fix FitIncidentSpectrum not correcting for different binning sizes

Framework/PythonInterface/plugins/algorithms/FitIncidentSpectrum.py

@@ -45,7 +45,7 @@ class FitIncidentSpectrum(PythonAlgorithm):
             doc='Output workspace containing the fit and it\'s first derivative.')
 
         self.declareProperty(
-            name='SpectrumIndex',
+            name='WorkspaceIndex',
             defaultValue=0,
             doc='Workspace index of the spectra to be fitted (Defaults to the first index.)')
 
@@ -72,7 +72,7 @@ class FitIncidentSpectrum(PythonAlgorithm):
     def _setup(self):
         self._input_ws = self.getProperty('InputWorkspace').value
         self._output_ws = self.getProperty('OutputWorkspace').valueAsStr
-        self._incident_index = self.getProperty('SpectrumIndex').value
+        self._incident_index = self.getProperty('WorkspaceIndex').value
         self._binning_for_calc = self.getProperty('BinningForCalc').value
         self._binning_for_fit = self.getProperty('BinningForFit').value
         self._fit_spectrum_with = self.getProperty('FitSpectrumWith').value
@@ -89,19 +89,19 @@ class FitIncidentSpectrum(PythonAlgorithm):
             y_fit = np.array(self._input_ws.readY(self._incident_index))
         else:
             rebinned = Rebin(
-                self._input_ws,
+                InputWorkspace=self._input_ws,
                 Params=self._binning_for_fit,
-                PreserveEvents=True,
-                StoreInADS=False)
+                PreserveEvents=True)
             x_fit = np.array(rebinned.readX(self._incident_index))
             y_fit = np.array(rebinned.readY(self._incident_index))
-
+        rebin_norm = x.size/x_fit.size
+        x_bin_centers = 0.5 * (x[:-1] + x[1:])
         if len(x_fit) != len(y_fit):
-            x_fit = x_fit[:-1]
+            x_fit = 0.5*(x_fit[:-1] + x_fit[1:])
 
         if self._fit_spectrum_with == 'CubicSpline':
             # Fit using cubic spline
-            fit, fit_prime = self.fit_cubic_spline(x_fit, y_fit, x[:-1], s=1e7)
+            fit, fit_prime = self.fit_cubic_spline(x_fit, y_fit, x_bin_centers, s=1e7)
         elif self._fit_spectrum_with == 'CubicSplineViaMantid':
             # Fit using cubic spline via Mantid
             fit, fit_prime = self.fit_cubic_spline_via_mantid_spline_smoothing(
@@ -112,13 +112,13 @@ class FitIncidentSpectrum(PythonAlgorithm):
                 MaxNumberOfBreaks=0)
         elif self._fit_spectrum_with == 'GaussConvCubicSpline':
             # Fit using Gauss conv cubic spline
-            fit, fit_prime = self.fit_cubic_spline_with_gauss_conv(x_fit, y_fit, x[:-1], sigma=0.5)
+            fit, fit_prime = self.fit_cubic_spline_with_gauss_conv(x_fit, y_fit, x_bin_centers, sigma=0.5)
 
         # Create output workspace
         unit = self._input_ws.getAxis(0).getUnit().unitID()
         output_workspace = CreateWorkspace(
             DataX=x,
-            DataY=np.append(fit, fit_prime),
+            DataY=np.append(fit, fit_prime)/rebin_norm,
             UnitX=unit,
             NSpec=2,
             Distribution=False,
@@ -126,7 +126,7 @@ class FitIncidentSpectrum(PythonAlgorithm):
             StoreInADS=False)
         self.setProperty("OutputWorkspace", output_workspace)
 
-    def fit_cubic_spline_with_gauss_conv(self, x_fit, y_fit, x, n_gouss=39, sigma=3):
+    def fit_cubic_spline_with_gauss_conv(self, x_fit, y_fit, x, n_gouss=39, sigma=3.0):
         # Fit with Cubic Spline using a Gaussian Convolution to get weights
         def moving_average(y, n=n_gouss, sig=sigma):
             b = signal.gaussian(n, sig)

docs/source/algorithms/FitIncidentSpectrum-v1.rst

@@ -102,10 +102,10 @@ Output:
 
 .. testoutput:: ExFitIncidentSpectrum
 
-    [ 5328.83700775  2330.08408285  1600.78200105  2543.59379589  3249.78956903
-      2797.87138465  2050.3366076   1417.4868309    965.23854845   659.79544224
-       456.54322031   320.88688262   229.29830975   166.5536716    122.89703604
-        92.0419568     69.89199835    53.75902111    41.84355559]
+    [ 3318.3489535   1760.07570573  1901.11829551  3081.98511847  3110.03374921
+      2423.17832412  1711.216875    1170.12096584   797.13759356   547.91281905
+       381.9735739    270.66392746   195.01094402   142.79582704   106.17020879
+        80.07657305    61.2047398     47.35976442]
 
 References
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