def __init__(self, entkey, rkey):
super(AnalysisReportWorker, self).__init__(rkey,
start_att="dt_created",
start_att_direction="")
self.enterprise = self.report.enterprise
self.FILTERS = [("enterprise =", self.enterprise)]
specs = self.report.getSpecs()
start = specs.get("start", 0)
end = specs.get("end", 0)
self.columns = specs.get('columns', [])
if isinstance(self.columns, basestring):
self.columns = self.columns.split(',')
sensortype_id = specs.get("sensortype_id")
self.report.generate_title("Analysis Report",
ts_start=start,
ts_end=end,
sensortype_id=sensortype_id)
if start:
self.FILTERS.append(("dt_created >=", tools.dt_from_ts(start)))
if end:
self.FILTERS.append(("dt_created <", tools.dt_from_ts(end)))
if sensortype_id:
self.FILTERS.append(("sensortype =",
db.Key.from_path('Enterprise',
self.enterprise.key().id(),
'SensorType',
int(sensortype_id))))
self.sensor_lookup = tools.lookupDict(
Sensor,
self.enterprise.sensor_set.fetch(limit=200),
valueTransform=lambda s: s.name)
self.headers = ["Key", "Sensor", "Created", "Updated"] + self.columns
def __init__(self, entkey, rkey):
super(AlarmReportWorker, self).__init__(rkey,
start_att="dt_start",
start_att_direction="")
self.enterprise = self.report.enterprise
self.FILTERS = [("enterprise =", self.enterprise)]
specs = self.report.getSpecs()
start = specs.get("start", 0)
end = specs.get("end", 0)
if start:
self.FILTERS.append(("dt_start >=", tools.dt_from_ts(start)))
if end:
self.FILTERS.append(("dt_start <", tools.dt_from_ts(end)))
self.report.generate_title("Alarm Report", ts_start=start, ts_end=end)
self.sensor_lookup = tools.lookupDict(
Sensor,
self.enterprise.sensor_set.fetch(limit=200),
valueTransform=lambda s: s.name,
keyprop="key_name")
self.rule_lookup = tools.lookupDict(
Rule, self.enterprise.rule_set.fetch(limit=100))
self.headers = [
"Alarm ID", "Sensor ID", "Sensor", "Rule ID", "Rule", "Apex",
"Start", "End"
]
def add_date_filters(self, start=None, end=None):
if start:
self.FILTERS.append(
"%s >= DATETIME('%s 00:00:00')" %
(self.start_att, tools.iso_date(tools.dt_from_ts(start))))
if end:
self.FILTERS.append(
"%s < DATETIME('%s 23:59:59')" %
(self.start_att, tools.iso_date(tools.dt_from_ts(end))))
def __init__(self, sensorkey, rkey):
super(SensorDataReportWorker, self).__init__(rkey, start_att="dt_recorded", start_att_direction="-")
self.sensor = Sensor.get(sensorkey)
self.FILTERS = [("sensor =", self.sensor)]
self.report.title = "Sensor Data Report [ %s ]" % (self.sensor)
specs = self.report.getSpecs()
ts_start = specs.get("ts_start", 0)
ts_end = specs.get("ts_end", 0)
if ts_start:
self.FILTERS.append(("dt_recorded >=", tools.dt_from_ts(ts_start)))
if ts_end:
self.FILTERS.append(("dt_recorded <", tools.dt_from_ts(ts_end)))
self.columns = specs.get('columns',[])
standard_cols = ["Date"]
self.headers = standard_cols + self.columns
def __init__(self, entkey, rkey):
super(AlarmReportWorker, self).__init__(rkey, start_att="dt_start", start_att_direction="-")
self.enterprise = self.report.enterprise
self.FILTERS = [("enterprise =", self.enterprise)]
specs = self.report.getSpecs()
start = specs.get("start", 0)
end = specs.get("end", 0)
if start:
self.FILTERS.append(("dt_start >=", tools.dt_from_ts(start)))
if end:
self.FILTERS.append(("dt_start <", tools.dt_from_ts(end)))
self.report.generate_title("Alarm Report", ts_start=start, ts_end=end)
self.sensor_lookup = tools.lookupDict(Sensor, self.enterprise.sensor_set.fetch(limit=200), valueTransform=lambda s : s.name)
self.rule_lookup = tools.lookupDict(Rule, self.enterprise.rule_set.fetch(limit=100))
self.headers = ["Sensor","Rule","Apex","Start","End"]
def __init__(self, sensorkey, rkey):
super(SensorDataReportWorker, self).__init__(rkey, start_att="dt_recorded", start_att_direction="-")
self.sensor = Sensor.get(sensorkey)
self.FILTERS = [("sensor =", self.sensor)]
self.report.title = "Sensor Data Report [ %s ]" % (self.sensor)
specs = self.report.getSpecs()
ts_start = specs.get("ts_start", 0)
ts_end = specs.get("ts_end", 0)
if ts_start:
self.FILTERS.append(("dt_recorded >=", tools.dt_from_ts(ts_start)))
if ts_end:
self.FILTERS.append(("dt_recorded <", tools.dt_from_ts(ts_end)))
self.columns = specs.get('columns',[])
standard_cols = ["Date"]
self.headers = standard_cols + self.columns
def list(self, d):
"""Fetch a list of targets
Args:
max: page size (1-500, default 100)
updated_since: (optional) timestamp (ms) of update cutoff
Returns:
JSON: 'targets' list of target objects
"""
success = False
message = None
_max = self.request.get_range('max', max_value=500, default=100)
ms_updated_since = self.request.get_range('updated_since', default=0) # ms
group_id = self.request.get_range("group_id")
updated_since = tools.dt_from_ts(ms_updated_since) if ms_updated_since else None
targets = Target.Fetch(d['user'], updated_since=updated_since, group_id=group_id, limit=_max)
success = True
data = {
'targets': [tgt.json() for tgt in targets]
}
self.json_out(data, success=success, message=message)
def test_project_report(self):
prj = Project.Create(self.u)
prj.Update(title="New Project",
subhead="Project subhead",
due=datetime(2017, 4, 5))
prj.set_progress(3)
prj.put()
self._test_report(
{'type': REPORT.PROJECT_REPORT},
[[
"Date Created", "Date Due", "Date Completed", "Date Archived",
"Title", "Subhead", "Links", "Starred", "Archived", "Progress",
'Progress 10%', 'Progress 20%', 'Progress 30%', 'Progress 40%',
'Progress 50%', 'Progress 60%', 'Progress 70%', 'Progress 80%',
'Progress 90%', 'Progress 100%'
],
[
tools.sdatetime(prj.dt_created, fmt="%Y-%m-%d %H:%M:%S %Z"),
tools.sdatetime(prj.dt_due, fmt="%Y-%m-%d %H:%M:%S %Z"),
tools.sdatetime(prj.dt_completed, fmt="%Y-%m-%d %H:%M:%S %Z"),
tools.sdatetime(prj.dt_archived,
fmt="%Y-%m-%d %H:%M:%S %Z"), "New Project",
"Project subhead", "", "0", "0", "30%", "N/A", "N/A",
tools.sdatetime(tools.dt_from_ts(prj.progress_ts[2]),
fmt="%Y-%m-%d %H:%M:%S %Z"), "N/A", "N/A",
"N/A", "N/A", "N/A", "N/A", "N/A"
]])
def __init__(self, entkey, rkey):
super(AnalysisReportWorker, self).__init__(rkey, start_att="dt_created", start_att_direction="-")
self.enterprise = self.report.enterprise
self.FILTERS = [("enterprise =", self.enterprise)]
specs = self.report.getSpecs()
start = specs.get("start", 0)
end = specs.get("end", 0)
self.columns = specs.get("columns", "").split(",")
sensortype_id = specs.get("sensortype_id")
self.report.generate_title("Analysis Report", ts_start=start, ts_end=end, sensortype_id=sensortype_id)
if start:
self.FILTERS.append(("dt_created >=", tools.dt_from_ts(start)))
if end:
self.FILTERS.append(("dt_created <", tools.dt_from_ts(end)))
if sensortype_id:
self.FILTERS.append(("sensortype =", db.Key.from_path('Enterprise', self.enterprise.key().id(), 'SensorType', int(sensortype_id))))
self.sensor_lookup = tools.lookupDict(Sensor, self.enterprise.sensor_set.fetch(limit=200), valueTransform=lambda s : s.name)
self.headers = ["Key","Sensor","Created","Updated"] + self.columns
def __init__(self, rkey):
super(APILogReportWorker, self).__init__(rkey,
start_att="date",
start_att_direction="")
self.enterprise = self.report.enterprise
self.FILTERS = [("enterprise =", self.enterprise)]
specs = self.report.getSpecs()
start = specs.get("start", 0)
end = specs.get("end", 0)
if start:
self.FILTERS.append(("date >=", tools.dt_from_ts(start)))
if end:
self.FILTERS.append(("date <", tools.dt_from_ts(end)))
self.report.generate_title("API Log Report",
ts_start=start,
ts_end=end)
self.headers = [
"Request ID", "User ID", "Date", "Path", "Method", "Request"
]
def list(self, d):
success = False
message = None
records = []
_max = self.request.get_range('max', max_value=500, default=100)
sensor_kn = self.request.get('sensor_kn')
dt_start = tools.dt_from_ts(self.request.get_range('ts_start'))
dt_end = tools.dt_from_ts(self.request.get_range('ts_end'))
downsample = self.request.get_range('downsample')
if sensor_kn:
s = Sensor.get_by_key_name(sensor_kn, parent=d['enterprise'])
if s:
records = Record.Fetch(s, dt_start=dt_start, dt_end=dt_end, downsample=downsample, limit=_max)
success = True
data = {
'records': [r.json() for r in records]
}
self.json_out(data, success=success, message=message)
def __init__(self, sensorkey, rkey):
super(SensorDataReportWorker, self).__init__(rkey,
start_att="dt_recorded",
start_att_direction="")
title_kwargs = {}
specs = self.report.getSpecs()
if sensorkey:
self.sensor = Sensor.get(sensorkey)
self.FILTERS = [("sensor =", self.sensor)]
title_kwargs['sensor'] = str(self.sensor)
elif specs.get('sensortype_id'):
sensortype_id = specs.get('sensortype_id')
title_kwargs['sensor_type'] = sensortype_id
self.FILTERS = [
("sensortype =",
db.Key.from_path('SensorType',
sensortype_id,
parent=self.report.enterprise.key()))
]
else:
# Enterprise wide
self.FILTERS = [("enterprise =", self.report.enterprise)]
start = specs.get("start", 0)
end = specs.get("end", 0)
if start:
self.FILTERS.append(("dt_recorded >=", tools.dt_from_ts(start)))
if end:
self.FILTERS.append(("dt_recorded <", tools.dt_from_ts(end)))
self.report.generate_title("Sensor Data Report",
ts_start=start,
ts_end=end,
**title_kwargs)
self.columns = specs.get('columns', [])
if isinstance(self.columns, basestring) and ',' in self.columns:
self.columns = self.columns.split(',')
standard_cols = ["Record ID", "Sensor ID", "Date"]
self.headers = standard_cols + self.columns
self.batch_size = 1000
def entityData(self, prj):
row = [
tools.sdatetime(prj.dt_created, fmt=DATE_FMT),
tools.sdatetime(prj.dt_due, fmt=DATE_FMT),
tools.sdatetime(prj.dt_completed, fmt=DATE_FMT),
tools.sdatetime(prj.dt_archived, fmt=DATE_FMT), prj.title,
prj.subhead, ', '.join(prj.urls), "1" if prj.starred else "0",
"1" if prj.archived else "0",
"%d%%" % (prj.progress * 10)
]
for i in range(10):
val = ""
if prj.progress_ts and len(prj.progress_ts) > i:
ms = prj.progress_ts[i]
val = tools.sdatetime(tools.dt_from_ts(ms), fmt=DATE_FMT)
row.append(val)
return row
def list(self, d):
success = False
message = None
_max = self.request.get_range('max', max_value=500, default=100)
with_records = self.request.get_range('with_records', default=0)
ms_updated_since = self.request.get_range('updated_since', default=0) # ms
target_id = self.request.get_range('target_id')
group_id = self.request.get_range('group_id')
updated_since = tools.dt_from_ts(ms_updated_since) if ms_updated_since else None
sensors = Sensor.Fetch(d['user'], updated_since=updated_since, target_id=target_id, group_id=group_id, limit=_max)
success = True
data = {
'sensors': [s.json(with_records=with_records) for s in sensors]
}
self.json_out(data, success=success, message=message)
def entityData(self, prj):
row = [
tools.sdatetime(prj.dt_created, fmt=DATE_FMT),
tools.sdatetime(prj.dt_due, fmt=DATE_FMT),
tools.sdatetime(prj.dt_completed, fmt=DATE_FMT),
tools.sdatetime(prj.dt_archived, fmt=DATE_FMT),
prj.title,
prj.subhead,
', '.join(prj.urls),
"1" if prj.starred else "0",
"1" if prj.archived else "0",
"%d%%" % (prj.progress * 10)
]
for i in range(10):
val = ""
if prj.progress_ts and len(prj.progress_ts) > i:
ms = prj.progress_ts[i]
val = tools.sdatetime(tools.dt_from_ts(ms), fmt=DATE_FMT)
row.append(val)
return row
def list(self, d):
success = False
message = None
key_names = self.request.get('key_names') # comma sep
_max = self.request.get_range('max', max_value=500, default=100)
with_records = self.request.get_range('with_records', default=0)
ms_updated_since = self.request.get_range('updated_since', default=0) # ms
target_id = self.request.get_range('target_id')
group_id = self.request.get_range('group_id')
updated_since = tools.dt_from_ts(ms_updated_since) if ms_updated_since else None
if key_names:
sensors = Sensor.get_by_key_name(key_names.split(','), parent=self.enterprise)
else:
sensors = Sensor.Fetch(d['user'], updated_since=updated_since, target_id=target_id, group_id=group_id, limit=_max)
success = True
data = {
'sensors': [s.json(with_records=with_records) for s in sensors]
}
self.json_out(data, success=success, message=message)
def test_project_report(self):
prj = Project.Create(self.u)
prj.Update(title="New Project", subhead="Project subhead", due=datetime(2017, 4, 5))
prj.set_progress(3)
prj.put()
self._test_report(
{'type': REPORT.PROJECT_REPORT},
[
["Date Created", "Date Due", "Date Completed", "Date Archived", "Title", "Subhead",
"Links", "Starred", "Archived", "Progress", 'Progress 10%', 'Progress 20%', 'Progress 30%',
'Progress 40%', 'Progress 50%', 'Progress 60%', 'Progress 70%', 'Progress 80%',
'Progress 90%', 'Progress 100%'],
[
tools.sdatetime(prj.dt_created, fmt="%Y-%m-%d %H:%M:%S %Z"),
tools.sdatetime(prj.dt_due, fmt="%Y-%m-%d %H:%M:%S %Z"),
tools.sdatetime(prj.dt_completed, fmt="%Y-%m-%d %H:%M:%S %Z"),
tools.sdatetime(prj.dt_archived, fmt="%Y-%m-%d %H:%M:%S %Z"),
"New Project",
"Project subhead",
"",
"0",
"0",
"30%",
"N/A",
"N/A",
tools.sdatetime(tools.dt_from_ts(prj.progress_ts[2]), fmt="%Y-%m-%d %H:%M:%S %Z"),
"N/A",
"N/A",
"N/A",
"N/A",
"N/A",
"N/A",
"N/A"
]
]
)
def sync(user, access_token, since_timestamp=0):
'''
Return JSON array {title, author, isbn, image}
Sample dict from pocket:
{u'resolved_url': u'https://arxiv.org/abs/1701.06538', u'given_title': u'', u'is_article': u'1', u'sort_id': 16, u'word_count': u'221', u'status': u'0', u'has_image': u'0', u'given_url': u'https://arxiv.org/abs/1701.06538', u'favorite': u'0', u'has_video': u'0', u'time_added': u'1485774143', u'time_updated': u'1485774143', u'time_read': u'0', u'excerpt': u'Authors: Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean Abstract: The capacity of a neural network to absorb information is limited by its number of parameters.', u'resolved_title': u'Title: Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer', u'authors': {u'32207876': {u'url': u'', u'author_id': u'32207876', u'item_id': u'1576987151', u'name': u'cscs.CLcs.NEstatstat.ML'}}, u'resolved_id': u'1576987151', u'item_id': u'1576987151', u'time_favorited': u'0', u'is_index': u'0'}
{u'resolved_url': u'http://lens.blogs.nytimes.com/2012/10/09/looking-into-the-eyes-of-made-in-china/', u'given_title': u'http://lens.blogs.nytimes.com/2012/10/09/looking-into-the-eyes-of-made-in-c', u'is_article': u'1', u'sort_id': 99, u'word_count': u'800', u'status': u'1', u'has_image': u'0', u'given_url': u'http://lens.blogs.nytimes.com/2012/10/09/looking-into-the-eyes-of-made-in-china/?partner=rss&emc=rss&smid=tw-nytimes', u'favorite': u'0', u'has_video': u'0', u'time_added': u'1349951324', u'time_updated': u'1482284773', u'time_read': u'1482284772', u'excerpt': u'Your clothes, your child\u2019s toys, even the device you use to read these words may have been made in China. They are among the $100 billion of goods that the United States imports from China each year \u2014 an exchange that has become an important issue in the 2012 presidential campaign.', u'resolved_title': u'Looking Into the Eyes of ‘Made in China’', u'authors': {u'3024958': {u'url': u'', u'author_id': u'3024958', u'item_id': u'233921121', u'name': u'KERRI MACDONALD'}}, u'resolved_id': u'233843309', u'item_id': u'233921121', u'time_favorited': u'0', u'is_index': u'0'}
'''
data = urllib.urlencode({
'access_token': access_token,
'consumer_key': POCKET_CONSUMER_KEY,
'detailType': 'complete',
'since': since_timestamp,
'state': 'all'
})
success = False
res = urlfetch.fetch(
url=GET_ENDPOINT,
payload=data,
method=urlfetch.POST,
deadline=60,
validate_certificate=True)
logging.debug(res.status_code)
latest_timestamp = 0
readables = []
if res.status_code == 200:
data = json.loads(res.content)
articles = data.get('list', {})
latest_timestamp = data.get('since', 0) #?
save = []
USE_RESOLVED_TITLE = True
if articles:
for id, article in articles.items():
source = 'pocket'
if USE_RESOLVED_TITLE:
title = article.get('resolved_title')
else:
title = article.get('given_title')
url = article.get('given_url')
status = article.get('status')
authors = article.get('authors')
excerpt = article.get('excerpt')
images = article.get('images')
time_added = int(article.get('time_added', 0)) * 1000
time_read = int(article.get('time_read', 0)) * 1000
dt_added = tools.dt_from_ts(time_added)
dt_read = tools.dt_from_ts(time_read) if time_read else None
tags = article.get('tags', {}).keys()
word_count = int(article.get('word_count', 0))
favorite = int(article.get('favorite', 0)) == 1
image_url = None
author = None
if images:
first_image = images.get('1')
if first_image:
image_url = first_image.get('src')
if authors:
author_keys = authors.keys()
if author_keys:
author = authors.get(author_keys[0], {}).get('name')
archived = int(status) == 1
read = archived and (not tags or 'unread' not in tags)
r = Readable.CreateOrUpdate(user, source_id=id, title=title, url=url,
image_url=image_url, author=author,
excerpt=excerpt, favorite=favorite,
dt_added=dt_added, word_count=word_count,
dt_read=dt_read,
tags=tags, source=source, read=read)
if r:
r.Update(read=archived, favorite=favorite, dt_read=dt_read)
save.append(r)
readables.append(r)
ndb.put_multi(save) # Save all
Readable.put_sd_batch(save)
success = True
else:
logging.debug(res.headers)
return (success, readables, latest_timestamp)
def sync(user, access_token):
'''
Return JSON array {title, author, isbn, image}
Sample dict from pocket:
{u'resolved_url': u'https://arxiv.org/abs/1701.06538', u'given_title': u'', u'is_article': u'1', u'sort_id': 16, u'word_count': u'221', u'status': u'0', u'has_image': u'0', u'given_url': u'https://arxiv.org/abs/1701.06538', u'favorite': u'0', u'has_video': u'0', u'time_added': u'1485774143', u'time_updated': u'1485774143', u'time_read': u'0', u'excerpt': u'Authors: Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean Abstract: The capacity of a neural network to absorb information is limited by its number of parameters.', u'resolved_title': u'Title: Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer', u'authors': {u'32207876': {u'url': u'', u'author_id': u'32207876', u'item_id': u'1576987151', u'name': u'cscs.CLcs.NEstatstat.ML'}}, u'resolved_id': u'1576987151', u'item_id': u'1576987151', u'time_favorited': u'0', u'is_index': u'0'}
{u'resolved_url': u'http://lens.blogs.nytimes.com/2012/10/09/looking-into-the-eyes-of-made-in-china/', u'given_title': u'http://lens.blogs.nytimes.com/2012/10/09/looking-into-the-eyes-of-made-in-c', u'is_article': u'1', u'sort_id': 99, u'word_count': u'800', u'status': u'1', u'has_image': u'0', u'given_url': u'http://lens.blogs.nytimes.com/2012/10/09/looking-into-the-eyes-of-made-in-china/?partner=rss&emc=rss&smid=tw-nytimes', u'favorite': u'0', u'has_video': u'0', u'time_added': u'1349951324', u'time_updated': u'1482284773', u'time_read': u'1482284772', u'excerpt': u'Your clothes, your child\u2019s toys, even the device you use to read these words may have been made in China. They are among the $100 billion of goods that the United States imports from China each year \u2014 an exchange that has become an important issue in the 2012 presidential campaign.', u'resolved_title': u'Looking Into the Eyes of ‘Made in China’', u'authors': {u'3024958': {u'url': u'', u'author_id': u'3024958', u'item_id': u'233921121', u'name': u'KERRI MACDONALD'}}, u'resolved_id': u'233843309', u'item_id': u'233921121', u'time_favorited': u'0', u'is_index': u'0'}
'''
dt = datetime.now() - timedelta(days=7)
init_sync_since = tools.unixtime(dt, ms=False)
TS_KEY = 'pocket_last_timestamp' # Seconds
since_timestamp = user.get_integration_prop(TS_KEY, init_sync_since)
data = urllib.urlencode({
'access_token': access_token,
'consumer_key': POCKET_CONSUMER_KEY,
'detailType': 'complete',
'since': since_timestamp,
'state': 'all'
})
success = False
logging.debug("Syncing pocket for %s since %s" % (user, dt))
res = urlfetch.fetch(
url=GET_ENDPOINT,
payload=data,
method=urlfetch.POST,
deadline=60,
validate_certificate=True)
logging.debug(res.status_code)
latest_timestamp = 0
readables = []
if res.status_code == 200:
data = json.loads(res.content)
articles = data.get('list', {})
latest_timestamp = data.get('since', 0) #?
save = []
USE_RESOLVED_TITLE = True
if articles:
for id, article in articles.items():
source = 'pocket'
if USE_RESOLVED_TITLE:
title = article.get('resolved_title')
else:
title = article.get('given_title')
url = article.get('given_url')
status = article.get('status')
authors = article.get('authors')
excerpt = article.get('excerpt')
images = article.get('images')
time_added = int(article.get('time_added', 0)) * 1000
time_read = int(article.get('time_read', 0)) * 1000
dt_added = tools.dt_from_ts(time_added)
dt_read = tools.dt_from_ts(time_read) if time_read else None
tags = article.get('tags', {}).keys()
word_count = int(article.get('word_count', 0))
favorite = int(article.get('favorite', 0)) == 1
image_url = None
author = None
if images:
first_image = images.get('1')
if first_image:
image_url = first_image.get('src')
if authors:
author_keys = authors.keys()
if author_keys:
author = authors.get(author_keys[0], {}).get('name')
archived = int(status) == 1
read = archived and (not tags or 'unread' not in tags)
r = Readable.CreateOrUpdate(user, source_id=id, title=title, url=url,
image_url=image_url, author=author,
excerpt=excerpt, favorite=favorite,
dt_added=dt_added, word_count=word_count,
dt_read=dt_read,
tags=tags, source=source, read=read)
if r:
r.Update(read=archived, favorite=favorite, dt_read=dt_read)
save.append(r)
readables.append(r)
ndb.put_multi(save) # Save all
Readable.put_sd_batch(save)
user.set_integration_prop(TS_KEY, latest_timestamp)
success = True
else:
logging.debug(res.headers)
return (success, readables, latest_timestamp)
def add_date_filters(self, start=None, end=None):
if start:
self.FILTERS.append("%s >= DATETIME('%s 00:00:00')" % (self.start_att, tools.iso_date(tools.dt_from_ts(start))))
if end:
self.FILTERS.append("%s < DATETIME('%s 23:59:59')" % (self.start_att, tools.iso_date(tools.dt_from_ts(end))))