Develop

MJOLNIR/Data/DataFile.py

@@ -79,15 +79,15 @@ def __init__(self,fileLocation=None):
                         self.Norm=np.array(f.get('entry/data/normalization'))
                     self.MonitorMode = np.array(f.get('entry/control/mode'))[0].decode()
                     self.MonitorPreset=np.array(f.get('entry/control/preset'))                
+                    self.startTime = np.array(f.get('entry/start_time'))[0]
                     if self.type == 'hdf':
                         self.Monitor = np.array(f.get('entry/control/data'))
                         if not self.MonitorMode == 't' and len(self.Monitor)>1: # If not counting on time and more than one point saved
-                            if self.Monitor.flatten()[0]!=self.MonitorPreset: # For all data in 2018 with wrong monitor saved
+                            if self.Monitor.flatten()[0]!=self.MonitorPreset and self.startTime[:4]=='2018': # For all data in 2018 with wrong monitor saved
                                 self.Monitor = np.ones_like(self.Monitor)*self.MonitorPreset ### TODO: Make Mark save the correct monitor!!
                     else:
                         self.Monitor=np.array(f.get('entry/data/monitor'))
                     self.Time = np.array(f.get('entry/control/time'))
-                    self.startTime = np.array(f.get('entry/start_time'))[0]
                     self.endTime = np.array(f.get('entry/end_time'))[0]
                     self.experimentIdentifier = np.array(f.get('entry/experiment_identifier'))[0]
                     self.comment = np.array(f.get('entry/comment'))[0]
@@ -165,7 +165,9 @@ def __init__(self,fileLocation=None):
                         calibrations.append([EfTable,A4,bound])
                     self.instrumentCalibrations = np.array(calibrations,dtype=object)
                     self.loadBinning(self.binning)
-
+
+                    self.twotheta = self.A4-self.A4Off
+
                     self.temperature = np.array(sample.get('temperature'))
                     self.magneticField = np.array(sample.get('magnetic_field'))
                     self.electricField = np.array(sample.get('electric_field'))
@@ -1787,11 +1789,11 @@ def extractData(files):
         scanParamUnit.append(datafile.scanUnits)
 
         Monitor.append(datafile.Monitor)
-        if np.array(datafile.A3Off).shape is ():
+        if np.array(datafile.A3Off).shape == ():
             datafile.A3Off = 0.0
         a3.append(datafile.A3-datafile.A3Off)
         a3Off.append(datafile.A3Off)
-        if np.array(datafile.A4Off).shape is ():
+        if np.array(datafile.A4Off).shape == ():
             datafile.A4Off = [0.0]
         a4.append(datafile.A4-datafile.A4Off)
         a4Off.append(datafile.A4Off)

MJOLNIR/Data/RLUAxes.py

@@ -280,7 +280,7 @@ def calculateTicks(ticks,angle,round=True):
         ax.basey = basey
 
     def set_axis(ax,v1,v2,*args):
-        if not args is ():
+        if args != ():
             points = np.concatenate([[v1,v2],[x for x in args]],axis=0)
         else:
             points = np.array([v1,v2])

MJOLNIR/Geometry/Instrument.py

@@ -367,14 +367,14 @@ def generateCalibration(self,Vanadiumdatafile,A4datafile=False,savelocation='cal
         if ignoreTubes is None:
             ignoreTubes = []
 
-        with hdf.File(Vanadiumdatafile,'r') as VanFile:
+        with hdf.File(Vanadiumdatafile,'r') as VanFile: # Open normalziation file
             if not A4datafile == False: # pragma: no cover
                 A4File = hdf.File(A4datafile,'r')
                 A4FileInstrument = getInstrument(A4File)
                 A4FileInstrumentType = A4FileInstrument.name.split('/')[-1]
 
 
-            VanFileInstrument = getInstrument(VanFile)
+            VanFileInstrument = getInstrument(VanFile) # Get the HDF object of the instrument (CAMEA)
 
 
             VanFileInstrumentType = VanFileInstrument.name.split('/')[-1]
@@ -390,8 +390,19 @@ def generateCalibration(self,Vanadiumdatafile,A4datafile=False,savelocation='cal
                 if savelocation[-1]!='/':
                     savelocation+='/'
 
-                Data = np.array(VanFileInstrument.get('detector/counts')).transpose(2,0,1).astype(float)
-                monitor = np.mean(np.array(VanFile.get('entry/control/data'))) # Extract monitor count
+                monitor = np.array(VanFile.get('entry/control/data')) # Extract monitor count
+                # Divide data with corresponding monitor by reshaping it correctly
+                Data = np.array(VanFileInstrument.get('detector/counts')).transpose(2,0,1).astype(float)/monitor[np.newaxis,:,np.newaxis]
+                if sampleMass != None: # A sample mass has been proivided
+
+                    sampleMolarMass = _tools.calculateMolarMass(sample,formulaUnitsPerUnitCell)
+
+                    # TODO: Check placement of sampleDebyeWallerFactor!
+                    vanadiumFactor = sampleDebyeWallerFactor*sampleMolarMass/(sampleMass*sampleIncoherent)
+                    Data*=vanadiumFactor # Multiply vanadium factor to perform absolut normalization 22-02-2021 JL
+                else:
+                    vanadiumFactor = 1.0
+
 
                 if False: #Mask pixels where spurion is present
                     Data[:,:,:100]=0
@@ -435,7 +446,7 @@ def generateCalibration(self,Vanadiumdatafile,A4datafile=False,savelocation='cal
                         plt.scatter(np.arange(pixels),np.sum(Data[:][i],axis=0),s=5)
                         plt.ylim(0,np.max(np.sum(Data[i],axis=0))*1.1)
                         plt.xlabel('Pixel')
-                        plt.ylabel('Intensity [arg]')
+                        plt.ylabel('Intensity [arb]')
                         plt.title('Vanadium normalization detector '+str(i))
                         plt.tight_layout()
                         plt.savefig(savelocation+'Raw/detector'+str(i)+'.png',format='png', dpi=150)
@@ -479,7 +490,7 @@ def generateCalibration(self,Vanadiumdatafile,A4datafile=False,savelocation='cal
                                 plt.plot([peakPos[i+13*k,j],peakPos[i+13*k,j]],[0,np.max(ESummedData[i+13*k])*1.1])
                             plt.plot(x,y,'k')
                             plt.xlabel('Pixel')
-                            plt.ylabel('Intensity [arg]')
+                            plt.ylabel('Intensity [arb]')
                             plt.title('Detector {}'.format(i+13*k))
                             plt.ylim(0,np.max(ESummedData[i+13*k])*1.1)
 
@@ -537,22 +548,14 @@ def generateCalibration(self,Vanadiumdatafile,A4datafile=False,savelocation='cal
                     if plot: # pragma: no cover
                         plt.ylim(0,np.max(ESummedData[i])*1.1)
                         plt.xlabel('Pixel')
-                        plt.ylabel('Intensity [arg]')
+                        plt.ylabel('Intensity [arb]')
                         plt.savefig(savelocation+'/Raw/Active_'+str(i)+'.png',format='png', dpi=150)
 
                 Eguess = np.zeros_like(peakPos,dtype=int)
                 for i in range(Eguess.shape[0]):
                     for j in range(analysers):
                         Eguess[i,j]=np.argmax(Data[i,:,int(pixelpos[i,j])])
 
-                if sampleMass != None: # A sample mass has been proivided
-
-                    sampleMolarMass = _tools.calculateMolarMass(sample,formulaUnitsPerUnitCell)
-
-                    # TODO: Check placement of sampleDebyeWallerFactor!
-                    vanadiumFactor = sampleDebyeWallerFactor*sampleMolarMass/(sampleMass*sampleIncoherent*monitor)
-                else:
-                    vanadiumFactor = 1.0
 
                 fitParameters = []
                 activePixelRanges = []
@@ -611,8 +614,8 @@ def generateCalibration(self,Vanadiumdatafile,A4datafile=False,savelocation='cal
                                 for k in range(detpixels):
                                     binPixelData = Data[i,:,pixelAreas[k]:pixelAreas[k+1]].sum(axis=1)
                                     ECenter = Ei[np.argmax(binPixelData)]
-                                    ECutLow = ECenter-0.4
-                                    ECutHigh= ECenter+0.4
+                                    ECutLow = ECenter-3.0
+                                    ECutHigh= ECenter+3.0
                                     TopId = np.argmin(np.abs(Ei-ECutHigh))
                                     BotId = np.argmin(np.abs(ECutLow-Ei))
                                     if TopId<BotId:
@@ -622,9 +625,9 @@ def generateCalibration(self,Vanadiumdatafile,A4datafile=False,savelocation='cal
                                     binPixelData = binPixelData[BotId:TopId]
                                     EiLocal = Ei[BotId:TopId]
                                     Bg = np.min(binPixelData[[0,-1]])
-                                    guess = np.array([np.max(binPixelData), ECenter,0.005,Bg],dtype=float)
+                                    guess = np.array([np.max(binPixelData), ECenter,0.08],dtype=float)
                                     try:
-                                        res = scipy.optimize.curve_fit(Gaussian,EiLocal,binPixelData.astype(float),p0=guess)
+                                        res = scipy.optimize.curve_fit(lambda x,A,mu,sigma:Gaussian(x,A,mu,sigma,0.0),EiLocal,binPixelData.astype(float),p0=guess)
 
                                     except: # pragma: no cover
                                         if not os.path.exists(savelocation+'/{}_pixels'.format(detpixels)):
@@ -633,16 +636,16 @@ def generateCalibration(self,Vanadiumdatafile,A4datafile=False,savelocation='cal
                                             plt.ioff
                                         plt.figure()
                                         plt.scatter(EiLocal,binPixelData)
-                                        plt.plot(Ei,Gaussian(Ei,*guess))
+                                        plt.plot(Ei,Gaussian(Ei,*guess,0.0))
 
                                         plt.savefig(savelocation+'/{}_pixels/Detector{}_{}.png'.format(detpixels,i,k),format='png',dpi=150)
                                         plt.close()
 
-                                    fittedParameters[i,j,k]=res[0]
+                                    fittedParameters[i,j,k]=np.concatenate([res[0],[0.0]])
                                     fittedParameters[i,j,k,0] *= np.sqrt(2*np.pi)*fittedParameters[i,j,k,2] #np.sum(binPixelData) # Use integrated intensity as amplitude 29-07-2020 JL
-                                    fittedParameters[i,j,k,0] *= vanadiumFactor # Multiply vanadium factor to perform absolut normalization 22-02-2021 JL
+                                     
                                     if plot: # pragma: no cover
-                                        plt.plot(EiX,GaussianNormalized(EiX,*fittedParameters[i,j,k]),color='black')
+                                        plt.plot(EiX,Gaussian(EiX,*fittedParameters[i,j,k])/(np.sqrt(2*np.pi)*fittedParameters[i,j,k,2]),color='black')
                                         plt.scatter(EiLocal,binPixelData,color=colors[:,k])
                                 activePixelAnalyser.append(np.linspace(-width/2.0,width/2.0,detpixels+1,dtype=int)+center+1)
                         activePixelDetector.append(activePixelAnalyser)
@@ -1326,7 +1329,7 @@ def converterToQxQy(A3,A4,Ei,Ef):
         Qx,Qy = (QV*q.reshape(-1,1))[:,:2].T
         return (Qx,Qy)
 
-    def converterToA3A4(Qx,Qy, Ei,Ef,A3Off=0.0):
+    def converterToA3A4(Qx,Qy, Ei,Ef,A3Off=0.0,A4Sign=-1):
         Qx = np.asarray(Qx)
         Qy = np.asarray(Qy)
 
@@ -1354,12 +1357,12 @@ def converterToA3A4(Qx,Qy, Ei,Ef,A3Off=0.0):
 
         A4 = ss*TasUBlib.arccosd(cos2t)
         theta = TasUBlib.calcTheta(ki, kf, A4)
-        A3 = -om + -ss*theta + A3Off
-        return A3,-A4
+        A3 = -om + np.sign(A4Sign)*ss*theta + A3Off
+        return A3,np.sign(A4Sign)*A4
 
 
     t = simpleDataSet(s)
-    fig = plt.figure(figsize=(26,13))
+    fig = plt.figure(figsize=(16,11))
     Ax = [RLUAxes.createRLUAxes(t,figure=fig,ids=(3,3,i+1)) for i in range(9)]
 
     MJOLNIRDir = os.path.dirname(_tools.__file__)
@@ -1381,8 +1384,8 @@ def converterToA3A4(Qx,Qy, Ei,Ef,A3Off=0.0):
     startColor = np.array([0.83921569, 0.15294118, 0.15686275, 0.5])
     diff = (endColor-startColor)/7.0
 
-    def format_axes_func(self,x,y,Ei,Ef,A3Off=s.theta):
-        A3,A4 = converterToA3A4(x,y,Ei,Ef,-A3Off)
+    def format_axes_func(self,x,y,Ei,Ef,A3Off=s.theta,A4Sign=-1.0):
+        A3,A4 = converterToA3A4(x,y,Ei,Ef,-A3Off,A4Sign=A4Sign)
         return ax.sample.format_coord(x,y)+', A3 = {:.2f} deg, A4 = {:.2f} deg, Ef = {:.2f}'.format(A3,A4,Ef)
 
     for i,(ax,Ef,A4) in enumerate(zip(Ax,EfInstrument.reshape(104,8).T,A4Instrument.reshape(104,8).T)):
@@ -1410,14 +1413,14 @@ def format_axes_func(self,x,y,Ei,Ef,A3Off=s.theta):
     Ax[-1].set_title('All Planes')
 
     fig.tight_layout()
-    Ax[0].format_coord = lambda x,y: format_axes_func(Ax[0],x,y,Ei,np.nanmean(EfInstrument,axis=0)[0],-s.theta)
-    Ax[1].format_coord = lambda x,y: format_axes_func(Ax[1],x,y,Ei,np.nanmean(EfInstrument,axis=0)[1],-s.theta)
-    Ax[2].format_coord = lambda x,y: format_axes_func(Ax[2],x,y,Ei,np.nanmean(EfInstrument,axis=0)[2],-s.theta)
-    Ax[3].format_coord = lambda x,y: format_axes_func(Ax[3],x,y,Ei,np.nanmean(EfInstrument,axis=0)[3],-s.theta)
-    Ax[4].format_coord = lambda x,y: format_axes_func(Ax[4],x,y,Ei,np.nanmean(EfInstrument,axis=0)[4],-s.theta)
-    Ax[5].format_coord = lambda x,y: format_axes_func(Ax[5],x,y,Ei,np.nanmean(EfInstrument,axis=0)[5],-s.theta)
-    Ax[6].format_coord = lambda x,y: format_axes_func(Ax[6],x,y,Ei,np.nanmean(EfInstrument,axis=0)[6],-s.theta)
-    Ax[7].format_coord = lambda x,y: format_axes_func(Ax[7],x,y,Ei,np.nanmean(EfInstrument,axis=0)[7],-s.theta)
+    Ax[0].format_coord = lambda x,y: format_axes_func(Ax[0],x,y,Ei,np.nanmean(EfInstrument,axis=0)[0],-s.theta,A4Sign=A4Positions[0])
+    Ax[1].format_coord = lambda x,y: format_axes_func(Ax[1],x,y,Ei,np.nanmean(EfInstrument,axis=0)[1],-s.theta,A4Sign=A4Positions[0])
+    Ax[2].format_coord = lambda x,y: format_axes_func(Ax[2],x,y,Ei,np.nanmean(EfInstrument,axis=0)[2],-s.theta,A4Sign=A4Positions[0])
+    Ax[3].format_coord = lambda x,y: format_axes_func(Ax[3],x,y,Ei,np.nanmean(EfInstrument,axis=0)[3],-s.theta,A4Sign=A4Positions[0])
+    Ax[4].format_coord = lambda x,y: format_axes_func(Ax[4],x,y,Ei,np.nanmean(EfInstrument,axis=0)[4],-s.theta,A4Sign=A4Positions[0])
+    Ax[5].format_coord = lambda x,y: format_axes_func(Ax[5],x,y,Ei,np.nanmean(EfInstrument,axis=0)[5],-s.theta,A4Sign=A4Positions[0])
+    Ax[6].format_coord = lambda x,y: format_axes_func(Ax[6],x,y,Ei,np.nanmean(EfInstrument,axis=0)[6],-s.theta,A4Sign=A4Positions[0])
+    Ax[7].format_coord = lambda x,y: format_axes_func(Ax[7],x,y,Ei,np.nanmean(EfInstrument,axis=0)[7],-s.theta,A4Sign=A4Positions[0])
     fig.tight_layout()
 
     return Ax

MJOLNIR/Marray.py

@@ -103,7 +103,7 @@ def __len__(self):
         if self.multidimensional:
             return len(self._data)
         else:
-            if not self._data.shape is ():
+            if self._data.shape != ():
                 return len(self._data)
             else:
                 return 0

MJOLNIR/__init__.py

@@ -8,7 +8,7 @@
 import sys,os
 sys.path.append('.')
 
-__version__ = '1.1.18'
+__version__ = '1.1.19'
 __author__ = 'Jakob Lass'
 
 __multiFLEXXNormalization__ = os.path.join(os.path.dirname(__file__),'CalibrationMultiFLEXX.csv') 

docs/conf.py

@@ -31,9 +31,9 @@
 author = u'Jakob Lass'
 
 # The short X.Y version
-version = u'1.1.18'
+version = u'1.1.19'
 # The full version, including alpha/beta/rc tags
-release = u'1.1.18'
+release = u'1.1.19'
 
 
 

setup.py

@@ -19,8 +19,7 @@
 
 setup(
     name='MJOLNIR',
-
-    version='1.1.18',
+    version='1.1.19',
     description=('Neutron Scattering software suite.'),
     long_description=long_description,
     author='Jakob Lass',