def test_k_fixed_with_background(self):
fit_options = self._setup_fit_options()
fit_options.background_order = 2
raw_ws = mtd['raw_ws']
raw_ws = ncs.preprocess(raw_ws, fit_options)
reduced_chi_square, params_ws = ncs.run_fit(raw_ws, fit_options)
ncs.display_fit_output(reduced_chi_square, params_ws,fit_options)
expected = [\
{'Name': 'f0.Width', 'Value': 4.11688961577, 'Error': 0.243634042057},\
{'Name': 'f0.FSECoeff', 'Value': 0.485180094118, 'Error': 0.0},\
{'Name': 'f0.C_0', 'Value': 20.0966608709, 'Error': 1.21251471312},\
{'Name': 'f1.Width', 'Value': 10.0, 'Error': 0.0},\
{'Name': 'f1.Intensity', 'Value': 4.09470837231, 'Error': 0.433950802885},\
{'Name': 'f2.Width', 'Value': 13.0, 'Error': 0.0},\
{'Name': 'f2.Intensity', 'Value': 2.93153580371, 'Error': 0.464917511735},\
{'Name': 'f3.Width', 'Value': 30.0, 'Error': 0.0},\
{'Name': 'f3.Intensity', 'Value': 0.969159274084, 'Error': 0.23026233512},\
{'Name': 'f4.A0', 'Value': -0.00656341362348, 'Error': 0.00289823424173},\
{'Name': 'f4.A1', 'Value': 18.9776869966, 'Error':19.1127629958},\
{'Name': 'f4.A2', 'Value': 93.5252719638, 'Error': 29084.614777},\
{'Name': 'Cost function value', 'Value': 3.38368838556, 'Error': 0.0}\
]
self._check_params(params_ws,expected)
def test_k_free_with_background(self):
fit_options = self._setup_fit_options()
#------------------- FIRST MASS --------------------------
fit_options.masses[0]['k_free'] = True
#-------------------------------------------------------------------
fit_options.background_order = 2
raw_ws = mtd['raw_ws']
raw_ws = ncs.preprocess(raw_ws, fit_options)
reduced_chi_square, params_ws = ncs.run_fit(raw_ws, fit_options)
ncs.display_fit_output(reduced_chi_square, params_ws,fit_options)
expected = [\
{'Name': 'f0.Width', 'Value': 4.12370499357, 'Error': 0.280633041767},
{'Name': 'f0.FSECoeff', 'Value': -32.2147778697, 'Error': 21.4446620068},\
{'Name': 'f0.C_0', 'Value': 20.6729781391, 'Error': 1.46423308815},\
{'Name': 'f1.Width', 'Value': 10.0, 'Error': 0.0},\
{'Name': 'f1.Intensity', 'Value': 4.11299150262, 'Error': 0.434739226944},\
{'Name': 'f2.Width', 'Value': 13.0, 'Error': 0.0},\
{'Name': 'f2.Intensity', 'Value': 2.93613295977, 'Error': 0.465065906128},\
{'Name': 'f3.Width', 'Value': 30.0, 'Error': 0.0},\
{'Name': 'f3.Intensity', 'Value': 0.963632631744, 'Error': 0.230636343676},\
{'Name': 'f4.A0', 'Value': -0.00518193477871, 'Error': 0.00300080764282},\
{'Name': 'f4.A1', 'Value': 15.8505498861, 'Error': 19.129998072},\
{'Name': 'f4.A2', 'Value': -30.6569391098, 'Error': 29179.377345725108},\
{'Name': 'Cost function value', 'Value': 3.34158168582, 'Error': 0.0}\
]
self._check_params(params_ws,expected)
def test_k_fixed_no_background(self):
fit_options = self._setup_fit_options()
raw_ws = mtd['raw_ws']
raw_ws = ncs.preprocess(raw_ws, fit_options)
reduced_chi_square, params_ws = ncs.run_fit(raw_ws, fit_options)
message = ncs.display_fit_output(reduced_chi_square, params_ws,fit_options)
expected = [\
{'Name': 'f0.Width', 'Value': 3.9527823429702624, 'Error': 0.18552724464340611},\
{'Name': 'f0.FSECoeff', 'Value': 0.465839866544787, 'Error': 0.0},\
{'Name': 'f0.C_0', 'Value': 21.228162833736132, 'Error': 0.7484061245669142},
{'Name': 'f1.Width', 'Value': 10.0, 'Error': 0.0},\
{'Name': 'f1.Intensity', 'Value': 4.31589459135, 'Error': 0.426413174495},\
{'Name': 'f2.Width', 'Value': 13.0, 'Error': 0.0},\
{'Name': 'f2.Intensity', 'Value': 2.92303015759, 'Error': 0.463509032992},\
{'Name': 'f3.Width', 'Value': 30.0, 'Error': 0.0},\
{'Name': 'f3.Intensity', 'Value': 0.931308549279, 'Error': 0.22624298123},\
{'Name': 'Cost function value', 'Value': 3.28100193547, 'Error': 0.0}\
]
self._check_params(params_ws,expected)
expected_msg = \
"""
Reduced Chi-Square =3.281002
Fitting in the TOF space
--------------------------------------------------------------------------------
The mass M(1)=1.007900
Parameters values in the Y space:
See results log for resolution parameters w_L1, w_L0 etc
St. dev. of momentum distr. = 3.952782 +/- 0.185527
Scatt. int. (area, normalised) = 0.722086 +/- 0.004042
Hermite polynomial expansion coefficient c0 = 1.000000 +/- 0.000000
Hermite polynomial expansion coefficient c2 = 0.000000 +/- 0.000000
Hermite polynomial expansion coefficient c4 = 0.000000 +/- 0.000000
Hermite polynomial expansion coefficient a0 = c0/(2^0*0!) = 1.000000 +/ 0.000000
Hermite polynomial expansion coefficient a2 = c2/(2^2*1!) = 0.000000 +/ 0.000000
Hermite polynomial expansion coefficient a4 = c4/(2^4*2!) = 0.000000 +/ 0.000000
FSE coefficient k by the k/q He_3(y) expansion member = 0.465840 +/- 0.021865
The coefficient k calculated in a harmonic oscillator model would be k = sigma*sqrt(2)/12 = 0.465840
--------------------------------------------------------------------------------
The mass M(2)=16.000000
Parameters values in the Y space:
See results log for resolution parameters w_L1, w_L0 etc
St. dev. of momentum distr. = 10.000000 +/- 0.000000
Scatt. int. (area, normalised) = 0.146807 +/- 0.010727
--------------------------------------------------------------------------------
The mass M(3)=27.000000
Parameters values in the Y space:
See results log for resolution parameters w_L1, w_L0 etc
St. dev. of momentum distr. = 13.000000 +/- 0.000000
Scatt. int. (area, normalised) = 0.099428 +/- 0.010590
--------------------------------------------------------------------------------
The mass M(4)=133.000000
Parameters values in the Y space:
See results log for resolution parameters w_L1, w_L0 etc
St. dev. of momentum distr. = 30.000000 +/- 0.000000
Scatt. int. (area, normalised) = 0.031679 +/- 0.010457
"""
self.assertEquals(expected_msg, message)
def test_k_free_no_background(self):
fit_options = self._setup_fit_options()
#------------------- FIRST MASS ------------------------------------
fit_options.masses[0]['k_free'] = True
#-------------------------------------------------------------------
raw_ws = mtd['raw_ws']
raw_ws = ncs.preprocess(raw_ws, fit_options)
reduced_chi_square, params_ws = ncs.run_fit(raw_ws, fit_options)
message = ncs.display_fit_output(reduced_chi_square, params_ws,fit_options)
expected = [\
{'Name': 'f0.Width', 'Value': 4.23594841483436, 'Error': 0.21738339873184126},\
{'Name': 'f0.FSECoeff', 'Value': -76.53672476106186, 'Error': 20.37825852906559},\
{'Name': 'f0.C_0', 'Value': 22.602517742831605, 'Error': 0.8424355253357285},\
{'Name': 'f1.Width', 'Value': 10.0, 'Error': 0.0},\
{'Name': 'f1.Intensity', 'Value': 4.315249149863499, 'Error': 0.42641324871786956},\
{'Name': 'f2.Width', 'Value': 13.0, 'Error': 0.0},\
{'Name': 'f2.Intensity', 'Value': 2.9233511235166656, 'Error': 0.463509049047},\
{'Name': 'f3.Width', 'Value': 30.0, 'Error': 0.0},\
{'Name': 'f3.Intensity', 'Value': 0.9310641107710553, 'Error': 0.226242997134},\
{'Name': 'Cost function value', 'Value': 3.2673708431428428, 'Error': 0.0}\
]
self._check_params(params_ws,expected)
expected_msg = \
"""
Reduced Chi-Square =3.267371
Fitting in the TOF space
--------------------------------------------------------------------------------
The mass M(1)=1.007900
Parameters values in the Y space:
See results log for resolution parameters w_L1, w_L0 etc
St. dev. of momentum distr. = 4.235948 +/- 0.217383
Scatt. int. (area, normalised) = 0.734511 +/- 0.003924
Hermite polynomial expansion coefficient c0 = 1.000000 +/- 0.000000
Hermite polynomial expansion coefficient c2 = 0.000000 +/- 0.000000
Hermite polynomial expansion coefficient c4 = 0.000000 +/- 0.000000
Hermite polynomial expansion coefficient a0 = c0/(2^0*0!) = 1.000000 +/ 0.000000
Hermite polynomial expansion coefficient a2 = c2/(2^2*1!) = 0.000000 +/ 0.000000
Hermite polynomial expansion coefficient a4 = c4/(2^4*2!) = 0.000000 +/ 0.000000
FSE coefficient k by the k/q He_3(y) expansion member = -3.386204 +/- 1.027802
The coefficient k calculated in a harmonic oscillator model would be k = sigma*sqrt(2)/12 = 0.499211
--------------------------------------------------------------------------------
The mass M(2)=16.000000
Parameters values in the Y space:
See results log for resolution parameters w_L1, w_L0 etc
St. dev. of momentum distr. = 10.000000 +/- 0.000000
Scatt. int. (area, normalised) = 0.140232 +/- 0.011058
--------------------------------------------------------------------------------
The mass M(3)=27.000000
Parameters values in the Y space:
See results log for resolution parameters w_L1, w_L0 etc
St. dev. of momentum distr. = 13.000000 +/- 0.000000
Scatt. int. (area, normalised) = 0.095000 +/- 0.010934
--------------------------------------------------------------------------------
The mass M(4)=133.000000
Parameters values in the Y space:
See results log for resolution parameters w_L1, w_L0 etc
St. dev. of momentum distr. = 30.000000 +/- 0.000000
Scatt. int. (area, normalised) = 0.030257 +/- 0.010815
"""
self.assertEquals(expected_msg,message)
fit_options.masses = [mass1, mass2, mass3, mass4]
## Intensity constraints
fit_options.constraints = ([0,1,0,-4])
## Load data & preprocess ##
tof_data = LoadVesuvio(Filename=runs, SpectrumList=spectra,
Mode=diff_type,InstrumentParFile=ip_file,SumSpectra=sum_spectra)
tof_data = CropWorkspace(tof_data,XMin=50.0,XMax=562.0)
tof_data = ncs.preprocess(tof_data, fit_options)
## Run fitting ##
fitted_ws, fitted_params = [], []
for idx in range(tof_data.getNumberHistograms()):
fit_options.workspace_index = idx
reduced_chi_square, params_ws = ncs.run_fit(tof_data, fit_options)
fitted_ws.append("fit_%d" % (idx+1))
RenameWorkspace("fit_Workspace", OutputWorkspace=fitted_ws[-1])
fitted_params.append("params_%d" % (idx+1))
RenameWorkspace("fit_Parameters", OutputWorkspace=fitted_params[-1])
# Group
fitted_data= GroupWorkspaces(fitted_ws)
fitted_pars = GroupWorkspaces(fitted_params)
## Print results to screen ##
#ncs.display_fit_output(reduced_chi_square, params_ws,fit_options)