1 files changed, 52 insertions, 22 deletions

diff --git a/parse/sched.py b/parse/sched.py
index 11a614e..49a945b 100644
--- a/parse/sched.py
+++ b/parse/sched.py
@@ -4,6 +4,9 @@ import re
 import struct
 import subprocess
 
+import scipy.stats as stats
+import numpy as np
+
 from collections import defaultdict,namedtuple
 from common import recordtype,log_once
 from point import Measurement
@@ -16,6 +19,8 @@ class TimeTracker:
     def __init__(self, is_valid_duration = lambda x: True, delay_buffer_size = 1, max_pending = -1):
         self.validator = is_valid_duration
         self.avg = self.max = self.num = 0
+        self.all_measurements = []
+        self.all_measurements_arr = None
 
         self.matches = 0
 
@@ -32,6 +37,18 @@ class TimeTracker:
         unmatched = len(self.start_records) + len(self.end_records)
         return self.discarded + unmatched
 
+    def stdev(self):
+        if self.all_measurements_arr is None:
+            self.all_measurements_arr = np.asarray(self.all_measurements)
+            self.all_measurements_arr.sort()
+        return np.std(self.all_measurements_arr)
+
+    def percentile(self, which):
+        if self.all_measurements_arr is None:
+            self.all_measurements_arr = np.asarray(self.all_measurements)
+            self.all_measurements_arr.sort()
+        return stats.scoreatpercentile(self.all_measurements_arr, which)
+
     def process_completed(self):
         completed = self.start_records.viewkeys() & self.end_records.viewkeys()
         self.matches += len(completed)
@@ -47,6 +64,7 @@ class TimeTracker:
                 old_avg = self.avg * self.num
                 self.num += 1
                 self.avg = (old_avg + dur) / float(self.num)
+                self.all_measurements.append(dur)
 
         # Give up on some jobs if they've been hanging around too long.
         # While not strictly needed, it helps improve performance and
@@ -423,38 +441,41 @@ def extract_sched_data(result, data_dir, work_dir):
         # start with basic task information
         stat_data["miss-ratio" ].append(miss_ratio)
 
-        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["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["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)
+#        WE DON'T REALLY CARE ABOUT THESE VALUES IN PGM
+#        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["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["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-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-std"].append(pgm_response.stdev()/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-response-prop-std"].append(pgm_response.stdev()/ 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-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-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)
+#        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:
@@ -471,9 +492,18 @@ def extract_sched_data(result, data_dir, work_dir):
             continue
 
         gstat_data["graph-response-max"].append(float(response.max)/NSEC_PER_MSEC)
+        gstat_data["graph-response-99p9tile"].append(float(response.percentile(99.9))/NSEC_PER_MSEC)
+        gstat_data["graph-response-99tile"].append(float(response.percentile(99))/NSEC_PER_MSEC)
+        gstat_data["graph-response-95tile"].append(float(response.percentile(95))/NSEC_PER_MSEC)
         gstat_data["graph-response-avg"].append(response.avg/NSEC_PER_MSEC)
+        gstat_data["graph-response-std"].append(response.stdev()/NSEC_PER_MSEC)
+
         gstat_data["graph-response-prop-max"].append(float(response.max) / gdata.params.etoe)
+        gstat_data["graph-response-prop-99p9tile"].append(float(response.percentile(99.9))/ gdata.params.etoe)
+        gstat_data["graph-response-prop-99tile"].append(float(response.percentile(99))/ gdata.params.etoe)
+        gstat_data["graph-response-prop-95tile"].append(float(response.percentile(95))/ gdata.params.etoe)
         gstat_data["graph-response-prop-avg"].append(response.avg / gdata.params.etoe)
+        gstat_data["graph-response-prop-std"].append(response.stdev()/ gdata.params.etoe)
 
     # Summarize value groups
     for name, data in stat_data.iteritems():