def testFilterLikelihodsInputType(self):
"""
Calls _filterLikelihoods with both input types -- numpy array of floats and
list of floats.
"""
l = [0.0, 0.0, 0.3, 0.3, 0.5]
l2 = an._filterLikelihoods(l)
n = numpy.array(l)
n2 = an._filterLikelihoods(n)
filtered = [0.0, 0.001, 0.3, 0.3, 0.5]
[
self.assertAlmostEqual(
l2[i],
filtered[i],
msg="Input of type list returns incorrect result")
for i in range(len(l))
]
[
self.assertAlmostEqual(
n2[i],
filtered[i],
msg="Input of type numpy array returns incorrect result")
for i in range(len(n))
]
def testFilterLikelihods2(self):
"""
Tests _filterLikelihoods function
"""
# redThreshold = 0.99999
yellowThreshold = 0.999
# Test first with a list of floats
# Since windowSize will be 3, the numbers with an underscore should
# be replaced with 1-yellowThreshold
l = [0.0, 0.0, 0.1, 0.2, 0.5, 0.6, 0.0, 0.0, 0.4, 0.0]
# ___ ___
l2 = copy.copy(l)
l2[1] = 1 - yellowThreshold
l2[7] = 1 - yellowThreshold
l3 = an._filterLikelihoods(l)
for i, v in enumerate(l3):
self.assertAlmostEqual(v, l2[i])
# This time test with numpy arrays using values we really got
# Indices 1-10 should be at 1 - yellowThreshold
l = numpy.array([
0.999978229, 0.999978229, 0.999999897, 1, 1, 1, 1, 0.999999994,
0.999999966, 0.999999966, 0.999994331, 0.999516576, 0.99744487
])
l = 1.0 - l
l2 = copy.copy(l)
for i in range(1, 11):
l2[i] = 1 - yellowThreshold
l3 = an._filterLikelihoods(l)
for i, v in enumerate(l3):
self.assertAlmostEqual(v, l2[i])
def testFilterLikelihods2(self):
"""
Tests _filterLikelihoods function
"""
# redThreshold = 0.99999
yellowThreshold = 0.999
# Test first with a list of floats
# Since windowSize will be 3, the numbers with an underscore should
# be replaced with 1-yellowThreshold
l = [0.0, 0.0, 0.1, 0.2, 0.5, 0.6, 0.0, 0.0, 0.4, 0.0]
# ___ ___
l2 = copy.copy(l)
l2[1] = 1 - yellowThreshold
l2[7] = 1 - yellowThreshold
l3 = an._filterLikelihoods(l)
for i, v in enumerate(l3):
self.assertAlmostEqual(v, l2[i])
# This time test with numpy arrays using values we really got
# Indices 1-10 should be at 1 - yellowThreshold
l = numpy.array([0.999978229, 0.999978229, 0.999999897, 1, 1, 1,
1, 0.999999994, 0.999999966, 0.999999966,
0.999994331, 0.999516576, 0.99744487])
l = 1.0 - l
l2 = copy.copy(l)
for i in range(1, 11):
l2[i] = 1 - yellowThreshold
l3 = an._filterLikelihoods(l)
for i, v in enumerate(l3):
self.assertAlmostEqual(v, l2[i])
def testFilterLikelihoods(self):
"""
Tests _filterLikelihoods function for several cases:
i. Likelihood goes straight to redzone, skipping over yellowzone, repeats
ii. Case (i) with different values, and numpy array instead of float list
iii. A scenario where changing the redzone from four to five 9s should
filter differently
"""
redThreshold = 0.9999
yellowThreshold = 0.999
# Case (i): values at indices 1 and 7 should be filtered to yellowzone
l = [1.0, 1.0, 0.9, 0.8, 0.5, 0.4, 1.0, 1.0, 0.6, 0.0]
l = [1 - x for x in l]
l2 = copy.copy(l)
l2[1] = 1 - yellowThreshold
l2[7] = 1 - yellowThreshold
l3 = an._filterLikelihoods(l, redThreshold=redThreshold)
for i in range(len(l2)):
self.assertAlmostEqual(l2[i], l3[i], msg="Failure in case (i)")
# Case (ii): values at indices 1-10 should be filtered to yellowzone
l = numpy.array([
0.999978229, 0.999978229, 0.999999897, 1, 1, 1, 1, 0.999999994,
0.999999966, 0.999999966, 0.999994331, 0.999516576, 0.99744487
])
l = 1.0 - l
l2 = copy.copy(l)
l2[1:11] = 1 - yellowThreshold
l3 = an._filterLikelihoods(l, redThreshold=redThreshold)
for i in range(len(l2)):
self.assertAlmostEqual(l2[i], l3[i], msg="Failure in case (ii)")
# Case (iii): redThreshold difference should be at index 2
l = numpy.array([
0.999968329, 0.999999897, 1, 1, 1, 1, 0.999999994, 0.999999966,
0.999999966, 0.999994331, 0.999516576, 0.99744487
])
l = 1.0 - l
l2a = copy.copy(l)
l2b = copy.copy(l)
l2a[1:10] = 1 - yellowThreshold
l2b[2:10] = 1 - yellowThreshold
l3a = an._filterLikelihoods(l, redThreshold=redThreshold)
l3b = an._filterLikelihoods(l, redThreshold=0.99999)
for i in range(len(l2a)):
self.assertAlmostEqual(l2a[i],
l3a[i],
msg="Failure in case (iii), list a")
for i in range(len(l2b)):
self.assertAlmostEqual(l2b[i],
l3b[i],
msg="Failure in case (iii), list b")
self.assertFalse(numpy.array_equal(l3a, l3b),
msg="Failure in case (iii), list 3")
def testFilterLikelihoods(self):
"""
Tests _filterLikelihoods function for several cases:
i. Likelihood goes straight to redzone, skipping over yellowzone, repeats
ii. Case (i) with different values, and numpy array instead of float list
iii. A scenario where changing the redzone from four to five 9s should
filter differently
"""
redThreshold = 0.9999
yellowThreshold = 0.999
# Case (i): values at indices 1 and 7 should be filtered to yellowzone
l = [1.0, 1.0, 0.9, 0.8, 0.5, 0.4, 1.0, 1.0, 0.6, 0.0]
l = [1 - x for x in l]
l2 = copy.copy(l)
l2[1] = 1 - yellowThreshold
l2[7] = 1 - yellowThreshold
l3 = an._filterLikelihoods(l, redThreshold=redThreshold)
for i in range(len(l2)):
self.assertAlmostEqual(l2[i], l3[i], msg="Failure in case (i)")
# Case (ii): values at indices 1-10 should be filtered to yellowzone
l = numpy.array([0.999978229, 0.999978229, 0.999999897, 1, 1, 1, 1,
0.999999994, 0.999999966, 0.999999966, 0.999994331,
0.999516576, 0.99744487])
l = 1.0 - l
l2 = copy.copy(l)
l2[1:11] = 1 - yellowThreshold
l3 = an._filterLikelihoods(l, redThreshold=redThreshold)
for i in range(len(l2)):
self.assertAlmostEqual(l2[i], l3[i], msg="Failure in case (ii)")
# Case (iii): redThreshold difference should be at index 2
l = numpy.array([0.999968329, 0.999999897, 1, 1, 1,
1, 0.999999994, 0.999999966, 0.999999966,
0.999994331, 0.999516576, 0.99744487])
l = 1.0 - l
l2a = copy.copy(l)
l2b = copy.copy(l)
l2a[1:10] = 1 - yellowThreshold
l2b[2:10] = 1 - yellowThreshold
l3a = an._filterLikelihoods(l, redThreshold=redThreshold)
l3b = an._filterLikelihoods(l, redThreshold=0.99999)
for i in range(len(l2a)):
self.assertAlmostEqual(l2a[i], l3a[i],
msg="Failure in case (iii), list a")
for i in range(len(l2b)):
self.assertAlmostEqual(l2b[i], l3b[i],
msg="Failure in case (iii), list b")
self.assertFalse(numpy.array_equal(l3a, l3b),
msg="Failure in case (iii), list 3")
def testFilterLikelihodsInputType(self):
"""
Calls _filterLikelihoods with both input types -- numpy array of floats and
list of floats.
"""
l = [0.0, 0.0, 0.3, 0.3, 0.5]
l2 = an._filterLikelihoods(l)
n = numpy.array(l)
n2 = an._filterLikelihoods(n)
filtered = [0.0, 0.001, 0.3, 0.3, 0.5]
for i in range(len(l)):
self.assertAlmostEqual(l2[i], filtered[i], msg="Input of type list returns incorrect result")
for i in range(len(n)):
self.assertAlmostEqual(n2[i], filtered[i], msg="Input of type numpy array returns incorrect result")
