Fix PYFFTW usage. Use numexpr
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@ -1,3 +1,4 @@
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import numexpr as ne
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import multiprocessing
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import pyfftw
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import weakref
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@ -13,14 +14,14 @@ class CubeFT(object):
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self.max_cpu = multiprocessing.cpu_count() if max_cpu < 0 else max_cpu
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self._dhat = pyfftw.n_byte_align_empty((self.N,self.N,self.N/2+1), self.align, dtype='complex64')
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self._density = pyfftw.n_byte_align_empty((self.N,self.N,self.N), self.align, dtype='float32')
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self.irfft = pyfftw.FFTW(self._dhat, self._density, axes=(0,1,2), direction='FFTW_BACKWARD', threads=self.max_cpu, normalize_idft=False)
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self.rfft = pyfftw.FFTW(self._density, self._dhat, axes=(0,1,2), threads=self.max_cpu, normalize_idft=False)
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self._irfft = pyfftw.FFTW(self._dhat, self._density, axes=(0,1,2), direction='FFTW_BACKWARD', threads=self.max_cpu, normalize_idft=False)
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self._rfft = pyfftw.FFTW(self._density, self._dhat, axes=(0,1,2), threads=self.max_cpu, normalize_idft=False)
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def rfft(self):
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return self.rfft()*(self.L/self.N)**3
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return ne.evaluate('c*a', local_dict={'c':self._rfft(normalise_idft=False),'a':(self.L/self.N)**3})
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def irfft(self):
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return self.irfft()/self.L**3
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return ne.evaluate('c*a', local_dict={'c':self._irfft(normalise_idft=False),'a':(1/self.L)**3})
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def get_dhat(self):
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return self._dhat
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@ -152,16 +153,18 @@ class LagrangianPerturbation(object):
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k2 = self._get_k2()
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k2[0,0,0] = 1
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potgen0 = lambda i: ne.evaluate('kdir**2*d/k2',local_dict={'kdir':self._kdir(i),'d':self.dhat,'k2':k2} )
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potgen = lambda i,j: ne.evaluate('kdir0*kdir1*d/k2',local_dict={'kdir0':self._kdir(i),'kdir1':self._kdir(j),'d':self.dhat,'k2':k2} )
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if 'lpt2_potential' not in self.cache:
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print("Rebuilding potential...")
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div_phi2 = np.zeros((self.N,self.N,self.N), dtype=np.float64)
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for j in xrange(3):
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q = self._do_irfft( self._kdir(j)**2*self.dhat / k2 ).copy()
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q = self._do_irfft( potgen0(j) ).copy()
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for i in xrange(j+1, 3):
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div_phi2 += q * self._do_irfft( self._kdir(i)**2*self.dhat / k2 )
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div_phi2 -= (self._do_irfft(self._kdir(j)*self._kdir(i)*self.dhat / k2 ) )**2
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div_phi2 += q * self._do_irfft( potgen0(i) )
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div_phi2 -= self._do_irfft(potgen(i,j))**2
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div_phi2 *= 1/self.L**6
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phi2_hat = -self._do_rfft(div_phi2) / k2
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#self.cache['lpt2_potential'] = phi2_hat
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del div_phi2
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@ -12,8 +12,8 @@ cosmo['SIGMA8']=0.8344
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cosmo['ns']=0.9624
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N0=256
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doSimulation=True
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simShift=True
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doSimulation=False
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simShift=False
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snap_id=int(sys.argv[1])
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astart=1/100.
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@ -37,7 +37,7 @@ if doSimulation:
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dsim_hat = np.fft.rfftn(dsim)*(L/N0)**3
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Psim, bsim = bic.bin_power(np.abs(dsim_hat)**2/L**3, L, range=(0,1.), bins=150)
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pos,_,density,N,L,_,_ = bic.run_generation("initial_density_2588.dat", 0.001, astart, cosmo, supersample=2, do_lpt2=True)
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pos,_,density,N,L,_,_ = bic.run_generation("initial_density_988.dat", 0.001, astart, cosmo, supersample=1, do_lpt2=True)
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dcic = ct.cicParticles(pos, L, N0)
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dcic /= np.average(np.average(np.average(dcic, axis=0), axis=0), axis=0)
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