Quick script for calculating linear fit with uncertainty from x, y values

linreg.py

import sys
import numpy as np

class LinFit:
    def __init__(self, x, y):
        d = x.size * np.sum(x ** 2) - np.sum(x) ** 2
        self.intercept = (np.sum(x ** 2) * np.sum(y) - np.sum(x) * np.sum(x * y)) / d
        self.slope = (x.size * np.sum(x * y) - np.sum(x) * np.sum(y)) / d
        self.s_y = np.sqrt(np.sum((y - self.intercept - self.slope * x) ** 2) / (x.size - 2))
        self.s_intercept = self.s_y * np.sqrt(np.sum(x ** 2) / d)
        self.s_slope = self.s_y * np.sqrt(x.size / d)

if __name__ == "__main__":
    if len(sys.argv) > 1:
        data = np.loadtxt('linreg.py', delimiter=',')
    else:
        data = np.loadtxt(__file__, delimiter=',', skiprows=30, comments='"')
    fit = LinFit(data[:,0], data[:,1])
    print("Linear fit results:")
    print(f"  slope: {fit.slope} +/- {fit.s_slope}")
    print(f"  intercept: {fit.intercept} +/- {fit.s_intercept}")
    print(f"  y uncertainty: {fit.s_y}")


# How to use:
# python linreg.py <data-file>
# where <data-file> is comma-separated x- and y-values
# or paste the values below, between the triple-quotes

"""
0.6,3
1.1,16
1.6,12
2.1,20
2.6,42
3.1,37
3.6,53
4.1,56
4.6,54
5.1,75
5.6,80
6.1,91
6.6,104
7.1,94
7.6,107
8.1,118
8.6,130
9.1,133
9.6,136
10.1,152
10.6,147
11.1,159
"""