python numpy.convolve 求解从 0 到 t 的卷积积分，而不是 -t 到 t

Question

I have a convolution integral of the type:

To solve this integral numerically, I would like to use numpy.convolve() . Now, as you can see in the online help , the convolution is formally done from -infinity to +infinity meaning that the arrays are moved along each other completely for evaluation - which is not what I need. I obviously need to be sure to pick the correct part of the convolution - can you confirm that this is the right way to do it or alternatively tell me how to do it right and (maybe even more important) why?

res = np.convolve(J_t, dF, mode="full")[:len(dF)]

J_t is an analytical function and I can evaluate as many points as I need, dF are derivatives of measurement data. for this attempt I choose len(J_t) = len(dF) because from my understanding I do not need more.

Thank you for your thoughts, as always, I appreciate your help!

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Background information (for those who might be interested)

These type of integrals can be used to evaluate viscoelastic behaviour of bodies (or the response of an electric circuit during change of voltage, if you feel more familiar on this topic). For viscoelasticity, J(t) is the creep compliance function and F(t) can be the deviatoric strains over time, then this integral would yield the deviatoric stresses. If you now eg have a J(t) of the form:

J_t = lambda p, t: p[0] + p[1]*N.exp(-t/p[2])

with p = [J_elastic, J_viscous, tau] this would be the "famous" standard linear solid . The integral limits are the start of the measurement t_0 = 0 and the moment of interest, t.

Answer 1

To get it right, I have chosen the following two functions:

a(t) = t
b(t) = t**2

It is easy to do the math and find that their "convolution" as defined in your case, takes on the values:

c(t) = t**4 / 12

So lets try them out:

>>> delta = 0.001
>>> t = np.arange(1000) * delta
>>> a = t
>>> b = t**2
>>> c = np.convolve(a, b) * delta
>>> d = t**4 / 12
>>> plt.plot(np.arange(len(c)) * delta, c)
[<matplotlib.lines.Line2D object at 0x00000000025C37B8>]
>>> plt.plot(t[::50], d[::50], 'o')
[<matplotlib.lines.Line2D object at 0x000000000637AB38>]
>>> plt.show()

So by doing the above, if both your a and b have n elements, you get the right convolution values in the first n elements of c .

Not sure if the following explanation will make any sense, but here it goes... If you think of convolution as mirroring one of the functions along the y-axis, then sliding it along the x axis and computing the integral of the product at each point, it is easy to see how, since outside of the area of definition numpy takes them as if padded with zeros, you are effectively setting an integration interval from 0 to t, since the first function is zero below zero, and the second is zero above t, since it originally was zero below zero, but has been mirrored and moved t to the right.

Answer 2

I was tackling this same problem and solved it using a highly inefficient but functionally correct algorithm:

def Jfunk(inz,t):

    c0 = inz[0]
    c1 = inz[1]
    c2 = inz[2]

    J = c0 - c1*np.exp(-t/c2)

    return J

def SLS_funk(inz, t, dl_dt):

    boltz_int = np.empty(shape=(0,))

    for i,v in enumerate(t, start=1):

        t_int = t[0:i]

        Jarg = v - t[0:i]

        J_int = Jfunk(inz,Jarg)

        dl_dt_int = dl_dt[0:i]

        inter_grand = np.multiply(J_int, dl_dt_int)

        boltz_int = np.append(boltz_int, simps (inter_grand, x=t_int) )

    return boltz_int

Thanks to this question and its answers, I was able to implement a much better solution based on the numpy convolution function suggested above. In case the OP was curious I did a time comparison of the two methods.

For an SLS (three parameter J function) with 20,000 time points:

Using Numpy convolution: ~0.1 seconds

Using Brute Force method: ~7.2 seconds

Answer 3

If if helps to get a feeling for the alignment, try convolving a pair of impulses. With matplotlib (using ipython --pylab ):

In [1]: a = numpy.zeros(20)
In [2]: b = numpy.zeros(20)
In [3]: a[0] = 1
In [4]: b[0] = 1
In [5]: c = numpy.convolve(a, b, mode='full')
In [6]: plot(c)

You can see from the resultant plot that the first sample in c corresponds to the first position of overlap. In this case, only the first samples of a and b overlap. All the rest are floating in undefined space. numpy.convolve effectively replaces this undefined space with zeros, which you can see if you set a second non-zero value:

In [9]: b[1] = 1
In [10]: plot(numpy.convolve(a, b, mode='full'))

In this case, the first value of the plot is 1, as before (showing that the second value of b is not contributing at all).

Answer 4

I have been struggling with similar question for past 2 days. The OP may have moved on, but I am still presenting my analysis here. Following two sources helped me:

1. Discussion on stackoverflow
2. These notes

I will consider time-series data defined on the same time series starting from time. Let the two series be A and B . Their (continuous) convolution is

Substitutingwith in the above equation we get what np.convolve(A,B) returns:

What you want is

Again making the same substitution, we get

which is same asabove because A for negative indices is extrapolated to zero and for i > ( j + m ) B[j - i + m] is zero.

If you look at the notes cited above, you can figure out thatcorresponds to timefor our time series. The next value in the list will correspond to and so on. Therefore, the correct answer will be

is equal to np.convolve(A,B)[0:M] , where M = len(A) = len(B) .

Here keep in mind that M*dt = T , where T is the last element of time array.

Disclaimer: I am not a programmer, mathematician or an engineer. I had to use convolution somewhere and have derived these conclusions from my own struggle with the problem. I will be happy to cite any book which has this analysis if someone can point it out.