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[tor-commits] [metrics-web/release] Remove unused code from the censorship detector.
commit 4a30e7a9fbdded5318506302543b5d5e07571670
Author: Karsten Loesing <karsten.loesing@xxxxxxx>
Date: Mon Mar 12 17:49:40 2018 +0100
Remove unused code from the censorship detector.
---
src/main/python/clients/detector.py | 199 +-----------------------------------
1 file changed, 2 insertions(+), 197 deletions(-)
diff --git a/src/main/python/clients/detector.py b/src/main/python/clients/detector.py
index 6cf1c7d..3d17bf0 100644
--- a/src/main/python/clients/detector.py
+++ b/src/main/python/clients/detector.py
@@ -60,8 +60,6 @@ import country_info
# write utf8 to file
import codecs
-days = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
-
def get_country_name_from_cc(country_code):
if (country_code.lower() in country_info.countries):
return country_info.countries[country_code.lower()]
@@ -208,145 +206,6 @@ def make_tendencies_minmax(l, INTERVAL = 1):
## print minx[-1], maxx[-1]
return minx, maxx
-# Makes pretty plots
-def raw_plot(series, minc, maxc, labels, xtitle):
- assert len(xtitle) == 3
- fname, stitle, slegend = xtitle
-
- font = {'family' : 'Bitstream Vera Sans',
- 'weight' : 'normal',
- 'size' : 8}
- matplotlib.rc('font', **font)
-
- ylim( (-max(series)*0.1, max(series)*1.1) )
- plot(labels, series, linewidth=1.0, label="Users")
-
- wherefill = []
- for mm,mx in zip(minc, maxc):
- wherefill += [not (mm == None and mx == None)]
- assert mm < mx or (mm == None and mx == None)
-
- fill_between(labels, minc, maxc, where=wherefill, color="gray", label="Prediction")
-
- vdown = []
- vup = []
- for i,v in enumerate(series):
- if minc[i] != None and v < minc[i]:
- vdown += [v]
- vup += [None]
- elif maxc[i] != None and v > maxc[i]:
- vdown += [None]
- vup += [v]
- else:
- vup += [None]
- vdown += [None]
-
- plot(labels, vdown, 'o', ms=10, lw=2, alpha=0.5, mfc='orange', label="Downturns")
- plot(labels, vup, 'o', ms=10, lw=2, alpha=0.5, mfc='green', label="Upturns")
-
- legend(loc=2)
-
- xlabel('Time (days)')
- ylabel('Users')
- title(stitle)
- grid(True)
- F = gcf()
-
- F.set_size_inches(10,5)
- F.savefig(fname, format="png", dpi = (150))
- close()
-
-def absolute_plot(series, minc, maxc, labels,INTERVAL, xtitle):
- in_minc = []
- in_maxc = []
- for i, v in enumerate(series):
- if i > 0 and i - INTERVAL >= 0 and series[i] != None and series[i-INTERVAL] != None and series[i-INTERVAL] != 0 and minc[i]!= None and maxc[i]!= None:
- in_minc += [minc[i] * poisson.ppf(1-0.9999, series[i-INTERVAL])]
- in_maxc += [maxc[i] * poisson.ppf(0.9999, series[i-INTERVAL])]
- if not in_minc[-1] < in_maxc[-1]:
- print in_minc[-1], in_maxc[-1], series[i-INTERVAL], minc[i], maxc[i]
- assert in_minc[-1] < in_maxc[-1]
- else:
- in_minc += [None]
- in_maxc += [None]
- raw_plot(series, in_minc, in_maxc, labels, xtitle)
-
-"""Return the number of downscores and upscores of a time series
-'series', given tendencies 'minc' and 'maxc' for the time interval
-'INTERVAL'.
-
-If 'scoring_interval' is specifed we only consider upscore/downscore
-that happened in the latest 'scoring_interval' days.
-"""
-def censor_score(series, minc, maxc, INTERVAL, scoring_interval=None):
- upscore = 0
- downscore = 0
-
- if scoring_interval is None:
- scoring_interval = len(series)
- assert(len(series) >= scoring_interval)
-
- for i, v in enumerate(series):
- if i > 0 and i - INTERVAL >= 0 and series[i] != None and series[i-INTERVAL] != None and series[i-INTERVAL] != 0 and minc[i]!= None and maxc[i]!= None:
- in_minc = minc[i] * poisson.ppf(1-0.9999, series[i-INTERVAL])
- in_maxc = maxc[i] * poisson.ppf(0.9999, series[i-INTERVAL])
- if (i >= (len(series) - scoring_interval)):
- downscore += 1 if minc[i] != None and v < in_minc else 0
- upscore += 1 if maxc[i] != None and v > in_maxc else 0
-
- return downscore, upscore
-
-def plot_target(tss, TARGET, xtitle, minx, maxx, DAYS=365, INTERV = 7):
- ctarget = tss.get_country_series(TARGET)
- c = n_day_rel(ctarget, INTERV)
- absolute_plot(ctarget[-DAYS:], minx[-DAYS:], maxx[-DAYS:], tss.all_dates[-DAYS:],INTERV, xtitle = xtitle)
-
-def write_censorship_report_prologue(report_file, dates, notification_period):
- if (notification_period == 1):
- date_str = "%s" % (dates[-1]) # no need for date range if it's just one day
- else:
- date_str = "%s to %s" % (dates[-notification_period], dates[-1])
-
- prologue = "=======================\n"
- prologue += "Automatic Censorship Report for %s\n" % (date_str)
- prologue += "=======================\n\n"
- report_file.write(prologue)
-
-## Make a league table of censorship + nice graphs
-def plot_all(tss, minx, maxx, INTERV, DAYS=None, rdir="img"):
- rdir = os.path.realpath(rdir)
- if not os.path.exists(rdir) or not os.path.isdir(rdir):
- print "ERROR: %s does not exist or is not a directory." % rdir
- return
-
- summary_file = file(os.path.join(rdir, "summary.txt"), "w")
-
- if DAYS == None:
- DAYS = 6*31
-
- s = tss.get_largest(200)
- scores = []
- for num, li in s:
- print ".",
- ds,us = censor_score(tss.get_country_series(li)[-DAYS:], minx[-DAYS:], maxx[-DAYS:], INTERV)
- # print ds, us
- scores += [(ds,num, us, li)]
- scores.sort()
- scores.reverse()
- s = "\n=======================\n"
- s+= "Report for %s to %s\n" % (tss.all_dates[-DAYS], tss.all_dates[-1])
- s+= "=======================\n"
- print s
- summary_file.write(s)
- for a,nx, b,c in scores:
- if a > 0:
- s = "%s -- down: %2d (up: %2d affected: %s)" % (c, a, b, nx)
- print s
- summary_file.write(s + "\n")
- xtitle = (os.path.join(rdir, "%03d-%s-censor.png" % (a,c)), "Tor report for %s -- down: %2d (up: %2d affected: %s)" % (c, a, b, nx),"")
- plot_target(tss, c,xtitle, minx, maxx, DAYS, INTERV)
- summary_file.close()
-
"""Write a CSV report on the minimum/maximum users of each country per date."""
def write_all(tss, minc, maxc, RANGES_FILE, INTERVAL=7):
ranges_file = file(RANGES_FILE, "w")
@@ -368,70 +227,16 @@ def write_all(tss, minc, maxc, RANGES_FILE, INTERVAL=7):
ranges_file.write("%s,%s,%s,%s\n" % (tss.all_dates[i], c, minv, maxv))
ranges_file.close()
-"""Return a URL that points to a graph in metrics.tpo that displays
-the number of direct Tor users in country 'country_code', for a
-'period'-days period.
-
-Let's hope that the metrics.tpo URL scheme doesn't change often.
-"""
-def get_tor_usage_graph_url_for_cc_and_date(country_code, dates, period):
- url = "https://metrics.torproject.org/users.html?graph=userstats-relay-country&start=%s&end=%s&country=%s&events=on#userstats-relay-country\n"; % \
- (dates[-period], dates[-1], country_code)
- return url
-
-"""Write a file containing a short censorship report over the last
-'notification_period' days.
-"""
-def write_ml_report(tss, minx, maxx, INTERV, DAYS, notification_period=None):
- if notification_period is None:
- notification_period = DAYS
-
- report_file = codecs.open('short_censorship_report.txt', 'w', 'utf-8')
- file_prologue_written = False
-
- s = tss.get_largest(None) # no restrictions, get 'em all.
- scores = []
- for num, li in s:
- ds,us = censor_score(tss.get_country_series(li)[-DAYS:], minx[-DAYS:], maxx[-DAYS:], INTERV, notification_period)
- scores += [(ds,num, us, li)]
- scores.sort()
- scores.reverse()
-
- for downscores,users_n,upscores,country_code in scores:
- if (downscores > 0) or (upscores > 0):
- if not file_prologue_written:
- write_censorship_report_prologue(report_file, tss.all_dates, notification_period)
- file_prologue_written = True
-
- if ((upscores > 0) and (downscores == 0)):
- s = "We detected an unusual spike of Tor users in %s (%d upscores, %d users):\n" % \
- (get_country_name_from_cc(country_code), upscores, users_n)
- else:
- s = "We detected %d potential censorship events in %s (users: %d, upscores: %d):\n" % \
- (downscores, get_country_name_from_cc(country_code), users_n, upscores)
-
- # Also give out a link for the appropriate usage graph for a 90-days period.
- s += get_tor_usage_graph_url_for_cc_and_date(country_code, tss.all_dates, 90)
-
- report_file.write(s + "\n")
-
- report_file.close()
-
# INTERV is the time interval to model connection rates;
# consider maximum DAYS days back.
def detect(CSV_FILE = "userstats-detector.csv",
- RANGES_FILE = "userstats-ranges.csv", GRAPH_DIR = "img",
- INTERV = 7, DAYS = 6 * 31, REPORT = True):
+ RANGES_FILE = "userstats-ranges.csv",
+ INTERV = 7, DAYS = 6 * 31):
tss = torstatstore(CSV_FILE)
l = tss.get_largest_locations(50)
minx, maxx = make_tendencies_minmax(l, INTERV)
- #plot_all(tss, minx, maxx, INTERV, DAYS, rdir=GRAPH_DIR)
write_all(tss, minx, maxx, RANGES_FILE, INTERV)
- if REPORT:
- # Make our short report; only consider events of the last day
- write_ml_report(tss, minx, maxx, INTERV, DAYS, 1)
-
def main():
detect()
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