gcfit.analysis.ModelVisualizer#

class gcfit.analysis.ModelVisualizer(model, observations=None)#

Analysis and visualization of a single model.

Provides a number of plotting methods useful for the analysis of a single (multimass) model instance, and it’s fit to all relevant observational datasets.

A number of relevant (plottable) quantities will be initially computed and stored to be used by the various plotting functions defined by the base class _ClusterVisualizer.

Parameters:

modelgcfit.Model: Model instance to visualize.
observationsgcfit.Observations or None, optional: The Observations instance corresponding to this cluster. If None, the observations will first try to be read from the model instance itself (model.observations). If no observations can be found, this object will be unable to plot any observational data alongside it’s modelled profiles.

See also

_ClusterVisualizer: Base class providing all common plotting functions.

Methods

`__init__`(model[, observations])
`from_chain`(chain, observations[, method])	Initialize a visualizer based on a full chain of parameters.
`from_theta`(theta, observations)	Initialize a visualizer based on a single set of parameters.
`plot_LOS`([fig, ax, show_obs, residuals, ...])	Plot the line-of-sight velocity dispersion profiles.
`plot_MF_fields`([fig, ax, unit, radii, grid, ...])	Plot the on-sky HST fields corresponding to each mass function PI.
`plot_all`([fig, sharex])	Plot all primary model radial profiles in one figure.
`plot_cumulative_mass`([fig, ax, kind, ...])	Plot model cumulative mass profiles.
`plot_density`([fig, ax, kind, x_unit, ...])	Plot model density profiles.
`plot_mass_func`([fig, show_obs, show_fields, ...])	Plot present day mass functions in various radial bins.
`plot_number_density`([fig, ax, ...])	Plot the projected number density profiles.
`plot_pm_R`([fig, ax, show_obs, residuals, ...])	Plot the radial proper motion dispersion profiles.
`plot_pm_T`([fig, ax, show_obs, residuals, ...])	Plot the tangential proper motion dispersion profiles.
`plot_pm_ratio`([fig, ax, show_obs, ...])	Plot the proper motion dispersion anisotropy profiles.
`plot_pm_tot`([fig, ax, show_obs, residuals, ...])	Plot the total proper motion dispersion profiles.
`plot_potential`([fig, ax, x_unit, ...])	Plot model potential profile.
`plot_remnant_fraction`([fig, ax, show_total, ...])	Plot model stellar and remnant mass fraction profiles.
`plot_surface_density`([fig, ax, kind, ...])	Plot model surface density profiles.
`plot_vesc`([fig, ax, x_unit, label_position, ...])	Plot model escape velocity profile.

Attributes

`chi2`	Compute χ^2 between the median model and all observational data.
`cmap`
`rlims`

property chi2#: Compute χ^2 between the median model and all observational data.

classmethod from_chain(chain, observations, method='median')#

Initialize a visualizer based on a full chain of parameters.

Classmethod which creates a single model visualizer object based on a full chain of parameter values, by reducing the chain to a single set of parameters (through the given method) and creating a Model from that to initialize this class with.

Parameters:

chainnp.ndarray[…, Nparams]: Array containing chain of parameters values. Final axis must be of the size of the number of model parameters (13).
observationsgcfit.Observations: The Observations instance corresponding to this cluster. Will be passed to FittableModel.
method{“median”, “mean”, “final”}: Method used to reduce the chain to a single set of parameters. “median” and “mean” find the average values, “final” will take the final iteration in the chain.

Returns:

ModelVisualizer: The created model visualization object.

See also

gcfit.FittableModel: Model subclass used to initialize the model.

classmethod from_theta(theta, observations)#

Initialize a visualizer based on a single set of parameters.

Classmethod which creates a single model visualizer object based on a set of parameter values, and uses that to initialize this class with.

Parameters:

thetadict or list: The set of model input parameters. Must either be a dict, or a full list of all 13 parameters.
observationsgcfit.Observations: The Observations instance corresponding to this cluster. Will be passed to FittableModel.

Returns:

ModelVisualizer: The created model visualization object.

See also

gcfit.FittableModel: Model subclass used to initialize the model.

plot_LOS(fig=None, ax=None, show_obs=True, residuals=False, *, x_unit='pc', y_unit='km/s', legend=True, label_position='top', verbose_label=True, blank_xaxis=False, res_kwargs=None, **kwargs)#

Plot the line-of-sight velocity dispersion profiles.

Plots the self.LOS model profiles, and the relevant observational datasets (variable “σ” within the dataset pattern “velocity_dispersion”), corresponding to the line-of-sight velocity dispersion profiles.

Parameters:

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 _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_obsbool, optional: Whether to also plot the corresponding observational datasets on top of the relevant model profile. Defaults to True.
residualsbool, optional: Whether to, if also plotting data, append a residuals ax to the figure. Defaults to False.
x_unit, y_unitu.Unit, optional: Units to convert the x and y axes to. By default, x-units are in parsecs and y-units are in kilometres per second.
label_position{‘top’, ‘left’, ‘right’}, optional: Where to place the quantity (y) label. If “top” (default), will be set as the ax title, otherwise will be set to one side. If on a side, will also attempt to correctly append the units to the end of the label.
verbose_labelbool, optional: If True (default), quantity label will be “LOS Velocity Dispersion”, otherwise “$sigma_{mathrm{LOS}}$”.
blank_xaxisbool, optional: If True, will remove the tick markers and label from the x-axis. May be useful if stacking multiple profiles vertically.
res_kwargsdict, optional: Optional arguments passed to _add_residuals.
**kwargsdict: All other arguments are passed to _plot_profile.

Returns:

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

plot_MF_fields(fig=None, ax=None, *, unit='arcmin', radii=('rh',), grid=True, label_grid=False, add_legend=True)#

Plot the on-sky HST fields corresponding to each mass function PI.

Plots the representation of the observed on-sky photometry footprints for each stored mass function groupings, with the origin on the cluster centre. Each grouping of mass functions bins (i.e. under a certain data source proposal) will be plotted with it’s own colour.

Parameters:

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 _setup_artist for more details.
axNone or matplotlib.axes.Axes, optional: An axes instance on which to plot the fields. Should be a part of the given fig.
unitu.Unit, optional: Units to convert the x and y axes and grid lines to. By default, the units are arcminutes.
radiilist of {‘rh’, ‘ra’, ‘rt’, ‘r0’, ‘rhp’, ‘rv’}, optional: A collection of radius values to be overplotted (as circles) on the plot. If the given radii have corresponding uncertainties, they will also be shaded in. By default plots the half-mass radius.
gridbool, optional: If True (default), adds a circular grid at 2 arcminute intervals, out to the tidal radius.
label_gridbool, optional: Optionally adds labels on to the figure noting the size of each grid interval, if grid is True.
add_legendbool, optional: If True (default), adds a legend with an entry for each grouping with the proposal PI name and ID.

Returns:

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

See also

util.mass.Field: Class representing a given mass function field, containing all associated plotting methods.

plot_all(fig=None, sharex=True, **kwargs)#

Plot all primary model radial profiles in one figure.

Plots the six primary radial profile quantities used for fitting (excluding notably the mass functions and pulsar accelerations). That is, clockwise from the top left, number density, total PM, tangential PM, radial PM, PM ratio and LOS dispersion profiles.

Simply sets up a figure with six axes and calls the various relevant profile plotting functions to populate each ax.

Parameters:

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 _setup_multi_artist for more details.
sharexbool, optional: If True, created subplots will all share the same x-axis. Will also remove the x axis ticks and labels on all but the bottom row.
**kwargsdict: All other arguments are passed to each plotting function.

Returns:

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

plot_cumulative_mass(fig=None, ax=None, kind='all', *, x_unit='pc', label_position='left', colors=None)#

Plot model cumulative mass profiles.

Plots the radial cumulative mass profiles of the total, main sequence, and dark remnant objects in this model.

Parameters:

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 _setup_artist for more details.
axNone or matplotlib.axes.Axes, optional: An axes instance on which to plot the profiles. Should be a part of the given fig.
kindlist of {‘tot’, ‘rem’, ‘MS’, ‘WD’, ‘NS’, ‘BH’}, optional: The mass profiles to plot. By default plots all of them.
x_unitu.Unit, optional: Units to convert the x axis to. By default, x-units are in parsecs.
label_position{‘top’, ‘left’, ‘right’}, optional: Where to place the quantity (y) label. If “top”, will be set as the ax title, otherwise will be set to one side. If on a side, will also attempt to correctly append the units to the end of the label. Defaults to the left.
colorsdict, optional: A dictionary of colours, where the keys correspond to the given kind. Each object type has it’s own default colour.

Returns:

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

plot_density(fig=None, ax=None, kind='all', *, x_unit='pc', label_position='left', colors=None)#

Plot model density profiles.

Plots the radial mass density (self.rho_*) profiles of the total, main sequence, and dark remnant objects in this model.

Parameters:

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 _setup_artist for more details.
axNone or matplotlib.axes.Axes, optional: An axes instance on which to plot the profiles. Should be a part of the given fig.
kindlist of {‘tot’, ‘rem’, ‘MS’, ‘WD’, ‘NS’, ‘BH’}, optional: The density profiles to plot. By default plots all of them.
x_unitu.Unit, optional: Units to convert the x axis to. By default, x-units are in parsecs.
label_position{‘top’, ‘left’, ‘right’}, optional: Where to place the quantity (y) label. If “top”, will be set as the ax title, otherwise will be set to one side. If on a side, will also attempt to correctly append the units to the end of the label. Defaults to the left.
colorsdict, optional: A dictionary of colours, where the keys correspond to the given kind. Each object type has it’s own default colour.

Returns:

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

plot_mass_func(fig=None, show_obs=True, show_fields=True, *, PI_legend=False, propid_legend=False, label_unit='arcmin', model_color=None, model_label=None, logscaled=False, field_kw=None, **kwargs)#

Plot present day mass functions in various radial bins.

Plots each of the stored self.mass_func radial (present-day) mass function bins (i.e. binned star counts by mean star mass), in a large figure of stacked axes, as well as the corresponding observational datasets.

Each grouping of mass functions bins (i.e. under a certain data source proposal) will be plotted with it’s own colour and sorted to be next to one another.

Optionally, a panel will be added to the left of the figure showing the related field outlines on the sky (using plot_MF_fields).

Parameters:

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 _setup_multi_artist for more details.
show_obsbool, optional: Whether to also plot the corresponding observational datasets on top of the relevant model profile. Defaults to True.
show_fieldsbool, optional: Whether to include an extra panel (on the left of the figure) showing the observed mass function fields (using plot_MF_fields).
PI_legendbool, optional: If True, also notes the PI name of each proposal grouping under each first radial bin annotation. Only applies if show_fields is False, as otherwise they will be noted in that panel better.
propid_legendbool, False: If True, also notes the ID of each proposal grouping under each first radial bin annotation. Only applies if show_fields is False, as otherwise they will be noted in that panel better.
label_unitu.Unit, optional: The unit used to denote each radial bin in it’s corresponding label. Does not change anything about what is plotted, only the label. Defaults to arcminutes (‘).
model_colorstr, optional: Optionally colour all model profiles with a specific colour. This will give the same colour to all radial bins, regardless of proposal grouping. By default, models will match the colour of their corresponding data, as dictated within self.mass_func, or as set by the default matplotlib colour cycler, if not set.
model_labelstr, optional: Optionally add a figure legend with this label corresponding to the model profiles. Only valid if a single specific model colour was given through model_color. The legend placement won’t be great, and this is mostly for inspection when overplotting multiple models on a single figure.
logscaledbool, optional: If True, applies a log scaling to the the x (i.e. mass) axis.
field_kwdict, optional: Optional arguments passed to plot_MF_fields if show_fields is True.
**kwargsdict: All other arguments are passed to each plt.errorbars call.

Returns:

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

plot_number_density(fig=None, ax=None, show_background=False, subtract_background=False, show_obs=True, residuals=False, *, legend=True, x_unit='pc', y_unit='1/pc2', scale_to='model', label_position='top', verbose_label=True, blank_xaxis=False, res_kwargs=None, data_kwargs=None, model_kwargs=None, **kwargs)#

Plot the projected number density profiles.

Plots the self.numdens model profiles, and the relevant observational datasets (variable “Σ” within the dataset pattern “number_density”), corresponding to the project number density profiles.

Parameters:

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 _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_backgroundbool, optional: If True, also plots a horizontal line representing the value of the background density, as taken from the observational datasets (if possible).
subtract_backgroundbool, optional: If True, subtracts the background density value from the number density model profiles and data.
show_obsbool, optional: Whether to also plot the corresponding observational datasets on top of the relevant model profile. Defaults to True.
residualsbool, optional: Whether to, if also plotting data, append a residuals ax to the figure. Defaults to False.
x_unit, y_unitu.Unit, optional: Units to convert the x and y axes to. By default, x-units are in parsecs and y-units are in stars per parsec squared.
scale_to{“model”, “data”}, optional: Number densities are typically scaled to match the normalization of the data in GCfit, as they are fit to the shape, not value of the number densities. This argument allows the absolute values of the plotted quantities to be scaled to either the model (data / K; default) or the data (model * K).
label_position{‘top’, ‘left’, ‘right’}, optional: Where to place the quantity (y) label. If “top” (default), will be set as the ax title, otherwise will be set to one side. If on a side, will also attempt to correctly append the units to the end of the label.
verbose_labelbool, optional: If True (default), quantity label will be “Tangential PM Dispersion”, otherwise “$sigma_{mathrm{PM},mathrm{T}}$”.
blank_xaxisbool, optional: If True, will remove the tick markers and label from the x-axis. May be useful if stacking multiple profiles vertically.
res_kwargs, data_kwargs, model_kwargsdict, optional: Optional arguments passed to _add_residuals, _plot_data, and _plot_model, respectively.
**kwargsdict: All other arguments are passed to _plot_profile.

Returns:

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

plot_pm_R(fig=None, ax=None, show_obs=True, residuals=False, *, x_unit='pc', y_unit='mas/yr', legend=True, label_position='top', verbose_label=True, blank_xaxis=False, res_kwargs=None, **kwargs)#

Plot the radial proper motion dispersion profiles.

Plots the self.pm_T model profiles, and the relevant observational datasets (variable “PM_T” within the dataset pattern “proper_motion”), corresponding to the radial proper motion dispersion profiles.

Parameters:

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 _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_obsbool, optional: Whether to also plot the corresponding observational datasets on top of the relevant model profile. Defaults to True.
residualsbool, optional: Whether to, if also plotting data, append a residuals ax to the figure. Defaults to False.
x_unit, y_unitu.Unit, optional: Units to convert the x and y axes to. By default, x-units are in parsecs and y-units are in milliarcseconds per year.
label_position{‘top’, ‘left’, ‘right’}, optional: Where to place the quantity (y) label. If “top” (default), will be set as the ax title, otherwise will be set to one side. If on a side, will also attempt to correctly append the units to the end of the label.
verbose_labelbool, optional: If True (default), quantity label will be “Radial PM Dispersion”, otherwise “$sigma_{mathrm{PM},mathrm{R}}$”.
blank_xaxisbool, optional: If True, will remove the tick markers and label from the x-axis. May be useful if stacking multiple profiles vertically.
res_kwargsdict, optional: Optional arguments passed to _add_residuals.
**kwargsdict: All other arguments are passed to _plot_profile.

Returns:

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

plot_pm_T(fig=None, ax=None, show_obs=True, residuals=False, *, x_unit='pc', y_unit='mas/yr', legend=True, label_position='top', verbose_label=True, blank_xaxis=False, res_kwargs=None, **kwargs)#

Plot the tangential proper motion dispersion profiles.

Plots the self.pm_T model profiles, and the relevant observational datasets (variable “PM_T” within the dataset pattern “proper_motion”), corresponding to the tangential proper motion dispersion profiles.

Parameters:

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 _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_obsbool, optional: Whether to also plot the corresponding observational datasets on top of the relevant model profile. Defaults to True.
residualsbool, optional: Whether to, if also plotting data, append a residuals ax to the figure. Defaults to False.
x_unit, y_unitu.Unit, optional: Units to convert the x and y axes to. By default, x-units are in parsecs and y-units are in milliarcseconds per year.
label_position{‘top’, ‘left’, ‘right’}, optional: Where to place the quantity (y) label. If “top” (default), will be set as the ax title, otherwise will be set to one side. If on a side, will also attempt to correctly append the units to the end of the label.
verbose_labelbool, optional: If True (default), quantity label will be “Tangential PM Dispersion”, otherwise “$sigma_{mathrm{PM},mathrm{T}}$”.
blank_xaxisbool, optional: If True, will remove the tick markers and label from the x-axis. May be useful if stacking multiple profiles vertically.
res_kwargsdict, optional: Optional arguments passed to _add_residuals.
**kwargsdict: All other arguments are passed to _plot_profile.

Returns:

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

plot_pm_ratio(fig=None, ax=None, show_obs=True, residuals=False, *, x_unit='pc', blank_xaxis=False, legend=True, label_position='top', verbose_label=True, res_kwargs=None, **kwargs)#

Plot the proper motion dispersion anisotropy profiles.

Plots the self.pm_ratio model profiles, and the relevant observational datasets (variable “PM_ratio” within the dataset pattern “proper_motion”), corresponding to the proper motion dispersion anisotropy profiles, defined as the ratio of the tangential over radial proper motion dispersions.

Parameters:

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 _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_obsbool, optional: Whether to also plot the corresponding observational datasets on top of the relevant model profile. Defaults to True.
residualsbool, optional: Whether to, if also plotting data, append a residuals ax to the figure. Defaults to False.
x_unitu.Unit, optional: Units to convert the x axis to. By default, x-units are in parsecs.
label_position{‘top’, ‘left’, ‘right’}, optional: Where to place the quantity (y) label. If “top” (default), will be set as the ax title, otherwise will be set to one side. If on a side, will also attempt to correctly append the units to the end of the label.
verbose_labelbool, optional: If True (default), quantity label will be “PM Anisotropy Ratio”, otherwise “$sigma_{mathrm{PM},mathrm{T}} / sigma_{mathrm{PM},mathrm{R}}$”.
blank_xaxisbool, optional: If True, will remove the tick markers and label from the x-axis. May be useful if stacking multiple profiles vertically.
res_kwargsdict, optional: Optional arguments passed to _add_residuals.
**kwargsdict: All other arguments are passed to _plot_profile.

Returns:

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

plot_pm_tot(fig=None, ax=None, show_obs=True, residuals=False, *, x_unit='pc', y_unit='mas/yr', legend=True, label_position='top', verbose_label=True, blank_xaxis=False, res_kwargs=None, **kwargs)#

Plot the total proper motion dispersion profiles.

Plots the self.pm_tot model profiles, and the relevant observational datasets (variable “PM_tot” within the dataset pattern “proper_motion”), corresponding to the total proper motion dispersion profiles.

Parameters:

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 _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_obsbool, optional: Whether to also plot the corresponding observational datasets on top of the relevant model profile. Defaults to True.
residualsbool, optional: Whether to, if also plotting data, append a residuals ax to the figure. Defaults to False.
x_unit, y_unitu.Unit, optional: Units to convert the x and y axes to. By default, x-units are in parsecs and y-units are in milliarcseconds per year.
label_position{‘top’, ‘left’, ‘right’}, optional: Where to place the quantity (y) label. If “top” (default), will be set as the ax title, otherwise will be set to one side. If on a side, will also attempt to correctly append the units to the end of the label.
verbose_labelbool, optional: If True (default), quantity label will be “Total PM Dispersion”, otherwise “$sigma_{mathrm{PM},mathrm{tot}}$”.
blank_xaxisbool, optional: If True, will remove the tick markers and label from the x-axis. May be useful if stacking multiple profiles vertically.
res_kwargsdict, optional: Optional arguments passed to _add_residuals.
**kwargsdict: All other arguments are passed to _plot_profile.

Returns:

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

plot_potential(fig=None, ax=None, *, x_unit='pc', label_position='left', verbose_label=True, **kwargs)#

Plot model potential profile.

Plots the total radial gravitational potential (self.phi) profile of this model.

Parameters:

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 _setup_artist for more details.
axNone or matplotlib.axes.Axes, optional: An axes instance on which to plot the profiles. Should be a part of the given fig.
x_unitu.Unit, optional: Units to convert the x axis to. By default, x-units are in parsecs.
label_position{‘top’, ‘left’, ‘right’}, optional: Where to place the quantity (y) label. If “top”, will be set as the ax title, otherwise will be set to one side. If on a side, will also attempt to correctly append the units to the end of the label. Defaults to the left.
verbose_labelbool, optional: If True (default), quantity label will be “Potential”, otherwise “$Phi$”.
**kwargsdict, optional: All other arguments are passed to _plot_profile.

Returns:

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

plot_remnant_fraction(fig=None, ax=None, *, show_total=True, x_unit='pc', label_position='left')#

Plot model stellar and remnant mass fraction profiles.

Plots the radial mass fraction profile of the main sequence stars (mass_MS / mass_tot) and the dark remnants (mass_rem / mass_tot).

Parameters:

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 _setup_artist for more details.
axNone or matplotlib.axes.Axes, optional: An axes instance on which to plot the profiles. Should be a part of the given fig.
show_totalbool, optional: If True (default), places an anchored box in the upper centre of the ax showing the total remnant mass fraction of the cluster.
x_unitu.Unit, optional: Units to convert the x axis to. By default, x-units are in parsecs.
label_position{‘top’, ‘left’, ‘right’}, optional: Where to place the quantity (y) label. If “top”, will be set as the ax title, otherwise will be set to one side. If on a side, will also attempt to correctly append the units to the end of the label. Defaults to the left.

Returns:

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

plot_surface_density(fig=None, ax=None, kind='all', *, x_unit='pc', label_position='left', colors=None)#

Plot model surface density profiles.

Plots the radial surface mass density (self.Sigma_*) profiles of the total, main sequence, and dark remnant objects in this model.

Parameters:

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 _setup_artist for more details.
axNone or matplotlib.axes.Axes, optional: An axes instance on which to plot the profiles. Should be a part of the given fig.
kindlist of {‘tot’, ‘rem’, ‘MS’, ‘WD’, ‘NS’, ‘BH’}, optional: The density profiles to plot. By default plots all of them.
x_unitu.Unit, optional: Units to convert the x axis to. By default, x-units are in parsecs.
label_position{‘top’, ‘left’, ‘right’}, optional: Where to place the quantity (y) label. If “top”, will be set as the ax title, otherwise will be set to one side. If on a side, will also attempt to correctly append the units to the end of the label. Defaults to the left.
colorsdict, optional: A dictionary of colours, where the keys correspond to the given kind. Each object type has it’s own default colour.

Returns:

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

plot_vesc(fig=None, ax=None, *, x_unit='pc', label_position='left', verbose_label=True, **kwargs)#

Plot model escape velocity profile.

Plots the escape velocity profile (self.vesc) of this model, as given by $v_{\mathrm{esc}}=\sqrt{2 |\phi(r)|}$.

Parameters:

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 _setup_artist for more details.
axNone or matplotlib.axes.Axes, optional: An axes instance on which to plot the profiles. Should be a part of the given fig.
x_unitu.Unit, optional: Units to convert the x axis to. By default, x-units are in parsecs.
label_position{‘top’, ‘left’, ‘right’}, optional: Where to place the quantity (y) label. If “top”, will be set as the ax title, otherwise will be set to one side. If on a side, will also attempt to correctly append the units to the end of the label. Defaults to the left.
verbose_labelbool, optional: If True (default), quantity label will be “Escape Velocity”, otherwise “$v_{mathrm{esc}}$”.
**kwargsdict, optional: All other arguments are passed to _plot_profile.

Returns:

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

gcfit.analysis.ModelVisualizer#

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