ttim.fit

Classes

Calibrate
Series
SeriesInWell

Module Contents

class ttim.fit.Calibrate(model)

model

parameters

seriesdict

seriesinwelldict

set_parameter(name=None, layers=None, initial=0, pmin=-np.inf, pmax=np.inf, inhoms=None)

Set parameter to be optimized.

Parameters:

• name (str) – name can be ‘kaq’, ‘Saq’, ‘c’, ‘Sll’ or ‘kzoverkh’.

• layers (int or list of ints) – layer number(s) for which the parameter is set. If an integer is passed, parameter is associated with a single layer. If a list of layers is passed, layers must be consecutive and parameter is set for each layer from min(layers) up to and including max(layers).

• initial (float, optional) – initial value for the parameter (the default is 0)

• pmin (float, optional) – lower bound for parameter value (the default is -np.inf)

• pmax (float, optional) – upper bound for paramater value (the default is np.inf)

• inhoms (str, list) – string with name of inhomogeneity, list with string names of inhomogeneities or list of inhomogeneities inhomogeneity(ies) for which the parameter is set. If a string is passed, parameter is associated with a single inhomogeneity. If a list of strings of inhomogeneities (or list of inhomogeneities) is passed, parameter is set for each inhomogeneity in the list. This allows linking of parameters across inhomogeneities.

set_parameter_by_reference(name=None, parameter=None, initial=0, pmin=-np.inf, pmax=np.inf)

Set parameter to be optimized.

Parameters:

• name (str) – parameter name

• parameter (np.array) – array reference containing the parameter to be optimized. must be specified as reference, i.e. w.rc[0:]

• initial (float, optional) – initial value for the parameter (the default is 0)

• pmin (float, optional) – lower bound for parameter value (the default is -np.inf)

• pmax (float, optional) – upper bound for paramater value (the default is np.inf)

series(name, x, y, layer, t, h, weights=None)

Method to add observations to Calibration object.

Parameters:

• name (str) – name of series

• x (float) – x-coordinate

• y (float) – y-coordinate

• layer (int) – layer number, 0-indexed

• t (np.array) – array containing timestamps of timeseries

• h (np.array) – array containing timeseries values, i.e. head observations

seriesinwell(name, element, t, h)

Method to add observations to Calibration object.

Parameters:

• name (str) – name of series

• element (element object with headinside function)

• t (np.array) – array containing timestamps of timeseries

• h (np.array) – array containing timeseries values, i.e. head observations

residuals(p, printdot=False, weighted=True, layers=None, series=None)

Method to calculate residuals given certain parameters.

Parameters:

• p (np.array) – array containing parameter values

• printdot (bool, optional) – print dot for each function call

Returns:

array containing all residuals

Return type:

np.array

residuals_lmfit(lmfitparams, printdot=False)

fit_least_squares(report=True, diff_step=0.0001, xtol=1e-08, method='lm')

fit_lmfit(report=False, printdot=True, **kwargs)

residuals_leastsq(logparams, printdot=False)

fit_leastsq(report=True, diff_step=0.0001, xtol=1e-08)

fit(report=False, printdot=True, **kwargs)

rmse(weighted=True, layers=None)

Calculate root-mean-squared-error.

Returns:

return rmse value

Return type:

float

topview(ax=None, layers=None, labels=True)

Plot topview of model with calibration points.

Parameters:

ax (matplotlib.axes.Axes, optional) – axes to plot on (the default is None, which creates a new figure)

xsection(ax=None, labels=True)

Plot cross-section of model with calibration points.

Parameters:

ax (matplotlib.axes.Axes, optional) – axes to plot on (the default is None, which creates a new figure)

class ttim.fit.Series(x, y, layer, t, h, weights=None)

x

y

layer

t

h

weights = None

class ttim.fit.SeriesInWell(element, t, h)

element

t

h