Changeset 3805

Timestamp:

Jan 28, 2019 5:20:01 PM (4 years ago)

Author:

vondreele

Message:

fix normalization of mag moments --> Kvector
add save of map peaks as csv file

Location:

trunk

Files:

• GSASIIdataGUI.py (modified) (2 diffs)
• GSASIIphsGUI.py (modified) (2 diffs)
• GSASIIstrMath.py (modified) (3 diffs)

trunk/GSASIIdataGUI.py

@@ -5598 +5598 @@
             
         # Phase / Map peaks tab
-        G2G.Define_wxId('wxID_PEAKSMOVE', 'wxID_PEAKSCLEAR','wxID_PEAKSUNIQUE', 'wxID_PEAKSDELETE',
+        G2G.Define_wxId('wxID_PEAKSMOVE', 'wxID_PEAKSCLEAR','wxID_PEAKSUNIQUE', 'wxID_PEAKSDELETE','wxID_PEAKSSAVE',
             'wxID_PEAKSDA','wxID_PEAKSDISTVP', 'wxID_PEAKSVIEWPT', 'wxID_FINDEQVPEAKS', 'wxID_SHOWBONDS',)
         self.MapPeaksMenu = wx.MenuBar()
@@ -5612 +5612 @@
         self.MapPeaksEdit.Append(G2G.wxID_FINDEQVPEAKS,'Equivalent peaks','Find equivalent peaks')
         self.MapPeaksEdit.Append(G2G.wxID_PEAKSUNIQUE,'Unique peaks','Select unique set')
+        self.MapPeaksEdit.Append(G2G.wxID_PEAKSSAVE,'Save peaks','Save peaks to csv file')
         self.MapPeaksEdit.Append(G2G.wxID_PEAKSDELETE,'Delete peaks','Delete selected peaks')
         self.MapPeaksEdit.Append(G2G.wxID_PEAKSCLEAR,'Clear peaks','Clear the map peak list')

trunk/GSASIIphsGUI.py

@@ -8956 +8956 @@
         G2plt.PlotStructure(G2frame,data)
         
+    def OnPeaksSave(event):
+        if 'Map Peaks' in data:
+            mapPeaks = data['Map Peaks']
+            pfName = PhaseName+'_peaks.csv'
+            pfFile = ''
+            pth = G2G.GetExportPath(G2frame)
+            dlg = wx.FileDialog(G2frame, 'Choose map peaks file name', pth, pfName, 
+                'csv (*.csv)|*.csv',wx.FD_SAVE|wx.FD_OVERWRITE_PROMPT)
+            try:
+                if dlg.ShowModal() == wx.ID_OK:
+                    pfFile = dlg.GetPath()
+            finally:
+                dlg.Destroy()
+                
+            if pfFile:
+                pf = open(pfFile,'w')
+                pf.write('"%s"\n'%(PhaseName))
+                pf.write(' mag, x, y, z, dzero, dcent \n')
+                for peak in mapPeaks:
+                    pf.write(' %.4f, %.4f, %.4f, %.4f, %.4f, %.4f \n'%(peak[0],peak[1],peak[2],peak[3],peak[4],peak[5]))
+                pf.close()
+        
     def OnPeaksDelete(event):
         if 'Map Peaks' in data:
@@ -9484 +9506 @@
         G2frame.Bind(wx.EVT_MENU, OnPeaksEquiv, id=G2G.wxID_FINDEQVPEAKS)
         G2frame.Bind(wx.EVT_MENU, OnPeaksUnique, id=G2G.wxID_PEAKSUNIQUE)
+        G2frame.Bind(wx.EVT_MENU, OnPeaksSave, id=G2G.wxID_PEAKSSAVE)
         G2frame.Bind(wx.EVT_MENU, OnPeaksDelete, id=G2G.wxID_PEAKSDELETE)
         G2frame.Bind(wx.EVT_MENU, OnPeaksClear, id=G2G.wxID_PEAKSCLEAR)

trunk/GSASIIstrMath.py

@@ -1054 +1054 @@
     Gdata = SGData['MagMom'][nxs,:,nxs]*Gdata   #flip vectors according to spin flip * det(opM)
     Mag = np.tile(Mag[:,nxs],Nops).T  #make Mag same length as Gdata
-    Kdata = np.inner(Gdata.T,uAmat.T).T/Mag     #Cartesian unit vectors
+    Kdata = np.inner(Gdata.T,uAmat).T     #Cartesian unit vectors
+    Kmean = np.mean(np.sqrt(np.sum(Kdata**2,axis=0)),axis=0)
+    Kdata /= Kmean
     Uij = np.array(G2lat.U6toUij(Uijdata))
     bij = Mast*Uij.T
@@ -1211 +1213 @@
     Gones = SGData['MagMom'][nxs,:,nxs]*Gones   #flip vectors according to spin flip
     Mag = np.tile(Mag[:,nxs],Nops).T  #make Mag same length as Gdata
-    Kdata = np.inner(Gdata.T,uAmat.T).T/Mag     #Cartesian unit vectors
+    Kdata = np.inner(Gdata.T,uAmat).T     #Cartesian unit vectors
+    Kmean = np.mean(np.sqrt(np.sum(Kdata**2,axis=0)),axis=0)
+    Kdata /= Kmean
     Uij = np.array(G2lat.U6toUij(Uijdata))
     bij = Mast*Uij.T
@@ -1518 +1522 @@
         GSdata = SGData['MagMom'][nxs,:,nxs,nxs]*GSdata   #flip vectors according to spin flip * det(opM)
         SMag = np.sqrt(np.sum((np.inner(GSdata.T,Ginv)*GSdata.T),axis=-1)).T
-        Kdata = np.inner(Gdata.T,uAmat.T).T/SMag[nxs,:,:,:]     #Cartesian unit vectors
+        Kdata = np.inner(GSdata.T,uAmat).T     #Cartesian unit vectors
+        Kmean = np.mean(np.sqrt(np.sum(Kdata**2,axis=0)),axis=0)    #normalization --> unit vectors
+        Kdata /= Kmean      #mxyz,nops,ntau,natm
 
     FF = np.zeros(len(Tdata))