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#!/bin/python
import json
from datetime import datetime, timedelta
import numpy as np
import scipy as sp
import scipy.stats
import sys
import os.path
from scipy import stats
from math import sqrt
cols = [
{
"type": "string",
"id": "commits",
"label": "Commit name"
},
{
"type": "datetime",
"id": "average",
"label": "Average time"
},
{
"type": "datetime",
"id": "i0",
"role": "interval",
"label": "Interval1"
},
{
"type": "datetime",
"id": "i1",
"role": "interval",
"label": "Interval2"
},
{
"type": "datetime",
"id": "i2",
"role": "interval",
"label": "Point1"
},
{
"type": "datetime",
"id": "i2",
"role": "interval",
"label": "Point2"
},
{
"type": "datetime",
"id": "i2",
"role": "interval",
"label": "Point3"
},
{
"role": "interval",
"type": "datetime",
"id": "i2",
"label": "Point4"
},
{
"role": "interval",
"type": "datetime",
"id": "i2",
"label": "Point5"
},
{
"role": "interval",
"type": "datetime",
"id": "i2",
"label": "Point6"
},
{
"role": "interval",
"type": "datetime",
"id": "i2",
"label": "Point7"
},
{
"role": "interval",
"type": "datetime",
"id": "i2",
"label": "Point8"
},
{
"role": "interval",
"type": "datetime",
"id": "i2",
"label": "Point9"
}
]
#
#Usage
#
if len(sys.argv)!= 3:
print "./compute_json.py accepts only one argument"
print "Usage: ./compute_json.py <arg> <input_file>"
exit(1)
elif type(int(sys.argv[1])) is not int:
print " Usage: ./copmute_json.py <arg> <input_file>"
print " where [arg] is int"
exit (1)
elif not os.path.isfile(sys.argv[2]):
print "USage: ./compute_json.py <arg> <input_file>"
exit (1)
option = int(sys.argv[1])
input = sys.argv[2]
#function check if line creates new column or adds to existing column
def check_col(rows, items):
commit = items[2]
# values = items[8].split(":")
# the items column is passed as a python script argument sys.argv[1]
timed = items[int(option)].split(".")
val = timed[0].split(":")
if len(val) == 2:
val.insert(0,"0")
values = val
# print "Values", values
build_time = timedelta(hours=int(values[0]), minutes=int(values[1]), seconds=int(values[2]))
for row in rows:
current_commit = row["c"][0]["v"]
if current_commit == commit:
row["sum"] += build_time
row["runs"] += 1
row["build_times"].append(build_time.total_seconds())
date_obj = datetime.now()
date_obj = date_obj.replace(hour=int(values[0]), minute=int(values[1]), second=int(values[2]))
fmt = "Date(%Y,%m,%d,%H,%M,%S,0)"
#printing point values to the HTML
point_format = timedelta(hours=int(val[0]), minutes=int(values[1]), seconds=int(values[2]))
# row["c"].append({"f": "Point{}".format(row["runs"]), "v": date_obj.strftime(fmt)})
row["c"].append({"f": "P" + str(row["runs"]) + ": " + str(point_format), "v": date_obj.strftime(fmt)})
return row
row = {"c": [{"v": commit}], "sum": timedelta(), "runs": 0, "build_times": []}
rows.append(row)
return check_col(rows, items)
rows = []
#with open ("input.csv", "r") as f:
#with open ("input.csv", "r") as f:
with open(input, "r") as f:
for line in f:
items = line.split(",")
commit = items[1]
check_col(rows, items)
for row in rows:
#compute confidence intervals based on standard deviation
## check if enugh runs are available to compute the average.
# TODO - this fails when there are diffrent git describe than git commits. the checking of no of commits is larger than 3 is done by clean-csv.py
if row["runs"] < 3:
print "current commit/git describe: " + row["c"][0]["v"]
print "Failed with no of runs under 3: " + str(row["runs"])
exit(1)
avg_td = row["sum"] // row["runs"]
avg = avg_td.total_seconds()
variance = map(lambda x: (x - avg) ** 2, row["build_times"])
std_dev = sqrt(sum(variance) / row["runs"])
se = stats.sem(row["build_times"])
h = se * stats.t._ppf((1 + 0.95) / 2., row["runs"] - 1)
# print(row["build_times"] + [std_dev, avg, avg-h, avg+h])
high_int = avg + h
# print high_int
low_int = avg -h
# print low_int
#make low_int = 0 if negative
if low_int < 0:
low_int = 0
# avg, low_int, high_int = mean_confidence_interval(row)
# low_int, high_int = stats.norm.interval(0.05, loc=avg, scale=std_dev)
# print(row["build_times"] + [std_dev, avg, low_int, high_int])
#second_attempt std dev
# mean_confidence_interval(row)
#write average time to JSON
hours, remainder = divmod(int(avg), 3600)
minutes, seconds = divmod(remainder, 60)
date_obj = datetime.now()
date_obj = date_obj.replace(hour=hours, minute=minutes, second=seconds)
fmt = "Date(%Y,%m,%d,%H,%M,%S,0)"
fmt_print = "%H:%M:%S"
row["c"].insert(1, {"f": date_obj.strftime(fmt_print), "v": date_obj.strftime(fmt)})
#compute and write top an bottom intervals based on the confidence intervals
# print
# print line
# print high_int
# print avg
hours, remainder = divmod(int(high_int), 3600)
minutes, seconds = divmod(remainder, 60)
date_obj = datetime.now()
date_obj = date_obj.replace(hour=hours, minute=minutes, second=seconds)
row["c"].insert(2, {"f": "C1: " + date_obj.strftime(fmt_print), "v": date_obj.strftime(fmt)})
hours, remainder = divmod(int(low_int), 3600)
minutes, seconds = divmod(remainder, 60)
date_obj = datetime.now()
date_obj = date_obj.replace(hour=hours, minute=minutes, second=seconds)
row["c"].insert(3, {"f": "C2: " + date_obj.strftime(fmt_print), "v": date_obj.strftime(fmt)})
del row["sum"]
del row["runs"]
del row["build_times"]
# break
print(json.dumps({"rows": rows, "cols": cols}))
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