"""
Time Series Forecasting Engine for Telecom Analytics
Provides forecasting methods for all four domains:
  1. Seasonal Usage Patterns
  2. Technology Adoption Curves
  3. Competitive Market Dynamics
  4. Economic Impact Forecasting
"""

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

# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------

def _moving_average_forecast(series, window=3, forecast_periods=12):
    """Weighted moving average forecast."""
    values = series.values.astype(float)
    weights = np.arange(1, window + 1, dtype=float)
    weights /= weights.sum()

    forecasts = list(values)
    for _ in range(forecast_periods):
        recent = np.array(forecasts[-window:])
        forecasts.append(float(np.dot(recent, weights)))
    return np.array(forecasts[-forecast_periods:])


def _seasonal_decompose_forecast(series, period=12, forecast_periods=12):
    """Simple seasonal decomposition + trend extrapolation."""
    values = series.values.astype(float)
    n = len(values)

    # Trend via centered moving average
    if n >= period:
        trend = pd.Series(values).rolling(window=period, center=True).mean().values
        # Fill edges
        for i in range(n):
            if np.isnan(trend[i]):
                trend[i] = values[i]
    else:
        trend = values.copy()

    # Seasonal component
    detrended = values - trend
    seasonal = np.zeros(period)
    for i in range(period):
        indices = list(range(i, n, period))
        seasonal[i] = np.mean(detrended[indices])

    # Extrapolate trend linearly
    x = np.arange(n)
    valid = ~np.isnan(trend)
    if valid.sum() > 1:
        coeffs = np.polyfit(x[valid], trend[valid], 1)
    else:
        coeffs = [0, values[-1]]

    forecast_trend = np.polyval(coeffs, np.arange(n, n + forecast_periods))
    forecast_seasonal = np.tile(seasonal, (forecast_periods // period + 2))[:forecast_periods]

    forecast = forecast_trend + forecast_seasonal
    return forecast


def _logistic_curve_forecast(current_values, k=0.65, forecast_periods=12):
    """Forecast S-curve / logistic adoption."""
    values = current_values / 100.0  # convert from pct
    n = len(values)

    # Fit logistic parameters by least squares grid search
    best_err = float('inf')
    best_x0 = n // 2
    best_L = 0.2

    for x0_try in range(max(1, n // 4), n + 12):
        for L_try in np.arange(0.05, 0.5, 0.05):
            t = np.arange(n)
            predicted = k / (1 + np.exp(-L_try * (t - x0_try)))
            err = np.sum((predicted - values) ** 2)
            if err < best_err:
                best_err = err
                best_x0 = x0_try
                best_L = L_try

    t_future = np.arange(n, n + forecast_periods)
    forecast = k / (1 + np.exp(-best_L * (t_future - best_x0)))
    return forecast * 100  # back to pct


def _exponential_smoothing(series, alpha=0.3, forecast_periods=12):
    """Simple exponential smoothing forecast."""
    values = series.values.astype(float)
    smoothed = [values[0]]
    for v in values[1:]:
        smoothed.append(alpha * v + (1 - alpha) * smoothed[-1])

    forecasts = []
    last = smoothed[-1]
    # Add slight trend
    if len(smoothed) > 1:
        trend = (smoothed[-1] - smoothed[-6]) / 6 if len(smoothed) >= 6 else 0
    else:
        trend = 0

    for i in range(forecast_periods):
        next_val = last + trend
        forecasts.append(next_val)
        last = next_val

    return np.array(forecasts)


# ---------------------------------------------------------------------------
# Forecast Functions per Domain
# ---------------------------------------------------------------------------

def forecast_seasonal_usage(seasonal_df, forecast_months=12):
    """Forecast seasonal usage patterns."""
    last_date = pd.to_datetime(seasonal_df['date']).max()
    forecast_dates = pd.date_range(start=last_date + pd.DateOffset(months=1),
                                   periods=forecast_months, freq='ME')

    # Forecast each metric
    data_usage_forecast = _seasonal_decompose_forecast(
        seasonal_df['avg_data_usage_gb'], period=12, forecast_periods=forecast_months)

    voice_forecast = _seasonal_decompose_forecast(
        seasonal_df['avg_voice_minutes'], period=12, forecast_periods=forecast_months)

    network_load_forecast = _moving_average_forecast(
        seasonal_df['network_load_factor'], window=4, forecast_periods=forecast_months)
    network_load_forecast = np.clip(network_load_forecast, 0.3, 0.98)

    peak_users_forecast = _seasonal_decompose_forecast(
        seasonal_df['peak_concurrent_users'], period=12, forecast_periods=forecast_months)

    # Predict holiday months
    holiday_flags = []
    for d in forecast_dates:
        holiday_flags.append(1 if d.month in [2, 7, 9, 11, 12] else 0)

    # Confidence intervals (wider for further out)
    ci_width = np.linspace(0.05, 0.20, forecast_months)

    result = {
        'dates': [d.strftime('%Y-%m') for d in forecast_dates],
        'data_usage': {
            'forecast': np.round(np.maximum(data_usage_forecast, 2), 2).tolist(),
            'upper': np.round(data_usage_forecast * (1 + ci_width), 2).tolist(),
            'lower': np.round(data_usage_forecast * (1 - ci_width), 2).tolist(),
        },
        'voice_minutes': {
            'forecast': np.round(np.maximum(voice_forecast, 50), 1).tolist(),
            'upper': np.round(voice_forecast * (1 + ci_width * 0.8), 1).tolist(),
            'lower': np.round(voice_forecast * (1 - ci_width * 0.8), 1).tolist(),
        },
        'network_load': {
            'forecast': np.round(network_load_forecast, 3).tolist(),
            'upper': np.round(np.clip(network_load_forecast + ci_width * 0.3, 0, 1), 3).tolist(),
            'lower': np.round(np.clip(network_load_forecast - ci_width * 0.3, 0, 1), 3).tolist(),
        },
        'peak_users': {
            'forecast': np.round(np.maximum(peak_users_forecast, 10000)).astype(int).tolist(),
            'upper': np.round(peak_users_forecast * (1 + ci_width)).astype(int).tolist(),
            'lower': np.round(peak_users_forecast * (1 - ci_width)).astype(int).tolist(),
        },
        'holiday_months': holiday_flags,
    }

    # Historical data for chart context
    hist = seasonal_df.tail(12)
    result['historical'] = {
        'dates': [pd.to_datetime(d).strftime('%Y-%m') for d in hist['date']],
        'data_usage': hist['avg_data_usage_gb'].round(2).tolist(),
        'voice_minutes': hist['avg_voice_minutes'].round(1).tolist(),
        'network_load': hist['network_load_factor'].round(3).tolist(),
        'peak_users': hist['peak_concurrent_users'].tolist(),
    }

    return result


def forecast_tech_adoption(tech_df, forecast_months=12):
    """Forecast 5G adoption and technology migration."""
    last_date = pd.to_datetime(tech_df['date']).max()
    forecast_dates = pd.date_range(start=last_date + pd.DateOffset(months=1),
                                   periods=forecast_months, freq='ME')

    # 5G adoption via logistic curve
    five_g_forecast = _logistic_curve_forecast(
        tech_df['five_g_adoption_pct'].values, k=0.65, forecast_periods=forecast_months)

    # Tower deployment trend
    towers_forecast = _exponential_smoothing(
        tech_df['five_g_towers_cumulative'], alpha=0.4, forecast_periods=forecast_months)
    towers_forecast = np.maximum(towers_forecast, tech_df['five_g_towers_cumulative'].iloc[-1])

    # Speed improvement
    speed_forecast = _moving_average_forecast(
        tech_df['avg_5g_speed_mbps'], window=4, forecast_periods=forecast_months)

    # Revenue premium
    premium_forecast = _exponential_smoothing(
        tech_df['five_g_revenue_premium_pct'], alpha=0.35, forecast_periods=forecast_months)

    # 4G and 3G derived
    four_g_forecast = np.maximum(100 - five_g_forecast - 5, 20)  # floor at 20%
    three_g_forecast = 100 - five_g_forecast - four_g_forecast

    ci_width = np.linspace(0.03, 0.15, forecast_months)

    result = {
        'dates': [d.strftime('%Y-%m') for d in forecast_dates],
        'five_g_adoption': {
            'forecast': np.round(five_g_forecast, 2).tolist(),
            'upper': np.round(five_g_forecast * (1 + ci_width), 2).tolist(),
            'lower': np.round(np.maximum(five_g_forecast * (1 - ci_width), 0), 2).tolist(),
        },
        'four_g_pct': np.round(four_g_forecast, 2).tolist(),
        'three_g_pct': np.round(np.maximum(three_g_forecast, 1), 2).tolist(),
        'towers_deployed': {
            'forecast': np.round(towers_forecast).astype(int).tolist(),
        },
        'avg_speed': {
            'forecast': np.round(speed_forecast, 1).tolist(),
        },
        'revenue_premium': {
            'forecast': np.round(premium_forecast, 1).tolist(),
        },
    }

    # Historical
    hist = tech_df.tail(12)
    result['historical'] = {
        'dates': [pd.to_datetime(d).strftime('%Y-%m') for d in hist['date']],
        'five_g_adoption': hist['five_g_adoption_pct'].round(2).tolist(),
        'four_g_pct': hist['four_g_pct'].round(2).tolist(),
        'three_g_pct': hist['three_g_pct'].round(2).tolist(),
        'towers_deployed': hist['five_g_towers_cumulative'].tolist(),
        'avg_speed': hist['avg_5g_speed_mbps'].round(1).tolist(),
    }

    return result


def forecast_competitive_dynamics(comp_df, forecast_months=12):
    """Forecast competitive market dynamics."""
    last_date = pd.to_datetime(comp_df['date']).max()
    forecast_dates = pd.date_range(start=last_date + pd.DateOffset(months=1),
                                   periods=forecast_months, freq='ME')

    # Market shares via exponential smoothing
    our_share_fc = _exponential_smoothing(comp_df['our_market_share'], alpha=0.35, forecast_periods=forecast_months)
    comp_a_fc = _exponential_smoothing(comp_df['competitor_a_share'], alpha=0.35, forecast_periods=forecast_months)
    comp_b_fc = _exponential_smoothing(comp_df['competitor_b_share'], alpha=0.35, forecast_periods=forecast_months)
    comp_c_fc = 100 - our_share_fc - comp_a_fc - comp_b_fc

    # Pricing forecast
    our_price_fc = _moving_average_forecast(comp_df['our_avg_price'], window=4, forecast_periods=forecast_months)
    market_price_fc = _moving_average_forecast(comp_df['market_avg_price'], window=4, forecast_periods=forecast_months)

    # Net subscriber adds
    net_adds_fc = _seasonal_decompose_forecast(comp_df['net_subscriber_adds'], period=12, forecast_periods=forecast_months)

    # Competitive churn
    comp_churn_fc = _moving_average_forecast(comp_df['competitive_churn_pct'], window=4, forecast_periods=forecast_months)

    # Pricing war risk assessment (based on price convergence)
    price_gap = np.abs(our_price_fc - market_price_fc)
    pricing_war_risk = np.clip(1 - price_gap / 10, 0, 1)

    ci_width = np.linspace(0.02, 0.12, forecast_months)

    result = {
        'dates': [d.strftime('%Y-%m') for d in forecast_dates],
        'market_shares': {
            'ours': np.round(our_share_fc, 2).tolist(),
            'competitor_a': np.round(comp_a_fc, 2).tolist(),
            'competitor_b': np.round(comp_b_fc, 2).tolist(),
            'competitor_c': np.round(np.maximum(comp_c_fc, 5), 2).tolist(),
        },
        'pricing': {
            'our_price': np.round(our_price_fc, 2).tolist(),
            'market_price': np.round(market_price_fc, 2).tolist(),
        },
        'net_adds': {
            'forecast': np.round(net_adds_fc).astype(int).tolist(),
            'upper': np.round(net_adds_fc * (1 + ci_width * 2)).astype(int).tolist(),
            'lower': np.round(net_adds_fc * (1 - ci_width * 2)).astype(int).tolist(),
        },
        'competitive_churn': {
            'forecast': np.round(np.maximum(comp_churn_fc, 0.3), 2).tolist(),
        },
        'pricing_war_risk': np.round(pricing_war_risk * 100, 1).tolist(),
    }

    # Historical
    hist = comp_df.tail(12)
    result['historical'] = {
        'dates': [pd.to_datetime(d).strftime('%Y-%m') for d in hist['date']],
        'our_share': hist['our_market_share'].round(2).tolist(),
        'competitor_a': hist['competitor_a_share'].round(2).tolist(),
        'competitor_b': hist['competitor_b_share'].round(2).tolist(),
        'competitor_c': hist['competitor_c_share'].round(2).tolist(),
        'our_price': hist['our_avg_price'].round(2).tolist(),
        'market_price': hist['market_avg_price'].round(2).tolist(),
        'net_adds': hist['net_subscriber_adds'].tolist(),
    }

    return result


def forecast_economic_impact(econ_df, forecast_months=12):
    """Forecast economic impact on telecom behavior."""
    last_date = pd.to_datetime(econ_df['date']).max()
    forecast_dates = pd.date_range(start=last_date + pd.DateOffset(months=1),
                                   periods=forecast_months, freq='ME')

    # GDP growth
    gdp_fc = _exponential_smoothing(econ_df['gdp_growth_rate'], alpha=0.3, forecast_periods=forecast_months)

    # Consumer confidence
    cci_fc = _exponential_smoothing(econ_df['consumer_confidence_index'], alpha=0.3, forecast_periods=forecast_months)

    # Unemployment
    unemp_fc = _exponential_smoothing(econ_df['unemployment_rate'], alpha=0.25, forecast_periods=forecast_months)

    # ARPU index
    arpu_fc = _exponential_smoothing(econ_df['arpu_index'], alpha=0.35, forecast_periods=forecast_months)

    # Downgrade rate
    downgrade_fc = _moving_average_forecast(econ_df['plan_downgrade_rate'], window=4, forecast_periods=forecast_months)

    # Delinquency
    delinquency_fc = _moving_average_forecast(econ_df['payment_delinquency_rate'], window=4, forecast_periods=forecast_months)

    # Revenue at risk
    risk_fc = _exponential_smoothing(econ_df['revenue_at_risk_millions'], alpha=0.3, forecast_periods=forecast_months)

    # Sentiment
    sentiment_fc = _exponential_smoothing(econ_df['customer_sentiment_index'], alpha=0.3, forecast_periods=forecast_months)

    # Recession probability (simple heuristic)
    recession_prob = np.clip((1.5 - gdp_fc) / 2.0 * 100, 0, 95)

    ci_width = np.linspace(0.03, 0.18, forecast_months)

    result = {
        'dates': [d.strftime('%Y-%m') for d in forecast_dates],
        'gdp_growth': {
            'forecast': np.round(gdp_fc, 2).tolist(),
            'upper': np.round(gdp_fc + ci_width * 3, 2).tolist(),
            'lower': np.round(gdp_fc - ci_width * 3, 2).tolist(),
        },
        'consumer_confidence': {
            'forecast': np.round(np.clip(cci_fc, 20, 100), 1).tolist(),
        },
        'unemployment': {
            'forecast': np.round(np.clip(unemp_fc, 2, 12), 1).tolist(),
        },
        'arpu_index': {
            'forecast': np.round(arpu_fc, 1).tolist(),
        },
        'downgrade_rate': {
            'forecast': np.round(np.maximum(downgrade_fc, 0.5), 2).tolist(),
        },
        'delinquency_rate': {
            'forecast': np.round(np.maximum(delinquency_fc, 0.5), 2).tolist(),
        },
        'revenue_at_risk': {
            'forecast': np.round(np.maximum(risk_fc, 0.5), 2).tolist(),
            'upper': np.round(np.maximum(risk_fc, 0.5) * (1 + ci_width), 2).tolist(),
            'lower': np.round(np.maximum(risk_fc, 0.5) * (1 - ci_width), 2).tolist(),
        },
        'sentiment_index': {
            'forecast': np.round(np.clip(sentiment_fc, 10, 100), 1).tolist(),
        },
        'recession_probability': np.round(recession_prob, 1).tolist(),
    }

    # Historical
    hist = econ_df.tail(12)
    result['historical'] = {
        'dates': [pd.to_datetime(d).strftime('%Y-%m') for d in hist['date']],
        'gdp_growth': hist['gdp_growth_rate'].round(2).tolist(),
        'consumer_confidence': hist['consumer_confidence_index'].round(1).tolist(),
        'unemployment': hist['unemployment_rate'].round(1).tolist(),
        'arpu_index': hist['arpu_index'].round(1).tolist(),
        'revenue_at_risk': hist['revenue_at_risk_millions'].round(2).tolist(),
        'sentiment_index': hist['customer_sentiment_index'].round(1).tolist(),
    }

    return result


def get_forecast_summary(seasonal_fc, tech_fc, comp_fc, econ_fc):
    """Generate a high-level summary of all forecasts for KPI cards."""
    summary = {}

    # Seasonal
    usage_trend = seasonal_fc['data_usage']['forecast']
    summary['data_usage_next_month'] = usage_trend[0]
    summary['data_usage_growth'] = round((usage_trend[-1] - usage_trend[0]) / usage_trend[0] * 100, 1)
    summary['peak_network_load'] = max(seasonal_fc['network_load']['forecast'])

    # Tech
    five_g = tech_fc['five_g_adoption']['forecast']
    summary['five_g_current'] = tech_fc['historical']['five_g_adoption'][-1]
    summary['five_g_forecast_end'] = five_g[-1]
    summary['five_g_growth'] = round(five_g[-1] - tech_fc['historical']['five_g_adoption'][-1], 1)

    # Competitive
    summary['market_share_current'] = comp_fc['historical']['our_share'][-1]
    summary['market_share_forecast'] = comp_fc['market_shares']['ours'][-1]
    summary['avg_pricing_war_risk'] = round(np.mean(comp_fc['pricing_war_risk']), 1)

    # Economic
    summary['recession_probability'] = econ_fc['recession_probability'][0]
    summary['revenue_at_risk'] = econ_fc['revenue_at_risk']['forecast'][0]
    summary['sentiment_forecast'] = econ_fc['sentiment_index']['forecast'][0]

    return summary