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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 dictionary (defined in ps.rcParams[“timeseries”]), or a dictionary with the settings to apply. For more information, refer to Time series settings section below.

  • sort_wells (bool, optional) – sort wells from closest to furthest, by default True.

  • metadata (Optional[list]) –

Time series settings
  • fill_nan ({“drop”, “mean”, “interpolate”} or float) –

    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

  • fill_before ({“mean”, “bfill”} or float) –

    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

  • fill_after ({“mean”, “ffill”} or float) –

    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

  • sample_up ({“mean”, “interpolate”, “divide”} or float) – 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).

  • sample_down ({“mean”, “drop”, “sum”, “min”, “max”}) – 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

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.