added scripts for transforming runs into sqlite db, and a file to analyze said db

gmsh/01_merge_output.py

@@ -0,0 +1,43 @@
+import sys
+import numpy as np
+
+indexes = {
+  2: {
+    'order' : 0,
+    'ep'    : 1,
+    'final' : 6,
+    'exact' : 7,
+    'time'  : 8,
+  },
+  3: {
+    'order' : 0,
+    'ep'    : 1,
+    'final' : 7,
+    'exact' : 8,
+    'time'  : 9,
+  },
+}
+
+
+if __name__ == '__main__':
+  files = sys.argv[1:]
+
+  prefixes = set((i[0] for i in files))
+  if len(prefixes) != 1:
+    print "must only feed either 3- or 2-D files, not mixed"
+    sys.exit(1)
+
+  dim = int(prefixes.pop())
+
+  for cur_filename in files:
+    resolution = int(cur_filename.split('.')[1])
+    lines = np.loadtxt(cur_filename)
+    for line in lines:
+      print "%d %d %d %e %e %e" % (
+        resolution,
+        line[indexes[dim]['order']],
+        line[indexes[dim]['ep'   ]],
+        line[indexes[dim]['final']],
+        line[indexes[dim]['exact']],
+        line[indexes[dim]['time' ]],
+      )

gmsh/02_merge_to_sqlite.py

@@ -0,0 +1,26 @@
+import sys
+import numpy as np
+import sqlite3
+from   optparse       import OptionParser
+
+if __name__ == '__main__':
+  parser = OptionParser(usage = "usage: %s <input text file> <output db file>")
+
+  (options, args) = parser.parse_args()
+
+  if len(args) != 2:
+    parser.print_usage()
+    sys.exit(1)
+
+  data_file_name, sql_file_name = args
+
+  data = np.loadtxt(data_file_name)
+
+  con = sqlite3.connect(sql_file_name)
+  cur = con.cursor()
+  cur.execute('DROP TABLE IF EXISTS results')
+  cur.execute('CREATE TABLE results (res INTEGER, ord INTEGER, ep INTEGER, final FLOAT, exact FLOAT, time FLOAT)')
+  cur.executemany("insert into results values(?,?,?,?,?,?)", data)
+  con.commit()
+  cur.close()
+  con.close()

gmsh/03_plotter.py

@@ -0,0 +1,23 @@
+import sqlite3
+import numpy as np
+
+con = sqlite3.connect('data.db')
+cur = con.cursor()
+
+
+orders = np.array(cur.execute('select distinct ord from results').fetchall())[:,0]
+eps    = np.array(cur.execute('select distinct ep  from results').fetchall())[:,0]
+print "#", orders, eps
+
+with open('time.out', 'w') as time_file:
+  for ep in eps:
+    time_file.write("%d " % ep)
+    for order in orders:
+      cur_results = cur.execute('select time from results where res = 1 and ep = ? and ord = ?',
+                               (int(ep),int(order)))
+      cur_time = np.array(cur_results.fetchall()).mean()
+      time_file.write("%e " % cur_time)
+    time_file.write("\n")
+
+
+print "%e" % np.array(cur.execute('select abs(exact - final) from results').fetchall()).mean()