def swap_and_compute_levels(tg_data, tg_info):
columns = tg_data["columns"]
swapped: Dict[Any, List[Any]] = {c: [] for c in columns}
for step in tg_data["points"]:
row = dict(zip(columns, step))
for k, v in row.items():
swapped[k].append(v)
if row["average"] is not None and row["stdev"] is not None:
upper_0, upper_1, lower_0, lower_1 = prediction.estimate_levels(
reference_value=row["average"],
stdev=row["stdev"],
levels_lower=tg_info.get("levels_lower"),
levels_upper=tg_info.get("levels_upper"),
levels_upper_lower_bound=tg_info.get("levels_upper_min"),
levels_factor=1.0,
)
swapped.setdefault("upper_warn", []).append(upper_0 or 0)
swapped.setdefault("upper_crit", []).append(upper_1 or 0)
swapped.setdefault("lower_warn", []).append(lower_0 or 0)
swapped.setdefault("lower_crit", []).append(lower_1 or 0)
else:
swapped.setdefault("upper_warn", []).append(0)
swapped.setdefault("upper_crit", []).append(0)
swapped.setdefault("lower_warn", []).append(0)
swapped.setdefault("lower_crit", []).append(0)
return swapped
def test_estimate_levels(reference_value, reference_deviation, params,
levels_factor, result):
assert prediction.estimate_levels(
reference_value=reference_value,
stdev=reference_deviation,
levels_lower=params.get("levels_lower"),
levels_upper=params.get("levels_upper"),
levels_upper_lower_bound=params.get("levels_upper_min"),
levels_factor=levels_factor,
) == result
def swap_and_compute_levels(tg_data, tg_info):
columns = tg_data["columns"]
swapped = dict([(c, []) for c in columns])
for step in tg_data["points"]:
row = dict(zip(columns, step))
for k, v in row.items():
swapped[k].append(v)
if row["average"] is not None and row["stdev"] is not None:
_, (upper_0, upper_1, lower_0, lower_1) = prediction.estimate_levels(row, tg_info, 1.0)
swapped.setdefault("upper_warn", []).append(upper_0 or 0)
swapped.setdefault("upper_crit", []).append(upper_1 or 0)
swapped.setdefault("lower_warn", []).append(lower_0 or 0)
swapped.setdefault("lower_crit", []).append(lower_1 or 0)
else:
swapped.setdefault("upper_warn", []).append(0)
swapped.setdefault("upper_crit", []).append(0)
swapped.setdefault("lower_warn", []).append(0)
swapped.setdefault("lower_crit", []).append(0)
return swapped
def test_estimate_levels(reference, params, levels_factor, result):
assert prediction.estimate_levels(reference, params, levels_factor) == result
