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