`vamtoolbox.response`#

Module Contents#

Classes#

ResponseModel

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

_default_gen_log_fun#

_default_linear#

_default_interpolation#

_map_glf(f: numpy.ndarray)#

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

_map_inv_glf(mapped: numpy.ndarray)#

_map_lin(f: numpy.ndarray)#

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

_map_inv_lin(mapped: numpy.ndarray)#

_map_id(f: numpy.ndarray)#

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

_map_inv_id(mapped: numpy.ndarray)#

_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.

_dmapdf_interp(f: numpy.ndarray)#: Map optical dose to response 1st derivative via interpolation.

_map_inv_interp(mapped: numpy.ndarray)#: Map material response back to optical dose via interpolation.

plotMap(fig=None, ax=None, lb=0, ub=1, n_pts=512, block=True, **plot_kwargs)#

plotDmapDf(fig=None, ax=None, lb=0, ub=1, n_pts=512, block=True, **plot_kwargs)#

plotMapInv(fig=None, ax=None, lb=0, ub=1, n_pts=512, block=True, **plot_kwargs)#

checkResponseTarget(f_T: numpy.ndarray)#

__repr__()#: Return repr(self).