hmf.density_field.filters.SharpKEllipsoid

class hmf.density_field.filters.SharpKEllipsoid(k, power, **model_parameters)

Fourier-space top-hat window function with ellipsoidal correction

See Schneider, Smith, Reed 2013.

Refer to Filter for more details.

Methods

__init__(k, power, **model_parameters)	Initialize self.
a3(r)
a3a1(e, p)	The short to long axis ratio of an ellipsoid given its ellipticity and prolateness
a3a2(e, p)	The short to medium axis ratio of an ellipsoid given its ellipticity and prolateness
dlnr_dlnm(r)	The derivative of log radius with log mass.
dlnss_dlnm(r)	The logarithmic slope of mass variance with mass.
dlnss_dlnr(r)	The derivative of the mass variance with radius.
dw_dlnkr(kr)	The derivative of the (fourier-transformed) filter with \(\ln(kr)\).
em(xm)	The average ellipticity of a patch as a function of peak tensor
gamma(r)	Bardeen et al. 1986 equation 6.17.
get_models()	Get a dictionary of all implemented models for this component.
k_space(kr)	Fourier-transform of the real-space filter.
mass_to_radius(m, rho_mean)	Return radius of a region of space given its mass.
nu(r[, delta_c])	Peak height, \(\frac{\delta_c^2}{\sigma^2(r)}\).
pm(xm)	The average prolateness of a patch as a function of peak tensor
r_a3(rmin, rmax)
radius_to_mass(r, rho_mean)	Return mass of a region of space given its radius
real_space(R, r)	Filter definition in real space.
sigma(r[, order])	Calculate the nth-moment of the smoothed density field, \(\sigma_n(r)\).
xi(pm, em)
xm(g, v)	Peak of the distribution of x, where x is the sum of the eigenvalues of the inertia tensor (?) of an ellipsoidal peak, divided by the second spectral moment.