[SciPy-User] bug in signal.lsim2
Warren Weckesser
warren.weckesser@enthought....
Tue Feb 9 12:17:33 CST 2010
josef.pktd@gmail.com wrote:
> On Wed, Feb 3, 2010 at 9:00 AM, Ryan Krauss <ryanlists@gmail.com> wrote:
>
>> FYI, I am quite happy with passing in an hmax value. I basically
>> copied and pasted lsim2 from signal.ltisys and adapted it just a
>> little to make it a method of my derived class. Then I added the hmas
>> kwarg that gets passed to odeint.
>>
>> Is there any reason not to allow the user to pass in a kwargs to lsim2
>> that gets passed to odeint?
>>
>
> I don't see a reason why we cannot add a **kwargs, it should be
> completely backwards compatible.
> Can you file a ticket and add your adjusted version or a patch? And
> even better, add your original example as a test case?
>
>
Josef,
I just created ticket #1112 for this. Unless Ryan wants to adapt his
change to lsim2, I can make a patch this week to implement the enhancement.
Warren
> Josef
>
>
>
>> On Fri, Jan 29, 2010 at 6:44 AM, Ryan Krauss <ryanlists@gmail.com> wrote:
>>
>>> Thanks to Warren and Josef for their time and thoughts. I feel like I
>>> now understand the underlying problem and have some good options to
>>> solve my short term issues (I assigned the project last night and they
>>> need to be able to start working on it immediately). I actually use a
>>> TransferFunction class that derives from ltisys. I could override its
>>> lsim2 method to try out some of these solutions quickly and fairly
>>> easily.
>>>
>>> Ryan
>>>
>>> On Thu, Jan 28, 2010 at 10:00 PM, <josef.pktd@gmail.com> wrote:
>>>
>>>> On Thu, Jan 28, 2010 at 10:33 PM, Warren Weckesser
>>>> <warren.weckesser@enthought.com> wrote:
>>>>
>>>>> josef.pktd@gmail.com wrote:
>>>>>
>>>>>> On Thu, Jan 28, 2010 at 8:50 PM, Warren Weckesser
>>>>>> <warren.weckesser@enthought.com> wrote:
>>>>>>
>>>>>>
>>>>>>> Ryan,
>>>>>>>
>>>>>>> The problem is that the ODE solver used by lsim2 is too good. :)
>>>>>>>
>>>>>>> It uses scipy.integrate.odeint, which in turn uses the Fortran library
>>>>>>> LSODA. Like any good solver, LSODA is an adaptive solver--it adjusts its
>>>>>>> step size to be as large as possible while keeping estimates of the error
>>>>>>> bounded. For the problem you are solving, with initial condition 0, the
>>>>>>> exact solution is initially exactly 0. This is such a nice smooth solution
>>>>>>> that the solver's step size quickly grows--so big, in fact, that it skips
>>>>>>> right over your pulse and never sees it.
>>>>>>>
>>>>>>> So how does it create all those intermediate points at the requested time
>>>>>>> values? It uses interpolation between the steps that it computed to create
>>>>>>> the solution values at the times that you requested. So using a finer grid
>>>>>>> of time values won't help. (If lsim2 gave you a hook into the parameters
>>>>>>> passed to odeint, you could set odeint's 'hmax' to a value smaller than your
>>>>>>> pulse width, which would force the solver to see the pulse. But there is no
>>>>>>> way to set that parameter from lsim2.)
>>>>>>>
>>>>>>>
>>>>>> It's something what I suspected. I don't know much about odeint, but
>>>>>> do you think it would be useful to let lsim2 pass through some
>>>>>> parameters to odeint?
>>>>>>
>>>>>>
>>>>>>
>>>>> Sounds useful to me. A simple implementation is an optional keyword
>>>>> argument that is a dict of odeint arguments. But this would almost
>>>>> certainly break if lsim2 were ever reimplemented with a different
>>>>> solver. So perhaps it should allow a common set of ODE solver
>>>>> parameters (e.g. absolute and relative error tolerances, max and min
>>>>> step sizes, others?).
>>>>>
>>>>> Perhaps this should wait until after the ODE solver redesign that is
>>>>> occasionally discussed:
>>>>> http://projects.scipy.org/scipy/wiki/OdeintRedesign
>>>>> Then the solver itself could be an optional argument to lsim2.
>>>>>
>>>> I was just thinking of adding to the argument list a **kwds argument
>>>> that is directly passed on to whatever ODE solver is used. This should
>>>> be pretty flexible for any changes and be backwards compatible.
>>>>
>>>> I've seen and used it in a similar way for calls to optimization
>>>> routines, e.g. also optimize.curve_fit, does it. What are actually
>>>> valid keywords would depend on which function is called.
>>>>
>>>> (But I'm not a user of lsim, I'm just stealing some ideas from lti and
>>>> friends for time series analysis.)
>>>>
>>>> Josef
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>> Warren
>>>>>
>>>>>
>>>>>> Josef
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>> The basic problem is you are passing in a discontinuous function to a solver
>>>>>>> that expects a smooth function. A better way to solve this problem is to
>>>>>>> explicitly account for the discontinuity. One possibility is the attached
>>>>>>> script.
>>>>>>>
>>>>>>> This is an excellent "learning opportunity" for your students on the hazards
>>>>>>> of numerical computing!
>>>>>>>
>>>>>>> Warren
>>>>>>>
>>>>>>>
>>>>>>> Ryan Krauss wrote:
>>>>>>>
>>>>>>>
>>>>>>>> I believe I have discovered a bug in signal.lsim2. I believe the
>>>>>>>> short attached script illustrates the problem. I was trying to
>>>>>>>> predict the response of a transfer function with a pure integrator:
>>>>>>>>
>>>>>>>> g
>>>>>>>> G = -------------
>>>>>>>> s(s+p)
>>>>>>>>
>>>>>>>> to a finite width pulse. lsim2 seems to handle the step response just
>>>>>>>> fine, but says that the pulse response is exactly 0.0 for the entire
>>>>>>>> time of the simulation. Obviously, this isn't the right answer.
>>>>>>>>
>>>>>>>> I am running scipy 0.7.0 and numpy 1.2.1 on Ubuntu 9.04, but I also
>>>>>>>> have the same problem on Windows running 0.7.1 and 1.4.0.
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>>
>>>>>>>> Ryan
>>>>>>>> ------------------------------------------------------------------------
>>>>>>>>
>>>>>>>> _______________________________________________
>>>>>>>> SciPy-User mailing list
>>>>>>>> SciPy-User@scipy.org
>>>>>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>>>>>>
>>>>>>>>
>>>>>>> from pylab import *
>>>>>>> from scipy import signal
>>>>>>>
>>>>>>>
>>>>>>> g = 100.0
>>>>>>> p = 15.0
>>>>>>> G = signal.ltisys.lti(g, [1,p,0])
>>>>>>>
>>>>>>> t = arange(0, 1.0, 0.002)
>>>>>>> N = len(t)
>>>>>>>
>>>>>>> # u for the whole interval (not used in lsim2, only for plotting later).
>>>>>>> amp = 50.0
>>>>>>> u = zeros(N)
>>>>>>> k1 = 50
>>>>>>> k2 = 100
>>>>>>> u[k1:k2] = amp
>>>>>>>
>>>>>>> # Create input functions for each smooth interval. (This could be simpler,
>>>>>>> since u
>>>>>>> # is constant on each interval.)
>>>>>>> a = float(k1)/N
>>>>>>> b = float(k2)/N
>>>>>>> T1 = linspace(0, a, 201)
>>>>>>> u1 = zeros_like(T1)
>>>>>>> T2 = linspace(a, b, 201)
>>>>>>> u2 = amp*ones_like(T2)
>>>>>>> T3 = linspace(b, 1.0, 201)
>>>>>>> u3 = zeros_like(T3)
>>>>>>>
>>>>>>> # Solve on each interval; use the final value of one solution as the
>>>>>>> starting
>>>>>>> # point of the next solution.
>>>>>>> # (We could skip the first calculation, since we know the solution will be
>>>>>>> 0.)
>>>>>>> (t1, y1, x1) = signal.lsim2(G,u1,T1)
>>>>>>> (t2, y2, x2) = signal.lsim2(G, u2, T2, X0=x1[-1])
>>>>>>> (t3, y3, x3) = signal.lsim2(G, u3, T3, X0=x2[-1])
>>>>>>>
>>>>>>> figure(1)
>>>>>>> clf()
>>>>>>> plot(t, u, 'k', linewidth=3)
>>>>>>> plot(t1, y1, 'y', linewidth=3)
>>>>>>> plot(t2, y2, 'b', linewidth=3)
>>>>>>> plot(t3, y3, 'g', linewidth=3)
>>>>>>>
>>>>>>> show()
>>>>>>>
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>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
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