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USANSSimulation.py
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# Mantid Repository : https://github.com/mantidproject/mantid
#
# Copyright © 2018 ISIS Rutherford Appleton Laboratory UKRI,
# NScD Oak Ridge National Laboratory, European Spallation Source,
# Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
# SPDX - License - Identifier: GPL - 3.0 +
#pylint: disable=no-init,invalid-name
from mantid.simpleapi import *
from mantid.api import *
from mantid.kernel import *
import math
import numpy
class USANSSimulation(PythonAlgorithm):
def category(self):
return "SANS"
def seeAlso(self):
return [ "USANSReduction" ]
def name(self):
return "USANSSimulation"
def summary(self):
return "Simulate a USANS workspace"
def PyInit(self):
self.declareProperty("TwoTheta", 0.01, "Scattering angle in degrees")
self.declareProperty(FloatArrayProperty("WavelengthPeaks", values=[0.72, 0.9, 1.2, 1.8, 3.6],
direction=Direction.Input), "Wavelength peaks out of the monochromator")
self.declareProperty("CountTime", 1000.0, "Fake count time")
# Model parameters
self.declareProperty("EmptyRun", False, "If True, the run is considered an empty run")
self.declareProperty("SphereRadius", 60.0, "Radius for the sphere model (Angstrom)")
self.declareProperty("Background", 0.0, "Background")
self.declareProperty("SigmaPeak", 0.01, "Width of the wavelength peaks")
self.declareProperty(MatrixWorkspaceProperty("OutputWorkspace", "", Direction.Output), "Output workspace")
self.declareProperty(MatrixWorkspaceProperty("MonitorWorkspace", "", Direction.Output), "Output monitor workspace")
#pylint: disable=too-many-locals
def PyExec(self):
workspace = self.getPropertyValue("OutputWorkspace")
out_ws = CreateSimulationWorkspace(Instrument="USANS",
BinParams="0,50,32000",
UnitX="TOF",
OutputWorkspace=workspace)
out_ws.setYUnitLabel("1/cm")
data_x = out_ws.dataX(0)
mon_ws_name = self.getPropertyValue("MonitorWorkspace")
mon_ws = CreateWorkspace(dataX=data_x, dataY=numpy.zeros(len(data_x)-1),
UnitX="TOF", OutputWorkspace=mon_ws_name)
mon_y = mon_ws.dataY(0)
mon_e = mon_ws.dataE(0)
# Number of pixels for the main detector
n_pixels = int(out_ws.getNumberHistograms()/2)
# Clean up the workspace
for j in range(n_pixels):
data_y = out_ws.dataY(j)
for i in range(len(data_y)):
data_y[i] = 0.0
# Fill monitor workspace with fake beam profile
count_time = self.getProperty("CountTime").value
for i in range(len(data_x)-1):
wl_i = 0.0039560/30.0*(data_x[i]+data_x[i+1])/2.0
mon_y[i] = count_time*math.exp(-wl_i)
mon_e[i] = math.sqrt(mon_y[i])
# Add analyzer theta value and monochromator angle theta_b in logs
two_theta = self.getProperty("TwoTheta").value
is_empty_run = self.getProperty("EmptyRun").value
if is_empty_run:
two_theta = 0.0
theta_b = 70.0
theta = theta_b + two_theta
out_ws.getRun().addProperty("AnalyzerTheta", theta, 'degree', True)
out_ws.getRun().addProperty("two_theta", two_theta, 'degree', True)
out_ws.getRun().addProperty("MonochromatorTheta", theta_b, 'degree', True)
out_ws.getRun().addProperty("run_title", "Simulated USANS", True)
out_ws.getRun().addProperty("run_number", "1234", True)
# List of wavelength peaks, and width of the peaks
wl_peaks = self.getProperty("WavelengthPeaks").value
sigma = self.getProperty("SigmaPeak").value
for wl in wl_peaks:
q = 6.28*math.sin(two_theta)/wl
Logger("USANS").notice( "wl = %g; Q = %g" % (wl, q))
for i in range(len(data_x)-1):
wl_i = 0.0039560/30.0*(data_x[i]+data_x[i+1])/2.0
# Scale the I(q) by a Gaussian to simulate the wavelength peaks selected by the monochromator
flux = 1.0e6/(sigma*math.sqrt(2.0*math.pi))*math.exp(-(wl_i-wl)*(wl_i-wl)/(2.0*sigma*sigma))
# Multiply by beam profile
flux *= mon_y[i]
# Account for transmission
if not is_empty_run:
flux *= math.exp(-wl_i/2.0)
# Transmission detector
for j in range(n_pixels, 2*n_pixels):
det_pos = out_ws.getInstrument().getDetector(j).getPos()
r = math.sqrt(det_pos.Y()*det_pos.Y()+det_pos.X()*det_pos.X())
sigma = 0.01
scale = math.exp(-r*r/(2.0*sigma*sigma))
data_y = out_ws.dataY(j)
data_y[i] += int(scale*flux)
data_e = out_ws.dataE(j)
data_e[i] = math.sqrt(data_e[i]*data_e[i]+scale*scale*flux*flux)
# If we have an empty run, there's no need to fill the main detector
if is_empty_run:
continue
# Compute I(q) and store the results
q_i = q*wl/wl_i
i_q = self._sphere_model(q_i, scale=flux)
for j in range(n_pixels):
det_pos = out_ws.getInstrument().getDetector(j).getPos()
r = math.sqrt(det_pos.Y()*det_pos.Y()+det_pos.X()*det_pos.X())
sigma = 0.01
scale = math.exp(-r*r/(2.0*sigma*sigma))
data_y = out_ws.dataY(j)
data_y[i] += int(i_q*scale)
data_e = out_ws.dataE(j)
data_e[i] = math.sqrt(data_e[i]*data_e[i]+i_q*i_q*scale*scale)
self.setProperty("OutputWorkspace", out_ws)
self.setProperty("MonitorWorkspace", mon_ws)
def _sphere_model(self, q, scale):
"""
Return I(q) for a sphere model
@param q: q-value
@param scale: normalization factor to give I(q)
"""
radius = self.getProperty("SphereRadius").value
bck = self.getProperty("Background").value
qr = q*radius
bes = 3.0*(math.sin(qr)-qr*math.cos(qr))/(qr*qr*qr) if not qr == 0.0 else 1.0
vol = 4.0*math.pi/3.0*radius*radius*radius
f2 = vol*bes*bes*1.0e-6
return scale*f2+bck
#############################################################################################
AlgorithmFactory.subscribe(USANSSimulation())