コード例 #1
0
ファイル: fspace.py プロジェクト: odlgroup/odl
    def _imagpart(self, x):
        """Function returning the imaginary part of a result."""
        x_call_oop = x._call_out_of_place

        def imagpart_oop(x):
            return np.asarray(x_call_oop(x), dtype=self.out_dtype).imag

        if is_real_dtype(self.out_dtype):
            return self.zero()
        else:
            rdtype = real_dtype(self.out_dtype)
            rspace = self.astype(rdtype)
            return rspace.element(imagpart_oop)
コード例 #2
0
ファイル: fspace.py プロジェクト: odlgroup/odl
    def _realpart(self, x):
        """Function returning the real part of a result."""
        x_call_oop = x._call_out_of_place

        def realpart_oop(x):
            return np.asarray(x_call_oop(x), dtype=self.out_dtype).real

        if is_real_dtype(self.out_dtype):
            return x
        else:
            rdtype = real_dtype(self.out_dtype)
            rspace = self.astype(rdtype)
            return rspace.element(realpart_oop)
コード例 #3
0
ファイル: base_ntuples.py プロジェクト: odlgroup/odl
    def __init__(self, size, dtype):
        """Initialize a new instance.

        Parameters
        ----------
        size : non-negative int
            Number of entries in a tuple.
        dtype :
            Data type for each tuple entry. Can be provided in any
            way the `numpy.dtype` function understands, most notably
            as built-in type, as one of NumPy's internal datatype
            objects or as string.
            Only scalar data types (numbers) are allowed.
        """
        NtuplesBase.__init__(self, size, dtype)

        if not is_scalar_dtype(self.dtype):
            raise TypeError('{!r} is not a scalar data type'.format(dtype))

        if is_real_dtype(self.dtype):
            field = RealNumbers()
            self.__is_real = True
            self.__real_dtype = self.dtype
            self.__real_space = self
            try:
                self.__complex_dtype = complex_dtype(self.dtype)
            except ValueError:
                self.__complex_dtype = None
            self.__complex_space = None  # Set in first call of astype
        else:
            field = ComplexNumbers()
            self.__is_real = False
            try:
                self.__real_dtype = real_dtype(self.dtype)
            except ValueError:
                self.__real_dtype = None
            self.__real_space = None  # Set in first call of astype
            self.__complex_dtype = self.dtype
            self.__complex_space = self

        self.__is_floating = is_floating_dtype(self.dtype)
        LinearSpace.__init__(self, field)
コード例 #4
0
ファイル: fspace.py プロジェクト: odlgroup/odl
    def __init__(self, domain, field=None, out_dtype=None):
        """Initialize a new instance.

        Parameters
        ----------
        domain : `Set`
            The domain of the functions
        field : `Field`, optional
            The range of the functions, usually the `RealNumbers` or
            `ComplexNumbers`. If not given, the field is either inferred
            from ``out_dtype``, or, if the latter is also ``None``, set
            to ``RealNumbers()``.
        out_dtype : optional
            Data type of the return value of a function in this space.
            Can be given in any way `numpy.dtype` understands, e.g. as
            string (``'float64'``) or data type (``float``).
            By default, ``'float64'`` is used for real and ``'complex128'``
            for complex spaces.
        """
        if not isinstance(domain, Set):
            raise TypeError('`domain` {!r} not a Set instance'.format(domain))

        if field is not None and not isinstance(field, Field):
            raise TypeError('`field` {!r} not a `Field` instance'
                            ''.format(field))

        # Data type: check if consistent with field, take default for None
        dtype, dtype_in = np.dtype(out_dtype), out_dtype

        # Default for both None
        if field is None and out_dtype is None:
            field = RealNumbers()
            out_dtype = np.dtype('float64')

        # field None, dtype given -> infer field
        elif field is None:
            if is_real_dtype(dtype):
                field = RealNumbers()
            elif is_complex_floating_dtype(dtype):
                field = ComplexNumbers()
            else:
                raise ValueError('{} is not a scalar data type'
                                 ''.format(dtype_in))

        # field given -> infer dtype if not given, else check consistency
        elif field == RealNumbers():
            if out_dtype is None:
                out_dtype = np.dtype('float64')
            elif not is_real_dtype(dtype):
                raise ValueError('{} is not a real data type'
                                 ''.format(dtype_in))
        elif field == ComplexNumbers():
            if out_dtype is None:
                out_dtype = np.dtype('complex128')
            elif not is_complex_floating_dtype(dtype):
                raise ValueError('{} is not a complex data type'
                                 ''.format(dtype_in))

        # Else: keep out_dtype=None, which results in lazy dtype determination

        LinearSpace.__init__(self, field)
        FunctionSet.__init__(self, domain, field, out_dtype)

        # Init cache attributes for real / complex variants
        if self.field == RealNumbers():
            self.__real_out_dtype = self.out_dtype
            self.__real_space = self
            self.__complex_out_dtype = complex_dtype(self.out_dtype,
                                                     default=np.dtype(object))
            self.__complex_space = None
        elif self.field == ComplexNumbers():
            self.__real_out_dtype = real_dtype(self.out_dtype)
            self.__real_space = None
            self.__complex_out_dtype = self.out_dtype
            self.__complex_space = self
        else:
            self.__real_out_dtype = None
            self.__real_space = None
            self.__complex_out_dtype = None
            self.__complex_space = None