def test_warnings(self):
"""
Verify that appropriate warnings are raised.
"""
msg = "some peaks have a prominence of 0"
for p in [0, 1, 2]:
with warns(PeakPropertyWarning, match=msg):
peak_prominences([1, 0, 2], [p,])
with warns(PeakPropertyWarning, match=msg):
peak_prominences([0, 1, 1, 1, 0], [2], wlen=2)
def test_wlen(self):
"""
Test if wlen actually shrinks the evaluation range correctly.
"""
x = [0, 1, 2, 3, 1, 0, -1]
peak = [3]
# Test rounding behavior of wlen
assert_equal(peak_prominences(x, peak), [3., 0, 6])
for wlen, i in [(8, 0), (7, 0), (6, 0), (5, 1), (3.2, 1), (3, 2), (1.1, 2)]:
assert_equal(peak_prominences(x, peak, wlen), [3. - i, 0 + i, 6 - i])
def test_warnings(self):
"""
Verify that appropriate warnings are raised.
"""
msg = "some peaks have a prominence of 0"
for p in [0, 1, 2]:
with warns(PeakPropertyWarning, match=msg):
peak_prominences([1, 0, 2], [p,])
with warns(PeakPropertyWarning, match=msg):
peak_prominences([0, 1, 1, 1, 0], [2], wlen=2)
def test_wlen(self):
"""
Test if wlen actually shrinks the evaluation range correctly.
"""
x = [0, 1, 2, 3, 1, 0, -1]
peak = [3]
# Test rounding behavior of wlen
assert_equal(peak_prominences(x, peak), [3., 0, 6])
for wlen, i in [(8, 0), (7, 0), (6, 0), (5, 1), (3.2, 1), (3, 2), (1.1, 2)]:
assert_equal(peak_prominences(x, peak, wlen), [3. - i, 0 + i, 6 - i])
def test_empty(self):
"""
Test if an empty array is returned if no peaks are provided.
"""
out = peak_prominences([1, 2, 3], [])
for arr, dtype in zip(out, [np.float64, np.intp, np.intp]):
assert_(arr.size == 0)
assert_(arr.dtype == dtype)
out = peak_prominences([], [])
for arr, dtype in zip(out, [np.float64, np.intp, np.intp]):
assert_(arr.size == 0)
assert_(arr.dtype == dtype)
def test_empty(self):
"""
Test if an empty array is returned if no peaks are provided.
"""
out = peak_prominences([1, 2, 3], [])
for arr, dtype in zip(out, [np.float64, np.intp, np.intp]):
assert_(arr.size == 0)
assert_(arr.dtype == dtype)
out = peak_prominences([], [])
for arr, dtype in zip(out, [np.float64, np.intp, np.intp]):
assert_(arr.size == 0)
assert_(arr.dtype == dtype)
def test_empty(self):
"""
Test if an empty array is returned if no peaks are provided.
"""
proms = peak_prominences([], [])[0]
assert_(isinstance(proms, np.ndarray))
assert_equal(proms.size, 0)
proms = peak_prominences([1, 2, 3], [])[0]
assert_(isinstance(proms, np.ndarray))
assert_equal(proms.size, 0)
out = peak_prominences([], [])
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.
"""
proms = peak_prominences([], [])[0]
assert_(isinstance(proms, np.ndarray))
assert_equal(proms.size, 0)
proms = peak_prominences([1, 2, 3], [])[0]
assert_(isinstance(proms, np.ndarray))
assert_equal(proms.size, 0)
out = peak_prominences([], [])
for arr in out:
assert_(isinstance(arr, np.ndarray))
assert_equal(arr.size, 0)
def test_wlen(self):
"""
Test if wlen actually shrinks the evaluation range.
"""
t = np.linspace(0, 4 * np.pi, 1000)
x = abs(np.sin(t))
peaks = argrelmax(x)[0]
# Raise 2 baseline of peaks in the middle
x[250:750] += 0.3
# If entire x is used the middle two peaks should have a prominence
# of approx. 1 + 0.3, otherwise minima between second and third peak
# should be lowest contour line and prominence should be < 1.1
proms_wlen = peak_prominences(x, peaks, wlen=600)[0]
assert_(np.all(proms_wlen < 1.1))
# If window length is 2
assert_equal(peak_prominences(x, peaks)[0],
peak_prominences(x, peaks, wlen=(x.size * 2))[0])
def test_wlen(self):
"""
Test if wlen actually shrinks the evaluation range.
"""
t = np.linspace(0, 4 * np.pi, 1000)
x = abs(np.sin(t))
peaks = argrelmax(x)[0]
# Raise 2 baseline of peaks in the middle
x[250:750] += 0.3
# If entire x is used the middle two peaks should have a prominence
# of approx. 1 + 0.3, otherwise minima between second and third peak
# should be lowest contour line and prominence should be < 1.1
proms_wlen = peak_prominences(x, peaks, wlen=600)[0]
assert_(np.all(proms_wlen < 1.1))
# If window length is 2
assert_equal(
peak_prominences(x, peaks)[0],
peak_prominences(x, peaks, wlen=(x.size * 2))[0])
def test_non_contiguous(self):
"""
Test with non-C-contiguous input arrays.
"""
x = np.repeat([-9, 9, 9, 0, 3, 1], 2)
peaks = np.repeat([1, 2, 4], 2)
proms, lbases, rbases = peak_prominences(x[::2], peaks[::2])
assert_equal(proms, [9, 9, 2])
assert_equal(lbases, [0, 0, 3])
assert_equal(rbases, [3, 3, 5])
def test_edge_cases(self):
"""
Test edge cases.
"""
# Peaks have same height, prominence and bases
x = [0, 2, 1, 2, 1, 2, 0]
peaks = [1, 3, 5]
proms, lbases, rbases = peak_prominences(x, peaks)
assert_equal(proms, [2, 2, 2])
assert_equal(lbases, [0, 0, 0])
assert_equal(rbases, [6, 6, 6])
# Peaks have same height & prominence but different bases
x = [0, 1, 0, 1, 0, 1, 0]
peaks = np.array([1, 3, 5])
proms, lbases, rbases = peak_prominences(x, peaks)
assert_equal(proms, [1, 1, 1])
assert_equal(lbases, peaks - 1)
assert_equal(rbases, peaks + 1)
def test_non_contiguous(self):
"""
Test with non-C-contiguous input arrays.
"""
x = np.repeat([-9, 9, 9, 0, 3, 1], 2)
peaks = np.repeat([1, 2, 4], 2)
proms, lbases, rbases = peak_prominences(x[::2], peaks[::2])
assert_equal(proms, [9, 9, 2])
assert_equal(lbases, [0, 0, 3])
assert_equal(rbases, [3, 3, 5])
def test_edge_cases(self):
"""
Test edge cases.
"""
# Peaks have same height, prominence and bases
x = [0, 2, 1, 2, 1, 2, 0]
peaks = [1, 3, 5]
proms, lbases, rbases = peak_prominences(x, peaks)
assert_equal(proms, [2, 2, 2])
assert_equal(lbases, [0, 0, 0])
assert_equal(rbases, [6, 6, 6])
# Peaks have same height & prominence but different bases
x = [0, 1, 0, 1, 0, 1, 0]
peaks = np.array([1, 3, 5])
proms, lbases, rbases = peak_prominences(x, peaks)
assert_equal(proms, [1, 1, 1])
assert_equal(lbases, peaks - 1)
assert_equal(rbases, peaks + 1)
def test_basic(self):
"""
Test if height of prominences is correctly calculated in signal with
rising baseline (peak widths are 1 sample).
"""
# Prepare basic signal
x = np.array([-1, 1.2, 1.2, 1, 3.2, 1.3, 2.88, 2.1])
peaks = np.array([1, 2, 4, 6])
lbases = np.array([0, 0, 0, 5])
rbases = np.array([3, 3, 5, 7])
proms = x[peaks] - np.max([x[lbases], x[rbases]], axis=0)
# Test if calculation matches handcrafted result
out = peak_prominences(x, peaks)
assert_equal(out[0], proms)
assert_equal(out[1], lbases)
assert_equal(out[2], rbases)
def test_basic(self):
"""
Test if height of prominences is correctly calculated in signal with
rising baseline (peak widths are 1 sample).
"""
x = np.linspace(1, 4, 7) # Rising baseline
peak_heights = [2, 4, 1.2] # Peak heights
peak_pos = [1, 3, 5]
desired = []
for h, p in zip(peak_heights, peak_pos):
x[p] += h
# Peak prominence is difference between vector[peak] and next
# sample to the right
desired.append(x[p] - x[p + 1])
actual = peak_prominences(x, [1, 3, 5])[0]
assert_equal(actual, desired)
def test_basic(self):
"""
Test if height of prominences is correctly calculated in signal with
rising baseline (peak widths are 1 sample).
"""
# Prepare basic signal
x = np.array([-1, 1.2, 1.2, 1, 3.2, 1.3, 2.88, 2.1])
peaks = np.array([1, 2, 4, 6])
lbases = np.array([0, 0, 0, 5])
rbases = np.array([3, 3, 5, 7])
proms = x[peaks] - np.max([x[lbases], x[rbases]], axis=0)
# Test if calculation matches handcrafted result
out = peak_prominences(x, peaks)
assert_equal(out[0], proms)
assert_equal(out[1], lbases)
assert_equal(out[2], rbases)
def test_basic(self):
"""
Test if height of prominences is correctly calculated in signal with
rising baseline (peak widths are 1 sample).
"""
x = np.linspace(1, 4, 7) # Rising baseline
peak_heights = [2, 4, 1.2] # Peak heights
peak_pos = [1, 3, 5]
desired = []
for h, p in zip(peak_heights, peak_pos):
x[p] += h
# Peak prominence is difference between vector[peak] and next
# sample to the right
desired.append(x[p] - x[p + 1])
actual = peak_prominences(x, [1, 3, 5])[0]
assert_equal(actual, desired)
def test_raises(self):
"""
Verfiy that argument validation works as intended.
"""
with raises(ValueError, match='dimension'):
# x with dimension > 1
peak_prominences(np.zeros((3, 4)), np.ones(3))
with raises(ValueError, match='dimension'):
# x with dimension < 1
peak_prominences(3, [0,])
with raises(ValueError, match='dimension'):
# peaks with dimension > 1
peak_prominences(np.arange(10), np.ones((3, 2)))
with raises(ValueError, match='dimension'):
# peaks with dimension < 1
peak_prominences(np.arange(10), 3)
with raises(ValueError, match='index'):
# peak pos exceeds x.size
peak_prominences(np.arange(10), [8, 11])
with raises(ValueError, match='index'):
# empty x with peaks supplied
peak_prominences([], [1, 2])
with raises(ValueError, match='integers'):
# peak is not of subtype int
peak_prominences(np.arange(10), [1.1, 2.3])
with raises(ValueError, match='wlen'):
# wlen < 3
peak_prominences(np.arange(10), [3, 5], wlen=2)
def test_exceptions(self):
"""
Verify that exceptions and warnings are raised.
"""
# x with dimension > 1
with raises(ValueError, match='1-D array'):
peak_prominences([[0, 1, 1, 0]], [1, 2])
# peaks with dimension > 1
with raises(ValueError, match='1-D array'):
peak_prominences([0, 1, 1, 0], [[1, 2]])
# x with dimension < 1
with raises(ValueError, match='1-D array'):
peak_prominences(3, [
0,
])
# empty x with supplied
with raises(ValueError, match='not a valid index'):
peak_prominences([], [0])
# invalid indices with non-empty x
for p in [-100, -1, 3, 1000]:
with raises(ValueError, match='not a valid index'):
peak_prominences([1, 0, 2], [p])
# peaks is not cast-able to np.intp
with raises(TypeError, match='cannot safely cast'):
peak_prominences([0, 1, 1, 0], [1.1, 2.3])
# wlen < 3
with raises(ValueError, match='wlen'):
peak_prominences(np.arange(10), [3, 5], wlen=1)
def test_raises(self):
"""
Verfiy that argument validation works as intended.
"""
with raises(ValueError, match='dimension'):
# x with dimension > 1
peak_prominences(np.zeros((3, 4)), np.ones(3))
with raises(ValueError, match='dimension'):
# x with dimension < 1
peak_prominences(3, [
0,
])
with raises(ValueError, match='dimension'):
# peaks with dimension > 1
peak_prominences(np.arange(10), np.ones((3, 2)))
with raises(ValueError, match='dimension'):
# peaks with dimension < 1
peak_prominences(np.arange(10), 3)
with raises(ValueError, match='index'):
# peak pos exceeds x.size
peak_prominences(np.arange(10), [8, 11])
with raises(ValueError, match='index'):
# empty x with peaks supplied
peak_prominences([], [1, 2])
with raises(ValueError, match='integers'):
# peak is not of subtype int
peak_prominences(np.arange(10), [1.1, 2.3])
with raises(ValueError, match='wlen'):
# wlen < 3
peak_prominences(np.arange(10), [3, 5], wlen=2)
def test_exceptions(self):
"""
Verify that exceptions and warnings are raised.
"""
# x with dimension > 1
with raises(ValueError, match='1D array'):
peak_prominences([[0, 1, 1, 0]], [1, 2])
# peaks with dimension > 1
with raises(ValueError, match='1D array'):
peak_prominences([0, 1, 1, 0], [[1, 2]])
# x with dimension < 1
with raises(ValueError, match='1D array'):
peak_prominences(3, [0,])
# empty x with supplied
with raises(ValueError, match='not a valid index'):
peak_prominences([], [0])
# invalid indices with non-empty x
for p in [-100, -1, 3, 1000]:
with raises(ValueError, match='not a valid index'):
peak_prominences([1, 0, 2], [p])
# peaks is not cast-able to np.intp
with raises(TypeError, match='cannot safely cast'):
peak_prominences([0, 1, 1, 0], [1.1, 2.3])
# wlen < 3
with raises(ValueError, match='wlen'):
peak_prominences(np.arange(10), [3, 5], wlen=1)
def test_raises(self):
"""
Verfiy that argument validation works as intended.
"""
# x with dimension > 1
with raises(ValueError, match='dimension'):
peak_prominences([[0, 1, 1, 0]], [1, 2])
# peaks with dimension > 1
with raises(ValueError, match='dimension'):
peak_prominences([0, 1, 1, 0], [[1, 2]])
# x with dimension < 1
with raises(ValueError, match='dimension'):
peak_prominences(3, [0,])
# empty x with peaks supplied
with raises(ValueError, match='not a valid peak'):
peak_prominences([], [1, 2])
# invalid peaks
for p in [-1, 0, 1, 2, 3]:
with raises(ValueError, match=str(p) + ' is not a valid peak'):
peak_prominences([1, 0, 2], [p,])
# peaks is not cast-able to np.intp
with raises(TypeError, match='Cannot safely cast'):
peak_prominences([0, 1, 1, 0], [1.1, 2.3])
# wlen < 3
with raises(ValueError, match='wlen'):
peak_prominences(np.arange(10), [3, 5], wlen=1)
def test_raises(self):
"""
Verfiy that argument validation works as intended.
"""
# x with dimension > 1
with raises(ValueError, match='dimension'):
peak_prominences([[0, 1, 1, 0]], [1, 2])
# peaks with dimension > 1
with raises(ValueError, match='dimension'):
peak_prominences([0, 1, 1, 0], [[1, 2]])
# x with dimension < 1
with raises(ValueError, match='dimension'):
peak_prominences(3, [
0,
])
# empty x with peaks supplied
with raises(ValueError, match='not a valid peak'):
peak_prominences([], [1, 2])
# invalid peaks
for p in [-1, 0, 1, 2, 3]:
with raises(ValueError, match=str(p) + ' is not a valid peak'):
peak_prominences([1, 0, 2], [
p,
])
# peaks is not cast-able to np.intp
with raises(TypeError, match='Cannot safely cast'):
peak_prominences([0, 1, 1, 0], [1.1, 2.3])
# wlen < 3
with raises(ValueError, match='wlen'):
peak_prominences(np.arange(10), [3, 5], wlen=1)
