Added script to parse directory data, create CSVs for every chagned value.

This change also makes SchedTrace and OverheadTrace events configurable.

1 files changed, 89 insertions, 0 deletions

diff --git a/parse/sched.py b/parse/sched.py
new file mode 100644
index 0000000..ec4d917
--- /dev/null
+++ b/parse/sched.py
@@ -0,0 +1,89 @@
+import config.config as conf
+import os
+import re
+import numpy as np
+import subprocess
+
+from collections import namedtuple
+from point import Measurement
+
+Task = namedtuple('Task', ['pid', 'period'])
+
+def get_st_output(data_dir, out_dir):
+    bin_files = conf.FILES['sched_data'].format(".*")
+    bins = [f for f in os.listdir(data_dir) if re.match(bin_files, f)]
+
+    output_file = "%s/out-st" % out_dir
+
+    if os.path.isfile(output_file):
+        return output_file
+
+    if len(bins) != 0:
+        cmd_arr = [conf.BINS['st_show']]
+        cmd_arr.extend(bins)
+        with open(output_file, "w") as f:
+            subprocess.call(cmd_arr, cwd=data_dir, stdout=f)
+    else:
+        return None
+    return output_file
+
+def get_tasks(data):
+    reg = r"PARAM.*?(\d+).*?cost:\s+[\d\.]+ms.*?period.*?([\d.]+)"
+    return [Task(x[0], x[1]) for x in re.findall(reg, data)]
+
+def extract_tardy_vals(data, exp_point):
+    ratios = []
+    tards = []
+
+    for t in get_tasks(data):
+        reg = r"TARDY.*?" + t.pid + "/(\d+).*?Tot.*?([\d.]+).*?ms.*([\d.]+).*?ms.*?([\d.]+)"
+        matches = re.findall(reg, data)
+        if len(matches) != 0:
+            jobs = float(matches[0][0])
+            total_tard = float(matches[0][1])
+            # max_tard = float(matches[0][2])
+            misses = float(matches[0][3])
+            rel_tard = (total_tard / jobs) / float(t.period)
+            if misses != 0:
+                miss_ratio = (misses / jobs)
+            else:
+                miss_ratio = 0
+
+            ratios.append(miss_ratio)
+            tards.append(rel_tard)
+
+    for (array, name) in ((tards, "rel-tard"), (ratios, "miss-ratio")):
+        exp_point[name] = Measurement().from_array(array)
+
+def extract_variance(data, exp_point):
+    varz = []
+    for t in get_tasks(data):
+        reg = r"COMPLETION.*?" + t.pid + r".*?([\d\.]+)ms"
+        matches = re.findall(reg, data)
+
+        if len(matches) == 0:
+            return 0
+
+        job_times = np.array(filter(lambda x: float(x) != 0, matches), dtype=np.float)
+
+        # Coefficient of variation
+        cv = job_times.std() / job_times.mean()
+        # Correction, assuming normal distributions
+        corrected = (1 + 1/(4 * len(job_times))) * cv
+
+        varz.append(corrected)
+
+    exp_point['var'] = Measurement().from_array(varz)
+
+def get_sched_data(data_file, result):
+    with open(data_file, 'r') as f:
+        data = f.read()
+
+        # if conf != BASE:
+        #     (our_values, their_values) = extract_exec_vals(our_data, their_data)
+        #     conf_result = get_stats(our_values, their_values)
+        #     for key in conf_result.keys():
+        #         result[key][conf] = conf_result[key]
+
+        extract_tardy_vals(data, result)
+        extract_variance(data, result)