Changeset 2616

Timestamp:

Jan 8, 2017 4:17:15 PM (5 years ago)

Author:

vondreele

Message:

add Durbin-Watson statistic to refinement results for PWDR data
some more spots work

Location:

trunk

Files:

• GSASIIgrid.py (modified) (3 diffs)
• GSASIIimage.py (modified) (2 diffs)
• GSASIIimgGUI.py (modified) (2 diffs)
• GSASIIplot.py (modified) (7 diffs)
• GSASIIstrIO.py (modified) (1 diff)
• GSASIIstrMath.py (modified) (1 diff)

trunk/GSASIIgrid.py

@@ -2119 +2119 @@
         self.MaskEdit.Append(help='Load mask from file; ignore threshold', 
             id=wxID_MASKLOADNOT, kind=wx.ITEM_NORMAL,text='Load mask w/o threshold')
-        self.MaskEdit.Append(help='Auto search for spot masks', 
+        self.MaskEdit.Append(help='Auto search for spot masks; NB: will clear old spot masks', 
             id=wxID_FINDSPOTS, kind=wx.ITEM_NORMAL,text='Auto spot masks')
         submenu.Append(help='Create an arc mask with mouse input', 
@@ -2129 +2129 @@
         submenu.Append(help='Create a ring mask with mouse input', 
             id=wxID_NEWMASKRING, kind=wx.ITEM_NORMAL,text='Ring mask')
-        submenu.Append(help='Create spot masks with mouse clicks', 
+        submenu.Append(help='Create spot masks with mouse input', 
             id=wxID_NEWMASKSPOT, kind=wx.ITEM_NORMAL,text='Spot mask')
         self.PostfillDataMenu()
@@ -4271 +4271 @@
         mainSizer.Add(wx.StaticText(G2frame.dataDisplay,-1,
             ' Data residual wR: %.3f%% on %d observations'%(data[0]['wR'],data[0]['Nobs'])))
+        mainSizer.Add(wx.StaticText(G2frame.dataDisplay,-1,
+            ' Durbin-Watson statistic: %.3f'%(data[0].get('Durbin-Watson',0.))))
         for value in data[0]:
             if 'Nref' in value:

trunk/GSASIIimage.py

@@ -1132 +1132 @@
     for roll in rolls:
         if np.any(roll):        #avoid [0,0]
-            spotMask = ma.array(spotMask,mask=(spotMask-rollImage(Image,roll)<=0.))
+            spotMask = ma.array(spotMask,mask=(spotMask-rollImage(Image,roll)<=0.10*np.mean(Image)))
     mags = spotMask[spotMask.nonzero()]
     indx = np.transpose(spotMask.nonzero())
@@ -1146 +1146 @@
                 jndx.append([ind[1]+.5,ind[0]+.5])
     print 'Spots found: ',len(jndx)
-    if len(jndx) > 100:
-        txt = 'Found: %d. Too many spots found; are rings spotty?'%(len(jndx))
-        return txt
     jndx = np.array(jndx)
     peaks = jndx*pixelSize/1000.
     tth = GetTth(peaks.T[0],peaks.T[1],Controls)
     Peakarray = np.vstack((tth,peaks.T)).T
-    Peakarray = np.array(G2mth.sortArray(Peakarray,0))  #now in 2theta order
+    Peakarray = np.array(G2mth.sortArray(Peakarray,0))  #now in 2theta 
+    if peaks.shape[0] > 100:
+        txt = 'More than 100 spots found: %d. Are rings spotty?'%(len(jndx))
+        return txt
+    #should be able to filter out spotty Bragg rings here
     Points = np.ones((peaks.shape[0],3))
     Points[:,:2] = Peakarray[:,1:]
-    Masks['Points'] = Points
+    Masks['Points'] = list(Points)
     return None
                     

trunk/GSASIIimgGUI.py

@@ -1303 +1303 @@
     def OnAutoSpotMask(event):
         'Do auto search for spot masks'
+        wx.MessageDialog(G2frame.dataDisplay,'NB: This will clear any old spot masks','Auto Spot Masks', wx.OK).ShowModal()
         Controls = copy.deepcopy(G2frame.PatternTree.GetItemPyData( 
-            G2gd.GetPatternTreeItemId(G2frame,G2frame.PatternId,'Image Controls')))
+        G2gd.GetPatternTreeItemId(G2frame,G2frame.PatternId,'Image Controls')))
         Error = G2img.AutoSpotMasks(G2frame.ImageZ,data,Controls)
         if not Error is None:
             G2frame.ErrorDialog('Auto spot search error',Error)
         wx.CallAfter(UpdateMasks,G2frame,data)
-        G2plt.PlotExposedImage(G2frame,event=event)                
+        wx.CallAfter(G2plt.PlotExposedImage,G2frame,event=event)                
             
     def ToggleSpotMaskMode(event):
@@ -1670 +1671 @@
     G2frame.dataDisplay.SetSize(Size)
     G2frame.dataFrame.setSizePosLeft(Size)    
-    wx.Yield()
     if startScroll: # reset scroll to saved position
         G2frame.dataDisplay.Scroll(0,startScroll) # set to saved scroll position

trunk/GSASIIplot.py

@@ -4349 +4349 @@
     def OnImPick(event):
         'A object has been picked'
+        
         def OnDragIntBound(event):
             'Respond to the dragging of one of the integration boundaries'
@@ -4375 +4376 @@
             # compute arcs, etc
             LRAzim = Data['LRazimuth']                  #NB: integers
-            Nazm = Data['outAzimuths']
-            delAzm = float(LRAzim[1]-LRAzim[0])/Nazm
             AzmthOff = Data['azmthOff']
             IOtth = Data['IOtth']
@@ -4415 +4414 @@
             Page.figure.gca().draw_artist(pick)
             Page.canvas.blit(Page.figure.gca().bbox)
+            
         def OnDragMask(event):
             'Respond to the dragging of a mask'
@@ -4423 +4423 @@
             Xpos,Ypos = [event.xdata,event.ydata]
             #if Page.toolbar._active: return # zoom/pan selected
-            itemPicked = str(G2frame.itemPicked)
             Page.canvas.restore_region(savedplot)
             try:
@@ -4578 +4577 @@
                 print('picktype {} should not happen!'.format(pickType))
                 GSASIIpath.IPyBreak()
-            saveLinestyle = [p.get_linestyle() for p in pl]
-            for p in pl: p.set_linestyle('dotted') # set line as dotted
-            Page.canvas.draw() # refresh without dotted line & save bitmap
-            savedplot = Page.canvas.copy_from_bbox(Page.figure.gca().bbox)
-            G2frame.cid = Page.canvas.mpl_connect('motion_notify_event', OnDragMask)
-            for p,s in zip(pl,saveLinestyle): p.set_linestyle(s) # set back to original
+            if event.mouseevent.button == 3:
+                if pickType == 'Spot':
+                    print 'delete',pick.center
+                    del Masks['Points'][pick.itemNumber]
+                    
+                    Page.canvas.draw() # refresh without dotted line & save bitmap
+            else:
+                saveLinestyle = [p.get_linestyle() for p in pl]
+                for p in pl: p.set_linestyle('dotted') # set line as dotted
+                Page.canvas.draw() # refresh without dotted line & save bitmap
+                savedplot = Page.canvas.copy_from_bbox(Page.figure.gca().bbox)
+                G2frame.cid = Page.canvas.mpl_connect('motion_notify_event', OnDragMask)
+                for p,s in zip(pl,saveLinestyle): p.set_linestyle(s) # set back to original
 
     def OnImRelease(event):
@@ -4779 +4785 @@
                 G2frame.Razim.SetValue(Data['LRazimuth'][1])
             elif pickType == "Spot" and treeItem == 'Masks':
-                # update the selected circle mask with the last drawn values
-                spotnum = G2frame.itemPicked.itemNumber
-                Masks['Points'][spotnum] = list(G2frame.itemPicked.center) + [
-                    2.*G2frame.itemPicked.radius]
+                if event.button == 3:
+                    # update the selected circle mask with the last drawn values
+                    spotnum = G2frame.itemPicked.itemNumber
+                    Masks['Points'][spotnum] = list(G2frame.itemPicked.center) + [
+                        2.*G2frame.itemPicked.radius]
                 G2imG.UpdateMasks(G2frame,Masks)
             elif pickType.startswith('Ring') and treeItem == 'Masks':
@@ -5192 +5199 @@
     atomData = data['Atoms']
     mapPeaks = []
-    vdWRadii = generalData['vdWRadii']
     if generalData.get('DisAglCtrls',{}):
         BondRadii = generalData['DisAglCtrls']['BondRadii']

trunk/GSASIIstrIO.py

@@ -2748 +2748 @@
                     print >>pFile,' Final refinement RF, RF^2 = %.2f%%, %.2f%% on %d reflections'   \
                         %(Histogram['Residuals'][pfx+'Rf'],Histogram['Residuals'][pfx+'Rf^2'],Histogram['Residuals'][pfx+'Nref'])
+                    print >>pFile,' Durbin-Watson statistic = %.3f'%(Histogram['Residuals']['Durbin-Watson'])
                     print >>pFile,' Bragg intensity sum = %.3g'%(Histogram['Residuals'][pfx+'sumInt'])
                 

trunk/GSASIIstrMath.py

@@ -3757 +3757 @@
             yd[xB:xF] = y[xB:xF]-yc[xB:xF]
             W = wtFactor*w
-            wdy = -ma.sqrt(W[xB:xF])*(yd[xB:xF])
+            wdy = -ma.sqrt(w[xB:xF])*(yd[xB:xF])
+            Histogram['Residuals']['Durbin-Watson'] = ma.sum(ma.diff(wdy)**2)/ma.sum(wdy**2)
+            wdy *= wtFactor
             Histogram['Residuals']['Nobs'] = ma.count(x[xB:xF])
             Nobs += Histogram['Residuals']['Nobs']