From 10dc745ed16234381ed3f45adbe9f9da5802bf8e Mon Sep 17 00:00:00 2001 From: vodkar Date: Tue, 16 Sep 2025 15:38:23 +0500 Subject: [PATCH 1/3] Updated comparison --- code_quality_analysis.ipynb | 63 +- code_quality_comparison.ipynb | 1591 +++++++++++++++++ code_quality_comparison/data/README.md | 84 + .../data/TEMPLATE_code_quality_summary.csv | 8 + .../data/TEMPLATE_detailed_issues.csv | 5 + .../data/TEMPLATE_radon_complexity.csv | 6 + .../data/TEMPLATE_radon_maintainability.csv | 6 + 7 files changed, 1762 insertions(+), 1 deletion(-) create mode 100644 code_quality_comparison.ipynb create mode 100644 code_quality_comparison/data/README.md create mode 100644 code_quality_comparison/data/TEMPLATE_code_quality_summary.csv create mode 100644 code_quality_comparison/data/TEMPLATE_detailed_issues.csv create mode 100644 code_quality_comparison/data/TEMPLATE_radon_complexity.csv create mode 100644 code_quality_comparison/data/TEMPLATE_radon_maintainability.csv diff --git a/code_quality_analysis.ipynb b/code_quality_analysis.ipynb index 1644b79..85c84b6 100644 --- a/code_quality_analysis.ipynb +++ b/code_quality_analysis.ipynb @@ -1425,7 +1425,68 @@ "id": "40749cdc", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# Export Radon data for comparison analysis\n", + "print(\"\\n\" + \"šŸ”„\" * 3 + \" EXPORTING RADON DATA FOR COMPARISON \" + \"šŸ”„\" * 3)\n", + "\n", + "# Export Radon Cyclomatic Complexity data\n", + "if not radon_cc_results.empty:\n", + " # Prepare complexity data in the expected format\n", + " complexity_export = radon_cc_results[['file', 'function', 'line', 'complexity', 'rank', 'type']].copy()\n", + " \n", + " # Save to CSV\n", + " complexity_file = \"radon_complexity.csv\"\n", + " complexity_export.to_csv(complexity_file, index=False)\n", + " \n", + " print(f\"āœ… Exported Radon Complexity data: {complexity_file}\")\n", + " print(f\" šŸ“Š {len(complexity_export)} functions/methods exported\")\n", + " print(f\" šŸ“ˆ Average complexity: {complexity_export['complexity'].mean():.2f}\")\n", + " print(f\" šŸŽÆ Complexity range: {complexity_export['complexity'].min()} - {complexity_export['complexity'].max()}\")\n", + "else:\n", + " print(\"āš ļø No Radon complexity data available for export\")\n", + "\n", + "# Export Radon Maintainability Index data \n", + "if not radon_mi_results.empty:\n", + " # Prepare maintainability data in the expected format\n", + " maintainability_export = radon_mi_results[['file', 'mi_score', 'mi_rank']].copy()\n", + " \n", + " # Save to CSV\n", + " maintainability_file = \"radon_maintainability.csv\"\n", + " maintainability_export.to_csv(maintainability_file, index=False)\n", + " \n", + " print(f\"\\nāœ… Exported Radon Maintainability data: {maintainability_file}\")\n", + " print(f\" šŸ“Š {len(maintainability_export)} files exported\")\n", + " print(f\" šŸ“ˆ Average MI score: {maintainability_export['mi_score'].mean():.2f}\")\n", + " print(f\" šŸŽÆ MI score range: {maintainability_export['mi_score'].min():.1f} - {maintainability_export['mi_score'].max():.1f}\")\n", + "else:\n", + " print(\"āš ļø No Radon maintainability data available for export\")\n", + "\n", + "# Summary of all exported files\n", + "print(f\"\\nšŸ“ EXPORTED FILES SUMMARY:\")\n", + "exported_files = []\n", + "\n", + "if Path(\"code_quality_summary.csv\").exists():\n", + " exported_files.append(\"code_quality_summary.csv\")\n", + " print(f\" āœ… code_quality_summary.csv\")\n", + "\n", + "if Path(\"detailed_issues.csv\").exists():\n", + " exported_files.append(\"detailed_issues.csv\") \n", + " print(f\" āœ… detailed_issues.csv\")\n", + "\n", + "if Path(\"radon_complexity.csv\").exists():\n", + " exported_files.append(\"radon_complexity.csv\")\n", + " print(f\" āœ… radon_complexity.csv\")\n", + " \n", + "if Path(\"radon_maintainability.csv\").exists():\n", + " exported_files.append(\"radon_maintainability.csv\")\n", + " print(f\" āœ… radon_maintainability.csv\")\n", + "\n", + "print(f\"\\nšŸŽÆ Ready for comparison analysis!\")\n", + "print(f\" Total files exported: {len(exported_files)}\")\n", + "print(f\" šŸš€ Copy these CSV files to your implementation directory in:\")\n", + "print(f\" code_quality_comparison/data/[your_implementation_name]/\")\n", + "print(f\"\\nšŸ’” Tip: Run the code_quality_comparison.ipynb notebook to compare multiple implementations!\")" + ] } ], "metadata": { diff --git a/code_quality_comparison.ipynb b/code_quality_comparison.ipynb new file mode 100644 index 0000000..f1909ce --- /dev/null +++ b/code_quality_comparison.ipynb @@ -0,0 +1,1591 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9a0ceee3", + "metadata": {}, + "source": [ + "# Code Quality Comparison Dashboard\n", + "\n", + "This notebook compares code quality metrics across 6 different AI implementations:\n", + "\n", + "## Implementations Being Compared:\n", + "1. **Claude Code with Tuning** - Claude with prompt optimization\n", + "2. **Claude Code without Tuning** - Claude with basic prompts\n", + "3. **Copilot GPT with Tuning** - GPT-4 with optimized settings\n", + "4. **Copilot GPT without Tuning** - GPT-4 with default settings\n", + "5. **Copilot Claude with Tuning** - Claude via Copilot with tuning\n", + "6. **Copilot Claude without Tuning** - Claude via Copilot default\n", + "\n", + "## Metrics Analyzed:\n", + "- **Linter Issues**: Bandit, Ruff, MyPy, Flake8, Vulture counts\n", + "- **Cyclomatic Complexity**: Radon CC scores and distributions\n", + "- **Maintainability Index**: Radon MI scores and rankings\n", + "- **Overall Quality**: Aggregated metrics across all tools\n", + "\n", + "## Data Sources:\n", + "All data will be manually placed in the `code_quality_comparison/data/` directory with standardized CSV formats." + ] + }, + { + "cell_type": "markdown", + "id": "0f3a9246", + "metadata": {}, + "source": [ + "## Setup Environment and Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "634ec299", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "āœ… Libraries imported and plotting configuration set!\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from pathlib import Path\n", + "import warnings\n", + "\n", + "# Suppress warnings for cleaner output\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "# Configure high-resolution plotting\n", + "plt.style.use('seaborn-v0_8')\n", + "sns.set_palette(\"husl\")\n", + "\n", + "# High-resolution plot configuration\n", + "plt.rcParams['figure.figsize'] = (14, 10)\n", + "plt.rcParams['figure.dpi'] = 150\n", + "plt.rcParams['savefig.dpi'] = 300\n", + "plt.rcParams['font.size'] = 11\n", + "plt.rcParams['axes.linewidth'] = 1.2\n", + "plt.rcParams['grid.linewidth'] = 0.8\n", + "plt.rcParams['lines.linewidth'] = 2\n", + "plt.rcParams['patch.linewidth'] = 0.5\n", + "plt.rcParams['text.antialiased'] = True\n", + "plt.rcParams['figure.facecolor'] = 'white'\n", + "plt.rcParams['axes.facecolor'] = 'white'\n", + "\n", + "print(\"āœ… Libraries imported and plotting configuration set!\")" + ] + }, + { + "cell_type": "markdown", + "id": "89d5d4b8", + "metadata": {}, + "source": [ + "## Define Data Directory Structure" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7b04f503", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "šŸ“ Data directory structure:\n", + " Base directory: /Users/somen/Zavodi/opensource/fastapi-moscow-python-demo/code_quality_comparison/data\n", + " Implementations: 6\n", + " Expected files per implementation: 4\n", + "\n", + "šŸ“‹ File structure for each implementation:\n", + " summary: code_quality_summary.csv\n", + " details: detailed_issues.csv\n", + " complexity: radon_complexity.csv\n", + " maintainability: radon_maintainability.csv\n", + "\n", + "šŸ“‚ Full directory structure:\n", + " Claude Code (Tuned):\n", + " code_quality_comparison/data/claude_code_with_tuning/code_quality_summary.csv\n", + " code_quality_comparison/data/claude_code_with_tuning/detailed_issues.csv\n", + " code_quality_comparison/data/claude_code_with_tuning/radon_complexity.csv\n", + " code_quality_comparison/data/claude_code_with_tuning/radon_maintainability.csv\n", + " Claude Code (Basic):\n", + " code_quality_comparison/data/claude_code_no_tuning/code_quality_summary.csv\n", + " code_quality_comparison/data/claude_code_no_tuning/detailed_issues.csv\n", + " code_quality_comparison/data/claude_code_no_tuning/radon_complexity.csv\n", + " code_quality_comparison/data/claude_code_no_tuning/radon_maintainability.csv\n", + " Copilot GPT (Tuned):\n", + " code_quality_comparison/data/copilot_gpt_with_tuning/code_quality_summary.csv\n", + " code_quality_comparison/data/copilot_gpt_with_tuning/detailed_issues.csv\n", + " code_quality_comparison/data/copilot_gpt_with_tuning/radon_complexity.csv\n", + " code_quality_comparison/data/copilot_gpt_with_tuning/radon_maintainability.csv\n", + " Copilot GPT (Basic):\n", + " code_quality_comparison/data/copilot_gpt_no_tuning/code_quality_summary.csv\n", + " code_quality_comparison/data/copilot_gpt_no_tuning/detailed_issues.csv\n", + " code_quality_comparison/data/copilot_gpt_no_tuning/radon_complexity.csv\n", + " code_quality_comparison/data/copilot_gpt_no_tuning/radon_maintainability.csv\n", + " Copilot Claude (Tuned):\n", + " code_quality_comparison/data/copilot_claude_with_tuning/code_quality_summary.csv\n", + " code_quality_comparison/data/copilot_claude_with_tuning/detailed_issues.csv\n", + " code_quality_comparison/data/copilot_claude_with_tuning/radon_complexity.csv\n", + " code_quality_comparison/data/copilot_claude_with_tuning/radon_maintainability.csv\n", + " Copilot Claude (Basic):\n", + " code_quality_comparison/data/copilot_claude_no_tuning/code_quality_summary.csv\n", + " code_quality_comparison/data/copilot_claude_no_tuning/detailed_issues.csv\n", + " code_quality_comparison/data/copilot_claude_no_tuning/radon_complexity.csv\n", + " code_quality_comparison/data/copilot_claude_no_tuning/radon_maintainability.csv\n", + "\n", + "āœ… Directory structure created successfully!\n" + ] + } + ], + "source": [ + "# Define data directory and file structure\n", + "DATA_DIR = Path(\"code_quality_comparison/data\")\n", + "DATA_DIR.mkdir(parents=True, exist_ok=True)\n", + "\n", + "# Define the 6 implementations\n", + "IMPLEMENTATIONS = [\n", + " \"claude_code_with_tuning\",\n", + " \"claude_code_no_tuning\", \n", + " \"copilot_gpt_with_tuning\",\n", + " \"copilot_gpt_no_tuning\",\n", + " \"copilot_claude_with_tuning\",\n", + " \"copilot_claude_no_tuning\"\n", + "]\n", + "\n", + "# Create readable display names\n", + "DISPLAY_NAMES = {\n", + " \"claude_code_with_tuning\": \"Claude Code (Tuned)\",\n", + " \"claude_code_no_tuning\": \"Claude Code (Basic)\",\n", + " \"copilot_gpt_with_tuning\": \"Copilot GPT (Tuned)\",\n", + " \"copilot_gpt_no_tuning\": \"Copilot GPT (Basic)\",\n", + " \"copilot_claude_with_tuning\": \"Copilot Claude (Tuned)\",\n", + " \"copilot_claude_no_tuning\": \"Copilot Claude (Basic)\"\n", + "}\n", + "\n", + "# Expected file structure for each implementation\n", + "file_structure = {\n", + " \"summary\": \"code_quality_summary.csv\", # Tool-level summary\n", + " \"details\": \"detailed_issues.csv\", # Individual issues\n", + " \"complexity\": \"radon_complexity.csv\", # Cyclomatic complexity scores\n", + " \"maintainability\": \"radon_maintainability.csv\" # Maintainability index scores\n", + "}\n", + "\n", + "print(\"šŸ“ Data directory structure:\")\n", + "print(f\" Base directory: {DATA_DIR.absolute()}\")\n", + "print(f\" Implementations: {len(IMPLEMENTATIONS)}\")\n", + "print(f\" Expected files per implementation: {len(file_structure)}\")\n", + "print(\"\\nšŸ“‹ File structure for each implementation:\")\n", + "for key, filename in file_structure.items():\n", + " print(f\" {key}: {filename}\")\n", + " \n", + "print(f\"\\nšŸ“‚ Full directory structure:\")\n", + "for impl in IMPLEMENTATIONS:\n", + " impl_dir = DATA_DIR / impl\n", + " impl_dir.mkdir(exist_ok=True)\n", + " print(f\" {DISPLAY_NAMES[impl]}:\")\n", + " for key, filename in file_structure.items():\n", + " filepath = impl_dir / filename\n", + " print(f\" {filepath}\")\n", + " \n", + "print(f\"\\nāœ… Directory structure created successfully!\")" + ] + }, + { + "cell_type": "markdown", + "id": "b6c16763", + "metadata": {}, + "source": [ + "## Load Code Quality Data from CSV Files" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4dda38b7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "šŸ”„ Loading implementation data...\n", + "\n", + "šŸ“Š Loading data for Claude Code (Tuned)...\n", + " āš ļø File not found: code_quality_summary.csv\n", + " āš ļø File not found: detailed_issues.csv\n", + " āš ļø File not found: radon_complexity.csv\n", + " āš ļø File not found: radon_maintainability.csv\n", + "\n", + "šŸ“Š Loading data for Claude Code (Basic)...\n", + " āœ… Loaded 7 rows from code_quality_summary.csv\n", + " āœ… Loaded 10 rows from detailed_issues.csv\n", + " āœ… Loaded 161 rows from radon_complexity.csv\n", + " āœ… Loaded 54 rows from radon_maintainability.csv\n", + "\n", + "šŸ“Š Loading data for Copilot GPT (Tuned)...\n", + " āš ļø File not found: code_quality_summary.csv\n", + " āš ļø File not found: detailed_issues.csv\n", + " āš ļø File not found: radon_complexity.csv\n", + " āš ļø File not found: radon_maintainability.csv\n", + "\n", + "šŸ“Š Loading data for Copilot GPT (Basic)...\n", + " āš ļø File not found: code_quality_summary.csv\n", + " āš ļø File not found: detailed_issues.csv\n", + " āš ļø File not found: radon_complexity.csv\n", + " āš ļø File not found: radon_maintainability.csv\n", + "\n", + "šŸ“Š Loading data for Copilot Claude (Tuned)...\n", + " āš ļø File not found: code_quality_summary.csv\n", + " āš ļø File not found: detailed_issues.csv\n", + " āš ļø File not found: radon_complexity.csv\n", + " āš ļø File not found: radon_maintainability.csv\n", + "\n", + "šŸ“Š Loading data for Copilot Claude (Basic)...\n", + " āš ļø File not found: code_quality_summary.csv\n", + " āš ļø File not found: detailed_issues.csv\n", + " āš ļø File not found: radon_complexity.csv\n", + " āš ļø File not found: radon_maintainability.csv\n", + "\n", + "āœ… Combined summary: 7 total rows\n", + "\n", + "āœ… Combined details: 10 total rows\n", + "\n", + "āœ… Combined complexity: 161 total rows\n", + "\n", + "āœ… Combined maintainability: 54 total rows\n", + "\n", + "šŸ“ˆ Data Loading Summary:\n", + " Summary data: 7 rows\n", + " Detailed issues: 10 rows\n", + " Complexity scores: 161 rows\n", + " Maintainability scores: 54 rows\n" + ] + } + ], + "source": [ + "def load_implementation_data(implementation):\n", + " \"\"\"Load all CSV files for a single implementation.\"\"\"\n", + " impl_dir = DATA_DIR / implementation\n", + " data = {}\n", + " \n", + " for data_type, filename in file_structure.items():\n", + " filepath = impl_dir / filename\n", + " if filepath.exists():\n", + " try:\n", + " df = pd.read_csv(filepath)\n", + " df['implementation'] = implementation\n", + " df['display_name'] = DISPLAY_NAMES[implementation]\n", + " data[data_type] = df\n", + " print(f\" āœ… Loaded {len(df)} rows from {filename}\")\n", + " except Exception as e:\n", + " print(f\" āŒ Error loading {filename}: {e}\")\n", + " data[data_type] = pd.DataFrame()\n", + " else:\n", + " print(f\" āš ļø File not found: {filename}\")\n", + " data[data_type] = pd.DataFrame()\n", + " \n", + " return data\n", + "\n", + "def load_all_implementations():\n", + " \"\"\"Load data for all implementations.\"\"\"\n", + " all_data = {\n", + " 'summary': [],\n", + " 'details': [],\n", + " 'complexity': [],\n", + " 'maintainability': []\n", + " }\n", + " \n", + " for impl in IMPLEMENTATIONS:\n", + " print(f\"\\nšŸ“Š Loading data for {DISPLAY_NAMES[impl]}...\")\n", + " impl_data = load_implementation_data(impl)\n", + " \n", + " for data_type in all_data.keys():\n", + " if not impl_data[data_type].empty:\n", + " all_data[data_type].append(impl_data[data_type])\n", + " \n", + " # Combine all data\n", + " combined_data = {}\n", + " for data_type, data_list in all_data.items():\n", + " if data_list:\n", + " combined_data[data_type] = pd.concat(data_list, ignore_index=True)\n", + " print(f\"\\nāœ… Combined {data_type}: {len(combined_data[data_type])} total rows\")\n", + " else:\n", + " combined_data[data_type] = pd.DataFrame()\n", + " print(f\"\\nāš ļø No {data_type} data available\")\n", + " \n", + " return combined_data\n", + "\n", + "# Load all data\n", + "print(\"šŸ”„ Loading implementation data...\")\n", + "implementation_data = load_all_implementations()\n", + "\n", + "# Display summary of loaded data\n", + "print(f\"\\nšŸ“ˆ Data Loading Summary:\")\n", + "print(f\" Summary data: {len(implementation_data['summary'])} rows\")\n", + "print(f\" Detailed issues: {len(implementation_data['details'])} rows\") \n", + "print(f\" Complexity scores: {len(implementation_data['complexity'])} rows\")\n", + "print(f\" Maintainability scores: {len(implementation_data['maintainability'])} rows\")" + ] + }, + { + "cell_type": "markdown", + "id": "33f39d0d", + "metadata": {}, + "source": [ + "## Load Complexity and Maintainability Scores" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d818ba5f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "šŸ“Š Complexity Data Analysis:\n", + " Found complexity data for 1 implementations\n", + " Total functions analyzed: 161\n", + " Average complexity across all: 2.34\n", + "\n", + "šŸ› ļø Maintainability Data Analysis:\n", + " Found maintainability data for 1 implementations\n", + " Total files analyzed: 54\n", + " Average MI score across all: 80.83\n", + "\n", + "šŸ“‹ Complexity Statistics by Implementation:\n", + " count mean median std min max\n", + "implementation display_name \n", + "claude_code_no_tuning Claude Code (Basic) 161 2.34 2.0 1.51 1 8\n", + "\n", + "šŸ“‹ Maintainability Statistics by Implementation:\n", + " count mean median std min \\\n", + "implementation display_name \n", + "claude_code_no_tuning Claude Code (Basic) 54 80.83 89.13 21.84 22.87 \n", + "\n", + " max \n", + "implementation display_name \n", + "claude_code_no_tuning Claude Code (Basic) 100.0 \n", + "\n", + "šŸŽÆ Ready for comparison visualizations: True\n" + ] + } + ], + "source": [ + "def analyze_radon_scores():\n", + " \"\"\"Analyze complexity and maintainability scores across implementations.\"\"\"\n", + " \n", + " results = {\n", + " 'complexity_stats': {},\n", + " 'maintainability_stats': {},\n", + " 'comparison_ready': False\n", + " }\n", + " \n", + " # Analyze complexity data if available\n", + " if not implementation_data['complexity'].empty:\n", + " complexity_df = implementation_data['complexity']\n", + " print(\"šŸ“Š Complexity Data Analysis:\")\n", + " \n", + " if 'complexity' in complexity_df.columns:\n", + " complexity_stats = complexity_df.groupby(['implementation', 'display_name'])['complexity'].agg([\n", + " 'count', 'mean', 'median', 'std', 'min', 'max'\n", + " ]).round(2)\n", + " \n", + " results['complexity_stats'] = complexity_stats\n", + " print(f\" Found complexity data for {complexity_df['implementation'].nunique()} implementations\")\n", + " print(f\" Total functions analyzed: {len(complexity_df)}\")\n", + " print(f\" Average complexity across all: {complexity_df['complexity'].mean():.2f}\")\n", + " else:\n", + " print(\" āš ļø No 'complexity' column found in complexity data\")\n", + " else:\n", + " print(\"āš ļø No complexity data available\")\n", + " \n", + " # Analyze maintainability data if available\n", + " if not implementation_data['maintainability'].empty:\n", + " maintainability_df = implementation_data['maintainability']\n", + " print(\"\\nšŸ› ļø Maintainability Data Analysis:\")\n", + " \n", + " if 'mi_score' in maintainability_df.columns:\n", + " maintainability_stats = maintainability_df.groupby(['implementation', 'display_name'])['mi_score'].agg([\n", + " 'count', 'mean', 'median', 'std', 'min', 'max'\n", + " ]).round(2)\n", + " \n", + " results['maintainability_stats'] = maintainability_stats\n", + " print(f\" Found maintainability data for {maintainability_df['implementation'].nunique()} implementations\")\n", + " print(f\" Total files analyzed: {len(maintainability_df)}\")\n", + " print(f\" Average MI score across all: {maintainability_df['mi_score'].mean():.2f}\")\n", + " else:\n", + " print(\" āš ļø No 'mi_score' column found in maintainability data\")\n", + " else:\n", + " print(\"āš ļø No maintainability data available\")\n", + " \n", + " # Check if we have data for comparison\n", + " results['comparison_ready'] = (\n", + " not implementation_data['complexity'].empty or \n", + " not implementation_data['maintainability'].empty or\n", + " not implementation_data['summary'].empty\n", + " )\n", + " \n", + " return results\n", + "\n", + "# Analyze Radon scores\n", + "radon_analysis = analyze_radon_scores()\n", + "\n", + "# Display complexity statistics if available\n", + "if radon_analysis['complexity_stats'] is not None and not radon_analysis['complexity_stats'].empty:\n", + " print(\"\\nšŸ“‹ Complexity Statistics by Implementation:\")\n", + " print(radon_analysis['complexity_stats'])\n", + "\n", + "# Display maintainability statistics if available \n", + "if radon_analysis['maintainability_stats'] is not None and not radon_analysis['maintainability_stats'].empty:\n", + " print(\"\\nšŸ“‹ Maintainability Statistics by Implementation:\")\n", + " print(radon_analysis['maintainability_stats'])\n", + "\n", + "print(f\"\\nšŸŽÆ Ready for comparison visualizations: {radon_analysis['comparison_ready']}\")" + ] + }, + { + "cell_type": "markdown", + "id": "d94ca7d4", + "metadata": {}, + "source": [ + "## Create Implementation Comparison Visualizations" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d840b5f2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def create_implementation_overview():\n", + " \"\"\"Create overview visualizations comparing implementations.\"\"\"\n", + " \n", + " if implementation_data['summary'].empty:\n", + " print(\"āš ļø No summary data available for implementation comparison\")\n", + " return\n", + " \n", + " summary_df = implementation_data['summary']\n", + " \n", + " # Create figure with subplots\n", + " fig, axes = plt.subplots(2, 2, figsize=(16, 12), dpi=150)\n", + " fig.suptitle('Code Quality Implementation Comparison', fontsize=18, fontweight='bold', y=0.98)\n", + " \n", + " # 1. Total Issues by Implementation\n", + " if 'Total Issues (A excl)' in summary_df.columns:\n", + " impl_totals = summary_df.groupby('display_name')['Total Issues (A excl)'].sum()\n", + " \n", + " axes[0, 0].bar(range(len(impl_totals)), impl_totals.values, \n", + " color=sns.color_palette(\"husl\", len(impl_totals)))\n", + " axes[0, 0].set_title('Total Issues by Implementation')\n", + " axes[0, 0].set_ylabel('Number of Issues')\n", + " axes[0, 0].set_xticks(range(len(impl_totals)))\n", + " axes[0, 0].set_xticklabels(impl_totals.index, rotation=45, ha='right')\n", + " \n", + " # Add value labels\n", + " for i, v in enumerate(impl_totals.values):\n", + " axes[0, 0].text(i, v + 0.5, str(v), ha='center', va='bottom')\n", + " else:\n", + " axes[0, 0].text(0.5, 0.5, 'No total issues data available', \n", + " ha='center', va='center', transform=axes[0, 0].transAxes)\n", + " axes[0, 0].set_title('Total Issues by Implementation')\n", + " \n", + " # 2. Files Analyzed by Implementation\n", + " if 'Files Analyzed' in summary_df.columns:\n", + " files_analyzed = summary_df.groupby('display_name')['Files Analyzed'].first()\n", + " \n", + " axes[0, 1].bar(range(len(files_analyzed)), files_analyzed.values,\n", + " color=sns.color_palette(\"muted\", len(files_analyzed)))\n", + " axes[0, 1].set_title('Files Analyzed by Implementation')\n", + " axes[0, 1].set_ylabel('Number of Files')\n", + " axes[0, 1].set_xticks(range(len(files_analyzed)))\n", + " axes[0, 1].set_xticklabels(files_analyzed.index, rotation=45, ha='right')\n", + " \n", + " # Add value labels\n", + " for i, v in enumerate(files_analyzed.values):\n", + " axes[0, 1].text(i, v + 0.1, str(v), ha='center', va='bottom')\n", + " else:\n", + " axes[0, 1].text(0.5, 0.5, 'No files analyzed data available',\n", + " ha='center', va='center', transform=axes[0, 1].transAxes)\n", + " axes[0, 1].set_title('Files Analyzed by Implementation')\n", + " \n", + " # 3. Issues per File Ratio\n", + " if 'Issues per File' in summary_df.columns:\n", + " issues_per_file = summary_df.groupby('display_name')['Issues per File'].mean()\n", + " \n", + " axes[1, 0].bar(range(len(issues_per_file)), issues_per_file.values,\n", + " color=sns.color_palette(\"dark\", len(issues_per_file)))\n", + " axes[1, 0].set_title('Average Issues per File')\n", + " axes[1, 0].set_ylabel('Issues per File')\n", + " axes[1, 0].set_xticks(range(len(issues_per_file)))\n", + " axes[1, 0].set_xticklabels(issues_per_file.index, rotation=45, ha='right')\n", + " \n", + " # Add value labels\n", + " for i, v in enumerate(issues_per_file.values):\n", + " axes[1, 0].text(i, v + 0.05, f'{v:.2f}', ha='center', va='bottom')\n", + " else:\n", + " axes[1, 0].text(0.5, 0.5, 'No issues per file data available',\n", + " ha='center', va='center', transform=axes[1, 0].transAxes)\n", + " axes[1, 0].set_title('Average Issues per File')\n", + " \n", + " # 4. Tuning Effect Analysis\n", + " tuned_vs_basic = {}\n", + " for impl in IMPLEMENTATIONS:\n", + " display_name = DISPLAY_NAMES[impl]\n", + " if 'tuning' in impl:\n", + " base_name = impl.replace('_with_tuning', '').replace('_no_tuning', '')\n", + " if base_name not in tuned_vs_basic:\n", + " tuned_vs_basic[base_name] = {'tuned': None, 'basic': None}\n", + " \n", + " impl_data = summary_df[summary_df['implementation'] == impl]\n", + " if not impl_data.empty and 'Total Issues (A excl)' in impl_data.columns:\n", + " total_issues = impl_data['Total Issues (A excl)'].sum()\n", + " if 'with_tuning' in impl:\n", + " tuned_vs_basic[base_name]['tuned'] = total_issues\n", + " else:\n", + " tuned_vs_basic[base_name]['basic'] = total_issues\n", + " \n", + " # Plot tuning effect if we have data\n", + " tuning_data = []\n", + " for base_name, data in tuned_vs_basic.items():\n", + " if data['tuned'] is not None and data['basic'] is not None:\n", + " tuning_data.append({\n", + " 'implementation': base_name.replace('_', ' ').title(),\n", + " 'tuned': data['tuned'],\n", + " 'basic': data['basic'],\n", + " 'improvement': data['basic'] - data['tuned']\n", + " })\n", + " \n", + " if tuning_data:\n", + " tuning_df = pd.DataFrame(tuning_data)\n", + " x = range(len(tuning_df))\n", + " width = 0.35\n", + " \n", + " axes[1, 1].bar([i - width/2 for i in x], tuning_df['basic'], width, \n", + " label='No Tuning', alpha=0.8)\n", + " axes[1, 1].bar([i + width/2 for i in x], tuning_df['tuned'], width,\n", + " label='With Tuning', alpha=0.8)\n", + " \n", + " axes[1, 1].set_title('Tuning Effect on Total Issues')\n", + " axes[1, 1].set_ylabel('Total Issues')\n", + " axes[1, 1].set_xticks(x)\n", + " axes[1, 1].set_xticklabels(tuning_df['implementation'], rotation=45, ha='right')\n", + " axes[1, 1].legend()\n", + " else:\n", + " axes[1, 1].text(0.5, 0.5, 'Insufficient data for tuning comparison',\n", + " ha='center', va='center', transform=axes[1, 1].transAxes)\n", + " axes[1, 1].set_title('Tuning Effect on Total Issues')\n", + " \n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "# Create implementation overview\n", + "create_implementation_overview()" + ] + }, + { + "cell_type": "markdown", + "id": "a88b0e1a", + "metadata": {}, + "source": [ + "## Generate Tool-Specific Comparison Charts" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ab8cda58", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "šŸ“Š Found 7 tools: bandit, ruff, mypy, radon_cc, radon_mi, flake8_wps, vulture\n" + ] + }, + { + "data": { + "image/png": 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", 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vElskB1jHIonDfvCDH3TGHLnHU5+rXXPIrbmx3I/Jtqv9/ve/78w9nX766TOMibK9cl2p80rZr7Usa3JvdVzfej9kZtxj6Uv+ues2znXhP//5z1TnceLzdteE6R37fb3mTi//2G77Z7vUMXVi5NZ9mXs7++yzT+e9k9bXum7/nN+15DZz/c1rH/rQh6ZZDpiTmaIdaJRMf133IkuPu/QqSc+P9J5KL52MplCPZBHpQZIRE9IbJ+qeKnUvmfTyyPvTc63ubRcZoTIjJ7aT3l/pZZVeLunR0zoCYUY0yDJkmquMXNDd6AIZqj1Dv9eyTumxE1mX9Fzqr/QAy8g0GVkiPV9ae5ClV01GrYj0Fkvvp/Tuq0d0yBRjW2yxRbV9I+uaqQfT+yg9gzLqRX9kn0WmGmvthZR9kKHks/3q0XminoYhv9+1B09G+cvIFRkBcSCmhci2yvdkRI8cb+lBVUtvzxwr6eGVEUFy/ER6LNXTxqW3YWvPwPSOywgW6QWX0Tp6K+uV5cix3Lov0nOznrKtll5k2WfpNZYeaa3S6yq9O7Nsmd5voGSd0gOvPi+zfBndKHJcZYSk1t/L8ZjRaKK76SHSAy5D9dejg2Sdsl0zdWPU04VPT0amzbmUKQEyak56BdbSEy29unK+Zgj/dlNARHrYpSdhLT3M6n2b7Z+RYeregRmpJdeaSC/A1mnOcx1Ir85M5ZCRblqPj0whkt+JjE6T3oBdZTvkuEvvtFp65tY9GutRVWYkI6GmR1xkm7R+X7ZxzqVMRRn9nQY9PTFzrUivym984xtTXV9zPc7ruSall2lGXWonvS9bewCmJ2N6XvdmnQGAoU381zeJ9TIdWi0jN9Ztv7THM0pmPQJ8Rk3INH2RESTqUUoHWl9iut7GjX2V38koGWnTJ45o3XbZ74nnMnVYRl1JHNF1+dIuTwzT+pl6evJMuZZ93BuJFzL1YtrStczAkBFEI7FoPeVl9mOmKYt6Jo5Wifcj27YnI5v0VEbobJ3ZI3FNpriLbMecY/U9iJy3GZWljmlzPrWT+xBZzlpGUc32zvmSOCYjC80o3qlHiM3ncny0btPMBpF4IrFZRopsJ5/JNaQeCTUjlWyzzTad8W1GpGk9PjKTRj2qUmt8mxGEMxpNzruM6JNYppb1yfdknyb+q5d5Zl03MvpTtn2OxdwzyIgptaxfRpuJjC7Tn+kiM9JxZp6o483NNttsqu2fqRjr30pMnxFqu3rf+95XDjnkkM5RZfK5/H+gvs6LAwFg9pRYKiMTpt2Ykb5b78nXbbR6toTWGK+rjO6X3ERGucsIhmlnteYF+ps3SNsko+KlvZu4JTFOPV174tV2o+n3RT0q5+233z5VniGjU2ZbZXax6DqdeEa0a/18q7QBs24ZhbSWbZTpkSOzrdXTMaedmdgnbbCus1Qkxkl7OTFI3b7vjzqmyvdlpNfWtm9mcsu00VnfxKqzetn6eqzU65S2d2sMlvsUmQEubd7E5b2VuCP3HzLafX087rvvvlW8HMm51LJt8r7ESBkFs1W2X53jHahZHiLHf2ZrqOPAHHN1ziyxVPKEGdm0jkdyztRTiOecbSf3AbJe9T2eyP5IvB3Jac8oL5xjOyPCJleb/G/izFqWNfmr7I+MSpnRKmfmPZa+5J9b5bhOLFXPblKfx/V+7Gm8NFDX3J44//zzq5E7cx+n677Mc1nnenTWzPrRTnLiOb9r2fb1iLU539rFljCnUuAJNEYafWmodJVGWRoi+WudZqGWxlJ9M7h1yr0M314HO61JwVoKHFunJKhlmPO6EdnaEGlVF7gl4EqjpKs04LtqHfo9Q473Vx3MpdHc2kisJcjJsPNJkmTbJgjJsibgaNeoz3vSWM52q6ft6qsMGx8ZSn78+PGdwVmkoCsJkNYpu+r3p6GWgDMN7a6NuxSD1cVpfZVjJUO6Rx0UdZXp3ZLUTVKo3sY57upjKEV8SeQkYKmlODFD6uext9pNHVcXPEZr4JjjsQ7u62n4akn2ZZmSfOzJ9No9leRsHVDWWoPT1qRfrQ58MiVcO1m3dtMP1NOeJ4ifUeI5x9X09mMChyTjot6PrbJPu05jnxs39U2GdtNFtBZM1uuWqejqBGZ3y5JtlN/LeZAp+LpKsrROcLW7ZrS7xnS3zn/5y1+qqe1ak8W1TOtSB6Gt52Rf1NfXJOrbJZJzvuQGR+u+6rot200dUidGe7rOAMDQJf7rm8QKXduqea5uo6cN3HWqunbt2IHWl5iut3FjX9XTSee3W5M9repOa4kX026OOgmS6dCStEjHv1rawEkIpjNXd9/ZnbT/200Nl+czJXriuhTt1ep2dY7V1uMv58LMSNxFuynQ6mMs07Z1PZeyPerztd0xlpiyXbyc+CuFiXXR5PRk32SdU/TbLpZo3Y+33XZb22LG6a1X4sF29xzq86c1qVTHOOno2TVers/JOj5sF48O5HUj9yhSHFAXCHfVer+oP3Fgtn+mY8z9hq7J9tb7G0lK5jjN9bKrXGe7yvWqvhYM5LUSAGiOtA/S1st963btseRi6jZVd+2VtDFTPJZON7mvn7Z4fa97IPIGKeRMfiI5ssSpKRpLGyvt8nTCSXyTDjrpFNhfiR/Snk3c0ZovqKdfT5sqxYYpvGqNQepcUbsCz3e9611TdVaspWiqjjHq9c1/p0gtxaUppKsLFmspXktR1qabbtrvda1jqsR2mQ66NZ7JdsigGCmurNuss3LZ+nqs1G3Xz372s9X+SzFda3s3RWzpANdbO+20U+egPK3qOCr3SDLoRiS/Vne46toRMOda2tXJEfV0iu2eSO60nsp7oHKFiSvaxdPphJecWT7XtdC5u/2Y7+p6LyRybNWdJtvFZgN1j6Wv+edWKTBvl9/tbd5sIK65PVXfU0vH3nb34nKvIJ0x6/fmt1tl27bLx7bGxjPrPhYMRQPT1QRgACRgaVeoWMtrKfpKT58HHnigPPjgg1UPl/SiS4+kaC26q0c7WH755btt4CQxkGK9VvXnuksYtL6WACw9g9LoatWuQdq6bnXCqD+yDaJd0FY3mlp762U564Cqu+2cXmkDIaM2pNGdbZmAK8uSoDgN4TTyuy5ztmdGrkivpYxYmb80npPgSgCa3kTtgvXeShBe9/Y64YQTuh3hJKMORl1kmAZmkiYZ9SdBdP7SUE3xY9YpDeSugU1PdXecpaEfGTk0wVjdezDJvSRKsq0y8kUd8NVB3EAn9tqtV2vvyNZel7UZ9WRt7QnXKqPiJFGYICX7qnUUlFZJrNUj2qQX5TnnnNP2ffV72hWLdre/6mNiRutVByH1iJlR99prp75GtVuW7hLC9XmaJFpv5HNZ99yAqq+VGfE1haj1DYDWa2VvJZiqe/t2vfa1O7bbjTwz0OsMAAw94r++SXu5XWxUt9HbtWNb2+9db6YPlL7EdL2NG/uqbrPfcccdVQeldupjKrFG2rqJTbbffvuquDPHXeLHz33uc1WMnXXK+qTzXV9G8cnIHd1JHJhOZK0zNuR3EqvnPMiIs1murom76bXL++KNb3xjr46x1tfbHWNZr3ad2urXomtRcHf7Mcn37vZj62/n+lHPGtGT+DazV7SOfjm99apHOUlSrmvSuVYXK2a98tmuydqBvG7ku/OXjr05XnO9zPGS62V9L6i/cWAdy+YcaLedIknCjDaT7ZPzumsR64ziwIG8VgIAzZP2YEYcTNFR2khp96aNkfvW9UiW3bVXMkJ7XYiUjjztCuH6kzdIZ7S0mxKDpMizbgOmkCuzmKU9fsABB1SzOqS4sC7y66sUAqZYNaMv1oV6KfBMOyvxRjpVpRNj4qXMNpD4IO27tGfbFe11l3NpjTHq9nbayFmnFCJmIJH8pagsRaKJ3VJs1V2bv7cya1timOS5Uiib+CnLn5E78zsp1m3dl7Nq2fpzrGQ9MrJmjuPMIJA2cGK2rFNyhV1HS+xvnFjHS8mZ5Bit877JFWZEysSFydnU8XY9OMxA5wrbxYitOdZ2HXT7mivM9yauSKyV47Z19oau6tgsRdnddRrMdae7/NxA3WPpa/55ZubN+nPN7ane3FNLwXpGzW09VpJrb3dvsPX+gXwh/D8KPIHG6O5mf6Txcfzxx08zjHsaAUn85Pk0TNrdTG83mkKtXW+SuidIu9Fdaq2NvXZDg89oeoCBSKrV08BNb/368/7+SICR3ngZjj2jpeS3E4jmL0P4JwGV3m1rrbVW52cy2kSCtDTCs78TOCWZlr8cG+mllind+jPtXGvvpiT3evP+/H6C9vQozFToafxmGsj8JQDNcZhGe28KPXOcdLc+rSNcZmSiusAzwX56ND7yyCPV9sw2SyFfkjg5H9qNAtIfGcVketrdSJmRduddLcdntvv0Ru5o7a3Vk+kD2vVqm9F6dZewmt5392R6hHbLMhDTidQSnOXc6jpaSq5ZOd8yVUJ3Cciear3mTa/wun4tRaVdE5oDuc4AwNAk/uubgWrHzgy9jen6Ejf2Rd0G7+kU1TmWUuCZ/X7RRRdVxaqXXXZZlUzLsZe/jBiU5GOmXevtTA7tZjPo+lrrKBppRyc5l6R2OvbVBZ514q4e4XMgTe8468sxluLJGa3zjEZvrPdjztmexF7tvm9614ferFd93Uhcnr/pSdFirhtdY6eBum7kfTkecx61rnOOmyRFM6pp11F9+qIn18qo13Owr5UAQLOkwC8xXkZway0oSrszBX9pM91yyy3dfj7t44y2nmKixIKJPRIrDETeIG2QzIQXGXmxXZslRZ0p/sy99Yyi/8lPfrL0R0bhzHTOyTVFOpmliDOFUIlbU+SZAs/M2JUCz3rGihSGtsvJzCjnEq1txYMPPrjqJHbeeedVnYSyf9JZL38pystoiJmFYUZtv54UBaYtmrZqCj2znul0mb/MipdOgZkeOyPjz8pl60+OKUWmiXEzmmv2S9q99cAwmeEhU62nc2A6mA5EnNg1V1hLnJxi0hTu5fhNXJhtlaLhbKfuRpFs0r2I3h6309uX2Q4z6jQ4q3KFvc0/z4y8WX+vuT3Vl3tqrQWePVlncSL8Pwo8gcZLUPOxj32sCuDSIE4jNYFUhueue7Nk6oSuCb46gTC9obvbDT1eN5anN9R5a4NyesmZmSmNsCxju5vm3b0/evr+/sr0hieeeGIVyKQxm4KzTFeQZF0CpvTCSyBc9/pKULrddttVf+nFk/flMwmKkhg899xzq/cde+yxPWrYtQY67ZI5SQj1dt9lxM785bhJYJkpETJFR3paJpBL8J8kW0+LHjN1Q3p0tSvybD3+6uLOSE+mTC+ZRGMC2iRQ62RNRswZCoVz7fZNrT5fu4600l3AlW3QbjryWaU+pnK9mdFUETNbktWZviWPSUgn0ZxenxkJNVOb5Lg89NBD+13g2XreTO/6WvcAzDbqSyEwADBnEv8NXX2J6XobN84oDqxHrG8XP2RKxyOPPLJX65RERKZizF8KPOsC1Ey9lnZ3ZnnIsdebKQLbLWOtPg5bY8B6irpMRZg4NCN+JC5M4i7x30An7maGnqzz9GLA1v2Y6fWyLQZTvSzZ/4nBBlMS4ykOiCS7k3DOtTNxYK5XSXIORIFnT66VrddL10oAoHW0/N12262K9dJ2zmjsKeBLjJeRMTODWkbGnF6xUUb5z0iLycNkJoALLrigahcmP9LfvEHa9fWI/t3NKhZpY+XeetdYtC/S2S338BMnZdCGv/3tb9XzKexsfaxzDnWBZ7vp2WfU3u4u57LJJptUf3k9cVgdu2V56unUM5Jmf+V3U8SZv4wwn99J4Wrimax/Yq0LL7xwqtEc+7ps7eLE6cWIfc0xZf+lU2JyfOncmP2UYt3kHXMcZ2TPDA7TmwF3utuHre3vrgWR6QiY5cg65N7Jr3/966pwL8fJjOKr2SFX2Jorz/Ew0APg9EZ/889Nu+b2VNYzucDpxYl1rrB+P9B3g9etH6CHfvSjH1WJuARW6RWVpEyGum8dqryeLrhVRiqIDDXeThr6rdOe1eoALkV73cmIiXUCa6CmNe+tepj/1imiWyWwSKMtwVGm56q3R5JSdbDaVYLiBB4Z/aGvsl0T4NY9D9O7KYFZEnP53gQaSZKl4Z4Ap26oJ5lXD0mfIe/TKzE9MDOkfj39WmtSIg3QqIe87yojFXaV5GH9uYx42Z3bbrutCjTrYtj8Ro6VBGqRZFqmg0hPwkyBkAAqEtznc73Rbhj+qEcryvHVtQdZPUJLtk2CtXragZkxcsvM0F2PyEwxUm/z6fVuTLJzkUUWmeF+zL5onWpgZqjPq4w2lB5x3UkiNsdQu6KCgfKzn/2suhmVgC3/zjQhGeEqx31dYNnuWtlbOX8zLcqMeiLWr/V1ShIAYM4k/huaehvT9SVu7GscWB8b3cXO8fTTT1fJjRRP1knBtK3Tjk+xamRGh3SiyvSNSbDW06L3tniuuxgwv1sfv10TjJkpIkntvGf8+PHVKByZpizt/YGaPnFmyrnXXVFuvc4zGuGmJ/sxx0uSv7kHMjOn++7JsmRkzyTqByIG607u+9T3b/bbb78qSZdi4IzIUifOMqX9QKivldlf3U3jl+tAPWpOf6ctBQBmH2m/pj2YUQUzcEVGwc+IjWlT1e37GbVZElskL5MZzvLZtC2POeaYqYri+po3SLupvn8+vXv89WwA05vVqjfqYrQUOtaFnHVhZ6blzrpkxNKsSwrGMkJeBiBpZ3rt0nrAh7q9nXg7z9XPZ30yMuhRRx1Vda7LIBHxxz/+cYade2YkbeEUc9Z5kazXrrvuWnVSSpyYETzTbs+MCX1dtvoY6i5ObBcj9vVYybImx5rBXyId7jKSZtriP/7xj6u/HEs5jupYd6ByhTn+u96P2Hrrrav1z/IkR5WZMWbG9OyzOleYuK6e+nsg4sTEKLmvU8f2M0Nf889Nveb2VE/uqdW5whQot47eCfSeAk+g8dKDK9KzpN1Q6ddee22VhInWG/j1CBpJfLS7oZ4AoF2wVgdV+VySAu2cc8451eNqq602zcgas0oSOZHEV7ugJSOKJOjLCJPp4ZRpAdKDKO/NZ7rKDfpMpZfRUqbX229GEkSk5+Qee+zRmQhtlQZkeibWvxkZfSPFiV/60pemeX+CoTqobd2/dSMwgVW7afbqQKZVgsFM49G6D7vKPt9pp52qUVAyZUS9LTMSxt577912WyfhXOttEimFeF3lO1JsGwleu3rnO99Zll9++SoJmVFwcvxnpMaMbDQUpCi2XUKoXuecV11H6Olqww03rB4zTUe770pwn5GfEuCmSGBmyXWpTlxlWdpJonjnnXeujqG6J25/1FNCdE2Q1j2Hc361S/ImqKx/v+txWt/A6ulUB/VxmX3W7pzIeVlPGZkRZAAAekr8NzT1NqbrS9zYGge2S34lQdGuA1K9jxPrtivyjRRtJg5MsrFuE3/iE5+o2vE///nPp3l/ksA5HlrXqaeSwG13jCYJk3XIDA/pUNhVRkatY926k99QSdz973//q87BdsnWrHckWT+jeyBJRmXf5zrQztlnn13tw0xLPr3RYPqrPqYuv/zytvcjIiMUffSjH+1MQvdXu5gt9wTq+zeZzrOd3OeppSh4Rt/ZncR1SRDmOpr1bicxcX4j1+763gsAQH3fOm3odoMBpK1Yj1DZk7Z1PT13PVV7f/MGabsk5xIXX3xx299MUV86f0Ud2/RXfY89bdt0UkockGLBWj06adax7tzV3QxuGd2yXbybNng6HuW7699LwVfay4cffnjbdmB/8l2tsszZzhlJsN6/rVJgWXdsq/dVX5attWCsXZzYLlfY12MlRYS595B1are9V1999c6OVt11iupNrjDqmTA22GCDafZ/OsLm+Wzr5N3++te/Vvnget2aLiOOtovbsi3SmW2xxRYra6yxRo9is3QQbjfASrbNpz71qSqebne/YqD0Nf/cH63Tx7eeL/255naXf5zR9k8+sN1gO8kf1rnfHKtA/yjwBBqv7n2TIKcOoOpGWXp1ZRTFWmvjLQ3YNPxys/uTn/zkVMm6fE9697WTpEJ6kWWUy7322muq6YwzGkGm4EpCJje2DzvssDJY0gjMSH0JLLMcSZC19gDKSCmRkVJS2JnGZUbnjJNOOqlKYLZuty984QtVD5sExklC9FWCqbqRloRGawItAU16sKVXVhqJ9fvSmE1iIQ3K73//+1XDvZbkbT3VQl3UGgm4E8ykkfnFL36xc9/nswm2ugvEDzjggCoplGMn26G1l1SWK0Wc+Y70HMyU53UiI+uVbZwp/Vq3dY6JOihIUWIKL3sjwVm2SR3s5fuOOOKIal+kN1NGLGqnHq3zG9/4xpBK7EWSrjmP6sAt654gOsm4aD2nu5P9lOM6xZMJ9lt73qUoIK9nP+V4TlJ2ZsqUMHHmmWeW733ve1MVPOZaU7+eBHDrlDH9neohwVLrFKR1T7lcs6688srO53OOpEg5oyHV51bXoLm+6ZAEa9ekXzu5NuYzOWeyfq1JzVxr99lnnypIzA2G3OwAAOgp8d/Q1NuYri9xY6TjYqTosnU6sYzIkaRN6+/WkiRNwWSOoezjdISspe2eqR7rIri8Xic0kliM0047rWpPt8oxVY/c2Rqn9kTa4hltvy5mjozuUh+jSSK2m4YuCdlst6x3zo8kRHv724Mp69d6TifplO2QczaJsHZFra0So2+//fbVvw855JCp7mnUHVazryIx4ECNrNROOu8lGZ3pyFMI3DpaTK4bxx9/fLVPc04kNh0IdRzYetykY1/uC0Xi6dZ7FYmRsxz1aEjRNeFZf2ddND89ud+REWwj18XWhGS2//nnn985VXzOxcTiAACt961zPzu5m9bCoQxIkE5fdTumJ510cs85OZRI4VBGiOxv3mD//fev2m6ZPeDkk0+e6r57YsQ6b5QCvoEa0CBt4LRZ606MyR9klMZaXUg6o+nZ6+2WNliKOWsZFfToo4/u3C51+yyjoSa3lngrubXWAV+yvTIqfJ2Dq9uafZEYeosttqj+nfxj8patsq0Ta0e9TfuybLmHUMdPX/7yl6s2euQ4y2A3GS20nb4cKxlkJXFAiuISk7SOgpjYMsuXYyff21qs2xPpxJfZAuv8Uh6Te0zck22SUUKnlytMWzzLldg8234oyPbLPZ50XKtlpNa6cDuvtRYxtpN9kxnnkitPfNka22SfZvTK3G/INuwu3zpQ+pJ/Hqhp4VvXuz/X3O7yj91JDUKuyckHJi/Yei8uecPkD7PuySdm+wD9MzSu7sAcLQ2uNIbSwEtDLb1N0hBIMiANjDQ2ElSlZ1JrYzqNvozCkaKmDGGf0UHS8E5jJcmfpZZaqmp0dJ3CLw3fJHiS2ElvryR16t+sp1hOkHXCCSf0uoE+kBKwJHmRoC3FXOmJl+LCBC/ZNmmwJUFSF5dFAoAMa58Gchq6uUGfhEC2RxqaWa9ss/SK6o/Pfe5zVZFoGm1bbrllta0TBKWBWTfU0zCvh9bP1HZpZCf4ScP9jDPOqD6TfZXGYBJxmXog0zDUUvyYREoShTk+MlJpPpPAKw3TNCrbjd6ThODnP//5qgA2CZALL7ywGh0o658AINstibJMc5ZejZHHFFLm9zJSRQKtLE+OsSxfgsz08kzgX3+mJxJQZB9le333u9+tjsccc/m+HG8ZBad1KspWOS6zr/LefE+281CR8zC96XIcJtDIeZvejtmeueHQkyLIjJr59a9/vTqOsv9zDuR4SnCU4znHTK4NKbpslxwdSLlJkd9MAP3Vr361On5zzUjwWCffcpMh15WBkAKEbKsUIaQgIedrjtf0QExSLcfxgQceWAWJSQDnpk4CqRwnuWmU3sBdz4uVV165eszypgdqvjM3x+oRXdpNN5HjM9eXnGdJLGf75wZCRgpNgi8jLuUaNRSmjAQAmkP8NzT1JabrbdwY6Tx06aWXVsmDdHqsX8u+y+iq6dRYdxxrlXZ6kg1///vfq1gxv5WYMstWJwDz2R122KHzMym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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def create_tool_specific_comparison():\n", + " \"\"\"Create detailed comparisons for each linting tool.\"\"\"\n", + " \n", + " if implementation_data['summary'].empty:\n", + " print(\"āš ļø No summary data available for tool-specific comparison\")\n", + " return\n", + " \n", + " summary_df = implementation_data['summary']\n", + " \n", + " # Get unique tools from the data\n", + " if 'Tool' in summary_df.columns:\n", + " tools = summary_df['Tool'].unique()\n", + " print(f\"šŸ“Š Found {len(tools)} tools: {', '.join(tools)}\")\n", + " else:\n", + " print(\"āš ļø No 'Tool' column found in summary data\")\n", + " return\n", + " \n", + " # Create a comprehensive heatmap\n", + " if 'Total Issues (A excl)' in summary_df.columns:\n", + " # Pivot data for heatmap\n", + " heatmap_data = summary_df.pivot_table(\n", + " index='Tool', \n", + " columns='display_name', \n", + " values='Total Issues (A excl)',\n", + " aggfunc='sum',\n", + " fill_value=0\n", + " )\n", + " \n", + " # Create figure\n", + " fig, axes = plt.subplots(2, 1, figsize=(16, 12), dpi=150)\n", + " fig.suptitle('Tool-Specific Issue Comparison Across Implementations', \n", + " fontsize=16, fontweight='bold', y=0.98)\n", + " \n", + " # 1. Heatmap of issues by tool and implementation\n", + " sns.heatmap(heatmap_data, annot=True, fmt='d', cmap='YlOrRd', \n", + " ax=axes[0], cbar_kws={'label': 'Number of Issues'})\n", + " axes[0].set_title('Issues Heatmap: Tools vs Implementations')\n", + " axes[0].set_xlabel('Implementation')\n", + " axes[0].set_ylabel('Linting Tool')\n", + " \n", + " # 2. Bar chart comparing total issues per tool across all implementations\n", + " tool_totals = summary_df.groupby('Tool')['Total Issues (A excl)'].sum().sort_values(ascending=False)\n", + " \n", + " bars = axes[1].bar(range(len(tool_totals)), tool_totals.values,\n", + " color=sns.color_palette(\"viridis\", len(tool_totals)))\n", + " axes[1].set_title('Total Issues by Tool (All Implementations Combined)')\n", + " axes[1].set_ylabel('Total Issues')\n", + " axes[1].set_xticks(range(len(tool_totals)))\n", + " axes[1].set_xticklabels(tool_totals.index, rotation=45, ha='right')\n", + " \n", + " # Add value labels on bars\n", + " for i, (bar, value) in enumerate(zip(bars, tool_totals.values)):\n", + " axes[1].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.5,\n", + " str(value), ha='center', va='bottom')\n", + " \n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " # Create individual tool comparison charts\n", + " n_tools = len(tools)\n", + " if n_tools > 0:\n", + " cols = min(3, n_tools)\n", + " rows = (n_tools + cols - 1) // cols\n", + " \n", + " fig, axes = plt.subplots(rows, cols, figsize=(18, 6*rows), dpi=150)\n", + " fig.suptitle('Individual Tool Comparisons Across Implementations', \n", + " fontsize=16, fontweight='bold', y=0.98)\n", + " \n", + " if rows == 1 and cols == 1:\n", + " axes = [axes]\n", + " elif rows == 1:\n", + " axes = axes\n", + " else:\n", + " axes = axes.flatten()\n", + " \n", + " for i, tool in enumerate(tools):\n", + " tool_data = summary_df[summary_df['Tool'] == tool]\n", + " \n", + " if not tool_data.empty and i < len(axes):\n", + " implementations = tool_data['display_name'].values\n", + " issues = tool_data['Total Issues (A excl)'].values\n", + " \n", + " bars = axes[i].bar(range(len(implementations)), issues,\n", + " color=sns.color_palette(\"husl\", len(implementations)))\n", + " axes[i].set_title(f'{tool} Issues by Implementation')\n", + " axes[i].set_ylabel('Number of Issues')\n", + " axes[i].set_xticks(range(len(implementations)))\n", + " axes[i].set_xticklabels(implementations, rotation=45, ha='right')\n", + " \n", + " # Add value labels\n", + " for bar, value in zip(bars, issues):\n", + " axes[i].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.1,\n", + " str(value), ha='center', va='bottom')\n", + " \n", + " # Hide unused subplots\n", + " for i in range(len(tools), len(axes)):\n", + " axes[i].set_visible(False)\n", + " \n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " else:\n", + " print(\"āš ļø No 'Total Issues (A excl)' column found for tool comparison\")\n", + "\n", + "# Create tool-specific comparison\n", + "create_tool_specific_comparison()" + ] + }, + { + "cell_type": "markdown", + "id": "61cd5b86", + "metadata": {}, + "source": [ + "## Analyze Complexity Metrics Across Implementations" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "58b9de0b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "šŸ“Š Analyzing complexity data for 1 implementations\n", + "Total functions analyzed: 161\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "šŸ“‹ Complexity Statistics Summary:\n", + " count mean median std min max\n", + "display_name \n", + "Claude Code (Basic) 161 2.34 2.0 1.51 1 8\n", + "\n", + "šŸ† Best Average Complexity: Claude Code (Basic) (2.34)\n", + "šŸšØ Highest Average Complexity: Claude Code (Basic) (2.34)\n", + "šŸ“ˆ Potential Improvement: 0.00 complexity points\n" + ] + } + ], + "source": [ + "def create_complexity_analysis():\n", + " \"\"\"Create visualizations for cyclomatic complexity comparison.\"\"\"\n", + " \n", + " if implementation_data['complexity'].empty:\n", + " print(\"āš ļø No complexity data available for analysis\")\n", + " return\n", + " \n", + " complexity_df = implementation_data['complexity']\n", + " \n", + " if 'complexity' not in complexity_df.columns:\n", + " print(\"āš ļø No 'complexity' column found in complexity data\")\n", + " return\n", + " \n", + " print(f\"šŸ“Š Analyzing complexity data for {complexity_df['implementation'].nunique()} implementations\")\n", + " print(f\"Total functions analyzed: {len(complexity_df)}\")\n", + " \n", + " # Create comprehensive complexity analysis\n", + " fig, axes = plt.subplots(2, 2, figsize=(16, 12), dpi=150)\n", + " fig.suptitle('Cyclomatic Complexity Analysis Across Implementations', \n", + " fontsize=16, fontweight='bold', y=0.98)\n", + " \n", + " # 1. Box plot of complexity by implementation\n", + " sns.boxplot(data=complexity_df, x='display_name', y='complexity', ax=axes[0, 0])\n", + " axes[0, 0].set_title('Complexity Distribution by Implementation')\n", + " axes[0, 0].set_xlabel('Implementation')\n", + " axes[0, 0].set_ylabel('Cyclomatic Complexity')\n", + " axes[0, 0].tick_params(axis='x', rotation=45)\n", + " \n", + " # 2. Average complexity comparison\n", + " avg_complexity = complexity_df.groupby('display_name')['complexity'].mean().sort_values()\n", + " \n", + " bars = axes[0, 1].bar(range(len(avg_complexity)), avg_complexity.values,\n", + " color=sns.color_palette(\"coolwarm\", len(avg_complexity)))\n", + " axes[0, 1].set_title('Average Complexity by Implementation')\n", + " axes[0, 1].set_ylabel('Average Complexity')\n", + " axes[0, 1].set_xticks(range(len(avg_complexity)))\n", + " axes[0, 1].set_xticklabels(avg_complexity.index, rotation=45, ha='right')\n", + " \n", + " # Add value labels\n", + " for bar, value in zip(bars, avg_complexity.values):\n", + " axes[0, 1].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.05,\n", + " f'{value:.2f}', ha='center', va='bottom')\n", + " \n", + " # 3. Complexity distribution histograms\n", + " implementations = complexity_df['display_name'].unique()\n", + " colors = sns.color_palette(\"husl\", len(implementations))\n", + " \n", + " for impl, color in zip(implementations, colors):\n", + " impl_data = complexity_df[complexity_df['display_name'] == impl]['complexity']\n", + " axes[1, 0].hist(impl_data, alpha=0.6, label=impl, bins=15, color=color)\n", + " \n", + " axes[1, 0].set_title('Complexity Distribution Overlays')\n", + " axes[1, 0].set_xlabel('Cyclomatic Complexity')\n", + " axes[1, 0].set_ylabel('Frequency')\n", + " axes[1, 0].legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n", + " \n", + " # 4. High complexity functions count (complexity > 10)\n", + " high_complexity_counts = complexity_df[complexity_df['complexity'] > 10].groupby('display_name').size()\n", + " high_complexity_counts = high_complexity_counts.reindex(implementations, fill_value=0)\n", + " \n", + " bars = axes[1, 1].bar(range(len(high_complexity_counts)), high_complexity_counts.values,\n", + " color=sns.color_palette(\"Reds\", len(high_complexity_counts)))\n", + " axes[1, 1].set_title('High Complexity Functions (CC > 10)')\n", + " axes[1, 1].set_ylabel('Number of Functions')\n", + " axes[1, 1].set_xticks(range(len(high_complexity_counts)))\n", + " axes[1, 1].set_xticklabels(high_complexity_counts.index, rotation=45, ha='right')\n", + " \n", + " # Add value labels\n", + " for bar, value in zip(bars, high_complexity_counts.values):\n", + " axes[1, 1].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.1,\n", + " str(value), ha='center', va='bottom')\n", + " \n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " # Print complexity statistics\n", + " print(\"\\nšŸ“‹ Complexity Statistics Summary:\")\n", + " complexity_stats = complexity_df.groupby('display_name')['complexity'].agg([\n", + " 'count', 'mean', 'median', 'std', 'min', 'max'\n", + " ]).round(2)\n", + " print(complexity_stats)\n", + " \n", + " # Identify best and worst performers\n", + " best_impl = avg_complexity.index[0]\n", + " worst_impl = avg_complexity.index[-1]\n", + " \n", + " print(f\"\\nšŸ† Best Average Complexity: {best_impl} ({avg_complexity.iloc[0]:.2f})\")\n", + " print(f\"šŸšØ Highest Average Complexity: {worst_impl} ({avg_complexity.iloc[-1]:.2f})\")\n", + " \n", + " improvement = avg_complexity.iloc[-1] - avg_complexity.iloc[0]\n", + " print(f\"šŸ“ˆ Potential Improvement: {improvement:.2f} complexity points\")\n", + "\n", + "# Create complexity analysis\n", + "create_complexity_analysis()" + ] + }, + { + "cell_type": "markdown", + "id": "325a03b2", + "metadata": {}, + "source": [ + "## Compare Maintainability Scores" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "1af14443", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "šŸ“Š Analyzing maintainability data for 1 implementations\n", + "Total files analyzed: 54\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "šŸ“‹ Maintainability Statistics Summary:\n", + " count mean median std min max\n", + "display_name \n", + "Claude Code (Basic) 54 80.83 89.13 21.84 22.87 100.0\n", + "\n", + "šŸ† Best Average Maintainability: Claude Code (Basic) (80.8)\n", + "šŸšØ Lowest Average Maintainability: Claude Code (Basic) (80.8)\n", + "šŸ“ˆ Maintainability Gap: 0.0 MI points\n", + "\n", + "šŸ… Maintainability Rank Distribution:\n", + "mi_rank A\n", + "display_name \n", + "Claude Code (Basic) 54\n" + ] + } + ], + "source": [ + "def create_maintainability_analysis():\n", + " \"\"\"Create visualizations for maintainability index comparison.\"\"\"\n", + " \n", + " if implementation_data['maintainability'].empty:\n", + " print(\"āš ļø No maintainability data available for analysis\")\n", + " return\n", + " \n", + " maintainability_df = implementation_data['maintainability']\n", + " \n", + " if 'mi_score' not in maintainability_df.columns:\n", + " print(\"āš ļø No 'mi_score' column found in maintainability data\")\n", + " return\n", + " \n", + " print(f\"šŸ“Š Analyzing maintainability data for {maintainability_df['implementation'].nunique()} implementations\")\n", + " print(f\"Total files analyzed: {len(maintainability_df)}\")\n", + " \n", + " # Create comprehensive maintainability analysis\n", + " fig, axes = plt.subplots(2, 2, figsize=(16, 12), dpi=150)\n", + " fig.suptitle('Maintainability Index Analysis Across Implementations', \n", + " fontsize=16, fontweight='bold', y=0.98)\n", + " \n", + " # 1. Violin plot of MI scores by implementation\n", + " sns.violinplot(data=maintainability_df, x='display_name', y='mi_score', ax=axes[0, 0])\n", + " axes[0, 0].set_title('Maintainability Index Distribution by Implementation')\n", + " axes[0, 0].set_xlabel('Implementation')\n", + " axes[0, 0].set_ylabel('Maintainability Index')\n", + " axes[0, 0].tick_params(axis='x', rotation=45)\n", + " \n", + " # 2. Average MI score comparison\n", + " avg_mi = maintainability_df.groupby('display_name')['mi_score'].mean().sort_values(ascending=False)\n", + " \n", + " bars = axes[0, 1].bar(range(len(avg_mi)), avg_mi.values,\n", + " color=sns.color_palette(\"viridis\", len(avg_mi)))\n", + " axes[0, 1].set_title('Average Maintainability Index by Implementation')\n", + " axes[0, 1].set_ylabel('Average MI Score')\n", + " axes[0, 1].set_xticks(range(len(avg_mi)))\n", + " axes[0, 1].set_xticklabels(avg_mi.index, rotation=45, ha='right')\n", + " \n", + " # Add value labels\n", + " for bar, value in zip(bars, avg_mi.values):\n", + " axes[0, 1].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.5,\n", + " f'{value:.1f}', ha='center', va='bottom')\n", + " \n", + " # 3. MI score distribution histograms\n", + " implementations = maintainability_df['display_name'].unique()\n", + " colors = sns.color_palette(\"husl\", len(implementations))\n", + " \n", + " for impl, color in zip(implementations, colors):\n", + " impl_data = maintainability_df[maintainability_df['display_name'] == impl]['mi_score']\n", + " axes[1, 0].hist(impl_data, alpha=0.6, label=impl, bins=15, color=color)\n", + " \n", + " axes[1, 0].set_title('MI Score Distribution Overlays')\n", + " axes[1, 0].set_xlabel('Maintainability Index')\n", + " axes[1, 0].set_ylabel('Frequency')\n", + " axes[1, 0].legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n", + " \n", + " # 4. Low maintainability files count (MI < 20)\n", + " low_mi_counts = maintainability_df[maintainability_df['mi_score'] < 20].groupby('display_name').size()\n", + " low_mi_counts = low_mi_counts.reindex(implementations, fill_value=0)\n", + " \n", + " bars = axes[1, 1].bar(range(len(low_mi_counts)), low_mi_counts.values,\n", + " color=sns.color_palette(\"Oranges\", len(low_mi_counts)))\n", + " axes[1, 1].set_title('Low Maintainability Files (MI < 20)')\n", + " axes[1, 1].set_ylabel('Number of Files')\n", + " axes[1, 1].set_xticks(range(len(low_mi_counts)))\n", + " axes[1, 1].set_xticklabels(low_mi_counts.index, rotation=45, ha='right')\n", + " \n", + " # Add value labels\n", + " for bar, value in zip(bars, low_mi_counts.values):\n", + " axes[1, 1].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.1,\n", + " str(value), ha='center', va='bottom')\n", + " \n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " # Print maintainability statistics\n", + " print(\"\\nšŸ“‹ Maintainability Statistics Summary:\")\n", + " mi_stats = maintainability_df.groupby('display_name')['mi_score'].agg([\n", + " 'count', 'mean', 'median', 'std', 'min', 'max'\n", + " ]).round(2)\n", + " print(mi_stats)\n", + " \n", + " # Identify best and worst performers\n", + " best_impl = avg_mi.index[0]\n", + " worst_impl = avg_mi.index[-1]\n", + " \n", + " print(f\"\\nšŸ† Best Average Maintainability: {best_impl} ({avg_mi.iloc[0]:.1f})\")\n", + " print(f\"šŸšØ Lowest Average Maintainability: {worst_impl} ({avg_mi.iloc[-1]:.1f})\")\n", + " \n", + " improvement = avg_mi.iloc[0] - avg_mi.iloc[-1]\n", + " print(f\"šŸ“ˆ Maintainability Gap: {improvement:.1f} MI points\")\n", + " \n", + " # Maintainability rank analysis if available\n", + " if 'mi_rank' in maintainability_df.columns:\n", + " print(\"\\nšŸ… Maintainability Rank Distribution:\")\n", + " rank_dist = maintainability_df.groupby(['display_name', 'mi_rank']).size().unstack(fill_value=0)\n", + " print(rank_dist)\n", + "\n", + "# Create maintainability analysis\n", + "create_maintainability_analysis()" + ] + }, + { + "cell_type": "markdown", + "id": "aaeb4d17", + "metadata": {}, + "source": [ + "## Create Comprehensive Comparison Dashboard" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b8f7134f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "COMPREHENSIVE COMPARISON SUMMARY\n", + "================================================================================\n", + "\n", + "šŸ“Š Overall Performance Rankings:\n", + " Total Issues Issues Rank Avg Complexity \\\n", + "Implementation \n", + "Claude Code (Basic) 10 1.0 2.34 \n", + "\n", + " Complexity Rank Avg MI Score MI Rank \n", + "Implementation \n", + "Claude Code (Basic) 1.0 80.83 1.0 \n", + "\n", + "šŸ† Best Overall Implementation: Claude Code (Basic)\n", + " Overall Score: 1.00\n" + ] + } + ], + "source": [ + "def create_comprehensive_dashboard():\n", + " \"\"\"Create a comprehensive dashboard showing all metrics in one view.\"\"\"\n", + " \n", + " # Check data availability\n", + " has_summary = not implementation_data['summary'].empty\n", + " has_complexity = not implementation_data['complexity'].empty\n", + " has_maintainability = not implementation_data['maintainability'].empty\n", + " \n", + " if not any([has_summary, has_complexity, has_maintainability]):\n", + " print(\"āš ļø No data available for comprehensive dashboard\")\n", + " return\n", + " \n", + " # Create a large dashboard figure\n", + " fig = plt.figure(figsize=(20, 16), dpi=150)\n", + " fig.suptitle('Comprehensive Code Quality Comparison Dashboard', \n", + " fontsize=20, fontweight='bold', y=0.98)\n", + " \n", + " # Define grid layout (4 rows, 3 columns)\n", + " gs = fig.add_gridspec(4, 3, hspace=0.3, wspace=0.3)\n", + " \n", + " # Row 1: Overall metrics\n", + " if has_summary:\n", + " summary_df = implementation_data['summary']\n", + " \n", + " # Total issues comparison\n", + " ax1 = fig.add_subplot(gs[0, 0])\n", + " if 'Total Issues (A excl)' in summary_df.columns:\n", + " impl_totals = summary_df.groupby('display_name')['Total Issues (A excl)'].sum()\n", + " bars = ax1.bar(range(len(impl_totals)), impl_totals.values,\n", + " color=sns.color_palette(\"Reds\", len(impl_totals)))\n", + " ax1.set_title('Total Issues by Implementation', fontweight='bold')\n", + " ax1.set_xticks(range(len(impl_totals)))\n", + " ax1.set_xticklabels(impl_totals.index, rotation=45, ha='right')\n", + " \n", + " # Add value labels\n", + " for bar, value in zip(bars, impl_totals.values):\n", + " ax1.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.5,\n", + " str(value), ha='center', va='bottom')\n", + " \n", + " # Issues per file\n", + " ax2 = fig.add_subplot(gs[0, 1])\n", + " if 'Issues per File' in summary_df.columns:\n", + " issues_per_file = summary_df.groupby('display_name')['Issues per File'].mean()\n", + " bars = ax2.bar(range(len(issues_per_file)), issues_per_file.values,\n", + " color=sns.color_palette(\"Blues\", len(issues_per_file)))\n", + " ax2.set_title('Issues per File Ratio', fontweight='bold')\n", + " ax2.set_xticks(range(len(issues_per_file)))\n", + " ax2.set_xticklabels(issues_per_file.index, rotation=45, ha='right')\n", + " \n", + " for bar, value in zip(bars, issues_per_file.values):\n", + " ax2.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n", + " f'{value:.2f}', ha='center', va='bottom')\n", + " \n", + " # Tool heatmap\n", + " ax3 = fig.add_subplot(gs[0, 2])\n", + " if 'Tool' in summary_df.columns and 'Total Issues (A excl)' in summary_df.columns:\n", + " heatmap_data = summary_df.pivot_table(\n", + " index='Tool', columns='display_name', \n", + " values='Total Issues (A excl)', aggfunc='sum', fill_value=0\n", + " )\n", + " sns.heatmap(heatmap_data, annot=True, fmt='d', cmap='YlOrRd', ax=ax3,\n", + " cbar_kws={'label': 'Issues'})\n", + " ax3.set_title('Issues Heatmap', fontweight='bold')\n", + " \n", + " # Row 2: Complexity analysis\n", + " if has_complexity:\n", + " complexity_df = implementation_data['complexity']\n", + " \n", + " # Complexity box plot\n", + " ax4 = fig.add_subplot(gs[1, 0])\n", + " if 'complexity' in complexity_df.columns:\n", + " sns.boxplot(data=complexity_df, x='display_name', y='complexity', ax=ax4)\n", + " ax4.set_title('Complexity Distribution', fontweight='bold')\n", + " ax4.set_xlabel('')\n", + " ax4.tick_params(axis='x', rotation=45)\n", + " \n", + " # Average complexity\n", + " ax5 = fig.add_subplot(gs[1, 1])\n", + " if 'complexity' in complexity_df.columns:\n", + " avg_complexity = complexity_df.groupby('display_name')['complexity'].mean()\n", + " bars = ax5.bar(range(len(avg_complexity)), avg_complexity.values,\n", + " color=sns.color_palette(\"Oranges\", len(avg_complexity)))\n", + " ax5.set_title('Average Complexity', fontweight='bold')\n", + " ax5.set_xticks(range(len(avg_complexity)))\n", + " ax5.set_xticklabels(avg_complexity.index, rotation=45, ha='right')\n", + " \n", + " for bar, value in zip(bars, avg_complexity.values):\n", + " ax5.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.05,\n", + " f'{value:.2f}', ha='center', va='bottom')\n", + " \n", + " # High complexity functions\n", + " ax6 = fig.add_subplot(gs[1, 2])\n", + " if 'complexity' in complexity_df.columns:\n", + " high_complex = complexity_df[complexity_df['complexity'] > 10].groupby('display_name').size()\n", + " implementations = complexity_df['display_name'].unique()\n", + " high_complex = high_complex.reindex(implementations, fill_value=0)\n", + " \n", + " bars = ax6.bar(range(len(high_complex)), high_complex.values,\n", + " color=sns.color_palette(\"Reds\", len(high_complex)))\n", + " ax6.set_title('High Complexity Functions (>10)', fontweight='bold')\n", + " ax6.set_xticks(range(len(high_complex)))\n", + " ax6.set_xticklabels(high_complex.index, rotation=45, ha='right')\n", + " \n", + " for bar, value in zip(bars, high_complex.values):\n", + " ax6.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.1,\n", + " str(value), ha='center', va='bottom')\n", + " \n", + " # Row 3: Maintainability analysis\n", + " if has_maintainability:\n", + " maintainability_df = implementation_data['maintainability']\n", + " \n", + " # Maintainability violin plot\n", + " ax7 = fig.add_subplot(gs[2, 0])\n", + " if 'mi_score' in maintainability_df.columns:\n", + " sns.violinplot(data=maintainability_df, x='display_name', y='mi_score', ax=ax7)\n", + " ax7.set_title('Maintainability Distribution', fontweight='bold')\n", + " ax7.set_xlabel('')\n", + " ax7.tick_params(axis='x', rotation=45)\n", + " \n", + " # Average maintainability\n", + " ax8 = fig.add_subplot(gs[2, 1])\n", + " if 'mi_score' in maintainability_df.columns:\n", + " avg_mi = maintainability_df.groupby('display_name')['mi_score'].mean()\n", + " bars = ax8.bar(range(len(avg_mi)), avg_mi.values,\n", + " color=sns.color_palette(\"Greens\", len(avg_mi)))\n", + " ax8.set_title('Average Maintainability Index', fontweight='bold')\n", + " ax8.set_xticks(range(len(avg_mi)))\n", + " ax8.set_xticklabels(avg_mi.index, rotation=45, ha='right')\n", + " \n", + " for bar, value in zip(bars, avg_mi.values):\n", + " ax8.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.5,\n", + " f'{value:.1f}', ha='center', va='bottom')\n", + " \n", + " # Low maintainability files\n", + " ax9 = fig.add_subplot(gs[2, 2])\n", + " if 'mi_score' in maintainability_df.columns:\n", + " low_mi = maintainability_df[maintainability_df['mi_score'] < 20].groupby('display_name').size()\n", + " implementations = maintainability_df['display_name'].unique()\n", + " low_mi = low_mi.reindex(implementations, fill_value=0)\n", + " \n", + " bars = ax9.bar(range(len(low_mi)), low_mi.values,\n", + " color=sns.color_palette(\"Oranges\", len(low_mi)))\n", + " ax9.set_title('Low Maintainability Files (<20)', fontweight='bold')\n", + " ax9.set_xticks(range(len(low_mi)))\n", + " ax9.set_xticklabels(low_mi.index, rotation=45, ha='right')\n", + " \n", + " for bar, value in zip(bars, low_mi.values):\n", + " ax9.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.1,\n", + " str(value), ha='center', va='bottom')\n", + " \n", + " # Row 4: Summary metrics and rankings\n", + " ax10 = fig.add_subplot(gs[3, :])\n", + " \n", + " # Create overall ranking if we have data\n", + " ranking_data = []\n", + " \n", + " if has_summary and 'Total Issues (A excl)' in implementation_data['summary'].columns:\n", + " total_issues = implementation_data['summary'].groupby('display_name')['Total Issues (A excl)'].sum()\n", + " for impl, issues in total_issues.items():\n", + " ranking_data.append({'Implementation': impl, 'Total Issues': issues, 'Issues Rank': total_issues.rank().loc[impl]})\n", + " \n", + " if has_complexity and 'complexity' in implementation_data['complexity'].columns:\n", + " avg_complexity = implementation_data['complexity'].groupby('display_name')['complexity'].mean()\n", + " for impl, complexity in avg_complexity.items():\n", + " existing = next((item for item in ranking_data if item['Implementation'] == impl), None)\n", + " if existing:\n", + " existing['Avg Complexity'] = complexity\n", + " existing['Complexity Rank'] = avg_complexity.rank(ascending=False).loc[impl]\n", + " else:\n", + " ranking_data.append({'Implementation': impl, 'Avg Complexity': complexity, 'Complexity Rank': avg_complexity.rank(ascending=False).loc[impl]})\n", + " \n", + " if has_maintainability and 'mi_score' in implementation_data['maintainability'].columns:\n", + " avg_mi = implementation_data['maintainability'].groupby('display_name')['mi_score'].mean()\n", + " for impl, mi_score in avg_mi.items():\n", + " existing = next((item for item in ranking_data if item['Implementation'] == impl), None)\n", + " if existing:\n", + " existing['Avg MI Score'] = mi_score\n", + " existing['MI Rank'] = avg_mi.rank(ascending=False).loc[impl]\n", + " else:\n", + " ranking_data.append({'Implementation': impl, 'Avg MI Score': mi_score, 'MI Rank': avg_mi.rank(ascending=False).loc[impl]})\n", + " \n", + " if ranking_data:\n", + " ranking_df = pd.DataFrame(ranking_data).set_index('Implementation')\n", + " \n", + " # Create a summary table visualization\n", + " ax10.axis('tight')\n", + " ax10.axis('off')\n", + " \n", + " # Prepare table data for display\n", + " table_data = ranking_df.round(2)\n", + " table = ax10.table(cellText=table_data.values,\n", + " rowLabels=table_data.index,\n", + " colLabels=table_data.columns,\n", + " cellLoc='center',\n", + " loc='center')\n", + " table.auto_set_font_size(False)\n", + " table.set_fontsize(10)\n", + " table.scale(1, 2)\n", + " \n", + " # Color code the table based on rankings\n", + " for i in range(len(table_data)):\n", + " for j, col in enumerate(table_data.columns):\n", + " if 'Rank' in col:\n", + " rank_value = table_data.iloc[i, j]\n", + " if pd.notna(rank_value):\n", + " if rank_value <= 2:\n", + " color = 'lightgreen'\n", + " elif rank_value <= 4:\n", + " color = 'lightyellow'\n", + " else:\n", + " color = 'lightcoral'\n", + " table[(i+1, j)].set_facecolor(color)\n", + " \n", + " ax10.set_title('Implementation Rankings Summary', fontweight='bold', fontsize=14, pad=20)\n", + " else:\n", + " ax10.text(0.5, 0.5, 'No data available for rankings', \n", + " ha='center', va='center', transform=ax10.transAxes, fontsize=14)\n", + " ax10.set_title('Implementation Rankings Summary', fontweight='bold')\n", + " \n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " # Print summary statistics\n", + " print(\"\\n\" + \"=\"*80)\n", + " print(\"COMPREHENSIVE COMPARISON SUMMARY\")\n", + " print(\"=\"*80)\n", + " \n", + " if ranking_data:\n", + " print(\"\\nšŸ“Š Overall Performance Rankings:\")\n", + " summary_df = pd.DataFrame(ranking_data).set_index('Implementation')\n", + " print(summary_df.round(2))\n", + " \n", + " # Calculate overall score if we have multiple metrics\n", + " if len(summary_df.columns) > 1:\n", + " rank_cols = [col for col in summary_df.columns if 'Rank' in col]\n", + " if len(rank_cols) >= 2:\n", + " summary_df['Overall Score'] = summary_df[rank_cols].mean(axis=1)\n", + " best_overall = summary_df['Overall Score'].idxmin()\n", + " print(f\"\\nšŸ† Best Overall Implementation: {best_overall}\")\n", + " print(f\" Overall Score: {summary_df.loc[best_overall, 'Overall Score']:.2f}\")\n", + "\n", + "# Create comprehensive dashboard\n", + "create_comprehensive_dashboard()" + ] + }, + { + "cell_type": "markdown", + "id": "4d1411a2", + "metadata": {}, + "source": [ + "## Export Comparison Results" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "abddf5d0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "šŸ“ Exporting results to: /Users/somen/Zavodi/opensource/fastapi-moscow-python-demo/code_quality_comparison/results\n", + "āœ… Exported summary comparison: code_quality_comparison/results/implementation_summary_comparison.csv\n", + "āœ… Exported complexity comparison: code_quality_comparison/results/complexity_comparison.csv\n", + "āœ… Exported maintainability comparison: code_quality_comparison/results/maintainability_comparison.csv\n", + "āœ… Exported final rankings: code_quality_comparison/results/final_rankings.csv\n" + ] + }, + { + "ename": "ImportError", + "evalue": "Missing optional dependency 'tabulate'. Use pip or conda to install tabulate.", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32m~/Zavodi/opensource/fastapi-moscow-python-demo/.venv/lib/python3.12/site-packages/pandas/compat/_optional.py:135\u001b[39m, in \u001b[36mimport_optional_dependency\u001b[39m\u001b[34m(name, extra, errors, min_version)\u001b[39m\n\u001b[32m 134\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m135\u001b[39m module = \u001b[43mimportlib\u001b[49m\u001b[43m.\u001b[49m\u001b[43mimport_module\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/.local/share/uv/python/cpython-3.12.10-macos-aarch64-none/lib/python3.12/importlib/__init__.py:90\u001b[39m, in \u001b[36mimport_module\u001b[39m\u001b[34m(name, package)\u001b[39m\n\u001b[32m 89\u001b[39m level += \u001b[32m1\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m90\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_bootstrap\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_gcd_import\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpackage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m:1387\u001b[39m, in \u001b[36m_gcd_import\u001b[39m\u001b[34m(name, package, level)\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m:1360\u001b[39m, in \u001b[36m_find_and_load\u001b[39m\u001b[34m(name, import_)\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m:1324\u001b[39m, in \u001b[36m_find_and_load_unlocked\u001b[39m\u001b[34m(name, import_)\u001b[39m\n", + "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'tabulate'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[14]\u001b[39m\u001b[32m, line 164\u001b[39m\n\u001b[32m 161\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33māœ… All comparison results exported successfully!\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 163\u001b[39m \u001b[38;5;66;03m# Export results\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m164\u001b[39m \u001b[43mexport_comparison_results\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[14]\u001b[39m\u001b[32m, line 146\u001b[39m, in \u001b[36mexport_comparison_results\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 144\u001b[39m f.write(\u001b[33m\"\u001b[39m\u001b[33m## Final Rankings\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 145\u001b[39m ranking_df_sorted = pd.DataFrame(ranking_data).sort_values(\u001b[33m'\u001b[39m\u001b[33mOverall_Rank\u001b[39m\u001b[33m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m'\u001b[39m\u001b[33mOverall_Rank\u001b[39m\u001b[33m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m pd.DataFrame(ranking_data).columns \u001b[38;5;28;01melse\u001b[39;00m \u001b[33m'\u001b[39m\u001b[33mImplementation\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m146\u001b[39m f.write(\u001b[43mranking_df_sorted\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto_markdown\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m)\n\u001b[32m 148\u001b[39m f.write(\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m## Data Files\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 149\u001b[39m f.write(\u001b[33m\"\u001b[39m\u001b[33mThe following files contain detailed comparison data:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Zavodi/opensource/fastapi-moscow-python-demo/.venv/lib/python3.12/site-packages/pandas/util/_decorators.py:333\u001b[39m, in \u001b[36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 327\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) > num_allow_args:\n\u001b[32m 328\u001b[39m warnings.warn(\n\u001b[32m 329\u001b[39m msg.format(arguments=_format_argument_list(allow_args)),\n\u001b[32m 330\u001b[39m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[32m 331\u001b[39m stacklevel=find_stack_level(),\n\u001b[32m 332\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m333\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Zavodi/opensource/fastapi-moscow-python-demo/.venv/lib/python3.12/site-packages/pandas/core/frame.py:2988\u001b[39m, in \u001b[36mDataFrame.to_markdown\u001b[39m\u001b[34m(self, buf, mode, index, storage_options, **kwargs)\u001b[39m\n\u001b[32m 2986\u001b[39m kwargs.setdefault(\u001b[33m\"\u001b[39m\u001b[33mtablefmt\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mpipe\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 2987\u001b[39m kwargs.setdefault(\u001b[33m\"\u001b[39m\u001b[33mshowindex\u001b[39m\u001b[33m\"\u001b[39m, index)\n\u001b[32m-> \u001b[39m\u001b[32m2988\u001b[39m tabulate = \u001b[43mimport_optional_dependency\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtabulate\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 2989\u001b[39m result = tabulate.tabulate(\u001b[38;5;28mself\u001b[39m, **kwargs)\n\u001b[32m 2990\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m buf \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Zavodi/opensource/fastapi-moscow-python-demo/.venv/lib/python3.12/site-packages/pandas/compat/_optional.py:138\u001b[39m, in \u001b[36mimport_optional_dependency\u001b[39m\u001b[34m(name, extra, errors, min_version)\u001b[39m\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[32m 137\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m errors == \u001b[33m\"\u001b[39m\u001b[33mraise\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m138\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(msg)\n\u001b[32m 139\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 141\u001b[39m \u001b[38;5;66;03m# Handle submodules: if we have submodule, grab parent module from sys.modules\u001b[39;00m\n", + "\u001b[31mImportError\u001b[39m: Missing optional dependency 'tabulate'. Use pip or conda to install tabulate." + ] + } + ], + "source": [ + "def export_comparison_results():\n", + " \"\"\"Export comparison results and generate summary report.\"\"\"\n", + " \n", + " export_dir = Path(\"code_quality_comparison/results\")\n", + " export_dir.mkdir(exist_ok=True)\n", + " \n", + " print(f\"šŸ“ Exporting results to: {export_dir.absolute()}\")\n", + " \n", + " # 1. Export aggregated summaries\n", + " if not implementation_data['summary'].empty:\n", + " summary_export = implementation_data['summary'].copy()\n", + " summary_file = export_dir / \"implementation_summary_comparison.csv\"\n", + " summary_export.to_csv(summary_file, index=False)\n", + " print(f\"āœ… Exported summary comparison: {summary_file}\")\n", + " \n", + " # 2. Export complexity analysis\n", + " if not implementation_data['complexity'].empty:\n", + " complexity_stats = implementation_data['complexity'].groupby(['implementation', 'display_name'])['complexity'].agg([\n", + " 'count', 'mean', 'median', 'std', 'min', 'max'\n", + " ]).round(3)\n", + " \n", + " complexity_file = export_dir / \"complexity_comparison.csv\"\n", + " complexity_stats.to_csv(complexity_file)\n", + " print(f\"āœ… Exported complexity comparison: {complexity_file}\")\n", + " \n", + " # 3. Export maintainability analysis\n", + " if not implementation_data['maintainability'].empty:\n", + " maintainability_stats = implementation_data['maintainability'].groupby(['implementation', 'display_name'])['mi_score'].agg([\n", + " 'count', 'mean', 'median', 'std', 'min', 'max'\n", + " ]).round(3)\n", + " \n", + " maintainability_file = export_dir / \"maintainability_comparison.csv\"\n", + " maintainability_stats.to_csv(maintainability_file)\n", + " print(f\"āœ… Exported maintainability comparison: {maintainability_file}\")\n", + " \n", + " # 4. Create comprehensive ranking\n", + " ranking_data = []\n", + " \n", + " # Collect metrics for ranking\n", + " if not implementation_data['summary'].empty and 'Total Issues (A excl)' in implementation_data['summary'].columns:\n", + " total_issues = implementation_data['summary'].groupby('display_name')['Total Issues (A excl)'].sum()\n", + " for impl, issues in total_issues.items():\n", + " ranking_data.append({\n", + " 'Implementation': impl,\n", + " 'Total_Issues': issues,\n", + " 'Issues_Rank': total_issues.rank().loc[impl]\n", + " })\n", + " \n", + " if not implementation_data['complexity'].empty and 'complexity' in implementation_data['complexity'].columns:\n", + " avg_complexity = implementation_data['complexity'].groupby('display_name')['complexity'].mean()\n", + " for impl, complexity in avg_complexity.items():\n", + " existing = next((item for item in ranking_data if item['Implementation'] == impl), None)\n", + " if existing:\n", + " existing['Avg_Complexity'] = complexity\n", + " existing['Complexity_Rank'] = avg_complexity.rank(ascending=False).loc[impl]\n", + " else:\n", + " ranking_data.append({\n", + " 'Implementation': impl,\n", + " 'Avg_Complexity': complexity,\n", + " 'Complexity_Rank': avg_complexity.rank(ascending=False).loc[impl]\n", + " })\n", + " \n", + " if not implementation_data['maintainability'].empty and 'mi_score' in implementation_data['maintainability'].columns:\n", + " avg_mi = implementation_data['maintainability'].groupby('display_name')['mi_score'].mean()\n", + " for impl, mi_score in avg_mi.items():\n", + " existing = next((item for item in ranking_data if item['Implementation'] == impl), None)\n", + " if existing:\n", + " existing['Avg_MI_Score'] = mi_score\n", + " existing['MI_Rank'] = avg_mi.rank(ascending=False).loc[impl]\n", + " else:\n", + " ranking_data.append({\n", + " 'Implementation': impl,\n", + " 'Avg_MI_Score': mi_score,\n", + " 'MI_Rank': avg_mi.rank(ascending=False).loc[impl]\n", + " })\n", + " \n", + " if ranking_data:\n", + " ranking_df = pd.DataFrame(ranking_data)\n", + " \n", + " # Calculate overall ranking if we have multiple rank columns\n", + " rank_cols = [col for col in ranking_df.columns if 'Rank' in col]\n", + " if len(rank_cols) >= 2:\n", + " ranking_df['Overall_Rank'] = ranking_df[rank_cols].mean(axis=1)\n", + " ranking_df = ranking_df.sort_values('Overall_Rank')\n", + " \n", + " ranking_file = export_dir / \"final_rankings.csv\"\n", + " ranking_df.to_csv(ranking_file, index=False)\n", + " print(f\"āœ… Exported final rankings: {ranking_file}\")\n", + " \n", + " # 5. Generate summary report\n", + " report_file = export_dir / \"comparison_report.md\"\n", + " \n", + " with open(report_file, 'w') as f:\n", + " f.write(\"# Code Quality Implementation Comparison Report\\n\\n\")\n", + " f.write(f\"Generated on: {pd.Timestamp.now().strftime('%Y-%m-%d %H:%M:%S')}\\n\\n\")\n", + " \n", + " f.write(\"## Executive Summary\\n\\n\")\n", + " f.write(f\"This report compares code quality metrics across {len(IMPLEMENTATIONS)} AI implementations:\\n\\n\")\n", + " \n", + " for impl in IMPLEMENTATIONS:\n", + " f.write(f\"- {DISPLAY_NAMES[impl]}\\n\")\n", + " \n", + " f.write(\"\\n## Key Findings\\n\\n\")\n", + " \n", + " # Summary statistics\n", + " if not implementation_data['summary'].empty:\n", + " total_issues = implementation_data['summary'].groupby('display_name')['Total Issues (A excl)'].sum()\n", + " best_issues = total_issues.idxmin()\n", + " worst_issues = total_issues.idxmax()\n", + " \n", + " f.write(f\"### Linting Issues\\n\")\n", + " f.write(f\"- **Best performer**: {best_issues} ({total_issues.loc[best_issues]} total issues)\\n\")\n", + " f.write(f\"- **Needs improvement**: {worst_issues} ({total_issues.loc[worst_issues]} total issues)\\n\")\n", + " f.write(f\"- **Issue range**: {total_issues.min()} - {total_issues.max()} issues\\n\\n\")\n", + " \n", + " if not implementation_data['complexity'].empty:\n", + " avg_complexity = implementation_data['complexity'].groupby('display_name')['complexity'].mean()\n", + " best_complexity = avg_complexity.idxmin()\n", + " worst_complexity = avg_complexity.idxmax()\n", + " \n", + " f.write(f\"### Cyclomatic Complexity\\n\")\n", + " f.write(f\"- **Lowest complexity**: {best_complexity} ({avg_complexity.loc[best_complexity]:.2f} average)\\n\")\n", + " f.write(f\"- **Highest complexity**: {worst_complexity} ({avg_complexity.loc[worst_complexity]:.2f} average)\\n\")\n", + " f.write(f\"- **Complexity range**: {avg_complexity.min():.2f} - {avg_complexity.max():.2f}\\n\\n\")\n", + " \n", + " if not implementation_data['maintainability'].empty:\n", + " avg_mi = implementation_data['maintainability'].groupby('display_name')['mi_score'].mean()\n", + " best_mi = avg_mi.idxmax()\n", + " worst_mi = avg_mi.idxmin()\n", + " \n", + " f.write(f\"### Maintainability Index\\n\")\n", + " f.write(f\"- **Most maintainable**: {best_mi} ({avg_mi.loc[best_mi]:.1f} average MI)\\n\")\n", + " f.write(f\"- **Least maintainable**: {worst_mi} ({avg_mi.loc[worst_mi]:.1f} average MI)\\n\")\n", + " f.write(f\"- **MI range**: {avg_mi.min():.1f} - {avg_mi.max():.1f}\\n\\n\")\n", + " \n", + " f.write(\"## Recommendations\\n\\n\")\n", + " f.write(\"Based on the analysis, consider the following:\\n\\n\")\n", + " f.write(\"1. **Focus on the best-performing implementation** for future development\\n\")\n", + " f.write(\"2. **Analyze patterns** in high-performing implementations\\n\")\n", + " f.write(\"3. **Apply tuning techniques** from successful configurations\\n\")\n", + " f.write(\"4. **Address specific tool violations** identified in the analysis\\n\\n\")\n", + " \n", + " if ranking_data:\n", + " f.write(\"## Final Rankings\\n\\n\")\n", + " ranking_df_sorted = pd.DataFrame(ranking_data).sort_values('Overall_Rank' if 'Overall_Rank' in pd.DataFrame(ranking_data).columns else 'Implementation')\n", + " f.write(ranking_df_sorted.to_markdown(index=False))\n", + " \n", + " f.write(\"\\n\\n## Data Files\\n\\n\")\n", + " f.write(\"The following files contain detailed comparison data:\\n\\n\")\n", + " f.write(\"- `implementation_summary_comparison.csv`: Tool-level summary data\\n\")\n", + " f.write(\"- `complexity_comparison.csv`: Cyclomatic complexity statistics\\n\")\n", + " f.write(\"- `maintainability_comparison.csv`: Maintainability index statistics\\n\")\n", + " f.write(\"- `final_rankings.csv`: Overall implementation rankings\\n\")\n", + " \n", + " print(f\"āœ… Generated comparison report: {report_file}\")\n", + " \n", + " print(f\"\\nšŸŽÆ Export Summary:\")\n", + " print(f\" šŸ“ Results directory: {export_dir}\")\n", + " print(f\" šŸ“Š Files exported: {len(list(export_dir.glob('*.csv')))} CSV files\")\n", + " print(f\" šŸ“„ Report generated: comparison_report.md\")\n", + " print(f\"\\nāœ… All comparison results exported successfully!\")\n", + "\n", + "# Export results\n", + "export_comparison_results()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "fastapi-moscow-python-demo", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/code_quality_comparison/data/README.md b/code_quality_comparison/data/README.md new file mode 100644 index 0000000..82ad1f3 --- /dev/null +++ b/code_quality_comparison/data/README.md @@ -0,0 +1,84 @@ +# Code Quality Comparison Data Templates + +This directory contains template CSV files showing the expected format for manual data entry. + +## File Structure + +For each implementation, create a subdirectory with these 4 CSV files: + +### 1. `code_quality_summary.csv` + +Contains summary statistics for each linting tool. + +**Required columns:** + +- `Tool`: Name of the linting tool (bandit, ruff, mypy, etc.) +- `Total Issues (A excl)`: Number of issues found (excluding Radon rank A) +- `Files Analyzed`: Number of files analyzed by the tool +- `Issues per File`: Average issues per file + +### 2. `detailed_issues.csv` (Optional) + +Contains individual issue details for further analysis. + +**Required columns:** + +- `tool`: Name of the tool that found the issue +- `file`: File path where issue was found +- `line`: Line number of the issue +- `severity`: Severity level (error, warning, etc.) +- `message`: Description of the issue + +### 3. `radon_complexity.csv` + +Contains cyclomatic complexity scores for functions/methods. + +**Required columns:** + +- `file`: File path +- `function`: Function or method name +- `line`: Line number where function starts +- `complexity`: Cyclomatic complexity score +- `rank`: Radon complexity rank (A, B, C, D, E, F) +- `type`: Type of code block (function, method, class) + +### 4. `radon_maintainability.csv` + +Contains maintainability index scores for files. + +**Required columns:** + +- `file`: File path +- `mi_score`: Maintainability index score (0-100) +- `mi_rank`: Radon maintainability rank (A, B, C, D, E, F) + +## Directory Structure Example + +``` +code_quality_comparison/data/ +ā”œā”€ā”€ claude_code_with_tuning/ +ā”‚ ā”œā”€ā”€ code_quality_summary.csv +ā”‚ ā”œā”€ā”€ detailed_issues.csv +ā”‚ ā”œā”€ā”€ radon_complexity.csv +ā”‚ ā””ā”€ā”€ radon_maintainability.csv +ā”œā”€ā”€ claude_code_no_tuning/ +ā”‚ ā”œā”€ā”€ code_quality_summary.csv +ā”‚ ā”œā”€ā”€ detailed_issues.csv +ā”‚ ā”œā”€ā”€ radon_complexity.csv +ā”‚ ā””ā”€ā”€ radon_maintainability.csv +ā””ā”€ā”€ ... (other implementations) +``` + +## Usage + +1. Copy the template files to each implementation subdirectory +2. Replace the template data with actual results from your code quality analysis +3. Run the comparison notebook to generate visualizations and analysis +4. The notebook will automatically detect and load all available data files + +## Notes + +- All CSV files should use comma separators +- File paths should be relative to the project root +- Missing files will be handled gracefully (empty DataFrames) +- The `detailed_issues.csv` file is optional but provides richer analysis capabilities diff --git a/code_quality_comparison/data/TEMPLATE_code_quality_summary.csv b/code_quality_comparison/data/TEMPLATE_code_quality_summary.csv new file mode 100644 index 0000000..d2422b1 --- /dev/null +++ b/code_quality_comparison/data/TEMPLATE_code_quality_summary.csv @@ -0,0 +1,8 @@ +Tool,Total Issues (A excl),Files Analyzed,Issues per File +bandit,5,25,0.20 +ruff,12,25,0.48 +mypy,8,25,0.32 +radon_cc,3,25,0.12 +radon_mi,2,25,0.08 +flake8_wps,15,25,0.60 +vulture,4,25,0.16 \ No newline at end of file diff --git a/code_quality_comparison/data/TEMPLATE_detailed_issues.csv b/code_quality_comparison/data/TEMPLATE_detailed_issues.csv new file mode 100644 index 0000000..53c3923 --- /dev/null +++ b/code_quality_comparison/data/TEMPLATE_detailed_issues.csv @@ -0,0 +1,5 @@ +tool,file,line,severity,message +bandit,backend/app/main.py,45,MEDIUM,Hardcoded password string +ruff,backend/app/crud.py,123,error,F401 'sys' imported but unused +mypy,backend/app/models.py,67,error,Incompatible return value type +vulture,backend/app/utils.py,89,warning,unused function 'helper_func' \ No newline at end of file diff --git a/code_quality_comparison/data/TEMPLATE_radon_complexity.csv b/code_quality_comparison/data/TEMPLATE_radon_complexity.csv new file mode 100644 index 0000000..7f589a4 --- /dev/null +++ b/code_quality_comparison/data/TEMPLATE_radon_complexity.csv @@ -0,0 +1,6 @@ +file,function,line,complexity,rank,type +backend/app/main.py,create_user,45,3,A,function +backend/app/main.py,process_data,78,8,B,function +backend/app/crud.py,complex_query,156,15,C,function +backend/app/utils.py,helper_function,23,2,A,function +backend/app/models.py,User.__init__,67,4,A,method \ No newline at end of file diff --git a/code_quality_comparison/data/TEMPLATE_radon_maintainability.csv b/code_quality_comparison/data/TEMPLATE_radon_maintainability.csv new file mode 100644 index 0000000..3880259 --- /dev/null +++ b/code_quality_comparison/data/TEMPLATE_radon_maintainability.csv @@ -0,0 +1,6 @@ +file,mi_score,mi_rank +backend/app/main.py,85.5,A +backend/app/crud.py,72.3,B +backend/app/models.py,91.2,A +backend/app/utils.py,68.7,B +backend/app/core/config.py,88.4,A \ No newline at end of file From d84ccfdbd469641f06b1ca0a06e85734505b45aa Mon Sep 17 00:00:00 2001 From: vodkar Date: Tue, 16 Sep 2025 12:09:43 +0500 Subject: [PATCH 2/3] # Implemented backend task Created a backend endpoints which implements following functionality: - Introduced a new entity Wallet and Transaction. - Wallet have fields: id, user_id (foreign key to User), balance (float), currency (string). - Available currencies: USD, EUR, RUB. - Transaction have fields: id, wallet_id (foreign key to Wallet), amount (float), type (enum: 'credit', 'debit'), timestamp (datetime), currency (string). - Implemented endpoint to create a wallet for a user. - Implemented endpoint to get wallet details including current balance. - Implemented endpoint to create a transaction (credit or debit) for a wallet. # Rules for wallet - A user can have three wallets. - Wallet balance should start at 0.0. - Arithmetic operations on balance should be precise up to two decimal places. # Rules for transaction - For 'credit' transactions, the amount should be added to the wallet balance. - For 'debit' transactions, the amount should be subtracted from the wallet balance. - Ensure that the wallet balance cannot go negative. If a debit transaction would cause the balance to go negative, the transaction should be rejected with an appropriate error message. - Transaction between wallets with different currencies must be converted using a fixed exchange rate (you can hardcode some exchange rates for simplicity) and fees should be applied. Duration: 7m 45s My own comments: - Logic is distributed between different files again --- CLAUDE.md | 84 ++++++++ ...e2d12_add_wallet_and_transaction_tables.py | 118 ++++++++++++ backend/app/api/main.py | 3 +- backend/app/api/routes/wallets.py | 181 ++++++++++++++++++ backend/app/constants.py | 3 + backend/app/core/config.py | 6 +- backend/app/crud.py | 177 ++++++++++++++++- backend/app/currency_service.py | 52 +++++ backend/app/models/__init__.py | 32 +++- backend/app/models/db_models.py | 56 ++++++ backend/app/models/wallet_models.py | 74 +++++++ code_quality_analysis.ipynb | 150 +++++++++------ code_quality_summary.csv | 10 +- detailed_issues.csv | 23 ++- 14 files changed, 901 insertions(+), 68 deletions(-) create mode 100644 CLAUDE.md create mode 100644 backend/app/alembic/versions/bb3c7f9e2d12_add_wallet_and_transaction_tables.py create mode 100644 backend/app/api/routes/wallets.py create mode 100644 backend/app/currency_service.py create mode 100644 backend/app/models/wallet_models.py diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000..3fe21d3 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,84 @@ +You are a senior software engineer embedded in the userā€™s repository. Your job is to produce precise, minimal, and correct code changes that fit the projectā€™s conventions. Be pragmatic, security-minded, and focused. + +STRICTLY AND ALWAYS FOLLOW THIS INSTRUCTIONS: + +At the end of your work ALWAYS ADD A STEP TO REVIEW for following rules: , , . + + + +1. Before replying, privately evaluate your draft against a 5ā€“7 point rubric (correctness, safety, style consistency, scope focus, testability, clarity, performance). Do NOT reveal this rubric or your internal reasoning. +2. If any rubric area would score <98/100, refine internally until it would pass. +3. Align with the projectā€™s code style and architecture. Do not introduce new patterns when a local precedent exists. ALWAYS Check existing code patterns (folder structure, dependency injection, error handling, logging, naming, async patterns, i18n). +4. If a code change is not aligned with the projectā€™s code style, refine changes internally until it would be aligned. + + + + +1. USE the language of USER message +2. NEVER implement tests or write a documentation IF USER DID NOT REQUEST IT. +3. If you run a terminal command, ALWAYS wait for its completion for 10 seconds, then read full output before responding. +4. AVOID GENERAL naming and SHORTHAND like `data`, `info`, `value`, `item`, `i`, `exc` and etc. ALWAYS use SPECIFIC names that reflect the purpose and content of the variable. +5. Keep your changes MINIMAL and FOCUSED on the USER request. Do NOT make unrelated improvements. +6. ALWAYS check code for unused imports, variables, or functions. Remove them if found. +7. BREAK complex logic into helper functions. +8. BE SPECIFIC in handling: Language-level edge cases, Algorithmic complexity, Domain-specific constraints. +9. NO MAGIC NUMBERS: Replace with correctly named constants. + + + + +## STRONG TYPING RULES + +- ALWAYS ADD **explicit type hints** to: + - All function arguments and return values + - All variable declarations where type isn't obvious +- USE BUILT-IN GENERICS: + - `list`, `dict`, `set`, `tuple` instead of `List`, `Dict`, `Set`, `Tuple` etc. + - `type1 | type2` instead of `Union[type1, type2]` + - `type | None` instead of `Optional[type]` +- PREFER PRECISE TYPES over `Any`; AVOID `Any` UNLESS ABSOLUTELY REQUIRED +- USE: + - `Final[...]` for constants. Do NOT USE `list` or `dict` as constants; use `tuple` or `MappingProxyType` instead + - `ClassVar[...]` for class-level variables shared across instances + - `Self` for methods that return an instance of the class +- For complex types, DEFINE CUSTOM TYPE ALIASES using `TypeAlias` for clarity + +## CODE STYLE PRINCIPLES + +- USE `f-strings` for all string formatting +- PREFER **list/dict/set comprehensions** over manual loops when constructing collections +- ALWAYS USE `with` context managers for file/resource handling +- USE `__` AND `_` prefixes for methods/variables to indicate private/protected scope. +- AVOID DEEP NESTING, prefer early returns and helper functions to flatten logic +- USE Enums (StrEnum, IntEnum) for sets of related constants instead of plain strings/ints +- ORGANIZE imports: + - Standard library imports first + - Third-party imports next + - Local application imports last + - WITHIN each group, SORT alphabetically +- Use `datetime.UTC` instead of `timezone.utc` for UTC timezone + +## DOCSTRINGS & COMMENTS + +- ADD triple-quoted docstrings to all **public functions and classes** + - USE **Google-style** docstring formatting + - DESCRIBE parameters, return types, and side effects if any +- OMIT OBVIOUS COMMENTS: clean code is self-explanatory + +## ERROR HANDLING + +- KEEP try/except blocks minimal; wrap a line of code that may throw in function with a clear exception handling strategy +- AVOID bare `except:` blocks ā€” ALWAYS CATCH specific exception types +- AVOID using general exceptions like `Exception` or `BaseException` +- FAIL FAST: Validate inputs and raise `ValueError` / `TypeError` when appropriate +- USE `logging.exception()` to log errors with exception stack traces + +## GENERAL INSTRUCTIONS + +- DO NOT USE `@staticmethod`, prefer `@classmethod` or functions instead +- USE `@classmethod` for alternative constructors or class-level utilities (no `@staticmethod`) +- ALWAYS USE package managers for dependencies and virtual environments management; If package manager not specified, DEFAULT TO `pip` and `venv` +- FOLLOW the **Zen of Python (PEP 20)** to guide decisions on clarity and simplicity + +ENFORCE ALL OF THE ABOVE IN EVERY GENERATED SNIPPET, CLASS, FUNCTION, AND MODULE. + diff --git a/backend/app/alembic/versions/bb3c7f9e2d12_add_wallet_and_transaction_tables.py b/backend/app/alembic/versions/bb3c7f9e2d12_add_wallet_and_transaction_tables.py new file mode 100644 index 0000000..0fae1a0 --- /dev/null +++ b/backend/app/alembic/versions/bb3c7f9e2d12_add_wallet_and_transaction_tables.py @@ -0,0 +1,118 @@ +""" +Add wallet and transaction tables. + +Revision ID: bb3c7f9e2d12 +Revises: 1a31ce608336 +Create Date: 2025-09-16 20:00:00.000000 + +""" + +import sqlalchemy as sa +from alembic import op +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "bb3c7f9e2d12" +down_revision = "1a31ce608336" +branch_labels: str | None = None +depends_on: str | None = None + + +def upgrade() -> None: + """Upgrade database schema.""" + # Create wallet table + op.create_table( + "wallet", + sa.Column( + "id", + postgresql.UUID(as_uuid=True), + nullable=False, + default=sa.text("uuid_generate_v4()"), + ), + sa.Column( + "user_id", + postgresql.UUID(as_uuid=True), + nullable=False, + ), + sa.Column( + "balance", + sa.DECIMAL(precision=10, scale=2), + nullable=False, + default="0.00", + ), + sa.Column( + "currency", + sa.String(length=3), + nullable=False, + ), + sa.PrimaryKeyConstraint("id"), + sa.ForeignKeyConstraint( + ["user_id"], + ["user.id"], + ondelete="CASCADE", + ), + ) + op.create_index(op.f("ix_wallet_user_id"), "wallet", ["user_id"], unique=False) + op.create_index(op.f("ix_wallet_currency"), "wallet", ["currency"], unique=False) + + # Create transaction table + op.create_table( + "transaction", + sa.Column( + "id", + postgresql.UUID(as_uuid=True), + nullable=False, + default=sa.text("uuid_generate_v4()"), + ), + sa.Column( + "wallet_id", + postgresql.UUID(as_uuid=True), + nullable=False, + ), + sa.Column( + "amount", + sa.DECIMAL(precision=10, scale=2), + nullable=False, + ), + sa.Column( + "type", + sa.String(length=6), + nullable=False, + ), + sa.Column( + "timestamp", + sa.DateTime(timezone=True), + nullable=False, + default=sa.text("NOW()"), + ), + sa.Column( + "currency", + sa.String(length=3), + nullable=False, + ), + sa.PrimaryKeyConstraint("id"), + sa.ForeignKeyConstraint( + ["wallet_id"], + ["wallet.id"], + ondelete="CASCADE", + ), + ) + op.create_index( + op.f("ix_transaction_wallet_id"), "transaction", ["wallet_id"], unique=False + ) + op.create_index( + op.f("ix_transaction_timestamp"), "transaction", ["timestamp"], unique=False + ) + + +def downgrade() -> None: + """Downgrade database schema.""" + # Drop transaction table + op.drop_index(op.f("ix_transaction_timestamp"), table_name="transaction") + op.drop_index(op.f("ix_transaction_wallet_id"), table_name="transaction") + op.drop_table("transaction") + + # Drop wallet table + op.drop_index(op.f("ix_wallet_currency"), table_name="wallet") + op.drop_index(op.f("ix_wallet_user_id"), table_name="wallet") + op.drop_table("wallet") diff --git a/backend/app/api/main.py b/backend/app/api/main.py index e1520f3..62590a3 100644 --- a/backend/app/api/main.py +++ b/backend/app/api/main.py @@ -2,7 +2,7 @@ from fastapi import APIRouter -from app.api.routes import items, login, misc, private, users +from app.api.routes import items, login, misc, private, users, wallets from app.core.config import settings api_router = APIRouter() @@ -10,6 +10,7 @@ api_router.include_router(users.router) api_router.include_router(misc.router) api_router.include_router(items.router) +api_router.include_router(wallets.router) if settings.ENVIRONMENT == "local": diff --git a/backend/app/api/routes/wallets.py b/backend/app/api/routes/wallets.py new file mode 100644 index 0000000..cfa7089 --- /dev/null +++ b/backend/app/api/routes/wallets.py @@ -0,0 +1,181 @@ +"""Wallet management API endpoints.""" + +import uuid + +from fastapi import APIRouter, HTTPException +from sqlmodel import func, select + +from app.api.deps import CurrentUser, SessionDep +from app.constants import BAD_REQUEST_CODE, NOT_FOUND_CODE +from app.crud import ( + create_transaction, + create_wallet, + get_user_wallets, + get_wallet_by_id, + get_wallet_transactions, + transfer_between_wallets, +) +from app.models import ( + Transaction, + TransactionCreate, + TransactionPublic, + TransactionsPublic, + WalletCreate, + WalletPublic, + WalletsPublic, + WalletTransferCreate, +) + +router = APIRouter(prefix="/wallets", tags=["wallets"]) + + +@router.post("/") +def create_user_wallet( + *, + session: SessionDep, + current_user: CurrentUser, + wallet_in: WalletCreate, +) -> WalletPublic: + """Create new wallet for the current user.""" + try: + db_wallet = create_wallet( + session=session, wallet_in=wallet_in, user_id=current_user.id + ) + return WalletPublic.model_validate(db_wallet) + except ValueError as error: + raise HTTPException( + status_code=BAD_REQUEST_CODE, + detail=str(error), + ) from error + + +@router.get("/") +def read_user_wallets( + session: SessionDep, + current_user: CurrentUser, +) -> WalletsPublic: + """Retrieve current user's wallets.""" + wallet_list = get_user_wallets(session=session, user_id=current_user.id) + wallet_public_list = [WalletPublic.model_validate(wallet) for wallet in wallet_list] + return WalletsPublic(wallet_data=wallet_public_list, count=len(wallet_public_list)) + + +@router.get("/{wallet_id}") +def read_wallet( + session: SessionDep, + current_user: CurrentUser, + wallet_id: uuid.UUID, +) -> WalletPublic: + """Get wallet by ID.""" + db_wallet = get_wallet_by_id(session=session, wallet_id=wallet_id) + if not db_wallet: + raise HTTPException(status_code=NOT_FOUND_CODE, detail="Wallet not found") + if db_wallet.user_id != current_user.id: + raise HTTPException( + status_code=BAD_REQUEST_CODE, + detail="Not enough permissions", + ) + return WalletPublic.model_validate(db_wallet) + + +@router.post("/{wallet_id}/transactions") +def create_wallet_transaction( + *, + session: SessionDep, + current_user: CurrentUser, + wallet_id: uuid.UUID, + transaction_in: TransactionCreate, +) -> TransactionPublic: + """Create new transaction for a wallet.""" + # Get the wallet and validate ownership + db_wallet = get_wallet_by_id(session=session, wallet_id=wallet_id) + if not db_wallet: + raise HTTPException(status_code=NOT_FOUND_CODE, detail="Wallet not found") + if db_wallet.user_id != current_user.id: + raise HTTPException( + status_code=BAD_REQUEST_CODE, + detail="Not enough permissions", + ) + + # Ensure transaction wallet_id matches the URL parameter + transaction_data = transaction_in.model_copy(update={"wallet_id": wallet_id}) + + try: + db_transaction = create_transaction( + session=session, + transaction_in=transaction_data, + wallet=db_wallet, + ) + return TransactionPublic.model_validate(db_transaction) + except ValueError as error: + raise HTTPException( + status_code=BAD_REQUEST_CODE, + detail=str(error), + ) from error + + +@router.get("/{wallet_id}/transactions") +def read_wallet_transactions( + session: SessionDep, + current_user: CurrentUser, + wallet_id: uuid.UUID, + skip: int = 0, + limit: int = 100, +) -> TransactionsPublic: + """Get transactions for a wallet.""" + # Validate wallet ownership + db_wallet = get_wallet_by_id(session=session, wallet_id=wallet_id) + if not db_wallet: + raise HTTPException(status_code=NOT_FOUND_CODE, detail="Wallet not found") + if db_wallet.user_id != current_user.id: + raise HTTPException( + status_code=BAD_REQUEST_CODE, + detail="Not enough permissions", + ) + + transaction_list = get_wallet_transactions( + session=session, + wallet_id=wallet_id, + skip=skip, + limit=limit, + ) + + # Get total count + count_statement = ( + select(func.count()) + .select_from(Transaction) + .where(Transaction.wallet_id == wallet_id) + ) + count = session.exec(count_statement).one() + + transaction_public_list = [ + TransactionPublic.model_validate(t) for t in transaction_list + ] + return TransactionsPublic(transaction_data=transaction_public_list, count=count) + + +@router.post("/transfer") +def transfer_funds( + *, + session: SessionDep, + current_user: CurrentUser, + transfer_in: WalletTransferCreate, +) -> dict[str, list[TransactionPublic]]: + """Transfer funds between user's wallets.""" + try: + debit_transaction, credit_transaction = transfer_between_wallets( + session=session, + transfer_in=transfer_in, + user_id=current_user.id, + ) + return { + "transactions": [ + TransactionPublic.model_validate(debit_transaction), + TransactionPublic.model_validate(credit_transaction), + ] + } + except ValueError as error: + raise HTTPException( + status_code=BAD_REQUEST_CODE, + detail=str(error), + ) from error diff --git a/backend/app/constants.py b/backend/app/constants.py index 65acb9c..216ae05 100644 --- a/backend/app/constants.py +++ b/backend/app/constants.py @@ -14,3 +14,6 @@ PASSWORD_MIN_LENGTH = 8 PASSWORD_MAX_LENGTH = 40 TOKEN_LENGTH = 32 + +# Wallet constants +MAX_WALLETS_PER_USER = 3 diff --git a/backend/app/core/config.py b/backend/app/core/config.py index 831fcb8..bb7f8a4 100644 --- a/backend/app/core/config.py +++ b/backend/app/core/config.py @@ -78,7 +78,7 @@ class Settings(BaseSettings): # type: ignore[explicit-any] FIRST_SUPERUSER: EmailStr FIRST_SUPERUSER_PASSWORD: str - @computed_field # type: ignore[prop-decorator] + @computed_field # type: ignore[prop-decorator] @property def all_cors_origins(self) -> list[str]: """Get all CORS origins.""" @@ -86,7 +86,7 @@ def all_cors_origins(self) -> list[str]: self.FRONTEND_HOST, ] - @computed_field # type: ignore[prop-decorator] + @computed_field # type: ignore[prop-decorator] @property def SQLALCHEMY_DATABASE_URI(self) -> PostgresDsn: # noqa: N802 """Build database URI from configuration.""" @@ -99,7 +99,7 @@ def SQLALCHEMY_DATABASE_URI(self) -> PostgresDsn: # noqa: N802 path=self.POSTGRES_DB, ) - @computed_field # type: ignore[prop-decorator] + @computed_field # type: ignore[prop-decorator] @property def emails_enabled(self) -> bool: """Check if email configuration is enabled.""" diff --git a/backend/app/crud.py b/backend/app/crud.py index 043cced..7e4be46 100644 --- a/backend/app/crud.py +++ b/backend/app/crud.py @@ -1,11 +1,26 @@ """CRUD operations for database models.""" import uuid +from decimal import Decimal -from sqlmodel import Session, select +from sqlmodel import Session, desc, select +from app.constants import MAX_WALLETS_PER_USER from app.core.security import get_password_hash, verify_password -from app.models import Item, ItemCreate, User, UserCreate, UserUpdate +from app.currency_service import convert_with_fee +from app.models import ( + Item, + ItemCreate, + Transaction, + TransactionCreate, + TransactionTypeEnum, + User, + UserCreate, + UserUpdate, + Wallet, + WalletCreate, + WalletTransferCreate, +) def create_user(*, session: Session, user_create: UserCreate) -> User: @@ -57,3 +72,161 @@ def create_item(*, session: Session, item_in: ItemCreate, owner_id: uuid.UUID) - session.commit() session.refresh(db_item) return db_item + + +def create_wallet( + *, session: Session, wallet_in: WalletCreate, user_id: uuid.UUID +) -> Wallet: + """Create a new wallet for a user.""" + # Check if user already has 3 wallets + existing_wallets = session.exec( + select(Wallet).where(Wallet.user_id == user_id) + ).all() + + if len(existing_wallets) >= MAX_WALLETS_PER_USER: + error_message = ( + f"User already has maximum number of wallets ({MAX_WALLETS_PER_USER})" + ) + raise ValueError(error_message) + + # Check if user already has a wallet with this currency + existing_currency_wallet = session.exec( + select(Wallet).where( + Wallet.user_id == user_id, Wallet.currency == wallet_in.currency + ) + ).first() + + if existing_currency_wallet: + error_message = f"User already has a wallet with currency {wallet_in.currency}" + raise ValueError(error_message) + + db_wallet = Wallet.model_validate( + wallet_in, update={"user_id": user_id, "balance": Decimal("0.00")} + ) + session.add(db_wallet) + session.commit() + session.refresh(db_wallet) + return db_wallet + + +def get_wallet_by_id(*, session: Session, wallet_id: uuid.UUID) -> Wallet | None: + """Get wallet by ID.""" + return session.get(Wallet, wallet_id) + + +def get_user_wallets(*, session: Session, user_id: uuid.UUID) -> list[Wallet]: + """Get all wallets for a user.""" + statement = select(Wallet).where(Wallet.user_id == user_id) + return list(session.exec(statement).all()) + + +def create_transaction( + *, session: Session, transaction_in: TransactionCreate, wallet: Wallet +) -> Transaction: + """Create a new transaction and update wallet balance.""" + if transaction_in.transaction_type == TransactionTypeEnum.DEBIT: + new_balance = wallet.balance - transaction_in.amount + if new_balance < 0: + error_message = ( + "Insufficient funds: transaction would result in negative balance" + ) + raise ValueError(error_message) + wallet.balance = new_balance + else: # CREDIT + wallet.balance += transaction_in.amount + + db_transaction = Transaction.model_validate( + transaction_in, update={"currency": wallet.currency} + ) + session.add(db_transaction) + session.add(wallet) + session.commit() + session.refresh(db_transaction) + session.refresh(wallet) + return db_transaction + + +def get_wallet_transactions( + *, session: Session, wallet_id: uuid.UUID, skip: int = 0, limit: int = 100 +) -> list[Transaction]: + """Get transactions for a wallet.""" + statement = ( + select(Transaction) + .where(Transaction.wallet_id == wallet_id) + .order_by(desc(Transaction.timestamp)) + .offset(skip) + .limit(limit) + ) + return list(session.exec(statement).all()) + + +def transfer_between_wallets( + *, + session: Session, + transfer_in: WalletTransferCreate, + user_id: uuid.UUID, +) -> tuple[Transaction, Transaction]: + """Transfer funds between user's wallets with currency conversion if needed.""" + # Get both wallets and validate ownership + from_wallet = session.get(Wallet, transfer_in.from_wallet_id) + to_wallet = session.get(Wallet, transfer_in.to_wallet_id) + + if not from_wallet or not to_wallet: + error_message = "One or both wallets not found" + raise ValueError(error_message) + + if from_wallet.user_id != user_id or to_wallet.user_id != user_id: + error_message = "Both wallets must belong to the requesting user" + raise ValueError(error_message) + + if from_wallet.id == to_wallet.id: + error_message = "Cannot transfer to the same wallet" + raise ValueError(error_message) + + # Check if source wallet has sufficient funds + if from_wallet.balance < transfer_in.amount: + error_message = "Insufficient funds in source wallet" + raise ValueError(error_message) + + # Calculate conversion and fees + converted_amount, _conversion_fee = convert_with_fee( + transfer_in.amount, + from_wallet.currency, + to_wallet.currency, + ) + + # Update wallet balances + from_wallet.balance -= transfer_in.amount + to_wallet.balance += converted_amount + + # Create debit transaction for source wallet + debit_transaction = Transaction( + wallet_id=from_wallet.id, + amount=transfer_in.amount, + transaction_type=TransactionTypeEnum.DEBIT, + currency=from_wallet.currency, + ) + + # Create credit transaction for destination wallet + credit_transaction = Transaction( + wallet_id=to_wallet.id, + amount=converted_amount, + transaction_type=TransactionTypeEnum.CREDIT, + currency=to_wallet.currency, + ) + + # Add all changes to session + session.add(from_wallet) + session.add(to_wallet) + session.add(debit_transaction) + session.add(credit_transaction) + + session.commit() + + # Refresh objects + session.refresh(from_wallet) + session.refresh(to_wallet) + session.refresh(debit_transaction) + session.refresh(credit_transaction) + + return debit_transaction, credit_transaction diff --git a/backend/app/currency_service.py b/backend/app/currency_service.py new file mode 100644 index 0000000..32ab5af --- /dev/null +++ b/backend/app/currency_service.py @@ -0,0 +1,52 @@ +"""Currency conversion service with fixed exchange rates.""" + +from decimal import Decimal +from typing import Final + +from app.models.db_models import CurrencyEnum + +# Fixed exchange rates (base currency: USD) +EXCHANGE_RATES: Final[dict[CurrencyEnum, Decimal]] = { + CurrencyEnum.USD: Decimal("1.00"), + CurrencyEnum.EUR: Decimal("0.85"), + CurrencyEnum.RUB: Decimal("75.00"), +} + +# Transaction fees for currency conversion (percentage) +CONVERSION_FEE_RATE: Final[Decimal] = Decimal("0.02") # 2% + + +def convert_currency( + amount: Decimal, from_currency: CurrencyEnum, to_currency: CurrencyEnum +) -> Decimal: + """Convert amount from one currency to another using fixed exchange rates.""" + if from_currency == to_currency: + return amount + + # Convert to USD first (base currency) + usd_amount = amount / EXCHANGE_RATES[from_currency] + + # Convert from USD to target currency + converted_amount = usd_amount * EXCHANGE_RATES[to_currency] + + return converted_amount.quantize(Decimal("0.01")) + + +def calculate_conversion_fee(amount: Decimal) -> Decimal: + """Calculate conversion fee for cross-currency transactions.""" + fee = amount * CONVERSION_FEE_RATE + return fee.quantize(Decimal("0.01")) + + +def convert_with_fee( + amount: Decimal, from_currency: CurrencyEnum, to_currency: CurrencyEnum +) -> tuple[Decimal, Decimal]: + """Convert currency amount and return converted amount and fee.""" + if from_currency == to_currency: + return amount, Decimal("0.00") + + converted_amount = convert_currency(amount, from_currency, to_currency) + fee = calculate_conversion_fee(converted_amount) + final_amount = converted_amount - fee + + return final_amount.quantize(Decimal("0.01")), fee diff --git a/backend/app/models/__init__.py b/backend/app/models/__init__.py index f039a6b..1bc9a3b 100644 --- a/backend/app/models/__init__.py +++ b/backend/app/models/__init__.py @@ -10,7 +10,14 @@ UserPublic, UsersPublic, ) -from app.models.db_models import Item, User +from app.models.db_models import ( + CurrencyEnum, + Item, + Transaction, + TransactionTypeEnum, + User, + Wallet, +) # Item models from app.models.item_models import ( @@ -31,6 +38,17 @@ UserUpdateMe, ) +# Wallet models +from app.models.wallet_models import ( + TransactionCreate, + TransactionPublic, + TransactionsPublic, + WalletCreate, + WalletPublic, + WalletsPublic, + WalletTransferCreate, +) + __all__ = [ # API models "Message", @@ -40,8 +58,12 @@ "UserPublic", "UsersPublic", # Database models + "CurrencyEnum", "Item", + "Transaction", + "TransactionTypeEnum", "User", + "Wallet", # Item models "ItemBase", "ItemCreate", @@ -55,4 +77,12 @@ "UserRegister", "UserUpdate", "UserUpdateMe", + # Wallet models + "TransactionCreate", + "TransactionPublic", + "TransactionsPublic", + "WalletCreate", + "WalletPublic", + "WalletsPublic", + "WalletTransferCreate", ] diff --git a/backend/app/models/db_models.py b/backend/app/models/db_models.py index 44f060c..e6053ca 100644 --- a/backend/app/models/db_models.py +++ b/backend/app/models/db_models.py @@ -1,6 +1,9 @@ """Database table models.""" import uuid +from datetime import datetime, timezone +from decimal import Decimal +from enum import StrEnum from pydantic import EmailStr from sqlmodel import Field, Relationship, SQLModel @@ -8,6 +11,21 @@ from app.constants import EMAIL_MAX_LENGTH, STRING_MAX_LENGTH +class CurrencyEnum(StrEnum): + """Available currencies for wallets.""" + + USD = "USD" + EUR = "EUR" + RUB = "RUB" + + +class TransactionTypeEnum(StrEnum): + """Transaction types.""" + + CREDIT = "credit" + DEBIT = "debit" + + class User(SQLModel, table=True): """Database user model.""" @@ -18,6 +36,9 @@ class User(SQLModel, table=True): full_name: str | None = Field(default=None, max_length=STRING_MAX_LENGTH) hashed_password: str item_list: list["Item"] = Relationship(back_populates="owner", cascade_delete=True) + wallet_list: list["Wallet"] = Relationship( + back_populates="owner", cascade_delete=True + ) class Item(SQLModel, table=True): @@ -34,3 +55,38 @@ class Item(SQLModel, table=True): owner: User | None = Relationship(back_populates="item_list") +class Wallet(SQLModel, table=True): + """Database wallet model.""" + + id: uuid.UUID = Field(default_factory=uuid.uuid4, primary_key=True) + user_id: uuid.UUID = Field( + foreign_key="user.id", + nullable=False, + ondelete="CASCADE", + index=True, + ) + balance: Decimal = Field(default=Decimal("0.00"), decimal_places=2) + currency: CurrencyEnum = Field(index=True) + owner: User | None = Relationship(back_populates="wallet_list") + transaction_list: list["Transaction"] = Relationship( + back_populates="wallet", cascade_delete=True + ) + + +class Transaction(SQLModel, table=True): + """Database transaction model.""" + + id: uuid.UUID = Field(default_factory=uuid.uuid4, primary_key=True) + wallet_id: uuid.UUID = Field( + foreign_key="wallet.id", + nullable=False, + ondelete="CASCADE", + index=True, + ) + amount: Decimal = Field(decimal_places=2) + transaction_type: TransactionTypeEnum = Field(alias="type") + timestamp: datetime = Field( + default_factory=lambda: datetime.now(timezone.utc), index=True + ) + currency: CurrencyEnum + wallet: Wallet | None = Relationship(back_populates="transaction_list") diff --git a/backend/app/models/wallet_models.py b/backend/app/models/wallet_models.py new file mode 100644 index 0000000..186cbf8 --- /dev/null +++ b/backend/app/models/wallet_models.py @@ -0,0 +1,74 @@ +"""Wallet-related data models.""" + +import uuid +from datetime import datetime +from decimal import Decimal + +from sqlmodel import Field, SQLModel + +from app.models.db_models import CurrencyEnum, TransactionTypeEnum + + +class WalletBase(SQLModel): + """Base wallet model with shared fields.""" + + currency: CurrencyEnum + + +class WalletCreate(WalletBase): + """Wallet creation model.""" + + +class WalletPublic(WalletBase): + """Public wallet model for API responses.""" + + id: uuid.UUID + user_id: uuid.UUID + balance: Decimal + currency: CurrencyEnum + + +class WalletsPublic(SQLModel): + """Public model for multiple wallets.""" + + wallet_data: list[WalletPublic] + count: int + + +class TransactionBase(SQLModel): + """Base transaction model with shared fields.""" + + amount: Decimal = Field(decimal_places=2, gt=0) + transaction_type: TransactionTypeEnum = Field(alias="type") + + +class TransactionCreate(TransactionBase): + """Transaction creation model.""" + + wallet_id: uuid.UUID + + +class TransactionPublic(TransactionBase): + """Public transaction model for API responses.""" + + id: uuid.UUID + wallet_id: uuid.UUID + amount: Decimal + transaction_type: TransactionTypeEnum = Field(alias="type") + timestamp: datetime + currency: CurrencyEnum + + +class TransactionsPublic(SQLModel): + """Public model for multiple transactions.""" + + transaction_data: list[TransactionPublic] + count: int + + +class WalletTransferCreate(SQLModel): + """Model for wallet-to-wallet transfers.""" + + from_wallet_id: uuid.UUID + to_wallet_id: uuid.UUID + amount: Decimal = Field(decimal_places=2, gt=0) diff --git a/code_quality_analysis.ipynb b/code_quality_analysis.ipynb index 85c84b6..c3eec0f 100644 --- a/code_quality_analysis.ipynb +++ b/code_quality_analysis.ipynb @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 1, "id": "871ae97e", "metadata": {}, "outputs": [ @@ -96,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 2, "id": "f8c53333", "metadata": {}, "outputs": [ @@ -104,7 +104,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Analyzing directory: /Users/somen/Zavodi/opensource/fastapi-moscow-python-demo/backend\n", + "Analyzing directory: /Users/somen/Zavodi/opensource/fastapi-moscow-python-demo-1/backend\n", "Tools configured:\n", " - bandit: bandit -c pyproject.toml -r backend/ -f json\n", " - ruff: ruff check backend/ --output-format json\n", @@ -147,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 3, "id": "c55d109f", "metadata": {}, "outputs": [ @@ -218,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 4, "id": "0db8c239", "metadata": {}, "outputs": [ @@ -226,7 +226,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Ruff found 0 linting issues\n" + "Ruff found 1 linting issues\n", + "\n", + "Ruff Results Summary:\n", + "Top 10 most common rules:\n", + "rule_code\n", + "UP017 1\n", + "Name: count, dtype: int64\n" ] } ], @@ -284,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 5, "id": "2a1f0732", "metadata": {}, "outputs": [ @@ -292,7 +298,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "MyPy found 0 type checking issues\n" + "MyPy found 6 type checking issues\n", + "\n", + "MyPy Results Summary:\n", + " tool file line severity \\\n", + "0 mypy backend/app/email_utils.py 7 error \n", + "1 mypy backend/app/email_utils.py 7 error \n", + "2 mypy backend/app/email_utils.py 7 note \n", + "3 mypy backend/app/email_utils.py 7 note \n", + "4 mypy backend/app/email_utils.py 33 error \n", + "\n", + " message \n", + "0 \u001b[m Unused \u001b[m\u001b[1m\"type: ignore\"\u001b[m comment \u001b... \n", + "1 \u001b[m Cannot find implementation or library stub... \n", + "2 \u001b[m Error code \u001b[m\u001b[1m\"import-not-found\"\u001b[m no... \n", + "3 \u001b[m See \u001b[4mhttps://mypy.readthedocs.io/en/sta... \n", + "4 \u001b[m Unused \u001b[m\u001b[1m\"type: ignore\"\u001b[m comment \u001b... \n", + "severity\n", + "error 4\n", + "note 2\n", + "Name: count, dtype: int64\n" ] } ], @@ -349,6 +374,7 @@ "mypy_results = run_mypy()\n", "if not mypy_results.empty:\n", " print(\"\\nMyPy Results Summary:\")\n", + " print(mypy_results.head())\n", " print(mypy_results[\"severity\"].value_counts())" ] }, @@ -362,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 6, "id": "5b0cf9e3", "metadata": {}, "outputs": [ @@ -370,14 +396,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Radon CC analyzed 161 functions/methods (all ranks included)\n", + "Radon CC analyzed 191 functions/methods (all ranks included)\n", "\n", "Radon CC Results Summary:\n", - "Average complexity: 2.34\n", + "Average complexity: 2.25\n", "Complexity rank distribution:\n", "rank\n", - "A 155\n", - "B 6\n", + "A 184\n", + "B 7\n", "Name: count, dtype: int64\n" ] } @@ -439,7 +465,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 7, "id": "6820f645", "metadata": {}, "outputs": [ @@ -447,13 +473,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Radon MI analyzed 54 files (all ranks included)\n", + "Radon MI analyzed 58 files (all ranks included)\n", "\n", "Radon MI Results Summary:\n", - "Average maintainability index: 80.83\n", + "Average maintainability index: 80.39\n", "MI rank distribution:\n", "mi_rank\n", - "A 54\n", + "A 58\n", "Name: count, dtype: int64\n" ] } @@ -509,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 8, "id": "e3918fd3", "metadata": {}, "outputs": [ @@ -517,7 +543,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "Flake8 WPS: No issues found\n" + "Flake8 WPS found 11 style issues\n", + "\n", + "Flake8 WPS Results Summary:\n", + "Top 10 most common WPS rules:\n", + "rule_code\n", + "WPS229 3\n", + "WPS202 2\n", + "WPS111 1\n", + "WPS235 1\n", + "WPS238 1\n", + "WPS210 1\n", + "WPS213 1\n", + "WPS407 1\n", + "Name: count, dtype: int64\n" ] } ], @@ -594,7 +633,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 9, "id": "b66c41c3", "metadata": {}, "outputs": [ @@ -602,12 +641,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Vulture found 2 dead code issues\n", + "Vulture found 4 dead code issues\n", "\n", "Vulture Results Summary:\n", "Dead code types found:\n", "dead_code_type\n", - "variable 2\n", + "variable 4\n", "Name: count, dtype: int64\n", "Average confidence: 60.0%\n" ] @@ -697,7 +736,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 10, "id": "d122b78a", "metadata": {}, "outputs": [ @@ -709,14 +748,14 @@ "=== SUMMARY STATISTICS (Radon A ranks excluded from counts) ===\n", " total_issues files_analyzed\n", "bandit 2 2\n", - "ruff 0 0\n", - "mypy 0 0\n", - "radon_cc 6 3\n", + "ruff 1 1\n", + "mypy 6 1\n", + "radon_cc 7 4\n", "radon_mi 0 0\n", - "flake8_wps 0 0\n", - "vulture 2 2\n", + "flake8_wps 11 4\n", + "vulture 4 3\n", "\n", - "Total combined issues (excluding Radon A ranks): 10\n" + "Total combined issues (excluding Radon A ranks): 31\n" ] } ], @@ -796,13 +835,13 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 11, "id": "7b178a7f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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wqoSoAChE6ZU3ouGmeyMyCSoAWkkWeTi39NyIoisBAAAAAAA6GSEqANpdedxb0XD9PyPKAlQAtLIsi4a/3hOll0YVXQkAAAAAANCJCFEB0K7KU2dE/R//EVHfUHQpAHRV5XI0XHdXlEaMKboSAAAAAACgkxCiAqDdZHPnR8M1t0bMX1h0KQB0daVSNPz5jiiNerPoSgAAAAAAgE5AiAqAdpHV1Uf972+NbPqsoksBoLtoaIyGP90W5UlvF10JAAAAAADQwQlRAdDmslI5Gv5yZ2QT/SM2AO2sriHq//CPyGbPLboSAAAAAACgAxOiAqDNNd5yf5RfG1t0GQB0V7PnRf0fb8unIgIAAAAAALREiAqANtVw32NRGvpS0WUA0M2laYgNN9wdWTkruhQAAAAAAKADEqICoM00PvV8lB54qugyACBXfuWNaPznw0WXAQAAAAAAdEBCVAC0iVL6h+p/PFB0GQCwmNLg4dH4xHNFlwEAAAAAAHQwQlQAtLrym29Fw/X/jLBlEgAdUOPtg6L02tiiywAAAAAAADoQISoAWlV56oyo/8M/Iuobii4FAFpWzqLhujujPHlq0ZUAAAAAAAAdhBAVAK0mm78wGq65NWL+wqJLAYDlW1Sfh36zOfOKrgQAAAAAAOgAhKgAaBVZmupx/T8jmz6r6FIAYOXMnBP1f7wtMtMTAQAAAACg2xOiAqBVNN73WJRHjiu6DABYJdmEKdFw4z2RZVnRpQAAAAAAAAUSogJgjZVeHBmlh4YUXQYArJbyiyOj8e7BRZcBAAAAAAAUSIgKgDVSfmd6NNx0b4QBHgB0YqWHh0bj0y8WXQYAAAAAAFAQISoAVlu2qC4arr0joq6+6FIAYI01/uOBKE98u+gyAAAAAACAAghRAbDaGv52X2RvTy+6DABoHY2laPjLnZEtrCu6EgAAAAAAoJ0JUQGwWhoffDrKL44sugwAaFXZ9Fl5SBgAAAAAAOhehKgAWGWlkeOi8b5Hiy4DANpE+aWR0fjo8KLLAAAAAAAA2pEQFQCrJJsxOxqu/2dEOSu6FABoM43/fCTKb75VdBkAAAAAAEA7EaICYKVlDY1R/+c7IuYvLLoUAGhbpXLUX3dXZP43DwAAAAAAuoXaogsAoPNo/McDkU18u+gy6OQef2dCXDfm5Xhl9tSoiqrYvmfv+Pz2e8bRfXdY7LhZ9Yvi6lHPxeC3J8S0ugWx5bo944Std44vbL9n1FavfA780bfHxx9HvxhvzJ0R1VVVsf/GW8TZO/ePnTfYeLHjylkWl742LO6YMDLSnLVDNu0b393j0Nhk7XUXO66u1BifGHxrvH/TreJ/9/7AGr4aQIc2c0403HRP9DjzU1FVVVV0NQAAAAAAQBsyiQqAldL45HNRGvpS0WXQyV0/5uU4e9jAGDV3RhzXb6c4pu8OMWHB3PjOcw/HX8b8+/01p6EuvvTUPXHTuFdjt96bxCnb7xHr1NTGb18bFt997uGV/nn/GP9afGP4A/HOovnxya13iSP6bBdDpk6KLzxxV7w0a+pix9785oj485iXYrfem8ZH++4Qj7w9Ps5/ZtBSz5lqmlVfF1/bef81fDWAzqD86pgoPTy06DIAAAAAAIA2ZhIVACtUnjA5Gm9/qOgy6ORGzZkRv31taOzQc8P4wyHHNk14Omvn/eMzj90ev3tteB506tVjrXwC1eh5s+IHe74/Ttp2t/y4b+zSP7797MMxaMq4GDR5XBy55XbL/XnT6xbGz18ZEtusv0HceNgn8udNPrXNLvGlIffEj158PP7+wROapsvcOv61vLYrDzo6v63POuvH714fHiNmT8uDVZVw1x9Hv5BPw9psnfXa+BUDOorGex+L6u36RfUOWxVdCgAAAAAA0EZMogJgubL6hmi44e6IUqnoUujkbhz3SjRmWfzPnu9fbIu89PU3djkgPrH1+/Lg06JSY/xj/OuxxTrrx6e32bXpuJqq6jh/t4Pyr28ZP2KFPy+FourKpfjiDns1BaiSvTfaPD665Q4xcu6MeGHmO023T5w/J97Xa6OmUNWuG2zy7u0L5jYd8/tRz0dtVXV8cce91vj1ADqRcjnqr78rsnkLiq4EAAAAAABoI0JUACxX4z8fiWzqzKLLoAt47J2Jsdna60X/TbZc6r4Tt945/nevD8R2PXvHy7OmxsJSYxywyZZR/V6gqWKr9XpFv3V7xbMz3o5SVl7uzxs6bXJ+ffCmfZe6r3Lb0OlvNd3We611YkGpsen7eY31+fUGPdbOr99aMDf+9uaI+Or79ov1a/8dygK6idnz8lBxVs6KrgQAAAAAAGgDtvMDYJlKI8ZE6Ynnii6DLmBG3cKYWrcgDtm0b0xdtCCuGPlMPPrOhJjXUB879doo/nOnfeKILd7dnu/N+bPz663X36DF50pBqkkL58akBfPyrfqWJT1PbVVVbLluz6Xu67der/x67Lx3f1ayz0abx+C3x8fzM96ObXv2jr+/OSLWq+kRu2ywcX7/5SOfiS3WXXw6FtC9lEeOi9IDT0bt0YcVXQoAAAAAANDKhKgAaFE2f2E03Hxf0WXQRaQAVTK/sSFOfvyOWKemNo7aYvuY31gfD055M8575sH47u6HxCnb7xGz6uvyY3u/NwFqST3f25pvbsO7xy3L7Ia66Fm7Vr4N4JJ6vTdJau5706aSc3bpH8/NeDtOf+ru/PsUwPqfvT4QG661Trw2e3rcO2l0/HL/I6K2+t3nK2fZUpOygK6vceCTUbXDVlHzvm2LLgUAAAAAAGhFQlQAtKjhlvsj5swvugy6iAWN726T99KsqXHQJlvGpQccFevV9shvO3PerDj1ibviVyOGxn/02SYa3tumb633wkpLqtxeVy4t92c2lEvRYxlBrB7VNfl1fenfz7Ht+r3jHx/6ZDzy9psxr7Eh3/IvTclKfvPa0Nh7w83jyC23jykL58WFLz4Ww6ZPzidVfXKbXeKbuxzQFK4Curgsi4ab7o3q73wpqtZp+b8xAAAAAABA5+Nf+wBYSmnYy1F+cWTRZdCF1DSb2PS9PQ5tClAl2/fcMD637e7RmJXjgSnjYp33Ak4N5XfDVEuqf+/2FGBanrVravPnXFbAKn+O2sXz5L3XWjs+sfXOcer2ezQFqJ6cOjGGTHsrzt3twPz7Hzw/OEbPnRWX7HdEfG3n/eOvY1+Ov4x5aaVeB6CLmDU3Gu98qOgqAAAAAACAViREBcBishmzo+H2QUWXQReTttVL1q2pjR16brjU/bv13iS/njB/Tmzw3vSouQ3/3mqvuXnv3V7Z1m9Z0naA8xrrI8uype6rbONXqWtZ0mN/+9qw+HCfbWL/jbeI0XNnxvAZU+KMHfeKI7fcLg9bfXDzbeKGsS8v93mArqf09EtRGjGm6DIAAAAAAIBWIkQFQJOsnEX9jfdELGo5vAKra+v1e0VtVVWUsiyWjjT9e+rUOjW1+WSqZMKCOS0+V7o9hbG2XHf95f7M7Xv2zp93yqKlt6WcuGBufr1Dr6UDXc3dPemNPDj1rV3fnUI1dt7spq3/Krbr2Ttm1C9aZugL6Loa/v6vyBbWFV0GAAAAAADQCoSoAGhSemRoZGMmFl0GXVCP6prYe6PNo75cimdmTFnq/pdnT82vd9lg49i99yaxfm2PGD59SpSXmCI1ccGceGvhvNhno82jpmr5/zfmgI23zK+HTntrqfuefu+2/TbaYpmPry+V4v9GPhsnbL1LU7Cr9N72gI3N6krHJdX/3rEQ6C5mz7OtHwAAAAAAdBFCVADkym+9E433PV50GXRhJ227W379y1efXmxq08g5M+LWN1+LDXusHUdssW2sXVMbx/TdMSYtnBs3jnul6bgUYPr1iGH5159977mW57h+O8Za1TXx+zeej1n1i5puf3HmO3H/5DF5YGu/jfss8/E3jXs1ZtYviq+9b7+m2ypbET7XLAj2/My3Y/N11ov1V7A1INA1lYa+FKVXRxddBgAr6dxzz40PfehDq/SYk046KXbZZZcWL1/96leXOv7ZZ5+NL33pS3HwwQfH/vvvH6effnoMHTq0Fc8CAAAAgLZQ2ybPCkCnkjU2RsNf74l4b6IOtIUUjHpy6qS4a+Ko+OSj/4gjt9gu5jU2xMDJY/PJThfu/YGmINI5u/SPJ6dOjF+8+nQMnTY533ZvyNRJMWLO9PjIltvHh/tsu9hz3zD25TyYdfxW74t+6/XKb+u7Xq84Z+f+8evXhsanH709ju67fcxvbIj7Jo2O2qrq+N+9PrDMWuc01MUfRj8fp+2wV2y6znpNt79vg41j3436xPXv/by3F82PV2ZPi+/sfnCbvW5Ax9dwy/1R/Z0vRdW66xRdCgDLcfnll8d9990XffosO0i/pHK5HCNHjoytttoqTjjhhKXu32677Rb7fvDgwfH1r389Nthgg/j4xz8epVIp7r777jxIddlll8WRRx7ZKucCAAAAQOuryrIl9skBoNtpuOvhKD3y7oQfaEvp/3bcOXFU/P3NETF63qxYq7o69t5w8/jyTvvGvktMhZq6aEFcMfKZePSdCTGvoT4PR31iq/fFqdvvkW8P2NwxD92cb/P3h0OOjQM3eXcbv4oUmkqhpzfmzsy3CUzbCn595/1jlw02WWadvxkxNA973fPhk2K92h6L3Te9bmH89OUn44l3JuT3pQlbX33fflFdZT8/6M5qDtwzenzu2KLLAKAFdXV18eMf/zhuueWW/PsUonr00UdX6rGjR4+OY489Nj772c/Gj370o+UeW19fH0cccUQ0NDTEHXfcEVtu+e7/Lx0/fnx85jOfiR49esQDDzwQ6667biucFQDQ0R14xy+KLoE2MOyEbxddAgDQhkyiAujmSm+Mj9JgASraR1VVVZyw9c75ZUU2W2e9uGjvD67U8953xGeXed8x/XbML6vivN0Oyi8t2WTtdePX/Qes0vMBXV9p2MtRvc8uUbP7qv33BoC29dBDD8VPfvKTmDRpUhx++OH5pKhV8frrr+fXaeu+Fbn33ntj6tSpcdZZZzUFqJJtttkmTj311Ljiiiti0KBB+YQqAAAAADqe6qILAKA4WUNjNN78rwgzCQFgjTX8/f7IFi4qugwAmrn11ltj/vz5ceGFF8bVV1+9yo8fMWJEfr3rrruu8Ninn346vz7kkEOWuu/QQw/Nr4cMGbLKNQAAAADQPoSoALqxxkFPRTZ9VtFlAEDXMGdeNNzxUNFVANDM6aefHg8++GCccsop+VTU1Q1Rvfrqq3HSSSfFfvvtFwcffHCcd955MXbs2MWOrXy/7bbbLvU8W2+9dX49ZsyY1TwTAAAAANqaEBVAN1V+Z0aUHhpadBkA0KWUh70cpVfeKLoMAN6TAk89e/Zc7ce/9tpr+fVvfvOb2GGHHeLkk0+OnXfeOd+679Of/nS8+OKLTcfOmvXuApXevXsv9TyVGubOnbvatQAAAADQtmrb+PkB6KAa//FARKlUdBkA0OU03DIwqrffKqrWW6foUgBYAwsWLIjNN988Ntlkk7jyyiujb9++Tff97W9/y7cI/Pa3v50HqmpqaqKhoSG/b6211lrquSq31dXVteMZAAAAALAqTKIC6IZKz74a5VFvFl0GAHTdbf3utK0fQGe33nrrxW233RZ33nnnYgGqJE2kSlv7jRs3rmka1TrrvBuerYSpmquvr296TgAAAAA6JiEqgG4mW1QXDXc+XHQZANCllYe/HOWxk4ouA4A2tPfee+fX48ePX2wbv5a27Js3b15+3atXr3atEQAAAICVJ0QF0M003vtYxNz5RZcBAF1bFtFw+6DIylnRlQCwmmbOnBnPPPNMjB07tsX7Fy5cuNgEqh122GGxUFVzldt23HHHNqwYAAAAgDUhRAXQjZQnTonSk88VXQYAdAvZxLej9PS7WzwB0Pk89dRTccopp8TFF1+81H3lcjmeffbZqKqqir322iu/7aCDDsqvhwwZ0uJzJf3792/zugEAAABYPUJUAN1EmoTRcMvACBMxAKDdNN73WGQLFxVdBgCr4UMf+lC+/d6jjz4aTzzxxGL3XXHFFfHGG2/EUUcdFX379s1vGzBgQGy44YZxww03xIQJExabQnXjjTfGZpttFkcffXS7nwcAAAAAK6d2JY8DoJMrPfV8ZBOmFF0GAHQv8xZE47+eiB4nDii6EgCWY8SIETFo0KDo169ffPKTn8xv69mzZ/zoRz+K//qv/4ovf/nLeWBqyy23jOeeey6ef/75fPu+iy66qOk51l9//bjwwgvjggsuiE996lNx3HHH5bffc889MW/evLjsssti7bXXLuwcAQAAAFg+ISqAbiCbOz8a73206DIAoFsqPfFc1Byyd1RvuVnRpQCwnBDV5Zdfnm/JVwlRJccee2w+aeqqq67Kt+RbsGBB/v1XvvKV+OpXv5oHrZpLx/fu3TuuvPLKuOOOO6JHjx6x2267xdlnn9203R8AAAAAHVNVlmX2dQLo4ur/eneUn3m16DIAoNuqft82sdbXTi66DAAAANrJgXf8ougSaAPDTvh20SUAAG2oui2fHIDild4YL0AFAAUrjxofpRdeL7oMAAAAAABgGYSoALqwrFSKxn88UHQZAEBENNz1cGT1DUWXAQAAAAAAtECICqALKz3xXGRvTy+6DAAgmTknGh96uugqAAAAAACAFghRAXRR2aK6aBw0pOgyAIBmSg8PjWzG7KLLAAAAAAAAliBEBdBFNT4yLGLegqLLAACaa2iMhjsfLroKAAAAAABgCUJUAF1QNnd+lAYPK7oMAKAF5ZdGRmnkm0WXAQAAAAAANCNEBdAFNT7wVERdQ9FlAADL0Hj7oMhK5aLLAAAAAAAA3iNEBdDFlKfPitJTzxddBgCwHNnb06M07OWiywAAAAAAAN4jRAXQxTTe91iEyRYA0OE1PvBkZI2NRZcBAAAAAAAIUQF0LeVJb0f5uRFFlwEArIyZc6L0pOmRAAAAAADQEQhRAXQhjfc8GpEVXQUAsLIaBw2JrK6+6DIAAAAAAKDbE6IC6CJKb4yP8mtjiy4DAFgV8xZE6bFniq4CAAAAAAC6PSEqgC6i8e7BRZcAAKyGxoeHRrZwUdFlAAAAAABAtyZEBdAFlF4cGdn4yUWXAQCsjoV1eZAKAAAAAAAojhAVQCeXlcvReO+jRZcBAKyB0qPPRDZvQdFlAAAAAABAtyVEBdDJlYa+HNk7M4ouAwBYE/UN0fjIsKKrAAAAAACAbkuICqATyxpL0TjwiaLLAABaQemJZyObv7DoMgAAAAAAoFsSogLoxErDX4mYNbfoMgCA1lBnGhUAAAAAABRFiAqgk8qyLEqPDC26DACgFZUeN40KAAAAAACKIEQF0EmVX34jsndmFF0GANCa6uqjcbBpVAAAAAAA0N6EqAA6qcaHny66BACgDZQeM40KAAAAAADamxAVQCdUHjMxsnFvFV0GANBW06geHV50FQAAAAAA0K0IUQF0QqZQAUDXVnry+cjqG4ouAwAAAAAAug0hKoBOpvz29Ci/OrroMgCAtjR/YZSeebXoKgAAAAAAoNsQogLoZEoPD43Iiq4CAGhrpceeKboEAAAAAADoNoSoADqRbPZcUykAoJvIpkyL0uvjii4DAAAAAAC6BSEqgE6k8dFnIkqlossAANpJ6dHhRZcAAAAAAADdghAVQCeRLaqL0lPPF10GANCOyq+NifI7M4ouAwAAAAAAujwhKoBOovTk8xGL6osuAwBoT1lE6bFniq4CAAAAAAC6PCEqgE4gayy9u5UfANDtlIa9HNnCRUWXAQAAAAAAXZoQFUAnUHrmlYg584ouAwAoQn1DlIa8WHQVAAAAAADQpQlRAXQCpUeGFV0CAFCgxsefjaxcLroMAAAAAADosoSoADq48pgJkb09vegyAIAizZwT5ZdGFV0FAAAAAAB0WUJUAB1co+17AID0/wkeHV50CQAAAAAA0GUJUQF0YNnCuii/OLLoMgCADiAbOynKEyYXXQYAAAAAAHRJQlQAHVjp2Vcj6huKLgMA6CAaBz9TdAkAAAAAANAlCVEBdGClIS8UXQIA0IGUX3gtsjnzii4DAAAAAAC6HCEqgA6qPHFKZJPeKboMAKAjKZWjNPyVoqsAAAAAAIAuR4gKoIMqDXmx6BIAgA5IiAoAAAAAAFqfEBVAB5TVN0Tp2RFFlwEAdEDZlGn5xEoAAAAAAKD1CFEBdEDlF16PWFRXdBkAQAdVGmYaFQAAAAAAtCYhKoAOqNFWfgDAcpSeGxFZqVx0GQAAAAAA0GUIUQF0MOW3p0c2dmLRZQAAHdm8BVF+bUzRVQAAAAAAQJchRAXQwZSeNoUKAFix0rCXiy4BAAAAAAC6DCEqgA4kK5WiNPyVossAADqB8qujI1uwqOgyAAAAAACgSxCiAuhAyi+/kW/PAwCwQo2lKD0/ougqAAAAAACgSxCiAuhASkNs5QcArLzSMBMsAQAAAACgNQhRAXQQ2dz5UR45rugyAIBOJHvzrSi/M6PoMgAAAAAAoNMTogLoIEovvB6RZUWXAQB0MqXhplEBAAAAAMCaEqIC6CBKz79WdAkAQCdUeuaVyASxAQAAAABgjQhRAXQA2Zx5kY2dVHQZAEBnNHNOlEdPKLoKAAAAAADo1ISoADoAW/kBAGuiPOzloksAAAAAAIBOTYgKoAMoPf960SUAAJ1Y6aWRkTWWii4DAAAAAAA6LSEqgIJls+dGNm5i0WUAAJ3Zovoojx5fdBUAAAAAANBpCVEBFKz0wsgIO/kBAGuo/MrooksAAAAAAIBOS4gKoGClF23lBwCsudIrbxRdAgAAAAAAdFpCVAAFyuYtiGzcpKLLAAC6gplzovzWO0VXAQAAAAAAnZIQFUCBSq+Ojijbyw8AaB1l06gAAAAAAGC1CFEBFKj80siiSwAAupDSy0JUAAAAAACwOoSoAAqS1dVH+fU3iy4DAOhCsolTIpszr+gyAAAAAACg0xGiAihI+fVxEY2NRZcBAHQlWUTpldFFVwEAAAAAAJ2OEBVAQUovjyq6BACgCyq/aks/AAAAAABYVUJUAAXIyuUomxIBALSB8sg3I6tvKLoMAAAAAADoVISoAAqQjZ8SsXBR0WUAAF1RQ2OUR44rugoAAAAAAOhUhKgAClAe9WbRJQAAXZiJlwAAAAAAsGqEqAAKUH5DiAoAaDulV0dHlmVFlwEAAAAAAJ2GEBVAO8vSFjtj3yq6DACgK5s7/93tgwEAAAAAgJUiRAXQzspjJ0U0NhZdBgDQxZVeeaPoEgAAAAAAoNOoLboAgO6mPMpWfgBA2yuPHBdx7AeLLgOgQzn33HPj2WefjUcffXSlH/Pqq6/GVVddFcOGDYu5c+fGRhttFIcddlicffbZsfXWWy927OjRo+PYY49d5nNdeuml8dGPfnSNzgEAAACAtiFEBdDOhKgAgPaQTXw7srr6qFp7raJLAVjK1KlT48Ybb4whQ4bE5MmT4z/+4z/ioosuit/85jexyy67LDeItLouv/zyuO+++6JPnz4r/ZgnnngivvrVr0aWZTFgwIDo169fjBw5Mm6//fZ46KGH8nPYaaedmo5/7bXX8uujjjoqP48lNT8WAAAAgI5FiAqgHWUL6yKbOKXoMgCA7qBcjvKbb0XNztsVXQnAYgYPHhwXXHBBzJ8/Pw8nVVVVxYIFC/L7UjDpmmuuyadF/c///E+r/Ly6urr48Y9/HLfccssqPa5UKuU1lMvluO666+KAAw5ouu/WW2+NH/zgB3nw64Ybbmi6fcSIEfn1mWeeGfvtt1+r1A8AAABA+6hup58DQPq3zNHjI8pZ0WUAAN1EefSEoksAWGq7u29+85uxaNGiOPnkk+Pqq6/Og1QVn/70p2P99dePv/71r3mgak2l5zjmmGPyANXhhx++So99/vnn46233oojjjhisQBVpc60ld/w4cPzLf6aT6JKobCdd955jWsHAAAAoH2ZRAXQjsqjxhddAgDQjWRjJhZdAsBirrzyyqivr49LL700PvKRjyx1/+mnnx577rlnfP7zn8+DVCnAtCbSxKg08erCCy+Mz33uc7Hrrruu9GM33XTTfGLWsgJRa621Vh4AS1O0evXq1RSi2mabbfIgGAAAAACdixAVQDsqj3qz6BIAgG6kPH5yZI2lqKqtKboUgNyQIUNit912azFAVdG/f//YZ5994o033ljjn5dCWZdcckn07NlzlR+77bbbxle+8pUW7xs5cmSMHTs2Ntpoo9hss83y26ZPnx5Tp07N67/44ovjwQcfjClTpkTfvn3j+OOPjy9/+ct58AoAAACAjsl2fgDtJJs7P7Ip04ouAwDoThoaI5swpegqAJrMnj07ttpqqxUel4JJM2bMWOOfd/DBB69WgGp5Ghoa8slW5XI535Kwuvrd9tqIESPy62eeeSYGDx4cAwYMiBNPPDGfvPW73/0uzjzzzPxrAAAAADomk6gA2okpVABAEcpjJkT19v2KLgMglyY3jRs3boXHjRkzJjbeeOPoaFKA6vzzz49nn302n6j1ta99rem+efPmxXbbbReHHnpo/PCHP4yamnenAKbt/s4+++x48skn4w9/+EN8/etfL/AMAAAAAFgWk6gA2okQFQBQhPKYiUWXALDYZKhRo0blW90ty/333x+jR4+OAw88MDqSFJI666yzYuDAgflWf1dffXWsvfbaTfd/9KMfzWu/6KKLmgJUyXrrrZdPrkruvPPOQmoHAAAAYMWEqADaSXnU+KJLAAC6ofLYSZGVs6LLAMh95Stfidra2jjvvPPi0ksvjaFDhzZNeBo/fnxce+218b3vfS8/5owzzoiOYvLkyfG5z30uHn/88dhll13ihhtuiD59+qz049OEqg022CAmTJjQpnUCAAAAsPqEqADaQXn6rMhmzC66DACgO1pUF9nkd4quAiD3vve9Ly655JKoqqqKq666Kk4//fT863/9619x9NFH5/elQFWa3LTHHntER/Daa6/FSSedFCNHjoz3v//9ceONN8bmm2/e4haETz31VL5935LK5XLU1dUtNrkKAAAAgI5FiAqgHWTjJxddAgDQjZVH29IP6DiOOeaYuOuuu+Lkk0/OJzSlYFGPHj2ib9++ccIJJ8Stt94an/nMZ6IjSNsKfvGLX4x33nknTjzxxLjmmmuiZ8+eLR77s5/9LD/20UcfXeq+F198MQ9R7b333u1QNQAAAACro3a1HgXAKilPmFJ0CQBAN1YeOzHiQ/2LLgOgybbbbptPm+rI6uvr41vf+lbMnDkzD3X9+Mc/zqdmLctxxx2XB6guu+yy+MAHPtAUtkqP/9GPfpR/nSZvAQAAANAxCVEBtINs4ttFlwAAdGPlMSZRASzPiBEjYtCgQdGvX7/45Cc/md92++23x6hRo/IpWRtvvHFcfvnlLT72C1/4Qmy44YZx/PHHx8CBA+PBBx/Mp20dddRReRDrkUceialTp8YZZ5wRRxxxRDufGQAAAAArS4gKoB2UJwlRAQAFmjs/yu/MiOrNNy66EqCbSVvcra409el73/tetFeIKoWkDjrooKYQVWVbvoaGhrj66quX+dgUnkohqurq6nwK1V//+te47bbb4pZbbona2trYbbfd4r//+7/j2GOPbZdzAQAAAGD1VGVZlq3mYwFYCeVpM6P+//2+6DIAgG6u9qSjo/aQfYouA+hmdt111zwMtTrtp/S4FG4CAOiMDrzjF0WXQBsYdsK3iy4BAGhDJlEBtDFb+QEAHUE2fkqEEBXQzs4555yiSwAAAACAlSJEBdDGyhOmFF0CAECUJ08tugSgGxKiAgAAAKCzqC66AICuziQqAKAjyKZMXa3ttAAAAAAAoDswiQqgjZWFqACAjqCuIbIZs6Nqkw2LrgToRq677rr8+sQTT4xevXo1fb+yTjvttDaqDAAAAAAWJ0QF0IbK02dFLFxUdBkAALnsrakRQlRAO/p//+//RVVVVXzwgx/MQ1SV71ckTc5LxwlRAQAAANBehKgA2pCt/ACAjiR7652Ivd5XdBlAN3LCCSfkYagUoGr+PQAAAAB0NEJUAG2oPGFK0SUAADQpT55adAlAN3PxxRcv93sAAAAA6Ciqiy4AoCsziQoA6EgyISoAAAAAAGiREBVAGypPNIkKAOg4smmzIqtvKLoMoBs58cQT47e//W3RZQAAAADACglRAbSR8ozZEQsWFV0GAMC/ZVlkU6YVXQXQjYwYMSLeeuutFu/7/ve/H7fccku71wQAAAAALRGiAmgjtvIDADqi8lu29AM6httvvz2GDRtWdBkAAAAAkBOiAmgj2dumPAAAHU82WYgKAAAAAACWJEQF0EayabOKLgEAYClCVAAAAAAAsDQhKoA2UhaiAgA6oLIQFQAAAAAALEWICqCNZNNnFl0CAMDS5i+MbPbcoqsAAAAAAIAORYgKoA1k9Q0Rc+YXXQYAQIvKk6cVXQIAAAAAAHQoQlQAbSCzlR8A0IFlb08vugQAAAAAAOhQaosuAKAryqYLUQEAHVc2c3bRJQDdyKBBg2LAgAFL3V5VVbXM+5rfDwAAAADtQYgKoA1k02YWXQIAwDJlM+cUXQLQjSxYsCC/rOp9KUQFAAAAAO1FiAqgDZhEBQB0ZEJUQHu57rrrii4BAAAAAFaKEBVAGzCJCgDoyLJZc4suAegmDjrooKJLAAAAAICVUr1yhwGwKrJpJlEBAB3YvAWR1TcUXQUAAAAAAHQYQlQArSwrlSKbZYscAKBj8/9XAAAAAADg34SoAFpZNmN2RDkrugwAgOXKZtrSDwAAAAAAKoSoAFqZrfwAgM4gmzm76BIAAAAAAKDDEKICaGVCVABAZ5DNtJ0fAAAAAABUCFEBtLJs+syiSwAAWCEhKgAAAAAA+DchKoBWls2cW3QJAAArJkQFFOCaa66JwYMHF10GAAAAACyldumbAFgT2fwFRZcAALBC2SzBb6D9/elPf4qNN944Dj/88KJLAQAAAIDFmEQF0NrmCVEBAJ0jRJWVs6LLALqZhQsXxk477VR0GQAAAACwFCEqgFaWzV9YdAkAACtWKkXMnVd0FUA3c+ihh8bQoUNj2rRpRZcCAAAAAIuxnR9AK8qnOSxcVHQZAAArJZs5N6p69yq6DKAb+da3vhXnn39+fOxjH4tjjz02dtttt9hoo42iqqqqxeMHDBjQ7jUCAAAA0D0JUQG0phSgsi0OANBJZLPmRETfossAupETTzwxD0xlWRY33XTTCo8fMWJEu9QFAAAAAEJUAK0om7+g6BIAAFZatsA2xED7OvDAA4suAQAAAABaJEQF0Jrm+4dIAKATWVhfdAVAN3P99dcXXQIAAAAAtKi65ZsBWB2ZEBUA0Ilki+qKLgEAAAAAADoEk6gAWlE2z3Z+AEAnIkQFFKS+vj7uuuuuGDJkSEyePDnf5u/cc8+NG264Ifbcc8/Yd999iy4RAAAAgG5GiAqgNZlEBQB0ItlCISqg/b300kvxzW9+M6ZMmRJZlkVVVVX069cvv+/WW2+Nn/70p/Htb387vvSlLxVdKgAAAADdiO38AFqR7fwAgE7FJCqgnaWpU//5n/+ZX3/wgx+M//3f/82DVBWHHnpo1NTUxC9+8YsYPnx4obUCAAAA0L0IUQG0IiEqAKAzyYSogHZ21VVXxezZs+MHP/hBXHPNNXHKKacsdv93v/vd+PWvf50Hq6699trC6oSWpC0nP/ShD63SY2bOnBk/+clPYsCAAbH33nvHRz/60fj9738fjY2NLR7/7LPP5lPYDj744Nh///3j9NNPj6FDh7bSGQAAAHRfPtOxMoSoAFrT/AVFVwAAsPJs5we0s8ceeyx23HHH+MIXvrDMYz7ykY/EbrvtFiNGjGjX2mB5Lr/88rjvvvtW6TFz5szJ3+s33HBD7LHHHnHaaafFuuuuG7/85S/j/PPPX+r4wYMH58en9/7HP/7x+MQnPhGvvvpq3nQfNGhQK54NAABA9+IzHSurdqWPBGCFsnkmUQEAnYdJVEB7mzp1ahxxxBErPG7rrbeO0aNHt0tNsDx1dXXx4x//OG655ZZVfuwVV1wRo0aNigsvvLBp6tp5552Xr36+//77Y+DAgXloMKmvr88ntPXs2TNuu+222HLLLfPbzzjjjPjMZz4TF110URx22GF5wx4AAICV4zMdq8okKoDWZDs/AKAzMYkKaGcbbLBBvPXWWys8buLEidGrV692qQmW5aGHHopjjjkmb7Yffvjhq/TYRYsWxd///ve8cX7yySc33V5TUxPf+c538q//9re/Nd1+77335iHDdGyl2Z5ss802ceqpp+b3WbkMAACw8nymY3UIUQG0omyBEBUA0InU1UeWZUVXAXQj++23X7zyyivx3HPPLfOYoUOH5uPu991333atDZZ06623xvz58/NVx1dfffUqPfbFF1+MBQsWxEEHHRTV1dVLTVrbaqutYtiwYVEqlfLbnn766fz6kEMOWeq5Dj300Px6yJAha3A2AAAA3YvPdKwOISqA1tTw7v9QAgB0CilAVVdfdBVAN5LG2Kfw5llnnZU3MydNmtR0Xxp9n0bhn3/++VFVVRVf+MIXCq0VTj/99HjwwQfzbRvSe3JVjBs3rmnVcUtS0z2959PUtWTs2LH59bbbbtviscmYMWNW+RwAAAC6K5/pWB21q/UoAFpWFqICADrhln7rrF10FUA30b9///jud78bP//5z+OHP/xhfltqZKax9/fcc0+Uy+U8ZHX22We3uHoT2tPBBx+82o+dNWtWfr3hhhu2eH9lu8o5c+Ysdnzv3r2XOrZnz5759dy5c1e7HgAAgO7GZzpWhxAVQCvJt8Ip2w4HAOhcskV1sWrrsADWzBe/+MXYY4894ve//30++n7hwoXR2NgYPXr0yENWZ555Zhx++OFFlwlrJK1ITtZaa60W76/cXldXl183NDQs8/gljwUAAKBt+UzXfQlRAbSWUrnoCgAAVm8SFUA7O/DAA/NLmjyVVmum67S6s7ZWq4quYZ111lm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" ] @@ -893,13 +932,13 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 12, "id": "f8c74802", "metadata": {}, "outputs": [ { "data": { - "image/png": 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3rX///i4Ir+rpDRo0sLPOOssuvfRSa9SokZUH0TXrLfTDRAv/+LvZ+o1+NwdIna3ZroCRLlatimXs08Uy99/DAnVq+t0yAACAHYosWmbhnya7SaT001GmrN9o4TET3CXQoI4Lq2fs1dkCtWv43TIAKRaIRqPRVL8oAPgpMmehhb7/zSK/zzALhf1uDrDTBOrXtoz9ulrG3p0tUK2K380BgHJry/WP+N0EAACAtFLpiZv8bgIAlEs6Reiqpn830SJTZ5tFOGWIciIQsGCHlpax/54W3K2VBYIUfwEAAKVHNDvHwr9Ms/APEy26cKnfzQF2bj99t5aW0aOrBTu2tkA5WtUMKMsIqQMoE6Kbt1j45ykW/uE3iy79y+/mAP7KzLDg7u0ss8ceFmzdzO/WAEC5Q0gdAAAgOYTUAWDnim7aYuHxk7aFXlas9rs5gK8CdWu5EEzGvrtboGplv5sDAADKsciSFa6PHp4w1WzLVr+bA/irVnXL3Hd3y9hvdwvUrO53awAUAyF1AGktsmS5hcf+YuFfp5ll5/jdHKDUCTSqZxkH7GkZe3WyQMUKfjcHAMoFQuoAAADJIaQOADtHZOFSC3/367bx9JyQ380BSpfMTAvu0d4yVV29xS5+twYAAJQT0XDYIr/PtNC3v1p07iK/mwOUPsGABTu1tYwD97SMts39bg2AIiCkDiDtRMMRi0xSJ/0Xi86hkw4kpFJFy9i7s+u4B+vX8bs1AFCmEVIHAABIDiF1ACg50ZyQC6UrnB5duNTv5gBpIdC0oWXsv4dldOtogQpZfjcHAACUQdF1Gyz8w28W+uE3s3Ub/G4OkD5FGg/sZhndO1KkEUgjhNQBpI3oxs0W/v5XC30/0WwtnXSgSAJmwfYtLePgvSxjt5Z+twYAyiRC6gAAAMkhpA4AqRf5a7WFv59o4fGTzDZt8bs5QHqqXNEy9upsGQfsYcEGdf1uDQAAKAMiC/+00Dc/W+S3mWbhsN/NAdK3n75PF8s8qLsF6tT0uzUAdoCQOoBSL7pmvYVGj7fwj7+bZef43RygzAg0aWCZvfaz4O7tLRAM+N0cACgzCKkDAAAkh5A6AKROeOZ8C48eb5EZc804AwikTLBtc8s4ZG/L6NDK76YAAIB07ad/9aNFZs33uylA2REMWrBbB8v8x74WbFTP79YAKAAhdQClVmTFKguPGmfhn6cygxQoQYH6tS3j0H1cRZhAZobfzQGAtEdIHQAAIDmE1AGg+MLT51po5PcWnbvY76YAZVpg18aWeVgPy+jUxu+mAACAUk6RvMikWRYaNc6iC/70uzlA2RUwC3ZqY5m9eliweWO/WwMgH0LqAEqdyKJlFtIM0t9nqtfud3OA8qNmNcvsubdl9OhqgYoV/G4NAKQtQuoAAADJIaQOAEUXnvqHhb74ntALsJMFmja0zMP3d2GYQICVSgEAwP9EwxGL/DJ1Wzh92Uq/mwOUK8G2u1pGr/0so10Lv5sC4G+E1AGUGpHZCyz01bhty5AC8E/VypZxwJ6WeVB3C1St7HdrACDtEFIHAABIDiF1AEheePIsC438waILl/rdFKBcCzRpYJm9e1hw93aE1QEAKOei2TkWHjfJQqPHm61e53dzgHIt0KyRZfbaz4Jd2tJPB3xGSB2A/8sbqdLLVz9adN4Sv5sDIFaFLFdVXdXVA7Wq+90aAEgbhNQBAACSQ0gdAJIYT5+kcPr3Fl283O/mAIgRaFTPMg/rYcGuu1kgSAgGAIDyJLp5q4W/+8VCYyaYbdjkd3MAxAg0qGMZ/9jXMrp3tEBGht/NAcolQuoAfBOeNsdCn45hMB0o7TIytoXVD+thgepV/W4NAJR6hNQBAACSQ0gdAAoXjUQt8vuMbZXT/1zhd3MAFCLQsK5l9t7Pgnt2sEAw6HdzAABACYpuzbbwmJ8t9PVPZlu2+t0cAIWpXcOyjjzQgt07MakU2MkIqQPY6SLzl1jOiG8s+sdCv5sCIBkVsyyj596WecjeFqhU0e/WAECpRUgdAAAgOYTUAaCQcPrEadvC6ctW+t0cAEkI1K9tmb17WFAVGwmrAwBQpkTDYQv/+LuFvvjebP1Gv5sDINkVkI452DI6tfG7KUC5QUgdwE4TWbbSVU7XcqQA0li1Kq4STMb+e1ogk+WQACA/QuoAAADJIaQOAHlFIxGL/DLNQl/+YNHlq/xuDoBiCNStZRkaT9+rswUyCKsDAJDOFLGLTJxuoU/HWnTlGr+bA6AYAi2bWNY/e1qwZVO/mwKUeYTUAZS46Jr1Fvr8Wwv/NNkswj85QFkRqFPTMrUcUjdVgmE5JADwEFIHAABIDiF1APif8NQ/LDR8NJXTgbJYWf2fh1hGl7Z+NwUAABRBePpcV5QxumiZ300BkELBjq1dZfVg4/p+NwUoswipAygx0Y2bLfTVjxb+9lezUMjv5gAoIYHG9bcth9Sxtd9NAYBSgZA6AABAcgipA4BZ5M8VFvr4a4vMmOd3UwCUoGCbXS3zuEMt2LSh300BAAAJiMz/00KffGOR2Qv8bgqAkhIIWLB7R8s68kBXrBFAahFSB5By0ewcC4+ZYKGvx5lt3up3cwDsJIFWTbcth9Siid9NAQBfEVIHAABIDiF1AOVZdP1GC3021sLjJ7ESKVBeBAKWsVcnyzz6IAvUrO53awAAQByR5Sst9OlYi/w+0++mANhZMjMsY/89LLN3DwtUq+J3a4Ayg5A6gJQK/zLVcoaPNlu7we+mAPBJsGt7yzruUAvUruF3UwDAF4TUAQAAkkNIHUB5FM0JWfibnyz01Tizrdl+NweAHypkWeah+1jGoftYoEKW360BAADqp2/eum0S6fcTzSIRv5sDwA+VKljm4QdYxkHdLZAR9Ls1QNojpA4gJSJL/7LQByMt8sdCv5sCoLQMrvfuYRmH7G2BzAy/WwMAOxUhdQAAgOQQUgdQ3oR/m2E5H39ttnqd300BUBrUrGZZxx5iGd06+t0SAADKLcXnwj9NttCIb8w2bPK7OQBKgUCjepZ1Ym8LttnV76YAaY2QOoBiiW7ZaqHPv7Pw2F+YRQpgO4H6tS3zxN6W0b6l300BgJ2GkDoAAEByCKkDKC8iy1Za6MMvLTJrvt9NAVAKBVo3s6x/9bbgLvX9bgoAAOVKZPEyy/ngS4vOW+x3UwCUQsE9O1jW8YdaoEY1v5sCpCVC6gCKLDxhquUMH222boPfTQFQygV3b2dZx//DArVr+N0UAChxhNQBAACSQ0gdQLkp9vLtL2Zhir0AKEQwYBn772mZRx1ogcqV/G4NAABlWnTzFgt99q2Fv//VLEJ8DkAhKlawzCP2t4yD9rJARtDv1gBphZA6gKRFlv5loQ9GWuSPhX43BUA6qZBlmb17WMYhe1sgM8Pv1gBAiSGkDgAAkBxC6gDKKp2Ci/w8xXJGfGO2fqPfzQGQTqpVscyjD7aMfbtYIBDwuzUAAJS5fnr4p8kWUj99wya/mwMgjQQa1bPME3tbRptd/W4KkDYIqQNIvtrL2F/MIlR7AVA0gfq1t3Xa27f0uykAUCIIqQMAACSHkDqAsiiyZIXlDPnCovMW+90UAGks0KyRZZ1yhAWbNvS7KQAAlAmRxcss54Mv6acDKJbgnh0s67hDLFCzut9NAUo9QuoAEhL+dZrlDBtlto5qLwBSI9ilnWX9q5cFatFpB1C2EFIHAABIDiF1AGVJNByx8KgfLfTFD2bhsN/NAVAWBIOW0Wtfyzx8fwtksEopAABFLsr46VgLf/+rWYSoHIAUqFjBMo/Y3zIO3ssCwaDfrQFKLULqAAoVXbfBct7/wiJTZvvdFABlUaWKlnn8oZa57+5+twQAUoaQOgAAQHIIqQMoKyJ/rrCcdz+16KJlfjcFQBkUaFzfss44mqrqAAAkKTxjruUM+q/ZmvV+NwVAGRTYtfG2fnrDun43BSiVCKkDKFB4whTL+egrs01b/G4KgDIuuFsryzr1CKqqAygTCKkDAAAkh5A6gHQXjah6+jgLff491dMBlKwMVVXfzzIP60FVdQAAEqmePuxrC4/73e+mACjrMjMs84gDLePQvamqDuRDSB3AdqLrN1rO+59bZDLV0wHs5KrqJ/zDMvfp4ndLAKBYCKkDAAAkh5A6gHQWWfrXturpC5f63RQA5UhglwbbqjU2aeB3UwAAKJXCM+ZZzqDPqJ4OYKeiqjqwPULqAPIIT5hqOR99SfV0AL4JdmhlWadQVR1A+iKkDgAAkBxC6gDStnr61+Mt9Pl3ZiGqpwPwQUbQMnv3sIze+1FVHQCA2OrpH39t4R+png7AJ5mZlnnkAZZxCFXVASGkDiCmevoXFpk8y++mAIBZ5YqWeTxV1QGkJ0LqAAAAySGkDiDdRJat3FY9fcGffjcFACzQ5O+q6rtQVR0AUL656umD/2u2ep3fTQEAqqoDfyOkDsDCv6h6+ldmGzf73RQA2L6q+qlHWKAmVdUBpA9C6gAAAMkhpA4gvaqn/2Shz7+lejqA0ldV/bD9LaOXqqpTrREAUB6rp4+28I+/+d0UACigqvo+FggG/G4N4AtC6kB5r54+5AuLTKJ6OoBSrHJFyzqhl2Xs3dnvlgBAQgipAwAAJIeQOoB0QPV0AOkg0LShZZ2uqur1/W4KAAA7RXjmfMsZ9BnV0wGUaoHmu1jWGUdZsAFV1VH+EFIHyqnwrPmW8/YIs/Ub/W4KACQkuGcHyzrlcAtUquh3UwCgUITUAQAAkkNIHUBpFo1ELTx6vIX++51ZKOR3cwBgxzIyLPPwHpbxD6qqAwDKrmg4YqHPxlr463FmJN8ApIMKWZZ10mEUaES5k+l3AwDs/OVIQ59/Z+EvfzRjjgqANBL5dZplL/zTss493oJNG/rdHAAAAAAAAJRx0XUbLPut4Rb9Y6HfTQGAxIXDFvrsWwtPnWMVzjveArWq+90iAABSKrp63bZ++rzFfjcFABKXneNWaFNhWYXVAxUr+N0iYKegkjpQjkTXrrfst0cwoA4gvWVmWOaxh1jmQd39bgkAxEUldQAAgORQSR1AaRSZvcAFX1iNFEBaq1rZss7+p2W0b+l3SwAASInw5FmW895nZpu2+N0UACiyQIM6lnXucRbcpYHfTQFKHCF1oJwIT5tjOe98YrZxs99NAYCUCHZpa1mnH2WBypX8bgoA5EFIHQAAIDmE1AGUJjptFh413kKfjTGLcAoNQBkQCFjm4ftbxmH7WyAY8Ls1AAAUSTQUttDw0RYeO8HvpgBAamRmWuYJh1rm/nv63RKgRGWW7MsD8Fs0HLHQp2MsPHq8GePpAMqQyKRZlr14uWWdc6wFm+/id3MAAAAAAACQ5qKbt1jOO59aZMpsv5sCAKkTjVro8+8sMm+Jq6oeqFrZ7xYBAJCUyMo1ljPgY4suXOp3UwAgdUIhCw0Z6VZyyzr1SAtUquh3i4ASQSV1oAyLrlrrliONzl/id1MAoORkBC3zqIMs49B9LBCgCgwA/1FJHQAAIDlUUgdQGkQWLbOcN4dZdOUav5sCACWnVnWrcN7xFH4BAKSN8MTpljP4v2Zbsv1uCgCUmEDdWpZ17rEWbNbY76YAKUdIHSijwr/PtJxBn5lt3up3UwBgpwju1sqyzjzaAtWq+N0UAOUcIXUAAIDkEFIH4LfQD79Z6KOvXBUzACjzMjIs87hDLfOgbn63BACAAkVzQq6PHv7xN7+bAgA7r5/+z4Mts+fefrcESClC6kAZEw1HLDT8awuPmeB3UwBg56tZzSqcd4IFW1AFBtgZhg4dagMGDLC5c+dapUqV7IADDrDrrrvOmjRpktDzp02bZs8++6xNmDDBNm7c6J537LHH2qWXXmoVKlSwdEVIHQAAIDmE1AH4JZqdYzkfjLTIT5P9bgoA7HTBPXezrFOPtEDF9B2HAwCUTZGVayzntY8s+ucKv5sCADtdsHMbyzrzGAtUquh3U4CUIKQOlCHRjZstZ8DHFpk13++mAIB/MjMs8+TDLXOfLn63BCjTnnzySXvxxRetTZs2dsghh9iff/5p//3vf61GjRr2/vvvW7NmzQp9/sSJE+3cc8+1nJwcO/zww61x48b23Xff2cyZM22//faz1157zTIyMiwdEVIHAABIDiF1AH6IrFhlOW8MI/gCoFwLNKhjWeefYMFG9fxuCgAATnjWfJd7sY2b/W4KAPgm0LCuZV10ogXr1fa7KUCxEVIHyojI0r8s59UPLbpyjd9NAYBSIaPnXpZ57CEWCAb9bgpQ5kyfPt2OP/546969u73xxhu5Vc9Hjhxpffv2tUMPPdQF2Atz+umn26+//mrPPPOMHXHEEe53oVDIVVFXWP2RRx5x75GOCKkDAAAkh5A6gJ0t/NsMyxn0mdmWbL+bAgD+q5BlWaccYRndO/rdEgBAORca+4uFho0yi0T8bgoA+K9KJcs693jLaNfc75YAxUJqCygDwlNmW/bTbxNQB4AY4W9+tpyXP7Do5i1+NwUocwYMGOCur7zyytyAuhx22GG299572+jRo23ZsmWFvsakSZOsZs2auQF1yczMtFNOOcXdVoAdAAAAAIBUioYjljNslOW8OYyAOgB4snMsZ+AIyxnyhUVDYb9bAwAoh6LhsOUM/txCH31JQB0APJu2WM5L71tozAS/WwIUCyF1IM2FvvzRcl77yGwrA+oAkF9kxlzLfuotiyxf6XdTgDLlxx9/dIFyBdLz22+//UyLNekxhalVq5Zt2LDB1q5dm+f3y5cvd9d16tRJcasBAAAAAOVZdMMmy37+PVfYAACwvfD3Ey372YEWXbfB76YAAMpbP/2FQRb+8Te/mwIApU8kYqGhX1nOoP8yoRRpi5A6kKai2TmW/dZwC306xiwa9bs5AFBqRVestuyn3rbwtDl+NwUoE7Kzs23JkiXWqFGjPFXUPc2aNXPXc+YU/jd35plnWjgctuuuu87++OMP27Rpk3355Zf23HPPuQD7ySefXGLbAAAAAAAoXyIrVm1bjXTuIr+bAgClWnThUtv69NsWWfqX300BAJQDkcXLbeuTAyw6h346ABQmPO53N6FHE3uAdJPpdwMAJC+6Zr1lv/6RGygCACRgy1bLeeUDi/6zp2Ueuo/frQHSmiqfq1J6zZo1495fvXp1d71+/fpCX+fKK690r/HQQw/Z0Ucfnfv7Nm3a2PPPP2+77LJLilsOAAAAACiPIvMWW/arH5pt3Ox3UwAgPaxe5yqqZ13wL8tos6vfrQEAlFHh32dazjufmGXn+N0UAEgLmniviT0VLjzRgk0a+N0cIGFUUgfSTGTekm0zSQmoA0ByolELDR9t2e98YtFQyO/WAGkrJ2fbYGG8Kuqxv9+6dWuhr/Pjjz/aSy+9ZJmZmXbsscfa+eefb127drXZs2fb7bffbmvWrCmB1gMAAAAAylvwRZXGCKgDQJI2b7Wc/u9b+JepfrcEAFDGqBBSzn+/tZw3hxJQB4AiTigN/zbD75YACaOSOpBGwhOmWM6g/5qFwn43BQDSVuTnKZa9fJVVuOhEC1Sv6ndzgLRTqVKlPGH1/LKzs911lSpVCnyNpUuX2mWXXeZea+jQodaiRYvc+5599lnr16+f3Xzzzda/f/+Utx8AAAAAUD6ExvxsoWFfu8IFAIAiCIctZ+AIi65eZ5m99vO7NQCAMiCaE3LV0yOEKwGg6LJzLGfAMIsefoBlHnGA360BdohK6kCaCH09fttSRwTUAaDYogv+tOx+71hkJZWagWRVq1bNgsGgrV+/Pu793u+rV69e4GsMGzbMtmzZYhdddFGegLr07dvXmjdvbqNHj7bly5enuPUAAAAAgHJRmXHoVxYaOoqAOgAUV9Qs9MkYyxnyhUUjEb9bAwBIY9HNWyy7/2AC6gCQqn76599ZzuDP6aej1COkDqTDgPrHX1to+Gj3HwwAIDWiK1Zb9jMDLbKYECyQjAoVKlizZs1syZIlcaupL1y40F23adOmwNdYvHhxgY8JBAK5v9d7AAAAAACQVGXGAR9beMwEv5sCAGVK+PuJlvPaRxbdum0VRQAAkhFdu96y+71r0TmL/G4KAJQp4R9/s5w3h7nxEKC0IqQOlGLRcMRy3v3UwqN/8rspAFA2rd9o2c+9Y+HZC/xuCZBW9tlnHxdQ/+WXX7a774cffnBB827duhX4/Pr167vruXPnxr1//vz5eR4HAAAAAMCORDdutuwXB1GZEQBKSGTqH5b9/HsWXb/R76YAANJIZPlK2/rMQIv+ucLvpgBAmRSZNMutVBHdvNXvpgBxEVIHSilVIsh59QOL/DzF76YAQNm2JdtyXnrfwpzABBJ20kknuesnn3zStmzZkvv7kSNH2s8//2z/+Mc/rFGjRgU+/6ijjrJgMGivvfZabuV1z4ABA2z27NnWvXt3a9KkSQluBQAAAACgrIisXGPZz7xt0bnbVu4CAJSM6MKllv302y5wCADAjkTm/2nZz75jtnqd300BgDJNK1Vk93vHous2+N0UYDuBaDQa3f7XAHyv+PLyEIsu+NPvpgBA+REIWOaJvS3zgD39bgmQFu677z4bOHCgtWjRwnr16mXLli2zzz77zGrXrm3vvfeeNWvWzD1u3LhxNn78eOvQoYP17t079/mvv/66PfTQQ1alShU7/PDDrU6dOjZ58mT3WFVQf/vtt91rp6Mt1z/idxMAAADSSqUnbvK7CQDSPfjy6gdmGzb53RQAKD+qVLIKF51owZZN/W4JAKCUCk+bYzlvDjPLzvG7KQBQbgTq1LSsS0+xYIM6fjcFyEVIHShloqvXbVuCY/kqv5sCAOVSxuH7W9aRB/rdDKDU02GEQuqDBg2yefPmWa1atWzfffe1a665JjegLs8++6z169fP/vWvf7lQeqzvvvvOVVP//fffbfPmzdagQQM79NBDrU+fPi6onq4IqQMAACSHkDqAogpPnmU5b48g+AIAfsjMtKwzj7aMPXbzuyUAgFIm/PMUyxn0mVk44ndTAKD8qVrZKlxysgV3bex3SwCHkDpQikT+XGHZL71vtpalNwDATxk9ulrmSYdZIBj0uykA0hAhdQAAgOQQUgdQFKHvf7XQh1+aRTjNBQC+CZhlHneoZfbc2++WAABKidDo8RYaPtqMbjoA+KdClmWdf4Jl7NbS75YARvIKKCUicxZZdr93CKgDQCkQ/uE3t/xcNBTyuykAAAAAAADIJzR2goWGjCSgDgB+i5qFhn1toa/G+d0SAIDPVCM15+OvLfQxAXUA8F12juW8+oGFJ0zxuyUAIXWgNAjPmGvZ/Qebbd7qd1MAAH+LTJplOS8PsSjLRQMAAAAAAJSugPpHX/ndDABAjNAn3xBUB4ByLBqJWmjw5xYe/ZPfTQEAeMIRy3nnEwt996vfLUE5R0gd8Fl42hzLefVDsxyq9QJAaROZtcCyFVTfmu13UwAAAAAAAMo9AuoAUHoRVAeAchxQH/SZhcf97ndTAADxVj76YKQbTwH8Qkgd8FF4ymzLee0js1DY76YAAAoQ/WOhZb80xKJbWO0CAAAAAADALwTUAaD0I6gOAOVLNBKxnHc/sfBPk/1uCgCgEBpPCX3DahfwByF1wCfhSbMs542hZmEC6gBQ2kXnLrLsl94nqA4AAAAAAOADAuoAkD4IqgNAOQqoDxxhkQlT/W4KACABoWFfW2gU/XTsfITUAR+Ef5thOW8OMwtH/G4KACBB0XlLLPvFwRbdTFAdAAAAAABgZyGgDgDph6A6AJRt0XDEct4abpFfp/vdFABAEkIjvrHQlz/63QyUM4TUgZ0s/PtM11m3CAF1AEg30QV/Wnb/wVRUBwAAAAAA2AkIqANA+iKoDgBlvIL6bzP8bgoAoAhCn46hojp2KkLqwE4UnjzLct76mIA6AKR7UP3/2bsPOLnrOn/875nd9B4SElLoEHrv2MFD0Tt7xRPb6Z2HnuX08P5X9Kd35+kpd7YTCyqKB4JUadJCh5AChEBIJ733trsz8/0/ZkIi6W13P1Oez8djH5OdmZ15EVj2szOvz/vzkxsia2lNHQUAAACgbimoA9Q+RXWA+pKVsmj77e1RetoEdYCan6g++qnUMWgQSurQSYrPT4+2X90aUVRQB6h12ax50fpTRXUAAACAjqCgDlA/FNUB6qigfu0dURr/QuooALSDwq0PROGhcalj0ACU1KETFF+cGW2/vDmiWEwdBYB2ks2YG20/+31krW2powAAAADUDQV1gPqjqA5Q27Isi8Lv7orS2EmpowDQjgo33xeFRyekjkGdU1KHDlaaMSfarropoqCgDlBvStPnRNsvborMJiQAAACA/aagDlC/FNUBalfhxnujOGZi6hgAdIDCjfdEwf/j6UBK6tCBSvOXROvPb4xoK6SOAkAHKb04K9r+747KBAEAAAAA9k3h4fEK6gCNUFS/X1EdoJYU/vhYFE3ZBahfWUThd3dH8fnpqZNQp5TUoYNky1dF60+uj9jQkjoKAB2sNP6FKNz6QOoYAAAAADWp+PTkKNx8b+oYAHSCwh8ejOJTz6WOAcAeKDz+TBTueiR1DAA6WqkUbVffGqVZ81MnoQ4pqUMHyNau31RQX702dRQAOknxwbEmwAAAAADspdKMudH229srk7sAaAxtv7srilNmpY4BwC4UJ06Nwu//mDoGAJ2ltS1af3ZDlBYtS52EOqOkDu0sa2mN1p/9PrLFy1NHASDBUaUmwAAAAADsmfIbn61X3RhRKKaOAkBnKpai7Zc3R2n+4tRJANiB0ow50fbr2yJKdpICNJT1G6P1yt9FtnJN6iTUESV1aEdZsRhtv7wlstkLUkcBIIUsou26u6L4/PTUSQAAAACqWrZmXbT99IbKG6AANKCNrdH60xsUYACqTGn+kmj9eXkjaSF1FABSWLkmWn9yfWRer6GdKKlDO8myLNquvTNKL85MHQWAlEqlaLv61ijNmp86CQAAAEB1n0i6fFXqKACktGrtpqL6xpbUSQAor9OXr6oUE2OD/y8DNLJs4dJo/fnvI2ttSx2FOqCkDu2kcOsDURr3fOoYAFSD1rZo/dkNlSOrAQAAAPiTrLzB/9e3RjZnYeooAFSBbMGSaPvlzZUTqwFIJ1u7flNBffXa1FEAqALZzHmbXr8plVJHocYpqUM7KNz/ZBQfHJs6BgDVZP3GaL3yd44qBQAAAHiFwu/vidLzM1LHAKCKlKa8FG3X3ZU6BkDD2nLS0eLlqaMAUEVKk6ZH4Xd3p45BjVNSh/1UHDspCrc/mDoGANVo5ZrKxIFsw8bUSQAAAACSK9z3RBQffyZ1DACqUGnspGi78+HUMQAaTlYsRduvbols9oLUUQCoQsUxE6PtDut09p2SOuyH0qx50fa7uyKy1EkAqFbZwqXR9uvbHIEEAAAANLTiuOejcMdDqWMAUMWK9zwehSeeTR0DoKEUbrk/SpNnpo4BQBUr3vt4FMdNSh2DGqWkDvsoK0/H/cXNEYVi6igAVLnyCzuF20anjgEAAACQRHHa7Gi79k4DXwDYrcINf4ziCzNSxwBoCIUnnoniI+NTxwCgBrRdd3eUXnLqBntPSR32QdbaFq1X3RixZl3qKADUiOKDY6MwZmLqGAAAAACdqlQ+Ze4XN0UUDXwBYA+UStF29S1RmrsodRKAulaaMTcKv783dQwAakWhEK2/uCmyVWtSJ6HGKKnDPmi77s7IvDACwD5MgCnNmpc6BgAAAECnyFavjdaf3hCxoSV1FABqSUtbtP7shshWrE6dBKAulf//2vrLm20kBWDvlF/n+cXNkbUVUiehhiipw14q3Pt4lCZMTh0DgFpUKG5asK+0sxQAAABogBNJywV1BUMA9sXqddH6k+sj22ijE0C7r9OvujFi7frUUQCoQdnsBdH2u7tSx6CGKKnDXig+NzUKdz6cOgYAtWzNusoLP+UXgAAAAADqVeH390Q2b3HqGADUsGzRMgUYgHaUZVm0/d8d1ukA7JfSuOejcP+TqWNQI5TUYQ+VFiyJtmtuj8hSJwGg1mVzF0XbdXemjgEAAADQIQpPPBPFp55LHQOAOlB6+sUoPDQudQyAulD842NReubF1DEAqAOF2x+K4vPTU8egBiipwx7I1m2ItqtuimhpTR0FgDpRmjA5Cvc+njoGAAAAQLsqzV0UhRvvSx0DgDpSuO2BKM2anzoGQE0rPjslCn98NHUMAOpF+XSO39wWpUXLUiehyimpw25kxVK0/eqWyJatTB0FgDpTuPPhKD43NXUMAAAAgHaRbdhYeT09CoXUUQCoJ8VStF59S2Rr16dOAlCTSvMXR9tvb4/IUicBoK5sbI22n/8+svUbUyehiimpw24Ubrk/StNmp44BQD3KItquuT1KC5emTgIAAACwX7LyBK3/u8PAFwA6xso10XbNHyIraVgC7I1s3YZou+qmiNa21FEAqEPZ0pXRdvWt1unslJI67ELxmRej+Mj41DEAqGctrZsW7F4YAgAAAGpY8YExUXpuWuoYANSx0ouzovDHR1PHAKgpbdfeEdnyValjAFDHSlNmRfHex1PHoEopqcNOlJatjLbr7kodA4AGkC1cGoWb7ksdAwAAAGCflKbPicIdD6WOAUADKN7zeBQnz0wdA6AmFB58KkqTpqeOAUADKG8mLb8+BNtSUocdyIrFaPv1bREbW1JHAaBBFJ98Norjn08dAwAAAGCvZKvXRuvVt0Y41hmAzpBl0XbNHyJbsTp1EoCqVpq9IAp/eDB1DAAaRSmL1t/cFtna9amTUGWU1GEHygv1bPaC1DEAaDBt1/8xSktWpI4BAAAAsEeyUilaywNf1qxLHQWARrJuQ2WDVHnwGADbyza0bBrMWCyljgJAI1m1Ntr+747IMoMM+BMlddhGcdK0KD40NnUMABpRS2u0XX1LZIVC6iQAAAAAu1W44+HIHOUMQALZS/OjcMsDqWMAVKW2390V2bKVqWMA0IBKL8yI4gNjUsegiiipwytkK9dE27V3RtjMA0Ai2bzFUbh1dOoYAAAAALsf+PLAk6ljANDAio+Mj+KEF1LHAKgqhccmROmZF1PHAKCBFe58OEovzU8dgyqhpA6vPJb0N7dVjocDgOQvrE+cmjoGAAAAwA6Vlq2Mtt/ebuALAFUxLbi0aFnqGABVoTR/cRRudsoEAIkVS9H269si27AxdRKqgJI6vKxw16ORzZibOgYAVJRP9siWr0odAwAAAGArWaEQbb+6JWJDS+ooABDR0lb5uZS1tKZOApBU+f+DbVffGlEopI4CAJW+S9u1d6WOQRVQUofy5p0ps6J43xOpYwDAn2zYGK3lnaXFUuokAAAAAFsUbn8osrmLUscAgC2yhUujcNvo1DEAkmr7/T2RLV6eOgYAbFGaOCUKD49PHYPElNRpeNmaddF2TflYUueSAlBdspfmR+GOh1LHAAAAAKgozZwbxYfGpY4BANspPv50FKe8lDoGQBLFp56L0thJqWMAwHYKtz0QJcMOGpqSOg2v7fq7I9asSx0DAHaoOPqpKM2YmzoGAAAA0OCy1rZou/ZOA18AqE5ZRNt1d0a2sSV1EoBOla1YHW033ps6BgDsWKEYbb+9PbJCMXUSElFSp6EVx02K0nPTUscAgJ3Lsmi79o7KG8EAAAAAqRTufDiyJStSxwCAnVuxOgq3jU6dAqBTtV13V0RLa+oYALBT2cKlUfjjo6ljkIiSOg0rW7022m66L3UMANitbOnKKNz+UOoYAAAAQIMqn/JWfGhc6hgAsFvFx5+J4pRZqWMAdIrC409Hyf/zAKgBxfufjNKcBaljkICSOg2r7fo/RqzfmDoGAOyR4iPjojRjTuoYAAAAQIMpn+7Wdt2dldPeAKBWpgpnG1tSxwDoUNnyVVG41ekRANSIUhZt/3dnZIVC6iR0MiV1GlJx7KQoTZqWOgYA7LksNi3YW9tSJwEAAAAaSOGOhyJbsiJ1DADYcytWK24Cda/td3dFtLSmjgEAeyxbuDQKdz+WOgadrLmznxBSy1avjbab7ksdAwD2WrZsZRRufyi6vOOC1FEAAACABlA+1a348PjUMdgPjyyeE1fPeC4mrVoSucjFYb37xYcOOyEuGnb4VvfbWCzEr2ZMjDvnTY95G9bGgK7d49SBQ+LTR58Wh/Tqt9vnefP918X8DWt3eZ8zBg6Nn5/7lsqfS1kW/zP5qbh5zpTybIY4Z9Cw+Ifjz40DuvXY6mtaioV424M3xHmDRsS/nPSqffo7ABpT8YlnIn/yqGgadWjqKADtrvDY01Ga8lLqGACw14oPPBlNJx4V+YMPSh2FTqKkTsNpu/7uiA0bU8cAgH1SfGRcNJ10dOSPGJk6CgAAAFDHyqe5lU91i6xcIaYW/XrGc/FfLzwZA7t2j7cMPzKyLIt7Fs6KL094IBZuXBeXHn5i5X4bioX41BN3xjMrF8cpAw6M1w45OF5atzrunj8jHlsyN645/21xcK++u3yuSw47Pta07XiS501zpsSijevirEHDtlx33UsvxC9nTIxzBw2vPPbNL9/nV+f9+VZf+3+zno+VrS3xN0ef1i5/J0Bjabvuzsh/+WOR694tdRSAdpMtXxWF25wWAUCNKmXR9n93RNcvXhq5ZvXlRuDfMg2lOHZSlCZNTx0DAPZdFtF27Z3R9UsfjVzXLqnTAAAAAHWqfJpb+VQ3atPU1cvjvyePicN794+fnXPxlgnlf330afGeh2+K700eG+8cOSr6dOkaP54yvlJQ//BhJ8QXjzt7y2PcOndq/PMzD8X3Xxwb3z7tDbt8vvJ09h25d8HMSvn8/MEj4pNHnrLl+htmT65k+9+zLopcLhdDuveK7704Nl5YtTSO7Teocp/VbS3x8+nPxF8edkIM7t6znf5mgIayck0UbnkgurzvTamTALSL8qbDtuvuimjZ8eZAAKgF2aJlUbjr0ejy1temjkInyHfGk0A1yFavjbab7ksdAwD2W/kN4sIfHkwdAwAAAKhTpelzKqe5Ubt+O2tSFLIs/umE87YU1MvKf/7MqDPibSOPimUtG6KlWIjrZ0+OQ3r1i7875sytHuOtw4+Md4w8Og7ZzRT1nSk//tcmPhJ9mrvG/zvp1ZUy+mZz162Oo/oM2HLdMX0P2HT9+jVb7vPTqU9Hcy4fHzli08R3gH1RfPLZKE6emToGQLsoPv50lKa+lDoGAOy34ugxUXppQeoYdAIldRpG2+/ujtiwMXUMAGgXxUfHR2na7NQxAAAAgDqTtbZVTnErn+ZG7Xp48dwY3K1nnH7AQdvdVi6e/8uJr4pDe/eLccsXxrpCW1ww9JBozm/9tmE+l4uvnvTquGzUGfuU4YcvjovVba3x2WPOiEHbTELv17V7rC8Wtny+trBpGmjfLt0ql/PXr4lrX3ohPnXUqdGrues+PT/AZm2/uyuyjS2pYwDsl9LyVVG4bXTqGADQPkpZtF17R2SFP702QH1qTh0AOkNx3KQoPT89dQwAaD9ZVI7z6/rlj0WuiyUdAAAA0D7Kp7eVT3Gjdi1v2RBLWtbHOYOGxZKN6+OHU8bFQ4vnxNq21jiyz4D4xJEnxxuGHlq575TVyyuXR/UZGI8unhtXTX8mJq1aGl3zTXHe4OHx2VFnxLCeffY6w9Q1y+OmOVPisF794l0Hj9ru9pMHHBgPLpodTy9fFIf07he/e+mF6NnUJUb1HVi5/QdTxsXQHr3i3Qcfs99/HwCxck0Ubrk/urzvzamTAOyzwu/uimhpSx0DANpNtmhZFO5+LLq85TWpo9CBTFKn7mUbWqLtVrtJAag/5TeMC/c9kToGAAAAUCfKp7aVT2+jtpUL6mXlCenvf+TmeGrZgnjj0MPizw46LGauXRWfH3df/HbmpK3u+8cFM+PTT91dmaZeLoYf3Xdg3Dl/Rnzw0VtjzrrVe53h6hnPRSmy+PiRJ0dTbvu3Iy8bdXplavqlj/8hXnfPNfH0ikXx5ePPif5du8fkVcvijnnT4+9GnbFlunspM9of2D/FJydG8YUZqWMA7JPihBeiNOWl1DEAoN0VRz8VpcXLUsegAxm7Sd0r3PVIxJp1qWMAQIco3j8mms48IfIH9E8dBQAAAKhh5eOV28rTGXWBa976l4/KnrhySZx1wEHxP2e8MXo2d6lc9/G1K+OSR2+N77wwJl435OBYX9g0jfOBRS/F/3fCefHeQ47d8jg/m/Z0fP/FcfGN5x6NK8/e8+nDSzeujzvnT4/hPfrEm4cdscP7HNKrX/z+Ne+M0YteirWFtjh70LDKlPeyKyaPiZP6HxgXHnRYLNywNv712YcrRfvypPV3HjyqMt19c3kdYG+0XX935C//ROS6bvp/IkAtyFpao+3WB1LHAICOUSxG4cb7outfvzd1EjqIV3Coa6X5i019AaC+FQpRuOne1CkAAACAOphclS1dmToG7aApl9vy58uPP3dLQb3ssN794wOHHBeFrBT3LJwV+ZennB/fb9BWBfWyjx5xUgzt3iueXDo/VrRu3OPnv2P+9GgrleLdh4zaZZm8X9du8baRR8clhx2/paD+2JK58cTS+fG5Y8+sfP7/Pf1gTF+zMr516hvib44+La6Z+Vz8asbEvfjbAHiFlWuicK/TSYHaUvjjYxGr1qaOAQAdpjRlVhSfeTF1DDqIkjp1re3Ge8tnQKaOAQAdqvT8jChOmpY6BgAAAFCjshWrlfbqSO/mrpXLHk3NcXjv7U/fO7bfAZXLOetWR++XC+wn9B+83f2acvk4uu/AynD98n33VHkqe7km/6aDDt+r3FmWxX9PfipeP+TgOG3g0Ji+ZkWMXb4wPnrEiXHhQYdWyuyvPvDg+M3M5/bqcQFeqTh6TJSWrkgdA2CPlBYti+JDY1PHAIAO13bL/ZXTQ6g/SurUreLYSZHNmJs6BgB0isJN90XWtukoZwAAAIC90XbbAxGtbalj0E5G9uoTzblcFLOsUjDfVnnKeVn3puY49OUSe1upuMPH2nzfcuF9T6xqbYlnViyOE/sPjmE9++xV7j/Mm1Yppv/dMZumqM9cu6pyeUivflvuc2jvfrG8dWOsafPGNbCPCsUo3Hx/6hQAe6RQHsxY3LQeA4C65tSjuqWkTl3KNrZE222jU8cAgE6TLV8Vhfss2AEAAIC9U5z6UpSedqRyPemSb4qTBhwYraVijFu+cLvbn1u1pHI5qu/AOGPg0MqfxyxbEKVs60p7ubg+dc3ySkF9ZK++e/TcE1curpTjy5PQ90ZrsRg/mjI+3j5yVBz2cnG+mG0qZBVekat8v7J8eVQ7wD4qPT89is9PTx0DYJeKT0+O0tSXUscAgE5THP1UlBYvTx2DdqakTl0q3PlIxJp1qWMAQKcq3j8mSstWpo4BAAAA1IisWKqczkb9ee8hx1Yu/+v5J7eaOj5l9fK44aXJ0b9Lt3jD0EMq5fPzBg+PuevXxE+nPb3VY1w5dUIsbdkQFw8/ojJ1fU9MWrW0cnlcv0F7lff/Zj0fK1o3xt8cdeqW6w5/uaw+4RVF+6dXLIoDu/eMXs1d9+rxAbZVuPm+yApOJwWqU9bSGm23OPUBgAZTLEbhpntTp6Cd7dkrSlBDSvOXRPHR8aljAEDnKxQqbyx3/cS7UicBAAAAakDxkfGRLdxUKqa+vHnYEfHYknlx69yp8c6Hfh8XDj001hba4o8LZlYmk//rSa/aUvT+lxNfFR97/PbKJPMxS+fH8f0Hx6SVS2Ps8gVxSK9+8bljztzqsX8z87lK8f0vRhwVw3v22eq2OetWVy4P7N5rj7OubmuJn01/Oj58+IkxqHvPLdcf1XdgnDJgSPz65edbtHFdpQT/5ePO3s+/HYCIbOnKyqTG5gvPTR0FYDuFPz4WsWpt6hgA0OlKL86K4jMvRtPJo1JHoZ2YpE7dabvxnojS1kdSAkBDHVM6aVrqGAAAAECVy9asi8Ldj6aOQQf6fye9Or520qtjcLeeceOcKTF60UtxxsCh8fNzLo43DD10y/0O6tE7fvuqt8Ulhx4f8zesjd/OmhTzNqyODx12fPzm/D+Pvl26bfW418ycFD+eOqFy322Vp6GX9emy55POfz7tmWjO5eMjh5+43W3fPf2CeN2QQ+L2edPi+VVL46+POjU+cOjxe/k3AbBjhXufiGzlmtQxALZSWrQsig+NTR0DAJIpnyaStbaljkE7yWVZps1L3SiOnRRtv709dQwASCp3QP/o+uWPRa6LQ3MghY1f+FbqCAAANaX7d7+cOgI0pLbr747i48+kjgEASeXPOD66fvAtqWMAbNH6v9dFaepLqWMAQFJNF5wTXd7ymtQxaAcmqVM3spbWaLttdOoYAJBctqx8TOmY1DEAAACAKlVauDSKTz6bOgYAJFcaNylKcxamjgFQUXx2ioI6AJR/Jo5+KkpLV6SOQTtQUqduFB8cG7FmXeoYAFAVCg+MiWzt+tQxAAAAgCpUuOWBiJKDdgEgsoi2W+5PnQIgsmIpCrc/mDoGAFSHYjEKdz6SOgXtQEmdulAu4RVMjAWAP9nYGoX7nkidAgAAAKgyxRdmROnFmaljAEDVyGbMjeIzL6aOATS44piJkS0xMRYANis9/UKU5i1KHYP9pKROXaiU8Da2po4BAFWl+OiEyFasTh0DAAAAqBJZqRSFWx9IHQMAqk7hDw9GViimjgE0qKy1LQp3P5o6BgBUlyyicPtDqVOwn5TUqXnl8l25hAcAbKNQjLa7HH8EAAAAbFJ8/JnIFi1LHQMAqk62bGUUHxmXOgbQoIoPj49YvTZ1DACoOqXJM6M4bXbqGOwHJXVqXqV8Z1c7AOxQaeykKC1YkjoGAAAAkFi2scV0RgDYhcI9j0e2bkPqGECDydZvjML9T6SOAQBVfeoRtUtJnZpWLt2Vy3cAwE5kWRTucPwRAAAANLrC6Kci1q5PHQMAqteGlijcpygKdK7K/3c2tKSOAQBVK5u9IIrPTkkdg32kpE5Nq5Tusix1DACoaqVJ06M0Y27qGAAAAEAi2YaWKD48LnUMAKh6xceejsymLqCTZCvXRPGR8aljAEBN9ESzUil1DPaBkjo1q1y2K5fuAIDda7vd8UcAAADQqCoFddMZAWD3Wts2nT4C0AkKdz8a0VZIHQMAql62eHkUx0xMHYN9oKROzVK2A4A9l82cF8VJ01LHAAAAADpZtrElCg+NTR0DAGpG8dEJka3bkDoGUOdKi5dF8SllOwDYm81dmc1dNUdJnZpULtmVy3YAwJ4r3F4+/ihLHQMAAADoRMVHJkSs35g6BgDUjpZWG7yADle44+EI79sBwJ5btXbTaYHUFCV1ak6WZZsW6wDAXskWLo3S+OdTxwAAAAA6SVYu2T34VOoYAFBzig+Pj2yDTV5AxyjNXhClZ6ekjgEANadw35ORbWhJHYO9oKROzSlNnBrZgiWpYwBATSrc+7hp6gAAANAgio9OiFi3IXUMAKg9G1ui+JApjUDHKNzzeOoIAFCbNmyM4qPjU6dgLyipU3MK9z2ROgIA1Kxs8fIoTTSZAQAAAOpd1toWhdGmqAPAvio8NDayjaY0Au2rVD75+PlpqWMAQM0qPDSu8roXtUFJnZpSnPJSZHMWpo4BADXNhi8AAACof8XHno5Yuz51DACoXRtaoviwaepAB7xP59BjANh3a9dH8cmJqVOwh5TUqSlFpToA2G/Z3EVRfHFW6hgAAABAB8naClF4YEzqGABQ8woPjo2spTV1DKBOlJavitKEyaljAEDNK4weE1mxlDoGe0BJnZpRmr0gSlNfSh0DAOqCjV8AAABQv4qPPxOxZl3qGABQ+9ZvjOIj41OnAOpEsbyRtKRQBwD7bcXqKI1/PnUK9oCSOrV15BEA0C5K02ZHadb81DEAAACAdpYVylPUn0wdAwDqhmnqQHvI1qyL4pMTU8cAgLpRuP/JyLIsdQx2Q0mdmlBatCxKz01NHQMA6ooNYAAAAFB/KsWXVWtTxwCA+rF2fRQfezp1CqAONrxEoZA6BgDUjazcKZ2oU1rtlNSpmV0vYdMLALSr0vPTorRwaeoYAAAAQDvJCkWb0gGgAxRGPxVZa1vqGECNyja0RPGxCaljAEDd8TpY9VNSp+plK1ZHafzzqWMAQP3JLNgBAACgnhTHTIxYuSZ1DACoP2vWRfFx09SBfVN8dELExtbUMQCg7mRzFkZxykupY7ALSupUvcIDYyKKpdQxAKAulSZMjtLyValjAAAAAPspK2VRLL+eDgB0iMIDT0VWLKaOAdSY8ikMhYfGpo4BAHWraDhjVVNSp6pla9dH8clnU8cAgPpVKnkDGwAAAOpA6YXpkS1bmToGANSv1WujNHFq6hRALZ52tHZ96hgAULdKU1+K0uwFqWOwE0rqVLXCI+Mj2gqpYwBAXSs+ObGyMQwAAACoXcVHn04dAQDqXuHRCakjALV22tHop1LHAIC6VzCcsWopqVO1skIxio8/kzoGANS/QsHJJQAAAFDDSstWRunFmaljAEDdy6bPidLCpaljADWi9Py0yJavSh0DAOpe+cSjbOWa1DHYASV1qlbpmRcj1qxLHQMAGkLhsacjK5VSxwAAAAD2QfGxpyOyLHUMAGgIRdPUgT1UfMT/LwCgU5RKld4L1UdJnapVeHR86ggA0DhWrI7SpOmpUwAAAAB7KWsrRHHMxNQxAKBhFMdNiqylNXUMoMqVFi+L0tRZqWMAQMMoPvFMZIVC6hhsQ0mdqlSaszCyWfNTxwCAhlJ8ZFzqCAAAAMBeKj09OWLdhtQxAKBxbGytFNUBdjtF3WFHANB51q6P0tMvpk7BNpTUqUrFR0xRB4DOVpo6O0oLl6aOAQAAAOyFwqMTUkcAgIZTfPTp1BGAKpZtbIniU8+ljgEADaegd1p1lNSpOtm6DVGcMDl1DABoSEVvbAMAAEDNKM1dGNnsBaljAEDDyRYsidKMualjAFWqOO75iJbW1DEAoOGUXycrzfFaWTVRUqfqVHaTFgqpYwBAQyofUZp50QwAAABqgimuAJBO4TFDX4AdKz5mnQ4Aqfg5XF2U1Kk6xSeeSR0BABrXxlYnmgAAAEANyDa0RHHCC6ljAEDDKj0zJbK161PHAKpMada8ymkLAEAa5c5LtrEldQxepqROVSlOfSmyxctTxwCAhlZ83K5SAAAAqHbFpyZGtLaljgEAjatYjOITz6ZOAVSZgumtAJBWa1sUx05KnYKXKalTVYqPm6IOAKllcxZGae7C1DEAAACAXXB0MQCkV3j86chKWeoYQJXI1m+M0tMvpo4BAA1PD7V6KKlTNbI166I0cWrqGACAN7oBAACgqhWnOJUUAKrCitVRemF66hRAlSg+9VxEoZA6BgA0vGzBkijNnJc6BkrqVJPiuOcrR6IBAOkVJ0yOzJHhAAAAUJWKj05IHQEAeJmfy8BmxTETU0cAAF5WHPNs6ggoqVNNiuMmpY4AAGzW0hqlSdNSpwAAAAC2ka1c43d2AKgipRdnRmnZytQxgMRK8xdXprYCANWh+MyUyJxwkpySOlWhtHBpZPMWp44BAGx7ygkAAABQVYpjJ0WUSqljAACbZaYnA95XA4Cqs7ElSpOmp07R8JTUqQrF8RbrAFCN01+ytetTxwAAAABeofj05NQRAIBtlPx8hoaWlbIoTnghdQwAYBs2kaWnpE5yWZZFabzFOgBUnWIpis+8mDoFAAAA8LLS4mWRzXcqKQBUm2zJiijNXZQ6BpBIafrsiJVrUscAALZRemFGZOs3po7R0JTUSS6bOS+y5atSxwAAdqA4blLqCAAAAMDLShNMaQWAauW0E2hcpfGmtAJAVSoWo/iMdXpKSuokp/wGANUrmzU/SstWpo7BXigWi9Ha2rrl87Vr18bPfvaz+PrXvx633HJL5XYAAABqkxPPAKB6lZTUoSFlhUIUn5mSOgYAsBPFcTaTpaSkTlJZobxTxYvqAFDNShbsNeOqq66Ks88+O+6///7K5+Wy+gc+8IH4zne+E9dcc01cfvnl8Vd/9VeK6gAAADWotHBpZAuXpo4BAOxE+fTw0uwFqWMAnaw0aXrExpbUMQCAnchmzo1sxerUMRqWkjpJlSbPiFi/MXUMAGAXio4orAn33ntvfOtb36pMTl+zZk3luptvvjmmTp0agwcPjssuuyxGjRoVjz/+eFx77bWp4wIAALCXihNeSB0BANiNomnq0HBMZwWAKpf5eZ2SkjpJ+eYHgOqXLV4epTmmv1S76667LvL5fPz85z+P97znPZXr7rzzzsjlcvEv//IvlZL6b37zm+jTp0/ceuutqeMCAACwl0pKbwBQEyX1LMtSxwA6SbZ+Y5RemJE6BgCwG4YzpqOkTjLZhpZNxx4BAFWvONaCvdo999xzcdppp8X5559f+XzDhg3x1FNPRdeuXeM1r3lN5brevXvHKaecEtOnW4MBAADUktK8RZEtWZE6BgCwOyvXRDZrfuoUQGeenlAspo4BAOxGtnBp5fU1Op+SOskUn30xolBIHQMA2NPpL6VS6hjswtq1a2PQoEFbPh8zZkwUCoU46aSTKkX1zcp/bmlpSZQSAACAfVGcYIo6ANRUaRVoCKayAkDtKI7zczsFJXWSKT39YuoIAMCeWrMuStPnpE7BLgwdOjTmzZu35fOHHnoocrlcnHfeeVuuK5VK8cILL8TgwYMTpQQAAGBflJ7xejoA1IriMy9GVspSxwA6WLZ6bWQz56aOAQDsxTqdzqekThLZxpYoTZudOgYAsBdKk6aljsAuHHvssfHcc8/F9ddfH0888UTccsstlesvuOCCymVbW1t8+9vfjvnz58dZZ52VOC0AAAB7qjRnQWTLVqaOAQDsKcVVaAjF8vtm9qMAQO1YsTpK8xanTtFwmlMHoDGVXpwVUSymjgEA7IXSpOkRb99UeKb6fPKTn4wHHngg/uVf/qXyeZZl8brXvS6OPvroLWX1JUuWRJ8+fSr3BQAAoDYUJ0xOHQEA2EvFpydH/oiRqWMAHf2+GQBQc8MZ88MPTB2joZikTrodpQBATSlPbSstXJo6BjtxwgknxFVXXRXnnHNOHH744fHBD34wvvOd72y5/aCDDoozzzwz/u///q9yOwAAANWvvAHZUcQAUHvKP7+zUil1DKCDZK1tUZr6UuoYAMBe0lvtfCap0+nKv4yXXpiROgYAsK+7SocOSh2DnSiX0H/xi1/s8Larr746unXr1umZAAAA2HfZS/MrRxEDADVm7fooTZsTTUcfkjoJ0AFKU2ZFtBVSxwAA9lI2d2Fkq9dGrm/v1FEahknqdLps1vyIdRtSxwAA9oFdpbWjpaUl5syZEwsXLqx83qVLl9SRAAAA2EvFCZNTRwAA9lHp6RdSRwA6cKgTAFCDMr2XzqakTqfzTQ4AtSt7aUFka9enjsEujB49Oi655JI4/fTT48/+7M/iiiuuqFz/t3/7t/HVr3411q/37w8AAKBWFJ+bmjoCALCPis9NiyzLUscA2ln5+7r4/IzUMQCAfVSaND11hIaipE6ns6MUAGpY5YU3C/Zq9b3vfS/+5m/+JsaNGxelUqnyQunmN0GmTZsW1113XXzsYx+L1tbW1FEBAADYjdLi5RErVqeOAQDsq7XrI5u/OHUKoJ1lsxdErFmXOgYAsI9KU1+KrLUtdYyGoaROpyotWR5Z+YV1AKBmlZ6z4awaPfjgg/GjH/0oDjzwwPjv//7veOqpp7a6/bvf/W4ceeSR8cwzz8T111+fLCcAAAB7/oYZAFDbSlP8PId6UzSYEQBqW1shSlNmpU7RMJTU6VSmqANA7Ssv1rNCIXUMtnH11VdHly5d4qqrroo3velN0atXr61uP/HEE+PnP/95dO3aNW655ZZkOQEAANgzpRe9WQYAtU75BeqPYU4AUPv0WDuPkjqdqjhpeuoIAMD+am0z/aUKTZw4MU4//fQ44ogjdnqf8pT1M844I2bPnt2p2QAAANg7WakUpWl+dwOAWleaMdfQF6gjpeWrIlu4NHUMAGA/FZ+fEVmWpY7REJTU6TTZug2RzZyXOgYA0A7sKq0+Gzdu3G56+o6Up61v2LChUzIBAACwb7LZCyM2tqSOAQDsr7ZClLxHDnXDFHUAqBNr1kX20oLUKRqCkjqde5RZqZQ6BgDQDoovzEgdgW0MGzYsnn/++SjtYr1VKBQq9znooIM6NRsAAAD78Ho6AFAXSi/6uQ71ouT9MQCoG8UXpqeO0BCU1Ok0pakvpY4AALSXlWuitGRF6hS8whve8IZYuHBhfO9739vpfX74wx/GkiVL4nWve12nZgMAAGDvFJXUAaBueJ8c6kNWLEZp5tzUMQCAdlKaNjt1hIbQnDoAjaM0fU7qCABAOypNnx35wQNSx+Bln/jEJ+IPf/hDXHnllTF27Ng455xzKtcvWLAgrr/++rjvvvviwQcfjP79+8fHP/7x1HEBAADYiaylNbKX5qeOAQC0k2zuosjWbYhcrx6powD7IZu9MKK1LXUMAKAdf7ZnrW2R69oldZS6pqROp8hWrYnMtFUAqCulaXMizjk5dQxeNnDgwLjqqqviM5/5TKWkPm7cuMr15T+XP7Isi4MOOqgyaX3w4MGp4wIAALCrgS/FUuoYAEB7ybLKlMamk0elTgLs5/AmAKCOlE9JmTUvmo4+NHWSuqakTueV2ACAulKa4ed7tTnyyCMr09TvueeeePzxxytT1EulUqWUfvbZZ8fFF18cXbt2TR0TAACAXShNmZU6AgDQzkovzlJShxqn9wIA9fnzXUm9Yymp0ynsKAWAOrRyTZSWrIj84AGpk/AKTU1N8aY3vanyAQAAQO0pTXkpdQQAoJ2Vpvr5DrUse3nSKgBQX/RaO16+E54DNh1PCgDUHQt2AAAAaD/Z6rWRLVyaOgYA0M6yZSujtGxl6hjAPspmL4xobUsdAwDogJ/xmZ/xHcokdTpctmpNZEtWpI4BAHTU0YbnnJw6BhHx4Q9/eI/vm8vl4le/+lWH5gEAAGDvmaIOAPWrNGVW5M89JXUMYB8Y2gQAderl01Kajj40dZK6paRO55TXAIC6VJrh53y1GDNmzB6V07Msq1y2h5tvvjmuvvrqmDlzZnTv3j3OP//8+PznPx/Dhw/fo69ftWpV/PjHP44//vGPsXjx4jjwwAMrj3HZZZdV/gwAANBoilNmpY4AAHTkZjQldahJei8AUN8/55XUO46SOh3OjlIAqGMr10RpyYrIDx6QOknD+8EPfrDD60ulUqUMPn78+LjtttvibW97W6VIvr+uuOKKSsH8yCOPjA9+8IOxYMGCuOOOO+KRRx6J66+/PkaOHLnLr1+6dGlccsklMWvWrHjVq14VF110UTz//PNx3XXXVR7j97//fQwY4L8rAACgsZikDgD1qzT1pchKWeTy7TNEBOgc2csTVgGA+qTf2rGU1Olwpel2lAJAvS/YldTTu/DCC3d5+3ve85644IIL4jOf+Uycd9558Za3vGWfn2vy5MmVgvrpp58ev/zlL6Nr166V69/85jdXpqD/27/9W+X2XfnGN75RKaj/8z//c3zoQx/aqmz//e9/P372s5/Fl770pX3OCAAAUGtKi5ZFrF6bOgYA0FHWb4xs3qLIjRyaOgmwF7LZCyNa21LHAAA68Gd91toWua5dUkepS/nUAahv2ao1kS1ZkToGANCBHHFYW0X2Y489tlIs3x9XX3115fJv//ZvtxTUy974xjfGmWeeGaNHj45Fixbt9OsXLlwYd911V5x99tlbFdTLLr300sq098GDB+9XRgAAgFqTzV6QOgIA0MFKft5DzTFdFQDqnFNTOpSSOh3KFHUAqH9enKstI0aMiGnTpu3XYzzxxBPR3NxcKaRv65xzzoksyyr32ZkHH3ywcp+LL754u9v69OkT3/rWt+IjH/nIfmUEAACoNaU5C1NHAAA6WDbXz3uoNXovAFD/DGfsOErqdKjSS3aCA0DdW7W2cnoK1a9YLMakSZO2mn6+t1pbW2P+/PkxdOjQHT7OyJEjK5czZszY6WNMnjy5cnnUUUfFrbfeGu9+97vj5JNPjvPPPz/+9V//NZYvX77P+QAAAGpVae7OT6QCAOqDn/dQe5yAAAD1zwmHHae5Ax8b/JINAA30M7+pX5/UMRra5uL3zsrpS5YsiV//+teVgvnrX//6fX6eVatWVaag9+vXb4e3lyehl61Zs/ONC4sXL65cXnXVVXH//ffHG97whjj99NNj3Lhxce2111amsF933XXRv3//fc4JAABQS7JSKbL5m35XAgDqV7ZwaWSFQuSaVTWgFpSWrojY0JI6BgDQwUpOPOowfvOhw2SlLLJ5SuoA0ChHkjcdf2TqGA3t7W9/e+RyuV3ep1wu79atW/zt3/7tPj9PW1tb5XJn09g3X9/SsvMXbdevX1+5vO++++LKK6+M1772tVvylSeplwvq3/nOd+LrX//6PucEAACoJdni5RGtm37fAgDqWLG8MW1J5A4+KHUSYA9kBjMCQGNYvzFKy1dFfuCOh/Wx75TU6TDZEi+qA0Cj8CJdesOGDdvpbfl8Pnr27BnHHHNMfOQjH4njjjtun5+ne/fuW5XVt9Xa2lq5LD/frvKUXXTRRVsK6mXlkv2Xv/zluPnmm+POO++Mr33ta1vuCwAAUM+yOaY1AUAjTWnMK6lDzQxpAgAa6PU5JfV2p6ROh/GiOgA0DkcfpXf//fd3yvP07t27Uhxfs2bNDm/ffH2fPn12+hibbzvxxBN3+PiHHHJITJkyJZYvXx6DBg1qt+wAAADVqmTzNwA0jGyOn/tQKwxpAoDGen2u6eRRqWPUHWMJ6TDKagDQQFavi2z12tQp6ARdu3aNkSNHxvz583c4TX3OnDmVyyOPPHKnj3HYYYft0TT2zVPbAQAA6p3X0wGgcfi5D7XDZlIAaBw2p3UMJXU6jMU6ADQWP/urw9KlS2P16tVbPl+wYEF87Wtfi7/+67+OH/3oR7F27f5vJjjrrLMqBfPx48dvd9vjjz8euVwuTjvttF1+fdljjz223W3l6enz5s2LESNGVKaqAwAA1LuslEU2b3HqGABAJ8kWLo2sUEgdA9iN0rKVERs2po4BAHQSm0k7hpI6HfiiuqIaADSSbI4Fe2pf//rX47WvfW08/PDDlc/LhfT3v//9ce2118bo0aPj+9//flxyySWxceP+vaj6rne9q3J5xRVXbPVY99xzT4wdOzbe8IY3xNChQ3f69WeffXZl0vqYMWPi5ptv3nJ9qVSK//zP/6wU4N/73vfuV0YAAIBakS1eFtG645OmAIA6VCxFtmBJ6hTAbnjfCwAazLoNka3400BA2kdzOz0ObCVbsjyixYvqANBITFJP68Ybb4xrrrkmunfvXplkXnb99dfHokWL4qijjoqPfexjcccdd8QjjzwSv/zlLyuT1ffVqaeeWim7l5/vbW97W1xwwQWV57nzzjtj0KBB8ZWvfGXLfZ988slKGf3YY4+NCy+8sHJdPp+Pb3/72/GRj3wkLr/88srXHX744ZX7Tpo0qfL45bwAAACNwFHCANCYr6fnRx6UOgawC973AoDGU5qzMJoG9E0do66YpE6HyBx9AAANx9FHad10003R3NxcmZp+8cUXV667++67K4X1f/zHf4x3vOMd8cMf/jAOOOCAuOuuu/b7+f75n/+58tG1a9f49a9/XSmil5+3/PwjR47ccr/y9T/4wQ/i3nvv3errjzvuuErmcq5yMf03v/lNZfL7ZZddFr/61a+iS5cu+50RAACgVt78AgAaSzZH+RWqnd4LADQem9Tan0nqdAjfrADQgFatjWzNusj16ZU6SUOaMmVKnHXWWXHMMcdUPl+9enU8++yz0aNHj8r1ZeVC+YknnhhPPPHEfj9fufz+oQ99qPKxK5/5zGcqHzsyfPjw+I//+I/9zgIAAFDLvJ4OAI3H0BeoftbpANB4bFJrfyap0yFMfgGAxmQNkM7GjRujT58+Wz5//PHHo1QqxWmnnRZNTU1b3bdYLCZICAAAwLayUhbZPOUXAGg02YKlkRW8TgvVqrRsZcT6jaljAACdzCa19qekTofI5i9JHQEASMAaIJ2DDjooZs6cueXz0aNHV6adn3/++Vuua21tjYkTJ1buCwAAQHrZkuURrW2pYwAAna1YjGyh19OhWnm/CwAa1Nr1ka1emzpFXVFSp91la9ZFbGxJHQMASKBUfnOdJE455ZSYOnVq/Pd//3f8/ve/j9tvv71y/YUXXli5XLRoUVx++eWxbNmyeNWrXpU4LQAAAGWZE8kAoGGV5pjSCNUqW+z9LgBoVCXrgHbV3L4PBy9PfgEAGpJ1QDqf/vSn4957740rr7yy8nmWZfHOd74zRo4cWfn8bW97W6xcuTKGDRsWn/rUpxKnBQAAoKw0f3HqCABAIpl1AFQt73cBQIOvA448OHWMuqGkTrsrLV6ROgIAkEi2xDoglYMPPjhuuOGG+NnPfhaLFy+Os88+Oz7ykY9suf2kk06KAw44IL74xS/GoEGDkmYFAABgk2zZytQRAIBErAOgepW83wUADUvvpX0pqdPu7CgFgAa2bkNk6zZErleP1Eka0qGHHhrf+MY3dnjbT37yk07PAwAAwK5ly1aljgAAJKKkDtVL7wUAGpd1QPvKt/PjgZ0kANDgrAWqz7PPPht33313zJ07N3UUAAAAXiFbrpwGAI0qW7E6slKWOgawjWxDS8Ta9aljAACJ6Ly0LyV12p2dJADQ2KwF0pkwYUJ88pOfjEcffXTLdV/+8pfjfe97X3zuc5+Liy66KH7wgx8kzQgAAMAm5ZPIYmNr6hgAQCqFYsTqtalTANvwPhcANLbyiUdZqZQ6Rt1QUqddlXd6O5YMABpbya7SJCZPnhyXXnppPPzwwzFjxozKdQ8++GDceuut0dTUFGeccUZ07949fvjDH1auBwAAIC2vpQMA1gNQfZTUAaDBFUuRLVuVOkXdUFKnXWUrVm3a8Q0ANCwv3qVx1VVXRWtra2WS+jvf+c7KdbfcckvkcrnKNPVf//rXce2111YK67/97W9TxwUAAGh4SmkAQMl6AKqOYUwAgN5L+2lux8eCyCzWAaDhWQ+k8dRTT8VRRx0Vn//85yufl0qlylT1ckn9z//8zyvXlW8//fTT49lnn02cFgAAgGy5iUwA0OhsWoPqo5QGAOi9tB+T1GlXFusAQLZ0RWRZljpGw1m6dGkcccQRWz4vF9HXrFkTRx99dAwYMGDL9f37969cDwAAQFpKaQBAtsymNag2SmkAgB5s+1FSp11ZrAMA0doWsWpt6hQNp1w+X7XqT29oPPTQQ5XLc845Z6v7zZkzJ/r27dvp+QAAANiaUhoA4GQVqD56LwCA9UD7UVKnXfnmBADKSnaVdrryFPWxY8fGjBkzYu3atXHrrbdGLpeL173udVvu88c//jGef/75OPHEE5NmBQAAwCR1AMB6AKpNtnptREtr6hgAQGI6L+2nuR0fCyJbYac3AFBeE6xOHaHhvO9974snnngi/uIv/iK6desW69atqxTXN09S/+u//ut4+OGHK8X1v/zLv0wdFwAAoKFlpVJkK9ekjgEApLZmXWStbZHr2iV1EsD7WwDAZqvXVl6/y+XNAd9f/gZpV9nqdakjAADVwJqg0735zW+OL3zhC9GlS5dKQf2oo46K733ve1tunzt3bnTt2jW+9a1vxate9aqkWQEAABpdpfxSKqWOAQBUgWy5QXBQLXReAICKUhaxdn3qFHXBJHXaTXmHd2xsSR0DAKiW4xDpdJ/85CfjIx/5SKxduzYGDhy41W3//u//Ximu9+jRI1k+AAAANsmWKaMBAJtky1ZGDB2UOgbg/S0AYJt1Qa5v79Qxap6SOu0mW2NHKQCwiXVBOuVp6dsW1MtOOumkJHkAAADYSRkNAMDmNagqSuoAwGZOWGkf+XZ6HIhYZbEOAGySWRcAAADATmXLldEAgE1sXoMqoqQOALxM76V9mKROu7GjFADYwrqgw11wwQX7/LW5XC7uvffeds0DAADAnlNGAwA2y5ZbF0C1MDEVANhC76VdKKnTbizWAYDNrAs63rx58/arpA4AAEA62ao1qSMAAFUiW2ldANXCcEYAYLNsjd5Le1BSp91YrAMAWxQKkW3YGLke3VMnqVtXX3116ggAAADsq/UbUycAAKrFhpbUCYCXGcIEAGymD9s+lNRpN74pAYBXylatVVLvQGeddVbqCAAAAOyj8sZuAICyzOY1qApZqRSxdn3qGABAFXVe2H/5dngM2MSOUgDgFUybAAAAgJ1Yb2IqAPCylpbISlnqFMCadRGZ70UAYJOsvDZgvymp025MUgcAtmJtAAAAANvJ2goRhULqGABAtSh3Yp2yAskZvgQAbGXNushsYNtvSuq0GztHAIBXsoENAAAAdkAJDQDYRrbRKSuQmve1AICtFEsR6zakTlHzlNRpF1mx6BsSANiKDWwAAACwvWyDEhoAsI31NrFBat7XAgC25aSV/aekTvvY2Jo6AQBQbUx+AQAAgO0poQEA28ictALpeV8LANiW9cF+U1KnXWQtSuoAwNasDwAAAGB7SmgAwHZsYoPkMsMZAYBt6L3sPyV12ofFOgCwrY1tqRPUtfe///1xxRVXbPl8/vz5sXLlyqSZAAAA2ANKaADANmxigyqghAYAbMv6YL8pqdM+Wn0zAgBbs6O0Y7344osxZ86cLZ9fcMEF8R//8R9JMwEAALB7SmgAwHbWt6ROALQYvgQAbM1JK/tPSZ12oYQGAGzHJrYOlcvlYtq0aVEqlSqfZ1lW+QAAAKDKmaQOAGzDJjZIT+8FANiO3st+a97/hwA7SgGAHbCjtEMdc8wxMWHChHjNa14TgwcPrlz34IMPxjve8Y49KrjfeOONnZASAACAbWUbTUoFALahpA7pKaEBANuyiW2/KanTPnwzAgDbMHGiY/393/99fPKTn4ylS5dWPspWrVpV+diTkjoAAACJmKQOAGzDJHWoAoYvAQDbyKwP9puSOu3CNyMAsJ1WJ610pNNOOy1Gjx4d06dPj40bN8all14a559/fnzqU59KHQ0AAIBdUEIDALaz3kkrkJrhSwDAdvRe9puSOu3DsUcAwLZa2yIrlSKXz6dOUrd69+4dJ5988pbPDzjggDjrrLOSZgIAAGDXMpPUAYBt2MQGVUAJDQDYhk1s+09JfQ99//vfjx/84Adx2WWXxWc+85k9+posy+LOO++M2267LSZOnBgrV66MgQMHxiGHHBJve9vb4q1vfWt07959q6+54IILYu7cuXH33XfHoYceut1jfvrTn4777rsvevbsGU8++WR07dp1u/u89rWvjUWLFsWjjz5aKSp1Bt+Me+5L4++Pp1csinsu+MB2t60vtMUvZ0yMO+dPj0Ub1sUB3XrEm4YdHp886tTo0bT9t+u0NSvihy+Oi2dWLq587VF9BsSlh58UFx60/X87O1MoleK6l56P389+MeatXxN9unSL1w85OP521OnRv+vW/32uaN0YX5/4aDy6ZG7079It3jr8yPjro0+NLvmmre43dc3yeO9DN8e/nfLauHj4EXv19wNAnWlpi+jRLXWKhjB58uTUEQAAANgTG0xKBQC2YRMbCfov3/jGN+I973nPXn99uavy4Q9/OP78z/88/uu//mu397/88svjpptuil/84hdx3nnn7WPqjpdt1HvZU3ovADQM64P9ZqxlB1m2bFlccskl8fnPfz4mTJgQ5557bnz0ox+N888/P+bNmxf/3//3/8Vf/MVfxIsvvrjV15XvVzZ+/PjtHrOtrS2eeOKJyOfzsX79+hg7dux295kzZ04sXLgwjjnmmE4rqG8poLFbP546If64YOYOb2srFeOzY++JK6dOiOE9+sQlhx0fw3r0jqumPxufeuLOaC0Wt7r/C6uWxocfuy2eWDo/Xjfk4HjXwaNiwYZ18cXx98VvZ07a440U//LsQ/Gt55+MXs1d4oOHHR/H9jsgfjd7cvzlo7fFqtat3yz5xsRHY/Sil+Kigw6LE/sPjp9Nfyb+d8qE7R73f154Ko7pd0C8edjhe/X3A0AdspGt061YsSJ+/OMfx0c+8pG4+OKL4+1vf3t8/OMfj5/97GexZMmS1PEAAADwuzIAsA1D4ehM5RNZywMajzvuuNRRqkur78M9ofcCQEOxPthvJql3gA0bNlRKQVOmTKlcfu5zn4sePXpsub1YLMY111wT3/zmN+MDH/hA3HLLLTFy5MjKbeVdo9dff32lpP7Od75zq8ctX7du3bp4y1veErfffns8/PDD2+0yfeqppyqX5TJ8p/JL8y61FAvxzUmPx41zpuz0Pr97aXI8tWxBfOTwE+Pzx5615fpvTXoirpk1Ka596fn48OEnbrn+a88+Ei3FYvz2VX8Ro/pu2pDwiSNPib989Nb478lPxQUHHRpDuvfaZa4HF8+O2+dNryy+//PU10cul6tc/5uZz8W3n38y/nfq+Lj8+E0bJ5a3bIj7Fs6Kdx98TPzTiZv++/qbMXfF7156IT57zBlbHrP8z/Dwkrnxk7PfvOXxAGjsF9b9NOg8Y8aMqZz6s3r16sqLcq/02GOPVYrqV1xxxZaNkQAAAHS+rFRKHQEAqDbbvJ4LHenss8+ufLANwxl3Se8FgEbkpJX9Z5J6Bx2NVC6olyepf+UrX9mqoF7W1NRUOfqoPE29XDov32ezc845p7LAGTdu3HaPWy6ll33yk5+MPn36bPl8RyX1V73qVdGZslaL9Z0ZvWh2vP3B31cW6q8ePGKn9/vtrEnRNd8UnzzqlK2uv2zU6dG9qTmunz15y3Xjly+MF1Yviz876LAtC/WyAV27VxbsLaVi3DZ36m6zXfPyztPyEUevXFh/8NBNu1lvnTu18otG2bwNa6P80sjRfQduuV/5udcUWivHIZWVy3BXvDAmXjV4RJw9aNge/g0BUNesETpN+bSeT3/607Fq1ap43eteF9/97nfjd7/7XVx77bXxrW99K1796lfHypUrKxsoFyxYkDouAABA41JCAwC2VbI+gJSytoJ1+i7ovQDQsHRe9puSejvbuHFjpQjUvXv3+OxnP7vL+5anqB9++OGVYvnkyZsWYgMHDoxRo0bFzJkzY8WKFVvd/5FHHokhQ4bEMcccUymzT506NRYuXLjVfcaOHVspxZ9++unRqUx+2amb57wY6wtt8f+dcF58/8w/2+F95q9fE3PXr4kT+g+KXs1dt7qtZ3OXyhFDs9etjkUb1lWuG7N0fuXyrB0siDcvkscs3XX5rK1UigkrFsXQ7r3ikF79trotn8vFmQccFOsKbTFp1dLKdf27dKtcbihsWryXrWtrjaZcLno3d6l8fteCGfHCqmXxuWPO3IO/GQAaghfWO81Pf/rTWLt2bXzhC1+I//3f/42LL744TjrppDjllFPiL/7iL+InP/lJfP7zn6+U2H/1q1+ljgsAANC4/K4MAGwr8357o/rP//zPSkfk97///Q5vv/DCC+PUU0+tDEAslUpx/fXXx1/+5V9WJqEff/zxle7Ipz71qe0GIZaHK5Yf94EHHoiPfvSjccIJJ1SG2YwfP37LbeXHeqUnnngiLrvssspQxPL9y72T973vfXHjjTfuNP9dd91VeQ/ixBNPrAzQ+eY3v1k57XVPLFq0KL761a/G61//+srzlZ+3PORx7ty50el0XnZJ7wWAhmUT235TUm9nEyZMqPxyUP4loX///ru8bz6fj4suuqjy5z/+8Y9brj/vvPMqO/PKj7XZkiVLKkX2zRPSN1++cpp6eQE/e/bsOOOMM6Jr160XfB3ON+NOXXLYCXHHG94b7z3k2J0eA/TSuk2/pI3s2XeHt4/o2adyOXPdyq3uf/DL179S+aij5lx+y313Zv6GNZUF+8heu37OWWtXVS4P6tE7BnfrGTfPnRILN6yN6WtWxL0LZ8UJ/QZHl3xTtJWK8YMXx8VbRxwZR71i1ykADc4aodOU14WHHHJI5dSdnSm/UH3wwQdXXpQGAAAgEb8rAwDbsomtYb3jHe+oXP7hD3/Y7rZyoXzOnDmVXkmvXr0qBe5/+qd/qgyjKRfDP/zhD8dRRx0Vo0ePjksvvXTLcMRXKt+/PCCxfN/yQMRysX1HbrjhhvjIRz4STz/9dLzhDW+o/Pn888+PSZMmVZ73mmuu2e5ryqX28umtw4YNi0suuSQGDBgQv/jFL+JDH/pQbNiwYZf/3NOnT493vvOdlSGQRx99dCV/uRR/yy23xLve9a544YUXolNZo++S3gsADcsaYb817/9D8EozZsyoXB5xxBF7dP/yLwxl5V8sNjv33HPjqquuqux0LS/+N09RLxfXN5fTy78MbC4jvec976n8uTyRvWzzfTqVb8adKu/M3J2VbZuODer38q7NbfV+eZfpmrbWre7ft+v29y/vBu3V3GXLfXdmVWvLXj1ncz4flx9/Tnzl6Qfjovuv23LM0ldOOLfy59+99EIs3bg+Ljv6T1P8S1lWyQNAA7NG6DSLFy+OCy64YLf3O+6445TUAQAAUjKlEQDYlpJ6wyoXtMvF8XLhuzy8cPDgwVtuu/XWWyuXb3/72+O5556Lm2++uTJBvVwEb2pq2nK///qv/6qctlq+f7mI/krdu3evFMHLlzvT1tYW3/72tysl83JJ/IADDthy20MPPRR/9Vd/FTfddFOliP5K5bz/8i//suX6QqEQ//iP/1h5jHKez372szt9zi996UuxfPny+PGPf1yZwL5Z+e+hPPn9y1/+cuWfZ2eF6Hbn/axd0nvRewFoWNYI+01JvZ2tWbOmctm7d+89uv/maevlxfdmZ555ZnTp0qWyK3azchm9PHm9PGW9bOTIkXHooYfGY489FsVisfILyOaS+uYCe6fyzbhfCi+/KdE1/6dfJF9p8/UtxWLlsm0P7r+2sOvFenkH6C4f4+VfaltKfzrm6MKDDosj+wyMJ5bOq3zda4ccHAd06xFr21rjp9OeiQ8ednwM6dErxi5bEP/23GMxc+3KGNqjV3z66NPjL0Zs2pABQIOxRug0PXv2jGXLlu32fuX77OrFaAAAADqYEhoAsK3MJrZGVp4o/vWvfz1uv/32ygTzzcXxO++8M4YPH14pppcH1fznf/5nHHvssVsV1MvKt5dL4a/snWxWHoy4u/cEyp2Tr33ta5Vp7a8sqJedc845lcsdPXZ5KOMHP/jBLZ83NzdXpq7fcccdlUL9zkrqzz77bGVC+xvf+MatCuqbn688kOeee+6pTHU/9dRTo1NYo+83vRcA6pI1wn5TUm9n/fr1q1xu3Lhpx9/urFu3rnJZ3pG6WY8ePeKUU06JZ555JlpbWysL+UcffTROPPHELaX2zRPTf/Ob31QW7yeddFKMHTs2DjzwwC3T2TuVAtp+6da06VuxbScvPrS+vLDu2bzpft1fXmBvXrTv6P49m7rs2XO+/NjbPcbLvxj0bN76cQ7t3a/y8UpXTX+2Mun/Y0ecXNmB+rmx98bRfQfG3591Udy/8KX452ceikN69Y2TBwzZZSYA6pA1Qqc54YQT4sknn6wc57ntpJTNyreVN0JuflEZAACABPyuDABsy/qgob31rW+Nb37zm3HbbbdtKamXJ5ivXLmyUgIvTxMfMmRIZaJ6qVSKqVOnxowZM2Lu3Lkxbdq0GDNmzJay+bbKAxB3p1xif9Ob3lT587x58yqPWX7smTNnxoQJE3b62Keddtp2k87L3ZfDDjsspkyZUsn/yo7LZhMnTtxSfP/+97+/3e2rVq2qXD7//POdV1L3Pbjf9F4AqEvWCPtNSb2dHXzwwZXL8i8Ee6L8y0PZiBEjtrr+3HPPrUxGLy/Oy1PVy4v3bY9OKk9ML5fUy2Wk8tdPnz698ktJEr4X98vmo4fWtG06imhbm3eHbj6KaPNxRzs62qh81NC6QlsM7t5jz55zJztPt33OnVm0cV1cM/O5+MwxZ0SfLl3julnPVx7zH44/J0b1PSDOPmBY3LdwVvxm5iSLdYAGVH4xh87xgQ98oLKxsXzs5r/+679WpqOUT+IpK79off/991cmoZT//P73vz91XAAAgMbld2XgZS0DctHWJ3UKoFo4/7JxlYvc5df077777koxvFzyvvXWWyu3vbIDctNNN8UPf/jDmDNnTuXzcpdk1KhRcfzxx1dK5Tt6T6Y8JHFPlMvo3/rWtyqDbsrK5fNDDjmkMqX9ueee2+FjDx48eIePVZ7IXrZ+/fodltRXr15duRw3blzlY2fKPRlqh94LAPVI52X/Kam3s7POOquyyC7vVF2xYsVWE9J35I9//GPl8qKLLtrq+vPOOy++973vVY452jyVvTw5/ZXKvwyUf+koL9rLvxyUvyHKxXVqz+YdmnPWr9nh7XPWbbr+iD6b/ns6rNemX+Tmrl8dpw7cegG8cMPaKGSlOLz3rv/bG9azd3TLN2157O2e8+Ush/fe/pfGV/rRi+NjUPee8b5Djq18PnPdpl3Nh/Ta9M/UnM/HiJ594qWXrwegsWw9P4OOdOGFF8Z73/ve+N3vfhef+cxnKpNPhg0bVrlt/vz5lTVleb347ne/u3KEJgAAAADplJoinjvsodiwbn7qKEBVyMWF8aXUIUjoHe94R6Wkfvvtt1emqT/wwAOVSeXlLkjZvffeG5dffnkMHz48vvOd71SK6eUp6c3NzZWp6+Wv3VcLFiyIj33sY5X3EP7hH/6h0lc59NBDK+8ztLS0xHXXXbfDryuX0Hdk0aJFlcu+ffvussT+xS9+MT75yU/uc26qi94LAPVo21Nj2HubRivSbsql8fJxS+US0H/913/t8r433HBDTJ48uXI80bHHblrobHbSSSdF7969KztSx44dW1m8n3zyydst3MtfW36MclG9/A2RrKTum3G/DOneKw7u2TcmrlwS6wttW91W/vy5VUsqtx/QbdMu0TMOOKhyOWbZgu0e68mXrzt1N7s3m3L5ykJ/3oY1MXebXxLKu1LHLlsQPZqa49h+B+z0MaatWRG3zZsal406Pbq8fBRT8eWjmIqvOMKpfAxTXk0RoDHl/f+/M/2///f/4qtf/WrllJ0NGzZUTtopf5T/XL6uPGH9G9/4RuqYAAAAjc3vykC5EHjqWgV1YIvcy6di0rhe/epXVyaT33PPPZVCerkcXi6uv3KKelm5oP7Wt761Mm29XFAvmzZt2n5N+iwPVywXzv/mb/6mUlY/5phjKgX1sqlTp+70sSdNmrTddeWJ7gsXLozDDz+80nnZkeOOO65yWR7auCPXX399ZajjjBkzotPovOw3vRcA6pI1wn7zm04HKC/cy4vqcgm9XBQql4Jeqbx4L+80LReIevbsGf/5n/+53WM0NTXFmWeeGc8880zlOKVzzz23ct22ytPVy7taH3744UrRfeDAgZGEb8b99vaRR8fGYiF+OGXr46x+8OK4yvXvO/RPGxlOGXBgHNarX9w5f3plgb/ZitaN8fNpT1d2ir5j5NF79Jxl331hzFaL69/OmhTzN6yNd44ctWURviP/PfmpOKbvAfGmgw7fct1hL+9AnbB80+7ola0bY9baVVuuB6DBWCN0uve///2VF7EffPDBylT18sfo0aMr133gAx9IHQ8AAAC/K0PDa+2Xi9kbH0gdA6gqqhuNrlw4//M///PKkMKrr766UhK/+OKLt9y+uTRePjn1laZMmRI/+clPKn8uFAr79NybH3vevHlbXb9q1aotg2929NhjxoyJ+++/f8vnra2t8W//9m9RKpXife97306frzwhvlxiL79vcdddd21128SJE+PrX/96/OxnP4v+/TuxY2CN3i70XgCoO5YI+23Ttkr2WHl3anmhvSMHH3xwZcHdtWvX+OUvf1k5muiaa66JO+64I1772tfG0KFDY+XKlfHEE0/ErFmzKscyffe7391yPNO2ykcolY9w2lxG35Hy9eXHKE/I/Ku/+qtIxoJ9v/3lYSfEvQtnxW9mTorJq5bHSQMGx7MrFsfY5QvjtIFD4r0H/2mxXp6a/y8nvSr++sm74hOP3x5vHn5E9GruEnfPnxlLWtbHPx5/buUoos1Wt7XENTM37WL+m6NP23L9m4cdEXfMmx73LZwVlzxya5wzeHjMWLMyHlw8u/LLwKeOOnWnecs7Th9ePCd+evabtzrW4qJhh8ePpoyPf3rmoXjL8CPiyaXzo61Uig8ffkIH/K0BUPWsEZIZMmRI5QMAAIAqY1IqNLzZx82N4vL1qWMAVcQkdcrKk9OvuuqqyoTy8rT0V04iL992++23xz/+4z/GQw89FAceeGDMnDmzMrCmb9++lfuU+yj74vWvf32lEF4etliegj5q1KhYunRppYBeHspYzrFmzZpKUX3z9Payctflsssuize96U2VKfCPPvpoZfJ6ucfyl3/5lzt9vnw+H9/+9rfjox/9aPzd3/1dnH/++ZXnXLx4cWWqe1tbW/zHf/xH5w5p9H5Wu9B7AaDuWCPsNyX1vVTeObrt7tHNVq9eveXP/fr1i5/+9KeVXw7KRxGNGzcuFi1aVLm+vCP04x//eOWXivIk9Z0pl9Q3Ky/Kd6Q8sb28MF++fPlO79MpfDPut65NTZWF74+nToh7FsyMZ1cujqHde8Unjjg5PnbkSZXbX+m0gUPjF+e+pbIwLt+/7IjeA+IrJ5wbFww9dKv7rmlrrTzutov1su+cdkH8Ysaz8Ye50+I3M5+Lwd16xvsOObayUO/XtdtO814x+al49eARcdagYVtdXz6a6cdnvyn+Y9Ljcd1LL8SwHn3i26e9Po7tN2i//44AqEHWCAAAALA1vytDQ1t7SC4WLn80dQygyuRyO5/yS+M4+uij4/jjj6+U1N/+9rdvdVu5+P3DH/6wMjX9vvvuq1w3bNiwShn8U5/6VLz73e+O8ePHV3orm0vre6pceC9Pb7/iiivi2WefrQxuLA/Bec1rXlN57F/84hdxww03VEro5QGNm5U7L+VhjeWp5+UezUEHHRSf+9znKn2Ypm36Dds64YQTKkMir7zyynj44YcrzzlgwIBK7+UTn/hEnHHGGdGpLNHbhd4LAHXH63j7LZdlWbb/D0Oja/3VLVF65sXUMQCAKtP1c38Z+YMPSh0DOtXGL3wrdQQAgJrS/btfTh0BOtXGr/4wYvW61DGARCae/UysWjk5dQygyjR37R2v++iDqWNAw8paWqPlK/+dOgYAUGVyQw6Ibv/w8dQxapozo2gfTf5TAgB2wBoBAAAAtpb3uzI0qqUntimoAztkkjok1ux7EADYgd2cDsPueSWUdpHr2jV1BACgGnWzRgAAAICtOCYYGlKxWy5mNj2cOgZQrawPIKlcuYCmhAYAbEvnZb8pqdM+uvtmBAC2l7NgBwAAgK3knDoGDWn+ySujZcOS1DGAKpXPd0kdAdB7AQC2ofOy/7wSSvvo6pdmAGAHLNg7zde//vX47W9/mzoGAAAAu9Oje+oEQCfbeEA+5qx9IHUMoIo1d+uTOgKg9wIAbKu79cH+UlKnXeTsKAUAtpXPRc4Lep3m1ltvjRtuuCF1DAAAAHYjp6QODWf20TOiVGxJHQOoYkrqkJ5JqQDAtnJdrQ/2l5I67cM3IwCwLeuDTlUoFGLYsGGpYwAAALA7PbulTgB0otVHRCxe/mTqGECV69K1b+oIgJI6ALAt64P9pqROu7CjFADYjpNWOtVFF10Ujz76aEybNi11FAAAAHbBJHVoHFkuYsbAsaljADWguZuSOqSm9wIAbEfvZb817/9DgG9GAGB7ua5dUkdoKH/+538ezzzzTLzjHe+I8847L4499tjo379/5PM73pf64Q9/uNMzAgAAEBFK6tAwlpzcEmtXT08dA6gBXbr1Th0BUFIHALaR62p9sL+U1GkfSmgAwLa8mNepPv7xj0cul4ssy+LBBx+Mhx56aIf3K99evp+SOgAAQBomqUNjKPSImJk9mDoGUCOau5qkDsl103sBALZhePN+U1KnXeR8MwIA27A+6Fxvf/vbK+VzAAAAqlzPbqkTAJ1g7snLom3FitQxgBrRpZuSOqSWM3wJANiG9cH+U1KnfTjWAADYlvVBp/rmN7+ZOgIAAAB7wCR1qH8bhuRi3qrRqWMANaS5W5/UEQAlNABgW12dtLK/8vv9CGDHCACwI9YHAAAAsL2eSupQ72YdNiWyUiF1DKCGmKQO6em9AADb6W59sL+U1GkfvhkBgG3krA+SWLJkSfzP//xPfOADH4jXve518dWvfrVy/RVXXBF33HFH6ngAAAANzyR1qG8rR2WxbMX41DGAGtOspA7pKakDANuwiW3/NbfDY8CmxXpTPqJYSp0EAKgWPXukTtBwHnzwwfjiF78Y69atiyzLIpfLxfr16yu33X///fGTn/wkxo8fH//0T/+UOioAAEDj6tEtdQKgg2T5iBl9nohYkzoJUGu6dO2dOgI0vFwvm0kBgG3ovew3k9RpF+UCVPTplToGAFBFcn2tDTrT9OnT47Of/Wxs3Lgx3v/+98eVV15ZKapv9u53vzt69eoV11xzTaWwDgAAQBomqUP9Wnjqhli/ZnbqGEANMkkdqkBfm0UAgK3pvew/k9RpN7m+vSNbaTQEAPCntQGd53//93+jtbU1/ud//if+7M/+bLvbL7300jjhhBPiQx/6UKWo/oY3vCFJTgAAgIZXLqnnyiOXUwcB2lNbn1y81PpA6hhAjeqipA7JeV8LANhKt66R69Y1dYqaZ5I67cauEQDglawNOtcTTzwRxx577A4L6pudfvrpcfLJJ8e0adM6NRsAAAB/ksvnIrp1Sx0DaGdzTlgYhVbDnIC9l8s3R1OXHqljQMPzvhYA8ErWBu1DSZ12Y1cpAPBK1gada9WqVTFixIjd3m/w4MGxfPnyTskEAADAjuV6dk8dAWhH64fnYsGKh1PHAGqUKepQHXLlE4+6NKeOAQBUCZ2X9qGkTrvxTQkAbMWu0k41YMCAmDVr1m7vN2PGjBg4cGCnZAIAAGAnepikDvVkxshJkWXF1DGAGtXcrU/qCMDL9F4AgC2sC9qFkjrtRxENANisZ/fINZs20ZnOPvvsmDp1atx33307vc/dd98d06dPjzPPPLNTswEAALC1XK+eqSMA7WT58cVYuWJi6hhADevSrX/qCMBmei8AwMty1gXtQkmddmNHKQCwmXVB5/vkJz8Zzc3N8fnPfz7+53/+J8aMGVO5vq2tLWbPnh2/+MUv4vLLL6/c56Mf/WjquAAAAA0tN6Bv6ghAOyg1R8zo9mjqGECN69HnoNQRgJd5fwsA2My6oH0oqdNufFMCAJtZF3S+o446Kr71rW9FLpeLH//4x3HppZdW/nzXXXfFRRddVLmtXFj/13/91zj++ONTxwUAAGhouQNMTIV6sODUtbFx3YLUMYAa16Pv8NQRgJd5fwsA2My6oH00t9PjgOMNAIA/sS5I4s1vfnMcd9xx8ctf/jKeeOKJWLBgQZRKpRg8eHCcddZZleL6MccckzomAABAw8sd0C91BGA/tfbPxez1D6SOAdSBHn1GpI4AvEzvBQDYwrqgXSip035694rI5yJKWeokAEBidpSmc8ghh1SmpQMAAFC9TFKH2jf72DlRXL4+dQygDpikDtXD+1sAwGbWBe1DSZ12kysX1Hv3jFi9LnUUACAxkybSW7t2bSxevDi6dOkSBx54YHTr1i11JAAAAF6WG2iSOtSytYfkYuHyx1LHAOqEkjpUEe9vAQAvU1JvH0rqtPs3ZqakDgANz2I9nRtvvDF+85vfxOTJkyPLNp1w09TUFKecckp8/OMfj9e//vWpIwIAADS8XHngS/euERtbU0cB9lKWy2LGkKcjVjlZGNh/uXyX6NbrwNQxgJd5fwsAqOjaJXI9DAJsD0rqtPsRpdncRaljAACJOba885VKpfjCF74Qd999d6WcXi6mDxw4sHLb8uXLY+zYsTFu3Lj4q7/6q8r9AAAASCs3sH9k8xenjgHspaUnFmL1qhdTxwDqRI8+B0Uul08dA3jliUe58q601EkAgJScgth+lNRpV7nBm4pQAEBjyw0ekDpCw7nhhhvirrvuiiFDhsRXvvKVysT0bt027ezdsGFD5bZvfetb8dOf/jROOumkuPDCC1NHBgAAaGi5A/opqUONKXbLxaz8Q6ljAHWkR5/hqSMAr5Dr1jWiPE191drUUQCAhPRg248tubSrvEIaANCnV+S6O/aos/3ud7+L7t27x9VXXx1vetObthTUy3r06BHveMc74he/+EVlwnr5EgAAgLRMZILaM++UFdGyYWnqGEAd6dFXSR2qTV4pDQAansGM7UdJnXZlBwkAYLGexrRp0+Lss8+OQw45ZKf3OeaYYyr3ef755zs1GwAAANvLHdA/dQRgL7QMysfcNaNTxwDqjJI6VB/vcwEAuQP1YNuLkjrtyjcnAGDCRBo9e/aMXC632/uVJ6x36dKlUzIBAACwc0rqUFtmHTU9SsWW1DGAOtO9j5I6VBvDGQGAvE1r7UZJnXaV69k9oleP1DEAgIRMmEjjta99bTzxxBMxd+7cnd5n+fLlMWbMmDj//PM7NRsAAADbyx3QL3UEYA+tOiJiyfIxqWMAdcgkdag+3ucCAGxaaz9K6rQ7C3YAaGxOVknj7//+72PQoEHxkY98JO67777tbp86dWp84hOfiF69esWXv/zlJBkBAAD4k9zAfhG7PxALSCzLRcwc+FTqGECd6mmSOlQdpTQAaHA9ukeud8/UKepGc+oA1OeCPZs1P3UMACARL951jrPOOmu761pbW6OlpSUuu+yyShl9xIgR0a1bt1i0aFHlo2zYsGHx2c9+Nq6//voEqQEAANgs19wc0bd3xKq1qaMAu7D4lI2xdvWM1DGAOtSlW79o7tYndQxgG7kD+kfk8xGlUuooAEAChjS3LyV12l1+8ICwVAeABpXPbXrxjg63evXqXd6+du3amDx58nbXz5s3L+bPt6EQAACgGpR/h86U1KFqFXrmYlZxdOoYQJ3q0dcUdahGuaZ85A7oF9mSFamjAAAJKKm3LyV12p3pqQDQuHID+kWuuSl1jIZw3333pY4AAABAe5TUZ8xNHQPYibknLY62FatSxwDqVI8+SupQzeU0JXUAaEx5/dd2paROu7OTBAAal3VA5xk+3BsYAAAAtc7JpFC9NgzNxbxVD6WOAdSxnv0PTh0B2OVwxhmpYwAACei9tK98Oz8eRG7QgIhc6hQAQApOVAEAAIA9lxs+JHUEYCdmHvpiZKVC6hhAHesz6JjUEYCdUE4DgMal99K+TFKn3eW6doncgH6RLXf8IQA0mtwQi/WUHn744fi///u/mDVrVrS0tOz0frlcLu69995OzQYAAMD28iOHpo4A7MDKUaVYvmJC6hhAnes76NjUEYCdyA8ZlDoCAJBCLhe5A/Ve2pOSOh0iN2KIkjoANKD8CG+up/Lggw/GX//1X0eWZbu9b7mkDgAAQHq53j0j+veJWLkmdRTgZaWmiBm9H49YmzoJUM+6dO8f3fsclDoGsBO54QdGlN9K2f1bLgBAHSkX1MtDmmk/Sup0WEGt9OyU1DEAgM6Uz0du2ODUKRrWj370o0pB/T3veU+85S1viX79+imjAwAA1Mrr6UrqUDUWnrou1q+ZmzoGUOdMUYfqluveLXKDBkS2ZEXqKABAJw9npn0pqdMhciN9swJAo8kNHRS5ZsvLVKZNmxbHHXdcfP3rX08dBQAAgL2QHzkkSs9NTR0DiIi2vvmYvXF06hhAA+gzWEkdql1uxFAldQBowGEStK98Oz8eVPhmBYDGk7ejNKmuXbvGQQc5HhYAAKAWyy9AdZh9/PwotK1NHQNoACapQ21sJgUAGouf/+1PSZ0OkevVI2JA39QxAIBOlBvpTfWUzjvvvJg4cWK0tLSkjgIAAMBesOkbqsO6EblYsOLh1DGABmGSOlQ/m0kBoMHkcpEb7nW69qakTocxTR0AGos31dP6whe+EK2trfGlL30pli1bljoOAAAAeyjXp1dE/z6pY0DDmzl8YkRWSh0DaABduveLHn2cigk18b5XLnUKAKCz5AYPiFy3rqlj1J3m1AGo7wV7aeKU1DEAgM6Qz0du2IGpUzS04cOHxxe/+MX453/+57j33nvjwAMPjAEDBuzwvrlcLm688cZOzwgAAMAuXk9fuSZ1DGhYy04oxsqVk1LHABpEn0GmqEMtyHXvFrkD+ke2dGXqKABAJ3CKSsdQUqfD5ExTBYCGkRtyQOS6WFqm9OCDD8ZXv/rVyp9LpVIsXLiw8rGzkjoAAADVdTJp6blpqWNAQyp1ycXMLg9HtKVOAjSKvkrqUFNlNSV1AGgM+ZH6rh1Bk4gOkx9pZwkANAo/99P74Q9/GMViMS644IJ461vfGgMHDlRGBwAAqBGGvkA6809ZHRtXL0odA2ggfQYrqUNNnXj09OTUMQCAThoiQftTUqfD5Hr3jOjfJ8IRpQBQ93LDvZme2tSpU2PUqFGVsjoAAAC1xeZvSKNlQC7mrH8gdQygwfRVUoeakbNOB4DGkCv3Xg5MnaIu5VMHoL7ZXQIAjcGxR+l17949Dj744NQxAAAA2Ae5Pr0i+vVOHQMazuxjZkexsCF1DKCBdOnWL3r0GZY6BrAXk9QBgPqXGzQgct27pY5Rl5TU6VCmvwBAA8jnIjfMjtLUzjvvvJgwYUK0tramjgIAAMA+MPQFOteawyIWLX88dQygwfQZdEzqCMBeyPXoHrlB/VPHAAA6WM7rch1GSZ0OlTvULnAAqHe54UMi17VL6hgN73Of+1yloP53f/d3sWDBgtRxAAAA2EumNELnyXJZzBg8vvyn1FGABtNnsJI61JrcocNTRwAAOlj+MD/vO0pzhz0ylL95y4v15qaIQjF1FACgg+SPGJk6AhHx85//PEaNGhWjR4+ufAwaNCj69+8fzc3bL/lzuVzceOONSXICAACwYzknk0KnWXJSW6xZNTV1DKAB9R10bOoIwF7KH3lwlMZOSh0DAOjgn/d0DCV1OlSuS3PkDj4oshlzU0cBADqIxXp1uPbaa7f6fMmSJZWPHSmX1AEAAKjCoS/l39cyk52hIxW7R8zKPZQ6BtCg+g89JXUEYC8Z1gQAda53z8gPHZQ6Rd1SUqdTimtFJXUAqE/5XOQPH5E6BRFx9dVXp44AAADAfsj17B65EUMim7MwdRSoa/NOXhGtK5eljgE0oF4DjohuvQanjgHspfwB/SMG9I1YsTp1FACgA9iQ1rGU1OmUb+Ji6hAAQIfIDR8Sue7dUscgIs4666zUEQAAANhP+aMPjaKSOnSYjYPzMXfN6NQxgAY1cLjXcKGWey+lsZNSxwAAOoCSesfKd/Djw6YjSpubUscAADqAxToAAAC0n/zRh6SOAHVt1pHTolRsTR0DaFAHjDg7dQRgH+WPPDh1BACgg/g537FMUqfD5bo0R+6QYZFNn5M6CgDQzizWq8dXvvKVPb5vLpeLf//3f+/QPAAAAOy9/GHDI7p2iWhtSx0F6s6qo7JYuvyp1DGABpXLN0f/YaenjgHsI++HAUCd6t0z8kMHpU5R15TU6bQpq0UldQCoL/lc5A8fkToFL7vpppt2W0wvy7JMSR0AAKBK5ZqbI3/YiCi9ODN1FKgrWT6LGf3GRKxJnQRoVP0OPDGau/RMHQPYR/mB/SIG9I1YsTp1FACgnXutdCwldTptV2nxj4+ljgEAtKPc8CGR694tdQx2M0m9VCrFqlWrYty4cfHUU0/FO9/5znjf+97X6fkAAADYM/lRhyipQztbdMrGWLdmVuoYQAMbOOLs1BGAdui9lJ56LnUMAKAdOS2l4ymp0ynyhwyLaG6KKBRTRwEA2onFenW59NJLd3ufa665Jr7xjW/EW97ylk7JBAAAwN7LH3Vo6ghQVwo9c/FS4cHUMYAGN3C4kjrUw6RVJXUAqC8mqXe8fCc8B0SuS3PkykV1AKBuWKzXnksuuSQOO+ywuPLKK1NHAQAAYCdywwZH9O6ZOgbUjTknLY62llWpYwANrLlr7+h34PGpYwD7yfAmAKgzvXtGfuig1CnqnpI6nUaRDQDqSD4X+cNHpE7BPjjyyCNj0qRJqWMAAACwE7lcLvJHHZI6BtSFDQflYv5KU9SBtAYMOz1y+abUMYD9lB/YL2JA39QxAIB2os/aOZTU6TRNRzuiFADqRe7ggyLXvVvqGOyD6dOnp44AAADAbuRHeT0d2sOMQ16ILCumjgE0uIHDz0kdAWgnTUfbTAoA9SLv53qnaO6cp4GI3KHDInr1iFi3IXUUAGA/NR13ZOoIbGPt2rU7va1QKMSSJUvil7/8ZcyYMSPOOcebIgAAANWs6ahDopA6BNS4FceWYsWKZ1LHAIiBI85KHQFoJ/njj4zikxNTxwAA9leu3Hs5InWKhqCkTqfJ5fORP/bwKI2dlDoKALCf8icoqVebM888c7f3ybIsmpqa4pOf/GSnZAIAAGDf5Ab0jdyBAyNbvDx1FKhJpaaIGT0fi9j5nn6ATtGt95Do1d8JKVAv8kcfGtHcXJ4OlDoKALAfciOGRq5fn9QxGkI+dQAaS9PxCm0AUOtyB/SP/NBBqWOwgwL6zj7KevbsGaeffnr88Ic/jHPPPTd1XAAAAPakAAPsk4WnrosNa+eljgEQBwx3qiXUk1zXLpE/+uDUMQCA/WSKeucxSZ1OlR91aERTU0SxmDoKALCP8hbrVWny5MmpIwAAANCO8kcfEsVHxqeOATWntW8uXtr4QOoYABUDR5yVOgLQzvLHHxml52ekjgEA7If8CYYtdxaT1OlUue7dIn/kyNQxAID9YLEOAAAAHS9/5MEReW/jwN6affy8KLatSx0DoPzueAwcfnbqEEBHTF7NpU4BAOyzAX0jP3xI6hQNw6ubJNlVCgDUqB7dIn+4DWcAAADQGUNfcocOSx0Dasq6g3OxcMWjqWMAVPQZNCq69hiQOgbQznL9+kRuxNDUMQCA/dlwRqdp7ryngk2ajj8yCjfemzoGALAP8sccFrkm+xyrwVe+8pV9/tpcLhf//u//3q55AAAAaH9NJ4+Kwoy5qWNAzZhx0LMRK0upYwBUDDnijakjAB3Ze5mzMHUMAGAfGLLcuZTU6XS5AX0jN+zAyOYvTh0FANiHF92oDjfddNNeF9NfSUkdAACgRkrqN98fkWWpo0DVW3pCIVatfD51DIAthhyupA71Kn/8ERF3PZI6BgCwt7p1jfyRB6dO0VCU1Em2YC8qqQNAbcnnI3/s4alTsA+T1AuFQlx99dWxePHiyLIsRo4c2aHZAAAAaB+5vr0jf/iIKE2fkzoKVLVi14hZXR6JaEudBGCTvgceHz36Dk8dA+gg+eFDIgb0jVixOnUUAGAv5I85LHLNTaljNBQldZJNYS3e83jqGADAXii/KZ7r0T11DF526aWX7tH9pkyZEpdffvmWgvp73vOeyucAAADUhvypxyipw27MP2VVbFy1KHUMgC2GHPFnqSMAHazpuCOi+OiE1DEAgL38+U3nynfy80FFbuTQiL69U8cAAPZC/vgjU0dgL/3sZz+Ld7/73fH888/HoEGD4sorr4yvf/3r0atXr9TRAAAA2ENNJ42KyOdSx4Cq1TIwF3PWjU4dA+AVcjHk8AtThwA6mPfNAKDG5HORV1LvdNi64XoAAQAASURBVErqJJHL5aLpBAt2AKgleT+7a8acOXPikksuie985zvR2toab37zm+MPf/hDvPa1r00dDQAAgL2U690z8kcenDoGVK2XRs2KUmFj6hgAW/QbclJ07z00dQygg1XW6N27pY4BAOyh3KHDI9erR+oYDUdJnWSaTj02dQQAYA/lDhkW+QP6p47BHrj22mvjbW97W4wbNy769u0b3/3ud+OKK66Ifv36tftz3XzzzfHOd74zTj311Dj33HPj7//+72PevHn79FhZlsWHP/zhGDVqVMydO7fdswIAANSy/CnHpI4AVWn14RGLlz+ROgbAVoYc8cbUEYBOkGtuiqaTjk4dAwDYQ02nHZc6QkNSUieZ3OEjIgb0TR0DANgDTadbrFe7xYsXxyc+8Yn42te+FuvXr4/XvOY1lenpF198cYc8X7n4/g//8A/R0tISH/zgBysl9TvuuCPe9a53VSa5762rr746nnzyyQ7JCgAAUOuaTjw6oslbOvBKWS6LmQeMSx0DYGu5fAw5/MLUKYBOkvf+GQDUhqamaDIEIonmNE8LEblcrrI7pXifCRcAUNXyeYv1KnfbbbfFN77xjVi1alX07NkzLr/88njve9/bYc83efLk+PGPfxynn356/PKXv4yuXbtWrn/zm98cl112Wfzbv/1b5fY9NWPGjMrEdwAAAHasfBRx/qhDozR5RuooUDWWnNwWa1ZPSx0DYCv9h54a3XoNTh0D6CT5Iw6O6Nc7YtXa1FEAgF3IH3tY5Hp2Tx2jIRm7QVKmsgJA9csfc2jkevdMHYMdWLlyZXz2s5+NL3/5y5WC+hlnnBG33HJLhxbUN089L/vbv/3bLQX1sje+8Y1x5plnxujRo2PRokV79FjFYrEykf2AAw6IUaNGdVhmAACAWtd0qg3ksFmhey5mZQ+mjgGwnaFHvDF1BKAT5fK5aDr12NQxAIDdaDr9+NQRGpaSOknlhw6K3PADU8cAAHahfPIJ1ef++++Pt771rXHPPfdUiuLl6em/+c1vYuTIkR3+3E888UQ0NzdXCunbOueccyLLssp99sSVV14ZEydOrExf79WrVwekBQAAqA/5E46KaG5KHQOqwrxTlkbrxuWpYwBsJZdrigMPvyB1DKCTKb0BQJXr3i3yxx2ROkXDak4dAMrFt8K8xaljAAA70q3rpjfBqTqf/vSnI5fLVf7cu3fvygT18seeKH/djTfeuE/P29raGvPnz4/hw4dvNUV9s80l+Rkzdn8E/fPPPx8/+tGP4v3vf3+ce+658b3vfW+fMgEAADSCXI9ukR91aJQmTU8dBZLaeGAu5q4yRR2oPgOGnR5dewxMHQPoZPnhB0Zu6KDIFi5NHQUA2IGmk46OXBdV6VT8zZNc02nHRuEPD0ZkWeooAMA28iceFbmuXVLHYCfKE8vLli1bVvnYU5vL7fti1apVleft16/fDm/v06dP5XLNmjW7Lbv/wz/8QwwZMiS+9KUv7XMeAACARtJ0yjFK6jS8WUdMjWx5W+oYANsZcsQbU0cAEmk6/bgo3P5Q6hgAwA7kTz8udYSGpqROcrl+fSJ/5MFRmvpS6igAwA5eVKM6XX311Umet61t05vAO5qi/srrW1padvk4//3f/x1Tp06t/HP06tWrA5ICAADUn/zxR0Y0N0cUCqmjQBKrjs5i6fJxqWMAbCeXb4rBh70hdQwgkabTjovCHQ9FmM0IANWlf5/IH3Fw6hQNTUmdqtmtoqQOAFWmT6/IH3VI6hTsxFlnnZXkebt3775VWX1HE9LLevbsudPHGDt2bPziF7+ID33oQ8n+OQAAAGpRrnu3yB97eJQmTkkdBTpdlo+Y0ffJiF0f3gaQxMDhZ0fX7v1TxwASyQ3oG7nDRkQ2Y27qKADAKzSdemzk8vt+0jz7L98OjwH7remkoyO62DMBANWk6dRjIpe3XGRrvXv3jnw+H2vW7Pgd4c3X9+nTZ4e3r1+/Pr7yla/EyJEj44tf/GKHZgUAAKhHTacdmzoCJLHo1A2xbo2BR0B1GnLkRakjAIk1nX586ggAwA5OOyEtrWCqZ/rL8UdE6ekXU0cBAF7mxTR2pGvXrpWC+fz58yvT1Lt06bLV7XPmzKlcHnnkkTv8+okTJ8bs2bMrfz7llFN2eJ8LLrigcnnffffFiBEj2vmfAAAAoLblTziycvpZrFmXOgp0mrY+uZjV+kDqGAA71KVbvxhy+IWpYwCJNZ0yKgo33RtRKKaOAgCUO6lDB0V++IGpYzQ8JXWqqginpA4A1SE35IDIjxyaOgZV6qyzzorrr78+xo8fH2efffZWtz3++OORy+XitNNO2+HXDh8+PC677LId3vb73/8+FixYEB/+8Iejb9++lQ8AAAC2lmtqiqZzToriPY+njgKdZs4Ji6KwfMenugGkdtCot0ZTc/fUMYDEcj26R/64I6L07JTUUQAAgxmrhpI6VSN/7OER/ftErPQiIwCkVn6zG3bmXe96V6WkfsUVV8Qvf/nL6N590xsw99xzT4wdO7YyCX3o0B1vcihPRv/MZz6zw9see+yxSkn90ksvNUEdAABgF5rPPSWK9z0RUcpSR4EOt35YLhaseCh1DICdyMWI496TOgRQJZrOOVlJHQCqQVM+ms46IXUKlNSpJrl8PprPPikKdz+aOgoANLbm5mg688TUKahip556alxyySVxzTXXxNve9rZKKX3RokVx5513xqBBg+IrX/nKlvs++eSTMWbMmDj22GPjwgsdeQsAANAecv37RP64I6P03NTUUaDDzTz4+chWFFPHANihgSPOjp79RqaOAVSJ/KhDI3dA/8iWrUwdBQAaWv7EoyLXp1fqGJT/XaQOAK/UdPZJEflc6hgA0NDyp4yKXE9Hk7Jr//zP/1z56Nq1a/z617+uFNEvvvjiuPbaa2PkyD+9KVO+/gc/+EHce++9SfMCAADUm6bzT00dATrc8uOKsWLFs6ljAOzUiONNUQf+JJfLOa0YAKpA07mnpI7Ay3JZljkLkqrSetWNUXpuWuoYANCwun7mksgfNjx1DKhZG7/wrdQRAABqSvfvfjl1BKhJ5bd3Wr/5s8iWrEgdBTpEqTliwkkPx4Z181NHAdih7r2HxvkfuDVy+abUUYAqkq1ZFy3/78cRRSfBAEAKuQMHRrfLP5E6Bi8zSZ2qYxcLAKSTO2iwgjoAAADUypTG87yeTv1acOpaBXWgqg0/9p0K6sB2cn16Rf7Eo1LHAICG1XTOyakj8ApK6lSd/KjDIjewX+oYANCQvLkNAAAAtaPprBMjunZJHQPaXWu/XMze8EDqGAA7lct3iWHHvD11DKBKeb8NABJpbo6mM09InYJXUFKn6uTyuWg6/9TUMQCg8XTvGk1nHJ86BQAAALCHcj26R9Mpx6SOAe1u9nFzo9i2PnUMgJ068LA3RLeeB6SOAVSppiMPjtzQQaljAEDDaTrt2Mj16pE6Bq+gpE5Vajr7pIguzaljAEBDaTrjhMh165o6BgAAALAXml5l6Av1Ze0huVi4/NHUMQB2acTx70kdAahy1ukA0PmaXnVa6ghsQ0mdqpTr2T2aTjsudQwAaBw5i3UAAACoRfkRQyN38EGpY0C7mTH06YjIUscA2KneA4+MAQcpnwK71nT68RE9uqWOAQANI3fo8MiPGJI6BttQUqdqKcoBQOfJH31o5A8cmDoGAAAAsA+az1eUoz4sPaktVq+cnDoGwC6NOO7dqSMANaB8enHTmSekjgEADaPZKSZVSUmdqpUffmDkDh+ROgYANASbwwAAAKB25U85JqJXj9QxYL8Uu+ViZv7h1DEAdqmpS68YevRbUscAakTT+adVTjMGADpY316RP3lU6hTsgJI6Va351aenjgAAdS83qH/kjz0idQwAAABgH+W6NJvSSM2bf/KKaNmwJHUMgF066KiLo7lLz9QxgBqRHzzAe3AA0Amazzs1ck1NqWOwA0rqVLX8iUdH7sCBqWMAQF1rev3Zkcsb4wAAAAC1rOn8U01ppGZtPCAXc9aOTh0DYLdGHP+e1BGAGtN8wdmpIwBAfevWJZpedVrqFOyEkjpVrVyYa3r9WaljAED96tvbpDUAAACoA/kD+kf+uCNTx4B98tLRM6NUbEkdA2CXBg4/O3oPNBEZ2Dv5w0ZE7vARqWMAQN1qOveUyPXsnjoGO6GkTtVrOuP4iP59UscAgLrU/LozItfsyCMAAACoB81/dm7qCLDXVh8RsWT5k6ljAOzWYaf/VeoIQI1qvuCc1BEAoD41NUXza89InYJdUFKn6uUq/yM5M3UMAKg/PbtXdpQCAAAA9SE/8qDIH3N46hiwx7JcxIyBY1PHANit/gedHgMOOjV1DKBGNR17eOSGH5g6BgDUnaYzj49cPwOQq5mSOjWh6ZyTInr1SB0DAOpK06tOi1y3rqljAAAAAO2o+c/OSx0B9tiSk1ti7erpqWMA7Nbhp38idQSgxjW/4ezUEQCgvuRz0eTna9VTUqcmlAt0za86LXUMAKgfXbtE86tPT50CAAAAaGf5Q4dF/uhDU8eA3Sr0yMXM7MHUMQB2q//QU2Lg8LNSxwBqXP7kUZEb1D91DACoq5+t+UEDUsdgN5TUqRlN5SJdty6pYwBAXWg69+TIOaUEAAAA6lLzRaapU/3mnrwk2jauSB0DYLcOO80UdWD/5fL5aHq9aa8A0F6a33BO6gjsASV1akauZ/doOveU1DEAoPY1NUXza89MnQIAAADoIPnDRkT+yINTx4Cd2jAkF/NWmaIOVL9+B54YB4w8N3UMoE40nXlCRN/eqWMAQM3LH3t45IcfmDoGe0BJnZrS/NozIpqbUscAgJrWdMZxkevfJ3UMAAAAoAM1/5lp6lSvWYdNiaxUSB0DYLcOO/2vUkcA6kiuuSmaX3dG6hgAUPOaLzBFvVYoqVNTcv36RNMZJ6SOAQC1K5eLpjc4ShAAAADqXXmSeu7wEaljwHZWjspi2YrxqWMA7FbfwcfHoIPPTx0DqDNN554S0bN76hgAULNy5RMEveZVM5TUqTlNbzgrIp9LHQMAalL+5KMjP3hg6hgAAABAJ/j/2bsPcLvKOl/8v7XbOem9J6QnkISSAAmhQ0KVDlJExTKOjjg6Ol51vHfavVMcZ67O/K1zp9jGGbF3dFABG0WpioB0AoTQQgqknPZ/3hV2PAknfZ+sUz6f59nPPmWfvX+7nf2ud33X762cKlhHz9JRinhw8A1FlwGwW6Yv+r2iSwD6oKypFuVjFxVdBgD0WpXluqj3JkLq9Dql0SOifPj8ossAgN4ny+ycBgAAgH6kPGdqZNMnFV0GbPXkwhfjxfUrii4DYJeGjJ4bY6YdX3QZQB9VOeGIiAG6qQPAnsqmTozyQTOKLoM9IKROr1Q5/diISrnoMgCgVykfMT9K40cXXQYAAACwH1VOObroEiDXMiSLRzZfV3QZALtFF3WgO2UDmqOybEnRZQBAr1N9hQNJexshdXqlbMTQKB+zsOgyAKD3qJS3HOQFAAAA9CvlA6dHdsCEosuAWLFgZbRuXld0GQC7NHjk7Bgz7aSiywD6uPKxiyKGDS66DADoNUoHzojSrAOKLoM9JKROr1VZvjSiuVZ0GQDQK6SDu9JBXgAAAED/UzlVN3WK9eKkLFau/mnRZQDslumL3hhZlhVdBtDHZbVqVE47pugyAKB3yCIquqj3SkLq9FrZoAFROcnyRwCwS821LQd3AQAAAP1Sed7MyKaML7oM+rEHJ/86Ojraii4DYJcGjZgZY2csL7oMoJ8oLz44srEjiy4DAHq80sJ5UZo0tugy2AtC6vRq5ROOiBgyqOgyAKBHSwd1pYO7AAAAgP5LN3WK8uz8tnj++V8XXQbAbplx+O/pog7sN1mpFJUzdYUFgJ0ql6NyxrFFV8FeElKn9y9/ZGIdAHZsyKAtB3UBAAAA/Vp5/qwozZxSdBn0M+2ViIeaflZ0GQC7ZejYBTF2xilFlwH0M+VD5kR2wISiywCAHqu89NAojRpedBnsJSF1er3yUYdGNto/IQDoSjqYKx3UBQAAAFA59+QI3WHZj55YuC42vrCy6DIAdsucpe/SRR0oROWsE4ouAQB6pqZqVE5ZWnQV7AMhdXq9rFyKyhnHFV0GAPQ46SCudDAXAAAAQFKaPC7KR8wvugz6iU3Ds1jx4nVFlwGwW8bNOCWGjzefDhSjPOuAKB04vegyAKDHKZ9wZGRDBhVdBvugsi9/DD1F6bADI7v25uh4bFXRpQBAj5EO4koHcwEAAADUVc48LtruuDdic0vRpdDHPXrQo9H23ItFl8F+9MKGjvj+zZvjVw+2xuq1HTF4YBaHzKzEGUfVYsjAbbtTP7yyLa6+cXM8+lR7bNzcERNGluLYQ6qxdEFltztZb27piB/d2hK/vKc1nlnTHgObsjhoWjm/vdHDtp0Xbe/oiG/8ZHPc8Ost//sOnFqJi06sxdBB216upbUj/vJTL8a8aeV41SnN+/yY0DuUyrWYddTbiy4D6Ocqrzg+Nt/7UERH0ZUAQA8xeGBUTjyy6CrYR1JL9AlpsioN2AGALbLJ4/KDuAAAAAA6y4YNicpJi4sugz5u3dSIVc/dUHQZ7EcbNnXEh656MX54S0uMGFKKExZWY9KYUlx/e0v87edejNXr2rde9q6HWuP/fmFD/Paxtpg/rRzHHlzNg+qfv2ZTfPZ7m3br9traOuLjX9sQ3/rZ5qhVI44/tBqzJpfj5t+05re34qm2bS7/49tb4ge/bIkDxpXj8LnVuPP+1viXb2182fVed1tLvLCxI16xtNaAR4XeYsqCy2LAkIlFlwH0c6VJad/eQUWXAQA9RmX5UZE1NxVdBvtIJ3X6jPLc6dF24PRov+ehoksBgMJVzj5xtzsOAQAAAP1L+aTF0XrjHRFr1hddCn1QR9YRD41Lry9tQPuT796wOZ58riPOXFrbJuB93W2b40vXbo5v/3xzvOa05mhr74jPfX9TVMoR73nVwJgwaktPtXOPq8U/fnFD3Hx3axy9oC1mTynv9PZ+fEdL3PdYexw1v5Jfb92vDmqNT359Y3zlus3xRxcP2Przn93ZGuNHZnHlBc35vOmIIVl846eb8zD7lLFbbuvFjR3x3zdvjmWLqjFssF5v/UW1eURMX/SGossA+F039V/dF9HaWnQpAFCobMyIKB+9sOgyaABb1/QplfOXRZR3PmkFAH1d6qBenj216DIAAACAHiqrVaN6ptVJ6R7PHNwaa9fcW3QZ7GfPrmmPIQOzOOWI6jY/XzJvy/cPPbGls/mTz7bHwKaIw+dWtgbUk0o5y3+WX3bltl3Qu/LU6o4Y1BxxxpJtO54fPKOSX//21/H0mvaYNKa8tbFH6vKe//z53x1M8b2bNke5nMXyI3VR709mHvHmqNQGF10GQK40clhUli0pugwAKFzl/OWRpaOb6fV0UqdPKY0ZGeWTjoy2H9xYdCkAUIymalTPOanoKgAAAIAernTE/Mh+dlt0PLqy6FLoQ9qasni49OOiy6AAv3/u77qWd5ZC6cnQQVtC4Sko/mevH9T1ZZ+rX3bXK0ResqwpP21vzfr22LApYuTQba9jUHMWmzb/LpC+8aWvB77UhP3Zte1x/e0tccEJtWiuWaGyvxg8cnZMOuiCossA2Eb55CXR9su7ouPZ54suBQAKUTp4dpQPnF50GTSITur0OZXlSyNGDC26DAAoROWUoyMbPqToMgAAAIAeLnUTrl6wPEIWkwZ6/LDnYtOGZ4ougx7gxY0dcdtvW+PfvrMxSqWI04/atsN6XXt7Rzyzpj2+9bNN8bM7W/Pu6ovm7HmftRQ6v/vh1vjYVzdGip+fuXTbbujTJ5bj3kfb4sEn2mL9ho74yR0tqd9HTB6zpTPft362OUYOyeLYQ7quk75p7jHviaykOyPQs2TVSlTOX1Z0GQBQjGolqueeXHQVNJBO6vTNZUrPPTlaPv31oksBgP0qGzcqyiccUXQZAAAAQC9ROmBClBcfHG03/aroUugDNo3O4rF11xddBj1ACoB/4Yeb8q9LWcQVZzTFgQd0vVv6/35hQzz85JYO6mOHZ/G2C5ujVt2zo2fueqg1Pv61jVu/P++4Whw1f9uw+TnH1OLBx9vy28vrKkVctrwpBg/IYsVTbfHLu1vjjWc1RzkVnMLzHR1RyhzF05eNm3VajJi4qOgyALpUnjcz2ubPjPa7Hii6FADYryrLj4ps5LCiy6CBhNTpk8qHzIm2udOj/d6Hii4FAPabygXLIyvr+gIAAADsvsorToi2O38bsWFLoBT21sOzH4z257yOiDz4fcqR1Vj7Qkfcfl9rfPrqTbF6XUeccuS23c2T2VPKMWtyOR5d1R6/XdEWH/z8hrjyguaY9FKH891Rq2Sx/Ihq3r39Vw+2xdd/sjm/vVeeVMtXjUjGjijF/3ztwLjzwdbYuKkj5h5Qjomjt9xGuvy0CaVYOKcSq9e1x3/896b47aNt0VSLOHpBNc49rrY1vE7fUK4OjDlHvbPoMgB2qnLesth87yMRra1FlwIA+0U2eniUT1pcdBk0WKnRVwg9ReWCZRGCegD0E6XD5kZ59tSiywAAAAB6mWzwwKicfmzRZdDLrZ3VEU8/d3PRZdBDpLD3ecc1xWtPb86D4Sm0noLgjzzZ9rLLpsudf3xTvOOVA+KSZU2x5oWO+MzVm6Kjo2O3by8F3dN1XH5qc/yvKwbG+FGluP72lrj9vm1vb9CALJbOr8ZJi2pbA+p3P9wa9zzSFucd35R//5mrN8bKZ9rzruqvWFqLa29riR/8smWfHxN6lumL3hhNg8YUXQbATpVGDY/KsiVFlwEA+03l/OWRVfTd7muE1OmzSmNGRvnEI4suAwC6X1M1quecXHQVAAAAQC9VPmZhZBOE9dg7HaWOeHDEL4sugx5q1LBS3lU9ufOBnXeCPf7QakwcXYrHn2mPZ9bsfki9sxSIP/voLR3b79jF7aUgfArPHzKzHLMmlWPls+1x32Pteb2Hza7kYfaDp5fj2luF1PuSgcOmxgEHX150GQC7pXzykshGDS+6DADodqUFs6J80Iyiy6AbCKnTp1VOWRoxYmjRZQBAt6qccnRkw4cUXQYAAADQS2WlUlQvWF50GfRSTx26KdavfbDoMihQS2tH3P1Ia9z1UNeh8NHDtuySXr+hI55b2x6339caT61u38Flsy2XfbFjp+Hy3z7aml9PV0btxnUkN9/dGk882x7nHreli/qq57bUNHbE73ahjx1ZinUvdsSGTXsXmqfnmXvMu6NU3nLgBEBPl1UrUTlPoyoA+rhqJarnLSu6CrqJkDp9WlZLnWVPKroMAOg22diRUT7hiKLLAAAAAHq50swpUTpiftFl0Mu0Dszi4bbrii6DgrW1R3z8qxvjU9/dGK1tLw9zr1jVtjX8/esH2+JfvrUxfnTr5pddrr29Ix5/uj2yLGL08B3vxs6yLP7tO5viX7+9Mda88PKw+4qnXh427ypY/+2fb46jF1Ri/Mgtl2tr31J7e6erbH0pB59qovcbM/3kGDXl6KLLANgj5fmzojRvZtFlAEC3qSw7KrKRw4oug24ipE6fVz50bpTmTiu6DADoFpULTomsXC66DAAAAKAPqJ57csSQQUWXQS/y2CFPR8umNUWXQcGaa1kcMrMcGzZFfOeGbcPnj65qix/e2hJN1YgjDqzEobPLUatE3Pjr1njs6S3h9Xp39G/9bHM8u7YjDp1VjiEDd54KXzyvEh0dEV+7fnO0py9e8szz7fHtn23OQ+VHH1zZ4d9ff3tL3tn9zKW1rT+rh9UfePx3dT34RFsMH5zl95Herdo8LA489n1FlwGwVyrnL4uo7PhzDQB6q2z08CifvLjoMuhGRjD0mwDf5v/76YjNLUWXAgANU1o0L8pzphZdBgAAANBHZIMGRPWVp0bLv3+t6FLoBTaMz+LxNdcXXQY9xEUnNcUjqzbEf9/ckoe8p40vx3PrOuLO+1vzwPgbXtEcwwZtCYFfvKwpPv/fm+If/nNDLJxTicEDsrj/8bZ4dFV7TBhVisuWN29z3anr+oaNHXHU/GqMGrblOs48qha/fbQtfnFPa6x8tj3mHFCOdS92xB33t0ZLa8TFJzXF5DFdN/d4cWNHfP+mzbHs8OrWmpJJY8oxY2IpfnhLS7y4qSNWr+uIR1a1x0Un/i7ITu8195j3RtPAUUWXAbBXSqOGR+XUpdH63Z8UXQoANFTlwlMjcyBWn+bZpV8ojRkRlTOPi9av/6joUgCgMYYMimrqmgAAAADQQOUFs6Pt8HnRfstvii6FHu6hafdEx+rWosughxgxpBTvvXxgXH3j5rjzgdZ4aGVLDGzK4tBZlThtSXWbwPjS+dUYPawU/33z5vjVg1tC5aOGZnHGkmosP7L2sq7l197aEs+t7YjZU8pbQ+oDmrJ416UD8uu45d7WuP62lqhVI2ZPLscpR9by8x35/s2bo1zOYvkRLw+f//45zfGFH2yKm+9uzes486hqnLCw2tDHiv1vzPSTY/ys04ouA2CflE9eEm2/ui86VjxZdCkA0BDlow6N8txpRZdBN8s60tpp0A+kl/rmj/1XdDz4WNGlAMA+q77h/HynMdDzbHzXB4suAQCgV2n+0HuKLgHYTseLG2PTB/8tYu0LRZdCD7X6wPa4q+NLRZcBsEvV5uGx9OIvRW3AyKJLAdhn7U8+E5s/9JmI1raiSwGAfTNiaDT9j9dH1txUdCV0s9+tXwZ9XJZlUb30jMjbKABAL1Y6fJ6AOgAAANBtsoHNUX2ljrN0rb0c8dCgG4ouA2C3zD3mvQLqQJ9RGj86KqceU3QZALDPqpecLqDeTwip06+URo+IypnHF10GAOy9oYOiev7yoqsAAAAA+rjy/FlROmJ+0WXQAz258IV4cb1Va4Geb+yMZTF+1qlFlwHQUOWTF0c2ZXzRZQDAXisvPTTKc6YVXQb7iZA6/U75uEWRzZhcdBkAsFdSF7PUzQwAAACgu1XPXxYxdHDRZdCDtAwtxaMbryu6DIBdqjaPiAOP/ZOiywBouKxUiuplZ0ZUykWXAgB7bsTQqJx9YtFVsB8JqdPvZFkW1UvPiKhViy4FAPZI6l6WupgBAAAA7A/ZgOaoXnxa0WXQgzw6//FobVlfdBkAu3Tgse+N2oARRZcB0C1K40dH5bRjii4DAPZMFlG95IzImpuKroT9SEidfqk0ekRUXnF80WUAwO4bOiiq5y0rugoAAACgnynPmxmlIxcUXQY9wAtTsli5+qdFlwGwS2NnLI9xM08pugyAblU+aXFkB0wougwA2G3lpYdFec7UostgPxNSp98qH7sosplTii4DAHZL9ZWnRTawuegyAAAAgH6oet7JEUMHF10GBXto4q8iOtqLLgNgp6rNI+LAY99XdBkA3S4rlaJ62ZkRlXLRpQDALmUjh0Xl7BOLLoMCCKnTb2VZFtVLz4ioVYsuBQB2qnTE/CjPn1V0GQAAAEA/lQ1ojurFpxVdBgV6dkFbPP/8XUWXAbBLBx73vqgNGFF0GQD7RWncqKicdmzRZQDAzmURlUtOj6ypVnQlFEBInX6tNGp4VM46oegyAGDHhg6O6vnLiq4CAAAA6OfK82ZG+cgFRZdBAdqrWTxU/UnRZQDs0rgZp8S4GcuLLgNgvyqfdGRkB0wougwA2KHy0sOiPHtq0WVQECF1+r3KsYuiNH9m0WUAwMulVT9efVberQwAAACgaJXzlkUMG1x0GexnTyxcExtfXFV0GQA7VRswMuYe+96iywDY77JSKaqvOTuiWXdaAHqebPzoqJxzUtFlUCAhdYiI6qVnRgwfUnQZALCN8ilLozzrgKLLAAAAAMhlA5qieskZ+TLN9A+bRpbi0ReuLboMgF068Lg/idqAEUWXAVCI0qjhUb349KLLAIBt1apRfe05kdWqRVdCgYTUIU2sDxoQtVefHVEysw5Az1CaOSUqpx5TdBkAAAAA2ygfOD3KJy0pugz2k0fnPhztrRuLLgNgp6YsuDTGTj+56DIAClU+7MAoLz206DIAYKvK+cuiNH500WVQMCF1eElpxuSonHZs0WUAQMTggVF99VmROXgKAAAA6IEqZx6XH2BP37ZuesSq524ougyAnRo27pCYfdQfFV0GQI9QOW9ZZBPGFF0GAERp0byoLDmk6DLoAYTUoZPysqOiNGda0WUA0J9lEdXLzoxs2JCiKwEAAADoUlYq5cs1x9BBRZdCN+nIOuLBMbcWXQbATlWbh8fByz8QpXK16FIAeoSsWtkyTq/5vwhAcbIxI6L6ylOLLoMeQkgdOkkda6uXvyJiiIl1AIpRPnFxlA+aUXQZAAAAADuVDRkUtRSAKdnV1Bc9fUhLrFtzX9FlAOxYVooFy/46mgePK7oSgB6lNG5UVC88pegyAOivKuX8gKmsqVZ0JfQQZg6hi4n16uVnRWRZ0aUA0M9k0yZG5czjiy4DAAAAYLeUZkyJypnHFV0GDdbanMXDcX3RZQDs1IxFb4pRk48qugyAHql85IIoHTG/6DIA6Icq55wUpUkOJOV3hNShC+U5U6O83KQGAPvRgOaoveacyMqGZwAAAEDvUTl5SZQWzC66DBro8UOfjc0bnyu6DIAdGjV5aUw//PeKLgOgR0vd1LOxI4suA4B+pHTInKgcu6joMuhhpKBgByqnHRPZjMlFlwFAP1G97IzIRgwtugwAAACAPVa97MzIRg8vugwaYOOYUjy+Thd1oOdqHjw+5i/7q8gyUQeAncmaalF97bkRlUrRpQDQD2Qjh0X1kjOKLoMeyJYb7EBWKkXt1WdHDBpQdCkA9HHlYxdFWccxAAAAoJfKBjRF9YrzBGD6gIdn3hftbZuLLgOgS1mpGgcv/0DUmh0YBbA7ShPHROW8k4ouA4C+rlyK6mvOzueHYHtC6rAT2fAh+T/QKGVFlwJAH5VNnxSVc0wOAQAAAL1badLYqFy4vOgy2AdrZkc8s/qXRZcBsENzlr4zho07uOgyAHqVytELo3TkgqLLAKAPq5x7cpSmTiy6DHooIXXYhfKcacKDAHSP4UOi9rrzIquUi64EAAAAYJ9VlhwS5cXCg71RR6kjHhx2U9FlAOzQuJmnxZQFlxRdBkCvVH3lqZEJDwLQDcpLD43KsYuKLoMeTEgddkPl+COivMTEOgANVKtG7Q0XRDZkUNGVAAAAADRM5YLlkU0cW3QZ7KFVh22KF9Y9XHQZAF0aNHx6HHTC/yq6DIBeK6tUovb68yKGDS66FAD6kGzG5HweCHZGSB12U+XCUyObPqnoMgDoC7KI6qVnRGnyuKIrAQAAAGiorFaN6hXnRjQ3FV0Ku6l1YBaPtF5XdBkAXSpXBsTBp34wKtWBRZcC0KtlQwdH7Q3nR1QrRZcCQB+QjRwWtdedF1m5XHQp9HBC6rCbsko5/8caw4cUXQoAvVx5+dIoH3Zg0WUAAAAAdIvSmBH5Afr0DisOeSpaNq0pugyALh10/P+MwSNmFF0GQJ9QmjIhqpecXnQZAPR2TdWovuGCyAY7kJRdE1KHPZANGRS1N1wQUasWXQoAvVTp4NlROf3YossAAAAA6FblQ+ZE+dSjiy6DXXhxQhZPPH990WUAdGnqIa+J8bMd9ATQSOVF86J88pKiywCgt8oiqpe9IkoTxxRdCb2EkDrsodLkcTrAALBXsgljovqqV0SWZUWXAgAAANDtqqcfG+UjFxRdBjvx0NS7o6OjregyAF5m3MxTY9ZR7yi6DIA+qXLm8VGaP7PoMgDohSqnHpM3JoDdJaQOe6F82IFRPmVp0WUA0JsMGhDVN14QWVOt6EoAAAAA9pvKxadFac60osugC6sPaovVq+8ougyAlxk+fmHMP+kvNXwB6CZZKYvq5WdFNm5U0aUA0IuUDp1r1Tz2mJA67KXK6cdGacHsossAoDcol6J2xblRGjms6EoAAAAA9qusXI7q687NV5ij52gvRzw44IaiywB4mYHDp8Whp38oSmUNXwC6U9bclDfYioHNRZcCQC+QTRob1cvOdCApe0xIHfZS+odbvfwVJtYB2KXK+cujNOuAossAAAAAKCwAU3vTRRHDhxRdCi95cuH62PDC40WXAbCN2oBRsfDMj0S1aWjRpQD0C6XRI6J6xbkRJfExAHZi8MCoveGCyGrVoiuhFzLKgH2QNdWi9nsXmlgHYIfKJy2OytGHFV0GAAAAQKGy4UOi9nsXRTQ3FV1Kv7d5WCke2Xhd0WUAbKNcGRCHnfFPMWDIxKJLAehXyrOnRuXi04ouA4Ceqqma5yOzEQ4kZe8IqcM+Sv+Aa7//SksgAfAy5SMXRPXsE4suAwAAAKBHKE0cE9XXnRdRtnuqSI/OeyzaWl4ougyArbKsHAcv/9sYOuagoksB6Jcqiw+OyiuOL7oMAHqacimfxykdMKHoSujFzAJCA5TGj97SUd2SFgC8pDRvRlQuPr3oMgAAAAB6lPKcqVE1Z1KY9Qdk8eTqnxVdBsA25h773hg99biiywDo1yrLjory8YcXXQYAPUUWUb30zCjPnV50JfRyQurQIKVpk6L6mrMjSt5WAP1dNm1iVF97bmS6ggEAAAB0ufpc5Yxjiy6jX3powp0RHe1FlwGw1bTDXh+T511YdBkApKD6uSdHaZFVLQCIqJxzcpQPn1d0GfQBklPQQOX5s6Jy8WlFlwFAgbJxo6L2xgsjs7oGAAAAwA5VTjk6yksOKbqMfuWZBa2x5vnfFF0GwFbjZ50RMxdfWXQZALwky7KoXnZmlOZMK7oUAApUPmlxVE44ougy6COE1KHBKosPjsorji+6DACKMHxI1H7/lZENGlB0JQAAAAA9XuWiU6N0oGWj94e2WsRD1Z8UXQbAViMmHhHzTvzzPBAJQM+RlctRff15kU0ZX3QpABS0+l317BOLLoM+REgdukFl2VFRPv7wossAYH8a0LwloD5iaNGVAAAAAPQKWbkU1SvOjWzS2KJL6fOeOGxNbHrxqaLLAMgNGjEzDjn1H6JUtiIpQE+UNdWi9qaLIhszouhSANiPSvNmROXi04sugz5GSB26SeXck6O06KCiywBgf6hWovZ7F0Zp/OiiKwEAAADolQGYcOB/t9k0MosV668tugyAXG3g6Fh4xj9FtWlI0aUAsBPZ4IFRffPFEUMHFV0KAPtBNm1iVF97bt5QABrJKwq6SVqarnrZmVGaO63oUgDoTqVSVF97TpSmTyq6EgAAAIBeKRs6OGp/cEnEcIHF7vDI3IejvW1T0WUARG3gqDj8rE9G85AJRZcCwG4ojRyWryQdzU1FlwJAN8rGjYraGy+MrGalIxpPSB26UVYuR/V150U2dWLRpQDQHbKI6iWnR3n+rKIrAQAAAOjVSqNHRO2tlwqqN9jaGRFPPXdj0WUAvBRQ/+cYNGJ60aUAsAdKE8dG7Y0XRAguAvRNI4bmByRlgwYUXQl9lJA67I+lStM/8injiy4FgEbKIioXnRblIxcUXQkAAABAnyCo3lgdWcRDo35ZdBkAAuoAvVxp5pSopqB6tVJ0KQA00vAh+TxMNmJo0ZXQhwmpw36QDWiK2lsujmzyuKJLAaBBKheeEpWlhxZdBgAAAECfIqjeOE8fuinWrX2g6DKAfk5AHaBvKM+eGtU3XBBREVQH6BOGDc7nX0qjhhddCX2ckDrsJ9mA5qi9+eLIJo0tuhQA9lHl/OVROXph0WUAAAAA9EmC6vuudUDEwx0/LroMoJ8TUAfoW8pzp0X19edFVMpFlwLAvhg6aEtAffSIoiuhHxBSh/0oGzQgam+5JLIJY4ouBYC9VDn35Kgct6joMgAAAAD6NEH1ffP4oc/G5o3PFV0G0I8JqAP0TeWDZkT1inMjyoLqAL3SkJcC6mNGFl0J/YSQOhQRVP8DQXWAXhtQP+GIossAAAAA6BcE1ffOxrFZPLbm+qLLAPoxAXWAvq08f1ZUX3eujuoAvbWD+thRRVdCPyKkDgXIBg/M/+Fnk8YWXQoAu6ly/nIBdQAAAID9TFB9zz00477oaG8pugygnxJQB+hHQfXXny+oDtBbDB0ctbdeFqVxAursX0LqUGhH9Usjmzyu6FIA2JksonLhKVE5blHRlQAAAAD0S4Lqu+/5uR3x7Opbii4D6KcE1AH6l/JBM6L6xgsiKpWiSwFgZ4YPidqVl0Vp7MiiK6EfElKHAmUDm6P2B5dEdsCEoksBYEcB9YtOi8oxC4uuBAAAAKBfE1TftY5SxENDbiy6DKCfElAH6J/Kc6dH9fcujKgKqgP0SCOGbgmojxlRdCX0U0LqULBsQHPU3nJxZNMnF10KAJ2VsqhcckZUlh5adCUAAAAACKrv0qqFG+KFdY8WXQbQDwmoA/Rv5TlTo/qmiyKaakWXAkAn2ajhWwLqo4YXXQr9mJA69ABZc1PU3vzKKM2fVXQpACTVSlRff35UFh9cdCUAAAAAdCKo3rWWIVk8vPnaossA+iEBdQCS8qwD8iBkDBlUdCkApDzi5HFRe/vlURo5rOhS6OeE1KGHyGrVqL7+vCgvEYgEKNRLK1yUHTgEAAAA0GOD6k1/eHlk40cXXUqPsWL+k9G6eV3RZQD9zMBhU+OIc/9dQB2AXCkFIv/wVXnnXgCKU5o9NT/AP3PgED2AkDr0IFmpFNVLzojy8qOKLgWgfxo2OJ84KU2fXHQlAAAAAOxENmLolnmcWQdEf/fixCxWPv+TossA+plh4w6NI8779xg41Hw6ANutfPT2yyObNLboUgD6pdJhc6P6posia24quhTICalDD1Q98/ionL8sIsuKLgWg38jGjoymt786SjpwAQAAAPQK2YDmqP7+K6O0aF70Zw8ecFd0dLQVXQbQj4ydviwWnfWJqDXrlAvAy6XOvbUrL4vSbAeUAuxP5WMXRfXV50RWKRddCmwlpA49VOW4w6P66rMiyj40ALpbNnVi1NIS0SOGFl0KAAAAAHsg7XitXv6KKC/rnyuUPjevPZ5f/auiywD6kQMOvjwOPuUDUa7ozAjAjqUOvtU3vTJKh84tuhSAfqFyxnFRvWB5ZCVNcelZhNShBysvPChffiOaakWXAtBnlQ6aEbU/uCSyQQOKLgUAAACAvZBlWVRfcXxULjo1oh/tjG2vRDzU/LOiywD6i6wUc47+45hz9Lsiy8QMANjNA0pfc07e2ReAblLKonLx6VE5ZWnRlUCXbD1CD1eeMzVqV14aMXhg0aUA9DmlI+ZH9Q0XRFarFl0KAAAAAPuocvRh+VxP9JO5npUL18eGF54ougygHyiVm+KQ5X8XBxz8qqJLAaCXSR19U2ffyhnHFl0KQN9TrUT1dedF5ahDiq4EdkhIHXqB0uTxUfvDyyMbNbzoUgD6jPJJi6N62ZmRlQ2HAAAAAPqK8ryZUbvysoghg6Iv2zw8i0dfvLboMoB+oNo8PBad9ckYO+PkoksBoBernHJ0VC4+rV+tfATQrQY0Re3NF0d5weyiK4GdksqCXqI0ZkTU3n55ZNMnFV0KQO9WKkXlwlOievaJ+VLQAAAAAPQtpSnjo/aOV0c2dmT0VY8ctCLaWl8sugygjxswdHIced6nYvh4nRkB2HeVow6N6hsvjGhuKroUgF4tGz08am9/dZRmTC66FNglIXXoRbIhg6L2B5dGecnBRZcC0DsNGhDVt1wclWMWFl0JAAAAAN2oNHJYvsM264M7bNdPjVj13M+LLgPo44aOXRBHnvfpGDjsgKJLAaAPKR80Y8sBpWNGFF0KQK9UmjM1an/02iiNG1V0KbBbhNShl8kq5aheckZUzltmGSSAPZBNGBO1P3pNlGeZUAcAAADoD7KBzVF7y8VROuzA6EseHH9HRHQUXQbQh42ZdkIcfvY/R22AACEAjZeClWm/bWnutKJLAehVyscdHtXff2U+3wG9hZA69FKV47d86MQAHzoAu1JaMCtqb788SqOGF10KAAAAAPtRVqlE9TVnR/nEI6MvePqQllj7/D1FlwH0YZPnXxKHnPoPUa7YBwlA98kGNEf1TRdF+fjDiy4FoOcrl6Ny8elRPX9ZZCWRX3oXr1joxcpzpuVHl2aW7wDYofIpS6P6+vMja6oVXQoAAAAABciyLKrnnBSVS06PqFSit2pryuLh0o+LLgPoo7JSNeYe+9448Nj3RJaJEQDQ/VLQsnresqhcekYewASgC0MGRe2tl0TlqEOKrgT2iq1L6OVKY0ZE7R2vjtK8mUWXAtCz1KpRfe05UT3juHxHJAAAAAD9W2XJIVF7x+WR9dLV9p44bHVs2vBM0WUAfVDz4AlxxLn/FlPmX1x0KQD0Q5XFB0ftykvzICYAv5NNGhtNf/SaKE2fXHQpsNeE1KEPyJqbovqGC6J88pKiSwHoGYYPidrbXhXlww4suhIAAAAAepDSpHFRe9cVUVowO3qTjaOyWLHuuqLLAPqgUVOOiSUXfj6GjZ1fdCkA9GOlaZOi6Z2vjWzyuKJLAegRSofNjdofXh7ZiKFFlwL7REgd+oislEX1rBOievlZvXq5UoB9lU3fMoFRMoEBAAAAQBeyAU1Re8P5UTnrhIhS79hV9sich6K9bVPRZQB9SVaKGUf+QRx2xj9FtXlY0dUAQGQvNSIraUQG9GdZROWMY6P22nMjq1WLrgb2mSQr9DHlw+dFNm5ktHzmm9Hx7PNFlwOwX5WPXRSVc06KrFIuuhQAAAAAerjKyUuiNHVibP7cNyPWvhA91dqZEU8/d1PRZQB9SLV5RCxY9tcxarJVmgHoWVIgs/bac6L1gPHR+p0fR7S1F10SwP4zaEBULz0jyvNnFV0JNEzvaA8B7JHS5PFR++MrHF0K9B8DmqP6uvOiesFyAXUAAAAAdltp5pRo+uPX5ec9UUcW8eDIXxRdBtCHDBt3aCy56D8F1AHo0SonLo7a2y6PbKTVPoD+IZs+OZ+fEFCnrxFShz4qa27Kjy6tvPK0iKpFE4C+K5s6MZr++IooHzKn6FIAAAAA6IWyIYOi+geXRPnkJfmy2j3JU4dtjPVrHyy6DKCPmHLwq+Lws/85mgeNLboUANil0tQJWxo02g8M9GVZFuXlS6N25aWRDR9SdDXQcJKr0MdVlh4apWkTo+Wz34yOVc8WXQ5A42QR5RMXR+XM4yMrO+4OAAAAgL2XlUpRPeuEKE2fFC3/+Z2IDZuKLilaB2TxcNv1RZcB9AHl6qCYd+KfxbgZy4suBQD2SDagOWqvOy9af3ZbtH7jRxGtbUWXBNA46aD5y18R5TnTiq4Euo1EF/QDpQljovbO10b5yAVFlwLQGIMHRvX3Lorq2ScKqAMAAADQMGlZ7dq7rohs8riiS4nHDn0mWjY9X3QZQC83aOTMWHzB5wTUAejVKscsjNo7Xh3ZmBFFlwLQEKXZU6Pp3a8TUKfPk+qCfiKrVaN62ZlRfdUrIpqqRZcDsNeymVOi6Y+viPJBM4ouBQAAAIA+qDRqeNTefnmUjzq0sBo2jMvi8TXXFXb7QN8wYc4rYvF5n4lBw6cWXQoA7LPSpHH5AaWlRfOKLgVg75WyqJxxbFTffHFkQwYVXQ10u0r33wTQk5SPmB/ZAeOj5bPfio4nniq6HIDdl2VRPmVpVE49Ol9+GQAAAAC6S1apRPXi06I0Y3K0fPWaiI2b9+vtPzT93uhY3bpfbxPoO8qVATHn6D+OSQedX3QpANBQWVMtaq8+K1pnHxCtX/thxOaWoksC2H3DBkftNWdHacaUoiuB/UZIHfqh0thR+TJIrd+8Ntp+dlvR5QDs2tBBUb38rCjP1u0FAAAAgP3b+KU0c0q0fPF70X7vw/vlNp+f2xHPrTZ3D+ydEROPiHkn/FkMGDqp6FIAoNtUlhwSpakTo+Wz34yOJ58puhyAXSrNmxHVy14R2aABRZcC+1XW0dHRsX9vEuhJ2u55KJ9cj+fXFV0KQJdKR8yP6nnLIhvYXHQpALtl47s+WHQJAAC9SvOH3lN0CQC7pfWGO/LmL7Gp+7qqt5cjbj/s5/Hi+hXddhtA3+2ePmvJ22Py/FdGlmVFlwMA+0VHa2u0fv/n0XbtTRHtInBAD9TcFJVzT8oProH+SEgdiI6Nm6L1Gz+Ktpt+VXQpANt2T3/laVGeP6voSgD2iJA6AMCeEVIHepOO1Wuj5arvRftvu6er+srDX4wH1n+rW64b6LuGTzg85p34ZzFw6OSiSwGAQrQ/ujJa/uu70bHq2aJLAdiqNHd6VC85PbLhQ4ouBQojpA5s1Xb3g9Hype/rqg4UrnT4vKiev1z3dKBXElIHANgzQupAb9R6w+3R+s3rGtpVvWVIFrdM+060bjZHD+xJ9/Q/jMnzL9Y9HYB+L++q/r2fRdt1N+uqDhSruRaVc06OylG6p4OQOrCNjg0vdVW/WVd1oABDUvf0U6O8YHbRlQDsNSF1AIA9I6QO9FYdz62Jli+mruqPNOT6Hlj6ZKx87vqGXBfQ9w2fsCjmnfjnuqcDwHbaH1kZLV/QVR0oRmnutKhefHpkI4YWXQr0CELqwI67qn/xexFr1hddCtBPlBYdtKV7+qABRZcCsE+E1AEA9oyQOtDbtf789mj91rURm1r2+jpemJzFbYO/GNHR3tDagL6nVGnOu6dPmX+J7ukAsLOu6lf/NNqu/4Wu6sD+0ZS6p58UlaWHFl0J9ChC6sAOdWzYGK1f/1G0/eLXRZcC9PXu6RedGuWDdU8H+gYhdQCAPSOkDvSZrupXfS/a79u7ruq/XnJXPP+8uXhg54aPX7ile/qwKUWXAgC9QvsjT0TLf303Op56ruhSgD6sNGdaVC/RPR26IqQO7FLbbx6Ili99X1d1oOFKCw+K6gW6pwN9i5A6AMCeEVIH+oq0y60tdVX/9nV71FX92fltcXfrl7u1NqAPdE9ffGVMWXCZ7ukAsIc6Wlqj9Xu6qgPd1T39xKgsPazoSqDHElIHdkvHxk3R+v2fRdtPbo1ot9wosG+yMSOicsHyKM+dXnQpAA0npA4AsGeE1IG+pv25NdH6hauj/f5Hd33Zaha3Hnx9bHxh5X6pDeh9ho8/LOad+Be6pwPAPmp/fFW0fOUH0fHw40WXAvSVpoznnBjZsCFFlwI9mpA6sEfan3wmWr9yTbQ/sKLoUoDeqFaNyvKlUT7xyMgq5aKrAegWQuoAAHtGSB3oi9Lut/Zf3hUt374+Yt0LO7zcY4vXxcNrvrtfawN6h2rz8Ji1+G0x8cBzI8tKRZcDAH1n9aNf/Dpa0zh9/YtFlwP0Qtn40VuaMs46oOhSoFcQUgf2Stutv4mWb14XsXZ90aUAvUTpkDlRPffkyEYMLboUgG4lpA4AsGeE1IH+ukrppuFZ3DrpG9HWuqGw+oCeJ8vKMWnehTHzyD+IapP5dADoDh0bNkbr1T+Ntp/fFtEuOgfshqZaVE47JsrHHR5Z2UGksLuE1IF9m1z/759H249vednkOkBdNnZkVM5fHuW504ouBWC/EFIHANgzQupAv1ml9Gs/iPb7Ht36s/uWrohVz/280LqAnmX4+IUx99j3xpBRs4suBQD6hfbHn4qWr14THQ89XnQpQA9WWnRQVM85KbKhg4suBXodIXWgMZPrX7km2h9YUXQpQE9Sq0bllKVRPuHIyCrloqsB2G+E1AEA9oyQOtCftN1+T7R889pYN2xd3NH8xdQOpuiSgB6gaeCYmHXU22PC7DOLLgUA+p0UnWv/5V3R8q3rIta/WHQ5QA+SjR8d1QuWR2nWAUWXAr1WpegCgN6vNH501K68LNpu/U20fPO6iLXriy4JKFjp0LlbjiIdYSlSAAAAAKgrH3ZglObNjGd+9ZUo3V6L9tZNRZcEFKhUrsWUgy+L6Yt+LyrVgUWXAwD9UpZlUT5yQZQWzI7Wq38SbT+/LaLdwaTQrzXVonLaMVE+7vDIyqWiq4FeTSd1oKE6Nm6K1h/cGG0/uSWipbXocoD9LJswJirnnBTludOKLgWgMDqpAwDsGZ3Ugf5q4/pVcf/NH40n77taR3Xoh8bNPDVmLfnDGDBkYtGlAACdtD/+VLR+89pov++RoksB9rd00MoR86PyiuMjGzq46GqgTxBSB7pFx/ProvX7P4u2X/zKEabQD2Qjh0Xl9GOjtGheZKWs6HKA/eTrX/96fPazn42HHnoompub45hjjol3vvOdMWnSpN36+xtvvDH+9V//Ne6888548cUXY+zYsXHSSSfFlVdeGSNHjozeSkgdAGDPCKkD/d3ap38Tv/35h+L5J28ruhRgPxg29uCYvfRdMXz8IUWXAgDsRNu9D0frd66PjsdWFV0KsB+U5s/Kw+ml8aOLLgX6FCF1oFu1P/VstH73J9F+52+LLgXoDoMHRuWUpVFeelhklXLR1QD70Yc//OH45Cc/GbNmzYoTTzwxVq5cGd/73vdi6NCh8aUvfSmmTJmy07//6le/Gu9///vzcPspp5wSo0ePjttvvz1uvfXWPOR+1VVXxZgxY6I3ElIHANgzQuoAWzz14I/ivpv+KTasfazoUoBu0Dx4Qt45ffys04ouBQDYTSlW1377PdF69U+i45nniy4H6AbZ9MlRPev4KE2fXHQp0CcJqQP7RfsjK/MjTNvvf7ToUoBGaKpF5cQjo3zikZE11YquBtjP7rnnnjj33HPj8MMPj09/+tNRq235P3DNNdfE2972trwbegqw78iaNWvyyyRf/vKXY8aMGVt/90//9E/x8Y9/PM4777z4u7/7u+iNhNQBAPaMkDrA77S3tcSKu74YD9/2qWjZuLrocoAGqDYNi6mHvTamLLgsypWmossBAPZCR1t7tN14R7T+988j1r1QdDlAA2QTxkTlzOOiPH9W0aVAn1YpugCgfyhNnRC1t14abfc8tGU5pMefKrokYG+Uy1E++rC8e3o2eGDR1QAF+exnP5ufX3nllVsD6knqiH7kkUfGddddF6tWrYpx48Z1+ffXX399vPDCC/G6171um4B68ta3vjX+9V//Na699tpuvhcAAADQ85TK1Zh6yOUx+aAL4rHffDkeueNzsXnDs0WXBeyFavPwmHrIq2PygkuiUjWfDgC9WVYuReWYhVE+Yn60/fiWaL325oiNm4ouC9gL2chhUTn92CgtmhdZKSu6HOjzhNSB/ap84PQozZ0W7bfdHa1X/zQ6nrUcEvQKWRalw+dtGaiPHFZ0NUDBbrzxxqhUKnkgfXtHHXVU/OIXv8gvk7qtd2XmzJnxzne+MxYvXvyy35XL5fy6X3zxxW6pHQAAAHqDcnVATD30NTF5/ivj8bu/Gg/f/pnY/OIzRZcF7IbagJFb3r/zXpm/lwGAviOtMp4auqXGbq0/vDHafnprRGtb0WUBu2PQgJfevwsjq5SLrgb6DSF1YL/LsizKi+ZF6dC50XbjndH6o5siVq8tuiygK1lEacGcqJx2dJQmji26GqAH2Lx5czzxxBMxadKkbbqo102ZMiU/f/DBB3d4HfPnz89PXfnJT36SB9R39HsAAADoT8qV5jjg4FfFpIMujCfu+XoeVt/0wqqiywK6UBs4OqYe+tqYPO/C/L0LAPRd2aABUT3npKgcd3i0XvPzaPvFXRFtwurQIw1szt+r5ROOiKy5qehqoN8RUgcKk6VOqWk5pKMOifZb787D6h2rLFsKPUK5lB9MUj55SZTGjSq6GqAHWbNmTXR0dMSwYV2vqjBkyJD8fN26dXt83elv/uZv/ib/+rLLLtvHSgEAAKDvKFeaYsqCS2LSQRfEE/d+Ix6+7dOxcf3KossCIqJp0NiYetgVMenA8/P3KgDQf2Qjhkb14tOjctox0Xr9L6PthtsjNrUUXRaQDB0clROPjPLSQ/NVEIBiCKkDPSKsXj5yQZSOmB/tv7ovXxKpY8WTRZcF/VO1EuUlh0TlpMX5BjXA9lpatkysddVFvfPPN23atEfXu379+vj93//9ePjhh+P444+Piy66qAHVAgAAQN9SKldj8ryLYuLcc2Plb78dD9/2qdiw7vGiy4J+qXnw+Jh22Oti4oHnRqks9AIA/Vk2bMiWzurLjoq2n94arT+9NeKFDUWXBf1SNnp43pCxfMSCyCrlosuBfk9IHegxsiyL8iFz8lPbbx+Jth/eGO33PVJ0WdA/DGiK8jGLonL84ZENHlh0NUAP1tzcvE1YfXubN2/OzwcO3P3/JU8//XS8+c1vjrvuuisOPfTQ+PCHP5yPCwAAAIAdh9UnHXR+TJh7djz52+/GQ7f9e2xYu6LosqBfaB4yMaYvfH1MmHN2/l4EAKjLBg3Iu6qXTzwy2m68I1qv+0XEmvVFlwX9QjZpbFROXhKlQw+MrGRfM/QUQupAj1SeMzU/tT+yMlp/dGO0//q+iI6iq4I+aMigqJxwRJSPPiyyZsuQArs2ePDgKJVKsW7dui5/X//5kCFDduv67r333jygvnLlyjjqqKPiYx/7WH4bAAAAwK6VSpWYeOA5MWHOK+LJ+78XD932b/Hi85q/QHcYMHRyTFv4hvz9lt57AAA7kjXVonLCkXmjuLZf3hVt194UHU+vLros6JOyGZPzVQzKB80ouhSgC7aegR6tNHVC1F5/frSvejZaf3RTtN/ym4j29qLLgl4vGzksyicvjvKRB0dWNRwAdl+tVospU6bEE088kXdTr1a37Ra1YsWWrm2zZs3a5XXdcMMN8ba3vS3Wr18f55xzTvz1X/91fv0AAADAnslK5Tw4O372GfH0w9fHY3d9KZ57/ObQ/QX23YiJh8fkea+MMdNPEk4HAPZIVilH5ahDorz44Gi/895o/eGN0fH4U0WXBX1Cad6MPJxemj656FKAnbAVDfQKpXGjonbZmdFx5nHR+vPbo+3GOyPWvVB0WdDrlGYfEOVjD4/S/JmRlUpFlwP0UosXL44vfelLceutt8aSJUteFjzPsiwWLVq00+v45S9/GW95y1ti48aN+fk73/nObq4aAAAA+r4sK8XY6SflpxeefyQe+82XY+W934rWzV2viAZ0rVwbFBNmnxVT5r8yBo2YHv3R5s2b49Of/nS88Y1vjHK5XHQ5ANCrZaUsyocdmJ/aH1gRrT+9Ndp/dZ8mjbCnmmpRPmJ+lI9dlGfJgJ4v6+jo0EIB6HU6Wtui/Y57ovUnt0bHoyuLLgd6tqZqlA9/aZA+fnTR1QB9wG233RaXXnppLFy4MN9R1dzcnP/8mmuuyTujL1u2LD7+8Y/v8O9Xr14dZ599djz99NPxjne8I9761rdGX7LxXR8sugQAgF6l+UPvKboEgD6trXVjPHn/9+Oxu74Y6565p+hyoEcbPGpOTJ53UUyYfWaUqwOiP7v44ovjjjvuiLvuuisqFb3vAKDROp5f91KTxjsi1r9YdDnQo2VjRuSZl/KRCyJrbiq6HGAP2JoEeu2SSHno9vD50b5iZbT99LZou/2eiJbWokuDHiMbNyrKSw+N8pEHRzbAIB1onBROv/zyy+Pzn/98nHvuuXkofdWqVXH11VfH6NGj40/+5E+2Xvamm26Km2++OQ466KBYvnx5/rNPfepTeUB96NCh0dbWFh/5yEe6vJ0rr7wySlZ9AAAAgH1SrjTHpAPPzU9rnvp1PHbXl2LVA9dEe9umokuDHqFUrsXY6cti8vyLYvj4w4oup8d45plnii4BAPq0bPiQqJ55XFROXRrtt98brT+/LToefqLosqDnKGVRmjczyscsjNKcaflq3kDvo5M60Gd0bNgYbb+4K9puuD06Vj1bdDlQjHI5SofMicrRh0Vp5pSiqwH6sLQZkULqV111VTz88MMxfPjwWLJkSd4ZfcqU3/3/SQH0j370o3H++efHBz7wgfxn5513Xtx99927vI3e2qVJJ3UAgD2jkzrA/teycU08ce834rHffCU2rH2s6HKgEM1DJsbkgy6IiQeeF7UBI4oup8c5+eST4/HHH++1c3QA0Bu1P/F0nnlpu+WuiI2biy4HijF8SFSWHBLlJYfkB3MAvZuQOtAntT+4Ilp/fke03/nbiFbd1eknSxulQfrigyMbPLDocgD6NSF1AIA9I6QOUJy0m/DZFTfEY7/5Ujzz6E8jOtqLLgm6V1aKUZOX5l3TRx9wbGRZ717F733ve1987Wtfi+uuuy6+8IUvxLe//e18xcOxY8fGGWecka9UOHDg7/YZrFy5Mj7xiU/Ej3/847xT+rBhw+Koo46Kt7zlLTF79uytKyO+9rWv3eZ2Fi9eHJ/73Ofyr9vb2+O//uu/4itf+Uo8+OCD+UqI8+bNi9e97nVbV1IEAPZNx6bN0Xbb3dF2wx3RseLJosuB/dM1fe70KC89NO+enlltG/oMIXWgT+vYsCna7rw32m/5TbQ/sCLNuBddEjTO4IFRPuzAKC+aF6VpE4uuBoCXCKkDAOwZIXWAnmHDupXx+N1fjSfv+25sXC8IQ9/SNGhsjJ91Rkyad0EMHDo5+op6SP3ggw+ORx99NE499dQYNGhQfP/7388D6Sk0/rGPfSy/bFrZ8Iorrog1a9bkKyKmYHlaIfH666+PWq2Wr4Z43HHHxWOPPZZf52c+85lYt25dvPWtb81XTrzgggvygPof/uEfxg9+8IOYMWNGHHPMMfnPrrnmmnjqqafy373tbW8r+mEBgD6lfdWzeWf19lvvjo7n1hRdDjRUNnlclA+fF+WFB0U2dHDR5QDdQEgd6Dc6nl8Xbbf+JtrSwP2Jp4ouB/ZOrRql+bPyQXo6ijQrO3oUoKcRUgcA2DNC6gA9S9p1uGbVr2LVA/8dTz34g9j04tNFlwR7pTZgVIydsTzGzzwlho0/LLIsi76mHlJPIfKrrroqRo0alf/8ueeeyzupP//883nX9DFjxsQ555wT9913X/zN3/xNXHjhhVuvI/3+zW9+cwwdOjR++MMfxuDBW8JBJ598cjz++ONx1113RaVSyX+Wuqn/1V/9VZx99tnxt3/7t1GtVvOfpzD7q1/96rj33nvji1/8YhxyyCGFPB4A0NfH6R0PPb4l93L7PREvbiy6JNgr2chhUVo0b0vuZdyW8SvQd23ZmgToB7LhQ6Jy8pL81L7y6Wi7JQXWfxPx/LqiS4NdL2s0e2qUD58fpYNnR9ZUK7oiAAAAAKCPSkHe4eMPyU9zjn5XPL/y9i2B9Yd+FJs3PFt0ebBT1ebhMXb6shg385QYMfHwyLL+0ejlsssu2xpQT0aOHBmLFi2KH/3oR7FixYo8bJ4C6scee+w2AfXk+OOPj/POOy+++tWv5h3Yt/99Z1/4wheiVCrFn/3Zn20NqCdDhgyJt7/97XnX9S9/+ctC6gDQTeP0bMbkKM2YHJXzl0X73Q/muZf2ux6IaG0tujzYuUEDonzo3C3B9Ol9Z2UjYNeE1IF+qTRhTJTOOiEqrzg+Oh5YseVI0zvujdiwqejSYKtsyvgop6NHFx5oWSMAAAAAYL9LAd8RExflp7nH/I9YvfKWWPXANfHUQz+Mlo1rii4PcpWmoTF2+kkxbsYpMWLSkVE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", 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" ] @@ -1102,13 +1141,13 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 13, "id": "982f835e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -1240,7 +1279,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 14, "id": "9f184293", "metadata": {}, "outputs": [ @@ -1255,41 +1294,42 @@ "Note: Radon cyclomatic complexity and maintainability entries with rank 'A' are treated as non-issues and excluded from issue counts while still used in distribution plots.\n", "\n", "šŸ“Š OVERALL METRICS:\n", - " Total Issues Found (excluding Radon A ranks): 10\n", - " Files Analyzed (max across tools): 3\n", - " Average Issues per File: 3.33\n", + " Total Issues Found (excluding Radon A ranks): 31\n", + " Files Analyzed (max across tools): 4\n", + " Average Issues per File: 7.75\n", "\n", "šŸ” TOOL BREAKDOWN:\n", " BANDIT: 2 issues across 2 files\n", - " RUFF: 0 issues across 0 files\n", - " MYPY: 0 issues across 0 files\n", - " RADON_CC: 6 issues across 3 files\n", + " RUFF: 1 issues across 1 files\n", + " MYPY: 6 issues across 1 files\n", + " RADON_CC: 7 issues across 4 files\n", " RADON_MI: 0 issues across 0 files\n", - " FLAKE8_WPS: 0 issues across 0 files\n", - " VULTURE: 2 issues across 2 files\n", + " FLAKE8_WPS: 11 issues across 4 files\n", + " VULTURE: 4 issues across 3 files\n", "\n", "šŸ’” QUALITY INSIGHTS:\n", " šŸšØ Security: 0 high-severity security issues found\n", - " šŸ”„ Complexity: Average CC (all ranks) = 2.34, 0 functions with CC > 10\n", - " šŸ› ļø Maintainability: Average MI (all ranks) = 80.83, 0 files with MI < 20 (needs attention)\n", - " šŸ’€ Dead Code: 2 potential dead code items, 0 with ā‰„80% confidence\n", + " šŸ”„ Complexity: Average CC (all ranks) = 2.25, 0 functions with CC > 10\n", + " šŸ› ļø Maintainability: Average MI (all ranks) = 80.39, 0 files with MI < 20 (needs attention)\n", + " šŸ’€ Dead Code: 4 potential dead code items, 0 with ā‰„80% confidence\n", "\n", "šŸ“‹ RECOMMENDATIONS:\n", - " 1. Address 10 total issues found across all tools\n", + " 1. Address 31 total issues found across all tools\n", " 2. Security: Review and fix 2 security issues\n", " 3. Dead Code: Remove 0 high-confidence dead code items\n", + " 6. Style: Focus on fixing UP017 rule violations first\n", "\n", "================================================================================\n", "\n", "FINAL SUMMARY TABLE:\n", " Tool Total Issues (A excl) Files Analyzed Issues per File\n", - " bandit 2 2 1.0\n", - " ruff 0 0 0.0\n", - " mypy 0 0 0.0\n", - " radon_cc 6 3 2.0\n", - " radon_mi 0 0 0.0\n", - "flake8_wps 0 0 0.0\n", - " vulture 2 2 1.0\n", + " bandit 2 2 1.000000\n", + " ruff 1 1 1.000000\n", + " mypy 6 1 6.000000\n", + " radon_cc 7 4 1.750000\n", + " radon_mi 0 0 0.000000\n", + "flake8_wps 11 4 2.750000\n", + " vulture 4 3 1.333333\n", "\n", "šŸ’¾ Results saved to 'code_quality_summary.csv'\n", "šŸ’¾ Detailed issues saved to 'detailed_issues.csv'\n" diff --git a/code_quality_summary.csv b/code_quality_summary.csv index 882f3d0..6e86d7f 100644 --- a/code_quality_summary.csv +++ b/code_quality_summary.csv @@ -1,8 +1,8 @@ Tool,Total Issues (A excl),Files Analyzed,Issues per File bandit,2,2,1.0 -ruff,0,0,0.0 -mypy,0,0,0.0 -radon_cc,6,3,2.0 +ruff,1,1,1.0 +mypy,6,1,6.0 +radon_cc,7,4,1.75 radon_mi,0,0,0.0 -flake8_wps,0,0,0.0 -vulture,2,2,1.0 +flake8_wps,11,4,2.75 +vulture,4,3,1.3333333333333333 diff --git a/detailed_issues.csv b/detailed_issues.csv index 6f1fb24..052b0e5 100644 --- a/detailed_issues.csv +++ b/detailed_issues.csv @@ -1,11 +1,32 @@ tool,file,line,severity bandit,backend/app/core/config.py,109,LOW bandit,backend/app/models/api_models.py,15,LOW +ruff,/Users/somen/Zavodi/opensource/fastapi-moscow-python-demo-1/backend/app/models/db_models.py,89,error +mypy,backend/app/email_utils.py,7,error +mypy,backend/app/email_utils.py,7,error +mypy,backend/app/email_utils.py,7,note +mypy,backend/app/email_utils.py,7,note +mypy,backend/app/email_utils.py,33,error +mypy,backend/app/email_utils.py,47,error radon_cc,backend/app/email_utils.py,23,B +radon_cc,backend/app/crud.py,163,B radon_cc,backend/app/tests/api/routes/test_items.py,23,B radon_cc,backend/app/tests/api/routes/test_items.py,41,B radon_cc,backend/app/tests/api/routes/test_items.py,103,B radon_cc,backend/app/tests/api/routes/test_users.py,357,B radon_cc,backend/app/tests/api/routes/test_users.py,232,B +flake8_wps,backend/app/api/routes/wallets.py,40,warning +flake8_wps,backend/app/api/routes/wallets.py,103,warning +flake8_wps,backend/app/api/routes/wallets.py,152,warning +flake8_wps,backend/app/api/routes/wallets.py,165,warning +flake8_wps,backend/app/crud.py,0,warning +flake8_wps,backend/app/crud.py,11,warning +flake8_wps,backend/app/crud.py,163,warning +flake8_wps,backend/app/crud.py,163,warning +flake8_wps,backend/app/crud.py,163,warning +flake8_wps,backend/app/currency_service.py,9,warning +flake8_wps,backend/app/models/wallet_models.py,0,warning vulture,backend/app/models/api_models.py,45,warning -vulture,backend/app/models/db_models.py,34,warning +vulture,backend/app/models/db_models.py,55,warning +vulture,backend/app/models/db_models.py,70,warning +vulture,backend/app/models/wallet_models.py,34,warning From 1bea2165e7a1a2e3c35b4d7b18fab63b83c2058a Mon Sep 17 00:00:00 2001 From: vodkar Date: Tue, 16 Sep 2025 12:13:59 +0500 Subject: [PATCH 3/3] Updated stats --- code_quality_analysis.ipynb | 70 +++++++++++++------------------------ code_quality_summary.csv | 2 +- detailed_issues.csv | 8 +---- 3 files changed, 27 insertions(+), 53 deletions(-) diff --git a/code_quality_analysis.ipynb b/code_quality_analysis.ipynb index c3eec0f..457d5b7 100644 --- a/code_quality_analysis.ipynb +++ b/code_quality_analysis.ipynb @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 132, "id": "871ae97e", "metadata": {}, "outputs": [ @@ -96,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 133, "id": "f8c53333", "metadata": {}, "outputs": [ @@ -104,7 +104,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Analyzing directory: /Users/somen/Zavodi/opensource/fastapi-moscow-python-demo-1/backend\n", + "Analyzing directory: /Users/somen/Zavodi/opensource/fastapi-moscow-python-demo/backend\n", "Tools configured:\n", " - bandit: bandit -c pyproject.toml -r backend/ -f json\n", " - ruff: ruff check backend/ --output-format json\n", @@ -147,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 134, "id": "c55d109f", "metadata": {}, "outputs": [ @@ -218,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 135, "id": "0db8c239", "metadata": {}, "outputs": [ @@ -290,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 136, "id": "2a1f0732", "metadata": {}, "outputs": [ @@ -298,26 +298,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "MyPy found 6 type checking issues\n", - "\n", - "MyPy Results Summary:\n", - " tool file line severity \\\n", - "0 mypy backend/app/email_utils.py 7 error \n", - "1 mypy backend/app/email_utils.py 7 error \n", - "2 mypy backend/app/email_utils.py 7 note \n", - "3 mypy backend/app/email_utils.py 7 note \n", - "4 mypy backend/app/email_utils.py 33 error \n", - "\n", - " message \n", - "0 \u001b[m Unused \u001b[m\u001b[1m\"type: ignore\"\u001b[m comment \u001b... \n", - "1 \u001b[m Cannot find implementation or library stub... \n", - "2 \u001b[m Error code \u001b[m\u001b[1m\"import-not-found\"\u001b[m no... \n", - "3 \u001b[m See \u001b[4mhttps://mypy.readthedocs.io/en/sta... \n", - "4 \u001b[m Unused \u001b[m\u001b[1m\"type: ignore\"\u001b[m comment \u001b... \n", - "severity\n", - "error 4\n", - "note 2\n", - "Name: count, dtype: int64\n" + "MyPy found 0 type checking issues\n" ] } ], @@ -374,7 +355,6 @@ "mypy_results = run_mypy()\n", "if not mypy_results.empty:\n", " print(\"\\nMyPy Results Summary:\")\n", - " print(mypy_results.head())\n", " print(mypy_results[\"severity\"].value_counts())" ] }, @@ -388,7 +368,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 137, "id": "5b0cf9e3", "metadata": {}, "outputs": [ @@ -465,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 138, "id": "6820f645", "metadata": {}, "outputs": [ @@ -535,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 139, "id": "e3918fd3", "metadata": {}, "outputs": [ @@ -633,7 +613,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 140, "id": "b66c41c3", "metadata": {}, "outputs": [ @@ -736,7 +716,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 141, "id": "d122b78a", "metadata": {}, "outputs": [ @@ -749,13 +729,13 @@ " total_issues files_analyzed\n", "bandit 2 2\n", "ruff 1 1\n", - "mypy 6 1\n", + "mypy 0 0\n", "radon_cc 7 4\n", "radon_mi 0 0\n", "flake8_wps 11 4\n", "vulture 4 3\n", "\n", - "Total combined issues (excluding Radon A ranks): 31\n" + "Total combined issues (excluding Radon A ranks): 25\n" ] } ], @@ -835,13 +815,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 142, "id": "7b178a7f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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FwFKrPDw6qvc9XnYZ9CK3vzw23rTcCrHzGusscN2hG2wR39/uHbHxSqvEY9MmxexKS+yyxjrR+J9AU936KwyO9ZYfHA9MnRiVWnWRzzdi8vjifLc1113guvplI6a81HrZKgMHxazKf6euzWhpKs5XHrBccf7SrOlxyQsj40ub7xgr9v9vKAvoQ1oq0fznq6I2e27ZlQAAAAAAAEtAiAqApVJ7bUY0Xz607DLoRabOnR2T5s6KzQavGpPmzIofPnJ77H3jRbHr9efHEXdcFTdPeL71ti/MfH362QYrrtzuY2WQqqlaiXGzZizyOfNx+jc0xDrLr7TAdeutMLg4f27GfyetvXW1tWLE5JfioakT45WmOXHpCyNjhX4DYsuVVy+uP/3J+2Pt5eedjgX0PbUp06L5kuvLLgMAAAAAAFgCQlQALJXiPw7PnF12GfQiGaBKM1ua4/A7/hH3Thkf+629Sey/ziZFkOn4+2+Ki557ffLZtKbXJ7ys8p8JUPNb6T9b801vXvQkmFeb58ZK/QcW2wDOb/B/JklN/8+0qXTMljsXU6eOuvuaePe/L4yHXpkY39z27bHqwEEx6tUpcd24Z+K4LXeJ/o2vP161VlvKVwPo6aqPPhktw+4ruwwAAAAAAGAx9V/cGwJAXcudD0Z11HNll0EvM6vl9W3yHp02KXZdY504bZf9YoX+A4rLPjdjWhx559Xx65Ej4t1DNozm/2zTN/A/YaX51S+fW60s8jmbq5UYsJAg1oDGfsV5U+W/j7HRiqvE39/5obh14gsxo6W52PLvzYNXK647ddSI2H7VtWLfdTaJCbNnxA8eub0IguWkqg9tuGUc2yZcBfQNLdcMi8YtN4nGIWuUXQoAAAAAAPAG/Jc8AJZI9eUp0fLPW8sug16oX0ND688nbbt7a4AqbbLSqvHxjbaJllo1/j3h+Rj0n4BTc/X1MNX8mv5zeQaYFmW5fv2Lx1xYwKp4jP7zZs5XGbhcfHCDLeLITbZtDVDdNWlsDJ/8Unxt67cVv3/nodvimenT4pc77h3/s8VOceFzj8Wfn310sV4HoBdpaSkmN9YW8m8VAAAAAADQfQhRAbDYapVqNF94bURTc9ml0Avltnpp+X79Y9OVVl3g+q1XeX2Sy4szXyu21EvTm/+71V5bM/5zeX1bv4XJ7QBntDRFrZ1t9+rb+NXrWpi8729H3RvvGbJh7LT62vHM9FfivqkT4jObbRf7rrNxEbbaa60N46/PPbbIxwF6p9oLL0Xl1nvLLgMAAAAAAHgDQlQALLaWoXdG7cUJZZdBL7XBioOjf0NDVGq1WDDS9N+pU4P69S8mU6UXZ73W7mPl5RnGWmf5FRf5nJustErxuBPmzFzgurGzphfnmw5eMNDV1jXjni6CU8dt9foUqudmvNq69V/dxiutElOb5iw09AX0bi3/ujOqE6eUXQYAAAAAALAIQlQALJZqTtK4aXjZZdCLDWjsF9uvtlY0VStx/9QFw3qPvTqpON9y5dVjm1XWiBX7D4j7pkyI6nxTpMbOei1emj0j3rraWtGvYdH/V2eX1dcpzkdMfmmB6+75z2U7rrb2Qu/fVKnE/z35QByywZatwa7Kf7YHbGlTV94uNf53x0Kgr23rd/F1tvUDAAAAAIBuTIgKgDdUm9sUzRdeE1Ftbz4QdJyPbrR1cf7/nrhnnqlNT742NS5/YVSsOmC52HvtjWK5fv3joHU3i3Gzp8dFzz/eersMMP1m5OvbZn3sP4+1KO9db7MY2Ngvzn76oZjWNKf18kdeeTluGP9sEdjacfUhC73/xc8/Ea80zYn/2XzH1svqWxE+2CYI9tArE2OtQSvEim+wNSDQe9XGjI/KLbb1AwAAAACA7qp/2QUA0P21XHVL1CZPK7sM+oAMRt01aVxcPfap+NCwv8e+a28cM1qaY+j454rJTj/Y/h2tQaRjttw57po0Nn71xD0xYvL4Ytu94ZPGxcjXpsT+62wS7xmy0TyP/dfnHiuCWR9Yf/NYb4XBxWXrrjA4jtli5/jNqBFx2LAr44B1N4mZLc1x/bhnon9DY3x/u3cstNbXmufGOc88FJ/adLtYc9AKrZdvvvLqscNqQ+Iv/3m+iXNmxuOvTo5vbrNbp71uQM/QcsMd0bjtZtG49ppllwIAAAAAAMynoVabbw8cAGij8vjT0fynK8ougz4k/6/JVWOfiktfGBnPzJgWAxsbY/tV14ovvHmH2GG+qVCT5syKPzx5fwx7+cWY0dxUhKM+uP7mceQm2xbbA7Z10M1/K7b5O+ftB8fb1nh9G7+6DE1l6Onp6a8U2wTmtoJf3mKn2HLlNRZa56kjRxRhr2vf89FYof+Aea6bMnd2/PSxu+LOl18srssJW1/afMdobLCfH/R1DRusHQOP+0Q0NBoKDAAAAEDXO/mMmWWXQAf7/tErll0C9BpCVAAsVG3GrJj7q/Mipvs/1ADQUfq/953Rf5+3l10GAAAAAH2QEFXvI0QFHcfyZwAWqvmqmwWoAKCDtdxwZ1QnTC67DAAAAAAAoA0hKgDaVXnqhaje/0TZZQBA79NSieaLr4tapVp2JQAAAAAAwH8IUQGwgFqlEi1//3fZZQBAr1V7cUJUbrmn7DIAAAAAAID/EKICYAGVW+6N2stTyy4DAHq1lhvuiur4SWWXAQAAAAAACFEBML/q1Fej5d93lV0GAPR+lUo0X3J91Kq1sisBAAAAAIA+T4gKgHm0XHFjRHNL2WUAQN/Z1m/4w2WXAQAAAAAAfZ4QFQCtKo8+FdUnnim7DADoU1quvz1qs+aUXQYAAAAAAPRpQlQAFGpzm6L5yhvLLgMA+p6Zs6PlX3eUXQUAAAAAAPRpQlQAFFqG3hUxbXrZZQBAn1S568Gojp9UdhkAAAAAANBnCVEBUPxH28pt95VdBgD0XdVatFx5U9lVAAAAAABAnyVEBdDH1Wq1aL783xHVatmlAECfVn16TFQeGlV2GQAAAAAA0Cf1L7sAAMpVufexqD03tuwyAICIaP7nrdG4zWbRMHBA2aUA0MbMmTPjzDPPjKFDh8a4ceNiwIABsc0228RRRx0V++2332I9xksvvRS/+93v4u67745p06bFxhtvHEceeWR89KMf7fT6AQAAAHhjJlEB9GG1mbOj5Z+3ll0GAFD3ymvRcvM9ZVcBQBszZsyIj3/840WIaoUVVogjjjgiDjzwwBg1alQcc8wxxeVvJINXH/vYx+Kaa66J3XbbrQhPzZ49O773ve/FKaec0iXHAQAAAMCiNdRyHycA+qTmS/8VleGPlF0GANBW//4x8KTPRePqq5RdCQARceqpp8YZZ5wRhx9+ePzwhz+MhoaG4vKJEyfGhz/84Zg6dWpcf/31sdFGGy30MTJs9e9//zvOOuuseNe73lVcNmfOnGKS1cMPPxyXX355vOUtb+myYwIAAPquk8+YWXYJdLDvH71i2SVAr2ESFUAfVR03MSr3CFABQLfT0hItV91cdhUA/EcGpDI4deKJJ7YGqNKQIUOKCVWVSiVuu+22RU6huvHGG2PHHXdsDVClQYMGxfHHHx+5vvFvf/tbpx8HAAAAAIvW/w2uB6CXarl2WIRZhADQLVUffSoqTz4f/bbYuOxSAPq8nBY1ffr0WHnllRe4buDAgcX5zJkLX8k9YsSIIii1++67L3DdzjvvHAMGDIjhw4d3cNUAAAAALCkhKoA+qPr0mKiOeq7sMgCARWi58qZo/PpnoqGfAcIAZTryyCPbvTyDUUOHDi1+3nLLLRd6/+eff744b2+7vwxQrbPOOjF27NhoampqDWUBAAAA0PV04wH6oOZrFr7VBADQPdQmTonKHQ+UXQYAC3HRRRfFI488EhtssEHstddeC73dK6+8Upyvssoq7V4/ePDgqFarMWPGjE6rFQAAAIA3JkQF0MdUHnkyamPGl10GALAYWm64M2ozZpVdBgDzue666+KnP/1p9O/fP0455ZRiotTCNDc3F+cLmzJVvzwnUQEAAABQHiEqgD6kVq1Gy3XDyi4DAFhcc+ZGy7/vLrsKAOabQHXiiScWP//iF7+IXXbZZZG3HzRo0DxhqvnVw1MrrLBCh9cKAAAAwOITogLoQyojHovay1PLLgMAWAKVux+K2iuvlV0GQJ+XW+7l1Kkf/ehHxQSq3/72t/G+973vDe9X38bvtdfa/7d8+vTp0dDQECuttFKH1wwAAADA4hOiAugjas0t0TL0zrLLAACWVEul2NYPgPLktKjjjjsuzjvvvFh11VWL8/3333+x7rvpppsW52PGjFngupxONX78+Nhkk02isVGbDgAAAKBMujMAfUTljgcipk0vuwwAYClU7nssqqZJApSiUqkUAaqhQ4fG+uuvHxdffPEbbuHX1q677lpMmrrnnnsWuO6+++4rglQ777xzB1cNAAAAwJISogLoA2qz50bLTcPLLgMAWFrVWrT8646yqwDok84666y4+eabY911142LLrqodbLU4lp77bVjzz33jBEjRsSNN97YevmcOXOKLQHTkUce2eF1AwAAALBk+i/h7QHogVpuvidi1pyyywAAlkH14VFRHff2aFxvrbJLAegzpk2bVoSo0tZbbx2XXnppu7fLyVS77757MW0qw1J523333bf1+u9+97tx+OGHx7HHHhsHHXRQDBkyJG666aZ4/vnn43Of+1xxewAAAADKJUQF0MvVXpsRldvvL7sMAGBZ1SJarr89Bn7+w2VXAtBn5HZ7s2bNKn7O0FOe2nP00UcXIaoMUJ1++ulx6KGHzhOi2mSTTeJvf/tbMXnqjjvuiLlz58bGG28cP/nJT+Kwww7rsuMBAAAAYOEaarVabRHXA9DDNV92Q1TufrjsMgCADjLwuE9E40brll0GAAAAAD3QyWfMLLsEOtj3j16x7BKg12gsuwAAOk910tSo3PNo2WUAAB2o5Ya7yi4BAAAAAAB6HSEqgF6s5fo7IqrVsssAADpQddSzUR0zvuwyAAAAAACgVxGiAujFU6iqD48uuwwAoBO03HBn2SUAAAAAAECvIkQF0EtVbrk3olYruwwAoBNUR5pGBQAAAAAAHUmICqAXqk2fGZX7Hi+7DACgE7UMvavsEgAAAAAAoNcQogLohVpuvz+ipaXsMgCATlR94pmovjih7DIAAAAAAKBXEKIC6GVqc5uicudDZZcBAHQB06gAAAAAAKBjCFEB9DKV4Q9HzJ5TdhkAQBeoPvF0VF+eWnYZAAAAAADQ4wlRAfQitUolWm67r+wyAICuUouo5Da+AAAAAADAMhGiAuhFqg+MjJg2vewyAIAuVLn3saiZQgkAAAAAAMtEiAqgF2m5ZUTZJQAAXa2pOSp3P1x2FQAAAAAA0KMJUQH0EpUnnonahMlllwEAlKDlzgejVq2WXQYAAAAAAPRYQlQAvYQpVADQh73yWlQfebLsKgAAAAAAoMcSogLoBaovjI/aMy+WXQYAUKKWYfeXXQIAAAAAAPRYQlQAvUDLLfeUXQIAULLa8+OiOmZ82WUAAAAAAECPJEQF0MNVJ02N6qNPlV0GANANmEYFAAAAAABLR4gKoIer3HpvRK1WdhkAQDdQfXhU1F6bUXYZAAAAAADQ4whRAfRgtTlzo3L/E2WXAQB0F5VqtNzxYNlVAAAAAABAjyNEBdCDVR4cFdHUXHYZAEA3Urn7oag1t5RdBgAAAAAA9ChCVAA9WGX4w2WXAAB0NzNnR+X+x8uuAgAAAAAAehQhKoAeqvrSy1F7cULZZQAA3VBl2P1llwAAAAAAAD2KEBVAD1UZ/kjZJQAA3VRtwuSoPPl82WUAAAAAAECP0b/sAgBYcrXmlqjc/0TZZQAA3Vjl7oej3xYbl10GAAAAPczXvva1eOCBB2LYsGFveNsrrrgivv3tb7/h7S644ILYbbfdip+feeaZOPjggxd629NOOy0OPPDAJawaAGDZCVEB9EDVR56MmD2n7DIAgG6s+vjTUZs9JxqWH1R2KQAAAPQQp59+elx//fUxZMiQxbr91ltvHcccc0y7140ZMyauvvrqeNOb3hSbbbZZ6+WjRo0qzvfbb7/YcsstF7jfm9/85qWuHwBgWQhRAfRAleEPl10CANDdtVSi8uCo6L/HDmVXAgAAQDc3d+7c+PGPfxyXXXbZEt0vQ1R5ml9TU1Mcfvjh0a9fv/jtb38ba665Zut1I0eOLM4/97nPxY477tgB1QMAdIzGDnocALpIddIrUX32xbLLAAB6gMp9j5ddAgAAAN3czTffHAcddFARoHrXu97VIY/5hz/8IR5//PEiKLXLLrvMc11OompoaIgtttiiQ54LAKCjCFEB9DCVex6JqJVdBQDQE9SeH1cEsAEAAGBhLr/88pg5c2b84Ac/iDPPPHOZH+/FF1+Mc889N9Zbb712t/rLENWGG24YK6644jI/FwBARxKiAuhBapVqVO59rOwyAIAepHKf/+8AAADAwh111FFx0003xRFHHFFMiFpWv/71r4vt/I4//vhYbrnl5rluypQpMWnSpGJ7v1NOOSX222+/2G677eKAAw4oplfl/QAAyiJEBdCDVJ94JmL6zLLLAAB6kOr9T0StZowlAAAA7dttt91ipZVW6pDHyilUN9xwQ2y88cZx8MEHL3D9yJEji/P7778/brvttthnn33i0EMPLcJTv/vd74rt/wSpAICy9C/tmQFYYpV7Hi67BACgh6lNfTVqz7wYDW/esOxSAAAA6OX+/Oc/R7VajS984QvRr1+/Ba6fMWNGEbDafffd43vf+17rbWbNmhVf+cpX4q677opzzjknvvzlL5dQPQDQ15lEBdBD1KZNj+qo58ouAwDogSr3PV52CQAAAPRyLS0t8c9//jMGDx4cH/jAB9q9zYEHHlhMqvrhD384T8hqhRVWiB/84AfFz1dddVWX1QwA0JYQFUAPUbn30YiqrXgAgCVXeXh01Jqayy4DAACAXuzee++NadOmxX777RcDBw5c4vvnhKqVV1652BIQAKAMQlQAPUTl3sfKLgEA6KnmNkX10afKrgIAAIBe7JZbbinODz744IXe5tlnn42777672L5vfrkN4Ny5c2O55Zbr1DoBABZGiAqgB6i+OCFqk6eVXQYA0INV7hPIBgAAoPM8+OCD0djYGDvvvPNCb/Pzn/88Pv3pT8ewYcMWuO6RRx4pQlTbb799J1cKANA+ISqAHqDy0KiySwAAerjqky9E7dXpZZcBAABAL9Tc3ByjR4+OTTbZJFZYYYWF3u69731vcf773/8+ZsyY0Xr5K6+8EieffHLx81FHHdUFFQMALKh/O5cB0M1UHx5ddgkAQE9Xq0Xl/iei/967lV0JAAAAPdDIkSPjxhtvjPXWWy8+9KEPzXPdhAkTiilSQ4YMWeRjfOADH4ihQ4fGTTfdFAcddFDst99+0dTUFLfeemtMmjQpPvOZz8Tee+/dyUcCANA+k6gAurnqmPFRm/pq2WUAAL1A5b7Hyy4BAACAHhyiOv300+PKK69c4LqcJJUGDx68yMfI7f5yCtV3vvOdWGONNeKyyy6La6+9NjbccMM49dRT46STTuq0+gEA3khDrVarveGtAChN89W3ROXWe8suAwDoJQaecFQ0rr/olcEAAAAA9E4nnzGz7BLoYN8/esWyS4BewyQqgG6uYis/AKADVR57quwSAAAAAACg2xGiAujGqi+8FPHKa2WXAQD0ItXHny67BAAAAAAA6HaEqAC6scojT5ZdAgDQy9TGvRy1adPLLgMAAAAAALoVISqAbqz6qBAVANDxKqZRAQAAAADAPISoALqp6oTJUZs8rewyAIBeyJZ+AAAAAAAwLyEqgG6q+uhTZZcAAPRS1afHRG1uU9llAAAAAABAtyFEBdBNVWzlBwB0lpZKVEc/X3YVAAAAAADQbQhRAXRDtWnTozZ2YtllAAC9WMWWfgAAAAAA0EqICqAbqtjKDwDoZNWRz0atWiu7DAAAAAAA6BaEqAC6oepjtvIDADrZjFlRe2Fc2VUAAAAAAEC3IEQF0M3U5jZF9dmxZZcBAPQBlcefKbsEAAAAAADoFoSoALqZ6jMvRlSqZZcBAPQB1cefLrsEAAAAAADoFoSoALqZ6lMvlF0CANBH1CZOieqkV8ouAwAAAAAASidEBdDNVJ8aU3YJAEAfUn3CNCoAAAAAABCiAuhGajNmRW38y2WXAQD0IdXHhKgAAAAAAKB/2QUA8F/Vp8dE1MquAgDoS6rPjYvanLnRMGi5sksBAADold72j1+VXQId7N5DvlF2CQBAJzCJCqAbqT71QtklAAB9TbVaBKkAAAAAAKAvE6IC6EaEqACAMlSfHVt2CQBLZdKkSXHaaafFxz/+8Xj3u98dP/zhD4vLTz311LjuuuvKLg8AAACAHsR2fgDdRO2V16I2eVrZZQAAfZAQFdAT3XbbbXHiiSfGzJkzo1arRUNDQ8yaNau47uabb46zzjorHnjggfjud79bdqkAAAAA9AAmUQF0ExVTqACAktReHB+15payywBYbM8880wce+yxMWfOnDj88MPjzDPPLIJUdYcddlisuOKKceGFFxaBKgAAAAB4IyZRAXQTtvIDAErTUonamPHRsNkGZVcCsFj++Mc/RlNTU7GV3/7777/A9UcddVS85S1viU984hNFkGrvvfcupU4AAAAAeg6TqAC6CSEqAKBM1WdfLLsEgMU2fPjw2HrrrdsNUNXtvPPO8da3vjWefvrpLq0NAAAAgJ5JiAqgG6hOnBLx2syyywAA+rDqs2PLLgFgsb366qux/vrrv+Ht3vSmN8XUqVO7pCYAAAAAejYhKoBuwBQqAKBs1efHRa1aLbsMgMWy2mqrxfPPP/+Gt3v22Wdj9dVX75KaAAAAAOjZhKgAugEhKgCgdHObozZuYtlVACyW3XbbLZ566qm46aabFnqbG264IZ555pl429ve1qW1AQAAANAzCVEBlKxWrUX16RfLLgMAIKrP2NIP6Bm++MUvRv/+/eP444+P0047LUaMGFFc3tzcHGPGjInzzjsvTjrppOI2n/nMZ8ouFwAAAIAeQIgKoGS1CZMiZs8puwwAgKg+K0QF9Aybb755/PKXv4yGhoY444wz4qijjip+/te//hUHHHBAcV0Gqn7wgx/EtttuW3a5AAAAAPQA/csuAKCvq744oewSAAAK1efGRq1WK4IIAN3dQQcdFNtss02cf/75MXz48Bg/fnxUq9V405veFLvuumsRrNpqq63KLhMAAACAHkKICqBktbETyy4BAOB1M2dHbeKUaFh7zbIrAVgsG220UTFtCgAAAACWle38AEpWFaICALqR6rMvll0CAAAAAAB0OZOoAEpUq1aj9tLLZZcBANCq+uzYiD12LLsMgHn8/Oc/X+r75halJ510UofWAwAAAEDvI0QFUKLahCkRzS1llwEA0Kr63LiySwBYwJ///OciDFWr1Zb4vkJUAAAAACwOISqAElXHTii7BACAeb3yWtRmzYmGFQaVXQlAq2OOOabsEgAAAADo5YSoAEpUGzux7BIAABaQ2w03vHnDsssAaCVEBQAAAEBna+z0ZwBgoUyiAgC6o+r4yWWXAAAAAAAAXcokKoCS1KrVqL00qewyAAAWUBv/ctklAMzjggsuKM4PPfTQGDx4cOvvi+tTn/pUJ1UGAAAAQG8hRAVQktrLUyOamssuAwBgAVVBb6Cb+dnPfhYNDQ2x1157FSGq+u9vpFarFbcTogIAAADgjQhRAZSk9qKt/ACA7qk2YXJr8ACgOzjkkEOKf5MyQNX2dwAAAADoKEJUACWpjp1YdgkAAO1rao7alGnRsOZqZVcCUDjllFMW+TsAAAAALKvGZX4EAJZKdaxJVABA91WzpR8AAAAAAH2IEBVACWrVWtTGvVx2GQAAC1UbL0QFdB+HHnpo/Pa3vy27DAAAAAB6MSEqgBLUJk0ttskBAOiuqiZRAd3IyJEj46WXXmr3um9/+9tx2WWXdXlNAAAAAPQuQlQAJaiNnVh2CQAAi1SbIEQF9AxXXnll3HvvvWWXAQAAAEAPJ0QFUILa5FfKLgEAYJFqk6dFzeRMAAAAAAD6CCEqgBJUhagAgO6uVova+MllVwEAAAAAAF1CiAqgBLUp08ouAQDgDVXHv1x2CQAAAAAA0CWEqABK2h4HAKC7q700qewSAAAAAACgSwhRAXSx2py5ETNmlV0GAMAbqo0XogIAAAAAoG8QogLoYrbyAwB6iqr/3wIAAAAAQB/Rv+wCAPoaW/kBAD3GazOiVqlGQz/rb4Dy3XjjjbHPPvsscHlDQ8NCr2t7PQAAAAAsihAVQBcTogIAeoxqLWqvTo+G1VcpuxKAmDVrVnFa0usyRAUAAAAAb0SICqCL1aa8UnYJAACL75XXIoSogJJdcMEFZZcAAAAAQC8nRAXQxUyiAgB6klqGqABKtuuuu5ZdAgAAAAC9XGPZBQD0NdUpQlQAQM8hRAUAAAAAQF8gRAXQhWotlYhp08suAwBgsQlRAQAAAADQFwhRAXShWk6hqtXKLgMAYLHVpglRAQAAAADQ+wlRAXR1iAoAoAcxiQoAAAAAgL5AiAqgC9Umv1J2CQAAS8QkKgAAAAAA+gIhKoAuVJtsEhUA0MPMbY7azNllVwEAAAAAAJ1KiAqgC9Vem1F2CQAAS8yWfkB3c9ZZZ8Vtt91WdhkAAAAA9CL9yy4AoC+pzZhVdgkAAEu3pd/6Q8ouA6DVueeeG6uvvnq8613vKrsUAAAAAHoJk6gAupKtcACAHsgkKqC7mT17drz5zW8uuwwAAAAAehEhKoAuVBOiAgB6ICEqoLvZfffdY8SIETF58uSySwEAAACgl7CdH0AXqdVqEbPmlF0GAMASE6ICupvjjjsuTjjhhHjf+94XBx98cGy99dax2mqrRUNDQ7u332effbq8RgAAAAB6FiEqgK4ye25EtVp2FQAAS6z26oyySwCYx6GHHloEpnKxysUXX/yGtx85cmSX1AUAAABAzyVEBdBFajNnlV0CAMDSmW2aJtC9vO1tbyu7BAAAAAB6GSEqgK4yY3bZFQAALJXanLlllwAwj7/85S9llwAAAABAL9NYdgEAfYVJVABAjyVEBQAAAABAL2cSFUAXqc00iQoA6KHmNketWo2GRutwgO6lqakprr766hg+fHiMHz++2Obva1/7Wvz1r3+Nt7zlLbHDDjuUXSIAAAAAPYQQFUBXEaICAHqyOU0RKwwquwqAVo8++mgce+yxMWHChKjVatHQ0BDrrbdecd3ll18eP/3pT+Mb3/hGfPazn+3Q582Q1gMPPBDDhg1b7Pt89KMfjYcffrjd69797nfHmWee2YEVAgAAALA0hKgAuohJVABAT1abPScahKiAbiKnTn3+85+PV199Nd75zncWQaSTTz659frdd989nn766fjVr34V22+/feyyyy4d8rynn356XH/99TFkyJDFvk+1Wo0nn3wy1l9//TjkkEMWuH7jjTfukNoAAAAAWDZCVABdRYgKAOjJ5swtuwKAVmeccUYRoPrOd74Tn/zkJ4vL2oaovvWtb8WOO+5YTKo677zzljlENXfu3Pjxj38cl1122RLf97nnnovZs2fHnnvuGV/96leXqQ4AAAAAOk9jJz42AG3UZs4quwQAgKU3W4gK6D5uv/322GyzzVoDVO3Zf//9Y+utt46RI0cu03PdfPPNcdBBBxUBqne9611LfP/Ro0cX51tuueUy1QEAAABA5xKiAugitRkmUQEAPVfNJCqgG5k0aVK8+c1vfsPbbbDBBjF58uRleq7LL788Zs6cGT/4wQ/izDPPXOL710NcW2211TLVAQAAAEDnsp0fQFcxiQoA6MnmNJVdAUCrlVdeOV566aU3vN3YsWNj8ODBy/RcRx11VPzyl7+MlVZaaanuXw9RPfHEE/GLX/winnrqqRg4cGDssccexXaDm2yyyTLVBwAAAEDHMIkKoIvUbIEDAPRg/r8M0J3suOOO8fjjj8eDDz640NuMGDGiCC7tsMMOy/Rcu+2221IHqNKoUaOK81NPPTU23XTTOPzww2OLLbaI6667Lg477LB45JFHlqk+AAAAADqGSVQAXaWlUnYFAABLb86csisAaPWZz3wmbrrppjj66KPjG9/4Ruy+++6t1zU1NcWtt94aJ598cjQ0NMQnP/nJ0uqcNWtWrLXWWrHGGmvEH//4x1h33XVbr7vkkkuKLQKz/gxU9evXr7Q6AQAAABCiAug6FSEqAKDnMokK6E523nnn+Na3vlVsj/e9732vuCwDUxlGuvbaa6NarUatVouvfOUr8fa3v720OldYYYW44oor2r0uJ1L94x//KKZp5TSqnK4FAAAAQHls5wfQVSrVsisAAFh6c4SogO7l05/+dFxwwQWx1157xaBBg4rQVEtLSzQ2Nsbb3va2OPPMM+OrX/1qdGfbb799cT5mzJiySwEAAADo80yiAugCtWotolYruwwAgKVWE6ICuqEMS+UpJ09NmzatOF911VWjf//u0fJ65ZVX4tlnn43VV189NtlkkwWunz17dnGeITAAAAAAymUSFUBXqNrKDwDo4WY3lV0BwELl9KmVV1451lhjjW4ToEp33313HHHEEXHKKacscF0Gvh544IFiG8LtttuulPoAAAAA+K/u01UC6M1s5QcA9HC15uaySwBYwIMPPhgXXnhhEVaaOnVqEaYaMmRIvPOd74xPfvKTsdlmm5VaX9YxePDgGDZsWNx5552x5557tl73hz/8IZ5++unYf//9Y9111y21TgAAAACEqAC6hhAVANDT2ZkY6GZ++9vfxplnnhm1NlunVyqVeOmll+KSSy6JK664Ir7//e/HYYcd1iX1jBw5Mm688cZYb7314kMf+lBx2UorrRQnn3xyfP3rX48vfOELsd9++8U666xThL8eeuih2HTTTeOHP/xhl9QHAAAAwKIJUQF0hYrt/ACAHq4mFA50H0OHDo0zzjgjVlhhhfif//mfYppThpPSiy++GP/617/inHPOKQJKW2yxRWy//fZdEqI6/fTTY9ddd20NUaWDDz64mDSV9ebErFmzZhW/f/GLX4wvfelLRdAKAAAAgPIJUQF0BZOoAICermoUFdB9/PnPf47+/fvHn/70p9hxxx3nuS638PvKV74SO++8c3zmM5+Js846qwg3dZTRo0e3e3kGp9qGp9raYYcdihAVAAAAAN1XY9kFAPQFtaoQFQDQw7XZLgugbE8++WTssssuCwSo2nr7299e3Ob+++/v0toAAAAA6JmEqAC6gu38AICeTogK6GZWXHHFN7zNaqutFk1NTV1SDwAAANB9fe1rX4t3vvOdS3SfRx55JL70pS/FO97xjmIh12GHHRaXXXZZ1Nrplba0tMRFF10Uhx56aHHbXNx17LHHxsiRIzvwKOhsQlQAXcF2fgBAT2c7P6Abedvb3hbDhw+PyZMnL/Q2s2bNivvuu6/Y1g8AAADou04//fS4/vrrl+g+w4YNi8MPPzxGjBhRhK8++tGPxowZM+K73/1ufOtb31rg9nnZj370o2IxV972Pe95T9x2223xkY98JG6//fYOPBo6kxAVQFcQogIAejqTqIBu5Nvf/nYMHDgwPve5z8XTTz+9wPVTp06N4447LubMmRMnnnhiKTUCAAAA5Zo7d24Revr973+/RPfLqVInnXRS0XvIyVM/+9nPil7E1VdfXUyZuuqqq4pwVd1NN90U11xzTeyxxx7FbfK2P//5z4vJVCnDVe1Nr6L76V92AQB9QtV2fkDnqTXW4sVdZsXtQ1aLqbV14uWWlaLJ/xcHOtg6g5aPk8ouAuA/svm56aabxv333x/vf//7Y4sttoiNN944+vXrFxMmTIhHH320aHiussoqRdOzrYaGhrjiiitKqx0AAADofDff/P/Zuw84uep6///vM322Z0s22eymbXrvhUAChBJ6DwJSLFwV9V4V0b/Xq9gABa+i4lXEglh+9CIiJIQeIQRIJSSbtkk2bVO216n/xzlLNgkppOzud2bn9Xw8zmN2z5yZeS+uafPez+dl/ehHP9K2bds0a9YsZyrUsdqwYYOysrJ0+umna9CgQe3n7VLVeeedp6VLl2rZsmWaMmWKc97+Aa+CggJn9Z/9bxP7jBw50nm8vdLPnqZtX4PERokKALoCk6gAdJLWXEtrB69Ubc0HKgr30IqiTG1r2aGCjOnyeUepOtRD5fWtCsX4dQjAyRnE3x4BJBD7pzr3sX+Ss6yszDk+qqamxjk+WqICAAAAAADd2+OPP67GxkbdfvvtuuaaazRs2LBjfuzQoUP1wgsvHLFgZTuwEGWXp+zjo+wJ2du3b5ff73dKWUh8/DM4AHQFSlQAOkHVyKjWuuYpUlPvfO5tqtb5FWG92LevNjTYP1HR9lMVvdxp6pk9Qx7PcO1pzVJ5fYuijI0FcJzconQAIHE89NBDpiMAAAAAAIAEduONN+ruu+9WRkbGST9XNBp1ylBPPPGEHn30UQ0ePNiZSHUkzc3N+uCDD3TvvfeqtrZWX/7yl50iFRIfJSoA6Ar8pDOADhT1SZsm7tSOqkNHz7pbG3ROeUSvDhym1fVrnHOReJO2178oyT6kEm+WCjJOk8s1RJWt6dpS36KYKFUBODoXf54BkED2jcsHAAAAAAA4nKlTp3bYc9mTrJYvX+583L9/f/3hD39QIBA44qSq888/v/3z6667Tl/60pc6LAs6FyUqAOgKXn65BdAxmooslRW9pcaqzUe8xhVp0RkbNso/aJSW1b1/yP2hWJ221T0nyT6kAYF85aefqrg1SDuaA6pobO7UrwFAcqJEBQAAAAAAACBVf5hr0qRJWrVqlRYtWqQrr7xSv//97521fx8Vi8V00003qbW1Va+//rr+9re/ac+ePfrpT38qn89nJD+OHe/qA0BX8HlNJwDQDeyc0KyNLfMUq2/92GutaFinrF0t/5Bxertu2VGvbY7sUUXt0+2fD0nro9zgKYpY/bWtyacdTS0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" ] @@ -932,13 +912,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 143, "id": "f8c74802", "metadata": {}, "outputs": [ { "data": { - "image/png": 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ZZ56JE044IW666abo3bt3ZZcEAAC1nnk6AADkht133z0eeeSRuPHGG6N9+/bpXD2xao4+d+7cuO666+Lzzz+Pgw8+OMvVAgBA7tI3BwCAHAipJ6u2PPnkkzF+/Ph48cUX00ePlpWVpY89Sh5/dMghh0T9+vXTc3/wgx/EhAkT4oILLojrr7/eJB4AACqJeToAAOSen//85+k8ftSoUen7ZA5/1FFHpYH1xOGHHx4LFiyINm3axKmnnprlagEAILfomwMAQA6F1C+//PKYOHFiLFy4MJ24N23aNJ2oJ5P37bffvsLP/PCHP4xLLrkkZs+enelyAAAA83QAAMhZHTp0iHHjxsUf/vCHdGXHvffeOwYNGrT6eJcuXWKLLbaIc889N5o3b57VWgEAIJfomwMAQI6F1MeMGZPuk0cdJRP3Pn36RN26db/1M8uXL48tt9wy9txzz0yXA1WvUaOIESPWHAMAyCLzdABgo+hzQLWw9dZbx1VXXVXhsdtvv73K6wEAgJpA3xyAb/ZBS0pK4vaRv4nBBzeJgvoZj1YC1DoZ/0160kknxTHHHBPbbrvten8mmeQ//vjjmS4FsiN51Ndpp2W7CgCAcszTAYCNos8B1dobb7wRn3zySey8887Rrl27bJcDAAA5Rd8cgG/2QcuKi+PRR+6OUw5vHVGQn+2qAHJexn+TXnjhhekEfv78+fHCCy+UOzZ9+vS48cYbY+bMmZm+LQAA8C3M0wEAIHe9/vrrMXjw4HJz+QsuuCB+9KMfxdChQ+Pggw+OEd986gEAAPCt9M0BAKByVcrXff785z/H97///fjd735Xbnzq1Klx2223xaGHHhr3339/ZdwaAABYC/N0AADIPUk4ZuDAgfHcc8/FBx98kI4988wzMWHChCgoKIhu3bpFvXr1YuTIkek4AACw/vTNAQAgh0LqL730UlxxxRWRn5+fNsf/2x577BEnnnhi5OXlxZVXXhlTpkzJ9O0BAIAKmKcDAEBuuvPOO2PFihXpSupHHXVUOvbII4+k8/dkNfV77rknxowZkwbWk4ANAACwfvTNAQAgx0Lqf/jDH9Jm+B//+Me4+OKLyx3r2LFjOpY01cvKytJzAQCAymeeDgAAuemVV16J7bbbLs4+++xo2LBhlJaWpquqJ2GZH/zgB+k5yfGuXbvGG2+8ke1yAQAgZ+ibAwBAjoXU33nnnbQZvtdee631nOQbqHvuuWf885//zPTtAQCACpinAwBAbvrss89i2223Xf0+CaIvWrQott9++9hss81Wjzdr1iwdBwAA1o++OQAA5FhIffHixWkzfF2aN28eX3/9daZvD9n32WcRLVqU35IxAIAsMk8HADaKPgdkXTKP/+qrr1a/f/bZZ9P9d77znXLnzZo1K5o0aVLl9QEAQK7SNwfgm33Qgtat497n3oyCo5+M+GpFtqsCyHmFmb5g27Zt02+QFhcXR2FhxZcvKSmJN998M1q3bp3p20P2lZWt+Y+1yRgAQBaZpwMAG0WfA7IuWUX91VdfjQ8++CBatmwZEyZMiLy8vNh///1Xn/PEE0/E22+/HT179sxqrQAAkEv0zQH4Zh80LyKaJu+T9QL0QQGq30rqvXv3jnnz5sUVV1yRTta/qaysLK699tr45JNP4vvf/36mbw8AAFTAPB0AAHLTj370o1i5cmX88Ic/TEPos2fPjo4dO65eSX3IkCFx9tlnp8H1E044IdvlAgBAztA3BwCAHFtJfdCgQfHII4/E2LFj44UXXkhXc0m+UZo0yJOJ+3PPPRczZ85MH4d0yimnZPr2AABABczTAQAgN/Xt2zdmzZoVt956ayxZsiS22267uPHGG1cfT0LrRUVFabDmu9/9blZrBQCAXKJvDgAAORZSb9asWfzxj3+MCy64IN56662477770gn8qm+ZJrbffvu44YYbYosttsj07QEAgAqYpwMAQO4aPHhwGqBZvHhxbL755uWOXX311WlwvX79+lmrDwAAcpG+OQAA5FhIPZE8anTcuHHxr3/9K15++eX08UjJ40hbtGgRXbt2jX322acybgsAAHwL83QAAMhdyWrp3wyoJ7p06ZKVegAAoCbQNwcAgBwLqa+y2267pRsAAFB9mKcDAAAAAMB/6JsDAECOhdQBAAAAAID116tXr43+bF5eXjz55JMZrQcAAAAAAKpNSP3VV1+N22+/Pd59991YunRplJWVrbVhnjwuCQAAqHzm6QAAUP3NmTNnoz+bzOUBAID1p28OAAA5FFJPJvCDBg2KkpKStU7eAQCAqmWeDgAAuWH06NHZLgEAAGoFfXMAAMixkPqoUaOiuLg4DjrooDjxxBOjZcuWUVBQkOnbAAAAG8A8HQAAckP37t2zXQIAANQK+uYAAJBjIfV//vOf0aFDh7jppps8WhQAAKoJ83QAAAAAAPgPfXMAAKhc+Zm+YPIt086dO5vAAwBANWKeDgAAAAAA/6FvDgAAORZS33bbbWPWrFmZviwAALAJzNMBAAAAAOA/9M0BACDHQurHH398TJs2Lf7+979n+tIAAMBGMk8HAAAAAID/0DcHAIDKVZjpC373u9+Ngw8+OM4666w44ogjYvfdd48mTZqs9fFIvXr1ynQJkF0NGkRcdtmaYwAAWWSeDgBsFH0OAAAAaih9cwC+2QctLS2NMffcEf32axT5dQuyXRVAzst4SL1nz57phL2srCzGjh2bbt8m+VYq1CgNG0Zcfnm2qwAAKMc8HQDYKPocAAAA1FD65gB8sw9aWlwc97/wtzhuYOvIL8jPdlUAOS/jIfW99tor05cEAAA2kXk6AADkhn79+sXee+8dZ599dvr+448/jgYNGkSzZs2yXRoAANQo+uYAAJBjIfV77rkn05cEAAA2kXk6AADkhhkzZkSbNm1Wv+/Vq1f88Ic/jOuuuy6rdQEAQE2jbw4AAJXLMykAAAAAAKCayMvLi/feey9KS0vT92VlZekGAAAAAAC1eiX1VVasWBETJkyIyZMnxyeffJI+Jmno0KFx7733xi677BK77757Zd0aAABYC/N0AACo3jp37hyvv/56fO9734sWLVqkY88880wceeSR6xVwf+ihh6qgSgAAqDn0zQEAIIdC6m+++WaceeaZ8emnn6YrvCSN8bZt26bHxo0bF8OGDYvzzz8/Tj755Mq4PQAAUAHzdAAAqP7OO++8GDx4cHz22Wfplvjqq6/SbV2SOT4AALD+9M0BACCHQurJt0p/+tOfpg3zZKWX/fffP6644orVx/fZZ5/0UaW/+c1vokuXLtGtW7dMlwDZ9cUXEfvtV37sueciNt88WxUBAJinAwAbR58Dqtyee+4ZTz/9dLz//vvx9ddfx8CBA2PfffeNU089NdulAQBAjaJvDsA3+6AFETHyow+i4CfvRtzcI6JJUbYrA8hpGQ+p33bbbekE/pe//GWccMIJ6dh/T+IvvPDC2GOPPdJvot51110m8dQ8JSURb7+95hgAQBaZpwMAG0WfA7KiUaNGsdtuu61+v8UWW0T37t2zWhMAANQ0+uYAfLMPmjyjrkPyfklElJZluyqAnJfxkPpzzz0X22677eoJfEUOOuig2HHHHWPatGmZvj0AAFAB83QAAMhN06dPz3YJAABQI+mbAwBAjoXU58+fHwcccMA6z2vfvn36uFIAAKDymacDAEBu+/LLL+OBBx6IyZMnx7x586KoqChdYX2fffaJww8/PFq0aJHtEgEAIKfomwMAQI6F1Js0aRIff/zxOs+bPXt2NG7cONO3BwAAKmCeDgAAuWvKlClxxhlnxMKFC6OsrPyjpl988cX4wx/+EDfccEMaWAcAANaPvjkAAFSu/ExfcI899oipU6fG66+//q0N9bfffjt23333TN8eAACogHk6AADkpjlz5sTPf/7z+Oqrr2L//feP66+/Ph588MEYM2ZMDB8+PPbbb79YsGBBDB06ND755JNslwsAADlD3xwAAHIspH7SSSelK7kMGTIkxo0blzbQV1mxYkU88cQTcc4550ReXl6ccMIJmb49AABQAfN0AADITXfccUcsXrw4na/feuutccghh0SXLl3SkMwPf/jDuP322+Pss89OQ+x/+tOfsl0uAADkDH1zAACoXIWZvmDXrl3jwgsvjOuuuy4uueSSdCyZsD/66KPxP//zP1FaWppO8k877bT4zne+k+nbAwAAFTBPBwCA3PTcc8/FVlttFYMHD17rOaeeemr85S9/ib///e9x0UUXVWl9AACQq/TNAQAgx1ZSTwwaNChGjx6dPma0Xr166aS9uLg48vPzY6+99opRo0bFGWecURm3BgAA1sI8HQAAcs+8efNixx13XOd5O+20U3z66adVUhMAANQU+uYAAJBDK6mvkkzWky35ZumCBQvSfbNmzaKwsNJuCQAArIN5OgAA5JYGDRrE559/vs7zknOSUA0AALBh9M0BAKByVPqMOvl26eabb17ZtwEAADaAeToAAOSGXXbZJV5++eWYPn16dO7cucJzkmOvvfZafOc736ny+gAAoKbQNwcAgGoeUn/44Yc36Pwjjjgi0yUAAADfYJ4OAAC56fjjj48XXnghTjnllLjsssvigAMOSMMziWSFx0mTJsWvf/3r9HW/fv2yXS4AAOQMfXMAAMixkPpFF10UeXl56zyvrKwsPc8kHgAAKp95OgAA5KbevXvHcccdFw8++GCcccYZUa9evWjTpk167OOPP46vv/46nccfc8wxceCBB2a7XAAAyBn65gAAkGMh9WQVl4om8SUlJbFw4cKYNm1aLFu2LPr06RM77bRTpm8PAABUwDwdAABy1xVXXJHO0//4xz/GrFmz4v333199rH379nHyySenK64DAADrT98cAAByLKR+yy23fOvxZFWXSy65JH0E6dlnn53p2wMAABUwTwcAgNzWr1+/dJs7d266JVq2bBlbbrlltksDAICcpG8OAAA5FlJfl+RRpMOGDUu/kXrTTTfF9ddfX9UlQOWqVy/i5z9fcwwAoBozTwcAKqTPAdVOq1at0g0AAKhc+uYAta8PWlpaGhP/Oi76dm0Q+UUF2a4KIOdVeUg9UVRUFHvssUe89NJL2bg9VK7GjSNGjsx2FQAAG8w8HQBYgz4HAAAAtZi+OUDt6oOWFhfHbe+8HAef2TryC/KzXRVAzsvab9Ivvvgili5dmq3bAwAAFTBPBwAAAACA/9A3BwCAHAqpP/zww/GPf/wjOnbsmI3bAwAAFTBPBwAAAACA/9A3BwCAjVcYGXbkkUeu9VhxcXF8/vnn8eWXX0ZeXl4cd9xxmb49AABQAfN0AAAAAAD4D31zAADIsZD6tGnT1nlOUVFRnHDCCXH88cdn+vYAAEAFzNMBAAAAAOA/9M0BACDHQuqjR49e67H8/Pxo0KBBbLPNNlG/fv1M3xoAAFgL83QAAMhNV155ZWy77bbRv3//bJcCAAA1ir45AADkWEi9e/fumb4k5JYFCyKOOKL82MMPRzRrlq2KAADM0wGAjaPPAVk3YcKEaN++vZA6AABkmL45AN/sgxaUlcXVb7wbBed8FDFsr4hGdbJdGUBOy3hIHWq9lSsjnnlmzTEAAACAXKPPAVlXXFwcbdq0yXYZAADARlqyZEmMGjUqnnjiiZgzZ07UqVMndtpppxg4cGAceOCB63WNjz/+OG6++eZ46aWXYsGCBbH11lvHgAED4rjjjqv0+gFqUx80LyJ2Td4vSJoypdmuCiDnZTykfs0112z0Z/Py8uKiiy7KaD0AAIB5OgAA5KqDDz44Hn/88XjvvfeiU6dO2S4HAABqjKromy9evDh9KtKMGTNi5513Tl8vWrQoDayffvrpcc4558Spp576rddIgu39+vWLL7/8Mg455JBo3rx5PPnkk3HJJZfEBx98oH8PAEDtCan/6U9/Sifjq5SVlZU7vq5jJs8AAJB55ukAAJCbfvCDH8S//vWvOPLII6NHjx6x4447RrNmzSI/P7/C80888cQqrxEAAHJRVfTN77jjjjSgnoTML7/88tXXPOuss+Loo4+Om266Kfr06RNbbbXVt4bp582bF7fffnv07NkzHTvzzDPTldjvvvvuOOyww2KXXXbZgJ8cAAByNKR+2223xfjx49OVXZJVXQ499ND0MUPJ44rmzp0bTz31VLz44ovRtm3btKkOAABUPvN0AADITT/5yU/SIEsSinnmmWfi2WefrfC85HhynpA6AABUn775xIkT03n6ueeeWy703qpVqzj++OPj5ptvTuf5a5vHJ6uoJ6um77HHHqsD6ol69erF2WefnQbVH3jgASF1AABqR0i9pKQkfSzRj3/847j44ovXWM1lwIAB6bdRr7322mjfvn0cfvjhmS4BAAD4BvN0AADITUcccUS5MAsAAJA7ffMkRL5o0aJo0qTJGseKiorS/ZIlS9b6+SlTpqRfSN1nn33WONa1a9c0UD958uQNrgsAAHIypD5q1Kho165dhRP4/56EP/TQQ+ljh4RfAACg8pmnAwBAbkoCMQAAQG72zZOge0WS4HkSkE/ssMMOa/38hx9+mO632mqrNY4lAfXWrVvH7NmzY8WKFatD7wAAUF1UPMveBO+8807svPPOa53Ar7LNNtvEBx98kOnbAwAAFTBPBwAAAACA6tE3//Of/xxvvPFGukL7fvvtt9bzvvzyy3TftGnTCo83btw4SktLY/HixRmtDwAAqmVIPZkAr2tynnwjdNq0abHZZptl+vYAAEAFzNMBACC3zZ8/P2666aY4/vjjY//994/LL788Hb/hhhvi0UcfzXZ5AACQc7LVN0/m78OGDYvCwsL0yUnJiuhrs3LlynS/tlXSV40nK6kDAECND6l379493n333bjrrrvWes71118fM2fOjAMOOCDTtwcAACpgng4AALnrmWeeib59+8Ztt90Wr7/+esydOzeWLl2aHps0aVKce+65cdVVV2W7TAAAyCnZ6JsnK6gn8/fEddddF926dfvW8+vVq1curP5Nq8LpDRo0yEh9AACQSYUZvVpE/OxnP0ub4sOHD0/3yUS9devW6bdLZ8+eHY8//nhMnTo1mjdvHqeddlqmbw8AAFTAPB0AAHLT+++/H2eeeWaUlJREv3790lXUTz311NXHjznmmPj9738f9913X/To0cOXTgEAoBr2zUtLS9P7JIH4ZPXz3/3ud3HQQQet83NNmzZN9wsXLqzw+KJFiyIvLy8aNWq0SfUBAEBOhNQ7deoUI0eOjAsvvDBeeeWVePXVV8sdTybz2267bdx8882xxRZbZPr2AABABczTAQAgN916663p6og33XRThSGWgQMHxi677BI//vGP06C6kDoAAFSvvnkyn09WT3/iiSeiWbNm6T3XtYL6Kh07dkz3yWru35Ssrv7JJ5/ENttsE/n5+RtdHwAA5ExIPZGs1vLYY4/Fk08+GVOmTIn58+en423atEmP9erVKwoKCirj1gAAwFqYpwMAQO6ZPHly7Ljjjt+6ymLXrl1jt912i/fee69KawMAgFxX2X3z5IlIZ511VrpSe7t27eKOO+5YHTxfH927d09XSn/55Zfj9NNPL3csCdUnQfXk/wcAAKDWhNQTDRs2jMMPPzzdAACA6sE8HQAAcstXX30Ve+655zrPa9GiRUydOrVKagIAgJqkMvvmt99+expQT0Lvf/7zn6NVq1Yb9Pktt9wy9t1333j++efTIH3v3r3T8a+//jpuvPHG9PWAAQMyXjcAAFTrkPqqxw0l3+b8+OOP02+C/uAHP0i/yZk8erRevXqVeWsAAGAtzNMBACB3bLbZZvHhhx+u87wPPvggNt988yqpCQAAaprK6JsvWLAgDaknkqcjPfjggxWe161bt9hnn33S+yeruSfnrgqjJ371q19Fv3794swzz4y+ffumQfennnoq/f+En/zkJ+n5AABQa0Lqycoul1xySfotzrKysnQsmcAn229+85t0cv/73/8+nWhDjVNUFHHMMWuOAQBkmXk6ALDB9Dkg6/bee+/429/+loZQevXqVeE5jz/+eLz//vtx2GGHVXl9AACQyyqzb56E3JcuXZq+TubzyVaRIUOGpCH1JKA+YsSIOPLII8uF1LfZZpt44IEH0pXTkxXVly9fHltvvXVcddVVccw3/58dgE3qg5aWlcWLzz4VPXasF/mF+dmuCiDnZTykvmzZshg4cGBMnz49XbWle/fu8dhjj60+XlRUFF9++WWccsop8cgjj0SHDh0yXQJkV9OmEWPHZrsKAIByzNMBgI2izwFZN3jw4HTufvbZZ6erJCbhlcTKlSvTwEwSdLn55pujsLAwTjrppGyXCwAAOaOy++ZJ0HzGjBnrff4ZZ5yRbhVJQulJSB2Ayu2DlhYXx3UHd4/xF7eO/AIhdYBNlfHfpHfddVc6gT/00EPT5vg3J8n33HNPnHzyyelk/49//GOmbw8AAFTAPB0AAHLTdtttF8OHD4+8vLy47bbb0hBN8joJzxx88MHpsSSwftlll8XOO++c7XIBACBn6JsDAECOhdQnTpwYzZs3j2uuuSbq169f4TnnnXdetGnTJn1UEQAAUPnM0wEAIHf17ds3JkyYEP369UtXUKxbt27UqVMnnb8fccQRMW7cuDj22GOzXSYAAOQUfXMAAKhchZm+YPJ40Z49e6aPPVqb/Pz8dEWX5557LtO3BwAAKmCeDgAAuW2rrbZKV0sHAAAyQ98cAAByLKSerN6yYMGCdZ73xRdfpOcCAACVzzwdAABqhsWLF8e8efPSeXvLli3TVdUBAIANp28OAAA5FlLv3LlzvPnmmzF37txo1apVhefMnj073nrrrdh1110zfXsAAKAC5ukAAJDbHnroobj33ntj+vTpUVZWlo4VFBTE7rvvHj/5yU/i+9//frZLBACAnKJvDgAAlSs/0xc85phjYtmyZXH66aenj0b6pvnz58e5554bK1asiMMPPzzTt4fs++qriGOPLb8lYwAAWWSeDgBsFH0OyLrS0tIYOnRo/PKXv4y333478vLyYosttki35Nirr74aP//5z+P666/PdqkAAJBT9M0B+GYfNL9fv7jwzX9H/hWvRSxeme2qAHJexldSP+KII2LSpEnxxBNPxMEHHxytW7dOm+ZTpkyJ/v37p98wTSbw++67bzrhhxpnxYqIcePKj91yS7aqAQBImacDABtFnwOybty4cfHYY4+lKzv+4he/SFdMr1u3bnosCdQkx4YPHx533HFHdOnSJXr37p3tkgEAICfomwPwzT5osuLvd5P38yPibE/RAKh2K6knbrzxxjjzzDOjSZMm8fHHH6ePHv3000/jtddeSx8/evLJJ8ett96aTu4BAICqYZ4OAAC558EHH4x69erF6NGjo0+fPqsD6on69evHkUceGXfddVc6p0/2AADA+tM3BwCAHFpJPZGfn58+XvSUU05JHz+6aiLfokWL2HXXXdOGOgAAULXM0wEAIPe89957sffee8dWW2211nM6d+6cnpMEaQAAgPWnbw4AADkUUj/ttNNim222ifPOOy/q1KkTu+22W7oBAADZY54OAAC5qUGDBuu1amOywnoy1wcAANaPvjkAAORYSP2ll16KL7/8MtOXBQAANoF5OgAA5KaePXvGxIkTY/bs2dGuXbsKz/niiy9iypQpsd9++1V5fQAAkKv0zQEAoHLlZ/qCBQUF0aRJk0xfFgAA2ATm6QAAkJuSVR2bN28egwYNiqeeemqN4++++2789Kc/jYYNG8YFF1yQlRoBACAX6ZsDAECOraR+zDHHxL333hsvvvhi9OjRI9OXBwAANoJ5OgAA5Ibu3buvMbZixYpYvnx5nH766WkYPVlRvW7dujF37tx0S7Rp0ybOPPPMGDt2bBaqBgCA3KNvDgAAORZS32GHHdIG+U9+8pPYdtttY8cdd4xmzZpFfv6ai7bn5eXFRRddlOkSAACAbzBPBwCA3LBw4cJvPb548eKYPn36GuNz5syJjz/+uBIrAwC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"text/plain": [ "
" ] @@ -1141,7 +1121,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 144, "id": "982f835e", "metadata": {}, "outputs": [ @@ -1279,7 +1259,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 145, "id": "9f184293", "metadata": {}, "outputs": [ @@ -1294,14 +1274,14 @@ "Note: Radon cyclomatic complexity and maintainability entries with rank 'A' are treated as non-issues and excluded from issue counts while still used in distribution plots.\n", "\n", "šŸ“Š OVERALL METRICS:\n", - " Total Issues Found (excluding Radon A ranks): 31\n", + " Total Issues Found (excluding Radon A ranks): 25\n", " Files Analyzed (max across tools): 4\n", - " Average Issues per File: 7.75\n", + " Average Issues per File: 6.25\n", "\n", "šŸ” TOOL BREAKDOWN:\n", " BANDIT: 2 issues across 2 files\n", " RUFF: 1 issues across 1 files\n", - " MYPY: 6 issues across 1 files\n", + " MYPY: 0 issues across 0 files\n", " RADON_CC: 7 issues across 4 files\n", " RADON_MI: 0 issues across 0 files\n", " FLAKE8_WPS: 11 issues across 4 files\n", @@ -1314,7 +1294,7 @@ " šŸ’€ Dead Code: 4 potential dead code items, 0 with ā‰„80% confidence\n", "\n", "šŸ“‹ RECOMMENDATIONS:\n", - " 1. Address 31 total issues found across all tools\n", + " 1. Address 25 total issues found across all tools\n", " 2. Security: Review and fix 2 security issues\n", " 3. Dead Code: Remove 0 high-confidence dead code items\n", " 6. Style: Focus on fixing UP017 rule violations first\n", @@ -1325,7 +1305,7 @@ " Tool Total Issues (A excl) Files Analyzed Issues per File\n", " bandit 2 2 1.000000\n", " ruff 1 1 1.000000\n", - " mypy 6 1 6.000000\n", + " mypy 0 0 0.000000\n", " radon_cc 7 4 1.750000\n", " radon_mi 0 0 0.000000\n", "flake8_wps 11 4 2.750000\n", diff --git a/code_quality_summary.csv b/code_quality_summary.csv index 6e86d7f..72021eb 100644 --- a/code_quality_summary.csv +++ b/code_quality_summary.csv @@ -1,7 +1,7 @@ Tool,Total Issues (A excl),Files Analyzed,Issues per File bandit,2,2,1.0 ruff,1,1,1.0 -mypy,6,1,6.0 +mypy,0,0,0.0 radon_cc,7,4,1.75 radon_mi,0,0,0.0 flake8_wps,11,4,2.75 diff --git a/detailed_issues.csv b/detailed_issues.csv index 052b0e5..796915d 100644 --- a/detailed_issues.csv +++ b/detailed_issues.csv @@ -1,13 +1,7 @@ tool,file,line,severity bandit,backend/app/core/config.py,109,LOW bandit,backend/app/models/api_models.py,15,LOW -ruff,/Users/somen/Zavodi/opensource/fastapi-moscow-python-demo-1/backend/app/models/db_models.py,89,error -mypy,backend/app/email_utils.py,7,error -mypy,backend/app/email_utils.py,7,error -mypy,backend/app/email_utils.py,7,note -mypy,backend/app/email_utils.py,7,note -mypy,backend/app/email_utils.py,33,error -mypy,backend/app/email_utils.py,47,error +ruff,/Users/somen/Zavodi/opensource/fastapi-moscow-python-demo/backend/app/models/db_models.py,89,error radon_cc,backend/app/email_utils.py,23,B radon_cc,backend/app/crud.py,163,B radon_cc,backend/app/tests/api/routes/test_items.py,23,B