[SciPy-dev] Is this a bugfix for scipy.hilbert?
josef.pktd@gmai...
josef.pktd@gmai...
Sat Jan 16 23:31:16 CST 2010
On Fri, Jan 15, 2010 at 4:20 PM, Ariel Rokem <arokem@berkeley.edu> wrote:
> Hi - I've never done this before, so it would be great if I could 'look over
> your shoulder' (in the sense that I know how this ticket came about :D), as
> you submit a ticket on this.
Done in http://projects.scipy.org/scipy/ticket/1093
tests pass at 14 decimals
Josef
>
> Thanks --
>
> Ariel
>
> On Fri, Jan 15, 2010 at 11:48 AM, <josef.pktd@gmail.com> wrote:
>>
>> On Fri, Jan 15, 2010 at 2:34 PM, Ariel Rokem <arokem@berkeley.edu> wrote:
>> > Hi -
>> >
>> > attached is a file with a couple of tests. I am not sure this tests the
>> > issues we were dealing with previously (the axis issues, etc.), but it
>> > has
>> > some sensible test-cases, which compare to what Matlab would give you
>> > (not
>> > quite 10by3 or 10by6, but as you can see, they make sense). Also - all
>> > the
>> > assertions are assert_almost_equal. Do you think that's OK? I think
>> > there
>> > are float-precision issues here, which would make assert_equal fail, but
>> > I
>> > am not sure - I would be happy to get any general comments on these
>> > tests,
>> > in case I am doing this all wrong.
>>
>> nice test cases, I like theoretical tests even better than verified
>> numbers from other packages.
>>
>> Besides some cosmetic changes to get them into a test function, the
>> only part to add is the precision of the tests.
>> The default precision of assert_almost_equal is only 6 decimals.
>>
>> For these kind of cases, I usually go to 12 to 15 depending on the
>> numerical precision of the algorithm. Usually, I go by trial and error
>> until the test breaks, or calculate max abs of the error.
>>
>> I can add some tests for the axis argument.
>>
>> Can you open a ticket for the record or shall I ?
>>
>> Josef
>>
>>
>> >
>> > Cheers,
>> >
>> > Ariel
>> >
>> > On Thu, Jan 14, 2010 at 11:10 PM, <josef.pktd@gmail.com> wrote:
>> >>
>> >> On Fri, Jan 15, 2010 at 1:44 AM, Ariel Rokem <arokem@berkeley.edu>
>> >> wrote:
>> >> > Yes - looks good. Except I would prefer to eventually set the axis to
>> >> > default to -1, to be consistent with signal.fft (and also np.fft.fft)
>> >> > which
>> >> > has axis=-1.
>> >>
>> >> I'm indifferent to the default axis, from a quick look and my
>> >> experience there are not many functions with axis arguments in signal.
>> >> So I'm fine with switching to axis=-1. We should do it with this
>> >> bugfix, since until now the function wasn't correct anyway for 2d.
>> >>
>> >> >
>> >> > As for whether it's doing what it's supposed to do, for what it's
>> >> > worth
>> >> > - it
>> >> > seems to do similar things to what Matlab's 'hilbert' function does
>> >> > on a
>> >> > few
>> >> > simple examples I tried out.
>> >>
>> >> I was reading briefly on wikipedia, and checked with fftpack.hilbert,
>> >> which returns the same array as signal.hilbert(a).imag, but I didn't
>> >> manage to figure out why fftpack.hilbert only allows 1d (i got lost
>> >> starting at convolve.pyf)
>> >>
>> >> Could you write a simple test case compared to matlab, e.g. 10by3 as
>> >> in my example, for both axis, or 10by6 if 10by3 doesn't make sense?
>> >>
>> >> If nobody objects, I can commit the change with axis=-1.
>> >>
>> >> Josef
>> >>
>> >> >
>> >> > Cheers,
>> >> >
>> >> > Ariel
>> >> >
>> >> >
>> >> >
>> >> > On Thu, Jan 14, 2010 at 8:53 PM, <josef.pktd@gmail.com> wrote:
>> >> >>
>> >> >> On Thu, Jan 14, 2010 at 11:27 PM, <josef.pktd@gmail.com> wrote:
>> >> >> > On Thu, Jan 14, 2010 at 11:02 PM, <josef.pktd@gmail.com> wrote:
>> >> >> >> On Thu, Jan 14, 2010 at 10:54 PM, <josef.pktd@gmail.com> wrote:
>> >> >> >>> On Thu, Jan 14, 2010 at 10:24 PM, Ariel Rokem
>> >> >> >>> <arokem@berkeley.edu>
>> >> >> >>> wrote:
>> >> >> >>>> Hi everyone,
>> >> >> >>>>
>> >> >> >>>> I have been trying to use scipy.signal.hilbert and I got the
>> >> >> >>>> following
>> >> >> >>>> puzzling result:
>> >> >> >>>>
>> >> >> >>>> In [22]: import scipy
>> >> >> >>>>
>> >> >> >>>> In [23]: scipy.__version__ #I have r6182
>> >> >> >>>> Out[23]: '0.8.0.dev'
>> >> >> >>>>
>> >> >> >>>> In [24]: import scipy.signal as signal
>> >> >> >>>>
>> >> >> >>>> In [25]: a = np.random.rand(100,100)
>> >> >> >>>>
>> >> >> >>>> In [26]: np.abs(signal.hilbert(a[-1]))
>> >> >> >>>> Out[26]:
>> >> >> >>>> array([ 0.57567681, 0.25918624, 0.50207097, 0.51834052,
>> >> >> >>>> 0.24293389,
>> >> >> >>>> 0.5779464 , 0.6515758 , 0.89973173, 1.00275444,
>> >> >> >>>> 0.37352935,
>> >> >> >>>> 0.62332717, 0.93599749, 0.40651376, 0.65088756,
>> >> >> >>>> 0.8332281
>> >> >> >>>> ,
>> >> >> >>>> 0.5770101 , 0.9288512 , 0.46671906, 0.41536055,
>> >> >> >>>> 0.71418068,
>> >> >> >>>> 0.81250913, 0.07652627, 0.72939072, 0.26755626,
>> >> >> >>>> 0.36396146,
>> >> >> >>>> 0.59725999, 1.02264694, 0.41227986, 0.98122853,
>> >> >> >>>> 0.71906675,
>> >> >> >>>> 0.58582611, 0.77288117, 0.3217015 , 0.65261394,
>> >> >> >>>> 0.11947618,
>> >> >> >>>> 0.75632703, 0.43432935, 0.52182485, 1.0277177 ,
>> >> >> >>>> 1.01104986,
>> >> >> >>>> 0.3023265 , 0.6024772 , 0.69257548, 0.55418735,
>> >> >> >>>> 0.46259052,
>> >> >> >>>> 0.25832231, 0.38278355, 0.45508532, 0.26215872,
>> >> >> >>>> 0.34207947,
>> >> >> >>>> 0.80704729, 0.80755477, 0.95317178, 0.97458885,
>> >> >> >>>> 0.58762294,
>> >> >> >>>> 0.82540618, 0.62005585, 0.82494646, 1.04221293,
>> >> >> >>>> 0.14983027,
>> >> >> >>>> 1.01571579, 0.99381328, 0.24158714, 0.84256569,
>> >> >> >>>> 0.53418924,
>> >> >> >>>> 0.24067628, 0.90489883, 1.02217747, 0.34988034,
>> >> >> >>>> 0.5310065
>> >> >> >>>> ,
>> >> >> >>>> 0.48135002, 1.03020269, 0.6013679 , 0.46062485,
>> >> >> >>>> 0.3918485
>> >> >> >>>> ,
>> >> >> >>>> 0.21554545, 0.31704519, 0.04868385, 0.1787766 ,
>> >> >> >>>> 0.37361852,
>> >> >> >>>> 0.21977912, 0.7649772 , 0.77867281, 0.37684278,
>> >> >> >>>> 0.64432638,
>> >> >> >>>> 0.77494951, 0.87106309, 0.77611484, 0.52666801,
>> >> >> >>>> 0.88683667,
>> >> >> >>>> 0.69164967, 0.98618191, 0.84811375, 0.35934198,
>> >> >> >>>> 0.32650478,
>> >> >> >>>> 0.1752677 , 0.60574454, 0.5109132 , 0.52332287,
>> >> >> >>>> 0.99777805])
>> >> >> >>>>
>> >> >> >>>> In [27]: np.abs(signal.hilbert(a))[-1]
>> >> >> >>>> Out[27]:
>> >> >> >>>> array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
>> >> >> >>>> 0.,
>> >> >> >>>> 0.,
>> >> >> >>>> 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
>> >> >> >>>> 0.,
>> >> >> >>>> 0.,
>> >> >> >>>> 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
>> >> >> >>>> 0.,
>> >> >> >>>> 0.,
>> >> >> >>>> 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
>> >> >> >>>> 0.,
>> >> >> >>>> 0.,
>> >> >> >>>> 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
>> >> >> >>>> 0.,
>> >> >> >>>> 0.,
>> >> >> >>>> 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
>> >> >> >>>> 0.,
>> >> >> >>>> 0.,
>> >> >> >>>> 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
>> >> >> >>>> 0.,
>> >> >> >>>> 0.,
>> >> >> >>>> 0., 0., 0., 0., 0., 0., 0., 0., 0.])
>> >> >> >>>>
>> >> >> >>>>
>> >> >> >>>>
>> >> >> >>>>
>> >> >> >>>>
>> >> >> >>>> ----------------------------------------------------------------------
>> >> >> >>>>
>> >> >> >>>> I was expecting both of these to have the same values - am I
>> >> >> >>>> missing
>> >> >> >>>> something?
>> >> >> >>>>
>> >> >> >>>> I think that the following solves this issue, but now I am not
>> >> >> >>>> that
>> >> >> >>>> sure
>> >> >> >>>> whether it does what it is supposed to do and I couldn't find a
>> >> >> >>>> test
>> >> >> >>>> for
>> >> >> >>>> this in test_signaltools.py. Does anyone know of a good
>> >> >> >>>> test-case
>> >> >> >>>> for
>> >> >> >>>> the
>> >> >> >>>> analytic signal, that I could create for this?
>> >> >> >>>>
>> >> >> >>>> Index: scipy/signal/signaltools.py
>> >> >> >>>>
>> >> >> >>>>
>> >> >> >>>> ===================================================================
>> >> >> >>>> --- scipy/signal/signaltools.py (revision 6182)
>> >> >> >>>> +++ scipy/signal/signaltools.py (working copy)
>> >> >> >>>> @@ -1062,13 +1062,13 @@
>> >> >> >>>> """
>> >> >> >>>> x = asarray(x)
>> >> >> >>>> if N is None:
>> >> >> >>>> - N = len(x)
>> >> >> >>>> + N = x.shape[-1]
>> >> >> >>>> if N <=0:
>> >> >> >>>> raise ValueError, "N must be positive."
>> >> >> >>>> if iscomplexobj(x):
>> >> >> >>>> print "Warning: imaginary part of x ignored."
>> >> >> >>>> x = real(x)
>> >> >> >>>> - Xf = fft(x,N,axis=0)
>> >> >> >>>> + Xf = fft(x,N,axis=-1)
>> >> >> >>>> h = zeros(N)
>> >> >> >>>> if N % 2 == 0:
>> >> >> >>>> h[0] = h[N/2] = 1
>> >> >> >>>> @@ -1078,7 +1078,7 @@
>> >> >> >>>> h[1:(N+1)/2] = 2
>> >> >> >>>>
>> >> >> >>>> if len(x.shape) > 1:
>> >> >> >>>> - h = h[:, newaxis]
>> >> >> >>>> + h = h[newaxis,:]
>> >> >> >>>> x = ifft(Xf*h)
>> >> >> >>>> return x
>> >> >> >>>
>> >> >> >>> I think your change would break the currently advertised
>> >> >> >>> behavior,
>> >> >> >>> axis=0 (The transformation is done along the first axis)
>> >> >> >>>
>> >> >> >>> but fft and ifft have default axis=-1
>> >> >> >>>
>> >> >> >>> fft in hilbert uses axis=0 as in docstring
>> >> >> >>> but ifft uses default axis=-1
>> >> >> >>>
>> >> >> >>> so, I would think the fix should be x = ifft(Xf*h, axis=0)
>> >> >> >>>
>> >> >> >>> But as it currently looks like the axis argument doesn't work
>> >> >> >>> anyway,
>> >> >> >>> there wouldn't be much breakage if the axis would be included as
>> >> >> >>> an
>> >> >> >>> argument and default to -1.
>> >> >> >>> However, I don't know what the "standard" for scipy.signal is
>> >> >> >>> for
>> >> >> >>> default axis.
>> >> >> >>>
>> >> >> >>> Josef
>> >> >> >>
>> >> >> >> after adding axis to ifft:
>> >> >> >>>>> print hilbert(aa).real
>> >> >> >> [[ 0.82584851 0.15215031 0.14767381]
>> >> >> >> [ 0.95021675 0.16803995 0.43562964]
>> >> >> >> [ 0.13033881 0.06198952 0.70729614]
>> >> >> >> [ 0.69409563 0.06962778 0.72552601]
>> >> >> >> [ 0.34297612 0.50579001 0.86463304]
>> >> >> >> [ 0.28355261 0.21626889 0.85165102]
>> >> >> >> [ 0.49481491 0.21290645 0.71416814]
>> >> >> >> [ 0.2645843 0.95783096 0.77514016]
>> >> >> >> [ 0.38735994 0.14274852 0.56344808]
>> >> >> >> [ 0.88084015 0.39879649 0.64949951]]
>> >> >> >>>>> print hilbert(aa[:,:1]).real
>> >> >> >> [[ 0.82584851]
>> >> >> >> [ 0.95021675]
>> >> >> >> [ 0.13033881]
>> >> >> >> [ 0.69409563]
>> >> >> >> [ 0.34297612]
>> >> >> >> [ 0.28355261]
>> >> >> >> [ 0.49481491]
>> >> >> >> [ 0.2645843 ]
>> >> >> >> [ 0.38735994]
>> >> >> >> [ 0.88084015]]
>> >> >> >>
>> >> >> >> but it treats a 1d array as row vector and transforms along zero
>> >> >> >> axis
>> >> >> >> of length 1, and not along the length of the array.
>> >> >> >> so another fix to handle 1d arrays correctly should be done
>> >> >> >>
>> >> >> >>>>> print hilbert(aa[:,1]).real
>> >> >> >> [ 0.15215031 0.16803995 0.06198952 0.06962778 0.50579001
>> >> >> >> 0.21626889
>> >> >> >> 0.21290645 0.95783096 0.14274852 0.39879649]
>> >> >> >>>>> aa[:,1]
>> >> >> >> array([ 0.15215031, 0.16803995, 0.06198952, 0.06962778,
>> >> >> >> 0.50579001,
>> >> >> >> 0.21626889, 0.21290645, 0.95783096, 0.14274852,
>> >> >> >> 0.39879649])
>> >> >> >>>>>
>> >> >> >
>> >> >> > there's something wrong with my example, the real part is the same
>> >> >> > which confused me
>> >> >> >
>> >> >> > it works correctly with 1d
>> >> >> >
>> >> >> >>>> np.abs(hilbert(aa[:,0]))
>> >> >> > array([ 0.83251128, 1.04487091, 0.27702083, 0.69901499,
>> >> >> > 0.49170197,
>> >> >> > 0.31227114, 0.49505637, 0.26461488, 0.61385196,
>> >> >> > 0.90716272])
>> >> >> >
>> >> >> >>>> np.abs(hilbert(aa[:,:1])).T
>> >> >> > array([[ 0.83251128, 1.04487091, 0.27702083, 0.69901499,
>> >> >> > 0.49170197,
>> >> >> > 0.31227114, 0.49505637, 0.26461488, 0.61385196,
>> >> >> > 0.90716272]])
>> >> >> >
>> >> >> >>>> np.abs(hilbert(aa))[:,0]
>> >> >> > array([ 0.83251128, 1.04487091, 0.27702083, 0.69901499,
>> >> >> > 0.49170197,
>> >> >> > 0.31227114, 0.49505637, 0.26461488, 0.61385196,
>> >> >> > 0.90716272])
>> >> >> >
>> >> >> > besides reading the docstring, I don't know what hilbert is
>> >> >> > supposed
>> >> >> > to be good for.
>> >> >>
>> >> >> Would something like the function in the attachment do ?
>> >> >>
>> >> >>
>> >> >>
>> >> >> > Josef
>> >> >> >
>> >> >> >
>> >> >> >> Josef
>> >> >> >>
>> >> >> >>
>> >> >> >>>
>> >> >> >>>>
>> >> >> >>>>
>> >> >> >>>> Cheers,
>> >> >> >>>>
>> >> >> >>>> Ariel
>> >> >> >>>> --
>> >> >> >>>> Ariel Rokem
>> >> >> >>>> Helen Wills Neuroscience Institute
>> >> >> >>>> University of California, Berkeley
>> >> >> >>>> http://argentum.ucbso.berkeley.edu/ariel
>> >> >> >>>>
>> >> >> >>>> _______________________________________________
>> >> >> >>>> SciPy-Dev mailing list
>> >> >> >>>> SciPy-Dev@scipy.org
>> >> >> >>>> http://mail.scipy.org/mailman/listinfo/scipy-dev
>> >> >> >>>>
>> >> >> >>>>
>> >> >> >>>
>> >> >> >>
>> >> >> >
>> >> >>
>> >> >> _______________________________________________
>> >> >> SciPy-Dev mailing list
>> >> >> SciPy-Dev@scipy.org
>> >> >> http://mail.scipy.org/mailman/listinfo/scipy-dev
>> >> >>
>> >> >
>> >> >
>> >> >
>> >> > --
>> >> > Ariel Rokem
>> >> > Helen Wills Neuroscience Institute
>> >> > University of California, Berkeley
>> >> > http://argentum.ucbso.berkeley.edu/ariel
>> >> >
>> >> > _______________________________________________
>> >> > SciPy-Dev mailing list
>> >> > SciPy-Dev@scipy.org
>> >> > http://mail.scipy.org/mailman/listinfo/scipy-dev
>> >> >
>> >> >
>> >> _______________________________________________
>> >> SciPy-Dev mailing list
>> >> SciPy-Dev@scipy.org
>> >> http://mail.scipy.org/mailman/listinfo/scipy-dev
>> >
>> >
>> >
>> > --
>> > Ariel Rokem
>> > Helen Wills Neuroscience Institute
>> > University of California, Berkeley
>> > http://argentum.ucbso.berkeley.edu/ariel
>> >
>> > _______________________________________________
>> > SciPy-Dev mailing list
>> > SciPy-Dev@scipy.org
>> > http://mail.scipy.org/mailman/listinfo/scipy-dev
>> >
>> >
>> _______________________________________________
>> SciPy-Dev mailing list
>> SciPy-Dev@scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
>
>
> --
> Ariel Rokem
> Helen Wills Neuroscience Institute
> University of California, Berkeley
> http://argentum.ucbso.berkeley.edu/ariel
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev@scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
>
More information about the SciPy-Dev
mailing list