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