def testAverage(self):
with Numpy() as numpy:
if numpy is None:
return
sys.stderr.write("\n")
self.compare("Average no data", Average(lambda x: x["empty"]), self.data, Average(lambda x: x), self.empty)
self.compare("Average noholes", Average(
lambda x: x["noholes"]), self.data, Average(lambda x: x), self.noholes)
self.compare("Average holes", Average(lambda x: x["withholes"]),
self.data, Average(lambda x: x), self.withholes)
def testAverage(self):
for i in xrange(11):
left, right = self.simple[:i], self.simple[i:]
leftAveraging = Average(named("something", lambda x: x))
rightAveraging = Average(named("something", lambda x: x))
for _ in left:
leftAveraging.fill(_)
for _ in right:
rightAveraging.fill(_)
if len(left) == 0:
self.assertTrue(math.isnan(leftAveraging.mean))
else:
self.assertAlmostEqual(leftAveraging.mean, self.mean(left))
if len(right) == 0:
self.assertTrue(math.isnan(rightAveraging.mean))
else:
self.assertAlmostEqual(rightAveraging.mean, self.mean(right))
finalResult = leftAveraging + rightAveraging
self.assertAlmostEqual(finalResult.mean, self.mean(self.simple))
self.checkScaling(leftAveraging)
self.checkScaling(leftAveraging.toImmutable())
self.checkJson(leftAveraging)
self.checkPickle(leftAveraging)
self.checkName(leftAveraging)
def testSparselyBinAverage(self):
with Numpy() as numpy:
if numpy is None:
return
sys.stderr.write("\n")
self.compare("SparselyBinAverage no data", SparselyBin(0.1, lambda x: x["empty"], Average(
lambda x: x["empty"])), self.data, SparselyBin(0.1, lambda x: x, Average(lambda x: x)), self.empty)
self.compare("SparselyBinAverage noholes", SparselyBin(0.1, lambda x: x["noholes"], Average(
lambda x: x["noholes"])), self.data, SparselyBin(0.1, lambda x: x, Average(lambda x: x)), self.noholes)
self.compare("SparselyBinAverage holes", SparselyBin(0.1, lambda x: x["withholes"], Average(
lambda x: x["withholes"])), self.data, SparselyBin(0.1, lambda x: x, Average(lambda x: x)), self.withholes)
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 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 testAverageWithWeightingFactor(self):
for i in xrange(11):
left, right = self.struct[:i], self.struct[i:]
leftAveraging = Select(lambda x: x.int,
Average(lambda x: x.double))
rightAveraging = Select(lambda x: x.int,
Average(lambda x: x.double))
for _ in left:
leftAveraging.fill(_)
for _ in right:
rightAveraging.fill(_)
if sum(map(lambda _: _.int if _.int > 0.0 else 0.0, left)) == 0.0:
self.assertTrue(math.isnan(leftAveraging.cut.mean))
else:
self.assertAlmostEqual(
leftAveraging.cut.mean,
self.meanWeighted(list(map(lambda _: _.double, left)),
list(map(lambda _: _.int, left))))
if sum(map(lambda _: _.int if _.int > 0.0 else 0.0, right)) == 0.0:
self.assertTrue(math.isnan(rightAveraging.cut.mean))
else:
self.assertAlmostEqual(
rightAveraging.cut.mean,
self.meanWeighted(list(map(lambda _: _.double, right)),
list(map(lambda _: _.int, right))))
finalResult = leftAveraging + rightAveraging
self.assertAlmostEqual(
finalResult.cut.mean,
self.meanWeighted(list(map(lambda _: _.double, self.struct)),
list(map(lambda _: _.int, self.struct))))
self.checkScaling(leftAveraging)
self.checkScaling(leftAveraging.toImmutable())
self.checkJson(leftAveraging)
self.checkPickle(leftAveraging)
self.checkName(leftAveraging)
def testAverageWithFilter(self):
for i in xrange(11):
left, right = self.struct[:i], self.struct[i:]
leftAveraging = Select(lambda x: x.bool,
Average(lambda x: x.double))
rightAveraging = Select(lambda x: x.bool,
Average(lambda x: x.double))
for _ in left:
leftAveraging.fill(_)
for _ in right:
rightAveraging.fill(_)
if len([_.double for _ in left if _.bool]) == 0:
self.assertTrue(math.isnan(leftAveraging.mean))
else:
self.assertAlmostEqual(
leftAveraging.cut.mean,
self.mean([_.double for _ in left if _.bool]))
if len([_.double for _ in right if _.bool]) == 0:
self.assertTrue(math.isnan(rightAveraging.mean))
else:
self.assertAlmostEqual(
rightAveraging.cut.mean,
self.mean([_.double for _ in right if _.bool]))
finalResult = leftAveraging + rightAveraging
self.assertAlmostEqual(
finalResult.cut.mean,
self.mean([_.double for _ in self.struct if _.bool]))
self.checkScaling(leftAveraging)
self.checkScaling(leftAveraging.toImmutable())
self.checkJson(leftAveraging)
self.checkPickle(leftAveraging)
self.checkName(leftAveraging)
def Profile(num, low, high, binnedQuantity, averagedQuantity):
"""Convenience function for creating binwise averages."""
return Bin.ing(num, low, high, binnedQuantity, Average.ing(averagedQuantity))
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 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 testCentrallyBinAverage(self):
with Numpy() as numpy:
if numpy is None:
return
sys.stderr.write("\n")
centers = [-3.0, -1.5, -1.0, -0.5, 0.0, 0.5, 1.0, 1.5, 3.0]
self.compare("CentrallyBinAverage no data", CentrallyBin(centers, lambda x: x["empty"], Average(
lambda x: x["empty"])), self.data, CentrallyBin(centers, lambda x: x, Average(lambda x: x)), self.empty)
self.compare("CentrallyBinAverage noholes", CentrallyBin(centers, lambda x: x["noholes"], Average(
lambda x: x["noholes"])), self.data, CentrallyBin(centers, lambda x: x, Average(lambda x: x)), self.noholes)
self.compare("CentrallyBinAverage holes", CentrallyBin(centers, lambda x: x["withholes"], Average(
lambda x: x["withholes"])), self.data, CentrallyBin(centers, lambda x: x, Average(lambda x: x)), self.withholes)
def testBinAverage(self):
with Numpy() as numpy:
if numpy is None:
return
sys.stderr.write("\n")
for bins in [10, 100]:
self.compare("BinAverage ({0} bins) no data".format(bins), Bin(bins, -3.0, 3.0, lambda x: x["empty"], Average(
lambda x: x["empty"])), self.data, Bin(bins, -3.0, 3.0, lambda x: x, Average(lambda x: x)), self.empty)
self.compare("BinAverage ({0} bins) noholes".format(bins), Bin(bins, -3.0, 3.0, lambda x: x["noholes"], Average(
lambda x: x["noholes"])), self.data, Bin(bins, -3.0, 3.0, lambda x: x, Average(lambda x: x)), self.noholes)
self.compare("BinAverage ({0} bins) holes".format(bins), Bin(bins, -3.0, 3.0, lambda x: x["withholes"], Average(
lambda x: x["withholes"])), self.data, Bin(bins, -3.0, 3.0, lambda x: x, Average(lambda x: x)), self.withholes)