Metadata-Version: 1.1
Name: pdkit
Version: 0.4.2
Summary: Parkinson`s Disease Kit
Home-page: https://github.com/pdkit/pdkit
Author: J.S. Pons
Author-email: joan@dcs.bbk.ac.uk
License: MIT
Description: .. image:: https://circleci.com/gh/pdkit/pdkit.svg?style=shield
            :target: https://circleci.com/gh/pdkit/pdkit
        
        .. image:: https://readthedocs.org/projects/pdkit/badge/
            :target: https://pdkit.readthedocs.org
        
        PDKIT
        #####
        
        TREMOR PROCESSOR
        ****************
        
        Example how to use pdkit to calculate tremor amplitude and frequency:
        
            >>> import pdkit
            >>> tp = pdkit.TremorProcessor()
            >>> ts = pdkit.TremorTimeSeries().load(filename)
            >>> amplitude, frequency = tp.amplitude(ts)
        
        where, `filename` is the data path to load, by default in the cloudUPDRS format.
        
        Pdkit can also read data in the MPower format, just like:
        
            >>> ts = pdkit.TremorTimeSeries().load(filename, 'mpower')
        
        where, `filename` is the data path to load in MPower format.
        
        To calculate Welch, as a robust alternative to using Fast Fourier Transform, use like:
        
            >>> amplitude, frequency = tp.amplitude(ts, 'welch')
        
        This  class also provides a method named `extract_features <http://pdkit.readthedocs.io/en/latest/tremor.html#tremor_processor.TremorProcessor.extract_features>`_
        to extract all the features available in `Tremor Processor <http://pdkit.readthedocs.io/en/latest/tremor.html>`_.
        
            >>> tp.extract_features(ts)
        
        BRADYKINESIA
        ************
        
            >>> import pdkit
            >>> ts = pdkit.TremorTimeSeries().load(filename)
            >>> tp = pdkit.TremorProcessor(lower_frequency=0.0, upper_frequency=4.0)
            >>> amplitude, frequency = tp.bradykinesia(ts)
        
        GAIT
        ****
        
        Example how to use pdkit to calculate various Gait features:
        
            >>> import pdkit
            >>> ts = pdkit.GaitTimeSeries().load(filename)
            >>> gp = pdkit.GaitProcessor()
            >>> freeze_times, freeze_indexes, locomotion_freezes = gp.freeze_of_gait(ts)
            >>> frequency_of_peaks = gp.frequency_of_peaks(ts)
            >>> speed_of_gait = gp.speed_of_gait(ts)
            >>> step_regularity, stride_regularity, walk_symmetry = gp.walk_regularity_symmetry(ts)
        
        where, `filename` is the data path to load, by default in the CloudUPDRS format.
        
        FINGER TAPPING
        **************
        
        Example how to use pdkit to calculate the mean alternate distance of the finger tapping tests:
        
            >>> import pdkit
            >>> ts = pdkit.FingerTappingTimeSeries().load(filename)
            >>> ftp = pdkit.FingerTappingProcessor()
            >>> ftp.mean_alnt_target_distance(ts)
        
        kinesia scores (the number of key taps)
        
            >>> ftp.kinesia_scores(ts)
        
        TEST RESULT SET
        ****************
        
        Pdkit can be used to extract all the features for different measurements (i.e. tremor, finger tapping, gait) placed in a single folder. The result
        is a `data frame` where the measurements are rows and the columns are the features extracted.
        
        >>> import pdkit
        >>> testResultSet = pdkit.TestResultSet(folderpath)
        >>> dataframe = testResultSet.process()
        
        where `folderpath` is the relative folder with the different measurements. For CloudUPDRS there are measurements in the following
        folder `./tests/data`.
        
        We can also write the `data frame` to a output file like:
        
        >>> testResultSet.write_output(dataframe, name)
Keywords: parkinson`s disease kit
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
