Is there a simple python 3 command that replicates matlab's interp1 command over multiple columns?
data_1 contains two parameters (1 per column) that go with the time_1 time vector (data_1 is a 5 by 2 array that isn't actually used in this example so can be ignored)
data_2 contains two parameters (1 per column) that go with the time_2 time vector
import numpy as np data_2 = np.array([ [ 0.43, -0.54], [ 0.32, -0.83], [ 0.26, -0.94], [ 0.51, -0.69], [ 0.63, -0.74] ]) time_1 = np.array([ 399.87, 399.89, 399.91, 399.93, 399.95 ]) time_2 = np.array([ 399.86, 399.88, 399.90, 399.92, 399.94 ]) I'd like to interpolate the data_2 2D array into the time_1 time vector so both data sets will have the same time vector.
Desired output (which is just the np.interp of the two data_2 columns into the time_1 time vector and merged back into an array) is:
data_2_i = np.array([[ 0.375, -0.685], [ 0.290, -0.885], [ 0.385, -0.815], [ 0.570, -0.715], [ 0.630, -0.740]]) Actual arrays will contain approx 20 columns (parameters) and thousands of rows (longer time range).
I know you can just loop over each column with np.interp but I was hoping there was a more compact and faster python 3 (numpy, scipy, pandas, etc.) method that I haven't been able to track down yet. I'm still pretty new to python (more familiar with matlab).
In matlab, you can just use interp1 on the entire multi-column array to get the multi-column result (although the edge cases are handled a bit differently - NaNs vs. last entry in this example - I'm not worried about the edge case differences here).
