def __init__(self, *args, **kwargs):
root = kwargs.pop('root', None)
if root is not None:
reader = SampleReader(root)
samples, logL, logL_birth = reader.samples()
params, tex = reader.paramnames()
columns = kwargs.pop('columns', params)
limits = reader.limits()
kwargs['label'] = kwargs.get('label', os.path.basename(root))
self.__init__(data=samples,
columns=columns,
logL=logL,
logL_birth=logL_birth,
tex=tex,
limits=limits,
*args,
**kwargs)
self.root = root
else:
logzero = kwargs.pop('logzero', -1e30)
self._beta = kwargs.pop('beta', 1.)
logL_birth = kwargs.pop('logL_birth', None)
if not isinstance(logL_birth, int) and logL_birth is not None:
logL_birth = np.where(logL_birth <= logzero, -np.inf,
logL_birth)
super().__init__(logzero=logzero, *args, **kwargs)
if logL_birth is not None:
self.recompute(logL_birth, inplace=True)
self._set_automatic_limits()
def __init__(self, *args, **kwargs):
root = kwargs.pop('root', None)
if root is not None:
reader = SampleReader(root)
if hasattr(reader, 'birth_file') or hasattr(reader, 'ev_file'):
raise ValueError("The file root %s seems to point to a Nested "
"Sampling chain. Please use NestedSamples "
"instead which has the same features as "
"MCMCSamples and more. MCMCSamples should be "
"used for MCMC chains only." % root)
burn_in = kwargs.pop('burn_in', False)
weights, logL, samples = reader.samples(burn_in=burn_in)
params, tex = reader.paramnames()
columns = kwargs.pop('columns', params)
limits = reader.limits()
kwargs['label'] = kwargs.get('label', os.path.basename(root))
self.__init__(data=samples,
columns=columns,
weights=weights,
logL=logL,
tex=tex,
limits=limits,
*args,
**kwargs)
self.root = root
else:
logzero = kwargs.pop('logzero', -1e30)
logL = kwargs.pop('logL', None)
if logL is not None:
logL = np.where(logL <= logzero, -np.inf, logL)
self.tex = kwargs.pop('tex', {})
self.limits = kwargs.pop('limits', {})
self.label = kwargs.pop('label', None)
self.root = None
super().__init__(*args, **kwargs)
if logL is not None:
self['logL'] = logL
self.tex['logL'] = r'$\log\mathcal{L}$'
self._set_automatic_limits()
def __init__(self, *args, **kwargs):
root = kwargs.pop('root', None)
if root is not None:
reader = SampleReader(root)
if hasattr(reader, 'birth_file') or hasattr(reader, 'ev_file'):
raise ValueError("The file root %s seems to point to a Nested "
"Sampling chain. Please use NestedSamples "
"instead which has the same features as "
"MCMCSamples and more. MCMCSamples should be "
"used for MCMC chains only." % root)
w, logL, samples = reader.samples()
params, tex = reader.paramnames()
columns = kwargs.pop('columns', params)
limits = reader.limits()
kwargs['label'] = kwargs.get('label', os.path.basename(root))
self.__init__(data=samples,
columns=columns,
w=w,
logL=logL,
tex=tex,
limits=limits,
*args,
**kwargs)
self.root = root
else:
logL = kwargs.pop('logL', None)
self.tex = kwargs.pop('tex', {})
self.limits = kwargs.pop('limits', {})
self.label = kwargs.pop('label', None)
self.root = None
super(MCMCSamples, self).__init__(*args, **kwargs)
if logL is not None:
self['logL'] = logL
self.tex['logL'] = r'$\log\mathcal{L}$'
if self._weight is not None:
self['weight'] = self.weight
self.tex['weight'] = r'MCMC weight'
def __init__(self, *args, **kwargs):
root = kwargs.pop('root', None)
if root is not None:
reader = SampleReader(root)
samples, logL, logL_birth = reader.samples()
params, tex = reader.paramnames()
columns = kwargs.pop('columns', params)
limits = reader.limits()
kwargs['label'] = kwargs.get('label', os.path.basename(root))
self.__init__(data=samples,
columns=columns,
logL=logL,
logL_birth=logL_birth,
tex=tex,
limits=limits,
*args,
**kwargs)
self.root = root
else:
self._beta = kwargs.pop('beta', 1.)
logL_birth = kwargs.pop('logL_birth', None)
super(NestedSamples, self).__init__(*args, **kwargs)
if logL_birth is not None:
self._compute_nlive(logL_birth)