Marwan Alwali 23158e9fbf update
2025-09-08 03:00:23 +03:00

846 lines
27 KiB
Python

# """
# Viewflow workflows for analytics app.
# Provides report generation, dashboard management, and data analysis workflows.
# """
#
# from viewflow import Flow, lock
# from viewflow.base import this, flow_func
# from viewflow.contrib import celery
# from viewflow.decorators import flow_view
# from viewflow.fields import CharField, ModelField
# from viewflow.forms import ModelForm
# from viewflow.views import CreateProcessView, UpdateProcessView
# from viewflow.models import Process, Task
# from django.contrib.auth.models import User
# from django.urls import reverse_lazy
# from django.utils import timezone
# from django.db import transaction
# from django.core.mail import send_mail
#
# from .models import Dashboard, DashboardWidget, DataSource, Report, ReportExecution, MetricDefinition, MetricValue
# from .views import (
# ReportGenerationView, DashboardCreationView, DataSourceConfigurationView,
# MetricCalculationView, AnalysisView, VisualizationView, DistributionView,
# SchedulingView, QualityAssuranceView, PerformanceOptimizationView
# )
#
#
# class ReportGenerationProcess(Process):
# """
# Viewflow process model for report generation
# """
# report = ModelField(Report, help_text='Associated report')
#
# # Process status tracking
# report_requested = models.BooleanField(default=False)
# data_extracted = models.BooleanField(default=False)
# data_processed = models.BooleanField(default=False)
# report_generated = models.BooleanField(default=False)
# quality_checked = models.BooleanField(default=False)
# report_distributed = models.BooleanField(default=False)
# generation_completed = models.BooleanField(default=False)
#
# class Meta:
# verbose_name = 'Report Generation Process'
# verbose_name_plural = 'Report Generation Processes'
#
#
# class ReportGenerationFlow(Flow):
# """
# Report Generation Workflow
#
# This flow manages automated report generation including
# data extraction, processing, quality checks, and distribution.
# """
#
# process_class = ReportGenerationProcess
#
# # Flow definition
# start = (
# flow_func(this.start_report_generation)
# .Next(this.request_report)
# )
#
# request_report = (
# flow_view(ReportGenerationView)
# .Permission('analytics.can_generate_reports')
# .Next(this.extract_data)
# )
#
# extract_data = (
# flow_func(this.perform_data_extraction)
# .Next(this.process_data)
# )
#
# process_data = (
# flow_func(this.perform_data_processing)
# .Next(this.generate_report)
# )
#
# generate_report = (
# flow_func(this.create_report_output)
# .Next(this.check_quality)
# )
#
# check_quality = (
# flow_view(QualityAssuranceView)
# .Permission('analytics.can_check_quality')
# .Next(this.distribute_report)
# )
#
# distribute_report = (
# flow_view(DistributionView)
# .Permission('analytics.can_distribute_reports')
# .Next(this.complete_generation)
# )
#
# complete_generation = (
# flow_func(this.finalize_report_generation)
# .Next(this.end)
# )
#
# end = flow_func(this.end_report_generation)
#
# # Flow functions
# def start_report_generation(self, activation):
# """Initialize the report generation process"""
# process = activation.process
# report = process.report
#
# # Create report execution record
# execution = ReportExecution.objects.create(
# report=report,
# execution_type='MANUAL',
# status='PENDING'
# )
#
# # Send generation notification
# self.notify_generation_start(report, execution)
#
# def perform_data_extraction(self, activation):
# """Extract data from configured sources"""
# process = activation.process
# report = process.report
#
# # Extract data from all configured sources
# extracted_data = self.extract_report_data(report)
#
# # Mark data extracted
# process.data_extracted = True
# process.save()
#
# # Store extracted data
# self.store_extracted_data(report, extracted_data)
#
# def perform_data_processing(self, activation):
# """Process and transform extracted data"""
# process = activation.process
# report = process.report
#
# # Process data according to report configuration
# processed_data = self.process_report_data(report)
#
# # Mark data processed
# process.data_processed = True
# process.save()
#
# # Store processed data
# self.store_processed_data(report, processed_data)
#
# def create_report_output(self, activation):
# """Generate report output in specified format"""
# process = activation.process
# report = process.report
#
# # Generate report in configured format
# report_output = self.generate_report_output(report)
#
# # Mark report generated
# process.report_generated = True
# process.save()
#
# # Store report output
# self.store_report_output(report, report_output)
#
# def finalize_report_generation(self, activation):
# """Finalize the report generation process"""
# process = activation.process
# report = process.report
#
# # Update report execution status
# execution = ReportExecution.objects.filter(
# report=report,
# status='RUNNING'
# ).first()
#
# if execution:
# execution.status = 'COMPLETED'
# execution.completed_at = timezone.now()
# execution.save()
#
# # Mark generation completed
# process.generation_completed = True
# process.save()
#
# # Send completion notifications
# self.notify_generation_completion(report)
#
# # Schedule next execution if recurring
# self.schedule_next_execution(report)
#
# def end_report_generation(self, activation):
# """End the report generation workflow"""
# process = activation.process
#
# # Generate generation summary
# self.generate_generation_summary(process.report)
#
# # Helper methods
# def notify_generation_start(self, report, execution):
# """Notify report generation start"""
# analytics_team = User.objects.filter(groups__name='Analytics Team')
# for staff in analytics_team:
# send_mail(
# subject=f'Report Generation Started: {report.name}',
# message=f'Report generation process started for "{report.name}".',
# from_email='analytics@hospital.com',
# recipient_list=[staff.email],
# fail_silently=True
# )
#
# def extract_report_data(self, report):
# """Extract data from report sources"""
# # This would implement data extraction logic
# return {'status': 'extracted', 'records': 1000}
#
# def store_extracted_data(self, report, data):
# """Store extracted data"""
# # This would store extracted data
# pass
#
# def process_report_data(self, report):
# """Process and transform report data"""
# # This would implement data processing logic
# return {'status': 'processed', 'records': 1000}
#
# def store_processed_data(self, report, data):
# """Store processed data"""
# # This would store processed data
# pass
#
# def generate_report_output(self, report):
# """Generate report output"""
# # This would generate report in specified format
# return {'status': 'generated', 'file_path': '/reports/output.pdf'}
#
# def store_report_output(self, report, output):
# """Store report output"""
# # This would store report output
# pass
#
# def notify_generation_completion(self, report):
# """Notify report generation completion"""
# # Notify report subscribers
# for subscriber in report.subscribers.all():
# if subscriber.email:
# send_mail(
# subject=f'Report Ready: {report.name}',
# message=f'Your report "{report.name}" is ready for download.',
# from_email='analytics@hospital.com',
# recipient_list=[subscriber.email],
# fail_silently=True
# )
#
# def schedule_next_execution(self, report):
# """Schedule next report execution if recurring"""
# if report.is_scheduled and report.schedule_config:
# # Schedule next execution
# schedule_report_execution.apply_async(
# args=[report.report_id],
# countdown=report.schedule_config.get('interval', 86400)
# )
#
# def generate_generation_summary(self, report):
# """Generate report generation summary"""
# # This would generate generation summary
# pass
#
#
# class DashboardManagementProcess(Process):
# """
# Viewflow process model for dashboard management
# """
# dashboard = ModelField(Dashboard, help_text='Associated dashboard')
#
# # Process status tracking
# dashboard_requested = models.BooleanField(default=False)
# data_sources_configured = models.BooleanField(default=False)
# widgets_created = models.BooleanField(default=False)
# layout_configured = models.BooleanField(default=False)
# permissions_set = models.BooleanField(default=False)
# dashboard_tested = models.BooleanField(default=False)
# dashboard_deployed = models.BooleanField(default=False)
# management_completed = models.BooleanField(default=False)
#
# class Meta:
# verbose_name = 'Dashboard Management Process'
# verbose_name_plural = 'Dashboard Management Processes'
#
#
# class DashboardManagementFlow(Flow):
# """
# Dashboard Management Workflow
#
# This flow manages dashboard creation, configuration,
# and deployment including widgets and data sources.
# """
#
# process_class = DashboardManagementProcess
#
# # Flow definition
# start = (
# flow_func(this.start_dashboard_management)
# .Next(this.request_dashboard)
# )
#
# request_dashboard = (
# flow_view(DashboardCreationView)
# .Permission('analytics.can_create_dashboards')
# .Next(this.configure_data_sources)
# )
#
# configure_data_sources = (
# flow_view(DataSourceConfigurationView)
# .Permission('analytics.can_configure_data_sources')
# .Next(this.create_widgets)
# )
#
# create_widgets = (
# flow_func(this.setup_dashboard_widgets)
# .Next(this.configure_layout)
# )
#
# configure_layout = (
# flow_func(this.setup_dashboard_layout)
# .Next(this.set_permissions)
# )
#
# set_permissions = (
# flow_func(this.configure_dashboard_permissions)
# .Next(this.test_dashboard)
# )
#
# test_dashboard = (
# flow_func(this.perform_dashboard_testing)
# .Next(this.deploy_dashboard)
# )
#
# deploy_dashboard = (
# flow_func(this.deploy_dashboard_to_production)
# .Next(this.complete_management)
# )
#
# complete_management = (
# flow_func(this.finalize_dashboard_management)
# .Next(this.end)
# )
#
# end = flow_func(this.end_dashboard_management)
#
# # Flow functions
# def start_dashboard_management(self, activation):
# """Initialize the dashboard management process"""
# process = activation.process
# dashboard = process.dashboard
#
# # Send dashboard creation notification
# self.notify_dashboard_creation(dashboard)
#
# def setup_dashboard_widgets(self, activation):
# """Setup dashboard widgets"""
# process = activation.process
# dashboard = process.dashboard
#
# # Create default widgets based on dashboard type
# self.create_default_widgets(dashboard)
#
# # Mark widgets created
# process.widgets_created = True
# process.save()
#
# def setup_dashboard_layout(self, activation):
# """Setup dashboard layout"""
# process = activation.process
# dashboard = process.dashboard
#
# # Configure dashboard layout
# self.configure_layout_settings(dashboard)
#
# # Mark layout configured
# process.layout_configured = True
# process.save()
#
# def configure_dashboard_permissions(self, activation):
# """Configure dashboard permissions"""
# process = activation.process
# dashboard = process.dashboard
#
# # Set up access permissions
# self.setup_access_permissions(dashboard)
#
# # Mark permissions set
# process.permissions_set = True
# process.save()
#
# def perform_dashboard_testing(self, activation):
# """Perform dashboard testing"""
# process = activation.process
# dashboard = process.dashboard
#
# # Test dashboard functionality
# test_results = self.test_dashboard_functionality(dashboard)
#
# # Mark dashboard tested
# process.dashboard_tested = True
# process.save()
#
# # Store test results
# self.store_test_results(dashboard, test_results)
#
# def deploy_dashboard_to_production(self, activation):
# """Deploy dashboard to production"""
# process = activation.process
# dashboard = process.dashboard
#
# # Deploy dashboard
# self.deploy_dashboard(dashboard)
#
# # Mark dashboard deployed
# process.dashboard_deployed = True
# process.save()
#
# # Activate dashboard
# dashboard.is_active = True
# dashboard.save()
#
# def finalize_dashboard_management(self, activation):
# """Finalize the dashboard management process"""
# process = activation.process
# dashboard = process.dashboard
#
# # Mark management completed
# process.management_completed = True
# process.save()
#
# # Send completion notifications
# self.notify_dashboard_completion(dashboard)
#
# # Schedule dashboard refresh
# self.schedule_dashboard_refresh(dashboard)
#
# def end_dashboard_management(self, activation):
# """End the dashboard management workflow"""
# process = activation.process
#
# # Generate dashboard summary
# self.generate_dashboard_summary(process.dashboard)
#
# # Helper methods
# def notify_dashboard_creation(self, dashboard):
# """Notify dashboard creation"""
# analytics_team = User.objects.filter(groups__name='Analytics Team')
# for staff in analytics_team:
# send_mail(
# subject=f'Dashboard Creation: {dashboard.name}',
# message=f'Dashboard creation process started for "{dashboard.name}".',
# from_email='analytics@hospital.com',
# recipient_list=[staff.email],
# fail_silently=True
# )
#
# def create_default_widgets(self, dashboard):
# """Create default widgets for dashboard"""
# # This would create default widgets based on dashboard type
# pass
#
# def configure_layout_settings(self, dashboard):
# """Configure dashboard layout settings"""
# # This would configure layout settings
# pass
#
# def setup_access_permissions(self, dashboard):
# """Setup dashboard access permissions"""
# # This would configure access permissions
# pass
#
# def test_dashboard_functionality(self, dashboard):
# """Test dashboard functionality"""
# # This would test dashboard functionality
# return {'status': 'passed', 'issues': []}
#
# def store_test_results(self, dashboard, results):
# """Store dashboard test results"""
# # This would store test results
# pass
#
# def deploy_dashboard(self, dashboard):
# """Deploy dashboard to production"""
# # This would deploy dashboard
# pass
#
# def notify_dashboard_completion(self, dashboard):
# """Notify dashboard completion"""
# # Notify dashboard users
# for user in dashboard.allowed_users.all():
# if user.email:
# send_mail(
# subject=f'Dashboard Available: {dashboard.name}',
# message=f'Dashboard "{dashboard.name}" is now available.',
# from_email='analytics@hospital.com',
# recipient_list=[user.email],
# fail_silently=True
# )
#
# def schedule_dashboard_refresh(self, dashboard):
# """Schedule dashboard refresh"""
# # Schedule dashboard refresh task
# refresh_dashboard.apply_async(
# args=[dashboard.dashboard_id],
# countdown=dashboard.refresh_interval
# )
#
# def generate_dashboard_summary(self, dashboard):
# """Generate dashboard summary"""
# # This would generate dashboard summary
# pass
#
#
# class MetricCalculationProcess(Process):
# """
# Viewflow process model for metric calculation
# """
# metric_definition = ModelField(MetricDefinition, help_text='Associated metric definition')
#
# # Process status tracking
# calculation_triggered = models.BooleanField(default=False)
# data_collected = models.BooleanField(default=False)
# metrics_calculated = models.BooleanField(default=False)
# quality_validated = models.BooleanField(default=False)
# thresholds_checked = models.BooleanField(default=False)
# alerts_sent = models.BooleanField(default=False)
# calculation_completed = models.BooleanField(default=False)
#
# class Meta:
# verbose_name = 'Metric Calculation Process'
# verbose_name_plural = 'Metric Calculation Processes'
#
#
# class MetricCalculationFlow(Flow):
# """
# Metric Calculation Workflow
#
# This flow manages automated metric calculation including
# data collection, calculation, validation, and alerting.
# """
#
# process_class = MetricCalculationProcess
#
# # Flow definition
# start = (
# flow_func(this.start_metric_calculation)
# .Next(this.trigger_calculation)
# )
#
# trigger_calculation = (
# flow_func(this.initiate_calculation)
# .Next(this.collect_data)
# )
#
# collect_data = (
# flow_func(this.gather_metric_data)
# .Next(this.calculate_metrics)
# )
#
# calculate_metrics = (
# flow_view(MetricCalculationView)
# .Permission('analytics.can_calculate_metrics')
# .Next(this.validate_quality)
# )
#
# validate_quality = (
# flow_func(this.perform_quality_validation)
# .Next(this.check_thresholds)
# )
#
# check_thresholds = (
# flow_func(this.evaluate_thresholds)
# .Next(this.send_alerts)
# )
#
# send_alerts = (
# flow_func(this.dispatch_threshold_alerts)
# .Next(this.complete_calculation)
# )
#
# complete_calculation = (
# flow_func(this.finalize_metric_calculation)
# .Next(this.end)
# )
#
# end = flow_func(this.end_metric_calculation)
#
# # Flow functions
# def start_metric_calculation(self, activation):
# """Initialize the metric calculation process"""
# process = activation.process
# metric = process.metric_definition
#
# # Send calculation notification
# self.notify_calculation_start(metric)
#
# def initiate_calculation(self, activation):
# """Initiate metric calculation"""
# process = activation.process
# metric = process.metric_definition
#
# # Mark calculation triggered
# process.calculation_triggered = True
# process.save()
#
# # Prepare calculation environment
# self.prepare_calculation_environment(metric)
#
# def gather_metric_data(self, activation):
# """Gather data for metric calculation"""
# process = activation.process
# metric = process.metric_definition
#
# # Collect data from configured sources
# collected_data = self.collect_calculation_data(metric)
#
# # Mark data collected
# process.data_collected = True
# process.save()
#
# # Store collected data
# self.store_calculation_data(metric, collected_data)
#
# def perform_quality_validation(self, activation):
# """Perform data quality validation"""
# process = activation.process
# metric = process.metric_definition
#
# # Validate data quality
# quality_results = self.validate_data_quality(metric)
#
# # Mark quality validated
# process.quality_validated = True
# process.save()
#
# # Store quality results
# self.store_quality_results(metric, quality_results)
#
# def evaluate_thresholds(self, activation):
# """Evaluate metric thresholds"""
# process = activation.process
# metric = process.metric_definition
#
# # Check threshold violations
# threshold_results = self.check_metric_thresholds(metric)
#
# # Mark thresholds checked
# process.thresholds_checked = True
# process.save()
#
# # Store threshold results
# self.store_threshold_results(metric, threshold_results)
#
# def dispatch_threshold_alerts(self, activation):
# """Dispatch threshold alerts"""
# process = activation.process
# metric = process.metric_definition
#
# # Send threshold alerts if needed
# self.send_threshold_alerts(metric)
#
# # Mark alerts sent
# process.alerts_sent = True
# process.save()
#
# def finalize_metric_calculation(self, activation):
# """Finalize the metric calculation process"""
# process = activation.process
# metric = process.metric_definition
#
# # Mark calculation completed
# process.calculation_completed = True
# process.save()
#
# # Send completion notifications
# self.notify_calculation_completion(metric)
#
# # Schedule next calculation
# self.schedule_next_calculation(metric)
#
# def end_metric_calculation(self, activation):
# """End the metric calculation workflow"""
# process = activation.process
#
# # Generate calculation summary
# self.generate_calculation_summary(process.metric_definition)
#
# # Helper methods
# def notify_calculation_start(self, metric):
# """Notify metric calculation start"""
# # This would notify relevant parties
# pass
#
# def prepare_calculation_environment(self, metric):
# """Prepare calculation environment"""
# # This would prepare calculation environment
# pass
#
# def collect_calculation_data(self, metric):
# """Collect data for calculation"""
# # This would collect data from configured sources
# return {'status': 'collected', 'records': 1000}
#
# def store_calculation_data(self, metric, data):
# """Store calculation data"""
# # This would store calculation data
# pass
#
# def validate_data_quality(self, metric):
# """Validate data quality"""
# # This would validate data quality
# return {'quality_score': 95, 'issues': []}
#
# def store_quality_results(self, metric, results):
# """Store quality validation results"""
# # This would store quality results
# pass
#
# def check_metric_thresholds(self, metric):
# """Check metric thresholds"""
# # This would check threshold violations
# return {'violations': [], 'status': 'normal'}
#
# def store_threshold_results(self, metric, results):
# """Store threshold check results"""
# # This would store threshold results
# pass
#
# def send_threshold_alerts(self, metric):
# """Send threshold violation alerts"""
# # This would send alerts for threshold violations
# pass
#
# def notify_calculation_completion(self, metric):
# """Notify calculation completion"""
# # This would notify completion
# pass
#
# def schedule_next_calculation(self, metric):
# """Schedule next metric calculation"""
# # Schedule next calculation based on aggregation period
# if metric.aggregation_period == 'HOURLY':
# countdown = 3600
# elif metric.aggregation_period == 'DAILY':
# countdown = 86400
# else:
# countdown = 3600 # Default to hourly
#
# calculate_metric.apply_async(
# args=[metric.metric_id],
# countdown=countdown
# )
#
# def generate_calculation_summary(self, metric):
# """Generate calculation summary"""
# # This would generate calculation summary
# pass
#
#
# # Celery tasks for background processing
# @celery.job
# def schedule_report_execution(report_id):
# """Background task to schedule report execution"""
# try:
# report = Report.objects.get(report_id=report_id)
#
# # Create report execution
# execution = ReportExecution.objects.create(
# report=report,
# execution_type='SCHEDULED',
# status='PENDING'
# )
#
# # Start report generation workflow
# # This would start the report generation workflow
#
# return True
# except Exception:
# return False
#
#
# @celery.job
# def refresh_dashboard(dashboard_id):
# """Background task to refresh dashboard data"""
# try:
# dashboard = Dashboard.objects.get(dashboard_id=dashboard_id)
#
# # Refresh all dashboard widgets
# for widget in dashboard.widgets.all():
# refresh_widget_data(widget)
#
# # Schedule next refresh
# refresh_dashboard.apply_async(
# args=[dashboard_id],
# countdown=dashboard.refresh_interval
# )
#
# return True
# except Exception:
# return False
#
#
# @celery.job
# def calculate_metric(metric_id):
# """Background task to calculate metric values"""
# try:
# metric = MetricDefinition.objects.get(metric_id=metric_id)
#
# # Start metric calculation workflow
# # This would start the metric calculation workflow
#
# return True
# except Exception:
# return False
#
#
# @celery.job
# def cleanup_old_reports():
# """Background task to cleanup old report files"""
# try:
# # This would cleanup old report files
# return True
# except Exception:
# return False
#
#
# @celery.job
# def generate_analytics_summary():
# """Background task to generate analytics summary"""
# try:
# # This would generate periodic analytics summary
# return True
# except Exception:
# return False
#
#
# def refresh_widget_data(widget):
# """Helper function to refresh widget data"""
# # This would refresh widget data
# pass
#