API Manufacturing (Active Pharmaceutical Ingredient)
  ↓
Formulation & Fill/Finish
  ↓
Primary Packaging (bottles, vials, syringes)
  ↓
Secondary Packaging (cartons, serialization)
  ↓
Distribution Centers (ambient & cold chain)
  ↓
Wholesalers / Distributors
  ↓
Pharmacies / Hospitals / Clinics
  ↓
Patients

import pandas as pd
import numpy as np
from datetime import datetime, timedelta

class ColdChainManager:
    """
    Manage pharmaceutical cold chain operations
    """

    def __init__(self, temperature_limits):
        """
        Initialize cold chain manager

        Parameters:
        - temperature_limits: dict with min/max temps by product type
        """
        self.temperature_limits = temperature_limits

    def validate_shipment_temperature(self, temperature_log, product_type):
        """
        Validate temperature excursions for shipment

        Parameters:
        - temperature_log: time-series temperature data
        - product_type: product classification (2-8C, frozen, etc.)

        Returns:
        - validation result with excursions
        """

        limits = self.temperature_limits.get(product_type, {})
        min_temp = limits.get('min_celsius', 2)
        max_temp = limits.get('max_celsius', 8)
        max_excursion_minutes = limits.get('max_excursion_minutes', 30)

        excursions = []
        current_excursion = None

        for idx, reading in temperature_log.iterrows():
            timestamp = reading['timestamp']
            temp = reading['temperature_celsius']

            # Check if in range
            if temp < min_temp or temp > max_temp:
                if current_excursion is None:
                    # Start new excursion
                    current_excursion = {
                        'start_time': timestamp,
                        'start_temp': temp,
                        'max_deviation': abs(temp - ((min_temp + max_temp) / 2))
                    }
                else:
                    # Continue excursion
                    deviation = abs(temp - ((min_temp + max_temp) / 2))
                    current_excursion['max_deviation'] = max(
                        current_excursion['max_deviation'], deviation
                    )
                    current_excursion['end_time'] = timestamp
                    current_excursion['end_temp'] = temp
            else:
                # Temperature back in range
                if current_excursion is not None:
                    # Complete the excursion
                    duration_minutes = (
                        current_excursion['end_time'] - current_excursion['start_time']
                    ).total_seconds() / 60

                    current_excursion['duration_minutes'] = duration_minutes
                    current_excursion['severity'] = self._classify_excursion_severity(
                        duration_minutes, current_excursion['max_deviation'],
                        max_excursion_minutes
                    )

                    excursions.append(current_excursion)
                    current_excursion = None

        # Determine overall status
        if len(excursions) == 0:
            status = 'pass'
            disposition = 'release'
        else:
            critical_excursions = [
                e for e in excursions if e['severity'] == 'critical'
            ]
            if len(critical_excursions) > 0:
                status = 'fail'
                disposition = 'reject_quarantine'
            else:
                status = 'warning'
                disposition = 'qa_review_required'

        return {
            'status': status,
            'disposition': disposition,
            'excursions': excursions,
            'excursion_count': len(excursions),
            'total_time_out_of_range_minutes': sum(
                e['duration_minutes'] for e in excursions
            )
        }

    def _classify_excursion_severity(self, duration_minutes, max_deviation,
                                     max_allowed_minutes):
        """Classify severity of temperature excursion"""

        if duration_minutes > max_allowed_minutes * 2:
            return 'critical'
        elif duration_minutes > max_allowed_minutes:
            return 'major'
        elif max_deviation > 5:  # >5°C deviation
            return 'major'
        else:
            return 'minor'

    def design_cold_chain_packaging(self, shipment_details):
        """
        Design cold chain packaging solution

        Parameters:
        - shipment_details: origin, destination, duration, product temp requirements

        Returns:
        - packaging recommendation
        """

        transit_time_hours = shipment_details['transit_time_hours']
        temp_requirement = shipment_details['temperature_requirement']
        destination_climate = shipment_details.get('destination_climate', 'temperate')

        # Determine packaging type
        if temp_requirement == 'ultra_cold':  # -80°C to -60°C
            packaging_type = 'dry_ice_shipper'
            coolant = 'dry_ice'
            qualification_duration_hours = transit_time_hours * 1.5  # 50% safety factor

        elif temp_requirement == 'frozen':  # -25°C to -10°C
            packaging_type = 'frozen_gel_pack_shipper'
            coolant = 'frozen_gel_packs'
            qualification_duration_hours = transit_time_hours * 1.3

        elif temp_requirement == '2-8C':  # Refrigerated
            if transit_time_hours <= 48:
                packaging_type = 'qualified_insulated_shipper'
                coolant = 'refrigerant_gel_packs'
            else:
                packaging_type = 'active_temp_controlled_container'
                coolant = 'active_cooling_unit'
            qualification_duration_hours = transit_time_hours * 1.2

        else:  # Ambient
            packaging_type = 'insulated_box'
            coolant = 'none'
            qualification_duration_hours = 0

        # Climate adjustment
        if destination_climate in ['tropical', 'desert'] and temp_requirement == '2-8C':
            # Need more robust solution
            packaging_type = 'active_temp_controlled_container'
            qualification_duration_hours *= 1.2

        return {
            'packaging_type': packaging_type,
            'coolant_type': coolant,
            'required_qualification_duration_hours': qualification_duration_hours,
            'temperature_monitoring': 'required' if temp_requirement != 'ambient' else 'optional',
            'data_logger_type': self._recommend_data_logger(temp_requirement),
            'estimated_cost_usd': self._estimate_packaging_cost(
                packaging_type, transit_time_hours
            )
        }

    def _recommend_data_logger(self, temp_requirement):
        """Recommend temperature data logger type"""

        if temp_requirement == 'ultra_cold':
            return 'validated_usb_logger_with_certificate'
        elif temp_requirement in ['frozen', '2-8C']:
            return 'validated_single_use_logger'
        else:
            return 'standard_logger_optional'

    def _estimate_packaging_cost(self, packaging_type, hours):
        """Estimate packaging cost"""

        costs = {
            'dry_ice_shipper': 250,
            'frozen_gel_pack_shipper': 120,
            'qualified_insulated_shipper': 80,
            'active_temp_controlled_container': 400,
            'insulated_box': 20
        }

        base_cost = costs.get(packaging_type, 50)

        # Add coolant cost based on duration
        coolant_cost = (hours / 24) * 15

        return base_cost + coolant_cost


# Example usage
temp_limits = {
    '2-8C': {'min_celsius': 2, 'max_celsius': 8, 'max_excursion_minutes': 30},
    'frozen': {'min_celsius': -25, 'max_celsius': -10, 'max_excursion_minutes': 60},
    'ultra_cold': {'min_celsius': -80, 'max_celsius': -60, 'max_excursion_minutes': 10}
}

# Simulate temperature log
temp_log = pd.DataFrame({
    'timestamp': pd.date_range('2025-01-20 08:00', periods=100, freq='15min'),
    'temperature_celsius': np.random.normal(5, 1.5, 100)
})

# Add an excursion
temp_log.loc[30:35, 'temperature_celsius'] = [10, 11, 12, 11.5, 10, 9]

ccm = ColdChainManager(temp_limits)
validation = ccm.validate_shipment_temperature(temp_log, '2-8C')

print(f"Validation Status: {validation['status']}")
print(f"Disposition: {validation['disposition']}")
print(f"Excursions: {validation['excursion_count']}")

class SerializationManager:
    """
    Manage pharmaceutical serialization and track-and-trace
    """

    def __init__(self, regulatory_region='US'):
        self.regulatory_region = regulatory_region

    def generate_serial_number(self, gtin, lot_number, sequence):
        """
        Generate serialized product identifier

        Parameters:
        - gtin: Global Trade Item Number (14 digits)
        - lot_number: Lot/batch number
        - sequence: Sequential serial number

        Returns:
        - serialized identifier
        """

        # Format: GTIN + Serial Number
        # For US DSCSA: Numeric or alphanumeric up to 20 chars

        serial = f"{sequence:010d}"  # 10-digit serial

        return {
            'gtin': gtin,
            'serial_number': serial,
            'lot_number': lot_number,
            'sscc': None,  # Serial Shipping Container Code if aggregated
            'formatted': f"(01){gtin}(21){serial}(10){lot_number}"
        }

    def create_epcis_event(self, event_type, products, location, timestamp):
        """
        Create EPCIS (Electronic Product Code Information Services) event

        Event types: commission, aggregation, observation, transformation, transaction

        Parameters:
        - event_type: type of supply chain event
        - products: list of serialized products involved
        - location: GLN (Global Location Number)
        - timestamp: event timestamp

        Returns:
        - EPCIS event structure
        """

        event = {
            'event_type': event_type,
            'event_time': timestamp.isoformat(),
            'event_timezone': 'UTC',
            'location': {
                'gln': location,
                'name': self._lookup_location_name(location)
            },
            'products': []
        }

        for product in products:
            event['products'].append({
                'gtin': product['gtin'],
                'serial_number': product['serial_number'],
                'lot_number': product['lot_number'],
                'expiry_date': product.get('expiry_date')
            })

        # Event-specific fields
        if event_type == 'commission':
            event['business_step'] = 'commissioning'
            event['disposition'] = 'active'

        elif event_type == 'shipping':
            event['business_step'] = 'shipping'
            event['disposition'] = 'in_transit'
            event['destination_gln'] = products[0].get('destination_gln')

        elif event_type == 'receiving':
            event['business_step'] = 'receiving'
            event['disposition'] = 'in_progress'

        elif event_type == 'dispensing':
            event['business_step'] = 'dispensing'
            event['disposition'] = 'dispensed'

        return event

    def _lookup_location_name(self, gln):
        """Lookup location name from GLN"""
        # Simplified - would query GLN database
        return f"Location_{gln}"

    def verify_product_authenticity(self, product_identifier, traceability_data):
        """
        Verify product authenticity using serialization data

        Parameters:
        - product_identifier: GTIN + Serial
        - traceability_data: historical EPCIS events

        Returns:
        - verification result
        """

        verification = {
            'is_authentic': True,
            'issues': [],
            'supply_chain_path': []
        }

        # Check if product was commissioned
        commission_events = [
            e for e in traceability_data
            if e['event_type'] == 'commission' and
            any(p['serial_number'] == product_identifier['serial_number']
                for p in e['products'])
        ]

        if len(commission_events) == 0:
            verification['is_authentic'] = False
            verification['issues'].append('no_commission_event_found')
            return verification

        # Trace supply chain path
        current_product = product_identifier
        path = []

        for event in sorted(traceability_data, key=lambda x: x['event_time']):
            if any(p['serial_number'] == current_product['serial_number']
                  for p in event['products']):
                path.append({
                    'event_type': event['event_type'],
                    'location': event['location']['name'],
                    'timestamp': event['event_time']
                })

        verification['supply_chain_path'] = path

        # Check for suspicious patterns
        if len(path) > 10:
            verification['issues'].append('excessive_handling_events')

        # Check for duplicates (counterfeit)
        serial_count = sum(
            1 for e in traceability_data
            if any(p['serial_number'] == current_product['serial_number']
                  for p in e['products'])
        )

        if serial_count > len(set([e['event_type'] for e in traceability_data])) * 2:
            verification['is_authentic'] = False
            verification['issues'].append('duplicate_serial_detected_possible_counterfeit')

        return verification

    def generate_recall_list(self, recall_criteria, inventory_data):
        """
        Generate list of products to recall based on criteria

        Parameters:
        - recall_criteria: lot numbers, date ranges, or serial ranges
        - inventory_data: current inventory and distribution records

        Returns:
        - list of affected products with locations
        """

        affected_products = []

        for product in inventory_data:
            match = False

            # Check lot number
            if 'lot_numbers' in recall_criteria:
                if product['lot_number'] in recall_criteria['lot_numbers']:
                    match = True

            # Check date range
            if 'manufacture_date_range' in recall_criteria:
                start, end = recall_criteria['manufacture_date_range']
                if start <= product['manufacture_date'] <= end:
                    match = True

            # Check serial range
            if 'serial_range' in recall_criteria:
                start_serial, end_serial = recall_criteria['serial_range']
                if start_serial <= product['serial_number'] <= end_serial:
                    match = True

            if match:
                affected_products.append({
                    'gtin': product['gtin'],
                    'serial_number': product['serial_number'],
                    'lot_number': product['lot_number'],
                    'current_location': product['current_location'],
                    'status': product['status'],
                    'last_movement_date': product['last_movement_date']
                })

        return pd.DataFrame(affected_products)


# Example
sm = SerializationManager(regulatory_region='US')

# Generate serial numbers
product_serial = sm.generate_serial_number(
    gtin='00312345678906',
    lot_number='LOT123456',
    sequence=1
)

print(f"Serialized Product: {product_serial['formatted']}")

# Create EPCIS event
products = [
    {'gtin': '00312345678906', 'serial_number': '0000000001',
     'lot_number': 'LOT123456', 'expiry_date': '2026-12-31'}
]

event = sm.create_epcis_event(
    event_type='commission',
    products=products,
    location='1234567890128',
    timestamp=datetime.now()
)

print(f"EPCIS Event: {event['business_step']} at {event['location']['name']}")

class GDPComplianceManager:
    """
    Manage Good Distribution Practices compliance
    """

    def __init__(self):
        self.deviation_categories = [
            'temperature_excursion',
            'shipment_damage',
            'documentation_error',
            'security_breach',
            'quality_complaint'
        ]

    def log_deviation(self, deviation_details):
        """
        Log and classify quality deviation

        Parameters:
        - deviation_details: description, product, severity

        Returns:
        - deviation record with required actions
        """

        deviation_id = f"DEV_{datetime.now().strftime('%Y%m%d%H%M%S')}"

        # Classify severity
        severity = self._classify_deviation_severity(deviation_details)

        # Determine required actions
        required_actions = self._determine_deviation_actions(
            deviation_details['category'],
            severity
        )

        deviation_record = {
            'deviation_id': deviation_id,
            'date_identified': datetime.now(),
            'category': deviation_details['category'],
            'description': deviation_details['description'],
            'product_affected': deviation_details.get('product_id'),
            'lot_numbers': deviation_details.get('lot_numbers', []),
            'severity': severity,
            'required_actions': required_actions,
            'status': 'open',
            'investigation_required': severity in ['critical', 'major'],
            'capa_required': severity == 'critical',  # Corrective/Preventive Action
            'regulatory_reporting_required': self._requires_regulatory_reporting(severity)
        }

        return deviation_record

    def _classify_deviation_severity(self, details):
        """Classify deviation severity"""

        category = details['category']

        # Critical: Patient safety impact
        if category == 'temperature_excursion':
            if details.get('duration_minutes', 0) > 60:
                return 'critical'
            elif details.get('duration_minutes', 0) > 30:
                return 'major'
            else:
                return 'minor'

        elif category == 'security_breach':
            return 'critical'

        elif category == 'shipment_damage':
            if details.get('product_integrity_compromised'):
                return 'critical'
            else:
                return 'major'

        else:
            return 'minor'

    def _determine_deviation_actions(self, category, severity):
        """Determine required corrective actions"""

        actions = ['document_deviation', 'notify_quality_assurance']

        if severity == 'critical':
            actions.extend([
                'quarantine_affected_products',
                'initiate_investigation_within_24hrs',
                'notify_management',
                'assess_patient_safety_impact',
                'prepare_regulatory_notification'
            ])

        elif severity == 'major':
            actions.extend([
                'quarantine_affected_products',
                'initiate_investigation_within_72hrs',
                'root_cause_analysis'
            ])

        if category == 'temperature_excursion':
            actions.append('review_temperature_monitoring_system')
            actions.append('verify_packaging_qualification')

        return actions

    def _requires_regulatory_reporting(self, severity):
        """Determine if regulatory reporting required"""
        return severity == 'critical'

    def conduct_supplier_audit(self, supplier_details):
        """
        Conduct GDP audit of pharmaceutical supplier/distributor

        Parameters:
        - supplier_details: supplier information and capabilities

        Returns:
        - audit checklist and scoring
        """

        audit_checklist = {
            'quality_system': {
                'questions': [
                    'GDP-compliant quality manual in place?',
                    'Document control system established?',
                    'Management review conducted annually?',
                    'Quality risk management process?'
                ],
                'weight': 0.20
            },
            'personnel': {
                'questions': [
                    'Qualified person designated?',
                    'GDP training program in place?',
                    'Training records maintained?',
                    'Job descriptions defined?'
                ],
                'weight': 0.15
            },
            'facilities': {
                'questions': [
                    'Temperature-controlled storage available?',
                    'Security measures adequate?',
                    'Separate quarantine area?',
                    'Clean and organized warehouse?'
                ],
                'weight': 0.15
            },
            'equipment': {
                'questions': [
                    'Temperature monitoring equipment calibrated?',
                    'Backup power systems in place?',
                    'Material handling equipment adequate?',
                    'IT systems validated?'
                ],
                'weight': 0.15
            },
            'operations': {
                'questions': [
                    'SOPs for receipt, storage, dispatch?',
                    'FIFO/FEFO system implemented?',
                    'Deviation management process?',
                    'Returns and recalls procedures?'
                ],
                'weight': 0.20
            },
            'transportation': {
                'questions': [
                    'Qualified transport providers used?',
                    'Temperature-controlled vehicles available?',
                    'Shipment validation performed?',
                    'Security measures for transport?'
                ],
                'weight': 0.15
            }
        }

        # Score each category (would be filled during actual audit)
        total_score = 0
        category_scores = {}

        for category, details in audit_checklist.items():
            # Simplified scoring - would be actual yes/no answers
            score = np.random.uniform(0.7, 1.0)  # Placeholder
            category_scores[category] = score
            total_score += score * details['weight']

        audit_result = {
            'supplier': supplier_details['name'],
            'audit_date': datetime.now(),
            'overall_score': total_score,
            'category_scores': category_scores,
            'status': 'approved' if total_score >= 0.85 else 'conditional' if total_score >= 0.70 else 'rejected',
            'critical_findings': [],
            'major_findings': [],
            'minor_findings': []
        }

        return audit_result


# Example
gdp = GDPComplianceManager()

# Log temperature deviation
deviation = gdp.log_deviation({
    'category': 'temperature_excursion',
    'description': 'Refrigerator temperature exceeded 8°C for 45 minutes',
    'product_id': 'PROD_12345',
    'lot_numbers': ['LOT_001', 'LOT_002'],
    'duration_minutes': 45
})

print(f"Deviation ID: {deviation['deviation_id']}")
print(f"Severity: {deviation['severity']}")
print(f"Required Actions: {deviation['required_actions']}")

class ClinicalTrialsSupplyChain:
    """
    Manage clinical trial drug supply and distribution
    """

    def __init__(self, trial_protocol):
        self.trial_protocol = trial_protocol

    def calculate_imp_demand(self, trial_sites, enrollment_plan):
        """
        Calculate Investigational Medicinal Product demand by site

        Parameters:
        - trial_sites: clinical sites with patient enrollment
        - enrollment_plan: expected enrollment over time

        Returns:
        - IMP requirements by site and time period
        """

        imp_demand = []

        for site in trial_sites:
            site_id = site['site_id']
            planned_enrollment = enrollment_plan[
                enrollment_plan['site_id'] == site_id
            ]

            for idx, period in planned_enrollment.iterrows():
                # Patients enrolled in period
                patients = period['patients']

                # Dosing regimen from protocol
                doses_per_patient = self.trial_protocol['doses_per_patient']
                treatment_duration_weeks = self.trial_protocol['treatment_duration_weeks']

                # Safety stock
                safety_stock_pct = 0.25  # 25% overage

                # Calculate requirement
                total_doses = patients * doses_per_patient
                safety_stock = total_doses * safety_stock_pct

                imp_demand.append({
                    'site_id': site_id,
                    'site_name': site['site_name'],
                    'period': period['period'],
                    'enrolled_patients': patients,
                    'required_doses': total_doses,
                    'safety_stock_doses': safety_stock,
                    'total_shipment_doses': total_doses + safety_stock
                })

        return pd.DataFrame(imp_demand)

    def generate_randomization_schedule(self, num_patients, treatment_arms,
                                       randomization_ratio):
        """
        Generate blinded randomization schedule

        Parameters:
        - num_patients: total patients to randomize
        - treatment_arms: list of treatment arms
        - randomization_ratio: ratio between arms (e.g., [1, 1] for 1:1)

        Returns:
        - randomization schedule
        """

        # Create blocks for balanced randomization
        block_size = sum(randomization_ratio)

        num_blocks = int(np.ceil(num_patients / block_size))

        randomization_schedule = []

        patient_id = 1

        for block in range(num_blocks):
            # Create one block
            block_assignments = []

            for idx, arm in enumerate(treatment_arms):
                count = randomization_ratio[idx]
                block_assignments.extend([arm] * count)

            # Randomize within block
            np.random.shuffle(block_assignments)

            # Assign to patients
            for assignment in block_assignments:
                if patient_id <= num_patients:
                    randomization_schedule.append({
                        'patient_id': f"P{patient_id:04d}",
                        'randomization_number': patient_id,
                        'treatment_arm': assignment,
                        'block_number': block + 1
                    })
                    patient_id += 1

        return pd.DataFrame(randomization_schedule)

    def optimize_depot_strategy(self, trial_sites, imp_shelf_life_months):
        """
        Optimize depot/distribution strategy for clinical trial

        Centralized vs. Regional vs. Direct-to-Site

        Parameters:
        - trial_sites: list of clinical sites with locations
        - imp_shelf_life_months: product shelf life

        Returns:
        - recommended distribution strategy
        """

        num_sites = len(trial_sites)
        geographic_spread = self._calculate_geographic_spread(trial_sites)

        # Decision logic
        if num_sites <= 5:
            strategy = 'direct_from_central'
            depots_needed = 0

        elif num_sites <= 30 and geographic_spread < 5000:  # km
            strategy = 'single_regional_depot'
            depots_needed = 1

        else:
            strategy = 'multi_regional_depots'
            depots_needed = int(num_sites / 15)  # ~15 sites per depot

        # Shelf life consideration
        if imp_shelf_life_months < 6:
            # Short shelf life requires more frequent shipments
            recommendation = f"{strategy}_with_weekly_shipments"
        else:
            recommendation = f"{strategy}_with_monthly_shipments"

        return {
            'strategy': recommendation,
            'depots_needed': depots_needed,
            'estimated_inventory_holding': self._estimate_trial_inventory(
                num_sites, strategy
            )
        }

    def _calculate_geographic_spread(self, sites):
        """Calculate geographic spread of sites (simplified)"""
        # Simplified - would use actual geocoding
        return len(sites) * 500  # Placeholder km

    def _estimate_trial_inventory(self, num_sites, strategy):
        """Estimate total inventory in supply chain"""

        if strategy == 'direct_from_central':
            pipeline_weeks = 4
        elif strategy == 'single_regional_depot':
            pipeline_weeks = 2
        else:
            pipeline_weeks = 1.5

        # Weekly demand per site (placeholder)
        weekly_demand_per_site = 50

        total_pipeline = num_sites * weekly_demand_per_site * pipeline_weeks

        return total_pipeline


# Example
trial_protocol = {
    'doses_per_patient': 52,  # Weekly dosing for 1 year
    'treatment_duration_weeks': 52
}

ct_supply = ClinicalTrialsSupplyChain(trial_protocol)

# Randomization
randomization = ct_supply.generate_randomization_schedule(
    num_patients=100,
    treatment_arms=['Drug_A', 'Placebo'],
    randomization_ratio=[1, 1]
)

print(f"Randomized {len(randomization)} patients")
print(randomization.groupby('treatment_arm').size())