metobs_toolkit.sensordata.SensorData#

class SensorData(stationname: str, datarecords: ~numpy.ndarray, timestamps: ~numpy.ndarray, obstype: ~metobs_toolkit.obstypes.Obstype, datadtype: type = <class 'numpy.float32'>, timezone: str | ~pandas._libs.tslibs.timedeltas.Timedelta = 'UTC', freq_estimation_method: ~typing.Literal['highest', 'median'] = 'median', freq_estimation_simplify_tolerance: ~pandas._libs.tslibs.timedeltas.Timedelta | str = Timedelta('0 days 00:01:00'), origin_simplify_tolerance: ~pandas._libs.tslibs.timedeltas.Timedelta | str = Timedelta('0 days 00:01:00'), timestamp_tolerance: ~pandas._libs.tslibs.timedeltas.Timedelta | str = Timedelta('0 days 00:04:00'))[source]#

Holds data for one station for one sensor.

Parameters:

stationname (str) – Name of the station.
datarecords (np.ndarray) – Array of data records.
timestamps (np.ndarray) – Array of timestamps.
obstype (Obstype) – Observation type.
datadtype (type, optional) – Data type of the records, by default np.float32.
timezone (str or pd.Timedelta, optional) – Timezone of the timestamps, by default ‘UTC’.
freq_estimation_method ({'highest', 'median'}, optional) – Method to estimate frequency, by default ‘median’.
freq_estimation_simplify_tolerance (pd.Timedelta or str, optional) – Tolerance for frequency estimation simplification, by default pd.Timedelta(‘1min’).
origin_simplify_tolerance (pd.Timedelta or str, optional) – Tolerance for origin simplification, by default pd.Timedelta(‘1min’).
timestamp_tolerance (pd.Timedelta or str, optional) – Tolerance for timestamp matching, by default pd.Timedelta(‘4min’).

__init__(stationname: str, datarecords: ~numpy.ndarray, timestamps: ~numpy.ndarray, obstype: ~metobs_toolkit.obstypes.Obstype, datadtype: type = <class 'numpy.float32'>, timezone: str | ~pandas._libs.tslibs.timedeltas.Timedelta = 'UTC', freq_estimation_method: ~typing.Literal['highest', 'median'] = 'median', freq_estimation_simplify_tolerance: ~pandas._libs.tslibs.timedeltas.Timedelta | str = Timedelta('0 days 00:01:00'), origin_simplify_tolerance: ~pandas._libs.tslibs.timedeltas.Timedelta | str = Timedelta('0 days 00:01:00'), timestamp_tolerance: ~pandas._libs.tslibs.timedeltas.Timedelta | str = Timedelta('0 days 00:04:00'))[source]#

Methods

`__init__`(stationname, datarecords, ...[, ...])
`convert_outliers_to_gaps`()	Convert all outliers to gaps.
`convert_to_standard_units`()	Convert the data records to the standard units defined in the observation type.
`duplicated_timestamp_check`()	Check for duplicated timestamps in the series.
`fill_gap_with_modeldata`(modeltimeseries[, ...])	Fill gaps using model data.
`get_info`([printout])	Retrieve and optionally print basic information about the sensor data.
`get_qc_freq_statistics`()	Generate quality control (QC) frequency statistics.
`gross_value_check`(**qckwargs)	Perform a gross value check on the series.
`interpolate_gaps`([method, max_consec_fill, ...])	Interpolate gaps in the data.
`invalid_value_check`(skip_records)	Check for invalid values in the series.
`persistence_check`(**qckwargs)	Perform a persistence check on the series.
`repetitions_check`(**qckwargs)	Perform a repetitions check on the series.
`resample`(target_freq[, shift_tolerance, ...])	Resample to a new time resolution.
`step_check`(**qckwargs)	Perform a step check on the series.
`window_variation_check`(**qckwargs)	Perform a window variation check on the series.

Attributes

`df`	Return a DataFrame of the sensor records.
`end_datetime`	Return the end datetime of the series.
`freq`	Return the frequency of the series.
`gapsdf`	Return a DataFrame of the gap records.
`outliersdf`	Return a DataFrame of the outlier records.
`start_datetime`	Return the start datetime of the series.
`stationname`	Return the name of the station this SensorData belongs to.
`tz`	Return the timezone of the stored timestamps.