ElasticIsoRotDiff¶

import numpy as np
from scipy.special import sph_jn
"""Generate resolution function with the following properties:
1. Normal distribution along the energy axis, same for all Q-values
2. FWHM = 0.005 meV
3. Dynamic range = [-0.1, 0.1] meV with spacing 0.0004 meV
"""
FWHM=0.005
sigma = FWHM/(2*np.sqrt(2*np.log(2)))
dE=0.0004  # spacing in the dynamic range
dataX = np.arange(-0.1,0.1,dE)  # dynamic range
Emin=min(dataX)
Emax=max(dataX)
nE=len(dataX)
dataY = np.exp(-0.5*(dataX/sigma)**2)  # the resolution function
Qs = np.array([0.3, 0.5, 0.7, 0.9, 1.1, 1.3, 1.5, 1.9])  # Q-values
nQ = len(Qs)
# Workspace containing resolution for each Q, the same in this case
resolution=CreateWorkspace(np.tile(dataX,nQ), np.tile(dataY,nQ), NSpec=nQ, UnitX="deltaE",
    VerticalAxisUnit="MomentumTransfer", VerticalAxisValues=Qs)

"""Generate a synthetic elastic signal for a particle undergoing isotropic rotational diffusion.
1. Radius of rotation = 2.5 Angstroms
2. Up to 10% of noise in the rotator signal
3. Linear background noise, up to 1% of the intensity
"""
R=2.5  # Nominal radius, a value to find in the fit
qdataY=np.empty(0)
H=2-np.random.random() # global intensity, a value to find in the fit
for Q in Qs:
    centre=dE*np.random.random()  # some shift along the energy axis
    dataY = np.exp(-0.5*((dataX-centre)/sigma)**2)  # resolution shifted by "centre"
    noise = dataY*np.random.random(nE)*0.1 # noise is up to 10% of the elastic signal
    background = np.random.random()+np.random.random()*dataX  # linear background
    background = (0.01*H*max(dataY)) * (background/max(np.abs(background))) # up to 1% of H
    j0 = sph_jn(0,Q*R)[0][0]
    qdataY=np.append(qdataY, H*j0**2*(dataY+noise) + background)
# Create data workspace
data=CreateWorkspace(np.tile(dataX,nQ), qdataY, NSpec=nQ, UnitX="deltaE",
    VerticalAxisUnit="MomentumTransfer", VerticalAxisValues=Qs)

"""Now we fit our model to the data workspace. Our model is:
    S(Q,E) = Convolution(resolution, ElasticIsoRotDiff) + LinearBackground
We do a global fit (all spectra) to find out the radius R and height H
"""
# Our initial guess are Height=1.0 and Radius=0.98. Here's a template of the
# model for each spectrum:
single_model_template="""(composite=Convolution,FixResolution=true,NumDeriv=true;
name=TabulatedFunction,Workspace=resolution,WorkspaceIndex=_WI_,Scaling=1,Shift=0,XScaling=1;
name=ElasticIsoRotDiff,Q=_Q_,Height=1,Centre=0,Radius=0.98);
name=LinearBackground,A0=0,A1=0"""
# Now create the string representation model for all spectra:
global_model="composite=MultiDomainFunction,NumDeriv=true;"
wi=0
for Q in Qs:
    single_model = single_model_template.replace("_Q_", str(Q))  # insert Q-value
    single_model = single_model.replace("_WI_", str(wi))  # insert workspace index
    global_model += "(composite=CompositeFunction,NumDeriv=true,$domains=i;{0});\n".format(single_model)
    wi+=1
# Introduce ties: Height and Radius same for all spectra
ties=['='.join(["f{0}.f0.f1.Radius".format(wi) for wi in reversed(range(nQ))]),
    '='.join(["f{0}.f0.f1.Height".format(wi) for wi in reversed(range(nQ))]) ]
global_model += "ties=("+','.join(ties)+')'  # introduce ties in the global model
# Now relate each domain(i.e. spectrum) to each single model
domain_model=dict()
for wi in range(nQ):
    if wi == 0:
        domain_model.update({"InputWorkspace": data.name(), "WorkspaceIndex": str(wi),
            "StartX": str(Emin), "EndX": str(Emax)})
    else:
        domain_model.update({"InputWorkspace_"+str(wi): data.name(), "WorkspaceIndex_"+str(wi): str(wi),
            "StartX_"+str(wi): str(Emin), "EndX_"+str(wi): str(Emax)})

"""Invoke the Fit algorithm using global_model and domain_model.
Output of the fit are three workspaces, but we are interested in workspace
with name glofit_data_Parameters, containing optimized values for Radius and Height
"""
output_workspace = "glofit_"+data.name()
Fit(Function=global_model, Output=output_workspace, CreateOutput=True, MaxIterations=500, **domain_model)
# Extract Height and Radius from workspace glofit_data_Parameters.
# Check optimal values are close to nominal ones
nparms=0
parameter_ws = mtd[output_workspace+"_Parameters"]
for irow in range(parameter_ws.rowCount()):
    row = parameter_ws.row(irow)
    if row["Name"]=="f0.f0.f1.Radius":
        Radius=row["Value"]  # Extract value of optimized Radius
        nparms+=1
    elif row["Name"]=="f0.f0.f1.Height":
        Height=row["Value"]  # Extract value of optimized Height
        nparms+=1
    if nparms==2:
        break
if abs(H-Height)/H < 0.1:
    print "Optimal Height within 10% of nominal value"
if abs(R-Radius)/R < 0.05:
    print "Optimal Radius within 5% of nominal value"