def simSpc(mat, e0, det, dose, ntraj, simDir):
"""
simSpc(mat, e0, det, dose, ntraj, simDir)
simulate a spectrum from a material and write it out.
"""
xrts = []
trs = mc3.suggestTransitions(mat, e0)
for tr in trs:
xrts.append(tr)
xtraParams = {}
xtraParams.update(mc3.configureXRayAccumulators(xrts, True, True, True))
xtraParams.update(mc3.configureOutput(simDir))
spc = mc3.simulate(mat, det, e0, dose, True, nTraj, True, True, xtraParams)
fmtS = "%s-at-%g-kV"
sName = fmtS % (mat.getName(), e0)
spc.rename(sName)
spc.display()
fi = simDir + "/"
fi += sName
fi += "-%g-Traj.msa" % (nTraj)
spc.save(fi)
return spc
def simBulkStd(mat, det, e0, nTraj, lt=100, pc=1.0, ctd=True):
"""simBulkStd(mat, det, e0, nTraj, lt=100, pc=1.0)
Use mc3 simulation to simulate an uncoated standard specimen
Parameters
----------
mat - a dtsa material.
Note the material must have an associated density. It should have a useful name.
det - a dtsa detector
Here is where we get the detector properties and calibration
e0 - float
The accelerating voltage in kV
nTraj - integer
The number of trajectories to run
lt - integer (100)
The live time (sec)
pc - float (1.0)
The probe current in nA
ctd - Boolean (True) - is C coated
Returns
-------
sim - DTSA scriptable spectrum
The simulated standard spectrum
Example
-------
import dtsa2.jmMC3 as jm3
det = findDetector("Oxford p4 05eV 2K")
cu = material("Cu", density=8.92)
a = jm3.simBulkStd(cu, det, 20.0, 100, 100, 1.0)
a.display()
"""
dose = pc * lt # na-sec"
xrts = []
trs = mc3.suggestTransitions(mat, e0)
for tr in trs:
xrts.append(tr)
xtraParams = {}
xtraParams.update(mc3.configureXRayAccumulators(xrts, True, True, True))
sim = mc3.simulate(mat,
det,
e0,
dose,
withPoisson=True,
nTraj=nTraj,
sf=True,
bf=True,
xtraParams=xtraParams)
sName = "%s-%g-kV" % (mat, e0)
sim.rename(sName)
sim.setAsStandard(mat)
return sim
def simSpc(mat, e0, det, dose, ntraj, simDir):
"""
simSpc(mat, e0, det, dose, ntraj, simDir)
simulate a spectrum from a material and write it out.
"""
xrts = []
trs = mc3.suggestTransitions(mat, e0)
for tr in trs:
xrts.append(tr)
xtraParams={}
xtraParams.update(mc3.configureXRayAccumulators(xrts,True, True, True))
xtraParams.update(mc3.configureOutput(simDir))
spc = mc3.simulate(mat, det, e0, dose, True,
nTraj, True, True, xtraParams)
fmtS = "%s-at-%g-kV"
sName = fmtS % (mat.getName(), e0)
spc.rename(sName)
spc.display()
fi = simDir + "/"
fi += sName
fi += "-%g-Traj.msa" % (nTraj)
spc.save(fi)
return spc
def simBulkStd(mat, det, e0, nTraj, lt=100, pc=1.0, ctd=True):
"""simBulkStd(mat, det, e0, nTraj, lt=100, pc=1.0)
Use mc3 simulation to simulate an uncoated standard specimen
Parameters
----------
mat - a dtsa material.
Note the material must have an associated density. It should have a useful name.
det - a dtsa detector
Here is where we get the detector properties and calibration
e0 - float
The accelerating voltage in kV
nTraj - integer
The number of trajectories to run
lt - integer (100)
The live time (sec)
pc - float (1.0)
The probe current in nA
ctd - Boolean (True) - is C coated
Returns
-------
sim - DTSA scriptable spectrum
The simulated standard spectrum
Example
-------
import dtsa2.jmMC3 as jm3
det = findDetector("Oxford p4 05eV 2K")
cu = material("Cu", density=8.92)
a = jm3.simBulkStd(cu, det, 20.0, 100, 100, 1.0)
a.display()
"""
dose = pc * lt # na-sec"
xrts = []
trs = mc3.suggestTransitions(mat, e0)
for tr in trs:
xrts.append(tr)
xtraParams={}
xtraParams.update(mc3.configureXRayAccumulators(xrts,True, True, True))
sim = mc3.simulate(mat, det, e0, dose, withPoisson=True, nTraj=nTraj,
sf=True, bf=True, xtraParams=xtraParams)
sName = "%s-%g-kV" % (mat, e0)
sim.rename(sName)
sim.setAsStandard(mat)
return sim
def simulate_spectrum(mat, e0, det, nTraj, dose, spcDir):
"""
simulate_spectrum(mat, e0, det, nTraj, dose, spcDir)
"""
xrts = []
trs = mc3.suggestTransitions(mat, e0)
for tr in trs:
xrts.append(tr)
xtraParams={}
xtraParams.update(mc3.configureXRayAccumulators(xrts,True, True, True))
# xtraParams.update(mc3.configureOutput(simDir))
spc = mc3.simulate(mat, det, e0, dose, True, nTraj, True, True, xtraParams)
sName = "%s std-%g-kV" % (mat.getName(), e0)
spc.rename(sName)
spc.setAsStandard(mat)
spc.display()
# Save the spectra if NTraj > 500
if nTraj > 500:
fi = spcDir + "/"
fi += sName
fi += "-%g-Traj.msa" % (nTraj)
spc.save(fi)
return (spc)
def sim_amc_coated_mat(mat, det, e0, nTraj, lt=100, pc=1.0, tc=20.0):
"""sim_amc_coated_mat(mat, det, e0, nTraj, lt=100, pc=1.0, tc=20.0)
Use mc3 multilayer simulation to simulate an am-C-ctd specimen
Parameters
----------
mat - a dtsa material.
Note the material must have an associated density. It should have a useful name.
det - a dtsa detector
Here is where we get the detector properties and calibration
e0 - float
The accelerating voltage in kV
nTraj - integer
The number of trajectories to run
lt - integer (100)
The live time (sec)
pc - float (1.0)
The probe current in nA
tc - float (20.0)
C thickness in nm
Returns
-------
sim - DTSA scriptable spectrum
The simulated spectrum
Example
-------
import dtsa2.jmMC3 as jm3
det = findDetector("Oxford p4 05eV 2K")
si = material("Si", density=2.3296)
a = jm3.simCarbonCoatedStd(mgo, det, 5.0, 100, 100, 1.0, 20.0)
a.display()
"""
dose = pc * lt # na-sec"
amc = material("C", density=1.35)
amcThickComment = "amC Thickness = %g nm %g trajectories" % (tc, nTraj)
layers = [[amc, tc * 1.0e-9], [mat, 50.0e-6]]
xrts = []
trs = mc3.suggestTransitions(amc, e0)
for tr in trs:
xrts.append(tr)
trs = mc3.suggestTransitions(mat, e0)
for tr in trs:
xrts.append(tr)
xtraParams = {}
xtraParams.update(mc3.configureXRayAccumulators(xrts, True, True, True))
sim = mc3.multiFilm(layers,
det,
e0,
withPoisson=True,
nTraj=nTraj,
dose=dose,
sf=True,
bf=True,
xtraParams=xtraParams)
sName = "%g-nm-amC-on-%s-%g-kV" % (tc, mat, e0)
sim.rename(sName)
sim.getProperties().setTextProperty(epq.SpectrumProperties.SpectrumComment,
amcThickComment)
return sim
def simCarbonCoatedStd(mat, det, e0, nTraj, lt=100, pc=1.0, tc=20.0):
"""simCarbonCoatedStd(mat, det, e0, nTraj, lt=100, pc=1.0, tc=20.0)
Use mc3 multilayer simulation to simulate a C-ctd standard specimen
Parameters
----------
mat - a dtsa material.
Note the material must have an associated density. It should have a useful name.
det - a dtsa detector
Here is where we get the detector properties and calibration
e0 - float
The accelerating voltage in kV
nTraj - integer
The number of trajectories to run
lt - integer (100)
The live time (sec)
pc - float (1.0)
The probe current in nA
tc - float (20.0)
C thickness in nm
Returns
-------
sim - DTSA scriptable spectrum
The simulated standard spectrum
Example
-------
import dtsa2.jmMC3 as jm3
det = findDetector("Si(Li)")
mgo = material("MgO", density=3.58)
a = jm3.simCarbonCoatedStd(mgo, det, 20.0, 100, 100, 1.0, 20.0)
a.display()
"""
dose = pc * lt # na-sec"
c = dt2.material("C", density=2.1)
layers = [[c, tc * 1.0e-9], [mat, 50.0e-6]]
xrts = []
trs = mc3.suggestTransitions(c, e0)
for tr in trs:
xrts.append(tr)
trs = mc3.suggestTransitions(mat, e0)
for tr in trs:
xrts.append(tr)
xtraParams = {}
xtraParams.update(mc3.configureXRayAccumulators(xrts, True, True, True))
sim = mc3.multiFilm(layers,
det,
e0,
withPoisson=True,
nTraj=nTraj,
dose=dose,
sf=True,
bf=True,
xtraParams=xtraParams)
sName = "%g-nm-C-on-%s-%g-kV-%g-Traj" % (tc, mat, e0, nTraj)
sim.rename(sName)
sim.setAsStandard(mat)
return sim
def uncoatedSimBulkStd(mat,
det,
e0,
nTraj,
outPath,
dim=5.0e-6,
lt=100,
pc=1.0,
emiSize=512):
"""
uncoatedSimBulkStd(mat, det, e0, nTraj, outPath,
dim=5.0e-6, lt=100, pc=1.0, emiSize=512)
Use mc3 simulation to simulate an uncoated standard specimen
Parameters
----------
mat - a dtsa material.
Note the material must have an associated density. It should
have a useful name.
det - a dtsa detector
Here is where we get the detector properties and calibration
e0 - float
The accelerating voltage in kV
nTraj - integer
The number of trajectories to run
outPath - string
The path to the directory for output
dim - float (5.0e-6)
The size of the emission images in um
lt - integer (100)
The live time (sec)
pc - float (1.0)
The probe current in nA
emiSize - int (default 512)
The width and depth of the emission images.
Returns
-------
sim - DTSA scriptable spectrum
The simulated standard spectrum
Example
-------
import dtsa2 as dtsa2
import dtsa2.jmMC3 as jm3
outPath = "path/to/yours/spc"
cu = material("Cu", density=8.92)
det = findDetector("Si(Li)")
a = jm3.uncoatedSimBulkStd(cu, det, 15.0, 100, outPath,
dim=5.0e-6, lt=100,
pc=1.0, emiSize=512)
a.display()
"""
start = time.time()
strMat = mat.getName()
dose = pc * lt # na-sec"
# specify the transitions to generate
xrts = []
trs = mc3.suggestTransitions(mat, e0)
for tr in trs:
xrts.append(tr)
# At 20 kV the images are best at 2.0e-6
xtraParams = {}
xtraParams.update(mc3.configureXRayAccumulators(xrts, True, True, True))
# note that the image size on the specimen is in meters...
xtraParams.update(mc3.configureEmissionImages(xrts, dim, emiSize))
xtraParams.update(mc3.configurePhiRhoZ(dim))
xtraParams.update(mc3.configureTrajectoryImage(dim, emiSize))
xtraParams.update(mc3.configureVRML(nElectrons=100))
xtraParams.update(mc3.configureOutput(outPath))
print("Output sent to %s") % (outPath)
sim = mc3.simulate(mat, det, e0, lt * pc, True, nTraj, True, True,
xtraParams)
sName = "%s-%g-kV" % (strMat, e0)
sim.rename(sName)
sim.setAsStandard(mat)
sim.display()
end = time.time()
delta = end - start
msg = "This simulation required %.f sec" % (delta)
print(msg)
msg = " %.f min" % (delta / 60.0)
print(msg)
msg = " %.f hr" % (delta / 360.0)
print("")
return (sim)
def simCoatedSubstrate(mat, ctg, thNm, det, e0, nTraj, lt=100, pc=1.0):
"""simCoatedSubstrate(mat, ctg, thNm, det, e0, nTraj, lt=100, pc=1.0)
Use mc3 multilayer simulation to simulate an coated substrate specimen
This ONLY generates a spectrum
Parameters
----------
mat - a dtsa material for the substrate.
Note the material must have an associated density.
It should have a useful name.
ctg - a dtsa material for the coating
thNm - coating thickness in nm
det - a dtsa detector
Here is where we get the detector properties and calibration
e0 - float
The accelerating voltage in kV
nTraj - integer
The number of trajectories to run
lt - integer (100)
The live time (sec)
pc - float (1.0)
The probe current in nA
tc - float (20.0)
C thickness in nm
Returns
-------
sim - DTSA scriptable spectrum
The simulated spectrum
Example
-------
import dtsa2 as dtsa2
import dtsa2.jmMC3 as jm3
det = findDetector("Si(Li)")
sio2 = material("SiO2", 2.65)
si = material("Si", 2.3296)
a = jm3.simCoatedSubstrate(si, sio2, 10.0, det, e0, nTraj, 100, 1.0)
a.display()
"""
dose = pc * lt # na-sec"
layers = [[ctg, thNm * 1.0e-9], [mat, 50.0e-6]]
xrts = []
trs = mc3.suggestTransitions(ctg, e0)
for tr in trs:
xrts.append(tr)
trs = mc3.suggestTransitions(mat, e0)
for tr in trs:
xrts.append(tr)
xtraParams = {}
xtraParams.update(mc3.configureXRayAccumulators(xrts, True, True, True))
sim = mc3.multiFilm(layers, det, e0, True, nTraj, dose, True, True,
xtraParams)
sName = "%g-nm-%s-on-%s-%g-kV" % (thNm, ctg, mat, e0)
sim.rename(sName)
return sim
def simCtdOxOnSi(det, e0, nTraj, lt=100, pc=1.0, tox=10.0, tc=20.0):
"""
simCtdOxOnSi(det, e0, nTraj, lt=100, pc=1.0, tox = 10.0, tc=20.0)
Use mc3 multilayer simulation to simulate a C-ctd silicon specimen
with a native oxide layer.
det - a dtsa detector
Here is where we get the detector properties and calibration
e0 - float
The accelerating voltage in kV
nTraj - integer
The number of trajectories to run
lt - integer (100)
The live time (sec)
pc - float (1.0)
The probe current in nA
tox - float (10.0)
The thickness of the native oxide in nm
tc - float (20.0)
C thickness in nm
Returns
-------
sim - DTSA scriptable spectrum
The simulated spectrum
Example
-------
import dtsa2.jmMC3 as jm3
det = findDetector("Si(Li)")
a = jm3.simCtdOxOnSi(det, 3.0, 100, 100, 1.0, 10.0, 20.0)
a.display()
"""
c = dt2.material("C", density=2.1)
si = dt2.material("Si", density=2.329)
sio2 = dt2.material("SiO2", density=2.65)
dose = pc * lt # na-sec"
layers = [[c, tc * 1.0e-9], [sio2, tox * 1.0e-9], [si, 50.0e-6]]
xrts = []
trs = mc3.suggestTransitions(c, e0)
for tr in trs:
xrts.append(tr)
trs = mc3.suggestTransitions(sio2, e0)
for tr in trs:
xrts.append(tr)
xtraParams = {}
xtraParams.update(mc3.configureXRayAccumulators(xrts, True, True, True))
sim = mc3.multiFilm(layers,
det,
e0,
withPoisson=True,
nTraj=nTraj,
dose=dose,
sf=True,
bf=True,
xtraParams=xtraParams)
sName = "%g-nm-C-on-%g-nm-SiO2-on-Si-%g-kV-%g-Traj" % (tc, tox, e0, nTraj)
sim.rename(sName)
return sim
def sim_amc_coated_mat(mat, det, e0, nTraj, lt=100, pc=1.0, tc=20.0):
"""sim_amc_coated_mat(mat, det, e0, nTraj, lt=100, pc=1.0, tc=20.0)
Use mc3 multilayer simulation to simulate an am-C-ctd specimen
Parameters
----------
mat - a dtsa material.
Note the material must have an associated density. It should have a useful name.
det - a dtsa detector
Here is where we get the detector properties and calibration
e0 - float
The accelerating voltage in kV
nTraj - integer
The number of trajectories to run
lt - integer (100)
The live time (sec)
pc - float (1.0)
The probe current in nA
tc - float (20.0)
C thickness in nm
Returns
-------
sim - DTSA scriptable spectrum
The simulated spectrum
Example
-------
import dtsa2.jmMC3 as jm3
det = findDetector("Oxford p4 05eV 2K")
si = material("Si", density=2.3296)
a = jm3.simCarbonCoatedStd(mgo, det, 20.0, 100, 100, 1.0, 20.0)
a.display()
"""
dose = pc * lt # na-sec"
amc = material("C", density=1.35)
amcThickComment = "amC Thickness = %g nm %g trajectories" % (tc, nTraj)
layers = [ [amc, tc*1.0e-9],
[mat, 50.0e-6]
]
xrts = []
trs = mc3.suggestTransitions(amc, e0)
for tr in trs:
xrts.append(tr)
trs = mc3.suggestTransitions(mat, e0)
for tr in trs:
xrts.append(tr)
xtraParams={}
xtraParams.update(mc3.configureXRayAccumulators(xrts,True, True, True))
sim = mc3.multiFilm(layers, det, e0, withPoisson=True, nTraj=nTraj,
dose=dose, sf=True, bf=True, xtraParams=xtraParams)
sName = "%g-nm-amC-on-%s-%g-kV" % (tc, mat, e0)
sim.rename(sName)
sim.getProperties().setTextProperty(epq.SpectrumProperties.SpectrumComment, amcThickComment)
return sim
def simLineInMatrixLimScan(lin,
linMat,
blk,
blkMat,
nmLinWid,
umBlock,
nmScan,
nPts,
trs,
outDir,
hdr,
det,
e0,
lt,
pc,
withPoisson=True,
nTraj=100,
sf=True,
bf=True,
iDigits=5,
bVerbose=False,
xtraParams={}):
"""simLineInMatrixLimScan(lin, linMat, blk, blkMat, nmLinWid, umBlock,
nmScan, nPts, trs, outDir, hdr, det, e0, lt, pc, withPoisson=True,
nTraj=nTraj, sf=True, bf=True, iDigits=5, bVerbose=False,
xtraParams={})
Simulate a line of width `nmLinWid' nm at the center of a block of
`umBlock' microns. The line is of material `lin' with a name `linMat'.
The block is of material `blk' with a name `blkMat'. We step a total
distance of nmScan across the center of the line.
We analyze an list `trs' of transitions, writing the K-ratios to a
.csv file with a header `hdr'. We use the detector `det', voltage `e0'
(kV) and live time `lt' sec and probe current `pc' nA. This will
compute the standard spectra, compute the spectra the scanned
region. It will then compute the K-ratios for each spectrum and write
them to a file `name' in outDir with a header `hdr' that matches the
transition order.
"""
# order is order of trs..
sc = 1.0e-6 # scale from microns to meters for positions
dose = lt * pc
lX = [] # an array for postions
lKlin = [] # an array for the K-ratio of the line
lKblk = [
] # an array for the K-ratio of the block. Title correspond to hdr string
umLine = nmLinWid * 1.0e-3
# start clean
dt2.DataManager.clearSpectrumList()
# create the standards
linStd = simulateBulkStandard(lin,
linMat,
det,
e0,
lt,
pc,
withPoisson=withPoisson,
nTraj=nTraj,
sf=sf,
bf=bf,
xtraParams={})
dt2.display(linStd)
blkStd = simulateBulkStandard(blk,
blkMat,
det,
e0,
lt,
pc,
withPoisson=withPoisson,
nTraj=nTraj,
sf=sf,
bf=sf,
xtraParams={})
dt2.display(blkStd)
lStd = {"El": dt2.element(linMat), "Spc": linStd}
bStd = {"El": dt2.element(blkMat), "Spc": blkStd}
stds = [lStd, bStd] # note: put the transitions in this order
iCount = 0
for x in range(-nPts / 2, (nPts / 2) + 1, 1):
xPosNm = x * nmScan / nPts
lX.append(round(xPosNm, iDigits))
xtraParams = {}
xtraParams.update(
mc3.configureXRayAccumulators(trs,
charAccum=sf,
charFluorAccum=sf,
bremFluorAccum=bf))
xtraParams.update(mc3.configureOutput(outDir))
xtraParams.update(mc3.configureBeam(xPosNm * 1.0e-09, 0, -0.099, 1.0))
spec = mc3.embeddedRectangle(lin,
[umLine * sc, umBlock * sc, umBlock * sc],
blk,
0,
det,
e0,
withPoisson=withPoisson,
nTraj=nTraj,
dose=dose,
sf=sf,
bf=bf,
xtraParams=xtraParams)
props = spec.getProperties()
props.setNumericProperty(epq.SpectrumProperties.LiveTime, lt)
props.setNumericProperty(epq.SpectrumProperties.FaradayBegin, pc)
props.setNumericProperty(epq.SpectrumProperties.FaradayEnd, pc)
props.setNumericProperty(epq.SpectrumProperties.BeamEnergy, e0)
spcName = "x = %.3f um" % x
epq.SpectrumUtils.rename(spec, spcName)
spec = epq.SpectrumUtils.addNoiseToSpectrum(spec, 1.0)
# display(spec)
a = jmg.compKRs(spec, stds, trs, det, e0)
iCount += 1
print(iCount, xPosNm)
lKlin.append(round(a[0], iDigits))
lKblk.append(round(a[1], iDigits))
basFile = "%gnm-%s-in-%gum-%s-%gkV-%g-Traj.csv" % (
nmLinWid, linMat, umBlock, blkMat, e0, nTraj)
strOutFile = outDir + "/" + basFile
f = open(strOutFile, 'w')
strLine = hdr + '\n'
f.write(strLine)
for i in range(iCount):
strLine = "%.3f" % lX[i] + ","
strLine = strLine + "%.5f" % lKlin[i] + ","
strLine = strLine + "%.5f" % lKblk[i] + "\n"
f.write(strLine)
f.close()
e0 = 5 # kV
nTraj = 10000 # trajectories
lt = 60 # sec
pc = 2.0 # nA
imgSzUm = 5.0 # physical size of images in microns
imgSizePx = 512 # size of images in pixels
vmrlEl = 40 # number of el for VMRL
dose = pc * lt # nA sec
DataManager.clearSpectrumList()
b = material("B", density=2.37)
bn = material("BN", density=2.1)
aln = material("AlN", density=3.26)
# Sim B
xrts = [transition("B K-L2"), transition("B K-L3")]
xtraParams={}
xtraParams.update(mc3.configureXRayAccumulators(xrts, charAccum=True, charFluorAccum=True, bremFluorAccum=True))
# note that the image size on the specimen is in meters...
xtraParams.update(mc3.configureEmissionImages(xrts, imgSzUm*1.0e-6, imgSizePx))
xtraParams.update(mc3.configurePhiRhoZ(imgSzUm*1.0e-6))
xtraParams.update(mc3.configureTrajectoryImage(imgSzUm*1.0e-6, imgSizePx))
xtraParams.update(mc3.configureVRML(nElectrons = vmrlEl))
xtraParams.update(mc3.configureOutput(spcDir))
b_spc = mc3.simulate(b, det, e0, dose = pc*lt, nTraj=nTraj, sf=True, bf=True, xtraParams = xtraParams)
b_spc.rename("B")
# b_spec.setAsStandard(b)
display(b_spc)
fi = spcDir + "/B-%g-kV-%g-Traj.msa" % (e0, nTraj)
b_spc.save(fi)
# Sim BN
xrts = [transition("B K-L2"), transition("B K-L3"),
transition("N K-L2"), transition("N K-L3")]
pyrDir = prjDir + "/mc3SphereOnSubstrate Results"
al2o3 = epq.Material(
epq.Composition([epq.Element.Al, epq.Element.O], [0.5293, 0.4707]),
epq.ToSI.gPerCC(3.95))
al2o3.setName("Al2O3")
c = epq.Material(epq.Composition([epq.Element.C], [1.0]),
epq.ToSI.gPerCC(2.62))
c.setName("C")
xrts = [transition("Al K-L3"), transition("O K-L3"), transition("C K-L3")]
xtraParams = {}
xtraParams.update(
mc3.configureXRayAccumulators(xrts,
charAccum=True,
charFluorAccum=True,
bremFluorAccum=True))
# note that the image size on the specimen is in meters...
xtraParams.update(
mc3.configureEmissionImages(xrts, imgSzUm * 1.0e-6, imgSizePx))
xtraParams.update(mc3.configurePhiRhoZ(imgSzUm * 1.0e-6))
xtraParams.update(mc3.configureTrajectoryImage(imgSzUm * 1.0e-6, imgSizePx))
xtraParams.update(mc3.configureVRML(nElectrons=vmrlEl))
xtraParams.update(mc3.configureOutput(simDir))
spc = mc3.sphere(al2o3,
radUm * 1.0e-6,
det,
e0,
withPoisson=True,
nTraj=nTraj,
def fullSimBulkStd(mat, det, e0, nTraj, outPath, dim=5.0e-6, lt=100, pc=1.0, emiSize=512, ctd=False):
"""
fullSimBulkStd(mat, det, e0, nTraj, outPath, dim=5.0e-6, lt=100, pc=1.0, emiSize=512, ctd=False)
Use mc3 simulation to simulate an uncoated standard specimen
Parameters
----------
mat - a dtsa material.
Note the material must have an associated density. It should have a useful name.
det - a dtsa detector
Here is where we get the detector properties and calibration
e0 - float
The accelerating voltage in kV
nTraj - integer
The number of trajectories to run
outPath - string
The path to the directory for output
dim - float (5.0e-6)
The size of the emission images
lt - integer (100)
The live time (sec)
pc - float (1.0)
The probe current in nA
emiSize - int (default 512)
The width and depth of the emission images.
ctd - Boolean (False) - is C coated
Returns
-------
sim - DTSA scriptable spectrum
The simulated standard spectrum
Example
-------
import dtsa2 as dtsa2
import dtsa2.mcSimulate3 as mc3
det = findDetector("Oxford p4 05eV 2K")
cu = material("Cu", density=8.92)
a = fullSimBulkStd(cu, det, 20.0, 100, 100, 1.0)
a.display()
"""
dose = pc * lt # na-sec"
xrts = []
trs = mc3.suggestTransitions(mat, e0)
for tr in trs:
xrts.append(tr)
mc3.configureEmissionImages(xrts, dim, emiSize)
xtraParams={}
xtraParams.update(mc3.configureXRayAccumulators(xrts,True, True, True))
# note that the image size on the specimen is in meters...
xtraParams.update(mc3.configureEmissionImages(xrts, 5.0e-6, 512))
xtraParams.update(mc3.configurePhiRhoZ(5.0e-6))
xtraParams.update(mc3.configureTrajectoryImage(5.0e-6, 512))
xtraParams.update(mc3.configureVRML(nElectrons=100))
xtraParams.update(mc3.configureOutput(outPath))
mc3.configureOutput(outPath)
print("Output sent to %s") % (outPath)
dose = lt*pc
sim = mc3.simulate(mat, det, e0, dose, withPoisson=True, nTraj=nTraj,
sf=True, bf=True, xtraParams=xtraParams)
sName = "%s-%g-kV" % (mat, e0)
sim.rename(sName)
sim.setAsStandard(mat)
sim.display()
fi = outPath + "/"
fi += sName
fi += "-%g-Traj.msa" % (nTraj)
print(fi)
sim.save(fi)
return(sim)
def fullSimBulkStd(mat, ctg, ctgThickNm, det, e0, nTraj, outPath,
dim=5.0e-6, lt=100, pc=1.0, emiSize=512, ctd=False):
"""
fullSimBulkStd(mat, ctg, ctgThickNm, det, e0, nTraj, outPath,
dim=5.0e-6, lt=100, pc=1.0, emiSize=512, ctd=False)
Use mc3 simulation to simulate an uncoated standard specimen
Parameters
----------
mat - a dtsa material.
Note the material must have an associated density. It should
have a useful name.
ctg - a dtsa2 material for the coating
det - a dtsa detector
Here is where we get the detector properties and calibration
e0 - float
The accelerating voltage in kV
nTraj - integer
The number of trajectories to run
outPath - string
The path to the directory for output
dim - float (5.0e-6)
The size of the emission images
lt - integer (100)
The live time (sec)
pc - float (1.0)
The probe current in nA
emiSize - int (default 512)
The width and depth of the emission images.
ctd - Boolean (False) - is C coated
Returns
-------
sim - DTSA scriptable spectrum
The simulated standard spectrum
Example
-------
import dtsa2 as dtsa2
import dtsa2.mcSimulate3 as mc3
det = findDetector("Oxford p4 05eV 4K")
cu = material("Cu", density=8.92)
outPath = "C:/Users/johnr/Documents/git/dtsa2Scripts/ben-buse/out"
a = fullSimBulkStd(cu, det, 15.0, 100, outPath,
dim=5.0e-6, lt=100,
pc=1.0, emiSize=512, ctd=False)
a.display()
"""
strCtg = ctg.getName()
strMat = mat.getName()
dose = pc * lt # na-sec"
# specify the transitions to generate
xrts = []
trs = mc3.suggestTransitions(mat, e0)
for tr in trs:
xrts.append(tr)
trs = mc3.suggestTransitions(ctg, e0)
for tr in trs:
xrts.append(tr)
# At 20 kV the images are best at 2.0e-6
xtraParams={}
xtraParams.update(mc3.configureXRayAccumulators(xrts,True, True, True))
# note that the image size on the specimen is in meters...
xtraParams.update(mc3.configureEmissionImages(xrts, 2.0e-6, 512))
xtraParams.update(mc3.configurePhiRhoZ(2.0e-6))
xtraParams.update(mc3.configureTrajectoryImage(2.0e-6, 512))
xtraParams.update(mc3.configureVRML(nElectrons=100))
xtraParams.update(mc3.configureOutput(outPath))
print("Output sent to %s") % (outPath)
layers = [ [ctg, ctgThickNm*1.0e-9],
[mat, 1.0e-3]]
sim = mc3.multiFilm(layers, det, e0, withPoisson=True, nTraj=nTraj,
dose=dose, sf=True, bf=True, xtraParams=xtraParams)
sName = "%g-nm-%s-on-%s" % (tNmC, strCtg, strMat)
sim.rename(sName)
sim.setAsStandard(mat)
sim.display()
return(sim)
# start clean
DataManager.clearSpectrumList()
# define the desired transitions (most intense for each line...)
# Ka1 Ka1 Ka1 La1
xrts = [
transition("C K-L3"),
transition("O K-L3"),
transition("Al K-L3"),
transition("Ag L3-M5")
]
# print(xrts)
# set up the extra parameters
xtraParams = {}
xtraParams.update(
mc3.configureXRayAccumulators(xrts,
charAccum=charF,
charFluorAccum=charF,
bremFluorAccum=bremF))
# note that the image size on the specimen is in meters...
xtraParams.update(mc3.configureEmissionImages(xrts, imgSzUm * 1.0e-6, imgSize))
xtraParams.update(mc3.configurePhiRhoZ(imgSzUm * 1.0e-6))
xtraParams.update(mc3.configureTrajectoryImage(imgSzUm * 1.0e-6, imgSize))
xtraParams.update(mc3.configureVRML(nElectrons=vmrlEl))
xtraParams.update(mc3.configureOutput(simDir))
print(xtraParams)
spc = jm3.coatedOverBlock(ag,
tBlk * 1.0e-6,
wBlk * 1.0e-6,
al2o3,
tFlm * 1.0e-6,
def simCtdOxOnSi(det, e0, nTraj, lt=100, pc=1.0, tox = 10.0, tc=20.0):
"""
simCtdOxOnSi(det, e0, nTraj, lt=100, pc=1.0, tox = 10.0, tc=20.0)
Use mc3 multilayer simulation to simulate a C-ctd silicon specimen
with a native oxide layer.
det - a dtsa detector
Here is where we get the detector properties and calibration
e0 - float
The accelerating voltage in kV
nTraj - integer
The number of trajectories to run
lt - integer (100)
The live time (sec)
pc - float (1.0)
The probe current in nA
tox - float (10.0)
The thickness of the native oxide in nm
tc - float (20.0)
C thickness in nm
Returns
-------
sim - DTSA scriptable spectrum
The simulated spectrum
Example
-------
import dtsa2.jmMC3 as jm3
det = findDetector("Oxford p4 05eV 2K")
a = jm3.simCtdOxOnSi(det, 3.0, 100, 100, 1.0, 10.0, 20.0)
a.display()
"""
c = dt2.material("C", density=2.1)
si = dt2.material("Si", density=2.329)
sio2 = dt2.material("SiO2", density=2.65)
dose = pc * lt # na-sec"
layers = [ [ c, tc*1.0e-9],
[sio2, tox*1.0e-9],
[si, 50.0e-6] ]
xrts = []
trs = mc3.suggestTransitions(c, e0)
for tr in trs:
xrts.append(tr)
trs = mc3.suggestTransitions(sio2, e0)
for tr in trs:
xrts.append(tr)
xtraParams={}
xtraParams.update(mc3.configureXRayAccumulators(xrts,True, True, True))
sim = mc3.multiFilm(layers, det, e0, withPoisson=True, nTraj=nTraj,
dose=dose, sf=True, bf=True, xtraParams=xtraParams)
sName = "%g-nm-C-on-%g-nm-SiO2-on-Si-%g-kV-%g-Traj" % (tc, tox, e0, nTraj)
sim.rename(sName)
return sim
cTS = epq.XRayTransitionSet(epq.Element.C,
epq.XRayTransitionSet.K_FAMILY)
cTrs = cTS.getTransitions()
for tr in cTrs:
xrts.append(tr)
# get and add the Cu-L family
siTS = epq.XRayTransitionSet(epq.Element.Si,
epq.XRayTransitionSet.K_FAMILY)
siTrs = siTS.getTransitions()
for tr in siTrs:
xrts.append(tr)
xp.update(mc3.configureXRayAccumulators(xrts,
charAccum=True,
charFluorAccum=True,
bremFluorAccum=True))
# xp.update(mc3.configureEmissionImages(xrts,imgSzUm*1.0e-6, imgSzPx))
# xp.update(mc3.configurePhiRhoZ(imgSzUm*1.0e-6))
# xp.update(mc3.configureTrajectoryImage(imgSzUm*1.0e-6, imgSzPx))
# xp.update(mc3.configureVRML(nElectrons = 40))
xp.update(mc3.configureOutput(outDir))
xp.update(mc3.configureGun(beamSzNm*1.0e-9))
ts = time.time()
# first sim bare Si
spc = mc3.simulate(si, det, e0, dose, True, nTraj, True, True, xp)
spc = epq.SpectrumUtils.addNoiseToSpectrum(spc, 1.0)
spc = wrap(spc)
rptDir = wrkDir + '/testMC3AdmiraltyBrass Results/'
simDir = homDir + "/Documents/git/dtsa2Scripts/sim-Admiralty-Brass"
dt2c.ensureDir(simDir)
DataManager.clearSpectrumList()
xrts = []
trs = mc3.suggestTransitions(mix, e0)
for tr in trs:
xrts.append(tr)
# print(xrts)
xtraParams={}
xtraParams.update(mc3.configurePhiRhoZ(przDepUm*1.0e-6))
xtraParams.update(mc3.configureXRayAccumulators(xrts,True, True, True))
# note that the image size on the specimen is in meters...
xtraParams.update(mc3.configureEmissionImages(xrts, imgSzUm*1.0e-6,
imgSize))
xtraParams.update(mc3.configureTrajectoryImage(imgSzUm*1.0e-6, imgSize))
xtraParams.update(mc3.configureVRML(nElectrons = vmrlEl))
xtraParams.update(mc3.configureOutput(simDir))
print(xtraParams)
sim = mc3.simulate(mix, det, e0, dose, True,
nTraj, True, True, xtraParams)
fmtS = "Admiralty-Brass-at-%g-kV"
sName = fmtS % (e0)
sim.rename(sName)
sim.display()
layers = [[c, tCNm * 1.0e-9], [eagleXG, 50.0e-6]]
xrts = []
trs = mc3.suggestTransitions(eagleXG, e0)
for tr in trs:
xrts.append(tr)
trs = mc3.suggestTransitions(c, e0)
for tr in trs:
xrts.append(tr)
xtraParams = {}
xtraParams.update(mc3.configurePhiRhoZ(przDepUm * 1.0e-6))
xtraParams.update(mc3.configureXRayAccumulators(xrts, True, True, True))
# note that the image size on the specimen is in meters...
xtraParams.update(mc3.configureEmissionImages(xrts, imgSzUm * 1.0e-6, imgSize))
xtraParams.update(mc3.configureTrajectoryImage(imgSzUm * 1.0e-6, imgSize))
xtraParams.update(mc3.configureVRML(nElectrons=vmrlEl))
xtraParams.update(mc3.configureOutput(simDir))
print(xtraParams)
fmtS = "%g-nm-C-on-EagleXG-at-%g-kV"
print("Starting simulation")
multiLaySim = mc3.multiFilm(layers, det, e0, True, nTraj, dose, True, True,
xtraParams)
sName = fmtS % (tCNm, e0)
jmg.ensureDir(csvDir)
# wd = homDir + relPrj + "/py/dtsa"
wd = homDir + relPrj + "/py/dtsa"
os.chdir(wd)
pyrDir = wd + "/simPdLineInCuMatrix Results"
#start clean
DataManager.clearSpectrumList()
# xrts=mc3.suggestTransitions("PdCu")
xrts = [epq.XRayTransition(epq.Element.Cu, epq.XRayTransition.LA1), epq.XRayTransition(epq.Element.Pd, epq.XRayTransition.LA1)]
xtraParams={}
xtraParams.update(mc3.configureXRayAccumulators(xrts, charAccum=charF, charFluorAccum=charF, bremFluorAccum=bremF))
xtraParams.update(mc3.configureOutput(simDir))
xtraParams.update(mc3.configureBeam(0.5*nmLinWid*1.0e-9, 0, -0.099, 1.0))
# xtraParams.update(mc3.configureGun(gun))
# mc3.useHeatMapPalette()
print(xtraParams)
# spc = jm3.lineInMatrix(lin, blk, nmLinWid, umBlock, det, e0, withPoisson=True, nTraj=nTraj, dose=dose, sf=charF, bf=bremF, xtraParams=xtraParams)
# spc = jm3.lineInMatrix(lin, blk, nmLinWid, umBlock, det, e0, poisN, nTraj, dose, charF, bremF, xtraParams)
#e0=20.0, dose=defaultDose, withPoisson=poisN, nTraj=defaultNumTraj, sf=defaultCharFluor, bf=defaultBremFluor, xtraParams=defaultXtraParams):
# spc = mc3.simulate(blk, det, e0, dose, poisN, nTraj, charF, bremF, xtraParams)
#
# Embedded Rectangle
#
# This works as expected.
def simLineInMatrixLimScan(lin, linMat, blk, blkMat, nmLinWid, umBlock,nmScan, nPts, trs, outDir, hdr, det, e0, lt, pc, withPoisson=True, nTraj=100, sf=True, bf=True, iDigits=5, bVerbose=False, xtraParams={}):
"""simLineInMatrixLimScan(lin, linMat, blk, blkMat, nmLinWid, umBlock,
nmScan, nPts, trs, outDir, hdr, det, e0, lt, pc, withPoisson=True,
nTraj=nTraj, sf=True, bf=True, iDigits=5, bVerbose=False,
xtraParams={})
Simulate a line of width `nmLinWid' nm at the center of a block of
`umBlock' microns. The line is of material `lin' with a name `linMat'.
The block is of material `blk' with a name `blkMat'. We step a total
distance of nmScan across the center of the line.
We analyze an list `trs' of transitions, writing the K-ratios to a
.csv file with a header `hdr'. We use the detector `det', voltage `e0'
(kV) and live time `lt' sec and probe current `pc' nA. This will
compute the standard spectra, compute the spectra the scanned
region. It will then compute the K-ratios for each spectrum and write
them to a file `name' in outDir with a header `hdr' that matches the
transition order.
"""
# order is order of trs..
sc = 1.0e-6 # scale from microns to meters for positions
dose = lt*pc
lX = [] # an array for postions
lKlin = [] # an array for the K-ratio of the line
lKblk = [] # an array for the K-ratio of the block. Title correspond to hdr string
umLine = nmLinWid * 1.0e-3
# start clean
dt2.DataManager.clearSpectrumList()
# create the standards
linStd = simulateBulkStandard(lin, linMat, det, e0, lt, pc, withPoisson=withPoisson, nTraj=nTraj, sf=sf, bf=bf, xtraParams={})
dt2.display(linStd)
blkStd = simulateBulkStandard(blk, blkMat, det, e0, lt, pc, withPoisson=withPoisson, nTraj=nTraj, sf=sf, bf=sf, xtraParams={})
dt2.display(blkStd)
lStd = {"El":dt2.element(linMat), "Spc":linStd}
bStd = {"El":dt2.element(blkMat), "Spc":blkStd}
stds = [lStd, bStd] # note: put the transitions in this order
iCount = 0
for x in range(-nPts/2, (nPts/2)+1, 1):
xPosNm = x * nmScan / nPts
lX.append(round(xPosNm, iDigits))
xtraParams={}
xtraParams.update(mc3.configureXRayAccumulators(trs, charAccum=sf, charFluorAccum=sf, bremFluorAccum=bf))
xtraParams.update(mc3.configureOutput(outDir))
xtraParams.update(mc3.configureBeam(xPosNm*1.0e-09, 0, -0.099, 1.0))
spec = mc3.embeddedRectangle(lin, [umLine*sc, umBlock*sc, umBlock*sc], blk, 0, det, e0, withPoisson=withPoisson, nTraj=nTraj, dose=dose, sf=sf, bf=bf, xtraParams=xtraParams)
props = spec.getProperties()
props.setNumericProperty(epq.SpectrumProperties.LiveTime, lt)
props.setNumericProperty(epq.SpectrumProperties.FaradayBegin, pc)
props.setNumericProperty(epq.SpectrumProperties.FaradayEnd, pc)
props.setNumericProperty(epq.SpectrumProperties.BeamEnergy, e0)
spcName = "x = %.3f um" % x
epq.SpectrumUtils.rename(spec, spcName)
spec = epq.SpectrumUtils.addNoiseToSpectrum(spec, 1.0)
# display(spec)
a = jmg.compKRs(spec, stds, trs, det, e0)
iCount += 1
print(iCount, xPosNm)
lKlin.append(round(a[0], iDigits))
lKblk.append(round(a[1], iDigits))
basFile ="%gnm-%s-in-%gum-%s-%gkV-%g-Traj.csv" % (nmLinWid, linMat, umBlock, blkMat, e0, nTraj)
strOutFile = outDir + "/" + basFile
f=open(strOutFile, 'w')
strLine = hdr + '\n'
f.write(strLine)
for i in range(iCount):
strLine = "%.3f" % lX[i] + ","
strLine = strLine + "%.5f" % lKlin[i] + ","
strLine = strLine + "%.5f" % lKblk[i] + "\n"
f.write(strLine)
f.close()
