Put both rank and pid into csv file

bin/process_measurements.py

@@ -8,19 +8,19 @@ import re
 def join_csv_files():
     with open('timings.csv', 'w') as out:
         for f in os.listdir(os.getcwd()):
-            match = re.match(".*pid-([0-9]*).csv", f)
+            match = re.match(".*rank-([0-9]*).*", f)
             if match:
                 for line in open(f, 'r'):
                     out.write("{} {}".format(match.group(1), line))
 
 
 def calculate_floprate():
-    frame = pandas.read_csv('timings.csv', header=None, names=('pid', 'ident', 'kernel', 'what', 'value'), delimiter=' ')
+    frame = pandas.read_csv('timings.csv', header=None, names=('rank', 'ident', 'kernel', 'what', 'value'), delimiter=' ')
     time = frame[frame.what == "time"]
     ops = frame[frame.what != "time"]
 
-    time = time.groupby(('pid', 'ident', 'kernel'))['value'].min().to_frame().reset_index().groupby(('ident', 'kernel'))['value'].max()
-    ops = ops.groupby(('pid', 'ident', 'kernel'))['value'].max().to_frame().reset_index().groupby(('ident', 'kernel'))['value'].max()
+    time = time.groupby(('rank', 'ident', 'kernel'))['value'].min().to_frame().reset_index().groupby(('ident', 'kernel'))['value'].max()
+    ops = ops.groupby(('rank', 'ident', 'kernel'))['value'].max().to_frame().reset_index().groupby(('ident', 'kernel'))['value'].max()
 
     with open('floprates.csv', 'w') as out:
         for key in time.keys():
@@ -30,12 +30,12 @@ def calculate_floprate():
 
 
 def calculate_doftimes():
-    frame = pandas.read_csv('timings.csv', header=None, names=('pid', 'ident', 'kernel', 'what', 'value'), delimiter=' ')
+    frame = pandas.read_csv('timings.csv', header=None, names=('rank', 'ident', 'kernel', 'what', 'value'), delimiter=' ')
     dofs = frame[frame.what == "dofs"]
     time = frame[frame.what == "time"]
 
-    dofs = dofs.groupby(('pid', 'ident', 'kernel'))['value'].max().to_frame().reset_index().groupby(('ident', 'kernel'))['value'].max()
-    time = time.groupby(('pid', 'ident', 'kernel'))['value'].min().to_frame().reset_index().groupby(('ident', 'kernel'))['value'].max()
+    dofs = dofs.groupby(('rank', 'ident', 'kernel'))['value'].max().to_frame().reset_index().groupby(('ident', 'kernel'))['value'].max()
+    time = time.groupby(('rank', 'ident', 'kernel'))['value'].min().to_frame().reset_index().groupby(('ident', 'kernel'))['value'].max()
 
     with open('doftimes.csv', 'w') as out:
         for key in time.keys():