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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
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