redback.result.RedbackResult

class redback.result.RedbackResult(*args: Any, **kwargs: Any)[source]

Bases: Result

__init__(label: str = 'no_label', outdir: str = '.', sampler: str = None, search_parameter_keys: list = None, fixed_parameter_keys: list = None, constraint_parameter_keys: list = None, priors: dict | bilby.core.prior.PriorDict = None, sampler_kwargs: dict = None, injection_parameters: dict = None, meta_data: dict = None, posterior: DataFrame = None, samples: DataFrame = None, nested_samples: DataFrame = None, log_evidence: float = nan, log_evidence_err: float = nan, information_gain: float = nan, log_noise_evidence: float = nan, log_bayes_factor: float = nan, log_likelihood_evaluations: ndarray = None, log_prior_evaluations: int = None, sampling_time: float = None, nburn: int = None, num_likelihood_evaluations: int = None, walkers: int = None, max_autocorrelation_time: float = None, use_ratio: bool = None, parameter_labels: list = None, parameter_labels_with_unit: list = None, version: str = None) → None[source]

Constructor for an extension of the regular bilby Result. This result adds the capability of utilising the plotting methods of the Transient such as plot_lightcurve. The class does this by reconstructing the Transient object that was used during the run by saving the required information in meta_data.

Parameters:

label (str, optional) – Labels of files produced by this class.
outdir (str, optional) – Output directory of the result. Default is the current directory.
sampler (str, optional) – The sampler used during the run.
search_parameter_keys (list, optional) – The parameters that were sampled in.
fixed_parameter_keys (list, optional) – Parameters that had a DeltaFunction prior
constraint_parameter_keys (list, optional) – Parameters that had a Constraint prior
priors (Union[dict, bilby.core.prior.PriorDict]) – Dictionary of priors.
sampler_kwargs (dict, optional) – Any keyword arguments passed to the sampling package.
injection_parameters (dict, optional) – True parameters if the dataset is simulated.
meta_data (dict, optional) – Additional dictionary. Contains the data used during the run and is used to reconstruct the Transient object used during the run.
posterior (pd.Dataframe, optional) – Posterior samples with log likelihood and log prior values.
samples (np.ndarray, optional) – An array of the output posterior samples.
nested_samples (np.ndarray, optional) – An array of the unweighted samples
log_evidence (float, optional) – The log evidence value if provided.
log_evidence_err (float, optional) – The log evidence error value if provided
information_gain (float, optional) – The information gain calculated.

:param log_noise_evidence:The log noise evidence. :type log_noise_evidence: float, optional :param log_bayes_factor:The log Bayes factor if we sampled using the likelihood ratio. :type log_bayes_factor: float, optional :param log_likelihood_evaluations: The evaluations of the likelihood for each sample point :type log_likelihood_evaluations: np.ndarray, optional :param log_prior_evaluations: Number of log prior evaluations. :type log_prior_evaluations: int, optional :param sampling_time: The time taken to complete the sampling in seconds. :type sampling_time: float, optional :param nburn: The number of burn-in steps discarded for MCMC samplers :type nburn: int, optional :param num_likelihood_evaluations: Number of total likelihood evaluations. :type num_likelihood_evaluations: int, optional :param walkers: The samplers taken by an ensemble MCMC samplers. :type walkers: array_like, optional :param max_autocorrelation_time: The estimated maximum autocorrelation time for MCMC samplers. :type max_autocorrelation_time: float, optional :param use_ratio:

A boolean stating whether the likelihood ratio, as opposed to the likelihood was used during sampling.

Parameters:

parameter_labels (list, optional) – List of the latex-formatted parameter labels.
parameter_labels_with_unit (list, optional) – List of the latex-formatted parameter labels with units.
version (str,) – Version information for software used to generate the result. Note, this information is generated when the result object is initialized.

__call__(*args: Any, **kwargs: Any) → Any: Call self as a function.

Methods

`__init__`([label, outdir, sampler, ...])	Constructor for an extension of the regular bilby Result.
`plot_data`(**kwargs)	Reconstructs the transient and calls the specific plot_data method.
`plot_lightcurve`([model])	Reconstructs the transient and calls the specific plot_lightcurve method. Detailed documentation appears below by running print(plot_lightcurve.__doc__) :param model: The model used to plot the lightcurve. :param filename: The output filename. Otherwise, use default which starts with the name attribute and ends with *lightcurve.png. :param axes: Axes to plot in if given. :param save:Whether to save the plot. :param show: Whether to show the plot. :param random_models: Number of random posterior samples plotted faintly. (Default value = 100) :param posterior: Posterior distribution to which to draw samples from. Is optional but must be given. :param outdir: Out directory in which to save the plot. Default is the current working directory. :param model_kwargs: Additional keyword arguments to be passed into the model. :param kwargs: Additional keyword arguments to pass in the Plotter methods. Available in the online documentation under at redback.plotting.Plotter.
`plot_multiband`(**kwargs)	Reconstructs the transient and calls the specific plot_multiband method. Detailed documentation appears below by running print(plot_multiband.__doc__) :param figure: Figure can be given if defaults are not satisfying. :param axes: Axes can be given if defaults are not satisfying. :param filename: Name of the file to be plotted in. :param outdir: The directory in which to save the file in. :param save: Whether to save the plot. (Default value = True) :param show: Whether to show the plot. (Default value = True) :param ncols: Number of columns to use on the plot. Default is 2. :param nrows: Number of rows to use on the plot. If None are given this will be inferred from ncols and the number of filters. :param figsize: Size of the figure. A default based on ncols and nrows will be used if None is given. :param filters: Which bands to plot. Will use default filters if None is given. :param kwargs: Additional keyword arguments to pass in the Plotter methods. Available in the online documentation under at redback.plotting.Plotter.
`plot_multiband_lightcurve`([model])	Reconstructs the transient and calls the specific plot_multiband_lightcurve method. Detailed documentation appears below by running print(plot_multiband_lightcurve.__doc__) :param model: The model used to plot the lightcurve. :param filename: The output filename. Otherwise, use default which starts with the name attribute and ends with *lightcurve.png. :param figure: Figure can be given if defaults are not satisfying. :param axes: Axes to plot in if given. :param save:Whether to save the plot. :param show: Whether to show the plot. :param random_models: Number of random posterior samples plotted faintly. (Default value = 100) :param posterior: Posterior distribution to which to draw samples from. Is optional but must be given. :param outdir: Out directory in which to save the plot. Default is the current working directory. :param model_kwargs: Additional keyword arguments to be passed into the model. :param kwargs: Additional keyword arguments to pass in the Plotter methods. Available in the online documentation under at redback.plotting.Plotter.
`plot_residual`([model])	Reconstructs the transient and calls the specific plot_residual method. Detailed documentation appears below by running print(plot_residual.__doc__) :param model: The model used to plot the lightcurve. :param filename: The output filename. Otherwise, use default which starts with the name attribute and ends with *lightcurve.png. :param axes: Axes to plot in if given. :param save:Whether to save the plot. :param show: Whether to show the plot. :param posterior: Posterior distribution to which to draw samples from. Is optional but must be given. :param outdir: Out directory in which to save the plot. Default is the current working directory. :param model_kwargs: Additional keyword arguments to be passed into the model. :param kwargs: Additional keyword arguments to pass in the Plotter methods. Available in the online documentation under at redback.plotting.Plotter.

Attributes

transient

Reconstruct the transient used during sampling time using the metadata information.

plot_data(**kwargs: None) → Axes[source]

Reconstructs the transient and calls the specific plot_data method. Detailed documentation appears below by running print(plot_data.__doc__) Plots the Transient data and returns Axes.

Parameters:

axes – Matplotlib axes to plot the lightcurve into. Useful for user specific modifications to the plot.
filename – Name of the file to be plotted in.
outdir – The directory in which to save the file in.
save – Whether to save the plot. (Default value = True)
show – Whether to show the plot. (Default value = True)
plot_others – Whether to plot inactive bands. (Default value = True)
color – Color of the data.
kwargs – Additional keyword arguments to pass in the Plotter methods.

Available in the online documentation under at redback.plotting.Plotter.

Keyword Arguments:

capsize – Same as matplotlib capsize.
bands_to_plot – List of bands to plot in plot lightcurve and multiband lightcurve. Default is active bands.
legend_location – Same as matplotlib legend location.
legend_cols – Same as matplotlib legend columns.
color – Color of the data points.
band_colors – A dictionary with the colors of the bands.
band_labels – List with the names of the bands.
band_scaling – Dict with the scaling for each band. First entry should be {type: ‘+’ or ‘x’} for different types.
dpi – Same as matplotlib dpi.
elinewidth – same as matplotlib elinewidth
errorbar_fmt – ‘fmt’ argument of ax.errorbar.
model – str or callable, the model to plot.
ms – Same as matplotlib markersize.
axis_tick_params_pad – pad argument in calls to ax.tick_params when setting the axes.
max_likelihood_alpha – alpha argument, i.e. transparency, when plotting the max likelihood curve.
random_sample_alpha – alpha argument, i.e. transparency, when plotting random sample curves.
uncertainty_band_alpha – alpha argument, i.e. transparency, when plotting a credible band.
max_likelihood_color – Color of the maximum likelihood curve.
random_sample_color – Color of the random sample curves.
bbox_inches – Setting for saving plots. Default is ‘tight’.
linewidth – Same as matplotlib linewidth
zorder – Same as matplotlib zorder
xy – For `ax.annotate’ x and y coordinates of the point to annotate.
xycoords – The coordinate system xy is given in. Default is ‘axes fraction’
horizontalalignment – Horizontal alignment of the annotation. Default is ‘right’
annotation_size – size argument of of ax.annotate.
fontsize_axes – Font size of the x and y labels.
fontsize_legend – Font size of the legend.
fontsize_figure – Font size of the figure. Relevant for multiband plots. Used on supxlabel and supylabel.
fontsize_ticks – Font size of the axis ticks.
hspace – Argument for subplots_adjust, sets horizontal spacing between panels.
wspace – Argument for subplots_adjust, sets horizontal spacing between panels.
plot_others – Whether to plot additional bands in the data plot, all in the same colors
random_models – Number of random draws to use to calculate credible bands or to plot.
uncertainty_mode – ‘random_models’: Plot random draws from the available parameter sets. ‘credible_intervals’: Plot a credible interval that is calculated based on the available parameter sets.
reference_mjd_date – Date to use as reference point for the x axis. Default is the first date in the data.
credible_interval_level – 0.9: Plot the 90% credible interval.
plot_max_likelihood – Plots the draw corresponding to the maximum likelihood. Default is ‘True’.
set_same_color_per_subplot – Sets the lightcurve to be the same color as the data per subplot. Default is ‘True’.
xlim_high_multiplier – Adjust the maximum xlim based on available x values.
xlim_low_multiplier – Adjust the minimum xlim based on available x values.
ylim_high_multiplier – Adjust the maximum ylim based on available x values.
ylim_low_multiplier – Adjust the minimum ylim based on available x values.

Returns:

The axes with the plot.

plot_lightcurve(model: callable | str = None, **kwargs: None) → Axes[source]

Reconstructs the transient and calls the specific plot_lightcurve method. Detailed documentation appears below by running print(plot_lightcurve.__doc__) :param model: The model used to plot the lightcurve. :param filename: The output filename. Otherwise, use default which starts with the name

attribute and ends with *lightcurve.png.

Parameters:: axes – Axes to plot in if given.

:param save:Whether to save the plot. :param show: Whether to show the plot. :param random_models: Number of random posterior samples plotted faintly. (Default value = 100) :param posterior: Posterior distribution to which to draw samples from. Is optional but must be given. :param outdir: Out directory in which to save the plot. Default is the current working directory. :param model_kwargs: Additional keyword arguments to be passed into the model. :param kwargs: Additional keyword arguments to pass in the Plotter methods. Available in the online documentation under at redback.plotting.Plotter.

Keyword Arguments:

capsize – Same as matplotlib capsize.
bands_to_plot – List of bands to plot in plot lightcurve and multiband lightcurve. Default is active bands.
legend_location – Same as matplotlib legend location.
legend_cols – Same as matplotlib legend columns.
color – Color of the data points.
band_colors – A dictionary with the colors of the bands.
band_labels – List with the names of the bands.
band_scaling – Dict with the scaling for each band. First entry should be {type: ‘+’ or ‘x’} for different types.
dpi – Same as matplotlib dpi.
elinewidth – same as matplotlib elinewidth
errorbar_fmt – ‘fmt’ argument of ax.errorbar.
model – str or callable, the model to plot.
ms – Same as matplotlib markersize.
axis_tick_params_pad – pad argument in calls to ax.tick_params when setting the axes.
max_likelihood_alpha – alpha argument, i.e. transparency, when plotting the max likelihood curve.
random_sample_alpha – alpha argument, i.e. transparency, when plotting random sample curves.
uncertainty_band_alpha – alpha argument, i.e. transparency, when plotting a credible band.
max_likelihood_color – Color of the maximum likelihood curve.
random_sample_color – Color of the random sample curves.
bbox_inches – Setting for saving plots. Default is ‘tight’.
linewidth – Same as matplotlib linewidth
zorder – Same as matplotlib zorder
xy – For `ax.annotate’ x and y coordinates of the point to annotate.
xycoords – The coordinate system xy is given in. Default is ‘axes fraction’
horizontalalignment – Horizontal alignment of the annotation. Default is ‘right’
annotation_size – size argument of of ax.annotate.
fontsize_axes – Font size of the x and y labels.
fontsize_legend – Font size of the legend.
fontsize_figure – Font size of the figure. Relevant for multiband plots. Used on supxlabel and supylabel.
fontsize_ticks – Font size of the axis ticks.
hspace – Argument for subplots_adjust, sets horizontal spacing between panels.
wspace – Argument for subplots_adjust, sets horizontal spacing between panels.
plot_others – Whether to plot additional bands in the data plot, all in the same colors
random_models – Number of random draws to use to calculate credible bands or to plot.
uncertainty_mode – ‘random_models’: Plot random draws from the available parameter sets. ‘credible_intervals’: Plot a credible interval that is calculated based on the available parameter sets.
reference_mjd_date – Date to use as reference point for the x axis. Default is the first date in the data.
credible_interval_level – 0.9: Plot the 90% credible interval.
plot_max_likelihood – Plots the draw corresponding to the maximum likelihood. Default is ‘True’.
set_same_color_per_subplot – Sets the lightcurve to be the same color as the data per subplot. Default is ‘True’.
xlim_high_multiplier – Adjust the maximum xlim based on available x values.
xlim_low_multiplier – Adjust the minimum xlim based on available x values.
ylim_high_multiplier – Adjust the maximum ylim based on available x values.
ylim_low_multiplier – Adjust the minimum ylim based on available x values.

Returns:

The axes.

plot_multiband(**kwargs: None) → Axes[source]

Reconstructs the transient and calls the specific plot_multiband method. Detailed documentation appears below by running print(plot_multiband.__doc__) :param figure: Figure can be given if defaults are not satisfying. :param axes: Axes can be given if defaults are not satisfying. :param filename: Name of the file to be plotted in. :param outdir: The directory in which to save the file in. :param save: Whether to save the plot. (Default value = True) :param show: Whether to show the plot. (Default value = True) :param ncols: Number of columns to use on the plot. Default is 2. :param nrows: Number of rows to use on the plot. If None are given this will

be inferred from ncols and the number of filters.

Parameters:

figsize – Size of the figure. A default based on ncols and nrows will be used if None is given.
filters – Which bands to plot. Will use default filters if None is given.
kwargs – Additional keyword arguments to pass in the Plotter methods.

Available in the online documentation under at redback.plotting.Plotter.

Keyword Arguments:

capsize – Same as matplotlib capsize.
bands_to_plot – List of bands to plot in plot lightcurve and multiband lightcurve. Default is active bands.
legend_location – Same as matplotlib legend location.
legend_cols – Same as matplotlib legend columns.
color – Color of the data points.
band_colors – A dictionary with the colors of the bands.
band_labels – List with the names of the bands.
band_scaling – Dict with the scaling for each band. First entry should be {type: ‘+’ or ‘x’} for different types.
dpi – Same as matplotlib dpi.
elinewidth – same as matplotlib elinewidth
errorbar_fmt – ‘fmt’ argument of ax.errorbar.
model – str or callable, the model to plot.
ms – Same as matplotlib markersize.
axis_tick_params_pad – pad argument in calls to ax.tick_params when setting the axes.
max_likelihood_alpha – alpha argument, i.e. transparency, when plotting the max likelihood curve.
random_sample_alpha – alpha argument, i.e. transparency, when plotting random sample curves.
uncertainty_band_alpha – alpha argument, i.e. transparency, when plotting a credible band.
max_likelihood_color – Color of the maximum likelihood curve.
random_sample_color – Color of the random sample curves.
bbox_inches – Setting for saving plots. Default is ‘tight’.
linewidth – Same as matplotlib linewidth
zorder – Same as matplotlib zorder
xy – For `ax.annotate’ x and y coordinates of the point to annotate.
xycoords – The coordinate system xy is given in. Default is ‘axes fraction’
horizontalalignment – Horizontal alignment of the annotation. Default is ‘right’
annotation_size – size argument of of ax.annotate.
fontsize_axes – Font size of the x and y labels.
fontsize_legend – Font size of the legend.
fontsize_figure – Font size of the figure. Relevant for multiband plots. Used on supxlabel and supylabel.
fontsize_ticks – Font size of the axis ticks.
hspace – Argument for subplots_adjust, sets horizontal spacing between panels.
wspace – Argument for subplots_adjust, sets horizontal spacing between panels.
plot_others – Whether to plot additional bands in the data plot, all in the same colors
random_models – Number of random draws to use to calculate credible bands or to plot.
uncertainty_mode – ‘random_models’: Plot random draws from the available parameter sets. ‘credible_intervals’: Plot a credible interval that is calculated based on the available parameter sets.
reference_mjd_date – Date to use as reference point for the x axis. Default is the first date in the data.
credible_interval_level – 0.9: Plot the 90% credible interval.
plot_max_likelihood – Plots the draw corresponding to the maximum likelihood. Default is ‘True’.
set_same_color_per_subplot – Sets the lightcurve to be the same color as the data per subplot. Default is ‘True’.
xlim_high_multiplier – Adjust the maximum xlim based on available x values.
xlim_low_multiplier – Adjust the minimum xlim based on available x values.
ylim_high_multiplier – Adjust the maximum ylim based on available x values.
ylim_low_multiplier – Adjust the minimum ylim based on available x values.

Returns:

The axes.

plot_multiband_lightcurve(model: callable | str = None, **kwargs: None) → Axes[source]

Reconstructs the transient and calls the specific plot_multiband_lightcurve method. Detailed documentation appears below by running print(plot_multiband_lightcurve.__doc__) :param model: The model used to plot the lightcurve. :param filename: The output filename. Otherwise, use default which starts with the name

attribute and ends with *lightcurve.png.

Parameters:

figure – Figure can be given if defaults are not satisfying.
axes – Axes to plot in if given.

:param save:Whether to save the plot. :param show: Whether to show the plot. :param random_models: Number of random posterior samples plotted faintly. (Default value = 100) :param posterior: Posterior distribution to which to draw samples from. Is optional but must be given. :param outdir: Out directory in which to save the plot. Default is the current working directory. :param model_kwargs: Additional keyword arguments to be passed into the model. :param kwargs: Additional keyword arguments to pass in the Plotter methods. Available in the online documentation under at redback.plotting.Plotter.

Keyword Arguments:

capsize – Same as matplotlib capsize.
bands_to_plot – List of bands to plot in plot lightcurve and multiband lightcurve. Default is active bands.
legend_location – Same as matplotlib legend location.
legend_cols – Same as matplotlib legend columns.
color – Color of the data points.
band_colors – A dictionary with the colors of the bands.
band_labels – List with the names of the bands.
band_scaling – Dict with the scaling for each band. First entry should be {type: ‘+’ or ‘x’} for different types.
dpi – Same as matplotlib dpi.
elinewidth – same as matplotlib elinewidth
errorbar_fmt – ‘fmt’ argument of ax.errorbar.
model – str or callable, the model to plot.
ms – Same as matplotlib markersize.
axis_tick_params_pad – pad argument in calls to ax.tick_params when setting the axes.
max_likelihood_alpha – alpha argument, i.e. transparency, when plotting the max likelihood curve.
random_sample_alpha – alpha argument, i.e. transparency, when plotting random sample curves.
uncertainty_band_alpha – alpha argument, i.e. transparency, when plotting a credible band.
max_likelihood_color – Color of the maximum likelihood curve.
random_sample_color – Color of the random sample curves.
bbox_inches – Setting for saving plots. Default is ‘tight’.
linewidth – Same as matplotlib linewidth
zorder – Same as matplotlib zorder
xy – For `ax.annotate’ x and y coordinates of the point to annotate.
xycoords – The coordinate system xy is given in. Default is ‘axes fraction’
horizontalalignment – Horizontal alignment of the annotation. Default is ‘right’
annotation_size – size argument of of ax.annotate.
fontsize_axes – Font size of the x and y labels.
fontsize_legend – Font size of the legend.
fontsize_figure – Font size of the figure. Relevant for multiband plots. Used on supxlabel and supylabel.
fontsize_ticks – Font size of the axis ticks.
hspace – Argument for subplots_adjust, sets horizontal spacing between panels.
wspace – Argument for subplots_adjust, sets horizontal spacing between panels.
plot_others – Whether to plot additional bands in the data plot, all in the same colors
random_models – Number of random draws to use to calculate credible bands or to plot.
uncertainty_mode – ‘random_models’: Plot random draws from the available parameter sets. ‘credible_intervals’: Plot a credible interval that is calculated based on the available parameter sets.
reference_mjd_date – Date to use as reference point for the x axis. Default is the first date in the data.
credible_interval_level – 0.9: Plot the 90% credible interval.
plot_max_likelihood – Plots the draw corresponding to the maximum likelihood. Default is ‘True’.
set_same_color_per_subplot – Sets the lightcurve to be the same color as the data per subplot. Default is ‘True’.
xlim_high_multiplier – Adjust the maximum xlim based on available x values.
xlim_low_multiplier – Adjust the minimum xlim based on available x values.
ylim_high_multiplier – Adjust the maximum ylim based on available x values.
ylim_low_multiplier – Adjust the minimum ylim based on available x values.

Returns:

The axes.

plot_residual(model: callable | str = None, **kwargs: None) → Axes[source]

Reconstructs the transient and calls the specific plot_residual method. Detailed documentation appears below by running print(plot_residual.__doc__) :param model: The model used to plot the lightcurve. :param filename: The output filename. Otherwise, use default which starts with the name

attribute and ends with *lightcurve.png.

Parameters:: axes – Axes to plot in if given.

:param save:Whether to save the plot. :param show: Whether to show the plot. :param posterior: Posterior distribution to which to draw samples from. Is optional but must be given. :param outdir: Out directory in which to save the plot. Default is the current working directory. :param model_kwargs: Additional keyword arguments to be passed into the model. :param kwargs: Additional keyword arguments to pass in the Plotter methods. Available in the online documentation under at redback.plotting.Plotter.

Keyword Arguments:

capsize – Same as matplotlib capsize.
bands_to_plot – List of bands to plot in plot lightcurve and multiband lightcurve. Default is active bands.
legend_location – Same as matplotlib legend location.
legend_cols – Same as matplotlib legend columns.
color – Color of the data points.
band_colors – A dictionary with the colors of the bands.
band_labels – List with the names of the bands.
band_scaling – Dict with the scaling for each band. First entry should be {type: ‘+’ or ‘x’} for different types.
dpi – Same as matplotlib dpi.
elinewidth – same as matplotlib elinewidth
errorbar_fmt – ‘fmt’ argument of ax.errorbar.
model – str or callable, the model to plot.
ms – Same as matplotlib markersize.
axis_tick_params_pad – pad argument in calls to ax.tick_params when setting the axes.
max_likelihood_alpha – alpha argument, i.e. transparency, when plotting the max likelihood curve.
random_sample_alpha – alpha argument, i.e. transparency, when plotting random sample curves.
uncertainty_band_alpha – alpha argument, i.e. transparency, when plotting a credible band.
max_likelihood_color – Color of the maximum likelihood curve.
random_sample_color – Color of the random sample curves.
bbox_inches – Setting for saving plots. Default is ‘tight’.
linewidth – Same as matplotlib linewidth
zorder – Same as matplotlib zorder
xy – For `ax.annotate’ x and y coordinates of the point to annotate.
xycoords – The coordinate system xy is given in. Default is ‘axes fraction’
horizontalalignment – Horizontal alignment of the annotation. Default is ‘right’
annotation_size – size argument of of ax.annotate.
fontsize_axes – Font size of the x and y labels.
fontsize_legend – Font size of the legend.
fontsize_figure – Font size of the figure. Relevant for multiband plots. Used on supxlabel and supylabel.
fontsize_ticks – Font size of the axis ticks.
hspace – Argument for subplots_adjust, sets horizontal spacing between panels.
wspace – Argument for subplots_adjust, sets horizontal spacing between panels.
plot_others – Whether to plot additional bands in the data plot, all in the same colors
random_models – Number of random draws to use to calculate credible bands or to plot.
uncertainty_mode – ‘random_models’: Plot random draws from the available parameter sets. ‘credible_intervals’: Plot a credible interval that is calculated based on the available parameter sets.
reference_mjd_date – Date to use as reference point for the x axis. Default is the first date in the data.
credible_interval_level – 0.9: Plot the 90% credible interval.
plot_max_likelihood – Plots the draw corresponding to the maximum likelihood. Default is ‘True’.
set_same_color_per_subplot – Sets the lightcurve to be the same color as the data per subplot. Default is ‘True’.
xlim_high_multiplier – Adjust the maximum xlim based on available x values.
xlim_low_multiplier – Adjust the minimum xlim based on available x values.
ylim_high_multiplier – Adjust the maximum ylim based on available x values.
ylim_low_multiplier – Adjust the minimum ylim based on available x values.

Returns:

The axes.

property transient: Transient

Reconstruct the transient used during sampling time using the metadata information.

Returns:: The reconstructed Transient.
Return type:: redback.transient.transient.Transient