Am 28.02.2013 um 00:33 schrieb MARK BAKER <[email protected]>:

> 
> yes your right Dimensional analysis..
> 
> if they have it covered why are they still debating whether there is 80% of 
> dark matter in the universe ???

Mark,

after finding the World Formular - how will you verify it?

> 
> -Mark
> 
> From: Joel Berger <[email protected]>
> To: MARK BAKER <[email protected]> 
> Cc: [email protected] 
> Sent: Wednesday, February 27, 2013 2:47 PM
> Subject: Re: [Perldl] radial power spectrum
> 
> Mark, I think you are just talking about unit transform, or dimensional 
> analysis? Am I correct? If this is the case, I think the astrophysicist have 
> that one covered :-)
> 
> Though of course I could be misunderstanding you.
> 
> Joel
> 
> On Wed, Feb 27, 2013 at 4:25 PM, MARK BAKER <[email protected]> wrote:
> 
> 
> That is really Fascinating, may be there might be a underlying representation 
> of 
> this tho not ordered (I think that is what you meant by Theoretical models)
> 
> I will do some googling  to see if I can perhaps find some commonly used
> units in astronomy, as well I will do some book search, It seems like 
> something like this, should be beneficial for both astronomy and molecular 
> physics, I will see if I can find a list of units to work with...
> 
> maybe this can help to find the dimension of Dark Matter...
> my guess is it would be a dimension integrated of  [mass]^-6 .. [mass]^-11
> 
> Cheers
> -Mark
> 
> From: Jarle Brinchmann <[email protected]>
> To: Craig DeForest <[email protected]> 
> Cc: MARK BAKER <[email protected]>; """[email protected]""" 
> <[email protected]> 
> Sent: Wednesday, February 27, 2013 1:47 PM
> 
> Subject: Re: [Perldl] radial power spectrum
> 
> To follow up on what Craig says, what you are describing, Mark, is indeed 
> something we do regularly in extra-galactic astronomy but the transformation 
> of image values has to go through a mapping which is ultimately based on 
> theoretical models. 
> 
> Thus if you have flux with an effective wavelength of l1 and l2, say, there 
> are functions that you can apply that can map
> 
>     G[ f(l1), f(l2)] -> Mass of stars
> 
> for instance. There are a number of technical problems with this, however, 
> not least that the function is often multi-valued - for a given x, y there 
> are a number of possible G(x, y).  These problems are usually even more 
> serious when you consider other possible physical quantities you might want 
> to extract from the images such as mean ages or metal content. However it is 
> a major area of research in astronomy and is indeed very valuable - if you 
> want an overview you can do worse than consult the very recent review article 
> on the subject: http://esoads.eso.org/abs/2013arXiv1301.7095C.
> 
> Other wavelengths than the optical provide other information but the general 
> idea is much the same although there are quantities that are more reliably 
> estimated from the data. And expanding the spectral dimension so that you do 
> not have several colours, but rather have a spectrum in each spatial pixel 
> provides even more of an improvement and optimally exploiting this dimension 
> is still work in progress I would say, at least for distant galaxies.
> 
>     Cheers,
>         Jarle.
> 
> 
> 
> On 27 Feb 2013, at 22:34, Craig DeForest wrote:
> 
> > Well, there are a lot of things different people have gotten from images - 
> > but, fundamentally, all that astronomers get from images is brightness 
> > versus arrival angle and wavelength. Spectral power density, in the context 
> > of an image, is a map of how much brightness exists at different spatial 
> > scales in the image -- i.e. if there are a lot of big, diffuse things, 
> > there will be a lot of spectral power at low spatial frequencies (big 
> > spatial scales), and not much at high spatial frequencies (small spatial 
> > scales).
> > 
> > You see astronomers talking about mass of this and mass of that, but those 
> > masses are inferred using methods that are far more sophisticated and 
> > specialized than PDL::Transform can accomplish.
> > 
> > 
> > 
> > On Feb 27, 2013, at 2:26 PM, MARK BAKER <[email protected]> wrote:
> > 
> >> Hey Craig,
> >> 
> >> [I'm not sure what you mean by, for example, "now we make a transformation 
> >> for Mass to any power".]
> >> 
> >> so based on the position of each individual pixel and it's color compared 
> >> to other pixels and there color...
> >> we should be able to map transformations of different Dimensions , yet to 
> >> do this simply we have to have
> >> known mapping transformations of known Dimensions, then we should be able 
> >> to derive 
> >> mapping transformations of any dimension.
> >>  
> >> This Idea would change the pixel colors and pixel position to highlight 
> >> different dimensional values.
> >> 
> >> if you can send me a list of commonly used Astronomical dimensions that 
> >> have been measured accurately 
> >> from a image , I should be able to show you  a better explanation 
> >> Mathematically
> >> 
> >> just send me a list of units like the "Spectral Power Density" used in  
> >> Astronomical transformations
> >> 
> >> 
> >> Cheers
> >> 
> >> -Mark
> >> 
> >> 
> >> From: Craig DeForest <[email protected]>
> >> To: MARK BAKER <[email protected]> 
> >> Cc: Craig DeForest <[email protected]>; John Lapeyre 
> >> <[email protected]>; ""[email protected]"" 
> >> <[email protected]> 
> >> Sent: Wednesday, February 27, 2013 1:04 PM
> >> Subject: Re: [Perldl] radial power spectrum
> >> 
> >> Mark, I'm not sure what you're getting at here.  The Transform module only 
> >> does coordinate transformations on data sets.  It modifies vectors or 
> >> images so that the components of the vector, or pixel indices of the 
> >> image, have a different geometry than they originally did.  
> >> 
> >> An image, for example, is a collection of values taken at a regular grid 
> >> of positions (X,Y): one pixel index is proportional to X position in the 
> >> image, and the other pixel index is proportional to Y position in the 
> >> image.  With Transform::map, you can resample the image so that the pixel 
> >> indices are proportional to some other parameter (like distance from a 
> >> particular point, or angle *around* that point).  
> >> 
> >> I'm not sure what you mean by, for example, "now we make a transformation 
> >> for Mass to any power".
> >> 
> >> Cheers,
> >> Craig
> >> 
> >> 
> >> On Feb 27, 2013, at 1:44 PM, MARK BAKER <[email protected]> wrote:
> >> 
> >>> 
> >>> you Might have hinted on to something very big here ...
> >>> I thought about this for a while , and here is what I have 
> >>> if you have a know dimension that can be found threw image processing
> >>> say  ([mass][length]^3[time]^-4[current]^-2)  and if you can find some 
> >>> other dimensions 
> >>> then you might be able to derive a image transform for each dimension 
> >>> 
> >>> as a example voltage/resistance = current  so now you have the I 
> >>> (current) dimension
> >>>  resistance * capacitance = time  so now we have our T (time) dimension 
> >>> speed of light / frequency = wave length  so now we have our L (length)) 
> >>> dimension (1/time = frequency)
> >>> and now  voltage * L^-2*T^3*I^1 = M so now we have our Mass dimension
> >>> 
> >>> now we make a transformation for Mass to any power 
> >>> now we make a transformation for Length to any power
> >>> now we make a transformation for Time to any power
> >>> now we make a transformation for current to any power
> >>> 
> >>> by mixing those dimensions now now we can process a value for any 
> >>> unit Dimension like the  Power spectral density = [Mass]*[Length]^2 * 
> >>> [Time]-2
> >>> 
> >>> with this Idea you can calculate all 194481 value in string theory of the 
> >>> image ...
> >>> 
> >>> if you can find a few different transformations and can send them to me
> >>> I would be happy to try to help build a multi-dimensional imaging engine 
> >>> ... 
> >>> 
> >>> Perfect Blessing's
> >>> -Mark
> >>> 
> >>> "sometimes I think perl is alive".
> >>> 
> >>> 
> >>> From: John Lapeyre <[email protected]>
> >>> To: Craig DeForest <[email protected]>; "[email protected]" 
> >>> <[email protected]> 
> >>> Sent: Saturday, February 23, 2013 1:26 PM
> >>> Subject: Re: [Perldl] radial power spectrum
> >>> 
> >>> 
> >>> Awesome. Thanks. Have fun!
> >>> 
> >>> On 02/23/2013 09:52 PM, Craig DeForest wrote:
> >>> > I fft rhem use PDL::Transform
> >>>      for the radial part. Periodic boundaries are your friend. Sorry
> >>>      for brief - on ski lift.
> >>> 
> >>> 
> >>>      >
> >>> 
> >>> 
> >>>      > (Mobile)
> >>> 
> >>> 
> >>>      >
> >>> 
> >>> 
> >>>      >
> >>> 
> >>> 
> >>>      > On Feb 23, 2013, at 11:40 AM, John Lapeyre
> >>>      
> >>> <[email protected]> wrote:
> >>> 
> >>>      >
> >>> 
> >>> 
> >>>      >> Greetings,
> >>> 
> >>> 
> >>>      >>
> >>> 
> >>> 
> >>>      >> I want to compute the power spectral density of an image,
> >>>      and then
> >>> 
> >>> 
> >>>      >> integrate over the azimuth to get a radial (in wavelengh)
> >>>      spectral
> >>> 
> >>> 
> >>>      >> density. I wonder if anyone has code to do this ? I am
> >>>      trying to cook
> >>> 
> >>> 
> >>>      >> up something with rvals, and whichND, but I don't want to
> >>>      waste time
> >>> 
> >>> 
> >>>      >> if it is already coded.
> >>> 
> >>> 
> >>>      >>
> >>> 
> >>> 
> >>>      >> Thanks,
> >>> 
> >>> 
> >>>      >> John
> >>> 
> >>> 
> >>>      >>
> >>> 
> >>> 
> >>>      >>
> >>> 
> >>> 
> >>>      >> _______________________________________________
> >>> 
> >>> 
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> >>>      >> 
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> >>> 
> >>>      >> 
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> >>> 
> >>>      >>
> >>> 
> >>> 
> >>> 
> >>> 
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> >> 
> >> 
> > 
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