metobs_toolkit.Dataset.get_missing_obs_info#

Dataset.get_missing_obs_info()[source]#

Print out detailed information of the missing observations.

Return type:: None.

Examples

>>> import metobs_toolkit
>>>
>>> # Import data into a Dataset
>>> dataset = metobs_toolkit.Dataset()
>>> dataset.update_settings(
...                         input_data_file=metobs_toolkit.demo_datafile,
...                         input_metadata_file=metobs_toolkit.demo_metadatafile,
...                         template_file=metobs_toolkit.demo_template,
...                         )
>>> dataset.import_data_from_file()
>>> dataset.coarsen_time_resolution(freq='1h')
>>>
>>> # Apply quality control on the temperature observations
>>> dataset.apply_quality_control(obstype='temp') #Using the default QC settings
>>>
>>> # Interpret the outliers as missing/gaps
>>> dataset.update_gaps_and_missing_from_outliers(obstype='temp')
>>> dataset
Dataset instance containing:
      *28 stations
      *['temp', 'humidity', 'wind_speed', 'wind_direction'] observation types
      *10080 observation records
      *0 records labeled as outliers
      *2 gaps
      *1473 missing observations
      *records range: 2022-09-01 00:00:00+00:00 --> 2022-09-15 23:00:00+00:00 (total duration:  14 days 23:00:00)
      *time zone of the records: UTC
      *Coordinates are available for all stations.
>>> dataset.get_missing_obs_info()
-------- Missing observations info --------
(Note: missing observations are defined on the frequency estimation of the native dataset.)
  * 1473 missing observations
  * For 28 stations
  * The missing observations are not filled.
(More details on the missing observation can be found in the .series and .fill_df attributes.)