Scientific research data analysis tools
Project description
# easy-data-analysis: plotting and statistical analysis tools for scientific research
## Overview
This package provides tools to analyze scientific research data, with a focus on plotting and statistical analysis.
## Compatibility and Installation
This package is written in Python 3.6 and was tested on Linux and MacOS 13. It depends on the following libraries: * matplotlib 3.1.3 * numpy 1.18.4 * pandas 0.25.3 * scipy 1.4.1
Installing these libraries is easily done with pip, simply run in a terminal: ` pip install --upgrade <library> `
easy-data-analysis is packaged and available through [PyPI](https://pypi.org), you can install it by running in a terminal: ` pip install --upgrade easy-data-analysis `
## Usage
At the moment easy-data-analysis is accessible through a command line interface, type this in your terminal for more information: ` eda --help `
Currently, the only available command is plot and give access to subommands spectrum and kinetics.
### spectrum subcommand
This subcommand plots absorption spectra from CSV files, run: ` eda plot spectrum [arguments] `
Arguments include: * -f or –file specify names of files to process * -l or –label specify labels on data for the plot legend
For example: ` eda plot spectrum -f file1.csv file2.csv -l experiment1 experiment2 `
For more information: ` eda plot spectrum -h `
### kinetics subcommand
This subcommand plots absorption kinetics curves from CSV files, run: ` eda plot kinetics [arguments] `
Arguments include: * -f or –file specify names of files to process * -l or –label specify labels on data for the plot legend * -m or –model is a flag indicating an exponential model should be fitted on the data
For example: ` eda plot kinetics -f file1.csv file2.csv -l experiment1 experiment2 -m `
For more information: ` eda plot kinetics -h `
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