#!/bin/bash
# Production research runner script

set -e

echo "=== Liquid Neural Networks Research Framework ==="
echo "Starting research execution..."

# Set environment
export PYTHONPATH="/app/python:$PYTHONPATH"
export RESEARCH_OUTPUT_DIR="/app/results"
export ARTIFACTS_DIR="/app/artifacts"

# Create output directories
mkdir -p "$RESEARCH_OUTPUT_DIR" "$ARTIFACTS_DIR" /app/logs

# Log system information
echo "System Information:" > /app/logs/system_info.log
python -c "
import platform
import psutil
import sys
print(f'Platform: {platform.platform()}')
print(f'Python: {sys.version}')
print(f'CPU Cores: {psutil.cpu_count()}')
print(f'Memory: {psutil.virtual_memory().total / (1024**3):.1f} GB')
" >> /app/logs/system_info.log

# Determine research mode
RESEARCH_MODE=${RESEARCH_MODE:-"demo"}

case $RESEARCH_MODE in
    "demo")
        echo "Running research demonstration..."
        python /app/examples/research_demo.py 2>&1 | tee /app/logs/research_demo.log
        ;;
    "comparative_study")
        echo "Running comparative study..."
        python -c "
from liquid_audio_nets.research import ComparativeStudyFramework
import numpy as np

# Run full comparative study
framework = ComparativeStudyFramework(random_seed=42)
print('Initializing comparative study framework...')

# Generate test data
np.random.seed(42)
train_data = (np.random.randn(1000, 40), np.random.randint(0, 8, 1000))
test_data = (np.random.randn(200, 40), np.random.randint(0, 8, 200))

print('Running comprehensive comparison...')
results = framework.run_comparative_study(
    lnn_model=None,  # Would be actual LNN model
    train_data=train_data,
    test_data=test_data,
    study_name='Production Comparative Study'
)
print('Comparative study completed successfully!')
" 2>&1 | tee /app/logs/comparative_study.log
        ;;
    "multi_objective")
        echo "Running multi-objective optimization..."
        python -c "
from liquid_audio_nets.research import MultiObjectiveOptimizer

# Run multi-objective optimization
optimizer = MultiObjectiveOptimizer(random_seed=42)
print('Starting multi-objective optimization...')

# Mock optimization
def mock_evaluation(params):
    import numpy as np
    accuracy = 0.9 + np.random.normal(0, 0.05)
    power = 1.0 + np.random.normal(0, 0.2)
    latency = 10.0 + np.random.normal(0, 1.0)
    return [accuracy, -power, -latency]  # Minimize power and latency

result = optimizer.optimize(
    evaluation_function=mock_evaluation,
    parameter_space={'hidden_dim': (32, 128), 'lr': (0.001, 0.1)},
    objective_functions=['accuracy', 'power', 'latency'],
    n_generations=20,
    population_size=50
)
print('Multi-objective optimization completed!')
print(f'Found {len(result.pareto_front)} Pareto optimal solutions')
" 2>&1 | tee /app/logs/multi_objective.log
        ;;
    "batch")
        echo "Running batch experiments..."
        if [ "$BATCH_MODE" = "true" ]; then
            # Run multiple experiments in parallel
            N_JOBS=${N_PARALLEL_JOBS:-4}
            echo "Running $N_JOBS parallel experiments..."
            
            for i in $(seq 1 $N_JOBS); do
                python -c "
from liquid_audio_nets.research import ExperimentalFramework
from pathlib import Path
import time

framework = ExperimentalFramework(Path('/app/results'))

def test_experiment(config):
    time.sleep(5)  # Simulate experiment time
    return {'metric': 0.9 + $i * 0.01}

from liquid_audio_nets.research.experimental_framework import ExperimentConfig
config = ExperimentConfig(f'Batch_Experiment_{$i}', 'Batch test')
result = framework.run_experiment(config, test_experiment)
print(f'Experiment {$i} completed: {result.success}')
" &
            done
            wait
            echo "All batch experiments completed!"
        fi
        ;;
    *)
        echo "Unknown research mode: $RESEARCH_MODE"
        echo "Available modes: demo, comparative_study, multi_objective, batch"
        exit 1
        ;;
esac

echo "Research execution completed!"
echo "Results available in: $RESEARCH_OUTPUT_DIR"
echo "Artifacts available in: $ARTIFACTS_DIR"
echo "Logs available in: /app/logs"