diff options
Diffstat (limited to 'numpy/lib/shape_base.py')
| -rw-r--r-- | numpy/lib/shape_base.py | 62 |
1 files changed, 31 insertions, 31 deletions
diff --git a/numpy/lib/shape_base.py b/numpy/lib/shape_base.py index c63f8140d..1363a3213 100644 --- a/numpy/lib/shape_base.py +++ b/numpy/lib/shape_base.py @@ -1,7 +1,7 @@ from __future__ import division, absolute_import, print_function -__all__ = ['column_stack','row_stack', 'dstack','array_split','split','hsplit', - 'vsplit','dsplit','apply_over_axes','expand_dims', +__all__ = ['column_stack', 'row_stack', 'dstack', 'array_split', 'split', 'hsplit', + 'vsplit', 'dsplit', 'apply_over_axes', 'expand_dims', 'apply_along_axis', 'kron', 'tile', 'get_array_wrap'] import numpy.core.numeric as _nx @@ -68,18 +68,18 @@ def apply_along_axis(func1d,axis,arr,*args): axis += nd if (axis >= nd): raise ValueError("axis must be less than arr.ndim; axis=%d, rank=%d." - % (axis,nd)) + % (axis, nd)) ind = [0]*(nd-1) - i = zeros(nd,'O') + i = zeros(nd, 'O') indlist = list(range(nd)) indlist.remove(axis) - i[axis] = slice(None,None) + i[axis] = slice(None, None) outshape = asarray(arr.shape).take(indlist) i.put(indlist, ind) res = func1d(arr[tuple(i.tolist())],*args) # if res is a number, then we have a smaller output array if isscalar(res): - outarr = zeros(outshape,asarray(res).dtype) + outarr = zeros(outshape, asarray(res).dtype) outarr[tuple(ind)] = res Ntot = product(outshape) k = 1 @@ -91,7 +91,7 @@ def apply_along_axis(func1d,axis,arr,*args): ind[n-1] += 1 ind[n] = 0 n -= 1 - i.put(indlist,ind) + i.put(indlist, ind) res = func1d(arr[tuple(i.tolist())],*args) outarr[tuple(ind)] = res k += 1 @@ -101,7 +101,7 @@ def apply_along_axis(func1d,axis,arr,*args): holdshape = outshape outshape = list(arr.shape) outshape[axis] = len(res) - outarr = zeros(outshape,asarray(res).dtype) + outarr = zeros(outshape, asarray(res).dtype) outarr[tuple(i.tolist())] = res k = 1 while k < Ntot: @@ -182,7 +182,7 @@ def apply_over_axes(func, a, axes): if res.ndim == val.ndim: val = res else: - res = expand_dims(res,axis) + res = expand_dims(res, axis) if res.ndim == val.ndim: val = res else: @@ -288,11 +288,11 @@ def column_stack(tup): """ arrays = [] for v in tup: - arr = array(v,copy=False,subok=True) + arr = array(v, copy=False, subok=True) if arr.ndim < 2: - arr = array(arr,copy=False,subok=True,ndmin=2).T + arr = array(arr, copy=False, subok=True, ndmin=2).T arrays.append(arr) - return _nx.concatenate(arrays,1) + return _nx.concatenate(arrays, 1) def dstack(tup): """ @@ -348,7 +348,7 @@ def _replace_zero_by_x_arrays(sub_arys): for i in range(len(sub_arys)): if len(_nx.shape(sub_arys[i])) == 0: sub_arys[i] = _nx.array([]) - elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]),0)): + elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)): sub_arys[i] = _nx.array([]) return sub_arys @@ -383,17 +383,17 @@ def array_split(ary,indices_or_sections,axis = 0): Nsections = int(indices_or_sections) if Nsections <= 0: raise ValueError('number sections must be larger than 0.') - Neach_section,extras = divmod(Ntotal,Nsections) + Neach_section, extras = divmod(Ntotal, Nsections) section_sizes = [0] + \ extras * [Neach_section+1] + \ (Nsections-extras) * [Neach_section] div_points = _nx.array(section_sizes).cumsum() sub_arys = [] - sary = _nx.swapaxes(ary,axis,0) + sary = _nx.swapaxes(ary, axis, 0) for i in range(Nsections): st = div_points[i]; end = div_points[i+1] - sub_arys.append(_nx.swapaxes(sary[st:end],axis,0)) + sub_arys.append(_nx.swapaxes(sary[st:end], axis, 0)) # there is a weird issue with array slicing that allows # 0x10 arrays and other such things. The following kludge is needed @@ -474,10 +474,10 @@ def split(ary,indices_or_sections,axis=0): N = ary.shape[axis] if N % sections: raise ValueError('array split does not result in an equal division') - res = array_split(ary,indices_or_sections,axis) + res = array_split(ary, indices_or_sections, axis) return res -def hsplit(ary,indices_or_sections): +def hsplit(ary, indices_or_sections): """ Split an array into multiple sub-arrays horizontally (column-wise). @@ -535,11 +535,11 @@ def hsplit(ary,indices_or_sections): if len(_nx.shape(ary)) == 0: raise ValueError('hsplit only works on arrays of 1 or more dimensions') if len(ary.shape) > 1: - return split(ary,indices_or_sections,1) + return split(ary, indices_or_sections, 1) else: - return split(ary,indices_or_sections,0) + return split(ary, indices_or_sections, 0) -def vsplit(ary,indices_or_sections): +def vsplit(ary, indices_or_sections): """ Split an array into multiple sub-arrays vertically (row-wise). @@ -588,9 +588,9 @@ def vsplit(ary,indices_or_sections): """ if len(_nx.shape(ary)) < 2: raise ValueError('vsplit only works on arrays of 2 or more dimensions') - return split(ary,indices_or_sections,0) + return split(ary, indices_or_sections, 0) -def dsplit(ary,indices_or_sections): +def dsplit(ary, indices_or_sections): """ Split array into multiple sub-arrays along the 3rd axis (depth). @@ -633,7 +633,7 @@ def dsplit(ary,indices_or_sections): """ if len(_nx.shape(ary)) < 3: raise ValueError('vsplit only works on arrays of 3 or more dimensions') - return split(ary,indices_or_sections,2) + return split(ary, indices_or_sections, 2) def get_array_prepare(*args): """Find the wrapper for the array with the highest priority. @@ -659,7 +659,7 @@ def get_array_wrap(*args): return wrappers[-1][-1] return None -def kron(a,b): +def kron(a, b): """ Kronecker product of two arrays. @@ -728,10 +728,10 @@ def kron(a,b): """ b = asanyarray(b) - a = array(a,copy=False,subok=True,ndmin=b.ndim) + a = array(a, copy=False, subok=True, ndmin=b.ndim) ndb, nda = b.ndim, a.ndim if (nda == 0 or ndb == 0): - return _nx.multiply(a,b) + return _nx.multiply(a, b) as_ = a.shape bs = b.shape if not a.flags.contiguous: @@ -745,7 +745,7 @@ def kron(a,b): else: bs = (1,)*(nda-ndb) + bs nd = nda - result = outer(a,b).reshape(as_+bs) + result = outer(a, b).reshape(as_+bs) axis = nd-1 for _ in range(nd): result = concatenate(result, axis=axis) @@ -819,14 +819,14 @@ def tile(A, reps): except TypeError: tup = (reps,) d = len(tup) - c = _nx.array(A,copy=False,subok=True,ndmin=d) + c = _nx.array(A, copy=False, subok=True, ndmin=d) shape = list(c.shape) - n = max(c.size,1) + n = max(c.size, 1) if (d < c.ndim): tup = (1,)*(c.ndim-d) + tup for i, nrep in enumerate(tup): if nrep!=1: - c = c.reshape(-1,n).repeat(nrep,0) + c = c.reshape(-1, n).repeat(nrep, 0) dim_in = shape[i] dim_out = dim_in*nrep shape[i] = dim_out |