vitabel 0.0.1

A toolbox for plotting and interactively labeling vital data.

vitabel: a toolbox for interactively annotating and labeling vital data

In a nutshell, the vitabel package allows loading, processing, and annotating vital data (e.g., medical time-series data e.g. from defibrillators, anesthesia charts or critical care) interactively in a Jupyter notebook.

Installation and Usage

The latest stable release of vitabel is distributed via PyPI and can be installed via

$ pip install vitabel

The latest development version can be installed from the main branch on GitHub by running

$ pip install git+https://github.com/UniGrazMath/vitabel.git

The main feature of vitabel, interactive plots that can be used to annotate data, is designed to work in Jupyter notebooks. Start a new server by running jupyter notebook (or create a new notebook in an existing server), then import the central Vitals class that acts as a container for the vital data. A set of data can be added using, for example, the Vitals.add_defibrillator_recording method, or Vitals.add_vital_db_recording; various output formats of defibrillators and VitalDB are supported.

A typical use of this package reads as follows:

from vitabel import Vitals, Label

# create case and load data
case = Vitals()
case.add_defibrillator_recording("path/to/ZOLL_data_file.json")

# use in-built methods for processing available data, compute etco2
# and predict circulatory state
case.compute_etco2_and_ventilations()
case.predict_circulation()

# create a new label for ROSC events
ROSC_label = Label('ROSC', plotstyle={'marker': '$\u2665$', 'color': 'red', 'ms': 10, 'linestyle': ''})
case.add_global_label(ROSC_label)

# display an interactive plot that allows annotations and further data adjustments
case.plot_interactive(
    channels=[['cpr_acceleration'], ['capnography'], ['ecg_pads'], []],
    labels = [['ROSC'], ['etco2_from_capnography', 'ROSC'], ['ROSC'], ['ROSC', 'rosc_probability']],
    channel_overviews=[['cpr_acceleration']],
    time_unit='s',
    subplots_kwargs={'figsize': (22, 9)}
)

More detailed explicit examples (including the required test data) are contained in the examples directory.

Development

Setup a development environment by using the Python project and environment management tool uv. To setup the environment, simply run

uv sync

Package tests are contained in the tests directory; run them locally via

uv run pytest

We use ruff for linting and formatting the code base, and semantic versioning for the release tags.