Merge branch 'master' of bitbucket.org:glavaux/cosmotool
This commit is contained in:
Guilhem Lavaux
commit
5395097fa7
@ -22,11 +22,11 @@ option(INTERNAL_HDF5 "Build internal version of HDF5" OFF)
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option(INTERNAL_NETCDF "Build internal version of NETCDF" OFF)
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option(INTERNAL_BOOST "Build internal version of BOOST" OFF)
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IF(NOT BUILD_SHARED_LIBS AND BUILD_STATIC_LIBS)
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SET(CosmoTool_local CosmoTool_static)
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ELSE(NOT BUILD_SHARED_LIBS AND BUILD_STATIC_LIBS)
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#IF(NOT BUILD_SHARED_LIBS AND BUILD_STATIC_LIBS)
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# SET(CosmoTool_local CosmoTool_static)
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#ELSE(NOT BUILD_SHARED_LIBS AND BUILD_STATIC_LIBS)
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SET(CosmoTool_local CosmoTool)
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ENDIF(NOT BUILD_SHARED_LIBS AND BUILD_STATIC_LIBS)
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#ENDIF(NOT BUILD_SHARED_LIBS AND BUILD_STATIC_LIBS)
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MESSAGE(STATUS "Using the target ${CosmoTool_local} to build python module")
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@ -23,16 +23,16 @@ class SimulationBare(PySimulationBase):
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def merge(self, other):
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def _safe_merge(a, b):
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if b:
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if a:
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if b is not None:
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if a is not None:
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a = [np.append(q, r) for q,r in zip(a,b)]
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else:
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a = b
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return a
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def _safe_merge0(a, b):
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if b:
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if a:
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if b is not None:
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if a is not None:
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a = np.append(a, b)
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else:
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a = b
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@ -100,13 +100,35 @@ def build_unit_vectors(N):
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return ux,uy,uz
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def generate_from_catalog(dmin,dmax,Nside,perturb=0.0,y=0.0,do_random=False,do_hubble=False):
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def compute_vcmb(l, b):
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# Motion is obtained from Tully (2007): sun_vs_LS + LS_vs_CMB
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motion = [-25.,-246.,277.];
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x = np.cos(l*np.pi/180) * np.cos(b*np.pi/180)
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y = np.sin(l*np.pi/180) * np.cos(b*np.pi/180)
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z = np.sin(b*np.pi/180)
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return x*motion[0] + y*motion[1] + z*motion[2]
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def compute_vlg(l,b):
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motion = [-79,296,-36]; # [-86, 305, -33];
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x = np.cos(l*np.pi/180) * np.cos(b*np.pi/180)
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y = np.sin(l*np.pi/180) * np.cos(b*np.pi/180)
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z = np.sin(b*np.pi/180)
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return x*motion[0] + y*motion[1] + z*motion[2]
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def generate_from_catalog(dmin,dmax,Nside,perturb=0.0,y=0.0,do_random=False,do_hubble=False,x=2.37,bright=-np.inf,bright_list=[],use_vlg=True,sculpt=-1):
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import progressbar as pbar
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cat = np.load("2m++.npy")
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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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# 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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@ -119,14 +141,19 @@ def generate_from_catalog(dmin,dmax,Nside,perturb=0.0,y=0.0,do_random=False,do_h
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ksz_hubble_template = np.zeros(ksz_template.size, dtype=np.float64)
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for i in pbar.ProgressBar(maxval = cat.size, widgets=[pbar.Bar(), pbar.ETA()])(cat):
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# Skip too point sources
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if i['name'] in bright_list:
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print("Object %s is in bright list" % i['name'])
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continue
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if do_random:
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l = np.random.rand()*360
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b = np.arcsin(2*np.random.rand()-1)*180/np.pi
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else:
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l,b=i['gal_long'],i['gal_lat']
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l0,b0=i['gal_long'],i['gal_lat']
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l=ne.evaluate('l*deg2rad')
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b=ne.evaluate('b*deg2rad')
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l=ne.evaluate('l0*deg2rad')
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b=ne.evaluate('b0*deg2rad')
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dtheta,dphi = np.random.randn(2)*perturb
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theta,l=move_direction(dtheta,dphi,0.5*np.pi - b, l)
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@ -137,10 +164,23 @@ def generate_from_catalog(dmin,dmax,Nside,perturb=0.0,y=0.0,do_random=False,do_h
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y0 = np.sin(l)*np.cos(b)
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z0 = np.sin(b)
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DA =i['distance']/100
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if use_vlg:
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vlg = i['best_velcmb'] - compute_vcmb(l0, b0) + compute_vlg(l0, b0)
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DA = vlg/100
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else:
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DA = i['best_velcmb'] / 100
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if DA < dmin or DA > dmax:
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continue
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Lgal = DA**2*10**(0.4*(tmpp_cat['Msun']-i['K2MRS']+25))
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profiler = ksz.KSZ_Isothermal(Lgal, 2.37, y=y)
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M_K=i['K2MRS']-5*np.log10(DA)-25
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# Skip too bright galaxies
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if M_K < bright:
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continue
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profiler = ksz.KSZ_Isothermal(Lgal, x, y=y, sculpt=sculpt)
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idx0 = hp.query_disc(Nside, (x0,y0,z0), 3*profiler.rGalaxy/DA)
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@ -177,12 +217,17 @@ def get_args():
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parser.add_argument('--depth_max', type=float, default=60)
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parser.add_argument('--ksz_map', type=str, required=True)
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parser.add_argument('--base_fig', type=str, default="kszfig.png")
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parser.add_argument('--build_dipole', type=bool, default=False)
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parser.add_argument('--build_dipole', action='store_true')
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parser.add_argument('--degrade', type=int, default=-1)
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parser.add_argument('--y',type=float,default=0.0)
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parser.add_argument('--random', type=bool, default=False)
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parser.add_argument('--x',type=float,default=2.37)
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parser.add_argument('--random', action='store_true')
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parser.add_argument('--perturb', type=float, default=0)
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parser.add_argument('--hubble_monopole', type=bool, default=False)
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parser.add_argument('--hubble_monopole', action='store_true')
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parser.add_argument('--remove_bright', type=float, default=-np.inf)
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parser.add_argument('--bright_file', type=str)
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parser.add_argument('--lg', action='store_true')
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parser.add_argument('--sculpt_beam', type=float, default=-1)
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return parser.parse_args()
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def main():
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@ -195,7 +240,16 @@ def main():
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print("Generating map...")
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r = generate_from_catalog(args.depth_min,args.depth_max,args.Nside,perturb=args.perturb,y=args.y,do_random=args.random,do_hubble=args.hubble_monopole)
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with open("crap.txt", mode="r") as f:
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bright_list = [l.split('#')[0].strip(" \t\n\r") for l in f]
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if args.bright_file:
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with open(args.bright_file, mode="r") as f:
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idx_name = f.readline().split(',').index('name_2')
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bright_list = bright_list + [l.split(',')[idx_name] for l in f]
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print("Built bright point source list: " + repr(bright_list))
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r = generate_from_catalog(args.depth_min,args.depth_max,args.Nside,perturb=args.perturb,y=args.y,do_random=args.random,do_hubble=args.hubble_monopole,x=args.x,bright=args.remove_bright,use_vlg=args.lg,bright_list=bright_list,sculpt=args.sculpt_beam)
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hubble_map = None
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if args.hubble_monopole:
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proj,mask,hubble_map = r
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@ -4,10 +4,11 @@ import cosmolopy as cpy
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from cosmogrowth import *
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import borgadaptor as ba
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@ct.timeit
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def gen_posgrid(N, L, delta=1, dtype=np.float32):
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""" Generate an ordered lagrangian grid"""
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ix = (np.arange(N)*L/N*delta).astype(dtype)
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ix = (np.arange(N)*(L/N*delta)).astype(dtype)
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x = ix[:,None,None].repeat(N, axis=1).repeat(N, axis=2)
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y = ix[None,:,None].repeat(N, axis=0).repeat(N, axis=2)
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@ -147,6 +148,8 @@ def run_generation(input_borg, a_borg, a_ic, cosmo, supersample=1, supergenerate
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D2 = -3./7 * D1_0**2
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if do_lpt2:
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psi2 = lpt.lpt2('all')
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for j in xrange(3):
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# Generate psi_j (displacement along j)
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print("LPT1 axis=%d" % j)
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@ -154,8 +157,7 @@ def run_generation(input_borg, a_borg, a_ic, cosmo, supersample=1, supergenerate
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psi = psi.reshape((psi.size,))
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if do_lpt2:
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print("LPT2 axis=%d" % j)
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psi2 = lpt.lpt2(j)
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psi += D2 * psi2.reshape((psi2.size,))
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psi += D2 * psi2[j].reshape((psi2[j].size,))
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# Generate posx
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posx.append(((posq[j] + psi)%L).astype(np.float32))
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# Generate vel
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@ -4,6 +4,7 @@ import pyfftw
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import weakref
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import numpy as np
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import cosmolopy as cpy
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import cosmotool as ct
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class CubeFT(object):
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def __init__(self, L, N, max_cpu=-1):
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@ -98,6 +99,7 @@ class LagrangianPerturbation(object):
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self._kz = self.ik[None,None,:(self.N/2+1)]
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self.cache = {}#weakref.WeakValueDictionary()
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@ct.timeit_quiet
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def upgrade_sampling(self, supersample):
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N2 = self.N * supersample
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N = self.N
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@ -113,14 +115,26 @@ class LagrangianPerturbation(object):
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self.N = N2
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self.cube = CubeFT(self.L, self.N, max_cpu=self.max_cpu)
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@ct.timeit_quiet
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def _gradient(self, phi, direction):
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if direction == 'all':
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dirs = [0,1,2]
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copy = True
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else:
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dirs = [direction]
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copy = False
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ret=[]
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for dir in dirs:
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ne.evaluate('phi_hat * i * kv / (kx**2 + ky**2 + kz**2)', out=self.cube.dhat,
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local_dict={'i':-1j, 'phi_hat':phi, 'kv':self._kdir(direction),
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local_dict={'i':-1j, 'phi_hat':phi, 'kv':self._kdir(dir),
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'kx':self._kx, 'ky':self._ky, 'kz':self._kz},casting='unsafe')
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# self.cube.dhat = self._kdir(direction)*1j*phi
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self.cube.dhat[0,0,0] = 0
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return self.cube.irfft()
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x = self.cube.irfft()
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ret.append(x.copy() if copy else x)
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return ret[0] if len(ret)==1 else ret
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@ct.timeit_quiet
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def lpt1(self, direction=0):
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return self._gradient(self.dhat, direction)
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@ -155,6 +169,7 @@ class LagrangianPerturbation(object):
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self.cube.density = array
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return self.cube.rfft()
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@ct.timeit_quiet
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def lpt2(self, direction=0):
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# k2 = self._get_k2()
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# k2[0,0,0] = 1
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@ -170,6 +185,7 @@ class LagrangianPerturbation(object):
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for j in xrange(3):
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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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with ct.time_block("LPT2 elemental (%d,%d)" %(i,j)):
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ne.evaluate('div + q * pot', out=div_phi2,
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local_dict={'div':div_phi2, 'q':q,'pot':self._do_irfft( potgen0(i), copy=False ) }
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)
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@ -27,6 +27,7 @@ tmpp_cat={'Msun':3.29,
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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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assert ksz_normalization < 0
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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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@ -10,7 +10,14 @@ class KSZ_Profile(object):
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R_star= 0.0 # 15 kpc/h
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L_gal0 = 10**(0.4*(tmpp_cat['Msun']-tmpp_cat['Mstar']))
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def __init__(self):
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def __init__(self,sculpt):
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"""Base class for KSZ profiles
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Arguments:
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sculpt (float): If negative, do not sculpt. If positive, there will be a 2d
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suppression of the profile with a radius given by sculpt (in arcmins).
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"""
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self.sculpt = sculpt * np.pi/180/60.
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self.rGalaxy = 1.0
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def evaluate_profile(self, r):
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@ -37,6 +44,11 @@ class KSZ_Profile(object):
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if tan_theta_2.size > 0:
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idx_mask = np.append(idx_mask,idx[tan_theta_2.argmin()])
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if self.sculpt > 0:
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theta = np.arctan(tan_theta)
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cond = theta < self.sculpt
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m[cond] *= (theta[cond]/self.sculpt)**2
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return idx,idx_mask,m
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@ -48,10 +60,20 @@ class KSZ_Isothermal(KSZ_Profile):
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sigma_FP=160e3 #m/s
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R_innergal = 0.030
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def __init__(self, Lgal, x, y=0.0):
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"Support for Isothermal profile"
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def __init__(self, Lgal, x, y=0.0, sculpt=-1):
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"""Support for Isothermal profile
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super(KSZ_Isothermal,self).__init__()
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Arguments:
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Lgal (float): Galaxy luminosity in solar units
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x (float): extent of halo in virial radius units
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Keyword arguments:
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y (float): Inner part where there is no halo
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sculpt (float): If negative, do not sculpt. If positive, there will be a 2d
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suppression of the profile with a radius given by sculpt (in arcmins).
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"""
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super(KSZ_Isothermal,self).__init__(sculpt)
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self.R_gal = 0.226 * x
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self.R_innergal *= y
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@ -71,7 +93,7 @@ class KSZ_Isothermal(KSZ_Profile):
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Q = np.zeros(r.size)
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cond = (r<=0)
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cond = (r<=1e-10)
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Q[cond] = rho0*2/Mpc * (rGalaxy-rInner)/(rGalaxy*rInner)
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cond = (r>0)*(r <= rInner)
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@ -84,7 +106,7 @@ class KSZ_Isothermal(KSZ_Profile):
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Q[cond] = ne.evaluate('rho0*2/(Mpc*r) * arctan(sqrt( (rGalaxy/r)**2 -1 ))',
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local_dict=D)
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return Q,np.where(r<rInner)[0]
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return Q,[] #np.where(r<rInner)[0]
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# -----------------------------------------------------------------------------
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Block a user