本文整理匯總了Python中numpy.core.numeric.ndim方法的典型用法代碼示例。如果您正苦於以下問題：Python numeric.ndim方法的具體用法？Python numeric.ndim怎麽用？Python numeric.ndim使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類numpy.core.numeric的用法示例。
在下文中一共展示了numeric.ndim方法的5個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: _make_along_axis_idx
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import ndim [as 別名]
def _make_along_axis_idx(arr_shape, indices, axis):
# compute dimensions to iterate over
if not _nx.issubdtype(indices.dtype, _nx.integer):
raise IndexError('`indices` must be an integer array')
if len(arr_shape) != indices.ndim:
raise ValueError(
"`indices` and `arr` must have the same number of dimensions")
shape_ones = (1,) * indices.ndim
dest_dims = list(range(axis)) + [None] + list(range(axis+1, indices.ndim))
# build a fancy index, consisting of orthogonal aranges, with the
# requested index inserted at the right location
fancy_index = []
for dim, n in zip(dest_dims, arr_shape):
if dim is None:
fancy_index.append(indices)
else:
ind_shape = shape_ones[:dim] + (-1,) + shape_ones[dim+1:]
fancy_index.append(_nx.arange(n).reshape(ind_shape))
return tuple(fancy_index) 示例2: column_stack
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import ndim [as 別名]
def column_stack(tup):
"""
Stack 1-D arrays as columns into a 2-D array.
Take a sequence of 1-D arrays and stack them as columns
to make a single 2-D array. 2-D arrays are stacked as-is,
just like with `hstack`. 1-D arrays are turned into 2-D columns
first.
Parameters
----------
tup : sequence of 1-D or 2-D arrays.
Arrays to stack. All of them must have the same first dimension.
Returns
-------
stacked : 2-D array
The array formed by stacking the given arrays.
See Also
--------
stack, hstack, vstack, concatenate
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.column_stack((a,b))
array([[1, 2],
[2, 3],
[3, 4]])
"""
_warn_for_nonsequence(tup)
arrays = []
for v in tup:
arr = array(v, copy=False, subok=True)
if arr.ndim < 2:
arr = array(arr, copy=False, subok=True, ndmin=2).T
arrays.append(arr)
return _nx.concatenate(arrays, 1) 示例3: _replace_zero_by_x_arrays
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import ndim [as 別名]
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 示例4: column_stack
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import ndim [as 別名]
def column_stack(tup):
"""
Stack 1-D arrays as columns into a 2-D array.
Take a sequence of 1-D arrays and stack them as columns
to make a single 2-D array. 2-D arrays are stacked as-is,
just like with `hstack`. 1-D arrays are turned into 2-D columns
first.
Parameters
----------
tup : sequence of 1-D or 2-D arrays.
Arrays to stack. All of them must have the same first dimension.
Returns
-------
stacked : 2-D array
The array formed by stacking the given arrays.
See Also
--------
stack, hstack, vstack, concatenate
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.column_stack((a,b))
array([[1, 2],
[2, 3],
[3, 4]])
"""
arrays = []
for v in tup:
arr = array(v, copy=False, subok=True)
if arr.ndim < 2:
arr = array(arr, copy=False, subok=True, ndmin=2).T
arrays.append(arr)
return _nx.concatenate(arrays, 1) 示例5: column_stack
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import ndim [as 別名]
def column_stack(tup):
"""
Stack 1-D arrays as columns into a 2-D array.
Take a sequence of 1-D arrays and stack them as columns
to make a single 2-D array. 2-D arrays are stacked as-is,
just like with `hstack`. 1-D arrays are turned into 2-D columns
first.
Parameters
----------
tup : sequence of 1-D or 2-D arrays.
Arrays to stack. All of them must have the same first dimension.
Returns
-------
stacked : 2-D array
The array formed by stacking the given arrays.
See Also
--------
hstack, vstack, concatenate
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.column_stack((a,b))
array([[1, 2],
[2, 3],
[3, 4]])
"""
arrays = []
for v in tup:
arr = array(v, copy=False, subok=True)
if arr.ndim < 2:
arr = array(arr, copy=False, subok=True, ndmin=2).T
arrays.append(arr)
return _nx.concatenate(arrays, 1)