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# Source code for tecplot.data.array

    from builtins import super

    import logging

    from ctypes import (addressof, cast, c_double, c_float, c_int8, c_int16,
                        c_int32, c_int64, c_void_p, POINTER)

    from ..tecutil import _tecutil
    from ..constant import *
    from ..exception import *
    from .. import session
    from ..tecutil import lock, log_setattr

    log = logging.getLogger(__name__)

    class Array(c_void_p):
        """Low-level accessor for underlying data within a `Dataset`.

        .. note::

            The data manipulation context referred to below is currently being
            developed and should show up in an up-coming revision.

        This object exposes a list-like interface to the underlying data array.
        Using it, values can be directly queried and modified. After any
        modification to the data, the Tecplot Engine will have to be notified of
        the change. This notification will happen automatically in most cases, but
        can be turned off using the data manipulation context for a significant
        performance increase on large datasets.

        Accessing values within an `Array` is done through the standard ``[]``
        syntax::

            >>> print(array[3])
            3.1415

        The numbers passed are interpreted just like Python's built-in
        :py:class:`slice` object::

            >>> # print the values at indices: 5, 7, 9
            >>> print(array[5:10:2])
            [1.0, 1.0, 1.0]

        Elements within an array can be manipulated in-place with the assignment
        operator::

            >>> array[3] = 5.0
            >>> print(array[3])
            5.0

        Element-by-element access is *not* guaranteed to be performant and users
        should avoid writing loops over indices in Python. Instead, whole arrays
        should be used. This will effectively push the loop down to the underlying
        native library and will be much faster in virtually all cases.

        Consider this array of 10k elements::

            >>> import tecplot as tp
            >>> ds = tp.active_frame().create_dataset('Dataset', ['x'])
            >>> zn = ds.add_ordered_zone('Zone', 10000)
            >>> array = zn.values('x')

        The following loop, which takes the sine of all values in the array will
        require several Python function calls per element which is a tremendous
        overhead::

            >>> import math
            >>> for i in range(len(ar)):
            ...     ar[i] = math.sin(ar[i])

        An immediate improvement on this can be made by looping over the elements
        in Python only when reading the values, but assigning them using the
        whole array. This will be several times faster for even modest arrays::

            >>> ar[:] = [math.sin(x) for x in ar]

        But there is still a large performance penalty for looping over elements
        directly in Python and PyTecplot supports two solutions for large arrays:
        `tecplot.data.operate.execute_equation` and `tecplot.extension.numpy`.
        Please refer to these for details. Continuing with the example above, we
        could accomplish the same thing with either of the following using
        `execute_equation` (assuming the array is identified by the first zone,
        first variable)::

            >>> from tecplot.data.operate import execute_equation
            >>> execute_equaion('V1 = SIN(V1)', zones=[dataset.zone(0)])

        or by using the `numpy` library::

            >>> import numpy as np
            >>> ar[:] = np.sin(ar[:])

        In both of these cases, the calculation of the sine and loop over elements
        is pushed to the low level library and is much faster.
        """
        def __init__(self, zone, variable):
            self.zone = zone
            self.variable = variable
            super().__init__(self._native_reference())

        @lock()
        def _native_reference(self, writable=False):
            args = (self.zone.dataset.uid, self.zone.index + 1,
                    self.variable.index + 1)
            if writable:
                return _tecutil.DataValueGetWritableNativeRefByUniqueID(*args)
            else:
                return _tecutil.DataValueGetReadableNativeRefByUniqueID(*args)

        @lock()
        def _raw_pointer(self, writable=False):
            if writable:
                ref = self._native_reference(writable=True)
                _tecutil.handle.tecUtilDataValueGetWritableRawPtrByRef.restype = \
                    POINTER(self.c_type)
                return _tecutil.DataValueGetWritableRawPtrByRef(ref)
            else:
                _tecutil.handle.tecUtilDataValueGetReadableRawPtrByRef.restype = \
                    POINTER(self.c_type)
                return _tecutil.DataValueGetReadableRawPtrByRef(self)

        def __eq__(self, other):
            self_addr = addressof(cast(self, POINTER(c_int64)).contents)
            other_addr = addressof(cast(other, POINTER(c_int64)).contents)
            return self_addr == other_addr

        def __ne__(self, other):
            return not (self == other)

        def __len__(self):
            return self.shape[0]

        @property
        def size(self):
            """The number of values in this array.

            :type: `integer <int>`

            Example showing size of ordered data::

                >>> x = dataset.zone('Zone').values('X')
                >>> print(x.shape)
                (10, 10, 10)
                >>> print(x.size)
                1000
            """
            return _tecutil.DataValueGetCountByRef(self)

        @property
        def location(self):
            """The location of the data points with respect to the elements.

            :type: `ValueLocation`

            Possible values are `ValueLocation.CellCentered` and
            `ValueLocation.Nodal`. Example usage::

                >>> print(dataset.variable('X').zone(0).location)
                ValueLocation.Nodal
            """
            return _tecutil.DataValueGetLocation(self.zone.index + 1,
                                                 self.variable.index + 1)

        @property
        def shape(self):
            """``(i, j, k)`` shape for this array.

            :type: `tuple` of `floats <float>`

            This is defined by the parent zone and can be used to reshape arrays.
            The following example assumes 32-bit floating point array and copies the Tecplot-owned ``data`` into the `numpy`-owned ``array``::

                >>> import numpy as np
                >>> data = dataset.zone('Zone').values('X')
                >>> array = np.empty(data.shape, dtype=np.float32)
                >>> arr_ptr = array.ctypes.data_as(POINTER(data.c_type))
                >>> memmove(arr_ptr, data.copy(), sizeof(data.c_type) * data.size)

            The data array presented is normally one-dimensional. For ordered data,
            you may wish to reshape the array indexing according to the
            dimensionality given by the ``shape`` attribute:

            .. code-block:: python
                :emphasize-lines: 10

                >>> import numpy as np
                >>> import tecplot as tp
                >>> frame = tp.active_frame()
                >>> dataset = frame.create_dataset('Dataset', ['x'])
                >>> zone = dataset.add_ordered_zone('Zone', shape=(3,3,3))
                >>> x = np.array(zone.values('X')[:])
                >>> print(x)
                [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.
                  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
                >>> x.shape = zone.values('X').shape
                >>> print(x)
                [[[ 0.  0.  0.]
                  [ 0.  0.  0.]
                  [ 0.  0.  0.]]

                 [[ 0.  0.  0.]
                  [ 0.  0.  0.]
                  [ 0.  0.  0.]]

                 [[ 0.  0.  0.]
                  [ 0.  0.  0.]
                  [ 0.  0.  0.]]]
            """
            if self.zone.zone_type is ZoneType.Ordered:
                array_shape = tuple(i for i in self.zone._shape if i > 1)
            else:
                array_shape = (self.zone.num_points,)
            if self.location is ValueLocation.CellCentered:
                array_shape = tuple(i - 1 for i in array_shape if i > 2)
            if not array_shape:
                array_shape = (1,)
            return array_shape

        @property
        def c_type(self):
            """`ctypes` compatible data type of this array.

            This is the `ctypes` equivalent of `Array.data_type` and will return
            one of the following:

                * `ctypes.c_float`
                * `ctypes.c_double`
                * `ctypes.c_int`
                * `ctypes.c_int16`
                * `ctypes.c_int8`

            and can be used to create a `ctypes` array to store a copy of the
            data::

                import tecplot as tp
                frame = tp.active_frame()
                dataset = frame.create_dataset('Dataset', ['x'])
                dataset.add_ordered_zone('Zone', (3,3,3))
                x = dataset.zone('Zone').values('x')
                # allocate array using Python's ctypes
                x_array = (x.c_type * x.size)()
                # copy values from Dataset into ctypes array
                x_array[:] = x[:]
            """
            _ctypes = {
                FieldDataType.Float:  c_float,
                FieldDataType.Double: c_double,
                FieldDataType.Int32:  c_int32,
                FieldDataType.Int16:  c_int16,
                FieldDataType.Byte:   c_int8}
            return _ctypes[self.data_type]

        @property
        def data_type(self):
            """`FieldDataType` indicating the underlying value type of this array.

            :type: `FieldDataType`

            Example usage::

                >>> print(dataset.zone('Zone').values('X').data_type)
                FieldDataType.Float
            """
            return _tecutil.DataValueGetRefType(self)

        @lock()
        def copy(self, offset=0, size=None):
            """Copy the whole or part of the array into a ctypes array.

            Parameters:
                offset (`integer <int>`, optional): Zero-based offset for starting
                    index to copy. (default: 0)
                size (`integer <int>`, optional): Number of values to copy into the
                    resulting array. A value of `None` will copy to the end of the
                    array. (default: `None`)

            Here we will copy out chunks of the data, do some operation and set the values back into the dataset::

                >>> import tecplot as tp
                >>> tp.new_layout()
                >>> frame = tp.active_frame()
                >>> dataset = frame.create_dataset('Dataset', ['x'])
                >>> dataset.add_ordered_zone('Zone', (2, 2, 2))
                >>> x = dataset.zone('Zone').values('x')
                >>> # loop over array copying out 4 values at a time
                >>> for i, offset in enumerate(range(0, x.size, 4)):
                ...     x_array = x.copy(offset, 4)
                ...     x_array[:] = [i] * 4
                ...     x[offset:offset + 4] = x_array
                >>> print(x[:])
                [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0]
            """
            size = (self.size - offset) if size is None else size
            arr = (self.c_type * size)()
            _tecutil.DataValueArrayGetByRef(self, offset + 1, size, arr)
            return arr

        def _slice_range(self, s):
            start = s.start or 0
            stop = s.stop or self.size
            step = s.step or 1
            return range(start, stop, step)

        def __getitem__(self, i):
            if isinstance(i, slice):
                data = self._raw_pointer()
                return [data[ii] for ii in self._slice_range(i)]
            else:
                return self._raw_pointer()[i]

        def __setitem__(self, i, val):
            state_change_opts = dict(
                variables=self.variable,
                zones=self.zone,
                dataset=self.zone.dataset)
            if isinstance(i, slice):
                try:
                    data = self._raw_pointer(True)
                    for ival, ii in enumerate(self._slice_range(i)):
                        data[ii] = val[ival]
                except IndexError:
                    msg = 'Array length mismatch: {} != {}'
                    slice_len = len(self._slice_range(i))
                    raise TecplotIndexError(msg.format(slice_len, len(val)))
            else:
                self._raw_pointer(True)[i] = val
                state_change_opts.update(index=i)
            session.state_changed(StateChange.VarsAltered, **state_change_opts)

        def __iter__(self):
            self.current_index = -1
            self.current_length = self.size
            return self

        def __next__(self):
            self.current_index += 1
            if self.current_index < self.current_length:
                return self.__getitem__(self.current_index)
            else:
                del self.current_index
                del self.current_length
                raise StopIteration

        def next(self): # if sys.version_info < (3,)
            return self.__next__()

        @property
        def minmax(self):
            """Limits of the values stored in this array.

            :type: 2-tuple of `floats <float>`

            This always returns `floats <float>` regardless of the underlying data
            type::

                >>> print(dataset.zone('Zone').values('x').minmax)
                (0, 10)
            """
            return _tecutil.DataValueGetMinMaxByRef(self)

        @property
        def min(self):
            """Lower bound of the values stored in this array.

            :type: `float`

            This always returns a `float` regardless of the underlying data type::

                >>> print(dataset.zone('Zone').values('x').min)
                0
            """
            return self.minmax[0]

        @property
        def max(self):
            """Upper bound of the values stored in this array.

            :type: `float`

            This always returns a `float` regardless of the underlying data type::

                >>> print(dataset.zone('Zone').values('x').max)
                10
            """
            return self.minmax[1]

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