Height Of Male Models ((free)) Official

@app.get("/analyze/outliers") async def detect_outliers(multiplier: float = Query(1.5, ge=1.0, le=3.0)): """Detect height outliers using IQR method""" # Implementation would use analyzer pass # Sample data sample_models = [ MaleModel("M001", "John Doe", 188, "6'2\"", "Elite Models", "runway"), MaleModel("M002", "Mike Smith", 185, "6'1\"", "IMG Models", "runway"), MaleModel("M003", "Alex Chen", 178, "5'10\"", "Wilhelmina", "commercial"), MaleModel("M004", "Chris Evans", 183, "6'0\"", "Ford Models", "fitness"), MaleModel("M005", "David Kim", 192, "6'3.6\"", "Next Models", "runway"), MaleModel("M006", "Tom Wilson", 175, "5'9\"", "Elite Models", "commercial"), MaleModel("M007", "James Brown", 195, "6'4.8\"", "IMG Models", "runway"), ] Analyze analyzer = MaleModelHeightAnalyzer(sample_models) print(analyzer.generate_height_report()) Visualize visualizer = HeightVisualizer(analyzer) visualizer.plot_height_distribution("height_analysis.png") Get category fit category_fit = analyzer.category_fit() for model_id, info in category_fit.items(): print(f"{info['name']}: {info['height_ft_in']} - Suitable for {', '.join(info['suitable_categories'])}")

def percentile_distribution(self) -> Dict: """Get height percentiles""" if not self.heights: return {} percentiles = [10, 25, 50, 75, 90, 95, 99] return { f"p{p}": round(statistics.quantiles(self.heights, n=100, method='exclusive')[p-1], 1) for p in percentiles if p-1 < len(statistics.quantiles(self.heights, n=100, method='exclusive')) } height of male models

def __post_init__(self): if not self.height_ft_in and self.height_cm: self.height_ft_in = self.cm_to_ft_in(self.height_cm) 0)} - Mean Height: {stats.get('mean'

def __init__(self, models: List[MaleModel]): self.models = models self.heights = [m.height_cm for m in models] 'N/A')} cm - Median Height: {stats.get('median'

def generate_height_report(self) -> str: """Generate comprehensive height analysis report""" stats = self.basic_statistics() percentiles = self.percentile_distribution() outliers = self.height_outliers() category_fit = self.category_fit() report = f""" ===== MALE MODEL HEIGHT ANALYSIS REPORT ===== BASIC STATISTICS: - Total Models: {stats.get('count', 0)} - Mean Height: {stats.get('mean', 'N/A')} cm - Median Height: {stats.get('median', 'N/A')} cm - Height Range: {stats.get('min', 'N/A')} - {stats.get('max', 'N/A')} cm - Standard Deviation: {stats.get('std_dev', 'N/A')} cm PERCENTILE DISTRIBUTION: {chr(10).join([f' - {k}: {v} cm' for k, v in percentiles.items()])} CATEGORY SUITABILITY: - Suitable for Runway: {sum(1 for v in category_fit.values() if v['is_ideal_runway'])} models - Below Industry Minimum: {sum(1 for v in category_fit.values() if 'short_for_industry' in v['suitable_categories'])} models - Above Industry Maximum: {sum(1 for v in category_fit.values() if 'tall_for_industry' in v['suitable_categories'])} models OUTLIERS DETECTED: {len(outliers)} {chr(10).join([f' - {o["name"]}: {o["height_ft_in"]} ({o["height_cm"]} cm) - {o["deviation"]} average' for o in outliers])} """ return report import matplotlib.pyplot as plt import seaborn as sns import numpy as np class HeightVisualizer: """Create visualizations for model height analysis"""

def plot_height_distribution(self, save_path: str = None): """Create histogram with KDE of height distribution""" fig, axes = plt.subplots(2, 2, figsize=(15, 10)) # Histogram with KDE axes[0, 0].hist(self.heights, bins=15, edgecolor='black', alpha=0.7, density=True) sns.kdeplot(self.heights, ax=axes[0, 0], color='red', linewidth=2) axes[0, 0].set_xlabel('Height (cm)') axes[0, 0].set_ylabel('Density') axes[0, 0].set_title('Height Distribution with KDE') axes[0, 0].axvline(statistics.mean(self.heights), color='green', linestyle='--', label='Mean') axes[0, 0].axvline(statistics.median(self.heights), color='orange', linestyle='--', label='Median') axes[0, 0].legend() # Box plot axes[0, 1].boxplot(self.heights, vert=True, patch_artist=True) axes[0, 1].set_ylabel('Height (cm)') axes[0, 1].set_title('Height Distribution Box Plot') axes[0, 1].grid(True, alpha=0.3) # Cumulative distribution sorted_heights = np.sort(self.heights) cumulative = np.arange(1, len(sorted_heights) + 1) / len(sorted_heights) axes[1, 0].plot(sorted_heights, cumulative, marker='.', linestyle='none', markersize=3) axes[1, 0].set_xlabel('Height (cm)') axes[1, 0].set_ylabel('Cumulative Probability') axes[1, 0].set_title('Cumulative Distribution Function') axes[1, 0].grid(True, alpha=0.3) # Category comparison cat_data = self.analyzer.distribution_by_category() categories = list(cat_data.keys()) means = [cat_data[cat]['mean'] for cat in categories] axes[1, 1].bar(categories, means, color=['blue', 'green', 'orange']) axes[1, 1].set_ylabel('Mean Height (cm)') axes[1, 1].set_title('Mean Height by Category') axes[1, 1].grid(True, alpha=0.3, axis='y') plt.tight_layout() if save_path: plt.savefig(save_path, dpi=300, bbox_inches='tight') plt.show()