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)
def binHistogram(self):
"""
_binHistogram_
Histograms of continuous data must be binned,
this takes care of that using given or optimal parameters.
Note that this modifies the data object,
and points can't be added to the histogram after this.
"""
if not self.nPoints:
return
self.binned = True
# Number of bins can be specified or calculated based on number of points
nBins = self.fixedNBins
if nBins is None:
nBins = int(
math.floor((5.0 / 3.0) * math.pow(self.nPoints, 1.0 / 3.0)))
# Define min and max
if not self.dropOutliers:
upperLimit = max(self.data.keys())
lowerLimit = min(self.data.keys())
else:
stdDev = calculateStdDevFromQ(self.QValue, self.nPoints)
upperLimit = self.average + (stdDev * self.sigmaLimit)
lowerLimit = self.average - (stdDev * self.sigmaLimit)
# Incremental delta
delta = abs(float(upperLimit - lowerLimit)) / nBins
# Build the bins, it's a list of tuples for now
bins = []
a = lowerLimit
b = lowerLimit + delta
while len(bins) < nBins:
bins.append((a, b))
a += delta
b += delta
# Go through data and populate the binned histogram
binnedHisto = {}
currentBin = 0
currentPoint = 0
sortedData = sorted(self.data.keys())
while currentPoint < len(sortedData):
point = sortedData[currentPoint]
encodedTuple = "%s,%s" % (bins[currentBin][0], bins[currentBin][1])
if encodedTuple not in binnedHisto:
binnedHisto[encodedTuple] = 0
if point > upperLimit or point < lowerLimit:
currentPoint += 1
elif currentBin == len(bins) - 1:
binnedHisto[encodedTuple] += self.data[point]
currentPoint += 1
elif point >= bins[currentBin][0] and point < bins[currentBin][1]:
binnedHisto[encodedTuple] += self.data[point]
currentPoint += 1
else:
currentBin += 1
self.data = binnedHisto
return
def binHistogram(self):
"""
_binHistogram_
Histograms of continuous data must be binned,
this takes care of that using given or optimal parameters.
Note that this modifies the data object,
and points can't be added to the histogram after this.
"""
if not self.nPoints:
return
self.binned = True
# Number of bins can be specified or calculated based on number of points
nBins = self.fixedNBins
if nBins is None:
nBins = int(math.floor((5.0 / 3.0) * math.pow(self.nPoints, 1.0 / 3.0)))
# Define min and max
if not self.dropOutliers:
upperLimit = max(self.data.keys())
lowerLimit = min(self.data.keys())
else:
stdDev = calculateStdDevFromQ(self.QValue, self.nPoints)
upperLimit = self.average + (stdDev * self.sigmaLimit)
lowerLimit = self.average - (stdDev * self.sigmaLimit)
# Incremental delta
delta = abs(float(upperLimit - lowerLimit)) / nBins
# Build the bins, it's a list of tuples for now
bins = []
a = lowerLimit
b = lowerLimit + delta
while len(bins) < nBins:
bins.append((a, b))
a += delta
b += delta
# Go through data and populate the binned histogram
binnedHisto = {}
currentBin = 0
currentPoint = 0
sortedData = sorted(self.data.keys())
while currentPoint < len(sortedData):
point = sortedData[currentPoint]
encodedTuple = "%s,%s" % (bins[currentBin][0], bins[currentBin][1])
if encodedTuple not in binnedHisto:
binnedHisto[encodedTuple] = 0
if point > upperLimit or point < lowerLimit:
currentPoint += 1
elif currentBin == len(bins) - 1:
binnedHisto[encodedTuple] += self.data[point]
currentPoint += 1
elif point >= bins[currentBin][0] and point < bins[currentBin][1]:
binnedHisto[encodedTuple] += self.data[point]
currentPoint += 1
else:
currentBin += 1
self.data = binnedHisto
return
