async def get_shaped_timeseries(self, query: ShapeQuery) -> ModelResult:
logger.debug(
"\n--->>> Shape Query Called successfully on %s Model!! <<<---" %
self.name)
logger.debug("Spatial Component is: \n%s" % str(query.spatial))
logger.debug("Temporal Component is: \n%s" % str(query.temporal))
logger.debug(
"\nDerived LAT1: %s\nDerived LON1: %s\nDerived LAT2: %s\nDerived LON2: %s"
% query.spatial.expanded(0.05))
sr = await (self.get_shaped_resultcube(query))
logger.debug(sr)
logger.debug(sr.data)
# Check our param exists in the shaped result set
if len(sr.data) > 0:
logger.debug(">> Crack a beer we got there!! <<")
dps = [
self.get_datapoint_for_param(
b=sr.isel(time=t),
param=self.outputs["readings"]["prefix"])
for t in range(0, len(sr["time"]))
]
return ModelResult(model_name=self.name, data_points=dps)
else:
return ModelResult(model_name=self.name, data_points=[])
async def get_shaped_timeseries(self, query: ShapeQuery) -> ModelResult:
logger.debug(
"\n--->>> Shape Query Called successfully on %s Model!! <<<---" % self.name)
# logger.debug("Spatial Component is: \n%s" % str(query.spatial))
# logger.debug("Temporal Component is: \n%s" % str(query.temporal))
#
# logger.debug("\nDerived LAT1: %s\nDerived LON1: %s\nDerived LAT2: %s\nDerived LON2: %s" %
# query.spatial.expanded(0.05))
sr = await (self.get_shaped_resultcube(query))
sr.load()
dps = []
for r in sr['time']:
t = r['time'].values
o = sr.sel(time=t)
p = self.outputs['readings']['prefix']
df = o[p].to_dataframe()
df = df[p]
# TODO - This is a quick hack to massage the datetime format into a markup suitable for D3 & ngx-charts!
m = df.mean()
dps.append(DataPoint(observation_time=str(t).replace('.000000000', '.000Z'),
value=m,
mean=m,
minimum=df.min(),
maximum=df.max(),
deviation=0))
asyncio.sleep(1)
return ModelResult(model_name=self.name, data_points=dps)
async def get_timeseries(self, query: SpatioTemporalQuery) -> ModelResult:
"""
Essentially just time slicing the resultcube.
DataPoint actually handles the creation of values from stats.
:param query:
:return:
"""
logger.debug(
"--->>> SpatioTemporal Query Called on %s Model!! <<<---" % self.name)
sr = await (self.get_resultcube(query))
sr.load()
asyncio.sleep(1)
dps = [self.get_datapoint_for_param(b=sr.isel(time=t), param="DFMC")
for t in range(0, len(sr["time"]))]
return ModelResult(model_name=self.name, data_points=dps)
def subscribe(self, observer):
if dev.DEBUG:
dps = []
for i in range(3):
dps.append(
DataPoint(observation_time=dt.date(2018, 5, 17),
value=i,
mean=i,
minimum=i,
maximum=i,
deviation=0))
observer.on_next(
ModelResult(model_name='test', data_points=dps))
observer.on_completed()
else:
dps = []
pass
def subscribe(self, observer):
if dev.DEBUG:
logger.debug("Got subscription. Building response.")
for model in self.models:
dps = []
logger.debug('Building dummy response for model: %s' %
model.name)
for j in range(30):
dps.append(DummyResults.dummy_single(j))
observer.on_next(
ModelResult(model_name=model.name, data_points=dps))
observer.on_completed()
else:
dps = []
for model in self.models:
model.subscribe(observer)
# rx.Observable.merge()
pass
async def get_shaped_timeseries(self, query: ShapeQuery) -> ModelResult:
logger.debug(
"\n--->>> Shape Query Called successfully on %s Model!! <<<---" % self.name)
logger.debug("Spatial Component is: \n%s" % str(query.spatial))
logger.debug("Temporal Component is: \n%s" % str(query.temporal))
logger.debug("\nDerived LAT1: %s\nDerived LON1: %s\nDerived LAT2: %s\nDerived LON2: %s" %
query.spatial.expanded(0.05))
dps = []
try:
sr = await (self.get_shaped_resultcube(query))
sr.load()
# Values in AWRA are in range 0..1
# Normalise to between 0-100%
# sr[self.outputs["readings"]["prefix"]] *= 100
if dev.DEBUG:
logger.debug("Shaped ResultCube is: \n%s" % sr)
for r in sr['time']:
t = r['time'].values
o = sr.sel(time=t)
p = self.outputs['readings']['prefix']
df = o[p].to_dataframe()
df = df[p]
# TODO - This is a quick hack to massage the datetime format into a markup suitable for D3 & ngx-charts!
m = df.median()
dps.append(DataPoint(observation_time=str(t).replace('.000000000', '.000Z'),
value=m,
mean=df.mean(),
minimum=df.min(),
maximum=df.max(),
deviation=0))
except FileNotFoundError:
logger.exception('Files not found for date range.')
asyncio.sleep(1)
return ModelResult(model_name=self.name, data_points=dps)
async def get_shaped_timeseries(self, query: ShapeQuery) -> ModelResult:
logger.debug(
"\n--->>> Shape Query Called successfully on %s Model!! <<<---" %
self.name)
logger.debug("Spatial Component is: \n%s" % str(query.spatial))
logger.debug("Temporal Component is: \n%s" % str(query.temporal))
logger.debug(
"\nDerived LAT1: %s\nDerived LON1: %s\nDerived LAT2: %s\nDerived LON2: %s"
% query.spatial.expanded(0.05))
sr = await (self.get_shaped_resultcube(query))
sr.load()
# Values in AWRA are in range 0..1
# Normalise to between 0-100%
sr[self.outputs["readings"]["prefix"]] *= 100
dps = [
self.get_datapoint_for_param(
b=sr.sel(time=t), param=self.outputs["readings"]["prefix"])
for t in sr["time"]
]
asyncio.sleep(1)
return ModelResult(model_name=self.name, data_points=dps)
def get_timeseries(self, query: SpatioTemporalQuery) -> ModelResult:
# MAGIC HAPPENS HERE
# An array of mockup DataPoints for testing only
dr = DummyResults()
dps = dr.dummy_data(query)
return ModelResult(self.name, dps)
