interpolation

I have a NetCDF file of a probability surface. It's a 30x30 grid of 0.25 degree lat/lon intervals with a probability surface described in the z dimension. I can easily import this into Panoply, a NetCDF viewer: And it's then a breeze (checking one box) to interpolate/smooth the raw data to a finer grid size: However, I don't just want to visualize the data, I want to plot it in R along with bathymetry and point data. That all is no problem, but I have not found a straightforward way to interpolate the gridded data in R. Here's the code I use to import and plot the data: library(RNetCDF) nc <-

I have a point dataset that I'm trying to interpolate on a grid. These points are aligned in grid fashion with some points missing see below: To complicate it, it's possible that other input pointsets may not align on a grid, so I'm trying to use scipy.interpolate.griddata to interpolate these values onto a regular grid. However, sometimes my underlying grid aligns perfectly with the sampling rate of the input point dataset and griddata hangs. According to this question scipy.interpolate.griddata operates poorly if the sample occurs across 3 points that are perfectly aligned which sometimes

Relative programming newbie here. I have trouble figuring out how to plot interpolated functions over a series of iterations, where as the iteration index increases, the plot would go from black to gradually lighter shades of grey. For example, import numpy as np import matplotlib.pyplot as plt from scipy.interpolate import interp1d for t in np.arange(0.,2., 0.4): x = np.linspace(0.,4, 100) y = np.sin(x-2*t) + 0.01 * np.random.normal(size=x.shape) yint = interp1d(x, y) plt.plot(x, yint(x)) plt.show() produces I would like the blue sinusoidal function to be black, and the rest becomes lighter

Right now, I have more than 25 vertices that form a model. I want to interpolate color linearly between the first and last vertex. The Problem is when I write the following code glColor3f(1.0,0.0,0.0); vertex3f(1.0,1.0,1.0); vertex3f(0.9,1.0,1.0); . .`<more vertices>; glColor3f(0.0,0.0,1.0); vertex3f(0.0,0.0,0.0); All the vertices except that last one are red. Now I am wondering if there is a way to interpolate color across these vertices without me having to manually interpolate color (instead natively, like how opengl does it automatically) at each vertex since, I will be having a lot more

I have some values (bytes) over a plane evenly distributed (the come from real measures) like for instance temperature. I'm trying to generate the whole surface. But I'm not successful. The main condition is that the number and position of the points will not be known and that the surface MUST keep the value in the points where is measured and the points in between will be interpolated. Ideally, if only one point is set the final surface should be a mountain. By the way, and just in the case that it may help. Im coding it on WPF (C#) and it would nice to not involve heavy libraries or whatever

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 logical from a mathematical perspective (although not to me), but it is undesired. How could I achieve