def cleanup(self):
day = 24 * 60 * 60
now = time.time()
q = []
self.progress_.show()
for grp, lim in [(30.0, Settings.get('group_month')),
(7.0, Settings.get('group_week')),
(1.0, Settings.get('group_day'))]:
w = now - day * lim
g = grp * day
q.extend(
DB.fetchall('''
select avg(w), data, type, agg_mean(time, count), sum(count), sum(mistakes), agg_median(viscosity)
from statistic where w <= %f
group by data, type, cast(w/%f as int)''' % (w, g)))
self.progress_.inc()
DB.execute('''delete from statistic where w <= ?''', (w, ))
self.progress_.inc()
DB.executemany(
'''insert into statistic (w, data, type, time, count, mistakes, viscosity)
VALUES (?, ?, ?, ?, ?, ?, ?)''', q)
self.progress_.inc()
DB.commit()
DB.execute('vacuum')
self.progress_.inc()
self.progress_.hide()
def removeSelected(self):
cats, texts = self.getSelected()
DB.executemany("delete from text where rowid = ?",
map(lambda x:(x, ), texts))
self.removeUnused()
self.update()
DB.commit()
def removeSelected(self):
cats, texts = self.getSelected()
DB.executemany("delete from text where rowid = ?",
map(lambda x: (x, ), texts))
self.removeUnused()
self.update()
DB.commit()
def cleanup(self):
s_in_day = 24*60*60
now = time.time()
pending = []
for idx, grp, lim in [
(1, 30, Settings.get("group_month")),
(2, 7, Settings.get("group_week")),
(3, 1, Settings.get("group_day")),
]:
minimum = now - s_in_day * lim
binsize = s_in_day * grp
pending.extend(DB.fetchall(f"""
select avg(w), data, type, agg_mean(time, count), sum(count), sum(mistakes),
agg_median(viscosity)
from statistic where w <= {minimum}
group by data, type, cast(w/{binsize} as int)"""))
self.progressbar.set_fraction(idx/5)
DB.executemany("""insert into statistic (w, data, type, time, count, mistakes, viscosity)
values (?,?,?,?,?,?,?)""", pending)
self.progressbar.set_fraction(4/5)
# FIXME vacuum not supported
# DB.execute("vacuum")
self.progressbar.set_fraction(5/5)
DB.commit()
self.progressbar.set_fraction(0)
def cleanup(self):
day = 24*60*60
now = time.time()
q = []
self.progress_.show()
for grp, lim in [(30.0, Settings.get('group_month')),
(7.0, Settings.get('group_week')),
(1.0, Settings.get('group_day'))]:
w = now - day*lim
g = grp * day
q.extend(DB.fetchall('''
select avg(w), data, type, agg_mean(time, count), sum(count), sum(mistakes), agg_median(viscosity)
from statistic where w <= %f
group by data, type, cast(w/%f as int)''' % (w, g)))
self.progress_.inc()
DB.execute('''delete from statistic where w <= ?''', (w, ))
self.progress_.inc()
DB.executemany('''insert into statistic (w, data, type, time, count, mistakes, viscosity)
VALUES (?, ?, ?, ?, ?, ?, ?)''', q)
self.progress_.inc()
DB.execute('vacuum')
self.progress_.inc()
DB.commit()
self.progress_.hide()
def disableSelected(self):
cats, texts = self.getSelected()
DB.setRegex(Settings.get('text_regex'))
DB.executemany("""update text set disabled = ifelse(disabled,NULL,1)
where rowid = ? and regex_match(text) = 1""",
map(lambda x:(x, ), texts))
DB.executemany("""update text set disabled = ifelse(disabled,NULL,1)
where source = ? and regex_match(text) = 1""",
map(lambda x:(x, ), cats))
self.update()
def disableSelected(self):
cats, texts = self.getSelected()
DB.setRegex(Settings.get('text_regex'))
DB.executemany("""update text set disabled = ifelse(disabled,NULL,1)
where rowid = ? and regex_match(text) = 1""",
[(x, ) for x in texts])
DB.executemany("""update text set disabled = ifelse(disabled,NULL,1)
where source = ? and regex_match(text) = 1""",
[(x, ) for x in cats])
self.update()
def disableAllSelected(self):
cats, texts = self.getSelected()
DB.setRegex(Settings.get('text_regex'))
DB.executemany(
"""update text set disabled = ifelse(disabled, NULL, 1) where rowid = ?""",
map(lambda x: (x, ), texts))
DB.executemany(
"""update text set disabled = ifelse(disabled, NULL, 1) where source = ?""",
map(lambda x: (x, ), cats))
self.update()
def disableSelected(self):
cats, texts = self.getSelected()
DB.setRegex(Settings.get("text_regex"))
DB.executemany(
"""update text set disabled = ifelse(disabled,NULL,1)
where rowid = ? and regex_match(text) = 1""",
map(lambda x: (x, ), texts),
)
DB.executemany(
"""update text set disabled = ifelse(disabled,NULL,1)
where source = ? and regex_match(text) = 1""",
map(lambda x: (x, ), cats),
)
self.update()
def done(self):
print("DONE")
# TODO split into smaller bits
now = timer()
elapsed, chars, times, mis, mistakes = self.typer.get_stats()
text = self.text[2]
assert chars == len(text)
accuracy = 1.0 - sum(1 for f in mis if f) / chars
spc = elapsed / chars
viscosity = sum((t / spc - 1)**2 for t in 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))
wpm_median, acc_median = DB.fetchone(
f"""select agg_median(wpm),agg_median(acc) from
(select wpm,100.0*accuracy as acc from result order by w desc limit
{Settings.get("def_group_by")})""", (0.0, 100.0))
self.result.set_text(
"Last: {:.1f}wpm ({:.1f}%), last 10 average: {:.1f}wpm ({:.1f}%)".
format(12.0 / spc, 100.0 * accuracy, wpm_median, acc_median))
self.emit("stats-changed")
stats = collections.defaultdict(Statistic)
viscs = collections.defaultdict(Statistic)
for char, time, mistake in zip(text, times, mis):
stats[char].append(time, mistake)
viscs[char].append((time / spc - 1)**2)
def gen_tup(start, end):
span = end - start
char_avg = sum(times[start:end]) / span
visc = sum((t / char_avg - 1)**2 for t in times[start:end]) / span
return (text[start:end], char_avg,
sum(1 for f in mis[start:end] if f), visc)
for trigraph, time, mist, visc in [
gen_tup(i, i + 3) for i in range(0, chars - 2)
]:
stats[trigraph].append(time, mist > 0)
viscs[trigraph].append(visc)
regex = re.compile(r"(\w|'(?![A-Z]))+(-\w(\w|')*)*")
for word, time, mist, visc in [
gen_tup(*m.span()) for m in regex.finditer(text)
if m.end() - m.start() > 3
]:
stats[word].append(time, mist > 0)
viscs[word].append(visc)
def kind(key):
if len(key) == 1:
return 0
if len(key) == 3:
return 1
return 2
vals = []
for key, stat in stats.items():
visc = viscs[key].median()
vals.append((stat.median(), visc * 100.0, now, len(stat),
stat.flawed(), kind(key), key))
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.items()])
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.set_target(self.text)
elif not is_lesson and Settings.get('auto_review'):
words = [x for x in vals if x[5] == 2]
if not words:
self.emit("want-text")
return
words.sort(key=lambda x: (x[4], x[0]), reverse=True)
i = 0
while words[i][4] != 0:
i += 1
i += (len(words) - i) // 4
# TODO support want-review
# self.emit("want-review", [x[6] for x in words[0:i]])
else:
self.emit("want-text")
