def testspline(fltfile, parfile, splinefile):
# print "Yop"
sys.stdout.flush()
os.system("rm test.flt")
os.system("fix_spline.py %s test.flt %s" % (fltfile, splinefile))
tr = transformer()
tr.loadfiltered("test.flt")
tr.loadfileparameters(parfile)
tr.fit()
def intorduce_gvectors(flt_file, par_file):
obj = transformer.transformer()
obj.loadfiltered(flt_file)
obj.loadfileparameters(par_file)
obj.compute_tth_eta()
obj.addcellpeaks()
obj.computegv()
obj.write_colfile(str(flt_file.split(".")[0]) + "_with_gvec.flt")
def __init__(self):
self.objects = {
"peakmerger": peakmerge.peakmerger(),
"transformer": transformer.transformer(),
"indexer": indexing.indexer(),
"solver": eps_sig_solver.solver(),
}
self.commandscript = \
"""# Create objects to manipulate - they hold your data
def gridgrains(
ul,
flt,
pars,
minx=-750,
maxx=750,
stepx=25,
miny=-750,
maxy=750,
stepy=25,
tol=0.05,
):
trn = transformer.transformer()
trn.loadfiltered(flt)
trn.parameterobj = parameters.parameters(**pars)
peaks = [trn.getcolumn(trn.xname), trn.getcolumn(trn.yname)]
peaks_xyz = transform.compute_xyz_lab(peaks,
**trn.parameterobj.get_parameters())
omega = trn.getcolumn(trn.omeganame)
trn.updateparameters()
tx = minx - stepx
n = 0
sys.stderr.write("Using tol = %f\n" % (tol))
while tx <= maxx:
tx = tx + stepx
ty = miny - stepy
while ty <= maxy:
ty = ty + stepy
trn.parameterobj.set_parameters({'t_x': tx, 't_y': ty})
pars = trn.parameterobj.get_parameters()
tth, eta = transform.compute_tth_eta_from_xyz(
peaks_xyz, omega, **pars)
if 'omegasign' in pars:
om_sgn = float(pars["omegasign"])
else:
om_sgn = 1.0
gv = transform.compute_g_vectors(tth, eta, omega * om_sgn, **pars)
print(tx, ty, end=' ')
for ubi in ul:
ns = score(ubi, gv.T, tol)
print(ns, end=' ')
n += ns
print()
return n
def test_many_points(args):
"""
Grid index - loop over points
Places the results in a multiprocessing Queue
"""
colfile, parameters, translations, gridpars = args
s = "Hello from %s %d" % (multiprocessing.current_process().name,
os.getpid())
s += " %d to do" % (len(translations))
s += "%s %s" % (colfile, parameters)
print(s)
mytransformer = transformer.transformer()
mytransformer.loadfiltered(colfile)
mytransformer.loadfileparameters(parameters)
w = mytransformer.parameterobj.get("wavelength")
first = True
ni = len(translations) / 100.0
for i, (t_x, t_y, t_z) in enumerate(translations):
mytransformer.updateparameters()
mytransformer.parameterobj.set_parameters({
't_x': t_x,
't_y': t_y,
't_z': t_z
})
mytransformer.compute_tth_eta()
mytransformer.computegv()
# mytransformer.savegv( tmp+".gve" )
gve = np.vstack((mytransformer.colfile.gx, mytransformer.colfile.gy,
mytransformer.colfile.gz))
if first:
first = False
grains = doindex(gve, t_x, t_y, t_z, w, gridpars)
ng = len(grains)
if ng > 0:
grains = domap(mytransformer.parameterobj, mytransformer.colfile,
grains, gridpars)
nk = len(grains)
if len(grains) > 0:
test_many_points.q.put(grains, False) # do not wait
else:
nk = 0
sys.stderr.write(" % 6.2f%% Position %d %d %d" %
(i / ni, t_x, t_y, t_z) +
" grains found %d kept %d\n" % (ng, nk))
def initgrid(fltfile, parfile, tmp, gridpars):
"""
Sets up a grid indexing by preparing the unitcell for indexing
and checking the columns we want are in the colfile
"""
mytransformer = transformer.transformer()
mytransformer.loadfiltered(fltfile)
mytransformer.loadfileparameters(parfile)
gridpars['UC'] = unitcell.unitcell_from_parameters(
mytransformer.parameterobj)
col = mytransformer.colfile
if not "drlv2" in col.titles:
col.addcolumn(np.ones(col.nrows, float), "drlv2")
if not "labels" in col.titles:
col.addcolumn(np.ones(col.nrows, float) - 2, "labels")
if not "sc" in col.titles:
assert "xc" in col.titles
col.addcolumn(col.xc.copy(), "sc")
if not "fc" in col.titles:
assert "yc" in col.titles
col.addcolumn(col.yc.copy(), "fc")
mytransformer.colfile.writefile("%s.flt" % (tmp))
return gridpars
for entry in uniq_ubis:
print "\n\n"
for i in range(3):
for j in range(3):
print "# ubi[%d,%d] = %f" % (i, j, ubi[i, j])
ubi = entry[0]
for (name, i) in entry[1]:
j, zh = get_z(name)
print j, "%7.5f" % (zh), name, i,
# we have the ubi matrix here.
# we want to refine and score this ubi against some data
try:
t = transformer.transformer()
t.loadfileparameters(parfilename)
t.loadfiltered(name.replace(".ubi", ""))
t.computegv()
except:
print name
raise
# bit of a hack - between 10 and 11 degrees
h = np.matrixmultiply(ubi, t.gv)
hint = np.floor(h + 0.5).astype(int) # rounds down
diff = h - hint
drlv2 = np.sum(diff * diff, 0)
ind = np.compress(drlv2 < tol * tol, np.arange(t.twotheta.shape[0]))
avg = 4
npix = 3
print()
print("Into output file %s"%(outf))
#if raw_input("OK? [y/n]") not in ["Y","y"]:
# sys.exit()
allpks = open(outf,"w")
allpeaks = {}
always_ignore = {}
goodthres = []
for v in thres:
mytransformer = transformer.transformer()
mytransformer.loadfileparameters( pars )
flt = "%s_t%d.flt"%(stem,v)
print(flt, end=' ')
try:
tc = columnfile( flt )
if tc.nrows == 0:
print("Skipped",tc," no peaks")
continue
except:
print("Skipped",v," Exception reading",flt)
continue
goodthres.append( v )
mytransformer.loadfiltered( flt )
mytransformer.compute_tth_eta( )
def fitspline(fltfile, parfile, splinefile):
tr = transformer()
tr.loadfiltered(fltfile)
tr.loadfileparameters(parfile)
print("fitting to assign peaks")
tr.fit()
tr.loadfileparameters(parfile)
tthobs, eta = tr.compute_tth_eta()
tthcalc = tthobs * 0.0 - 1
for tthc, inds in zip(tr.tthc, tr.indices): # assignments
tthcalc[inds] = tthc
# project the error in the two directions
sineta = np.sin(np.radians(eta))
coseta = np.cos(np.radians(eta))
# convert differences to pixels
r = np.sqrt(tr.colfile.yl**2 + tr.colfile.zl**2)
ps = tr.parameterobj.get("y_size")
pix_obs = r / ps
pix_calc = r * tthcalc / tthobs / ps
diffs = pix_calc - pix_obs
print(diffs)
mask = tthcalc > 0
xvals = np.compress(mask, tr.colfile.s_raw)
yvals = np.compress(mask, tr.colfile.f_raw)
dsvals = np.compress(mask, sineta * diffs)
dfvals = np.compress(mask, coseta * diffs)
w = np.ones(len(yvals))
ss = 0.25
for i in range(2):
m = len(yvals)
s = (m - np.sqrt(2 * m)) * ss
print("s=", s)
rets = bisplrep(yvals,
xvals,
dsvals,
w=w,
kx=3,
ky=3,
xb=0,
xe=4096,
yb=0,
ye=4096,
full_output=0,
s=s,
task=0)
retf = bisplrep(yvals,
xvals,
dfvals,
w=w,
kx=3,
ky=3,
xb=0,
xe=4096,
yb=0,
ye=4096,
full_output=0,
s=s,
task=0)
print(rets, retf)
dscalc = [bisplev(y, x, rets) for y, x in zip(yvals, xvals)]
dfcalc = [bisplev(y, x, retf) for y, x in zip(yvals, xvals)]
es = dscalc - dsvals
ef = dfcalc - dfvals
pylab.ion()
pylab.figure(1)
pylab.clf()
pylab.subplot(121)
pylab.xlim(0, 4096)
pylab.ylim(0, 4096)
pylab.scatter(xvals, yvals, c=es, edgecolors='none')
pylab.colorbar()
pylab.subplot(122)
pylab.scatter(xvals, yvals, c=ef, edgecolors='none')
pylab.xlim(0, 4096)
pylab.ylim(0, 4096)
pylab.colorbar()
pylab.figure(2)
pylab.clf()
pylab.hist(es, bins=128)
pylab.hist(ef, bins=128)
pylab.show()
print("stddev=", ((np.std(es) + np.std(ef)) / 2))
# co = ((np.std(es) + np.std(ef))/2)*3
co = float(input("cutoff"))
print("Using cutoff", co)
m = (np.abs(es) < co) & (np.abs(ef) < co)
yvals = np.compress(m, yvals)
xvals = np.compress(m, xvals)
dsvals = np.compress(m, dsvals)
dfvals = np.compress(m, dfvals)
w = np.ones(len(yvals)) / ((np.std(es) + np.std(ef)) / 2)
print("w avg = ", w.mean())
m = len(yvals)
s = (m - np.sqrt(2 * m)) * ss
print("s=", s)
rets = bisplrep(yvals,
xvals,
dsvals,
w=w,
kx=3,
ky=3,
xb=0,
xe=4096,
yb=0,
ye=4096,
full_output=0,
s=s,
task=0)
retf = bisplrep(yvals,
xvals,
dfvals,
w=w,
kx=3,
ky=3,
xb=0,
xe=4096,
yb=0,
ye=4096,
full_output=0,
s=s,
task=0)
print(rets, retf)
write_spline(rets, retf, splinefile)
pylab.show()
input("End?")
