galapy.analysis.plot

Plotting utilities for the analysis of the results and to generate easily plots from GalaPy structures and functions.

Functions

corner_res(res[, handler, which_params, ...])

Plot the triangle plot with the 2D posteriors of a sampling run and 1D marginals on the diagonal.

format_axes_ticks(fig[, labelsize])

photometric_system(obj[, colors, ax, ax_kwargs])

Generates a visualisation of the photometric system bandpass transmissions.

sed_1sigma2sigma(ll, flux, err[, center, ...])

sed_components(ll, components[, redshift, ...])

sed_flux(ll, flux[, color, label, redshift, ...])

sed_flux_res(res[, model, observation, ...])

Plots the formatted SED flux from a galapy.sampling.Results instance.

sed_layout(redshift, frame[, ax])

Produces/modifies a matplotlib.axes.Axes instance with the grid a labels for plotting SED fluxes.

sed_obs(ll, ff, ee, lo[, redshift, frame, ...])

sed_residuals_res(res[, frame, ...])

Plots the formatted residuals with respect to the best-fit model from a sampling run.

show_default_dict([name])

Shows the default dictionary for the kwargs dictionary with name 'name'.
galapy.analysis.plot.show_default_dict(name=None)

Shows the default dictionary for the kwargs dictionary with name ‘name’.

galapy.analysis.plot.sed_layout(redshift, frame, ax=None, **kwargs)

Produces/modifies a matplotlib.axes.Axes instance with the grid a labels for plotting SED fluxes.

Parameters:

  • redshift (scalar) – the redshift of the observed frame

  • frame (str) – one among (‘obs’, ‘rest’, ‘both’), whether to scale the x-axis to the observed frame, the rest-frame or both. In the latter case a secondary x-axis will be added on top of the grid

  • ax (matplotlib.axes.Axes) – (Optional) an instance of matplotlib axes

  • kwargs (dictionary) – (Optional) the eventual keyword arguments to pass to the `set` function of `ax`.

Returns:

the generated axes

Return type:

matplotlib.axes.Axes

galapy.analysis.plot.sed_flux_res(res, model=None, observation=None, plot_observation=False, plot_components=False, plot_contours=True, frame='both', show_legend=True, legend_kwargs={}, ax=None, ax_kwargs={})

Plots the formatted SED flux from a galapy.sampling.Results instance. Optional specs that can be activated or de-activated are the observed points, 1- and 2-sigma contours around the mean of the sampled SEDs and the different physical components contributing to the total best-fit SED.

Parameters:

  • res (Results instance) – A Results instance from a sampling run.

  • model (GXY instance) – (Optional) A galaxy model or any class derived from galapy.Galaxy.GXY.

  • observation (Observation instance) – (Optional) an Observation instance. Note that if plot_observation=False it will be ignored anyways.

  • plot_observation (bool) – Whether to plot the observational data used in the sampling (dafault is False).

  • plot_components (bool) – Whether to plot the different components contributing to the best-fit SED (dafault is False).

  • plot_contours (bool) – Whether to plot the 1- and 2-sigma contours around the mean SED (dafault is False).

  • frame (string) – One among {'rest', 'obs', 'both'}, choose the frame, observed- or rest-frame, to plot the wavelength-axis (default is 'both')

  • show_legend (bool) – (Optional)

  • legend_kwargs (dict) – (Optional)

  • ax (matplotlib.axes.Axes) – (Optional) an instance of matplotlib axes

  • ax_kwargs (dict) – (Optional) keyword arguments to pass to the function sed_layout

Returns:

An instance of matplotlib.axes.Axes with the plotted fluxes

Return type:

matplotlib.axes.Axes

galapy.analysis.plot.sed_residuals_res(res, frame='both', plot_contours=False, plot_chi2=True, ax=None, text_kwargs={}, ax_kwargs={})

Plots the formatted residuals with respect to the best-fit model from a sampling run.

Parameters:

  • res (Results instance) – A Results instance from a sampling run.

  • frame (string) – One among {'rest', 'obs', 'both'}, choose the frame, observed- or rest-frame, to plot the wavelength-axis (default is 'both')

  • plot_contours (bool) – Whether to plot the 1- and 2-sigma contours around the mean SED (dafault is False).

  • plot_chi2 (bool) – Whether to plot a text box with the best-fit value of the reduced chi2 (dafault is True).

  • ax (matplotlib.axes.Axes) – (Optional) an instance of matplotlib axes

  • text_kwargs (dict) – (Optional) keyword arguments to pass to the matplotlib.offsetbox.AnchoredText class (regulates shape and text-formatting of the text-box for the eventual chi2 plot)

  • ax_kwargs (dict) – (Optional) keyword arguments to pass to the function sed_layout

Returns:

An instance of matplotlib.axes.Axes with the plotted fluxes

Return type:

matplotlib.axes.Axes

galapy.analysis.plot.photometric_system(obj, colors=None, ax=None, ax_kwargs={})

Generates a visualisation of the photometric system bandpass transmissions.

Parameters:

  • obj (object) – An instance of galapy.PhotometricSystem.PMS or any other object with a pms attribute which is an instance of galapy.PhotometricSystem.PMS (e.g. galapy.Galaxy.GXY or galapy.Observation.OBS)

  • colors (iterable) – (Optional) a list or iterable of valid colors. One color per bandpass is necessary

  • ax (matplotlib.axes.Axes) – (Optional) an instance of matplotlib axes

  • ax_kwargs (dictionary) – (Optional) the eventual keyword arguments to pass to the set function of ax.

Returns:

  • matplotlib.axes.Axes

  • an instance of matplotlib axes

galapy.analysis.plot.corner_res(res, handler=None, which_params=None, getdist_settings=None, param_limits='auto', plot_titles=True, mark='bestfit', titles_kw={}, triangle_kw={}, marker_kw={})

Plot the triangle plot with the 2D posteriors of a sampling run and 1D marginals on the diagonal.

Parameters:

  • res (Results instance) – A Results instance from a sampling run.

  • handler (ModelParameters instance) – (Optional) The ModelParameters corresponding to the given sampling run

  • which_params (str or sequence of str) – Either a single string or a sequence of strings. Name of the parameters to show on the triangle plot. Also accepts wildcards (e.g. which_params = 'sfh*' will show all the parameters that contain the sub-string sfh).

  • getdist_settings (dict) – a dictionary of custom analysis settings to pass to the getdist.MCSamples settings argument (for further informations see the documentation of getdist) (to see defaults call galapy.analysis.plots.show_default_dict('getdist_settings'))

  • param_limits (str or sequence or dict) –

    If a string is passed it has to be one among

    • ’auto’ : set the axes limits automatically

    • ’prior’ : set the axes to the limits of the prior

    Otherwise it can be a

    • 2D sequence (list or tuple) with dimensions (ndim, 2), where ndim is the number of free parameters of the sampling run. Each of the ndim couples will be assigned to the ordered list of free-parameters.

    • a dictionary where the keys are the names of the free parameters of the sampling run and the values are sequences of length = 2 with the limits for the corresponding free parameter.

  • plot_titles (bool) – Whether to plot titles above the diagonal marginal posteriors with summary statistics computed from the corresponding posteriors (default summary is the median with 68% uncertainty, to change this behaviour modify the argument titles_kw)

  • mark (str) – What position in the posteriors space to highlight with lines, default is the ‘bestfit’, other available positions are ‘mean’ and ‘median’.

  • titles_kw (dict) – dictionary to modify the titles above the diagonal marginal posteriors. It will be passed to function galapy.analysis.funcs.get_parameters_summary_strings() (to see defaults call galapy.analysis.plots.show_default_dict('titles_kw'))

  • triangle_kw (dict) – Dictionary to modify the aspect of the triangle plot, these are the keyword arguments passed to triangle_plot() function of getdist (to see defaults call galapy.analysis.plots.show_default_dict('triangle_kw'))

  • marker_kw (dict) – Dictionary to modify the aspect of the markers (color, linewidth, …) (to see defaults call galapy.analysis.plots.show_default_dict('marker_kw'))

Returns:

  • matplotlib.pyplot.Figure instance

  • matplotlib.axes.Axes instance