def evaluate_time_series_expression(expression, rrd_data) -> List[TimeSeries]:
if rrd_data:
sample_data = next(iter(rrd_data.values()))
num_points = len(sample_data)
twindow = sample_data.twindow
else:
# no data, default clean graph, use for pure scalars on custom graphs
num_points = 1
twindow = (0, 60, 60)
if expression[0] == "operator":
operator_id, operands = expression[1:]
operands_evaluated_l = [evaluate_time_series_expression(a, rrd_data) for a in operands]
operands_evaluated = [item for lists in operands_evaluated_l for item in lists]
return [time_series_math(operator_id, operands_evaluated)]
if expression[0] == "transformation":
(transform, conf), operands = expression[1:]
operands_evaluated = evaluate_time_series_expression(operands[0], rrd_data)
return evaluate_timeseries_transformation(transform, conf, operands_evaluated)
if expression[0] == "rrd":
key = tuple(expression[1:])
if key in rrd_data:
return [rrd_data[key]]
return [TimeSeries([None] * num_points, twindow)]
if expression[0] == "constant":
return [TimeSeries([expression[1]] * num_points, twindow)]
if expression[0] == "combined":
metrics = resolve_combined_single_metric_spec(expression[1])
curves = []
for m in metrics:
for curve in evaluate_time_series_expression(m['expression'], rrd_data):
curve.metadata = {k: m[k] for k in m if k in ['line_type', 'title']}
curves.append(curve)
return curves
raise NotImplementedError()
def evaluate_time_series_expression(expression, rrd_data):
if rrd_data:
sample_data = next(iter(rrd_data.values()))
num_points = len(sample_data)
twindow = sample_data.twindow
else:
# no data, default clean graph, use for pure scalars on custom graphs
num_points = 1
twindow = (0, 60, 60)
if expression[0] == "operator":
operator_id, operands = expression[1:]
operands_evaluated = [evaluate_time_series_expression(a, rrd_data) for a in operands]
return time_series_math(operator_id, operands_evaluated)
if expression[0] == "transformation":
(transform, conf), operands = expression[1:]
operands_evaluated = evaluate_time_series_expression(operands[0], rrd_data)
return evaluate_timeseries_transformation(transform, conf, operands_evaluated)
if expression[0] == "rrd":
key = tuple(expression[1:])
if key in rrd_data:
return rrd_data[key]
return TimeSeries([None] * num_points, twindow)
if expression[0] == "constant":
return TimeSeries([expression[1]] * num_points, twindow)
if expression[0] == "combined":
metrics = resolve_combined_single_metric_spec(expression[1])
return [(m["line_type"], m["color"], m['title'],
evaluate_time_series_expression(m['expression'], rrd_data)) for m in metrics]
raise NotImplementedError()
def resolve_combined_single_metric_spec(expression):
return evaluate_timeseries_transformation(None, None, None)
# no data, default clean graph, use for pure scalars on custom graphs
num_points = 1
twindow = (0, 60, 60)
if expression[0] == "operator":
operator_id, operands = expression[1:]
operands_evaluated = list(
chain.from_iterable(evaluate_time_series_expression(a, rrd_data) for a in operands))
if result := time_series_math(operator_id, operands_evaluated):
return [result]
return []
if expression[0] == "transformation":
(transform, conf), operands = expression[1:]
operands_evaluated = evaluate_time_series_expression(operands[0], rrd_data)
return evaluate_timeseries_transformation(transform, conf, operands_evaluated)
if expression[0] == "rrd":
key = tuple(expression[1:])
if key in rrd_data:
return [rrd_data[key]]
return [TimeSeries([None] * num_points, twindow)]
if expression[0] == "constant":
return [TimeSeries([expression[1]] * num_points, twindow)]
if expression[0] == "combined":
metrics = resolve_combined_single_metric_spec(expression[1])
curves = []
for m in metrics:
for curve in evaluate_time_series_expression(m['expression'], rrd_data):