def bboxstring2list(bbox_string):
    """Convert bounding box string to list

    Input
        bbox_string: String of bounding box coordinates of the form 'W,S,E,N'
    Output
        bbox: List of floating point numbers with format [W, S, E, N]
    """

    msg = ('Bounding box must be a string with coordinates following the '
           'format 105.592,-7.809,110.159,-5.647\n'
           'Instead I got %s of type %s.' % (str(bbox_string),
                                             type(bbox_string)))
    verify(isinstance(bbox_string, basestring), msg)

    fields = bbox_string.split(',')
    msg = ('Bounding box string must have 4 coordinates in the form '
           '"W,S,E,N". I got bbox == "%s"' % bbox_string)
    verify(len(fields) == 4, msg)

    for x in fields:
        try:
            float(x)
        except ValueError, e:
            msg = ('Bounding box %s contained non-numeric entry %s, '
                   'original error was "%s".' % (bbox_string, x, e))
            raise AssertionError(msg)

def grid_to_points(A, x, y):
    """Convert grid data to point data

    :param A: Array of pixel values
    :type A: numpy.ndarray

    :param x: Longitudes corresponding to columns in A (west->east)
    :type x: numpy.ndarray

    :param y: Latitudes corresponding to rows in A (south->north)
    :type y: numpy.ndarray

    Returns:
        * P: Nx2 array of point coordinates
        * V: N array of point values
    """

    msg = ('Longitudes must be increasing (west to east). I got %s' % str(x))
    verify(x[0] < x[1], msg)

    msg = ('Latitudes must be increasing (south to north). I got %s' % str(y))
    verify(y[0] < y[1], msg)

    # Create Nx2 array of x, y points corresponding to each
    # element in A.
    points = axes_to_points(x, y)

    # Create flat 1D row-major view of A cast as
    # one column vector of length MxN where M, N = A.shape
    #values = A.reshape((-1, 1))
    values = A.reshape(-1)

    # Return Nx3 array with rows: x, y, value
    return points, values

def convert_polygons_to_centroids(V):
    """Convert polygon vector data to point vector data

    :param V: Vector layer with polygon data
    :type V: Vector

    :returns: Vector layer with point data and the same attributes as V
    :rtype: Vector
    """

    msg = 'Input data %s must be polygon vector data' % V
    verify(V.is_polygon_data, msg)

    geometry = V.get_geometry()
    N = len(V)

    # Calculate points for each polygon
    centroids = []
    for i in range(N):
        c = calculate_polygon_centroid(geometry[i])
        centroids.append(c)

    # Create new point vector layer with same attributes and return
    V = Vector(data=V.get_data(),
               projection=V.get_projection(),
               geometry=centroids,
               name='%s_centroid_data' % V.get_name(),
               keywords=V.get_keywords())
    return V

    def interpolate(self, X, name=None):
        """Interpolate values of this raster layer to other layer

        Input
            X: Layer object defining target
            name: Optional name of interpolated layer.
                  If name is None, the name of self is used.

        Output
            Y: Layer object with values of this raster layer interpolated to
               geometry of input layer X

        Note: If target geometry is polygon, data will be interpolated to
        its centroids and the output is a point data set.
        """

        if X.is_raster:
            if self.get_geotransform() != X.get_geotransform():
                # Need interpolation between grids
                msg = 'Intergrid interpolation not yet implemented'
                raise Exception(msg)
            else:
                # Rasters are aligned, no need to interpolate
                return self
        else:
            # Interpolate this raster layer to geometry of X
            msg = ('Name must be either a string or None. I got %s'
                   % (str(type(X)))[1:-1])
            verify(name is None or isinstance(name, basestring), msg)

            return interpolate_raster_vector(self, X, name)

def bboxlist2string(bbox, decimals=6):
    """Convert bounding box list to comma separated string

    Input
        bbox: List of coordinates of the form [W, S, E, N]
    Output
        bbox_string: Format 'W,S,E,N' - each will have 6 decimal points
    """

    msg = 'Got string %s, but expected bounding box as a list' % str(bbox)
    verify(not isinstance(bbox, basestring), msg)

    try:
        bbox = list(bbox)
    except:
        msg = 'Could not coerce bbox %s into a list' % str(bbox)
        raise Exception(msg)

    msg = ('Bounding box must have 4 coordinates [W, S, E, N]. '
           'I got %s' % str(bbox))
    verify(len(bbox) == 4, msg)

    for x in bbox:
        try:
            float(x)
        except ValueError, e:
            msg = ('Bounding box %s contained non-numeric entry %s, '
                   'original error was "%s".' % (bbox, x, e))
            raise AssertionError(msg)

def convert_line_to_points(V, delta):
    """Convert line vector data to point vector data

    Input
        V: Vector layer with line data
        delta: Incremental step to find the points
    Output
        Vector layer with point data and the same attributes as V
    """

    msg = 'Input data %s must be line vector data' % V
    verify(V.is_line_data, msg)

    geometry = V.get_geometry()
    data = V.get_data()
    N = len(V)

    # Calculate centroids for each polygon
    points = []
    new_data = []
    for i in range(N):
        c = points_along_line(geometry[i], delta)
        # We need to create a data entry for each point.
        new_data.extend([data[i] for thing in c])
        points.extend(c)

    # Create new point vector layer with same attributes and return
    V = Vector(data=new_data,
               projection=V.get_projection(),
               geometry=points,
               name='%s_point_data' % V.get_name(),
               keywords=V.get_keywords())
    return V

def geotransform_to_axes(G, nx, ny):
    """Convert geotransform to coordinate axes

    :param G: GDAL geotransform (6-tuple).
        (top left x, w-e pixel resolution, rotation,
        top left y, rotation, n-s pixel resolution).
    :type G: tuple

    :param nx: Number of cells in the w-e direction
    :type nx: int

    :param ny: Number of cells in the n-s direction
    :type nx: int


    :returns: Two vectors (longitudes and latitudes) representing the grid
        defined by the geotransform.

        The values are offset by half a pixel size to correspond to
        pixel registration.

        I.e. If the grid origin (top left corner) is (105, 10) and the
        resolution is 1 degrees in each direction, then the vectors will
        take the form

        longitudes = [100.5, 101.5, ..., 109.5]
        latitudes = [0.5, 1.5, ..., 9.5]
    """

    lon_ul = float(G[0])  # Longitude of upper left corner
    lat_ul = float(G[3])  # Latitude of upper left corner
    dx = float(G[1])      # Longitudinal resolution
    dy = - float(G[5])    # Latitudinal resolution (always(?) negative)

    verify(dx > 0)
    verify(dy > 0)

    # Coordinates of lower left corner
    lon_ll = lon_ul
    lat_ll = lat_ul - ny * dy

    # Coordinates of upper right corner
    lon_ur = lon_ul + nx * dx

    # Define pixel centers along each directions
    # This is to achieve pixel registration rather
    # than gridline registration
    dx2 = dx / 2
    dy2 = dy / 2

    # Define longitudes and latitudes for each axes
    x = numpy.linspace(lon_ll + dx2,
                       lon_ur - dx2, nx)
    y = numpy.linspace(lat_ll + dy2,
                       lat_ul - dy2, ny)

    # Return
    return x, y

def axes_to_points(x, y):
    """Generate all combinations of grid point coordinates from x and y axes

    :param x: x coordinates (array)
    :type x: numpy.ndarray

    :param y: y coordinates (array)
    :type y: numpy.ndarray

    :returns:
        * P: Nx2 array consisting of coordinates for all
             grid points defined by x and y axes. The x coordinate
             will vary the fastest to match the way 2D numpy
             arrays are laid out by default ('C' order). That way,
             the x and y coordinates will match a corresponding
             2D array A when flattened (A.flat[:] or A.reshape(-1))

    Note:
        Example

        x = [1, 2, 3]
        y = [10, 20]

        P = [[1, 10],
             [2, 10],
             [3, 10],
             [1, 20],
             [2, 20],
             [3, 20]]
    """

    # Reverse y coordinates to have them start at bottom of array
    y = numpy.flipud(y)

    # Repeat x coordinates for each y (fastest varying)
    # noinspection PyTypeChecker
    X = numpy.kron(numpy.ones(len(y)), x)

    # Repeat y coordinates for each x (slowest varying)
    Y = numpy.kron(y, numpy.ones(len(x)))

    # Check
    N = len(X)
    verify(len(Y) == N)

    # Create Nx2 array of x and y coordinates
    X = numpy.reshape(X, (N, 1))
    Y = numpy.reshape(Y, (N, 1))
    P = numpy.concatenate((X, Y), axis=1)

    # Return
    return P

    def get_geometry(self):
        """Return longitudes and latitudes (the axes) for grid.

        Return two vectors (longitudes and latitudes) corresponding to
        grid. The values are offset by half a pixel size to correspond to
        pixel registration.

        I.e. If the grid origin (top left corner) is (105, 10) and the
        resolution is 1 degrees in each direction, then the vectors will
        take the form

        longitudes = [100.5, 101.5, ..., 109.5]
        latitudes = [0.5, 1.5, ..., 9.5]
        """

        # Get parameters for axes
        g = self.get_geotransform()

        lon_ul = g[0]  # Longitude of upper left corner
        lat_ul = g[3]  # Latitude of upper left corner
        dx = g[1]      # Longitudinal resolution
        dy = - g[5]    # Latitudinal resolution (always(?) negative)
        nx = self.columns
        ny = self.rows

        verify(dx > 0)
        verify(dy > 0)

        # Coordinates of lower left corner
        lon_ll = lon_ul
        lat_ll = lat_ul - ny * dy

        # Coordinates of upper right corner
        lon_ur = lon_ul + nx * dx

        # Define pixel centers along each directions
        dy2 = dy / 2
        dx2 = dx / 2

        # Define longitudes and latitudes for each axes
        x = numpy.linspace(lon_ll + dx2,
                           lon_ur - dx2, nx)
        y = numpy.linspace(lat_ll + dy2,
                           lat_ul - dy2, ny)

        # Return
        return x, y

    def write_to_file(self, filename):
        """Save raster data to file

        Input
            filename: filename with extension .tif
        """

        # Check file format
        basename, extension = os.path.splitext(filename)

        msg = ('Invalid file type for file %s. Only extension '
               'tif allowed.' % filename)
        verify(extension in ['.tif', '.asc'], msg)
        format = DRIVER_MAP[extension]

        # Get raster data
        A = self.get_data()

        # Get Dimensions. Note numpy and Gdal swap order
        N, M = A.shape

        # Create empty file.
        # FIXME (Ole): It appears that this is created as single
        #              precision even though Float64 is specified
        #              - see issue #17
        driver = gdal.GetDriverByName(format)
        fid = driver.Create(filename, M, N, 1, gdal.GDT_Float64)
        if fid is None:
            msg = ('Gdal could not create filename %s using '
                   'format %s' % (filename, format))
            raise Exception(msg)

        # Write metada
        fid.SetProjection(str(self.projection))
        fid.SetGeoTransform(self.geotransform)

        # Write data
        fid.GetRasterBand(1).WriteArray(A)

        # Write keywords if any
        write_keywords(self.keywords, basename + '.keywords')

def convert_line_to_points(V, delta):
    """Convert line vector data to point vector data

    :param V: Vector layer with line data
    :type V: Vector

    :param delta: Incremental step to find the points
    :type delta: float

    :returns: Vector layer with point data and the same attributes as V
    :rtype: Vector
    """

    msg = 'Input data %s must be line vector data' % V
    verify(V.is_line_data, msg)

    geometry = V.get_geometry()
    data = V.get_data()
    N = len(V)

    # Calculate centroids for each polygon
    points = []
    new_data = []
    for i in range(N):
        c = points_along_line(geometry[i], delta)
        # We need to create a data entry for each point.
        # FIXME (Ole): What on earth is this?
        # pylint: disable=W0621
        new_data.extend([data[i] for _ in c])
        # pylint: enable=W0621
        points.extend(c)

    # Create new point vector layer with same attributes and return
    V = Vector(data=new_data,
               projection=V.get_projection(),
               geometry=points,
               name='%s_point_data' % V.get_name(),
               keywords=V.get_keywords())
    return V

    def get_bins(self, N=10, quantiles=False):
        """Get N values between the min and the max occurred in this dataset.

        Return sorted list of length N+1 where the first element is min and
        the last is max. Intermediate values depend on the keyword quantiles:
        If quantiles is True, they represent boundaries between quantiles.
        If quantiles is False, they represent equidistant interval boundaries.
        """

        min, max = self.get_extrema()

        levels = []
        if quantiles is False:
            # Linear intervals
            d = (max - min) / N

            for i in range(N):
                levels.append(min + i * d)
        else:
            # Quantiles
            # FIXME (Ole): Not 100% sure about this algorithm,
            # but it is close enough

            A = self.get_data(nan=True).flat[:]

            mask = numpy.logical_not(numpy.isnan(A))  # Omit NaN's
            A = A.compress(mask)

            A.sort()

            verify(len(A) == A.shape[0])

            d = float(len(A) + 0.5) / N
            for i in range(N):
                levels.append(A[int(i * d)])

        levels.append(max)

        return levels

    def get_data(self, attribute=None, index=None):
        """Get vector attributes

        Data is returned as a list where each entry is a dictionary of
        attributes for one feature. Entries in get_geometry() and
        get_data() are related as 1-to-1

        If optional argument attribute is specified and a valid name,
        then the list of values for that attribute is returned.

        If optional argument index is specified on the that value will
        be returned. Any value of index is ignored if attribute is None.
        """

        if hasattr(self, 'data'):
            if attribute is None:
                return self.data
            else:
                msg = ('Specified attribute %s does not exist in '
                       'vector layer %s. Valid names are %s'
                       '' % (attribute, self, self.data[0].keys()))
                verify(attribute in self.data[0], msg)

                if index is None:
                    # Return all values for specified attribute
                    return [x[attribute] for x in self.data]
                else:
                    # Return value for specified attribute and index
                    msg = ('Specified index must be either None or '
                           'an integer. I got %s' % index)
                    verify(type(index) == type(0), msg)

                    msg = ('Specified index must lie within the bounds '
                           'of vector layer %s which is [%i, %i]'
                           '' % (self, 0, len(self) - 1))
                    verify(0 <= index < len(self), msg)

                    return self.data[index][attribute]
        else:
            msg = 'Vector data instance does not have any attributes'
            raise Exception(msg)

    def get_topN(self, attribute, N=10):
        """Get top N features

        Input
            attribute: The name of attribute where values are sought
            N: How many

        Output
            layer: New vector layer with selected features
        """

        # FIXME (Ole): Maybe generalise this to arbitrary expressions

        # Input checks
        msg = ('Specfied attribute must be a string. '
               'I got %s' % (type(attribute)))
        verify(isinstance(attribute, basestring), msg)

        msg = 'Specified attribute was empty'
        verify(attribute != '', msg)

        msg = 'N must be a positive number. I got %i' % N
        verify(N > 0, msg)

        # Create list of values for specified attribute
        values = self.get_data(attribute)

        # Sort and select using Schwarzian transform
        A = zip(values, self.data, self.geometry)
        A.sort()

        # Pick top N and unpack
        _, data, geometry = zip(*A[-N:])

        # Create new Vector instance and return
        return Vector(data=data,
                      projection=self.get_projection(),
                      geometry=geometry)

    def get_topN(self, attribute, N=10):
        """Get top N features

        :param attribute: The name of attribute where values are sought
        :type attribute: str

        :param N: How many
        :type N: int

        :returns: New vector layer with selected features
        """

        # Input checks
        msg = ('Specfied attribute must be a string. '
               'I got %s' % (type(attribute)))
        verify(isinstance(attribute, basestring), msg)

        msg = 'Specified attribute was empty'
        verify(attribute != '', msg)

        msg = 'N must be a positive number. I got %i' % N
        verify(N > 0, msg)

        # Create list of values for specified attribute
        values = self.get_data(attribute)

        # Sort and select using Schwarzian transform
        A = zip(values, self.data, self.geometry)
        A.sort()

        # Pick top N and unpack
        data, geometry = zip(*A[-N:])[1:]

        # Create new Vector instance and return
        return Vector(data=data,
                      projection=self.get_projection(),
                      geometry=geometry,
                      keywords=self.get_keywords())

            raise Exception(msg)

        if native:
            keywords = self.get_keywords()
            if 'resolution' in keywords:

                resolution = keywords['resolution']
                try:
                    res = float(resolution)
                except:
                    # Assume resolution is a string of the form:
                    # (0.00045228819716044, 0.00045228819716044)

                    msg = ('Unknown format for resolution keyword: %s'
                           % resolution)
                    verify((resolution.startswith('(') and
                            resolution.endswith(')')), msg)

                    dx, dy = [float(s) for s in resolution[1:-1].split(',')]
                    if not isotropic:
                        res = (dx, dy)
                    else:
                        msg = ('Resolution of layer "%s" was not isotropic: '
                               '[dx, dy] == %s' % (self.get_name(), res))
                        verify(numpy.allclose(dx, dy,
                                              rtol=1.0e-12, atol=1.0e-12), msg)
                        res = dx
                else:
                    if not isotropic:
                        res = (res, res)

        # Return either 2-tuple or scale depending on isotropic

    def write_to_file(self, filename, sublayer=None):
        """Save vector data to file

        :param filename: filename with extension .shp or .gml
        :type filename: str

        :param sublayer: Optional parameter for writing a sublayer. Ignored
            unless we are writing to an sqlite file.
        :type sublayer: str

        :raises: WriteLayerError

        Note:
            Shp limitation, if attribute names are longer than 10
            characters they will be truncated. This is due to limitations in
            the shp file driver and has to be done here since gdal v1.7 onwards
            has changed its handling of this issue:
            http://www.gdal.org/ogr/drv_shapefile.html

            **For this reason we recommend writing to spatialite.**

        """

        # Check file format
        base_name, extension = os.path.splitext(filename)

        msg = ('Invalid file type for file %s. Only extensions '
               'sqlite, shp or gml allowed.' % filename)
        verify(extension in ['.sqlite', '.shp', '.gml'], msg)
        driver = DRIVER_MAP[extension]

        # FIXME (Ole): Tempory flagging of GML issue (ticket #18)
        if extension == '.gml':
            msg = ('OGR GML driver does not store geospatial reference.'
                   'This format is disabled for the time being. See '
                   'https://github.com/AIFDR/riab/issues/18')
            raise WriteLayerError(msg)

        # Derive layer_name from filename (excluding preceding dirs)
        if sublayer is None or extension == '.shp':
            layer_name = os.path.split(base_name)[-1]
        else:
            layer_name = sublayer

        # Get vector data
        if self.is_polygon_data:
            geometry = self.get_geometry(as_geometry_objects=True)
        else:
            geometry = self.get_geometry()
        data = self.get_data()

        N = len(geometry)

        # Clear any previous file of this name (ogr does not overwrite)
        try:
            os.remove(filename)
        except OSError:
            pass

        # Create new file with one layer
        drv = ogr.GetDriverByName(driver)
        if drv is None:
            msg = 'OGR driver %s not available' % driver
            raise WriteLayerError(msg)

        ds = drv.CreateDataSource(get_string(filename))
        if ds is None:
            msg = 'Creation of output file %s failed' % filename
            raise WriteLayerError(msg)

        lyr = ds.CreateLayer(get_string(layer_name),
                             self.projection.spatial_reference,
                             self.geometry_type)
        if lyr is None:
            msg = 'Could not create layer %s' % layer_name
            raise WriteLayerError(msg)

        # Define attributes if any
        store_attributes = False
        fields = []
        if data is not None:
            if len(data) > 0:
                try:
                    fields = data[0].keys()
                except:
                    msg = ('Input parameter "attributes" was specified '
                           'but it does not contain list of dictionaries '
                           'with field information as expected. The first '
                           'element is %s' % data[0])
                    raise WriteLayerError(msg)
                else:
                    # Establish OGR types for each element
                    ogr_types = {}
                    for name in fields:
                        att = data[0][name]
                        py_type = type(att)
                        msg = ('Unknown type for storing vector '
                               'data: %s, %s' % (name, str(py_type)[1:-1]))
                        verify(py_type in TYPE_MAP, msg)
                        ogr_types[name] = TYPE_MAP[py_type]
#.........这里部分代码省略.........

    def get_data(self, attribute=None, index=None, copy=False):
        """Get vector attributes.

        :param attribute: Specify an attribute name of which to return data.
        :type attribute: str

        :param index: Indicates a specific value on which to call the
            attribute. Ignored if no attribute is set.
        :type index: int

        :param copy: Indicate whether to return a pointer to the data,
            or a copy of.
        :type copy: bool

        :raises: GetDataError

        :returns: A list where each entry is a dictionary of attributes for one
            feature.
        :rtype: list,

        Note:
            Data is returned as a list where each entry is a dictionary of
            attributes for one feature. Entries in get_geometry() and
            get_data() are related as 1-to-1

            If optional argument attribute is specified and a valid name,
            then the list of values for that attribute is returned.

            If optional argument index is specified on the that value will
            be returned. Any value of index is ignored if attribute is None.

            If optional argument copy is True and all attributes are requested,
            a copy will be returned. Otherwise a pointer to the data is
            returned.
        """

        if hasattr(self, 'data'):
            if attribute is None:
                if copy:
                    return copy_module.deepcopy(self.data)
                else:
                    return self.data
            else:
                msg = ('Specified attribute %s does not exist in '
                       'vector layer %s. Valid names are %s'
                       '' % (attribute, self, self.data[0].keys()))
                verify(attribute in self.data[0], msg)

                if index is None:
                    # Return all values for specified attribute
                    return [x[attribute] for x in self.data]
                else:
                    # Return value for specified attribute and index
                    msg = ('Specified index must be either None or '
                           'an integer. I got %s' % index)
                    verify(isinstance(index, int), msg)

                    msg = ('Specified index must lie within the bounds '
                           'of vector layer %s which is [%i, %i]'
                           '' % (self, 0, len(self) - 1))
                    verify(0 <= index < len(self), msg)

                    return self.data[index][attribute]
        else:
            msg = 'Vector data instance does not have any attributes'
            raise GetDataError(msg)

    def get_data(self, nan=True, scaling=None):
        """Get raster data as numeric array

        Input
            nan: Optional flag controlling handling of missing values.
                 If nan is True (default), nodata values will be replaced
                 with numpy.nan
                 If keyword nan has a numeric value, nodata values will
                 be replaced by that value. E.g. to set missing values to 0,
                 do get_data(nan=0.0)
            scaling: Optional flag controlling if data is to be scaled
                     if it has been resampled. Admissible values are
                     False: data is retrieved without modification.
                     True: Data is rescaled based on the squared ratio between
                           its current and native resolution. This is typically
                           required if raster data represents a density
                           such as population per km^2
                     None: The behaviour will depend on the keyword
                           "population" associated with the layer. If
                           it is "density", scaling will be applied
                           otherwise not. This is the default.
                     scalar value: If scaling takes a numerical scalar value,
                                   that will be use to scale the data

        """

        if hasattr(self, 'data'):
            A = self.data
            verify(A.shape[0] == self.rows and A.shape[1] == self.columns)
        else:
            # Read from raster file
            A = self.band.ReadAsArray()

            # Convert to double precision (issue #75)
            A = numpy.array(A, dtype=numpy.float64)

            # Self check
            M, N = A.shape
            msg = ('Dimensions of raster array do not match those of '
                   'raster file %s' % self.filename)
            verify(M == self.rows, msg)
            verify(N == self.columns, msg)

        # Handle no data value
        if nan is False:
            pass
        else:
            if nan is True:
                NAN = numpy.nan
            else:
                NAN = nan

            # Replace NODATA_VALUE with NaN
            nodata = self.get_nodata_value()

            NaN = numpy.ones(A.shape, A.dtype) * NAN
            A = numpy.where(A == nodata, NaN, A)

        # Take care of possible scaling
        if scaling is None:
            # Redefine scaling from density keyword if possible
            kw = self.get_keywords()
            if 'datatype' in kw and kw['datatype'].lower() == 'density':
                scaling = True
            else:
                scaling = False

        if scaling is False:
            # No change
            sigma = 1
        elif scaling is True:
            # Calculate scaling based on resolution change

            actual_res = self.get_resolution(isotropic=True)
            native_res = self.get_resolution(isotropic=True, native=True)
            #print
            #print 'Actual res', actual_res
            #print 'Native res', native_res
            sigma = (actual_res / native_res) ** 2
            #print 'Scaling', sigma
        else:
            # See if scaling can work as a scalar value
            try:
                sigma = float(scaling)
            except Exception, e:
                msg = ('Keyword scaling "%s" could not be converted to a '
                       'number. It must be either True, False, None or a '
                       'number: %s' % (scaling, str(e)))
                raise Exception(msg)

    def __init__(
            self,
            data=None,
            projection=None,
            geometry=None,
            geometry_type=None,
            name=None,
            keywords=None,
            style_info=None,
            sublayer=None):
        """Initialise object with either geometry or filename

        NOTE: Doc strings in constructor are not harvested and exposed in
        online documentation. Hence the details are specified in the
        class docstring.
        """

        # Invoke common layer constructor
        Layer.__init__(
            self,
            name=name,
            projection=projection,
            keywords=keywords,
            style_info=style_info,
            sublayer=sublayer)

        # Input checks
        if data is None and geometry is None:
            # Instantiate empty object
            self.geometry_type = None
            self.extent = [0, 0, 0, 0]
            return

        if isinstance(data, basestring):
            self.read_from_file(data)
        # check QGIS_IS_AVAILABLE to avoid QgsVectorLayer undefined error
        elif QGIS_IS_AVAILABLE and isinstance(data, QgsVectorLayer):
            self.read_from_qgis_native(data)
        else:
            # Assume that data is provided as sequences provided as
            # arguments to the Vector constructor
            # with extra keyword arguments supplying metadata

            msg = 'Geometry must be specified'
            verify(geometry is not None, msg)

            msg = 'Geometry must be a sequence'
            verify(is_sequence(geometry), msg)

            if len(geometry) > 0 and isinstance(geometry[0], Polygon):
                self.geometry_type = ogr.wkbPolygon
                self.geometry = geometry
            else:
                self.geometry_type = get_geometry_type(geometry, geometry_type)

                if self.is_polygon_data:
                    # Convert to objects if input is a list of simple arrays
                    self.geometry = [Polygon(outer_ring=x) for x in geometry]
                else:
                    # Convert to list if input is an array
                    if isinstance(geometry, numpy.ndarray):
                        self.geometry = geometry.tolist()
                    else:
                        self.geometry = geometry

            if data is None:
                # Generate default attribute as OGR will do that anyway
                # when writing
                data = []
                for i in range(len(geometry)):
                    data.append({'ID': i})

            # Check data
            self.data = data
            if data is not None:
                msg = 'Data must be a sequence'
                verify(is_sequence(data), msg)

                msg = ('The number of entries in geometry (%s) and data (%s)'
                       'must be the same' % (len(geometry), len(data)))
                verify(len(geometry) == len(data), msg)

            # Establish extent
            if len(geometry) == 0:
                # Degenerate layer
                self.extent = [0, 0, 0, 0]
                return

            # Compute bounding box for each geometry type
            minx = miny = sys.maxint
            maxx = maxy = -minx
            if self.is_point_data:
                A = numpy.array(self.get_geometry())
                minx = min(A[:, 0])
                maxx = max(A[:, 0])
                miny = min(A[:, 1])
                maxy = max(A[:, 1])
            elif self.is_line_data:
                for g in self.get_geometry():
                    A = numpy.array(g)
#.........这里部分代码省略.........