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style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![CI GH actions](https://github.com/MyPyDavid/raman-fitting/actions/workflows/build-test-codecov.yml/badge.svg)](https://github.com/MyPyDavid/raman-fitting/actions/workflows/build-test-codecov.yml)\n[![codecov](https://codecov.io/gh/MyPyDavid/raman-fitting/branch/main/graph/badge.svg?token=II9JZAODJY)](https://codecov.io/gh/MyPyDavid/raman-fitting)\n[![Test \u0026 Upload to TestPyPI](https://github.com/MyPyDavid/raman-fitting/actions/workflows/upload-to-testpypi.yml/badge.svg)](https://github.com/MyPyDavid/raman-fitting/actions/workflows/upload-to-testpypi.yml)\n\n\u003cp align=\"center\" width=\"100%\"\u003e\n  \u003cimg src=\"https://user-images.githubusercontent.com/13996213/140090631-ed7c9f51-7630-49b6-9081-fb0675a5a4c9.png\" alt=\"raman_cover_img\"  width=\"200px\" height=\"100px\"/\u003e\n\u003c/p\u003e\n\n\n# raman-fitting\n A Python framework that performs a deconvolution on typical parts of interest on the spectrum of carbonaceous materials.\n The deconvolutions are done with models which are composed of collections of lineshapes or peaks that are typically assigned to these spectra in scientific literature.\n\nIn batch processing mode this package will index the raman data files in a chosen folder.\nFirst, it will try to extract a sample ID and position number from the filenames and create an index of the files in a dataframe. Over this index a preprocessing, fitting and exporting loop will start.\nThere are several models, each with a different combination of typical peaks, used for fitting. Each individual typical peak is defined as a class in the deconvolution/default_models folder with some added literature reference in the docstring. Here, the individual peak parameter settings can also be easily adjusted for initial values, limits, shape (eg. Lorentzian, Gaussian and Voigt) or be fixed at certain initial values.\nExport is done with plots and excel files for the spectral data and fitting parameters for further analysis.\n\n\n### Example plots\n\nhttps://github.com/MyPyDavid/raman-fitting/wiki\n\n\n### Set up virtual environment and install the package\n\nA release is now available on PyPI, installation can be done with these commands in a terminal.\n``` bash\n# Setting up and activating a virtual environment\npython -m venv env # python 3.11 is recommended\nsource env/bin/activate\n\n# Installation from PyPI\npython -m pip install raman_fitting\n```\n\n#### From source installation\n\nThe following shows how to install the package from this source repository.\nDownload or clone this repository in a certain folder.\n``` bash\ngit clone https://github.com/MyPyDavid/raman-fitting.git\n\n# set up and activate venv ...\n\n# regular install\npython -m pip install raman-fitting/\n\n# editable/develop mode\npython -m pip install -e raman-fitting/\n```\n\n### Usage\n\n#### Post installation test run\n\nIn order to test the package after installation, please try the following command in a terminal CLI.\n``` bash\nraman_fitting run examples\n```\nor these commands in the Python interpreter or in a Jupyter Notebook.\n``` python\nimport raman_fitting\nraman_fitting.make_examples()\n```\nThis test run should yield the resulting plots and files in the following folder. Where home means the local user home directory depending on the OS.\n``` bash\n# Linux\nhome/.raman_fitting/example_results\n\n# For Other OSs, log messages will show:\n# Results saved in ...\n\n```\n\n#### Fitting your own datafiles\nPlace your data files in the default location or change this default setting in the config.\n``` bash\nhome/.raman_fitting/datafiles\n```\nThe following command will attempt the indexing, preprocessing, fitting and plotting on all the files found in this folder.\n``` bash\n# default run mode is \"normal\" means over all the files found in the index\nraman_fitting\n\n# If you add a lot of files, try to check if the index is properly constructed\n# before fitting them.\nraman_fitting make index\n\n# Location of index\nhome/.raman_fitting/datafiles/results/raman_fitting_index.csv\n```\n\n#### Datafiles\n\nThe raman data files should be .txt files with two columns of data values.\nThe first column should contain the Raman shift values and the second one the measured intensity.\nFilenames will be parsed into a sampleID and position, in order to take the mean of the measured intensity\nof several positions on the same sample.\n\nAn example of filename formatting and parsing result:\n``` python\nsamplename1_pos1.txt =\u003e sampleID = 'samplename1', position = 1\nsample2-100_3.txt =\u003e sampleID = 'sample2-100', position = 3\n```\n### Version\n\nThe current version is v0.8.0\n\n### Dependencies\n\n- python \u003e= 3.11\n- lmfit \u003e= 1.2.0\n- pandas \u003e= 2.0.0\n- scipy \u003e= 1.10.1\n- matplotlib \u003e= 3.7.2\n- numpy \u003e= 1.24.2\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmypydavid%2Framan-fitting","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmypydavid%2Framan-fitting","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmypydavid%2Framan-fitting/lists"}