def mono_dTdQ():
slits = res.slit_widths(T=Ts, slits_at_Tlo=sTlo, Tlo=Tlo, Thi=Thi,
slits_below=sbelow, slits_above=sabove)
dT = res.divergence(T=Ts, slits=slits, distance=(d1,d2),
sample_broadening=0.01, sample_width=10)
dQ = res.dTdL2dQ(T=Ts,dT=dT,L=L,dL=dL)
return dT,dQ
def poly_dTdQ():
low,high = poly.wavelength
L = res.bins(low,high,poly.dLoL)[::-1] # reversed to be in Q order
dL = res.binwidths(L)
slits = (s1,s2)
dT = res.divergence(T=T, slits=slits, distance=(d1,d2),
sample_broadening=0.01, sample_width=10)
dQ = res.dTdL2dQ(T=T,dT=dT,L=L,dL=dL)
return dT,dQ,L,dL
def poly_dTdQ():
low, high = poly.wavelength
L = res.bins(low, high, poly.dLoL)[::-1] # reversed to be in Q order
dL = res.binwidths(L)
slits = (s1, s2)
dT = res.divergence(T=T,
slits=slits,
distance=(d1, d2),
sample_broadening=0.01,
sample_width=10)
dQ = res.dTdL2dQ(T=T, dT=dT, L=L, dL=dL)
return dT, dQ, L, dL
def mono_dTdQ():
slits = res.slit_widths(T=Ts,
slits_at_Tlo=sTlo,
Tlo=Tlo,
Thi=Thi,
slits_below=sbelow,
slits_above=sabove)
dT = res.divergence(T=Ts,
slits=slits,
distance=(d1, d2),
sample_broadening=0.01,
sample_width=10)
dQ = res.dTdL2dQ(T=Ts, dT=dT, L=L, dL=dL)
return dT, dQ
def test():
# Test constants
Q, dQ = 0.5, 0.01
T, dT, L, dL = 0.5, 0.5, 2.5, 0.05
FWHM = sqrt(log(256))
Trad, dTrad = radians(T), radians(dT)
# Resolution primitives
assert norm(res.FWHM2sigma(1) - 1 / FWHM) < 1e-14
assert norm(res.sigma2FWHM(1) - FWHM) < 1e-14
assert norm(res.QL2T(Q=Q, L=L) - degrees(asin(Q * L / 4 / pi))) < 1e-14
assert norm(res.TL2Q(T=T, L=L) - 4 * pi * sin(Trad) / L) < 1e-14
assert norm(
res.dTdL2dQ(T=T, dT=dT, L=L, dL=dL) -
4 * pi * sin(Trad) / L * sqrt((dL / L)**2 +
(dTrad / tan(Trad))**2) / FWHM) < 1e-14
Q1 = res.TL2Q(T=T, L=L)
dQ1 = res.dTdL2dQ(T=T, dT=dT, L=L, dL=dL)
assert norm(
res.dQdT2dLoL(Q=Q1, dQ=dQ1, T=T, dT=dT) -
sqrt((dQ1 * FWHM / Q1)**2 - (dTrad / tan(Trad))**2)) < 1e-14
# For spallation sources, bin edges are at A r**n where r is 1+resolution.
# Bin edges from 1 to 2 at 20% [1, 1.2, 1.44, 1.728, 2.0736]
# Centers of each range 1.1 1.32 1.584 1.9008
Lp = res.bins(1, 2, 0.2)
dLp = res.binwidths(Lp)
assert norm(Lp - [1.1, 1.32, 1.584, 1.9008]) < 1e-14
assert norm(dLp - [0.2, 0.24, 0.288, 0.3456]) < 1e-14
# Slit openings are assumed to be linear in angle; since footprint
# goes as the sine of the angle, this is correct in the small angle
# approximation.
Tlo, Thi = 0.2, 0.5
sTlo, sbelow, sabove = 0.2, 0.4, 3
Ts = numpy.array([Tlo / 2, Tlo, (Tlo + Thi) / 2, Thi, Thi * 2])
slits = (sbelow, sTlo, sTlo * (Tlo + Thi) / 2 / Tlo, sTlo * Thi / Tlo,
sabove)
assert norm(
res.slit_widths(T=Ts,
slits_at_Tlo=sTlo,
Tlo=Tlo,
Thi=Thi,
slits_below=sbelow,
slits_above=sabove)[0] - slits) < 1e-14
# FWHM angular divergence is average slit opening / slit separation
# For tiny samples, use the sample itself as a slit.
d1, d2 = 3000, 1000
s1, s2 = 0.1, 0.3
d, savg = d1 - d2, (s1 + s2) / 2
expected = degrees(savg / d)
assert norm(
res.divergence(T=T, slits=(s1, s2), distance=(d1, d2)) -
expected) < 1e-14
assert norm(
res.divergence(
T=T, slits=(s1, s2), distance=(d1, d2), sample_broadening=0.01) -
(expected + 0.01)) < 1e-14
assert norm(
res.divergence(T=T, slits=(s1, s2), distance=(
d1, d2), sample_width=1) - degrees((s1 + sin(Trad)) /
(2 * d1))) < 1e-14
# Simulate an scanning reflectometer
mono = inst.Monochromatic(d_s1=d1,
d_s2=d2,
wavelength=L,
dLoL=dL / L,
slits_at_Tlo=sTlo,
Tlo=Tlo,
Thi=Thi,
slits_below=sbelow,
slits_above=sabove)
sim = mono.probe(T=Ts, sample_broadening=0.01, sample_width=10)
# Check the result
def mono_dTdQ():
slits = res.slit_widths(T=Ts,
slits_at_Tlo=sTlo,
Tlo=Tlo,
Thi=Thi,
slits_below=sbelow,
slits_above=sabove)
dT = res.divergence(T=Ts,
slits=slits,
distance=(d1, d2),
sample_broadening=0.01,
sample_width=10)
dQ = res.dTdL2dQ(T=Ts, dT=dT, L=L, dL=dL)
return dT, dQ
e_dT, e_dQ = mono_dTdQ()
assert norm(sim.T - Ts) < 1e-14
assert norm(sim.dT - e_dT) < 1e-14
assert norm(sim.L - L) < 1e-14
assert norm(sim.dL - dL) < 1e-14
assert norm(sim.Q - res.TL2Q(Ts, L)) < 1e-14
assert norm(sim.dQ - e_dQ) < 1e-14
assert isinstance(str(mono), type(""))
assert isinstance(ncnrdata.NG1.defaults(), type(""))
# Check the subclassing interface
class Mono(inst.Monochromatic):
instrument = "mono"
d_s1, d_s2 = d1, d2
wavelength = L
dLoL = dL / L
# Set some default parameters
mono2 = Mono(slits_at_Tlo=sTlo,
Tlo=Tlo,
Thi=Thi,
slits_below=sbelow,
slits_above=sabove)
sim = mono2.probe(T=Ts, sample_broadening=0.01, sample_width=10)
assert norm(sim.dQ - e_dQ) < 1e-14
# Simulate an TOF reflectometer
poly = inst.Pulsed(d_s1=d1, d_s2=d2, wavelength=(1, 2), dLoL=0.2)
sim = poly.probe(T=T,
slits=(s1, s2),
sample_broadening=0.01,
sample_width=10)
# Check the result
def poly_dTdQ():
low, high = poly.wavelength
L = res.bins(low, high, poly.dLoL)[::-1] # reversed to be in Q order
dL = res.binwidths(L)
slits = (s1, s2)
dT = res.divergence(T=T,
slits=slits,
distance=(d1, d2),
sample_broadening=0.01,
sample_width=10)
dQ = res.dTdL2dQ(T=T, dT=dT, L=L, dL=dL)
return dT, dQ, L, dL
e_dT, e_dQ, Ls, e_dL = poly_dTdQ()
assert norm(sim.T - T) < 1e-14
assert norm(sim.dT - e_dT) < 1e-14
assert norm(sim.L - Ls) < 1e-14
assert norm(sim.dL - e_dL) < 1e-14
assert norm(sim.Q - res.TL2Q(T, Ls)) < 1e-14
assert norm(sim.dQ - e_dQ) < 1e-14
assert isinstance(str(poly), type(""))
assert isinstance(snsdata.Liquids.defaults(), type(""))
# Make sure that the string reps don't crash
_ = (str(mono), str(ncnrdata.NG1.defaults()), str(ncnrdata.NG1()),
str(poly), str(snsdata.Liquids.defaults()))
if 1:
for s in _:
print(s)
def test():
# Test constants
Q,dQ = 0.5,0.01
T,dT,L,dL=0.5,0.5,2.5,0.05
FWHM = sqrt(log(256))
Trad,dTrad = radians(T),radians(dT)
# Resolution primitives
assert norm(res.FWHM2sigma(1) - 1/FWHM) < 1e-14
assert norm(res.sigma2FWHM(1) - FWHM) < 1e-14
assert norm(res.QL2T(Q=Q,L=L) - degrees(asin(Q*L/4/pi))) < 1e-14
assert norm(res.TL2Q(T=T,L=L) - 4*pi*sin(Trad)/L) < 1e-14
assert norm(res.dTdL2dQ(T=T,dT=dT,L=L,dL=dL) - 4*pi*sin(Trad)/L
* sqrt( (dL/L)**2 + (dTrad/tan(Trad))**2 )/FWHM) < 1e-14
Q1 = res.TL2Q(T=T,L=L)
dQ1 = res.dTdL2dQ(T=T,dT=dT,L=L,dL=dL)
assert norm(res.dQdT2dLoL(Q=Q1,dQ=dQ1,T=T,dT=dT) -
sqrt((dQ1*FWHM/Q1)**2 - (dTrad/tan(Trad))**2)) < 1e-14
# For spallation sources, bin edges are at A r**n where r is 1+resolution.
# Bin edges from 1 to 2 at 20% [1, 1.2, 1.44, 1.728, 2.0736]
# Centers of each range 1.1 1.32 1.584 1.9008
Lp = res.bins(1,2,0.2)
dLp = res.binwidths(Lp)
assert norm(Lp-[1.1, 1.32, 1.584, 1.9008]) < 1e-14
assert norm(dLp-[0.2, 0.24, 0.288, 0.3456]) < 1e-14
# Slit openings are assumed to be linear in angle; since footprint
# goes as the sine of the angle, this is correct in the small angle
# approximation.
Tlo,Thi = 0.2,0.5
sTlo,sbelow,sabove = 0.2,0.4,3
Ts = numpy.array([Tlo/2,Tlo,(Tlo+Thi)/2,Thi,Thi*2])
slits = (sbelow,sTlo,sTlo*(Tlo+Thi)/2/Tlo,sTlo*Thi/Tlo,sabove)
assert norm(res.slit_widths(T=Ts,slits_at_Tlo=sTlo,Tlo=Tlo,Thi=Thi,
slits_below=sbelow,slits_above=sabove)[0]-slits) < 1e-14
# FWHM angular divergence is average slit opening / slit separation
# For tiny samples, use the sample itself as a slit.
d1,d2 = 3000,1000
s1,s2 = 0.1,0.3
d,savg = d1-d2,(s1+s2)/2
expected = degrees(savg/d)
assert norm(res.divergence(T=T,slits=(s1,s2),distance=(d1,d2)) - expected) < 1e-14
assert norm(res.divergence(T=T,slits=(s1,s2),distance=(d1,d2),
sample_broadening=0.01) - (expected+0.01)) < 1e-14
assert norm(res.divergence(T=T,slits=(s1,s2),distance=(d1,d2),
sample_width=1) - degrees((s1+sin(Trad))/(2*d1))) < 1e-14
# Simulate an scanning reflectometer
mono = inst.Monochromatic(d_s1=d1, d_s2=d2, wavelength=L, dLoL=dL/L,
slits_at_Tlo=sTlo, Tlo=Tlo, Thi=Thi,
slits_below=sbelow, slits_above=sabove)
sim = mono.probe(T=Ts, sample_broadening=0.01, sample_width=10)
# Check the result
def mono_dTdQ():
slits = res.slit_widths(T=Ts, slits_at_Tlo=sTlo, Tlo=Tlo, Thi=Thi,
slits_below=sbelow, slits_above=sabove)
dT = res.divergence(T=Ts, slits=slits, distance=(d1,d2),
sample_broadening=0.01, sample_width=10)
dQ = res.dTdL2dQ(T=Ts,dT=dT,L=L,dL=dL)
return dT,dQ
e_dT, e_dQ = mono_dTdQ()
assert norm(sim.T - Ts) < 1e-14
assert norm(sim.dT - e_dT) < 1e-14
assert norm(sim.L - L) < 1e-14
assert norm(sim.dL - dL) < 1e-14
assert norm(sim.Q - res.TL2Q(Ts,L)) < 1e-14
assert norm(sim.dQ - e_dQ) < 1e-14
assert isinstance(str(mono),type(""))
assert isinstance(ncnrdata.NG1.defaults(),type(""))
# Check the subclassing interface
class Mono(inst.Monochromatic):
instrument="mono"
d_s1,d_s2 = d1,d2
wavelength=L
dLoL=dL/L
# Set some default parameters
mono2 = Mono(slits_at_Tlo=sTlo, Tlo=Tlo, Thi=Thi,
slits_below=sbelow, slits_above=sabove)
sim = mono2.probe(T=Ts, sample_broadening=0.01, sample_width=10)
assert norm(sim.dQ - e_dQ) < 1e-14
# Simulate an TOF reflectometer
poly = inst.Pulsed(d_s1=d1, d_s2=d2, wavelength=(1,2), dLoL=0.2)
sim = poly.probe(T=T, slits=(s1,s2),
sample_broadening=0.01, sample_width=10)
# Check the result
def poly_dTdQ():
low,high = poly.wavelength
L = res.bins(low,high,poly.dLoL)[::-1] # reversed to be in Q order
dL = res.binwidths(L)
slits = (s1,s2)
dT = res.divergence(T=T, slits=slits, distance=(d1,d2),
sample_broadening=0.01, sample_width=10)
dQ = res.dTdL2dQ(T=T,dT=dT,L=L,dL=dL)
return dT,dQ,L,dL
e_dT, e_dQ, Ls, e_dL = poly_dTdQ()
assert norm(sim.T - T) < 1e-14
assert norm(sim.dT - e_dT) < 1e-14
assert norm(sim.L - Ls) < 1e-14
assert norm(sim.dL - e_dL) < 1e-14
assert norm(sim.Q - res.TL2Q(T,Ls)) < 1e-14
assert norm(sim.dQ - e_dQ) < 1e-14
assert isinstance(str(poly),type(""))
assert isinstance(snsdata.Liquids.defaults(),type(""))
# Make sure that the string reps don't crash
_ = (str(mono),str(ncnrdata.NG1.defaults()),str(ncnrdata.NG1()),
str(poly),str(snsdata.Liquids.defaults()))
if 1:
for s in _: print(s)