def handle(self, **options):
grower = GrowerInfo.objects.get(pk=1)
basin = grower.basin_set.all()[0]
self.ds = FewsJdbcDataSource(basin)
tbl = DemandTable()
model = CalculationModel(tbl, self.ds)
now = datetime.datetime.now(pytz.utc)
history = now - datetime.timedelta(days=1)
future = now + datetime.timedelta(days=5)
now = round_date(now)
history = round_date(history)
future = round_date(future)
self.mkplot('history', history, now)
self.mkplot('span', history, future)
self.mkplot('future', now, future)
now = mktim(2012, 8, 5, 8, 0) # some rain fell here
now = round_date(datetime.datetime.now(tz=pytz.utc))
future = now + settings.CONTROLNEXT_FILL_PREDICT_FUTURE
ts = model.predict_fill(now, future, 20, 100, 100)
plot('predict_fill', ts['scenarios']['mean']['prediction'],
ts['history'])
# plot('predict_fill_rain', ts['rain'])
plot('predict_fill_uitstroom',
ts['scenarios']['mean']['intermediate']['uitstroom'])
plot('predict_fill_toestroom',
ts['scenarios']['mean']['intermediate']['toestroom'])
plot('predict_fill_max_uitstroom',
ts['intermediate']['max_uitstroom'])
plot('predict_fill_watervraag', ts['intermediate']['demand'])
def prediction(self, t0, desired_fill_pct, demand_exaggerate, rain_exaggerate):
tbl = DemandTable()
ds = FewsJdbcDataSource()
model = CalculationModel(tbl, ds)
future = t0 + fill_predict_future
prediction = model.predict_fill(t0, future, desired_fill_pct, demand_exaggerate, rain_exaggerate)
# TODO should use dict comprehension in py > 2.6
data = dict(
[(name, series_to_js(scenario["prediction"])) for name, scenario in prediction["scenarios"].items()]
)
data["history"] = series_to_js(prediction["history"])
graph_info = {
"data": data,
"x0": datetime_to_js(t0),
"y_marking_min": min_berging_pct,
"y_marking_max": max_berging_pct,
"x_marking_omslagpunt": datetime_to_js(prediction["scenarios"]["mean"]["omslagpunt"]),
"y_marking_desired_fill": desired_fill_pct,
"desired_fill": desired_fill_pct,
}
result = {
"graph_info": graph_info,
"overflow_24h": prediction["scenarios"]["mean"]["overstort_24h"],
"overflow_5d": prediction["scenarios"]["mean"]["overstort_5d"],
"demand_week": tbl.get_week_demand_on(t0),
"demand_24h": tbl.get_total_demand(t0, t0 + datetime.timedelta(hours=24)),
"current_fill": prediction["current_fill"],
}
return result
def advanced(self, t0, desired_fill_pct, demand_exaggerate, rain_exaggerate, graph_type):
tbl = DemandTable()
ds = FewsJdbcDataSource()
model = CalculationModel(tbl, ds)
future = t0 + fill_predict_future
prediction = model.predict_fill(t0, future, desired_fill_pct, demand_exaggerate, rain_exaggerate)
data = []
unit = ""
if graph_type == "demand":
data = prediction["intermediate"]["demand"]
unit = "m3"
elif graph_type == "max_uitstroom":
data = prediction["intermediate"]["max_uitstroom"]
unit = "m3"
elif graph_type == "toestroom":
data = prediction["scenarios"]["mean"]["intermediate"]["toestroom"]
unit = "m3"
elif graph_type == "uitstroom":
data = prediction["scenarios"]["mean"]["intermediate"]["uitstroom"]
unit = "m3"
result = {"graph_info": {"data": series_to_js(data), "x0": datetime_to_js(t0), "unit": unit}}
return result
def prediction(self, t0, outflow_open, outflow_closed, outflow_capacity):
tbl = EvaporationTable(self.basin, self.constants.rain_flood_surface)
ds = FewsJdbcDataSource(self.basin, self.constants)
model = CalculationModel(tbl, ds)
future = t0 + settings.CONTROLNEXT_FILL_PREDICT_FUTURE
prediction = model.predict_fill(t0, future, outflow_open,
outflow_closed, outflow_capacity)
data = {name: self.series_to_js(scenario['prediction'])
for name, scenario in prediction['scenarios'].items()}
data['history'] = self.series_to_js(prediction['history'])
graph_info = {
'data': data,
}
result = {
'graph_info': graph_info,
'overflow_24h': prediction['scenarios']['mean']['overstort_24h'],
'overflow_5d': prediction['scenarios']['mean']['overstort_5d'],
}
return result
class CalculationModelTest(TestCase):
def setUp(self):
tbl = DemandTable()
self.tbl = tbl
jdbc_source = JdbcSourceFactory.create(
connector_string=('jdbc:vjdbc:rmi://p-fews-jd-d3.external-nens.'
'local:2001/VJdbc,FewsDataStore'),
customfilter='',
filter_tree_root='',
jdbc_tag_name='controlnext_delfland',
jdbc_url=('jdbc:vjdbc:rmi://p-fews-jd-d3.external-nens.local:2001'
'/VJdbc,FewsDataStore'),
name='controlnext',
slug='controlnext',
usecustomfilter=False,
timezone_string='UTC'
)
self.grower_info = GrowerInfoFactory.create(jdbc_source=jdbc_source)
basin = BasinFactory.create(owner=self.grower_info,
jdbc_source=jdbc_source)
ds = FewsJdbcDataSource(basin)
self.ds = ds
self.model = CalculationModel(tbl, ds)
@mock.patch('controlnext.fews_data.FewsJdbcDataSource.get_fill',
get_fill_mock_data)
@mock.patch('controlnext.fews_data.FewsJdbcDataSource.get_rain',
get_rain_mock_data)
def test_calc_model(self):
now = mktim(2012, 8, 5, 8, 0) # some rain fell here
now = round_date(datetime.datetime.now(tz=pytz.utc))
future = now + settings.CONTROLNEXT_FILL_PREDICT_FUTURE
ts = self.model.predict_fill(now, future, 20, 100, 100)
self.assertGreater(len(ts['scenarios']['mean']['prediction']), 10)
def setUp(self):
tbl = DemandTable()
self.tbl = tbl
jdbc_source = JdbcSourceFactory.create(
connector_string=('jdbc:vjdbc:rmi://p-fews-jd-d3.external-nens.'
'local:2001/VJdbc,FewsDataStore'),
customfilter='',
filter_tree_root='',
jdbc_tag_name='controlnext_delfland',
jdbc_url=('jdbc:vjdbc:rmi://p-fews-jd-d3.external-nens.local:2001'
'/VJdbc,FewsDataStore'),
name='controlnext',
slug='controlnext',
usecustomfilter=False,
timezone_string='UTC'
)
self.grower_info = GrowerInfoFactory.create(jdbc_source=jdbc_source)
basin = BasinFactory.create(owner=self.grower_info,
jdbc_source=jdbc_source)
ds = FewsJdbcDataSource(basin)
self.ds = ds
self.model = CalculationModel(tbl, ds)