70 lines
2.0 KiB
Python
70 lines
2.0 KiB
Python
import numpy as np
|
||
import pandas as pd
|
||
import matplotlib.pyplot as plt
|
||
from scipy.optimize import curve_fit
|
||
|
||
# --- Load CSV ---
|
||
data = pd.read_csv(r'C:\Users\k28ad\OneDrive\Documents\sensor\Sensor3DataNew.csv')
|
||
x = data["x"].values
|
||
# Stack them into a 2D array (shape: 5 × 100)
|
||
# stacked = np.column_stack((data["y"].values, data["y2"].values, data["y3"].values, data["y4"].values, data["y5"].values))
|
||
|
||
# Take the average across the 5 columns for each row
|
||
# y = np.mean(data["y"], axis=1)
|
||
# print(y)
|
||
y = data["y"].values
|
||
# --- Define 5-parameter logistic function ---
|
||
def logistic_5pl(x, A, K, B, C, nu):
|
||
# Clip to avoid overflow in exp for large values
|
||
z = -B * (x - C)
|
||
z = np.clip(z, -500, 500)
|
||
return A + (K - A) / (1.0 + np.exp(z))**(1/nu)
|
||
|
||
# --- Initial parameter guesses ---
|
||
A0 = np.min(y) # lower asymptote
|
||
K0 = np.max(y) # upper asymptote
|
||
B0 = 1.0 # slope
|
||
C0 = np.median(x) # inflection point
|
||
nu0 = 1.0 # asymmetry (1 = symmetric, same as 4PL)
|
||
p0 = [A0, K0, B0, C0, nu0]
|
||
|
||
# --- Fit the curve ---
|
||
params, covariance = curve_fit(
|
||
logistic_5pl, x, y, p0=p0, maxfev=20000,
|
||
bounds=([-np.inf, -np.inf, 0, -np.inf, 0.1], [np.inf, np.inf, np.inf, np.inf, 10])
|
||
)
|
||
|
||
A_fit, K_fit, B_fit, C_fit, nu_fit = params
|
||
print("Fitted parameters:")
|
||
print(f"A = {A_fit}")
|
||
print(f"K = {K_fit}")
|
||
print(f"B = {B_fit}")
|
||
print(f"C = {C_fit}")
|
||
print(f"nu = {nu_fit}")
|
||
|
||
# --- Predicted values ---
|
||
y_pred = logistic_5pl(x, *params)
|
||
|
||
# --- Goodness of fit (R²) ---
|
||
residuals = y - y_pred
|
||
ss_res = np.sum(residuals**2)
|
||
ss_tot = np.sum((y - np.mean(y))**2)
|
||
r_squared = 1 - (ss_res / ss_tot)
|
||
print(f"\nGoodness of fit: R² = {r_squared:.6f}")
|
||
|
||
# --- Plot ---
|
||
x_fit = np.linspace(min(x), max(x), 400)
|
||
y_fit = logistic_5pl(x_fit, *params)
|
||
y_pred = logistic_5pl(x, *params)
|
||
|
||
|
||
plt.figure(figsize=(8, 5))
|
||
plt.scatter(x, y, label="Data", color="blue")
|
||
plt.plot(x_fit, y_fit, 'r-', label="5PL Fit")
|
||
plt.title("5-Parameter Logistic Fit")
|
||
plt.xlabel("x")
|
||
plt.ylabel("y")
|
||
plt.legend()
|
||
plt.grid(True, linestyle="--", alpha=0.5)
|
||
plt.show()
|