def getStatValues(documents):
statValues = {}
triggerValues = []
unmergingValues = []
argValues = []
for doc in documents:
for event in doc.events:
for value in sorted(event.trigger.triggerScoreDict.values()):
triggerValues.append(value)
if hasattr(event.trigger, "unmergingScoreDict"):
for value in sorted(event.trigger.unmergingScoreDict.values()):
unmergingValues.append(value)
for argScoreDict in event.argScoreDicts:
for value in sorted(argScoreDict.values()):
argValues.append(value)
for relation in doc.relations:
for argScoreDict in relation.argScoreDicts:
for value in sorted(argScoreDict.values()):
argValues.append(value)
#print triggerValues, unmergingValues, argValues
if len(triggerValues) > 0:
statValues["trigger-stdev"] = stats.lstdev(triggerValues)
statValues["trigger-mean"] = stats.lmean(triggerValues)
if len(unmergingValues) > 0:
statValues["unmerging-stdev"] = stats.lstdev(unmergingValues)
statValues["unmerging-mean"] = stats.lmean(unmergingValues)
statValues["arg-stdev"] = stats.lstdev(argValues)
statValues["arg-mean"] = stats.lmean(argValues)
return statValues
def getStatValues(documents):
statValues = {}
triggerValues = []
unmergingValues = []
argValues = []
for doc in documents:
for event in doc.events:
for value in sorted(event.trigger.triggerScoreDict.values()):
triggerValues.append(value)
if hasattr(event.trigger, "unmergingScoreDict"):
for value in sorted(event.trigger.unmergingScoreDict.values()):
unmergingValues.append(value)
for argScoreDict in event.argScoreDicts:
for value in sorted(argScoreDict.values()):
argValues.append(value)
for relation in doc.relations:
for argScoreDict in relation.argScoreDicts:
for value in sorted(argScoreDict.values()):
argValues.append(value)
#print triggerValues, unmergingValues, argValues
if len(triggerValues) > 0:
statValues["trigger-stdev"] = stats.lstdev(triggerValues)
statValues["trigger-mean"] = stats.lmean(triggerValues)
if len(unmergingValues) > 0:
statValues["unmerging-stdev"] = stats.lstdev(unmergingValues)
statValues["unmerging-mean"] = stats.lmean(unmergingValues)
statValues["arg-stdev"] = stats.lstdev(argValues)
statValues["arg-mean"] = stats.lmean(argValues)
return statValues
def get_modules(self, cutoff=.05):
modules = []
for e in self:
if e.val < min(e.lo_min, e.hi_min, cutoff):
if self.datatype=="continuous":
e.desc = "lo" if lmean(e.a) < lmean(e.b) else "hi"
else:
e.desc = "enriched"
modules.append(e)
else:
modules += e.get_modules(cutoff=cutoff)
return modules
def get_modules(self, cutoff=.05):
modules = []
for e in self:
if e.val < min(e.lo_min, e.hi_min, cutoff):
if self.datatype == "continuous":
e.desc = "lo" if lmean(e.a) < lmean(e.b) else "hi"
else:
e.desc = "enriched"
modules.append(e)
else:
modules += e.get_modules(cutoff=cutoff)
return modules
def eu_std_razlika_vzorcnih_arit_sred(var, year):
s,f = seznam_vzorec(var, year, 'new')
as_n = stats.lmean(s)
nn = len(s)
s,f = seznam_vzorec(var, year, 'old')
as_s = stats.lmean(s)
ns = len(s)
s = eu_skupni_std_odklon(var, year)[0]
h = float((ns + nn) /( float(ns) * float(nn)))
te = (as_s - as_n) / (s * math.sqrt(h) )
return te
def _get_id_stats(glyphs, k=None):
import stats
if len(glyphs) < 3:
return (len(glyphs),1.0, 1.0, 1.0)
if k is None:
k = kNN()
distances = k.unique_distances(glyphs)
return (len(glyphs),stats.lmean(distances), stats.lstdev(distances), stats.lmedian(distances))
def _get_id_stats(glyphs, k=None):
import stats
if len(glyphs) < 3:
return (len(glyphs),1.0, 1.0, 1.0)
if k is None:
k = kNN()
distances = k.unique_distances(glyphs)
return (len(glyphs),stats.lmean(distances), stats.lstdev(distances), stats.lmedian(distances))
def vzorcna_varianca(var, year):
s,f = seznam_vzorec(var, year)
n = len(s) - 1
asredina = stats.lmean(s)
x = 0
for i in s:
x = x + (i - asredina)*(i - asredina)
vv = x / float(n)
return vv, math.sqrt(vv)
def log_normal_distribution(self):
# take logs of sequence
log_sequence = []
for number in self.sequence:
log_sequence.append(math.log(number,math.e))
mean= stats.lmean(log_sequence)
stdev = stats.stdev(log_sequence)
number_of_points = len(self.sequence)
distribution = log_sequence
for each_value in range(number_of_points):
distribution[each_value]=(distribution[each_value] - mean)/stdev
return distribution
def eu_vzorcna_varianca(var, year, eu):
s,f = seznam_vzorec(var, year, eu)
asredina = stats.lmean(s)
n = len(s)
m = 0
for i in s:
m = m + ((i - asredina) * (i - asredina))
s2 = m / (n - 1)
vso = math.sqrt(s2)
return vso, s2
def log_normal_distribution(self):
# take logs of sequence
log_sequence = []
for number in self.sequence:
log_sequence.append(math.log(number, math.e))
mean = stats.lmean(log_sequence)
stdev = stats.stdev(log_sequence)
number_of_points = len(self.sequence)
distribution = log_sequence
for each_value in range(number_of_points):
distribution[each_value] = (distribution[each_value] -
mean) / stdev
return distribution
def zaupanje_aritmeticna(var, year):
s,f = seznam_vzorec(var, year)
n = len(s)
asredina = stats.lmean(s)
#z = 2.093
# print n, "artimeticna", var, year
probability = 0.10
z = round(statistics.tinv(probability, n-1), 2)
# print "z is", z
std = math.sqrt(vzorcna_varianca(var, year)[0])
interval1 = asredina - (z * (std / math.sqrt(n)) )
interval2 = asredina + (z * (std / math.sqrt(n)) )
return interval1, interval2
def get_avg_box_width():
box_widths = []
filename = './image/test_bi3.jpg'
image = cvLoadImage(filename, CV_8UC1)
storage = cvCreateMemStorage(0)
input_image = cvCloneImage(image)
# output_image = cvCloneImage(image)
output_image = cvCreateImage(cvGetSize(input_image), 8, 3)
cvCvtColor(input_image, output_image, CV_GRAY2BGR)
count, contours = cvFindContours (input_image, storage, sizeof_CvContour, CV_RETR_CCOMP, CV_CHAIN_APPROX_NONE, cvPoint (0,0))
for contour in contours.hrange():
bbox = cvBoundingRect(contour, 0)
box_width = bbox.width
if 100 > box_width > 10:
box_widths.append(box_width)
# return box_widths
width_mean = mean(box_widths)
width_lmean = lmean(box_widths)
width_stdev = stdev(box_widths)
width_lstdev = lstdev(box_widths)
return (width_mean,width_lmean,width_stdev,width_lstdev)
def get_avg_box_width():
box_widths = []
filename = './image/test_bi3.jpg'
image = cvLoadImage(filename, CV_8UC1)
storage = cvCreateMemStorage(0)
input_image = cvCloneImage(image)
# output_image = cvCloneImage(image)
output_image = cvCreateImage(cvGetSize(input_image), 8, 3)
cvCvtColor(input_image, output_image, CV_GRAY2BGR)
count, contours = cvFindContours(input_image, storage, sizeof_CvContour,
CV_RETR_CCOMP, CV_CHAIN_APPROX_NONE,
cvPoint(0, 0))
for contour in contours.hrange():
bbox = cvBoundingRect(contour, 0)
box_width = bbox.width
if 100 > box_width > 10:
box_widths.append(box_width)
# return box_widths
width_mean = mean(box_widths)
width_lmean = lmean(box_widths)
width_stdev = stdev(box_widths)
width_lstdev = lstdev(box_widths)
return (width_mean, width_lmean, width_stdev, width_lstdev)
def eu_arit_sred(var, year, eu):
s,f = seznam_vzorec(var, year, eu)
asredina = stats.lmean(s)
return asredina
def vzorcna_aritmeticna(var, year, eu=None):
s = seznam_vzorec(var, year, eu)
arit = stats.lmean(s[0])
return arit
def nal_32b(var1, year1, var2, year2):
v1 = seznam_vzorec(var1, year1)[1]
v2 = seznam(var2, year2)[1]
for i in v2.copy():
if not v1.has_key(i):
del(v2[i])
# iz drugega seznama je treba zbrisat tiste vnose, ki jih ni v prvem
temp = dict(v1)
for i in temp:
try:
v2[i]
except KeyError:
v1.__delitem__(i)
temp = dict(v2)
for i in temp:
try:
v1[i]
except KeyError:
v2.__delitem__(i)
n = len(v2)
sez1 = []
for i in range(1,29):
try:
sez1.append(v1['c'+str(i)])
except KeyError:
pass
sez2 = []
for i in range(1,29):
try:
sez2.append(v2['c'+str(i)])
except KeyError:
pass
as1 = stats.lmean(sez1)
as2 = stats.lmean(sez2)
c = 0
d = 0
e = 0
for i in range(0, n):
c = c + (sez1[i] - as1)*(sez2[i] - as2)
d = d + (sez1[i] - as1)**2
e = e + (sez2[i] - as2)**2
# print sez1[i], sez2[i], c, d, e
rxy = round(c / math.sqrt(d*e),4)
texp = (rxy*math.sqrt(n-2)) / math.sqrt(1 - rxy**2)
s = math.sqrt( d / (n - 1))
mm1 = c / d
mm2 = c / e
X = as2
Y = as1
regr1a = Y + mm2*((-1)*X)
regr1b = mm2
regr2a = X + mm1*((-1)*Y)
regr2b = mm1
R = rxy**2
o = s * math.sqrt(1 - R)
return as1, as2, rxy, texp, (regr1a, regr1b), (regr2a, regr2b), R, o
def aritmeticna(var, year):
s = seznam(var, year)
arit = stats.lmean(s[0])
return arit
score = 0 data = s.read(4) if len(data) != 4: print "test failed: invalid score bytes" failed += 1 if failed >= options.failures: print "ERROR: maximum read failures reached" sys.exit(1) else: continue for value, shift in zip(data, [0, 8, 16, 24]): score += ord(value) << shift print "read score: %8d (0x%08X)" % (score, score) scores.append(score) mean = stats.lmean(scores) print "average score: %.2f" % mean out = open(options.output, "w") print >> out, mean if options.samples > 1 and mean != 0: stdev = stats.lstdev(scores) rating = mean / stdev print "standard deviation: %.2f" % stdev print "rating: %.6f" % rating
def run(self, num_trials):
""" Runs the given number of trials.
If num_trials is 1, runs a single trial and graphs the result.
Otherwise, graphs averaged results over all trials.
"""
if num_trials == 1:
self.run_single(-1)
self.graph_single()
return
for trial in range(num_trials):
print "********Running Trial %s**********" % trial
self.run_single(trial)
filename = "plot_%s.gp" % self.file_prefix
gnuplot_file = open(filename, 'w')
gnuplot_file.write("set terminal postscript color 'Helvetica' 14\n")
#gnuplot_file.write("set size .5, .5\n")
gnuplot_file.write("set output 'graphs/%s.ps'\n" % self.file_prefix)
gnuplot_file.write("set xlabel 'Utilization'\n")
gnuplot_file.write("set ylabel 'Response Time (ms)'\n")
gnuplot_file.write("set yrange [0:700]\n")
gnuplot_file.write("set grid ytics\n")
#gnuplot_file.write("set xtics 0.25\n")
extra = ""
gnuplot_file.write("set title 'Effect of Load Probing on Response "
"Time%s'\n" % extra)
#gnuplot_file.write("set key font 'Helvetica,10' left width -5"
# "title 'Probes:Tasks' samplen 2\n")
gnuplot_file.write("set key left\n")
gnuplot_file.write("plot ")
for i, probes_ratio in enumerate(self.probes_ratio_values):
# Aggregate results and write to a file.
# Map of utilization to response times for that utilization.
results = {}
for trial in range(num_trials):
results_filename = ("raw_results/%s_response_time" %
self.get_prefix(trial, probes_ratio))
results_file = open(results_filename, "r")
index = 0
for line in results_file:
values = line.split("\t")
if values[0] == "n":
continue
# Use median response time.
normalized_response_time = float(values[6])
if self.remove_delay:
normalized_response_time -= 3*float(values[9])
utilization = float(values[2])
if utilization not in results:
results[utilization] = []
results[utilization].append(normalized_response_time)
agg_output_filename = ("raw_results/agg_%s_%f" %
(self.file_prefix, probes_ratio))
agg_output_file = open(agg_output_filename, "w")
agg_output_file.write("Utilization\tResponseTime\tStdDev\n")
for utilization in sorted(results.keys()):
avg_response_time = stats.lmean(results[utilization])
std_dev = stats.lstdev(results[utilization])
agg_output_file.write("%f\t%f\t%f\n" %
(utilization, avg_response_time, std_dev))
# Plot aggregated results.
if i > 0:
gnuplot_file.write(', \\\n')
title = "Probes/Tasks = %s" % probes_ratio
if probes_ratio == -1:
title = "Ideal"
gnuplot_file.write(("'%s' using 1:2 title '%s' lc %d lw 4 with l,"
"\\\n") %
(agg_output_filename, title, i))
gnuplot_file.write(("'%s' using 1:2:3 notitle lt %d lw 4 with "
"errorbars") % (agg_output_filename, i))
subprocess.call(["gnuplot", filename])
def run(self, num_trials):
for trial in range(num_trials):
self.run_single(trial)
filename = "plot_final_network_delay.gp"
gnuplot_file = open(filename, 'w')
gnuplot_file.write("set terminal postscript color\n")
gnuplot_file.write("set size 0.5,0.5\n")
gnuplot_file.write("set output 'graphs/final_network_delay.ps'\n")
gnuplot_file.write("set xlabel 'Utilization'\n")
gnuplot_file.write("set ylabel 'Normalized Response Time (ms)'\n")
gnuplot_file.write("set yrange [100:500]\n")
gnuplot_file.write("set grid ytics\n")
gnuplot_file.write("set xtics 0.25\n")
#gnuplot_file.write("set title 'Effect of Network Delay on Response "
# "Time'\n")
gnuplot_file.write("set key font 'Helvetica,10' left samplen 2 invert "
"\n")
gnuplot_file.write("plot ")
for i, (network_delay, probes_ratio) in enumerate(
zip(self.delay_values, self.probes_ratio_values)):
# Aggregate results and write to a file.
# Map of utilization to response times for that utilization.
results = {}
for trial in range(num_trials):
results_filename = ("raw_results/%s_response_time" %
self.get_prefix(trial, network_delay,
probes_ratio))
results_file = open(results_filename, "r")
index = 0
for line in results_file:
values = line.split("\t")
if values[0] == "n":
continue
normalized_response_time = (float(values[3]) -
3*float(values[6]))
utilization = float(values[2])
if utilization not in results:
results[utilization] = []
results[utilization].append(normalized_response_time)
agg_output_filename = ("raw_results/agg_final_delay_%d_%s" %
(network_delay, probes_ratio))
agg_output_file = open(agg_output_filename, "w")
agg_output_file.write("Utilization\tResponseTime\tStdDev\n")
for utilization in sorted(results.keys()):
avg_response_time = stats.lmean(results[utilization])
std_dev = stats.lstdev(results[utilization])
agg_output_file.write("%f\t%f\t%f\n" %
(utilization, avg_response_time, std_dev))
# Plot aggregated results.
if i > 0:
gnuplot_file.write(', \\\n')
title = "%dms" % network_delay
if probes_ratio == 1.0:
title = "Random"
if probes_ratio == -1:
title = "Ideal"
gnuplot_file.write(("'%s' using 1:2 title '%s' lt %d lw 4 with l"
",\\\n") % (agg_output_filename, title, i))
gnuplot_file.write(("'%s' using 1:2:3 notitle lt %d lw 4 with "
"errorbars") % (agg_output_filename, i))
subprocess.call(["gnuplot", filename])
def calculate_ellipse_data(xdata, ydata):
xcenter = stats.lmean(xdata)
xradius = stats.lstdev(xdata)
ycenter = stats.lmean(ydata)
yradius = stats.lstdev(ydata)
return (xcenter, ycenter), (xradius, yradius)
def mean(self):
return float('%1.4f'%stats.lmean(self.datapoints))
def run(self, num_trials):
for trial in range(num_trials):
self.run_single(trial)
filename = "plot_final_network_delay.gp"
gnuplot_file = open(filename, 'w')
gnuplot_file.write("set terminal postscript color\n")
gnuplot_file.write("set size 0.5,0.5\n")
gnuplot_file.write("set output 'graphs/final_network_delay.ps'\n")
gnuplot_file.write("set xlabel 'Utilization'\n")
gnuplot_file.write("set ylabel 'Normalized Response Time (ms)'\n")
gnuplot_file.write("set yrange [100:500]\n")
gnuplot_file.write("set grid ytics\n")
gnuplot_file.write("set xtics 0.25\n")
#gnuplot_file.write("set title 'Effect of Network Delay on Response "
# "Time'\n")
gnuplot_file.write("set key font 'Helvetica,10' left samplen 2 invert "
"\n")
gnuplot_file.write("plot ")
for i, (network_delay, probes_ratio) in enumerate(
zip(self.delay_values, self.probes_ratio_values)):
# Aggregate results and write to a file.
# Map of utilization to response times for that utilization.
results = {}
for trial in range(num_trials):
results_filename = (
"raw_results/%s_response_time" %
self.get_prefix(trial, network_delay, probes_ratio))
results_file = open(results_filename, "r")
index = 0
for line in results_file:
values = line.split("\t")
if values[0] == "n":
continue
normalized_response_time = (float(values[3]) -
3 * float(values[6]))
utilization = float(values[2])
if utilization not in results:
results[utilization] = []
results[utilization].append(normalized_response_time)
agg_output_filename = ("raw_results/agg_final_delay_%d_%s" %
(network_delay, probes_ratio))
agg_output_file = open(agg_output_filename, "w")
agg_output_file.write("Utilization\tResponseTime\tStdDev\n")
for utilization in sorted(results.keys()):
avg_response_time = stats.lmean(results[utilization])
std_dev = stats.lstdev(results[utilization])
agg_output_file.write(
"%f\t%f\t%f\n" % (utilization, avg_response_time, std_dev))
# Plot aggregated results.
if i > 0:
gnuplot_file.write(', \\\n')
title = "%dms" % network_delay
if probes_ratio == 1.0:
title = "Random"
if probes_ratio == -1:
title = "Ideal"
gnuplot_file.write(("'%s' using 1:2 title '%s' lt %d lw 4 with l"
",\\\n") % (agg_output_filename, title, i))
gnuplot_file.write(("'%s' using 1:2:3 notitle lt %d lw 4 with "
"errorbars") % (agg_output_filename, i))
subprocess.call(["gnuplot", filename])
def run(self, num_trials):
""" Runs the given number of trials.
If num_trials is 1, runs a single trial and graphs the result.
Otherwise, graphs averaged results over all trials.
"""
if num_trials == 1:
self.run_single(-1)
self.graph_single()
return
for trial in range(num_trials):
print "********Running Trial %s**********" % trial
self.run_single(trial)
filename = "plot_%s.gp" % self.file_prefix
gnuplot_file = open(filename, 'w')
gnuplot_file.write("set terminal postscript color 'Helvetica' 14\n")
#gnuplot_file.write("set size .5, .5\n")
gnuplot_file.write("set output 'graphs/%s.ps'\n" % self.file_prefix)
gnuplot_file.write("set xlabel 'Utilization'\n")
gnuplot_file.write("set ylabel 'Response Time (ms)'\n")
gnuplot_file.write("set yrange [0:700]\n")
gnuplot_file.write("set grid ytics\n")
#gnuplot_file.write("set xtics 0.25\n")
extra = ""
gnuplot_file.write("set title 'Effect of Load Probing on Response "
"Time%s'\n" % extra)
#gnuplot_file.write("set key font 'Helvetica,10' left width -5"
# "title 'Probes:Tasks' samplen 2\n")
gnuplot_file.write("set key left\n")
gnuplot_file.write("plot ")
for i, probes_ratio in enumerate(self.probes_ratio_values):
# Aggregate results and write to a file.
# Map of utilization to response times for that utilization.
results = {}
for trial in range(num_trials):
results_filename = ("raw_results/%s_response_time" %
self.get_prefix(trial, probes_ratio))
results_file = open(results_filename, "r")
index = 0
for line in results_file:
values = line.split("\t")
if values[0] == "n":
continue
# Use median response time.
normalized_response_time = float(values[6])
if self.remove_delay:
normalized_response_time -= 3 * float(values[9])
utilization = float(values[2])
if utilization not in results:
results[utilization] = []
results[utilization].append(normalized_response_time)
agg_output_filename = ("raw_results/agg_%s_%f" %
(self.file_prefix, probes_ratio))
agg_output_file = open(agg_output_filename, "w")
agg_output_file.write("Utilization\tResponseTime\tStdDev\n")
for utilization in sorted(results.keys()):
avg_response_time = stats.lmean(results[utilization])
std_dev = stats.lstdev(results[utilization])
agg_output_file.write(
"%f\t%f\t%f\n" % (utilization, avg_response_time, std_dev))
# Plot aggregated results.
if i > 0:
gnuplot_file.write(', \\\n')
title = "Probes/Tasks = %s" % probes_ratio
if probes_ratio == -1:
title = "Ideal"
gnuplot_file.write(("'%s' using 1:2 title '%s' lc %d lw 4 with l,"
"\\\n") % (agg_output_filename, title, i))
gnuplot_file.write(("'%s' using 1:2:3 notitle lt %d lw 4 with "
"errorbars") % (agg_output_filename, i))
subprocess.call(["gnuplot", filename])