redback.result.RedbackResult
- class redback.result.RedbackResult(*args: Any, **kwargs: Any)[source]
Bases:
Result- __init__(label: str = 'no_label', outdir: str = '.', sampler: Optional[str] = None, search_parameter_keys: Optional[list] = None, fixed_parameter_keys: Optional[list] = None, constraint_parameter_keys: Optional[list] = None, priors: Optional[Union[dict, bilby.core.prior.PriorDict]] = None, sampler_kwargs: Optional[dict] = None, injection_parameters: Optional[dict] = None, meta_data: Optional[dict] = None, posterior: Optional[DataFrame] = None, samples: Optional[DataFrame] = None, nested_samples: Optional[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: Optional[ndarray] = None, log_prior_evaluations: Optional[int] = None, sampling_time: Optional[float] = None, nburn: Optional[int] = None, num_likelihood_evaluations: Optional[int] = None, walkers: Optional[int] = None, max_autocorrelation_time: Optional[float] = None, use_ratio: Optional[bool] = None, parameter_labels: Optional[list] = None, parameter_labels_with_unit: Optional[list] = None, version: Optional[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
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: Optional[Union[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: Optional[Union[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: Optional[Union[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.