def __init__(self, fb, fs, pairs=None):
Estimator.__init__(self, fs, pairs)
self.csd_nfft = 256
self.csd_noverlap = self.csd_nfft / 2.0
self.fb = fb
self.fs = fs
def __init__(self, fname=None):
# Default values, see explanations below:
taskDic = {
'taskName': 'total energy',
'tolerance': '1',
'nMaxSteps': '10'
}
Estimator.__init__(self,fname)
Launcher.__init__(self,fname)
# value to converge with respect to k-points or energy cutoffs
# currently can be 'total energy', 'single phonon', or 'geometry':
self.taskName = self.config.get('Task', 'taskName')
# convergence criteria in percents:
self.tolerance = self.config.getfloat('Task','tolerance')
# maximum number of optimization steps:
self.nMaxSteps = self.config.getint('Task','nMaxSteps')
self.lookupTable = {
'total energy' : (self.pwscfLauncher, self.getTotalEnergy),
'single phonon': (self.singlePhononLauncher, self.getSinglePhonon),
'geometry' : (self.pwscfLauncher, self.getLatticeParameters),
'multiple phonon': (self.multiPhononLauncher, self.getMultiPhonon)
}
assert self.lookupTable.has_key(self.taskName), "Convergence \
def __init__(self,priors,data,model=None):
self.data = data
self.model = model
Estimator.__init__(self,priors)
def __init__(self, priors, data, model):
self.data = data
self.model = model
Estimator.__init__(self, priors)
def __init__(self, fb, fs, pairs=None):
Estimator.__init__(self, fs, pairs)
self.fb = fb
self.fs = fs
self.data_type = np.complex