sensor data

sensor/analogFitter-Exponential.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.optimize import curve_fit
+import pandas as pd
+
+# Data from the chart
+data = pd.read_csv(r'C:\Users\k28ad\OneDrive\Documents\sensor\oldData\data.csv')
+x = data["x"].values
+y = data["y"].values
+
+# Define a possible fitting model (exponential fit in this case)
+def exponential(x, a, b, c):
+    return a * np.exp(b * x) + c
+
+# Perform the curve fitting
+params, covariance = curve_fit(exponential, x, y)
+
+# Plot the data and the fitting curve
+x_fit = np.linspace(2, 20, 100)
+y_fit = exponential(x_fit, *params)
+
+plt.scatter(x, y, color='blue', label='Data points')
+plt.plot(x_fit, y_fit, color='red', label='Exponential fit')
+plt.xlabel('x')
+plt.ylabel('y')
+plt.legend()
+plt.title('Exponential Fit')
+plt.grid(True)
+plt.show()
+
+# Output the fitted parameters
+print("Fitted parameters:")
+print(f"a = {params[0]}, b = {params[1]}, c = {params[2]}")