├── .github └── workflows │ ├── ci.yml │ ├── dependabot.yml │ ├── dockerpublish.yml │ └── pythonpublish.yml ├── .gitignore ├── .readthedocs.yml ├── .travis.yml ├── Dockerfile ├── LICENSE ├── MANIFEST.in ├── README.md ├── assets ├── PyProphet_Logo.png ├── PyProphet_Logo_paths.svg └── PyProphet_Logo_transparent_bg.png ├── dist-scripts ├── create-manylinux.sh └── manylinux.patch ├── docs ├── Makefile ├── README.md ├── _static │ └── custom.css ├── api │ ├── config.rst │ ├── index.rst │ ├── io.rst │ ├── ipf.rst │ ├── levels_context.rst │ └── scoring.rst ├── cli.rst ├── conf.py ├── file_formats.rst ├── index.rst ├── templates │ └── class.rst └── user_guide │ ├── file_conversion.rst │ ├── index.rst │ └── pyprophet_workflow.rst ├── pyproject.toml ├── pyprophet ├── __init__.py ├── _base.py ├── _config.py ├── cli │ ├── __init__.py │ ├── export.py │ ├── ipf.py │ ├── levels_context.py │ ├── merge.py │ ├── score.py │ └── util.py ├── export │ ├── __init__.py │ ├── export_compound.py │ └── export_report.py ├── filter.py ├── glyco │ ├── __init__.py │ ├── export.py │ ├── glycoform.py │ ├── pepmass │ │ ├── __init__.py │ │ ├── glycomass.py │ │ ├── modmass.py │ │ └── pepmass.py │ ├── report.py │ ├── scoring.py │ └── stats.py ├── io │ ├── __init__.py │ ├── _base.py │ ├── dispatcher.py │ ├── export │ │ ├── __init__.py │ │ ├── osw.py │ │ ├── parquet.py │ │ ├── split_parquet.py │ │ └── sqmass.py │ ├── ipf │ │ ├── __init__.py │ │ ├── osw.py │ │ ├── parquet.py │ │ ├── split_parquet.py │ │ └── tsv.py │ ├── levels_context │ │ ├── __init__.py │ │ ├── osw.py │ │ ├── parquet.py │ │ └── split_parquet.py │ ├── scoring │ │ ├── __init__.py │ │ ├── osw.py │ │ ├── parquet.py │ │ ├── split_parquet.py │ │ └── tsv.py │ └── util.py ├── ipf.py ├── levels_contexts.py ├── main.py ├── report.py ├── scoring │ ├── __init__.py │ ├── _optimized.c │ ├── _optimized.pyx │ ├── classifiers.py │ ├── data_handling.py │ ├── optimized.py │ ├── pyprophet.py │ ├── runner.py │ └── semi_supervised.py ├── split.py ├── stats.py └── util.py ├── requirements.txt ├── sandbox ├── compare.py ├── createFakeOSW.ipynb ├── dummyOSWScoredData.osw ├── export_parquet.py ├── fakeLib.tsv ├── generate_storey_ref_data.R ├── ludovic.py ├── p_values.txt ├── reproduce_r.sh ├── test_pyprophet_export_parquet.py └── test_qvalue_ref_data.csv ├── setup.cfg ├── setup.py └── tests ├── .gitignore ├── Create_OSW_test.ipynb ├── README.md ├── __init__.py ├── _regtest_outputs ├── test_pyprophet_export.test_compound_0.out ├── test_pyprophet_export.test_compound_1.out ├── test_pyprophet_export.test_compound_ms1.out ├── test_pyprophet_export.test_compound_ms2.out ├── test_pyprophet_export.test_compound_unscored.out ├── test_pyprophet_export.test_ipf_0.out ├── test_pyprophet_export.test_ipf_1.out ├── test_pyprophet_export.test_ipf_2.out ├── test_pyprophet_export.test_ipf_3.out ├── test_pyprophet_export.test_ipf_analysis[False-augmented].out ├── test_pyprophet_export.test_ipf_analysis[False-disable].out ├── test_pyprophet_export.test_ipf_analysis[False-peptidoform].out ├── test_pyprophet_export.test_ipf_analysis[True-disable].out ├── test_pyprophet_export.test_osw_0.out ├── test_pyprophet_export.test_osw_1.out ├── test_pyprophet_export.test_osw_2.out ├── test_pyprophet_export.test_osw_3.out ├── test_pyprophet_export.test_osw_analysis[osw-False-False-False].out ├── test_pyprophet_export.test_osw_analysis[osw-False-False-True].out ├── test_pyprophet_export.test_osw_analysis[osw-False-True-False].out ├── test_pyprophet_export.test_osw_analysis[osw-True-False-False].out ├── test_pyprophet_export.test_osw_analysis[parquet-False-False-False].out ├── test_pyprophet_export.test_osw_analysis[parquet-False-False-True].out ├── test_pyprophet_export.test_osw_analysis[parquet-False-True-False].out ├── test_pyprophet_export.test_osw_analysis[parquet-True-False-False].out ├── test_pyprophet_export.test_osw_analysis[split_parquet-False-False-False].out ├── test_pyprophet_export.test_osw_analysis[split_parquet-False-False-True].out ├── test_pyprophet_export.test_osw_analysis[split_parquet-False-True-False].out ├── test_pyprophet_export.test_osw_analysis[split_parquet-True-False-False].out ├── test_pyprophet_export.test_osw_unscored.out ├── test_pyprophet_export.test_osw_unscored[osw].out ├── test_pyprophet_export.test_osw_unscored[parquet].out ├── test_pyprophet_export.test_osw_unscored[split_parquet].out ├── test_pyprophet_export_parquet.test_osw_to_parquet_scoring_format_0.out ├── test_pyprophet_export_parquet.test_osw_to_parquet_scoring_format_1.out ├── test_pyprophet_export_parquet.test_osw_to_parquet_scoring_format_2.out ├── test_pyprophet_export_parquet.test_osw_to_parquet_scoring_format_3.out ├── test_pyprophet_ipf.test_ipf_0.out ├── test_pyprophet_ipf.test_ipf_1.out ├── test_pyprophet_ipf.test_ipf_2.out ├── test_pyprophet_ipf.test_ipf_3.out ├── test_pyprophet_ipf.test_ipf_4.out ├── test_pyprophet_ipf.test_ipf_scoring[osw-h0_off-ms2_off-ms1_off].out ├── test_pyprophet_ipf.test_ipf_scoring[osw-h0_off-ms2_off-ms1_on].out ├── test_pyprophet_ipf.test_ipf_scoring[osw-h0_off-ms2_on-ms1_off].out ├── test_pyprophet_ipf.test_ipf_scoring[osw-h0_off-ms2_on-ms1_on].out ├── test_pyprophet_ipf.test_ipf_scoring[osw-h0_on-ms2_off-ms1_off].out ├── test_pyprophet_ipf.test_ipf_scoring[osw-h0_on-ms2_off-ms1_on].out ├── test_pyprophet_ipf.test_ipf_scoring[osw-h0_on-ms2_on-ms1_off].out ├── test_pyprophet_ipf.test_ipf_scoring[osw-h0_on-ms2_on-ms1_on].out ├── test_pyprophet_ipf.test_ipf_scoring[parquet-h0_off-ms2_off-ms1_off].out ├── test_pyprophet_ipf.test_ipf_scoring[parquet-h0_off-ms2_off-ms1_on].out ├── test_pyprophet_ipf.test_ipf_scoring[parquet-h0_off-ms2_on-ms1_off].out ├── test_pyprophet_ipf.test_ipf_scoring[parquet-h0_off-ms2_on-ms1_on].out ├── test_pyprophet_ipf.test_ipf_scoring[parquet-h0_on-ms2_off-ms1_off].out ├── test_pyprophet_ipf.test_ipf_scoring[parquet-h0_on-ms2_off-ms1_on].out ├── test_pyprophet_ipf.test_ipf_scoring[parquet-h0_on-ms2_on-ms1_off].out ├── test_pyprophet_ipf.test_ipf_scoring[parquet-h0_on-ms2_on-ms1_on].out ├── test_pyprophet_ipf.test_ipf_scoring[split_parquet-h0_off-ms2_off-ms1_off].out ├── test_pyprophet_ipf.test_ipf_scoring[split_parquet-h0_off-ms2_off-ms1_on].out ├── test_pyprophet_ipf.test_ipf_scoring[split_parquet-h0_off-ms2_on-ms1_off].out ├── test_pyprophet_ipf.test_ipf_scoring[split_parquet-h0_off-ms2_on-ms1_on].out ├── test_pyprophet_ipf.test_ipf_scoring[split_parquet-h0_on-ms2_off-ms1_off].out ├── test_pyprophet_ipf.test_ipf_scoring[split_parquet-h0_on-ms2_off-ms1_on].out ├── test_pyprophet_ipf.test_ipf_scoring[split_parquet-h0_on-ms2_on-ms1_off].out ├── test_pyprophet_ipf.test_ipf_scoring[split_parquet-h0_on-ms2_on-ms1_on].out ├── test_pyprophet_levels_contexts.test_ipf_1.out ├── test_pyprophet_levels_contexts.test_levels_contexts_0.out ├── test_pyprophet_levels_contexts.test_peptide_levels[osw-experiment-wide].out ├── test_pyprophet_levels_contexts.test_peptide_levels[osw-global].out ├── test_pyprophet_levels_contexts.test_peptide_levels[osw-run-specific].out ├── test_pyprophet_levels_contexts.test_peptide_levels[parquet-experiment-wide].out ├── test_pyprophet_levels_contexts.test_peptide_levels[parquet-global].out ├── test_pyprophet_levels_contexts.test_peptide_levels[parquet-run-specific].out ├── test_pyprophet_levels_contexts.test_peptide_levels[split_parquet-experiment-wide].out ├── test_pyprophet_levels_contexts.test_peptide_levels[split_parquet-global].out ├── test_pyprophet_levels_contexts.test_peptide_levels[split_parquet-run-specific].out ├── test_pyprophet_levels_contexts.test_protein_levels[osw-experiment-wide].out ├── test_pyprophet_levels_contexts.test_protein_levels[osw-global].out ├── test_pyprophet_levels_contexts.test_protein_levels[osw-run-specific].out ├── test_pyprophet_levels_contexts.test_protein_levels[parquet-experiment-wide].out ├── test_pyprophet_levels_contexts.test_protein_levels[parquet-global].out ├── test_pyprophet_levels_contexts.test_protein_levels[parquet-run-specific].out ├── test_pyprophet_levels_contexts.test_protein_levels[split_parquet-experiment-wide].out ├── test_pyprophet_levels_contexts.test_protein_levels[split_parquet-global].out ├── test_pyprophet_levels_contexts.test_protein_levels[split_parquet-run-specific].out ├── test_pyprophet_score.test_multi_split_parquet_0.out ├── test_pyprophet_score.test_multi_split_parquet_1.out ├── test_pyprophet_score.test_multi_split_parquet_2.out ├── test_pyprophet_score.test_multi_split_parquet_3.out ├── test_pyprophet_score.test_multi_split_parquet_6.out ├── test_pyprophet_score.test_multi_split_parquet_7.out ├── test_pyprophet_score.test_multi_split_parquet_8.out ├── test_pyprophet_score.test_multi_split_parquet_9.out ├── test_pyprophet_score.test_multi_split_parquet_apply_weights.out ├── test_pyprophet_score.test_osw_0.out ├── test_pyprophet_score.test_osw_1.out ├── test_pyprophet_score.test_osw_10.out ├── test_pyprophet_score.test_osw_2.out ├── test_pyprophet_score.test_osw_3.out ├── test_pyprophet_score.test_osw_4.out ├── test_pyprophet_score.test_osw_5.out ├── test_pyprophet_score.test_osw_6.out ├── test_pyprophet_score.test_osw_7.out ├── test_pyprophet_score.test_osw_8.out ├── test_pyprophet_score.test_osw_9.out ├── test_pyprophet_score.test_parquet_0.out ├── test_pyprophet_score.test_parquet_1.out ├── test_pyprophet_score.test_parquet_2.out ├── test_pyprophet_score.test_parquet_3.out ├── test_pyprophet_score.test_parquet_6.out ├── test_pyprophet_score.test_parquet_7.out ├── test_pyprophet_score.test_parquet_8.out ├── test_pyprophet_score.test_parquet_9.out ├── test_pyprophet_score.test_parquet_apply_weights.out ├── test_pyprophet_score.test_split_parquet_0.out ├── test_pyprophet_score.test_split_parquet_1.out ├── test_pyprophet_score.test_split_parquet_2.out ├── test_pyprophet_score.test_split_parquet_3.out ├── test_pyprophet_score.test_split_parquet_6.out ├── test_pyprophet_score.test_split_parquet_7.out ├── test_pyprophet_score.test_split_parquet_8.out ├── test_pyprophet_score.test_split_parquet_9.out ├── test_pyprophet_score.test_split_parquet_apply_weights.out ├── test_pyprophet_score.test_tsv_0.out ├── test_pyprophet_score.test_tsv_1.out ├── test_pyprophet_score.test_tsv_2.out ├── test_pyprophet_score.test_tsv_3.out ├── test_pyprophet_score.test_tsv_apply_weights.out ├── test_stats.test_lfdr.out ├── test_stats.test_qvalue.out ├── test_stats.test_random.out └── test_stats.test_stat_metrics.out ├── data ├── test_data.osw ├── test_data.oswpq │ ├── precursors_features.parquet │ └── transition_features.parquet ├── test_data.oswpqd │ └── napedro_L120420_010_SW.oswpq │ │ ├── precursors_features.parquet │ │ └── transition_features.parquet ├── test_data.parquet ├── test_data.txt ├── test_data_compound.osw ├── test_data_compound_ms1.tsv ├── test_data_compound_ms2.tsv ├── test_invalid_data.txt ├── test_lfdr_ref_data.csv └── test_qvalue_ref_data.csv ├── test.ipynb ├── test_data_handling.py ├── test_io_ipf.py ├── test_io_levels_contexts.py ├── test_io_scoring.py ├── test_ipf.py ├── test_optimized.py ├── test_pyprophet_export.py ├── test_pyprophet_ipf.py ├── test_pyprophet_levels_contexts.py ├── test_pyprophet_score.py └── test_stats.py /.github/workflows/ci.yml: -------------------------------------------------------------------------------- 1 | name: continuous-integration 2 | 3 | on: [push] 4 | 5 | jobs: 6 | test: 7 | # Add concurrency to cancel previous runs 8 | concurrency: 9 | group: ${{ github.workflow }}-${{ github.ref }} 10 | cancel-in-progress: true 11 | 12 | runs-on: ${{ matrix.os }} 13 | strategy: 14 | matrix: 15 | os: [ubuntu-latest] 16 | # Requirements file generated with python=3.11 17 | python-version: ["3.11"] 18 | steps: 19 | - uses: actions/checkout@v4 20 | 21 | - name: Set up Python ${{ matrix.python-version }} 22 | uses: actions/setup-python@v5 23 | with: 24 | python-version: ${{ matrix.python-version }} 25 | - name: Install dependencies 26 | run: | 27 | python -m pip install --upgrade pip 28 | pip install -r requirements.txt # test with requirements file so can easily bump with dependabot 29 | pip install . 30 | 31 | - name: Compile cython module 32 | run: python setup.py build_ext --inplace 33 | 34 | - name: Test 35 | run: | 36 | python -m pytest -n auto tests/ -------------------------------------------------------------------------------- /.github/workflows/dependabot.yml: -------------------------------------------------------------------------------- 1 | version: 2 2 | updates: 3 | - package-ecosystem: "pip" 4 | directory: "/" # Location of your pyproject.toml or requirements.txt 5 | schedule: 6 | interval: "weekly" # Checks for updates every week 7 | commit-message: 8 | prefix: "deps" # Prefix for pull request titles 9 | open-pull-requests-limit: 5 # Limit the number of open PRs at a time 10 | -------------------------------------------------------------------------------- /.github/workflows/dockerpublish.yml: -------------------------------------------------------------------------------- 1 | name: Upload Docker image 2 | 3 | on: 4 | release: 5 | types: [published] 6 | 7 | jobs: 8 | push_to_registries: 9 | name: Push Docker image to multiple registries 10 | runs-on: ubuntu-latest 11 | permissions: 12 | packages: write 13 | contents: read 14 | steps: 15 | - name: Check out the repo 16 | uses: actions/checkout@v3 17 | 18 | - name: Log in to Docker Hub 19 | uses: docker/login-action@f054a8b539a109f9f41c372932f1ae047eff08c9 20 | with: 21 | username: ${{ secrets.DOCKER_USERNAME }} 22 | password: ${{ secrets.DOCKER_PASSWORD }} 23 | 24 | - name: Log in to the Container registry 25 | uses: docker/login-action@f054a8b539a109f9f41c372932f1ae047eff08c9 26 | with: 27 | registry: ghcr.io 28 | username: ${{ github.actor }} 29 | password: ${{ secrets.GITHUB_TOKEN }} 30 | 31 | - name: Extract metadata (tags, labels) for Docker 32 | id: meta 33 | uses: docker/metadata-action@98669ae865ea3cffbcbaa878cf57c20bbf1c6c38 34 | with: 35 | images: | 36 | pyprophet/pyprophet 37 | ghcr.io/${{ github.repository }} 38 | 39 | - name: Build and push Docker images 40 | uses: docker/build-push-action@ad44023a93711e3deb337508980b4b5e9bcdc5dc 41 | with: 42 | context: . 43 | push: true 44 | tags: ${{ steps.meta.outputs.tags }} 45 | labels: ${{ steps.meta.outputs.labels }} 46 | -------------------------------------------------------------------------------- /.github/workflows/pythonpublish.yml: -------------------------------------------------------------------------------- 1 | name: Upload Python Package 2 | 3 | on: 4 | release: 5 | types: [published] 6 | 7 | jobs: 8 | deploy: 9 | runs-on: ubuntu-latest 10 | steps: 11 | - uses: actions/checkout@v1 12 | - name: Set up Python 13 | uses: actions/setup-python@v1 14 | with: 15 | python-version: '3.x' 16 | - name: Install dependencies 17 | run: | 18 | python -m pip install --upgrade pip numpy cython 19 | pip install setuptools wheel twine 20 | - name: Build and publish 21 | env: 22 | TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} 23 | TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} 24 | run: | 25 | python setup.py sdist 26 | twine upload dist/* 27 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | *.py[cod] 2 | 3 | # C extensions 4 | *.so 5 | 6 | # Packages 7 | *.egg 8 | *.egg-info 9 | dist 10 | build 11 | eggs 12 | parts 13 | bin 14 | var 15 | sdist 16 | develop-eggs 17 | .installed.cfg 18 | lib 19 | lib64 20 | 21 | # Installer logs 22 | pip-log.txt 23 | 24 | # Unit test / coverage reports 25 | .coverage 26 | .tox 27 | nosetests.xml 28 | 29 | # Translations 30 | *.mo 31 | 32 | # Mr Developer 33 | .mr.developer.cfg 34 | .project 35 | .pydevproject 36 | 37 | # vim 38 | *.sw[opqrs] 39 | *~ 40 | 41 | # docs 42 | docs/_build/* 43 | docs/api/generated/* -------------------------------------------------------------------------------- /.readthedocs.yml: -------------------------------------------------------------------------------- 1 | version: 2 2 | build: 3 | os: ubuntu-22.04 4 | tools: 5 | python: "3.10" 6 | sphinx: 7 | configuration: docs/conf.py 8 | python: 9 | install: 10 | - requirements: requirements.txt 11 | - method: pip 12 | path: . 13 | extra_requirements: 14 | - docs -------------------------------------------------------------------------------- /.travis.yml: -------------------------------------------------------------------------------- 1 | sudo: required 2 | dist: xenial 3 | language: python 4 | python: 5 | - "3.6" 6 | - "3.7" 7 | - "3.8" 8 | before_install: 9 | - sudo apt-get update 10 | install: 11 | - pip install -U setuptools 12 | - pip install -U pytest 13 | - pip install -U pytest-regtest 14 | - pip install -U Click 15 | - pip install -U numpy 16 | - pip install -U scipy 17 | - pip install -U cython 18 | - pip install -U pandas 19 | - pip install -U scikit-learn 20 | - pip install -U numexpr 21 | - pip install -U statsmodels 22 | - pip install -U matplotlib 23 | - pip install -U networkx 24 | - travis_retry python setup.py develop 25 | script: 26 | - if [[ $TRAVIS_PYTHON_VERSION == 3.6* ]]; then py.test tests/; fi 27 | - if [[ $TRAVIS_PYTHON_VERSION == 3.7* ]]; then py.test tests/; fi 28 | - if [[ $TRAVIS_PYTHON_VERSION == 3.8* ]]; then py.test tests/; fi 29 | cache: pip 30 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | # PyProphet Dockerfile 2 | FROM python:3.9.1 3 | 4 | # install numpy & cython 5 | RUN pip install numpy cython 6 | 7 | # install duckdb and its extensions before pyprophet 8 | RUN pip install duckdb 9 | RUN pip install seaborn 10 | RUN pip install psutil 11 | 12 | 13 | # install PyProphet and dependencies 14 | ADD . /pyprophet 15 | WORKDIR /pyprophet 16 | # RUN python setup.py install 17 | RUN pip install . 18 | RUN python -c "import duckdb; conn = duckdb.connect(); conn.execute(\"INSTALL 'sqlite_scanner'\"); conn.execute(\"LOAD 'sqlite_scanner'\");" 19 | # import duckdb 20 | # conn = duckdb.connect() 21 | # conn.execute("INSTALL 'sqlite_scanner'") 22 | # conn.execute("LOAD 'sqlite_scanner'") 23 | WORKDIR / 24 | RUN rm -rf /pyprophet 25 | 26 | # Set final working directory, useful for when binding to a local mount 27 | WORKDIR /data/ 28 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Copyright (c) 2013, Uwe Schmitt, mineway GmbH All rights reserved. 2 | 3 | Redistribution and use in source and binary forms, with or without 4 | modification, are permitted provided that the following conditions are met: 5 | 6 | Redistributions of source code must retain the above copyright notice, this 7 | list of conditions and the following disclaimer. 8 | 9 | Redistributions in binary form must reproduce the above copyright notice, this 10 | list of conditions and the following disclaimer in the documentation and/or 11 | other materials provided with the distribution. 12 | 13 | Neither the name of the mineway GmbH nor the names of its contributors may be 14 | used to endorse or promote products derived from this software without specific 15 | prior written permission. 16 | 17 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND 18 | ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED 19 | WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 20 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE 21 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL 22 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR 23 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 24 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, 25 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 26 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 27 | -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | global-include *.pyx 2 | -------------------------------------------------------------------------------- /assets/PyProphet_Logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyProphet/pyprophet/54e18bd69c91ce7c2dcc8bd2ee0ecd853fe3d297/assets/PyProphet_Logo.png -------------------------------------------------------------------------------- /assets/PyProphet_Logo_transparent_bg.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyProphet/pyprophet/54e18bd69c91ce7c2dcc8bd2ee0ecd853fe3d297/assets/PyProphet_Logo_transparent_bg.png -------------------------------------------------------------------------------- /dist-scripts/create-manylinux.sh: -------------------------------------------------------------------------------- 1 | # Create manylinux packages from current release using the docker image 2 | # 3 | # based on https://github.com/pypa/python-manylinux-demo/blob/master/travis/build-wheels.sh 4 | # 5 | # Execute as: 6 | # 7 | # sudo docker run --net=host -v `pwd`:/data quay.io/pypa/manylinux1_x86_64 /bin/bash /data/create-manylinux.sh 8 | # 9 | 10 | git clone https://github.com/PyProphet/pyprophet.git 11 | cd pyprophet 12 | # Apply patch that sets dependency to matplotlib 1.5.3 which does not depend on 13 | # subprocess32 (which cannot be properly installed under CentOS 5). 14 | # See https://github.com/matplotlib/matplotlib/issues/8361 15 | # See https://github.com/google/python-subprocess32/issues/12 16 | git apply /data/manylinux.patch 17 | 18 | # Compile wheels 19 | for PYBIN in /opt/python/cp27*/bin /opt/python/cp3[4-9]*/bin; do 20 | "${PYBIN}/pip" install Cython 21 | "${PYBIN}/pip" install numpy 22 | "${PYBIN}/pip" install pandas 23 | "${PYBIN}/pip" wheel . -w wheelhouse_tmp/ 24 | done 25 | 26 | # Bundle external shared libraries into the wheels 27 | for whl in wheelhouse_tmp/pyprophet*.whl; do 28 | auditwheel repair "$whl" -w wheelhouse/ 29 | done 30 | 31 | # upload / store result 32 | mv wheelhouse /data/ 33 | 34 | 35 | -------------------------------------------------------------------------------- /dist-scripts/manylinux.patch: -------------------------------------------------------------------------------- 1 | diff --git a/setup.py b/setup.py 2 | index 23412c3..383f23d 100644 3 | --- a/setup.py 4 | +++ b/setup.py 5 | @@ -47,8 +47,7 @@ setup(name='pyprophet', 6 | "scipy >= 0.9.0", 7 | "numexpr >= 2.1", 8 | "scikit-learn >= 0.17", 9 | - "matplotlib", 10 | - "seaborn" 11 | + "matplotlib == 1.5.3" 12 | ], 13 | entry_points={ 14 | 'console_scripts': [ 15 | -------------------------------------------------------------------------------- /docs/Makefile: -------------------------------------------------------------------------------- 1 | # Minimal makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line, and also 5 | # from the environment for the first two. 6 | SPHINXOPTS ?= 7 | SPHINXBUILD ?= sphinx-build 8 | SOURCEDIR = source 9 | BUILDDIR = build 10 | 11 | # Put it first so that "make" without argument is like "make help". 12 | help: 13 | @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 14 | 15 | .PHONY: help Makefile 16 | 17 | # Catch-all target: route all unknown targets to Sphinx using the new 18 | # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). 19 | %: Makefile 20 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 21 | -------------------------------------------------------------------------------- /docs/README.md: -------------------------------------------------------------------------------- 1 | # Building locally 2 | 3 | To build the documentation locally 4 | 5 | ```bash 6 | # In the docs directory 7 | sphinx-build -b html ./ ./_build 8 | ``` -------------------------------------------------------------------------------- /docs/_static/custom.css: -------------------------------------------------------------------------------- 1 | /* Style for badge container */ 2 | .badge-container { 3 | margin: 1em 0; 4 | text-align: center; 5 | } 6 | 7 | .badge-container img { 8 | display: inline-block; 9 | margin: 0 5px 5px 0; 10 | height: 20px; 11 | } -------------------------------------------------------------------------------- /docs/api/config.rst: -------------------------------------------------------------------------------- 1 | Configuration Data Classes 2 | ===================================================== 3 | 4 | For scoring, IPF and levels context inference, PyProphet uses configuration data classes to manage settings and parameters. These classes are designed to be easily extensible and provide a structured way to handle configuration options. 5 | 6 | .. automodule:: pyprophet._base 7 | :no-members: 8 | :no-inherited-members: 9 | 10 | Abstract Base Classes 11 | --------------------- 12 | 13 | .. currentmodule:: pyprophet._base 14 | 15 | .. autosummary:: 16 | :nosignatures: 17 | :toctree: generated/ 18 | :template: class.rst 19 | 20 | BaseIOConfig 21 | 22 | 23 | Scoring Configuration 24 | ---------------------- 25 | 26 | .. automodule:: pyprophet._config 27 | :no-members: 28 | :no-inherited-members: 29 | 30 | .. currentmodule:: pyprophet._config 31 | 32 | .. autosummary:: 33 | :nosignatures: 34 | :toctree: generated/ 35 | :template: class.rst 36 | 37 | RunnerIOConfig 38 | RunnerConfig 39 | ErrorEstimationConfig 40 | 41 | IPF Configuration 42 | ----------------- 43 | 44 | .. currentmodule:: pyprophet._config 45 | 46 | .. autosummary:: 47 | :nosignatures: 48 | :toctree: generated/ 49 | :template: class.rst 50 | 51 | IPFIOConfig 52 | 53 | Levels Context Configuration 54 | ---------------------------- 55 | 56 | .. currentmodule:: pyprophet._config 57 | 58 | .. autosummary:: 59 | :nosignatures: 60 | :toctree: generated/ 61 | :template: class.rst 62 | 63 | LevelContextIOConfig 64 | 65 | Export Configuration 66 | ---------------------- 67 | 68 | .. currentmodule:: pyprophet._config 69 | 70 | .. autosummary:: 71 | :nosignatures: 72 | :toctree: generated/ 73 | :template: class.rst 74 | 75 | ExportIOConfig -------------------------------------------------------------------------------- /docs/api/index.rst: -------------------------------------------------------------------------------- 1 | API Reference 2 | ============= 3 | 4 | .. toctree:: 5 | :maxdepth: 2 6 | 7 | io 8 | config 9 | scoring 10 | ipf 11 | levels_context -------------------------------------------------------------------------------- /docs/api/io.rst: -------------------------------------------------------------------------------- 1 | IO: Reading and Writing Data 2 | ===================================================== 3 | 4 | 5 | 6 | .. automodule:: pyprophet.io 7 | :members: 8 | :inherited-members: 9 | 10 | Abstract Base Classes 11 | ---------------------- 12 | 13 | .. currentmodule:: pyprophet.io 14 | 15 | .. autosummary:: 16 | :nosignatures: 17 | :toctree: generated/ 18 | :template: class.rst 19 | 20 | _base.BaseReader 21 | _base.BaseWriter 22 | _base.BaseOSWReader 23 | _base.BaseOSWWriter 24 | _base.BaseParquetReader 25 | _base.BaseParquetWriter 26 | _base.BaseSplitParquetReader 27 | _base.BaseSplitParquetWriter 28 | dispatcher.ReaderDispatcher 29 | dispatcher.WriterDispatcher 30 | 31 | These submodules provide specific implementations for reading and writing data for specific algorithms. 32 | 33 | Scoring 34 | ---------------------- 35 | 36 | .. automodule:: pyprophet.io.scoring 37 | :no-members: 38 | :no-inherited-members: 39 | 40 | .. currentmodule:: pyprophet.io.scoring 41 | 42 | .. autosummary:: 43 | :nosignatures: 44 | :toctree: generated/ 45 | :template: class.rst 46 | 47 | osw.OSWReader 48 | osw.OSWWriter 49 | parquet.ParquetReader 50 | parquet.ParquetWriter 51 | split_parquet.SplitParquetReader 52 | split_parquet.SplitParquetWriter 53 | tsv.TSVReader 54 | tsv.TSVWriter 55 | 56 | IPF 57 | ---------------------- 58 | 59 | .. automodule:: pyprophet.io.ipf 60 | :no-members: 61 | :no-inherited-members: 62 | 63 | .. currentmodule:: pyprophet.io.ipf 64 | 65 | .. autosummary:: 66 | :nosignatures: 67 | :toctree: generated/ 68 | :template: class.rst 69 | 70 | osw.OSWReader 71 | osw.OSWWriter 72 | parquet.ParquetReader 73 | parquet.ParquetWriter 74 | split_parquet.SplitParquetReader 75 | split_parquet.SplitParquetWriter 76 | 77 | Levels Context 78 | ---------------------- 79 | 80 | .. automodule:: pyprophet.io.levels_context 81 | :no-members: 82 | :no-inherited-members: 83 | 84 | .. currentmodule:: pyprophet.io.levels_context 85 | 86 | .. autosummary:: 87 | :nosignatures: 88 | :toctree: generated/ 89 | :template: class.rst 90 | 91 | osw.OSWReader 92 | osw.OSWWriter 93 | parquet.ParquetReader 94 | parquet.ParquetWriter 95 | split_parquet.SplitParquetReader 96 | split_parquet.SplitParquetWriter 97 | 98 | Export 99 | ---------------------- 100 | 101 | .. automodule:: pyprophet.io.export 102 | :no-members: 103 | :no-inherited-members: 104 | 105 | .. currentmodule:: pyprophet.io.export 106 | 107 | .. autosummary:: 108 | :nosignatures: 109 | :toctree: generated/ 110 | :template: class.rst 111 | 112 | osw.OSWReader 113 | osw.OSWWriter 114 | parquet.ParquetReader 115 | parquet.ParquetWriter 116 | split_parquet.SplitParquetReader 117 | split_parquet.SplitParquetWriter -------------------------------------------------------------------------------- /docs/api/ipf.rst: -------------------------------------------------------------------------------- 1 | Inference of Peptidoforms Documentation 2 | ========================== 3 | 4 | .. automodule:: pyprophet.ipf 5 | :no-members: 6 | :no-inherited-members: 7 | 8 | .. currentmodule:: pyprophet.ipf 9 | 10 | .. autosummary:: 11 | :nosignatures: 12 | :toctree: generated/ 13 | :template: class.rst 14 | 15 | infer_peptidoforms 16 | peptidoform_inference 17 | precursor_inference 18 | apply_bm 19 | prepare_transition_bm 20 | transfer_confident_evidence_across_runs 21 | prepare_precursor_bm 22 | compute_model_fdr -------------------------------------------------------------------------------- /docs/api/levels_context.rst: -------------------------------------------------------------------------------- 1 | Context and FDR Estimation Documentation 2 | ======================================== 3 | 4 | .. automodule:: pyprophet.levels_contexts 5 | :no-members: 6 | :no-inherited-members: 7 | 8 | .. currentmodule:: pyprophet.levels_contexts 9 | 10 | .. autosummary:: 11 | :nosignatures: 12 | :toctree: generated/ 13 | :template: class.rst 14 | 15 | statistics_report 16 | infer_peptides 17 | infer_glycopeptides 18 | infer_proteins 19 | infer_genes -------------------------------------------------------------------------------- /docs/api/scoring.rst: -------------------------------------------------------------------------------- 1 | Semi-Supervised Scoring Documentation 2 | ========================== 3 | 4 | .. automodule:: pyprophet.scoring 5 | :no-members: 6 | :no-inherited-members: 7 | 8 | .. currentmodule:: pyprophet.scoring 9 | 10 | .. autosummary:: 11 | :nosignatures: 12 | :toctree: generated/ 13 | :template: class.rst 14 | 15 | scoring 16 | 17 | Runner 18 | ---------------- 19 | 20 | .. currentmodule:: pyprophet.scoring.runner 21 | 22 | .. autosummary:: 23 | :nosignatures: 24 | :toctree: generated/ 25 | :template: class.rst 26 | 27 | PyProphetRunner 28 | PyProphetLearner 29 | PyProphetWeightApplier 30 | 31 | PyProphet 32 | ---------------- 33 | 34 | .. currentmodule:: pyprophet.scoring.pyprophet 35 | 36 | .. autosummary:: 37 | :nosignatures: 38 | :toctree: generated/ 39 | :template: class.rst 40 | 41 | PyProphet 42 | Scorer 43 | 44 | Semi-Supervised 45 | ---------------- 46 | 47 | .. currentmodule:: pyprophet.scoring.semi_supervised 48 | 49 | .. autosummary:: 50 | :nosignatures: 51 | :toctree: generated/ 52 | :template: class.rst 53 | 54 | AbstractSemiSupervisedLearner 55 | StandardSemiSupervisedLearner 56 | 57 | Classifiers 58 | ---------------- 59 | 60 | .. currentmodule:: pyprophet.scoring.classifiers 61 | 62 | .. autosummary:: 63 | :nosignatures: 64 | :toctree: generated/ 65 | :template: class.rst 66 | 67 | AbstractLearner 68 | LinearLearner 69 | LDALearner 70 | SVMLearner 71 | XGBLearner 72 | 73 | Data Handling 74 | ---------------- 75 | 76 | .. currentmodule:: pyprophet.scoring.data_handling 77 | 78 | .. autosummary:: 79 | :nosignatures: 80 | :toctree: generated/ 81 | :template: class.rst 82 | 83 | Experiment 84 | prepare_data_table 85 | cleanup_and_check 86 | check_for_unique_blocks 87 | update_chosen_main_score_in_table 88 | use_metabolomics_scores -------------------------------------------------------------------------------- /docs/index.rst: -------------------------------------------------------------------------------- 1 | .. PyProphet documentation master file 2 | 3 | .. raw:: html 4 | 5 |
6 | 7 | PyProphet 8 | ========= 9 | 10 | .. raw:: html 11 | 12 |
13 | 14 | .. image:: https://raw.githubusercontent.com/PyProphet/pyprophet/master/assets/PyProphet_Logo.png 15 | :width: 300px 16 | :align: center 17 | :target: https://github.com/PyProphet/pyprophet 18 | :alt: PyProphet Logo 19 | 20 | .. container:: badge-container 21 | 22 | .. image:: https://github.com/PyProphet/pyprophet/actions/workflows/ci.yml/badge.svg?branch=master 23 | :target: https://github.com/PyProphet/pyprophet/actions/workflows/ci.yml 24 | :alt: Continuous Integration 25 | 26 | .. image:: https://www.openhub.net/p/PyProphet/widgets/project_thin_badge.gif 27 | :target: https://www.openhub.net/p/PyProphet 28 | :alt: Project Stats 29 | 30 | .. image:: https://img.shields.io/pypi/pyversions/pyprophet 31 | :target: https://pypi.org/project/pyprophet/ 32 | :alt: Python Versions 33 | 34 | .. image:: https://img.shields.io/pypi/v/pyprophet 35 | :target: https://pypi.org/project/pyprophet/ 36 | :alt: PyPI Version 37 | 38 | .. image:: https://img.shields.io/docker/v/pyprophet/pyprophet?label=Docker 39 | :target: https://hub.docker.com/r/pyprophet/pyprophet 40 | :alt: Docker Version 41 | 42 | .. image:: https://img.shields.io/readthedocs/pyprophet/latest 43 | :target: https://pyprophet.readthedocs.io/en/latest/index.html 44 | :alt: Documentation Status 45 | 46 | PyProphet is a Python package designed for scoring and post-processing of DIA-MS data, particularly in the context of OpenSWATH and related workflows. It provides tools for scoring, inference of levels context, and exporting results in various formats. For more information on the general usage of pyprophet, consult the `OpenSWATH website `_. 47 | 48 | .. toctree:: 49 | :maxdepth: 2 50 | 51 | user_guide/index.rst 52 | file_formats 53 | cli 54 | api/index.rst 55 | 56 | -------------------------------------------------------------------------------- /docs/templates/class.rst: -------------------------------------------------------------------------------- 1 | .. 2 | The empty line below should not be removed. It is added such that the `rst_prolog` 3 | is added before the :mod: directive. Otherwise, the rendering will show as a 4 | paragraph instead of a header. 5 | 6 | {{objname}} 7 | {{ underline }}============== 8 | 9 | .. currentmodule:: {{ module }} 10 | 11 | .. autoclass:: {{ objname }} 12 | :members: 13 | :special-members: 14 | :private-members: 15 | :show-inheritance: 16 | 17 | -------------------------------------------------------------------------------- /docs/user_guide/file_conversion.rst: -------------------------------------------------------------------------------- 1 | File Conversion 2 | ========================================= 3 | 4 | 5 | Feature File Conversion 6 | ----------------------------------------- 7 | 8 | The output from OpenSWATH is usually an sqlite-based (.osw) file, which is great for storing different levels of information and data. However, if you have a large input peptide query parameter and a lot of data, the OSW file can become very large and unwieldy. In such cases, you may benefit from converting the OSW file to a parquet file, which is a columnar storage format that is more efficient for large datasets. This can be acheived using with pyprophet using the following command: 9 | 10 | .. code-block:: bash 11 | 12 | $ pyprophet export parquet --in input.osw --out output.parquet 13 | 14 | 15 | This will convert the OSW file to a single parquet file, containing precursor metadata and feature data, as well as transition metadata and feature data. See the :ref:`Parquet file format documentation ` for more information on the structure of the parquet file. 16 | 17 | If your OSW file is really large, you may want to split the precursor and transition data into separate parquet files. This can be done using the following command: 18 | 19 | .. code-block:: bash 20 | 21 | $ pyprophet export parquet --in input.osw --out output.oswpq --split_transition_data 22 | 23 | This will create two parquet files in the `output.oswpq` directory: `precursors_features.parquet` and `transition_features.parquet`. The `precursors_features.parquet` file contains precursor metadata and feature data, while the `transition_features.parquet` file contains transition metadata and feature data. This can be useful if you want to work with the precursor and transition data separately. The transition data will generally be much larger than the precursor data, so splitting the data can help with performance and memory usage. See the :ref:`Split parquet file format documentation ` for more information on the structure of the parquet files. 24 | 25 | If you have multiple runs in your OSW file, you can further split the data by run. This can be done using the following command: 26 | 27 | .. code-block:: bash 28 | 29 | $ pyprophet export parquet --in input.osw --out output.oswpqd --split_transition_data --split_runs 30 | 31 | This will create a directory for each run in the `output.oswpqd` directory, with the precursor and transition data split into separate parquet files for each run. See the :ref:`Split parquet file format documentation ` for more information on the structure of the parquet files. 32 | 33 | 34 | Extracted Ion Chromatogram File Conversion 35 | ------------------------------------------ 36 | 37 | If you run OpenSWATH and output an XIC file, it will typically be in an sqlite-based format (.sqMass). Similar to the OSW file, this file can become quite large if you have a lot of data. PyProphet provides a way to convert this XIC file to a parquet file, which can be more efficient for large datasets. This can be done using the following command: 38 | 39 | .. code-block:: bash 40 | 41 | $ pyprophet export parquet --in input.sqMass --out output.parquet -------------------------------------------------------------------------------- /docs/user_guide/index.rst: -------------------------------------------------------------------------------- 1 | User Guide 2 | ============= 3 | 4 | Some useful guides to help you get started with some functionalities of PyProphet. 5 | 6 | .. toctree:: 7 | :maxdepth: 2 8 | 9 | file_conversion 10 | pyprophet_workflow -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [build-system] 2 | requires = ["setuptools", "wheel", "numpy", "cython"] # Dependencies needed to build the package 3 | build-backend = "setuptools.build_meta" 4 | 5 | [project] 6 | name = "pyprophet" 7 | version = "3.0.0" 8 | description = "PyProphet: Semi-supervised learning and scoring of OpenSWATH results." 9 | readme = { file = "README.md", content-type = "text/markdown" } 10 | license = { text = "BSD" } 11 | authors = [{ name = "The PyProphet Developers", email = "rocksportrocker@gmail.com" }] 12 | classifiers = [ 13 | "Development Status :: 3 - Alpha", 14 | "Environment :: Console", 15 | "Intended Audience :: Science/Research", 16 | "License :: OSI Approved :: BSD License", 17 | "Operating System :: OS Independent", 18 | "Topic :: Scientific/Engineering :: Bio-Informatics", 19 | "Topic :: Scientific/Engineering :: Chemistry" 20 | ] 21 | keywords = ["bioinformatics", "openSWATH", "mass spectrometry"] 22 | requires-python = ">=3.9, <=3.13" 23 | 24 | # Dependencies required for runtime 25 | dependencies = [ 26 | "Click", 27 | "loguru", 28 | "duckdb", 29 | "duckdb-extensions", 30 | "duckdb-extension-sqlite-scanner", 31 | "numpy >= 1.26.4", 32 | "scipy", 33 | "pandas >= 2.0", 34 | "polars >= 1.28.1", 35 | "cython", 36 | "numexpr >= 2.10.1", 37 | "scikit-learn >= 1.5", 38 | "xgboost >= 2.1.4", 39 | "statsmodels >= 0.8.0", 40 | "matplotlib", 41 | "seaborn", 42 | "tabulate", 43 | "pyarrow", 44 | "pypdf", 45 | "psutil", 46 | "pyopenms" 47 | ] 48 | 49 | # Optional dependencies 50 | [project.optional-dependencies] 51 | testing = ["pytest", "pytest-regtest", "pytest-xdist"] 52 | docs = ["sphinx", "sphinx-copybutton", "sphinx_rtd_theme", "pydata_sphinx_theme", "sphinx-click"] 53 | 54 | # Define console entry points 55 | [project.scripts] 56 | pyprophet = "pyprophet.main:cli" 57 | 58 | [tool.setuptools] 59 | packages = { find = { exclude = ["ez_setup", "examples", "tests"] } } 60 | include-package-data = true 61 | zip-safe = false 62 | -------------------------------------------------------------------------------- /pyprophet/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyProphet/pyprophet/54e18bd69c91ce7c2dcc8bd2ee0ecd853fe3d297/pyprophet/__init__.py -------------------------------------------------------------------------------- /pyprophet/cli/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyProphet/pyprophet/54e18bd69c91ce7c2dcc8bd2ee0ecd853fe3d297/pyprophet/cli/__init__.py -------------------------------------------------------------------------------- /pyprophet/export/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyProphet/pyprophet/54e18bd69c91ce7c2dcc8bd2ee0ecd853fe3d297/pyprophet/export/__init__.py -------------------------------------------------------------------------------- /pyprophet/glyco/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyProphet/pyprophet/54e18bd69c91ce7c2dcc8bd2ee0ecd853fe3d297/pyprophet/glyco/__init__.py -------------------------------------------------------------------------------- /pyprophet/glyco/pepmass/__init__.py: -------------------------------------------------------------------------------- 1 | from .pepmass import PeptideMassCalculator 2 | from .modmass import ModifiedPeptideMassCalculator 3 | from .glycomass import GlycoPeptideMassCalculator -------------------------------------------------------------------------------- /pyprophet/io/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | The `io` module provides tools and utilities for handling input and output operations 3 | in PyProphet. It supports various file formats, including SQLite (OSW), 4 | Parquet, Split Parquet, and TSV, and provides functionality for reading, writing, 5 | and validating data. 6 | 7 | Submodules: 8 | ----------- 9 | - `util`: Contains utility functions for file validation, schema inspection, and logging. 10 | - `dispatcher`: Provides dispatcher classes for routing I/O configurations to the appropriate 11 | reader and writer implementations based on file type and context. 12 | - `_base`: Defines abstract base classes and utility methods for implementing custom readers 13 | and writers for different data formats. 14 | 15 | Dependencies: 16 | ------------- 17 | - `pandas` 18 | - `pyarrow` 19 | - `duckdb` 20 | - `sqlite3` 21 | - `loguru` 22 | - `click` 23 | 24 | """ 25 | -------------------------------------------------------------------------------- /pyprophet/io/export/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyProphet/pyprophet/54e18bd69c91ce7c2dcc8bd2ee0ecd853fe3d297/pyprophet/io/export/__init__.py -------------------------------------------------------------------------------- /pyprophet/io/ipf/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyProphet/pyprophet/54e18bd69c91ce7c2dcc8bd2ee0ecd853fe3d297/pyprophet/io/ipf/__init__.py -------------------------------------------------------------------------------- /pyprophet/io/ipf/tsv.py: -------------------------------------------------------------------------------- 1 | from typing import Literal 2 | import pandas as pd 3 | import click 4 | 5 | from .._base import BaseReader, BaseWriter 6 | from ..._config import IPFIOConfig 7 | 8 | 9 | class TSVReader(BaseReader): 10 | """ 11 | Class for reading and processing data from OpenSWATH results stored in a tsv format. 12 | 13 | The TSVReader class provides methods to read different levels of data from the split parquet files and process it accordingly. 14 | It supports reading data for semi-supervised learning, IPF analysis, context level analysis. 15 | 16 | Attributes: 17 | infile (str): Input file path. 18 | outfile (str): Output file path. 19 | classifier (str): Classifier used for semi-supervised learning. 20 | level (str): Level used in semi-supervised learning (e.g., 'ms1', 'ms2', 'ms1ms2', 'transition', 'alignment'), or context level used peptide/protein/gene inference (e.g., 'global', 'experiment-wide', 'run-specific'). 21 | glyco (bool): Flag indicating whether analysis is glycoform-specific. 22 | 23 | Methods: 24 | read(): Read data from the input file based on the alogorithm. 25 | """ 26 | 27 | def __init__(self, config: IPFIOConfig): 28 | super().__init__(config) 29 | 30 | def read( 31 | self, level: Literal["peakgroup_precursor", "transition", "alignment"] 32 | ) -> pd.DataFrame: 33 | raise NotImplementedError("IPF read method is not implemented for TSVReader") 34 | 35 | 36 | class TSVWriter(BaseWriter): 37 | """ 38 | Class for writing OpenSWATH results to a tsv format. 39 | 40 | Attributes: 41 | infile (str): Input file path. 42 | outfile (str): Output file path. 43 | classifier (str): Classifier used for semi-supervised learning. 44 | level (str): Level used in semi-supervised learning (e.g., 'ms1', 'ms2', 'ms1ms2', 'transition', 'alignment'), or context level used peptide/protein/gene inference (e.g., 'global', 'experiment-wide', 'run-specific'). 45 | glyco (bool): Flag indicating whether analysis is glycoform-specific. 46 | 47 | Methods: 48 | save_results(result, pi0): Save the results to the output file based on the module using this class. 49 | save_weights(weights): Save the weights to the output file. 50 | """ 51 | 52 | def __init__(self, config: IPFIOConfig): 53 | super().__init__(config) 54 | 55 | def save_results(self, result): 56 | raise NotImplementedError( 57 | "IPF save_results method is not implemented for TSVWriter" 58 | ) 59 | -------------------------------------------------------------------------------- /pyprophet/io/levels_context/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyProphet/pyprophet/54e18bd69c91ce7c2dcc8bd2ee0ecd853fe3d297/pyprophet/io/levels_context/__init__.py -------------------------------------------------------------------------------- /pyprophet/io/scoring/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyProphet/pyprophet/54e18bd69c91ce7c2dcc8bd2ee0ecd853fe3d297/pyprophet/io/scoring/__init__.py -------------------------------------------------------------------------------- /pyprophet/scoring/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | This module provides the main tools for statistical scoring, error estimation, and hypothesis testing 3 | in targeted proteomics and glycoproteomics workflows. It includes modules for semi-supervised 4 | learning, feature scaling, classifier integration, and context-specific inference. 5 | 6 | Submodules: 7 | ----------- 8 | - `data_handling`: Utilities for handling and processing data, including feature scaling, 9 | ranking, and validation. 10 | - `classifiers`: Implements various classifiers (e.g., LDA, SVM, XGBoost) for scoring. 11 | - `semi_supervised`: Implements semi-supervised learning workflows for iterative scoring. 12 | - `runner`: Defines workflows for running PyProphet, including learning and weight application. 13 | - `pyprophet`: Core functionality for orchestrating scoring and error estimation workflows. 14 | 15 | Dependencies: 16 | ------------- 17 | - `numpy` 18 | - `pandas` 19 | - `scikit-learn` 20 | - `xgboost` 21 | - `loguru` 22 | - `click` 23 | """ 24 | -------------------------------------------------------------------------------- /pyprophet/scoring/optimized.py: -------------------------------------------------------------------------------- 1 | from ._optimized import * 2 | -------------------------------------------------------------------------------- /sandbox/compare.py: -------------------------------------------------------------------------------- 1 | # encoding: utf-8 2 | from __future__ import print_function 3 | 4 | import math 5 | 6 | import numpy as np 7 | import pandas as pd 8 | 9 | """ 10 | We compare the implementaion of FDR in pyprophet to the one from Johan Telemann. 11 | 12 | """ 13 | 14 | p_values = np.loadtxt("p_values.txt") 15 | p_values = np.sort((p_values))[::-1] 16 | 17 | from pyprophet.stats import get_error_table_from_pvalues_new 18 | 19 | 20 | def calc(pvalues, lamb): 21 | """ meaning pvalues presorted i descending order""" 22 | 23 | m = len(pvalues) 24 | pi0 = (pvalues > lamb).sum() / ((1 - lamb)*m) 25 | 26 | pFDR = np.ones(m) 27 | print("pFDR y Pr fastPow") 28 | for i in range(m): 29 | y = pvalues[i] 30 | Pr = max(1, m - i) / float(m) 31 | pFDR[i] = (pi0 * y) / (Pr * (1 - math.pow(1-y, m))) 32 | print(i, pFDR[i], y, Pr, 1.0 - math.pow(1-y, m)) 33 | 34 | 35 | num_null = pi0*m 36 | num_alt = m - num_null 37 | num_negs = np.array(range(m)) 38 | num_pos = m - num_negs 39 | pp = num_pos / float(m) 40 | 41 | qvalues = np.ones(m) 42 | qvalues[0] = pFDR[0] 43 | for i in range(m-1): 44 | qvalues[i+1] = min(qvalues[i], pFDR[i+1]) 45 | 46 | sens = ((1.0 - qvalues) * num_pos) / num_alt 47 | sens[sens > 1.0] = 1.0 48 | 49 | df = pd.DataFrame(dict( 50 | pvalue=pvalues, 51 | qvalue=qvalues, 52 | FDR=pFDR, 53 | percentile_positive=pp, 54 | sens=sens 55 | )) 56 | 57 | df["svalue"] = df.sens[::-1].cummax()[::-1] 58 | 59 | return df, num_null, m 60 | 61 | errstat = get_error_table_from_pvalues_new(p_values, 0.4, True) 62 | fdr_pyprophet = errstat.df["FDR"] 63 | df, __, __ = calc(p_values, 0.4) 64 | fdr_storey = df["FDR"] 65 | fdrs = pd.DataFrame(dict(fdr_pp=fdr_pyprophet, fdr_storey=fdr_storey)) 66 | print(fdrs[:34]) 67 | print(fdrs[:]) 68 | -------------------------------------------------------------------------------- /sandbox/dummyOSWScoredData.osw: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyProphet/pyprophet/54e18bd69c91ce7c2dcc8bd2ee0ecd853fe3d297/sandbox/dummyOSWScoredData.osw -------------------------------------------------------------------------------- /sandbox/fakeLib.tsv: -------------------------------------------------------------------------------- 1 | PrecursorMz ProductMz LibraryIntensity NormalizedRetentionTime ProteinId PeptideSequence ModifiedPeptideSequence PrecursorCharge FragmentType FragmentSeriesNumber ProductCharge GeneName LibraryDriftTime Decoy 2 | 100 101 101 10 ProtY YYYYYYYYYYYK YYYYYYYYYYYK 2 b 1 2 Y 10 0 3 | 100 102 201 10 ProtY YYYYYYYYYYYK YYYYYYYYYYYK 2 y 2 2 Y 10 0 4 | 100 103 301 10 ProtY YYYYYYYYYYYK YYYYYYYYYYYK 2 b 3 2 Y 10 0 5 | 200 201 102 20 ProtY YYYYYR YYYYYR 2 b 1 2 Y 20 0 6 | 200 202 202 20 ProtY YYYYYR YYYYYR 2 y 2 2 Y 20 0 7 | 200 203 302 20 ProtY YYYYYR YYYYYR 2 b 3 2 Y 20 0 8 | 220 221 122 20 ProtY YYYYYR YYYYYR 3 b 1 2 Y 20 0 9 | 220 222 222 20 ProtY YYYYYR YYYYYR 3 y 2 2 Y 20 0 10 | 400 401 104 40 ProtG GGGGGGGGGGR GGGGGGGGGGR 4 b 1 2 G 40 0 11 | 400 402 204 40 ProtG GGGGGGGGGGR GGGGGGGGGGR 4 y 2 2 G 40 0 12 | 400 403 403 40 ProtG GGGGGGGGGGR GGGGGGGGGGR 4 b 3 2 G 40 0 13 | 400 404 404 40 ProtG GGGGGGGGGGR GGGGGGGGGGR 4 y 4 2 G 40 0 14 | 500 501 105 50 ProtT TTTTTTTR TTTTTTTR 2 b 1 2 T 50 0 15 | 500 502 205 50 ProtT TTTTTTTR TTTTTTTR 2 y 2 2 T 50 0 16 | 500 503 305 50 ProtT TTTTTTTR TTTTTTTR 2 b 3 2 T 50 0 17 | 600 601 106 60 ProtT TTTTTTTTTTTTK TTTTTTTTTTTTK 2 b 1 2 T 60 0 18 | 600 602 206 60 ProtT TTTTTTTTTTTTK TTTTTTTTTTTTK 2 y 2 2 T 60 0 19 | 700 701 107 70 ProtT TTR TTR 3 b 1 3 T 70 0 20 | 700 702 207 70 ProtT TTR TTR 3 y 2 3 T 70 0 21 | 700 703 307 70 ProtT TTR TTR 3 b 3 3 T 70 0 22 | 800 801 808 80 Decoy_ProtT TTK TTK 3 b 1 3 Decoy_T 80 1 23 | 800 802 808 80 Decoy_ProtT TTK TTK 3 y 2 3 Decoy_T 80 1 24 | 800 803 808 80 Decoy_ProtT TTK TTK 3 b 3 3 Decoy_T 80 1 25 | -------------------------------------------------------------------------------- /sandbox/generate_storey_ref_data.R: -------------------------------------------------------------------------------- 1 | library(qvalue) 2 | data(hedenfalk) 3 | p <- hedenfalk$p 4 | pi0<-pi0est(p)$pi0 # 0.669926 5 | 6 | # qvalue validation 7 | qv_q<-qvalue(p) 8 | qv_q_pfdr<-qvalue(p, pfdr=TRUE) 9 | 10 | qvalue_ref_data<-data.frame("p"=p, "q_default"=qv_q$qvalues, "q_pfdr"=qv_q_pfdr$qvalues) 11 | write.csv(qvalue_ref_data, file="test_qvalue_ref_data.csv", row.names=FALSE) 12 | 13 | # pi0est validation 14 | nullRatio <- pi0est(p)$pi0 15 | nullRatioS <- pi0est(p, lambda=seq(0.40, 0.95, 0.05), smooth.log.pi0="TRUE")$pi0 16 | nullRatioM <- pi0est(p, pi0.method="bootstrap")$pi0 17 | 18 | # lfdr validation 19 | lfdr_default <- lfdr(p,pi0) 20 | lfdr_monotone_false <- lfdr(p, pi0, monotone=FALSE) 21 | lfdr_transf_logit <- lfdr(p, pi0, transf="logit") 22 | lfdr_eps <- lfdr(p, pi0, eps=10^-2) 23 | 24 | lfdr_ref_data<-data.frame("p"=p, "lfdr_default"=lfdr_default, "lfdr_monotone_false"=lfdr_monotone_false, "lfdr_transf_logit"=lfdr_transf_logit, "lfdr_eps"=lfdr_eps) 25 | write.csv(lfdr_ref_data, file="test_lfdr_ref_data.csv", row.names=FALSE) 26 | -------------------------------------------------------------------------------- /sandbox/ludovic.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import pylab 3 | import pyprophet.stats as stats 4 | import random 5 | import numpy as np 6 | 7 | path = sys.argv[1] 8 | 9 | lines = open(path).readlines() 10 | header = [h.lower() for h in lines[0].split()] 11 | 12 | data_lines = [line.split() for line in lines[1:]] 13 | 14 | if header == ["decoy", "score"]: 15 | data_lines = [(np.log(float(s.strip())), l.strip()) for (l, s) in data_lines] 16 | targets = [s for (s, l) in data_lines if l.upper() == "FALSE"] 17 | decoys = [s for (s, l) in data_lines if l.upper() == "TRUE"] 18 | invalid = [(s, l) for (s, l) in data_lines if l.upper() not in ("FALSE", "TRUE")] 19 | elif header == ["score", "decoy"]: 20 | data_lines = [(np.log(float(s.strip())), l.strip()) for (s, l) in data_lines] 21 | targets = [s for (s, l) in data_lines if l.upper() == "FALSE"] 22 | decoys = [s for (s, l) in data_lines if l.upper() == "TRUE"] 23 | invalid = [(s, l) for (s, l) in data_lines if l.upper() not in ("FALSE", "TRUE")] 24 | else: 25 | raise NotImplementedError("this kind of header %r not supported yet" % header) 26 | 27 | 28 | print 29 | print "COUNTS" 30 | print 31 | print "targets =", len(targets) 32 | print "decoys =", len(decoys) 33 | print "targests + decoys =", len(targets) + len(decoys) 34 | print "data lines =", len(data_lines) 35 | print "invalid lines =", len(invalid) 36 | 37 | df, __, __ = stats.get_error_stat_from_null(targets, decoys, 0.4) 38 | 39 | errt= stats.summary_err_table(df, (0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, .08, .09, .1, 40 | .15, .2, .3, .4, .5)) 41 | 42 | errt["exp_cutoff"] = np.exp(errt.cutoff.values.astype(float)) 43 | 44 | print 45 | print "STATS" 46 | print 47 | print errt.to_string() # avoid line break 48 | -------------------------------------------------------------------------------- /sandbox/reproduce_r.sh: -------------------------------------------------------------------------------- 1 | NUM_XVAL=20 2 | NUM_FRACTION=0.5 3 | NUM_SEMISV_ITER=5 4 | 5 | 6 | pyprophet --xeval.num_processes=10 \ 7 | --xeval.num_iter=$NUM_XVAL\ 8 | --xeval.fraction=$NUM_FRACTION\ 9 | --target.overwrite=1\ 10 | --semi_supervised_learner.num_iter=$NUM_SEMISV_ITER\ 11 | --is_test=0\ 12 | ../orig_r_code/testfile.csv 13 | 14 | 15 | cp ../orig_r_code/testfile_with_dscore.csv ../orig_r_code/testfile_with_dscore2.csv 16 | 17 | pyprophet --xeval.num_processes=1 \ 18 | --xeval.num_iter=$NUM_XVAL\ 19 | --xeval.fraction=$NUM_FRACTION\ 20 | --target.overwrite=1\ 21 | --semi_supervised_learner.num_iter=$NUM_SEMISV_ITER\ 22 | --is_test=0\ 23 | ../orig_r_code/testfile.csv 24 | -------------------------------------------------------------------------------- /setup.cfg: -------------------------------------------------------------------------------- 1 | [nosetests] 2 | logging-level = INFO 3 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup, Extension, find_packages 2 | from Cython.Build import cythonize 3 | import numpy 4 | 5 | try: 6 | from Cython.Build import cythonize 7 | except ImportError: 8 | use_cython = False 9 | else: 10 | use_cython = True 11 | 12 | ext_modules = [] 13 | if use_cython: 14 | ext_modules += [ 15 | Extension("pyprophet.scoring._optimized", ["pyprophet/scoring/_optimized.pyx"]) 16 | ] 17 | ext_modules = cythonize(ext_modules) 18 | else: 19 | ext_modules += [ 20 | Extension("pyprophet.scoring._optimized", ["pyprophet/scoring/_optimized.c"]) 21 | ] 22 | 23 | setup(name="pyprophet", ext_modules=ext_modules, include_dirs=[numpy.get_include()]) 24 | -------------------------------------------------------------------------------- /tests/.gitignore: -------------------------------------------------------------------------------- 1 | *.csv 2 | *.pdf 3 | *.bin 4 | -------------------------------------------------------------------------------- /tests/README.md: -------------------------------------------------------------------------------- 1 | README 2 | ====== 3 | 4 | The scripts should be run with `py.test` (>=3.4.1) with installed plugin `pytest-regest` 5 | (>=1.0.14 see https://pypi.python.org/pypi/pytest-regtest). 6 | 7 | The plugin allows recording of approved output so that later test runs will check if 8 | the output is still the same. It is simple to use as you can see in `test_via_regression.py`. 9 | 10 | In order to record output you have to use the `regtest` fixture like in the following example. 11 | This `regtest` behaves like a file handle, so you can write to it as usual: 12 | 13 | ```` 14 | def test_0(regtest): 15 | print >> regtest, "this is the recorded output" 16 | ```` 17 | 18 | If you now create a new test function `test_0` in a file `test_xyz.py`, first run 19 | 20 | ```` 21 | $ py.test tests/test_xyz.py::test_0 22 | ```` 23 | 24 | which will show you the yet not approved output. You can approve this output using 25 | 26 | ```` 27 | $ py.test --regtest-reset tests/test_xyz.py::test_0 28 | ```` 29 | 30 | Which will create a file in `tests/_regtest_outputs/test_xyz.test_0.out` which you should not forget to 31 | commit with `git`. 32 | 33 | 34 | Later runs like 35 | ```` 36 | $ py.test tests/test_xyz.py 37 | ```` 38 | 39 | will then check if the recorded output is still the same. 40 | 41 | If you want to only run certain tests and certain combination of fixture paramaters, you can use the `-k` option of `py.test`: 42 | 43 | ```` 44 | # Run all combinations for OSW input only 45 | pytest -k "test_ipf_scoring and osw" 46 | 47 | # Run specific parameter combination 48 | pytest -k "test_ipf_scoring and ms1_on and ms2_off and h0_on" 49 | 50 | # Run all peptide tests for OSW input 51 | pytest -k "test_peptide_levels and osw" 52 | 53 | # Run protein tests for specific context 54 | pytest -k "test_protein_levels and experiment-wide" 55 | 56 | # Run all tests for parquet input 57 | pytest -k "parquet" 58 | ```` 59 | 60 | To run tests in parallel, you need the `pytest-xdist` plugin. You can then use the `-n` option to specify the number of parallel workers: 61 | 62 | ```` 63 | pytest -n 4 64 | ```` -------------------------------------------------------------------------------- /tests/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyProphet/pyprophet/54e18bd69c91ce7c2dcc8bd2ee0ecd853fe3d297/tests/__init__.py -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_export.test_osw_3.out: -------------------------------------------------------------------------------- 1 | Empty DataFrame 2 | Columns: [Charge, FullPeptideName, Intensity, ProteinName, RT, Sequence, aggr_prec_Peak_Apex, aggr_prec_Peak_Area, assay_iRT, assay_rt, d_score, decoy, delta_iRT, delta_rt, filename, iRT, id, leftWidth, m_score, m_score_protein_experiment_wide, m_score_protein_global, m_score_protein_run_specific, mz, peak_group_rank, rightWidth, run_id, transition_group_id] 3 | Index: [] 4 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_export.test_osw_analysis[osw-False-False-True].out: -------------------------------------------------------------------------------- 1 | Empty DataFrame 2 | Columns: [Charge, FullPeptideName, Intensity, ProteinName, RT, Sequence, aggr_prec_Peak_Apex, aggr_prec_Peak_Area, assay_iRT, assay_rt, d_score, decoy, delta_iRT, delta_rt, filename, iRT, id, leftWidth, m_score, m_score_protein_experiment_wide, m_score_protein_global, m_score_protein_run_specific, mz, peak_group_rank, rightWidth, run_id, transition_group_id] 3 | Index: [] 4 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_0.out: -------------------------------------------------------------------------------- 1 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 2 | 0 -4409520928686189639 0.0003 0.0031 0.0063 3 | 1 -7771919224870429764 0.0003 0.0031 0.0007 4 | 2 -1732939685941081620 0.0003 0.0031 0.0007 5 | 3 -7157712673420189723 0.0003 0.0031 0.0133 6 | 4 909047282828920049 0.0003 0.0031 0.0147 7 | .. ... ... ... ... 8 | 95 6840593155190879143 0.0003 0.0031 0.0017 9 | 96 -6764315401363298084 0.0003 0.0031 0.0007 10 | 97 8943629340769664660 1.0000 0.0225 0.0121 11 | 98 -6034887541083502974 0.0003 0.0031 0.0017 12 | 99 7291105701317857435 0.0261 0.0031 0.0541 13 | 14 | [100 rows x 4 columns] 15 | feature_id pep pvalue qvalue rank score transition_id 16 | 0 -4409520928686189639 0.0063 0.0047 0.0016 1 2.1349 1334 17 | 1 -4409520928686189639 1.0000 0.6411 0.1162 1 -0.3597 1335 18 | 2 -4409520928686189639 1.0000 0.5897 0.1075 1 -0.1966 1336 19 | 3 -4409520928686189639 0.0161 0.0121 0.0033 1 1.9360 1337 20 | 4 -4409520928686189639 0.2419 0.1084 0.0226 1 1.2209 1343 21 | .. ... ... ... ... ... ... ... 22 | 95 -1732939685941081620 0.0075 0.0056 0.0019 1 2.0916 1451 23 | 96 -1732939685941081620 0.0097 0.0075 0.0023 1 2.0680 1455 24 | 97 -1732939685941081620 0.5444 0.2206 0.0434 1 0.8410 1456 25 | 98 -1732939685941081620 0.3426 0.1477 0.0299 1 1.0588 1457 26 | 99 -1732939685941081620 0.0075 0.0056 0.0019 1 2.0930 1461 27 | 28 | [100 rows x 7 columns] 29 | feature_id pep peptide_id precursor_peakgroup_pep qvalue 30 | 0 -9078977811506172301 0.0 305 7.2449e-09 0.0 31 | 1 -9009602369958523731 0.0 309 6.4212e-08 0.0 32 | 2 -8990894093332793487 0.0 1169 7.2449e-09 0.0 33 | 3 -8915955323477460297 0.0 1214 1.1667e-08 0.0 34 | 4 -8858715981476206597 0.0 862 7.0822e-10 0.0 35 | .. ... ... ... ... ... 36 | 95 -3196707605593292319 0.0 1355 6.0432e-09 0.0 37 | 96 -3129995828656718688 0.0 590 4.6346e-09 0.0 38 | 97 -3096050638984928024 0.0 1365 1.5983e-09 0.0 39 | 98 -2959420398616195477 0.0 1254 8.3354e-09 0.0 40 | 99 -2912234918591861719 0.0 1029 7.0822e-10 0.0 41 | 42 | [100 rows x 5 columns] 43 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_1.out: -------------------------------------------------------------------------------- 1 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 2 | 0 -4409520928686189639 0.0003 0.0031 0.0063 3 | 1 -7771919224870429764 0.0003 0.0031 0.0007 4 | 2 -1732939685941081620 0.0003 0.0031 0.0007 5 | 3 -7157712673420189723 0.0003 0.0031 0.0133 6 | 4 909047282828920049 0.0003 0.0031 0.0147 7 | .. ... ... ... ... 8 | 95 6840593155190879143 0.0003 0.0031 0.0017 9 | 96 -6764315401363298084 0.0003 0.0031 0.0007 10 | 97 8943629340769664660 1.0000 0.0225 0.0121 11 | 98 -6034887541083502974 0.0003 0.0031 0.0017 12 | 99 7291105701317857435 0.0261 0.0031 0.0541 13 | 14 | [100 rows x 4 columns] 15 | feature_id pep pvalue qvalue rank score transition_id 16 | 0 -4409520928686189639 0.0063 0.0047 0.0016 1 2.1349 1334 17 | 1 -4409520928686189639 1.0000 0.6411 0.1162 1 -0.3597 1335 18 | 2 -4409520928686189639 1.0000 0.5897 0.1075 1 -0.1966 1336 19 | 3 -4409520928686189639 0.0161 0.0121 0.0033 1 1.9360 1337 20 | 4 -4409520928686189639 0.2419 0.1084 0.0226 1 1.2209 1343 21 | .. ... ... ... ... ... ... ... 22 | 95 -1732939685941081620 0.0075 0.0056 0.0019 1 2.0916 1451 23 | 96 -1732939685941081620 0.0097 0.0075 0.0023 1 2.0680 1455 24 | 97 -1732939685941081620 0.5444 0.2206 0.0434 1 0.8410 1456 25 | 98 -1732939685941081620 0.3426 0.1477 0.0299 1 1.0588 1457 26 | 99 -1732939685941081620 0.0075 0.0056 0.0019 1 2.0930 1461 27 | 28 | [100 rows x 7 columns] 29 | feature_id pep peptide_id precursor_peakgroup_pep qvalue 30 | 0 -9078977811506172301 0.0 305 2.3908e-05 0.0 31 | 1 -9009602369958523731 0.0 309 2.1186e-04 0.0 32 | 2 -8990894093332793487 0.0 1169 2.3908e-05 0.0 33 | 3 -8915955323477460297 0.0 1214 3.8501e-05 0.0 34 | 4 -8858715981476206597 0.0 862 2.3372e-06 0.0 35 | .. ... ... ... ... ... 36 | 95 -3196707605593292319 0.0 1355 1.9943e-05 0.0 37 | 96 -3129995828656718688 0.0 590 1.5294e-05 0.0 38 | 97 -3096050638984928024 0.0 1365 5.2745e-06 0.0 39 | 98 -2959420398616195477 0.0 1254 2.7507e-05 0.0 40 | 99 -2912234918591861719 0.0 1029 2.3372e-06 0.0 41 | 42 | [100 rows x 5 columns] 43 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_2.out: -------------------------------------------------------------------------------- 1 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 2 | 0 -4409520928686189639 0.0003 0.0031 0.0063 3 | 1 -7771919224870429764 0.0003 0.0031 0.0007 4 | 2 -1732939685941081620 0.0003 0.0031 0.0007 5 | 3 -7157712673420189723 0.0003 0.0031 0.0133 6 | 4 909047282828920049 0.0003 0.0031 0.0147 7 | .. ... ... ... ... 8 | 95 6840593155190879143 0.0003 0.0031 0.0017 9 | 96 -6764315401363298084 0.0003 0.0031 0.0007 10 | 97 8943629340769664660 1.0000 0.0225 0.0121 11 | 98 -6034887541083502974 0.0003 0.0031 0.0017 12 | 99 7291105701317857435 0.0261 0.0031 0.0541 13 | 14 | [100 rows x 4 columns] 15 | feature_id pep pvalue qvalue rank score transition_id 16 | 0 -4409520928686189639 0.0063 0.0047 0.0016 1 2.1349 1334 17 | 1 -4409520928686189639 1.0000 0.6411 0.1162 1 -0.3597 1335 18 | 2 -4409520928686189639 1.0000 0.5897 0.1075 1 -0.1966 1336 19 | 3 -4409520928686189639 0.0161 0.0121 0.0033 1 1.9360 1337 20 | 4 -4409520928686189639 0.2419 0.1084 0.0226 1 1.2209 1343 21 | .. ... ... ... ... ... ... ... 22 | 95 -1732939685941081620 0.0075 0.0056 0.0019 1 2.0916 1451 23 | 96 -1732939685941081620 0.0097 0.0075 0.0023 1 2.0680 1455 24 | 97 -1732939685941081620 0.5444 0.2206 0.0434 1 0.8410 1456 25 | 98 -1732939685941081620 0.3426 0.1477 0.0299 1 1.0588 1457 26 | 99 -1732939685941081620 0.0075 0.0056 0.0019 1 2.0930 1461 27 | 28 | [100 rows x 7 columns] 29 | feature_id pep peptide_id precursor_peakgroup_pep qvalue 30 | 0 -9078977811506172301 0.0 305 9.5518e-07 0.0 31 | 1 -9009602369958523731 0.0 309 9.5518e-07 0.0 32 | 2 -8990894093332793487 0.0 1169 9.5518e-07 0.0 33 | 3 -8915955323477460297 0.0 1214 9.5518e-07 0.0 34 | 4 -8858715981476206597 0.0 862 9.5518e-07 0.0 35 | .. ... ... ... ... ... 36 | 95 -3836935184308268027 0.0 327 9.5518e-07 0.0 37 | 96 -3751206597519588606 0.0 922 9.5518e-07 0.0 38 | 97 -3723393495768556771 0.0 1218 9.5518e-07 0.0 39 | 98 -3691251994571895144 0.0 448 9.5518e-07 0.0 40 | 99 -3689374433140347909 0.0 688 9.5518e-07 0.0 41 | 42 | [100 rows x 5 columns] 43 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_3.out: -------------------------------------------------------------------------------- 1 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 2 | 0 -4409520928686189639 0.0003 0.0031 0.0063 3 | 1 -7771919224870429764 0.0003 0.0031 0.0007 4 | 2 -1732939685941081620 0.0003 0.0031 0.0007 5 | 3 -7157712673420189723 0.0003 0.0031 0.0133 6 | 4 909047282828920049 0.0003 0.0031 0.0147 7 | .. ... ... ... ... 8 | 95 6840593155190879143 0.0003 0.0031 0.0017 9 | 96 -6764315401363298084 0.0003 0.0031 0.0007 10 | 97 8943629340769664660 1.0000 0.0225 0.0121 11 | 98 -6034887541083502974 0.0003 0.0031 0.0017 12 | 99 7291105701317857435 0.0261 0.0031 0.0541 13 | 14 | [100 rows x 4 columns] 15 | feature_id pep pvalue qvalue rank score transition_id 16 | 0 -4409520928686189639 0.0063 0.0047 0.0016 1 2.1349 1334 17 | 1 -4409520928686189639 1.0000 0.6411 0.1162 1 -0.3597 1335 18 | 2 -4409520928686189639 1.0000 0.5897 0.1075 1 -0.1966 1336 19 | 3 -4409520928686189639 0.0161 0.0121 0.0033 1 1.9360 1337 20 | 4 -4409520928686189639 0.2419 0.1084 0.0226 1 1.2209 1343 21 | .. ... ... ... ... ... ... ... 22 | 95 -1732939685941081620 0.0075 0.0056 0.0019 1 2.0916 1451 23 | 96 -1732939685941081620 0.0097 0.0075 0.0023 1 2.0680 1455 24 | 97 -1732939685941081620 0.5444 0.2206 0.0434 1 0.8410 1456 25 | 98 -1732939685941081620 0.3426 0.1477 0.0299 1 1.0588 1457 26 | 99 -1732939685941081620 0.0075 0.0056 0.0019 1 2.0930 1461 27 | 28 | [100 rows x 7 columns] 29 | feature_id pep peptide_id precursor_peakgroup_pep qvalue 30 | 0 -9078977811506172301 0.0 305 0.0031 0.0 31 | 1 -9009602369958523731 0.0 309 0.0031 0.0 32 | 2 -8990894093332793487 0.0 1169 0.0031 0.0 33 | 3 -8915955323477460297 0.0 1214 0.0031 0.0 34 | 4 -8858715981476206597 0.0 862 0.0031 0.0 35 | .. ... ... ... ... ... 36 | 95 -4029731266955352788 0.0 1240 0.0031 0.0 37 | 96 -3993387312722337069 0.0 489 0.0031 0.0 38 | 97 -3836935184308268027 0.0 327 0.0031 0.0 39 | 98 -3751206597519588606 0.0 922 0.0031 0.0 40 | 99 -3723393495768556771 0.0 1218 0.0031 0.0 41 | 42 | [100 rows x 5 columns] 43 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_4.out: -------------------------------------------------------------------------------- 1 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 2 | 0 -4409520928686189639 0.0003 0.0031 0.0063 3 | 1 -7771919224870429764 0.0003 0.0031 0.0007 4 | 2 -1732939685941081620 0.0003 0.0031 0.0007 5 | 3 -7157712673420189723 0.0003 0.0031 0.0133 6 | 4 909047282828920049 0.0003 0.0031 0.0147 7 | .. ... ... ... ... 8 | 95 6840593155190879143 0.0003 0.0031 0.0017 9 | 96 -6764315401363298084 0.0003 0.0031 0.0007 10 | 97 8943629340769664660 1.0000 0.0225 0.0121 11 | 98 -6034887541083502974 0.0003 0.0031 0.0017 12 | 99 7291105701317857435 0.0261 0.0031 0.0541 13 | 14 | [100 rows x 4 columns] 15 | feature_id pep pvalue qvalue rank score transition_id 16 | 0 -4409520928686189639 0.0063 0.0047 0.0016 1 2.1349 1334 17 | 1 -4409520928686189639 1.0000 0.6411 0.1162 1 -0.3597 1335 18 | 2 -4409520928686189639 1.0000 0.5897 0.1075 1 -0.1966 1336 19 | 3 -4409520928686189639 0.0161 0.0121 0.0033 1 1.9360 1337 20 | 4 -4409520928686189639 0.2419 0.1084 0.0226 1 1.2209 1343 21 | .. ... ... ... ... ... ... ... 22 | 95 -1732939685941081620 0.0075 0.0056 0.0019 1 2.0916 1451 23 | 96 -1732939685941081620 0.0097 0.0075 0.0023 1 2.0680 1455 24 | 97 -1732939685941081620 0.5444 0.2206 0.0434 1 0.8410 1456 25 | 98 -1732939685941081620 0.3426 0.1477 0.0299 1 1.0588 1457 26 | 99 -1732939685941081620 0.0075 0.0056 0.0019 1 2.0930 1461 27 | 28 | [100 rows x 7 columns] 29 | feature_id pep peptide_id precursor_peakgroup_pep qvalue 30 | 0 -9078977811506172301 0.0 305 7.2449e-09 0.0 31 | 1 -9009602369958523731 0.0 309 6.4212e-08 0.0 32 | 2 -8990894093332793487 0.0 1169 7.2449e-09 0.0 33 | 3 -8915955323477460297 0.0 1214 1.1667e-08 0.0 34 | 4 -8858715981476206597 0.0 862 7.0822e-10 0.0 35 | .. ... ... ... ... ... 36 | 95 -3196707605593292319 0.0 1355 6.0432e-09 0.0 37 | 96 -3129995828656718688 0.0 590 4.6346e-09 0.0 38 | 97 -3096050638984928024 0.0 1365 1.5983e-09 0.0 39 | 98 -2959420398616195477 0.0 1254 8.3354e-09 0.0 40 | 99 -2912234918591861719 0.0 1029 7.0822e-10 0.0 41 | 42 | [100 rows x 5 columns] 43 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_scoring[osw-h0_off-ms2_off-ms1_off].out: -------------------------------------------------------------------------------- 1 | Precursor Data: 2 | feature_id ms2_peakgroup_pep ms1_precursor_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0031 4 | 1 -9059007664292712863 0.3615 5 | 2 -9009602369958523731 0.0031 6 | 3 -8990894093332793487 0.0031 7 | 4 -8915955323477460297 0.0031 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0031 10 | 96 -4495976808403190115 0.0031 11 | 97 -4474179539802460946 0.0031 12 | 98 -4409520928686189639 0.0031 13 | 99 -4399716566495748799 0.0031 14 | 15 | [100 rows x 4 columns] 16 | Transition Data: 17 | feature_id transition_id pep peptide_id bmask num_peptidoforms 18 | 0 -9078977811506172301 3275 0.0075 -1 0.0 1 19 | 1 -9078977811506172301 3275 0.0075 305 1.0 1 20 | 2 -9078977811506172301 3276 0.0133 -1 0.0 1 21 | 3 -9078977811506172301 3276 0.0133 305 1.0 1 22 | 4 -9078977811506172301 3277 0.0048 -1 0.0 1 23 | .. ... ... ... ... ... ... 24 | 95 -8858715981476206597 7986 0.0017 862 1.0 1 25 | 96 -8858715981476206597 7990 0.0017 -1 0.0 1 26 | 97 -8858715981476206597 7990 0.0017 862 1.0 1 27 | 98 -8858715981476206597 7991 0.0007 -1 0.0 1 28 | 99 -8858715981476206597 7991 0.0007 862 1.0 1 29 | 30 | [100 rows x 6 columns] 31 | IPF Data: 32 | feature_id peptide_id precursor_peakgroup_pep qvalue pep 33 | 0 -9078977811506172301 305 0.0031 0.0 0.0 34 | 1 -9009602369958523731 309 0.0031 0.0 0.0 35 | 2 -8990894093332793487 1169 0.0031 0.0 0.0 36 | 3 -8915955323477460297 1214 0.0031 0.0 0.0 37 | 4 -8858715981476206597 862 0.0031 0.0 0.0 38 | .. ... ... ... ... ... 39 | 95 -4029731266955352788 1240 0.0031 0.0 0.0 40 | 96 -3993387312722337069 489 0.0031 0.0 0.0 41 | 97 -3836935184308268027 327 0.0031 0.0 0.0 42 | 98 -3751206597519588606 922 0.0031 0.0 0.0 43 | 99 -3723393495768556771 1218 0.0031 0.0 0.0 44 | 45 | [100 rows x 5 columns] 46 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_scoring[osw-h0_off-ms2_off-ms1_on].out: -------------------------------------------------------------------------------- 1 | Precursor Data: 2 | feature_id ms2_peakgroup_pep ms1_precursor_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0031 0.0003 4 | 1 -9059007664292712863 0.3615 0.2419 5 | 2 -9009602369958523731 0.0031 0.0003 6 | 3 -8990894093332793487 0.0031 0.0003 7 | 4 -8915955323477460297 0.0031 0.0003 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0031 0.0003 10 | 96 -4495976808403190115 0.0031 0.0003 11 | 97 -4474179539802460946 0.0031 0.0003 12 | 98 -4409520928686189639 0.0031 0.0003 13 | 99 -4399716566495748799 0.0031 0.0003 14 | 15 | [100 rows x 4 columns] 16 | Transition Data: 17 | feature_id transition_id pep peptide_id bmask num_peptidoforms 18 | 0 -9078977811506172301 3275 0.0075 -1 0.0 1 19 | 1 -9078977811506172301 3275 0.0075 305 1.0 1 20 | 2 -9078977811506172301 3276 0.0133 -1 0.0 1 21 | 3 -9078977811506172301 3276 0.0133 305 1.0 1 22 | 4 -9078977811506172301 3277 0.0048 -1 0.0 1 23 | .. ... ... ... ... ... ... 24 | 95 -8858715981476206597 7986 0.0017 862 1.0 1 25 | 96 -8858715981476206597 7990 0.0017 -1 0.0 1 26 | 97 -8858715981476206597 7990 0.0017 862 1.0 1 27 | 98 -8858715981476206597 7991 0.0007 -1 0.0 1 28 | 99 -8858715981476206597 7991 0.0007 862 1.0 1 29 | 30 | [100 rows x 6 columns] 31 | IPF Data: 32 | feature_id peptide_id precursor_peakgroup_pep qvalue pep 33 | 0 -9078977811506172301 305 9.5518e-07 0.0 0.0 34 | 1 -9009602369958523731 309 9.5518e-07 0.0 0.0 35 | 2 -8990894093332793487 1169 9.5518e-07 0.0 0.0 36 | 3 -8915955323477460297 1214 9.5518e-07 0.0 0.0 37 | 4 -8858715981476206597 862 9.5518e-07 0.0 0.0 38 | .. ... ... ... ... ... 39 | 95 -3836935184308268027 327 9.5518e-07 0.0 0.0 40 | 96 -3751206597519588606 922 9.5518e-07 0.0 0.0 41 | 97 -3723393495768556771 1218 9.5518e-07 0.0 0.0 42 | 98 -3691251994571895144 448 9.5518e-07 0.0 0.0 43 | 99 -3689374433140347909 688 9.5518e-07 0.0 0.0 44 | 45 | [100 rows x 5 columns] 46 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_scoring[osw-h0_off-ms2_on-ms1_off].out: -------------------------------------------------------------------------------- 1 | Precursor Data: 2 | feature_id ms2_peakgroup_pep ms1_precursor_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0031 0.0075 4 | 1 -9009602369958523731 0.0031 0.0630 5 | 2 -8990894093332793487 0.0031 0.0075 6 | 3 -8915955323477460297 0.0031 0.0121 7 | 4 -8858715981476206597 0.0031 0.0007 8 | .. ... ... ... ... 9 | 95 -3220457216356394124 0.0031 0.0007 10 | 96 -3212703409469281429 0.0031 0.0017 11 | 97 -3196707605593292319 0.0031 0.0063 12 | 98 -3129995828656718688 0.0031 0.0048 13 | 99 -3096050638984928024 0.0031 0.0017 14 | 15 | [100 rows x 4 columns] 16 | Transition Data: 17 | feature_id transition_id pep peptide_id bmask num_peptidoforms 18 | 0 -9078977811506172301 3275 0.0075 -1 0.0 1 19 | 1 -9078977811506172301 3275 0.0075 305 1.0 1 20 | 2 -9078977811506172301 3276 0.0133 -1 0.0 1 21 | 3 -9078977811506172301 3276 0.0133 305 1.0 1 22 | 4 -9078977811506172301 3277 0.0048 -1 0.0 1 23 | .. ... ... ... ... ... ... 24 | 95 -8858715981476206597 7986 0.0017 862 1.0 1 25 | 96 -8858715981476206597 7990 0.0017 -1 0.0 1 26 | 97 -8858715981476206597 7990 0.0017 862 1.0 1 27 | 98 -8858715981476206597 7991 0.0007 -1 0.0 1 28 | 99 -8858715981476206597 7991 0.0007 862 1.0 1 29 | 30 | [100 rows x 6 columns] 31 | IPF Data: 32 | feature_id peptide_id precursor_peakgroup_pep qvalue pep 33 | 0 -9078977811506172301 305 2.3908e-05 0.0 0.0 34 | 1 -9009602369958523731 309 2.1186e-04 0.0 0.0 35 | 2 -8990894093332793487 1169 2.3908e-05 0.0 0.0 36 | 3 -8915955323477460297 1214 3.8501e-05 0.0 0.0 37 | 4 -8858715981476206597 862 2.3372e-06 0.0 0.0 38 | .. ... ... ... ... ... 39 | 95 -3196707605593292319 1355 1.9943e-05 0.0 0.0 40 | 96 -3129995828656718688 590 1.5294e-05 0.0 0.0 41 | 97 -3096050638984928024 1365 5.2745e-06 0.0 0.0 42 | 98 -2959420398616195477 1254 2.7507e-05 0.0 0.0 43 | 99 -2912234918591861719 1029 2.3372e-06 0.0 0.0 44 | 45 | [100 rows x 5 columns] 46 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_scoring[osw-h0_off-ms2_on-ms1_on].out: -------------------------------------------------------------------------------- 1 | Precursor Data: 2 | feature_id ms2_peakgroup_pep ms1_precursor_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0031 0.0003 0.0075 4 | 1 -9009602369958523731 0.0031 0.0003 0.0630 5 | 2 -8990894093332793487 0.0031 0.0003 0.0075 6 | 3 -8915955323477460297 0.0031 0.0003 0.0121 7 | 4 -8858715981476206597 0.0031 0.0003 0.0007 8 | .. ... ... ... ... 9 | 95 -3220457216356394124 0.0031 0.0003 0.0007 10 | 96 -3212703409469281429 0.0031 0.0003 0.0017 11 | 97 -3196707605593292319 0.0031 0.0003 0.0063 12 | 98 -3129995828656718688 0.0031 0.0003 0.0048 13 | 99 -3096050638984928024 0.0031 0.0003 0.0017 14 | 15 | [100 rows x 4 columns] 16 | Transition Data: 17 | feature_id transition_id pep peptide_id bmask num_peptidoforms 18 | 0 -9078977811506172301 3275 0.0075 -1 0.0 1 19 | 1 -9078977811506172301 3275 0.0075 305 1.0 1 20 | 2 -9078977811506172301 3276 0.0133 -1 0.0 1 21 | 3 -9078977811506172301 3276 0.0133 305 1.0 1 22 | 4 -9078977811506172301 3277 0.0048 -1 0.0 1 23 | .. ... ... ... ... ... ... 24 | 95 -8858715981476206597 7986 0.0017 862 1.0 1 25 | 96 -8858715981476206597 7990 0.0017 -1 0.0 1 26 | 97 -8858715981476206597 7990 0.0017 862 1.0 1 27 | 98 -8858715981476206597 7991 0.0007 -1 0.0 1 28 | 99 -8858715981476206597 7991 0.0007 862 1.0 1 29 | 30 | [100 rows x 6 columns] 31 | IPF Data: 32 | feature_id peptide_id precursor_peakgroup_pep qvalue pep 33 | 0 -9078977811506172301 305 7.2449e-09 0.0 0.0 34 | 1 -9009602369958523731 309 6.4212e-08 0.0 0.0 35 | 2 -8990894093332793487 1169 7.2449e-09 0.0 0.0 36 | 3 -8915955323477460297 1214 1.1667e-08 0.0 0.0 37 | 4 -8858715981476206597 862 7.0822e-10 0.0 0.0 38 | .. ... ... ... ... ... 39 | 95 -3196707605593292319 1355 6.0432e-09 0.0 0.0 40 | 96 -3129995828656718688 590 4.6346e-09 0.0 0.0 41 | 97 -3096050638984928024 1365 1.5983e-09 0.0 0.0 42 | 98 -2959420398616195477 1254 8.3354e-09 0.0 0.0 43 | 99 -2912234918591861719 1029 7.0822e-10 0.0 0.0 44 | 45 | [100 rows x 5 columns] 46 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_scoring[osw-h0_on-ms2_off-ms1_off].out: -------------------------------------------------------------------------------- 1 | Precursor Data: 2 | feature_id ms2_peakgroup_pep ms1_precursor_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0031 4 | 1 -9059007664292712863 0.3615 5 | 2 -9009602369958523731 0.0031 6 | 3 -8990894093332793487 0.0031 7 | 4 -8915955323477460297 0.0031 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0031 10 | 96 -4495976808403190115 0.0031 11 | 97 -4474179539802460946 0.0031 12 | 98 -4409520928686189639 0.0031 13 | 99 -4399716566495748799 0.0031 14 | 15 | [100 rows x 4 columns] 16 | Transition Data: 17 | feature_id transition_id pep peptide_id bmask num_peptidoforms 18 | 0 -9078977811506172301 3275 0.0075 -1 0.0 1 19 | 1 -9078977811506172301 3275 0.0075 305 1.0 1 20 | 2 -9078977811506172301 3276 0.0133 -1 0.0 1 21 | 3 -9078977811506172301 3276 0.0133 305 1.0 1 22 | 4 -9078977811506172301 3277 0.0048 -1 0.0 1 23 | .. ... ... ... ... ... ... 24 | 95 -8858715981476206597 7986 0.0017 862 1.0 1 25 | 96 -8858715981476206597 7990 0.0017 -1 0.0 1 26 | 97 -8858715981476206597 7990 0.0017 862 1.0 1 27 | 98 -8858715981476206597 7991 0.0007 -1 0.0 1 28 | 99 -8858715981476206597 7991 0.0007 862 1.0 1 29 | 30 | [100 rows x 6 columns] 31 | IPF Data: 32 | feature_id peptide_id precursor_peakgroup_pep qvalue pep 33 | 0 -9078977811506172301 305 0.0031 0.0 0.0 34 | 1 -9009602369958523731 309 0.0031 0.0 0.0 35 | 2 -8990894093332793487 1169 0.0031 0.0 0.0 36 | 3 -8915955323477460297 1214 0.0031 0.0 0.0 37 | 4 -8858715981476206597 862 0.0031 0.0 0.0 38 | .. ... ... ... ... ... 39 | 95 -4029731266955352788 1240 0.0031 0.0 0.0 40 | 96 -3993387312722337069 489 0.0031 0.0 0.0 41 | 97 -3836935184308268027 327 0.0031 0.0 0.0 42 | 98 -3751206597519588606 922 0.0031 0.0 0.0 43 | 99 -3723393495768556771 1218 0.0031 0.0 0.0 44 | 45 | [100 rows x 5 columns] 46 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_scoring[osw-h0_on-ms2_off-ms1_on].out: -------------------------------------------------------------------------------- 1 | Precursor Data: 2 | feature_id ms2_peakgroup_pep ms1_precursor_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0031 0.0003 4 | 1 -9059007664292712863 0.3615 0.2419 5 | 2 -9009602369958523731 0.0031 0.0003 6 | 3 -8990894093332793487 0.0031 0.0003 7 | 4 -8915955323477460297 0.0031 0.0003 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0031 0.0003 10 | 96 -4495976808403190115 0.0031 0.0003 11 | 97 -4474179539802460946 0.0031 0.0003 12 | 98 -4409520928686189639 0.0031 0.0003 13 | 99 -4399716566495748799 0.0031 0.0003 14 | 15 | [100 rows x 4 columns] 16 | Transition Data: 17 | feature_id transition_id pep peptide_id bmask num_peptidoforms 18 | 0 -9078977811506172301 3275 0.0075 -1 0.0 1 19 | 1 -9078977811506172301 3275 0.0075 305 1.0 1 20 | 2 -9078977811506172301 3276 0.0133 -1 0.0 1 21 | 3 -9078977811506172301 3276 0.0133 305 1.0 1 22 | 4 -9078977811506172301 3277 0.0048 -1 0.0 1 23 | .. ... ... ... ... ... ... 24 | 95 -8858715981476206597 7986 0.0017 862 1.0 1 25 | 96 -8858715981476206597 7990 0.0017 -1 0.0 1 26 | 97 -8858715981476206597 7990 0.0017 862 1.0 1 27 | 98 -8858715981476206597 7991 0.0007 -1 0.0 1 28 | 99 -8858715981476206597 7991 0.0007 862 1.0 1 29 | 30 | [100 rows x 6 columns] 31 | IPF Data: 32 | feature_id peptide_id precursor_peakgroup_pep qvalue pep 33 | 0 -9078977811506172301 305 9.5518e-07 0.0 0.0 34 | 1 -9009602369958523731 309 9.5518e-07 0.0 0.0 35 | 2 -8990894093332793487 1169 9.5518e-07 0.0 0.0 36 | 3 -8915955323477460297 1214 9.5518e-07 0.0 0.0 37 | 4 -8858715981476206597 862 9.5518e-07 0.0 0.0 38 | .. ... ... ... ... ... 39 | 95 -3836935184308268027 327 9.5518e-07 0.0 0.0 40 | 96 -3751206597519588606 922 9.5518e-07 0.0 0.0 41 | 97 -3723393495768556771 1218 9.5518e-07 0.0 0.0 42 | 98 -3691251994571895144 448 9.5518e-07 0.0 0.0 43 | 99 -3689374433140347909 688 9.5518e-07 0.0 0.0 44 | 45 | [100 rows x 5 columns] 46 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_scoring[osw-h0_on-ms2_on-ms1_off].out: -------------------------------------------------------------------------------- 1 | Precursor Data: 2 | feature_id ms2_peakgroup_pep ms1_precursor_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0031 0.0075 4 | 1 -9009602369958523731 0.0031 0.0630 5 | 2 -8990894093332793487 0.0031 0.0075 6 | 3 -8915955323477460297 0.0031 0.0121 7 | 4 -8858715981476206597 0.0031 0.0007 8 | .. ... ... ... ... 9 | 95 -3220457216356394124 0.0031 0.0007 10 | 96 -3212703409469281429 0.0031 0.0017 11 | 97 -3196707605593292319 0.0031 0.0063 12 | 98 -3129995828656718688 0.0031 0.0048 13 | 99 -3096050638984928024 0.0031 0.0017 14 | 15 | [100 rows x 4 columns] 16 | Transition Data: 17 | feature_id transition_id pep peptide_id bmask num_peptidoforms 18 | 0 -9078977811506172301 3275 0.0075 -1 0.0 1 19 | 1 -9078977811506172301 3275 0.0075 305 1.0 1 20 | 2 -9078977811506172301 3276 0.0133 -1 0.0 1 21 | 3 -9078977811506172301 3276 0.0133 305 1.0 1 22 | 4 -9078977811506172301 3277 0.0048 -1 0.0 1 23 | .. ... ... ... ... ... ... 24 | 95 -8858715981476206597 7986 0.0017 862 1.0 1 25 | 96 -8858715981476206597 7990 0.0017 -1 0.0 1 26 | 97 -8858715981476206597 7990 0.0017 862 1.0 1 27 | 98 -8858715981476206597 7991 0.0007 -1 0.0 1 28 | 99 -8858715981476206597 7991 0.0007 862 1.0 1 29 | 30 | [100 rows x 6 columns] 31 | IPF Data: 32 | feature_id peptide_id precursor_peakgroup_pep qvalue pep 33 | 0 -9078977811506172301 305 2.3908e-05 0.0 0.0 34 | 1 -9009602369958523731 309 2.1186e-04 0.0 0.0 35 | 2 -8990894093332793487 1169 2.3908e-05 0.0 0.0 36 | 3 -8915955323477460297 1214 3.8501e-05 0.0 0.0 37 | 4 -8858715981476206597 862 2.3372e-06 0.0 0.0 38 | .. ... ... ... ... ... 39 | 95 -3196707605593292319 1355 1.9943e-05 0.0 0.0 40 | 96 -3129995828656718688 590 1.5294e-05 0.0 0.0 41 | 97 -3096050638984928024 1365 5.2745e-06 0.0 0.0 42 | 98 -2959420398616195477 1254 2.7507e-05 0.0 0.0 43 | 99 -2912234918591861719 1029 2.3372e-06 0.0 0.0 44 | 45 | [100 rows x 5 columns] 46 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_scoring[osw-h0_on-ms2_on-ms1_on].out: -------------------------------------------------------------------------------- 1 | Precursor Data: 2 | feature_id ms2_peakgroup_pep ms1_precursor_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0031 0.0003 0.0075 4 | 1 -9009602369958523731 0.0031 0.0003 0.0630 5 | 2 -8990894093332793487 0.0031 0.0003 0.0075 6 | 3 -8915955323477460297 0.0031 0.0003 0.0121 7 | 4 -8858715981476206597 0.0031 0.0003 0.0007 8 | .. ... ... ... ... 9 | 95 -3220457216356394124 0.0031 0.0003 0.0007 10 | 96 -3212703409469281429 0.0031 0.0003 0.0017 11 | 97 -3196707605593292319 0.0031 0.0003 0.0063 12 | 98 -3129995828656718688 0.0031 0.0003 0.0048 13 | 99 -3096050638984928024 0.0031 0.0003 0.0017 14 | 15 | [100 rows x 4 columns] 16 | Transition Data: 17 | feature_id transition_id pep peptide_id bmask num_peptidoforms 18 | 0 -9078977811506172301 3275 0.0075 -1 0.0 1 19 | 1 -9078977811506172301 3275 0.0075 305 1.0 1 20 | 2 -9078977811506172301 3276 0.0133 -1 0.0 1 21 | 3 -9078977811506172301 3276 0.0133 305 1.0 1 22 | 4 -9078977811506172301 3277 0.0048 -1 0.0 1 23 | .. ... ... ... ... ... ... 24 | 95 -8858715981476206597 7986 0.0017 862 1.0 1 25 | 96 -8858715981476206597 7990 0.0017 -1 0.0 1 26 | 97 -8858715981476206597 7990 0.0017 862 1.0 1 27 | 98 -8858715981476206597 7991 0.0007 -1 0.0 1 28 | 99 -8858715981476206597 7991 0.0007 862 1.0 1 29 | 30 | [100 rows x 6 columns] 31 | IPF Data: 32 | feature_id peptide_id precursor_peakgroup_pep qvalue pep 33 | 0 -9078977811506172301 305 7.2449e-09 0.0 0.0 34 | 1 -9009602369958523731 309 6.4212e-08 0.0 0.0 35 | 2 -8990894093332793487 1169 7.2449e-09 0.0 0.0 36 | 3 -8915955323477460297 1214 1.1667e-08 0.0 0.0 37 | 4 -8858715981476206597 862 7.0822e-10 0.0 0.0 38 | .. ... ... ... ... ... 39 | 95 -3196707605593292319 1355 6.0432e-09 0.0 0.0 40 | 96 -3129995828656718688 590 4.6346e-09 0.0 0.0 41 | 97 -3096050638984928024 1365 1.5983e-09 0.0 0.0 42 | 98 -2959420398616195477 1254 8.3354e-09 0.0 0.0 43 | 99 -2912234918591861719 1029 7.0822e-10 0.0 0.0 44 | 45 | [100 rows x 5 columns] 46 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_scoring[parquet-h0_off-ms2_off-ms1_off].out: -------------------------------------------------------------------------------- 1 | Precursor Data: 2 | feature_id ms2_peakgroup_pep ms1_precursor_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0031 4 | 1 -9059007664292712863 0.3615 5 | 2 -9009602369958523731 0.0031 6 | 3 -8990894093332793487 0.0031 7 | 4 -8915955323477460297 0.0031 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0031 10 | 96 -4495976808403190115 0.0031 11 | 97 -4474179539802460946 0.0031 12 | 98 -4409520928686189639 0.0031 13 | 99 -4399716566495748799 0.0031 14 | 15 | [100 rows x 4 columns] 16 | Transition Data: 17 | feature_id transition_id pep peptide_id bmask num_peptidoforms 18 | 0 -9078977811506172301 3275 0.0075 -1 0.0 1 19 | 1 -9078977811506172301 3275 0.0075 305 1.0 1 20 | 2 -9078977811506172301 3276 0.0133 -1 0.0 1 21 | 3 -9078977811506172301 3276 0.0133 305 1.0 1 22 | 4 -9078977811506172301 3277 0.0048 -1 0.0 1 23 | .. ... ... ... ... ... ... 24 | 95 -8858715981476206597 7986 0.0017 862 1.0 1 25 | 96 -8858715981476206597 7990 0.0017 -1 0.0 1 26 | 97 -8858715981476206597 7990 0.0017 862 1.0 1 27 | 98 -8858715981476206597 7991 0.0007 -1 0.0 1 28 | 99 -8858715981476206597 7991 0.0007 862 1.0 1 29 | 30 | [100 rows x 6 columns] 31 | IPF Data: 32 | run_id feature_id peptide_id precursor_peakgroup_pep qvalue pep 33 | 0 -8670811102654833664 -9211032279639747584 976 NaN NaN NaN 34 | 1 -8670811102654833664 -9209834744278113280 1157 NaN NaN NaN 35 | 2 -8670811102654833664 -9204568338203973632 76 NaN NaN NaN 36 | 3 -8670811102654833664 -9202066408251325440 547 NaN NaN NaN 37 | 4 -8670811102654833664 -9194114845888269312 562 NaN NaN NaN 38 | .. ... ... ... ... ... ... 39 | 95 -8670811102654833664 -8647629181891982336 661 NaN NaN NaN 40 | 96 -8670811102654833664 -8646966398648626176 529 NaN NaN NaN 41 | 97 -8670811102654833664 -8646066421910515712 431 0.0031 0.0 0.0 42 | 98 -8670811102654833664 -8638785506020690944 409 NaN NaN NaN 43 | 99 -8670811102654833664 -8630914225771745280 578 NaN NaN NaN 44 | 45 | [100 rows x 6 columns] 46 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_scoring[parquet-h0_off-ms2_on-ms1_off].out: -------------------------------------------------------------------------------- 1 | Precursor Data: 2 | Empty DataFrame 3 | Columns: [feature_id, ms2_peakgroup_pep, ms1_precursor_pep, ms2_precursor_pep] 4 | Index: [] 5 | Transition Data: 6 | feature_id transition_id pep peptide_id bmask num_peptidoforms 7 | 0 -9078977811506172301 3275 0.0075 -1 0.0 1 8 | 1 -9078977811506172301 3275 0.0075 305 1.0 1 9 | 2 -9078977811506172301 3276 0.0133 -1 0.0 1 10 | 3 -9078977811506172301 3276 0.0133 305 1.0 1 11 | 4 -9078977811506172301 3277 0.0048 -1 0.0 1 12 | .. ... ... ... ... ... ... 13 | 95 -8858715981476206597 7986 0.0017 862 1.0 1 14 | 96 -8858715981476206597 7990 0.0017 -1 0.0 1 15 | 97 -8858715981476206597 7990 0.0017 862 1.0 1 16 | 98 -8858715981476206597 7991 0.0007 -1 0.0 1 17 | 99 -8858715981476206597 7991 0.0007 862 1.0 1 18 | 19 | [100 rows x 6 columns] 20 | IPF Data: 21 | run_id feature_id peptide_id precursor_peakgroup_pep qvalue pep 22 | 0 -8670811102654833664 -9211032279639747584 976 NaN NaN NaN 23 | 1 -8670811102654833664 -9209834744278113280 1157 NaN NaN NaN 24 | 2 -8670811102654833664 -9204568338203973632 76 NaN NaN NaN 25 | 3 -8670811102654833664 -9202066408251325440 547 NaN NaN NaN 26 | 4 -8670811102654833664 -9194114845888269312 562 NaN NaN NaN 27 | .. ... ... ... ... ... ... 28 | 95 -8670811102654833664 -8647629181891982336 661 NaN NaN NaN 29 | 96 -8670811102654833664 -8646966398648626176 529 NaN NaN NaN 30 | 97 -8670811102654833664 -8646066421910515712 431 NaN NaN NaN 31 | 98 -8670811102654833664 -8638785506020690944 409 NaN NaN NaN 32 | 99 -8670811102654833664 -8630914225771745280 578 NaN NaN NaN 33 | 34 | [100 rows x 6 columns] 35 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_scoring[parquet-h0_off-ms2_on-ms1_on].out: -------------------------------------------------------------------------------- 1 | Precursor Data: 2 | Empty DataFrame 3 | Columns: [feature_id, ms2_peakgroup_pep, ms1_precursor_pep, ms2_precursor_pep] 4 | Index: [] 5 | Transition Data: 6 | feature_id transition_id pep peptide_id bmask num_peptidoforms 7 | 0 -9078977811506172301 3275 0.0075 -1 0.0 1 8 | 1 -9078977811506172301 3275 0.0075 305 1.0 1 9 | 2 -9078977811506172301 3276 0.0133 -1 0.0 1 10 | 3 -9078977811506172301 3276 0.0133 305 1.0 1 11 | 4 -9078977811506172301 3277 0.0048 -1 0.0 1 12 | .. ... ... ... ... ... ... 13 | 95 -8858715981476206597 7986 0.0017 862 1.0 1 14 | 96 -8858715981476206597 7990 0.0017 -1 0.0 1 15 | 97 -8858715981476206597 7990 0.0017 862 1.0 1 16 | 98 -8858715981476206597 7991 0.0007 -1 0.0 1 17 | 99 -8858715981476206597 7991 0.0007 862 1.0 1 18 | 19 | [100 rows x 6 columns] 20 | IPF Data: 21 | run_id feature_id peptide_id precursor_peakgroup_pep qvalue pep 22 | 0 -8670811102654833664 -9211032279639747584 976 NaN NaN NaN 23 | 1 -8670811102654833664 -9209834744278113280 1157 NaN NaN NaN 24 | 2 -8670811102654833664 -9204568338203973632 76 NaN NaN NaN 25 | 3 -8670811102654833664 -9202066408251325440 547 NaN NaN NaN 26 | 4 -8670811102654833664 -9194114845888269312 562 NaN NaN NaN 27 | .. ... ... ... ... ... ... 28 | 95 -8670811102654833664 -8647629181891982336 661 NaN NaN NaN 29 | 96 -8670811102654833664 -8646966398648626176 529 NaN NaN NaN 30 | 97 -8670811102654833664 -8646066421910515712 431 NaN NaN NaN 31 | 98 -8670811102654833664 -8638785506020690944 409 NaN NaN NaN 32 | 99 -8670811102654833664 -8630914225771745280 578 NaN NaN NaN 33 | 34 | [100 rows x 6 columns] 35 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_scoring[parquet-h0_on-ms2_on-ms1_off].out: -------------------------------------------------------------------------------- 1 | Precursor Data: 2 | Empty DataFrame 3 | Columns: [feature_id, ms2_peakgroup_pep, ms1_precursor_pep, ms2_precursor_pep] 4 | Index: [] 5 | Transition Data: 6 | feature_id transition_id pep peptide_id bmask num_peptidoforms 7 | 0 -9078977811506172301 3275 0.0075 -1 0.0 1 8 | 1 -9078977811506172301 3275 0.0075 305 1.0 1 9 | 2 -9078977811506172301 3276 0.0133 -1 0.0 1 10 | 3 -9078977811506172301 3276 0.0133 305 1.0 1 11 | 4 -9078977811506172301 3277 0.0048 -1 0.0 1 12 | .. ... ... ... ... ... ... 13 | 95 -8858715981476206597 7986 0.0017 862 1.0 1 14 | 96 -8858715981476206597 7990 0.0017 -1 0.0 1 15 | 97 -8858715981476206597 7990 0.0017 862 1.0 1 16 | 98 -8858715981476206597 7991 0.0007 -1 0.0 1 17 | 99 -8858715981476206597 7991 0.0007 862 1.0 1 18 | 19 | [100 rows x 6 columns] 20 | IPF Data: 21 | run_id feature_id peptide_id precursor_peakgroup_pep qvalue pep 22 | 0 -8670811102654833664 -9211032279639747584 976 NaN NaN NaN 23 | 1 -8670811102654833664 -9209834744278113280 1157 NaN NaN NaN 24 | 2 -8670811102654833664 -9204568338203973632 76 NaN NaN NaN 25 | 3 -8670811102654833664 -9202066408251325440 547 NaN NaN NaN 26 | 4 -8670811102654833664 -9194114845888269312 562 NaN NaN NaN 27 | .. ... ... ... ... ... ... 28 | 95 -8670811102654833664 -8647629181891982336 661 NaN NaN NaN 29 | 96 -8670811102654833664 -8646966398648626176 529 NaN NaN NaN 30 | 97 -8670811102654833664 -8646066421910515712 431 NaN NaN NaN 31 | 98 -8670811102654833664 -8638785506020690944 409 NaN NaN NaN 32 | 99 -8670811102654833664 -8630914225771745280 578 NaN NaN NaN 33 | 34 | [100 rows x 6 columns] 35 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_scoring[parquet-h0_on-ms2_on-ms1_on].out: -------------------------------------------------------------------------------- 1 | Precursor Data: 2 | Empty DataFrame 3 | Columns: [feature_id, ms2_peakgroup_pep, ms1_precursor_pep, ms2_precursor_pep] 4 | Index: [] 5 | Transition Data: 6 | feature_id transition_id pep peptide_id bmask num_peptidoforms 7 | 0 -9078977811506172301 3275 0.0075 -1 0.0 1 8 | 1 -9078977811506172301 3275 0.0075 305 1.0 1 9 | 2 -9078977811506172301 3276 0.0133 -1 0.0 1 10 | 3 -9078977811506172301 3276 0.0133 305 1.0 1 11 | 4 -9078977811506172301 3277 0.0048 -1 0.0 1 12 | .. ... ... ... ... ... ... 13 | 95 -8858715981476206597 7986 0.0017 862 1.0 1 14 | 96 -8858715981476206597 7990 0.0017 -1 0.0 1 15 | 97 -8858715981476206597 7990 0.0017 862 1.0 1 16 | 98 -8858715981476206597 7991 0.0007 -1 0.0 1 17 | 99 -8858715981476206597 7991 0.0007 862 1.0 1 18 | 19 | [100 rows x 6 columns] 20 | IPF Data: 21 | run_id feature_id peptide_id precursor_peakgroup_pep qvalue pep 22 | 0 -8670811102654833664 -9211032279639747584 976 NaN NaN NaN 23 | 1 -8670811102654833664 -9209834744278113280 1157 NaN NaN NaN 24 | 2 -8670811102654833664 -9204568338203973632 76 NaN NaN NaN 25 | 3 -8670811102654833664 -9202066408251325440 547 NaN NaN NaN 26 | 4 -8670811102654833664 -9194114845888269312 562 NaN NaN NaN 27 | .. ... ... ... ... ... ... 28 | 95 -8670811102654833664 -8647629181891982336 661 NaN NaN NaN 29 | 96 -8670811102654833664 -8646966398648626176 529 NaN NaN NaN 30 | 97 -8670811102654833664 -8646066421910515712 431 NaN NaN NaN 31 | 98 -8670811102654833664 -8638785506020690944 409 NaN NaN NaN 32 | 99 -8670811102654833664 -8630914225771745280 578 NaN NaN NaN 33 | 34 | [100 rows x 6 columns] 35 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_scoring[split_parquet-h0_off-ms2_off-ms1_off].out: -------------------------------------------------------------------------------- 1 | Precursor Data: 2 | feature_id ms2_peakgroup_pep ms1_precursor_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0031 4 | 1 -9059007664292712863 0.3615 5 | 2 -9009602369958523731 0.0031 6 | 3 -8990894093332793487 0.0031 7 | 4 -8915955323477460297 0.0031 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0031 10 | 96 -4495976808403190115 0.0031 11 | 97 -4474179539802460946 0.0031 12 | 98 -4409520928686189639 0.0031 13 | 99 -4399716566495748799 0.0031 14 | 15 | [100 rows x 4 columns] 16 | Transition Data: 17 | feature_id transition_id pep peptide_id bmask num_peptidoforms 18 | 0 -9078977811506172301 3275 0.0075 -1 0.0 1 19 | 1 -9078977811506172301 3275 0.0075 305 1.0 1 20 | 2 -9078977811506172301 3276 0.0133 -1 0.0 1 21 | 3 -9078977811506172301 3276 0.0133 305 1.0 1 22 | 4 -9078977811506172301 3277 0.0048 -1 0.0 1 23 | .. ... ... ... ... ... ... 24 | 95 -8858715981476206597 7986 0.0017 862 1.0 1 25 | 96 -8858715981476206597 7990 0.0017 -1 0.0 1 26 | 97 -8858715981476206597 7990 0.0017 862 1.0 1 27 | 98 -8858715981476206597 7991 0.0007 -1 0.0 1 28 | 99 -8858715981476206597 7991 0.0007 862 1.0 1 29 | 30 | [100 rows x 6 columns] 31 | IPF Data: 32 | feature_id peptide_id precursor_peakgroup_pep qvalue pep 33 | 0 -9211032279639747263 976 NaN NaN NaN 34 | 1 -9209834744278112856 1157 NaN NaN NaN 35 | 2 -9204568338203974043 76 NaN NaN NaN 36 | 3 -9202066408251325127 547 NaN NaN NaN 37 | 4 -9194114845888269381 562 NaN NaN NaN 38 | .. ... ... ... ... ... 39 | 95 -8647629181891982027 661 NaN NaN NaN 40 | 96 -8646966398648626586 529 NaN NaN NaN 41 | 97 -8646066421910515352 431 0.0031 0.0 0.0 42 | 98 -8638785506020691374 409 NaN NaN NaN 43 | 99 -8630914225771744781 578 NaN NaN NaN 44 | 45 | [100 rows x 5 columns] 46 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_scoring[split_parquet-h0_off-ms2_off-ms1_on].out: -------------------------------------------------------------------------------- 1 | Precursor Data: 2 | feature_id ms2_peakgroup_pep ms1_precursor_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0031 0.0003 4 | 1 -9059007664292712863 0.3615 0.2419 5 | 2 -9009602369958523731 0.0031 0.0003 6 | 3 -8990894093332793487 0.0031 0.0003 7 | 4 -8915955323477460297 0.0031 0.0003 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0031 0.0003 10 | 96 -4495976808403190115 0.0031 0.0003 11 | 97 -4474179539802460946 0.0031 0.0003 12 | 98 -4409520928686189639 0.0031 0.0003 13 | 99 -4399716566495748799 0.0031 0.0003 14 | 15 | [100 rows x 4 columns] 16 | Transition Data: 17 | feature_id transition_id pep peptide_id bmask num_peptidoforms 18 | 0 -9078977811506172301 3275 0.0075 -1 0.0 1 19 | 1 -9078977811506172301 3275 0.0075 305 1.0 1 20 | 2 -9078977811506172301 3276 0.0133 -1 0.0 1 21 | 3 -9078977811506172301 3276 0.0133 305 1.0 1 22 | 4 -9078977811506172301 3277 0.0048 -1 0.0 1 23 | .. ... ... ... ... ... ... 24 | 95 -8858715981476206597 7986 0.0017 862 1.0 1 25 | 96 -8858715981476206597 7990 0.0017 -1 0.0 1 26 | 97 -8858715981476206597 7990 0.0017 862 1.0 1 27 | 98 -8858715981476206597 7991 0.0007 -1 0.0 1 28 | 99 -8858715981476206597 7991 0.0007 862 1.0 1 29 | 30 | [100 rows x 6 columns] 31 | IPF Data: 32 | feature_id peptide_id precursor_peakgroup_pep qvalue pep 33 | 0 -9211032279639747263 976 NaN NaN NaN 34 | 1 -9209834744278112856 1157 NaN NaN NaN 35 | 2 -9204568338203974043 76 NaN NaN NaN 36 | 3 -9202066408251325127 547 NaN NaN NaN 37 | 4 -9194114845888269381 562 NaN NaN NaN 38 | .. ... ... ... ... ... 39 | 95 -8647629181891982027 661 NaN NaN NaN 40 | 96 -8646966398648626586 529 NaN NaN NaN 41 | 97 -8646066421910515352 431 9.5518e-07 0.0 0.0 42 | 98 -8638785506020691374 409 NaN NaN NaN 43 | 99 -8630914225771744781 578 NaN NaN NaN 44 | 45 | [100 rows x 5 columns] 46 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_scoring[split_parquet-h0_off-ms2_on-ms1_off].out: -------------------------------------------------------------------------------- 1 | Precursor Data: 2 | feature_id ms2_peakgroup_pep ms1_precursor_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0031 0.0075 4 | 1 -9059007664292712863 0.3615 NaN 5 | 2 -9009602369958523731 0.0031 0.0630 6 | 3 -8990894093332793487 0.0031 0.0075 7 | 4 -8915955323477460297 0.0031 0.0121 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0031 0.0121 10 | 96 -4495976808403190115 0.0031 0.0007 11 | 97 -4474179539802460946 0.0031 0.0048 12 | 98 -4409520928686189639 0.0031 0.0063 13 | 99 -4399716566495748799 0.0031 NaN 14 | 15 | [100 rows x 4 columns] 16 | Transition Data: 17 | feature_id transition_id pep peptide_id bmask num_peptidoforms 18 | 0 -9078977811506172301 3275 0.0075 -1 0.0 1 19 | 1 -9078977811506172301 3275 0.0075 305 1.0 1 20 | 2 -9078977811506172301 3276 0.0133 -1 0.0 1 21 | 3 -9078977811506172301 3276 0.0133 305 1.0 1 22 | 4 -9078977811506172301 3277 0.0048 -1 0.0 1 23 | .. ... ... ... ... ... ... 24 | 95 -8858715981476206597 7986 0.0017 862 1.0 1 25 | 96 -8858715981476206597 7990 0.0017 -1 0.0 1 26 | 97 -8858715981476206597 7990 0.0017 862 1.0 1 27 | 98 -8858715981476206597 7991 0.0007 -1 0.0 1 28 | 99 -8858715981476206597 7991 0.0007 862 1.0 1 29 | 30 | [100 rows x 6 columns] 31 | IPF Data: 32 | feature_id peptide_id precursor_peakgroup_pep qvalue pep 33 | 0 -9211032279639747263 976 NaN NaN NaN 34 | 1 -9209834744278112856 1157 NaN NaN NaN 35 | 2 -9204568338203974043 76 NaN NaN NaN 36 | 3 -9202066408251325127 547 NaN NaN NaN 37 | 4 -9194114845888269381 562 NaN NaN NaN 38 | .. ... ... ... ... ... 39 | 95 -8647629181891982027 661 NaN NaN NaN 40 | 96 -8646966398648626586 529 NaN NaN NaN 41 | 97 -8646066421910515352 431 1.5294e-05 0.0 0.0 42 | 98 -8638785506020691374 409 NaN NaN NaN 43 | 99 -8630914225771744781 578 NaN NaN NaN 44 | 45 | [100 rows x 5 columns] 46 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_scoring[split_parquet-h0_off-ms2_on-ms1_on].out: -------------------------------------------------------------------------------- 1 | Precursor Data: 2 | feature_id ms2_peakgroup_pep ms1_precursor_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0031 0.0003 0.0075 4 | 1 -9059007664292712863 0.3615 0.2419 NaN 5 | 2 -9009602369958523731 0.0031 0.0003 0.0630 6 | 3 -8990894093332793487 0.0031 0.0003 0.0075 7 | 4 -8915955323477460297 0.0031 0.0003 0.0121 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0031 0.0003 0.0121 10 | 96 -4495976808403190115 0.0031 0.0003 0.0007 11 | 97 -4474179539802460946 0.0031 0.0003 0.0048 12 | 98 -4409520928686189639 0.0031 0.0003 0.0063 13 | 99 -4399716566495748799 0.0031 0.0003 NaN 14 | 15 | [100 rows x 4 columns] 16 | Transition Data: 17 | feature_id transition_id pep peptide_id bmask num_peptidoforms 18 | 0 -9078977811506172301 3275 0.0075 -1 0.0 1 19 | 1 -9078977811506172301 3275 0.0075 305 1.0 1 20 | 2 -9078977811506172301 3276 0.0133 -1 0.0 1 21 | 3 -9078977811506172301 3276 0.0133 305 1.0 1 22 | 4 -9078977811506172301 3277 0.0048 -1 0.0 1 23 | .. ... ... ... ... ... ... 24 | 95 -8858715981476206597 7986 0.0017 862 1.0 1 25 | 96 -8858715981476206597 7990 0.0017 -1 0.0 1 26 | 97 -8858715981476206597 7990 0.0017 862 1.0 1 27 | 98 -8858715981476206597 7991 0.0007 -1 0.0 1 28 | 99 -8858715981476206597 7991 0.0007 862 1.0 1 29 | 30 | [100 rows x 6 columns] 31 | IPF Data: 32 | feature_id peptide_id precursor_peakgroup_pep qvalue pep 33 | 0 -9211032279639747263 976 NaN NaN NaN 34 | 1 -9209834744278112856 1157 NaN NaN NaN 35 | 2 -9204568338203974043 76 NaN NaN NaN 36 | 3 -9202066408251325127 547 NaN NaN NaN 37 | 4 -9194114845888269381 562 NaN NaN NaN 38 | .. ... ... ... ... ... 39 | 95 -8647629181891982027 661 NaN NaN NaN 40 | 96 -8646966398648626586 529 NaN NaN NaN 41 | 97 -8646066421910515352 431 4.6346e-09 0.0 0.0 42 | 98 -8638785506020691374 409 NaN NaN NaN 43 | 99 -8630914225771744781 578 NaN NaN NaN 44 | 45 | [100 rows x 5 columns] 46 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_scoring[split_parquet-h0_on-ms2_off-ms1_off].out: -------------------------------------------------------------------------------- 1 | Precursor Data: 2 | feature_id ms2_peakgroup_pep ms1_precursor_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0031 4 | 1 -9059007664292712863 0.3615 5 | 2 -9009602369958523731 0.0031 6 | 3 -8990894093332793487 0.0031 7 | 4 -8915955323477460297 0.0031 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0031 10 | 96 -4495976808403190115 0.0031 11 | 97 -4474179539802460946 0.0031 12 | 98 -4409520928686189639 0.0031 13 | 99 -4399716566495748799 0.0031 14 | 15 | [100 rows x 4 columns] 16 | Transition Data: 17 | feature_id transition_id pep peptide_id bmask num_peptidoforms 18 | 0 -9078977811506172301 3275 0.0075 -1 0.0 1 19 | 1 -9078977811506172301 3275 0.0075 305 1.0 1 20 | 2 -9078977811506172301 3276 0.0133 -1 0.0 1 21 | 3 -9078977811506172301 3276 0.0133 305 1.0 1 22 | 4 -9078977811506172301 3277 0.0048 -1 0.0 1 23 | .. ... ... ... ... ... ... 24 | 95 -8858715981476206597 7986 0.0017 862 1.0 1 25 | 96 -8858715981476206597 7990 0.0017 -1 0.0 1 26 | 97 -8858715981476206597 7990 0.0017 862 1.0 1 27 | 98 -8858715981476206597 7991 0.0007 -1 0.0 1 28 | 99 -8858715981476206597 7991 0.0007 862 1.0 1 29 | 30 | [100 rows x 6 columns] 31 | IPF Data: 32 | feature_id peptide_id precursor_peakgroup_pep qvalue pep 33 | 0 -9211032279639747263 976 NaN NaN NaN 34 | 1 -9209834744278112856 1157 NaN NaN NaN 35 | 2 -9204568338203974043 76 NaN NaN NaN 36 | 3 -9202066408251325127 547 NaN NaN NaN 37 | 4 -9194114845888269381 562 NaN NaN NaN 38 | .. ... ... ... ... ... 39 | 95 -8647629181891982027 661 NaN NaN NaN 40 | 96 -8646966398648626586 529 NaN NaN NaN 41 | 97 -8646066421910515352 431 0.0031 0.0 0.0 42 | 98 -8638785506020691374 409 NaN NaN NaN 43 | 99 -8630914225771744781 578 NaN NaN NaN 44 | 45 | [100 rows x 5 columns] 46 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_scoring[split_parquet-h0_on-ms2_off-ms1_on].out: -------------------------------------------------------------------------------- 1 | Precursor Data: 2 | feature_id ms2_peakgroup_pep ms1_precursor_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0031 0.0003 4 | 1 -9059007664292712863 0.3615 0.2419 5 | 2 -9009602369958523731 0.0031 0.0003 6 | 3 -8990894093332793487 0.0031 0.0003 7 | 4 -8915955323477460297 0.0031 0.0003 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0031 0.0003 10 | 96 -4495976808403190115 0.0031 0.0003 11 | 97 -4474179539802460946 0.0031 0.0003 12 | 98 -4409520928686189639 0.0031 0.0003 13 | 99 -4399716566495748799 0.0031 0.0003 14 | 15 | [100 rows x 4 columns] 16 | Transition Data: 17 | feature_id transition_id pep peptide_id bmask num_peptidoforms 18 | 0 -9078977811506172301 3275 0.0075 -1 0.0 1 19 | 1 -9078977811506172301 3275 0.0075 305 1.0 1 20 | 2 -9078977811506172301 3276 0.0133 -1 0.0 1 21 | 3 -9078977811506172301 3276 0.0133 305 1.0 1 22 | 4 -9078977811506172301 3277 0.0048 -1 0.0 1 23 | .. ... ... ... ... ... ... 24 | 95 -8858715981476206597 7986 0.0017 862 1.0 1 25 | 96 -8858715981476206597 7990 0.0017 -1 0.0 1 26 | 97 -8858715981476206597 7990 0.0017 862 1.0 1 27 | 98 -8858715981476206597 7991 0.0007 -1 0.0 1 28 | 99 -8858715981476206597 7991 0.0007 862 1.0 1 29 | 30 | [100 rows x 6 columns] 31 | IPF Data: 32 | feature_id peptide_id precursor_peakgroup_pep qvalue pep 33 | 0 -9211032279639747263 976 NaN NaN NaN 34 | 1 -9209834744278112856 1157 NaN NaN NaN 35 | 2 -9204568338203974043 76 NaN NaN NaN 36 | 3 -9202066408251325127 547 NaN NaN NaN 37 | 4 -9194114845888269381 562 NaN NaN NaN 38 | .. ... ... ... ... ... 39 | 95 -8647629181891982027 661 NaN NaN NaN 40 | 96 -8646966398648626586 529 NaN NaN NaN 41 | 97 -8646066421910515352 431 9.5518e-07 0.0 0.0 42 | 98 -8638785506020691374 409 NaN NaN NaN 43 | 99 -8630914225771744781 578 NaN NaN NaN 44 | 45 | [100 rows x 5 columns] 46 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_scoring[split_parquet-h0_on-ms2_on-ms1_off].out: -------------------------------------------------------------------------------- 1 | Precursor Data: 2 | feature_id ms2_peakgroup_pep ms1_precursor_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0031 0.0075 4 | 1 -9059007664292712863 0.3615 NaN 5 | 2 -9009602369958523731 0.0031 0.0630 6 | 3 -8990894093332793487 0.0031 0.0075 7 | 4 -8915955323477460297 0.0031 0.0121 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0031 0.0121 10 | 96 -4495976808403190115 0.0031 0.0007 11 | 97 -4474179539802460946 0.0031 0.0048 12 | 98 -4409520928686189639 0.0031 0.0063 13 | 99 -4399716566495748799 0.0031 NaN 14 | 15 | [100 rows x 4 columns] 16 | Transition Data: 17 | feature_id transition_id pep peptide_id bmask num_peptidoforms 18 | 0 -9078977811506172301 3275 0.0075 -1 0.0 1 19 | 1 -9078977811506172301 3275 0.0075 305 1.0 1 20 | 2 -9078977811506172301 3276 0.0133 -1 0.0 1 21 | 3 -9078977811506172301 3276 0.0133 305 1.0 1 22 | 4 -9078977811506172301 3277 0.0048 -1 0.0 1 23 | .. ... ... ... ... ... ... 24 | 95 -8858715981476206597 7986 0.0017 862 1.0 1 25 | 96 -8858715981476206597 7990 0.0017 -1 0.0 1 26 | 97 -8858715981476206597 7990 0.0017 862 1.0 1 27 | 98 -8858715981476206597 7991 0.0007 -1 0.0 1 28 | 99 -8858715981476206597 7991 0.0007 862 1.0 1 29 | 30 | [100 rows x 6 columns] 31 | IPF Data: 32 | feature_id peptide_id precursor_peakgroup_pep qvalue pep 33 | 0 -9211032279639747263 976 NaN NaN NaN 34 | 1 -9209834744278112856 1157 NaN NaN NaN 35 | 2 -9204568338203974043 76 NaN NaN NaN 36 | 3 -9202066408251325127 547 NaN NaN NaN 37 | 4 -9194114845888269381 562 NaN NaN NaN 38 | .. ... ... ... ... ... 39 | 95 -8647629181891982027 661 NaN NaN NaN 40 | 96 -8646966398648626586 529 NaN NaN NaN 41 | 97 -8646066421910515352 431 1.5294e-05 0.0 0.0 42 | 98 -8638785506020691374 409 NaN NaN NaN 43 | 99 -8630914225771744781 578 NaN NaN NaN 44 | 45 | [100 rows x 5 columns] 46 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_ipf.test_ipf_scoring[split_parquet-h0_on-ms2_on-ms1_on].out: -------------------------------------------------------------------------------- 1 | Precursor Data: 2 | feature_id ms2_peakgroup_pep ms1_precursor_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0031 0.0003 0.0075 4 | 1 -9059007664292712863 0.3615 0.2419 NaN 5 | 2 -9009602369958523731 0.0031 0.0003 0.0630 6 | 3 -8990894093332793487 0.0031 0.0003 0.0075 7 | 4 -8915955323477460297 0.0031 0.0003 0.0121 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0031 0.0003 0.0121 10 | 96 -4495976808403190115 0.0031 0.0003 0.0007 11 | 97 -4474179539802460946 0.0031 0.0003 0.0048 12 | 98 -4409520928686189639 0.0031 0.0003 0.0063 13 | 99 -4399716566495748799 0.0031 0.0003 NaN 14 | 15 | [100 rows x 4 columns] 16 | Transition Data: 17 | feature_id transition_id pep peptide_id bmask num_peptidoforms 18 | 0 -9078977811506172301 3275 0.0075 -1 0.0 1 19 | 1 -9078977811506172301 3275 0.0075 305 1.0 1 20 | 2 -9078977811506172301 3276 0.0133 -1 0.0 1 21 | 3 -9078977811506172301 3276 0.0133 305 1.0 1 22 | 4 -9078977811506172301 3277 0.0048 -1 0.0 1 23 | .. ... ... ... ... ... ... 24 | 95 -8858715981476206597 7986 0.0017 862 1.0 1 25 | 96 -8858715981476206597 7990 0.0017 -1 0.0 1 26 | 97 -8858715981476206597 7990 0.0017 862 1.0 1 27 | 98 -8858715981476206597 7991 0.0007 -1 0.0 1 28 | 99 -8858715981476206597 7991 0.0007 862 1.0 1 29 | 30 | [100 rows x 6 columns] 31 | IPF Data: 32 | feature_id peptide_id precursor_peakgroup_pep qvalue pep 33 | 0 -9211032279639747263 976 NaN NaN NaN 34 | 1 -9209834744278112856 1157 NaN NaN NaN 35 | 2 -9204568338203974043 76 NaN NaN NaN 36 | 3 -9202066408251325127 547 NaN NaN NaN 37 | 4 -9194114845888269381 562 NaN NaN NaN 38 | .. ... ... ... ... ... 39 | 95 -8647629181891982027 661 NaN NaN NaN 40 | 96 -8646966398648626586 529 NaN NaN NaN 41 | 97 -8646066421910515352 431 4.6346e-09 0.0 0.0 42 | 98 -8638785506020691374 409 NaN NaN NaN 43 | 99 -8630914225771744781 578 NaN NaN NaN 44 | 45 | [100 rows x 5 columns] 46 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_levels_contexts.test_ipf_1.out: -------------------------------------------------------------------------------- 1 | context pep protein_id pvalue qvalue run_id score 2 | 0 run-specific 0.3674 9 0.0625 0.0625 -8.6708e+18 6.0052 3 | 1 run-specific 0.3674 8 0.0625 0.0625 -8.6708e+18 5.9861 4 | 2 run-specific 0.3674 1 0.0625 0.0625 -8.6708e+18 5.9765 5 | 3 run-specific 0.3674 13 0.0625 0.0625 -8.6708e+18 5.9682 6 | 4 run-specific 0.3674 12 0.0625 0.0625 -8.6708e+18 5.9529 7 | .. ... ... ... ... ... ... ... 8 | 91 global 0.3674 25 0.0625 0.0625 NaN 1.3888 9 | 92 global 0.3674 20 0.0625 0.0625 NaN 1.3329 10 | 93 global 0.3674 16 0.0625 0.0625 NaN 0.9266 11 | 94 global 0.3674 22 0.0625 0.0625 NaN 0.7471 12 | 95 global 0.3674 23 0.0625 0.0625 NaN 0.7306 13 | 14 | [96 rows x 7 columns] 15 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_levels_contexts.test_levels_contexts_0.out: -------------------------------------------------------------------------------- 1 | context pep peptide_id pvalue qvalue run_id score 2 | 0 run-specific 0.0031 1116 0.0029 0.0033 -8670811102654834151 6.0052 3 | 1 run-specific 0.0031 485 0.0029 0.0033 -8670811102654834151 5.9964 4 | 2 run-specific 0.0031 504 0.0029 0.0033 -8670811102654834151 5.9861 5 | 3 run-specific 0.0031 831 0.0029 0.0033 -8670811102654834151 5.9765 6 | 4 run-specific 0.0031 1239 0.0029 0.0033 -8670811102654834151 5.9719 7 | .. ... ... ... ... ... ... ... 8 | 95 run-specific 0.0031 13 0.0029 0.0033 -8670811102654834151 5.6965 9 | 96 run-specific 0.0031 431 0.0029 0.0033 -8670811102654834151 5.6964 10 | 97 run-specific 0.0031 1255 0.0029 0.0033 -8670811102654834151 5.6963 11 | 98 run-specific 0.0031 1360 0.0029 0.0033 -8670811102654834151 5.6930 12 | 99 run-specific 0.0031 882 0.0029 0.0033 -8670811102654834151 5.6918 13 | 14 | [100 rows x 7 columns] 15 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_levels_contexts.test_peptide_levels[osw-experiment-wide].out: -------------------------------------------------------------------------------- 1 | Peptide Data: 2 | context run_id peptide_id score pvalue qvalue pep 3 | 0 experiment-wide -8670811102654834151 2 5.5390 0.0029 0.0033 0.0031 4 | 1 experiment-wide -8670811102654834151 3 0.1314 0.3988 0.4000 1.0000 5 | 2 experiment-wide -8670811102654834151 5 5.8582 0.0029 0.0033 0.0031 6 | 3 experiment-wide -8670811102654834151 7 0.3975 0.2493 0.2507 1.0000 7 | 4 experiment-wide -8670811102654834151 8 -0.4131 0.4692 0.4692 1.0000 8 | .. ... ... ... ... ... ... ... 9 | 95 experiment-wide -8670811102654834151 243 0.7509 0.1994 0.2018 1.0000 10 | 96 experiment-wide -8670811102654834151 244 0.9504 0.1994 0.2018 1.0000 11 | 97 experiment-wide -8670811102654834151 248 5.5804 0.0029 0.0033 0.0031 12 | 98 experiment-wide -8670811102654834151 250 5.5397 0.0029 0.0033 0.0031 13 | 99 experiment-wide -8670811102654834151 251 0.1822 0.3988 0.4000 1.0000 14 | 15 | [100 rows x 7 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_levels_contexts.test_peptide_levels[osw-global].out: -------------------------------------------------------------------------------- 1 | Peptide Data: 2 | context run_id peptide_id score pvalue qvalue pep 3 | 0 global None 2 5.5390 0.0029 0.0033 0.0031 4 | 1 global None 3 0.1314 0.3988 0.4000 1.0000 5 | 2 global None 5 5.8582 0.0029 0.0033 0.0031 6 | 3 global None 7 0.3975 0.2493 0.2507 1.0000 7 | 4 global None 8 -0.4131 0.4692 0.4692 1.0000 8 | .. ... ... ... ... ... ... ... 9 | 95 global None 243 0.7509 0.1994 0.2018 1.0000 10 | 96 global None 244 0.9504 0.1994 0.2018 1.0000 11 | 97 global None 248 5.5804 0.0029 0.0033 0.0031 12 | 98 global None 250 5.5397 0.0029 0.0033 0.0031 13 | 99 global None 251 0.1822 0.3988 0.4000 1.0000 14 | 15 | [100 rows x 7 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_levels_contexts.test_peptide_levels[osw-run-specific].out: -------------------------------------------------------------------------------- 1 | Peptide Data: 2 | context run_id peptide_id score pvalue qvalue pep 3 | 0 run-specific -8670811102654834151 2 5.5390 0.0029 0.0033 0.0031 4 | 1 run-specific -8670811102654834151 3 0.1314 0.3988 0.4000 1.0000 5 | 2 run-specific -8670811102654834151 5 5.8582 0.0029 0.0033 0.0031 6 | 3 run-specific -8670811102654834151 7 0.3975 0.2493 0.2507 1.0000 7 | 4 run-specific -8670811102654834151 8 -0.4131 0.4692 0.4692 1.0000 8 | .. ... ... ... ... ... ... ... 9 | 95 run-specific -8670811102654834151 243 0.7509 0.1994 0.2018 1.0000 10 | 96 run-specific -8670811102654834151 244 0.9504 0.1994 0.2018 1.0000 11 | 97 run-specific -8670811102654834151 248 5.5804 0.0029 0.0033 0.0031 12 | 98 run-specific -8670811102654834151 250 5.5397 0.0029 0.0033 0.0031 13 | 99 run-specific -8670811102654834151 251 0.1822 0.3988 0.4000 1.0000 14 | 15 | [100 rows x 7 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_levels_contexts.test_peptide_levels[parquet-experiment-wide].out: -------------------------------------------------------------------------------- 1 | Peptide Data: 2 | RUN_ID FEATURE_ID PEPTIDE_ID SCORE_PEPTIDE_EXPERIMENT_WIDE_SCORE SCORE_PEPTIDE_EXPERIMENT_WIDE_P_VALUE SCORE_PEPTIDE_EXPERIMENT_WIDE_Q_VALUE SCORE_PEPTIDE_EXPERIMENT_WIDE_PEP 3 | 0 -8670811102654833664 -9211032279639747584 976.0 5.0806 0.0029 0.0033 0.0031 4 | 1 -8670811102654833664 -9209834744278113280 1157.0 -0.2284 0.4692 0.4692 1.0000 5 | 2 -8670811102654833664 -9204568338203973632 76.0 0.1455 0.3988 0.4000 1.0000 6 | 3 -8670811102654833664 -9202066408251325440 547.0 5.8667 0.0029 0.0033 0.0031 7 | 4 -8670811102654833664 -9194114845888269312 562.0 5.2743 0.0029 0.0033 0.0031 8 | .. ... ... ... ... ... ... ... 9 | 95 -8670811102654833664 -8647629181891982336 661.0 0.0611 0.4692 0.4692 1.0000 10 | 96 -8670811102654833664 -8646966398648626176 529.0 5.8459 0.0029 0.0033 0.0031 11 | 97 -8670811102654833664 -8646066421910515712 431.0 5.6964 0.0029 0.0033 0.0031 12 | 98 -8670811102654833664 -8638785506020690944 409.0 0.9357 0.1994 0.2018 1.0000 13 | 99 -8670811102654833664 -8630914225771745280 578.0 -0.5617 0.4692 0.4692 1.0000 14 | 15 | [100 rows x 7 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_levels_contexts.test_peptide_levels[parquet-global].out: -------------------------------------------------------------------------------- 1 | Peptide Data: 2 | RUN_ID FEATURE_ID PEPTIDE_ID SCORE_PEPTIDE_GLOBAL_SCORE SCORE_PEPTIDE_GLOBAL_P_VALUE SCORE_PEPTIDE_GLOBAL_Q_VALUE SCORE_PEPTIDE_GLOBAL_PEP 3 | 0 -8670811102654833664 -9211032279639747584 976.0 5.0806 0.0029 0.0033 0.0031 4 | 1 -8670811102654833664 -9209834744278113280 1157.0 -0.2284 0.4692 0.4692 1.0000 5 | 2 -8670811102654833664 -9204568338203973632 76.0 0.1455 0.3988 0.4000 1.0000 6 | 3 -8670811102654833664 -9202066408251325440 547.0 5.8667 0.0029 0.0033 0.0031 7 | 4 -8670811102654833664 -9194114845888269312 562.0 5.2743 0.0029 0.0033 0.0031 8 | .. ... ... ... ... ... ... ... 9 | 95 -8670811102654833664 -8647629181891982336 661.0 0.0611 0.4692 0.4692 1.0000 10 | 96 -8670811102654833664 -8646966398648626176 529.0 5.8459 0.0029 0.0033 0.0031 11 | 97 -8670811102654833664 -8646066421910515712 431.0 5.6964 0.0029 0.0033 0.0031 12 | 98 -8670811102654833664 -8638785506020690944 409.0 0.9357 0.1994 0.2018 1.0000 13 | 99 -8670811102654833664 -8630914225771745280 578.0 -0.5617 0.4692 0.4692 1.0000 14 | 15 | [100 rows x 7 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_levels_contexts.test_peptide_levels[parquet-run-specific].out: -------------------------------------------------------------------------------- 1 | Peptide Data: 2 | RUN_ID FEATURE_ID PEPTIDE_ID SCORE_PEPTIDE_RUN_SPECIFIC_SCORE SCORE_PEPTIDE_RUN_SPECIFIC_P_VALUE SCORE_PEPTIDE_RUN_SPECIFIC_Q_VALUE SCORE_PEPTIDE_RUN_SPECIFIC_PEP 3 | 0 -8670811102654833664 -9211032279639747584 976.0 5.0806 0.0029 0.0033 0.0031 4 | 1 -8670811102654833664 -9209834744278113280 1157.0 -0.2284 0.4692 0.4692 1.0000 5 | 2 -8670811102654833664 -9204568338203973632 76.0 0.1455 0.3988 0.4000 1.0000 6 | 3 -8670811102654833664 -9202066408251325440 547.0 5.8667 0.0029 0.0033 0.0031 7 | 4 -8670811102654833664 -9194114845888269312 562.0 5.2743 0.0029 0.0033 0.0031 8 | .. ... ... ... ... ... ... ... 9 | 95 -8670811102654833664 -8647629181891982336 661.0 0.0611 0.4692 0.4692 1.0000 10 | 96 -8670811102654833664 -8646966398648626176 529.0 5.8459 0.0029 0.0033 0.0031 11 | 97 -8670811102654833664 -8646066421910515712 431.0 5.6964 0.0029 0.0033 0.0031 12 | 98 -8670811102654833664 -8638785506020690944 409.0 0.9357 0.1994 0.2018 1.0000 13 | 99 -8670811102654833664 -8630914225771745280 578.0 -0.5617 0.4692 0.4692 1.0000 14 | 15 | [100 rows x 7 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_levels_contexts.test_peptide_levels[split_parquet-experiment-wide].out: -------------------------------------------------------------------------------- 1 | Peptide Data: 2 | RUN_ID FEATURE_ID PEPTIDE_ID SCORE_PEPTIDE_EXPERIMENT_WIDE_SCORE SCORE_PEPTIDE_EXPERIMENT_WIDE_P_VALUE SCORE_PEPTIDE_EXPERIMENT_WIDE_Q_VALUE SCORE_PEPTIDE_EXPERIMENT_WIDE_PEP 3 | 0 -8670811102654833664 -9211032279639747584 976.0 5.0806 0.0029 0.0033 0.0031 4 | 1 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 5 | 2 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 6 | 3 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 7 | 4 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 8 | .. ... ... ... ... ... ... ... 9 | 95 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 10 | 96 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 11 | 97 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 12 | 98 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 13 | 99 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 14 | 15 | [100 rows x 7 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_levels_contexts.test_peptide_levels[split_parquet-global].out: -------------------------------------------------------------------------------- 1 | Peptide Data: 2 | RUN_ID FEATURE_ID PEPTIDE_ID SCORE_PEPTIDE_GLOBAL_SCORE SCORE_PEPTIDE_GLOBAL_P_VALUE SCORE_PEPTIDE_GLOBAL_Q_VALUE SCORE_PEPTIDE_GLOBAL_PEP 3 | 0 -8670811102654833664 -9211032279639747584 976.0 5.0806 0.0029 0.0033 0.0031 4 | 1 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 5 | 2 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 6 | 3 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 7 | 4 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 8 | .. ... ... ... ... ... ... ... 9 | 95 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 10 | 96 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 11 | 97 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 12 | 98 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 13 | 99 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 14 | 15 | [100 rows x 7 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_levels_contexts.test_peptide_levels[split_parquet-run-specific].out: -------------------------------------------------------------------------------- 1 | Peptide Data: 2 | RUN_ID FEATURE_ID PEPTIDE_ID SCORE_PEPTIDE_RUN_SPECIFIC_SCORE SCORE_PEPTIDE_RUN_SPECIFIC_P_VALUE SCORE_PEPTIDE_RUN_SPECIFIC_Q_VALUE SCORE_PEPTIDE_RUN_SPECIFIC_PEP 3 | 0 -8670811102654833664 -9211032279639747584 976.0 5.0806 0.0029 0.0033 0.0031 4 | 1 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 5 | 2 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 6 | 3 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 7 | 4 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 8 | .. ... ... ... ... ... ... ... 9 | 95 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 10 | 96 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 11 | 97 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 12 | 98 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 13 | 99 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 14 | 15 | [100 rows x 7 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_levels_contexts.test_protein_levels[osw-experiment-wide].out: -------------------------------------------------------------------------------- 1 | Protein Data: 2 | context run_id protein_id score pvalue qvalue pep 3 | 0 experiment-wide -8670811102654834151 0 5.8401 0.0625 0.0625 0.3674 4 | 1 experiment-wide -8670811102654834151 1 5.9765 0.0625 0.0625 0.3674 5 | 2 experiment-wide -8670811102654834151 2 5.8995 0.0625 0.0625 0.3674 6 | 3 experiment-wide -8670811102654834151 3 5.8824 0.0625 0.0625 0.3674 7 | 4 experiment-wide -8670811102654834151 4 5.8600 0.0625 0.0625 0.3674 8 | 5 experiment-wide -8670811102654834151 5 5.8587 0.0625 0.0625 0.3674 9 | 6 experiment-wide -8670811102654834151 6 5.7515 0.0625 0.0625 0.3674 10 | 7 experiment-wide -8670811102654834151 7 5.8338 0.0625 0.0625 0.3674 11 | 8 experiment-wide -8670811102654834151 8 5.9861 0.0625 0.0625 0.3674 12 | 9 experiment-wide -8670811102654834151 9 6.0052 0.0625 0.0625 0.3674 13 | 10 experiment-wide -8670811102654834151 10 5.8290 0.0625 0.0625 0.3674 14 | 11 experiment-wide -8670811102654834151 11 5.8485 0.0625 0.0625 0.3674 15 | 12 experiment-wide -8670811102654834151 12 5.9529 0.0625 0.0625 0.3674 16 | 13 experiment-wide -8670811102654834151 13 5.9682 0.0625 0.0625 0.3674 17 | 14 experiment-wide -8670811102654834151 14 5.6069 0.0625 0.0625 0.3674 18 | 15 experiment-wide -8670811102654834151 15 5.9295 0.0625 0.0625 0.3674 19 | 16 experiment-wide -8670811102654834151 16 0.9266 0.0625 0.0625 0.3674 20 | 17 experiment-wide -8670811102654834151 17 2.3906 0.0625 0.0625 0.3674 21 | 18 experiment-wide -8670811102654834151 18 1.6554 0.0625 0.0625 0.3674 22 | 19 experiment-wide -8670811102654834151 19 2.0251 0.0625 0.0625 0.3674 23 | 20 experiment-wide -8670811102654834151 20 1.3329 0.0625 0.0625 0.3674 24 | 21 experiment-wide -8670811102654834151 21 1.5227 0.0625 0.0625 0.3674 25 | 22 experiment-wide -8670811102654834151 22 0.7471 0.0625 0.0625 0.3674 26 | 23 experiment-wide -8670811102654834151 23 0.7306 0.0625 0.0625 0.3674 27 | 24 experiment-wide -8670811102654834151 24 2.0202 0.0625 0.0625 0.3674 28 | 25 experiment-wide -8670811102654834151 25 1.3888 0.0625 0.0625 0.3674 29 | 26 experiment-wide -8670811102654834151 26 4.5483 0.0625 0.0625 0.3674 30 | 27 experiment-wide -8670811102654834151 27 2.3784 0.0625 0.0625 0.3674 31 | 28 experiment-wide -8670811102654834151 28 4.4832 0.0625 0.0625 0.3674 32 | 29 experiment-wide -8670811102654834151 29 1.6475 0.0625 0.0625 0.3674 33 | 30 experiment-wide -8670811102654834151 30 2.8909 0.0625 0.0625 0.3674 34 | 31 experiment-wide -8670811102654834151 31 1.9599 0.0625 0.0625 0.3674 35 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_levels_contexts.test_protein_levels[osw-global].out: -------------------------------------------------------------------------------- 1 | Protein Data: 2 | context run_id protein_id score pvalue qvalue pep 3 | 0 global None 0 5.8401 0.0625 0.0625 0.3674 4 | 1 global None 1 5.9765 0.0625 0.0625 0.3674 5 | 2 global None 2 5.8995 0.0625 0.0625 0.3674 6 | 3 global None 3 5.8824 0.0625 0.0625 0.3674 7 | 4 global None 4 5.8600 0.0625 0.0625 0.3674 8 | 5 global None 5 5.8587 0.0625 0.0625 0.3674 9 | 6 global None 6 5.7515 0.0625 0.0625 0.3674 10 | 7 global None 7 5.8338 0.0625 0.0625 0.3674 11 | 8 global None 8 5.9861 0.0625 0.0625 0.3674 12 | 9 global None 9 6.0052 0.0625 0.0625 0.3674 13 | 10 global None 10 5.8290 0.0625 0.0625 0.3674 14 | 11 global None 11 5.8485 0.0625 0.0625 0.3674 15 | 12 global None 12 5.9529 0.0625 0.0625 0.3674 16 | 13 global None 13 5.9682 0.0625 0.0625 0.3674 17 | 14 global None 14 5.6069 0.0625 0.0625 0.3674 18 | 15 global None 15 5.9295 0.0625 0.0625 0.3674 19 | 16 global None 16 0.9266 0.0625 0.0625 0.3674 20 | 17 global None 17 2.3906 0.0625 0.0625 0.3674 21 | 18 global None 18 1.6554 0.0625 0.0625 0.3674 22 | 19 global None 19 2.0251 0.0625 0.0625 0.3674 23 | 20 global None 20 1.3329 0.0625 0.0625 0.3674 24 | 21 global None 21 1.5227 0.0625 0.0625 0.3674 25 | 22 global None 22 0.7471 0.0625 0.0625 0.3674 26 | 23 global None 23 0.7306 0.0625 0.0625 0.3674 27 | 24 global None 24 2.0202 0.0625 0.0625 0.3674 28 | 25 global None 25 1.3888 0.0625 0.0625 0.3674 29 | 26 global None 26 4.5483 0.0625 0.0625 0.3674 30 | 27 global None 27 2.3784 0.0625 0.0625 0.3674 31 | 28 global None 28 4.4832 0.0625 0.0625 0.3674 32 | 29 global None 29 1.6475 0.0625 0.0625 0.3674 33 | 30 global None 30 2.8909 0.0625 0.0625 0.3674 34 | 31 global None 31 1.9599 0.0625 0.0625 0.3674 35 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_levels_contexts.test_protein_levels[osw-run-specific].out: -------------------------------------------------------------------------------- 1 | Protein Data: 2 | context run_id protein_id score pvalue qvalue pep 3 | 0 run-specific -8670811102654834151 0 5.8401 0.0625 0.0625 0.3674 4 | 1 run-specific -8670811102654834151 1 5.9765 0.0625 0.0625 0.3674 5 | 2 run-specific -8670811102654834151 2 5.8995 0.0625 0.0625 0.3674 6 | 3 run-specific -8670811102654834151 3 5.8824 0.0625 0.0625 0.3674 7 | 4 run-specific -8670811102654834151 4 5.8600 0.0625 0.0625 0.3674 8 | 5 run-specific -8670811102654834151 5 5.8587 0.0625 0.0625 0.3674 9 | 6 run-specific -8670811102654834151 6 5.7515 0.0625 0.0625 0.3674 10 | 7 run-specific -8670811102654834151 7 5.8338 0.0625 0.0625 0.3674 11 | 8 run-specific -8670811102654834151 8 5.9861 0.0625 0.0625 0.3674 12 | 9 run-specific -8670811102654834151 9 6.0052 0.0625 0.0625 0.3674 13 | 10 run-specific -8670811102654834151 10 5.8290 0.0625 0.0625 0.3674 14 | 11 run-specific -8670811102654834151 11 5.8485 0.0625 0.0625 0.3674 15 | 12 run-specific -8670811102654834151 12 5.9529 0.0625 0.0625 0.3674 16 | 13 run-specific -8670811102654834151 13 5.9682 0.0625 0.0625 0.3674 17 | 14 run-specific -8670811102654834151 14 5.6069 0.0625 0.0625 0.3674 18 | 15 run-specific -8670811102654834151 15 5.9295 0.0625 0.0625 0.3674 19 | 16 run-specific -8670811102654834151 16 0.9266 0.0625 0.0625 0.3674 20 | 17 run-specific -8670811102654834151 17 2.3906 0.0625 0.0625 0.3674 21 | 18 run-specific -8670811102654834151 18 1.6554 0.0625 0.0625 0.3674 22 | 19 run-specific -8670811102654834151 19 2.0251 0.0625 0.0625 0.3674 23 | 20 run-specific -8670811102654834151 20 1.3329 0.0625 0.0625 0.3674 24 | 21 run-specific -8670811102654834151 21 1.5227 0.0625 0.0625 0.3674 25 | 22 run-specific -8670811102654834151 22 0.7471 0.0625 0.0625 0.3674 26 | 23 run-specific -8670811102654834151 23 0.7306 0.0625 0.0625 0.3674 27 | 24 run-specific -8670811102654834151 24 2.0202 0.0625 0.0625 0.3674 28 | 25 run-specific -8670811102654834151 25 1.3888 0.0625 0.0625 0.3674 29 | 26 run-specific -8670811102654834151 26 4.5483 0.0625 0.0625 0.3674 30 | 27 run-specific -8670811102654834151 27 2.3784 0.0625 0.0625 0.3674 31 | 28 run-specific -8670811102654834151 28 4.4832 0.0625 0.0625 0.3674 32 | 29 run-specific -8670811102654834151 29 1.6475 0.0625 0.0625 0.3674 33 | 30 run-specific -8670811102654834151 30 2.8909 0.0625 0.0625 0.3674 34 | 31 run-specific -8670811102654834151 31 1.9599 0.0625 0.0625 0.3674 35 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_levels_contexts.test_protein_levels[parquet-experiment-wide].out: -------------------------------------------------------------------------------- 1 | Protein Data: 2 | RUN_ID FEATURE_ID PROTEIN_ID SCORE_PROTEIN_EXPERIMENT_WIDE_SCORE SCORE_PROTEIN_EXPERIMENT_WIDE_P_VALUE SCORE_PROTEIN_EXPERIMENT_WIDE_Q_VALUE SCORE_PROTEIN_EXPERIMENT_WIDE_PEP 3 | 0 -8670811102654833664 -9211032279639747584 2.0 5.8995 0.0625 0.0625 0.3674 4 | 1 -8670811102654833664 -9209834744278113280 21.0 1.5227 0.0625 0.0625 0.3674 5 | 2 -8670811102654833664 -9204568338203973632 31.0 1.9599 0.0625 0.0625 0.3674 6 | 3 -8670811102654833664 -9202066408251325440 12.0 5.9529 0.0625 0.0625 0.3674 7 | 4 -8670811102654833664 -9194114845888269312 10.0 5.8290 0.0625 0.0625 0.3674 8 | .. ... ... ... ... ... ... ... 9 | 95 -8670811102654833664 -8647629181891982336 31.0 1.9599 0.0625 0.0625 0.3674 10 | 96 -8670811102654833664 -8646966398648626176 15.0 5.9295 0.0625 0.0625 0.3674 11 | 97 -8670811102654833664 -8646066421910515712 4.0 5.8600 0.0625 0.0625 0.3674 12 | 98 -8670811102654833664 -8638785506020690944 27.0 2.3784 0.0625 0.0625 0.3674 13 | 99 -8670811102654833664 -8630914225771745280 23.0 0.7306 0.0625 0.0625 0.3674 14 | 15 | [100 rows x 7 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_levels_contexts.test_protein_levels[parquet-global].out: -------------------------------------------------------------------------------- 1 | Protein Data: 2 | RUN_ID FEATURE_ID PROTEIN_ID SCORE_PROTEIN_GLOBAL_SCORE SCORE_PROTEIN_GLOBAL_P_VALUE SCORE_PROTEIN_GLOBAL_Q_VALUE SCORE_PROTEIN_GLOBAL_PEP 3 | 0 -8670811102654833664 -9211032279639747584 2.0 5.8995 0.0625 0.0625 0.3674 4 | 1 -8670811102654833664 -9209834744278113280 21.0 1.5227 0.0625 0.0625 0.3674 5 | 2 -8670811102654833664 -9204568338203973632 31.0 1.9599 0.0625 0.0625 0.3674 6 | 3 -8670811102654833664 -9202066408251325440 12.0 5.9529 0.0625 0.0625 0.3674 7 | 4 -8670811102654833664 -9194114845888269312 10.0 5.8290 0.0625 0.0625 0.3674 8 | .. ... ... ... ... ... ... ... 9 | 95 -8670811102654833664 -8647629181891982336 31.0 1.9599 0.0625 0.0625 0.3674 10 | 96 -8670811102654833664 -8646966398648626176 15.0 5.9295 0.0625 0.0625 0.3674 11 | 97 -8670811102654833664 -8646066421910515712 4.0 5.8600 0.0625 0.0625 0.3674 12 | 98 -8670811102654833664 -8638785506020690944 27.0 2.3784 0.0625 0.0625 0.3674 13 | 99 -8670811102654833664 -8630914225771745280 23.0 0.7306 0.0625 0.0625 0.3674 14 | 15 | [100 rows x 7 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_levels_contexts.test_protein_levels[parquet-run-specific].out: -------------------------------------------------------------------------------- 1 | Protein Data: 2 | RUN_ID FEATURE_ID PROTEIN_ID SCORE_PROTEIN_RUN_SPECIFIC_SCORE SCORE_PROTEIN_RUN_SPECIFIC_P_VALUE SCORE_PROTEIN_RUN_SPECIFIC_Q_VALUE SCORE_PROTEIN_RUN_SPECIFIC_PEP 3 | 0 -8670811102654833664 -9211032279639747584 2.0 5.8995 0.0625 0.0625 0.3674 4 | 1 -8670811102654833664 -9209834744278113280 21.0 1.5227 0.0625 0.0625 0.3674 5 | 2 -8670811102654833664 -9204568338203973632 31.0 1.9599 0.0625 0.0625 0.3674 6 | 3 -8670811102654833664 -9202066408251325440 12.0 5.9529 0.0625 0.0625 0.3674 7 | 4 -8670811102654833664 -9194114845888269312 10.0 5.8290 0.0625 0.0625 0.3674 8 | .. ... ... ... ... ... ... ... 9 | 95 -8670811102654833664 -8647629181891982336 31.0 1.9599 0.0625 0.0625 0.3674 10 | 96 -8670811102654833664 -8646966398648626176 15.0 5.9295 0.0625 0.0625 0.3674 11 | 97 -8670811102654833664 -8646066421910515712 4.0 5.8600 0.0625 0.0625 0.3674 12 | 98 -8670811102654833664 -8638785506020690944 27.0 2.3784 0.0625 0.0625 0.3674 13 | 99 -8670811102654833664 -8630914225771745280 23.0 0.7306 0.0625 0.0625 0.3674 14 | 15 | [100 rows x 7 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_levels_contexts.test_protein_levels[split_parquet-experiment-wide].out: -------------------------------------------------------------------------------- 1 | Protein Data: 2 | RUN_ID FEATURE_ID PROTEIN_ID SCORE_PROTEIN_EXPERIMENT_WIDE_SCORE SCORE_PROTEIN_EXPERIMENT_WIDE_P_VALUE SCORE_PROTEIN_EXPERIMENT_WIDE_Q_VALUE SCORE_PROTEIN_EXPERIMENT_WIDE_PEP 3 | 0 -8670811102654833664 -9211032279639747584 2.0 5.8995 0.0625 0.0625 0.3674 4 | 1 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 5 | 2 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 6 | 3 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 7 | 4 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 8 | .. ... ... ... ... ... ... ... 9 | 95 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 10 | 96 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 11 | 97 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 12 | 98 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 13 | 99 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 14 | 15 | [100 rows x 7 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_levels_contexts.test_protein_levels[split_parquet-global].out: -------------------------------------------------------------------------------- 1 | Protein Data: 2 | RUN_ID FEATURE_ID PROTEIN_ID SCORE_PROTEIN_GLOBAL_SCORE SCORE_PROTEIN_GLOBAL_P_VALUE SCORE_PROTEIN_GLOBAL_Q_VALUE SCORE_PROTEIN_GLOBAL_PEP 3 | 0 -8670811102654833664 -9211032279639747584 2.0 5.8995 0.0625 0.0625 0.3674 4 | 1 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 5 | 2 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 6 | 3 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 7 | 4 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 8 | .. ... ... ... ... ... ... ... 9 | 95 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 10 | 96 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 11 | 97 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 12 | 98 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 13 | 99 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 14 | 15 | [100 rows x 7 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_levels_contexts.test_protein_levels[split_parquet-run-specific].out: -------------------------------------------------------------------------------- 1 | Protein Data: 2 | RUN_ID FEATURE_ID PROTEIN_ID SCORE_PROTEIN_RUN_SPECIFIC_SCORE SCORE_PROTEIN_RUN_SPECIFIC_P_VALUE SCORE_PROTEIN_RUN_SPECIFIC_Q_VALUE SCORE_PROTEIN_RUN_SPECIFIC_PEP 3 | 0 -8670811102654833664 -9211032279639747584 2.0 5.8995 0.0625 0.0625 0.3674 4 | 1 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 5 | 2 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 6 | 3 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 7 | 4 -8670811102654833664 -9211032279639747584 NaN NaN NaN NaN NaN 8 | .. ... ... ... ... ... ... ... 9 | 95 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 10 | 96 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 11 | 97 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 12 | 98 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 13 | 99 -8670811102654833664 -9202066408251325440 NaN NaN NaN NaN NaN 14 | 15 | [100 rows x 7 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_multi_split_parquet_0.out: -------------------------------------------------------------------------------- 1 | 96259 2 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0064 0.0031 0.0987 4 | 1 -9059007664292712863 1.0000 0.3615 NaN 5 | 2 -9009602369958523731 0.0064 0.0031 0.4402 6 | 3 -8990894093332793487 0.0064 0.0031 0.0460 7 | 4 -8915955323477460297 0.0064 0.0031 0.0090 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0064 0.0031 0.0460 10 | 96 -4495976808403190115 0.0064 0.0031 0.0227 11 | 97 -4474179539802460946 0.0064 0.0031 0.0090 12 | 98 -4409520928686189639 0.0064 0.0031 0.1062 13 | 99 -4399716566495748799 0.0064 0.0031 NaN 14 | 15 | [100 rows x 4 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_multi_split_parquet_1.out: -------------------------------------------------------------------------------- 1 | 96259 2 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0005 9.9581e-08 0.1118 4 | 1 -9059007664292712863 1.0000 8.6993e-01 NaN 5 | 2 -9009602369958523731 0.0005 8.9398e-07 0.4155 6 | 3 -8990894093332793487 0.0005 2.8346e-07 0.0409 7 | 4 -8915955323477460297 0.0003 1.8926e-07 0.0181 8 | .. ... ... ... ... 9 | 95 -4554654845515399609 0.0003 3.8685e-08 NaN 10 | 96 -4539808410625597778 0.0094 1.2626e-06 0.0352 11 | 97 -4495976808403190115 0.0002 3.8685e-08 0.0378 12 | 98 -4474179539802460946 0.0002 3.8685e-08 0.0157 13 | 99 -4409520928686189639 0.0002 3.8685e-08 0.1650 14 | 15 | [100 rows x 4 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_multi_split_parquet_2.out: -------------------------------------------------------------------------------- 1 | 96259 2 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0064 0.0031 0.0987 4 | 1 -9059007664292712863 1.0000 0.3615 NaN 5 | 2 -9009602369958523731 0.0064 0.0031 0.4402 6 | 3 -8990894093332793487 0.0064 0.0031 0.0460 7 | 4 -8915955323477460297 0.0064 0.0031 0.0090 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0064 0.0031 0.0460 10 | 96 -4495976808403190115 0.0064 0.0031 0.0227 11 | 97 -4474179539802460946 0.0064 0.0031 0.0090 12 | 98 -4409520928686189639 0.0064 0.0031 0.1062 13 | 99 -4399716566495748799 0.0064 0.0031 NaN 14 | 15 | [100 rows x 4 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_multi_split_parquet_3.out: -------------------------------------------------------------------------------- 1 | 96259 2 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0064 0.0031 0.0987 4 | 1 -9059007664292712863 1.0000 0.3615 NaN 5 | 2 -9009602369958523731 0.0064 0.0031 0.4402 6 | 3 -8990894093332793487 0.0064 0.0031 0.0460 7 | 4 -8915955323477460297 0.0064 0.0031 0.0090 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0064 0.0031 0.0460 10 | 96 -4495976808403190115 0.0064 0.0031 0.0227 11 | 97 -4474179539802460946 0.0064 0.0031 0.0090 12 | 98 -4409520928686189639 0.0064 0.0031 0.1062 13 | 99 -4399716566495748799 0.0064 0.0031 NaN 14 | 15 | [100 rows x 4 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_multi_split_parquet_6.out: -------------------------------------------------------------------------------- 1 | 96259 2 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0064 0.0034 0.0997 4 | 1 -9009602369958523731 0.0064 0.0034 0.4270 5 | 2 -8990894093332793487 0.0064 0.0034 0.0463 6 | 3 -8915955323477460297 0.0064 0.0034 0.0089 7 | 4 -8858715981476206597 0.0064 0.0034 0.0089 8 | .. ... ... ... ... 9 | 95 -4213710682827324837 0.0064 0.0034 0.0089 10 | 96 -4209714083879967850 0.5256 0.0218 NaN 11 | 97 -4195322252177179725 0.0064 0.0034 0.0227 12 | 98 -4146541955602089926 0.0064 0.0034 0.0463 13 | 99 -4109405113780929799 0.0064 0.0034 0.0050 14 | 15 | [100 rows x 4 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_multi_split_parquet_7.out: -------------------------------------------------------------------------------- 1 | 96259 2 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0064 0.0034 0.0997 4 | 1 -9009602369958523731 0.0064 0.0034 0.4270 5 | 2 -8990894093332793487 0.0064 0.0034 0.0463 6 | 3 -8915955323477460297 0.0064 0.0034 0.0089 7 | 4 -8858715981476206597 0.0064 0.0034 0.0089 8 | .. ... ... ... ... 9 | 95 -4213710682827324837 0.0064 0.0034 0.0089 10 | 96 -4209714083879967850 0.5256 0.0218 NaN 11 | 97 -4195322252177179725 0.0064 0.0034 0.0227 12 | 98 -4146541955602089926 0.0064 0.0034 0.0463 13 | 99 -4109405113780929799 0.0064 0.0034 0.0050 14 | 15 | [100 rows x 4 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_multi_split_parquet_8.out: -------------------------------------------------------------------------------- 1 | 96259 2 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0064 0.0031 0.0987 4 | 1 -9059007664292712863 1.0000 0.3615 NaN 5 | 2 -9009602369958523731 0.0064 0.0031 0.4402 6 | 3 -8990894093332793487 0.0064 0.0031 0.0460 7 | 4 -8915955323477460297 0.0064 0.0031 0.0090 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0064 0.0031 0.0460 10 | 96 -4495976808403190115 0.0064 0.0031 0.0227 11 | 97 -4474179539802460946 0.0064 0.0031 0.0090 12 | 98 -4409520928686189639 0.0064 0.0031 0.1062 13 | 99 -4399716566495748799 0.0064 0.0031 NaN 14 | 15 | [100 rows x 4 columns] 16 | 96259 17 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 18 | 0 -9078977811506172301 0.0064 0.0031 0.0987 19 | 1 -9059007664292712863 1.0000 0.3615 NaN 20 | 2 -9009602369958523731 0.0064 0.0031 0.4402 21 | 3 -8990894093332793487 0.0064 0.0031 0.0460 22 | 4 -8915955323477460297 0.0064 0.0031 0.0090 23 | .. ... ... ... ... 24 | 95 -4539808410625597778 0.0064 0.0031 0.0460 25 | 96 -4495976808403190115 0.0064 0.0031 0.0227 26 | 97 -4474179539802460946 0.0064 0.0031 0.0090 27 | 98 -4409520928686189639 0.0064 0.0031 0.1062 28 | 99 -4399716566495748799 0.0064 0.0031 NaN 29 | 30 | [100 rows x 4 columns] 31 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_multi_split_parquet_9.out: -------------------------------------------------------------------------------- 1 | 96259 2 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0064 0.0031 0.0987 4 | 1 -9059007664292712863 1.0000 0.3615 NaN 5 | 2 -9009602369958523731 0.0064 0.0031 0.4402 6 | 3 -8990894093332793487 0.0064 0.0031 0.0460 7 | 4 -8915955323477460297 0.0064 0.0031 0.0090 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0064 0.0031 0.0460 10 | 96 -4495976808403190115 0.0064 0.0031 0.0227 11 | 97 -4474179539802460946 0.0064 0.0031 0.0090 12 | 98 -4409520928686189639 0.0064 0.0031 0.1062 13 | 99 -4399716566495748799 0.0064 0.0031 NaN 14 | 15 | [100 rows x 4 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_multi_split_parquet_apply_weights.out: -------------------------------------------------------------------------------- 1 | 3410 2 | 96259 3 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 4 | 0 -9211032279639747263 1.5282e-01 5 | 1 -9202066408251325127 6.6976e-02 6 | 2 -9194114845888269381 1.5282e-01 7 | 3 -9157656806856886367 1.5282e-01 8 | 4 -9154199948799956056 1.5282e-01 9 | .. ... ... ... ... 10 | 95 -7954403927701730016 1.5358e-05 11 | 96 -7945183889919201418 1.5282e-01 12 | 97 -7921043005244251597 5.9199e-02 13 | 98 -7915076791408573751 1.5282e-01 14 | 99 -7898872943061400987 1.1083e-02 15 | 16 | [100 rows x 4 columns] 17 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_osw_0.out: -------------------------------------------------------------------------------- 1 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 2 | 0 -9078977811506172301 0.0064 0.0031 0.0987 3 | 1 -9009602369958523731 0.0064 0.0031 0.4402 4 | 2 -8990894093332793487 0.0064 0.0031 0.0460 5 | 3 -8915955323477460297 0.0064 0.0031 0.0090 6 | 4 -8858715981476206597 0.0064 0.0031 0.0090 7 | .. ... ... ... ... 8 | 95 -3220457216356394124 0.0064 0.0031 0.0090 9 | 96 -3212703409469281429 0.0064 0.0031 0.0090 10 | 97 -3196707605593292319 0.0064 0.0031 0.0460 11 | 98 -3129995828656718688 0.0064 0.0031 0.0460 12 | 99 -3096050638984928024 0.0064 0.0031 0.0227 13 | 14 | [100 rows x 4 columns] 15 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_osw_1.out: -------------------------------------------------------------------------------- 1 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 2 | 0 -9078977811506172301 0.0005 9.9581e-08 0.1118 3 | 1 -9009602369958523731 0.0005 8.9398e-07 0.4155 4 | 2 -8990894093332793487 0.0005 2.8346e-07 0.0409 5 | 3 -8915955323477460297 0.0003 1.8926e-07 0.0181 6 | 4 -8858715981476206597 0.0002 3.8685e-08 0.0144 7 | .. ... ... ... ... 8 | 95 -3220457216356394124 0.0002 4.4892e-08 0.0274 9 | 96 -3212703409469281429 0.0008 7.2903e-07 0.0154 10 | 97 -3196707605593292319 0.0002 4.1300e-08 0.0483 11 | 98 -3129995828656718688 0.0002 3.8685e-08 0.0435 12 | 99 -3096050638984928024 0.0002 9.0820e-08 0.0420 13 | 14 | [100 rows x 4 columns] 15 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_osw_10.out: -------------------------------------------------------------------------------- 1 | level score weight 2 | 0 ms1 main_var_xcorr_shape 3.6907e+00 3 | 1 ms1ms2 main_var_xcorr_shape 1.6380e+00 4 | 2 ms1ms2 var_intensity_score 2.8489e+00 5 | 3 ms1 var_isotope_correlation_score 2.8452e-02 6 | 4 ms1ms2 var_isotope_correlation_score 9.0028e-01 7 | 5 ms1 var_isotope_overlap_score -5.8129e-02 8 | 6 ms1ms2 var_isotope_overlap_score -3.5869e-01 9 | 7 ms1ms2 var_library_corr 9.1134e-01 10 | 8 ms1 var_massdev_score 2.1145e-02 11 | 9 ms1ms2 var_massdev_score -3.1249e-02 12 | 10 ms1ms2 var_ms1_isotope_correlation_score 3.8243e-02 13 | 11 ms1ms2 var_ms1_isotope_overlap_score 5.8031e-02 14 | 12 ms1ms2 var_ms1_massdev_score -5.9398e-05 15 | 13 ms1ms2 var_ms1_xcorr_coelution -9.5609e-02 16 | 14 ms1ms2 var_norm_rt_score -3.7204e+01 17 | 15 ms1 var_xcorr_coelution -5.1548e-02 18 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_osw_2.out: -------------------------------------------------------------------------------- 1 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 2 | 0 -9078977811506172301 0.0064 0.0031 0.0987 3 | 1 -9009602369958523731 0.0064 0.0031 0.4402 4 | 2 -8990894093332793487 0.0064 0.0031 0.0460 5 | 3 -8915955323477460297 0.0064 0.0031 0.0090 6 | 4 -8858715981476206597 0.0064 0.0031 0.0090 7 | .. ... ... ... ... 8 | 95 -3220457216356394124 0.0064 0.0031 0.0090 9 | 96 -3212703409469281429 0.0064 0.0031 0.0090 10 | 97 -3196707605593292319 0.0064 0.0031 0.0460 11 | 98 -3129995828656718688 0.0064 0.0031 0.0460 12 | 99 -3096050638984928024 0.0064 0.0031 0.0227 13 | 14 | [100 rows x 4 columns] 15 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_osw_3.out: -------------------------------------------------------------------------------- 1 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 2 | 0 -9078977811506172301 0.0064 0.0031 0.0987 3 | 1 -9009602369958523731 0.0064 0.0031 0.4402 4 | 2 -8990894093332793487 0.0064 0.0031 0.0460 5 | 3 -8915955323477460297 0.0064 0.0031 0.0090 6 | 4 -8858715981476206597 0.0064 0.0031 0.0090 7 | .. ... ... ... ... 8 | 95 -3220457216356394124 0.0064 0.0031 0.0090 9 | 96 -3212703409469281429 0.0064 0.0031 0.0090 10 | 97 -3196707605593292319 0.0064 0.0031 0.0460 11 | 98 -3129995828656718688 0.0064 0.0031 0.0460 12 | 99 -3096050638984928024 0.0064 0.0031 0.0227 13 | 14 | [100 rows x 4 columns] 15 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_osw_4.out: -------------------------------------------------------------------------------- 1 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 2 | 0 -9078977811506172301 0.0009 0.0024 0.3929 3 | 1 -9009602369958523731 0.0009 0.0024 0.0252 4 | 2 -8990894093332793487 0.0009 0.0024 0.1486 5 | 3 -8915955323477460297 0.0009 0.0024 0.0421 6 | 4 -8858715981476206597 0.0009 0.0024 0.3929 7 | .. ... ... ... ... 8 | 95 -2872329084347808160 0.0009 0.0024 0.3929 9 | 96 -2789098353857361973 0.0009 0.0024 0.3929 10 | 97 -2788620575140019858 0.0009 0.0024 0.3929 11 | 98 -2741276427609241638 0.0106 0.0024 0.1486 12 | 99 -2709746704397488063 0.0009 0.0024 0.3929 13 | 14 | [100 rows x 4 columns] 15 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_osw_5.out: -------------------------------------------------------------------------------- 1 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 2 | 0 -9078977811506172301 0.0176 0.0048 0.5736 3 | 1 -9009602369958523731 0.0176 0.0048 0.1118 4 | 2 -8990894093332793487 0.0176 0.0048 0.3435 5 | 3 -8915955323477460297 0.0176 0.0048 0.1118 6 | 4 -8858715981476206597 0.0176 0.0048 0.5736 7 | .. ... ... ... ... 8 | 95 -3212703409469281429 0.0176 0.0048 0.1118 9 | 96 -3196707605593292319 0.0176 0.0048 0.5736 10 | 97 -3129995828656718688 0.0176 0.0048 0.5736 11 | 98 -3096050638984928024 0.0176 0.0048 0.5736 12 | 99 -2959420398616195477 1.0000 0.0048 0.5736 13 | 14 | [100 rows x 4 columns] 15 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_osw_6.out: -------------------------------------------------------------------------------- 1 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 2 | 0 -9078977811506172301 0.0064 0.0034 0.0997 3 | 1 -9009602369958523731 0.0064 0.0034 0.4270 4 | 2 -8990894093332793487 0.0064 0.0034 0.0463 5 | 3 -8915955323477460297 0.0064 0.0034 0.0089 6 | 4 -8858715981476206597 0.0064 0.0034 0.0089 7 | .. ... ... ... ... 8 | 95 -3220457216356394124 0.0064 0.0034 0.0089 9 | 96 -3212703409469281429 0.0064 0.0034 0.0089 10 | 97 -3196707605593292319 0.0064 0.0034 0.0463 11 | 98 -3129995828656718688 0.0064 0.0034 0.0463 12 | 99 -3096050638984928024 0.0064 0.0034 0.0227 13 | 14 | [100 rows x 4 columns] 15 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_osw_7.out: -------------------------------------------------------------------------------- 1 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 2 | 0 -9078977811506172301 0.0064 0.0034 0.0997 3 | 1 -9009602369958523731 0.0064 0.0034 0.4270 4 | 2 -8990894093332793487 0.0064 0.0034 0.0463 5 | 3 -8915955323477460297 0.0064 0.0034 0.0089 6 | 4 -8858715981476206597 0.0064 0.0034 0.0089 7 | .. ... ... ... ... 8 | 95 -3220457216356394124 0.0064 0.0034 0.0089 9 | 96 -3212703409469281429 0.0064 0.0034 0.0089 10 | 97 -3196707605593292319 0.0064 0.0034 0.0463 11 | 98 -3129995828656718688 0.0064 0.0034 0.0463 12 | 99 -3096050638984928024 0.0064 0.0034 0.0227 13 | 14 | [100 rows x 4 columns] 15 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_osw_8.out: -------------------------------------------------------------------------------- 1 | level score weight 2 | 0 ms1 main_var_xcorr_shape 9.5599e-01 3 | 1 ms2 main_var_xcorr_shape -2.5707e+00 4 | 2 ms2 var_bseries_score 2.9013e-15 5 | 3 ms2 var_dotprod_score 4.8268e+00 6 | 4 ms2 var_intensity_score 1.8417e+00 7 | 5 ms1 var_isotope_correlation_score 9.9777e-02 8 | 6 ms2 var_isotope_correlation_score 3.4469e-01 9 | 7 ms1 var_isotope_overlap_score -8.4481e-02 10 | 8 ms2 var_isotope_overlap_score 1.5073e-01 11 | 9 ms2 var_library_corr 5.6312e-01 12 | 10 ms2 var_library_dotprod -7.0580e+00 13 | 11 ms2 var_library_manhattan -2.2549e+00 14 | 12 ms2 var_library_rmsd -6.9756e+00 15 | 13 ms2 var_library_rootmeansquare -1.0792e+01 16 | 14 ms2 var_library_sangle 6.1293e+00 17 | 15 ms2 var_log_sn_score 4.2718e-01 18 | 16 ms2 var_manhattan_score 7.6499e-01 19 | 17 ms1 var_massdev_score -1.8875e-03 20 | 18 ms2 var_massdev_score -1.7858e-02 21 | 19 ms2 var_massdev_score_weighted -1.4218e-02 22 | 20 ms2 var_norm_rt_score -6.1086e+01 23 | 21 ms1 var_xcorr_coelution -4.6643e-02 24 | 22 ms2 var_xcorr_coelution -6.4718e-02 25 | 23 ms1 var_xcorr_coelution_combined 1.2248e+00 26 | 24 ms1 var_xcorr_coelution_contrast -1.2179e+00 27 | 25 ms2 var_xcorr_coelution_weighted 1.3923e-01 28 | 26 ms1 var_xcorr_shape_combined -2.5555e+01 29 | 27 ms1 var_xcorr_shape_contrast 2.9255e+01 30 | 28 ms2 var_xcorr_shape_weighted 6.7453e-01 31 | 29 ms2 var_yseries_score 0.0000e+00 32 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_osw_9.out: -------------------------------------------------------------------------------- 1 | level score weight 2 | 0 ms1 main_var_xcorr_shape 3.6907 3 | 1 ms2 main_var_xcorr_shape 1.8019 4 | 2 ms2 var_intensity_score 2.9725 5 | 3 ms1 var_isotope_correlation_score 0.0285 6 | 4 ms2 var_isotope_correlation_score 0.7824 7 | 5 ms1 var_isotope_overlap_score -0.0581 8 | 6 ms2 var_isotope_overlap_score -0.3541 9 | 7 ms2 var_library_corr 0.9053 10 | 8 ms1 var_massdev_score 0.0211 11 | 9 ms2 var_massdev_score -0.0305 12 | 10 ms2 var_norm_rt_score -60.9504 13 | 11 ms1 var_xcorr_coelution -0.0515 14 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_parquet_0.out: -------------------------------------------------------------------------------- 1 | 97964 2 | Empty DataFrame 3 | Columns: [feature_id, ms1_precursor_pep, ms2_peakgroup_pep, ms2_precursor_pep] 4 | Index: [] 5 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_parquet_1.out: -------------------------------------------------------------------------------- 1 | 97964 2 | Empty DataFrame 3 | Columns: [feature_id, ms1_precursor_pep, ms2_peakgroup_pep, ms2_precursor_pep] 4 | Index: [] 5 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_parquet_2.out: -------------------------------------------------------------------------------- 1 | 97964 2 | Empty DataFrame 3 | Columns: [feature_id, ms1_precursor_pep, ms2_peakgroup_pep, ms2_precursor_pep] 4 | Index: [] 5 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_parquet_3.out: -------------------------------------------------------------------------------- 1 | 97964 2 | Empty DataFrame 3 | Columns: [feature_id, ms1_precursor_pep, ms2_peakgroup_pep, ms2_precursor_pep] 4 | Index: [] 5 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_parquet_6.out: -------------------------------------------------------------------------------- 1 | 97964 2 | Empty DataFrame 3 | Columns: [feature_id, ms1_precursor_pep, ms2_peakgroup_pep, ms2_precursor_pep] 4 | Index: [] 5 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_parquet_7.out: -------------------------------------------------------------------------------- 1 | 97964 2 | Empty DataFrame 3 | Columns: [feature_id, ms1_precursor_pep, ms2_peakgroup_pep, ms2_precursor_pep] 4 | Index: [] 5 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_parquet_8.out: -------------------------------------------------------------------------------- 1 | 97964 2 | Empty DataFrame 3 | Columns: [feature_id, ms1_precursor_pep, ms2_peakgroup_pep, ms2_precursor_pep] 4 | Index: [] 5 | 97964 6 | Empty DataFrame 7 | Columns: [feature_id, ms1_precursor_pep, ms2_peakgroup_pep, ms2_precursor_pep] 8 | Index: [] 9 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_parquet_9.out: -------------------------------------------------------------------------------- 1 | 97964 2 | Empty DataFrame 3 | Columns: [feature_id, ms1_precursor_pep, ms2_peakgroup_pep, ms2_precursor_pep] 4 | Index: [] 5 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_parquet_apply_weights.out: -------------------------------------------------------------------------------- 1 | 97964 2 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 3 | 0 -9211032279639747263 1.5282e-01 4 | 1 -9202066408251325127 6.6976e-02 5 | 2 -9194114845888269381 1.5282e-01 6 | 3 -9157656806856886367 1.5282e-01 7 | 4 -9154199948799956056 1.5282e-01 8 | .. ... ... ... ... 9 | 95 -7954403927701730016 1.5358e-05 10 | 96 -7945183889919201418 1.5282e-01 11 | 97 -7921043005244251597 5.9199e-02 12 | 98 -7915076791408573751 1.5282e-01 13 | 99 -7898872943061400987 1.1083e-02 14 | 15 | [100 rows x 4 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_split_parquet_0.out: -------------------------------------------------------------------------------- 1 | 96259 2 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0064 0.0031 0.0987 4 | 1 -9059007664292712863 1.0000 0.3615 NaN 5 | 2 -9009602369958523731 0.0064 0.0031 0.4402 6 | 3 -8990894093332793487 0.0064 0.0031 0.0460 7 | 4 -8915955323477460297 0.0064 0.0031 0.0090 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0064 0.0031 0.0460 10 | 96 -4495976808403190115 0.0064 0.0031 0.0227 11 | 97 -4474179539802460946 0.0064 0.0031 0.0090 12 | 98 -4409520928686189639 0.0064 0.0031 0.1062 13 | 99 -4399716566495748799 0.0064 0.0031 NaN 14 | 15 | [100 rows x 4 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_split_parquet_1.out: -------------------------------------------------------------------------------- 1 | 96259 2 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0005 9.9581e-08 0.1118 4 | 1 -9059007664292712863 1.0000 8.6993e-01 NaN 5 | 2 -9009602369958523731 0.0005 8.9398e-07 0.4155 6 | 3 -8990894093332793487 0.0005 2.8346e-07 0.0409 7 | 4 -8915955323477460297 0.0003 1.8926e-07 0.0181 8 | .. ... ... ... ... 9 | 95 -4554654845515399609 0.0003 3.8685e-08 NaN 10 | 96 -4539808410625597778 0.0094 1.2626e-06 0.0352 11 | 97 -4495976808403190115 0.0002 3.8685e-08 0.0378 12 | 98 -4474179539802460946 0.0002 3.8685e-08 0.0157 13 | 99 -4409520928686189639 0.0002 3.8685e-08 0.1650 14 | 15 | [100 rows x 4 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_split_parquet_2.out: -------------------------------------------------------------------------------- 1 | 96259 2 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0064 0.0031 0.0987 4 | 1 -9059007664292712863 1.0000 0.3615 NaN 5 | 2 -9009602369958523731 0.0064 0.0031 0.4402 6 | 3 -8990894093332793487 0.0064 0.0031 0.0460 7 | 4 -8915955323477460297 0.0064 0.0031 0.0090 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0064 0.0031 0.0460 10 | 96 -4495976808403190115 0.0064 0.0031 0.0227 11 | 97 -4474179539802460946 0.0064 0.0031 0.0090 12 | 98 -4409520928686189639 0.0064 0.0031 0.1062 13 | 99 -4399716566495748799 0.0064 0.0031 NaN 14 | 15 | [100 rows x 4 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_split_parquet_3.out: -------------------------------------------------------------------------------- 1 | 96259 2 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0064 0.0031 0.0987 4 | 1 -9059007664292712863 1.0000 0.3615 NaN 5 | 2 -9009602369958523731 0.0064 0.0031 0.4402 6 | 3 -8990894093332793487 0.0064 0.0031 0.0460 7 | 4 -8915955323477460297 0.0064 0.0031 0.0090 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0064 0.0031 0.0460 10 | 96 -4495976808403190115 0.0064 0.0031 0.0227 11 | 97 -4474179539802460946 0.0064 0.0031 0.0090 12 | 98 -4409520928686189639 0.0064 0.0031 0.1062 13 | 99 -4399716566495748799 0.0064 0.0031 NaN 14 | 15 | [100 rows x 4 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_split_parquet_6.out: -------------------------------------------------------------------------------- 1 | 96259 2 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0064 0.0034 0.0997 4 | 1 -9009602369958523731 0.0064 0.0034 0.4270 5 | 2 -8990894093332793487 0.0064 0.0034 0.0463 6 | 3 -8915955323477460297 0.0064 0.0034 0.0089 7 | 4 -8858715981476206597 0.0064 0.0034 0.0089 8 | .. ... ... ... ... 9 | 95 -4213710682827324837 0.0064 0.0034 0.0089 10 | 96 -4209714083879967850 0.5256 0.0218 NaN 11 | 97 -4195322252177179725 0.0064 0.0034 0.0227 12 | 98 -4146541955602089926 0.0064 0.0034 0.0463 13 | 99 -4109405113780929799 0.0064 0.0034 0.0050 14 | 15 | [100 rows x 4 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_split_parquet_7.out: -------------------------------------------------------------------------------- 1 | 96259 2 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0064 0.0034 0.0997 4 | 1 -9009602369958523731 0.0064 0.0034 0.4270 5 | 2 -8990894093332793487 0.0064 0.0034 0.0463 6 | 3 -8915955323477460297 0.0064 0.0034 0.0089 7 | 4 -8858715981476206597 0.0064 0.0034 0.0089 8 | .. ... ... ... ... 9 | 95 -4213710682827324837 0.0064 0.0034 0.0089 10 | 96 -4209714083879967850 0.5256 0.0218 NaN 11 | 97 -4195322252177179725 0.0064 0.0034 0.0227 12 | 98 -4146541955602089926 0.0064 0.0034 0.0463 13 | 99 -4109405113780929799 0.0064 0.0034 0.0050 14 | 15 | [100 rows x 4 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_split_parquet_8.out: -------------------------------------------------------------------------------- 1 | 96259 2 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0064 0.0031 0.0987 4 | 1 -9059007664292712863 1.0000 0.3615 NaN 5 | 2 -9009602369958523731 0.0064 0.0031 0.4402 6 | 3 -8990894093332793487 0.0064 0.0031 0.0460 7 | 4 -8915955323477460297 0.0064 0.0031 0.0090 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0064 0.0031 0.0460 10 | 96 -4495976808403190115 0.0064 0.0031 0.0227 11 | 97 -4474179539802460946 0.0064 0.0031 0.0090 12 | 98 -4409520928686189639 0.0064 0.0031 0.1062 13 | 99 -4399716566495748799 0.0064 0.0031 NaN 14 | 15 | [100 rows x 4 columns] 16 | 96259 17 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 18 | 0 -9078977811506172301 0.0064 0.0031 0.0987 19 | 1 -9059007664292712863 1.0000 0.3615 NaN 20 | 2 -9009602369958523731 0.0064 0.0031 0.4402 21 | 3 -8990894093332793487 0.0064 0.0031 0.0460 22 | 4 -8915955323477460297 0.0064 0.0031 0.0090 23 | .. ... ... ... ... 24 | 95 -4539808410625597778 0.0064 0.0031 0.0460 25 | 96 -4495976808403190115 0.0064 0.0031 0.0227 26 | 97 -4474179539802460946 0.0064 0.0031 0.0090 27 | 98 -4409520928686189639 0.0064 0.0031 0.1062 28 | 99 -4399716566495748799 0.0064 0.0031 NaN 29 | 30 | [100 rows x 4 columns] 31 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_split_parquet_9.out: -------------------------------------------------------------------------------- 1 | 96259 2 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 3 | 0 -9078977811506172301 0.0064 0.0031 0.0987 4 | 1 -9059007664292712863 1.0000 0.3615 NaN 5 | 2 -9009602369958523731 0.0064 0.0031 0.4402 6 | 3 -8990894093332793487 0.0064 0.0031 0.0460 7 | 4 -8915955323477460297 0.0064 0.0031 0.0090 8 | .. ... ... ... ... 9 | 95 -4539808410625597778 0.0064 0.0031 0.0460 10 | 96 -4495976808403190115 0.0064 0.0031 0.0227 11 | 97 -4474179539802460946 0.0064 0.0031 0.0090 12 | 98 -4409520928686189639 0.0064 0.0031 0.1062 13 | 99 -4399716566495748799 0.0064 0.0031 NaN 14 | 15 | [100 rows x 4 columns] 16 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_pyprophet_score.test_split_parquet_apply_weights.out: -------------------------------------------------------------------------------- 1 | 3410 2 | 96259 3 | feature_id ms1_precursor_pep ms2_peakgroup_pep ms2_precursor_pep 4 | 0 -9211032279639747263 1.5282e-01 5 | 1 -9202066408251325127 6.6976e-02 6 | 2 -9194114845888269381 1.5282e-01 7 | 3 -9157656806856886367 1.5282e-01 8 | 4 -9154199948799956056 1.5282e-01 9 | .. ... ... ... ... 10 | 95 -7954403927701730016 1.5358e-05 11 | 96 -7945183889919201418 1.5282e-01 12 | 97 -7921043005244251597 5.9199e-02 13 | 98 -7915076791408573751 1.5282e-01 14 | 99 -7898872943061400987 1.1083e-02 15 | 16 | [100 rows x 4 columns] 17 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_stats.test_lfdr.out: -------------------------------------------------------------------------------- 1 | [0.00655898 0.00863043 0.00938367 ... 1. 1. 1. ] 2 | [0.00655898 0.00863043 0.00938367 ... 0.67595967 0.49910994 0.34936792] 3 | [0.00962478 0.01460633 0.01460633 ... 1. 1. 1. ] 4 | [0.10028765 0.10028765 0.10028765 ... 1. 1. 1. ] 5 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_stats.test_qvalue.out: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyProphet/pyprophet/54e18bd69c91ce7c2dcc8bd2ee0ecd853fe3d297/tests/_regtest_outputs/test_stats.test_qvalue.out -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_stats.test_random.out: -------------------------------------------------------------------------------- 1 | 1 1 2 | [1.] 3 | 1 2 4 | [1.] 5 | 1 5 6 | [1.] 7 | 1 10 8 | [0.3] 9 | 1 100 10 | [0.68] 11 | 2 1 12 | [1. 1.] 13 | 2 2 14 | [0.5 0.5] 15 | 2 5 16 | [0.4 0.2] 17 | 2 10 18 | [0.4 0.1] 19 | 2 100 20 | [0.99 0.89] 21 | 5 1 22 | [1. 1. 1. 1. 1.] 23 | 5 2 24 | [1. 1. 1. 0.5 0.5] 25 | 5 5 26 | [0.2 0.8 0.2 0.2 0.8] 27 | 5 10 28 | [0.1 0.5 1. 0.8 0.8] 29 | 5 100 30 | [0.27 0.49 0.23 0.43 0.54] 31 | 10 1 32 | [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] 33 | 10 2 34 | [0.5 1. 1. 1. 0.5 1. 0.5 0.5 0.5 0.5] 35 | 10 5 36 | [0.8 0.2 0.4 1. 1. 1. 1. 0.4 1. 1. ] 37 | 10 10 38 | [0.6 0.6 0.3 0.6 0.3 0.9 0.4 0.9 0.5 0.9] 39 | 10 100 40 | [0.12 0.01 0.26 0.24 0.12 0.59 0.75 0.84 0.89 0.2 ] 41 | 100 1 42 | [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 43 | 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 44 | 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 45 | 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 46 | 1. 1. 1. 1.] 47 | 100 2 48 | [0.5 0.5 0.5 0.5 0.5 0.5 1. 0.5 0.5 0.5 0.5 0.5 0.5 0.5 1. 0.5 0.5 0.5 49 | 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 1. 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 50 | 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 51 | 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 1. 0.5 0.5 0.5 0.5 0.5 0.5 0.5 52 | 0.5 0.5 0.5 0.5 1. 0.5 1. 0.5 0.5 1. 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 53 | 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] 54 | 100 5 55 | [1. 0.6 0.8 0.4 0.8 0.4 0.6 0.4 0.8 0.6 0.8 0.8 0.8 0.8 0.8 0.8 0.2 0.8 56 | 0.2 0.6 0.8 0.4 0.6 0.8 0.4 0.8 0.8 0.8 0.6 0.8 0.4 0.8 0.8 0.8 0.4 0.4 57 | 0.8 0.6 0.4 0.8 0.4 0.8 0.6 0.6 0.8 1. 0.8 0.2 0.8 0.4 0.8 0.8 0.8 0.6 58 | 0.6 0.6 0.8 0.6 0.8 0.8 0.8 0.8 1. 0.6 0.6 0.6 0.6 0.8 0.8 0.8 0.4 0.8 59 | 0.8 0.8 0.8 0.8 0.8 0.8 0.4 0.4 0.8 0.6 0.8 0.8 0.8 0.4 0.6 0.6 0.8 0.8 60 | 0.2 0.2 0.6 0.8 0.6 0.8 0.8 0.8 0.6 0.4] 61 | 100 10 62 | [1. 1. 0.6 1. 0.3 1. 0.7 1. 0.2 0.3 0.6 1. 0.2 0.6 0.2 0.5 0.5 1. 63 | 0.3 0.6 0.9 0.6 0.6 0.5 0.1 0.6 0.2 0.3 0.1 0.6 0.9 0.1 1. 0.1 0.3 0.6 64 | 0.1 1. 0.5 1. 0.3 1. 0.3 0.1 0.2 0.5 0.2 0.2 0.5 0.5 1. 0.6 1. 0.2 65 | 0.5 0.5 0.1 0.5 0.6 0.8 1. 0.6 1. 0.7 0.5 0.1 1. 0.2 0.5 0.2 1. 1. 66 | 0.6 0.2 1. 0.2 0.7 1. 1. 0.6 0.6 0.3 1. 1. 1. 1. 0.3 0.6 0.7 0.3 67 | 0.1 0.2 0.2 0.1 1. 1. 0.5 0.6 0.1 0.5] 68 | 100 100 69 | [0.85 0.91 0.09 0.75 0.61 0.48 0.5 0.93 0.09 0.03 0.16 0.71 0.5 0.64 70 | 0.49 0.01 0.68 0.34 0.66 0.19 0.66 0.07 0.23 0.19 0.53 0.78 0.95 0.66 71 | 0.95 0.31 0.34 0.58 0.19 0.49 0.95 0.85 0.81 0.64 0.95 0.37 0.5 0.35 72 | 0.58 0.11 0.55 0.98 0.09 0.34 0.13 0.03 0.95 0.76 0.94 0.6 0.1 0.92 73 | 0.53 0.87 0.19 0.13 0.95 0.52 0.75 0.03 0.04 0.36 0.01 0.64 0.07 0.58 74 | 0.75 0.04 0.32 0.94 0.09 0.29 0.37 0.25 0.73 0.39 0.16 0.95 0.86 0.13 75 | 0.35 0.5 0.73 0.7 0.09 0.6 0.94 0.07 0.78 0.13 0.08 0.52 0.41 0.5 76 | 0.34 0.11] 77 | -------------------------------------------------------------------------------- /tests/_regtest_outputs/test_stats.test_stat_metrics.out: -------------------------------------------------------------------------------- 1 | fdr fn fnr fp fpr svalue tn tp 2 | 0 2.1133e-06 -2123.6588 0.0000 0.0067 3.1546e-06 1.0 2123.6588 3169.9933 3 | 1 1.0570e-05 -2122.6320 0.0000 0.0335 1.5773e-05 1.0 2123.6320 3168.9665 4 | 2 1.4803e-05 -2121.6186 0.0000 0.0469 2.2082e-05 1.0 2123.6186 3167.9531 5 | 3 2.3269e-05 -2120.5918 0.0000 0.0737 3.4700e-05 1.0 2123.5918 3166.9263 6 | 4 2.5392e-05 -2119.5851 0.0000 0.0804 3.7855e-05 1.0 2123.5851 3165.9196 7 | ... ... ... ... ... ... ... ... ... 8 | 3165 1.0000e+00 3161.7576 0.9990 2120.4231 9.9847e-01 0.0 3.2424 -2115.4231 9 | 3166 1.0000e+00 3164.4458 0.9995 2122.1113 9.9927e-01 0.0 1.5542 -2118.1113 10 | 3167 1.0000e+00 3165.5463 0.9995 2122.2118 9.9932e-01 0.0 1.4537 -2119.2118 11 | 3168 1.0000e+00 3167.2765 0.9998 2122.9420 9.9966e-01 0.0 0.7235 -2120.9420 12 | 3169 1.0000e+00 3168.6851 0.9999 2123.3506 9.9985e-01 0.0 0.3149 -2122.3506 13 | 14 | [3170 rows x 8 columns] 15 | fdr fn fnr fp fpr svalue tn tp 16 | 0 0.0002 -2123.6588 0.0 0.0067 3.1546e-06 1.0 2123.6588 3169.9933 17 | 1 0.0002 -2122.6320 0.0 0.0335 1.5773e-05 1.0 2123.6320 3168.9665 18 | 2 0.0002 -2121.6186 0.0 0.0469 2.2082e-05 1.0 2123.6186 3167.9531 19 | 3 0.0002 -2120.5918 0.0 0.0737 3.4700e-05 1.0 2123.5918 3166.9263 20 | 4 0.0002 -2119.5851 0.0 0.0804 3.7855e-05 1.0 2123.5851 3165.9196 21 | ... ... ... ... ... ... ... ... ... 22 | 3165 1.0000 3161.7576 1.0 2120.4231 9.9847e-01 0.0 3.2424 -2115.4231 23 | 3166 1.0000 3164.4458 1.0 2122.1113 9.9927e-01 0.0 1.5542 -2118.1113 24 | 3167 1.0000 3165.5463 1.0 2122.2118 9.9932e-01 0.0 1.4537 -2119.2118 25 | 3168 1.0000 3167.2765 1.0 2122.9420 9.9966e-01 0.0 0.7235 -2120.9420 26 | 3169 1.0000 3168.6851 1.0 2123.3506 9.9985e-01 0.0 0.3149 -2122.3506 27 | 28 | [3170 rows x 8 columns] 29 | -------------------------------------------------------------------------------- /tests/data/test_data.osw: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyProphet/pyprophet/54e18bd69c91ce7c2dcc8bd2ee0ecd853fe3d297/tests/data/test_data.osw -------------------------------------------------------------------------------- /tests/data/test_data.oswpq/precursors_features.parquet: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyProphet/pyprophet/54e18bd69c91ce7c2dcc8bd2ee0ecd853fe3d297/tests/data/test_data.oswpq/precursors_features.parquet -------------------------------------------------------------------------------- /tests/data/test_data.oswpq/transition_features.parquet: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyProphet/pyprophet/54e18bd69c91ce7c2dcc8bd2ee0ecd853fe3d297/tests/data/test_data.oswpq/transition_features.parquet -------------------------------------------------------------------------------- /tests/data/test_data.oswpqd/napedro_L120420_010_SW.oswpq/precursors_features.parquet: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyProphet/pyprophet/54e18bd69c91ce7c2dcc8bd2ee0ecd853fe3d297/tests/data/test_data.oswpqd/napedro_L120420_010_SW.oswpq/precursors_features.parquet -------------------------------------------------------------------------------- /tests/data/test_data.oswpqd/napedro_L120420_010_SW.oswpq/transition_features.parquet: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyProphet/pyprophet/54e18bd69c91ce7c2dcc8bd2ee0ecd853fe3d297/tests/data/test_data.oswpqd/napedro_L120420_010_SW.oswpq/transition_features.parquet -------------------------------------------------------------------------------- /tests/data/test_data.parquet: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyProphet/pyprophet/54e18bd69c91ce7c2dcc8bd2ee0ecd853fe3d297/tests/data/test_data.parquet -------------------------------------------------------------------------------- /tests/data/test_data_compound.osw: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyProphet/pyprophet/54e18bd69c91ce7c2dcc8bd2ee0ecd853fe3d297/tests/data/test_data_compound.osw -------------------------------------------------------------------------------- /tests/test_data_handling.py: -------------------------------------------------------------------------------- 1 | # encoding: utf-8 2 | from __future__ import print_function 3 | 4 | from pyprophet.scoring.data_handling import check_for_unique_blocks 5 | 6 | 7 | def test_ok(): 8 | assert check_for_unique_blocks([1]) is True 9 | assert check_for_unique_blocks([1, 1]) is True 10 | assert check_for_unique_blocks([1, 2]) is True 11 | assert check_for_unique_blocks([1, 2, 3, 4, 5]) is True 12 | assert check_for_unique_blocks([1, 2, 3, 4, 5, 5]) is True 13 | assert check_for_unique_blocks([1, 1, 1]) is True 14 | assert check_for_unique_blocks([1, 1, 1, 2]) is True 15 | assert check_for_unique_blocks([1, 2, 2, 2]) is True 16 | assert check_for_unique_blocks([1, 1, 2, 2, 3, 3, 4, 4]) is True 17 | assert check_for_unique_blocks([1, 1, 2, 2, 3, 3, 4, 4, 5]) is True 18 | 19 | assert check_for_unique_blocks(map(str, [1])) is True 20 | assert check_for_unique_blocks(map(str, [1, 1])) is True 21 | assert check_for_unique_blocks(map(str, [1, 2])) is True 22 | assert check_for_unique_blocks(map(str, [1, 2, 3, 4, 5])) is True 23 | assert check_for_unique_blocks(map(str, [1, 2, 3, 4, 5, 5])) is True 24 | assert check_for_unique_blocks(map(str, [1, 1, 1])) is True 25 | assert check_for_unique_blocks(map(str, [1, 1, 1, 2])) is True 26 | assert check_for_unique_blocks(map(str, [1, 2, 2, 2])) is True 27 | assert check_for_unique_blocks(map(str, [1, 1, 2, 2, 3, 3, 4, 4])) is True 28 | assert check_for_unique_blocks(map(str, [1, 1, 2, 2, 3, 3, 4, 4, 5])) is True 29 | 30 | 31 | def test_not_ok(): 32 | assert check_for_unique_blocks([1, 2, 1]) is False 33 | assert check_for_unique_blocks([1, 2, 3, 4, 1]) is False 34 | assert check_for_unique_blocks([1, 2, 3, 4, 5, 1]) is False 35 | assert check_for_unique_blocks([1, 1, 2, 2, 1]) is False 36 | assert check_for_unique_blocks([1, 1, 1, 2, 1]) is False 37 | assert check_for_unique_blocks([1, 2, 2, 2, 1]) is False 38 | assert check_for_unique_blocks([1, 1, 2, 2, 3, 3, 4, 4, 3]) is False 39 | assert check_for_unique_blocks([1, 1, 2, 2, 3, 3, 4, 4, 5, 4]) is False 40 | 41 | assert check_for_unique_blocks(map(str, [1, 2, 1])) is False 42 | assert check_for_unique_blocks(map(str, [1, 2, 3, 4, 1])) is False 43 | assert check_for_unique_blocks(map(str, [1, 2, 3, 4, 5, 1])) is False 44 | assert check_for_unique_blocks(map(str, [1, 1, 2, 2, 1])) is False 45 | assert check_for_unique_blocks(map(str, [1, 1, 1, 2, 1])) is False 46 | assert check_for_unique_blocks(map(str, [1, 2, 2, 2, 1])) is False 47 | assert check_for_unique_blocks(map(str, [1, 1, 2, 2, 3, 3, 4, 4, 3])) is False 48 | assert check_for_unique_blocks(map(str, [1, 1, 2, 2, 3, 3, 4, 4, 5, 4])) is False 49 | --------------------------------------------------------------------------------