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: (mandatory)

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

accepted values: [‘linear’]

Optional arguments

parameter: (optional)

Parameter name on which the transform works on

accepted type: str

component: (optional)

Component name on which the transform works on

accepted type: str

orig_parameter_plg: (optional)

Plugin object on which the transform works on

accepted type: Plugin

orig_component_plg: (optional)

Corresponding component object on which the transform works on

accepted type: Plugin

successor: (optional)

Name of the successor transform

accepted type: str

precursor: (optional)

Name of the precursor transform

accepted type: str

recombine_periods: (optional): 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

accepted type: str

sparse_in: (optional): False

Whether inputs are sparse data

accepted type: bool

sparse_out: (optional): False

Whether outputs are sparse data

accepted type: bool

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