def testSparselyBin(self):
one = SparselyBin(1.0, named("something", lambda x: x))
for _ in self.simple:
one.fill(_)
self.assertEqual([(i, v.entries) for i, v in sorted(one.bins.items())],
[(-5, 1.0), (-3, 1.0), (-2, 2.0), (0, 2.0), (1, 1.0),
(2, 1.0), (3, 1.0), (7, 1.0)])
self.assertEqual(one.numFilled, 8)
self.assertEqual(one.num, 13)
self.assertEqual(one.low, -5.0)
self.assertEqual(one.high, 8.0)
self.checkScaling(one)
self.checkScaling(one.toImmutable())
self.checkJson(one)
self.checkPickle(one)
self.checkName(one)
two = SparselyBin(1.0, named("something", lambda x: x),
Sum(named("elsie", lambda x: x)))
for _ in self.simple:
two.fill(_)
self.checkScaling(two)
self.checkScaling(two.toImmutable())
self.checkJson(two)
self.checkPickle(two)
self.checkName(two)
def TwoDimensionallySparselyHistogram(xbinWidth, xquantity,
ybinWidth, yquantity,
selection=unweighted,
xorigin=0.0, yorigin=0.0):
"""Convenience function for creating a sparsely binned, two-dimensional histogram."""
return Select.ing(selection,
SparselyBin.ing(xbinWidth, xquantity,
SparselyBin.ing(ybinWidth, yquantity,
Count.ing(), Count.ing(), yorigin), Count.ing(), xorigin))
def SparselyProfileErr(binWidth,
binnedQuantity,
averagedQuantity,
selection=unweighted,
origin=0.0):
"""Convenience function for creating a physicist's sparsely binned "profile plot," which is a Profile with variances."""
return Select.ing(
selection,
SparselyBin.ing(binWidth, binnedQuantity,
Deviate.ing(averagedQuantity), Count.ing(), origin))
def SparselyProfile(binWidth,
binnedQuantity,
averagedQuantity,
selection=unweighted,
origin=0.0):
"""Convenience function for creating sparsely binned binwise averages."""
return Select.ing(
selection,
SparselyBin.ing(binWidth, binnedQuantity,
Average.ing(averagedQuantity), Count.ing(), origin))
def SparselyHistogram(binWidth, quantity=identity, origin=0.0):
"""Create a sparsely binned histogram that is only capable of being added.
Parameters:
binWidth (float): the width of a bin.
quantity (function returning float or string): function that computes the quantity of interest from
the data. pass on all values by default. If a string is given, quantity is set to identity(string),
in which case that column is picked up from a pandas df.
origin (float): the left edge of the bin whose index is zero.
"""
return SparselyBin.ing(binWidth, quantity, Count.ing(), Count.ing(), origin)
def testSparselyBinDeviate(self):
with Numpy() as numpy:
if numpy is None:
return
sys.stderr.write("\n")
self.compare("SparselyBinDeviate no data", SparselyBin(0.1, lambda x: x["empty"], Deviate(
lambda x: x["empty"])), self.data, SparselyBin(0.1, lambda x: x, Deviate(lambda x: x)), self.empty)
self.compare("SparselyBinDeviate noholes", SparselyBin(0.1, lambda x: x["noholes"], Deviate(
lambda x: x["noholes"])), self.data, SparselyBin(0.1, lambda x: x, Deviate(lambda x: x)), self.noholes)
self.compare("SparselyBinDeviate holes", SparselyBin(0.1, lambda x: x["withholes"], Deviate(
lambda x: x["withholes"])), self.data, SparselyBin(0.1, lambda x: x, Deviate(lambda x: x)), self.withholes)
def testSparselyBinTrans(self):
with Numpy() as numpy:
if numpy is None:
return
sys.stderr.write("\n")
self.compare("SparselyBinTrans no data", SparselyBin(0.1, lambda x: x["empty"], Count(
lambda x: 0.5*x)), self.data, SparselyBin(0.1, lambda x: x, Count(lambda x: 0.5*x)), self.empty)
self.compare("SparselyBinTrans noholes", SparselyBin(0.1, lambda x: x["noholes"], Count(
lambda x: 0.5*x)), self.data, SparselyBin(0.1, lambda x: x, Count(lambda x: 0.5*x)), self.noholes)
self.compare("SparselyBinTrans holes", SparselyBin(0.1, lambda x: x["withholes"], Count(
lambda x: 0.5*x)), self.data, SparselyBin(0.1, lambda x: x, Count(lambda x: 0.5*x)), self.withholes)
def SparselyProfileErr(binWidth, binnedQuantity, averagedQuantity, origin=0.0):
"""Convenience function for creating a sparsely binned profile plot
This is a Profile with variances.
"""
return SparselyBin.ing(binWidth, binnedQuantity, Deviate.ing(averagedQuantity), Count.ing(), origin)
def SparselyHistogram(binWidth, quantity, selection=unweighted, origin=0.0):
"Convenience function for creating a sparsely binned histogram."
return Select.ing(
selection,
SparselyBin.ing(binWidth, quantity, Count.ing(), Count.ing(), origin))
def SparselyProfileErr(binWidth, binnedQuantity, averagedQuantity, selection=unweighted, origin=0.0):
"""Convenience function for creating a physicist's sparsely binned "profile plot," which is a Profile with variances."""
return Select.ing(selection,
SparselyBin.ing(binWidth, binnedQuantity,
Deviate.ing(averagedQuantity), Count.ing(), origin))
def SparselyProfile(binWidth, binnedQuantity, averagedQuantity, selection=unweighted, origin=0.0):
"""Convenience function for creating sparsely binned binwise averages."""
return Select.ing(selection,
SparselyBin.ing(binWidth, binnedQuantity,
Average.ing(averagedQuantity), Count.ing(), origin))
def SparselyHistogram(binWidth, quantity, selection=unweighted, origin=0.0):
"""Convenience function for creating a sparsely binned histogram."""
return Select.ing(selection,
SparselyBin.ing(binWidth, quantity, Count.ing(), Count.ing(), origin))