def test__2d__via_light_obj_from(lp_0, grid_2d_7x7):
visuals_2d = aplt.Visuals2D(vectors=2)
include_2d = aplt.Include2D(origin=True,
mask=True,
border=True,
light_profile_centres=True)
get_visuals = GetVisuals2D(include=include_2d, visuals=visuals_2d)
visuals_2d_via = get_visuals.via_light_obj_from(light_obj=lp_0,
grid=grid_2d_7x7)
assert visuals_2d_via.origin.in_list == [(0.0, 0.0)]
assert (visuals_2d_via.mask == grid_2d_7x7.mask).all()
assert (visuals_2d_via.border == grid_2d_7x7.mask.border_grid_sub_1.binned
).all()
assert visuals_2d_via.light_profile_centres.in_list == [lp_0.centre]
assert visuals_2d_via.vectors == 2
include_2d = aplt.Include2D(origin=False,
mask=False,
border=False,
light_profile_centres=False)
get_visuals = GetVisuals2D(include=include_2d, visuals=visuals_2d)
visuals_2d_via = get_visuals.via_light_obj_from(light_obj=lp_0,
grid=grid_2d_7x7)
assert visuals_2d_via.origin is None
assert visuals_2d_via.mask is None
assert visuals_2d_via.border is None
assert visuals_2d_via.light_profile_centres is None
assert visuals_2d_via.vectors == 2
def test__2d__via_light_mass_obj(gal_x1_lp_x1_mp, grid_2d_7x7):
visuals_2d = aplt.Visuals2D(vectors=2)
include_2d = aplt.Include2D(
origin=True,
mask=True,
border=True,
light_profile_centres=True,
mass_profile_centres=True,
critical_curves=True,
)
get_visuals = GetVisuals2D(include=include_2d, visuals=visuals_2d)
visuals_2d_via = get_visuals.via_light_mass_obj_from(
light_mass_obj=gal_x1_lp_x1_mp, grid=grid_2d_7x7)
assert visuals_2d_via.origin.in_list == [(0.0, 0.0)]
assert (visuals_2d_via.mask == grid_2d_7x7.mask).all()
assert (visuals_2d_via.border == grid_2d_7x7.mask.border_grid_sub_1.binned
).all()
assert visuals_2d_via.light_profile_centres.in_list == [
gal_x1_lp_x1_mp.light_profile_0.centre
]
assert visuals_2d_via.mass_profile_centres.in_list == [
gal_x1_lp_x1_mp.mass_profile_0.centre
]
assert (visuals_2d_via.critical_curves[0] ==
gal_x1_lp_x1_mp.critical_curves_from(grid=grid_2d_7x7)[0]).all()
assert visuals_2d_via.vectors == 2
include_2d = aplt.Include2D(
origin=False,
mask=False,
border=False,
light_profile_centres=False,
mass_profile_centres=False,
critical_curves=False,
)
get_visuals = GetVisuals2D(include=include_2d, visuals=visuals_2d)
visuals_2d_via = get_visuals.via_light_mass_obj_from(
light_mass_obj=gal_x1_lp_x1_mp, grid=grid_2d_7x7)
assert visuals_2d_via.origin is None
assert visuals_2d_via.mask is None
assert visuals_2d_via.border is None
assert visuals_2d_via.light_profile_centres is None
assert visuals_2d_via.mass_profile_centres is None
assert visuals_2d_via.critical_curves is None
assert visuals_2d_via.vectors == 2
def test__via_fit_imaging_from(fit_imaging_x2_galaxy_7x7, grid_2d_7x7):
visuals_2d = aplt.Visuals2D(origin=(1.0, 1.0), vectors=2)
include_2d = aplt.Include2D(
origin=True,
mask=True,
border=True,
light_profile_centres=True,
mass_profile_centres=True,
critical_curves=True,
)
get_visuals = GetVisuals2D(include=include_2d, visuals=visuals_2d)
visuals_2d_via = get_visuals.via_fit_imaging_from(
fit=fit_imaging_x2_galaxy_7x7)
assert visuals_2d_via.origin == (1.0, 1.0)
assert (visuals_2d_via.mask == fit_imaging_x2_galaxy_7x7.mask).all()
assert (visuals_2d_via.border ==
fit_imaging_x2_galaxy_7x7.mask.border_grid_sub_1.binned).all()
assert visuals_2d_via.light_profile_centres.in_list == [(0.0, 0.0),
(0.0, 0.0)]
assert visuals_2d_via.mass_profile_centres.in_list == [(0.0, 0.0)]
assert (visuals_2d_via.critical_curves[0] == fit_imaging_x2_galaxy_7x7.
plane.critical_curves_from(grid=grid_2d_7x7)[0]).all()
assert visuals_2d_via.vectors == 2
include_2d = aplt.Include2D(
origin=False,
mask=False,
border=False,
light_profile_centres=False,
mass_profile_centres=False,
critical_curves=False,
)
get_visuals = GetVisuals2D(include=include_2d, visuals=visuals_2d)
visuals_2d_via = get_visuals.via_fit_imaging_from(
fit=fit_imaging_x2_galaxy_7x7)
assert visuals_2d_via.origin == (1.0, 1.0)
assert visuals_2d_via.mask is None
assert visuals_2d_via.border is None
assert visuals_2d_via.light_profile_centres is None
assert visuals_2d_via.mass_profile_centres is None
assert visuals_2d_via.critical_curves is None
assert visuals_2d_via.vectors == 2
def test__visuals_with_include_2d(gal_x1_lp_x1_mp, grid_2d_7x7):
visuals_2d = aplt.Visuals2D(vector_field=2)
include = aplt.Include2D(
origin=True,
mask=True,
border=True,
light_profile_centres=True,
mass_profile_centres=True,
critical_curves=True,
caustics=True,
)
galaxy_plotter = aplt.GalaxyPlotter(
galaxy=gal_x1_lp_x1_mp,
grid=grid_2d_7x7,
visuals_2d=visuals_2d,
include_2d=include,
)
assert galaxy_plotter.visuals_with_include_2d.origin.in_list == [(0.0, 0.0)
]
assert (
galaxy_plotter.visuals_with_include_2d.mask == grid_2d_7x7.mask).all()
assert (galaxy_plotter.visuals_with_include_2d.border ==
grid_2d_7x7.mask.border_grid_sub_1.binned).all()
assert galaxy_plotter.visuals_with_include_2d.light_profile_centres.in_list == [
gal_x1_lp_x1_mp.light_profile_0.centre
]
assert galaxy_plotter.visuals_with_include_2d.mass_profile_centres.in_list == [
gal_x1_lp_x1_mp.mass_profile_0.centre
]
assert galaxy_plotter.visuals_with_include_2d.vector_field == 2
include = aplt.Include2D(origin=False, mask=False, border=False)
galaxy_plotter = aplt.GalaxyPlotter(
galaxy=gal_x1_lp_x1_mp,
grid=grid_2d_7x7,
visuals_2d=visuals_2d,
include_2d=include,
)
assert galaxy_plotter.visuals_with_include_2d.origin == None
assert galaxy_plotter.visuals_with_include_2d.mask == None
assert galaxy_plotter.visuals_with_include_2d.border == None
assert galaxy_plotter.visuals_with_include_2d.vector_field == 2
def __init__(
self,
fit: FitImaging,
mat_plot_2d: aplt.MatPlot2D = aplt.MatPlot2D(),
visuals_2d: aplt.Visuals2D = aplt.Visuals2D(),
include_2d: aplt.Include2D = aplt.Include2D(),
):
"""
Plots the attributes of `FitImaging` objects using the matplotlib method `imshow()` and many other matplotlib
functions which customize the plot's appearance.
The `mat_plot_2d` attribute wraps 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 `Visuals2D` object. Attributes may be extracted from
the `FitImaging` and plotted via the visuals object, if the corresponding entry is `True` in the `Include2D`
object or the `config/visualize/include.ini` file.
Parameters
----------
fit
The fit to an imaging dataset the plotter plots.
mat_plot_2d
Contains objects which wrap the matplotlib function calls that make the plot.
visuals_2d
Contains visuals that can be overlaid on the plot.
include_2d
Specifies which attributes of the `Array2D` are extracted and plotted as visuals.
"""
super().__init__(mat_plot_2d=mat_plot_2d,
include_2d=include_2d,
visuals_2d=visuals_2d)
self.fit = fit
self._fit_imaging_meta_plotter = FitImagingPlotterMeta(
fit=self.fit,
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._fit_imaging_meta_plotter.figures_2d
self.subplot = self._fit_imaging_meta_plotter.subplot
self.subplot_fit_imaging = self._fit_imaging_meta_plotter.subplot_fit_imaging
def __init__(
self,
subhalo_result: SubhaloResult,
fit_imaging_detect,
use_log_evidences: bool = True,
use_stochastic_log_likelihoods: bool = False,
mat_plot_2d: aplt.MatPlot2D = aplt.MatPlot2D(),
visuals_2d: aplt.Visuals2D = aplt.Visuals2D(),
include_2d: aplt.Include2D = aplt.Include2D(),
):
super().__init__(mat_plot_2d=mat_plot_2d,
include_2d=include_2d,
visuals_2d=visuals_2d)
self.subhalo_result = subhalo_result
self.fit_imaging_detect = fit_imaging_detect
self.use_log_evidences = use_log_evidences
self.use_stochastic_log_likelihoods = use_stochastic_log_likelihoods
def __init__(
self,
fit: FitPointDataset,
mat_plot_1d: aplt.MatPlot1D = aplt.MatPlot1D(),
visuals_1d: aplt.Visuals1D = aplt.Visuals1D(),
include_1d: aplt.Include1D = aplt.Include1D(),
mat_plot_2d: aplt.MatPlot2D = aplt.MatPlot2D(),
visuals_2d: aplt.Visuals2D = aplt.Visuals2D(),
include_2d: aplt.Include2D = aplt.Include2D(),
):
super().__init__(
mat_plot_1d=mat_plot_1d,
visuals_1d=visuals_1d,
include_1d=include_1d,
mat_plot_2d=mat_plot_2d,
include_2d=include_2d,
visuals_2d=visuals_2d,
)
self.fit = fit
def __init__(
self,
point_dict: PointDict,
mat_plot_1d: aplt.MatPlot1D = aplt.MatPlot1D(),
visuals_1d: aplt.Visuals1D = aplt.Visuals1D(),
include_1d: aplt.Include1D = aplt.Include1D(),
mat_plot_2d: aplt.MatPlot2D = aplt.MatPlot2D(),
visuals_2d: aplt.Visuals2D = aplt.Visuals2D(),
include_2d: aplt.Include2D = aplt.Include2D(),
):
super().__init__(
mat_plot_1d=mat_plot_1d,
visuals_1d=visuals_1d,
include_1d=include_1d,
mat_plot_2d=mat_plot_2d,
include_2d=include_2d,
visuals_2d=visuals_2d,
)
self.point_dict = point_dict
def test__visuals_with_include_2d(lp_0, grid_2d_7x7):
visuals_2d = aplt.Visuals2D(vector_field=2)
include = aplt.Include2D(
origin=True,
mask=True,
border=True,
light_profile_centres=True,
critical_curves=True,
caustics=True,
)
light_profile_plotter = aplt.LightProfilePlotter(
light_profile=lp_0, grid=grid_2d_7x7, visuals_2d=visuals_2d, include_2d=include
)
assert light_profile_plotter.visuals_with_include_2d.origin.in_list == [(0.0, 0.0)]
assert (
light_profile_plotter.visuals_with_include_2d.mask == grid_2d_7x7.mask
).all()
assert (
light_profile_plotter.visuals_with_include_2d.border
== grid_2d_7x7.mask.border_grid_sub_1.binned
).all()
assert (
light_profile_plotter.visuals_with_include_2d.light_profile_centres.in_list
== [lp_0.centre]
)
assert light_profile_plotter.visuals_with_include_2d.vector_field == 2
include = aplt.Include2D(origin=False, mask=False, border=False)
light_profile_plotter = aplt.LightProfilePlotter(
light_profile=lp_0, grid=grid_2d_7x7, visuals_2d=visuals_2d, include_2d=include
)
assert light_profile_plotter.visuals_with_include_2d.origin == None
assert light_profile_plotter.visuals_with_include_2d.mask == None
assert light_profile_plotter.visuals_with_include_2d.border == None
assert light_profile_plotter.visuals_with_include_2d.vector_field == 2
def visualize_image_with_positions(
self, image: aa.Array2D, positions: aa.Grid2DIrregular
):
"""
Visualizes the positions of a model-fit, where these positions are used to resample lens models where
the positions to do trace within an input threshold of one another in the source-plane.
Images are output to the `image` folder of the `visualize_path` in a subfolder called `positions`. When
used with a non-linear search the `visualize_path` points to the search's results folder.
The visualization is an image of the strong lens with the positions overlaid.
The images output by the `Visualizer` are customized using the file `config/visualize/plots.ini` under the
[ray_tracing] header.
Parameters
----------
imaging
The imaging dataset whose image the positions are overlaid.
position
The 2D (y,x) arc-second positions used to resample inaccurate mass models.
"""
def should_plot(name):
return plot_setting(section=["positions"], name=name)
mat_plot_2d = self.mat_plot_2d_from(subfolders="positions")
if positions is not None:
visuals_2d = aplt.Visuals2D(positions=positions)
image_plotter = aplt.Array2DPlotter(
array=image,
mat_plot_2d=mat_plot_2d,
include_2d=self.include_2d,
visuals_2d=visuals_2d,
)
image_plotter.set_filename("image_with_positions")
if should_plot("image_with_positions"):
image_plotter.figure_2d()
def __init__(
self,
tracer: Tracer,
grid: aa.type.Grid2DLike,
mat_plot_1d: aplt.MatPlot1D = aplt.MatPlot1D(),
visuals_1d: aplt.Visuals1D = aplt.Visuals1D(),
include_1d: aplt.Include1D = aplt.Include1D(),
mat_plot_2d: aplt.MatPlot2D = aplt.MatPlot2D(),
visuals_2d: aplt.Visuals2D = aplt.Visuals2D(),
include_2d: aplt.Include2D = aplt.Include2D(),
):
"""
Plots the attributes of `Tracer` 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
----------
tracer
The tracer the plotter plots.
grid
The 2D (y,x) grid of coordinates used to evaluate the tracer's light and mass 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_1d=mat_plot_1d,
visuals_1d=visuals_1d,
include_1d=include_1d,
mat_plot_2d=mat_plot_2d,
include_2d=include_2d,
visuals_2d=visuals_2d,
)
self.tracer = tracer
self.grid = grid
self._mass_plotter = MassPlotter(
mass_obj=self.tracer,
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,
)