79 lines
2.0 KiB
Python
79 lines
2.0 KiB
Python
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 your CSV ---
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data = pd.read_csv(r'C:\Users\k28ad\OneDrive\Documents\sensor\dataPrecise.csv')
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x = data["x"].values
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y = data["y"].values
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# --- Define 4-parameter logistic function (A, K, B, C) ---
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def logistic_4pl(x, A, K, B, C):
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return A + (K - A) / (1.0 + np.exp(-B * (x - C)))
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# --- Initial guesses ---
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A0 = np.min(y)
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K0 = np.max(y)
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B0 = 1.0
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C0 = np.median(x)
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p0 = [A0, K0, B0, C0]
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# --- Fit the curve ---
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params, covariance = curve_fit(logistic_4pl, x, y, p0=p0, maxfev=10000)
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A_fit, K_fit, B_fit, C_fit = params
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print(f"Fitted parameters:")
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print(f"A (lower asymptote) = {A_fit:.4f}")
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print(f"K (upper asymptote) = {K_fit:.4f}")
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print(f"B (slope) = {B_fit:.4f}")
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print(f"C (midpoint) = {C_fit:.4f}")
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# --- Fitted curve ---
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x_fit = np.linspace(min(x), max(x), 400)
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y_fit = logistic_4pl(x_fit, *params)
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# --- Plot data vs fitted curve ---
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plt.figure(figsize=(8, 5))
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plt.scatter(x, y, label="Data")
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plt.plot(x_fit, y_fit, 'r-', label="Fitted Logistic Curve")
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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.title("4-Parameter Logistic Fit")
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plt.show()
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# --- Goodness of fit ---
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y_pred = logistic_4pl(x, *params)
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offsets = y_pred - y
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ss_res = np.sum((y - y_pred) ** 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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rmse = np.sqrt(np.mean((y - y_pred) ** 2))
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mae = np.mean(np.abs(y - y_pred))
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n = len(y)
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p = len(params)
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r2_adj = 1 - (1 - r_squared) * (n - 1) / (n - p - 1)
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print("\nGoodness of fit:")
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# for value in y_pred:
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# print(value)
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# for value in y:
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# print (value)
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print(f"R² = {r_squared:.4f}")
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print(f"Adjusted R² = {r2_adj:.4f}")
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print(f"RMSE = {rmse:.4f}")
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print(f"MAE = {mae:.4f}")
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# --- Residual plot ---
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residuals = y - y_pred
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plt.figure(figsize=(8, 4))
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plt.scatter(x, residuals)
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plt.axhline(0, color='red', linestyle='--')
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plt.xlabel('x')
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plt.ylabel('Residual (y - y_fit)')
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plt.title('Residual Plot')
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plt.show()
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