Preprocessing module#

pyneon.preprocess.interpolate(new_ts: ndarray, data: DataFrame, float_kind: str = 'linear', other_kind: str = 'nearest') → DataFrame#

Interpolate a data stream to a new set of timestamps.

Parameters:

  • new_ts (numpy.ndarray) – An array of new timestamps (in nanoseconds) at which to evaluate the interpolant.

  • data (pandas.DataFrame) – Source data to interpolate. Must have a monotonically increasing index named timestamp [ns].

  • float_kind (str, optional) – Kind of interpolation applied on columns of float type, For details see scipy.interpolate.interp1d. Defaults to "linear".

  • other_kind (str, optional) – Kind of interpolation applied on columns of other types, For details see scipy.interpolate.interp1d. Defaults to "nearest".

Returns:

Interpolated data with the same columns and dtypes as data and indexed by new_ts.

Return type:

pandas.DataFrame

pyneon.preprocess.interpolate_events(data: DataFrame, events: Events, buffer: Number | tuple[Number, Number] = 0.05, float_kind: str = 'linear', other_kind: str = 'nearest') → DataFrame#

Interpolate data in the duration of events in the stream data. Similar to mne.preprocessing.eyetracking.interpolate_blinks().

Parameters:

  • data (pandas.DataFrame) – Data to interpolate. Must have a monotonically increasing index named timestamp [ns].

  • events (Events) – Events object containing the events to interpolate. The events must have start timestamp [ns] and end timestamp [ns] columns.

  • buffer (numbers.Number or , optional) – The time before and after an event (in seconds) to consider invalid. If a single number is provided, the same buffer is applied to both before and after the event. Defaults to 0.05.

  • float_kind (str, optional) – Kind of interpolation applied on columns of float type, For details see scipy.interpolate.interp1d. Defaults to "linear".

  • other_kind (str, optional) – Kind of interpolation applied on columns of other types, For details see scipy.interpolate.interp1d. Defaults to "nearest".

Returns:

Interpolated data with the same columns and dtypes as data and indexed by data.index.

Return type:

pandas.DataFrame

pyneon.preprocess.window_average(new_ts: ndarray, data: DataFrame, window_size: int | None = None) → DataFrame#

Take the average over a time window to obtain smoothed data at new timestamps.

Parameters:

  • new_ts (numpy.ndarray) –

    An array of new timestamps (in nanoseconds) at which to evaluate the averaged signal. Must be coarser than the source sampling, i.e.:

    >>> np.median(np.diff(new_ts)) > np.median(np.diff(data.index))

  • data (pandas.DataFrame) – Source data to apply window average to. Must have a monotonically increasing index named timestamp [ns].

  • window_size (int, optional) – The size of the time window (in nanoseconds) over which to compute the average around each new timestamp. If None (default), the window size is set to the median interval between the new timestamps, i.e., np.median(np.diff(new_ts)). The window size must be larger than the median interval between the original data timestamps, i.e., window_size > np.median(np.diff(data.index)).

Returns:

Data with window average applied, carrying the same columns and dtypes as data and indexed by new_ts. Non-float columns are rounded back to their original integer type after averaging.

Return type:

pandas.DataFrame

pyneon.preprocess.concat_streams(rec: Recording, stream_names: str | list[str] = 'all', sampling_freq: Number | str = 'min', interp_float_kind: str = 'linear', interp_other_kind: str = 'nearest', inplace: bool = False) → DataFrame#

Concatenate data from different streams under common timestamps. Since the streams may have different timestamps and sampling frequencies, interpolation of all streams to a set of common timestamps is performed. The latest start timestamp and earliest last timestamp of the selected streams are used to define the common timestamps.

Parameters:

  • rec (Recording) – Recording object containing the streams to concatenate.

  • stream_names (str or list of str) – Stream names to concatenate. If “all” (default), then all streams will be used. If a list, items must be in {"gaze", "imu", "eye_states"} ("3d_eye_states") is also tolerated as an alias for "eye_states").

  • sampling_freq (numbers.Number or str, optional) – Sampling frequency of the concatenated streams. If numeric, the streams will be interpolated to this frequency. If “min” (default), the lowest nominal sampling frequency of the selected streams will be used. If “max”, the highest nominal sampling frequency will be used.

  • interp_float_kind (str, optional) – Kind of interpolation applied on columns of float type, Defaults to "linear". For details see scipy.interpolate.interp1d.

  • interp_other_kind (str, optional) – Kind of interpolation applied on columns of other types. Defaults to "nearest".

  • inplace (bool, optional) – Replace selected stream data with interpolated data during concatenation if``True``. Defaults to False.

Returns:

concat_data – Concatenated data.

Return type:

pandas.DataFrame

pyneon.preprocess.concat_events(rec: Recording, event_names: str | list[str]) → DataFrame#

Concatenate different events. All columns in the selected event type will be present in the final DataFrame. An additional type column denotes the event type. If "events" is in event_names, its timestamp [ns] column will be renamed to start timestamp [ns], and the name and type columns will be renamed to message name and message type respectively to prevent confusion between physiological events and user-supplied messages.

Parameters:

  • rec (Recording) – Recording object containing the events to concatenate.

  • event_names (list of str) – List of event names to concatenate. Event names must be in {"blinks", "fixations", "saccades", "events"} (singular forms are tolerated).

Returns:

Concatenated events.

Return type:

pandas.DataFrame

pyneon.preprocess.smooth_camera_pose(camera_position_raw: DataFrame, initial_state_noise: float = 0.1, process_noise: float = 0.1, measurement_noise: float = 0.01, gating_threshold: float = 2.0, bidirectional: bool = False) → DataFrame#

Apply a Kalman filter to smooth camera positions, with optional forward-backward smoothing (RTS smoother). Handles missing measurements and propagates predictions.

Parameters:

  • camera_position_raw (pandas.DataFrame) – DataFrame containing ‘frame_idx’ and ‘camera_pos’ columns.

  • initial_state_noise (float, optional) – Initial state covariance scaling factor. Default is 0.1.

  • process_noise (float, optional) – Process noise covariance scaling factor. Default is 0.1.

  • measurement_noise (float, optional) – Measurement noise covariance scaling factor. Default is 0.01.

  • gating_threshold (float, optional) – Mahalanobis distance threshold for gating outliers. Default is 2.0.

  • bidirectional (bool, optional) – If True, applies forward-backward RTS smoothing. Default is False.

Returns:

A DataFrame with ‘frame_idx’ and ‘smoothed_camera_pos’.

Return type:

pd.DataFrame