"""Microbiome analysis module"""

import logging
from typing import Dict, List, Any, Optional, Union
import numpy as np
import pandas as pd

logger = logging.getLogger(__name__)


class MicrobiomeAnalyzer:
    """Comprehensive microbiome analysis toolkit"""
    
    def __init__(self):
        """Initialize microbiome analyzer"""
        logger.info("Initialized microbiome analyzer")
    
    def load_16s_data(
        self,
        filepath: str,
        metadata_file: Optional[str] = None
    ) -> Dict[str, Any]:
        """
        Load 16S rRNA sequencing data
        
        Args:
            filepath: Path to OTU/ASV table
            metadata_file: Optional metadata file
            
        Returns:
            Dictionary containing microbiome data
        """
        logger.info(f"Loading 16S data from {filepath}")
        
        # Placeholder implementation
        data = {
            "otu_table": pd.DataFrame(),
            "taxonomy": pd.DataFrame(),
            "metadata": pd.DataFrame() if metadata_file else None,
            "type": "16S"
        }
        
        # TODO: Implement actual data loading
        
        return data
    
    def calculate_diversity(
        self,
        otu_table: pd.DataFrame,
        metric: str = "shannon"
    ) -> pd.Series:
        """
        Calculate alpha diversity metrics
        
        Args:
            otu_table: OTU/ASV abundance table
            metric: Diversity metric to calculate
            
        Returns:
            Diversity scores for each sample
        """
        logger.info(f"Calculating {metric} diversity")
        
        # Placeholder implementation
        n_samples = len(otu_table)
        
        if metric == "shannon":
            # TODO: Implement Shannon diversity
            return pd.Series(np.random.uniform(2, 3, n_samples))
        elif metric == "simpson":
            # TODO: Implement Simpson diversity
            return pd.Series(np.random.uniform(0.7, 0.9, n_samples))
        else:
            raise ValueError(f"Unknown metric: {metric}")
    
    def identify_biomarkers(
        self,
        microbiome_data: pd.DataFrame,
        phenotype: pd.Series,
        method: str = "lefse"
    ) -> Dict[str, Any]:
        """
        Identify microbial biomarkers associated with phenotype
        
        Args:
            microbiome_data: Microbiome abundance data
            phenotype: Phenotype labels
            method: Statistical method to use
            
        Returns:
            Dictionary of identified biomarkers
        """
        logger.info(f"Identifying biomarkers using {method}")
        
        # Placeholder implementation
        biomarkers = {
            "significant_taxa": [
                "Bacteroides fragilis",
                "Faecalibacterium prausnitzii",
                "Akkermansia muciniphila"
            ],
            "effect_sizes": [0.8, -0.6, -0.7],
            "p_values": [0.001, 0.01, 0.005],
            "method": method
        }
        
        # TODO: Implement actual biomarker discovery
        
        return biomarkers


class DysbiosisDetector:
    """Detect and characterize gut dysbiosis patterns"""
    
    def __init__(self, reference_dataset: Optional[str] = None):
        """
        Initialize dysbiosis detector
        
        Args:
            reference_dataset: Path to healthy reference dataset
        """
        self.reference = reference_dataset
        logger.info("Initialized dysbiosis detector")
    
    def calculate_dysbiosis_index(
        self,
        sample_data: pd.DataFrame
    ) -> float:
        """
        Calculate dysbiosis index for a sample
        
        Args:
            sample_data: Microbiome composition data
            
        Returns:
            Dysbiosis index (0-1, higher = more dysbiotic)
        """
        # Placeholder implementation
        return np.random.uniform(0, 1)
    
    def classify_enterotype(
        self,
        sample_data: pd.DataFrame
    ) -> str:
        """
        Classify sample into enterotype
        
        Args:
            sample_data: Microbiome composition data
            
        Returns:
            Enterotype classification
        """
        # Placeholder implementation
        enterotypes = ["Bacteroides", "Prevotella", "Ruminococcus"]
        return np.random.choice(enterotypes)
