TAUP-DATKit 0.2.2

A package for chromatographic data analysis

Overview

TAUP_DATKit (TAU Protein - Data Analysis Tool Kit) is a package designed for data analysis on chromatographic data. It includes various functions for:

• Loading data from different sources, such as CSV and Excel files.

• Merging data and homogenizing spectra using data interpolation techniques.

• Complex filtering of elements.

• Representation and visualization of distances and similarities between elements.

Project Context

This package is part of the TAU Protein: “Systematic manipulation of tau protein aggregation: bridging biochemical and pathological properties”.

Library Structure

The library structure of TAUP_DATKit is organized as follows:

• “TAUP_DATKit/”: The main directory containing the core functionality of the package.

  • “analysis_reporting.py”: Functions for the generation of the PDF report based in the results of the analysis.

  • “data_filtering.py”: Functions for filtering chromatographic data elements by inclusion/exclusion or by distance to other elements.

  • “data_integration.py”: Functions for integrating data from different sources.

  • “data_loading.py”: Functions for loading CSV or Excel data.

  • “data_visualization.py”: Functions for the representation and visualization of distances and similarities.

  • “distance_computing.py”: Functions for the calculation of distances and linkage of chromatographic elements.

  • “demo/”: Directory containing a simple demo for the library functionality.

    • “Data/”: Example input data.

    • “Saves/”: Example output information.

    • “example_process.py”: Script that defines all the required parameters and use the main functions of the library.

    • “log.log”: Example log of the execution.

  • “docs/”: Documentation for using the package and understanding its functionality.

    • “build/”: Documentation generated.

      • “html/”: Documentation in HTML format.

    • “rst/”: Documentation in .rst format.

    • “source/”: Configuration of the documentation generation.

  • “properties/”: Functions that allow defining and applying the basic configuration of the library.

    • “config_prop_loader.py”: Script that loads the configuration parameters from “config.properties”.

    • “config.properties”: Configuration properties.

    • “process_prop_loader.py”: Script that loads the processing parameters from “process.properties”.

    • “process.properties”: Execution properties.

    • “prop_parser.py”: Function for parsing config parameters from .properties files.

  • “tools/”:

    • “chart_tools.py”: Functions for generating plots.

  • “utils/”: Functions to manage other resources.

    • “image_utils.py”: Functions for managing images.

    • “interpolation_utils.py”: Functions for defining interpolation algorithms.

Requirements

DATKit requires the following libraries to work properly. Please ensure that you install the versions indicated (or those within the specified ranges) to avoid compatibility issues:

• CairoSVG: approximately version 2.7.1 (~=2.7.1)

• matplotlib: approximately version 3.10.0 (~=3.10.0)

• numpy: version between 1.26.0 (inclusive) and 2.0.0 (exclusive) (>=1.26.0, <2.0.0)

• pandas: version between 2.2.2 (inclusive) and 2.3.0 (exclusive) (>=2.2.2, <2.3.0)

• reportlab: approximately version 4.2.5 (~=4.2.5)

• scikit_learn: approximately version 1.6.0 (~=1.6.0)

• scipy: version between 1.7.0 (inclusive) and 1.14.0 (exclusive) (>=1.7.0, <1.14.0)

• seaborn: approximately version 0.13.2 (~=0.13.2)

• setuptools: version 60.0.0 or higher (>=60.0.0)

` CairoSVG~=2.7.1 matplotlib~=3.10.0 numpy>=1.26.0,<2.0.0 pandas>=2.2.2,<2.3.0 reportlab~=4.2.5 scikit_learn~=1.6.0 scipy>=1.7.0,<1.14.0 seaborn~=0.13.2 setuptools>=60.0.0 `

For detailed instructions on installation and usage, see the documentation in the docs/ directory.