def read_mc_chain(f):
"""
Reads the mcmc chain created with emcee
:param f: chain_file
:return:
"""
# read the file
lines = read_text_file(f)
# key counter and ouput dictionary
chainlog = {}
hkcounter = 0
# define header keys
head_keys = ['name', 'component', 'group']
for l in lines:
d = l.split()
# print d
for hk in head_keys:
if l.find(hk) > -1:
# groups are integers of course
if hk == 'group':
d[2:] = map(int, d[2:])
else:
d[2:] = d[2:]
# append the header info
chainlog[hk] = d[2:]
hkcounter += 1
break
# once we read all data, we end
if hkcounter == 3:
break
# load the file
d = np.loadtxt(f)
# get fit properties
nwalkers = int(np.max(d[:, 0])) + 1
niter = len(d[:, 0]) / nwalkers
npars = len(d[0]) - 2
# remove the first column with numbering
chainlog['data'] = d[:, 1:]
return chainlog, nwalkers, niter, npars
def read_mc_chain(f):
"""
Reads the mcmc chain created with emcee
:param f: chain_file
:return:
"""
# read the file
lines = read_text_file(f)
# key counter and ouput dictionary
chainlog = {}
hkcounter = 0
# define header keys
head_keys = ['name', 'component', 'group']
for l in lines:
d = l.split()
# print d
for hk in head_keys:
if l.find(hk) > -1:
# groups are integers of course
if hk == 'group':
d[2:] = map(int, d[2:])
else:
d[2:] = d[2:]
# append the header info
chainlog[hk] = d[2:]
hkcounter += 1
break
# once we read all data, we end
if hkcounter == 3:
break
# load the file
d = np.loadtxt(f)
# get fit properties
nwalkers = int(np.max(d[:, 0])) + 1
niter = len(d[:, 0]) / nwalkers
npars = len(d[0]) - 2
# remove the first column with numbering
chainlog['data'] = d[:, 1:]
return chainlog, nwalkers, niter, npars
def read_fitlog(f):
"""
Reads the fitting log and stores it within a dictionary.
:param f:
:return:
"""
# read the file
lines = read_text_file(f)
# key counter and ouput dictionary
fitlog = {}
hkcounter = 0
# define header keys
head_keys = ['name', 'component', 'group']
for l in lines:
d = l.split()
# print d
for hk in head_keys:
if l.find(hk) > -1:
# groups are integers of course
if hk == 'group':
d[2:] = map(int, d[2:])
else:
d[2:] = d[2:]
# append the header info
fitlog[hk] = d[2:]
hkcounter += 1
break
# once we read all data, we end
if hkcounter == 3:
break
# print fitlog
# append data
fitlog['data'] = np.loadtxt(f)
return fitlog
def read_fitlog(f):
"""
Reads the fitting log and stores it within a dictionary.
:param f:
:return:
"""
# read the file
lines = read_text_file(f)
# key counter and ouput dictionary
fitlog = {}
hkcounter = 0
# define header keys
head_keys = ['name', 'component', 'group']
for l in lines:
d = l.split()
# print d
for hk in head_keys:
if l.find(hk) > -1:
# groups are integers of course
if hk == 'group':
d[2:] = map(int, d[2:])
else:
d[2:] = d[2:]
# append the header info
fitlog[hk] = d[2:]
hkcounter += 1
break
# once we read all data, we end
if hkcounter == 3:
break
# print fitlog
# append data
fitlog['data'] = np.loadtxt(f)
return fitlog
def load(self, f):
"""
Loads the text representation of the class from
a file f.
:param f
:return:
"""
# read the file
lines = read_text_file(f)
data_start = len(lines)
for i, l in enumerate(lines):
if l.find('FITTER') > -1:
data_start = i
break
# check that there are actually some data in the file
if data_start >= len(lines):
return False
# create the class
fitter = Fitter()
name = None
fit_kwargs = {}
# from here the file is actually being read
for i, l in enumerate(lines[data_start + 1:]):
# once we reach FITTER again we end
if l.find('FITTER') > -1:
break
# split the line
d = l.split()
# print d
# save the name
if d[0].find('fitter:') > -1:
name = d[1]
# save the kwargs
elif d[0].find('fit_parameters:') > -1:
d = d[1:]
if len(d) < 2:
continue
fit_kwargs = {
d[i].strip(':'): float(d[i + 1])
for i in range(0, len(d), 2)
}
# do the same for enviromental keys
if d[0].find('env_keys:') > -1:
# the first string is just identification
d = d[1:]
# secure corrct types
recs = ['debug', 'verbose', 'fitlog']
cast_types = [string2bool, string2bool, str]
cdict = {
d[i].rstrip(':'): d[i + 1]
for i in range(0, len(d), 2)
}
for k in cdict.keys():
if k in recs:
i = recs.index(k)
ctype = cast_types[i]
cdict[k] = ctype(cdict[k])
# assign the vlues
setattr(fitter, k, cdict[k])
# choose the fitter
if name != 'None':
fitter.choose_fitter(name, **fit_kwargs)
else:
return False
# finally assign everything to self
attrs = ['debug', 'fittername', 'verbose', 'fitlog', 'fit_kwargs']
for attr in attrs:
setattr(self, attr, getattr(fitter, attr))
# if we got here, we loaded the data
return True
def load(self, f):
"""
Loads the text representation of the class from
a file f.
:param f
:return:
"""
# read the file
lines = read_text_file(f)
data_start = len(lines)
for i, l in enumerate(lines):
if l.find('FITTER') > -1:
data_start = i
break
# check that there are actually some data in the file
if data_start >= len(lines):
return False
# create the class
fitter = Fitter()
name = None
fit_kwargs = {}
# from here the file is actually being read
for i, l in enumerate(lines[data_start+1:]):
# once we reach FITTER again we end
if l.find('FITTER') > -1:
break
# split the line
d = l.split()
# print d
# save the name
if d[0].find('fitter:') > -1:
name = d[1]
# save the kwargs
elif d[0].find('fit_parameters:') > -1:
d = d[1:]
if len(d) < 2:
continue
fit_kwargs = {d[i].strip(':'): float(d[i+1]) for i in range(0, len(d), 2)}
# do the same for enviromental keys
if d[0].find('env_keys:') > -1:
# the first string is just identification
d = d[1:]
# secure corrct types
recs = ['debug', 'verbose', 'fitlog']
cast_types = [string2bool, string2bool, str]
cdict = {d[i].rstrip(':'): d[i+1] for i in range(0, len(d), 2)}
for k in cdict.keys():
if k in recs:
i = recs.index(k)
ctype = cast_types[i]
cdict[k] = ctype(cdict[k])
# assign the vlues
setattr(fitter, k, cdict[k])
# choose the fitter
if name != 'None':
fitter.choose_fitter(name, **fit_kwargs)
else:
return False
# finally assign everything to self
attrs = ['debug', 'fittername', 'verbose', 'fitlog', 'fit_kwargs']
for attr in attrs:
setattr(self, attr, getattr(fitter, attr))
# if we got here, we loaded the data
return True