Remnant class

Treats the post-merger remnant of MW-M31 as a galaxy.

class galaxy.remnant.Remnant(snap=801, datadir=None, usesql=False, stride=1, ptype=(2, 3))

A class to work with the MW-M31 post-merger remant.

Args:

snap (int):

Snap number, equivalent to time elapsed. Defaults to the last timepoint.

datadir (str):

Directory to search first for the required file. Optional, and a default list of locations will be searched.

usesql (bool):

If True, data will be taken from a PostgreSQL database instead of text files.

stride (int):

Optional. For stride=n, get every nth row in the table. Only valid with usesql=True.

ptype (int or iterable of int):

Particle type: 1, 2, 3 or a combination of these

Class attributes:

data (np.ndarray):

type, mass, position_xyz, velocity_xyz for each particle

read_db(stride)

Get relevant data from a PostgreSQL database and format it to be identical to that read from test files.

Ex-disk and ex-bulge particles are included, not DM particles.

Args:

stride (int):

Optional. For stride=n, get every nth row in the table.

Changes:

self.time, self.particle_count and self.data are set.

Returns: nothing

xyz()

Convenience method to get positions as a np.array of shape (3,N)

vxyz()

Convenience method to get velocities as a np.array of shape (3,N)

I_tensor(m, x, y, z)

Args:

m, x, y, z:

1-D arrays with mass and coordinates (no units)

Returns:

3x3 array representing the moment of inertia tensor

ellipsoid_axes(m, x, y, z, r_lim=None)

Args:

m, x, y, z:

1-D arrays with mass and coordinates (no units)

r_lim : float

Radius to include in calculation (implicit kpc, no units)

Returns:

Two 3-tuples: relative semimajor axes and principal axis vectors

sub_mass_enclosed(radii, m, xyz)

Calculate the mass within a given radius of the origin. Based on code in MassProfile, but this version assumes CoM-centric coordinates are supplied.

Args:

radii (array of distances): spheres to integrate over m (array of masses): shape (N,) xyz (array of Cartesian coordinates): shape (3,N)

Returns:

array of masses, in units of M_sun

hernquist_mass(r, a, M_halo)

Calculate the mass enclosed for a theoretical profile

Args:

r (Quantity, units of kpc):

distance from center

a (Quantity, units of kpc):

scale radius

M_halo (Quantity, units of M_sun):

total DM mass

Returns:

Total DM mass enclosed within r (M_sun)

fit_hernquist_a(m, xyz, r_inner=1, r_outer=100)

Get scipy.optimize to do a non-linear least squares fit to find the best scale radius a for the Hernquist profile.

Args:

r_inner (numeric):

Optional. Minimum radius to consider (implicit kpc). Avoid values < 1 as they cause numeric problems.

r_outer (numeric):

Optional. Maximum radius to consider (implicit kpc).

sersic(R, Re, n, Mtot)

Function that returns Sersic Profile for an Elliptical System (See in-class lab 6)

Input

R:

radius (kpc)

Re:

half mass radius (kpc)

n:

sersic index

Mtot:

total stellar mass

Returns

Surface Brightness profile in Lsun/kpc^2

Re(R, m, xyz)

Find the radius enclosing half the mass.

Args:

R (array of Quantity):

Radii to consider (kpc)

m (array of float):

masses (no units)

xyz (array of float with shape (3,N)):

coordinates (implicit kpc)

Returns:

sub_Re (Quantity) :

Radius enclosing half light/mass (kpc)

sub_total (numeric):

Mass of entire system (M_sun, no units)

subI (array of Quantity):

Surface brightness at radii R (kpc^-2), assuming M/L=1

fit_sersic_n(R, sub_Re, sub_total, subI)

Get scipy.optimize to do a non-linear least squares fit to find the best value of n for a Sersic profile.

Args:

R (array of quantity):

Radii at which to calculate fit (kpc)

Re (Quantity) :

Radius enclosing half light/mass (kpc)

bulge_total (numeric):

Mass of entire bulge (M_sun, no units)

bulgeI (array of Quantity):

Surface brightness at radii R (kpc^-2)

Returns:

best n value and error estimate