Numpy: sort by key function
your approach is right, it is similar to the Schwartzian transform or Decorate-Sort-Undecorate (DSU) idiom
As I said you can use the numpy function np.argsort. It does the work of your order_to_index.
For a more explicit answer, suppose we have an array x and want to sort the rows according to some function func which takes a row of x and outputs a scalar.
x[np.apply_along_axis(func, axis=1, arr=x).argsort()]For this example
c1, c2 = 4, 7x = np.array([ [0, 1], [2, 3], [4, -5]])x[np.apply_along_axis(lambda row: c1 * / c2 * row[1] + row[0], 1, x).argsort()]Out:
array([[ 0, 1], [ 4, -5], [ 2, 3]])In this case, np.apply_along_axis isn't even necessary.
x[(c1 / c2 * x[:,1] + x[:,0]).argsort()]Out:
array([[ 0, 1], [ 4, -5], [ 2, 3]])