##________automated analysis________##

import pandas as pd
import numpy as np
from scipy import stats

class Analyzer:
    def __init__(self, df, schema):
        self.df = df
        self.schema = schema
        self.insights = []

    def run_full_analysis(self):
        """run all analysis methods"""
        print("Running automated analysis....")

        analysis = {
            'descriptive_stats': self.descriptive_statistics(),
            'correlations': self.correlation_analysis(),
            'trends': self.trend_detection(),
            'group_analysis': self.group_by_analysis(),
            'outliers': self.detect_outliers(),
            'distributions': self.get_distributions()
        }

        return analysis
    
    def descriptive_statistics(self):
        """basic statistics for numeric columns"""

        stats = {}
        for col in self.schema['numeric']:
            stats[col] = {
                'mean': self.df[col].mean(),
                'median': self.df[col].median(),
                'std': self.df[col].std(),
                'min': self.df[col].min(),
                'max': self.df[col].max(),
                'q1': self.df[col].quantile(0.25),
                'q3': self.df[col].quantile(0.75)
            }
        return stats
    
    def correlation_analysis(self):
        """fins correlations between numeric columns"""

        if len(self.schema['numeric']) >= 2:
            corr_matrix = self.df[self.schema['numeric']].corr()

            ## ind strong correlations
            strong_corrs = []
            for i in range(len(corr_matrix.columns)):
                for j in range(i+1, len(corr_matrix.columns)):
                    corr_value = corr_matrix.iloc[i,j]
                    if abs(corr_value) > 0.5:  # strong correlation threshold
                        strong_corrs.append({
                            'col1': corr_matrix.columns[i],
                            'col2': corr_matrix.columns[j],
                            'correlation': corr_value,
                            'strength': 'positive' if corr_value > 0 else 'negative'
                        })
            return strong_corrs
        return []
    
    def trend_detection(self):
        """detect trends in time series data"""

        trends = []
        for date_col in self.schema['datetime']:
            for num_col in self.schema['numeric']:
                #group by date and calculate mean
                trend_data = self.df.groupby(pd.Grouper(key=date_col, freq='M'))[num_col].mean()
                
                if len(trend_data) > 1:
                    # simple trend detection: compare first and last
                    first_val = trend_data.iloc[0]
                    last_val = trend_data.iloc[-1]
                    percent_change = ((last_val - first_val) / first_val) * 100 if first_val != 0 else 0
                    
                    trends.append({
                        'column': num_col,
                        'time_column': date_col,
                        'percent_change': percent_change,
                        'direction': 'increasing' if percent_change > 0 else 'decreasing',
                        'first_value': first_val,
                        'last_value': last_val
                    })
        return trends
    
    def group_by_analysis(self):
        """analyze data by categorical groups"""

        group_analysis = {}
        
        for cat_col in self.schema['categorical']:
            group_analysis[cat_col] = {}
            for num_col in self.schema['numeric']:
                grouped = self.df.groupby(cat_col)[num_col].agg(['mean', 'sum', 'count'])
                
                #find top performer
                top_category = grouped['mean'].idxmax() if len(grouped) > 0 else None
                top_value = grouped['mean'].max() if len(grouped) > 0 else 0
                
                group_analysis[cat_col][num_col] = {
                    'grouped_data': grouped.to_dict(),
                    'top_category': top_category,
                    'top_value': top_value,
                    'total_categories': len(grouped)
                }
        
        return group_analysis
    
    def detect_outliers(self):
        """detect outliers using IQR method"""

        outliers = {}
        
        for col in self.schema['numeric']:
            Q1 = self.df[col].quantile(0.25)
            Q3 = self.df[col].quantile(0.75)
            IQR = Q3 - Q1
            lower_bound = Q1 - 1.5 * IQR
            upper_bound = Q3 + 1.5 * IQR
            
            outlier_count = len(self.df[(self.df[col] < lower_bound) | (self.df[col] > upper_bound)])
            
            if outlier_count > 0:
                outliers[col] = {
                    'count': outlier_count,
                    'percentage': (outlier_count / len(self.df)) * 100,
                    'lower_bound': lower_bound,
                    'upper_bound': upper_bound
                }
        
        return outliers
    
    def get_distributions(self):
        """get distribution information for numeric columns"""

        distributions = {}
        
        for col in self.schema['numeric']:
            distributions[col] = {
                'skewness': self.df[col].skew(),
                'kurtosis': self.df[col].kurtosis(),
                'unique_values': self.df[col].nunique()
            }
            
            #determine distribution shape
            skew = distributions[col]['skewness']
            if skew > 1:
                distributions[col]['shape'] = 'right-skewed'
            elif skew < -1:
                distributions[col]['shape'] = 'left-skewed'
            else:
                distributions[col]['shape'] = 'approximately normal'
        
        return distributions