graphs/model_characteristics.py

import plotly.graph_objects as go
import plotly.express as px

def create_concentration_chart(
    df,
    period_col,
    metric_col,
    value_col,
    order,
    palette
): 
    fig = go.Figure()

    # Create stacked area traces
    for i, metric in enumerate(order):
        metric_data = df[df[metric_col] == metric]

        # Sort by time and get values
        metric_data = metric_data.sort_values(period_col)
        x_vals = metric_data[period_col]
        y_vals = metric_data[value_col]
        
        # Add area trace
        fig.add_trace(
            go.Scatter(
                x=x_vals,
                y=y_vals,
                name=metric,
                mode='lines',
                line=dict(width=0),
                fill='tonexty' if i > 0 else 'tozeroy',
                fillcolor=palette[i % len(palette)],
                stackgroup='one',
                hovertemplate='<b>%{fullData.name}</b><br>' +
                             'Time: %{x}<br>' +
                             'Value: %{y}<extra></extra>'
            )
        )
    
    fig.update_layout(
        autosize=True,
        font_size=14,
        showlegend=True,
        legend=dict(
            title="Language Concentration",
            orientation="v",
            yanchor="top",
            y=1,
            xanchor="left",
            x=1.02
        ),
        margin=dict(l=60, r=150, t=40, b=60),  # Extra right margin for legend
        plot_bgcolor='white',
        hovermode='x unified'
    )
    
    fig.update_xaxes(
        title_text="",
        showgrid=True,
        gridcolor='lightgray',
        gridwidth=1
    )
    
    fig.update_yaxes(
        title_text="",
        showgrid=True,
        gridcolor='lightgray',
        gridwidth=1
    )
    
    return fig

def create_line_plot(
    df,
    plot_choices,
    color_palette=None
):
    fig = go.Figure()
    
    groups = df['status'].unique()
    
    if color_palette is None:
        color_palette = px.colors.qualitative.Set1
    
    for i, group in enumerate(groups):
        group_data = df[df['status'] == group]
        group_data = group_data.sort_values('period')
        
        x_vals = group_data['period']
        y_vals = group_data[plot_choices["y_col"]]

        if plot_choices.get("y_format") == "percent":
            y_vals = y_vals * 100
        
        fig.add_trace(
            go.Scatter(
                x=x_vals,
                y=y_vals,
                name=group,
                mode='lines',
                line=dict(
                    color=color_palette[i % len(color_palette)],
                    width=3
                ),
                opacity=0.85,
                hovertemplate='<b>%{fullData.name}</b><br>' +
                             'Period: %{x}<br>' +
                             'Value: %{y:.2f}%<extra></extra>' if plot_choices.get("y_format") == "percent" 
                             else '<b>%{fullData.name}</b><br>Period: %{x}<br>Value: %{y}<extra></extra>'
            )
        )
    
    fig.update_layout(
        width=1125,
        height=225,
        showlegend=True,
        legend=dict(
            orientation="h",
            yanchor="bottom",
            y=1.02,
            xanchor="right",
            x=1
        ),
        margin=dict(l=60, r=60, t=60, b=60),
        plot_bgcolor='white',
        hovermode='x unified'
    )
    
    fig.update_xaxes(
        title_text="Period",
        showgrid=False,
        zeroline=False
    )

    y_title = plot_choices["y_col"]
    if plot_choices.get("y_format") == "percent":
        y_title += " (%)"
    
    fig.update_yaxes(
        title_text=y_title,
        showgrid=False,
        zeroline=False,
        type='log' if plot_choices.get("y_log") else 'linear'
    )
    
    return fig