data_generators.py

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

def generate_physician_segments():
    """Generate simulated physician segment data"""
    segments = [
        "High Volume PCPs",
        "Early Adopter Endocrinologists",
        "Conservative PCPs",
        "Academic Endocrinologists",
        "Urban Health System PCPs",
        "Rural Independent PCPs",
        "Diabetes-Focused PCPs",
        "Cardiologists with Diabetes Interest",
        "Nurse Practitioners in Primary Care",
        "Physician Assistants in Endocrinology"
    ]
    
    data = []
    for segment in segments:
        data.append({
            "Segment": segment,
            "Size": random.randint(1000, 15000),
            "Prescribing Volume": random.randint(50, 200),
            "Digital Engagement": random.uniform(0.1, 0.9),
            "XenoGlip Affinity": random.uniform(0.2, 0.8),
            "Message Receptivity": random.uniform(0.3, 0.9)
        })
    
    return pd.DataFrame(data)

def generate_prescription_data():
    """Generate simulated prescription data for the past year"""
    # Create date range for the past year
    end_date = datetime.now()
    start_date = end_date - timedelta(days=365)
    dates = pd.date_range(start=start_date, end=end_date, freq='W')
    
    # Competitors
    competitors = ["XenoGlip", "CompDPP4", "GLP1-A", "GLP1-B", "SGLT2-A", "SGLT2-B"]
    
    # Generate data
    data = []
    for date in dates:
        # Base values
        base_values = {
            "XenoGlip": 8000 + random.randint(-500, 500),
            "CompDPP4": 12000 + random.randint(-800, 800),
            "GLP1-A": 9000 + random.randint(-600, 600),
            "GLP1-B": 7500 + random.randint(-500, 500),
            "SGLT2-A": 11000 + random.randint(-700, 700),
            "SGLT2-B": 6500 + random.randint(-400, 400)
        }
        
        # Add trend over time
        week_num = (date - start_date).days / 7
        growth_factor = 1 + (week_num / 52) * 0.15  # 15% annual growth for XenoGlip
        base_values["XenoGlip"] = int(base_values["XenoGlip"] * growth_factor)
        
        # Add data points
        for comp in competitors:
            data.append({
                "Date": date,
                "Product": comp,
                "Prescriptions": base_values[comp]
            })
    
    return pd.DataFrame(data)

def generate_key_drivers():
    """Generate key prescription drivers data"""
    drivers = [
        "Efficacy in A1C reduction",
        "Safety profile",
        "Tolerability",
        "Once-daily dosing",
        "Formulary status",
        "Patient cost",
        "Cardiovascular benefits",
        "Weight neutrality",
        "Renal considerations",
        "Low hypoglycemia risk"
    ]
    
    segments = ["PCP", "Endocrinologist", "Cardiologist"]
    
    data = []
    for driver in drivers:
        for segment in segments:
            data.append({
                "Driver": driver,
                "Segment": segment,
                "Importance": random.uniform(0.5, 0.95)
            })
    
    return pd.DataFrame(data)

def generate_regional_data():
    """Generate regional prescription data"""
    regions = ["Northeast", "Southeast", "Midwest", "Southwest", "West"]
    
    data = []
    for region in regions:
        data.append({
            "Region": region,
            "Market Share": random.uniform(0.05, 0.25),
            "Growth Rate": random.uniform(-0.05, 0.15),
            "Prescription Volume": random.randint(5000, 20000),
            "Physician Adoption": random.uniform(0.2, 0.6)
        })
    
    return pd.DataFrame(data)

def generate_formulary_scenario_data():
    """Generate formulary scenario impact data"""
    scenarios = [
        "Current (Tier 3, PA required)",
        "Tier 2, PA required",
        "Tier 3, No PA",
        "Tier 2, No PA",
        "Tier 1, No PA"
    ]
    
    impact_metrics = ["New Rx Growth", "Overall Share", "Switch from Competitors", "Adherence"]
    
    data = []
    baselines = {
        "New Rx Growth": 0.0,
        "Overall Share": 0.11,
        "Switch from Competitors": 0.0,
        "Adherence": 0.68
    }
    
    # Improvements for each scenario, relative to baseline
    improvements = {
        "Tier 2, PA required": {"New Rx Growth": 0.15, "Overall Share": 0.02, "Switch from Competitors": 0.08, "Adherence": 0.03},
        "Tier 3, No PA": {"New Rx Growth": 0.22, "Overall Share": 0.015, "Switch from Competitors": 0.12, "Adherence": 0.05},
        "Tier 2, No PA": {"New Rx Growth": 0.35, "Overall Share": 0.04, "Switch from Competitors": 0.25, "Adherence": 0.08},
        "Tier 1, No PA": {"New Rx Growth": 0.65, "Overall Share": 0.07, "Switch from Competitors": 0.38, "Adherence": 0.12}
    }
    
    for scenario in scenarios:
        for metric in impact_metrics:
            if scenario == "Current (Tier 3, PA required)":
                value = baselines[metric]
            else:
                value = baselines[metric] + improvements[scenario][metric]
                
            data.append({
                "Scenario": scenario,
                "Metric": metric,
                "Value": value
            })
    
    return pd.DataFrame(data)

def generate_message_testing_data():
    """Generate message testing data"""
    messages = [
        "Once-daily dosing for simplicity",
        "Proven efficacy in A1C reduction",
        "Established cardiovascular safety",
        "Minimal hypoglycemia risk",
        "Suitable for renal impairment patients",
        "Weight neutral option",
        "Extensive clinical experience"
    ]
    
    segments = ["High Volume PCPs", "Early Adopter Endocrinologists", "Conservative PCPs", "Academic Endocrinologists"]
    
    data = []
    for message in messages:
        for segment in segments:
            data.append({
                "Message": message,
                "Segment": segment,
                "Receptivity": random.uniform(0.3, 0.9),
                "Impact Score": random.uniform(2.5, 9.5)
            })
    
    return pd.DataFrame(data)

def generate_patient_profile_data():
    """Generate patient profile data"""
    # Patient profiles
    profiles = []
    
    # Age groups
    age_groups = ["30-45", "46-60", "61-75", "76+"]
    
    # Comorbidities
    comorbidities = ["Hypertension", "Obesity", "Dyslipidemia", "CKD", "CVD", "None"]
    
    # A1C ranges
    a1c_ranges = ["<7.0", "7.0-7.9", "8.0-8.9", "9.0+"]
    
    # Medications
    current_meds = ["Metformin only", "Met+SU", "Met+DPP4", "Met+SGLT2", "Met+GLP1", "Complex regimen"]
    
    # Generate 50 profiles
    for i in range(50):
        profile = {
            "ID": i + 1,
            "Age Group": random.choice(age_groups),
            "Gender": random.choice(["Male", "Female"]),
            "BMI Category": random.choice(["Normal", "Overweight", "Obese", "Severely Obese"]),
            "A1C Range": random.choice(a1c_ranges),
            "Primary Comorbidity": random.choice(comorbidities),
            "Secondary Comorbidity": random.choice(comorbidities),
            "Current Medication": random.choice(current_meds),
            "Insurance": random.choice(["Commercial", "Medicare", "Medicaid", "Uninsured"]),
            "Years with T2DM": random.randint(1, 20)
        }
        profiles.append(profile)
    
    return pd.DataFrame(profiles)

def generate_competitive_analysis_data():
    """Generate competitive analysis data"""
    products = [
        "XenoGlip (DPP-4)",
        "CompDPP4",
        "GLP1-A",
        "GLP1-B",
        "SGLT2-A",
        "SGLT2-B"
    ]
    
    attributes = [
        "A1C Reduction",
        "Weight Effect",
        "Hypoglycemia Risk",
        "Cardiovascular Benefit",
        "Renal Benefit",
        "GI Side Effects",
        "Injection Required",
        "Cost to Patient",
        "Formulary Status"
    ]
    
    # Values for each product-attribute combination
    values = {
        "XenoGlip (DPP-4)": {
            "A1C Reduction": 0.7,
            "Weight Effect": 0.0,
            "Hypoglycemia Risk": 0.05,
            "Cardiovascular Benefit": 0.0,
            "Renal Benefit": 0.1,
            "GI Side Effects": 0.1,
            "Injection Required": 0.0,
            "Cost to Patient": 0.5,
            "Formulary Status": 0.6
        },
        "CompDPP4": {
            "A1C Reduction": 0.65,
            "Weight Effect": 0.0,
            "Hypoglycemia Risk": 0.05,
            "Cardiovascular Benefit": 0.0,
            "Renal Benefit": 0.1,
            "GI Side Effects": 0.1,
            "Injection Required": 0.0,
            "Cost to Patient": 0.5,
            "Formulary Status": 0.7
        },
        "GLP1-A": {
            "A1C Reduction": 1.2,
            "Weight Effect": -0.8,
            "Hypoglycemia Risk": 0.1,
            "Cardiovascular Benefit": 0.8,
            "Renal Benefit": 0.5,
            "GI Side Effects": 0.7,
            "Injection Required": 1.0,
            "Cost to Patient": 0.85,
            "Formulary Status": 0.5
        },
        "GLP1-B": {
            "A1C Reduction": 1.4,
            "Weight Effect": -0.9,
            "Hypoglycemia Risk": 0.1,
            "Cardiovascular Benefit": 0.8,
            "Renal Benefit": 0.6,
            "GI Side Effects": 0.8,
            "Injection Required": 1.0,
            "Cost to Patient": 0.9,
            "Formulary Status": 0.4
        },
        "SGLT2-A": {
            "A1C Reduction": 0.8,
            "Weight Effect": -0.5,
            "Hypoglycemia Risk": 0.05,
            "Cardiovascular Benefit": 0.7,
            "Renal Benefit": 0.8,
            "GI Side Effects": 0.2,
            "Injection Required": 0.0,
            "Cost to Patient": 0.7,
            "Formulary Status": 0.6
        },
        "SGLT2-B": {
            "A1C Reduction": 0.7,
            "Weight Effect": -0.4,
            "Hypoglycemia Risk": 0.05,
            "Cardiovascular Benefit": 0.6,
            "Renal Benefit": 0.7,
            "GI Side Effects": 0.2,
            "Injection Required": 0.0,
            "Cost to Patient": 0.6,
            "Formulary Status": 0.5
        }
    }
    
    data = []
    for product in products:
        for attribute in attributes:
            data.append({
                "Product": product,
                "Attribute": attribute,
                "Value": values[product][attribute]
            })
    
    return pd.DataFrame(data)