With FFTW and MPI, given a two-dimensional array that is the transform of a real function, represented in complex space, is it possible to output the real-space array transposed?
For example, suppose there is a 2x4 array in real space. If the code calls fftw_mpi_plan_dft_r2c_2d, then it will output a 2x3 complex array. If the flag FFTW_MPI_TRANSPOSED_OUT is added, then the output is a 3x2 complex array, the transpose of the former array. I can easily produce this behavior.
My question: is it possible to go the other way? Starting with a 2x3 complex array that is the complex-space transform of a 2x4 real-space array, is it possible to use fftw_mpi_plan_dft_c2r_2d with suitable arguments to produce the transposed, 4x2 real-space array?
Note, this is in 2D. In 3D, everything works fine, indicating that in 2D it may have to do with the last dimension representing only half of the complex plane conflicting with FTTW's expectation of the layout of the complex transpose.
Related
I'm using Julia's FFT implementation to perform a 2D real FFT on a couple of arrays but I can't be sure of the order of the frequencies in the output. Consider the MWE
N=64
U = rand(Float64, N, N);
FFTW.set_num_threads(2)
prfor = plan_rfft(U, (1,2), flags=FFTW.MEASURE);
size(prfor*U)
The output is an array of size (33, 64).
Julia doesn't have a rfftfreq function like Numpy does, and the fact that Julia's output is different from Numpy's fft.rfftn default output makes me not want to use Numpy's default here. I read the documentation but it's not clear how the frequencies are organized just by reading that.
Is there anywhere that tells us the order of the frequencies?
I'm not sure what you are seeking exactly, but if you use DSP.jl, its util.jl file probably has what you may need:
https://github.com/JuliaDSP/DSP.jl/blob/master/src/util.jl
"""
rfftfreq(n, fs=1)
Return discrete fourier transform sample frequencies for use with
`rfft`. The returned Frequencies object is an AbstractVector
containing the frequency bin centers at every sample point. `fs`
is the sample rate of the input signal.
"""
As per the title, is the best way to calculate the n-dimensional cross product just using the determinant definition and using the LU Decomposition method of doing as such or could you guys suggest a better one?
Thanks
Edit: for clarity I mean http://en.wikipedia.org/wiki/Cross_product and not the Cartesian Product
Edit: It also seems that using the Leibniz Formula might help - though I don't know how that compares to LU Decomp. at the moment.
From your comment, it seems like you are looking for an operation which takes n −1 vectors as input and computes a single vector as its result, which will be orthogonal to all the input vectors and perhaps have a well-defined length as well.
With defined length
You can characterize the 3-dimensional cross product v =a ×b using the identity v ∙w =det(a,b,w). In other words, taking the cross product of the input vectors and then computing the dot product with any other vector w is the same as plugging the input vectors and that other vector into a matrix and computing its determinant.
This definition can be generalized to arbitrary dimensions. Due to the way a determinant can be computed using Laplace expansion along the last column, the resulting coordinates of that cross product will be the values of all (n −1)×(n −1) sub-determinants you can form from the input vectors, with alternating signs. So yes, Leibniz might be useful in theory, although it is hardly suitable for real-world computations. In practice, you'll soon have to figure out ways to avoid repeating computationswhile computing these n determinants. But wait for the last section of this answer…
Just the direction
Most applications however can do with a weaker requirement. They don't care about the length of the resulting vector, but only about its direction. In that case, what you are asking for is the kernel of the (n −1)×n matrix you can form by taking the input vectors as rows. Any element of that kernel will be orthogonal to the input vectors, and since computing kernels is a common task, you can build on a lot of existing implementations, e.g. Lapack. Details might depend on the language you are using.
Combining these
You can even combine the two approaches above: compute one element of the kernel, and for a non-zero entry of that vector, also compute the corresponding (n −1)×(n −1) determinant which would give you that single coordinate using the first approach. You can then simply scale the vector so that the selected coordinate reaches the computed value, and all the other coordinates will match that one.
I have an n-dimensional array with column-major order. I need to find the address of any element in this array(in memory).
On the Internet, I found only formulas for 1-,2-,3-,4- dimensional arrays here. However, even with them I can't get the address of an element in a multi-dimensional array.
Does somebody knows formula for this?
If an is the coordinate in dimension n ans sn is the size of dimension n then the element (a1,a2,...,an) has the address:
a1+s1(a2+s2(a3+s3(a4+...)))
For multivariable arrays column-major vs row-major doesn't make much sence, but it should just be to swap a1,s1 with a2,s2 to make it column-major.
Maybe this question is better suited in the math section of the site but I guess stackoverflow is suited too. In mathematics, a vector has a position and a direction, but in programming, a vector is usually defined as:
Vector v (3, 1, 5);
Where is the direction and magnitude? For me, this is a point, not a vector... So what gives? Probably I am not getting something so if anybody can explain this to me it would be very appreciated.
If we are working in cartesian coordinates, and assume (0,0,0) to be the origin, then a point p=(3,1,5) can be written as
where i, j and k are the unit vectors in the x, y and z directions. For convenience sake, the unit vectors are dropped from programming constructs.
The magnitude of the vector is
and its direction cosines are
respectively, both of which can be done programmatically. You can also take dot products and cross-products, which I'm sure you know about. So the usage is consistent between programming and mathematics. The difference in notations is mostly because of convenience.
However as Tomas pointed out, in programming, it is also common to define a vector of strings or objects, which really have no mathematical meaning. You can consider such vectors to be a one dimensional array or a list of items that can be accessed or manipulated easily by indexing.
In mathematics, it is easy to represent a vector by a point - just say that the "base" of the vector is implied to be the origin. Thus, a mathematical point for all practical purposes is also a representation of a mathematical vector, and the vector in your example has the magnitude sqrt(3^2 + 1^2 + 5^2) = 6 and the direction (1/2, 1/6, 5/6) (a normalized vector from the origin).
However, a vector in programming usually has no geometrical use, which means you really aren't interested in things like magnitude or direction. A vector in programming is rather just an ordered list of items. Important here is that the items need not be numbers - it can be anything handled by the language in question! Thus, ("Hello", "little", "world") is also a vector in programming, although it (obviously) has no vector interpretation in the mathematical sense.
Practically speaking (!):
A vector in mathematics is only a direction without a position (actually something more general, but to stay in your terminology). In programming you often use vectors for points. You can think of your vector as the vector pointing from the origin (0,0,0) to the point (3,1,5), called the location vector of the point. Consult texts on analytical and affine geometry for more insight.
A Vector in computer science is an "one dimensional" data structure (array) (can be thought as direction) with an usually dynamic size (length/magnitude). For that reason it is called as vector. But it's an array at least.
A vector also means a set of coordinates. This is how it is used in programming. Just as a set of numbers. You might want to represent position vectors, velocity vectors, momentum vectors, force vectors with a vector object, or you may wish to represent it any way that suits you.
Many times vector quantities may be represented by 4 coordinates instead of 3 (see homogeneous coordinates in computer graphics) so a physical vector is represented by a computer vector with 4 elements. Alternatively you can store direction and magnitude separately, or encode them with 3, 4 or more coordinates.
I guess what I am getting to, is that computer languages are designed to represent physical models, but abstract data containers that the programmer use as tools for his/hers modeling.
Vector in math is an element of n-dimensional space over some field(e.g. real/complex number, functions, string). It may have infinite dimension, e.g. functional space L^2. I don't remember infite-dimensional vectors were used in programming (infinite vectors are not vectors with non-limited length, but vector with infite number of elements)
The most rigorous statement is that a mathematical vector is a first-order tensor that transforms from one coordinate system to another according to tensor transformation rules. The physical idea to keep in mind is that vectors have both magnitude and direction.
Programming vectors are data structures that need not transform according to any rules and may or may not have a notion of a coordinate system as reference. If you happen to use a vector data structure to hold numbers, they may conform to the mathematical definition. But if you have a vector of objects, it's unlikely that they have anything to do with coordinate transformations.
If I think of the x,y coordinate plane, x,y is the common notation for an ordered pair, but if I use a two-dime array I have myArray[row][col] and row is the y and col is the x. Is that backwards or am I just thinking about it wrong? I was thinking it would look like myArray[x][y] but that's wrong if I want real rows and columns (like in a gameboard.) Wouldn't it be myArray[y][x] to truly mimic a row column board?
You have it right, and it does feel a bit backwards. The row number is a y coordinate, and the column number is an x coordinate, and yet we usually write row,col but we also usually write x,y.
Whether you want to write your array as [y][x] or [x][y] depends mostly on how much you actually care about the layout of your array in memory (and if you do, what language you use). And whether you want to write functions/methods that can operate on rows or columns in isolation.
If you are writing C/C++ code, arrays are stored in Row Major Order which means that a single row of data can be treated as 1 dimensional array. But a single column of data cannot. If I remember correctly, VB uses column major order, so languages vary. I'd be surprised of C# isn't also row major order, but I don't know.
This is what I do for my own sanity:
int x = array[0].length;
int y = array.length;
And then for every single array call I make, I write:
array[y][x]
This is particulary useful for graphing algorithms and horizontal/vertical matrix flipping.
It doesn't matter how you store your data in the array ([x][y] or [y][x]). What does matter is that you always loop over the array in a contiguous way. A java two dimensional array is essentially a one dimensional array storing the second array (eg. in the case of [y][x] you have a long array of [y] in which each y holds the corresponding [x] arrays for that line of y).
To efficiently run through the whole array, it's important to access the data in a way so that you don't continuously have to do searches in that array, jumping from one y-array-of-xarrays to another y-array-of-xarrays. What you want to do is access one y element and access all the x's in there before moving to the next y element.
So in an Array[y][x] situation. always have the first variable in the outer loop and the second in the inner loop:
for (int ys = 0; ys < Array.length; ys++)
for (int xs = 0; xs < Array[y].length; xs++)
{
do your stuff here
}
And of course pre-allocate both Array.lengths out of the loop to prevent having to get those values every cycle.
I love the question. You’re absolutely right. Most of the time we are either thinking (x, y) or (row, col). It was years before I questioned it. Then one day I realized that I always processed for loops as if x was a row and y was a column, though in plane geometry it’s actually the opposite. As mentioned by many, it really doesn’t matter in most cases, but consistency is a beautiful thing.
Actually, It's up to you. There is no right of thinking in your question. For example i usually think of a one-dimension array as a row of cell. So, in my mind it is array[col][row]. But it is really up to you...
I bet there are a lot of differing opinions on this one. Bottom line is, it doesn't really matter as long as you are consistent. If you have other libraries or similar that is going to use the same data it might make sense to do whatever they do for easier integration.
If this is strictly in your own code, do whatever you feel comfortable with. My personal preference would be to use myArray[y][x]. If they are large, there might be performance benefits of keeping the items that you are going to access a lot at the same time together. But I wouldn't worry about that until at a very late stage if at all.
Well not really, if you think of a row as elements on the x axis and then a 2d array is a bunch of row elements on the y axis, then it's normal to use y to operate on a row, as you already know the x (for that particular row x is always the same, it's y that's changing with its indices) and then use x to operate on the multiple row elements (the rows are stacked vertically, each one at a particular y value)
For better or for worse, the inconsistent notation was inherited from math.
Multidimensional arrays follow matrix notation where Mi,j represents the matrix element on row i and column j.
Multidimensional arrays therefore are not backward if used to represent a matrix, but they will seem backward if used to represent a 2D Cartesian plane where (x, y) is the typical ordering for a coordinate.
Also note that 2D Cartesian planes typically are oriented with the y-axis growing upward. However, that also is backward from how 2D arrays/matrices are typically visualized (and with the coordinate systems for most raster images).