import sys
import numpy as np
import vtk
from vtk.util import numpy_support
from scipy.ndimage import map_coordinates
from fury.colormap import line_colors


def set_input(vtk_object, inp):
    """Set Generic input function which takes into account VTK 5 or 6.

    Parameters
    ----------
    vtk_object: vtk object
    inp: vtkPolyData or vtkImageData or vtkAlgorithmOutput

    Returns
    -------
    vtk_object

    Notes
    -------
    This can be used in the following way::
        from fury.utils import set_input
        poly_mapper = set_input(vtk.vtkPolyDataMapper(), poly_data)

    """
    if isinstance(inp, vtk.vtkPolyData) or isinstance(inp, vtk.vtkImageData):
        vtk_object.SetInputData(inp)
    elif isinstance(inp, vtk.vtkAlgorithmOutput):
        vtk_object.SetInputConnection(inp)
    vtk_object.Update()
    return vtk_object


def numpy_to_vtk_points(points):
    """Convert Numpy points array to a vtk points array.

    Parameters
    ----------
    points : ndarray

    Returns
    -------
    vtk_points : vtkPoints()

    """
    vtk_points = vtk.vtkPoints()
    vtk_points.SetData(numpy_support.numpy_to_vtk(np.asarray(points),
                                                  deep=True))
    return vtk_points


def numpy_to_vtk_colors(colors):
    """Convert Numpy color array to a vtk color array.

    Parameters
    ----------
    colors: ndarray

    Returns
    -------
    vtk_colors : vtkDataArray

    Notes
    -----
    If colors are not already in UNSIGNED_CHAR you may need to multiply by 255.

    Examples
    --------
    >>> import numpy as np
    >>> from fury.utils import numpy_to_vtk_colors
    >>> rgb_array = np.random.rand(100, 3)
    >>> vtk_colors = numpy_to_vtk_colors(255 * rgb_array)

    """
    vtk_colors = numpy_support.numpy_to_vtk(np.asarray(colors), deep=True,
                                            array_type=vtk.VTK_UNSIGNED_CHAR)
    return vtk_colors


def map_coordinates_3d_4d(input_array, indices):
    """Evaluate the input_array data at the given indices
    using trilinear interpolation.

    Parameters
    ----------
    input_array : ndarray,
        3D or 4D array
    indices : ndarray

    Returns
    -------
    output : ndarray
        1D or 2D array

    """
    if input_array.ndim <= 2 or input_array.ndim >= 5:
        raise ValueError("Input array can only be 3d or 4d")

    if input_array.ndim == 3:
        return map_coordinates(input_array, indices.T, order=1)

    if input_array.ndim == 4:
        values_4d = []
        for i in range(input_array.shape[-1]):
            values_tmp = map_coordinates(input_array[..., i],
                                         indices.T, order=1)
            values_4d.append(values_tmp)
        return np.ascontiguousarray(np.array(values_4d).T)


def lines_to_vtk_polydata(lines, colors="RGB"):
    """Create a vtkPolyData with lines and colors.

    Parameters
    ----------
    lines : list
        list of N curves represented as 2D ndarrays
    colors : array (N, 3), list of arrays, tuple (3,), array (K,), "RGB"
        If None or False, no coloring is done
        If "RGB" then a standard orientation colormap is used for every line.
        If one tuple of color is used. Then all streamlines will have the same
        colour.
        If an array (N, 3) is given, where N is equal to the number of lines.
        Then every line is coloured with a different RGB color.
        If a list of RGB arrays is given then every point of every line takes
        a different color.
        If an array (K, 3) is given, where K is the number of points of all
        lines then every point is colored with a different RGB color.
        If an array (K,) is given, where K is the number of points of all
        lines then these are considered as the values to be used by the
        colormap.
        If an array (L,) is given, where L is the number of streamlines then
        these are considered as the values to be used by the colormap per
        streamline.
        If an array (X, Y, Z) or (X, Y, Z, 3) is given then the values for the
        colormap are interpolated automatically using trilinear interpolation.

    Returns
    -------
    poly_data : vtkPolyData
    color_is_scalar : bool, true if the color array is a single scalar
        Scalar array could be used with a colormap lut
        None if no color was used

    """
    # Get the 3d points_array
    points_array = np.vstack(lines)

    nb_lines = len(lines)
    nb_points = len(points_array)

    lines_range = range(nb_lines)

    # Get lines_array in vtk input format
    lines_array = []
    # Using np.intp (instead of int64), because of a bug in numpy:
    # https://github.com/nipy/dipy/pull/789
    # https://github.com/numpy/numpy/issues/4384
    points_per_line = np.zeros([nb_lines], np.intp)
    current_position = 0
    for i in lines_range:
        current_len = len(lines[i])
        points_per_line[i] = current_len

        end_position = current_position + current_len
        lines_array += [current_len]
        lines_array += range(current_position, end_position)
        current_position = end_position

    lines_array = np.array(lines_array)

    # Set Points to vtk array format
    vtk_points = numpy_to_vtk_points(points_array)

    # Set Lines to vtk array format
    vtk_lines = vtk.vtkCellArray()
    vtk_lines.GetData().DeepCopy(numpy_support.numpy_to_vtk(lines_array))
    vtk_lines.SetNumberOfCells(nb_lines)

    # Create the poly_data
    poly_data = vtk.vtkPolyData()
    poly_data.SetPoints(vtk_points)
    poly_data.SetLines(vtk_lines)

    # Get colors_array (reformat to have colors for each points)
    #           - if/else tested and work in normal simple case
    color_is_scalar = False
    if colors is None or colors is False:
        # No color array is used
        return poly_data, None
    elif isinstance(colors, str) and colors.lower() == "rgb":
        # set automatic rgb colors
        cols_arr = line_colors(lines)
        colors_mapper = np.repeat(lines_range, points_per_line, axis=0)
        vtk_colors = numpy_to_vtk_colors(255 * cols_arr[colors_mapper])
    else:
        cols_arr = np.asarray(colors)
        if cols_arr.dtype == np.object:  # colors is a list of colors
            vtk_colors = numpy_to_vtk_colors(255 * np.vstack(colors))
        else:
            if len(cols_arr) == nb_points:
                if cols_arr.ndim == 1:  # values for every point
                    vtk_colors = numpy_support.numpy_to_vtk(cols_arr,
                                                            deep=True)
                    color_is_scalar = True
                elif cols_arr.ndim == 2:  # map color to each point
                    vtk_colors = numpy_to_vtk_colors(255 * cols_arr)

            elif cols_arr.ndim == 1:
                if len(cols_arr) == nb_lines:  # values for every streamline
                    cols_arrx = []
                    for (i, value) in enumerate(colors):
                        cols_arrx += lines[i].shape[0]*[value]
                    cols_arrx = np.array(cols_arrx)
                    vtk_colors = numpy_support.numpy_to_vtk(cols_arrx,
                                                            deep=True)
                    color_is_scalar = True
                else:  # the same colors for all points
                    vtk_colors = numpy_to_vtk_colors(
                        np.tile(255 * cols_arr, (nb_points, 1)))

            elif cols_arr.ndim == 2:  # map color to each line
                colors_mapper = np.repeat(lines_range, points_per_line, axis=0)
                vtk_colors = numpy_to_vtk_colors(255 * cols_arr[colors_mapper])
            else:  # colormap
                #  get colors for each vertex
                cols_arr = map_coordinates_3d_4d(cols_arr, points_array)
                vtk_colors = numpy_support.numpy_to_vtk(cols_arr, deep=True)
                color_is_scalar = True

    vtk_colors.SetName("Colors")
    poly_data.GetPointData().SetScalars(vtk_colors)
    return poly_data, color_is_scalar


def get_polydata_lines(line_polydata):
    """Convert vtk polydata to a list of lines ndarrays.

    Parameters
    ----------
    line_polydata : vtkPolyData

    Returns
    -------
    lines : list
        List of N curves represented as 2D ndarrays

    """
    lines_vertices = numpy_support.vtk_to_numpy(line_polydata.GetPoints().
                                                GetData())
    lines_idx = numpy_support.vtk_to_numpy(line_polydata.GetLines().GetData())

    lines = []
    current_idx = 0
    while current_idx < len(lines_idx):
        line_len = lines_idx[current_idx]

        next_idx = current_idx + line_len + 1
        line_range = lines_idx[current_idx + 1: next_idx]

        lines += [lines_vertices[line_range]]
        current_idx = next_idx
    return lines


def get_polydata_triangles(polydata):
    """Get triangles (ndarrays Nx3 int) from a vtk polydata.

    Parameters
    ----------
    polydata : vtkPolyData

    Returns
    -------
    output : array (N, 3)
        triangles

    """
    vtk_polys = numpy_support.vtk_to_numpy(polydata.GetPolys().GetData())
    # test if its really triangles
    if not (vtk_polys[::4] == 3).all():
        raise AssertionError("Shape error: this is not triangles")
    return np.vstack([vtk_polys[1::4], vtk_polys[2::4], vtk_polys[3::4]]).T


def get_polydata_vertices(polydata):
    """Get vertices (ndarrays Nx3 int) from a vtk polydata.

    Parameters
    ----------
    polydata : vtkPolyData

    Returns
    -------
    output : array (N, 3)
        points, represented as 2D ndarrays

    """
    return numpy_support.vtk_to_numpy(polydata.GetPoints().GetData())


def get_polydata_normals(polydata):
    """Get vertices normal (ndarrays Nx3 int) from a vtk polydata.

    Parameters
    ----------
    polydata : vtkPolyData

    Returns
    -------
    output : array (N, 3)
        Normals, represented as 2D ndarrays (Nx3). None if there are no normals
        in the vtk polydata.

    """
    vtk_normals = polydata.GetPointData().GetNormals()
    if vtk_normals is None:
        return None
    else:
        return numpy_support.vtk_to_numpy(vtk_normals)


def get_polydata_colors(polydata):
    """Get points color (ndarrays Nx3 int) from a vtk polydata.

    Parameters
    ----------
    polydata : vtkPolyData

    Returns
    -------
    output : array (N, 3)
        Colors. None if no normals in the vtk polydata.

    """
    vtk_colors = polydata.GetPointData().GetScalars()
    if vtk_colors is None:
        return None
    else:
        return numpy_support.vtk_to_numpy(vtk_colors)


def set_polydata_triangles(polydata, triangles):
    """Set polydata triangles with a numpy array (ndarrays Nx3 int).

    Parameters
    ----------
    polydata : vtkPolyData
    triangles : array (N, 3)
        triangles, represented as 2D ndarrays (Nx3)

    """
    isize = vtk.vtkIdTypeArray().GetDataTypeSize()
    req_dtype = np.int32 if isize == 4 else np.int64
    vtk_triangles = np.hstack(
        np.c_[np.ones(len(triangles), dtype=req_dtype) * 3,
              triangles.astype(req_dtype)])
    vtk_triangles = numpy_support.numpy_to_vtkIdTypeArray(vtk_triangles,
                                                          deep=True)
    vtk_cells = vtk.vtkCellArray()
    vtk_cells.SetCells(len(triangles), vtk_triangles)
    polydata.SetPolys(vtk_cells)
    return polydata


def set_polydata_vertices(polydata, vertices):
    """Set polydata vertices with a numpy array (ndarrays Nx3 int).

    Parameters
    ----------
    polydata : vtkPolyData
    vertices : vertices, represented as 2D ndarrays (Nx3)

    """
    vtk_points = vtk.vtkPoints()
    vtk_points.SetData(numpy_support.numpy_to_vtk(vertices, deep=True))
    polydata.SetPoints(vtk_points)
    return polydata


def set_polydata_normals(polydata, normals):
    """Set polydata normals with a numpy array (ndarrays Nx3 int).

    Parameters
    ----------
    polydata : vtkPolyData
    normals : normals, represented as 2D ndarrays (Nx3) (one per vertex)

    """
    vtk_normals = numpy_support.numpy_to_vtk(normals, deep=True)
    polydata.GetPointData().SetNormals(vtk_normals)
    return polydata


def set_polydata_colors(polydata, colors):
    """Set polydata colors with a numpy array (ndarrays Nx3 int).

    Parameters
    ----------
    polydata : vtkPolyData
    colors : colors, represented as 2D ndarrays (Nx3)
        colors are uint8 [0,255] RGB for each points

    """
    vtk_colors = numpy_support.numpy_to_vtk(colors, deep=True,
                                            array_type=vtk.VTK_UNSIGNED_CHAR)
    vtk_colors.SetNumberOfComponents(3)
    vtk_colors.SetName("RGB")
    polydata.GetPointData().SetScalars(vtk_colors)
    return polydata


def update_polydata_normals(polydata):
    """Generate and update polydata normals.

    Parameters
    ----------
    polydata : vtkPolyData

    """
    normals_gen = set_input(vtk.vtkPolyDataNormals(), polydata)
    normals_gen.ComputePointNormalsOn()
    normals_gen.ComputeCellNormalsOn()
    normals_gen.SplittingOff()
    # normals_gen.FlipNormalsOn()
    # normals_gen.ConsistencyOn()
    # normals_gen.AutoOrientNormalsOn()
    normals_gen.Update()

    vtk_normals = normals_gen.GetOutput().GetPointData().GetNormals()
    polydata.GetPointData().SetNormals(vtk_normals)


def get_polymapper_from_polydata(polydata):
    """Get vtkPolyDataMapper from a vtkPolyData.

    Parameters
    ----------
    polydata : vtkPolyData

    Returns
    -------
    poly_mapper : vtkPolyDataMapper

    """
    poly_mapper = set_input(vtk.vtkPolyDataMapper(), polydata)
    poly_mapper.ScalarVisibilityOn()
    poly_mapper.InterpolateScalarsBeforeMappingOn()
    poly_mapper.Update()
    poly_mapper.StaticOn()
    return poly_mapper


def get_actor_from_polymapper(poly_mapper):
    """Get vtkActor from a vtkPolyDataMapper.

    Parameters
    ----------
    poly_mapper : vtkPolyDataMapper

    Returns
    -------
    actor : vtkActor

    """
    actor = vtk.vtkActor()
    actor.SetMapper(poly_mapper)
    actor.GetProperty().BackfaceCullingOn()
    actor.GetProperty().SetInterpolationToPhong()

    return actor


def get_actor_from_polydata(polydata):
    """Get vtkActor from a vtkPolyData.

    Parameters
    ----------
    polydata : vtkPolyData

    Returns
    -------
    actor : vtkActor

    """
    poly_mapper = get_polymapper_from_polydata(polydata)
    return get_actor_from_polymapper(poly_mapper)


def get_actor_from_primitive(vertices, triangles, colors=None,
                             normals=None, backface_culling=True):
    """Get vtkActor from a vtkPolyData.

    Parameters
    ----------
    vertices : (Mx3) ndarray
        XYZ coordinates of the object
    triangles: (Nx3) ndarray
        Indices into vertices; forms triangular faces.
    colors: (Nx3) ndarray
        N is equal to the number of lines. Every line is coloured with a
        different RGB color.
    normals: (Nx3) ndarray
        normals, represented as 2D ndarrays (Nx3) (one per vertex)
    backface_culling: bool
        culling of polygons based on orientation of normal with respect to
        camera. If backface culling is True, polygons facing away from camera
        are not drawn. Default: True


    Returns
    -------
    actor : vtkActor

    """
    # Create a Polydata
    pd = vtk.vtkPolyData()
    set_polydata_vertices(pd, vertices)
    set_polydata_triangles(pd, triangles)
    if isinstance(colors, np.ndarray):
        set_polydata_colors(pd, colors)
    if isinstance(normals, np.ndarray):
        set_polydata_normals(pd, normals)

    current_actor = get_actor_from_polydata(pd)
    current_actor.GetProperty().SetBackfaceCulling(backface_culling)
    return current_actor


def repeat_sources(centers, colors, active_scalars=1., directions=None,
                   source=None, vertices=None, faces=None):
    """Transform a vtksource to glyph.

    """
    if source is None and faces is None:
        raise IOError("A source or faces should be defined")

    if np.array(colors).ndim == 1:
        colors = np.tile(colors, (len(centers), 1))

    pts = numpy_to_vtk_points(np.ascontiguousarray(centers))
    cols = numpy_to_vtk_colors(255 * np.ascontiguousarray(colors))
    cols.SetName('colors')
    if isinstance(active_scalars, (float, int)):
        active_scalars = np.tile(active_scalars, (len(centers), 1))
    if isinstance(active_scalars, np.ndarray):
        ascalars = numpy_support.numpy_to_vtk(np.asarray(active_scalars),
                                              deep=True,
                                              array_type=vtk.VTK_DOUBLE)
        ascalars.SetName('active_scalars')

    if directions is not None:
        directions_fa = numpy_support.numpy_to_vtk(np.asarray(directions),
                                                   deep=True,
                                                   array_type=vtk.VTK_DOUBLE)
        directions_fa.SetName('directions')

    polydata_centers = vtk.vtkPolyData()
    polydata_geom = vtk.vtkPolyData()

    if faces is not None:
        set_polydata_vertices(polydata_geom, vertices.astype(np.int8))
        set_polydata_triangles(polydata_geom, faces)

    polydata_centers.SetPoints(pts)
    polydata_centers.GetPointData().AddArray(cols)
    if directions is not None:
        polydata_centers.GetPointData().AddArray(directions_fa)
        polydata_centers.GetPointData().SetActiveVectors('directions')
    if isinstance(active_scalars, np.ndarray):
        polydata_centers.GetPointData().AddArray(ascalars)
        polydata_centers.GetPointData().SetActiveScalars('active_scalars')

    glyph = vtk.vtkGlyph3D()
    if faces is None:
        glyph.SetSourceConnection(source.GetOutputPort())
    else:
        glyph.SetSourceData(polydata_geom)

    glyph.SetInputData(polydata_centers)
    glyph.SetOrient(True)
    glyph.SetScaleModeToScaleByScalar()
    glyph.SetVectorModeToUseVector()
    glyph.Update()

    mapper = vtk.vtkPolyDataMapper()
    mapper.SetInputData(glyph.GetOutput())
    mapper.SetScalarModeToUsePointFieldData()
    mapper.SelectColorArray('colors')

    actor = vtk.vtkActor()
    actor.SetMapper(mapper)
    return actor


def apply_affine(aff, pts):
    """Apply affine matrix `aff` to points `pts`.

    Returns result of application of `aff` to the *right* of `pts`.  The
    coordinate dimension of `pts` should be the last.
    For the 3D case, `aff` will be shape (4,4) and `pts` will have final axis
    length 3 - maybe it will just be N by 3. The return value is the
    transformed points, in this case::
    res = np.dot(aff[:3,:3], pts.T) + aff[:3,3:4]
    transformed_pts = res.T
    This routine is more general than 3D, in that `aff` can have any shape
    (N,N), and `pts` can have any shape, as long as the last dimension is for
    the coordinates, and is therefore length N-1.

    Parameters
    ----------
    aff : (N, N) array-like
        Homogenous affine, for 3D points, will be 4 by 4. Contrary to first
        appearance, the affine will be applied on the left of `pts`.
    pts : (..., N-1) array-like
        Points, where the last dimension contains the coordinates of each
        point.  For 3D, the last dimension will be length 3.

    Returns
    -------
    transformed_pts : (..., N-1) array
        transformed points

    Notes
    -----
    Copied from nibabel to remove dependency.

    Examples
    --------
    >>> aff = np.array([[0,2,0,10],[3,0,0,11],[0,0,4,12],[0,0,0,1]])
    >>> pts = np.array([[1,2,3],[2,3,4],[4,5,6],[6,7,8]])
    >>> apply_affine(aff, pts) #doctest: +ELLIPSIS
    array([[14, 14, 24],
           [16, 17, 28],
           [20, 23, 36],
           [24, 29, 44]]...)
    Just to show that in the simple 3D case, it is equivalent to:
    >>> (np.dot(aff[:3,:3], pts.T) + aff[:3,3:4]).T #doctest: +ELLIPSIS
    array([[14, 14, 24],
           [16, 17, 28],
           [20, 23, 36],
           [24, 29, 44]]...)
    But `pts` can be a more complicated shape:
    >>> pts = pts.reshape((2,2,3))
    >>> apply_affine(aff, pts) #doctest: +ELLIPSIS
    array([[[14, 14, 24],
            [16, 17, 28]],
    <BLANKLINE>
           [[20, 23, 36],
            [24, 29, 44]]]...)

    """
    aff = np.asarray(aff)
    pts = np.asarray(pts)
    shape = pts.shape
    pts = pts.reshape((-1, shape[-1]))
    # rzs == rotations, zooms, shears
    rzs = aff[:-1, :-1]
    trans = aff[:-1, -1]
    res = np.dot(pts, rzs.T) + trans[None, :]
    return res.reshape(shape)


def asbytes(s):
    if sys.version_info[0] >= 3:
        if isinstance(s, bytes):
            return s
        return s.encode('latin1')
    else:
        return str(s)


def vtk_matrix_to_numpy(matrix):
    """Convert VTK matrix to numpy array."""
    if matrix is None:
        return None

    size = (4, 4)
    if isinstance(matrix, vtk.vtkMatrix3x3):
        size = (3, 3)

    mat = np.zeros(size)
    for i in range(mat.shape[0]):
        for j in range(mat.shape[1]):
            mat[i, j] = matrix.GetElement(i, j)

    return mat


def numpy_to_vtk_matrix(array):
    """Convert a numpy array to a VTK matrix."""
    if array is None:
        return None

    if array.shape == (4, 4):
        matrix = vtk.vtkMatrix4x4()
    elif array.shape == (3, 3):
        matrix = vtk.vtkMatrix3x3()
    else:
        raise ValueError("Invalid matrix shape: {0}".format(array.shape))

    for i in range(array.shape[0]):
        for j in range(array.shape[1]):
            matrix.SetElement(i, j, array[i, j])

    return matrix


def get_bounding_box_sizes(actor):
    """Get the bounding box sizes of an actor."""
    X1, X2, Y1, Y2, Z1, Z2 = actor.GetBounds()
    return (X2-X1, Y2-Y1, Z2-Z1)


def get_grid_cells_position(shapes, aspect_ratio=16/9., dim=None):
    """Construct a XY-grid based on the cells content shape.

    This function generates the coordinates of every grid cell. The width and
    height of every cell correspond to the largest width and the largest height
    respectively. The grid dimensions will automatically be adjusted to respect
    the given aspect ratio unless they are explicitly specified.

    The grid follows a row-major order with the top left corner being at
    coordinates (0,0,0) and the bottom right corner being at coordinates
    (nb_cols*cell_width, -nb_rows*cell_height, 0). Note that the X increases
    while the Y decreases.

    Parameters
    ----------
    shapes : list of tuple of int
        The shape (width, height) of every cell content.
    aspect_ratio : float (optional)
        Aspect ratio of the grid (width/height). Default: 16:9.
    dim : tuple of int (optional)
        Dimension (nb_rows, nb_cols) of the grid, if provided.

    Returns
    -------
    ndarray
        3D coordinates of every grid cell.

    """
    cell_shape = np.r_[np.max(shapes, axis=0), 0]
    cell_aspect_ratio = cell_shape[0] / cell_shape[1]

    count = len(shapes)
    if dim is None:
        # Compute the number of rows and columns.
        n_cols = np.ceil(np.sqrt(count*aspect_ratio / cell_aspect_ratio))
        n_rows = np.ceil(count / n_cols)
    else:
        n_rows, n_cols = dim

    if n_cols * n_rows < count:
        msg = "Size is too small, it cannot contain at least {} elements."
        raise ValueError(msg.format(count))

    # Use indexing="xy" so the cells are in row-major (C-order). Also,
    # the Y coordinates are negative so the cells are order from top to bottom.
    X, Y, Z = np.meshgrid(np.arange(n_cols), -np.arange(n_rows),
                          [0], indexing="xy")
    return cell_shape * np.array([X.flatten(), Y.flatten(), Z.flatten()]).T


def shallow_copy(vtk_object):
    """Create a shallow copy of a given `vtkObject` object."""
    copy = vtk_object.NewInstance()
    copy.ShallowCopy(vtk_object)
    return copy


def rotate(actor, rotation=(90, 1, 0, 0)):
    """Rotate actor around axis by angle.

    Parameters
    ----------
    actor : vtkActor or other prop
    rotation : tuple
        Rotate with angle w around axis x, y, z. Needs to be provided
        in the form (w, x, y, z).

    """
    prop3D = actor
    center = np.array(prop3D.GetCenter())

    oldMatrix = prop3D.GetMatrix()
    orig = np.array(prop3D.GetOrigin())

    newTransform = vtk.vtkTransform()
    newTransform.PostMultiply()
    if prop3D.GetUserMatrix() is not None:
        newTransform.SetMatrix(prop3D.GetUserMatrix())
    else:
        newTransform.SetMatrix(oldMatrix)

    newTransform.Translate(*(-center))
    newTransform.RotateWXYZ(*rotation)
    newTransform.Translate(*center)

    # now try to get the composit of translate, rotate, and scale
    newTransform.Translate(*(-orig))
    newTransform.PreMultiply()
    newTransform.Translate(*orig)

    if prop3D.GetUserMatrix() is not None:
        newTransform.GetMatrix(prop3D.GetUserMatrix())
    else:
        prop3D.SetPosition(newTransform.GetPosition())
        prop3D.SetScale(newTransform.GetScale())
        prop3D.SetOrientation(newTransform.GetOrientation())


def rgb_to_vtk(data):
    """RGB or RGBA images to VTK arrays.

    Parameters
    ----------
    data : ndarray
        Shape can be (X, Y, 3) or (X, Y, 4)

    Returns
    -------
    vtkImageData

    """
    grid = vtk.vtkImageData()
    grid.SetDimensions(data.shape[1], data.shape[0], 1)
    nd = data.shape[-1]
    vtkarr = numpy_support.numpy_to_vtk(
        np.flip(data.swapaxes(0, 1), axis=1).reshape((-1, nd), order='F'))
    vtkarr.SetName('Image')
    grid.GetPointData().AddArray(vtkarr)
    grid.GetPointData().SetActiveScalars('Image')
    grid.GetPointData().Update()
    return grid


def normalize_v3(arr):
    """Normalize a numpy array of 3 component vectors shape=(N, 3).

    Parameters
    -----------
    array : ndarray
        Shape (N, 3)

    Returns
    -------
    norm_array

    """
    lens = np.sqrt(arr[:, 0] ** 2 + arr[:, 1] ** 2 + arr[:, 2] ** 2)
    arr[:, 0] /= lens
    arr[:, 1] /= lens
    arr[:, 2] /= lens
    return arr


def normals_from_v_f(vertices, faces):
    """Calculate normals from vertices and faces.

    Parameters
    ----------
    verices : ndarray
    faces : ndarray

    Returns
    -------
    normals : ndarray
        Shape same as vertices

    """
    norm = np.zeros(vertices.shape, dtype=vertices.dtype)
    tris = vertices[faces]
    n = np.cross(tris[::, 1] - tris[::, 0], tris[::, 2] - tris[::, 0])
    normalize_v3(n)
    norm[faces[:, 0]] += n
    norm[faces[:, 1]] += n
    norm[faces[:, 2]] += n
    normalize_v3(norm)
    return norm


def what_order(verts, tri):
    """Determines the winding order of a given set of vertices and a triangle

    Parameter
    ---------
    verts: ndarray
        array of vertices making up a shape
    tri: ndarray

    Returns
    -------
    0 or 1: int
        If the order is counter clockwise, returns 0.
        Otherwise, returns 1.
    """
    v1 = verts[tri[0] - 1]
    v2 = verts[tri[1] - 1]
    v3 = verts[tri[2] - 1]

    x1 = v1[0]
    x2 = v2[0]
    x3 = v3[0]

    y1 = v1[1]
    y2 = v2[1]
    y3 = v3[1]

    val = (y2 - y1) * (x3 - x2) - (y3 - y2) * (x2 - x1)

    if val < 0:
        return 1
    return 0


def change_order(tri):
    """ Changes the order of a given triangle

    Parameter
    ---------
    tri: ndarray
        array of 3 vertices making up a triangle

    Returns
    -------
    np.array(newVert): ndarray
        new array of vertices making up a triangle in the opposite winding order
        of the given parameter
    """

    newVert = [tri[2], tri[1], tri[0]]

    return np.array(newVert)


def check_order(vert, triarr):
    """

    Parameter
    ---------
    vert: ndarray
        array of vertices corresponding to a shape
    triarr: ndarray
        array of triangles corresponding to a shape

    Returns
    -------
    correct_vert: ndarray
        The corrected order of the vert parameter

    """

    shape = triarr.shape
    correct_vert = np.empty(shape)
    correct_order = what_order(vert, triarr[0])
    for nb, i in enumerate(triarr):
        order2 = what_order(vert, i)
        if correct_order != order2:
            temp = change_order(i)
            correct_vert[nb] = temp
        else:
            correct_vert[nb] = i
    return correct_vert
