interpolation

问题 Let's say I have this code: import numpy as np import time from datetime import datetime class Measurements(): def __init__(self, time_var, value): self.time_var = time_var self.value = value a = np.array([ Measurements('30-01-2017 12:02:15.880922', 100), Measurements('30-01-2017 12:02:16.880922', 100), Measurements('30-01-2017 12:02:17.880922', 110), Measurements('30-01-2017 12:02:18.880922', 99), Measurements('30-01-2017 12:02:19.880922', 96)]) b = np.array([ Measurements('30-01-2017 12:02

问题 Let's say I have Freemarker variable A that contains the name of another variable on the hash tree, say. "B." I'd like to use a to read the value of B so, for example, if B contained "C" I could tell Freemarker to output C using A: ${${A}} should result in the output of "C". Naturally, this doesn't work in Freemarker, but is there a way to accomplish this? 回答1: Use the .vars special variable, which is a hash (map) of the variables, and hence you can use the aHash[aKeyExpression] syntax: ${

问题 I have many (~ 1 million) irregularly spaced points, P, along a 1D line. These mark segments of the line, such that if the points are {0, x_a, x_b, x_c, x_d, ...}, the segments go from 0->x_a, x_a->x_b, x_b->x_c, x_c->x_d, etc. I also have a y-value for each segment, which I wish to interpret as a depth of colour. I need to plot this line as an image, but there might only be (say) 1000 pixels available to represent the entire length of the line. These pixels, of course, correspond to

问题 Is there a way to interpolate a vector-valued function using NumPy/SciPy? There are plenty of offerings that work on scalar-valued functions, and I guess I can use one of those to estimate each component of the vector separately, but is there a way to do it more efficiently? To elaborate, I have a function f(x) = V , where x is scalar and V is a vector. I also have a collection of xs and their corresponding Vs . I would like to use it to interpolate and estimate V for an arbitrary x . 回答1:

问题 I use slerp to interpolate between two quaternions representing rotations. The resulting rotation is then extracted as Euler angles to be fed into a graphics lib. This kind of works, but I have the following problem; when rotating around two (one works just fine) axes in the direction of the green arrow as shown in the left frame here the rotation soon jumps around to rotate from the opposite site to the opposite visual direction, as indicated by the red arrow in the right frame. This may be

问题 I got this code for scaling image by bilinear interpolation.I know this works but i can't figure out one thing what if the approximated pixel value is an edge(by edge i mean it is in the last row or last column) pixel in the input image then i can i gt a pixel of coordinate (x+1,y+1) ,this should lead to a array index out of range error but no such error occurs why? The code is: public int[] resizeBilinearGray(int[] pixels, int w, int h, int w2, int h2) { int[] temp = new int[w2*h2] ; int A,

问题 I have two data points x and y : x = 5 (value corresponding to 95%) y = 17 (value corresponding to 102.5%) No I would like to calculate the value for xi which should correspond to 100%. x = 5 (value corresponding to 95%) xi = ?? (value corresponding to 100%) y = 17 (value corresponding to 102.5%) How should I do this using python? 回答1: is that what you want? In [145]: s = pd.Series([5, np.nan, 17], index=[95, 100, 102.5]) In [146]: s Out[146]: 95.0 5.0 100.0 NaN 102.5 17.0 dtype: float64 In

问题 I'm in the process of rewriting a coworkers IDL code into python and am coming up with some differences that I'm confused about. According to other SO questions and mailing list threads I've found if you use scipy.ndimage.interpolation.map_coordinates and specify order=1 it is supposed to do bilinear interpolation. When comparing results between the IDL code (run in GDL) and python (map_coordinates) I got different results. Then I tried using mpl_toolkits.basemap.interp and I got the same

问题 I'm getting the following error in one of my functions: Error: [$interpolate:interr] http://errors.angularjs.org/1.3.0-rc.4/$interpolate/interr?p0=%7B%7B%20crea…&p1=TypeError%3A%20Cannot%20read%20property%20'startDate'%20of%20undefined at Error (native) at file:///C:/Users/Zuh/Desktop/MSF_Juba_2014/Radio%20Room%20App/angular.min.js:6:421 at z.exp (file:///C:/Users/Zuh/Desktop/MSF_Juba_2014/Radio%20Room%20App/angular.min.js:86:481) at file:///C:/Users/Zuh/Desktop/MSF_Juba_2014/Radio%20Room

问题 I tried the following: spline= interpolate.InterpolatedUnivariateSpline(X, Y, k=3) coefs= spline.get_coeffs() With five values in each of X and Y , I ended up with coefs also having five values. Given that five data points implies four spline sections, and that a cubic polynomial has four coefficients, I would have expected to get four times four= 16 coefficients. Does anyone know how to interpret the values that are returned by the get_coeffs method? Is there any place where this is