def insertStats(self, now, vals):
DB.executemany_(
'''insert into statistic
(time, viscosity, w, count, mistakes, type, data, source) values (?, ?, ?, ?, ?, ?, ?, ?)''',
vals)
DB.executemany_(
'insert into mistake (w, target, mistake, count) values (?, ?, ?, ?)',
[(now, k[0], k[1], v)
for k, v in self.typer.getMistakes().items()])
def done(self):
now = time.time()
elapsed, chars, times, mis, mistakes = self.typer.getStats()
assert chars == len(self.text[2])
accuracy = 1.0 - len(filter(None, mis)) / chars
spc = elapsed / chars
viscosity = sum(map(lambda x: ((x-spc)/spc)**2, times)) / chars
DB.execute('insert into result (w,text_id,source,wpm,accuracy,viscosity) values (?,?,?,?,?,?)',
(now, self.text[0], self.text[1], 12.0/spc, accuracy, viscosity))
v2 = DB.fetchone("""select agg_median(wpm),agg_median(acc) from
(select wpm,100.0*accuracy as acc from result order by w desc limit %d)""" % Settings.get('def_group_by'), (0.0, 100.0))
self.result.setText("Last: %.1fwpm (%.1f%%), last 10 average: %.1fwpm (%.1f%%)"
% ((12.0/spc, 100.0*accuracy) + v2))
self.emit(SIGNAL("statsChanged"))
stats = collections.defaultdict(Statistic)
visc = collections.defaultdict(Statistic)
text = self.text[2]
for c, t, m in zip(text, times, mis):
stats[c].append(t, m)
visc[c].append(((t-spc)/spc)**2)
def gen_tup(s, e):
perch = sum(times[s:e])/(e-s)
visc = sum(map(lambda x: ((x-perch)/perch)**2, times[s:e]))/(e-s)
return (text[s:e], perch, len(filter(None, mis[s:e])), visc)
for tri, t, m, v in [gen_tup(i, i+3) for i in xrange(0, chars-2)]:
stats[tri].append(t, m > 0)
visc[tri].append(v)
regex = re.compile(r"(\w|'(?![A-Z]))+(-\w(\w|')*)*")
for w, t, m, v in [gen_tup(*x.span()) for x in regex.finditer(text) if x.end()-x.start() > 3]:
stats[w].append(t, m > 0)
visc[w].append(v)
def type(k):
if len(k) == 1:
return 0
elif len(k) == 3:
return 1
return 2
vals = []
for k, s in stats.iteritems():
v = visc[k].median()
vals.append( (s.median(), v*100.0, now, len(s), s.flawed(), type(k), k) )
is_lesson = DB.fetchone("select discount from source where rowid=?", (None,), (self.text[1], ))[0]
if Settings.get('use_lesson_stats') or not is_lesson:
DB.executemany_('''insert into statistic
(time,viscosity,w,count,mistakes,type,data) values (?,?,?,?,?,?,?)''', vals)
DB.executemany_('insert into mistake (w,target,mistake,count) values (?,?,?,?)',
[(now, k[0], k[1], v) for k, v in mistakes.iteritems()])
if is_lesson:
mins = (Settings.get("min_lesson_wpm"), Settings.get("min_lesson_acc"))
else:
mins = (Settings.get("min_wpm"), Settings.get("min_acc"))
if 12.0/spc < mins[0] or accuracy < mins[1]/100.0:
self.setText(self.text)
elif not is_lesson and Settings.get('auto_review'):
ws = filter(lambda x: x[5] == 2, vals)
if len(ws) == 0:
self.emit(SIGNAL("wantText"))
return
ws.sort(key=lambda x: (x[4],x[0]), reverse=True)
i = 0
while ws[i][4] != 0:
i += 1
i += (len(ws) - i) // 4
self.emit(SIGNAL("wantReview"), map(lambda x:x[6], ws[0:i]))
else:
self.emit(SIGNAL("wantText"))
def insertStats(self, now, vals):
DB.executemany_('''insert into statistic
(time, viscosity, w, count, mistakes, type, data, source) values (?, ?, ?, ?, ?, ?, ?, ?)''', vals)
DB.executemany_('insert into mistake (w, target, mistake, count) values (?, ?, ?, ?)',
[(now, k[0], k[1], v) for k, v in self.typer.getMistakes().iteritems()])
def done(self):
now = time.time()
elapsed, chars, times, mis, mistakes = self.typer.getStats()
assert chars == len(self.text[2])
accuracy = 1.0 - len(filter(None, mis)) / chars
spc = elapsed / chars
viscosity = sum(map(lambda x: ((x - spc) / spc)**2, times)) / chars
DB.execute(
'insert into result (w,text_id,source,wpm,accuracy,viscosity) values (?,?,?,?,?,?)',
(now, self.text[0], self.text[1], 12.0 / spc, accuracy, viscosity))
v2 = DB.fetchone(
"""select agg_median(wpm),agg_median(acc) from
(select wpm,100.0*accuracy as acc from result order by w desc limit %d)"""
% Settings.get('def_group_by'), (0.0, 100.0))
self.result.setText(
"Last: %.1fwpm (%.1f%%), last 10 average: %.1fwpm (%.1f%%)" %
((12.0 / spc, 100.0 * accuracy) + v2))
self.emit(SIGNAL("statsChanged"))
stats = collections.defaultdict(Statistic)
visc = collections.defaultdict(Statistic)
text = self.text[2]
for c, t, m in zip(text, times, mis):
stats[c].append(t, m)
visc[c].append(((t - spc) / spc)**2)
def gen_tup(s, e):
perch = sum(times[s:e]) / (e - s)
visc = sum(map(lambda x:
((x - perch) / perch)**2, times[s:e])) / (e - s)
return (text[s:e], perch, len(filter(None, mis[s:e])), visc)
for tri, t, m, v in [gen_tup(i, i + 3) for i in xrange(0, chars - 2)]:
stats[tri].append(t, m > 0)
visc[tri].append(v)
regex = re.compile(r"(\w|'(?![A-Z]))+(-\w(\w|')*)*")
for w, t, m, v in [
gen_tup(*x.span()) for x in regex.finditer(text)
if x.end() - x.start() > 3
]:
stats[w].append(t, m > 0)
visc[w].append(v)
def type(k):
if len(k) == 1:
return 0
elif len(k) == 3:
return 1
return 2
vals = []
for k, s in stats.iteritems():
v = visc[k].median()
vals.append(
(s.median(), v * 100.0, now, len(s), s.flawed(), type(k), k))
is_lesson = DB.fetchone("select discount from source where rowid=?",
(None, ), (self.text[1], ))[0]
if Settings.get('use_lesson_stats') or not is_lesson:
DB.executemany_(
'''insert into statistic
(time,viscosity,w,count,mistakes,type,data) values (?,?,?,?,?,?,?)''',
vals)
DB.executemany_(
'insert into mistake (w,target,mistake,count) values (?,?,?,?)',
[(now, k[0], k[1], v) for k, v in mistakes.iteritems()])
if is_lesson:
mins = (Settings.get("min_lesson_wpm"),
Settings.get("min_lesson_acc"))
else:
mins = (Settings.get("min_wpm"), Settings.get("min_acc"))
if 12.0 / spc < mins[0] or accuracy < mins[1] / 100.0:
self.setText(self.text)
elif not is_lesson and Settings.get('auto_review'):
ws = filter(lambda x: x[5] == 2, vals)
if len(ws) == 0:
self.emit(SIGNAL("wantText"))
return
ws.sort(key=lambda x: (x[4], x[0]), reverse=True)
i = 0
while ws[i][4] != 0:
i += 1
i += (len(ws) - i) // 4
self.emit(SIGNAL("wantReview"), map(lambda x: x[6], ws[0:i]))
else:
self.emit(SIGNAL("wantText"))
