Dataset

The dataset is the heart of the MetObs Toolkit. It holds the observations and corresponding methods.

Constructor

Dataset()

Extension on the metobs_toolkit.Dataset class with QC related methods

General methods and attributes

Dataset.show([show_all_settings, ...])	Show detailed information of the Dataset.
Dataset.get_info([show_all_settings, ...])	Alias of show().
Dataset.show_settings()	Show detailed information of the stored Settings.
Dataset.get_gaps_df()	List all gaps into an overview dataframe.
Dataset.get_gaps_info()	Print out detailed information of the gaps.
Dataset.get_missing_obs_info()	Print out detailed information of the missing observations.
Dataset.combine_all_to_obsspace([...])	Make one dataframe with all observations and their labels.

Common methods

Dataset.get_station(stationname)	Filter out one station of the Dataset.
Dataset.update_gaps_and_missing_from_outliers([...])	Interpret the outliers as missing observations.
Dataset.fill_gaps_linear([obstype, ...])	Fill the gaps using linear interpolation.
Dataset.fill_gaps_era5(modeldata[, method, ...])	Fill the gaps using a diurnal debiased modeldata approach.
Dataset.fill_gaps_automatic(modeldata[, ...])	Fill the gaps by using linear interpolation or debiased modeldata.
Dataset.fill_missing_obs_linear([obstype])	Interpolate missing observations.
Dataset.get_analysis([add_gapfilled_values])	Create an Analysis instance from the Dataframe.
Dataset.apply_quality_control([obstype, ...])	Apply quality control methods to the dataset.
Dataset.apply_buddy_check([obstype, ...])	Apply the buddy check on the observations.
Dataset.apply_titan_buddy_check([obstype, ...])	Apply the TITAN buddy check on the observations.
Dataset.apply_titan_sct_resistant_check([...])	Apply the TITAN spatial consistency test (resistant).

Extracting data

Dataset.get_modeldata([modelname, ...])	Make Modeldata for the Dataset.
Dataset.get_lcz()	Extract local climate zones for all stations.
Dataset.get_altitude()	Extract Altitudes for all stations.
Dataset.get_landcover([buffers, aggregate, ...])	Extract landcover for all stations.

Updating Settings

Dataset.update_settings([output_folder, ...])	Update the most common input-output (IO) settings.
Dataset.add_new_observationtype(Obstype)	Add a new observation type to the known observation types.
Dataset.add_new_unit(obstype, new_unit[, ...])	Add a new unit to a known observation type.
Dataset.update_timezone(timezonestr)	Change the timezone of the input data.
Dataset.update_default_name(default_name)	Update the default name (the name of the station).
Dataset.update_gap_and_missing_fill_settings([...])	Update fill settings for gaps and missing observations.
Dataset.update_qc_settings([obstype, ...])	Update the QC settings for the specified observation type.
Dataset.update_titan_qc_settings([obstype, ...])	Update the TITAN QC settings for the specified observation type.

Reading and writing related

Dataset.import_data_from_file([...])	Read observations from a csv file.
Dataset.save_dataset([outputfolder, ...])	Save a Dataset instance to a (pickle) file.
Dataset.import_dataset([folder_path, filename])	Import a Dataset instance from a (pickle) file.
Dataset.write_to_csv([obstype, filename, ...])	Write Dataset to a csv file.
Dataset.sync_observations(tolerance[, ...])	Simplify and syncronize the observation timestamps.
Dataset.coarsen_time_resolution([origin, ...])	Resample the observations to coarser timeresolution.

Plotting methods

Dataset.make_plot([stationnames, obstype, ...])	This function creates a timeseries plot for the dataset.
Dataset.make_interactive_plot([obstype, ...])	Make interactive geospatial plot with time evolution.
Dataset.make_geo_plot([variable, title, ...])	Make geospatial plot.
Dataset.make_gee_plot(gee_map[, ...])	Make an interactive plot of a google earth dataset.
Dataset.get_qc_stats([obstype, stationname, ...])	Get quality control statistics.