Formatting tweaks.

gprMax/gprMax.py

@@ -113,6 +113,7 @@ def run_main(args):
     # Process for benchmarking simulation
     elif args.benchmark:
         run_benchmark_sim(args, inputfile, usernamespace)
+        print('\nSimulation completed.\n{}\n'.format('-' * get_terminal_size()[0]))
 
     # Process for standard simulation
     else:
@@ -443,8 +444,7 @@ def run_model(args, modelrun, numbermodelruns, inputfile, usernamespace):
             outputfile = inputfileparts[0] + '.out'
         else:
             outputfile = inputfileparts[0] + str(modelrun) + '.out'
-        sys.stdout.write('\nOutput to file: {}\n'.format(outputfile))
+        print('\nOutput to file: {}\n'.format(outputfile))
-        sys.stdout.flush()
 
         ####################################
         #  Start - Main FDTD calculations  #

gprMax/optimisation_taguchi.py

@@ -16,10 +16,11 @@
 # You should have received a copy of the GNU General Public License
 # along with gprMax.  If not, see <http://www.gnu.org/licenses/>.
 
+from collections import OrderedDict
 import importlib
 import os
 import pickle
-from collections import OrderedDict
+from shutil import get_terminal_size
 
 import numpy as np
 
@@ -68,7 +69,7 @@ def run_opt_sim(args, numbermodelruns, inputfile, usernamespace):
 
     # Select OA
     OA, N, cols, k, s, t = construct_OA(optparams)
-    print('\n{}\nTaguchi optimisation...\n'.format(68 * '*'))
+    print('\n{}\nTaguchi optimisation...\n'.format('-' * get_terminal_size()[0]))
     print('\tOrthogonal array: {:g} experiments per iteration, {:g} parameters ({:g} will be used), {:g} levels, and strength {:g}'.format(N, cols, k, s, t))
     tmp = [(k, v) for k, v in optparams.items()]
     print('\tParameters to optimise with ranges: {}'.format(str(tmp).strip('[]')))
@@ -131,7 +132,7 @@ def run_opt_sim(args, numbermodelruns, inputfile, usernamespace):
         # Rename confirmation experiment output file so that it is retained for each iteraction
         os.rename(outputfile, os.path.splitext(outputfile)[0] + '_final' + str(iteration + 1) + '.out')
 
-        print('\nTaguchi optimisation, iteration {} completed. History of optimal parameter values {} and of fitness values {}'.format(iteration + 1, dict(optparamshist), fitnessvalueshist, 68 * '*'))
+        print('\nTaguchi optimisation, iteration {} completed. History of optimal parameter values {} and of fitness values {}'.format(iteration + 1, dict(optparamshist), fitnessvalueshist, '-' * get_terminal_size()[0]))
         iteration += 1
 
         # Stop optimisation if stopping criterion has been reached
@@ -154,7 +155,7 @@ def run_opt_sim(args, numbermodelruns, inputfile, usernamespace):
         pickle.dump(fitnessvalueshist, f)
         pickle.dump(optparamsinit, f)
 
-    print('\n{}\nTaguchi optimisation completed after {} iteration(s).\nHistory of optimal parameter values {} and of fitness values {}\n{}\n'.format(68 * '*', iteration, dict(optparamshist), fitnessvalueshist, 68 * '*'))
+    print('\n{}\nTaguchi optimisation completed after {} iteration(s).\nHistory of optimal parameter values {} and of fitness values {}\n{}\n'.format('-' * get_terminal_size()[0], iteration, dict(optparamshist), fitnessvalueshist, '-' * get_terminal_size()[0]))
 
 
 def taguchi_code_blocks(inputfile, taguchinamespace):