def test_findrfc():
t = linspace(0, 7 * pi, 250)
x = sin(t) + 0.1 * sin(50 * t)
ind = findextrema(x)
assert_array_almost_equal(
ind,
np.array(
[1, 3, 4, 6, 7, 9, 11, 13, 14, 16, 18, 19, 21,
23, 25, 26, 28, 29, 31, 33, 35, 36, 38, 39, 41, 43,
45, 46, 48, 50, 51, 53, 55, 56, 58, 60, 61, 63, 65,
67, 68, 70, 71, 73, 75, 77, 78, 80, 81, 83, 85, 87,
88, 90, 92, 93, 95, 97, 99, 100, 102, 103, 105, 107, 109,
110, 112, 113, 115, 117, 119, 120, 122, 124, 125, 127, 129, 131,
132, 134, 135, 137, 139, 141, 142, 144, 145, 147, 149, 151, 152,
154, 156, 157, 159, 161, 162, 164, 166, 167, 169, 171, 173, 174,
176, 177, 179, 181, 183, 184, 186, 187, 189, 191, 193, 194, 196,
198, 199, 201, 203, 205, 206, 208, 209, 211, 213, 215, 216, 218,
219, 221, 223, 225, 226, 228, 230, 231, 233, 235, 237, 238, 240,
241, 243, 245, 247, 248]))
_ti, tp = t[ind], x[ind]
for method in ['clib', 2, 1, 0]:
ind1 = findrfc(tp, 0.3, method=method)
if method in [1, 0]:
ind1 = ind1[:-1]
assert_array_almost_equal(
ind1,
np.array([0, 9, 32, 53, 74, 95, 116, 137]))
assert_array_almost_equal(
tp[ind1],
np.array(
[-0.00743352, 1.08753972, -1.07206545, 1.09550837, -1.07940458,
1.07849396, -1.0995006, 1.08094452]))
def test_findrfc():
t = linspace(0, 7 * pi, 250)
x = sin(t) + 0.1 * sin(50 * t)
ind = findextrema(x)
assert_array_almost_equal(
ind,
np.array([
1, 3, 4, 6, 7, 9, 11, 13, 14, 16, 18, 19, 21, 23, 25, 26, 28, 29,
31, 33, 35, 36, 38, 39, 41, 43, 45, 46, 48, 50, 51, 53, 55, 56, 58,
60, 61, 63, 65, 67, 68, 70, 71, 73, 75, 77, 78, 80, 81, 83, 85, 87,
88, 90, 92, 93, 95, 97, 99, 100, 102, 103, 105, 107, 109, 110, 112,
113, 115, 117, 119, 120, 122, 124, 125, 127, 129, 131, 132, 134,
135, 137, 139, 141, 142, 144, 145, 147, 149, 151, 152, 154, 156,
157, 159, 161, 162, 164, 166, 167, 169, 171, 173, 174, 176, 177,
179, 181, 183, 184, 186, 187, 189, 191, 193, 194, 196, 198, 199,
201, 203, 205, 206, 208, 209, 211, 213, 215, 216, 218, 219, 221,
223, 225, 226, 228, 230, 231, 233, 235, 237, 238, 240, 241, 243,
245, 247, 248
]))
_ti, tp = t[ind], x[ind]
ind1 = findrfc(tp, 0.3)
assert_array_almost_equal(ind1, np.array([0, 9, 32, 53, 74, 95, 116, 137]))
assert_array_almost_equal(
tp[ind1],
np.array([
-0.00743352, 1.08753972, -1.07206545, 1.09550837, -1.07940458,
1.07849396, -1.0995006, 1.08094452
]))
def test_findrfc():
t = linspace(0, 7 * pi, 250)
x = sin(t) + 0.1 * sin(50 * t)
ind = findextrema(x)
assert_allclose(ind,
[1, 3, 4, 6, 7, 9, 11, 13, 14, 16, 18, 19, 21,
23, 25, 26, 28, 29, 31, 33, 35, 36, 38, 39, 41, 43,
45, 46, 48, 50, 51, 53, 55, 56, 58, 60, 61, 63, 65,
67, 68, 70, 71, 73, 75, 77, 78, 80, 81, 83, 85, 87,
88, 90, 92, 93, 95, 97, 99, 100, 102, 103, 105, 107, 109,
110, 112, 113, 115, 117, 119, 120, 122, 124, 125, 127, 129, 131,
132, 134, 135, 137, 139, 141, 142, 144, 145, 147, 149, 151, 152,
154, 156, 157, 159, 161, 162, 164, 166, 167, 169, 171, 173, 174,
176, 177, 179, 181, 183, 184, 186, 187, 189, 191, 193, 194, 196,
198, 199, 201, 203, 205, 206, 208, 209, 211, 213, 215, 216, 218,
219, 221, 223, 225, 226, 228, 230, 231, 233, 235, 237, 238, 240,
241, 243, 245, 247, 248])
_ti, tp = t[ind], x[ind]
for method in ['clib', 2, 1, 0]:
ind1 = findrfc(tp, 0.3, method=method)
if method in [1, 0]:
ind1 = ind1[:-1]
assert_allclose(ind1, [0, 9, 32, 53, 74, 95, 116, 137])
# print(tp[ind1].tolist())
truth = [-0.007433524853697526, 1.0875397175924215, -1.0720654490829054,
1.0955083650755328, -1.0794045843842426, 1.0784939627613357,
-1.0995005995649583, 1.0809445217915996]
assert_allclose(tp[ind1], truth)
def test_rfcfilter():
# 1. Filtered signal y is the turning points of x.
x = sea()
y = rfcfilter(x[:, 1], h=0.0, method=1)
assert_array_almost_equal(
y[0:5],
np.array([-1.2004945, 0.83950546, -0.09049454,
-0.02049454, -0.09049454]))
# 2. This removes all rainflow cycles with range less than 0.5.
y1 = rfcfilter(x[:, 1], h=0.5, method=0)
assert_array_almost_equal(
y1[0:5],
np.array([-1.2004945, 0.83950546, -0.43049454,
0.34950546, -0.51049454]))
# return
t = linspace(0, 7 * pi, 250)
x = sin(t) + 0.1 * sin(50 * t)
ind = findextrema(x)
assert_array_almost_equal(
ind,
np.array(
[1, 3, 4, 6, 7, 9, 11, 13, 14, 16, 18, 19, 21,
23, 25, 26, 28, 29, 31, 33, 35, 36, 38, 39, 41, 43,
45, 46, 48, 50, 51, 53, 55, 56, 58, 60, 61, 63, 65,
67, 68, 70, 71, 73, 75, 77, 78, 80, 81, 83, 85, 87,
88, 90, 92, 93, 95, 97, 99, 100, 102, 103, 105, 107, 109,
110, 112, 113, 115, 117, 119, 120, 122, 124, 125, 127, 129, 131,
132, 134, 135, 137, 139, 141, 142, 144, 145, 147, 149, 151, 152,
154, 156, 157, 159, 161, 162, 164, 166, 167, 169, 171, 173, 174,
176, 177, 179, 181, 183, 184, 186, 187, 189, 191, 193, 194, 196,
198, 199, 201, 203, 205, 206, 208, 209, 211, 213, 215, 216, 218,
219, 221, 223, 225, 226, 228, 230, 231, 233, 235, 237, 238, 240,
241, 243, 245, 247, 248]))
_ti, tp = t[ind], x[ind]
tp03 = rfcfilter(tp, 0.3)
assert_array_almost_equal(
tp03,
np.array(
[-0.00743352, 1.08753972, -1.07206545, 1.09550837, -1.07940458,
1.07849396, -1.0995006, 1.08094452, 0.11983423]))
tp3 = findrfc_astm(tp)
assert_array_almost_equal((77, 3), tp3.shape)
# print(tp3[-5:])
assert_array_almost_equal(tp3[-5:],
[[0.01552179, 0.42313414, 1.],
[1.09750448, -0.00199612, 0.5],
[1.09022256, -0.00927804, 0.5],
[0.48055514, 0.60038938, 0.5],
[0.03200274, 0.15183698, 0.5]])
assert_array_almost_equal(tp3[:5],
[[0.03578165, 0.28906389, 1.],
[0.03602834, 0.56726584, 1.],
[0.03816623, 0.76461446, 1.],
[0.0638364, 0.92381302, 1.],
[0.07759006, 0.99628738, 1.]])
def test_rfcfilter(sea1):
# 1. Filtered signal y is the turning points of x.
x = sea1
y = rfcfilter(x[:, 1], h=0.0, method=1)
assert_allclose(
y[0:5],
[-1.2004945, 0.83950546, -0.09049454, -0.02049454, -0.09049454])
# 2. This removes all rainflow cycles with range less than 0.5.
y1 = rfcfilter(x[:, 1], h=0.5, method=0)
assert_allclose(
y1[0:5],
[-1.2004945, 0.83950546, -0.43049454, 0.34950546, -0.51049454])
# return
t = np.linspace(0, 7 * pi, 250)
x = sin(t) + 0.1 * sin(50 * t)
ind = findextrema(x)
assert_allclose(ind, [
1, 3, 4, 6, 7, 9, 11, 13, 14, 16, 18, 19, 21, 23, 25, 26, 28, 29, 31,
33, 35, 36, 38, 39, 41, 43, 45, 46, 48, 50, 51, 53, 55, 56, 58, 60, 61,
63, 65, 67, 68, 70, 71, 73, 75, 77, 78, 80, 81, 83, 85, 87, 88, 90, 92,
93, 95, 97, 99, 100, 102, 103, 105, 107, 109, 110, 112, 113, 115, 117,
119, 120, 122, 124, 125, 127, 129, 131, 132, 134, 135, 137, 139, 141,
142, 144, 145, 147, 149, 151, 152, 154, 156, 157, 159, 161, 162, 164,
166, 167, 169, 171, 173, 174, 176, 177, 179, 181, 183, 184, 186, 187,
189, 191, 193, 194, 196, 198, 199, 201, 203, 205, 206, 208, 209, 211,
213, 215, 216, 218, 219, 221, 223, 225, 226, 228, 230, 231, 233, 235,
237, 238, 240, 241, 243, 245, 247, 248
])
_ti, tp = t[ind], x[ind]
tp03 = rfcfilter(tp, 0.3)
# print(tp03.tolist())
truth = [
-0.007433524853697526, 1.0875397175924215, -1.0720654490829054,
1.0955083650755328, -1.0794045843842426, 1.0784939627613357,
-1.0995005995649583, 1.0809445217915996, 0.11983423290349654
]
assert_allclose(tp03, truth)
tp3 = findrfc_astm(tp)
assert_allclose((77, 3), tp3.shape)
# print(tp3[-5:].tolist())
assert_allclose(tp3[-5:],
[[0.01552179103405038, 0.4231341427960734, 1.0],
[1.0975044823202456, -0.001996117244712714, 0.5],
[1.090222560678279, -0.00927803888667933, 0.5],
[0.48055514444405156, 0.600389377347548, 0.5],
[0.032002742614076624, 0.15183697551757316, 0.5]])
# print(tp3[:5].tolist())
assert_allclose(tp3[:5], [[0.035781645324019146, 0.28906389183961456, 1.0],
[0.03602834384593512, 0.5672658361052029, 1.0],
[0.038166226239640555, 0.7646144604852383, 1.0],
[0.06383640016547976, 0.9238130173264235, 1.0],
[0.07759005562881188, 0.9962873791766909, 1.0]])
def test_rfcfilter():
# 1. Filtered signal y is the turning points of x.
x = sea()
y = rfcfilter(x[:, 1], h=0.0, method=1)
assert_array_almost_equal(
y[0:5],
np.array(
[-1.2004945, 0.83950546, -0.09049454, -0.02049454, -0.09049454]))
# 2. This removes all rainflow cycles with range less than 0.5.
y1 = rfcfilter(x[:, 1], h=0.5, method=0)
assert_array_almost_equal(
y1[0:5],
np.array(
[-1.2004945, 0.83950546, -0.43049454, 0.34950546, -0.51049454]))
# return
t = linspace(0, 7 * pi, 250)
x = sin(t) + 0.1 * sin(50 * t)
ind = findextrema(x)
assert_array_almost_equal(
ind,
np.array([
1, 3, 4, 6, 7, 9, 11, 13, 14, 16, 18, 19, 21, 23, 25, 26, 28, 29,
31, 33, 35, 36, 38, 39, 41, 43, 45, 46, 48, 50, 51, 53, 55, 56, 58,
60, 61, 63, 65, 67, 68, 70, 71, 73, 75, 77, 78, 80, 81, 83, 85, 87,
88, 90, 92, 93, 95, 97, 99, 100, 102, 103, 105, 107, 109, 110, 112,
113, 115, 117, 119, 120, 122, 124, 125, 127, 129, 131, 132, 134,
135, 137, 139, 141, 142, 144, 145, 147, 149, 151, 152, 154, 156,
157, 159, 161, 162, 164, 166, 167, 169, 171, 173, 174, 176, 177,
179, 181, 183, 184, 186, 187, 189, 191, 193, 194, 196, 198, 199,
201, 203, 205, 206, 208, 209, 211, 213, 215, 216, 218, 219, 221,
223, 225, 226, 228, 230, 231, 233, 235, 237, 238, 240, 241, 243,
245, 247, 248
]))
_ti, tp = t[ind], x[ind]
tp03 = rfcfilter(tp, 0.3)
assert_array_almost_equal(
tp03,
np.array([
-0.00743352, 1.08753972, -1.07206545, 1.09550837, -1.07940458,
1.07849396, -1.0995006, 1.08094452, 0.11983423
]))
tp3 = findrfc_astm(tp)
assert_array_almost_equal((77, 3), tp3.shape)
# print(tp3[-5:])
assert_array_almost_equal(
tp3[-5:],
[[0.01552179, 0.42313414, 1.], [1.09750448, -0.00199612, 0.5],
[1.09022256, -0.00927804, 0.5], [0.48055514, 0.60038938, 0.5],
[0.03200274, 0.15183698, 0.5]])
assert_array_almost_equal(
tp3[:5], [[0.03578165, 0.28906389, 1.], [0.03602834, 0.56726584, 1.],
[0.03816623, 0.76461446, 1.], [0.0638364, 0.92381302, 1.],
[0.07759006, 0.99628738, 1.]])
def _remove_index_to_data_too_close_to_each_other(ix_e, is_too_small, di_e, ti_e, tmin):
is_too_close = np.hstack((is_too_small[0], is_too_small[:-1] | is_too_small[1:],
is_too_small[-1]))
# Find opening (no) and closing (nc) index for data beeing to close:
iy = findextrema(np.hstack([0, 0, is_too_small, 0]))
no = iy[:2] - 1
nc = iy[1::2]
for start, stop in zip(no, nc):
iz = slice(start, stop)
i_ok = _find_ok_peaks(di_e[iz], ti_e[iz], tmin)
if len(i_ok):
is_too_close[start + i_ok] = 0
# Remove data which is too close to other data.
if is_too_close.any():
i_ok, = where(1 - is_too_close)
ix_e = ix_e[i_ok]
return ix_e
def test_rfcfilter():
# 1. Filtered signal y is the turning points of x.
x = sea()
y = rfcfilter(x[:, 1], h=0, method=1)
assert_array_almost_equal(
y[0:5],
np.array(
[-1.2004945, 0.83950546, -0.09049454, -0.02049454, -0.09049454]))
# 2. This removes all rainflow cycles with range less than 0.5.
y1 = rfcfilter(x[:, 1], h=0.5)
assert_array_almost_equal(
y1[0:5],
np.array(
[-1.2004945, 0.83950546, -0.43049454, 0.34950546, -0.51049454]))
t = linspace(0, 7 * pi, 250)
x = sin(t) + 0.1 * sin(50 * t)
ind = findextrema(x)
assert_array_almost_equal(
ind,
np.array([
1, 3, 4, 6, 7, 9, 11, 13, 14, 16, 18, 19, 21, 23, 25, 26, 28, 29,
31, 33, 35, 36, 38, 39, 41, 43, 45, 46, 48, 50, 51, 53, 55, 56, 58,
60, 61, 63, 65, 67, 68, 70, 71, 73, 75, 77, 78, 80, 81, 83, 85, 87,
88, 90, 92, 93, 95, 97, 99, 100, 102, 103, 105, 107, 109, 110, 112,
113, 115, 117, 119, 120, 122, 124, 125, 127, 129, 131, 132, 134,
135, 137, 139, 141, 142, 144, 145, 147, 149, 151, 152, 154, 156,
157, 159, 161, 162, 164, 166, 167, 169, 171, 173, 174, 176, 177,
179, 181, 183, 184, 186, 187, 189, 191, 193, 194, 196, 198, 199,
201, 203, 205, 206, 208, 209, 211, 213, 215, 216, 218, 219, 221,
223, 225, 226, 228, 230, 231, 233, 235, 237, 238, 240, 241, 243,
245, 247, 248
]))
_ti, tp = t[ind], x[ind]
tp03 = rfcfilter(tp, 0.3)
assert_array_almost_equal(
tp03,
np.array([
-0.00743352, 1.08753972, -1.07206545, 1.09550837, -1.07940458,
1.07849396, -1.0995006, 1.08094452, 0.11983423
]))
def test_rfcfilter():
# 1. Filtered signal y is the turning points of x.
x = sea()
y = rfcfilter(x[:, 1], h=0, method=1)
assert_array_almost_equal(
y[0:5],
np.array([-1.2004945, 0.83950546, -0.09049454,
-0.02049454, -0.09049454]))
# 2. This removes all rainflow cycles with range less than 0.5.
y1 = rfcfilter(x[:, 1], h=0.5)
assert_array_almost_equal(
y1[0:5],
np.array([-1.2004945, 0.83950546, -0.43049454,
0.34950546, -0.51049454]))
t = linspace(0, 7 * pi, 250)
x = sin(t) + 0.1 * sin(50 * t)
ind = findextrema(x)
assert_array_almost_equal(
ind,
np.array(
[1, 3, 4, 6, 7, 9, 11, 13, 14, 16, 18, 19, 21,
23, 25, 26, 28, 29, 31, 33, 35, 36, 38, 39, 41, 43,
45, 46, 48, 50, 51, 53, 55, 56, 58, 60, 61, 63, 65,
67, 68, 70, 71, 73, 75, 77, 78, 80, 81, 83, 85, 87,
88, 90, 92, 93, 95, 97, 99, 100, 102, 103, 105, 107, 109,
110, 112, 113, 115, 117, 119, 120, 122, 124, 125, 127, 129, 131,
132, 134, 135, 137, 139, 141, 142, 144, 145, 147, 149, 151, 152,
154, 156, 157, 159, 161, 162, 164, 166, 167, 169, 171, 173, 174,
176, 177, 179, 181, 183, 184, 186, 187, 189, 191, 193, 194, 196,
198, 199, 201, 203, 205, 206, 208, 209, 211, 213, 215, 216, 218,
219, 221, 223, 225, 226, 228, 230, 231, 233, 235, 237, 238, 240,
241, 243, 245, 247, 248]))
_ti, tp = t[ind], x[ind]
tp03 = rfcfilter(tp, 0.3)
assert_array_almost_equal(
tp03,
np.array(
[-0.00743352, 1.08753972, -1.07206545, 1.09550837, -1.07940458,
1.07849396, -1.0995006, 1.08094452, 0.11983423]))
def test_findextrema():
t = linspace(0, 7 * pi, 250)
x = sin(t)
ind = findextrema(x)
assert_array_almost_equal(ind, np.array([18, 53, 89, 125, 160, 196, 231]))
def test_findextrema():
t = linspace(0, 7 * pi, 250)
x = sin(t)
ind = findextrema(x)
assert_allclose(ind, [18, 53, 89, 125, 160, 196, 231])
def test_findextrema():
t = linspace(0, 7 * pi, 250)
x = sin(t)
ind = findextrema(x)
assert_array_almost_equal(ind, np.array([18, 53, 89, 125, 160, 196, 231]))
def findpot(data, t=None, thresh=None, tmin=1):
'''
Retrun indices to Peaks over threshold values
Parameters
----------
data, t : array-like
data-values and sampling-times, respectively.
thresh : real scalar
minimum threshold for levels in data.
tmin : real scalar
minimum distance to another peak [same unit as t] (default 1)
Returns
-------
Ie : ndarray
indices to extreme values, i.e., all data > tresh which are at least
tmin distance apart.
Example
-------
>>> import pylab
>>> import wafo.data
>>> from wafo.misc import findtc
>>> x = wafo.data.sea()
>>> t, data = x.T
>>> itc, iv = findtc(data,0,'dw')
>>> ytc, ttc = data[itc], t[itc]
>>> ymin = 2*data.std()
>>> tmin = 10 # sec
>>> I = findpot(data, t, ymin, tmin)
>>> yp, tp = data[I], t[I]
>>> Ie = findpot(yp, tp, ymin,tmin)
>>> ye, te = yp[Ie], tp[Ie]
>>> h = pylab.plot(t,data,ttc,ytc,'ro',
... t,zeros(len(t)),':',
... te, ye,'k.',tp,yp,'+')
See also
--------
fitgenpar, decluster, extremalidx
'''
Data = arr(data)
if t is None:
ti = np.arange(len(Data))
else:
ti = arr(t)
Ie, = where(Data > thresh)
Ye = Data[Ie]
Te = ti[Ie]
if len(Ye) <= 1:
return Ie
dT = np.diff(Te)
notSorted = np.any(dT < 0)
if notSorted:
I = np.argsort(Te)
Te = Te[I]
Ie = Ie[I]
Ye = Ye[I]
dT = np.diff(Te)
isTooSmall = (dT <= tmin)
if np.any(isTooSmall):
isTooClose = np.hstack(
(isTooSmall[0], isTooSmall[:-1] | isTooSmall[1:], isTooSmall[-1]))
# Find opening (NO) and closing (NC) index for data beeing to close:
iy = findextrema(np.hstack([0, 0, isTooSmall, 0]))
NO = iy[::2] - 1
NC = iy[1::2]
for no, nc in zip(NO, NC):
iz = slice(no, nc)
iOK = _find_ok_peaks(Ye[iz], Te[iz], tmin)
if len(iOK):
isTooClose[no + iOK] = 0
# Remove data which is too close to other data.
if isTooClose.any():
# len(tooClose)>0:
iOK, = where(1 - isTooClose)
Ie = Ie[iOK]
return Ie
def findpot(data, t=None, thresh=None, tmin=1):
'''
Retrun indices to Peaks over threshold values
Parameters
----------
data, t : array-like
data-values and sampling-times, respectively.
thresh : real scalar
minimum threshold for levels in data.
tmin : real scalar
minimum distance to another peak [same unit as t] (default 1)
Returns
-------
Ie : ndarray
indices to extreme values, i.e., all data > tresh which are at least
tmin distance apart.
Example
-------
>>> import pylab
>>> import wafo.data
>>> from wafo.misc import findtc
>>> x = wafo.data.sea()
>>> t, data = x.T
>>> itc, iv = findtc(data,0,'dw')
>>> ytc, ttc = data[itc], t[itc]
>>> ymin = 2*data.std()
>>> tmin = 10 # sec
>>> I = findpot(data, t, ymin, tmin)
>>> yp, tp = data[I], t[I]
>>> Ie = findpot(yp, tp, ymin,tmin)
>>> ye, te = yp[Ie], tp[Ie]
>>> h = pylab.plot(t,data,ttc,ytc,'ro',t,zeros(len(t)),':',te, ye,'k.',tp,yp,'+')
See also
--------
fitgenpar, decluster, extremalidx
'''
Data = arr(data)
if t is None:
ti = np.arange(len(Data))
else:
ti = arr(t)
Ie, = where(Data > thresh);
Ye = Data[Ie]
Te = ti[Ie]
if len(Ye) <= 1:
return Ie
dT = np.diff(Te)
notSorted = np.any(dT < 0);
if notSorted:
I = np.argsort(Te)
Te = Te[I]
Ie = Ie[I]
Ye = Ye[I]
dT = np.diff(Te)
isTooSmall = (dT <= tmin)
if np.any(isTooSmall):
isTooClose = np.hstack((isTooSmall[0], isTooSmall[:-1] | isTooSmall[1:], isTooSmall[-1]))
#Find opening (NO) and closing (NC) index for data beeing to close:
iy = findextrema(np.hstack([0, 0, isTooSmall, 0]))
NO = iy[::2] - 1
NC = iy[1::2]
for no, nc in zip(NO, NC):
iz = slice(no, nc)
iOK = _find_ok_peaks(Ye[iz], Te[iz], tmin)
if len(iOK):
isTooClose[no + iOK] = 0
# Remove data which is too close to other data.
if isTooClose.any():
#len(tooClose)>0:
iOK, = where(1 - isTooClose)
Ie = Ie[iOK]
return Ie
