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