Curve fitting using matplotlib

Question

I have two 1d arrays shape.x=[701,] and shape.y=[701,]. This gives me a curve shown in the image below. How can I make a curve fit for this?

Answer 1

Have a look at

https://docs.scipy.org/doc/scipy-0.19.0/reference/generated/scipy.optimize.curve_fit.html,

there is an example at the bottom, which pretty much does what you are after.

Edit: Reply to comment

import matplotlib.pyplot as plt;
import numpy as np;
import scipy.optimize as opt;

# This is the function we are trying to fit to the data.
def func(x, a, b, c):
     return a * np.exp(-b * x) + c

# Generate some data, you don't have to do this, as you already have your data
xdata = np.linspace(0, 4, 50)
y = func(xdata, 2.5, 1.3, 0.5)
y_noise = 0.2 * np.random.normal(size=xdata.size)
ydata = y + y_noise

# Plot the actual data
plt.plot(xdata, ydata, ".", label="Data");

# The actual curve fitting happens here
optimizedParameters, pcov = opt.curve_fit(func, xdata, ydata);

# Use the optimized parameters to plot the best fit
plt.plot(xdata, func(xdata, *optimizedParameters), label="fit");

# Show the graph
plt.legend();
plt.show();

The x, y data are the xdata and ydata variables.

So if you want to use this code, just take out the bit where the data is generated, and define your x, y data arrays as "xdata" and "ydata".