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Copy pathDSPC_bilayer_example.py
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Copy pathDSPC_bilayer_example.py
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204 lines (189 loc) · 10.5 KB
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import numpy as np
from rat import rat_core
if __name__ == '__main__':
control = rat_core.Control()
problem = rat_core.ProblemDefinition()
limits = rat_core.Limits()
cells = rat_core.Cells()
priors = rat_core.Priors()
#------------------------------------------------------------------------------------
# Control
control.procedure = 'calculate'
control.parallel = 'single'
control.display = 'iter'
control.calcSldDuringFit = False
control.resamPars = [0.9000, 50]
# control.procedure = 'simplex'
# control.tolX = 1e-6
# control.tolFun = 1e-6
# control.maxFunEvals = 10000
# control.maxIter = 1
# control.updateFreq = -1
# control.updatePlotFreq = 1
# control.procedure = 'dream'
# control.nSamples = 100
# control.nChains = 10
# control.jumpProbability = 0.5
# control.pUnitGamma = 0.2
# control.boundHandling = 'fold'
# control.adaptPCR = False;
# control.procedure = 'de'
# control.populationSize = 10
# control.fWeight = 0.5
# control.crossoverProbability = 0.8
# control.strategy = 4
# control.targetValue = 1
# control.numGenerations = 500
# control.procedure = 'ns'
# control.Nlive = 150
# control.Nmcmc = 0
# control.propScale = 0.1
# control.nsTolerance = 0.1
control.checks.fitParam = [1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1]
control.checks.fitBackgroundParam = np.ones((2))
control.checks.fitQzshift = [0]
control.checks.fitScalefactor = [0, 0]
control.checks.fitBulkIn = [0]
control.checks.fitBulkOut = [0, 0]
control.checks.fitResolutionParam = [0]
#------------------------------------------------------------------------------------
# ProblemDef
problem.contrastBackgrounds = [1, 2]
problem.contrastBackgroundsType = [1, 1]
problem.TF = 'non polarised'
problem.resample = [0, 0]
problem.dataPresent = [1, 1]
problem.oilChiDataPresent = [0, 0]
problem.numberOfContrasts = 2
problem.geometry = 'substrate/liquid'
problem.useImaginary = False
problem.contrastQzshifts = [1, 1]
problem.contrastScalefactors = [1, 1]
problem.contrastBulkIns = [1, 1]
problem.contrastBulkOuts = [1, 2]
problem.contrastResolutions = [1, 1]
problem.backgroundParams = [2.28525214157571e-06, 3.10246417982706e-06]
problem.qzshifts = [0]
problem.scalefactors = [0.102860901766649, 0.151378164199993]
problem.bulkIn = [2.07399989449186e-06]
problem.bulkOut = [5.98067802097389e-06, 2.21000000000000e-06]
problem.resolutionParams = [0.0300]
problem.params = [5.08929897223891, 19.2830495317839, 3.39317612702561e-06, 21.5451088784623, -4.01001212540204e-07, 1.24963548580913, 8.09797576127018, 20.3854719276630,
0, 5.79580504624961, 1.75686012656988e-06, 10.0916971312704, 11.7066921282983, 1.47000000000000e-06, 5.63694566866001, 17.8733566725932, -4.61089672942576e-07,
17.8556570737753, 41.4142113901161, 100, 25.6893163532023]
problem.numberOfLayers = 6
problem.modelType = 'standard layers'
problem.contrastCustomFiles = [np.NAN, np.NaN]
problem.contrastDomainRatios = [0, 0]
problem.domainRatio = []
problem.numberOfDomainContrasts = 0
problem.fitParams = [5.08929897223891, 19.2830495317839, 21.5451088784623, 1.24963548580913, 8.09797576127018,
20.3854719276630, 5.79580504624961, 10.0916971312704, 11.7066921282983, 5.63694566866001,
17.8733566725932, 17.8556570737753, 41.4142113901161, 25.6893163532023, 2.28525214157571e-06,
3.10246417982706e-06]
problem.otherParams = [3.39317612702561e-06, -4.01001212540204e-07, 0, 1.75686012656988e-06, 1.47e-06, -4.61089672942576e-07,
100, 0.102860901766649, 0.151378164199993,0, 2.07399989449186e-06, 5.98067802097389e-06, 2.21e-06, 0.030]
problem.fitLimits = [[2, 8], [5, 60], [15, 35], [1, 50], [1, 15], [10, 28], [5, 17],
[10, 50], [7, 17], [2, 15], [14, 22], [10, 50], [10, 50], [0, 60], [5.0e-10, 7.0e-06],
[1.0e-10, 4.99999987368938e-06]]
problem.otherLimits = [[3.39e-06, 3.41e-06], [-5.0e-07, -3.0e-07], [0, 1.0e-09], [1.0e-07, 2.0e-06], [5.0e-07, 1.5e-06],
[-5.0e-07, 0], [99.9, 100], [0.050, 0.20], [0.050, 0.20], [-0.00010, 0.00010], [2.0e-06, 2.1e-06],
[5.50000004295725e-06, 6.4e-06], [2.0e-06, 4.99999987368938e-06], [0.010, 0.050]]
#------------------------------------------------------------------------------------
# Limits
limits.param = [[2, 8], [5, 60], [0, 0], [15, 35], [-0, -0], [1, 50], [1, 15],
[10, 28], [0, 0], [5, 17], [0, 0], [10, 50], [7, 17], [0, 0], [2, 15],
[14, 22], [-0, 0], [10, 50], [10, 50], [99.9,100], [0, 60]]
limits.backgroundParam = [[5.0000e-10, 7.0000e-06], [1.0000e-10, 5.0000e-06]]
limits.scalefactor = [[0.0500, 0.2000], [0.0500, 0.2000]]
limits.qzshift = [[-1.0000e-04, 1.0000e-04]]
limits.bulkIn = [[2.0000e-06, 2.1000e-06]]
limits.bulkOut = [[5.5000e-06, 6.4000e-06], [2.0000e-06, 5.0000e-06]]
limits.resolutionParam = [[0.0100, 0.0500]]
limits.domainRatio = []
#-------------------------------------------------------------------------------------
# Cells
cells.f1 = [[0, 1], [0, 1]]
cells.f2 = [np.loadtxt('data/dspc_bilayer/data1.csv', delimiter=','),
np.loadtxt('data/dspc_bilayer/data2.csv', delimiter=',')]
cells.f3 = [[0.011403, 0.59342], [0.011403, 0.59342]]
cells.f4 = [[0.011403, 0.70956], [0.011403, 0.59342]]
cells.f5 = [[1, 2, 3, 4, 5, 6, 6, 5], [1, 2, 3, 4, 5, 6, 6, 5]]
cells.f6 = [[2, 3, 1, 21, 2],
[4, 5, 7, 6, 2],
[10, 11, 7, 12, 2],
[8, 9, 15, 20, 2],
[13, 14, 15, 19, 2],
[16, 17, 15, 18, 2]]
cells.f7 = ['Substrate Roughness', 'Oxide thick', 'Oxide SLD', 'Sam tails thick', 'Sam tails SLD',
'Sam tails hydration', 'Sam rough', 'cw thick', 'cw SLD', 'SAM head thick', 'SAM head SLD',
'SAM head hydration', 'Bilayer head thick', 'Bilayer head SLD', 'Bilayer rough',
'Bilayer tails thick', 'Bilayer tails SLD', 'Bilayer tails hydr', 'Bilayer heads hydr', 'cw hydration', 'Oxide Hydration']
cells.f8 = ['Backs parameter 1', 'Backs parameter 2']
cells.f9 = ['Scalefactor 1', 'Scalefactor 2']
cells.f10 = ['Qz shift 1']
cells.f11 = ['Air']
cells.f12 = ['D2O', 'SMW']
cells.f13 = ['Resolution par 1']
# cells.f14 = [['', '', '']]
cells.f15 = ['constant', 'constant']
cells.f16 = ['constant']
# cells.f17 = [[], []]
# cells.f18 = []
# cells.f19 = []
# cells.f20 = []
#------------------------------------------------------------------------------------
# Priors
priors.param = [['Substrate Roughness', 'uniform', 0, np.Inf],
['Oxide thick', 'uniform', 0, np.Inf],
['Oxide SLD', 'uniform', 0, np.Inf],
['Sam tails thick', 'uniform', 0, np.Inf],
['Sam tails SLD', 'uniform', 0, np.Inf],
['Sam tails hydra…', 'uniform', 0, np.Inf],
['Sam rough', 'uniform', 0, np.Inf],
['cw thick', 'uniform', 0, np.Inf],
['cw SLD', 'uniform', 0, np.Inf],
['SAM head thick', 'uniform', 0, np.Inf],
['SAM head SLD', 'uniform', 0, np.Inf],
['SAM head hydrat…', 'uniform', 0, np.Inf],
['Bilayer head th…', 'uniform', 0, np.Inf],
['Bilayer head SLD', 'uniform', 0, np.Inf],
['Bilayer rough', 'uniform', 0, np.Inf],
['Bilayer tails t…', 'uniform', 0, np.Inf],
['Bilayer tails SLD', 'uniform', 0, np.Inf],
['Bilayer tails h…', 'uniform', 0, np.Inf],
['Bilayer heads h…', 'uniform', 0, np.Inf],
['cw hydration', 'uniform', 0, np.Inf],
['Oxide Hydration', 'uniform', 0, np.Inf]]
priors.backgroundParam = [['Backs parameter 1', 'uniform', 0, np.Inf], ['Backs parameter 2', 'uniform', 0, np.Inf]]
priors.scalefactor = [['Scalefactor 1', 'uniform', 0, np.Inf], ['Scalefactor 2', 'uniform', 0, np.Inf]]
priors.qzshift = [['Qz shift 1', 'uniform', 0, np.Inf]]
priors.bulkIn = [['Air', 'uniform', 0, np.Inf]]
priors.bulkOut = [['D2O', 'uniform', 0, np.Inf], ['SMW', 'uniform', 0, np.Inf]]
priors.resolutionParam = [['Resolution par 1', 'uniform', 0, np.Inf]]
priors.priorNames = ['Substrate Roughness', 'Oxide thick', 'Oxide SLD',
'Sam tails thick', 'Sam tails SLD', 'Sam tails hydration', 'Sam rough',
'cw thick', 'cw SLD', 'SAM head thick', 'SAM head SLD', 'SAM head hydration',
'Bilayer head thick', 'Bilayer head SLD', 'Bilayer rough', 'Bilayer tails thick',
'Bilayer tails SLD', 'Bilayer tails hydr', 'Bilayer heads hydr', 'cw hydration',
'Oxide Hydration','Backs parameter 1', 'Backs parameter 2', 'Resolution par 1',
'Air', 'D2O', 'SMW', 'Qz shift 1', 'Scalefactor 1', 'Scalefactor 2']
priors.priorValues = [[1, 0, np.Inf],[1, 0, np.Inf],[1, 0, np.Inf],[1, 0, np.Inf],[1, 0, np.Inf],
[1, 0, np.Inf],[1, 0, np.Inf],[1, 0, np.Inf],[1, 0, np.Inf],[1, 0, np.Inf],
[1, 0, np.Inf],[1, 0, np.Inf],[1, 0, np.Inf],[1, 0, np.Inf],[1, 0, np.Inf],
[1, 0, np.Inf],[1, 0, np.Inf],[1, 0, np.Inf],[1, 0, np.Inf],[1, 0, np.Inf],
[1, 0, np.Inf],[1, 0, np.Inf],[1, 0, np.Inf],[1, 0, np.Inf],[1, 0, np.Inf],
[1, 0, np.Inf],[1, 0, np.Inf],[1, 0, np.Inf],[1, 0, np.Inf],[1, 0, np.Inf]]
problem, contrast_params, result, bayes_results = rat_core.RATMain(problem, cells, limits, control, priors)
print(contrast_params.ssubs)
print(contrast_params.backgroundParams)
print(contrast_params.qzshifts)
print(contrast_params.scalefactors)
print(contrast_params.bulkIn)
print(contrast_params.bulkOut)
print(contrast_params.resolutionParams)
print(contrast_params.calculations.allChis)
print(contrast_params.calculations.sumChi)
print(contrast_params.allSubRough)
print(contrast_params.resample)
#print(output)
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