def runTest(self):
ws = DirectILLCollectData('ILL/IN4/084446.nxs', ElasticChannel='Default Elastic Channel',
FlatBkg='Flat Bkg OFF', Normalisation='Normalisation OFF')
for i in range(ws.getNumberHistograms()):
ws.dataY(i).fill(1)
dE = ConvertUnits(ws, 'DeltaE', 'Direct')
corr = DetectorEfficiencyCorUser(dE)
Ei = corr.run().get('Ei').value
def rosace_corr(x):
return 1.0 - numpy.exp(-6.1343 / numpy.sqrt(x))
def wide_angle_corr(x):
return 0.951 * numpy.exp(-0.0887 / numpy.sqrt(x)) * (1 - numpy.exp(-5.597 / numpy.sqrt(x)))
def eff_factor(x, corr_func):
return corr_func(Ei) / corr_func(x)
# Wide-angle detectors are at ws indices 0-299
for i in range(0, 300):
x = (corr.readX(i)[:-1] + corr.readX(i)[1:]) / 2.
e = Ei - x
assert_almost_equal(corr.readY(i), eff_factor(e, wide_angle_corr))
# Rosace detectors are at ws indices 300-395
for i in range(300, 396):
x = (corr.readX(i)[:-1] + corr.readX(i)[1:]) / 2.
e = Ei - x
assert_almost_equal(corr.readY(i), eff_factor(e, rosace_corr))
def runTest(self):
ws = DirectILLCollectData('ILL/IN4/084446.nxs', ElasticChannel='Default Elastic Channel',
FlatBkg='Flat Bkg OFF', Normalisation='Normalisation OFF')
for i in range(ws.getNumberHistograms()):
ws.dataY(i).fill(1)
dE = ConvertUnits(ws, 'DeltaE', 'Direct')
corr = DetectorEfficiencyCorUser(dE)
Ei = corr.run().get('Ei').value
def rosace_corr(x):
return 1.0 - numpy.exp(-6.1343 / numpy.sqrt(x))
def wide_angle_corr(x):
return 0.951 * numpy.exp(-0.0887 / numpy.sqrt(x)) * (1 - numpy.exp(-5.597 / numpy.sqrt(x)))
def eff_factor(x, corr_func):
return corr_func(Ei) / corr_func(x)
# Wide-angle detectors are at ws indices 0-299
for i in range(0, 300):
x = (corr.readX(i)[:-1] + corr.readX(i)[1:]) / 2.
e = Ei - x
numpy.testing.assert_almost_equal(corr.readY(i), eff_factor(e, wide_angle_corr))
# Rosace detectors are at ws indices 300-395
for i in range(300, 396):
x = (corr.readX(i)[:-1] + corr.readX(i)[1:]) / 2.
e = Ei - x
numpy.testing.assert_almost_equal(corr.readY(i), eff_factor(e, rosace_corr))
def runTest(self):
ws = DirectILLCollectData('ILL/IN6/164192.nxs',
ElasticChannel='Default Elastic Channel',
FlatBkg='Flat Bkg OFF',
Normalisation='Normalisation OFF')
for i in range(ws.getNumberHistograms()):
ws.dataY(i).fill(1)
dE = ConvertUnits(ws, 'DeltaE', 'Direct')
corr = DetectorEfficiencyCorUser(dE)
Ei = corr.run().get('Ei').value
def det_corr(x):
high = x > 5.113
low = x <= 5.113
c = numpy.empty_like(x)
c[high] = 0.94 * (1. - numpy.exp(-3.284 / numpy.sqrt(x[high])))
c[low] = numpy.exp(-0.0565 / numpy.sqrt(x[low])) * (
1. - numpy.exp(-3.284 / numpy.sqrt(x[low])))
return c
corr_at_Ei = det_corr(numpy.array([Ei]))[0]
for i in range(corr.getNumberHistograms()):
x = (corr.readX(i)[:-1] + corr.readX(i)[1:]) / 2.
e = Ei - x
numpy.testing.assert_almost_equal(corr.readY(i),
corr_at_Ei / det_corr(e))
def runTest(self):
ws = DirectILLCollectData('ILL/IN5/104007.nxs', ElasticChannel='Default Elastic Channel',
FlatBkg='Flat Bkg OFF', Normalisation='Normalisation OFF')
for i in range(ws.getNumberHistograms()):
ws.dataY(i).fill(1)
dE = ConvertUnits(ws, 'DeltaE', 'Direct')
corr = DetectorEfficiencyCorUser(dE)
Ei = corr.run().get('Ei').value
def tube_corr(x):
return 1. - numpy.exp(-5.6 / numpy.sqrt(x))
corr_at_Ei = tube_corr(Ei)
for i in range(corr.getNumberHistograms()):
x = (corr.readX(i)[:-1] + corr.readX(i)[1:]) / 2.
e = Ei - x
assert_almost_equal(corr.readY(i), corr_at_Ei / tube_corr(e))
def runTest(self):
ws = DirectILLCollectData('ILL/IN5/104007.nxs', ElasticChannel='Default Elastic Channel',
FlatBkg='Flat Bkg OFF', Normalisation='Normalisation OFF')
for i in range(ws.getNumberHistograms()):
ws.dataY(i).fill(1)
dE = ConvertUnits(ws, 'DeltaE', 'Direct')
corr = DetectorEfficiencyCorUser(dE)
Ei = corr.run().get('Ei').value
def tube_corr(x):
return 1. - numpy.exp(-5.6 / numpy.sqrt(x))
corr_at_Ei = tube_corr(Ei)
for i in range(corr.getNumberHistograms()):
x = (corr.readX(i)[:-1] + corr.readX(i)[1:]) / 2.
e = Ei - x
numpy.testing.assert_almost_equal(corr.readY(i), corr_at_Ei / tube_corr(e))
def runTest(self):
ws = DirectILLCollectData('ILL/IN6/164192.nxs', ElasticChannel='Default Elastic Channel',
FlatBkg='Flat Bkg OFF', Normalisation='Normalisation OFF')
for i in range(ws.getNumberHistograms()):
ws.dataY(i).fill(1)
dE = ConvertUnits(ws, 'DeltaE', 'Direct')
corr = DetectorEfficiencyCorUser(dE)
Ei = corr.run().get('Ei').value
def det_corr(x):
high = x > 5.113
low = x <= 5.113
c = numpy.empty_like(x)
c[high] = 0.94 * (1. - numpy.exp(-3.284/numpy.sqrt(x[high])))
c[low] = numpy.exp(-0.0565 / numpy.sqrt(x[low])) * (1.- numpy.exp(-3.284 / numpy.sqrt(x[low])))
return c
corr_at_Ei = det_corr(numpy.array([Ei]))[0]
for i in range(corr.getNumberHistograms()):
x = (corr.readX(i)[:-1] + corr.readX(i)[1:]) / 2.
e = Ei - x
numpy.testing.assert_almost_equal(corr.readY(i), corr_at_Ei / det_corr(e))