threeML.io.plotting.model_plot module¶
-
class
threeML.io.plotting.model_plot.
SpectralContourPlot
(n_total, xscale='log', yscale='log', show_legend=True, plot_kwargs=None, contour_kwargs=None, legend_kwargs=None, emin=None, emax=None, subplot=None)[source]¶ Bases:
object
-
add_dimensionless_model
(energy_range, best_fit, color, upper_error=None, lower_error=None, contour_color=None, label='model')[source]¶
-
-
threeML.io.plotting.model_plot.
plot_spectra
(*analysis_results, **kwargs) → matplotlib.figure.Figure[source]¶ plotting routine for fitted point source spectra
- Parameters
analysis_results – fitted JointLikelihood or BayesianAnalysis objects
sources_to_use – (optional) list of PointSource string names to plot from the analysis
energy_unit – (optional) astropy energy unit in string form (can also be frequency)
flux_unit – (optional) astropy flux unit in string form
confidence_level – (optional) confidence level to use (default: 0.68)
ene_min – (optional) minimum energy to plot
ene_max – (optional) maximum energy to plot
num_ene – (optional) number of energies to plot
use_components – (optional) True or False to plot the spectral components
components_to_use – (optional) list of string names of the components to plot: including ‘total’
will also plot the total spectrum :param sum_sources: (optional) some all the MLE and Bayesian sources :param show_contours: (optional) True or False to plot the contour region :param plot_style_kwargs: (optional) dictionary of MPL plot styling for the best fit curve :param contour_style_kwargs: (optional) dictionary of MPL plot styling for the contour regions :param fit_cmap: MPL color map to iterate over for plotting multiple analyses :param contour_cmap: MPL color map to iterate over for plotting contours for multiple analyses :param subplot: subplot to use :param xscale: ‘log’ or ‘linear’ :param yscale: ‘log’ or ‘linear’ :param include_extended: True or False, also plot extended source spectra. :return: