correcting tests for new spatial values (#108)

Author: fariza

emva1288/unittests/test_data.py

@@ -58,8 +58,7 @@ def test_data(self):
         photons = round(self.dataset.cam.get_photons(radiance), 3)
         self.assertEqual(data['spatial']['u_p'], photons)
         # spatial data are images
-        for typ in ('avg', 'avg_dark', 'pvar', 'pvar_dark', 'sum',
-                    'sum_dark', 'var', 'var_dark'):
+        for typ in ('sum_dark', 'sum'):
             self.assertTrue(typ in data['spatial'].keys())
             self.assertEqual(data['spatial'][typ].shape, (self._height,
                                                           self._width))

emva1288/unittests/test_results.py

@@ -200,9 +200,8 @@ def test_results_exposure_variation(self):
         self.assertAlmostEqual(self.results.PRNU1288,
                                np.sqrt(self.results.s_2_y -
                                        self.results.s_2_y_dark) * 100 /
-                               (np.mean(self.data.data['spatial']['avg']) -
-                                np.mean(self.data.data['spatial']['avg_'
-                                                                  'dark'])))
+                               (self.data.data['spatial']['avg_mean'] -
+                                self.data.data['spatial']['avg_mean_dark']))
 
         # Test that histograms contains relevant keys and are numpy arrays
         hists = ('histogram_PRNU', 'histogram_PRNU_accumulated',
@@ -293,8 +292,8 @@ def test_nans(self):
         self.assertIs(r.u_I_var, np.nan)
 
         # Test that a negative s2y_dark will yield a Nan for DSNU1288
-        data['spatial'] = {'avg_dark': [0, 0, 0],
-                           'var_dark': [1, 1, 1],
+        data['spatial'] = {'avg_var_dark': 0.,
+                           'var_mean_dark': 1.,
                            'L_dark': 3}
         r = Results1288(data)
         self.assertIs(r.DSNU1288, np.nan)