cosmotool/python_sample/icgen/cosmogrowth.py
2014-07-03 15:36:58 +02:00

172 lines
5.1 KiB
Python

import multiprocessing
import pyfftw
import weakref
import numpy as np
import cosmolopy as cpy
class CubeFT(object):
def __init__(self, L, N, max_cpu=-1):
self.N = N
self.align = pyfftw.simd_alignment
self.L = L
self.max_cpu = multiprocessing.cpu_count() if max_cpu < 0 else max_cpu
self._dhat = pyfftw.n_byte_align_empty((self.N,self.N,self.N/2+1), self.align, dtype='complex64')
self._density = pyfftw.n_byte_align_empty((self.N,self.N,self.N), self.align, dtype='float32')
self.irfft = pyfftw.FFTW(self._dhat, self._density, axes=(0,1,2), direction='FFTW_BACKWARD', threads=self.max_cpu, normalize_idft=False)
self.rfft = pyfftw.FFTW(self._density, self._dhat, axes=(0,1,2), threads=self.max_cpu, normalize_idft=False)
def rfft(self):
return self.rfft()*(self.L/self.N)**3
def irfft(self):
return self.irfft()/self.L**3
def get_dhat(self):
return self._dhat
def set_dhat(self, in_dhat):
self._dhat[:] = in_dhat
dhat = property(get_dhat, set_dhat, None)
def get_density(self):
return self._density
def set_density(self, d):
self._density[:] = d
density = property(get_density, set_density, None)
class CosmoGrowth(object):
def __init__(self, **cosmo):
self.cosmo = cosmo
def D(self, a):
return cpy.perturbation.fgrowth(1/a-1, self.cosmo['omega_M_0'], unnormed=True)
def compute_E(self, a):
om = self.cosmo['omega_M_0']
ol = self.cosmo['omega_lambda_0']
ok = self.cosmo['omega_k_0']
E = np.sqrt(om/a**3 + ol + ok/a**2)
H2 = -3*om/a**4 - 2*ok/a**3
Eprime = 0.5*H2/E
return E,Eprime
def Ddot(self, a):
E,Eprime = self.compute_E(a)
D = self.D(a)
Ddot_D = Eprime/E + 2.5 * self.cosmo['omega_M_0']/(a**3*E**2*D)
Ddot_D *= a
return Ddot_D
def compute_velmul(self, a):
E,_ = self.compute_E(a)
velmul = self.Ddot(a)
velmul *= 100 * a * E
return velmul
class LagrangianPerturbation(object):
def __init__(self,density,L, fourier=False, supersample=1, max_cpu=-1):
self.L = L
self.N = density.shape[0]
self.max_cpu = max_cpu
self.cube = CubeFT(self.L, self.N, max_cpu=max_cpu)
if not fourier:
self.cube.density = density
self.dhat = self.cube.rfft().copy()
else:
self.dhat = density.copy()
if supersample > 1:
self.upgrade_sampling(supersample)
self.ik = np.fft.fftfreq(self.N, d=L/self.N)*2*np.pi
self.cache = {}#weakref.WeakValueDictionary()
def upgrade_sampling(self, supersample):
N2 = self.N * supersample
N = self.N
dhat_new = np.zeros((N2, N2, N2/2+1), dtype=np.complex128)
hN = N/2
dhat_new[:hN, :hN, :hN+1] = self.dhat[:hN, :hN, :]
dhat_new[:hN, (N2-hN):N2, :hN+1] = self.dhat[:hN, hN:, :]
dhat_new[(N2-hN):N2, (N2-hN):N2, :hN+1] = self.dhat[hN:, hN:, :]
dhat_new[(N2-hN):N2, :hN, :hN+1] = self.dhat[hN:, :hN, :]
self.dhat = dhat_new
self.N = N2
self.cube = CubeFT(self.L, self.N, max_cpu=self.max_cpu)
def _gradient(self, phi, direction):
self.cube.dhat = self._kdir(direction)*1j*phi
return self.cube.irfft()
def lpt1(self, direction=0):
k2 = self._get_k2()
k2[0,0,0] = 1
return self._gradient(self.dhat/k2, direction)
def new_shape(self,direction, q=3, half=False):
N0 = (self.N/2+1) if half else self.N
return ((1,)*direction) + (N0,) + ((1,)*(q-1-direction))
def _kdir(self, direction, q=3):
if direction != q-1:
return self.ik.reshape(self.new_shape(direction, q=q))
else:
return self.ik[:self.N/2+1].reshape(self.new_shape(direction, q=q, half=True))
def _get_k2(self, q=3):
if 'k2' in self.cache:
return self.cache['k2']
k2 = self._kdir(0, q=q)**2
for d in xrange(1,q):
k2 = k2 + self._kdir(d, q=q)**2
self.cache['k2'] = k2
return k2
def _do_irfft(self, array):
self.cube.dhat = array
return self.cube.irfft()
def _do_rfft(self, array):
self.cube.density = array
return self.cube.rfft()
def lpt2(self, direction=0):
k2 = self._get_k2()
k2[0,0,0] = 1
if 'lpt2_potential' not in self.cache:
print("Rebuilding potential...")
div_phi2 = np.zeros((self.N,self.N,self.N), dtype=np.float64)
for j in xrange(3):
q = self._do_irfft( self._kdir(j)**2*self.dhat / k2 ).copy()
for i in xrange(j+1, 3):
div_phi2 += q * self._do_irfft( self._kdir(i)**2*self.dhat / k2 )
div_phi2 -= (self._do_irfft(self._kdir(j)*self._kdir(i)*self.dhat / k2 ) )**2
div_phi2 *= 1/self.L**6
phi2_hat = -self._do_rfft(div_phi2) / k2
#self.cache['lpt2_potential'] = phi2_hat
del div_phi2
else:
phi2_hat = self.cache['lpt2_potential']
return self._gradient(phi2_hat, direction)