def TwoDimensionallyHistogram(xnum, xlow, xhigh, xquantity,
ynum, ylow, yhigh, yquantity,
selection=unweighted):
"""Convenience function for creating a conventional, two-dimensional histogram."""
return Select.ing(selection,
Bin.ing(xnum, xlow, xhigh, xquantity,
Bin.ing(ynum, ylow, yhigh, yquantity)))
def testUntypedLabelBin(self):
with Numpy() as numpy:
if numpy is None:
return
sys.stderr.write("\n")
self.compare("UntypedLabelBin no data", UntypedLabel(
x=Bin(100, -3.0, 3.0, lambda x: x["empty"])), self.data, UntypedLabel(x=Bin(100, -3.0, 3.0, lambda x: x)), self.empty)
self.compare("UntypedLabelBin noholes", UntypedLabel(
x=Bin(100, -3.0, 3.0, lambda x: x["noholes"])), self.data, UntypedLabel(x=Bin(100, -3.0, 3.0, lambda x: x)), self.noholes)
self.compare("UntypedLabelBin holes", UntypedLabel(x=Bin(
100, -3.0, 3.0, lambda x: x["withholes"])), self.data, UntypedLabel(x=Bin(100, -3.0, 3.0, lambda x: x)), self.withholes)
def testSelectBin(self):
with Numpy() as numpy:
if numpy is None:
return
sys.stderr.write("\n")
self.compare("SelectBin no data", Select(lambda x: x["empty"], Bin(
100, -3.0, 3.0, lambda x: x["empty"])), self.data, Select(lambda x: x, Bin(100, -3.0, 3.0, lambda x: x)), self.empty)
self.compare("SelectBin noholes", Select(lambda x: x["noholes"], Bin(
100, -3.0, 3.0, lambda x: x["noholes"])), self.data, Select(lambda x: x, Bin(100, -3.0, 3.0, lambda x: x)), self.noholes)
self.compare("SelectBin holes", Select(lambda x: x["withholes"], Bin(
100, -3.0, 3.0, lambda x: x["withholes"])), self.data, Select(lambda x: x, Bin(100, -3.0, 3.0, lambda x: x)), self.withholes)
def testStackBin(self):
with Numpy() as numpy:
if numpy is None:
return
sys.stderr.write("\n")
cuts = [-3.0, -1.5, -1.0, -0.5, 0.0, 0.5, 1.0, 1.5, 3.0]
self.compare("StackBin no data", Stack(cuts, lambda x: x["empty"], Bin(
100, -3.0, 3.0, lambda x: x["empty"])), self.data, Stack(cuts, lambda x: x, Bin(100, -3.0, 3.0, lambda x: x)), self.empty)
self.compare("StackBin noholes", Stack(cuts, lambda x: x["noholes"], Bin(
100, -3.0, 3.0, lambda x: x["noholes"])), self.data, Stack(cuts, lambda x: x, Bin(100, -3.0, 3.0, lambda x: x)), self.noholes)
self.compare("StackBin holes", Stack(cuts, lambda x: x["withholes"], Bin(
100, -3.0, 3.0, lambda x: x["withholes"])), self.data, Stack(cuts, lambda x: x, Bin(100, -3.0, 3.0, lambda x: x)), self.withholes)
def testBin(self):
with Numpy() as numpy:
if numpy is None:
return
sys.stderr.write("\n")
for bins in [10, 100]:
self.compare("Bin ({0} bins) no data".format(bins), Bin(bins, -3.0, 3.0,
lambda x: x["empty"]), self.data, Bin(bins, -3.0, 3.0, lambda x: x), self.empty)
self.compare("Bin ({0} bins) noholes".format(bins), Bin(bins, -3.0, 3.0,
lambda x: x["noholes"]), self.data, Bin(bins, -3.0, 3.0, lambda x: x), self.noholes)
self.compare("Bin ({0} bins) holes".format(bins), Bin(
bins, -3.0, 3.0, lambda x: x["withholes"]), self.data, Bin(bins, -3.0, 3.0, lambda x: x), self.withholes)
def ProfileErr(num,
low,
high,
binnedQuantity,
averagedQuantity,
selection=unweighted):
"""Convenience function for creating a physicist's "profile plot," which is a Profile with variances."""
return Select.ing(
selection,
Bin.ing(num, low, high, binnedQuantity, Deviate.ing(averagedQuantity)))
def Profile(num,
low,
high,
binnedQuantity,
averagedQuantity,
selection=unweighted):
"""Convenience function for creating binwise averages."""
return Select.ing(
selection,
Bin.ing(num, low, high, binnedQuantity, Average.ing(averagedQuantity)))
def Histogram(num, low, high, quantity=identity):
"""Create a conventional histogram that is capable of being filled and added.
Parameters:
num (int): the number of bins; must be at least one.
low (float): the minimum-value edge of the first bin.
high (float): the maximum-value edge of the last bin; must be strictly greater than `low`.
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.
"""
return Bin.ing(num, low, high, quantity, Count.ing(), Count.ing(), Count.ing(), Count.ing())
def testBinWithSum(self):
one = Bin(5, -3.0, 7.0, named("xaxis", lambda x: x),
Sum(named("yaxis", lambda x: 10.0)), Sum(lambda x: 10.0),
Sum(lambda x: 10.0), Sum(lambda x: 10.0))
for _ in self.simple:
one.fill(_)
self.assertEqual(list(map(lambda _: _.sum, one.values)),
[30.0, 20.0, 20.0, 10.0, 0.0])
self.assertEqual(one.underflow.sum, 10.0)
self.assertEqual(one.overflow.sum, 10.0)
self.assertEqual(one.nanflow.sum, 0.0)
two = Select(
lambda x: x.bool,
Bin(5, -3.0, 7.0, lambda x: x.double, Sum(lambda x: 10.0),
Sum(lambda x: 10.0), Sum(lambda x: 10.0), Sum(lambda x: 10.0)))
for _ in self.struct:
two.fill(_)
self.assertEqual(list(map(lambda _: _.sum, two.cut.values)),
[20.0, 10.0, 10.0, 10.0, 0.0])
self.assertEqual(two.cut.underflow.sum, 0.0)
self.assertEqual(two.cut.overflow.sum, 0.0)
self.assertEqual(two.cut.nanflow.sum, 0.0)
self.checkScaling(one)
self.checkScaling(one.toImmutable())
self.checkJson(one)
self.checkPickle(one)
self.checkName(one)
self.checkScaling(two)
self.checkScaling(two.toImmutable())
self.checkJson(two)
self.checkPickle(two)
self.checkName(two)
def testBin(self):
one = Bin(5, -3.0, 7.0, named("xaxis", lambda x: x))
for _ in self.simple:
one.fill(_)
self.assertEqual(list(map(lambda _: _.entries, one.values)),
[3.0, 2.0, 2.0, 1.0, 0.0])
self.assertEqual(one.underflow.entries, 1.0)
self.assertEqual(one.overflow.entries, 1.0)
self.assertEqual(one.nanflow.entries, 0.0)
two = Select(lambda x: x.bool, Bin(5, -3.0, 7.0, lambda x: x.double))
for _ in self.struct:
two.fill(_)
self.assertEqual(list(map(lambda _: _.entries, two.cut.values)),
[2.0, 1.0, 1.0, 1.0, 0.0])
self.assertEqual(two.cut.underflow.entries, 0.0)
self.assertEqual(two.cut.overflow.entries, 0.0)
self.assertEqual(two.cut.nanflow.entries, 0.0)
self.checkScaling(one)
self.checkScaling(one.toImmutable())
self.checkJson(one)
self.checkPickle(one)
self.checkName(one)
self.checkScaling(two)
self.checkScaling(two.toImmutable())
self.checkJson(two)
self.checkPickle(two)
self.checkName(two)
def HistogramCut(num, low, high, quantity=identity, selection=unweighted):
"""Create a conventional histogram that is capable of being filled and added, with a selection cut.
Parameters:
num (int): the number of bins; must be at least one.
low (float): the minimum-value edge of the first bin.
high (float): the maximum-value edge of the last bin; must be strictly greater than `low`.
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.
selection (function returning boolean): function that computes if data point is accepted or not.
default is: lamba x: True
"""
return Select.ing(selection, Bin.ing(num, low, high, quantity, Count.ing(), Count.ing(), Count.ing(), Count.ing()))
def ProfileErr(num, low, high, binnedQuantity, averagedQuantity, selection=unweighted):
"""Convenience function for creating a physicist's "profile plot," which is a Profile with variances."""
return Select.ing(selection,
Bin.ing(num, low, high, binnedQuantity,
Deviate.ing(averagedQuantity)))
def Profile(num, low, high, binnedQuantity, averagedQuantity, selection=unweighted):
"""Convenience function for creating binwise averages."""
return Select.ing(selection,
Bin.ing(num, low, high, binnedQuantity,
Average.ing(averagedQuantity)))
def Histogram(num, low, high, quantity, selection=unweighted):
"""Convenience function for creating a conventional histogram."""
return Select.ing(selection, Bin.ing(num, low, high, quantity,
Count.ing(), Count.ing(), Count.ing(), Count.ing()))
def Profile(num, low, high, binnedQuantity, averagedQuantity):
"""Convenience function for creating binwise averages."""
return Bin.ing(num, low, high, binnedQuantity, Average.ing(averagedQuantity))
def ProfileErr(num, low, high, binnedQuantity, averagedQuantity):
"""Convenience function for creating a profile plot
This is a Profile with variances.
"""
return Bin.ing(num, low, high, binnedQuantity, Deviate.ing(averagedQuantity))
def Histogram(num, low, high, quantity, selection=unweighted):
"""Convenience function for creating a conventional histogram."""
return Select.ing(
selection,
Bin.ing(num, low, high, quantity, Count.ing(), Count.ing(),
Count.ing(), Count.ing()))
