extrapolation

I've got a 3D dataset that I want to interpolate AND extrapolate linearly. The interpolation can be done easily with scipy.interpolate.LinearNDInterpolator . The module can only fill in a constant/nan for values outside the parameter range, but I don't see why it would not offer an option to turn on extrapolation. Looking at the code, I see that the module is written in cython. With no experience in cython, it is hard to play around with the code to implement extrapolation. I can write it in pure python code, but maybe someone else here has a better idea? My particular case involves a constant

I have a graph where the extrapolation does not match the initial interpolation. I would like the heatmap to fill the entire image. First, the interpolation code: library(akima) library(reshape2) xmin <- signif(min(CBLo2$MD1)) xmax <- signif(max(CBLo2$MD1)) ymin <- signif(min(CBLo2$MD2)) ymax <- signif(max(CBLo2$MD2)) gridint <- 100 fld <- with(CBLo2, interp(x = MD1, y = MD2, z = Abundance, xo=seq(xmin, xmax, length=gridint), yo=seq(ymin, ymax, length=gridint) )) df <- melt(fld$z, na.rm = TRUE) names(df) <- c("MD1", "MD2", "Abundance") df$MD1 <- fld$x[df$MD1] df$MD2 <- fld$y[df$MD2] contour

I am doing a 2-D interpolation using interp2 . For some data values, the interp2 command returns NaN because one of the dimensions are outside of the range defined by the vector of known values. Its possible to extrapolate with the interp1 command. However, Is there a way to do this for interp2 ? Thanks Here is the code in which I am using the interp2 command: function [Cla] = AirfoilLiftCurveSlope(obj,AFdata,Rc,M) % Input: % AFdata: Airfoil coordinates. % Rc: Local Reynolds number. % M: Mach number for Prandtle Glauert compressibility correction. % Output: % Cla: 2 dimensional lift curve

I'm running about 45,000 local linear regressions (essentially) on about 1.2 million observations, so I'd appreciate some help trying to speed things up because I'm impatient. I'm basically trying to construct year-by-position wage contracts--the function wage(experience given firm,year,position)--for a bunch of firms. Here's the data (basic structure of) set I'm working with: > wages firm year position exp salary 1: 0007 1996 4 1 20029 2: 0007 1996 4 1 23502 3: 0007 1996 4 1 22105 4: 0007 1996 4 2 23124 5: 0007 1996 4 2 22700 --- 1175141: 994 2012 5 2 47098 1175142: 994 2012 5 2 45488 1175143

I have a pandas DataFrame with time as index (1 min Freq) and several columns worth of data. Sometimes the data contains NaN. If so, I want to interpolate only if the gap is not longer than 5 Minutes. In this case this would be a maximum of 5 consecutive NaNs. The data may look like this (several test cases, which show the problems): import numpy as np import pandas as pd from datetime import datetime start = datetime(2014,2,21,14,50) data = pd.DataFrame(index=[start + timedelta(minutes=1*x) for x in range(0, 8)], data={'a': [123.5, np.NaN, 136.3, 164.3, 213.0, 164.3, 213.0, 221.1], 'b': [433