Obsparsers (obsparser)

Available Obsparsers (obsparser)

The following obsparsers are implemented in pyCIF so far:

• CO2M pseudo_data (CO2M / pseudo_data)
  • Description
  • Yaml arguments
    • Optional arguments
  • Yaml template
• Integrated Carbon Observing System (ICOS) data (ICOS / std)
  • Description
  • Yaml arguments
    • Optional arguments
  • Yaml template
• NOAA-ESRL Observation Package (ObsPack) Data Products (obspack / std)
  • Description
  • Yaml arguments
  • Yaml template
• TROPOMI XCH4 retrievals – Official product (TROPOMI / CH4-official)
  • Description
  • Yaml arguments
    • Optional arguments
  • Yaml template
• TROPOMI XCH4 retrievals from SRON (TROPOMI / CH4-SRON)
  • Description
  • Yaml arguments
    • Optional arguments
  • Yaml template
• TROPOMI XCH4 retrievals from the University of Bremen (TROPOMI / CH4-WFMD)
  • Description
  • Yaml arguments
    • Optional arguments
  • Yaml template
• Template plugin for observation parsers (template / std)
  • Description
  • Yaml arguments
    • Optional arguments
  • Yaml template
• VERIFY / std
  • Yaml arguments
  • Yaml template
• WDCGG / std
  • Description
  • Yaml arguments
    • Optional arguments
  • Yaml template

Description

The obsparser (Observation Parser) class parses and formats raw observation files into the standard pyCIF observations format (see here for details).

obsparser objects are called by the standard/std measurement object.

Required parameters, dependencies and functions

do_parse

pycif.plugins.obsparsers.template.do_parse(self, obs_file, **kwargs)

Parse function for a file from template observations

Parameters:

obs_file (str) – Path to input file

Returns:

Dataframe from input file df[parameter][station]

Return type:

pandas.DataFrame

parse_multiple_files (optional)

The parse_multiple_files is defined by default in the ObsParser class. It loops on files fitting a path pattern and parses them individually by calling the function do_parse.

If files cannot be processed individually, the function parse_multiple_files should be implemented in the corresponding obsparser plugin.

It should return the dataframe with all required observations.

class pycif.utils.classes.obsparsers.ObsParser(plg_orig=None, orig_name='', **kwargs)

Class for handling time series parsing from different data providers and data file formats.

parse_multiple_files(**kwargs)

Parses multiple files specified by a glob pattern and stores the content into a datastore.

Parameters:

self – the plugin with its describing arguments (in particular dir_obs)

Returns:

{obs_file} = df[obssite_id, parameter]

Return type:

dict

Note

By default, the function calls self.parse_file, which filters out NaNs and check that all required columns are available.