"""Property transfer models."""
import numpy as np
from sklearn.base import BaseEstimator
from sklearn.neighbors import NearestNeighbors
[docs]class PropertiesTransfer(BaseEstimator):
"""Model to transfer rock properties using Nearest Neighbors algorithm."""
def __init__(self):
self._nn = None
self._neigh_dist = None
self._neigh_ind = None
self._new_shape = None
[docs] def fit(self, original_grid, new_grid, n_neighbors=5, normalize_vector=(1, 1, 1)):
"""Fit Nearest Neighbors model.
Parameters
----------
original_grid : geology.Grid
Original grid.
new_grid : geology.Grid
New grid.
n_neighbors : int, optional
number of neighbors to use, by default 5.
normalize_vector : list, optional
vector to normalize distance, by default [1, 1, 1].
"""
self._nn = NearestNeighbors(n_neighbors=n_neighbors)
self._nn.fit(original_grid.cell_centroids.reshape(-1, 3) / np.asarray(normalize_vector))
self._neigh_dist, self._neigh_ind = self._nn.kneighbors(
new_grid.cell_centroids.reshape(-1, 3) / np.asarray(normalize_vector))
self._new_shape = new_grid.dimens
return self
[docs] def dump(self, path):
"""Dump to a file (*.npz).
Parameters
----------
path : str or pathlib.Path
Path to file, ``.npz`` extension will be appended to the file name if it is not
already there.
"""
np.savez(path, **{att: getattr(self, att) for att in ('_neigh_dist',
'_neigh_ind', '_new_shape')},
)
return self
[docs] def load(self, path):
"""Load from file.
Parameters
----------
path : str or pathlib.Path
File path.
"""
npzfile = np.load(path, allow_pickle=True)
for att in npzfile.files:
value = npzfile[att]
setattr(self, att, value if (value.ndim > 0) or (value[()] is not None) else None)
return self
[docs] def predict(self, original_properties,
aggr=lambda values, dist: np.average(values, axis=1, weights=1/dist)):
"""Transfer property values to the new grid.
Parameters
----------
original_properties : numpy.ndarray
Properties on original grid.
aggr : Callable, optional
Function to aggregate nearest neighbors values, by default weighted average.
Returns
-------
array : ndarray
New values.
"""
reshaped_properties = original_properties.reshape([-1] + list(original_properties.shape[-3:]))
new_values = np.stack([
aggr(prop.ravel()[self._neigh_ind], self._neigh_dist) for prop in reshaped_properties
])
return new_values.reshape(list(original_properties.shape[:-3]) + list(self._new_shape))