Source code for autogalaxy.plane.plane

import json
import numpy as np
from typing import Dict, List, Optional, Type

import autoarray as aa

from autoconf.dictable import Dictable

from autogalaxy import exc
from autogalaxy.galaxy.galaxy import Galaxy
from autogalaxy.galaxy.galaxy import HyperGalaxy
from autogalaxy.plane.to_inversion import PlaneToInversion
from autogalaxy.profiles.light_profiles.light_profiles import LightProfile
from autogalaxy.profiles.light_profiles.light_profiles_snr import LightProfileSNR
from autogalaxy.operate.image import OperateImageGalaxies
from autogalaxy.operate.deflections import OperateDeflections

from autogalaxy.util import plane_util


[docs]class Plane(OperateImageGalaxies, OperateDeflections, Dictable):
[docs] def __init__( self, galaxies, redshift: Optional[float] = None, profiling_dict: Optional[Dict] = None, ): """ A plane of galaxies where all galaxies are at the same redshift. Parameters ----------- redshift or None The redshift of the plane. galaxies : [Galaxy] The list of galaxies in this plane. """ if redshift is None: if not galaxies: raise exc.PlaneException( "No redshift and no galaxies were input to a Plane. A redshift for the Plane therefore cannot be" "determined" ) elif not all( [galaxies[0].redshift == galaxy.redshift for galaxy in galaxies] ): redshift = np.mean([galaxy.redshift for galaxy in galaxies]) else: redshift = galaxies[0].redshift self.redshift = redshift self.galaxies = galaxies self.profiling_dict = profiling_dict
def dict(self) -> Dict: plane_dict = super().dict() plane_dict["galaxies"] = [galaxy.dict() for galaxy in self.galaxies] return plane_dict def output_to_json(self, file_path: str): with open(file_path, "w+") as f: json.dump(self.dict(), f, indent=4) @property def galaxy_redshifts(self) -> List[float]: return [galaxy.redshift for galaxy in self.galaxies] def has(self, cls: Type) -> bool: if self.galaxies is not None: return any(list(map(lambda galaxy: galaxy.has(cls=cls), self.galaxies))) return False def cls_list_from(self, cls:Type) -> List: """ Returns a list of objects in the plane which are an instance of the input `cls`. For example: - If the input is `cls=ag.lp.LightProfile`, a list containing all light profiles in the plane is returned. Returns ------- The list of objects in the plane that inherit from input `cls`. """ cls_list = [] for galaxy in self.galaxies: if galaxy.has(cls=cls): for cls_galaxy in galaxy.cls_list_from(cls=cls): cls_list.append(cls_galaxy) return cls_list def galaxies_with_cls_list_from(self, cls:Type) -> List[Galaxy]: return list( filter(lambda galaxy: galaxy.has(cls=aa.pix.Pixelization), self.galaxies) ) @aa.grid_dec.grid_2d_to_structure def image_2d_from( self, grid: aa.type.Grid2DLike, operated_only: Optional[bool] = None ) -> aa.Array2D: """ Returns the profile-image plane image of the list of galaxies of the plane's sub-grid, by summing the individual images of each galaxy's light profile. If the `operated_only` input is included, the function omits light profiles which are parents of the `LightProfileOperated` object, which signifies that the light profile represents emission that has already had the instrument operations (e.g. PSF convolution, a Fourier transform) applied to it. If the plane has no galaxies (or no galaxies have mass profiles) an arrays of all zeros the shape of the plane's sub-grid is returned. Parameters ----------- grid The 2D (y, x) coordinates where values of the image are evaluated. operated_only By default, the image is the sum of light profile images (irrespective of whether they have been operatd on or not). If this input is included as a bool, only images which are or are not already operated are summed and returned. """ if self.galaxies: return sum(self.image_2d_list_from(grid=grid, operated_only=operated_only)) return np.zeros((grid.shape[0],)) def image_2d_list_from( self, grid: aa.type.Grid2DLike, operated_only: Optional[bool] = None ) -> List[aa.Array2D]: return [ galaxy.image_2d_from(grid=grid, operated_only=operated_only) for galaxy in self.galaxies ] def galaxy_image_2d_dict_from( self, grid: aa.type.Grid2DLike, operated_only: Optional[bool] = None ) -> {Galaxy: np.ndarray}: """ Returns a dictionary associating every `Galaxy` object in the `Plane` with its corresponding 2D image, using the instance of each galaxy as the dictionary keys. This object is used for hyper-features, which use the image of each galaxy in a model-fit in order to adapt quantities like a pixelization or regularization scheme to the surface brightness of the galaxies being fitted. By inheriting from `OperateImageGalaxies` functions which apply operations of this dictionary are accessible, for example convolving every image with a PSF or applying a Fourier transform to create a galaxy-visibilities dictionary. Parameters ---------- grid The 2D (y,x) coordinates of the (masked) grid, in its original geometric reference frame. Returns ------- A dictionary associated every galaxy in the plane with its corresponding 2D image. """ galaxy_image_2d_dict = dict() image_2d_list = self.image_2d_list_from(grid=grid, operated_only=operated_only) for (galaxy_index, galaxy) in enumerate(self.galaxies): galaxy_image_2d_dict[galaxy] = image_2d_list[galaxy_index] return galaxy_image_2d_dict def plane_image_2d_from(self, grid: aa.type.Grid2DLike) -> "PlaneImage": return plane_util.plane_image_of_galaxies_from( shape=grid.mask.shape, grid=grid.mask.unmasked_grid_sub_1, galaxies=self.galaxies, ) @aa.grid_dec.grid_2d_to_vector_yx @aa.grid_dec.grid_2d_to_structure def deflections_yx_2d_from(self, grid: aa.type.Grid2DLike) -> np.ndarray: if self.galaxies: return sum( map(lambda g: g.deflections_yx_2d_from(grid=grid), self.galaxies) ) return np.zeros(shape=(grid.shape[0], 2)) @aa.grid_dec.grid_2d_to_structure def convergence_2d_from(self, grid: aa.type.Grid2DLike) -> np.ndarray: """ Returns the convergence of the list of galaxies of the plane's sub-grid, by summing the individual convergences \ of each galaxy's mass profile. The convergence is calculated on the sub-grid and binned-up to the original grid by taking the mean value of every set of sub-pixels, provided the *returned_binned_sub_grid* bool is `True`. If the plane has no galaxies (or no galaxies have mass profiles) an arrays of all zeros the shape of the plane's sub-grid is returned. Internally data structures are treated as ndarrays, however the decorator `grid_2d_to_structure` converts the output to an `Array2D` using the input `grid`'s attributes. Parameters ----------- grid : Grid2D The grid (or sub) of (y,x) arc-second coordinates at the centre of every unmasked pixel which the \ potential is calculated on. galaxies : [Galaxy] The galaxies whose mass profiles are used to compute the surface densities. """ if self.galaxies: return sum(map(lambda g: g.convergence_2d_from(grid=grid), self.galaxies)) return np.zeros((grid.shape[0],)) @aa.grid_dec.grid_2d_to_structure def potential_2d_from(self, grid: aa.type.Grid2DLike) -> np.ndarray: """ Returns the potential of the list of galaxies of the plane's sub-grid, by summing the individual potentials \ of each galaxy's mass profile. The potential is calculated on the sub-grid and binned-up to the original grid by taking the mean value of every set of sub-pixels, provided the *returned_binned_sub_grid* bool is `True`. If the plane has no galaxies (or no galaxies have mass profiles) an arrays of all zeros the shape of the plane's sub-grid is returned. Internally data structures are treated as ndarrays, however the decorator `grid_2d_to_structure` converts the output to an `Array2D` using the input `grid`'s attributes. Parameters ----------- grid : Grid2D The grid (or sub) of (y,x) arc-second coordinates at the centre of every unmasked pixel which the \ potential is calculated on. galaxies : [Galaxy] The galaxies whose mass profiles are used to compute the surface densities. """ if self.galaxies: return sum(map(lambda g: g.potential_2d_from(grid=grid), self.galaxies)) return np.zeros((grid.shape[0],)) @aa.grid_dec.grid_2d_to_structure def traced_grid_from(self, grid: aa.type.Grid2DLike) -> aa.type.Grid2DLike: """Trace this plane's grid_stacks to the next plane, using its deflection angles.""" return grid - self.deflections_yx_2d_from(grid=grid) @property def hyper_galaxies_with_pixelization_image_list(self) -> List[aa.Array2D]: return [galaxy.hyper_galaxy_image for galaxy in self.galaxies_with_cls_list_from(cls=aa.pix.Pixelization)] def hyper_noise_map_from(self, noise_map) -> aa.Array2D: hyper_noise_maps = self.hyper_noise_map_list_from(noise_map=noise_map) return sum(hyper_noise_maps) def hyper_noise_map_list_from(self, noise_map) -> List[aa.Array2D]: """ For a contribution map and noise-map, use the model hyper_galaxy galaxies to compute a hyper noise-map. Parameters ----------- noise_map : imaging.NoiseMap or ndarray An arrays describing the RMS standard deviation error in each pixel, preferably in units of electrons per second. """ hyper_noise_maps = [] for galaxy in self.galaxies: if galaxy.has(cls=HyperGalaxy): hyper_noise_map_1d = galaxy.hyper_galaxy.hyper_noise_map_via_hyper_images_from( noise_map=noise_map, hyper_model_image=galaxy.hyper_model_image, hyper_galaxy_image=galaxy.hyper_galaxy_image, ) hyper_noise_maps.append(hyper_noise_map_1d) else: hyper_noise_map = aa.Array2D.manual_mask( array=np.zeros(noise_map.mask.mask_sub_1.pixels_in_mask), mask=noise_map.mask.mask_sub_1, ) hyper_noise_maps.append(hyper_noise_map) return hyper_noise_maps @property def contribution_map(self) -> Optional[aa.Array2D]: contribution_map_list = self.contribution_map_list contribution_map_list = [i for i in contribution_map_list if i is not None] if contribution_map_list: return sum(contribution_map_list) else: return None @property def contribution_map_list(self) -> List[aa.Array2D]: contribution_map_list = [] for galaxy in self.galaxies: if galaxy.hyper_galaxy is not None: contribution_map_list.append(galaxy.contribution_map) else: contribution_map_list.append(None) return contribution_map_list @property def to_inversion(self): return PlaneToInversion(plane=self) def extract_attribute(self, cls, attr_name): """ Returns an attribute of a class in `Plane` as a `ValueIrregular` or `Grid2DIrregular` object. For example, if a plane has a galaxy which two light profiles and we want its axis-ratios, the following: `plane.extract_attribute(cls=LightProfile, name="axis_ratio")` would return: ValuesIrregular(values=[axis_ratio_0, axis_ratio_1]) If a galaxy has three mass profiles and we want their centres, the following: `plane.extract_attribute(cls=MassProfile, name="centres")` would return: GridIrregular2D(grid=[(centre_y_0, centre_x_0), (centre_y_1, centre_x_1), (centre_y_2, centre_x_2)]) This is used for visualization, for example plotting the centres of all mass profiles colored by their profile. """ def extract(value, name): try: return getattr(value, name) except (AttributeError, IndexError): return None attributes = [ extract(value, attr_name) for galaxy in self.galaxies for value in galaxy.__dict__.values() if isinstance(value, cls) ] if attributes == []: return None elif isinstance(attributes[0], float): return aa.ValuesIrregular(values=attributes) elif isinstance(attributes[0], tuple): return aa.Grid2DIrregular(grid=attributes) def extract_attributes_of_galaxies(self, cls, attr_name, filter_nones=False): """ Returns an attribute of a class in the plane as a list of `ValueIrregular` or `Grid2DIrregular` objects, where the list indexes correspond to each galaxy in the plane.. For example, if a plane has two galaxies which each have a light profile the following: `plane.extract_attributes_of_galaxies(cls=LightProfile, name="axis_ratio")` would return: [ValuesIrregular(values=[axis_ratio_0]), ValuesIrregular(values=[axis_ratio_1])] If a plane has two galaxies, the first with a mass profile and the second with two mass profiles ,the following: `plane.extract_attributes_of_galaxies(cls=MassProfile, name="centres")` would return: [ Grid2DIrregular(grid=[(centre_y_0, centre_x_0)]), Grid2DIrregular(grid=[(centre_y_0, centre_x_0), (centre_y_1, centre_x_1)]) ] If a Profile does not have a certain entry, it is replaced with a None. Nones can be removed by setting `filter_nones=True`. This is used for visualization, for example plotting the centres of all mass profiles colored by their profile. """ if filter_nones: return [ galaxy.extract_attribute(cls=cls, attr_name=attr_name) for galaxy in self.galaxies if galaxy.extract_attribute(cls=cls, attr_name=attr_name) is not None ] else: return [ galaxy.extract_attribute(cls=cls, attr_name=attr_name) for galaxy in self.galaxies ] def set_snr_of_snr_light_profiles( self, grid: aa.type.Grid2DLike, exposure_time: float, background_sky_level: float = 0.0, ): for galaxy in self.galaxies: for light_profile in galaxy.cls_list_from(cls=LightProfile): if isinstance(light_profile, LightProfileSNR): light_profile.set_intensity_from( grid=grid, exposure_time=exposure_time, background_sky_level=background_sky_level, )
class PlaneImage: def __init__(self, array, grid): self.array = array self.grid = grid