def SimInt_ID3(FITPAR):
TPARs=np.zeros([Trapnumber+1,2])
TPARs[:,0:2]=np.reshape(FITPAR[0:(Trapnumber+1)*2],(Trapnumber+1,2))
SPAR=FITPAR[Trapnumber*2+2:Trapnumber*2+5]
X1=FITPAR[Trapnumber*2+5]
(Coord)= CD.ID2CoordAssign(TPAR,SLD,Trapnumber,Pitch,X1)
F1 = CD.FreeFormTrapezoid(Coord[:,:,0],Qx,Qz,Trapnumber)
F2 = CD.FreeFormTrapezoid(Coord[:,:,1],Qx,Qz,Trapnumber)
F3 = CD.FreeFormTrapezoid(Coord[:,:,2],Qx,Qz,Trapnumber)
M=np.power(np.exp(-1*(np.power(Qx,2)+np.power(Qz,2))*np.power(SPAR[0],2)),0.5)
Formfactor=(F1+F2+F3)*M
Formfactor=abs(Formfactor)
SimInt = np.power(Formfactor,2)*SPAR[1]+SPAR[2]
return (SimInt,Formfactor)
def Uncertainty2(ReSampledMatrix):
Xi = np.zeros([101, 4, len(ReSampledMatrix[:, 0])])
Yi = np.zeros([101, 1, len(ReSampledMatrix[:, 0])])
PopWidth = np.zeros([len(ReSampledMatrix[:, 0])])
PopHeight = np.zeros([len(ReSampledMatrix[:, 0])])
TrapHeight = np.zeros([Trapnumber + 1, len(ReSampledMatrix[:, 0])])
S1 = np.zeros(len(ReSampledMatrix[:, 0]))
for PopNumber in range(len(ReSampledMatrix[:, 0])):
TPARU = np.zeros([Trapnumber + 1, 2])
TPARU[:, 0:2] = np.reshape(
ReSampledMatrix[PopNumber, 0:(Trapnumber + 1) * 2],
(Trapnumber + 1, 2))
X1fit = ReSampledMatrix[PopNumber, Trapnumber * 2 + 5]
(CoordUnc) = CD.ID2CoordAssign(TPARU, SLD, Trapnumber, Pitch, X1fit)
for i in np.arange(1, Trapnumber + 1, 1):
TrapHeight[i, PopNumber] = TrapHeight[i - 1,
PopNumber] + TPARU[i - 1, 1]
for LineNumber in range(2):
EffTrapnumber = 0
#LeftSide
X1L = CoordUnc[EffTrapnumber, 0, LineNumber]
X2L = CoordUnc[EffTrapnumber + 1, 0, LineNumber]
X1R = CoordUnc[EffTrapnumber, 1, LineNumber]
X2R = CoordUnc[EffTrapnumber + 1, 1, LineNumber]
Y1 = TrapHeight[EffTrapnumber, PopNumber]
Y2 = TrapHeight[EffTrapnumber + 1, PopNumber]
Disc = 0
for c in np.arange(0, 101, 1):
if Disc > Y2 and Disc < TrapHeight[Trapnumber, PopNumber]:
EffTrapnumber = EffTrapnumber + 1
X1L = CoordUnc[EffTrapnumber, 0, LineNumber]
X2L = CoordUnc[EffTrapnumber + 1, 0, LineNumber]
X1R = CoordUnc[EffTrapnumber, 1, LineNumber]
X2R = CoordUnc[EffTrapnumber + 1, 1, LineNumber]
Y1 = TrapHeight[EffTrapnumber, PopNumber]
Y2 = TrapHeight[EffTrapnumber + 1, PopNumber]
ML = (Y2 - Y1) / (X2L - X1L)
MR = (Y2 - Y1) / (X2R - X1R)
BL = Y1 - ML * X1L
BR = Y1 - MR * X1R
Xi[c, LineNumber * 2, PopNumber] = (Disc - BL) / ML
Xi[c, LineNumber * 2 + 1, PopNumber] = (Disc - BR) / MR
Yi[c, 0, PopNumber] = Disc
Disc = Disc + TrapHeight[Trapnumber, PopNumber] / 100
Xi[:, :, PopNumber] = Xi[:, :, PopNumber] - (Xi[0, 1, PopNumber] -
Xi[0, 0, PopNumber]) / 2
PopWidth[PopNumber] = Xi[50, 1, PopNumber] - Xi[50, 0, PopNumber]
PopHeight[PopNumber] = Yi[100, 0, PopNumber]
S1[PopNumber] = Xi[0, 0, PopNumber] - Xi[0, 2, PopNumber]
S = np.std(Xi, 2) * 1.96
Center = np.average(Xi, 2)
Sy = np.std(Yi, 2) * 1.96
YC = np.average(Yi, 2)
OuterEdge = Center
YInner = YC - Sy
YOuter = YC + Sy
OuterEdge[:, 0] = OuterEdge[:, 0] - S[:, 0]
OuterEdge[:, 1] = OuterEdge[:, 1] + S[:, 1]
InnerEdge = Center
InnerEdge[:, 0] = InnerEdge[:, 0] + S[:, 0]
InnerEdge[:, 1] = InnerEdge[:, 1] - S[:, 1]
LinePlot = np.zeros([2 * 101, 2])
InnerPlot = np.zeros([2 * 101, 2])
OuterPlot = np.zeros([2 * 101, 2])
LinePlot[0:101, 0] = Center[:, 0]
LinePlot[101:202, 0] = np.flipud(Center[:, 1])
LinePlot[0:101, 1] = YC[:, 0]
LinePlot[101:202, 1] = np.flipud(YC[:, 0])
InnerPlot[0:101, 0] = InnerEdge[:, 0]
InnerPlot[101:202, 0] = np.flipud(InnerEdge[:, 1])
InnerPlot[0:101, 1] = YInner[:, 0]
InnerPlot[101:202, 1] = np.flipud(YInner[:, 0])
OuterPlot[0:101, 0] = OuterEdge[:, 0]
OuterPlot[101:202, 0] = np.flipud(OuterEdge[:, 1])
OuterPlot[0:101, 1] = YOuter[:, 0]
OuterPlot[101:202, 1] = np.flipud(YOuter[:, 0])
vi = np.zeros([100, 1])
vo = np.zeros([100, 1])
vc = np.zeros([100, 1])
for l in np.arange(1, 1 + 0.0001, 2):
for h in np.arange(1, 101, 1):
vi[h - 1,
l / 2] = 0.5 * (YInner[h, l - 1] - YInner[h - 1, l - 1]) * (
(InnerEdge[h - 1, l] - InnerEdge[h - 1, l - 1]) +
(InnerEdge[h, l] - InnerEdge[h, l - 1]))
vo[h - 1,
l / 2] = 0.5 * (YOuter[h, l - 1] - YOuter[h - 1, l - 1]) * (
(OuterEdge[h - 1, l] - OuterEdge[h - 1, l - 1]) +
(OuterEdge[h, l] - OuterEdge[h, l - 1]))
vc[h - 1, l / 2] = 0.5 * (YC[h, l - 1] - YC[h - 1, l - 1]) * (
(Center[h - 1, l] - Center[h - 1, l - 1]) +
(Center[h, l] - Center[h, l - 1]))
vd = vo - vi
vt = np.sum(vd)
vct = np.sum(vc)
WidthAvg = np.average(PopWidth)
HeightAvg = np.average(PopHeight)
WidthStd = np.std(PopWidth)
HeightStd = np.std(PopHeight)
S1Avg = np.average(S1)
S1Std = np.std(S1)
viLower = np.zeros([10, 1])
voLower = np.zeros([10, 1])
vcLower = np.zeros([10, 1])
for l in np.arange(1, 1 + 0.0001, 2):
for h in np.arange(1, 11, 1):
viLower[h - 1, l /
2] = 0.5 * (YInner[h, l - 1] - YInner[h - 1, l - 1]) * (
(InnerEdge[h - 1, l] - InnerEdge[h - 1, l - 1]) +
(InnerEdge[h, l] - InnerEdge[h, l - 1]))
voLower[h - 1, l /
2] = 0.5 * (YOuter[h, l - 1] - YOuter[h - 1, l - 1]) * (
(OuterEdge[h - 1, l] - OuterEdge[h - 1, l - 1]) +
(OuterEdge[h, l] - OuterEdge[h, l - 1]))
vcLower[h - 1, l / 2] = 0.5 * (YC[h, l - 1] - YC[h - 1, l - 1]) * (
(Center[h - 1, l] - Center[h - 1, l - 1]) +
(Center[h, l] - Center[h, l - 1]))
vdLower = voLower - viLower
vtLower = np.sum(vdLower)
vctLower = np.sum(vcLower)
viUpper = np.zeros([10, 1])
voUpper = np.zeros([10, 1])
vcUpper = np.zeros([10, 1])
for l in np.arange(1, 1 + 0.0001, 2):
for h in np.arange(91, 101, 1):
viUpper[h - 91, l /
2] = 0.5 * (YInner[h, l - 1] - YInner[h - 1, l - 1]) * (
(InnerEdge[h - 1, l] - InnerEdge[h - 1, l - 1]) +
(InnerEdge[h, l] - InnerEdge[h, l - 1]))
voUpper[h - 91, l /
2] = 0.5 * (YOuter[h, l - 1] - YOuter[h - 1, l - 1]) * (
(OuterEdge[h - 1, l] - OuterEdge[h - 1, l - 1]) +
(OuterEdge[h, l] - OuterEdge[h, l - 1]))
vcUpper[h - 91,
l / 2] = 0.5 * (YC[h, l - 1] - YC[h - 1, l - 1]) * (
(Center[h - 1, l] - Center[h - 1, l - 1]) +
(Center[h, l] - Center[h, l - 1]))
vdUpper = voUpper - viUpper
vtUpper = np.sum(vdUpper)
vctUpper = np.sum(vcUpper)
RA = vt / vct
RAL = vtLower / vctLower
RAU = vtUpper / vctUpper
return (vt, vct, RA, vtLower, vctLower, RAL, vtUpper, vctUpper, RAU,
WidthAvg, HeightAvg, WidthStd, HeightStd, S1Avg, S1Std, LinePlot,
InnerPlot, OuterPlot)
X1 = Param[SampleNumber, 4]
W = Param[SampleNumber, 0]
H = Param[SampleNumber, 1]
WidthLoad = 'W' + str(int(W)) + '.txt'
HeightLoad = 'H' + str(int(H)) + '.txt'
Width = np.loadtxt(WidthLoad)
Height = np.loadtxt(HeightLoad)
TPAR[:, 0] = Width
TPAR[:, 1] = Height
SLD[0, 0] = SLD1
SLD[1, 0] = SLD1
SLD[2, 0] = SLD1
SLD[3, 0] = SLD1
Coord = CD.ID2CoordAssign(TPAR, SLD, Trapnumber, Pitch, X1)
#CDp.plotID1(Coord,Trapnumber,Pitch)
(FITPAR, FITPARLB, FITPARUB) = CD.PBA_ID2(TPAR, SPAR, Trapnumber, X1)
R = np.random.normal(0, 0.225, [len(Qx[:, 0]), len(Qx[0, :])])
(Intensity, Amplitude) = SimInt_ID2(FITPAR)
N = (1 / (np.power(Intensity, 0.5))) * Intensity # Generates noise
N = N * R
Intensity2 = Intensity + R
# Applies Noise
C = CD.Misfit(Intensity, Intensity2)
Chi2 = np.sum((C))
MCPAR = np.zeros([7])
