{ "info": { "author": "Paul McCarthy", "author_email": "pauldmccarthy@gmail.com", "bugtrack_url": null, "classifiers": [ "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7" ], "description": "``ukbparse`` - the FMRIB UK BioBank data parser\n===============================================\n\n\n.. note:: ``ukbparse`` has been superseded by ``funpack`` and will no longer\n be developed. Head to https://git.fmrib.ox.ac.uk/fsl/ukbparse for\n more information.\n\n\n.. image:: https://img.shields.io/pypi/v/ukbparse.svg\n :target: https://pypi.python.org/pypi/ukbparse/\n\n.. image:: https://anaconda.org/conda-forge/ukbparse/badges/version.svg\n :target: https://anaconda.org/conda-forge/ukbparse\n\n.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1997626.svg\n :target: https://doi.org/10.5281/zenodo.1997626\n\n.. image:: https://git.fmrib.ox.ac.uk/fsl/ukbparse/badges/master/coverage.svg\n :target: https://git.fmrib.ox.ac.uk/fsl/ukbparse/commits/master/\n\n\n``ukbparse`` is a Python library for pre-processing of UK BioBank data.\n\n\n ``ukbparse`` is developed at the Wellcome Centre for Integrative\n Neuroimaging (WIN@FMRIB), University of Oxford. ``ukbparse`` is in no way\n endorsed, sanctioned, or validated by the `UK BioBank\n `_.\n\n ``ukbparse`` comes bundled with metadata about the variables present in UK\n BioBank data sets. This metadata can be obtained from the `UK BioBank\n online data showcase `_\n\n\nInstallation\n------------\n\n\nInstall ``ukbparse`` via pip::\n\n\n pip install ukbparse\n\n\nOr from ``conda-forge``::\n\n conda install -c conda-forge ukbparse\n\n\nIntroductory notebook\n---------------------\n\n\nThe ``ukbparse_demo`` command will start a Jupyter Notebook which introduces\nthe main features provided by ``ukbparse``. To run it, you need to install a\nfew additional dependencies::\n\n\n pip install ukbparse[demo]\n\n\nYou can then start the demo by running ``ukbparse_demo``.\n\n\n.. note:: The introductory notebook uses ``bash``, so is unlikely to work on\n Windows.\n\n\nUsage\n-----\n\n\nGeneral usage is as follows::\n\n\n ukbparse [options] output.tsv input1.tsv input2.tsv\n\n\nYou can get information on all of the options by typing ``ukbparse --help``.\n\n\nOptions can be specified on the command line, and/or stored in a configuration\nfile. For example, the options in the following command line::\n\n\n ukbparse \\\n --overwrite \\\n --import_all \\\n --log_file log.txt \\\n --icd10_map_file icd_codes.tsv \\\n --category 10 \\\n --category 11 \\\n output.tsv input1.tsv input2.tsv\n\n\nCould be stored in a configuration file ``config.txt``::\n\n\n overwrite\n import_all\n log_file log.txt\n icd10_map_file icd_codes.tsv\n category 10\n category 11\n\n\nAnd then executed as follows::\n\n\n ukbparse -cfg config.txt output.tsv input1.tsv input2.tsv\n\n\nCustomising\n-----------\n\n\n``ukbparse`` contains a large number of built-in rules which have been\nspecifically written to pre-process UK BioBank data variables. These rules are\nstored in the following files:\n\n\n * ``ukbparse/data/variables_*.tsv``: Cleaning rules for individual variables\n * ``ukbparse/data/datacodings_*.tsv``: Cleaning rules for data codings\n * ``ukbparse/data/types.tsv``: Cleaning rules for specific types\n * ``ukbparse/data/processing.tsv``: Processing steps\n\n\nYou can customise or replace these files as you see fit. You can also pass\nyour own versions of these files to ``ukbparse`` via the ``--variable_file``,\n``--datacoding_file``, ``--type_file`` and ``--processing_file`` command-line\noptions respectively. ``ukbparse`` will load all variable and datacoding files,\nand merge them into a single table which contains the cleaning rules for each\nvariable.\n\nFinally, you can use the ``--no_builtins`` option to bypass all of the\nbuilt-in cleaning and processing rules.\n\n\nOutput\n------\n\n\nThe main output of ``ukbparse`` is a plain-text tab-delimited[*]_ file which\ncontains the input data, after cleaning and processing, potentially with\nsome columns removed, and new columns added.\n\n\nIf you used the ``--non_numeric_file`` option, the main output file will only\ncontain the numeric columns; non-numeric columns will be saved to a separate\nfile.\n\n\nYou can use any tool of your choice to load this output file, such as Python,\nMATLAB, or Excel. It is also possible to pass the output back into\n``ukbparse``.\n\n\n.. [*] You can change the delimiter via the ``--tsv_sep`` / ``-ts`` option.\n\n\nLoading output into MATLAB\n^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\n.. |readtable| replace:: ``readtable``\n.. _readtable: https://uk.mathworks.com/help/matlab/ref/readtable.html\n\n.. |table| replace:: ``table``\n.. _table: https://uk.mathworks.com/help/matlab/ref/table.html\n\n\nIf you are using MATLAB, you have several options for loading the ``ukbparse``\noutput. The best option is |readtable|_, which will load column names, and\nwill handle both non-numeric data and missing values. Use ``readtable`` like\nso::\n\n data = readtable('out.tsv', 'FileType', 'text');\n\n\nThe ``readtable`` function returns a |table|_ object, which stores each column\nas a separate vector (or cell-array for non-numeric columns). If you are only\ninterested in numeric columns, you can retrieve them as an array like this::\n\n rawdata = data(:, vartype('numeric')).Variables;\n\n\nThe ``readtable`` function will potentially rename the column names to ensure\nthat they are are valid MATLAB identifiers. You can retrieve the original\nnames from the ``table`` object like so::\n\n colnames = data.Properties.VariableDescriptions;\n colnames = regexp(colnames, '''(.+)''', 'tokens', 'once');\n empty = cellfun(@isempty, colnames);\n colnames(empty) = data.Properties.VariableNames(empty);\n colnames = vertcat(colnames{:});\n\n\nIf you have used the ``--description_file`` option, you can load in the\ndescriptions for each column as follows::\n\n descs = readtable('descriptions.tsv', ...\n 'FileType', 'text', ...\n 'Delimiter', '\\t', ...\n 'ReadVariableNames',false);\n descs = [descs; {'eid', 'ID'}];\n idxs = cellfun(@(x) find(strcmp(descs.Var1, x)), colnames, ...\n 'UniformOutput', false);\n idxs = cell2mat(idxs);\n descs = descs.Var2(idxs);\n\n\nTests\n-----\n\n\nTo run the test suite, you need to install some additional dependencies::\n\n\n pip install ukbparse[test]\n\n\nThen you can run the test suite using ``pytest``::\n\n pytest\n\n\nCiting\n------\n\n\nIf you would like to cite 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