Merge branch 'master' of bitbucket.org:glavaux/cosmotool
This commit is contained in:
Guilhem Lavaux
commit
adf14da4b4
@ -7,8 +7,9 @@ include(GetGitRevisionDescription)
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include(ExternalProject)
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include(FindOpenMP)
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include(FindPythonLibs)
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include(FindPythonInterp)
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set(NumPy_FIND_REQUIRED TRUE)
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include(${CMAKE_SOURCE_DIR}/FindPyLibs.cmake)
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include(${CMAKE_SOURCE_DIR}/FindNumPy.cmake)
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30
FindPyLibs.cmake
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30
FindPyLibs.cmake
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@ -0,0 +1,30 @@
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execute_process(COMMAND "${PYTHON_EXECUTABLE}" "-c"
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"import distutils.sysconfig as cs; import os; v=cs.get_config_vars(); print(os.path.join(v['LIBDIR'],v['LDLIBRARY']));"
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RESULT_VARIABLE _PYLIB_SEARCH_SUCCESS
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OUTPUT_VARIABLE _PYLIB_VALUES_OUTPUT
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ERROR_VARIABLE _PYLIB_ERROR_VALUE
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OUTPUT_STRIP_TRAILING_WHITESPACE)
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execute_process(COMMAND "${PYTHON_EXECUTABLE}" "-c"
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"import distutils.sysconfig as cs; import os; v=cs.get_config_vars(); print(v['INCLUDEPY']);"
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RESULT_VARIABLE _PYINC_SEARCH_SUCCESS
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OUTPUT_VARIABLE _PYINC_VALUES_OUTPUT
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ERROR_VARIABLE _PYINC_ERROR_VALUE
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OUTPUT_STRIP_TRAILING_WHITESPACE)
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if(NOT _PYLIB_SEARCH_SUCCESS MATCHES 0)
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message(FATAL_ERROR
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"PyLib search failure:\n${_PYLIB_ERROR_VALUE}")
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return()
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endif()
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if(NOT _PYINC_SEARCH_SUCCESS MATCHES 0)
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message(FATAL_ERROR
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"PyInc search failure:\n${_PYINC_ERROR_VALUE}")
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return()
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endif()
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set(PYTHON_LIBRARY ${_PYLIB_VALUES_OUTPUT} CACHE PATH "Python runtime library path")
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set(PYTHON_INCLUDE_PATH ${_PYINC_VALUES_OUTPUT} CACHE PATH "Python runtime include path")
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@ -1 +1,2 @@
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from _cosmotool import *
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from borg import read_borg_vol
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265
python/cosmotool/borg.py
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265
python/cosmotool/borg.py
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@ -0,0 +1,265 @@
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###
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### BORG code is from J. Jasche
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###
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import StringIO
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import numpy as np
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from numpy import *
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import os.path
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import array
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import glob
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class BorgVolume(object):
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def __init__(self, density, ranges):
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self._density = density
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self._ranges = ranges
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property density:
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def __get__(self):
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return self._density
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property ranges:
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def __get__(self):
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return self._ranges
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def build_filelist(fdir):
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#builds list of all borg density fields which may be distributed over several directories
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fname_0=glob.glob(fdir[0]+'initial_density_*')
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fname_1=glob.glob(fdir[0]+'final_density_*')
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fdir=fdir[1:] #eliminate first element
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for fd in fdir:
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fname_0=fname_0+glob.glob(fd+'initial_density_*')
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fname_1=fname_1+glob.glob(fd+'final_density_*')
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return fname_0, fname_1
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def read_borg_vol(BORGFILE):
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""" Reading routine for BORG data
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"""
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openfile=open(BORGFILE,'rb')
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period=0
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N0=0
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N1=0
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N2=0
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xmin=0
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xmax=0
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ymin=0
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ymax=0
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zmin=0
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zmax=0
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nlines=0
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while True:
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line=openfile.readline()
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s=line.rstrip('\n')
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r=s.rsplit(' ')
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if size(r)==5 :
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if r[0] =="define":
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if r[1]=="Lattice" : N0=int(r[2])
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N1=int(r[3])
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N2=int(r[4])
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if size(r)==11 :
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if r[4] =="BoundingBox": xmin=float(r[5])
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xmax=float(r[6])
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ymin=float(r[7])
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ymax=float(r[8])
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zmin=float(r[9])
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zmax=float(r[10].rstrip(','))
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if r[0]=='@1': break
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ranges=[]
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ranges.append(xmin)
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ranges.append(xmax)
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ranges.append(ymin)
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ranges.append(ymax)
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ranges.append(zmin)
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ranges.append(zmax)
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#now read data
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data=np.fromfile(openfile, '>f4')
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data=data.reshape(N2,N0,N1)
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return BorgVolume(data,ranges)
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def read_spec( fname ):
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""" Reading routine for ARES spectrum samples
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"""
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x,y=np.loadtxt( fname ,usecols=(0,1),unpack=True)
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return x , y
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def read_bias_nmean( fname ):
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""" Reading routine for ARES bias data
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"""
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x,b0,b1,nmean=np.loadtxt( fname ,usecols=(0,1,2,3),unpack=True)
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return x , b0, b1, nmean
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def read_nmean( fname ):
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""" Reading routine for BORG bias data
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"""
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x,nmean=np.loadtxt( fname ,usecols=(0,1),unpack=True)
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return x, nmean
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def get_grid_values(xx,data, ranges):
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""" return values at grid positions
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"""
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xmin=ranges[0]
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xmax=ranges[1]
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ymin=ranges[2]
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ymax=ranges[3]
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zmin=ranges[4]
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zmax=ranges[5]
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Lx= xmax-xmin
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Ly= ymax-ymin
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Lz= zmax-zmin
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Nx=shape(data)[0]
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Ny=shape(data)[1]
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Nz=shape(data)[2]
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dx=Lx/float(Nx)
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dy=Ly/float(Ny)
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dz=Lz/float(Nz)
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idx=(xx[:,0]-xmin)/dx
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idy=(xx[:,1]-ymin)/dz
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idz=(xx[:,2]-zmin)/dy
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idx=idx.astype(int)
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idy=idy.astype(int)
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idz=idz.astype(int)
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idflag=np.where( (idx>-1)*(idx<Nx)*(idy>-1)*(idy<Ny)*(idz>-1)*(idz<Nz) )
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flag=[False]*len(xx)
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vals=[-999.]*len(xx)
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flag=np.array(flag)
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vals=np.array(vals)
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flag[idflag]=True
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vals[idflag]=data[idx[idflag],idy[idflag],idz[idflag]]
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return vals,flag
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def get_mean_density(fdir, smin, step):
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""" estimate ensemble mean
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"""
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print '-'*60
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print 'Get 3D ensemble mean density field'
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print '-'*60
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fname0 = fdir + 'initial_density_'+str(0)+'.dat'
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fname1 = fdir + 'final_density_'+str(0)+'.dat'
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MEAN0,ranges=read_borg_vol(fname0)
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MEAN0=MEAN0*0.;
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VAR0=copy(MEAN0)
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MEAN1=copy(MEAN0)
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VAR1=copy(MEAN0)
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norm=0.
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idat=smin
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fname0 = fdir + 'initial_density_'+str(idat)+'.dat'
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fname1 = fdir + 'final_density_'+str(idat)+'.dat'
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#and (idat<smin+1000)
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while((os.path.exists(fname0))):
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auxdata0,auxranges0=read_borg_vol(fname0)
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auxdata1,auxranges1=read_borg_vol(fname1)
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auxx0=auxdata0
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auxx1=auxdata1
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MEAN0+=auxx0
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VAR0+=auxx0**2
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MEAN1+=auxx1
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VAR1+=auxx1**2
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norm+=1
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idat+=step
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fname0 = fdir + 'initial_density_'+str(idat)+'.dat'
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fname1 = fdir + 'final_density_'+str(idat)+'.dat'
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del auxranges0
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del auxdata0
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del auxranges1
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del auxdata1
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MEAN0/=norm
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VAR0/=norm
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VAR0-=MEAN0**2
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VAR0=sqrt(fabs(VAR0))
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MEAN1/=norm
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VAR1/=norm
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VAR1-=MEAN1**2
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VAR1=sqrt(fabs(VAR1))
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return MEAN0,VAR0,MEAN1,VAR1,ranges
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def get_mean_density_fdir(fdir,init,steps):
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""" estimate ensemble mean
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"""
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print '-'*60
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print 'Get 3D ensemble mean density field'
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print '-'*60
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fname0,fname1=build_filelist(fdir)
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fname0=fname0[init::steps]
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fname1=fname1[init::steps]
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MEAN0,ranges=read_borg_vol(fname0[0])
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MEAN0=MEAN0*0.;
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VAR0=copy(MEAN0)
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MEAN1=copy(MEAN0)
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VAR1=copy(MEAN0)
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norm0=0.
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norm1=0.
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for fn in fname0:
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auxdata0,auxranges0=read_borg_vol(fn)
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MEAN0+=auxdata0
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VAR0+=auxdata0**2.
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norm0+=1.
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del auxranges0
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del auxdata0
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for fn in fname1:
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auxdata1,auxranges1=read_borg_vol(fn)
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MEAN1+=auxdata1
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VAR1+=auxdata1**2.
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norm1+=1.
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del auxranges1
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del auxdata1
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MEAN0/=norm0
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VAR0/=norm0
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VAR0-=MEAN0**2
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VAR0=sqrt(fabs(VAR0))
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MEAN1/=norm1
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VAR1/=norm1
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VAR1-=MEAN1**2
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VAR1=sqrt(fabs(VAR1))
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return MEAN0,VAR0,MEAN1,VAR1,ranges
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