Metadata-Version: 2.1
Name: matchms
Version: 0.19.0
Summary: Python library for large-scale comparisons and processing of tandem mass spectral data
Home-page: https://github.com/matchms/matchms
Author: matchms developer team
Author-email: florian.huber@hs-duesseldorf.de
License: Apache Software License 2.0
Description: `fair-software.nl <https://fair-software.nl/>`_ recommendations:
        
        |GitHub Badge|
        |License Badge|
        |Conda Badge| |Pypi Badge| |Research Software Directory Badge|
        |Zenodo Badge|
        |CII Best Practices Badge| |Howfairis Badge|
        
        Code quality checks:
        
        |CI Build|
        |ReadTheDocs Badge|
        |Sonarcloud Quality Gate Badge| |Sonarcloud Coverage Badge|
        
        .. 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
        
        
        .. |GitHub Badge| image:: https://img.shields.io/badge/github-repo-000.svg?logo=github&labelColor=gray&color=blue
           :target: https://github.com/matchms/matchms
           :alt: GitHub Badge
        
        .. |License Badge| image:: https://img.shields.io/github/license/matchms/matchms
           :target: https://github.com/matchms/matchms
           :alt: License Badge
        
        .. |Conda Badge| image:: https://anaconda.org/bioconda/matchms/badges/version.svg
           :target: https://anaconda.org/bioconda/matchms
           :alt: Conda Badge
        
        .. |Pypi Badge| image:: https://img.shields.io/pypi/v/matchms?color=blue
           :target: https://pypi.org/project/matchms/
           :alt: Pypi Badge
        
        .. |Research Software Directory Badge| image:: https://img.shields.io/badge/rsd-matchms-00a3e3.svg
           :target: https://www.research-software.nl/software/matchms
           :alt: Research Software Directory Badge
        
        .. |Zenodo Badge| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3859772.svg
           :target: https://doi.org/10.5281/zenodo.3859772
           :alt: Zenodo Badge
        
        .. |JOSS Badge| image:: https://joss.theoj.org/papers/10.21105/joss.02411/status.svg
           :target: https://doi.org/10.21105/joss.02411
           :alt: JOSS Badge
        
        .. |CII Best Practices Badge| image:: https://bestpractices.coreinfrastructure.org/projects/3792/badge
           :target: https://bestpractices.coreinfrastructure.org/projects/3792
           :alt: CII Best Practices Badge
        
        .. |Howfairis Badge| image:: https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F-green
           :target: https://fair-software.eu
           :alt: Howfairis badge
        
        .. |CI Build| image:: https://github.com/matchms/matchms/actions/workflows/CI_build.yml/badge.svg
            :alt: Continuous integration workflow
            :target: https://github.com/matchms/matchms/actions/workflows/CI_build.yml
        
        .. |ReadTheDocs Badge| image:: https://readthedocs.org/projects/matchms/badge/?version=latest
            :alt: Documentation Status
            :scale: 100%
            :target: https://matchms.readthedocs.io/en/latest/?badge=latest
        
        .. |Sonarcloud Quality Gate Badge| image:: https://sonarcloud.io/api/project_badges/measure?project=matchms_matchms&metric=alert_status
           :target: https://sonarcloud.io/dashboard?id=matchms_matchms
           :alt: Sonarcloud Quality Gate
        
        .. |Sonarcloud Coverage Badge| image:: https://sonarcloud.io/api/project_badges/measure?project=matchms_matchms&metric=coverage
           :target: https://sonarcloud.io/component_measures?id=matchms_matchms&metric=Coverage&view=list
           :alt: Sonarcloud Coverage
        
        **********************************
        Latest changes (matchms >= 0.18.0)
        **********************************
        
        Pipeline class
        ==============
        
        To make typical matchms workflows (data import, processing, score computations) more accessible to users, matchms now offers a `Pipeline` class to handle complex workflows. This also allows to define, import, export, or modify workflows using yaml files. See code examples below (and soon: updated tutorial).
        
        Sparse scores array
        ===================
        
        We realized that many matchms-based workflows aim to compare many-to-many spectra whereby not all pairs and scores are equally important. Often, for instance, it will be about searching similar or related spectra/compounds. This also means that often not all scores need to be stored (or computed). For this reason we now shifted to a sparse handling of scores in matchms (that means: only storing actuallly computed, non-null values).
        
        .. image:: readthedocs/_static/matchms_sketch.png
           :target: readthedocs/_static/matchms_sketch.png
           :align: left
           :alt: matchms code design
        
        
        
        **********************************
        Latest changes (matchms >= 0.14.0)
        **********************************
        
        Metadata class
        ==============
        
        This is the first of a few releases to work our way towards matchms 1.0.0, which also means that a few things in the API will likely change. Here the main change is that `Spectrum.metadata` is no longer a simple Python dictionary but became a ``Metadata`` object. In this context metadata field-names/keys will now be harmonized by default (e.g. "Precursor Mass" will become "precursor_mz). For list of conversions see `matchms key conversion table <https://github.com/matchms/matchms/blob/master/matchms/data/known_key_conversions.csv>`_.
        
        - metadata is now stored using new ``Metadata`` class which automatically applied restrictions to used field names/keys to avoid confusion between different format styles
        - all metadata keys must be lower-case, spaces will be changed to underscores.
        - Known key conversions are applied to metadata entries using a `matchms key conversion table <https://github.com/matchms/matchms/blob/master/matchms/data/known_key_conversions.csv>`_.
        - new ``MetadataMatch`` similarity measure in matchms.similarity. This can be used to find matches between metadata entries and currently supports either full string matches or matches of numerical entries within a specified tolerance
        - new ``interpret_pepmass()`` filter to handle different pepmass entries found in data 
        - ``Spikes`` class has become ``Fragments`` class
        
        
        ***********************
        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, 3.9 or 3.10, (higher versions should work as well, but are not yet tested systematically)
        - Anaconda (recommended)
        
        We recommend installing matchms in a new virtual environment to avoid dependency clashes
        
        .. code-block:: console
        
          conda create --name matchms python=3.9
          conda activate matchms
          conda install --channel bioconda --channel conda-forge matchms
        
        Alternatively, matchms can also be installed using ``pip``. In the most basic version matchms will then come without ``rdkit`` so that several filter functions related to processing and cleaning chemical metadata will not run. To include ``rdkit`` install matchms as ``matchms[chemistry]``:
        
        .. code-block:: console
        
          pip install matchms  # simple install w/o rdkit
          pip install matchms[chemistry]  # full install including rdkit
        
        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>`_.
        
        Below is a small example of using matchms to calculate the Cosine score between mass Spectrums in the `tests/testdata/pesticides.mgf <https://github.com/matchms/matchms/blob/master/tests/testdata/pesticides.mgf>`_ file.
        
        .. code-block:: python
        
            from matchms import Pipeline
            
            pipeline = Pipeline()
            
            # Read spectrums from a MGF formatted file, for other formats see https://matchms.readthedocs.io/en/latest/api/matchms.importing.html 
            pipeline.query_files = "tests/testdata/pesticides.mgf"
            pipeline.filter_steps_queries = [
                ["default_filters"],
                ["add_parent_mass"],
                ["normalize_intensities"],
                ["select_by_intensity", {"intensity_from": 0.001, "intensity_to": 1.0}],
                ["select_by_mz", {"mz_from": 0, "mz_to": 1000}],
                ["require_minimum_number_of_peaks", {"n_required": 5}]
            ]
            pipeline.score_computations = [["precursormzmatch",  {"tolerance": 100.0}],
                                           ["cosinegreedy", {"tolerance": 1.0}],
                                           ["filter_by_range", {"name": "CosineGreedy_score", "low": 0.2}]]
        
            pipeline.logging_file = "my_pipeline.log"  # for pipeline and logging message
            pipeline.logging_level = "INFO"
            pipeline.run()
        
        
        Alternatively, in particular if you need more room to add custom functions and steps, the individual
        steps can run without using the matchms ``Pipeline``:
        
        .. code-block:: python
            
            from matchms.importing import load_from_mgf
            from matchms.filtering import default_filters, 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/testdata/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())
        
            # Matchms allows to get the best matches for any query using scores_by_query
            query = spectrums[15]  # just an example
            best_matches = scores.scores_by_query(query, 'CosineGreedy_score', sort=True)
        
            # Print the calculated scores for each spectrum pair
            for (reference, score) in best_matches[:10]
                # Ignore scores between same spectrum
                if reference is not query:
                    print(f"Reference scan id: {reference.metadata['scans']}")
                    print(f"Query scan id: {query.metadata['scans']}")
                    print(f"Score: {score[0]:.4f}")
                    print(f"Number of matching peaks: {score[1]}")
                    print("----------------------------")
        
        Different spectrum similarity scores
        ====================================
        
        Matchms comes with numerous different scoring methods in `matchms.similarity` and can furthe seemlessly work with `Spec2Vec` or `MS2DeepScore`.
        
        Code example: 
        
        .. code-block:: python
        
            from matchms.importing import load_from_usi
            import matchms.filtering as msfilters
            import matchms.similarity as mssim
        
        
            usi1 = "mzspec:GNPS:GNPS-LIBRARY:accession:CCMSLIB00000424840"
            usi2 = "mzspec:MSV000086109:BD5_dil2x_BD5_01_57213:scan:760"
        
            mz_tolerance = 0.1
        
            spectrum1 = load_from_usi(usi1)
            spectrum1 = msfilters.select_by_mz(spectrum1, 0, spectrum1.get("precursor_mz"))
            spectrum1 = msfilters.remove_peaks_around_precursor_mz(spectrum1,
                                                                   mz_tolerance=0.1)
        
            spectrum2 = load_from_usi(usi2)
            spectrum2 = msfilters.select_by_mz(spectrum2, 0, spectrum1.get("precursor_mz"))
            spectrum2 = msfilters.remove_peaks_around_precursor_mz(spectrum2,
                                                                   mz_tolerance=0.1)
            # Compute scores:
            similarity_cosine = mssim.CosineGreedy(tolerance=mz_tolerance).pair(spectrum1, spectrum2)
            similarity_modified_cosine = mssim.ModifiedCosine(tolerance=mz_tolerance).pair(spectrum1, spectrum2)
            similarity_neutral_losses = mssim.NeutralLossesCosine(tolerance=mz_tolerance).pair(spectrum1, spectrum2)
        
            print(f"similarity_cosine: {similarity_cosine}")
            print(f"similarity_modified_cosine: {similarity_modified_cosine}")
            print(f"similarity_neutral_losses: {similarity_neutral_losses}")
        
            spectrum1.plot_against(spectrum2)
        
        
        ****************************
        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 .
        
        Files will be changed in place and need to be committed manually. If you only want to inspect the isort suggestions then simply run:
        
        .. code-block:: console
        
          isort --check-only --diff .
        
        
        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
