def loop(self):
id, grain_params = self._jobs.get(False)
# skips the first loop if have_estimate is True
have_estimate = not np.all(grain_params[-9] == [0,0,0,1,1,1,0,0,0])
iterations = (have_estimate, len(self._p['eta_tol']))
for iteration in range(*iterations):
self.pull_spots(id, grain_params, iteration)
grain_params, compl = self.fit_grains(id, grain_params)
if compl == 0:
break
eMat = logm(np.linalg.inv(vecMVToSymm(grain_params[6:])))
dFunc, dParams = self._p['distortion']
resd = objFuncFitGrain(
grain_params[gFlag], grain_params, gFlag,
self._p['detector_params'],
self._p['xyo_det'], self._p['hkls'],
self._p['bMat'], self._p['wlen'],
bVec_ref, eta_ref,
dFunc, dParams,
self._p['omega_period'],
simOnly=False
)
self._results.append((id, grain_params, compl, eMat, sum(resd**2)))
self._jobs.task_done()
def dump_grain(self, grain_id, completeness, chisq, grain_params):
assert len(grain_params) == 12, \
"len(grain_params) must be 12, not %d" % len(grain_params)
# extract strain
emat = logm(np.linalg.inv(mutil.vecMVToSymm(grain_params[6:])))
evec = mutil.symmToVecMV(emat, scale=False)
res = [int(grain_id), completeness, chisq] \
+ grain_params.tolist() \
+ evec.tolist()
output_str = self._delim.join([
self._delim.join(['{:<12d}', '{:<12f}',
'{:<12e}']).format(*res[:3]),
self._delim.join(np.tile('{:<23.16e}',
len(res) - 3)).format(*res[3:])
])
print(output_str, file=self.fid)
return output_str
def dump_grain(self, grain_id, completeness, chisq,
grain_params):
assert len(grain_params) == 12, \
"len(grain_params) must be 12, not %d" % len(grain_params)
# extract strain
emat = logm(np.linalg.inv(mutil.vecMVToSymm(grain_params[6:])))
evec = mutil.symmToVecMV(emat, scale=False)
res = [int(grain_id), completeness, chisq] \
+ grain_params.tolist() \
+ evec.tolist()
output_str = self._delim.join(
[self._delim.join(
['{:<12d}', '{:<12f}', '{:<12e}']
).format(*res[:3]),
self._delim.join(
np.tile('{:<23.16e}', len(res) - 3)
).format(*res[3:])]
)
print(output_str, file=self.fid)
return output_str
def get_e_mat(self, grain_params):
"""
strain tensor calculation
"""
return logm(np.linalg.inv(vecMVToSymm(grain_params[6:])))
def get_e_mat(self, grain_params):
"""
strain tensor calculation
"""
return logm(np.linalg.inv(vecMVToSymm(grain_params[6:])))
def get_e_mat(self, grain_params):
# TODO: document what is this?
return logm(np.linalg.inv(vecMVToSymm(grain_params[6:])))
def get_e_mat(self, grain_params):
# TODO: document what is this?
return logm(np.linalg.inv(vecMVToSymm(grain_params[6:])))
fid = open(filename %grainID, 'w')
sd = xrdutil.pullSpots(pd, detector_params, g_refined, reader,
distortion=(dFunc, dParams),
eta_range=etaRange, ome_period=ome_period,
tth_tol=tth_tol_r, eta_tol=eta_tol_r, ome_tol=ome_tol_r,
panel_buff=[10, 10],
npdiv=2, threshold=threshold,
use_closest=True, doClipping=False,
filename=fid)
fid.close()
pass
else:
g_refined = grain_params
break
pass
eMat = logm(np.linalg.inv(mutil.vecMVToSymm(g_refined[6:])))
resd_f2 = fitting.objFuncFitGrain(g_refined[gFlag], g_refined, gFlag,
detector_params,
xyo_det, hkls, bMat, wlen,
bVec_ref, eta_ref,
dFunc, dParams,
ome_period,
simOnly=False)
print >> grains_file, \
"%d\t%1.7e\t%1.7e\t" % (grainID, sum(idx)/float(len(idx)), sum(resd_f2**2)) + \
"%1.7e\t%1.7e\t%1.7e\t" % tuple(g_refined[:3]) + \
"%1.7e\t%1.7e\t%1.7e\t" % tuple(g_refined[3:6]) + \
"%1.7e\t%1.7e\t%1.7e\t%1.7e\t%1.7e\t%1.7e\t" % tuple(g_refined[6:]) + \
"%1.7e\t%1.7e\t%1.7e\t%1.7e\t%1.7e\t%1.7e" % (eMat[0, 0], eMat[1, 1], eMat[2, 2], eMat[1, 2], eMat[0, 2], eMat[0, 1])
elapsed = (time.clock() - start)
