API Reference

Binning

binit.bin.bin_array_around_event(arr: ndarray, timestamps_to_bin_around: ndarray, binsize: float) → ndarray[source]

Get counts of timestamps of one array occuring around an timestamps specified in another array.

Parameters:

  • arr (np.ndarray) – Array of timestamps to be counted

  • timestamps_to_bin_around (np.ndarray) – Array of timestamps to bin around

  • binsize (float) – Size of the window around the timestamps to calculate counts over

Returns:

Array of timestamp counts

Return type:

np.ndarray

binit.bin.binarize_array(arr: ndarray) → ndarray[source]

Binarize an array

Parameters:

arr (np.ndarray) – Input array

Returns:

An array whose non-zero values have been replaced with 1

Return type:

np.ndarray

binit.bin.binned_array_bins_provided(arr: ndarray, bins: ndarray) → Tuple[ndarray, ndarray][source]

Bin an array of timestamps into pre-specified time bins

Parameters:

  • arr (np.ndarray) – Input array of timestamps

  • bins (np.ndarray) – Time bins to use for binning

Returns:

Bin edges, event counts

Return type:

Tuple[np.ndarray, np.ndarray]

binit.bin.binned_array_regular_interval(arr: ndarray, binwidth: float, t_start: Optional[float] = None, t_stop: Optional[float] = None) → Tuple[ndarray, ndarray][source]

Bin an array of timestamps into bins at a regular interval

Parameters:

  • arr (np.ndarray) – Input array of timestamps

  • binwidth (float) – Length of bins

  • t_start (Optional[float], optional) – Sets the first bin to start at this timepoint. Defaults to None.

  • t_stop (Optional[float], optional) – Sets the last bin to stop before this timepoint. Defaults to None.

Returns:

Bin edges, event counts

Return type:

Tuple[np.ndarray, np.ndarray]

binit.bin.split_by_bin(arr: ndarray, bins: ndarray, max_latency: Optional[float] = None, time_before: float = None) → OrderedDict[float, np.ndarray][source]

Split an array of timestamps by bin and transform their values to be latencies to that bin.

Parameters:

  • arr (np.ndarray) – Array of timestamps

  • bins (np.ndarray) – Array of bins

  • max_latency (Optional[float], optional) – Exclude timestamps occuring at a latency to the final bin greater than this value. Defaults to None.

  • time_before (Optional[float], optional) – By default, values are binned into the closest preceding bin but if this value is specified, timestamps falling this value before a following bin are binned to that bin. Defaults to None.

Returns:

An ordered dict whose keys are the bins and whose values are arrays of vatency to these bins.

Return type:

OrderedDict[float, np.ndarray]

binit.bin.which_bin(arr: ndarray, bin_edges: ndarray, time_before: Optional[float] = 0, time_after: Optional[float] = 1) → ndarray[source]

For each element of an input array, get the corresponding bin it would be binned into

Parameters:

  • arr (np.ndarray) – Input array of timestamps

  • bin_edges (np.ndarray) – Array of bins

  • time_before (Optional[float], optional) – By default, values are binned into the closest preceding bin but if this value is specified, timestamps falling this value before a following bin are binned to that bin. Defaults to None.

  • nan_vals_before_first_bin (bool, optional) – If True, returns np.nan for values of input timestamps occuring before the after the first bin. Defaults to True.

  • time_after (Optional[float], optional) – If specified, return np.nan for values occuring this latency after the event. Defaults to None.

Returns:

Bin values

Return type:

np.ndarray

binit.bin.which_bin_idx(arr: ndarray, bin_edges: ndarray, time_before: Optional[float] = 0, time_after: Optional[float] = 1) → ndarray[source]

For each element of an input array, get the corresponding index of the bin it would be binned into

Parameters:

  • arr (np.ndarray) – Input array of timestamps

  • bin_edges (np.ndarray) – Array of bins

  • time_before (Optional[float], optional) – By default, values are binned into the closest preceding bin but if this value is specified, timestamps falling this value before a following bin are binned to that bin. Defaults to None.

  • nan_vals_before_first_bin (bool, optional) – If True, returns np.nan for values of input timestamps occuring before the after the first bin. Defaults to True.

  • time_after (Optional[float], optional) – If specified, return np.nan for values occuring this latency after the event. Defaults to None.

Returns:

Bin values

Return type:

np.ndarray

Alignment

binit.align.align_around(to_be_aligned: ndarray, to_align_to: ndarray, t_before: Optional[float] = None, max_latency: Optional[float] = None, drop: bool = False) → ndarray[source]

Align one array to another.

Useful for aligning data to events. Default behaviour is to align to closest smaller event. If t_before is specified

Parameters:

  • to_be_aligned – A numpy array to align

  • to_align_to – A numpy to align to (events)

  • t_before – The time window before each aligning event.

  • max_latency – Maximum aligned latency. Latencies above this threshold will be returned as NaN

  • drop – Whether to drop NaN elements of the aligned array

Returns:

A numpy array of aligned data