[SciPy-User] leastsq error

[Matt Newville]

Hi Stephen,

On Sun, Oct 28, 2012 at 8:02 PM, stephen lukacs <sjlukacs at gmail.com> wrote:
> hello one and all,
>
> i am having a terrible time with optimize.leastsq, even fitting a line, so
> please help.  here is my python code and error
>
>>>> import numpy
>>>> from scipy import optimize
>>>> def expr_conductance(x, a, b, c):
> ...     return a*0 + b*x + c
> ...
>>>> def residual_conductance(p,x,y):
> ...     a, b, c = p
> ...     return y - expr_conductance(x, a, b, c)
> ...
>>>> x = [0.99771057137610752, 0.49976145827781415, 0.24821831884394957,
>>>> 0.12480215949109599, 0.06315070141095365, 0.03065779901355976,
>>>> 0.015669312142317458, 0.0078799613766362755, 0.0039027338918740067]
>>>> len(x)
> 9
>>>> y = [2.9954211427522148, 1.9995229165556283, 1.496436637687899,
>>>> 1.2496043189821919, 1.1263014028219074, 1.0613155980271196,
>>>> 1.031338624284635, 1.0157599227532725, 1.007805467783748]
>>>> len(y)
> 9
>>>> [a, b, c], conv = optimize.leastsq(residual_conductance, [0,10000.,5.],
>>>> args = (x,y))
> Traceback (most recent call last):
>   File "<stdin>", line 1, in <module>
>   File "/usr/lib/python2.6/site-packages/scipy/optimize/minpack.py", line
> 276, in leastsq
>     m = _check_func('leastsq', 'func', func, x0, args, n)[0]
>   File "/usr/lib/python2.6/site-packages/scipy/optimize/minpack.py", line
> 13, in _check_func
>     res = atleast_1d(thefunc(*((x0[:numinputs],) + args)))
>   File "<stdin>", line 3, in residual_conductance
> ValueError: operands could not be broadcast together with shapes (9) (90000)
>>>>
>
>  i have tried many permutations and looked all over for syntax, but i can
> not find why this error is there or how to deal with it.  i have scipy
> 0.10.1 and numpy 1.6.1 under python 2.6.6 on a centos 6.3 system.
> ultimately i want to put make the fit function a*numpy.power(x,(3/2))+b*x+c,
> which is a bit nonlinear but at this point i can't even get a line to fit.
>
> thank you in advance and have a great day.  lucas
>
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Try making your x and y numpy arrays instead of python lists.

--Matt