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]
