1 files changed, 35 insertions, 34 deletions

diff --git a/parse/sched.py b/parse/sched.py
index 0ab16ce..d773c8a 100644
--- a/parse/sched.py
+++ b/parse/sched.py
@@ -44,9 +44,9 @@ class TimeTracker:
             dur = etime - stime
             if self.validator(dur):
                 self.max = max(self.max, dur)
-                self.avg *= float(self.num / (self.num + 1))
+                old_avg = self.avg * self.num
                 self.num += 1
-                self.avg += dur / float(self.num)
+                self.avg = (old_avg + dur) / float(self.num)
 
         # Give up on some jobs if they've been hanging around too long.
         # While not strictly needed, it helps improve performance and
@@ -248,12 +248,13 @@ class ReleaseRecord(SchedRecord):
         data.pgm_misses.start_time(self, self.deadline)
         data.pgm_lateness.start_time(self, self.deadline)
 
-        ntype = task_dict[self.pid].pgm_params.node_type
-        if ntype == PGM_SRC or ntype == PGM_SRC_SINK:
-            gid = task_dict[self.pid].pgm_params.gid
-            gdata = graph_dict[gid]
-            gdata.jobs += 1
-            gdata.response.start_time(self, self.when)
+        if data.pgm_params:
+            ntype = data.pgm_params.node_type
+            if ntype == PGM_SRC or ntype == PGM_SRC_SINK:
+                gid = data.pgm_params.gid
+                gdata = graph_dict[gid]
+                gdata.jobs += 1
+                gdata.response.start_time(self, self.when)
 
 class CompletionRecord(SchedRecord):
     FIELDS = [('when', c_uint64)]
@@ -422,38 +423,38 @@ def extract_sched_data(result, data_dir, work_dir):
         # start with basic task information
         stat_data["miss-ratio" ].append(miss_ratio)
 
-        stat_data["max-response"].append(float(response.max)/NSEC_PER_MSEC)
-        stat_data["avg-response"].append(response.avg/NSEC_PER_MSEC)
-        stat_data["max-response-prop"].append(float(response.max) / tdata.params.period)
-        stat_data["avg-response-prop"].append(response.avg / tdata.params.period)
+        stat_data["response-max"].append(float(response.max)/NSEC_PER_MSEC)
+        stat_data["response-avg"].append(response.avg/NSEC_PER_MSEC)
+        stat_data["response-prop-max"].append(float(response.max) / tdata.params.period)
+        stat_data["response-prop-avg"].append(response.avg / tdata.params.period)
 
-        stat_data["max-tard"].append(float(miss.max)/NSEC_PER_MSEC)
-        stat_data["avg-tard"].append(avg_tard/NSEC_PER_MSEC)
-        stat_data["max-tard-prop"].append(float(miss.max) / tdata.params.period)
-        stat_data["avg-tard-prop"].append(avg_tard / tdata.params.period)
+        stat_data["tard-max"].append(float(miss.max)/NSEC_PER_MSEC)
+        stat_data["tard-avg"].append(avg_tard/NSEC_PER_MSEC)
+        stat_data["tard-prop-max"].append(float(miss.max) / tdata.params.period)
+        stat_data["tard-prop-avg"].append(avg_tard / tdata.params.period)
 
-        stat_data["max-response"].append(float(lateness.max)/NSEC_PER_MSEC)
-        stat_data["avg-response"].append(lateness.avg/NSEC_PER_MSEC)
-        stat_data["max-response-prop"].append(float(lateness.max) / tdata.params.period)
-        stat_data["avg-response-prop"].append(lateness.avg / tdata.params.period)
+        stat_data["lateness-max"].append(float(lateness.max)/NSEC_PER_MSEC)
+        stat_data["lateness-avg"].append(lateness.avg/NSEC_PER_MSEC)
+        stat_data["lateness-prop-max"].append(float(lateness.max) / tdata.params.period)
+        stat_data["lateness-prop-avg"].append(lateness.avg / tdata.params.period)
 
         # same data, but with PGM-adjusted release times (shifted deadlines)
         stat_data["pgm-miss-ratio" ].append(pgm_miss_ratio)
 
-        stat_data["pgm-max-response"].append(float(pgm_response.max)/NSEC_PER_MSEC)
-        stat_data["pgm-avg-response"].append(pgm_response.avg/NSEC_PER_MSEC)
-        stat_data["pgm-max-response-prop"].append(float(pgm_response.max) / tdata.params.period)
-        stat_data["pgm-avg-response-prop"].append(pgm_response.avg / tdata.params.period)
+        stat_data["pgm-response-max"].append(float(pgm_response.max)/NSEC_PER_MSEC)
+        stat_data["pgm-response-avg"].append(pgm_response.avg/NSEC_PER_MSEC)
+        stat_data["pgm-response-prop-max"].append(float(pgm_response.max) / tdata.params.period)
+        stat_data["pgm-response-prop-avg"].append(pgm_response.avg / tdata.params.period)
 
-        stat_data["pgm-max-tard"].append(float(pgm_miss.max)/NSEC_PER_MSEC)
-        stat_data["pgm-avg-tard"].append(pgm_avg_tard/NSEC_PER_MSEC)
-        stat_data["pgm-max-tard-prop"].append(float(pgm_miss.max) / tdata.params.period)
-        stat_data["pgm-avg-tard-prop"].append(pgm_avg_tard / tdata.params.period)
+        stat_data["pgm-tard-max"].append(float(pgm_miss.max)/NSEC_PER_MSEC)
+        stat_data["pgm-tard-avg"].append(pgm_avg_tard/NSEC_PER_MSEC)
+        stat_data["pgm-tard-prop-max"].append(float(pgm_miss.max) / tdata.params.period)
+        stat_data["pgm-tard-prop-avg"].append(pgm_avg_tard / tdata.params.period)
 
-        stat_data["pgm-max-response"].append(float(pgm_lateness.max)/NSEC_PER_MSEC)
-        stat_data["pgm-avg-response"].append(pgm_lateness.avg/NSEC_PER_MSEC)
-        stat_data["pgm-max-response-prop"].append(float(pgm_lateness.max) / tdata.params.period)
-        stat_data["pgm-avg-response-prop"].append(pgm_lateness.avg / tdata.params.period)
+        stat_data["pgm-lateness-max"].append(float(pgm_lateness.max)/NSEC_PER_MSEC)
+        stat_data["pgm-lateness-avg"].append(pgm_lateness.avg/NSEC_PER_MSEC)
+        stat_data["pgm-lateness-prop-max"].append(float(pgm_lateness.max) / tdata.params.period)
+        stat_data["pgm-lateness-prop-avg"].append(pgm_lateness.avg / tdata.params.period)
 
     for gid, gdata in graph_dict.iteritems():
         if not gdata.params:
@@ -469,8 +470,8 @@ def extract_sched_data(result, data_dir, work_dir):
             log_once(LOSS_MSG)
             continue
 
-        gstat_data["graph-max-response"].append(float(response.max)/NSEC_PER_MSEC)
-        gstat_data["graph-avg-response"].append(response.avg/NSEC_PER_MSEC)
+        gstat_data["graph-response-max"].append(float(response.max)/NSEC_PER_MSEC)
+        gstat_data["graph-response-avg"].append(response.avg/NSEC_PER_MSEC)
 
     # Summarize value groups
     for name, data in stat_data.iteritems():