Added parallelization to _project.pyx code

python_sample/build_nbody_skymap.py

@@ -1,57 +0,0 @@
-import healpy as hp
-import numpy as np
-import cosmotool as ct
-import h5py as h5
-from matplotlib import pyplot as plt
-
-L=600.
-Nside=128
-
-INDATA="/nethome/lavaux/Copy/PlusSimulation/BORG/Input_Data/2m++.npy"
-tmpp = np.load(INDATA)
-
-def build_sky_proj(density, dmax=60.,dmin=0):
-
-  N = density.shape[0]
-  ix = (np.arange(N)-0.5)*L/N - 0.5 * L
-
-
-  dist2 = (ix[:,None,None]**2 + ix[None,:,None]**2 + ix[None,None,:]**2)
-
-  flux = density.transpose().astype(ct.DTYPE) # / dist2
-  dmax=N*dmax/L
-  dmin=N*dmin/L
-  projsky1 = ct.spherical_projection(Nside, flux, dmin, dmax, integrator_id=1)
-#  projsky0 = ct.spherical_projection(Nside, flux, 0, 52, integrator_id=0)
-
-  return projsky1*L/N#,projsky0
-
-l,b = tmpp['gal_long'],tmpp['gal_lat']
-
-l = np.radians(l)
-b = np.pi/2 - np.radians(b)
-
-dcmb = tmpp['velcmb']/100.
-
-idx = np.where((dcmb>10)*(dcmb<60))
-
-plt.figure(1)
-plt.clf()
-if False:
-  with h5.File("fields.h5", mode="r") as f:
-    d = f["density"][:]
-    d /= np.average(np.average(np.average(d,axis=0),axis=0),axis=0)
-    proj = build_sky_proj(f["density"][:], dmin=10,dmax=60.)
-else:
-  d = np.load("icgen/dcic0.npy")
-  proj0 = build_sky_proj(1+d, dmin=10,dmax=60.)
-  d = np.load("icgen/dcic1.npy")
-  proj1 = build_sky_proj(1+d, dmin=10,dmax=60.)
-
-hp.mollview(proj0, fig=1, coord='CG', max=60, cmap=plt.cm.coolwarm)
-hp.projscatter(b[idx], l[idx], lw=0, color='g', s=5.0, alpha=0.8)
-
-plt.figure(2)
-plt.clf()
-hp.mollview(proj1, fig=2, coord='CG', max=60, cmap=plt.cm.coolwarm)
-hp.projscatter(b[idx], l[idx], lw=0, color='g', s=5.0, alpha=0.8)

python_sample/build_nbody_skymap.py

@@ -0,0 +1 @@
+/nethome/lavaux/wuala/WualaDrive/g_lavaux/PythonCode/BORG/build_nbody_skymap.py

python_sample/icgen/borgicgen.py

@@ -93,17 +93,67 @@ def run_generation(input_borg, a_borg, a_ic, cosmo, supersample=1, do_lpt2=True,
 
     density = np.fft.irfftn(lpt.dhat*D1_0)*(supersample*N/L)**3
 
-    return posx,vel,density,N*supersample,L,a_ic
+    return posx,vel,density,N*supersample,L,a_ic,cosmo
     
-def write_icfiles(*generated_ic, **cosmo):
-    """Write the initial conditions from the tuple returned by run_generation"""
-    posx,vel,density,N,L,a_ic = generated_ic
+def whitify(density, L, cosmo, supergenerate=1, func='HU_WIGGLES'):
+  N = density.shape[0]
+  ik = np.fft.fftfreq(N, d=L/N)*2*np.pi
 
-    ct.simpleWriteGadget("borg.gad", posx, velocities=vel, boxsize=L, Hubble=cosmo['h'], Omega_M=cosmo['omega_M_0'], time=a_ic) 
-    for i,c in enumerate(["x","y","z"]):
-        ct.writeGrafic("ic_velc%s" % c, vel[i].reshape((N,N,N)), L, a_ic, **cosmo)
-# This used to be necessary. However this has been fixed in writeGrafic now
-#        ct.writeGrafic("ic_velc%s" % c, vel[i].reshape((N,N,N)).transpose(), L, a_ic, **cosmo)
+  k = np.sqrt(ik[:,None,None]**2 + ik[None,:,None]**2 + ik[None,None,:(N/2+1)]**2)
+  p = ct.CosmologyPower(**cosmo)
+  p.setFunction(func)
+  p.normalize(cosmo['SIGMA8'])
+
+  Pk = p.compute(k)*cosmo['h']**3*L**3
+  Pk[0,0,0]=1
+
+  density_hat = np.fft.rfftn(density)/N**3/np.sqrt(Pk)
+
+  Ns = N*supergenerate
+
+  density_hat_super = np.zeros((Ns,Ns,Ns/2+1), dtype=np.complex128)
+  density_hat_super[:] = np.nan
+  # Copy density hat in place
+
+  hN = N/2
+  density_hat_super[:hN, :hN, :hN+1]               = density_hat[:hN, :hN, :]
+  density_hat_super[:hN, (Ns-hN):Ns, :hN+1]        = density_hat[:hN, hN:, :]
+  density_hat_super[(Ns-hN):Ns, (Ns-hN):Ns, :hN+1] = density_hat[hN:, hN:, :]
+  density_hat_super[(Ns-hN):Ns, :hN, :hN+1]        = density_hat[hN:, :hN, :]
+
+  # The moved nyquist place is untouched (so loss of "noise") to keep the structure
+  # now we just add some noise term
+  cond=np.isnan(density_hat_super)
+  x = np.random.randn(np.count_nonzero(cond),2)/np.sqrt(2.0)
+  density_hat_super[cond] = x[:,0] + 1j * x[:,1]
+  
+  # Now we have to fix the Nyquist plane
+  hNs = Ns/2
+  nyquist = density_hat_super[:, :, :hNs]
+  Nplane = nyquist.size
+  nyquist.flat[:Nplane/2] = nyquist.flat[Nplane:Nplane/2:-1].conj()
+
+  return np.fft.irfftn(density_hat_super)
+    
+def write_icfiles(*generated_ic, **kwargs):
+  """Write the initial conditions from the tuple returned by run_generation"""
+  
+  supergenerate=1
+  if 'supergenerate' in kwargs:
+    supergenerate=kwargs['supergenerate']
+  posx,vel,density,N,L,a_ic,cosmo = generated_ic
+
+  ct.simpleWriteGadget("borg.gad", posx, velocities=vel, boxsize=L, Hubble=cosmo['h'], Omega_M=cosmo['omega_M_0'], time=a_ic) 
+  for i,c in enumerate(["x","y","z"]):
+    ct.writeGrafic("ic_velc%s" % c, vel[i].reshape((N,N,N)), L, a_ic, **cosmo)
+
+  ct.writeGrafic("ic_deltab", density, L, a_ic, **cosmo)
+  
+  ct.writeWhitePhase("white.dat", whitify(density, L, cosmo, supergenerate=supergenerate))
+  
+  with file("white_params", mode="w") as f:
+    f.write("4\n%lg, %lg, %lg\n" % (cosmo['omega_M_0'], cosmo['omega_lambda_0'], 100*cosmo['h']))
+    f.write("%lg\n%lg\n-%lg\n0,0\n" % (cosmo['omega_B_0'],cosmo['ns'],cosmo['SIGMA8']))
+    f.write("-%lg\n1\n0\n\n\n\n\n" % L)
+    f.write("2\n\n0\nwhite.dat\n0\npadding_white.dat\n")
 
-    ct.writeGrafic("ic_deltab", density, L, a_ic, **cosmo)
-    ct.writeWhitePhase("white.dat", density)

python_sample/icgen/gen_ic_from_borg.py

@@ -14,4 +14,4 @@ astart=1/(1.+zstart)
 halfPixelShift=True
 
 if __name__=="__main__":
-    bic.write_icfiles(*bic.run_generation("initial_condition_borg.dat", 0.001, astart, cosmo, supersample=2, shiftPixel=halfPixelShift, do_lpt2=False), **cosmo)
+    bic.write_icfiles(*bic.run_generation("initial_condition_borg.dat", 0.001, astart, cosmo, supersample=2, shiftPixel=halfPixelShift, do_lpt2=False))

python_sample/icgen/test_ic_from_borg.py

@@ -10,7 +10,7 @@ cosmo['omega_k_0'] = 0
 cosmo['omega_B_0']=0.049
 cosmo['SIGMA8']=0.8344
 cosmo['ns']=0.9624
-N0=256
+N0=128
 
 doSimulation=True
 
@@ -29,7 +29,7 @@ if doSimulation:
   dsim_hat = np.fft.rfftn(dsim)*(L/N0)**3
   Psim, bsim = bic.bin_power(np.abs(dsim_hat)**2/L**3, L, range=(0,1.), bins=150)
 
-pos,_,density,N,L,_ = bic.run_generation("initial_condition_borg.dat", 0.001, astart, cosmo, supersample=2, do_lpt2=True)
+pos,_,density,N,L,_,_ = bic.run_generation("initial_density_1380.dat", 0.001, astart, cosmo, supersample=2, do_lpt2=True)
 
 dcic = ct.cicParticles(pos, L, N0)
 dcic /= np.average(np.average(np.average(dcic, axis=0), axis=0), axis=0)
@@ -50,5 +50,5 @@ Pref, bref = bic.compute_ref_power(L, N0, cosmo, range=(0,1.), bins=150)
 Pcic /= D1_0**2
 Pdens /= D1_0**2
 
-borg_evolved = ct.read_borg_vol("final_density_380.dat")
+borg_evolved = ct.read_borg_vol("final_density_1380.dat")
 

python_sample/test_spheric_proj.py

@@ -1,20 +0,0 @@
-import cosmotool as ct
-import numpy as np
-import healpy as hp
-
-d = np.zeros((64,64,64), ct.DTYPE)
-
-d[32,32,32] = 1
-
-ii=np.arange(256)*64/256.-32
-xx = ii[:,None,None].repeat(256,axis=1).repeat(256,axis=2).reshape(256**3)
-yy = ii[None,:,None].repeat(256,axis=0).repeat(256,axis=2).reshape(256**3)
-zz = ii[None,None,:].repeat(256,axis=0).repeat(256,axis=1).reshape(256**3)
-
-d_high = ct.interp3d(xx, yy, zz, d, 64, periodic=True)
-d_high = d_high.reshape((256,256,256))
-
-proj0 = ct.spherical_projection(64, d, 0, 20, integrator_id=0, shifter=np.array([0.5,0.5,0.5]))
-proj1 = ct.spherical_projection(64, d, 0, 20, integrator_id=1)
-
-proj0_high = ct.spherical_projection(256, d_high, 0, 30, integrator_id=0, shifter=np.array([0.5,0.5,0.5]))

python_sample/test_spheric_proj.py

@@ -0,0 +1 @@
+/nethome/lavaux/wuala/WualaDrive/g_lavaux/PythonCode/BORG/test_spheric_proj.py