Python utilities.verify函数代码示例

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())