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
________________________________
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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