:py:mod:`vamtoolbox.response`
=============================

.. py:module:: vamtoolbox.response


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

Classes
~~~~~~~

.. autoapisummary::

   vamtoolbox.response.ResponseModel




.. py:class:: ResponseModel(type: str = 'interpolation', form: str = 'gen_log_fun', **kwargs)


   .. py:attribute:: _default_gen_log_fun

      

   .. py:attribute:: _default_linear

      

   .. py:attribute:: _default_interpolation

      

   .. py:method:: _map_glf(f: numpy.ndarray)


   .. py:method:: _dmapdf_glf(f: numpy.ndarray, use_cached_result: bool = False)


   .. py:method:: _map_inv_glf(mapped: numpy.ndarray)


   .. py:method:: _map_lin(f: numpy.ndarray)


   .. py:method:: _dmapdf_lin(f: numpy.ndarray, use_cached_result: bool = False)


   .. py:method:: _map_inv_lin(mapped: numpy.ndarray)


   .. py:method:: _map_id(f: numpy.ndarray)


   .. py:method:: _dmapdf_id(f: numpy.ndarray, use_cached_result: bool = False)


   .. py:method:: _map_inv_id(mapped: numpy.ndarray)


   .. py:method:: _map_interp(f: numpy.ndarray)

      Map optical dose to response via interpolation.
      More robust for asymptote values and potentially faster than computing exponentials in generalized logistic function.


   .. py:method:: _dmapdf_interp(f: numpy.ndarray)

      Map optical dose to response 1st derivative via interpolation.


   .. py:method:: _map_inv_interp(mapped: numpy.ndarray)

      Map material response back to optical dose via interpolation.


   .. py:method:: plotMap(fig=None, ax=None, lb=0, ub=1, n_pts=512, block=True, **plot_kwargs)


   .. py:method:: plotDmapDf(fig=None, ax=None, lb=0, ub=1, n_pts=512, block=True, **plot_kwargs)


   .. py:method:: plotMapInv(fig=None, ax=None, lb=0, ub=1, n_pts=512, block=True, **plot_kwargs)


   .. py:method:: checkResponseTarget(f_T: numpy.ndarray)


   .. py:method:: __repr__()

      Return repr(self).