"""
A gridder that uses scipy.interpolate as the backend.
"""
from warnings import warn
import numpy as np
from sklearn.utils.validation import check_is_fitted
from scipy.interpolate import (
LinearNDInterpolator,
NearestNDInterpolator,
CloughTocher2DInterpolator,
)
from .base import BaseGridder, check_fit_input
from .coordinates import get_region
[docs]class ScipyGridder(BaseGridder):
"""
A scipy.interpolate based gridder for scalar Cartesian data.
Provides a verde gridder interface to the scipy interpolators
:class:`scipy.interpolate.LinearNDInterpolator`,
:class:`scipy.interpolate.NearestNDInterpolator`, and
:class:`scipy.interpolate.CloughTocher2DInterpolator` (cubic).
Parameters
----------
method : str
The interpolation method. Either ``'linear'``, ``'nearest'``, or
``'cubic'``.
extra_args : None or dict
Extra keyword arguments to pass to the scipy interpolator class. See
the documentation for each interpolator for a list of possible
arguments.
Attributes
----------
interpolator_ : scipy interpolator class
An instance of the corresponding scipy interpolator class.
region_ : tuple
The boundaries (``[W, E, S, N]``) of the data used to fit the
interpolator. Used as the default region for the
:meth:`~verde.ScipyGridder.grid` and
:meth:`~verde.ScipyGridder.scatter` methods.
"""
def __init__(self, method="cubic", extra_args=None):
self.method = method
self.extra_args = extra_args
[docs] def fit(self, coordinates, data, weights=None):
"""
Fit the interpolator to the given data.
Any keyword arguments passed as the ``extra_args`` attribute will be
used when instantiating the scipy interpolator.
The data region is captured and used as default for the
:meth:`~verde.ScipyGridder.grid` and
:meth:`~verde.ScipyGridder.scatter` methods.
Parameters
----------
coordinates : tuple of arrays
Arrays with the coordinates of each data point. Should be in the
following order: (easting, northing, vertical, ...). Only easting
and northing will be used, all subsequent coordinates will be
ignored.
data : array
The data values that will be interpolated.
weights : None or array
If not None, then the weights assigned to each data point.
Typically, this should be 1 over the data uncertainty squared.
Ignored for this interpolator. Only present for compatibility with
other gridder.
Returns
-------
self : verde.ScipyGridder
Returns this gridder instance for chaining operations.
"""
classes = dict(
linear=LinearNDInterpolator,
nearest=NearestNDInterpolator,
cubic=CloughTocher2DInterpolator,
)
if self.method not in classes:
raise ValueError(
"Invalid interpolation method '{}'. Must be one of {}.".format(
self.method, str(classes.keys())
)
)
if self.extra_args is None:
kwargs = {}
else:
kwargs = self.extra_args
if weights is not None:
warn(
"{} does not support weights and they will be ignored.".format(
self.__class__.__name__
)
)
coordinates, data, weights = check_fit_input(coordinates, data, weights)
easting, northing = coordinates[:2]
self.region_ = get_region((easting, northing))
points = np.column_stack((np.ravel(easting), np.ravel(northing)))
self.interpolator_ = classes[self.method](points, np.ravel(data), **kwargs)
return self
[docs] def predict(self, coordinates):
"""
Interpolate data on the given set of points.
Requires a fitted gridder (see :meth:`~verde.ScipyGridder.fit`).
Parameters
----------
coordinates : tuple of arrays
Arrays with the coordinates of each data point. Should be in the
following order: (easting, northing, vertical, ...). Only easting
and northing will be used, all subsequent coordinates will be
ignored.
Returns
-------
data : array
The data values interpolated on the given points.
"""
check_is_fitted(self, ["interpolator_"])
easting, northing = coordinates[:2]
return self.interpolator_((easting, northing))