total_mass_enclosed_per_cylinder

halotools.mock_observables.total_mass_enclosed_per_cylinder(centers, particles, particle_masses, downsampling_factor, rp_bins, period, num_threads=1, approx_cell1_size=None, approx_cell2_size=None)[source] [edit on github]

Calculate the total mass enclosed in a set of cylinders of infinite length.

Parameters:

centers : array_like

Numpy array of shape (num_cyl, 3) containing 3-d positions of galaxies. Length units are comoving and assumed to be in Mpc/h, here and throughout Halotools.

See the Formatting your xyz coordinates for Mock Observables calculations documentation page for instructions on how to transform your coordinate position arrays into the format accepted by the galaxies and particles arguments.

particles : array_like

Numpy array of shape (num_ptcl, 3) containing 3-d positions of particles.

Length units are comoving and assumed to be in Mpc/h, here and throughout Halotools.

particle_masses : array_like

Float or array of shape (num_ptcl, ) storing the mass of each particle in units of Msun with h=1 units. If every particle has the same mass (i.e., if your simulation is DM-only), you can pass in a single float.

downsampling_factor : float

Factor by which the particles have been randomly downsampled. Should be unity if all simulation particles have been chosen.

rp_bins : array_like

Numpy array of shape (num_rbins+1, ) of projected radial boundaries defining the bins in which the result is calculated.

Length units are comoving and assumed to be in Mpc/h, here and throughout Halotools.

period : array_like

Length-3 sequence defining the periodic boundary conditions in each dimension. If you instead provide a single scalar, Lbox, period is assumed to be the same in all Cartesian directions. Length units are assumed to be in Mpc/h, here and throughout Halotools.

num_threads : int, optional

Number of threads to use in calculation, where parallelization is performed using the python multiprocessing module. Default is 1 for a purely serial calculation, in which case a multiprocessing Pool object will never be instantiated. A string ‘max’ may be used to indicate that the pair counters should use all available cores on the machine.

approx_cell1_size : array_like, optional

Length-3 array serving as a guess for the optimal manner by how points will be apportioned into subvolumes of the simulation box. The optimum choice unavoidably depends on the specs of your machine. Default choice is to use Lbox/10 in each dimension, which will return reasonable result performance for most use-cases. Performance can vary sensitively with this parameter, so it is highly recommended that you experiment with this parameter when carrying out performance-critical calculations.

approx_cell2_size : array_like, optional

Analogous to approx_cell1_size, but for sample2. See comments for approx_cell1_size for details.

Returns:

total_mass_enclosed : array_like

Numpy array of shape (num_cyl, num_rbins) storing the sum of all particle masses enclosed in each of the input cylinders.

Examples

>>> period = 100.
>>> num_cyl, num_ptcl = 100, 1000
>>> centers = np.random.random((num_cyl, 3))*period
>>> particles = np.random.random((num_ptcl, 3))*period
>>> masses = np.random.rand(num_ptcl)
>>> downsampling_factor = 1.
>>> rp_bins = np.logspace(-1, 1, 15)
>>> mass_encl = total_mass_enclosed_per_cylinder(centers, particles, masses, downsampling_factor, rp_bins, period)

The mass enclosed in cylinder i with radius j is given by:

>>> ith_cylinder_jth_radius_mass = mass_encl[i, j]