interpolation

问题 I want to make a text box in .NET "glow" yellow, and then "fade" to white (basically, by incrementally increasing the brightness). I think Stackoverflow does this after you've posted an answer. I know that increasing brightness is not all that simple (it's not just uniformly increasing/decreasing RGB), but I'm not sure how to do this. Perfect color accuracy is not important for this. I am using C#, although VB examples would be just fine, too. Edit: This is for Winforms. 回答1: This may be more

问题 I have two lists to describe the function y(x): x = [0,1,2,3,4,5] y = [12,14,22,39,58,77] I would like to perform cubic spline interpolation so that given some value u in the domain of x, e.g. u = 1.25 I can find y(u). I found this in SciPy but I am not sure how to use it. 回答1: Short answer: from scipy import interpolate def f(x): x_points = [ 0, 1, 2, 3, 4, 5] y_points = [12,14,22,39,58,77] tck = interpolate.splrep(x_points, y_points) return interpolate.splev(x, tck) print(f(1.25)) Long

问题 I'm converting a matlab script to python and I have it a roadblock. In order to use cubic spline interpolation on a signal. The script uses the command spline with three inputs. f_o, c_signal and freq. so it looks like the following. cav_sig_freq = spline(f_o, c_signal, freq) f_o = 1x264, c_signal = 1x264 and freq = 1x264 From the documentation in matlab it reads that "s = spline(x,y,xq) returns a vector of interpolated values s corresponding to the query points in xq. The values of s are

问题 I need to create thousands and thousands of interpolation splines, each based on 5 pairs of (x, y) values. I would like to save them in a database (or csv file). How can I export / import them, say in a text format or as an array of real parameters to rebuild each function when I need them? 回答1: If you are using the splinefun function from R base package stats , it is very easy to export its construction information. set.seed(0) xk <- c(0, 1, 2) yk <- round(runif(3), 2) f <- splinefun(xk, yk,

问题 I have csv files which are 1200 Rows x 3 Columns. Number of rows can differ from as low as 500 to as large as 5000 but columns remain same. I want to create a feature vector from these files which will thus maintain consistent cells/vector length & thus help in finding out the distance between these vectors. FILE_1 A, B, C (267.09669678867186, 6.3664069175720197, 1257325.5809999991), (368.24070923984374, 9.0808353424072301, 49603.662999999884), (324.21470826328124, 11.489830970764199, 244391

问题 I am trying to interpolate data from an unstructured mesh M1 to another unstructured mesh M2 . For this, scipy.interpolate.griddata seems good. However, I will need to interpolate many times from M1 to M2 , changing only the data not the meshes. I guess that, internally, the scipy.interpolate.griddata defines some weight coefficients when interpolating from M1 to M2 and that this may be one of the expensive parts of the computation. Therefore, I would like to avoid re-compute these weigths

问题 Assume that we have quarterly GDP change data like the following: Country 1999Q3 0.01 1999Q4 0.01 2000Q1 0.02 2000Q2 0.00 2000Q3 -0.01 Now, I would like to turn this into a monthly series based on e.g. the mean of the previous two quarters, as one measure to represent the economic conditions. I.e. with the above data I would like to produce the following: Country 2000-01 0.01 2000-02 0.01 2000-03 0.01 2000-04 0.015 2000-05 0.015 2000-06 0.015 2000-07 0.01 2000-08 0.01 2000-09 0.01 2000-10 -0

问题 For a project, I have a matrix<float> which is rotated few degrees. I have no control over this process (assume it is using nearest neighbour), I want to reverse this rotation operation and obtain the initial matrix (or a matrix very close to it). My initial assumption was if I rotate the rotated matrix with -angle and crop the middle part, I'd have the original matrix but the results indicate the quality drops dramatically. Consider my original matrix (the first image in the figure ) is

问题 I am having a problem using Scipy's griddata inside a loop. Basically what happens is that the memory grows without bound while the loop is running. To reproduce the problem just put the example in http://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.griddata.html inside a loop: for i in range(100000): grid_z1 = griddata(points, values, (grid_x, grid_y), method='linear') My Python version is 2.7.3, my numpy version is 1.7.0 and my scipy version is 0.12.0b1. I'm running it on

问题 I have a 3D array (z, y, x) with shape=(92, 4800, 4800) where each value along axis 0 represents a different point in time. The acquisition of values in the time domain failed in a few instances causing some values to be np.NaN . In other instances no values have been acquired and all values along z are np.NaN . What is the most efficient way to use linear interpolation to fill np.NaN along axis 0 disregarding instances where all values are np.NaN ? Here is a working example of what I'm doing