本文整理汇总了Python中Data.DB.executemany_方法的典型用法代码示例。如果您正苦于以下问题：Python DB.executemany_方法的具体用法？Python DB.executemany_怎么用？Python DB.executemany_使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Data.DB的用法示例。
在下文中一共展示了DB.executemany_方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: done
# 需要导入模块: from Data import DB [as 别名]
# 或者: from Data.DB import executemany_ [as 别名]
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"))