I'm using a fluid simulation software which can create .vtk files of scalars x-velocity, y-velocity, and z-velocity. I'm trying to view streamlines using ParaView, however that requires vectorized data. Is there an easy way to combine the scalar .vtk files to produce a vectorized .vtk file?
Thanks a lot!
You can use the calculator filter in ParaView to combine the components to a vector.
The required entities are iHat, jHat, and kHat, i.e. the vector constants representing unit vectors in the X, Y, and Z directions, respectively.
In your case the required line would look something like iHat*Xvel+jHat*Yvel+kHat*Zvel with
Xvel, Yvel and Zvel are the x, y, and z velocity components.
You can find your scalar data in the dropdown list 'Scalars'.
As an example, the following shows an example combining the coordinates (scalars) to a coordinate vector.
Related
I am struggling with GNUPLOT binary data handling.
I have a binary file, printed by MATLAB frite function, which prints in column order.
I am printing a Nx2 array, that is a collection of points on xy plane, that I guess is stored as x1..xn y1..yn, as consecutive records in the binary file. Do you agree? Consider that I still have a not clear idea of what binary storage means. I am used to ASCII files, with nice separators and \n's.
So I want to plot these points with gnuplot. I have been reading the binary general documentation and I ended trying this:
plot 'datafile.bin' binary array=N:N w l
that means that my data file is made by two arrays, each one of N elements. Gnuplot produces one line, first following the values of the first array, then following the values of the second array, both of them on the interval 1:N.
I tried to use the first array as x axis of my plot and the second array as y axis, So I try:
plot 'datafile.bin' binary array=N:N u 1:2 w l
It plots the two arrays again consecutively, not in a xy plot. Where am I wrong?
Many thanks
EDIT: I tried to apply the scan=xy keyword to both the lines, but he told me that my file is a unidimensional record. So I guess that u 1:2 has no sense
I don't think gnuplot can handle the data type you describe. It doesn't know about arrays and matrices like matlab does.
Write your data file with pairs of x,y values.
Then you can
plot dataf binary format='%float%float' using 1:2".
(if your x,y values are both floats).
The "array" keyword is meant for the case when your file only contains the function values and you want gnuplot to construct the independent variable(s). Totally different.
I'm looking to preallocate a sparse matrix in R (using simple_triplet_matrix) by providing the dimensions of the matrix, m x n, and also the number of non-zero elements I expect to have. Matlab has the function "spalloc" (see below), but I have not been able to find an equivalent in R. Any suggestions?
S = spalloc(m,n,nzmax) creates an all zero sparse matrix S of size m-by-n with room to hold nzmax nonzeros.
Whereas it may make sense to preallocate a traditional dense matrix in R (in the same way it is much more efficient to preallocate a regular (atomic) vector rather than increasing its size one by one,
I'm pretty sure it will not pay to preallocate sparse matrices in R, in most situations.
Why?
For dense matrices, you allocate and then assign "piece by piece", e.g.,
m[i,j] <- value
For sparse matrices, however that is very different: If you do something like
S[i,j] <- value
the internal code has to check if [i,j] is an existing entry (typically non-zero) or not. If it is, it can change the value, but otherwise, one way or the other, the triplet (i,j, value) needs to be stored and that means extending the current structure etc. If you do this piece by piece, it is inefficient... mostly irrespectively if you had done some preallocation or not.
If, on the other hand, you already know in advance all the [i,j] combinations which will contain non-zeroes, you could "pre-allocate", but in this case,
just store the vector i and j of length nnzero, say. And then use your underlying "algorithm" to also construct a vector x of the same length which contains all the corresponding values, i.e., entries.
Now, indeed, as #Pafnucy suggested, use spMatrix() or sparseMatrix(), two slightly different versions of the same functionality: Constructing a sparse matrix, given its contents.
I am happy to help further, as I am the maintainer of the Matrix package.
Is there a fast way in numpy to add a vector to every row or column of a matrix.
Lately, I have been tiling the vector to the size of the matrix, which can use a lot of memory. For example
mat=np.arange(15)
mat.shape=(5,3)
vec=np.ones(3)
mat+=np.tile(vec, (5,1))
The other way I can think of is using a python loop, but loops are slow:
for i in xrange(len(mat)):
mat[i,:]+=vec
Is there a fast way to do this in numpy without resorting to C extensions?
It would be nice to be able to virtually tile a vector, like a more flexible version of broadcasting. Or to be able to iterate an operation row-wise or column-wise, which you may almost be able to do with some of the ufunc methods.
For adding a 1d array to every row, broadcasting already takes care of things for you:
mat += vec
However more generally you can use np.newaxis to coerce the array into a broadcastable form. For example:
mat + np.ones(3)[np.newaxis,:]
While not necessary for adding the array to every row, this is necessary to do the same for column-wise addition:
mat + np.ones(5)[:,np.newaxis]
EDIT: as Sebastian mentions, for row addition, mat + vec already handles the broadcasting correctly. It is also faster than using np.newaxis. I've edited my original answer to make this clear.
Numpy broadcasting will automatically add a compatible size vector (1D array) to a matrix (2D array, not numpy matrix). It does this by matching shapes based on dimension from right to left, "stretching" missing or value 1 dimensions to match the other. This is explained in https://numpy.org/doc/stable/user/basics.broadcasting.html:
mat: 5 x 3
vec: 3
vec (broadcasted): 5 x 3
By default, numpy arrays are row-major ("C order"), with axis 0 is "matrix row" and axis 1 is "matrix col", so the broadcasting clones the vector as matrix rows along axis 0.
I have assignment using R and have a little problem. In the assignment several matrices have to be generated with random number of rows and later used for various calculations. Everything works perfect, unless number of rows is 1.
In the calculations I use nrow(matrix) in different ways, for example if (i <= nrow(matrix) ) {action} and also statements like matrix[,4] and so on.
So in case number of rows is 1 (I know it is actually vector) R give errors, definitely because nrow(1-dimensional matrix)=NULL. Is there simple way to deal with this? Otherwise probably whole code have to be rewritten, but I'm very short in time :(
It is not that single-row/col matrices in R have ncol/nrow set to NULL -- in R everything is a 1D vector which can behave like matrix (i.e. show as a matrix, accept matrix indexing, etc.) when it has a dim attribute set. It seems otherwise because simple indexing a matrix to a single row or column drops dim and leaves the data in its default (1D vector) state.
Thus you can accomplish your goal either by directly recreating dim attribute of a vector (say it is called x):
dim(x)<-c(length(x),1)
x #Now a single column matrix
dim(x)<-c(1,length(x))
x #Now a single row matrix
OR by preventing [] operator from dropping dim by adding drop=FALSE argument:
x<-matrix(1:12,3,4)
x #OK, matrix
x[,3] #Boo, vector
x[,3,drop=FALSE] #Matrixicity saved!
Let's call your vector x. Try using matrix(x) or t(matrix(x)) to convert it into a proper (2D) matrix.
New to R and having problem with a very simple task! I have read a few columns of .csv data into R, the contents of which contains of variables that are in the natural numbers plus zero, and have missing values. After trying to use the non-parametric package, I have two problems: first, if I use the simple command bw=npregbw(ydat=y, xdat=x, na.omit), where x and y are column vectors, I get the error that "number of regression data and response data do not match". Why do I get this, as I have the same number of elements in each vector?
Second, I would like to call the data ordered and tell npregbw this, using the command bw=npregbw(ydat=y, xdat=ordered(x)). When I do that, I get the error that x must be atomic for sort.list. But how is x not atomic, it is just a vector with natural numbers and NA's?
Any clarifications would be greatly appreciated!
1) You probably have a different number of NA's in y and x.
2) Can't be sure about this, since there is no example. If it is of following type:
x <- c(3,4,NA,2)
Then ordered(x) should work fine. Please provide an example of your case.
EDIT: You of course tried bw=npregbw(ydat=y, xdat=x)? ordered() makes your vector an ordered factor (see ?ordered), which is not an atomic vector (see 2.1.1 link and ?factor)
EDIT2: So the problem was the way of subsetting data. Note the difference in various ways of subsetting. data$x and data[,i] (where i = column number of column x) give you vectors, while data[c("x")] and data[i] give a data frame. Functions expect vectors, unless they call for data = (your data). In that case they work with column names