def process_timestamp(self, elem):
if self._skip_revision:
return
revision_time = mwlib.ts2dt(elem.text)
if ((self.detailed_end and revision_time > self.detailed_end) or
(self.detailed_start and revision_time < self.detailed_start)):
self._skip_revision = True
else:
self._date = revision_time
del revision_time
def process_timestamp(self, elem):
if self._skip_revision:
return
revision_time = mwlib.ts2dt(elem.text)
if ((self.detailed_end and revision_time > self.detailed_end) or
(self.detailed_start and revision_time < self.detailed_start)):
self._skip_revision = True
else:
self._date = revision_time
del revision_time
def process_timestamp(self, elem):
if self._skip_revision: return
self._time = elem.text
# Used only because there are two id tags. We're intrested in the
# id child of contributor. As timestamp is before contributor is good
# to clear self._id, self._username now.
self._id = None
self._username = None
revision_time = mwlib.ts2dt(elem.text)
if (self.time_end and revision_time > self.time_end):
self._skip_revision = True
def process_timestamp(self, elem):
if self._skip_revision: return
self._time = elem.text
# Used only because there are two id tags. We're intrested in the
# id child of contributor. As timestamp is before contributor is good
# to clear self._id, self._username now.
self._id = None
self._username = None
revision_time = mwlib.ts2dt(elem.text)
if (self.time_end and revision_time > self.time_end):
self._skip_revision = True
def process_timestamp(self, elem):
if self._skip_revision:
return
revision_time = mwlib.ts2dt(elem.text)
if ((self.time_end and revision_time > self.time_end) or
(self.time_start and revision_time < self.time_start)):
self._skip_revision = True
else:
self._time = revision_time
del revision_time
# Used only because there are two id tags. We're intrested in the
# id child of contributor. As timestamp is before contributor is good
# to clear self._id, self._username, self._ip now.
self.delattr(("_id", "_username", "_ip"))
def process_timestamp(self, elem):
if self._skip_revision:
return
revision_time = mwlib.ts2dt(elem.text)
if ((self.time_end and revision_time > self.time_end)
or (self.time_start and revision_time < self.time_start)):
self._skip_revision = True
else:
self._time = revision_time
del revision_time
# Used only because there are two id tags. We're intrested in the
# id child of contributor. As timestamp is before contributor is good
# to clear self._id, self._username, self._ip now.
self.delattr(("_id", "_username", "_ip"))
def first_time(self):
if self.data[7] == 0:
api_base = 'http://%s.wikipedia.org/w/api.php' % self.lang
options = {
'action': 'query',
'list': 'usercontribs',
'ucuser': smart_str(self.user),
'ucdir': 'newer',
'uclimit': 1,
'format': 'json'
}
url = api_base + '?' + urllib.urlencode(options)
result = simplejson.load(urllib.urlopen(url))
try:
dtime = mwlib.ts2dt(result["query"]["usercontribs"][0]["timestamp"])
self.data[7] = int(time.mktime(dtime.timetuple()))
except (IndexError, KeyError):
logging.warn("Error while fetching firstedit %s", url)
self.data[7] = self.current_time
self.deleted = True
return datetime.fromtimestamp(self.data[7])
def first_time(self):
if self.data[7] == 0:
api_base = 'http://%s.wikipedia.org/w/api.php' % self.lang
options = {
'action': 'query',
'list': 'usercontribs',
'ucuser': smart_str(self.user),
'ucdir': 'newer',
'uclimit': 1,
'format': 'json'
}
url = api_base + '?' + urllib.urlencode(options)
result = simplejson.load(urllib.urlopen(url))
try:
dtime = mwlib.ts2dt(
result["query"]["usercontribs"][0]["timestamp"])
self.data[7] = int(time.mktime(dtime.timetuple()))
except (IndexError, KeyError):
logging.warn("Error while fetching firstedit %s", url)
self.data[7] = self.current_time
self.deleted = True
return datetime.fromtimestamp(self.data[7])
def save(self):
if self._text is None: # difflib doesn't like NoneType
self._text = ""
if self.clean:
self._text = self.textcleaner.clean_all(self._text)
text_words = len(self.rwords.findall(self._text))
prev_words = len(self.rwords.findall(self._prev_text))
if text_words < 1000 or text_words <= 2 * prev_words:
diff = _diff_text(self._prev_text,
self._text,
timeout=self.diff_timeout)[0]
self.pywc.parse_col(diff)
if not self.data.has_key(self._type):
self.data[self._type] = {}
current = self.data[self._type]
date = mwlib.ts2dt(self._date)
date_str = date.strftime("%Y/%m/%d")
tmp = {"date": date_str,
"qmarks": self.pywc._qmarks,
"unique": len(self.pywc._unique),
"dic": self.pywc._dic,
"sixltr": self.pywc._sixltr,
"total": self.pywc._total}
for x in self.pywc.categories:
tmp[x] = self.pywc._results[x]
if not current.has_key(date_str):
current[date_str] = tmp
current[date_str]["edits"] = 1
else:
for elem in tmp:
if elem != "date":
current[date_str][elem] += tmp[elem]
current[date_str]["edits"] += 1
del tmp
else:
logging.warn("Revert detected: skipping... (%s)", self._date)
self._prev_text = self._text
def main():
import optparse
from sonet.lib import SonetOption
p = optparse.OptionParser(
usage="usage: %prog [options] input_file output_file",
option_class=SonetOption)
p.add_option('-v', action="store_true", dest="verbose", default=False,
help="Verbose output")
p.add_option('-i', '--ignorecols', action="store", dest="ignorecols",
help="Columns numbers of the source file to ignore"
"(comma separated and starting from 0)")
p.add_option('-I', '--id', action="store", dest="id_col", type="int",
help="Id column number (starting from 0)", default=0)
p.add_option('-o', '--onlycols', action="store", dest="onlycols",
help="Select only this set of columns" + \
"(comma separated and starting from 0)")
p.add_option('-p', '--percentages', action="store_true", dest="perc",
help="Use percentages instead of absolute value")
p.add_option('-w', '--window', action="store", dest="window", type=int,
help="Collapse days")
p.add_option('-S', '--sliding', action="store", dest="smooth", type=int,
help="Sliding window")
p.add_option('--exclude-less-than', action="store",
dest="excludelessthan", type=int,
help="Exclude lines with totals (or dic if -d option is used) " + \
"smaller than this parameter")
p.add_option('--exclude-more-than', action="store",
dest="excludemorethan", type=int,
help="Exclude lines with totals (or dic if -d option is used) " + \
"greater than this parameter")
p.add_option('-s', '--start', action="store",
dest='start', type="yyyymmdd", metavar="YYYYMMDD", default=None,
help="Look for revisions starting from this date")
p.add_option('-e', '--end', action="store",
dest='end', type="yyyymmdd", metavar="YYYYMMDD", default=None,
help="Look for revisions until this date")
p.add_option('-d', '--dic', action="store_true", dest="dic", default=False,
help="Calculate percentage over dic column instead of total")
opts, files = p.parse_args()
if len(files) != 2:
p.error("Wrong parameters")
if opts.verbose:
logging.basicConfig(stream=sys.stderr,
level=logging.DEBUG,
format='%(asctime)s %(levelname)s %(message)s',
datefmt='%Y-%m-%d %H:%M:%S')
csv_reader = csv.reader(open(files[0]), delimiter="\t")
onlycols = None
ignorecols = None
if opts.onlycols:
onlycols = [int(x) for x in opts.onlycols.split(",")]
if opts.ignorecols:
ignorecols = [int(x) for x in opts.ignorecols.split(",")]
# content contains all the csv file
content = [row for row in csv_reader]
# CSV header, only of interesting columns
header = [x for x in _gen_data(content[0], opts.id_col,
ignorecols, onlycols)]
# Creates a matrix (list) with percentages of the occurrencies of every
# category. Don't count id, total, text, ignore columns. If onlycols is set
# consider only them.
mat = []
timestamps = []
totals = []
tot_index = -2
if opts.dic:
tot_index = -4
for line in content[1:]:
#filter only pages with total (or dic is -d) greater or smaller than X
if opts.excludemorethan:
if float(line[tot_index]) > opts.excludemorethan:
continue
if opts.excludelessthan:
if float(line[tot_index]) < opts.excludelessthan:
continue
mat.append([x for x in _gen_data(line, opts.id_col,
ignorecols, onlycols)])
totals.append(float(line[tot_index]))
timestamps.append(ts2dt(line[opts.id_col]))
del content
mat = np.array(mat, dtype=np.float).transpose()
logging.info("Input file read. Ready to plot")
pdf_pag = PdfPages(files[1])
with Timr("Plotting"):
for i, series in enumerate(mat):
logging.info("Plotting page %d", i + 1)
# Don't plot zeros and skip zero revisions!
#ser = [x for x in series if x != 0]
#time = [x for k, x in enumerate(timestamps) if series[k] != 0]
#tot = [x for k, x in enumerate(totals) if series[k] != 0]
ser = [x for k, x in enumerate(series) \
if (not opts.start or timestamps[k] >= opts.start) and \
(not opts.end or timestamps[k] <= opts.end)]
time = [x for k, x in enumerate(timestamps) \
if (not opts.start or x >= opts.start) and \
(not opts.end or x <= opts.end)]
tot = [x for k, x in enumerate(totals) \
if (not opts.start or timestamps[k] >= opts.start) and \
(not opts.end or timestamps[k] <= opts.end)]
if opts.smooth and len(time) and len(ser) and len(tot):
time, ser, tot = smooth_values(time, ser, tot,
opts.smooth)
if opts.window and len(time) and len(ser) and len(tot):
time, ser, tot = collapse_values(time, ser, tot,
opts.window)
mean = float(sum(series)) / len(series)
#rel_mean is the mean for the period [opts.end, opts.start]
rel_mean = float(sum(ser)) / len(ser)
if opts.perc:
try:
mean = float(sum(series)) / sum(totals)
rel_mean = float(sum(ser)) / sum(tot)
except ZeroDivisionError:
mean = 0
rel_mean = 0
# Calculate percentages
ser = [calc_perc(x, tot[k]) for k, x in enumerate(ser)]
# Set axis limit 0-1 IS IT GOOD OR BAD?
#axis.set_ylim(0, 1)
plt.ylabel("%")
first_time = time[0].date()
last_time = time[-1].date()
plt.clf()
plt.subplots_adjust(bottom=0.25)
plt.xticks(rotation=90)
fig = plt.gcf()
fig.set_size_inches(11.7, 8.3)
axis = plt.gca()
axis.xaxis.set_major_formatter(md.DateFormatter('%Y-%m-%d'))
axis.set_xlim(matplotlib.dates.date2num(first_time),
matplotlib.dates.date2num(last_time))
if last_time - first_time < timedelta(days=30):
axis.xaxis.set_major_locator(md.DayLocator(interval=1))
axis.xaxis.set_minor_locator(md.DayLocator(interval=1))
else:
axis.xaxis.set_minor_locator(md.MonthLocator(interval=1))
#auto_loc = md.AutoDateLocator(minticks=8, maxticks=12, interval_multiples=True)
#auto_loc.intervald[md.MONTHLY] = [6]
rule = md.rrulewrapper(md.MONTHLY, interval=4)
auto_loc = md.RRuleLocator(rule)
axis.xaxis.set_major_locator(auto_loc)
axis.tick_params(labelsize='x-small')
plt.xlabel("Revisions Timestamp")
if len(time) and len(ser):
if opts.window:
time = [t.date() for t in time]
logging.info("Mean: %f", mean)
logging.info("Relative Mean: %f", rel_mean)
if header[i] == "negemo" or header[i] == "posemo":
print ser # ONLY FOR TESTING, FIXME WHEN FINISHED
plt.plot(matplotlib.dates.date2num(time), ser, "b.-")
plt.axhline(y=mean, color="r")
plt.title("%s - Mean: %.5f - Relative mean: %.5f" % (header[i], round(mean, 5), round(rel_mean, 5)))
pdf_pag.savefig()
pdf_pag.close()
def main():
import optparse
from sonet.lib import SonetOption
p = optparse.OptionParser(
usage="usage: %prog [options] input_file output_file",
option_class=SonetOption)
p.add_option('-v',
action="store_true",
dest="verbose",
default=False,
help="Verbose output")
p.add_option('-i',
'--ignorecols',
action="store",
dest="ignorecols",
help="Columns numbers of the source file to ignore"
"(comma separated and starting from 0)")
p.add_option('-I',
'--id',
action="store",
dest="id_col",
type="int",
help="Id column number (starting from 0)",
default=0)
p.add_option('-o', '--onlycols', action="store", dest="onlycols",
help="Select only this set of columns" + \
"(comma separated and starting from 0)")
p.add_option('-p',
'--percentages',
action="store_true",
dest="perc",
help="Use percentages instead of absolute value")
p.add_option('-w',
'--window',
action="store",
dest="window",
type=int,
help="Collapse days")
p.add_option('-S',
'--sliding',
action="store",
dest="smooth",
type=int,
help="Sliding window")
p.add_option('--exclude-less-than', action="store",
dest="excludelessthan", type=int,
help="Exclude lines with totals (or dic if -d option is used) " + \
"smaller than this parameter")
p.add_option('--exclude-more-than', action="store",
dest="excludemorethan", type=int,
help="Exclude lines with totals (or dic if -d option is used) " + \
"greater than this parameter")
p.add_option('-s',
'--start',
action="store",
dest='start',
type="yyyymmdd",
metavar="YYYYMMDD",
default=None,
help="Look for revisions starting from this date")
p.add_option('-e',
'--end',
action="store",
dest='end',
type="yyyymmdd",
metavar="YYYYMMDD",
default=None,
help="Look for revisions until this date")
p.add_option('-d',
'--dic',
action="store_true",
dest="dic",
default=False,
help="Calculate percentage over dic column instead of total")
opts, files = p.parse_args()
if len(files) != 2:
p.error("Wrong parameters")
if opts.verbose:
logging.basicConfig(stream=sys.stderr,
level=logging.DEBUG,
format='%(asctime)s %(levelname)s %(message)s',
datefmt='%Y-%m-%d %H:%M:%S')
csv_reader = csv.reader(open(files[0]), delimiter="\t")
onlycols = None
ignorecols = None
if opts.onlycols:
onlycols = [int(x) for x in opts.onlycols.split(",")]
if opts.ignorecols:
ignorecols = [int(x) for x in opts.ignorecols.split(",")]
# content contains all the csv file
content = [row for row in csv_reader]
# CSV header, only of interesting columns
header = [
x for x in _gen_data(content[0], opts.id_col, ignorecols, onlycols)
]
# Creates a matrix (list) with percentages of the occurrencies of every
# category. Don't count id, total, text, ignore columns. If onlycols is set
# consider only them.
mat = []
timestamps = []
totals = []
tot_index = -2
if opts.dic:
tot_index = -4
for line in content[1:]:
#filter only pages with total (or dic is -d) greater or smaller than X
if opts.excludemorethan:
if float(line[tot_index]) > opts.excludemorethan:
continue
if opts.excludelessthan:
if float(line[tot_index]) < opts.excludelessthan:
continue
mat.append(
[x for x in _gen_data(line, opts.id_col, ignorecols, onlycols)])
totals.append(float(line[tot_index]))
timestamps.append(ts2dt(line[opts.id_col]))
del content
mat = np.array(mat, dtype=np.float).transpose()
logging.info("Input file read. Ready to plot")
pdf_pag = PdfPages(files[1])
with Timr("Plotting"):
for i, series in enumerate(mat):
logging.info("Plotting page %d", i + 1)
# Don't plot zeros and skip zero revisions!
#ser = [x for x in series if x != 0]
#time = [x for k, x in enumerate(timestamps) if series[k] != 0]
#tot = [x for k, x in enumerate(totals) if series[k] != 0]
ser = [x for k, x in enumerate(series) \
if (not opts.start or timestamps[k] >= opts.start) and \
(not opts.end or timestamps[k] <= opts.end)]
time = [x for k, x in enumerate(timestamps) \
if (not opts.start or x >= opts.start) and \
(not opts.end or x <= opts.end)]
tot = [x for k, x in enumerate(totals) \
if (not opts.start or timestamps[k] >= opts.start) and \
(not opts.end or timestamps[k] <= opts.end)]
if opts.smooth and len(time) and len(ser) and len(tot):
time, ser, tot = smooth_values(time, ser, tot, opts.smooth)
if opts.window and len(time) and len(ser) and len(tot):
time, ser, tot = collapse_values(time, ser, tot, opts.window)
mean = float(sum(series)) / len(series)
#rel_mean is the mean for the period [opts.end, opts.start]
rel_mean = float(sum(ser)) / len(ser)
if opts.perc:
try:
mean = float(sum(series)) / sum(totals)
rel_mean = float(sum(ser)) / sum(tot)
except ZeroDivisionError:
mean = 0
rel_mean = 0
# Calculate percentages
ser = [calc_perc(x, tot[k]) for k, x in enumerate(ser)]
# Set axis limit 0-1 IS IT GOOD OR BAD?
#axis.set_ylim(0, 1)
plt.ylabel("%")
first_time = time[0].date()
last_time = time[-1].date()
plt.clf()
plt.subplots_adjust(bottom=0.25)
plt.xticks(rotation=90)
fig = plt.gcf()
fig.set_size_inches(11.7, 8.3)
axis = plt.gca()
axis.xaxis.set_major_formatter(md.DateFormatter('%Y-%m-%d'))
axis.set_xlim(matplotlib.dates.date2num(first_time),
matplotlib.dates.date2num(last_time))
if last_time - first_time < timedelta(days=30):
axis.xaxis.set_major_locator(md.DayLocator(interval=1))
axis.xaxis.set_minor_locator(md.DayLocator(interval=1))
else:
axis.xaxis.set_minor_locator(md.MonthLocator(interval=1))
#auto_loc = md.AutoDateLocator(minticks=8, maxticks=12, interval_multiples=True)
#auto_loc.intervald[md.MONTHLY] = [6]
rule = md.rrulewrapper(md.MONTHLY, interval=4)
auto_loc = md.RRuleLocator(rule)
axis.xaxis.set_major_locator(auto_loc)
axis.tick_params(labelsize='x-small')
plt.xlabel("Revisions Timestamp")
if len(time) and len(ser):
if opts.window:
time = [t.date() for t in time]
logging.info("Mean: %f", mean)
logging.info("Relative Mean: %f", rel_mean)
if header[i] == "negemo" or header[i] == "posemo":
print ser # ONLY FOR TESTING, FIXME WHEN FINISHED
plt.plot(matplotlib.dates.date2num(time), ser, "b.-")
plt.axhline(y=mean, color="r")
plt.title("%s - Mean: %.5f - Relative mean: %.5f" %
(header[i], round(mean, 5), round(rel_mean, 5)))
pdf_pag.savefig()
pdf_pag.close()
def process_timestamp(self, elem):
if self._skip:
return
self._date = ts2dt(elem.text)
def process_timestamp(self, elem):
if self._skip:
return
self._date = ts2dt(elem.text)
