interpolation

问题 I have implemented a code for image warping using bilinear interpolation: Matlab image rotation I would like to improve the code by using bicubic interpolation to rotate the image WITHOUT using the built-in functions like imrotate or imwarp and interp functions in MATLAB. 回答1: I successfully managed to implement a full working example. Code is based on Anna1994's code: Matlab image rotation Biqubic code is also based on Java (and C++) implementation posted here: http://www.paulinternet.nl/

问题 I've been struggling to find a solution to find the area of overlap between two curves. I'm not dealing with probability density functions with known parameters but curves obtained from smoothing of empirical data points. The only hint I found is to calculate the area that is not over-lapping as in this code (from here): x <- seq(-6,6,by = 0.01) y1 <- dnorm(x,0,1) y2 <- pnorm(x,1,1.1) f1 <- approxfun(x, y1-y2) f2 <- function(z) abs(f1(z)) dif <- integrate(f2, min(x), max(x)) plot(x,y1,type="l

问题 I have this matrix in MATLAB: x = [NaN -2 -1 0 1 2; 1 0.21 0.15 0.34 0.11 0.32; 2 0.14 0.10 0.16 0.31 0.11]; The first row represents the location of the values following X coordinates. I shift the first row by -0.63, so x becomes: New_x = [NaN -2.63 -1.63 -0.63 0.37 1.37; 1 0.21 0.15 0.34 0.11 0.32; 2 0.14 0.10 0.16 0.31 0.11]; How can I use interpolation to get the values at specific coordinates of the New_x matrix that we have in the x matrix? ( [-2 -1 0 1 2] points) New_xInterp = [NaN -2

问题 In my application their is some text which is coming from a constant file which is declared like this: export const EmpStrings = { data: "Welcome {{employee.name}}" } And In my component file there is an object called employee . public employee = { name: 'xyz', dept: 'EE' } Now In my HTML I want to use it like this: <div class='e-data' [innerHTML] = "EmpStrings.data"></div> But this didn't seems to be working. I tried various variations: [inner-html] = "EmpStrings.data" [innerHTML] = {

问题 I recognize there are several related questions, but I seem to be stumbling somewhere here. I followed this thread as best I could: Interpolating timeseries, but get error messages (see below) : My dataset contains samples collected every four hours everyday. I would like to interpolate these data into hourly values. Below is a subsample of my much larger dataset: vis <- structure(list(datetime = structure(1:24, .Label = c("2002-05-01-00", "2002-05-01-06", "2002-05-01-12", "2002-05-01-18",

问题 I was trying out the 2d example given in the scipy.interpolation.griddata help file. It works for interpolation with 'nearest'. But it gives a matrix filled with nan while using any other interpolation like 'linear' or 'cubic'. If I give the argument fill_value=5 , it gives the matrix filled with 5. Is this due to some installation problem? I was trying the exact same thing they have given in the help document. But somehow it is giving the result as if the points i asked to interpolating is

问题 I was trying out the 2d example given in the scipy.interpolation.griddata help file. It works for interpolation with 'nearest'. But it gives a matrix filled with nan while using any other interpolation like 'linear' or 'cubic'. If I give the argument fill_value=5 , it gives the matrix filled with 5. Is this due to some installation problem? I was trying the exact same thing they have given in the help document. But somehow it is giving the result as if the points i asked to interpolating is

问题 Modified from this example: import io import pandas as pd import matplotlib.pyplot as plt data = io.StringIO('''\ Values 1992-08-27 07:46:48,1 1992-08-27 08:00:48,2 1992-08-27 08:33:48,4 1992-08-27 08:43:48,3 1992-08-27 08:48:48,1 1992-08-27 08:51:48,5 1992-08-27 08:53:48,4 1992-08-27 08:56:48,2 1992-08-27 09:03:48,1 ''') s = pd.read_csv(data, squeeze=True) s.index = pd.to_datetime(s.index) res = s.resample('4s').interpolate('linear') print(res) plt.plot(res, '.-') plt.plot(s, 'o') plt.grid

问题 Modified from this example: import io import pandas as pd import matplotlib.pyplot as plt data = io.StringIO('''\ Values 1992-08-27 07:46:48,1 1992-08-27 08:00:48,2 1992-08-27 08:33:48,4 1992-08-27 08:43:48,3 1992-08-27 08:48:48,1 1992-08-27 08:51:48,5 1992-08-27 08:53:48,4 1992-08-27 08:56:48,2 1992-08-27 09:03:48,1 ''') s = pd.read_csv(data, squeeze=True) s.index = pd.to_datetime(s.index) res = s.resample('4s').interpolate('linear') print(res) plt.plot(res, '.-') plt.plot(s, 'o') plt.grid

来源： https://stackoverflow.com/questions/64829772/how-to-fill-irregularly-missing-time-series-values-with-linear-interepolation-by