""" PDF export utility for dashboard data Generates professional PDF reports with charts and maps using matplotlib """ import io from datetime import datetime from reportlab.lib import colors from reportlab.lib.pagesizes import letter, A4 from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle from reportlab.lib.units import inch from reportlab.platypus import SimpleDocTemplate, Table, TableStyle, Paragraph, Spacer, PageBreak, Image from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_RIGHT import matplotlib matplotlib.use('Agg') # Use non-interactive backend import matplotlib.pyplot as plt import numpy as np try: import contextily as cx HAS_CONTEXTILY = True except ImportError: HAS_CONTEXTILY = False class DashboardPDFExporter: """Export dashboard data to PDF with charts and maps""" def __init__(self, pagesize=letter): self.pagesize = pagesize self.styles = getSampleStyleSheet() self._setup_custom_styles() def _setup_custom_styles(self): """Setup custom paragraph styles""" self.styles.add(ParagraphStyle( name='CustomTitle', parent=self.styles['Heading1'], fontSize=24, textColor=colors.HexColor('#2c3e50'), spaceAfter=30, alignment=TA_CENTER )) self.styles.add(ParagraphStyle( name='SectionHeader', parent=self.styles['Heading2'], fontSize=16, textColor=colors.HexColor('#34495e'), spaceAfter=12, spaceBefore=12 )) def generate_pdf(self, buffer, data): """ Generate PDF report Args: buffer: BytesIO buffer to write PDF to data: Dictionary containing dashboard data """ doc = SimpleDocTemplate(buffer, pagesize=self.pagesize, rightMargin=72, leftMargin=72, topMargin=72, bottomMargin=18) story = [] # Title title = Paragraph("Participatory Planning Dashboard Report", self.styles['CustomTitle']) story.append(title) story.append(Spacer(1, 12)) # Metadata view_mode_label = "Sentence-Level" if data['view_mode'] == 'sentences' else "Submission-Level" metadata = Paragraph( f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
" f"Analysis Mode: {view_mode_label}", self.styles['Normal'] ) story.append(metadata) story.append(Spacer(1, 24)) # Summary Statistics story.append(Paragraph("Summary Statistics", self.styles['SectionHeader'])) story.extend(self._create_summary_stats(data)) story.append(Spacer(1, 24)) # Category Distribution Chart story.append(Paragraph("Category Distribution", self.styles['SectionHeader'])) category_chart = self._create_category_chart(data['category_stats']) if category_chart: story.append(category_chart) story.append(Spacer(1, 24)) # Contributor Type Distribution story.append(Paragraph("Contributor Type Distribution", self.styles['SectionHeader'])) contributor_chart = self._create_contributor_chart(data['contributor_stats']) if contributor_chart: story.append(contributor_chart) story.append(PageBreak()) # Breakdown Table story.append(Paragraph("Category Breakdown by Contributor Type", self.styles['SectionHeader'])) breakdown_table = self._create_breakdown_table(data['breakdown'], data['contributor_types']) story.append(breakdown_table) story.append(Spacer(1, 24)) # Map if data['geotagged_submissions']: story.append(PageBreak()) story.append(Paragraph("Geographic Distribution", self.styles['SectionHeader'])) map_image = self._create_map(data['geotagged_submissions'], data['categories']) if map_image: story.append(map_image) # Build PDF doc.build(story) return buffer def _create_summary_stats(self, data): """Create summary statistics section""" elements = [] total_items = sum(count for _, count in data['category_stats']) total_submissions = len(data['submissions']) total_geotagged = len(data['geotagged_submissions']) # Create metrics table metrics_data = [ ['Total Submissions', str(total_submissions)], ['Total Items Analyzed', str(total_items)], ['Geotagged Items', str(total_geotagged)], ['Categories', str(len([c for c, count in data['category_stats'] if count > 0]))] ] metrics_table = Table(metrics_data, colWidths=[3*inch, 2*inch]) metrics_table.setStyle(TableStyle([ ('FONTNAME', (0, 0), (0, -1), 'Helvetica-Bold'), ('FONTNAME', (1, 0), (1, -1), 'Helvetica'), ('FONTSIZE', (0, 0), (-1, -1), 12), ('TEXTCOLOR', (0, 0), (0, -1), colors.HexColor('#2c3e50')), ('TEXTCOLOR', (1, 0), (1, -1), colors.HexColor('#3498db')), ('ALIGN', (1, 0), (1, -1), 'RIGHT'), ('VALIGN', (0, 0), (-1, -1), 'MIDDLE'), ('BOTTOMPADDING', (0, 0), (-1, -1), 12), ])) elements.append(metrics_table) return elements def _create_category_chart(self, category_stats): """Create category distribution pie chart using matplotlib""" if not category_stats: return None try: # Prepare data labels = [cat for cat, _ in category_stats] values = [count for _, count in category_stats] # Create matplotlib figure fig, ax = plt.subplots(figsize=(6, 5)) colors_list = ['#3498db', '#2ecc71', '#f39c12', '#e74c3c', '#9b59b6', '#1abc9c'] wedges, texts, autotexts = ax.pie(values, labels=labels, autopct='%1.1f%%', colors=colors_list[:len(labels)], startangle=90) # Make percentage text more readable for autotext in autotexts: autotext.set_color('white') autotext.set_fontsize(10) autotext.set_weight('bold') ax.set_title('Category Distribution', fontsize=14, fontweight='bold') # Convert to image img_buffer = io.BytesIO() plt.tight_layout() plt.savefig(img_buffer, format='png', dpi=150, bbox_inches='tight') plt.close(fig) img_buffer.seek(0) img = Image(img_buffer, width=5*inch, height=4*inch) return img except Exception as e: print(f"Error creating category chart: {e}") return None def _create_contributor_chart(self, contributor_stats): """Create contributor type bar chart using matplotlib""" if not contributor_stats: return None try: # Prepare data types = [ctype for ctype, _ in contributor_stats] counts = [count for _, count in contributor_stats] # Create matplotlib figure fig, ax = plt.subplots(figsize=(6, 4)) bars = ax.bar(types, counts, color='#3498db', edgecolor='#2980b9', linewidth=1.5) # Add value labels on bars for bar in bars: height = bar.get_height() ax.text(bar.get_x() + bar.get_width()/2., height, f'{int(height)}', ha='center', va='bottom', fontsize=10, fontweight='bold') ax.set_xlabel('Contributor Type', fontsize=11, fontweight='bold') ax.set_ylabel('Count', fontsize=11, fontweight='bold') ax.set_title('Submissions by Contributor Type', fontsize=14, fontweight='bold') ax.grid(axis='y', alpha=0.3) plt.xticks(rotation=45, ha='right') # Convert to image img_buffer = io.BytesIO() plt.tight_layout() plt.savefig(img_buffer, format='png', dpi=150, bbox_inches='tight') plt.close(fig) img_buffer.seek(0) img = Image(img_buffer, width=5*inch, height=3.5*inch) return img except Exception as e: print(f"Error creating contributor chart: {e}") return None def _create_breakdown_table(self, breakdown, contributor_types): """Create category breakdown table""" # Prepare table data headers = ['Category'] + [ct['label'] for ct in contributor_types] data = [headers] for category, counts in breakdown.items(): row = [category] for ct in contributor_types: row.append(str(counts.get(ct['value'], 0))) data.append(row) # Calculate column widths num_cols = len(headers) col_width = 6.5 * inch / num_cols table = Table(data, colWidths=[col_width] * num_cols) table.setStyle(TableStyle([ ('BACKGROUND', (0, 0), (-1, 0), colors.HexColor('#3498db')), ('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke), ('ALIGN', (0, 0), (-1, -1), 'CENTER'), ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'), ('FONTSIZE', (0, 0), (-1, -1), 10), ('BOTTOMPADDING', (0, 0), (-1, 0), 12), ('GRID', (0, 0), (-1, -1), 1, colors.grey), ('ROWBACKGROUNDS', (0, 1), (-1, -1), [colors.white, colors.HexColor('#ecf0f1')]) ])) return table def _create_map(self, geotagged_submissions, categories): """Create geographic distribution map with real OpenStreetMap tiles""" if not geotagged_submissions: return None try: # Prepare data lats = [s.latitude for s in geotagged_submissions] lons = [s.longitude for s in geotagged_submissions] cats = [s.category for s in geotagged_submissions] # Create matplotlib figure fig, ax = plt.subplots(figsize=(10, 8)) # Color map for categories category_colors = { 'Vision': '#3498db', 'Problem': '#e74c3c', 'Objectives': '#2ecc71', 'Directives': '#f39c12', 'Values': '#9b59b6', 'Actions': '#1abc9c' } # Plot points by category for category in set(cats): cat_lats = [lat for lat, cat in zip(lats, cats) if cat == category] cat_lons = [lon for lon, cat in zip(lons, cats) if cat == category] color = category_colors.get(category, '#95a5a6') ax.scatter(cat_lons, cat_lats, c=color, label=category, s=150, alpha=0.8, edgecolors='white', linewidths=2, zorder=5) # Add OpenStreetMap basemap if contextily is available if HAS_CONTEXTILY: try: # Add map tiles cx.add_basemap(ax, crs='EPSG:4326', source=cx.providers.OpenStreetMap.Mapnik, attribution=False, alpha=0.8) except Exception as e: print(f"Could not add basemap: {e}") # Fallback to grid ax.grid(True, alpha=0.3) else: # Fallback: simple grid ax.grid(True, alpha=0.3) ax.set_xlabel('Longitude', fontsize=12, fontweight='bold') ax.set_ylabel('Latitude', fontsize=12, fontweight='bold') ax.set_title('Geographic Distribution of Submissions', fontsize=16, fontweight='bold', pad=20) # Legend outside plot area ax.legend(loc='upper left', bbox_to_anchor=(1.02, 1), fontsize=10, frameon=True, fancybox=True, shadow=True) # Add attribution text if using OpenStreetMap if HAS_CONTEXTILY: fig.text(0.99, 0.01, '© OpenStreetMap contributors', ha='right', va='bottom', fontsize=7, style='italic', alpha=0.7) # Convert to image img_buffer = io.BytesIO() plt.tight_layout() plt.savefig(img_buffer, format='png', dpi=200, bbox_inches='tight') plt.close(fig) img_buffer.seek(0) img = Image(img_buffer, width=7*inch, height=5.5*inch) return img except Exception as e: print(f"Error creating map: {e}") import traceback traceback.print_exc() return None