Changeset 32dffae4 in sasview

Timestamp:

May 8, 2008 7:20:22 PM (17 years ago)

Author:

Mathieu Doucet <doucetm@…>

Branches:

master, ESS_GUI, ESS_GUI_Docs, ESS_GUI_batch_fitting, ESS_GUI_bumps_abstraction, ESS_GUI_iss1116, ESS_GUI_iss879, ESS_GUI_iss959, ESS_GUI_opencl, ESS_GUI_ordering, ESS_GUI_sync_sascalc, costrafo411, magnetic_scatt, release-4.1.1, release-4.1.2, release-4.2.2, release_4.0.1, ticket-1009, ticket-1094-headless, ticket-1242-2d-resolution, ticket-1243, ticket-1249, ticket885, unittest-saveload

Children:

f71287f4

Parents:

4f63160

Message:

Added alpha estimator and help

Location:

prview/perspectives/pr

Files:

• inversion_panel.py (modified) (6 diffs)
• pars_dialog.py (deleted)
• pr.py (modified) (5 diffs)
• pr_thread.py (modified) (2 diffs)
• requirements.txt (modified) (1 diff)

prview/perspectives/pr/inversion_panel.py

@@ -150 +150 @@
             self.osc_ctl.SetValue("%-5.2g" % value)
         elif name=='alpha_estimate':
-            self.alpha_estimate_ctl.SetValue("%-5.2g" % value)
+            self.alpha_estimate_ctl.SetValue("%-3.1g" % value)
         elif name=='plotname':
             self.plot_data.SetValue(str(value))
@@ -226 +226 @@
         boxsizer2.SetMinSize((320,50))
         
+        explanation  = "P(r) is found by fitting a set of base functions to I(Q). "
+        explanation += "The minimization involves a regularization term to ensure "
+        explanation += "a smooth P(r). The alpha parameter gives the size of that "  
+        explanation += "term. The suggested value is the value above which the"
+        explanation += "output P(r) will have only one peak."
+        label_explain = wx.StaticText(self, -1, explanation, size=(280,80))
+        boxsizer2.Add(label_explain,  wx.LEFT|wx.BOTTOM, 5)
+        
+        
+        
         label_nfunc = wx.StaticText(self, -1, "Number of terms")
         label_nfunc.SetMinSize((120,20))
@@ -233 +243 @@
         
         self.nfunc_ctl = wx.TextCtrl(self, -1, size=(60,20))
+        self.nfunc_ctl.SetToolTipString("Number of terms in the expansion.")
         self.alpha_ctl = wx.TextCtrl(self, -1, size=(60,20))
+        self.alpha_ctl.SetToolTipString("Control parameter for the size of the regularization term.")
         self.dmax_ctl  = wx.TextCtrl(self, -1, size=(60,20))
+        self.dmax_ctl.SetToolTipString("Maximum distance between any two points in the system.")
         self.alpha_estimate_ctl  = wx.TextCtrl(self, -1, size=(60,20))
         self.alpha_estimate_ctl.Enable(False)
+        self.alpha_estimate_ctl.SetToolTipString("Value of alpha below which P(r) may have multiple peaks.")
         
         # EVT_TEXT would trigger an event for each character entered
@@ -247 +261 @@
 
         iy = 0
-        sizer_params.Add(label_sugg, (iy,2), (1,1), wx.EXPAND|wx.ADJUST_MINSIZE, 15)
-        iy += 1
-        sizer_params.Add(label_nfunc, (iy,0), (1,1), wx.LEFT|wx.EXPAND|wx.ADJUST_MINSIZE, 15)
-        sizer_params.Add(self.nfunc_ctl,   (iy,1), (1,1), wx.RIGHT|wx.EXPAND|wx.ADJUST_MINSIZE, 15)
-        iy += 1
-        sizer_params.Add(label_alpha, (iy,0), (1,1), wx.LEFT|wx.EXPAND|wx.ADJUST_MINSIZE, 15)
-        sizer_params.Add(self.alpha_ctl,   (iy,1), (1,1), wx.RIGHT|wx.EXPAND|wx.ADJUST_MINSIZE, 15)
-        sizer_params.Add(self.alpha_estimate_ctl,   (iy,2), (1,1), wx.RIGHT|wx.EXPAND|wx.ADJUST_MINSIZE, 15)
-        iy += 1
-        sizer_params.Add(label_dmax, (iy,0), (1,1), wx.LEFT|wx.EXPAND|wx.ADJUST_MINSIZE, 15)
-        sizer_params.Add(self.dmax_ctl,   (iy,1), (1,1), wx.RIGHT|wx.EXPAND|wx.ADJUST_MINSIZE, 15)
+        sizer_params.Add(label_sugg,       (iy,2), (1,1), wx.LEFT, 15)
+        iy += 1
+        sizer_params.Add(label_nfunc,      (iy,0), (1,1), wx.LEFT, 15)
+        sizer_params.Add(self.nfunc_ctl,   (iy,1), (1,1), wx.RIGHT, 0)
+        iy += 1
+        sizer_params.Add(label_alpha,      (iy,0), (1,1), wx.LEFT, 15)
+        sizer_params.Add(self.alpha_ctl,   (iy,1), (1,1), wx.RIGHT, 0)
+        sizer_params.Add(self.alpha_estimate_ctl, (iy,2), (1,1), wx.LEFT, 15)
+        iy += 1
+        sizer_params.Add(label_dmax, (iy,0), (1,1), wx.LEFT, 15)
+        sizer_params.Add(self.dmax_ctl,   (iy,1), (1,1), wx.RIGHT, 0)
 
         boxsizer2.Add(sizer_params, 0)
@@ -275 +289 @@
         self.time_ctl = wx.TextCtrl(self, -1, size=(60,20))
         self.time_ctl.SetEditable(False)
+        self.time_ctl.SetToolTipString("Computation time for the last inversion, in seconds.")
+        
         self.chi2_ctl = wx.TextCtrl(self, -1, size=(60,20))
         self.chi2_ctl.SetEditable(False)
+        self.chi2_ctl.SetToolTipString("Chi^2 over degrees of freedom.")
+        
         self.osc_ctl = wx.TextCtrl(self, -1, size=(60,20))
         self.osc_ctl.SetEditable(False)
+        self.osc_ctl.SetToolTipString("Oscillation parameter. P(r) for a sphere has an oscillation parameter of 1.1.")
         
         sizer_res = wx.GridBagSizer(5,5)
@@ -302 +321 @@
         id = wx.NewId()
         button_OK = wx.Button(self, id, "Compute")
+        button_OK.SetToolTipString("Perform P(r) inversion.")
         self.Bind(wx.EVT_BUTTON, self._on_invert, id = id)   
         #button_Cancel = wx.Button(self, wx.ID_CANCEL, "Cancel")

prview/perspectives/pr/pr.py

@@ -291 +291 @@
         wx.PostEvent(self.parent, StatusEvent(status=error))
     
-    def _estimate_completed(self, alpha, elapsed):
+    def _estimate_completed(self, alpha, message, elapsed):
         """
             Parameter estimation completed, 
@@ -301 +301 @@
         self.elapsed = elapsed
         self.control_panel.alpha_estimate = alpha
+        if not message==None:
+            wx.PostEvent(self.parent, StatusEvent(status=str(message)))
     
     def _completed(self, out, cov, pr, elapsed):
@@ -322 +324 @@
         self.control_panel.oscillation = pr.oscillations(out)
         #print "OSCILL", pr.oscillations(out)
+        print "PEAKS:", pr.get_peaks(out)
         
         for i in range(len(out)):
@@ -428 +431 @@
         
     def perform_estimate(self):
-        print "ESTIMATE"
         from pr_thread import EstimatePr
         from copy import deepcopy
         
+        wx.PostEvent(self.parent, StatusEvent(status=''))
         # If a thread is already started, stop it
         if self.estimation_thread != None and self.estimation_thread.isrunning():
@@ -548 +551 @@
             [Somehow openGL needs this call]
         """
-        pass
-    
+        self.parent.set_perspective(self.perspective)
+    

prview/perspectives/pr/pr_thread.py

@@ -75 +75 @@
         except KeyboardInterrupt:
             printEVT("P(r) calc interrupted")
-            raise KeyboardInterrupt
+            raise KeyboardInterrupt    
         
     def compute(self):
         import time
-        try:
-            
-            print "Alpha  Oscill"
-            
+        try:            
             self.starttime = time.time()
             # If the current alpha is zero, try
@@ -89 +86 @@
                 self.pr.alpha = 0.0001
                  
+            # Perform inversion to find the largest alpha
             out, cov = self.pr.lstsq(self.nfunc)
             elapsed = time.time()-self.starttime
-            
+            initial_alpha = self.pr.alpha
+            initial_peaks = self.pr.get_peaks(out)
 
-            # Take the default and try to find 
-            # a better value
-            best_alpha = self.pr.alpha
-            best_osc   = self.pr.oscillations(out) 
-            
-            print best_alpha, best_osc
-            
-            alpha = self.pr.suggested_alpha
-            print "initial:", alpha
+            # Try the inversion with the estimated alpha
+            self.pr.alpha = self.pr.suggested_alpha
+            out, cov = self.pr.lstsq(self.nfunc)
 
-            # Look at smaller values
-            for i in range(5):
-                self.pr.alpha = (0.1)**(i)*alpha
-                out, cov = self.pr.lstsq(self.nfunc)
-                osc = self.pr.oscillations(out) 
-                print self.pr.alpha, osc
-                if  osc < best_osc:
-                    best_osc = osc
-                    best_alpha = alpha
-            
-            ## Look at larger values
-            #for i in range(4):
-            #    self.pr.alpha = (10.0)**(i+1)*alpha
-            #    out, cov = self.pr.lstsq(self.nfunc)
-            #    osc = self.pr.oscillations(out) 
-            #    print self.pr.alpha, osc
-            #    if  osc < best_osc:
-            #        best_osc = osc
-            #        best_alpha = alpha
-            
-            
-            self.complete(alpha=best_alpha, elapsed=elapsed)
-            
-            
-            
-            
+            npeaks = self.pr.get_peaks(out)
+            # if more than one peak to start with
+            # just return the estimate
+            if npeaks>1:
+                message = "Your P(r) is not smooth, please check your inversion parameters"
+                self.complete(alpha=self.pr.suggested_alpha, message=message, elapsed=elapsed)
+            else:
+                
+                # Look at smaller values
+                # We assume that for the suggested alpha, we have 1 peak
+                # if not, send a message to change parameters
+                alpha = self.pr.suggested_alpha
+                best_alpha = self.pr.suggested_alpha
+                found = False
+                for i in range(10):
+                    self.pr.alpha = (0.33)**(i+1)*alpha
+                    out, cov = self.pr.lstsq(self.nfunc)
+                    #osc = self.pr.oscillations(out) 
+                    #print self.pr.alpha, osc
+                    
+                    peaks = self.pr.get_peaks(out)
+                    print self.pr.alpha, peaks
+                    if peaks>1:
+                        found = True
+                        break
+                    best_alpha = self.pr.alpha
+                    
+                # If we didn't find a turning point for alpha and
+                # the initial alpha already had only one peak,
+                # just return that
+                if not found and initial_peaks==1 and initial_alpha<best_alpha:
+                    best_alpha = initial_alpha
+                    
+                # Check whether the size makes sense
+                message=None
+                
+                if not found:
+                    message = "None"
+                elif best_alpha>=0.5*self.pr.suggested_alpha:
+                    # best alpha is too big, return a 
+                    # reasonable value
+                    message  = "The estimated alpha for your system is too large. "
+                    message += "Try increasing your maximum distance."
+                
+                self.complete(alpha=best_alpha, message=None, elapsed=elapsed)
+
         except:
             if not self.error_func==None:

prview/perspectives/pr/requirements.txt

@@ -18 +18 @@
 14. Clean up the InversionDialog
 15. Add online help for inputs and outputs (especially figures of merit)
+16. Add help text to the interface (description of what we are minimizing, to explain alpha and nfunc).