diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 00000000..22ece28c --- /dev/null +++ b/.gitattributes @@ -0,0 +1,14 @@ +*.py diff=python + +*.anI binary +*.atr binary +*.d[0-9] binary +*.dat binary +*.edf binary +*.gz binary +*.mat binary +*.qrs binary +*.wabp binary +*.wav binary +*.wqrs binary +*.xyz binary diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 00000000..c9a58941 --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,23 @@ +name: Build + +on: + push: + branches: + - main + pull_request: + branches: + - main + +jobs: + docs: + runs-on: ubuntu-22.04 + steps: + - uses: actions/checkout@v3 + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r docs/requirements.txt + - name: Build documentation + run: | + cd docs + make html diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml new file mode 100644 index 00000000..10f97426 --- /dev/null +++ b/.github/workflows/test.yml @@ -0,0 +1,67 @@ +name: Test + +on: + push: + branches: + - main + pull_request: + branches: + - main + +env: + NPY_PROMOTION_STATE: weak_and_warn + +jobs: + test: + name: Python ${{ matrix.python-version }} / ${{ matrix.os }} / ${{ matrix.numpy }} + strategy: + fail-fast: false + matrix: + os: [windows-latest, ubuntu-latest, macos-latest] + python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"] + numpy: ["numpy"] + include: + - os: ubuntu-latest + python-version: "3.9" + numpy: "numpy==1.26.4" + runs-on: ${{ matrix.os }} + steps: + - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v3 + - name: Install Python ${{ matrix.python-version }} + run: uv python install ${{ matrix.python-version }} + - name: Install libsndfile + if: startsWith(matrix.os, 'ubuntu') + run: sudo apt-get install -y libsndfile1 + - name: Run tests + run: uv run --with ${{ matrix.numpy }} --extra dev pytest + - name: Check style + run: uv run --extra dev black --check --diff . + + test-deb11-i386: + name: Python 3.7 on Debian 11 i386 + runs-on: ubuntu-latest + container: i386/debian:11 + steps: + - name: Install dependencies + run: | + apt-get update + apt-get install -y --no-install-recommends \ + python3-fsspec \ + python3-matplotlib \ + python3-numpy \ + python3-pandas \ + python3-pip \ + python3-requests \ + python3-scipy \ + python3-soundfile \ + python3-pytest \ + git + python3 --version + # Note: "actions/checkout@v2" requires libstdc++6:amd64 to be + # installed in the container. To keep things simple, use + # "actions/checkout@v1" instead. + # https://github.com/actions/checkout/issues/334 + - uses: actions/checkout@v1 + - name: Run tests + run: pytest-3 diff --git a/.gitignore b/.gitignore index a13de5a1..ebdd3e46 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,5 @@ # For private use -private_tests.ipynb +private-tests.ipynb notes.md # Byte-compiled / optimized / DLL files @@ -12,6 +12,8 @@ __pycache__/ # Distribution / packaging .Python +.vscode +venv/ env/ build/ develop-eggs/ @@ -67,3 +69,6 @@ target/ # OSX .DS_Store .DS_Store + +# pyenv +.python-version diff --git a/.readthedocs.yml b/.readthedocs.yml new file mode 100644 index 00000000..7d45d867 --- /dev/null +++ b/.readthedocs.yml @@ -0,0 +1,17 @@ +# File: .readthedocs.yaml + +version: 2 + +build: + os: "ubuntu-20.04" + tools: + python: "3.10" + +# Build from the docs/ directory with Sphinx +sphinx: + configuration: docs/conf.py + +# Explicitly set the version of Python and its requirements +python: + install: + - requirements: docs/requirements.txt diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index aa7138b7..00000000 --- a/.travis.yml +++ /dev/null @@ -1,32 +0,0 @@ -# Link repository with Travis CI -# https://travis-ci.org/ - -# Master branch only -branches: - except: - - gh-pages - -# Start the databases -# See https://docs.travis-ci.com/user/database-setup/ -# addons: -# postgresql: "9.4" - -# services: -# - postgresql - -# Set the language -language: python -python: - - "2.7.13" - - "3.5" - -# before_script: -# - psql -c 'create database mimic_test;' -U postgres - -# command to install dependencies -install: - - pip install -r requirements.txt - - pip install . - -# command to run tests -script: nosetests diff --git a/DEVELOPING.md b/DEVELOPING.md new file mode 100644 index 00000000..cf3ec53b --- /dev/null +++ b/DEVELOPING.md @@ -0,0 +1,82 @@ +# Developing WFDB-Python + +## Guidelines + +We welcome community contributions in the form of pull requests. When contributing code, please ensure: + +- Documentation is provided. New functions and classes should have numpy/scipy style [docstrings](https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt). +- Unit tests are written for new features that are not covered by [existing tests](https://github.com/MIT-LCP/wfdb-python/tree/main/tests). +- The code style is consistent with the project's formating standards. + +Run the formatter with: + +```sh +black . +``` + +## Package and Dependency Management + +This project uses [uv](https://docs.astral.sh/uv/) for package management and distribution. + +Development dependencies are specified as optional dependencies, at least for now and until [development dependencies](https://docs.astral.sh/uv/concepts/dependencies/#development-dependencies) become more widely used. + +```sh +uv add --optional +``` + +## Creating Distributions + +1. Bump the version in [version.py](./wfdb/version.py). + +2. Add a summary of the changes to [the changelog](https://github.com/MIT-LCP/wfdb-python/blob/main/docs/changes.rst). You may also need to update [the documentation](https://github.com/MIT-LCP/wfdb-python/tree/main/docs). For example, if function arguments have been updated, this change will need to be captured. Open a pull request to merge these changes to the main branch. + +3. After the pull requests above have been merged, go to https://github.com/MIT-LCP/wfdb-python/releases and click "Draft new release" to create a new tag/release of the package. Set the tag to the new version number and draft the release notes (or click "Generate release notes"!). + +4. Publish the project to PyPI, the [Python Package Index](https://pypi.org/project/wfdb/). + +It may be useful to publish to testpypi and preview the changes before publishing to PyPi. However, the project dependencies likely will not be available when trying to install from there. + +Setup: configure access to repositories: + +```sh +# Create an API token, then add it +poetry config pypi-token.pypi + +# For testpypi +poetry config repositories.test-pypi https://test.pypi.org/legacy/ +poetry config pypi-token.test-pypi +``` + +To build and upload a new distribution: + +```sh +uv build + +uv publish --publish-url https://test.pypi.org/legacy/ +uv publish +``` + +## Creating Documentation + +The project's documentation is generated by [Sphinx](https://docs.readthedocs.io/en/stable/intro/getting-started-with-sphinx.html) using the content in the [docs](./docs) directory. The generated content is then hosted on readthedocs (RTD) at: + +To manage the content on RTD, request yourself to be added to the [wfdb](https://readthedocs.org/projects/wfdb/) project. The project has already been configured to import content from the GitHub repository. Documentation for new releases should be automatically built and uploaded. See the [import guide](https://docs.readthedocs.io/en/stable/intro/import-guide.html) for more details. + +There is some redundancy in specifying the Sphinx requirements between pyproject.toml and [docs/requirements.txt](./docs/requirements.txt), the latter of which is used by RTD. Make sure that the content is consistent across the two files. + +To generate the HTML content locally, install the required dependencies and run from the `docs` directory: + +```sh +make html +``` + +## Tests + +Run tests using pytest: + +```sh +pytest +# Distribute tests across multiple cores. +# https://github.com/pytest-dev/pytest-xdist +pytest -n auto +``` diff --git a/MANIFEST.in b/MANIFEST.in deleted file mode 100644 index 0ff3f716..00000000 --- a/MANIFEST.in +++ /dev/null @@ -1,12 +0,0 @@ -# Include the license file -include LICENSE - -# Include readme file -include README.rst - -# Include the data files -# recursive-include data * - -# If using Python 2.6 or less, then have to include package data, even though -# it's already declared in setup.py -# include sample/*.dat \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 00000000..d05eb73b --- /dev/null +++ b/README.md @@ -0,0 +1,54 @@ +# The WFDB Python Package + +![signals](https://raw.githubusercontent.com/MIT-LCP/wfdb-python/main/demo-img.png) + +[![tests workflow](https://github.com/MIT-LCP/wfdb-python/actions/workflows/run-tests.yml/badge.svg)](https://github.com/MIT-LCP/wfdb-python/actions?query=workflow%3Arun-tests+event%3Apush+branch%3Amain) +[![PyPI Downloads](https://img.shields.io/pypi/dm/wfdb.svg?label=PyPI%20downloads)](https://pypi.org/project/wfdb/) +[![PhysioNet Project](https://img.shields.io/badge/DOI-10.13026%2Fegpf--2788-blue)](https://doi.org/10.13026/egpf-2788) +[![Supported Python Versions](https://img.shields.io/pypi/pyversions/wfdb.svg)](https://pypi.org/project/wfdb) + +## Introduction + +A Python-native package for reading, writing, processing, and plotting physiologic signal and annotation data. The core I/O functionality is based on the Waveform Database (WFDB) [specifications](https://github.com/wfdb/wfdb-spec/). + +This package is heavily inspired by the original [WFDB Software Package](https://www.physionet.org/content/wfdb/), and initially aimed to replicate many of its command-line APIs. However, the projects are independent, and there is no promise of consistency between the two, beyond each package adhering to the core specifications. + +## Documentation and Usage + +See the [documentation site](http://wfdb.readthedocs.io) for the public APIs. + +See the [demo.ipynb](https://github.com/MIT-LCP/wfdb-python/blob/main/demo.ipynb) notebook file for example use cases. + +## Installation + +The distribution is hosted on PyPI at: . The package can be directly installed from PyPI using pip: + +```sh +pip install wfdb +``` + +On some less-common systems, you may need to install `libsndfile` separately. See the [soundfile installation notes](https://pypi.org/project/soundfile/) for more information. + +The development version is hosted at: . This repository also contains demo scripts and example data. To install the development version, clone or download the repository, navigate to the base directory, and run: + +```sh +pip install . +``` + +If you intend to make changes to the repository, you can install additional packages that are useful for development by running: + +```sh +pip install ".[dev]" +``` + +## Developing + +Please see the [DEVELOPING.md](https://github.com/MIT-LCP/wfdb-python/blob/main/DEVELOPING.md) document for contribution/development instructions. + +### Creating a new release + +For guidance on creating a new release, see: https://github.com/MIT-LCP/wfdb-python/blob/main/DEVELOPING.md#creating-distributions + +## Citing + +When using this resource, please cite the software [publication](https://physionet.org/content/wfdb-python/) on PhysioNet. diff --git a/README.rst b/README.rst deleted file mode 100644 index c75ef54f..00000000 --- a/README.rst +++ /dev/null @@ -1,826 +0,0 @@ -wfdb-python -=========== - -|Build Status| - -.. figure:: https://raw.githubusercontent.com/MIT-LCP/wfdb-python/master/demoimg.png - :alt: wfdb signals - -Introduction ------------- - -Native python scripts for reading and writing WFDB signals and annotations. Package to be expanded with other useful functionalities. - - -Installation ------------- - -The distribution is hosted on pypi and directly installable via pip without needing clone or download this repository. Note that the pypi package does not contain the demo scripts or the example data. To install the package from pypi, run from your terminal: -``pip install wfdb`` - -Download or clone this repository https://github.com/MIT-LCP/wfdb-python for the latest development version, the demo scripts, and the example data. To install the downloaded package, change directory into the base directory of the repository and run: -``pip install .`` - - -Usage ------ - -See the **demo.ipynb** file for example cases. - -Objects -~~~~~~~ - -As of version 1.0.0, wfdb records are stored in **Record** or **MultiRecord** objects, and annotations are stored in **Annotation** objects. To see all attributes of an object, call `object.__dict__` - - -**Record** - The class representing WFDB headers, and single segment WFDB records. - -Record objects can be created using the constructor, by reading a WFDB header -with 'rdheader', or a WFDB record (header and associated dat files) with rdsamp' -or 'srdsamp'. - -The attributes of the Record object give information about the record as specified -by https://www.physionet.org/physiotools/wag/header-5.htm - -In addition, the d_signals and p_signals attributes store the digital and physical -signals of WFDB records with at least one channel. - -Contructor function: -:: - - def __init__(self, p_signals=None, d_signals=None, - recordname=None, nsig=None, - fs=None, counterfreq=None, basecounter=None, - siglen=None, basetime=None, basedate=None, - filename=None, fmt=None, sampsperframe=None, - skew=None, byteoffset=None, adcgain=None, - baseline=None, units=None, adcres=None, - adczero=None, initvalue=None, checksum=None, - blocksize=None, signame=None, comments=None) - -Example Usage: -:: - - import wfdb - record1 = wfdb.Record(recordname='r1', fs=250, nsig=2, siglen=1000, - filename=['r1.dat','r1.dat']) - - -**MultiRecord** - The class representing multi-segment WFDB records. - -MultiRecord objects can be created using the constructor, or by reading a multi-segment -WFDB record using 'rdsamp' with the 'm2s' (multi to single) input parameter set to False. - -The attributes of the MultiRecord object give information about the entire record as specified -by https://www.physionet.org/physiotools/wag/header-5.htm - -In addition, the 'segments' parameter is a list of Record objects representing each -individual segment, or 'None' representing empty segments, of the entire multi-segment record. - -Noteably, this class has no attribute representing the signals as a whole. The 'multi_to_single' -instance method can be called on MultiRecord objects to return a single segment representation -of the record as a Record object. The resulting Record object will have its 'p_signals' field set. - -Contructor function: -:: - - def __init__(self, segments = None, layout = None, - recordname=None, nsig=None, fs=None, - counterfreq=None, basecounter=None, - siglen=None, basetime=None, basedate=None, - segname = None, seglen = None, comments=None) - -Example Usage: -:: - - import wfdb - recordM = wfdb.MultiRecord(recordname='rm', fs=50, nsig=8, siglen=9999, - segname=['rm_1', '~', rm_2'], - seglen=[800, 200, 900]) - - recordL = wfdb.rdsamp('s00001-2896-10-10-00-31', m2s = False) - recordL = recordL.multi_to_single() - - -**Annotation** - The class representing WFDB annotations. - -Annotation objects can be created using the constructor, or by reading a WFDB annotation -file with 'rdann'. - -The attributes of the Annotation object give information about the annotation as specified -by https://www.physionet.org/physiotools/wag/annot-5.htm: - -- ``recordname``: The base file name (without extension) of the record that the annotation is attached to. -- ``extension``: The file extension of the file the annotation is stored in. -- ``sample``: The annotation locations in samples relative to the beginning of the record. -- ``symbol``: The annotation type according the the standard WFDB codes. -- ``subtype``: The marked class/category of each annotation. -- ``chan``: The signal channel associated with each annotations. -- ``num``: The labelled annotation number for each annotation. -- ``aux_note``: The auxiliary information string for each annotation. -- ``fs``: The sampling frequency of the record, if available. -- ``label_store``: The integer value used to store/encode each annotation label -- ``description``: The descriptive string of each annotation label -- ``custom_labels``: The custom annotation labels defined in the annotation file. - Maps the relationship between the three label fields. - The data type is a pandas DataFrame with three columns: ['label_store', 'symbol', 'description'] -- ``contained_labels``: The unique labels contained in this annotation. Same structure - as custom_labels. - - -Constructor function: -:: - def __init__(self, recordname, extension, sample, symbol=None, subtype=None, - chan=None, num=None, aux_note=None, fs=None, label_store=None, - description=None, custom_labels=None, contained_labels=None) - -Call `showanncodes()` to see the list of standard annotation codes. Any text used to label annotations that are not one of these codes should go in the 'aux_note' field rather than the 'symbol' field. - -Example usage: -:: - - import wfdb - ann1 = wfdb.Annotation(recordname='ann1', annotator='atr', sample=[10,20,400], - symbol = ['N','N','['], aux_note=[None, None, 'Serious Vfib']) - -Reading Signals -~~~~~~~~~~~~~~~ - - -**rdsamp** - Read a WFDB record and return the signal and record descriptors as attributes in a Record or MultiRecord object. - -:: - - record = rdsamp(recordname, sampfrom=0, sampto=None, channels=None, - physical=True, pbdir = None, m2s=True) - -Example Usage: - -:: - - import wfdb - ecgrecord = wfdb.rdsamp('sampledata/test01_00s', sampfrom=800, channels = [1,3]) - -Input Arguments: - -- ``recordname`` (required): The name of the WFDB record to be read (without any file extensions). -- ``sampfrom`` (default=0): The starting sample number to read for each channel. -- ``sampto`` (default=length of entire signal)- The final sample number to read for each channel. -- ``channels`` (default=all channels): Indices specifying the channels to be returned. -- ``physical`` (default=True): Flag that specifies whether to return signals in physical (True) or digital (False) units. -- ``pbdir`` (default=None): Option used to stream data from Physiobank. The Physiobank database directory from which to find the required record files. eg. For record '100' in 'http://physionet.org/physiobank/database/mitdb', pbdir = 'mitdb'. -- ``m2s`` (default=True): Flag used only for multi-segment records. Specifies whether to convert the returned wfdb.MultiRecord object into a wfdb.Record object (True) or not (False). -- ``smoothframes`` (default=True): Flag used when reading records with signals having multiple samples per frame. Specifies whether to smooth the samples in signals with more than one sample per frame and return an mxn uniform numpy array as the d_signals or p_signals field (True), or to return a list of 1d numpy arrays containing every expanded sample as the e_d_sign.als or e_p_signals field (False). -- ``ignoreskew`` (default=False): Flag used when reading records with at least one skewed signal. Specifies whether to apply the skew to align the signals in the output variable (False), or to ignore the skew field and load in all values contained in the dat files unaligned (True). -- ``returnres`` (default=64): The numpy array dtype of the returned signals. Options are: 64, 32, 16, and 8, where the value represents the numpy int or float dtype. Note that the value cannot be 8 when physical is True since there is no float8 format. - -Output Arguments: - -- ``record`` - The wfdb Record or MultiRecord object representing the contents of the record read. - -**srdsamp** - A simplified wrapper function around rdsamp. Read a WFDB record and return the physical signal and a few important descriptor fields. - -:: - - signals, fields = srdsamp(recordname, sampfrom=0, sampto=None, channels=None, pbdir=None) - -Example Usage: - -:: - - import wfdb - sig, fields = wfdb.srdsamp('sampledata/test01_00s', sampfrom=800, channels = [1,3]) - -Input arguments: - -- ``recordname`` (required): The name of the WFDB record to be read (without any file extensions). If the argument contains any path delimiter characters, the argument will be interpreted as PATH/baserecord and the data files will be searched for in the local path. -- ``sampfrom`` (default=0): The starting sample number to read for each channel. -- ``sampto`` (default=None): The sample number at which to stop reading for each channel. -- ``channels`` (default=all): Indices specifying the channel to be returned. - -Output arguments: - -- ``signals``: A 2d numpy array storing the physical signals from the record. -- ``fields``: A dictionary specifying several key attributes of the read record: -- ``fs``: The sampling frequency of the record -- ``units``: The units for each channel -- ``signame``: The signal name for each channel -- ``comments``: Any comments written in the header - - -Converting between Analog and Digital Values -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -When reading signal sample values into ``record`` objects using ``rdsamp``, the samples are stored in either the ``p_signals`` or the ``d_signals`` field depending on the specified return type (``physical`` = ``True`` or ``False`` respectively). - -One can also use existing objects to obtain physical values from digital values and vice versa, without having to re-read the wfdb file with a different set of options. The two following instance methods perform the conversions. - - -**adc** - Performs analogue to digital conversion of the physical signal stored in p_signals if expanded is False, or e_p_signals if expanded is True. The p_signals/e_p_signals, fmt, gain, and baseline fields must all be valid. If inplace is True, the adc will be performed inplace on the variable, the d_signals/e_d_signals attribute will be set, and the p_signals/e_p_signals field will be set to None. - -:: - - record.adc(self, expanded=False, inplace=False) - -Input arguments: - -- ``expanded`` (default=False): Boolean specifying whether to transform the e_p_signals attribute (True) or the p_signals attribute (False). -- ``inplace`` (default=False): Boolean specifying whether to automatically set the object's corresponding digital signal attribute and set the physical signal attribute to None (True), or to return the converted signal as a separate variable without changing the original physical signal attribute (False). - -Possible output argument: - -- ``d_signals``: The digital conversion of the signal. Either a 2d numpy array or a list of 1d numpy arrays. - -Example Usage: - -:: - - import wfdb - record = wfdb.rdsamp('sampledata/100') - d_signal = record.adc() - record.adc(inplace=True) - record.dac(inplace=True) - - -**dac** - Performs digital to analogue conversion of the digital signal stored in d_signals if expanded is False, or e_d_signals if expanded is True. The d_signals/e_d_signals, fmt, gain, and baseline fields must all be valid. If inplace is True, the dac will be performed inplace on the variable, the p_signals/e_p_signals attribute will be set, and the d_signals/e_d_signals field will be set to None. - -:: - - record.dac(self, expanded=False, inplace=False) - -Input arguments: - -- ``expanded`` (default=False): Boolean specifying whether to transform the e_d_signals attribute (True) or the d_signals attribute (False). -- ``inplace`` (default=False): Boolean specifying whether to automatically set the object's corresponding physical signal attribute and set the digital signal attribute to None (True), or to return the converted signal as a separate variable without changing the original digital signal attribute (False). - -Possible output argument: - -- ``p_signals``: The physical conversion of the signal. Either a 2d numpy array or a list of 1d numpy arrays. - -Example Usage: - -:: - - import wfdb - record = wfdb.rdsamp('sampledata/100', physical=False) - p_signal = record.dac() - record.dac(inplace=True) - record.adc(inplace=True) - - -Writing Signals -~~~~~~~~~~~~~~~ - -The Record class has a **wrsamp** instance method for writing wfdb record files. Create a valid Record object and call ``record.wrsamp()``. If you choose this more advanced method, see also the `setdefaults`, `set_d_features`, and `set_p_features` instance methods to help populate attributes. In addition, there is also the following simpler module level **wrsamp** function. - - -**wrsamp** - Write a single segment WFDB record, creating a WFDB header file and any associated dat files. - -:: - - wrsamp(recordname, fs, units, signames, p_signals = None, d_signals=None, - fmt = None, gain = None, baseline = None, comments = None) - -Example Usage: - -:: - - import wfdb - sig, fields = wfdb.srdsamp('a103l', sampfrom = 50000, channels = [0,1], - pbdir = 'challenge/2015/training') - wfdb.wrsamp('ecgrecord', fs = 250, units = ['mV', 'mV'], - signames = ['I', 'II'], p_signals = sig, fmt = ['16', '16']) - -Input Arguments: - -- ``recordname`` (required): The string name of the WFDB record to be written (without any file extensions). -- ``fs`` (required): The numerical sampling frequency of the record. -- ``units`` (required): A list of strings giving the units of each signal channel. -- ``signames`` (required): A list of strings giving the signal name of each signal channel. -- ``p_signals`` (default=None): An MxN 2d numpy array, where M is the signal length. Gives the physical signal - values intended to be written. Either p_signals or d_signals must be set, but not both. If p_signals - is set, this method will use it to perform analogue-digital conversion, writing the resultant digital - values to the dat file(s). If fmt is set, gain and baseline must be set or unset together. If fmt is - unset, gain and baseline must both be unset. -- ``d_signals`` (default=None): An MxN 2d numpy array, where M is the signal length. Gives the digital signal - values intended to be directly written to the dat file(s). The dtype must be an integer type. Either - p_signals or d_signals must be set, but not both. In addition, if d_signals is set, fmt, gain and baseline - must also all be set. -- ``fmt`` (default=None): A list of strings giving the WFDB format of each file used to store each channel. - Accepted formats are: "80","212","16","24", and "32". There are other WFDB formats but this library - will not write (though it will read) those file types. -- ``gain`` (default=None): A list of integers specifying the ADC gain. -- ``baseline`` (default=None): A list of integers specifying the digital baseline. -- ``comments`` (default-None): A list of string comments to be written to the header file. - - -Reading Annotations -~~~~~~~~~~~~~~~~~~~ - -**rdann** - Read a WFDB annotation file ``recordname.annot`` and return an Annotation object. - -:: - - annotation = rdann(recordname, extension, sampfrom=0, sampto=None, shiftsamps=False, - pbdir=None, return_label_elements=['symbol'], summarize_labels=False) - -Example Usage: -:: - - import wfdb - ann = wfdb.rdann('sampledata/100', 'atr', sampto = 300000) - -Input Arguments: - -- ``recordname`` (required): The record name of the WFDB annotation file. ie. for file `100.atr`, recordname='100' -- ``extension`` (required): The annotatator extension of the annotation file. ie. for - file '100.atr', extension='atr' -- ``sampfrom`` (default=0): The minimum sample number for annotations to be returned. -- ``sampto`` (default=None): The maximum sample number for annotations to be returned. -- ``shiftsamps`` (default=False): Boolean flag that specifies whether to return the - sample indices relative to 'sampfrom' (True), or sample 0 (False). Annotation files - store exact sample locations. -- ``pbdir`` (default=None): Option used to stream data from Physiobank. The Physiobank database - directory from which to find the required annotation file. - eg. For record '100' in 'http://physionet.org/physiobank/database/mitdb', pbdir = 'mitdb'. -- ``return_label_elements`` (default=['symbol']): The label elements that are to be returned - from reading the annotation file. A list with at least one of the following: 'symbol', - 'label_store', 'description'. -- ``summarize_labels`` (default=False): Assign a summary table of the set of annotation labels - contained in the file to the 'contained_labels' attribute of the returned object. - This table will contain the columns: ['label_store', 'symbol', 'description', 'n_occurences'] - -Output arguments: - -- ``annotation``: The Annotation object. Contains the following attributes: - - ``recordname``: The base file name (without extension) of the record that the annotation is attached to. - - ``extension``: The file extension of the file the annotation is stored in. - - ``sample``: The annotation locations in samples relative to the beginning of the record. - - ``symbol``: The annotation type according the the standard WFDB codes. - - ``subtype``: The marked class/category of each annotation. - - ``chan``: The signal channel associated with each annotations. - - ``num``: The labelled annotation number for each annotation. - - ``aux_note``: The auxiliary information string for each annotation. - - ``fs``: The sampling frequency of the record, if available. - - ``label_store``: The integer value used to store/encode each annotation label - - ``description``: The descriptive string of each annotation label - - ``custom_labels``: The custom annotation labels defined in the annotation file. - Maps the relationship between the three label fields. - The data type is a pandas DataFrame with three columns: ['label_store', 'symbol', 'description'] - - ``contained_labels``: The unique labels contained in this annotation. Same structure - as custom_labels. - -\*\ **NOTE**: In annotation files, every annotation contains the ‘sample’ and ‘symbol’ field. All other fields default to 0 or empty if not present. - -**show_ann_labels** - Display the annotation symbols and the codes they represent according to the standard WFDB library 10.5.24 - -:: - - show_ann_labels() - -Writing Annotations -~~~~~~~~~~~~~~~~~~~ - -The Annotation class has a **wrann** instance method for writing wfdb annotation files. Create a valid Annotation object and call ``annotation.wrsamp()``. In addition, there is also the following simpler module level **wrann** function. - -**wrann** - Write a WFDB annotation file. - -:: - - wrann(recordname, extension, sample, symbol=None, subtype=None, chan=None, - num=None, aux_note=None, label_store=None, fs=None, custom_labels=None) - -Example Usage: - -:: - - import wfdb - annotation = wfdb.rdann('b001', 'atr', pbdir='cebsdb') - wfdb.wrann('b001', 'cpy', annotation.sample, annotation.symbol) - -Input Arguments: - -- ``recordname`` (required): The string name of the WFDB record to be written (without any file extensions). -- ``extension`` (required): The string annotation file extension. -- ``sample`` (required): The annotation location in samples relative to the beginning of the record. Numpy array. -- ``symbol`` (default=None): The symbols used to display the annotation labels. List or numpy array. If this field is present, 'label_store' must not be present. -- ``subtype`` (default=None): The marked class/category of each annotation. Numpy array. -- ``chan`` (default=None): The signal channel associated with each annotation. Numpy array. -- ``num`` (default=None): The labelled annotation number of each annotation. Numpy array. -- ``aux_note`` (default=None): The auxiliary information strings. List or numpy array. -- ``label_store`` (default=None): The integer values used to store the annotation labels. Numpy array. If this field is present, 'symbol' must not be present. -- ``fs`` (default=None): The numerical sampling frequency of the record to be written to the file. -- ``custom_labels`` (default=None): The map of custom defined annotation labels used for this annotation, in addition to the standard WFDB annotation labels. The custom labels are defined by two or three fields: - - - The integer values used to store custom annotation labels in the file (optional) - - Their short display symbols - - Their long descriptions. - - This input argument may come in four formats: - - 1. A pandas.DataFrame object with columns: ['label_store', 'symbol', 'description'] - 2. A pandas.DataFrame object with columns: ['symbol', 'description'] - If this option is chosen, label_store values are automatically chosen. - 3. A list or tuple of tuple triplets, with triplet elements representing: (label_store, symbol, description). - 4. A list or tuple of tuple pairs, with pair elements representing: (symbol, description). - If this option is chosen, label_store values are automatically chosen. - - If the 'label_store' field is given for this function, and 'custom_labels' is defined, 'custom_labels' - must contain 'label_store' in its mapping. ie. it must come in format 1 or 3 above. - -\*\ **NOTE**: Each annotation stored in a WFDB annotation file contains a sample and a label field. All other fields may or may not be present. Therefore in order to save space, when writing additional string features such as 'aux_note' that are not present for every annotation, it is recommended to make the field a list, with empty indices set to None so that they are not written to the file. - - -Plotting Data -~~~~~~~~~~~~~ - -**plotrec** - Subplot and label each channel of a WFDB Record. Optionally, subplot annotation locations over selected channels. - -:: - - plotrec(record=None, title = None, annotation = None, timeunits='samples', - sigstyle='', annstyle='r*', plotannsym=False, figsize=None, - returnfig=False, ecggrids=[]): - -Example Usage: - -:: - - import wfdb - record = wfdb.rdsamp('sampledata/100', sampto = 3000) - annotation = wfdb.rdann('sampledata/100', 'atr', sampto = 3000) - - wfdb.plotrec(record, annotation = annotation, - title='Record 100 from MIT-BIH Arrhythmia Database', - timeunits = 'seconds', figsize = (10,4), ecggrids = 'all') - -Input Arguments: - -- ``record`` (required): A wfdb Record object. The p_signals attribute will be plotted. -- ``title`` (default=None): A string containing the title of the graph. -- ``annotation`` (default=None): A list of Annotation objects or numpy arrays. The locations of the Annotation objects' 'sample' attribute, or the locations of the numpy arrays' values, will be overlaid on the signals. The list index of the annotation item corresponds to the signal channel that each annotation set will be plotted on. For channels without annotations to plot, put None in the list. This argument may also be just an Annotation object or numpy array, which will be plotted over channel 0. -- ``timeunits`` (default='samples'): String specifying the x axis unit. Allowed options are: 'samples', 'seconds', 'minutes', and 'hours'. -- ``sigstyle`` (default=''): String, or list of strings, specifying the styling of the matplotlib plot for the signals. If 'sigstyle' is a string, each channel will have the same style. If it is a list, each channel's style will correspond to the list element. ie. sigtype=['r','b','k']. -- ``annstyle`` (default='r*'): String, or list of strings, specifying the styling of the matplotlib plot for the annotations. If 'annstyle' is a string, each channel will have the same style. If it is a list, each channel's style will correspond to the list element. -- ``plotannsym`` (default=False): Specifies whether to plot the annotation symbols at their locations. -- ``figsize`` (default=None): Tuple pair specifying the width, and height of the figure. Same as the 'figsize' argument passed into matplotlib.pyplot's figure() function. -- ``returnfig`` (default=False): Specifies whether the figure is to be returned as an output argument -- ``ecggrids`` (default=[]): List of integers specifying channels in which to plot ecg grids. May be set to [] for no channels, or 'all' for all channels. Major grids at 0.5mV, and minor grids at 0.125mV. All channels to be plotted with grids must have units equal to 'uV', 'mV', or 'V'. - -Output argument: -- ``figure``: The matplotlib figure generated. Only returned if the 'returnfig' option is set to True. - - -**plotann** - Plot sample locations of an Annotation object. - -:: - - plotann(annotation, title = None, timeunits = 'samples', returnfig = False) - -Example Usage: - -:: - - import wfdb - record = wfdb.rdsamp('sampledata/100', sampto = 15000) - annotation = wfdb.rdann('sampledata/100', 'atr', sampto = 15000) - - wfdb.plotrec(record, annotation = annotation, - title='Record 100 from MIT-BIH Arrhythmia Database', - timeunits = 'seconds') - - -Input Arguments: - -- ``annotation`` (required): An Annotation object. The sample attribute locations will be overlaid on the signal. -- ``title`` (default=None): A string containing the title of the graph. -- ``annotation`` (default=None): An Annotation object. The sample attribute locations will be overlaid on the signal. -- ``timeunits`` (default='samples'): String specifying the x axis unit. Allowed options are: 'samples', 'seconds', 'minutes', and 'hours'. -- ``returnfig`` (default=False): Specifies whether the figure is to be returned as an output argument - -Output argument: -- ``figure``: The matplotlib figure generated. Only returned if the 'returnfig' option is set to True. - -Downloading Physiobank Content -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Download files from various Physiobank databases. The Physiobank index page located at http://physionet.org/physiobank/database lists all available databases. - - -**getdblist** - Return a list of all the physiobank databases available. - -:: - - dblist = wfdb.getdblist() - -Example Usage: - -:: - - import wfdb - dblist = wfdb.getdblist() - -**dldatabase** - Download WFDB record (and optionally annotation) files from a Physiobank database. The database must contain a 'RECORDS' file in its base directory which lists its WFDB records. - -:: - - dldatabase(pbdb, dlbasedir, records = 'all', annotators = 'all' , - keepsubdirs = True, overwrite = False) - -Example Usage: - -:: - - import wfdb - wfdb.dldatabase('ahadb', os.getcwd()) - -Input arguments: - -- ``pbdb`` (required): The Physiobank database directory to download. eg. For database 'http://physionet.org/physiobank/database/mitdb', pbdb = 'mitdb'. -- ``dlbasedir`` (required): The full local directory path in which to download the files. -- ``records`` (default='all'): Specifier of the WFDB records to download. Is either a list of strings which each specify a record, or 'all' to download all records listed in the database's RECORDS file. eg. records = ['test01_00s', test02_45s] for database https://physionet.org/physiobank/database/macecgdb/ -- ``annotators`` (default='all'): Specifier of the WFDB annotation file types to download along with the record files. Is either None to skip downloading any annotations, 'all' to download all annotation types as specified by the ANNOTATORS file, or a list of strings which each specify an annotation extension. eg. annotators = ['anI'] for database https://physionet.org/physiobank/database/prcp/ -- ``keepsubdirs`` (default=True): Whether to keep the relative subdirectories of downloaded files as they are organized in Physiobank (True), or to download all files into the same base directory (False). -- ``overwrite`` (default=False): If set to True, all files will be redownloaded regardless. If set to False, existing files with the same name and relative subdirectory will be checked. If the local file is the same size as the online file, the download is skipped. If the local file is larger, it will be deleted and the file will be redownloaded. If the local file is smaller, the file will be assumed to be partially downloaded and the remaining bytes will be downloaded and appended. - - -**dldatabasefiles** - Download specified files from a Physiobank database. - -:: - - dldatabasefiles(pbdb, dlbasedir, files, keepsubdirs = True, overwrite = False) - -Example Usage: - -:: - - import wfdb - wfdb.dldatabasefiles('ahadb', os.getcwd(), ['STAFF-Studies-bibliography-2016.pdf', 'data/001a.hea', 'data/001a.dat']) - -Input arguments: - -- ``pbdb`` (required): The Physiobank database directory to download. eg. For database 'http://physionet.org/physiobank/database/mitdb', pbdb = 'mitdb'. -- ``dlbasedir`` (required): The full local directory path in which to download the files. -- ``files`` (required): A list of strings specifying the file names to download relative to the database base directory -- ``keepsubdirs`` (default=True): Whether to keep the relative subdirectories of downloaded files as they are organized in Physiobank (True), or to download all files into the same base directory (False). -- ``overwrite`` (default=False): If set to True, all files will be redownloaded regardless. If set to False, existing files with the same name and relative subdirectory will be checked. If the local file is the same size as the online file, the download is skipped. If the local file is larger, it will be deleted and the file will be redownloaded. If the local file is smaller, the file will be assumed to be partially downloaded and the remaining bytes will be downloaded and appended. - - -Signal Processing ------------------ - -Basic Functionalities -~~~~~~~~~~~~~~~~~~~~~ - -**resample_sig** - Resample a single-channel signal - -:: - - resample_sig(x, fs, fs_target) - -Example Usage: - -:: - - import wfdb - sig, fields = wfdb.srdsamp('sampledata/100', sampto=10000) - x, _ = wfdb.processing.resample_sig(x=sig[:,0], fs=fields['fs'], fs_target=128) - -Input arguments: - -- ``x`` (required): The signal. -- ``fs`` (required): The signal frequency. -- ``fs_target`` (required): The target signal frequency. - - -**resample_singlechan** - Resample a single-channel signal and its annotation. - -:: - - resample_singlechan(x, ann, fs, fs_target) - -Example Usage: - -:: - - import wfdb - sig, fields = wfdb.srdsamp('sampledata/100') - ann = wfdb.rdann('sampledata/100', 'atr') - new_sig, new_ann = wfdb.processing.resample_singlechan(x=sig[:, 0], ann=ann, fs=fields['fs'], fs_target=50) - -Input arguments: - -- ``x`` (required): The signal. -- ``ann`` (required): The signal Annotation. -- ``fs`` (required): The signal frequency. -- ``fs_target`` (required): The target signal frequency. - - - -**resample_multichan** - Resample a multi-channel signal and its annotation. - -:: - - resample_multichan(sig, ann, fs, fs_target) - -Example Usage: - -:: - - import wfdb - sig, fields = wfdb.srdsamp('sampledata/100') - ann = wfdb.rdann('sampledata/100', 'atr') - new_sig, new_ann = wfdb.processing.resample_multichan(sig=sig, ann=ann, fs=fields['fs'], fs_target=50) - -Input arguments: - -- ``x`` (required): The signal. -- ``ann`` (required): The signal Annotation. -- ``fs`` (required): The signal frequency. -- ``fs_target`` (required): The target signal frequency. - - - -**normalize** - Resizes a signal between a lower and upper bound - -:: - - normalize(x, lb=0, ub=1) - -Example Usage: - -:: - - import wfdb - sig, _ = wfdb.srdsamp('sampledata/100') - x = wfdb.processing.normalize(x=sig[:, 0], lb=-2, ub=15) - -Input arguments: - -- ``x`` (required): The signal. -- ``lb`` (required): The lower bound. -- ``ub`` (required): The upper bound. - - - -**smooth** - Signal smoothing - -:: - - smooth(x, window_size) - -Example Usage: - -:: - - import wfdb - sig, _ = wfdb.srdsamp('sampledata/100') - x = smooth(x=sig[:,0], window_size=150) - -Input arguments: - -- ``x`` (required): The signal. -- ``window_size`` (required): The smoothing window width. - - -Peak Detection -~~~~~~~~~~~~~~ - -**gqrs_detect** - The GQRS detector function - -:: - - gqrs_detect(x, fs, adcgain, adczero, threshold=1.0, hr=75, RRdelta=0.2, - RRmin=0.28, RRmax=2.4, QS=0.07, QT=0.35, RTmin=0.25, RTmax=0.33, - QRSa=750, QRSamin=130): - -Example Usage: - -:: - - import wfdb - t0 = 10000 - tf = 20000 - record = wfdb.rdsamp("sampledata/100", sampfrom=t0, sampto=tf, channels=[0]) - d_signal = record.adc()[:,0] - peak_indices = wfdb.processing.gqrs_detect(x=d_signal, fs=record.fs, adcgain=record.adcgain[0], adczero=record.adczero[0], threshold=1.0) - -Input arguments: - -- ``x`` (required): The digital signal as a numpy array -- ``fs`` (required): The sampling frequency of the signal -- ``adcgain``: The gain of the signal (the number of adus (q.v.) per physical unit) -- ``adczero`` (required): The value produced by the ADC given a 0 volt input. -- ``threshold`` (default=1.0): The threshold for detection -- ``hr`` (default=75): Typical heart rate, in beats per minute -- ``RRdelta`` (default=0.2): Typical difference between successive RR intervals in seconds -- ``RRmin`` (default=0.28): Minimum RR interval ("refractory period"), in seconds -- ``RRmax`` (default=2.4): Maximum RR interval, in seconds; thresholds will be adjusted if no peaks are detected within this interval -- ``QS`` (default=0.07): Typical QRS duration, in seconds -- ``QT`` (default=0.35): Typical QT interval, in seconds -- ``RTmin`` (default=0.25): Minimum interval between R and T peaks, in seconds -- ``RTmax`` (default=0.33): Maximum interval between R and T peaks, in seconds -- ``QRSa`` (default=750): Typical QRS peak-to-peak amplitude, in microvolts -- ``QRSamin`` (default=130): Minimum QRS peak-to-peak amplitude, in microvolts - -Output Arguments: - -- ``peak_indices``: A python list containing the peak indices. - - -**correct_peaks** - A post-processing algorithm to correct peaks position. - -See code comments for details about the algorithm. - - -:: - - correct_peaks(x, peak_indices, min_gap, max_gap, smooth_window) - -Input arguments: - -- ``x`` (required): The signal. -- ``peak_indices`` (required): The location of the peaks. -- ``min_gap`` (required): The minimum gap in samples between two peaks. -- ``max_gap`` (required): The maximum gap in samples between two peaks. -- ``smooth_window`` (required): The size of the smoothing window. - -Output Arguments: - -- ``new_indices``: A python list containing the new peaks indices. - - -Example Usage: - -:: - - import wfdb - t0 = 10000 - tf = 20000 - record = wfdb.rdsamp('sampledata/100', sampfrom=t0, sampto=tf, channels=[0]) - d_signal = record.adc()[:,0] - peak_indices = wfdb.processing.gqrs_detect(d_signal, fs=record.fs, - adcgain=record.adcgain[0], - adczero=record.adczero[0], - threshold=1.0) - min_bpm = 10 - max_bpm = 350 - min_gap = record.fs*60/min_bpm - max_gap = record.fs*60/max_bpm - new_indices = wfdb.processing.correct_peaks(d_signal, peak_indices=peak_indices, - min_gap=min_gap, max_gap=max_gap, - smooth_window=150) - - -Heart Rate -~~~~~~~~~~~~~~ - -**compute_hr** - Compute instantaneous heart rate from peak indices and signal frequency. - -:: - - compute_hr(siglen, peak_indices, fs) - -Input arguments: - -- ``siglen`` (required): The length of the corresponding signal. -- ``peak_indices`` (required): The peak indices. -- ``fs`` (required): The corresponding signal's sampling frequency. - - -Output Arguments: - -- ``hr``: A numpy array of the instantaneous heart rate, with the length of the corresponding signal. Contains numpy.nan where heart rate could not be computed. - - -Example Usage: - -:: - - import wfdb - t0 = 10000 - tf = 20000 - record = wfdb.rdsamp("sampledata/100", sampfrom=t0, sampto=tf, channels=[0]) - peak_indices = wfdb.processing.gqrs_detect(record.adc(), fs=record.fs, - adcgain=record.adcgain[0], - adczero=record.adczero[0], - threshold=1.0) - hr = wfdb.processing.compute_hr(siglen=tf-t0, peak_indices=peak_indices, fs=record.fs) - - - - -Based on the original WFDB software package specifications ----------------------------------------------------------- - -| `WFDB Software Package`_ -| `WFDB Applications Guide`_ -| `WFDB Header File Specifications`_ - -.. _WFDB Software Package: http://physionet.org/physiotools/wfdb.shtml -.. _WFDB Applications Guide: http://physionet.org/physiotools/wag/ -.. _WFDB Header File Specifications: https://physionet.org/physiotools/wag/header-5.htm - - -.. |Build Status| image:: https://travis-ci.org/MIT-LCP/wfdb-python.svg?branch=master - :target: https://travis-ci.org/MIT-LCP/wfdb-python diff --git a/demoimg.png b/demo-img.png similarity index 100% rename from demoimg.png rename to demo-img.png diff --git a/demo.ipynb b/demo.ipynb index a5a34776..66958a3d 100644 --- a/demo.ipynb +++ b/demo.ipynb @@ -6,130 +6,34784 @@ "source": [ "# Demo Scripts for the wfdb-python package\n", "\n", - "Run this script from the base directory of the git repository to access the included demo files" + "Run this notebook from the base directory of the git repository to access the included demo files" ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "cell_type": "markdown", + "metadata": {}, "source": [ - "import wfdb\n", - "import numpy as np\n", - "import os\n", - "import matplotlib.pyplot as plt\n", - "from IPython.display import display" + "## Documentation Site\n", + "\n", + "http://wfdb.readthedocs.io/" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 1, + "metadata": {}, "outputs": [], "source": [ - "# See the help documentation for the read functions\n", + "from IPython.display import display\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import numpy as np\n", + "import os\n", + "import shutil\n", + "import posixpath\n", "\n", - "#help(wfdb.rdsamp)\n", - "#help(wfdb.srdsamp)\n", - "#help(wfdb.rdann)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reading Records and Annotations" + "import wfdb" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "{'record_name': 'a103l',\n", + " 'n_sig': 3,\n", + " 'fs': 250,\n", + " 'counter_freq': None,\n", + " 'base_counter': None,\n", + " 'sig_len': 82500,\n", + " 'base_time': None,\n", + " 'base_date': None,\n", + " 'comments': ['Asystole', 'False alarm'],\n", + " 'sig_name': ['II', 'V', 'PLETH'],\n", + " 'p_signal': array([[-0.02359597, 0.86758555, 0.48220271],\n", + " [-0.03698082, 0.98298479, 0.5443735 ],\n", + " [-0.06292259, 0.85979087, 0.47821229],\n", + " ...,\n", + " [-0.04084449, 0.7493346 , 0.5150838 ],\n", + " [-0.04719194, 0.7581749 , 0.50957702],\n", + " [-0.04677798, 0.7615019 , 0.5028731 ]]),\n", + " 'd_signal': None,\n", + " 'e_p_signal': None,\n", + " 'e_d_signal': None,\n", + " 'file_name': ['a103l.mat', 'a103l.mat', 'a103l.mat'],\n", + " 'fmt': ['16', '16', '16'],\n", + " 'samps_per_frame': [1, 1, 1],\n", + " 'skew': [None, None, None],\n", + " 'byte_offset': [24, 24, 24],\n", + " 'adc_gain': [7247.0, 10520.0, 12530.0],\n", + " 'baseline': [0, 0, 0],\n", + " 'units': ['mV', 'mV', 'NU'],\n", + " 'adc_res': [16, 16, 16],\n", + " 'adc_zero': [0, 0, 0],\n", + " 'init_value': [-171, 9127, 6042],\n", + " 'checksum': [-27403, -301, -17391],\n", + " 'block_size': [0, 0, 0]}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Demo 1 - Read a wfdb record using the 'rdsamp' function into a wfdb.Record object.\n", + "# Demo 1 - Read a WFDB record using the 'rdrecord' function into a wfdb.Record object.\n", "# Plot the signals, and show the data.\n", - "record = wfdb.rdsamp('sampledata/a103l') \n", - "wfdb.plotrec(record, title='Record a103l from Physionet Challenge 2015') \n", + "record = wfdb.rdrecord('sample-data/a103l') \n", + "wfdb.plot_wfdb(record=record, title='Record a103l from PhysioNet Challenge 2015') \n", "display(record.__dict__)\n", "\n", - "\n", - "# Can also read the same files hosted on Physiobank https://physionet.org/physiobank/database/\n", - "# in the challenge/2015/training/ database subdirectory. Full url = https://physionet.org/physiobank/database/challenge/2015/training/\n", - "record2 = wfdb.rdsamp('a103l', pbdir = 'challenge/2015/training/')" + "# Can also read the same files hosted on PhysioNet https://physionet.org/content/challenge-2015/1.0.0\n", + "# in the /training/ database subdirectory.\n", + "record2 = wfdb.rdrecord('a103l', pn_dir='challenge-2015/training/')" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.0335, -0.167 , -0.237 , 0.1165],\n", + " [ 0.0355, -0.1615, -0.2395, 0.119 ],\n", + " [ 0.0385, -0.168 , -0.2465, 0.116 ],\n", + " ...,\n", + " [-0.0445, 0.008 , 0.033 , 0.045 ],\n", + " [-0.044 , 0.0175, 0.042 , 0.052 ],\n", + " [-0.044 , 0.0245, 0.0365, 0.05 ]])" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "{'fs': 1000,\n", + " 'sig_len': 14900,\n", + " 'n_sig': 4,\n", + " 'base_date': None,\n", + " 'base_time': None,\n", + " 'units': ['mV', 'mV', 'mV', 'mV'],\n", + " 'sig_name': ['vz', 'i', 'avf', 'v5'],\n", + " 'comments': ['age: 81',\n", + " 'sex: female',\n", + " 'ECG date: 01/10/1990',\n", + " 'Diagnose:',\n", + " 'Reason for admission: Myocardial infarction',\n", + " 'Acute infarction (localization): infero-latera',\n", + " 'Former infarction (localization): no',\n", + " 'Additional diagnoses: Diabetes mellitus',\n", + " 'Smoker: no',\n", + " 'Number of coronary vessels involved: 1',\n", + " 'Infarction date (acute): 29-Sep-90',\n", + " 'Previous infarction (1) date: n/a',\n", + " 'Previous infarction (2) date: n/a',\n", + " 'Hemodynamics:',\n", + " 'Catheterization date: 16-Oct-90',\n", + " 'Ventriculography: Akinesia inferior wall',\n", + " 'Chest X-ray: Heart size upper limit of norm',\n", + " 'Peripheral blood Pressure (syst/diast): 140/80 mmHg',\n", + " 'Pulmonary artery pressure (at rest) (syst/diast): n/a',\n", + " 'Pulmonary artery pressure (at rest) (mean): n/a',\n", + " 'Pulmonary capillary wedge pressure (at rest): n/a',\n", + " 'Cardiac output (at rest): n/a',\n", + " 'Cardiac index (at rest): n/a',\n", + " 'Stroke volume index (at rest): n/a',\n", + " 'Pulmonary artery pressure (laod) (syst/diast): n/a',\n", + " 'Pulmonary artery pressure (laod) (mean): n/a',\n", + " 'Pulmonary capillary wedge pressure (load): n/a',\n", + " 'Cardiac output (load): n/a',\n", + " 'Cardiac index (load): n/a',\n", + " 'Stroke volume index (load): n/a',\n", + " 'Aorta (at rest) (syst/diast): 160/64 cmH2O',\n", + " 'Aorta (at rest) mean: 106 cmH2O',\n", + " 'Left ventricular enddiastolic pressure: 11 cmH2O',\n", + " 'Left coronary artery stenoses (RIVA): RIVA 70% proximal to ramus diagonalis_2',\n", + " 'Left coronary artery stenoses (RCX): No stenoses',\n", + " 'Right coronary artery stenoses (RCA): No stenoses',\n", + " 'Echocardiography: n/a',\n", + " 'Therapy:',\n", + " 'Infarction date: 29-Sep-90',\n", + " 'Catheterization date: 16-Oct-90',\n", + " 'Admission date: 29-Sep-90',\n", + " 'Medication pre admission: Isosorbit-Dinitrate Digoxin Glibenclamide',\n", + " 'Start lysis therapy (hh.mm): 19:45',\n", + " 'Lytic agent: Gamma-TPA',\n", + " 'Dosage (lytic agent): 30 mg',\n", + " 'Additional medication: Heparin Isosorbit-Mononitrate ASA Diazepam',\n", + " 'In hospital medication: ASA Isosorbit-Mononitrate Ca-antagonist Amiloride+Chlorothiazide Glibenclamide Insulin',\n", + " 'Medication after discharge: ASA Isosorbit-Mononitrate Amiloride+Chlorothiazide Glibenclamide']}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Demo 2 - Read certain channels and sections of the WFDB record using the simplified 'srdsamp' function\n", + "# Demo 2 - Read certain channels and sections of the WFDB record using the simplified 'rdsamp' function\n", "# which returns a numpy array and a dictionary. Show the data.\n", - "signals, fields=wfdb.srdsamp('sampledata/s0010_re', channels=[14, 0, 5, 10], sampfrom=100, sampto=15000)\n", + "signals, fields = wfdb.rdsamp('sample-data/s0010_re', channels=[14, 0, 5, 10], sampfrom=100, sampto=15000)\n", "display(signals)\n", "display(fields)\n", "\n", - "# Can also read the same files hosted on Physiobank \n", - "signals2, fields2=wfdb.srdsamp('s0010_re', channels=[14, 0, 5, 10], sampfrom=100, sampto=15000, pbdir = 'ptbdb/patient001/')" + "# Can also read the same files hosted on Physionet\n", + "signals2, fields2 = wfdb.rdsamp('s0010_re', channels=[14, 0, 5, 10], sampfrom=100, sampto=15000, pn_dir='ptbdb/patient001/')" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'record_name': 'drive02',\n", + " 'n_sig': 5,\n", + " 'fs': 15.5,\n", + " 'counter_freq': None,\n", + " 'base_counter': None,\n", + " 'sig_len': 78056,\n", + " 'base_time': None,\n", + " 'base_date': None,\n", + " 'comments': [],\n", + " 'sig_name': ['ECG', 'foot GSR', 'HR', 'marker', 'RESP'],\n", + " 'p_signal': None,\n", + " 'd_signal': None,\n", + " 'e_p_signal': None,\n", + " 'e_d_signal': None,\n", + " 'file_name': ['drive02.dat',\n", + " 'drive02.dat',\n", + " 'drive02.dat',\n", + " 'drive02.dat',\n", + " 'drive02.dat'],\n", + " 'fmt': ['16', '16', '16', '16', '16'],\n", + " 'samps_per_frame': [32, 2, 1, 1, 2],\n", + " 'skew': [None, None, None, None, None],\n", + " 'byte_offset': [None, None, None, None, None],\n", + " 'adc_gain': [1000.0, 1000.0, 1.0001, 100.0, 500.0],\n", + " 'baseline': [0, 0, 0, 0, 0],\n", + " 'units': ['mV', 'mV', 'bpm', 'mV', 'mV'],\n", + " 'adc_res': [16, 16, 16, 16, 16],\n", + " 'adc_zero': [0, 0, 0, 0, 0],\n", + " 'init_value': [-1236, 1802, 75, 0, 5804],\n", + " 'checksum': [14736, 13501, -19070, -9226, -14191],\n", + " 'block_size': [0, 0, 0, 0, 0]}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Demo 3 - Read a WFDB header file only (without the signals)\n", - "record = wfdb.rdheader('sampledata/drive02')\n", + "record = wfdb.rdheader('sample-data/drive02')\n", "display(record.__dict__)\n", "\n", - "# Can also read the same file hosted on Physiobank\n", - "record2 = wfdb.rdheader('drive02', pbdir = 'drivedb')" + "# Can also read the same file hosted on Physionet\n", + "record2 = wfdb.rdheader('drive02', pn_dir='drivedb')" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Demo 4 - Read part of a WFDB annotation file into a wfdb.Annotation object, and plot the samples\n", - "annotation = wfdb.rdann('sampledata/100', 'atr', sampfrom = 100000, sampto = 110000)\n", + "annotation = wfdb.rdann('sample-data/100', 'atr', sampfrom=100000, sampto=110000)\n", "annotation.fs = 360\n", - "wfdb.plotann(annotation, timeunits = 'minutes')\n", + "wfdb.plot_wfdb(annotation=annotation, time_units='minutes')\n", "\n", - "# Can also read the same file hosted on PhysioBank \n", - "annotation2 = wfdb.rdann('100', 'atr', sampfrom = 100000, sampto = 110000, pbdir = 'mitdb')" + "# Can also read the same file hosted on PhysioNet \n", + "annotation2 = wfdb.rdann('100', 'atr', sampfrom=100000, sampto=110000, pn_dir='mitdb')" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Demo 5 - Read a WFDB record and annotation. Plot all channels, and the annotation on top of channel 0.\n", - "record = wfdb.rdsamp('sampledata/100', sampto = 15000)\n", - "annotation = wfdb.rdann('sampledata/100', 'atr', sampto = 15000)\n", + "record = wfdb.rdrecord('sample-data/100', sampto = 15000)\n", + "annotation = wfdb.rdann('sample-data/100', 'atr', sampto = 15000)\n", + "\n", + "wfdb.plot_wfdb(record=record, annotation=annotation,\n", + " title='Record 100 from MIT-BIH Arrhythmia Database',\n", + " time_units='seconds')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multiple sample/frame examples\n", + "\n", + "Although there can only be one base sampling frequency per record, a single WFDB record can store multiple channels with different sampling frequencies, as long as their sampling frequencies can all be expressed by an integer multiple of a base value. This is done by using the `samps_per_frame` attribute in each channel, which indicates the number of samples of each channel present in each frame.\n", + "\n", + "ie: To capture three signals with sampling frequencies of 120, 240, and 360 Hz, in a single record, they can be combined into a record with `fs=120` and `samps_per_frame = [1, 2, 3]`.\n", + "\n", + "#### Reading Options\n", + "\n", + "This package allows signals in records with multiple samples/frame to be read in two ways:\n", + "1. smoothed - An uniform mxn numpy is returned as the d_signal or p_signal field. Channels with multiple samples/frame have their values averaged within each frame. This is like the behaviour of the `rdsamp` function of the original WFDB c package. Note that `wfdb.plot_record` only works if the record object has the `p_signals` field.\n", + "2. expanded - A list of 1d numpy arrays is returned as the e_d_signal or e_p_signal field. All samples for each channel are returned in its respective numpy array. The arrays may have different lengths depending on their `samps_per_frame` values.\n", "\n", - "wfdb.plotrec(record, annotation = annotation, title='Record 100 from MIT-BIH Arrhythmia Database', timeunits = 'seconds')" + "Set the `smooth_frames` *(default=True)* option in `rdrecord` to return the desired signal type." ] }, { @@ -146,87 +34800,11592 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(array([[ nan],\n", + " [ nan],\n", + " [ nan],\n", + " ...,\n", + " [0.52156863],\n", + " [0.51764706],\n", + " [0.51764706]]),\n", + " {'fs': 125,\n", + " 'sig_len': 2000,\n", + " 'n_sig': 1,\n", + " 'base_date': None,\n", + " 'base_time': datetime.time(16, 57, 39),\n", + " 'units': ['NU'],\n", + " 'sig_name': ['PLETH'],\n", + " 'comments': []})" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Demo 6 - Read the multi-segment record and plot waveforms from the MIMIC matched waveform database. \n", - "import wfdb\n", - "from IPython.display import display\n", - "record=wfdb.rdsamp('sampledata/multisegment/s25047/s25047-2704-05-04-10-44')\n", - "wfdb.plotrec(record, title='Record s25047-2704-05-04-10-44') \n", + "record = wfdb.rdrecord('sample-data/multi-segment/p000878/3269321_0001')\n", + "wfdb.plot_wfdb(record=record, title='Record p000878/3269321_0001') \n", "display(record.__dict__)\n", "\n", - "# Can also read the same files hosted on PhysioBank (takes long to stream the many large files)\n", - "#record2 = wfdb.rdsamp('s25047-2704-05-04-10-44', pbdir = 'mimic2wdb/matched/s25047')" + "# Can also read the same files hosted on PhysioNet (takes long to stream the many large files)\n", + "signals, fields = wfdb.rdsamp('3269321_0001', pn_dir = 'mimic3wdb/matched/p00/p000878')\n", + "wfdb.plot_items(signal=signals, fs=fields['fs'], title='Record p000878/3269321_0001')\n", + "display((signals, fields))" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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None,\n", + " 'file_name': ['3269321_0001.dat'],\n", + " 'fmt': ['80'],\n", + " 'samps_per_frame': [1],\n", + " 'skew': [None],\n", + " 'byte_offset': [None],\n", + " 'adc_gain': [255.0],\n", + " 'baseline': [-128],\n", + " 'units': ['NU'],\n", + " 'adc_res': [8],\n", + " 'adc_zero': [0],\n", + " 'init_value': [-18],\n", + " 'checksum': [62211],\n", + " 'block_size': [0]}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Demo 7 - Read the multi-segment record and plot waveforms from the MIMIC matched waveform database.\n", "# Notice that some channels have no valid values to plot\n", - "record = wfdb.rdsamp('sampledata/multisegment/s00001/s00001-2896-10-10-00-31', sampfrom = 3000000, sampto = 4000000)\n", - "wfdb.plotrec(record, title='Record s00001/s00001-2896-10-10-00-31') \n", + "record = wfdb.rdrecord('sample-data/multi-segment/p000878/3269321_0001',\n", + " sampfrom=300, sampto=1000)\n", + "wfdb.plot_wfdb(record, title='Record p000878/3269321_0001') \n", "display(record.__dict__)\n", "\n", - "# Can also read the same files hosted on PhysioBank\n", - "record2 = wfdb.rdsamp('s00001-2896-10-10-00-31', sampfrom = 3000000, sampto = 4000000, pbdir = 'mimic2wdb/matched/s00001')" + "# Can also read the same files hosted on Physionet\n", + "record2 = wfdb.rdrecord('3269321_0001', sampfrom=300, sampto=1000,\n", + " pn_dir = 'mimic3wdb/matched/p00/p000878')" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 42, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "### Multiple sample/frame examples\n", - "\n", - "Although there can only be one base sampling frequency per record, a single wfdb record can store multiple channels with different sampling frequencies, as long as their sampling frequencies can all be expressed by an integer multiple of a base value. This is done by using the `sampsperframe` attribute in each channel, which indicates the number of samples of each channel present in each frame.\n", - "\n", - "ie: To capture three signals with `fs = 120, 240, and 360 Hz` in a single record, they can be combined into a record with `fs = 120` and `sampsperframe = [1, 2, 3]`.\n", - "\n", - "#### Reading Options\n", - "\n", - "This package allows signals in records with multiple samples/frame to be read in two ways:\n", - "1. smoothed - An uniform mxn numpy is returned as the d_signals or p_signals field. Channels with multiple samples/frame have their values averaged within each frame. This is like the behaviour of the `rdsamp` function of the original WFDB c package. Note that `wfdb.plotrec` only works if the record object has the `p_signals` field.\n", - "2. expanded - A list of 1d numpy arrays is returned as the e_d_signals or e_p_signals field. All samples for each channel are returned in its respective numpy array. The arrays may have different lengths depending on their `sampsperframe` values. \n", - "\n", - "Set the `smoothframes` *(default=True)* option in `rdsamp` to return the desired signal type." + "# Demo 8 - Read a WFDB record in which one channel has multiple samples/frame. Return a smoothed uniform array.\n", + "record = wfdb.rdrecord('sample-data/test01_00s_frame')\n", + "wfdb.plot_wfdb(record)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 44, + "metadata": {}, "outputs": [], "source": [ - "# Demo 8 - Read a wfdb record in which one channel has multiple samples/frame. Return a smoothed uniform array.\n", - "record = wfdb.rdsamp('sampledata/test01_00s_frame')\n", - "wfdb.plotrec(record)" + "from IPython.display import display\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import numpy as np\n", + "import os\n", + "import shutil\n", + "import posixpath\n", + "\n", + "import wfdb" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([ 0.1 , 0.11, 0.13, ..., -0.27, -0.27, -0.26]),\n", + " array([-0.08, -0.57, -0.06, ..., 0.13, -0.18, 0.12]),\n", + " array([-0.66, -0.66, -0.67, ..., 0.15, 0.15, 0.16])]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4000, 8000, 4000]\n" + ] + } + ], "source": [ - "# Demo 9 - Read a wfdb record in which one channel has multiple samples/frame. Return a list of all the expanded samples.\n", - "record = wfdb.rdsamp('sampledata/test01_00s_frame', smoothframes = False)\n", + "# Demo 9 - Read a WFDB record in which one channel has multiple samples/frame. Return a list of all the expanded samples.\n", + "record = wfdb.rdrecord('sample-data/test01_00s_frame', smooth_frames=False)\n", "\n", - "display(record.e_p_signals)\n", + "display(record.e_p_signal)\n", "# Show that different channels have different lengths. Channel 1 has 2 samples/frame, hence has 2x as many samples.\n", - "print([len(s) for s in record.e_p_signals])\n", + "print([len(s) for s in record.e_p_signal])\n", "\n", "# wfdb.plotrec doesn't work because the Record object is missing its p_signals field." ] @@ -240,228 +46399,24798 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ - "# Demo 10 - Read a WFDB record's digital samples and create a copy via the wrsamp() instance method \n", - "# of the Record object.\n", + "# Demo 10 - Read a WFDB record's digital samples and create a copy via the wrsamp() instance method of the Record object.\n", "\n", "# Read a record as a Record object.\n", - "record = wfdb.rdsamp('sampledata/100', physical = False)\n", - "record.recordname = '100x'\n", + "record = wfdb.rdrecord('sample-data/100', physical=False)\n", + "record.record_name = '100x'\n", "\n", "# Call the instance method of the object\n", "record.wrsamp()\n", "\n", "# The new file can be read\n", - "recordx = wfdb.rdsamp('100x')" + "record_x = wfdb.rdrecord('100x')\n", + "\n", + "# Cleanup: delete the created files\n", + "# os.remove('100x.hea')\n", + "# os.remove('100.dat')" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 8, + "metadata": {}, "outputs": [], "source": [ "# Demo 11 - Write a WFDB record without using a Record object via the gateway wrsamp function.\n", "# This is the basic way to write physical signals to a WFDB file. \n", "\n", - "# Read part of a record from Physiobank\n", - "sig, fields = wfdb.srdsamp('a103l', sampfrom = 50000, channels = [0,1], pbdir = 'challenge/2015/training')\n", + "# Read part of a record from Physionet\n", + "sig, fields = wfdb.rdsamp('a103l', sampfrom=50000, channels=[0,1], pn_dir='challenge-2015/training')\n", "\n", "# Call the gateway wrsamp function, manually inserting fields as function input parameters\n", - "wfdb.wrsamp('ecgrecord', fs = 250, units = ['mV', 'mV'], signames = ['I', 'II'], p_signals = sig, fmt = ['16', '16'])\n", + "wfdb.wrsamp('ecg-record', fs=250, units=['mV', 'mV'], sig_name=['I', 'II'], p_signal=sig, fmt=['16', '16'])\n", "\n", "# The new file can be read\n", - "recordecg = wfdb.rdsamp('ecgrecord')" + "record = wfdb.rdrecord('ecg-record')\n", + "\n", + "# Cleanup: delete the created files\n", + "# os.remove('ecg-record.hea')\n", + "# os.remove('ecg-record.dat')" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "wfdb.plot_wfdb(record)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import display\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import numpy as np\n", + "import os\n", + "import shutil\n", + "import posixpath\n", + "\n", + "\n", + "import wfdb" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "# Demo 12 - Write a WFDB record with multiple samples/frame in a channel\n", "\n", "# Read a record as a Record object.\n", - "record = wfdb.rdsamp('sampledata/test01_00s_frame', physical = False, smoothframes=False)\n", - "record.recordname = 'test01_00s_framex'\n", + "record = wfdb.rdrecord('sample-data/test01_00s_frame', physical=False, smooth_frames=False)\n", + "record.record_name = 'test01_00s_framex'\n", "\n", - "# Call the instance method of the object with expanded=True to write the record using the e_d_signals field\n", + "# Call the instance method of the object with expanded=True to write the record using the e_d_signal field\n", "record.wrsamp(expanded=True)\n", "\n", "# The new file can be read\n", - "recordx = wfdb.rdsamp('test01_00s_framex')" + "recordx = wfdb.rdrecord('test01_00s_framex')\n", + "\n", + "# Cleanup: deleted the created files\n", + "# os.remove('test01_00s_framex.hea')\n", + "# os.remove('test01_00s.dat')" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 11, + "metadata": {}, "outputs": [], "source": [ "# Demo 13 - Read a WFDB annotation file and create a copy via the wrann() instance method\n", "# of the Annotation object\n", "\n", - "# Read an annotation from Physiobank\n", - "annotation = wfdb.rdann('sampledata/100', 'atr')\n", - "annotation.annotator = 'cpy'\n", + "# Read an annotation from Physionet\n", + "annotation = wfdb.rdann('sample-data/100', 'atr')\n", + "annotation.extension = 'cpy'\n", "\n", "# Call the instance method of the object\n", "annotation.wrann()\n", "\n", "# The new file can be read\n", - "ann100copy = wfdb.rdann('100', 'cpy')" + "annotation_copy = wfdb.rdann('100', 'cpy')\n", + "\n", + "# Cleanup: deleted the created files\n", + "# os.remove('100.cpy')" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 12, + "metadata": {}, "outputs": [], "source": [ - "# Demo 14 - Write a WFDB annotation file without using an Annotator object via the gateway wrann function.\n", + "# Demo 14 - Write a WFDB annotation file without using an Annotator\n", + "# object via the gateway wrann function.\n", "\n", "# Read an annotation as an Annotation object\n", - "annotation = wfdb.rdann('b001', 'atr', pbdir='cebsdb')\n", + "annotation = wfdb.rdann('b001', 'atr', pn_dir='cebsdb')\n", "\n", "# Call the gateway wrann function, manually inserting fields as function input parameters\n", - "wfdb.wrann('b001', 'cpy', annotation.sample, annotation.anntype)\n", + "wfdb.wrann('b001', 'cpy', annotation.sample, annotation.symbol)\n", "\n", "# The new file can be read\n", - "annbcopy = wfdb.rdann('b001', 'cpy')" + "annotation_copy = wfdb.rdann('b001', 'cpy')\n", + "\n", + "# Cleanup: deleted the created files\n", + "# os.remove('b001.cpy')" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "label_store symbol description\n", + "0 0 Not an actual annotation\n", + "1 1 N Normal beat\n", + "2 2 L Left bundle branch block beat\n", + "3 3 R Right bundle branch block beat\n", + "4 4 a Aberrated atrial premature beat\n", + "5 5 V Premature ventricular contraction\n", + "6 6 F Fusion of ventricular and normal beat\n", + "7 7 J Nodal (junctional) premature beat\n", + "8 8 A Atrial premature contraction\n", + "9 9 S Premature or ectopic supraventricular beat\n", + "10 10 E Ventricular escape beat\n", + "11 11 j Nodal (junctional) escape beat\n", + "12 12 / Paced beat\n", + "13 13 Q Unclassifiable beat\n", + "14 14 ~ Signal quality change\n", + "16 16 | Isolated QRS-like artifact\n", + "18 18 s ST change\n", + "19 19 T T-wave change\n", + "20 20 * Systole\n", + "21 21 D Diastole\n", + "22 22 \" Comment annotation\n", + "23 23 = Measurement annotation\n", + "24 24 p P-wave peak\n", + "25 25 B Left or right bundle branch block\n", + "26 26 ^ Non-conducted pacer spike\n", + "27 27 t T-wave peak\n", + "28 28 + Rhythm change\n", + "29 29 u U-wave peak\n", + "30 30 ? Learning\n", + "31 31 ! Ventricular flutter wave\n", + "32 32 [ Start of ventricular flutter/fibrillation\n", + "33 33 ] End of ventricular flutter/fibrillation\n", + "34 34 e Atrial escape beat\n", + "35 35 n Supraventricular escape beat\n", + "36 36 @ Link to external data (aux_note contains URL)\n", + "37 37 x Non-conducted P-wave (blocked APB)\n", + "38 38 f Fusion of paced and normal beat\n", + "39 39 ( Waveform onset\n", + "40 40 ) Waveform end\n", + "41 41 r R-on-T premature ventricular contraction\n" + ] + } + ], "source": [ - "# Demo 15 - View what the 'anntype' symbols mean in the standard WFDB library\n", - "wfdb.showanncodes()" + "# Demo 15 - View the standard WFDB annotation labels\n", + "wfdb.show_ann_labels()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Downloading Content from Physiobank\n", + "## Downloading Content from Physionet\n", "\n", "- The downloads are made via http\n", - "- See the above demos for examples on streaming WFDB files stored in Physiobank without downloading them to local disk\n", - "- Physionet has rsync modules for downloading entire databases for users who have access to rsync. " + "- See the above demos for examples on streaming WFDB files stored in PhysioNet without downloading them to local disk\n", + "- PhysioNet has rsync modules for downloading entire databases for users who have access to rsync. " ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[['aami-ec13', 'ANSI/AAMI EC13 Test Waveforms'],\n", + " ['adfecgdb', 'Abdominal and Direct Fetal ECG Database'],\n", + " ['afdb', 'MIT-BIH Atrial Fibrillation Database'],\n", + " ['afpdb', 'PAF Prediction Challenge Database'],\n", + " ['aftdb', 'AF Termination Challenge Database'],\n", + " ['ahadb', 'AHA Database Sample Excluded Record'],\n", + " ['apnea-ecg', 'Apnea-ECG Database'],\n", + " ['bidmc', 'BIDMC PPG and Respiration Dataset'],\n", + " ['bpssrat', 'Blood Pressure in Salt-Sensitive Dahl Rats'],\n", + " ['capslpdb', 'CAP Sleep Database'],\n", + " ['cdb', 'MIT-BIH ECG Compression Test Database'],\n", + " ['cebsdb', 'Combined measurement of ECG, Breathing and Seismocardiograms'],\n", + " ['cerebral-vasoreg-diabetes', 'Cerebral Vasoregulation in Diabetes'],\n", + " ['charisdb', 'CHARIS database'],\n", + " ['chbmit', 'CHB-MIT Scalp EEG Database'],\n", + " ['chf2db', 'Congestive Heart Failure RR Interval Database'],\n", + " ['chfdb', 'BIDMC Congestive Heart Failure Database'],\n", + " ['crisdb', 'CAST RR Interval Sub-Study Database'],\n", + " ['ct-ich',\n", + " 'Computed Tomography Images for Intracranial Hemorrhage Detection and Segmentation'],\n", + " ['ct-ich',\n", + " 'Computed Tomography Images for Intracranial Hemorrhage Detection and Segmentation'],\n", + " ['ct-ich',\n", + " 'Computed Tomography Images for Intracranial Hemorrhage Detection and Segmentation'],\n", + " ['ct-ich',\n", + " 'Computed Tomography Images for Intracranial Hemorrhage Detection and Segmentation'],\n", + " ['ctu-uhb-ctgdb', 'CTU-CHB Intrapartum Cardiotocography Database'],\n", + " ['cudb', 'CU Ventricular Tachyarrhythmia Database'],\n", + " ['cuiless16', 'CUILESS2016'],\n", + " ['culm', 'Complex Upper-Limb Movements'],\n", + " ['cves', 'Cerebral Vasoregulation in Elderly with Stroke'],\n", + " ['deidentifiedmedicaltext', 'Deidentified Medical Text'],\n", + " ['drivedb', 'Stress Recognition in Automobile Drivers'],\n", + " ['earh', 'Evoked Auditory Responses in Heading Impaired'],\n", + " ['earndb', 'Evoked Auditory Responses in Normals'],\n", + " ['ecgcipa', 'CiPA ECG Validation Study'],\n", + " ['ecgdmmld',\n", + " 'ECG Effects of Dofetilide, Moxifloxacin, Dofetilide+Mexiletine, Dofetilide+Lidocaine and Moxifloxacin+Diltiazem'],\n", + " ['ecgiddb', 'ECG-ID Database'],\n", + " ['ecgrdvq',\n", + " 'ECG Effects of Ranolazine, Dofetilide, Verapamil, and Quinidine'],\n", + " ['edb', 'European ST-T Database'],\n", + " ['eegmat', 'EEG During Mental Arithmetic Tasks'],\n", + " ['eegmmidb', 'EEG Motor Movement/Imagery Dataset'],\n", + " ['ehgdb', 'Icelandic 16-electrode Electrohysterogram Database'],\n", + " ['eicu-crd', 'eICU Collaborative Research Database'],\n", + " ['eicu-crd-demo', 'eICU Collaborative Research Database Demo'],\n", + " ['emgdb', 'Examples of Electromyograms'],\n", + " ['erpbci', 'ERP-based Brain-Computer Interface recordings'],\n", + " ['excluded', 'Recordings excluded from the NSR DB'],\n", + " ['fantasia', 'Fantasia Database'],\n", + " ['fecgsyndb', 'Fetal ECG Synthetic Database'],\n", + " ['fpcgdb', 'Fetal PCG Database'],\n", + " ['gait-maturation-db', 'Gait Maturation Database'],\n", + " ['gaitdb', 'Gait in Aging and Disease Database'],\n", + " ['gaitndd', 'Gait in Neurodegenerative Disease Database'],\n", + " ['gaitpdb', \"Gait in Parkinson's Disease\"],\n", + " ['hbedb', 'Human Balance Evaluation Database'],\n", + " ['iafdb', 'Intracardiac Atrial Fibrillation Database'],\n", + " ['images', 'Samples of MR Images'],\n", + " ['incartdb', 'St Petersburg INCART 12-lead Arrhythmia Database'],\n", + " ['inipdmsa',\n", + " 'Safety and Preliminary Efficacy of Intranasal Insulin for Cognitive Impairment in Parkinson Disease and Multiple System Atrophy'],\n", + " ['kinematic-actors-emotions',\n", + " 'Kinematic dataset of actors expressing emotions'],\n", + " ['ltafdb', 'Long Term AF Database'],\n", + " ['ltdb', 'MIT-BIH Long-Term ECG Database'],\n", + " ['ltmm', 'Long Term Movement Monitoring Database'],\n", + " ['ltrsvp', 'EEG Signals from an RSVP Task'],\n", + " ['ltstdb', 'Long Term ST Database'],\n", + " ['macecgdb', 'Motion Artifact Contaminated ECG Database'],\n", + " ['maternal-visceral-adipose',\n", + " 'Visceral adipose tissue measurements during pregnancy'],\n", + " ['meditation', 'Heart Rate Oscillations during Meditation'],\n", + " ['mednli',\n", + " 'MedNLI - A Natural Language Inference Dataset For The Clinical Domain'],\n", + " ['mednli-bionlp19', 'MedNLI for Shared Task at ACL BioNLP 2019'],\n", + " ['mednli-bionlp19', 'MedNLI for Shared Task at ACL BioNLP 2019'],\n", + " ['mghdb', 'MGH/MF Waveform Database'],\n", + " ['mimic-cxr', 'MIMIC-CXR Database'],\n", + " ['mimic-cxr', 'MIMIC-CXR Database'],\n", + " ['mimic-cxr-jpg', 'MIMIC-CXR-JPG - chest radiographs with structured labels'],\n", + " ['mimic3wdb', 'MIMIC-III Waveform Database'],\n", + " ['mimic3wdb-matched', 'MIMIC-III Waveform Database Matched Subset'],\n", + " ['mimicdb', 'MIMIC Database'],\n", + " ['mimiciii', 'MIMIC-III Clinical Database'],\n", + " ['mimiciii-demo', 'MIMIC-III Clinical Database Demo'],\n", + " ['mitdb', 'MIT-BIH Arrhythmia Database'],\n", + " ['mmgdb', 'MMG Database'],\n", + " ['motion-artifact', 'Motion Artifact Contaminated fNIRS and EEG Data'],\n", + " ['mssvepdb', 'MAMEM SSVEP Database'],\n", + " ['mvtdb', 'Spontaneous Ventricular Tachyarrhythmia Database'],\n", + " ['nesfdb', 'Noise Enhancement of Sensorimotor Function'],\n", + " ['nifeadb', 'Non-Invasive Fetal ECG Arrhythmia Database'],\n", + " ['nifecgdb', 'Non-Invasive Fetal ECG Database'],\n", + " ['noneeg', 'Non-EEG Dataset for Assessment of Neurological Status'],\n", + " ['nqmitcsxpd', 'neuroQWERTY MIT-CSXPD Dataset'],\n", + " ['nsr2db', 'Normal Sinus Rhythm RR Interval Database'],\n", + " ['nsrdb', 'MIT-BIH Normal Sinus Rhythm Database'],\n", + " ['nstdb', 'MIT-BIH Noise Stress Test Database'],\n", + " ['ob1db', 'OB-1 Fetal ECG Database'],\n", + " ['osv', 'Pattern Analysis of Oxygen Saturation Variability'],\n", + " ['phdsm', 'Permittivity of Healthy and Diseased Skeletal Muscle'],\n", + " ['phdsm', 'Permittivity of Healthy and Diseased Skeletal Muscle'],\n", + " ['phenotype-annotations-mimic',\n", + " 'Phenotype Annotations for Patient Notes in the MIMIC-III Database'],\n", + " ['physiozoo', 'PhysioZoo - mammalian NSR databases'],\n", + " ['picdb', 'Paediatric Intensive Care database'],\n", + " ['picsdb', 'Preterm Infant Cardio-Respiratory Signals Database'],\n", + " ['plantar', 'Modulation of Plantar Pressure and Muscle During Gait'],\n", + " ['pmd', 'A Pressure Map Dataset for In-bed Posture Classification'],\n", + " ['prcp', 'Physiologic Response to Changes in Posture'],\n", + " ['ptb-xl', 'PTB-XL, a large publicly available electrocardiography dataset'],\n", + " ['ptb-xl', 'PTB-XL, a large publicly available electrocardiography dataset'],\n", + " ['ptbdb', 'PTB Diagnostic ECG Database'],\n", + " ['pwave', 'MIT-BIH Arrhythmia Database P-Wave Annotations'],\n", + " ['qde', 'Quantitative Dehydration Estimation'],\n", + " ['qtdb', 'QT Database'],\n", + " ['rvmh1', 'Response to Valsalva Maneuver in Humans'],\n", + " ['santa-fe', 'Santa Fe Time Series Competition Data Set B'],\n", + " ['sddb', 'Sudden Cardiac Death Holter Database'],\n", + " ['sgamp', 'Squid Giant Axon Membrane Potential'],\n", + " ['shareedb',\n", + " 'Smart Health for Assessing the Risk of Events via ECG Database'],\n", + " ['shhpsgdb', 'Sleep Heart Health Study PSG Database'],\n", + " ['simfpcgdb', 'Simulated Fetal Phonocardiograms'],\n", + " ['sleep-accel',\n", + " 'Motion and heart rate from a wrist-worn wearable and labeled sleep from polysomnography'],\n", + " ['sleep-edf', 'Sleep-EDF Database'],\n", + " ['sleep-edfx', 'Sleep-EDF Database Expanded'],\n", + " ['sleepbrl', 'Sleep Bioradiolocation Database'],\n", + " ['slpdb', 'MIT-BIH Polysomnographic Database'],\n", + " ['staffiii', 'STAFF III Database'],\n", + " ['stdb', 'MIT-BIH ST Change Database'],\n", + " ['sufhsdb', 'Shiraz University Fetal Heart Sounds Database'],\n", + " ['svdb', 'MIT-BIH Supraventricular Arrhythmia Database'],\n", + " ['szdb', 'Post-Ictal Heart Rate Oscillations in Partial Epilepsy'],\n", + " ['taichidb', 'Tai Chi, Physiological Complexity, and Healthy Aging - Gait'],\n", + " ['tappy', 'Tappy Keystroke Data'],\n", + " ['tns', 'Surrogate Data with Correlations, Trends, and Nonstationarities'],\n", + " ['tpehgdb', 'Term-Preterm EHG Database'],\n", + " ['tpehgt', 'Term-Preterm EHG DataSet with Tocogram'],\n", + " ['tremordb', 'Effect of Deep Brain Stimulation on Parkinsonian Tremor'],\n", + " ['twadb', 'T-Wave Alternans Challenge Database'],\n", + " ['ucddb',\n", + " \"St. Vincent's University Hospital / University College Dublin Sleep Apnea Database\"],\n", + " ['umwdb', 'Long-term Recordings of Gait Dynamics'],\n", + " ['unicaprop', 'UniCA ElectroTastegram Database (PROP)'],\n", + " ['vfdb', 'MIT-BIH Malignant Ventricular Ectopy Database'],\n", + " ['videopulse', 'Video Pulse Signals in Stationary and Motion Conditions'],\n", + " ['voiced', 'VOice ICar fEDerico II Database'],\n", + " ['wctecgdb', 'Wilson Central Terminal ECG Database'],\n", + " ['wctecgdb', 'Wilson Central Terminal ECG Database'],\n", + " ['wrist', 'Wrist PPG During Exercise']]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Demo 16 - List the Physiobank Databases\n", + "# Demo 16 - List the PhysioNet Databases\n", "\n", - "dbs = wfdb.getdblist()\n", + "dbs = wfdb.get_dbs()\n", "display(dbs)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating record list for: 0001\n", + "Generating record list for: 0201\n", + "Generating list of all files for: 0001\n", + "Generating list of all files for: 0201\n", + "Created local base download directory: /Users/lucasmc/Documents/wfdb-python/tmp_dl_dir\n", + "Downloading files...\n", + "Finished downloading files\n" + ] + }, + { + "data": { + "text/plain": [ + "['0001.dat', '0201.dat', '0001.hea', '0201.hea', '0201.atr', '0001.atr']" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Demo 17 - Download all the WFDB records and annotations from a small Physiobank Database\n", + "# Demo 17 - Download all the WFDB records and annotations from a small PhysioNet Database\n", "\n", "# Make a temporary download directory in your current working directory\n", "cwd = os.getcwd()\n", - "dldir = os.path.join(cwd, 'tmp_dl_dir')\n", - "# Make sure to use a new directory\n", - "while os.path.exists(dldir):\n", - " dldir = dldir+'1'\n", + "dl_dir = os.path.join(cwd, 'tmp_dl_dir')\n", "\n", "# Download all the WFDB content\n", - "wfdb.dldatabase('ahadb', dlbasedir = dldir)\n", + "wfdb.dl_database('ahadb', dl_dir=dl_dir)\n", "\n", "# Display the downloaded content in the folder\n", - "display(os.listdir(dldir))" + "display(os.listdir(dl_dir))\n", + "\n", + "# Cleanup: delete the downloaded directory\n", + "# shutil.rmtree(dl_dir)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading files...\n", + "Finished downloading files\n" + ] + }, + { + "data": { + "text/plain": [ + "['STAFF-Studies-bibliography-2016.pdf',\n", + " '0001.dat',\n", + " '0201.dat',\n", + " '0001.hea',\n", + " '0201.hea',\n", + " '0201.atr',\n", + " '0001.atr',\n", + " 'data']" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "['001b.dat', '001a.dat', '001b.hea', '001a.hea']" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Demo 18 - Download specified files from a Physiobank database\n", + "# Demo 18 - Download specified files from a PhysioNet database\n", "\n", "# The files to download\n", - "filelist = ['STAFF-Studies-bibliography-2016.pdf', 'data/001a.hea', 'data/001a.dat', 'data/001b.hea', 'data/001b.dat']\n", + "file_list = ['STAFF-Studies-bibliography-2016.pdf',\n", + " 'data/001a.hea',\n", + " 'data/001a.dat',\n", + " 'data/001b.hea',\n", + " 'data/001b.dat']\n", "\n", "# Make a temporary download directory in your current working directory\n", "cwd = os.getcwd()\n", - "dldir = os.path.join(cwd, 'tmp_dl_dir')\n", - "# Make sure to use a new directory\n", - "while os.path.exists(dldir):\n", - " dldir = dldir+'1'\n", + "dl_dir = os.path.join(cwd, 'tmp_dl_dir')\n", "\n", "# Download the listed files\n", - "wfdb.dldatabasefiles('staffiii', dldir, filelist)\n", + "wfdb.dl_files('staffiii', dl_dir, file_list)\n", "\n", "# Display the downloaded content in the folder\n", - "display(os.listdir(dldir))\n", - "display(os.listdir(os.path.join(dldir, 'data')))" + "display(os.listdir(dl_dir))\n", + "display(os.listdir(os.path.join(dl_dir, 'data')))\n", + "\n", + "# Cleanup: delete the downloaded directory\n", + "# shutil.rmtree(dl_dir)" ] }, { "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "metadata": {}, "source": [ - "## ECG Peak Detection" + "## ECG Processing" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 20, + "metadata": {}, "outputs": [], "source": [ - "def peaks_hr(x, peak_indices, fs, title, figsize=(20, 10), saveto=None):\n", - " \n", + "import wfdb\n", + "from wfdb import processing" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Demo 19 - Use the GQRS detection algorithm and correct the peaks\n", + "\n", + "def peaks_hr(sig, peak_inds, fs, title, figsize=(20, 10), saveto=None):\n", + " \"Plot a signal with its peaks and heart rate\"\n", " # Calculate heart rate\n", - " hrs = wfdb.processing.compute_hr(siglen=x.shape[0], peak_indices=peak_indices, fs=fs)\n", + " hrs = processing.hr.compute_hr(sig_len=sig.shape[0], qrs_inds=peak_inds, fs=fs)\n", " \n", - " N = x.shape[0]\n", + " N = sig.shape[0]\n", " \n", " fig, ax_left = plt.subplots(figsize=figsize)\n", " ax_right = ax_left.twinx()\n", " \n", - " ax_left.plot(x, color='#3979f0', label='Signal')\n", - " ax_left.plot(peak_indices, x[peak_indices], 'rx', marker='x', color='#8b0000', label='Peak', markersize=12)\n", + " ax_left.plot(sig, color='#3979f0', label='Signal')\n", + " ax_left.plot(peak_inds, sig[peak_inds], 'rx', marker='x', \n", + " color='#8b0000', label='Peak', markersize=12)\n", " ax_right.plot(np.arange(N), hrs, label='Heart rate', color='m', linewidth=2)\n", "\n", " ax_left.set_title(title)\n", @@ -476,47 +71205,7268 @@ " plt.savefig(saveto, dpi=600)\n", " plt.show()\n", "\n", + "# Load the WFDB record and the physical samples\n", + "record = wfdb.rdrecord('sample-data/100', sampfrom=0, sampto=10000, channels=[0])\n", "\n", - "recordname = 'sampledata/100'\n", + "# Use the GQRS algorithm to detect QRS locations in the first channel\n", + "qrs_inds = processing.qrs.gqrs_detect(sig=record.p_signal[:,0], fs=record.fs)\n", "\n", - "def gqrs_plot(recordname, t0=0, tf=10000):\n", - " # Load the wfdb record and the physical samples\n", - " record = wfdb.rdsamp(recordname, sampfrom=t0, sampto=tf, channels=[0])\n", + "# Plot results\n", + "peaks_hr(sig=record.p_signal, peak_inds=qrs_inds, fs=record.fs,\n", + " title=\"GQRS peak detection on record 100\")\n", " \n", - " # Use the gqrs algorithm to find peaks in the first channel\n", - " # The gqrs_detect argument expects a digital signal for the first argument.\n", - " d_signal = record.adc()[:,0]\n", - " peak_indices = wfdb.processing.gqrs_detect(d_signal, fs=record.fs, adcgain=record.adcgain[0], adczero=record.adczero[0], threshold=1.0)\n", - " print('gqrs detected peak indices:', peak_indices)\n", - " peaks_hr(x=record.p_signals, peak_indices=peak_indices, fs=record.fs, title=\"GQRS peak detection on sampledata/100\")\n", - " \n", - " # Correct the peaks by applying constraints\n", - " min_bpm = 20\n", - " max_bpm = 230\n", - " min_gap = record.fs*60/min_bpm\n", - " max_gap = record.fs*60/max_bpm\n", - " peak_indices = wfdb.processing.correct_peaks(d_signal, peak_indices=peak_indices, min_gap=min_gap, max_gap=max_gap, smooth_window=150)\n", - " print('corrected gqrs detected peak indices:', sorted(peak_indices))\n", - " peaks_hr(x=record.p_signals, peak_indices=sorted(peak_indices), fs=record.fs, title=\"Corrected GQRS peak detection on sampledata/100\")\n", + "# Correct the peaks shifting them to local maxima\n", + "min_bpm = 20\n", + "max_bpm = 230\n", + "#min_gap = record.fs * 60 / min_bpm\n", + "# Use the maximum possible bpm as the search radius\n", + "search_radius = int(record.fs * 60 / max_bpm)\n", + "corrected_peak_inds = processing.peaks.correct_peaks(record.p_signal[:,0], \n", + " peak_inds=qrs_inds,\n", + " search_radius=search_radius, \n", + " smooth_window_size=150)\n", "\n", - "gqrs_plot(recordname)" + "# Display results\n", + "print('Corrected GQRS detected peak indices:', sorted(corrected_peak_inds))\n", + "peaks_hr(sig=record.p_signal, peak_inds=sorted(corrected_peak_inds), fs=record.fs,\n", + " title=\"Corrected GQRS peak detection on sampledata/100\")\n", + " " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning initial signal parameters...\n", + "Found 8 beats during learning. Initializing using learned parameters\n", + "Running QRS detection...\n", + "QRS detection complete.\n", + "51 reference annotations, 51 test annotations\n", + "\n", + "True Positives (matched samples): 51\n", + "False Positives (unmatched test samples): 0\n", + "False Negatives (unmatched reference samples): 0\n", + "\n", + "Sensitivity: 1.0000 (51/51)\n", + "Positive Predictivity: 1.0000 (51/51)\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Demo 20 - Use the XQRS detection algorithm and compare results to reference annotations\n", + "import wfdb\n", + "from wfdb import processing\n", + "sig, fields = wfdb.rdsamp('sample-data/100', channels=[0], sampto=15000)\n", + "ann_ref = wfdb.rdann('sample-data/100','atr', sampto=15000)\n", + "\n", + "# Run QRS detection on signal\n", + "xqrs = processing.XQRS(sig=sig[:,0], fs=fields['fs'])\n", + "xqrs.detect()\n", + "# Alternatively, use the gateway function to get the QRS indices directly\n", + "# qrs_inds = processing.xqrs_detect(sig=sig[:,0], fs=fields['fs'])\n", + "\n", + "# Compare detected QRS complexes to reference annotation.\n", + "# Note, first sample in 100.atr is not a QRS.\n", + "comparitor = processing.compare_annotations(ref_sample=ann_ref.sample[1:],\n", + " test_sample=xqrs.qrs_inds,\n", + " window_width=int(0.1 * fields['fs']),\n", + " signal=sig[:,0])\n", + "\n", + "# Print and plot the results\n", + "comparitor.print_summary()\n", + "comparitor.plot(title='xqrs detected QRS vs reference annotations')" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Demo 21 - Convert records to dataframes\n", + "import wfdb\n", + "record = wfdb.rdrecord('sample-data/100', sampto=2000)\n", + "df = record.to_dataframe()\n", + "df.plot();" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": { - "collapsed": true + "pycharm": { + "name": "#%%\n" + } }, "outputs": [], - "source": [] + "source": [ + "# Cleanup for all demos\n", + "for file in ['100x.hea', '100.dat', # demo 10\n", + " 'ecg-record.hea', 'ecg-record.dat', # demo 11 \n", + " 'test01_00s_framex.hea', 'test01_00s.dat', # demo 12\n", + " '100.cpy', # demo 13\n", + " 'b001.cpy', # demo 14\n", + " ]:\n", + " if os.path.isfile(file):\n", + " os.remove(file)\n", + "\n", + "dl_dir = os.path.join(cwd, 'tmp_dl_dir') # demo 17, 18\n", + "if os.path.isdir(dl_dir):\n", + " shutil.rmtree(dl_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import wfdb\n", + "record_no_skew = wfdb.rdrecord('sample-data/test01_00s_skewframe',\n", + " physical=False,\n", + " smooth_frames=False, ignore_skew=True)\n", + "record_no_skew.wrsamp(expanded=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "int_types = (int, np.int64, np.int32, np.int16, np.int8)\n", + "\n", + "int_types[0]('10')" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python (wfdb)", "language": "python", - "name": "python3" + "name": "wfdb" }, "language_info": { "codemirror_mode": { @@ -528,9 +78478,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.8.10" } }, "nbformat": 4, - "nbformat_minor": 1 -} + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/devtests.ipynb b/devtests.ipynb deleted file mode 100644 index cee09f77..00000000 --- a/devtests.ipynb +++ /dev/null @@ -1,79 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

This script is for internal developer testing.

\n", - "\n", - "This is different from the unit tests located in the `tests` directory." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import wfdb" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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ZYM/WoLM1te0RmoPJRCP8CfBVTPTBFTQ4aoBS6kTgNGAWsBQ4SWv99zHqfhn4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\naTpP7MXlmWcgtrZlxowIfr+LggJ4+OEIDz7YOk7z3Llh9tijmoEDS9l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OAAAg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\ncPjhlS0NVZWtuEKcrROVP/+ED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dyyFQ+Q0RyUrSlTBB9/DK++CvF2u+eepEW9Y8cCwuG+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\nVIpEIkFlZWX9KtLNpeWybTqdmUK3R85xcond3nFail2IOM3FK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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import wfdb\n", - "rec = wfdb.rdsamp('sampledata/100', sampto=2000)\n", - "wfdb.plotrec(rec,timeunits='seconds', ecggrids='all', figsize = (10, 4))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 00000000..cbb0a435 --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +SPHINXPROJ = wfdb +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file diff --git a/wfdb/readwrite/__init__.py b/docs/_static/.gitkeep similarity index 100% rename from wfdb/readwrite/__init__.py rename to docs/_static/.gitkeep diff --git a/docs/_templates/.gitkeep b/docs/_templates/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/docs/changes.rst b/docs/changes.rst new file mode 100644 index 00000000..6f0e13cf --- /dev/null +++ b/docs/changes.rst @@ -0,0 +1,93 @@ +Recent Changes +============== + +This page lists recent changes in the `wfdb` package (since version 4.0.0) that may be relevant if you are upgrading from an older version of the package. For the complete history of changes in the package, please refer to the `development repository`_ on GitHub. + +.. _development repository: https://github.com/MIT-LCP/wfdb-python + +Version 4.3.0 (Apr 2025) +----------------------------- + +**Bump Sphinx to 7.0.0** + Bump Sphinx to 7.0.0 + +**Integrate `fsspec` for reading WFDB files from the cloud** + Enables reading WFDB files from cloud URLs + + +Version 4.2.0 (Jan 2025) +----------------------------- + +**Add support for Numpy 2.0** + Fixes were added to address [changes to type promotion](https://numpy.org/devdocs/numpy_2_0_migration_guide.html#changes-to-numpy-data-type-promotion) that led to overflow errors (e.g. https://github.com/MIT-LCP/wfdb-python/issues/493). + +**Fix UnboundLocalError in GQRS algorithm** + Fixes the GQRS algorithm to address an `UnboundLocalError`. + +**Support write directory in `csv_to_wfdb`** + `write_dir` can now be specified when calling `csv_to_wfdb`. + +**Use uv for for package management** + Moves package management from poetry to uv. + +**Fix misordered arguments in `util.lines_to_file`** + Fixes misordered arguments in `util.lines_to_file`. + +**Allow signals to be written with unique samples per frame** + Adds capability to write signal with unique samps_per_frame to `wfdb.io.wrsamp`. + +**Allow expanded physical signal in `calc_adc_params`** + Updates `calc_adc_params` to allow an expanded physical signal to be passed. Previously only a non-expanded signal was allowed. + +**Allow selection of channels when converting to EDF** + Fixes the `wfdb-to_edf()` function to support an optional channels argument. + +**Migrates Ricker wavelet from SciPy to WFDB after deprecation** + The Ricker wavelet (`scipy.signal.ricker`) was removed in SciPy v1.15, so the original implementation was migrated to the WFDB package. + +**Miscellaneous style and typing fixes** + Various fixes were made to code style and handling of data types. + + +Version 4.1.2 (June 2023) +----------------------------- + +**Handle more than 8 compressed signals in wrsamp** + Previously, the package did not support writing of compressed records with more than 8 channels. + +**Use int64 instead of int for ann2rr** + Fixes 'np has no attribute np.int' error raised when running ann2rr. + +Version 4.1.1 (April 2023) +----------------------------- + +**Remove upper bound on dependencies** + Previously, the package provided restrictive caps on version number of dependencies. These caps have been removed. + +**Miscellaneous style and typing fixes** + Various fixes were made to code style and handling of data types. + + +Version 4.1.0 (December 2022) +----------------------------- + +**Converting a record into a DataFrame** + The new method :meth:`wfdb.Record.to_dataframe` can be used to convert signal data from a Record object into a Pandas DataFrame, which can then be manipulated using Pandas methods. + +**Locating signals in a multi-segment record** + The new method :meth:`wfdb.MultiRecord.contained_ranges` can be used to search for time intervals within a record that contain a specific channel. The method :meth:`wfdb.MultiRecord.contained_combined_ranges` searches for time intervals that contain several specific channels at once. + +**Writing custom annotation symbols** + The :func:`wfdb.wrann` function can now be used to write annotations with custom annotation types (``symbol`` strings.) Custom annotation types must be defined using the ``custom_labels`` argument. + +**Correct rounding when converting floating-point signal data** + When calling :func:`wfdb.wrsamp` with a ``p_signal`` argument, input values will be *rounded* to the nearest sample value rather than being *truncated* towards zero. The same applies to the :meth:`wfdb.Record.adc` method. + +**Writing signals in compressed format** + The :func:`wfdb.wrsamp` function, and the :meth:`wfdb.Record.wrsamp` method, now support writing compressed signal files. To write a compressed file, set the ``fmt`` value to ``"508"`` (for an 8-bit channel), ``"516"`` (for a 16-bit channel), or ``"524"`` (for a 24-bit channel). + +**Decoding non-ASCII text in EDF files** + The :func:`wfdb.io.convert.edf.read_edf` and :func:`wfdb.io.convert.edf.rdedfann` functions now take an optional argument ``encoding``, which specifies the character encoding for text fields. ISO-8859-1 encoding is used by default, in contrast to older versions of the package which used UTF-8. + +**Bug fixes when writing signal metadata** + When calling :meth:`wfdb.Record.wrsamp`, the checksum and samples-per-frame fields in the header file will correctly match the signal data, rather than relying on attributes of the Record object. diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 00000000..86ca68f0 --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,183 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# WFDB documentation build configuration file, created by +# sphinx-quickstart on Tue Jan 2 13:19:02 2018. +# +# This file is execfile()d with the current directory set to its +# containing dir. +# +# Note that not all possible configuration values are present in this +# autogenerated file. +# +# All configuration values have a default; values that are commented out +# serve to show the default. + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import sys + +sys.path.insert(0, os.path.abspath("..")) + +# For dependencies +from unittest.mock import MagicMock + + +class Mock(MagicMock): + @classmethod + def __getattr__(cls, name): + return MagicMock() + + +MOCK_MODULES = ["numpy", "matplotlib", "matplotlib.pyplot", "pandas", "scipy"] +sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) + +# -- General configuration ------------------------------------------------ + +# If your documentation needs a minimal Sphinx version, state it here. +# +# needs_sphinx = '1.0' + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + "sphinx.ext.autodoc", + "numpydoc", +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ["_templates"] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# +# source_suffix = ['.rst', '.md'] +source_suffix = ".rst" + +# The master toctree document. +master_doc = "index" + +# General information about the project. +project = "wfdb" +copyright = "2018, MIT Lab for Computational Physiology" +author = "MIT Lab for Computational Physiology" + +with open("../wfdb/version.py") as f: + __version__ = f.read().split()[-1].strip("'") +# The version info for the project you're documenting, acts as replacement for +# |version| and |release|, also used in various other places throughout the +# built documents. +# +# The short X.Y version. +version = __version__ +# The full version, including alpha/beta/rc tags. +release = __version__ + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = "en" + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This patterns also effect to html_static_path and html_extra_path +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = "sphinx" + +# If true, `todo` and `todoList` produce output, else they produce nothing. +todo_include_todos = False + + +# -- Options for HTML output ---------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = "classic" + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +# +# html_theme_options = {} + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ["_static"] + +# Custom sidebar templates, must be a dictionary that maps document names +# to template names. +# +# This is required for the alabaster theme +# refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars +html_sidebars = { + "**": [ + "relations.html", # needs 'show_related': True theme option to display + "searchbox.html", + ] +} + + +# -- Options for HTMLHelp output ------------------------------------------ + +# Output file base name for HTML help builder. +htmlhelp_basename = "wfdbdoc" + + +# -- Options for LaTeX output --------------------------------------------- + +latex_elements = { + # The paper size ('letterpaper' or 'a4paper'). + # + # 'papersize': 'letterpaper', + # The font size ('10pt', '11pt' or '12pt'). + # + # 'pointsize': '10pt', + # Additional stuff for the LaTeX preamble. + # + # 'preamble': '', + # Latex figure (float) alignment + # + # 'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, "wfdb.tex", "wfdb Documentation", author, "manual"), +] + + +# -- Options for manual page output --------------------------------------- + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [(master_doc, "wfdb", "wfdb Documentation", [author], 1)] + + +# -- Options for Texinfo output ------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + ( + master_doc, + "wfdb", + "wfdb Documentation", + author, + "wfdb", + "One line description of project.", + "Miscellaneous", + ), +] diff --git a/docs/convert.rst b/docs/convert.rst new file mode 100644 index 00000000..1fa8f570 --- /dev/null +++ b/docs/convert.rst @@ -0,0 +1,35 @@ +Format Conversions +================== + +The wfdb.io.convert subpackage contains functions to read and write +non-WFDB files commonly used for waveforms. + +CSV +--- + +.. automodule:: wfdb.io.convert.csv + :members: csv_to_wfdb + +EDF +--- + +.. automodule:: wfdb.io.convert.edf + :members: + +Matlab +------ + +.. automodule:: wfdb.io.convert.matlab + :members: + +TFF +--- + +.. automodule:: wfdb.io.convert.tff + :members: + +WAV +--- + +.. automodule:: wfdb.io.convert.wav + :members: diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 00000000..8794c347 --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,53 @@ +wfdb +==== + +Introduction +------------ + +The native Python waveform-database (WFDB) package. A library of tools +for reading, writing, and processing WFDB signals and annotations. + +See the project home page and description at: https://github.com/MIT-LCP/wfdb-python + + +API Reference +-------------- + +The exact API of all accessible functions and classes, as given by the docstrings, grouped by subpackage: + +.. toctree:: + :maxdepth: 2 + + io + plot + processing + + +Core Components +--------------- + +A subset of the above components are accessible by directly importing the base package. + +.. toctree:: + :maxdepth: 2 + + wfdb + + +Other Content +------------- +.. toctree:: + :maxdepth: 2 + + installation + changes + wfdb-specifications + convert + + +Indices and tables +------------------ + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/installation.rst b/docs/installation.rst new file mode 100644 index 00000000..c9f28c7d --- /dev/null +++ b/docs/installation.rst @@ -0,0 +1,10 @@ +Installation +------------ + +The distribution is hosted on pypi at: https://pypi.python.org/pypi/wfdb/. To directly install the package from pypi without needing to explicitly download content, run from your terminal:: + + $ pip install wfdb + +The development version is hosted at: https://github.com/MIT-LCP/wfdb-python. This repository also contains demo scripts and example data. To install the development version, clone or download the repository, navigate to the base directory, and run:: + + $ pip install . \ No newline at end of file diff --git a/docs/io.rst b/docs/io.rst new file mode 100644 index 00000000..80831a4b --- /dev/null +++ b/docs/io.rst @@ -0,0 +1,37 @@ +io +=== + +The input/output subpackage contains classes used to represent WFDB +objects, and functions to read, write, and download WFDB files. + + +WFDB Records +--------------- + +.. automodule:: wfdb.io + :members: rdrecord, rdheader, rdsamp, wrsamp + +.. autoclass:: wfdb.io.Record + :members: get_frame_number, get_elapsed_time, get_absolute_time, + wrsamp, adc, dac, to_dataframe + +.. autoclass:: wfdb.io.MultiRecord + :members: get_frame_number, get_elapsed_time, get_absolute_time, + multi_to_single, contained_ranges, contained_combined_ranges + + +WFDB Annotations +--------------- + +.. automodule:: wfdb.io + :members: rdann, wrann, show_ann_labels, show_ann_classes + +.. autoclass:: wfdb.io.Annotation + :members: wrann + + +Downloading +----------- + +.. automodule:: wfdb.io + :members: dl_files, dl_database, get_dbs, get_record_list, set_db_index_url diff --git a/docs/plot.rst b/docs/plot.rst new file mode 100644 index 00000000..ef947d3a --- /dev/null +++ b/docs/plot.rst @@ -0,0 +1,5 @@ +plot +---- + +.. automodule:: wfdb.plot + :members: plot_items, plot_wfdb, plot_all_records diff --git a/docs/processing.rst b/docs/processing.rst new file mode 100644 index 00000000..96c1a19a --- /dev/null +++ b/docs/processing.rst @@ -0,0 +1,46 @@ +processing +========== + +The processing subpackage contains signal-processing tools. + + +Basic Utility +------------- + +Basic signal processing functions + +.. automodule:: wfdb.processing + :members: resample_ann, resample_sig, resample_singlechan, + resample_multichan, normalize_bound, get_filter_gain + +Heart Rate +---------- + +.. automodule:: wfdb.processing + :members: compute_hr, calc_rr, calc_mean_hr, ann2rr, rr2ann + + +Peaks +----- + +.. automodule:: wfdb.processing + :members: find_peaks, find_local_peaks, correct_peaks + +Filters +------- + +.. automodule:: wfdb.processing + :members: sigavg + +QRS Detectors +------------- + +.. automodule:: wfdb.processing + :members: XQRS, xqrs_detect, gqrs_detect + + +Annotation Evaluators +--------------------- + +.. automodule:: wfdb.processing + :members: Comparitor, compare_annotations, benchmark_mitdb diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 00000000..96e61db3 --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,5 @@ +numpydoc==1.7.0 +sphinx==7.0.0 +sphinx_rtd_theme==3.0.0 +readthedocs-sphinx-search==0.3.2 +fsspec>=2023.10.0 diff --git a/docs/wfdb-specifications.rst b/docs/wfdb-specifications.rst new file mode 100644 index 00000000..e8ceef4e --- /dev/null +++ b/docs/wfdb-specifications.rst @@ -0,0 +1,16 @@ +WFDB Specifications +------------------- + +The wfdb-python package is built according to the specifications of the original WFDB package. + +* `WFDB Software Package`_ +* `WFDB Applications Guide`_ +* `WFDB Header Specifications`_ +* `WFDB Signal Specifications`_ +* `WFDB Annotation Specifications`_ + +.. _WFDB Software Package: http://physionet.org/physiotools/wfdb.shtml +.. _WFDB Applications Guide: http://physionet.org/physiotools/wag/ +.. _WFDB Header Specifications: https://physionet.org/physiotools/wag/header-5.htm +.. _WFDB Signal Specifications: https://physionet.org/physiotools/wag/signal-5.htm +.. _WFDB Annotation Specifications: https://physionet.org/physiotools/wag/annot-5.htm diff --git a/docs/wfdb.rst b/docs/wfdb.rst new file mode 100644 index 00000000..62d3b7b2 --- /dev/null +++ b/docs/wfdb.rst @@ -0,0 +1,44 @@ +wfdb +==== + +These core components are accessible by importing the `wfdb` package +directly, as well as from their respective subpackages. + + +WFDB Records +--------------- + +.. automodule:: wfdb + :members: rdrecord, rdheader, rdsamp, wrsamp + +.. autoclass:: wfdb.Record + :members: get_frame_number, get_elapsed_time, get_absolute_time, + wrsamp, adc, dac, to_dataframe + +.. autoclass:: wfdb.MultiRecord + :members: get_frame_number, get_elapsed_time, get_absolute_time, + multi_to_single, contained_ranges, contained_combined_ranges + + +WFDB Annotations +--------------- + +.. automodule:: wfdb + :members: rdann, wrann, show_ann_labels, show_ann_classes + +.. autoclass:: wfdb.Annotation + :members: wrann + + +Downloading +----------- + +.. automodule:: wfdb + :members: dl_files, dl_database, get_dbs, get_record_list, set_db_index_url + + +Plotting +-------- + +.. automodule:: wfdb + :members: plot_items, plot_wfdb, plot_all_records diff --git a/pylintrc b/pylintrc new file mode 100644 index 00000000..3253109f --- /dev/null +++ b/pylintrc @@ -0,0 +1,564 @@ +[MASTER] + +# Specify a configuration file. +#rcfile= + +# Python code to execute, usually for sys.path manipulation such as +# pygtk.require(). +#init-hook= + +# Files or directories to be skipped. They should be base names, not +# paths. +ignore=CVS + +# Add files or directories matching the regex patterns to the ignore-list. The +# regex matches against paths and can be in Posix or Windows format. +ignore-paths= + +# Files or directories matching the regex patterns are skipped. The regex +# matches against base names, not paths. +ignore-patterns=^\.# + +# Pickle collected data for later comparisons. +persistent=yes + +# List of plugins (as comma separated values of python modules names) to load, +# usually to register additional checkers. +load-plugins= + pylint.extensions.check_elif, + pylint.extensions.bad_builtin, + pylint.extensions.docparams, + pylint.extensions.for_any_all, + pylint.extensions.set_membership, + pylint.extensions.code_style, + pylint.extensions.overlapping_exceptions, + pylint.extensions.typing, + pylint.extensions.redefined_variable_type, + pylint.extensions.comparison_placement, + +# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the +# number of processors available to use. +jobs=1 + +# When enabled, pylint would attempt to guess common misconfiguration and emit +# user-friendly hints instead of false-positive error messages. +suggestion-mode=yes + +# Allow loading of arbitrary C extensions. Extensions are imported into the +# active Python interpreter and may run arbitrary code. +unsafe-load-any-extension=no + +# A comma-separated list of package or module names from where C extensions may +# be loaded. Extensions are loading into the active Python interpreter and may +# run arbitrary code +extension-pkg-allow-list= + +# Minimum supported python version +py-version = 3.7.2 + +# Control the amount of potential inferred values when inferring a single +# object. This can help the performance when dealing with large functions or +# complex, nested conditions. +limit-inference-results=100 + +# Specify a score threshold to be exceeded before program exits with error. +fail-under=10.0 + +# Return non-zero exit code if any of these messages/categories are detected, +# even if score is above --fail-under value. Syntax same as enable. Messages +# specified are enabled, while categories only check already-enabled messages. +fail-on= + + +[MESSAGES CONTROL] + +# Only show warnings with the listed confidence levels. Leave empty to show +# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED +# confidence= + +# Enable the message, report, category or checker with the given id(s). You can +# either give multiple identifier separated by comma (,) or put this option +# multiple time (only on the command line, not in the configuration file where +# it should appear only once). See also the "--disable" option for examples. +enable= + use-symbolic-message-instead, + useless-suppression, + fixme + +# Disable the message, report, category or checker with the given id(s). You +# can either give multiple identifiers separated by comma (,) or put this +# option multiple times (only on the command line, not in the configuration +# file where it should appear only once).You can also use "--disable=all" to +# disable everything first and then re-enable specific checks. For example, if +# you want to run only the similarities checker, you can use "--disable=all +# --enable=similarities". If you want to run only the classes checker, but have +# no Warning level messages displayed, use"--disable=all --enable=classes +# --disable=W" + +disable= + attribute-defined-outside-init, + duplicate-code, + invalid-name, + missing-docstring, + protected-access, + too-few-public-methods, + # handled by black + format, + + +[REPORTS] + +# Set the output format. Available formats are text, parseable, colorized, msvs +# (visual studio) and html. You can also give a reporter class, eg +# mypackage.mymodule.MyReporterClass. +output-format=text + +# Tells whether to display a full report or only the messages +reports=no + +# Python expression which should return a note less than 10 (10 is the highest +# note). You have access to the variables 'fatal', 'error', 'warning', 'refactor', 'convention' +# and 'info', which contain the number of messages in each category, as +# well as 'statement', which is the total number of statements analyzed. This +# score is used by the global evaluation report (RP0004). +evaluation=max(0, 0 if fatal else 10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)) + +# Template used to display messages. This is a python new-style format string +# used to format the message information. See doc for all details +#msg-template= + +# Activate the evaluation score. +score=yes + + +[LOGGING] + +# Logging modules to check that the string format arguments are in logging +# function parameter format +logging-modules=logging + +# The type of string formatting that logging methods do. `old` means using % +# formatting, `new` is for `{}` formatting. +logging-format-style=old + + +[MISCELLANEOUS] + +# List of note tags to take in consideration, separated by a comma. +notes=FIXME,XXX,TODO + +# Regular expression of note tags to take in consideration. +#notes-rgx= + + +[SIMILARITIES] + +# Minimum lines number of a similarity. +min-similarity-lines=4 + +# Ignore comments when computing similarities. +ignore-comments=yes + +# Ignore docstrings when computing similarities. +ignore-docstrings=yes + +# Ignore imports when computing similarities. +ignore-imports=no + +# Signatures are removed from the similarity computation +ignore-signatures=no + + +[VARIABLES] + +# Tells whether we should check for unused import in __init__ files. +init-import=no + +# A regular expression matching the name of dummy variables (i.e. expectedly +# not used). +dummy-variables-rgx=_$|dummy + +# List of additional names supposed to be defined in builtins. Remember that +# you should avoid defining new builtins when possible. +additional-builtins= + +# List of strings which can identify a callback function by name. A callback +# name must start or end with one of those strings. +callbacks=cb_,_cb + +# Tells whether unused global variables should be treated as a violation. +allow-global-unused-variables=yes + +# List of names allowed to shadow builtins +allowed-redefined-builtins= + +# Argument names that match this expression will be ignored. Default to name +# with leading underscore. +ignored-argument-names=_.* + +# List of qualified module names which can have objects that can redefine +# builtins. +redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io + + +[FORMAT] + +# Maximum number of characters on a single line. +max-line-length=100 + +# Regexp for a line that is allowed to be longer than the limit. +ignore-long-lines=^\s*(# )??$ + +# Allow the body of an if to be on the same line as the test if there is no +# else. +single-line-if-stmt=no + +# Allow the body of a class to be on the same line as the declaration if body +# contains single statement. +single-line-class-stmt=no + +# Maximum number of lines in a module +max-module-lines=2000 + +# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1 +# tab). +indent-string=' ' + +# Number of spaces of indent required inside a hanging or continued line. +indent-after-paren=4 + +# Expected format of line ending, e.g. empty (any line ending), LF or CRLF. +expected-line-ending-format= + + +[BASIC] + +# Good variable names which should always be accepted, separated by a comma +good-names=i,j,k,ex,Run,_ + +# Good variable names regexes, separated by a comma. If names match any regex, +# they will always be accepted +good-names-rgxs= + +# Bad variable names which should always be refused, separated by a comma +bad-names=foo,bar,baz,toto,tutu,tata + +# Bad variable names regexes, separated by a comma. If names match any regex, +# they will always be refused +bad-names-rgxs= + +# Colon-delimited sets of names that determine each other's naming style when +# the name regexes allow several styles. +name-group= + +# Include a hint for the correct naming format with invalid-name +include-naming-hint=no + +# Naming style matching correct function names. +function-naming-style=snake_case + +# Regular expression matching correct function names +function-rgx=[a-z_][a-z0-9_]{2,30}$ + +# Naming style matching correct variable names. +variable-naming-style=snake_case + +# Regular expression matching correct variable names +variable-rgx=[a-z_][a-z0-9_]{2,30}$ + +# Naming style matching correct constant names. +const-naming-style=UPPER_CASE + +# Regular expression matching correct constant names +const-rgx=(([A-Z_][A-Z0-9_]*)|(__.*__))$ + +# Naming style matching correct attribute names. +attr-naming-style=snake_case + +# Regular expression matching correct attribute names +attr-rgx=[a-z_][a-z0-9_]{2,}$ + +# Naming style matching correct argument names. +argument-naming-style=snake_case + +# Regular expression matching correct argument names +argument-rgx=[a-z_][a-z0-9_]{2,30}$ + +# Naming style matching correct class attribute names. +class-attribute-naming-style=any + +# Regular expression matching correct class attribute names +class-attribute-rgx=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$ + +# Naming style matching correct class constant names. +class-const-naming-style=UPPER_CASE + +# Regular expression matching correct class constant names. Overrides class- +# const-naming-style. +#class-const-rgx= + +# Naming style matching correct inline iteration names. +inlinevar-naming-style=any + +# Regular expression matching correct inline iteration names +inlinevar-rgx=[A-Za-z_][A-Za-z0-9_]*$ + +# Naming style matching correct class names. +class-naming-style=PascalCase + +# Regular expression matching correct class names +class-rgx=[A-Z_][a-zA-Z0-9]+$ + + +# Naming style matching correct module names. +module-naming-style=snake_case + +# Regular expression matching correct module names +module-rgx=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$ + + +# Naming style matching correct method names. +method-naming-style=snake_case + +# Regular expression matching correct method names +method-rgx=[a-z_][a-z0-9_]{2,}$ + +# Regular expression which can overwrite the naming style set by typevar-naming-style. +#typevar-rgx= + +# Regular expression which should only match function or class names that do +# not require a docstring. Use ^(?!__init__$)_ to also check __init__. +no-docstring-rgx=__.*__ + +# Minimum line length for functions/classes that require docstrings, shorter +# ones are exempt. +docstring-min-length=-1 + +# List of decorators that define properties, such as abc.abstractproperty. +property-classes=abc.abstractproperty + + +[TYPECHECK] + +# Regex pattern to define which classes are considered mixins if ignore-mixin- +# members is set to 'yes' +mixin-class-rgx=.*MixIn + +# List of module names for which member attributes should not be checked +# (useful for modules/projects where namespaces are manipulated during runtime +# and thus existing member attributes cannot be deduced by static analysis). It +# supports qualified module names, as well as Unix pattern matching. +ignored-modules= + +# List of class names for which member attributes should not be checked (useful +# for classes with dynamically set attributes). This supports the use of +# qualified names. +ignored-classes=SQLObject, optparse.Values, thread._local, _thread._local + +# List of members which are set dynamically and missed by pylint inference +# system, and so shouldn't trigger E1101 when accessed. Python regular +# expressions are accepted. +generated-members=REQUEST,acl_users,aq_parent,argparse.Namespace + +# List of decorators that create context managers from functions, such as +# contextlib.contextmanager. +contextmanager-decorators=contextlib.contextmanager + +# Tells whether to warn about missing members when the owner of the attribute +# is inferred to be None. +ignore-none=yes + +# This flag controls whether pylint should warn about no-member and similar +# checks whenever an opaque object is returned when inferring. The inference +# can return multiple potential results while evaluating a Python object, but +# some branches might not be evaluated, which results in partial inference. In +# that case, it might be useful to still emit no-member and other checks for +# the rest of the inferred objects. +ignore-on-opaque-inference=yes + +# Show a hint with possible names when a member name was not found. The aspect +# of finding the hint is based on edit distance. +missing-member-hint=yes + +# The minimum edit distance a name should have in order to be considered a +# similar match for a missing member name. +missing-member-hint-distance=1 + +# The total number of similar names that should be taken in consideration when +# showing a hint for a missing member. +missing-member-max-choices=1 + +[SPELLING] + +# Spelling dictionary name. Available dictionaries: none. To make it working +# install python-enchant package. +spelling-dict= + +# List of comma separated words that should not be checked. +spelling-ignore-words= + +# List of comma separated words that should be considered directives if they +# appear and the beginning of a comment and should not be checked. +spelling-ignore-comment-directives=fmt: on,fmt: off,noqa:,noqa,nosec,isort:skip,mypy: + +# A path to a file that contains private dictionary; one word per line. +spelling-private-dict-file= + +# Tells whether to store unknown words to indicated private dictionary in +# --spelling-private-dict-file option instead of raising a message. +spelling-store-unknown-words=no + +# Limits count of emitted suggestions for spelling mistakes. +max-spelling-suggestions=4 + + +[DESIGN] + +# Maximum number of arguments for function / method +max-args=10 + +# Maximum number of locals for function / method body +max-locals=25 + +# Maximum number of return / yield for function / method body +max-returns=11 + +# Maximum number of branch for function / method body +max-branches=27 + +# Maximum number of statements in function / method body +max-statements=100 + +# Maximum number of parents for a class (see R0901). +max-parents=7 + +# List of qualified class names to ignore when counting class parents (see R0901). +ignored-parents= + +# Maximum number of attributes for a class (see R0902). +max-attributes=11 + +# Minimum number of public methods for a class (see R0903). +min-public-methods=2 + +# Maximum number of public methods for a class (see R0904). +max-public-methods=25 + +# Maximum number of boolean expressions in an if statement (see R0916). +max-bool-expr=5 + +# List of regular expressions of class ancestor names to +# ignore when counting public methods (see R0903). +exclude-too-few-public-methods= + +[CLASSES] + +# List of method names used to declare (i.e. assign) instance attributes. +defining-attr-methods=__init__,__new__,setUp,__post_init__ + +# List of valid names for the first argument in a class method. +valid-classmethod-first-arg=cls + +# List of valid names for the first argument in a metaclass class method. +valid-metaclass-classmethod-first-arg=mcs + +# List of member names, which should be excluded from the protected access +# warning. +exclude-protected=_asdict,_fields,_replace,_source,_make + +# Warn about protected attribute access inside special methods +check-protected-access-in-special-methods=no + +[IMPORTS] + +# List of modules that can be imported at any level, not just the top level +# one. +allow-any-import-level= + +# Allow wildcard imports from modules that define __all__. +allow-wildcard-with-all=no + +# Analyse import fallback blocks. This can be used to support both Python 2 and +# 3 compatible code, which means that the block might have code that exists +# only in one or another interpreter, leading to false positives when analysed. +analyse-fallback-blocks=no + +# Deprecated modules which should not be used, separated by a comma +deprecated-modules=regsub,TERMIOS,Bastion,rexec + +# Create a graph of every (i.e. internal and external) dependencies in the +# given file (report RP0402 must not be disabled) +import-graph= + +# Create a graph of external dependencies in the given file (report RP0402 must +# not be disabled) +ext-import-graph= + +# Create a graph of internal dependencies in the given file (report RP0402 must +# not be disabled) +int-import-graph= + +# Force import order to recognize a module as part of the standard +# compatibility libraries. +known-standard-library= + +# Force import order to recognize a module as part of a third party library. +known-third-party=enchant + +# Couples of modules and preferred modules, separated by a comma. +preferred-modules= + + +[EXCEPTIONS] + +# Exceptions that will emit a warning when being caught. Defaults to +# "Exception" +overgeneral-exceptions=Exception + + +[TYPING] + +# Set to ``no`` if the app / library does **NOT** need to support runtime +# introspection of type annotations. If you use type annotations +# **exclusively** for type checking of an application, you're probably fine. +# For libraries, evaluate if some users what to access the type hints at +# runtime first, e.g., through ``typing.get_type_hints``. Applies to Python +# versions 3.7 - 3.9 +runtime-typing = no + + +[DEPRECATED_BUILTINS] + +# List of builtins function names that should not be used, separated by a comma +bad-functions=map,input + + +[REFACTORING] + +# Maximum number of nested blocks for function / method body +max-nested-blocks=5 + +# Complete name of functions that never returns. When checking for +# inconsistent-return-statements if a never returning function is called then +# it will be considered as an explicit return statement and no message will be +# printed. +never-returning-functions=sys.exit,argparse.parse_error + + +[STRING] + +# This flag controls whether inconsistent-quotes generates a warning when the +# character used as a quote delimiter is used inconsistently within a module. +check-quote-consistency=no + +# This flag controls whether the implicit-str-concat should generate a warning +# on implicit string concatenation in sequences defined over several lines. +check-str-concat-over-line-jumps=no + + +[CODE_STYLE] + +# Max line length for which to sill emit suggestions. Used to prevent optional +# suggestions which would get split by a code formatter (e.g., black). Will +# default to the setting for ``max-line-length``. +#max-line-length-suggestions= diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..bd5f10c7 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,51 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "wfdb" +description = "The WFDB Python package: tools for reading, writing, and processing physiologic signals and annotations." +authors = [{name = "The Laboratory for Computational Physiology", email = "contact@physionet.org"}] +license = {text = "MIT License"} +readme = "README.md" +requires-python = ">= 3.9" +dependencies = [ + "numpy >= 1.26.4", + "scipy >= 1.13.0", + "pandas >= 2.2.3", + "soundfile >= 0.10.0", + "matplotlib >= 3.2.2", + "requests >= 2.8.1", + "fsspec >= 2023.10.0", + "aiohttp >= 3.10.11", +] +dynamic = ["version"] + +[project.optional-dependencies] +dev = [ + "pytest >= 7.1.1", + "pytest-xdist >= 2.5.0", + "pylint >= 2.13.7", + "black >= 22.3.0", + "sphinx >= 7.0.0", +] + +[project.urls] +homepage = "https://github.com/MIT-LCP/wfdb-python/" +repository = "https://github.com/MIT-LCP/wfdb-python/" +documentation = "https://wfdb.readthedocs.io/" + +[tool.black] +line-length = 80 +target-version = ["py39"] + +[tool.hatch.build.targets.sdist] +exclude = [ + "/tests", + "/sample-data", + "/demo-img.png", + "/demo.ipynb", +] + +[tool.hatch.version] +path = "wfdb/version.py" diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index d18fe3ec..00000000 --- a/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ -nose>=1.3.7 -numpy>=1.11.0 -matplotlib>=1.5.1 -requests>=2.10.0 -pandas>=0.19.1 -scipy>=0.19.0 diff --git a/sampledata/03700181.dat b/sample-data/03700181.dat similarity index 100% rename from sampledata/03700181.dat rename to sample-data/03700181.dat diff --git a/sample-data/03700181.gqrsh b/sample-data/03700181.gqrsh new file mode 100644 index 00000000..6390b193 Binary files /dev/null and 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+binformats.d4 160 200/mV 16 0 -32763 747 0 sig 4, fmt 160 +binformats.d5 212 200/mV 12 0 -2042 -6824 0 sig 5, fmt 212 +binformats.d6 310 200/mV 10 0 -505 -1621 0 sig 6, fmt 310 +binformats.d7 311 200/mV 10 0 -504 -2145 0 sig 7, fmt 311 +binformats.d8 24 200/mV 24 0 -8388599 11715 0 sig 8, fmt 24 +binformats.d9 32 200/mV 32 0 -2147483638 19035 0 sig 9, fmt 32 diff --git a/sample-data/drive02-no-len.hea b/sample-data/drive02-no-len.hea new file mode 100644 index 00000000..cae31a6a --- /dev/null +++ b/sample-data/drive02-no-len.hea @@ -0,0 +1,6 @@ +drive02-no-len 5 15.5 +drive02.dat 16x32 1000 16 0 -1236 14736 0 ECG +drive02.dat 16x2 1000 16 0 1802 13501 0 foot GSR +drive02.dat 16 1.0001/bpm 16 0 75 -19070 0 HR +drive02.dat 16 100 16 0 0 -9226 0 marker +drive02.dat 16x2 500 16 0 5804 -14191 0 RESP diff --git a/sampledata/drive02.dat b/sample-data/drive02.dat similarity index 100% rename from sampledata/drive02.dat rename to sample-data/drive02.dat diff --git a/sampledata/drive02.hea b/sample-data/drive02.hea similarity index 100% rename from sampledata/drive02.hea rename to sample-data/drive02.hea diff --git a/sample-data/flac_3_constant.dat b/sample-data/flac_3_constant.dat new file mode 100644 index 00000000..a2376502 Binary files /dev/null and b/sample-data/flac_3_constant.dat differ diff --git a/sample-data/flac_3_constant.hea b/sample-data/flac_3_constant.hea new file mode 100644 index 00000000..b8fd85f7 --- /dev/null +++ b/sample-data/flac_3_constant.hea @@ -0,0 +1,4 @@ +flac_3_constant 3 250 5600000 +flac_3_constant.dat 508 200 8 0 25 15104 0 col 0 +flac_3_constant.dat 508 200 8 0 50 30208 0 col 1 +flac_3_constant.dat 508 200 8 0 75 -20224 0 col 2 diff --git a/sample-data/flacformats.d0 b/sample-data/flacformats.d0 new file mode 100644 index 00000000..7e485ae6 Binary files /dev/null and b/sample-data/flacformats.d0 differ diff --git a/sample-data/flacformats.d1 b/sample-data/flacformats.d1 new file mode 100644 index 00000000..7a9543a4 Binary files /dev/null and b/sample-data/flacformats.d1 differ diff --git a/sample-data/flacformats.d2 b/sample-data/flacformats.d2 new file mode 100644 index 00000000..34b10fb9 Binary files /dev/null and b/sample-data/flacformats.d2 differ diff --git a/sample-data/flacformats.hea b/sample-data/flacformats.hea new file mode 100644 index 00000000..d8df787b --- /dev/null +++ b/sample-data/flacformats.hea @@ -0,0 +1,4 @@ +flacformats 3 200 499 +flacformats.d0 508 200/mV 8 0 -127 -484 0 sig 0, fmt 508 +flacformats.d1 516 200/mV 16 0 -32766 -750 0 sig 1, fmt 516 +flacformats.d2 524 200/mV 24 0 -8388605 8721 0 sig 2, fmt 524 diff --git a/sample-data/huge.qrs b/sample-data/huge.qrs new file mode 100644 index 00000000..f48e8e03 Binary files /dev/null and b/sample-data/huge.qrs differ diff --git a/sample-data/mixedsignals.hea b/sample-data/mixedsignals.hea new file mode 100644 index 00000000..baab78fc --- /dev/null +++ b/sample-data/mixedsignals.hea @@ -0,0 +1,7 @@ +mixedsignals 6 62.4725/999.56 14400 +mixedsignals_e.dat 516x4 200/mV 14 8192 0 24460 0 II +mixedsignals_e.dat 516x4 200/mV 14 8192 0 19772 0 III +mixedsignals_e.dat 516x4 200/mV 14 8192 0 22261 0 V +mixedsignals_p.dat 516x2 16(800)/mmHg 12 2048 0 49347 0 ABP +mixedsignals_p.dat 516x2 4096(0)/NU 12 2048 0 36026 0 Pleth +mixedsignals_r.dat 516 4093(2)/Ohm 12 2048 0 35395 0 Resp diff --git a/sample-data/mixedsignals_e.dat b/sample-data/mixedsignals_e.dat new file mode 100644 index 00000000..08bfcde1 Binary files /dev/null and b/sample-data/mixedsignals_e.dat differ diff --git a/sample-data/mixedsignals_p.dat b/sample-data/mixedsignals_p.dat new file mode 100644 index 00000000..d3c4072a Binary files /dev/null and b/sample-data/mixedsignals_p.dat differ diff --git a/sample-data/mixedsignals_r.dat b/sample-data/mixedsignals_r.dat new file mode 100644 index 00000000..db46b7ee Binary files /dev/null and b/sample-data/mixedsignals_r.dat differ diff --git a/sample-data/multi-segment/041s/041s.hea b/sample-data/multi-segment/041s/041s.hea new file mode 100644 index 00000000..74844c25 --- /dev/null +++ b/sample-data/multi-segment/041s/041s.hea @@ -0,0 +1,3 @@ +041s/2 7 125 2000 8:26:04 26/10/1994 +041s01 1000 +041s02 1000 diff --git a/sample-data/multi-segment/041s/041s01.dat b/sample-data/multi-segment/041s/041s01.dat new file mode 100644 index 00000000..221d4014 Binary files /dev/null and b/sample-data/multi-segment/041s/041s01.dat differ diff --git a/sample-data/multi-segment/041s/041s01.hea b/sample-data/multi-segment/041s/041s01.hea new file mode 100644 index 00000000..d5d52779 --- /dev/null +++ b/sample-data/multi-segment/041s/041s01.hea @@ -0,0 +1,9 @@ +041s01 7 125 1000 8:26:04 26/10/1994 +041s01.dat 212x4 2000 12 0 168 -2716 0 III +041s01.dat 212x4 2000 12 0 2 -25019 0 I +041s01.dat 212x4 2000 12 0 155 -12467 0 V +041s01.dat 212 20(-1600)/mmHg 12 0 -242 -18875 0 ABP +041s01.dat 212 80(-1600)/mmHg 12 0 706 -5338 0 PAP +041s01.dat 212 2000 12 0 -841 30145 0 PLETH +041s01.dat 212 2000 12 0 401 3712 0 RESP +#Produced by xform from record mimicdb/041/04100001, beginning at s74000 diff --git a/sample-data/multi-segment/041s/041s02.dat b/sample-data/multi-segment/041s/041s02.dat new file mode 100644 index 00000000..0df680db Binary files /dev/null and b/sample-data/multi-segment/041s/041s02.dat differ diff --git a/sample-data/multi-segment/041s/041s02.hea b/sample-data/multi-segment/041s/041s02.hea new file mode 100644 index 00000000..46d856bb --- /dev/null +++ b/sample-data/multi-segment/041s/041s02.hea @@ -0,0 +1,9 @@ +041s02 7 125 1000 8:26:12 26/10/1994 +041s02.dat 212x4 2000 12 0 -103 -862 0 III +041s02.dat 212x4 2000 12 0 -64 14967 0 I +041s02.dat 212x4 2000 12 0 89 13162 0 V +041s02.dat 212 20(-1600)/mmHg 12 0 -715 -21117 0 ABP +041s02.dat 212 80(-1600)/mmHg 12 0 -583 -31770 0 PAP +041s02.dat 212 2000 12 0 -840 -31041 0 PLETH +041s02.dat 212 2000 12 0 -861 -31272 0 RESP +#Produced by xform from record mimicdb/041/04100002, beginning at 0:0 diff --git a/sampledata/multisegment/fixed1/v102s.dat b/sample-data/multi-segment/fixed1/v102s.dat similarity index 100% rename from sampledata/multisegment/fixed1/v102s.dat rename to sample-data/multi-segment/fixed1/v102s.dat diff --git a/sampledata/multisegment/fixed1/v102s.hea b/sample-data/multi-segment/fixed1/v102s.hea similarity index 100% rename from sampledata/multisegment/fixed1/v102s.hea rename to sample-data/multi-segment/fixed1/v102s.hea diff --git a/sampledata/multisegment/fixed1/v102s_1.hea b/sample-data/multi-segment/fixed1/v102s_1.hea similarity index 100% rename from sampledata/multisegment/fixed1/v102s_1.hea rename to sample-data/multi-segment/fixed1/v102s_1.hea diff --git a/sampledata/multisegment/fixed1/v102s_2.hea b/sample-data/multi-segment/fixed1/v102s_2.hea similarity index 100% rename from sampledata/multisegment/fixed1/v102s_2.hea rename to sample-data/multi-segment/fixed1/v102s_2.hea diff --git a/sampledata/multisegment/fixed1/v102s_3.hea b/sample-data/multi-segment/fixed1/v102s_3.hea similarity index 100% rename from sampledata/multisegment/fixed1/v102s_3.hea rename to sample-data/multi-segment/fixed1/v102s_3.hea diff --git a/sample-data/multi-segment/p000878/3269321_0001.dat b/sample-data/multi-segment/p000878/3269321_0001.dat new file mode 100644 index 00000000..06fedde4 Binary files /dev/null and b/sample-data/multi-segment/p000878/3269321_0001.dat differ diff --git a/sample-data/multi-segment/p000878/3269321_0001.hea b/sample-data/multi-segment/p000878/3269321_0001.hea new file mode 100644 index 00000000..2d1c7565 --- /dev/null +++ b/sample-data/multi-segment/p000878/3269321_0001.hea @@ -0,0 +1,2 @@ +3269321_0001 1 125 2000 16:57:39 +3269321_0001.dat 80 255(-128)/NU 8 0 -128 -25757 0 PLETH diff --git a/sample-data/multi-segment/p000878/3269321_0002.dat b/sample-data/multi-segment/p000878/3269321_0002.dat new file mode 100644 index 00000000..e4bc1f0d Binary files /dev/null and b/sample-data/multi-segment/p000878/3269321_0002.dat differ diff --git a/sample-data/multi-segment/p000878/3269321_0002.hea b/sample-data/multi-segment/p000878/3269321_0002.hea new file mode 100644 index 00000000..ca90366e --- /dev/null +++ b/sample-data/multi-segment/p000878/3269321_0002.hea @@ -0,0 +1,3 @@ +3269321_0002 2 125 1750 16:57:55 +3269321_0002.dat 80 127(-64)/mV 8 0 -128 -17878 0 II +3269321_0002.dat 80 255(-128)/NU 8 0 4 5246 0 PLETH diff --git a/sample-data/multi-segment/p000878/3269321_layout.hea b/sample-data/multi-segment/p000878/3269321_layout.hea new file mode 100644 index 00000000..7caedfcc --- /dev/null +++ b/sample-data/multi-segment/p000878/3269321_layout.hea @@ -0,0 +1,5 @@ +3269321_layout 4 125 0 16:57:09 +~ 0 255(-128)/NU 8 0 -128 0 0 PLETH +~ 0 127(-64)/mV 8 0 -128 0 0 II +~ 0 127(-64)/mV 8 0 -128 0 0 V +~ 0 1.28(-109)/mmHg 8 0 -128 0 0 ABP diff --git a/sample-data/multi-segment/p000878/p000878-2137-10-26-16-57.hea b/sample-data/multi-segment/p000878/p000878-2137-10-26-16-57.hea new file mode 100644 index 00000000..a2713d4a --- /dev/null +++ b/sample-data/multi-segment/p000878/p000878-2137-10-26-16-57.hea @@ -0,0 +1,38 @@ +p000878-2137-10-26-16-57/36 4 125 52725000 16:57:09.000 26/10/2137 +3269321_layout 0 +~ 3750 +3269321_0001 2000 +3269321_0002 1750 +3269321_0003 4875375 +3269321_0004 2923750 +3269321_0005 293375 +~ 274750 +3269321_0006 1616500 +3269321_0007 750 +3269321_0008 2435875 +3269321_0009 125 +3269321_0010 5977000 +~ 40375 +3269321_0011 125 +3269321_0012 1844625 +3269321_0013 6000 +3269321_0014 28375 +3269321_0015 1750 +3269321_0016 927750 +3269321_0017 2375 +3269321_0018 4500 +3269321_0019 2375 +3269321_0020 8148875 +3269321_0021 61250 +3269321_0022 1806000 +3269321_0023 125 +3269321_0024 6334875 +3269321_0025 750 +3269321_0026 750 +3269321_0027 250 +3269321_0028 1520250 +3269321_0029 125 +3269321_0030 500 +3269321_0031 750 +3269321_0032 13587250 +# Location: micu diff --git a/sampledata/multisegment/s00001/3248903_layout.hea b/sample-data/multi-segment/s00001/3248903_layout.hea similarity index 100% rename from sampledata/multisegment/s00001/3248903_layout.hea rename to sample-data/multi-segment/s00001/3248903_layout.hea diff --git a/sampledata/multisegment/s00001/3248903n.dat b/sample-data/multi-segment/s00001/3248903n.dat similarity index 100% rename from sampledata/multisegment/s00001/3248903n.dat rename to sample-data/multi-segment/s00001/3248903n.dat diff --git a/sampledata/multisegment/s00001/3975656_0001.dat b/sample-data/multi-segment/s00001/3975656_0001.dat similarity index 100% rename from sampledata/multisegment/s00001/3975656_0001.dat rename to sample-data/multi-segment/s00001/3975656_0001.dat diff --git a/sampledata/multisegment/s00001/3975656_0001.hea b/sample-data/multi-segment/s00001/3975656_0001.hea similarity index 100% rename from sampledata/multisegment/s00001/3975656_0001.hea rename to sample-data/multi-segment/s00001/3975656_0001.hea diff --git a/sampledata/multisegment/s00001/3975656_0002.dat b/sample-data/multi-segment/s00001/3975656_0002.dat similarity index 100% rename from sampledata/multisegment/s00001/3975656_0002.dat rename to sample-data/multi-segment/s00001/3975656_0002.dat diff --git a/sampledata/multisegment/s00001/3975656_0002.hea b/sample-data/multi-segment/s00001/3975656_0002.hea similarity index 100% rename from sampledata/multisegment/s00001/3975656_0002.hea rename to sample-data/multi-segment/s00001/3975656_0002.hea diff --git a/sampledata/multisegment/s00001/3975656_0003.dat b/sample-data/multi-segment/s00001/3975656_0003.dat similarity index 100% rename from sampledata/multisegment/s00001/3975656_0003.dat rename to sample-data/multi-segment/s00001/3975656_0003.dat diff --git a/sampledata/multisegment/s00001/3975656_0003.hea b/sample-data/multi-segment/s00001/3975656_0003.hea similarity index 100% rename from sampledata/multisegment/s00001/3975656_0003.hea rename to sample-data/multi-segment/s00001/3975656_0003.hea diff --git a/sampledata/multisegment/s00001/3975656_0004.dat b/sample-data/multi-segment/s00001/3975656_0004.dat similarity index 100% rename from sampledata/multisegment/s00001/3975656_0004.dat rename to sample-data/multi-segment/s00001/3975656_0004.dat diff --git a/sampledata/multisegment/s00001/3975656_0004.hea b/sample-data/multi-segment/s00001/3975656_0004.hea similarity index 100% rename from sampledata/multisegment/s00001/3975656_0004.hea rename to sample-data/multi-segment/s00001/3975656_0004.hea diff --git a/sampledata/multisegment/s00001/3975656_0005.dat b/sample-data/multi-segment/s00001/3975656_0005.dat similarity index 100% rename from sampledata/multisegment/s00001/3975656_0005.dat rename to sample-data/multi-segment/s00001/3975656_0005.dat diff --git a/sampledata/multisegment/s00001/3975656_0005.hea b/sample-data/multi-segment/s00001/3975656_0005.hea similarity index 100% rename from sampledata/multisegment/s00001/3975656_0005.hea rename to sample-data/multi-segment/s00001/3975656_0005.hea diff --git a/sampledata/multisegment/s00001/3975656_0006.dat b/sample-data/multi-segment/s00001/3975656_0006.dat similarity index 100% rename from sampledata/multisegment/s00001/3975656_0006.dat rename to sample-data/multi-segment/s00001/3975656_0006.dat diff --git a/sampledata/multisegment/s00001/3975656_0006.hea b/sample-data/multi-segment/s00001/3975656_0006.hea similarity index 100% rename from sampledata/multisegment/s00001/3975656_0006.hea rename to sample-data/multi-segment/s00001/3975656_0006.hea diff --git a/sampledata/multisegment/s00001/3975656_0007.dat b/sample-data/multi-segment/s00001/3975656_0007.dat similarity index 100% rename from sampledata/multisegment/s00001/3975656_0007.dat rename to sample-data/multi-segment/s00001/3975656_0007.dat diff --git a/sampledata/multisegment/s00001/3975656_0007.hea b/sample-data/multi-segment/s00001/3975656_0007.hea similarity index 100% rename from sampledata/multisegment/s00001/3975656_0007.hea rename to sample-data/multi-segment/s00001/3975656_0007.hea diff --git a/sampledata/multisegment/s00001/3975656_0008.dat b/sample-data/multi-segment/s00001/3975656_0008.dat similarity index 100% rename from sampledata/multisegment/s00001/3975656_0008.dat rename to sample-data/multi-segment/s00001/3975656_0008.dat diff --git a/sampledata/multisegment/s00001/3975656_0008.hea b/sample-data/multi-segment/s00001/3975656_0008.hea similarity index 100% rename from sampledata/multisegment/s00001/3975656_0008.hea rename to sample-data/multi-segment/s00001/3975656_0008.hea diff --git a/sampledata/multisegment/s00001/3975656_0009.dat b/sample-data/multi-segment/s00001/3975656_0009.dat similarity index 100% rename from sampledata/multisegment/s00001/3975656_0009.dat rename to sample-data/multi-segment/s00001/3975656_0009.dat diff --git a/sampledata/multisegment/s00001/3975656_0009.hea b/sample-data/multi-segment/s00001/3975656_0009.hea similarity index 100% rename from sampledata/multisegment/s00001/3975656_0009.hea rename to sample-data/multi-segment/s00001/3975656_0009.hea diff --git a/sampledata/multisegment/s00001/3975656_0010.dat b/sample-data/multi-segment/s00001/3975656_0010.dat similarity index 100% rename from sampledata/multisegment/s00001/3975656_0010.dat rename to sample-data/multi-segment/s00001/3975656_0010.dat diff --git a/sampledata/multisegment/s00001/3975656_0010.hea b/sample-data/multi-segment/s00001/3975656_0010.hea similarity index 100% rename from sampledata/multisegment/s00001/3975656_0010.hea rename to sample-data/multi-segment/s00001/3975656_0010.hea diff --git a/sampledata/multisegment/s00001/3975656_0011.dat b/sample-data/multi-segment/s00001/3975656_0011.dat similarity index 100% rename from sampledata/multisegment/s00001/3975656_0011.dat rename to sample-data/multi-segment/s00001/3975656_0011.dat diff --git a/sampledata/multisegment/s00001/3975656_0011.hea b/sample-data/multi-segment/s00001/3975656_0011.hea similarity index 100% rename from sampledata/multisegment/s00001/3975656_0011.hea rename to sample-data/multi-segment/s00001/3975656_0011.hea diff --git a/sampledata/multisegment/s00001/3975656_0012.dat b/sample-data/multi-segment/s00001/3975656_0012.dat similarity index 100% rename from sampledata/multisegment/s00001/3975656_0012.dat rename to sample-data/multi-segment/s00001/3975656_0012.dat diff --git a/sampledata/multisegment/s00001/3975656_0012.hea b/sample-data/multi-segment/s00001/3975656_0012.hea similarity index 100% rename from sampledata/multisegment/s00001/3975656_0012.hea rename to sample-data/multi-segment/s00001/3975656_0012.hea diff --git a/sampledata/multisegment/s00001/3975656_0013.dat b/sample-data/multi-segment/s00001/3975656_0013.dat similarity index 100% rename from sampledata/multisegment/s00001/3975656_0013.dat rename to sample-data/multi-segment/s00001/3975656_0013.dat diff --git a/sampledata/multisegment/s00001/3975656_0013.hea b/sample-data/multi-segment/s00001/3975656_0013.hea similarity index 100% rename from sampledata/multisegment/s00001/3975656_0013.hea rename to sample-data/multi-segment/s00001/3975656_0013.hea diff --git a/sampledata/multisegment/s00001/3975656_0014.dat b/sample-data/multi-segment/s00001/3975656_0014.dat similarity index 100% rename from sampledata/multisegment/s00001/3975656_0014.dat rename to sample-data/multi-segment/s00001/3975656_0014.dat diff --git a/sampledata/multisegment/s00001/3975656_0014.hea b/sample-data/multi-segment/s00001/3975656_0014.hea similarity index 100% rename from sampledata/multisegment/s00001/3975656_0014.hea rename to sample-data/multi-segment/s00001/3975656_0014.hea diff --git a/sampledata/multisegment/s00001/3975656_0015.dat b/sample-data/multi-segment/s00001/3975656_0015.dat similarity index 100% rename from sampledata/multisegment/s00001/3975656_0015.dat rename to sample-data/multi-segment/s00001/3975656_0015.dat diff --git a/sampledata/multisegment/s00001/3975656_0015.hea b/sample-data/multi-segment/s00001/3975656_0015.hea similarity index 100% rename from sampledata/multisegment/s00001/3975656_0015.hea rename to sample-data/multi-segment/s00001/3975656_0015.hea diff --git a/sampledata/multisegment/s00001/3975656_0016.dat b/sample-data/multi-segment/s00001/3975656_0016.dat similarity index 100% rename from sampledata/multisegment/s00001/3975656_0016.dat rename to sample-data/multi-segment/s00001/3975656_0016.dat diff --git a/sampledata/multisegment/s00001/3975656_0016.hea b/sample-data/multi-segment/s00001/3975656_0016.hea similarity index 100% rename from sampledata/multisegment/s00001/3975656_0016.hea rename to sample-data/multi-segment/s00001/3975656_0016.hea diff --git a/sampledata/multisegment/s00001/3975656_layout.hea b/sample-data/multi-segment/s00001/3975656_layout.hea similarity index 100% rename from sampledata/multisegment/s00001/3975656_layout.hea rename to sample-data/multi-segment/s00001/3975656_layout.hea diff --git a/sampledata/multisegment/s00001/3975656n.dat b/sample-data/multi-segment/s00001/3975656n.dat similarity index 100% rename from sampledata/multisegment/s00001/3975656n.dat rename to sample-data/multi-segment/s00001/3975656n.dat diff --git a/sampledata/multisegment/s00001/s00001-2896-10-09-01-56.hea b/sample-data/multi-segment/s00001/s00001-2896-10-09-01-56.hea similarity index 100% rename from sampledata/multisegment/s00001/s00001-2896-10-09-01-56.hea rename to sample-data/multi-segment/s00001/s00001-2896-10-09-01-56.hea diff --git a/sampledata/multisegment/s00001/s00001-2896-10-09-01-56n.hea b/sample-data/multi-segment/s00001/s00001-2896-10-09-01-56n.hea similarity index 100% rename from sampledata/multisegment/s00001/s00001-2896-10-09-01-56n.hea rename to sample-data/multi-segment/s00001/s00001-2896-10-09-01-56n.hea diff --git a/sampledata/multisegment/s00001/s00001-2896-10-10-00-31.hea b/sample-data/multi-segment/s00001/s00001-2896-10-10-00-31.hea similarity index 100% rename from sampledata/multisegment/s00001/s00001-2896-10-10-00-31.hea rename to sample-data/multi-segment/s00001/s00001-2896-10-10-00-31.hea diff --git a/sampledata/multisegment/s00001/s00001-2896-10-10-00-31n.hea b/sample-data/multi-segment/s00001/s00001-2896-10-10-00-31n.hea similarity index 100% rename from sampledata/multisegment/s00001/s00001-2896-10-10-00-31n.hea rename to sample-data/multi-segment/s00001/s00001-2896-10-10-00-31n.hea diff --git a/sampledata/multisegment/s25047/3234460_0001.dat b/sample-data/multi-segment/s25047/3234460_0001.dat similarity index 100% rename from sampledata/multisegment/s25047/3234460_0001.dat rename to sample-data/multi-segment/s25047/3234460_0001.dat diff --git a/sampledata/multisegment/s25047/3234460_0001.hea b/sample-data/multi-segment/s25047/3234460_0001.hea similarity index 100% rename from sampledata/multisegment/s25047/3234460_0001.hea rename to sample-data/multi-segment/s25047/3234460_0001.hea diff --git a/sampledata/multisegment/s25047/3234460_0002.dat b/sample-data/multi-segment/s25047/3234460_0002.dat similarity index 100% rename from sampledata/multisegment/s25047/3234460_0002.dat rename to sample-data/multi-segment/s25047/3234460_0002.dat diff --git a/sampledata/multisegment/s25047/3234460_0002.hea b/sample-data/multi-segment/s25047/3234460_0002.hea similarity index 100% rename from sampledata/multisegment/s25047/3234460_0002.hea rename to sample-data/multi-segment/s25047/3234460_0002.hea diff --git a/sampledata/multisegment/s25047/3234460_0003.dat b/sample-data/multi-segment/s25047/3234460_0003.dat similarity index 100% rename from sampledata/multisegment/s25047/3234460_0003.dat rename to sample-data/multi-segment/s25047/3234460_0003.dat diff --git a/sampledata/multisegment/s25047/3234460_0003.hea b/sample-data/multi-segment/s25047/3234460_0003.hea similarity index 100% rename from sampledata/multisegment/s25047/3234460_0003.hea rename to sample-data/multi-segment/s25047/3234460_0003.hea diff --git a/sampledata/multisegment/s25047/3234460_0004.dat b/sample-data/multi-segment/s25047/3234460_0004.dat similarity index 100% rename from sampledata/multisegment/s25047/3234460_0004.dat rename to sample-data/multi-segment/s25047/3234460_0004.dat diff --git a/sampledata/multisegment/s25047/3234460_0004.hea b/sample-data/multi-segment/s25047/3234460_0004.hea similarity index 100% rename from sampledata/multisegment/s25047/3234460_0004.hea rename to sample-data/multi-segment/s25047/3234460_0004.hea diff --git a/sampledata/multisegment/s25047/3234460_0005.dat b/sample-data/multi-segment/s25047/3234460_0005.dat similarity index 100% rename from sampledata/multisegment/s25047/3234460_0005.dat rename to sample-data/multi-segment/s25047/3234460_0005.dat diff --git a/sampledata/multisegment/s25047/3234460_0005.hea 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index 100% rename from sampledata/multisegment/s25047/s25047-2704-05-04-10-44n.hea rename to sample-data/multi-segment/s25047/s25047-2704-05-04-10-44n.hea diff --git a/sample-data/n16.dat b/sample-data/n16.dat new file mode 100644 index 00000000..d0d6ced3 Binary files /dev/null and b/sample-data/n16.dat differ diff --git a/sample-data/n16.edf b/sample-data/n16.edf new file mode 100644 index 00000000..f0669a9b Binary files /dev/null and b/sample-data/n16.edf differ diff --git a/sample-data/n16.hea b/sample-data/n16.hea new file mode 100644 index 00000000..81474c8f --- /dev/null +++ b/sample-data/n16.hea @@ -0,0 +1,6 @@ +n16 5 100 3080000 22:34:47 01/01/2006 +n16.dat 16 44.9629231183/uV 15 0 -161 -9172 0 Fp2-F4 +n16.dat 16 44.9629231183/uV 15 0 1926 29657 0 F4-C4 +n16.dat 16 44.9629231183/uV 15 0 -4623 23837 0 C4-P4 +n16.dat 16 44.9629231183/uV 15 0 2472 25301 0 P4-O2 +n16.dat 16 44.9629231183/uV 15 0 -2354 -17711 0 C4-A1 diff --git a/sample-data/n8_evoked_raw_95_F1_R9.dat b/sample-data/n8_evoked_raw_95_F1_R9.dat new file mode 100644 index 00000000..ec9f2870 Binary files /dev/null and b/sample-data/n8_evoked_raw_95_F1_R9.dat differ diff --git a/sample-data/n8_evoked_raw_95_F1_R9.hea b/sample-data/n8_evoked_raw_95_F1_R9.hea new file mode 100644 index 00000000..01c39bfa --- /dev/null +++ b/sample-data/n8_evoked_raw_95_F1_R9.hea @@ -0,0 +1,4 @@ +n8_evoked_raw_95_F1_R9 2 48000 2022144 +n8_evoked_raw_95_F1_R9.dat 24 2.4011e-11/nV +n8_evoked_raw_95_F1_R9.dat 24 6.8094e-14/V +##: 95 : 1 : r : 2002 : Normal \ No newline at end of file diff --git a/sampledata/p10143.dat b/sample-data/p10143.dat similarity index 100% rename from sampledata/p10143.dat rename to sample-data/p10143.dat diff --git a/sampledata/p10143.hea b/sample-data/p10143.hea similarity index 100% rename from sampledata/p10143.hea rename to sample-data/p10143.hea diff --git a/sampledata/s0010_re.dat b/sample-data/s0010_re.dat similarity index 100% rename from sampledata/s0010_re.dat rename to 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rename to sample-data/test01_00s_skew.hea diff --git a/sampledata/test01_00s_skewframe.hea b/sample-data/test01_00s_skewframe.hea similarity index 100% rename from sampledata/test01_00s_skewframe.hea rename to sample-data/test01_00s_skewframe.hea diff --git a/sample-data/test_edfann.edf b/sample-data/test_edfann.edf new file mode 100644 index 00000000..745958ce Binary files /dev/null and b/sample-data/test_edfann.edf differ diff --git a/sample-data/test_generator_2.dat b/sample-data/test_generator_2.dat new file mode 100644 index 00000000..17c2622f Binary files /dev/null and b/sample-data/test_generator_2.dat differ diff --git a/sample-data/test_generator_2.edf b/sample-data/test_generator_2.edf new file mode 100644 index 00000000..9a243be1 Binary files /dev/null and b/sample-data/test_generator_2.edf differ diff --git a/sample-data/test_generator_2.hea b/sample-data/test_generator_2.hea new file mode 100644 index 00000000..1879c76c --- /dev/null +++ b/sample-data/test_generator_2.hea @@ -0,0 +1,13 @@ +test_generator_2 12 1 600 12:44:02 10/12/2009 +test_generator_2.dat 16x200 32.7675/uV 15 0 3276 0 0 squarewave +test_generator_2.dat 16x200 32.7675/uV 15 0 -3276 480 0 ramp +test_generator_2.dat 16x200 32.7675/uV 15 0 3276 -1920 0 pulse +test_generator_2.dat 16x200 32.7675/uV 15 0 0 21000 0 ECG +test_generator_2.dat 16x200 32.7675/uV 15 0 2752 -12878 0 noise +test_generator_2.dat 16x200 32.7675/uV 15 0 102 0 0 sine 1 Hz +test_generator_2.dat 16x200 32.7675/uV 15 0 814 0 0 sine 8 Hz +test_generator_2.dat 16x200 32.7675/uV 15 0 864 0 0 sine 8.5 Hz +test_generator_2.dat 16x200 32.7675/ 15 0 1487 0 0 sine 15 Hz +test_generator_2.dat 16x200 32.7675/ 15 0 1668 0 0 sine 17 Hz +test_generator_2.dat 16x200 32.7675/uV 15 0 3276 0 0 sine 50 Hz +test_generator_2.dat 16x51 32767.5 15 0 12331 29678 0 EDF Annotations diff --git a/sampledata/v102s.dat b/sample-data/v102s.dat similarity index 100% rename from sampledata/v102s.dat rename to sample-data/v102s.dat diff --git a/sampledata/v102s.hea b/sample-data/v102s.hea similarity index 100% rename from sampledata/v102s.hea rename to sample-data/v102s.hea diff --git a/sample-data/wave_4.dat b/sample-data/wave_4.dat new file mode 100644 index 00000000..71b39ffe Binary files /dev/null and b/sample-data/wave_4.dat differ diff --git a/sample-data/wave_4.edf b/sample-data/wave_4.edf new file mode 100644 index 00000000..ec568f3f Binary files /dev/null and b/sample-data/wave_4.edf differ diff --git a/sample-data/wave_4.hea b/sample-data/wave_4.hea new file mode 100644 index 00000000..906663d2 --- /dev/null +++ b/sample-data/wave_4.hea @@ -0,0 +1,8 @@ +wave_4 7 1 3659 0:00 01/01/2001 +wave_4.dat 16x1000 1(1)/uV 15 0 5393 -12624 0 ECG +wave_4.dat 16x100 2(1)/mg 13 0 1371 18209 0 Accelerometer_X +wave_4.dat 16x100 2(1)/mg 13 0 1273 29556 0 Accelerometer_Y +wave_4.dat 16x100 2(1)/mg 13 0 -473 -31584 0 Accelerometer_Z +wave_4.dat 16 1(1) 15 0 32766 -29151 0 Marker +wave_4.dat 16x5 1(-32767)/ms 15 0 -32768 13833 0 HRV +wave_4.dat 16 -19.3447902992(3064)/degC 11 2048 2461 27731 0 DEV_Temperature diff --git a/setup.py b/setup.py deleted file mode 100644 index fe0b35c5..00000000 --- a/setup.py +++ /dev/null @@ -1,93 +0,0 @@ -"""The WFDB Python Toolbox. -See: https://www.physionet.org/physiotools/wfdb.shtml -""" - -# Always prefer setuptools over distutils -from setuptools import setup, find_packages -# To use a consistent encoding -from codecs import open -from os import path - -here = path.abspath(path.dirname(__file__)) - -# Get the long description from the README file -with open(path.join(here, 'README.rst'), encoding='utf-8') as f: - long_description = f.read() - -# Get the version number from the version.py file -from wfdb.version import __version__ - -# Get the requirements -with open('requirements.txt') as f: - requirements = f.read().splitlines() - -setup( - name='wfdb', - - # Versions should comply with PEP440. For a discussion on single-sourcing - # the version across setup.py and the project code, see - # https://packaging.python.org/en/latest/single_source_version.html - version=__version__, - - description='The WFDB Python Toolbox', - long_description=long_description, - - # The project's main homepage. - url='https://github.com/MIT-LCP/wfdb-python', - - # Author details - author='The Laboratory for Computational Physiology', - author_email='support@physionet.org', - - # Choose your license - license='MIT', - - # What does your project relate to? - keywords='WFDB clinical waveform', - - # You can just specify the packages manually here if your project is - # simple. Or you can use find_packages(). - # packages=find_packages(exclude=['contrib', 'docs', 'tests']), - packages=find_packages(), - - # Alternatively, if you want to distribute just a my_module.py, uncomment - # this: - # py_modules=["my_module"], - - # List run-time dependencies here. These will be installed by pip when - # your project is installed. For an analysis of "install_requires" vs pip's - # requirements files see: - # https://packaging.python.org/en/latest/requirements.html - install_requires=requirements, - - # List additional groups of dependencies here (e.g. development - # dependencies). You can install these using the following syntax, - # for example: - # $ pip install -e .[dev,test] - # extras_require={ - # 'dev': ['check-manifest'], - # 'test': ['coverage'], - # }, - - # If there are data files included in your packages that need to be - # installed, specify them here. If using Python 2.6 or less, then these - # have to be included in MANIFEST.in as well. - # package_data={'wfdb': ['wfdb.config'], - # }, - - # Although 'package_data' is the preferred approach, in some case you may - # need to place data files outside of your packages. See: - # http://docs.python.org/3.4/distutils/setupscript.html#installing-additional-files # noqa - # In this case, 'data_file' will be installed into '/my_data' - # data_files=[('my_data', ['data/data_file'])], - # data_files=[('config', ['wfdb.config'])], - - # To provide executable scripts, use entry points in preference to the - # "scripts" keyword. Entry points provide cross-platform support and allow - # pip to create the appropriate form of executable for the target platform. - # entry_points={ - # 'console_scripts': [ - # 'sample=sample:main', - # ], - # }, -) diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 00000000..b280897f --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1,16 @@ +import numpy as np + + +_np_error_state = {} + + +def setup_module(): + # Raise exceptions for arithmetic errors, except underflow + global _np_error_state + _np_error_state = np.seterr() + np.seterr("raise", under="ignore") + + +def teardown_module(): + # Restore original error handling state + np.seterr(**_np_error_state) diff --git a/tests/io/test_convert.py b/tests/io/test_convert.py new file mode 100644 index 00000000..cf97f700 --- /dev/null +++ b/tests/io/test_convert.py @@ -0,0 +1,179 @@ +import os +import shutil +import unittest + +import numpy as np + +from wfdb.io.record import rdrecord +from wfdb.io.convert.edf import read_edf +from wfdb.io.convert.csv import csv_to_wfdb + + +class TestEdfToWfdb: + """ + Tests for the io.convert.edf module. + """ + + def test_edf_uniform(self): + """ + EDF format conversion to MIT for uniform sample rates. + """ + # Uniform sample rates + record_MIT = rdrecord("sample-data/n16").__dict__ + record_EDF = read_edf("sample-data/n16.edf").__dict__ + + fields = list(record_MIT.keys()) + # Original MIT format method of checksum is outdated, sometimes + # the same value though + fields.remove("checksum") + # Original MIT format units are less comprehensive since they + # default to mV if unknown.. therefore added more default labels + fields.remove("units") + + test_results = [] + for field in fields: + # Signal value will be slightly off due to C to Python type conversion + if field == "p_signal": + true_array = np.array(record_MIT[field]).flatten() + pred_array = np.array(record_EDF[field]).flatten() + # Prevent divide by zero warning + for i, v in enumerate(true_array): + if v == 0: + true_array[i] = 1 + pred_array[i] = 1 + sig_diff = np.abs((pred_array - true_array) / true_array) + sig_diff[sig_diff == -np.inf] = 0 + sig_diff[sig_diff == np.inf] = 0 + sig_diff = np.nanmean(sig_diff, 0) + # 5% tolerance + if np.max(sig_diff) <= 5: + test_results.append(True) + else: + test_results.append(False) + elif field == "init_value": + signal_diff = [ + abs(record_MIT[field][i] - record_EDF[field][i]) + for i in range(len(record_MIT[field])) + ] + if abs(max(min(signal_diff), max(signal_diff), key=abs)) <= 2: + test_results.append(True) + else: + test_results.append(False) + else: + test_results.append(record_MIT[field] == record_MIT[field]) + + target_results = len(fields) * [True] + assert np.array_equal(test_results, target_results) + + def test_edf_non_uniform(self): + """ + EDF format conversion to MIT for non-uniform sample rates. + """ + # Non-uniform sample rates + record_MIT = rdrecord("sample-data/wave_4").__dict__ + record_EDF = read_edf("sample-data/wave_4.edf").__dict__ + + fields = list(record_MIT.keys()) + # Original MIT format method of checksum is outdated, sometimes + # the same value though + fields.remove("checksum") + # Original MIT format units are less comprehensive since they + # default to mV if unknown.. therefore added more default labels + fields.remove("units") + + test_results = [] + for field in fields: + # Signal value will be slightly off due to C to Python type conversion + if field == "p_signal": + true_array = np.array(record_MIT[field]).flatten() + pred_array = np.array(record_EDF[field]).flatten() + # Prevent divide by zero warning + for i, v in enumerate(true_array): + if v == 0: + true_array[i] = 1 + pred_array[i] = 1 + sig_diff = np.abs((pred_array - true_array) / true_array) + sig_diff[sig_diff == -np.inf] = 0 + sig_diff[sig_diff == np.inf] = 0 + sig_diff = np.nanmean(sig_diff, 0) + # 5% tolerance + if np.max(sig_diff) <= 5: + test_results.append(True) + else: + test_results.append(False) + elif field == "init_value": + signal_diff = [ + abs(record_MIT[field][i] - record_EDF[field][i]) + for i in range(len(record_MIT[field])) + ] + if abs(max(min(signal_diff), max(signal_diff), key=abs)) <= 2: + test_results.append(True) + else: + test_results.append(False) + else: + test_results.append(record_MIT[field] == record_MIT[field]) + + target_results = len(fields) * [True] + assert np.array_equal(test_results, target_results) + + +class TestCsvToWfdb(unittest.TestCase): + """ + Tests for the io.convert.csv module. + """ + + def setUp(self): + """ + Create a temporary directory containing data for testing. + + Load 100.dat file for comparison to 100.csv file. + """ + self.test_dir = "test_output" + os.makedirs(self.test_dir, exist_ok=True) + + self.record_100_csv = "sample-data/100.csv" + self.record_100_dat = rdrecord("sample-data/100", physical=True) + + def tearDown(self): + """ + Remove the temporary directory after the test. + """ + if os.path.exists(self.test_dir): + shutil.rmtree(self.test_dir) + + def test_write_dir(self): + """ + Call the function with the write_dir argument. + """ + csv_to_wfdb( + file_name=self.record_100_csv, + fs=360, + units="mV", + write_dir=self.test_dir, + ) + + # Check if the output files are created in the specified directory + base_name = os.path.splitext(os.path.basename(self.record_100_csv))[0] + expected_dat_file = os.path.join(self.test_dir, f"{base_name}.dat") + expected_hea_file = os.path.join(self.test_dir, f"{base_name}.hea") + + self.assertTrue(os.path.exists(expected_dat_file)) + self.assertTrue(os.path.exists(expected_hea_file)) + + # Check that newly written file matches the 100.dat file + record_write = rdrecord(os.path.join(self.test_dir, base_name)) + + self.assertEqual(record_write.fs, 360) + self.assertEqual(record_write.fs, self.record_100_dat.fs) + self.assertEqual(record_write.units, ["mV", "mV"]) + self.assertEqual(record_write.units, self.record_100_dat.units) + self.assertEqual(record_write.sig_name, ["MLII", "V5"]) + self.assertEqual(record_write.sig_name, self.record_100_dat.sig_name) + self.assertEqual(record_write.p_signal.size, 1300000) + self.assertEqual( + record_write.p_signal.size, self.record_100_dat.p_signal.size + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/targetoutputdata/anntarget1 b/tests/target-output/ann-1 similarity index 100% rename from tests/targetoutputdata/anntarget1 rename to tests/target-output/ann-1 diff --git a/tests/targetoutputdata/anntarget2 b/tests/target-output/ann-2 similarity index 100% rename from tests/targetoutputdata/anntarget2 rename to tests/target-output/ann-2 diff --git a/tests/targetoutputdata/anntarget3 b/tests/target-output/ann-3 similarity index 100% rename from tests/targetoutputdata/anntarget3 rename to tests/target-output/ann-3 diff --git a/tests/targetoutputdata/target1a b/tests/target-output/record-1a similarity index 100% rename from tests/targetoutputdata/target1a rename to tests/target-output/record-1a diff --git a/tests/targetoutputdata/target1b b/tests/target-output/record-1b similarity index 100% rename from tests/targetoutputdata/target1b rename to tests/target-output/record-1b diff --git a/tests/targetoutputdata/target1c b/tests/target-output/record-1c similarity index 100% rename from tests/targetoutputdata/target1c rename to tests/target-output/record-1c diff --git a/tests/targetoutputdata/target1d b/tests/target-output/record-1d similarity index 100% rename from tests/targetoutputdata/target1d rename to tests/target-output/record-1d diff --git a/tests/target-output/record-1e.gz b/tests/target-output/record-1e.gz new file mode 100644 index 00000000..13684843 Binary files /dev/null and b/tests/target-output/record-1e.gz differ diff --git a/tests/target-output/record-1f.gz b/tests/target-output/record-1f.gz new file mode 100644 index 00000000..674baad3 Binary files /dev/null and b/tests/target-output/record-1f.gz differ diff --git a/tests/targetoutputdata/target2a b/tests/target-output/record-2a similarity index 100% rename from tests/targetoutputdata/target2a rename to tests/target-output/record-2a diff --git a/tests/targetoutputdata/target2b b/tests/target-output/record-2b similarity index 100% rename from tests/targetoutputdata/target2b rename to tests/target-output/record-2b diff --git a/tests/targetoutputdata/target2c b/tests/target-output/record-2c similarity index 100% rename from tests/targetoutputdata/target2c rename to tests/target-output/record-2c diff --git a/tests/targetoutputdata/target2d b/tests/target-output/record-2d similarity index 100% rename from tests/targetoutputdata/target2d rename to tests/target-output/record-2d diff --git a/tests/targetoutputdata/target2e b/tests/target-output/record-2e similarity index 100% rename from tests/targetoutputdata/target2e rename to tests/target-output/record-2e diff --git a/tests/targetoutputdata/target3a b/tests/target-output/record-3a similarity index 100% rename from tests/targetoutputdata/target3a rename to tests/target-output/record-3a diff --git a/tests/targetoutputdata/target3b b/tests/target-output/record-3b similarity index 100% rename from tests/targetoutputdata/target3b rename to tests/target-output/record-3b diff --git a/tests/targetoutputdata/target4a b/tests/target-output/record-4a similarity index 100% rename from tests/targetoutputdata/target4a rename to tests/target-output/record-4a diff --git a/tests/targetoutputdata/target4b b/tests/target-output/record-4b similarity index 100% rename from tests/targetoutputdata/target4b rename to tests/target-output/record-4b diff --git a/tests/targetoutputdata/target4c b/tests/target-output/record-4c similarity index 100% rename from tests/targetoutputdata/target4c rename to tests/target-output/record-4c diff --git a/tests/targetoutputdata/target4d b/tests/target-output/record-4d similarity index 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TestAnnotation(unittest.TestCase): + """ + Testing read and write of WFDB annotations, including Physionet + streaming. + + Target files created using the original WFDB Software Package + version 10.5.24 + """ + + def test_1(self): + """ + Target file created with: + rdann -r sample-data/100 -a atr > ann-1 + """ + annotation = wfdb.rdann("sample-data/100", "atr") + + # This is not the fault of the script. The annotation file specifies a + # length 3 + annotation.aux_note[0] = "(N" + # aux_note field with a null written after '(N' which the script correctly picks up. I am just + # getting rid of the null in this unit test to compare with the regexp output below which has + # no null to detect in the output text file of rdann. + + # Target data from WFDB software package + with open("tests/target-output/ann-1", "r") as f: + lines = tuple(f) + nannot = len(lines) + + target_time = [None] * nannot + target_sample = np.empty(nannot, dtype="object") + target_symbol = [None] * nannot + target_subtype = np.empty(nannot, dtype="object") + target_chan = np.empty(nannot, dtype="object") + target_num = np.empty(nannot, dtype="object") + target_aux_note = [None] * nannot + + RXannot = re.compile( + r"[ \t]*(?P