def test_export_import():
log_scale = LogScale(low=-1, high=1, log_base=2)
log_scale_export = log_scale.export()
assert log_scale_export["width"] == 2
assert log_scale_export["class"] == "LogScale"
assert (scale_factory(log_scale.export())) == log_scale
linear_scale = Scale(low=1, high=10000)
assert (scale_factory(linear_scale.export())) == linear_scale
linear_date_scale = TimeScale(low=631152000, high=946684800)
assert (scale_factory(linear_date_scale.export())) == linear_date_scale
def test_serialization():
assert hash(Scale(0, 100)) == hash(Scale(0, 100))
assert hash(Scale(0, 100)) != hash(Scale(100, 200))
assert hash(LogScale(0, 100, 10)) == hash(LogScale(0, 100, 10))
assert hash(LogScale(0, 100, 10)) != hash(LogScale(0, 100, 100))
assert hash(TimeScale(946684800, 1592914415)) == hash(
TimeScale(946684800, 1592914415))
assert hash(TimeScale(631152000, 1592914415)) != hash(
TimeScale(946684800, 1592914415))
assert (hash(LogScale(0, 100, 1)) != hash(Scale(0, 100)) != hash(
TimeScale(631152000, 946684800)))
def __init__(
self,
id: int,
metaculus: Any,
data: Dict,
name=None,
):
super().__init__(id, metaculus, data, name)
self.scale = TimeScale(
self.date_to_timestamp(self.possibilities["scale"]["min"]),
self.date_to_timestamp(self.possibilities["scale"]["max"]),
)
1.6001953904517954,
2.0558220666186604,
2.5114487427855257,
2.9670754189523905,
]
)
densities = np.array(
[
0.05020944540593859,
0.3902426887736647,
0.5887675161478794,
0.19516571803813396,
0.33712516238248535,
0.4151935926066581,
0.16147625748938946,
0.03650993407810862,
]
)
return {"xs": xs, "densities": densities}
scales_to_test = [
Scale(0, 1),
Scale(0, 10000),
Scale(-1, 1),
LogScale(0.01, 100, 10),
LogScale(0.01, 1028, 2),
TimeScale(631152000, 946684800),
TimeScale(2000, 2051222400),
]