code
string | signature
string | docstring
string | loss_without_docstring
float64 | loss_with_docstring
float64 | factor
float64 |
|---|---|---|---|---|---|
'''
Alternative constructor which accepts a path as taken from URL and uses
the given app or the current app config to get the real path.
If class has attribute `generic` set to True, `directory_class` or
`file_class` will be used as type.
:param path: relative path as from URL
:param app: optional, flask application
:return: file object pointing to path
:rtype: File
'''
app = app or current_app
base = app.config['directory_base']
path = urlpath_to_abspath(path, base)
if not cls.generic:
kls = cls
elif os.path.isdir(path):
kls = cls.directory_class
else:
kls = cls.file_class
return kls(path=path, app=app) | def from_urlpath(cls, path, app=None) | Alternative constructor which accepts a path as taken from URL and uses
the given app or the current app config to get the real path.
If class has attribute `generic` set to True, `directory_class` or
`file_class` will be used as type.
:param path: relative path as from URL
:param app: optional, flask application
:return: file object pointing to path
:rtype: File | 4.869467 | 1.801076 | 2.703643 |
pattern = patterns[0]
patterns = patterns[1:]
has_wildcard = is_pattern(pattern)
using_globstar = pattern == "**"
# This avoids os.listdir() for performance
if has_wildcard:
entries = [x.name for x in scandir(current_dir)]
else:
entries = [pattern]
if using_globstar:
matching_subdirs = map(lambda x: x[0], walk(current_dir))
else:
subdirs = [e for e in entries
if os.path.isdir(os.path.join(current_dir, e))]
matching_subdirs = match_entries(subdirs, pattern)
# For terminal globstar, add a pattern for all files in subdirs
if using_globstar and not patterns:
patterns = ['*']
if patterns: # we've still got more directories to traverse
for subdir in matching_subdirs:
absolute_path = os.path.join(current_dir, subdir)
for match in self._find_paths(absolute_path, patterns):
yield match
else: # we've got the last pattern
if not has_wildcard:
entries = [pattern + '.wsp', pattern + '.wsp.gz']
files = [e for e in entries
if os.path.isfile(os.path.join(current_dir, e))]
matching_files = match_entries(files, pattern + '.*')
for _basename in matching_files + matching_subdirs:
yield os.path.join(current_dir, _basename) | def _find_paths(self, current_dir, patterns) | Recursively generates absolute paths whose components
underneath current_dir match the corresponding pattern in
patterns | 3.276847 | 3.380353 | 0.96938 |
disjoint_intervals = []
for interval in intervals:
if disjoint_intervals and disjoint_intervals[-1].overlaps(interval):
disjoint_intervals[-1] = disjoint_intervals[-1].union(interval)
else:
disjoint_intervals.append(interval)
return disjoint_intervals | def union_overlapping(intervals) | Union any overlapping intervals in the given set. | 1.999864 | 2.062285 | 0.969732 |
for node in app.store.find(query):
if node.is_leaf:
index.add(node.path)
else:
recurse('{0}.*'.format(node.path), index) | def recurse(query, index) | Recursively walk across paths, adding leaves to the index as they're found. | 5.259458 | 4.37425 | 1.202368 |
fh = None
try:
fh = open(path,'r+b')
if LOCK:
fcntl.flock( fh.fileno(), fcntl.LOCK_EX )
packedMetadata = fh.read(metadataSize)
try:
(aggregationType,maxRetention,xff,archiveCount) = struct.unpack(metadataFormat,packedMetadata)
except:
raise CorruptWhisperFile("Unable to read header", fh.name)
try:
newAggregationType = struct.pack( longFormat, aggregationMethodToType[aggregationMethod] )
except KeyError:
raise InvalidAggregationMethod("Unrecognized aggregation method: %s" %
aggregationMethod)
if xFilesFactor is not None:
#use specified xFilesFactor
xff = struct.pack( floatFormat, float(xFilesFactor) )
else:
#retain old value
xff = struct.pack( floatFormat, xff )
#repack the remaining header information
maxRetention = struct.pack( longFormat, maxRetention )
archiveCount = struct.pack(longFormat, archiveCount)
packedMetadata = newAggregationType + maxRetention + xff + archiveCount
fh.seek(0)
#fh.write(newAggregationType)
fh.write(packedMetadata)
if AUTOFLUSH:
fh.flush()
os.fsync(fh.fileno())
if CACHE_HEADERS and fh.name in __headerCache:
del __headerCache[fh.name]
finally:
if fh:
fh.close()
return aggregationTypeToMethod.get(aggregationType, 'average') | def setAggregationMethod(path, aggregationMethod, xFilesFactor=None) | setAggregationMethod(path,aggregationMethod,xFilesFactor=None)
path is a string
aggregationMethod specifies the method to use when propagating data (see ``whisper.aggregationMethods``)
xFilesFactor specifies the fraction of data points in a propagation interval that must have known values for a propagation to occur. If None, the existing xFilesFactor in path will not be changed | 3.96046 | 4.013458 | 0.986795 |
if not archiveList:
raise InvalidConfiguration("You must specify at least one archive configuration!")
archiveList = sorted(archiveList, key=lambda a: a[0]) #sort by precision (secondsPerPoint)
for i,archive in enumerate(archiveList):
if i == len(archiveList) - 1:
break
nextArchive = archiveList[i+1]
if not archive[0] < nextArchive[0]:
raise InvalidConfiguration("A Whisper database may not configured having"
"two archives with the same precision (archive%d: %s, archive%d: %s)" %
(i, archive, i + 1, nextArchive))
if nextArchive[0] % archive[0] != 0:
raise InvalidConfiguration("Higher precision archives' precision "
"must evenly divide all lower precision archives' precision "
"(archive%d: %s, archive%d: %s)" %
(i, archive[0], i + 1, nextArchive[0]))
retention = archive[0] * archive[1]
nextRetention = nextArchive[0] * nextArchive[1]
if not nextRetention > retention:
raise InvalidConfiguration("Lower precision archives must cover "
"larger time intervals than higher precision archives "
"(archive%d: %s seconds, archive%d: %s seconds)" %
(i, retention, i + 1, nextRetention))
archivePoints = archive[1]
pointsPerConsolidation = nextArchive[0] // archive[0]
if not archivePoints >= pointsPerConsolidation:
raise InvalidConfiguration("Each archive must have at least enough points "
"to consolidate to the next archive (archive%d consolidates %d of "
"archive%d's points but it has only %d total points)" %
(i + 1, pointsPerConsolidation, i, archivePoints)) | def validateArchiveList(archiveList) | Validates an archiveList.
An ArchiveList must:
1. Have at least one archive config. Example: (60, 86400)
2. No archive may be a duplicate of another.
3. Higher precision archives' precision must evenly divide all lower precision archives' precision.
4. Lower precision archives must cover larger time intervals than higher precision archives.
5. Each archive must have at least enough points to consolidate to the next archive
Returns True or False | 3.236243 | 2.546325 | 1.270946 |
# Set default params
if xFilesFactor is None:
xFilesFactor = 0.5
if aggregationMethod is None:
aggregationMethod = 'average'
#Validate archive configurations...
validateArchiveList(archiveList)
#Looks good, now we create the file and write the header
if os.path.exists(path):
raise InvalidConfiguration("File %s already exists!" % path)
fh = None
try:
fh = open(path,'wb')
if LOCK:
fcntl.flock( fh.fileno(), fcntl.LOCK_EX )
aggregationType = struct.pack( longFormat, aggregationMethodToType.get(aggregationMethod, 1) )
oldest = max([secondsPerPoint * points for secondsPerPoint,points in archiveList])
maxRetention = struct.pack( longFormat, oldest )
xFilesFactor = struct.pack( floatFormat, float(xFilesFactor) )
archiveCount = struct.pack(longFormat, len(archiveList))
packedMetadata = aggregationType + maxRetention + xFilesFactor + archiveCount
fh.write(packedMetadata)
headerSize = metadataSize + (archiveInfoSize * len(archiveList))
archiveOffsetPointer = headerSize
for secondsPerPoint,points in archiveList:
archiveInfo = struct.pack(archiveInfoFormat, archiveOffsetPointer, secondsPerPoint, points)
fh.write(archiveInfo)
archiveOffsetPointer += (points * pointSize)
#If configured to use fallocate and capable of fallocate use that, else
#attempt sparse if configure or zero pre-allocate if sparse isn't configured.
if CAN_FALLOCATE and useFallocate:
remaining = archiveOffsetPointer - headerSize
fallocate(fh, headerSize, remaining)
elif sparse:
fh.seek(archiveOffsetPointer - 1)
fh.write('\x00')
else:
remaining = archiveOffsetPointer - headerSize
chunksize = 16384
zeroes = b'\x00' * chunksize
while remaining > chunksize:
fh.write(zeroes)
remaining -= chunksize
fh.write(zeroes[:remaining])
if AUTOFLUSH:
fh.flush()
os.fsync(fh.fileno())
finally:
if fh:
fh.close() | def create(path,archiveList,xFilesFactor=None,aggregationMethod=None,sparse=False,useFallocate=False) | create(path,archiveList,xFilesFactor=0.5,aggregationMethod='average')
path is a string
archiveList is a list of archives, each of which is of the form (secondsPerPoint,numberOfPoints)
xFilesFactor specifies the fraction of data points in a propagation interval that must have known values for a propagation to occur
aggregationMethod specifies the function to use when propagating data (see ``whisper.aggregationMethods``) | 3.940441 | 3.910127 | 1.007753 |
value = float(value)
fh = None
try:
fh = open(path,'r+b')
return file_update(fh, value, timestamp)
finally:
if fh:
fh.close() | def update(path,value,timestamp=None) | update(path,value,timestamp=None)
path is a string
value is a float
timestamp is either an int or float | 3.791528 | 4.472087 | 0.847821 |
if not points: return
points = [ (int(t),float(v)) for (t,v) in points]
points.sort(key=lambda p: p[0],reverse=True) #order points by timestamp, newest first
fh = None
try:
fh = open(path,'r+b')
return file_update_many(fh, points)
finally:
if fh:
fh.close() | def update_many(path,points) | update_many(path,points)
path is a string
points is a list of (timestamp,value) points | 3.732583 | 3.47739 | 1.073387 |
fh = None
try:
fh = open(path,'rb')
return __readHeader(fh)
finally:
if fh:
fh.close()
return None | def info(path) | info(path)
path is a string | 4.262611 | 4.915082 | 0.867251 |
fh = None
try:
fh = open(path,'rb')
return file_fetch(fh, fromTime, untilTime, now)
finally:
if fh:
fh.close() | def fetch(path,fromTime,untilTime=None,now=None) | fetch(path,fromTime,untilTime=None)
path is a string
fromTime is an epoch time
untilTime is also an epoch time, but defaults to now.
Returns a tuple of (timeInfo, valueList)
where timeInfo is itself a tuple of (fromTime, untilTime, step)
Returns None if no data can be returned | 3.334802 | 4.23107 | 0.78817 |
fromInterval = int( fromTime - (fromTime % archive['secondsPerPoint']) ) + archive['secondsPerPoint']
untilInterval = int( untilTime - (untilTime % archive['secondsPerPoint']) ) + archive['secondsPerPoint']
if fromInterval == untilInterval:
# Zero-length time range: always include the next point
untilInterval += archive['secondsPerPoint']
fh.seek(archive['offset'])
packedPoint = fh.read(pointSize)
(baseInterval,baseValue) = struct.unpack(pointFormat,packedPoint)
if baseInterval == 0:
step = archive['secondsPerPoint']
points = (untilInterval - fromInterval) // step
timeInfo = (fromInterval,untilInterval,step)
valueList = [None] * points
return (timeInfo,valueList)
#Determine fromOffset
timeDistance = fromInterval - baseInterval
pointDistance = timeDistance // archive['secondsPerPoint']
byteDistance = pointDistance * pointSize
fromOffset = archive['offset'] + (byteDistance % archive['size'])
#Determine untilOffset
timeDistance = untilInterval - baseInterval
pointDistance = timeDistance // archive['secondsPerPoint']
byteDistance = pointDistance * pointSize
untilOffset = archive['offset'] + (byteDistance % archive['size'])
#Read all the points in the interval
fh.seek(fromOffset)
if fromOffset < untilOffset: #If we don't wrap around the archive
seriesString = fh.read(untilOffset - fromOffset)
else: #We do wrap around the archive, so we need two reads
archiveEnd = archive['offset'] + archive['size']
seriesString = fh.read(archiveEnd - fromOffset)
fh.seek(archive['offset'])
seriesString += fh.read(untilOffset - archive['offset'])
#Now we unpack the series data we just read (anything faster than unpack?)
byteOrder,pointTypes = pointFormat[0],pointFormat[1:]
points = len(seriesString) // pointSize
seriesFormat = byteOrder + (pointTypes * points)
unpackedSeries = struct.unpack(seriesFormat, seriesString)
#And finally we construct a list of values (optimize this!)
valueList = [None] * points #pre-allocate entire list for speed
currentInterval = fromInterval
step = archive['secondsPerPoint']
for i in xrange(0,len(unpackedSeries),2):
pointTime = unpackedSeries[i]
if pointTime == currentInterval:
pointValue = unpackedSeries[i+1]
valueList[i//2] = pointValue #in-place reassignment is faster than append()
currentInterval += step
timeInfo = (fromInterval,untilInterval,step)
return (timeInfo,valueList) | def __archive_fetch(fh, archive, fromTime, untilTime) | Fetch data from a single archive. Note that checks for validity of the time
period requested happen above this level so it's possible to wrap around the
archive on a read and request data older than the archive's retention | 3.171871 | 3.192875 | 0.993422 |
fh_from = open(path_from, 'rb')
fh_to = open(path_to, 'rb+')
return file_merge(fh_from, fh_to) | def merge(path_from, path_to) | Merges the data from one whisper file into another. Each file must have
the same archive configuration | 3.300328 | 3.493909 | 0.944595 |
fh_from = open(path_from, 'rb')
fh_to = open(path_to, 'rb')
diffs = file_diff(fh_from, fh_to, ignore_empty)
fh_to.close()
fh_from.close()
return diffs | def diff(path_from, path_to, ignore_empty = False) | Compare two whisper databases. Each file must have the same archive configuration | 2.05817 | 2.164644 | 0.950812 |
# Dont add if empty
if not nonempty(data):
for d in self.data[path]:
if nonempty(d['values']):
return
# Add data to path
for expr in exprs:
self.paths[expr].add(path)
self.data[path].append({
'time_info': time_info,
'values': data
}) | def add_data(self, path, time_info, data, exprs) | Stores data before it can be put into a time series | 4.500783 | 4.737862 | 0.949961 |
v1, v2 = pattern.find('{'), pattern.find('}')
if v1 > -1 and v2 > v1:
variations = pattern[v1+1:v2].split(',')
variants = [pattern[:v1] + v + pattern[v2+1:] for v in variations]
else:
variants = [pattern]
return list(_deduplicate(variants)) | def extract_variants(pattern) | Extract the pattern variants (ie. {foo,bar}baz = foobaz or barbaz). | 2.853052 | 2.577074 | 1.10709 |
matching = []
for variant in expand_braces(pattern):
matching.extend(fnmatch.filter(entries, variant))
return list(_deduplicate(matching)) | def match_entries(entries, pattern) | A drop-in replacement for fnmatch.filter that supports pattern
variants (ie. {foo,bar}baz = foobaz or barbaz). | 8.207303 | 6.238034 | 1.315687 |
res = set()
# Used instead of s.strip('{}') because strip is greedy.
# We want to remove only ONE leading { and ONE trailing }, if both exist
def remove_outer_braces(s):
if s[0] == '{' and s[-1] == '}':
return s[1:-1]
return s
match = EXPAND_BRACES_RE.search(pattern)
if match is not None:
sub = match.group(1)
v1, v2 = match.span(1)
if "," in sub:
for pat in sub.strip('{}').split(','):
subpattern = pattern[:v1] + pat + pattern[v2:]
res.update(expand_braces(subpattern))
else:
subpattern = pattern[:v1] + remove_outer_braces(sub) + pattern[v2:]
res.update(expand_braces(subpattern))
else:
res.add(pattern.replace('\\}', '}'))
return list(res) | def expand_braces(pattern) | Find the rightmost, innermost set of braces and, if it contains a
comma-separated list, expand its contents recursively (any of its items
may itself be a list enclosed in braces).
Return the full list of expanded strings. | 3.252937 | 3.219795 | 1.010293 |
key = self.keyfunc(metric)
nodes = []
servers = set()
for node in self.hash_ring.get_nodes(key):
server, instance = node
if server in servers:
continue
servers.add(server)
nodes.append(node)
if len(servers) >= self.replication_factor:
break
available = [n for n in nodes if self.is_available(n)]
return random.choice(available or nodes) | def select_host(self, metric) | Returns the carbon host that has data for the given metric. | 3.729457 | 3.71841 | 1.002971 |
return (arg for arg in args
if arg is not None and not math.isnan(arg) and not math.isinf(arg)) | def safeArgs(args) | Iterate over valid, finite values in an iterable.
Skip any items that are None, NaN, or infinite. | 3.603601 | 2.912023 | 1.237491 |
missingValues = any(None in series for series in data)
finiteData = [series for series in data
if not series.options.get('drawAsInfinite')]
yMinValue = safeMin(safeMin(series) for series in finiteData)
if yMinValue is None:
# This can only happen if there are no valid, non-infinite data.
return (0.0, 1.0)
if yMinValue > 0.0 and drawNullAsZero and missingValues:
yMinValue = 0.0
if stacked:
length = safeMin(len(series) for series in finiteData)
sumSeries = []
for i in range(0, length):
sumSeries.append(safeSum(series[i] for series in finiteData))
yMaxValue = safeMax(sumSeries)
else:
yMaxValue = safeMax(safeMax(series) for series in finiteData)
if yMaxValue < 0.0 and drawNullAsZero and missingValues:
yMaxValue = 0.0
return (yMinValue, yMaxValue) | def dataLimits(data, drawNullAsZero=False, stacked=False) | Return the range of values in data as (yMinValue, yMaxValue).
data is an array of TimeSeries objects. | 2.808434 | 2.680368 | 1.047779 |
if v is None:
return 0, ''
for prefix, size in UnitSystems[system]:
if condition(v, size, step):
v2 = v / size
if v2 - math.floor(v2) < 0.00000000001 and v > 1:
v2 = float(math.floor(v2))
if units:
prefix = "%s%s" % (prefix, units)
return v2, prefix
if v - math.floor(v) < 0.00000000001 and v > 1:
v = float(math.floor(v))
if units:
prefix = units
else:
prefix = ''
return v, prefix | def format_units(v, step=None, system="si", units=None) | Format the given value in standardized units.
``system`` is either 'binary' or 'si'
For more info, see:
http://en.wikipedia.org/wiki/SI_prefix
http://en.wikipedia.org/wiki/Binary_prefix | 2.708692 | 2.71613 | 0.997261 |
if math.isnan(value):
raise GraphError('Encountered NaN %s' % (name,))
elif math.isinf(value):
raise GraphError('Encountered infinite %s' % (name,))
return value | def checkFinite(value, name='value') | Check that value is a finite number.
If it is, return it. If not, raise GraphError describing the
problem, using name in the error message. | 3.046703 | 2.579493 | 1.181125 |
if self.minValue < self.maxValue:
# The limits are already OK.
return
minFixed = (self.minValueSource in ['min'])
maxFixed = (self.maxValueSource in ['max', 'limit'])
if minFixed and maxFixed:
raise GraphError('The %s must be less than the %s' %
(self.minValueSource, self.maxValueSource))
elif minFixed:
self.maxValue = self.minValue + self.chooseDelta(self.minValue)
elif maxFixed:
self.minValue = self.maxValue - self.chooseDelta(self.maxValue)
else:
delta = self.chooseDelta(max(abs(self.minValue),
abs(self.maxValue)))
average = (self.minValue + self.maxValue) / 2.0
self.minValue = average - delta
self.maxValue = average + delta | def reconcileLimits(self) | If self.minValue is not less than self.maxValue, fix the problem.
If self.minValue is not less than self.maxValue, adjust
self.minValue and/or self.maxValue (depending on which was not
specified explicitly by the user) to make self.minValue <
self.maxValue. If the user specified both limits explicitly, then
raise GraphError. | 2.739247 | 2.347432 | 1.166912 |
if axisMin is not None and not math.isnan(axisMin):
self.minValueSource = 'min'
self.minValue = self.checkFinite(axisMin, 'axis min')
if axisMax == 'max':
self.maxValueSource = 'extremum'
elif axisMax is not None and not math.isnan(axisMax):
self.maxValueSource = 'max'
self.maxValue = self.checkFinite(axisMax, 'axis max')
if axisLimit is None or math.isnan(axisLimit):
self.axisLimit = None
elif axisLimit < self.maxValue:
self.maxValue = self.checkFinite(axisLimit, 'axis limit')
self.maxValueSource = 'limit'
# The limit has already been imposed, so there is no need to
# remember it:
self.axisLimit = None
elif math.isinf(axisLimit):
# It must be positive infinity, which is the same as no limit:
self.axisLimit = None
else:
# We still need to remember axisLimit to avoid rounding top to
# a value larger than axisLimit:
self.axisLimit = axisLimit
self.reconcileLimits() | def applySettings(self, axisMin=None, axisMax=None, axisLimit=None) | Apply the specified settings to this axis.
Set self.minValue, self.minValueSource, self.maxValue,
self.maxValueSource, and self.axisLimit reasonably based on the
parameters provided.
Arguments:
axisMin -- a finite number, or None to choose a round minimum
limit that includes all of the data.
axisMax -- a finite number, 'max' to use the maximum value
contained in the data, or None to choose a round maximum limit
that includes all of the data.
axisLimit -- a finite number to use as an upper limit on maxValue,
or None to impose no upper limit. | 3.247133 | 2.863027 | 1.134161 |
value, prefix = format_units(value, self.step,
system=self.unitSystem)
span, spanPrefix = format_units(self.span, self.step,
system=self.unitSystem)
if prefix:
prefix += " "
if value < 0.1:
return "%g %s" % (float(value), prefix)
elif value < 1.0:
return "%.2f %s" % (float(value), prefix)
if span > 10 or spanPrefix != prefix:
if type(value) is float:
return "%.1f %s" % (value, prefix)
else:
return "%d %s" % (int(value), prefix)
elif span > 3:
return "%.1f %s" % (float(value), prefix)
elif span > 0.1:
return "%.2f %s" % (float(value), prefix)
else:
return "%g %s" % (float(value), prefix) | def makeLabel(self, value) | Create a label for the specified value.
Create a label string containing the value and its units (if any),
based on the values of self.step, self.span, and self.unitSystem. | 2.487205 | 2.365164 | 1.051599 |
self.checkFinite(minStep)
if self.binary:
base = 2.0
mantissas = [1.0]
exponent = math.floor(math.log(minStep, 2) - EPSILON)
else:
base = 10.0
mantissas = [1.0, 2.0, 5.0]
exponent = math.floor(math.log10(minStep) - EPSILON)
while True:
multiplier = base ** exponent
for mantissa in mantissas:
value = mantissa * multiplier
if value >= minStep * (1.0 - EPSILON):
yield value
exponent += 1 | def generateSteps(self, minStep) | Generate allowed steps with step >= minStep in increasing order. | 2.820836 | 2.704493 | 1.043019 |
bottom = step * math.floor(self.minValue / float(step) + EPSILON)
top = bottom + step * divisor
if top >= self.maxValue - EPSILON * step:
return max(top - self.maxValue, self.minValue - bottom)
else:
return None | def computeSlop(self, step, divisor) | Compute the slop that would result from step and divisor.
Return the slop, or None if this combination can't cover the full
range. See chooseStep() for the definition of "slop". | 5.997557 | 5.355998 | 1.119783 |
self.binary = binary
if divisors is None:
divisors = [4, 5, 6]
else:
for divisor in divisors:
self.checkFinite(divisor, 'divisor')
if divisor < 1:
raise GraphError('Divisors must be greater than or equal '
'to one')
if self.minValue == self.maxValue:
if self.minValue == 0.0:
self.maxValue = 1.0
elif self.minValue < 0.0:
self.minValue *= 1.1
self.maxValue *= 0.9
else:
self.minValue *= 0.9
self.maxValue *= 1.1
variance = self.maxValue - self.minValue
bestSlop = None
bestStep = None
for step in self.generateSteps(variance / float(max(divisors))):
if (
bestSlop is not None and
step * min(divisors) >= 2 * bestSlop + variance
):
break
for divisor in divisors:
slop = self.computeSlop(step, divisor)
if slop is not None and (bestSlop is None or slop < bestSlop):
bestSlop = slop
bestStep = step
self.step = bestStep | def chooseStep(self, divisors=None, binary=False) | Choose a nice, pretty size for the steps between axis labels.
Our main constraint is that the number of divisions must be taken
from the divisors list. We pick a number of divisions and a step
size that minimizes the amount of whitespace ("slop") that would
need to be included outside of the range [self.minValue,
self.maxValue] if we were to push out the axis values to the next
larger multiples of the step size.
The minimum step that could possibly cover the variance satisfies
minStep * max(divisors) >= variance
or
minStep = variance / max(divisors)
It's not necessarily possible to cover the variance with a step
that size, but we know that any smaller step definitely *cannot*
cover it. So we can start there.
For a sufficiently large step size, it is definitely possible to
cover the variance, but at some point the slop will start growing.
Let's define the slop to be
slop = max(minValue - bottom, top - maxValue)
Then for a given, step size, we know that
slop >= (1/2) * (step * min(divisors) - variance)
(the factor of 1/2 is for the best-case scenario that the slop is
distributed equally on the two sides of the range). So suppose we
already have a choice that yields bestSlop. Then there is no need
to choose steps so large that the slop is guaranteed to be larger
than bestSlop. Therefore, the maximum step size that we need to
consider is
maxStep = (2 * bestSlop + variance) / min(divisors) | 2.657688 | 2.394295 | 1.110009 |
pathExpressions = sorted(set([s.pathExpression for s in seriesList]))
return ','.join(pathExpressions) | def formatPathExpressions(seriesList) | Returns a comma-separated list of unique path expressions. | 4.078918 | 2.952191 | 1.381658 |
if not seriesLists or not any(seriesLists):
return []
seriesList, start, end, step = normalize(seriesLists)
name = "sumSeries(%s)" % formatPathExpressions(seriesList)
values = (safeSum(row) for row in zip_longest(*seriesList))
series = TimeSeries(name, start, end, step, values)
series.pathExpression = name
return [series] | def sumSeries(requestContext, *seriesLists) | Short form: sum()
This will add metrics together and return the sum at each datapoint. (See
integral for a sum over time)
Example::
&target=sum(company.server.application*.requestsHandled)
This would show the sum of all requests handled per minute (provided
requestsHandled are collected once a minute). If metrics with different
retention rates are combined, the coarsest metric is graphed, and the sum
of the other metrics is averaged for the metrics with finer retention
rates. | 5.275437 | 5.891492 | 0.895433 |
newSeries = {}
newNames = list()
for series in seriesList:
newname = '.'.join(map(lambda x: x[1],
filter(lambda i: i[0] not in positions,
enumerate(series.name.split('.')))))
if newname in newSeries:
newSeries[newname] = sumSeries(requestContext,
(series, newSeries[newname]))[0]
else:
newSeries[newname] = series
newNames.append(newname)
newSeries[newname].name = newname
return [newSeries[name] for name in newNames] | def sumSeriesWithWildcards(requestContext, seriesList, *positions) | Call sumSeries after inserting wildcards at the given position(s).
Example::
&target=sumSeriesWithWildcards(host.cpu-[0-7].cpu-{user,system}.value,
1)
This would be the equivalent of::
&target=sumSeries(host.*.cpu-user.value)&target=sumSeries(
host.*.cpu-system.value) | 2.802584 | 3.000559 | 0.934021 |
matchedList = defaultdict(list)
for series in seriesList:
newname = '.'.join(map(lambda x: x[1],
filter(lambda i: i[0] not in positions,
enumerate(series.name.split('.')))))
matchedList[newname].append(series)
result = []
for name in matchedList:
[series] = averageSeries(requestContext, (matchedList[name]))
series.name = name
result.append(series)
return result | def averageSeriesWithWildcards(requestContext, seriesList, *positions) | Call averageSeries after inserting wildcards at the given position(s).
Example::
&target=averageSeriesWithWildcards(
host.cpu-[0-7].cpu-{user,system}.value, 1)
This would be the equivalent of::
&target=averageSeries(host.*.cpu-user.value)&target=averageSeries(
host.*.cpu-system.value) | 3.508277 | 3.676976 | 0.95412 |
positions = [position] if isinstance(position, int) else position
newSeries = {}
newNames = []
for series in seriesList:
new_name = ".".join(map(lambda x: x[1],
filter(lambda i: i[0] not in positions,
enumerate(series.name.split('.')))))
if new_name in newSeries:
[newSeries[new_name]] = multiplySeries(requestContext,
(newSeries[new_name],
series))
else:
newSeries[new_name] = series
newNames.append(new_name)
newSeries[new_name].name = new_name
return [newSeries[name] for name in newNames] | def multiplySeriesWithWildcards(requestContext, seriesList, *position) | Call multiplySeries after inserting wildcards at the given position(s).
Example::
&target=multiplySeriesWithWildcards(
web.host-[0-7].{avg-response,total-request}.value, 2)
This would be the equivalent of::
&target=multiplySeries(web.host-0.{avg-response,total-request}.value)
&target=multiplySeries(web.host-1.{avg-response,total-request}.value)
... | 2.798698 | 3.298851 | 0.848386 |
if not seriesLists or not any(seriesLists):
return []
seriesList, start, end, step = normalize(seriesLists)
name = "rangeOfSeries(%s)" % formatPathExpressions(seriesList)
values = (safeSubtract(max(row),
min(row)) for row in zip_longest(*seriesList))
series = TimeSeries(name, start, end, step, values)
series.pathExpression = name
return [series] | def rangeOfSeries(requestContext, *seriesLists) | Takes a wildcard seriesList.
Distills down a set of inputs into the range of the series
Example::
&target=rangeOfSeries(Server*.connections.total) | 5.819502 | 6.893213 | 0.844236 |
if n <= 0:
raise ValueError(
'The requested percent is required to be greater than 0')
if not seriesList:
return []
name = 'percentileOfSeries(%s,%g)' % (seriesList[0].pathExpression, n)
start, end, step = normalize([seriesList])[1:]
values = [_getPercentile(row, n, interpolate)
for row in zip_longest(*seriesList)]
resultSeries = TimeSeries(name, start, end, step, values)
resultSeries.pathExpression = name
return [resultSeries] | def percentileOfSeries(requestContext, seriesList, n, interpolate=False) | percentileOfSeries returns a single series which is composed of the
n-percentile values taken across a wildcard series at each point.
Unless `interpolate` is set to True, percentile values are actual values
contained in one of the supplied series. | 4.95934 | 5.05615 | 0.980853 |
for series in seriesList:
series.name = "keepLastValue(%s)" % (series.name)
series.pathExpression = series.name
consecutiveNones = 0
for i, value in enumerate(series):
series[i] = value
# No 'keeping' can be done on the first value because we have no
# idea what came before it.
if i == 0:
continue
if value is None:
consecutiveNones += 1
else:
if 0 < consecutiveNones <= limit:
# If a non-None value is seen before the limit of Nones is
# hit, backfill all the missing datapoints with the last
# known value.
for index in range(i - consecutiveNones, i):
series[index] = series[i - consecutiveNones - 1]
consecutiveNones = 0
# If the series ends with some None values, try to backfill a bit to
# cover it.
if 0 < consecutiveNones <= limit:
for index in range(len(series) - consecutiveNones, len(series)):
series[index] = series[len(series) - consecutiveNones - 1]
return seriesList | def keepLastValue(requestContext, seriesList, limit=INF) | Takes one metric or a wildcard seriesList, and optionally a limit to the
number of 'None' values to skip over. Continues the line with the last
received value when gaps ('None' values) appear in your data, rather than
breaking your line.
Example::
&target=keepLastValue(Server01.connections.handled)
&target=keepLastValue(Server01.connections.handled, 10) | 3.500152 | 3.813124 | 0.917922 |
for series in seriesList:
series.name = "interpolate(%s)" % (series.name)
series.pathExpression = series.name
consecutiveNones = 0
for i, value in enumerate(series):
series[i] = value
# No 'keeping' can be done on the first value because we have no
# idea what came before it.
if i == 0:
continue
if value is None:
consecutiveNones += 1
elif consecutiveNones == 0:
# Have a value but no need to interpolate
continue
elif series[i - consecutiveNones - 1] is None:
# Have a value but can't interpolate: reset count
consecutiveNones = 0
continue
else:
# Have a value and can interpolate. If a non-None value is
# seen before the limit of Nones is hit, backfill all the
# missing datapoints with the last known value.
if consecutiveNones > 0 and consecutiveNones <= limit:
lastIndex = i - consecutiveNones - 1
lastValue = series[lastIndex]
for index in range(i - consecutiveNones, i):
nextValue = lastValue + (index - lastIndex)
nextValue = nextValue * (value - lastValue)
nextValue = nextValue / (consecutiveNones + 1)
series[index] = nextValue
consecutiveNones = 0
return seriesList | def interpolate(requestContext, seriesList, limit=INF) | Takes one metric or a wildcard seriesList, and optionally a limit to the
number of 'None' values to skip over. Continues the line with the last
received value when gaps ('None' values) appear in your data, rather than
breaking your line.
Example::
&target=interpolate(Server01.connections.handled)
&target=interpolate(Server01.connections.handled, 10) | 3.836159 | 4.012927 | 0.95595 |
for series in seriesList:
series.name = series.pathExpression = 'changed(%s)' % series.name
previous = None
for index, value in enumerate(series):
if previous is None:
series[index] = 0
elif value is not None and previous != value:
series[index] = 1
else:
series[index] = 0
previous = value
return seriesList | def changed(requestContext, seriesList) | Takes one metric or a wildcard seriesList.
Output 1 when the value changed, 0 when null or the same
Example::
&target=changed(Server01.connections.handled) | 3.378631 | 4.030995 | 0.838163 |
if len(dividendSeriesList) != len(divisorSeriesList):
raise ValueError("dividendSeriesList and divisorSeriesList argument\
must have equal length")
results = []
for dividendSeries, divisorSeries in zip(dividendSeriesList,
divisorSeriesList):
name = "divideSeries(%s,%s)" % (dividendSeries.name,
divisorSeries.name)
bothSeries = (dividendSeries, divisorSeries)
step = reduce(lcm, [s.step for s in bothSeries])
for s in bothSeries:
s.consolidate(step // s.step)
start = min([s.start for s in bothSeries])
end = max([s.end for s in bothSeries])
end -= (end - start) % step
values = (safeDiv(v1, v2) for v1, v2 in zip(*bothSeries))
quotientSeries = TimeSeries(name, start, end, step, values)
results.append(quotientSeries)
return results | def divideSeriesLists(requestContext, dividendSeriesList, divisorSeriesList) | Iterates over a two lists and divides list1[0] by list2[0], list1[1] by
list2[1] and so on. The lists need to be the same length | 2.896017 | 3.021196 | 0.958566 |
if len(divisorSeriesList) == 0:
for series in dividendSeriesList:
series.name = "divideSeries(%s,MISSING)" % series.name
series.pathExpression = series.name
for i in range(len(series)):
series[i] = None
return dividendSeriesList
if len(divisorSeriesList) > 1:
raise ValueError(
"divideSeries second argument must reference exactly 1 series"
" (got {0})".format(len(divisorSeriesList)))
[divisorSeries] = divisorSeriesList
results = []
for dividendSeries in dividendSeriesList:
name = "divideSeries(%s,%s)" % (dividendSeries.name,
divisorSeries.name)
bothSeries = (dividendSeries, divisorSeries)
step = reduce(lcm, [s.step for s in bothSeries])
for s in bothSeries:
s.consolidate(step / s.step)
start = min([s.start for s in bothSeries])
end = max([s.end for s in bothSeries])
end -= (end - start) % step
values = (safeDiv(v1, v2) for v1, v2 in zip_longest(*bothSeries))
quotientSeries = TimeSeries(name, start, end, step, values)
quotientSeries.pathExpression = name
results.append(quotientSeries)
return results | def divideSeries(requestContext, dividendSeriesList, divisorSeriesList) | Takes a dividend metric and a divisor metric and draws the division result.
A constant may *not* be passed. To divide by a constant, use the scale()
function (which is essentially a multiplication operation) and use the
inverse of the dividend. (Division by 8 = multiplication by 1/8 or 0.125)
Example::
&target=divideSeries(Series.dividends,Series.divisors) | 3.201283 | 3.399921 | 0.941576 |
if not seriesLists or not any(seriesLists):
return []
seriesList, start, end, step = normalize(seriesLists)
if len(seriesList) == 1:
return seriesList
name = "multiplySeries(%s)" % ','.join([s.name for s in seriesList])
product = map(lambda x: safeMul(*x), zip_longest(*seriesList))
resultSeries = TimeSeries(name, start, end, step, product)
resultSeries.pathExpression = name
return [resultSeries] | def multiplySeries(requestContext, *seriesLists) | Takes two or more series and multiplies their points. A constant may not be
used. To multiply by a constant, use the scale() function.
Example::
&target=multiplySeries(Series.dividends,Series.divisors) | 4.088712 | 4.536291 | 0.901334 |
if isinstance(nodes, int):
nodes = [nodes]
sortedSeries = {}
for seriesAvg, seriesWeight in zip_longest(
seriesListAvg, seriesListWeight):
key = ''
for node in nodes:
key += seriesAvg.name.split(".")[node]
sortedSeries.setdefault(key, {})
sortedSeries[key]['avg'] = seriesAvg
key = ''
for node in nodes:
key += seriesWeight.name.split(".")[node]
sortedSeries.setdefault(key, {})
sortedSeries[key]['weight'] = seriesWeight
productList = []
for key in sortedSeries:
if 'weight' not in sortedSeries[key]:
continue
if 'avg' not in sortedSeries[key]:
continue
seriesWeight = sortedSeries[key]['weight']
seriesAvg = sortedSeries[key]['avg']
productValues = [safeMul(val1, val2)
for val1, val2
in zip_longest(seriesAvg, seriesWeight)]
name = 'product(%s,%s)' % (seriesWeight.name, seriesAvg.name)
productSeries = TimeSeries(name, seriesAvg.start, seriesAvg.end,
seriesAvg.step, productValues)
productSeries.pathExpression = name
productList.append(productSeries)
if not productList:
return []
[sumProducts] = sumSeries(requestContext, productList)
[sumWeights] = sumSeries(requestContext, seriesListWeight)
resultValues = [safeDiv(val1, val2)
for val1, val2 in zip_longest(sumProducts, sumWeights)]
name = "weightedAverage(%s, %s, %s)" % (
','.join(sorted(set(s.pathExpression for s in seriesListAvg))),
','.join(sorted(set(s.pathExpression for s in seriesListWeight))),
','.join(map(str, nodes)))
resultSeries = TimeSeries(name, sumProducts.start, sumProducts.end,
sumProducts.step, resultValues)
resultSeries.pathExpression = name
return resultSeries | def weightedAverage(requestContext, seriesListAvg, seriesListWeight, *nodes) | Takes a series of average values and a series of weights and
produces a weighted average for all values.
The corresponding values should share one or more zero-indexed nodes.
Example::
&target=weightedAverage(*.transactions.mean,*.transactions.count,0)
&target=weightedAverage(*.transactions.mean,*.transactions.count,1,3,4) | 2.314298 | 2.345124 | 0.986855 |
# EMA = C * (current_value) + (1 - C) + EMA
# C = 2 / (windowSize + 1)
# The following was copied from movingAverage, and altered for ema
if not seriesList:
return []
windowInterval = None
if isinstance(windowSize, six.string_types):
delta = parseTimeOffset(windowSize)
windowInterval = abs(delta.seconds + (delta.days * 86400))
# set previewSeconds and constant based on windowSize string or integer
if windowInterval:
previewSeconds = windowInterval
constant = (float(2) / (int(windowInterval) + 1))
else:
previewSeconds = max([s.step for s in seriesList]) * int(windowSize)
constant = (float(2) / (int(windowSize) + 1))
# ignore original data and pull new, including our preview
# data from earlier is needed to calculate the early results
newContext = requestContext.copy()
newContext['startTime'] = (requestContext['startTime'] -
timedelta(seconds=previewSeconds))
previewList = evaluateTokens(newContext, requestContext['args'][0])
result = []
for series in previewList:
if windowInterval:
windowPoints = windowInterval // series.step
else:
windowPoints = int(windowSize)
if isinstance(windowSize, six.string_types):
newName = 'exponentialMovingAverage(%s,"%s")' % (
series.name, windowSize)
else:
newName = "exponentialMovingAverage(%s,%s)" % (
series.name, windowSize)
newSeries = TimeSeries(newName, series.start + previewSeconds,
series.end, series.step, [])
newSeries.pathExpression = newName
window_sum = safeSum(series[:windowPoints]) or 0
count = safeLen(series[:windowPoints])
ema = safeDiv(window_sum, count)
newSeries.append(ema)
if ema is None:
ema = 0.0
else:
ema = float(ema)
for i in range(windowPoints, len(series) - 1):
if series[i] is not None:
ema = (float(constant) * float(series[i]) +
(1 - float(constant)) * float(ema))
newSeries.append(round(ema, 3))
else:
newSeries.append(None)
result.append(newSeries)
return result | def exponentialMovingAverage(requestContext, seriesList, windowSize) | Takes a series of values and a window size and produces an exponential
moving average utilizing the following formula:
ema(current) = constant * (Current Value) + (1 - constant) * ema(previous)
The Constant is calculated as:
constant = 2 / (windowSize + 1)
The first period EMA uses a simple moving average for its value.
Example::
&target=exponentialMovingAverage(*.transactions.count, 10)
&target=exponentialMovingAverage(*.transactions.count, '-10s') | 3.930558 | 3.830369 | 1.026156 |
if not seriesList:
return []
windowInterval = None
if isinstance(windowSize, six.string_types):
delta = parseTimeOffset(windowSize)
windowInterval = to_seconds(delta)
if windowInterval:
previewSeconds = windowInterval
else:
previewSeconds = max([s.step for s in seriesList]) * int(windowSize)
# ignore original data and pull new, including our preview
# data from earlier is needed to calculate the early results
newContext = requestContext.copy()
newContext['startTime'] = (requestContext['startTime'] -
timedelta(seconds=previewSeconds))
previewList = evaluateTokens(newContext, requestContext['args'][0])
result = []
for series in previewList:
if windowInterval:
windowPoints = windowInterval // series.step
else:
windowPoints = int(windowSize)
if isinstance(windowSize, six.string_types):
newName = 'movingMedian(%s,"%s")' % (series.name, windowSize)
else:
newName = "movingMedian(%s,%s)" % (series.name, windowSize)
newSeries = TimeSeries(newName, series.start + previewSeconds,
series.end, series.step, [])
newSeries.pathExpression = newName
for i in range(windowPoints, len(series)):
window = series[i - windowPoints:i]
nonNull = [v for v in window if v is not None]
if nonNull:
m_index = len(nonNull) // 2
newSeries.append(sorted(nonNull)[m_index])
else:
newSeries.append(None)
result.append(newSeries)
return result | def movingMedian(requestContext, seriesList, windowSize) | Graphs the moving median of a metric (or metrics) over a fixed number of
past points, or a time interval.
Takes one metric or a wildcard seriesList followed by a number N of
datapoints or a quoted string with a length of time like '1hour' or '5min'
(See ``from / until`` in the render\_api_ for examples of time formats).
Graphs the median of the preceding datapoints for each point on the graph.
Example::
&target=movingMedian(Server.instance01.threads.busy,10)
&target=movingMedian(Server.instance*.threads.idle,'5min') | 3.872353 | 4.061271 | 0.953483 |
for series in seriesList:
series.name = "scale(%s,%g)" % (series.name, float(factor))
series.pathExpression = series.name
for i, value in enumerate(series):
series[i] = safeMul(value, factor)
return seriesList | def scale(requestContext, seriesList, factor) | Takes one metric or a wildcard seriesList followed by a constant, and
multiplies the datapoint by the constant provided at each point.
Example::
&target=scale(Server.instance01.threads.busy,10)
&target=scale(Server.instance*.threads.busy,10) | 4.197841 | 5.760663 | 0.728708 |
for series in seriesList:
series.name = "scaleToSeconds(%s,%d)" % (series.name, seconds)
series.pathExpression = series.name
factor = seconds * 1.0 / series.step
for i, value in enumerate(series):
series[i] = safeMul(value, factor)
return seriesList | def scaleToSeconds(requestContext, seriesList, seconds) | Takes one metric or a wildcard seriesList and returns "value per seconds"
where seconds is a last argument to this functions.
Useful in conjunction with derivative or integral function if you want
to normalize its result to a known resolution for arbitrary retentions | 3.696594 | 4.105643 | 0.900369 |
for series in seriesList:
series.name = "pow(%s,%g)" % (series.name, float(factor))
series.pathExpression = series.name
for i, value in enumerate(series):
series[i] = safePow(value, factor)
return seriesList | def pow(requestContext, seriesList, factor) | Takes one metric or a wildcard seriesList followed by a constant, and
raises the datapoint by the power of the constant provided at each point.
Example::
&target=pow(Server.instance01.threads.busy,10)
&target=pow(Server.instance*.threads.busy,10) | 3.757124 | 5.397134 | 0.696133 |
if not seriesLists or not any(seriesLists):
return []
seriesList, start, end, step = normalize(seriesLists)
name = "powSeries(%s)" % ','.join([s.name for s in seriesList])
values = []
for row in zip_longest(*seriesList):
first = True
tmpVal = None
for element in row:
# If it is a first iteration - tmpVal needs to be element
if first:
tmpVal = element
first = False
else:
tmpVal = safePow(tmpVal, element)
values.append(tmpVal)
series = TimeSeries(name, start, end, step, values)
series.pathExpression = name
return [series] | def powSeries(requestContext, *seriesLists) | Takes two or more series and pows their points. A constant line may be
used.
Example::
&target=powSeries(Server.instance01.app.requests,
Server.instance01.app.replies) | 3.95599 | 4.609529 | 0.85822 |
for series in seriesList:
series.name = "squareRoot(%s)" % (series.name)
for i, value in enumerate(series):
series[i] = safePow(value, 0.5)
return seriesList | def squareRoot(requestContext, seriesList) | Takes one metric or a wildcard seriesList, and computes the square root
of each datapoint.
Example::
&target=squareRoot(Server.instance01.threads.busy) | 3.084404 | 4.664082 | 0.66131 |
for series in seriesList:
series.name = "absolute(%s)" % (series.name)
series.pathExpression = series.name
for i, value in enumerate(series):
series[i] = safeAbs(value)
return seriesList | def absolute(requestContext, seriesList) | Takes one metric or a wildcard seriesList and applies the mathematical abs
function to each datapoint transforming it to its absolute value.
Example::
&target=absolute(Server.instance01.threads.busy)
&target=absolute(Server.instance*.threads.busy) | 3.979023 | 5.239015 | 0.759498 |
for series in seriesList:
series.name = "offset(%s,%g)" % (series.name, float(factor))
series.pathExpression = series.name
for i, value in enumerate(series):
if value is not None:
series[i] = value + factor
return seriesList | def offset(requestContext, seriesList, factor) | Takes one metric or a wildcard seriesList followed by a constant, and adds
the constant to each datapoint.
Example::
&target=offset(Server.instance01.threads.busy,10) | 3.479943 | 4.884774 | 0.712406 |
for series in seriesList:
series.name = "offsetToZero(%s)" % (series.name)
minimum = safeMin(series)
for i, value in enumerate(series):
if value is not None:
series[i] = value - minimum
return seriesList | def offsetToZero(requestContext, seriesList) | Offsets a metric or wildcard seriesList by subtracting the minimum
value in the series from each datapoint.
Useful to compare different series where the values in each series
may be higher or lower on average but you're only interested in the
relative difference.
An example use case is for comparing different round trip time
results. When measuring RTT (like pinging a server), different
devices may come back with consistently different results due to
network latency which will be different depending on how many
network hops between the probe and the device. To compare different
devices in the same graph, the network latency to each has to be
factored out of the results. This is a shortcut that takes the
fastest response (lowest number in the series) and sets that to zero
and then offsets all of the other datapoints in that series by that
amount. This makes the assumption that the lowest response is the
fastest the device can respond, of course the more datapoints that
are in the series the more accurate this assumption is.
Example::
&target=offsetToZero(Server.instance01.responseTime)
&target=offsetToZero(Server.instance*.responseTime) | 3.298953 | 4.802119 | 0.686979 |
if not seriesList:
return []
windowInterval = None
if isinstance(windowSize, six.string_types):
delta = parseTimeOffset(windowSize)
windowInterval = to_seconds(delta)
if windowInterval:
previewSeconds = windowInterval
else:
previewSeconds = max([s.step for s in seriesList]) * int(windowSize)
# ignore original data and pull new, including our preview
# data from earlier is needed to calculate the early results
newContext = requestContext.copy()
newContext['startTime'] = (requestContext['startTime'] -
timedelta(seconds=previewSeconds))
previewList = evaluateTokens(newContext, requestContext['args'][0])
result = []
for series in previewList:
if windowInterval:
windowPoints = windowInterval // series.step
else:
windowPoints = int(windowSize)
if isinstance(windowSize, six.string_types):
newName = 'movingAverage(%s,"%s")' % (series.name, windowSize)
else:
newName = "movingAverage(%s,%s)" % (series.name, windowSize)
newSeries = TimeSeries(newName, series.start + previewSeconds,
series.end, series.step, [])
newSeries.pathExpression = newName
windowSum = safeSum(series[:windowPoints]) or 0
count = safeLen(series[:windowPoints])
newSeries.append(safeDiv(windowSum, count))
for n, last in enumerate(series[windowPoints:-1]):
if series[n] is not None:
windowSum -= series[n]
count -= 1
if last is not None:
windowSum += last
count += 1
newSeries.append(safeDiv(windowSum, count))
result.append(newSeries)
return result | def movingAverage(requestContext, seriesList, windowSize) | Graphs the moving average of a metric (or metrics) over a fixed number of
past points, or a time interval.
Takes one metric or a wildcard seriesList followed by a number N of
datapoints or a quoted string with a length of time like '1hour' or '5min'
(See ``from / until`` in the render\_api_ for examples of time formats).
Graphs the average of the preceding datapoints for each point on the graph.
Example::
&target=movingAverage(Server.instance01.threads.busy,10)
&target=movingAverage(Server.instance*.threads.idle,'5min') | 4.004721 | 4.215869 | 0.949916 |
if not seriesList:
return []
windowInterval = None
if isinstance(windowSize, six.string_types):
delta = parseTimeOffset(windowSize)
windowInterval = abs(delta.seconds + (delta.days * 86400))
if windowInterval:
previewSeconds = windowInterval
else:
previewSeconds = max([s.step for s in seriesList]) * int(windowSize)
# ignore original data and pull new, including our preview
# data from earlier is needed to calculate the early results
newContext = requestContext.copy()
newContext['startTime'] = (requestContext['startTime'] -
timedelta(seconds=previewSeconds))
previewList = evaluateTokens(newContext, requestContext['args'][0])
result = []
for series in previewList:
if windowInterval:
windowPoints = windowInterval // series.step
else:
windowPoints = int(windowSize)
if isinstance(windowSize, six.string_types):
newName = 'movingSum(%s,"%s")' % (series.name, windowSize)
else:
newName = "movingSum(%s,%s)" % (series.name, windowSize)
newSeries = TimeSeries(newName, series.start + previewSeconds,
series.end, series.step, [])
newSeries.pathExpression = newName
window_sum = safeSum(series[:windowPoints])
newSeries.append(window_sum)
for n, last in enumerate(series[windowPoints:-1]):
if series[n] is not None:
window_sum -= series[n]
if last is not None:
window_sum = (window_sum or 0) + last
newSeries.append(window_sum)
result.append(newSeries)
return result | def movingSum(requestContext, seriesList, windowSize) | Graphs the moving sum of a metric (or metrics) over a fixed number of
past points, or a time interval.
Takes one metric or a wildcard seriesList followed by a number N of
datapoints or a quoted string with a length of time like '1hour' or '5min'
(See ``from / until`` in the render\_api_ for examples of time formats).
Graphs the sum of the preceeding datapoints for each point on the graph.
Example::
&target=movingSum(Server.instance01.requests,10)
&target=movingSum(Server.instance*.errors,'5min') | 3.977894 | 4.251676 | 0.935606 |
if not seriesList:
return []
windowInterval = None
if isinstance(windowSize, six.string_types):
delta = parseTimeOffset(windowSize)
windowInterval = abs(delta.seconds + (delta.days * 86400))
if windowInterval:
previewSeconds = windowInterval
else:
previewSeconds = max([s.step for s in seriesList]) * int(windowSize)
# ignore original data and pull new, including our preview
# data from earlier is needed to calculate the early results
newContext = requestContext.copy()
newContext['startTime'] = (requestContext['startTime'] -
timedelta(seconds=previewSeconds))
previewList = evaluateTokens(newContext, requestContext['args'][0])
result = []
for series in previewList:
if windowInterval:
windowPoints = windowInterval // series.step
else:
windowPoints = int(windowSize)
if isinstance(windowSize, six.string_types):
newName = 'movingMax(%s,"%s")' % (series.name, windowSize)
else:
newName = "movingMax(%s,%s)" % (series.name, windowSize)
newSeries = TimeSeries(newName, series.start + previewSeconds,
series.end, series.step, [])
newSeries.pathExpression = newName
for i in range(windowPoints, len(series)):
window = series[i - windowPoints:i]
newSeries.append(safeMax(window))
result.append(newSeries)
return result | def movingMax(requestContext, seriesList, windowSize) | Graphs the moving maximum of a metric (or metrics) over a fixed number of
past points, or a time interval.
Takes one metric or a wildcard seriesList followed by a number N of
datapoints or a quoted string with a length of time like '1hour' or '5min'
(See ``from / until`` in the render\_api_ for examples of time formats).
Graphs the maximum of the preceeding datapoints for each point on the
graph.
Example::
&target=movingMax(Server.instance01.requests,10)
&target=movingMax(Server.instance*.errors,'5min') | 4.083945 | 4.418615 | 0.924259 |
for series in seriesList:
# datalib will throw an exception, so it's not necessary to validate
# here
series.consolidationFunc = consolidationFunc
series.name = 'consolidateBy(%s,"%s")' % (series.name,
series.consolidationFunc)
series.pathExpression = series.name
return seriesList | def consolidateBy(requestContext, seriesList, consolidationFunc) | Takes one metric or a wildcard seriesList and a consolidation function
name.
Valid function names are 'sum', 'average', 'min', and 'max'.
When a graph is drawn where width of the graph size in pixels is smaller
than the number of datapoints to be graphed, Graphite consolidates the
values to to prevent line overlap. The consolidateBy() function changes
the consolidation function from the default of 'average' to one of 'sum',
'max', or 'min'. This is especially useful in sales graphs, where
fractional values make no sense and a 'sum' of consolidated values is
appropriate.
Example::
&target=consolidateBy(Sales.widgets.largeBlue, 'sum')
&target=consolidateBy(Servers.web01.sda1.free_space, 'max') | 5.764908 | 7.52754 | 0.765842 |
results = []
for series in seriesList:
newValues = []
prev = []
for val in series:
if len(prev) < steps:
newValues.append(None)
prev.append(val)
continue
newValues.append(prev.pop(0))
prev.append(val)
newName = "delay(%s,%d)" % (series.name, steps)
newSeries = TimeSeries(newName, series.start, series.end, series.step,
newValues)
newSeries.pathExpression = newName
results.append(newSeries)
return results | def delay(requestContext, seriesList, steps) | This shifts all samples later by an integer number of steps. This can be
used for custom derivative calculations, among other things. Note: this
will pad the early end of the data with None for every step shifted.
This complements other time-displacement functions such as timeShift and
timeSlice, in that this function is indifferent about the step intervals
being shifted.
Example::
&target=divideSeries(server.FreeSpace,delay(server.FreeSpace,1))
This computes the change in server free space as a percentage of the
previous free space. | 2.678699 | 3.003256 | 0.891932 |
results = []
for series in seriesList:
newValues = []
current = 0.0
for val in series:
if val is None:
newValues.append(None)
else:
current += val
newValues.append(current)
newName = "integral(%s)" % series.name
newSeries = TimeSeries(newName, series.start, series.end, series.step,
newValues)
newSeries.pathExpression = newName
results.append(newSeries)
return results | def integral(requestContext, seriesList) | This will show the sum over time, sort of like a continuous addition
function. Useful for finding totals or trends in metrics that are
collected per minute.
Example::
&target=integral(company.sales.perMinute)
This would start at zero on the left side of the graph, adding the sales
each minute, and show the total sales for the time period selected at the
right side, (time now, or the time specified by '&until='). | 2.712554 | 2.910528 | 0.93198 |
intervalDuration = int(to_seconds(parseTimeOffset(intervalUnit)))
startTime = int(epoch(requestContext['startTime']))
results = []
for series in seriesList:
newValues = []
# current time within series iteration
currentTime = series.start
# current accumulated value
current = 0.0
for val in series:
# reset integral value if crossing an interval boundary
if (
((currentTime - startTime) // intervalDuration) !=
((currentTime - startTime - series.step) // intervalDuration)
):
current = 0.0
if val is None:
# keep previous value since val can be None when resetting
# current to 0.0
newValues.append(current)
else:
current += val
newValues.append(current)
currentTime += series.step
newName = "integralByInterval(%s,'%s')" % (series.name, intervalUnit)
newSeries = TimeSeries(newName, series.start, series.end, series.step,
newValues)
newSeries.pathExpression = newName
results.append(newSeries)
return results | def integralByInterval(requestContext, seriesList, intervalUnit) | This will do the same as integral() funcion, except resetting the total
to 0 at the given time in the parameter "from"
Useful for finding totals per hour/day/week/..
Example::
&target=integralByInterval(company.sales.perMinute,
"1d")&from=midnight-10days
This would start at zero on the left side of the graph, adding the sales
each minute, and show the evolution of sales per day during the last 10
days. | 4.274975 | 4.521508 | 0.945476 |
results = []
for series in seriesList:
newValues = []
prev = None
for val in series:
if None in (prev, val):
newValues.append(None)
prev = val
continue
diff = val - prev
if diff >= 0:
newValues.append(diff)
elif maxValue is not None and maxValue >= val:
newValues.append((maxValue - prev) + val + 1)
else:
newValues.append(None)
prev = val
newName = "nonNegativeDerivative(%s)" % series.name
newSeries = TimeSeries(newName, series.start, series.end, series.step,
newValues)
newSeries.pathExpression = newName
results.append(newSeries)
return results | def nonNegativeDerivative(requestContext, seriesList, maxValue=None) | Same as the derivative function above, but ignores datapoints that trend
down. Useful for counters that increase for a long time, then wrap or
reset. (Such as if a network interface is destroyed and recreated by
unloading and re-loading a kernel module, common with USB / WiFi cards.
Example::
&target=nonNegativederivative(
company.server.application01.ifconfig.TXPackets) | 2.655868 | 2.913683 | 0.911516 |
if 'totalStack' in requestContext:
totalStack = requestContext['totalStack'].get(stackName, [])
else:
requestContext['totalStack'] = {}
totalStack = []
results = []
for series in seriesLists:
newValues = []
for i in range(len(series)):
if len(totalStack) <= i:
totalStack.append(0)
if series[i] is not None:
totalStack[i] += series[i]
newValues.append(totalStack[i])
else:
newValues.append(None)
# Work-around for the case when legend is set
if stackName == '__DEFAULT__':
newName = "stacked(%s)" % series.name
else:
newName = series.name
newSeries = TimeSeries(newName, series.start, series.end, series.step,
newValues)
newSeries.options['stacked'] = True
newSeries.pathExpression = newName
results.append(newSeries)
requestContext['totalStack'][stackName] = totalStack
return results | def stacked(requestContext, seriesLists, stackName='__DEFAULT__') | Takes one metric or a wildcard seriesList and change them so they are
stacked. This is a way of stacking just a couple of metrics without having
to use the stacked area mode (that stacks everything). By means of this a
mixed stacked and non stacked graph can be made
It can also take an optional argument with a name of the stack, in case
there is more than one, e.g. for input and output metrics.
Example::
&target=stacked(company.server.application01.ifconfig.TXPackets, 'tx') | 2.672555 | 2.912284 | 0.917684 |
if len(seriesLists) == 1:
[seriesLists] = seriesLists
assert len(seriesLists) == 2, ("areaBetween series argument must "
"reference *exactly* 2 series")
lower, upper = seriesLists
if len(lower) == 1:
[lower] = lower
if len(upper) == 1:
[upper] = upper
lower.options['stacked'] = True
lower.options['invisible'] = True
upper.options['stacked'] = True
lower.name = upper.name = "areaBetween(%s)" % upper.pathExpression
return [lower, upper] | def areaBetween(requestContext, *seriesLists) | Draws the vertical area in between the two series in seriesList. Useful for
visualizing a range such as the minimum and maximum latency for a service.
areaBetween expects **exactly one argument** that results in exactly two
series (see example below). The order of the lower and higher values
series does not matter. The visualization only works when used in
conjunction with ``areaMode=stacked``.
Most likely use case is to provide a band within which another metric
should move. In such case applying an ``alpha()``, as in the second
example, gives best visual results.
Example::
&target=areaBetween(service.latency.{min,max})&areaMode=stacked
&target=alpha(areaBetween(service.latency.{min,max}),0.3)&areaMode=stacked
If for instance, you need to build a seriesList, you should use the
``group`` function, like so::
&target=areaBetween(group(minSeries(a.*.min),maxSeries(a.*.max))) | 3.752607 | 4.971285 | 0.754856 |
try:
seriesList.name = re.sub(search, replace, seriesList.name)
except AttributeError:
for series in seriesList:
series.name = re.sub(search, replace, series.name)
return seriesList | def aliasSub(requestContext, seriesList, search, replace) | Runs series names through a regex search/replace.
Example::
&target=aliasSub(ip.*TCP*,"^.*TCP(\d+)","\\1") | 2.111422 | 2.916682 | 0.723912 |
try:
seriesList.name = newName
except AttributeError:
for series in seriesList:
series.name = newName
return seriesList | def alias(requestContext, seriesList, newName) | Takes one metric or a wildcard seriesList and a string in quotes.
Prints the string instead of the metric name in the legend.
Example::
&target=alias(Sales.widgets.largeBlue,"Large Blue Widgets") | 3.354186 | 6.430655 | 0.521593 |
def fmt(x):
if system:
if units:
return "%.2f %s" % format_units(x, system=system, units=units)
else:
return "%.2f%s" % format_units(x, system=system)
else:
if units:
return "%.2f %s" % (x, units)
else:
return "%.2f" % x
nameLen = max([0] + [len(series.name) for series in seriesList])
lastLen = max([0] + [len(fmt(int(safeLast(series) or 3)))
for series in seriesList]) + 3
maxLen = max([0] + [len(fmt(int(safeMax(series) or 3)))
for series in seriesList]) + 3
minLen = max([0] + [len(fmt(int(safeMin(series) or 3)))
for series in seriesList]) + 3
for series in seriesList:
last = safeLast(series)
maximum = safeMax(series)
minimum = safeMin(series)
if last is None:
last = NAN
else:
last = fmt(float(last))
if maximum is None:
maximum = NAN
else:
maximum = fmt(float(maximum))
if minimum is None:
minimum = NAN
else:
minimum = fmt(float(minimum))
series.name = "%*s Current:%*s Max:%*s Min:%*s " % (
-nameLen, series.name, -lastLen, last,
-maxLen, maximum, -minLen, minimum)
return seriesList | def cactiStyle(requestContext, seriesList, system=None, units=None) | Takes a series list and modifies the aliases to provide column aligned
output with Current, Max, and Min values in the style of cacti. Optionally
takes a "system" value to apply unit formatting in the same style as the
Y-axis, or a "unit" string to append an arbitrary unit suffix.
NOTE: column alignment only works with monospace fonts such as terminus.
Example::
&target=cactiStyle(ganglia.*.net.bytes_out,"si")
&target=cactiStyle(ganglia.*.net.bytes_out,"si","b") | 2.10303 | 2.1176 | 0.99312 |
tokens = grammar.parseString(name)
pathExpression = None
while pathExpression is None:
if tokens.pathExpression:
pathExpression = tokens.pathExpression
elif tokens.expression:
tokens = tokens.expression
elif tokens.call:
tokens = tokens.call.args[0]
else:
break
return pathExpression | def _getFirstPathExpression(name) | Returns the first metric path in an expression. | 3.406969 | 3.565708 | 0.955482 |
for series in seriesList:
pathExpression = _getFirstPathExpression(series.name)
metric_pieces = pathExpression.split('.')
series.name = '.'.join(metric_pieces[n] for n in nodes)
return seriesList | def aliasByNode(requestContext, seriesList, *nodes) | Takes a seriesList and applies an alias derived from one or more "node"
portion/s of the target name. Node indices are 0 indexed.
Example::
&target=aliasByNode(ganglia.*.cpu.load5,1) | 6.218773 | 8.864464 | 0.70154 |
valueFuncs = {
'avg': lambda s: safeDiv(safeSum(s), safeLen(s)),
'total': safeSum,
'min': safeMin,
'max': safeMax,
'last': safeLast,
}
system = None
if valueTypes[-1] in ('si', 'binary'):
system = valueTypes[-1]
valueTypes = valueTypes[:-1]
for valueType in valueTypes:
valueFunc = valueFuncs.get(valueType, lambda s: '(?)')
if system is None:
for series in seriesList:
series.name += " (%s: %s)" % (valueType, valueFunc(series))
else:
for series in seriesList:
value = valueFunc(series)
formatted = None
if value is not None:
formatted = "%.2f%s" % format_units(value, system=system)
series.name = "%-20s%-5s%-10s" % (series.name, valueType,
formatted)
return seriesList | def legendValue(requestContext, seriesList, *valueTypes) | Takes one metric or a wildcard seriesList and a string in quotes.
Appends a value to the metric name in the legend. Currently one or several
of: `last`, `avg`, `total`, `min`, `max`. The last argument can be `si`
(default) or `binary`, in that case values will be formatted in the
corresponding system.
Example::
&target=legendValue(Sales.widgets.largeBlue, 'avg', 'max', 'si') | 3.210658 | 2.94787 | 1.089145 |
for series in seriesList:
series.options['alpha'] = alpha
return seriesList | def alpha(requestContext, seriesList, alpha) | Assigns the given alpha transparency setting to the series. Takes a float
value between 0 and 1. | 4.185523 | 5.684774 | 0.736269 |
for series in seriesList:
series.color = theColor
return seriesList | def color(requestContext, seriesList, theColor) | Assigns the given color to the seriesList
Example::
&target=color(collectd.hostname.cpu.0.user, 'green')
&target=color(collectd.hostname.cpu.0.system, 'ff0000')
&target=color(collectd.hostname.cpu.0.idle, 'gray')
&target=color(collectd.hostname.cpu.0.idle, '6464ffaa') | 3.045916 | 11.348255 | 0.268404 |
for series in seriesList:
left = series.name.rfind('(') + 1
right = series.name.find(')')
if right < 0:
right = len(series.name)+1
cleanName = series.name[left:right:].split('.')
if int(stop) == 0:
series.name = '.'.join(cleanName[int(start)::])
else:
series.name = '.'.join(cleanName[int(start):int(stop):])
# substr(func(a.b,'c'),1) becomes b instead of b,'c'
series.name = re.sub(',.*$', '', series.name)
return seriesList | def substr(requestContext, seriesList, start=0, stop=0) | Takes one metric or a wildcard seriesList followed by 1 or 2 integers.
Assume that the metric name is a list or array, with each element
separated by dots. Prints n - length elements of the array (if only one
integer n is passed) or n - m elements of the array (if two integers n and
m are passed). The list starts with element 0 and ends with element
(length - 1).
Example::
&target=substr(carbon.agents.hostname.avgUpdateTime,2,4)
The label would be printed as "hostname.avgUpdateTime". | 3.93625 | 4.769338 | 0.825324 |
results = []
for series in seriesList:
newValues = []
for val in series:
if val is None:
newValues.append(None)
elif val <= 0:
newValues.append(None)
else:
newValues.append(math.log(val, base))
newName = "log(%s, %s)" % (series.name, base)
newSeries = TimeSeries(newName, series.start, series.end, series.step,
newValues)
newSeries.pathExpression = newName
results.append(newSeries)
return results | def logarithm(requestContext, seriesList, base=10) | Takes one metric or a wildcard seriesList, a base, and draws the y-axis in
logarithmic format. If base is omitted, the function defaults to base 10.
Example::
&target=log(carbon.agents.hostname.avgUpdateTime,2) | 2.229671 | 2.535084 | 0.879526 |
results = []
for series in seriesList:
val = safeMax(series)
if val is None or val <= n:
results.append(series)
return results | def maximumBelow(requestContext, seriesList, n) | Takes one metric or a wildcard seriesList followed by a constant n.
Draws only the metrics with a maximum value below n.
Example::
&target=maximumBelow(system.interface.eth*.packetsSent,1000)
This would only display interfaces which always sent less than 1000
packets/min. | 3.71496 | 5.778328 | 0.642913 |
results = []
for series in seriesList:
val = safeMin(series)
if val is None or val <= n:
results.append(series)
return results | def minimumBelow(requestContext, seriesList, n) | Takes one metric or a wildcard seriesList followed by a constant n.
Draws only the metrics with a minimum value below n.
Example::
&target=minimumBelow(system.interface.eth*.packetsSent,1000)
This would only display interfaces which sent at one point less than
1000 packets/min. | 3.487108 | 5.454971 | 0.639253 |
result_list = sorted(seriesList, key=lambda s: safeMax(s))[-n:]
return sorted(result_list, key=lambda s: max(s), reverse=True) | def highestMax(requestContext, seriesList, n=1) | Takes one metric or a wildcard seriesList followed by an integer N.
Out of all metrics passed, draws only the N metrics with the highest
maximum value in the time period specified.
Example::
&target=highestMax(server*.instance*.threads.busy,5)
Draws the top 5 servers who have had the most busy threads during the time
period specified. | 4.401247 | 6.874042 | 0.640271 |
results = []
for series in seriesList:
val = safeLast(series)
if val is not None and val >= n:
results.append(series)
return results | def currentAbove(requestContext, seriesList, n) | Takes one metric or a wildcard seriesList followed by an integer N.
Out of all metrics passed, draws only the metrics whose value is above N
at the end of the time period specified.
Example::
&target=currentAbove(server*.instance*.threads.busy,50)
Draws the servers with more than 50 busy threads. | 3.607757 | 5.212544 | 0.69213 |
results = []
for series in seriesList:
val = safeAvg(series)
if val is not None and val >= n:
results.append(series)
return results | def averageAbove(requestContext, seriesList, n) | Takes one metric or a wildcard seriesList followed by an integer N.
Out of all metrics passed, draws only the metrics with an average value
above N for the time period specified.
Example::
&target=averageAbove(server*.instance*.threads.busy,25)
Draws the servers with average values above 25. | 3.481952 | 4.796837 | 0.725885 |
sortedPoints = sorted(not_none(points))
if len(sortedPoints) == 0:
return None
fractionalRank = (n/100.0) * (len(sortedPoints) + 1)
rank = int(fractionalRank)
rankFraction = fractionalRank - rank
if not interpolate:
rank += int(math.ceil(rankFraction))
if rank == 0:
percentile = sortedPoints[0]
elif rank - 1 == len(sortedPoints):
percentile = sortedPoints[-1]
else:
percentile = sortedPoints[rank - 1] # Adjust for 0-index
if interpolate:
if rank != len(sortedPoints): # if a next value exists
nextValue = sortedPoints[rank]
percentile = percentile + rankFraction * (nextValue - percentile)
return percentile | def _getPercentile(points, n, interpolate=False) | Percentile is calculated using the method outlined in the NIST Engineering
Statistics Handbook:
http://www.itl.nist.gov/div898/handbook/prc/section2/prc252.htm | 2.9654 | 2.960524 | 1.001647 |
assert n, 'The requested percent is required to be greater than 0'
results = []
for s in seriesList:
# Create a sorted copy of the TimeSeries excluding None values in the
# values list.
s_copy = TimeSeries(s.name, s.start, s.end, s.step,
sorted(not_none(s)))
if not s_copy:
continue # Skip this series because it is empty.
perc_val = _getPercentile(s_copy, n)
if perc_val is not None:
name = 'nPercentile(%s, %g)' % (s_copy.name, n)
point_count = int((s.end - s.start)/s.step)
perc_series = TimeSeries(name, s_copy.start, s_copy.end,
s_copy.step, [perc_val] * point_count)
perc_series.pathExpression = name
results.append(perc_series)
return results | def nPercentile(requestContext, seriesList, n) | Returns n-percent of each series in the seriesList. | 3.567999 | 3.563801 | 1.001178 |
averages = [safeAvg(s) for s in seriesList]
if n < 50:
n = 100 - n
lowPercentile = _getPercentile(averages, 100 - n)
highPercentile = _getPercentile(averages, n)
return [s for s in seriesList
if not lowPercentile < safeAvg(s) < highPercentile] | def averageOutsidePercentile(requestContext, seriesList, n) | Removes functions lying inside an average percentile interval | 3.290918 | 3.277635 | 1.004053 |
if n < 50:
n = 100 - n
transposed = list(zip_longest(*seriesList))
lowPercentiles = [_getPercentile(col, 100-n) for col in transposed]
highPercentiles = [_getPercentile(col, n) for col in transposed]
return [l for l in seriesList
if sum([not lowPercentiles[index] < val < highPercentiles[index]
for index, val in enumerate(l)]) > 0] | def removeBetweenPercentile(requestContext, seriesList, n) | Removes lines who do not have an value lying in the x-percentile of all
the values at a moment | 3.588663 | 3.495529 | 1.026644 |
for s in seriesList:
s.name = 'removeAboveValue(%s, %g)' % (s.name, n)
s.pathExpression = s.name
for (index, val) in enumerate(s):
if val is None:
continue
if val > n:
s[index] = None
return seriesList | def removeAboveValue(requestContext, seriesList, n) | Removes data above the given threshold from the series or list of series
provided. Values above this threshold are assigned a value of None. | 3.502664 | 3.553268 | 0.985758 |
for s in seriesList:
s.name = 'removeBelowPercentile(%s, %g)' % (s.name, n)
s.pathExpression = s.name
try:
percentile = nPercentile(requestContext, [s], n)[0][0]
except IndexError:
continue
for (index, val) in enumerate(s):
if val is None:
continue
if val < percentile:
s[index] = None
return seriesList | def removeBelowPercentile(requestContext, seriesList, n) | Removes data below the nth percentile from the series or list of series
provided. Values below this percentile are assigned a value of None. | 3.494081 | 3.687175 | 0.947631 |
if natural:
return list(sorted(seriesList, key=lambda x: paddedName(x.name)))
else:
return list(sorted(seriesList, key=lambda x: x.name)) | def sortByName(requestContext, seriesList, natural=False) | Takes one metric or a wildcard seriesList.
Sorts the list of metrics by the metric name using either alphabetical
order or natural sorting. Natural sorting allows names containing numbers
to be sorted more naturally, e.g:
- Alphabetical sorting: server1, server11, server12, server2
- Natural sorting: server1, server2, server11, server12 | 2.708469 | 3.360578 | 0.805953 |
return list(sorted(seriesList, key=safeSum, reverse=True)) | def sortByTotal(requestContext, seriesList) | Takes one metric or a wildcard seriesList.
Sorts the list of metrics by the sum of values across the time period
specified. | 7.948934 | 12.299452 | 0.646284 |
newSeries = []
for series in seriesList:
newname = re.sub(search, replace, series.name)
if safeMax(series) > value:
n = evaluateTarget(requestContext, newname)
if n is not None and len(n) > 0:
newSeries.append(n[0])
return newSeries | def useSeriesAbove(requestContext, seriesList, value, search, replace) | Compares the maximum of each series against the given `value`. If the
series maximum is greater than `value`, the regular expression search and
replace is applied against the series name to plot a related metric.
e.g. given useSeriesAbove(ganglia.metric1.reqs,10,'reqs','time'),
the response time metric will be plotted only when the maximum value of the
corresponding request/s metric is > 10
Example::
&target=useSeriesAbove(ganglia.metric1.reqs,10,"reqs","time") | 4.569159 | 5.076903 | 0.899989 |
deviants = []
for series in seriesList:
mean = safeAvg(series)
if mean is None:
continue
square_sum = sum([(value - mean) ** 2 for value in series
if value is not None])
sigma = safeDiv(square_sum, safeLen(series))
if sigma is None:
continue
deviants.append((sigma, series))
return [series for sig, series in sorted(deviants, # sort by sigma
key=itemgetter(0),
reverse=True)][:n] | def mostDeviant(requestContext, seriesList, n) | Takes one metric or a wildcard seriesList followed by an integer N.
Draws the N most deviant metrics.
To find the deviants, the standard deviation (sigma) of each series
is taken and ranked. The top N standard deviations are returned.
Example::
&target=mostDeviant(server*.instance*.memory.free, 5)
Draws the 5 instances furthest from the average memory free. | 3.709413 | 3.482422 | 1.065182 |
# For this we take the standard deviation in terms of the moving average
# and the moving average of series squares.
for seriesIndex, series in enumerate(seriesList):
stdevSeries = TimeSeries("stdev(%s,%d)" % (series.name, int(points)),
series.start, series.end, series.step, [])
stdevSeries.pathExpression = "stdev(%s,%d)" % (series.name,
int(points))
validPoints = 0
currentSum = 0
currentSumOfSquares = 0
for index, newValue in enumerate(series):
# Mark whether we've reached our window size - dont drop points
# out otherwise
if index < points:
bootstrapping = True
droppedValue = None
else:
bootstrapping = False
droppedValue = series[index - points]
# Track non-None points in window
if not bootstrapping and droppedValue is not None:
validPoints -= 1
if newValue is not None:
validPoints += 1
# Remove the value that just dropped out of the window
if not bootstrapping and droppedValue is not None:
currentSum -= droppedValue
currentSumOfSquares -= droppedValue**2
# Add in the value that just popped in the window
if newValue is not None:
currentSum += newValue
currentSumOfSquares += newValue**2
if (
validPoints > 0 and
float(validPoints) / points >= windowTolerance
):
try:
deviation = math.sqrt(validPoints * currentSumOfSquares -
currentSum**2) / validPoints
except ValueError:
deviation = None
stdevSeries.append(deviation)
else:
stdevSeries.append(None)
seriesList[seriesIndex] = stdevSeries
return seriesList | def stdev(requestContext, seriesList, points, windowTolerance=0.1) | Takes one metric or a wildcard seriesList followed by an integer N.
Draw the Standard Deviation of all metrics passed for the past N
datapoints. If the ratio of null points in the window is greater than
windowTolerance, skip the calculation. The default for windowTolerance is
0.1 (up to 10% of points in the window can be missing). Note that if this
is set to 0.0, it will cause large gaps in the output anywhere a single
point is missing.
Example::
&target=stdev(server*.instance*.threads.busy,30)
&target=stdev(server*.instance*.cpu.system,30,0.0) | 3.592196 | 3.696762 | 0.971714 |
for series in seriesList:
series.options['secondYAxis'] = True
series.name = 'secondYAxis(%s)' % series.name
return seriesList | def secondYAxis(requestContext, seriesList) | Graph the series on the secondary Y axis. | 4.020801 | 4.420883 | 0.909502 |
previewSeconds = 7 * 86400 # 7 days
# ignore original data and pull new, including our preview
newContext = requestContext.copy()
newContext['startTime'] = (requestContext['startTime'] -
timedelta(seconds=previewSeconds))
previewList = evaluateTokens(newContext, requestContext['args'][0])
results = []
for series in previewList:
analysis = holtWintersAnalysis(series)
predictions = analysis['predictions']
windowPoints = previewSeconds // predictions.step
result = TimeSeries("holtWintersForecast(%s)" % series.name,
predictions.start + previewSeconds,
predictions.end, predictions.step,
predictions[windowPoints:])
result.pathExpression = result.name
results.append(result)
return results | def holtWintersForecast(requestContext, seriesList) | Performs a Holt-Winters forecast using the series as input data. Data from
one week previous to the series is used to bootstrap the initial forecast. | 5.908636 | 6.386492 | 0.925177 |
previewSeconds = 7 * 86400 # 7 days
# ignore original data and pull new, including our preview
newContext = requestContext.copy()
newContext['startTime'] = (requestContext['startTime'] -
timedelta(seconds=previewSeconds))
previewList = evaluateTokens(newContext, requestContext['args'][0])
results = []
for series in previewList:
analysis = holtWintersAnalysis(series)
data = analysis['predictions']
windowPoints = previewSeconds // data.step
forecast = TimeSeries(data.name, data.start + previewSeconds,
data.end, data.step, data[windowPoints:])
forecast.pathExpression = data.pathExpression
data = analysis['deviations']
windowPoints = previewSeconds // data.step
deviation = TimeSeries(data.name, data.start + previewSeconds,
data.end, data.step, data[windowPoints:])
deviation.pathExpression = data.pathExpression
seriesLength = len(forecast)
i = 0
upperBand = list()
lowerBand = list()
while i < seriesLength:
forecast_item = forecast[i]
deviation_item = deviation[i]
i = i + 1
if forecast_item is None or deviation_item is None:
upperBand.append(None)
lowerBand.append(None)
else:
scaled_deviation = delta * deviation_item
upperBand.append(forecast_item + scaled_deviation)
lowerBand.append(forecast_item - scaled_deviation)
upperName = "holtWintersConfidenceUpper(%s)" % series.name
lowerName = "holtWintersConfidenceLower(%s)" % series.name
upperSeries = TimeSeries(upperName, forecast.start, forecast.end,
forecast.step, upperBand)
lowerSeries = TimeSeries(lowerName, forecast.start, forecast.end,
forecast.step, lowerBand)
upperSeries.pathExpression = series.pathExpression
lowerSeries.pathExpression = series.pathExpression
results.append(lowerSeries)
results.append(upperSeries)
return results | def holtWintersConfidenceBands(requestContext, seriesList, delta=3) | Performs a Holt-Winters forecast using the series as input data and plots
upper and lower bands with the predicted forecast deviations. | 2.696231 | 2.694159 | 1.000769 |
results = []
for series in seriesList:
confidenceBands = holtWintersConfidenceBands(requestContext, [series],
delta)
lowerBand = confidenceBands[0]
upperBand = confidenceBands[1]
aberration = list()
for i, actual in enumerate(series):
if actual is None:
aberration.append(0)
elif upperBand[i] is not None and actual > upperBand[i]:
aberration.append(actual - upperBand[i])
elif lowerBand[i] is not None and actual < lowerBand[i]:
aberration.append(actual - lowerBand[i])
else:
aberration.append(0)
newName = "holtWintersAberration(%s)" % series.name
results.append(TimeSeries(newName, series.start, series.end,
series.step, aberration))
return results | def holtWintersAberration(requestContext, seriesList, delta=3) | Performs a Holt-Winters forecast using the series as input data and plots
the positive or negative deviation of the series data from the forecast. | 2.212947 | 2.262194 | 0.97823 |
bands = holtWintersConfidenceBands(requestContext, seriesList, delta)
results = areaBetween(requestContext, bands)
for series in results:
series.name = series.name.replace('areaBetween',
'holtWintersConfidenceArea')
return results | def holtWintersConfidenceArea(requestContext, seriesList, delta=3) | Performs a Holt-Winters forecast using the series as input data and plots
the area between the upper and lower bands of the predicted forecast
deviations. | 4.242509 | 4.647117 | 0.912934 |
n = safeLen(series)
sumI = sum([i for i, v in enumerate(series) if v is not None])
sumV = sum([v for i, v in enumerate(series) if v is not None])
sumII = sum([i * i for i, v in enumerate(series) if v is not None])
sumIV = sum([i * v for i, v in enumerate(series) if v is not None])
denominator = float(n * sumII - sumI * sumI)
if denominator == 0:
return None
else:
factor = (n * sumIV - sumI * sumV) / denominator / series.step
offset = sumII * sumV - sumIV * sumI
offset = offset / denominator - factor * series.start
return factor, offset | def linearRegressionAnalysis(series) | Returns factor and offset of linear regression function by least
squares method. | 2.582288 | 2.402622 | 1.074779 |
from .app import evaluateTarget
results = []
sourceContext = requestContext.copy()
if startSourceAt is not None:
sourceContext['startTime'] = parseATTime(startSourceAt)
if endSourceAt is not None:
sourceContext['endTime'] = parseATTime(endSourceAt)
sourceList = []
for series in seriesList:
source = evaluateTarget(sourceContext, series.pathExpression)
sourceList.extend(source)
for source, series in zip(sourceList, seriesList):
newName = 'linearRegression(%s, %s, %s)' % (
series.name,
int(epoch(sourceContext['startTime'])),
int(epoch(sourceContext['endTime'])))
forecast = linearRegressionAnalysis(source)
if forecast is None:
continue
factor, offset = forecast
values = [offset + (series.start + i * series.step) * factor
for i in range(len(series))]
newSeries = TimeSeries(newName, series.start, series.end,
series.step, values)
newSeries.pathExpression = newSeries.name
results.append(newSeries)
return results | def linearRegression(requestContext, seriesList, startSourceAt=None,
endSourceAt=None) | Graphs the liner regression function by least squares method.
Takes one metric or a wildcard seriesList, followed by a quoted string
with the time to start the line and another quoted string with the time
to end the line. The start and end times are inclusive (default range is
from to until). See ``from / until`` in the render\_api_ for examples of
time formats. Datapoints in the range is used to regression.
Example::
&target=linearRegression(Server.instance01.threads.busy,'-1d')
&target=linearRegression(Server.instance*.threads.busy,
"00:00 20140101","11:59 20140630") | 3.204118 | 3.547956 | 0.903089 |
for series in seriesList:
series.options['drawAsInfinite'] = True
series.name = 'drawAsInfinite(%s)' % series.name
return seriesList | def drawAsInfinite(requestContext, seriesList) | Takes one metric or a wildcard seriesList.
If the value is zero, draw the line at 0. If the value is above zero, draw
the line at infinity. If the value is null or less than zero, do not draw
the line.
Useful for displaying on/off metrics, such as exit codes. (0 = success,
anything else = failure.)
Example::
drawAsInfinite(Testing.script.exitCode) | 3.794523 | 6.635935 | 0.571814 |
for series in seriesList:
series.options['lineWidth'] = width
return seriesList | def lineWidth(requestContext, seriesList, width) | Takes one metric or a wildcard seriesList, followed by a float F.
Draw the selected metrics with a line width of F, overriding the default
value of 1, or the &lineWidth=X.X parameter.
Useful for highlighting a single metric out of many, or having multiple
line widths in one graph.
Example::
&target=lineWidth(server01.instance01.memory.free,5) | 3.541772 | 22.88199 | 0.154784 |
for series in seriesList:
series.name = 'dashed(%s, %g)' % (series.name, dashLength)
series.options['dashed'] = dashLength
return seriesList | def dashed(requestContext, seriesList, dashLength=5) | Takes one metric or a wildcard seriesList, followed by a float F.
Draw the selected metrics with a dotted line with segments of length F
If omitted, the default length of the segments is 5.0
Example::
&target=dashed(server01.instance01.memory.free,2.5) | 3.420565 | 6.722641 | 0.508813 |
# Default to negative. parseTimeOffset defaults to +
if timeShiftUnit[0].isdigit():
timeShiftUnit = '-' + timeShiftUnit
delta = parseTimeOffset(timeShiftUnit)
# if len(seriesList) > 1, they will all have the same pathExpression,
# which is all we care about.
series = seriesList[0]
results = []
timeShiftStartint = int(timeShiftStart)
timeShiftEndint = int(timeShiftEnd)
for shft in range(timeShiftStartint, timeShiftEndint):
myContext = requestContext.copy()
innerDelta = delta * shft
myContext['startTime'] = requestContext['startTime'] + innerDelta
myContext['endTime'] = requestContext['endTime'] + innerDelta
for shiftedSeries in evaluateTarget(myContext, series.pathExpression):
shiftedSeries.name = 'timeShift(%s, %s, %s)' % (shiftedSeries.name,
timeShiftUnit,
shft)
shiftedSeries.pathExpression = shiftedSeries.name
shiftedSeries.start = series.start
shiftedSeries.end = series.end
results.append(shiftedSeries)
return results | def timeStack(requestContext, seriesList, timeShiftUnit, timeShiftStart,
timeShiftEnd) | Takes one metric or a wildcard seriesList, followed by a quoted string
with the length of time (See ``from / until`` in the render\_api_ for
examples of time formats). Also takes a start multiplier and end
multiplier for the length of time-
Create a seriesList which is composed the original metric series stacked
with time shifts starting time shifts from the start multiplier through
the end multiplier.
Useful for looking at history, or feeding into averageSeries or
stddevSeries.
Example::
# create a series for today and each of the previous 7 days
&target=timeStack(Sales.widgets.largeBlue,"1d",0,7) | 3.753199 | 4.22713 | 0.887883 |
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