本文整理匯總了Python中numpy.core.numeric.concatenate方法的典型用法代碼示例。如果您正苦於以下問題：Python numeric.concatenate方法的具體用法？Python numeric.concatenate怎麽用？Python numeric.concatenate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.concatenate方法的8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: _from_string
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import concatenate [as 別名]
def _from_string(str, gdict, ldict):
rows = str.split(';')
rowtup = []
for row in rows:
trow = row.split(',')
newrow = []
for x in trow:
newrow.extend(x.split())
trow = newrow
coltup = []
for col in trow:
col = col.strip()
try:
thismat = ldict[col]
except KeyError:
try:
thismat = gdict[col]
except KeyError:
raise KeyError("%s not found" % (col,))
coltup.append(thismat)
rowtup.append(concatenate(coltup, axis=-1))
return concatenate(rowtup, axis=0) 示例2: _stack
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import concatenate [as 別名]
def _stack(arrays, axis=0):
arrays = [np.asanyarray(arr) for arr in arrays]
if not arrays:
raise ValueError('need at least one array to stack')
shapes = set(arr.shape for arr in arrays)
if len(shapes) != 1:
raise ValueError('all input arrays must have the same shape')
result_ndim = arrays[0].ndim + 1
if not -result_ndim <= axis < result_ndim:
msg = 'axis {0} out of bounds [-{1}, {1})'.format(axis, result_ndim)
raise np.IndexError(msg)
if axis < 0:
axis += result_ndim
sl = (slice(None),) * axis + (numeric.newaxis,)
expanded_arrays = [arr[sl] for arr in arrays]
return numeric.concatenate(expanded_arrays, axis=axis) 示例3: __setitem__
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import concatenate [as 別名]
def __setitem__(self, key, val):
ind = self.order - key
if key < 0:
raise ValueError("Does not support negative powers.")
if key > self.order:
zr = NX.zeros(key-self.order, self.coeffs.dtype)
self._coeffs = NX.concatenate((zr, self.coeffs))
ind = 0
self._coeffs[ind] = val
return 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 concatenate [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5: column_stack
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import concatenate [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6: __setitem__
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import concatenate [as 別名]
def __setitem__(self, key, val):
ind = self.order - key
if key < 0:
raise ValueError("Does not support negative powers.")
if key > self.order:
zr = NX.zeros(key-self.order, self.coeffs.dtype)
self.__dict__['coeffs'] = NX.concatenate((zr, self.coeffs))
self.__dict__['order'] = key
ind = 0
self.__dict__['coeffs'][ind] = val
return 示例7: column_stack
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import concatenate [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) 示例8: concatenate
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import concatenate [as 別名]
def concatenate (arrays, axis=0):
"Concatenate the arrays along the given axis"
d = []
for x in arrays:
d.append(filled(x))
d = numeric.concatenate(d, axis)
for x in arrays:
if getmask(x) is not nomask: break
else:
return masked_array(d)
dm = []
for x in arrays:
dm.append(getmaskarray(x))
dm = numeric.concatenate(dm, axis)
return masked_array(d, mask=dm)