def __init__(
self,
units: wrap_base.Units = wrap_base.Units(),
figure: wrap_base.Figure = wrap_base.Figure(),
axis: wrap_base.Axis = wrap_base.Axis(),
cmap: wrap_base.Cmap = wrap_base.Cmap(),
colorbar: wrap_base.Colorbar = wrap_base.Colorbar(),
colorbar_tickparams: wrap_base.ColorbarTickParams = wrap_base.
ColorbarTickParams(),
tickparams: wrap_base.TickParams = wrap_base.TickParams(),
yticks: wrap_base.YTicks = wrap_base.YTicks(),
xticks: wrap_base.XTicks = wrap_base.XTicks(),
title: wrap_base.Title = wrap_base.Title(),
ylabel: wrap_base.YLabel = wrap_base.YLabel(),
xlabel: wrap_base.XLabel = wrap_base.XLabel(),
legend: wrap_base.Legend = wrap_base.Legend(),
output: wrap_base.Output = wrap_base.Output(),
yx_plot: wrap_1d.YXPlot = wrap_1d.YXPlot(),
half_light_radius_axvline: lensing_wrap.
HalfLightRadiusAXVLine = lensing_wrap.HalfLightRadiusAXVLine(),
einstein_radius_axvline: lensing_wrap.
EinsteinRadiusAXVLine = lensing_wrap.EinsteinRadiusAXVLine(),
):
"""
Visualizes 1D data structures as a y versus x plot using Matplotlib.
The `Plotter` is passed objects from the `wrap_base` package which wrap matplotlib plot functions and customize
the appearance of the plots of the data structure. If the values of these matplotlib wrapper objects are not
manually specified, they assume the default values provided in the `config.visualize.mat_*` `.ini` config files.
Parameters
----------
units : wrap_base.Units
The units of the figure used to plot the data structure which sets the y and x ticks and labels.
figure : wrap_base.Figure
Opens the matplotlib figure before plotting via `plt.figure` and closes it once plotting is complete
via `plt.close`.
axis : wrap_base.Axis
Sets the extent of the figure axis via `plt.axis` and allows for a manual axis range.
cmap : wrap_base.Cmap
Customizes the colormap of the plot and its normalization via matplotlib `colors` objects such
as `colors.Normalize` and `colors.LogNorm`.
colorbar : wrap_base.Colorbar
Plots the colorbar of the plot via `plt.colorbar` and customizes its tick labels and values using method
like `cb.set_yticklabels`.
colorbar_tickparams : wrap_base.ColorbarTickParams
Customizes the yticks of the colorbar plotted via `plt.colorbar`.
tickparams : wrap_base.TickParams
Customizes the appearances of the y and x ticks on the plot, (e.g. the fontsize), using `plt.tick_params`.
yticks : wrap_base.YTicks
Sets the yticks of the plot, including scaling them to new units depending on the `Units` object, via
`plt.yticks`.
xticks : wrap_base.XTicks
Sets the xticks of the plot, including scaling them to new units depending on the `Units` object, via
`plt.xticks`.
title : wrap_base.Title
Sets the figure title and customizes its appearance using `plt.title`.
ylabel : wrap_base.YLabel
Sets the figure ylabel and customizes its appearance using `plt.ylabel`.
xlabel : wrap_base.XLabel
Sets the figure xlabel and customizes its appearance using `plt.xlabel`.
legend : wrap_base.Legend
Sets whether the plot inclues a legend and customizes its appearance and labels using `plt.legend`.
output : wrap_base.Output
Sets if the figure is displayed on the user's screen or output to `.png` using `plt.show` and `plt.savefig`
yx_plot : wrap_1d.YXPlot
Sets how the y versus x plot appears, for example if it each axis is linear or log, using `plt.plot`.
half_light_radius_axvline : lensing_wrap.HalfLightRadiusAXVLine
Sets how a vertical line representing the half light radius of a `LightProfile` is plotted on the figure
using the `plt.axvline` method.
half_light_radius_axvline : lensing_wrap.HalfLightRadiusAXVLine
Sets how a vertical line representing the Einstein radius of a `LensingObj` (e.g. a `MassProfile`) is
plotted on the figure using the `plt.axvline` method.
"""
super().__init__(
units=units,
figure=figure,
axis=axis,
cmap=cmap,
colorbar=colorbar,
colorbar_tickparams=colorbar_tickparams,
tickparams=tickparams,
yticks=yticks,
xticks=xticks,
title=title,
ylabel=ylabel,
xlabel=xlabel,
legend=legend,
output=output,
yx_plot=yx_plot,
)
self.half_light_radius_axvline = half_light_radius_axvline
self.einstein_radius_axvline = einstein_radius_axvline
def __init__(
self,
units: wrap_base.Units = wrap_base.Units(),
figure: wrap_base.Figure = wrap_base.Figure(),
axis: wrap_base.Axis = wrap_base.Axis(),
cmap: wrap_base.Cmap = wrap_base.Cmap(),
colorbar: wrap_base.Colorbar = wrap_base.Colorbar(),
colorbar_tickparams: wrap_base.ColorbarTickParams = wrap_base.
ColorbarTickParams(),
tickparams: wrap_base.TickParams = wrap_base.TickParams(),
yticks: wrap_base.YTicks = wrap_base.YTicks(),
xticks: wrap_base.XTicks = wrap_base.XTicks(),
title: wrap_base.Title = wrap_base.Title(),
ylabel: wrap_base.YLabel = wrap_base.YLabel(),
xlabel: wrap_base.XLabel = wrap_base.XLabel(),
legend: wrap_base.Legend = wrap_base.Legend(),
output: wrap_base.Output = wrap_base.Output(),
):
"""
Visualizes data structures (e.g an `Array2D`, `Grid2D`, `VectorField`, etc.) using Matplotlib.
The `Plotter` is passed objects from the `wrap_base` package which wrap matplotlib plot functions and customize
the appearance of the plots of the data structure. If the values of these matplotlib wrapper objects are not
manually specified, they assume the default values provided in the `config.visualize.mat_*` `.ini` config files.
The following data structures can be plotted using the following matplotlib functions:
- `Array2D`:, using `plt.imshow`.
- `Grid2D`: using `plt.scatter`.
- `Line`: using `plt.plot`, `plt.semilogy`, `plt.loglog` or `plt.scatter`.
- `VectorField`: using `plt.quiver`.
- `RectangularMapper`: using `plt.imshow`.
- `VoronoiMapper`: using `plt.fill`.
Parameters
----------
units : wrap_base.Units
The units of the figure used to plot the data structure which sets the y and x ticks and labels.
figure : wrap_base.Figure
Opens the matplotlib figure before plotting via `plt.figure` and closes it once plotting is complete
via `plt.close`
axis : wrap_base.Axis
Sets the extent of the figure axis via `plt.axis` and allows for a manual axis range.
cmap : wrap_base.Cmap
Customizes the colormap of the plot and its normalization via matplotlib `colors` objects such
as `colors.Normalize` and `colors.LogNorm`.
colorbar : wrap_base.Colorbar
Plots the colorbar of the plot via `plt.colorbar` and customizes its tick labels and values using method
like `cb.set_yticklabels`.
colorbar_tickparams : wrap_base.ColorbarTickParams
Customizes the yticks of the colorbar plotted via `plt.colorbar`.
tickparams : wrap_base.TickParams
Customizes the appearances of the y and x ticks on the plot (e.g. the fontsize) using `plt.tick_params`.
yticks : wrap_base.YTicks
Sets the yticks of the plot, including scaling them to new units depending on the `Units` object, via
`plt.yticks`.
xticks : wrap_base.XTicks
Sets the xticks of the plot, including scaling them to new units depending on the `Units` object, via
`plt.xticks`.
title : wrap_base.Title
Sets the figure title and customizes its appearance using `plt.title`.
ylabel : wrap_base.YLabel
Sets the figure ylabel and customizes its appearance using `plt.ylabel`.
xlabel : wrap_base.XLabel
Sets the figure xlabel and customizes its appearance using `plt.xlabel`.
legend : wrap_base.Legend
Sets whether the plot inclues a legend and customizes its appearance and labels using `plt.legend`.
output : wrap_base.Output
Sets if the figure is displayed on the user's screen or output to `.png` using `plt.show` and `plt.savefig`
"""
self.units = units
self.figure = figure
self.axis = axis
self.cmap = cmap
self.colorbar = colorbar
self.colorbar_tickparams = colorbar_tickparams
self.tickparams = tickparams
self.title = title
self.yticks = yticks
self.xticks = xticks
self.ylabel = ylabel
self.xlabel = xlabel
self.legend = legend
self.output = output
self.number_subplots = None
self.subplot_index = None
def __init__(
self,
units: wrap_base.Units = wrap_base.Units(),
figure: wrap_base.Figure = wrap_base.Figure(),
axis: wrap_base.Axis = wrap_base.Axis(),
cmap: wrap_base.Cmap = wrap_base.Cmap(),
colorbar: wrap_base.Colorbar = wrap_base.Colorbar(),
colorbar_tickparams: wrap_base.ColorbarTickParams = wrap_base.
ColorbarTickParams(),
tickparams: wrap_base.TickParams = wrap_base.TickParams(),
yticks: wrap_base.YTicks = wrap_base.YTicks(),
xticks: wrap_base.XTicks = wrap_base.XTicks(),
title: wrap_base.Title = wrap_base.Title(),
ylabel: wrap_base.YLabel = wrap_base.YLabel(),
xlabel: wrap_base.XLabel = wrap_base.XLabel(),
legend: wrap_base.Legend = wrap_base.Legend(),
output: wrap_base.Output = wrap_base.Output(),
array_overlay: wrap_2d.ArrayOverlay = wrap_2d.ArrayOverlay(),
grid_scatter: wrap_2d.GridScatter = wrap_2d.GridScatter(),
grid_plot: wrap_2d.GridPlot = wrap_2d.GridPlot(),
vector_field_quiver: wrap_2d.VectorFieldQuiver = wrap_2d.
VectorFieldQuiver(),
patch_overlay: wrap_2d.PatchOverlay = wrap_2d.PatchOverlay(),
voronoi_drawer: wrap_2d.VoronoiDrawer = wrap_2d.VoronoiDrawer(),
origin_scatter: wrap_2d.OriginScatter = wrap_2d.OriginScatter(),
mask_scatter: wrap_2d.MaskScatter = wrap_2d.MaskScatter(),
border_scatter: wrap_2d.BorderScatter = wrap_2d.BorderScatter(),
positions_scatter: wrap_2d.PositionsScatter = wrap_2d.PositionsScatter(
),
index_scatter: wrap_2d.IndexScatter = wrap_2d.IndexScatter(),
pixelization_grid_scatter: wrap_2d.PixelizationGridScatter = wrap_2d.
PixelizationGridScatter(),
light_profile_centres_scatter: lensing_wrap.
LightProfileCentresScatter = lensing_wrap.LightProfileCentresScatter(),
mass_profile_centres_scatter: lensing_wrap.
MassProfileCentresScatter = lensing_wrap.MassProfileCentresScatter(),
multiple_images_scatter: lensing_wrap.
MultipleImagesScatter = lensing_wrap.MultipleImagesScatter(),
critical_curves_plot: lensing_wrap.CriticalCurvesPlot = lensing_wrap.
CriticalCurvesPlot(),
caustics_plot: lensing_wrap.CausticsPlot = lensing_wrap.CausticsPlot(),
):
"""
Visualizes data structures (e.g an `Array2D`, `Grid2D`, `VectorField`, etc.) using Matplotlib.
The `Plotter` is passed objects from the `mat_wrap` package which wrap matplotlib plot functions and
customize the appearance of the plots of the data structure. If the values of these matplotlib wrapper
objects are not manually specified, they assume the default values provided in
the `config.visualize.mat_*` `.ini` config files.
The following data structures can be plotted using the following matplotlib functions:
- `Array2D`:, using `plt.imshow`.
- `Grid2D`: using `plt.scatter`.
- `Line`: using `plt.plot`, `plt.semilogy`, `plt.loglog` or `plt.scatter`.
- `VectorField`: using `plt.quiver`.
- `RectangularMapper`: using `plt.imshow`.
- `VoronoiMapper`: using `plt.fill`.
Parameters
----------
units : wrap_base.Units
The units of the figure used to plot the data structure which sets the y and x ticks and labels.
figure : wrap_base.Figure
Opens the matplotlib figure before plotting via `plt.figure` and closes it once plotting is complete
via `plt.close`.
axis : mat_wrap.Axis
Sets the extent of the figure axis via `plt.axis` and allows for a manual axis range.
cmap : wrap_base.Cmap
Customizes the colormap of the plot and its normalization via matplotlib `colors` objects such
as `colors.Normalize` and `colors.LogNorm`.
colorbar : mat_wrap.Colorbar
Plots the colorbar of the plot via `plt.colorbar` and customizes its tick labels and values using method
like `cb.set_yticklabels`.
colorbar_tickparams : mat_wrap.ColorbarTickParams
Customizes the yticks of the colorbar plotted via `plt.colorbar`.
tickparams : wrap_base.TickParams
Customizes the appearances of the y and x ticks on the plot, (e.g. the fontsize), using `plt.tick_params`.
yticks : wrap_base.YTicks
Sets the yticks of the plot, including scaling them to new units depending on the `Units` object, via
`plt.yticks`.
xticks : wrap_base.XTicks
Sets the xticks of the plot, including scaling them to new units depending on the `Units` object, via
`plt.xticks`.
title : wrap_base.Title
Sets the figure title and customizes its appearance using `plt.title`.
ylabel : wrap_base.YLabel
Sets the figure ylabel and customizes its appearance using `plt.ylabel`.
xlabel : wrap_base.XLabel
Sets the figure xlabel and customizes its appearance using `plt.xlabel`.
legend : wrap_base.Legend
Sets whether the plot inclues a legend and customizes its appearance and labels using `plt.legend`.
output : wrap_base.Output
Sets if the figure is displayed on the user's screen or output to `.png` using `plt.show` and `plt.savefig`
array_overlay: wrap_2d.ArrayOverlay
Overlays an input `Array2D` over the figure using `plt.imshow`.
grid_scatter : wrap_2d.GridScatter
Scatters a `Grid2D` of (y,x) coordinates over the figure using `plt.scatter`.
grid_plot: wrap_2d.LinePlot
Plots lines of data (e.g. a y versus x plot via `plt.plot`, vertical lines via `plt.avxline`, etc.)
vector_field_quiver: wrap_2d.VectorFieldQuiver
Plots a `VectorField` object using the matplotlib function `plt.quiver`.
patch_overlay: wrap_2d.PatchOverlay
Overlays matplotlib `patches.Patch` objects over the figure, such as an `Ellipse`.
voronoi_drawer: wrap_2d.VoronoiDrawer
Draws a colored Voronoi mesh of pixels using `plt.fill`.
origin_scatter : wrap_2d.OriginScatter
Scatters the (y,x) origin of the data structure on the figure.
mask_scatter : wrap_2d.MaskScatter
Scatters an input `Mask2d` over the plotted data structure's figure.
border_scatter : wrap_2d.BorderScatter
Scatters the border of an input `Mask2d` over the plotted data structure's figure.
positions_scatter : wrap_2d.PositionsScatter
Scatters specific (y,x) coordinates input as a `Grid2DIrregular` object over the figure.
index_scatter : wrap_2d.IndexScatter
Scatters specific coordinates of an input `Grid2D` based on input values of the `Grid2D`'s 1D or 2D indexes.
pixelization_grid_scatter : wrap_2d.PixelizationGridScatter
Scatters the `PixelizationGrid` of a `Pixelization` object.
light_profile_centres_scatter : lensing_wrap_2d.LightProfileCentresScatter
Scatters the (y,x) centres of all `LightProfile`'s in the plotted object (e.g. a `Tracer`).
mass_profile_centres_scatter : lensing_wrap_2d.MassProfileCentresScatter
Scatters the (y,x) centres of all `MassProfile`'s in the plotted object (e.g. a `Tracer`).
light_profile_centres_scatter : lensing_wrap_2d.MultipleImagesScatter
Scatters the (y,x) coordinates of the multiple image locations of the lens mass model.
critical_curves_plot : lensing_wrap_2d.CriticalCurvesPlot
Plots the critical curves of the lens mass model as colored lines.
caustics_plot : lensing_wrap_2d.CauticsPlot
Plots the caustics of the lens mass model as colored lines.
"""
self.light_profile_centres_scatter = light_profile_centres_scatter
self.mass_profile_centres_scatter = mass_profile_centres_scatter
self.multiple_images_scatter = multiple_images_scatter
self.critical_curves_plot = critical_curves_plot
self.caustics_plot = caustics_plot
super(MatPlot2D, self).__init__(
units=units,
figure=figure,
axis=axis,
cmap=cmap,
colorbar=colorbar,
colorbar_tickparams=colorbar_tickparams,
legend=legend,
title=title,
tickparams=tickparams,
yticks=yticks,
xticks=xticks,
ylabel=ylabel,
xlabel=xlabel,
output=output,
origin_scatter=origin_scatter,
mask_scatter=mask_scatter,
border_scatter=border_scatter,
grid_scatter=grid_scatter,
positions_scatter=positions_scatter,
index_scatter=index_scatter,
pixelization_grid_scatter=pixelization_grid_scatter,
vector_field_quiver=vector_field_quiver,
patch_overlay=patch_overlay,
array_overlay=array_overlay,
grid_plot=grid_plot,
voronoi_drawer=voronoi_drawer,
)
def mat_plot_2d_from(self, subfolders, format="png"):
return lensing_mat_plot.MatPlot2D(output=wrap_base.Output(
path=path.join(self.visualize_path, subfolders), format=format))