Source code for autogalaxy.plot.mat_plot.two_d

from typing import List, Optional, Union

import autoarray.plot as aplt
from autogalaxy.plot import wrap as w

[docs]class MatPlot2D(aplt.MatPlot2D): def __init__( self, units: Optional[aplt.Units] = None, figure: Optional[aplt.Figure] = None, axis: Optional[aplt.Axis] = None, cmap: Optional[aplt.Cmap] = None, colorbar: Optional[aplt.Colorbar] = None, colorbar_tickparams: Optional[aplt.ColorbarTickParams] = None, tickparams: Optional[aplt.TickParams] = None, yticks: Optional[aplt.YTicks] = None, xticks: Optional[aplt.XTicks] = None, title: Optional[aplt.Title] = None, ylabel: Optional[aplt.YLabel] = None, xlabel: Optional[aplt.XLabel] = None, text: Optional[Union[aplt.Text, List[aplt.Text]]] = None, annotate: Optional[Union[aplt.Annotate, List[aplt.Annotate]]] = None, legend: Optional[aplt.Legend] = None, output: Optional[aplt.Output] = None, array_overlay: Optional[aplt.ArrayOverlay] = None, contour: Optional[aplt.Contour] = None, grid_scatter: Optional[aplt.GridScatter] = None, grid_plot: Optional[aplt.GridPlot] = None, vector_yx_quiver: Optional[aplt.VectorYXQuiver] = None, patch_overlay: Optional[aplt.PatchOverlay] = None, interpolated_reconstruction: Optional[aplt.InterpolatedReconstruction] = None, voronoi_drawer: Optional[aplt.VoronoiDrawer] = None, origin_scatter: Optional[aplt.OriginScatter] = None, mask_scatter: Optional[aplt.MaskScatter] = None, border_scatter: Optional[aplt.BorderScatter] = None, positions_scatter: Optional[aplt.PositionsScatter] = None, index_scatter: Optional[aplt.IndexScatter] = None, mesh_grid_scatter: Optional[aplt.MeshGridScatter] = None, light_profile_centres_scatter: Optional[w.LightProfileCentresScatter] = None, mass_profile_centres_scatter: Optional[w.MassProfileCentresScatter] = None, multiple_images_scatter: Optional[w.MultipleImagesScatter] = None, tangential_critical_curves_plot: Optional[ w.TangentialCriticalCurvesPlot ] = None, radial_critical_curves_plot: Optional[w.RadialCriticalCurvesPlot] = None, tangential_caustics_plot: Optional[w.TangentialCausticsPlot] = None, radial_caustics_plot: Optional[w.RadialCausticsPlot] = None, use_log10: bool = False, ): """ 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`. - `MapperVoronoiNoInterp`: using `plt.fill`. Parameters ---------- units The units of the figure used to plot the data structure which sets the y and x ticks and labels. figure Opens the matplotlib figure before plotting via `plt.figure` and closes it once plotting is complete via `plt.close`. axis Sets the extent of the figure axis via `plt.axis` and allows for a manual axis range. cmap Customizes the colormap of the plot and its normalization via matplotlib `colors` objects such as `colors.Normalize` and `colors.LogNorm`. 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 Customizes the yticks of the colorbar plotted via `plt.colorbar`. tickparams Customizes the appearances of the y and x ticks on the plot, (e.g. the fontsize), using `plt.tick_params`. yticks Sets the yticks of the plot, including scaling them to new units depending on the `Units` object, via `plt.yticks`. xticks Sets the xticks of the plot, including scaling them to new units depending on the `Units` object, via `plt.xticks`. title Sets the figure title and customizes its appearance using `plt.title`. ylabel Sets the figure ylabel and customizes its appearance using `plt.ylabel`. xlabel Sets the figure xlabel and customizes its appearance using `plt.xlabel`. text Sets any text on the figure and customizes its appearance using `plt.text`. annotate Sets any annotations on the figure and customizes its appearance using `plt.annotate`. legend Sets whether the plot inclues a legend and customizes its appearance and labels using `plt.legend`. output Sets if the figure is displayed on the user's screen or output to `.png` using `` and `plt.savefig` array_overlay Overlays an input `Array2D` over the figure using `plt.imshow`. contour Overlays contours of an input `Array2D` over the figure using `plt.contour`. grid_scatter Scatters a `Grid2D` of (y,x) coordinates over the figure using `plt.scatter`. grid_plot Plots lines of data (e.g. a y versus x plot via `plt.plot`, vertical lines via `plt.avxline`, etc.) vector_yx_quiver Plots a `VectorField` object using the matplotlib function `plt.quiver`. patch_overlay Overlays matplotlib `patches.Patch` objects over the figure, such as an `Ellipse`. voronoi_drawer Interpolations the reconstruction of a `Mapper` object from its irregular grid (e.g. Delaunay, Voronoi) to a uniform 2D array and plots it via `plt.imshow()`. voronoi_drawer Draws a colored Voronoi mesh of pixels using `plt.fill`. origin_scatter Scatters the (y,x) origin of the data structure on the figure. mask_scatter Scatters an input `Mask2d` over the plotted data structure's figure. border_scatter Scatters the border of an input `Mask2d` over the plotted data structure's figure. positions_scatter Scatters specific (y,x) coordinates input as a `Grid2DIrregular` object over the figure. index_scatter Scatters specific coordinates of an input `Grid2D` based on input values of the `Grid2D`'s 1D or 2D indexes. mesh_grid_scatter Scatters the `PixelizationGrid` of a `Pixelization` object. light_profile_centres_scatter Scatters the (y,x) centres of all `LightProfile`'s in the plotted object (e.g. a `Tracer`). mass_profile_centres_scatter Scatters the (y,x) centres of all `MassProfile`'s in the plotted object (e.g. a `Tracer`). light_profile_centres_scatter Scatters the (y,x) coordinates of the multiple image locations of the lens mass model. tangential_critical_curves_plot Plots the tangential critical curves of the lens mass model as colored lines. radial_critical_curves_plot Plots the radial critical curves of the lens mass model as colored lines. tangential_caustics_plot Plots the tangential caustics of the lens mass model as colored lines. radial_caustics_plot Plots the radial caustics of the lens mass model as colored lines. """ self.light_profile_centres_scatter = ( light_profile_centres_scatter or w.LightProfileCentresScatter(is_default=True) ) self.mass_profile_centres_scatter = ( mass_profile_centres_scatter or w.MassProfileCentresScatter(is_default=True) ) self.multiple_images_scatter = ( multiple_images_scatter or w.MultipleImagesScatter(is_default=True) ) self.tangential_critical_curves_plot = ( tangential_critical_curves_plot or w.TangentialCriticalCurvesPlot(is_default=True) ) self.radial_critical_curves_plot = ( radial_critical_curves_plot or w.RadialCriticalCurvesPlot() ) self.tangential_caustics_plot = ( tangential_caustics_plot or w.TangentialCausticsPlot(is_default=True) ) self.radial_caustics_plot = radial_caustics_plot or w.RadialCausticsPlot() super().__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, text=text, annotate=annotate, 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, mesh_grid_scatter=mesh_grid_scatter, vector_yx_quiver=vector_yx_quiver, patch_overlay=patch_overlay, array_overlay=array_overlay, contour=contour, grid_plot=grid_plot, interpolated_reconstruction=interpolated_reconstruction, voronoi_drawer=voronoi_drawer, use_log10=use_log10, )