def _load_prediction_information(
*,
tg_name: Optional[str],
pred_dir: str,
) -> Tuple[Tuple[str, prediction.PredictionInfo], Sequence[Tuple[str, str]]]:
# Load all prediction information, sort by time of generation
if not os.path.exists(pred_dir):
raise MKGeneralException(
_("There is currently no prediction information "
"available for this service."))
selected_timegroup: Optional[Tuple[str, prediction.PredictionInfo]] = None
timegroups: List[Tuple[str, prediction.PredictionInfo]] = []
now = time.time()
for f in os.listdir(pred_dir):
if not f.endswith(".info"):
continue
tg_info = prediction.retrieve_data_for_prediction(
pred_dir + "/" + f,
"<unknown>"
if selected_timegroup is None else str(selected_timegroup[1]),
)
if tg_info is None:
continue
current_tg_name = f[:-5]
timegroups.append((current_tg_name, tg_info))
if current_tg_name == tg_name or (
tg_name is None and
(tg_info["range"][0] <= now <= tg_info["range"][1])):
selected_timegroup = (current_tg_name, tg_info)
timegroups.sort(key=lambda x: x[1]["range"][0])
choices: List[Tuple[str, str]] = [(name, name.title())
for name, _tg_info in timegroups]
if selected_timegroup is None:
if not timegroups:
raise MKGeneralException(_("Missing prediction information."))
selected_timegroup = timegroups[0]
return selected_timegroup, choices
def page_graph():
host = html.request.get_str_input_mandatory("host")
service = html.request.get_str_input_mandatory("service")
dsname = html.request.get_str_input_mandatory("dsname")
html.header(_("Prediction for %s - %s - %s") % (host, service, dsname))
# Get current value from perf_data via Livestatus
current_value = get_current_perfdata(host, service, dsname)
pred_dir = prediction.predictions_dir(host, service, dsname)
if not os.path.exists(pred_dir):
raise MKGeneralException(
_("There is currently no prediction information "
"available for this service."))
# Load all prediction information, sort by time of generation
tg_name = html.request.var("timegroup")
timegroup = None
timegroups = [] # type: List[prediction.PredictionInfo]
now = time.time()
for f in os.listdir(pred_dir):
if not f.endswith(".info"):
continue
tg_info = prediction.retrieve_data_for_prediction(
pred_dir + "/" + f,
"<unknown>" if timegroup is None else timegroup)
if tg_info is None:
continue
tg_info["name"] = f[:-5]
timegroups.append(tg_info)
if tg_info["name"] == tg_name or (
tg_name is None and
(tg_info["range"][0] <= now <= tg_info["range"][1])):
timegroup = tg_info
tg_name = tg_info["name"]
timegroups.sort(key=lambda x: x["range"][0])
choices = [(tg_info_["name"], tg_info_["name"].title())
for tg_info_ in timegroups]
if not timegroup:
if not timegroups:
raise MKGeneralException(_("Missing prediction information."))
timegroup = timegroups[0]
tg_name = choices[0][0]
if tg_name is None:
raise Exception("should not happen")
html.begin_form("prediction")
html.write(_("Show prediction for "))
html.dropdown("timegroup",
choices,
deflt=tg_name,
onchange="document.prediction.submit();")
html.hidden_fields()
html.end_form()
# Get prediction data
path = pred_dir + "/" + timegroup["name"]
tg_data = prediction.retrieve_data_for_prediction(path, tg_name)
if tg_data is None:
raise MKGeneralException(_("Missing prediction data."))
swapped = swap_and_compute_levels(tg_data, timegroup['params'])
vertical_range = compute_vertical_range(swapped)
legend = [
("#000000", _("Reference")),
("#ffffff", _("OK area")),
("#ffff00", _("Warning area")),
("#ff0000", _("Critical area")),
]
if current_value is not None:
legend.append(("#0000ff", _("Current value: %.2f") % current_value))
create_graph(timegroup["name"], graph_size, timegroup["range"],
vertical_range, legend)
if "levels_upper" in timegroup['params']:
render_dual_area(swapped["upper_warn"], swapped["upper_crit"],
"#fff000", 0.4)
render_area_reverse(swapped["upper_crit"], "#ff0000", 0.1)
if "levels_lower" in timegroup['params']:
render_dual_area(swapped["lower_crit"], swapped["lower_warn"],
"#fff000", 0.4)
render_area(swapped["lower_crit"], "#ff0000", 0.1)
vscala_low = vertical_range[0]
vscala_high = vertical_range[1]
vert_scala = compute_vertical_scala(vscala_low, vscala_high)
time_scala = [[timegroup["range"][0] + i * 3600,
"%02d:00" % i] for i in range(0, 25, 2)]
render_coordinates(vert_scala, time_scala)
if "levels_lower" in timegroup['params']:
render_dual_area(swapped["average"], swapped["lower_warn"], "#ffffff",
0.5)
render_curve(swapped["lower_warn"], "#e0e000", square=True)
render_curve(swapped["lower_crit"], "#f0b0a0", square=True)
if "levels_upper" in timegroup['params']:
render_dual_area(swapped["upper_warn"], swapped["average"], "#ffffff",
0.5)
render_curve(swapped["upper_warn"], "#e0e000", square=True)
render_curve(swapped["upper_crit"], "#f0b0b0", square=True)
render_curve(swapped["average"], "#000000")
render_curve(swapped["average"], "#000000") # repetition makes line bolder
# Try to get current RRD data and render it also
from_time, until_time = timegroup["range"]
now = time.time()
if from_time <= now <= until_time:
timeseries = prediction.get_rrd_data(host, service, dsname, "MAX",
from_time, until_time)
rrd_data = timeseries.values
render_curve(rrd_data, "#0000ff", 2)
if current_value is not None:
rel_time = (now - prediction.timezone_at(now)) % timegroup["slice"]
render_point(timegroup["range"][0] + rel_time, current_value,
"#0000ff")
html.footer()
def page_graph():
host = html.request.get_str_input_mandatory("host")
service = html.request.get_str_input_mandatory("service")
dsname = html.request.get_str_input_mandatory("dsname")
breadcrumb = make_service_breadcrumb(host, service)
html.header(
_("Prediction for %s - %s - %s") % (host, service, dsname), breadcrumb)
# Get current value from perf_data via Livestatus
current_value = get_current_perfdata(host, service, dsname)
pred_dir = prediction.predictions_dir(host, service, dsname)
(tg_name, timegroup), choices = _load_prediction_information(
tg_name=html.request.var("timegroup"),
pred_dir=pred_dir,
)
html.begin_form("prediction")
html.write(_("Show prediction for "))
html.dropdown("timegroup",
choices,
deflt=tg_name,
onchange="document.prediction.submit();")
html.hidden_fields()
html.end_form()
# Get prediction data
path = pred_dir + "/" + tg_name
tg_data = prediction.retrieve_data_for_prediction(path, tg_name)
if tg_data is None:
raise MKGeneralException(_("Missing prediction data."))
swapped = swap_and_compute_levels(tg_data, timegroup['params'])
vertical_range = compute_vertical_range(swapped)
legend = [
("#000000", _("Reference")),
("#ffffff", _("OK area")),
("#ffff00", _("Warning area")),
("#ff0000", _("Critical area")),
]
if current_value is not None:
legend.append(("#0000ff", _("Current value: %.2f") % current_value))
create_graph(tg_name, graph_size, timegroup["range"], vertical_range,
legend)
if "levels_upper" in timegroup['params']:
render_dual_area(swapped["upper_warn"], swapped["upper_crit"],
"#fff000", 0.4)
render_area_reverse(swapped["upper_crit"], "#ff0000", 0.1)
if "levels_lower" in timegroup['params']:
render_dual_area(swapped["lower_crit"], swapped["lower_warn"],
"#fff000", 0.4)
render_area(swapped["lower_crit"], "#ff0000", 0.1)
vscala_low = vertical_range[0]
vscala_high = vertical_range[1]
vert_scala = compute_vertical_scala(vscala_low, vscala_high)
time_scala = [[timegroup["range"][0] + i * 3600,
"%02d:00" % i] for i in range(0, 25, 2)]
render_coordinates(vert_scala, time_scala)
if "levels_lower" in timegroup['params']:
render_dual_area(swapped["average"], swapped["lower_warn"], "#ffffff",
0.5)
render_curve(swapped["lower_warn"], "#e0e000", square=True)
render_curve(swapped["lower_crit"], "#f0b0a0", square=True)
if "levels_upper" in timegroup['params']:
render_dual_area(swapped["upper_warn"], swapped["average"], "#ffffff",
0.5)
render_curve(swapped["upper_warn"], "#e0e000", square=True)
render_curve(swapped["upper_crit"], "#f0b0b0", square=True)
render_curve(swapped["average"], "#000000")
render_curve(swapped["average"], "#000000") # repetition makes line bolder
# Try to get current RRD data and render it also
from_time, until_time = timegroup["range"]
now = time.time()
if from_time <= now <= until_time:
timeseries = prediction.get_rrd_data(host, service, dsname, "MAX",
from_time, until_time)
rrd_data = timeseries.values
render_curve(rrd_data, "#0000ff", 2)
if current_value is not None:
rel_time = (now - prediction.timezone_at(now)) % timegroup["slice"]
render_point(timegroup["range"][0] + rel_time, current_value,
"#0000ff")
html.footer()