gcfit.analysis.RunCollection#

class gcfit.analysis.RunCollection(runs, *, sort=True)#

Analysis and visualization of an collection of multiple runs.

Provides a number of flexible plotting, output and summary methods useful for the analysis of distributions, relationships and correlations between the results of multiple runs at once. This class is meant to enable the analysis of a larger population of fits of different clusters at once, and explore the relationships in cluster parameters and make comparisons with results from the literature. Multiple different runs of fitting on the same cluster may also be given, though care should be taken that each has it’s own .name to avoid confusion.

Parameters:

runs, *, sort=True
runslist of _SingleRunAnalysis: List of run objects which will make up this collection. In theory, MCMC and Nested sampling runs can both be used interchangeably.
sortbool, optional: If True (default), the given list of runs will be sorted by their .name attributes. Sorting decides the positioning of the runs in some plot functions.

Methods

`__init__`(runs, *[, sort])
`add_residuals`(ax, y1, y2, e1, e2[, clrs, ...])	Append an extra axis to ax for plotting residuals.
`filter_runs`(pattern[, sort_by, sort])	Filter all runs based on names and return a new object with them.
`from_dir`(directory[, pattern, strict, ...])	Initialize a run collection based on run files found in a directory.
`from_files`(file_list[, strict, sampler, ...])	Initialize a run collection based on a list of run files.
`get_CImodels`([N, Nprocesses, add_errors, ...])	Return a CI ModelCollection instance corresponding to these runs.
`get_models`(**kwargs)	Return a ModelCollection instance corresponding to these runs.
`get_run`(name)	Return the a single run from this collection with a given name.
`iter_plots`(plot_func[, yield_run])	Iterator yielding a call to plot_func for each run.
`output_summary`([outfile, params, style, ...])	Output a table of the median and 1σ param values for all runs.
`plot_a3_FeH`([fig, ax, show_kroupa])	Special case of plot_relation with "a3" and "FeH".
`plot_lit_comp`(param, truths[, e_truths, ...])	Plot comparison between parameter values and "truths".
`plot_lit_dist`(param, truths[, e_truths, ...])	Plot hist of the fractional difference between param and "truths".
`plot_lit_relation`(param, lit[, e_lit, ...])	Plot comparison between parameter values and arbitrary data.
`plot_param_bar`(param[, fig, ax, clr_param, ...])	Plot the mean and 1σ values of param along all runs as a bar chart.
`plot_param_corner`([params, fig, ...])	Plot a "corner plot" showing relationship between parameters.
`plot_param_hist`(param[, fig, ax, kde, ...])	Plot a histogram representing the sum of all distributions of param.
`plot_param_means`(param[, fig, ax, ...])	Plot the mean and 1σ values of param along all runs.
`plot_param_violins`(param[, fig, ax, ...])	Plot a violin plot showing the parameter distributions for all runs.
`plot_relation`(param1, param2[, fig, ax, ...])	Plot relationship between two parameters across all runs.
`save_plots`(plot_func[, fn_pattern, save_kw, ...])	Iterate over calls to plot_func on each run and save the figures.
`summary_dataframe`(*[, params, include_FeH, ...])	Return a Dataframe with the median and 1σ param values for all runs.

Attributes

`cmap`
`models`
`names`	List of `.name`s of each run in this collection.

add_residuals(ax, y1, y2, e1, e2, clrs=None, res_ax=None, loc='bottom', size='15%', pad=0.1)#

Append an extra axis to ax for plotting residuals.

Automatically appends a new axis to the the bottom of the given ax, and plots the residuals between the two given quantities (and their errors) on it, as a percentage.

Parameters:

axmatplotlib.axes.Axes: An axes instance on which to plot this observational data.
y1, y2np.ndarray: Arrays of data values to plot the residual between. Residuals are of (y2 - y1) / y1.
e1, e2np.ndarray: Arrays of errors on each datapoint.
clrscolor, optional: Colour used for all datapoints, passed to errorbar and scatter.
res_axmatplotlib.axes.Axes, optional: Optionally provide an already created axis to plot residuals on. This is useful for overplotting multiple residuals (i.e. for multiple datasets).
loc{“left”, “right”, “bottom”, “top”}, optional: Where the new axes is positioned relative to the main axes.
sizestr or float, optional: The size of the appended residuals axes, with respect to the primary axes. See mpl_toolkits.axes_grid1.axes_divider.AxesDivider.append_axes for more information. Defaults to “15%”.
padfloat, optional: Padding between the axes. Defaults to 0.1.

Returns:

matplotlib.axes.Axes: The created axes instance containing the residuals plot.

filter_runs(pattern, sort_by=None, sort=True, **kwargs)#

Filter all runs based on names and return a new object with them.

Based on a given string pattern, filters out all runs within this collection matching this pattern (as based on fnmatch.filter) and returns a new RunCollection instance with only those filtered runs.

Parameters:

patternstr or list of str: A pattern used to filter all run names, using the glob rules provided by fnmatch. Also allowed is a list of cluster names, which will filter on the matching runs. A list of names will not use pattern matching, and the given names must match exactly.
sort_by{‘old’, ‘new’, None}, optional: Sort runs in new collection by either the order in this collection or the list of names (if it is a list, otherwise does None). If None (default), simply passes sort to the new collection init and sorting is handled there, by name. This argument is only used if sort is True.
sortbool, optional: Whether or not to sort this run. If sort_by is None, this argument is passed to the new run collection init.
**kwargsdict: All other arguments are passed to the new RunCollection object.

Returns:

RunCollection: A new run collection instance based on the filtered out runs.

classmethod from_dir(directory, pattern='**/*hdf', strict=False, *args, sampler='nested', run_kwargs=None, **kwargs)#

Initialize a run collection based on run files found in a directory.

Search for run output files (as created by the relevant fitting functions) in a given directory and create a new RunCollection instance based on the run objects created from them.

NestedRun or MCMCRun instances will be created for found file. Which class is used must be consistent over all runs, and specified a priori.

Parameters:

directorystr or pathlib.Path: Path to the directory to search for files within.
patternstr, optional: The glob pattern used to find all files within the given directory. Should be tuned in order to only return valid run files. Default is to search (recursively) for all HDF files within the directory.
strictbool, optional: If True, will raise an RuntimeError if any discovered file fails when creating a run class.
sampler{‘nested’, ‘mcmc’}, optional: Whether to initialize each run as either a NestedRun or MCMCRun.
run_kwargsdict, optional: Optional arguments passed to all individual run initialization.
*args, **kwargs: All other arguments are passed to the new RunCollection object.

classmethod from_files(file_list, strict=False, *args, sampler='nested', run_kwargs=None, **kwargs)#

Initialize a run collection based on a list of run files.

Given a list of paths to a number of run output files (as created by the relevant fitting functions), creates a new RunCollection instance based on the run objects created from them.

NestedRun or MCMCRun instances will be created for found file. Which class is used must be consistent over all runs, and specified a priori.

Parameters:

file_listlist of str: A list of paths to valid run output files.
strictbool, optional: If True, will raise an RuntimeError if any discovered file fails when creating a run class.
sampler{‘nested’, ‘mcmc’}, optional: Whether to initialize each run as either a NestedRun or MCMCRun.
run_kwargsdict, optional: Optional arguments passed to all individual run initialization.
*args, **kwargs: All other arguments are passed to the new RunCollection object.

get_CImodels(N=100, Nprocesses=1, add_errors=False, shuffle=True, load=True)#

Return a CI ModelCollection instance corresponding to these runs.

The visualizer collection is initialized through the ModelCollection.from_chains classmethod, with the chains from each run in this collection and using N samples, if load is False, otherwise will attempt to use the ModelCollection.load classmethod, assuming a CI model has already been created and saved to this same file, under the model group, in each run.

Parameters:

Nint, optional: The number of samples to use in computing the confidence intervals.
Nprocessesint, optional: The number of processes to use in a multiprocessing.Pool passed to the CI model initializer. Defaults to only 1 cpu.
add_errorsbool, optional: Optionally add the statistical and sampling errors, not normally accounted for, to the chain of samples used. Only relevant to nested sampling runs.
shufflebool, optional: Optionally shuffle the chains before passing on. Useful when N is smaller than the full sample size, to avoid biasing the resulting CIs.
loadbool, optional: If True, will attempt to load CI models, rather than creating a new one.

Returns:

CIModelVisualizer: The created model visualization (with confidence intervals) object.

get_models(**kwargs)#

Return a ModelCollection instance corresponding to these runs.

The visualizer collection is initialized through the ModelCollection.from_chains classmethod, with the chains from each run in this collection, and based on the single average model.

Parameters:

**kwargsdict: All arguments are passed to the from_chains classmethod, with ci=False.

Returns:

ModelCollection: The created model collection visualization object.

get_run(name)#: Return the a single run from this collection with a given name.

iter_plots(plot_func, yield_run=False, *args, **kwargs)#: Iterator yielding a call to plot_func for each run.

property names#: List of `.name`s of each run in this collection.

output_summary(outfile=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>, params='all', style='latex', *, include_FeH=False, include_BH=False, math_labels=False, substack_errors=False, **kwargs)#

Output a table of the median and 1σ param values for all runs.

Constructs and writes out a table of the median and 1σ values of all included parameters, with each row representing a run.

Parameters:

outfilefile, optional: Output file handler to write the summary to. Defaults to printing to “stdout”.
paramslist of str, optional: The list of parameters to include in the outputted table. If not given, defaults to the typical 13 free model parameters, and any included by the include_* arguments.
style{‘table’, ‘latex’, ‘hdf’, ‘csv’, ‘html’}, optional: Type of output file to create. Defaults to ‘latex’. Most formats use the corresponding to_* method on a pandas.Dataframe.
include_FeHbool, optional: If True, the metallicity FeH is included in the default params.
include_BHbool, optional: If True, the black hole related parameters (BH_mass, BH_num, f_BH, f_rem) are included in the default params.
math_labelsbool, optional: If True, the column names will be labelled with latex math. See _get_latex_labels for more information.
substack_errorsbool, optional: If True, and the given style is latex, the errors will be written, not as a separate column, but within a “substack” on each value.
**kwargsdict: All other arguments are passed to the corresponding to_* method on a pandas.Dataframe.

Returns:

pandas.Dataframe: The dataframe used to write the output file.

plot_a3_FeH(fig=None, ax=None, show_kroupa=False, *args, **kwargs)#: Special case of plot_relation with “a3” and “FeH”.

plot_lit_comp(param, truths, e_truths=None, src_truths='', fig=None, ax=None, *, annotate=False, annotate_kwargs=None, clr_param=None, clr_kwargs=None, residuals=False, diagonal=True, force_model=False, label=None, marker='o', **kwargs)#

Plot comparison between parameter values and “truths”.

Plots a scatter plot (with errorbars) of param by the values provided in the truths array, using the median and 1σ error values from each run in this collection.

This is meant to compare one-to-one the results of fits against “true” values, representing the same parameter, from the literature.

Parameters:

paramstr: Name of the parameter to plot.
truthsnp.ndarray[Nruns]: Array of “truth” values, to plot on the y-axis.
e_truthsnp.ndarray[Nruns], optional: Array of uncertainties on the “truth” values.
src_truthsstr, optional: The source of the “truths”, included in the y-axis label.
figNone or matplotlib.figure.Figure, optional: Figure to place the ax on. If None (default), a new figure will be created, otherwise the given figure should be empty, or already have the correct number of axes. See _RunAnalysis._setup_artist for more details.
axNone or matplotlib.axes.Axes, optional: An axes instance on which to plot this relation. Should be a part of the given fig.
annotatebool, optional: Optionally create a hook to this figure allowing the interactive annotating of selected cluster names. See _Annotator for more details.
annotate_kwargsdict, optional: Optional arguments passed to the _Annotator instance.
clr_paramstr, optional: Defines the colour of the plotted points. If the name of a parameter, will colour each point by the respective value of that parameter in each run, otherwise will accept a single colour, or array of colours for each run.
clr_kwargsdict, optional: Optional arguments passed to the _add_colours function.
residualsbool, optional: Add an ax to the bottom of the figure showing the residuals between the run results and the “truths”.
diagonalbool, optional: Include a background one-to-one line along the diagonal.
force_modelbool, optional: Force these parameter values to be taken from model quantities. Can be useful when some parameter names overlap (e.g. “ra”).
labelstr, optional: Set a label that will be displayed in the legend.
markerstr, optional: The marker style. See matplotlib.markers for more information.
**kwargsdict: All other arguments are passed to ax.errorbar and ax.scatter.

Returns:

matplotlib.figure.Figure: The corresponding figure, containing all axes and plot artists.

plot_lit_dist(param, truths, e_truths=None, src_truths='', fig=None, ax=None, *, kde=True, show_normal=True, kde_color='tab:blue', show_FWHM=True, annotate=False, annotate_kwargs=None, residuals=False, force_model=False, **kwargs)#

Plot hist of the fractional difference between param and “truths”.

Plots a scatter plot (with errorbars) of param by the values provided in the truths array, using the median and 1σ error values from each run in this collection.

This is meant to compare one-to-one the results of fits against “true” values, representing the same parameter, from the literature.

Plots a histogram (or smoothed KDE) of the fractional difference distribution between the run parameters values “true” values of the same parameter, from the literature.

The fractional difference is given by (param - truths) / sqrt(e_param^2 + e_truths^2) which, if in perfect agreement, should resemble a Gaussian centred on 0 with a width of 1.

Parameters:

paramstr: Name of the parameter to plot.
truthsnp.ndarray[Nruns]: Array of “truth” values.
e_truthsnp.ndarray[Nruns], optional: Array of uncertainties on the “truth” values. If None, will be taken as all 0.
src_truthsstr, optional: The source of the “truths”, included in the y-axis label.
figNone or matplotlib.figure.Figure, optional: Figure to place the ax on. If None (default), a new figure will be created, otherwise the given figure should be empty, or already have the correct number of axes. See _RunAnalysis._setup_artist for more details.
axNone or matplotlib.axes.Axes, optional: An axes instance on which to plot this relation. Should be a part of the given fig.
kdebool, optional: Whether to plot a smooth Gaussian KDE, or a simple histogram.
show_normalbool, optional: Optionally overplot a Gaussian centred at 0 with a width of 1, representing perfect agreement.
kde_colorstr, optional: The colour of the filled KDE.
show_FWHMbool, optional: If show_normal is True, also place in a text box the difference in the FWHM between the KDE and the normal plot.
annotatebool, optional: Optionally create a hook to this figure allowing the interactive annotating of selected cluster names. See _Annotator for more details.
annotate_kwargsdict, optional: Optional arguments passed to the _Annotator instance.
residualsbool, optional: Add an ax to the bottom of the figure showing the residuals between the run results and the “truths”.
force_modelbool, optional: Force these parameter values to be taken from model quantities. Can be useful when some parameter names overlap (e.g. “ra”).
**kwargsdict: All other arguments are passed to ax.fill_between.

Returns:

matplotlib.figure.Figure: The corresponding figure, containing all axes and plot artists.

plot_lit_relation(param, lit, e_lit=None, param_lit='', src_lit='', fig=None, ax=None, *, lit_on_x=False, clr_param=None, clr_kwargs=None, residuals=False, annotate=False, annotate_kwargs=None, force_model=False, label=None, marker='o', **kwargs)#

Plot comparison between parameter values and arbitrary data.

Plots a scatter plot (with errorbars) of param by the values provided in the lit array, using the median and 1σ error values from each run in this collection.

This is meant to showcase relationships between the parameter results and other external parameter values, ostensibly from the literature.

Parameters:

paramstr: Name of the parameter to plot.
litnp.ndarray[Nruns]: Array of external data values, to plot on the y-axis.
e_litnp.ndarray[Nruns], optional: Array of uncertainties on the lit values.
param_litstr, optional: The name of the parameter represented in the lit values, included in the y-axis label.
src_litstr, optional: The source of the lit, included in the y-axis label.
figNone or matplotlib.figure.Figure, optional: Figure to place the ax on. If None (default), a new figure will be created, otherwise the given figure should be empty, or already have the correct number of axes. See _RunAnalysis._setup_artist for more details.
axNone or matplotlib.axes.Axes, optional: An axes instance on which to plot this relation. Should be a part of the given fig.
lit_on_xbool, optional: Optionally flip the axes, plotting the lit values on the x-axis and the runs on the y-axis.
clr_paramstr, optional: Defines the colour of the plotted points. If the name of a parameter, will colour each point by the respective value of that parameter in each run, otherwise will accept a single colour, or array of colours for each run.
clr_kwargsdict, optional: Optional arguments passed to the _add_colours function.
residualsbool, optional: Add an ax to the bottom of the figure showing the residuals between the run results and the “truths”.
annotatebool, optional: Optionally create a hook to this figure allowing the interactive annotating of selected cluster names. See _Annotator for more details.
annotate_kwargsdict, optional: Optional arguments passed to the _Annotator instance.
force_modelbool, optional: Force these parameter values to be taken from model quantities. Can be useful when some parameter names overlap (e.g. “ra”).
labelstr, optional: Set a label that will be displayed in the legend.
markerstr, optional: The marker style. See matplotlib.markers for more information.
**kwargsdict: All other arguments are passed to ax.errorbar and ax.scatter.

Returns:

matplotlib.figure.Figure: The corresponding figure, containing all axes and plot artists.

plot_param_bar(param, fig=None, ax=None, clr_param=None, clr_kwargs=None, force_model=False, **kwargs)#

Plot the mean and 1σ values of param along all runs as a bar chart.

Plots, with error bars, the mean values of the given parameter for each run as a bar chart beginning at 0.0, spaced equally along the x-axis, sorted by run name.

Parameters:

paramstr: Name of the parameter to plot.
figNone or matplotlib.figure.Figure, optional: Figure to place the ax on. If None (default), a new figure will be created, otherwise the given figure should be empty, or already have the correct number of axes. See _RunAnalysis._setup_artist for more details.
axNone or matplotlib.axes.Axes, optional: An axes instance on which to plot this parameter. Should be a part of the given fig.
clr_paramstr, optional: Defines the colour of the plotted points. If the name of a parameter, will colour each point by the respective value of that parameter in each run, otherwise will accept a single colour, or array of colours for each run.
clr_kwargsdict, optional: Optional arguments passed to the _add_colours function.
force_modelbool, optional: Force these parameter values to be taken from model quantities. Can be useful when some parameter names overlap (e.g. “ra”).
**kwargsdict: All other arguments are passed to ax.bar.

Returns:

matplotlib.figure.Figure: The corresponding figure, containing all axes and plot artists.

plot_param_corner(params=None, fig=None, *, include_FeH=True, include_BH=False, include_rt=False, log_radii=False, force_model=False, **kwargs)#

Plot a “corner plot” showing relationship between parameters.

Plots a Nparam-Nparam lower-triangular “corner” plot showing the mean and 1σ values for all parameters for all runs.

Parameters:

paramslist of str, optional: The list of parameters to plot. If not given, defaults to the typical 13 free model parameters, and any included by the include_* arguments.
figNone or matplotlib.figure.Figure, optional: Figure to place all axes on. If None (default), a new figure will be created, otherwise the given figure should be empty, or already have the correct number of axes. See _RunAnalysis._setup_multi_artist for more details.
include_FeHbool, optional: If True, the metallicity FeH is included in the default params.
include_BHbool, optional: If True, the black hole mass BH_mass is included in the default params.
include_rtbool, optional: If True, the tidal radius rt is included in the default params.
log_radiibool, optional: If True, the radii included in the default params are logged.
force_modelbool, optional: Force these parameter values to be taken from model quantities. Can be useful when some parameter names overlap (e.g. “ra”).
**kwargsdict: All other arguments are passed to plot_relation.

Returns:

matplotlib.figure.Figure: The corresponding figure, containing all axes and plot artists.

plot_param_hist(param, fig=None, ax=None, kde=False, force_model=False, **kwargs)#

Plot a histogram representing the sum of all distributions of param.

Plots a histogram (or smoothed Gaussian KDE) representing the sum (or convolution) of the distributions of this parameter over all runs.

Parameters:

paramstr: Name of the parameter to plot.
figNone or matplotlib.figure.Figure, optional: Figure to place the ax on. If None (default), a new figure will be created, otherwise the given figure should be empty, or already have the correct number of axes. See _RunAnalysis._setup_artist for more details.
axNone or matplotlib.axes.Axes, optional: An axes instance on which to plot this parameter. Should be a part of the given fig.
kdebool, optional: Whether to plot a smooth Gaussian KDE, or a simple histogram.
force_modelbool, optional: Force these parameter values to be taken from model quantities. Can be useful when some parameter names overlap (e.g. “ra”).
**kwargsdict: All other arguments are passed to ax.fill_between or ax.hist.

Returns:

matplotlib.figure.Figure: The corresponding figure, containing all axes and plot artists.

plot_param_means(param, fig=None, ax=None, clr_param=None, clr_kwargs=None, force_model=False, **kwargs)#

Plot the mean and 1σ values of param along all runs.

Plots, with error bars, the mean values of the given parameter for each run, spaced equally along the x-axis, sorted by run name.

Parameters:

paramstr: Name of the parameter to plot.
figNone or matplotlib.figure.Figure, optional: Figure to place the ax on. If None (default), a new figure will be created, otherwise the given figure should be empty, or already have the correct number of axes. See _RunAnalysis._setup_artist for more details.
axNone or matplotlib.axes.Axes, optional: An axes instance on which to plot this parameter. Should be a part of the given fig.
clr_paramstr, optional: Defines the colour of the plotted points. If the name of a parameter, will colour each point by the respective value of that parameter in each run, otherwise will accept a single colour, or array of colours for each run.
clr_kwargsdict, optional: Optional arguments passed to the _add_colours function.
force_modelbool, optional: Force these parameter values to be taken from model quantities. Can be useful when some parameter names overlap (e.g. “ra”).
**kwargsdict: All other arguments are passed to ax.errorbar and ax.scatter.

Returns:

matplotlib.figure.Figure: The corresponding figure, containing all axes and plot artists.

plot_param_violins(param, fig=None, ax=None, clr_param=None, clr_kwargs=None, color=None, alpha=0.3, edgecolor='k', edgewidth=1.0, quantiles=[0.9772, 0.8413, 0.5, 0.1587, 0.0228], force_model=False, **kwargs)#

Plot a violin plot showing the parameter distributions for all runs.

Plots a violin plot with the full posterior distributions of a parameter for each run, spaced equally along the x-axis, sorted by run name.

Parameters:

paramstr: Name of the parameter to plot.
figNone or matplotlib.figure.Figure, optional: Figure to place the ax on. If None (default), a new figure will be created, otherwise the given figure should be empty, or already have the correct number of axes. See _RunAnalysis._setup_artist for more details.
axNone or matplotlib.axes.Axes, optional: An axes instance on which to plot this parameter. Should be a part of the given fig.
clr_paramstr, optional: Defines the colour of the plotted points. If the name of a parameter, will colour each point by the respective value of that parameter in each run, otherwise will accept a single colour, or array of colours for each run.
clr_kwargsdict, optional: Optional arguments passed to the _add_colours function.
colorstr, optional: Fallback default (single) colour for all distributions. clr_param has precedence over this.
alphafloat, optional: Transparency value applied to all distributions.
edgecolorstr, optional: The color of the border placed around each distribution.
edgewidthfloat, optional: The width of the border placed around each distribution.
quantileslist of float, optional: The quantiles shown as ticks on the central errorbars inside the distributions.
force_modelbool, optional: Force these parameter values to be taken from model quantities. Can be useful when some parameter names overlap (e.g. “ra”).
**kwargsdict: All other arguments are passed to ax.violinplot.

Returns:

matplotlib.figure.Figure: The corresponding figure, containing all axes and plot artists.

plot_relation(param1, param2, fig=None, ax=None, *, show_pearsonr=False, force_model=False, annotate=False, annotate_kwargs=None, clr_param=None, clr_kwargs=None, label=None, marker='o', **kwargs)#

Plot relationship between two parameters across all runs.

Plots a scatter plot (with errorbars) of param1 by param2 using the median and 1σ error values from each run in this collection.

Parameters:

param1str: Name of the parameter to plot on the x-axis.
param2str: Name of the parameter to plot on the y-axis.
figNone or matplotlib.figure.Figure, optional: Figure to place the ax on. If None (default), a new figure will be created, otherwise the given figure should be empty, or already have the correct number of axes. See _RunAnalysis._setup_artist for more details.
axNone or matplotlib.axes.Axes, optional: An axes instance on which to plot this relation. Should be a part of the given fig.
show_pearsonrbool, optional: Optionally compute the “Pearson-r” statistic for this data and place it in a text box in the bottom right of the ax.
force_modelbool, optional: Force these parameter values to be taken from model quantities. Can be useful when some parameter names overlap (e.g. “ra”).
annotatebool, optional: Optionally create a hook to this figure allowing the interactive annotating of selected cluster names. See _Annotator for more details.
annotate_kwargsdict, optional: Optional arguments passed to the _Annotator instance.
clr_paramstr, optional: Defines the colour of the plotted points. If the name of a parameter, will colour each point by the respective value of that parameter in each run, otherwise will accept a single colour, or array of colours for each run.
clr_kwargsdict, optional: Optional arguments passed to the _add_colours function.
labelstr, optional: Set a label that will be displayed in the legend.
markerstr, optional: The marker style. See matplotlib.markers for more information.
**kwargsdict: All other arguments are passed to ax.errorbar and ax.scatter.

Returns:

matplotlib.figure.Figure: The corresponding figure, containing all axes and plot artists.

save_plots(plot_func, fn_pattern=None, save_kw=None, size=None, remove_name=True, *args, **kwargs)#

Iterate over calls to plot_func on each run and save the figures.

Iterates over the iter_plots function and saves all individual figures to separate files, under a custom iterative file naming schema.

Parameters:

plot_funcstr: The name of the plotting function called on each run.
fn_patternstr, optional: A format string, which is passed the argument “cluster” representing each run’s name, and is used to create the filename each figure is saved under. Defaults to f’./{cluster}_{plot_func[5:]}’.
save_kwdict, optional: Optional arguments are passed to the fig.savefig function.
size2-tuple of float, optional: Optional resizing of the figure, using fig.set_size_inches.
remove_namebool, optional: Remove the sometimes present cluster name placed into the figure’s suptitle.
*args, **kwargs: All other arguments are passed to iter_plots.

summary_dataframe(*, params='all', include_FeH=True, include_BH=False, math_labels=False)#

Return a Dataframe with the median and 1σ param values for all runs.

Constructs and returns a table (in the form of a pandas.Dataframe) of the median and 1σ values of all included parameters, with each row representing a run.

Parameters:

paramslist of str, optional: The list of parameters to include in the returned table. If not given, defaults to the typical 13 free model parameters, and any included by the include_* arguments.
include_FeHbool, optional: If True, the metallicity FeH is included in the default params.
include_BHbool, optional: If True, the black hole related parameters (BH_mass, BH_num, f_BH, f_rem) are included in the default params.
math_labelsbool, optional: If True, the column names will be labelled with latex math. See _get_latex_labels for more information.

Returns:

pandas.Dataframe: The full table of parameter values.

gcfit.analysis.RunCollection#

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