class LensModelPlot(object):
"""
class that manages the summary plots of a lens model
"""
def __init__(self, kwargs_data, kwargs_psf, kwargs_numerics, kwargs_model, kwargs_lens, kwargs_source,
kwargs_lens_light, kwargs_ps, arrow_size=0.02, cmap_string="gist_heat"):
"""
:param kwargs_options:
:param kwargs_data:
:param arrow_size:
:param cmap_string:
"""
self._kwargs_data = kwargs_data
if isinstance(cmap_string, str) or isinstance(cmap_string, unicode):
cmap = plt.get_cmap(cmap_string)
else:
cmap = cmap_string
cmap.set_bad(color='k', alpha=1.)
cmap.set_under('k')
self._cmap = cmap
self._arrow_size = arrow_size
data = Data(kwargs_data)
self._coords = data._coords
nx, ny = np.shape(kwargs_data['image_data'])
Mpix2coord = kwargs_data['transform_pix2angle']
self._Mpix2coord = Mpix2coord
self._deltaPix = self._coords.pixel_size
self._frame_size = self._deltaPix * nx
x_grid, y_grid = data.coordinates
self._x_grid = util.image2array(x_grid)
self._y_grid = util.image2array(y_grid)
self._imageModel = class_creator.create_image_model(kwargs_data, kwargs_psf, kwargs_numerics, kwargs_model)
self._analysis = LensAnalysis(kwargs_model)
self._lensModel = LensModel(lens_model_list=kwargs_model.get('lens_model_list', []),
z_source=kwargs_model.get('z_source', None),
redshift_list=kwargs_model.get('redshift_list', None),
multi_plane=kwargs_model.get('multi_plane', False))
self._lensModelExt = LensModelExtensions(self._lensModel)
model, error_map, cov_param, param = self._imageModel.image_linear_solve(kwargs_lens, kwargs_source,
kwargs_lens_light, kwargs_ps, inv_bool=True)
self._kwargs_lens = kwargs_lens
self._kwargs_source = kwargs_source
self._kwargs_lens_light = kwargs_lens_light
self._kwargs_else = kwargs_ps
self._model = model
self._data = kwargs_data['image_data']
self._cov_param = cov_param
self._norm_residuals = self._imageModel.reduced_residuals(model, error_map=error_map)
self._reduced_x2 = self._imageModel.reduced_chi2(model, error_map=error_map)
log_model = np.log10(model)
log_model[np.isnan(log_model)] = -5
self._v_min_default = max(np.min(log_model), -5)
self._v_max_default = min(np.max(log_model), 10)
print("reduced chi^2 = ", self._reduced_x2)
def _critical_curves(self):
if not hasattr(self, '_ra_crit_list') or not hasattr(self, '_dec_crit_list'):
self._ra_crit_list, self._dec_crit_list = self._lensModelExt.critical_curve_tiling(self._kwargs_lens,
compute_window=self._frame_size,
start_scale=self._deltaPix / 5.,
max_order=10)
return self._ra_crit_list, self._dec_crit_list
def _caustics(self):
if not hasattr(self, '_ra_caustic_list') or not hasattr(self, '_dec_caustic_list'):
ra_crit_list, dec_crit_list = self._critical_curves()
self._ra_caustic_list, self._dec_caustic_list = self._lensModel.ray_shooting(ra_crit_list,
dec_crit_list, self._kwargs_lens)
return self._ra_caustic_list, self._dec_caustic_list
def data_plot(self, ax, v_min=None, v_max=None, text='Observed'):
"""
:param ax:
:return:
"""
if v_min is None:
v_min = self._v_min_default
if v_max is None:
v_max = self._v_max_default
im = ax.matshow(np.log10(self._data), origin='lower',
extent=[0, self._frame_size, 0, self._frame_size], cmap=self._cmap, vmin=v_min, vmax=v_max) # , vmin=0, vmax=2
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
scale_bar(ax, self._frame_size, dist=1, text='1"')
text_description(ax, self._frame_size, text=text, color="w", backgroundcolor='k')
coordinate_arrows(ax, self._frame_size, self._coords, arrow_size=self._arrow_size)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax, orientation='vertical')
cb.set_label(r'log$_{10}$ flux', fontsize=15)
return ax
def model_plot(self, ax, v_min=None, v_max=None, image_names=False):
"""
:param ax:
:param model:
:param v_min:
:param v_max:
:return:
"""
if v_min is None:
v_min = self._v_min_default
if v_max is None:
v_max = self._v_max_default
im = ax.matshow(np.log10(self._model), origin='lower', vmin=v_min, vmax=v_max,
extent=[0, self._frame_size, 0, self._frame_size], cmap=self._cmap)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
scale_bar(ax, self._frame_size, dist=1, text='1"')
text_description(ax, self._frame_size, text="Reconstructed", color="w", backgroundcolor='k')
coordinate_arrows(ax, self._frame_size, self._coords, arrow_size=self._arrow_size)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax)
cb.set_label(r'log$_{10}$ flux', fontsize=15)
#plot_line_set(ax, self._coords, self._ra_caustic_list, self._dec_caustic_list, color='b')
#plot_line_set(ax, self._coords, self._ra_crit_list, self._dec_crit_list, color='r')
if image_names is True:
ra_image, dec_image = self._imageModel.image_positions(self._kwargs_else, self._kwargs_lens)
image_position_plot(ax, self._coords, ra_image, dec_image)
#source_position_plot(ax, self._coords, self._kwargs_source)
def convergence_plot(self, ax, v_min=None, v_max=None):
"""
:param x_grid:
:param y_grid:
:param kwargs_lens:
:param kwargs_else:
:return:
"""
kappa_result = util.array2image(self._lensModel.kappa(self._x_grid, self._y_grid, self._kwargs_lens))
im = ax.matshow(np.log10(kappa_result), origin='lower',
extent=[0, self._frame_size, 0, self._frame_size], cmap=self._cmap, vmin=v_min, vmax=v_max)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
scale_bar(ax, self._frame_size, dist=1, text='1"', color='w')
coordinate_arrows(ax, self._frame_size, self._coords, color='w', arrow_size=self._arrow_size)
text_description(ax, self._frame_size, text="Convergence", color="w", backgroundcolor='k', flipped=False)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax)
cb.set_label(r'log$_{10}$ $\kappa$', fontsize=15)
return ax
def normalized_residual_plot(self, ax, v_min=-6, v_max=6, **kwargs):
"""
:param ax:
:param v_min:
:param v_max:
:param kwargs: kwargs to send to matplotlib.pyplot.matshow()
:return:
"""
if not 'cmap' in kwargs:
kwargs['cmap'] = 'bwr'
im = ax.matshow(self._norm_residuals, vmin=v_min, vmax=v_max,
extent=[0, self._frame_size, 0, self._frame_size], origin='lower', **kwargs)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
scale_bar(ax, self._frame_size, dist=1, text='1"', color='k')
text_description(ax, self._frame_size, text="Normalized Residuals", color="k", backgroundcolor='w')
coordinate_arrows(ax, self._frame_size, self._coords, color='k', arrow_size=self._arrow_size)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax)
cb.set_label(r'(f$_{model}$-f$_{data}$)/$\sigma$', fontsize=15)
return ax
def absolute_residual_plot(self, ax, v_min=-1, v_max=1):
"""
:param ax:
:param residuals:
:return:
"""
im = ax.matshow(self._model - self._data, vmin=v_min, vmax=v_max,
extent=[0, self._frame_size, 0, self._frame_size], cmap='bwr', origin='lower')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
scale_bar(ax, self._frame_size, dist=1, text='1"', color='k')
text_description(ax, self._frame_size, text="Residuals", color="k", backgroundcolor='w')
coordinate_arrows(ax, self._frame_size, self._coords, color='k', arrow_size=self._arrow_size)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax)
cb.set_label(r'(f$_{model}$-f$_{data}$)', fontsize=15)
return ax
def source_plot(self, ax, numPix, deltaPix_source, source_sigma=0.001, convolution=False, v_min=None, v_max=None, with_caustics=False):
"""
:param ax:
:param coords_source:
:param source:
:return:
"""
if v_min is None:
v_min = self._v_min_default
if v_max is None:
v_max = self._v_max_default
d_s = numPix * deltaPix_source
x_grid_source, y_grid_source = util.make_grid_transformed(numPix,
self._Mpix2coord * deltaPix_source / self._deltaPix)
if len(self._kwargs_source) > 0:
x_center = self._kwargs_source[0]['center_x']
y_center = self._kwargs_source[0]['center_y']
x_grid_source += x_center
y_grid_source += y_center
coords_source = Coordinates(self._Mpix2coord * deltaPix_source / self._deltaPix, ra_at_xy_0=x_grid_source[0],
dec_at_xy_0=y_grid_source[0])
source = self._imageModel.SourceModel.surface_brightness(x_grid_source, y_grid_source, self._kwargs_source)
source = util.array2image(source)
if convolution is True:
source = ndimage.filters.gaussian_filter(source, sigma=source_sigma / deltaPix_source, mode='nearest',
truncate=20)
im = ax.matshow(np.log10(source), origin='lower', extent=[0, d_s, 0, d_s],
cmap=self._cmap, vmin=v_min, vmax=v_max) # source
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax)
cb.set_label(r'log$_{10}$ flux', fontsize=15)
if with_caustics:
ra_caustic_list, dec_caustic_list = self._caustics()
plot_line_set(ax, coords_source, ra_caustic_list, dec_caustic_list, color='b')
scale_bar(ax, d_s, dist=0.1, text='0.1"', color='w', flipped=False)
coordinate_arrows(ax, d_s, coords_source, arrow_size=self._arrow_size, color='w')
text_description(ax, d_s, text="Reconstructed source", color="w", backgroundcolor='k', flipped=False)
source_position_plot(ax, coords_source, self._kwargs_source)
return ax
def error_map_source_plot(self, ax, numPix, deltaPix_source, v_min=None, v_max=None, with_caustics=False):
x_grid_source, y_grid_source = util.make_grid_transformed(numPix,
self._Mpix2coord * deltaPix_source / self._deltaPix)
x_center = self._kwargs_source[0]['center_x']
y_center = self._kwargs_source[0]['center_y']
x_grid_source += x_center
y_grid_source += y_center
coords_source = Coordinates(self._Mpix2coord * deltaPix_source / self._deltaPix, ra_at_xy_0=x_grid_source[0],
dec_at_xy_0=y_grid_source[0])
error_map_source = self._analysis.error_map_source(self._kwargs_source, x_grid_source, y_grid_source, self._cov_param)
error_map_source = util.array2image(error_map_source)
d_s = numPix * deltaPix_source
im = ax.matshow(error_map_source, origin='lower', extent=[0, d_s, 0, d_s],
cmap=self._cmap, vmin=v_min, vmax=v_max) # source
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax)
cb.set_label(r'error variance', fontsize=15)
if with_caustics:
ra_caustic_list, dec_caustic_list = self._caustics()
plot_line_set(ax, coords_source, ra_caustic_list, dec_caustic_list, color='b')
scale_bar(ax, d_s, dist=0.1, text='0.1"', color='w', flipped=False)
coordinate_arrows(ax, d_s, coords_source, arrow_size=self._arrow_size, color='w')
text_description(ax, d_s, text="Error map in source", color="w", backgroundcolor='k', flipped=False)
source_position_plot(ax, coords_source, self._kwargs_source)
return ax
def magnification_plot(self, ax, v_min=-10, v_max=10, with_caustics=False, image_name_list=None, **kwargs):
"""
:param ax:
:param v_min:
:param v_max:
:param with_caustics:
:param kwargs: kwargs to send to matplotlib.pyplot.matshow()
:return:
"""
if not 'cmap' in kwargs:
kwargs['cmap'] = self._cmap
if not 'alpha' in kwargs:
kwargs['alpha'] = 0.5
mag_result = util.array2image(self._lensModel.magnification(self._x_grid, self._y_grid, self._kwargs_lens))
im = ax.matshow(mag_result, origin='lower', extent=[0, self._frame_size, 0, self._frame_size],
vmin=v_min, vmax=v_max, **kwargs)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
scale_bar(ax, self._frame_size, dist=1, text='1"', color='k')
coordinate_arrows(ax, self._frame_size, self._coords, color='k', arrow_size=self._arrow_size)
text_description(ax, self._frame_size, text="Magnification model", color="k", backgroundcolor='w')
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax)
cb.set_label(r'det(A$^{-1}$)', fontsize=15)
if with_caustics:
ra_crit_list, dec_crit_list = self._critical_curves()
ra_caustic_list, dec_caustic_list = self._caustics()
plot_line_set(ax, self._coords, ra_caustic_list, dec_caustic_list, color='b')
plot_line_set(ax, self._coords, ra_crit_list, dec_crit_list, color='r')
ra_image, dec_image = self._imageModel.image_positions(self._kwargs_else, self._kwargs_lens)
image_position_plot(ax, self._coords, ra_image, dec_image, color='k', image_name_list=image_name_list)
source_position_plot(ax, self._coords, self._kwargs_source)
return ax
def deflection_plot(self, ax, v_min=None, v_max=None, axis=0, with_caustics=False, image_name_list=None):
"""
:param kwargs_lens:
:param kwargs_else:
:return:
"""
alpha1, alpha2 = self._lensModel.alpha(self._x_grid, self._y_grid, self._kwargs_lens)
alpha1 = util.array2image(alpha1)
alpha2 = util.array2image(alpha2)
if axis == 0:
alpha = alpha1
else:
alpha = alpha2
im = ax.matshow(alpha, origin='lower', extent=[0, self._frame_size, 0, self._frame_size],
vmin=v_min, vmax=v_max, cmap=self._cmap, alpha=0.5)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
scale_bar(ax, self._frame_size, dist=1, text='1"', color='k')
coordinate_arrows(ax, self._frame_size, self._coords, color='k', arrow_size=self._arrow_size)
text_description(ax, self._frame_size, text="Deflection model", color="k", backgroundcolor='w')
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax)
cb.set_label(r'arcsec', fontsize=15)
if with_caustics:
ra_crit_list, dec_crit_list = self._critical_curves()
ra_caustic_list, dec_caustic_list = self._caustics()
plot_line_set(ax, self._coords, ra_caustic_list, dec_caustic_list, color='b')
plot_line_set(ax, self._coords, ra_crit_list, dec_crit_list, color='r')
ra_image, dec_image = self._imageModel.image_positions(self._kwargs_else, self._kwargs_lens)
image_position_plot(ax, self._coords, ra_image, dec_image, image_name_list=image_name_list)
source_position_plot(ax, self._coords, self._kwargs_source)
return ax
def decomposition_plot(self, ax, text='Reconstructed', v_min=None, v_max=None, unconvolved=False, point_source_add=False, source_add=False, lens_light_add=False, **kwargs):
"""
:param ax:
:param text:
:param v_min:
:param v_max:
:param unconvolved:
:param point_source_add:
:param source_add:
:param lens_light_add:
:param kwargs: kwargs to send matplotlib.pyplot.matshow()
:return:
"""
model = self._imageModel.image(self._kwargs_lens, self._kwargs_source, self._kwargs_lens_light,
self._kwargs_else, unconvolved=unconvolved, source_add=source_add,
lens_light_add=lens_light_add, point_source_add=point_source_add)
if v_min is None:
v_min = self._v_min_default
if v_max is None:
v_max = self._v_max_default
if not 'cmap' in kwargs:
kwargs['cmap'] = self._cmap
im = ax.matshow(np.log10(model), origin='lower', vmin=v_min, vmax=v_max,
extent=[0, self._frame_size, 0, self._frame_size], **kwargs)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
scale_bar(ax, self._frame_size, dist=1, text='1"')
text_description(ax, self._frame_size, text=text, color="w", backgroundcolor='k')
coordinate_arrows(ax, self._frame_size, self._coords, arrow_size=self._arrow_size)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax)
cb.set_label(r'log$_{10}$ flux', fontsize=15)
return ax
def subtract_from_data_plot(self, ax, text='Subtracted', v_min=None, v_max=None, point_source_add=False, source_add=False, lens_light_add=False):
model = self._imageModel.image(self._kwargs_lens, self._kwargs_source, self._kwargs_lens_light,
self._kwargs_else, unconvolved=False, source_add=source_add,
lens_light_add=lens_light_add, point_source_add=point_source_add)
if v_min is None:
v_min = self._v_min_default
if v_max is None:
v_max = self._v_max_default
im = ax.matshow(np.log10(self._data - model), origin='lower', vmin=v_min, vmax=v_max,
extent=[0, self._frame_size, 0, self._frame_size], cmap=self._cmap)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
scale_bar(ax, self._frame_size, dist=1, text='1"')
text_description(ax, self._frame_size, text=text, color="w", backgroundcolor='k')
coordinate_arrows(ax, self._frame_size, self._coords, arrow_size=self._arrow_size)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax)
cb.set_label(r'log$_{10}$ flux', fontsize=15)
return ax
def plot_main(self):
"""
print the main plots together in a joint frame
:return:
"""
f, axes = plt.subplots(2, 3, figsize=(16, 8))
self.data_plot(ax=axes[0, 0])
self.model_plot(ax=axes[0, 1])
self.normalized_residual_plot(ax=axes[0, 2], v_min=-6, v_max=6)
self.source_plot(ax=axes[1, 0], convolution=False, deltaPix_source=0.01, numPix=100)
self.convergence_plot(ax=axes[1, 1], v_max=1)
self.magnification_plot(ax=axes[1, 2])
f.tight_layout()
f.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0., hspace=0.05)
return f, axes
def plot_separate(self):
"""
plot the different model components separately
:return:
"""
f, axes = plt.subplots(2, 3, figsize=(16, 8))
self.decomposition_plot(ax=axes[0, 0], text='Lens light', lens_light_add=True, unconvolved=True)
self.decomposition_plot(ax=axes[1, 0], text='Lens light convolved', lens_light_add=True)
self.decomposition_plot(ax=axes[0, 1], text='Source light', source_add=True, unconvolved=True)
self.decomposition_plot(ax=axes[1, 1], text='Source light convolved', source_add=True)
self.decomposition_plot(ax=axes[0, 2], text='All components', source_add=True, lens_light_add=True,
unconvolved=True)
self.decomposition_plot(ax=axes[1, 2], text='All components convolved', source_add=True,
lens_light_add=True, point_source_add=True)
f.tight_layout()
f.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0., hspace=0.05)
return f, axes
def plot_subtract_from_data_all(self):
"""
subtract model components from data
:return:
"""
f, axes = plt.subplots(2, 3, figsize=(16, 8))
self.subtract_from_data_plot(ax=axes[0, 0], text='Data')
self.subtract_from_data_plot(ax=axes[0, 1], text='Data - Point Source', point_source_add=True)
self.subtract_from_data_plot(ax=axes[0, 2], text='Data - Lens Light', lens_light_add=True)
self.subtract_from_data_plot(ax=axes[1, 0], text='Data - Source Light', source_add=True)
self.subtract_from_data_plot(ax=axes[1, 1], text='Data - Source Light - Point Source', source_add=True,
point_source_add=True)
self.subtract_from_data_plot(ax=axes[1, 2], text='Data - Lens Light - Point Source', lens_light_add=True,
point_source_add=True)
f.tight_layout()
f.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0., hspace=0.05)
return f, axes
class LensModelPlot(object):
"""
class that manages the summary plots of a lens model
"""
def __init__(self,
kwargs_data,
kwargs_psf,
kwargs_numerics,
kwargs_model,
kwargs_lens,
kwargs_source,
kwargs_lens_light,
kwargs_ps,
arrow_size=0.1,
cmap_string="gist_heat",
high_res=5):
"""
:param kwargs_options:
:param kwargs_data:
:param arrow_size:
:param cmap_string:
"""
self._kwargs_data = kwargs_data
if isinstance(cmap_string, str) or isinstance(cmap_string, unicode):
cmap = plt.get_cmap(cmap_string)
else:
cmap = cmap_string
cmap.set_bad(color='k', alpha=1.)
cmap.set_under('k')
self._cmap = cmap
self._arrow_size = arrow_size
data = Data(kwargs_data)
self._coords = data._coords
nx, ny = np.shape(kwargs_data['image_data'])
Mpix2coord = kwargs_data['transform_pix2angle']
self._Mpix2coord = Mpix2coord
self._deltaPix = self._coords.pixel_size
self._frame_size = self._deltaPix * nx
self._x_grid, self._y_grid = data.coordinates
self._imageModel = class_creator.creat_image_model(
kwargs_data, kwargs_psf, kwargs_numerics, kwargs_model)
self._analysis = LensAnalysis(kwargs_model)
self._lensModel = LensModelExtensions(
lens_model_list=kwargs_model.get('lens_model_list', ['NONE']),
z_source=kwargs_model.get('z_source', None),
redshift_list=kwargs_model.get('redshift_list', None),
multi_plane=kwargs_model.get('multi_plane', False))
self._ra_crit_list, self._dec_crit_list, self._ra_caustic_list, self._dec_caustic_list = self._lensModel.critical_curve_caustics(
kwargs_lens, compute_window=self._frame_size, grid_scale=0.01)
model, error_map, cov_param, param = self._imageModel.image_linear_solve(
kwargs_lens,
kwargs_source,
kwargs_lens_light,
kwargs_ps,
inv_bool=True)
self._kwargs_lens = kwargs_lens
self._kwargs_source = kwargs_source
self._kwargs_lens_light = kwargs_lens_light
self._kwargs_else = kwargs_ps
self._model = model
self._data = kwargs_data['image_data']
self._cov_param = cov_param
self._norm_residuals = self._imageModel.reduced_residuals(
model, error_map=error_map)
self._reduced_x2 = self._imageModel.reduced_chi2(model,
error_map=error_map)
log_model = np.log10(model)
log_model[np.isnan(log_model)] = -5
self._v_min_default = max(np.min(log_model), -5)
self._v_max_default = min(np.max(log_model), 10)
print("reduced chi^^ = ", self._reduced_x2)
def data_plot(self, ax, v_min=None, v_max=None):
"""
:param ax:
:return:
"""
if v_min is None:
v_min = self._v_min_default
if v_max is None:
v_max = self._v_max_default
im = ax.matshow(np.log10(self._data),
origin='lower',
extent=[0, self._frame_size, 0, self._frame_size],
cmap=self._cmap,
vmin=v_min,
vmax=v_max) # , vmin=0, vmax=2
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
scale_bar(ax, self._frame_size, dist=1, text='1"')
text_description(ax,
self._frame_size,
text="Observed",
color="w",
backgroundcolor='k')
coordinate_arrows(ax,
self._frame_size,
self._coords,
arrow_size=self._arrow_size)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax)
cb.set_label(r'log$_{10}$ flux', fontsize=15)
return ax
def model_plot(self, ax, v_min=None, v_max=None):
"""
:param ax:
:param model:
:param v_min:
:param v_max:
:return:
"""
if v_min is None:
v_min = self._v_min_default
if v_max is None:
v_max = self._v_max_default
im = ax.matshow(np.log10(self._model),
origin='lower',
vmin=v_min,
vmax=v_max,
extent=[0, self._frame_size, 0, self._frame_size],
cmap=self._cmap)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
scale_bar(ax, self._frame_size, dist=1, text='1"')
text_description(ax,
self._frame_size,
text="Reconstructed",
color="w",
backgroundcolor='k')
coordinate_arrows(ax,
self._frame_size,
self._coords,
arrow_size=self._arrow_size)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax)
cb.set_label(r'log$_{10}$ flux', fontsize=15)
plot_line_set(ax,
self._coords,
self._ra_caustic_list,
self._dec_caustic_list,
color='b')
plot_line_set(ax,
self._coords,
self._ra_crit_list,
self._dec_crit_list,
color='r')
ra_image, dec_image = self._imageModel.image_positions(
self._kwargs_else, self._kwargs_lens)
image_position_plot(ax, self._coords, ra_image[0], dec_image[0])
source_position_plot(ax, self._coords, self._kwargs_source)
def convergence_plot(self, ax, v_min=None, v_max=None):
"""
:param x_grid:
:param y_grid:
:param kwargs_lens:
:param kwargs_else:
:return:
"""
kappa_result = util.array2image(
self._lensModel.kappa(self._x_grid, self._y_grid,
self._kwargs_lens))
im = ax.matshow(np.log10(kappa_result),
origin='lower',
extent=[0, self._frame_size, 0, self._frame_size],
cmap=self._cmap,
vmin=v_min,
vmax=v_max)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
scale_bar(ax, self._frame_size, dist=1, text='1"', color='w')
coordinate_arrows(ax,
self._frame_size,
self._coords,
color='w',
arrow_size=self._arrow_size)
text_description(ax,
self._frame_size,
text="Convergence",
color="w",
backgroundcolor='k',
flipped=False)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax)
cb.set_label(r'log$_{10}$ $\kappa$', fontsize=15)
return ax
def normalized_residual_plot(self, ax, v_min=-6, v_max=6):
"""
:param ax:
:param residuals:
:return:
"""
im = ax.matshow(self._norm_residuals,
vmin=v_min,
vmax=v_max,
extent=[0, self._frame_size, 0, self._frame_size],
cmap='bwr',
origin='lower')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
scale_bar(ax, self._frame_size, dist=1, text='1"', color='k')
text_description(ax,
self._frame_size,
text="Normalized Residuals",
color="k",
backgroundcolor='w')
coordinate_arrows(ax,
self._frame_size,
self._coords,
color='k',
arrow_size=self._arrow_size)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax)
cb.set_label(r'(f$_{model}$-f$_{data}$)/$\sigma$', fontsize=15)
return ax
def absolute_residual_plot(self, ax, v_min=-1, v_max=1):
"""
:param ax:
:param residuals:
:return:
"""
im = ax.matshow(self._model - self._data,
vmin=v_min,
vmax=v_max,
extent=[0, self._frame_size, 0, self._frame_size],
cmap='bwr',
origin='lower')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
scale_bar(ax, self._frame_size, dist=1, text='1"', color='k')
text_description(ax,
self._frame_size,
text="Residuals",
color="k",
backgroundcolor='w')
coordinate_arrows(ax,
self._frame_size,
self._coords,
color='k',
arrow_size=self._arrow_size)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax)
cb.set_label(r'(f$_{model}$-f$_{data}$)', fontsize=15)
return ax
def source_plot(self,
ax,
numPix,
deltaPix_source,
source_sigma=0.001,
convolution=False,
v_min=None,
v_max=None):
"""
:param ax:
:param coords_source:
:param source:
:return:
"""
if v_min is None:
v_min = self._v_min_default
if v_max is None:
v_max = self._v_max_default
d_s = numPix * deltaPix_source
x_grid_source, y_grid_source = util.make_grid_transformed(
numPix, self._Mpix2coord * deltaPix_source / self._deltaPix)
x_center = self._kwargs_source[0]['center_x']
y_center = self._kwargs_source[0]['center_y']
x_grid_source += x_center
y_grid_source += y_center
coords_source = Coordinates(self._Mpix2coord * deltaPix_source /
self._deltaPix,
ra_at_xy_0=x_grid_source[0],
dec_at_xy_0=y_grid_source[0])
source = self._imageModel.SourceModel.surface_brightness(
x_grid_source, y_grid_source, self._kwargs_source)
source = util.array2image(source)
if convolution:
source = ndimage.filters.gaussian_filter(source,
sigma=source_sigma /
deltaPix_source,
mode='nearest',
truncate=20)
im = ax.matshow(np.log10(source),
origin='lower',
extent=[0, d_s, 0, d_s],
cmap=self._cmap,
vmin=v_min,
vmax=v_max) # source
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax)
cb.set_label(r'log$_{10}$ flux', fontsize=15)
plot_line_set(ax,
coords_source,
self._ra_caustic_list,
self._dec_caustic_list,
color='b')
scale_bar(ax, d_s, dist=0.1, text='0.1"', color='w', flipped=False)
coordinate_arrows(ax,
d_s,
coords_source,
arrow_size=self._arrow_size,
color='w')
text_description(ax,
d_s,
text="Reconstructed source",
color="w",
backgroundcolor='k',
flipped=False)
source_position_plot(ax, coords_source, self._kwargs_source)
return ax
def error_map_source_plot(self,
ax,
numPix,
deltaPix_source,
v_min=None,
v_max=None):
x_grid_source, y_grid_source = util.make_grid_transformed(
numPix, self._Mpix2coord * deltaPix_source / self._deltaPix)
x_center = self._kwargs_source[0]['center_x']
y_center = self._kwargs_source[0]['center_y']
x_grid_source += x_center
y_grid_source += y_center
coords_source = Coordinates(self._Mpix2coord * deltaPix_source /
self._deltaPix,
ra_at_xy_0=x_grid_source[0],
dec_at_xy_0=y_grid_source[0])
error_map_source = self._analysis.error_map_source(
self._kwargs_source, x_grid_source, y_grid_source, self._cov_param)
error_map_source = util.array2image(error_map_source)
d_s = numPix * deltaPix_source
im = ax.matshow(error_map_source,
origin='lower',
extent=[0, d_s, 0, d_s],
cmap=self._cmap,
vmin=v_min,
vmax=v_max) # source
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax)
cb.set_label(r'error variance', fontsize=15)
plot_line_set(ax,
coords_source,
self._ra_caustic_list,
self._dec_caustic_list,
color='b')
scale_bar(ax, d_s, dist=0.1, text='0.1"', color='w', flipped=False)
coordinate_arrows(ax,
d_s,
coords_source,
arrow_size=self._arrow_size,
color='w')
text_description(ax,
d_s,
text="Error map in source",
color="w",
backgroundcolor='k',
flipped=False)
source_position_plot(ax, coords_source, self._kwargs_source)
return ax
def magnification_plot(self, ax, v_min=-10, v_max=10):
"""
:param ax:
:return:
"""
mag_result = util.array2image(
self._lensModel.magnification(self._x_grid, self._y_grid,
self._kwargs_lens))
im = ax.matshow(mag_result,
origin='lower',
extent=[0, self._frame_size, 0, self._frame_size],
vmin=v_min,
vmax=v_max,
cmap=self._cmap,
alpha=0.5)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
scale_bar(ax, self._frame_size, dist=1, text='1"', color='k')
coordinate_arrows(ax,
self._frame_size,
self._coords,
color='k',
arrow_size=self._arrow_size)
text_description(ax,
self._frame_size,
text="Magnification model",
color="k",
backgroundcolor='w')
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax)
cb.set_label(r'det(A$^{-1}$)', fontsize=15)
plot_line_set(ax,
self._coords,
self._ra_caustic_list,
self._dec_caustic_list,
color='b')
plot_line_set(ax,
self._coords,
self._ra_crit_list,
self._dec_crit_list,
color='r')
ra_image, dec_image = self._imageModel.image_positions(
self._kwargs_else, self._kwargs_lens)
image_position_plot(ax,
self._coords,
ra_image[0],
dec_image[0],
color='k')
source_position_plot(ax, self._coords, self._kwargs_source)
return ax
def deflection_plot(self, ax, v_min=None, v_max=None, axis=0):
"""
:param kwargs_lens:
:param kwargs_else:
:return:
"""
alpha1, alpha2 = self._lensModel.alpha(self._x_grid, self._y_grid,
self._kwargs_lens)
alpha1 = util.array2image(alpha1)
alpha2 = util.array2image(alpha2)
if axis == 0:
alpha = alpha1
else:
alpha = alpha2
im = ax.matshow(alpha,
origin='lower',
extent=[0, self._frame_size, 0, self._frame_size],
vmin=v_min,
vmax=v_max,
cmap=self._cmap,
alpha=0.5)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
scale_bar(ax, self._frame_size, dist=1, text='1"', color='k')
coordinate_arrows(ax,
self._frame_size,
self._coords,
color='k',
arrow_size=self._arrow_size)
text_description(ax,
self._frame_size,
text="Deflection model",
color="k",
backgroundcolor='w')
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax)
cb.set_label(r'arcsec', fontsize=15)
plot_line_set(ax,
self._coords,
self._ra_caustic_list,
self._dec_caustic_list,
color='b')
plot_line_set(ax,
self._coords,
self._ra_crit_list,
self._dec_crit_list,
color='r')
ra_image, dec_image = self._imageModel.image_positions(
self._kwargs_else, self._kwargs_lens)
image_position_plot(ax, self._coords, ra_image[0], dec_image[0])
source_position_plot(ax, self._coords, self._kwargs_source)
return ax
def decomposition_plot(self,
ax,
text='Reconstructed',
v_min=None,
v_max=None,
unconvolved=False,
point_source_add=False,
source_add=False,
lens_light_add=False):
model = self._imageModel.image(self._kwargs_lens,
self._kwargs_source,
self._kwargs_lens_light,
self._kwargs_else,
unconvolved=unconvolved,
source_add=source_add,
lens_light_add=lens_light_add,
point_source_add=point_source_add)
if v_min is None:
v_min = self._v_min_default
if v_max is None:
v_max = self._v_max_default
im = ax.matshow(np.log10(model),
origin='lower',
vmin=v_min,
vmax=v_max,
extent=[0, self._frame_size, 0, self._frame_size],
cmap=self._cmap)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
scale_bar(ax, self._frame_size, dist=1, text='1"')
text_description(ax,
self._frame_size,
text=text,
color="w",
backgroundcolor='k')
coordinate_arrows(ax,
self._frame_size,
self._coords,
arrow_size=self._arrow_size)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax)
cb.set_label(r'log$_{10}$ flux', fontsize=15)
return ax
def subtract_from_data_plot(self,
ax,
text='Subtracted',
v_min=None,
v_max=None,
point_source_add=False,
source_add=False,
lens_light_add=False):
model = self._imageModel.image(self._kwargs_lens,
self._kwargs_source,
self._kwargs_lens_light,
self._kwargs_else,
unconvolved=False,
source_add=source_add,
lens_light_add=lens_light_add,
point_source_add=point_source_add)
if v_min is None:
v_min = self._v_min_default
if v_max is None:
v_max = self._v_max_default
im = ax.matshow(np.log10(self._data - model),
origin='lower',
vmin=v_min,
vmax=v_max,
extent=[0, self._frame_size, 0, self._frame_size],
cmap=self._cmap)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
scale_bar(ax, self._frame_size, dist=1, text='1"')
text_description(ax,
self._frame_size,
text=text,
color="w",
backgroundcolor='k')
coordinate_arrows(ax,
self._frame_size,
self._coords,
arrow_size=self._arrow_size)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax)
cb.set_label(r'log$_{10}$ flux', fontsize=15)
return ax