A comprehensive Python library for scientific data analysis with uncertainty propagation.

LabTools is an integrated toolkit for handling experimental data in scientific laboratories. It provides a streamlined workflow for:

• Loading and processing data with uncertainties
• Performing various types of regression (linear, polynomial, and custom curves)
• Propagating uncertainties through mathematical expressions
• Visualizing data with proper error representation
• Exporting results to Excel and CSV formats

The package is designed with a focus on proper uncertainty handling throughout the entire data analysis process.

• numpy
• pandas
• matplotlib
• scipy
• sympy
• openpyxl
• xlsxwriter

The package includes an example that demonstrate its capabilities. One key example is the Decay module, which shows how to:

• Load data from radioactive decay experiments
• Apply linear and exponential curve fitting with uncertainty propagation
• Visualize decay curves with proper uncertainty bands
• Calculate half-life values with uncertainties

You can find this example in the Decay.ipynb notebook and the related files in the decay directory.

• Load from Excel, CSV and other formats
• Extract data with their associated uncertainties
• Export processed data with properly formatted uncertainties

• Linear Regression: Weighted and unweighted regression with uncertainty analysis
• Polynomial Regression: Fit polynomials of any degree with proper error propagation
• Custom Curve Fitting: Orthogonal distance regression for arbitrary functions
• Uncertainty Propagation: Apply formulas to data with proper error propagation
• Statistical Analysis: Calculate means, standard deviations with uncertainty weighting

• Error bars on data points
• Uncertainty bands on regression curves
• Multiple regression comparison plots
• LaTeX-formatted labels and equations

The main interface that unifies all functionality:

lab = LabTools(
 foldername=None, # Directory for data files (optional)
 filename=None, # Input data file (without extension)
 variables=None, # Variables in comma-separated format
 equations=None, # Formula equations
 x_label=None, # X-axis label for plots
 y_label=None, # Y-axis label for plots 
 title=None # Plot title
)

# Initialize with experiment details
lab = LabTools(
 filename="pendulum_data",
 x_label="Length (m)",
 y_label="Period2 (s2)",
 title="Pendulum Period vs Length"
)
# Load and analyze data
lab.load_data()
lab.linear_regression()
# Display results
print(f"Slope: {lab.regression_results['coefficients'][1]} ± {lab.regression_results['errors'][1]}")
print(f"Correlation coefficient: {lab.regression_results['r']}")
# Visualize and save
lab.plot_regression()

# Calculate acceleration from force and mass with uncertainties
lab = LabTools(
 filename="force_measurements",
 variables="F,m",
 equations="a = F/m"
)
# Load data and apply formula
lab.load_data()
computed_df, summary = lab.apply_formula()
# Export results
lab.unload_data(new_filename="acceleration_results")

import numpy as np
# Define fitting function
def exponential_decay(params, x):
 amplitude, decay_constant = params
 return amplitude * np.exp(-decay_constant * x)
# Setup analysis
lab = LabTools(
 filename="decay_data",
 x_label="Time (s)",
 y_label="Activity (Bq)",
 title="Radioactive Decay"
)
# Load data and fit curve
lab.load_data()
lab.curve_regression(
 function=exponential_decay,
 initial_params=[100, 0.1]
)
# Visualize results
lab.plot_regression()

MIT License