32 lines
1018 B
Python
32 lines
1018 B
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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# If you exported from Google Sheets with headers "x,y":
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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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print(x, y)
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# --- Define logistic function with 3 parameters (a, b, c) ---
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def logistic(x, a, b, c):
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return a / (1 + np.exp(-b * (x - c)))
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# return c - ((1-b) * np.log((a - x)/x))
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# --- Fit the curve ---
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# p0 are initial guesses for (a, b, c) — tweak depending on your data
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params, covariance = curve_fit(logistic, x, y, p0=[max(y), 1, np.median(x)])
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a_fit, b_fit, c_fit = params
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print(f"Fitted parameters:\na = {a_fit}\nb = {b_fit}\nc = {c_fit}")
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# --- Plot data vs fitted curve ---
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x_fit = np.linspace(min(x), max(x), 200) # smooth x range
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y_fit = logistic(x_fit, *params)
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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.legend()
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plt.show()
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