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        "author": "Christopher H. Jordan",
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        "description": "sslf - A Simple Spectral Line Finder\n====================================\n\n``sslf`` is designed to be a dead-simple, effective and useful spectral line finder for 1D data. It utilises the continuous wavelet transform from scipy, which is a productive way to find even weak spectral lines.\n\nBecause there are many definitions for a spectral line, this package can not conceivably work in all cases. Here, I have designed ``sslf`` for Gaussian-profiled spectral lines. A big part of my work focuses on recovering weak signals in the noise, so ``sslf`` aims to identify lines, flag them, and subtract the non-zero bandpass around them.\n\nUsage\n-----\nWith a 1D spectrum (``spectral_data``) and a range of scales to test (e.g. 1 through 20), statistically significant spectral peaks can be found with::\n\n    import numpy as np\n    from sslf.sslf import Spectrum\n\n    s = Spectrum(spectral_data)\n    s.find_cwt_peaks(scales=np.arange(1, 20), snr=6.5)\n    s.subtract_bandpass()\n\nThe flattened spectrum is then contained in ``s.modified``, and peak locations at ``s.channel_peaks``. Other parameters related to the peaks are ``s.peak_snrs``, ``s.vel_peaks`` and ``s.channel_edges``.\n\nNote that the ``snr`` parameter specified to ``find_cwt_peaks`` actually refers to the signal-to-noise ratio in the wavelet domain, not the input-signal domain; for this reason, if you wanted all signals above 5-sigma, you would have to put in a little more work to determine what your spectrum's noise RMS is and how it maps to the wavelet RMS.\n\nAlso note that ``channel_edges`` can be quite inaccurate; by default, this variable is populated by the wavelet scale that found the spectral line. The wavelet need not match very well; it only needs to be significant enough to be picked up by ``sslf``. The ``Spectrum`` object has an optional ``refine_line_widths`` method which (hopefully) does a better job of finding the channel extent of all detected spectral lines. Read the documentation of ``sslf`` functions for more information.\n\n``find_cwt_peaks`` can optionally take the wavelet to be used in the wavelet transformation (Ricker wavelet by default). ``subtract_bandpass`` is a little harder to explain; more complicated examples of usage can be found in the ``notebooks`` directory. A reasonably complex example of ``sslf`` usage is `here <https://gitlab.com/chjordan/brewmaster/blob/master/brewmaster/merge.py>`_.\n\nContributions and Suggestions\n-----------------------------\nI created this software because I could not find anything in the community that functioned similarly. So, I would welcome contributions to make this software more generally flexible, to be suitable for a wide audience. It's really simple, just look at the code!\n\nIssues? Bugs? Inconsistencies?\n------------------------------\nAbsolutely! Please raise a GitLab issue if you find something odd, with your inputs and expected outputs, and I'll attempt to fix it ASAP.\n\nInstallation\n------------\n::\n\n    pip install sslf\n\nor\n\n- clone this repo (``git clone https://gitlab.com/cjordan/sslf.git``)\n- within the directory, run: ``python setup.py install --optimize=1``\n\nDependencies\n------------\n- python 2.7.x or 3.x\n- numpy 1.8+\n- scipy\n- future\n\n\n",
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