def plot_noise_map(
fit,
mask=None,
extract_array_from_mask=False,
zoom_around_mask=False,
positions=None,
as_subplot=False,
units="arcsec",
kpc_per_arcsec=None,
figsize=(7, 7),
aspect="square",
cmap="jet",
norm="linear",
norm_min=None,
norm_max=None,
linthresh=0.05,
linscale=0.01,
cb_ticksize=10,
cb_fraction=0.047,
cb_pad=0.01,
cb_tick_values=None,
cb_tick_labels=None,
title="Fit Noise-Map",
titlesize=16,
xlabelsize=16,
ylabelsize=16,
xyticksize=16,
mask_pointsize=10,
position_pointsize=10,
output_path=None,
output_format="show",
output_filename="fit_noise_map",
):
"""Plot the noise-map of a lens fit.
Set *autolens.datas.array.plotters.array_plotters* for a description of all input parameters not described below.
Parameters
-----------
image : datas.imaging.datas.Imaging
The datas-datas, which includes the observed datas, noise_map-map, PSF, signal-to-noise_map-map, etc.
plot_origin : True
If true, the origin of the datas's coordinate system is plotted as a 'x'.
"""
noise_map = fit.noise_map(return_in_2d=True)
array_plotters.plot_array(
array=noise_map,
mask=mask,
extract_array_from_mask=extract_array_from_mask,
zoom_around_mask=zoom_around_mask,
positions=positions,
as_subplot=as_subplot,
units=units,
kpc_per_arcsec=kpc_per_arcsec,
figsize=figsize,
aspect=aspect,
cmap=cmap,
norm=norm,
norm_min=norm_min,
norm_max=norm_max,
linthresh=linthresh,
linscale=linscale,
cb_ticksize=cb_ticksize,
cb_fraction=cb_fraction,
cb_pad=cb_pad,
cb_tick_values=cb_tick_values,
cb_tick_labels=cb_tick_labels,
title=title,
titlesize=titlesize,
xlabelsize=xlabelsize,
ylabelsize=ylabelsize,
xyticksize=xyticksize,
mask_pointsize=mask_pointsize,
position_pointsize=position_pointsize,
output_path=output_path,
output_format=output_format,
output_filename=output_filename,
)
def plot_model_image_of_plane(
fit,
plane_index,
mask=None,
extract_array_from_mask=False,
zoom_around_mask=False,
positions=None,
plot_mass_profile_centres=True,
as_subplot=False,
units="arcsec",
kpc_per_arcsec=None,
figsize=(7, 7),
aspect="square",
cmap="jet",
norm="linear",
norm_min=None,
norm_max=None,
linthresh=0.05,
linscale=0.01,
cb_ticksize=10,
cb_fraction=0.047,
cb_pad=0.01,
cb_tick_values=None,
cb_tick_labels=None,
title="Model Image",
titlesize=16,
xlabelsize=16,
ylabelsize=16,
xyticksize=16,
mask_pointsize=10,
position_pointsize=10,
output_path=None,
output_format="show",
output_filename="fit_model_image_of_plane",
):
"""Plot the model image of a specific plane of a lens fit.
Set *autolens.datas.array.plotters.array_plotters* for a description of all input parameters not described below.
Parameters
-----------
fit : datas.fitting.fitting.AbstractFitter
The fit to the datas, which includes a list of every model image, residual_map, chi-squareds, etc.
plane_indexes : [int]
The plane from which the model image is generated.
"""
output_filename += "_" + str(plane_index)
centres = get_mass_profile_centes(
plot_mass_profile_centres=plot_mass_profile_centres, fit=fit)
array_plotters.plot_array(
array=fit.model_images_of_planes(return_in_2d=True)[plane_index],
mask=mask,
extract_array_from_mask=extract_array_from_mask,
zoom_around_mask=zoom_around_mask,
positions=positions,
centres=centres,
as_subplot=as_subplot,
units=units,
kpc_per_arcsec=kpc_per_arcsec,
figsize=figsize,
aspect=aspect,
cmap=cmap,
norm=norm,
norm_min=norm_min,
norm_max=norm_max,
linthresh=linthresh,
linscale=linscale,
cb_ticksize=cb_ticksize,
cb_fraction=cb_fraction,
cb_pad=cb_pad,
cb_tick_values=cb_tick_values,
cb_tick_labels=cb_tick_labels,
title=title,
titlesize=titlesize,
xlabelsize=xlabelsize,
ylabelsize=ylabelsize,
xyticksize=xyticksize,
mask_pointsize=mask_pointsize,
position_pointsize=position_pointsize,
output_path=output_path,
output_format=output_format,
output_filename=output_filename,
)
def plot_contribution_maps(
fit,
mask=None,
extract_array_from_mask=False,
zoom_around_mask=False,
positions=None,
as_subplot=False,
units="arcsec",
kpc_per_arcsec=None,
figsize=(7, 7),
aspect="square",
cmap="jet",
norm="linear",
norm_min=None,
norm_max=None,
linthresh=0.05,
linscale=0.01,
cb_ticksize=10,
cb_fraction=0.047,
cb_pad=0.01,
cb_tick_values=None,
cb_tick_labels=None,
title="Contributions",
titlesize=16,
xlabelsize=16,
ylabelsize=16,
xyticksize=16,
mask_pointsize=10,
position_pointsize=10,
output_path=None,
output_format="show",
output_filename="fit_contribution_maps",
):
"""Plot the summed contribution maps of a hyper_galaxies-fit.
Set *autolens.datas.array.plotters.array_plotters* for a description of all input parameters not described below.
Parameters
-----------
fit : datas.fitting.fitting.AbstractLensHyperFit
The hyper_galaxies-fit to the datas, which includes a list of every model image, residual_map, chi-squareds, etc.
image_index : int
The index of the datas in the datas-set of which the contribution_maps are plotted.
"""
if len(fit.contribution_maps) > 1:
contribution_map = sum(fit.contribution_maps)
else:
contribution_map = fit.contribution_maps[0]
array_plotters.plot_array(
array=contribution_map,
mask=mask,
extract_array_from_mask=extract_array_from_mask,
zoom_around_mask=zoom_around_mask,
positions=positions,
as_subplot=as_subplot,
units=units,
kpc_per_arcsec=kpc_per_arcsec,
figsize=figsize,
aspect=aspect,
cmap=cmap,
norm=norm,
norm_min=norm_min,
norm_max=norm_max,
linthresh=linthresh,
linscale=linscale,
cb_ticksize=cb_ticksize,
cb_fraction=cb_fraction,
cb_pad=cb_pad,
cb_tick_values=cb_tick_values,
cb_tick_labels=cb_tick_labels,
title=title,
titlesize=titlesize,
xlabelsize=xlabelsize,
ylabelsize=ylabelsize,
xyticksize=xyticksize,
mask_pointsize=mask_pointsize,
position_pointsize=position_pointsize,
output_path=output_path,
output_format=output_format,
output_filename=output_filename,
)
def plot_reconstructed_image(
inversion,
mask=None,
positions=None,
grid=None,
as_subplot=False,
extract_array_from_mask=False,
zoom_around_mask=False,
units="arcsec",
kpc_per_arcsec=None,
figsize=(7, 7),
aspect="square",
cmap="jet",
norm="linear",
norm_min=None,
norm_max=None,
linthresh=0.05,
linscale=0.01,
cb_ticksize=10,
cb_fraction=0.047,
cb_pad=0.01,
cb_tick_values=None,
cb_tick_labels=None,
title="Reconstructed Image",
titlesize=16,
xlabelsize=16,
ylabelsize=16,
xyticksize=16,
output_path=None,
output_format="show",
output_filename="reconstructed_inversion_image",
):
array_plotters.plot_array(
array=inversion.reconstructed_data_2d,
mask=mask,
positions=positions,
grid=grid,
as_subplot=as_subplot,
extract_array_from_mask=extract_array_from_mask,
zoom_around_mask=zoom_around_mask,
units=units,
kpc_per_arcsec=kpc_per_arcsec,
figsize=figsize,
aspect=aspect,
cmap=cmap,
norm=norm,
norm_min=norm_min,
norm_max=norm_max,
linthresh=linthresh,
linscale=linscale,
cb_ticksize=cb_ticksize,
cb_fraction=cb_fraction,
cb_pad=cb_pad,
cb_tick_values=cb_tick_values,
cb_tick_labels=cb_tick_labels,
title=title,
titlesize=titlesize,
xlabelsize=xlabelsize,
ylabelsize=ylabelsize,
xyticksize=xyticksize,
output_path=output_path,
output_format=output_format,
output_filename=output_filename,
)
from autolens.array import mask as msk
from test.simulation import simulation_util
from autolens.plotters import array_plotters
# In this tutorial, we'll introduce a new pixelization, called an adaptive-pixelization. This pixelization doesn't use
# uniform grid of rectangular pixels, but instead uses ir'Voronoi' pixels. So, why would we want to do that?
# Lets take another look at the rectangular grid, and think about its weakness.
# Lets quickly remind ourselves of the image, and the 3.0" circular mask we'll use to mask it.
imaging_data = simulation_util.load_test_imaging_data(
data_type="lens_light_dev_vaucouleurs", data_resolution="LSST")
array = imaging_data.image
mask = al.Mask.elliptical(
shape=imaging_data.shape,
pixel_scale=imaging_data.pixel_scale,
major_axis_radius_arcsec=3.0,
axis_ratio=1.0,
phi=0.0,
centre=(0.0, 0.0),
)
array_plotters.plot_array(array=array)
def plot_deflections_x(
tracer,
grid,
mask=None,
extract_array_from_mask=False,
zoom_around_mask=False,
as_subplot=False,
units="arcsec",
figsize=(7, 7),
aspect="square",
cmap="jet",
norm="linear",
norm_min=None,
norm_max=None,
linthresh=0.05,
linscale=0.01,
cb_ticksize=10,
cb_fraction=0.047,
cb_pad=0.01,
cb_tick_values=None,
cb_tick_labels=None,
title="Tracer Deflections (x)",
titlesize=16,
xlabelsize=16,
ylabelsize=16,
xyticksize=16,
output_path=None,
output_format="show",
output_filename="tracer_deflections_x",
):
deflections = tracer.deflections_from_grid(
grid=grid, return_in_2d=False, return_binned=True
)
deflections_x = grid.mapping.scaled_array_2d_from_array_1d(
array_1d=deflections[:, 1]
)
array_plotters.plot_array(
array=deflections_x,
mask=mask,
extract_array_from_mask=extract_array_from_mask,
zoom_around_mask=zoom_around_mask,
as_subplot=as_subplot,
units=units,
kpc_per_arcsec=tracer.image_plane.kpc_per_arcsec,
figsize=figsize,
aspect=aspect,
cmap=cmap,
norm=norm,
norm_min=norm_min,
norm_max=norm_max,
linthresh=linthresh,
linscale=linscale,
cb_ticksize=cb_ticksize,
cb_fraction=cb_fraction,
cb_pad=cb_pad,
cb_tick_values=cb_tick_values,
cb_tick_labels=cb_tick_labels,
title=title,
titlesize=titlesize,
xlabelsize=xlabelsize,
ylabelsize=ylabelsize,
xyticksize=xyticksize,
output_path=output_path,
output_format=output_format,
output_filename=output_filename,
)
def plot_magnification(
mass_profile,
grid,
mask=None,
extract_array_from_mask=False,
zoom_around_mask=False,
positions=None,
as_subplot=False,
plot_critical_curves=False,
plot_caustics=False,
units="arcsec",
kpc_per_arcsec=None,
figsize=(7, 7),
aspect="square",
cmap="jet",
norm="linear",
norm_min=None,
norm_max=None,
linthresh=0.05,
linscale=0.01,
cb_ticksize=10,
cb_fraction=0.047,
cb_pad=0.01,
cb_tick_values=None,
cb_tick_labels=None,
title="Magnification",
titlesize=16,
xlabelsize=16,
ylabelsize=16,
xyticksize=16,
mask_pointsize=10,
position_pointsize=10.0,
grid_pointsize=1,
output_path=None,
output_format="show",
output_filename="magnification",
):
"""Plot the magnification of a mass profile, on a grid of (y,x) coordinates.
Set *autolens.hyper_galaxies.array.plotters.array_plotters* for a description of all innput parameters not described below.
Parameters
-----------
mass_profile : model.profiles.mass_profiles.MassProfile
The mass profile whose magnification is plotted.
grid : ndarray or hyper_galaxies.array.grid_stacks.Grid
The (y,x) coordinates of the grid, in an array of shape (total_coordinates, 2)
"""
magnification = mass_profile.magnification_from_grid(
grid=grid, bypass_decorator=False)
lines = plotter_util.get_critical_curve_and_caustic(
obj=mass_profile,
grid=grid,
plot_critical_curve=plot_critical_curves,
plot_caustics=plot_caustics,
)
array_plotters.plot_array(
array=magnification,
mask=mask,
extract_array_from_mask=extract_array_from_mask,
zoom_around_mask=zoom_around_mask,
positions=positions,
lines=lines,
as_subplot=as_subplot,
units=units,
kpc_per_arcsec=kpc_per_arcsec,
figsize=figsize,
aspect=aspect,
cmap=cmap,
norm=norm,
norm_min=norm_min,
norm_max=norm_max,
linthresh=linthresh,
linscale=linscale,
cb_ticksize=cb_ticksize,
cb_fraction=cb_fraction,
cb_pad=cb_pad,
cb_tick_values=cb_tick_values,
cb_tick_labels=cb_tick_labels,
title=title,
titlesize=titlesize,
xlabelsize=xlabelsize,
ylabelsize=ylabelsize,
xyticksize=xyticksize,
mask_pointsize=mask_pointsize,
position_pointsize=position_pointsize,
grid_pointsize=grid_pointsize,
output_path=output_path,
output_format=output_format,
output_filename=output_filename,
)
from autolens.array import scaled_array
from autolens.plotters import array_plotters
import numpy as np
array = scaled_array.ScaledSquarePixelArray(array=np.ones((50, 50)),
pixel_scale=0.1)
# array_plotters.plot_array(array=array, centres=[[(1.0, 1.0)], [(-1.0, 1.0)], [(-2.0, -2.0), (-3.0, -3.0)]])
array_plotters.plot_array(array=array,
centres=[[(0.0, 0.0)]],
axis_ratios=[[0.5]],
phis=[[45.0]])
stop
array_plotters.plot_array(
array=array,
centres=[[(0.0, 0.0)], [(-1.0, 1.0)], [(-2.0, -2.0), (-3.0, -3.0)]],
axis_ratios=[[0.5], [0.5], [0.3, 0.3]],
phis=[[0.0], [90.0], [45.0, 15.0]],
)
array_plotters.plot_array(array=array,
positions=[[[1.0, 1.0], [2.0, 2.0]], [[-1.0, -1.0]]])
def plot_image(
image,
plot_origin=True,
grid=None,
mask=None,
extract_array_from_mask=False,
zoom_around_mask=False,
should_plot_border=False,
positions=None,
as_subplot=False,
units="arcsec",
kpc_per_arcsec=None,
figsize=(7, 7),
aspect="square",
cmap="jet",
norm="linear",
norm_min=None,
norm_max=None,
linthresh=0.05,
linscale=0.01,
cb_ticksize=10,
cb_fraction=0.047,
cb_pad=0.01,
cb_tick_values=None,
cb_tick_labels=None,
title="Image",
titlesize=16,
xlabelsize=16,
ylabelsize=16,
xyticksize=16,
mask_pointsize=10,
position_pointsize=30,
grid_pointsize=1,
output_path=None,
output_format="show",
output_filename="image",
):
"""Plot the observed image of the imaging data_type.
Set *autolens.data_type.array.plotters.array_plotters* for a description of all input parameters not described below.
Parameters
-----------
image : ScaledSquarePixelArray
The image of the data.
plot_origin : True
If true, the origin of the data's coordinate system is plotted as a 'x'.
image_plane_pix_grid : ndarray or data_type.array.grid_stacks.PixGrid
If an adaptive pixelization whose pixels are formed by tracing pixels from the data, this plots those pixels \
over the immage.
"""
origin = get_origin(array=image, plot_origin=plot_origin)
array_plotters.plot_array(
array=image,
origin=origin,
grid=grid,
mask=mask,
extract_array_from_mask=extract_array_from_mask,
zoom_around_mask=zoom_around_mask,
should_plot_border=should_plot_border,
positions=positions,
as_subplot=as_subplot,
units=units,
kpc_per_arcsec=kpc_per_arcsec,
figsize=figsize,
aspect=aspect,
cmap=cmap,
norm=norm,
norm_min=norm_min,
norm_max=norm_max,
linthresh=linthresh,
linscale=linscale,
cb_ticksize=cb_ticksize,
cb_fraction=cb_fraction,
cb_pad=cb_pad,
cb_tick_values=cb_tick_values,
cb_tick_labels=cb_tick_labels,
title=title,
titlesize=titlesize,
xlabelsize=xlabelsize,
ylabelsize=ylabelsize,
xyticksize=xyticksize,
mask_pointsize=mask_pointsize,
position_pointsize=position_pointsize,
grid_pointsize=grid_pointsize,
output_path=output_path,
output_format=output_format,
output_filename=output_filename,
)
def plot_deflections_y(
plane,
grid,
mask=None,
extract_array_from_mask=False,
zoom_around_mask=False,
plot_critical_curves=False,
plot_caustics=False,
as_subplot=False,
units="arcsec",
figsize=(7, 7),
aspect="square",
cmap="jet",
norm="linear",
norm_min=None,
norm_max=None,
linthresh=0.05,
linscale=0.01,
cb_ticksize=10,
cb_fraction=0.047,
cb_pad=0.01,
cb_tick_values=None,
cb_tick_labels=None,
title="Plane Deflections (y)",
titlesize=16,
xlabelsize=16,
ylabelsize=16,
xyticksize=16,
output_path=None,
output_format="show",
output_filename="plane_deflections_y",
):
deflections = plane.deflections_from_grid(grid=grid,
return_in_2d=False,
return_binned=True)
deflections_y = grid.mapping.scaled_array_2d_from_array_1d(
array_1d=deflections[:, 0])
lines = plotter_util.get_critical_curve_and_caustic(
obj=plane,
grid=grid,
plot_critical_curve=plot_critical_curves,
plot_caustics=plot_caustics,
)
array_plotters.plot_array(
array=deflections_y,
mask=mask,
extract_array_from_mask=extract_array_from_mask,
zoom_around_mask=zoom_around_mask,
lines=lines,
as_subplot=as_subplot,
units=units,
kpc_per_arcsec=plane.kpc_per_arcsec,
figsize=figsize,
aspect=aspect,
cmap=cmap,
norm=norm,
norm_min=norm_min,
norm_max=norm_max,
linthresh=linthresh,
linscale=linscale,
cb_ticksize=cb_ticksize,
cb_fraction=cb_fraction,
cb_pad=cb_pad,
cb_tick_values=cb_tick_values,
cb_tick_labels=cb_tick_labels,
title=title,
titlesize=titlesize,
xlabelsize=xlabelsize,
ylabelsize=ylabelsize,
xyticksize=xyticksize,
output_path=output_path,
output_format=output_format,
output_filename=output_filename,
)
def plot_magnification(
plane,
grid,
mask=None,
extract_array_from_mask=False,
zoom_around_mask=False,
plot_critical_curves=False,
plot_caustics=False,
as_subplot=False,
units="arcsec",
figsize=(7, 7),
aspect="square",
cmap="jet",
norm="linear",
norm_min=None,
norm_max=None,
linthresh=0.05,
linscale=0.01,
cb_ticksize=10,
cb_fraction=0.047,
cb_pad=0.01,
cb_tick_values=None,
cb_tick_labels=None,
title="Plane Magnification",
titlesize=16,
xlabelsize=16,
ylabelsize=16,
xyticksize=16,
output_path=None,
output_format="show",
output_filename="plane_magnification",
):
magnification = plane.magnification_from_grid(grid=grid,
bypass_decorator=False)
lines = plotter_util.get_critical_curve_and_caustic(
obj=plane,
grid=grid,
plot_critical_curve=plot_critical_curves,
plot_caustics=plot_caustics,
)
array_plotters.plot_array(
array=magnification,
mask=mask,
extract_array_from_mask=extract_array_from_mask,
zoom_around_mask=zoom_around_mask,
lines=lines,
as_subplot=as_subplot,
units=units,
kpc_per_arcsec=plane.kpc_per_arcsec,
figsize=figsize,
aspect=aspect,
cmap=cmap,
norm=norm,
norm_min=norm_min,
norm_max=norm_max,
linthresh=linthresh,
linscale=linscale,
cb_ticksize=cb_ticksize,
cb_fraction=cb_fraction,
cb_pad=cb_pad,
cb_tick_values=cb_tick_values,
cb_tick_labels=cb_tick_labels,
title=title,
titlesize=titlesize,
xlabelsize=xlabelsize,
ylabelsize=ylabelsize,
xyticksize=xyticksize,
output_path=output_path,
output_format=output_format,
output_filename=output_filename,
)
def plot_plane_image(
plane,
grid,
plot_origin=True,
positions=None,
plot_grid=True,
lines=None,
as_subplot=False,
units="arcsec",
figsize=(7, 7),
aspect="square",
cmap="jet",
norm="linear",
norm_min=None,
norm_max=None,
linthresh=0.05,
linscale=0.01,
cb_ticksize=10,
cb_fraction=0.047,
cb_pad=0.01,
cb_tick_values=None,
cb_tick_labels=None,
title="Plane Image",
titlesize=16,
xlabelsize=16,
ylabelsize=16,
xyticksize=16,
position_pointsize=10,
grid_pointsize=1,
output_path=None,
output_format="show",
output_filename="plane_plane_image",
):
plane_image = plane.plane_image_from_grid(grid=grid)
if not plot_grid:
grid = None
if plot_origin:
origin = plane_image.origin
else:
origin = None
array_plotters.plot_array(
array=plane_image,
origin=origin,
positions=positions,
grid=grid,
lines=lines,
as_subplot=as_subplot,
units=units,
kpc_per_arcsec=plane.kpc_per_arcsec,
figsize=figsize,
aspect=aspect,
cmap=cmap,
norm=norm,
norm_min=norm_min,
norm_max=norm_max,
linthresh=linthresh,
linscale=linscale,
cb_ticksize=cb_ticksize,
cb_fraction=cb_fraction,
cb_pad=cb_pad,
cb_tick_values=cb_tick_values,
cb_tick_labels=cb_tick_labels,
title=title,
titlesize=titlesize,
xlabelsize=xlabelsize,
ylabelsize=ylabelsize,
xyticksize=xyticksize,
position_pointsize=position_pointsize,
grid_pointsize=grid_pointsize,
output_path=output_path,
output_format=output_format,
output_filename=output_filename,
)
def plot_profile_image(
plane,
grid,
mask=None,
extract_array_from_mask=False,
zoom_around_mask=False,
positions=None,
plot_grid=False,
plot_critical_curves=False,
plot_caustics=False,
as_subplot=False,
units="arcsec",
figsize=(7, 7),
aspect="square",
cmap="jet",
norm="linear",
norm_min=None,
norm_max=None,
linthresh=0.05,
linscale=0.01,
cb_ticksize=10,
cb_fraction=0.047,
cb_pad=0.01,
cb_tick_values=None,
cb_tick_labels=None,
title="Plane Profile Image",
titlesize=16,
xlabelsize=16,
ylabelsize=16,
xyticksize=16,
mask_pointsize=10,
position_pointsize=10.0,
grid_pointsize=1,
output_path=None,
output_format="show",
output_filename="plane_profile_image",
):
profile_image = plane.profile_image_from_grid(grid=grid,
bypass_decorator=False)
if plane.has_mass_profile:
lines = plotter_util.get_critical_curve_and_caustic(
obj=plane,
grid=grid,
plot_critical_curve=plot_critical_curves,
plot_caustics=plot_caustics,
)
else:
lines = None
if not plot_grid:
grid = None
array_plotters.plot_array(
array=profile_image,
mask=mask,
extract_array_from_mask=extract_array_from_mask,
zoom_around_mask=zoom_around_mask,
positions=positions,
grid=grid,
lines=lines,
as_subplot=as_subplot,
units=units,
kpc_per_arcsec=plane.kpc_per_arcsec,
figsize=figsize,
aspect=aspect,
cmap=cmap,
norm=norm,
norm_min=norm_min,
norm_max=norm_max,
linthresh=linthresh,
linscale=linscale,
cb_ticksize=cb_ticksize,
cb_fraction=cb_fraction,
cb_pad=cb_pad,
cb_tick_values=cb_tick_values,
cb_tick_labels=cb_tick_labels,
title=title,
titlesize=titlesize,
xlabelsize=xlabelsize,
ylabelsize=ylabelsize,
xyticksize=xyticksize,
mask_pointsize=mask_pointsize,
position_pointsize=position_pointsize,
grid_pointsize=grid_pointsize,
output_path=output_path,
output_format=output_format,
output_filename=output_filename,
)
def plot_deflections_x(
galaxy,
grid,
mask=None,
extract_array_from_mask=False,
zoom_around_mask=False,
positions=None,
plot_critical_curves=False,
plot_caustics=False,
as_subplot=False,
units="arcsec",
kpc_per_arcsec=None,
figsize=(7, 7),
aspect="square",
cmap="jet",
norm="linear",
norm_min=None,
norm_max=None,
linthresh=0.05,
linscale=0.01,
cb_ticksize=10,
cb_fraction=0.047,
cb_pad=0.01,
cb_tick_values=None,
cb_tick_labels=None,
title="Galaxy Deflections (x)",
titlesize=16,
xlabelsize=16,
ylabelsize=16,
xyticksize=16,
mask_pointsize=10,
position_pointsize=10.0,
grid_pointsize=1,
output_path=None,
output_format="show",
output_filename="galaxy_deflections_x",
):
"""Plot the x component of the deflection angles of a galaxy, on a grid of (y,x) coordinates.
Set *autolens.datas.array.plotters.array_plotters* for a description of all innput parameters not described below.
Parameters
-----------
galaxy : model.galaxy.galaxy.Galaxy
The galaxy whose x deflecton angles are plotted.
grid : ndarray or datas.array.grid_stacks.Grid
The (y,x) coordinates of the grid, in an array of shape (total_coordinates, 2)
"""
deflections = galaxy.deflections_from_grid(grid,
return_in_2d=False,
return_binned=True)
deflections_x = grid.mapping.scaled_array_2d_from_array_1d(deflections[:,
1])
lines = plotter_util.get_critical_curve_and_caustic(
obj=galaxy,
grid=grid,
plot_critical_curve=plot_critical_curves,
plot_caustics=plot_caustics,
)
array_plotters.plot_array(
array=deflections_x,
mask=mask,
extract_array_from_mask=extract_array_from_mask,
zoom_around_mask=zoom_around_mask,
positions=positions,
lines=lines,
as_subplot=as_subplot,
units=units,
kpc_per_arcsec=kpc_per_arcsec,
figsize=figsize,
aspect=aspect,
cmap=cmap,
norm=norm,
norm_min=norm_min,
norm_max=norm_max,
linthresh=linthresh,
linscale=linscale,
cb_ticksize=cb_ticksize,
cb_fraction=cb_fraction,
cb_pad=cb_pad,
cb_tick_values=cb_tick_values,
cb_tick_labels=cb_tick_labels,
title=title,
titlesize=titlesize,
xlabelsize=xlabelsize,
ylabelsize=ylabelsize,
xyticksize=xyticksize,
mask_pointsize=mask_pointsize,
position_pointsize=position_pointsize,
grid_pointsize=grid_pointsize,
output_path=output_path,
output_format=output_format,
output_filename=output_filename,
)
# Lets quickly remind ourselves of the image, and the 3.0" circular mask we'll use to mask it.
imaging_data = simulation_util.load_test_imaging_data(
data_type="lens_light_dev_vaucouleurs", data_resolution="LSST")
array = imaging_data.image
mask = al.Mask.circular(
shape=imaging_data.shape,
pixel_scale=imaging_data.pixel_scale,
radius_arcsec=5.0,
centre=(0.0, 0.0),
)
array_plotters.plot_array(
array=array,
mask=mask,
positions=[[[1.0, 1.0]]],
centres=[[(0.0, 0.0)]],
zoom_around_mask=True,
extract_array_from_mask=True,
)
imaging_data = simulation_util.load_test_imaging_data(
data_type="lens_sis__source_smooth__offset_centre", data_resolution="LSST")
array = imaging_data.image
mask = al.Mask.circular(
shape=imaging_data.shape,
pixel_scale=imaging_data.pixel_scale,
radius_arcsec=5.0,
centre=(1.0, 1.0),
)
array_plotters.plot_array(array=array,
def plot_potential_chi_squared_map(
potential_chi_squared_map,
plot_origin=True,
mask=None,
extract_array_from_mask=False,
zoom_around_mask=False,
as_subplot=False,
units="arcsec",
kpc_per_arcsec=None,
figsize=(7, 7),
aspect="square",
cmap="jet",
norm="linear",
norm_min=None,
norm_max=None,
linthresh=0.05,
linscale=0.01,
cb_ticksize=10,
cb_fraction=0.047,
cb_pad=0.01,
cb_tick_values=None,
cb_tick_labels=None,
title="Potential Chi-Squared Map",
titlesize=16,
xlabelsize=16,
ylabelsize=16,
xyticksize=16,
mask_pointsize=10,
output_path=None,
output_format="show",
output_filename="potential_chi_squared_map",
):
"""Plot the signal-to-noise_map of the imaging data_type.
Set *autolens.data_type.array.plotters.array_plotters* for a description of all input parameters not described below.
Parameters
-----------
potential_chi_squared_map : ScaledSquarePixelArray
The signal-to-noise map of the data.
plot_origin : True
If true, the origin of the data's coordinate system is plotted as a 'x'.
"""
origin = get_origin(array=potential_chi_squared_map,
plot_origin=plot_origin)
array_plotters.plot_array(
array=potential_chi_squared_map,
origin=origin,
mask=mask,
extract_array_from_mask=extract_array_from_mask,
zoom_around_mask=zoom_around_mask,
as_subplot=as_subplot,
units=units,
kpc_per_arcsec=kpc_per_arcsec,
figsize=figsize,
aspect=aspect,
cmap=cmap,
norm=norm,
norm_min=norm_min,
norm_max=norm_max,
linthresh=linthresh,
linscale=linscale,
cb_ticksize=cb_ticksize,
cb_fraction=cb_fraction,
cb_pad=cb_pad,
cb_tick_values=cb_tick_values,
cb_tick_labels=cb_tick_labels,
title=title,
titlesize=titlesize,
xlabelsize=xlabelsize,
ylabelsize=ylabelsize,
xyticksize=xyticksize,
mask_pointsize=mask_pointsize,
output_path=output_path,
output_format=output_format,
output_filename=output_filename,
)
def plot_potential(
tracer,
grid,
mask=None,
extract_array_from_mask=False,
zoom_around_mask=False,
as_subplot=False,
units="arcsec",
figsize=(7, 7),
aspect="square",
cmap="jet",
norm="linear",
norm_min=None,
norm_max=None,
linthresh=0.05,
linscale=0.01,
cb_ticksize=10,
cb_fraction=0.047,
cb_pad=0.01,
cb_tick_values=None,
cb_tick_labels=None,
title="Tracer Potential",
titlesize=16,
xlabelsize=16,
ylabelsize=16,
xyticksize=16,
output_path=None,
output_format="show",
output_filename="tracer_potential",
):
potential = tracer.potential_from_grid(
grid=grid, bypass_decorator=False
)
array_plotters.plot_array(
array=potential,
mask=mask,
extract_array_from_mask=extract_array_from_mask,
zoom_around_mask=zoom_around_mask,
as_subplot=as_subplot,
units=units,
kpc_per_arcsec=tracer.image_plane.kpc_per_arcsec,
figsize=figsize,
aspect=aspect,
cmap=cmap,
norm=norm,
norm_min=norm_min,
norm_max=norm_max,
linthresh=linthresh,
linscale=linscale,
cb_ticksize=cb_ticksize,
cb_fraction=cb_fraction,
cb_pad=cb_pad,
cb_tick_values=cb_tick_values,
cb_tick_labels=cb_tick_labels,
title=title,
titlesize=titlesize,
xlabelsize=xlabelsize,
ylabelsize=ylabelsize,
xyticksize=xyticksize,
output_path=output_path,
output_format=output_format,
output_filename=output_filename,
)
def plot_primary_beam(
primary_beam,
plot_origin=True,
as_subplot=False,
units="arcsec",
kpc_per_arcsec=None,
figsize=(7, 7),
aspect="square",
cmap="jet",
norm="linear",
norm_min=None,
norm_max=None,
linthresh=0.05,
linscale=0.01,
cb_ticksize=10,
cb_fraction=0.047,
cb_pad=0.01,
cb_tick_values=None,
cb_tick_labels=None,
title="Primary Beam",
titlesize=16,
xlabelsize=16,
ylabelsize=16,
xyticksize=16,
output_path=None,
output_format="show",
output_filename="primary_beam",
):
"""Plot the PSF of the imaging data_type.
Set *autolens.data_type.array.plotters.array_plotters* for a description of all input parameters not described below.
Parameters
-----------
signal_to_noise_map : ScaledSquarePixelArray
The primary_beam of the data.
plot_origin : True
If true, the origin of the data's coordinate system is plotted as a 'x'.
"""
origin = get_origin(array=primary_beam, plot_origin=plot_origin)
array_plotters.plot_array(
array=primary_beam,
origin=origin,
as_subplot=as_subplot,
units=units,
kpc_per_arcsec=kpc_per_arcsec,
figsize=figsize,
aspect=aspect,
cmap=cmap,
norm=norm,
norm_min=norm_min,
norm_max=norm_max,
linthresh=linthresh,
linscale=linscale,
cb_ticksize=cb_ticksize,
cb_fraction=cb_fraction,
cb_pad=cb_pad,
cb_tick_values=cb_tick_values,
cb_tick_labels=cb_tick_labels,
title=title,
titlesize=titlesize,
xlabelsize=xlabelsize,
ylabelsize=ylabelsize,
xyticksize=xyticksize,
output_path=output_path,
output_format=output_format,
output_filename=output_filename,
)
def plot_galaxy_data_array(
galaxy_data,
mask=None,
extract_array_from_mask=False,
zoom_around_mask=False,
positions=None,
as_subplot=False,
units="arcsec",
kpc_per_arcsec=None,
figsize=None,
aspect="square",
cmap="jet",
norm="linear",
norm_min=None,
norm_max=None,
linthresh=0.05,
linscale=0.01,
cb_ticksize=10,
cb_fraction=0.047,
cb_pad=0.01,
cb_tick_values=None,
cb_tick_labels=None,
titlesize=10,
xlabelsize=10,
ylabelsize=10,
xyticksize=10,
mask_pointsize=10,
position_pointsize=10.0,
grid_pointsize=1,
output_path=None,
output_filename="galaxy_data",
output_format="show",
):
if galaxy_data.use_image:
title = "Galaxy Data Image"
elif galaxy_data.use_convergence:
title = "Galaxy Data Convergence"
elif galaxy_data.use_potential:
title = "Galaxy Data Potential"
elif galaxy_data.use_deflections_y:
title = "Galaxy Data Deflections (y)"
elif galaxy_data.use_deflections_x:
title = "Galaxy Data Deflections (x)"
else:
raise exc.PlottingException(
"The galaxy data_type array does not have a True use_profile_type")
array_plotters.plot_array(
array=galaxy_data.image(return_in_2d=True),
mask=mask,
extract_array_from_mask=extract_array_from_mask,
zoom_around_mask=zoom_around_mask,
positions=positions,
as_subplot=as_subplot,
units=units,
kpc_per_arcsec=kpc_per_arcsec,
figsize=figsize,
aspect=aspect,
cmap=cmap,
norm=norm,
norm_min=norm_min,
norm_max=norm_max,
linthresh=linthresh,
linscale=linscale,
cb_ticksize=cb_ticksize,
cb_fraction=cb_fraction,
cb_pad=cb_pad,
cb_tick_values=cb_tick_values,
cb_tick_labels=cb_tick_labels,
title=title,
titlesize=titlesize,
xlabelsize=xlabelsize,
ylabelsize=ylabelsize,
xyticksize=xyticksize,
mask_pointsize=mask_pointsize,
position_pointsize=position_pointsize,
grid_pointsize=grid_pointsize,
output_path=output_path,
output_format=output_format,
output_filename=output_filename,
)
def plot_normalized_residual_map(
fit,
mask=None,
extract_array_from_mask=False,
zoom_around_mask=False,
positions=None,
as_subplot=False,
units="arcsec",
kpc_per_arcsec=None,
figsize=(7, 7),
aspect="square",
cmap="jet",
norm="linear",
norm_min=None,
norm_max=None,
linthresh=0.05,
linscale=0.01,
cb_ticksize=10,
cb_fraction=0.047,
cb_pad=0.01,
cb_tick_values=None,
cb_tick_labels=None,
title="Fit Normalized Residuals",
titlesize=16,
xlabelsize=16,
ylabelsize=16,
xyticksize=16,
mask_pointsize=10,
position_pointsize=10,
output_path=None,
output_format="show",
output_filename="fit_normalized_residual_map",
):
"""Plot the residual-map of a lens fit.
Set *autolens.datas.array.plotters.array_plotters* for a description of all input parameters not described below.
Parameters
-----------
fit : datas.fitting.fitting.AbstractFitter
The fit to the datas, which includes a list of every model image, normalized_residual_map, chi-squareds, etc.
image_index : int
The index of the datas in the datas-set of which the normalized_residual_map are plotted.
"""
normalized_residual_map = fit.normalized_residual_map(return_in_2d=True)
array_plotters.plot_array(
array=normalized_residual_map,
mask=mask,
extract_array_from_mask=extract_array_from_mask,
zoom_around_mask=zoom_around_mask,
positions=positions,
as_subplot=as_subplot,
units=units,
kpc_per_arcsec=kpc_per_arcsec,
figsize=figsize,
aspect=aspect,
cmap=cmap,
norm=norm,
norm_min=norm_min,
norm_max=norm_max,
linthresh=linthresh,
linscale=linscale,
cb_ticksize=cb_ticksize,
cb_fraction=cb_fraction,
cb_pad=cb_pad,
cb_tick_values=cb_tick_values,
cb_tick_labels=cb_tick_labels,
title=title,
titlesize=titlesize,
xlabelsize=xlabelsize,
ylabelsize=ylabelsize,
xyticksize=xyticksize,
mask_pointsize=mask_pointsize,
position_pointsize=position_pointsize,
output_path=output_path,
output_format=output_format,
output_filename=output_filename,
)
difference_y = deflections[:, 0] - true_deflections[:, 0]
difference_x = deflections[:, 1] - true_deflections[:, 1]
print("interpolation y error: ", np.mean(difference_y))
print("interpolation y uncertainty: ", np.std(difference_y))
print("interpolation y max error: ", np.max(difference_y))
print("interpolation x error: ", np.mean(difference_x))
print("interpolation x uncertainty: ", np.std(difference_x))
print("interpolation x max error: ", np.max(difference_x))
difference_y_2d = lens_data.grid.scaled_array_2d_with_sub_dimensions_from_sub_array_1d(
sub_array_1d=difference_y)
difference_x_2d = lens_data.grid.scaled_array_2d_with_sub_dimensions_from_sub_array_1d(
sub_array_1d=difference_x)
array_plotters.plot_array(array=true_deflections_y_2d)
array_plotters.plot_array(array=difference_y_2d)
array_plotters.plot_array(array=true_deflections_x_2d)
array_plotters.plot_array(array=difference_x_2d)
# difference_percent_y = (np.abs(difference_y) / np.abs(true_deflections[:,0]))*100.0
# difference_percent_x = (np.abs(difference_x) / np.abs(true_deflections[:,1]))*100.0
#
# print("interpolation y mean percent difference: ", np.mean(difference_percent_y))
# print("interpolation y std percent difference: ", np.std(difference_percent_y))
# print("interpolation y max percent difference: ", np.max(difference_percent_y))
# print("interpolation x mean percent difference: ", np.mean(difference_percent_x))
# print("interpolation x std percent difference: ", np.std(difference_percent_x))
# print("interpolation x mean percent difference: ", np.max(difference_percent_x))
#
def plot_subtracted_image_of_plane(
fit,
plane_index,
mask=None,
extract_array_from_mask=False,
zoom_around_mask=False,
positions=None,
image_plane_pix_grid=None,
as_subplot=False,
units="arcsec",
kpc_per_arcsec=None,
figsize=(7, 7),
aspect="square",
cmap="jet",
norm="linear",
norm_min=None,
norm_max=None,
linthresh=0.05,
linscale=0.01,
cb_ticksize=10,
cb_fraction=0.047,
cb_pad=0.01,
cb_tick_values=None,
cb_tick_labels=None,
title="Fit Model Image",
titlesize=16,
xlabelsize=16,
ylabelsize=16,
xyticksize=16,
mask_pointsize=10,
position_pointsize=10,
output_path=None,
output_format="show",
output_filename="fit_subtracted_image_of_plane",
):
"""Plot the model image of a specific plane of a lens fit.
Set *autolens.datas.array.plotters.array_plotters* for a description of all input parameters not described below.
Parameters
-----------
fit : datas.fitting.fitting.AbstractFitter
The fit to the datas, which includes a list of every model image, residual_map, chi-squareds, etc.
image_index : int
The index of the datas in the datas-set of which the model image is plotted.
plane_indexes : int
The plane from which the model image is generated.
"""
output_filename += "_" + str(plane_index)
if fit.tracer.total_planes > 1:
other_planes_model_images_2d = [
model_image_2d for i, model_image_2d in enumerate(
fit.model_images_of_planes(return_in_2d=True))
if i != plane_index
]
subtracted_image = fit.image(
return_in_2d=True) - sum(other_planes_model_images_2d)
else:
subtracted_image = fit.image(return_in_2d=True)
array_plotters.plot_array(
array=subtracted_image,
mask=mask,
extract_array_from_mask=extract_array_from_mask,
grid=image_plane_pix_grid,
zoom_around_mask=zoom_around_mask,
positions=positions,
as_subplot=as_subplot,
units=units,
kpc_per_arcsec=kpc_per_arcsec,
figsize=figsize,
aspect=aspect,
cmap=cmap,
norm=norm,
norm_min=norm_min,
norm_max=norm_max,
linthresh=linthresh,
linscale=linscale,
cb_ticksize=cb_ticksize,
cb_fraction=cb_fraction,
cb_pad=cb_pad,
cb_tick_values=cb_tick_values,
cb_tick_labels=cb_tick_labels,
title=title,
titlesize=titlesize,
xlabelsize=xlabelsize,
ylabelsize=ylabelsize,
xyticksize=xyticksize,
mask_pointsize=mask_pointsize,
position_pointsize=position_pointsize,
output_path=output_path,
output_format=output_format,
output_filename=output_filename,
)
def plot_inversion_with_source_values(
inversion,
source_pixel_values,
plot_origin=True,
positions=None,
should_plot_centres=False,
should_plot_grid=False,
should_plot_border=False,
image_pixels=None,
source_pixels=None,
as_subplot=False,
units="arcsec",
kpc_per_arcsec=None,
figsize=(7, 7),
aspect="square",
cmap="jet",
norm="linear",
norm_min=None,
norm_max=None,
linthresh=0.05,
linscale=0.01,
cb_ticksize=10,
cb_fraction=0.047,
cb_pad=0.01,
cb_tick_values=None,
cb_tick_labels=None,
title="Reconstructed Pixelization",
titlesize=16,
xlabelsize=16,
ylabelsize=16,
xyticksize=16,
output_path=None,
output_format="show",
output_filename="pixelization_source_values",
):
if isinstance(inversion.mapper, mappers.RectangularMapper):
reconstructed_pixelization = inversion.mapper.reconstructed_pixelization_from_solution_vector(
solution_vector=source_pixel_values)
origin = get_origin(image=reconstructed_pixelization,
plot_origin=plot_origin)
array_plotters.plot_array(
array=reconstructed_pixelization,
origin=origin,
positions=positions,
as_subplot=True,
units=units,
kpc_per_arcsec=kpc_per_arcsec,
figsize=figsize,
aspect=aspect,
cmap=cmap,
norm=norm,
norm_min=norm_min,
norm_max=norm_max,
linthresh=linthresh,
linscale=linscale,
cb_ticksize=cb_ticksize,
cb_fraction=cb_fraction,
cb_pad=cb_pad,
cb_tick_values=cb_tick_values,
cb_tick_labels=cb_tick_labels,
title=title,
titlesize=titlesize,
xlabelsize=xlabelsize,
ylabelsize=ylabelsize,
xyticksize=xyticksize,
output_filename=output_filename,
)
mapper_plotters.plot_rectangular_mapper(
mapper=inversion.mapper,
should_plot_centres=should_plot_centres,
should_plot_grid=should_plot_grid,
should_plot_border=should_plot_border,
image_pixels=image_pixels,
source_pixels=source_pixels,
as_subplot=True,
units=units,
kpc_per_arcsec=kpc_per_arcsec,
title=title,
titlesize=titlesize,
xlabelsize=xlabelsize,
ylabelsize=ylabelsize,
xyticksize=xyticksize,
)
plotter_util.output_figure(
array=reconstructed_pixelization,
as_subplot=as_subplot,
output_path=output_path,
output_filename=output_filename,
output_format=output_format,
)
elif isinstance(inversion.mapper, mappers.VoronoiMapper):
mapper_plotters.plot_voronoi_mapper(
mapper=inversion.mapper,
source_pixel_values=source_pixel_values,
should_plot_centres=should_plot_centres,
should_plot_grid=should_plot_grid,
should_plot_border=should_plot_border,
image_pixels=image_pixels,
source_pixels=source_pixels,
as_subplot=True,
units=units,
kpc_per_arcsec=kpc_per_arcsec,
title=title,
titlesize=titlesize,
xlabelsize=xlabelsize,
ylabelsize=ylabelsize,
xyticksize=xyticksize,
)
plotter_util.output_figure(
array=None,
as_subplot=as_subplot,
output_path=output_path,
output_filename=output_filename,
output_format=output_format,
)
def plot_hyper_chi_squared_map(
hyper_chi_squared_map,
mask=None,
extract_array_from_mask=False,
zoom_around_mask=False,
positions=None,
image_plane_pix_grid=None,
as_subplot=False,
units="arcsec",
kpc_per_arcsec=None,
figsize=(7, 7),
aspect="square",
cmap="jet",
norm="linear",
norm_min=None,
norm_max=None,
linthresh=0.05,
linscale=0.01,
cb_ticksize=10,
cb_fraction=0.047,
cb_pad=0.01,
cb_tick_values=None,
cb_tick_labels=None,
title="Hyper Chi-Squared Map",
titlesize=16,
xlabelsize=16,
ylabelsize=16,
xyticksize=16,
grid_pointsize=1,
mask_pointsize=10,
position_pointsize=10,
output_path=None,
output_format="show",
output_filename="hyper_chi_squared_map",
):
"""Plot the image of a hyper_galaxies galaxy image.
Set *autolens.datas.array.plotters.array_plotters* for a description of all input parameters not described below.
Parameters
-----------
hyper_chi_squared_map : datas.imaging.datas.Imaging
The hyper_galaxies galaxy image.
plot_origin : True
If true, the origin of the datas's coordinate system is plotted as a 'x'.
"""
array_plotters.plot_array(
array=hyper_chi_squared_map,
mask=mask,
extract_array_from_mask=extract_array_from_mask,
zoom_around_mask=zoom_around_mask,
grid=image_plane_pix_grid,
positions=positions,
as_subplot=as_subplot,
units=units,
kpc_per_arcsec=kpc_per_arcsec,
figsize=figsize,
aspect=aspect,
cmap=cmap,
norm=norm,
norm_min=norm_min,
norm_max=norm_max,
linthresh=linthresh,
linscale=linscale,
cb_ticksize=cb_ticksize,
cb_fraction=cb_fraction,
cb_pad=cb_pad,
cb_tick_values=cb_tick_values,
cb_tick_labels=cb_tick_labels,
title=title,
titlesize=titlesize,
xlabelsize=xlabelsize,
ylabelsize=ylabelsize,
xyticksize=xyticksize,
grid_pointsize=grid_pointsize,
mask_pointsize=mask_pointsize,
position_pointsize=position_pointsize,
output_path=output_path,
output_format=output_format,
output_filename=output_filename,
)
