Source code for autoarray.inversion.pixelization.image_mesh.kmeans

from __future__ import annotations
import numpy as np
from sklearn.cluster import KMeans as ScipyKMeans
from typing import TYPE_CHECKING, Optional
import sys
import warnings

    from autoarray.structures.grids.uniform_2d import Grid2D

from autoarray.inversion.pixelization.image_mesh.abstract_weighted import (
from autoarray.structures.grids.irregular_2d import Grid2DIrregular

from autoarray import exc

[docs]class KMeans(AbstractImageMeshWeighted): def __init__( self, pixels=10.0, weight_floor=0.0, weight_power=0.0, ): """ Computes an image-mesh by running a weighted KMeans clustering algorithm. This requires an adapt-image, which is the image that the KMeans algorithm adapts to in order to compute the image mesh. This could simply be the image itself, or a model fit to the image which removes certain features or noise. For example, using the adapt image, the image mesh is computed as follows: 1) Convert the adapt image to a weight map, which is a 2D array of weight values. 2) Run the KMeans algorithm on the weight map, such that the image mesh pixels cluster around the weight map values with higher values. Parameters ---------- total_pixels The total number of pixels in the image mesh and input into the KMeans algortihm. weight_power """ super().__init__( pixels=pixels, weight_floor=weight_floor, weight_power=weight_power, )
[docs] def image_plane_mesh_grid_from( self, grid: Grid2D, adapt_data: Optional[np.ndarray], settings=None ) -> Grid2DIrregular: """ Returns an image mesh by running a KMeans clustering algorithm on the weight map. See the `__init__` docstring for a full description of how this is performed. Parameters ---------- grid The grid of (y,x) coordinates of the image data the pixelization fits, which the KMeans algorithm adapts to. adapt_data The weights defining the regions of the image the KMeans algorithm adapts to. Returns ------- """ if self.pixels > grid.shape[0]: print( """ The number of pixels passed to the KMeans object exceeds the number of image-pixels in the mask of the data being fitted. This is not allowed by the KMenas algorithm and will cause an error. To fix this, you should reduce the number of pixels in the KMeans object to be less than the number of image-pixels in the mask of the data being fitted. If you are performing model-fitting, you should update the priors to have an upper limit which does not exceed the number of image-pixels in the mask of the data being fitted. For adaptive fitting, the KMeans object has been superseeded by the Hilbert object, which does not have this limitation and performs better in general. You should therefore consider using the Hilbert object instead. """ ) sys.exit() warnings.filterwarnings("ignore") weight_map = self.weight_map_from(adapt_data=adapt_data) kmeans = ScipyKMeans( n_clusters=int(self.pixels), random_state=1, n_init=1, max_iter=5, ) try: kmeans =, sample_weight=weight_map) except ValueError or OverflowError: raise exc.InversionException() return Grid2DIrregular( values=kmeans.cluster_centers_, )