mean_radial_velocity_vs_r¶

halotools.mock_observables.
mean_radial_velocity_vs_r
(sample1, velocities1, rbins_absolute=None, rbins_normalized=None, normalize_rbins_by=None, sample2=None, velocities2=None, period=None, num_threads=1, approx_cell1_size=None, approx_cell2_size=None, seed=None)[source] [edit on github]¶ Calculate the mean pairwise velocity, \(\bar{v}_{12}(r)\).
Example calls to this function appear in the documentation below. 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
sample1
andsample2
arguments.See also Galaxy Catalog Analysis Example: Mean infall velocity into cluster BCGs.
Parameters: sample1 : array_like
Numpy array of shape (npts1, 3) containing the 3D positions of points.
velocities1 : array_like
Numpy array of shape (npts1, 3) containing the 3D velocities.
rbins_absolute : array_like, optional
Array of shape (num_rbins+1, ) defining the boundaries of bins in which mean radial velocities are computed.
Either
rbins_absolute
must be passed, orrbins_normalized
andnormalize_rbins_by
must be passed.Length units are comoving and assumed to be in Mpc/h, here and throughout Halotools.
rbins_normalized : array_like, optional
Array of shape (num_rbins+1, ) defining the bin boundaries x, where \(x = r / R_{\rm vir}\), in which mean radial velocity profile is computed. The quantity \(R_{\rm vir}\) can vary from point to point in
sample1
and is passed in via thenormalize_rbins_by
argument. While scaling by \(R_{\rm vir}\) is common, you are not limited to this normalization choice; in principle you can use therbins_normalized
andnormalize_rbins_by
arguments to scale your distances by any lengthscale associated with points insample1
.Default is None, in which case the
rbins_absolute
argument must be passed.normalize_rbins_by : array_like, optional
Numpy array of shape (npts1, ) defining how the distance between each pair of points will be normalized. For example, if
normalize_rbins_by
is defined to be the virial radius of each point insample1
, then the input numerical values x stored inrbins_normalized
will be interpreted as referring to bins of \(x = r / R_{\rm vir}\). Default is None, in which case the inputrbins_absolute
argument must be passed instead ofrbins_normalized
.Pay special attention to lengthunits in whatever halo catalog you are using: while Halotoolsprovided catalogs will always have length units preprocessed to be Mpc/h, commonly used default settings for ASCII catalogs produced by Rockstar return the
Rvir
column in kpc/h units, but halo centers in Mpc/h units.sample2 : array_like, optional
Numpy array of shape (npts2, 3) containing the 3D positions of points.
velocities2 : array_like, optional
Numpy array of shape (npts2, 3) containing the 3D velocities.
period : array_like, optional
Length3 array defining periodic boundary conditions. If only one number, Lbox, is specified, period is assumed to be [Lbox, Lbox, Lbox]. Default is None, for no PBCs.
num_threads : int, optional
number of threads to use in calculation. Default is 1. 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
Length3 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 max(rbins) in each dimension, which will return reasonable result performance for most usecases. Performance can vary sensitively with this parameter, so it is highly recommended that you experiment with this parameter when carrying out performancecritical calculations.
approx_cell2_size : array_like, optional
Analogous to
approx_cell1_size
, but forsample2
. See comments forapprox_cell1_size
for details.Returns: v_12 : numpy.array
Array of shape (num_rbins, ) containing the mean pairwise radial velocity
Notes
The pairwise velocity, \(v_{12}(r)\), is defined as:
\[v_{12}(r) = \vec{v}_{\rm 1, pec} \cdot \vec{r}_{12}\vec{v}_{\rm 2, pec} \cdot \vec{r}_{12}\]where \(\vec{v}_{\rm 1, pec}\) is the peculiar velocity of object 1, and \(\vec{r}_{12}\) is the radial vector connecting object 1 and 2.
\(\bar{v}_{12}(r)\) is the mean of that quantity calculated in radial bins.
For radial separation bins in which there are zero pairs, function returns zero.
Examples
For demonstration purposes we will work with halos in the
FakeSim
. Here we’ll just demonstrate basic usage, referring to Galaxy Catalog Analysis Example: Mean infall velocity into cluster BCGs for a more detailed demo.>>> from halotools.sim_manager import FakeSim >>> halocat = FakeSim()
>>> x = halocat.halo_table['halo_x'] >>> y = halocat.halo_table['halo_y'] >>> z = halocat.halo_table['halo_z']
We transform our x, y, z points into the array shape used by the paircounter by taking the transpose of the result of
numpy.vstack
. This boilerplate transformation is used throughout themock_observables
subpackage:>>> sample1 = np.vstack((x,y,z)).T
We will do the same to get a random set of velocities.
>>> vx = halocat.halo_table['halo_vx'] >>> vy = halocat.halo_table['halo_vy'] >>> vz = halocat.halo_table['halo_vz'] >>> velocities = np.vstack((vx,vy,vz)).T
>>> rbins = np.logspace(1, 1, 10) >>> v_12 = mean_radial_velocity_vs_r(sample1, velocities, rbins_absolute=rbins, period=halocat.Lbox)