Source code for verde.scipygridder

"""
A gridder that uses scipy.interpolate as the backend.
"""
from warnings import warn

import numpy as np
from scipy.interpolate import (
    LinearNDInterpolator,
    NearestNDInterpolator,
    CloughTocher2DInterpolator,
)
from sklearn.utils.validation import check_is_fitted

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): super().__init__() 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))