Height Of Male Models Instant

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

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 height of male models

@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}\"" def __init__(self, models: List[MaleModel]): self

@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" = model.height_cm &lt

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 height_outliers(self, multiplier: float = 1.5) -> List[Dict]: """Detect height outliers using IQR method""" if len(self.heights) < 4: return [] q1 = statistics.quantiles(self.heights, n=4)[0] q3 = statistics.quantiles(self.heights, n=4)[2] iqr = q3 - q1 lower_bound = q1 - multiplier * iqr upper_bound = q3 + multiplier * iqr outliers = [] for model in self.models: if model.height_cm < lower_bound or model.height_cm > upper_bound: outliers.append({ "id": model.id, "name": model.name, "height_cm": model.height_cm, "height_ft_in": model.height_ft_in, "deviation": "below" if model.height_cm < lower_bound else "above" }) return outliers

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