interpolation

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

问题 Using numpy.interp I am able to compute the one-dimensional piecewise linear interpolant to a function with given values at discrete data-points. Is it a similar function to return me the logarithmic interpolation? 回答1: In the past, I've just wrapped the normal interpolation to do it in log-space, i.e. def log_interp(zz, xx, yy): logz = np.log10(zz) logx = np.log10(xx) logy = np.log10(yy) return np.power(10.0, np.interp(logz, logx, logy)) Personally, I much prefer the scipy interpolation

问题 Using numpy.interp I am able to compute the one-dimensional piecewise linear interpolant to a function with given values at discrete data-points. Is it a similar function to return me the logarithmic interpolation? 回答1: In the past, I've just wrapped the normal interpolation to do it in log-space, i.e. def log_interp(zz, xx, yy): logz = np.log10(zz) logx = np.log10(xx) logy = np.log10(yy) return np.power(10.0, np.interp(logz, logx, logy)) Personally, I much prefer the scipy interpolation

问题 Using numpy.interp I am able to compute the one-dimensional piecewise linear interpolant to a function with given values at discrete data-points. Is it a similar function to return me the logarithmic interpolation? 回答1: In the past, I've just wrapped the normal interpolation to do it in log-space, i.e. def log_interp(zz, xx, yy): logz = np.log10(zz) logx = np.log10(xx) logy = np.log10(yy) return np.power(10.0, np.interp(logz, logx, logy)) Personally, I much prefer the scipy interpolation

问题 How to correct this program, please? There is an error: ValueError: One of the requested xi is out of bounds in dimension 0. There is a function for defining the magnetic field according to equations in cylindric coordinates. Then, it should compute steamline using yt module and compute a trajectory of charges particle. Without the last two lines, it works. However, I am not sure wheater it works correct but there is no error. import numpy as np import matplotlib.pyplot as plt from numpy

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 I am having some issues with interpolation of a Pandas dataframe. Basically, I have a dataframe of 295339 rows and have artificially generated nan's to study different sampling rates and completion methods. The issue is that when I do some combinations of my sampling rates and completion methods it all works out while for others I get the following error message, ValueError: The number of derivatives at boundaries does not match: expected. 1, got 0+0. The type of ValueError depends on the

问题 I'm attempting to use scipy.interpolate.bisplrep and scipy.interpolate.bisplev to perform a 2D regression on the differences between two datasets, based on a small set of known differences. The code is: splineRT = interp.bisplrep(diffPoints[0], diffPoints[1], RTdiffs) allDiffs = interp.bisplev(features[0], features[1], splineRT) When I run this, bisplev throws the inscrutable exception "ValueError: Invalid input data", which is in response from an error code returned from the underlying

问题 I'm attempting to use scipy.interpolate.bisplrep and scipy.interpolate.bisplev to perform a 2D regression on the differences between two datasets, based on a small set of known differences. The code is: splineRT = interp.bisplrep(diffPoints[0], diffPoints[1], RTdiffs) allDiffs = interp.bisplev(features[0], features[1], splineRT) When I run this, bisplev throws the inscrutable exception "ValueError: Invalid input data", which is in response from an error code returned from the underlying

问题 I'm attempting to use scipy.interpolate.bisplrep and scipy.interpolate.bisplev to perform a 2D regression on the differences between two datasets, based on a small set of known differences. The code is: splineRT = interp.bisplrep(diffPoints[0], diffPoints[1], RTdiffs) allDiffs = interp.bisplev(features[0], features[1], splineRT) When I run this, bisplev throws the inscrutable exception "ValueError: Invalid input data", which is in response from an error code returned from the underlying