physicslab-coda 0.2.0

This library contains the code usefull in physics lab

Physicslab-coda

A Python library for data analysis in physics laboratories, including:

• linear interpolation with uncertainties
• statistical variables
• common statistical tests (χ², t-Student, F-test, etc.)

The goal is to provide ready-to-use tools for students and researchers working with experimental data.

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Statistics

Interpolation

Class for linear interpolation with experimental uncertainties.

• linear_function(x, a, b)
  Simple linear function.

• erry_var_errx_null(x, y, erry, verbose=False)
  Linear interpolation with variable error on Y and negligible error on X.

• linear_interpolation(x, y, errx, erry, threshold=30, tries=10, verbose=False)
  Linear interpolation with variable errors on both X and Y.
  Automatically chooses the best approximation depending on error magnitudes.

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StatisticalVariables

Class for calculating basic statistical variables.

• r(x, y)
  Computes the sample correlation coefficient.

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StatisticalTests

Class for applying common statistical tests.

• chi2(parameters, y_original, y_calculated, erry)
  Compute chi-squared.

• reduced_chi2(parameters, y_original, y_calculated, erry)
  Compute reduced chi-squared.

• post_error(parameters, y_original, y_calculated)
  Compute posterior error.

• r_t_student(x, y)
  Compute Student’s t-value for correlation.

• t_student(mean, stddev, n, mu)
  Compute Student’s t-value for a sample.

• f_test(var1, var2, n1, n2)
  Perform F-test to compare two variances.

• double_var_t_student(mean1, mean2, var1, var2, n1, n2)
  Student’s t-test for two samples with different variances.

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Installation

git clone https://github.com/yourusername/physics-lab-data-analysis.git
cd physics-lab-data-analysis
pip install -r requirements.txt

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Usage Example

import numpy as np
from physicslab-coda import Interpolation, StatisticalVariables, StatisticalTests

# Example data
x = np.array([1, 2, 3, 4])
y = np.array([2.1, 4.2, 5.9, 8.2])
erry = np.array([0.1, 0.2, 0.1, 0.3])
errx = np.array([0.05, 0.05, 0.05, 0.05])

# Linear interpolation
interp = Interpolation()
a, b, s_a, s_b = interp.linear_interpolation(x, y, errx, erry, verbose=True)
print("a =", a, " b =", b, " σa =", s_a, " σb =", s_b)

# Correlation coefficient
r = StatisticalVariables.r(x, y)
print("r =", r)

# Reduced chi-squared
tests = StatisticalTests()
y_fit = a + b*x
rchi2, df = tests.reduced_chi2(parameters=2, y_original=y, y_calculated=y_fit, erry=erry)
print("Reduced χ² =", rchi2, " with degrees of freedom =", df)

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Author

Andrea Codarin