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hdcmCalib_tdiode.py
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hdcmCalib_tdiode.py
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import numpy
import string
from matplotlib import pyplot
import subprocess
import scipy as sp
import scipy.optimize
import x3toAthenaSetup as xa
import srxpeak
#plan:
#1. load 4 scans - Ti(5), Fe(7), Cu (8), Se (12)
#2. calculate their edge DCM location
#3. fit the E vs Bragg RBV with four values, provide fitting results: dtheta, dlatticeSpace
def scanderive(xaxis,yaxis):
##def plotscan(scan,**kwargs):
# fp=open(scan,'r')
# line=fp.readline()
# length=0
# TWOTHREE=False
# if kwargs.has_key('lines'):
# length=int(kwargs['lines'])
# scname=line.split(',')[0][2:]
# for i in range(0,len(line.split(','))):
# if line.split(',')[i][:9]==' --xnumst':
# print i, line.split(',')[i], line.split(',')[i].split('=')[1]
# length=int(line.replace('\n','').split(',')[i].split('=')[1])
# if line.split(',')[i][:9]==' --config' and not kwargs.has_key('lines'):
# tmp=subprocess.check_output(['wc','-l',line.replace('\n','').split(',')[i].split('=')[1]])
# length=int(tmp.split()[0])
# TWOTHREE=True
# if length==0:
# print "failed to find length"
# return 2
# fp.readline()
# fp.readline()
## fp.readline()
# offset=fp.tell()
# xaxis=numpy.zeros(length)
# yaxis=numpy.zeros(length)
# i0=numpy.zeros(length)
#
# if kwargs.has_key('x'):
# x=kwargs['x']
# else:
# x=2
# if kwargs.has_key('y'):
# y=kwargs['y']
# else:
# y=7
# if kwargs.has_key('i0'):
# i0col=kwargs['i0']
# if kwargs.has_key('Ugap'):
# ugap=kwargs['Ugap']
# if kwargs.has_key('derivative'):
# dxaxis=numpy.zeros(length-1)
# dyaxis=numpy.zeros(length-1)
# fp.seek(offset)
# for i in range(0,length):
# line=fp.readline()
#
# if scname=='srx-scan-slits.py':
# print line.split()[x]
# xaxis[i]=float(line.split()[x].strip(','))
# yaxis[i]=float(line.split()[y].strip(','))
# else:
# #print line
# xaxis[i]=float(line.split()[x])
# yaxis[i]=float(line.split()[y])
# if kwargs.has_key('i0'):
# i0[i]=float(line.split()[i0col])
# yaxis[i]=yaxis[i]/i0[i]*(-1)
# if kwargs.has_key('Ugap'):
# [i]=float(line.split()[ugap])
# print 'Ugap', ugap
# fp.close()
#if data were collected in absoprtion/transmission mode, instead of fluo.
# if kwargs.has_key('abso'):
# yaxis=yaxis
# #print yaxis
# yaxis=-numpy.log(yaxis)
#
# if kwargs.has_key('norm'):
# norm0=numpy.average(yaxis[:5])
# norm1=numpy.average(yaxis[-5:])
# yaxis=(yaxis-norm0)/(norm1-norm0)
# if kwargs.has_key('derivative'):
length=len(xaxis)
dxaxis=xaxis[0:-1]
dyaxis=yaxis[0:-1]
for i in range(0,length-1):
dxaxis[i]=(xaxis[i]+xaxis[i+1])/2.
dyaxis[i]=(yaxis[i+1]-yaxis[i])/(xaxis[i+1]-xaxis[i])
#print "Deriv. max value is ",dyaxis.max()," at ", dxaxis[dyaxis.argmax()]
#print "Deriv. min value is ",dyaxis.min()," at ", dxaxis[dyaxis.argmin()]
#pyplot.plot(dxaxis,dyaxis,'+')
p=pyplot.plot(dxaxis,dyaxis*(-1),'-')
#make the useoffset = False
ax = pyplot.gca()
ax.ticklabel_format(useOffset=False)
#edge = dxaxis[dyaxis.argmin()]
edge = dxaxis[dyaxis.argmin()]
return p, dxaxis,dyaxis, edge
fitfunc = lambda pa, x: 12.3984/(2*pa[0]*numpy.sin((x+pa[1])*numpy.pi/180))
errfunc = lambda pa, x, y: fitfunc(pa,x) - y
scandir='/nfs/xf05id1/data/'
#try to work out automatic energy calibration for HDCM
energyDic={'Cu':8.979, 'Se': 12.658, 'Zr':17.998, 'Nb':18.986, 'Fe':7.112,
'Ti':4.966, 'Cr': 5.989, 'Co': 7.709}
#datalogDic={'Cu':'11_42', 'Se':'14_31', 'Fe':'14_41', 'Co':'17_56', 'Ti':'15_56'}
#datalogDic={'Cu':'11_42', 'Ti':'15_56'}
#dateDic={'Cu':'2015/4/6', 'Se':'2015/3/23', 'Fe':'2015/2/21', 'Co':'2015/2/21', 'Ti':'2015/4/6'}
#BraggRBVDic={'Cu':12.5, 'Se':8.7, 'Co': 14.6, 'Fe':15.9, 'Ti':23.0}
#2015 cycle 1 calibration
#dateDic={'Se':'2015/4/10', 'Cu':'2015/4/10', 'Fe':'2015/4/10', 'Ti':'2015/4/10'}
#datalogDic={'Ti':'20_19', 'Fe':'20_37', 'Cu':'20_55', 'Se':'22_8'}
#2015 cycle 2 calibration
dateDic={'Ti':'2015/6/13', 'Fe':'2015/6/13', 'Cu':'2015/6/13', 'Se':'2015/6/13'}
datalogDic={'Ti':'21_15', 'Fe':'21_51', 'Cu':'22_4', 'Se':'22_39'}
#2015 cycle 2 calibration repeat
dateDic={'Ti':'2015/6/17', 'Fe':'2015/6/17', 'Cu':'2015/6/17', 'Se':'2015/6/17'}
datalogDic={'Ti':'17_1', 'Fe':'17_19', 'Cu':'17_46', 'Se':'17_59'}
dateDic={'Ti':'2015/10/5', 'Fe':'2015/10/5', 'Cu':'2015/10/5', 'Se':'2015/10/5'}
datalogDic={'Ti':'20_58', 'Fe':'20_7', 'Cu':'19_47', 'Se':'20_33'}
roiDic={'Ti':[433, 488], 'Cr':[520, 550], 'Fe':[620,640], 'Cu':[770,815], 'Se':[1101,1138]}
#datalogDic={'Ti':'2015_11_10_0_8',
# 'Cr':'2015_11_9_23_48',
# 'Fe':'2015_11_10_0_24',
# 'Cu':'2015_11_10_0_53',
# 'Se':'2015_11_10_1_12'
# }
datalogDic={'Ti':'2016_1_27_10_4',
'Fe':'2016_1_27_9_17',
'Cu':'2016_1_27_10_33',
'Se':'2016_1_27_10_59'
}
usesdd = False
baseline = 8.5e-10
BraggRBVDic={}
fitBragg=[]
fitEnergy=[]
for element in datalogDic:
print 'element:', element
print 'K-edge:', energyDic[element]
print 'data:', datalogDic[element]
#scanfile = scandir+dateDic[element]+'/log_'+datalogDic[element]+'_srx-coord-energy.py.txt'
if usesdd == True:
rawx,rawy = xa.x3toAthena(fileprefix=datalogDic[element], roil=roiDic[element][0], roih=roiDic[element][1],
plot=True, normalization=True, savetoAthena=False, returnarray=True, sampleinfo=element,
xbragg=True)
else:
rawx, rawy, scanproperty = srxpeak.dataread(fileprefix=datalogDic[element], scriptname='srx-coord-energy-timeout-e.py',
xcol = 3, ycol = 8, chalist = [',', '(',')','[',']',':'])
rawy = -numpy.log(rawy-baseline)
p, xaxis, yaxis, edge = scanderive(rawx,rawy)
#p, xaxis, yaxis, edge = plotscan(scanfile, x=1, y=9, i0=8, abso=True, norm=True, derivative=True)
#p, xaxis, yaxis, edge = plotscan(scanfile, x=1, y=9, i0=8, abso=True, norm=True)
BraggRBVDic[element] = round(edge,3)
print 'Edge position is at Braggg RBV', BraggRBVDic[element]
pyplot.show(p)
fitBragg.append(BraggRBVDic[element])
fitEnergy.append(energyDic[element])
#print BraggRBVDic
#print fitBragg
#print fitEnergy
#
#fitfunc = lambda dSi111, dtheta, fitBragg: 12.3984/(2*dSi111*numpy.sin(numpy.pi+dtheta))
#errfunc = lambda dSi111, dtheta, fitBragg, fitEnergy: fitfunc(dSi111, dtheta, fitBragg) - fitEnergy
#
#dSi111 =
##
fitEnergy=numpy.sort(fitEnergy)
fitBragg=numpy.sort(fitBragg)[-1::-1]
guess = [3.1356, 0.32]
fitted_dcm, success = sp.optimize.leastsq(errfunc, guess, args = (fitBragg, fitEnergy))
print '(111) d spacing:', fitted_dcm[0]
print 'Bragg RBV offset:', fitted_dcm[1]
print 'success:', success
newEnergy=fitfunc(fitted_dcm, fitBragg)
print fitBragg
print newEnergy
pyplot.figure(1)
pyplot.plot(fitBragg, fitEnergy,'b^', label = 'raw scan')
bragg = numpy.linspace(fitBragg[0], fitBragg[-1], 200)
pyplot.plot(bragg, fitfunc(fitted_dcm, bragg), 'k-', label = 'fitting')
#plt.show(p)
pyplot.legend()
pyplot.xlabel('Bragg RBV (deg)')
pyplot.ylabel('Energy(keV)')
pyplot.show()
print '(111) d spacing:', fitted_dcm[0]
print 'Bragg RBV offset:', fitted_dcm[1]