def time_series_aggregated(
self, aggregation, aggregation_period,
dt_start=None, dt_end=None):
"""
Aggregated time series.
Result is a dictionary with timeseries. The keys are: (location,
parameter, option), where option is (GraphItemMixin.)TIME_SERIES_ALL,
TIME_SERIES_POSITIVE or TIME_SERIES_NEGATIVE
aggregation: see PredefinedGraph
'avg', 'sum'
aggregation_period: see PredefinedGraph
'day', 'month', 'quarter', 'year'
"""
source = self.fews_norm_source
series = self.series()
result = {}
for single_series in series:
obj = {
GraphItemMixin.TIME_SERIES_ALL: timeseries.TimeSeries(),
GraphItemMixin.TIME_SERIES_POSITIVE: timeseries.TimeSeries(),
GraphItemMixin.TIME_SERIES_NEGATIVE: timeseries.TimeSeries()}
# Somehow get the events with aggregation in it
# aggregation == PredefinedGraph.AGGREGATION_AVG, AGGREGATION_SUM
# aggregation_period == PredefinedGraph.PERIOD_YEAR, PERIOD_MONTH,
# PERIOD_QUARTER, PERIOD_DAY
# between dt_start and dt_end
events = Event.agg_from_raw(
single_series, dt_start=dt_start, dt_end=dt_end,
schema_prefix=source.database_schema_name,
agg_function=aggregation,
agg_period=aggregation_period).using(source.database_name)
# Put the events in Timeseries objects in obj
for event in events:
obj[GraphItemMixin.TIME_SERIES_ALL][event.timestamp] = (
event.value, event.flag, event.comment)
if event.value >= 0:
obj[GraphItemMixin.TIME_SERIES_POSITIVE][
event.timestamp] = (
event.value, event.flag, event.comment)
else:
obj[GraphItemMixin.TIME_SERIES_NEGATIVE][
event.timestamp] = (
event.value, event.flag, event.comment)
# Now put the timeseries in the result
for k in obj.keys():
obj[k].location_id = single_series.location
obj[k].parameter_id = single_series.parameter
obj[k].time_step = single_series.timestep
obj[k].units = single_series.unit
result[single_series.location,
single_series.parameter, k] = obj[k]
return result
def cached_time_series(identifier, start, end):
"""
Cached time series
"""
def time_series_key(identifier, start, end):
return str(hash(('ts::%s::%s:%s' % (
str(identifier), start, end)).replace(' ', '_')))
cache_key = time_series_key(identifier, start, end)
ts = cache.get(cache_key)
if ts is None:
# Actually fetching time series
source_name = identifier['fews_norm_source_slug']
source = FewsNormSource.objects.get(slug=source_name)
params = {}
if 'geo_ident' in identifier:
params['location'] = identifier['geo_ident']
if 'par_ident' in identifier:
params['parameter'] = identifier['par_ident']
if 'mod_ident' in identifier:
params['moduleinstance'] = identifier['mod_ident']
if 'stp_ident' in identifier:
params['timestep'] = identifier['stp_ident']
if 'qua_ident' in identifier and identifier['qua_ident']:
params['qualifierset'] = identifier['qua_ident']
series = Series.from_raw(
schema_prefix=source.database_schema_name,
params=params).using(source.database_name)
ts = Event.time_series(source, series, start, end)
cache.set(cache_key, ts)
return ts
def get(self, request):
deadline = datetime.strptime(request.GET['timestamp'],
"%Y-%m-%dT%H:%M:%S")
lppairs = [tuple(k.split(':')) for k in request.GET['keys'].split(',')]
filtered_series = Series.from_lppairs(lppairs)
if filtered_series:
e = Event.filter_latest_before_deadline(filtered_series, deadline)
return [(i.series.location.id, i.series.parameter.id, i.timestamp,
i.value, i.flag, i.comment) for i in e]
else:
return None
def time_series(
self, series=None, dt_start=None, dt_end=None, with_comments=False):
"""
Return dictionary of timeseries.
Low level fewsnorm stuff.
Keys are (location, parameter), value is timeseries object.
1) which series
2) retrieve events (with comments) for each series
Note: with_comments does nothing anymore, comments are always
returned. The option is still there for compatibility.
"""
if not series:
series = self.series()
source = self.fews_norm_source
events = Event.time_series(source, series, dt_start, dt_end)
return events