From 8140ec785b9ca4d5a1bbc256b62cfd673d8d5ebd Mon Sep 17 00:00:00 2001 From: Per A Brodtkorb Date: Tue, 20 Dec 2016 12:07:55 +0100 Subject: [PATCH] Fixed failing doctests --- wafo/kdetools/gridding.py | 58 ++++++++++++++++++++------------------- 1 file changed, 30 insertions(+), 28 deletions(-) diff --git a/wafo/kdetools/gridding.py b/wafo/kdetools/gridding.py index 8afc712..792e9eb 100644 --- a/wafo/kdetools/gridding.py +++ b/wafo/kdetools/gridding.py @@ -36,9 +36,9 @@ def accumsum(accmap, a, shape, dtype=None): [-1, 8, 9]]) >>> # Sum the diagonals. >>> accmap = array([[0,1,2],[2,0,1],[1,2,0]]) - >>> s = accumsum(accmap, a, (3,) - >>> s - array([ 9, 7, 15]) + >>> s = accumsum(accmap, a, (3,)) + >>> np.allclose(s.toarray().T, [ 9, 7, 15]) + True """ if dtype is None: @@ -67,17 +67,17 @@ def accumsum2(accmap, a, shape): 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 = accumsum2(accmap, a, (3,) - >>> s - array([ 9, 7, 15]) + + >>> accmap = array([[0,1,2],[2,0,1],[1,2,0]]) # Sum the diagonals. + >>> s = accumsum2(accmap, a, (3,)) + >>> np.allclose(s, [ 9, 7, 15]) + True """ return np.bincount(accmap.ravel(), a.ravel(), np.array(shape).max()) -def accum(accmap, a, func=None, size=None, fill_value=0, dtype=None): +def accum(accmap, a, func=None, shape=None, fill_value=0, dtype=None): """An accumulation function similar to Matlab's `accumarray` function. Parameters @@ -99,8 +99,8 @@ def accum(accmap, a, func=None, size=None, fill_value=0, dtype=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 + shape : ndarray or None + The shape of the output array. If None, the shape will be determined from `accmap`. fill_value : scalar The default value for elements of the output array. @@ -113,7 +113,7 @@ def accum(accmap, a, func=None, size=None, fill_value=0, dtype=None): out : ndarray The accumulated results. - The shape of `out` is `size` if `size` is given. Otherwise the + The shape of `out` is `shape` if `shape` is given. Otherwise the shape is determined by the (lexicographically) largest indices of the output found in `accmap`. @@ -126,32 +126,34 @@ def accum(accmap, a, func=None, size=None, fill_value=0, dtype=None): array([[ 1, 2, 3], [ 4, -1, 6], [-1, 8, 9]]) - >>> # Sum the diagonals. - >>> accmap = array([[0,1,2],[2,0,1],[1,2,0]]) + >>> accmap = array([[0,1,2],[2,0,1],[1,2,0]]) # Sum the diagonals. >>> s = accum(accmap, a) >>> s array([ 9, 7, 15]) - >>> # A 2D output, from sub-arrays with shapes and positions like this: - >>> # [ (2,2) (2,1)] - >>> # [ (1,2) (1,1)] + + # 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. + + # 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. + + # 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) """ - def create_array_of_python_lists(accmap, a, size): - vals = np.empty(size, dtype='O') - for s in product(*[range(k) for k in size]): + def create_array_of_python_lists(accmap, a, shape): + vals = np.empty(shape, dtype='O') + for s in product(*[range(k) for k in shape]): vals[s] = [] for s in product(*[range(k) for k in a.shape]): @@ -172,16 +174,16 @@ def accum(accmap, a, func=None, size=None, fill_value=0, dtype=None): 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) + if shape is None: + shape = 1 + np.squeeze(np.apply_over_axes(np.max, accmap, axes=adims)) + shape = np.atleast_1d(shape) # Create an array of python lists of values. - vals = create_array_of_python_lists(accmap, a, size) + vals = create_array_of_python_lists(accmap, a, shape) # Create the output array. - out = np.empty(size, dtype=dtype) - for s in np.product(*[range(k) for k in size]): + out = np.empty(shape, dtype=dtype) + for s in product(*[range(k) for k in shape]): if vals[s] == []: out[s] = fill_value else: