python numpy vectorize函數

Init signature:
np.vectorize(
pyfunc,
otypes=None,
doc=None,
excluded=None,
cache=False,
signature=None,
)
Docstring:
vectorize(pyfunc, otypes=None, doc=None, excluded=None, cache=False,
signature=None)

Generalized function class.

Define a vectorized function which takes a nested sequence of objects or
numpy arrays as inputs and returns a single numpy array or a tuple of numpy
arrays. The vectorized function evaluates pyfunc over successive tuples
of the input arrays like the python map function, except it uses the
broadcasting rules of numpy.

The data type of the output of vectorized is determined by calling
the function with the first element of the input. This can be avoided
by specifying the otypes argument.

Parameters

pyfunc : callable
A python function or method.
otypes : str or list of dtypes, optional
The output data type. It must be specified as either a string of
typecode characters or a list of data type specifiers. There should
be one data type specifier for each output.
doc : str, optional
The docstring for the function. If None, the docstring will be the
pyfunc.__doc__.
excluded : set, optional
Set of strings or integers representing the positional or keyword
arguments for which the function will not be vectorized. These will be
passed directly to pyfunc unmodified.

.. versionadded:: 1.7.0

cache : bool, optional
If True, then cache the first function call that determines the number
of outputs if otypes is not provided.

.. versionadded:: 1.7.0

signature : string, optional
Generalized universal function signature, e.g., (m,n),(n)->(m) for
vectorized matrix-vector multiplication. If provided, pyfunc will
be called with (and expected to return) arrays with shapes given by the
size of corresponding core dimensions. By default, pyfunc is
assumed to take scalars as input and output.

.. versionadded:: 1.12.0

Returns

vectorized : callable
Vectorized function.

See Also

frompyfunc : Takes an arbitrary Python function and returns a ufunc

Notes

The vectorize function is provided primarily for convenience, not for
performance. The implementation is essentially a for loop.

If otypes is not specified, then a call to the function with the
first argument will be used to determine the number of outputs. The
results of this call will be cached if cache is True to prevent
calling the function twice. However, to implement the cache, the
original function must be wrapped which will slow down subsequent
calls, so only do this if your function is expensive.

The new keyword argument interface and excluded argument support
further degrades performance.

References

… [1] NumPy Reference, section Generalized Universal Function API <https://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html>_.

Examples

def myfunc(a, b):
… “Return a-b if a>b, otherwise return a+b”
… if a > b:
… return a - b
… else:
… return a + b

vfunc = np.vectorize(myfunc)
vfunc([1, 2, 3, 4], 2)
array([3, 4, 1, 2])

The docstring is taken from the input function to vectorize unless it
is specified:

vfunc.doc
‘Return a-b if a>b, otherwise return a+b’

vfunc = np.vectorize(myfunc, doc='Vectorized myfunc')
vfunc.doc
'Vectorized myfunc’

The output type is determined by evaluating the first element of the input,
unless it is specified:

out = vfunc([1, 2, 3, 4], 2)
type(out[0])
<class ‘numpy.int64’>

vfunc = np.vectorize(myfunc, otypes=[float])
out = vfunc([1, 2, 3, 4], 2)
type(out[0])
<class ‘numpy.float64’>

The excluded argument can be used to prevent vectorizing over certain
arguments. This can be useful for array-like arguments of a fixed length
such as the coefficients for a polynomial as in polyval:

def mypolyval(p, x):
… _p = list§
… res = _p.pop(0)
… while _p:
… res = res*x + _p.pop(0)
… return res

vpolyval = np.vectorize(mypolyval, excluded=[‘p’])
vpolyval(p=[1, 2, 3], x=[0, 1])
array([3, 6])

Positional arguments may also be excluded by specifying their position:

vpolyval.excluded.add(0)
vpolyval([1, 2, 3], x=[0, 1])
array([3, 6])

The signature argument allows for vectorizing functions that act on
non-scalar arrays of fixed length. For example, you can use it for a
vectorized calculation of Pearson correlation coefficient and its p-value:

import scipy.stats
pearsonr = np.vectorize(scipy.stats.pearsonr,
… signature=’(n),(n)->(),()’)

pearsonr([[0, 1, 2, 3]], [[1, 2, 3, 4], [4, 3, 2, 1]])
(array([ 1., -1.]), array([ 0., 0.]))

Or for a vectorized convolution:

convolve = np.vectorize(np.convolve, signature=’(n),(m)->(k)’)
convolve(np.eye(4), [1, 2, 1])
array([[1., 2., 1., 0., 0., 0.],
[0., 1., 2., 1., 0., 0.],
[0., 0., 1., 2., 1., 0.],
[0., 0., 0., 1., 2., 1.]])
File: ~/.local/lib/python3.6/site-packages/numpy/init.py
Type: type
Subclasses: