Changes in / [dea2f6e:f552ff3] in sasview

Files:

• .gitattributes (added)
• src/sas/fit/AbstractFitEngine.py (modified) (2 diffs)
• src/sas/fit/BumpsFitting.py (modified) (2 diffs)
• src/sas/models/qsmearing.py (modified) (2 diffs)
• src/sas/models/resolution.py (added)
• src/sas/perspectives/pr/explore_dialog.py (modified) (2 diffs)
• src/sas/perspectives/pr/inversion_panel.py (modified) (2 diffs)
• src/sas/pr/num_term.py (modified) (2 diffs)
• test/pr_inversion/test/test_output.txt (modified) (1 diff)
• test/sasfit/test/utest_fit_smeared.py (modified) (2 diffs)

src/sas/fit/AbstractFitEngine.py

@@ -161 +161 @@
         # TODO: Should provide an option for users to set it like percent,
         # constant, or dy data
-        if dy == None or dy == [] or dy.all() == 0:
+        if dy is None or dy == [] or dy.all() == 0:
             self.dy = numpy.ones(len(y))
         else:
@@ -260 +260 @@
         """
         return []
-    
-    
+
+
 class FitData2D(Data2D):
     """

src/sas/fit/BumpsFitting.py

@@ -107 +107 @@
         self.model = model.model
         self.data = data
+        if self.data.smearer is not None:
+            self.data.smearer.model = self.model
         self._define_pars()
         self._init_pars(kw)
@@ -174 +176 @@
     def _recalculate(self):
         if self._dirty:
-            self._residuals, self._theory = self.data.residuals(self.model.evalDistribution)
+            self._residuals, self._theory \
+                = self.data.residuals(self.model.evalDistribution)
             self._dirty = False
 

src/sas/models/qsmearing.py

@@ -81 +81 @@
     # If we found slit smearing data, return a slit smearer
     if _found_slit == True:
-        return SlitSmearer(data1D, model)
+        return PySlitSmearer(data1D, model)
     return None
             
@@ -583 +583 @@
     return nbins_low, nbins_high, \
              new_width[data_x_ext > 0], data_x_ext[data_x_ext > 0]
+
+
+
+from .resolution import Slit1D
+class PySlitSmearer(object):
+    def __init__(self, data1D, model = None):
+        self.model = model
+
+        q = data1D.x
+        width = data1D.dxw if data1D.dxw is not None else 0
+        height = data1D.dxl if data1D.dxl is not None else 0
+        # TODO: width and height seem to be reversed
+        self.resolution = Slit1D(q, height, width)
+
+    def __call__(self, iq_in, first_bin=0, last_bin=None):
+        if last_bin is None or last_bin >= len(iq_in):
+            last_bin = len(iq_in) - 1
+        q_calc = self.resolution.q_calc
+        iq_calc = numpy.empty_like(q_calc)
+        iq_calc[:first_bin] = 0
+        iq_calc[first_bin:last_bin+1] = iq_in
+        if last_bin < len(q_calc)-1:
+            iq_calc[last_bin:] = self.model.evalDistribution(q_calc[last_bin:])
+        iq_out = self.resolution.apply(iq_calc)
+        return iq_out[first_bin:last_bin+1]
+
+    def get_bin_range(self, q_min=None, q_max=None):
+        """
+
+        :param q_min: minimum q-value to smear
+        :param q_max: maximum q-value to smear
+
+        """
+        # assume the data values are sorted
+        first = numpy.searchsorted(self.resolution.q, q_min)
+        # assume that the resolution is much larger than the q range
+        last = len(self.resolution.q)-1
+        return first, last

src/sas/perspectives/pr/explore_dialog.py

@@ -257 +257 @@
         or when a new computation is finished.
         """
+        self.npts_ctl.SetFocus()
         # Get the output type selection
         output_type = self.output_box.GetString(self.output_box.GetSelection())
@@ -329 +330 @@
         # Ouput selection box
         selection_msg = wx.StaticText(self, -1, "Select a dependent variable:")
-        self.output_box = wx.ComboBox(self, -1)
+        self.output_box = wx.ComboBox(self, -1, style=wx.CB_READONLY)
         for item in self.results.outputs.keys():
             self.output_box.Append(item, "")
         self.output_box.SetStringSelection(DEFAULT_OUTPUT)
+        self.output_box.SetEditable(False)
 
         output_sizer = wx.GridBagSizer(5, 5)

src/sas/perspectives/pr/inversion_panel.py

@@ -703 +703 @@
         try:
             alpha = self.alpha_estimate_ctl.GetLabel()
-            self.alpha_ctl.SetValue(float(alpha))
+            # Check that we have a number
+            float(alpha)
+            self.alpha_ctl.SetValue(alpha)
+        except ValueError:
+            logging.error("InversionControl._on_accept_alpha got a value that was not a number: %s" % alpha )
         except:
             # No estimate or bad estimate, either do nothing
@@ -715 +719 @@
         try:
             nterms = self.nterms_estimate_ctl.GetLabel()
-            self.nfunc_ctl.SetValue(float(nterms))
+            # Check that we have a number
+            float(nterms)
+            self.nfunc_ctl.SetValue(nterms)
+        except ValueError:
+            logging.error("InversionControl._on_accept_nterms got a value that was not a number: %s" % nterms )
         except:
             # No estimate or bad estimate, either do nothing

src/sas/pr/num_term.py

@@ -107 +107 @@
         """
         # Generate data
-        # ls_osc = self.get0_out()
+        self.get0_out()
         med = self.median_osc()
 
@@ -144 +144 @@
             return nt, self.alpha_list[nt - 10], self.mess_list[nt - 10]
         except:
-            return self.nterm_min, self.alpha_list[10], self.mess_list[10]
+            #TODO: check the logic above and make sure it doesn't 
+            # rely on the try-except.
+            return self.nterm_min, self.invertor.alpha, ''
 
 

test/pr_inversion/test/test_output.txt

@@ -3 +3 @@
 #alpha=0.0007
 #chi2=836.797
-#elapsed=0.00199986
+#elapsed=0.000377893
 #qmin=None
 #qmax=None

test/sasfit/test/utest_fit_smeared.py

@@ -165 +165 @@
     def test_slit(self):
         smear = smear_selection(self.data_slit)
-        self.assertEqual(smear.__class__.__name__, 'SlitSmearer')
+        #self.assertEqual(smear.__class__.__name__, 'SlitSmearer')
+        self.assertEqual(smear.__class__.__name__, 'PySlitSmearer')
 
         fitter = Fit('bumps')
@@ -181 +182 @@
         
         result1, = fitter.fit()
-        
+
         #print "v",result1.pvec
         #print "dv",result1.stderr
         #print "chisq(v)",result1.fitness
-        
-        self.assertTrue( math.fabs(result1.pvec[0]-2340) < 20 )
-        self.assertTrue( math.fabs(result1.pvec[1]-0.010) < 0.002 )
+
+        numpy.testing.assert_allclose(result1.pvec, [2323.466,0.22137], rtol=0.001)
 
 if __name__ == '__main__':