pastas.stressmodels.WellModel ============================= .. toctree:: :hidden: /api/pastas/stressmodels/WellModel.set_init_parameters /api/pastas/stressmodels/WellModel.get_stress /api/pastas/stressmodels/WellModel.get_parameters /api/pastas/stressmodels/WellModel.dump_stress /api/pastas/stressmodels/WellModel.to_dict /api/pastas/stressmodels/WellModel.variance_gain .. py:class:: pastas.stressmodels.WellModel(stress: list[pandas.Series], name: str, distances: pastas.typing.ArrayLike, rfunc: pastas.typing.RFunc | None = None, up: bool = False, settings: str | pastas.typing.StressSettingsDict = 'well', sort_wells: bool = True, metadata: list[dict[str, Any]] = None, max_cache_size: int = None) Convolution of one or more stresses with a single scaled response function. :param stress: list containing the stresses time series. :type stress: list :param name: name of the stressmodel. :type name: str :param distances: array_like of distances between the stresses (wells) and the oseries (monitoring well), must be in the same order as the stresses. This distance is used to scale the HantushWellModel response function for each stress. :type distances: array_like :param rfunc: this model only works with the HantushWellModel response function, default is None which will initialize a HantushWellModel response function. :type rfunc: pastas.rfunc instance, optional :param up: whether a positive stress has an increasing or decreasing effect on the model, by default False, in which case positive stress lowers e.g., the groundwater level. :type up: bool, optional :param settings: The settings of the stress. By default this is "well". This can be a string referring to a predefined settings dictionary (defined in ps.rcParams["timeseries"]), or a dictionary with the settings to apply. For more information, refer to Time series settings section below. :type settings: str, list of dict, optional :param sort_wells: sort wells from closest to furthest, by default True. :type sort_wells: bool, optional :param max_cache_size: Maximum size of the cache (in number of entries). Only used when cachetools is installed and caching is enabled (see ps.set_use_cache()). :type max_cache_size: int, optional :param Time series settings: :param fill_nan: Method for filling NaNs. * `drop`: drop NaNs from time series * `mean`: fill NaNs with mean value of time series * `interpolate`: fill NaNs by interpolating between finite values * `float`: fill NaN with provided value, e.g. 0.0 :type fill_nan: {"drop", "mean", "interpolate"} or float :param fill_before: Method for extending time series into past. * `mean`: extend time series into past with mean value of time series * `bfill`: extend time series into past by back-filling first value * `float`: extend time series into past with provided value, e.g. 0.0 :type fill_before: {"mean", "bfill"} or float :param fill_after: Method for extending time series into future. * `mean`: extend time series into future with mean value of time series * `ffill`: extend time series into future by forward-filling last value * `float`: extend time series into future with provided value, e.g. 0.0 :type fill_after: {"mean", "ffill"} or float :param sample_up: Method for up-sampling time series (increasing frequency, e.g. going from weekly to daily values). * `bfill` or `backfill`: fill up-sampled time steps by back-filling current values * `ffill` or `pad`: fill up-sampled time steps by forward-filling current values * `mean`: fill up-sampled time steps with mean of timeseries * `interpolate`: fill up-sampled time steps by interpolating between current values * `divide`: fill up-sampled steps with current value divided by length of current time steps (i.e. spread value over new time steps). :type sample_up: {"mean", "interpolate", "divide"} or float :param sample_down: Method for down-sampling time series (decreasing frequency, e.g. going from daily to weekly values). * `mean`: resample time series by taking the mean * `drop`: resample the time series by taking the mean, dropping any NaN-values * `sum`: resample time series by summing values * `max`: resample time series with maximum value * `min`: resample time series with minimum value :type sample_down: {"mean", "drop", "sum", "min", "max"} .. rubric:: Notes This class implements convolution of multiple series with the same response function. This can be applied when dealing with multiple wells in a time series model. The distance(s) from the pumping well(s) to the monitoring well have to be provided for each stress. See :cite:t:`brakenhoff_application_2022` for more details on the methods for this model. Only works with the HantushWellModel response function. .. !! processed by numpydoc !! Methods ------- .. autoapisummary:: pastas.stressmodels.WellModel.set_init_parameters pastas.stressmodels.WellModel.get_stress pastas.stressmodels.WellModel.get_parameters pastas.stressmodels.WellModel.dump_stress pastas.stressmodels.WellModel.to_dict pastas.stressmodels.WellModel.variance_gain