Source code for autogalaxy.profiles.plot.mass_profile_plotters

import math
from typing import List, Optional

import autoarray as aa
import autoarray.plot as aplt

from autogalaxy.plot.mass_plotter import MassPlotter
from autogalaxy.plot.abstract_plotters import Plotter
from autogalaxy.profiles.mass_profiles import MassProfile
from autogalaxy.plot.mat_wrap.mat_plot import MatPlot1D
from autogalaxy.plot.mat_wrap.mat_plot import MatPlot2D
from autogalaxy.plot.mat_wrap.visuals import Visuals1D
from autogalaxy.plot.mat_wrap.visuals import Visuals2D
from autogalaxy.plot.mat_wrap.include import Include1D
from autogalaxy.plot.mat_wrap.include import Include2D

from autogalaxy.util import error_util


[docs]class MassProfilePlotter(Plotter):
[docs] def __init__( self, mass_profile: MassProfile, grid: aa.type.Grid2DLike, mat_plot_1d: MatPlot1D = MatPlot1D(), visuals_1d: Visuals1D = Visuals1D(), include_1d: Include1D = Include1D(), mat_plot_2d: MatPlot2D = MatPlot2D(), visuals_2d: Visuals2D = Visuals2D(), include_2d: Include2D = Include2D(), ): """ Plots the attributes of `MassProfile` objects using the matplotlib methods `plot()` and `imshow()` and many other matplotlib functions which customize the plot's appearance. The `mat_plot_1d` and `mat_plot_2d` attributes wrap matplotlib function calls to make the figure. By default, the settings passed to every matplotlib function called are those specified in the `config/visualize/mat_wrap/*.ini` files, but a user can manually input values into `MatPlot2D` to customize the figure's appearance. Overlaid on the figure are visuals, contained in the `Visuals1D` and `Visuals2D` objects. Attributes may be extracted from the `MassProfile` and plotted via the visuals object, if the corresponding entry is `True` in the `Include1D` or `Include2D` object or the `config/visualize/include.ini` file. Parameters ---------- mass_profile The mass profile the plotter plots. grid The 2D (y,x) grid of coordinates used to evaluate the mass profile quantities that are plotted. mat_plot_1d Contains objects which wrap the matplotlib function calls that make 1D plots. visuals_1d Contains 1D visuals that can be overlaid on 1D plots. include_1d Specifies which attributes of the `MassProfile` are extracted and plotted as visuals for 1D plots. mat_plot_2d Contains objects which wrap the matplotlib function calls that make 2D plots. visuals_2d Contains 2D visuals that can be overlaid on 2D plots. include_2d Specifies which attributes of the `MassProfile` are extracted and plotted as visuals for 2D plots. """ super().__init__( mat_plot_2d=mat_plot_2d, include_2d=include_2d, visuals_2d=visuals_2d, mat_plot_1d=mat_plot_1d, include_1d=include_1d, visuals_1d=visuals_1d, ) self.mass_profile = mass_profile self.grid = grid self._mass_plotter = MassPlotter( mass_obj=self.mass_profile, grid=self.grid, get_visuals_2d=self.get_visuals_2d, mat_plot_2d=self.mat_plot_2d, include_2d=self.include_2d, visuals_2d=self.visuals_2d, ) self.figures_2d = self._mass_plotter.figures_2d
def get_visuals_1d(self) -> Visuals1D: return self.get_1d.via_mass_obj_from(mass_obj=self.mass_profile, grid=self.grid) def get_visuals_2d(self) -> Visuals2D: return self.get_2d.via_mass_obj_from(mass_obj=self.mass_profile, grid=self.grid) def figures_1d(self, convergence: bool = False, potential: bool = False): """ Plots the individual attributes of the plotter's `MassProfile` object in 1D, which are computed via the plotter's grid object. If the plotter has a 1D grid object this is used to evaluate each quantity. If it has a 2D grid, a 1D grid is computed from the mass profile. This is performed by aligning a 1D grid with the major-axis of the mass profile in projection, uniformly computing 1D values based on the 2D grid's size and pixel-scale. The API is such that every plottable attribute of the `MassProfile` object is an input parameter of type bool of the function, which if switched to `True` means that it is plotted. Parameters ---------- convergence Whether or not to make a 1D plot (via `imshow`) of the convergence. potential Whether or not to make a 1D plot (via `imshow`) of the potential. """ if self.mat_plot_1d.yx_plot.plot_axis_type is None: plot_axis_type_override = "semilogy" else: plot_axis_type_override = None if convergence: convergence_1d = self.mass_profile.convergence_1d_from(grid=self.grid) self.mat_plot_1d.plot_yx( y=convergence_1d, x=convergence_1d.grid_radial, visuals_1d=self.get_visuals_1d(), auto_labels=aplt.AutoLabels( title="Convergence vs Radius", ylabel="Convergence ", xlabel="Radius", legend=self.mass_profile.__class__.__name__, filename="convergence_1d", ), plot_axis_type_override=plot_axis_type_override, ) if potential: potential_1d = self.mass_profile.potential_1d_from(grid=self.grid) self.mat_plot_1d.plot_yx( y=potential_1d, x=potential_1d.grid_radial, visuals_1d=self.get_visuals_1d(), auto_labels=aplt.AutoLabels( title="Potential vs Radius", ylabel="Potential ", xlabel="Radius", legend=self.mass_profile.__class__.__name__, filename="potential_1d", ), plot_axis_type_override=plot_axis_type_override, )
[docs]class MassProfilePDFPlotter(MassProfilePlotter):
[docs] def __init__( self, mass_profile_pdf_list: List[MassProfile], grid: aa.Grid2D, mat_plot_1d: MatPlot1D = MatPlot1D(), visuals_1d: Visuals1D = Visuals1D(), include_1d: Include1D = Include1D(), mat_plot_2d: MatPlot2D = MatPlot2D(), visuals_2d: Visuals2D = Visuals2D(), include_2d: Include2D = Include2D(), sigma: Optional[float] = 3.0, ): """ Plots the attributes of a list of `MassProfile` objects using the matplotlib methods `plot()` and `imshow()` and many other matplotlib functions which customize the plot's appearance. Figures plotted by this object average over a list mass profiles to computed the average value of each attribute with errors, where the 1D regions within the errors are plotted as a shaded region to show the range of plausible models. Therefore, the input list of galaxies is expected to represent the probability density function of an inferred model-fit. The `mat_plot_1d` and `mat_plot_2d` attributes wrap matplotlib function calls to make the figure. By default, the settings passed to every matplotlib function called are those specified in the `config/visualize/mat_wrap/*.ini` files, but a user can manually input values into `MatPlot2D` to customize the figure's appearance. Overlaid on the figure are visuals, contained in the `Visuals1D` and `Visuals2D` objects. Attributes may be extracted from the `MassProfile` and plotted via the visuals object, if the corresponding entry is `True` in the `Include1D` or `Include2D` object or the `config/visualize/include.ini` file. Parameters ---------- mass_profile_pdf_list The list of mass profiles whose mean and error values the plotter plots. grid The 2D (y,x) grid of coordinates used to evaluate the mass profile quantities that are plotted. mat_plot_1d Contains objects which wrap the matplotlib function calls that make 1D plots. visuals_1d Contains 1D visuals that can be overlaid on 1D plots. include_1d Specifies which attributes of the `MassProfile` are extracted and plotted as visuals for 1D plots. mat_plot_2d Contains objects which wrap the matplotlib function calls that make 2D plots. visuals_2d Contains 2D visuals that can be overlaid on 2D plots. include_2d Specifies which attributes of the `MassProfile` are extracted and plotted as visuals for 2D plots. sigma The confidence interval in terms of a sigma value at which the errors are computed (e.g. a value of sigma=3.0 uses confidence intevals at ~0.01 and 0.99 the PDF). """ super().__init__( mass_profile=None, grid=grid, mat_plot_1d=mat_plot_1d, visuals_1d=visuals_1d, include_1d=include_1d, mat_plot_2d=mat_plot_2d, visuals_2d=visuals_2d, include_2d=include_2d, ) self.mass_profile_pdf_list = mass_profile_pdf_list self.sigma = sigma self.low_limit = (1 - math.erf(sigma / math.sqrt(2))) / 2
def figures_1d(self, convergence=False, potential=False): """ Plots the individual attributes of the plotter's list of ` MassProfile` object in 1D, which are computed via the plotter's grid object. This averages over a list mass profiles to compute the average value of each attribute with errors, where the 1D regions within the errors are plotted as a shaded region to show the range of plausible models. Therefore, the input list of galaxies is expected to represent the probability density function of an inferred model-fit. If the plotter has a 1D grid object this is used to evaluate each quantity. If it has a 2D grid, a 1D grid is computed from each mass profile. This is performed by aligning a 1D grid with the major-axis of each mass profile in projection, uniformly computing 1D values based on the 2D grid's size and pixel-scale. The API is such that every plottable attribute of the `MassProfile` object is an input parameter of type bool of the function, which if switched to `True` means that it is plotted. Parameters ---------- convergence Whether or not to make a 1D plot (via `imshow`) of the convergence. potential Whether or not to make a 1D plot (via `imshow`) of the potential. """ if self.mat_plot_1d.yx_plot.plot_axis_type is None: plot_axis_type_override = "semilogy" else: plot_axis_type_override = None if convergence: convergence_1d_list = [ mass_profile.convergence_1d_from(grid=self.grid) for mass_profile in self.mass_profile_pdf_list ] min_index = min( [convergence_1d.shape[0] for convergence_1d in convergence_1d_list] ) convergence_1d_list = [ convergence_1d[0:min_index] for convergence_1d in convergence_1d_list ] median_convergence_1d, errors_convergence_1d = error_util.profile_1d_median_and_error_region_via_quantile( profile_1d_list=convergence_1d_list, low_limit=self.low_limit ) visuals_1d_via_lensing_obj_list = self.get_1d.via_mass_obj_list_from( mass_obj_list=self.mass_profile_pdf_list, grid=self.grid, low_limit=self.low_limit, ) visuals_1d_with_shaded_region = self.visuals_1d.__class__( shaded_region=errors_convergence_1d ) visuals_1d = visuals_1d_via_lensing_obj_list + visuals_1d_with_shaded_region self.mat_plot_1d.plot_yx( y=median_convergence_1d, x=convergence_1d_list[0].grid_radial, visuals_1d=visuals_1d, auto_labels=aplt.AutoLabels( title="Convergence vs Radius", ylabel="Convergence ", xlabel="Radius", legend=self.mass_profile_pdf_list[0].__class__.__name__, filename="convergence_1d", ), plot_axis_type_override=plot_axis_type_override, ) if potential: potential_1d_list = [ mass_profile.potential_1d_from(grid=self.grid) for mass_profile in self.mass_profile_pdf_list ] min_index = min( [potential_1d.shape[0] for potential_1d in potential_1d_list] ) potential_1d_list = [ potential_1d[0:min_index] for potential_1d in potential_1d_list ] median_potential_1d, errors_potential_1d = error_util.profile_1d_median_and_error_region_via_quantile( profile_1d_list=potential_1d_list, low_limit=self.low_limit ) visuals_1d_via_lensing_obj_list = self.get_1d.via_mass_obj_list_from( mass_obj_list=self.mass_profile_pdf_list, grid=self.grid, low_limit=self.low_limit, ) visuals_1d_with_shaded_region = self.visuals_1d.__class__( shaded_region=errors_potential_1d ) visuals_1d = visuals_1d_via_lensing_obj_list + visuals_1d_with_shaded_region self.mat_plot_1d.plot_yx( y=median_potential_1d, x=potential_1d_list[0].grid_radial, visuals_1d=visuals_1d, auto_labels=aplt.AutoLabels( title="Potential vs Radius", ylabel="Potential ", xlabel="Radius", legend=self.mass_profile_pdf_list[0].__class__.__name__, filename="potential_1d", ), plot_axis_type_override=plot_axis_type_override, )