Source code for pyCP_APR.numpy_backend.accum

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Source: https://scipy-cookbook.readthedocs.io/items/AccumarrayLike.html
"""
from itertools import product
import numpy as np


[docs]def accum(accmap, a, func=None, size=None, fill_value=0, dtype=None): """ An accumulation function similar to Matlab's `accumarray` function. Parameters ---------- accmap : ndarray This is the "accumulation map". It maps input (i.e. indices into `a`) to their destination in the output array. The first `a.ndim` dimensions of `accmap` must be the same as `a.shape`. That is, `accmap.shape[:a.ndim]` must equal `a.shape`. For example, if `a` has shape (15,4), then `accmap.shape[:2]` must equal (15,4). In this case `accmap[i,j]` gives the index into the output array where element (i,j) of `a` is to be accumulated. If the output is, say, a 2D, then `accmap` must have shape (15,4,2). The value in the last dimension give indices into the output array. If the output is 1D, then the shape of `accmap` can be either (15,4) or (15,4,1) a : ndarray The input data to be accumulated. func : callable or None The accumulation function. The function will be passed a list of values from `a` to be accumulated. If None, numpy.sum is assumed. size : ndarray or None The size of the output array. If None, the size will be determined from `accmap`. fill_value : scalar The default value for elements of the output array. dtype : numpy data type, or None The data type of the output array. If None, the data type of `a` is used. Returns ------- out : ndarray The accumulated results. The shape of `out` is `size` if `size` is given. Otherwise the shape is determined by the (lexicographically) largest indices of the output found in `accmap`. Examples -------- >>> from numpy import array, prod >>> a = array([[1,2,3],[4,-1,6],[-1,8,9]]) >>> a array([[ 1, 2, 3], [ 4, -1, 6], [-1, 8, 9]]) >>> # Sum the diagonals. >>> accmap = array([[0,1,2],[2,0,1],[1,2,0]]) >>> s = accum(accmap, a) array([9, 7, 15]) >>> # A 2D output, from sub-arrays with shapes and positions like this: >>> # [ (2,2) (2,1)] >>> # [ (1,2) (1,1)] >>> accmap = array([ [[0,0],[0,0],[0,1]], [[0,0],[0,0],[0,1]], [[1,0],[1,0],[1,1]], ]) >>> # Accumulate using a product. >>> accum(accmap, a, func=prod, dtype=float) array([[ -8., 18.], [ -8., 9.]]) >>> # Same accmap, but create an array of lists of values. >>> accum(accmap, a, func=lambda x: x, dtype='O') array([[[1, 2, 4, -1], [3, 6]], [[-1, 8], [9]]], dtype=object) """ # Check for bad arguments and handle the defaults. if accmap.shape[:a.ndim] != a.shape: raise ValueError("The initial dimensions of accmap must be the same as a.shape") if func is None: func = np.sum if dtype is None: dtype = a.dtype if accmap.shape == a.shape: accmap = np.expand_dims(accmap, -1) adims = tuple(range(a.ndim)) if size is None: size = 1 + np.squeeze(np.apply_over_axes(np.max, accmap, axes=adims)) size = np.atleast_1d(size) # Create an array of python lists of values. vals = np.empty(size, dtype='O') for s in product(*[range(k) for k in size]): vals[s] = [] for s in product(*[range(k) for k in a.shape]): indx = tuple(accmap[s]) val = a[s] vals[indx].append(val) # Create the output array. out = np.empty(size, dtype=dtype) for s in product(*[range(k) for k in size]): if vals[s] == []: out[s] = fill_value else: out[s] = func(vals[s]) return out