def __init__(self, visualize_path: str):
"""
Visualizes a model-fit, including components of the model and fit objects.
The `Visualizer` is typically used in the `Analysis` class of a non-linear search to visualize the maximum
log likelihood model of the model-fit so far.
The methods of the `Visualizer` are called throughout a non-linear search using the `Analysis`
classes `visualize` method.
The images output by the `Visualizer` are customized using the file `config/visualize/plots.ini`.
Parameters
----------
visualize_path
The path on the hard-disk to the `image` folder of the non-linear searches results.
"""
self.visualize_path = visualize_path
self.plot_fit_no_hyper = plot_setting("hyper", "fit_no_hyper")
self.include_2d = Include2D()
try:
os.makedirs(visualize_path)
except FileExistsError:
pass
def __init__(
self,
mat_plot_2d: MatPlot2D = MatPlot2D(),
visuals_2d: Visuals2D = Visuals2D(),
include_2d: Include2D = Include2D(),
):
super().__init__(mat_plot_2d=mat_plot_2d,
include_2d=include_2d,
visuals_2d=visuals_2d)
def __init__(
self,
light_profile: LightProfile,
grid: aa.type.Grid1D2DLike,
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 `LightProfile` 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 `LightProfile` 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
----------
light_profile
The light profile the plotter plots.
grid
The 2D (y,x) grid of coordinates used to evaluate the light 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 `LightProfile` 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 `LightProfile` are extracted and plotted as visuals for 2D plots.
"""
self.light_profile = light_profile
self.grid = grid
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,
)
def make_include_2d_all():
return Include2D(
origin=True,
mask=True,
border=True,
positions=True,
light_profile_centres=True,
mass_profile_centres=True,
critical_curves=False,
caustics=False,
multiple_images=False,
mapper_source_pixelization_grid=True,
mapper_data_pixelization_grid=True,
)
def __init__(
self,
mass_obj,
grid: aa.type.Grid2DLike,
get_visuals_2d: Callable,
mat_plot_2d: MatPlot2D = MatPlot2D(),
visuals_2d: Visuals2D = Visuals2D(),
include_2d: Include2D = Include2D(),
):
super().__init__(
mat_plot_2d=mat_plot_2d, include_2d=include_2d, visuals_2d=visuals_2d
)
self.mass_obj = mass_obj
self.grid = grid
self._get_visuals_2d = get_visuals_2d
def __init__(
self,
fit: FitQuantity,
mat_plot_2d: MatPlot2D = MatPlot2D(),
visuals_2d: Visuals2D = Visuals2D(),
include_2d: Include2D = Include2D(),
):
"""
Plots the attributes of `FitQuantity` objects using the matplotlib method `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 `FitQuantity` 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
----------
fit
The fit to an interferometer dataset the plotter 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 `FitQuantity` 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)
self.fit = fit
def __init__(
self,
light_profile_pdf_list: List[LightProfile],
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(),
sigma: Optional[float] = 3.0,
):
"""
Plots the attributes of a list of `LightProfile` 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 light 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 `LightProfile` 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
----------
light_profile_pdf_list
The list of light profiles whose mean and error values the plotter plots.
grid
The 2D (y,x) grid of coordinates used to evaluate the light 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 `LightProfile` 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 `LightProfile` 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__(
light_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.light_profile_pdf_list = light_profile_pdf_list
self.sigma = sigma
self.low_limit = (1 - math.erf(sigma / math.sqrt(2))) / 2