Changeset 6939bd4 in sasview for Invariant/test

Timestamp:

Jan 11, 2010 6:44:59 PM (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:

3082632

Parents:

885857e

Message:

invariant: added unit test to test low-q extrapolation

File:

• Invariant/test/utest_data_handling.py (modified) (3 diffs)

Invariant/test/utest_data_handling.py

@@ -9 +9 @@
 """
 import unittest
-import numpy
+import numpy, math
 from DataLoader.loader import  Loader
 from DataLoader.data_info import Data1D
@@ -53 +53 @@
         # Test results
         self.assertTrue(math.fabs(a-1.0)<0.05)
-        self.assertTrue(math.fabs(b)<0.05)        
+        self.assertTrue(math.fabs(b)<0.1)        
     
     
@@ -101 +101 @@
     
     
+class TestGuinierExtrapolation(unittest.TestCase):
+    """
+        Generate a Guinier distribution and verify that the extrapolation
+        produce the correct ditribution.
+    """
+    
+    def setUp(self):
+        """
+            Generate a Guinier distribution. After extrapolating, we will
+            verify that we obtain the scale and rg parameters
+        """
+        self.scale = 1.5
+        self.rg = 70.0
+        x = numpy.arange(0.0001, 0.1, 0.0001)
+        y = numpy.asarray([self.scale * math.exp( -(q*self.rg)**2 / 3.0 ) for q in x])
+        dy = y*.1
+        self.data = Data1D(x=x, y=y, dy=dy)
+        
+    def test_low_q(self):
+        """
+            Invariant with low-Q extrapolation
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data)
+        # Set the extrapolation parameters for the low-Q range
+        inv.set_extrapolation(range='low', npts=20, function='guinier')
+        
+        self.assertEqual(inv._low_extrapolation_npts, 20)
+        self.assertEqual(inv._low_extrapolation_function.__name__, 'guinier')
+        
+        # Data boundaries for fiiting
+        qmin = inv._data.x[0]
+        qmax = inv._data.x[inv._low_extrapolation_npts - 1]
+        
+        # Extrapolate the low-Q data
+        a, b = inv._fit(function=inv._low_extrapolation_function,
+                          qmin=qmin,
+                          qmax=qmax,
+                          power=inv._low_extrapolation_power)
+        self.assertAlmostEqual(self.scale, a, 6)
+        self.assertAlmostEqual(self.rg, b, 6)
+    
+
+class TestPowerLawExtrapolation(unittest.TestCase):
+    """
+        Generate a power law distribution and verify that the extrapolation
+        produce the correct ditribution.
+    """
+    
+    def setUp(self):
+        """
+            Generate a power law distribution. After extrapolating, we will
+            verify that we obtain the scale and m parameters
+        """
+        self.scale = 1.5
+        self.m = 3.0
+        x = numpy.arange(0.0001, 0.1, 0.0001)
+        y = numpy.asarray([self.scale * math.pow(q ,-1.0*self.m) for q in x])                
+        dy = y*.1
+        self.data = Data1D(x=x, y=y, dy=dy)
+        
+    def test_low_q(self):
+        """
+            Invariant with low-Q extrapolation
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data)
+        # Set the extrapolation parameters for the low-Q range
+        inv.set_extrapolation(range='low', npts=20, function='power_law')
+        
+        self.assertEqual(inv._low_extrapolation_npts, 20)
+        self.assertEqual(inv._low_extrapolation_function.__name__, 'power_law')
+        
+        # Data boundaries for fitting
+        qmin = inv._data.x[0]
+        qmax = inv._data.x[inv._low_extrapolation_npts - 1]
+        
+        # Extrapolate the low-Q data
+        a, b = inv._fit(function=inv._low_extrapolation_function,
+                          qmin=qmin,
+                          qmax=qmax,
+                          power=inv._low_extrapolation_power)
+        
+        self.assertAlmostEqual(self.scale, a, 6)
+        self.assertAlmostEqual(self.m, b, 6)
+