pdkit

PDKIT

INSTALL INSTRUCTIONS

Regular install

$ pip install pdkit

$ pip install git+git://github.com/pdkit/pdkit.git

For “editable” install

$ pip install -e git://github.com/pdkit/pdkit.git#egg=pdkit

For development install

$ git clone https://github.com/pdkit/pdkit.git
$ pip install -r requirements.txt
$ pip install .

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 to extract all the features available in Tremor Processor.

>>> 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) 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)
>>> testResultSet.process()

where folderpath is the relative folder with the different measurements. For CloudUPDRS there are measurements in the following folder ./tests/data. The resulting dataframe with all the features processed is saved in testResultSet.features

We can also write the data frame to a output file like:

>>> testResultSet.write_output(dataframe, name)

UPDRS

Pdkit can calculate the UPDRS score for a given testResultSet.

>>> import pdkit
>>> updrs = pdkit.UPDRS(data_frame)

The UPDRS scores can be written to a file. You can pass the name of a filename and the output_format

>>> updrs.write_model(filename='scores', output_format='csv')

To score a new measurement against the trained knn clusters.

>>> updrs.score(measurement)

To read the testResultSet data from a file. See TestResultSet class for more details.

>>> updrs = pdkit.UPDRS(data_frame_file_path=file_path_to_testResultSet_file)

Clinical UPDRS

Pdkit uses the clinical data to calculates classifiers implementing the k-nearest neighbors vote.

>>> import pdkit
>>> clinical_UPDRS = pdkit.Clinical_UPDRS(labels_file_path, data_frame)

where the labels_file_path is the path to the clinical data file, data_frame is the result of the testResultSet.

To score a new measurement against the trained knn clusters.

>>> clinical_UPDRS.predict(measurement)

To read the testResultSet data from a file. See TestResultSet class for more details.

>>> clinical_UPDRS = pdkit.Clinical_UPDRS(labels_file_path, data_frame_file_path=file_path_to_testResultSet_file)

Metadata

Release files for pdkit 1.3.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pdkit 1.3.2
File	Size	Uploaded
pdkit-1.3.2.tar.gz	47.9 kB	Jan 31, 2020	Details

Built distribution (wheel)

Table of built distributions (wheels) for pdkit 1.3.2
File	Interpreter	ABI	Platform	Reset
pdkit-1.3.2-py2.py3-none-any.whl 58.3 kB Jan 31, 2020	Python 3, Python 2	none	any	Details

Total release size: 106.2 kB

Release files / pdkit-1.3.2.tar.gz

Download URL	pdkit-1.3.2.tar.gz
Size	47.9 kB
Tags	Source
SHA-256 checksum How to use checksums	`9d40ab50366ce0c5b178a9835e33123487b5a8ab6f7c9ad96998625b34f85d05`
BLAKE2b-256 checksum How to use checksums	`0f32e195919c540d4d6426ec8d5a118dcde9a6b2b555a27a3bfc923b7ac089ff`
Upload date	Jan 31, 2020
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.1.1 pkginfo/1.4.2 requests/2.21.0 setuptools/45.1.0 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.7.6`

Release files / pdkit-1.3.2-py2.py3-none-any.whl

Download URL	pdkit-1.3.2-py2.py3-none-any.whl
Size	58.3 kB
Tags	Python 2 Python 3
SHA-256 checksum How to use checksums	`ae0069abdcab58e5df74f58d5d7b9d7ee4c01eb60b539fc0085b44029b717365`
BLAKE2b-256 checksum How to use checksums	`5518a69d0f780ee01ed5a7df54da8106e6a8e28a69c6993c654dd92ef8496818`
Upload date	Jan 31, 2020
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.1.1 pkginfo/1.4.2 requests/2.21.0 setuptools/45.1.0 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.7.6`