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| import numpy as np | |
| import plotly.graph_objs as go | |
| import plotly.io as pio | |
| def calculate_fat_free_mass(weight_kg, body_fat_percentage): | |
| """ | |
| Calculate fat-free mass in kilograms. | |
| :param weight_kg: Weight in kilograms [kg]. | |
| :param body_fat_percentage: Body fat percentage [%]. | |
| :return: Fat-free mass in kilograms [kg]. | |
| """ | |
| fat_free_mass = weight_kg * (1 - (body_fat_percentage / 100)) | |
| return fat_free_mass | |
| def calculate_ffmi(fat_free_mass_kg, height_m): | |
| """ | |
| Calculate FFMI (Fat-Free Mass Index). | |
| :param fat_free_mass_kg: Fat-free mass in kilograms [kg]. | |
| :param height_m: Height in meters [m]. | |
| :return: FFMI in kg/m². | |
| """ | |
| ffmi = fat_free_mass_kg / (height_m ** 2) | |
| return ffmi | |
| def plot_ffmi_curves(height_m, gender): | |
| """ | |
| Generate a plot of FFMI (Fat-Free Mass Index) curves based on weight | |
| and body fat percentage for a specified gender. | |
| :param height_m: Height of the individual in meters [m]. | |
| :param gender: Gender of the individual ('Male' or 'Female'), which determines | |
| the FFMI ranges used for categorization. | |
| :return: A Plotly Figure object containing the scatter plot of FFMI curves. | |
| """ | |
| weight_kg_range = np.linspace(45, 160, 100, endpoint=True) | |
| body_fat_percentage_range = np.linspace(1, 50, 100, endpoint=True) | |
| if gender == "Male": | |
| ffmi_ranges = [ | |
| {"min": 0, "max": 18, "description": "Below average", "color": "blue"}, | |
| {"min": 18, "max": 20, "description": "Average", "color": "green"}, | |
| {"min": 20, "max": 22, "description": "Above average", "color": "yellow"}, | |
| {"min": 22, "max": 23, "description": "Excellent", "color": "orange"}, | |
| {"min": 23, "max": 26, "description": "Superior", "color": "red"}, | |
| {"min": 26, "max": 28, "description": "Suspicion of FAKEE", "color": "purple"}, | |
| {"min": 28, "max": float("inf"), "description": "FAKEE", "color": "black"} | |
| ] | |
| else: | |
| ffmi_ranges = [ | |
| {"min": 0, "max": 15, "description": "Below average", "color": "blue"}, | |
| {"min": 15, "max": 17, "description": "Average", "color": "green"}, | |
| {"min": 17, "max": 18, "description": "Above average", "color": "yellow"}, | |
| {"min": 18, "max": 19, "description": "Excellent", "color": "orange"}, | |
| {"min": 19, "max": 21.5, "description": "Superior", "color": "red"}, | |
| {"min": 21.5, "max": 25, "description": "Suspicion of FAKEE", "color": "purple"}, | |
| {"min": 25, "max": float("inf"), "description": "FAKEE", "color": "black"} | |
| ] | |
| fig = go.Figure() | |
| for ffmi_range in ffmi_ranges: | |
| weight_kg_values = [] | |
| body_fat_values = [] | |
| ffmi_values = [] | |
| for weight in weight_kg_range: | |
| for body_fat_percentage in body_fat_percentage_range: | |
| fat_free_mass = calculate_fat_free_mass(weight, body_fat_percentage) | |
| ffmi = calculate_ffmi(fat_free_mass, height_m) | |
| if ffmi_range["min"] <= ffmi < ffmi_range["max"]: | |
| weight_kg_values.append(weight) | |
| body_fat_values.append(body_fat_percentage) | |
| ffmi_values.append(ffmi) | |
| fig.add_trace(go.Scatter( | |
| x=weight_kg_values, | |
| y=body_fat_values, | |
| mode='markers', | |
| name=ffmi_range["description"], | |
| marker=dict( | |
| color=ffmi_range.get("marker_color", ffmi_range["color"]), | |
| size=5, opacity=0.6, | |
| line=dict(color=ffmi_range.get("line_color", ffmi_range["color"])) | |
| ), | |
| hovertext=[ | |
| f'FFMI: {ffmi:.2f}<br>Weight: {weight_kg:.2f}<br>BodyFat: {body_fat:.2f}<br>{ffmi_range["description"]}' for ffmi,weight_kg, body_fat in | |
| zip(ffmi_values, weight_kg_values, body_fat_values)], | |
| hoverinfo='text' | |
| )) | |
| fig.update_layout( | |
| title='FFMI Curves for Different Body Fat Percentages and Weight', | |
| xaxis_title='Weight (kg)', | |
| yaxis_title='Body Fat Percentage (%)', | |
| legend_title='FFMI Range', | |
| hovermode='closest', | |
| template='plotly_dark' | |
| ) | |
| return fig | |