Height Of Male Models !!install!! May 2026

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')) }

def __init__(self, models: List[MaleModel]): self.models = models self.heights = [m.height_cm for m in 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 def percentile_distribution(self) -&gt

class ModelInput(BaseModel): name: str height_cm: float agency: Optional[str] = None category: Optional[str] = None method='exclusive')) } def __init__(self