sensor data

sensor/analogFitter-Logistic3Parameters.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 ---
+# If you exported from Google Sheets with headers "x,y":
+data = pd.read_csv(r'C:\Users\k28ad\OneDrive\Documents\sensor\dataPrecise.csv')
+x = data["x"].values
+y = data["y"].values
+print(x, y)
+# --- Define logistic function with 3 parameters (a, b, c) ---
+def logistic(x, a, b, c):
+    return a / (1 + np.exp(-b * (x - c)))
+    # return c - ((1-b) * np.log((a - x)/x))
+
+# --- Fit the curve ---
+# p0 are initial guesses for (a, b, c) — tweak depending on your data
+params, covariance = curve_fit(logistic, x, y, p0=[max(y), 1, np.median(x)])
+
+a_fit, b_fit, c_fit = params
+print(f"Fitted parameters:\na = {a_fit}\nb = {b_fit}\nc = {c_fit}")
+
+# --- Plot data vs fitted curve ---
+x_fit = np.linspace(min(x), max(x), 200)  # smooth x range
+y_fit = logistic(x_fit, *params)
+
+plt.scatter(x, y, label="Data")
+plt.plot(x_fit, y_fit, 'r-', label="Fitted Logistic Curve")
+plt.legend()
+plt.show()