我对Passport的import pandas as pd
from collections import namedtuple
from datetime import timedelta
Interval = namedtuple('Interval', 'field_name start_time end_time delta')
class IntervalMatch(object):
def __init__(self):
pass
def per_delta(self,interval: Interval, include_start: bool):
current_interval = interval.start_time
if not include_start:
current_interval += pd.DateOffset(seconds=interval.delta)
while current_interval < interval.end_time:
yield current_interval
current_interval += pd.DateOffset(seconds=interval.delta)
def _copy(self, row, columns: pd.Index):
values = pd.Series(row).values
return pd.DataFrame([values], columns=columns.values).copy(True)
def interval_split(self, interval: Interval, base_row: pd.Series, columns: pd.Index, include_start: bool):
for time in self.per_delta(interval, include_start):
extended_row = self._copy(base_row, columns)
extended_row.at[(0, interval.field_name)] = time
yield extended_row
def get_exploded_records(self, data_to_examine: pd.DataFrame, time_field_name: str):
last_row = None
results = pd.DataFrame()
delta = 1 # second
time_col_index = data_to_examine.columns.get_loc(time_field_name)
# process each row. It is possible there is a map/reduce/fluent way of doing this w/ Pandas
intermediate_results = {}
current_row = -1
for row in data_to_examine.itertuples(index=False):
current_row += 1
if last_row is None:
last_row = row
intermediate_results[current_row] = row
continue
total_seconds = (row[time_col_index] - last_row[time_col_index]).total_seconds()
if total_seconds > 1 and total_seconds < 100:
# there is a gap, so we want to explode the gap into the data and fill it with last_row values.
interval = Interval(time_field_name, last_row[time_col_index], row[time_col_index], delta)
for intrvl in self.interval_split(interval, last_row, data_to_examine.columns, False):
# we must unroll the list of rows to just the first row (since there is only one)
intermediate_results[current_row] = intrvl.values[0]
current_row += 1
# append the current row
intermediate_results[current_row] = row
last_row = row
results = pd.DataFrame.from_dict(intermediate_results, orient='index') #, columns=data_to_examine.columns)
return results
def test():
print("Preparing Data")
timestamps = ['2016-01-01 09:24:20', '2016-01-01 09:24:21',
'2016-01-01 09:24:23', '2016-01-01 09:24:24', '2016-01-01 09:24:40']
data_with_gaps = pd.DataFrame({'timestamp':[pd.Timestamp(timestamp) for timestamp in timestamps],
'names':['Torial', 'Torial', 'Knut', 'Knut', 'Torial'],
'action':['Add','Edit','Add', 'Edit','Delete']})
interval = IntervalMatch()
print("Getting Exploded Records")
exploded = interval.get_exploded_records(data_with_gaps, 'timestamp')
print(f"Data with Gaps: {data_with_gaps}")
print(f"Exploded: {exploded}")
exploded.to_csv("Exploded_test.csv")
方法有疑问。没有调用提供给该方法的回调。
代码如下：
authenticate答案 0 :(得分：0)
我错过了我基于代码的示例的关键部分。我不得不将import { Router, Request, Response, NextFunction } from 'express';
import jwt from 'jsonwebtoken';
import passport from 'passport';
// ... some stuff
router.post('/login',
(req: Request, res: Response, next: NextFunction) => {
passport.authenticate('local', { session: false }, (err, user, info) => {
// not reaching this part
if (err) {
return next(err);
}
if (err || !user) {
return res.status(400).json({
message: info ? info.message : 'Login failed',
user,
});
}
req.login(user, { session: false }, loginErr => {
if (loginErr) {
res.send(loginErr);
}
const token = jwt.sign(user, secrets.secret);
return res.json({ user, token });
});
return res.status(500).send('Shouldn\'t come here');
});
},
(err: any, req: Request, res: Response, next: NextFunction) => {
return res.status(err.status || 500).send(err.message)
});
用作中间件。因此，我使用passport.authenticate生成了一个函数，然后将passport.authenticate，req和res传递给它，如下所示：
next一切正常。