tlpipe.timestream.accumulate.Accum¶

class tlpipe.timestream.accumulate.Accum(parameter_file_or_dict=None, feedback=2)[source]¶

Accumulate data.

This task accumulates data abserved in different (sidereal) days and records the weight (i.e., the number of valid or un-masked data) of each data point.

Note

This should be done after the data has been calibrated and re-ordered to a same LST (or RA).

Attributes

`cacheable`	Override to return True if caching results is implemented.
`embarrassingly_parallelizable`	Override to return True if next() is trivially parallelizeable.
`history`	History that will be added to the output file.
`iteration`	Current iteration when iterable is True, None else.
`params_init`
`prefix`

__init__(parameter_file_or_dict=None, feedback=2)[source]¶

Methods

`cast_input`(input)	Override to support accepting pipeline inputs of various types.
`copy_input`(tod)	Return a copy of tod, so the original tod would not be changed.
`data_select`(tod)	Data select.
`finish`()	Final analysis stage of pipeline task.
`next`([input])	Should not need to override.
`process`(ts)
`read_input`()	Method for reading time ordered data input.
`read_output`(filenames)	Override to implement reading outputs from disk.
`read_process_write`(tod)	Reads input, executes any processing and writes output.
`restart_iteration`()	Re-start the iteration.
`setup`()
`show_params`()	Show all parameters that can be set and their default values of this task.
`stop_iteration`([force_stop])	Determine whether to stop the iteration.
`subset_select`(tod)	Data subset select.
`write_output`(output)	Method for writing time ordered data output.