ttim.fit
========

.. py:module:: ttim.fit


Classes
-------

.. autoapisummary::

   ttim.fit.Calibrate
   ttim.fit.Series
   ttim.fit.SeriesInWell


Module Contents
---------------

.. py:class:: Calibrate(model)

   .. py:attribute:: model


   .. py:attribute:: parameters


   .. py:attribute:: seriesdict


   .. py:attribute:: seriesinwelldict


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

      
      Set parameter to be optimized.

      :param name: name can be 'kaq', 'Saq', 'c', 'Sll' or 'kzoverkh'.
      :type name: str
      :param layers: 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).
      :type layers: int or list of ints
      :param initial: initial value for the parameter (the default is 0)
      :type initial: float, optional
      :param pmin: lower bound for parameter value (the default is -np.inf)
      :type pmin: float, optional
      :param pmax: upper bound for paramater value (the default is np.inf)
      :type pmax: float, optional
      :param inhoms: 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.
      :type inhoms: str, list















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

      
      Set parameter to be optimized.

      :param name: parameter name
      :type name: str
      :param parameter: array reference containing the parameter to be optimized. must be
                        specified as reference, i.e. w.rc[0:]
      :type parameter: np.array
      :param initial: initial value for the parameter (the default is 0)
      :type initial: float, optional
      :param pmin: lower bound for parameter value (the default is -np.inf)
      :type pmin: float, optional
      :param pmax: upper bound for paramater value (the default is np.inf)
      :type pmax: float, optional















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   .. py:method:: series(name, x, y, layer, t, h, weights=None)

      
      Method to add observations to Calibration object.

      :param name: name of series
      :type name: str
      :param x: x-coordinate
      :type x: float
      :param y: y-coordinate
      :type y: float
      :param layer: layer number, 0-indexed
      :type layer: int
      :param t: array containing timestamps of timeseries
      :type t: np.array
      :param h: array containing timeseries values, i.e. head observations
      :type h: np.array















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   .. py:method:: seriesinwell(name, element, t, h)

      
      Method to add observations to Calibration object.

      :param name: name of series
      :type name: str
      :param element:
      :type element: element object with headinside function
      :param t: array containing timestamps of timeseries
      :type t: np.array
      :param h: array containing timeseries values, i.e. head observations
      :type h: np.array















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   .. py:method:: residuals(p, printdot=False, weighted=True, layers=None, series=None)

      
      Method to calculate residuals given certain parameters.

      :param p: array containing parameter values
      :type p: np.array
      :param printdot: print dot for each function call
      :type printdot: bool, optional

      :returns: array containing all residuals
      :rtype: np.array















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   .. py:method:: residuals_lmfit(lmfitparams, printdot=False)


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


   .. py:method:: fit_lmfit(report=False, printdot=True, **kwargs)


   .. py:method:: residuals_leastsq(logparams, printdot=False)


   .. py:method:: fit_leastsq(report=True, diff_step=0.0001, xtol=1e-08)


   .. py:method:: fit(report=False, printdot=True, **kwargs)


   .. py:method:: rmse(weighted=True, layers=None)

      
      Calculate root-mean-squared-error.

      :returns: return rmse value
      :rtype: float















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   .. py:method:: topview(ax=None, layers=None, labels=True)

      
      Plot topview of model with calibration points.

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















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   .. py:method:: xsection(ax=None, labels=True)

      
      Plot cross-section of model with calibration points.

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















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.. py:class:: Series(x, y, layer, t, h, weights=None)

   .. py:attribute:: x


   .. py:attribute:: y


   .. py:attribute:: layer


   .. py:attribute:: t


   .. py:attribute:: h


   .. py:attribute:: weights
      :value: None



.. py:class:: SeriesInWell(element, t, h)

   .. py:attribute:: element


   .. py:attribute:: t


   .. py:attribute:: h