A python module to boost first-step data analysis after measurements.
Problem and Motivation
It is very complicated and tedious to deal with data in my physics lab course. Hence, I develop such a package to boost the process. After the first-step analysis, you can turn to complicated and advanced tools or just finish the report if the experiment is simple.
- Directly get the value and error with multi-measurements.
- Simple linear regession (based on scipy now).
- Simple plotting with just a parameter to choose the style.
- Plus, minus, multiply, divide with errors.
The package has not been published to
pip although I aim to do so. Currently, you can install it by running
git clone https://github.com/JasonQSY/measurepy && cd measurepy pip3 install .
For the multimeasure feature,
import numpy as np from measurepy.multimeasure import * A = np.array([1, 2, 3, 4, 5]) mean, delta_a, error = cal_error(A, 0.01) print(mean) print(delta_a) print(error)
For linear regression,
import numpy as np from measurepy.regression import * x = np.array([1, 2, 3, 4, 5]) y = np.array(x**2 + 1) slope, intercept, slope_err, intercept_err, rsquare = lineareg(x, y)