Fit data with scipy

Question

I have two datasets, calculated from different equations, now I would like to fit both datasets to the same trend line, or to fit one dataset to another. I would like to constrain the blue dataset, with the orange dataset and to get the red trendline, I was wondering if I could use SciPy (scipy.optimize.leastsq or scipy.optimize.curve_fit) but I don't know how, and I don't understand the instruction, because they ask for a function as input, and this is exactly what I want, the function, I have the points (x,y).

Answer 1

You can do this using numpy.polynomial.polynomial by fitting a second-degree polynomial to your data which uses a least-squares method.

import numpy as np
import numpy.polynomial.polynomial as poly
import matplotlib.pyplot as plt

x = [1, 2, 3] # --> assuming your x-data
y = [0.1, 1, 10] # --> assuming your y-data

coefs = poly.polyfit(x, y, 2)
ffit = poly.Polynomial(coefs)
x_new = np.linspace(1, 3, 10) # --> more data points for a smooth curve

plt.plot(x, y, label="Data")
plt.plot(x_new, ffit(x_new), label="Fit")
plt.legend()
plt.show()

Just replace x, y, and x_new with your desired values.