aerosandbox.modeling.interpolation_unstructured#

Module Contents#

Classes#

UnstructuredInterpolatedModel

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

Attributes#

x

class aerosandbox.modeling.interpolation_unstructured.UnstructuredInterpolatedModel(x_data, y_data, x_data_resample=10, resampling_interpolator=interpolate.RBFInterpolator, resampling_interpolator_kwargs=None, fill_value=np.nan, interpolated_model_kwargs=None)[source]#

Bases: aerosandbox.modeling.interpolation.InterpolatedModel

A model that is interpolated to unstructured (i.e., point cloud) 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 UnstructuredInterpolatedModel.

Parameters:

  • x_data (Union[aerosandbox.numpy.ndarray, Dict[str, aerosandbox.numpy.ndarray]]) –

  • y_data (aerosandbox.numpy.ndarray) –

  • x_data_resample (Union[int, Dict[str, Union[int, aerosandbox.numpy.ndarray]]]) –

  • resampling_interpolator (object) –

  • resampling_interpolator_kwargs (Dict[str, Any]) –

  • interpolated_model_kwargs (Dict[str, Any]) –

aerosandbox.modeling.interpolation_unstructured.x[source]#