raise ValueError + return valor + except ValueError: + print("Valor inválido. Usando el valor predeterminado.") + return valor_por_defecto + + +def solicitar_rango_max(valor_por_defecto=1.15, valor=None): + try: + if valor is None: + valor = input(f"Ingrese el rango máximo de MTOW (0-2, predeterminado {valor_por_defecto * 100:.0f}): ") + valor = float(valor) / 100 if valor else valor_por_defecto + if not (0.01 <= valor <= 2.00): + raise ValueError + return valor + except ValueError: + print("Valor inválido. Usando el valor predeterminado.") + return valor_por_defecto + + +def solicitar_confianza_min_similitud(valor_por_defecto=0.5, valor=None): + try: + if valor is None: + valor = input(f"Ingrese el nivel mínimo de confianza (0-1, predeterminado {valor_por_defecto}): ") + valor = float(valor) if valor else valor_por_defecto + if not (0 <= valor <= 1): + raise ValueError + return valor + except ValueError: + print("Valor inválido. Usando el valor predeterminado.") + return valor_por_defecto + +def solicitar_umbral_correlacion(valor_por_defecto=0.7): + """ + Solicita al usuario el umbral mínimo de correlación. + Si no se proporciona, se utiliza un valor predeterminado. + """ + try: + valor = input(f"Ingrese el umbral mínimo de correlación (0-1, predeterminado {valor_por_defecto}): ") + umbral = float(valor) if valor else valor_por_defecto + if not 0 <= umbral <= 1: + raise ValueError("El umbral de correlación debe estar entre 0 y 1.") + return umbral + except ValueError: + print("Entrada inválida. Usando el valor predeterminado.") + return valor_por_defecto + +def solicitar_confianza_min_correlacion(valor_por_defecto=0.5): + """ + Solicita al usuario el nivel mínimo de confianza para correlación. + Si no se proporciona, se utiliza un valor predeterminado. + """ + try: + valor = input(f"Ingrese el nivel mínimo de confianza para correlación (0-1, predeterminado {valor_por_defecto}): ") + confianza = float(valor) if valor else valor_por_defecto + if not 0 <= confianza <= 1: + raise ValueError("El nivel de confianza debe estar entre 0 y 1.") + return confianza + except ValueError: + print("Entrada inválida. Usando el valor predeterminado.") + return valor_por_defecto diff --git a/ADRpy/analisis/README.md b/ADRpy/analisis/README.md new file mode 100644 index 00000000..e69de29b diff --git a/ADRpy/analisis/Results/Datos_imputados.xlsx b/ADRpy/analisis/Results/Datos_imputados.xlsx new file mode 100644 index 00000000..ed01aed4 Binary files /dev/null and b/ADRpy/analisis/Results/Datos_imputados.xlsx differ diff --git a/ADRpy/analisis/Results/Datos_imputados11.xlsx b/ADRpy/analisis/Results/Datos_imputados11.xlsx new file mode 100644 index 00000000..d0f0418b Binary files /dev/null and b/ADRpy/analisis/Results/Datos_imputados11.xlsx differ diff --git a/ADRpy/analisis/Results/archivo_salida antes de transpo.xlsx b/ADRpy/analisis/Results/archivo_salida antes de transpo.xlsx new file mode 100644 index 00000000..078e97cc Binary files /dev/null and b/ADRpy/analisis/Results/archivo_salida antes de transpo.xlsx differ diff --git a/ADRpy/analisis/Results/archivo_salida.xlsx b/ADRpy/analisis/Results/archivo_salida.xlsx new file mode 100644 index 00000000..60e3575b Binary files /dev/null and b/ADRpy/analisis/Results/archivo_salida.xlsx differ diff --git a/ADRpy/analisis/aaa.ipynb b/ADRpy/analisis/aaa.ipynb new file mode 100644 index 00000000..2791ec64 --- /dev/null +++ b/ADRpy/analisis/aaa.ipynb @@ -0,0 +1,27232 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "b70a4a82", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DEBUG: ruta_archivo antes de validar: 'C:\\Users\\delpi\\OneDrive\\Tesis\\ADRpy-VTOL\\ADRpy\\analisis\\Data\\Datos_aeronaves.xlsx'\n", + "=== Cargando datos desde el archivo: C:\\Users\\delpi\\OneDrive\\Tesis\\ADRpy-VTOL\\ADRpy\\analisis\\Data\\Datos_aeronaves.xlsx ===\n", + "\n", + "=== Resumen inicial del DataFrame cargado ===\n", + "\n", + "Index: 37 entries, Stalker XE to Reach\n", + "Data columns (total 42 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Distancia de carrera requerida para despegue 37 non-null object \n", + " 1 Altitud a la que se realiza el crucero 37 non-null int64 \n", + " 2 Velocidad a la que se realiza el crucero (KTAS) 37 non-null object \n", + " 3 Techo de servicio máximo 37 non-null object \n", + " 4 Velocidad de pérdida limpia (KCAS) 37 non-null object \n", + " 5 Área del ala 37 non-null object \n", + " 6 Relación de aspecto del ala 37 non-null object \n", + " 7 Longitud del fuselaje 37 non-null object \n", + " 8 Profundidad del fuselaje 37 non-null object \n", + " 9 Ancho del fuselaje 37 non-null object \n", + " 10 Peso máximo al despegue (MTOW) 37 non-null float64\n", + " 11 Alcance de la aeronave 37 non-null object \n", + " 12 Autonomía de la aeronave 37 non-null object \n", + " 13 Velocidad máxima (KIAS) 37 non-null object \n", + " 14 Velocidad de pérdida (KCAS) 37 non-null object \n", + " 15 Tasa de ascenso 37 non-null object \n", + " 16 Radio de giro 37 non-null object \n", + " 17 envergadura 37 non-null object \n", + " 18 Cuerda 37 non-null object \n", + " 19 payload 37 non-null object \n", + " 20 duracion en VTOL 37 non-null object \n", + " 21 Crucero KIAS 37 non-null object \n", + " 22 RTF (dry weight) 37 non-null object \n", + " 23 RTF (Including fuel & Batteries) 37 non-null object \n", + " 24 Empty weight 37 non-null object \n", + " 25 Maximum Crosswind 37 non-null object \n", + " 26 Rango de comunicación 37 non-null object \n", + " 27 Wing Loading 37 non-null object \n", + " 28 Potencia específica (P/W) 37 non-null object \n", + " 29 Capacidad combustible 37 non-null object \n", + " 30 Consumo 37 non-null object \n", + " 31 Potencia Watts 37 non-null object \n", + " 32 Potencia HP 37 non-null object \n", + " 33 Precio 37 non-null object \n", + " 34 Tiempo de emergencia en vuelo 37 non-null object \n", + " 35 Distancia de aterrizaje 37 non-null object \n", + " 36 Despegue 37 non-null int64 \n", + " 37 Propulsión horizontal 37 non-null int64 \n", + " 38 Propulsión vertical 37 non-null int64 \n", + " 39 Cantidad de motores propulsión vertical 37 non-null int64 \n", + " 40 Cantidad de motores propulsión horizontal 37 non-null int64 \n", + " 41 Misión 37 non-null int64 \n", + "dtypes: float64(1), int64(7), object(34)\n", + "memory usage: 12.4+ KB\n", + "None\n", + "Datos cargados correctamente desde: C:\\Users\\delpi\\OneDrive\\Tesis\\ADRpy-VTOL\\ADRpy\\analisis\\Data\\Datos_aeronaves.xlsx\n", + "\n", + "=== Validando datos cargados ===\n", + "\n", + "=== Continuando con el procesamiento de datos ===\n", + "\n", + "Encabezados iniciales cargados:\n", + "['Distancia de carrera requerida para despegue', 'Altitud a la que se realiza el crucero', 'Velocidad a la que se realiza el crucero (KTAS)', 'Techo de servicio máximo', 'Velocidad de pérdida limpia (KCAS)', 'Área del ala', 'Relación de aspecto del ala', 'Longitud del fuselaje', 'Profundidad del fuselaje', 'Ancho del fuselaje', 'Peso máximo al despegue (MTOW)', 'Alcance de la aeronave', 'Autonomía de la aeronave', 'Velocidad máxima (KIAS)', 'Velocidad de pérdida (KCAS)', 'Tasa de ascenso', 'Radio de giro', 'envergadura', 'Cuerda', 'payload', 'duracion en VTOL', 'Crucero KIAS', 'RTF (dry weight)', 'RTF (Including fuel & Batteries)', 'Empty weight', 'Maximum Crosswind', 'Rango de comunicación', 'Wing Loading', 'Potencia específica (P/W)', 'Capacidad combustible', 'Consumo', 'Potencia Watts', 'Potencia HP', 'Precio', 'Tiempo de emergencia en vuelo', 'Distancia de aterrizaje', 'Despegue', 'Propulsión horizontal', 'Propulsión vertical', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal', 'Misión']\n", + "\n", + "=== Mostrando datos iniciales en formato HTML ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

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Distancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeProfundidad del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Tasa de ascensoRadio de giroenvergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (dry weight)RTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónWing LoadingPotencia específica (P/W)Capacidad combustibleConsumoPotencia WattsPotencia HPPrecioTiempo de emergencia en vueloDistancia de aterrizajeDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
Modelo
Stalker XE06000.00016.88037912000Nan0.8715.3012552.1Nan0.21113.600370820NanNanNan3.6570.2392.494756215.43332NanNan10.886208Nan59NanNanNanNanNanNanNanNanNan1.0002.0005.0000.0001.0001.000
Stalker VXE3006000.00017.60237312000Nan1.15828315.3264492.5908Nan0.219.958433825.034211NanNanNan4.87680.3181952.494756Nan16.093422NanNan17.463292Nan161NanNanNanNanNanNanNanNanNan1.0002.0005.0000.0001.0001.000
Aerosonde Mk. 4.7 Fixed WingNan6000.00027.3440514700Nan1.5512.53Nan0.27742.200Nan19.833.43886NanNanNan4.40.35214.5Nan25Nan27.7NanNan140NanNanNan0.629804NanNanNan1.0002.0005.0000.0001.0001.000
Aerosonde Mk. 4.7 VTOL06000.00027.344059700Nan1.5512.53Nan0.27753.500Nan1233.43886NanNanNan4.40.35211.3Nan25Nan42.2NanNan140NanNanNan0.629804NanNanNan2.0002.0001.0004.0001.0001.000
Aerosonde Mk. 4.8 Fixed wingNan6000.00027.3440518200Nan1.5512.53Nan0.27754.400Nan19.833.43886NanNanNan4.40.35217.7Nan25Nan36.7NanNan140NanNanNanNanNanNanNanNanNan1.0002.0005.0000.0001.0001.000
Aerosonde Mk. 4.8 VTOL FTUAS06000.000Nan15000NanNan12.5NanNanNan93.000Nan14NanNanNanNanNanNan22.7NanNanNan70.3NanNanNanNanNanNanNanNanNanNanNanNan2.0002.0001.0004.0001.0001.000
AAI AerosondeNan5500.000Nan15000Nan0.5714.7543861.7NanNan13.10032702630.845725NanNanNan2.90.196552NanNanNanNanNan10Nan1502398NanNan12801.74NanNanNan2.0002.0001.0004.0001.0001.000
Fulmar XNan6000.00030.4065849.842NanNanNan1.2NanNan20.000800841.7NanNanNan3NanNanNan27.8NanNanNanNanNanNanNanNanNanNanNanNanNanNan1.0002.0005.0000.0001.0001.000
Orbiter 4Nan6000.000NanNanNanNanNan1.2NanNan55.000Nan2436NanNanNan5.2Nan12NanNanNanNanNanNan150NanNanNanNanNanNanNanNanNan1.0002.0005.0000.0001.0001.000
Orbiter 3Nan6000.000NanNanNanNanNan1.2NanNan32.00050636NanNanNan4.4Nan5.5NanNanNanNanNanNan50NanNanNanNanNanNanNanNanNan1.0001.0005.0000.0001.0001.000
MantisNan6000.00018.265826NanNanNanNan1.48NanNan6.50025225.6NanNanNan2.1NanNanNan16.7NanNanNanNan25NanNanNanNanNanNanNanNanNan1.0001.0005.0000.0001.0001.000
ScanEagleNan6000.00030.62533619500NanNanNan1.71NanNan26.500Nan1841.2NanNanNan3.1Nan5Nan28NanNanNanNan101.86NanNanNanNanNanNanNanNanNan1.0002.0005.0000.0001.0001.000
IntegratorNan6000.00030.95346519500NanNanNan2.5NanNan74.800Nan2446.3NanNanNan4.8Nan18Nan28.3NanNanNanNan92.6NanNanNanNanNanNanNanNanNan1.0002.0005.0000.0001.0001.000
Integrator VTOL05000.000NanNanNanNanNanNanNanNan75.000Nan16NanNanNanNanNanNan18NanNanNanNanNan30NanNanNanNanNanNanNanNanNan02.0002.0001.0004.0001.0001.000
Integrator Extended RangeNan6000.000Nan19500NanNanNan2.5NanNan74.8005001946.3NanNanNan4.8Nan18NanNanNanNanNanNanNanNanNanNanNanNanNanNanNanNan1.0002.0005.0000.0001.0001.000
ScanEagle 3Nan6000.00025.70340720NanNanNan2.4NanNan36.300Nan1841.2NanNanNan4Nan8.6Nan23.5NanNanNanNanNanNanNanNanNan170NanNanNanNan1.0002.0005.0000.0001.0001.000
RQ Nan 21A BlackjackNan6000.00033.79724620NanNanNan2.5NanNan61.000Nan1646.3NanNanNan4.8Nan17.7Nan30.9NanNanNanNan92.6NanNanNanNanNan8NanNanNan1.0002.0005.0000.0001.0001.000
DeltaQuad Evo06000.00018.09082413Nan0.84Nan0.75NanNan10.0002704.53NanNanNanNan2.69Nan34.5316.544.86.84.845NanNanNanNanNanNanNanNanNan02.0001.0001.0004.0001.0001.000
DeltaQuad Pro #MAP06000.00017.50019213.123NanNanNan0.9NanNan6.2001001.83NanNanNanNan2.35Nan1.2Nan16NanNanNan5050NanNanNanNanNanNanNanNan02.0001.0001.0004.0001.0001.000
DeltaQuad Pro #CARGO06000.00017.50019213.123NanNanNan0.9NanNan6.2001001.83NanNanNanNan2.35Nan1.2Nan16NanNanNan5030NanNanNanNanNanNanNanNan02.0001.0001.0004.0001.0001.000
V2106000.00019.68771648800140.8Nan0.93NanNan10.000Nan33314Nan1002.15Nan1.5Nan18NanNan2.65Nan3012.5NanNanNanNanNan39990.108Nan2.0001.0001.0004.0001.0001.000
V2506000.00021.875241600015.50.52Nan0.93NanNan12.500Nan43315.5Nan1202.45Nan2.2Nan20NanNan3.45Nan3024NanNanNanNanNan46790.108Nan2.0001.0001.0004.0001.0001.000
V3206000.00021.875241600017NanNan1NanNan23.500Nan4.53317Nan1503.2Nan5Nan20NanNan6.45Nan3025NanNanNanNanNan699990.108Nan2.0002.0001.0004.0001.0001.000
V3506000.00027.3440516000NanNanNan1.88NanNan32.000Nan2.833NanNanNan3.5Nan10Nan25NanNanNanNan30NanNanNanNanNanNan7999NanNan2.0002.0001.0004.0001.0001.000
V3906000.00027.3440516000NanNanNanNanNanNan24.000Nan4.533NanNanNan3.9Nan5Nan25NanNanNanNan30NanNanNanNanNanNan8999NanNan2.0002.0001.0004.0001.0001.000
Volitation VT37006000.00027.3440517000NanNanNan2.02NanNan40.000Nan1533NanNanNan6.5Nan18Nan25NanNanNanNanNanNanNan130.96NanNan8999NanNan2.0002.0001.0004.0001.0001.000
Skyeye 2600Nan6000.00036.094147Nan100.88Nan2.05NanNan15.000Nan2Nan10NanNan2.6Nan4Nan33NanNan6.5NanNanNanNanNanNanNanNan2299NanNan2.0002.0001.0004.0001.0001.000
Skyeye 2930 VTOL06000.00026.250288Nan181Nan2.03NanNan28.000Nan33018NanNan2.93Nan6Nan24NanNan7.1NanNanNanNanNanNanNanNan6799NanNan2.0002.0001.0004.0001.0001.000
Skyeye 3600506000.000NanNan12.51.33Nan2.488NanNan28.000Nan4.5Nan12.5NanNan3.6Nan10NanNanNanNan11.5NanNanNanNan11.5NanNanNan4999NanNan3.0002.0005.0000.0001.0001.000
Skyeye 3600 VTOL06000.00032.81286Nan241.32Nan2.42NanNan40.00030063324NanNan3.6Nan10Nan30NanNan11NanNanNanNan11.5NanNanNan6999NanNan2.0002.0001.0004.0001.0001.000
Skyeye 5000606000.00036.094147Nan152.615Nan3.5Nan0.37590.000Nan84215NanNan5Nan20Nan33NanNan32NanNanNanNan281.2NanNan9999NanNan3.0002.0005.0000.0001.0001.000
Skyeye 5000 VTOL06000.00030.625336NanNan2.615Nan3.5Nan0.375100.000800842NanNanNan5Nan25Nan28NanNanNanNanNanNanNan28NanNanNan13900NanNan2.0002.0001.0004.0001.0001.000
Skyeye 5000 VTOL octo06000.000NanNanNan2.615Nan3.5Nan0.375100.000NanNan3824NanNan5Nan15Nan35NanNan35NanNanNanNan28NanNanNan15999NanNan2.0002.0001.0008.0001.0001.000
Volitation VT51006000.00032.812861700025NanNan2.905NanNan100.000Nan55025NanNan5.1Nan25Nan30NanNanNanNanNanNanNan255NanNan16599NanNan2.0002.0001.0004.0001.0001.000
Ascend06000.00021.8752410000NanNanNan1.562NanNan9.500Nan63013NanNan2Nan0.60.052068.9315NanNanNanNanNanNanNanNanNanNan2.0002.0001.0004.0001.0001.000
Transition06000.00021.8752413000NanNanNan2.3NanNan18.000Nan123013NanNan3Nan1.50.052011.816.55.815NanNanNanNanNanNanNanNanNanNan2.0002.0001.0004.0001.0001.000
Reach06000.00027.3440516000NanNanNan4.712NanNan91.000Nan203513NanNan6Nan70.052554843115NanNanNanNanNanNanNanNanNanNan2.0002.0001.0004.0001.0001.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Procesando los datos ===\n", + "=== Inicio del procesamiento de datos ===\n", + "\n", + "=== Comprobación de duplicados ===\n", + "No se encontraron duplicados en índices o columnas.\n", + "\n", + "=== Convirtiendo valores a numéricos donde sea posible ===\n", + "=== Procesamiento completado ===\n", + "\n", + "✅ Los encabezados se preservaron correctamente.\n", + "\n", + "=== Mostrando datos procesados en formato HTML ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Datos Procesados

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Distancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeProfundidad del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Tasa de ascensoRadio de giroenvergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (dry weight)RTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónWing LoadingPotencia específica (P/W)Capacidad combustibleConsumoPotencia WattsPotencia HPPrecioTiempo de emergencia en vueloDistancia de aterrizajeDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
Modelo
Stalker XE0.06000.00016.88037912000.0NaN0.8715.3012552.1NaN0.21113.600370.08.020.0NaNNaNNaN3.6570.2392.4947562.015.43332NaNNaN10.886208NaN59.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Stalker VXE300.06000.00017.60237312000.0NaN1.15828315.3264492.5908NaN0.219.958433.08.025.034211NaNNaNNaN4.87680.3181952.494756NaN16.093422NaNNaN17.463292NaN161.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Aerosonde Mk. 4.7 Fixed WingNaN6000.00027.3440514700.0NaN1.5512.53.0NaN0.27742.200NaN19.833.43886NaNNaNNaN4.40.35214.5NaN25.0NaN27.7NaNNaN140.0NaNNaNNaN0.62980.04.0NaNNaNNaN1.0002.0005.0000.0001.0001.000
Aerosonde Mk. 4.7 VTOL0.06000.00027.344059700.0NaN1.5512.53.0NaN0.27753.500NaN12.033.43886NaNNaNNaN4.40.35211.3NaN25.0NaN42.2NaNNaN140.0NaNNaNNaN0.62980.04.0NaNNaNNaN2.0002.0001.0004.0001.0001.000
Aerosonde Mk. 4.8 Fixed wingNaN6000.00027.3440518200.0NaN1.5512.53.0NaN0.27754.400NaN19.833.43886NaNNaNNaN4.40.35217.7NaN25.0NaN36.7NaNNaN140.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Aerosonde Mk. 4.8 VTOL FTUAS0.06000.000NaN15000.0NaNNaN12.5NaNNaNNaN93.000NaN14.0NaNNaNNaNNaNNaNNaN22.7NaNNaNNaN70.3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0002.0001.0004.0001.0001.000
AAI AerosondeNaN5500.000NaN15000.0NaN0.5714.7543861.7NaNNaN13.1003270.026.030.845725NaNNaNNaN2.90.196552NaNNaNNaNNaNNaN10.0NaN150.023.098.0NaNNaN1280.01.74NaNNaNNaN2.0002.0001.0004.0001.0001.000
Fulmar XNaN6000.00030.4065849.842NaNNaNNaN1.2NaNNaN20.000800.08.041.7NaNNaNNaN3.0NaNNaNNaN27.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Orbiter 4NaN6000.000NaNNaNNaNNaNNaN1.2NaNNaN55.000NaN24.036.0NaNNaNNaN5.2NaN12.0NaNNaNNaNNaNNaNNaN150.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Orbiter 3NaN6000.000NaNNaNNaNNaNNaN1.2NaNNaN32.00050.06.036.0NaNNaNNaN4.4NaN5.5NaNNaNNaNNaNNaNNaN50.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0001.0005.0000.0001.0001.000
MantisNaN6000.00018.265826NaNNaNNaNNaN1.48NaNNaN6.50025.02.025.6NaNNaNNaN2.1NaNNaNNaN16.7NaNNaNNaNNaN25.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0001.0005.0000.0001.0001.000
ScanEagleNaN6000.00030.62533619500.0NaNNaNNaN1.71NaNNaN26.500NaN18.041.2NaNNaNNaN3.1NaN5.0NaN28.0NaNNaNNaNNaN101.86NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
IntegratorNaN6000.00030.95346519500.0NaNNaNNaN2.5NaNNaN74.800NaN24.046.3NaNNaNNaN4.8NaN18.0NaN28.3NaNNaNNaNNaN92.6NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Integrator VTOL0.05000.000NaNNaNNaNNaNNaNNaNNaNNaN75.000NaN16.0NaNNaNNaNNaNNaNNaN18.0NaNNaNNaNNaNNaN30.0NaNNaNNaNNaNNaNNaNNaNNaNNaN0.02.0002.0001.0004.0001.0001.000
Integrator Extended RangeNaN6000.000NaN19500.0NaNNaNNaN2.5NaNNaN74.800500.019.046.3NaNNaNNaN4.8NaN18.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
ScanEagle 3NaN6000.00025.70340720.0NaNNaNNaN2.4NaNNaN36.300NaN18.041.2NaNNaNNaN4.0NaN8.6NaN23.5NaNNaNNaNNaNNaNNaNNaNNaNNaN170.0NaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
RQ Nan 21A BlackjackNaN6000.00033.79724620.0NaNNaNNaN2.5NaNNaN61.000NaN16.046.3NaNNaNNaN4.8NaN17.7NaN30.9NaNNaNNaNNaN92.6NaNNaNNaNNaNNaN8.0NaNNaNNaN1.0002.0005.0000.0001.0001.000
DeltaQuad Evo0.06000.00018.09082413.0NaN0.84NaN0.75NaNNaN10.000270.04.53NaNNaNNaNNaN2.69NaN3.04.5316.544.86.84.845.0NaNNaNNaNNaNNaNNaNNaNNaNNaN0.02.0001.0001.0004.0001.0001.000
DeltaQuad Pro #MAP0.06000.00017.50019213.123NaNNaNNaN0.9NaNNaN6.200100.01.83NaNNaNNaNNaN2.35NaN1.2NaN16.0NaNNaNNaN50.050.0NaNNaNNaNNaNNaNNaNNaNNaN0.02.0001.0001.0004.0001.0001.000
DeltaQuad Pro #CARGO0.06000.00017.50019213.123NaNNaNNaN0.9NaNNaN6.200100.01.83NaNNaNNaNNaN2.35NaN1.2NaN16.0NaNNaNNaN50.030.0NaNNaNNaNNaNNaNNaNNaNNaN0.02.0001.0001.0004.0001.0001.000
V210.06000.00019.68771648800.014.00.8NaN0.93NaNNaN10.000NaN3.033.014.0NaN100.02.15NaN1.5NaN18.0NaNNaN2.65NaN30.012.5NaNNaNNaNNaNNaN3999.00.108NaN2.0001.0001.0004.0001.0001.000
V250.06000.00021.8752416000.015.50.52NaN0.93NaNNaN12.500NaN4.033.015.5NaN120.02.45NaN2.2NaN20.0NaNNaN3.45NaN30.024.0NaNNaNNaNNaNNaN4679.00.108NaN2.0001.0001.0004.0001.0001.000
V320.06000.00021.8752416000.017.0NaNNaN1.0NaNNaN23.500NaN4.533.017.0NaN150.03.2NaN5.0NaN20.0NaNNaN6.45NaN30.025.0NaNNaNNaNNaNNaN69999.00.108NaN2.0002.0001.0004.0001.0001.000
V350.06000.00027.3440516000.0NaNNaNNaN1.88NaNNaN32.000NaN2.833.0NaNNaNNaN3.5NaN10.0NaN25.0NaNNaNNaNNaN30.0NaNNaNNaNNaNNaNNaN7999.0NaNNaN2.0002.0001.0004.0001.0001.000
V390.06000.00027.3440516000.0NaNNaNNaNNaNNaNNaN24.000NaN4.533.0NaNNaNNaN3.9NaN5.0NaN25.0NaNNaNNaNNaN30.0NaNNaNNaNNaNNaNNaN8999.0NaNNaN2.0002.0001.0004.0001.0001.000
Volitation VT3700.06000.00027.3440517000.0NaNNaNNaN2.02NaNNaN40.000NaN15.033.0NaNNaNNaN6.5NaN18.0NaN25.0NaNNaNNaNNaNNaNNaNNaN13.00.96NaNNaN8999.0NaNNaN2.0002.0001.0004.0001.0001.000
Skyeye 2600NaN6000.00036.094147NaN10.00.88NaN2.05NaNNaN15.000NaN2.0NaN10.0NaNNaN2.6NaN4.0NaN33.0NaNNaN6.5NaNNaNNaNNaNNaNNaNNaNNaN2299.0NaNNaN2.0002.0001.0004.0001.0001.000
Skyeye 2930 VTOL0.06000.00026.250288NaN18.01.0NaN2.03NaNNaN28.000NaN3.030.018.0NaNNaN2.93NaN6.0NaN24.0NaNNaN7.1NaNNaNNaNNaNNaNNaNNaNNaN6799.0NaNNaN2.0002.0001.0004.0001.0001.000
Skyeye 360050.06000.000NaNNaN12.51.33NaN2.488NaNNaN28.000NaN4.5NaN12.5NaNNaN3.6NaN10.0NaNNaNNaNNaN11.5NaNNaNNaNNaN11.5NaNNaNNaN4999.0NaNNaN3.0002.0005.0000.0001.0001.000
Skyeye 3600 VTOL0.06000.00032.81286NaN24.01.32NaN2.42NaNNaN40.000300.06.033.024.0NaNNaN3.6NaN10.0NaN30.0NaNNaN11.0NaNNaNNaNNaN11.5NaNNaNNaN6999.0NaNNaN2.0002.0001.0004.0001.0001.000
Skyeye 500060.06000.00036.094147NaN15.02.615NaN3.5NaN0.37590.000NaN8.042.015.0NaNNaN5.0NaN20.0NaN33.0NaNNaN32.0NaNNaNNaNNaN28.01.2NaNNaN9999.0NaNNaN3.0002.0005.0000.0001.0001.000
Skyeye 5000 VTOL0.06000.00030.625336NaNNaN2.615NaN3.5NaN0.375100.000800.08.042.0NaNNaNNaN5.0NaN25.0NaN28.0NaNNaNNaNNaNNaNNaNNaN28.0NaNNaNNaN13900.0NaNNaN2.0002.0001.0004.0001.0001.000
Skyeye 5000 VTOL octo0.06000.000NaNNaNNaN2.615NaN3.5NaN0.375100.000NaNNaN38.024.0NaNNaN5.0NaN15.0NaN35.0NaNNaN35.0NaNNaNNaNNaN28.0NaNNaNNaN15999.0NaNNaN2.0002.0001.0008.0001.0001.000
Volitation VT5100.06000.00032.8128617000.025.0NaNNaN2.905NaNNaN100.000NaN5.050.025.0NaNNaN5.1NaN25.0NaN30.0NaNNaNNaNNaNNaNNaNNaN25.05.0NaNNaN16599.0NaNNaN2.0002.0001.0004.0001.0001.000
Ascend0.06000.00021.8752410000.0NaNNaNNaN1.562NaNNaN9.500NaN6.030.013.0NaNNaN2.0NaN0.60.0520.06.08.93.015.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0002.0001.0004.0001.0001.000
Transition0.06000.00021.8752413000.0NaNNaNNaN2.3NaNNaN18.000NaN12.030.013.0NaNNaN3.0NaN1.50.0520.011.816.55.815.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0002.0001.0004.0001.0001.000
Reach0.06000.00027.3440516000.0NaNNaNNaN4.712NaNNaN91.000NaN20.035.013.0NaNNaN6.0NaN7.00.0525.054.084.031.015.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0002.0001.0004.0001.0001.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parámetros disponibles en df_procesado antes de seleccionar:\n", + "['Distancia de carrera requerida para despegue', 'Altitud a la que se realiza el crucero', 'Velocidad a la que se realiza el crucero (KTAS)', 'Techo de servicio máximo', 'Velocidad de pérdida limpia (KCAS)', 'Área del ala', 'Relación de aspecto del ala', 'Longitud del fuselaje', 'Profundidad del fuselaje', 'Ancho del fuselaje', 'Peso máximo al despegue (MTOW)', 'Alcance de la aeronave', 'Autonomía de la aeronave', 'Velocidad máxima (KIAS)', 'Velocidad de pérdida (KCAS)', 'Tasa de ascenso', 'Radio de giro', 'envergadura', 'Cuerda', 'payload', 'duracion en VTOL', 'Crucero KIAS', 'RTF (dry weight)', 'RTF (Including fuel & Batteries)', 'Empty weight', 'Maximum Crosswind', 'Rango de comunicación', 'Wing Loading', 'Potencia específica (P/W)', 'Capacidad combustible', 'Consumo', 'Potencia Watts', 'Potencia HP', 'Precio', 'Tiempo de emergencia en vuelo', 'Distancia de aterrizaje', 'Despegue', 'Propulsión horizontal', 'Propulsión vertical', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal', 'Misión']\n", + "\n", + "=== Selección de Parámetros ===\n", + "Parámetros disponibles:\n", + "1. Distancia de carrera requerida para despegue\n", + "2. Altitud a la que se realiza el crucero\n", + "3. Velocidad a la que se realiza el crucero (KTAS)\n", + "4. Techo de servicio máximo\n", + "5. Velocidad de pérdida limpia (KCAS)\n", + "6. Área del ala\n", + "7. Relación de aspecto del ala\n", + "8. Longitud del fuselaje\n", + "9. Profundidad del fuselaje\n", + "10. Ancho del fuselaje\n", + "11. Peso máximo al despegue (MTOW)\n", + "12. Alcance de la aeronave\n", + "13. Autonomía de la aeronave\n", + "14. Velocidad máxima (KIAS)\n", + "15. Velocidad de pérdida (KCAS)\n", + "16. Tasa de ascenso\n", + "17. Radio de giro\n", + "18. envergadura\n", + "19. Cuerda\n", + "20. payload\n", + "21. duracion en VTOL\n", + "22. Crucero KIAS\n", + "23. RTF (dry weight)\n", + "24. RTF (Including fuel & Batteries)\n", + "25. Empty weight\n", + "26. Maximum Crosswind\n", + "27. Rango de comunicación\n", + "28. Wing Loading\n", + "29. Potencia específica (P/W)\n", + "30. Capacidad combustible\n", + "31. Consumo\n", + "32. Potencia Watts\n", + "33. Potencia HP\n", + "34. Precio\n", + "35. Tiempo de emergencia en vuelo\n", + "36. Distancia de aterrizaje\n", + "37. Despegue\n", + "38. Propulsión horizontal\n", + "39. Propulsión vertical\n", + "40. Cantidad de motores propulsión vertical\n", + "41. Cantidad de motores propulsión horizontal\n", + "42. Misión\n", + "\n", + "Preseleccionados: 3, 4, 6, 7, 8, 11, 12, 13, 14, 15, 5, 18, 19, 20, 25\n", + "Parámetros seleccionados después de filtrar:\n", + "['Velocidad a la que se realiza el crucero (KTAS)', 'Techo de servicio máximo', 'Área del ala', 'Relación de aspecto del ala', 'Longitud del fuselaje', 'Peso máximo al despegue (MTOW)', 'Alcance de la aeronave', 'Autonomía de la aeronave', 'Velocidad máxima (KIAS)', 'Velocidad de pérdida (KCAS)', 'Velocidad de pérdida limpia (KCAS)', 'envergadura', 'Cuerda', 'payload', 'Empty weight']\n", + "Parámetros seleccionados después de filtrar:\n", + "['Velocidad a la que se realiza el crucero (KTAS)', 'Techo de servicio máximo', 'Área del ala', 'Relación de aspecto del ala', 'Longitud del fuselaje', 'Peso máximo al despegue (MTOW)', 'Alcance de la aeronave', 'Autonomía de la aeronave', 'Velocidad máxima (KIAS)', 'Velocidad de pérdida (KCAS)', 'Velocidad de pérdida limpia (KCAS)', 'envergadura', 'Cuerda', 'payload', 'Empty weight']\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Datos Filtrados por Parámetros (df_filtrado)

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Velocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
Modelo
Stalker XE16.88037912000.00.8715.3012552.113.600370.08.020.0NaNNaN3.6570.2392.49475610.886208
Stalker VXE3017.60237312000.01.15828315.3264492.590819.958433.08.025.034211NaNNaN4.87680.3181952.49475617.463292
Aerosonde Mk. 4.7 Fixed Wing27.3440514700.01.5512.53.042.200NaN19.833.43886NaNNaN4.40.35214.5NaN
Aerosonde Mk. 4.7 VTOL27.344059700.01.5512.53.053.500NaN12.033.43886NaNNaN4.40.35211.3NaN
Aerosonde Mk. 4.8 Fixed wing27.3440518200.01.5512.53.054.400NaN19.833.43886NaNNaN4.40.35217.7NaN
Aerosonde Mk. 4.8 VTOL FTUASNaN15000.0NaN12.5NaN93.000NaN14.0NaNNaNNaNNaNNaN22.7NaN
AAI AerosondeNaN15000.00.5714.7543861.713.1003270.026.030.845725NaNNaN2.90.196552NaN10.0
Fulmar X30.4065849.842NaNNaN1.220.000800.08.041.7NaNNaN3.0NaNNaNNaN
Orbiter 4NaNNaNNaNNaN1.255.000NaN24.036.0NaNNaN5.2NaN12.0NaN
Orbiter 3NaNNaNNaNNaN1.232.00050.06.036.0NaNNaN4.4NaN5.5NaN
Mantis18.265826NaNNaNNaN1.486.50025.02.025.6NaNNaN2.1NaNNaNNaN
ScanEagle30.62533619500.0NaNNaN1.7126.500NaN18.041.2NaNNaN3.1NaN5.0NaN
Integrator30.95346519500.0NaNNaN2.574.800NaN24.046.3NaNNaN4.8NaN18.0NaN
Integrator VTOLNaNNaNNaNNaNNaN75.000NaN16.0NaNNaNNaNNaNNaN18.0NaN
Integrator Extended RangeNaN19500.0NaNNaN2.574.800500.019.046.3NaNNaN4.8NaN18.0NaN
ScanEagle 325.70340720.0NaNNaN2.436.300NaN18.041.2NaNNaN4.0NaN8.6NaN
RQ Nan 21A Blackjack33.79724620.0NaNNaN2.561.000NaN16.046.3NaNNaN4.8NaN17.7NaN
DeltaQuad Evo18.09082413.00.84NaN0.7510.000270.04.53NaNNaNNaN2.69NaN3.04.8
DeltaQuad Pro #MAP17.50019213.123NaNNaN0.96.200100.01.83NaNNaNNaN2.35NaN1.2NaN
DeltaQuad Pro #CARGO17.50019213.123NaNNaN0.96.200100.01.83NaNNaNNaN2.35NaN1.2NaN
V2119.68771648800.00.8NaN0.9310.000NaN3.033.014.014.02.15NaN1.52.65
V2521.8752416000.00.52NaN0.9312.500NaN4.033.015.515.52.45NaN2.23.45
V3221.8752416000.0NaNNaN1.023.500NaN4.533.017.017.03.2NaN5.06.45
V3527.3440516000.0NaNNaN1.8832.000NaN2.833.0NaNNaN3.5NaN10.0NaN
V3927.3440516000.0NaNNaNNaN24.000NaN4.533.0NaNNaN3.9NaN5.0NaN
Volitation VT37027.3440517000.0NaNNaN2.0240.000NaN15.033.0NaNNaN6.5NaN18.0NaN
Skyeye 260036.094147NaN0.88NaN2.0515.000NaN2.0NaN10.010.02.6NaN4.06.5
Skyeye 2930 VTOL26.250288NaN1.0NaN2.0328.000NaN3.030.018.018.02.93NaN6.07.1
Skyeye 3600NaNNaN1.33NaN2.48828.000NaN4.5NaN12.512.53.6NaN10.011.5
Skyeye 3600 VTOL32.81286NaN1.32NaN2.4240.000300.06.033.024.024.03.6NaN10.011.0
Skyeye 500036.094147NaN2.615NaN3.590.000NaN8.042.015.015.05.0NaN20.032.0
Skyeye 5000 VTOL30.625336NaN2.615NaN3.5100.000800.08.042.0NaNNaN5.0NaN25.0NaN
Skyeye 5000 VTOL octoNaNNaN2.615NaN3.5100.000NaNNaN38.024.0NaN5.0NaN15.035.0
Volitation VT51032.8128617000.0NaNNaN2.905100.000NaN5.050.025.025.05.1NaN25.0NaN
Ascend21.8752410000.0NaNNaN1.5629.500NaN6.030.013.0NaN2.0NaN0.63.0
Transition21.8752413000.0NaNNaN2.318.000NaN12.030.013.0NaN3.0NaN1.55.8
Reach27.3440516000.0NaNNaN4.71291.000NaN20.035.013.0NaN6.0NaN7.031.0
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Filas con datos faltantes ===\n", + "1. Stalker XE\n", + "2. Stalker VXE30\n", + "3. Aerosonde Mk. 4.7 Fixed Wing\n", + "4. Aerosonde Mk. 4.7 VTOL\n", + "5. Aerosonde Mk. 4.8 Fixed wing\n", + "6. Aerosonde Mk. 4.8 VTOL FTUAS\n", + "7. AAI Aerosonde\n", + "8. Fulmar X\n", + "9. Orbiter 4\n", + "10. Orbiter 3\n", + "11. Mantis\n", + "12. ScanEagle\n", + "13. Integrator\n", + "14. Integrator VTOL\n", + "15. Integrator Extended Range\n", + "16. ScanEagle 3\n", + "17. RQ Nan 21A Blackjack\n", + "18. DeltaQuad Evo\n", + "19. DeltaQuad Pro #MAP\n", + "20. DeltaQuad Pro #CARGO\n", + "21. V21\n", + "22. V25\n", + "23. V32\n", + "24. V35\n", + "25. V39\n", + "26. Volitation VT370\n", + "27. Skyeye 2600\n", + "28. Skyeye 2930 VTOL\n", + "29. Skyeye 3600\n", + "30. Skyeye 3600 VTOL\n", + "31. Skyeye 5000\n", + "32. Skyeye 5000 VTOL\n", + "33. Skyeye 5000 VTOL octo\n", + "34. Volitation VT510\n", + "35. Ascend\n", + "36. Transition\n", + "37. Reach\n", + "🔁 Entrada inválida o vacía. Seleccionando la primera fila por defecto.\n", + "\n", + "=== Analizando celdas faltantes en la fila: 'Stalker XE' ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Celdas Faltantes Identificadas en df_filtrado (df_celdas_faltantes)

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Valores
Índice
Velocidad de pérdida (KCAS)NaN
Velocidad de pérdida limpia (KCAS)NaN
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Resumen de Valores Faltantes de df_filtrado

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FilaValores Faltantes
0Stalker XE2.000
1Stalker VXE302.000
2Aerosonde Mk. 4.7 Fixed Wing4.000
3Aerosonde Mk. 4.7 VTOL4.000
4Aerosonde Mk. 4.8 Fixed wing4.000
5Aerosonde Mk. 4.8 VTOL FTUAS10.000
6AAI Aerosonde4.000
7Fulmar X7.000
8Orbiter 49.000
9Orbiter 38.000
10Mantis8.000
11ScanEagle7.000
12Integrator7.000
13Integrator VTOL12.000
14Integrator Extended Range7.000
15ScanEagle 37.000
16RQ Nan 21A Blackjack7.000
17DeltaQuad Evo5.000
18DeltaQuad Pro #MAP7.000
19DeltaQuad Pro #CARGO7.000
20V213.000
21V253.000
22V324.000
23V357.000
24V398.000
25Volitation VT3707.000
26Skyeye 26005.000
27Skyeye 2930 VTOL4.000
28Skyeye 36006.000
29Skyeye 3600 VTOL3.000
30Skyeye 50004.000
31Skyeye 5000 VTOL6.000
32Skyeye 5000 VTOL octo7.000
33Volitation VT5105.000
34Ascend5.000
35Transition5.000
36Reach5.000
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Sumatoria Total de Valores Faltantes

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ResumenCantidad
0Total de Valores Faltantes215.000
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Tabla de Correlaciones con todos los parametros(tabla_completa)

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Distancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeProfundidad del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Tasa de ascensoRadio de giroenvergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (dry weight)RTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónWing LoadingPotencia específica (P/W)Capacidad combustibleConsumoPotencia WattsPotencia HPPrecioTiempo de emergencia en vueloDistancia de aterrizajeDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
Distancia de carrera requerida para despegue1.0000.0630.467nan-0.5050.363nan0.260nan0.4250.168nan-0.0680.316-0.308nannan0.145nan0.229nan0.389nannan0.357nannannannan-0.018-0.240nannan-0.156nannan0.7350.1540.671-0.598nannan
Altitud a la que se realiza el crucero0.0631.000nan-0.038nan0.301-0.3510.081nannan-0.095-0.955-0.2800.128nannannan0.1360.761-0.183nannannannan0.0600.038-0.325-0.215nannannan0.2770.690nannannan-0.119-0.1090.187-0.159nannan
Velocidad a la que se realiza el crucero (KTAS)0.467nan1.0000.0410.1280.587-0.9990.535nan0.9360.6630.6650.3360.8150.257nan0.8030.4720.8460.696-0.6941.0000.9150.7230.426-0.8550.3590.997nan0.4910.4611.0001.000-0.296nannan0.1130.6190.126-0.126nannan
Techo de servicio máximonan-0.0380.0411.000-0.502-0.152-0.3140.082nan0.3690.1370.4280.079-0.111-0.071nan-0.8030.0570.0170.087-0.8750.0410.6770.579-0.138-0.961-0.120-0.986nannan0.5150.653-0.933-0.257nannan0.1250.007-0.1250.125nannan
Velocidad de pérdida limpia (KCAS)-0.505nan0.128-0.5021.0000.097nan0.260nannan0.546nan0.4010.4461.000nan0.9930.505nan0.536nan0.128nannan0.038nannan0.900nan0.0681.000nannan0.163nannan-0.3450.231-0.3450.345nannan
Área del ala0.3630.3010.587-0.1520.0971.000-0.8310.867nan0.9840.977-0.3010.0810.7370.423nan-1.0000.8410.9840.899-1.0000.675nan0.9230.941nan0.595-0.996nan1.0001.0001.0001.0000.899nannan0.2360.4530.1720.055nannan
Relación de aspecto del alanan-0.351-0.999-0.314nan-0.8311.000-0.790nan-0.996-0.823-0.998-0.305-0.859nannannan-0.349-0.744-0.888nan-0.999nannan0.622nan-0.298nannannannan-1.000-1.000nannannan-0.247nan0.247-0.247nannan
Longitud del fuselaje0.2600.0810.5350.0820.2600.867-0.7901.000nan0.9380.7860.1500.3890.2560.180nan0.9180.6930.9950.599-0.6170.5760.9660.9400.880-0.7180.6760.263nan0.9290.0360.6860.359-0.210nannan0.1110.6150.093-0.004nannan
Profundidad del fuselajenannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Ancho del fuselaje0.425nan0.9360.369nan0.984-0.9960.938nan1.0000.9860.982-0.0900.940nannannan0.6710.7600.868nan0.944nannan0.955nan0.323nannannan1.000nannannannannan0.794nan-0.5350.574nannan
Peso máximo al despegue (MTOW)0.168-0.0950.6630.1370.5460.977-0.8230.786nan0.9861.0000.0250.4340.6780.539nan0.9730.7910.8580.875-0.4010.7080.9990.9790.947-0.4640.5140.628nan0.9760.7580.5590.8550.052nannan0.0900.4670.0230.075nannan
Alcance de la aeronavenan-0.9550.6650.428nan-0.301-0.9980.150nan0.9820.0251.0000.8430.042nannannan-0.010-0.7550.804-1.0000.665nannan-0.059-1.0000.670nannan1.000nannannan1.000nannan0.2620.474-0.2620.262nannan
Autonomía de la aeronave-0.068-0.2800.3360.0790.4010.081-0.3050.389nan-0.0900.4340.8431.0000.297-0.164nan0.9540.532-0.2010.461-0.5940.3360.9400.6340.428-0.7150.8020.268nan-0.113-0.732-0.391-0.5770.011nannan-0.4240.4770.361-0.361nannan
Velocidad máxima (KIAS)0.3160.1280.815-0.1110.4460.737-0.8590.256nan0.9400.6780.0420.2971.0000.539nannan0.4000.5120.715-0.9270.7750.9940.8570.517nan0.094-0.215nan0.7050.910-0.6240.9700.015nannan-0.0410.2080.174-0.133nannan
Velocidad de pérdida (KCAS)-0.308nan0.257-0.0711.0000.423nan0.180nannan0.539nan-0.1640.5391.000nan0.9930.401nan0.627nan0.417nannan0.321nannan0.900nan0.2301.000nannan0.160nannan-0.2440.154-0.2440.444nannan
Tasa de ascensonannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Radio de gironannan0.803-0.8030.993-1.000nan0.918nannan0.973nan0.954nan0.993nan1.0000.992nan0.976nan0.803nannan0.979nannan0.844nannannannannan0.921nannannan0.918nannannannan
envergadura0.1450.1360.4720.0570.5050.841-0.3490.693nan0.6710.791-0.0100.5320.4000.401nan0.9921.0000.8850.734-0.2580.5010.9830.9360.924-0.4520.6480.780nan0.2970.0850.5220.8350.032nannan-0.1240.5080.239-0.164nannan
Cuerdanan0.7610.8460.017nan0.984-0.7440.995nan0.7600.858-0.755-0.2010.512nannannan0.8851.0000.776nan0.846nannan0.971nan0.354nannannannan1.0001.000nannannan-0.313nan0.313-0.313nannan
payload0.229-0.1830.6960.0870.5360.899-0.8880.599nan0.8680.8750.8040.4610.7150.627nan0.9760.7340.7761.000-0.0240.6940.9150.5590.778-0.1420.5460.707nan0.7110.8460.8410.866-0.008nannan-0.0040.4770.162-0.111nannan
duracion en VTOLnannan-0.694-0.875nan-1.000nan-0.617nannan-0.401-1.000-0.594-0.927nannannan-0.258nan-0.0241.000-0.694-0.408-0.402-0.3151.000nannannannannannannannannannan-0.188-0.9040.188-0.188nannan
Crucero KIAS0.389nan1.0000.0410.1280.675-0.9990.576nan0.9440.7080.6650.3360.7750.417nan0.8030.5010.8460.694-0.6941.0000.9150.7230.583-0.8550.3590.997nan0.5810.4611.0001.000-0.243nannan0.1430.6080.0650.063nannan
RTF (dry weight)nannan0.9150.677nannannan0.966nannan0.999nan0.9400.994nannannan0.983nan0.915-0.4080.9151.0001.0000.995-0.408nannannannannannannannannannannan0.408nannannannan
RTF (Including fuel & Batteries)nannan0.7230.579nan0.923nan0.940nannan0.979nan0.6340.857nannannan0.936nan0.559-0.4020.7231.0001.0000.996-0.402nannannannannannannannannannan0.0970.428-0.0970.097nannan
Empty weight0.3570.0600.426-0.1380.0380.9410.6220.880nan0.9550.947-0.0590.4280.5170.321nan0.9790.9240.9710.778-0.3150.5830.9950.9961.000-0.3190.8320.530nan0.995nannannan-0.029nannan0.1820.4040.3070.004nannan
Maximum Crosswindnan0.038-0.855-0.961nannannan-0.718nannan-0.464-1.000-0.715nannannannan-0.452nan-0.1421.000-0.855-0.408-0.402-0.3191.000nannannannannannannannannannannan-0.943nannannannan
Rango de comunicaciónnan-0.3250.359-0.120nan0.595-0.2980.676nan0.3230.5140.6700.8020.094nannannan0.6480.3540.546nan0.359nannan0.832nan1.0000.215nannannan-1.000-0.972nannannan-0.4300.6040.430-0.430nannan
Wing Loadingnan-0.2150.997-0.9860.900-0.996nan0.263nannan0.628nan0.268-0.2150.900nan0.8440.780nan0.707nan0.997nannan0.530nan0.2151.000nannannannannan0.568nannannan0.572nannannannan
Potencia específica (P/W)nannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Capacidad combustible-0.018nan0.491nan0.0681.000nan0.929nannan0.9761.000-0.1130.7050.230nannan0.297nan0.711nan0.581nannan0.995nannannannan1.0000.377nannan0.817nannan-0.080nan-0.0800.270nannan
Consumo-0.240nan0.4610.5151.0001.000nan0.036nan1.0000.758nan-0.7320.9101.000nannan0.085nan0.846nan0.461nannannannannannannan0.3771.000nannan0.998nannan0.113nan-0.3750.375nannan
Potencia Wattsnan0.2771.0000.653nan1.000-1.0000.686nannan0.559nan-0.391-0.624nannannan0.5221.0000.841nan1.000nannannannan-1.000nannannannan1.0001.000nannannan0.232nan-0.2320.232nannan
Potencia HPnan0.6901.000-0.933nan1.000-1.0000.359nannan0.855nan-0.5770.970nannannan0.8351.0000.866nan1.000nannannannan-0.972nannannannan1.0001.000nannannan-0.694nan0.694-0.694nannan
Precio-0.156nan-0.296-0.2570.1630.899nan-0.210nannan0.0521.0000.0110.0150.160nan0.9210.032nan-0.008nan-0.243nannan-0.029nannan0.568nan0.8170.998nannan1.000nannan-0.1380.217-0.1380.134nannan
Tiempo de emergencia en vuelonannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Distancia de aterrizajenannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Despegue0.735-0.1190.1130.125-0.3450.236-0.2470.111nan0.7940.0900.262-0.424-0.041-0.244nannan-0.124-0.313-0.004-0.1880.143nan0.0970.182nan-0.430nannan-0.0800.1130.232-0.694-0.138nannan1.000-0.010-0.6390.610nannan
Propulsión horizontal0.154-0.1090.6190.0070.2310.453nan0.615nannan0.4670.4740.4770.2080.154nan0.9180.508nan0.477-0.9040.6080.4080.4280.404-0.9430.6040.572nannannannannan0.217nannan-0.0101.0000.118-0.083nannan
Propulsión vertical0.6710.1870.126-0.125-0.3450.1720.2470.093nan-0.5350.023-0.2620.3610.174-0.244nannan0.2390.3130.1620.1880.065nan-0.0970.307nan0.430nannan-0.080-0.375-0.2320.694-0.138nannan-0.6390.1181.000-0.954nannan
Cantidad de motores propulsión vertical-0.598-0.159-0.1260.1250.3450.055-0.247-0.004nan0.5740.0750.262-0.361-0.1330.444nannan-0.164-0.313-0.111-0.1880.063nan0.0970.004nan-0.430nannan0.2700.3750.232-0.6940.134nannan0.610-0.083-0.9541.000nannan
Cantidad de motores propulsión horizontalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Misiónnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
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Resumen de la Tabla

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ResumenCantidad
0Total de valores1764.000
1Valores numéricos907.000
2Valores NaN857.000
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Tabla de Correlaciones Filtradas por parametros seleccionados (Para Heatmap)

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Velocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
Velocidad a la que se realiza el crucero (KTAS)1.0000.0410.587-0.9990.5350.6630.6650.3360.8150.2570.1280.4720.8460.6960.426
Techo de servicio máximo0.0411.000-0.152-0.3140.0820.1370.4280.079-0.111-0.071-0.5020.0570.0170.087-0.138
Área del ala0.587-0.1521.000-0.8310.8670.977-0.3010.0810.7370.4230.0970.8410.9840.8990.941
Relación de aspecto del ala-0.999-0.314-0.8311.000-0.790-0.823-0.998-0.305-0.859nannan-0.349-0.744-0.8880.622
Longitud del fuselaje0.5350.0820.867-0.7901.0000.7860.1500.3890.2560.1800.2600.6930.9950.5990.880
Peso máximo al despegue (MTOW)0.6630.1370.977-0.8230.7861.0000.0250.4340.6780.5390.5460.7910.8580.8750.947
Alcance de la aeronave0.6650.428-0.301-0.9980.1500.0251.0000.8430.042nannan-0.010-0.7550.804-0.059
Autonomía de la aeronave0.3360.0790.081-0.3050.3890.4340.8431.0000.297-0.1640.4010.532-0.2010.4610.428
Velocidad máxima (KIAS)0.815-0.1110.737-0.8590.2560.6780.0420.2971.0000.5390.4460.4000.5120.7150.517
Velocidad de pérdida (KCAS)0.257-0.0710.423nan0.1800.539nan-0.1640.5391.0001.0000.401nan0.6270.321
Velocidad de pérdida limpia (KCAS)0.128-0.5020.097nan0.2600.546nan0.4010.4461.0001.0000.505nan0.5360.038
envergadura0.4720.0570.841-0.3490.6930.791-0.0100.5320.4000.4010.5051.0000.8850.7340.924
Cuerda0.8460.0170.984-0.7440.9950.858-0.755-0.2010.512nannan0.8851.0000.7760.971
payload0.6960.0870.899-0.8880.5990.8750.8040.4610.7150.6270.5360.7340.7761.0000.778
Empty weight0.426-0.1380.9410.6220.8800.947-0.0590.4280.5170.3210.0380.9240.9710.7781.000
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Resumen de la Tabla

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ResumenCantidad
0Total de valores225.000
1Valores numéricos213.000
2Valores NaN12.000
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Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= 0.7)

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Velocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
Velocidad a la que se realiza el crucero (KTAS)nannannan-0.999nannannannan0.815nannannan0.846nannan
Techo de servicio máximonannannannannannannannannannannannannannannan
Área del alanannannan-0.8310.8670.977nannan0.737nannan0.8410.9840.8990.941
Relación de aspecto del ala-0.999nan-0.831nan-0.790-0.823-0.998nan-0.859nannannan-0.744-0.888nan
Longitud del fuselajenannan0.867-0.790nan0.786nannannannannannan0.995nan0.880
Peso máximo al despegue (MTOW)nannan0.977-0.8230.786nannannannannannan0.7910.8580.8750.947
Alcance de la aeronavenannannan-0.998nannannan0.843nannannannan-0.7550.804nan
Autonomía de la aeronavenannannannannannan0.843nannannannannannannannan
Velocidad máxima (KIAS)0.815nan0.737-0.859nannannannannannannannannan0.715nan
Velocidad de pérdida (KCAS)nannannannannannannannannannannannannannannan
Velocidad de pérdida limpia (KCAS)nannannannannannannannannannannannannannannan
envergaduranannan0.841nannan0.791nannannannannannan0.8850.7340.924
Cuerda0.846nan0.984-0.7440.9950.858-0.755nannannannan0.885nan0.7760.971
payloadnannan0.899-0.888nan0.8750.804nan0.715nannan0.7340.776nan0.778
Empty weightnannan0.941nan0.8800.947nannannannannan0.9240.9710.778nan
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Resumen de la Tabla

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ResumenCantidad
0Total de valores225.000
1Valores numéricos68.000
2Valores NaN157.000
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Distancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeProfundidad del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Tasa de ascensoRadio de giroenvergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (dry weight)RTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónWing LoadingPotencia específica (P/W)Capacidad combustibleConsumoPotencia WattsPotencia HPPrecioTiempo de emergencia en vueloDistancia de aterrizajeDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
Modelo
Stalker XE0.06000.00016.88037912000.0NaN0.8715.3012552.1NaN0.21113.600370.08.020.0NaNNaNNaN3.6570.2392.4947562.015.43332NaNNaN10.886208NaN59.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Stalker VXE300.06000.00017.60237312000.0NaN1.15828315.3264492.5908NaN0.219.958433.08.025.034211NaNNaNNaN4.87680.3181952.494756NaN16.093422NaNNaN17.463292NaN161.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Aerosonde Mk. 4.7 Fixed WingNaN6000.00027.3440514700.0NaN1.5512.53.0NaN0.27742.200NaN19.833.43886NaNNaNNaN4.40.35214.5NaN25.0NaN27.7NaNNaN140.0NaNNaNNaN0.62980.04.0NaNNaNNaN1.0002.0005.0000.0001.0001.000
Aerosonde Mk. 4.7 VTOL0.06000.00027.344059700.0NaN1.5512.53.0NaN0.27753.500NaN12.033.43886NaNNaNNaN4.40.35211.3NaN25.0NaN42.2NaNNaN140.0NaNNaNNaN0.62980.04.0NaNNaNNaN2.0002.0001.0004.0001.0001.000
Aerosonde Mk. 4.8 Fixed wingNaN6000.00027.3440518200.0NaN1.5512.53.0NaN0.27754.400NaN19.833.43886NaNNaNNaN4.40.35217.7NaN25.0NaN36.7NaNNaN140.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Aerosonde Mk. 4.8 VTOL FTUAS0.06000.000NaN15000.0NaNNaN12.5NaNNaNNaN93.000NaN14.0NaNNaNNaNNaNNaNNaN22.7NaNNaNNaN70.3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0002.0001.0004.0001.0001.000
AAI AerosondeNaN5500.000NaN15000.0NaN0.5714.7543861.7NaNNaN13.1003270.026.030.845725NaNNaNNaN2.90.196552NaNNaNNaNNaNNaN10.0NaN150.023.098.0NaNNaN1280.01.74NaNNaNNaN2.0002.0001.0004.0001.0001.000
Fulmar XNaN6000.00030.4065849.842NaNNaNNaN1.2NaNNaN20.000800.08.041.7NaNNaNNaN3.0NaNNaNNaN27.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Orbiter 4NaN6000.000NaNNaNNaNNaNNaN1.2NaNNaN55.000NaN24.036.0NaNNaNNaN5.2NaN12.0NaNNaNNaNNaNNaNNaN150.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Orbiter 3NaN6000.000NaNNaNNaNNaNNaN1.2NaNNaN32.00050.06.036.0NaNNaNNaN4.4NaN5.5NaNNaNNaNNaNNaNNaN50.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0001.0005.0000.0001.0001.000
MantisNaN6000.00018.265826NaNNaNNaNNaN1.48NaNNaN6.50025.02.025.6NaNNaNNaN2.1NaNNaNNaN16.7NaNNaNNaNNaN25.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0001.0005.0000.0001.0001.000
ScanEagleNaN6000.00030.62533619500.0NaNNaNNaN1.71NaNNaN26.500NaN18.041.2NaNNaNNaN3.1NaN5.0NaN28.0NaNNaNNaNNaN101.86NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
IntegratorNaN6000.00030.95346519500.0NaNNaNNaN2.5NaNNaN74.800NaN24.046.3NaNNaNNaN4.8NaN18.0NaN28.3NaNNaNNaNNaN92.6NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Integrator VTOL0.05000.000NaNNaNNaNNaNNaNNaNNaNNaN75.000NaN16.0NaNNaNNaNNaNNaNNaN18.0NaNNaNNaNNaNNaN30.0NaNNaNNaNNaNNaNNaNNaNNaNNaN0.02.0002.0001.0004.0001.0001.000
Integrator Extended RangeNaN6000.000NaN19500.0NaNNaNNaN2.5NaNNaN74.800500.019.046.3NaNNaNNaN4.8NaN18.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
ScanEagle 3NaN6000.00025.70340720.0NaNNaNNaN2.4NaNNaN36.300NaN18.041.2NaNNaNNaN4.0NaN8.6NaN23.5NaNNaNNaNNaNNaNNaNNaNNaNNaN170.0NaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
RQ Nan 21A BlackjackNaN6000.00033.79724620.0NaNNaNNaN2.5NaNNaN61.000NaN16.046.3NaNNaNNaN4.8NaN17.7NaN30.9NaNNaNNaNNaN92.6NaNNaNNaNNaNNaN8.0NaNNaNNaN1.0002.0005.0000.0001.0001.000
DeltaQuad Evo0.06000.00018.09082413.0NaN0.84NaN0.75NaNNaN10.000270.04.53NaNNaNNaNNaN2.69NaN3.04.5316.544.86.84.845.0NaNNaNNaNNaNNaNNaNNaNNaNNaN0.02.0001.0001.0004.0001.0001.000
DeltaQuad Pro #MAP0.06000.00017.50019213.123NaNNaNNaN0.9NaNNaN6.200100.01.83NaNNaNNaNNaN2.35NaN1.2NaN16.0NaNNaNNaN50.050.0NaNNaNNaNNaNNaNNaNNaNNaN0.02.0001.0001.0004.0001.0001.000
DeltaQuad Pro #CARGO0.06000.00017.50019213.123NaNNaNNaN0.9NaNNaN6.200100.01.83NaNNaNNaNNaN2.35NaN1.2NaN16.0NaNNaNNaN50.030.0NaNNaNNaNNaNNaNNaNNaNNaN0.02.0001.0001.0004.0001.0001.000
V210.06000.00019.68771648800.014.00.8NaN0.93NaNNaN10.000NaN3.033.014.0NaN100.02.15NaN1.5NaN18.0NaNNaN2.65NaN30.012.5NaNNaNNaNNaNNaN3999.00.108NaN2.0001.0001.0004.0001.0001.000
V250.06000.00021.8752416000.015.50.52NaN0.93NaNNaN12.500NaN4.033.015.5NaN120.02.45NaN2.2NaN20.0NaNNaN3.45NaN30.024.0NaNNaNNaNNaNNaN4679.00.108NaN2.0001.0001.0004.0001.0001.000
V320.06000.00021.8752416000.017.0NaNNaN1.0NaNNaN23.500NaN4.533.017.0NaN150.03.2NaN5.0NaN20.0NaNNaN6.45NaN30.025.0NaNNaNNaNNaNNaN69999.00.108NaN2.0002.0001.0004.0001.0001.000
V350.06000.00027.3440516000.0NaNNaNNaN1.88NaNNaN32.000NaN2.833.0NaNNaNNaN3.5NaN10.0NaN25.0NaNNaNNaNNaN30.0NaNNaNNaNNaNNaNNaN7999.0NaNNaN2.0002.0001.0004.0001.0001.000
V390.06000.00027.3440516000.0NaNNaNNaNNaNNaNNaN24.000NaN4.533.0NaNNaNNaN3.9NaN5.0NaN25.0NaNNaNNaNNaN30.0NaNNaNNaNNaNNaNNaN8999.0NaNNaN2.0002.0001.0004.0001.0001.000
Volitation VT3700.06000.00027.3440517000.0NaNNaNNaN2.02NaNNaN40.000NaN15.033.0NaNNaNNaN6.5NaN18.0NaN25.0NaNNaNNaNNaNNaNNaNNaN13.00.96NaNNaN8999.0NaNNaN2.0002.0001.0004.0001.0001.000
Skyeye 2600NaN6000.00036.094147NaN10.00.88NaN2.05NaNNaN15.000NaN2.0NaN10.0NaNNaN2.6NaN4.0NaN33.0NaNNaN6.5NaNNaNNaNNaNNaNNaNNaNNaN2299.0NaNNaN2.0002.0001.0004.0001.0001.000
Skyeye 2930 VTOL0.06000.00026.250288NaN18.01.0NaN2.03NaNNaN28.000NaN3.030.018.0NaNNaN2.93NaN6.0NaN24.0NaNNaN7.1NaNNaNNaNNaNNaNNaNNaNNaN6799.0NaNNaN2.0002.0001.0004.0001.0001.000
Skyeye 360050.06000.000NaNNaN12.51.33NaN2.488NaNNaN28.000NaN4.5NaN12.5NaNNaN3.6NaN10.0NaNNaNNaNNaN11.5NaNNaNNaNNaN11.5NaNNaNNaN4999.0NaNNaN3.0002.0005.0000.0001.0001.000
Skyeye 3600 VTOL0.06000.00032.81286NaN24.01.32NaN2.42NaNNaN40.000300.06.033.024.0NaNNaN3.6NaN10.0NaN30.0NaNNaN11.0NaNNaNNaNNaN11.5NaNNaNNaN6999.0NaNNaN2.0002.0001.0004.0001.0001.000
Skyeye 500060.06000.00036.094147NaN15.02.615NaN3.5NaN0.37590.000NaN8.042.015.0NaNNaN5.0NaN20.0NaN33.0NaNNaN32.0NaNNaNNaNNaN28.01.2NaNNaN9999.0NaNNaN3.0002.0005.0000.0001.0001.000
Skyeye 5000 VTOL0.06000.00030.625336NaNNaN2.615NaN3.5NaN0.375100.000800.08.042.0NaNNaNNaN5.0NaN25.0NaN28.0NaNNaNNaNNaNNaNNaNNaN28.0NaNNaNNaN13900.0NaNNaN2.0002.0001.0004.0001.0001.000
Skyeye 5000 VTOL octo0.06000.000NaNNaNNaN2.615NaN3.5NaN0.375100.000NaNNaN38.024.0NaNNaN5.0NaN15.0NaN35.0NaNNaN35.0NaNNaNNaNNaN28.0NaNNaNNaN15999.0NaNNaN2.0002.0001.0008.0001.0001.000
Volitation VT5100.06000.00032.8128617000.025.0NaNNaN2.905NaNNaN100.000NaN5.050.025.0NaNNaN5.1NaN25.0NaN30.0NaNNaNNaNNaNNaNNaNNaN25.05.0NaNNaN16599.0NaNNaN2.0002.0001.0004.0001.0001.000
Ascend0.06000.00021.8752410000.0NaNNaNNaN1.562NaNNaN9.500NaN6.030.013.0NaNNaN2.0NaN0.60.0520.06.08.93.015.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0002.0001.0004.0001.0001.000
Transition0.06000.00021.8752413000.0NaNNaNNaN2.3NaNNaN18.000NaN12.030.013.0NaNNaN3.0NaN1.50.0520.011.816.55.815.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0002.0001.0004.0001.0001.000
Reach0.06000.00027.3440516000.0NaNNaNNaN4.712NaNNaN91.000NaN20.035.013.0NaNNaN6.0NaN7.00.0525.054.084.031.015.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0002.0001.0004.0001.0001.000
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df_filtrado_base

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Velocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
Modelo
Stalker XE16.88037912000.00.8715.3012552.113.600370.08.020.0NaNNaN3.6570.2392.49475610.886208
Stalker VXE3017.60237312000.01.15828315.3264492.590819.958433.08.025.034211NaNNaN4.87680.3181952.49475617.463292
Aerosonde Mk. 4.7 Fixed Wing27.3440514700.01.5512.53.042.200NaN19.833.43886NaNNaN4.40.35214.5NaN
Aerosonde Mk. 4.7 VTOL27.344059700.01.5512.53.053.500NaN12.033.43886NaNNaN4.40.35211.3NaN
Aerosonde Mk. 4.8 Fixed wing27.3440518200.01.5512.53.054.400NaN19.833.43886NaNNaN4.40.35217.7NaN
Aerosonde Mk. 4.8 VTOL FTUASNaN15000.0NaN12.5NaN93.000NaN14.0NaNNaNNaNNaNNaN22.7NaN
AAI AerosondeNaN15000.00.5714.7543861.713.1003270.026.030.845725NaNNaN2.90.196552NaN10.0
Fulmar X30.4065849.842NaNNaN1.220.000800.08.041.7NaNNaN3.0NaNNaNNaN
Orbiter 4NaNNaNNaNNaN1.255.000NaN24.036.0NaNNaN5.2NaN12.0NaN
Orbiter 3NaNNaNNaNNaN1.232.00050.06.036.0NaNNaN4.4NaN5.5NaN
Mantis18.265826NaNNaNNaN1.486.50025.02.025.6NaNNaN2.1NaNNaNNaN
ScanEagle30.62533619500.0NaNNaN1.7126.500NaN18.041.2NaNNaN3.1NaN5.0NaN
Integrator30.95346519500.0NaNNaN2.574.800NaN24.046.3NaNNaN4.8NaN18.0NaN
Integrator VTOLNaNNaNNaNNaNNaN75.000NaN16.0NaNNaNNaNNaNNaN18.0NaN
Integrator Extended RangeNaN19500.0NaNNaN2.574.800500.019.046.3NaNNaN4.8NaN18.0NaN
ScanEagle 325.70340720.0NaNNaN2.436.300NaN18.041.2NaNNaN4.0NaN8.6NaN
RQ Nan 21A Blackjack33.79724620.0NaNNaN2.561.000NaN16.046.3NaNNaN4.8NaN17.7NaN
DeltaQuad Evo18.09082413.00.84NaN0.7510.000270.04.53NaNNaNNaN2.69NaN3.04.8
DeltaQuad Pro #MAP17.50019213.123NaNNaN0.96.200100.01.83NaNNaNNaN2.35NaN1.2NaN
DeltaQuad Pro #CARGO17.50019213.123NaNNaN0.96.200100.01.83NaNNaNNaN2.35NaN1.2NaN
V2119.68771648800.00.8NaN0.9310.000NaN3.033.014.014.02.15NaN1.52.65
V2521.8752416000.00.52NaN0.9312.500NaN4.033.015.515.52.45NaN2.23.45
V3221.8752416000.0NaNNaN1.023.500NaN4.533.017.017.03.2NaN5.06.45
V3527.3440516000.0NaNNaN1.8832.000NaN2.833.0NaNNaN3.5NaN10.0NaN
V3927.3440516000.0NaNNaNNaN24.000NaN4.533.0NaNNaN3.9NaN5.0NaN
Volitation VT37027.3440517000.0NaNNaN2.0240.000NaN15.033.0NaNNaN6.5NaN18.0NaN
Skyeye 260036.094147NaN0.88NaN2.0515.000NaN2.0NaN10.010.02.6NaN4.06.5
Skyeye 2930 VTOL26.250288NaN1.0NaN2.0328.000NaN3.030.018.018.02.93NaN6.07.1
Skyeye 3600NaNNaN1.33NaN2.48828.000NaN4.5NaN12.512.53.6NaN10.011.5
Skyeye 3600 VTOL32.81286NaN1.32NaN2.4240.000300.06.033.024.024.03.6NaN10.011.0
Skyeye 500036.094147NaN2.615NaN3.590.000NaN8.042.015.015.05.0NaN20.032.0
Skyeye 5000 VTOL30.625336NaN2.615NaN3.5100.000800.08.042.0NaNNaN5.0NaN25.0NaN
Skyeye 5000 VTOL octoNaNNaN2.615NaN3.5100.000NaNNaN38.024.0NaN5.0NaN15.035.0
Volitation VT51032.8128617000.0NaNNaN2.905100.000NaN5.050.025.025.05.1NaN25.0NaN
Ascend21.8752410000.0NaNNaN1.5629.500NaN6.030.013.0NaN2.0NaN0.63.0
Transition21.8752413000.0NaNNaN2.318.000NaN12.030.013.0NaN3.0NaN1.55.8
Reach27.3440516000.0NaNNaN4.71291.000NaN20.035.013.0NaN6.0NaN7.031.0
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Configuración Inicial ===\n", + "\n", + "Valores configurados: Rango MTOW [85% - 115%], Confianza Mínima: 0.50\n", + "\n", + "================================================================================\n", + "\u001b[1m=== INICIO DE ITERACIÓN 1 ===\u001b[0m\n", + "================================================================================\n", + "\n", + "=== Iteración 1: Resumen antes de imputaciones ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Resumen de Valores Faltantes Antes de Iteración 1

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FilaValores Faltantes
0Stalker XE17.000
1Stalker VXE3018.000
2Aerosonde Mk. 4.7 Fixed Wing17.000
3Aerosonde Mk. 4.7 VTOL16.000
4Aerosonde Mk. 4.8 Fixed wing20.000
5Aerosonde Mk. 4.8 VTOL FTUAS28.000
6AAI Aerosonde19.000
7Fulmar X26.000
8Orbiter 428.000
9Orbiter 327.000
10Mantis26.000
11ScanEagle25.000
12Integrator25.000
13Integrator VTOL29.000
14Integrator Extended Range27.000
15ScanEagle 325.000
16RQ Nan 21A Blackjack24.000
17DeltaQuad Evo18.000
18DeltaQuad Pro #MAP22.000
19DeltaQuad Pro #CARGO22.000
20V2116.000
21V2516.000
22V3217.000
23V3523.000
24V3924.000
25Volitation VT37022.000
26Skyeye 260023.000
27Skyeye 2930 VTOL21.000
28Skyeye 360023.000
29Skyeye 3600 VTOL19.000
30Skyeye 500018.000
31Skyeye 5000 VTOL21.000
32Skyeye 5000 VTOL octo22.000
33Volitation VT51020.000
34Ascend19.000
35Transition19.000
36Reach19.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Sumatoria Total de Valores Faltantes

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ResumenCantidad
0Total de Valores Faltantes801.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[1m*** IMPUTACIÓN POR SIMILITUD - ITERACIÓN 1 ***\u001b[0m\n", + "--------------------------------------------------------------------------------\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 VTOL FTUAS - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'envergadura' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 5.0, Objetivo: 14.0, d: 64.29%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 20.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.15000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.150\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 0.770\n", + " vecino 'Volitation VT510' → sim_i: 0.770\n", + " vecino 'Reach' → sim_i: 0.795\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.770', '0.770', '0.795']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.778\n", + " Media de valores (y): 30.261\n", + " Coeficiente de variación (CV): 0.851\n", + " Dispersión: 2.247\n", + " Penalización por cantidad de vecinos (k): 0.355\n", + " Confianza en base a la calidad y cantidad de datos: 0.504\n", + " Confianza final: 39.244%\n", + "✅ Valor imputado: 30.229 (conf 0.392, datos 3, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: AAI Aerosonde - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.88, Objetivo: 0.57, d: 54.39%, g: -100.00, Bono: -0.05000\n", + " Parámetro: envergadura, Vecino: 2.6, Objetivo: 2.9, d: 10.34%, g: 94.70, Bono: 0.04735\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.05, Objetivo: 1.7, d: 20.59%, g: -99.99, Bono: -0.04999\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': -0.05265\n", + "⚠️ Diferencia NaN para el parámetro 'Potencia específica (P/W)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 2.0, Objetivo: 26.0, d: 92.31%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: -0.053\n", + " Bono prestacional: -0.050\n", + " vecino 'Skyeye 2600' → sim_i: 0.664\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.664']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.664\n", + " Media de valores (y): 36.094\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.517%\n", + "✅ Valor imputado: 36.094 (conf 0.035, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 5.2, d: 15.38%, g: 74.14, Bono: 0.03707\n", + " Parámetro: envergadura, Vecino: 4.8, Objetivo: 5.2, d: 7.69%, g: 96.72, Bono: 0.04836\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.0, Objetivo: 1.2, d: 150.00%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.5, Objetivo: 1.2, d: 108.33%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.01457\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.8, Objetivo: 24.0, d: 17.50%, g: 49.96, Bono: 0.02498\n", + " Parámetro: Autonomía de la aeronave, Vecino: 16.0, Objetivo: 24.0, d: 33.33%, g: -86.96, Bono: -0.04348\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.43886, Objetivo: 36.0, d: 7.11%, g: 96.98, Bono: 0.04849\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 46.3, Objetivo: 36.0, d: 28.61%, g: -96.24, Bono: -0.04812\n", + " Bono total para 'prest': -0.01814\n", + " Bono geométrico: -0.015\n", + " Bono prestacional: -0.018\n", + " vecino 'Aerosonde Mk. 4.8 Fixed wing' → sim_i: 0.916\n", + " vecino 'RQ Nan 21A Blackjack' → sim_i: 0.859\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.916', '0.859']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.889\n", + " Media de valores (y): 30.571\n", + " Coeficiente de variación (CV): 0.789\n", + " Dispersión: 3.227\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.373\n", + " Confianza final: 33.168%\n", + "✅ Valor imputado: 30.466 (conf 0.332, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.1, Objetivo: 4.4, d: 29.55%, g: -95.56, Bono: -0.04778\n", + " Parámetro: envergadura, Vecino: 4.0, Objetivo: 4.4, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.71, Objetivo: 1.2, d: 42.50%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.4, Objetivo: 1.2, d: 100.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.09982\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 18.0, Objetivo: 6.0, d: 200.00%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 18.0, Objetivo: 6.0, d: 200.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 41.2, Objetivo: 36.0, d: 14.44%, g: 81.06, Bono: 0.04053\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 41.2, Objetivo: 36.0, d: 14.44%, g: 81.06, Bono: 0.04053\n", + " Bono total para 'prest': -0.01894\n", + " Bono geométrico: -0.100\n", + " Bono prestacional: -0.019\n", + " vecino 'ScanEagle' → sim_i: 0.262\n", + " vecino 'ScanEagle 3' → sim_i: 0.487\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.262', '0.487']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.408\n", + " Media de valores (y): 28.164\n", + " Coeficiente de variación (CV): 0.825\n", + " Dispersión: 2.461\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.384\n", + " Confianza final: 15.678%\n", + "✅ Valor imputado: 27.426 (conf 0.157, datos 2, familia F2)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.8, Objetivo: 4.8, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: envergadura, Vecino: 4.8, Objetivo: 4.8, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.5, Objetivo: 2.5, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.5, Objetivo: 2.5, d: 0.00%, g: 99.94, Bono: 0.04997\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.19988\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 24.0, Objetivo: 19.0, d: 26.32%, g: -97.32, Bono: -0.04866\n", + " Parámetro: Autonomía de la aeronave, Vecino: 16.0, Objetivo: 19.0, d: 15.79%, g: 70.51, Bono: 0.03526\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 46.3, Objetivo: 46.3, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 46.3, Objetivo: 46.3, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Bono total para 'prest': 0.08654\n", + " Bono geométrico: 0.200\n", + " Bono prestacional: 0.087\n", + " vecino 'Integrator' → sim_i: 1.236\n", + " vecino 'RQ Nan 21A Blackjack' → sim_i: 0.611\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.236', '0.611']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.029\n", + " Media de valores (y): 32.375\n", + " Coeficiente de variación (CV): 0.912\n", + " Dispersión: 1.422\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.410\n", + " Confianza final: 42.216%\n", + "✅ Valor imputado: 31.894 (conf 0.422, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL octo - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad a la que se realiza el crucero (KTAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 2.615, Objetivo: 2.615, d: 0.00%, g: 99.94, Bono: 0.04997\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.0, Objetivo: 5.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: envergadura, Vecino: 5.1, Objetivo: 5.0, d: 2.00%, g: 99.57, Bono: 0.04979\n", + " Parámetro: envergadura, Vecino: 6.0, Objetivo: 5.0, d: 20.00%, g: 0.00, Bono: 0.00000\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.5, Objetivo: 3.5, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.905, Objetivo: 3.5, d: 17.00%, g: 56.93, Bono: 0.02847\n", + " Parámetro: Longitud del fuselaje, Vecino: 4.712, Objetivo: 3.5, d: 34.63%, g: -79.86, Bono: -0.03993\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.18823\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Autonomía de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 42.0, Objetivo: 38.0, d: 10.53%, g: 94.45, Bono: 0.04723\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 50.0, Objetivo: 38.0, d: 31.58%, g: -92.58, Bono: -0.04629\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 35.0, Objetivo: 38.0, d: 7.89%, g: 96.63, Bono: 0.04831\n", + " Bono total para 'prest': 0.04925\n", + " Bono geométrico: 0.188\n", + " Bono prestacional: 0.049\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 1.062\n", + " vecino 'Volitation VT510' → sim_i: 1.062\n", + " vecino 'Reach' → sim_i: 1.029\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.062', '1.062', '1.029']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.051\n", + " Media de valores (y): 30.261\n", + " Coeficiente de variación (CV): 0.851\n", + " Dispersión: 2.247\n", + " Penalización por cantidad de vecinos (k): 0.355\n", + " Confianza en base a la calidad y cantidad de datos: 0.504\n", + " Confianza final: 53.011%\n", + "✅ Valor imputado: 30.291 (conf 0.530, datos 3, familia F1)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 5.2, d: 15.38%, g: 74.14, Bono: 0.03707\n", + " Parámetro: envergadura, Vecino: 4.8, Objetivo: 5.2, d: 7.69%, g: 96.72, Bono: 0.04836\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.0, Objetivo: 1.2, d: 150.00%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.5, Objetivo: 1.2, d: 108.33%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.01457\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.8, Objetivo: 24.0, d: 17.50%, g: 49.96, Bono: 0.02498\n", + " Parámetro: Autonomía de la aeronave, Vecino: 16.0, Objetivo: 24.0, d: 33.33%, g: -86.96, Bono: -0.04348\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.43886, Objetivo: 36.0, d: 7.11%, g: 96.98, Bono: 0.04849\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 46.3, Objetivo: 36.0, d: 28.61%, g: -96.24, Bono: -0.04812\n", + " Bono total para 'prest': -0.01814\n", + " Bono geométrico: -0.015\n", + " Bono prestacional: -0.018\n", + " vecino 'Aerosonde Mk. 4.8 Fixed wing' → sim_i: 0.916\n", + " vecino 'RQ Nan 21A Blackjack' → sim_i: 0.859\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.916', '0.859']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.889\n", + " Media de valores (y): 9110.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 9090.000\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.137\n", + " Confianza final: 12.136%\n", + "✅ Valor imputado: 9403.635 (conf 0.121, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.1, Objetivo: 4.4, d: 29.55%, g: -95.56, Bono: -0.04778\n", + " Parámetro: envergadura, Vecino: 4.0, Objetivo: 4.4, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.71, Objetivo: 1.2, d: 42.50%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.4, Objetivo: 1.2, d: 100.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.09982\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 18.0, Objetivo: 6.0, d: 200.00%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 18.0, Objetivo: 6.0, d: 200.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 41.2, Objetivo: 36.0, d: 14.44%, g: 81.06, Bono: 0.04053\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 41.2, Objetivo: 36.0, d: 14.44%, g: 81.06, Bono: 0.04053\n", + " Bono total para 'prest': -0.01894\n", + " Bono geométrico: -0.100\n", + " Bono prestacional: -0.019\n", + " vecino 'ScanEagle' → sim_i: 0.262\n", + " vecino 'ScanEagle 3' → sim_i: 0.487\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.262', '0.487']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.408\n", + " Media de valores (y): 9760.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 9740.000\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.137\n", + " Confianza final: 5.574%\n", + "✅ Valor imputado: 6839.145 (conf 0.056, datos 2, familia F2)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2600 - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.57, Objetivo: 0.88, d: 35.23%, g: -75.44, Bono: -0.03772\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.9, Objetivo: 2.6, d: 11.54%, g: 92.67, Bono: 0.04633\n", + " Parámetro: envergadura, Vecino: 3.0, Objetivo: 2.6, d: 15.38%, g: 74.14, Bono: 0.03707\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.7, Objetivo: 2.05, d: 17.07%, g: 55.96, Bono: 0.02798\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.3, Objetivo: 2.05, d: 12.20%, g: 91.03, Bono: 0.04551\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.11918\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 26.0, Objetivo: 2.0, d: 1200.00%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 12.0, Objetivo: 2.0, d: 500.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 21.875240329888257, Objetivo: 36.09414654431562, d: 39.39%, g: -14.71, Bono: -0.00736\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.10736\n", + " Bono geométrico: 0.119\n", + " Bono prestacional: -0.107\n", + " vecino 'AAI Aerosonde' → sim_i: 0.863\n", + " vecino 'Transition' → sim_i: 0.012\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.863', '0.012']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.851\n", + " Media de valores (y): 14000.000\n", + " Coeficiente de variación (CV): 0.857\n", + " Dispersión: 1000.000\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.394\n", + " Confianza final: 33.507%\n", + "✅ Valor imputado: 14972.956 (conf 0.335, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2930 VTOL - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.2, Objetivo: 2.93, d: 9.22%, g: 95.83, Bono: 0.04791\n", + " Parámetro: envergadura, Vecino: 3.5, Objetivo: 2.93, d: 19.45%, g: 13.34, Bono: 0.00667\n", + " Parámetro: envergadura, Vecino: 3.9, Objetivo: 2.93, d: 33.11%, g: -87.92, Bono: -0.04396\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.0, Objetivo: 2.03, d: 50.74%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.88, Objetivo: 2.03, d: 7.39%, g: 96.86, Bono: 0.04843\n", + "⚠️ Diferencia NaN para el parámetro 'Longitud del fuselaje'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.00905\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 4.5, Objetivo: 3.0, d: 50.00%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 2.8, Objetivo: 3.0, d: 6.67%, g: 97.17, Bono: 0.04858\n", + " Parámetro: Autonomía de la aeronave, Vecino: 4.5, Objetivo: 3.0, d: 50.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 21.875240329888257, Objetivo: 26.250288395865905, d: 16.67%, g: 61.11, Bono: 0.03056\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 26.250288395865905, d: 4.17%, g: 98.38, Bono: 0.04919\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 26.250288395865905, d: 4.17%, g: 98.38, Bono: 0.04919\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 30.0, d: 10.00%, g: 95.11, Bono: 0.04755\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 30.0, d: 10.00%, g: 95.11, Bono: 0.04755\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 30.0, d: 10.00%, g: 95.11, Bono: 0.04755\n", + " Bono total para 'prest': 0.22017\n", + " Bono geométrico: 0.009\n", + " Bono prestacional: 0.220\n", + " vecino 'V32' → sim_i: 0.873\n", + " vecino 'V35' → sim_i: 1.009\n", + " vecino 'V39' → sim_i: 1.009\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.873', '1.009', '1.009']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.968\n", + " Media de valores (y): 16000.000\n", + " Coeficiente de variación (CV): 1.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.355\n", + " Confianza en base a la calidad y cantidad de datos: 0.549\n", + " Confianza final: 53.099%\n", + "✅ Valor imputado: 16000.000 (conf 0.531, datos 3, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 VTOL - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.5, Objetivo: 3.6, d: 2.78%, g: 99.17, Bono: 0.04958\n", + " Parámetro: envergadura, Vecino: 6.5, Objetivo: 3.6, d: 80.56%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.88, Objetivo: 2.42, d: 22.31%, g: -99.42, Bono: -0.04971\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.02, Objetivo: 2.42, d: 16.53%, g: 62.74, Bono: 0.03137\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.01875\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 2.8, Objetivo: 6.0, d: 53.33%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 15.0, Objetivo: 6.0, d: 150.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 32.812860494832385, d: 16.67%, g: 61.11, Bono: 0.03056\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 32.812860494832385, d: 16.67%, g: 61.11, Bono: 0.03056\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 33.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 33.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Bono total para 'prest': 0.06105\n", + " Bono geométrico: -0.019\n", + " Bono prestacional: 0.061\n", + " vecino 'V35' → sim_i: 0.042\n", + " vecino 'Volitation VT370' → sim_i: 0.992\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.042', '0.992']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.953\n", + " Media de valores (y): 16500.000\n", + " Coeficiente de variación (CV): 0.939\n", + " Dispersión: 500.000\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.418\n", + " Confianza final: 39.867%\n", + "✅ Valor imputado: 16959.092 (conf 0.399, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'envergadura'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.1, Objetivo: 5.0, d: 2.00%, g: 99.57, Bono: 0.04979\n", + " Parámetro: envergadura, Vecino: 6.0, Objetivo: 5.0, d: 20.00%, g: 0.00, Bono: 0.00000\n", + "⚠️ Diferencia NaN para el parámetro 'Longitud del fuselaje'. Vecino: nan. Bono = 0.\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.905, Objetivo: 3.5, d: 17.00%, g: 56.93, Bono: 0.02847\n", + " Parámetro: Longitud del fuselaje, Vecino: 4.712, Objetivo: 3.5, d: 34.63%, g: -79.86, Bono: -0.03993\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.03832\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 14.0, Objetivo: 8.0, d: 75.00%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 5.0, Objetivo: 8.0, d: 37.50%, g: -49.96, Bono: -0.02498\n", + " Parámetro: Autonomía de la aeronave, Vecino: 20.0, Objetivo: 8.0, d: 150.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 32.812860494832385, Objetivo: 30.625336461843556, d: 7.14%, g: 96.96, Bono: 0.04848\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 30.625336461843556, d: 10.71%, g: 94.18, Bono: 0.04709\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 50.0, Objetivo: 42.0, d: 19.05%, g: 22.32, Bono: 0.01116\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 35.0, Objetivo: 42.0, d: 16.67%, g: 61.11, Bono: 0.03056\n", + " Bono total para 'prest': 0.01231\n", + " Bono geométrico: 0.038\n", + " Bono prestacional: 0.012\n", + " vecino 'Aerosonde Mk. 4.8 VTOL FTUAS' → sim_i: 0.972\n", + " vecino 'Volitation VT510' → sim_i: 1.000\n", + " vecino 'Reach' → sim_i: 0.963\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.972', '1.000', '0.963']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.979\n", + " Media de valores (y): 16000.000\n", + " Coeficiente de variación (CV): 0.898\n", + " Dispersión: 816.497\n", + " Penalización por cantidad de vecinos (k): 0.355\n", + " Confianza en base a la calidad y cantidad de datos: 0.518\n", + " Confianza final: 50.706%\n", + "✅ Valor imputado: 16009.436 (conf 0.507, datos 3, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL octo - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'envergadura'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.1, Objetivo: 5.0, d: 2.00%, g: 99.57, Bono: 0.04979\n", + " Parámetro: envergadura, Vecino: 6.0, Objetivo: 5.0, d: 20.00%, g: 0.00, Bono: 0.00000\n", + "⚠️ Diferencia NaN para el parámetro 'Longitud del fuselaje'. Vecino: nan. Bono = 0.\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.905, Objetivo: 3.5, d: 17.00%, g: 56.93, Bono: 0.02847\n", + " Parámetro: Longitud del fuselaje, Vecino: 4.712, Objetivo: 3.5, d: 34.63%, g: -79.86, Bono: -0.03993\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.03832\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Autonomía de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 50.0, Objetivo: 38.0, d: 31.58%, g: -92.58, Bono: -0.04629\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 35.0, Objetivo: 38.0, d: 7.89%, g: 96.63, Bono: 0.04831\n", + " Bono total para 'prest': 0.00202\n", + " Bono geométrico: 0.038\n", + " Bono prestacional: 0.002\n", + " vecino 'Aerosonde Mk. 4.8 VTOL FTUAS' → sim_i: 0.841\n", + " vecino 'Volitation VT510' → sim_i: 0.865\n", + " vecino 'Reach' → sim_i: 0.832\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.841', '0.865', '0.832']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.846\n", + " Media de valores (y): 16000.000\n", + " Coeficiente de variación (CV): 0.898\n", + " Dispersión: 816.497\n", + " Penalización por cantidad de vecinos (k): 0.355\n", + " Confianza en base a la calidad y cantidad de datos: 0.518\n", + " Confianza final: 43.846%\n", + "✅ Valor imputado: 16009.476 (conf 0.438, datos 3, familia F1)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 VTOL FTUAS - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'envergadura' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.050\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 0.870\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.870']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.870\n", + " Media de valores (y): 2.615\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.607%\n", + "✅ Valor imputado: 2.615 (conf 0.046, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Fulmar X - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.8768, Objetivo: 3.0, d: 62.56%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.5908, Objetivo: 1.2, d: 115.90%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.10000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 8.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Alcance de la aeronave, Vecino: 433.0, Objetivo: 800.0, d: 45.88%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 17.602373279430935, Objetivo: 30.406584058544677, d: 42.11%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 25.034211403264, Objetivo: 41.7, d: 39.97%, g: -0.88, Bono: -0.00044\n", + " Bono total para 'prest': -0.05047\n", + " Bono geométrico: -0.100\n", + " Bono prestacional: -0.050\n", + " vecino 'Stalker VXE30' → sim_i: 0.799\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.799']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.799\n", + " Media de valores (y): 1.158\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.235%\n", + "✅ Valor imputado: 1.158 (conf 0.042, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 5.2, d: 15.38%, g: 74.14, Bono: 0.03707\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.0, Objetivo: 1.2, d: 150.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.01293\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.8, Objetivo: 24.0, d: 17.50%, g: 49.96, Bono: 0.02498\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.43886, Objetivo: 36.0, d: 7.11%, g: 96.98, Bono: 0.04849\n", + " Bono total para 'prest': 0.07347\n", + " Bono geométrico: -0.013\n", + " Bono prestacional: 0.073\n", + " vecino 'Aerosonde Mk. 4.8 Fixed wing' → sim_i: 1.010\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.010']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.010\n", + " Media de valores (y): 1.550\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.349%\n", + "✅ Valor imputado: 1.550 (conf 0.053, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Área del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Área del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Área del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Área del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 4.0, d: 10.00%, g: 95.10, Bono: 0.04755\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.0, Objetivo: 2.4, d: 25.00%, g: -97.93, Bono: -0.04897\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.00141\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.8, Objetivo: 18.0, d: 10.00%, g: 95.10, Bono: 0.04755\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 25.7034073876187, d: 6.38%, g: 97.29, Bono: 0.04864\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.43886, Objetivo: 41.2, d: 18.84%, g: 26.66, Bono: 0.01333\n", + " Bono total para 'prest': 0.10952\n", + " Bono geométrico: -0.001\n", + " Bono prestacional: 0.110\n", + " vecino 'Aerosonde Mk. 4.7 Fixed Wing' → sim_i: 0.733\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.733']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.733\n", + " Media de valores (y): 1.550\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.885%\n", + "✅ Valor imputado: 1.550 (conf 0.039, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 4.8, d: 8.33%, g: 96.40, Bono: 0.04820\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.0, Objetivo: 2.5, d: 20.00%, g: 0.00, Bono: 0.00000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.04820\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.8, Objetivo: 16.0, d: 23.75%, g: -98.61, Bono: -0.04931\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 33.797246309677355, d: 19.09%, g: 21.34, Bono: 0.01067\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.43886, Objetivo: 46.3, d: 27.78%, g: -96.68, Bono: -0.04834\n", + " Bono total para 'prest': -0.08698\n", + " Bono geométrico: 0.048\n", + " Bono prestacional: -0.087\n", + " vecino 'Aerosonde Mk. 4.8 Fixed wing' → sim_i: 0.854\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.854']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.854\n", + " Media de valores (y): 1.550\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.526%\n", + "✅ Valor imputado: 1.550 (conf 0.045, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V32 - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.93, Objetivo: 3.2, d: 8.44%, g: 96.34, Bono: 0.04817\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.03, Objetivo: 1.0, d: 103.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.00183\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 4.5, d: 33.33%, g: -86.96, Bono: -0.04348\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 26.250288395865905, Objetivo: 21.875240329888257, d: 20.00%, g: 0.00, Bono: 0.00000\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.0, Objetivo: 33.0, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Bono total para 'prest': 0.00448\n", + " Bono geométrico: -0.002\n", + " Bono prestacional: 0.004\n", + " vecino 'Skyeye 2930 VTOL' → sim_i: 0.194\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.194']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.194\n", + " Media de valores (y): 1.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 1.028%\n", + "✅ Valor imputado: 1.000 (conf 0.010, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: V35 - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.93, Objetivo: 3.5, d: 16.29%, g: 65.47, Bono: 0.03274\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.03, Objetivo: 1.88, d: 7.98%, g: 96.58, Bono: 0.04829\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.08103\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 2.8, d: 7.14%, g: 96.96, Bono: 0.04848\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 26.250288395865905, Objetivo: 27.344050412360318, d: 4.00%, g: 98.47, Bono: 0.04924\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.0, Objetivo: 33.0, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Bono total para 'prest': 0.14568\n", + " Bono geométrico: 0.081\n", + " Bono prestacional: 0.146\n", + " vecino 'Skyeye 2930 VTOL' → sim_i: 1.083\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.083']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.083\n", + " Media de valores (y): 1.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.736%\n", + "✅ Valor imputado: 1.000 (conf 0.057, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: V39 - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.93, Objetivo: 3.9, d: 24.87%, g: -98.00, Bono: -0.04900\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.04900\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 4.5, d: 33.33%, g: -86.96, Bono: -0.04348\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 26.250288395865905, Objetivo: 27.344050412360318, d: 4.00%, g: 98.47, Bono: 0.04924\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.0, Objetivo: 33.0, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Bono total para 'prest': 0.05371\n", + " Bono geométrico: -0.049\n", + " Bono prestacional: 0.054\n", + " vecino 'Skyeye 2930 VTOL' → sim_i: 0.585\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.585']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.585\n", + " Media de valores (y): 1.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.101%\n", + "✅ Valor imputado: 1.000 (conf 0.031, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT370 - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.6, Objetivo: 6.5, d: 44.62%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.42, Objetivo: 2.02, d: 19.80%, g: 5.01, Bono: 0.00251\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.04749\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 6.0, Objetivo: 15.0, d: 60.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 32.812860494832385, Objetivo: 27.344050412360318, d: 20.00%, g: -99.94, Bono: -0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 33.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: -0.047\n", + " Bono prestacional: -0.050\n", + " vecino 'Skyeye 3600 VTOL' → sim_i: 0.852\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.852']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.852\n", + " Media de valores (y): 1.320\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.513%\n", + "✅ Valor imputado: 1.320 (conf 0.045, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT510 - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.0, Objetivo: 5.1, d: 1.96%, g: 99.59, Bono: 0.04979\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.5, Objetivo: 2.905, d: 20.48%, g: -99.99, Bono: -0.04999\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.00020\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 5.0, d: 60.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 30.625336461843556, Objetivo: 32.812860494832385, d: 6.67%, g: 97.17, Bono: 0.04858\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 42.0, Objetivo: 50.0, d: 16.00%, g: 68.46, Bono: 0.03423\n", + " Bono total para 'prest': 0.03281\n", + " Bono geométrico: -0.000\n", + " Bono prestacional: 0.033\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 0.982\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.982']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.982\n", + " Media de valores (y): 2.615\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.203%\n", + "✅ Valor imputado: 2.615 (conf 0.052, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Ascend - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.69, Objetivo: 2.0, d: 34.50%, g: -80.70, Bono: -0.04035\n", + " Parámetro: envergadura, Vecino: 2.15, Objetivo: 2.0, d: 7.50%, g: 96.81, Bono: 0.04840\n", + " Parámetro: Longitud del fuselaje, Vecino: 0.75, Objetivo: 1.562, d: 51.98%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 0.93, Objetivo: 1.562, d: 40.46%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.09195\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 4.53, Objetivo: 6.0, d: 24.50%, g: -98.19, Bono: -0.04910\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 6.0, d: 50.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 18.090823752817585, Objetivo: 21.875240329888257, d: 17.30%, g: 52.85, Bono: 0.02643\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 19.68771629689943, Objetivo: 21.875240329888257, d: 10.00%, g: 95.11, Bono: 0.04755\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 30.0, d: 10.00%, g: 95.11, Bono: 0.04755\n", + " Bono total para 'prest': 0.02243\n", + " Bono geométrico: -0.092\n", + " Bono prestacional: 0.022\n", + " vecino 'DeltaQuad Evo' → sim_i: 0.615\n", + " vecino 'V21' → sim_i: 0.615\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.615', '0.615']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.615\n", + " Media de valores (y): 0.820\n", + " Coeficiente de variación (CV): 0.951\n", + " Dispersión: 0.020\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.422\n", + " Confianza final: 25.952%\n", + "✅ Valor imputado: 0.820 (conf 0.260, datos 2, familia F2)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Transition - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.6, Objetivo: 3.0, d: 13.33%, g: 86.96, Bono: 0.04348\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.05, Objetivo: 2.3, d: 10.87%, g: 93.93, Bono: 0.04697\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.09045\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 2.0, Objetivo: 12.0, d: 83.33%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 36.09414654431562, Objetivo: 21.875240329888257, d: 65.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': -0.10000\n", + " Bono geométrico: 0.090\n", + " Bono prestacional: -0.100\n", + " vecino 'Skyeye 2600' → sim_i: 0.571\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.571']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.571\n", + " Media de valores (y): 0.880\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.025%\n", + "✅ Valor imputado: 0.880 (conf 0.030, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Reach - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.0, Objetivo: 6.0, d: 16.67%, g: 61.11, Bono: 0.03056\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.5, Objetivo: 4.712, d: 25.72%, g: -97.58, Bono: -0.04879\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.01823\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 20.0, d: 60.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 30.625336461843556, Objetivo: 27.344050412360318, d: 12.00%, g: 91.56, Bono: 0.04578\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 42.0, Objetivo: 35.0, d: 20.00%, g: 0.00, Bono: 0.00000\n", + " Bono total para 'prest': -0.00422\n", + " Bono geométrico: -0.018\n", + " Bono prestacional: -0.004\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 0.882\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.882']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.882\n", + " Media de valores (y): 2.615\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.674%\n", + "✅ Valor imputado: 2.615 (conf 0.047, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Fulmar X - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.8768, Objetivo: 3.0, d: 62.56%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.5908, Objetivo: 1.2, d: 115.90%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.10000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 8.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Alcance de la aeronave, Vecino: 433.0, Objetivo: 800.0, d: 45.88%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 17.602373279430935, Objetivo: 30.406584058544677, d: 42.11%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 25.034211403264, Objetivo: 41.7, d: 39.97%, g: -0.88, Bono: -0.00044\n", + " Bono total para 'prest': -0.05047\n", + " Bono geométrico: -0.100\n", + " Bono prestacional: -0.050\n", + " vecino 'Stalker VXE30' → sim_i: 0.799\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.799']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.799\n", + " Media de valores (y): 15.326\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.235%\n", + "✅ Valor imputado: 15.326 (conf 0.042, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 5.2, d: 15.38%, g: 74.14, Bono: 0.03707\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.0, Objetivo: 1.2, d: 150.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.01293\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.8, Objetivo: 24.0, d: 17.50%, g: 49.96, Bono: 0.02498\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.43886, Objetivo: 36.0, d: 7.11%, g: 96.98, Bono: 0.04849\n", + " Bono total para 'prest': 0.07347\n", + " Bono geométrico: -0.013\n", + " Bono prestacional: 0.073\n", + " vecino 'Aerosonde Mk. 4.8 Fixed wing' → sim_i: 1.010\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.010']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.010\n", + " Media de valores (y): 12.500\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.349%\n", + "✅ Valor imputado: 12.500 (conf 0.053, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 4.0, d: 10.00%, g: 95.10, Bono: 0.04755\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.0, Objetivo: 2.4, d: 25.00%, g: -97.93, Bono: -0.04897\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.00141\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.8, Objetivo: 18.0, d: 10.00%, g: 95.10, Bono: 0.04755\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 25.7034073876187, d: 6.38%, g: 97.29, Bono: 0.04864\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.43886, Objetivo: 41.2, d: 18.84%, g: 26.66, Bono: 0.01333\n", + " Bono total para 'prest': 0.10952\n", + " Bono geométrico: -0.001\n", + " Bono prestacional: 0.110\n", + " vecino 'Aerosonde Mk. 4.7 Fixed Wing' → sim_i: 0.733\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.733']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.733\n", + " Media de valores (y): 12.500\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.885%\n", + "✅ Valor imputado: 12.500 (conf 0.039, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 4.8, d: 8.33%, g: 96.40, Bono: 0.04820\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.0, Objetivo: 2.5, d: 20.00%, g: 0.00, Bono: 0.00000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.04820\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.8, Objetivo: 16.0, d: 23.75%, g: -98.61, Bono: -0.04931\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 33.797246309677355, d: 19.09%, g: 21.34, Bono: 0.01067\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.43886, Objetivo: 46.3, d: 27.78%, g: -96.68, Bono: -0.04834\n", + " Bono total para 'prest': -0.08698\n", + " Bono geométrico: 0.048\n", + " Bono prestacional: -0.087\n", + " vecino 'Aerosonde Mk. 4.8 Fixed wing' → sim_i: 0.854\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.854']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.854\n", + " Media de valores (y): 12.500\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.526%\n", + "✅ Valor imputado: 12.500 (conf 0.045, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Evo - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V21 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V25 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.57, Objetivo: 0.52, d: 9.62%, g: 95.49, Bono: 0.04775\n", + " Parámetro: envergadura, Vecino: 2.9, Objetivo: 2.45, d: 18.37%, g: 35.68, Bono: 0.01784\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.7, Objetivo: 0.93, d: 82.80%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.01558\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 26.0, Objetivo: 4.0, d: 550.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.845724764736, Objetivo: 33.0, d: 6.53%, g: 97.23, Bono: 0.04861\n", + " Bono total para 'prest': -0.00139\n", + " Bono geométrico: 0.016\n", + " Bono prestacional: -0.001\n", + " vecino 'AAI Aerosonde' → sim_i: 0.700\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.700']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.700\n", + " Media de valores (y): 14.754\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.711%\n", + "✅ Valor imputado: 14.754 (conf 0.037, datos 1, familia F2)\n", + "\u001b[1m\n", + "=== Imputación por similitud: V32 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V35 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V39 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT370 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2600 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.57, Objetivo: 0.88, d: 35.23%, g: -75.44, Bono: -0.03772\n", + " Parámetro: envergadura, Vecino: 2.9, Objetivo: 2.6, d: 11.54%, g: 92.67, Bono: 0.04633\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.7, Objetivo: 2.05, d: 17.07%, g: 55.96, Bono: 0.02798\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.03660\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 26.0, Objetivo: 2.0, d: 1200.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: 0.037\n", + " Bono prestacional: -0.050\n", + " vecino 'AAI Aerosonde' → sim_i: 0.837\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.837']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.837\n", + " Media de valores (y): 14.754\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.436%\n", + "✅ Valor imputado: 14.754 (conf 0.044, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2930 VTOL - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 VTOL - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'envergadura'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Longitud del fuselaje'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 14.0, Objetivo: 8.0, d: 75.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.050\n", + " vecino 'Aerosonde Mk. 4.8 VTOL FTUAS' → sim_i: 0.872\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.872']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.872\n", + " Media de valores (y): 12.500\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.618%\n", + "✅ Valor imputado: 12.500 (conf 0.046, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL octo - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'envergadura'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Longitud del fuselaje'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Autonomía de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': 0.00000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: 0.000\n", + " vecino 'Aerosonde Mk. 4.8 VTOL FTUAS' → sim_i: 0.800\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.800']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.800\n", + " Media de valores (y): 12.500\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.241%\n", + "✅ Valor imputado: 12.500 (conf 0.042, datos 1, familia F1)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT510 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'envergadura'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Longitud del fuselaje'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 14.0, Objetivo: 5.0, d: 180.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.050\n", + " vecino 'Aerosonde Mk. 4.8 VTOL FTUAS' → sim_i: 0.872\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.872']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.872\n", + " Media de valores (y): 12.500\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.618%\n", + "✅ Valor imputado: 12.500 (conf 0.046, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Ascend - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Transition - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Reach - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'envergadura'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Longitud del fuselaje'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 14.0, Objetivo: 20.0, d: 30.00%, g: -95.11, Bono: -0.04755\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': -0.04755\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.048\n", + " vecino 'Aerosonde Mk. 4.8 VTOL FTUAS' → sim_i: 0.897\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.897']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.897\n", + " Media de valores (y): 12.500\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.755%\n", + "✅ Valor imputado: 12.500 (conf 0.048, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 VTOL FTUAS - Longitud del fuselaje ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'envergadura' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 5.0, Objetivo: 14.0, d: 64.29%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 20.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.15000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.150\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 0.770\n", + " vecino 'Volitation VT510' → sim_i: 0.770\n", + " vecino 'Reach' → sim_i: 0.795\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.770', '0.770', '0.795']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.778\n", + " Media de valores (y): 3.706\n", + " Coeficiente de variación (CV): 0.594\n", + " Dispersión: 0.752\n", + " Penalización por cantidad de vecinos (k): 0.355\n", + " Confianza en base a la calidad y cantidad de datos: 0.427\n", + " Confianza final: 33.237%\n", + "✅ Valor imputado: 3.717 (conf 0.332, datos 3, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Longitud del fuselaje ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V39 - Longitud del fuselaje ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.2, Objetivo: 3.9, d: 17.95%, g: 42.93, Bono: 0.02147\n", + " Parámetro: envergadura, Vecino: 2.93, Objetivo: 3.9, d: 24.87%, g: -98.00, Bono: -0.04900\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.02753\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 4.5, Objetivo: 4.5, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 4.5, d: 33.33%, g: -86.96, Bono: -0.04348\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 21.875240329888257, Objetivo: 27.344050412360318, d: 20.00%, g: 0.00, Bono: 0.00000\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 26.250288395865905, Objetivo: 27.344050412360318, d: 4.00%, g: 98.47, Bono: 0.04924\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 33.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.0, Objetivo: 33.0, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Bono total para 'prest': 0.15365\n", + " Bono geométrico: -0.028\n", + " Bono prestacional: 0.154\n", + " vecino 'V32' → sim_i: 1.072\n", + " vecino 'Skyeye 2930 VTOL' → sim_i: 0.707\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.072', '0.707']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.927\n", + " Media de valores (y): 1.515\n", + " Coeficiente de variación (CV): 0.320\n", + " Dispersión: 0.515\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.233\n", + " Confianza final: 21.554%\n", + "✅ Valor imputado: 1.409 (conf 0.216, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 Fixed Wing - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 VTOL - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 Fixed wing - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 VTOL FTUAS - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'envergadura' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.050\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 0.870\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.870']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.870\n", + " Media de valores (y): 800.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.607%\n", + "✅ Valor imputado: 800.000 (conf 0.046, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.8, Objetivo: 4.8, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.5, Objetivo: 2.5, d: 0.00%, g: 99.94, Bono: 0.04997\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.09994\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.0, Objetivo: 24.0, d: 20.83%, g: -99.96, Bono: -0.04998\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 46.3, Objetivo: 46.3, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Bono total para 'prest': -0.00001\n", + " Bono geométrico: 0.100\n", + " Bono prestacional: -0.000\n", + " vecino 'Integrator Extended Range' → sim_i: 1.049\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.049']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.049\n", + " Media de valores (y): 500.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.559%\n", + "✅ Valor imputado: 500.000 (conf 0.056, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 4.0, d: 10.00%, g: 95.10, Bono: 0.04755\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.2, Objetivo: 2.4, d: 50.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.00245\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 6.0, Objetivo: 18.0, d: 66.67%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 36.0, Objetivo: 41.2, d: 12.62%, g: 89.71, Bono: 0.04485\n", + " Bono total para 'prest': -0.00515\n", + " Bono geométrico: -0.002\n", + " Bono prestacional: -0.005\n", + " vecino 'Orbiter 3' → sim_i: 0.636\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.636']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.636\n", + " Media de valores (y): 50.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.370%\n", + "✅ Valor imputado: 50.000 (conf 0.034, datos 1, familia F2)\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V21 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.84, Objetivo: 0.8, d: 5.00%, g: 97.93, Bono: 0.04897\n", + " Parámetro: envergadura, Vecino: 2.69, Objetivo: 2.15, d: 25.12%, g: -97.87, Bono: -0.04894\n", + " Parámetro: Longitud del fuselaje, Vecino: 0.75, Objetivo: 0.93, d: 19.35%, g: 15.60, Bono: 0.00780\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.00783\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 4.53, Objetivo: 3.0, d: 51.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 18.090823752817585, Objetivo: 19.68771629689943, d: 8.11%, g: 96.52, Bono: 0.04826\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': -0.00174\n", + " Bono geométrico: 0.008\n", + " Bono prestacional: -0.002\n", + " vecino 'DeltaQuad Evo' → sim_i: 0.956\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.956']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.956\n", + " Media de valores (y): 270.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.062%\n", + "✅ Valor imputado: 270.000 (conf 0.051, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: V25 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.84, Objetivo: 0.52, d: 61.54%, g: -100.00, Bono: -0.05000\n", + " Parámetro: envergadura, Vecino: 2.69, Objetivo: 2.45, d: 9.80%, g: 95.32, Bono: 0.04766\n", + " Parámetro: Longitud del fuselaje, Vecino: 0.75, Objetivo: 0.93, d: 19.35%, g: 15.60, Bono: 0.00780\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.00546\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 4.53, Objetivo: 4.0, d: 13.25%, g: 87.32, Bono: 0.04366\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 18.090823752817585, Objetivo: 21.875240329888257, d: 17.30%, g: 52.85, Bono: 0.02643\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': 0.07009\n", + " Bono geométrico: 0.005\n", + " Bono prestacional: 0.070\n", + " vecino 'DeltaQuad Evo' → sim_i: 0.076\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.076']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.076\n", + " Media de valores (y): 270.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 0.400%\n", + "✅ Valor imputado: 270.000 (conf 0.004, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: V32 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V35 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V39 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT370 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.6, Objetivo: 6.5, d: 44.62%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.42, Objetivo: 2.02, d: 19.80%, g: 5.01, Bono: 0.00251\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.04749\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 6.0, Objetivo: 15.0, d: 60.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 32.812860494832385, Objetivo: 27.344050412360318, d: 20.00%, g: -99.94, Bono: -0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 33.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: -0.047\n", + " Bono prestacional: -0.050\n", + " vecino 'Skyeye 3600 VTOL' → sim_i: 0.852\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.852']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.852\n", + " Media de valores (y): 300.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.513%\n", + "✅ Valor imputado: 300.000 (conf 0.045, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2600 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.57, Objetivo: 0.88, d: 35.23%, g: -75.44, Bono: -0.03772\n", + " Parámetro: envergadura, Vecino: 2.9, Objetivo: 2.6, d: 11.54%, g: 92.67, Bono: 0.04633\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.7, Objetivo: 2.05, d: 17.07%, g: 55.96, Bono: 0.02798\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.03660\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 26.0, Objetivo: 2.0, d: 1200.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: 0.037\n", + " Bono prestacional: -0.050\n", + " vecino 'AAI Aerosonde' → sim_i: 0.837\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.837']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.837\n", + " Media de valores (y): 3270.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.436%\n", + "✅ Valor imputado: 3270.000 (conf 0.044, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2930 VTOL - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL octo - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 2.615, Objetivo: 2.615, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: envergadura, Vecino: 5.0, Objetivo: 5.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.5, Objetivo: 3.5, d: 0.00%, g: 99.94, Bono: 0.04997\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.14991\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Autonomía de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 42.0, Objetivo: 38.0, d: 10.53%, g: 94.45, Bono: 0.04723\n", + " Bono total para 'prest': 0.04723\n", + " Bono geométrico: 0.150\n", + " Bono prestacional: 0.047\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 1.022\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.022']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.022\n", + " Media de valores (y): 800.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.412%\n", + "✅ Valor imputado: 800.000 (conf 0.054, datos 1, familia F1)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT510 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.0, Objetivo: 5.1, d: 1.96%, g: 99.59, Bono: 0.04979\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.5, Objetivo: 2.905, d: 20.48%, g: -99.99, Bono: -0.04999\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.00020\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 5.0, d: 60.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 30.625336461843556, Objetivo: 32.812860494832385, d: 6.67%, g: 97.17, Bono: 0.04858\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 42.0, Objetivo: 50.0, d: 16.00%, g: 68.46, Bono: 0.03423\n", + " Bono total para 'prest': 0.03281\n", + " Bono geométrico: -0.000\n", + " Bono prestacional: 0.033\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 0.982\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.982']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.982\n", + " Media de valores (y): 800.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.203%\n", + "✅ Valor imputado: 800.000 (conf 0.052, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Ascend - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.69, Objetivo: 2.0, d: 34.50%, g: -80.70, Bono: -0.04035\n", + " Parámetro: Longitud del fuselaje, Vecino: 0.75, Objetivo: 1.562, d: 51.98%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.09035\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 4.53, Objetivo: 6.0, d: 24.50%, g: -98.19, Bono: -0.04910\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 18.090823752817585, Objetivo: 21.875240329888257, d: 17.30%, g: 52.85, Bono: 0.02643\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': -0.02267\n", + " Bono geométrico: -0.090\n", + " Bono prestacional: -0.023\n", + " vecino 'DeltaQuad Evo' → sim_i: 0.572\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.572']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.572\n", + " Media de valores (y): 270.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.028%\n", + "✅ Valor imputado: 270.000 (conf 0.030, datos 1, familia F2)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Transition - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Reach - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.0, Objetivo: 6.0, d: 16.67%, g: 61.11, Bono: 0.03056\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.5, Objetivo: 4.712, d: 25.72%, g: -97.58, Bono: -0.04879\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.01823\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 20.0, d: 60.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 30.625336461843556, Objetivo: 27.344050412360318, d: 12.00%, g: 91.56, Bono: 0.04578\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 42.0, Objetivo: 35.0, d: 20.00%, g: 0.00, Bono: 0.00000\n", + " Bono total para 'prest': -0.00422\n", + " Bono geométrico: -0.018\n", + " Bono prestacional: -0.004\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 0.882\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.882']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.882\n", + " Media de valores (y): 800.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.674%\n", + "✅ Valor imputado: 800.000 (conf 0.047, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL octo - Autonomía de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Autonomía de la aeronave'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + " Parámetro: Área del ala, Vecino: 2.615, Objetivo: 2.615, d: 0.00%, g: 99.94, Bono: 0.04997\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'envergadura'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.0, Objetivo: 5.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: envergadura, Vecino: 5.1, Objetivo: 5.0, d: 2.00%, g: 99.57, Bono: 0.04979\n", + " Parámetro: envergadura, Vecino: 6.0, Objetivo: 5.0, d: 20.00%, g: 0.00, Bono: 0.00000\n", + "⚠️ Diferencia NaN para el parámetro 'Longitud del fuselaje'. Vecino: nan. Bono = 0.\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.5, Objetivo: 3.5, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.905, Objetivo: 3.5, d: 17.00%, g: 56.93, Bono: 0.02847\n", + " Parámetro: Longitud del fuselaje, Vecino: 4.712, Objetivo: 3.5, d: 34.63%, g: -79.86, Bono: -0.03993\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.18823\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Autonomía de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 42.0, Objetivo: 38.0, d: 10.53%, g: 94.45, Bono: 0.04723\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 50.0, Objetivo: 38.0, d: 31.58%, g: -92.58, Bono: -0.04629\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 35.0, Objetivo: 38.0, d: 7.89%, g: 96.63, Bono: 0.04831\n", + " Bono total para 'prest': 0.04925\n", + " Bono geométrico: 0.188\n", + " Bono prestacional: 0.049\n", + " vecino 'Aerosonde Mk. 4.8 VTOL FTUAS' → sim_i: 1.038\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 1.062\n", + " vecino 'Volitation VT510' → sim_i: 1.062\n", + " vecino 'Reach' → sim_i: 1.029\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.038', '1.062', '1.062', '1.029']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.048\n", + " Media de valores (y): 11.750\n", + " Coeficiente de variación (CV): 0.019\n", + " Dispersión: 5.761\n", + " Penalización por cantidad de vecinos (k): 0.530\n", + " Confianza en base a la calidad y cantidad de datos: 0.376\n", + " Confianza final: 39.457%\n", + "✅ Valor imputado: 11.673 (conf 0.395, datos 4, familia F1)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 VTOL FTUAS - Velocidad máxima (KIAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'envergadura' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 5.0, Objetivo: 14.0, d: 64.29%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 20.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.15000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.150\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 0.770\n", + " vecino 'Volitation VT510' → sim_i: 0.770\n", + " vecino 'Reach' → sim_i: 0.795\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.770', '0.770', '0.795']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.778\n", + " Media de valores (y): 42.333\n", + " Coeficiente de variación (CV): 0.710\n", + " Dispersión: 6.128\n", + " Penalización por cantidad de vecinos (k): 0.355\n", + " Confianza en base a la calidad y cantidad de datos: 0.462\n", + " Confianza final: 35.952%\n", + "✅ Valor imputado: 42.253 (conf 0.360, datos 3, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Velocidad máxima (KIAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Evo - Velocidad máxima (KIAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.8, Objetivo: 0.84, d: 4.76%, g: 98.05, Bono: 0.04903\n", + " Parámetro: envergadura, Vecino: 2.15, Objetivo: 2.69, d: 20.07%, g: -99.96, Bono: -0.04998\n", + " Parámetro: Longitud del fuselaje, Vecino: 0.93, Objetivo: 0.75, d: 24.00%, g: -98.47, Bono: -0.04924\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.05019\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 4.53, d: 33.77%, g: -84.88, Bono: -0.04244\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 19.68771629689943, Objetivo: 18.090823752817585, d: 8.83%, g: 96.11, Bono: 0.04805\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': 0.00561\n", + " Bono geométrico: -0.050\n", + " Bono prestacional: 0.006\n", + " vecino 'V21' → sim_i: 0.905\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.905']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.905\n", + " Media de valores (y): 33.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.794%\n", + "✅ Valor imputado: 33.000 (conf 0.048, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Velocidad máxima (KIAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Velocidad máxima (KIAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2600 - Velocidad máxima (KIAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.57, Objetivo: 0.88, d: 35.23%, g: -75.44, Bono: -0.03772\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.9, Objetivo: 2.6, d: 11.54%, g: 92.67, Bono: 0.04633\n", + " Parámetro: envergadura, Vecino: 3.0, Objetivo: 2.6, d: 15.38%, g: 74.14, Bono: 0.03707\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.7, Objetivo: 2.05, d: 17.07%, g: 55.96, Bono: 0.02798\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.3, Objetivo: 2.05, d: 12.20%, g: 91.03, Bono: 0.04551\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.11918\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 26.0, Objetivo: 2.0, d: 1200.00%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 12.0, Objetivo: 2.0, d: 500.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 21.875240329888257, Objetivo: 36.09414654431562, d: 39.39%, g: -14.71, Bono: -0.00736\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.10736\n", + " Bono geométrico: 0.119\n", + " Bono prestacional: -0.107\n", + " vecino 'AAI Aerosonde' → sim_i: 0.863\n", + " vecino 'Transition' → sim_i: 0.012\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.863', '0.012']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.851\n", + " Media de valores (y): 30.423\n", + " Coeficiente de variación (CV): 0.972\n", + " Dispersión: 0.423\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.428\n", + " Confianza final: 36.444%\n", + "✅ Valor imputado: 30.834 (conf 0.364, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Velocidad máxima (KIAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Stalker XE - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Stalker VXE30 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 Fixed Wing - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 VTOL - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 Fixed wing - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 VTOL FTUAS - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'envergadura' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 5.0, Objetivo: 14.0, d: 64.29%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 20.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.10000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.100\n", + " vecino 'Volitation VT510' → sim_i: 0.820\n", + " vecino 'Reach' → sim_i: 0.845\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.820', '0.845']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.833\n", + " Media de valores (y): 19.000\n", + " Coeficiente de variación (CV): 0.368\n", + " Dispersión: 6.000\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.247\n", + " Confianza final: 20.573%\n", + "✅ Valor imputado: 18.907 (conf 0.206, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: AAI Aerosonde - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.88, Objetivo: 0.57, d: 54.39%, g: -100.00, Bono: -0.05000\n", + " Parámetro: envergadura, Vecino: 2.6, Objetivo: 2.9, d: 10.34%, g: 94.70, Bono: 0.04735\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.05, Objetivo: 1.7, d: 20.59%, g: -99.99, Bono: -0.04999\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': -0.05265\n", + "⚠️ Diferencia NaN para el parámetro 'Potencia específica (P/W)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 2.0, Objetivo: 26.0, d: 92.31%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: -0.053\n", + " Bono prestacional: -0.050\n", + " vecino 'Skyeye 2600' → sim_i: 0.664\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.664']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.664\n", + " Media de valores (y): 10.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.517%\n", + "✅ Valor imputado: 10.000 (conf 0.035, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Fulmar X - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Evo - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.8, Objetivo: 0.84, d: 4.76%, g: 98.05, Bono: 0.04903\n", + " Parámetro: envergadura, Vecino: 2.15, Objetivo: 2.69, d: 20.07%, g: -99.96, Bono: -0.04998\n", + " Parámetro: Longitud del fuselaje, Vecino: 0.93, Objetivo: 0.75, d: 24.00%, g: -98.47, Bono: -0.04924\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.05019\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 4.53, d: 33.77%, g: -84.88, Bono: -0.04244\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 19.68771629689943, Objetivo: 18.090823752817585, d: 8.83%, g: 96.11, Bono: 0.04805\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': 0.00561\n", + " Bono geométrico: -0.050\n", + " Bono prestacional: 0.006\n", + " vecino 'V21' → sim_i: 0.905\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.905']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.905\n", + " Media de valores (y): 14.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.794%\n", + "✅ Valor imputado: 14.000 (conf 0.048, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V35 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.93, Objetivo: 3.5, d: 16.29%, g: 65.47, Bono: 0.03274\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.03, Objetivo: 1.88, d: 7.98%, g: 96.58, Bono: 0.04829\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.08103\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 2.8, d: 7.14%, g: 96.96, Bono: 0.04848\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 26.250288395865905, Objetivo: 27.344050412360318, d: 4.00%, g: 98.47, Bono: 0.04924\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.0, Objetivo: 33.0, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Bono total para 'prest': 0.14568\n", + " Bono geométrico: 0.081\n", + " Bono prestacional: 0.146\n", + " vecino 'Skyeye 2930 VTOL' → sim_i: 1.083\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.083']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.083\n", + " Media de valores (y): 18.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.736%\n", + "✅ Valor imputado: 18.000 (conf 0.057, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: V39 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.2, Objetivo: 3.9, d: 17.95%, g: 42.93, Bono: 0.02147\n", + " Parámetro: envergadura, Vecino: 2.93, Objetivo: 3.9, d: 24.87%, g: -98.00, Bono: -0.04900\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.02753\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 4.5, Objetivo: 4.5, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 4.5, d: 33.33%, g: -86.96, Bono: -0.04348\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 21.875240329888257, Objetivo: 27.344050412360318, d: 20.00%, g: 0.00, Bono: 0.00000\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 26.250288395865905, Objetivo: 27.344050412360318, d: 4.00%, g: 98.47, Bono: 0.04924\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 33.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.0, Objetivo: 33.0, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Bono total para 'prest': 0.15365\n", + " Bono geométrico: -0.028\n", + " Bono prestacional: 0.154\n", + " vecino 'V32' → sim_i: 1.072\n", + " vecino 'Skyeye 2930 VTOL' → sim_i: 0.707\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.072', '0.707']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.927\n", + " Media de valores (y): 17.500\n", + " Coeficiente de variación (CV): 0.943\n", + " Dispersión: 0.500\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.419\n", + " Confianza final: 38.865%\n", + "✅ Valor imputado: 17.397 (conf 0.389, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT370 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.6, Objetivo: 6.5, d: 44.62%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.42, Objetivo: 2.02, d: 19.80%, g: 5.01, Bono: 0.00251\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.04749\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 6.0, Objetivo: 15.0, d: 60.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 32.812860494832385, Objetivo: 27.344050412360318, d: 20.00%, g: -99.94, Bono: -0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 33.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: -0.047\n", + " Bono prestacional: -0.050\n", + " vecino 'Skyeye 3600 VTOL' → sim_i: 0.852\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.852']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.852\n", + " Media de valores (y): 24.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.513%\n", + "✅ Valor imputado: 24.000 (conf 0.045, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.1, Objetivo: 5.0, d: 2.00%, g: 99.57, Bono: 0.04979\n", + " Parámetro: envergadura, Vecino: 6.0, Objetivo: 5.0, d: 20.00%, g: 0.00, Bono: 0.00000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.905, Objetivo: 3.5, d: 17.00%, g: 56.93, Bono: 0.02847\n", + " Parámetro: Longitud del fuselaje, Vecino: 4.712, Objetivo: 3.5, d: 34.63%, g: -79.86, Bono: -0.03993\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.03832\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 5.0, Objetivo: 8.0, d: 37.50%, g: -49.96, Bono: -0.02498\n", + " Parámetro: Autonomía de la aeronave, Vecino: 20.0, Objetivo: 8.0, d: 150.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 32.812860494832385, Objetivo: 30.625336461843556, d: 7.14%, g: 96.96, Bono: 0.04848\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 30.625336461843556, d: 10.71%, g: 94.18, Bono: 0.04709\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 50.0, Objetivo: 42.0, d: 19.05%, g: 22.32, Bono: 0.01116\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 35.0, Objetivo: 42.0, d: 16.67%, g: 61.11, Bono: 0.03056\n", + " Bono total para 'prest': 0.06231\n", + " Bono geométrico: 0.038\n", + " Bono prestacional: 0.062\n", + " vecino 'Volitation VT510' → sim_i: 1.050\n", + " vecino 'Reach' → sim_i: 1.013\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.050', '1.013']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.032\n", + " Media de valores (y): 19.000\n", + " Coeficiente de variación (CV): 0.368\n", + " Dispersión: 6.000\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.247\n", + " Confianza final: 25.490%\n", + "✅ Valor imputado: 19.109 (conf 0.255, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Stalker XE - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Stalker VXE30 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 Fixed Wing - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 VTOL - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 Fixed wing - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 VTOL FTUAS - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'envergadura' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 5.0, Objetivo: 14.0, d: 64.29%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.050\n", + " vecino 'Volitation VT510' → sim_i: 0.870\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.870']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.870\n", + " Media de valores (y): 25.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.607%\n", + "✅ Valor imputado: 25.000 (conf 0.046, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: AAI Aerosonde - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.88, Objetivo: 0.57, d: 54.39%, g: -100.00, Bono: -0.05000\n", + " Parámetro: envergadura, Vecino: 2.6, Objetivo: 2.9, d: 10.34%, g: 94.70, Bono: 0.04735\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.05, Objetivo: 1.7, d: 20.59%, g: -99.99, Bono: -0.04999\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': -0.05265\n", + "⚠️ Diferencia NaN para el parámetro 'Potencia específica (P/W)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 2.0, Objetivo: 26.0, d: 92.31%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: -0.053\n", + " Bono prestacional: -0.050\n", + " vecino 'Skyeye 2600' → sim_i: 0.664\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.664']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.664\n", + " Media de valores (y): 10.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.517%\n", + "✅ Valor imputado: 10.000 (conf 0.035, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Fulmar X - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Evo - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.8, Objetivo: 0.84, d: 4.76%, g: 98.05, Bono: 0.04903\n", + " Parámetro: envergadura, Vecino: 2.15, Objetivo: 2.69, d: 20.07%, g: -99.96, Bono: -0.04998\n", + " Parámetro: Longitud del fuselaje, Vecino: 0.93, Objetivo: 0.75, d: 24.00%, g: -98.47, Bono: -0.04924\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.05019\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 4.53, d: 33.77%, g: -84.88, Bono: -0.04244\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 19.68771629689943, Objetivo: 18.090823752817585, d: 8.83%, g: 96.11, Bono: 0.04805\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': 0.00561\n", + " Bono geométrico: -0.050\n", + " Bono prestacional: 0.006\n", + " vecino 'V21' → sim_i: 0.905\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.905']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.905\n", + " Media de valores (y): 14.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.794%\n", + "✅ Valor imputado: 14.000 (conf 0.048, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V35 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.93, Objetivo: 3.5, d: 16.29%, g: 65.47, Bono: 0.03274\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.03, Objetivo: 1.88, d: 7.98%, g: 96.58, Bono: 0.04829\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.08103\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 2.8, d: 7.14%, g: 96.96, Bono: 0.04848\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 26.250288395865905, Objetivo: 27.344050412360318, d: 4.00%, g: 98.47, Bono: 0.04924\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.0, Objetivo: 33.0, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Bono total para 'prest': 0.14568\n", + " Bono geométrico: 0.081\n", + " Bono prestacional: 0.146\n", + " vecino 'Skyeye 2930 VTOL' → sim_i: 1.083\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.083']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.083\n", + " Media de valores (y): 18.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.736%\n", + "✅ Valor imputado: 18.000 (conf 0.057, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: V39 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.2, Objetivo: 3.9, d: 17.95%, g: 42.93, Bono: 0.02147\n", + " Parámetro: envergadura, Vecino: 2.93, Objetivo: 3.9, d: 24.87%, g: -98.00, Bono: -0.04900\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.02753\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 4.5, Objetivo: 4.5, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 4.5, d: 33.33%, g: -86.96, Bono: -0.04348\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 21.875240329888257, Objetivo: 27.344050412360318, d: 20.00%, g: 0.00, Bono: 0.00000\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 26.250288395865905, Objetivo: 27.344050412360318, d: 4.00%, g: 98.47, Bono: 0.04924\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 33.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.0, Objetivo: 33.0, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Bono total para 'prest': 0.15365\n", + " Bono geométrico: -0.028\n", + " Bono prestacional: 0.154\n", + " vecino 'V32' → sim_i: 1.072\n", + " vecino 'Skyeye 2930 VTOL' → sim_i: 0.707\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.072', '0.707']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.927\n", + " Media de valores (y): 17.500\n", + " Coeficiente de variación (CV): 0.943\n", + " Dispersión: 0.500\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.419\n", + " Confianza final: 38.865%\n", + "✅ Valor imputado: 17.397 (conf 0.389, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT370 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.6, Objetivo: 6.5, d: 44.62%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.42, Objetivo: 2.02, d: 19.80%, g: 5.01, Bono: 0.00251\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.04749\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 6.0, Objetivo: 15.0, d: 60.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 32.812860494832385, Objetivo: 27.344050412360318, d: 20.00%, g: -99.94, Bono: -0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 33.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: -0.047\n", + " Bono prestacional: -0.050\n", + " vecino 'Skyeye 3600 VTOL' → sim_i: 0.852\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.852']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.852\n", + " Media de valores (y): 24.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.513%\n", + "✅ Valor imputado: 24.000 (conf 0.045, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.1, Objetivo: 5.0, d: 2.00%, g: 99.57, Bono: 0.04979\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.905, Objetivo: 3.5, d: 17.00%, g: 56.93, Bono: 0.02847\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.07825\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 5.0, Objetivo: 8.0, d: 37.50%, g: -49.96, Bono: -0.02498\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 32.812860494832385, Objetivo: 30.625336461843556, d: 7.14%, g: 96.96, Bono: 0.04848\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 50.0, Objetivo: 42.0, d: 19.05%, g: 22.32, Bono: 0.01116\n", + " Bono total para 'prest': 0.03466\n", + " Bono geométrico: 0.078\n", + " Bono prestacional: 0.035\n", + " vecino 'Volitation VT510' → sim_i: 1.062\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.062']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.062\n", + " Media de valores (y): 25.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.628%\n", + "✅ Valor imputado: 25.000 (conf 0.056, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL octo - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.1, Objetivo: 5.0, d: 2.00%, g: 99.57, Bono: 0.04979\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.905, Objetivo: 3.5, d: 17.00%, g: 56.93, Bono: 0.02847\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.07825\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Autonomía de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 50.0, Objetivo: 38.0, d: 31.58%, g: -92.58, Bono: -0.04629\n", + " Bono total para 'prest': -0.04629\n", + " Bono geométrico: 0.078\n", + " Bono prestacional: -0.046\n", + " vecino 'Volitation VT510' → sim_i: 0.856\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.856']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.856\n", + " Media de valores (y): 25.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.537%\n", + "✅ Valor imputado: 25.000 (conf 0.045, datos 1, familia F1)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Ascend - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.15, Objetivo: 2.0, d: 7.50%, g: 96.81, Bono: 0.04840\n", + " Parámetro: Longitud del fuselaje, Vecino: 0.93, Objetivo: 1.562, d: 40.46%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.00160\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 6.0, d: 50.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 19.68771629689943, Objetivo: 21.875240329888257, d: 10.00%, g: 95.11, Bono: 0.04755\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 30.0, d: 10.00%, g: 95.11, Bono: 0.04755\n", + " Bono total para 'prest': 0.04511\n", + " Bono geométrico: -0.002\n", + " Bono prestacional: 0.045\n", + " vecino 'V21' → sim_i: 0.728\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.728']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.728\n", + " Media de valores (y): 14.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.857%\n", + "✅ Valor imputado: 14.000 (conf 0.039, datos 1, familia F2)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Transition - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.6, Objetivo: 3.0, d: 13.33%, g: 86.96, Bono: 0.04348\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.05, Objetivo: 2.3, d: 10.87%, g: 93.93, Bono: 0.04697\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.09045\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 2.0, Objetivo: 12.0, d: 83.33%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 36.09414654431562, Objetivo: 21.875240329888257, d: 65.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': -0.10000\n", + " Bono geométrico: 0.090\n", + " Bono prestacional: -0.100\n", + " vecino 'Skyeye 2600' → sim_i: 0.571\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.571']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.571\n", + " Media de valores (y): 10.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.025%\n", + "✅ Valor imputado: 10.000 (conf 0.030, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Reach - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 5.1, Objetivo: 6.0, d: 15.00%, g: 77.21, Bono: 0.03861\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.905, Objetivo: 4.712, d: 38.35%, g: -36.01, Bono: -0.01801\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.02060\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 5.0, Objetivo: 20.0, d: 75.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 32.812860494832385, Objetivo: 27.344050412360318, d: 20.00%, g: -99.94, Bono: -0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 50.0, Objetivo: 35.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + " Bono total para 'prest': -0.14997\n", + " Bono geométrico: 0.021\n", + " Bono prestacional: -0.150\n", + " vecino 'Volitation VT510' → sim_i: 0.775\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.775']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.775\n", + " Media de valores (y): 25.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.107%\n", + "✅ Valor imputado: 25.000 (conf 0.041, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 VTOL FTUAS - envergadura ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'envergadura' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 5.0, Objetivo: 14.0, d: 64.29%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Autonomía de la aeronave, Vecino: 20.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.15000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.150\n", + " vecino 'Skyeye 5000 VTOL' → sim_i: 0.770\n", + " vecino 'Volitation VT510' → sim_i: 0.770\n", + " vecino 'Reach' → sim_i: 0.795\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.770', '0.770', '0.795']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.778\n", + " Media de valores (y): 5.367\n", + " Coeficiente de variación (CV): 0.832\n", + " Dispersión: 0.450\n", + " Penalización por cantidad de vecinos (k): 0.355\n", + " Confianza en base a la calidad y cantidad de datos: 0.499\n", + " Confianza final: 38.799%\n", + "✅ Valor imputado: 5.374 (conf 0.388, datos 3, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - envergadura ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 VTOL FTUAS - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Fulmar X - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.8768, Objetivo: 3.0, d: 62.56%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.5908, Objetivo: 1.2, d: 115.90%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.10000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 8.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Alcance de la aeronave, Vecino: 433.0, Objetivo: 800.0, d: 45.88%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 17.602373279430935, Objetivo: 30.406584058544677, d: 42.11%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 25.034211403264, Objetivo: 41.7, d: 39.97%, g: -0.88, Bono: -0.00044\n", + " Bono total para 'prest': -0.05047\n", + " Bono geométrico: -0.100\n", + " Bono prestacional: -0.050\n", + " vecino 'Stalker VXE30' → sim_i: 0.799\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.799']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.799\n", + " Media de valores (y): 0.318\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.235%\n", + "✅ Valor imputado: 0.318 (conf 0.042, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 5.2, d: 15.38%, g: 74.14, Bono: 0.03707\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.0, Objetivo: 1.2, d: 150.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.01293\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.8, Objetivo: 24.0, d: 17.50%, g: 49.96, Bono: 0.02498\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.43886, Objetivo: 36.0, d: 7.11%, g: 96.98, Bono: 0.04849\n", + " Bono total para 'prest': 0.07347\n", + " Bono geométrico: -0.013\n", + " Bono prestacional: 0.073\n", + " vecino 'Aerosonde Mk. 4.8 Fixed wing' → sim_i: 1.010\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.010']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.010\n", + " Media de valores (y): 0.352\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.349%\n", + "✅ Valor imputado: 0.352 (conf 0.053, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 4.0, d: 10.00%, g: 95.10, Bono: 0.04755\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.0, Objetivo: 2.4, d: 25.00%, g: -97.93, Bono: -0.04897\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.00141\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.8, Objetivo: 18.0, d: 10.00%, g: 95.10, Bono: 0.04755\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 25.7034073876187, d: 6.38%, g: 97.29, Bono: 0.04864\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.43886, Objetivo: 41.2, d: 18.84%, g: 26.66, Bono: 0.01333\n", + " Bono total para 'prest': 0.10952\n", + " Bono geométrico: -0.001\n", + " Bono prestacional: 0.110\n", + " vecino 'Aerosonde Mk. 4.7 Fixed Wing' → sim_i: 0.733\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.733']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.733\n", + " Media de valores (y): 0.352\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.885%\n", + "✅ Valor imputado: 0.352 (conf 0.039, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.4, Objetivo: 4.8, d: 8.33%, g: 96.40, Bono: 0.04820\n", + " Parámetro: Longitud del fuselaje, Vecino: 3.0, Objetivo: 2.5, d: 20.00%, g: 0.00, Bono: 0.00000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.04820\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 19.8, Objetivo: 16.0, d: 23.75%, g: -98.61, Bono: -0.04931\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 33.797246309677355, d: 19.09%, g: 21.34, Bono: 0.01067\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.43886, Objetivo: 46.3, d: 27.78%, g: -96.68, Bono: -0.04834\n", + " Bono total para 'prest': -0.08698\n", + " Bono geométrico: 0.048\n", + " Bono prestacional: -0.087\n", + " vecino 'Aerosonde Mk. 4.8 Fixed wing' → sim_i: 0.854\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.854']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.854\n", + " Media de valores (y): 0.352\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.526%\n", + "✅ Valor imputado: 0.352 (conf 0.045, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Evo - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V21 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V25 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.57, Objetivo: 0.52, d: 9.62%, g: 95.49, Bono: 0.04775\n", + " Parámetro: envergadura, Vecino: 2.9, Objetivo: 2.45, d: 18.37%, g: 35.68, Bono: 0.01784\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.7, Objetivo: 0.93, d: 82.80%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.01558\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 26.0, Objetivo: 4.0, d: 550.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.845724764736, Objetivo: 33.0, d: 6.53%, g: 97.23, Bono: 0.04861\n", + " Bono total para 'prest': -0.00139\n", + " Bono geométrico: 0.016\n", + " Bono prestacional: -0.001\n", + " vecino 'AAI Aerosonde' → sim_i: 0.700\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.700']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.700\n", + " Media de valores (y): 0.197\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.711%\n", + "✅ Valor imputado: 0.197 (conf 0.037, datos 1, familia F2)\n", + "\u001b[1m\n", + "=== Imputación por similitud: V32 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V35 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V39 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT370 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2600 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.57, Objetivo: 0.88, d: 35.23%, g: -75.44, Bono: -0.03772\n", + " Parámetro: envergadura, Vecino: 2.9, Objetivo: 2.6, d: 11.54%, g: 92.67, Bono: 0.04633\n", + " Parámetro: Longitud del fuselaje, Vecino: 1.7, Objetivo: 2.05, d: 17.07%, g: 55.96, Bono: 0.02798\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.03660\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 26.0, Objetivo: 2.0, d: 1200.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad a la que se realiza el crucero (KTAS)'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: 0.037\n", + " Bono prestacional: -0.050\n", + " vecino 'AAI Aerosonde' → sim_i: 0.837\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.837']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.837\n", + " Media de valores (y): 0.197\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.436%\n", + "✅ Valor imputado: 0.197 (conf 0.044, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2930 VTOL - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 VTOL - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL octo - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT510 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Ascend - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Transition - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Reach - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: AAI Aerosonde - payload ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + " Parámetro: Área del ala, Vecino: 0.88, Objetivo: 0.57, d: 54.39%, g: -100.00, Bono: -0.05000\n", + " Parámetro: envergadura, Vecino: 2.6, Objetivo: 2.9, d: 10.34%, g: 94.70, Bono: 0.04735\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.05, Objetivo: 1.7, d: 20.59%, g: -99.99, Bono: -0.04999\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': -0.05265\n", + "⚠️ Diferencia NaN para el parámetro 'Potencia específica (P/W)'. Vecino: nan. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 2.0, Objetivo: 26.0, d: 92.31%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Velocidad máxima (KIAS)'. Vecino: nan. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: -0.053\n", + " Bono prestacional: -0.050\n", + " vecino 'Skyeye 2600' → sim_i: 0.664\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.664']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.664\n", + " Media de valores (y): 4.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 3.517%\n", + "✅ Valor imputado: 4.000 (conf 0.035, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Fulmar X - payload ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.8768, Objetivo: 3.0, d: 62.56%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.5908, Objetivo: 1.2, d: 115.90%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.10000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 8.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Alcance de la aeronave, Vecino: 433.0, Objetivo: 800.0, d: 45.88%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 17.602373279430935, Objetivo: 30.406584058544677, d: 42.11%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 25.034211403264, Objetivo: 41.7, d: 39.97%, g: -0.88, Bono: -0.00044\n", + " Bono total para 'prest': -0.05047\n", + " Bono geométrico: -0.100\n", + " Bono prestacional: -0.050\n", + " vecino 'Stalker VXE30' → sim_i: 0.799\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.799']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.799\n", + " Media de valores (y): 2.495\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.235%\n", + "✅ Valor imputado: 2.495 (conf 0.042, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - payload ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 Fixed Wing - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 VTOL - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 Fixed wing - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 VTOL FTUAS - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'envergadura' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Diferencia NaN para el parámetro 'Relación de aspecto del ala'. Vecino: nan. Bono = 0.\n", + " Bono total para 'geom': 0.00000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 20.0, Objetivo: 14.0, d: 42.86%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad a la que se realiza el crucero (KTAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Velocidad máxima (KIAS)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: 0.000\n", + " Bono prestacional: -0.050\n", + " vecino 'Reach' → sim_i: 0.895\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.895']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.895\n", + " Media de valores (y): 31.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.743%\n", + "✅ Valor imputado: 31.000 (conf 0.047, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Fulmar X - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 4.8768, Objetivo: 3.0, d: 62.56%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.5908, Objetivo: 1.2, d: 115.90%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.10000\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 8.0, Objetivo: 8.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Alcance de la aeronave, Vecino: 433.0, Objetivo: 800.0, d: 45.88%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 17.602373279430935, Objetivo: 30.406584058544677, d: 42.11%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 25.034211403264, Objetivo: 41.7, d: 39.97%, g: -0.88, Bono: -0.00044\n", + " Bono total para 'prest': -0.05047\n", + " Bono geométrico: -0.100\n", + " Bono prestacional: -0.050\n", + " vecino 'Stalker VXE30' → sim_i: 0.799\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.799']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.799\n", + " Media de valores (y): 17.463\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.235%\n", + "✅ Valor imputado: 17.463 (conf 0.042, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Empty weight'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Empty weight'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Empty weight'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Empty weight'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V35 - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 2.93, Objetivo: 3.5, d: 16.29%, g: 65.47, Bono: 0.03274\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.03, Objetivo: 1.88, d: 7.98%, g: 96.58, Bono: 0.04829\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': 0.08103\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 2.8, d: 7.14%, g: 96.96, Bono: 0.04848\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 26.250288395865905, Objetivo: 27.344050412360318, d: 4.00%, g: 98.47, Bono: 0.04924\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.0, Objetivo: 33.0, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Bono total para 'prest': 0.14568\n", + " Bono geométrico: 0.081\n", + " Bono prestacional: 0.146\n", + " vecino 'Skyeye 2930 VTOL' → sim_i: 1.083\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.083']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 1.083\n", + " Media de valores (y): 7.100\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 5.736%\n", + "✅ Valor imputado: 7.100 (conf 0.057, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: V39 - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.2, Objetivo: 3.9, d: 17.95%, g: 42.93, Bono: 0.02147\n", + " Parámetro: envergadura, Vecino: 2.93, Objetivo: 3.9, d: 24.87%, g: -98.00, Bono: -0.04900\n", + "⚠️ Parámetro 'Longitud del fuselaje' no tiene valor en la aeronave objetivo. Bono = 0.\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.02753\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 4.5, Objetivo: 4.5, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Autonomía de la aeronave, Vecino: 3.0, Objetivo: 4.5, d: 33.33%, g: -86.96, Bono: -0.04348\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 21.875240329888257, Objetivo: 27.344050412360318, d: 20.00%, g: 0.00, Bono: 0.00000\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 26.250288395865905, Objetivo: 27.344050412360318, d: 4.00%, g: 98.47, Bono: 0.04924\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 33.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 30.0, Objetivo: 33.0, d: 9.09%, g: 95.92, Bono: 0.04796\n", + " Bono total para 'prest': 0.15365\n", + " Bono geométrico: -0.028\n", + " Bono prestacional: 0.154\n", + " vecino 'V32' → sim_i: 1.072\n", + " vecino 'Skyeye 2930 VTOL' → sim_i: 0.707\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['1.072', '0.707']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.927\n", + " Media de valores (y): 6.775\n", + " Coeficiente de variación (CV): 0.904\n", + " Dispersión: 0.325\n", + " Penalización por cantidad de vecinos (k): 0.195\n", + " Confianza en base a la calidad y cantidad de datos: 0.408\n", + " Confianza final: 37.786%\n", + "✅ Valor imputado: 6.708 (conf 0.378, datos 2, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT370 - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 3.6, Objetivo: 6.5, d: 44.62%, g: -100.00, Bono: -0.05000\n", + " Parámetro: Longitud del fuselaje, Vecino: 2.42, Objetivo: 2.02, d: 19.80%, g: 5.01, Bono: 0.00251\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.04749\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 6.0, Objetivo: 15.0, d: 60.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 32.812860494832385, Objetivo: 27.344050412360318, d: 20.00%, g: -99.94, Bono: -0.04997\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 33.0, Objetivo: 33.0, d: 0.00%, g: 99.94, Bono: 0.04997\n", + " Bono total para 'prest': -0.05000\n", + " Bono geométrico: -0.047\n", + " Bono prestacional: -0.050\n", + " vecino 'Skyeye 3600 VTOL' → sim_i: 0.852\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.852']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.852\n", + " Media de valores (y): 11.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.513%\n", + "✅ Valor imputado: 11.000 (conf 0.045, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Diferencia NaN para el parámetro 'Área del ala'. Vecino: nan. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 6.0, Objetivo: 5.0, d: 20.00%, g: 0.00, Bono: 0.00000\n", + " Parámetro: Longitud del fuselaje, Vecino: 4.712, Objetivo: 3.5, d: 34.63%, g: -79.86, Bono: -0.03993\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.03993\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 20.0, Objetivo: 8.0, d: 150.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Diferencia NaN para el parámetro 'Alcance de la aeronave'. Vecino: nan. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 30.625336461843556, d: 10.71%, g: 94.18, Bono: 0.04709\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 35.0, Objetivo: 42.0, d: 16.67%, g: 61.11, Bono: 0.03056\n", + " Bono total para 'prest': 0.02765\n", + " Bono geométrico: -0.040\n", + " Bono prestacional: 0.028\n", + " vecino 'Reach' → sim_i: 0.900\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.900']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.900\n", + " Media de valores (y): 31.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.766%\n", + "✅ Valor imputado: 31.000 (conf 0.048, datos 1, familia F0)\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT510 - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "⚠️ Parámetro 'Área del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: envergadura, Vecino: 6.0, Objetivo: 5.1, d: 17.65%, g: 47.74, Bono: 0.02387\n", + " Parámetro: Longitud del fuselaje, Vecino: 4.712, Objetivo: 2.905, d: 62.20%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Relación de aspecto del ala' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Bono total para 'geom': -0.02613\n", + "⚠️ Parámetro 'Potencia específica (P/W)' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Autonomía de la aeronave, Vecino: 20.0, Objetivo: 5.0, d: 300.00%, g: -100.00, Bono: -0.05000\n", + "⚠️ Parámetro 'Alcance de la aeronave' no tiene valor en la aeronave objetivo. Bono = 0.\n", + " Parámetro: Velocidad a la que se realiza el crucero (KTAS), Vecino: 27.344050412360318, Objetivo: 32.812860494832385, d: 16.67%, g: 61.11, Bono: 0.03056\n", + " Parámetro: Velocidad máxima (KIAS), Vecino: 35.0, Objetivo: 50.0, d: 30.00%, g: -95.11, Bono: -0.04755\n", + " Bono total para 'prest': -0.06700\n", + " Bono geométrico: -0.026\n", + " Bono prestacional: -0.067\n", + " vecino 'Reach' → sim_i: 0.819\n", + "\n", + "Detalles del cálculo de confianza:\n", + " Confianza que tan similares son los vecinos (familia x Mtow + Bonos): ['0.819']\n", + " Promedio ponderado de confianza de similitud de aeronaves: 0.819\n", + " Media de valores (y): 31.000\n", + " Coeficiente de variación (CV): 0.000\n", + " Dispersión: 0.000\n", + " Penalización por cantidad de vecinos (k): 0.076\n", + " Confianza en base a la calidad y cantidad de datos: 0.053\n", + " Confianza final: 4.338%\n", + "✅ Valor imputado: 31.000 (conf 0.043, datos 1, familia F0)\n", + "\u001b[1m>>> RESULTADOS DE IMPUTACIÓN POR SIMILITUD\u001b[0m\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[1m*** IMPUTACIÓN POR CORRELACIÓN - ITERACIÓN 1 ***\u001b[0m\n", + "--------------------------------------------------------------------------------\n", + "\n", + "=== DataFrame inicial ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

DataFrame antes de realizar imputacion por correlacion (df_procesado.copy())

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Distancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeProfundidad del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Tasa de ascensoRadio de giroenvergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (dry weight)RTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónWing LoadingPotencia específica (P/W)Capacidad combustibleConsumoPotencia WattsPotencia HPPrecioTiempo de emergencia en vueloDistancia de aterrizajeDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
Modelo
Stalker XE0.06000.00016.88037912000.0NaN0.8715.3012552.1NaN0.21113.600370.08.020.0NaNNaNNaN3.6570.2392.4947562.015.43332NaNNaN10.886208NaN59.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Stalker VXE300.06000.00017.60237312000.0NaN1.15828315.3264492.5908NaN0.219.958433.08.025.034211NaNNaNNaN4.87680.3181952.494756NaN16.093422NaNNaN17.463292NaN161.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Aerosonde Mk. 4.7 Fixed WingNaN6000.00027.3440514700.0NaN1.5512.53.0NaN0.27742.200NaN19.833.43886NaNNaNNaN4.40.35214.5NaN25.0NaN27.7NaNNaN140.0NaNNaNNaN0.62980.04.0NaNNaNNaN1.0002.0005.0000.0001.0001.000
Aerosonde Mk. 4.7 VTOL0.06000.00027.344059700.0NaN1.5512.53.0NaN0.27753.500NaN12.033.43886NaNNaNNaN4.40.35211.3NaN25.0NaN42.2NaNNaN140.0NaNNaNNaN0.62980.04.0NaNNaNNaN2.0002.0001.0004.0001.0001.000
Aerosonde Mk. 4.8 Fixed wingNaN6000.00027.3440518200.0NaN1.5512.53.0NaN0.27754.400NaN19.833.43886NaNNaNNaN4.40.35217.7NaN25.0NaN36.7NaNNaN140.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Aerosonde Mk. 4.8 VTOL FTUAS0.06000.000NaN15000.0NaNNaN12.5NaNNaNNaN93.000NaN14.0NaNNaNNaNNaNNaNNaN22.7NaNNaNNaN70.3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0002.0001.0004.0001.0001.000
AAI AerosondeNaN5500.000NaN15000.0NaN0.5714.7543861.7NaNNaN13.1003270.026.030.845725NaNNaNNaN2.90.196552NaNNaNNaNNaNNaN10.0NaN150.023.098.0NaNNaN1280.01.74NaNNaNNaN2.0002.0001.0004.0001.0001.000
Fulmar XNaN6000.00030.4065849.842NaNNaNNaN1.2NaNNaN20.000800.08.041.7NaNNaNNaN3.0NaNNaNNaN27.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Orbiter 4NaN6000.000NaNNaNNaNNaNNaN1.2NaNNaN55.000NaN24.036.0NaNNaNNaN5.2NaN12.0NaNNaNNaNNaNNaNNaN150.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Orbiter 3NaN6000.000NaNNaNNaNNaNNaN1.2NaNNaN32.00050.06.036.0NaNNaNNaN4.4NaN5.5NaNNaNNaNNaNNaNNaN50.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0001.0005.0000.0001.0001.000
MantisNaN6000.00018.265826NaNNaNNaNNaN1.48NaNNaN6.50025.02.025.6NaNNaNNaN2.1NaNNaNNaN16.7NaNNaNNaNNaN25.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0001.0005.0000.0001.0001.000
ScanEagleNaN6000.00030.62533619500.0NaNNaNNaN1.71NaNNaN26.500NaN18.041.2NaNNaNNaN3.1NaN5.0NaN28.0NaNNaNNaNNaN101.86NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
IntegratorNaN6000.00030.95346519500.0NaNNaNNaN2.5NaNNaN74.800NaN24.046.3NaNNaNNaN4.8NaN18.0NaN28.3NaNNaNNaNNaN92.6NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Integrator VTOL0.05000.000NaNNaNNaNNaNNaNNaNNaNNaN75.000NaN16.0NaNNaNNaNNaNNaNNaN18.0NaNNaNNaNNaNNaN30.0NaNNaNNaNNaNNaNNaNNaNNaNNaN0.02.0002.0001.0004.0001.0001.000
Integrator Extended RangeNaN6000.000NaN19500.0NaNNaNNaN2.5NaNNaN74.800500.019.046.3NaNNaNNaN4.8NaN18.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
ScanEagle 3NaN6000.00025.70340720.0NaNNaNNaN2.4NaNNaN36.300NaN18.041.2NaNNaNNaN4.0NaN8.6NaN23.5NaNNaNNaNNaNNaNNaNNaNNaNNaN170.0NaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
RQ Nan 21A BlackjackNaN6000.00033.79724620.0NaNNaNNaN2.5NaNNaN61.000NaN16.046.3NaNNaNNaN4.8NaN17.7NaN30.9NaNNaNNaNNaN92.6NaNNaNNaNNaNNaN8.0NaNNaNNaN1.0002.0005.0000.0001.0001.000
DeltaQuad Evo0.06000.00018.09082413.0NaN0.84NaN0.75NaNNaN10.000270.04.53NaNNaNNaNNaN2.69NaN3.04.5316.544.86.84.845.0NaNNaNNaNNaNNaNNaNNaNNaNNaN0.02.0001.0001.0004.0001.0001.000
DeltaQuad Pro #MAP0.06000.00017.50019213.123NaNNaNNaN0.9NaNNaN6.200100.01.83NaNNaNNaNNaN2.35NaN1.2NaN16.0NaNNaNNaN50.050.0NaNNaNNaNNaNNaNNaNNaNNaN0.02.0001.0001.0004.0001.0001.000
DeltaQuad Pro #CARGO0.06000.00017.50019213.123NaNNaNNaN0.9NaNNaN6.200100.01.83NaNNaNNaNNaN2.35NaN1.2NaN16.0NaNNaNNaN50.030.0NaNNaNNaNNaNNaNNaNNaNNaN0.02.0001.0001.0004.0001.0001.000
V210.06000.00019.68771648800.014.00.8NaN0.93NaNNaN10.000NaN3.033.014.0NaN100.02.15NaN1.5NaN18.0NaNNaN2.65NaN30.012.5NaNNaNNaNNaNNaN3999.00.108NaN2.0001.0001.0004.0001.0001.000
V250.06000.00021.8752416000.015.50.52NaN0.93NaNNaN12.500NaN4.033.015.5NaN120.02.45NaN2.2NaN20.0NaNNaN3.45NaN30.024.0NaNNaNNaNNaNNaN4679.00.108NaN2.0001.0001.0004.0001.0001.000
V320.06000.00021.8752416000.017.0NaNNaN1.0NaNNaN23.500NaN4.533.017.0NaN150.03.2NaN5.0NaN20.0NaNNaN6.45NaN30.025.0NaNNaNNaNNaNNaN69999.00.108NaN2.0002.0001.0004.0001.0001.000
V350.06000.00027.3440516000.0NaNNaNNaN1.88NaNNaN32.000NaN2.833.0NaNNaNNaN3.5NaN10.0NaN25.0NaNNaNNaNNaN30.0NaNNaNNaNNaNNaNNaN7999.0NaNNaN2.0002.0001.0004.0001.0001.000
V390.06000.00027.3440516000.0NaNNaNNaNNaNNaNNaN24.000NaN4.533.0NaNNaNNaN3.9NaN5.0NaN25.0NaNNaNNaNNaN30.0NaNNaNNaNNaNNaNNaN8999.0NaNNaN2.0002.0001.0004.0001.0001.000
Volitation VT3700.06000.00027.3440517000.0NaNNaNNaN2.02NaNNaN40.000NaN15.033.0NaNNaNNaN6.5NaN18.0NaN25.0NaNNaNNaNNaNNaNNaNNaN13.00.96NaNNaN8999.0NaNNaN2.0002.0001.0004.0001.0001.000
Skyeye 2600NaN6000.00036.094147NaN10.00.88NaN2.05NaNNaN15.000NaN2.0NaN10.0NaNNaN2.6NaN4.0NaN33.0NaNNaN6.5NaNNaNNaNNaNNaNNaNNaNNaN2299.0NaNNaN2.0002.0001.0004.0001.0001.000
Skyeye 2930 VTOL0.06000.00026.250288NaN18.01.0NaN2.03NaNNaN28.000NaN3.030.018.0NaNNaN2.93NaN6.0NaN24.0NaNNaN7.1NaNNaNNaNNaNNaNNaNNaNNaN6799.0NaNNaN2.0002.0001.0004.0001.0001.000
Skyeye 360050.06000.000NaNNaN12.51.33NaN2.488NaNNaN28.000NaN4.5NaN12.5NaNNaN3.6NaN10.0NaNNaNNaNNaN11.5NaNNaNNaNNaN11.5NaNNaNNaN4999.0NaNNaN3.0002.0005.0000.0001.0001.000
Skyeye 3600 VTOL0.06000.00032.81286NaN24.01.32NaN2.42NaNNaN40.000300.06.033.024.0NaNNaN3.6NaN10.0NaN30.0NaNNaN11.0NaNNaNNaNNaN11.5NaNNaNNaN6999.0NaNNaN2.0002.0001.0004.0001.0001.000
Skyeye 500060.06000.00036.094147NaN15.02.615NaN3.5NaN0.37590.000NaN8.042.015.0NaNNaN5.0NaN20.0NaN33.0NaNNaN32.0NaNNaNNaNNaN28.01.2NaNNaN9999.0NaNNaN3.0002.0005.0000.0001.0001.000
Skyeye 5000 VTOL0.06000.00030.625336NaNNaN2.615NaN3.5NaN0.375100.000800.08.042.0NaNNaNNaN5.0NaN25.0NaN28.0NaNNaNNaNNaNNaNNaNNaN28.0NaNNaNNaN13900.0NaNNaN2.0002.0001.0004.0001.0001.000
Skyeye 5000 VTOL octo0.06000.000NaNNaNNaN2.615NaN3.5NaN0.375100.000NaNNaN38.024.0NaNNaN5.0NaN15.0NaN35.0NaNNaN35.0NaNNaNNaNNaN28.0NaNNaNNaN15999.0NaNNaN2.0002.0001.0008.0001.0001.000
Volitation VT5100.06000.00032.8128617000.025.0NaNNaN2.905NaNNaN100.000NaN5.050.025.0NaNNaN5.1NaN25.0NaN30.0NaNNaNNaNNaNNaNNaNNaN25.05.0NaNNaN16599.0NaNNaN2.0002.0001.0004.0001.0001.000
Ascend0.06000.00021.8752410000.0NaNNaNNaN1.562NaNNaN9.500NaN6.030.013.0NaNNaN2.0NaN0.60.0520.06.08.93.015.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0002.0001.0004.0001.0001.000
Transition0.06000.00021.8752413000.0NaNNaNNaN2.3NaNNaN18.000NaN12.030.013.0NaNNaN3.0NaN1.50.0520.011.816.55.815.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0002.0001.0004.0001.0001.000
Reach0.06000.00027.3440516000.0NaNNaNNaN4.712NaNNaN91.000NaN20.035.013.0NaNNaN6.0NaN7.00.0525.054.084.031.015.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0002.0001.0004.0001.0001.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Convertir todo a numérico ===\n", + "\n", + "\n", + "=== PASO 1: CÁLCULO DE CORRELACIONES ENTRE PARÁMETROS ===\n", + "\n", + "Umbral seleccionado para correlaciones significativas: 0.7\n", + "\n", + "=== Cálculo de tabla completa ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Tabla de Correlaciones con todos los parametros(tabla_completa)

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Distancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeProfundidad del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Tasa de ascensoRadio de giroenvergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (dry weight)RTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónWing LoadingPotencia específica (P/W)Capacidad combustibleConsumoPotencia WattsPotencia HPPrecioTiempo de emergencia en vueloDistancia de aterrizajeDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
Distancia de carrera requerida para despegue1.0000.0630.467nan-0.5050.363nan0.260nan0.4250.168nan-0.0680.316-0.308nannan0.145nan0.229nan0.389nannan0.357nannannannan-0.018-0.240nannan-0.156nannan0.7350.1540.671-0.598nannan
Altitud a la que se realiza el crucero0.0631.000nan-0.038nan0.301-0.3510.081nannan-0.095-0.955-0.2800.128nannannan0.1360.761-0.183nannannannan0.0600.038-0.325-0.215nannannan0.2770.690nannannan-0.119-0.1090.187-0.159nannan
Velocidad a la que se realiza el crucero (KTAS)0.467nan1.0000.0410.1280.587-0.9990.535nan0.9360.6630.6650.3360.8150.257nan0.8030.4720.8460.696-0.6941.0000.9150.7230.426-0.8550.3590.997nan0.4910.4611.0001.000-0.296nannan0.1130.6190.126-0.126nannan
Techo de servicio máximonan-0.0380.0411.000-0.502-0.152-0.3140.082nan0.3690.1370.4280.079-0.111-0.071nan-0.8030.0570.0170.087-0.8750.0410.6770.579-0.138-0.961-0.120-0.986nannan0.5150.653-0.933-0.257nannan0.1250.007-0.1250.125nannan
Velocidad de pérdida limpia (KCAS)-0.505nan0.128-0.5021.0000.097nan0.260nannan0.546nan0.4010.4461.000nan0.9930.505nan0.536nan0.128nannan0.038nannan0.900nan0.0681.000nannan0.163nannan-0.3450.231-0.3450.345nannan
Área del ala0.3630.3010.587-0.1520.0971.000-0.8310.867nan0.9840.977-0.3010.0810.7370.423nan-1.0000.8410.9840.899-1.0000.675nan0.9230.941nan0.595-0.996nan1.0001.0001.0001.0000.899nannan0.2360.4530.1720.055nannan
Relación de aspecto del alanan-0.351-0.999-0.314nan-0.8311.000-0.790nan-0.996-0.823-0.998-0.305-0.859nannannan-0.349-0.744-0.888nan-0.999nannan0.622nan-0.298nannannannan-1.000-1.000nannannan-0.247nan0.247-0.247nannan
Longitud del fuselaje0.2600.0810.5350.0820.2600.867-0.7901.000nan0.9380.7860.1500.3890.2560.180nan0.9180.6930.9950.599-0.6170.5760.9660.9400.880-0.7180.6760.263nan0.9290.0360.6860.359-0.210nannan0.1110.6150.093-0.004nannan
Profundidad del fuselajenannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Ancho del fuselaje0.425nan0.9360.369nan0.984-0.9960.938nan1.0000.9860.982-0.0900.940nannannan0.6710.7600.868nan0.944nannan0.955nan0.323nannannan1.000nannannannannan0.794nan-0.5350.574nannan
Peso máximo al despegue (MTOW)0.168-0.0950.6630.1370.5460.977-0.8230.786nan0.9861.0000.0250.4340.6780.539nan0.9730.7910.8580.875-0.4010.7080.9990.9790.947-0.4640.5140.628nan0.9760.7580.5590.8550.052nannan0.0900.4670.0230.075nannan
Alcance de la aeronavenan-0.9550.6650.428nan-0.301-0.9980.150nan0.9820.0251.0000.8430.042nannannan-0.010-0.7550.804-1.0000.665nannan-0.059-1.0000.670nannan1.000nannannan1.000nannan0.2620.474-0.2620.262nannan
Autonomía de la aeronave-0.068-0.2800.3360.0790.4010.081-0.3050.389nan-0.0900.4340.8431.0000.297-0.164nan0.9540.532-0.2010.461-0.5940.3360.9400.6340.428-0.7150.8020.268nan-0.113-0.732-0.391-0.5770.011nannan-0.4240.4770.361-0.361nannan
Velocidad máxima (KIAS)0.3160.1280.815-0.1110.4460.737-0.8590.256nan0.9400.6780.0420.2971.0000.539nannan0.4000.5120.715-0.9270.7750.9940.8570.517nan0.094-0.215nan0.7050.910-0.6240.9700.015nannan-0.0410.2080.174-0.133nannan
Velocidad de pérdida (KCAS)-0.308nan0.257-0.0711.0000.423nan0.180nannan0.539nan-0.1640.5391.000nan0.9930.401nan0.627nan0.417nannan0.321nannan0.900nan0.2301.000nannan0.160nannan-0.2440.154-0.2440.444nannan
Tasa de ascensonannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Radio de gironannan0.803-0.8030.993-1.000nan0.918nannan0.973nan0.954nan0.993nan1.0000.992nan0.976nan0.803nannan0.979nannan0.844nannannannannan0.921nannannan0.918nannannannan
envergadura0.1450.1360.4720.0570.5050.841-0.3490.693nan0.6710.791-0.0100.5320.4000.401nan0.9921.0000.8850.734-0.2580.5010.9830.9360.924-0.4520.6480.780nan0.2970.0850.5220.8350.032nannan-0.1240.5080.239-0.164nannan
Cuerdanan0.7610.8460.017nan0.984-0.7440.995nan0.7600.858-0.755-0.2010.512nannannan0.8851.0000.776nan0.846nannan0.971nan0.354nannannannan1.0001.000nannannan-0.313nan0.313-0.313nannan
payload0.229-0.1830.6960.0870.5360.899-0.8880.599nan0.8680.8750.8040.4610.7150.627nan0.9760.7340.7761.000-0.0240.6940.9150.5590.778-0.1420.5460.707nan0.7110.8460.8410.866-0.008nannan-0.0040.4770.162-0.111nannan
duracion en VTOLnannan-0.694-0.875nan-1.000nan-0.617nannan-0.401-1.000-0.594-0.927nannannan-0.258nan-0.0241.000-0.694-0.408-0.402-0.3151.000nannannannannannannannannannan-0.188-0.9040.188-0.188nannan
Crucero KIAS0.389nan1.0000.0410.1280.675-0.9990.576nan0.9440.7080.6650.3360.7750.417nan0.8030.5010.8460.694-0.6941.0000.9150.7230.583-0.8550.3590.997nan0.5810.4611.0001.000-0.243nannan0.1430.6080.0650.063nannan
RTF (dry weight)nannan0.9150.677nannannan0.966nannan0.999nan0.9400.994nannannan0.983nan0.915-0.4080.9151.0001.0000.995-0.408nannannannannannannannannannannan0.408nannannannan
RTF (Including fuel & Batteries)nannan0.7230.579nan0.923nan0.940nannan0.979nan0.6340.857nannannan0.936nan0.559-0.4020.7231.0001.0000.996-0.402nannannannannannannannannannan0.0970.428-0.0970.097nannan
Empty weight0.3570.0600.426-0.1380.0380.9410.6220.880nan0.9550.947-0.0590.4280.5170.321nan0.9790.9240.9710.778-0.3150.5830.9950.9961.000-0.3190.8320.530nan0.995nannannan-0.029nannan0.1820.4040.3070.004nannan
Maximum Crosswindnan0.038-0.855-0.961nannannan-0.718nannan-0.464-1.000-0.715nannannannan-0.452nan-0.1421.000-0.855-0.408-0.402-0.3191.000nannannannannannannannannannannan-0.943nannannannan
Rango de comunicaciónnan-0.3250.359-0.120nan0.595-0.2980.676nan0.3230.5140.6700.8020.094nannannan0.6480.3540.546nan0.359nannan0.832nan1.0000.215nannannan-1.000-0.972nannannan-0.4300.6040.430-0.430nannan
Wing Loadingnan-0.2150.997-0.9860.900-0.996nan0.263nannan0.628nan0.268-0.2150.900nan0.8440.780nan0.707nan0.997nannan0.530nan0.2151.000nannannannannan0.568nannannan0.572nannannannan
Potencia específica (P/W)nannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Capacidad combustible-0.018nan0.491nan0.0681.000nan0.929nannan0.9761.000-0.1130.7050.230nannan0.297nan0.711nan0.581nannan0.995nannannannan1.0000.377nannan0.817nannan-0.080nan-0.0800.270nannan
Consumo-0.240nan0.4610.5151.0001.000nan0.036nan1.0000.758nan-0.7320.9101.000nannan0.085nan0.846nan0.461nannannannannannannan0.3771.000nannan0.998nannan0.113nan-0.3750.375nannan
Potencia Wattsnan0.2771.0000.653nan1.000-1.0000.686nannan0.559nan-0.391-0.624nannannan0.5221.0000.841nan1.000nannannannan-1.000nannannannan1.0001.000nannannan0.232nan-0.2320.232nannan
Potencia HPnan0.6901.000-0.933nan1.000-1.0000.359nannan0.855nan-0.5770.970nannannan0.8351.0000.866nan1.000nannannannan-0.972nannannannan1.0001.000nannannan-0.694nan0.694-0.694nannan
Precio-0.156nan-0.296-0.2570.1630.899nan-0.210nannan0.0521.0000.0110.0150.160nan0.9210.032nan-0.008nan-0.243nannan-0.029nannan0.568nan0.8170.998nannan1.000nannan-0.1380.217-0.1380.134nannan
Tiempo de emergencia en vuelonannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Distancia de aterrizajenannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Despegue0.735-0.1190.1130.125-0.3450.236-0.2470.111nan0.7940.0900.262-0.424-0.041-0.244nannan-0.124-0.313-0.004-0.1880.143nan0.0970.182nan-0.430nannan-0.0800.1130.232-0.694-0.138nannan1.000-0.010-0.6390.610nannan
Propulsión horizontal0.154-0.1090.6190.0070.2310.453nan0.615nannan0.4670.4740.4770.2080.154nan0.9180.508nan0.477-0.9040.6080.4080.4280.404-0.9430.6040.572nannannannannan0.217nannan-0.0101.0000.118-0.083nannan
Propulsión vertical0.6710.1870.126-0.125-0.3450.1720.2470.093nan-0.5350.023-0.2620.3610.174-0.244nannan0.2390.3130.1620.1880.065nan-0.0970.307nan0.430nannan-0.080-0.375-0.2320.694-0.138nannan-0.6390.1181.000-0.954nannan
Cantidad de motores propulsión vertical-0.598-0.159-0.1260.1250.3450.055-0.247-0.004nan0.5740.0750.262-0.361-0.1330.444nannan-0.164-0.313-0.111-0.1880.063nan0.0970.004nan-0.430nannan0.2700.3750.232-0.6940.134nannan0.610-0.083-0.9541.000nannan
Cantidad de motores propulsión horizontalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Misiónnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
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Resumen de la Tabla

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ResumenCantidad
0Total de valores1764.000
1Valores numéricos907.000
2Valores NaN857.000
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Tabla de Correlaciones Filtradas por parametros seleccionados (Para Heatmap)

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Velocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
Velocidad a la que se realiza el crucero (KTAS)1.0000.0410.587-0.9990.5350.6630.6650.3360.8150.2570.1280.4720.8460.6960.426
Techo de servicio máximo0.0411.000-0.152-0.3140.0820.1370.4280.079-0.111-0.071-0.5020.0570.0170.087-0.138
Área del ala0.587-0.1521.000-0.8310.8670.977-0.3010.0810.7370.4230.0970.8410.9840.8990.941
Relación de aspecto del ala-0.999-0.314-0.8311.000-0.790-0.823-0.998-0.305-0.859nannan-0.349-0.744-0.8880.622
Longitud del fuselaje0.5350.0820.867-0.7901.0000.7860.1500.3890.2560.1800.2600.6930.9950.5990.880
Peso máximo al despegue (MTOW)0.6630.1370.977-0.8230.7861.0000.0250.4340.6780.5390.5460.7910.8580.8750.947
Alcance de la aeronave0.6650.428-0.301-0.9980.1500.0251.0000.8430.042nannan-0.010-0.7550.804-0.059
Autonomía de la aeronave0.3360.0790.081-0.3050.3890.4340.8431.0000.297-0.1640.4010.532-0.2010.4610.428
Velocidad máxima (KIAS)0.815-0.1110.737-0.8590.2560.6780.0420.2971.0000.5390.4460.4000.5120.7150.517
Velocidad de pérdida (KCAS)0.257-0.0710.423nan0.1800.539nan-0.1640.5391.0001.0000.401nan0.6270.321
Velocidad de pérdida limpia (KCAS)0.128-0.5020.097nan0.2600.546nan0.4010.4461.0001.0000.505nan0.5360.038
envergadura0.4720.0570.841-0.3490.6930.791-0.0100.5320.4000.4010.5051.0000.8850.7340.924
Cuerda0.8460.0170.984-0.7440.9950.858-0.755-0.2010.512nannan0.8851.0000.7760.971
payload0.6960.0870.899-0.8880.5990.8750.8040.4610.7150.6270.5360.7340.7761.0000.778
Empty weight0.426-0.1380.9410.6220.8800.947-0.0590.4280.5170.3210.0380.9240.9710.7781.000
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Resumen de la Tabla

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ResumenCantidad
0Total de valores225.000
1Valores numéricos213.000
2Valores NaN12.000
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Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= 0.7)

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Velocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
Velocidad a la que se realiza el crucero (KTAS)nannannan-0.999nannannannan0.815nannannan0.846nannan
Techo de servicio máximonannannannannannannannannannannannannannannan
Área del alanannannan-0.8310.8670.977nannan0.737nannan0.8410.9840.8990.941
Relación de aspecto del ala-0.999nan-0.831nan-0.790-0.823-0.998nan-0.859nannannan-0.744-0.888nan
Longitud del fuselajenannan0.867-0.790nan0.786nannannannannannan0.995nan0.880
Peso máximo al despegue (MTOW)nannan0.977-0.8230.786nannannannannannan0.7910.8580.8750.947
Alcance de la aeronavenannannan-0.998nannannan0.843nannannannan-0.7550.804nan
Autonomía de la aeronavenannannannannannan0.843nannannannannannannannan
Velocidad máxima (KIAS)0.815nan0.737-0.859nannannannannannannannannan0.715nan
Velocidad de pérdida (KCAS)nannannannannannannannannannannannannannannan
Velocidad de pérdida limpia (KCAS)nannannannannannannannannannannannannannannan
envergaduranannan0.841nannan0.791nannannannannannan0.8850.7340.924
Cuerda0.846nan0.984-0.7440.9950.858-0.755nannannannan0.885nan0.7760.971
payloadnannan0.899-0.888nan0.8750.804nan0.715nannan0.7340.776nan0.778
Empty weightnannan0.941nan0.8800.947nannannannannan0.9240.9710.778nan
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Resumen de la Tabla

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ResumenCantidad
0Total de valores225.000
1Valores numéricos68.000
2Valores NaN157.000
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", 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Tabla de correlaciones con filtro de umbral de correlación

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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== PASO 2: IMPUTACIÓN DE VALORES ===\n", + "\n", + "=== Velocidad a la que se realiza el crucero (KTAS): Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Techo de servicio máximo: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Área del ala: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Relación de aspecto del ala: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Longitud del fuselaje: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Peso máximo al despegue (MTOW): Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Alcance de la aeronave: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Autonomía de la aeronave: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Velocidad máxima (KIAS): Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Velocidad de pérdida (KCAS): Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Velocidad de pérdida limpia (KCAS): Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== envergadura: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Cuerda: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== payload: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Empty weight: Sin correlaciones significativas (|r| < 0.7) ===\n", + "La columna 'Nivel de Confianza' no está presente en df_reporte.\n", + "\u001b[1mNo se realizaron imputaciones por correlación en esta iteración.\u001b[0m\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Aerosonde Mk. 4.8 VTOL FTUAS = 30.228668858925072 (Similitud)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - AAI Aerosonde = 36.09414654431562 (Similitud)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Orbiter 4 = 30.466419244333917 (Similitud)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Orbiter 3 = 27.426371766294327 (Similitud)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Integrator Extended Range = 31.89437620999355 (Similitud)\n", + "Imputación final aplicada: Velocidad a la que se realiza el crucero (KTAS) - Skyeye 5000 VTOL octo = 30.290908946357952 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Orbiter 4 = 9403.635180379342 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Orbiter 3 = 6839.1446057940275 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 2600 = 14972.955913461341 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 2930 VTOL = 15999.999999999998 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 3600 VTOL = 16959.091874090493 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 5000 VTOL = 16009.435943246384 (Similitud)\n", + "Imputación final aplicada: Techo de servicio máximo - Skyeye 5000 VTOL octo = 16009.476366047784 (Similitud)\n", + "Imputación final aplicada: Área del ala - Aerosonde Mk. 4.8 VTOL FTUAS = 2.615 (Similitud)\n", + "Imputación final aplicada: Área del ala - Fulmar X = 1.1582832 (Similitud)\n", + "Imputación final aplicada: Área del ala - Orbiter 4 = 1.55 (Similitud)\n", + "Imputación final aplicada: Área del ala - ScanEagle 3 = 1.5499999999999998 (Similitud)\n", + "Imputación final aplicada: Área del ala - RQ Nan 21A Blackjack = 1.55 (Similitud)\n", + "Imputación final aplicada: Área del ala - V32 = 1.0 (Similitud)\n", + "Imputación final aplicada: Área del ala - V35 = 1.0 (Similitud)\n", + "Imputación final aplicada: Área del ala - V39 = 1.0 (Similitud)\n", + "Imputación final aplicada: Área del ala - Volitation VT370 = 1.32 (Similitud)\n", + "Imputación final aplicada: Área del ala - Volitation VT510 = 2.615 (Similitud)\n", + "Imputación final aplicada: Área del ala - Ascend = 0.8200000000000001 (Similitud)\n", + "Imputación final aplicada: Área del ala - Transition = 0.8800000000000001 (Similitud)\n", + "Imputación final aplicada: Área del ala - Reach = 2.615 (Similitud)\n", + "Imputación final aplicada: Relación de aspecto del ala - Fulmar X = 15.326448834651856 (Similitud)\n", + "Imputación final aplicada: Relación de aspecto del ala - Orbiter 4 = 12.500000000000002 (Similitud)\n", + "Imputación final aplicada: Relación de aspecto del ala - ScanEagle 3 = 12.500000000000002 (Similitud)\n", + "Imputación final aplicada: Relación de aspecto del ala - RQ Nan 21A Blackjack = 12.500000000000002 (Similitud)\n", + "Imputación final aplicada: Relación de aspecto del ala - V25 = 14.754385964912283 (Similitud)\n", + "Imputación final aplicada: Relación de aspecto del ala - Skyeye 2600 = 14.75438596491228 (Similitud)\n", + "Imputación final aplicada: Relación de aspecto del ala - Skyeye 5000 VTOL = 12.5 (Similitud)\n", + "Imputación final aplicada: Relación de aspecto del ala - Skyeye 5000 VTOL octo = 12.500000000000002 (Similitud)\n", + "Imputación final aplicada: Relación de aspecto del ala - Volitation VT510 = 12.5 (Similitud)\n", + "Imputación final aplicada: Relación de aspecto del ala - Reach = 12.500000000000002 (Similitud)\n", + "Imputación final aplicada: Longitud del fuselaje - Aerosonde Mk. 4.8 VTOL FTUAS = 3.7167351527516352 (Similitud)\n", + "Imputación final aplicada: Longitud del fuselaje - V39 = 1.4093116957279586 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Aerosonde Mk. 4.8 VTOL FTUAS = 800.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Integrator = 499.99999999999994 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - ScanEagle 3 = 50.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - V21 = 270.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - V25 = 270.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Volitation VT370 = 300.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Skyeye 2600 = 3270.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Skyeye 5000 VTOL octo = 800.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Volitation VT510 = 800.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Ascend = 270.0 (Similitud)\n", + "Imputación final aplicada: Alcance de la aeronave - Reach = 800.0 (Similitud)\n", + "Imputación final aplicada: Autonomía de la aeronave - Skyeye 5000 VTOL octo = 11.672906868436865 (Similitud)\n", + "Imputación final aplicada: Velocidad máxima (KIAS) - Aerosonde Mk. 4.8 VTOL FTUAS = 42.25267526977939 (Similitud)\n", + "Imputación final aplicada: Velocidad máxima (KIAS) - DeltaQuad Evo = 33.0 (Similitud)\n", + "Imputación final aplicada: Velocidad máxima (KIAS) - Skyeye 2600 = 30.8342888378733 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Aerosonde Mk. 4.8 VTOL FTUAS = 18.90746548752 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - AAI Aerosonde = 10.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - DeltaQuad Evo = 14.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - V35 = 18.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - V39 = 17.397389995852386 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Volitation VT370 = 24.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida (KCAS) - Skyeye 5000 VTOL = 19.109224697504906 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Aerosonde Mk. 4.8 VTOL FTUAS = 25.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - AAI Aerosonde = 10.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - DeltaQuad Evo = 14.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - V35 = 18.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - V39 = 17.397389995852386 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Volitation VT370 = 24.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Skyeye 5000 VTOL = 25.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Skyeye 5000 VTOL octo = 25.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Ascend = 14.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Transition = 10.0 (Similitud)\n", + "Imputación final aplicada: Velocidad de pérdida limpia (KCAS) - Reach = 25.0 (Similitud)\n", + "Imputación final aplicada: envergadura - Aerosonde Mk. 4.8 VTOL FTUAS = 5.373632590337233 (Similitud)\n", + "Imputación final aplicada: Cuerda - Fulmar X = 0.31819504 (Similitud)\n", + "Imputación final aplicada: Cuerda - Orbiter 4 = 0.352 (Similitud)\n", + "Imputación final aplicada: Cuerda - ScanEagle 3 = 0.3519999999999999 (Similitud)\n", + "Imputación final aplicada: Cuerda - RQ Nan 21A Blackjack = 0.352 (Similitud)\n", + "Imputación final aplicada: Cuerda - V25 = 0.196551724137931 (Similitud)\n", + "Imputación final aplicada: Cuerda - Skyeye 2600 = 0.196551724137931 (Similitud)\n", + "Imputación final aplicada: payload - AAI Aerosonde = 4.0 (Similitud)\n", + "Imputación final aplicada: payload - Fulmar X = 2.4947559999999998 (Similitud)\n", + "Imputación final aplicada: Empty weight - Aerosonde Mk. 4.8 VTOL FTUAS = 31.0 (Similitud)\n", + "Imputación final aplicada: Empty weight - Fulmar X = 17.463292 (Similitud)\n", + "Imputación final aplicada: Empty weight - V35 = 7.1 (Similitud)\n", + "Imputación final aplicada: Empty weight - V39 = 6.708303497304052 (Similitud)\n", + "Imputación final aplicada: Empty weight - Volitation VT370 = 10.999999999999998 (Similitud)\n", + "Imputación final aplicada: Empty weight - Skyeye 5000 VTOL = 31.0 (Similitud)\n", + "Imputación final aplicada: Empty weight - Volitation VT510 = 30.999999999999996 (Similitud)\n", + "\n", + "=== Iteración 1: Resumen después de imputaciones ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Resumen de Valores Faltantes Después de Iteración 1

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FilaValores Faltantes
0Stalker XE2.000
1Stalker VXE302.000
2Aerosonde Mk. 4.7 Fixed Wing4.000
3Aerosonde Mk. 4.7 VTOL4.000
4Aerosonde Mk. 4.8 Fixed wing4.000
5Aerosonde Mk. 4.8 VTOL FTUAS1.000
6AAI Aerosonde0.000
7Fulmar X2.000
8Orbiter 44.000
9Orbiter 36.000
10Mantis8.000
11ScanEagle7.000
12Integrator6.000
13Integrator VTOL12.000
14Integrator Extended Range6.000
15ScanEagle 33.000
16RQ Nan 21A Blackjack4.000
17DeltaQuad Evo2.000
18DeltaQuad Pro #MAP7.000
19DeltaQuad Pro #CARGO7.000
20V212.000
21V250.000
22V323.000
23V353.000
24V393.000
25Volitation VT3702.000
26Skyeye 26000.000
27Skyeye 2930 VTOL3.000
28Skyeye 36006.000
29Skyeye 3600 VTOL2.000
30Skyeye 50004.000
31Skyeye 5000 VTOL1.000
32Skyeye 5000 VTOL octo1.000
33Volitation VT5101.000
34Ascend2.000
35Transition3.000
36Reach1.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Sumatoria Total de Valores Faltantes

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ResumenCantidad
0Total de Valores Faltantes128.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "\u001b[1m=== FIN DE ITERACIÓN 1 ===\u001b[0m\n", + "================================================================================\n", + "\n", + "================================================================================\n", + "\u001b[1m=== INICIO DE ITERACIÓN 2 ===\u001b[0m\n", + "================================================================================\n", + "\n", + "=== Iteración 2: Resumen antes de imputaciones ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Resumen de Valores Faltantes Antes de Iteración 2

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FilaValores Faltantes
0Stalker XE17.000
1Stalker VXE3018.000
2Aerosonde Mk. 4.7 Fixed Wing17.000
3Aerosonde Mk. 4.7 VTOL16.000
4Aerosonde Mk. 4.8 Fixed wing20.000
5Aerosonde Mk. 4.8 VTOL FTUAS19.000
6AAI Aerosonde15.000
7Fulmar X21.000
8Orbiter 423.000
9Orbiter 325.000
10Mantis26.000
11ScanEagle25.000
12Integrator24.000
13Integrator VTOL29.000
14Integrator Extended Range26.000
15ScanEagle 321.000
16RQ Nan 21A Blackjack21.000
17DeltaQuad Evo15.000
18DeltaQuad Pro #MAP22.000
19DeltaQuad Pro #CARGO22.000
20V2115.000
21V2513.000
22V3216.000
23V3519.000
24V3919.000
25Volitation VT37017.000
26Skyeye 260018.000
27Skyeye 2930 VTOL20.000
28Skyeye 360023.000
29Skyeye 3600 VTOL18.000
30Skyeye 500018.000
31Skyeye 5000 VTOL16.000
32Skyeye 5000 VTOL octo16.000
33Volitation VT51016.000
34Ascend16.000
35Transition17.000
36Reach15.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Sumatoria Total de Valores Faltantes

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ResumenCantidad
0Total de Valores Faltantes714.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[1m*** IMPUTACIÓN POR SIMILITUD - ITERACIÓN 2 ***\u001b[0m\n", + "--------------------------------------------------------------------------------\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Velocidad a la que se realiza el crucero (KTAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 - Techo de servicio máximo ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Techo de servicio máximo'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Área del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Área del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Área del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Área del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Área del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Evo - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V21 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V32 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V35 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V39 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT370 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2930 VTOL - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 VTOL - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Relación de aspecto del ala'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Ascend - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Transition - Relación de aspecto del ala ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Longitud del fuselaje ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 Fixed Wing - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 VTOL - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 Fixed wing - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V32 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V35 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V39 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2930 VTOL - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Alcance de la aeronave'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Transition - Alcance de la aeronave ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Velocidad máxima (KIAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Velocidad máxima (KIAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Velocidad máxima (KIAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Velocidad máxima (KIAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Stalker XE - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Stalker VXE30 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 Fixed Wing - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 VTOL - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 Fixed wing - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Fulmar X - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Velocidad de pérdida (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Stalker XE - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Stalker VXE30 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 Fixed Wing - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 VTOL - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 Fixed wing - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Fulmar X - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Velocidad de pérdida limpia (KCAS)'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Velocidad de pérdida limpia (KCAS) ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - envergadura ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 VTOL FTUAS - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Evo - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V21 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V32 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V35 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: V39 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT370 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 2930 VTOL - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 3600 VTOL - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F2 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Skyeye 5000 VTOL octo - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Cuerda'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Volitation VT510 - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Ascend - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Transition - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Reach - Cuerda ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - payload ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 Fixed Wing - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.7 VTOL - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Aerosonde Mk. 4.8 Fixed wing - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 4 - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Orbiter 3 - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Empty weight'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Empty weight'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Mantis - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F0 tiene 'Empty weight'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Ningún dron en F1 tiene 'Empty weight'.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator VTOL - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: Integrator Extended Range - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: ScanEagle 3 - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: RQ Nan 21A Blackjack - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #MAP - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1m\n", + "=== Imputación por similitud: DeltaQuad Pro #CARGO - Empty weight ===\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F0: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal', 'Cantidad de motores propulsión vertical', 'Cantidad de motores propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F0.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F1: criterios ['Misión', 'Despegue', 'Propulsión vertical', 'Propulsión horizontal'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F1.\u001b[0m\n", + "\u001b[1m\n", + "--- Capa F2: criterios ['Misión', 'Despegue'] ---\u001b[0m\n", + "\u001b[1m❌ Sin vecinos ±20% MTOW en F2.\u001b[0m\n", + "\u001b[1m⚠️ No se pudo imputar en ninguna capa. Delegar a correlación...\u001b[0m\n", + "\u001b[1mNo se realizaron imputaciones por similitud en esta iteración.\u001b[0m\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[1m*** IMPUTACIÓN POR CORRELACIÓN - ITERACIÓN 2 ***\u001b[0m\n", + "--------------------------------------------------------------------------------\n", + "\n", + "=== DataFrame inicial ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

DataFrame antes de realizar imputacion por correlacion (df_procesado.copy())

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Distancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeProfundidad del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Tasa de ascensoRadio de giroenvergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (dry weight)RTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónWing LoadingPotencia específica (P/W)Capacidad combustibleConsumoPotencia WattsPotencia HPPrecioTiempo de emergencia en vueloDistancia de aterrizajeDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
Modelo
Stalker XE0.06000.00016.88037912000.0NaN0.8715.3012552.1NaN0.21113.600370.08.020.0NaNNaNNaN3.6570.2392.4947562.015.43332NaNNaN10.886208NaN59.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Stalker VXE300.06000.00017.60237312000.0NaN1.15828315.3264492.5908NaN0.219.958433.08.025.034211NaNNaNNaN4.87680.3181952.494756NaN16.093422NaNNaN17.463292NaN161.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Aerosonde Mk. 4.7 Fixed WingNaN6000.00027.3440514700.0NaN1.5512.53.0NaN0.27742.200NaN19.833.43886NaNNaNNaN4.40.35214.5NaN25.0NaN27.7NaNNaN140.0NaNNaNNaN0.62980.04.0NaNNaNNaN1.0002.0005.0000.0001.0001.000
Aerosonde Mk. 4.7 VTOL0.06000.00027.344059700.0NaN1.5512.53.0NaN0.27753.500NaN12.033.43886NaNNaNNaN4.40.35211.3NaN25.0NaN42.2NaNNaN140.0NaNNaNNaN0.62980.04.0NaNNaNNaN2.0002.0001.0004.0001.0001.000
Aerosonde Mk. 4.8 Fixed wingNaN6000.00027.3440518200.0NaN1.5512.53.0NaN0.27754.400NaN19.833.43886NaNNaNNaN4.40.35217.7NaN25.0NaN36.7NaNNaN140.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Aerosonde Mk. 4.8 VTOL FTUAS0.06000.00030.22866915000.025.02.61512.53.716735NaNNaN93.000800.014.042.25267518.907465NaNNaN5.373633NaN22.7NaNNaNNaN70.331.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0002.0001.0004.0001.0001.000
AAI AerosondeNaN5500.00036.09414715000.010.00.5714.7543861.7NaNNaN13.1003270.026.030.84572510.0NaNNaN2.90.1965524.0NaNNaNNaNNaN10.0NaN150.023.098.0NaNNaN1280.01.74NaNNaNNaN2.0002.0001.0004.0001.0001.000
Fulmar XNaN6000.00030.4065849.842NaN1.15828315.3264491.2NaNNaN20.000800.08.041.7NaNNaNNaN3.00.3181952.494756NaN27.8NaNNaN17.463292NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Orbiter 4NaN6000.00030.4664199403.63518NaN1.5512.51.2NaNNaN55.000NaN24.036.0NaNNaNNaN5.20.35212.0NaNNaNNaNNaNNaNNaN150.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Orbiter 3NaN6000.00027.4263726839.144606NaNNaNNaN1.2NaNNaN32.00050.06.036.0NaNNaNNaN4.4NaN5.5NaNNaNNaNNaNNaNNaN50.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0001.0005.0000.0001.0001.000
MantisNaN6000.00018.265826NaNNaNNaNNaN1.48NaNNaN6.50025.02.025.6NaNNaNNaN2.1NaNNaNNaN16.7NaNNaNNaNNaN25.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0001.0005.0000.0001.0001.000
ScanEagleNaN6000.00030.62533619500.0NaNNaNNaN1.71NaNNaN26.500NaN18.041.2NaNNaNNaN3.1NaN5.0NaN28.0NaNNaNNaNNaN101.86NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
IntegratorNaN6000.00030.95346519500.0NaNNaNNaN2.5NaNNaN74.800500.024.046.3NaNNaNNaN4.8NaN18.0NaN28.3NaNNaNNaNNaN92.6NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
Integrator VTOL0.05000.000NaNNaNNaNNaNNaNNaNNaNNaN75.000NaN16.0NaNNaNNaNNaNNaNNaN18.0NaNNaNNaNNaNNaN30.0NaNNaNNaNNaNNaNNaNNaNNaNNaN0.02.0002.0001.0004.0001.0001.000
Integrator Extended RangeNaN6000.00031.89437619500.0NaNNaNNaN2.5NaNNaN74.800500.019.046.3NaNNaNNaN4.8NaN18.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
ScanEagle 3NaN6000.00025.70340720.0NaN1.5512.52.4NaNNaN36.30050.018.041.2NaNNaNNaN4.00.3528.6NaN23.5NaNNaNNaNNaNNaNNaNNaNNaNNaN170.0NaNNaNNaNNaN1.0002.0005.0000.0001.0001.000
RQ Nan 21A BlackjackNaN6000.00033.79724620.0NaN1.5512.52.5NaNNaN61.000NaN16.046.3NaNNaNNaN4.80.35217.7NaN30.9NaNNaNNaNNaN92.6NaNNaNNaNNaNNaN8.0NaNNaNNaN1.0002.0005.0000.0001.0001.000
DeltaQuad Evo0.06000.00018.09082413.014.00.84NaN0.75NaNNaN10.000270.04.5333.014.0NaNNaN2.69NaN3.04.5316.544.86.84.845.0NaNNaNNaNNaNNaNNaNNaNNaNNaN0.02.0001.0001.0004.0001.0001.000
DeltaQuad Pro #MAP0.06000.00017.50019213.123NaNNaNNaN0.9NaNNaN6.200100.01.83NaNNaNNaNNaN2.35NaN1.2NaN16.0NaNNaNNaN50.050.0NaNNaNNaNNaNNaNNaNNaNNaN0.02.0001.0001.0004.0001.0001.000
DeltaQuad Pro #CARGO0.06000.00017.50019213.123NaNNaNNaN0.9NaNNaN6.200100.01.83NaNNaNNaNNaN2.35NaN1.2NaN16.0NaNNaNNaN50.030.0NaNNaNNaNNaNNaNNaNNaNNaN0.02.0001.0001.0004.0001.0001.000
V210.06000.00019.68771648800.014.00.8NaN0.93NaNNaN10.000270.03.033.014.0NaN100.02.15NaN1.5NaN18.0NaNNaN2.65NaN30.012.5NaNNaNNaNNaNNaN3999.00.108NaN2.0001.0001.0004.0001.0001.000
V250.06000.00021.8752416000.015.50.5214.7543860.93NaNNaN12.500270.04.033.015.5NaN120.02.450.1965522.2NaN20.0NaNNaN3.45NaN30.024.0NaNNaNNaNNaNNaN4679.00.108NaN2.0001.0001.0004.0001.0001.000
V320.06000.00021.8752416000.017.01.0NaN1.0NaNNaN23.500NaN4.533.017.0NaN150.03.2NaN5.0NaN20.0NaNNaN6.45NaN30.025.0NaNNaNNaNNaNNaN69999.00.108NaN2.0002.0001.0004.0001.0001.000
V350.06000.00027.3440516000.018.01.0NaN1.88NaNNaN32.000NaN2.833.018.0NaNNaN3.5NaN10.0NaN25.0NaNNaN7.1NaN30.0NaNNaNNaNNaNNaNNaN7999.0NaNNaN2.0002.0001.0004.0001.0001.000
V390.06000.00027.3440516000.017.397391.0NaN1.409312NaNNaN24.000NaN4.533.017.39739NaNNaN3.9NaN5.0NaN25.0NaNNaN6.708303NaN30.0NaNNaNNaNNaNNaNNaN8999.0NaNNaN2.0002.0001.0004.0001.0001.000
Volitation VT3700.06000.00027.3440517000.024.01.32NaN2.02NaNNaN40.000300.015.033.024.0NaNNaN6.5NaN18.0NaN25.0NaNNaN11.0NaNNaNNaNNaN13.00.96NaNNaN8999.0NaNNaN2.0002.0001.0004.0001.0001.000
Skyeye 2600NaN6000.00036.09414714972.95591310.00.8814.7543862.05NaNNaN15.0003270.02.030.83428910.0NaNNaN2.60.1965524.0NaN33.0NaNNaN6.5NaNNaNNaNNaNNaNNaNNaNNaN2299.0NaNNaN2.0002.0001.0004.0001.0001.000
Skyeye 2930 VTOL0.06000.00026.25028816000.018.01.0NaN2.03NaNNaN28.000NaN3.030.018.0NaNNaN2.93NaN6.0NaN24.0NaNNaN7.1NaNNaNNaNNaNNaNNaNNaNNaN6799.0NaNNaN2.0002.0001.0004.0001.0001.000
Skyeye 360050.06000.000NaNNaN12.51.33NaN2.488NaNNaN28.000NaN4.5NaN12.5NaNNaN3.6NaN10.0NaNNaNNaNNaN11.5NaNNaNNaNNaN11.5NaNNaNNaN4999.0NaNNaN3.0002.0005.0000.0001.0001.000
Skyeye 3600 VTOL0.06000.00032.8128616959.09187424.01.32NaN2.42NaNNaN40.000300.06.033.024.0NaNNaN3.6NaN10.0NaN30.0NaNNaN11.0NaNNaNNaNNaN11.5NaNNaNNaN6999.0NaNNaN2.0002.0001.0004.0001.0001.000
Skyeye 500060.06000.00036.094147NaN15.02.615NaN3.5NaN0.37590.000NaN8.042.015.0NaNNaN5.0NaN20.0NaN33.0NaNNaN32.0NaNNaNNaNNaN28.01.2NaNNaN9999.0NaNNaN3.0002.0005.0000.0001.0001.000
Skyeye 5000 VTOL0.06000.00030.62533616009.43594325.02.61512.53.5NaN0.375100.000800.08.042.019.109225NaNNaN5.0NaN25.0NaN28.0NaNNaN31.0NaNNaNNaNNaN28.0NaNNaNNaN13900.0NaNNaN2.0002.0001.0004.0001.0001.000
Skyeye 5000 VTOL octo0.06000.00030.29090916009.47636625.02.61512.53.5NaN0.375100.000800.011.67290738.024.0NaNNaN5.0NaN15.0NaN35.0NaNNaN35.0NaNNaNNaNNaN28.0NaNNaNNaN15999.0NaNNaN2.0002.0001.0008.0001.0001.000
Volitation VT5100.06000.00032.8128617000.025.02.61512.52.905NaNNaN100.000800.05.050.025.0NaNNaN5.1NaN25.0NaN30.0NaNNaN31.0NaNNaNNaNNaN25.05.0NaNNaN16599.0NaNNaN2.0002.0001.0004.0001.0001.000
Ascend0.06000.00021.8752410000.014.00.82NaN1.562NaNNaN9.500270.06.030.013.0NaNNaN2.0NaN0.60.0520.06.08.93.015.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0002.0001.0004.0001.0001.000
Transition0.06000.00021.8752413000.010.00.88NaN2.3NaNNaN18.000NaN12.030.013.0NaNNaN3.0NaN1.50.0520.011.816.55.815.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0002.0001.0004.0001.0001.000
Reach0.06000.00027.3440516000.025.02.61512.54.712NaNNaN91.000800.020.035.013.0NaNNaN6.0NaN7.00.0525.054.084.031.015.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0002.0001.0004.0001.0001.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Convertir todo a numérico ===\n", + "\n", + "\n", + "=== PASO 1: CÁLCULO DE CORRELACIONES ENTRE PARÁMETROS ===\n", + "\n", + "Umbral seleccionado para correlaciones significativas: 0.7\n", + "\n", + "=== Cálculo de tabla completa ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Tabla de Correlaciones con todos los parametros(tabla_completa)

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Distancia de carrera requerida para despegueAltitud a la que se realiza el cruceroVelocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoVelocidad de pérdida limpia (KCAS)Área del alaRelación de aspecto del alaLongitud del fuselajeProfundidad del fuselajeAncho del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Tasa de ascensoRadio de giroenvergaduraCuerdapayloadduracion en VTOLCrucero KIASRTF (dry weight)RTF (Including fuel & Batteries)Empty weightMaximum CrosswindRango de comunicaciónWing LoadingPotencia específica (P/W)Capacidad combustibleConsumoPotencia WattsPotencia HPPrecioTiempo de emergencia en vueloDistancia de aterrizajeDespeguePropulsión horizontalPropulsión verticalCantidad de motores propulsión verticalCantidad de motores propulsión horizontalMisión
Distancia de carrera requerida para despegue1.0000.0630.425nan-0.3420.237nan0.235nan0.4250.168nan-0.0780.287-0.311nannan0.123nan0.229nan0.389nannan0.221nannannannan-0.018-0.240nannan-0.156nannan0.7350.1540.671-0.598nannan
Altitud a la que se realiza el crucero0.0631.000-0.278-0.0350.3310.242-0.2780.081nannan-0.095-0.656-0.2780.1240.343nannan0.1390.462-0.110nannannannan0.0880.038-0.325-0.215nannannan0.2770.690nannannan-0.119-0.1090.187-0.159nannan
Velocidad a la que se realiza el crucero (KTAS)0.425-0.2781.0000.1050.2080.454-0.3280.455nan0.9170.5970.6330.4510.6450.161nan0.8030.456-0.0640.602-0.6940.9730.9150.7900.474-0.8550.4800.346nan0.3650.461-0.115-0.016-0.290nannan0.0810.5840.101-0.060nannan
Techo de servicio máximonan-0.0350.1051.0000.007-0.030-0.0780.136nan0.5460.1430.1200.031-0.0780.026nan-0.8030.036-0.4620.135-0.8750.0990.6770.579-0.094-0.961-0.118-0.986nan-0.7570.5150.653-0.933-0.171nannan0.1920.068-0.1920.191nannan
Velocidad de pérdida limpia (KCAS)-0.3420.3310.2080.0071.0000.727-0.9700.591nannan0.768-0.4980.1600.6040.817nan0.9930.771nan0.705-0.1810.4280.9100.9360.677-0.181-0.8740.161nan0.2240.532nannan0.118nannan-0.2530.261-0.2530.365nannan
Área del ala0.2370.2420.454-0.0300.7271.000-0.7480.826nan0.9840.976-0.1350.2450.6660.460nan0.5160.7630.9610.804-0.3670.6280.9940.9780.959-0.3380.552-0.119nan0.9900.6750.2770.6900.049nannan0.1480.3590.0630.058nannan
Relación de aspecto del alanan-0.278-0.328-0.078-0.970-0.7481.000-0.606nan-0.776-0.7950.257-0.418-0.596-0.726nannan-0.712-0.724-0.7901.000-0.475nannan-0.874nan-0.341nannan0.000-0.816-0.277-0.690-0.981nannan-0.125-0.2780.125-0.175nannan
Longitud del fuselaje0.2350.0810.4550.1360.5910.826-0.6061.000nan0.9380.8050.1590.4010.3070.239nan0.9180.7030.5150.652-0.6170.5650.9660.9340.856-0.7180.6810.263nan0.9290.0360.6860.359-0.187nannan0.1180.5940.0650.014nannan
Profundidad del fuselajenannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Ancho del fuselaje0.425nan0.9170.546nan0.984-0.7760.938nan1.0000.9860.988-0.0890.940nannannan0.6710.7600.868nan0.944nannan0.955nan0.323nannannan1.000nannannannannan0.794nan-0.5350.574nannan
Peso máximo al despegue (MTOW)0.168-0.0950.5970.1430.7680.976-0.7950.805nan0.9861.0000.0220.4200.6940.523nan0.9730.8020.8290.880-0.4010.7080.9990.9790.940-0.4640.5140.628nan0.9760.7580.5590.8550.052nannan0.0900.4670.0230.075nannan
Alcance de la aeronavenan-0.6560.6330.120-0.498-0.1350.2570.159nan0.9880.0221.0000.279-0.048-0.509nannan-0.055-0.619-0.006-0.5080.5881.0000.990-0.030-0.7420.6580.430nan0.9871.0001.000nan-0.268nannan0.2730.392-0.2730.251nannan
Autonomía de la aeronave-0.078-0.2780.4510.0310.1600.245-0.4180.401nan-0.0890.4200.2791.0000.327-0.098nan0.9540.5340.4170.382-0.5940.3370.9400.6340.368-0.7150.8020.268nan0.056-0.732-0.391-0.5770.033nannan-0.4200.4780.353-0.314nannan
Velocidad máxima (KIAS)0.2870.1240.645-0.0780.6040.666-0.5960.307nan0.9400.694-0.0480.3271.0000.513nannan0.4400.4790.684-0.0770.6820.7620.7260.6610.2720.094-0.215nan0.7050.910-0.6240.9700.064nannan-0.0400.2150.165-0.128nannan
Velocidad de pérdida (KCAS)-0.3110.3430.1610.0260.8170.460-0.7260.239nannan0.523-0.509-0.0980.5131.000nan0.9930.559nan0.6401.0000.414-0.4080.4340.4121.000-0.8740.161nan0.0360.532nannan0.121nannan-0.2220.211-0.2220.381nannan
Tasa de ascensonannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Radio de gironannan0.803-0.8030.9930.516nan0.918nannan0.973nan0.954nan0.993nan1.0000.992nan0.976nan0.803nannan0.979nannan0.844nannannannannan0.921nannannan0.918nannannannan
envergadura0.1230.1390.4560.0360.7710.763-0.7120.703nan0.6710.802-0.0550.5340.4400.559nan0.9921.0000.8340.759-0.2580.5010.9830.9440.770-0.4520.6480.780nan0.2970.0850.5220.8350.032nannan-0.1010.5120.200-0.132nannan
Cuerdanan0.462-0.064-0.462nan0.961-0.7240.515nan0.7600.829-0.6190.4170.479nannannan0.8341.0000.713nan0.121nannan0.931nan0.539nannannannan0.2770.690nannannan-0.6690.4620.669-0.669nannan
payload0.229-0.1100.6020.1350.7050.804-0.7900.652nan0.8680.880-0.0060.3820.6840.640nan0.9760.7590.7131.000-0.0240.6600.9150.5590.759-0.1420.4640.723nan0.7110.8460.6710.897-0.008nannan0.0210.4410.136-0.087nannan
duracion en VTOLnannan-0.694-0.875-0.181-0.3671.000-0.617nannan-0.401-0.508-0.594-0.0771.000nannan-0.258nan-0.0241.000-0.694-0.408-0.402-0.3151.000nannannannannannannannannannan-0.188-0.9040.188-0.188nannan
Crucero KIAS0.389nan0.9730.0990.4280.628-0.4750.565nan0.9440.7080.5880.3370.6820.414nan0.8030.5010.1210.660-0.6941.0000.9150.7230.600-0.8550.3590.997nan0.5810.4611.0001.000-0.243nannan0.1430.6080.0650.063nannan
RTF (dry weight)nannan0.9150.6770.9100.994nan0.966nannan0.9991.0000.9400.762-0.408nannan0.983nan0.915-0.4080.9151.0001.0000.995-0.408nannannannannannannannannannannan0.408nannannannan
RTF (Including fuel & Batteries)nannan0.7900.5790.9360.978nan0.934nannan0.9790.9900.6340.7260.434nannan0.944nan0.559-0.4020.7231.0001.0000.988-0.402nannannannannannannannannannan0.0970.428-0.0970.097nannan
Empty weight0.2210.0880.474-0.0940.6770.959-0.8740.856nan0.9550.940-0.0300.3680.6610.412nan0.9790.7700.9310.759-0.3150.6000.9950.9881.000-0.3190.8190.530nan0.9890.509nannan0.031nannan0.1040.3850.1610.031nannan
Maximum Crosswindnan0.038-0.855-0.961-0.181-0.338nan-0.718nannan-0.464-0.742-0.7150.2721.000nannan-0.452nan-0.1421.000-0.855-0.408-0.402-0.3191.000nannannannannannannannannannannan-0.943nannannannan
Rango de comunicaciónnan-0.3250.480-0.118-0.8740.552-0.3410.681nan0.3230.5140.6580.8020.094-0.874nannan0.6480.5390.464nan0.359nannan0.819nan1.0000.215nannannan-1.000-0.972nannannan-0.4300.6040.430-0.430nannan
Wing Loadingnan-0.2150.346-0.9860.161-0.119nan0.263nannan0.6280.4300.268-0.2150.161nan0.8440.780nan0.723nan0.997nannan0.530nan0.2151.000nannannannannan0.568nannannan0.572nannannannan
Potencia específica (P/W)nannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Capacidad combustible-0.018nan0.365-0.7570.2240.9900.0000.929nannan0.9760.9870.0560.7050.036nannan0.297nan0.711nan0.581nannan0.989nannannannan1.0000.377nannan0.817nannan-0.080nan-0.0800.270nannan
Consumo-0.240nan0.4610.5150.5320.675-0.8160.036nan1.0000.7581.000-0.7320.9100.532nannan0.085nan0.846nan0.461nannan0.509nannannannan0.3771.000nannan0.998nannan0.113nan-0.3750.375nannan
Potencia Wattsnan0.277-0.1150.653nan0.277-0.2770.686nannan0.5591.000-0.391-0.624nannannan0.5220.2770.671nan1.000nannannannan-1.000nannannannan1.0001.000nannannan0.232nan-0.2320.232nannan
Potencia HPnan0.690-0.016-0.933nan0.690-0.6900.359nannan0.855nan-0.5770.970nannannan0.8350.6900.897nan1.000nannannannan-0.972nannannannan1.0001.000nannannan-0.694nan0.694-0.694nannan
Precio-0.156nan-0.290-0.1710.1180.049-0.981-0.187nannan0.052-0.2680.0330.0640.121nan0.9210.032nan-0.008nan-0.243nannan0.031nannan0.568nan0.8170.998nannan1.000nannan-0.1380.217-0.1380.134nannan
Tiempo de emergencia en vuelonannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Distancia de aterrizajenannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Despegue0.735-0.1190.0810.192-0.2530.148-0.1250.118nan0.7940.0900.273-0.420-0.040-0.222nannan-0.101-0.6690.021-0.1880.143nan0.0970.104nan-0.430nannan-0.0800.1130.232-0.694-0.138nannan1.000-0.010-0.6390.610nannan
Propulsión horizontal0.154-0.1090.5840.0680.2610.359-0.2780.594nannan0.4670.3920.4780.2150.211nan0.9180.5120.4620.441-0.9040.6080.4080.4280.385-0.9430.6040.572nannannannannan0.217nannan-0.0101.0000.118-0.083nannan
Propulsión vertical0.6710.1870.101-0.192-0.2530.0630.1250.065nan-0.5350.023-0.2730.3530.165-0.222nannan0.2000.6690.1360.1880.065nan-0.0970.161nan0.430nannan-0.080-0.375-0.2320.694-0.138nannan-0.6390.1181.000-0.954nannan
Cantidad de motores propulsión vertical-0.598-0.159-0.0600.1910.3650.058-0.1750.014nan0.5740.0750.251-0.314-0.1280.381nannan-0.132-0.669-0.087-0.1880.063nan0.0970.031nan-0.430nannan0.2700.3750.232-0.6940.134nannan0.610-0.083-0.9541.000nannan
Cantidad de motores propulsión horizontalnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
Misiónnannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannannan
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Resumen de la Tabla

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ResumenCantidad
0Total de valores1764.000
1Valores numéricos973.000
2Valores NaN791.000
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Tabla de Correlaciones Filtradas por parametros seleccionados (Para Heatmap)

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Velocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
Velocidad a la que se realiza el crucero (KTAS)1.0000.1050.454-0.3280.4550.5970.6330.4510.6450.1610.2080.456-0.0640.6020.474
Techo de servicio máximo0.1051.000-0.030-0.0780.1360.1430.1200.031-0.0780.0260.0070.036-0.4620.135-0.094
Área del ala0.454-0.0301.000-0.7480.8260.976-0.1350.2450.6660.4600.7270.7630.9610.8040.959
Relación de aspecto del ala-0.328-0.078-0.7481.000-0.606-0.7950.257-0.418-0.596-0.726-0.970-0.712-0.724-0.790-0.874
Longitud del fuselaje0.4550.1360.826-0.6061.0000.8050.1590.4010.3070.2390.5910.7030.5150.6520.856
Peso máximo al despegue (MTOW)0.5970.1430.976-0.7950.8051.0000.0220.4200.6940.5230.7680.8020.8290.8800.940
Alcance de la aeronave0.6330.120-0.1350.2570.1590.0221.0000.279-0.048-0.509-0.498-0.055-0.619-0.006-0.030
Autonomía de la aeronave0.4510.0310.245-0.4180.4010.4200.2791.0000.327-0.0980.1600.5340.4170.3820.368
Velocidad máxima (KIAS)0.645-0.0780.666-0.5960.3070.694-0.0480.3271.0000.5130.6040.4400.4790.6840.661
Velocidad de pérdida (KCAS)0.1610.0260.460-0.7260.2390.523-0.509-0.0980.5131.0000.8170.559nan0.6400.412
Velocidad de pérdida limpia (KCAS)0.2080.0070.727-0.9700.5910.768-0.4980.1600.6040.8171.0000.771nan0.7050.677
envergadura0.4560.0360.763-0.7120.7030.802-0.0550.5340.4400.5590.7711.0000.8340.7590.770
Cuerda-0.064-0.4620.961-0.7240.5150.829-0.6190.4170.479nannan0.8341.0000.7130.931
payload0.6020.1350.804-0.7900.6520.880-0.0060.3820.6840.6400.7050.7590.7131.0000.759
Empty weight0.474-0.0940.959-0.8740.8560.940-0.0300.3680.6610.4120.6770.7700.9310.7591.000
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Resumen de la Tabla

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ResumenCantidad
0Total de valores225.000
1Valores numéricos221.000
2Valores NaN4.000
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Tabla de parametros seleccionados, filtrada por Correlaciones Significativas (Umbral >= 0.7)

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Velocidad a la que se realiza el crucero (KTAS)Techo de servicio máximoÁrea del alaRelación de aspecto del alaLongitud del fuselajePeso máximo al despegue (MTOW)Alcance de la aeronaveAutonomía de la aeronaveVelocidad máxima (KIAS)Velocidad de pérdida (KCAS)Velocidad de pérdida limpia (KCAS)envergaduraCuerdapayloadEmpty weight
Velocidad a la que se realiza el crucero (KTAS)nannannannannannannannannannannannannannannan
Techo de servicio máximonannannannannannannannannannannannannannannan
Área del alanannannan-0.7480.8260.976nannannannan0.7270.7630.9610.8040.959
Relación de aspecto del alanannan-0.748nannan-0.795nannannan-0.726-0.970-0.712-0.724-0.790-0.874
Longitud del fuselajenannan0.826nannan0.805nannannannannan0.703nannan0.856
Peso máximo al despegue (MTOW)nannan0.976-0.7950.805nannannannannan0.7680.8020.8290.8800.940
Alcance de la aeronavenannannannannannannannannannannannannannannan
Autonomía de la aeronavenannannannannannannannannannannannannannannan
Velocidad máxima (KIAS)nannannannannannannannannannannannannannannan
Velocidad de pérdida (KCAS)nannannan-0.726nannannannannannan0.817nannannannan
Velocidad de pérdida limpia (KCAS)nannan0.727-0.970nan0.768nannannan0.817nan0.771nan0.705nan
envergaduranannan0.763-0.7120.7030.802nannannannan0.771nan0.8340.7590.770
Cuerdanannan0.961-0.724nan0.829nannannannannan0.834nan0.7130.931
payloadnannan0.804-0.790nan0.880nannannannan0.7050.7590.713nan0.759
Empty weightnannan0.959-0.8740.8560.940nannannannannan0.7700.9310.759nan
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Resumen de la Tabla

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ResumenCantidad
0Total de valores225.000
1Valores numéricos64.000
2Valores NaN161.000
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Tabla de correlaciones con filtro de umbral de correlación

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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== PASO 2: IMPUTACIÓN DE VALORES ===\n", + "\n", + "=== Velocidad a la que se realiza el crucero (KTAS): Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Techo de servicio máximo: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Área del ala: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Relación de aspecto del ala: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Longitud del fuselaje: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Peso máximo al despegue (MTOW): Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Alcance de la aeronave: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Autonomía de la aeronave: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Velocidad máxima (KIAS): Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Velocidad de pérdida (KCAS): Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Velocidad de pérdida limpia (KCAS): Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== envergadura: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Cuerda: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== payload: Sin correlaciones significativas (|r| < 0.7) ===\n", + "\n", + "=== Empty weight: Sin correlaciones significativas (|r| < 0.7) ===\n", + "La columna 'Nivel de Confianza' no está presente en df_reporte.\n", + "\u001b[1mNo se realizaron imputaciones por correlación en esta iteración.\u001b[0m\n", + "\n", + "=== Iteración 2: Resumen después de imputaciones ===\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Resumen de Valores Faltantes Después de Iteración 2

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FilaValores Faltantes
0Stalker XE2.000
1Stalker VXE302.000
2Aerosonde Mk. 4.7 Fixed Wing4.000
3Aerosonde Mk. 4.7 VTOL4.000
4Aerosonde Mk. 4.8 Fixed wing4.000
5Aerosonde Mk. 4.8 VTOL FTUAS1.000
6AAI Aerosonde0.000
7Fulmar X2.000
8Orbiter 44.000
9Orbiter 36.000
10Mantis8.000
11ScanEagle7.000
12Integrator6.000
13Integrator VTOL12.000
14Integrator Extended Range6.000
15ScanEagle 33.000
16RQ Nan 21A Blackjack4.000
17DeltaQuad Evo2.000
18DeltaQuad Pro #MAP7.000
19DeltaQuad Pro #CARGO7.000
20V212.000
21V250.000
22V323.000
23V353.000
24V393.000
25Volitation VT3702.000
26Skyeye 26000.000
27Skyeye 2930 VTOL3.000
28Skyeye 36006.000
29Skyeye 3600 VTOL2.000
30Skyeye 50004.000
31Skyeye 5000 VTOL1.000
32Skyeye 5000 VTOL octo1.000
33Volitation VT5101.000
34Ascend2.000
35Transition3.000
36Reach1.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + "

Sumatoria Total de Valores Faltantes

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ResumenCantidad
0Total de Valores Faltantes128.000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mNo se realizaron nuevas imputaciones. Finalizando...\u001b[0m\n", + "=== Exportando datos al archivo: C:\\Users\\delpi\\OneDrive\\Tesis\\ADRpy-VTOL\\ADRpy\\analisis\\Results\\Datos_imputados.xlsx ===\n", + "Exportación completada. El archivo se guardó como 'C:\\Users\\delpi\\OneDrive\\Tesis\\ADRpy-VTOL\\ADRpy\\analisis\\Results\\Datos_imputados.xlsx'.\n", + "\n", + "=== Flujo completado. Verifique el archivo generado. ===\n", + "✅ Script finalizado.\n" + ] + } + ], + "source": [ + "%run main.py" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (adrpy)", + "language": "python", + "name": "adrpy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ADRpy/analisis/archivo_imputaciones.xlsx b/ADRpy/analisis/archivo_imputaciones.xlsx new file mode 100644 index 00000000..a4c49d13 Binary files /dev/null and b/ADRpy/analisis/archivo_imputaciones.xlsx differ diff --git a/ADRpy/analisis/flujo_datos_adrpy.md b/ADRpy/analisis/flujo_datos_adrpy.md new file mode 100644 index 00000000..5fca5e52 --- /dev/null +++ b/ADRpy/analisis/flujo_datos_adrpy.md @@ -0,0 +1,51 @@ +# 📊 Diagrama de flujo de datos - Proyecto ADRpy + +Este diagrama representa el flujo principal de los datos en el proceso de análisis e imputación de aeronaves. + + +```mermaid +graph TD + A[Archivo Excel original] --> B[Procesamiento y limpieza de datos] + B --> C[Analisis de correlaciones] + B --> D[Imputacion por similitud MTOW] + C --> E[Imputacion por correlacion] + D --> F[Bucle de imputacion hasta completar valores] + E --> F + F --> G[Exportacion a nuevo Excel con formato y comentarios] + G --> H[Visualizacion con Data Wrangler o HTML] +``` + +```mermaid +graph TD + A[📁 Excel original] --> B[🧹 Limpieza de datos] + B --> C[📈 Correlaciones] + B --> D[🧮 Similitud MTOW] + C --> E[🔗 Correlaciones significativas] + D --> F[🔁 Bucle de imputacion] + E --> F + F --> G[📤 Exportar a Excel] + G --> H[🖥️ Visualizar con Data Wrangler] +``` +```mermaid +graph TD + classDef inicio fill=#cce5ff,stroke=#004085,color=#004085,stroke-width:2px; + classDef proceso fill=#e2e3e5,stroke=#383d41,color=#383d41; + classDef importante fill=#fff3cd,stroke=#856404,color=#856404,stroke-width:2px; + classDef salida fill=#d4edda,stroke=#155724,color=#155724; + + A[📁 Excel original]:::inicio --> B[🧹 Limpieza de datos]:::proceso + B --> C[📈 Correlaciones]:::proceso + B --> D[🧮 Similitud MTOW]:::proceso + C --> E[🔗 Correlación significativa]:::importante + D --> F[🔁 Bucle de imputación]:::importante + E --> F + F --> G[📤 Exportación Excel]:::salida + G --> H[🖥️ Visualización HTML o Data Wrangler]:::salida + +--- + +## 📌 Notas: +- Cada bloque representa una etapa clave del proyecto. +- Las flechas indican la evolución del DataFrame a través de funciones. +- Las funciones clave están agrupadas en módulos dentro de la carpeta `Modulos`. + diff --git a/ADRpy/analisis/inicial.drawio b/ADRpy/analisis/inicial.drawio new file mode 100644 index 00000000..61c07fd1 --- /dev/null +++ b/ADRpy/analisis/inicial.drawio @@ -0,0 +1,1391 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/ADRpy/analisis/main.py b/ADRpy/analisis/main.py new file mode 100644 index 00000000..7a6e3c44 --- /dev/null +++ b/ADRpy/analisis/main.py @@ -0,0 +1,258 @@ +# 🛠 Watch: +# df_procesado +# df_filtrado +# parametros_seleccionados +# df_resultado_final +# resumen_imputaciones +# df_procesado.shape +# df_filtrado.isnull().sum() + +# TODO: Esto es algo que tengo que hacer más adelante +# FIXME: Esto está fallando o tiene errores +# ! Esto es importante +# ? Esto es una duda o algo que quiero revisar +# * Esto es algo que quiero hacer +# Normal comment: Este es un comentario normal + +import argparse + +# ===================== # +# ARGUMENT PARSER # +# ===================== # +parser = argparse.ArgumentParser(description="Ejecución del script ADRpy con parámetros opcionales") +parser.add_argument("--ruta_archivo", type=str, help="Ruta del archivo Excel original", default=None) +parser.add_argument("--archivo_destino", type=str, help="Ruta para guardar el archivo Excel exportado", default=None) +parser.add_argument("--debug_mode", action="store_true") +parser.add_argument("--umbral_heat_map", type=float, help="Umbral mínimo de correlación significativa", default=None) +parser.add_argument("--nivel_confianza_min_similitud", type=float, help="Nivel de confianza mínimo para imputaciones", default=None) +parser.add_argument("--rango_min", type=float, help="Rango mínimo de MTOW para imputación por similitud", default=None) +parser.add_argument("--rango_max", type=float, help="Rango máximo de MTOW para imputación por similitud", default=None) +parser.add_argument("--parametros", type=str, help="Parámetros seleccionados como lista de índices (ej. '4,5,6')", default=None) +parser.add_argument("--columna", type=str, help="Nombre de la columna para analizar celdas faltantes", default=None) +parser.add_argument("--umbral_correlacion", type=float, help="Umbral mínimo de correlación significativa", default=None) +parser.add_argument("--nivel_confianza_min_correlacion", type=float, help="Nivel de confianza mínimo correlacion para imputaciones", default=None) + + +args = parser.parse_args() + + +# Ahora args.ruta_archivo, args.archivo_destino, etc. están disponibles + + +# ===================== # +# IMPORTACIONES # +# ===================== # + +# Librerías estándar +import os + +# Librerías externas +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +from openpyxl import load_workbook +from openpyxl.styles import Font, PatternFill +from openpyxl.utils.dataframe import dataframe_to_rows +from openpyxl.comments import Comment +from tkinter import simpledialog, messagebox +import tkinter as tk +from sklearn.linear_model import LinearRegression +from sklearn.metrics import r2_score +from IPython.display import display, HTML + +# ===================== # +# IMPORTAR MÓDULOS # +# ===================== # + +from Modulos.config_and_loading import configurar_entorno, cargar_datos, normalizar_encabezados +from Modulos.data_processing import procesar_datos_y_manejar_duplicados +from Modulos.user_interaction import seleccionar_parametros_por_indices, solicitar_umbral +from Modulos.correlation_analysis import calcular_correlaciones_y_generar_heatmap_con_resumen +from Modulos.similarity_imputation import imputacion_similitud_con_rango, imprimir_detalles_imputacion +from Modulos.correlation_imputation import Imputacion_por_correlacion +from Modulos.imputation_loop import bucle_imputacion_similitud_correlacion +from Modulos.excel_export import exportar_excel_con_imputaciones +from Modulos.html_utils import convertir_a_html +from Modulos.data_processing import mostrar_celdas_faltantes_con_seleccion, generar_resumen_faltantes +from Modulos.imputacion_similitud_flexible import configurar_similitud +from Modulos.imputacion_similitud_flexible import imputar_por_similitud +# Paso 1: Configurar entorno +configurar_entorno(max_rows=20, max_columns=10) + +# Paso 2: Cargar datos +try: + df_inicial, ruta_archivo = cargar_datos(ruta_archivo=args.ruta_archivo) + print(f"Datos cargados correctamente desde: {ruta_archivo}") +except ValueError as e: + print(f"Error al cargar datos: {e}") + exit(1) # Detiene el programa si hay un error + +# Normalizar encabezados del DataFrame +#print("\n=== Normalizando encabezados del DataFrame ===") +#df_inicial = normalizar_encabezados(df_inicial) + +# Validar que los datos se hayan cargado correctamente +print("\n=== Validando datos cargados ===") +if df_inicial.empty: + print("El archivo cargado no contiene datos. Verifica el archivo y vuelve a intentarlo.") + exit(1) + +# Continuar con el siguiente paso solo si los datos son válidos +print("\n=== Continuando con el procesamiento de datos ===") + +# Validar encabezados iniciales +print("\nEncabezados iniciales cargados:") +print(df_inicial.columns.tolist()) + +# Paso 3: Mostrar datos iniciales en HTML +print("\n=== Mostrando datos iniciales en formato HTML ===") +convertir_a_html(df_inicial, titulo="Datos Iniciales", mostrar=True) + +# Paso 4: Procesar datos +print("\n=== Procesando los datos ===") +df_procesado = procesar_datos_y_manejar_duplicados(df_inicial) + +# Validar encabezados después del procesamiento +#print("\nEncabezados después del procesamiento:") +#print(df_procesado.columns.tolist()) + +# Comparar encabezados antes y después del procesamiento +if df_inicial.columns.tolist() == df_procesado.columns.tolist(): + print("\n✅ Los encabezados se preservaron correctamente.") +else: + print("\n❌ Los encabezados fueron modificados durante el procesamiento.") + +# Paso 5: Mostrar en HTML +print("\n=== Mostrando datos procesados en formato HTML ===") +convertir_a_html(df_procesado, titulo="Datos Procesados", mostrar=True) + +# Paso 6: Selección de parámetros + +# Parámetros disponibles en el índice del DataFrame +parametros_disponibles = df_procesado.columns.tolist() +print("Parámetros disponibles en df_procesado antes de seleccionar:") +print(parametros_disponibles) + +# Parámetros preseleccionados de interés +parametros_preseleccionados = [ + "Velocidad a la que se realiza el crucero (KTAS)", + "Techo de servicio máximo", + "Área del ala", + "Relación de aspecto del ala", + "Longitud del fuselaje", + "Peso máximo al despegue (MTOW)", + "Alcance de la aeronave", + "Autonomía de la aeronave", + "Velocidad máxima (KIAS)", + "Velocidad de pérdida (KCAS)", + "Velocidad de pérdida limpia (KCAS)", + "envergadura", + "Cuerda", + "payload", + "Empty weight" +] +# Filtrar preseleccionados para mantener solo los parámetros válidos +parametros_preseleccionados = [p for p in parametros_preseleccionados if p in parametros_disponibles] + +# Imprimir parámetros preseleccionados válidos +#print("Parámetros preseleccionados válidos:") +#print(parametros_preseleccionados) + +parametros_seleccionados = seleccionar_parametros_por_indices(parametros_disponibles, parametros_preseleccionados, args.parametros) +# Imprimir parámetros seleccionados después de filtrar +print("Parámetros seleccionados después de filtrar:") +print(parametros_seleccionados) + +# Filtrar el DataFrame por los parámetros seleccionados +try: + df_filtrado = df_procesado[parametros_seleccionados] +except KeyError as e: + print(f"Error al filtrar df_procesado: {e}") + print(f"Parámetros seleccionados inválidos: {set(parametros_seleccionados) - set(df_procesado.index.tolist())}") + raise + +# Mostrar la tabla en formato HTML con 3 cifras significativas (sin notación científica) +convertir_a_html(df_filtrado, titulo="Datos Filtrados por Parámetros (df_filtrado)", mostrar=True) + +# Paso 7: Mostrar celdas faltantes con selección de columna + +# Analizar celdas faltantes en la fila seleccionada +df_celdas_faltantes = mostrar_celdas_faltantes_con_seleccion( + df_filtrado, + fila_seleccionada=args.columna, + debug_mode=args.debug_mode +) + +# Verificar si hay celdas faltantes +if df_celdas_faltantes.empty: + print("No se encontraron valores faltantes en la columna seleccionada.") +else: + # Mostrar resultados en formato HTML + convertir_a_html(df_celdas_faltantes, titulo="Celdas Faltantes Identificadas en df_filtrado (df_celdas_faltantes)", mostrar=True) + +# Paso 8: Resumen de valores faltantes por columna +print("\n=== Generando resumen de valores faltantes por columna ===") +resumen_faltantes = generar_resumen_faltantes(df_filtrado, titulo="Resumen de Valores Faltantes de df_filtrado") + +# Paso 9: Calculando correlaciones y generando heatmap +print("\n=== Calculando correlaciones y generando heatmap ===") +tabla_completa = calcular_correlaciones_y_generar_heatmap_con_resumen( + df_procesado, + parametros_seleccionados, + umbral_heat_map=args.umbral_heat_map if args.debug_mode else None, + devolver_tabla=True +) + +# Paso 10: Ajustar rango e imputar valores faltantes +#print("\n=== Paso 8: Imputación con ajuste de rango ===") +#imputacion_similitud_con_rango(df_filtrado, df_procesado) + #Paso 11: Ajustar rango e imputar valores faltantes por correlación +#Imputacion_por_correlacion(df_procesado, parametros_preseleccionados, tabla_completa, parametros_seleccionados, umbral_correlacion=0.7, min_datos_validos=5, max_lineas_consola=250) +# Separar atributos y parámetros como antes: + +# Cargar configuración de similitud +bloques_rasgos, filas_familia, capas_familia = configurar_similitud() + +# Cambiar la lógica para seleccionar columnas en lugar de filas +# df_atributos debe seleccionar las columnas correspondientes a filas_familia +df_atributos = df_procesado[filas_familia] +# df_parametros debe excluir las columnas seleccionadas en filas_familia +df_parametros = df_procesado.drop(columns=filas_familia) + +# Paso 10: Llamar a la función principal +# IMPORTANTE: revisa que en los scripts de imputación y exportación se acceda a los datos como df.loc[aeronave, parametro] +df_procesado_actualizado, resumen_imputaciones = bucle_imputacion_similitud_correlacion( + df_parametros=df_parametros, + df_atributos=df_atributos, + parametros_preseleccionados=parametros_preseleccionados, + bloques_rasgos=bloques_rasgos, + capas_familia=capas_familia, + df_procesado=df_procesado, + df_filtrado=df_filtrado, + tabla_completa=tabla_completa, + parametros_seleccionados=parametros_seleccionados, + rango_min=args.rango_min if args.debug_mode else None, + rango_max=args.rango_max if args.debug_mode else None, + nivel_confianza_min_similitud=args.nivel_confianza_min_similitud if args.debug_mode else None, + umbral_correlacion=args.umbral_correlacion if args.debug_mode else None, + nivel_confianza_min_correlacion=args.nivel_confianza_min_similitud if args.debug_mode else None, + debug_mode=args.debug_mode +) + +# Paso 11: Exportar resultados a Excel +archivo_destino = args.archivo_destino +if not archivo_destino: + archivo_destino = input("Ingrese la ruta donde desea guardar el archivo con las imputaciones (incluya .xlsx): ") +if not archivo_destino: + archivo_destino = r"C:\Users\delpi\OneDrive\Tesis\ADRpy-VTOL\ADRpy\analisis\Results\Datos_imputados.xlsx" + +exportar_excel_con_imputaciones( + archivo_origen=ruta_archivo, + df_procesado=df_procesado_actualizado, + resumen_imputaciones=resumen_imputaciones, + archivo_destino=archivo_destino +) +print("\n=== Flujo completado. Verifique el archivo generado. ===") +print("✅ Script finalizado.") + diff --git a/ADRpy/analisis/requirements.txt b/ADRpy/analisis/requirements.txt new file mode 100644 index 00000000..8ea863fe --- /dev/null +++ b/ADRpy/analisis/requirements.txt @@ -0,0 +1,8 @@ +pandas +numpy +matplotlib +seaborn +openpyxl +tk +scikit-learn +ipython \ No newline at end of file diff --git a/ADRpy/analisis/tempCodeRunnerFile.py b/ADRpy/analisis/tempCodeRunnerFile.py new file mode 100644 index 00000000..5742efc4 --- /dev/null +++ b/ADRpy/analisis/tempCodeRunnerFile.py @@ -0,0 +1 @@ +2, 3, 2, 3, 2, 3, 2, 3, 2, 3 \ No newline at end of file diff --git a/docs/ADRpy/notebooks/Example from Small Unmanned Fixed-wing Aircraft Design (Keane et al.).ipynb b/docs/ADRpy/notebooks/Example from Small Unmanned Fixed-wing Aircraft Design (Keane et al.).ipynb index c5e6b077..6532cd65 100644 --- a/docs/ADRpy/notebooks/Example from Small Unmanned Fixed-wing Aircraft Design (Keane et al.).ipynb +++ b/docs/ADRpy/notebooks/Example from Small Unmanned Fixed-wing Aircraft Design (Keane et al.).ipynb @@ -218,7 +218,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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\n", 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", 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\n", 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", 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" ] @@ -400,9 +400,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python (adrpy)", "language": "python", - "name": "python3" + "name": "adrpy" }, "language_info": { "codemirror_mode": { @@ -414,7 +414,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/tests/test_imputacion_correlacion.py b/tests/test_imputacion_correlacion.py new file mode 100644 index 00000000..5fa131ad --- /dev/null +++ b/tests/test_imputacion_correlacion.py @@ -0,0 +1,11 @@ +import pandas as pd +from ADRpy.analisis.Modulos.imputacion_correlacion import imputacion_correlacion + + +def test_imputacion_correlacion_basica(): + df, reporte = imputacion_correlacion('ADRpy/analisis/Data/Datos_aeronaves.xlsx') + assert not df.isna().any().any(), "Deberia imputar todos los valores faltantes" + # Verificamos que el valor imputado para Potencia en la fila 2 sea cercano al calculo esperado + valor = df.loc[2, 'Potencia'] + assert round(valor, 3) == round(25.9691788448, 3) + assert 'Confianza' in reporte.columns