I should include the scipy response too I guess.
scipy.linalg.eig(STIFM, MASSM)
(array([ 3937.15984097+0.j, 3937.15984097+0.j, 3937.15984097+0.j,
3923.07692308+0.j, 3923.07692308+0.j, 7846.15384615+0.j]),
array([[ 1., 0., 0., 0., 0., 0.],
[ 0., 1., 0., 0., 0., 0.],
[ 0., 0., 1., 0., 0., 0.],
[ 0., 0., 0., 1., 0., 0.],
[ 0., 0., 0., 0., 1., 0.],
[ 0., 0., 0., 0., 0., 1.]]))
On Tue, Dec 20, 2011 at 9:14 PM, Fahreddın Basegmez <mangab...@gmail.com>wrote:
> If I can get the same response as Matlab I would be all set.
>
>
> Octave results
>
> >> STIFM
> STIFM =
>
> Diagonal Matrix
>
> 1020 0 0 0 0 0
> 0 1020 0 0 0 0
> 0 0 1020 0 0 0
> 0 0 0 102000 0 0
> 0 0 0 0 102000 0
> 0 0 0 0 0 204000
>
> >> MASSM
> MASSM =
>
> Diagonal Matrix
>
> 0.25907 0 0 0 0 0
> 0 0.25907 0 0 0 0
> 0 0 0.25907 0 0 0
> 0 0 0 26.00000 0 0
> 0 0 0 0 26.00000 0
> 0 0 0 0 0 26.00000
>
> >> [a, b] = eig(STIFM, MASSM)
> a =
>
> 0.00000 0.00000 0.00000 1.96468 0.00000 0.00000
> 0.00000 0.00000 0.00000 0.00000 1.96468 0.00000
> 0.00000 0.00000 1.96468 0.00000 0.00000 0.00000
> 0.19612 0.00000 0.00000 0.00000 0.00000 0.00000
> 0.00000 0.19612 0.00000 0.00000 0.00000 0.00000
> 0.00000 0.00000 0.00000 0.00000 0.00000 0.19612
>
> b =
>
> Diagonal Matrix
>
> 3923.1 0 0 0 0 0
> 0 3923.1 0 0 0 0
> 0 0 3937.2 0 0 0
> 0 0 0 3937.2 0 0
> 0 0 0 0 3937.2 0
> 0 0 0 0 0 7846.2
>
>
> Numpy Results
>
> >>> STIFM
> array([[ 1020., 0., 0., 0., 0., 0.],
> [ 0., 1020., 0., 0., 0., 0.],
> [ 0., 0., 1020., 0., 0., 0.],
> [ 0., 0., 0., 102000., 0., 0.],
> [ 0., 0., 0., 0., 102000., 0.],
> [ 0., 0., 0., 0., 0., 204000.]])
>
> >>> MASSM
>
> array([[ 0.25907, 0. , 0. , 0. , 0. , 0. ],
> [ 0. , 0.25907, 0. , 0. , 0. , 0. ],
> [ 0. , 0. , 0.25907, 0. , 0. , 0. ],
> [ 0. , 0. , 0. , 26. , 0. , 0. ],
> [ 0. , 0. , 0. , 0. , 26. , 0. ],
> [ 0. , 0. , 0. , 0. , 0. , 26. ]])
>
> >>> a, b = linalg.eig(dot( linalg.pinv(MASSM), STIFM))
>
> >>> a
>
> array([ 3937.15984097, 3937.15984097, 3937.15984097, 3923.07692308,
> 3923.07692308, 7846.15384615])
>
> >>> b
>
> array([[ 1., 0., 0., 0., 0., 0.],
> [ 0., 1., 0., 0., 0., 0.],
> [ 0., 0., 1., 0., 0., 0.],
> [ 0., 0., 0., 1., 0., 0.],
> [ 0., 0., 0., 0., 1., 0.],
> [ 0., 0., 0., 0., 0., 1.]])
>
> On Tue, Dec 20, 2011 at 8:40 PM, Olivier Delalleau <sh...@keba.be> wrote:
>
>> Hmm... ok ;) (sorry, I can't follow you there)
>>
>> Anyway, what kind of non-normalization are you after? I looked at the doc
>> for Matlab and it just says eigenvectors are not normalized, without
>> additional details... so it looks like it could be anything.
>>
>>
>> -=- Olivier
>>
>> 2011/12/20 Fahreddın Basegmez <mangab...@gmail.com>
>>
>>> I am computing normal-mode frequency response of a mass-spring system.
>>> The algorithm I am using requires it.
>>>
>>> On Tue, Dec 20, 2011 at 8:10 PM, Olivier Delalleau <sh...@keba.be>wrote:
>>>
>>>> I'm probably missing something, but... Why would you want
>>>> non-normalized eigenvectors?
>>>>
>>>> -=- Olivier
>>>>
>>>>
>>>> 2011/12/20 Fahreddın Basegmez <mangab...@gmail.com>
>>>>
>>>>> Howdy,
>>>>>
>>>>> Is it possible to get non-normalized eigenvectors from
>>>>> scipy.linalg.eig(a, b)? Preferably just by using numpy.
>>>>>
>>>>> BTW, Matlab/Octave provides this with its eig(a, b) function but I
>>>>> would like to use numpy for obvious reasons.
>>>>>
>>>>> Regards,
>>>>>
>>>>> Fahri
>>>>>
>>>>
>> _______________________________________________
>> NumPy-Discussion mailing list
>> NumPy-Discussion@scipy.org
>> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>>
>>
>
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