Files
backend/api/reports/views.py

1977 lines
91 KiB
Python

from warnings import filters
from rest_framework.decorators import api_view, permission_classes
from rest_framework.permissions import IsAuthenticated
from api.customs.models import Pedimento, Cove, EDocument, Partida
from api.record.models import Document
from api.organization.models import Organizacion
from django.db.models import Count, Q
# Registrar endpoint en urls.py:
# path('dashboard/summary/', dashboard_summary)
import csv
import io
from drf_yasg.utils import swagger_auto_schema
from drf_yasg import openapi
from .serializers import ExportModelSerializer
from rest_framework.response import Response
from django.http import HttpResponse
import openpyxl
from django.apps import apps
from rest_framework import status
from django.shortcuts import render
from rest_framework import viewsets
from .serializers import ExportModelSerializer
from core.permissions import (
IsSameOrganization,
IsSameOrganizationDeveloper,
IsSameOrganizationAndAdmin,
IsSuperUser
)
from rest_framework.permissions import IsAuthenticated
import csv
import io
import openpyxl
from django.http import HttpResponse
from django.apps import apps
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status
from drf_yasg.utils import swagger_auto_schema
from drf_yasg import openapi
from rest_framework.permissions import IsAuthenticated
from core.permissions import (
IsSameOrganization,
IsSameOrganizationDeveloper,
IsSameOrganizationAndAdmin,
IsSuperUser
)
from .serializers import ExportModelSerializer
import uuid
import datetime
import zipfile
from django.db import models
def export_model_to_csv(request, model_name, fields, module='datastage', filters=None):
model = apps.get_model(module, model_name)
queryset = model.objects.filter(**(filters or {})).values(*fields)
response = HttpResponse(content_type='text/csv')
response['Content-Disposition'] = f'attachment; filename="{model_name}.csv"'
writer = csv.DictWriter(response, fieldnames=fields)
writer.writeheader()
for row in queryset:
writer.writerow(row)
return response
def export_model_to_excel(request, model_name, fields, module='datastage', filters=None):
model = apps.get_model(module, model_name)
queryset = model.objects.filter(**(filters or {})).values(*fields)
wb = openpyxl.Workbook()
ws = wb.active
ws.append(fields)
for row in queryset:
# Convertir cada valor a string para asegurar compatibilidad con Excel
row_values = []
for field in fields:
value = row[field]
# Si es UUID u otro objeto, convertirlo a string
if hasattr(value, '__str__'):
value = str(value)
row_values.append(value)
ws.append(row_values)
output = io.BytesIO()
wb.save(output)
output.seek(0)
response = HttpResponse(output.read(
), content_type='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet')
response['Content-Disposition'] = f'attachment; filename="{model_name}.xlsx"'
return response
# class ControlPedimentoView(APIView):
# my_tags = ['Control-Pedimento']
# permission_classes = [IsAuthenticated & (IsSameOrganization | IsSameOrganizationAndAdmin | IsSameOrganizationDeveloper | IsSuperUser)]
# @swagger_auto_schema(request_body=ExportModelSerializer, responses={200: 'Archivo generado (Excel o CSV)'})
# def post(self, request, *args, **kwargs):
# """
# Endpoint específico para exportación de DataStage con soporte múltiple
# """
# # Verificar si es modo múltiple
# modo = request.data.get('modo', 'simple')
# if modo == 'multiple':
# return self.handle_multiple_export(request)
# else:
# return self.handle_simple_export(request)
class ExportDataStageView(APIView):
my_tags = ['Reportes-DataStage']
permission_classes = [IsAuthenticated & (IsSameOrganization | IsSameOrganizationAndAdmin | IsSameOrganizationDeveloper | IsSuperUser)]
# Constantes para partición
# MAX_RECORDS_PER_FILE = 100 # Límite seguro por archivo
MAX_RECORDS_PER_FILE = 120000 # Límite seguro por archivo
def safe_excel_value(self, value):
"""
Convierte cualquier valor a un formato seguro para Excel
"""
if value is None:
return ''
elif isinstance(value, (uuid.UUID,)):
return str(value)
elif hasattr(value, 'uuid'):
return str(value.uuid)
elif hasattr(value, 'id'):
return str(value.id)
elif isinstance(value, (datetime.datetime, datetime.date)):
return value.isoformat()
elif isinstance(value, (dict, list)):
return str(value)
else:
return str(value)
@swagger_auto_schema(request_body=ExportModelSerializer, responses={200: 'Archivo generado (Excel o CSV)'})
def post(self, request, *args, **kwargs):
"""
Endpoint específico para exportación de DataStage con soporte múltiple
"""
# Verificar si es modo múltiple
modo = request.data.get('modo', 'simple')
if modo == 'multiple':
return self.handle_multiple_export(request)
else:
return self.handle_simple_export(request)
def handle_simple_export(self, request):
"""Maneja exportación simple de DataStage (un solo modelo)"""
model_name = request.data.get('model')
fields = request.data.get('fields')
global_filters = request.data.get('globalFilters', {})
export_type = request.data.get('format', 'csv')
module = 'datastage'
if not model_name or not fields:
return Response({'error': 'model and fields are required'}, status=status.HTTP_400_BAD_REQUEST)
try:
model = apps.get_model(module, model_name)
filters = self.apply_global_filters_to_model(global_filters, model, request.user)
queryset = model.objects.filter(**filters).values(*fields)
total_records = queryset.count()
if export_type == 'excel':
# Verificar si necesita partición
if total_records > self.MAX_RECORDS_PER_FILE:
return self.export_single_model_partitioned(request, model_name, fields, filters, total_records)
else:
return export_model_to_excel(request, model_name, fields, module, filters)
else:
if total_records > self.MAX_RECORDS_PER_FILE:
return self.export_single_model_csv_partitioned(request, model_name, fields, filters, total_records)
else:
return export_model_to_csv(request, model_name, fields, module, filters)
except LookupError:
return Response({'error': f'Model {model_name} not found'}, status=status.HTTP_404_NOT_FOUND)
def handle_multiple_export(self, request):
"""Maneja exportación múltiple de DataStage (varios modelos)"""
models_data = request.data.get('models', [])
export_type = request.data.get('format', 'csv')
global_filters = request.data.get('globalFilters', {})
if not models_data:
return Response({'error': 'models are required for multiple export'}, status=status.HTTP_400_BAD_REQUEST)
related_keys = self.get_related_keys_from_filters(global_filters, models_data, request.user)
if export_type == 'excel':
# Siempre usar el método particionado inteligente para Excel
return self.export_datastage_multiple_partitioned_excel_agrupados(request, models_data, global_filters, related_keys)
else:
# Para CSV, podemos mantener la lógica actual o mejorarla
total_estimated_records = self.estimate_total_records(models_data, global_filters, related_keys, request.user)
if total_estimated_records > self.MAX_RECORDS_PER_FILE:
return self.export_datastage_multiple_partitioned_csv(request, models_data, global_filters, related_keys)
else:
return self.export_datastage_multiple_to_csv(request, models_data, global_filters, related_keys)
def estimate_total_records(self, models_data, global_filters, related_keys, user):
"""Estima el total de registros para todos los modelos"""
total = 0
for model_data in models_data:
model_name = model_data.get('model')
try:
model = apps.get_model('datastage', model_name)
filters = self.apply_related_filters(global_filters, model, related_keys, user)
total += model.objects.filter(**filters).count()
except:
continue
return total
def export_datastage_multiple_to_excel(self, request, models_data, global_filters, related_keys):
"""Exporta múltiples modelos de DataStage con filtrado relacionado (múltiples hojas)"""
wb = openpyxl.Workbook()
wb.remove(wb.active)
for model_data in models_data:
model_name = model_data.get('model')
fields = model_data.get('fields', [])
if not model_name or not fields:
continue
try:
model = apps.get_model('datastage', model_name)
# 🔥 APLICAR FILTROS RELACIONADOS
filters = self.apply_related_filters(global_filters, model, related_keys, request.user)
# Si hay filtros, aplicarlos; si no, obtener todos los registros
if filters:
queryset = model.objects.filter(**filters).values(*fields)
else:
queryset = model.objects.none() # No obtener nada si no hay filtros
# Si no hay registros, saltar este modelo
if queryset.count() == 0:
continue
# Crear hoja (limitar nombre a 31 caracteres)
sheet_name = model_name[:31]
ws = wb.create_sheet(title=sheet_name)
# Escribir encabezados
ws.append(fields)
# Escribir datos
for row in queryset:
row_values = []
for field in fields:
value = row[field]
# 🔥 USAR safe_excel_value para convertir valores
row_values.append(self.safe_excel_value(value))
ws.append(row_values)
except LookupError:
continue
# Si no se crearon hojas, crear una vacía
if len(wb.sheetnames) == 0:
ws = wb.create_sheet(title="Sin datos")
ws.append(["No se encontraron datos para los modelos especificados"])
output = io.BytesIO()
wb.save(output)
output.seek(0)
response = HttpResponse(
output.read(),
content_type='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
)
response['Content-Disposition'] = 'attachment; filename="datastage_related_report.xlsx"'
return response
def export_datastage_multiple_partitioned_excel_agrupados(self, request, models_data, global_filters, related_keys):
"""Exporta múltiples modelos de DataStage agrupados en la misma hoja de Excel, con particionado por límite de registros"""
try:
zip_buffer = io.BytesIO()
# 🔥 PRECARGAR ORGANIZACIONES para mapeo rápido
from api.organization.models import Organizacion
organizaciones = Organizacion.objects.all()
org_mapping = {str(org.id): org.nombre for org in organizaciones}
with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zip_file:
# 1. Recopilar todos los datos de cada modelo
all_models_data = {} # Ahora será una lista por clave
model_field_mappings = {}
for model_data in models_data:
model_name = model_data.get('model')
fields = model_data.get('fields', [])
if not model_name or not fields:
continue
# Normalizar nombres de campo entrantes: si se pasó "Organizacion"
# (cualquier capitalización), usar el campo real de la BD `organizacion_id`.
normalized_fields = []
for f in fields:
try:
key = f.strip() if isinstance(f, str) else f
except Exception:
key = f
if isinstance(key, str) and key.lower() == 'organizacion':
if 'organizacion_id' not in normalized_fields:
normalized_fields.append('organizacion_id')
else:
if key not in normalized_fields:
normalized_fields.append(key)
fields = normalized_fields
# Asegurar que tenemos los campos de relación
required_fields = ['seccion_aduanera', 'patente', 'pedimento']
for field in required_fields:
if field not in fields:
fields.append(field)
# 🔥 Añadir organizacion_id a los campos si no está y existe en el modelo
if 'organizacion_id' not in fields and 'organizacion_id' in [f.name for f in apps.get_model('datastage', model_name)._meta.get_fields()]:
fields.append('organizacion_id')
try:
model = apps.get_model('datastage', model_name)
filters = self.apply_related_filters(global_filters, model, related_keys, request.user)
if filters:
queryset = model.objects.filter(**filters).values(*fields)
else:
queryset = model.objects.none()
total_records = queryset.count()
if total_records == 0:
continue
# Determinar campos de relación disponibles en este modelo
relation_fields = []
for field_name in ['seccion_aduanera', 'patente', 'pedimento']:
if field_name in fields:
relation_fields.append(field_name)
if not relation_fields:
# Si no hay campos de relación, usar un identificador único
relation_fields = ['datastage_id'] if 'datastage_id' in fields else [fields[0]]
# Guardar mapeo de campos para este modelo
if model_name not in model_field_mappings:
model_field_mappings[model_name] = fields
# Procesar cada registro
for record in queryset:
# Crear clave de relación
key_parts = []
for rel_field in relation_fields:
if rel_field in record and record[rel_field] is not None:
key_parts.append(str(record[rel_field]))
if not key_parts:
# Si no hay campos de relación, usar un hash del registro
import hashlib
record_str = str(sorted(record.items()))
key = hashlib.md5(record_str.encode()).hexdigest()[:10]
else:
key = "_".join(key_parts)
# 🔥 PROCESAR CAMPO organizacion_id para convertirlo a nombre
processed_record = {}
for field_name, value in record.items():
# Convertir organizacion_id a nombre
if field_name == 'organizacion_id' and value:
org_id_str = str(value)
# Usar el nombre de la organización si está en el mapeo
if org_id_str in org_mapping:
processed_value = org_mapping[org_id_str]
else:
# Si no se encuentra, intentar obtener de la base de datos
try:
org = Organizacion.objects.filter(id=value).first()
processed_value = org.nombre if org else str(value)
# Actualizar mapeo para futuras referencias
org_mapping[org_id_str] = processed_value
except:
processed_value = str(value)
else:
processed_value = value
# Agregar prefijo del modelo a los campos para evitar colisiones
if field_name in relation_fields:
prefixed_field_name = field_name
else:
prefixed_field_name = f"{model_name}_{field_name}"
# 🔥 RENOMBRAR organizacion_id a organizacion_nombre
if field_name == 'organizacion_id':
prefixed_field_name = prefixed_field_name.replace('organizacion_id', 'organizacion_nombre')
processed_record[prefixed_field_name] = self.safe_excel_value(processed_value)
# 🔥 CORRECIÓN: Ahora almacenamos una LISTA de registros por clave
if key not in all_models_data:
all_models_data[key] = {
'relation_fields': {}, # Campos de relación compartidos
'model_records': {} # Diccionario de listas por modelo
}
# Guardar campos de relación (solo una vez, ya que son los mismos)
for rel_field in relation_fields:
if rel_field in record:
all_models_data[key]['relation_fields'][rel_field] = record[rel_field]
# 🔥 GUARDAR COMO LISTA: Crear lista si no existe
if model_name not in all_models_data[key]['model_records']:
all_models_data[key]['model_records'][model_name] = []
# Agregar este registro a la lista del modelo
all_models_data[key]['model_records'][model_name].append(processed_record)
except LookupError:
continue
# Si no hay datos, retornar error
if not all_models_data:
return Response({'error': 'No se encontraron datos para exportar'}, status=status.HTTP_404_NOT_FOUND)
# 2. Crear estructura de filas combinadas
# Ahora necesitamos expandir las filas cuando hay múltiples registros con la misma clave
combined_rows = []
for key, data in all_models_data.items():
relation_fields = data['relation_fields']
model_records = data['model_records']
# 🔥 NUEVO: Calcular cuántas filas necesitamos para esta clave
# Encontrar el modelo con más registros para esta clave
max_records_per_key = 1
for model_name, records in model_records.items():
if len(records) > max_records_per_key:
max_records_per_key = len(records)
# 🔗 CREAR UNA FILA POR CADA COMBINACIÓN
for i in range(max_records_per_key):
row_data = {}
# Campos de relación (mismos para todas las filas con esta clave)
for rel_field, rel_value in relation_fields.items():
row_data[rel_field] = self.safe_excel_value(rel_value)
# Datos de cada modelo
for model_name, records in model_records.items():
# Si hay un registro en esta posición i
if i < len(records):
record = records[i]
for field_name, value in record.items():
row_data[field_name] = value
else:
# Si no hay más registros para este modelo, poner campos vacíos
for field_name in model_field_mappings.get(model_name, []):
if field_name in ['seccion_aduanera', 'patente', 'pedimento', 'organizacion_id']:
# Los campos de relación ya están llenados o transformados
continue
prefixed_field_name = f"{model_name}_{field_name}"
# 🔥 RENOMBRAR organizacion_id a organizacion_nombre
if field_name == 'organizacion_id':
prefixed_field_name = prefixed_field_name.replace('organizacion_id', 'organizacion_nombre')
row_data[prefixed_field_name] = ''
combined_rows.append(row_data)
# 3. Determinar todos los campos únicos para los encabezados
all_fields_set = set()
# Campos de relación primero
common_relation_fields = ['seccion_aduanera', 'patente', 'pedimento']
# Agregar todos los campos de todas las filas
for row in combined_rows:
all_fields_set.update(row.keys())
# Ordenar campos: relación primero, luego alfabéticamente
all_fields = []
for rel_field in common_relation_fields:
if rel_field in all_fields_set:
all_fields.append(rel_field)
all_fields_set.remove(rel_field)
# 🔥 Mover organizacion_nombre cerca de los campos de relación
org_fields = [f for f in all_fields_set if 'organizacion' in f.lower()]
for org_field in sorted(org_fields):
all_fields.append(org_field)
all_fields_set.remove(org_field)
# Agregar el resto de campos ordenados alfabéticamente
all_fields.extend(sorted(all_fields_set))
total_records = len(combined_rows)
# 4. Manejar particionado
from django.core.paginator import Paginator
paginator = Paginator(combined_rows, self.MAX_RECORDS_PER_FILE)
for page_num in paginator.page_range:
page = paginator.page(page_num)
# Crear nuevo workbook para cada partición
current_wb = openpyxl.Workbook()
current_ws = current_wb.active
# Nombre de hoja limitado a 31 caracteres
sheet_name = f"Datastage_p{page_num}"
if len(sheet_name) > 31:
sheet_name = sheet_name[:31]
current_ws.title = sheet_name
# Escribir encabezados
current_ws.append(all_fields)
# Escribir datos de esta página
for row_data in page.object_list:
row_values = [row_data.get(field, '') for field in all_fields]
current_ws.append(row_values)
# Autoajustar anchos de columna
for column in current_ws.columns:
max_length = 0
column_letter = column[0].column_letter
for cell in column:
try:
if len(str(cell.value)) > max_length:
max_length = len(str(cell.value))
except:
pass
adjusted_width = min(max_length + 2, 50)
current_ws.column_dimensions[column_letter].width = adjusted_width
# Guardar archivo en ZIP
part_buffer = io.BytesIO()
current_wb.save(part_buffer)
part_buffer.seek(0)
zip_file.writestr(f"datastage_part{page_num}.xlsx", part_buffer.getvalue())
# Información de depuración
print(f"Creada partición {page_num} con {len(page.object_list)} registros combinados")
print(f"Total de claves únicas: {len(all_models_data)}")
print(f"Total de filas expandidas: {total_records}")
zip_buffer.seek(0)
response = HttpResponse(zip_buffer.read(), content_type='application/zip')
response['Content-Disposition'] = 'attachment; filename="datastage_combinado.zip"'
return response
except Exception as e:
import traceback
error_details = traceback.format_exc()
print(f"Error en exportación: {error_details}")
return Response({'error': f'Error en exportación combinada: {str(e)}'}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
def export_datastage_multiple_partitioned_excel_test_3(self, request, models_data, global_filters, related_keys):
"""Exporta múltiples modelos de DataStage agrupados en la misma hoja de Excel, con particionado por límite de registros"""
try:
zip_buffer = io.BytesIO()
with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zip_file:
# 1. Recopilar todos los datos de cada modelo
all_models_data = {} # Ahora será una lista por clave
model_field_mappings = {}
for model_data in models_data:
model_name = model_data.get('model')
fields = model_data.get('fields', [])
if not model_name or not fields:
continue
# Asegurar que tenemos los campos de relación
required_fields = ['seccion_aduanera', 'patente', 'pedimento']
for field in required_fields:
if field not in fields:
fields.append(field)
try:
model = apps.get_model('datastage', model_name)
filters = self.apply_related_filters(global_filters, model, related_keys, request.user)
if filters:
queryset = model.objects.filter(**filters).values(*fields)
else:
queryset = model.objects.none()
total_records = queryset.count()
if total_records == 0:
continue
# Determinar campos de relación disponibles en este modelo
relation_fields = []
for field_name in ['seccion_aduanera', 'patente', 'pedimento']:
if field_name in fields:
relation_fields.append(field_name)
if not relation_fields:
# Si no hay campos de relación, usar un identificador único
relation_fields = ['datastage_id'] if 'datastage_id' in fields else [fields[0]]
# Guardar mapeo de campos para este modelo
if model_name not in model_field_mappings:
model_field_mappings[model_name] = fields
# Procesar cada registro
for record in queryset:
# Crear clave de relación
key_parts = []
for rel_field in relation_fields:
if rel_field in record and record[rel_field] is not None:
key_parts.append(str(record[rel_field]))
if not key_parts:
# Si no hay campos de relación, usar un hash del registro
import hashlib
record_str = str(sorted(record.items()))
key = hashlib.md5(record_str.encode()).hexdigest()[:10]
else:
key = "_".join(key_parts)
# Agregar prefijo del modelo a los campos para evitar colisiones
prefixed_fields = {}
for field_name, value in record.items():
# Solo agregar prefijo si no es un campo de relación
if field_name in relation_fields:
prefixed_field_name = field_name
else:
prefixed_field_name = f"{model_name}_{field_name}"
prefixed_fields[prefixed_field_name] = self.safe_excel_value(value)
# 🔥 CORRECIÓN: Ahora almacenamos una LISTA de registros por clave
if key not in all_models_data:
all_models_data[key] = {
'relation_fields': {}, # Campos de relación compartidos
'model_records': {} # Diccionario de listas por modelo
}
# Guardar campos de relación (solo una vez, ya que son los mismos)
for rel_field in relation_fields:
if rel_field in record:
all_models_data[key]['relation_fields'][rel_field] = record[rel_field]
# 🔥 GUARDAR COMO LISTA: Crear lista si no existe
if model_name not in all_models_data[key]['model_records']:
all_models_data[key]['model_records'][model_name] = []
# Agregar este registro a la lista del modelo
all_models_data[key]['model_records'][model_name].append(prefixed_fields)
except LookupError:
continue
# Si no hay datos, retornar error
if not all_models_data:
return Response({'error': 'No se encontraron datos para exportar'}, status=status.HTTP_404_NOT_FOUND)
# 2. Crear estructura de filas combinadas
# Ahora necesitamos expandir las filas cuando hay múltiples registros con la misma clave
combined_rows = []
for key, data in all_models_data.items():
relation_fields = data['relation_fields']
model_records = data['model_records']
# 🔥 NUEVO: Calcular cuántas filas necesitamos para esta clave
# Encontrar el modelo con más registros para esta clave
max_records_per_key = 1
for model_name, records in model_records.items():
if len(records) > max_records_per_key:
max_records_per_key = len(records)
# 🔗 CREAR UNA FILA POR CADA COMBINACIÓN
for i in range(max_records_per_key):
row_data = {}
# Campos de relación (mismos para todas las filas con esta clave)
for rel_field, rel_value in relation_fields.items():
row_data[rel_field] = self.safe_excel_value(rel_value)
# Datos de cada modelo
for model_name, records in model_records.items():
# Si hay un registro en esta posición i
if i < len(records):
record = records[i]
for field_name, value in record.items():
row_data[field_name] = value
else:
# Si no hay más registros para este modelo, poner campos vacíos
for field_name in model_field_mappings.get(model_name, []):
if field_name in ['seccion_aduanera', 'patente', 'pedimento']:
# Los campos de relación ya están llenados
continue
prefixed_field_name = f"{model_name}_{field_name}"
row_data[prefixed_field_name] = ''
combined_rows.append(row_data)
# 3. Determinar todos los campos únicos para los encabezados
all_fields_set = set()
# Campos de relación primero
common_relation_fields = ['seccion_aduanera', 'patente', 'pedimento']
# Agregar todos los campos de todas las filas
for row in combined_rows:
all_fields_set.update(row.keys())
# Ordenar campos: relación primero, luego alfabéticamente
all_fields = []
for rel_field in common_relation_fields:
if rel_field in all_fields_set:
all_fields.append(rel_field)
all_fields_set.remove(rel_field)
# Agregar el resto de campos ordenados alfabéticamente
all_fields.extend(sorted(all_fields_set))
total_records = len(combined_rows)
# 4. Manejar particionado
from django.core.paginator import Paginator
paginator = Paginator(combined_rows, self.MAX_RECORDS_PER_FILE)
for page_num in paginator.page_range:
page = paginator.page(page_num)
# Crear nuevo workbook para cada partición
current_wb = openpyxl.Workbook()
current_ws = current_wb.active
# Nombre de hoja limitado a 31 caracteres
sheet_name = f"Datastage_p{page_num}"
if len(sheet_name) > 31:
sheet_name = sheet_name[:31]
current_ws.title = sheet_name
# Escribir encabezados
current_ws.append(all_fields)
# Escribir datos de esta página
for row_data in page.object_list:
row_values = [row_data.get(field, '') for field in all_fields]
current_ws.append(row_values)
# Autoajustar anchos de columna
for column in current_ws.columns:
max_length = 0
column_letter = column[0].column_letter
for cell in column:
try:
if len(str(cell.value)) > max_length:
max_length = len(str(cell.value))
except:
pass
adjusted_width = min(max_length + 2, 50)
current_ws.column_dimensions[column_letter].width = adjusted_width
# Guardar archivo en ZIP
part_buffer = io.BytesIO()
current_wb.save(part_buffer)
part_buffer.seek(0)
zip_file.writestr(f"datastage_part{page_num}.xlsx", part_buffer.getvalue())
# Información de depuración
print(f"Creada partición {page_num} con {len(page.object_list)} registros combinados")
print(f"Total de claves únicas: {len(all_models_data)}")
print(f"Total de filas expandidas: {total_records}")
zip_buffer.seek(0)
response = HttpResponse(zip_buffer.read(), content_type='application/zip')
response['Content-Disposition'] = 'attachment; filename="datastage_combinado.zip"'
return response
except Exception as e:
import traceback
error_details = traceback.format_exc()
print(f"Error en exportación: {error_details}")
return Response({'error': f'Error en exportación combinada: {str(e)}'}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
def export_datastage_multiple_partitioned_excel_test_2(self, request, models_data, global_filters, related_keys):
"""Exporta múltiples modelos de DataStage agrupados en la misma hoja de Excel, con particionado por límite de registros"""
try:
zip_buffer = io.BytesIO()
with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zip_file:
# 1. Recopilar todos los datos de cada modelo por clave (aduana, patente, pedimento)
all_models_data = {}
model_field_mappings = {}
for model_data in models_data:
model_name = model_data.get('model')
fields = model_data.get('fields', [])
if not model_name or not fields:
continue
required_fields = ['seccion_aduanera', 'patente', 'pedimento']
for field in required_fields:
if field not in fields:
fields.append(field)
try:
model = apps.get_model('datastage', model_name)
filters = self.apply_related_filters(global_filters, model, related_keys, request.user)
if filters:
queryset = model.objects.filter(**filters).values(*fields)
else:
queryset = model.objects.none()
total_records = queryset.count()
if total_records == 0:
continue
# Determinar campos de relación disponibles en este modelo
relation_fields = []
for field_name in ['seccion_aduanera', 'patente', 'pedimento']:
if field_name in fields:
relation_fields.append(field_name)
if not relation_fields:
# Si no hay campos de relación, usar un identificador único
relation_fields = ['datastage_id'] if 'datastage_id' in fields else [fields[0]]
# Procesar cada registro
for record in queryset:
# Crear clave de relación
key_parts = []
for rel_field in relation_fields:
if rel_field in record and record[rel_field] is not None:
key_parts.append(str(record[rel_field]))
if not key_parts:
# Si no hay campos de relación, usar un hash del registro
import hashlib
record_str = str(sorted(record.items()))
key = hashlib.md5(record_str.encode()).hexdigest()[:10]
else:
key = "_".join(key_parts)
# Agregar prefijo del modelo a los campos para evitar colisiones
prefixed_fields = {}
for field_name, value in record.items():
prefixed_field_name = f"{model_name}_{field_name}"
prefixed_fields[prefixed_field_name] = self.safe_excel_value(value)
# Registrar mapeo de campos
if model_name not in model_field_mappings:
model_field_mappings[model_name] = []
if field_name not in model_field_mappings[model_name]:
model_field_mappings[model_name].append(field_name)
# Guardar datos bajo la clave
if key not in all_models_data:
all_models_data[key] = {
'relation_fields': {},
'model_data': {}
}
# Guardar campos de relación
for rel_field in relation_fields:
if rel_field in record:
all_models_data[key]['relation_fields'][rel_field] = record[rel_field]
# Guardar datos del modelo
all_models_data[key]['model_data'][model_name] = prefixed_fields
except LookupError:
continue
# Si no hay datos, retornar error
if not all_models_data:
return Response({'error': 'No se encontraron datos para exportar'}, status=status.HTTP_404_NOT_FOUND)
# 2. Determinar todos los campos únicos que necesitaremos
all_fields_set = set()
# Primero agregar campos de relación comunes
common_relation_fields = ['seccion_aduanera', 'patente', 'pedimento']
for key, data in all_models_data.items():
# Agregar campos de relación
for rel_field in common_relation_fields:
if rel_field in data['relation_fields']:
all_fields_set.add(rel_field)
# Agregar campos de todos los modelos para esta clave
for model_name, model_fields in data['model_data'].items():
for field_name in model_fields.keys():
all_fields_set.add(field_name)
# Convertir a lista ordenada (campos de relación primero)
all_fields = []
for rel_field in common_relation_fields:
if rel_field in all_fields_set:
all_fields.append(rel_field)
all_fields_set.remove(rel_field)
# Luego agregar el resto de campos ordenados alfabéticamente
all_fields.extend(sorted(all_fields_set))
# 3. Crear datos combinados por fila
combined_rows = []
for key, data in all_models_data.items():
row_data = {}
# Campos de relación
for rel_field in common_relation_fields:
if rel_field in data['relation_fields']:
row_data[rel_field] = self.safe_excel_value(data['relation_fields'][rel_field])
else:
row_data[rel_field] = ''
# Datos de cada modelo
for model_name, model_fields in data['model_data'].items():
for field_name, value in model_fields.items():
row_data[field_name] = value
# Rellenar campos faltantes con vacío
for field in all_fields:
if field not in row_data:
row_data[field] = ''
combined_rows.append(row_data)
total_records = len(combined_rows)
# 4. Manejar particionado
from django.core.paginator import Paginator
paginator = Paginator(combined_rows, self.MAX_RECORDS_PER_FILE)
for page_num in paginator.page_range:
page = paginator.page(page_num)
# Crear nuevo workbook para cada partición
current_wb = openpyxl.Workbook()
current_ws = current_wb.active
# Nombre de hoja limitado a 31 caracteres
sheet_name = f"Datastage_p{page_num}"
if len(sheet_name) > 31:
sheet_name = sheet_name[:31]
current_ws.title = sheet_name
# Escribir encabezados
current_ws.append(all_fields)
# Escribir datos de esta página
for row_data in page.object_list:
row_values = [row_data.get(field, '') for field in all_fields]
current_ws.append(row_values)
# Autoajustar anchos de columna (opcional)
for column in current_ws.columns:
max_length = 0
column_letter = column[0].column_letter
for cell in column:
try:
if len(str(cell.value)) > max_length:
max_length = len(str(cell.value))
except:
pass
adjusted_width = min(max_length + 2, 50) # Máximo 50 caracteres
current_ws.column_dimensions[column_letter].width = adjusted_width
# Guardar archivo en ZIP
part_buffer = io.BytesIO()
current_wb.save(part_buffer)
part_buffer.seek(0)
zip_file.writestr(f"datastage_part{page_num}.xlsx", part_buffer.getvalue())
# Información de depuración
print(f"Creada partición {page_num} con {len(page.object_list)} registros combinados")
zip_buffer.seek(0)
response = HttpResponse(zip_buffer.read(), content_type='application/zip')
response['Content-Disposition'] = 'attachment; filename="datastage_combinado.zip"'
return response
except Exception as e:
import traceback
error_details = traceback.format_exc()
print(f"Error en exportación: {error_details}")
return Response({'error': f'Error en exportación combinada: {str(e)}'}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
def export_datastage_multiple_partitioned_excel_test(self, request, models_data, global_filters, related_keys):
"""Exporta múltiples modelos de DataStage agrupados en la misma hoja de Excel, con particionado por límite de registros"""
try:
zip_buffer = io.BytesIO()
with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zip_file:
file_counter = 1
current_wb = None
current_ws = None
current_record_count = 0
combined_fields = [] # Almacenar todos los campos únicos
combined_data = [] # Almacenar todos los datos
# 1. Primero recopilar todos los campos y datos
all_models_data = {}
for model_data in models_data:
model_name = model_data.get('model')
fields = model_data.get('fields', [])
if not model_name or not fields:
continue
try:
model = apps.get_model('datastage', model_name)
filters = self.apply_related_filters(global_filters, model, related_keys, request.user)
if filters:
queryset = model.objects.filter(**filters).values(*fields)
else:
queryset = model.objects.none()
total_records = queryset.count()
if total_records == 0:
continue
# Almacenar los datos de este modelo
all_models_data[model_name] = {
'fields': fields,
'data': list(queryset),
'total_records': total_records
}
# Agregar campos únicos a la lista combinada
for field in fields:
if field not in combined_fields:
combined_fields.append(field)
except LookupError:
continue
# Si no hay datos, retornar error
if not all_models_data:
return Response({'error': 'No se encontraron datos para exportar'}, status=status.HTTP_404_NOT_FOUND)
# 2. Crear estructura de datos combinada
# Primero, preparar los datos combinados
for model_name, model_info in all_models_data.items():
fields = model_info['fields']
data = model_info['data']
for record in data:
combined_record = {}
# Para cada campo en la lista combinada
for combined_field in combined_fields:
if combined_field in fields:
# Si el campo existe en este modelo, usar su valor
value = record.get(combined_field)
combined_record[combined_field] = self.safe_excel_value(value)
else:
# Si no existe, poner vacío
combined_record[combined_field] = ''
# Agregar columna para identificar el modelo origen
combined_record['_modelo_origen'] = model_name
combined_data.append(combined_record)
# Agregar campo de modelo origen a la lista de campos si no está ya
if '_modelo_origen' not in combined_fields:
combined_fields.append('_modelo_origen')
total_combined_records = len(combined_data)
# 3. Manejar particionado
from django.core.paginator import Paginator
paginator = Paginator(combined_data, self.MAX_RECORDS_PER_FILE)
for page_num in paginator.page_range:
page = paginator.page(page_num)
# Crear nuevo workbook para cada partición
current_wb = openpyxl.Workbook()
current_ws = current_wb.active
current_ws.title = f"Todos_Modelos_p{page_num}"[:31]
# Escribir encabezados
current_ws.append(combined_fields)
# Escribir datos de esta página
for record in page.object_list:
row_values = [record.get(field, '') for field in combined_fields]
current_ws.append(row_values)
# Guardar archivo en ZIP
part_buffer = io.BytesIO()
current_wb.save(part_buffer)
part_buffer.seek(0)
zip_file.writestr(f"datastage_combinado_part{page_num}.xlsx", part_buffer.getvalue())
# Información de depuración (opcional)
print(f"Creada partición {page_num} con {len(page.object_list)} registros")
zip_buffer.seek(0)
response = HttpResponse(zip_buffer.read(), content_type='application/zip')
response['Content-Disposition'] = 'attachment; filename="datastage_combinado.zip"'
return response
except Exception as e:
import traceback
error_details = traceback.format_exc()
print(f"Error en exportación: {error_details}")
return Response({'error': f'Error en exportación combinada: {str(e)}'}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
def export_datastage_multiple_partitioned_excel(self, request, models_data, global_filters, related_keys):
"""Exporta múltiples modelos de DataStage a múltiples archivos Excel particionados inteligentemente"""
try:
zip_buffer = io.BytesIO()
with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zip_file:
file_counter = 1
current_wb = None
current_file_records_count = 0
MAX_SHEETS_PER_FILE = 10 # Límite de hojas por archivo Excel
for model_data in models_data:
model_name = model_data.get('model')
fields = model_data.get('fields', [])
if not model_name or not fields:
continue
try:
model = apps.get_model('datastage', model_name)
filters = self.apply_related_filters(global_filters, model, related_keys, request.user)
if filters:
queryset = model.objects.filter(**filters).values(*fields)
else:
queryset = model.objects.none()
total_records = queryset.count()
if total_records == 0:
continue
# Si el modelo necesita particionarse (más de MAX_RECORDS_PER_FILE)
if total_records > self.MAX_RECORDS_PER_FILE:
from django.core.paginator import Paginator
paginator = Paginator(queryset, self.MAX_RECORDS_PER_FILE)
for page_num in paginator.page_range:
page = paginator.page(page_num)
# Verificar si necesitamos crear nuevo archivo
# 1. Si no hay archivo actual
# 2. Si ya tenemos muchas hojas en este archivo
# 3. Si este archivo ya está "lleno" (muchos registros)
if (current_wb is None or
len(current_wb.sheetnames) >= MAX_SHEETS_PER_FILE or
current_file_records_count > self.MAX_RECORDS_PER_FILE * 3): # ~150K registros
if current_wb is not None:
# Guardar archivo actual en ZIP
part_buffer = io.BytesIO()
current_wb.save(part_buffer)
part_buffer.seek(0)
zip_file.writestr(f"datastage_part{file_counter}.xlsx", part_buffer.getvalue())
file_counter += 1
# Crear nuevo workbook
current_wb = openpyxl.Workbook()
current_wb.remove(current_wb.active) # Remover hoja por defecto
current_file_records_count = 0
# Crear hoja para esta parte del modelo
sheet_name = f"{model_name[:25]}_p{page_num}"
ws = current_wb.create_sheet(title=sheet_name[:31])
ws.append(fields)
# Escribir datos
for row in page.object_list:
row_values = [self.safe_excel_value(row[field]) for field in fields]
ws.append(row_values)
current_file_records_count += len(page.object_list)
else:
# Modelo pequeño (≤ MAX_RECORDS_PER_FILE)
# Verificar si necesitamos nuevo archivo
if (current_wb is None or
len(current_wb.sheetnames) >= MAX_SHEETS_PER_FILE or
current_file_records_count + total_records > self.MAX_RECORDS_PER_FILE * 3):
if current_wb is not None:
# Guardar archivo actual
part_buffer = io.BytesIO()
current_wb.save(part_buffer)
part_buffer.seek(0)
zip_file.writestr(f"datastage_part{file_counter}.xlsx", part_buffer.getvalue())
file_counter += 1
# Crear nuevo workbook
current_wb = openpyxl.Workbook()
current_wb.remove(current_wb.active)
current_file_records_count = 0
# Crear hoja para este modelo
sheet_name = model_name[:31]
ws = current_wb.create_sheet(title=sheet_name)
ws.append(fields)
# Escribir datos
for row in queryset:
row_values = [self.safe_excel_value(row[field]) for field in fields]
ws.append(row_values)
current_file_records_count += total_records
except LookupError:
continue
# Guardar el último workbook si existe
if current_wb is not None:
part_buffer = io.BytesIO()
current_wb.save(part_buffer)
part_buffer.seek(0)
zip_file.writestr(f"datastage_part{file_counter}.xlsx", part_buffer.getvalue())
zip_buffer.seek(0)
response = HttpResponse(zip_buffer.read(), content_type='application/zip')
response['Content-Disposition'] = 'attachment; filename="datastage_reports.zip"'
return response
except Exception as e:
return Response({'error': f'Error en exportación particionada: {str(e)}'}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
def export_datastage_multiple_to_csv(self, request, models_data, global_filters, related_keys):
"""Exporta múltiples modelos de DataStage a múltiples archivos CSV en ZIP"""
zip_buffer = io.BytesIO()
with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zip_file:
for model_data in models_data:
model_name = model_data.get('model')
fields = model_data.get('fields', [])
if not model_name or not fields:
continue
try:
model = apps.get_model('datastage', model_name)
filters = self.apply_related_filters(global_filters, model, related_keys, request.user)
queryset = model.objects.filter(**filters).values(*fields)
total_records = queryset.count()
if total_records == 0:
continue
csv_buffer = io.StringIO()
writer = csv.writer(csv_buffer)
writer.writerow(fields)
for row in queryset:
row_values = [self.safe_excel_value(row[field]) for field in fields]
writer.writerow(row_values)
# Agregar al ZIP
filename = f"{model_name}.csv"
zip_file.writestr(filename, csv_buffer.getvalue())
except LookupError:
continue
zip_buffer.seek(0)
response = HttpResponse(zip_buffer.read(), content_type='application/zip')
response['Content-Disposition'] = 'attachment; filename="datastage_reports.zip"'
return response
def export_datastage_multiple_partitioned_csv(self, request, models_data, global_filters, related_keys):
"""Exporta múltiples modelos de DataStage a múltiples archivos CSV particionados en ZIP"""
try:
zip_buffer = io.BytesIO()
with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zip_file:
for model_data in models_data:
model_name = model_data.get('model')
fields = model_data.get('fields', [])
if not model_name or not fields:
continue
try:
model = apps.get_model('datastage', model_name)
filters = self.apply_related_filters(global_filters, model, related_keys, request.user)
queryset = model.objects.filter(**filters).values(*fields)
total_records = queryset.count()
if total_records == 0:
continue
if total_records > self.MAX_RECORDS_PER_FILE:
from django.core.paginator import Paginator
paginator = Paginator(queryset, self.MAX_RECORDS_PER_FILE)
for page_num in paginator.page_range:
page = paginator.page(page_num)
csv_buffer = io.StringIO()
writer = csv.writer(csv_buffer)
writer.writerow(fields)
for row in page.object_list:
row_values = [self.safe_excel_value(row[field]) for field in fields]
writer.writerow(row_values)
# Agregar al ZIP
filename = f"{model_name}_part{page_num}.csv"
zip_file.writestr(filename, csv_buffer.getvalue())
else:
# Modelo pequeño, exportar completo
csv_buffer = io.StringIO()
writer = csv.writer(csv_buffer)
# Escribir encabezados
writer.writerow(fields)
# Escribir datos
for row in queryset:
row_values = [self.safe_excel_value(row[field]) for field in fields]
writer.writerow(row_values)
# Agregar al ZIP
filename = f"{model_name}.csv"
zip_file.writestr(filename, csv_buffer.getvalue())
except LookupError as e:
continue
except Exception as e:
continue
zip_buffer.seek(0)
response = HttpResponse(zip_buffer.read(), content_type='application/zip')
response['Content-Disposition'] = 'attachment; filename="datastage_reports.zip"'
return response
except Exception as e:
return Response({'error': f'Error en exportación CSV particionada: {str(e)}'}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
def export_single_model_partitioned(self, request, model_name, fields, filters, total_records):
"""Exporta un solo modelo particionado a ZIP"""
try:
zip_buffer = io.BytesIO()
module = 'datastage'
model = apps.get_model(module, model_name)
queryset = model.objects.filter(**filters).values(*fields)
with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zip_file:
from django.core.paginator import Paginator
paginator = Paginator(queryset, self.MAX_RECORDS_PER_FILE)
for page_num in paginator.page_range:
page = paginator.page(page_num)
# Crear Excel para esta parte
wb = openpyxl.Workbook()
ws = wb.active
ws.title = f"Parte_{page_num}"[:31]
ws.append(fields)
for row in page.object_list:
row_values = [self.safe_excel_value(row[field]) for field in fields]
ws.append(row_values)
part_buffer = io.BytesIO()
wb.save(part_buffer)
part_buffer.seek(0)
filename = f"{model_name}_part{page_num}.xlsx"
zip_file.writestr(filename, part_buffer.getvalue())
zip_buffer.seek(0)
zip_content = zip_buffer.getvalue()
response = HttpResponse(zip_content, content_type='application/zip')
response['Content-Disposition'] = f'attachment; filename="{model_name}_particionado.zip"'
response['Content-Length'] = len(zip_content)
return response
except Exception as e:
return Response({'error': f'Error exportando modelo: {str(e)}'}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
def export_single_model_csv_partitioned(self, request, model_name, fields, filters, total_records):
"""Exporta un solo modelo CSV particionado a ZIP"""
try:
zip_buffer = io.BytesIO()
module = 'datastage'
model = apps.get_model(module, model_name)
queryset = model.objects.filter(**filters).values(*fields)
with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zip_file:
from django.core.paginator import Paginator
paginator = Paginator(queryset, self.MAX_RECORDS_PER_FILE)
for page_num in paginator.page_range:
page = paginator.page(page_num)
csv_buffer = io.StringIO()
writer = csv.writer(csv_buffer)
writer.writerow(fields)
for row in page.object_list:
row_values = [self.safe_excel_value(row[field]) for field in fields]
writer.writerow(row_values)
# Agregar al ZIP
filename = f"{model_name}_part{page_num}.csv"
zip_file.writestr(filename, csv_buffer.getvalue())
zip_buffer.seek(0)
zip_content = zip_buffer.getvalue()
response = HttpResponse(zip_content, content_type='application/zip')
response['Content-Disposition'] = f'attachment; filename="{model_name}_particionado.zip"'
response['Content-Length'] = len(zip_content)
return response
except Exception as e:
return Response({'error': f'Error exportando modelo CSV: {str(e)}'}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
def get_related_keys_from_filters(self, global_filters, models_data, user):
"""
Obtiene patentes, pedimentos y datastages que cumplen EXACTAMENTE con TODOS los filtros globales
VERSIÓN SIMPLIFICADA - Usa la MISMA lógica que apply_global_filters_to_model
"""
related_keys = {
'patentes': set(),
'pedimentos': set(),
'datastage_ids': set()
}
# Si no hay filtros, retornar vacío
if not any(v for v in global_filters.values() if v not in [None, '']):
return {}
all_records_with_filters = []
for model_data in models_data:
model_name = model_data.get('model')
try:
model = apps.get_model('datastage', model_name)
# ¡USAR LA MISMA FUNCIÓN QUE EN MODO SINGULAR!
filters = self.apply_global_filters_to_model(global_filters, model, user)
if filters:
# EJECUTAR CONSULTA - IDÉNTICO A MODO SINGULAR
queryset = model.objects.filter(**filters)
total = queryset.count()
# VERIFICACIÓN ESPECIAL PARA RFC
if 'rfc' in filters:
rfc_value = filters['rfc']
# Doble verificación: contar registros con ese RFC exacto
rfc_exact_count = queryset.filter(rfc=rfc_value).count()
if rfc_exact_count != total:
try:
other_rfcs = queryset.exclude(rfc=rfc_value).values_list('rfc', flat=True).distinct()[:5]
except:
pass
# Obtener registros
records = queryset.values('patente', 'pedimento', 'datastage_id')
all_records_with_filters.extend(list(records))
except LookupError:
continue
if not all_records_with_filters:
return {'patentes': set(), 'pedimentos': set(), 'datastage_ids': set()}
for record in all_records_with_filters:
if record.get('patente'):
related_keys['patentes'].add(record['patente'])
if record.get('pedimento'):
related_keys['pedimentos'].add(record['pedimento'])
if record.get('datastage_id'):
related_keys['datastage_ids'].add(record['datastage_id'])
return {k: list(v) for k, v in related_keys.items() if v}
def apply_global_filters_to_model(self, global_filters, model, user):
"""
Aplica filtros globales - VERSIÓN CORREGIDA CON UUID
"""
filters = {}
model_fields = [f.name for f in model._meta.get_fields()]
# ORGANIZACIÓN - Manejar como UUID
org_value = global_filters.get('organizacion')
if org_value and org_value != '' and 'organizacion' in model_fields:
field = model._meta.get_field('organizacion')
if hasattr(field, 'related_model'): # Es ForeignKey
# Convertir string a UUID
try:
import uuid
org_uuid = uuid.UUID(org_value)
filters['organizacion_id'] = org_uuid
except Exception as e:
# Fallback: dejar como string (puede no funcionar)
filters['organizacion_id'] = org_value
else: # Es CharField
filters['organizacion'] = org_value
# RFC - Manejar normalmente
rfc_value = global_filters.get('rfc')
if rfc_value and rfc_value != '' and 'rfc' in model_fields:
filters['rfc'] = rfc_value
# PATENTE
if global_filters.get('patente'):
filters['patente'] = global_filters['patente']
# PEDIMENTO
if global_filters.get('pedimento'):
filters['pedimento'] = global_filters['pedimento']
# FECHAS
if 'fecha_pago_real' in model_fields:
if global_filters.get('fecha_pago_desde'):
filters['fecha_pago_real__gte'] = global_filters['fecha_pago_desde']
if global_filters.get('fecha_pago_hasta'):
filters['fecha_pago_real__lte'] = global_filters['fecha_pago_hasta']
return filters
def apply_related_filters(self, global_filters, model, related_keys, user):
filters = {}
model_fields = [f.name for f in model._meta.get_fields()]
# 1. Organización
if 'organizacion' in model_fields and global_filters.get('organizacion'):
filters['organizacion'] = global_filters['organizacion']
# 2. RFC (¡ESTO ES LO QUE FALTA!)
if 'rfc' in model_fields and global_filters.get('rfc'):
filters['rfc'] = global_filters['rfc']
# 3. Fechas (SIEMPRE se aplican)
if 'fecha_pago_real' in model_fields:
if global_filters.get('fecha_pago_desde'):
filters['fecha_pago_real__gte'] = global_filters['fecha_pago_desde']
if global_filters.get('fecha_pago_hasta'):
filters['fecha_pago_real__lte'] = global_filters['fecha_pago_hasta']
# 🔥 SEGUNDO: Si hay related_keys, AÑADIRLAS a los filtros existentes
if any(related_keys.values()):
# Añadir patentes si existen
if related_keys.get('patentes') and 'patente' in model_fields:
filters['patente__in'] = related_keys['patentes']
# Añadir pedimentos si existen
if related_keys.get('pedimentos') and 'pedimento' in model_fields:
filters['pedimento__in'] = related_keys['pedimentos']
# Añadir datastage_ids si existen
if related_keys.get('datastage_ids') and 'datastage_id' in model_fields:
filters['datastage_id__in'] = related_keys['datastage_ids']
else:
# Solo patente y pedimento específicos (no listas)
if 'patente' in model_fields and global_filters.get('patente'):
filters['patente'] = global_filters['patente']
if 'pedimento' in model_fields and global_filters.get('pedimento'):
filters['pedimento'] = global_filters['pedimento']
return filters
def estimate_excel_file_size(self, num_records, num_columns):
"""Estima tamaño aproximado del archivo Excel"""
# Estimación aproximada: 100 bytes por celda
return num_records * num_columns * 100
def export_with_size_control(self, request, models_data, global_filters, related_keys):
"""Versión con control de tamaño de archivo"""
try:
zip_buffer = io.BytesIO()
with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zip_file:
file_counter = 1
current_wb = None
current_file_size_estimate = 0
MAX_FILE_SIZE_ESTIMATE = 50 * 1024 * 1024 # 50MB estimado
for model_data in models_data:
model_name = model_data.get('model')
fields = model_data.get('fields', [])
if not model_name or not fields:
continue
try:
model = apps.get_model('datastage', model_name)
filters = self.apply_related_filters(global_filters, model, related_keys, request.user)
if filters:
queryset = model.objects.filter(**filters).values(*fields)
else:
queryset = model.objects.none()
total_records = queryset.count()
if total_records == 0:
continue
# Calcular tamaño estimado para este modelo
model_size_estimate = self.estimate_excel_file_size(total_records, len(fields))
# Si el modelo es muy grande o no cabe en el archivo actual
needs_new_file = (
current_wb is None or
current_file_size_estimate + model_size_estimate > MAX_FILE_SIZE_ESTIMATE or
(total_records > self.MAX_RECORDS_PER_FILE and current_file_size_estimate > 0)
)
if needs_new_file and current_wb is not None:
# Guardar archivo actual
part_buffer = io.BytesIO()
current_wb.save(part_buffer)
part_buffer.seek(0)
zip_file.writestr(f"datastage_part{file_counter}.xlsx", part_buffer.getvalue())
file_counter += 1
current_wb = None
current_file_size_estimate = 0
if current_wb is None:
current_wb = openpyxl.Workbook()
current_wb.remove(current_wb.active)
# Manejar modelos que exceden el límite por hoja
if total_records > self.MAX_RECORDS_PER_FILE:
from django.core.paginator import Paginator
paginator = Paginator(queryset, self.MAX_RECORDS_PER_FILE)
for page_num in paginator.page_range:
page = paginator.page(page_num)
# Crear hoja para esta parte
sheet_name = f"{model_name[:20]}_p{page_num}"[:31]
ws = current_wb.create_sheet(title=sheet_name)
ws.append(fields)
for row in page.object_list:
row_values = [self.safe_excel_value(row[field]) for field in fields]
ws.append(row_values)
# Actualizar tamaño estimado
page_size = self.estimate_excel_file_size(len(page.object_list), len(fields))
current_file_size_estimate += page_size
else:
# Modelo pequeño, una hoja
sheet_name = model_name[:31]
ws = current_wb.create_sheet(title=sheet_name)
ws.append(fields)
for row in queryset:
row_values = [self.safe_excel_value(row[field]) for field in fields]
ws.append(row_values)
current_file_size_estimate += model_size_estimate
except LookupError:
continue
# Guardar último archivo si existe
if current_wb is not None:
part_buffer = io.BytesIO()
current_wb.save(part_buffer)
part_buffer.seek(0)
zip_file.writestr(f"datastage_part{file_counter}.xlsx", part_buffer.getvalue())
zip_buffer.seek(0)
response = HttpResponse(zip_buffer.read(), content_type='application/zip')
response['Content-Disposition'] = 'attachment; filename="datastage_reports.zip"'
return response
except Exception as e:
return Response({'error': f'Error: {str(e)}'}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
class ExportModelView(APIView):
my_tags = ['Reportes']
permission_classes = [IsAuthenticated & (IsSameOrganization | IsSameOrganizationAndAdmin | IsSameOrganizationDeveloper | IsSuperUser)]
@swagger_auto_schema(
manual_parameters=[
openapi.Parameter('model', openapi.IN_QUERY, description="Nombre del modelo (ejemplo: Registro500)",
type=openapi.TYPE_STRING, required=True)
],
responses={200: openapi.Response('Campos disponibles', schema=openapi.Schema(
type=openapi.TYPE_OBJECT,
properties={
'fields': openapi.Schema(type=openapi.TYPE_ARRAY, items=openapi.Items(type=openapi.TYPE_STRING))
}
))}
)
def get(self, request, *args, **kwargs):
"""
Devuelve los campos disponibles para el modelo solicitado.
Ejemplo: /api/reports/exportmodel/?model=Registro500
"""
model_name = request.query_params.get('model')
module = request.query_params.get('module', 'datastage')
if not model_name:
return Response({'error': 'model is required'}, status=status.HTTP_400_BAD_REQUEST)
try:
model = apps.get_model(module, model_name)
except LookupError:
return Response({'error': f'Model {model_name} not found in app {module}'}, status=status.HTTP_404_NOT_FOUND)
fields = [f.name for f in model._meta.fields]
return Response({'fields': fields})
@swagger_auto_schema(
request_body=ExportModelSerializer,
responses={200: 'Archivo generado (Excel o CSV)'}
)
def post(self, request, *args, **kwargs):
model_name = request.data.get('model')
fields = request.data.get('fields')
filters = request.data.get('filters', {})
export_type = request.data.get('type', 'csv')
module = request.data.get('module', 'datastage')
if not model_name or not fields:
return Response({'error': 'model and fields are required'}, status=status.HTTP_400_BAD_REQUEST)
if export_type == 'excel':
return export_model_to_excel(request, model_name, fields, module, filters)
else:
return export_model_to_csv(request, model_name, fields, module, filters)
# Create your views here.
class ExportModelView(APIView):
my_tags = ['Reportes']
permission_classes = [IsAuthenticated & (
IsSameOrganization | IsSameOrganizationAndAdmin | IsSameOrganizationDeveloper | IsSuperUser)]
@swagger_auto_schema(request_body=ExportModelSerializer, esponses={200: 'Archivo generado (Excel o CSV)'})
def post(self, request, *args, **kwargs):
model_name = request.data.get('model')
fields = request.data.get('fields')
filters = request.data.get('filters', {})
filters['organizacion__id'] = self.request.user.organizacion.id if hasattr(request.user, 'organizacion') and request.user.organizacion else None
export_type = request.data.get('type', 'csv')
if not model_name or not fields:
return Response({'error': 'model and fields are required'}, status=status.HTTP_400_BAD_REQUEST)
module = request.data.get('module', 'datastage')
if export_type == 'excel':
return export_model_to_excel(request, model_name, fields, module, filters)
else:
return export_model_to_csv(request, model_name, fields, module, filters)
# Resumen general para dashboard
@api_view(['GET'])
@permission_classes([
IsAuthenticated
])
def dashboard_summary(request):
org_id = request.query_params.get('organizacion_id')
filters = {}
user = request.user
pedimento_app = request.query_params.get('pedimento_app')
aduana = request.query_params.get('aduana')
patente = request.query_params.get('patente')
regimen = request.query_params.get('regimen')
agente_aduanal = request.query_params.get('agente_aduanal')
tipo_operacion = request.query_params.get('tipo_operacion')
fecha_pago_gte = request.query_params.get('fecha_pago__gte')
fecha_pago_lte = request.query_params.get('fecha_pago__lte')
contribuyente__rfc = request.query_params.get('contribuyente__rfc')
# Si no se especifica organización y el usuario tiene organización, usarla
if not org_id and hasattr(user, 'organizacion') and user.organizacion:
org_id = user.organizacion.id
# Si no es superusuario, filtrar por organización
if org_id and not getattr(user, 'is_superuser', False):
filters['organizacion_id'] = org_id
# Si el usuario pertenece al grupo Importador, filtrar por RFC
if user.groups.filter(name='Importador').exists():
rfc = getattr(user, 'rfc', None)
if rfc:
filters['contribuyente__rfc'] = rfc
if pedimento_app:
filters['pedimento_app'] = pedimento_app
if aduana:
filters['aduana'] = aduana
if patente:
filters['patente'] = patente
if regimen:
filters['regimen'] = regimen
if agente_aduanal:
filters['agente_aduanal'] = agente_aduanal
if tipo_operacion:
filters['tipo_operacion__tipo'] = tipo_operacion
if fecha_pago_gte:
filters['fecha_pago__gte'] = fecha_pago_gte
if fecha_pago_lte:
filters['fecha_pago__lte'] = fecha_pago_lte
if contribuyente__rfc:
filters['contribuyente__rfc'] = contribuyente__rfc
# Filtrar pedimentos
pedimentos_qs = Pedimento.objects.filter(**filters)
pedimentos_total = pedimentos_qs.count()
pedimentos_completos = pedimentos_qs.filter(existe_expediente=True).count()
pedimentos_pendientes = pedimentos_total - pedimentos_completos
# Usar los IDs de pedimentos filtrados para los demás modelos
pedimento_ids = list(pedimentos_qs.values_list('id', flat=True))
coves_total = Cove.objects.filter(pedimento_id__in=pedimento_ids).count()
coves_procesados = Cove.objects.filter(
pedimento_id__in=pedimento_ids, cove_descargado=True).count()
acuse_coves_procesados = Cove.objects.filter(
pedimento_id__in=pedimento_ids, acuse_cove_descargado=True).count()
acuse_coves_pendientes = coves_total - acuse_coves_procesados
coves_pendientes = coves_total - coves_procesados
edocs_total = EDocument.objects.filter(
pedimento_id__in=pedimento_ids).count()
edocs_descargados = EDocument.objects.filter(
pedimento_id__in=pedimento_ids, edocument_descargado=True).count()
acuse_descargados = EDocument.objects.filter(
pedimento_id__in=pedimento_ids, acuse_descargado=True).count()
edocs_pendientes = edocs_total - edocs_descargados
acuses_pendientes = edocs_total - acuse_descargados
remesas_total = Document.objects.filter(
document_type__id=3, pedimento_id__in=pedimento_ids).count()
documentos_descargados = Document.objects.filter(
pedimento_id__in=pedimento_ids).count()
partidas_total = Partida.objects.filter(
pedimento_id__in=pedimento_ids).count()
partidas_descargadas = Partida.objects.filter(
pedimento_id__in=pedimento_ids, descargado=True).count()
partidas_pendientes = partidas_total - partidas_descargadas
# Indicadores de cumplimiento
cumplimiento_pedimentos = (
pedimentos_completos / pedimentos_total * 100) if pedimentos_total else 0
cumplimiento_acuse_coves = (
acuse_coves_procesados / coves_total * 100) if coves_total else 0
cumplimiento_coves = (
coves_procesados / coves_total * 100) if coves_total else 0
cumplimiento_edocs = (edocs_descargados /
edocs_total * 100) if edocs_total else 0
cumplimiento_acuses = (acuse_descargados /
edocs_total * 100) if edocs_total else 0
cumplimiento_partidas = (partidas_descargadas /
partidas_total * 100) if partidas_total else 0
# Calcular cumplimiento total (promedio de todos los indicadores)
indicadores = [
cumplimiento_pedimentos,
cumplimiento_coves,
cumplimiento_acuse_coves,
cumplimiento_edocs,
cumplimiento_acuses,
cumplimiento_partidas
]
cumplimiento_total = sum(indicadores) / len(indicadores) if indicadores else 0
return Response({
"cumplimiento_total": round(cumplimiento_total, 2),
"pedimentos": {
"total": pedimentos_total,
"completos": pedimentos_completos,
"pendientes": pedimentos_pendientes,
"cumplimiento": round(cumplimiento_pedimentos, 2)
},
"coves": {
"total": coves_total,
"coves_procesados": coves_procesados,
"coves_pendientes": coves_pendientes,
"coves_cumplimiento": round(cumplimiento_coves, 2),
},
"acuse_coves": {
"total": coves_total,
"acuse_coves_procesados": acuse_coves_procesados,
"acuse_coves_pendientes": acuse_coves_pendientes,
"acuse_coves_cumplimiento": round(cumplimiento_acuse_coves, 2)
},
"edocuments": {
"total": edocs_total,
"edocs_descargados": edocs_descargados,
"edocs_pendientes": edocs_pendientes,
"edocs_cumplimiento": round(cumplimiento_edocs, 2),
},
"acuses":{
"total": edocs_total,
"acuse_descargados": acuse_descargados,
"acuses_pendientes": acuses_pendientes,
"acuses_cumplimiento": round(cumplimiento_acuses, 2)
},
"remesas": {
"total": remesas_total
},
"documentos": {
"descargados": documentos_descargados
},
"partidas": {
"total": partidas_total,
"partidas_descargadas": partidas_descargadas,
"partidas_pendientes": partidas_pendientes,
"cumplimiento": round(cumplimiento_partidas, 2)
}
})