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