resampling

There are a couple similar questions on SO, but none of them really helped. Basically I am trying to resize a simple png image, as seen here: http://media.spiralknights.com/wiki-images/3/3e/Equipment-Proto_Sword_icon.png (from the mmo Spiral Knights, copyright Three Rings Entertainment) I had originally implemented a utility which uses these images in php, and the resizing utility there worked perfectly well. I used the method described on the imagecopyresampled page in PHP's documentation. Honestly I can't even get it to resize better in Photoshop, but the results are almost disastrous in

This algorithm has been in my mind for a long time, but I cannot find it described anywhere. It's so simple though that I can't be the only one who has thought of it. Here's how it works: You start with an image. Say, 7x7px: You need to resample it say, to 5x5px: So all you do is take the average color of each new square: This isn't the nearest-neighbor, because that takes the color of just one pixel, not fractional pixels who happen to overlay the source pixel. It's also not bilinear, bicubic, lanczos or anything else interpolating. So - what is it? It intuitively seems to me that this should

It's easy to resample an array like a = numpy.array([1,2,3,4,5,6,7,8,9,10]) with an integer resampling factor . For instance, with a factor 2 : b = a[::2] # [1 3 5 7 9] But with a non-integer resampling factor, it doesn't work so easily : c = a[::1.5] # [1 2 3 4 5 6 7 8 9 10] => not what is needed... It should be (with linear interpolation): [1 2.5 4 5.5 7 8.5 10] or (by taking the nearest neighbour in the array) [1 3 4 6 7 9 10] How to resample a numpy array with a non-integer resampling factor? Example of application: audio signal resampling / repitching NumPy has numpy.interp which does

I have 2 data frames with identical columns but different datetime indices. I want to resample one of them to use the index of the other and forward fill data from the one on any dates in the index of the other in which there wasn't data for. import pandas as pd import numpy as np from datetime import datetime as dt a_values = np.random.randn(4, 4) a_index = [dt(2012, 3, 16), dt(2012, 3, 19), dt(2012, 3, 20), dt(2012, 3, 21)] a = pd.DataFrame(data=a_values, index=a_index) b_values = np.trunc(np.random.randn(3, 4) * 1000) b_index = [dt(2012, 3, 16), dt(2012, 3, 19), dt(2012, 3, 21)] b = pd