""" Executive Dashboard Functions Handles KPI calculations, metrics, and dashboard visualizations VERSION: 1.0.0 - Modularized dashboard functionality """ import plotly.graph_objects as go from datetime import datetime, timedelta from typing import List, Dict # Import constants from const_ui import ( UAB_GREEN, UAB_DARK_GREEN, UAB_LIGHT_GREEN, UAB_PALE_GREEN, CHART_HEIGHT_SMALL, CHART_HEIGHT_MEDIUM ) from const_data import ( AVERAGE_TIME_TO_IND, CURRENT_IND_READY_COUNT, TIME_BINS_LABELS, TIME_BINS_COUNTS, QUARTERLY_DELIVERIES ) def calculate_executive_metrics(candidates: List, categories: List, models_available: bool) -> Dict: """ Calculate KPIs for executive dashboard Args: candidates: List of DrugCandidate objects categories: List of SUDCategory objects models_available: Whether ML/DL models are loaded Returns: Dictionary containing all executive metrics """ total_candidates = len(candidates) active_projects = sum(len(c.attached_projects) for c in candidates) total_cohorts = sum(c.cohort_count for c in candidates) num_sud_types = len(categories) avg_time_ind = AVERAGE_TIME_TO_IND quarterly_deliveries = QUARTERLY_DELIVERIES # Count real vs synthetic scores real_scores = sum(1 for c in candidates if c.score_type == "Real") synthetic_scores = total_candidates - real_scores return { 'total_candidates': total_candidates, 'active_projects': active_projects, 'total_cohorts': total_cohorts, 'num_sud_types': num_sud_types, 'avg_time_ind_ready': avg_time_ind, 'quarterly_deliveries': quarterly_deliveries, 'real_scores': real_scores, 'synthetic_scores': synthetic_scores } def calculate_stage_distribution_over_time(candidates: List) -> List[Dict]: """ Calculate stage distribution over last 12 months Args: candidates: List of DrugCandidate objects Returns: List of dictionaries containing stage counts per month """ months = [] current_date = datetime.now() for i in range(12): month_date = current_date - timedelta(days=i*30) month_name = month_date.strftime('%b') stage_counts = {f'S{i}': 0 for i in range(7)} for candidate in candidates: candidate_stage = 'S0' for stage, date in candidate.stage_history: if date <= month_date: candidate_stage = stage else: break stage_counts[candidate_stage] += 1 months.append({ 'month': month_name, 'date': month_date, **stage_counts }) return list(reversed(months)) def calculate_portfolio_breakdown(candidates: List, categories: List) -> List[Dict]: """ Calculate active portfolio breakdown by SUD category Args: candidates: List of DrugCandidate objects categories: List of SUDCategory objects Returns: List of dictionaries containing category breakdown """ breakdown = [] for category in categories: count = len([c for c in candidates if c.target_sud_subtype == category.name]) if count > 0: breakdown.append({ 'category': category.name, 'count': count, 'color': category.hex_color }) return breakdown def create_time_to_ind_distribution() -> go.Figure: """ Create bar chart showing distribution of time to IND-ready Returns: Plotly figure object """ bin_labels = TIME_BINS_LABELS bin_counts = TIME_BINS_COUNTS colors = [ UAB_LIGHT_GREEN, '#4A9B7A', UAB_GREEN, '#1E7B52', UAB_DARK_GREEN, '#16533E', '#0F3E2E' ] y_max = max(bin_counts) fig = go.Figure() fig.add_trace(go.Bar( x=bin_labels, y=bin_counts, marker_color=colors, text=bin_counts, textposition='outside', textfont=dict(size=11, color=UAB_DARK_GREEN), cliponaxis=False, showlegend=False )) fig.update_layout( title="Distribution 12 months", xaxis_title="Months", height=CHART_HEIGHT_SMALL, margin=dict(l=25, r=10, t=55, b=45), plot_bgcolor='#F0F9F6', paper_bgcolor='white', font=dict(family="Times New Roman, serif", color=UAB_DARK_GREEN, size=12), showlegend=False, yaxis=dict( showticklabels=False, showgrid=False, zeroline=False, range=[0, y_max * 1.18], ) ) return fig def create_quarterly_deliveries_chart(quarterly_data: List[Dict]) -> go.Figure: """ Create line chart showing quarterly IND-ready deliveries Args: quarterly_data: List of quarterly delivery metrics Returns: Plotly figure object """ labels = [q['quarter'] for q in quarterly_data] cumulative = [q['cumulative'] for q in quarterly_data] display_percentages = ['0%', '4%', '6.5%', '20%', '21%', '14.5%'][:len(labels)] y_max = max(cumulative) if cumulative else 1.0 fig = go.Figure() fig.add_trace(go.Scatter( x=labels, y=cumulative, mode='lines+markers+text', line=dict(color=UAB_GREEN, width=3), marker=dict(size=11, color=UAB_GREEN), text=display_percentages, textposition='top center', textfont=dict(size=12, family="Times New Roman, serif", color=UAB_DARK_GREEN), cliponaxis=False, showlegend=False )) fig.update_layout( title="% IND-Ready Start", xaxis_title="", yaxis_title="Cumulative", height=CHART_HEIGHT_SMALL, margin=dict(l=55, r=15, t=55, b=45), plot_bgcolor='#F0F9F6', paper_bgcolor='white', font=dict(family="Times New Roman, serif", color=UAB_DARK_GREEN, size=12), showlegend=False, yaxis=dict( range=[0, y_max * 1.35], showgrid=True, gridcolor="rgba(30,107,82,0.12)", zeroline=False ) ) return fig def create_pipeline_progression_chart(stage_data: List[Dict]) -> go.Figure: """ Create stacked area chart for pipeline progression over time Args: stage_data: List of stage distribution data by month Returns: Plotly figure object """ months = [d['month'] for d in stage_data] fig = go.Figure() stages = ['S0', 'S1', 'S2', 'S3', 'S4', 'S5', 'S6'] colors_map = { 'S0': '#CBD5E0', 'S1': '#A0AEC0', 'S2': UAB_LIGHT_GREEN, 'S3': UAB_GREEN, 'S4': UAB_GREEN, 'S5': UAB_DARK_GREEN, 'S6': UAB_DARK_GREEN, } for stage in stages: values = [d[stage] for d in stage_data] fig.add_trace(go.Scatter( x=months, y=values, mode='lines', name=stage, stackgroup='one', fillcolor=colors_map[stage], line=dict(width=0.5, color=colors_map[stage]), )) fig.update_layout( title="", xaxis_title="", yaxis_title="Candidates", height=CHART_HEIGHT_MEDIUM, margin=dict(l=40, r=20, t=50, b=40), plot_bgcolor='white', paper_bgcolor='white', font=dict(family="Times New Roman, serif", color=UAB_DARK_GREEN, size=10), legend=dict( orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1 ), hovermode='x unified' ) return fig def create_portfolio_breakdown_chart(breakdown_data: List[Dict]) -> go.Figure: """ Create donut chart for active portfolio breakdown by SUD category Args: breakdown_data: List of category breakdowns Returns: Plotly figure object """ labels = [d['category'] for d in breakdown_data] values = [d['count'] for d in breakdown_data] colors = [d['color'] for d in breakdown_data] fig = go.Figure(data=[go.Pie( labels=labels, values=values, marker=dict(colors=colors), textinfo='label+percent', textposition='auto', textfont=dict(size=10, family="Times New Roman, serif"), hole=0.4 )]) fig.update_layout( title="", height=CHART_HEIGHT_MEDIUM, margin=dict(l=20, r=20, t=50, b=20), paper_bgcolor='white', font=dict(family="Times New Roman, serif", color=UAB_DARK_GREEN, size=10), showlegend=True, legend=dict( orientation="v", yanchor="middle", y=0.5, xanchor="left", x=1.05, font=dict(size=9) ) ) return fig def render_executive_dashboard(candidates: List, categories: List, models_available: bool) -> tuple: """ Render complete executive dashboard with all components Args: candidates: List of DrugCandidate objects categories: List of SUDCategory objects models_available: Whether ML/DL models are loaded Returns: Tuple of (html, time_fig, quarterly_fig, pipeline_fig, portfolio_fig, avg_time, ind_ready) """ # Calculate all metrics metrics = calculate_executive_metrics(candidates, categories, models_available) stage_data = calculate_stage_distribution_over_time(candidates) breakdown_data = calculate_portfolio_breakdown(candidates, categories) # Create all charts time_dist_fig = create_time_to_ind_distribution() quarterly_fig = create_quarterly_deliveries_chart(metrics['quarterly_deliveries']) pipeline_fig = create_pipeline_progression_chart(stage_data) portfolio_fig = create_portfolio_breakdown_chart(breakdown_data) current_ind_ready = CURRENT_IND_READY_COUNT # Add ML/DL status indicator ml_color = UAB_GREEN if models_available else "#FFA500" # Generate HTML html = f"""

Executive Dashboard: SUD Repositioning KPI Overview

{metrics['total_candidates']}

Drug Candidates

{metrics['active_projects']}

Evidence Projects

{metrics['total_cohorts']}

Patient Cohorts

{metrics['num_sud_types']}

Types of SUD Indications

""" return html, time_dist_fig, quarterly_fig, pipeline_fig, portfolio_fig, metrics['avg_time_ind_ready'], current_ind_ready if __name__ == "__main__": print("="*70) print("DASHBOARD FUNCTIONS - TESTING") print("="*70) print("This module provides executive dashboard functionality.") print("Import and use: render_executive_dashboard(candidates, categories, models_available)") print("="*70)