WellModel#

class WellModel(stress, name, distances, rfunc=None, up=False, settings='well', sort_wells=True, metadata=None)[source]#

Convolution of one or more stresses with a single scaled response function.

Parameters:

stress (list) – list containing the stresses time series.
name (str) – name of the stressmodel.
distances (array_like) – 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.
rfunc (pastas.rfunc instance, optional) – this model only works with the HantushWellModel response function, default is None which will initialize a HantushWellModel response function.
up (bool, optional) – 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.
settings (str, list of dict, optional) – The settings of the stress. By default this is “well”. This can be a string referring to a predefined settings dict (defined in ps.rcParams[“timeseries”]), or a dict with the settings to apply. Refer to the docs of pastas.Timeseries for further information.
sort_wells (bool, optional) – sort wells from closest to furthest, by default True.
metadata (Optional[list]) –

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.

Only works with the HantushWellModel response function.

Attributes#

nparam

Methods#

`__init__`
`dump_stress`	Method to dump all stresses in the stresses list.
`get_distances`
`get_nsplit`	Determine in how many time series the contribution can be split.
`get_parameters`	Get parameters including distance to observation point and return as array (dimensions = (nstresses, 4)).
`get_settings`	Method to obtain the settings of the stresses.
`get_stress`	Returns the stress(es) of the time series object as a pandas DataFrame.
`set_init_parameters`	Set the initial parameters (back) to their default values.
`simulate`
`to_dict`	Method to export the WellModel object.
`update_stress`	Method to update the settings of the all stresses in the stress model.
`variance_gain`	Calculate variance of the gain for WellModel.