Auto-scales failure with short inputs, default configs

[rmallof]

Description

Hi. Thanks for the library, is being really handy!

I found a way to crash the icwt function in the current version - 0.6.3

Provided are the time series and code required to reproduce the issue

data

Error message:

---

ValueError Traceback (most recent call last)
Input In [2], in <cell line: 80>()
76 wavelet = 'morlet'
78 _, cwt, _, scales = ssq_cwt(data, wavelet=wavelet)
---> 80 icwt(cwt, wavelet=wavelet, scales=scales)

File /opt/conda/envs/gst/lib/python3.8/site-packages/ssqueezepy/_cwt.py:391, in icwt(Wx, wavelet, scales, nv, one_int, x_len, x_mean, padtype, rpadded, l1_norm)
389 idx = logscale_transition_idx(scales)
390 x = icwt(Wx[:idx], scales=scales[:idx], **kw)
--> 391 x += icwt(Wx[idx:], scales=scales[idx:], **kw)
392 return x
393 ##########################################################################
394
395 #### Invert ##############################################################

File /opt/conda/envs/gst/lib/python3.8/site-packages/ssqueezepy/_cwt.py:378, in icwt(Wx, wavelet, scales, nv, one_int, x_len, x_mean, padtype, rpadded, l1_norm)
376 wavelet = Wavelet._init_if_not_isinstance(wavelet)
377 # will override nv to match scales's
--> 378 scales, scaletype, _, nv = process_scales(scales, x_len, wavelet, nv=nv,
379 get_params=True)
380 assert (len(scales) == na), "%s != %s" % (len(scales), na)
382 #### Handle piecewise scales case ########################################
383 # nv must be left unspecified so it's inferred automatically from scales
384 # in process_scales for each piecewise case

File /opt/conda/envs/gst/lib/python3.8/site-packages/ssqueezepy/utils/cwt_utils.py:247, in process_scales(scales, N, wavelet, nv, get_params, use_padded_N)
244 nv = int(nv)
245 return scaletype, nv, preset
--> 247 scaletype, nv, preset = _process_args(scales, nv, wavelet)
248 if isinstance(scales, (np.ndarray, torch.Tensor)):
249 scales = scales.reshape(-1, 1)

File /opt/conda/envs/gst/lib/python3.8/site-packages/ssqueezepy/utils/cwt_utils.py:228, in process_scales.._process_args(scales, nv, wavelet)
225 if scales.squeeze().ndim != 1:
226 raise ValueError("scales, if array, must be 1D "
227 "(got shape %s)" % str(scales.shape))
--> 228 scaletype, _nv = infer_scaletype(scales)
229 if scaletype == 'log':
230 if nv is not None and _nv != nv:

File /opt/conda/envs/gst/lib/python3.8/site-packages/ssqueezepy/utils/cwt_utils.py:289, in infer_scaletype(scales)
286 # ceil to avoid faulty float-int roundoffs
287 nv = int(np.round(1 / np.diff(np.log2(scales), axis=0)[0]))
--> 289 elif logscale_transition_idx(scales) is None:
290 raise ValueError("could not infer scaletype from scales; "
291 "scales array must be linear or exponential. "
292 "(got diff(scales)=%s..." % np.diff(scales, axis=0)[:4])
294 else:

File /opt/conda/envs/gst/lib/python3.8/site-packages/ssqueezepy/utils/cwt_utils.py:380, in logscale_transition_idx(scales)
378 scales = asnumpy(scales)
379 scales_diff2 = np.abs(np.diff(np.log(scales), 2, axis=0))
--> 380 idx = np.argmax(scales_diff2) + 2
381 diff2_max = scales_diff2.max()
382 # every other value must be zero, assert it is so

File <array_function internals>:180, in argmax(*args, **kwargs)

File /opt/conda/envs/gst/lib/python3.8/site-packages/numpy/core/fromnumeric.py:1216, in argmax(a, axis, out, keepdims)
1129 """
1130 Returns the indices of the maximum values along an axis.
1131
(...)
1213 (2, 1, 4)
1214 """
1215 kwds = {'keepdims': keepdims} if keepdims is not np._NoValue else {}
-> 1216 return _wrapfunc(a, 'argmax', axis=axis, out=out, **kwds)

File /opt/conda/envs/gst/lib/python3.8/site-packages/numpy/core/fromnumeric.py:57, in _wrapfunc(obj, method, *args, **kwds)
54 return _wrapit(obj, method, *args, **kwds)
56 try:
---> 57 return bound(*args, **kwds)
58 except TypeError:
59 # A TypeError occurs if the object does have such a method in its
60 # class, but its signature is not identical to that of NumPy's. This
(...)
64 # Call _wrapit from within the except clause to ensure a potential
65 # exception has a traceback chain.
66 return _wrapit(obj, method, *args, **kwds)

ValueError: attempt to get argmax of an empty sequence

Code for reproduction

from ssqueezepy import ssq_cwt, icwt
import numpy as np

data = np.array([ 41135817341,  29083562061,  26175547452,  16588370958,
        17264085441,  31947336829,  40770974039,  30242059107,
        21692004719,  29867476527,  27246574439,  34163220274,
        68204556440,  50913575242,  54912007015,  31183975654,
        27132421514,  42009436760,  35329942625,  30818458597,
        28970212744,  28574793478,  26188097173,  24957784918,
        18372538715,  18027170497,  20965695707,  21381535161,
        23552740328,  26267239923,  30767551159,  18100418740,
        16390821947,  21594638208,  26715546990,  24598943708,
        25814972520,  49899834488,  29641127858,  28688807249,
        24150249025,  25810220018,  33042430345,  31158743333,
        25905575359,  24302954056,  22927802083,  39974475562,
        48765202697,  42932549127,  33631012204,  31421555646,
        24021799169,  23565495303,  35574561406,  28624673855,
        31758955233,  40212386158,  35887249746,  28148218301,
        23553591896,  25849159141,  28389250717,  26288169966,
        25120229769,  28881249043,  15978259885,  15886817043,
        28575544847,  23555719219,  32837431722,  37127036580,
        27265804688,  22987346289,  22994133555,  35123501685,
        27753685646,  30931623076,  23747613147,  40509610260,
        27595671000,  23102307723,  31666498758,  31878280659,
        31962253368,  31028679593,  42326789564,  30116729776,
        24366810591,  32637854078,  34483360283,  33225232872,
        30182031010,  29123998928,  23613051457,  25245861652,
        28813460025,  43403978910,  35239757134,  32194477850,
        48469528171,  36913738894,  34493951963,  50212088965,
        51091116622,  37872380889,  36389011503,  30123362273,
        24957448100,  31254779144,  40177002624,  36791346508,
        46363793975,  41135767926,  38896078052,  26149643168,
        23359966112,  44148798321,  58571439619,  53071298734,
        41037843771,  43975248085,  18719537670,  20765955327,
        30484729489,  35887278685,  33223790572,  34711412966,
        29227315390,  16437423167,  16837262532,  27425022774,
        28711532910,  24950173846,  44219840004,  38452356727,
        16192235532,  17988916650,  27472552998,  30580012344,
        22425387184,  24493974420,  32459287866,  16104440957,
        22128794335,  30202235805,  47761524910,  58895950537,
        49625110402,  43994715910,  40369840645,  31486345556,
        45668466815,  39819303159,  55552169483,  43228750179,
        64072727950,  37846047609,  35082693210,  53510852236,
       118992465607, 102905151606,  83202283721,  55871616488,
        29717699419,  27209183682,  49630243054,  36599436183,
        33925512989,  27868914022,  26862218609,  16106223492,
        21313378652,  37429485518,  30726828760,  32958875628,
        26129037414,  18678255976,  18000008764,  20443898509,
        27743025156,  23581685468,  29523576583,  22895392882,
        19539705127,  16217776704,  16824520830,  22209086834,
        19889922369,  19675404389,  20496603770,  20328426366,
        12706781969,  14122486832,  19617581341,  26634741631,
        25534481470,  20964448341,  24031608960,  14463581825,
        10924354698,  17221074814,  22722096615,  14882945045,
        16441573050,  15329265213,   9744636213,  11656379938,
        11886957804,  15748580239,  17005713920,  14472237479,
        15929162910,  11239186456,   9244361700,  12097775227,
        13903079207,  18421743322,  13692758566,  14413662913,
         7714767174,   9768827914,  18624736866,  15808338949,
        18372283782,  34971338710,  29225029694,  38967784639,
        19298407543,  26792494050,  24999983362,  30005625418,
        21152848261,  28799154319,  32442278429,  24746386230,
        26518700512,  26405069715,  30685366709,  26357839322,
        25383335641,  14712928379,  27423687259,  27205595568,
        22837828665,  26683255504,  32066936882,  27083066007,
        15639298538,  19564262605,  23825006542,  27187964471,
        25371367758,  32572572185,  27078406594,  16356226232,
        17821046406,  23918742607,  26792596581,  32483312909,
        39316664596,  41358451255,  19625427158,  25555105670,
        28987376573,  31252098714,  30199996781,  30476264066,
        26811744928,  16100721565,  16644534842,  22660763494,
        20535363434,  24662841200,  20386398516,  26062404610,
        11166012913,  13488941056])


wavelet = 'morlet'

_, cwt, _, scales = ssq_cwt(data, wavelet=wavelet)

icwt(cwt, wavelet=wavelet, scales=scales)

[OverLordGoldDragon]

Thanks for the report, you've reproduced an edge case with short inputs.

Until it's fixed, try one of: 1) higher nv (e.g. 40); 2) scales='log'; 3) longer data.

[rmallof]

Thank you for the response and the suggestions. It does work with longer data indeed. In this case, setting a higher nv parameter or the scales='log' still gives errors. Adding them here for convenience:

icwt(cwt, wavelet=wavelet, nv=40)

---> 80 icwt(cwt, wavelet=wavelet, nv=40)

File /opt/conda/envs/gst/lib/python3.8/site-packages/ssqueezepy/_cwt.py:380, in icwt(Wx, wavelet, scales, nv, one_int, x_len, x_mean, padtype, rpadded, l1_norm)
377 # will override nv to match scales's
378 scales, scaletype, _, nv = process_scales(scales, x_len, wavelet, nv=nv,
379 get_params=True)
--> 380 assert (len(scales) == na), "%s != %s" % (len(scales), na)
382 #### Handle piecewise scales case ########################################
383 # nv must be left unspecified so it's inferred automatically from scales
384 # in process_scales for each piecewise case
385 if scaletype == 'log-piecewise':

AssertionError: 276 != 222

icwt(cwt, wavelet=wavelet, scales='log')
---> 80 icwt(cwt, wavelet=wavelet, scales='log')

File /opt/conda/envs/gst/lib/python3.8/site-packages/ssqueezepy/_cwt.py:380, in icwt(Wx, wavelet, scales, nv, one_int, x_len, x_mean, padtype, rpadded, l1_norm)
377 # will override nv to match scales's
378 scales, scaletype, _, nv = process_scales(scales, x_len, wavelet, nv=nv,
379 get_params=True)
--> 380 assert (len(scales) == na), "%s != %s" % (len(scales), na)
382 #### Handle piecewise scales case ########################################
383 # nv must be left unspecified so it's inferred automatically from scales
384 # in process_scales for each piecewise case
385 if scaletype == 'log-piecewise':

AssertionError: 168 != 222

Please, let me know if I'm missing something. Again, great library :)

[OverLordGoldDragon]

Right, I meant in cwt (and therefore also in icwt).