Changeset c4f52e3 in sasview for Invariant

Timestamp:

Mar 27, 2010 8:11:58 AM (15 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:

d1b43cd

Parents:

18e6c5e

Message:

invariant: minor clean up

File:

• Invariant/invariant.py (modified) (8 diffs)

Invariant/invariant.py

@@ -134 +134 @@
         self.dscale = math.exp(constant)*dconstant
         self.dradius = -3.0/2.0/math.sqrt(-3 * slope)*dslope
+        
+        return [self.radius, self.scale], [self.dradius, self.dscale]
         
     def evaluate_model(self, x):
@@ -195 +197 @@
         # Errors
         self.dscale = math.exp(constant)*dconstant
-        self.dradius = -dslope
+        self.dpower = -dslope
+        
+        return [self.power, self.scale], [self.dpower, self.dscale]
         
     def evaluate_model(self, x):
@@ -210 +214 @@
         """
         p1 = numpy.array([self.dscale * math.pow(q, -self.power) for q in x])
-        p2 = numpy.array([self.scale * self.power * math.pow(q, -self.power-1) * self.dradius for q in x])
+        p2 = numpy.array([self.scale * self.power * math.pow(q, -self.power-1) * self.dpower for q in x])
         diq2 = p1*p1 + p2*p2        
         return numpy.array( [math.sqrt(err) for err in diq2] )
@@ -393 +397 @@
         return  new_data
      
-    def _fit(self, model, qmin=Q_MINIMUM, qmax=Q_MAXIMUM, power=None, range=None):
+    def _fit(self, model, qmin=Q_MINIMUM, qmax=Q_MAXIMUM, power=None):
         """
             fit data with function using 
@@ -410 +414 @@
         extrapolator = Extrapolator(data=self._data, model=model)
         p, dp = extrapolator.fit(power=power, qmin=qmin, qmax=qmax) 
-        if range is not None and range == 'low':
-            self._low_extrapolation_power_fitted = -1*p[0]
-        if range is not None and range == 'high':
-            self._high_extrapolation_power_fitted = -1*p[0]
         
         return model.extract_model_parameters(constant=p[1], slope=p[0], dconstant=dp[1], dslope=dp[0])
@@ -519 +519 @@
     def get_extrapolation_power(self, range='high'):
         """
-            return the fitted power for power law function at a givenr ange
+            return the fitted power for power law function for a given extrapolation range
         """
         if range == 'low':
@@ -540 +540 @@
         
         # Extrapolate the low-Q data
-        self._fit(model=self._low_extrapolation_function,
-                         qmin=qmin,
-                         qmax=qmax,
-                         power=self._low_extrapolation_power,
-                         range='low')
+        p, dp = self._fit(model=self._low_extrapolation_function,
+                              qmin=qmin,
+                          qmax=qmax,
+                          power=self._low_extrapolation_power)
+        self._low_extrapolation_power_fitted = p[0]
         
         # Distribution starting point
@@ -578 +578 @@
         
         # fit the data with a model to get the appropriate parameters
-        self._fit(model=self._high_extrapolation_function,
-                         qmin=qmin,
-                         qmax=qmax,
-                         power=self._high_extrapolation_power,
-                         range='high')
+        p, dp = self._fit(model=self._high_extrapolation_function,
+                          qmin=qmin,
+                          qmax=qmax,
+                          power=self._high_extrapolation_power)
+        self._high_extrapolation_power_fitted = p[0]
         
         #create new Data1D to compute the invariant