Temporal interpolation and re-indexing time_interpolation/std

Temporal interpolation and re-indexing time_interpolation/std#

YAML arguments#

The following arguments are used to configure the plugin. pyCIF will return an exception at the initialization if mandatory arguments are not specified, or if any argument does not fit accepted values or type:

Mandatory arguments#

method : “linear”, mandatory

Method by which the original data is temporally interpolated onto the output time-scale

Optional arguments#

parameter : str, optional

Parameter name on which the transform works on

component : str, optional

Component name on which the transform works on

orig_parameter_plg : Plugin, optional

Plugin object on which the transform works on

orig_component_plg : Plugin, optional

Corresponding component object on which the transform works on

successor : str, optional

Name of the successor transform

precursor : str, optional

Name of the precursor transform

recombine_periods : str, optional, default True

Recombine inputs from different sub-periods. If False, data overlapping several periods will be taken from the period with the biggest overlap with the outputs

sparse_in : bool, optional, default False

Whether inputs are sparse data

sparse_out : bool, optional, default False

Whether outputs are sparse data

YAML template#

Please find below a template for a YAML configuration:

 1transform:
 2  plugin:
 3    name: time_interpolation
 4    version: std
 5    type: transform
 6
 7    # Mandatory arguments
 8    method: XXXXX  # linear
 9
10    # Optional arguments
11    parameter: XXXXX  # str
12    component: XXXXX  # str
13    orig_parameter_plg: XXXXX  # Plugin
14    orig_component_plg: XXXXX  # Plugin
15    successor: XXXXX  # str
16    precursor: XXXXX  # str
17    recombine_periods: XXXXX  # str
18    sparse_in: XXXXX  # bool
19    sparse_out: XXXXX  # bool