[SciPy-user] Fitting with global parameters optimize.leastsq
elcorto at gmx.net
Tue Nov 7 06:37:55 CST 2006
Iain Day wrote:
>>> I'm messing about with scipy.reshape at the moment and sort of getting
>>> somewhere. The residuals is now a 1D array, as is t and y. However, when
>>> I use leastsq I get the following error:
>>> File "/sw/lib/python2.4/site-packages/scipy/optimize/minpack.py", line
>>> 229, in leastsq
>>> retval =
>>> minpack.error: Result from function call is not a proper array of floats.
>> Could you post an example code producing this error?
> I hope the line wrapping doesn't hamper things too much. The model
> function is working fine, and the simple fit of the model to one trace
> works fine, its the extension to simutaneous fitting of several traces
> thats got me stumped.
> Thanks for your help,
> import scipy, scipy.optimize, scipy.io
> rawdata = scipy.io.read_array(open(datadir + filename + ".csv", "rb"),
> data_points = rawdata[:, 0]
> raw_signals = rawdata[:, 1:]
> # These are the guesses. I want to use the same fitted value to
> # B across all traces, it should be a common parameter.
> A = [40.0, 40.0, 40.0, 40.0, 40.0, 40.0]
> B = 0.3
> C = [45.0, 45.0, 45.0, 3.0, 3.0, 15.0]
> p0 = scipy.r_[A, B, C]
> fix0 = [x, y, z]
> expdata = scipy.reshape(raw_signals, (1, len(A)*len(data_points)))
> p1, success = scipy.optimize.leastsq(residuals, p0, args=(fix0,
> data_points, expdata))
> def residuals(varpars, fixpars, points, expdata):
> ntraces = (len(varpars) - 1) / 2
> A = varpars[:ntraces]
> B = varpars[ntraces]
> C = varpars[ntraces+1:]
> err = scipy.zeros((len(points), ntraces), dtype=float)
> raw_data = scipy.zeros((len(points), ntraces), dtype=float)
> raw_data = scipy.reshape(expdata, (len(points), ntraces))
> for i in range(ntraces):
> err[:, i] = raw_data[:, i] - model([A[i], B, C[i]], fixpars,
> err = scipy.reshape(err, (1, ntraces*len(points)))
> return err
What's x, y, z and def model(...) is missing :). I thought more of a
copy-paste-and-run example (e.g. with rawdata = numpy.random.randn(100,
7) where 100 == len(data_points)) that produces the error :-)
1) The error comes from the shape of err in your residuals(). leastsq
expects an array of shape (<number>,), not (1, <number>) (i.e.
len(err.shape) = 1, not 2.
You would have to do reshape(err, (ntraces*len(points),)).
I get this error when I replace
def residuals(x, t, y):
return y - model(t, x)
def residuals(x, t, y):
err = y - model(t, x)
err = scipy.reshape(err, (1, len(err)))
print "### err.shape:", err.shape
2) You're trying to fit all data sets (t == data_points, y ==
raw_signals[:, i]) at once and I guess that won't work. You have to loop
over A (or C or raw_signals[:, i]) building one data set and one initial
guess (x0 = numpy.array([A[i], B, C[i]])) per loop.
Random number generation is the art of producing pure gibberish as
quickly as possible.
More information about the SciPy-user