from_mult_files

classmethod caput.containers.tod.TOData.from_mult_files(files: caput.memdata._memh5.FileLike | collections.abc.Sequence[caput.memdata._memh5.FileLike], data_group: caput.memdata._memh5.GroupLike | None = None, start: int | dict | None = None, stop: int | dict | None = None, datasets: collections.abc.Sequence[str] | None = None, dataset_filter: collections.abc.Callable | None = None, **kwargs: Any) → TOData

Create new data object by concatenating a series of files.

Accepts any parameter supported by concatenate() (which controls the concatenation) or this class’s constructor (which controls the initialization of each file). By default, each file is opened with ondisk=True and mode=’r’.

Parameters:

filesFileLike

These are assumed to be identical in every way except along the axis representing time, over which they are concatenated. All other data and attributes are simply copied from the first entry of the list.

data_groupGroupLike

Underlying hdf5 like container that will store the data for the BaseData instance.

startint | dict, optional

In the aggregate datasets at what index to start. Every thing before this index is excluded. If provided as a dict, the keys should be data_list[0].time_axes.

stopint | dict, optional

In the aggregate datasets at what index to stop. Every thing after this index is excluded. If provided as a dict, the keys should be data_list[0].time_axes.

datasetslist[str], optional

Which datasets to include. Default is all of them.

dataset_filtercallable(), optional

Function for preprocessing all datasets. Useful for changing data types etc. Takes a dataset as an argument and should return a dataset (either h5py or memdata). Optionally may accept a second argument that is slice along the time axis, which the filter should apply.

**kwargsAny

Other keyword arguments are passed on to the class’s from_file method.

Returns:

datasetTOData

Concatenated time-ordered data.