Python __builtin__.any方法代碼示例

# 需要導入模塊: import __builtin__ [as 別名]
# 或者: from __builtin__ import any [as 別名]
def updatelocals(self, **vardict):
        '''Update variables in the local scope.

        This is a shortcut function to inject variables in the local scope
        without extensive checks (as in define()). Vardict must not contain any
        entries which have been made global via addglobal() before. In order to
        ensure this, updatelocals() should be called immediately after
        openscope(), or with variable names, which are warrantedly not globals
        (e.g variables starting with forbidden prefix)

        Args:
            **vardict: variable definitions.
        '''
        self._scope.update(vardict)
        if self._locals is not None:
            self._locals.update(vardict) 

# 需要導入模塊: import __builtin__ [as 別名]
# 或者: from __builtin__ import any [as 別名]
def _quantile_is_valid(q):
    # avoid expensive reductions, relevant for arrays with < O(1000) elements
    if q.ndim == 1 and q.size < 10:
        for i in range(q.size):
            if q[i] < 0.0 or q[i] > 1.0:
                return False
    else:
        # faster than any()
        if np.count_nonzero(q < 0.0) or np.count_nonzero(q > 1.0):
            return False
    return True 

# 需要導入模塊: import __builtin__ [as 別名]
# 或者: from __builtin__ import any [as 別名]
def any(items):
        for item in items:
            if item:
                return True
        return False

# ---all() from Python 2.5 --- 

# 需要導入模塊: import __builtin__ [as 別名]
# 或者: from __builtin__ import any [as 別名]
def any(it):
        for i in it:
            if i:
                return True
        return False 

# 需要導入模塊: import __builtin__ [as 別名]
# 或者: from __builtin__ import any [as 別名]
def updateglobals(self, **vardict):
        '''Update variables in the global scope.

        This is a shortcut function to inject protected variables in the global
        scope without extensive checks (as in define()). Vardict must not
        contain any global entries which can be shadowed in local scopes
        (e.g. should only contain variables with forbidden prefix).

        Args:
            **vardict: variable definitions.

        '''
        self._scope.update(vardict)
        if self._locals is not None:
            self._globals.update(vardict) 

# 需要導入模塊: import __builtin__ [as 別名]
# 或者: from __builtin__ import any [as 別名]
def __call__(self, line):
        '''Returns the entire line without any folding.

        Returns:
            list of str: Components of folded line. They should be
                assembled via ``\\n.join()`` to obtain the string
                representation.
        '''
        return [line] 

# 需要導入模塊: import __builtin__ [as 別名]
# 或者: from __builtin__ import any [as 別名]
def _get_ufunc_and_otypes(self, func, args):
        """Return (ufunc, otypes)."""
        # frompyfunc will fail if args is empty
        if not args:
            raise ValueError('args can not be empty')

        if self.otypes is not None:
            otypes = self.otypes
            nout = len(otypes)

            # Note logic here: We only *use* self._ufunc if func is self.pyfunc
            # even though we set self._ufunc regardless.
            if func is self.pyfunc and self._ufunc is not None:
                ufunc = self._ufunc
            else:
                ufunc = self._ufunc = frompyfunc(func, len(args), nout)
        else:
            # Get number of outputs and output types by calling the function on
            # the first entries of args.  We also cache the result to prevent
            # the subsequent call when the ufunc is evaluated.
            # Assumes that ufunc first evaluates the 0th elements in the input
            # arrays (the input values are not checked to ensure this)
            args = [asarray(arg) for arg in args]
            if builtins.any(arg.size == 0 for arg in args):
                raise ValueError('cannot call `vectorize` on size 0 inputs '
                                 'unless `otypes` is set')

            inputs = [arg.flat[0] for arg in args]
            outputs = func(*inputs)

            # Performance note: profiling indicates that -- for simple
            # functions at least -- this wrapping can almost double the
            # execution time.
            # Hence we make it optional.
            if self.cache:
                _cache = [outputs]

                def _func(*vargs):
                    if _cache:
                        return _cache.pop()
                    else:
                        return func(*vargs)
            else:
                _func = func

            if isinstance(outputs, tuple):
                nout = len(outputs)
            else:
                nout = 1
                outputs = (outputs,)

            otypes = ''.join([asarray(outputs[_k]).dtype.char
                              for _k in range(nout)])

            # Performance note: profiling indicates that creating the ufunc is
            # not a significant cost compared with wrapping so it seems not
            # worth trying to cache this.
            ufunc = frompyfunc(_func, len(args), nout)

        return ufunc, otypes 

# 需要導入模塊: import __builtin__ [as 別名]
# 或者: from __builtin__ import any [as 別名]
def _median(a, axis=None, out=None, overwrite_input=False):
    # can't be reasonably be implemented in terms of percentile as we have to
    # call mean to not break astropy
    a = np.asanyarray(a)

    # Set the partition indexes
    if axis is None:
        sz = a.size
    else:
        sz = a.shape[axis]
    if sz % 2 == 0:
        szh = sz // 2
        kth = [szh - 1, szh]
    else:
        kth = [(sz - 1) // 2]
    # Check if the array contains any nan's
    if np.issubdtype(a.dtype, np.inexact):
        kth.append(-1)

    if overwrite_input:
        if axis is None:
            part = a.ravel()
            part.partition(kth)
        else:
            a.partition(kth, axis=axis)
            part = a
    else:
        part = partition(a, kth, axis=axis)

    if part.shape == ():
        # make 0-D arrays work
        return part.item()
    if axis is None:
        axis = 0

    indexer = [slice(None)] * part.ndim
    index = part.shape[axis] // 2
    if part.shape[axis] % 2 == 1:
        # index with slice to allow mean (below) to work
        indexer[axis] = slice(index, index+1)
    else:
        indexer[axis] = slice(index-1, index+1)
    indexer = tuple(indexer)

    # Check if the array contains any nan's
    if np.issubdtype(a.dtype, np.inexact) and sz > 0:
        # warn and return nans like mean would
        rout = mean(part[indexer], axis=axis, out=out)
        return np.lib.utils._median_nancheck(part, rout, axis, out)
    else:
        # if there are no nans
        # Use mean in odd and even case to coerce data type
        # and check, use out array.
        return mean(part[indexer], axis=axis, out=out) 

# 需要導入模塊: import __builtin__ [as 別名]
# 或者: from __builtin__ import any [as 別名]
def append(arr, values, axis=None):
    """
    Append values to the end of an array.

    Parameters
    ----------
    arr : array_like
        Values are appended to a copy of this array.
    values : array_like
        These values are appended to a copy of `arr`.  It must be of the
        correct shape (the same shape as `arr`, excluding `axis`).  If
        `axis` is not specified, `values` can be any shape and will be
        flattened before use.
    axis : int, optional
        The axis along which `values` are appended.  If `axis` is not
        given, both `arr` and `values` are flattened before use.

    Returns
    -------
    append : ndarray
        A copy of `arr` with `values` appended to `axis`.  Note that
        `append` does not occur in-place: a new array is allocated and
        filled.  If `axis` is None, `out` is a flattened array.

    See Also
    --------
    insert : Insert elements into an array.
    delete : Delete elements from an array.

    Examples
    --------
    >>> np.append([1, 2, 3], [[4, 5, 6], [7, 8, 9]])
    array([1, 2, 3, 4, 5, 6, 7, 8, 9])

    When `axis` is specified, `values` must have the correct shape.

    >>> np.append([[1, 2, 3], [4, 5, 6]], [[7, 8, 9]], axis=0)
    array([[1, 2, 3],
           [4, 5, 6],
           [7, 8, 9]])
    >>> np.append([[1, 2, 3], [4, 5, 6]], [7, 8, 9], axis=0)
    Traceback (most recent call last):
    ...
    ValueError: arrays must have same number of dimensions

    """
    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) 

# 需要導入模塊: import __builtin__ [as 別名]
# 或者: from __builtin__ import any [as 別名]
def _median(a, axis=None, out=None, overwrite_input=False):
    # can't be reasonably be implemented in terms of percentile as we have to
    # call mean to not break astropy
    a = np.asanyarray(a)

    # Set the partition indexes
    if axis is None:
        sz = a.size
    else:
        sz = a.shape[axis]
    if sz % 2 == 0:
        szh = sz // 2
        kth = [szh - 1, szh]
    else:
        kth = [(sz - 1) // 2]
    # Check if the array contains any nan's
    if np.issubdtype(a.dtype, np.inexact):
        kth.append(-1)

    if overwrite_input:
        if axis is None:
            part = a.ravel()
            part.partition(kth)
        else:
            a.partition(kth, axis=axis)
            part = a
    else:
        part = partition(a, kth, axis=axis)

    if part.shape == ():
        # make 0-D arrays work
        return part.item()
    if axis is None:
        axis = 0

    indexer = [slice(None)] * part.ndim
    index = part.shape[axis] // 2
    if part.shape[axis] % 2 == 1:
        # index with slice to allow mean (below) to work
        indexer[axis] = slice(index, index+1)
    else:
        indexer[axis] = slice(index-1, index+1)

    # Check if the array contains any nan's
    if np.issubdtype(a.dtype, np.inexact) and sz > 0:
        # warn and return nans like mean would
        rout = mean(part[indexer], axis=axis, out=out)
        return np.lib.utils._median_nancheck(part, rout, axis, out)
    else:
        # if there are no nans
        # Use mean in odd and even case to coerce data type
        # and check, use out array.
        return mean(part[indexer], axis=axis, out=out)