Python numpy.ufunc方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ufunc [as 別名]
def get_module_functions(modules):
  """Finds functions that do not have implemented derivatives.

  Args:
    modules: A list of Python modules. Functions contained in these modules
        will be checked for membership in 'implemented', and if not found,
        will be added to an 'unimplemented' set
    implemented: A Python object containing implemented derivatives. A function
        should be checkable for membership using the `fn in implemented` syntax.

  Returns:
    module_fns: A set of functions, builtins or ufuncs in `modules`.
  """
  module_fns = set()
  for module in modules:
    for key in dir(module):
      attr = getattr(module, key)
      if isinstance(
          attr, (types.BuiltinFunctionType, types.FunctionType, numpy.ufunc)):
        module_fns.add(attr)
  return module_fns 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ufunc [as 別名]
def __init__(self, scalar_op, inplace_pattern=None, name=None,
                 nfunc_spec=None, openmp=None):
        if inplace_pattern is None:
            inplace_pattern = {}
        self.name = name
        self.scalar_op = scalar_op
        self.inplace_pattern = inplace_pattern
        self.destroy_map = dict((o, [i]) for o, i in inplace_pattern.items())

        self.ufunc = None
        self.nfunc = None
        if nfunc_spec is None:
            nfunc_spec = getattr(scalar_op, 'nfunc_spec', None)
        self.nfunc_spec = nfunc_spec
        if nfunc_spec:
            self.nfunc = getattr(numpy, nfunc_spec[0])

        # precompute the hash of this node
        self._rehash()
        super(Elemwise, self).__init__(openmp=openmp) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ufunc [as 別名]
def set_ufunc(self, scalar_op):
        # This is probably a speed up of the implementation
        if isinstance(scalar_op, theano.scalar.basic.Add):
            self.ufunc = numpy.add
        elif isinstance(scalar_op, theano.scalar.basic.Mul):
            self.ufunc = numpy.multiply
        elif isinstance(scalar_op, theano.scalar.basic.Maximum):
            self.ufunc = numpy.maximum
        elif isinstance(scalar_op, theano.scalar.basic.Minimum):
            self.ufunc = numpy.minimum
        elif isinstance(scalar_op, theano.scalar.basic.AND):
            self.ufunc = numpy.bitwise_and
        elif isinstance(scalar_op, theano.scalar.basic.OR):
            self.ufunc = numpy.bitwise_or
        elif isinstance(scalar_op, theano.scalar.basic.XOR):
            self.ufunc = numpy.bitwise_xor
        else:
            self.ufunc = numpy.frompyfunc(scalar_op.impl, 2, 1) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ufunc [as 別名]
def _get_ufuncs():
    ufuncs = []
    ufunc_names = []
    for name in sorted(sc.__dict__):
        obj = sc.__dict__[name]
        if not isinstance(obj, np.ufunc):
            continue
        msg = KNOWNFAILURES.get(obj)
        if msg is None:
            ufuncs.append(obj)
            ufunc_names.append(name)
        else:
            fail = pytest.mark.xfail(run=False, reason=msg)
            ufuncs.append(pytest.param(obj, marks=fail))
            ufunc_names.append(name)
    return ufuncs, ufunc_names 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ufunc [as 別名]
def __ua_convert__(value, dispatch_type, coerce):
    if dispatch_type is ufunc:
        return getattr(np, value.name)

    if value is None:
        return None

    if dispatch_type is ndarray:
        if not coerce:
            if not isinstance(value, sparse.SparseArray):
                return NotImplemented

        if isinstance(value, sparse.SparseArray):
            return value

        return sparse.as_coo(np.asarray(value))

    return value 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ufunc [as 別名]
def __ua_convert__(value, dispatch_type, coerce):
        if dispatch_type is ufunc and hasattr(fn, value.name):
            return getattr(fn, value.name)

        if value is None:
            return None

        if dispatch_type is ndarray:
            if not coerce and not isinstance(value, xnd.xnd):
                return NotImplemented

            return convert(value, coerce=coerce)

        if dispatch_type is dtype:
            return ndt(str(value))

        return NotImplemented 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ufunc [as 別名]
def astype(self, dtype, copy=True):
        """
        Copy of the array, cast to a specified type.

        See also
        --------
        scipy.sparse.coo_matrix.astype : SciPy sparse equivalent function
        numpy.ndarray.astype : NumPy equivalent ufunc.
        :obj:`COO.elemwise`: Apply an arbitrary element-wise function to one or two
            arguments.
        """
        # this matches numpy's behavior
        if self.dtype == dtype and not copy:
            return self
        return self.__array_ufunc__(
            np.ndarray.astype, "__call__", self, dtype=dtype, copy=copy
        ) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ufunc [as 別名]
def _transform_batch(self, func, return_schema):
        from databricks.koalas.series import Series
        from databricks import koalas as ks

        if isinstance(func, np.ufunc):
            f = func
            func = lambda *args, **kwargs: f(*args, **kwargs)

        if return_schema is None:
            # TODO: In this case, it avoids the shortcut for now (but only infers schema)
            #  because it returns a series from a different DataFrame and it has a different
            #  anchor. We should fix this to allow the shortcut or only allow to infer
            #  schema.
            limit = ks.get_option("compute.shortcut_limit")
            pser = self._kser.head(limit)._to_internal_pandas()
            transformed = pser.transform(func)
            kser = Series(transformed)
            spark_return_type = kser.spark.data_type
        else:
            spark_return_type = return_schema

        pudf = pandas_udf(func, returnType=spark_return_type, functionType=PandasUDFType.SCALAR)
        return self._kser._with_new_scol(scol=pudf(self._kser.spark.column)).rename(self._kser.name) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ufunc [as 別名]
def __array_prepare__(self, result, context=None):
        """
        Gets called prior to a ufunc
        """

        # nice error message for non-ufunc types
        if context is not None and not isinstance(self._values, np.ndarray):
            obj = context[1][0]
            raise TypeError("{obj} with dtype {dtype} cannot perform "
                            "the numpy op {op}".format(
                                obj=type(obj).__name__,
                                dtype=getattr(obj, 'dtype', None),
                                op=context[0].__name__))
        return result

    # complex 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ufunc [as 別名]
def test_coverage(self):
        """Test that we cover all ufunc's"""

        all_np_ufuncs = set([ufunc for ufunc in np.core.umath.__dict__.values()
                             if isinstance(ufunc, np.ufunc)])

        all_q_ufuncs = (qh.UNSUPPORTED_UFUNCS |
                        set(qh.UFUNC_HELPERS.keys()))
        # Check that every numpy ufunc is covered.
        assert all_np_ufuncs - all_q_ufuncs == set()
        # Check that all ufuncs we cover come from numpy or erfa.
        # (Since coverage for erfa is incomplete, we do not check
        # this the other way).
        all_erfa_ufuncs = set([ufunc for ufunc in erfa_ufunc.__dict__.values()
                               if isinstance(ufunc, np.ufunc)])
        assert (all_q_ufuncs - all_np_ufuncs - all_erfa_ufuncs == set()) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ufunc [as 別名]
def __array_wrap__(self, result, context=None):
        """
        Gets called after a ufunc. Needs additional handling as
        PeriodIndex stores internal data as int dtype

        Replace this to __numpy_ufunc__ in future version
        """
        if isinstance(context, tuple) and len(context) > 0:
            func = context[0]
            if func is np.add:
                pass
            elif func is np.subtract:
                name = self.name
                left = context[1][0]
                right = context[1][1]
                if (isinstance(left, PeriodIndex) and
                        isinstance(right, PeriodIndex)):
                    name = left.name if left.name == right.name else None
                    return Index(result, name=name)
                elif isinstance(left, Period) or isinstance(right, Period):
                    return Index(result, name=name)
            elif isinstance(func, np.ufunc):
                if 'M->M' not in func.types:
                    msg = "ufunc '{0}' not supported for the PeriodIndex"
                    # This should be TypeError, but TypeError cannot be raised
                    # from here because numpy catches.
                    raise ValueError(msg.format(func.__name__))

        if is_bool_dtype(result):
            return result
        # the result is object dtype array of Period
        # cannot pass _simple_new as it is
        return type(self)(result, freq=self.freq, name=self.name) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ufunc [as 別名]
def __array_wrap__(self, result, context=None):
        """
        Gets called after a ufunc.
        """
        return self._constructor(result, index=self.index,
                                 copy=False).__finalize__(self) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ufunc [as 別名]
def _infer_df_func_returns(self, in_dtypes, dtypes, index):
        if isinstance(self._func, np.ufunc):
            output_type, new_dtypes, index_value, new_elementwise = \
                OutputType.dataframe, None, 'inherit', True
        else:
            output_type, new_dtypes, index_value, new_elementwise = None, None, None, False

        try:
            empty_df = build_empty_df(in_dtypes, index=pd.RangeIndex(2))
            with np.errstate(all='ignore'):
                infer_df = empty_df.apply(self._func, axis=self._axis, raw=self._raw,
                                          result_type=self._result_type, args=self.args, **self.kwds)
            if index_value is None:
                if infer_df.index is empty_df.index:
                    index_value = 'inherit'
                else:
                    index_value = parse_index(pd.RangeIndex(-1))

            if isinstance(infer_df, pd.DataFrame):
                output_type = output_type or OutputType.dataframe
                new_dtypes = new_dtypes or infer_df.dtypes
            else:
                output_type = output_type or OutputType.series
                new_dtypes = new_dtypes or infer_df.dtype
            new_elementwise = False if new_elementwise is None else new_elementwise
        except:  # noqa: E722  # nosec
            pass

        self.output_types = [output_type] if not self.output_types else self.output_types
        dtypes = new_dtypes if dtypes is None else dtypes
        index_value = index_value if index is None else parse_index(index)
        self._elementwise = new_elementwise if self._elementwise is None else self._elementwise
        return dtypes, index_value 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ufunc [as 別名]
def __getstate__(self):
        d = copy(self.__dict__)
        d.pop('ufunc')
        d.pop('nfunc')
        d.pop('__epydoc_asRoutine', None)
        d.pop('_hashval')
        return d 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ufunc [as 別名]
def __setstate__(self, d):
        super(Elemwise, self).__setstate__(d)
        self.ufunc = None
        self.nfunc = None
        if getattr(self, 'nfunc_spec', None):
            self.nfunc = getattr(numpy, self.nfunc_spec[0])
        elif 0 < self.scalar_op.nin < 32:
            self.ufunc = numpy.frompyfunc(self.scalar_op.impl,
                                          self.scalar_op.nin,
                                          self.scalar_op.nout)
        self._rehash()