added time_measurement tools

Author: StegSchreck

.idea/runConfigurations/OFM_functional_tests.xml

@@ -0,0 +1,92 @@
+import datetime
+import os
+import re
+import statistics
+import time
+from os import listdir
+from os.path import isfile, join
+
+FILE_ENDING_RESULT = '.result'
+FILE_ENDING_SUMMARIZED = '.summarized'
+FILE_ENDING_ANALYZED = '.analyzed'
+RESULTS_FOLDER = 'time_measurements'
+
+
+def timing(f):
+    def wrap(*args):
+        time1 = time.time()
+        ret = f(*args)
+        time2 = time.time()
+
+        _write_time_measure_result_to_file(time1, time2)
+
+        return ret
+
+    def _write_time_measure_result_to_file(time1, time2):
+        date = _get_timestamp(time2)
+        result = '%s.%s, %0.3f' % (f.__module__, f.__qualname__, (time2 - time1) * 1000.0)
+        _append_line_to_file(date + FILE_ENDING_RESULT, result)
+
+    return wrap
+
+
+def _append_line_to_file(filename, result):
+    with open(os.path.join(RESULTS_FOLDER, filename), 'a+', encoding='utf8') as file:
+        file.write(result + '\n')
+
+
+def _get_timestamp(time2):
+    return datetime.datetime.fromtimestamp(time2).strftime('%Y-%m-%d_%H:%M')
+
+
+def export_analyzed_results_as_csv():
+    results_map = _get_results_from_files()
+
+    date = _get_timestamp(time.time())
+    csv_header = 'func, MAX, MIN, AVG, MED, STD, VAR'
+    _append_line_to_file(date + FILE_ENDING_ANALYZED, csv_header)
+
+    for key in sorted(results_map, key=lambda k: statistics.mean(results_map[k]), reverse=True):
+        csv_line = key + ',' + \
+                   str(max(results_map[key])) + ',' + \
+                   str(min(results_map[key])) + ',' + \
+                   str(statistics.mean(results_map[key])) + ',' + \
+                   str(statistics.median(results_map[key])) + ',' + \
+                   str(statistics.stdev(results_map[key])) + ',' + \
+                   str(statistics.variance(results_map[key]))
+        _append_line_to_file(date + FILE_ENDING_ANALYZED, csv_line)
+
+
+def export_results_as_csv():
+    results_map = _get_results_from_files()
+    date = _get_timestamp(time.time())
+
+    for key in sorted(results_map, key=lambda k: statistics.mean(results_map[k]), reverse=True):
+        values_as_csv = ",".join([str(v) for v in results_map[key]])
+        csv_line = key + ',' + values_as_csv
+        _append_line_to_file(date + FILE_ENDING_SUMMARIZED, csv_line)
+
+
+def _get_results_from_files():
+    results_map = {}
+    result_files = [f for f in listdir(RESULTS_FOLDER)
+                    if isfile(join(RESULTS_FOLDER, f)) and re.match(r'.*' + FILE_ENDING_RESULT, f)]
+
+    for f in result_files:
+        _read_results_from_file_to_map(f, results_map)
+
+    return results_map
+
+
+def _read_results_from_file_to_map(f, results_map):
+    file = open(os.path.join(RESULTS_FOLDER, f))
+    results = file.readlines()
+    for r in results:
+        _put_result_line_to_map(r, results_map)
+
+
+def _put_result_line_to_map(r, results_map):
+    function_name, function_time = r.split(',')
+    if function_name not in results_map.keys():
+        results_map[function_name] = []
+    results_map[function_name].append(float(function_time))