def test_mismatching_prominence_data(self):
"""Test with mismatching peak and / or prominence data."""
x = [0, 1, 0]
peak = [1]
for i, (peaks, left_bases, right_bases) in enumerate([
((1., ), (-1, ), (2, )), # left base not in x
((1., ), (0, ), (3, )), # right base not in x
((1., ), (1, ), (2, )), # left base same as peak
((1., ), (0, ), (1, )), # right base same as peak
((1., 1.), (0, 0), (2, 2)), # array shapes don't match peaks
((1., 1.), (0, ), (2, )), # arrays with different shapes
((1., ), (0, 0), (2, )), # arrays with different shapes
((1., ), (0, ), (2, 2)) # arrays with different shapes
]):
# Make sure input is matches output of signal.prominences
prominence_data = (np.array(peaks, dtype=np.float64),
np.array(left_bases, dtype=np.intp),
np.array(right_bases, dtype=np.intp))
# Test for correct exception
if i < 4:
match = "prominence data is invalid for peak"
else:
match = "arrays in `prominence_data` must have the same shape"
with raises(ValueError, match=match):
peak_widths(x, peak, prominence_data=prominence_data)
def test_mismatching_prominence_data(self):
"""Test with mismatching peak and / or prominence data."""
x = [0, 1, 0]
peak = [1]
for i, (peaks, left_bases, right_bases) in enumerate([
((1.,), (-1,), (2,)), # left base not in x
((1.,), (0,), (3,)), # right base not in x
((1.,), (1,), (2,)), # left base same as peak
((1.,), (0,), (1,)), # right base same as peak
((1., 1.), (0, 0), (2, 2)), # array shapes don't match peaks
((1., 1.), (0,), (2,)), # arrays with different shapes
((1.,), (0, 0), (2,)), # arrays with different shapes
((1.,), (0,), (2, 2)) # arrays with different shapes
]):
# Make sure input is matches output of signal.prominences
prominence_data = (np.array(peaks, dtype=np.float64),
np.array(left_bases, dtype=np.intp),
np.array(right_bases, dtype=np.intp))
# Test for correct exception
if i < 4:
match = "prominence data is invalid for peak"
else:
match = "arrays in `prominence_data` must have the same shape"
with raises(ValueError, match=match):
peak_widths(x, peak, prominence_data=prominence_data)
def test_basic(self):
"""
Test a simple use case with easy to verify results at different relative
heights.
"""
x = np.array([1, 0, 1, 2, 1, 0, -1])
prominence = 2
iteration = [
# rh, w_true, lip_true, rip_true
(0., 0., 3., 3.),
(0.25, 1., 2.5, 3.5),
(0.5, 2., 2., 4.),
(0.75, 3., 1.5, 4.5),
(1., 4., 1., 5.),
(2., 5., 1., 6.),
(3., 5., 1., 6.)
]
for rh, w_true, lip_true, rip_true in iteration:
w_calc, height, lip_calc, rip_calc = peak_widths(x, [3], rh)
assert_(w_calc == w_true)
assert_(height == 2 - rh * prominence)
assert_(lip_calc == lip_true)
assert_(rip_calc == rip_true)
# Additional test without argument ret_pos
assert_(peak_widths([1, 2, 1], [1])[0], 1)
def test_basic(self):
"""
Test a simple use case with easy to verify results at different relative
heights.
"""
x = np.array([1, 0, 1, 2, 1, 0, -1])
prominence = 2
iteration = [
# rh, w_true, lip_true, rip_true
(0., 0., 3., 3.),
(0.25, 1., 2.5, 3.5),
(0.5, 2., 2., 4.),
(0.75, 3., 1.5, 4.5),
(1., 4., 1., 5.),
(2., 5., 1., 6.),
(3., 5., 1., 6.)
]
for rh, w_true, lip_true, rip_true in iteration:
w_calc, height, lip_calc, rip_calc = peak_widths(x, [3], rh)
assert_(w_calc == w_true)
assert_(height == 2 - rh * prominence)
assert_(lip_calc == lip_true)
assert_(rip_calc == rip_true)
# Additional test without argument ret_pos
assert_(peak_widths([1, 2, 1], [1])[0], 1)
def test_intersection_rules(self):
"""Test if x == eval_height counts as an intersection."""
# Flatt peak with two possible intersection points if evaluated at 1
x = [0, 1, 2, 1, 3, 3, 3, 1, 2, 1, 0]
# relative height is 0 -> width is 0 as well, raises warning
assert_allclose(peak_widths(x, peaks=[5], rel_height=0),
[(0.,), (3.,), (5.,), (5.,)])
# width_height == x counts as intersection -> nearest 1 is chosen
assert_allclose(peak_widths(x, peaks=[5], rel_height=2/3),
[(4.,), (1.,), (3.,), (7.,)])
def test_intersection_rules(self):
"""Test if x == eval_height counts as an intersection."""
# Flatt peak with two possible intersection points if evaluated at 1
x = [0, 1, 2, 1, 3, 3, 3, 1, 2, 1, 0]
# relative height is 0 -> width is 0 as well
assert_allclose(peak_widths(x, peaks=[5], rel_height=0),
[(0.,), (3.,), (5.,), (5.,)])
# width_height == x counts as intersection -> nearest 1 is chosen
assert_allclose(peak_widths(x, peaks=[5], rel_height=2/3),
[(4.,), (1.,), (3.,), (7.,)])
def test_empty(self):
"""
Test if an empty array is returned if no peaks are provided.
"""
widths = peak_widths([], [])[0]
assert_(isinstance(widths, np.ndarray))
assert_equal(widths.size, 0)
widths = peak_widths([1, 2, 3], [])[0]
assert_(isinstance(widths, np.ndarray))
assert_equal(widths.size, 0)
out = peak_widths([], [])
for arr in out:
assert_(isinstance(arr, np.ndarray))
assert_equal(arr.size, 0)
def test_empty(self):
"""
Test if an empty array is returned if no peaks are provided.
"""
widths = peak_widths([], [])[0]
assert_(isinstance(widths, np.ndarray))
assert_equal(widths.size, 0)
widths = peak_widths([1, 2, 3], [])[0]
assert_(isinstance(widths, np.ndarray))
assert_equal(widths.size, 0)
out = peak_widths([], [])
for arr in out:
assert_(isinstance(arr, np.ndarray))
assert_equal(arr.size, 0)
def test_warnings(self):
"""
Verify that appropriate warnings are raised.
"""
msg = "some peaks have a width of 0"
with warns(PeakPropertyWarning, match=msg):
# Case: rel_height is 0
peak_widths([0, 1, 0], [1], rel_height=0)
with warns(PeakPropertyWarning, match=msg):
# Case: prominence is 0 and bases are identical
peak_widths([0, 1, 1, 1, 0], [2],
prominence_data=(np.array([0.], np.float64),
np.array([2], np.intp),
np.array([2], np.intp)))
def test_warnings(self):
"""
Verify that appropriate warnings are raised.
"""
msg = "some peaks have a width of 0"
with warns(PeakPropertyWarning, match=msg):
# Case: rel_height is 0
peak_widths([0, 1, 0], [1], rel_height=0)
with warns(PeakPropertyWarning, match=msg):
# Case: prominence is 0 and bases are identical
peak_widths(
[0, 1, 1, 1, 0], [2],
prominence_data=(np.array([0.], np.float64),
np.array([2], np.intp),
np.array([2], np.intp))
)
def test_non_contiguous(self):
"""
Test with non-C-contiguous input arrays.
"""
x = np.repeat([0, 100, 50], 4)
peaks = np.repeat([1], 3)
result = peak_widths(x[::4], peaks[::3])
assert_equal(result, [0.75, 75, 0.75, 1.5])
def test_non_contiguous(self):
"""
Test with non-C-contiguous input arrays.
"""
x = np.repeat([0, 100, 50], 4)
peaks = np.repeat([1], 3)
result = peak_widths(x[::4], peaks[::3])
assert_equal(result, [0.75, 75, 0.75, 1.5])
def test_basic(self):
"""
Test a simple use case with easy to verify results at different relative
heights.
"""
x = np.array([1, 0, 1, 2, 1, 0, -1])
prominence = 2
for rel_height, width_true, lip_true, rip_true in [
(0., 0., 3., 3.), (0.25, 1., 2.5, 3.5), (0.5, 2., 2., 4.),
(0.75, 3., 1.5, 4.5), (1., 4., 1., 5.), (2., 5., 1., 6.),
(3., 5., 1., 6.)
]:
width_calc, height, lip_calc, rip_calc = peak_widths(
x, [3], rel_height)
assert_allclose(width_calc, width_true)
assert_allclose(height, 2 - rel_height * prominence)
assert_allclose(lip_calc, lip_true)
assert_allclose(rip_calc, rip_true)
def test_basic(self):
"""
Test a simple use case with easy to verify results at different relative
heights.
"""
x = np.array([1, 0, 1, 2, 1, 0, -1])
prominence = 2
for rel_height, width_true, lip_true, rip_true in [
(0., 0., 3., 3.),
(0.25, 1., 2.5, 3.5),
(0.5, 2., 2., 4.),
(0.75, 3., 1.5, 4.5),
(1., 4., 1., 5.),
(2., 5., 1., 6.),
(3., 5., 1., 6.)
]:
width_calc, height, lip_calc, rip_calc = peak_widths(
x, [3], rel_height)
assert_allclose(width_calc, width_true)
assert_allclose(height, 2 - rel_height * prominence)
assert_allclose(lip_calc, lip_true)
assert_allclose(rip_calc, rip_true)
def test_raises(self):
"""
Verfiy that argument validation works as intended.
"""
with raises(ValueError, match='dimension'):
# x with dimension > 1
peak_widths(np.zeros((3, 4)), np.ones(3))
with raises(ValueError, match='dimension'):
# x with dimension < 1
peak_widths(3, [
0,
])
with raises(ValueError, match='dimension'):
# peaks with dimension > 1
peak_widths(np.arange(10), np.ones((3, 2)))
with raises(ValueError, match='dimension'):
# peaks with dimension < 1
peak_widths(np.arange(10), 3)
with raises(ValueError, match='index'):
# peak pos exceeds x.size
peak_widths(np.arange(10), [8, 11])
with raises(ValueError, match='index'):
# empty x with peaks supplied
peak_widths([], [1, 2])
with raises(ValueError, match='integers'):
# peak is not of subtype int
peak_widths(np.arange(10), [1.1, 2.3])
with raises(ValueError, match='rel_height'):
# rel_height is < 0
peak_widths(np.arange(10), [1, 2], rel_height=-1)
def test_exceptions(self):
"""
Verfiy that argument validation works as intended.
"""
with raises(ValueError, match='dimension'):
# x with dimension > 1
peak_widths(np.zeros((3, 4)), np.ones(3))
with raises(ValueError, match='dimension'):
# x with dimension < 1
peak_widths(3, [0])
with raises(ValueError, match='dimension'):
# peaks with dimension > 1
peak_widths(np.arange(10), np.ones((3, 2), dtype=np.intp))
with raises(ValueError, match='dimension'):
# peaks with dimension < 1
peak_widths(np.arange(10), 3)
with raises(ValueError, match='not a valid peak'):
# peak pos exceeds x.size
peak_widths(np.arange(10), [8, 11])
with raises(ValueError, match='not a valid peak'):
# empty x with peaks supplied
peak_widths([], [1, 2])
with raises(TypeError, match='Cannot safely cast'):
# peak cannot be safely casted to intp
peak_widths(np.arange(10), [1.1, 2.3])
with raises(ValueError, match='rel_height'):
# rel_height is < 0
peak_widths(np.arange(10), [1, 2], rel_height=-1)
with raises(TypeError, match='None'):
# prominence data contains None
peak_widths([1, 2, 1], [1], prominence_data=(None, None, None))
def test_raises(self):
"""
Verfiy that argument validation works as intended.
"""
with raises(ValueError, match='dimension'):
# x with dimension > 1
peak_widths(np.zeros((3, 4)), np.ones(3))
with raises(ValueError, match='dimension'):
# x with dimension < 1
peak_widths(3, [0, ])
with raises(ValueError, match='dimension'):
# peaks with dimension > 1
peak_widths(np.arange(10), np.ones((3, 2)))
with raises(ValueError, match='dimension'):
# peaks with dimension < 1
peak_widths(np.arange(10), 3)
with raises(ValueError, match='index'):
# peak pos exceeds x.size
peak_widths(np.arange(10), [8, 11])
with raises(ValueError, match='index'):
# empty x with peaks supplied
peak_widths([], [1, 2])
with raises(ValueError, match='integers'):
# peak is not of subtype int
peak_widths(np.arange(10), [1.1, 2.3])
with raises(ValueError, match='rel_height'):
# rel_height is < 0
peak_widths(np.arange(10), [1, 2], rel_height=-1)
def test_exceptions(self):
"""
Verify that argument validation works as intended.
"""
with raises(ValueError, match='1-D array'):
# x with dimension > 1
peak_widths(np.zeros((3, 4)), np.ones(3))
with raises(ValueError, match='1-D array'):
# x with dimension < 1
peak_widths(3, [0])
with raises(ValueError, match='1-D array'):
# peaks with dimension > 1
peak_widths(np.arange(10), np.ones((3, 2), dtype=np.intp))
with raises(ValueError, match='1-D array'):
# peaks with dimension < 1
peak_widths(np.arange(10), 3)
with raises(ValueError, match='not a valid index'):
# peak pos exceeds x.size
peak_widths(np.arange(10), [8, 11])
with raises(ValueError, match='not a valid index'):
# empty x with peaks supplied
peak_widths([], [1, 2])
with raises(TypeError, match='cannot safely cast'):
# peak cannot be safely casted to intp
peak_widths(np.arange(10), [1.1, 2.3])
with raises(ValueError, match='rel_height'):
# rel_height is < 0
peak_widths([0, 1, 0, 1, 0], [1, 3], rel_height=-1)
with raises(TypeError, match='None'):
# prominence data contains None
peak_widths([1, 2, 1], [1], prominence_data=(None, None, None))
