Create analytics.py

analytics.py

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+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from typing import Dict, Optional, List
+
+from config import get_chart_theme, DESIGN_SYSTEM
+
+def get_material_stats(df: pd.DataFrame) -> Dict:
+    stats = {}
+    total = df['weight_kg'].sum()
+    total_work_days = df['date'].nunique()
+    
+    for material in df['material_type'].unique():
+        data = df[df['material_type'] == material]
+        work_days = data['date'].nunique()
+        daily_avg = data.groupby('date')['weight_kg'].sum().mean()
+        
+        stats[material] = {
+            'total': data['weight_kg'].sum(),
+            'percentage': (data['weight_kg'].sum() / total) * 100,
+            'daily_avg': daily_avg,
+            'work_days': work_days,
+            'records': len(data)
+        }
+    
+    stats['_total_'] = {
+        'total': total,
+        'percentage': 100.0,
+        'daily_avg': df.groupby('date')['weight_kg'].sum().mean(),
+        'work_days': total_work_days,
+        'records': len(df)
+    }
+    
+    return stats
+
+def detect_outliers(df: pd.DataFrame) -> Dict:
+    outliers = {}
+    
+    for material in df['material_type'].unique():
+        material_data = df[df['material_type'] == material]
+        data = material_data['weight_kg']
+        
+        Q1, Q3 = data.quantile(0.25), data.quantile(0.75)
+        IQR = Q3 - Q1
+        lower, upper = Q1 - 1.5 * IQR, Q3 + 1.5 * IQR
+        
+        outlier_mask = (data < lower) | (data > upper)
+        outlier_dates = material_data[outlier_mask]['date'].dt.strftime('%Y-%m-%d').tolist()
+        
+        outliers[material] = {
+            'count': len(outlier_dates),
+            'range': f"{lower:.0f} - {upper:.0f} kg",
+            'dates': outlier_dates
+        }
+    
+    return outliers
+
+def create_total_production_chart(df: pd.DataFrame, time_period: str = 'daily'):
+    if time_period == 'daily':
+        grouped = df.groupby('date')['weight_kg'].sum().reset_index()
+        fig = px.line(grouped, x='date', y='weight_kg', 
+                     title='Total Production Trend',
+                     labels={'weight_kg': 'Weight (kg)', 'date': 'Date'})
+    elif time_period == 'weekly':
+        df_copy = df.copy()
+        df_copy['week'] = df_copy['date'].dt.isocalendar().week
+        df_copy['year'] = df_copy['date'].dt.year
+        grouped = df_copy.groupby(['year', 'week'])['weight_kg'].sum().reset_index()
+        grouped['week_label'] = grouped['year'].astype(str) + '-W' + grouped['week'].astype(str)
+        fig = px.bar(grouped, x='week_label', y='weight_kg',
+                    title='Total Production Trend (Weekly)',
+                    labels={'weight_kg': 'Weight (kg)', 'week_label': 'Week'})
+    else:
+        df_copy = df.copy()
+        df_copy['month'] = df_copy['date'].dt.to_period('M')
+        grouped = df_copy.groupby('month')['weight_kg'].sum().reset_index()
+        grouped['month'] = grouped['month'].astype(str)
+        fig = px.bar(grouped, x='month', y='weight_kg',
+                    title='Total Production Trend (Monthly)',
+                    labels={'weight_kg': 'Weight (kg)', 'month': 'Month'})
+    
+    fig.update_layout(**get_chart_theme()['layout'], height=400, showlegend=False)
+    return fig
+
+def create_materials_trend_chart(df: pd.DataFrame, time_period: str = 'daily', 
+                                selected_materials: Optional[List[str]] = None):
+    df_copy = df.copy()
+    
+    if selected_materials:
+        df_copy = df_copy[df_copy['material_type'].isin(selected_materials)]
+    
+    if time_period == 'daily':
+        grouped = df_copy.groupby(['date', 'material_type'])['weight_kg'].sum().reset_index()
+        fig = px.line(grouped, x='date', y='weight_kg', color='material_type',
+                     title='Materials Production Trends',
+                     labels={'weight_kg': 'Weight (kg)', 'date': 'Date', 'material_type': 'Material'})
+    elif time_period == 'weekly':
+        df_copy['week'] = df_copy['date'].dt.isocalendar().week
+        df_copy['year'] = df_copy['date'].dt.year
+        grouped = df_copy.groupby(['year', 'week', 'material_type'])['weight_kg'].sum().reset_index()
+        grouped['week_label'] = grouped['year'].astype(str) + '-W' + grouped['week'].astype(str)
+        fig = px.bar(grouped, x='week_label', y='weight_kg', color='material_type',
+                    title='Materials Production Trends (Weekly)',
+                    labels={'weight_kg': 'Weight (kg)', 'week_label': 'Week', 'material_type': 'Material'})
+    else:
+        df_copy['month'] = df_copy['date'].dt.to_period('M')
+        grouped = df_copy.groupby(['month', 'material_type'])['weight_kg'].sum().reset_index()
+        grouped['month'] = grouped['month'].astype(str)
+        fig = px.bar(grouped, x='month', y='weight_kg', color='material_type',
+                    title='Materials Production Trends (Monthly)',
+                    labels={'weight_kg': 'Weight (kg)', 'month': 'Month', 'material_type': 'Material'})
+    
+    fig.update_layout(**get_chart_theme()['layout'], height=400)
+    return fig
+
+def create_shift_trend_chart(df: pd.DataFrame, time_period: str = 'daily'):
+    if time_period == 'daily':
+        grouped = df.groupby(['date', 'shift'])['weight_kg'].sum().reset_index()
+        pivot_data = grouped.pivot(index='date', columns='shift', values='weight_kg').fillna(0)
+        
+        fig = go.Figure()
+        
+        if 'day' in pivot_data.columns:
+            fig.add_trace(go.Bar(
+                x=pivot_data.index, 
+                y=pivot_data['day'], 
+                name='Day Shift',
+                marker_color=DESIGN_SYSTEM['colors']['warning'],
+                text=pivot_data['day'].round(0), 
+                textposition='inside'
+            ))
+        
+        if 'night' in pivot_data.columns:
+            fig.add_trace(go.Bar(
+                x=pivot_data.index, 
+                y=pivot_data['night'], 
+                name='Night Shift',
+                marker_color=DESIGN_SYSTEM['colors']['primary'],
+                base=pivot_data['day'] if 'day' in pivot_data.columns else 0,
+                text=pivot_data['night'].round(0), 
+                textposition='inside'
+            ))
+        
+        fig.update_layout(
+            **get_chart_theme()['layout'],
+            title='Daily Shift Production Trends (Stacked)',
+            xaxis_title='Date', 
+            yaxis_title='Weight (kg)',
+            barmode='stack', 
+            height=400, 
+            showlegend=True
+        )
+    else:
+        grouped = df.groupby(['date', 'shift'])['weight_kg'].sum().reset_index()
+        fig = px.bar(grouped, x='date', y='weight_kg', color='shift',
+                    title=f'{time_period.title()} Shift Production Trends',
+                    barmode='stack')
+        fig.update_layout(**get_chart_theme()['layout'], height=400)
+    
+    return fig