Height Of Male Models !!exclusive!! 【2026 Release】

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()

def __init__(self, analyzer: MaleModelHeightAnalyzer): self.analyzer = analyzer self.heights = analyzer.heights height of male models

class ModelInput(BaseModel): name: str height_cm: float agency: Optional[str] = None category: Optional[str] = None axes = plt.subplots(2

class HeightStatsResponse(BaseModel): mean: float median: float min_height: float max_height: float std_dev: float total_models: int 10)) # Histogram with KDE axes[0

def plot_height_by_agency(self, agency_colors: Dict[str, str] = None): """Plot height distribution grouped by agency""" agency_groups = {} for model in self.analyzer.models: if model.agency: if model.agency not in agency_groups: agency_groups[model.agency] = [] agency_groups[model.agency].append(model.height_cm) if not agency_groups: print("No agency data available") return fig, ax = plt.subplots(figsize=(12, 6)) positions = range(len(agency_groups)) bp = ax.boxplot(agency_groups.values(), positions=positions, widths=0.6, patch_artist=True, showmeans=True) # Color boxes if agency_colors: for i, box in enumerate(bp['boxes']): agency = list(agency_groups.keys())[i] box.set_facecolor(agency_colors.get(agency, 'lightblue')) ax.set_xticklabels(agency_groups.keys(), rotation=45, ha='right') ax.set_ylabel('Height (cm)') ax.set_title('Height Distribution by Modeling Agency') ax.grid(True, alpha=0.3, axis='y') plt.tight_layout() plt.show() from fastapi import FastAPI, HTTPException, Query from typing import List, Optional from pydantic import BaseModel app = FastAPI(title="Male Model Height Analysis API")

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"""