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templateMantidEV.py
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import matplotlib.pyplot as plt
from lin_abs_coef import lin_abs_coef
from matplotlib import colors
from matplotlib.ticker import LogFormatter
from mpl_toolkits.mplot3d import proj3d
import numpy as np
from mantid.kernel import ConfigService
from mantid.kernel import V3D
from mantid.geometry import OrientedLattice, PointGroupFactory
from mantid import config
ConfigService.setConsoleLogLevel(2)
config['Q.convention'] = 'Crystallography'
from scipy.optimize import basinhopping
from multiprocessing.dummy import Pool as ThreadPool
from multiprocessing.dummy import current_process
class MantidEV():
def __init__(self):
self.eventFileName = "{eventFileName}"
self.phi = {phi}
self.chi = {chi}
self.omega = {omega}
self.calFileName = "{calFileName}"
self.minDSpacing = {minDSpacing}
self.minWavelength = {minWavelength}
self.maxWavelength = {maxWavelength}
self.sampleRadius = {sampleRadius}
self.molecularFormula = "{molecularFormula}"
self.Z = {Z}
self.unitCellVolume = {unitCellVolume}
self.minQ = {minQ}
self.maxQ = {maxQ}
self.numPeaksToFind = {numPeaksToFind}
self.abcMin = {abcMin}
self.abcMax = {abcMax}
self.tolerance = {tolerance}
self.pointGroup = "{pointGroup}"
self.centering = "{centering}"
self.peakRadius = {peakRadius}
self.minIntensity = {minIntensity}
self.nGrid = {nGrid}
self.predictPeaks = {predictPeaks}
self.outputDirectory = "{outputDirectory}"
self.LorentzCorr = True
self.numOrientations = {numOrientations}
self.pcharge = {pcharge}
self.edgePixels = {edgePixels}
self.changePhi = {changePhi}
self.changeChi = {changeChi}
self.changeOmega = {changeOmega}
self.useSymmetry = {useSymmetry}
self.addOrientations = {addOrientations}
self.seconds = {seconds}
def select_wksp(self):
try:
props=input.run().getProperties()
self._wksp = input
SetGoniometer(Workspace=self._wksp,Axis0="omega,0,1,0,1",Axis1="chi,0,0,1,1",Axis2="phi,0,1,0,1")
self.last = 1
except:
self._wksp = Load(Filename=self.eventFileName,OutputWorkspace="eventWksp",
FilterByTimeStart = self.time_start, FilterByTimeStop = self.time_stop)
self.time_start = self.time_stop
self.time_stop += int(self.seconds)
duration = self._wksp.getRun().getProperty("duration").value
if self.time_start > duration:
print ("No events next time")
self.last = 1
angles = self._wksp.run().getGoniometer().getEulerAngles('YZY')
if angles[1] == 0.0:
AddSampleLog(Workspace=self._wksp, LogName='phi', LogText=str(self.phi), LogType='Number')
AddSampleLog(Workspace=self._wksp, LogName='chi', LogText=str(self.chi), LogType='Number')
AddSampleLog(Workspace=self._wksp, LogName='omega', LogText=str(self.omega), LogType='Number')
SetGoniometer(Workspace=self._wksp,Axis0="omega,0,1,0,1",Axis1="chi,0,0,1,1",Axis2="phi,0,1,0,1")
angles = self._wksp.run().getGoniometer().getEulerAngles('YZY')
print ("phi,chi,omega=",angles[0],angles[1],angles[2])
if self.calFileName:
LoadIsawDetCal(InputWorkspace=self._wksp,Filename=str(self.calFileName)) # load cal file
try:
self._wksp = ConvertUnits(InputWorkspace = self._wksp,OutputWorkspace="eventWksp",Target="dSpacing",EMode="Elastic")
self._wksp = CropWorkspace(InputWorkspace = self._wksp, XMin = self.minDSpacing,OutputWorkspace="eventWksp")
self._wksp = ConvertUnits(InputWorkspace = self._wksp,OutputWorkspace="eventWksp",Target="Wavelength",EMode="Elastic")
self._wksp = CropWorkspace(InputWorkspace = self._wksp, XMin = self.minWavelength, XMax = self.maxWavelength,OutputWorkspace="eventWksp")
except:
pass
if self.sampleRadius > 0:
linSca, linAbs, calculatedRadius = lin_abs_coef(self.molecularFormula, self.Z, self.unitCellVolume, 0)
self._wksp = AnvredCorrection(InputWorkspace = self._wksp,LinearScatteringCoef = linSca,LinearAbsorptionCoef = linAbs,
Radius = self.sampleRadius,PowerLambda = 4.0,OutputWorkspace="eventWksp")
# Lorentz correction already added so not in ConvertToMD
self.LorentzCorr = False
if self._wksp:
try:
output = ConvertToMD(InputWorkspace = self._wksp, QDimensions = 'Q3D', dEAnalysisMode = 'Elastic',
LorentzCorrection = self.LorentzCorr, OutputWorkspace='output',
Q3DFrames = 'Q_sample', QConversionScales = 'Q in A^-1',
MinValues = str(self.minQ)+','+str(self.minQ)+','+str(self.minQ),
MaxValues = str(self.maxQ)+','+str(self.maxQ)+','+str(self.maxQ))
except:
print ("Viewer failed with no data: decrease minimum intensity")
sys.exit()
if self.time_start <= self.seconds:
self._md = CloneMDWorkspace(InputWorkspace=output, OutputWorkspace='sum_output')
else:
self._md += output
self.events = self._md.getNEvents()
else:
self.events = 0
def crystalplan(self):
angles = self.peaks_ws.run().getGoniometer().getEulerAngles('YZY')
X0 = []
self.numAngles = 0
if self.changePhi:
self.numAngles += 1
X0.append(angles[0])
if self.changeChi:
self.numAngles += 1
X0.append(angles[1])
if self.changeOmega:
self.numAngles += 1
X0.append(angles[2])
pool = ThreadPool(2)
threadNo = range(2)
results = pool.map(self.optimize, threadNo)
minPeaks = 0
for i in range(len(results)):
if results[i].fun < minPeaks:
X = results[i].x
minPeaks = results[i].fun
pool.close()
pool.join()
if (self.addOrientations):
X = np.concatenate((X0, X))
self.csv_write(X)
self.fopt(X)
def csv_write(self, X):
import csv
fileName = self.outputDirectory+"/CrystalPlan"+str(self.events)+".csv"
print ("Goniometer plan: ", fileName)
f = open(fileName, 'wt')
comment0 = '\"measured\"'
commentn = '\"optimized\"'
try:
writer = csv.writer(f, quotechar = "'")
writer.writerow( ('#Title:','\"text\"'))
writer.writerow( ('#Comment:',''))
if self.numAngles == 1:
writer.writerow( ('Phi', 'CountFor', 'CountValue', 'Comment') )
elif self.numAngles == 2:
writer.writerow( ('Phi', 'Omega', 'CountFor', 'CountValue', 'Comment') )
elif self.numAngles == 2:
writer.writerow( ('Phi','Chi', 'Omega', 'CountFor', 'CountValue', 'Comment') )
for i in range(len(X)/self.numAngles):
if i == 0 and self.addOrientations:
comment = comment0
else:
comment = commentn
if self.numAngles == 1:
writer.writerow((X[self.numAngles*i], 'pcharge', self.pcharge, comment))
elif self.numAngles == 2:
writer.writerow((X[self.numAngles*i], X[self.numAngles*i+1], 'pcharge', self.pcharge, comment))
elif self.numAngles == 3:
writer.writerow((X[self.numAngles*i], X[self.numAngles*i+1], X[self.numAngles*i+2], 'pcharge', self.pcharge, comment))
finally:
f.close()
print (open(fileName, 'rt').read())
def optimize(self, seed):
# the starting point
np.random.seed(seed)
# the starting point
x0 = []
for i in range(2*self.numOrientations):
x0.append(np.random.uniform(0., 360.0))
# define the new step taking routine and pass it to basinhopping
mytakestep = RandomDisplacementBounds(0.0, 360.0)
# use method
minimizer_kwargs = dict(method="Nelder-Mead", options={{'disp': False, 'ftol':1.00, 'maxiter':3, 'maxfev':15}})
result = basinhopping(self.f, x0, niter=1, minimizer_kwargs=minimizer_kwargs, take_step=mytakestep)
return result
def orthogonal_proj(self, zfront, zback):
a = (zfront+zback)/(zfront-zback)
b = -2*(zfront*zback)/(zfront-zback)
return np.array([[1,0,0,0],
[0,1,0,0],
[0,0,a,b],
[0,0,-0.0001,zback]])
def plot_Q(self):
proj3d.persp_transformation = self.orthogonal_proj
try:
# Find size of screen
import curses
stdscr = curses.initscr()
self.screen_x,self.screen_y = stdscr.getmaxyx()
self.screen_x = min(self.screen_x, self.screen_y)
self.screen_y = self.screen_x
except:
self.screen_x = 40
self.screen_y = 40
Box = BinMD(InputWorkspace = self._md,
AlignedDim0 = 'Q_sample_x,'+str(self.minQ)+','+str(self.maxQ)+','+str(self.nGrid),
AlignedDim1 = 'Q_sample_y,'+str(self.minQ)+','+str(self.maxQ)+','+str(self.nGrid),
AlignedDim2 = 'Q_sample_z,'+str(self.minQ)+','+str(self.maxQ)+','+str(self.nGrid))
signal = Box.getSignalArray()
step = float(self.maxQ-self.minQ)/(self.nGrid-1)
self.c = []
self.x = []
self.y = []
self.z = []
self.s = []
for Hj in range(self.nGrid):
for Kj in range(self.nGrid):
for Lj in range(self.nGrid):
if signal[Lj,Kj,Hj] >= self.minIntensity:
self.c.append(signal[Lj,Kj,Hj])
self.x.append(self.minQ+step*Lj)
self.y.append(self.minQ+step*Kj)
self.z.append(self.minQ+step*Hj)
self.s.append(10)
if (len(self.c)) < 1:
print ("Viewer failed with no data: decrease minimum intensity")
sys.exit()
fig = plt.figure("ReciprocalSpace"+str(self.events),figsize = (self.screen_x, self.screen_y))
ax = fig.gca(projection = '3d')
vmin = min(self.c)
vmax = max(self.c)
cm = plt.cm.get_cmap('rainbow')
logNorm = colors.LogNorm(vmin = vmin, vmax = vmax)
se = ax.scatter(self.x, self.y, self.z, c = self.c, vmin = vmin, vmax = vmax, cmap = cm, norm = logNorm, s = self.s, alpha = 1.0, picker = True)
ax.text2D(0.05, 0.90, "# Events = "+str(self.events) +
"\nHold mouse button to rotate." +
"\nClose window to continue." ,
fontsize = 20, transform = ax.transAxes)
ax.set_xlabel('Q$_X$')
ax.set_ylabel('Q$_Y$')
ax.set_zlabel('Q$_Z$')
formatter = LogFormatter(10, labelOnlyBase = False)
plt.colorbar(se,ticks = [10,20,50,100,200,500,1000,2000,5000,10000,20000,50000,1e5,2e5,5e5,1e6,2e6,5e6,1e7,2e7,2e8], format = formatter)
# Create cubic bounding box to simulate equal aspect ratio
max_range = max([max(self.x)-min(self.x), max(self.y)-min(self.y), max(self.z)-min(self.z)])
Xb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][0].flatten() + 0.5*(max(self.x)+min(self.x))
Yb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][1].flatten() + 0.5*(max(self.y)+min(self.y))
Zb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][2].flatten() + 0.5*(max(self.z)+min(self.z))
# Comment or uncomment following both lines to test the fake bounding box:
for xb, yb, zb in zip(Xb, Yb, Zb):
ax.plot([xb], [yb], [zb], 'w')
plt.show()
def find_peaks(self):
distance_threshold = 0.9 * 6.28 / float(self.abcMax)
#End Input from GUI
self.bkg_inner_radius = self.peakRadius
self.bkg_outer_radius = self.peakRadius * 1.25992105 # A factor of 2 ^ (1/3)
#Create peaks for all 3D grid point about minIntensity
self.peaks_ws = FindPeaksMD( self._md, MaxPeaks = self.numPeaksToFind, OutputWorkspace="peaks",
PeakDistanceThreshold = distance_threshold )
self.instrument = self.peaks_ws.getInstrument()
self.npeaks = self.peaks_ws.getNumberPeaks()
try:
FindUBUsingFFT( PeaksWorkspace = self.peaks_ws, MinD = self.abcMin, MaxD = self.abcMax, Tolerance = self.tolerance)
SaveIsawUB(self.peaks_ws, self.outputDirectory+"/Peaks"+str(self.events)+".mat")
print ("UB matrix: ", self.outputDirectory+"/Peaks"+str(self.events)+".mat")
self.numInd,errInd = IndexPeaks( PeaksWorkspace = self.peaks_ws, Tolerance = self.tolerance, RoundHKLs = False)
try:
pg = PointGroupFactory.createPointGroup(self.pointGroup)
self.numInd,errInd = SelectCellOfType( PeaksWorkspace=self.peaks_ws, CellType=str(pg.getLatticeSystem()),
Centering=self.centering, AllowPermutations=True, Apply=True, Tolerance=self.tolerance )
SaveIsawUB(self.peaks_ws, self.outputDirectory+"/PeaksSym"+str(self.events)+".mat")
print ("UB matrix: ", self.outputDirectory+"/PeaksSym"+str(self.events)+".mat")
except:
print ("Point group symmetry failed")
if self.predictPeaks or self.addOrientations:
self.peaks_ws = PredictPeaks(InputWorkspace=self.peaks_ws, WavelengthMin=self.minWavelength,
EdgePixels=self.edgePixels,
WavelengthMax=self.maxWavelength, MinDSpacing=self.minDSpacing, OutputWorkspace="peaks")
CopySample(InputWorkspace = self.peaks_ws,OutputWorkspace = self._wksp,CopyName = '0',CopyMaterial = '0',CopyEnvironment = '0',CopyShape = '0')
except:
self.numInd = 0
print ("UB matrix not found")
# this is number of predicted peaks if that option is selected
nPeaks = self.peaks_ws.getNumberPeaks()
self.sumIsigI = 0.0
self.sumIsigI2 = 0.0
self.sumIsigI5 = 0.0
for i in range(nPeaks):
peak = self.peaks_ws.getPeak(i)
qsample = peak.getQSampleFrame()
intensity = peak.getIntensity()
sigI = peak.getSigmaIntensity()
if sigI != 0.0:
IsigI = intensity/sigI
if (IsigI > 10.):
self.sumIsigI += 1.0
if (IsigI > 5.):
self.sumIsigI5 += 1.0
if (IsigI > 2.):
self.sumIsigI2 += 1.0
else:
IsigI = 0.0
detID = int(peak.getDetectorID())
bank = self.instrument.getDetector(detID).getName()
self.c = np.append(self.c,max(self.minIntensity,intensity))
self.x = np.append(self.x,qsample.X())
self.y = np.append(self.y,qsample.Y())
self.z = np.append(self.z,qsample.Z())
self.s = np.append(self.s,320)
self.sumIsigI /= nPeaks/100.0
self.sumIsigI5 /= nPeaks/100.0
self.sumIsigI2 /= nPeaks/100.0
def plot_peaks(self, xy):
plt.rcParams.update({{'font.size': 6}})
figS, axs = plt.subplots(10, 10, sharex=False, sharey=False, figsize=(self.screen_x, self.screen_y))
figS.canvas.set_window_title('Peaks'+str(self.events))
axs = axs.flatten()
if xy:
peakRadius2 = self.peakRadius
peakRadius3 = 0.05
bins = '100,100,1'
else:
peakRadius2 = 0.05
peakRadius3 = self.peakRadius
bins = '100,1,100'
nPeaks = self.peaks_ws.getNumberPeaks()
for i in range(nPeaks):
j = i%len(axs)
peak = self.peaks_ws.getPeak(i)
hkl = str(int(peak.getH()+0.5))+','+str(int(peak.getK()+0.5))+','+str(int(peak.getL()+0.5))
qsample = peak.getQSampleFrame()
sl2d = BinMD(InputWorkspace=self._md, AxisAligned=False, BasisVector0='Q_sample_x,Angstrom^-1,1,0,0',
BasisVector1='Q_sample_y,Angstrom^-1,0,1,0', BasisVector2='Q_sample_z,Angstrom^-1,0,0,1',
OutputExtents=str(qsample.X()-self.peakRadius)+','+str(qsample.X()+self.peakRadius)+','+str(qsample.Y()-peakRadius2)
+','+str(qsample.Y()+peakRadius2)+','+str(qsample.Z()-peakRadius3)+','+str(qsample.Z()+peakRadius3),
OutputBins=bins, Parallel=True)
#10 subplots per page
pcm=self.Plot2DMD(axs[j],sl2d, hkl, NumEvNorm=False)
figS.colorbar(pcm,ax=axs[j])
if j == len(axs)-1 and i < nPeaks-1:
plt.show()
figS, axs = plt.subplots(10, 10, sharex=False, sharey=False, figsize=(self.screen_x, self.screen_y))
figS.canvas.set_window_title('Peaks'+str(self.events))
axs = axs.flatten()
# do not draw axes for non-existant peaks
for k in range(j+1,len(axs)):
axs[k].axis('off')
plt.show()
def plot_Qpeaks(self):
plt.rcParams.update({{'font.size': 10}})
self.ws = CreatePeaksWorkspace(InstrumentWorkspace=self._wksp, NumberOfPeaks=0, OutputWorkspace="events")
CopySample(InputWorkspace = self.peaks_ws,OutputWorkspace = self.ws,CopyName = '0',CopyMaterial = '0',CopyEnvironment = '0',CopyShape = '0')
figP = plt.figure("EventsAndPeaks"+str(self.events),figsize = (self.screen_x, self.screen_y))
self.axP = figP.gca(projection = '3d')
self.plot_lattice('green')
self.c[self.c<=1] = 1.
vmin = min(self.c)
vmax = max(self.c)
cm = plt.cm.get_cmap('rainbow')
logNorm = colors.LogNorm(vmin = vmin, vmax = vmax)
sp = self.axP.scatter(self.x, self.y, self.z, c = self.c, vmin = vmin, vmax = vmax, cmap = cm, norm = logNorm, s = self.s, alpha = 0.2, picker = True)
self.props = dict(boxstyle = 'round', facecolor = 'wheat', alpha = 1.0)
cid = figP.canvas.mpl_connect('pick_event', self.onpick3)
try:
lattice = self.peaks_ws.sample().getOrientedLattice()
except:
lattice = OrientedLattice(1,1,1)
self.axP.text2D(0.05, 0.70, "# Events = "+str(self.events)+
"\nPeaks with I/sigI > 10 = "+'%.1f'%(self.sumIsigI) +
"%\nPeaks with I/sigI > 5 = "+'%.1f'%(self.sumIsigI5) +
"%\nPeaks with I/sigI > 2 = "+'%.1f'%(self.sumIsigI2) +
"%\n# peaks indexed = "+str(self.numInd) + " out of " + str(self.npeaks) +
"\nLattice = " + " " + "{{:.2f}}".format(lattice.a()) + " " + "{{:.2f}}".format(lattice.b()) + " " + "{{:.2f}}".format(lattice.c()) + " " +
"{{:.2f}}".format(lattice.alpha()) + " " + "{{:.2f}}".format(lattice.beta()) + " " + "{{:.2f}}".format(lattice.gamma()) +
"\nError = " + " " + "{{:.2E}}".format(lattice.errora()) + " " + "{{:.2E}}".format(lattice.errorb()) + " " + "{{:.2E}}".format(lattice.errorc()) + " " +
"{{:.2E}}".format(lattice.erroralpha()) + " " + "{{:.2E}}".format(lattice.errorbeta()) + " " + "{{:.2E}}".format(lattice.errorgamma()) +
"\nClick on peak to see peak info." +
"\nHold mouse button to rotate." +
"\nClose window to continue." ,
fontsize = 20, transform = self.axP.transAxes)
self.axP.set_xlabel('Q$_X$')
self.axP.set_ylabel('Q$_Y$')
self.axP.set_zlabel('Q$_Z$')
formatter = LogFormatter(10, labelOnlyBase = False)
plt.colorbar(sp,ticks = [1,2,5,10,20,50,100,200,500,1000,2000,5000,10000,20000,50000,100000], format = formatter)
# Create cubic bounding box to simulate equal aspect ratio
max_range = max([max(self.x)-min(self.x), max(self.y)-min(self.y), max(self.z)-min(self.z)])
Xb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][0].flatten() + 0.5*(max(self.x)+min(self.x))
Yb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][1].flatten() + 0.5*(max(self.y)+min(self.y))
Zb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][2].flatten() + 0.5*(max(self.z)+min(self.z))
# Comment or uncomment following both lines to test the fake bounding box:
for xb, yb, zb in zip(Xb, Yb, Zb):
self.axP.plot([xb], [yb], [zb], 'w')
plt.show()
figP.canvas.mpl_disconnect(cid)
def plot_lattice(self, latColor):
predict_peaks_ws = PredictPeaks(InputWorkspace=self.peaks_ws, WavelengthMin=self.minWavelength,
WavelengthMax=self.maxWavelength, MinDSpacing=self.minDSpacing,
OutputWorkspace="predict")
self.predict_dict = {{}}
for i in range(predict_peaks_ws.getNumberPeaks()):
peak = predict_peaks_ws.getPeak(i)
self.predict_dict.update({{peak.getHKL(): peak.getQSampleFrame()}})
for Hj in range(int(self.minQ),int(self.maxQ)+1):
for Kj in range(int(self.minQ),int(self.maxQ)+1):
for Lj in range(int(self.minQ),int(self.maxQ)+1):
if V3D(Hj,Kj,Lj) in self.predict_dict:
xyz0 = self.predict_dict[V3D(Hj,Kj,Lj)]
if V3D(Hj,Kj,Lj+1) in self.predict_dict:
qx = []
qy = []
qz = []
qx = np.append(qx, xyz0.getX())
qy = np.append(qy, xyz0.getY())
qz = np.append(qz, xyz0.getZ())
xyz = self.predict_dict[V3D(Hj,Kj,Lj+1)]
qx = np.append(qx, xyz.getX())
qy = np.append(qy, xyz.getY())
qz = np.append(qz, xyz.getZ())
self.axP.plot(qx,qy,qz, color=latColor, linewidth=1, alpha=0.2)
if V3D(Hj,Kj+1,Lj) in self.predict_dict:
qx = []
qy = []
qz = []
qx = np.append(qx, xyz0.getX())
qy = np.append(qy, xyz0.getY())
qz = np.append(qz, xyz0.getZ())
xyz = self.predict_dict[V3D(Hj,Kj+1,Lj)]
qx = np.append(qx, xyz.getX())
qy = np.append(qy, xyz.getY())
qz = np.append(qz, xyz.getZ())
self.axP.plot(qx,qy,qz, color=latColor, linewidth=1, alpha=0.2)
if V3D(Hj+1,Kj,Lj) in self.predict_dict:
qx = []
qy = []
qz = []
qx = np.append(qx, xyz0.getX())
qy = np.append(qy, xyz0.getY())
qz = np.append(qz, xyz0.getZ())
xyz = self.predict_dict[V3D(Hj+1,Kj,Lj)]
qx = np.append(qx, xyz.getX())
qy = np.append(qy, xyz.getY())
qz = np.append(qz, xyz.getZ())
self.axP.plot(qx,qy,qz, color=latColor, linewidth=1, alpha=0.2)
def plot_crystalplan(self, unique, completeness, redundancy, multiple):
self.x = []
self.y = []
self.z = []
self.c = []
self.s = []
plt.rcParams.update({{'font.size': 10}})
npeaksTotal = self.ws.getNumberPeaks()
for i in range(npeaksTotal):
peak = self.ws.getPeak(i)
qsample = peak.getQSampleFrame()
intensity = peak.getIntensity()
self.c = np.append(self.c,max(self.minIntensity,intensity))
self.x = np.append(self.x,qsample.X())
self.y = np.append(self.y,qsample.Y())
self.z = np.append(self.z,qsample.Z())
self.s = np.append(self.s,40)
figP = plt.figure("CrystalPlan"+str(self.events),figsize = (self.screen_x, self.screen_y))
self.axP = figP.gca(projection = '3d')
self.c[self.c<=1] = 1.
vmin = min(self.c)
vmax = max(self.c)
cm = plt.cm.get_cmap('rainbow')
logNorm = colors.LogNorm(vmin = vmin, vmax = vmax)
sp = self.axP.scatter(self.x, self.y, self.z, c = self.c, vmin = vmin, vmax = vmax, cmap = cm, norm = logNorm, s = self.s, alpha = 0.2, picker = True)
self.props = dict(boxstyle = 'round', facecolor = 'wheat', alpha = 1.0)
cid = figP.canvas.mpl_connect('pick_event', self.onpick3)
try:
lattice = self.peaks_ws.sample().getOrientedLattice()
except:
lattice = OrientedLattice(1,1,1)
self.axP.text2D(0.05, 0.70, "Peaks = "+str(npeaksTotal)+
'\nUnique: {{0}}'.format(unique)+
'\nCompleteness: {{0}}%'.format(round(completeness * 100, 2))+
'\nRedundancy: {{0}}'.format(round(redundancy, 2))+
'\nMeasured multiple times: {{0}}%'.format(round(multiple*100, 2))+
"\nLattice = " + " " + "{{:.2f}}".format(lattice.a()) + " " + "{{:.2f}}".format(lattice.b()) + " " + "{{:.2f}}".format(lattice.c()) + " " +
"{{:.2f}}".format(lattice.alpha()) + " " + "{{:.2f}}".format(lattice.beta()) + " " + "{{:.2f}}".format(lattice.gamma()) +
"\nError = " + " " + "{{:.2E}}".format(lattice.errora()) + " " + "{{:.2E}}".format(lattice.errorb()) + " " + "{{:.2E}}".format(lattice.errorc()) + " " +
"{{:.2E}}".format(lattice.erroralpha()) + " " + "{{:.2E}}".format(lattice.errorbeta()) + " " + "{{:.2E}}".format(lattice.errorgamma()) +
"\nHold mouse button to rotate." +
"\nClose window to continue." ,
fontsize = 20, transform = self.axP.transAxes)
self.axP.set_xlabel('Q$_X$')
self.axP.set_ylabel('Q$_Y$')
self.axP.set_zlabel('Q$_Z$')
formatter = LogFormatter(10, labelOnlyBase = False)
plt.colorbar(sp,ticks = [1,2,5,10,20,50,100,200,500,1000,2000,5000,10000,20000,50000,100000], format = formatter)
# Create cubic bounding box to simulate equal aspect ratio
max_range = max([max(self.x)-min(self.x), max(self.y)-min(self.y), max(self.z)-min(self.z)])
Xb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][0].flatten() + 0.5*(max(self.x)+min(self.x))
Yb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][1].flatten() + 0.5*(max(self.y)+min(self.y))
Zb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][2].flatten() + 0.5*(max(self.z)+min(self.z))
# Comment or uncomment following both lines to test the fake bounding box:
for xb, yb, zb in zip(Xb, Yb, Zb):
self.axP.plot([xb], [yb], [zb], 'w')
plt.show()
figP.canvas.mpl_disconnect(cid)
def onpick3(self, event):
ind = event.ind
zdirs = None
xp = np.take(self.x, ind)
yp = np.take(self.y, ind)
zp = np.take(self.z, ind)
cp = np.take(self.c, ind)
qSample=V3D(xp[0],yp[0],zp[0])
R=self._wksp.run().getGoniometer().getR()
qLab = R.dot(qSample)
peak = self.ws.createPeak(qLab)
peak.setQSampleFrame(qSample)
peak.setBinCount(cp[0])
self.ws.addPeak(peak)
try:
IndexPeaks( PeaksWorkspace = self.ws, Tolerance = 1.0, RoundHKLs = False )
except:
print ("UB matrix not found")
self.ws = IntegratePeaksMD( InputWorkspace = self._md, PeakRadius = self.peakRadius,
CoordinatesToUse = "Q (sample frame)",
BackgroundOuterRadius = self.bkg_outer_radius,
BackgroundInnerRadius = self.bkg_inner_radius,
PeaksWorkspace = self.ws, OutputWorkspace="events")
peak = self.ws.getPeak(0)
if peak.getSigmaIntensity() != 0.0:
IsigI = peak.getIntensity()/peak.getSigmaIntensity()
else:
IsigI = 0.0
detID = int(peak.getDetectorID())
bank = self.instrument.getDetector(detID).getName()
label = 'Counts:'+'%.3f'%(peak.getBinCount())+ \
'\nIntegral, I/sigI:'+'%.3f %.3f'%(peak.getIntensity(),IsigI)+ \
'\nDetector Number:'+bank+ \
'\nh,k,l:'+'%.3f %.3f %.3f'%(peak.getH(),peak.getK(),peak.getL())+ \
'\nQ$_X$,Q$_Y$,Q$_Z$:'+'%.3f %.3f %.3f'%(xp[0],yp[0],zp[0])+ \
'\nQ($\AA^{{-1}}, 2\pi/d$):'+'%.3f'%(2*np.pi/peak.getDSpacing())+ \
'\nd-Spacing($\AA$):'+'%.3f'%(peak.getDSpacing())+ \
'\nWavelength($\AA$):'+'%.3f'%(peak.getWavelength())+ \
'\n2$\\theta$($^\circ$):'+'%.3f'%(peak.getScattering())+ \
'\nTime($\mu$s):'+'%.3f'%(peak.getTOF())+ \
'\nE(meV):'+'%.3f'%(peak.getFinalEnergy())
self.ws.removePeak(0)
txt = self.axP.text(xp[0],yp[0],zp[0],label,zdirs, bbox = self.props)
plt.pause(2)
txt.remove()
def dim2array(self, d,center=True):
"""
Create a numpy array containing bin centers along the dimension d
input: d - IMDDimension
return: numpy array, from min+st/2 to max-st/2 with step st
"""
dmin=d.getMinimum()
dmax=d.getMaximum()
dstep=d.getX(1)-d.getX(0)
if center:
return np.arange(dmin+dstep/2,dmax,dstep)
else:
return np.linspace(dmin,dmax,d.getNBins()+1)
def Plot2DMD(self, ax,ws, hkl, NumEvNorm=False,**kwargs):
"""
Plot a 2D slice from an MDHistoWorkspace (assume all other dimensions are 1)
input: ax - axis object
input: ws - handle to the workspace
input: NumEvNorm - must be set to true if data was converted to MD from a histo workspace (like NXSPE) and no MDNorm... algorithms were used
input: kwargs - arguments that are passed to plot, such as plotting symbol, color, label, etc.
"""
dims=ws.getNonIntegratedDimensions()
if len(dims)!=2:
raise ValueError("The workspace dimensionality is not 2")
dimx=dims[0]
x=self.dim2array(dimx,center=False)
dimy=dims[1]
y=self.dim2array(dimy,center=False)
intensity=ws.getSignalArray()*1.
if NumEvNorm:
nev=ws.getNumEventsArray()
intensity/=nev
intensity=intensity.squeeze()
intensity=np.ma.masked_where(np.isnan(intensity),intensity)
XX,YY=np.meshgrid(x,y,indexing='ij')
pcm=ax.pcolorfast(XX,YY,intensity,**kwargs)
ax.set_xlabel(dimx.getName())
ax.set_ylabel(hkl + " " + dimy.getName())
ax.set(aspect=1)
for tick in ax.get_xticklabels():
tick.set_rotation(90)
return pcm
def meshgrid2(self, *arrs):
arrs = tuple(reversed(arrs))
lens = map(len, arrs)
dim = len(arrs)
sz = 1
for s in lens:
sz *= s
ans = []
for i, arr in enumerate(arrs):
slc = [1]*dim
slc[i] = lens[i]
arr2 = np.asarray(arr).reshape(slc)
for j, sz in enumerate(lens):
if j != i:
arr2 = arr2.repeat(sz, axis=j)
ans.append(arr2)
return tuple(ans)
def addOrientation(self, x, id):
ivar = 0
if self.changePhi:
AddSampleLog(Workspace=self._wksp, LogName='phi', LogText=str(x[ivar]), LogType='Number')
ivar += 1
if self.changeChi:
AddSampleLog(Workspace=self._wksp, LogName='chi', LogText=str(x[ivar]), LogType='Number')
ivar += 1
if self.changeOmega:
AddSampleLog(Workspace=self._wksp, LogName='omega', LogText=str(x[ivar]), LogType='Number')
ivar += 1
SetGoniometer(Workspace=self._wksp,Axis0="omega,0,1,0,1",Axis1="chi,0,0,1,1",Axis2="phi,0,1,0,1")
peaks = PredictPeaks(InputWorkspace=self._wksp, WavelengthMin=self.minWavelength,
EdgePixels=self.edgePixels,
WavelengthMax=self.maxWavelength, MinDSpacing=self.minDSpacing, OutputWorkspace='result'+str(id)+current_process().name)
return peaks
def f(self, x):
listoflists = zip(*[iter(x)]*self.numAngles)
if (self.addOrientations):
peaks = self.peaks_ws
else:
peaks = CreatePeaksWorkspace(InstrumentWorkspace=self._wksp, NumberOfPeaks=0, OutputWorkspace='peaks'+current_process().name)
CopySample(InputWorkspace = self.peaks_ws,OutputWorkspace = peaks,CopyName = '0',CopyMaterial = '0',CopyEnvironment = '0',CopyShape = '0')
for i in range(len(listoflists)):
result = self.addOrientation(listoflists[i], i)
peaks = CombinePeaksWorkspaces(peaks, result, OutputWorkspace='peaks'+current_process().name)
AnalysisDataService.remove( result.getName() )
if self.useSymmetry:
unique, completeness, redundancy, multiple = CountReflections(peaks, PointGroup=self.pointGroup,
LatticeCentering=self.centering, MinDSpacing=self.minDSpacing,
MissingReflectionsWorkspace='')
else:
unique, completeness, redundancy, multiple = CountReflections(peaks, PointGroup='-1',
LatticeCentering='P', MinDSpacing=self.minDSpacing,
MissingReflectionsWorkspace='')
AnalysisDataService.remove( peaks.getName() )
return -unique
def fopt(self, x):
listoflists = zip(*[iter(x)]*self.numAngles)
count = []
complete = []
if (self.addOrientations):
self.ws = self.peaks_ws
else:
self.ws = CreatePeaksWorkspace(InstrumentWorkspace=self._wksp, NumberOfPeaks=0, OutputWorkspace='peaks'+current_process().name)
CopySample(InputWorkspace = self.peaks_ws,OutputWorkspace = self.ws,CopyName = '0',CopyMaterial = '0',CopyEnvironment = '0',CopyShape = '0')
for i in range(len(listoflists)):
result = self.addOrientation(listoflists[i], i)
self.ws = CombinePeaksWorkspaces(self.ws, result, OutputWorkspace='peaks'+current_process().name)
AnalysisDataService.remove( result.getName() )
if self.useSymmetry:
unique, completeness, redundancy, multiple = CountReflections(self.ws, PointGroup=self.pointGroup,
LatticeCentering=self.centering, MinDSpacing=self.minDSpacing,
MissingReflectionsWorkspace='')
else:
unique, completeness, redundancy, multiple = CountReflections(self.ws, PointGroup='-1',
LatticeCentering='P', MinDSpacing=self.minDSpacing,
MissingReflectionsWorkspace='')
count.append(i+1)
complete.append(round(completeness * 100, 2))
self.plot_crystalplan(unique, completeness, redundancy, multiple)
plt.rcParams.update({{'font.size': 40}})
plt.figure("Completeness"+str(self.events),figsize = (self.screen_x, self.screen_y))
plt.plot(count, complete, 'ro')
plt.ylabel('Orientations')
plt.ylabel('Completeness (%)')
plt.show()
def func(self, x):
fx = self.f(x)
""" Derivative of objective function """
eps = 10
dfdx = []
for i in range(self.numAngles*self.numOrientations):
x2 = list(x)
x2[i] = x[i]+eps
dfdx.append(self.f(x2)-fx)
dfdx_array = np.array(dfdx)
return fx, dfdx_array
class RandomDisplacementBounds(object):
"""random displacement with bounds"""
def __init__(self, xmin, xmax, stepsize=30.0):
self.xmin = xmin
self.xmax = xmax
self.stepsize = stepsize
def __call__(self, x):
"""take a random step but ensure the new position is within the bounds"""
while True:
xnew = x + np.random.uniform(-self.stepsize, self.stepsize, np.shape(x))
if np.all(xnew < self.xmax) and np.all(xnew > self.xmin):
break
return xnew
if __name__ == '__main__': # if we're running file directly and not importing it
print ("Wait a few minutes for events in reciprocal space")
test = MantidEV() # run the main function
test.time_start = 0
test.time_stop = test.time_start + test.seconds
test.last = 0
while test.last == 0:
test.select_wksp()
if test.events > 0:
test.plot_Q()
test.find_peaks()
test.plot_peaks(True)
test.plot_peaks(False)
test.plot_Qpeaks()
if test.numOrientations > 0:
test.crystalplan()
# else:
# print ("No events")
print ("Plotting finished for current data")