interpolation

问题 I\'m plotting data using the command files discussed here: gnuplot contour line color: set style line and set linetype not working I want to provide different output options. PNG works well, as does the wxt terminal, however, these have fixed resolution, e.g. when I \"zoom in\" on the plot it gets grainier. If I use pdf or pdfcairo for the terminal, the resulting file has a Moire pattern. The region in the image over which the Moire pattern is observed can be reduced by using increasing

问题 I am trying to remove NA s from my data frame by interpolation with na.approx() but can\'t remove all of the NA s. My data frame is a 4096x4096 with 270.15 as flag for non valid value. I need data to be continous in all points to feed a meteorological model. Yesterday I asked, and obtained an answer, on how to replace values in a data frame based in another data frame. But after that I came to na.approx() and then decided to replace the 270.15 values with NA and try na.approx() to interpolate

问题 I have several values that are defined on the same irregular grid (x, y, z) that I want to interpolate onto a new grid (x1, y1, z1) . i.e., I have f(x, y, z), g(x, y, z), h(x, y, z) and I want to calculate f(x1, y1, z1), g(x1, y1, z1), h(x1, y1, z1) . At the moment I am doing this using scipy.interpolate.griddata and it works well. However, because I have to perform each interpolation separately and there are many points, it is quite slow, with a great deal of duplication in the calculation

问题 So, I have three numpy arrays which store latitude, longitude, and some property value on a grid -- that is, I have LAT(y,x), LON(y,x), and, say temperature T(y,x), for some limits of x and y. The grid isn\'t necessarily regular -- in fact, it\'s tripolar. I then want to interpolate these property (temperature) values onto a bunch of different lat/lon points (stored as lat1(t), lon1(t), for about 10,000 t...) which do not fall on the actual grid points. I\'ve tried matplotlib.mlab.griddata,

问题 I have a set of isoline points (or contour points) such as this: alt text http://www.rcad.eu/triang&isolines%20example1.jpg Each point on an isoline has its own respective X, Y, and Z coordinate. Since they are isolines, that means that each point will have a unique X-Y pair, but points on the same line will have the same Z coordinate. Now, is there any algorithm or any software packages (either in C# or C++ or MATLAB) that I can use to interpolate the isoline points into a full 3-D surface?

问题 I have two set of samples that are time independent. I would like to merge them and calculate the missing values for the times where I do not have values of both. Simplified example: A <- cbind(time=c(10, 20, 30, 40, 50, 60, 70, 80, 90, 100), Avalue=c(1, 2, 3, 2, 1, 2, 3, 2, 1, 2)) B <- cbind(time=c(15, 30, 45, 60), Bvalue=c(100, 200, 300, 400)) C <- merge(A,B, all=TRUE) time Avalue Bvalue 1 10 1 NA 2 15 NA 100 3 20 2 NA 4 30 3 200 5 40 2 NA 6 45 NA 300 7 50 1 NA 8 60 2 400 9 70 3 NA 10 80 2

问题 I know this is long, but please bear with me. The problem is easy to understand, just takes some writing to fully explain it. Right now I\'m getting this error Error: [$interpolate:noconcat] Error while interpolating: Strict Contextual Escaping disallows interpolations that concatenate multiple expressions when a trusted value is required. See http://docs.angularjs.org/api/ng.$sce I\'ve done all the reading in the documentation, but I still can\'t find a workaround for my problem. I\'m using

问题 BOUNTY UPDATE Following Denis\'s link, this is how to use the threeblindmiceandamonkey code: // the destination rect is our \'in\' quad int dw = 300, dh = 250; double in[4][4] = {{0,0},{dw,0},{dw,dh},{0,dh}}; // the quad in the source image is our \'out\' double out[4][5] = {{171,72},{331,93},{333,188},{177,210}}; double homo[3][6]; const int ret = mapQuadToQuad(in,out,homo); // homo can be used for calculating the x,y of any destination point // in the source, e.g. for(int i=0; i<4; i++) {

问题 I\'m writing a Java game and I want to implement a power meter for how hard you are going to shoot something. I need to write a function that takes a int between 0 - 100, and based on how high that number is, it will return a color between Green (0 on the power scale) and Red (100 on the power scale). Similar to how volume controls work: What operation do I need to do on the Red, Green, and Blue components of a color to generate the colors between Green and Red? So, I could run say, getColor

问题 I am interested in a general root finding problem for an interpolation function. Suppose I have the following (x, y) data: set.seed(0) x <- 1:10 + runif(10, -0.1, 0.1) y <- rnorm(10, 3, 1) as well as a linear interpolation and a cubic spline interpolation: f1 <- approxfun(x, y) f3 <- splinefun(x, y, method = \"fmm\") How can I find x -values where these interpolation functions cross a horizontal line y = y0 ? The following is a graphical illustration with y0 = 2.85 . par(mfrow = c(1, 2))