def __init__(self,
title,
xLabel,
yLabel=None,
roundingDecimals=2,
nBins=None,
dropOutliers=False,
sigmaLimit=5,
storeHistogram=True):
"""
__init__
Initialize a more complex histogram structure, containing different
data to calculate online average and standard deviations. This data is also
stored in the JSON to allow rebuilding and adding histograms.
All histograms are binned when requested, the resolution can be specified
through nBins, otherwise the value used is the one recommended in:
Wand, M.P. (1997), "Data-Based Choice of Histogram Bin Width," The American Statistician, 51, 59-64.
If specified, outlier farther than sigmaLimit standard deviations from the
mean will not be included in the binned histogram.
"""
# Initialize the parent object
SummaryHistogram.__init__(self, title, xLabel)
# Indicate this is a discrete histogram
self.continuous = True
# Add data only used in the continuous version
self.yLabel = yLabel
self.nPoints = 0
self.QValue = None
self.average = None
# Configuration parameters for the continuous histograms
self.roundingDecimals = roundingDecimals
self.fixedNBins = nBins
self.dropOutliers = dropOutliers
self.sigmaLimit = sigmaLimit
self.binned = False
self.storeHistogram = storeHistogram
# Override initialization of some attributes
self.average = 0.0
self.stdDev = 0.0
return
def __init__(self, title, xLabel, yLabel = None,
roundingDecimals = 2, nBins = None,
dropOutliers = False, sigmaLimit = 5,
storeHistogram = True):
"""
__init__
Initialize a more complex histogram structure, containing different
data to calculate online average and standard deviations. This data is also
stored in the JSON to allow rebuilding and adding histograms.
All histograms are binned when requested, the resolution can be specified
through nBins, otherwise the value used is the one recommended in:
Wand, M.P. (1997), "Data-Based Choice of Histogram Bin Width," The American Statistician, 51, 59-64.
If specified, outlier farther than sigmaLimit standard deviations from the
mean will not be included in the binned histogram.
"""
# Initialize the parent object
SummaryHistogram.__init__(self, title, xLabel)
# Indicate this is a discrete histogram
self.continuous = True
# Add data only used in the continuous version
self.yLabel = yLabel
self.nPoints = 0
self.QValue = None
self.average = None
# Configuration parameters for the continuous histograms
self.roundingDecimals = roundingDecimals
self.fixedNBins = nBins
self.dropOutliers = dropOutliers
self.sigmaLimit = sigmaLimit
self.binned = False
self.storeHistogram = storeHistogram
# Override initialization of some attributes
self.average = 0.0
self.stdDev = 0.0
return
def __init__(self, title, xLabel):
"""
__init__
Initialize a simpler histogram that only stores
the histogram. Everything else is calculated when the JSON is requested.
"""
# Initialize the parent object
SummaryHistogram.__init__(self, title, xLabel)
# Indicate this is a discrete histogram
self.continuous = False
# Add data only used in the discrete version
self.yLabels = set()
# Override initialization of some attributes
self.average = {}
self.stdDev = {}
return
def __init__(self, title, xLabel):
"""
__init__
Initialize a simpler histogram that only stores
the histogram. Everything else is calculated when the JSON is requested.
"""
# Initialize the parent object
SummaryHistogram.__init__(self, title, xLabel)
# Indicate this is a discrete histogram
self.continuous = False
# Add data only used in the discrete version
self.yLabels = set()
# Override initialization of some attributes
self.average = {}
self.stdDev = {}
return
def toJSON(self):
"""
_toJSON_
Calculate average and standard deviation, store it
and call the parent class toJSON method
"""
for yLabel in self.yLabels:
numList = []
for xValue in self.data:
numList.append(self.data[xValue][yLabel])
(self.average[yLabel], self.stdDev[yLabel]) = getAverageStdDev(numList)
return SummaryHistogram.toJSON(self)
def toJSON(self):
"""
_toJSON_
Calculate average and standard deviation, store it
and call the parent class toJSON method
"""
for yLabel in self.yLabels:
numList = []
for xValue in self.data:
numList.append(self.data[xValue][yLabel])
(self.average[yLabel],
self.stdDev[yLabel]) = getAverageStdDev(numList)
return SummaryHistogram.toJSON(self)
def toJSON(self):
"""
_toJSON_
Bin the histogram if any, calculate the standard deviation. Store
the internal data needed for reconstruction of the histogram
from JSON and call superclass toJSON method.
"""
if self.nPoints:
self.stdDev = calculateStdDevFromQ(self.QValue, self.nPoints)
if not self.binned and self.storeHistogram:
self.binHistogram()
self.jsonInternal = {}
self.jsonInternal['yLabel'] = self.yLabel
self.jsonInternal['QValue'] = self.QValue
self.jsonInternal['nPoints'] = self.nPoints
return SummaryHistogram.toJSON(self)
def toJSON(self):
"""
_toJSON_
Bin the histogram if any, calculate the standard deviation. Store
the internal data needed for reconstruction of the histogram
from JSON and call superclass toJSON method.
"""
if self.nPoints:
self.stdDev = calculateStdDevFromQ(self.QValue, self.nPoints)
if not self.binned and self.storeHistogram:
self.binHistogram()
self.jsonInternal = {}
self.jsonInternal['yLabel'] = self.yLabel
self.jsonInternal['QValue'] = self.QValue
self.jsonInternal['nPoints'] = self.nPoints
return SummaryHistogram.toJSON(self)
