def loads(cls, raw: str, *, name: Timegroup) -> 'PredictionInfo':
data = json.loads(raw)
range_ = data["range"]
return cls(
name=name, # explicitly passed. (not in `data` before 2.1)
time=int(data["time"]),
range=(Timestamp(range_[0]), Timestamp(range_[1])),
cf=ConsolidationFunctionName(data["cf"]),
dsname=MetricName(data["dsname"]),
slice=int(data["slice"]),
params=dict(data["params"]),
)
def loads(cls, raw: str) -> 'PredictionData':
data = json.loads(raw)
return cls(
columns=[str(e) for e in data["columns"]],
points=[[None if e is None else float(e) for e in elist] for elist in data["points"]],
num_points=int(data["num_points"]),
data_twindow=[Timestamp(e) for e in data["data_twindow"]],
step=Seconds(data["step"]),
)