Built-in plotting functions

Plotting functions are available to visualize the various profiles of the deprojected Sérsic mass distributions:

Function	Description
deproj_sersic.plot.plot_profiles	Plot all profiles
deproj_sersic.plot.plot_vcirc	Plot circular velocity
deproj_sersic.plot.plot_enclosed_mass	Plot enclosed mass (in spheres)
deproj_sersic.plot.plot_density	Plot mass density
deproj_sersic.plot.plot_dlnrho_dlnR	Plot log density slope
deproj_sersic.plot.plot_surface_density	Plot projected mass surface density
deproj_sersic.plot.plot_projected_enclosed_mass	Plot projected enclosed mass (in ellipses)

Note that a range of inputs can be passed to these plotting functions, as demonstrated below. These include either either individual pre-computed tables or DeprojSersicModel instances, or a list of either of these types.

import os
import numpy as np
import matplotlib.pyplot as plt
import deprojected_sersic_models as deproj_sersic
table_dir = os.getenv('DEPROJECTED_SERSIC_MODELS_DATADIR')

# Basic plotting example
%config InlineBackend.figure_format = 'svg'   # Tutorial plot configuration

total_mass = 1.e11
Reff = 5.0
n = 1.0
invq = 5.
R = np.arange(0., 30.1, 0.1)

Plot circular velocity

# Load & interpolate circular velocity profile
vc = deproj_sersic.interpolate_sersic_profile_VC(R=R, total_mass=total_mass, Reff=Reff,
                                       n=n, invq=invq, path=table_dir)

# Make pseudo table & plot
table_gather = {'R': R, 'vcirc': vc, 'n': n, 'invq': invq, 'q': 1./invq,
                    'total_mass': total_mass, 'Reff': Reff }


deproj_sersic.plot.plot_vcirc(table_gather)

Plot enclosed mass

# Load & interpolate enclosed mass profile
menc = deproj_sersic.interpolate_sersic_profile_menc(R=R, total_mass=total_mass, Reff=Reff,
                                           n=n, invq=invq, path=table_dir)

# Make pseudo table & plot
table_gather = {'R': R, 'menc3D_sph': menc, 'n': n, 'invq': invq, 'q': 1./invq,
                    'total_mass': total_mass, 'Reff': Reff }

deproj_sersic.plot.plot_enclosed_mass(table_gather)

Plot all profiles for a table

table = deproj_sersic.interpolate_entire_table(R=R, total_mass=total_mass, Reff=Reff,
                                     n=n, invq=invq, path=table_dir)

# Plot all profiles in table
deproj_sersic.plot.plot_profiles(table)

Ploting multiple tables

invq_arr = [1., 2.5, 3.33, 5., 10.]
tables = []
# Load & interpolate table for each n, invq
for invq in invq_arr:
    tables.append(deproj_sersic.interpolate_entire_table(R=R, total_mass=total_mass, Reff=Reff,
                                               n=n, invq=invq, path=table_dir))

# Plot all profiles in tables
fig_kwargs = {'legend_fontsize': 7.}
deproj_sersic.plot.plot_profiles(tables, fig_kwargs=fig_kwargs)

Customization

It is also possible to plot subsets of the different profiles, and change the plotting settings (including rlog, ylog, plot_kwargs, and fig_kwargs).

# Customize
linestyles = ['-', '--', ':', '-.', '-']
colors = ['black', 'blue', 'red', 'green', 'purple']
lws = [1., 1., 1., 1., 2.]
markers = [None, None, '*', None, None]
markersizes = [None, None, 10, None, None]
markerfacecolors = [None, None, 'red', None, None]
markeredgecolors = [None, None, 'turquoise', None, None]
markeverys = [None, None, 50, None, None]
plot_kwargs = []
for i, invq in enumerate(invq_arr):
    # plot_kwargs can be a dictionary, or a list of dictionaries.
    # The keywords can include any keyword taken by matplotlib.pyplot.plot.
    plot_kwargs.append({'label': 'q={:0.2f}'.format(1./invq),
                        'ls': linestyles[i],
                        'color': colors[i],
                        'lw': lws[i],
                        'marker': markers[i],
                        'ms': markersizes[i],
                        'mfc': markerfacecolors[i],
                        'mec': markeredgecolors[i],
                        'markevery': markeverys[i]})

# fig_kwargs contains a few odds and ends customizations.
# This includes any accepted keywords for plt.figure with 'figure_' prepended,
# or any accepted keyword for matplotlib.axes.Axes.legend with 'legend_' prepended
fig_kwargs = {'figure_figsize': (8., 7.),
              'legend_title': 'Intrinic axis ratio',
              'legend_title_fontsize': 7.,
              'legend_fontsize': 13.,
              'legend_labelcolor': 'linecolor'}

deproj_sersic.plot.plot_profiles(tables,
                       prof_names=['v_circ', 'enclosed_mass', 'density', 'dlnrho_dlnR'],
                       rlog = [False, True, True, False],
                       ylog = [False, True, True, False],
                       plot_kwargs=plot_kwargs, fig_kwargs=fig_kwargs)