interpolation

问题 I'm currently working on an little project in C++ and OpenGL and am trying to implement a colour selection tool similar to that in photoshop, as below. However I am having trouble with interpolation of the large square. Working on my desktop computer with a 8800 GTS the result was similar but the blending wasn't as smooth. This is the code I am using: GLfloat swatch[] = { 0,0,0, 1,1,1, mR,mG,mB, 0,0,0 }; GLint swatchVert[] = { 400,700, 400,500, 600,500, 600,700 }; glVertexPointer(2, GL_INT, 0

问题 I have a climate time series with missing Dates (not missing values). For example: n = 15 full.dates = seq(Sys.Date(), by = 'day', length = n) serie.dates = full.dates[c(1:10, 12, 15)] # missing 11, 13, 14 y = rnorm(n) require(zoo) serie = zoo(y, serie.dates) How can i 'fill' (using interpolation) these missing points, given the 'full.dates' vector? Thanks! 回答1: Merge with an "empty" object that has all the dates you want, then use na.approx (or na.spline , etc.) to fill in the missing values

问题 Users, I'd like to have some tips for a ternaryplot ("vcd"). I have this dataframe: a <- c(0.1, 0.5, 0.5, 0.6, 0.2, 0, 0, 0.004166667, 0.45) b <- c(0.75,0.5,0,0.1,0.2,0.951612903,0.918103448,0.7875,0.45) c <- c(0.15,0,0.5,0.3,0.6,0.048387097,0.081896552,0.208333333,0.1) d <- c(500,2324.90,2551.44,1244.50, 551.22,-644.20,-377.17,-100, 2493.04) df <- data.frame(a, b, c, d) and I'm building a ternary plot: ternaryplot(df[,1:3], df$d) How can I map the continuous variable d , obtaining a result

问题 Users, I'd like to have some tips for a ternaryplot ("vcd"). I have this dataframe: a <- c(0.1, 0.5, 0.5, 0.6, 0.2, 0, 0, 0.004166667, 0.45) b <- c(0.75,0.5,0,0.1,0.2,0.951612903,0.918103448,0.7875,0.45) c <- c(0.15,0,0.5,0.3,0.6,0.048387097,0.081896552,0.208333333,0.1) d <- c(500,2324.90,2551.44,1244.50, 551.22,-644.20,-377.17,-100, 2493.04) df <- data.frame(a, b, c, d) and I'm building a ternary plot: ternaryplot(df[,1:3], df$d) How can I map the continuous variable d , obtaining a result

问题 To do a linear interpolation between two variables a and b given a fraction f , I'm currently using this code: float lerp(float a, float b, float f) { return (a * (1.0 - f)) + (b * f); } I think there's probably a more efficient way of doing it. I'm using a microcontroller without an FPU, so floating point operations are done in software. They are reasonably fast, but it's still something like 100 cycles to add or multiply. Any suggestions? n.b. for the sake of clarity in the equation in the

问题 First of all: what am I trying to do? I have an application to view images. It uses the canvas element to render the image. You can zoom in, you can zoom out, and you can drag it around. This part works perfectly right now. But let's say I have an image with a lot of text. It has a resolution of 1200x1700, and my canvas has 1200x900. Initially, when zoomed out, this leads to a rendered resolution of ~560x800. My actual drawing looks like this: drawImage(src, srcOffsetX, srcOffsetY,

问题 This is a follow-up question to my previous post: Python/Scipy Interpolation (map_coordinates) Let's say I want to interpolate over a 2d rectangular area. My variable 'z' contains the data as shown below. Each column is at a constant value, however, each row of the array may be at a different value as shown in the comment below. from scipy import interpolate from numpy import array import numpy as np # # 0.0000, 0.1750, 0.8170, 1.0000 z = array([[-2.2818,-2.2818,-0.9309,-0.9309], # 0.0000, 0

问题 I have the following MATLAB code which I want to transport into C++ Assume Gr is 2d matrix and 1/newscale == 0.5 Gr = imresize(Gr, 1 / newScale); in the MATLAB documentation: B = imresize(A, scale) returns image B that is scale times the size of A. The input image A can be a grayscale, RGB, or binary image. If scale is between 0 and 1.0, B is smaller than A. If scale is greater than 1.0, B is larger than A. So this means I will get a 2D matrix == matrix_width/2 and matrix_height/2 How do I

问题 I have 2 rotation matrices (lets call them A and B) where: A = 1 0 0 0 0 -1 0 1 0 and B = -1 0 0 0 0 -1 0 -1 0 This is basically just a rotation where the camera spins around to look behind itself. Obviously I can't just interpolate the values in the matrices directly because it looks weird. I have tried converting the matrices to Euler angles which yields 2 sets of X,Y,Z angles and trying to determine which angles to use based on the minimum distance between each component of the X,Y,Z angle

问题 The Question: What is the best way to calculate inverse distance weighted (IDW) interpolation in Python, for point locations? Some Background: Currently I'm using RPy2 to interface with R and its gstat module. Unfortunately, the gstat module conflicts with arcgisscripting which I got around by running RPy2 based analysis in a separate process. Even if this issue is resolved in a recent/future release, and efficiency can be improved, I'd still like to remove my dependency on installing R. The