def cast(self, time, retro=False):
'''force time to be in workhours'''
if self.isworktime(time):
return time # ok
if retro:
if not self.isworkday(time) or time.time() < self.start:
return datetimef(self.prevworkday(time.date()), self.end)
# only remaining case is time>self.end on a work day
return datetimef(time.date(), self.end)
else:
if not self.isworkday(time) or time.time() > self.end:
return datetimef(self.nextworkday(time.date()), self.start)
# only remaining case is time<self.start on a work day
return datetimef(time.date(), self.start)
def cast(self,time,retro=False):
'''force time to be in workhours'''
if self.isworktime(time):
return time #ok
if retro:
if not self.isworkday(time) or time.time()<self.start:
return _datetime(self.prevworkday(time.date()),self.end)
#only remaining case is time>self.end on a work day
return _datetime(time.date(),self.end)
else:
if not self.isworkday(time) or time.time()>self.end:
return _datetime(self.nextworkday(time.date()),self.start)
#only remaining case is time<self.start on a work day
return _datetime(time.date(),self.start)
def _generate_smbg(self, d):
"""Generate timestamps and smbg values from a non-uniform pool of potential timestamps."""
readings = []
i = 0
while i < self.readings_per_day:
hour = random.choice(HOURS)
timestamp = dt(d.year, d.month, d.day, hour, random.choice(SIXTY), random.choice(SIXTY), random.choice(MICRO))
near = []
for reading in self.dexcom:
t = reading['deviceTime']
if t.date() == d:
if t.hour == hour:
near.append(reading)
jump = random.randint(-26, 26)
try:
value = random.choice(near)['value'] + jump
readings.append({'value': value, 'deviceTime': timestamp})
# exception occurs when can't find a near enough timestamp because data starts with datetime.now()
# which could be middle of the afternoon, but this method will always try to generate some morning timestamps
except IndexError:
pass
i += 1
return readings
def test_create_report_error_people_seen(self):
region = 'CC'
trail_name = 'test_trail'
trailhead = create_trail_and_trailhead(name=trail_name,
region=region,
coordinates=fake.word(),
filters=None)
time = datetime.now()
path = reverse('reports_trailhead',
args=(
region,
trailhead.trail.id,
trailhead.id,
))
response = self.client.post(
path, {
'trail': trailhead.trail.id,
'trailhead': trailhead.id,
'date_hiked': time.date(),
'day_hiked': 'Th',
'trail_begin': time.time(),
'trail_end': time.time(),
'pkg_location': 'P',
'pkg_estimate_begin': 29,
'pkg_estimate_end': 34,
'cars_seen': 34,
'people_seen': -344,
})
self.assertEqual(response.status_code, 200)
self.assertTemplateUsed('reports.html')
self.assertContains(response,
'Ensure this value is greater than or equal to 0')
def _generate_smbg(self, d):
"""Generate timestamps and smbg values from a non-uniform pool of potential timestamps."""
readings = []
i = 0
while i < self.readings_per_day:
hour = random.choice(HOURS)
timestamp = dt(d.year, d.month, d.day, hour, random.choice(SIXTY),
random.choice(SIXTY), random.choice(MICRO))
near = []
for reading in self.dexcom:
t = reading['deviceTime']
if t.date() == d:
if t.hour == hour:
near.append(reading)
jump = random.randint(-26, 26)
try:
value = random.choice(near)['value'] + jump
readings.append({'value': value, 'deviceTime': timestamp})
# exception occurs when can't find a near enough timestamp because data starts with datetime.now()
# which could be middle of the afternoon, but this method will always try to generate some morning timestamps
except IndexError:
pass
i += 1
return readings
def get_time_events(self, time, **kwargs):
all_day = kwargs.get('all_day')
delta = kwargs.get('delta')
if all_day:
return [event for event in self.event_list if time.date() == event.start_time.date() and event.all_day==True]
elif delta:
return [[event, event.get_duration()] for event in self.event_list if event.start_time >= time and event.start_time < time + delta and event.all_day==False]
else:
return []
def test_create_report_view(self):
region = 'CC'
trail_name = 'test_trail'
trailhead = create_trail_and_trailhead(name=trail_name,
region=region,
coordinates=fake.word(),
filters=None)
time = datetime.now()
path = reverse('reports_trailhead',
args=(
region,
trailhead.trail.id,
trailhead.id,
))
post_response = self.client.post(
path, {
'trail': trailhead.trail.id,
'trailhead': trailhead.id,
'date_hiked': time.date(),
'day_hiked': 'Th',
'trail_begin': time.time(),
'trail_end': time.time(),
'car_type': 'Suv',
'weather_type': 'S',
'temperature': 'C',
'bathroom_status': 'C',
'bathroom_type': 'FP',
'access': 'FS',
'access_distance': 5.0,
'access_condition': 'P+',
'pkg_location': 'P',
'pkg_estimate_begin': 29,
'pkg_estimate_end': 34,
'cars_seen': 34,
'people_seen': 344,
'horses_seen': False,
'dogs_seen': True
})
self.assertRedirects(post_response, path)
get_response = self.client.get(path,
args=(
region,
trailhead.trail.id,
trailhead.id,
))
reports = get_response.context['reports_list']
self.assertEqual(get_response.status_code, 200)
self.assertTemplateUsed('reports.html')
self.assertContains(get_response, 'Reports (1)')
self.assertEqual(len(reports), 1)
self.assertEqual(reports[0].trail.name, 'test_trail')
def prepare_data(self):
if self._data is not None:
return self._data
time_offset = timedelta(days=SAMPLE_DATE_OFFSET)
time_bias = timedelta(days=SAMPLE_DATE_BIAS)
self._limit_sample_start = self.date - time_offset - time_bias
self._limit_sample_end = self._limit_sample_start + time_offset
sql = "SELECT \
MIN(vol) as vol_min, AVG(vol) as vol_avg, MAX(vol) as vol_max, \
MIN(close) as vol_min, AVG(close) as vol_avg, MAX(close) as vol_max, \
MIN(count) as count_min, MAX(count) as count_max \
FROM {0} \
WHERE `code`='{1}' AND `time`>='{2}' AND `time`<='{3}' ".format(
TABLE_NAME_5MIN, self.code, self._limit_sample_start, self._limit_sample_end)
rs = self.db.execute(sql)
self._vol_min, self._vol_avg, self._vol_max, \
self._price_min, self._price_avg, self._price_max, \
self._count_min, self._count_max = rs.fetchone()
if self._count_min is None:
raise RuntimeError("Cannot fetch sample data for stock {} at {}".format(self.code, self.date))
return
shifted_date = self._get_shifted_startdate()
if shifted_date is None:
return
rs = self.db.execute(
"SELECT `date`, ROUND((`traded_market_value`/`close`)) as total_vol "
"FROM {0} "
"WHERE `code`='{1}' AND `date`>='{2}' AND `date`<='{3}' "
"ORDER BY `date` ASC".format(
TABLE_NAME_DAILY, self.code, shifted_date, self.date))
daily_df = pd.DataFrame(rs.fetchall())
daily_df.columns = ['date', 'total_vol']
self._daily_df = daily_df.set_index(['date'], drop=True)
rs = self.db.execute(
"SELECT * "
"FROM {0} "
"WHERE `code`='{1}' AND `time`>='{2}' AND `time`<='{3}' "
"ORDER BY time ASC".format(
TABLE_NAME_5MIN, self.code, shifted_date, self.date + timedelta(days=1)))
df = pd.DataFrame(rs.fetchall())
df.columns = ['code', 'time', 'open', 'high', 'low', 'close', 'vol', 'amount', 'count']
df = df.set_index(['time'], drop=True)
df = df.drop(labels='code', axis=1)
df['date'] = [time.date() for time in df.index.tolist()]
self._data = df
return self._data
def mark_attendance_from_checkin(checkin, employee, time):
att_time = time.time()
att_date = time.date()
in_time = ''
out_time = ''
checkins = frappe.db.sql(
"""select name,time from `tabEmployee Checkin` where employee = "%s" and date(time) = '%s' order by time """
% (employee, att_date),
as_dict=True)
if checkins:
if len(checkins) >= 2:
in_time = checkins[0].time
out_time = checkins[-1].time
elif len(checkins) == 1:
in_time = checkins[0].time
if in_time and out_time:
status = 'Present'
else:
status = 'Absent'
att = frappe.db.exists('Attendance', {
'employee': employee,
'attendance_date': att_date,
'docstatus': 0
})
if not att:
att = frappe.new_doc("Attendance")
att.employee = employee
att.attendance_date = att_date
att.shift = 'G'
att.status = status
att.in_time = in_time
att.out_time = out_time
att.save(ignore_permissions=True)
frappe.db.commit()
return att.name
else:
if in_time:
frappe.db.set_value("Attendance", att, 'in_time', in_time)
if out_time:
frappe.db.set_value("Attendance", att, 'out_time', out_time)
frappe.db.set_value("Attendance", att, 'shift', 'G')
frappe.db.set_value("Attendance", att, 'status', status)
return att
def timesFix():
# need to sort the time values into buckets of values by states
# should resemble
#
# Texas 10PM
# different time values tweets at times
# state by
tweetsUsed = []
startTime = 0
endTime = 0
with open("data/usefulTweets.json") as tw:
tweetsUsed = json.load(tw)
# 3 files (15,30,1 hour)
#
masterNorm = []
masterTotal = []
tweetsUsed.reverse()
for tweet in tweetsUsed:
if tweet["location"] in TOPFIVESTATES:
time = tweet["created_at"]
try:
tweet["created_at"] = datetime.strptime(
time, '%Y-%m-%d %H:%M:%S')
except TypeError:
pass
masterTotal.append(tweet)
if tweet["location"] in NORMTOPFIVESTATES:
time = tweet["created_at"]
try:
tweet["created_at"] = datetime.strptime(
time, '%Y-%m-%d %H:%M:%S')
except TypeError:
pass
masterNorm.append(tweet)
masterNormTime = list(map(lambda t: t["created_at"], masterNorm))
indexBeg = 0
indexEnd = 0
for i, time in enumerate(masterNormTime):
if time.date() == datetime(2018, 2, 27).date() and not indexBeg:
indexBeg = i
if time.date() == datetime(2018, 3, 3).date() and not indexEnd:
indexEnd = i
else:
continue
masterNorm = masterNorm[indexBeg:indexEnd]
startTime = masterNorm[0]["created_at"].date()
endTimeTry1 = masterNorm[-1]["created_at"].date()
endTime = endTimeTry1 + timedelta(days=1)
loop15Minutes = list(
rrule(MINUTELY, interval=15, dtstart=startTime, until=endTime))
loop30Minutes = list(
rrule(MINUTELY, interval=30, dtstart=startTime, until=endTime))
loopHourly = list(
rrule(HOURLY, interval=1, dtstart=startTime, until=endTime))
indexLoop15 = list(
map(lambda t: t.strftime('%Y-%m-%d %H:%M:%S'), loop15Minutes))
indexLoop30 = list(
map(lambda t: t.strftime('%Y-%m-%d %H:%M:%S'), loop30Minutes))
indexLoop60 = list(
map(lambda t: t.strftime('%Y-%m-%d %H:%M:%S'), loopHourly))
states15N = {key: [0] * len(loop15Minutes) for key in NORMTOPFIVESTATES}
states30N = {key: [0] * len(loop30Minutes) for key in NORMTOPFIVESTATES}
states60N = {key: [0] * len(loopHourly) for key in NORMTOPFIVESTATES}
counter = 0
index = 0
for time in loop15Minutes:
working = True
while working:
if not len(masterNorm) == index:
twTime = masterNorm[index]["created_at"]
twLocation = masterNorm[index]["location"]
if twTime >= time and twTime <= loop15Minutes[counter + 1]:
states15N[twLocation][counter] += 1
index += 1
pass
else:
working = False
else:
working = False
# for state, items in states15N.items():
# items[counter] = (
# (items[counter] /
# STATES_POPULATION[state])
# * 1000000
# )
# states15N[state] = items
counter += 1
continue
counter = 0
index = 0
for time in loop30Minutes:
working = True
while working:
if not len(masterNorm) == index:
twTime = masterNorm[index]["created_at"]
twLocation = masterNorm[index]["location"]
if twTime >= time and twTime <= loop30Minutes[counter + 1]:
states30N[twLocation][counter] += 1
index += 1
pass
else:
working = False
else:
working = False
# for state, items in states30N.items():
# items[counter] = (
# (items[counter] /
# STATES_POPULATION[state])
# * 1000000
# )
# states30N[state] = items
counter += 1
continue
counter = 0
index = 0
for time in loopHourly:
working = True
while working:
if not len(masterNorm) == index:
twTime = masterNorm[index]["created_at"]
twLocation = masterNorm[index]["location"]
if twTime >= time and twTime <= loopHourly[counter + 1]:
states60N[twLocation][counter] += 1
index += 1
pass
else:
working = False
else:
working = False
# for state, items in states60N.items():
# items[counter] = (
# (items[counter] /
# STATES_POPULATION[state])
# * 1000000
# )
counter += 1
continue
##This edit was made to help with R autocorrelation//Temporal Analysis
states15N["Times"] = indexLoop15
states30N["Times"] = indexLoop30
states60N["Times"] = indexLoop60
newDict15 = {"Location": [], "Tweets": [], "Times": []}
newDict30 = {"Location": [], "Tweets": [], "Times": []}
newDict60 = {"Location": [], "Tweets": [], "Times": []}
for keys, items in states15N.items():
if not keys == "Times":
for idx, location in enumerate(items):
newDict15["Location"].append(keys)
newDict15["Tweets"].append(location)
newDict15["Times"].append(states15N["Times"][idx])
for keys, items in states30N.items():
if not keys == "Times":
for idx, location in enumerate(items):
newDict30["Location"].append(keys)
newDict30["Tweets"].append(location)
newDict30["Times"].append(states30N["Times"][idx])
for keys, items in states60N.items():
if not keys == "Times":
for idx, location in enumerate(items):
newDict60["Location"].append(keys)
newDict60["Tweets"].append(location)
newDict60["Times"].append(states60N["Times"][idx])
tweets15 = DataFrame(newDict15)
tweets30 = DataFrame(newDict30)
tweets60 = DataFrame(newDict60)
tweets15.to_csv("data/tweets15Days.csv")
tweets30.to_csv("data/tweets30Days.csv")
tweets60.to_csv("data/tweets60Days.csv")