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        "author": "Charles Le Losq",
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        "description": "# RamPy\n=======\n\nCopyright (2015-2019) C. Le Losq.\n\ncharles.lelosq@anu.edu.au\n\n[![Build Status](https://travis-ci.org/charlesll/rampy.svg?branch=master)](https://travis-ci.org/charlesll/rampy)\n\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1168729.svg)](https://doi.org/10.5281/zenodo.1168729)\n\nRampy is a Python library that aims at helping processing spectroscopic data, such as Raman, Infrared or XAS spectra. It offers, for instance, functions to subtract baselines as well as to stack, resample or smooth spectra. It aims at facilitating the use of Python in processing spectroscopic data. It integrates within a workflow that uses Numpy/Scipy as well as optimisation libraries such as lmfit or emcee, for instance.\n\nThe /examples/ folder contain various examples.\n\n# REQUIREMENTS\n\nRampy is tested on Python 2.7 and 3.6 (see Travis badge; no garantee that it works on other Python versions)\n\nThe following libraries are required and indicated in setup.py:\n\n- Scipy\n- Numpy >= 1.12\n- sklearn\n- pandas\n\nOptional dependencies:\n\n- gcvspline (you need a working FORTRAN compiler for its installation. To avoid this problem under Windows, wheels for Python 2.7, 3.4 and 3.6 are provided for 64 bit Windows, and a wheel for Python 3.6 is provided for Windows 32 bits. If installation fails, please check if is due to a fortran compiler issue.)\n\n*Installation of gcvspline is necessary for use of the `rampy.rameau()` class.*\n\n- cvxpy v 1.0 or higher. As for gcvspline, the installation of cvxpy can cause problems for Windows users due to missing compiler. See instructions from cvxpy in this case.\n\n*Installation of gcvspline is necessary for use of the `rampy.mixing()` class.*\n\nAdditional libraries for model fitting may be wanted:\n\n- lmfit & aeval (http://cars9.uchicago.edu/software/python/lmfit/)\n- emcee\n\n# INSTALLATION\n\nInstall with pip:\n\n  `pip install rampy`\n\nIf you want to use gcvspline and cvxpy, also install it:\n\n  `pip install gcvspline`\n\n  `pip install cvxpy`\n\n# EXAMPLES\n\nGiven a signal [x y] containing a peak, and recorded in a text file myspectrum.txt.\n\nYou can import it, remove a automatic background, plot the result, and print the centroid of the peak as:\n\n```\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport rampy as rp\n\nspectrum = np.genfromtxt(\"myspectrum.txt\")\n\nbir = np.array([[0,100., 200., 1000]]) # the frequency regions devoid of signal, used by rp.baseline()\ny_corrected, background = rp.baseline(spectrum[:,0],spectrum[:,1],bir,\"arPLS\",lam=10**10)\n\nplt.figure()\nplt.plot(spectrum[:,0],spectrum[:,1],\"k\",label=\"raw data\")\nplt.plot(spectrum[:,0],background,\"k\",label=\"background\")\nplt.plot(spectrum[:,0],y_corrected,\"k\",label=\"corrected signal\")\nplt.show()\n\nprint(\"Signal centroid is %.2f\" % rp.centroid(spectrum[:,0],y_corrected))\n```\n\nSee the /example folder for further examples.\n\nUpdated May 2019\n\n\n",
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