linear-interpolation

问题 I have known data points and their values described as arrays, speed and power speed = [2, 6, 8, 10, 12] power = [200, 450, 500, 645, 820], I want to estimate the power value that corresponds to speed value (e.g. 7) that is not listed within source array (2 through 12). I.e. I am seeking a way to perform interpolation 回答1: The simplest way to do that is to perceive your power-from-speed dependency as a collection of lines and assuming your target point falls between two known points of a

问题 I have known data points and their values described as arrays, speed and power speed = [2, 6, 8, 10, 12] power = [200, 450, 500, 645, 820], I want to estimate the power value that corresponds to speed value (e.g. 7) that is not listed within source array (2 through 12). I.e. I am seeking a way to perform interpolation 回答1: The simplest way to do that is to perceive your power-from-speed dependency as a collection of lines and assuming your target point falls between two known points of a

问题 I tried to do something similar to this: CSS Poly Fluid Sizing using calc(), vw, breakpoints and linear equations The wrapper shall span 100% for viewports of 600px and smaller. The wrapper shall span 70% for a viewport of 1800px. Interpolated values for all remaining viewports. This is what I came up with so far: #square1 { background-color: blue; margin: 10px 0; width: 100%; height: 50px; } #square2 { background-color: yellow; margin: 10px auto; width: calc(100% - 20 * (100vw - 600px)/40);

问题 I tried to do something similar to this: CSS Poly Fluid Sizing using calc(), vw, breakpoints and linear equations The wrapper shall span 100% for viewports of 600px and smaller. The wrapper shall span 70% for a viewport of 1800px. Interpolated values for all remaining viewports. This is what I came up with so far: #square1 { background-color: blue; margin: 10px 0; width: 100%; height: 50px; } #square2 { background-color: yellow; margin: 10px auto; width: calc(100% - 20 * (100vw - 600px)/40);

问题 In some cases you might need to resample your data. How can this be done platform-independently with gnuplot? Below is one attempt. The dataset $Dummy contains the interpolated values, however, with a lot of unnecessary lines containing NaN . The dataset $DataResampled finally contains the desired data. My question is: can this be done simpler? The code: ### resampling data with linear interpolation reset session $Data <<EOD 0 1 1 4 2 10 5 15 10 5 20 11 EOD # get the x-range stats $Data u 1

问题 I have data which has missing values irregulaly, and I'd like to convert it with a certain interval with liner interpolation using BigQuery Standard SQL. Specifically, I have data like this: # data is missing irregulary +------+-------+ | time | value | +------+-------+ | 1 | 3.0 | | 5 | 5.0 | | 7 | 1.0 | | 9 | 8.0 | | 10 | 4.0 | +------+-------+ and I'd like to convert this table as follows: # interpolated with interval of 1 +------+--------------------+ | time | value_interpolated | +------

问题 I have been wanting to implement smooth scrolling in vanilla JS for some time, but could not find any adequate implementations or methods. However, I recently came across the concept of linear interpolation (LERP), which seems adequate unlike every other method. Although content about LERP smooth scrolling seems scarce on the web, I just found two great articles (one, two) about LERP in general and an example of LERP being used for smooth scrolling on codepen. The example, notably, seems

问题 I would like to interpolate missing values within groups in dataframe using preceding and following rows value. Here is the df (there are more records within a group but for this example I left 3 per group): import numpy as np import pandas as pd df = pd.DataFrame({'Group': ['a','a','a','b','b','b','c','c','c'],'Yval': [1,np.nan,5,2,np.nan,8,5,np.nan,10],'Xval': [0,3,2,4,5,8,3,1,9],'PTC': [0,1,0,0,1,0,0,1,0]}) df: Group Yval Xval PTC 0 a 1.0 0 0 1 a NaN 3 1 2 a 5.0 2 0 3 b 2.0 4 0 4 b NaN 5 1

问题 I have a stream of metrics that are unevenly sampled. I want to linearly interpolate and upsample these metrics to a specific sampling frequency. I have tried to use the Accelerate Framework and the SIMD framework but I am not really sure what to do. The problem itself is as follows: let original_times:[Double] = [0.0, 2.0, 3.0, 6.0, 10.0] let original_values: [Double] = [50.0, 20.0, 30.0, 40.0, 10.0] let new_times:[Double] = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0] So I am

问题 In a problem I'm working on now, I compute some values in a matrix x and I then for each element in x need to find the index of the closest element below in a monotonically increasing vector X as well as the relative proximity of the x elements to the first elements on their either side. (This is essentially linear interpolation without doing the actual interpolation.) I'm doing this maaaany times so I really super extra interested in it being as fast as possible. I have written a function