Concatenation of observations from parsers (standard / std)

Description

This plugin is the entry point to pre-process observations from various datastreams, concatenate them into a single datastore (see here for details on the observation format) and feed the observation vector.

Various observations datastreams are implemented as obsparsers in pyCIF.

Note

It is possible to either specify a list of providers or a single one:

  1. list of providers: as a Yaml paragraph, with each key a sub-paragraph specifying each provider’s arguments (the name of the keys in the main Yaml paragraph, naming each provider sub-paragraph does not impact the computation of the pre-processing),

  2. single provider: the providers arguments are given at the same Yaml level as the measurement paragraph.

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:

Optional arguments

specname: (optional)

Species name to use if different than the one in the corresponding datavect paragraph

accepted type: <class ‘str’>

dump_type: (optional): nc

How to dump intermediate observation datastores

accepted type: <class ‘str’>

file_monitor: (optional)

Path to a pre-formatted monitor file.

accepted type: <class ‘str’>

providers: (optional)

List of providers and corresponding arguments to parse. See the documentation of obsparser objects for further details: here

provider: (optional)

Used only if providers is not specified. Can be used if only one provider is to be used. In that case, the provider arguments are directly specified at the same level.

accepted type: <class ‘str’>

Yaml template

Please find below a template for a Yaml configuration:

 1measurements:
 2  plugin:
 3    name: standard
 4    version: std
 5    type: measurements
 6
 7
 8  # Optional arguments
 9  specname: XXXXX
10  dump_type: XXXXX
11  file_monitor: XXXXX
12  providers: XXXXX
13  provider: XXXXX