Changeset 4329

Timestamp:

Mar 28, 2007, 1:34:29 PM (17 years ago)

Author:

ole

Message:

Refactored automated_validation_tests/okushiri_tank_validation/compare_timeseries_with_measures.py

File:

• anuga_validation/automated_validation_tests/okushiri_tank_validation/compare_timeseries_with_measures.py (modified) (5 diffs)

anuga_validation/automated_validation_tests/okushiri_tank_validation/compare_timeseries_with_measures.py

@@ -24 +24 @@
 # No plotting for automated unittest (unless one wishes to run
 # this manually and look at the outputs)
-plotting = False
+plotting = True
 
 
@@ -273 +273 @@
 
 
+def report_difference(name, computed_value, reference_value, eps):
+
+    print '%s = %.18e' %(name, computed_value)
+
+    if computed_value < reference_value-eps:
+        print '  Result is better than expected by: %.18e'\
+              %(computed_value - reference_value)
+        print '  Expect = %.18e' %reference_value    
+    elif computed_value > reference_value+eps:
+        print '  FAIL: Result is worse than expected by: %.18e'\
+                                %(computed_value-reference_value)
+        print '  Expect = %.18e' %reference_value
+    else:
+        pass
+
+    if abs(reference_value) > 0:
+        msg = '%s (expected, computed): (%.18e, %.18e): Relative error=%.18e'\
+              %(name, reference_value, computed_value, abs(reference_value-computed_value)/reference_value)
+    else:
+        msg = '%s (expected, computed): (%.18e, %.18e): Absolute error=%.18e'\
+              %(name, reference_value, computed_value, abs(reference_value-computed_value))        
+    print msg
+    
+    #assert allclose(expected_covariance[name], res), msg
+    
+
+
 #--------------------------------------------------
 # Compare model output to validation data
@@ -278 +305 @@
 
 eps = get_machine_precision()
+print 'Machine precision: ', eps
 for k, name in enumerate(gauge_names):
     sqsum = 0
@@ -289 +317 @@
 
 
-    # Covariance measures    
-    res = cov(observed_timeseries, model)     
-    print 'Covariance = %.18e' %res
-
-    if res < expected_covariance[name]-eps:
-        print '  Result is better than expected by: %.18e'\
-              %(res-expected_covariance[name])
-        print '  Expect = %.18e' %expected_covariance[name]    
-    elif res > expected_covariance[name]+eps:
-        print '  FAIL: Result is worse than expected by: %.18e'\
-                                %(res-expected_covariance[name])
-        print '  Expect = %.18e' %expected_covariance[name]
-    else:
-        pass
-
-    msg = 'Covariance (expected, computed): (%.18e, %.18e)'\
-          %(expected_covariance[name], res)             
-    assert res-eps <= expected_covariance[name] <= res+eps, msg
-    
-    
-
+    # Covariance measure    
+    res = cov(observed_timeseries, model)
+    report_difference('Covariance', res, expected_covariance[name], eps)
+     
     # Difference measures    
-    res = sum(abs(observed_timeseries-model))/len(model)     
-    print 'Accumulated difference = %.18e' %res
-
-    if res < expected_difference[name]-eps:
-        print '  Result is better than expected by: %.18e'\
-              %(res-expected_difference[name])
-        print '  Expect = %.18e' %expected_difference[name]    
-    elif res > expected_difference[name]+eps:
-        print '  FAIL: Result is worse than expected by: %.18e'\
-                                %(res-expected_difference[name])
-        print '  Expect = %.18e' %expected_difference[name]
-    else:
-        pass
-
-
-    assert res-eps <= expected_difference[name] <= res+eps
+    res = sum(abs(observed_timeseries-model))/len(model)
+    report_difference('Accumulated difference', res, expected_difference[name], eps)    
 
     # Extrema
     res = abs(max(observed_timeseries)-max(model))
-    print 'Difference in maxima = %.18e' %res
-
-    if res < expected_maximum_diff[name]-eps:
-        print '  Result is better than expected by: %.18e'\
-              %(res-expected_maximum_diff[name])
-        print '  Expect = %.18e' %expected_maximum_diff[name]    
-    elif res > expected_maximum_diff[name]+eps:
-        print '  FAIL: Result is worse than expected by: %.18e'\
-                                %(res-expected_maximum_diff[name])
-        print '  Expect = %.18e' %expected_maximum_diff[name]
-    else:
-        pass
-
-    assert res-eps <= expected_maximum_diff[name] <= res+eps    
-
+    report_difference('Difference in maxima', res, expected_maximum_diff[name], eps)
     
     res = abs(min(observed_timeseries)-min(model))
-    print 'Difference in minima = %.18e' %res
-
-    if res < expected_minimum_diff[name]-eps:
-        print '  Result is better than expected by: %.18e'\
-              %(res-expected_minimum_diff[name])
-        print '  Expect = %.18e' %expected_minimum_diff[name]    
-    elif res > expected_minimum_diff[name]+eps:
-        print '  FAIL: Result is worse than expected by: %.18e'\
-                                %(res-expected_minimum_diff[name])
-        print '  Expect = %.18e' %expected_minimum_diff[name]
-    else:
-        pass
-
-    assert res-eps <= expected_minimum_diff[name] <= res+eps
-    
+    report_difference('Difference in minima', res, expected_minimum_diff[name], eps)    
 
     # Locations of extrema
@@ -367 +336 @@
     i1 = argmax(model)
     res = abs(reference_time[i1] - reference_time[i0])
-    print 'Timelag between maxima = %.18e' %res
-
-    if res < expected_argmax_timelag[name]-eps:
-        print '  Result is better than expected by: %.18e'\
-              %(res-expected_argmax_timelag[name])
-        print '  Expect = %.18e' %expected_argmax_timelag[name]    
-    elif res > expected_argmax_timelag[name]+eps:
-        print '  FAIL: Result is worse than expected by: %.18e'\
-                                %(res-expected_argmax_timelag[name])
-        print '  Expect = %.18e' %expected_argmax_timelag[name]
-    else:
-        pass
-    
-    assert res-eps <= expected_argmax_timelag[name] <= res+eps
+    report_difference('Timelag between maxima', res, expected_argmax_timelag[name], eps)    
+    
 
     i0 = argmin(observed_timeseries)
     i1 = argmin(model)
     res = abs(reference_time[i1] - reference_time[i0])
-    print 'Timelag between minima = %.18e' %res
-    if res < expected_argmin_timelag[name]-eps:
-        print '  Result is better than expected by: %.18e'\
-              %(res-expected_argmin_timelag[name])
-        print '  Expect = %.18e' %expected_argmin_timelag[name]    
-    elif res > expected_argmin_timelag[name]+eps:
-        print '  FAIL: Result is worse than expected by: %.18e'\
-                                %(res-expected_argmin_timelag[name])
-        print '  Expect = %.18e' %expected_argmin_timelag[name]
-    else:
-        pass
-
-    assert res-eps <= expected_argmin_timelag[name] <= res+eps
+    report_difference('Timelag between minima', res, expected_argmin_timelag[name], eps)