def test_getspacegroup():
# Good cases
assert spacegroups.get_spacegroup(1) == \
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
assert spacegroups.get_spacegroup('Pbca') == \
[3, 2, 2, 1, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0]
# Error cases
assert raises(NicosError, spacegroups.get_spacegroup, 'Pbbb')
assert raises(NicosError, spacegroups.get_spacegroup, 300)
def test_canreflect():
# P1 all reflection types are allowed
sg = spacegroups.get_spacegroup('P1')
assert spacegroups.can_reflect(sg, 0, 0, 0)
assert spacegroups.can_reflect(sg, 1, 0, 0)
assert spacegroups.can_reflect(sg, 0, 1, 0)
assert spacegroups.can_reflect(sg, 0, 0, 1)
assert spacegroups.can_reflect(sg, 1, 1, 1)
def generate_hkls(self, offset=None, origin=False, sgroup=True):
if offset is None:
offset = array([0, 0, 0])
else:
offset = array(offset)
o1 = array(self.cell._orient1)
o2 = array(self.cell._orient2)
hkls = []
sg = None
if sgroup:
sg = get_spacegroup(self.tasinfo['spacegroup'])
for i in range(-10, 11):
for j in range(-10, 11):
base = i * o1 + j * o2
if i == j == 0:
if not origin:
continue
elif sg and not can_reflect(sg, *base):
continue
hkls.append(base + offset)
return self.check_hkls(hkls)
def powderfit(powder,
scans=None,
peaks=None,
ki=None,
dmono=3.355,
spacegroup=1):
"""Fit powder peaks of a powder sample to calibrate instrument wavelength.
First argument is either a string that names a known material (currently
only ``'YIG'`` is available) or a cubic lattice parameter. Then you need
to give either scan numbers (*scans*) or peak positions (*peaks*) and a
neutron wavevector (*ki*). Examples:
>>> powderfit('YIG', scans=[1382, 1383, 1384, ...])
>>> powderfit(12.377932, peaks=[45.396, 61.344, 66.096, ...], ki=1.4)
As a further argument, *dmono* is the lattice constant of the monochromator
(only used to calculate monochromator 2-theta offsets), it defaults to PG
(3.355 A).
"""
maxhkl = 10 # max H/K/L to consider when looking for d-values
maxdd = 0.2 # max distance in d-value when looking for peak indices
ksteps = 50 # steps with different ki
dki = 0.002 # relative ki stepsize
if powder == 'YIG':
a = 12.377932
spacegroup = 230
session.log.info('YIG: using cubic lattice constant of %.6f A', a)
session.log.info('')
else:
if not isinstance(powder, float):
raise UsageError('first argument must be either "YIG" or a '
'lattice constant')
a = powder
sg = get_spacegroup(spacegroup)
# calculate (possible) d-values
# loop through some hkl-sets, also consider higher harmonics...
dhkls = {}
for h in range(maxhkl):
for k in range(maxhkl):
for l in range(maxhkl):
if h + k + l > 0: # assume all reflections are possible
if not can_reflect(sg, h, k, l):
continue
G = sqrt(h * h + k * k + l * l)
dhkls[a / G] = '(%d %d %d)' % (h, k, l)
dhkls[a / (2 * G)] = '(%d %d %d)/2' % (h, k, l)
dhkls[a / (3 * G)] = '(%d %d %d)/3' % (h, k, l)
dhkls[a / (4 * G)] = '(%d %d %d)/4' % (h, k, l)
dhkls[a / (5 * G)] = '(%d %d %d)/5' % (h, k, l)
# generate list from dict
dvals = sorted(dhkls)
# fit and helper functions
def dk2tt(d, k):
return 2.0 * degrees(arcsin(pi / (d * k)))
def model(x, k, stt0):
return stt0 + dk2tt(x, k)
data = {}
if not peaks:
if not scans:
raise UsageError('please give either scans or peaks argument')
for dataset in session.experiment.data.getLastScans():
num = dataset.counter
if num not in scans:
continue
res = _extract_powder_data(num, dataset)
session.log.debug('powder_data from %d: %s', num, res)
if res:
ki, peaks = res
data.setdefault(ki, []).extend(
[None, p, dp, '#%d ' % num] for (p, dp) in peaks)
if not data:
session.log.warning('no data found, check the scan numbers!')
return
else:
if scans:
raise UsageError('please give either scans or peaks argument')
if not ki:
raise UsageError('please give ki argument together with peaks')
data[float(ki)] = [[None, p, 0.1, ''] for p in peaks]
beststt0s = []
bestmtt0s = []
bestrms = 1.0
bestlines = []
orig_data = data
for j in [0] + [i * s for i in range(1, ksteps) for s in (-1, 1)]:
out = []
p = out.append
data = deepcopy(orig_data)
# now iterate through data (for all ki and for all peaks) and try to
# assign a d-value assuming the ki not to be completely off!
for ki1 in sorted(data):
new_ki = ki1 + j * dki * ki1
# iterate over ki specific list, start at last element
for el in reversed(data[ki1]):
tdval = pi / new_ki / sin(abs(radians(
el[1] / 2.))) # dvalue from scan
distances = [(abs(d - tdval), i)
for (i, d) in enumerate(dvals)]
mindist = min(distances)
if mindist[0] > maxdd:
p('%speak at %7.3f -> no hkl found' % (el[3], el[1]))
data[ki1].remove(el)
else:
el[0] = dvals[mindist[1]]
if el[1] < 0:
el[0] *= -1
p('%speak at %7.3f could be %s at d = %-7.4f' %
(el[3], el[1], dhkls[abs(el[0])], el[0]))
p('')
restxt = []
restxt.append('___final_results___')
restxt.append('ki_exp #peaks | ki_fit dki_fit mtt_0 lambda | '
'stt_0 dstt_0 | chisqr')
stt0s = []
mtt0s = []
rms = 0
for ki1 in sorted(data):
new_ki = ki1 + j * dki * ki1
peaks = data[ki1]
failed = True
if len(peaks) > 2:
fit = Fit('ki', model, ['ki', 'stt0'], [new_ki, 0])
res = fit.run([el[0] for el in peaks], [el[1] for el in peaks],
[el[2] for el in peaks])
failed = res._failed
if failed:
restxt.append('%4.3f %-6d | No fit!' % (ki1, len(peaks)))
rms += 1e6
continue
mtt0 = dk2tt(dmono, res.ki) - dk2tt(dmono, ki1)
restxt.append('%5.3f %-6d | %-7.4f %-7.4f %-7.4f %-7.4f | '
'%-7.4f %-7.4f | %.2f' %
(ki1, len(peaks), res.ki, res.dki, mtt0,
2 * pi / res.ki, res.stt0, res.dstt0, res.chi2))
stt0s.append(res.stt0)
mtt0s.append(mtt0)
peaks_fit = [model(el[0], res.ki, res.stt0) for el in peaks]
p('___fitted_peaks_for_ki=%.3f___' % ki1)
p('peak dval measured fitpos delta')
for i, el in enumerate(peaks):
p('%-10s %7.3f %7.3f %7.3f %7.3f%s' %
(dhkls[abs(el[0])], el[0], el[1], peaks_fit[i], peaks_fit[i]
- el[1], '' if abs(peaks_fit[i] - el[1]) < 0.10 else " **"))
p('')
rms += sum(
(pobs - pfit)**2
for (pobs,
pfit) in zip([el[1]
for el in peaks], peaks_fit)) / len(peaks)
out.extend(restxt)
session.log.debug('')
session.log.debug('-' * 80)
session.log.debug('result from run with j=%d (RMS = %g):', j, rms)
for line in out:
session.log.debug(line)
if rms < bestrms:
beststt0s = stt0s
bestmtt0s = mtt0s
bestrms = rms
bestlines = out
session.log.debug('')
session.log.debug('*** new best result: RMS = %g', rms)
if not beststt0s:
session.log.warning('No successful fit results!')
if ki is not None:
session.log.warning('Is the initial guess for ki too far off?')
return
for line in bestlines:
session.log.info(line)
meanstt0 = sum(beststt0s) / len(beststt0s)
meanmtt0 = sum(bestmtt0s) / len(bestmtt0s)
session.log.info('Check errors (dki, dstt0)! RMS = %.3g', bestrms)
session.log.info('')
session.log.info('Adjust using:')
# TODO: fix suggestions using adjust()
session.log.info('mtt.offset += %.4f', meanmtt0)
session.log.info('mth.offset += %.4f', meanmtt0 / 2)
session.log.info('stt.offset += %.4f', meanstt0)
return CommandLineFitResult((meanmtt0, meanstt0))
def doWriteSpacegroup(self, val):
try:
get_spacegroup(val)
except NicosError as err:
self.log.warning('%s; new value will be ignored', err)
def __init__(self, spgr):
self.spgr = get_spacegroup(spgr)