Merge branch 'master' of bitbucket.org:glavaux/cosmotool
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commit
01059f145b
@ -136,8 +136,12 @@ def whitify(density, L, cosmo, supergenerate=1, func='HU_WIGGLES'):
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if supergenerate > 1:
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if supergenerate > 1:
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cond=np.isnan(density_hat_super)
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cond=np.isnan(density_hat_super)
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x = np.random.randn(np.count_nonzero(cond),2)/np.sqrt(2.0)
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print np.where(np.isnan(density_hat_super))[0].size
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density_hat_super[cond] = x[:,0] + 1j * x[:,1]
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Nz = np.count_nonzero(cond)
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density_hat_super.real[cond] = np.random.randn(Nz)
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density_hat_super.imag[cond] = np.random.randn(Nz)
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density_hat_super[cond] /= np.sqrt(2.0)
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print np.where(np.isnan(density_hat_super))[0].size
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# Now we have to fix the Nyquist plane
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# Now we have to fix the Nyquist plane
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hNs = Ns/2
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hNs = Ns/2
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@ -145,6 +149,8 @@ def whitify(density, L, cosmo, supergenerate=1, func='HU_WIGGLES'):
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Nplane = nyquist.size
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Nplane = nyquist.size
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nyquist.flat[:Nplane/2] = np.sqrt(2.0)*nyquist.flat[Nplane:Nplane/2:-1].conj()
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nyquist.flat[:Nplane/2] = np.sqrt(2.0)*nyquist.flat[Nplane:Nplane/2:-1].conj()
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print np.where(np.isnan(density_hat_super))[0].size
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return np.fft.irfftn(density_hat_super)*Ns**1.5
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return np.fft.irfftn(density_hat_super)*Ns**1.5
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@ -10,26 +10,34 @@ cosmo['omega_k_0'] = 0
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cosmo['omega_B_0']=0.049
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cosmo['omega_B_0']=0.049
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cosmo['SIGMA8']=0.8344
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cosmo['SIGMA8']=0.8344
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cosmo['ns']=0.9624
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cosmo['ns']=0.9624
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N0=128
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N0=256
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doSimulation=True
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doSimulation=True
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simShift=True
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snap_id=int(sys.argv[1])
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snap_id=int(sys.argv[1])
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astart=1/100.
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astart=1/100.
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if doSimulation:
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if doSimulation:
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s = ct.loadRamsesAll("/nethome/lavaux/remote2/borgsim/", snap_id, doublePrecision=True)
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s = ct.loadRamsesAll("/nethome/lavaux/remote2/borgsim2/", snap_id, doublePrecision=True)
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astart=s.getTime()
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astart=s.getTime()
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L = s.getBoxsize()
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L = s.getBoxsize()
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dsim = ct.cicParticles(s.getPositions(), L, N0)
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p = s.getPositions()
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Nsim = int( np.round( p[0].size**(1./3)) )
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print("Nsim = %d" % Nsim)
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if simShift:
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p = [(q-0.5*L/Nsim)%L for q in p]
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dsim = ct.cicParticles(p[::-1], L, N0)
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dsim /= np.average(np.average(np.average(dsim, axis=0), axis=0), axis=0)
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dsim /= np.average(np.average(np.average(dsim, axis=0), axis=0), axis=0)
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dsim -= 1
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dsim -= 1
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dsim_hat = np.fft.rfftn(dsim)*(L/N0)**3
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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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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_1380.dat", 0.001, astart, cosmo, supersample=2, do_lpt2=True)
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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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dcic = ct.cicParticles(pos, L, N0)
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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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dcic /= np.average(np.average(np.average(dcic, axis=0), axis=0), axis=0)
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@ -50,7 +58,7 @@ Pref, bref = bic.compute_ref_power(L, N0, cosmo, range=(0,1.), bins=150)
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Pcic /= D1_0**2
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Pcic /= D1_0**2
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Pdens /= D1_0**2
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Pdens /= D1_0**2
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borg_evolved = ct.read_borg_vol("final_density_1380.dat")
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#borg_evolved = ct.read_borg_vol("final_density_1380.dat")
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dborg_hat = np.fft.rfftn(borg_evolved.density)*L**3/borg_evolved.density.size
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#dborg_hat = np.fft.rfftn(borg_evolved.density)*L**3/borg_evolved.density.size
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Pborg, bborg = bic.bin_power(np.abs(dborg_hat)**2/L**3, L, range=(0,1.),bins=150)
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#Pborg, bborg = bic.bin_power(np.abs(dborg_hat)**2/L**3, L, range=(0,1.),bins=150)
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