Merge branch 'master' of bitbucket.org:glavaux/cosmotool
This commit is contained in:
Guilhem Lavaux
commit
15e824a097
@ -1,3 +1,5 @@
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import matplotlib
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matplotlib.use('Agg')
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import healpy as hp
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import numpy as np
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import cosmotool as ct
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@ -21,7 +23,8 @@ parser.add_argument('--iid', type=int, default=0)
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parser.add_argument('--proj_cat', type=bool, default=False)
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args = parser.parse_args()
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INDATA="/nethome/lavaux/Copy/PlusSimulation/BORG/Input_Data/2m++.npy"
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#INDATA="/nethome/lavaux/Copy/PlusSimulation/BORG/Input_Data/2m++.npy"
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INDATA="2m++.npy"
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tmpp = np.load(INDATA)
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L = args.boxsize
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@ -3,6 +3,8 @@ import numpy as np
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import cosmotool as ct
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import argparse
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import h5py as h5
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import matplotlib
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matplotlib.use('Agg')
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from matplotlib import pyplot as plt
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import ksz
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from ksz.constants import *
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@ -54,48 +56,60 @@ def build_unit_vectors(N):
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return ux,uy,uz
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def build_radial_v(v):
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N = v[0].shape[0]
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u = build_unit_vectors(N)
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vr = v[0] * u[0]
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vr = v[0] * u[2]
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vr += v[1] * u[1]
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vr += v[2] * u[2]
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return vr
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vr += v[2] * u[0]
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def generate_from_catalog(vfield,Boxsize):
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return vr.transpose()
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def generate_from_catalog(vfield,Boxsize,dmin,dmax):
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import progressbar as pbar
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cat = np.load("2m++.npy")
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profiler = KSZ_Isothermal(2.37)
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cat['distance'] = cat['best_velcmb']
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cat = cat[np.where((cat['distance']>100*dmin)*(cat['distance']<dmax*100))]
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deg2rad = np.pi/180
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Npix = 12*Nside**2
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xp,yp,zp = hp.pix2vec(Nside, np.arange(Npix))
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N2 = np.sqrt(xp**2+yp**2+zp**2)
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ksz_template = np.zeros(12*Nside**2, dtype=np.float64)
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ksz_mask = np.zeros(12**Nside**2, dtype=np.uint8)
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ksz_template = np.zeros(Npix, dtype=np.float64)
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ksz_mask = np.zeros(Npix, dtype=np.uint8)
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pb = pbar.ProgressBar(maxval = cat.size, widgets=[pb.Bar(), pb.ETA()]).start()
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pb = pbar.ProgressBar(maxval = cat.size, widgets=[pbar.Bar(), pbar.ETA()]).start()
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for k,i in np.ndenumerate(cat):
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pb.update(k)
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pb.update(k[0])
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l,b=i['gal_long'],i['gal_lat']
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ra,dec=i['ra'],i['dec']
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l *= deg2rad
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b *= deg2rad
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x0 = np.cos(l)*np.cos(b)
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y0 = np.sin(l)*np.cos(b)
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z0 = np.sin(b)
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ra *= deg2rad
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dec *= deg2rad
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# x0 = np.cos(l)*np.cos(b)
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# y0 = np.sin(l)*np.cos(b)
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# z0 = np.sin(b)
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x0 = xra = np.cos(ra)*np.cos(dec)
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y0 = yra = np.sin(ra)*np.cos(dec)
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z0 = zra = np.sin(dec)
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DA =i['distance']/100
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Lgal = DA**2*10**(0.4*(tmpp_cat['Msun']-i['K2MRS']+25))
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idx0 = hp.query_disc(Nside, (x0,y0,z0), 3*rGalaxy/DA)
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profiler = ksz.KSZ_Isothermal(Lgal, 2.37)
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vr = interp3d(DA * x0, DA * y0, DA * z0, vfield, Boxsize)
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idx0 = hp.query_disc(Nside, (x0,y0,z0), 3*profiler.rGalaxy/DA)
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vr = interp3d(DA * xra, DA * yra, DA * zra, vfield, Boxsize)
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xp1 = xp[idx0]
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yp1 = yp[idx0]
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@ -119,18 +133,33 @@ for i in xrange(args.start,args.end,args.step):
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ff=plt.figure(1)
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plt.clf()
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v=[]
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with h5.File(args.base_h5 % i, mode="r") as f:
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p = wrapper_impulsion(f)
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v.append(p[i] / f['density'][:])
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fname = args.base_h5 % i
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if False:
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print("Opening %s..." % fname)
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with h5.File(fname, mode="r") as f:
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p = wrapper_impulsion(f)
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for j in xrange(3):
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v.append(p[j] / f['density'][:])
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print("Building radial velocities...")
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# Rescale by Omega_b / Omega_m
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vr = build_radial_v(v)
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vr *= -ksz_normalization*rho_mean_matter*1e6
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del v
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vr = build_radial_v(v)
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# _,_,vr = build_unit_vectors(128)
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# vr *= 1000 * 500
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vr *= ksz_normalization*1e6
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del v
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# np.save("vr.npy", vr)
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else:
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print("Loading vrs...")
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vr = np.load("vr.npy")
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proj = generate_from_catalog(vfield,Boxsize)
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print("Generating map...")
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proj,mask = generate_from_catalog(vr,args.boxsize,args.depth_min,args.depth_max)
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hp.write_map(args.ksz_map % i, proj)
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hp.write_map((args.ksz_map % i) + "_mask", mask)
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hp.mollview(proj, fig=1, coord='CG', cmap=plt.cm.coolwarm, title='Sample %d' % i, min=args.minval,
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max=args.maxval)
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@ -53,7 +53,7 @@ def compute_ref_power(L, N, cosmo, bins=10, range=(0,1), func='HU_WIGGLES'):
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return bin_power(p.compute(k)*cosmo['h']**3, L, bins=bins, range=range)
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def run_generation(input_borg, a_borg, a_ic, cosmo, supersample=1, do_lpt2=True, shiftPixel=False, needvel=True):
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def run_generation(input_borg, a_borg, a_ic, cosmo, supersample=1, do_lpt2=True, shiftPixel=False, psi_instead=False, needvel=True):
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""" Generate particles and velocities from a BORG snapshot. Returns a tuple of
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(positions,velocities,N,BoxSize,scale_factor)."""
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@ -74,7 +74,7 @@ def run_generation(input_borg, a_borg, a_ic, cosmo, supersample=1, do_lpt2=True,
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# Compute LPT scaling coefficient
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D1 = cgrowth.D(a_ic)
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D1_0 = D1/cgrowth.D(a_borg)
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velmul = cgrowth.compute_velmul(a_ic)
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velmul = cgrowth.compute_velmul(a_ic) if not psi_instead else 1
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D2 = -3./7 * D1_0**2
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@ -119,7 +119,7 @@ def whitify(density, L, cosmo, supergenerate=1, func='HU_WIGGLES'):
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Pk = build_Pk()
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Pk[0,0,0]=1
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cube = CubeFT(N, L)
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cube = CubeFT(L, N)
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cube.density = density
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density_hat = cube.rfft()
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density_hat /= np.sqrt(Pk)
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@ -170,15 +170,15 @@ def write_icfiles(*generated_ic, **kwargs):
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supergenerate=kwargs['supergenerate']
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posx,vel,density,N,L,a_ic,cosmo = generated_ic
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ct.simpleWriteGadget("borg.gad", posx, velocities=vel, boxsize=L, Hubble=cosmo['h'], Omega_M=cosmo['omega_M_0'], time=a_ic)
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ct.simpleWriteGadget("Data/borg.gad", posx, velocities=vel, boxsize=L, Hubble=cosmo['h'], Omega_M=cosmo['omega_M_0'], time=a_ic)
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for i,c in enumerate(["x","y","z"]):
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ct.writeGrafic("ic_velc%s" % c, vel[i].reshape((N,N,N)), L, a_ic, **cosmo)
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ct.writeGrafic("Data/ic_velc%s" % c, vel[i].reshape((N,N,N)), L, a_ic, **cosmo)
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ct.writeGrafic("ic_deltab", density, L, a_ic, **cosmo)
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ct.writeGrafic("Data/ic_deltab", density, L, a_ic, **cosmo)
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ct.writeWhitePhase("white.dat", whitify(density, L, cosmo, supergenerate=supergenerate))
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ct.writeWhitePhase("Data/white.dat", whitify(density, L, cosmo, supergenerate=supergenerate))
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with file("white_params", mode="w") as f:
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with file("Data/white_params", mode="w") as f:
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f.write("4\n%lg, %lg, %lg\n" % (cosmo['omega_M_0'], cosmo['omega_lambda_0'], 100*cosmo['h']))
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f.write("%lg\n%lg\n-%lg\n0,0\n" % (cosmo['omega_B_0'],cosmo['ns'],cosmo['SIGMA8']))
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f.write("-%lg\n1\n0\n\n\n\n\n" % L)
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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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@ -9,10 +9,10 @@ cosmo['omega_B_0']=0.049
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cosmo['SIGMA8']=0.8344
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cosmo['ns']=0.9624
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supergen=8
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supergen=2
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zstart=50
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astart=1/(1.+zstart)
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halfPixelShift=False
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if __name__=="__main__":
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bic.write_icfiles(*bic.run_generation("initial_condition_borg.dat", 0.001, astart, cosmo, supersample=1, shiftPixel=halfPixelShift, do_lpt2=False), supergenerate=supergen)
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bic.write_icfiles(*bic.run_generation("initial_condition_borg.dat", 0.001, astart, cosmo, supersample=2, shiftPixel=halfPixelShift, do_lpt2=False), supergenerate=supergen)
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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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@ -18,11 +18,15 @@ frac_electron = 1.0 # Hmmmm
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frac_gas_galaxy = 0.14
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mu = 1/(1-0.5*Y)
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tmpp_cat={'Msun':3.29,'alpha':-0.7286211634758224,'Mstar':-23.172904033796893,'PhiStar':0.0113246633636846,'lbar':393109973.22508669}
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tmpp_cat={'Msun':3.29,
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'alpha':-0.7286211634758224,
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'Mstar':-23.172904033796893,
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'PhiStar':0.0113246633636846,
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'lbar':393109973.22508669}
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baryon_fraction = Omega_baryon / Omega_matter
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ksz_normalization = T_cmb*sigmaT*v_unit/(lightspeed*mu*mp) * baryon_fraction
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ksz_normalization = -T_cmb*sigmaT*v_unit/(lightspeed*mu*mp) * baryon_fraction
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rho_mean_matter = Omega_matter * (3*(100e3/Mpc)**2/(8*np.pi*G))
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Lbar = tmpp_cat['lbar'] / Mpc**3
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M_over_L_galaxy = rho_mean_matter / Lbar
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@ -1,3 +1,4 @@
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import numpy as np
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from .constants import *
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# -----------------------------------------------------------------------------
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@ -11,27 +12,28 @@ class KSZ_Profile(object):
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def __init__(self):
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self.rGalaxy = 1.0
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def evaluate_profile(r):
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def evaluate_profile(self, r):
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raise NotImplementedError("Abstract function")
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def projected_profile(cos_theta,angularDistance):
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def projected_profile(self, cos_theta,angularDistance):
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idx = np.where(cos_theta > 0)[0]
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tan_theta_2 = 1/(cos_theta[idx]**2) - 1
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tan_theta_2_max = (self.rGalaxy/angularDistance)**2
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tan_theta_2_min = (R_star/angularDistance)**2
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tan_theta_2_min = (self.R_star/angularDistance)**2
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idx0 = np.where((tan_theta_2 < tan_theta_2_max))
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idx = idx[idx0]
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tan_theta_2 = tan_theta_2[idx0]
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tan_theta = np.sqrt(tan_theta_2)
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r = (tan_theta*DA)
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r = (tan_theta*angularDistance)
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m,idx_mask = self.evaluate_profile(r)
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idx_mask = np.append(idx_mask,np.where(tan_theta_2<tan_theta_2_min)[0])
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idx_mask = np.append(idx_mask,[tan_theta_2.argmin()])
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if tan_theta_2.size > 0:
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idx_mask = np.append(idx_mask,[tan_theta_2.argmin()])
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return idx,idx_mask,m
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@ -75,7 +77,7 @@ class KSZ_Isothermal(KSZ_Profile):
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self._table = x,profile
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def evaluate_profile(r):
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def evaluate_profile(self,r):
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rho0, rGalaxy, rInner = self.rho0, self.rGalaxy, self.rInnerGalaxy
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Q=rho0*2/r*np.arctan(np.sqrt((rGalaxy/r)**2 - 1))/Mpc
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@ -140,7 +142,7 @@ class KSZ_NFW(KSZ_Profile):
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return exp(0.971 - 0.094*log(Mvir/(1e12*MassSun)))
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def evaluate_profile(r):
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def evaluate_profile(self,r):
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cs = self._get_concentration(self.Mvir)
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rs = self.Rvir/cs
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