Metadata-Version: 2.1
Name: matchms
Version: 0.13.0
Summary: Python library for fuzzy comparison of mass spectrum data and other Python objects
Home-page: https://github.com/matchms/matchms
Author: Netherlands eScience Center
Author-email: generalization@esciencecenter.nl
License: Apache Software License 2.0
Description: .. image:: readthedocs/_static/matchms_header.png
           :target: readthedocs/_static/matchms.png
           :align: left
           :alt: matchms
        
        Matchms is an open-source Python package to import, process, clean, and compare mass spectrometry data (MS/MS). It allows to implement and run an easy-to-follow, easy-to-reproduce workflow from raw mass spectra to pre- and post-processed spectral data. Spectral data can be imported from common formats such mzML, mzXML, msp, metabolomics-USI, MGF, or json (e.g. GNPS-syle json files). Matchms then provides filters for metadata cleaning and checking, as well as for basic peak filtering. Finally, matchms was build to import and apply different similarity measures to compare large amounts of spectra. This includes common Cosine scores, but can also easily be extended by custom measures. Example for spectrum similarity measures that were designed to work in matchms are `Spec2Vec <https://github.com/iomega/spec2vec>`_ and `MS2DeepScore <https://github.com/matchms/ms2deepscore>`_.
        
        If you use matchms in your research, please cite the following software paper:  
        
        F Huber, S. Verhoeven, C. Meijer, H. Spreeuw, E. M. Villanueva Castilla, C. Geng, J.J.J. van der Hooft, S. Rogers, A. Belloum, F. Diblen, J.H. Spaaks, (2020). matchms - processing and similarity evaluation of mass spectrometry data. Journal of Open Source Software, 5(52), 2411, https://doi.org/10.21105/joss.02411
        
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        **********************************
        Latest changes (matchms >= 0.11.0)
        **********************************
        
        Matchms now allows proper logging. Matchms functions and method report unexpected or 
        undesired behavior as logging WARNING, and additional information as INFO.
        The default logging level is set to WARNING. If you want to output additional
        logging messages, you can lower the logging level to INFO using set_matchms_logger_level:
        
        .. code-block:: python
        
            from matchms import set_matchms_logger_level
        
            set_matchms_logger_level("INFO")
        
        If you want to suppress logging warnings, you can also raise the logging level
        to ERROR by:
        
        .. code-block:: python
        
            set_matchms_logger_level("ERROR")
        
        To write logging entries to a local file, you can do the following:
        
        .. code-block:: python
        
            from matchms.logging_functions import add_logging_to_file
        
            add_logging_to_file("sample.log", loglevel="INFO")
        
        If you want to write the logging messages to a local file while silencing the
        stream of such messages, you can do the following:
        
        .. code-block:: python
        
            from matchms.logging_functions import add_logging_to_file
        
            add_logging_to_file("sample.log", loglevel="INFO",
                                remove_stream_handlers=True)
        
        
        **********************************
        Latest changes (matchms >= 0.10.0)
        **********************************
        
        - 2 new filters in ``matchms.filtering``: ``add_retention_time()`` and ``add_retention_index()``, to consistently add retention time/index to the spectrum metadata
        - Hashes! ``Spectrum``-objects now allow to compute different hashes:
        
        .. code-block:: python
        
            from matchms.importing import load_from_mgf
        
            # Read spectrums from a MGF formatted file, for other formats see https://matchms.readthedocs.io/en/latest/api/matchms.importing.html 
            spectrums = list(load_from_mgf("tests/pesticides.mgf"))
            
            # Spectrum hashes are generated based on MS/MS peak m/z and intensities
            # Those will change if any processing step affects the peaks.
            spectrum_hashes = [s.spectrum_hash() for s in spectrums]
            # Metadata hashes are generated based on the spectrum metadata
            # Those will change if any processing step affects the metadata.
            metadata_hashes = [s.metadata_hash() for s in spectrums]
            # `hash(spectrum)` will return a hash that is a combination of spectrum and metadata hash
            # Those will hence change if any processing step affects the peaks and/or the metadata.
            hashes = [hash(s) for s in spectrums]
        
        ***********************
        Documentation for users
        ***********************
        For more extensive documentation `see our readthedocs <https://matchms.readthedocs.io/en/latest/>`_ and our `matchms introduction tutorial <https://blog.esciencecenter.nl/build-your-own-mass-spectrometry-analysis-pipeline-in-python-using-matchms-part-i-d96c718c68ee>`_.
        
        Installation
        ============
        
        Prerequisites:  
        
        - Python 3.7, 3.8 or 3.9
        - Anaconda (recommended)
        
        We recommend installing matchms from Anaconda Cloud with
        
        .. code-block:: console
        
          # install matchms in a new virtual environment to avoid dependency clashes
          conda create --name matchms python=3.8
          conda activate matchms
          conda install --channel bioconda --channel conda-forge matchms
        
        Alternatively, matchms can also be installed using ``pip`` but users will then either have to install ``rdkit`` on their own or won't be able to use the entire functionality. Without ``rdkit`` installed several filter functions related to processing and cleaning chemical metadata will not run.
        To install matchms with ``pip`` simply run
        
        .. code-block:: console
        
          pip install matchms
        
        matchms ecosystem -> additional functionalities
        ===============================================
        
        Matchms functionalities can be complemented by additional packages.  
        To date we are aware of:
        
        + `Spec2Vec <https://github.com/iomega/spec2vec>`_ an alternative machine-learning spectral similarity score that can simply be installed by `pip install spec2vec` and be imported as `from spec2vec import Spec2Vec` following the same API as the scores in `matchms.similarity`.
        
        + `MS2DeepScore <https://github.com/matchms/ms2deepscore>`_ a supervised, deep-learning based spectral similarity score that can simply be installed by `pip install ms2deepscore` and be imported as `from ms2deepscore import MS2DeepScore` following the same API as the scores in `matchms.similarity`.
        
        + `matchmsextras <https://github.com/matchms/matchmsextras>`_ which contains additional functions to create networks based on spectral similarities, to run spectrum searchers against `PubChem`, or additional plotting methods.
        
        + `memo <https://github.com/mandelbrot-project/memo>`_ a method allowing a Retention Time (RT) agnostic alignment of metabolomics samples using the fragmentation spectra (MS2) of their consituents.
        
        *(if you know of any other packages that are fully compatible with matchms, let us know!)*
        
        Introduction
        ============
        
        To get started with matchms, we recommend following our `matchms introduction tutorial <https://blog.esciencecenter.nl/build-your-own-mass-spectrometry-analysis-pipeline-in-python-using-matchms-part-i-d96c718c68ee>`_.
        
        Alternatively, here below is a small example of using matchms to calculate the Cosine score between mass Spectrums in the `tests/pesticides.mgf <https://github.com/matchms/matchms/blob/master/tests/pesticides.mgf>`_ file.
        
        .. code-block:: python
        
            from matchms.importing import load_from_mgf
            from matchms.filtering import default_filters
            from matchms.filtering import normalize_intensities
            from matchms import calculate_scores
            from matchms.similarity import CosineGreedy
        
            # Read spectrums from a MGF formatted file, for other formats see https://matchms.readthedocs.io/en/latest/api/matchms.importing.html 
            file = load_from_mgf("tests/pesticides.mgf")
        
            # Apply filters to clean and enhance each spectrum
            spectrums = []
            for spectrum in file:
                # Apply default filter to standardize ion mode, correct charge and more.
                # Default filter is fully explained at https://matchms.readthedocs.io/en/latest/api/matchms.filtering.html .
                spectrum = default_filters(spectrum)
                # Scale peak intensities to maximum of 1
                spectrum = normalize_intensities(spectrum)
                spectrums.append(spectrum)
        
            # Calculate Cosine similarity scores between all spectrums
            # For other similarity score methods see https://matchms.readthedocs.io/en/latest/api/matchms.similarity.html .
            scores = calculate_scores(references=spectrums,
                                      queries=spectrums,
                                      similarity_function=CosineGreedy())
        
            # Print the calculated scores for each spectrum pair
            for score in scores:
                (reference, query, score) = score
                # Ignore scores between same spectrum and
                # pairs which have less than 20 peaks in common
                if reference is not query and score["matches"] >= 20:
                    print(f"Reference scan id: {reference.metadata['scans']}")
                    print(f"Query scan id: {query.metadata['scans']}")
                    print(f"Score: {score['score']:.4f}")
                    print(f"Number of matching peaks: {score['matches']}")
                    print("----------------------------")
        
        Glossary of terms
        =================
        
        .. list-table::
           :header-rows: 1
        
           * - Term
             - Description
           * - adduct / addition product
             - During ionization in a mass spectrometer, the molecules of the injected compound break apart
               into fragments. When fragments combine into a new compound, this is known as an addition
               product, or adduct.  `Wikipedia <https://en.wikipedia.org/wiki/Adduct>`__
           * - GNPS
             - Knowledge base for sharing of mass spectrometry data (`link <https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp>`__).
           * - InChI / :code:`INCHI`
             - InChI is short for International Chemical Identifier. InChIs are useful
               in retrieving information associated with a certain molecule from a
               database.
           * - InChIKey / InChI key / :code:`INCHIKEY`
             - An identifier for molecules. For example, the InChI key for carbon
               dioxide is :code:`InChIKey=CURLTUGMZLYLDI-UHFFFAOYSA-N` (yes, it
               includes the substring :code:`InChIKey=`).
           * - MGF File / Mascot Generic Format
             - A plan ASCII file format to store peak list data from a mass spectrometry experiment. Links: `matrixscience.com <http://www.matrixscience.com/help/data_file_help.html#GEN>`__,
               `fiehnlab.ucdavis.edu <https://fiehnlab.ucdavis.edu/projects/lipidblast/mgf-files>`__.
           * - parent mass / :code:`parent_mass`
             - Actual mass (in Dalton) of the original compound prior to fragmentation.
               It can be recalculated from the precursor m/z by taking
               into account the charge state and proton/electron masses.
           * - precursor m/z / :code:`precursor_mz`
             - Mass-to-charge ratio of the compound targeted for fragmentation.
           * - SMILES
             - A line notation for describing the structure of chemical species using
               short ASCII strings. For example, water is encoded as :code:`O[H]O`,
               carbon dioxide is encoded as :code:`O=C=O`, etc. SMILES-encoded species may be converted to InChIKey `using a resolver like this one <https://cactus.nci.nih.gov/chemical/structure>`__. The Wikipedia entry for SMILES is `here <https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system>`__.
        
        
        ****************************
        Documentation for developers
        ****************************
        
        Installation
        ============
        
        To install matchms, do:
        
        .. code-block:: console
        
          git clone https://github.com/matchms/matchms.git
          cd matchms
          conda create --name matchms-dev python=3.8
          conda activate matchms-dev
          # Install rdkit using conda, rest of dependencies can be installed with pip
          conda install -c conda-forge rdkit
          python -m pip install --upgrade pip
          pip install --editable .[dev]
        
        Run the linter with:
        
        .. code-block:: console
        
          prospector
        
        Automatically fix incorrectly sorted imports:
        
        .. code-block:: console
        
          isort --recursive .
        
        Files will be changed in place and need to be committed manually.
        
        Run tests (including coverage) with:
        
        .. code-block:: console
        
          pytest
        
        
        Conda package
        =============
        
        The conda packaging is handled by a `recipe at Bioconda <https://github.com/bioconda/bioconda-recipes/blob/master/recipes/matchms/meta.yaml>`_.
        
        Publishing to PyPI will trigger the creation of a `pull request on the bioconda recipes repository <https://github.com/bioconda/bioconda-recipes/pulls?q=is%3Apr+is%3Aopen+matchms>`_
        Once the PR is merged the new version of matchms will appear on `https://anaconda.org/bioconda/matchms <https://anaconda.org/bioconda/matchms>`_
        
        Flowchart
        =========
        
        .. figure:: paper/flowchart_matchms.png
          :width: 400
          :alt: Flowchart
          
          Flowchart of matchms workflow. Reference and query spectrums are filtered using the same
          set of set filters (here: filter A and filter B). Once filtered, every reference spectrum is compared to
          every query spectrum using the matchms.Scores object.
        
        Contributing
        ============
        
        If you want to contribute to the development of matchms,
        have a look at the `contribution guidelines <CONTRIBUTING.md>`_.
        
        *******
        License
        *******
        
        Copyright (c) 2021, Netherlands eScience Center
        
        Licensed under the Apache License, Version 2.0 (the "License");
        you may not use this file except in compliance with the License.
        You may obtain a copy of the License at
        
        http://www.apache.org/licenses/LICENSE-2.0
        
        Unless required by applicable law or agreed to in writing, software
        distributed under the License is distributed on an "AS IS" BASIS,
        WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
        See the License for the specific language governing permissions and
        limitations under the License.
        
        *******
        Credits
        *******
        
        This package was created with `Cookiecutter
        <https://github.com/audreyr/cookiecutter>`_ and the `NLeSC/python-template
        <https://github.com/NLeSC/python-template>`_.
        
Keywords: similarity measures,mass spectrometry,fuzzy matching,fuzzy search
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.7
Description-Content-Type: text/x-rst
Provides-Extra: dev
Provides-Extra: chemistry
