sensor data

sensor/analogFitter-Logistic5Parameters.py

@@ -0,0 +1,69 @@
+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()