Changes in / [d9410c5:66d4370] in sasview

Location:

src/sas

Files:

• qtgui/Perspectives/Inversion/InversionPerspective.py (modified) (2 diffs)
• sascalc/pr/invertor.py (modified) (5 diffs)

src/sas/qtgui/Perspectives/Inversion/InversionPerspective.py

@@ -309 +309 @@
                                             and not self.isCalculating)
         self.removeButton.setEnabled(self.logic.data_is_loaded)
-        self.explorerButton.setEnabled(self.logic.data_is_loaded)
+        self.explorerButton.setEnabled(self.logic.data_is_loaded
+                                       and np.all(self.logic.data.dy != 0))
         self.stopButton.setVisible(self.isCalculating)
         self.regConstantSuggestionButton.setEnabled(
@@ -640 +641 @@
         self.calcThread.ready(2.5)
 
-        #Perform estimate should be done on value enter this should solve delay problem
-        self.performEstimate()
-
     def stopCalcThread(self):
         """ Stops a thread if it exists and is running """

src/sas/sascalc/pr/invertor.py

@@ -71 +71 @@
         A[j][i] = (Fourier transformed base function for point j)
 
-    We then choose a number of r-points, n_r, to evaluate the second
+    We them choose a number of r-points, n_r, to evaluate the second
     derivative of P(r) at. This is used as our regularization term.
     For a vector r of length n_r, the following n_r rows are set to ::
@@ -144 +144 @@
         x, y, err, d_max, q_min, q_max and alpha
         """
-        if name == 'x':
+        if   name == 'x':
             if 0.0 in value:
                 msg = "Invertor: one of your q-values is zero. "
@@ -227 +227 @@
         return None
 
-    def add_errors(self, yvalues):
-        """
-        Adds errors to data set is they are not avaialble
-        :return:
-        """
-        stats_errors = np.zeros(len(yvalues))
-        for i in range(len(yvalues)):
-            # Scale the error so that we can fit over several decades of Q
-            scale = 0.05 * np.sqrt(yvalues[i])
-            min_err = 0.01 * yvalues[i]
-            stats_errors[i] = scale * np.sqrt(np.fabs(yvalues[i])) + min_err
-        logger.warning("Simulated errors have been added to the data set\n")
-        return stats_errors
-
     def clone(self):
         """
@@ -258 +244 @@
         invertor.x = self.x
         invertor.y = self.y
-        if np.size(self.err) == 0 or np.all(self.err) == 0:
-            invertor.err = self.add_errors(self.y)
-        else:
-            invertor.err = self.err
+        invertor.err = self.err
         invertor.est_bck = self.est_bck
         invertor.background = self.background
@@ -285 +268 @@
             A[i][j] = (Fourier transformed base function for point j)
 
-        We then choose a number of r-points, n_r, to evaluate the second
+        We them choose a number of r-points, n_r, to evaluate the second
         derivative of P(r) at. This is used as our regularization term.
         For a vector r of length n_r, the following n_r rows are set to ::