I don't think I can do that. I can go to the normalized results but not the other way.<div><br><br><div class="gmail_quote">On Tue, Dec 20, 2011 at 9:45 PM, Olivier Delalleau <span dir="ltr"><<a href="mailto:shish@keba.be" target="_blank">shish@keba.be</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Hmm, sorry, I don't see any obvious logic that would explain how Octave obtains this result, although of course there is probably some logic...<br>
<br>Anyway, since you seem to know what you want, can't you obtain the same result by doing whatever un-normalizing operation you are after?<div><div><br>
<br>-=- Olivier<br><br><div class="gmail_quote">2011/12/20 Fahreddın Basegmez <span dir="ltr"><<a href="mailto:mangabasi@gmail.com" target="_blank">mangabasi@gmail.com</a>></span><br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
I should include the scipy response too I guess.<div><br></div><div><br></div><div><div>scipy.linalg.eig(STIFM, MASSM)</div><div>(array([ 3937.15984097+0.j, 3937.15984097+0.j, 3937.15984097+0.j,</div><div> 3923.07692308+0.j, 3923.07692308+0.j, 7846.15384615+0.j]), array([[ 1., 0., 0., 0., 0., 0.],</div>
<div>
<div> [ 0., 1., 0., 0., 0., 0.],</div><div> [ 0., 0., 1., 0., 0., 0.],</div><div> [ 0., 0., 0., 1., 0., 0.],</div><div> [ 0., 0., 0., 0., 1., 0.],</div></div><div> [ 0., 0., 0., 0., 0., 1.]]))</div>
<div><div>
<br><div class="gmail_quote">On Tue, Dec 20, 2011 at 9:14 PM, Fahreddın Basegmez <span dir="ltr"><<a href="mailto:mangabasi@gmail.com" target="_blank">mangabasi@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
If I can get the same response as Matlab I would be all set. <div><br></div><div><br></div><div><div>Octave results</div><div><br></div><div>>> STIFM</div><div>STIFM =</div><div><br></div>
<div>Diagonal Matrix</div><div><br></div><div> 1020 0 0 0 0 0</div><div> 0 1020 0 0 0 0</div><div> 0 0 1020 0 0 0</div>
<div> 0 0 0 102000 0 0</div><div> 0 0 0 0 102000 0</div><div> 0 0 0 0 0 204000</div><div>
<br></div><div>>> MASSM</div><div>MASSM =</div><div><br></div><div>Diagonal Matrix</div><div><br></div><div> 0.25907 0 0 0 0 0</div>
<div> 0 0.25907 0 0 0 0</div><div> 0 0 0.25907 0 0 0</div><div> 0 0 0 26.00000 0 0</div>
<div> 0 0 0 0 26.00000 0</div><div> 0 0 0 0 0 26.00000</div><div><br></div><div>>> [a, b] = eig(STIFM, MASSM)</div>
<div>a =</div><div><br></div><div> 0.00000 0.00000 0.00000 1.96468 0.00000 0.00000</div><div> 0.00000 0.00000 0.00000 0.00000 1.96468 0.00000</div><div> 0.00000 0.00000 1.96468 0.00000 0.00000 0.00000</div>
<div> 0.19612 0.00000 0.00000 0.00000 0.00000 0.00000</div><div> 0.00000 0.19612 0.00000 0.00000 0.00000 0.00000</div><div> 0.00000 0.00000 0.00000 0.00000 0.00000 0.19612</div>
<div><br></div><div>b =</div><div><br></div><div>Diagonal Matrix</div><div><br></div><div> 3923.1 0 0 0 0 0</div><div> 0 3923.1 0 0 0 0</div>
<div> 0 0 3937.2 0 0 0</div><div> 0 0 0 3937.2 0 0</div><div> 0 0 0 0 3937.2 0</div><div>
0 0 0 0 0 7846.2</div><div><br></div><div><br></div><div>Numpy Results</div><div><br></div><div><div>>>> STIFM</div><div>array([[ 1020., 0., 0., 0., 0., 0.],</div>
<div> [ 0., 1020., 0., 0., 0., 0.],</div><div> [ 0., 0., 1020., 0., 0., 0.],</div><div> [ 0., 0., 0., 102000., 0., 0.],</div>
<div> [ 0., 0., 0., 0., 102000., 0.],</div><div> [ 0., 0., 0., 0., 0., 204000.]])</div><div><br></div><div>>>> MASSM</div><div><br></div><div>
array([[ 0.25907, 0. , 0. , 0. , 0. , 0. ],</div><div> [ 0. , 0.25907, 0. , 0. , 0. , 0. ],</div><div> [ 0. , 0. , 0.25907, 0. , 0. , 0. ],</div>
<div> [ 0. , 0. , 0. , 26. , 0. , 0. ],</div><div> [ 0. , 0. , 0. , 0. , 26. , 0. ],</div><div> [ 0. , 0. , 0. , 0. , 0. , 26. ]])</div>
<div><br></div><div>>>> a, b = linalg.eig(dot( linalg.pinv(MASSM), STIFM))</div><div><br></div><div>>>> a</div><div><br></div><div>array([ 3937.15984097, 3937.15984097, 3937.15984097, 3923.07692308,</div>
<div> 3923.07692308, 7846.15384615])</div><div><br></div><div>>>> b</div><div><br></div><div>array([[ 1., 0., 0., 0., 0., 0.],</div><div> [ 0., 1., 0., 0., 0., 0.],</div><div> [ 0., 0., 1., 0., 0., 0.],</div>
<div> [ 0., 0., 0., 1., 0., 0.],</div><div> [ 0., 0., 0., 0., 1., 0.],</div><div> [ 0., 0., 0., 0., 0., 1.]])</div></div><br><div class="gmail_quote"><div><div>On Tue, Dec 20, 2011 at 8:40 PM, Olivier Delalleau <span dir="ltr"><<a href="mailto:shish@keba.be" target="_blank">shish@keba.be</a>></span> wrote:<br>
</div></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div>Hmm... ok ;) (sorry, I can't follow you there)<br><br>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.<div>
<div><br>
<br>-=- Olivier<br><br><div class="gmail_quote">2011/12/20 Fahreddın Basegmez <span dir="ltr"><<a href="mailto:mangabasi@gmail.com" target="_blank">mangabasi@gmail.com</a>></span><br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
I am computing normal-mode frequency response of a mass-spring system. The algorithm I am using requires it.<br><br><div class="gmail_quote"><div><div>On Tue, Dec 20, 2011 at 8:10 PM, Olivier Delalleau <span dir="ltr"><<a href="mailto:shish@keba.be" target="_blank">shish@keba.be</a>></span> wrote:<br>
</div></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div>I'm probably missing something, but... Why would you want non-normalized eigenvectors?<span><font color="#888888"><br>
<br>-=- Olivier</font></span><div><div><br><br><div class="gmail_quote">2011/12/20 Fahreddın Basegmez <span dir="ltr"><<a href="mailto:mangabasi@gmail.com" target="_blank">mangabasi@gmail.com</a>></span><br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Howdy,<div><br></div><div>Is it possible to get non-normalized eigenvectors from scipy.linalg.eig(a, b)? Preferably just by using numpy.</div>
<div><br></div><div>BTW, Matlab/Octave provides this with its eig(a, b) function but I would like to use numpy for obvious reasons.</div>
<div><br></div><div>Regards,</div><div><br></div><div>Fahri <br></div></blockquote></div>
</div></div></div></div></blockquote></div></blockquote></div>
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