Metadata-Version: 2.1
Name: spectrapepper
Version: 0.0.11
Summary: A Python package to simplify and accelerate analysis of spectroscopy data
Home-page: https://github.com/enricgrau/spectrapepper
Author: Enric Tomas Grau-Luque
Author-email: patenric@gmail.com
License: MIT license
Description: # Spectrapepper
        
        [![image](https://img.shields.io/pypi/v/spectrapepper.svg)](https://pypi.python.org/pypi/spectrapepper)
        [![image](https://img.shields.io/conda/vn/conda-forge/spectrapepper.svg)](https://anaconda.org/conda-forge/spectrapepper)
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        [![image](https://img.shields.io/lgtm/grade/python/g/enricgrau/spectrapepper.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/enricgrau/spectrapepper/context:python)
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        [![Downloads](https://static.pepy.tech/personalized-badge/spectrapepper?period=total&units=none&left_color=grey&right_color=blue&left_text=pypi%20downloads)](https://pepy.tech/project/spectrapepper)
        [![image](https://img.shields.io/conda/dn/conda-forge/spectrapepper.svg?label=conda%20downloads)](https://anaconda.org/conda-forge/spectrapepper)
        [![image](https://img.shields.io/badge/stackoverflow-Ask%20a%20question-blue)](https://stackoverflow.com/questions/tagged/spectrapepper)
        
        **A Python package to simplify and accelerate analysis of spectroscopy data.**
        
        * GitHub repo: https://github.com/enricgrau/spectrapepper
        * Documentation: https://enricgrau.github.io/spectrapepper
        * PyPI: https://pypi.python.org/pypi/spectrapepper
        * Conda-forge: https://anaconda.org/conda-forge/spectrapepper
        * Free software: MIT license
        
        # Introduction
        
        **Spectrapepper** is a Python package that makes advanced analysis of spectroscopic data easy and accessible
        through straightforward, simple, and intuitive code. This library contains functions for every stage of spectroscopic
        methodologies, including data acquisition, pre-processing, processing, and analysis. In particular, advanced and high
        statistic methods are intended to facilitate, namely combinatorial analysis and machine learning, allowing also
        fast and automated traditional methods.
        
        # Features
        
        The following is a short list of some main procedures that **SpectraPepper** package enables.
        
        * Automatic and user-defined baseline removal.
        * Several normalization methods.
        * Noise filters, trimming, and other pre-processing tools.
        * Cosmic Ray filters.
        * Combinatorial analysis tools, including Spearman, Pearson, and n-dimensional correlation coefficients.
        * Tools for Machine Learning applications, such as data merging, randomization, and decision map.
        * Easy export of data to text files to use visualization software, such as Origin.
        
        # Quickstart
        
        1. Install this library using ``pip``::
        
                pip install spectrapepper
        
        2. Install this library using ``conda-forge``::
        
                conda install -c conda-forge spectrapepper
        
        3. Test it by printing the logo!::
        
                import spectrapepper as spep
                spep.logo()
        
        
        # Credits
        
        
        This package was created with [Cookiecutter](https://github.com/cookiecutter/cookiecutter) and the
        [giswqs/pypackage](https://github.com/giswqs/pypackage) project template.
        
Keywords: spectrapepper
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.5
Description-Content-Type: text/markdown
