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Dmax.py
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import pandas as pd
import numpy as np
from scipy.stats import linregress
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from scipy.optimize import fsolve
from scipy.interpolate import interp1d
# 准备数据的函数，这里使用示例数据
def prepare_data():
data = {
"step": [0, 1, 2, 3, 4, 5, 6, 7],
"length": [0, 3, 3, 3, 3, 3, 3, 2],
"intensity": [0, 50, 75, 100, 125, 150, 175, 191],
"lactate": [0.93, 0.98, 1.23, 1.88, 2.8, 4.21, 6.66, 8.64],
"heart_rate": [96, 114, 134, 154, 170, 182, 193, 198]
}
df = pd.DataFrame(data)
return {"data": [df]}
# ——————————————————————————————————————————————————————————————————————————————————
# 曲线拟合函数
def prepare_fit(data, fit="3rd degree polynomial", include_baseline=False, sport="cycling"):
if data is None:
raise ValueError("No data provided. Please include your data in the function.")
if "intensity" not in data.columns or "lactate" not in data.columns:
raise ValueError("It looks like you didn't prepare your data. Please call `prepare_data()` before.")
sport_options = ["cycling", "running", "swimming"]
if sport not in sport_options:
raise ValueError("Invalid sport. Choose one of 'cycling', 'running', or 'swimming'.")
# Adjust intensity values to ensure no zero values
to_subtract = data['intensity'].iloc[2] - data['intensity'].iloc[1]
data.loc[data['intensity'] == 0, 'intensity'] = data['intensity'].iloc[1] - to_subtract
if sport == "cycling" or sport == "running":
interpolation_factor = 0.1
elif sport == "swimming":
interpolation_factor = 0.01
if include_baseline:
data_for_modeling = data
else:
data_for_modeling = data.iloc[1:]
intensity_range = np.arange(min(data_for_modeling['intensity']), max(data_for_modeling['intensity']), interpolation_factor)
if fit == "3rd degree polynomial":
def polynomial_fit(x, a, b, c, d):
return a * x**3 + b * x**2 + c * x + d
params, _ = curve_fit(polynomial_fit, data_for_modeling['intensity'], data_for_modeling['lactate'])
lactate_estimate = polynomial_fit(intensity_range, *params)
elif fit == "4th degree polynomial":
def polynomial_fit(x, a, b, c, d, e):
return a * x**4 + b * x**3 + c * x**2 + d * x + e
params, _ = curve_fit(polynomial_fit, data_for_modeling['intensity'], data_for_modeling['lactate'])
lactate_estimate = polynomial_fit(intensity_range, *params)
elif fit == "B-spline":
# Perform B-spline interpolation
spline = interp1d(data_for_modeling['intensity'], data_for_modeling['lactate'], kind='cubic')
lactate_estimate = spline(intensity_range)
elif fit == "Exponential":
def exponential_fit(x, a, b, c):
return a + b * np.exp(c * x)
params, _ = curve_fit(exponential_fit, data_for_modeling['intensity'], data_for_modeling['lactate'])
lactate_estimate = exponential_fit(intensity_range, *params)
else:
raise ValueError("Invalid fit method. Choose one of '3rd degree polynomial', '4th degree polynomial', 'B-spline', or 'Exponential'.")
result = pd.DataFrame({
'intensity': intensity_range,
'lactate_estimate': lactate_estimate
})
return result
# ——————————————————————————————————————————————————————————————————————————————————
def retrieve_heart_rate(raw_data, intensity_value):
# Remove baseline value
raw_data = raw_data.iloc[1:]
# Linear model heart rate vs intensity
model = linregress(raw_data["intensity"], raw_data["heart_rate"])
# Predict heart rate based on intensity value
out = model.intercept + model.slope * intensity_value
return round(float(out), 0)
# ——————————————————————————————————————————————————————————————————————————————————
def retrieve_lactate(model, intensity_value):
if 'B-spline' in model:
intensity_range = model['data_augmented']['intensity']
lactate_range = model['data_augmented']['.fitted']
f1 = interp1d(intensity_range, lactate_range, kind='cubic')
method_intensity = fsolve(lambda x: f1(x) - intensity_value, intensity_range[0])
else:
fitted_values = model['model'].fittedvalues
intensity_values = model['model'].model.exog[:, 1]
try:
method_intensity = np.interp(intensity_value, fitted_values, intensity_values)
except ValueError:
# Handle cases where lactate curve goes down and then increases again
start_increasing = np.where(np.diff(fitted_values) < 0)[0][-1]
fitted_values = fitted_values[start_increasing:]
intensity_values = intensity_values[start_increasing:]
method_intensity = np.interp(intensity_value, fitted_values, intensity_values)
return round(float(max(method_intensity)), 1)
# ——————————————————————————————————————————————————————————————————————————————————
# 辅助函数：计算Dmax方法
def helper_dmax(data_prepared, sport, plot):
data_prepared_dmax = prepare_fit(data_prepared['data'][0], "3rd degree polynomial", False, sport)
model_coefficients = data_prepared_dmax['model'][0].params
data_dmax = data_prepared_dmax['data'][0].iloc[1:]
diff_lactate = np.diff(np.array(data_dmax['lactate']))
diff_intensity = np.diff(np.array(data_dmax['intensity']))
max_intensity = data_dmax['intensity'].max()
lin_beta = diff_lactate / diff_intensity
d_max_roots = np.roots(
[model_coefficients[3], model_coefficients[2] - lin_beta, model_coefficients[1], model_coefficients[0]])
d_max = np.real(d_max_roots[d_max_roots > 0])
d_max = d_max[d_max <= max_intensity]
model_intensity = max(d_max)
model_lactate = retrieve_lactate(data_prepared_dmax['model'][0], model_intensity)
if model_lactate > 8:
model_intensity = min(d_max)
model_lactate = retrieve_lactate(data_prepared_dmax['model'][0], model_intensity)
if plot:
data_plot_line = data_dmax.iloc[[0, -1]]
fitting = "3rd degree polynomial (default)"
intensity = model_intensity
lactate = model_lactate
data_plot_line = [data_plot_line]
else:
data_plot_line = None
fitting = "3rd degree polynomial (default)"
intensity = model_intensity
lactate = model_lactate
if "heart_rate" in data_prepared['data'][0].columns:
heart_rate = retrieve_heart_rate(data_prepared['data'][0], intensity)
else:
heart_rate = None
if sport == "cycling":
intensity = round(intensity, 1)
elif sport == "running":
intensity = round(intensity, 2)
elif sport == "swimming":
intensity = round(intensity, 3)
result = {
"fitting": fitting,
"intensity": intensity,
"lactate": lactate,
"heart_rate": heart_rate,
"data_plot_line": data_plot_line
}
return pd.DataFrame([result])
# 画出乳酸曲线的函数
def plot_lactate(data, method):
plt.figure(figsize=(8, 6))
plt.plot(data['intensity'], data['lactate'], marker='o', linestyle='-', label=f'{method} Fit')
plt.xlabel("Intensity")
plt.ylabel("Lactate")
plt.title(f"Lactate Threshold Estimation - {method} Method")
plt.legend()
plt.grid(True)
plt.show()
# 调用方法计算乳酸阈值并绘制数据曲线
data_prepared = prepare_data()
result = helper_dmax(data_prepared, "cycling", plot=True)
print(result)
# 绘制乳酸曲线
plot_lactate(result, "Dmax")