Hi Seth, The function you’re looking for is `np.digitize`:
In [1]: t = np.array([0.5,1,1.5,2.5,3,10]) ...: table = np.array([[0,3],[1,0],[2,5],[3,-1]]) ...: In [2]: lookup, values = table[:, 0], table[:, 1:] In [3]: values = np.concatenate((values[0:1], values), axis=0) In [4]: indices = np.digitize(t, lookup) In [5]: values[indices] Out[5]: array([[ 3], [ 0], [ 0], [ 5], [-1], [-1]]) Note the call to concatenate. Depending on how exactly you want your bins to align, you might need to concatenate at the end or at the start of the `values` array. Hope this helps! Juan. On 16 Oct 2017, 1:17 PM +1100, Seth Ghandi <seth.ghandi.2...@gmail.com>, wrote: > Hi everybody, > > I am new to newpy and am trying to define a variant of piecewise or zero > holder interpolation function, say ZeroOrderInterpolation(t,a), where t is an > 1D array of size, say p, consisting of real numbers, and a is a 2D array of > size, say nxm, with first column consisting of increasing real numbers. This > function should return an array, say y, of size px(m-1) such that y[i,:] is > equal to > a[n,1:] if a[n,0] <= t[i], and > a[k,1:] if k < n and a[k,0] <= t[i] < a[k+1,0]. > Note that t[0] is assumed to be at least equal to a[0,0]. > > I have the following script made of "for loops" and I am trying to vectorize > it so as to make it faster for large arrays. > > def ZeroOrderInterpolation(t,a): > import numpy as np > p = t.shape[0] > n, m = a.shape > if n == 1: > return a[0,1:] > y = np.zeros((p,m-1)) > for i in range(p): > if a[n-1,0] <= t[i]: > y[i] = a[n-1,1:] > else: > for j in range(n-1): > if (a[j,0] <= t[i]) and (t[i] <= a[j+1,0]): > y[i] = a[j,1:] > return y > > import numpy as np > t = np.array([0.5,1,1.5,2.5,3,10]) > table = np.array([[0,3],[1,0],[2,5],[3,-1]]) > ZeroOrderInterpolation(t,table) > > [Out]: array([[ 3.], > [ 0.], > [ 0.], > [ 5.], > [-1.], > [-1.]]) > > > Any help for a vectorization "à la numpy" of this fucntion will be > apprecaited. > > Best regards, > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion
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