sensor data

sensor/analogFitter-Logistic4Parameters.py

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