def _replace_zero_by_x_arrays(sub_arys): for i in range(len(sub_arys)): if _nx.ndim(sub_arys[i]) == 0: sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype) elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)): sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype) return sub_arys
def forward_fill(marr, maxgap=None): """forward_fill(marr, maxgap=None) Forward fills masked values in a 1-d array when there are less maxgap consecutive masked values. If maxgap is None, then forward fill all masked values.""" # Initialization .................. if numeric.ndim(marr) > 1: raise ValueError,"The input array should be 1D only!" marr = marray(marr, copy=True) if getmask(marr) is nomask or marr.size == 0: return marr # currGap = 0 if maxgap is not None: for i in range(1, marr.size): if marr._mask[i]: currGap += 1 if currGap <= maxgap and not marr._mask[i-1]: marr._data[i] = marr._data[i-1] marr._mask[i] = False elif currGap == maxgap + 1: marr._mask[i-maxgap:i] = True else: currGap = 0 else: for i in range(1, marr.size): # CHECK: We should probable be able to speed things up here if marr._mask[i] and not marr._mask[i-1]: marr._data[i] = marr._data[i-1] marr._mask[i] = False return marr
def vsplit(ary, indices_or_sections): """ Split an array into multiple sub-arrays vertically (row-wise). Please refer to the ``split`` documentation. ``vsplit`` is equivalent to ``split`` with `axis=0` (default), the array is always split along the first axis regardless of the array dimension. See Also -------- split : Split an array into multiple sub-arrays of equal size. Examples -------- >>> x = np.arange(16.0).reshape(4, 4) >>> x array([[ 0., 1., 2., 3.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.], [ 12., 13., 14., 15.]]) >>> np.vsplit(x, 2) [array([[ 0., 1., 2., 3.], [ 4., 5., 6., 7.]]), array([[ 8., 9., 10., 11.], [ 12., 13., 14., 15.]])] >>> np.vsplit(x, np.array([3, 6])) [array([[ 0., 1., 2., 3.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.]]), array([[ 12., 13., 14., 15.]]), array([], dtype=float64)] With a higher dimensional array the split is still along the first axis. >>> x = np.arange(8.0).reshape(2, 2, 2) >>> x array([[[ 0., 1.], [ 2., 3.]], [[ 4., 5.], [ 6., 7.]]]) >>> np.vsplit(x, 2) [array([[[ 0., 1.], [ 2., 3.]]]), array([[[ 4., 5.], [ 6., 7.]]])] """ if _nx.ndim(ary) < 2: raise ValueError('vsplit only works on arrays of 2 or more dimensions') return split(ary, indices_or_sections, 0)
def dsplit(ary, indices_or_sections): """ Split array into multiple sub-arrays along the 3rd axis (depth). Please refer to the `split` documentation. `dsplit` is equivalent to `split` with ``axis=2``, the array is always split along the third axis provided the array dimension is greater than or equal to 3. See Also -------- split : Split an array into multiple sub-arrays of equal size. Examples -------- >>> x = np.arange(16.0).reshape(2, 2, 4) >>> x array([[[ 0., 1., 2., 3.], [ 4., 5., 6., 7.]], [[ 8., 9., 10., 11.], [ 12., 13., 14., 15.]]]) >>> np.dsplit(x, 2) [array([[[ 0., 1.], [ 4., 5.]], [[ 8., 9.], [ 12., 13.]]]), array([[[ 2., 3.], [ 6., 7.]], [[ 10., 11.], [ 14., 15.]]])] >>> np.dsplit(x, np.array([3, 6])) [array([[[ 0., 1., 2.], [ 4., 5., 6.]], [[ 8., 9., 10.], [ 12., 13., 14.]]]), array([[[ 3.], [ 7.]], [[ 11.], [ 15.]]]), array([], dtype=float64)] """ if _nx.ndim(ary) < 3: raise ValueError('dsplit only works on arrays of 3 or more dimensions') return split(ary, indices_or_sections, 2)
def _block_check_depths_match(arrays, parent_index=[]): """ Copied from NumPy 1.14.1. """ def format_index(index): idx_str = ''.join('[{}]'.format(i) for i in index if i is not None) return 'arrays' + idx_str if type(arrays) is tuple: # not strictly necessary, but saves us from: # - more than one way to do things - no point treating tuples like # lists # - horribly confusing behaviour that results when tuples are # treated like ndarray raise TypeError( '{} is a tuple. ' 'Only lists can be used to arrange blocks, and np.block does ' 'not allow implicit conversion from tuple to ndarray.'.format( format_index(parent_index) ) ) elif type(arrays) is list and len(arrays) > 0: idxs_ndims = (_block_check_depths_match(arr, parent_index + [i]) for i, arr in enumerate(arrays)) first_index, max_arr_ndim = next(idxs_ndims) for index, ndim in idxs_ndims: if ndim > max_arr_ndim: max_arr_ndim = ndim if len(index) != len(first_index): raise ValueError( "List depths are mismatched. First element was at depth " "{}, but there is an element at depth {} ({})".format( len(first_index), len(index), format_index(index) ) ) return first_index, max_arr_ndim elif type(arrays) is list and len(arrays) == 0: # We've 'bottomed out' on an empty list return parent_index + [None], 0 else: # We've 'bottomed out' - arrays is either a scalar or an array return parent_index, _nx.ndim(arrays)
def interp_masked1d(marr, kind='linear'): """interp_masked1d(marr, king='linear') Interpolates masked values in marr according to method kind. kind must be one of 'constant', 'linear', 'cubic', quintic' """ if numeric.ndim(marr) > 1: raise ValueError("array must be 1 dimensional!") # marr = marray(marr, copy=True) if getmask(marr) is nomask: return marr # unmaskedIndices = (~marr._mask).nonzero()[0] if unmaskedIndices.size < 2: return marr # kind = kind.lower() if kind == 'constant': return forward_fill(marr) try: k = {'linear' : 1, 'cubic' : 3, 'quintic' : 5}[kind.lower()] except KeyError: raise ValueError("Unsupported interpolation type.") first_unmasked, last_unmasked = flatnotmasked_edges(marr) vals = marr.data[unmaskedIndices] tck = fitpack.splrep(unmaskedIndices, vals, k=k) maskedIndices = marr._mask.nonzero()[0] interpIndices = maskedIndices[(maskedIndices > first_unmasked) & \ (maskedIndices < last_unmasked)] marr[interpIndices] = fitpack.splev(interpIndices, tck).astype(marr.dtype) return marr
def __getitem__(self, key): # handle matrix builder syntax if isinstance(key, str): frame = sys._getframe().f_back mymat = matrixlib.bmat(key, frame.f_globals, frame.f_locals) return mymat if not isinstance(key, tuple): key = (key, ) # copy attributes, since they can be overridden in the first argument trans1d = self.trans1d ndmin = self.ndmin matrix = self.matrix axis = self.axis objs = [] scalars = [] arraytypes = [] scalartypes = [] for k, item in enumerate(key): scalar = False if isinstance(item, slice): step = item.step start = item.start stop = item.stop if start is None: start = 0 if step is None: step = 1 if isinstance(step, complex): size = int(abs(step)) newobj = function_base.linspace(start, stop, num=size) else: newobj = _nx.arange(start, stop, step) if ndmin > 1: newobj = array(newobj, copy=False, ndmin=ndmin) if trans1d != -1: newobj = newobj.swapaxes(-1, trans1d) elif isinstance(item, str): if k != 0: raise ValueError("special directives must be the " "first entry.") if item in ('r', 'c'): matrix = True col = (item == 'c') continue if ',' in item: vec = item.split(',') try: axis, ndmin = [int(x) for x in vec[:2]] if len(vec) == 3: trans1d = int(vec[2]) continue except Exception: raise ValueError("unknown special directive") try: axis = int(item) continue except (ValueError, TypeError): raise ValueError("unknown special directive") elif type(item) in ScalarType: newobj = array(item, ndmin=ndmin) scalars.append(len(objs)) scalar = True scalartypes.append(newobj.dtype) else: item_ndim = ndim(item) newobj = array(item, copy=False, subok=True, ndmin=ndmin) if trans1d != -1 and item_ndim < ndmin: k2 = ndmin - item_ndim k1 = trans1d if k1 < 0: k1 += k2 + 1 defaxes = list(range(ndmin)) axes = defaxes[:k1] + defaxes[k2:] + defaxes[k1:k2] newobj = newobj.transpose(axes) objs.append(newobj) if not scalar and isinstance(newobj, _nx.ndarray): arraytypes.append(newobj.dtype) # Ensure that scalars won't up-cast unless warranted final_dtype = find_common_type(arraytypes, scalartypes) if final_dtype is not None: for k in scalars: objs[k] = objs[k].astype(final_dtype) res = self.concatenate(tuple(objs), axis=axis) if matrix: oldndim = res.ndim res = self.makemat(res) if oldndim == 1 and col: res = res.T return res
def hsplit(ary, indices_or_sections): """ Split an array into multiple sub-arrays horizontally (column-wise). Please refer to the `split` documentation. `hsplit` is equivalent to `split` with ``axis=1``, the array is always split along the second axis regardless of the array dimension. See Also -------- split : Split an array into multiple sub-arrays of equal size. Examples -------- >>> x = np.arange(16.0).reshape(4, 4) >>> x array([[ 0., 1., 2., 3.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.], [12., 13., 14., 15.]]) >>> np.hsplit(x, 2) [array([[ 0., 1.], [ 4., 5.], [ 8., 9.], [12., 13.]]), array([[ 2., 3.], [ 6., 7.], [10., 11.], [14., 15.]])] >>> np.hsplit(x, np.array([3, 6])) [array([[ 0., 1., 2.], [ 4., 5., 6.], [ 8., 9., 10.], [12., 13., 14.]]), array([[ 3.], [ 7.], [11.], [15.]]), array([], shape=(4, 0), dtype=float64)] With a higher dimensional array the split is still along the second axis. >>> x = np.arange(8.0).reshape(2, 2, 2) >>> x array([[[0., 1.], [2., 3.]], [[4., 5.], [6., 7.]]]) >>> np.hsplit(x, 2) [array([[[0., 1.]], [[4., 5.]]]), array([[[2., 3.]], [[6., 7.]]])] """ if _nx.ndim(ary) == 0: raise ValueError("hsplit only works on arrays of 1 or more dimensions") if ary.ndim > 1: return split(ary, indices_or_sections, 1) else: return split(ary, indices_or_sections, 0)
def hsplit(ary, indices_or_sections): """ Split an array into multiple sub-arrays horizontally (column-wise). Please refer to the `split` documentation. `hsplit` is equivalent to `split` with ``axis=1``, the array is always split along the second axis regardless of the array dimension. See Also -------- split : Split an array into multiple sub-arrays of equal size. Examples -------- >>> x = np.arange(16.0).reshape(4, 4) >>> x array([[ 0., 1., 2., 3.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.], [ 12., 13., 14., 15.]]) >>> np.hsplit(x, 2) [array([[ 0., 1.], [ 4., 5.], [ 8., 9.], [ 12., 13.]]), array([[ 2., 3.], [ 6., 7.], [ 10., 11.], [ 14., 15.]])] >>> np.hsplit(x, np.array([3, 6])) [array([[ 0., 1., 2.], [ 4., 5., 6.], [ 8., 9., 10.], [ 12., 13., 14.]]), array([[ 3.], [ 7.], [ 11.], [ 15.]]), array([], dtype=float64)] With a higher dimensional array the split is still along the second axis. >>> x = np.arange(8.0).reshape(2, 2, 2) >>> x array([[[ 0., 1.], [ 2., 3.]], [[ 4., 5.], [ 6., 7.]]]) >>> np.hsplit(x, 2) [array([[[ 0., 1.]], [[ 4., 5.]]]), array([[[ 2., 3.]], [[ 6., 7.]]])] """ if _nx.ndim(ary) == 0: raise ValueError('hsplit only works on arrays of 1 or more dimensions') if ary.ndim > 1: return split(ary, indices_or_sections, 1) else: return split(ary, indices_or_sections, 0)
def __getitem__(self, key): # handle matrix builder syntax if isinstance(key, str): frame = sys._getframe().f_back mymat = matrixlib.bmat(key, frame.f_globals, frame.f_locals) return mymat if not isinstance(key, tuple): key = (key,) # copy attributes, since they can be overridden in the first argument trans1d = self.trans1d ndmin = self.ndmin matrix = self.matrix axis = self.axis objs = [] scalars = [] arraytypes = [] scalartypes = [] for k, item in enumerate(key): scalar = False if isinstance(item, slice): step = item.step start = item.start stop = item.stop if start is None: start = 0 if step is None: step = 1 if isinstance(step, complex): size = int(abs(step)) newobj = function_base.linspace(start, stop, num=size) else: newobj = _nx.arange(start, stop, step) if ndmin > 1: newobj = array(newobj, copy=False, ndmin=ndmin) if trans1d != -1: newobj = newobj.swapaxes(-1, trans1d) elif isinstance(item, str): if k != 0: raise ValueError("special directives must be the " "first entry.") if item in ('r', 'c'): matrix = True col = (item == 'c') continue if ',' in item: vec = item.split(',') try: axis, ndmin = [int(x) for x in vec[:2]] if len(vec) == 3: trans1d = int(vec[2]) continue except Exception: raise ValueError("unknown special directive") try: axis = int(item) continue except (ValueError, TypeError): raise ValueError("unknown special directive") elif type(item) in ScalarType: newobj = array(item, ndmin=ndmin) scalars.append(len(objs)) scalar = True scalartypes.append(newobj.dtype) else: item_ndim = ndim(item) newobj = array(item, copy=False, subok=True, ndmin=ndmin) if trans1d != -1 and item_ndim < ndmin: k2 = ndmin - item_ndim k1 = trans1d if k1 < 0: k1 += k2 + 1 defaxes = list(range(ndmin)) axes = defaxes[:k1] + defaxes[k2:] + defaxes[k1:k2] newobj = newobj.transpose(axes) objs.append(newobj) if not scalar and isinstance(newobj, _nx.ndarray): arraytypes.append(newobj.dtype) # Ensure that scalars won't up-cast unless warranted final_dtype = find_common_type(arraytypes, scalartypes) if final_dtype is not None: for k in scalars: objs[k] = objs[k].astype(final_dtype) res = self.concatenate(tuple(objs), axis=axis) if matrix: oldndim = res.ndim res = self.makemat(res) if oldndim == 1 and col: res = res.T return res