Height Of Male | Models [top]

@staticmethod def cm_to_ft_in(cm: float) -> str: total_inches = cm / 2.54 feet = int(total_inches // 12) inches = round(total_inches % 12, 1) return f"{feet}'{inches}\""

@staticmethod def ft_in_to_cm(ft: int, inches: float) -> float: return (ft * 12 + inches) * 2.54 class MaleModelHeightAnalyzer: """Feature for analyzing male model heights""" # Industry standard height ranges (cm) RUNWAY_MIN = 185 # 6'1" RUNWAY_MAX = 192 # 6'3.6" COMMERCIAL_MIN = 175 # 5'9" COMMERCIAL_MAX = 190 # 6'2.8" FITNESS_MIN = 178 # 5'10" FITNESS_MAX = 188 # 6'2"

@app.post("/analyze/upload-models") async def upload_models(models: List[ModelInput]): """Upload multiple male models for analysis""" model_objects = [MaleModel( id=f"M{idx:04d}", name=m.name, height_cm=m.height_cm, agency=m.agency, category=m.category ) for idx, m in enumerate(models)] height of male models

def category_fit(self) -> Dict[str, Dict]: """Categorize models based on industry standards""" results = {} for model in self.models: fits = [] if self.RUNWAY_MIN <= model.height_cm <= self.RUNWAY_MAX: fits.append("runway") if self.COMMERCIAL_MIN <= model.height_cm <= self.COMMERCIAL_MAX: fits.append("commercial") if self.FITNESS_MIN <= model.height_cm <= self.FITNESS_MAX: fits.append("fitness") # Special classifications if model.height_cm < self.COMMERCIAL_MIN: fits.append("short_for_industry") elif model.height_cm > self.RUNWAY_MAX: fits.append("tall_for_industry") results[model.id] = { "name": model.name, "height_cm": model.height_cm, "height_ft_in": model.height_ft_in, "suitable_categories": fits, "is_ideal_runway": self.RUNWAY_MIN <= model.height_cm <= self.RUNWAY_MAX } return results

analyzer = MaleModelHeightAnalyzer(model_objects) return { "message": f"Successfully uploaded {len(model_objects)} models", "basic_stats": analyzer.basic_statistics(), "category_fit": analyzer.category_fit() } @app.get("/analyze/height-stats", response_model=HeightStatsResponse) async def get_height_statistics(min_height: Optional[float] = None, max_height: Optional[float] = None): """Get aggregated height statistics""" # Implementation would query database pass @staticmethod def cm_to_ft_in(cm: float) -&gt

def __init__(self, analyzer: MaleModelHeightAnalyzer): self.analyzer = analyzer self.heights = analyzer.heights

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() inches: float) -&gt

def distribution_by_category(self) -> Dict: """Analyze height distribution by modeling category""" categories = { "runway": [], "commercial": [], "fitness": [] } for model in self.models: if self.RUNWAY_MIN <= model.height_cm <= self.RUNWAY_MAX: categories["runway"].append(model.height_cm) if self.COMMERCIAL_MIN <= model.height_cm <= self.COMMERCIAL_MAX: categories["commercial"].append(model.height_cm) if self.FITNESS_MIN <= model.height_cm <= self.FITNESS_MAX: categories["fitness"].append(model.height_cm) results = {} for cat, heights in categories.items(): if heights: results[cat] = { "count": len(heights), "mean": round(statistics.mean(heights), 1), "range": f"{min(heights)}-{max(heights)}" } return results