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=...)chunk
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 dimensions.
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] whenissues 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.
|