spatial-interpolation | 易学教程

Geographic Interpolate in Python [closed]

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问题 Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 4 months ago . Improve this question I have 500 points with longitude x, latitude y, altitude z, and the value at these points. On the other hand, I have other 200 points than I would like to interpolate, where the latitude, longitude, and altitude are known. I would like to interpolate considering

Polygon boundary interpolation in R

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问题 I have a polygon dataframe: With vertice coordinates: 43740.95 40726.46 43741.36 40720.19 43742.67 40729.28 43743.99 40716.16 43745.52 40730.97 43748.72 40714.19 43748.72 40731.14 43748.72 40714.19 43752.23 40714.76 43752.86 40729.43 43755.27 40716.68 43756.77 40723.24 43757.19 40719.73 Is there any way in R to interpolate spatial objects such that the boundary could be more smooth? 回答1: You can use the smoothr package for smoothing an sf object. I created an example based on your data where

OpenCV remap interpolation error?

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问题 I'm using opencv remap function to map an image to another coordinate system. However, my initial tests indicate that there are some issues with the interpolation. Here, I give a simple example of a constant 0.1 pixel shift for a image that is 0 everywhere but at position [50,50]. import cv2 import numpy as np prvs = np.zeros((100,80), dtype=np.float32) prvs[50:51, 50:51] = 1. grid_x, grid_y = np.meshgrid(np.arange(prvs.shape[1]), np.arange(prvs.shape[0])) grid_y = grid_y.astype(np.float32)

Interpolation over regular grid in Python [closed]

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问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 7 months ago . I have been struggling to inteprolate the data for "empty" pixels in my 2D matrix. Basically, I understand (but not deeply) interpolation techniques such as Inverse Distance Weighting, Kriging, Bicubic etc. I dont know the starting point exactly (either in the statement of the

Interpolating Between Two Planes in 3d space

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问题 I'm developing a tool that lets you circle/enclose things on a 3d "volume." I want to save time by labelling "slices" 1 and 3, and "filling in" slice 2 from that information. Two easy solutions are to: 1. slice2 = slice1 AND slice3 (gets the overlap between the two) 2. slice2 = slice2 OR slice3 (true for any pixel true in either image) These are ok and fast, but I would prefer to do something more intelligent by making the shape some sort of average/ interpolation between the two. You can

How to interpolate between n colors by a fractional contribution for each color?

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问题 How can I interpolate between n colors. Simple case of 2 colors Consider a more simple case first, where we want to find the mid-point of 2 colors. Color1 is RGB ( 255, 0, 0 ) // Red Color2 is RGB ( 128, 128, 128 ) // Grey The solution being the mid-point between each R, G, B considered separately. RGB ( 128 + 64, 128 / 2, 128 / 2 ) = RGB ( 192, 64, 64 ) Since the mid-point is exactly in between the two and there is a linear relationship to the interpolation, then its possiable to interpolate

How to make Ordinary Kriging by using gstat predict

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问题 I am trying to write a code in R that use gstat library in order to create an interpolation. I have already read the gstat manual and based on some examples on internet I had managed to write this code (this is only a part): g <- gstat(id="tec", formula=TEC ~ 1, data=data) ##I create an object v <- variogram(g) # plot the empirical variogram plot(v) mod<-vgm(sill=var(data$TEC),model="Sph",range=200,nugget=200) #create the variogram model v.fit <- fit.variogram(v, model=mod,fit.method=1) #fit

Define function as interpolation of x,y data

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I have 2 columns of x y data in data.txt like this: 0 0 1 1 2 4 3 9 4 16 5 25 Now I want to define a function f(x) where x is the first column and f(x) is the second column, and then be able to print values of this function like so: f(2) Which should give me 4. How do I achieve this? Assuming that you want some return value for numbers between the ones you have as reference, you can use linear interpolation: function y= linearLut(x) xl = [0 1 2 3 4 5]; yl = [0 1 4 9 16 25]; y = interp1(xl,yl,x); end A more generic version of the function might be: function y= linearLut(xl,yl,x) y = interp1(xl

OpenCV remap interpolation error?

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I'm using opencv remap function to map an image to another coordinate system. However, my initial tests indicate that there are some issues with the interpolation. Here, I give a simple example of a constant 0.1 pixel shift for a image that is 0 everywhere but at position [50,50]. import cv2 import numpy as np prvs = np.zeros((100,80), dtype=np.float32) prvs[50:51, 50:51] = 1. grid_x, grid_y = np.meshgrid(np.arange(prvs.shape[1]), np.arange(prvs.shape[0])) grid_y = grid_y.astype(np.float32) grid_x = grid_x.astype(np.float32) + 0.1 prvs_remapped = cv2.remap(prvs, grid_x, grid_y, interpolation

Interpolation over regular grid in Python [closed]

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I have been struggling to inteprolate the data for "empty" pixels in my 2D matrix. Basically, I understand (but not deeply) interpolation techniques such as Inverse Distance Weighting, Kriging, Bicubic etc. I dont know the starting point exactly (either in the statement of the problem or Python case). The problem definition: I have MxN matrix (regular grid) in which each pixel represents certain measurement value ( figure below and data used in this figure is here ). I wanted to interpolate the data for "question mark space" (white space which also consists of the same sized but empty pixels)