Some fine tuning done

-Allowing to specify a ma number of iterations to consider as a command line argument
- Fixed one issue with negative integral quantities, these could not converge before

Author: tomrobin-teschner

optimalStoppingCriterion/findOptimalIteration.py

@@ -27,7 +27,7 @@ def get_start_of_monotonic_convergence(self):
   def get_iteration_with_highest_acceptable_error(self, average, threshold):
     for iteration in range(len(self.values) - self.window_size - 1, 0, -1):
       residual = fabs(self.averages[iteration] - average)
-      residual /= average
+      residual /= fabs(average)
 
       if residual > threshold:
         return iteration

optimalStoppingCriterion/handleCLAs.py

@@ -8,7 +8,8 @@ def __init__(self, args):
     self.min_window = 10
     self.max_window = 100
     self.increments = 5
-    self.convergence_threshold = 0.01
+    self.asymptotic_convergence_threshold = 0.01
+    self.max_iterations = 0
     self._process_cla()
 
   def _process_cla(self):
@@ -39,14 +40,19 @@ def _process_cla(self):
       print('If you want to change these defaults, see the help message with -h or --help\n')
 
 
-    if '-ct' in self.args:
-      self.convergence_threshold = float(self.args[int(self.args.index('-ct') + 1)])
-    elif'--convergence-threshold' in self.args:
-      self.convergence_threshold = float(self.args[int(self.args.index('--convergence-threshold') + 1)])
+    if '-act' in self.args:
+      self.asymptotic_convergence_threshold = float(self.args[int(self.args.index('-act') + 1)])
+    elif'--asymptotic-convergence-threshold' in self.args:
+      self.asymptotic_convergence_threshold = float(self.args[int(self.args.index('--asymptotic-convergence-threshold') + 1)])
     else:
       print('No asymptotic convergence threshold specified.')
       print('Using default asymptotic convergence threshold of 0.01 (1%)\n')
 
+    if '-mi' in self.args:
+      self.max_iterations = int(self.args[int(self.args.index('-mi') + 1)])
+    elif '--max-iterations' in self.args:
+      self.max_iterations = int(self.args[int(self.args.index('--max-iterations') + 1)])
+
     if '-h' in self.args or '--help' in self.args:
       self._print_help()
 
@@ -56,25 +62,29 @@ def get_case(self):
   def get_window_sizes(self):
     return self.min_window, self.max_window, self.increments
 
-  def get_convergence_threshold(self):
-    return self.convergence_threshold
+  def get_asymptotic_convergence_threshold(self):
+    return self.asymptotic_convergence_threshold
+  
+  def get_max_iterations(self):
+    return self.max_iterations
 
 
   def _print_help(self):
     if '-h' in self.args or '--help' in self.args:
       print('Usage: python3 pyOSC.py -c <case>\n')
-      print('  -c, --case <case>  Case to run')
-      print('  -h, --help         Show this help message and exit')
-      print('  -w, --window       Window size to use for convergence analysis')
-      print('                     Specified as a list of integers separated by spaces')
-      print('                     Use the format <smallest window> <largest window> <increments>')
-      print('                     Example: -w 10 100 10. Smallest window = 10')
-      print('                     largest window = 100, and window increments = 10')
-      print('                     If no window size is specified, the default 10 100 5 is used')
+      print('  -c, --case <case>    Case to run')
+      print('  -h, --help           Show this help message and exit')
+      print('  -w, --window         Window size to use for convergence analysis')
+      print('                       Specified as a list of integers separated by spaces')
+      print('                       Use the format <smallest window> <largest window> <increments>')
+      print('                       Example: -w 10 100 10. Smallest window = 10')
+      print('                       largest window = 100, and window increments = 10')
+      print('                       If no window size is specified, the default 10 100 5 is used')
       print('  -act, --asymptotic-convergence-threshold')
-      print('                     Asymptotic convergence threshold to use for convergence analysis.')
-      print('                     This value will be used to check if coefficients have converged')
-      print('                     to the asymptotic value at the end of the simulation.')
+      print('                       Asymptotic convergence threshold to use for convergence analysis.')
+      print('                       This value will be used to check if coefficients have converged')
+      print('                       to the asymptotic value at the end of the simulation.')
+      print('  -mi, --max-iteration Max number of iterations to consider in convergence analysis')
 
       exit()
 

optimalStoppingCriterion/windowAveraging.py

@@ -45,7 +45,12 @@ def determine_best_residual_threshold(self):
         window_residual[index] = fabs(averages[index + 1] - averages[index])
         window_residual[index] /= fabs(averages[index])
 
-      residual_at_optimum = window_residual[self.earliest_iteration_to_stop]
+      assert self.earliest_iteration_to_stop > window_size, \
+        'optimal iteration to stop reached before largest window size. Reduce your window sizes!\n' \
+        f'Largest window:            {self.window_sizes[-1]}\n' \
+        f'Optimal iteration to stop: {self.earliest_iteration_to_stop}\n' \
+        f'Max window size possible:  {self.earliest_iteration_to_stop}\n'
+      residual_at_optimum = window_residual[self.earliest_iteration_to_stop - window_size]
 
       has_lower_residual_before_optimum = False
       iteration_before_optimum = 0

pycfd-osc.py

@@ -10,7 +10,7 @@ def main():
 
   case = cla.get_case()
   min_window_size, max_window_size, window_increments = cla.get_window_sizes()
-  asymptotic_convergence_threshold = cla.get_convergence_threshold()
+  asymptotic_convergence_threshold = cla.get_asymptotic_convergence_threshold()
 
   # initialise dictionary holding convergence results
   convergence_data = dict()
@@ -35,6 +35,9 @@ def main():
 
       # read values from file
       values = osc.fluent_file_reader(path_and_filename).get_values()
+      max_iterations = cla.get_max_iterations()
+      if max_iterations > 0:
+        values = values[:max_iterations]
 
       # determine the averaged (asymptotic) coefficient value at the end of the simulation
       asymptotic_values = osc.find_asymptotic_values(values)