visualizations.py

import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
import pandas as pd
import numpy as np
from typing import Dict, Any

def create_time_series_plot(df: pd.DataFrame, date_column: str, value_column: str) -> go.Figure:
    """Create an interactive time series plot"""
    fig = px.line(df, x=date_column, y=value_column,
                  title=f'{value_column} Over Time')
    fig.update_layout(
        xaxis_title=date_column,
        yaxis_title=value_column,
        template='plotly_white'
    )
    return fig

def create_correlation_heatmap(df: pd.DataFrame) -> go.Figure:
    """Create an interactive correlation heatmap"""
    numeric_df = df.select_dtypes(include=[np.number])
    correlation_matrix = numeric_df.corr()
    
    fig = go.Figure(data=go.Heatmap(
        z=correlation_matrix,
        x=correlation_matrix.columns,
        y=correlation_matrix.columns,
        colorscale='RdBu',
        zmin=-1,
        zmax=1
    ))
    
    fig.update_layout(
        title='Correlation Heatmap',
        template='plotly_white'
    )
    return fig

def create_distribution_plots(df: pd.DataFrame, column: str) -> go.Figure:
    """Create distribution plots for numerical columns"""
    fig = go.Figure()
    fig.add_trace(go.Histogram(x=df[column], name='Histogram'))
    fig.add_trace(go.Box(x=df[column], name='Box Plot'))
    
    fig.update_layout(
        title=f'Distribution Analysis: {column}',
        template='plotly_white'
    )
    return fig

def create_summary_dashboard(kpis: Dict[str, float]) -> go.Figure:
    """Create a dashboard of KPI metrics"""
    # Filter out non-numeric KPIs
    numeric_kpis = {k: v for k, v in kpis.items() if isinstance(v, (int, float))}
    
    # Create a subplot for each KPI
    fig = go.Figure()
    
    # Calculate layout dimensions
    n_kpis = len(numeric_kpis)
    n_rows = (n_kpis + 1) // 2  # 2 columns layout
    
    for idx, (metric, value) in enumerate(numeric_kpis.items()):
        row = idx // 2 + 1
        col = idx % 2 + 1
        
        fig.add_trace(go.Indicator(
            mode="number+gauge+delta",
            value=value,
            title={'text': metric},
            domain={'row': row, 'column': col},
            gauge={
                'axis': {'range': [0, max(100, value * 1.2)]},
                'bar': {'color': "royalblue"},
                'steps': [
                    {'range': [0, value], 'color': 'lightgray'}
                ]
            }
        ))
    
    fig.update_layout(
        grid={'rows': n_rows, 'columns': 2, 'pattern': "independent"},
        height=200 * n_rows,
        title='Financial KPIs Dashboard',
        template='plotly_white'
    )
    return fig

def create_trend_analysis(df: pd.DataFrame, date_column: str, metrics: list) -> go.Figure:
    """Create trend analysis for multiple metrics"""
    fig = go.Figure()
    
    for metric in metrics:
        fig.add_trace(go.Scatter(
            x=df[date_column],
            y=df[metric],
            name=metric,
            mode='lines+markers'
        ))
    
    fig.update_layout(
        title='Trend Analysis',
        xaxis_title=date_column,
        yaxis_title='Value',
        template='plotly_white',
        showlegend=True
    )
    return fig

def create_categorical_analysis(df: pd.DataFrame, cat_column: str, value_column: str) -> go.Figure:
    """Create analysis for categorical variables"""
    grouped_data = df.groupby(cat_column)[value_column].agg(['mean', 'sum', 'count']).reset_index()
    
    fig = go.Figure()
    
    # Bar chart for total values
    fig.add_trace(go.Bar(
        x=grouped_data[cat_column],
        y=grouped_data['sum'],
        name='Total',
        marker_color='royalblue'
    ))
    
    # Line chart for average values
    fig.add_trace(go.Scatter(
        x=grouped_data[cat_column],
        y=grouped_data['mean'],
        name='Average',
        mode='lines+markers',
        line=dict(color='red')
    ))
    
    fig.update_layout(
        title=f'{value_column} by {cat_column}',
        xaxis_title=cat_column,
        yaxis_title=value_column,
        template='plotly_white',
        showlegend=True,
        barmode='group'
    )
    return fig