aerosandbox.modeling.interpolation
==================================

.. py:module:: aerosandbox.modeling.interpolation


Classes
-------

.. autoapisummary::

   aerosandbox.modeling.interpolation.InterpolatedModel


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

.. py:class:: InterpolatedModel(x_data_coordinates, y_data_structured, method = 'bspline', fill_value=np.nan)

   Bases: :py:obj:`aerosandbox.modeling.surrogate_model.SurrogateModel`


   A model that is interpolated to structured (i.e., gridded) N-dimensional data. Maps from R^N -> R^1.

   You can evaluate this model at a given point by calling it just like a function, e.g.:

   >>> y = my_interpolated_model(x)

   The input to the model (`x` in the example above) is of the type:
       * in the general N-dimensional case, a dictionary where: keys are variable names and values are float/array
       * in the case of a 1-dimensional input (R^1 -> R^1), it can optionally just be a float/array.
   If you're not sure what the input type of `my_interpolated_model` should be, just do:

   >>> print(my_interpolated_model) # Displays the valid input type to the model

   The output of the model (`y` in the example above) is always a float or array.

   See the docstring __init__ method of InterpolatedModel for more details of how to instantiate and use InterpolatedModel.

   One might have expected a interpolated model to be a literal Python function rather than a Python class - the
   benefit of having InterpolatedModel as a class rather than a function is that you can easily save (pickle) classes
   including data (e.g. parameters, x_data, y_data), but you can't do that with functions. And, because the
   InterpolatedModel class has a __call__ method, you can basically still just think of it like a function.



   .. py:attribute:: x_data_coordinates


   .. py:attribute:: x_data_coordinates_values


   .. py:attribute:: y_data_structured


   .. py:attribute:: method
      :value: 'bspline'



   .. py:attribute:: fill_value


   .. py:attribute:: x_data


   .. py:attribute:: y_data


   .. py:method:: __call__(x)

      Evaluates the surrogate model at some given input x.

      The input `x` is of the type:
          * in the general N-dimensional case, a dictionary where keys are variable names and values are float/array.
          * in the case of a 1-dimensional input (R^1 -> R^2), a float/array.