[SciPy-User] bug in signal.lsim2

josef.pktd@gmai... josef.pktd@gmai...
Wed Feb 3 08:16:35 CST 2010


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


>
> 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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>>
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