Station

The Station holds all the data related to a single station. This includes:

• observational data, stored as SensorData,

• metadata, stored as a Site,

• and timeseries of a model source, stored as ModelTimeSeries.

Constructor

Station(stationname, site, all_sensor_data)

Represents a weather station, holding metadata, sensor data, and model data.

Data attributes

A summary of all the attributes that hold or return data.

Station.sensordata	The SensorData related to the station, as a dictionary.
Station.df	Construct a DataFrame representation of the observations.
Station.metadf	Construct a DataFrame representation of metadata.
Station.outliersdf	Construct a DataFrame representation of all the outliers.
Station.gapsdf	Construct a DataFrame representation of all the gaps.
Station.modeldatadf	Construct a DataFrame representation of all the present model data.
Station.present_observations	Get a list of all the present observation types.

General methods and attributes

Station.get_sensor(obstype)	Get the SensorData instance for a specific observation type.
Station.get_info([printout])	Retrieve and optionally print detailed information about the station.
Station.resample(target_freq[, ...])	Resample observation data to a specified frequency.

GEE related methods

Station.modeldatadf	Construct a DataFrame representation of all the present model data.
Station.get_static_gee_point_data(...[, ...])	Extract static data from GEE dataset at Station locations.
Station.get_static_gee_buffer_fraction_data(...)	Extract circular buffer fractions of a GEE dataset at Station locations.
Station.get_LCZ([overwrite, initialize_gee])	Retrieve Local Climate Zone (LCZ) for the station using Google Earth Engine (GEE).
Station.get_altitude([overwrite, initialize_gee])	Retrieve altitude for the station using Google Earth Engine (GEE).
Station.get_landcover_fractions([buffers, ...])	Get landcover fractions for a circular buffer at the station using GEE.
Station.get_gee_timeseries_data(...[, ...])	Extract time series data from GEE.

Gaps related methods

Station.gapsdf	Construct a DataFrame representation of all the gaps.
Station.convert_outliers_to_gaps([...])	Convert outlier values in the observation data to gaps.
Station.interpolate_gaps(target_obstype[, ...])	Fill the gap(s) using interpolation of SensorData.
Station.fill_gaps_with_raw_modeldata(...[, ...])	Fill the gap(s) using model data without correction.
Station.fill_gaps_with_debiased_modeldata(...)	Fill the gaps using model data corrected for the bias.
Station.fill_gaps_with_diurnal_debiased_modeldata(...)	Fill the gaps using model data corrected for the diurnal bias.
Station.fill_gaps_with_weighted_diurnal_debiased_modeldata(...)	Fill the gaps using a weighted sum of model data corrected for the diurnal bias and weights with respect to the start of the gap.

Quality control related methods

Station.outliersdf	Construct a DataFrame representation of all the outliers.
Station.gross_value_check([target_obstype, ...])	Identify outliers based on thresholds.
Station.persistence_check([target_obstype, ...])	Check if values are not constant in a moving time window.
Station.repetitions_check([target_obstype, ...])	Test if an observation changes after a number of repetitions.
Station.step_check([target_obstype, ...])	Check for 'spikes' and 'dips' in a time series.
Station.window_variation_check([...])	Test if the increase/decrease in a time window exceeds a threshold.
Station.get_qc_stats([target_obstype, make_plot])	Generate quality control (QC) frequency statistics.
Station.convert_outliers_to_gaps([...])	Convert outlier values in the observation data to gaps.

Visualisations

Station.make_plot_of_modeldata([obstype, ...])	Generate a time series plot of model data for a specific observation type.
Station.make_plot([obstype, colorby, ...])	Generate a time series plot for observational data.