Metadata-Version: 2.1
Name: PyMassSpec
Version: 2.3.0
Summary: Python Toolkit for Mass Spectrometry
Home-page: https://github.com/domdfcoding/PyMassSpec
Author: Dominic Davis-Foster
Author-email: dominic@davis-foster.co.uk
License: GNU General Public License v2 (GPLv2)
Project-URL: Documentation, https://PyMassSpec.readthedocs.io
Project-URL: Issue_Tracker, https://github.com/domdfcoding/PyMassSpec/issues
Project-URL: Source_Code, https://github.com/domdfcoding/PyMassSpec
Description: ************
        PyMassSpec
        ************
        
        .. start short_desc
        
        **Python Toolkit for Mass Spectrometry**
        
        .. end short_desc
        
        .. start shields
        
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        .. |pypi-version| image:: https://img.shields.io/pypi/v/PyMassSpec
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        .. end shields
        
        PyMassSpec is a Python_ package for processing gas chromatography-mass spectrometry data.
        PyMassSpec provides a framework and a set of components for rapid development and testing of methods for processing of chromatography--mass spectrometry data.
        PyMassSpec can be used interactively through the Python shell, in a `Jupyter Notebook <https://jupyter.org/>`_, or the functions can be collected into scripts when it is preferable to perform data processing in the batch mode.
        
        |
        
        Forked from the original PyMS Repository: https://github.com/ma-bio21/pyms.
        Originally by Andrew Isaac, Sean O'Callaghan and Vladimir Likić. The original publication can be found here: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-13-115
        
        The original project seems to have been abandoned as there has been no activity since 2017.
        
        |
        
        .. contents:: Table of Contents
            :local:
        
        
        
        The PyMassSpec project
        =========================
        
        The directory structure of PyMassSpec is as follows:
        
        .. code-block:: text
        
            /
            ├── pyms:      The PyMassSpec code
            │
            ├── pyms-data: Example GC-MS data files
            │
            ├── pyms-demo: Examples of how to use PyMassSpec
            │
            ├── tests: pytest tests
            │
            └── doc-source: Sphinx source for documentation
        
        Features
        =========
        
        Installation
        ==============
        
        PyMassSpec can be installed with the following command:
        
        .. code-block:: bash
        
            $ pip --user install PyMassSpec
        
        This will also install the following dependencies:
        
        .. code-block:: bash
        
            numpy >= 1.16.2
            scipy >= 1.2.1
            pymzml >= 2.2.1
            matplotlib >= 3.0.2
            openpyxl >= 2.6.2
            netCDF4 >= 1.5.0
            biopython >= 1.74
            deprecation >= 2.0.6
        
        
        PyMassSpec can also make use of 'mpi4py' if it is installed. See https://mpi4py.readthedocs.io/en/stable/ for further information.
        
        
        Usage
        =======
        
        A tutorial illustrating various PyMassSpec features in detail is provided
        in subsequent chapters of this User Guide. The commands executed
        interactively are grouped together by example, and can be found
        `here <https://pymassspec.readthedocs.io/en/master/pyms-demo/introduction.html#pyms-demo>`__.
        
        .. If you are viewing this source, the examples can be found in the pyms-demo directory, and the data files in pyms-data
        
        The data used in the PyMassSpec documentation and examples is available
        `here <https://pymassspec.readthedocs.io/en/master/pyms-demo/data-files.html>`__.
        
        In the "`Demos and Examples`_" section there
        is a page corresponding to each example, coded with the chapter number
        (ie. "``pyms-demo/20a/``" corresponds to the Example 20a, from Chapter 2).
        
        Each example has a script named 'proc.py' which contains the commands given in the example.
        These scripts can be run with the following command:
        
        .. code-block:: bash
        
            $ python3 proc.py
        
        Example processing GC-MS data
        -------------------------------
        
        Download the file ``gc01_0812_066.jdx`` and save it in the folder ``data``.
        This file contains GC-MS data in the the JCAMP-DX format.
        
        First the raw data is loaded:
        
            >>> from pyms.GCMS.IO.JCAMP import JCAMP_reader
            >>> jcamp_file = "data/gc01_0812_066.jdx"
            >>> data = JCAMP_reader(jcamp_file)
            -> Reading JCAMP file 'Data/gc01_0812_066.jdx'
            >>> data
            <pyms.GCMS.Class.GCMS_data at 0x7f3ec77da0b8>
        
        The intensity matrix object is then built by binning the data:
        
            >>> from pyms.IntensityMatrix import build_intensity_matrix_i
            >>> im = build_intensity_matrix_i(data)
        
        In this example, we show how to obtain the dimensions of the
        newly created intensity matrix, then loop over all ion chromatograms,
        and for each ion chromatogram apply Savitzky-Golay noise filter
        and tophat baseline correction:
        
            >>> n_scan, n_mz = im.size
            >>> from pyms.Noise.SavitzkyGolay import savitzky_golay
            >>> from pyms.TopHat import tophat
            >>> for ii in range(n_mz):
            ...     print("working on IC", ii)
            ...     ic = im.get_ic_at_index(ii)
            ...     ic1 = savitzky_golay(ic)
            ...     ic_smooth = savitzky_golay(ic1)
            ...     ic_base = tophat(ic_smooth, struct="1.5m")
            ...     im.set_ic_at_index(ii, ic_base)
        
        The resulting noise and baseline corrected ion chromatogram is saved back into the intensity matrix.
        
        Further examples can be found in the `documentation`_
        
        Contributing
        ==============
        
        Contributions are very welcome. Tests can be run with `pytest`_.
        Please ensure the coverage is at least |coveralls|
        before you submit a pull request.
        
        For further information see the section `Contributing to PyMassSpec`_
        
        License
        =========
        PyMassSpec is Free and Open Source software released under the `GNU General Public License version 2 <GPL_>`__.
        
        
        Issues
        ========
        
        If you encounter any problems, please `file an issue`_ along with a
        detailed description.
        
        
        .. _`documentation`: https://pymassspec.readthedocs.io
        .. _`Contributing to PyMassSpec`: https://pymassspec.readthedocs.io/en/master/Contributing/Contributing.html
        .. _`pytest`: https://pytest.org/
        .. _`file an issue`: https://github.com/domdfcoding/pymassspec/issues
        .. _Python: https://www.python.org/
        .. _GPL: https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html
        .. _Demos and Examples: https://pymassspec.readthedocs.io/en/master/pyms-demo/introduction.html#pyms-demo
        
Platform: Windows
Platform: macOS
Platform: Linux
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Education
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6.1
Description-Content-Type: text/x-rst
Provides-Extra: all
