Updating check script to be python3 compatible.

Profugus/Source/Scripts/check_profugus.py

@@ -4,110 +4,110 @@ import numpy as np
 import os.path
 
 def check_solution(path_to_results):
-	# Load reference flux
-	ref_flux_filename = os.path.join(path_to_results,'c5g7_3d_flux_ref.h5')
-	assert os.path.isfile(ref_flux_filename)
-	ref_flux_file = h5py.File(ref_flux_filename,'r')
-	ref_flux     = np.array(ref_flux_file['flux_mean'])
-	ref_flux_std = np.array(ref_flux_file['flux_std_dev'])
-
-	# Load reference k
-	ref_k_filename = os.path.join(path_to_results,'c5g7_3d_output_ref.h5')
-	assert os.path.isfile(ref_k_filename)
-	ref_k_file = h5py.File(ref_k_filename,'r')
-	ref_k_dset     = ref_k_file['/keff/mean']
-	ref_k_var_dset = ref_k_file['/keff/variance']
-	ref_k = ref_k_dset[0]
-	ref_k_std = np.sqrt(ref_k_var_dset[0])
-
-	print '\nReference k = {0:.6f} +/- {1:.3e}'.format(ref_k,ref_k_std)
-
-	# Load flux file for comparison
-	flux_filename = os.path.join(path_to_results,'c5g7_3d_flux.h5')
-	assert os.path.isfile(flux_filename)
-	flux_file = h5py.File(flux_filename,'r')
-	flux     = np.array(flux_file['flux_mean'])
-	flux_std = np.array(flux_file['flux_std_dev'])
-
-	# Load k file for comparison
-	k_filename = os.path.join(path_to_results,'c5g7_3d_output.h5')
-	assert os.path.isfile(k_filename)
-	k_file = h5py.File(k_filename,'r')
-	k_dset     = k_file['/keff/mean']
-	k_var_dset = k_file['/keff/variance']
-	k = k_dset[0]
-	k_std = np.sqrt(k_var_dset[0])
-
-	print 'New k = {0:.6f} +/- {1:.3e}'.format(k,k_std)
-
-	passes = True
-
-	# Test to 4 standard deviations, should *almost* never fail
-	safety_factor = 4
-
-	# Check k values
-	kdiff = np.abs(k - ref_k)
-	tol = safety_factor*np.sqrt(k_std*k_std + ref_k_std*ref_k_std)
-	print 'Difference in k = {0:.2e} with tolerance of {1:.2e}'.format(kdiff,tol)
-	passes = kdiff < tol
-
-	if passes:
-	    'Eigenvalue difference is acceptable'
-	else:
-	    'Eigenvalue difference is outside expected tolerance'
-
-	# Cycle-to-cycle correlations cause reported standard deviation to
-	# be under-reported for tally values.  Increase safety factor on flux
-	# checks to eliminate false positives.
-	safety_factor = 10
-
-	# Check flux
-	N = len(ref_flux)
-	assert N == len(ref_flux_std)
-	assert N == len(flux)
-	assert N == len(flux_std)
-
-	failed_indices = []
-	max_ratio = 0.0
-	for i in xrange(N):
-	    ref_val = ref_flux[i]
-	    ref_std = ref_flux_std[i]
-	    val     = flux[i]
-	    std     = flux_std[i]
-	    tol = safety_factor*np.sqrt(ref_std*ref_std + std*std)
-	    flux_diff = np.abs(val - ref_val)
-	    ratio = flux_diff / tol
-	    max_ratio = max(ratio,max_ratio)
-	    if flux_diff > tol:
-		failed_indices.append(i)
-
-	num_failed = len(failed_indices)
-
-	print '\nMaximum relative error is {0:.3f} times tolerance'.format(max_ratio)
-
-	# Allow one outlier
-	if num_failed > 1:
-	    passes = False
-
-	if num_failed > 0:
-	    print '\nThe following flux values were outside of expected tolerance:'
-	    print ' Index   Reference   Value       Difference Tolerance'
-	    for ind in failed_indices:
-		ref_val = ref_flux[ind]
-		ref_std = ref_flux_std[ind]
-		val     = flux[ind]
-		std     = flux_std[ind]
-		tol = safety_factor*np.sqrt(ref_std*ref_std + std*std)
-		flux_diff = np.abs(val - ref_val)
-		print '{0:5}    {1:.4e}  {2:.4e}  {3:.3e}  {4:.3e}'.format(
-		    ind,ref_val,val,flux_diff,tol)
-
-	# Print pass/fail status
-	print '\n',
-	if passes:
-	    print 'Solution PASSED'
-            return 0
-	else:
-	    print 'Solution FAILED'
-            return 1
+    # Load reference flux
+    ref_flux_filename = os.path.join(path_to_results,'c5g7_3d_flux_ref.h5')
+    assert os.path.isfile(ref_flux_filename)
+    ref_flux_file = h5py.File(ref_flux_filename,'r')
+    ref_flux     = np.array(ref_flux_file['flux_mean'])
+    ref_flux_std = np.array(ref_flux_file['flux_std_dev'])
+
+    # Load reference k
+    ref_k_filename = os.path.join(path_to_results,'c5g7_3d_output_ref.h5')
+    assert os.path.isfile(ref_k_filename)
+    ref_k_file = h5py.File(ref_k_filename,'r')
+    ref_k_dset     = ref_k_file['/keff/mean']
+    ref_k_var_dset = ref_k_file['/keff/variance']
+    ref_k = ref_k_dset[0]
+    ref_k_std = np.sqrt(ref_k_var_dset[0])
+
+    print('\nReference k = {0:.6f} +/- {1:.3e}'.format(ref_k,ref_k_std))
+
+    # Load flux file for comparison
+    flux_filename = os.path.join(path_to_results,'c5g7_3d_flux.h5')
+    assert os.path.isfile(flux_filename)
+    flux_file = h5py.File(flux_filename,'r')
+    flux     = np.array(flux_file['flux_mean'])
+    flux_std = np.array(flux_file['flux_std_dev'])
+
+    # Load k file for comparison
+    k_filename = os.path.join(path_to_results,'c5g7_3d_output.h5')
+    assert os.path.isfile(k_filename)
+    k_file = h5py.File(k_filename,'r')
+    k_dset     = k_file['/keff/mean']
+    k_var_dset = k_file['/keff/variance']
+    k = k_dset[0]
+    k_std = np.sqrt(k_var_dset[0])
+
+    print('New k = {0:.6f} +/- {1:.3e}'.format(k,k_std))
+
+    passes = True
+
+    # Test to 4 standard deviations, should *almost* never fail
+    safety_factor = 4
+
+    # Check k values
+    kdiff = np.abs(k - ref_k)
+    tol = safety_factor*np.sqrt(k_std*k_std + ref_k_std*ref_k_std)
+    print('Difference in k = {0:.2e} with tolerance of {1:.2e}'.format(kdiff,tol))
+    passes = kdiff < tol
+
+    if passes:
+        'Eigenvalue difference is acceptable'
+    else:
+        'Eigenvalue difference is outside expected tolerance'
+
+    # Cycle-to-cycle correlations cause reported standard deviation to
+    # be under-reported for tally values.  Increase safety factor on flux
+    # checks to eliminate false positives.
+    safety_factor = 10
+
+    # Check flux
+    N = len(ref_flux)
+    assert N == len(ref_flux_std)
+    assert N == len(flux)
+    assert N == len(flux_std)
+
+    failed_indices = []
+    max_ratio = 0.0
+    for i in xrange(N):
+        ref_val = ref_flux[i]
+        ref_std = ref_flux_std[i]
+        val     = flux[i]
+        std     = flux_std[i]
+        tol = safety_factor*np.sqrt(ref_std*ref_std + std*std)
+        flux_diff = np.abs(val - ref_val)
+        ratio = flux_diff / tol
+        max_ratio = max(ratio,max_ratio)
+        if flux_diff > tol:
+            failed_indices.append(i)
+
+    num_failed = len(failed_indices)
+
+    print('\nMaximum relative error is {0:.3f} times tolerance'.format(max_ratio))
+
+    # Allow one outlier
+    if num_failed > 1:
+        passes = False
+
+    if num_failed > 0:
+        print('\nThe following flux values were outside of expected tolerance:')
+        print(' Index   Reference   Value       Difference Tolerance')
+        for ind in failed_indices:
+            ref_val = ref_flux[ind]
+            ref_std = ref_flux_std[ind]
+            val     = flux[ind]
+            std     = flux_std[ind]
+            tol = safety_factor*np.sqrt(ref_std*ref_std + std*std)
+            flux_diff = np.abs(val - ref_val)
+            print('{0:5}    {1:.4e}  {2:.4e}  {3:.3e}  {4:.3e}'.format(
+                ind,ref_val,val,flux_diff,tol))
+
+    # Print pass/fail status
+    print('\n'),
+    if passes:
+        print('Solution PASSED')
+        return 0
+    else:
+        print('Solution FAILED')
+        return 1