/usr/lib/python3/dist-packages/gnocchi/rest/aggregates/operations.py is in python3-gnocchi 4.2.0-0ubuntu5.
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#
# Copyright © 2016-2017 Red Hat, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import numbers
import numpy
from numpy.lib.stride_tricks import as_strided
from gnocchi import carbonara
from gnocchi.rest.aggregates import exceptions
AGG_MAP = {
'mean': numpy.nanmean,
'median': numpy.nanmedian,
'std': numpy.nanstd,
'min': numpy.nanmin,
'max': numpy.nanmax,
'sum': numpy.nansum,
'var': numpy.nanvar,
'count': lambda values, axis: numpy.count_nonzero(
~numpy.isnan(values), axis=axis),
}
# TODO(sileht): expose all operators in capability API
binary_operators = {
u"=": numpy.equal,
u"==": numpy.equal,
u"eq": numpy.equal,
u"<": numpy.less,
u"lt": numpy.less,
u">": numpy.greater,
u"gt": numpy.greater,
u"<=": numpy.less_equal,
u"≤": numpy.less_equal,
u"le": numpy.less_equal,
u">=": numpy.greater_equal,
u"≥": numpy.greater_equal,
u"ge": numpy.greater_equal,
u"!=": numpy.not_equal,
u"≠": numpy.not_equal,
u"ne": numpy.not_equal,
u"%": numpy.mod,
u"mod": numpy.mod,
u"+": numpy.add,
u"add": numpy.add,
u"-": numpy.subtract,
u"sub": numpy.subtract,
u"*": numpy.multiply,
u"×": numpy.multiply,
u"mul": numpy.multiply,
u"/": numpy.true_divide,
u"÷": numpy.true_divide,
u"div": numpy.true_divide,
u"**": numpy.power,
u"^": numpy.power,
u"pow": numpy.power,
}
# TODO(sileht): adds, numpy.around, but it take a decimal argument to handle
unary_operators = {
u"abs": numpy.absolute,
u"absolute": numpy.absolute,
u"neg": numpy.negative,
u"negative": numpy.negative,
u"cos": numpy.cos,
u"sin": numpy.sin,
u"tan": numpy.tan,
u"floor": numpy.floor,
u"ceil": numpy.ceil,
}
unary_operators_with_timestamps = {
u"rateofchange": lambda t, v: (t[1:], numpy.diff(v.T).T)
}
def handle_unary_operator(nodes, granularity, timestamps, initial_values,
is_aggregated, references):
op = nodes[0]
granularity, timestamps, values, is_aggregated = evaluate(
nodes[1], granularity, timestamps, initial_values,
is_aggregated, references)
if op in unary_operators:
values = unary_operators[op](values)
else:
timestamps, values = unary_operators_with_timestamps[op](
timestamps, values)
return granularity, timestamps, values, is_aggregated
def handle_binary_operator(nodes, granularity, timestamps,
initial_values, is_aggregated, references):
op = nodes[0]
g1, t1, v1, is_a1 = evaluate(nodes[1], granularity, timestamps,
initial_values, is_aggregated, references)
g2, t2, v2, is_a2 = evaluate(nodes[2], granularity, timestamps,
initial_values, is_aggregated, references)
is_aggregated = is_a1 or is_a2
# We keep the computed timeseries
if isinstance(v1, numpy.ndarray) and isinstance(v2, numpy.ndarray):
if not numpy.array_equal(t1, t2) or g1 != g2:
raise exceptions.UnAggregableTimeseries(
references,
"Can't compute timeseries with different "
"granularity %s <> %s" % (nodes[1], nodes[2]))
timestamps = t1
granularity = g1
is_aggregated = True
elif isinstance(v2, numpy.ndarray):
timestamps = t2
granularity = g2
else:
timestamps = t1
granularity = g1
values = binary_operators[op](v1, v2)
return granularity, timestamps, values, is_aggregated
def handle_aggregate(agg, granularity, timestamps, values, is_aggregated,
references):
values = numpy.array([AGG_MAP[agg](values, axis=1)]).T
if values.shape[1] != 1:
raise RuntimeError("Unexpected resulting aggregated array shape: %s" %
values)
return (granularity, timestamps, values, True)
def handle_rolling(agg, granularity, timestamps, values, is_aggregated,
references, window):
if window > len(values):
raise exceptions.UnAggregableTimeseries(
references,
"Rolling window '%d' is greater than serie length '%d'" %
(window, len(values))
)
timestamps = timestamps[window - 1:]
values = values.T
# rigtorp.se/2011/01/01/rolling-statistics-numpy.html
shape = values.shape[:-1] + (values.shape[-1] - window + 1, window)
strides = values.strides + (values.strides[-1],)
new_values = AGG_MAP[agg](as_strided(values, shape=shape, strides=strides),
axis=-1)
return granularity, timestamps, new_values.T, is_aggregated
def handle_resample(agg, granularity, timestamps, values, is_aggregated,
references, sampling):
# TODO(sileht): make a more optimised version that
# compute the data across the whole matrix
new_values = None
result_timestamps = timestamps
for ts in values.T:
ts = carbonara.AggregatedTimeSerie.from_data(None, agg, timestamps, ts)
ts = ts.resample(sampling)
result_timestamps = ts["timestamps"]
if new_values is None:
new_values = numpy.array([ts["values"]])
else:
new_values = numpy.concatenate((new_values, [ts["values"]]))
return sampling, result_timestamps, new_values.T, is_aggregated
def handle_aggregation_operator(nodes, granularity, timestamps, initial_values,
is_aggregated, references):
op = aggregation_operators[nodes[0]]
agg = nodes[1]
subnodes = nodes[-1]
args = nodes[2:-1]
granularity, timestamps, values, is_aggregated = evaluate(
subnodes, granularity, timestamps, initial_values,
is_aggregated, references)
return op(agg, granularity, timestamps, values, is_aggregated,
references, *args)
aggregation_operators = {
u"aggregate": handle_aggregate,
u"rolling": handle_rolling,
u"resample": handle_resample,
}
def sanity_check(method):
# NOTE(sileht): This is important checks, because caller may use zip and
# build an incomplete timeseries without we notice the result is
# unexpected.
def inner(*args, **kwargs):
granularity, timestamps, values, is_aggregated = method(
*args, **kwargs)
t_len = len(timestamps)
if t_len > 2 and not ((timestamps[1] - timestamps[0]) /
granularity).is_integer():
# NOTE(sileht): numpy.mod is not possible with timedelta64,
# we don't really care about the remainder value, instead we just
# check we don't have remainder, by using floor_divide and checking
# the result is an integer.
raise RuntimeError("timestamps and granularity doesn't match: "
"%s vs %s" % (timestamps[1] - timestamps[0],
granularity))
elif isinstance(values, numpy.ndarray) and t_len != len(values):
raise RuntimeError("timestamps and values length are different: "
"%s vs %s" % (t_len, len(values)))
return granularity, timestamps, values, is_aggregated
return inner
@sanity_check
def evaluate(nodes, granularity, timestamps, initial_values, is_aggregated,
references):
if isinstance(nodes, numbers.Number):
return granularity, timestamps, nodes, is_aggregated
elif nodes[0] in aggregation_operators:
return handle_aggregation_operator(nodes, granularity, timestamps,
initial_values, is_aggregated,
references)
elif nodes[0] in binary_operators:
return handle_binary_operator(nodes, granularity, timestamps,
initial_values, is_aggregated,
references)
elif (nodes[0] in unary_operators or
nodes[0] in unary_operators_with_timestamps):
return handle_unary_operator(nodes, granularity, timestamps,
initial_values, is_aggregated,
references)
elif nodes[0] == "metric":
if isinstance(nodes[1], list):
predicat = lambda r: r in nodes[1:]
else:
predicat = lambda r: r == nodes[1:]
indexes = [i for i, r in enumerate(references) if predicat(r)]
return (granularity, timestamps, initial_values.T[indexes].T,
is_aggregated)
else:
raise RuntimeError("Operation node tree is malformed: %s" % nodes)
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