def test_extract(self):
f = 'qmc.s000.stat.h5'
# Old format
# base = 'Observables/BackPropagated/FullOneRDM/Average_0'
dm = 2*extract_observable(f, ix=0)
self.assertAlmostEqual(numpy.sum(dm.real), 499.49878268185375)
self.assertAlmostEqual(numpy.sum(dm.imag), 1.2453436020111393)
def compute_one_body(filename, hcore, skip, group):
energies = []
# weights can be ignored if not using free projection.
dm = extract_observable(filename, ix=group).reshape((-1, ) + hcore.shape)
for d in dm[skip:]:
e1b = numpy.einsum('ij,ij->', hcore, d)
energies.append(e1b.real)
av = numpy.mean(energies)
err = scipy.stats.sem(energies)
return av, err
def average_observable(filename,
name,
eqlb=1,
estimator='back_propagated',
ix=None,
skip=1):
"""Compute mean and error bar for AFQMC HDF5 observable.
Parameters
----------
filename : string
QMCPACK output containing density matrix (*.h5 file).
name : string
Name of observable (see estimates.py for list).
eqlb : int
Number of blocks for equilibration. Default 1.
estimator : string
Estimator type to analyse. Options: back_propagated or mixed.
Default: back_propagated.
skip : int
Number of blocks to skip in between measurements equilibration.
Default 1 (use all data).
ix : int
Back propagation path length to average. Optional.
Default: None (chooses longest path).
Returns
-------
mean : :class:`numpy.ndarray`
Averaged quantity.
err : :class:`numpy.ndarray`
Error bars for quantity.
"""
md = get_metadata(filename)
free_proj = md['FreeProjection']
if free_proj:
mean = None
err = None
print("# Error analysis for free projection not implemented.")
else:
data = extract_observable(filename,
name=name,
estimator=estimator,
ix=ix)
mean = numpy.mean(data[eqlb:len(data):skip], axis=0)
err = scipy.stats.sem(data[eqlb:len(data):skip].real, axis=0)
return mean, err
def test_extract_single(self):
f = 'qmc.s000.stat.h5'
# base = 'Observables/BackPropagated/FullOneRDM/Average_3'
dm = extract_observable(f, ix=3, sample=37)
self.assertAlmostEqual(numpy.sum(dm).real, 5.00103256421043)