def OnSaveHistogram(self, evt):
toolkit = self.toolkit
# get histogram
key = self.focus
histogram = self.histograms.get(key)
# no histogram, alert
if histogram is None:
toolkit.messageDialog(None, "Error", "No histogram is on focus")
return
# save
filetypes = 'h5'
filename = toolkit.savefileDialog(
None, "Save histogram to file", filetypes)
if filename:
import os
if os.path.exists(filename):
toolkit.messageDialog(
None, "Error", "Overwrite is not supported yet.")
return
import histogram.hdf as hh
hh.dump(histogram, filename, '/', 'c')
return
def computeFocusedSpectraForRealMonitors(E, m2sout, out):
from mcni.utils.conversion import e2v
v = e2v(E)
from pyre.units.time import second
import histogram.hdf as hh, histogram as H
m1 = hh.load(os.path.join(m2sout, "mon1-tof.h5"), "I(tof)")
L1 = 11.831
t1 = L1 / v # * second
m1p = m1[(t1 * 0.9, t1 * 1.1)]
m1pc = H.histogram("I(tof)", m1p.axes(), data=m1p.I, errors=m1p.E2)
m1pc.setAttribute("title", "Monitor 1 I(tof)")
hh.dump(m1pc, os.path.join(out, "mon1-itof-focused.h5"), "/", "c")
m2 = hh.load(os.path.join(m2sout, "mon2-tof.h5"), "I(tof)")
L2 = 18.5
t2 = L2 / v # * second
m2p = m2[(t2 * 0.9, t2 * 1.1)]
m2pc = H.histogram("I(tof)", m2p.axes(), data=m2p.I, errors=m2p.E2)
m2pc.setAttribute("title", "Monitor 2 I(tof)")
hh.dump(m2pc, os.path.join(out, "mon2-itof-focused.h5"), "/", "c")
return
def testdump2(self):
'dump two histograms to one hdf'
filename = 'testdump1.h5'
import os
if os.path.exists( filename):
os.remove( filename )
from h5py import File
fs = File( filename, 'w' )
from histogram import histogram, arange
h = histogram('h',
[('x', arange(0,100, 1.) ),
('y', arange(100, 180, 1.) ),],
unit = 'meter',
)
dump( h, None, '/', fs = fs )
h2 = histogram('h2',
[('x', arange(0,100, 1.) ),
('y', arange(100, 180, 1.) ),],
unit = 'meter',
)
dump( h2, None, '/', fs = fs )
#load histogram
h2c = load( filename, '/h2', fs = fs )
print h2c
self.assert_( os.path.exists( filename ))
def process(path):
basename = os.path.basename(path)
from nslice.Run import Run
run = Run(path)
print "instrument=%s, Ei=%s, psi=%s" % (
run.instrument, run.Ei, run.psi)
from nslice.XtalOrientation import XtalOrientation
a = b = 8.87; c = 5.2
from math import pi
twopi = 2*pi
ra,rb,rc = [twopi/a, 0,0], [0,twopi/b,0], [0,0,twopi/c]
u,v = [1,0,0], [0,1,0]
xtal_ori = XtalOrientation(ra,rb,rc, u,v, run.psi)
h,k,l,E = run.compute_hklE(xtal_ori)
I, error = run.read_data()
h.shape = k.shape = l.shape = E.shape = I.shape = error.shape = -1
hklEIE = np.vstack((h,k,l,E,I,error))
from nslice.slice import slice_hE
H, edges = slice_hE(
hklEIE,
k=[0.95,1.05], l=[-1,1],
h=(-1, 6, 0.02), E=(-5, 10, 0.1),
)
import histogram, histogram.hdf as hh
axes = [
histogram.axis('h', boundaries=edges[0]),
histogram.axis('E', unit='meV', boundaries=edges[1]),
]
h = histogram.histogram('I(h,E)', axes=axes, data=H)
hh.dump(h, 'I_hE.h5')
return
def run( eventdatafilename, nevents, h5filename, pixparams ):
print "eventdatafilename = %s" % eventdatafilename
print "nevents = %s" % nevents
print "output h5filename = %s" % h5filename
print 'pixelID params = %s' % (pixparams, )
if os.path.exists(h5filename):
raise IOError, "%s already exists" % h5filename
pixbegin, pixend, pixstep = pixparams
ipixdat = 'Ipix.%s-%s-%s.dat' % (
eventdatafilename, nevents, pixparams)
cmd = 'ipix "%s" %s %s %s %s "%s" ' % (
eventdatafilename, nevents,
pixbegin, pixend, pixstep,
ipixdat )
if os.system( cmd ): raise "%s failed" % cmd
if not os.path.exists( ipixdat ): raise "%s was not created" % ipixdat
from arcseventdata.histogramFrom2colascii import convert
h = convert( ipixdat, name = "I(pix)", xname = 'pixelID', xunit = '1' )
from histogram.hdf import dump
dump(h, h5filename, '/', 'c' )
return
def run(
ncount=1e7,
nodes=5,
Ei=700.,
E_Q="Q*Q/3",
S_Q="1",
sigma_Q='Q/2.',
Qmin=0,
Qmax=10.,
Qstep=0.1,
Emin=0,
Emax=50.,
Estep=1.,
mod2sample='../mod2sample',
):
Ei_user = Ei
Ei = computeAverageEnergy()
if abs(Ei - Ei_user) / Ei > 0.1:
raise ValueError, "nominal energy %s is too different from average energy at sample position %s" % (
Ei_user, Ei)
import os
incident_neutrons = 'incident-neutrons'
if not os.path.exists(incident_neutrons):
os.link('../mod2sample/out/neutrons', 'incident-neutrons')
# create scattering kernel file
createScatteringKernel(
E_Q=E_Q,
S_Q=S_Q,
sigma_Q=sigma_Q,
Qmin=Qmin,
Qmax=Qmax,
)
# run main sim
cmd = './sssd --ncount=%s --mpirun.nodes=%s' % (ncount, nodes)
execute(cmd)
# reduce events to S(Q,E)
eventsdat = 'out/events.dat'
Qaxis = Qmin, Qmax, Qstep
Eaxis = Emin, Emax, Estep
Ei, toffset = getEiToffset(mod2sample)
iqe = reduceToIQE(eventsdat, Ei, toffset, Qaxis, Eaxis)
from histogram.hdf import dump
dump(iqe, 'iqe.h5', '/', 'c')
global interactive
if interactive:
from histogram.plotter import defaultPlotter
defaultPlotter.plot(iqe)
# quick analysis of S(Q,E) (without detector and sample size effects)
cmd = ['./analyze-sqe ']
cmd.append('--mpirun.nodes=%s' % nodes)
cmd.append('--ncount=%s --monitor.Ei=%s' % (ncount, Ei))
cmd.append(' --monitor.Qmin=%s --monitor.Qmax=%s' % (Qmin, Qmax))
cmd.append(' --monitor.Emin=%s --monitor.Emax=%s' % (Emin, Emax))
cmd = ' '.join(cmd)
# execute(cmd)
return
def testdump2(self):
'dump two histograms to one hdf'
filename = 'testdump1.h5'
import os
if os.path.exists(filename):
os.remove(filename)
from h5py import File
fs = File(filename, 'w')
from histogram import histogram, arange
h = histogram(
'h',
[
('x', arange(0, 100, 1.)),
('y', arange(100, 180, 1.)),
],
unit='meter',
)
dump(h, None, '/', fs=fs)
h2 = histogram(
'h2',
[
('x', arange(0, 100, 1.)),
('y', arange(100, 180, 1.)),
],
unit='meter',
)
dump(h2, None, '/', fs=fs)
#load histogram
h2c = load(filename, '/h2', fs=fs)
print(h2c)
self.assertTrue(os.path.exists(filename))
def _saveResult(self, res, directory):
"""save result to the given directory"""
from histogram.hdf import dump
import os
p = os.path.join(directory, self._getHistogramFilename())
dump(res, p, '/', 'c')
return
def test2(self):
datadir = self.datadir
from mcvine.phonon.powderSQE.IDF import from_data_dir
import mcvine.phonon.powderSQE.IDF as psidf
from mccomponents.sample import phonon as mcphonon
doshist = mcphonon.read_dos.dos_fromidf(os.path.join(datadir, 'DOS')).doshist
disp = psidf.disp_from_datadir(datadir)
IQEhist, mphhist = psidf.from_data_dir(
datadir=datadir,
disp=disp,
N = int(1e6),
Q_bins=np.arange(0, 14, 0.1), E_bins=np.arange(0,90,.5),
doshist=doshist,
T=300., Ei=120., max_det_angle=140.,
include_multiphonon=True,
)
IQEhist = IQEhist + mphhist
hh.dump(IQEhist, 'Si-iqe-test2.h5')
expected = hh.load(os.path.join(here, 'saved_results/Si-all-phonon-Ei_120-T_300-N_1e6.h5'))
max = np.nanmax(expected.I)
reldiff = IQEhist-expected
reldiff.I/=max; reldiff.E2/=max*max
Nbigdiff = (np.abs(reldiff.I)>0.03).sum()
Ngood = (IQEhist.I==IQEhist.I).sum()
Ntotal = IQEhist.size()
self.assertTrue(Ngood*1./Ntotal>.65)
self.assertTrue(Nbigdiff*1./Ngood<.10)
return
def _saveResult(self, res, directory):
"""save result to the given directory"""
from histogram.hdf import dump
import os
p = os.path.join(directory, self._getHistogramFilename())
dump(res, p, '/', 'c')
return
def normalize(self, IQE):
'normalize IQE'
# only the master node need to do normalization
if self.mpiRank != 0: return
#for debug
from histogram.hdf import dump
filename = 'IQE-nosolidanglenormalization.h5'
import os
if os.path.exists( filename ): os.remove( filename )
dump( IQE, filename, '/', 'c' )
info.log( 'node %s: convert I(Q,E) datatype from integer to double'
% self.mpiRank)
from histogram import histogram
newIQE = histogram( IQE.name(), IQE.axes() )
newIQE[(), ()] = IQE[(), ()]
Ei = self.Ei
pixelPositions = self.pixelPositions
pixelSolidAngles = self.pixelSolidAngles
from arcseventdata.normalize_iqe import normalize_iqe
info.log( 'node %s: normalize I(Q,E) by solid angle: Ei=%s, positions.shape=%s, solidangles.shape=%s'
% (self.mpiRank, Ei, pixelPositions.shape, pixelSolidAngles.shape))
import time
t0 = time.time()
normalize_iqe( newIQE, Ei, pixelPositions, pixelSolidAngles )
t1 = time.time()
info.log( 'node %s: normalization done: %s seconds' % (
self.mpiRank, t1-t0))
return newIQE
def slice2hist(ifile, ofile):
import histogram as H, histogram.hdf as hh
from mantid import simpleapi as msa
def eliminateUnitDimension(shape):
for d in shape:
if d>1: yield d
return
ws = msa.Load(ifile)
I = ws.getSignalArray()
I.shape = tuple(eliminateUnitDimension(I.shape))
E2 = ws.getErrorSquaredArray()
E2.shape = I.shape
axes = []
for i in range(ws.getNumDims()):
dim = ws.getDimension(i)
if dim.getNBins() > 1:
axis = H.axis(
dim.getName(),
unit="1",
centers = [dim.getX(ind) for ind in range(dim.getNBins())]
)
axes.append(axis)
continue
h = H.histogram("slice", axes, data=I, errors=E2)
hh.dump(h, ofile)
return
def saveSQE(Q, E, S, name):
h = H.histogram(
name,
[('Q', Q, 'angstrom**-1'),
('E', E, 'meV')],
S)
hh.dump(h, '%s.h5' % (name,))
return
def run(scattering_rundir, nodes, ncount=None):
# sendneutronstodetsys(scattering_rundir, nodes, ncount=ncount)
eventsdat = 'out/events.dat'
iqe = reduceToIQE(eventsdat)
from histogram.hdf import dump
dump(iqe, 'iqe.h5', '/', 'c')
from histogram.plotter import defaultPlotter
defaultPlotter.plot(iqe)
return
def onGridSQE(self, gridsqe):
sqehist = gridsqe.sqehist
from histogram.hdf import dump
filename = 'sqehist.h5'
h5path = 'S(Q,E)'
dump(sqehist, filename, '/', 'c')
self._write('<GridSQE histogram-hdf-path="%s"/>' %
'/'.join([filename, h5path]))
return
def test(self):
"wrap IQE_monitor"
from mcstas2 import componentfactory
category = 'monitors'
componentname = 'IQE_monitor'
factory = componentfactory(category, componentname)
Qmin = 0
Qmax = 13.
nQ = 130
Emin = -50
Emax = 50.
nE = 100
component = factory(
'component',
Ei=Ei,
Qmin=Qmin,
Qmax=Qmax,
nQ=nQ,
Emin=Emin,
Emax=Emax,
nE=nE,
max_angle_out_of_plane=30,
min_angle_out_of_plane=-30,
max_angle_in_plane=120,
min_angle_in_plane=-30,
)
scatterer = makeScatterer()
import mcni
N = 10000
neutrons = mcni.neutron_buffer(N)
for i in range(N):
neutron = mcni.neutron(r=(0, 0, 0), v=(0, 0, vi), time=0, prob=1)
scatterer.scatter(neutron)
neutrons[i] = neutron
#print neutrons[i]
continue
component.process(neutrons)
hist = get_histogram(component)
import os
f = os.path.basename(__file__)
filename = 'IQE-%s.h5' % f
if os.path.exists(filename): os.remove(filename)
import histogram.hdf as hh
hh.dump(hist, filename, '/', 'c')
if self.interactive:
from histogram.plotter import defaultPlotter
defaultPlotter.plot(hist)
return
def saveHistogram(histogram, filename, overwrite=False):
if os.path.exists(filename):
if overwrite:
os.remove( filename )
else:
raise IOError, "%s already exists" % filename
#
from histogram.hdf import dump
dump( histogram, filename, '/', 'c')
return
def test1(self):
Itof = H.histogram("itof",
[('tof', np.arange(2000, 3000.), 'microsecond')])
Itof.I[:] = 0
Itof.I[500] = 10
dir = 'tmp.bpptest'
if not os.path.exists(dir): os.makedirs(dir)
hh.dump(Itof, os.path.join(dir, 'itof.h5'))
print((bpp.computeFWHM(dir)))
return
def onGridSQE(self, gridsqe):
sqehist = gridsqe.sqehist
from histogram.hdf import dump
filename = 'sqehist.h5'
h5path = 'S(Q,E)'
dump(sqehist, filename, '/', 'c')
self._write(
'<GridSQE histogram-hdf-path="%s"/>' % '/'.join( [filename, h5path] )
)
return
def test(self):
from idf2histogram.Omega2 import read
material = 'fccNi-phonondisp-from-bvk-N20'
h = read(material)
out = '%s-omega2.h5' % material
import os
if os.path.exists(out): os.remove(out)
import histogram.hdf as hh
hh.dump(h, out, '/', 'c')
return
def test_load_slice_and_dump(self):
'load a histogram and dump a slice of it'
tmpfile = 'test_load_slice_and_dump.h5'
if os.path.exists(tmpfile):
os.remove(tmpfile)
import histogram.hdf as hh
h = hh.load('testload.h5', '/h')
s = h[(10, 30), ()]
hh.dump(s, tmpfile, '/', 'c')
return
def test_load_slice_and_dump(self):
'load a histogram and dump a slice of it'
tmpfile = 'test_load_slice_and_dump.h5'
if os.path.exists( tmpfile ):
os.remove( tmpfile )
import histogram.hdf as hh
h = hh.load('testload.h5', '/h')
s = h[(10, 30), ()]
hh.dump(s, tmpfile, '/', 'c')
return
def _dumpData(self, dir):
h = self.engine.histogram
title = self.inventory.title
h.setAttribute('title', title)
from histogram.hdf import dump
f = self._getHistogramFilename()
import os
f = os.path.join(dir, f)
dump(h, f, '/', 'c')
return
def _dumpData(self, dir):
h = self.engine.histogram
title = self.inventory.title
h.setAttribute('title', title)
from histogram.hdf import dump
f = self._getHistogramFilename()
import os
f = os.path.join(dir, f)
dump(h, f, '/', 'c')
return
def test2(self):
from idf2histogram.Polarizations import read
material = 'fccNi-phonondisp-from-phon-N10'
h = read(material)
out = '%s-pols.h5' % material
import os
if os.path.exists(out): os.remove(out)
import histogram.hdf as hh
hh.dump(h, out, '/', 'c')
return
def test3(self):
datadir = os.path.join(here, '..', '..', 'data', 'graphite')
doshist = hh.load(os.path.join(datadir, 'exp_DOS.h5'))
from mcvine.phonon.powderSQE._calc import multiphononSQE
IQEhist = multiphononSQE(
T=300., # kelvin
doshist=doshist, # DOS histogram
Q_bins=np.arange(0, 23, 0.1),
E_bins=np.arange(0, 250, 1),
)
hh.dump(IQEhist, 'graphite-multiephonon-T_300.h5')
return
def test_slice_and_dump(self):
'slice a histogram and dump the slice'
tmpfile = 'test_slice_and_dump.h5'
if os.path.exists(tmpfile):
os.remove(tmpfile)
from histogram import histogram
x = y = list(range(100))
h = histogram('h', [('x', x)], data=y, errors=y)
s = h[(3, 10)]
import histogram.hdf as hh
hh.dump(s, tmpfile, '/', 'c')
return
def run(
ncount=1e7,
nodes=5,
Ei=700.,
E_Q="Q*Q/3",
S_Q="1",
sigma_Q='Q/2.',
Qmin=0,
Qmax=10.,
Qstep=0.1,
Emin=0,
Emax=50.,
Estep=1.,
):
# create scattering kernel file
createScatteringKernel(
E_Q=E_Q,
S_Q=S_Q,
sigma_Q=sigma_Q,
Qmin=Qmin,
Qmax=Qmax,
)
# run main sim
cmd = './sssd --source.energy=%s --ncount=%s --mpirun.nodes=%s' % (
Ei, ncount, nodes)
execute(cmd)
# reduce events to S(Q,E)
eventsdat = 'out/events.dat'
Qaxis = Qmin, Qmax, Qstep
Eaxis = Emin, Emax, Estep
toffset = 0
iqe = reduceToIQE(eventsdat, Ei, toffset, Qaxis, Eaxis)
from histogram.hdf import dump
dump(iqe, 'iqe.h5', '/', 'c')
global interactive
if interactive:
from histogram.plotter import defaultPlotter
defaultPlotter.plot(iqe)
# quick analysis of S(Q,E) (without detector and sample size effects)
cmd = ['./analyze-sqe ']
cmd.append('--mpirun.nodes=%s' % nodes)
cmd.append('--ncount=%s --monitor.Ei=%s' % (ncount, Ei))
cmd.append(' --monitor.Qmin=%s --monitor.Qmax=%s' % (Qmin, Qmax))
cmd.append(' --monitor.Emin=%s --monitor.Emax=%s' % (Emin, Emax))
cmd = ' '.join(cmd)
# execute(cmd)
return
def main():
from sim_params import instrument, tofparams, eventsdat, Idpt_filename as filename
from mccomponents.detector.reduction_utils import readevents
events = readevents( eventsdat )
Idpt = events2Idpt( events, instrument, tofparams )
#save to file
import os
if os.path.exists(filename): os.remove( filename )
import histogram.hdf as hh
hh.dump( Idpt, filename, '/', 'c' )
return
def testdump0a(self):
""" histogram.hdf: dump with compression"""
from histogram import histogram, arange
h = histogram('h',
[('x', arange(0,100, 1.) ),
('y', arange(100, 180, 1.) ),],
unit = 'meter',
)
filename = 'test0-compressed.h5'
import os
if os.path.exists( filename): os.remove( filename )
dump( h, filename, '/', mode = 'c', compression=6 )
return
def main():
from sim_params import instrument, tofparams, eventsdat, Idpt_filename as filename
from mccomponents.detector.reduction_utils import readevents
events = readevents(eventsdat)
Idpt = events2Idpt(events, instrument, tofparams)
#save to file
import os
if os.path.exists(filename): os.remove(filename)
import histogram.hdf as hh
hh.dump(Idpt, filename, '/', 'c')
return
def getSqeHistogramFromMantidWS(reduced, outfile, qaxis=None, eaxis=None):
from mantid import simpleapi as msa
# if eaxis is not specified, use the data in reduced workspace
if eaxis is None:
Edim = reduced.getXDimension()
emin = Edim.getMinimum()
emax = Edim.getMaximum()
de = Edim.getX(1) - Edim.getX(0)
eaxis = emin, de, emax
qmin, dq, qmax = qaxis
nq = int(round((qmax - qmin) / dq))
emin, de, emax = eaxis
ne = int(round((emax - emin) / de))
md = msa.ConvertToMD(
InputWorkspace=reduced,
QDimensions='|Q|',
dEAnalysisMode='Direct',
MinValues="%s,%s" % (qmin, emin),
MaxValues="%s,%s" % (qmax, emax),
)
binned = msa.BinMD(
InputWorkspace=md,
AxisAligned=1,
AlignedDim0="|Q|,%s,%s,%s" % (qmin, qmax, nq),
AlignedDim1="DeltaE,%s,%s,%s" % (emin, emax, ne),
)
# convert to histogram
import histogram as H, histogram.hdf as hh
data = binned.getSignalArray().copy()
err2 = binned.getErrorSquaredArray().copy()
nev = binned.getNumEventsArray()
data /= nev
err2 /= (nev * nev)
import numpy as np
qaxis = H.axis('Q',
boundaries=np.arange(qmin, qmax + dq / 2., dq),
unit='1./angstrom')
eaxis = H.axis('E',
boundaries=np.arange(emin, emax + de / 2., de),
unit='meV')
hist = H.histogram('IQE', (qaxis, eaxis), data=data, errors=err2)
if outfile.endswith('.nxs'):
import warnings
warnings.warn(
"reduce function no longer writes iqe.nxs nexus file. it only writes iqe.h5 histogram file"
)
outfile = outfile[:-4] + '.h5'
hh.dump(hist, outfile)
return
def test1(self):
"multiphonon.forward.dos2sqe"
from dos import loadDOS
E, g = loadDOS()
Eaxis = H.axis('E', unit='meV', centers=E)
doshist = H.histogram('DOS', [Eaxis], g)
dE = E[1] - E[0]
iqe = hh.load(os.path.join(datadir, 'V-iqe.h5'))
from multiphonon.sqe import interp
newiqe = interp(iqe, newE=np.arange(iqe.energy[0], 80., dE))
hh.dump(newiqe, 'V-iqe-interped.h5')
from multiphonon.forward import dos2sqe
sqe = dos2sqe(doshist, 0.01, newiqe, 300, 50.94, 120.)
return
def run(
ncount=1e7, nodes=5,
Ei=700.,
E_Q="Q*Q/3", S_Q="1",
Qmin=0, Qmax=10., Qstep=0.1,
Emin=0, Emax=50., Estep=1.,
mod2sample='../mod2sample',
):
Ei_user = Ei
Ei = computeAverageEnergy()
if abs(Ei-Ei_user)/Ei > 0.1:
raise ValueError, "nominal energy %s is too different from average energy at sample position %s" % (Ei_user, Ei)
import os
incident_neutrons = 'incident-neutrons'
if not os.path.exists(incident_neutrons):
os.link('../mod2sample/out/neutrons', 'incident-neutrons')
# create scattering kernel file
createScatteringKernel(
E_Q=E_Q, S_Q=S_Q,
Qmin=Qmin, Qmax=Qmax,
)
# run main sim
cmd = './sssd --ncount=%s --mpirun.nodes=%s' % (ncount, nodes)
execute(cmd)
# reduce events to S(Q,E)
eventsdat = 'out/events.dat'
Qaxis = Qmin, Qmax, Qstep
Eaxis = Emin, Emax, Estep
Ei, toffset = getEiToffset(mod2sample)
iqe = reduceToIQE(eventsdat, Ei, toffset, Qaxis, Eaxis)
from histogram.hdf import dump
dump(iqe, 'iqe.h5', '/', 'c')
global interactive
if interactive:
from histogram.plotter import defaultPlotter
defaultPlotter.plot(iqe)
# quick analysis of S(Q,E) (without detector and sample size effects)
cmd = ['./analyze-sqe ']
cmd.append('--mpirun.nodes=%s' % nodes)
cmd.append('--ncount=%s --monitor.Ei=%s' % (ncount, Ei))
cmd.append(' --monitor.Qmin=%s --monitor.Qmax=%s' % (Qmin, Qmax))
cmd.append(' --monitor.Emin=%s --monitor.Emax=%s' % (Emin, Emax))
cmd = ' '.join(cmd)
execute(cmd)
return
def test_slice_and_dump(self):
'slice a histogram and dump the slice'
tmpfile = 'test_slice_and_dump.h5'
if os.path.exists( tmpfile ):
os.remove( tmpfile )
from histogram import histogram
x = y = range(100)
h = histogram(
'h',
[ ('x', x) ],
data = y, errors = y )
s = h[(3,10)]
import histogram.hdf as hh
hh.dump(s, tmpfile, '/', 'c')
return
def test2a(self):
"sqe2dos: V exp"
iqehist = hh.load(os.path.join(datadir, "V-iqe.h5"))
from multiphonon.sqe import interp
newiqe = interp(iqehist, newE=np.arange(-15, 80, 1.))
hh.dump(newiqe, 'V-iqe-interped.h5')
iterdos = sqe2dos.sqe2dos(newiqe,
T=300,
Ecutoff=55.,
elastic_E_cutoff=(-12., 6.7),
M=50.94,
C_ms=.2,
Ei=120.,
workdir='work-V')
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter('always')
for i, dos in enumerate(iterdos):
# print dos
# plot
if interactive:
# print '*' * 70
pylab.plot(dos.E, dos.I, label='%d' % i)
pass
# check warning
for w in ws:
assert 'Scaling factor' not in str(w)
path = os.path.join(here, 'expected_results',
'sqe2dos-test2a-final-dos.h5')
# hh.dump(dos, path)
expected = hh.load(path)
self.assert_(np.allclose(dos.I, expected.I))
self.assert_(np.allclose(dos.E2, expected.E2))
if interactive:
pylab.figure()
pylab.errorbar(dos.E,
dos.I + dos.I.max() / 5.,
dos.E2**.5,
label='new')
pylab.errorbar(expected.E,
expected.I,
expected.E2**.5,
label='expected')
pylab.legend()
pylab.show()
return
def run(Q=20, E_Q=None, dQ=0.15, dE=60, iqe=None):
"""
Q: center of Q
E_Q: E(Q) function
dQ: (Q-dQ, Q+dQ) is the Q range to sum over
dE: (-dE, dE) is the E range the result is on
iqe: I(Q,E) histogram
"""
Qcenter = Q
Qarr = iqe.Q
Earr = iqe.E
qstep = Qarr[1] - Qarr[0]
estep = Earr[1] - Earr[0]
# q values to sum over
Qs = np.arange(Q - dQ, Q + dQ, qstep)
# e values of result histogram
Es = np.arange(-dE, dE, estep)
# intensities
intensities = np.zeros(len(Es))
#
for Q in Qs:
# slice
slice = iqe[Q, ()]
# center E from E(Q) function
Ecenter = E_Q(Q)
# slice in the region of interest
Emin = Ecenter - dE
Emax = Ecenter + dE
subslice = slice[(Emin, Emax)]
subslice = subslice.I[:len(Es)]
#
intensities += subslice
continue
xaxis = H.axis('dE', Es, unit='meV')
h = H.histogram('I(dE)', [xaxis], data=intensities)
hh.dump(h, 'I_dE-Q=%s.h5' % Qcenter)
return
def test(self):
"wrap IQE_monitor"
from mcstas2 import componentfactory
category = 'monitors'
componentname = 'IQE_monitor'
factory = componentfactory( category, componentname )
Qmin=0; Qmax=13.; nQ=130
Emin=-50; Emax=50.; nE=100
component = factory(
'component',
Ei=Ei,
Qmin=Qmin, Qmax=Qmax, nQ=nQ,
Emin=Emin, Emax=Emax, nE=nE,
max_angle_out_of_plane=30, min_angle_out_of_plane=-30,
max_angle_in_plane=120, min_angle_in_plane=-30,
)
scatterer = makeScatterer()
import mcni
N = 10000
neutrons = mcni.neutron_buffer( N )
for i in range(N):
neutron = mcni.neutron(r=(0,0,0), v=(0,0,vi), time=0, prob=1)
scatterer.scatter(neutron)
neutrons[i] = neutron
#print neutrons[i]
continue
component.process( neutrons )
hist = get_histogram(component)
import os
f = os.path.basename(__file__)
filename = 'IQE-%s.h5' % f
if os.path.exists(filename): os.remove(filename)
import histogram.hdf as hh
hh.dump(hist, filename, '/', 'c')
if self.interactive:
from histogram.plotter import defaultPlotter
defaultPlotter.plot(hist)
return
def testdump0a(self):
""" histogram.hdf: dump with compression"""
from histogram import histogram, arange
h = histogram(
'h',
[
('x', arange(0, 100, 1.)),
('y', arange(100, 180, 1.)),
],
unit='meter',
)
filename = 'test0-compressed.h5'
import os
if os.path.exists(filename): os.remove(filename)
dump(h, filename, '/', mode='c', compression=6)
return
def run(scan=None, poolsize=None, output=None, **volume_opts):
scan = load_mod(scan)['scan']
def worker(work_q, H, sa):
while not work_q.empty():
f = work_q.get()
print f
one(f, H, sa, scan, volume_opts)
continue
return
shape, edges = scan.volumeOutputDims(**volume_opts)
size = shape[0] * shape[1] * shape[2]
from multiprocessing import Process, Queue, Array
shared_H = Array('d', size)
shared_sa = Array('d', size)
work_q = Queue()
for path in scan.paths:
work_q.put(path)
processes = []
for w in xrange(poolsize):
p = Process(target=worker, args=(work_q, shared_H, shared_sa))
p.start()
processes.append(p)
continue
for p in processes:
p.join()
import histogram
axes = [
histogram.axis(volume_opts['x'], boundaries=edges[0]),
histogram.axis(volume_opts['y'], boundaries=edges[1]),
histogram.axis(volume_opts['z'], boundaries=edges[2]),
]
H = mparr2nparr(shared_H)
sa = mparr2nparr(shared_sa)
H.shape = sa.shape = shape
h = histogram.histogram('I(%(x)s,%(y)s,%(z)s)' % volume_opts,
axes=axes,
data=H / sa)
import histogram.hdf as hh
hh.dump(h, output)
return
def testdump_and_load2(self):
'dump and load in the same process'
tmpfile = 'test_dump_load2.h5'
if os.path.exists(tmpfile):
os.remove(tmpfile)
from h5py import File
fs = File(tmpfile, 'w')
from histogram import histogram
x = y = list(range(10))
h = histogram('h', [('x', x)], data=y, errors=y)
import histogram.hdf as hh
hh.dump(h, tmpfile, '/', fs=fs)
h = load(tmpfile, 'h')
#print(h[1])
self.assertVectorAlmostEqual(h[1], (1, 1))
return
def testdump1(self):
""" histogram.hdf: dump with fs specified"""
filename = 'test1.h5'
if os.path.exists( filename): os.remove( filename )
from h5py import File
fs = File( filename, 'w' )
from histogram import histogram, arange
h = histogram('h',
[('x', arange(0, 100, 1.) ),
('y', arange(100, 180, 1.) ),],
unit = 'meter',
)
#fs will take over
dump( h, 'abc', '/', mode = 'w', fs = fs )
self.assert_( os.path.exists( filename ))
return
def test2a(self):
datadir = os.path.join(here, '..', '..', 'data', 'graphite')
doshist = hh.load(os.path.join(datadir, 'exp_DOS.h5'))
from mcvine.phonon.powderSQE.use_phonopy import from_FORCE_CONSTANTS
N = int(1e5)
IQEhist = from_FORCE_CONSTANTS(
datadir,
Ei=30., # meV
T=300., # kelvin
doshist=doshist, # DOS histogram
supercell=(6, 6, 1),
Q_bins=np.arange(0, 4, 0.04),
E_bins=np.arange(0, 30, .2),
workdir='_tmp.test2a',
N=N,
include_multiphonon=False,
max_det_angle=60.,
)
hh.dump(IQEhist,
'graphite-single-phonon-Ei_30-T_300.h5') # save for inspection
expectedIQEhist = hh.load(
os.path.join(
here,
'saved_results/graphite-single-phonon-Ei_30-T_300-N_3e6.h5'))
expected = expectedIQEhist.I
# scale it to sth that is easy to get "errorbar"
N = 3e6
scale = N / np.nansum(expected)
expected *= scale
max = np.nanmax(expected)
this = IQEhist.I
this *= scale
if plot:
plt.figure(figsize=(6, 3))
plt.subplot(1, 2, 1)
mpsqe.plot(IQEhist)
plt.clim(0, max / 50)
plt.subplot(1, 2, 2)
mpsqe.plot(expectedIQEhist)
plt.clim(0, max / 50)
plt.tight_layout()
plt.show()
return
def test2b(self):
iqehist = hh.load(os.path.join(datadir, "Al-iqe.h5"))
from multiphonon.sqe import interp
newiqe = interp(iqehist, newE=np.arange(-40, 70, 1.0))
hh.dump(newiqe, "Al-iqe-interped.h5")
iterdos = sqe2dos.sqe2dos(
newiqe, T=300, Ecutoff=50.0, elastic_E_cutoff=(-10.0, 7), M=26.98, C_ms=0.2, Ei=80.0, workdir="work-Al"
)
for i, dos in enumerate(iterdos):
# print dos
# plot
if interactive:
# print '*' * 70
pylab.plot(dos.E, dos.I, label="%d" % i)
if interactive:
pylab.legend()
pylab.show()
return
def run(scan=None, poolsize=None, output=None, **volume_opts):
scan = load_mod(scan)['scan']
def worker(work_q, H, sa):
while not work_q.empty():
f = work_q.get()
print f
one(f, H, sa, scan, volume_opts)
continue
return
shape, edges = scan.volumeOutputDims(**volume_opts)
size = shape[0] * shape[1] * shape[2]
from multiprocessing import Process, Queue, Array
shared_H = Array('d', size)
shared_sa = Array('d', size)
work_q = Queue()
for path in scan.paths: work_q.put(path)
processes = []
for w in xrange(poolsize):
p = Process(target=worker, args=(work_q, shared_H, shared_sa))
p.start()
processes.append(p)
continue
for p in processes:
p.join()
import histogram
axes = [
histogram.axis(volume_opts['x'], boundaries=edges[0]),
histogram.axis(volume_opts['y'], boundaries=edges[1]),
histogram.axis(volume_opts['z'], boundaries=edges[2]),
]
H = mparr2nparr(shared_H)
sa = mparr2nparr(shared_sa)
H.shape = sa.shape = shape
h = histogram.histogram(
'I(%(x)s,%(y)s,%(z)s)'%volume_opts, axes=axes, data=H/sa)
import histogram.hdf as hh
hh.dump(h, output)
return
def getDOS(sample_nxs, mt_nxs=None, mt_fraction=0.9,
Emin=-100, Emax=100, dE=1.,
Qmin=0, Qmax=15., dQ=0.1, T=300, Ecutoff=50.,
elastic_E_cutoff=(-20., 7), M=50.94,
C_ms=0.3, Ei=116.446, workdir='work',
iqe_nxs="iqe.nxs", iqe_h5="iqe.h5"):
# prepare paths
if not os.path.exists(workdir):
os.makedirs(workdir)
if not os.path.isabs(iqe_nxs):
iqe_nxs = os.path.abspath(os.path.join(workdir, iqe_nxs))
if not os.path.isabs(iqe_h5):
iqe_h5 = os.path.abspath(os.path.join(workdir, iqe_h5))
# reduce
Eaxis = Emin, Emax, dE
Qaxis = Qmin, Qmax, dQ
raw2iqe(sample_nxs, iqe_nxs, iqe_h5, Eaxis, Qaxis)
iqehist = hh.load(iqe_h5)
if mt_nxs is not None:
_tomtpath = lambda p: os.path.join(
os.path.dirname(p), 'mt-'+os.path.basename(p))
mtiqe_nxs = _tomtpath(iqe_nxs)
mtiqe_h5 = _tomtpath(iqe_h5)
raw2iqe(mt_nxs, mtiqe_nxs, mtiqe_h5, Eaxis, Qaxis)
iqehist -= hh.load(mtiqe_h5) * (mt_fraction, 0)
# to DOS
# interpolate data
from .sqe import interp
# probably don't need this line
newiqe = interp(iqehist, newE = np.arange(Emin, Emax, dE))
# save interpolated data
hh.dump(newiqe, 'iqe-interped.h5')
# create processing engine
from .backward import sqe2dos
print("iterative computation of DOS...")
iterdos = sqe2dos.sqe2dos(
newiqe, T=T, Ecutoff=Ecutoff,
elastic_E_cutoff=elastic_E_cutoff, M=M,
C_ms=C_ms, Ei=Ei, workdir='work')
doslist = list(iterdos)
print("done.")
return doslist
def _fixEaxis(iqe_h5_path, Eaxis):
"""when iqe is obtained from a nxs or nxspe file where
tof axis is already converted to E, the reduced data may
not have the Eaxis as desired. this method fix it by
interpolation
"""
h = hh.load(iqe_h5_path)
eaxis = h.axes()[1]
centers = eaxis.binCenters()
emin, emax, de = Eaxis
centers1 = np.arange(emin, emax, de)
if centers.size == centers1.size and np.allclose(centers, centers1):
return
# save a copy of the original histogram
import shutil
shutil.copyfile(iqe_h5_path, iqe_h5_path + '.bkup-wrongEaxis')
from .sqe import interp
h1 = interp(h, centers1)
hh.dump(h1, iqe_h5_path)
return
def extract_iqe(mantid_nxs, histogram):
"extract iqe from a mantid-saved h5 file and save to a histogram"
import h5py, numpy as np
inpath, outpath = mantid_nxs, histogram
f = h5py.File(inpath)
w = f['mantid_workspace_1']['workspace']
e = np.array(w['axis1'])
de = e[1] - e[0]
ee = (e+de/2.)[:-1]
q = np.array(w['axis2'])
dq = q[1] - q[0]
qq = (q+dq/2.)[:-1]
I = np.array(np.array(w['values']))
# I[I!=I] = 0
E2 = np.array(np.array(w['errors'])**2)
import histogram as H
iqe = H.histogram('iqe', [('Q',qq, 'angstrom**-1'), ('energy', ee, 'meV')], data=I, errors = E2)
import histogram.hdf as hh
hh.dump(iqe, outpath)
return
def run( monitordatafilename, h5filename,
tof_params = None,
nevents = None,
):
print "monitordatafilename = %s" % monitordatafilename
print "nevents = %s" % nevents
print "output h5filename = %s" % h5filename
print 'tof_params (unit: us) = %s' % (tof_params, )
if os.path.exists(h5filename):
raise IOError, "%s already exists" % h5filename
from arcseventdata.monitorData import readHistogram
h = readHistogram( monitordatafilename )
from histogram.hdf import dump
dump(h, h5filename, '/', 'c' )
return
def run(
ncount=1e7, nodes=5,
Ei=700.,
E_Q="Q*Q/3", S_Q="1", sigma_Q='Q/2.',
Qmin=0, Qmax=10., Qstep=0.1,
Emin=0, Emax=50., Estep=1.,
):
# create scattering kernel file
createScatteringKernel(
E_Q=E_Q, S_Q=S_Q, sigma_Q=sigma_Q,
Qmin=Qmin, Qmax=Qmax,
)
# run main sim
cmd = './sssd --source.energy=%s --ncount=%s --mpirun.nodes=%s' % (
Ei, ncount, nodes)
execute(cmd)
# reduce events to S(Q,E)
eventsdat = 'out/events.dat'
Qaxis = Qmin, Qmax, Qstep
Eaxis = Emin, Emax, Estep
toffset = 0
iqe = reduceToIQE(eventsdat, Ei, toffset, Qaxis, Eaxis)
from histogram.hdf import dump
dump(iqe, 'iqe.h5', '/', 'c')
global interactive
if interactive:
from histogram.plotter import defaultPlotter
defaultPlotter.plot(iqe)
# quick analysis of S(Q,E) (without detector and sample size effects)
cmd = ['./analyze-sqe ']
cmd.append('--mpirun.nodes=%s' % nodes)
cmd.append('--ncount=%s --monitor.Ei=%s' % (ncount, Ei))
cmd.append(' --monitor.Qmin=%s --monitor.Qmax=%s' % (Qmin, Qmax))
cmd.append(' --monitor.Emin=%s --monitor.Emax=%s' % (Emin, Emax))
cmd = ' '.join(cmd)
# execute(cmd)
return
def process(path):
basename, ext = os.path.splitext(os.path.basename(path))
from nslice.Run import Run
run = Run(path)
print "instrument=%s, Ei=%s, psi=%s" % (run.instrument, run.Ei, run.psi)
from nslice.XtalOrientation import XtalOrientation
a = b = 8.87
c = 5.2
from math import pi
twopi = 2 * pi
ra, rb, rc = [twopi / a, 0, 0], [0, twopi / b, 0], [0, 0, twopi / c]
u, v = [1, 0, 0], [0, 1, 0]
xtal_ori = XtalOrientation(ra, rb, rc, u, v, run.psi)
h, k, l, E = run.compute_hklE(xtal_ori)
I, error = run.read_data()
h.shape = k.shape = l.shape = E.shape = I.shape = error.shape = -1
hklEIE = np.vstack((h, k, l, E, I, error))
from nslice.slice import slice
H, edges = slice(
hklEIE,
x='h',
y='k',
u='l',
v='E',
E=[-2, 2],
l=[-5, 5],
h=(-5, 5, 0.02),
k=(-5, 5, 0.02),
)
import histogram, histogram.hdf as hh
axes = [
histogram.axis('h', boundaries=edges[0]),
histogram.axis('k', boundaries=edges[1]),
]
h = histogram.histogram('I(h,k)', axes=axes, data=H)
hh.dump(h, '%s-I_hk.h5' % basename)
return
def test(self):
datadir = self.datadir
doshist = hh.load(os.path.join(datadir, 'exp_DOS.h5'))
import mcvine.phonon.powderSQE.IDF as psidf
disp = psidf.disp_from_datadir(datadir)
IQEhist, mphhist = psidf.from_data_dir(
datadir=datadir,
disp=disp,
N=int(1e6),
Q_bins=np.arange(0, 23, 0.1),
E_bins=np.arange(0, 250, 1.),
doshist=doshist,
T=300.,
Ei=300.,
max_det_angle=140.,
include_multiphonon=True,
)
IQEhist += mphhist
hh.dump(IQEhist, 'graphite-allphonon-Ei_300-T_300-IDF.h5')
expected = hh.load(
os.path.join(
here, 'saved_results/graphite-allphonon-Ei_300-T_300-IDF.h5'))
max = np.nanmax(expected.I)
reldiff = IQEhist - expected
reldiff.I /= max
reldiff.E2 /= max * max
Nbigdiff = (np.abs(reldiff.I) > 0.03).sum()
Ngood = (IQEhist.I == IQEhist.I).sum()
Ntotal = IQEhist.size()
self.assertTrue(Ngood * 1. / Ntotal > .65)
self.assertTrue(Nbigdiff * 1. / Ngood < .10)
if plot:
plt.figure(figsize=(6, 3))
max = np.nanmax(IQEhist.I)
median = np.nanmedian(IQEhist.I[IQEhist.I > 0])
mpsqe.plot(IQEhist)
plt.clim(0, median * 3)
# plt.subplot(1,2,1); mpsqe.plot(IQEhist); plt.clim(0, max/50)
# plt.subplot(1,2,2); mpsqe.plot(expectedIQEhist); plt.clim(0, max/50)
plt.show()
return
def testdump_and_load2(self):
'dump and load in the same process'
tmpfile = 'test_dump_load2.h5'
if os.path.exists( tmpfile ):
os.remove( tmpfile )
from h5py import File
fs = File( tmpfile, 'w' )
from histogram import histogram
x = y = range(10)
h = histogram(
'h',
[ ('x', x) ],
data = y, errors = y )
import histogram.hdf as hh
hh.dump( h, tmpfile, '/', fs = fs )
h = load( tmpfile, 'h')
#print h[1]
self.assertVectorAlmostEqual( h[1], (1,1) )
return
def run( pixelPositionsFilename, h5filename,
npacks = 115, ndetsperpack = 8, npixelsperdet = 128):
print "pixel-positions-filename=%s" % pixelPositionsFilename
print "output h5filename = %s" % h5filename
if os.path.exists(h5filename):
raise IOError, "%s already exists" % h5filename
s = open( pixelPositionsFilename ).read()
from numpy import fromstring
positions = fromstring( s, 'd' )
import arcseventdata
phi_p, psi_p = arcseventdata.pixelpositions2angles(
positions, npacks, ndetsperpack, npixelsperdet)
from histogram.hdf import dump
dump(phi_p, h5filename, '/', 'c' )
dump(psi_p, h5filename, '/', 'w' )
return
def computeDirtyDOS(sqe, dos, M, T, workdir):
"""dirty dos calculation is procedure that quickly
"correct" sqe using the one-phonon Q multiplier.
After correction, the sqe would look like mostly Q-independent,
and the sum over Q axis can give a very rough estimate of the DOS.
This is mostly for double-checking the calculations.
"""
if not os.path.exists(workdir):
os.makedirs(workdir)
from ..forward.phonon import computeSNQ, DWExp, kelvin2mev, gamma0
beta = 1./(T*kelvin2mev)
E = dos.E; Q = sqe.Q; g = dos.I
dE = E[1] - E[0]
DW2 = DWExp(Q, M, E, g, beta, dE)
sq = computeSNQ(DW2, 1)
sqe1 = sqe.copy()
sqe1.I /= sq[:, np.newaxis]
sqe1.E2 /= sq[:, np.newaxis] * sq[:, np.newaxis]
hh.dump(sqe1, os.path.join(workdir, 'corrected-sqe.h5'))
# compute a sum to obtain S(E)
Qdiff = Q[-1]-Q[0]
# take the middle part. 1/6 is kind of arbitrary
se1 = sqe1[ (Q[0]+Qdiff/6., Q[-1]-Qdiff/6.), (E[0], None) ].sum('Q')
hh.dump(se1, os.path.join(workdir, 'se.h5'))
assert np.allclose(se1.E, E)
#
g0 = gamma0(E,g, beta, dE)
fE = (1-np.exp(-se1.E*beta)) * se1.E * g0
ddos = se1.copy()
ddos.I *= fE
ddos.E2 *= fE*fE
hh.dump(ddos, os.path.join(workdir, 'ddos.h5'))
return
