welltestpy.estimate.ExtTheis3D

class ExtTheis3D(name, campaign, val_ranges=None, val_fix=None, val_fit_type=None, val_fit_name=None, testinclude=None, generate=False)[source]

Bases: TransientPumping

Class for an estimation of stochastic subsurface parameters.

With this class you can run an estimation of statistical subsurface parameters. It utilizes the extended Theis solution in 3D which assumes a log-normal distributed conductivity field with a gaussian correlation function and an anisotropy ratio 0 < e <= 1.

Available values for fitting:

cond_gmean: geometric mean conductivity
var: variance of log-conductivity
len_scale: correlation length scale of log-conductivity
anis: anisotropy between horizontal and vertical correlation length
storage: storage

Parameters

name (str) – Name of the Estimation.
campaign (welltestpy.data.Campaign) – The pumping test campaign which should be used to estimate the parameters
val_ranges (dict, optional) – Dictionary containing the fit-ranges for each value in the type-curve. Names should be as in the type-curve signature. Ranges should be a tuple containing min and max value. Will default to default_ranges
val_fix (dict, optional) – Dictionary containing fixed values for the type-curve. Names should be as in the type-curve signature. Default: None
val_fit_type (dict, optional) – Dictionary containing fitting transformation type for each value. Names should be as in the type-curve signature. val_fit_type can be “lin”, “log”, “exp”, “sqrt”, “quad”, “inv” or a tuple of two callable functions where the first is the transformation and the second is its inverse. “log” is for example equivalent to (np.log, np.exp). By default, conductivity and storage will be fitted logarithmically and other values linearly. Default: None
val_fit_name (dict, optional) – Display name of the fitting transformation. Will be the val_fit_type string if it is a predefined one, or f if it is a given callable as default for each value. Default: None
testinclude (dict, optional) – Dictionary of which tests should be included. If None is given, all available tests are included. Default: None
generate (bool, optional) – State if time stepping, processed observation data and estimation setup should be generated with default values. Default: False

Methods

`gen_data`()	Generate the observed drawdown at given time points.
`gen_setup`([prate_kw, rad_kw, time_kw, dummy])	Generate the Spotpy Setup.
`run`([rep, parallel, run, folder, dbname, ...])	Run the estimation.
`sensitivity`([rep, parallel, folder, dbname, ...])	Run the sensitivity analysis.
`setpumprate`([prate])	Set a uniform pumping rate at all pumpingwells wells.
`settime`([time, tmin, tmax, typ, steps])	Set uniform time points for the observations.

gen_data()

Generate the observed drawdown at given time points.

It will also generate an array containing all radii of all well combinations.

gen_setup(prate_kw='rate', rad_kw='rad', time_kw='time', dummy=False)

Generate the Spotpy Setup.

Parameters

prate_kw (str, optional) – Keyword name for the pumping rate in the used type curve. Default: “rate”
rad_kw (str, optional) – Keyword name for the radius in the used type curve. Default: “rad”
time_kw (str, optional) – Keyword name for the time in the used type curve. Default: “time”
dummy (bool, optional) – Add a dummy parameter to the model. This could be used to equalize sensitivity analysis. Default: False

run(rep=5000, parallel='seq', run=True, folder=None, dbname=None, traceplotname=None, fittingplotname=None, interactplotname=None, estname=None, plot_style='WTP')

Run the estimation.

Parameters

rep (int, optional) – The number of repetitions within the SCEua algorithm in spotpy. Default: 5000
parallel (str, optional) –

State if the estimation should be run in parallel or not. Options:
- "seq": sequential on one CPU
- "mpi": use the mpi4py package
Default: "seq"
run (bool, optional) – State if the estimation should be executed. Otherwise all plots will be done with the previous results. Default: True
folder (str, optional) – Path to the output folder. If None the CWD is used. Default: None
dbname (str, optional) – File-name of the database of the spotpy estimation. If None, it will be the current time + "_db". Default: None
traceplotname (str, optional) – File-name of the parameter trace plot of the spotpy estimation. If None, it will be the current time + "_paratrace.pdf". Default: None
fittingplotname (str, optional) – File-name of the fitting plot of the estimation. If None, it will be the current time + "_fit.pdf". Default: None
interactplotname (str, optional) – File-name of the parameter interaction plot of the spotpy estimation. If None, it will be the current time + "_parainteract.pdf". Default: None
estname (str, optional) – File-name of the results of the spotpy estimation. If None, it will be the current time + "_estimate". Default: None
plot_style (str, optional) – Plot style. The default is “WTP”.

sensitivity(rep=None, parallel='seq', folder=None, dbname=None, plotname=None, traceplotname=None, sensname=None, plot_style='WTP')

Run the sensitivity analysis.

Parameters

rep (int, optional) – The number of repetitions within the FAST algorithm in spotpy. Default: estimated
parallel (str, optional) –

State if the estimation should be run in parallel or not. Options:
- "seq": sequential on one CPU
- "mpi": use the mpi4py package
Default: "seq"
folder (str, optional) – Path to the output folder. If None the CWD is used. Default: None
dbname (str, optional) – File-name of the database of the spotpy estimation. If None, it will be the current time + "_sensitivity_db". Default: None
plotname (str, optional) – File-name of the result plot of the sensitivity analysis. If None, it will be the current time + "_sensitivity.pdf". Default: None
traceplotname (str, optional) – File-name of the parameter trace plot of the spotpy sensitivity analysis. If None, it will be the current time + "_senstrace.pdf". Default: None
sensname (str, optional) – File-name of the results of the FAST estimation. If None, it will be the current time + "_estimate". Default: None
plot_style (str, optional) – Plot style. The default is “WTP”.

setpumprate(prate=-1.0)

Set a uniform pumping rate at all pumpingwells wells.

We assume linear scaling by the pumpingrate.

Parameters: prate (float, optional) – Pumping rate. Default: -1.0

settime(time=None, tmin=10.0, tmax=inf, typ='quad', steps=10)

Set uniform time points for the observations.

Parameters

time (numpy.ndarray, optional) – Array of specified time points. If None is given, they will be determined by the observation data. Default: None
tmin (float, optional) – Minimal time value. It will set a minimal value of 10s. Default: 10
tmax (float, optional) – Maximal time value. Default: inf
typ (str or float, optional) –

Typ of the time selection. You can select from:
- "exp": for exponential behavior
- "log": for logarithmic behavior
- "geo": for geometric behavior
- "lin": for linear behavior
- "quad": for quadratic behavior
- "cub": for cubic behavior
- float: here you can specifi any exponent (“quad” would be equivalent to 2)
Default: “quad”
steps (int, optional) – Number of generated time steps. Default: 10