def write_parnames(par_keys, latexpars=None):
basename = output_dir + 'mcmc_chain' + output_tag + '.txt'
basename = basename[0:-6]
info_message('writing paramnames file...')
parnames = open(basename + '.paramnames', 'w')
if latexpars == None:
latexpars = {}
for key in par_keys:
latexpars[key] = key
for key in par_keys:
parnames.write(key + '\t' + latexpars[key] + '\n')
parnames.close()
def write_parnames( par_keys, latexpars=None ):
basename = output_dir+'mcmc_chain'+output_tag+'.txt'
basename = basename[0:-6]
info_message( 'writing paramnames file...' )
parnames = open( basename+'.paramnames', 'w')
if latexpars == None:
latexpars = {}
for key in par_keys:
latexpars[key] = key
for key in par_keys:
parnames.write( key + '\t' + latexpars[key] + '\n' )
parnames.close()
def write_chain( chain ): info_message( ' Saving chains...' ) ofile = output_dir+'mcmc_chain'+output_tag+'.txt' #print ofile ch = [] for c in chains: a = [ c['weight'], c['chi2'] ] a.extend( c['parameters'].values() ) #print a ch.append( a ) ch = np.array( ch ) #print ch[0:2,:] #print ch.shape np.savetxt( ofile, ch, fmt='%-16.7E' ) info_message( ' Done.\n' )
def write_chain(chain):
info_message(' Saving chains...')
ofile = output_dir + 'mcmc_chain' + output_tag + '.txt'
#print ofile
ch = []
for c in chains:
a = [c['weight'], c['chi2']]
a.extend(c['parameters'].values())
#print a
ch.append(a)
ch = np.array(ch)
#print ch[0:2,:]
#print ch.shape
np.savetxt(ofile, ch, fmt='%-16.7E')
info_message(' Done.\n')
def marginalize(self, chains, nbins=21, skiplines=0.3, thin=1):
# ------------------------------
def extract_parameter(chains, par_key):
w = []
v = []
clen = len(chains)
for i in range(clen):
w.append(chains[i]['weight'])
v.append(chains[i]['parameters'][par_key])
v = np.array(v)
w = np.array(w)
return v, w
# ------------------------------
def compute_histogram(value, weight, nbins=21):
minv = np.min(value)
maxv = np.max(value)
deltav = (maxv - minv) / (nbins)
intervals = np.array(range(nbins + 1)) * deltav + minv
#print intervals
v = []
m = []
for ibin in range(nbins):
v.append((intervals[ibin] + intervals[ibin + 1]) / 2.)
select = np.array((value >= intervals[ibin])) * np.array(
(value < intervals[ibin + 1]))
m.append(np.sum(weight[select]))
v = np.array(v)
m = np.array(m, dtype=np.float) / np.sum(m)
return v, m
chlen = len(chains)
if isinstance(skiplines, float):
chlen = int(chlen * skiplines)
elif isinstance(skiplines, int):
chlen = skiplines
chains = chains[chlen::thin]
info_message('marginalize: Processing ' + str(len(chains)) +
' samples')
npars = len(chains[0]['parameters'])
par_keys = chains[0]['parameters'].keys()
marginals = {}
for key in par_keys:
v, w = extract_parameter(chains, key)
v, m = compute_histogram(v, w, nbins=nbins)
marginals[key] = (v, m)
return marginals
def marginalize( self, chains, nbins=21, skiplines=0.3, thin=1):
# ------------------------------
def extract_parameter( chains, par_key):
w = []
v = []
clen = len( chains )
for i in range(clen):
w.append( chains[i]['weight'])
v.append( chains[i]['parameters'][par_key])
v = np.array(v)
w = np.array(w)
return v,w
# ------------------------------
def compute_histogram( value, weight, nbins=21):
minv = np.min( value )
maxv = np.max( value )
deltav = (maxv-minv)/(nbins)
intervals = np.array(range(nbins+1)) * deltav + minv
#print intervals
v = []
m = []
for ibin in range(nbins):
v.append( (intervals[ibin]+intervals[ibin+1])/2. )
select = np.array( (value >= intervals[ibin]) ) * np.array( (value < intervals[ibin+1]) )
m.append( np.sum(weight[select]) )
v = np.array(v)
m = np.array(m, dtype=np.float) / np.sum(m)
return v, m
chlen = len( chains )
if isinstance(skiplines,float):
chlen = int( chlen * skiplines )
elif isinstance(skiplines, int):
chlen = skiplines
chains = chains[chlen::thin]
info_message( 'marginalize: Processing '+str(len(chains))+' samples' )
npars = len( chains[0]['parameters'] )
par_keys = chains[0]['parameters'].keys()
marginals = {}
for key in par_keys:
v,w = extract_parameter( chains, key )
v,m = compute_histogram( v, w, nbins=nbins )
marginals[key] = (v,m)
return marginals
def get_covmat(self, chains, skiplines=0.3, thin=1, print_key=False):
chlen = len(chains)
if isinstance(skiplines, float):
chlen = int(chlen * skiplines)
elif isinstance(skiplines, int):
chlen = skiplines
chains = chains[chlen::thin]
chlen = len(chains)
info_message('get_covmat: Processing ' + str(chlen) + ' samples')
npars = len(chains[0]['parameters'])
par_keys = chains[0]['parameters'].keys()
covmat = np.zeros((npars, npars), dtype=np.float64)
meanpars = np.zeros(npars, dtype=np.float64)
# Compute mean parameter values and reshaping parameter chains
pchains = []
# print_key = False
for ch in chains:
prow = []
for ikey, key in enumerate(par_keys):
if print_key:
print key
prow.append(ch['parameters'][key])
meanpars[ikey] += ch['parameters'][key]
pchains.append(prow)
if print_key:
print_key = False
meanpars /= chlen
pchains = np.array(pchains)
pchains.shape
for ich in range(chlen):
for ipar in range(npars):
covmat[ipar, :] += (pchains[ich, ipar] - meanpars[ipar]) * (
pchains[ich, :] - meanpars[:])
covmat /= chlen
return covmat
def get_covmat( self, chains, skiplines=0.3, thin=1, print_key=False ): chlen = len( chains ) if isinstance(skiplines,float): chlen = int( chlen * skiplines ) elif isinstance(skiplines, int): chlen = skiplines chains = chains[chlen::thin] chlen = len( chains ) info_message( 'get_covmat: Processing '+str(chlen)+' samples' ) npars = len( chains[0]['parameters'] ) par_keys = chains[0]['parameters'].keys() covmat = np.zeros( (npars,npars), dtype=np.float64 ) meanpars = np.zeros( npars, dtype=np.float64 ) # Compute mean parameter values and reshaping parameter chains pchains = [] # print_key = False for ch in chains: prow = [] for ikey,key in enumerate(par_keys): if print_key: print key prow.append( ch['parameters'][key] ) meanpars[ikey] += ch['parameters'][key] pchains.append( prow ) if print_key: print_key = False meanpars /= chlen pchains = np.array( pchains ) pchains.shape for ich in range(chlen): for ipar in range(npars): covmat[ipar,:] += (pchains[ich,ipar]-meanpars[ipar]) * (pchains[ich,:]-meanpars[:]) covmat /= chlen return covmat
basename = output_dir+'mcmc_chain'+output_tag+'.txt'
basename = basename[0:-6]
info_message( 'writing paramnames file...' )
parnames = open( basename+'.paramnames', 'w')
if latexpars == None:
latexpars = {}
for key in par_keys:
latexpars[key] = key
for key in par_keys:
parnames.write( key + '\t' + latexpars[key] + '\n' )
parnames.close()
# ------
code = 'sampler'
info_message( 'Starting MCMC...' )
info_message( '\t output_tag: '+output_tag )
if len(output_tag) != 0 and output_tag[0] != '_':
output_tag = '_'+output_tag
if check_point != None:
if check_point[0] != '_':
check_point = '_'+check_point
loginf = 1.e2
zero_like = np.exp( -0.5*loginf )
npars = len( parameters )
if Verbosity:
info_message( 'INFO - Number of parameters: '+str(npars), code=code )
basename = basename[0:-6]
info_message('writing paramnames file...')
parnames = open(basename + '.paramnames', 'w')
if latexpars == None:
latexpars = {}
for key in par_keys:
latexpars[key] = key
for key in par_keys:
parnames.write(key + '\t' + latexpars[key] + '\n')
parnames.close()
# ------
code = 'sampler'
info_message('Starting MCMC...')
info_message('\t output_tag: ' + output_tag)
if len(output_tag) != 0 and output_tag[0] != '_':
output_tag = '_' + output_tag
if check_point != None:
if check_point[0] != '_':
check_point = '_' + check_point
loginf = 1.e2
zero_like = np.exp(-0.5 * loginf)
npars = len(parameters)
if Verbosity:
info_message('INFO - Number of parameters: ' + str(npars),
code=code)
