def polyadd(a1, a2):
"""
Find the sum of two polynomials.
Returns the polynomial resulting from the sum of two input polynomials.
Each input must be either a poly1d object or a 1D sequence of polynomial
coefficients, from highest to lowest degree.
Parameters
----------
a1, a2 : array_like or poly1d object
Input polynomials.
Returns
-------
out : ndarray or poly1d object
The sum of the inputs. If either input is a poly1d object, then the
output is also a poly1d object. Otherwise, it is a 1D array of
polynomial coefficients from highest to lowest degree.
See Also
--------
poly1d : A one-dimensional polynomial class.
poly, polyadd, polyder, polydiv, polyfit, polyint, polysub, polyval
Examples
--------
>>> np.polyadd([1, 2], [9, 5, 4])
array([9, 6, 6])
Using poly1d objects:
>>> p1 = np.poly1d([1, 2])
>>> p2 = np.poly1d([9, 5, 4])
>>> print p1
1 x + 2
>>> print p2
2
9 x + 5 x + 4
>>> print np.polyadd(p1, p2)
2
9 x + 6 x + 6
"""
truepoly = (isinstance(a1, poly1d) or isinstance(a2, poly1d))
a1 = atleast_1d(a1)
a2 = atleast_1d(a2)
diff = len(a2) - len(a1)
if diff == 0:
val = a1 + a2
elif diff > 0:
zr = NX.zeros(diff, a1.dtype)
val = NX.concatenate((zr, a1)) + a2
else:
zr = NX.zeros(abs(diff), a2.dtype)
val = a1 + NX.concatenate((zr, a2))
if truepoly:
val = poly1d(val)
return val
def vstack(tup):
""" Stack arrays in sequence vertically (row wise)
Description:
Take a sequence of arrays and stack them vertically
to make a single array. All arrays in the sequence
must have the same shape along all but the first axis.
vstack will rebuild arrays divided by vsplit.
Arguments:
tup -- sequence of arrays. All arrays must have the same
shape.
Examples:
>>> import numpy
>>> a = array((1,2,3))
>>> b = array((2,3,4))
>>> numpy.vstack((a,b))
array([[1, 2, 3],
[2, 3, 4]])
>>> a = array([[1],[2],[3]])
>>> b = array([[2],[3],[4]])
>>> numpy.vstack((a,b))
array([[1],
[2],
[3],
[2],
[3],
[4]])
"""
return _nx.concatenate(map(atleast_2d,tup),0)
def hstack(tup):
""" Stack arrays in sequence horizontally (column wise)
Description:
Take a sequence of arrays and stack them horizontally
to make a single array. All arrays in the sequence
must have the same shape along all but the second axis.
hstack will rebuild arrays divided by hsplit.
Arguments:
tup -- sequence of arrays. All arrays must have the same
shape.
Examples:
>>> import numpy
>>> a = array((1,2,3))
>>> b = array((2,3,4))
>>> numpy.hstack((a,b))
array([1, 2, 3, 2, 3, 4])
>>> a = array([[1],[2],[3]])
>>> b = array([[2],[3],[4]])
>>> numpy.hstack((a,b))
array([[1, 2],
[2, 3],
[3, 4]])
"""
return _nx.concatenate(map(atleast_1d,tup),1)
def column_stack(tup):
""" Stack 1D arrays as columns into a 2D array
Description:
Take a sequence of 1D arrays and stack them as columns
to make a single 2D array. All arrays in the sequence
must have the same first dimension. 2D arrays are
stacked as-is, just like with hstack. 1D arrays are turned
into 2D columns first.
Arguments:
tup -- sequence of 1D or 2D arrays. All arrays must have the same
first dimension.
Examples:
>>> import numpy
>>> a = array((1,2,3))
>>> b = array((2,3,4))
>>> numpy.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)
def dstack(tup):
""" Stack arrays in sequence depth wise (along third dimension)
Description:
Take a sequence of arrays and stack them along the third axis.
All arrays in the sequence must have the same shape along all
but the third axis. This is a simple way to stack 2D arrays
(images) into a single 3D array for processing.
dstack will rebuild arrays divided by dsplit.
Arguments:
tup -- sequence of arrays. All arrays must have the same
shape.
Examples:
>>> import numpy
>>> a = array((1,2,3))
>>> b = array((2,3,4))
>>> numpy.dstack((a,b))
array([[[1, 2],
[2, 3],
[3, 4]]])
>>> a = array([[1],[2],[3]])
>>> b = array([[2],[3],[4]])
>>> numpy.dstack((a,b))
array([[[1, 2]],
<BLANKLINE>
[[2, 3]],
<BLANKLINE>
[[3, 4]]])
"""
return _nx.concatenate(map(atleast_3d,tup),2)
def kron(a,b):
"""kronecker product of a and b
Kronecker product of two arrays is block array
[[ a[ 0 ,0]*b, a[ 0 ,1]*b, ... , a[ 0 ,n-1]*b ],
[ ... ... ],
[ a[m-1,0]*b, a[m-1,1]*b, ... , a[m-1,n-1]*b ]]
"""
wrapper = get_array_wrap(a, b)
b = asanyarray(b)
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)
as_ = a.shape
bs = b.shape
if not a.flags.contiguous:
a = reshape(a, as_)
if not b.flags.contiguous:
b = reshape(b, bs)
nd = ndb
if (ndb != nda):
if (ndb > nda):
as_ = (1,)*(ndb-nda) + as_
else:
bs = (1,)*(nda-ndb) + bs
nd = nda
result = outer(a,b).reshape(as_+bs)
axis = nd-1
for _ in xrange(nd):
result = concatenate(result, axis=axis)
if wrapper is not None:
result = wrapper(result)
return result
def polysub(a1, a2):
"""
Returns difference from subtraction of two polynomials input as sequences.
Returns difference of polynomials; `a1` - `a2`. Input polynomials are
represented as an array_like sequence of terms or a poly1d object.
Parameters
----------
a1 : {array_like, poly1d}
Minuend polynomial as sequence of terms.
a2 : {array_like, poly1d}
Subtrahend polynomial as sequence of terms.
Returns
-------
out : {ndarray, poly1d}
Array representing the polynomial terms.
See Also
--------
polyval, polydiv, polymul, polyadd
Examples
--------
.. math:: (2 x^2 + 10 x - 2) - (3 x^2 + 10 x -4) = (-x^2 + 2)
>>> np.polysub([2, 10, -2], [3, 10, -4])
array([-1, 0, 2])
"""
truepoly = (isinstance(a1, poly1d) or isinstance(a2, poly1d))
a1 = atleast_1d(a1)
a2 = atleast_1d(a2)
diff = len(a2) - len(a1)
if diff == 0:
val = a1 - a2
elif diff > 0:
zr = NX.zeros(diff, a1.dtype)
val = NX.concatenate((zr, a1)) - a2
else:
zr = NX.zeros(abs(diff), a2.dtype)
val = a1 - NX.concatenate((zr, a2))
if truepoly:
val = poly1d(val)
return val
def stack(arrays, axis=0):
"""
Join a sequence of arrays along a new axis.
The `axis` parameter specifies the index of the new axis in the dimensions
of the result. For example, if ``axis=0`` it will be the first dimension
and if ``axis=-1`` it will be the last dimension.
.. versionadded:: 1.10.0
Parameters
----------
arrays : sequence of array_like
Each array must have the same shape.
axis : int, optional
The axis in the result array along which the input arrays are stacked.
Returns
-------
stacked : ndarray
The stacked array has one more dimension than the input arrays.
See Also
--------
concatenate : Join a sequence of arrays along an existing axis.
split : Split array into a list of multiple sub-arrays of equal size.
Examples
--------
>>> arrays = [np.random.randn(3, 4) for _ in range(10)]
>>> np.stack(arrays, axis=0).shape
(10, 3, 4)
>>> np.stack(arrays, axis=1).shape
(3, 10, 4)
>>> np.stack(arrays, axis=2).shape
(3, 4, 10)
>>> a = np.array([1, 2, 3])
>>> b = np.array([2, 3, 4])
>>> np.stack((a, b))
array([[1, 2, 3],
[2, 3, 4]])
>>> np.stack((a, b), axis=-1)
array([[1, 2],
[2, 3],
[3, 4]])
"""
arrays = [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 IndexError(msg)
if axis < 0:
axis += result_ndim
sl = (slice(None),) * axis + (_nx.newaxis,)
expanded_arrays = [arr[sl] for arr in arrays]
return _nx.concatenate(expanded_arrays, axis=axis)
def polysub(a1, a2):
"""
Difference (subtraction) of two polynomials.
Given two polynomials `a1` and `a2`, returns ``a1 - a2``.
`a1` and `a2` can be either array_like sequences of the polynomials'
coefficients (including coefficients equal to zero), or `poly1d` objects.
Parameters
----------
a1, a2 : array_like or poly1d
Minuend and subtrahend polynomials, respectively.
Returns
-------
out : ndarray or poly1d
Array or `poly1d` object of the difference polynomial's coefficients.
See Also
--------
polyval, polydiv, polymul, polyadd
Examples
--------
.. math:: (2 x^2 + 10 x - 2) - (3 x^2 + 10 x -4) = (-x^2 + 2)
>>> np.polysub([2, 10, -2], [3, 10, -4])
array([-1, 0, 2])
"""
truepoly = (isinstance(a1, poly1d) or isinstance(a2, poly1d))
a1 = atleast_1d(a1)
a2 = atleast_1d(a2)
diff = len(a2) - len(a1)
if diff == 0:
val = a1 - a2
elif diff > 0:
zr = NX.zeros(diff, a1.dtype)
val = NX.concatenate((zr, a1)) - a2
else:
zr = NX.zeros(abs(diff), a2.dtype)
val = a1 - NX.concatenate((zr, a2))
if truepoly:
val = poly1d(val)
return val
def polysub(a1, a2):
"""
Difference (subtraction) of two polynomials.
Given two polynomials `a1` and `a2`, returns ``a1 - a2``.
`a1` and `a2` can be either array_like sequences of the polynomials'
coefficients (including coefficients equal to zero), or `poly1d` objects.
Parameters
----------
a1, a2 : array_like or poly1d
Minuend and subtrahend polynomials, respectively.
Returns
-------
out : ndarray or poly1d
Array or `poly1d` object of the difference polynomial's coefficients.
See Also
--------
polyval, polydiv, polymul, polyadd
Examples
--------
.. math:: (2 x^2 + 10 x - 2) - (3 x^2 + 10 x -4) = (-x^2 + 2)
>>> np.polysub([2, 10, -2], [3, 10, -4])
array([-1, 0, 2])
"""
truepoly = (isinstance(a1, poly1d) or isinstance(a2, poly1d))
a1 = atleast_1d(a1)
a2 = atleast_1d(a2)
diff = len(a2) - len(a1)
if diff == 0:
val = a1 - a2
elif diff > 0:
zr = NX.zeros(diff, a1.dtype)
val = NX.concatenate((zr, a1)) - a2
else:
zr = NX.zeros(abs(diff), a2.dtype)
val = a1 - NX.concatenate((zr, a2))
if truepoly:
val = poly1d(val)
return val
def polysub(a1, a2):
"""Subtracts two polynomials represented as sequences
"""
truepoly = (isinstance(a1, poly1d) or isinstance(a2, poly1d))
a1 = atleast_1d(a1)
a2 = atleast_1d(a2)
diff = len(a2) - len(a1)
if diff == 0:
val = a1 - a2
elif diff > 0:
zr = NX.zeros(diff, a1.dtype)
val = NX.concatenate((zr, a1)) - a2
else:
zr = NX.zeros(abs(diff), a2.dtype)
val = a1 - NX.concatenate((zr, a2))
if truepoly:
val = poly1d(val)
return val
def _leading_trailing(a):
import numpy.core.numeric as _nc
if a.ndim == 1:
if len(a) > 2*_summaryEdgeItems:
b = _nc.concatenate((a[:_summaryEdgeItems],
a[-_summaryEdgeItems:]))
else:
b = a
else:
if len(a) > 2*_summaryEdgeItems:
l = [_leading_trailing(a[i]) for i in range(
min(len(a), _summaryEdgeItems))]
l.extend([_leading_trailing(a[-i]) for i in range(
min(len(a), _summaryEdgeItems),0,-1)])
else:
l = [_leading_trailing(a[i]) for i in range(0, len(a))]
b = _nc.concatenate(tuple(l))
return b
def block_recursion(arrays, depth=0):
if depth < max_depth:
if len(arrays) == 0:
raise ValueError('Lists cannot be empty')
arrs = [block_recursion(arr, depth+1) for arr in arrays]
return _nx.concatenate(arrs, axis=-(max_depth-depth))
else:
# We've 'bottomed out' - arrays is either a scalar or an array
# type(arrays) is not list
return atleast_nd(arrays, result_ndim)
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
def append(arr, values, axis=None):
"""Append to the end of an array along axis (ravel first if None)
"""
arr = asanyarray(arr)
if axis is None:
if arr.ndim != 1:
arr = arr.ravel()
values = ravel(values)
axis = arr.ndim-1
return concatenate((arr, values), axis=axis)
def block_recursion(arrays, depth=0):
if depth < max_depth:
if len(arrays) == 0:
raise ValueError('Lists cannot be empty')
arrs = [block_recursion(arr, depth+1) for arr in arrays]
return _nx.concatenate(arrs, axis=-(max_depth-depth))
else:
# We've 'bottomed out' - arrays is either a scalar or an array
# type(arrays) is not list
return atleast_nd(arrays, result_ndim)
def _leading_trailing(a):
import numpy.core.numeric as _nc
if a.ndim == 1:
if len(a) > 2 * _summaryEdgeItems:
b = _nc.concatenate((a[:_summaryEdgeItems],
a[-_summaryEdgeItems:]))
else:
b = a
else:
if len(a) > 2 * _summaryEdgeItems:
l = [_leading_trailing(a[i]) for i in range(
min(len(a), _summaryEdgeItems))]
l.extend([_leading_trailing(a[-i]) for i in range(
min(len(a), _summaryEdgeItems), 0, -1)])
else:
l = [_leading_trailing(a[i]) for i in range(0, len(a))]
b = _nc.concatenate(tuple(l))
return b
def append(arr, values, axis=None):
"""Append to the end of an array along axis (ravel first if None)
"""
arr = asanyarray(arr)
if axis is None:
if arr.ndim != 1:
arr = arr.ravel()
values = ravel(values)
axis = arr.ndim - 1
return concatenate((arr, values), axis=axis)
def dstack(tup):
"""
Stack arrays in sequence depth wise (along third axis).
Takes a sequence of arrays and stack them along the third axis
to make a single array. Rebuilds arrays divided by `dsplit`.
This is a simple way to stack 2D arrays (images) into a single
3D array for processing.
This function continues to be supported for backward compatibility, but
you should prefer ``np.concatenate`` or ``np.stack``. The ``np.stack``
function was added in NumPy 1.10.
Parameters
----------
tup : sequence of arrays
Arrays to stack. All of them must have the same shape along all
but the third axis.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
stack : Join a sequence of arrays along a new axis.
vstack : Stack along first axis.
hstack : Stack along second axis.
concatenate : Join a sequence of arrays along an existing axis.
dsplit : Split array along third axis.
Notes
-----
Equivalent to ``np.concatenate(tup, axis=2)`` if `tup` contains arrays that
are at least 3-dimensional.
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.dstack((a,b))
array([[[1, 2],
[2, 3],
[3, 4]]])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.dstack((a,b))
array([[[1, 2]],
[[2, 3]],
[[3, 4]]])
"""
return _nx.concatenate([atleast_3d(_m) for _m in tup], 2)
def dstack(tup):
"""
Stack arrays in sequence depth wise (along third axis).
This is equivalent to concatenation along the third axis after 2-D arrays
of shape `(M,N)` have been reshaped to `(M,N,1)` and 1-D arrays of shape
`(N,)` have been reshaped to `(1,N,1)`. Rebuilds arrays divided by
`dsplit`.
This function makes most sense for arrays with up to 3 dimensions. For
instance, for pixel-data with a height (first axis), width (second axis),
and r/g/b channels (third axis). The functions `concatenate`, `stack` and
`block` provide more general stacking and concatenation operations.
Parameters
----------
tup : sequence of arrays
The arrays must have the same shape along all but the third axis.
1-D or 2-D arrays must have the same shape.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays, will be at least 3-D.
See Also
--------
stack : Join a sequence of arrays along a new axis.
vstack : Stack along first axis.
hstack : Stack along second axis.
concatenate : Join a sequence of arrays along an existing axis.
dsplit : Split array along third axis.
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.dstack((a,b))
array([[[1, 2],
[2, 3],
[3, 4]]])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.dstack((a,b))
array([[[1, 2]],
[[2, 3]],
[[3, 4]]])
"""
if not overrides.ARRAY_FUNCTION_ENABLED:
# raise warning if necessary
_arrays_for_stack_dispatcher(tup, stacklevel=2)
return _nx.concatenate([atleast_3d(_m) for _m in tup], 2)
def fast_hstack(tup):
"""
Stack arrays in sequence horizontally (column wise).
Faster version of hstack (traditional hstack calls asanyarray too many times)
:param tup Collection[array]: input arrays in a tuple(see hstack)
:return array: stacked array see hstack
"""
arrays = []
for a in tup:
assert isinstance(a, np.ndarray)
# this is fast implementation of atleast_1d for arrays
if len(a.shape) == 0:
a = a.reshape(1)
arrays.append(a)
# As a special case, dimension 0 of 1-dimensional arrays is "horizontal"
if arrays[0].ndim == 1:
return concatenate(arrays, 0)
else:
return concatenate(arrays, 1)
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
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
def dstack(tup):
"""
Stack arrays in sequence depth wise (along third axis).
This is equivalent to concatenation along the third axis after 2-D arrays
of shape `(M,N)` have been reshaped to `(M,N,1)` and 1-D arrays of shape
`(N,)` have been reshaped to `(1,N,1)`. Rebuilds arrays divided by
`dsplit`.
This function makes most sense for arrays with up to 3 dimensions. For
instance, for pixel-data with a height (first axis), width (second axis),
and r/g/b channels (third axis). The functions `concatenate`, `stack` and
`block` provide more general stacking and concatenation operations.
Parameters
----------
tup : sequence of arrays
The arrays must have the same shape along all but the third axis.
1-D or 2-D arrays must have the same shape.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays, will be at least 3-D.
See Also
--------
stack : Join a sequence of arrays along a new axis.
vstack : Stack along first axis.
hstack : Stack along second axis.
concatenate : Join a sequence of arrays along an existing axis.
dsplit : Split array along third axis.
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.dstack((a,b))
array([[[1, 2],
[2, 3],
[3, 4]]])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.dstack((a,b))
array([[[1, 2]],
[[2, 3]],
[[3, 4]]])
"""
_warn_for_nonsequence(tup)
return _nx.concatenate([atleast_3d(_m) for _m in tup], 2)
def polyadd(a1, a2):
"""
Returns sum of two polynomials.
Returns sum of polynomials; `a1` + `a2`. Input polynomials are
represented as an array_like sequence of terms or a poly1d object.
Parameters
----------
a1 : {array_like, poly1d}
Polynomial as sequence of terms.
a2 : {array_like, poly1d}
Polynomial as sequence of terms.
Returns
-------
out : {ndarray, poly1d}
Array representing the polynomial terms.
See Also
--------
polyval, polydiv, polymul, polyadd
"""
truepoly = (isinstance(a1, poly1d) or isinstance(a2, poly1d))
a1 = atleast_1d(a1)
a2 = atleast_1d(a2)
diff = len(a2) - len(a1)
if diff == 0:
val = a1 + a2
elif diff > 0:
zr = NX.zeros(diff, a1.dtype)
val = NX.concatenate((zr, a1)) + a2
else:
zr = NX.zeros(abs(diff), a2.dtype)
val = a1 + NX.concatenate((zr, a2))
if truepoly:
val = poly1d(val)
return val
def polyadd(a1, a2):
"""
Returns sum of two polynomials.
Returns sum of polynomials; `a1` + `a2`. Input polynomials are
represented as an array_like sequence of terms or a poly1d object.
Parameters
----------
a1 : {array_like, poly1d}
Polynomial as sequence of terms.
a2 : {array_like, poly1d}
Polynomial as sequence of terms.
Returns
-------
out : {ndarray, poly1d}
Array representing the polynomial terms.
See Also
--------
polyval, polydiv, polymul, polyadd
"""
truepoly = (isinstance(a1, poly1d) or isinstance(a2, poly1d))
a1 = atleast_1d(a1)
a2 = atleast_1d(a2)
diff = len(a2) - len(a1)
if diff == 0:
val = a1 + a2
elif diff > 0:
zr = NX.zeros(diff, a1.dtype)
val = NX.concatenate((zr, a1)) + a2
else:
zr = NX.zeros(abs(diff), a2.dtype)
val = a1 + NX.concatenate((zr, a2))
if truepoly:
val = poly1d(val)
return val
def vstack(tup):
"""
Stack arrays in sequence vertically (row wise).
Take a sequence of arrays and stack them vertically to make a single
array. Rebuild arrays divided by `vsplit`.
Parameters
----------
tup : sequence of ndarrays
Tuple containing arrays to be stacked. The arrays must have the same
shape along all but the first axis.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
hstack : Stack arrays in sequence horizontally (column wise).
dstack : Stack arrays in sequence depth wise (along third dimension).
concatenate : Join a sequence of arrays together.
vsplit : Split array into a list of multiple sub-arrays vertically.
Notes
-----
Equivalent to ``np.concatenate(tup, axis=0)``
Examples
--------
>>> a = np.array([1, 2, 3])
>>> b = np.array([2, 3, 4])
>>> np.vstack((a,b))
array([[1, 2, 3],
[2, 3, 4]])
>>> a = np.array([[1], [2], [3]])
>>> b = np.array([[2], [3], [4]])
>>> np.vstack((a,b))
array([[1],
[2],
[3],
[2],
[3],
[4]])
"""
return _nx.concatenate(map(atleast_2d, tup), 0)
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 as e:
raise NameError(f"name {col!r} is not defined") from None
coltup.append(thismat)
rowtup.append(concatenate(coltup, axis=-1))
return concatenate(rowtup, axis=0)
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)
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)
def vstack(tup):
"""
Stack arrays in sequence vertically (row wise).
Take a sequence of arrays and stack them vertically to make a single
array. Rebuild arrays divided by `vsplit`.
Parameters
----------
tup : sequence of ndarrays
Tuple containing arrays to be stacked. The arrays must have the same
shape along all but the first axis.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
hstack : Stack arrays in sequence horizontally (column wise).
dstack : Stack arrays in sequence depth wise (along third dimension).
concatenate : Join a sequence of arrays together.
vsplit : Split array into a list of multiple sub-arrays vertically.
Notes
-----
Equivalent to ``np.concatenate(tup, axis=0)``
Examples
--------
>>> a = np.array([1, 2, 3])
>>> b = np.array([2, 3, 4])
>>> np.vstack((a,b))
array([[1, 2, 3],
[2, 3, 4]])
>>> a = np.array([[1], [2], [3]])
>>> b = np.array([[2], [3], [4]])
>>> np.vstack((a,b))
array([[1],
[2],
[3],
[2],
[3],
[4]])
"""
return _nx.concatenate(map(atleast_2d,tup),0)
def dstack(tup):
"""
Stack arrays in sequence depth wise (along third axis).
Takes a sequence of arrays and stack them along the third axis
to make a single array. Rebuilds arrays divided by `dsplit`.
This is a simple way to stack 2D arrays (images) into a single
3D array for processing.
Parameters
----------
tup : sequence of arrays
Arrays to stack. All of them must have the same shape along all
but the third axis.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
stack : Join a sequence of arrays along a new axis.
vstack : Stack along first axis.
hstack : Stack along second axis.
concatenate : Join a sequence of arrays along an existing axis.
dsplit : Split array along third axis.
Notes
-----
Equivalent to ``np.concatenate(tup, axis=2)``.
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.dstack((a,b))
array([[[1, 2],
[2, 3],
[3, 4]]])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.dstack((a,b))
array([[[1, 2]],
[[2, 3]],
[[3, 4]]])
"""
return _nx.concatenate([atleast_3d(_m) for _m in tup], 2)
def dstack(tup):
"""
Stack arrays in sequence depth wise (along third axis).
Takes a sequence of arrays and stack them along the third axis
to make a single array. Rebuilds arrays divided by `dsplit`.
This is a simple way to stack 2D arrays (images) into a single
3D array for processing.
Parameters
----------
tup : sequence of arrays
Arrays to stack. All of them must have the same shape along all
but the third axis.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
stack : Join a sequence of arrays along a new axis.
vstack : Stack along first axis.
hstack : Stack along second axis.
concatenate : Join a sequence of arrays along an existing axis.
dsplit : Split array along third axis.
Notes
-----
Equivalent to ``np.concatenate(tup, axis=2)``.
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.dstack((a,b))
array([[[1, 2],
[2, 3],
[3, 4]]])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.dstack((a,b))
array([[[1, 2]],
[[2, 3]],
[[3, 4]]])
"""
return _nx.concatenate([atleast_3d(_m) for _m in tup], 2)
def cov(m, y=None, rowvar=1, bias=0):
"""Estimate the covariance matrix.
If m is a vector, return the variance. For matrices return the
covariance matrix.
If y is given it is treated as an additional (set of)
variable(s).
Normalization is by (N-1) where N is the number of observations
(unbiased estimate). If bias is 1 then normalization is by N.
If rowvar is non-zero (default), then each row is a variable with
observations in the columns, otherwise each column
is a variable and the observations are in the rows.
"""
X = array(m, ndmin=2, dtype=float)
if X.shape[0] == 1:
rowvar = 1
if rowvar:
axis = 0
tup = (slice(None),newaxis)
else:
axis = 1
tup = (newaxis, slice(None))
if y is not None:
y = array(y, copy=False, ndmin=2, dtype=float)
X = concatenate((X,y),axis)
X -= X.mean(axis=1-axis)[tup]
if rowvar:
N = X.shape[1]
else:
N = X.shape[0]
if bias:
fact = N*1.0
else:
fact = N-1.0
if not rowvar:
return (dot(X.T, X.conj()) / fact).squeeze()
else:
return (dot(X, X.T.conj()) / fact).squeeze()
def cov(x, y=None, rowvar=True, bias=False, strict=False):
"""
Estimate the covariance matrix.
If x is a vector, return the variance. For matrices, returns the covariance
matrix.
If y is given, it is treated as an additional (set of) variable(s).
Normalization is by (N-1) where N is the number of observations (unbiased
estimate). If bias is True then normalization is by N.
If rowvar is non-zero (default), then each row is a variable with observations
in the columns, otherwise each column is a variable and the observations are
in the rows.
If strict is True, masked values are propagated: if a masked value appears in
a row or column, the whole row or column is considered masked.
"""
X = narray(x, ndmin=2, subok=True, dtype=float)
if X.shape[0] == 1:
rowvar = True
if rowvar:
axis = 0
tup = (slice(None),None)
else:
axis = 1
tup = (None, slice(None))
#
if y is not None:
y = narray(y, copy=False, ndmin=2, subok=True, dtype=float)
X = concatenate((X,y),axis)
#
X -= X.mean(axis=1-axis)[tup]
n = X.count(1-axis)
#
if bias:
fact = n*1.0
else:
fact = n-1.0
#
if not rowvar:
return (dot(X.T, X.conj(), strict=False) / fact).squeeze()
else:
return (dot(X, X.T.conj(), strict=False) / fact).squeeze()
def fast_vstack(tup):
"""
Stack arrays in sequence vertically (row wise).
Faster version of vstack (traditional vstack calls asanyarray too many times)
:param tup Collection[array]: input arrays in a tuple(see vstack)
:return array: stacked array see vstack
"""
arrays = []
for a in tup:
assert isinstance(a, np.ndarray)
# this is fast implementation of atleast_2d for arrays
if len(a.shape) == 0:
a = a.reshape(1, 1)
elif len(a.shape) == 1:
a = a[np.newaxis, :]
arrays.append(a)
return concatenate(arrays, 0)
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
Notes
-----
This function is equivalent to ``np.vstack(tup).T``.
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)
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]])
"""
if not overrides.ARRAY_FUNCTION_ENABLED:
# raise warning if necessary
_arrays_for_stack_dispatcher(tup, stacklevel=2)
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)
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
Notes
-----
This function is equivalent to ``np.vstack(tup).T``.
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)
def vstack(tup):
"""
Stack arrays vertically.
`vstack` can be used to rebuild arrays divided by `vsplit`.
Parameters
----------
tup : sequence of arrays
Tuple containing arrays to be stacked. The arrays must have the same
shape along all but the first axis.
See Also
--------
array_split : Split an array into a list of multiple sub-arrays of
near-equal size.
split : Split array into a list of multiple sub-arrays of equal size.
vsplit : Split array into a list of multiple sub-arrays vertically.
dsplit : Split array into a list of multiple sub-arrays along the 3rd axis
(depth).
concatenate : Join arrays together.
hstack : Stack arrays in sequence horizontally (column wise).
dstack : Stack arrays in sequence depth wise (along third dimension).
Examples
--------
>>> a = np.array([1, 2, 3])
>>> b = np.array([2, 3, 4])
>>> np.vstack((a,b))
array([[1, 2, 3],
[2, 3, 4]])
>>> a = np.array([[1], [2], [3]])
>>> b = np.array([[2], [3], [4]])
>>> np.vstack((a,b))
array([[1],
[2],
[3],
[2],
[3],
[4]])
"""
return _nx.concatenate(map(atleast_2d, tup), 0)
def hstack(tup):
"""
Stack arrays in sequence horizontally (column wise)
Take a sequence of arrays and stack them horizontally to make
a single array. Rebuild arrays divided by ``hsplit``.
Parameters
----------
tup : sequence of ndarrays
All arrays must have the same shape along all but the second axis.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
vstack : Stack along first axis.
dstack : Stack along third axis.
concatenate : Join arrays.
hsplit : Split array along second axis.
Notes
-----
Equivalent to ``np.concatenate(tup, axis=1)``
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.hstack((a,b))
array([1, 2, 3, 2, 3, 4])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.hstack((a,b))
array([[1, 2],
[2, 3],
[3, 4]])
"""
return _nx.concatenate(map(atleast_1d, tup), 1)
def hstack(tup):
"""
Stack arrays in sequence horizontally (column wise).
Take a sequence of arrays and stack them horizontally to make
a single array. Rebuild arrays divided by ``hsplit``.
Parameters
----------
tup : sequence of ndarrays
All arrays must have the same shape along all but the second axis.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
vstack : Stack arrays in sequence vertically (row wise).
dstack : Stack arrays in sequence depth wise (along third axis).
concatenate : Join a sequence of arrays together.
hsplit : Split array along second axis.
Notes
-----
Equivalent to ``np.concatenate(tup, axis=1)``
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.hstack((a,b))
array([1, 2, 3, 2, 3, 4])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.hstack((a,b))
array([[1, 2],
[2, 3],
[3, 4]])
"""
return _nx.concatenate(map(atleast_1d,tup),1)
def unique(x):
"""Return sorted unique items from an array or sequence.
Example:
>>> unique([5,2,4,0,4,4,2,2,1])
array([0, 1, 2, 4, 5])
"""
try:
tmp = x.flatten()
if tmp.size == 0:
return tmp
tmp.sort()
idx = concatenate(([True], tmp[1:] != tmp[:-1]))
return tmp[idx]
except AttributeError:
items = list(set(x))
items.sort()
return asarray(items)
def unique(x):
"""Return sorted unique items from an array or sequence.
Example:
>>> unique([5,2,4,0,4,4,2,2,1])
array([0, 1, 2, 4, 5])
"""
try:
tmp = x.flatten()
if tmp.size == 0:
return tmp
tmp.sort()
idx = concatenate(([True],tmp[1:]!=tmp[:-1]))
return tmp[idx]
except AttributeError:
items = list(set(x))
items.sort()
return asarray(items)
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)
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 IndexError(msg)
if axis < 0:
axis += result_ndim
sl = (slice(None), ) * axis + (_nx.newaxis, )
expanded_arrays = [arr[sl] for arr in arrays]
return _nx.concatenate(expanded_arrays, axis=axis)
def transform(self, positions, R_i=None, t_i=None, s_i=None, flip=False):
"""
Return subclusters with (randomly) rotated translated and scaled
positions. If R_i, s_i or t_i is given then that part of transformation
is not random.
"""
for sub in positions:
t = t_i or rand(2)*10
s = s_i or rand()*2
if R_i is None:
th = 2*pi*rand()
# ccw
R = array([[cos(th), -sin(th)], [sin(th), cos(th)]])
else:
R = R_i
if flip:
#TODO: make R with flip
pass
for node, pos in sub.items():
sub[node] = concatenate((dot(dot(s, R), pos[:2])+t, [nan]))
def transform(self, positions, R_i=None, t_i=None, s_i=None, flip=False):
"""
Return subclusters with (randomly) rotated translated and scaled
positions. If R_i, s_i or t_i is given then that part of transformation
is not random.
"""
for sub in positions:
t = t_i or rand(2) * 10
s = s_i or rand() * 2
if R_i is None:
th = 2 * pi * rand()
# ccw
R = array([[cos(th), -sin(th)], [sin(th), cos(th)]])
else:
R = R_i
if flip:
#TODO: make R with flip
pass
for node, pos in sub.items():
sub[node] = concatenate((dot(dot(s, R), pos[:2]) + t, [nan]))
def bmat(obj, ldict=None, gdict=None):
"""
Build a matrix object from a string, nested sequence, or array.
Parameters
----------
obj : str or array_like
Input data. Names of variables in the current scope may be
referenced, even if `obj` is a string.
ldict : dict, optional
A dictionary that replaces local operands in current frame.
Ignored if `obj` is not a string or `gdict` is `None`.
gdict : dict, optional
A dictionary that replaces global operands in current frame.
Ignored if `obj` is not a string.
Returns
-------
out : matrix
Returns a matrix object, which is a specialized 2-D array.
See Also
--------
matrix
Examples
--------
>>> A = np.mat('1 1; 1 1')
>>> B = np.mat('2 2; 2 2')
>>> C = np.mat('3 4; 5 6')
>>> D = np.mat('7 8; 9 0')
All the following expressions construct the same block matrix:
>>> np.bmat([[A, B], [C, D]])
matrix([[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 4, 7, 8],
[5, 6, 9, 0]])
>>> np.bmat(np.r_[np.c_[A, B], np.c_[C, D]])
matrix([[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 4, 7, 8],
[5, 6, 9, 0]])
>>> np.bmat('A,B; C,D')
matrix([[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 4, 7, 8],
[5, 6, 9, 0]])
"""
if isinstance(obj, str):
if gdict is None:
# get previous frame
frame = sys._getframe().f_back
glob_dict = frame.f_globals
loc_dict = frame.f_locals
else:
glob_dict = gdict
loc_dict = ldict
return matrix(_from_string(obj, glob_dict, loc_dict))
if isinstance(obj, (tuple, list)):
# [[A,B],[C,D]]
arr_rows = []
for row in obj:
if isinstance(row, N.ndarray): # not 2-d
return matrix(concatenate(obj, axis=-1))
else:
arr_rows.append(concatenate(row, axis=-1))
return matrix(concatenate(arr_rows, axis=0))
if isinstance(obj, N.ndarray):
return matrix(obj)
def kron(a, b):
"""
Kronecker product of two arrays.
Computes the Kronecker product, a composite array made of blocks of the
second array scaled by the first.
Parameters
----------
a, b : array_like
Returns
-------
out : ndarray
See Also
--------
outer : The outer product
Notes
-----
The function assumes that the number of dimensions of `a` and `b`
are the same, if necessary prepending the smallest with ones.
If `a.shape = (r0,r1,..,rN)` and `b.shape = (s0,s1,...,sN)`,
the Kronecker product has shape `(r0*s0, r1*s1, ..., rN*SN)`.
The elements are products of elements from `a` and `b`, organized
explicitly by::
kron(a,b)[k0,k1,...,kN] = a[i0,i1,...,iN] * b[j0,j1,...,jN]
where::
kt = it * st + jt, t = 0,...,N
In the common 2-D case (N=1), the block structure can be visualized::
[[ a[0,0]*b, a[0,1]*b, ... , a[0,-1]*b ],
[ ... ... ],
[ a[-1,0]*b, a[-1,1]*b, ... , a[-1,-1]*b ]]
Examples
--------
>>> np.kron([1,10,100], [5,6,7])
array([ 5, 6, 7, 50, 60, 70, 500, 600, 700])
>>> np.kron([5,6,7], [1,10,100])
array([ 5, 50, 500, 6, 60, 600, 7, 70, 700])
>>> np.kron(np.eye(2), np.ones((2,2)))
array([[ 1., 1., 0., 0.],
[ 1., 1., 0., 0.],
[ 0., 0., 1., 1.],
[ 0., 0., 1., 1.]])
>>> a = np.arange(100).reshape((2,5,2,5))
>>> b = np.arange(24).reshape((2,3,4))
>>> c = np.kron(a,b)
>>> c.shape
(2, 10, 6, 20)
>>> I = (1,3,0,2)
>>> J = (0,2,1)
>>> J1 = (0,) + J # extend to ndim=4
>>> S1 = (1,) + b.shape
>>> K = tuple(np.array(I) * np.array(S1) + np.array(J1))
>>> c[K] == a[I]*b[J]
True
"""
b = asanyarray(b)
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)
as_ = a.shape
bs = b.shape
if not a.flags.contiguous:
a = reshape(a, as_)
if not b.flags.contiguous:
b = reshape(b, bs)
nd = ndb
if (ndb != nda):
if (ndb > nda):
as_ = (1, ) * (ndb - nda) + as_
else:
bs = (1, ) * (nda - ndb) + bs
nd = nda
result = outer(a, b).reshape(as_ + bs)
axis = nd - 1
for _ in range(nd):
result = concatenate(result, axis=axis)
wrapper = get_array_prepare(a, b)
if wrapper is not None:
result = wrapper(result)
wrapper = get_array_wrap(a, b)
if wrapper is not None:
result = wrapper(result)
return result
def polyint(p, m=1, k=None):
"""
Return an antiderivative (indefinite integral) of a polynomial.
The returned order `m` antiderivative `P` of polynomial `p` satisfies
:math:`\\frac{d^m}{dx^m}P(x) = p(x)` and is defined up to `m - 1`
integration constants `k`. The constants determine the low-order
polynomial part
.. math:: \\frac{k_{m-1}}{0!} x^0 + \\ldots + \\frac{k_0}{(m-1)!}x^{m-1}
of `P` so that :math:`P^{(j)}(0) = k_{m-j-1}`.
Parameters
----------
p : array_like or poly1d
Polynomial to differentiate.
A sequence is interpreted as polynomial coefficients, see `poly1d`.
m : int, optional
Order of the antiderivative. (Default: 1)
k : list of `m` scalars or scalar, optional
Integration constants. They are given in the order of integration:
those corresponding to highest-order terms come first.
If ``None`` (default), all constants are assumed to be zero.
If `m = 1`, a single scalar can be given instead of a list.
See Also
--------
polyder : derivative of a polynomial
poly1d.integ : equivalent method
Examples
--------
The defining property of the antiderivative:
>>> p = np.poly1d([1,1,1])
>>> P = np.polyint(p)
>>> P
poly1d([ 0.33333333, 0.5 , 1. , 0. ])
>>> np.polyder(P) == p
True
The integration constants default to zero, but can be specified:
>>> P = np.polyint(p, 3)
>>> P(0)
0.0
>>> np.polyder(P)(0)
0.0
>>> np.polyder(P, 2)(0)
0.0
>>> P = np.polyint(p, 3, k=[6,5,3])
>>> P
poly1d([ 0.01666667, 0.04166667, 0.16666667, 3. , 5. , 3. ])
Note that 3 = 6 / 2!, and that the constants are given in the order of
integrations. Constant of the highest-order polynomial term comes first:
>>> np.polyder(P, 2)(0)
6.0
>>> np.polyder(P, 1)(0)
5.0
>>> P(0)
3.0
"""
m = int(m)
if m < 0:
raise ValueError("Order of integral must be positive (see polyder)")
if k is None:
k = NX.zeros(m, float)
k = atleast_1d(k)
if len(k) == 1 and m > 1:
k = k[0] * NX.ones(m, float)
if len(k) < m:
raise ValueError(
"k must be a scalar or a rank-1 array of length 1 or >m.")
truepoly = isinstance(p, poly1d)
p = NX.asarray(p)
if m == 0:
if truepoly:
return poly1d(p)
return p
else:
# Note: this must work also with object and integer arrays
y = NX.concatenate((p.__truediv__(NX.arange(len(p), 0, -1)), [k[0]]))
val = polyint(y, m - 1, k=k[1:])
if truepoly:
return poly1d(val)
return val
def polyint(p, m=1, k=None):
"""
Return an antiderivative (indefinite integral) of a polynomial.
The returned order `m` antiderivative `P` of polynomial `p` satisfies
:math:`\\frac{d^m}{dx^m}P(x) = p(x)` and is defined up to `m - 1`
integration constants `k`. The constants determine the low-order
polynomial part
.. math:: \\frac{k_{m-1}}{0!} x^0 + \\ldots + \\frac{k_0}{(m-1)!}x^{m-1}
of `P` so that :math:`P^{(j)}(0) = k_{m-j-1}`.
Parameters
----------
p : {array_like, poly1d}
Polynomial to differentiate.
A sequence is interpreted as polynomial coefficients, see `poly1d`.
m : int, optional
Order of the antiderivative. (Default: 1)
k : {None, list of `m` scalars, scalar}, optional
Integration constants. They are given in the order of integration:
those corresponding to highest-order terms come first.
If ``None`` (default), all constants are assumed to be zero.
If `m = 1`, a single scalar can be given instead of a list.
See Also
--------
polyder : derivative of a polynomial
poly1d.integ : equivalent method
Examples
--------
The defining property of the antiderivative:
>>> p = np.poly1d([1,1,1])
>>> P = np.polyint(p)
>>> P
poly1d([ 0.33333333, 0.5 , 1. , 0. ])
>>> np.polyder(P) == p
True
The integration constants default to zero, but can be specified:
>>> P = np.polyint(p, 3)
>>> P(0)
0.0
>>> np.polyder(P)(0)
0.0
>>> np.polyder(P, 2)(0)
0.0
>>> P = np.polyint(p, 3, k=[6,5,3])
>>> P
poly1d([ 0.01666667, 0.04166667, 0.16666667, 3. , 5. , 3. ])
Note that 3 = 6 / 2!, and that the constants are given in the order of
integrations. Constant of the highest-order polynomial term comes first:
>>> np.polyder(P, 2)(0)
6.0
>>> np.polyder(P, 1)(0)
5.0
>>> P(0)
3.0
"""
m = int(m)
if m < 0:
raise ValueError("Order of integral must be positive (see polyder)")
if k is None:
k = NX.zeros(m, float)
k = atleast_1d(k)
if len(k) == 1 and m > 1:
k = k[0]*NX.ones(m, float)
if len(k) < m:
raise ValueError(
"k must be a scalar or a rank-1 array of length 1 or >m.")
truepoly = isinstance(p, poly1d)
p = NX.asarray(p)
if m == 0:
if truepoly:
return poly1d(p)
return p
else:
# Note: this must work also with object and integer arrays
y = NX.concatenate((p.__truediv__(NX.arange(len(p), 0, -1)), [k[0]]))
val = polyint(y, m - 1, k=k[1:])
if truepoly:
return poly1d(val)
return val
def __getitem__(self, key):
trans1d = self.trans1d
ndmin = self.ndmin
if isinstance(key, str):
frame = sys._getframe().f_back
mymat = matrix.bmat(key, frame.f_globals, frame.f_locals)
return mymat
if type(key) is not tuple:
key = (key, )
objs = []
scalars = []
arraytypes = []
scalartypes = []
for k in range(len(key)):
scalar = False
if type(key[k]) is slice:
step = key[k].step
start = key[k].start
stop = key[k].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(key[k], str):
if k != 0:
raise ValueError("special directives must be the "
"first entry.")
key0 = key[0]
if key0 in 'rc':
self.matrix = True
self.col = (key0 == 'c')
continue
if ',' in key0:
vec = key0.split(',')
try:
self.axis, ndmin = \
[int(x) for x in vec[:2]]
if len(vec) == 3:
trans1d = int(vec[2])
continue
except:
raise ValueError("unknown special directive")
try:
self.axis = int(key[k])
continue
except (ValueError, TypeError):
raise ValueError("unknown special directive")
elif type(key[k]) in ScalarType:
newobj = array(key[k], ndmin=ndmin)
scalars.append(k)
scalar = True
scalartypes.append(newobj.dtype)
else:
newobj = key[k]
if ndmin > 1:
tempobj = array(newobj, copy=False, subok=True)
newobj = array(newobj, copy=False, subok=True, ndmin=ndmin)
if trans1d != -1 and tempobj.ndim < ndmin:
k2 = ndmin - tempobj.ndim
if (trans1d < 0):
trans1d += k2 + 1
defaxes = range(ndmin)
k1 = trans1d
axes = defaxes[:k1] + defaxes[k2:] + \
defaxes[k1:k2]
newobj = newobj.transpose(axes)
del tempobj
objs.append(newobj)
if not scalar and isinstance(newobj, _nx.ndarray):
arraytypes.append(newobj.dtype)
# Esure 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 = _nx.concatenate(tuple(objs), axis=self.axis)
return self._retval(res)
def histogram(a, bins=10, range=None, normed=False):
"""Compute the histogram from a set of data.
Parameters:
a : array
The data to histogram. n-D arrays will be flattened.
bins : int or sequence of floats
If an int, then the number of equal-width bins in the given range.
Otherwise, a sequence of the lower bound of each bin.
range : (float, float)
The lower and upper range of the bins. If not provided, then
(a.min(), a.max()) is used. Values outside of this range are
allocated to the closest bin.
normed : bool
If False, the result array will contain the number of samples in
each bin. If True, the result array is the value of the
probability *density* function at the bin normalized such that the
*integral* over the range is 1. Note that the sum of all of the
histogram values will not usually be 1; it is not a probability
*mass* function.
Returns:
hist : array
The values of the histogram. See `normed` for a description of the
possible semantics.
lower_edges : float array
The lower edges of each bin.
SeeAlso:
histogramdd
"""
a = asarray(a).ravel()
if (range is not None):
mn, mx = range
if (mn > mx):
raise AttributeError, 'max must be larger than min in range parameter.'
if not iterable(bins):
if range is None:
range = (a.min(), a.max())
mn, mx = [mi + 0.0 for mi in range]
if mn == mx:
mn -= 0.5
mx += 0.5
bins = linspace(mn, mx, bins, endpoint=False)
else:
if (any(bins[1:] - bins[:-1] < 0)):
raise AttributeError, 'bins must increase monotonically.'
# best block size probably depends on processor cache size
block = 65536
n = sort(a[:block]).searchsorted(bins)
for i in xrange(block, a.size, block):
n += sort(a[i:i + block]).searchsorted(bins)
n = concatenate([n, [len(a)]])
n = n[1:] - n[:-1]
if normed:
db = bins[1] - bins[0]
return 1.0 / (a.size * db) * n, bins
else:
return n, bins
def kron(a,b):
"""
Kronecker product of two arrays.
Computes the Kronecker product, a composite array made of blocks of the
second array scaled by the first.
Parameters
----------
a, b : array_like
Returns
-------
out : ndarray
See Also
--------
outer : The outer product
Notes
-----
The function assumes that the number of dimenensions of `a` and `b`
are the same, if necessary prepending the smallest with ones.
If `a.shape = (r0,r1,..,rN)` and `b.shape = (s0,s1,...,sN)`,
the Kronecker product has shape `(r0*s0, r1*s1, ..., rN*SN)`.
The elements are products of elements from `a` and `b`, organized
explicitly by::
kron(a,b)[k0,k1,...,kN] = a[i0,i1,...,iN] * b[j0,j1,...,jN]
where::
kt = it * st + jt, t = 0,...,N
In the common 2-D case (N=1), the block structure can be visualized::
[[ a[0,0]*b, a[0,1]*b, ... , a[0,-1]*b ],
[ ... ... ],
[ a[-1,0]*b, a[-1,1]*b, ... , a[-1,-1]*b ]]
Examples
--------
>>> np.kron([1,10,100], [5,6,7])
array([ 5, 6, 7, 50, 60, 70, 500, 600, 700])
>>> np.kron([5,6,7], [1,10,100])
array([ 5, 50, 500, 6, 60, 600, 7, 70, 700])
>>> np.kron(np.eye(2), np.ones((2,2)))
array([[ 1., 1., 0., 0.],
[ 1., 1., 0., 0.],
[ 0., 0., 1., 1.],
[ 0., 0., 1., 1.]])
>>> a = np.arange(100).reshape((2,5,2,5))
>>> b = np.arange(24).reshape((2,3,4))
>>> c = np.kron(a,b)
>>> c.shape
(2, 10, 6, 20)
>>> I = (1,3,0,2)
>>> J = (0,2,1)
>>> J1 = (0,) + J # extend to ndim=4
>>> S1 = (1,) + b.shape
>>> K = tuple(np.array(I) * np.array(S1) + np.array(J1))
>>> c[K] == a[I]*b[J]
True
"""
b = asanyarray(b)
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)
as_ = a.shape
bs = b.shape
if not a.flags.contiguous:
a = reshape(a, as_)
if not b.flags.contiguous:
b = reshape(b, bs)
nd = ndb
if (ndb != nda):
if (ndb > nda):
as_ = (1,)*(ndb-nda) + as_
else:
bs = (1,)*(nda-ndb) + bs
nd = nda
result = outer(a,b).reshape(as_+bs)
axis = nd-1
for _ in range(nd):
result = concatenate(result, axis=axis)
wrapper = get_array_prepare(a, b)
if wrapper is not None:
result = wrapper(result)
wrapper = get_array_wrap(a, b)
if wrapper is not None:
result = wrapper(result)
return result
def polyadd(a1, a2):
"""
Find the sum of two polynomials.
.. note::
This forms part of the old polynomial API. Since version 1.4, the
new polynomial API defined in `numpy.polynomial` is preferred.
A summary of the differences can be found in the
:doc:`transition guide </reference/routines.polynomials>`.
Returns the polynomial resulting from the sum of two input polynomials.
Each input must be either a poly1d object or a 1D sequence of polynomial
coefficients, from highest to lowest degree.
Parameters
----------
a1, a2 : array_like or poly1d object
Input polynomials.
Returns
-------
out : ndarray or poly1d object
The sum of the inputs. If either input is a poly1d object, then the
output is also a poly1d object. Otherwise, it is a 1D array of
polynomial coefficients from highest to lowest degree.
See Also
--------
poly1d : A one-dimensional polynomial class.
poly, polyadd, polyder, polydiv, polyfit, polyint, polysub, polyval
Examples
--------
>>> np.polyadd([1, 2], [9, 5, 4])
array([9, 6, 6])
Using poly1d objects:
>>> p1 = np.poly1d([1, 2])
>>> p2 = np.poly1d([9, 5, 4])
>>> print(p1)
1 x + 2
>>> print(p2)
2
9 x + 5 x + 4
>>> print(np.polyadd(p1, p2))
2
9 x + 6 x + 6
"""
truepoly = (isinstance(a1, poly1d) or isinstance(a2, poly1d))
a1 = atleast_1d(a1)
a2 = atleast_1d(a2)
diff = len(a2) - len(a1)
if diff == 0:
val = a1 + a2
elif diff > 0:
zr = NX.zeros(diff, a1.dtype)
val = NX.concatenate((zr, a1)) + a2
else:
zr = NX.zeros(abs(diff), a2.dtype)
val = a1 + NX.concatenate((zr, a2))
if truepoly:
val = poly1d(val)
return val