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extreme_deconvolution.py
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extreme_deconvolution.py
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import sys
import os, os.path, platform
import ctypes
import ctypes.util
import numpy as nu
from numpy.ctypeslib import ndpointer
#Find and load the library
_lib = None
if platform.system()=='Darwin': #pragma: no cover
_libraryname= 'libextremedeconvolution.dylib'
else:
_libraryname= 'libextremedeconvolution.so'
_libname = ctypes.util.find_library(_libraryname)
if _libname: #pragma: no cover
_lib = ctypes.CDLL(_libname)
if _lib is None: #Hack
p = os.path.join('/usr/local/lib/',_libraryname)
if os.path.exists(p):
_lib = ctypes.CDLL(p)
if _lib is None: #pragma: no cover
raise IOError(_libraryname+' library not found')
_PY3= sys.version_info[0] > 2
if _PY3: #pragma: no cover
long= int
chr= lambda x: bytes([x]) # Back to python 2 chr...
def _fix2chararray(fix,ngauss):
"""Internal function to process the fix* inputs"""
if fix is None:
fix= [chr(False) for kk in range(ngauss)]
else: #fix is set
try:
if len(fix) == 1 and ngauss != 1:
fix= [chr(fix[0]) for kk in range(ngauss)]
else: #We assume all values are set
fix= [chr(fix[kk]) for kk in range(ngauss)]
except TypeError: #fixamp == Bool
fix= [chr(fix) for kk in range(ngauss)]
if _PY3: #pragma: no cover
return b''.join(fix)
else:
return ''.join(fix)
def extreme_deconvolution(ydata,ycovar,
xamp,xmean,xcovar,
projection=None,
weight=None,
fixamp=None,fixmean=None,fixcovar=None,
tol=1.e-6,maxiter=long(1e9),w=0.,logfile=None,
splitnmerge=0,maxsnm=False,likeonly=False,
logweight=False):
"""
NAME:
extreme_deconvolution
PURPOSE:
run the underlying C-extreme-deconvolution code
INPUT:
ydata - [ndata,dy] numpy array of observed quantities
ycovar - [ndata,dy(,dy)] numpy array of observational error covariances
(if [ndata,dy] then the error correlations are assumed to vanish)
xamp - [ngauss] numpy array of initial amplitudes (*not* [1,ngauss])
xmean - [ngauss,dx] numpy array of initial means
xcovar - [ngauss,dx,dx] numpy array of initial covariances
OPTIONAL INPUTS:
projection - [ndata,dy,dx] numpy array of projection matrices
weight - [ndata] numpy array of weights to be applied to the data points
logweight - (bool, default=False) if True, weight is actually
log(weight)
fixamp - (default=None) None, True/False, or list of bools
fixmean - (default=None) None, True/False, or list of bools
fixcovar - (default=None) None, True/False, or list of bools
tol - (double, default=1.e-6) tolerance for convergence
maxiter - (long, default= 10**9) maximum number of iterations to perform
w - (double, default=0.) covariance regularization parameter
(of the conjugate prior)
logfile - basename for several logfiles (_c.log has output from
the c-routine; _loglike.log has the log likelihood path of
all the accepted routes, i.e. only parts which increase
the likelihood are included, during splitnmerge)
splitnmerge - (int, default=0) depth to go down the splitnmerge path
maxsnm - (Bool, default=False) use the maximum number of split 'n'
merge steps, K*(K-1)*(K-2)/2
likeonly - (Bool, default=False) only compute the total log
likelihood of the data
OUTPUT:
avgloglikedata after convergence,
+updated xamp, xmean, xcovar
HISTORY:
2010-02-10 - Written - Bovy (NYU)
DOCTEST:
>>> import numpy as nu
>>> ydata= nu.array([[ 2.62434536e+00],
... [ 3.88243586e-01],
... [ 4.71828248e-01],
... [ -7.29686222e-02],
... [ 1.86540763e+00],
... [ -1.30153870e+00],
... [ 2.74481176e+00],
... [ 2.38793099e-01],
... [ 1.31903910e+00],
... [ 7.50629625e-01],
... [ 2.46210794e+00],
... [ -1.06014071e+00],
... [ 6.77582796e-01],
... [ 6.15945645e-01],
... [ 2.13376944e+00],
... [ -9.98912673e-02],
... [ 8.27571792e-01],
... [ 1.22141582e-01],
... [ 1.04221375e+00],
... [ 1.58281521e+00],
... [ -1.00619177e-01],
... [ 2.14472371e+00],
... [ 1.90159072e+00],
... [ 1.50249434e+00],
... [ 1.90085595e+00],
... [ 3.16272141e-01],
... [ 8.77109774e-01],
... [ 6.42305657e-02],
... [ 7.32111920e-01],
... [ 1.53035547e+00],
... [ 3.08339248e-01],
... [ 6.03246473e-01],
... [ 3.12827300e-01],
... [ 1.54794359e-01],
... [ 3.28753869e-01],
... [ 9.87335401e-01],
... [ -1.17310349e-01],
... [ 1.23441570e+00],
... [ 2.65980218e+00],
... [ 1.74204416e+00],
... [ 8.08164448e-01],
... [ 1.12371036e-01],
... [ 2.52841706e-01],
... [ 2.69245460e+00],
... [ 1.05080775e+00],
... [ 3.63004353e-01],
... [ 1.19091548e+00],
... [ 3.10025514e+00],
... [ 1.12015895e+00],
... [ 1.61720311e+00],
... [ 1.30017032e+00],
... [ 6.47750154e-01],
... [ -1.42518198e-01],
... [ 6.50657278e-01],
... [ 7.91105767e-01],
... [ 1.58662319e+00],
... [ 1.83898341e+00],
... [ 1.93110208e+00],
... [ 1.28558733e+00],
... [ 1.88514116e+00],
... [ 2.45602059e-01],
... [ 2.25286816e+00],
... [ 1.51292982e+00],
... [ 7.01907165e-01],
... [ 1.48851815e+00],
... [ 9.24428287e-01],
... [ 2.13162939e+00],
... [ 2.51981682e+00],
... [ 3.18557541e+00],
... [ -3.96496335e-01],
... [ -4.44113805e-01],
... [ 4.95534137e-01],
... [ 1.16003707e+00],
... [ 1.87616892e+00],
... [ 1.31563495e+00],
... [ -1.02220122e+00],
... [ 6.93795987e-01],
... [ 1.82797464e+00],
... [ 1.23009474e+00],
... [ 1.76201118e+00],
... [ 7.77671857e-01],
... [ 7.99241931e-01],
... [ 1.18656139e+00],
... [ 1.41005165e+00],
... [ 1.19829972e+00],
... [ 1.11900865e+00],
... [ 3.29337714e-01],
... [ 1.37756379e+00],
... [ 1.12182127e+00],
... [ 2.12948391e+00],
... [ 2.19891788e+00],
... [ 1.18515642e+00],
... [ 6.24715050e-01],
... [ 3.61269593e-01],
... [ 1.42349435e+00],
... [ 1.07734007e+00],
... [ 6.56146324e-01],
... [ 1.04359686e+00],
... [ 3.79999156e-01],
... [ 1.69803203e+00],
... [ 5.52871435e-01],
... [ 2.22450770e+00],
... [ 1.40349164e+00],
... [ 1.59357852e+00],
... [ -9.49118457e-02],
... [ 1.16938243e+00],
... [ 1.74055645e+00],
... [ 4.62993982e-02],
... [ 7.33781494e-01],
... [ 1.03261455e+00],
... [ -3.73117320e-01],
... [ 1.31515939e+00],
... [ 1.84616065e+00],
... [ 1.40484059e-01],
... [ 1.35054598e+00],
... [ -3.12283411e-01],
... [ 9.61304491e-01],
... [ -6.15772355e-01],
... [ 2.12141771e+00],
... [ 1.40890054e+00],
... [ 9.75383044e-01],
... [ 2.24838381e-01],
... [ 2.27375593e+00],
... [ 2.96710175e+00],
... [ -8.57981864e-01],
... [ 2.23616403e+00],
... [ 2.62765075e+00],
... [ 1.33801170e+00],
... [ -1.99268032e-01],
... [ 1.86334532e+00],
... [ 8.19079698e-01],
... [ 3.96079372e-01],
... [ -2.30058136e-01],
... [ 1.55053750e+00],
... [ 1.79280687e+00],
... [ 3.76469270e-01],
... [ 1.52057634e+00],
... [ -1.44341390e-01],
... [ 1.80186103e+00],
... [ 1.04656730e+00],
... [ 8.13430228e-01],
... [ 8.98254127e-01],
... [ 1.86888616e+00],
... [ 1.75041164e+00],
... [ 1.52946532e+00],
... [ 1.13770121e+00],
... [ 1.07782113e+00],
... [ 1.61838026e+00],
... [ 1.23249456e+00],
... [ 1.68255141e+00],
... [ 6.89883226e-01],
... [ -1.43483776e+00],
... [ 2.03882460e+00],
... [ 3.18697965e+00],
... [ 1.44136444e+00],
... [ 8.99844767e-01],
... [ 8.63555256e-01],
... [ 8.80945812e-01],
... [ 1.01740941e+00],
... [ -1.22018729e-01],
... [ 4.82905542e-01],
... [ 2.97317235e-03],
... [ 1.24879916e+00],
... [ 7.03358848e-01],
... [ 1.49521132e+00],
... [ 8.25296840e-01],
... [ 1.98633519e+00],
... [ 1.21353390e+00],
... [ 3.19069973e+00],
... [ -8.96360923e-01],
... [ 3.53083312e-01],
... [ 1.90148689e+00],
... [ 3.52832571e+00],
... [ 7.51365222e-01],
... [ 1.04366899e+00],
... [ 7.73685757e-01],
... [ 2.33145711e+00],
... [ 7.12692137e-01],
... [ 1.68006984e+00],
... [ 6.80198401e-01],
... [ -2.72558755e-01],
... [ 1.31354772e+00],
... [ 1.50318481e+00],
... [ 2.29322588e+00],
... [ 8.89552974e-01],
... [ 3.82637936e-01],
... [ 1.56276110e+00],
... [ 1.24073709e+00],
... [ 1.28066508e+00],
... [ 9.26887296e-01],
... [ 2.16033857e+00],
... [ 1.36949272e+00],
... [ 2.90465871e+00],
... [ 2.11105670e+00],
... [ 1.65904980e+00],
... [ -6.27438341e-01],
... [ 1.60231928e+00],
... [ 1.42028220e+00],
... [ 1.81095167e+00],
... [ 2.04444209e+00]])
>>> ndata= len(ydata)
>>> ycovar= nu.reshape(nu.ones(ndata)*.01,(ndata,1))
>>> projection= nu.zeros((ndata,1,2))
>>> xamp= nu.ones(2)/2.
>>> xmean= nu.array([[ 0.86447943,0.322681 ],
... [ 0.67078879,0.45087394]])
>>> xcovar= nu.array([[[ 0.03821028, 0.04108113],
... [ 0.04014796, 0.03173839]],
... [[ 0.06219194, 0.04302473],
... [ 0.09738021, 0.06778009]]])
>>> for ii in range(ndata):
... if (ii % 2) == 0:
... projection[ii,:,:]= nu.array([1.,0.])
... else:
... projection[ii,:,:]= nu.array([0.,1.])
...
>>> l= extreme_deconvolution(ydata,ycovar,xamp,xmean,xcovar,projection=projection)
>>> assert (l--1.3114744655258121)**2. < 10.**-8
>>> assert (xmean[0,0]-2.30368235)**2. < 10.**-5
>>> assert (xmean[0,1]-1.70701517)**2. < 10.**-5
>>> assert (xmean[1,0]-1.08009397)**2. < 10.**-5
>>> assert (xmean[1,1]-0.8888667)**2. < 10.**-5
>>> assert (xcovar[0,0,0]-0.445645987259)**2. < 10.**-5.
>>> assert (xamp[0]-0.11968415)**2. < 10.**-5
>>> assert (xamp[1]-0.880315852981)**2. < 10.**-5
>>> ydata= nu.asfortranarray(ydata)
>>> ydata.flags['F_CONTIGUOUS']
True
>>> xamp= nu.ones(2)/2.
>>> xmean= nu.array([[ 0.86447943,0.322681 ],
... [ 0.67078879,0.45087394]])
>>> xcovar= nu.array([[[ 0.03821028, 0.04108113],
... [ 0.04014796, 0.03173839]],
... [[ 0.06219194, 0.04302473],
... [ 0.09738021, 0.06778009]]])
>>> xamp= nu.asfortranarray(xamp)
>>> xmean= nu.asfortranarray(xmean)
>>> xcovar= nu.asfortranarray(xcovar)
>>> xamp.flags['F_CONTIGUOUS']
True
>>> xmean.flags['F_CONTIGUOUS']
True
>>> xmean.flags['C_CONTIGUOUS']
False
>>> l= extreme_deconvolution(ydata,ycovar,xamp,xmean,xcovar,projection=projection)
>>> assert (l--1.3114744655258121)**2. < 10.**-8
>>> assert (xmean[0,0]-2.30368235)**2. < 10.**-5
>>> assert (xmean[0,1]-1.70701517)**2. < 10.**-5
>>> assert (xmean[1,0]-1.08009397)**2. < 10.**-5
>>> assert (xmean[1,1]-0.8888667)**2. < 10.**-5
>>> assert (xcovar[0,0,0]-0.445645987259)**2. < 10.**-5.
>>> assert (xamp[0]-0.11968415)**2. < 10.**-5
>>> assert (xamp[1]-0.880315852981)**2. < 10.**-5
>>> ydata.flags['F_CONTIGUOUS']
True
"""
ndata= ydata.shape[0]
dataDim= ydata.shape[1]
ngauss= len(xamp)
gaussDim= xmean.shape[1]
if len(ycovar.shape) == 2:
diagerrors= True
else:
diagerrors= False
fixamp= _fix2chararray(fixamp,ngauss)
fixmean= _fix2chararray(fixmean,ngauss)
fixcovar= _fix2chararray(fixcovar,ngauss)
avgloglikedata= ctypes.pointer(ctypes.c_double(0.))
if logfile is None:
clog= ''
clog2= ''
n_clog= 0
n_clog2= 0
else:
clog= logfile + '_c.log'
n_clog= len(clog)
clog2= logfile + '_loglike.log'
n_clog2= len(clog2)
if _PY3: #pragma: no cover
clog= clog.encode('utf8')
clog2= clog2.encode('utf8')
if maxsnm:
splitnmerge = long(ngauss*(ngauss-1)*(ngauss-2)/2)
if projection is None:
noprojection= True
projection= nu.zeros(1)
else:
noprojection= False
if weight is None:
noweight= True
logweights= nu.zeros(1)
elif not logweight:
noweight= False
logweights= nu.log(weight)
else:
noweight= False
logweights= weight
ndarrayFlags= ('C_CONTIGUOUS','WRITEABLE')
exdeconvFunc= _lib.proj_gauss_mixtures_IDL
exdeconvFunc.argtypes = [ndpointer(dtype=nu.float64,flags=ndarrayFlags),
ndpointer(dtype=nu.float64,flags=ndarrayFlags),
ndpointer(dtype=nu.float64,flags=ndarrayFlags),
ndpointer(dtype=nu.float64,flags=ndarrayFlags),
ctypes.c_int,
ctypes.c_int,
ndpointer(dtype=nu.float64,flags=ndarrayFlags),
ndpointer(dtype=nu.float64,flags=ndarrayFlags),
ndpointer(dtype=nu.float64,flags=ndarrayFlags),
ctypes.c_int,
ctypes.c_int,
ctypes.c_char_p,
ctypes.c_char_p,
ctypes.c_char_p,
ctypes.POINTER(ctypes.c_double),
ctypes.c_double,
ctypes.c_int,
ctypes.c_char,
ctypes.c_double,
ctypes.c_char_p,
ctypes.c_int,
ctypes.c_int,
ctypes.c_char_p,
ctypes.c_int,
ctypes.c_char,
ctypes.c_char,
ctypes.c_char]
#Requirements, first store old order
f_cont= [ydata.flags['F_CONTIGUOUS'],
ycovar.flags['F_CONTIGUOUS'],
projection.flags['F_CONTIGUOUS'],
logweights.flags['F_CONTIGUOUS'],
xamp.flags['F_CONTIGUOUS'],
xmean.flags['F_CONTIGUOUS'],
xcovar.flags['F_CONTIGUOUS']]
ydata= nu.require(ydata,dtype=nu.float64,requirements=['C','W'])
ycovar= nu.require(ycovar,dtype=nu.float64,requirements=['C','W'])
projection= nu.require(projection,dtype=nu.float64,requirements=['C','W'])
logweights= nu.require(logweights,dtype=nu.float64,requirements=['C','W'])
xamp_tmp= nu.require(xamp,dtype=nu.float64,requirements=['C','W'])
xmean_tmp= nu.require(xmean,dtype=nu.float64,requirements=['C','W'])
xcovar_tmp= nu.require(xcovar,dtype=nu.float64,requirements=['C','W'])
exdeconvFunc(ydata,
ycovar,
projection,
logweights,
ctypes.c_int(ndata),
ctypes.c_int(dataDim),
xamp_tmp,
xmean_tmp,
xcovar_tmp,
ctypes.c_int(gaussDim),
ctypes.c_int(ngauss),
ctypes.c_char_p(fixamp),
ctypes.c_char_p(fixmean),
ctypes.c_char_p(fixcovar),
avgloglikedata,
ctypes.c_double(tol),
ctypes.c_int(maxiter),
ctypes.c_char(chr(likeonly)),
ctypes.c_double(w),
ctypes.create_string_buffer(clog),
ctypes.c_int(n_clog),
ctypes.c_int(splitnmerge),
ctypes.create_string_buffer(clog2),
ctypes.c_int(n_clog2),
ctypes.c_char(chr(noprojection)),
ctypes.c_char(chr(diagerrors)),
ctypes.c_char(chr(noweight)))
#Reset input arrays
if f_cont[0]: ydata= nu.asfortranarray(ydata)
if f_cont[1]: ycovar= nu.asfortranarray(ycovar)
if f_cont[2]: projection= nu.asfortranarray(projection)
if f_cont[3]: logweights= nu.asfortranarray(logweights)
if f_cont[4]: xamp_tmp= nu.asfortranarray(xamp_tmp)
if f_cont[5]: xmean_tmp= nu.asfortranarray(xmean_tmp)
if f_cont[6]: xcovar_tmp= nu.asfortranarray(xcovar_tmp)
xamp[0:ngauss]= xamp_tmp
xmean[0:ngauss,0:gaussDim]= xmean_tmp
xcovar[0:ngauss,0:gaussDim,0:gaussDim]= xcovar_tmp
return avgloglikedata.contents.value
if __name__ == '__main__': #pragma: no cover
import doctest
doctest.testmod(verbose=True)