"""
Data Profiler Agent

Profiles and analyzes datasets to understand structure, quality,
patterns, and statistical characteristics.
"""

from typing import Any, Dict, List
from agents.base import BaseAgent


class DataProfilerAgent(BaseAgent):
    """
    Profiles and analyzes datasets.

    Supports:
    - Statistical profiling
    - Data type inference
    - Missing value analysis
    - Distribution analysis
    - Correlation detection
    - Pattern recognition
    """

    def __init__(self):
        super().__init__(
            name='data-profiler',
            description='Profile and analyze datasets',
            category='data',
            version='1.0.0',
            tags=['profiling', 'data-quality', 'analysis', 'statistics']
        )

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Profile a dataset.

        Args:
            params: {
                'data_source': str,
                'columns': List[str],  # Specific columns to profile, or all if empty
                'options': {
                    'include_statistics': bool,
                    'include_distributions': bool,
                    'include_correlations': bool,
                    'include_patterns': bool,
                    'sample_size': int,
                    'detect_outliers': bool
                }
            }

        Returns:
            {
                'status': 'success|failed',
                'dataset_name': str,
                'total_rows': int,
                'total_columns': int,
                'memory_usage_mb': float,
                'column_profiles': List[Dict[str, Any]],
                'correlations': Dict[str, Any],
                'data_quality_score': float,
                'execution_time_seconds': float,
                'insights': List[str],
                'recommendations': List[str]
            }
        """
        data_source = params.get('data_source')
        columns = params.get('columns', [])
        options = params.get('options', {})

        self.logger.info(f"Profiling dataset from '{data_source}'")

        # Mock profiling results
        column_profiles = self._generate_column_profiles(columns or 12)

        return {
            'status': 'success',
            'dataset_name': data_source,
            'total_rows': 125000,
            'total_columns': len(column_profiles),
            'memory_usage_mb': 45.8,
            'profiled_columns': len(column_profiles),
            'execution_time_seconds': 5.3,
            'column_profiles': column_profiles,
            'correlations': {
                'age_income': 0.65,
                'experience_salary': 0.78,
                'education_income': 0.52
            } if options.get('include_correlations') else {},
            'data_quality_score': 0.87,
            'quality_metrics': {
                'completeness': 0.92,
                'validity': 0.85,
                'consistency': 0.88,
                'accuracy': 0.84,
                'uniqueness': 0.89
            },
            'missing_values': {
                'total_missing': 3420,
                'percentage': 2.7,
                'columns_with_missing': 8
            },
            'duplicates': {
                'total_duplicates': 234,
                'percentage': 0.19,
                'duplicate_rows': [125, 456, 789]
            },
            'outliers_detected': 456 if options.get('detect_outliers') else 0,
            'patterns_found': [
                'Email addresses follow standard format',
                'Phone numbers have consistent country code',
                'Dates are within expected range',
                'Numeric values follow normal distribution'
            ] if options.get('include_patterns') else [],
            'insights': [
                'Dataset is 92% complete with good overall quality',
                'Strong correlation between experience and salary',
                'Age and income show moderate positive correlation',
                'Missing values concentrated in optional fields',
                'No systematic data quality issues detected'
            ],
            'recommendations': [
                'Implement validation for email and phone fields',
                'Consider imputation strategy for missing income values',
                'Add constraints to prevent duplicate records',
                'Review outliers in salary column for data entry errors',
                'Standardize date formats across all date columns'
            ]
        }

    def validate_params(self, params: Dict[str, Any]) -> bool:
        """Validate profiling parameters."""
        if 'data_source' not in params:
            self.logger.error("Missing required field: data_source")
            return False
        return True

    def _generate_column_profiles(self, count: int) -> List[Dict[str, Any]]:
        """Generate mock column profiles."""
        profiles = []
        column_examples = [
            {
                'name': 'id',
                'type': 'integer',
                'unique_count': 125000,
                'null_count': 0,
                'min': 1,
                'max': 125000,
                'mean': 62500.5,
                'median': 62500,
                'std_dev': 36084.4
            },
            {
                'name': 'email',
                'type': 'string',
                'unique_count': 124766,
                'null_count': 234,
                'most_common': 'example@email.com',
                'pattern': '^[\\w\\.-]+@[\\w\\.-]+\\.\\w+$',
                'avg_length': 24.5
            },
            {
                'name': 'age',
                'type': 'integer',
                'unique_count': 83,
                'null_count': 142,
                'min': 18,
                'max': 95,
                'mean': 42.3,
                'median': 41,
                'std_dev': 15.2,
                'quartiles': [30, 41, 54]
            }
        ]

        for i in range(min(count, len(column_examples))):
            profiles.append(column_examples[i])

        return profiles
