xarray-events 0.2.13

An open-source API for handling events based on xarray.

xarray-events: An open-source extension of xarray that supports events handling

xarray-events is an open-source API based on xarray. It provides sophisticated mechanisms to handle events easily.

Events data is something very natural to conceive, yet it’s rather infrequent to see native support for it in common data analysis libraries. Our aim is to fill this gap in a very general way, so that scientists from any domain can take benefit from this. We’re building all of this on top of xarray because this is already a well-established open-source library that provides exciting new ways of handling multi-dimensional labelled data, with applications in a wide range of domains of science.

This library makes it possible to extend a Dataset by introducing events based on the data. Internally it works as an accessor to xarray that provides new methods to deal with new data in the form of events and also extends the existing ones already provided by it to add compatibility with this new kind of data.

We hope that this project inspires you to rethink how you currently handle data and, if needed, improve it.

Example

Assume we have a DataFrame (in a variable called ds) of events and a Dataset (in a variable called events) of sports data in such a way that the events are a meaningful complement to the data stored in the Dataset.

events = pd.DataFrame(
    {
        'event_type': ['pass', 'goal', 'pass', 'pass'],
        'start_frame': [1, 175, 251, 376],
        'end_frame': [174, 250, 375, 500]
    }
)

ds = xr.Dataset(
    data_vars={
        'ball_trajectory': (
            ['frame', 'cartesian_coords'],
            np.exp(np.linspace((-6, -8), (3, 2), 500))
        )
    },
    coords={'frame': np.arange(1, 501), 'cartesian_coords': ['x', 'y']},
    attrs={'match_id': 12, 'resolution_fps': 25, '_events': events}
)

With this API we can do the following:

ds
.events.load(events, {'frame': ('start_frame', 'end_frame')})
.events.sel({
    'frame': range(175, 376),
    'start_frame': lambda frame: frame >= 175,
    'end_frame': lambda frame: frame < 376
})
.events.groupby_events('ball_trajectory')
.mean()

This will:

• Load the events DataFrame specifying that the columns start_frame and end_frame define the span of the events as per the Dataset’s coordinate frame.

• Perform a selection constraining the frames to be only in the range [175, 375].

• Group the DataVariable ball_trajectory by the events.

• Compute the mean of each group.

<xarray.DataArray 'ball_trajectory' (event_index: 2, cartesian_coords: 2)>
array([[0.12144595, 0.02556095],
       [0.84426861, 0.22346441]])
Coordinates:
  * cartesian_coords  (cartesian_coords) <U1 'x' 'y'
  * event_index       (event_index) int64 1 2

This result can be interpreted as the mean 2D position of the ball over the span of each event during the frames [175, 375]. This is a very powerful set of operations performed via some simple and intuitive function calls. This is the beauty of this API.