plot_mask#

pyvisgrid.plotting.plot_mask(grid_data, mode: str = 'hist', crop: tuple[list[float | None]] = ([None, None], [None, None]), norm: str | Normalize = None, cmap: str | Colormap | None = None, plot_args: dict | None = None, fig_args: dict | None = None, save_to: str | PathLike | None = None, save_args: dict | None = None, fig: Figure | None = None, ax: Axes | None = None) → tuple[Figure, Axes][source]#

Plots the (u,v) mask (the binned visibilities) of the gridded interferometric image.

Parameters:
grid_datapyvisgrid.GridData

The gridded data from the pyvisgrid.Gridder.grid method. This always represents the gridded visibilities of one Stokes component.

modestr, optional

The mode specifying which values of the mask should be plotted. Possible values are:

  • hist: Plots the number of (u,v) points which are sorted in

    each pixel of the image in the (u,v) space.

  • abs / amp: Plots the absolute value of the gridded visibilities,

    meaning the magnitude of the complex numbers in Euler representation.

  • phase: Plots the phase angle of the gridded visibilities,

    meaning the angle in the exponent of the complex numbers in Euler representation.

  • real: Plots the real part of the gridded visibilities.

  • imag: Plots the imaginary part of the gridded visibilities.

Default is hist.

croptuple[list[float | None]], optional

The crop of the image. This has to have the format ([x_left, x_right], [y_left, y_right]), where the left and right values for each axis are the upper and lower limits of the axes which should be shown. IMPORTANT: If one supplies the plt.imshow an extent parameter via the plot_args parameter, this will be the scale in which one has to give the crop! If not, the crop has to be in pixels.

normstr | matplotlib.colors.Normalize | None, optional

The name of the norm or a matplotlib norm. Possible values are:

  • log: Returns a logarithmic norm with clipping on (!), meaning

    values above the maximum will be mapped to the maximum and values below the minimum will be mapped to the minimum, thus avoiding the appearance of a colormaps ‘over’ and ‘under’ colors (e.g. in case of negative values). Depending on the use case this is desirable but in case that it is not, one can set the norm to log_noclip or provide a custom norm.

  • log_noclip: Returns a logarithmic norm with clipping off.

  • centered: Returns a linear norm which centered around zero.

  • sqrt: Returns a power norm with exponent 0.5, meaning the

    square-root of the values.

  • other: A value not declared above will be returned as is, meaning

    that this could be any value which exists in matplotlib itself.

Default is None, meaning no norm will be applied.

cmap: str | matplotlib.colors.Colormap | None, optional

The colormap to be used for the plot. Default is None, meaning the colormap will be default to a value fitting for the chosen mode.

plot_argsdict, optional

The additional arguments passed to the scatter plot. Default is {"color":"royalblue"}.

fig_argsdict | None, optional

The additional arguments passed to the figure. If a figure object is given in the fig parameter, this value will be discarded. Default is None.

save_tostr | PathLike | None, optional

The name of the file to save the plot to. Default is None, meaning the plot won’t be saved.

save_argsdict | None, optional

The additional arguments passed to the fig.savefig call. Default is {"bbox_inches":"tight"}.

figmatplotlib.figure.Figure | None, optional

A custom figure object. If set to None, the ax parameter also has to be None! Default is None.

axmatplotlib.axes.Axes | None, optional

A custom axes object. If set to None, the fig parameter also has to be None! Default is None.

Returns:
figmatplotlib.figure.Figure

The figure object.

axmatplotlib.axes.Axes

The axes object.