interpolation

来源： https://stackoverflow.com/questions/41190176/creating-dynamic-classes-in-jade-pug

来源： https://stackoverflow.com/questions/50816375/scipy-griddata-with-linear-and-cubic-yields-nan

问题 Follow up on R's survey package interpolation handling for median estimates, which has not attracted many feedback. I have managed to boil down the issue to the following: I'm using R's survey package to get the median estimation for a set of data. The data to replicate this issue is available as a dput text here. The design I'm using is a class svyrep.design defined as the following: design <- svydesign(id = ~id_directorio, strata = ~estrato, weights = ~f_pers, check.strata = TRUE, data =

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 reasonably large 1000 x 4000 pixel xr.DataArray returned from an OpenDataCube query, and a large set (> 200,000) of xy point values. I need to sample the array to return a value under each xy point, and return interpolated values (e.g. if the point lands halfway between a 0 and a 1.0 pixel, the value returned should be 0.5 ). xr.interp lets me easily sample interpolated values, but it returns a huge matrix of every combination of all the x and y values, rather than just the values

问题 I am working on a new model which is very sensitive to the interpolation/fit used to describe a certain dataset. I have some success with linear splines and logarithmic fits but I think there is still significant room for improvement. My supervisor suggested I take a look at exponential splines. I have found some books on the theory of exponential spines but no reference to a library or code example to follow. Is there a library that I am unaware of that supports this feature? 来源： https:/

问题 I am working on a new model which is very sensitive to the interpolation/fit used to describe a certain dataset. I have some success with linear splines and logarithmic fits but I think there is still significant room for improvement. My supervisor suggested I take a look at exponential splines. I have found some books on the theory of exponential spines but no reference to a library or code example to follow. Is there a library that I am unaware of that supports this feature? 来源： https:/

问题 I am working on a new model which is very sensitive to the interpolation/fit used to describe a certain dataset. I have some success with linear splines and logarithmic fits but I think there is still significant room for improvement. My supervisor suggested I take a look at exponential splines. I have found some books on the theory of exponential spines but no reference to a library or code example to follow. Is there a library that I am unaware of that supports this feature? 来源： https:/

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

问题 I'm reposting the question asked here hoping maybe to get a little more visibility. This is a question concerning Lumley's survey package for R. Specifically, its handling of interpolation for median estimation, after several hours of looking into the matter. I'm using a svyrep design which has the following form: design <- svydesign(id = ~id_directorio, strata = ~estrato, weights = ~f_pers, check.strata = TRUE, data = datos) options(survey.lonely.psu="remove") set.seed(234262762) SB2K_2 = as