chunk

method of dascore.core.spool.DataFrameSpool

chunk(
    self ,
    overlap: int | float | str | numpy.datetime64 | pandas._libs.tslibs.timestamps.Timestamp | None[int, float, str, datetime64, Timestamp, None] = None,
    keep_partial: bool = False,
    snap_coords: bool = True,
    tolerance: float = 1.5,
    conflict: Literal[‘drop’, ‘raise’, ‘keep_first’] = raise,
    **kwargs ,
)-> ‘Self’

Chunk the data in the spool along specified dimension.

Parameters

Parameter	Description
overlap	The amount of overlap between each segment, starting with the end of first patch. Negative values can be used to create gaps.
keep_partial	If True, keep the segments which are smaller than chunk size. This often occurs because of data gaps or at end of chunks.
snap_coords	If True, snap the coords on joined patches such that the spacing remains constant.
tolerance	The number of samples a block of data can be spaced and still be considered contiguous.
conflict	Indicates how to handle conflicts in attributes other than those indicated by dim (eg tag, history, station, etc). If “drop” simply drop conflicting attributes, or attributes not shared by all models. If “raise” raise an [AttributeMergeError](`dascore.exceptions.AttributeMergeError`] when issues are encountered. If “keep_first”, just keep the first value for each attribute.

kwargs	kwargs are used to specify the dimension along which to chunk, eg: `time=10` chunks along the time axis in 10 second increments.

Examples

import dascore as dc
from dascore.units import s

spool = dc.get_example_spool("random_das")
# get spools with time duration of 10 seconds
time_chunked = spool.chunk(time=10, overlap=1)
# merge along time axis
time_merged = spool.chunk(time=...)

Note

Spool.concatenate performs a similar operation but disregards the coordinate values.